Merge branch 'develop' of https://github.com/PaddlePaddle/Paddle into add_dist_unit_test

Dockerfile

 # A image for building paddle binaries
 # Use cuda devel base image for both cpu and gpu environment
-FROM nvidia/cuda:8.0-cudnn5-devel-ubuntu16.04
+FROM nvidia/cuda:8.0-cudnn7-devel-ubuntu16.04
 MAINTAINER PaddlePaddle Authors <paddle-dev@baidu.com>
 
 ARG UBUNTU_MIRROR
@@ -57,7 +57,7 @@ RUN localedef -i en_US -f UTF-8 en_US.UTF-8
 # specify sphinx version as 1.5.6 and remove -U option for [pip install -U
 # sphinx-rtd-theme] since -U option will cause sphinx being updated to newest
 # version(1.7.1 for now), which causes building documentation failed.
-RUN pip install --upgrade pip && \
+RUN pip install --upgrade pip==9.0.3 && \
     pip install -U wheel && \
     pip install -U docopt PyYAML sphinx==1.5.6 && \
     pip install sphinx-rtd-theme==0.1.9 recommonmark

cmake/cblas.cmake

@@ -78,7 +78,7 @@ if(NOT CMAKE_CROSSCOMPILING)
     /usr/lib/reference/
   )
 else()
-  # Diable the finding of reference cblas under host's system path
+  # Disable the finding of reference cblas under host's system path
   set(REFERENCE_CBLAS_INCLUDE_SEARCH_PATHS ${REFERENCE_CBLAS_ROOT}/include)
   set(REFERENCE_CBLAS_LIB_SEARCH_PATHS ${REFERENCE_CBLAS_ROOT}/lib)
 endif()

doc/fluid/design/motivation/fluid.md

@@ -119,7 +119,7 @@ An actual Fluid example is described  [here](https://github.com/PaddlePaddle/Pad
 
 From the example, the Fluid programs look very similar to their PyTorch equivalent programs, except that Fluid's loop structure, wrapped with Python's `with` statement, could run much faster than just a Python loop.
 
-We have more examples of the [`if-then-else`](https://github.com/PaddlePaddle/Paddle/blob/develop/doc/design/if_else_op.md) structure of Fluid.
+We have more examples of the [`if-then-else`](https://github.com/PaddlePaddle/Paddle/blob/develop/doc/fluid/design/execution/if_else_op.md) structure of Fluid.
 
 ## Turing Completeness
 

paddle/fluid/framework/details/CMakeLists.txt

 cc_library(var_handle SRCS var_handle.cc DEPS place)
-cc_library(op_handle_base SRCS op_handle_base.cc DEPS var_handle device_context)
+cc_library(op_handle_base SRCS op_handle_base.cc DEPS var_handle device_context lod_tensor)
 cc_library(scale_loss_grad_op_handle SRCS scale_loss_grad_op_handle.cc DEPS op_handle_base scope lod_tensor ddim memory)
 cc_library(fetch_op_handle SRCS fetch_op_handle.cc DEPS op_handle_base scope lod_tensor ddim memory)
 nv_library(nccl_all_reduce_op_handle SRCS nccl_all_reduce_op_handle.cc DEPS op_handle_base scope lod_tensor ddim memory
@@ -20,3 +20,11 @@ cc_library(multi_devices_graph_builder SRCS multi_devices_graph_builder.cc DEPS
 cc_library(ssa_graph_executor SRCS ssa_graph_executor.cc DEPS ssa_graph framework_proto)
 cc_library(threaded_ssa_graph_executor SRCS threaded_ssa_graph_executor.cc DEPS fetch_op_handle ssa_graph_executor scope
         simple_threadpool device_context)
+
+cc_library(broadcast_op_handle SRCS broadcast_op_handle.cc DEPS op_handle_base scope ddim memory)
+cc_library(gather_op_handle SRCS gather_op_handle.cc DEPS op_handle_base scope ddim memory)
+
+cc_test(broadcast_op_test SRCS broadcast_op_handle_test.cc DEPS var_handle op_handle_base scope ddim memory
+        device_context broadcast_op_handle)
+cc_test(gather_op_test SRCS gather_op_handle_test.cc DEPS var_handle op_handle_base scope ddim memory
+        device_context gather_op_handle)

paddle/fluid/framework/details/broadcast_op_handle.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/framework/details/broadcast_op_handle.h"
+
+namespace paddle {
+namespace framework {
+namespace details {
+
+Tensor *GetTensorFromVar(Variable *in_var) {
+  if (in_var->IsType<LoDTensor>()) {
+    return in_var->GetMutable<LoDTensor>();
+  } else if (in_var->IsType<SelectedRows>()) {
+    return in_var->GetMutable<SelectedRows>()->mutable_value();
+  } else {
+    PADDLE_THROW("Var should be LoDTensor or SelectedRows");
+  }
+  return nullptr;
+}
+
+BroadcastOpHandle::BroadcastOpHandle(const std::vector<Scope *> &local_scopes,
+                                     const std::vector<platform::Place> &places)
+    : local_scopes_(local_scopes), places_(places) {}
+
+void BroadcastOpHandle::RunImpl() {
+  // the input may have dummy var.
+  std::vector<VarHandle *> in_var_handle;
+  for (auto *in : inputs_) {
+    auto *out_handle = dynamic_cast<VarHandle *>(in);
+    if (out_handle) {
+      in_var_handle.push_back(out_handle);
+    }
+  }
+  PADDLE_ENFORCE_EQ(in_var_handle.size(), 1,
+                    "The number of input should be one.");
+
+  // the output may have dummy var.
+  std::vector<VarHandle *> out_var_handles;
+  for (auto *out : outputs_) {
+    auto *out_handle = dynamic_cast<VarHandle *>(out);
+    if (out_handle) {
+      out_var_handles.push_back(out_handle);
+    }
+  }
+
+  PADDLE_ENFORCE_EQ(
+      out_var_handles.size(), places_.size(),
+      "The number of output should equal to the number of places.");
+
+  // Wait input done, this Wait is asynchronous operation
+  auto &in_place = in_var_handle[0]->place_;
+  if (in_var_handle[0]->generated_op_) {
+    for (auto *out : out_var_handles) {
+      auto &out_p = out->place_;
+      in_var_handle[0]->generated_op_->Wait(dev_ctxes_[out_p]);
+    }
+  }
+
+  //
+  auto in_scope_idx = in_var_handle[0]->scope_idx_;
+  auto in_var =
+      local_scopes_.at(in_scope_idx)->FindVar(in_var_handle[0]->name_);
+  Tensor *in_tensor = GetTensorFromVar(in_var);
+
+  for (auto *out : out_var_handles) {
+    auto &out_p = out->place_;
+    auto out_var = local_scopes_.at(out->scope_idx_)->FindVar(out->name_);
+
+    PADDLE_ENFORCE_EQ(out_p.which(), in_place.which(),
+                      "Places must be all on CPU or all on CUDA.");
+
+    if (in_var->IsType<framework::SelectedRows>()) {
+      auto &in_sr = in_var->Get<framework::SelectedRows>();
+      auto out_sr = out_var->GetMutable<framework::SelectedRows>();
+      if (&in_sr == out_sr) continue;
+      out_sr->set_height(in_sr.height());
+      out_sr->set_rows(in_sr.rows());
+      out_sr->mutable_value()->Resize(in_sr.value().dims());
+      out_sr->mutable_value()->mutable_data(out_p, in_sr.value().type());
+    } else if (in_var->IsType<framework::LoDTensor>()) {
+      auto in_lod = in_var->Get<framework::LoDTensor>();
+      auto out_lod = out_var->GetMutable<framework::LoDTensor>();
+      if (&in_lod == out_lod) continue;
+      out_lod->set_lod(in_lod.lod());
+      out_lod->Resize(in_lod.dims());
+      out_lod->mutable_data(out_p, in_lod.type());
+    } else {
+      PADDLE_THROW("Var should be LoDTensor or SelectedRows.");
+    }
+
+    Tensor *out_tensor = GetTensorFromVar(out_var);
+    paddle::framework::TensorCopy(*in_tensor, out_p, *(dev_ctxes_[in_place]),
+                                  out_tensor);
+  }
+}
+
+std::string BroadcastOpHandle::Name() const { return "broadcast"; }
+}  // namespace details
+}  // namespace framework
+}  // namespace paddle

paddle/fluid/framework/details/broadcast_op_handle.h

+//   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.
+
+#pragma once
+
+#include <map>
+#include <string>
+#include <vector>
+
+#include "paddle/fluid/framework/details/op_handle_base.h"
+#include "paddle/fluid/framework/lod_tensor.h"
+#include "paddle/fluid/framework/scope.h"
+#include "paddle/fluid/framework/selected_rows.h"
+#include "paddle/fluid/platform/device_context.h"
+
+namespace paddle {
+namespace framework {
+namespace details {
+
+struct BroadcastOpHandle : public OpHandleBase {
+  const std::vector<Scope *> &local_scopes_;
+  const std::vector<platform::Place> &places_;
+
+  BroadcastOpHandle(const std::vector<Scope *> &local_scopes,
+                    const std::vector<platform::Place> &places);
+
+  std::string Name() const override;
+
+  bool IsMultiDeviceTransfer() override { return false; };
+
+ protected:
+  void RunImpl() override;
+};
+
+}  // namespace details
+}  // namespace framework
+}  // namespace paddle

paddle/fluid/framework/details/broadcast_op_handle_test.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/framework/details/broadcast_op_handle.h"
+#include "gtest/gtest.h"
+
+#include "paddle/fluid/platform/device_context.h"
+
+namespace paddle {
+namespace framework {
+namespace details {
+
+namespace f = paddle::framework;
+namespace p = paddle::platform;
+
+// test data amount
+const f::DDim kDims = {20, 20};
+
+struct TestBroadcastOpHandle {
+  std::vector<std::unique_ptr<p::DeviceContext>> ctxs_;
+  std::vector<Scope*> local_scopes_;
+  Scope g_scope_;
+  std::unique_ptr<OpHandleBase> op_handle_;
+  std::vector<std::unique_ptr<VarHandleBase>> vars_;
+  std::vector<p::Place> gpu_list_;
+
+  void WaitAll() {
+    for (size_t j = 0; j < ctxs_.size(); ++j) {
+      ctxs_[j]->Wait();
+    }
+  }
+
+  void InitCtxOnGpu(bool use_gpu) {
+    if (use_gpu) {
+#ifdef PADDLE_WITH_CUDA
+      int count = p::GetCUDADeviceCount();
+      if (count <= 1) {
+        LOG(WARNING) << "Cannot test multi-gpu Broadcast, because the CUDA "
+                        "device count is "
+                     << count;
+        exit(0);
+      }
+      for (int i = 0; i < count; ++i) {
+        auto p = p::CUDAPlace(i);
+        gpu_list_.push_back(p);
+        ctxs_.emplace_back(new p::CUDADeviceContext(p));
+      }
+#else
+      PADDLE_THROW("CUDA is not support.");
+#endif
+    } else {
+      int count = 8;
+      for (int i = 0; i < count; ++i) {
+        auto p = p::CPUPlace();
+        gpu_list_.push_back(p);
+        ctxs_.emplace_back(new p::CPUDeviceContext(p));
+      }
+    }
+  }
+
+  void InitBroadcastOp(size_t input_scope_idx) {
+    for (size_t j = 0; j < gpu_list_.size(); ++j) {
+      local_scopes_.push_back(&(g_scope_.NewScope()));
+      local_scopes_[j]->Var("out");
+    }
+    local_scopes_[input_scope_idx]->Var("input");
+
+    op_handle_.reset(new BroadcastOpHandle(local_scopes_, gpu_list_));
+
+    vars_.emplace_back(new VarHandle());
+    VarHandle* in_var_handle = static_cast<VarHandle*>(vars_.back().get());
+    in_var_handle->place_ = gpu_list_[input_scope_idx];
+    in_var_handle->name_ = "input";
+    in_var_handle->version_ = 1;
+    in_var_handle->scope_idx_ = input_scope_idx;
+    in_var_handle->generated_op_ = nullptr;
+    op_handle_->AddInput(in_var_handle);
+
+    // add dummy var
+    vars_.emplace_back(new DummyVarHandle());
+    DummyVarHandle* dummy_var_handle =
+        static_cast<DummyVarHandle*>(vars_.back().get());
+    dummy_var_handle->generated_op_ = nullptr;
+    op_handle_->AddInput(dummy_var_handle);
+
+    for (size_t j = 0; j < gpu_list_.size(); ++j) {
+      op_handle_->dev_ctxes_[gpu_list_[j]] = ctxs_[j].get();
+      vars_.emplace_back(new VarHandle());
+      VarHandle* out_var_handle = static_cast<VarHandle*>(vars_.back().get());
+      out_var_handle->place_ = gpu_list_[j];
+      out_var_handle->name_ = "out";
+      out_var_handle->version_ = 2;
+      out_var_handle->scope_idx_ = j;
+      op_handle_->AddOutput(out_var_handle);
+    }
+
+    // add dummy var
+    vars_.emplace_back(new DummyVarHandle());
+    DummyVarHandle* out_dummy_var_handle =
+        static_cast<DummyVarHandle*>(vars_.back().get());
+    out_dummy_var_handle->generated_op_ = nullptr;
+    op_handle_->AddOutput(out_dummy_var_handle);
+  }
+
+  void TestBroadcastLodTensor(size_t input_scope_idx) {
+    auto in_var = local_scopes_[input_scope_idx]->Var("input");
+    auto in_lod_tensor = in_var->GetMutable<f::LoDTensor>();
+    in_lod_tensor->mutable_data<float>(kDims, gpu_list_[input_scope_idx]);
+
+    std::vector<float> send_vector(static_cast<size_t>(f::product(kDims)));
+    for (size_t k = 0; k < send_vector.size(); ++k) {
+      send_vector[k] = k;
+    }
+    f::LoD lod{{0, 10, 20}};
+    paddle::framework::TensorFromVector<float>(
+        send_vector, *(ctxs_[input_scope_idx]), in_lod_tensor);
+    in_lod_tensor->set_lod(lod);
+
+    op_handle_->Run(false);
+
+    WaitAll();
+
+    p::CPUPlace cpu_place;
+    for (size_t j = 0; j < gpu_list_.size(); ++j) {
+      auto out_var = local_scopes_[j]->Var("out");
+      auto out_tensor = out_var->Get<f::LoDTensor>();
+      PADDLE_ENFORCE_EQ(out_tensor.lod(), lod, "lod is not equal.");
+
+      f::Tensor result_tensor;
+      f::TensorCopy(out_tensor, cpu_place, *(ctxs_[j]), &result_tensor);
+      float* ct = result_tensor.mutable_data<float>(cpu_place);
+
+      for (int64_t i = 0; i < f::product(kDims); ++i) {
+        ASSERT_NEAR(ct[i], send_vector[i], 1e-5);
+      }
+    }
+  }
+
+  void TestBroadcastSelectedRows(size_t input_scope_idx) {
+    auto in_var = local_scopes_[input_scope_idx]->Var("input");
+    auto in_selected_rows = in_var->GetMutable<f::SelectedRows>();
+    auto value = in_selected_rows->mutable_value();
+    value->mutable_data<float>(kDims, gpu_list_[input_scope_idx]);
+    int height = static_cast<int>(kDims[0]) * 2;
+    std::vector<int64_t> rows{0, 1, 2, 3, 3, 0, 14, 7, 3, 1,
+                              2, 4, 6, 3, 1, 1, 1,  1, 3, 7};
+    in_selected_rows->set_height(height);
+    in_selected_rows->set_rows(rows);
+
+    std::vector<float> send_vector(static_cast<size_t>(f::product(kDims)));
+    for (size_t k = 0; k < send_vector.size(); ++k) {
+      send_vector[k] = k;
+    }
+    paddle::framework::TensorFromVector<float>(
+        send_vector, *(ctxs_[input_scope_idx]), value);
+
+    op_handle_->Run(false);
+
+    WaitAll();
+
+    p::CPUPlace cpu_place;
+    for (size_t j = 0; j < gpu_list_.size(); ++j) {
+      auto out_var = local_scopes_[j]->Var("out");
+      auto& out_select_rows = out_var->Get<f::SelectedRows>();
+      auto rt = out_select_rows.value();
+
+      PADDLE_ENFORCE_EQ(out_select_rows.height(), height,
+                        "height is not equal.");
+      for (size_t k = 0; k < out_select_rows.rows().size(); ++k) {
+        PADDLE_ENFORCE_EQ(out_select_rows.rows()[k], rows[k]);
+      }
+
+      f::Tensor result_tensor;
+      f::TensorCopy(rt, cpu_place, *(ctxs_[j]), &result_tensor);
+      float* ct = result_tensor.data<float>();
+
+      for (int64_t i = 0; i < f::product(kDims); ++i) {
+        ASSERT_NEAR(ct[i], send_vector[i], 1e-5);
+      }
+    }
+  }
+};
+
+TEST(BroadcastTester, TestCPUBroadcastTestLodTensor) {
+  TestBroadcastOpHandle test_op;
+  size_t input_scope_idx = 0;
+  test_op.InitCtxOnGpu(false);
+  test_op.InitBroadcastOp(input_scope_idx);
+  test_op.TestBroadcastLodTensor(input_scope_idx);
+}
+
+TEST(BroadcastTester, TestCPUBroadcastTestSelectedRows) {
+  TestBroadcastOpHandle test_op;
+  size_t input_scope_idx = 0;
+  test_op.InitCtxOnGpu(false);
+  test_op.InitBroadcastOp(input_scope_idx);
+  test_op.TestBroadcastSelectedRows(input_scope_idx);
+}
+
+#ifdef PADDLE_WITH_CUDA
+TEST(BroadcastTester, TestGPUBroadcastTestLodTensor) {
+  TestBroadcastOpHandle test_op;
+  size_t input_scope_idx = 0;
+  test_op.InitCtxOnGpu(true);
+  test_op.InitBroadcastOp(input_scope_idx);
+  test_op.TestBroadcastLodTensor(input_scope_idx);
+}
+
+TEST(BroadcastTester, TestGPUBroadcastTestSelectedRows) {
+  TestBroadcastOpHandle test_op;
+  size_t input_scope_idx = 0;
+  test_op.InitCtxOnGpu(true);
+  test_op.InitBroadcastOp(input_scope_idx);
+  test_op.TestBroadcastSelectedRows(input_scope_idx);
+}
+#endif
+
+}  // namespace details
+}  // namespace framework
+}  // namespace paddle

paddle/fluid/framework/details/computation_op_handle.cc

@@ -35,7 +35,9 @@ void ComputationOpHandle::RunImpl() {
     }
   }
 
-  op_->Run(*scope_->FindVar(kLocalExecScopeName)->Get<Scope *>(), place_);
+  this->RunAndRecordEvent([this] {
+    op_->Run(*scope_->FindVar(kLocalExecScopeName)->Get<Scope *>(), place_);
+  });
 }
 
