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

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

CMakeLists.txt

@@ -25,7 +25,6 @@ message(STATUS "CXX compiler: ${CMAKE_CXX_COMPILER}, version: "
 message(STATUS "C compiler: ${CMAKE_C_COMPILER}, version: "
         "${CMAKE_C_COMPILER_ID} ${CMAKE_C_COMPILER_VERSION}")
 
-find_package(Sphinx)
 if(NOT CMAKE_CROSSCOMPILING)
     find_package(CUDA QUIET)
 endif(NOT CMAKE_CROSSCOMPILING)
@@ -226,5 +225,7 @@ if(WITH_PYTHON)
 endif()
 
 if(WITH_DOC)
+    find_package(Sphinx REQUIRED)
+    find_python_module(recommonmark REQUIRED)
     add_subdirectory(doc)
 endif()

cmake/external/mkldnn.cmake

@@ -56,6 +56,8 @@ ExternalProject_Add(
     GIT_TAG             "v0.14"
     PREFIX              ${MKLDNN_SOURCES_DIR}
     UPDATE_COMMAND      ""
+    # Patch MKLDNN to compile with gcc 4.8, the related issue is in intel/mkl-dnn#237.
+    PATCH_COMMAND       ${CMAKE_COMMAND} -E copy_if_different ${CMAKE_CURRENT_SOURCE_DIR}/patches/mkldnn.hpp ${MKLDNN_SOURCES_DIR}/src/extern_mkldnn/include/mkldnn.hpp
     CMAKE_ARGS          -DCMAKE_INSTALL_PREFIX=${MKLDNN_INSTALL_DIR}
     CMAKE_ARGS          -DCMAKE_BUILD_TYPE=${CMAKE_BUILD_TYPE} 
     CMAKE_ARGS          -DMKLROOT=${MKLML_ROOT}

doc/v2/build_and_install/build_from_source_cn.rst

@@ -19,8 +19,9 @@
 ----------------
 
 PaddlePaddle需要使用Docker环境完成编译，这样可以免去单独安装编译依赖的步骤，可选的不同编译环境Docker镜像
-可以在 `这里 <https://hub.docker.com/r/paddlepaddle/paddle_manylinux_devel/tags/>`_ 找到。或者
-参考下述可选步骤，从源码中构建用于编译PaddlePaddle的Docker镜像。
+可以在 `这里 <https://hub.docker.com/r/paddlepaddle/paddle_manylinux_devel/tags/>`_ 找到，您也可以
+在 `这里 <https://github.com/PaddlePaddle/Paddle/tree/develop/tools/manylinux1/>`_ 找到 paddle_manylinux_devel
+镜像的编译以及使用方法。或者参考下述可选步骤，从源码中构建用于编译PaddlePaddle的Docker镜像。
 
 如果您选择不使用Docker镜像，则需要在本机安装下面章节列出的 `编译依赖`_ 之后才能开始编译的步骤。
 

doc/v2/build_and_install/build_from_source_en.rst

@@ -22,6 +22,8 @@ How To Build
 You need to use Docker to build PaddlePaddle
 to avoid installing dependencies by yourself. We have several pre-built
 Docker images `here <https://hub.docker.com/r/paddlepaddle/paddle_manylinux_devel/tags/>`_ ,
+you can also find how to build and use paddle_manylinux_devel Docker image from
+`here <https://github.com/PaddlePaddle/Paddle/tree/develop/tools/manylinux1/>`_
 Or you can build your own image from source as the optional step below:
 
 .. code-block:: bash

paddle/fluid/framework/operator.h

@@ -192,6 +192,10 @@ class ExecutionContext {
     return op_.Attr<T>(name);
   }
 
+  bool HasInput(const std::string& name) const { return op_.HasInputs(name); }
+
+  bool HasOutput(const std::string& name) const { return op_.HasOutputs(name); }
+
   size_t InputSize(const std::string& name) const {
     return op_.Inputs(name).size();
   }

paddle/fluid/framework/parallel_executor.cc

@@ -58,7 +58,8 @@ ParallelExecutor::ParallelExecutor(
     const std::unordered_set<std::string> &bcast_vars,
     const ProgramDesc &main_program, const std::string &loss_var_name,
     Scope *scope, const std::vector<Scope *> &local_scopes, bool allow_op_delay,
-    bool use_default_grad_scale, bool balance_parameter_opt_between_cards)
+    bool use_default_grad_scale, bool balance_parameter_opt_between_cards,
+    size_t num_trainers, size_t trainer_id)
     : member_(new ParallelExecutorPrivate(places)) {
   member_->global_scope_ = scope;
 