 std::string ComputationOpHandle::Name() const { return op_->Type(); }

paddle/fluid/framework/details/gather_op_handle.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/framework/details/gather_op_handle.h"
+
+namespace paddle {
+namespace framework {
+namespace details {
+
+GatherOpHandle::GatherOpHandle(const std::vector<Scope *> &local_scopes,
+                               const std::vector<platform::Place> &places)
+    : local_scopes_(local_scopes), places_(places) {}
+
+void GatherOpHandle::RunImpl() {
+  // the input may have dummy var.
+  std::vector<VarHandle *> in_var_handles;
+  for (auto *in : inputs_) {
+    auto *in_handle = dynamic_cast<VarHandle *>(in);
+    if (in_handle) {
+      in_var_handles.push_back(in_handle);
+    }
+  }
+  PADDLE_ENFORCE_EQ(
+      in_var_handles.size(), places_.size(),
+      "The number of output should equal to the number of places.");
+
+  // the output may have dummy var.
+  std::vector<VarHandle *> out_var_handles;
+  for (auto *out : outputs_) {
+    auto *out_handle = dynamic_cast<VarHandle *>(out);
+    if (out_handle) {
+      out_var_handles.push_back(out_handle);
+    }
+  }
+  PADDLE_ENFORCE_EQ(out_var_handles.size(), 1,
+                    "The number of output should be one.");
+
+  auto in_0_handle = static_cast<VarHandle *>(in_var_handles[0]);
+  auto pre_in_var =
+      local_scopes_[in_0_handle->scope_idx_]->FindVar(in_0_handle->name_);
+  auto pre_place = in_0_handle->place_;
+
+  PADDLE_ENFORCE(pre_in_var->IsType<framework::SelectedRows>(),
+                 "Currently, gather_op only can gather SelectedRows.");
+
+  PADDLE_ENFORCE_EQ(out_var_handles[0]->place_.which(), pre_place.which(),
+                    "The place of input and output should be the same.");
+
+  // Wait input done, this Wait is asynchronous operation
+  for (auto *in : in_var_handles) {
+    if (in->generated_op_) {
+      in->generated_op_->Wait(dev_ctxes_[in->place_]);
+    }
+  }
+
+  std::vector<int64_t> out_rows;
+  std::vector<Tensor> in_tensors;
+  std::vector<platform::Place> in_places;
+
+  auto &pre_in = pre_in_var->Get<framework::SelectedRows>();
+  // gather the inputs
+  for (auto *in : in_var_handles) {
+    auto in_handle = static_cast<VarHandle *>(in);
+    auto in_p = in_handle->place_;
+    in_places.push_back(in_p);
+    PADDLE_ENFORCE_EQ(in_p.which(), pre_place.which(),
+                      "Places must be all on CPU or all on CUDA.");
+    auto in_var =
+        local_scopes_.at(in_handle->scope_idx_)->FindVar(in_handle->name_);
+    auto &in_sr = in_var->Get<framework::SelectedRows>();
+
+    PADDLE_ENFORCE_EQ(in_sr.value().type(), pre_in.value().type(),
+                      "The type of input is not consistent.");
+    PADDLE_ENFORCE_EQ(pre_in.height(), in_sr.height(),
+                      "The height of inputs is not consistent.");
+    PADDLE_ENFORCE_EQ(pre_in.GetCompleteDims(), in_sr.GetCompleteDims(), ,
+                      "The dims of inputs is not consistent.");
+
+    auto in_sr_rows = in_sr.rows();
+    out_rows.insert(out_rows.end(), in_sr_rows.begin(), in_sr_rows.end());
+
+    in_tensors.emplace_back(in_sr.value());
+  }
+
+  // write the output
+  auto &out_place = out_var_handles[0]->place_;
+  auto out_scope_idx = out_var_handles[0]->scope_idx_;
+  auto out_var =
+      local_scopes_[out_scope_idx]->FindVar(out_var_handles[0]->name_);
+
+  auto out = out_var->GetMutable<framework::SelectedRows>();
+  out->set_height(pre_in.height());
+  out->set_rows(out_rows);
+  size_t rows = out_rows.size();
+  DDim out_dim = pre_in.GetCompleteDims();
+  out_dim[0] = static_cast<int64_t>(rows);
+  out->mutable_value()->Resize(out_dim);
+  out->mutable_value()->mutable_data(out_place, pre_in.value().type());
+  Tensor *out_tensor = out->mutable_value();
+
+  // copy
+  int s = 0, e = 0;
+  for (size_t j = 0; j < in_tensors.size(); ++j) {
+    e += in_tensors[j].dims()[0];
+    auto sub_out = out_tensor->Slice(s, e);
+    paddle::framework::TensorCopy(in_tensors[j], out_place,
+                                  *(dev_ctxes_[in_places[j]]), &sub_out);
+    s = e;
+  }
+}
+
+std::string GatherOpHandle::Name() const { return "gather"; }
+}  // namespace details
+}  // namespace framework
+}  // namespace paddle

paddle/fluid/framework/details/gather_op_handle.h

+//   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.
+
+#pragma once
+
+#include <map>
+#include <string>
+#include <vector>
+
+#include "paddle/fluid/framework/details/op_handle_base.h"
+#include "paddle/fluid/framework/lod_tensor.h"
+#include "paddle/fluid/framework/scope.h"
+#include "paddle/fluid/framework/selected_rows.h"
+#include "paddle/fluid/platform/device_context.h"
+
+namespace paddle {
+namespace framework {
+namespace details {
+
+struct GatherOpHandle : public OpHandleBase {
+  const std::vector<Scope *> &local_scopes_;
+  const std::vector<platform::Place> &places_;
+
+  GatherOpHandle(const std::vector<Scope *> &local_scopes,
+                 const std::vector<platform::Place> &places);
+
+  std::string Name() const override;
+
+  bool IsMultiDeviceTransfer() override { return false; };
+
+ protected:
+  void RunImpl() override;
+};
+
+}  // namespace details
+}  // namespace framework
+}  // namespace paddle

paddle/fluid/framework/details/gather_op_handle_test.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/framework/details/gather_op_handle.h"
+#include "gtest/gtest.h"
+
+#include "paddle/fluid/platform/device_context.h"
+
+namespace paddle {
+namespace framework {
+namespace details {
+namespace f = paddle::framework;
+namespace p = paddle::platform;
+
+// test data amount
+const f::DDim kDims = {20, 20};
+
+struct TestGatherOpHandle {
+  std::vector<std::unique_ptr<p::DeviceContext>> ctxs_;
+  std::vector<Scope*> local_scopes_;
+  Scope g_scope_;
+  std::unique_ptr<OpHandleBase> op_handle_;
+  std::vector<std::unique_ptr<VarHandleBase>> vars_;
+  std::vector<p::Place> gpu_list_;
+
+  void WaitAll() {
+    for (size_t j = 0; j < ctxs_.size(); ++j) {
+      ctxs_[j]->Wait();
+    }
+  }
+
+  void InitCtxOnGpu(bool use_gpu) {
+    if (use_gpu) {
+#ifdef PADDLE_WITH_CUDA
+      int count = p::GetCUDADeviceCount();
+      if (count <= 1) {
+        LOG(WARNING) << "Cannot test multi-gpu Broadcast, because the CUDA "
+                        "device count is "
+                     << count;
+        exit(0);
+      }
+      for (int i = 0; i < count; ++i) {
+        auto p = p::CUDAPlace(i);
+        gpu_list_.push_back(p);
+        ctxs_.emplace_back(new p::CUDADeviceContext(p));
+      }
+#else
+      PADDLE_THROW("CUDA is not support.");
+#endif
+    } else {
+      int count = 8;
+      for (int i = 0; i < count; ++i) {
+        auto p = p::CPUPlace();
+        gpu_list_.push_back(p);
+        ctxs_.emplace_back(new p::CPUDeviceContext(p));
+      }
+    }
+  }
+
+  void InitGatherOp(size_t input_scope_idx) {
+    for (size_t j = 0; j < gpu_list_.size(); ++j) {
+      local_scopes_.push_back(&(g_scope_.NewScope()));
+      local_scopes_[j]->Var("out");
+    }
+    local_scopes_[input_scope_idx]->Var("input");
+
+    op_handle_.reset(new GatherOpHandle(local_scopes_, gpu_list_));
+    // add input
+    for (size_t j = 0; j < gpu_list_.size(); ++j) {
+      op_handle_->dev_ctxes_[gpu_list_[j]] = ctxs_[j].get();
+      vars_.emplace_back(new VarHandle());
+      VarHandle* in_var_handle = static_cast<VarHandle*>(vars_.back().get());
+      in_var_handle->place_ = gpu_list_[j];
+      in_var_handle->name_ = "input";
+      in_var_handle->version_ = 1;
+      in_var_handle->scope_idx_ = j;
+      in_var_handle->generated_op_ = nullptr;
+      op_handle_->AddInput(in_var_handle);
+    }
+
+    // add dummy var
+    vars_.emplace_back(new DummyVarHandle());
+    DummyVarHandle* in_dummy_var_handle =
+        static_cast<DummyVarHandle*>(vars_.back().get());
+    in_dummy_var_handle->generated_op_ = nullptr;
+    op_handle_->AddInput(in_dummy_var_handle);
+
+    // add output
+    vars_.emplace_back(new VarHandle());
+    VarHandle* out_var_handle = static_cast<VarHandle*>(vars_.back().get());
+    out_var_handle->place_ = gpu_list_[input_scope_idx];
+    out_var_handle->name_ = "out";
+    out_var_handle->version_ = 2;
+    out_var_handle->scope_idx_ = input_scope_idx;
+    op_handle_->AddOutput(out_var_handle);
+
+    // add dummy var
+    vars_.emplace_back(new DummyVarHandle());
+    DummyVarHandle* dummy_var_handle =
+        static_cast<DummyVarHandle*>(vars_.back().get());
+    op_handle_->AddOutput(dummy_var_handle);
+  }
+
+  void TestGatherSelectedRows(size_t output_scope_idx) {
+    int height = kDims[0] * 2;
+    std::vector<int64_t> rows{0, 1, 2, 3, 3, 0, 14, 7, 3, 1,
+                              2, 4, 6, 3, 1, 1, 1,  1, 3, 7};
+    std::vector<float> send_vector(f::product(kDims));
+    for (size_t k = 0; k < send_vector.size(); ++k) {
+      send_vector[k] = k;
+    }
+
+    for (size_t input_scope_idx = 0; input_scope_idx < gpu_list_.size();
+         ++input_scope_idx) {
+      auto in_var = local_scopes_[input_scope_idx]->Var("input");
+      auto in_selected_rows = in_var->GetMutable<f::SelectedRows>();
+      auto value = in_selected_rows->mutable_value();
+      value->mutable_data<float>(kDims, gpu_list_[input_scope_idx]);
+
+      in_selected_rows->set_height(height);
+      in_selected_rows->set_rows(rows);
+
+      paddle::framework::TensorFromVector<float>(
+          send_vector, *(ctxs_[input_scope_idx]), value);
+      value->Resize(kDims);
+    }
+
+    auto out_var = local_scopes_[output_scope_idx]->Var("out");
+    auto out_selected_rows = out_var->GetMutable<f::SelectedRows>();
+
+    auto in_var = local_scopes_[output_scope_idx]->Var("input");
+    auto in_selected_rows = in_var->GetMutable<f::SelectedRows>();
+
+    out_selected_rows->mutable_value()->ShareDataWith(
+        in_selected_rows->value());
+
+    op_handle_->Run(false);
+
+    WaitAll();
+
+    p::CPUPlace cpu_place;
+
+    auto& out_select_rows = out_var->Get<f::SelectedRows>();
+    auto rt = out_select_rows.value();
+
+    PADDLE_ENFORCE_EQ(out_select_rows.height(), height, "height is not equal.");
+    for (size_t k = 0; k < out_select_rows.rows().size(); ++k) {
+      PADDLE_ENFORCE_EQ(out_select_rows.rows()[k], rows[k % rows.size()]);
+    }
+
+    f::Tensor result_tensor;
+    f::TensorCopy(rt, cpu_place, *(ctxs_[output_scope_idx]), &result_tensor);
+    float* ct = result_tensor.data<float>();
+
+    for (int64_t j = 0; j < f::product(kDims); ++j) {
+      ASSERT_NEAR(ct[j], send_vector[j % send_vector.size()], 1e-5);
+    }
+  }
+};
+
+TEST(GatherTester, TestCPUGatherTestSelectedRows) {
+  TestGatherOpHandle test_op;
+  size_t input_scope_idx = 0;
+  test_op.InitCtxOnGpu(false);
+  test_op.InitGatherOp(input_scope_idx);
+  test_op.TestGatherSelectedRows(input_scope_idx);
+}
+
+#ifdef PADDLE_WITH_CUDA
+
+TEST(GatherTester, TestGPUGatherTestSelectedRows) {
+  TestGatherOpHandle test_op;
+  size_t input_scope_idx = 0;
+  test_op.InitCtxOnGpu(false);
+  test_op.InitGatherOp(input_scope_idx);
+  test_op.TestGatherSelectedRows(input_scope_idx);
+}
+#endif
+}  // namespace details
+}  // namespace framework
+}  // namespace paddle

paddle/fluid/framework/details/nccl_all_reduce_op_handle.cc

@@ -14,6 +14,8 @@
 
 #include "paddle/fluid/framework/details/nccl_all_reduce_op_handle.h"
 
+#include <algorithm>
+
 namespace paddle {
 namespace framework {
 namespace details {
@@ -27,6 +29,32 @@ NCCLAllReduceOpHandle::NCCLAllReduceOpHandle(
   }
 }
 
+struct ReduceLoDTensor {
+  const std::vector<LoDTensor> &src_tensors_;
+  LoDTensor &dst_tensor_;
+
+  ReduceLoDTensor(const std::vector<LoDTensor> &src, LoDTensor *dst)
+      : src_tensors_(src), dst_tensor_(*dst) {}
+
+  template <typename T>
+  void operator()() const {
+    PADDLE_ENFORCE(!src_tensors_.empty());
+    auto &t0 = src_tensors_[0];
+    PADDLE_ENFORCE_NE(t0.numel(), 0);
+    dst_tensor_.Resize(t0.dims());
+    T *dst = dst_tensor_.mutable_data<T>(platform::CPUPlace());
+    std::copy(t0.data<T>(), t0.data<T>() + t0.numel(), dst);
+
+    for (size_t i = 1; i < src_tensors_.size(); ++i) {
+      auto &t = src_tensors_[i];
+      PADDLE_ENFORCE_EQ(t.dims(), t0.dims());
+      PADDLE_ENFORCE_EQ(t.type(), t0.type());
+      std::transform(t.data<T>(), t.data<T>() + t.numel(), dst, dst,
+                     [](T a, T b) -> T { return a + b; });
+    }
+  }
+};
+
 void NCCLAllReduceOpHandle::RunImpl() {
   if (inputs_.size() == 1) {
     return;  // No need to all reduce when GPU count = 1;
@@ -41,37 +69,66 @@ void NCCLAllReduceOpHandle::RunImpl() {
     int dtype = -1;
     size_t numel = 0;
 
-    std::vector<std::function<void()>> all_reduce_calls;
+    std::vector<LoDTensor> lod_tensors;
 
     for (size_t i = 0; i < local_scopes_.size(); ++i) {
-      auto &p = places_[i];
       auto *s = local_scopes_[i];
-      int dev_id = boost::get<platform::CUDAPlace>(p).device;
 
       auto &lod_tensor = s->FindVar(var_name)->Get<LoDTensor>();
-      void *buffer = const_cast<void *>(lod_tensor.data<void>());
+      lod_tensors.emplace_back(lod_tensor);
+    }
 
-      if (dtype == -1) {
-        dtype = platform::ToNCCLDataType(lod_tensor.type());
-      }
+    if (platform::is_gpu_place(lod_tensors[0].place())) {
+      std::vector<std::function<void()>> all_reduce_calls;
+      for (size_t i = 0; i < local_scopes_.size(); ++i) {
+        auto &p = places_[i];
+        auto &lod_tensor = lod_tensors[i];
+        void *buffer = const_cast<void *>(lod_tensor.data<void>());
 
-      if (numel == 0) {
-        numel = static_cast<size_t>(lod_tensor.numel());
-      }
+        if (dtype == -1) {
+          dtype = platform::ToNCCLDataType(lod_tensor.type());
+        }
 
-      auto &nccl_ctx = nccl_ctxs_.at(dev_id);
-      auto stream = nccl_ctx.stream();
-      auto comm = nccl_ctx.comm_;
-      all_reduce_calls.emplace_back([=] {
-        PADDLE_ENFORCE(platform::dynload::ncclAllReduce(
-            buffer, buffer, numel, static_cast<ncclDataType_t>(dtype), ncclSum,
-            comm, stream));
+        if (numel == 0) {
+          numel = static_cast<size_t>(lod_tensor.numel());
+        }
+
+        int dev_id = boost::get<platform::CUDAPlace>(p).device;
+        auto &nccl_ctx = nccl_ctxs_.at(dev_id);
+        auto stream = nccl_ctx.stream();
+        auto comm = nccl_ctx.comm_;
+        all_reduce_calls.emplace_back([=] {
+          PADDLE_ENFORCE(platform::dynload::ncclAllReduce(
+              buffer, buffer, numel, static_cast<ncclDataType_t>(dtype),
+              ncclSum, comm, stream));
+        });
+      }
+      this->RunAndRecordEvent([&] {
+        platform::NCCLGroupGuard guard;
+        for (auto &call : all_reduce_calls) {
+          call();
+        }
       });
-    }
+    } else {  // Special handle CPU only Operator's gradient. Like CRF
+      auto &trg =
+          *this->local_scopes_[0]->Var()->GetMutable<framework::LoDTensor>();
+
+      // Reduce All Tensor to trg in CPU
+      ReduceLoDTensor func(lod_tensors, &trg);
+      VisitDataType(ToDataType(lod_tensors[0].type()), func);
 
-    platform::NCCLGroupGuard guard;
-    for (auto &call : all_reduce_calls) {
-      call();
+      for (size_t i = 0; i < local_scopes_.size(); ++i) {
+        auto &scope = local_scopes_[i];
+        auto &p = places_[i];
+        auto *var = scope->FindVar(var_name);
+        auto *dev_ctx = dev_ctxes_[p];
+
+        RunAndRecordEvent(p, [&trg, var, dev_ctx, p] {
+          auto &tensor_gpu = *var->GetMutable<framework::LoDTensor>();
+          auto &tensor_cpu = trg;
+          TensorCopy(tensor_cpu, p, *dev_ctx, &tensor_gpu);
+        });
+      }
     }
   }
 }

paddle/fluid/framework/details/op_handle_base.cc

@@ -54,17 +54,6 @@ void OpHandleBase::Run(bool use_event) {
 #endif
 
   RunImpl();
-
-#ifdef PADDLE_WITH_CUDA
-  if (use_event) {
-    for (auto &p : dev_ctxes_) {
-      int dev_id = boost::get<platform::CUDAPlace>(p.first).device;
-      auto stream =
-          static_cast<platform::CUDADeviceContext *>(p.second)->stream();
-      PADDLE_ENFORCE(cudaEventRecord(events_.at(dev_id), stream));
-    }
-  }
-#endif
 }
 
 void OpHandleBase::Wait(platform::DeviceContext *waited_dev) {
@@ -97,6 +86,43 @@ void OpHandleBase::AddOutput(VarHandleBase *out) {
   out->generated_op_ = this;
 }
 
+void OpHandleBase::RunAndRecordEvent(const std::function<void()> &callback) {
+#ifdef PADDLE_WITH_CUDA
+  if (!events_.empty()) {  // Use event
+    std::function<void()> method = callback;
+
+    for (auto &p : dev_ctxes_) {
+      method = [method, p, this]() {
+        static_cast<platform::CUDADeviceContext *>(p.second)->RecordEvent(
+            events_.at(boost::get<platform::CUDAPlace>(p.first).device),
+            method);
+      };
+    }
+    method();
+  } else {
+#endif
+    callback();
+#ifdef PADDLE_WITH_CUDA
+  }
+#endif
+}
+
+void OpHandleBase::RunAndRecordEvent(platform::Place p,
+                                     const std::function<void()> &callback) {
+#ifdef PADDLE_WITH_CUDA
+  if (platform::is_cpu_place(p) || events_.empty()) {
+    callback();
+  } else {
+    auto *ctx = dev_ctxes_.at(p);
+    auto *cuda_ctx = static_cast<platform::CUDADeviceContext *>(ctx);
+    cuda_ctx->RecordEvent(events_.at(boost::get<platform::CUDAPlace>(p).device),
+                          callback);
+  }
+#else
+  callback();
+#endif
+}
+
 }  // namespace details
 }  // namespace framework
 }  // namespace paddle

paddle/fluid/framework/details/op_handle_base.h

@@ -62,6 +62,11 @@ class OpHandleBase {
   virtual bool IsMultiDeviceTransfer() { return false; }
 
  protected:
+  void RunAndRecordEvent(const std::function<void()> &callback);
+
+  void RunAndRecordEvent(platform::Place p,
+                         const std::function<void()> &callback);
+
   virtual void RunImpl() = 0;
 };
 

paddle/fluid/framework/details/scale_loss_grad_op_handle.cc

@@ -14,6 +14,8 @@
 
 #include "paddle/fluid/framework/details/scale_loss_grad_op_handle.h"
 
+#include <string>
+
 namespace paddle {
 namespace framework {
 namespace details {
@@ -37,11 +39,13 @@ void ScaleLossGradOpHandle::RunImpl() {
     *tmp = coeff_;
   } else {
 #ifdef PADDLE_WITH_CUDA
-    auto stream =
-        static_cast<platform::CUDADeviceContext *>(this->dev_ctxes_[place_])
-            ->stream();
-    memory::Copy(boost::get<platform::CUDAPlace>(place_), tmp,
-                 platform::CPUPlace(), &coeff_, sizeof(float), stream);
+    this->RunAndRecordEvent([&] {
+      auto stream =
+          static_cast<platform::CUDADeviceContext *>(this->dev_ctxes_[place_])
+              ->stream();
+      memory::Copy(boost::get<platform::CUDAPlace>(place_), tmp,
+                   platform::CPUPlace(), &coeff_, sizeof(float), stream);
+    });
 #endif
   }
 }

paddle/fluid/framework/details/send_op_handle.cc

@@ -34,7 +34,7 @@ void SendOpHandle::RunImpl() {
     }
     in->generated_op_->Wait(dev_ctxes_[p]);
   }
-  op_->Run(*local_scope_, place_);
+  this->RunAndRecordEvent([&] { op_->Run(*local_scope_, place_); });
 }
 
 std::string SendOpHandle::Name() const { return "send"; }

paddle/fluid/framework/details/threaded_ssa_graph_executor.cc

@@ -196,10 +196,12 @@ void ThreadedSSAGraphExecutor::RunOp(
     BlockingQueue<VarHandleBase *> *ready_var_q, details::OpHandleBase *op) {
   auto op_run = [ready_var_q, op, this] {
     try {
-      VLOG(10) << op->Name() << " : " << op->DebugString();
+      VLOG(10) << op << " " << op->Name() << " : " << op->DebugString();
       op->Run(use_event_);
+      VLOG(10) << op << " " << op->Name() << " Done ";
       running_ops_--;
       ready_var_q->Extend(op->outputs_);
+      VLOG(10) << op << " " << op->Name() << "Signal posted";
     } catch (platform::EnforceNotMet ex) {
       exception_.reset(new platform::EnforceNotMet(ex));
     } catch (...) {

paddle/fluid/framework/details/var_handle.h

@@ -50,6 +50,7 @@ struct VarHandle : public VarHandleBase {
   // version field currently is not used, however, just store the version to
   // debug easily.
   size_t version_;
+  size_t scope_idx_;
   std::string name_;
   platform::Place place_;
 };

paddle/fluid/framework/executor.cc

@@ -83,8 +83,8 @@ static void CheckTensorNANOrInf(const std::string& name,
   if (tensor.memory_size() == 0) {
     return;
   }
-  if (tensor.type().hash_code() != typeid(float).hash_code() &&
-      tensor.type().hash_code() != typeid(double).hash_code()) {
+  if (tensor.type().hash_code() != typeid(float).hash_code() &&   // NOLINT
+      tensor.type().hash_code() != typeid(double).hash_code()) {  // NOLINT
     return;
   }
   PADDLE_ENFORCE(!framework::TensorContainsInf(tensor),
@@ -145,12 +145,13 @@ void Executor::Run(const ProgramDesc& pdesc, Scope* scope, int block_id,
 // Return true if the block has feed operators and holder of matching info.
 static bool has_feed_operators(
     const BlockDesc& block,
-    std::map<std::string, const LoDTensor*>& feed_targets,
+    const std::map<std::string, const LoDTensor*>& feed_targets,
     const std::string& feed_holder_name) {
   size_t feed_count = 0;
   for (auto* op : block.AllOps()) {
     if (op->Type() == kFeedOpType) {
       feed_count++;
+      // The input variable's name of feed_op should be feed_holder_name.
       PADDLE_ENFORCE_EQ(op->Input("X")[0], feed_holder_name,
                         "Input to feed op should be '%s'", feed_holder_name);
       std::string feed_target_name = op->Output("Out")[0];
@@ -166,13 +167,15 @@ static bool has_feed_operators(
         feed_count, feed_targets.size(),
         "The number of feed operators should match 'feed_targets'");
 
-    // When feed operator are present, so should be feed_holder
-    auto var = block.FindVar(feed_holder_name);
-    PADDLE_ENFORCE_NOT_NULL(var, "Block should already have a '%s' variable",
-                            feed_holder_name);
-    PADDLE_ENFORCE_EQ(var->GetType(), proto::VarType::FEED_MINIBATCH,
-                      "'%s' variable should be 'FEED_MINIBATCH' type",
-                      feed_holder_name);
+    if (!feed_holder_name.empty()) {
+      // When feed operator are present, so should be feed_holder.
+      auto var = block.FindVar(feed_holder_name);
+      PADDLE_ENFORCE_NOT_NULL(var, "Block should already have a '%s' variable",
+                              feed_holder_name);
+      PADDLE_ENFORCE_EQ(var->GetType(), proto::VarType::FEED_MINIBATCH,
+                        "'%s' variable should be 'FEED_MINIBATCH' type",
+                        feed_holder_name);
+    }
   }
 
   return feed_count > 0;
@@ -185,12 +188,14 @@ static bool has_feed_operators(
 // and fetch_holder_name. Raise exception when any mismatch is found.
 // Return true if the block has fetch operators and holder of matching info.
 static bool has_fetch_operators(
-    const BlockDesc& block, std::map<std::string, LoDTensor*>& fetch_targets,
+    const BlockDesc& block,
+    const std::map<std::string, LoDTensor*>& fetch_targets,
     const std::string& fetch_holder_name) {
   size_t fetch_count = 0;
   for (auto* op : block.AllOps()) {
     if (op->Type() == kFetchOpType) {
       fetch_count++;
+      // The output variable's name of fetch_op should be fetch_holder_name.
       PADDLE_ENFORCE_EQ(op->Output("Out")[0], fetch_holder_name,
                         "Output of fetch op should be '%s'", fetch_holder_name);
       std::string fetch_target_name = op->Input("X")[0];
@@ -206,13 +211,15 @@ static bool has_fetch_operators(
         fetch_count, fetch_targets.size(),
         "The number of fetch operators should match 'fetch_targets'");
 
-    // When fetch operator are present, so should be fetch_holder
-    auto var = block.FindVar(fetch_holder_name);
-    PADDLE_ENFORCE_NOT_NULL(var, "Block should already have a '%s' variable",
-                            fetch_holder_name);
-    PADDLE_ENFORCE_EQ(var->GetType(), proto::VarType::FETCH_LIST,
-                      "'%s' variable should be 'FETCH_LIST' type",
-                      fetch_holder_name);
+    if (!fetch_holder_name.empty()) {
+      // When fetch operator are present, so should be fetch_holder.
+      auto var = block.FindVar(fetch_holder_name);
+      PADDLE_ENFORCE_NOT_NULL(var, "Block should already have a '%s' variable",
+                              fetch_holder_name);
+      PADDLE_ENFORCE_EQ(var->GetType(), proto::VarType::FETCH_LIST,
+                        "'%s' variable should be 'FETCH_LIST' type",
+                        fetch_holder_name);
+    }
   }
 
   return fetch_count > 0;
@@ -259,16 +266,6 @@ void Executor::Run(const ProgramDesc& program, Scope* scope,
     }
   }
 
-  // map the data of feed_targets to feed_holder
-  for (auto* op : global_block->AllOps()) {
-    if (op->Type() == kFeedOpType) {
-      std::string feed_target_name = op->Output("Out")[0];
-      int idx = boost::get<int>(op->GetAttr("col"));
-      SetFeedVariable(scope, *feed_targets[feed_target_name], feed_holder_name,
-                      idx);
-    }
-  }
-
   if (!has_fetch_ops) {
     // create fetch_holder variable
     auto* fetch_holder = global_block->Var(fetch_holder_name);
@@ -292,17 +289,9 @@ void Executor::Run(const ProgramDesc& program, Scope* scope,
     }
   }
 
-  Run(*copy_program, scope, 0, create_vars, create_vars);
-
-  // obtain the data of fetch_targets from fetch_holder
-  for (auto* op : global_block->AllOps()) {
-    if (op->Type() == kFetchOpType) {
-      std::string fetch_target_name = op->Input("X")[0];
-      int idx = boost::get<int>(op->GetAttr("col"));
-      *fetch_targets[fetch_target_name] =
-          GetFetchVariable(*scope, fetch_holder_name, idx);
-    }
-  }
+  auto ctx = Prepare(*copy_program, 0);
+  RunPreparedContext(ctx.get(), scope, feed_targets, fetch_targets, create_vars,
+                     feed_holder_name, fetch_holder_name);
 }
 
 std::unique_ptr<ExecutorPrepareContext> Executor::Prepare(
@@ -370,5 +359,42 @@ void Executor::RunPreparedContext(ExecutorPrepareContext* ctx, Scope* scope,
   }
 }
 
+void Executor::RunPreparedContext(
+    ExecutorPrepareContext* ctx, Scope* scope,
+    std::map<std::string, const LoDTensor*>& feed_targets,
+    std::map<std::string, LoDTensor*>& fetch_targets, bool create_vars,
+    const std::string& feed_holder_name, const std::string& fetch_holder_name) {
+  auto& global_block = ctx->prog_.Block(ctx->block_id_);
+
+  PADDLE_ENFORCE(
+      has_feed_operators(global_block, feed_targets, feed_holder_name),
+      "Program in ExecutorPrepareContext should has feed_ops.");
+  PADDLE_ENFORCE(
+      has_fetch_operators(global_block, fetch_targets, fetch_holder_name),
+      "Program in the prepared context should has fetch_ops.");
+
+  // map the data of feed_targets to feed_holder
+  for (auto* op : global_block.AllOps()) {
+    if (op->Type() == kFeedOpType) {
+      std::string feed_target_name = op->Output("Out")[0];
+      int idx = boost::get<int>(op->GetAttr("col"));
+      SetFeedVariable(scope, *feed_targets[feed_target_name], feed_holder_name,
+                      idx);
+    }
+  }
+
+  RunPreparedContext(ctx, scope, create_vars, create_vars);
+
+  // obtain the data of fetch_targets from fetch_holder
+  for (auto* op : global_block.AllOps()) {
+    if (op->Type() == kFetchOpType) {
+      std::string fetch_target_name = op->Input("X")[0];
+      int idx = boost::get<int>(op->GetAttr("col"));
+      *fetch_targets[fetch_target_name] =
+          GetFetchVariable(*scope, fetch_holder_name, idx);
+    }
+  }
+}
+
 }  // namespace framework
 }  // namespace paddle

paddle/fluid/framework/executor.h

@@ -14,6 +14,9 @@ limitations under the License. */
 
 #pragma once
 
+#include <map>
+#include <string>
+#include <vector>
 #include "paddle/fluid/framework/op_info.h"
 #include "paddle/fluid/framework/program_desc.h"
 #include "paddle/fluid/framework/scope.h"
@@ -70,6 +73,13 @@ class Executor {
                           bool create_local_scope = true,
                           bool create_vars = true);
 
+  void RunPreparedContext(ExecutorPrepareContext* ctx, Scope* scope,
+                          std::map<std::string, const LoDTensor*>& feed_targets,
+                          std::map<std::string, LoDTensor*>& fetch_targets,
+                          bool create_vars = true,
+                          const std::string& feed_holder_name = "feed",
+                          const std::string& fetch_holder_name = "fetch");
+
  private:
   const platform::Place place_;
 };

paddle/fluid/framework/tensor_util.cc

@@ -11,8 +11,10 @@
    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/framework/tensor_util.h"
+#include <algorithm>
+#include <limits>
+#include <vector>
 
 namespace paddle {
 namespace framework {
@@ -65,8 +67,6 @@ void TensorCopy(const Tensor& src, const platform::Place& dst_place,
     auto dst_gpu_place = boost::get<platform::CUDAPlace>(dst_place);
     auto ctx_place = ctx.GetPlace();
     PADDLE_ENFORCE(platform::is_gpu_place(ctx_place));
-    auto ctx_gpu_place = boost::get<platform::CUDAPlace>(ctx_place);
-    PADDLE_ENFORCE_EQ(src_gpu_place, ctx_gpu_place);
     memory::Copy(
         dst_gpu_place, dst_ptr, src_gpu_place, src_ptr, size,
         reinterpret_cast<const platform::CUDADeviceContext&>(ctx).stream());

paddle/fluid/framework/threadpool.cc

@@ -14,8 +14,12 @@
 
 #include "paddle/fluid/framework/threadpool.h"
 
+#include "gflags/gflags.h"
 #include "paddle/fluid/platform/enforce.h"
 
+DEFINE_int32(io_threadpool_size, 100,
+             "number of threads used for doing IO, default 100");
+
 namespace paddle {
 namespace framework {
 
@@ -91,5 +95,20 @@ void ThreadPool::TaskLoop() {
   }
 }
 
+std::unique_ptr<ThreadPool> ThreadPoolIO::io_threadpool_(nullptr);
+std::once_flag ThreadPoolIO::io_init_flag_;
+
+ThreadPool* ThreadPoolIO::GetInstanceIO() {
+  std::call_once(io_init_flag_, &ThreadPoolIO::InitIO);
+  return io_threadpool_.get();
+}
+
+void ThreadPoolIO::InitIO() {
+  if (io_threadpool_.get() == nullptr) {
+    // TODO(typhoonzero1986): make this configurable
+    io_threadpool_.reset(new ThreadPool(FLAGS_io_threadpool_size));
+  }
+}
+
 }  // namespace framework
 }  // namespace paddle

paddle/fluid/framework/threadpool.h

@@ -14,12 +14,12 @@ limitations under the License. */
 
 #pragma once
 
-#include <condition_variable>
+#include <condition_variable>  // NOLINT
 #include <functional>
-#include <future>
-#include <mutex>
+#include <future>  // NOLINT
+#include <mutex>   // NOLINT
 #include <queue>
-#include <thread>
+#include <thread>  // NOLINT
 #include <vector>
 #include "glog/logging.h"
 #include "paddle/fluid/platform/enforce.h"
@@ -28,6 +28,22 @@ limitations under the License. */
 namespace paddle {
 namespace framework {
 
+struct ExceptionHandler {
+  mutable std::future<std::unique_ptr<platform::EnforceNotMet>> future_;
+  explicit ExceptionHandler(
+      std::future<std::unique_ptr<platform::EnforceNotMet>>&& f)
+      : future_(std::move(f)) {}
+  void operator()() const {
+    auto ex = this->future_.get();
+    if (ex != nullptr) {
+      LOG(FATAL) << "The exception is thrown inside the thread pool. You "
+                    "should use RunAndGetException to handle the exception.\n"
+                    "The default exception handler is LOG(FATAL)."
+                 << ex->what();
+    }
+  }
+};
+
 // ThreadPool maintains a queue of tasks, and runs them using a fixed
 // number of threads.
 class ThreadPool {
@@ -87,22 +103,6 @@ class ThreadPool {
   void Wait();
 
  private:
-  struct ExceptionHandler {
-    mutable std::future<std::unique_ptr<platform::EnforceNotMet>> future_;
-    explicit ExceptionHandler(
-        std::future<std::unique_ptr<platform::EnforceNotMet>>&& f)
-        : future_(std::move(f)) {}
-    void operator()() const {
-      auto ex = this->future_.get();
-      if (ex != nullptr) {
-        LOG(FATAL) << "The exception is thrown inside the thread pool. You "
-                      "should use RunAndGetException to handle the exception.\n"
-                      "The default exception handler is LOG(FATAL)."
-                   << ex->what();
-      }
-    }
-  };
-
   DISABLE_COPY_AND_ASSIGN(ThreadPool);
 
   // If the task queue is empty and avaialbe is equal to the number of
@@ -135,6 +135,17 @@ class ThreadPool {
   std::condition_variable completed_;
 };
 
+class ThreadPoolIO : ThreadPool {
+ public:
+  static ThreadPool* GetInstanceIO();
+  static void InitIO();
+
+ private:
+  // NOTE: threadpool in base will be inhereted here.
+  static std::unique_ptr<ThreadPool> io_threadpool_;
+  static std::once_flag io_init_flag_;
+};
+
 // Run a function asynchronously.
 // NOTE: The function must return void. If the function need to return a value,
 // you can use lambda to capture a value pointer.
@@ -143,5 +154,10 @@ std::future<void> Async(Callback callback) {
   return ThreadPool::GetInstance()->Run(callback);
 }
 
+template <typename Callback>
+std::future<void> AsyncIO(Callback callback) {
+  return ThreadPoolIO::GetInstanceIO()->Run(callback);
+}
+
 }  // namespace framework
 }  // namespace paddle

paddle/fluid/inference/io.cc

@@ -23,7 +23,7 @@ limitations under the License. */
 namespace paddle {
 namespace inference {
 
-// Temporarilly add this function for exposing framework::InitDevices() when
+// Temporarily add this function for exposing framework::InitDevices() when
 // linking the inference shared library.
 void Init(bool init_p2p) { framework::InitDevices(init_p2p); }
 

paddle/fluid/inference/tests/book/test_inference_image_classification.cc

@@ -46,8 +46,8 @@ TEST(inference, image_classification) {
 
   // Run inference on CPU
   LOG(INFO) << "--- CPU Runs: ---";
-  TestInference<paddle::platform::CPUPlace, false>(dirname, cpu_feeds,
-                                                   cpu_fetchs1, FLAGS_repeat);
+  TestInference<paddle::platform::CPUPlace, false, true>(
+      dirname, cpu_feeds, cpu_fetchs1, FLAGS_repeat);
   LOG(INFO) << output1.dims();
 
 #ifdef PADDLE_WITH_CUDA
@@ -57,8 +57,8 @@ TEST(inference, image_classification) {
 
   // Run inference on CUDA GPU
   LOG(INFO) << "--- GPU Runs: ---";
-  TestInference<paddle::platform::CUDAPlace, false>(dirname, cpu_feeds,
-                                                    cpu_fetchs2, FLAGS_repeat);
+  TestInference<paddle::platform::CUDAPlace, false, true>(
+      dirname, cpu_feeds, cpu_fetchs2, FLAGS_repeat);
   LOG(INFO) << output2.dims();
 
   CheckError<float>(output1, output2);

paddle/fluid/inference/tests/test_helper.h

@@ -89,7 +89,7 @@ void CheckError(const paddle::framework::LoDTensor& output1,
   EXPECT_EQ(count, 0U) << "There are " << count << " different elements.";
 }
 
-template <typename Place, bool CreateVars = true>
+template <typename Place, bool CreateVars = true, bool PrepareContext = false>
 void TestInference(const std::string& dirname,
                    const std::vector<paddle::framework::LoDTensor*>& cpu_feeds,
                    const std::vector<paddle::framework::LoDTensor*>& cpu_fetchs,
@@ -175,8 +175,15 @@ void TestInference(const std::string& dirname,
     }
 
     // Ignore the profiling results of the first run
-    executor.Run(*inference_program, scope, feed_targets, fetch_targets,
-                 CreateVars);
+    std::unique_ptr<paddle::framework::ExecutorPrepareContext> ctx;
+    if (PrepareContext) {
+      ctx = executor.Prepare(*inference_program, 0);
+      executor.RunPreparedContext(ctx.get(), scope, feed_targets, fetch_targets,
+                                  CreateVars);
+    } else {
+      executor.Run(*inference_program, scope, feed_targets, fetch_targets,
+                   CreateVars);
+    }
 
     // Enable the profiler
     paddle::platform::EnableProfiler(state);
@@ -187,8 +194,15 @@ void TestInference(const std::string& dirname,
           "run_inference",
           paddle::platform::DeviceContextPool::Instance().Get(place));
 
-      executor.Run(*inference_program, scope, feed_targets, fetch_targets,
-                   CreateVars);
+      if (PrepareContext) {
+        // Note: if you change the inference_program, you need to call
+        // executor.Prepare() again to get a new ExecutorPrepareContext.
+        executor.RunPreparedContext(ctx.get(), scope, feed_targets,
+                                    fetch_targets, CreateVars);
+      } else {
+        executor.Run(*inference_program, scope, feed_targets, fetch_targets,
+                     CreateVars);
+      }
     }
 
     // Disable the profiler and print the timing information

paddle/fluid/operators/CMakeLists.txt

@@ -245,9 +245,17 @@ op_library(channel_send_op DEPS concurrency)
 op_library(channel_recv_op DEPS concurrency)
 
 list(REMOVE_ITEM GENERAL_OPS ${DEPS_OPS})
+
+# The fully connected layer is deleted when the WITH_MKLDNN flag is OFF
+# Because the fully connected layer has only one MKLDNN's operator
+if(NOT WITH_MKLDNN)
+    list(REMOVE_ITEM GENERAL_OPS fc_op)
+endif(NOT WITH_MKLDNN)
+
 foreach(src ${GENERAL_OPS})
     op_library(${src})
 endforeach()
+
 file(APPEND ${pybind_file} "USE_OP(less_than);\nUSE_OP(logical_and);\nUSE_NO_KERNEL_OP(read_from_array);\n")
 
 add_subdirectory(reader)

paddle/fluid/operators/average_accumulates_op.cu

@@ -25,12 +25,14 @@ void GetAccumulators<paddle::platform::CUDADeviceContext>(
   auto* in_num_accumulates = ctx.Input<Tensor>("in_num_accumulates");
   auto* in_num_updates = ctx.Input<Tensor>("in_num_updates");
   auto stream = ctx.cuda_device_context().stream();
-  memory::Copy(platform::CPUPlace(), old_num_accumulates_,
-               platform::CUDAPlace(), in_old_num_accumulates->data<int64_t>(),
-               sizeof(int64_t), stream);
-  memory::Copy(platform::CPUPlace(), num_accumulates_, platform::CUDAPlace(),
+  auto cuda_place =
+      boost::get<platform::CUDAPlace>(in_old_num_accumulates->place());
+  memory::Copy(platform::CPUPlace(), old_num_accumulates_, cuda_place,
+               in_old_num_accumulates->data<int64_t>(), sizeof(int64_t),
+               stream);
+  memory::Copy(platform::CPUPlace(), num_accumulates_, cuda_place,
                in_num_accumulates->data<int64_t>(), sizeof(int64_t), stream);
-  memory::Copy(platform::CPUPlace(), num_updates_, platform::CUDAPlace(),
+  memory::Copy(platform::CPUPlace(), num_updates_, cuda_place,
                in_num_updates->data<int64_t>(), sizeof(int64_t), stream);
 }
 
@@ -42,14 +44,16 @@ void SetAccumulators<paddle::platform::CUDADeviceContext>(
   auto* out_old_num_accumulates = ctx.Output<Tensor>("out_old_num_accumulates");
   auto* out_num_accumulates = ctx.Output<Tensor>("out_num_accumulates");
   auto* out_num_updates = ctx.Output<Tensor>("out_num_updates");
+  auto cuda_place =
+      boost::get<platform::CUDAPlace>(out_old_num_accumulates->place());
 
-  memory::Copy(platform::CUDAPlace(), out_old_num_accumulates->data<int64_t>(),
+  memory::Copy(cuda_place, out_old_num_accumulates->data<int64_t>(),
                platform::CPUPlace(), &old_num_accumulates_, sizeof(int64_t),
                stream);
-  memory::Copy(platform::CUDAPlace(), out_num_accumulates->data<int64_t>(),
+  memory::Copy(cuda_place, out_num_accumulates->data<int64_t>(),
                platform::CPUPlace(), &num_accumulates_, sizeof(int64_t),
                stream);
-  memory::Copy(platform::CUDAPlace(), out_num_updates->data<int64_t>(),
+  memory::Copy(cuda_place, out_num_updates->data<int64_t>(),
                platform::CPUPlace(), &num_updates_, sizeof(int64_t), stream);
 }
 

paddle/fluid/operators/batch_norm_op.cu.cc

@@ -114,23 +114,11 @@ class BatchNormKernel<platform::CUDADeviceContext, T>
     const auto *bias = ctx.Input<Tensor>("Bias");
 
     auto *y = ctx.Output<Tensor>("Y");
-    auto *mean_out = ctx.Output<Tensor>("MeanOut");
-    auto *variance_out = ctx.Output<Tensor>("VarianceOut");
-    auto *saved_mean = ctx.Output<Tensor>("SavedMean");
-    auto *saved_variance = ctx.Output<Tensor>("SavedVariance");
 
     // alloc memory
     y->mutable_data<T>(ctx.GetPlace());
-    mean_out->mutable_data<BatchNormParamType<T>>(ctx.GetPlace());
-    variance_out->mutable_data<BatchNormParamType<T>>(ctx.GetPlace());
-    saved_mean->mutable_data<BatchNormParamType<T>>(ctx.GetPlace());
-    saved_variance->mutable_data<BatchNormParamType<T>>(ctx.GetPlace());
 
     auto &dev_ctx = ctx.template device_context<platform::CUDADeviceContext>();
-    math::SetConstant<platform::CUDADeviceContext, BatchNormParamType<T>>
-        functor;
-    functor(dev_ctx, saved_mean, static_cast<BatchNormParamType<T>>(0));
-    functor(dev_ctx, saved_variance, static_cast<BatchNormParamType<T>>(0));
 
     auto handle = dev_ctx.cudnn_handle();
 
@@ -159,6 +147,21 @@ class BatchNormKernel<platform::CUDADeviceContext, T>
       // Run training mode.
       // obtain running mean and running inv var, and see if we need to
       // initialize them.
+
+      auto *mean_out = ctx.Output<Tensor>("MeanOut");
+      auto *variance_out = ctx.Output<Tensor>("VarianceOut");
+      mean_out->mutable_data<BatchNormParamType<T>>(ctx.GetPlace());
+      variance_out->mutable_data<BatchNormParamType<T>>(ctx.GetPlace());
+
+      auto *saved_mean = ctx.Output<Tensor>("SavedMean");
+      auto *saved_variance = ctx.Output<Tensor>("SavedVariance");
+      saved_mean->mutable_data<BatchNormParamType<T>>(ctx.GetPlace());
+      saved_variance->mutable_data<BatchNormParamType<T>>(ctx.GetPlace());
+      math::SetConstant<platform::CUDADeviceContext, BatchNormParamType<T>>
+          functor;
+      functor(dev_ctx, saved_mean, static_cast<BatchNormParamType<T>>(0));
+      functor(dev_ctx, saved_variance, static_cast<BatchNormParamType<T>>(0));
+
       double this_factor = 1. - momentum;
 
       CUDNN_ENFORCE(platform::dynload::cudnnBatchNormalizationForwardTraining(

paddle/fluid/operators/detail/grpc_client.cc

@@ -35,7 +35,8 @@ bool RPCClient::AsyncSendVariable(const std::string& ep,
   const framework::Scope* p_scope = &scope;
   const auto ch = GetChannel(ep_val);
 
-  framework::Async([var_name_val, p_ctx, ep_val, p_scope, time_out, ch, this] {
+  framework::AsyncIO([var_name_val, p_ctx, ep_val, p_scope, time_out, ch,
+                      this] {
     auto* var = p_scope->FindVar(var_name_val);
 
     ::grpc::ByteBuffer req;
@@ -89,7 +90,8 @@ bool RPCClient::AsyncGetVariable(const std::string& ep,
   const framework::Scope* p_scope = &scope;
   const auto ch = GetChannel(ep_val);
 
-  framework::Async([var_name_val, ep_val, p_scope, p_ctx, time_out, ch, this] {
+  framework::AsyncIO([var_name_val, ep_val, p_scope, p_ctx, time_out, ch,
+                      this] {
     // prepare input
     sendrecv::VariableMessage req;
     req.set_varname(var_name_val);
@@ -132,8 +134,8 @@ bool RPCClient::AsyncPrefetchVariable(const std::string& ep,
   const framework::Scope* p_scope = &scope;
   const auto ch = GetChannel(ep_val);
 
-  framework::Async([in_var_name_val, out_var_name_val, ep_val, p_scope, p_ctx,
-                    time_out, ch, this] {
+  framework::AsyncIO([in_var_name_val, out_var_name_val, ep_val, p_scope, p_ctx,
+                      time_out, ch, this] {
     auto* var = p_scope->FindVar(in_var_name_val);
 
     ::grpc::ByteBuffer req;
@@ -196,7 +198,7 @@ bool RPCClient::Wait() {
   std::vector<std::future<void>> waits(req_count_);
 
   for (int i = 0; i < req_count_; i++) {
-    waits[i] = framework::Async([i, &a, this] { a[i] = Proceed(); });
+    waits[i] = framework::AsyncIO([i, &a, this] { a[i] = Proceed(); });
   }
 
   for (int i = 0; i < req_count_; i++) {

paddle/fluid/operators/detail/grpc_server.cc

@@ -217,10 +217,10 @@ void AsyncGRPCServer::RunSyncUpdate() {
   std::function<void()> prefetch_register =
       std::bind(&AsyncGRPCServer::TryToRegisterNewPrefetchOne, this);
 
+  // TODO(wuyi): Run these "HandleRequest" in thread pool
   t_send_.reset(
       new std::thread(std::bind(&AsyncGRPCServer::HandleRequest, this,
                                 cq_send_.get(), "cq_send", send_register)));
-
   t_get_.reset(
       new std::thread(std::bind(&AsyncGRPCServer::HandleRequest, this,
                                 cq_get_.get(), "cq_get", get_register)));

paddle/fluid/operators/math/math_function.cu

@@ -268,6 +268,7 @@ void batched_gemm<platform::CUDADeviceContext, float16>(
     const CBLAS_TRANSPOSE transB, const int M, const int N, const int K,
     const float16 alpha, const float16* A, const float16* B, const float16 beta,
     float16* C, const int batchCount, const int strideA, const int strideB) {
+#if CUDA_VERSION >= 8000
   // Note that cublas follows fortran order, so the order is different from
   // the cblas convention.
   int lda = (transA == CblasNoTrans) ? K : M;
@@ -288,9 +289,13 @@ void batched_gemm<platform::CUDADeviceContext, float16>(
   // TODO(kexinzhao): add processing code for compute capability < 53 case
   PADDLE_ENFORCE_GE(context.GetComputeCapability(), 53,
                     "cublas Hgemm requires GPU compute capability >= 53");
+
   PADDLE_ENFORCE(platform::dynload::cublasHgemmStridedBatched(
       context.cublas_handle(), cuTransB, cuTransA, N, M, K, &h_alpha, h_B, ldb,
       strideB, h_A, lda, strideA, &h_beta, h_C, ldc, strideC, batchCount));
+#else
+  PADDLE_ENFORCE(false, "HgemmStridedBatched is not supported on cuda <= 7.5");
+#endif
 }
 
 template <>
@@ -299,6 +304,7 @@ void batched_gemm<platform::CUDADeviceContext, float>(
     const CBLAS_TRANSPOSE transB, const int M, const int N, const int K,
     const float alpha, const float* A, const float* B, const float beta,
     float* C, const int batchCount, const int strideA, const int strideB) {
+#if CUDA_VERSION >= 8000
   // Note that cublas follows fortran order, so the order is different from
   // the cblas convention.
   int lda = (transA == CblasNoTrans) ? K : M;
@@ -313,6 +319,9 @@ void batched_gemm<platform::CUDADeviceContext, float>(
   PADDLE_ENFORCE(platform::dynload::cublasSgemmStridedBatched(
       context.cublas_handle(), cuTransB, cuTransA, N, M, K, &alpha, B, ldb,
       strideB, A, lda, strideA, &beta, C, ldc, strideC, batchCount));
+#else
+  PADDLE_ENFORCE(false, "SgemmStridedBatched is not supported on cuda <= 7.5");
+#endif
 }
 
 template <>
@@ -321,6 +330,7 @@ void batched_gemm<platform::CUDADeviceContext, double>(
     const CBLAS_TRANSPOSE transB, const int M, const int N, const int K,
     const double alpha, const double* A, const double* B, const double beta,
     double* C, const int batchCount, const int strideA, const int strideB) {
+#if CUDA_VERSION >= 8000
   // Note that cublas follows fortran order, so the order is different from
   // the cblas convention.
   int lda = (transA == CblasNoTrans) ? K : M;
@@ -335,6 +345,9 @@ void batched_gemm<platform::CUDADeviceContext, double>(
   PADDLE_ENFORCE(platform::dynload::cublasDgemmStridedBatched(
       context.cublas_handle(), cuTransB, cuTransA, N, M, K, &alpha, B, ldb,
       strideB, A, lda, strideA, &beta, C, ldc, strideC, batchCount));
+#else
+  PADDLE_ENFORCE(false, "DgemmStridedBatched is not supported on cuda <= 7.5");
+#endif
 }
 
 template <>

paddle/fluid/operators/pad_op.h

@@ -14,6 +14,8 @@ limitations under the License. */
 
 #pragma once
 
+#include <utility>
+#include <vector>
 #include "paddle/fluid/framework/eigen.h"
 #include "paddle/fluid/framework/op_registry.h"
 

paddle/fluid/operators/pool_mkldnn_op.cc

@@ -83,9 +83,11 @@ class PoolMKLDNNOpKernel : public paddle::framework::OpKernel<T> {
     dev_ctx.SetBlob(key_pool_workspace_memory, workspace_memory);
 
     auto src_memory =
-        mkldnn::memory({src_md, mkldnn_engine}, (void*)input_data);
+        mkldnn::memory({src_md, mkldnn_engine},
+                       static_cast<void*>(const_cast<T*>(input_data)));
     auto dst_memory =
-        mkldnn::memory({dst_md, mkldnn_engine}, (void*)output_data);
+        mkldnn::memory({dst_md, mkldnn_engine},
+                       static_cast<void*>(const_cast<T*>(output_data)));
 
     auto pool_prim = mkldnn::pooling_forward(*pool_pd, src_memory, dst_memory,
                                              *workspace_memory);
@@ -195,9 +197,11 @@ class PoolMKLDNNGradOpKernel : public paddle::framework::OpKernel<T> {
         pool_bwd_desc, mkldnn_engine, *pool_pd);
 
     auto diff_src_memory =
-        mkldnn::memory({diff_src_md, mkldnn_engine}, (void*)in_x_grad_data);
+        mkldnn::memory({diff_src_md, mkldnn_engine},
+                       static_cast<void*>(const_cast<T*>(in_x_grad_data)));
     auto diff_dst_memory =
-        mkldnn::memory({diff_dst_md, mkldnn_engine}, (void*)out_grad_data);
+        mkldnn::memory({diff_dst_md, mkldnn_engine},
+                       static_cast<void*>(const_cast<T*>(out_grad_data)));
 
     auto bwd_prim = mkldnn::pooling_backward(
         pool_bwd_pd, diff_dst_memory, *workspace_memory, diff_src_memory);

paddle/fluid/operators/pool_op.h

@@ -14,6 +14,8 @@ limitations under the License. */
 
 #pragma once
 
+#include <string>
+#include <vector>
 #include "paddle/fluid/framework/eigen.h"
 #include "paddle/fluid/framework/op_registry.h"
 #include "paddle/fluid/operators/math/math_function.h"

paddle/fluid/operators/pool_with_index_op.h

@@ -14,6 +14,7 @@ limitations under the License. */
 
 #pragma once
 
+#include <vector>
 #include "paddle/fluid/framework/eigen.h"
 #include "paddle/fluid/framework/op_registry.h"
 #include "paddle/fluid/operators/math/math_function.h"

paddle/fluid/operators/prelu_op.cc

@@ -13,7 +13,6 @@ See the License for the specific language governing permissions and
 limitations under the License. */
 
 #include "paddle/fluid/operators/prelu_op.h"
-
 #include <string>
 
 namespace paddle {

paddle/fluid/operators/prior_box_op.cc

@@ -45,7 +45,7 @@ class PriorBoxOp : public framework::OperatorWithKernel {
     bool flip = ctx->Attrs().Get<bool>("flip");
 
     std::vector<float> aspect_ratios_vec;
-    ExpandAspectRatios(aspect_ratios, flip, aspect_ratios_vec);
+    ExpandAspectRatios(aspect_ratios, flip, &aspect_ratios_vec);
 
     size_t num_priors = aspect_ratios_vec.size() * min_sizes.size();
     if (max_sizes.size() > 0) {

paddle/fluid/operators/prior_box_op.cu

@@ -96,7 +96,7 @@ class PriorBoxOpCUDAKernel : public framework::OpKernel<T> {
     auto clip = ctx.Attr<bool>("clip");
 
     std::vector<float> aspect_ratios;
-    ExpandAspectRatios(input_aspect_ratio, flip, aspect_ratios);
+    ExpandAspectRatios(input_aspect_ratio, flip, &aspect_ratios);
 
     T step_w = static_cast<T>(ctx.Attr<float>("step_w"));
     T step_h = static_cast<T>(ctx.Attr<float>("step_h"));

paddle/fluid/operators/prior_box_op.h

@@ -13,6 +13,8 @@ See the License for the specific language governing permissions and
 limitations under the License. */
 
 #pragma once
+#include <algorithm>
+#include <vector>
 #include "paddle/fluid/framework/op_registry.h"
 #include "paddle/fluid/operators/math/math_function.h"
 #include "paddle/fluid/platform/transform.h"
@@ -22,23 +24,23 @@ namespace operators {
 
 inline void ExpandAspectRatios(const std::vector<float>& input_aspect_ratior,
                                bool flip,
-                               std::vector<float>& output_aspect_ratior) {
+                               std::vector<float>* output_aspect_ratior) {
   constexpr float epsilon = 1e-6;
-  output_aspect_ratior.clear();
-  output_aspect_ratior.push_back(1.0f);
+  output_aspect_ratior->clear();
+  output_aspect_ratior->push_back(1.0f);
   for (size_t i = 0; i < input_aspect_ratior.size(); ++i) {
     float ar = input_aspect_ratior[i];
     bool already_exist = false;
-    for (size_t j = 0; j < output_aspect_ratior.size(); ++j) {
-      if (fabs(ar - output_aspect_ratior[j]) < epsilon) {
+    for (size_t j = 0; j < output_aspect_ratior->size(); ++j) {
+      if (fabs(ar - output_aspect_ratior->at(j)) < epsilon) {
         already_exist = true;
         break;
       }
     }
     if (!already_exist) {
-      output_aspect_ratior.push_back(ar);
+      output_aspect_ratior->push_back(ar);
       if (flip) {
-        output_aspect_ratior.push_back(1.0f / ar);
+        output_aspect_ratior->push_back(1.0f / ar);
       }
     }
   }
@@ -68,7 +70,7 @@ class PriorBoxOpKernel : public framework::OpKernel<T> {
     auto clip = ctx.Attr<bool>("clip");
 
     std::vector<float> aspect_ratios;
-    ExpandAspectRatios(input_aspect_ratio, flip, aspect_ratios);
+    ExpandAspectRatios(input_aspect_ratio, flip, &aspect_ratios);
 
     T step_w = static_cast<T>(ctx.Attr<float>("step_w"));
     T step_h = static_cast<T>(ctx.Attr<float>("step_h"));

paddle/fluid/operators/rank_loss_op.cc

@@ -13,6 +13,7 @@ See the License for the specific language governing permissions and
 limitations under the License. */
 
 #include "paddle/fluid/operators/rank_loss_op.h"
+#include <string>
 
 namespace paddle {
 namespace operators {

paddle/fluid/operators/reader/create_double_buffer_reader_op.cc

@@ -33,28 +33,14 @@ static constexpr size_t kChannelSize = 0;  // kCacheSize - 2
 
 class DoubleBufferReader : public framework::DecoratedReader {
  public:
-  struct Item {
-    Item() : ctx_(nullptr) {}
-    Item(Item&& b) {
-      payloads_ = std::move(b.payloads_);
-      ctx_ = std::move(b.ctx_);
-    }
-    Item& operator=(Item&& b) {
-      payloads_ = std::move(b.payloads_);
-      ctx_ = std::move(b.ctx_);
-      return *this;
-    }
-
-    std::vector<framework::LoDTensor> payloads_;
-    platform::DeviceContext* ctx_;
-  };
-
   explicit DoubleBufferReader(
       ReaderBase* reader, platform::Place target_place = platform::CPUPlace())
       : DecoratedReader(reader), place_(target_place) {
+    cpu_tensor_cache_.resize(kCacheSize);
+    gpu_tensor_cache_.resize(kCacheSize);
 #ifdef PADDLE_WITH_CUDA
-    for (size_t i = 0; i < kCacheSize; ++i) {
-      if (platform::is_gpu_place(place_)) {
+    if (platform::is_gpu_place(place_)) {
+      for (size_t i = 0; i < kCacheSize; ++i) {
         ctxs_.emplace_back(new platform::CUDADeviceContext(
             boost::get<platform::CUDAPlace>(place_)));
       }
@@ -72,7 +58,7 @@ class DoubleBufferReader : public framework::DecoratedReader {
   bool HasNext() const;
 
   void StartPrefetcher() {
-    channel_ = framework::MakeChannel<Item>(kChannelSize);
+    channel_ = framework::MakeChannel<size_t>(kChannelSize);
     prefetcher_ = std::thread([this] { PrefetchThreadFunc(); });
   }
 
@@ -88,8 +74,10 @@ class DoubleBufferReader : public framework::DecoratedReader {
   void PrefetchThreadFunc();
 
   std::thread prefetcher_;
-  framework::Channel<Item>* channel_;
+  framework::Channel<size_t>* channel_;
   platform::Place place_;
+  std::vector<std::vector<framework::LoDTensor>> cpu_tensor_cache_;
+  std::vector<std::vector<framework::LoDTensor>> gpu_tensor_cache_;
   std::vector<std::unique_ptr<platform::DeviceContext>> ctxs_;
 };
 
@@ -153,11 +141,14 @@ class CreateDoubleBufferReaderOpMaker : public DecoratedReaderMakerBase {
 void DoubleBufferReader::ReadNext(std::vector<framework::LoDTensor>* out) {
   out->clear();
   if (HasNext()) {
-    Item batch;
-    channel_->Receive(&batch);
-    *out = batch.payloads_;
-    if (batch.ctx_) {
-      batch.ctx_->Wait();
+    size_t cached_tensor_id;
+    channel_->Receive(&cached_tensor_id);
+    if (platform::is_gpu_place(place_)) {
+      *out = gpu_tensor_cache_[cached_tensor_id];
+      ctxs_[cached_tensor_id]->Wait();
+    } else {
+      // CPU place
+      *out = cpu_tensor_cache_[cached_tensor_id];
     }
   }
 }
@@ -176,42 +167,33 @@ bool DoubleBufferReader::HasNext() const {
 
 void DoubleBufferReader::PrefetchThreadFunc() {
   VLOG(5) << "A new prefetch thread starts.";
-  std::vector<std::vector<framework::LoDTensor>> cpu_tensor_cache(kCacheSize);
-  std::vector<std::vector<framework::LoDTensor>> gpu_tensor_cache(kCacheSize);
   size_t cached_tensor_id = 0;
-
   while (true) {
-    Item batch;
-    auto& cpu_batch = cpu_tensor_cache[cached_tensor_id];
+    auto& cpu_batch = cpu_tensor_cache_[cached_tensor_id];
     reader_->ReadNext(&cpu_batch);
     if (cpu_batch.empty()) {
       // The underlying reader have no next data.
       break;
     }
     if (platform::is_gpu_place(place_)) {
-      auto& gpu_batch = gpu_tensor_cache[cached_tensor_id];
+      auto& gpu_batch = gpu_tensor_cache_[cached_tensor_id];
       auto* gpu_ctx = ctxs_[cached_tensor_id].get();
       gpu_batch.resize(cpu_batch.size());
       for (size_t i = 0; i < cpu_batch.size(); ++i) {
         framework::TensorCopy(cpu_batch[i], place_, *gpu_ctx, &gpu_batch[i]);
         gpu_batch[i].set_lod(cpu_batch[i].lod());
       }
-      batch.payloads_ = gpu_batch;
-      batch.ctx_ = gpu_ctx;
-    } else {
-      // CPUPlace
-      batch.payloads_ = cpu_batch;
     }
-    ++cached_tensor_id;
-    cached_tensor_id %= kCacheSize;
-
     try {
-      channel_->Send(&batch);
+      size_t tmp = cached_tensor_id;
+      channel_->Send(&tmp);
     } catch (paddle::platform::EnforceNotMet e) {
       VLOG(5) << "WARNING: The double buffer channel has been closed. The "
                  "prefetch thread will terminate.";
       break;
     }
+    ++cached_tensor_id;
+    cached_tensor_id %= kCacheSize;
   }
   channel_->Close();
   VLOG(5) << "Prefetch thread terminates.";

paddle/fluid/operators/recv_op.cc

@@ -12,6 +12,7 @@ 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 <future>  // NOLINT
 #include <ostream>
 
 #include "paddle/fluid/framework/data_type.h"
@@ -19,7 +20,6 @@ limitations under the License. */
 #include "paddle/fluid/framework/lod_tensor.h"
 #include "paddle/fluid/framework/op_registry.h"
 
-#include <future>
 #include "paddle/fluid/operators/detail/grpc_client.h"
 
 namespace paddle {

paddle/fluid/operators/reshape_op.h

@@ -60,7 +60,7 @@ class ReshapeOp : public framework::OperatorWithKernel {
   static framework::DDim ValidateShape(const std::vector<int> shape,
                                        const framework::DDim &in_dims) {
     const int64_t in_size = framework::product(in_dims);
-    // only one dimension canbe set to -1, whose size will be automatically
+    // 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;
@@ -119,13 +119,15 @@ class ReshapeKernel : public framework::OpKernel<T> {
     auto *shape_tensor = ctx.Input<framework::LoDTensor>("Shape");
 
     framework::DDim out_dims = out->dims();
+
     if (shape_tensor) {
       auto *shape_data = shape_tensor->data<int>();
+      framework::Tensor cpu_shape_tensor;
       if (platform::is_gpu_place(ctx.GetPlace())) {
-        framework::Tensor cpu_shape_tensor;
         TensorCopy(*shape_tensor, platform::CPUPlace(), ctx.device_context(),
                    &cpu_shape_tensor);
         shape_data = cpu_shape_tensor.data<int>();
+        ctx.device_context().Wait();
       }
       auto shape =
           std::vector<int>(shape_data, shape_data + shape_tensor->numel());
@@ -145,6 +147,7 @@ class ReshapeKernel : public framework::OpKernel<T> {
     if (!inplace) {
       out->mutable_data<T>(ctx.GetPlace());
       framework::TensorCopy(*in, ctx.GetPlace(), ctx.device_context(), out);
+      ctx.device_context().Wait();
       // TensorCopy will resize to in_dims.
       out->Resize(out_dims);
     } else {
@@ -167,6 +170,7 @@ class ReshapeGradKernel : public framework::OpKernel<T> {
     auto in_dims = d_x->dims();
     if (!inplace) {
       framework::TensorCopy(*d_out, ctx.GetPlace(), ctx.device_context(), d_x);
+      ctx.device_context().Wait();
       d_x->Resize(in_dims);
     } else {
       d_x->ShareDataWith(*d_out);

paddle/fluid/operators/roi_pool_op.h

@@ -13,6 +13,8 @@ See the License for the specific language governing permissions and
 limitations under the License. */
 
 #pragma once
+#include <algorithm>
+#include <limits>
 #include "paddle/fluid/framework/op_registry.h"
 #include "paddle/fluid/operators/math/math_function.h"
 

paddle/fluid/operators/scale_op.cc

@@ -13,7 +13,6 @@ See the License for the specific language governing permissions and
 limitations under the License. */
 
 #include "paddle/fluid/operators/scale_op.h"
-
 #include <string>
 
 namespace paddle {

paddle/fluid/operators/scatter_op.cu

@@ -12,9 +12,10 @@ 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 "gather.cu.h"
+#include "paddle/fluid/operators/gather.cu.h"
 #include "paddle/fluid/operators/gather_op.h"
-#include "scatter.cu.h"
+#include "paddle/fluid/operators/scatter.cu.h"
+#include "paddle/fluid/operators/scatter_op.h"
 
 namespace paddle {
 namespace operators {

paddle/fluid/operators/scatter_op.h

@@ -13,10 +13,10 @@ See the License for the specific language governing permissions and
 limitations under the License. */
 
 #pragma once
-#include "gather.h"
 #include "paddle/fluid/framework/eigen.h"
 #include "paddle/fluid/framework/op_registry.h"
-#include "scatter.h"
+#include "paddle/fluid/operators/gather.h"
+#include "paddle/fluid/operators/scatter.h"
 
 namespace paddle {
 namespace operators {

paddle/fluid/operators/scatter_test.cc

@@ -13,44 +13,48 @@ See the License for the specific language governing permissions and
 limitations under the License. */
 
 #include "paddle/fluid/operators/scatter.h"
-#include "paddle/fluid/framework/ddim.h"
-#include "paddle/fluid/framework/tensor.h"
-#include "paddle/fluid/platform/place.h"
-
 #include <gtest/gtest.h>
 #include <iostream>
 #include <string>
+#include "paddle/fluid/framework/ddim.h"
+#include "paddle/fluid/framework/tensor.h"
+#include "paddle/fluid/platform/place.h"
 
 TEST(scatter, ScatterUpdate) {
-  using namespace paddle::framework;
-  using namespace paddle::platform;
-  using namespace paddle::operators;
+  // using namespace paddle::framework;
+  // using namespace paddle::platform;
+  // using namespace paddle::operators;
 
-  Tensor* src = new Tensor();
-  Tensor* index = new Tensor();
-  Tensor* output = new Tensor();
+  paddle::framework::Tensor* src = new paddle::framework::Tensor();
+  paddle::framework::Tensor* index = new paddle::framework::Tensor();
+  paddle::framework::Tensor* output = new paddle::framework::Tensor();
 
   float* p_src = nullptr;
   int* p_index = nullptr;
-  p_src = src->mutable_data<float>(make_ddim({1, 4}), CPUPlace());
-  p_index = index->mutable_data<int>(make_ddim({1}), CPUPlace());
+  p_src = src->mutable_data<float>(paddle::framework::make_ddim({1, 4}),
+                                   paddle::platform::CPUPlace());
+  p_index = index->mutable_data<int>(paddle::framework::make_ddim({1}),
+                                     paddle::platform::CPUPlace());
 
-  for (size_t i = 0; i < 4; ++i) p_src[i] = float(i);
+  for (size_t i = 0; i < 4; ++i) p_src[i] = static_cast<float>(i);
   p_index[0] = 1;
 
-  float* p_output = output->mutable_data<float>(make_ddim({4, 4}), CPUPlace());
+  float* p_output = output->mutable_data<float>(
+      paddle::framework::make_ddim({4, 4}), paddle::platform::CPUPlace());
 
   auto* cpu_place = new paddle::platform::CPUPlace();
   paddle::platform::CPUDeviceContext ctx(*cpu_place);
-  ScatterAssign<float>(ctx, *src, *index, output);
+  paddle::operators::ScatterAssign<float>(ctx, *src, *index, output);
 
-  for (size_t i = 0; i < 4; ++i) EXPECT_EQ(p_output[i], float(0));
-  for (size_t i = 0; i < 4; ++i) EXPECT_EQ(output->data<float>()[i], float(0));
-  for (size_t i = 4; i < 8; ++i) EXPECT_EQ(p_output[i], float(i - 4));
+  for (size_t i = 0; i < 4; ++i) EXPECT_EQ(p_output[i], 0.0f);
+  for (size_t i = 0; i < 4; ++i) EXPECT_EQ(output->data<float>()[i], 0.0f);
+  for (size_t i = 4; i < 8; ++i) {
+    EXPECT_EQ(p_output[i], static_cast<float>(i - 4));
+  }
   for (size_t i = 4; i < 8; ++i)
-    EXPECT_EQ(output->data<float>()[i], float(i - 4));
-  for (size_t i = 8; i < 16; ++i) EXPECT_EQ(p_output[i], float(0));
-  for (size_t i = 8; i < 16; ++i) EXPECT_EQ(output->data<float>()[i], float(0));
+    EXPECT_EQ(output->data<float>()[i], static_cast<float>(i - 4));
+  for (size_t i = 8; i < 16; ++i) EXPECT_EQ(p_output[i], 0.0f);
+  for (size_t i = 8; i < 16; ++i) EXPECT_EQ(output->data<float>()[i], 0.0f);
 
   delete src;
   delete index;

paddle/fluid/operators/send_barrier_op.cc

@@ -12,6 +12,7 @@ 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 <future>  // NOLINT
 #include <ostream>
 
 #include "paddle/fluid/framework/data_type.h"
@@ -19,7 +20,6 @@ limitations under the License. */
 #include "paddle/fluid/framework/lod_tensor.h"
 #include "paddle/fluid/framework/op_registry.h"
 
-#include <future>
 #include "paddle/fluid/operators/detail/grpc_client.h"
 
 namespace paddle {

paddle/fluid/operators/send_op.cc

@@ -12,7 +12,7 @@ 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 <future>
+#include <future>  // NOLINT
 #include <ostream>
 
 #include "paddle/fluid/framework/data_type.h"

paddle/fluid/operators/send_recv_util.h

@@ -12,6 +12,9 @@ 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 <string>
+
 namespace paddle {
 namespace operators {
 

paddle/fluid/operators/sequence_concat_op.h

@@ -13,6 +13,7 @@ See the License for the specific language governing permissions and
 limitations under the License. */
 
 #pragma once
+#include <vector>
 #include "paddle/fluid/framework/op_registry.h"
 #include "paddle/fluid/operators/strided_memcpy.h"
 

paddle/fluid/operators/sequence_conv_op.h

@@ -13,6 +13,7 @@ See the License for the specific language governing permissions and
 limitations under the License. */
 
 #pragma once
+#include <algorithm>
 #include "paddle/fluid/framework/op_registry.h"
 #include "paddle/fluid/operators/math/context_project.h"
 #include "paddle/fluid/operators/math/math_function.h"

paddle/fluid/operators/sequence_erase_op.cc

@@ -13,6 +13,7 @@ See the License for the specific language governing permissions and
 limitations under the License. */
 
 #include "paddle/fluid/operators/sequence_erase_op.h"
+#include <vector>
 
 namespace paddle {
 namespace operators {

paddle/fluid/operators/sequence_erase_op.h

@@ -14,6 +14,7 @@ limitations under the License. */
 
 #pragma once
 
+#include <vector>
 #include "paddle/fluid/framework/op_registry.h"
 
 namespace paddle {

paddle/fluid/operators/sequence_expand_op.cc

@@ -84,12 +84,11 @@ class SequenceExpandOp : public framework::OperatorWithKernel {
         }
       }
       out_dims[0] = out_first_dim;
-      ctx->SetOutputDim("Out", out_dims);
     } else {
       out_dims[0] = -1;
-      ctx->SetOutputDim("Out", out_dims);
-      ctx->ShareLoD("X", /*->*/ "Out");
     }
+    ctx->SetOutputDim("Out", out_dims);
+    ctx->ShareLoD("X", /*->*/ "Out");
   }
 };
 

paddle/fluid/operators/sequence_expand_op.cu

@@ -12,8 +12,135 @@ 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. */
 
-#define EIGEN_USE_GPU
+#include <algorithm>
 #include "paddle/fluid/operators/sequence_expand_op.h"
+#include "paddle/fluid/platform/cuda_helper.h"
+
+namespace paddle {
+namespace operators {
+
+using LoDTensor = framework::LoDTensor;
+
+template <typename T>
+__global__ void sequence_expand_kernel(const T* x_data, const size_t* x_lod,
+                                       const size_t* ref_lod,
+                                       const size_t* offset,
+                                       const size_t lod_size,
+                                       /* default=1,
+                                          the instance length*/
+                                       const int x_item_length, T* out_data) {
+  int bid = blockIdx.x;
+  if (bid >= lod_size - 1) return;
+
+  int x_item_count = x_lod[bid + 1] - x_lod[bid];
+  int repeats = ref_lod[bid + 1] - ref_lod[bid];
+  int out_offset = static_cast<int>(offset[bid]);
+  int x_offset = x_lod[bid];
+  for (int tid_z = threadIdx.z; tid_z < repeats; tid_z += blockDim.z) {
+    for (int tid_y = threadIdx.y; tid_y < x_item_count; tid_y += blockDim.y) {
+      for (int tid_x = threadIdx.x; tid_x < x_item_length;
+           tid_x += blockDim.x) {
+        out_data[(out_offset + tid_z * x_item_count + tid_y) * x_item_length +
+                 tid_x] = x_data[(x_offset + tid_y) * x_item_length + tid_x];
+      }
+    }
+  }
+}
+
+template <typename T>
+__global__ void sequence_expand_grad_kernel(
+    const T* dout_data, const size_t* ref_lod, const size_t* dx_lod,
+    const size_t* offset, const size_t lod_size,
+    /* default=1,
+       the instance length*/
+    const int x_item_length, T* dx_data) {
+  int bid = blockIdx.x;
+  if (bid >= lod_size - 1) return;
+  int x_item_count = dx_lod[bid + 1] - dx_lod[bid];
+  int repeats = ref_lod[bid + 1] - ref_lod[bid];
+  int out_offset = static_cast<int>(offset[bid]);
+  int x_offset = dx_lod[bid];
+
+  for (int tid_z = threadIdx.z; tid_z < repeats; tid_z += blockDim.z) {
+    for (int tid_y = threadIdx.y; tid_y < x_item_count; tid_y += blockDim.y) {
+      for (int tid_x = threadIdx.x; tid_x < x_item_length;
+           tid_x += blockDim.x) {
+        platform::CudaAtomicAdd(
+            &dx_data[(x_offset + tid_y) * x_item_length + tid_x],
+            dout_data[(out_offset + tid_z * x_item_count + tid_y) *
+                          x_item_length +
+                      tid_x]);
+      }
+    }
+  }
+}
+
+void GetOutputOffset(const framework::Vector<size_t>& x_lod,
+                     const framework::Vector<size_t>& ref_lod,
+                     framework::Vector<size_t>* out_offset) {
+  size_t offset = 0;
+  int lod_size = static_cast<int>(x_lod.size());
+  for (int i = 0; i < static_cast<int>(x_lod.size()); ++i) {
+    (*out_offset)[i] = offset;
+    if (i < lod_size - 1) {
+      offset += (ref_lod[i + 1] - ref_lod[i]) * (x_lod[i + 1] - x_lod[i]);
+    }
+  }
+}
+
+template <typename T>
+struct SequenceExpandFunctor<platform::CUDADeviceContext, T> {
+  void operator()(
+      const platform::CUDADeviceContext& context, const LoDTensor& x,
+      const framework::Vector<size_t>& x_lod,   /*expand source lod*/
+      const framework::Vector<size_t>& ref_lod, /*expand referenced lod*/
+      LoDTensor* out) {
+    int x_item_length = x.numel() / x.dims()[0];
+    framework::Vector<size_t> out_offset(x_lod.size());
+    GetOutputOffset(x_lod, ref_lod, &out_offset);
+
+    int thread_x = std::min(32, std::max(static_cast<int>(ref_lod.size()), 16));
+    int thread_y = 16;
+    int thread_z = 1024 / thread_x / thread_y;
+    int block_x = static_cast<int>(ref_lod.size());
+    dim3 block_size(thread_x, thread_y, thread_z);
+    dim3 grid_size(block_x, 1);
+
+    sequence_expand_kernel<<<grid_size, block_size, 0, context.stream()>>>(
+        x.data<T>(), x_lod.CUDAData(context.GetPlace()),
+        ref_lod.CUDAData(context.GetPlace()),
+        out_offset.CUDAData(context.GetPlace()), x_lod.size(), x_item_length,
+        out->mutable_data<T>(context.GetPlace()));
+  }
+};
+
+template <typename T>
+struct SequenceExpandGradFunctor<platform::CUDADeviceContext, T> {
+  void operator()(const platform::CUDADeviceContext& context,
+                  const LoDTensor& dout,
+                  const framework::Vector<size_t>& x_lod, /*expand source lod*/
+                  const framework::Vector<size_t>& ref_lod, /*expand based lod*/
+                  LoDTensor* dx) {
+    int x_item_length = framework::product(dx->dims()) / dx->dims()[0];
+    framework::Vector<size_t> out_offset(x_lod.size());
+    GetOutputOffset(x_lod, ref_lod, &out_offset);
+
+    int thread_x = std::min(32, std::max(static_cast<int>(ref_lod.size()), 16));
+    int thread_y = 16;
+    int thread_z = 1024 / thread_x / thread_y;
+    int block_x = static_cast<int>(ref_lod.size());
+    dim3 block_size(thread_x, thread_y, thread_z);
+    dim3 grid_size(block_x, 1);
+    sequence_expand_grad_kernel<<<grid_size, block_size, 0, context.stream()>>>(
+        dout.data<T>(), ref_lod.CUDAData(context.GetPlace()),
+        x_lod.CUDAData(context.GetPlace()),
+        out_offset.CUDAData(context.GetPlace()), ref_lod.size(), x_item_length,
+        dx->mutable_data<T>(context.GetPlace()));
+  }
+};
+
+}  // namespace operators
+}  // namespace paddle
 
 namespace ops = paddle::operators;
 REGISTER_OP_CUDA_KERNEL(

paddle/fluid/operators/sequence_expand_op.h

@@ -13,6 +13,7 @@ See the License for the specific language governing permissions and
 limitations under the License. */
 
 #pragma once
+#include <numeric>  // std::iota
 
 #include "paddle/fluid/framework/op_registry.h"
 #include "paddle/fluid/memory/memcpy.h"
@@ -26,6 +27,57 @@ template <typename T, int MajorType = Eigen::RowMajor,
           typename IndexType = Eigen::DenseIndex>
 using EigenMatrix = framework::EigenMatrix<T, MajorType, IndexType>;
 
+template <typename DeviceContext, typename T>
+struct SequenceExpandFunctor {
+  void operator()(
+      const DeviceContext& ctx, const LoDTensor& x,
+      const framework::Vector<size_t>& x_lod,   /*expand source lod*/
+      const framework::Vector<size_t>& ref_lod, /*expand referenced lod*/
+      LoDTensor* out);
+};
+
+template <typename DeviceContext, typename T>
+struct SequenceExpandGradFunctor {
+  void operator()(
+      const DeviceContext& ctx, const LoDTensor& dout,
+      const framework::Vector<size_t>& x_lod,   /*expand source lod*/
+      const framework::Vector<size_t>& ref_lod, /*expand referenced lod*/
+      LoDTensor* dx);
+};
+
+template <typename T>
+struct SequenceExpandFunctor<platform::CPUDeviceContext, T> {
+  void operator()(
+      const platform::CPUDeviceContext& context, const LoDTensor& x,
+      const framework::Vector<size_t>& x_lod,   /*expand source lod*/
+      const framework::Vector<size_t>& ref_lod, /*expand referenced lod*/
+      LoDTensor* out) {
+    int out_offset = 0;
+    auto& eigen_place = *context.eigen_device();
+    for (size_t i = 1; i < ref_lod.size(); ++i) {
+      int repeat_num = ref_lod[i] - ref_lod[i - 1];
+      int x_start = x_lod[i - 1];
+      int x_end = x_lod[i];
+      int x_seq_len = x_end - x_start;
+      if (repeat_num > 0) {
+        auto x_sub_tensor = x.Slice(x_start, x_end);
+        x_sub_tensor.Resize({1, x_sub_tensor.numel()});
+        int out_start = out_offset;
+        if (out->lod().size() == 1) {
+          out_start = out->lod()[0][out_offset];
+        }
+        auto out_sub_tensor =
+            out->Slice(out_start, out_start + x_seq_len * repeat_num);
+        out_sub_tensor.Resize({repeat_num, x_sub_tensor.dims()[1]});
+        EigenMatrix<T>::From(out_sub_tensor).device(eigen_place) =
+            EigenMatrix<T>::From(x_sub_tensor)
+                .broadcast(Eigen::array<int, 2>({{repeat_num, 1}}));
+      }
+      out_offset += repeat_num;
+    }
+  }
+};
+
 template <typename DeviceContext, typename T>
 class SequenceExpandKernel : public framework::OpKernel<T> {
  public:
@@ -47,45 +99,36 @@ class SequenceExpandKernel : public framework::OpKernel<T> {
       return;
     }
 
-    auto& out_lod = *out->mutable_lod();
+    // x lod level is at most 1.
+    framework::Vector<size_t> out_lod;
     if (x_lod.size() == 1) {
-      out_lod.resize(1);
-      out_lod[0] = {0};
-    }
-
-    int out_offset = 0;
-    auto& eigen_place =
-        *context.template device_context<DeviceContext>().eigen_device();
-    for (size_t i = 1; i < y_lod[ref_level].size(); ++i) {
-      int repeat_num = y_lod[ref_level][i] - y_lod[ref_level][i - 1];
-      int x_start = i - 1;
-      int x_end = i;
-      if (x_lod.size() == 1) {
-        x_start = x_lod[0][i - 1];
-        x_end = x_lod[0][i];
-      }
-      int x_seq_len = x_end - x_start;
-      if (repeat_num > 0) {
-        auto x_sub_tensor = x->Slice(x_start, x_end);
-        x_sub_tensor.Resize({1, x_sub_tensor.numel()});
-        int out_start = out_offset;
-        if (x_lod.size() == 1) {
-          out_start = out_lod[0][out_offset];
-        }
-        auto out_sub_tensor =
-            out->Slice(out_start, out_start + x_seq_len * repeat_num);
-        out_sub_tensor.Resize({repeat_num, x_sub_tensor.dims()[1]});
-        EigenMatrix<T>::From(out_sub_tensor).device(eigen_place) =
-            EigenMatrix<T>::From(x_sub_tensor)
-                .broadcast(Eigen::array<int, 2>({{repeat_num, 1}}));
-      }
-      for (int j = 0; j < repeat_num; ++j) {
-        if (x_lod.size() == 1) {
-          out_lod[0].push_back(out_lod[0].back() + x_seq_len);
+      out_lod.push_back(0);
+      int out_offset = 0;
+      for (size_t i = 1; i < y_lod[ref_level].size(); ++i) {
+        int repeat_num = y_lod[ref_level][i] - y_lod[ref_level][i - 1];
+        int x_start = x_lod[0][i - 1];
+        int x_end = x_lod[0][i];
+        int x_seq_len = x_end - x_start;
+        for (int j = 0; j < repeat_num; ++j) {
+          out_lod.push_back(out_lod.back() + x_seq_len);
+          out_offset++;
         }
-        out_offset++;
       }
+      // write lod to out if x has lod
+      auto& ref_lod = *out->mutable_lod();
+      ref_lod[0] = out_lod;
     }
+    framework::Vector<size_t> ref_x_lod;
+    if (x->lod().size() == 1) {
+      ref_x_lod = x->lod()[0];
+    } else {
+      // x_lod doesn't has lod, use fake x lod, level = 0
+      ref_x_lod.resize(x->dims()[0] + 1);
+      std::iota(ref_x_lod.begin(), ref_x_lod.end(), 0);
+    }
+    SequenceExpandFunctor<DeviceContext, T> functor;
+    functor(context.template device_context<DeviceContext>(), *x, ref_x_lod,
+            y_lod[ref_level], out);
   }
 };
 
@@ -101,6 +144,36 @@ class SequenceExpandKernel : public framework::OpKernel<T> {
  *    Grad(X).lod = Input(X).lod
  *
  * */
+template <typename T>
+struct SequenceExpandGradFunctor<platform::CPUDeviceContext, T> {
+  void operator()(
+      const platform::CPUDeviceContext& context, const LoDTensor& dout,
+      const framework::Vector<size_t>& x_lod,   /*expand source lod*/
+      const framework::Vector<size_t>& ref_lod, /*expand referenced lod*/
+      LoDTensor* dx) {
+    math::SetConstant<platform::CPUDeviceContext, T> set_zero;
+    set_zero(context, dx, static_cast<T>(0));
+
+    int dout_offset = 0;
+    for (size_t i = 1; i < ref_lod.size(); ++i) {
+      int repeat_num = ref_lod[i] - ref_lod[i - 1];
+      if (repeat_num > 0) {
+        int x_start = x_lod[i - 1];
+        int x_end = x_lod[i];
+        int x_seq_len = x_end - x_start;
+        auto dx_sub = dx->Slice(x_start, x_end);
+        dx_sub.Resize(flatten_to_1d(dx_sub.dims()));
+        int dout_end = dout_offset + repeat_num * x_seq_len;
+        auto dout_sub = dout.Slice(dout_offset, dout_end);
+        dout_sub.Resize({repeat_num, dx_sub.dims()[0]});
+        math::ColwiseSum<platform::CPUDeviceContext, T> col_sum;
+        col_sum(context, dout_sub, &dx_sub);
+        dout_offset += repeat_num * x_seq_len;
+      }
+    }
+  }
+};
+
 template <typename DeviceContext, typename T>
 class SequenceExpandGradKernel : public framework::OpKernel<T> {
  public:
@@ -114,43 +187,26 @@ class SequenceExpandGradKernel : public framework::OpKernel<T> {
     g_x->mutable_data<T>(context.GetPlace());
     g_x->set_lod(x->lod());
 
-    auto& x_lod = x->lod();
     auto& y_lod = y->lod();
-
     if (ref_level == -1) ref_level = y_lod.size() - 1;
-
     // just copy the gradient
     if (y_lod[ref_level].size() <= 1) {
       framework::TensorCopy(*g_out, context.GetPlace(), g_x);
       return;
     }
 
-    auto& dev_ctx = context.template device_context<DeviceContext>();
-
-    math::SetConstant<DeviceContext, T> set_zero;
-    set_zero(dev_ctx, g_x, static_cast<T>(0));
-
-    int g_out_offset = 0;
-    for (size_t i = 1; i < y_lod[ref_level].size(); ++i) {
-      int repeat_num = y_lod[ref_level][i] - y_lod[ref_level][i - 1];
-      if (repeat_num > 0) {
-        int x_start = i - 1;
-        int x_end = i;
-        if (x_lod.size() == 1) {
-          x_start = x_lod[0][i - 1];
-          x_end = x_lod[0][i];
-        }
-        int x_seq_len = x_end - x_start;
-        auto g_x_sub = g_x->Slice(x_start, x_end);
-        g_x_sub.Resize(flatten_to_1d(g_x_sub.dims()));
-        int g_out_end = g_out_offset + repeat_num * x_seq_len;
-        auto g_out_sub = g_out->Slice(g_out_offset, g_out_end);
-        g_out_sub.Resize({repeat_num, g_x_sub.dims()[0]});
-        math::ColwiseSum<DeviceContext, T> col_sum;
-        col_sum(dev_ctx, g_out_sub, &g_x_sub);
-        g_out_offset += repeat_num * x_seq_len;
-      }
+    framework::Vector<size_t> ref_x_lod;
+    framework::Vector<size_t> ref_lod = y_lod[ref_level];
+    if (x->lod().size() == 1) {
+      ref_x_lod = x->lod()[0];
+    } else {
+      // x_lod doesn't has lod, use fake x lod, level = 0
+      ref_x_lod.resize(x->dims()[0] + 1);
+      std::iota(ref_x_lod.begin(), ref_x_lod.end(), 0);
     }
+    SequenceExpandGradFunctor<DeviceContext, T> functor;
+    functor(context.template device_context<DeviceContext>(), *g_out, ref_x_lod,
+            ref_lod, g_x);
   }
 };
 

paddle/fluid/operators/sequence_pool_op.cc

@@ -13,6 +13,7 @@ See the License for the specific language governing permissions and
 limitations under the License. */
 
 #include "paddle/fluid/operators/sequence_pool_op.h"
+#include <string>
 
 namespace paddle {
 namespace operators {

paddle/fluid/operators/sequence_pool_op.h

@@ -13,6 +13,7 @@ See the License for the specific language governing permissions and
 limitations under the License. */
 
 #pragma once
+#include <string>
 #include "paddle/fluid/framework/eigen.h"
 #include "paddle/fluid/framework/op_registry.h"
 #include "paddle/fluid/operators/math/math_function.h"

paddle/fluid/operators/sequence_softmax_op.cc

@@ -13,6 +13,7 @@ See the License for the specific language governing permissions and
 limitations under the License. */
 
 #include "paddle/fluid/operators/sequence_softmax_op.h"
+#include <string>
 
 namespace paddle {
 namespace operators {

paddle/fluid/operators/sgd_op.h

@@ -65,7 +65,8 @@ class SGDOpKernel : public framework::OpKernel<T> {
         auto &grad_rows = grad->rows();
 
         size_t grad_row_numel = grad_value.numel() / grad_rows.size();
-        PADDLE_ENFORCE_EQ(grad_row_numel, param_out->numel() / grad_height);
+        PADDLE_ENFORCE_EQ(static_cast<int64_t>(grad_row_numel),
+                          param_out->numel() / grad_height);
 
         auto *grad_data = grad_value.data<T>();
         auto *out_data = param_out->data<T>();
@@ -73,7 +74,7 @@ class SGDOpKernel : public framework::OpKernel<T> {
         for (size_t i = 0; i < grad_rows.size(); i++) {
           PADDLE_ENFORCE(grad_rows[i] < grad_height,
                          "Input rows index should less than height");
-          for (int64_t j = 0; j < grad_row_numel; j++) {
+          for (size_t j = 0; j < grad_row_numel; j++) {
             out_data[grad_rows[i] * grad_row_numel + j] -=
                 lr[0] * grad_data[i * grad_row_numel + j];
           }
@@ -107,7 +108,7 @@ class SGDOpKernel : public framework::OpKernel<T> {
         PADDLE_ENFORCE(grad.rows()[i] < grad.height(),
                        "Input rows index should less than height");
         int64_t id_index = param.index(grad.rows()[i]);
-        for (int64_t j = 0; j < grad_row_width; j++) {
+        for (size_t j = 0; j < grad_row_width; j++) {
           out_data[id_index * grad_row_width + j] -=
               lr[0] * grad_data[i * grad_row_width + j];
         }

paddle/fluid/operators/softmax_mkldnn_op.cc

@@ -12,12 +12,11 @@ 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 <iostream>
 #include "mkldnn.hpp"
 #include "paddle/fluid/operators/softmax_op.h"
 #include "paddle/fluid/platform/mkldnn_helper.h"
 
-#include <iostream>
-
 namespace paddle {
 namespace operators {
 
@@ -63,9 +62,11 @@ class SoftmaxMKLDNNKernel : public paddle::framework::OpKernel<T> {
                                               softmax_md, 1 /*dim: C*/);
     // create memory primitives
     auto softmax_src_memory =
-        memory({softmax_md, mkldnn_engine}, (void*)input_data);
+        memory({softmax_md, mkldnn_engine},
+               static_cast<void*>(const_cast<T*>(input_data)));
     auto softmax_dst_memory =
-        memory({softmax_md, mkldnn_engine}, (void*)output_data);
+        memory({softmax_md, mkldnn_engine},
+               static_cast<void*>(const_cast<T*>(output_data)));
     auto softmax_prim_desc =
         softmax_forward::primitive_desc(softmax_desc, mkldnn_engine);
     auto softmax = softmax_forward(softmax_prim_desc, softmax_src_memory,

paddle/fluid/operators/split_ids_op.h

@@ -60,7 +60,9 @@ class SplitIdsOpKernel : public framework::OpKernel<T> {
     } else if (ids_var->IsType<framework::SelectedRows>()) {
       const auto *ids_selected_rows = ctx.Input<framework::SelectedRows>("Ids");
       auto &ids_dims = ids_selected_rows->value().dims();
-      PADDLE_ENFORCE_EQ(ids_dims[0], ids_selected_rows->rows().size(), "");
+      PADDLE_ENFORCE_EQ(ids_dims[0],
+                        static_cast<int64_t>(ids_selected_rows->rows().size()),
+                        "");
       const T *ids = ids_selected_rows->value().data<T>();
       const auto &ids_rows = ids_selected_rows->rows();
       auto outs = ctx.MultiOutput<framework::SelectedRows>("Out");
@@ -77,7 +79,7 @@ class SplitIdsOpKernel : public framework::OpKernel<T> {
         framework::DDim ddim = framework::make_ddim(
             {static_cast<int64_t>(out->rows().size()), row_width});
         T *output = out->mutable_value()->mutable_data<T>(ddim, place);
-        for (size_t i = 0; i < ddim[0]; ++i) {
+        for (int64_t i = 0; i < ddim[0]; ++i) {
           memcpy(output + i * row_width, ids + out->rows()[i] * row_width,
                  row_width * sizeof(T));
         }

paddle/fluid/operators/split_op.h

@@ -14,7 +14,7 @@ limitations under the License. */
 
 #pragma once
 
-#include <chrono>
+#include <chrono>  // NOLINT
 #include <vector>
 #include "paddle/fluid/framework/op_registry.h"
 #include "paddle/fluid/operators/strided_memcpy.h"

paddle/fluid/operators/strided_memcpy.h

@@ -37,8 +37,8 @@ inline void StridedMemcpy(const platform::DeviceContext& dev_ctx, const T* src,
                           const framework::DDim& src_stride,
                           const framework::DDim& dst_dim,
                           const framework::DDim& dst_stride, T* dst) {
-  using namespace detail;
-  StridedCopyDimVisitor<T> func(dev_ctx, src, src_stride, dst_stride, dst);
+  paddle::operators::detail::StridedCopyDimVisitor<T> func(
+      dev_ctx, src, src_stride, dst_stride, dst);
   boost::apply_visitor(func, dst_dim);
 }
 

paddle/fluid/operators/top_k_op.cu

@@ -13,6 +13,7 @@ See the License for the specific language governing permissions and
 limitations under the License. */
 
 #include "paddle/fluid/framework/op_registry.h"
+#include "paddle/fluid/operators/top_k_op.h"
 #include "paddle/fluid/platform/assert.h"
 
 namespace paddle {
@@ -133,71 +134,71 @@ __device__ __forceinline__ void GetTopK(Pair<T> topk[], const T* val, int* col,
 }
 
 template <typename T, int MaxLength, int BlockSize>
-__device__ __forceinline__ void ThreadGetTopK(Pair<T> topk[], int& beam,
+__device__ __forceinline__ void ThreadGetTopK(Pair<T> topk[], int* beam,
                                               int beam_size, const T* src,
-                                              bool& firstStep, bool& is_empty,
-                                              Pair<T>& max, int dim,
+                                              bool* firstStep, bool* is_empty,
+                                              Pair<T>* max, int dim,
                                               const int tid) {
-  if (beam > 0) {
-    int length = beam < beam_size ? beam : beam_size;
-    if (firstStep) {
-      firstStep = false;
+  if (*beam > 0) {
+    int length = (*beam) < beam_size ? *beam : beam_size;
+    if (*firstStep) {
+      *firstStep = false;
       GetTopK<T, BlockSize>(topk, src, tid, dim, length);
     } else {
       for (int k = 0; k < MaxLength; k++) {
-        if (k < MaxLength - beam) {
-          topk[k] = topk[k + beam];
+        if (k < MaxLength - (*beam)) {
+          topk[k] = topk[k + *beam];
         } else {
           topk[k].set(-INFINITY, -1);
         }
       }
-      if (!is_empty) {
-        GetTopK<T, BlockSize>(topk + MaxLength - beam, src, tid, dim, max,
+      if (!(*is_empty)) {
+        GetTopK<T, BlockSize>(topk + MaxLength - *beam, src, tid, dim, *max,
                               length);
       }
     }
 
-    max = topk[MaxLength - 1];
-    if (max.v == -1) is_empty = true;
-    beam = 0;
+    *max = topk[MaxLength - 1];
+    if ((*max).v == -1) *is_empty = true;
+    *beam = 0;
   }
 }
 
 template <typename T, int MaxLength, int BlockSize>
-__device__ __forceinline__ void ThreadGetTopK(Pair<T> topk[], int& beam,
+__device__ __forceinline__ void ThreadGetTopK(Pair<T> topk[], int* beam,
                                               int beam_size, const T* val,
-                                              int* col, bool& firstStep,
-                                              bool& is_empty, Pair<T>& max,
+                                              int* col, bool* firstStep,
+                                              bool* is_empty, Pair<T>* max,
                                               int dim, const int tid) {
-  if (beam > 0) {
-    int length = beam < beam_size ? beam : beam_size;
-    if (firstStep) {
-      firstStep = false;
+  if (*beam > 0) {
+    int length = (*beam) < beam_size ? *beam : beam_size;
+    if (*firstStep) {
+      *firstStep = false;
       GetTopK<T, BlockSize>(topk, val, col, tid, dim, length);
     } else {
       for (int k = 0; k < MaxLength; k++) {
-        if (k < MaxLength - beam) {
-          topk[k] = topk[k + beam];
+        if (k < MaxLength - *beam) {
+          topk[k] = topk[k + *beam];
         } else {
           topk[k].set(-INFINITY, -1);
         }
       }
-      if (!is_empty) {
-        GetTopK<T, BlockSize>(topk + MaxLength - beam, val, col, tid, dim, max,
+      if (!(*is_empty)) {
+        GetTopK<T, BlockSize>(topk + MaxLength - *beam, val, col, tid, dim, max,
                               length);
       }
     }
 
-    max = topk[MaxLength - 1];
-    if (max.v == -1) is_empty = true;
-    beam = 0;
+    *max = topk[MaxLength - 1];
+    if ((*max).v == -1) *is_empty = true;
+    *beam = 0;
   }
 }
 
 template <typename T, int MaxLength, int BlockSize>
 __device__ __forceinline__ void BlockReduce(Pair<T>* sh_topk, int* maxid,
                                             Pair<T> topk[], T** topVal,
-                                            int64_t** topIds, int& beam, int& k,
+                                            int64_t** topIds, int* beam, int* k,
                                             const int tid, const int warp) {
   while (true) {
     __syncthreads();
@@ -225,17 +226,17 @@ __device__ __forceinline__ void BlockReduce(Pair<T>* sh_topk, int* maxid,
       (*topVal)++;
       (*topIds)++;
     }
-    if (tid == maxid[0]) beam++;
-    if (--k == 0) break;
+    if (tid == maxid[0]) (*beam)++;
+    if (--(*k) == 0) break;
     __syncthreads();
 
     if (tid == maxid[0]) {
-      if (beam < MaxLength) {
-        sh_topk[tid] = topk[beam];
+      if (*beam < MaxLength) {
+        sh_topk[tid] = topk[*beam];
       }
     }
     if (maxid[0] / 32 == warp) {
-      if (__shfl(beam, (maxid[0]) % 32, 32) == MaxLength) break;
+      if (__shfl(*beam, (maxid[0]) % 32, 32) == MaxLength) break;
     }
   }
 }
@@ -268,13 +269,13 @@ __global__ void KeMatrixTopK(T* output, int output_stride, int64_t* indices,
     topk[k].set(-INFINITY, -1);
   }
   while (k) {
-    ThreadGetTopK<T, MaxLength, BlockSize>(topk, beam, k,
-                                           src + blockIdx.x * lds, firststep,
-                                           is_empty, max, dim, tid);
+    ThreadGetTopK<T, MaxLength, BlockSize>(topk, &beam, k,
+                                           src + blockIdx.x * lds, &firststep,
+                                           &is_empty, &max, dim, tid);
 
     sh_topk[tid] = topk[0];
     BlockReduce<T, MaxLength, BlockSize>(sh_topk, maxid, topk, &output,
-                                         &indices, beam, k, tid, warp);
+                                         &indices, &beam, &k, tid, warp);
   }
 }
 
@@ -308,9 +309,9 @@ class TopkOpCUDAKernel : public framework::OpKernel<T> {
     KeMatrixTopK<T, 5, 256><<<
         grid, threads, 0, reinterpret_cast<const platform::CUDADeviceContext&>(
                               ctx.device_context())
-                              .stream()>>>(output_data, output->dims()[1],
-                                           indices_data, input_data,
-                                           input_width, input_width, int(k));
+                              .stream()>>>(
+        output_data, output->dims()[1], indices_data, input_data, input_width,
+        input_width, static_cast<int>(k));
   }
 };
 

paddle/fluid/operators/uniform_random_op.cc

@@ -24,7 +24,19 @@ template <typename T>
 class CPUUniformRandomKernel : public framework::OpKernel<T> {
  public:
   void Compute(const framework::ExecutionContext& ctx) const override {
-    auto* tensor = ctx.Output<framework::Tensor>("Out");
+    framework::Tensor* tensor = nullptr;
+    auto out_var = ctx.OutputVar("Out");
+    if (out_var->IsType<framework::LoDTensor>()) {
+      tensor = out_var->GetMutable<framework::LoDTensor>();
+    } else if (out_var->IsType<framework::SelectedRows>()) {
+      auto shape = ctx.Attr<std::vector<int>>("shape");
+      tensor = out_var->GetMutable<framework::SelectedRows>()->mutable_value();
+      tensor->Resize(framework::make_ddim(shape));
+    } else {
+      PADDLE_THROW(
+          "uniform_random_op's output only"
+          "supports SelectedRows and Tensor");
+    }
     T* data = tensor->mutable_data<T>(ctx.GetPlace());
     unsigned int seed = static_cast<unsigned int>(ctx.Attr<int>("seed"));
     std::minstd_rand engine;

paddle/fluid/operators/uniform_random_op.cu

@@ -43,7 +43,19 @@ template <typename T>
 class GPUUniformRandomKernel : public framework::OpKernel<T> {
  public:
   void Compute(const framework::ExecutionContext& context) const override {
-    auto* tensor = context.Output<framework::Tensor>("Out");
+    framework::Tensor* tensor = nullptr;
+    auto out_var = context.OutputVar("Out");
+    if (out_var->IsType<framework::LoDTensor>()) {
+      tensor = out_var->GetMutable<framework::LoDTensor>();
+    } else if (out_var->IsType<framework::SelectedRows>()) {
+      auto shape = context.Attr<std::vector<int>>("shape");
+      tensor = out_var->GetMutable<framework::SelectedRows>()->mutable_value();
+      tensor->Resize(framework::make_ddim(shape));
+    } else {
+      PADDLE_THROW(
+          "uniform_random_op's output only"
+          "supports SelectedRows and Tensor");
+    }
     T* data = tensor->mutable_data<T>(context.GetPlace());
     unsigned int seed = static_cast<unsigned int>(context.Attr<int>("seed"));
     if (seed == 0) {

paddle/fluid/platform/cuda_profiler.h

@@ -11,12 +11,13 @@ 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. */
-
 #pragma once
+
 #include <cuda_profiler_api.h>
-#include <stdio.h>
-#include <stdlib.h>
-#include <string.h>
+
+#include <string>
+
+#include "paddle/fluid/platform/enforce.h"
 
 namespace paddle {
 namespace platform {

paddle/fluid/platform/details/device_ptr_cast.h → paddle/fluid/platform/details/cuda_transform_iterator_cast.h

@@ -18,16 +18,22 @@ limitations under the License. */
 #error device_ptr_cast must be include by .cu file
 #endif
 
-#include <thrust/device_ptr.h>
+#include <type_traits>  // For std::remove_pointer and std::is_pointer.
+
+#include "thrust/device_ptr.h"
 
 namespace paddle {
 namespace platform {
 namespace details {
+
+// PointerToThrustDevicePtr has two speicalizations, one casts a (CUDA
+// device) pointer into thrust::device_ptr, the other keeps rest types
+// un-casted.
 template <typename T, bool is_ptr>
-struct DevicePtrCast;
+struct PointerToThrustDevicePtr;
 
 template <typename T>
-struct DevicePtrCast<T, true> {
+struct PointerToThrustDevicePtr<T, true> {
   using ELEM = typename std::remove_pointer<T>::type;
   using RTYPE = thrust::device_ptr<ELEM>;
 
@@ -37,17 +43,26 @@ struct DevicePtrCast<T, true> {
 };
 
 template <typename T>
-struct DevicePtrCast<T, false> {
+struct PointerToThrustDevicePtr<T, false> {
   using RTYPE = T;
   inline RTYPE operator()(RTYPE it) const { return it; }
 };
 
-// Cast T to thrust::device_ptr if T is a pointer.
-// Otherwise, e.g., T is a iterator, return T itself.
+// CastToCUDATransformIterator casts a pointer to thrust::device_ptr
+// so it could be used as the iterator of thrust::transform.  It
+// doesn't cast other types.
+//
+// We need CastToCUDATransformIterator because it is often that we
+// want to use device memory pointers as transform iterators, e.g., to
+// transform a block of float32 to float16.  In this case, we want
+// CastToCUDATransformIterator to cast float16/32 pointers to
+// thrust::device_ptr, otherwise they cannot work as the iterator
+// required by thrust::transform.  At the same time, we don't want to
+// cast thrust::device_ptr to thrust::device_ptr repeatedly.
 template <typename T>
-auto DevPtrCast(T t) ->
-    typename DevicePtrCast<T, std::is_pointer<T>::value>::RTYPE {
-  DevicePtrCast<T, std::is_pointer<T>::value> cast;
+auto CastToCUDATransformIterator(T t) ->
+    typename PointerToThrustDevicePtr<T, std::is_pointer<T>::value>::RTYPE {
+  PointerToThrustDevicePtr<T, std::is_pointer<T>::value> cast;
   return cast(t);
 }
 

paddle/fluid/platform/device_context.cc

@@ -175,7 +175,7 @@ CUDADeviceContext::~CUDADeviceContext() {
 Place CUDADeviceContext::GetPlace() const { return place_; }
 
 void CUDADeviceContext::Wait() const {
-  std::lock_guard<std::mutex> guard(mutex_);
+  std::lock_guard<std::recursive_mutex> guard(mutex_);
   PADDLE_ENFORCE(cudaStreamSynchronize(stream_));
   PADDLE_ENFORCE(cudaGetLastError());
 }

paddle/fluid/platform/device_context.h

@@ -98,13 +98,20 @@ class CUDADeviceContext : public DeviceContext {
   /*! \brief  Return cuda stream in the device context. */
   cudaStream_t stream() const;
 
+  template <typename Callback>
+  void RecordEvent(cudaEvent_t ev, Callback callback) {
+    std::lock_guard<std::recursive_mutex> guard(mutex_);
+    callback();
+    PADDLE_ENFORCE(cudaEventRecord(ev, stream_));
+  }
+
  private:
   CUDAPlace place_;
 
   std::unique_ptr<Eigen::GpuDevice> eigen_device_;
   std::unique_ptr<EigenCudaStreamDevice> eigen_stream_;
 
-  mutable std::mutex mutex_;
+  mutable std::recursive_mutex mutex_;
   cudaStream_t stream_;
   cudnnHandle_t cudnn_handle_;
   cublasHandle_t cublas_handle_;

paddle/fluid/platform/transform.h

@@ -14,29 +14,44 @@ limitations under the License. */
 
 #pragma once
 
+#include <algorithm>
+#include <type_traits>
+
 #include "paddle/fluid/platform/device_context.h"
 #include "paddle/fluid/platform/enforce.h"
 #include "paddle/fluid/platform/hostdevice.h"
 #include "paddle/fluid/platform/place.h"
 
-#include <algorithm>
-#include <type_traits>
 #ifdef __NVCC__
 #include <thrust/execution_policy.h>
 #include <thrust/transform.h>
-#include "paddle/fluid/platform/details/device_ptr_cast.h"
+#include "paddle/fluid/platform/details/cuda_transform_iterator_cast.h"
 #endif
 
 namespace paddle {
 namespace platform {
 
-// Transform on host or device. It provides the same API in std library.
+// Transform applys a unary or a binary functor on each element in a
+// range defined by a pair of iterators.
+//
+// - The specialization for CPU calls std::transform.
+// - The specialization for CUDA calls thrust::tranform.
+//
+// NOTE: We need to define InputIter and OutputIter defined as
+//       different types, because the InputIter points op's inputs and
+//       OutputIter pints to op's outputs.
+//
+// NOTE: We don't assume that InputIter to be const InputType* and
+//       OutputIter to be OutputType*, because we might use a iterator
+//       class, paddle::fluid::operators::RowwiseTRansformIterator.
 template <typename DeviceContext>
 struct Transform {
+  // The unary version.
   template <typename InputIter, typename OutputIter, typename UnaryOperation>
   void operator()(const DeviceContext& context, InputIter first, InputIter last,
                   OutputIter result, UnaryOperation op);
 
+  // The binary version.
   template <typename InputIter1, typename InputIter2, typename OutputIter,
             typename BinaryOperation>
   void operator()(const DeviceContext& context, InputIter1 first1,
@@ -70,8 +85,9 @@ struct Transform<platform::CUDADeviceContext> {
     auto place = context.GetPlace();
     PADDLE_ENFORCE(is_gpu_place(place), "It must use GPU place.");
     thrust::transform(thrust::cuda::par.on(context.stream()),
-                      details::DevPtrCast(first), details::DevPtrCast(last),
-                      details::DevPtrCast(result), op);
+                      details::CastToCUDATransformIterator(first),
+                      details::CastToCUDATransformIterator(last),
+                      details::CastToCUDATransformIterator(result), op);
   }
 
   template <typename InputIter1, typename InputIter2, typename OutputIter,
@@ -82,9 +98,10 @@ struct Transform<platform::CUDADeviceContext> {
     auto place = context.GetPlace();
     PADDLE_ENFORCE(is_gpu_place(place), "It must use GPU place.");
     thrust::transform(thrust::cuda::par.on(context.stream()),
-                      details::DevPtrCast(first1), details::DevPtrCast(last1),
-                      details::DevPtrCast(first2), details::DevPtrCast(result),
-                      op);
+                      details::CastToCUDATransformIterator(first1),
+                      details::CastToCUDATransformIterator(last1),
+                      details::CastToCUDATransformIterator(first2),
+                      details::CastToCUDATransformIterator(result), op);
   }
 };
 #endif

paddle/fluid/platform/transform_test.cu

@@ -18,11 +18,12 @@ limitations under the License. */
 #include "paddle/fluid/platform/hostdevice.h"
 #include "paddle/fluid/platform/transform.h"
 
+namespace {
+
 template <typename T>
 class Scale {
  public:
   explicit Scale(const T& scale) : scale_(scale) {}
-
   HOSTDEVICE T operator()(const T& a) const { return a * scale_; }
 
  private:
@@ -35,11 +36,23 @@ class Multiply {
   HOSTDEVICE T operator()(const T& a, const T& b) const { return a * b; }
 };
 
+}  // namespace
+
+using paddle::memory::Alloc;
+using paddle::memory::Free;
+using paddle::memory::Copy;
+
+using paddle::platform::CPUPlace;
+using paddle::platform::CUDAPlace;
+using paddle::platform::CPUDeviceContext;
+using paddle::platform::CUDADeviceContext;
+
+using paddle::platform::Transform;
+
 TEST(Transform, CPUUnary) {
-  using namespace paddle::platform;
   CPUDeviceContext ctx;
   float buf[4] = {0.1, 0.2, 0.3, 0.4};
-  Transform<paddle::platform::CPUDeviceContext> trans;
+  Transform<CPUDeviceContext> trans;
   trans(ctx, buf, buf + 4, buf, Scale<float>(10));
   for (int i = 0; i < 4; ++i) {
     ASSERT_NEAR(buf[i], static_cast<float>(i + 1), 1e-5);
@@ -47,14 +60,12 @@ TEST(Transform, CPUUnary) {
 }
 
 TEST(Transform, GPUUnary) {
-  using namespace paddle::platform;
-  using namespace paddle::memory;
   CUDAPlace gpu0(0);
   CUDADeviceContext ctx(gpu0);
   float cpu_buf[4] = {0.1, 0.2, 0.3, 0.4};
   float* gpu_buf = static_cast<float*>(Alloc(gpu0, sizeof(float) * 4));
   Copy(gpu0, gpu_buf, CPUPlace(), cpu_buf, sizeof(cpu_buf), ctx.stream());
-  Transform<paddle::platform::CUDADeviceContext> trans;
+  Transform<CUDADeviceContext> trans;
   trans(ctx, gpu_buf, gpu_buf + 4, gpu_buf, Scale<float>(10));
   ctx.Wait();
   Copy(CPUPlace(), cpu_buf, gpu0, gpu_buf, sizeof(cpu_buf), ctx.stream());
@@ -65,10 +76,8 @@ TEST(Transform, GPUUnary) {
 }
 
 TEST(Transform, CPUBinary) {
-  using namespace paddle::platform;
-  using namespace paddle::memory;
   int buf[4] = {1, 2, 3, 4};
-  Transform<paddle::platform::CPUDeviceContext> trans;
+  Transform<CPUDeviceContext> trans;
   CPUDeviceContext ctx;
   trans(ctx, buf, buf + 4, buf, buf, Multiply<int>());
   for (int i = 0; i < 4; ++i) {
@@ -77,14 +86,12 @@ TEST(Transform, CPUBinary) {
 }
 
 TEST(Transform, GPUBinary) {
-  using namespace paddle::platform;
-  using namespace paddle::memory;
   int buf[4] = {1, 2, 3, 4};
   CUDAPlace gpu0(0);
   CUDADeviceContext ctx(gpu0);
   int* gpu_buf = static_cast<int*>(Alloc(gpu0, sizeof(buf)));
   Copy(gpu0, gpu_buf, CPUPlace(), buf, sizeof(buf), ctx.stream());
-  Transform<paddle::platform::CUDADeviceContext> trans;
+  Transform<CUDADeviceContext> trans;
   trans(ctx, gpu_buf, gpu_buf + 4, gpu_buf, gpu_buf, Multiply<int>());
   ctx.Wait();
   Copy(CPUPlace(), buf, gpu0, gpu_buf, sizeof(buf), ctx.stream());

paddle/fluid/platform/variant.h

@@ -14,29 +14,25 @@ limitations under the License. */
 
 #pragma once
 
-#ifdef __CUDACC__
-#ifdef __CUDACC_VER_MAJOR__
-// CUDA 9 define `__CUDACC_VER__` as a warning message, manually define
-// __CUDACC_VER__ instead.
+// Boost 1.41.0 requires __CUDACC_VER__, but in CUDA 9 __CUDACC_VER__
+// is removed, so we have to manually define __CUDACC_VER__ instead.
+// For details, please refer to
+// https://github.com/PaddlePaddle/Paddle/issues/6626
+#if defined(__CUDACC__) && defined(__CUDACC_VER_MAJOR__)
 #undef __CUDACC_VER__
-
-#define __CUDACC_VER__                                         \
-  (__CUDACC_VER_MAJOR__ * 10000 + __CUDACC_VER_MINOR__ * 100 + \
-   __CUDACC_VER_BUILD__)
-#endif
-
+#define __CUDACC_VER__                                  \
+  __CUDACC_VER_BUILD__ + __CUDACC_VER_MAJOR__ * 10000 + \
+      __CUDACC_VER_MINOR__ * 100
 #endif
 
-#include <boost/config.hpp>
+#include "boost/config.hpp"
 
-#ifdef PADDLE_WITH_CUDA
-
-// Because boost's variadic templates has bug on nvcc, boost will disable
-// variadic template support when GPU enabled on nvcc.
-// Define BOOST_NO_CXX11_VARIADIC_TEMPLATES on gcc/clang to generate same
-// function symbols.
-//
+// Because Boost 1.41.0's variadic templates has bug on nvcc, boost
+// will disable variadic template support in NVCC mode.  Define
+// BOOST_NO_CXX11_VARIADIC_TEMPLATES on gcc/clang to generate same
+// function symbols.  For details,
 // https://github.com/PaddlePaddle/Paddle/issues/3386
+#ifdef PADDLE_WITH_CUDA
 #ifndef BOOST_NO_CXX11_VARIADIC_TEMPLATES
 #define BOOST_NO_CXX11_VARIADIC_TEMPLATES
 #endif

paddle/fluid/recordio/chunk_test.cc

@@ -43,5 +43,5 @@ TEST(Chunk, Compressor) {
 
   ch.Clear();
   ch.Parse(ss);
-  ASSERT_EQ(ch.NumBytes(), 18);
+  ASSERT_EQ(ch.NumBytes(), 18ul);
 }

python/CMakeLists.txt

 file(GLOB UTILS_PY_FILES . ./paddle/utils/*.py)
-file(GLOB_RECURSE FLUID_PY_FILES ./paddle/fluid/ *.py)
+file(GLOB_RECURSE FLUID_PY_FILES ./paddle/fluid/*.py)
 set(PY_FILES paddle/__init__.py
   ${UTILS_PY_FILES}
   ${FLUID_PY_FILES})
@@ -7,7 +7,7 @@ set(PY_FILES paddle/__init__.py
 if(NOT WITH_FLUID_ONLY)
   file(GLOB TRAINER_PY_FILES . ./paddle/trainer/*.py)
   file(GLOB HELPERS_PY_FILES . ./paddle/trainer_config_helpers/*.py)
-  file(GLOB_RECURSE V2_PY_FILES ./paddle/v2/ *.py)
+  file(GLOB_RECURSE V2_PY_FILES ./paddle/v2/*.py)
   set(PY_FILES ${PY_FILES}
     ${TRAINER_PY_FILES}
     ${HELPERS_PY_FILES}
@@ -55,7 +55,7 @@ add_custom_target(copy_paddle_pybind ALL DEPENDS ${PADDLE_BINARY_DIR}/python/pad
 
 add_custom_command(OUTPUT ${PADDLE_PYTHON_BUILD_DIR}/.timestamp
     COMMAND touch stub.cc
-    COMMAND ${CMAKE_COMMAND} -E copy_directory ${PADDLE_SOURCE_DIR}/python/paddle ${PADDLE_BINARY_DIR}/python/paddle
+    COMMAND cp -r ${PADDLE_SOURCE_DIR}/python/paddle ${PADDLE_BINARY_DIR}/python
     COMMAND cp -r ${PADDLE_SOURCE_DIR}/paddle/py_paddle ${PADDLE_BINARY_DIR}/python/
     COMMAND env ${py_env} ${PYTHON_EXECUTABLE} setup.py bdist_wheel
     COMMAND ${CMAKE_COMMAND} -E touch ${PADDLE_PYTHON_BUILD_DIR}/.timestamp

python/paddle/fluid/distribute_transpiler.py

@@ -1115,4 +1115,6 @@ class DistributeTranspiler:
             for op2 in find_ops:
                 if ufind.is_connected(op1, op2):
                     lr_ops.append(op1)
+                    # we only need to append op for once
+                    break
         return lr_ops

python/paddle/fluid/memory_optimization_transpiler.py

@@ -29,17 +29,20 @@ dtype_to_size = {
     core.VarDesc.VarType.BOOL: 1
 }
 
-sub_block_ops = [
+SUB_BLOCK_OPS = [
     "while", "while_grad", "parallel_do", "parallel_do_grad",
     "conditional_block", "conditional_block_grad"
 ]
 
+SUB_BLOCK_PAIR = [("while", "while_grad"), ("parallel_do", "parallel_do_grad"),
+                  ("conditional_block", "conditional_block_grad")]
+
 PRINT_LOG = False
 
 
 class ControlFlowGraph(object):
-    def __init__(self, Program, ops, forward_num, skip_opt):
-        self._program = Program
+    def __init__(self, program, ops, forward_num, skip_opt):
+        self._program = program
         self._ops = ops
         self._forward_num = forward_num
         self._successors = defaultdict(set)
@@ -51,6 +54,7 @@ class ControlFlowGraph(object):
         self._skip_opt = skip_opt
 
     def _add_connections(self, connections):
+        """Populates _successors and _presuccessors for two neighbor nodes."""
         for node1, node2 in connections:
             self._add(node1, node2)
 
@@ -58,7 +62,11 @@ class ControlFlowGraph(object):
         self._successors[node1].add(node2)
         self._presuccessors[node2].add(node1)
 
+    # TODO(panyx0718): We need to have a unified way of building intermediate
+    # representation.
     def _build_graph(self):
+        """Build a graph based on op sequence.
+        """
         self.op_size = len(self._ops)
         op_node_connections = [(i, i + 1) for i in range(self.op_size - 1)]
         self._add_connections(op_node_connections)
@@ -82,15 +90,14 @@ class ControlFlowGraph(object):
                 self._live_out[i].add(new_name)
 
     def _reach_fixed_point(self, live_in, live_out):
+        """Check if the liveness set has stablized."""
         if len(live_in) != len(self._live_in):
             return False
         if len(live_out) != len(self._live_out):
             return False
         for i in range(self.op_size):
-            if live_in[i] != self._live_in[i]:
-                return False
-        for i in range(self.op_size):
-            if live_out[i] != self._live_out[i]:
+            if (live_in[i] != self._live_in[i] or
+                    live_out[i] != self._live_out[i]):
                 return False
         return True
 
@@ -98,6 +105,8 @@ class ControlFlowGraph(object):
         self._build_graph()
         live_in = defaultdict(set)
         live_out = defaultdict(set)
+        # Repeatedly apply liveness updates until the algorithm stablize
+        # on a complete set live input vars and live output vars.
         while True:
             for i in range(self.op_size, 0, -1):
                 live_in[i] = set(self._live_in[i])
@@ -141,6 +150,8 @@ class ControlFlowGraph(object):
             return False
         return True
 
+    # TODO(panyx0718): This needs to be less hacky. It seems memory optimization
+    # doesn't consider vars copied between cpu and gpu.
     def _update_skip_opt_set(self):
         for i in range(self.op_size):
             op = self._ops[i]
@@ -154,7 +165,7 @@ class ControlFlowGraph(object):
         bwd_id = 0
         for i in range(self.op_size):
             op = self._ops[i]
-            if op.type() in sub_block_ops:
+            if op.type() in SUB_BLOCK_OPS:
                 continue
             block_desc = op.block()
             is_forward = i < self._forward_num
@@ -177,13 +188,15 @@ class ControlFlowGraph(object):
         def compare_shape(x_shape, cache_shape, opt_level):
             if opt_level == 0:
                 return x_shape == cache_shape
-            if opt_level == 1:
+            elif opt_level == 1:
                 if (x_shape[0] == -1) ^ (cache_shape[0] == -1):
                     return False
                 x_size = abs(reduce(lambda x, y: x * y, x_shape))
                 cache_size = abs(reduce(lambda x, y: x * y, cache_shape))
                 if x_size <= cache_size:
                     return True
+            else:
+                raise ValueError("only support opt_level 0 or 1.")
             return False
 
         self._dataflow_analyze()
@@ -191,10 +204,9 @@ class ControlFlowGraph(object):
         self.pool = []
         for i in range(self.op_size):
             op = self._ops[i]
-            if op.type() in sub_block_ops:
+            if op.type() in SUB_BLOCK_OPS:
                 continue
             block_desc = op.block()
-            self.current_block_desc = block_desc
             is_forward = i < self._forward_num
             if self.pool:
                 defs_can_optimize = filter(
@@ -211,37 +223,40 @@ class ControlFlowGraph(object):
                     for index, cache_pair in enumerate(self.pool):
                         cache_var = cache_pair[0]
                         cache_shape = cache_pair[1]
-                        if compare_shape(x_shape, cache_shape, level):
-                            if self._has_var(block_desc, cache_var, is_forward):
-                                x_dtype = self._find_var(block_desc, x,
-                                                         is_forward).dtype()
-                                cache_dtype = self._find_var(
-                                    block_desc, cache_var, is_forward).dtype()
-                                # TODO(qijun): actually, we should compare dtype_to_size[x_dtype]
-                                # and dtype_to_size[cache_dtype]
-                                if x_dtype == cache_dtype:
-                                    if PRINT_LOG:
-                                        print(
-                                            ("Hit Cache !!!! cache pool index "
-                                             "is %d, var name is %s, "
-                                             "cached var name is %s, "
-                                             "var shape is %s ") %
-                                            (index, x, cache_var,
-                                             str(cache_shape)))
-                                    self.pool.pop(index)
-                                    if x == cache_var:
-                                        break
-                                    _rename_arg_(
-                                        self._ops, x, cache_var, begin_idx=i)
-                                    self._program.block(block_desc.id).var(
-                                        str(x)).desc = self._find_var(
-                                            block_desc, cache_var, is_forward)
-                                    self._update_graph(
-                                        x, cache_var, begin_idx=i)
-                                    break
-
-            in_diff, out_diff = self._get_diff(self._live_in[i],
-                                               self._live_out[i])
+                        if not compare_shape(x_shape, cache_shape, level):
+                            continue
+
+                        if not self._has_var(block_desc, cache_var, is_forward):
+                            continue
+
+                        x_dtype = self._find_var(block_desc, x,
+                                                 is_forward).dtype()
+                        cache_dtype = self._find_var(block_desc, cache_var,
+                                                     is_forward).dtype()
+                        # TODO(qijun): actually, we should compare
+                        # dtype_to_size[x_dtype] and dtype_to_size[cache_dtype]
+                        if x_dtype != cache_dtype:
+                            continue
+
+                        if PRINT_LOG:
+                            print(("Hit Cache !!!! cache pool index "
+                                   "is %d, var name is %s, "
+                                   "cached var name is %s, "
+                                   "var shape is %s ") % (index, x, cache_var,
+                                                          str(cache_shape)))
+                        self.pool.pop(index)
+                        if x == cache_var:
+                            break
+                        # Rename the var to the cache var already with
+                        # memory allocated in order to reuse the memory.
+                        _rename_arg_(self._ops, x, cache_var, begin_idx=i)
+                        self._program.block(block_desc.id).var(str(
+                            x)).desc = self._find_var(block_desc, cache_var,
+                                                      is_forward)
+                        self._update_graph(x, cache_var, begin_idx=i)
+                        break
+
+            in_diff, _ = self._get_diff(self._live_in[i], self._live_out[i])
             can_optimize = filter(
                 lambda x: self._check_var_validity(block_desc, x, is_forward),
                 in_diff)
@@ -252,6 +267,19 @@ class ControlFlowGraph(object):
 
 
 def _process_sub_block_pair(pdesc, sub_block_pair):
+    """Creates a list of tuple each of which tracks info of a subblock.
+
+      Note: this function doesn't handle nested subblocks yet.
+      TODO(panyx0718): assert if case nested subblocks happen.
+
+    :param pdesc: ProgramDesc.
+    :param sub_block_pair: A list op pairs. Each op pair is the forward
+        op and backward op. The ops in the list are special that they contain
+        a subblock of ops.
+    :return: A list of tuples, each tuple is (all ops in a subblock pair
+        including forward and backward, number of forward ops,
+        all output args names of the ops in the subblock pairs).
+    """
     ops_list = []
     block_desc = pdesc.block(0)
     op_size = block_desc.op_size()
@@ -308,6 +336,11 @@ def _process_sub_block_pair(pdesc, sub_block_pair):
 
 
 def _get_cfgs(input_program):
+    """Process each block and create ControlFlowGraph for each of them.
+
+    :param input_program: Program object.
+    :return: A list of ControlFlowGraph, each corresponds to a block.
+    """
     ops_list = []
     pdesc = input_program.get_desc()
     block_desc = pdesc.block(0)
@@ -316,11 +349,8 @@ def _get_cfgs(input_program):
     ops_list.append(
         ([block_desc.op(i) for i in range(op_size)], op_size, set()))
 
-    sub_block_pair = [("while", "while_grad"), ("parallel_do",
-                                                "parallel_do_grad"),
-                      ("conditional_block", "conditional_block_grad")]
-
-    ops_list.extend(_process_sub_block_pair(pdesc, sub_block_pair))
+    # Only process one level of nested subblock.
+    ops_list.extend(_process_sub_block_pair(pdesc, SUB_BLOCK_PAIR))
 
     cfgs = [
         ControlFlowGraph(input_program, ops, forward_num, skip_opt)
@@ -330,6 +360,17 @@ def _get_cfgs(input_program):
 
 
 def memory_optimize(input_program, print_log=False, level=0):
+    """Optimize memory by reusing var memory.
+
+      Note: it doesn't not support subblock nested in subblock.
+
+    :param input_program: Input Program
+    :param print_log: whether to print debug log.
+    :param level: If level=0, reuse if the shape is completely equal, o
+    :return:
+    """
+    if level != 0 and level != 1:
+        raise ValueError("only support opt_level 0 or 1.")
     global PRINT_LOG
     PRINT_LOG = print_log
     cfgs = _get_cfgs(input_program)

python/paddle/fluid/tests/book/test_label_semantic_roles.py

@@ -12,17 +12,16 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 
+import contextlib
 import math
-
 import numpy as np
+import os
+import time
+import unittest
+
 import paddle
 import paddle.dataset.conll05 as conll05
 import paddle.fluid as fluid
-from paddle.fluid.initializer import init_on_cpu
-import contextlib
-import time
-import unittest
-import os
 
 word_dict, verb_dict, label_dict = conll05.get_dict()
 word_dict_len = len(word_dict)
@@ -37,7 +36,7 @@ depth = 8
 mix_hidden_lr = 1e-3
 
 IS_SPARSE = True
-PASS_NUM = 10
+PASS_NUM = 100
 BATCH_SIZE = 10
 
 embedding_name = 'emb'
@@ -77,7 +76,8 @@ def db_lstm(word, predicate, ctx_n2, ctx_n1, ctx_0, ctx_p1, ctx_p2, mark,
     emb_layers.append(mark_embedding)
 
     hidden_0_layers = [
-        fluid.layers.fc(input=emb, size=hidden_dim) for emb in emb_layers
+        fluid.layers.fc(input=emb, size=hidden_dim, act='tanh')
+        for emb in emb_layers
     ]
 
     hidden_0 = fluid.layers.sums(input=hidden_0_layers)
@@ -94,8 +94,8 @@ def db_lstm(word, predicate, ctx_n2, ctx_n1, ctx_0, ctx_p1, ctx_p2, mark,
 
     for i in range(1, depth):
         mix_hidden = fluid.layers.sums(input=[
-            fluid.layers.fc(input=input_tmp[0], size=hidden_dim),
-            fluid.layers.fc(input=input_tmp[1], size=hidden_dim)
+            fluid.layers.fc(input=input_tmp[0], size=hidden_dim, act='tanh'),
+            fluid.layers.fc(input=input_tmp[1], size=hidden_dim, act='tanh')
         ])
 
         lstm = fluid.layers.dynamic_lstm(
@@ -109,8 +109,8 @@ def db_lstm(word, predicate, ctx_n2, ctx_n1, ctx_0, ctx_p1, ctx_p2, mark,
         input_tmp = [mix_hidden, lstm]
 
     feature_out = fluid.layers.sums(input=[
-        fluid.layers.fc(input=input_tmp[0], size=label_dict_len),
-        fluid.layers.fc(input=input_tmp[1], size=label_dict_len)
+        fluid.layers.fc(input=input_tmp[0], size=label_dict_len, act='tanh'),
+        fluid.layers.fc(input=input_tmp[1], size=label_dict_len, act='tanh')
     ])
 
     return feature_out
@@ -171,7 +171,7 @@ def train(use_cuda, save_dirname=None, is_local=True):
     # check other optimizers and check why out will be NAN
     sgd_optimizer = fluid.optimizer.SGD(
         learning_rate=fluid.layers.exponential_decay(
-            learning_rate=0.0001,
+            learning_rate=0.01,
             decay_steps=100000,
             decay_rate=0.5,
             staircase=True))
@@ -233,7 +233,7 @@ def train(use_cuda, save_dirname=None, is_local=True):
                         print("second per batch: " + str((time.time(
                         ) - start_time) / batch_id))
                     # Set the threshold low to speed up the CI test
-                    if float(pass_precision) > 0.05:
+                    if float(pass_precision) > 0.01:
                         if save_dirname is not None:
                             # TODO(liuyiqun): Change the target to crf_decode
                             fluid.io.save_inference_model(save_dirname, [

python/paddle/fluid/tests/book/test_recognize_digits.py

@@ -157,7 +157,6 @@ def train(nn_type,
         for ip in pserver_ips.split(","):
             eplist.append(':'.join([ip, port]))
         pserver_endpoints = ",".join(eplist)  # ip:port,ip:port...
-        pserver_endpoints = os.getenv("PSERVERS")
         trainers = int(os.getenv("TRAINERS"))
         current_endpoint = os.getenv("POD_IP") + ":" + port
         trainer_id = int(os.getenv("PADDLE_INIT_TRAINER_ID"))

python/paddle/fluid/tests/unittests/CMakeLists.txt

 file(GLOB TEST_OPS RELATIVE "${CMAKE_CURRENT_SOURCE_DIR}" "test_*.py")
 string(REPLACE ".py" "" TEST_OPS "${TEST_OPS}")
 
+# The fully connected test is removed whe the WITH_MKLDNN flag is OFF
+# Because the fully connected layer has only one kernel (MKLDNN)
+if(NOT WITH_MKLDNN)
+    list(REMOVE_ITEM TEST_OPS test_fc_op)
+endif(NOT WITH_MKLDNN)
+
 if(NOT WITH_DISTRIBUTE)
     list(REMOVE_ITEM TEST_OPS test_recv_op)
 endif(NOT WITH_DISTRIBUTE)
@@ -28,6 +34,8 @@ function(py_test_modules TARGET_NAME)
   endif()
 endfunction()
 
+list(REMOVE_ITEM TEST_OPS test_sequence_expand)
+
 # test time consuming OPs in a separate process for expliot parallism
 list(REMOVE_ITEM TEST_OPS test_parallel_executor)
 list(REMOVE_ITEM TEST_OPS test_warpctc_op)
@@ -64,6 +72,8 @@ else()
     endforeach(TEST_OP)
 endif(WITH_FAST_BUNDLE_TEST)
 
+#
+py_test_modules(test_sequence_expand MODULES test_sequence_expand)
 # tests with high overhead
 py_test_modules(test_parallel_executor MODULES test_parallel_executor)
 py_test_modules(test_warpctc_op MODULES test_warpctc_op ENVS FLAGS_warpctc_dir=${WARPCTC_LIB_DIR})

python/paddle/fluid/tests/unittests/test_parallel_executor.py

@@ -505,3 +505,148 @@ class ParallelExecutorTestingDuringTraining(unittest.TestCase):
                         train_loss, test_loss, atol=1e-8),
                     "Train loss: " + str(train_loss) + "\n Test loss:" +
                     str(test_loss))
+
+
+import paddle.dataset.conll05 as conll05
+import paddle.fluid as fluid
+
+word_dict, verb_dict, label_dict = conll05.get_dict()
+word_dict_len = len(word_dict)
+label_dict_len = len(label_dict)
+pred_dict_len = len(verb_dict)
+mark_dict_len = 2
+word_dim = 32
+mark_dim = 5
+hidden_dim = 512
+depth = 8
+mix_hidden_lr = 1e-3
+embedding_name = 'emb'
+
+
+def db_lstm(word, predicate, ctx_n2, ctx_n1, ctx_0, ctx_p1, ctx_p2, mark,
+            **ignored):
+    # 8 features
+    predicate_embedding = fluid.layers.embedding(
+        input=predicate,
+        size=[pred_dict_len, word_dim],
+        dtype='float32',
+        param_attr='vemb')
+
+    mark_embedding = fluid.layers.embedding(
+        input=mark, size=[mark_dict_len, mark_dim], dtype='float32')
+
+    word_input = [word, ctx_n2, ctx_n1, ctx_0, ctx_p1, ctx_p2]
+    emb_layers = [
+        fluid.layers.embedding(
+            size=[word_dict_len, word_dim],
+            input=x,
+            param_attr=fluid.ParamAttr(
+                name=embedding_name, trainable=False)) for x in word_input
+    ]
+    emb_layers.append(predicate_embedding)
+    emb_layers.append(mark_embedding)
+
+    hidden_0_layers = [
+        fluid.layers.fc(input=emb, size=hidden_dim, act='tanh')
+        for emb in emb_layers
+    ]
+
+    hidden_0 = fluid.layers.sums(input=hidden_0_layers)
+
+    lstm_0 = fluid.layers.dynamic_lstm(
+        input=hidden_0,
+        size=hidden_dim,
+        candidate_activation='relu',
+        gate_activation='sigmoid',
+        cell_activation='sigmoid')
+
+    # stack L-LSTM and R-LSTM with direct edges
+    input_tmp = [hidden_0, lstm_0]
+
+    for i in range(1, depth):
+        mix_hidden = fluid.layers.sums(input=[
+            fluid.layers.fc(input=input_tmp[0], size=hidden_dim, act='tanh'),
+            fluid.layers.fc(input=input_tmp[1], size=hidden_dim, act='tanh')
+        ])
+
+        lstm = fluid.layers.dynamic_lstm(
+            input=mix_hidden,
+            size=hidden_dim,
+            candidate_activation='relu',
+            gate_activation='sigmoid',
+            cell_activation='sigmoid',
+            is_reverse=((i % 2) == 1))
+
+        input_tmp = [mix_hidden, lstm]
+
+    feature_out = fluid.layers.sums(input=[
+        fluid.layers.fc(input=input_tmp[0], size=label_dict_len, act='tanh'),
+        fluid.layers.fc(input=input_tmp[1], size=label_dict_len, act='tanh')
+    ])
+
+    return feature_out
+
+
+class TestCRFModel(unittest.TestCase):
+    def test_all(self):
+        main = fluid.Program()
+        startup = fluid.Program()
+        with fluid.program_guard(main, startup):
+            word = fluid.layers.data(
+                name='word_data', shape=[1], dtype='int64', lod_level=1)
+            predicate = fluid.layers.data(
+                name='verb_data', shape=[1], dtype='int64', lod_level=1)
+            ctx_n2 = fluid.layers.data(
+                name='ctx_n2_data', shape=[1], dtype='int64', lod_level=1)
+            ctx_n1 = fluid.layers.data(
+                name='ctx_n1_data', shape=[1], dtype='int64', lod_level=1)
+            ctx_0 = fluid.layers.data(
+                name='ctx_0_data', shape=[1], dtype='int64', lod_level=1)
+            ctx_p1 = fluid.layers.data(
+                name='ctx_p1_data', shape=[1], dtype='int64', lod_level=1)
+            ctx_p2 = fluid.layers.data(
+                name='ctx_p2_data', shape=[1], dtype='int64', lod_level=1)
+            mark = fluid.layers.data(
+                name='mark_data', shape=[1], dtype='int64', lod_level=1)
+            feature_out = db_lstm(**locals())
+            target = fluid.layers.data(
+                name='target', shape=[1], dtype='int64', lod_level=1)
+            crf_cost = fluid.layers.linear_chain_crf(
+                input=feature_out,
+                label=target,
+                param_attr=fluid.ParamAttr(
+                    name='crfw', learning_rate=1e-1))
+            avg_cost = fluid.layers.mean(crf_cost)
+
+            sgd_optimizer = fluid.optimizer.SGD(
+                learning_rate=fluid.layers.exponential_decay(
+                    learning_rate=0.01,
+                    decay_steps=100000,
+                    decay_rate=0.5,
+                    staircase=True))
+            sgd_optimizer.minimize(avg_cost)
+
+            train_data = paddle.batch(
+                paddle.reader.shuffle(
+                    paddle.dataset.conll05.test(), buf_size=8192),
+                batch_size=16)
+
+            place = fluid.CUDAPlace(0)
+            exe = fluid.Executor(place)
+            exe.run(startup)
+
+            pe = fluid.ParallelExecutor(use_cuda=True, loss_name=avg_cost.name)
+
+            feeder = fluid.DataFeeder(
+                feed_list=[
+                    word, ctx_n2, ctx_n1, ctx_0, ctx_p1, ctx_p2, predicate,
+                    mark, target
+                ],
+                place=fluid.CPUPlace())
+
+            data = train_data()
+            for i in xrange(10):
+                cur_batch = next(data)
+                print map(numpy.array,
+                          pe.run(feed_dict=feeder.feed(cur_batch),
+                                 fetch_list=[avg_cost.name]))[0]

python/paddle/fluid/tests/unittests/test_sequence_expand.py

@@ -47,8 +47,10 @@ class TestSequenceExpand(OpTest):
             x_len = x_idx[i] - x_idx[i - 1]
             if repeat_num > 0:
                 x_sub = x_data[x_idx[i - 1]:x_idx[i], :]
-                x_sub = np.repeat(x_sub, repeat_num, axis=0)
-                out = np.vstack((out, x_sub))
+                stacked_x_sub = x_sub
+                for r in range(repeat_num - 1):
+                    stacked_x_sub = np.vstack((stacked_x_sub, x_sub))
+                out = np.vstack((out, stacked_x_sub))
                 if x_lod is not None:
                     for j in xrange(repeat_num):
                         out_lod[0].append(out_lod[0][-1] + x_len)
@@ -101,11 +103,11 @@ class TestSequenceExpandCase3(TestSequenceExpand):
 
 class TestSequenceExpandCase4(TestSequenceExpand):
     def set_data(self):
-        data = [0.1, 0.3, 0.2, 0.15, 0.25, 0.2, 0.15, 0.25, 0.1, 0.3]
+        data = np.random.uniform(0.1, 1, [5 * 2, 1])
         x_data = np.array(data).reshape([5, 2]).astype('float32')
         x_lod = [[0, 2, 5]]
-        y_data = np.random.uniform(0.1, 1, [2, 1]).astype('float32')
-        y_lod = [[0, 1, 2], [0, 1, 2]]
+        y_data = np.random.uniform(0.1, 1, [3, 1]).astype('float32')
+        y_lod = [[0, 1, 3], [0, 1, 3]]
         self.inputs = {'X': (x_data, x_lod), 'Y': (y_data, y_lod)}
 
 

python/paddle/fluid/tests/unittests/test_uniform_random_op.py

@@ -15,6 +15,16 @@
 import unittest
 import numpy as np
 from op_test import OpTest
+import paddle.fluid.core as core
+from paddle.fluid.op import Operator
+
+
+def output_hist(out):
+    hist, _ = np.histogram(out, range=(-5, 10))
+    hist = hist.astype("float32")
+    hist /= float(out.size)
+    prob = 0.1 * np.ones((10))
+    return hist, prob
 
 
 class TestUniformRandomOp(OpTest):
@@ -33,11 +43,37 @@ class TestUniformRandomOp(OpTest):
         self.check_output_customized(self.verify_output)
 
     def verify_output(self, outs):
-        tensor = outs[0]
-        hist, _ = np.histogram(outs[0], range=(-5, 10))
-        hist = hist.astype("float32")
-        hist /= float(outs[0].size)
-        prob = 0.1 * np.ones((10))
+        hist, prob = output_hist(np.array(outs[0]))
+        self.assertTrue(
+            np.allclose(
+                hist, prob, rtol=0, atol=0.01), "hist: " + str(hist))
+
+
+class TestUniformRandomOpSelectedRows(unittest.TestCase):
+    def get_places(self):
+        places = [core.CPUPlace()]
+        if core.is_compiled_with_cuda():
+            places.append(core.CUDAPlace(0))
+        return places
+
+    def test_check_output(self):
+        for place in self.get_places():
+            self.check_with_place(place)
+
+    def check_with_place(self, place):
+        scope = core.Scope()
+        out = scope.var("X").get_selected_rows()
+
+        op = Operator(
+            "uniform_random",
+            Out="X",
+            shape=[4, 784],
+            min=-5.0,
+            max=10.0,
+            seed=10)
+        op.run(scope, place)
+        self.assertEqual(out.get_tensor().shape(), [4, 784])
+        hist, prob = output_hist(np.array(out.get_tensor()))
         self.assertTrue(
             np.allclose(
                 hist, prob, rtol=0, atol=0.01), "hist: " + str(hist))