@@ -80,7 +81,13 @@ ParallelExecutor::ParallelExecutor(
 
 // Bcast Parameters to all GPUs
 #ifdef PADDLE_WITH_CUDA
-  member_->nccl_ctxs_.reset(new platform::NCCLContextMap(member_->places_));
+  auto *nccl_id_var = scope->FindVar(NCCL_ID_VARNAME);
+  ncclUniqueId *nccl_id = nullptr;
+  if (nccl_id_var != nullptr) {
+    nccl_id = nccl_id_var->GetMutable<ncclUniqueId>();
+  }
+  member_->nccl_ctxs_.reset(new platform::NCCLContextMap(
+      member_->places_, nccl_id, num_trainers, trainer_id));
 #endif
   if (platform::is_gpu_place(places[0]) && member_->local_scopes_.size() != 1 &&
       local_scopes.empty()) {  // Is CUDA

paddle/fluid/framework/parallel_executor.h

@@ -41,7 +41,8 @@ class ParallelExecutor {
                             const std::string& loss_var_name, Scope* scope,
                             const std::vector<Scope*>& local_scopes,
                             bool allow_op_delay, bool use_default_grad_scale,
-                            bool balance_parameter_opt_between_cards);
+                            bool balance_parameter_opt_between_cards,
+                            size_t num_trainers = 1, size_t trainer_id = 0);
 
   ~ParallelExecutor();
 

paddle/fluid/inference/analysis/CMakeLists.txt

-cc_library(dot SRCS dot.cc)
+cc_library(analysis SRCS dot.cc node.cc node.h)
+cc_test(test_node SRCS node_tester.cc DEPS analysis)

paddle/fluid/inference/analysis/device.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. */
+
+namespace paddle {
+namespace inference {
+namespace analysis {
+
+enum class Device { CPU, GPU };
+
+}  // namespace analysis
+}  // namespace inference
+}  // namespace paddle

paddle/fluid/inference/analysis/dot.h

@@ -21,6 +21,7 @@
 
 #include <glog/logging.h>
 #include <sstream>
+#include <string>
 #include <unordered_map>
 #include <vector>
 

paddle/fluid/inference/analysis/dot_tester.cc

+//   Copyright (c) 2018 PaddlePaddle Authors. All Rights Reserved.
+//
+// Licensed under the Apache License, Version 2.0 (the "License");
+// you may not use this file except in compliance with the License.
+// You may obtain a copy of the License at
+//
+//     http://www.apache.org/licenses/LICENSE-2.0
+//
+// Unless required by applicable law or agreed to in writing, software
+// distributed under the License is distributed on an "AS IS" BASIS,
+// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+// See the License for the specific language governing permissions and
+// limitations under the License.
+
+#include "paddle/fluid/inference/analysis/dot.h"
+
+#include <gtest/gtest.h>
+#include <memory>
+#include "paddle/fluid/inference/analysis/data_flow_graph.h"
+
+namespace paddle {
+namespace inference {
+namespace analysis {
+
+class DotTester : public ::testing::Test {
+ protected:
+  void SetUp() override {
+    std::vector<Dot::Attr> attrs({{"title", "hello"}});
+    dot.reset(new Dot(attrs));
+    dot->AddNode("a", {Dot::Attr{"shape", "box"}, Dot::Attr("color", "blue")});
+    dot->AddNode("b", {});
+    dot->AddNode("c", {});
+    dot->AddEdge("a", "b", {});
+    dot->AddEdge("b", "c", {});
+    dot->AddEdge("a", "c", {});
+  }
+
+  std::unique_ptr<Dot> dot;
+};
+
+TEST_F(DotTester, Build) {
+  auto codes = dot->Build();
+  // Output the DOT language code, the generated codes are too long to compare
+  // the string.
+  //
+  // The output is
+  //
+  // digraph G {
+  //   title="hello"
+  //   node_1
+  //   node_2
+  //   node_0[label="a" shape="box" color="blue"]
+  //   node_0->node_1
+  //   node_1->node_2
+  //   node_0->node_2
+  // } // end G
+  LOG(INFO) << '\n' << codes;
+}
+
+}  // namespace analysis
+}  // namespace inference
+}  // namespace paddle

paddle/fluid/inference/analysis/helper.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 <string>
+#include <unordered_map>
+#include <vector>
+
+#include "paddle/fluid/platform/enforce.h"
+
+namespace paddle {
+namespace inference {
+namespace analysis {
+
+template <typename IteratorT>
+class iterator_range {
+  IteratorT begin_, end_;
+
+ public:
+  template <typename Container>
+  explicit iterator_range(Container &&c) : begin_(c.begin()), end_(c.end()) {}
+
+  iterator_range(const IteratorT &begin, const IteratorT &end)
+      : begin_(begin), end_(end) {}
+
+  const IteratorT &begin() const { return begin_; }
+  const IteratorT &end() const { return end_; }
+};
+
+/*
+ * An registry helper class, with its records keeps the order they registers.
+ */
+template <typename T>
+class OrderedRegistry {
+ public:
+  T *Register(const std::string &name, T *x) {
+    PADDLE_ENFORCE(!dic_.count(name));
+    dic_[name] = data_.size();
+    data_.emplace_back(std::unique_ptr<T>(x));
+    return data_.back().get();
+  }
+
+  T *Lookup(const std::string &name) {
+    auto it = dic_.find(name);
+    if (it == dic_.end()) return nullptr;
+    return data_[it->second].get();
+  }
+
+ protected:
+  std::unordered_map<std::string, int> dic_;
+  std::vector<std::unique_ptr<T>> data_;
+};
+
+}  // namespace analysis
+}  // namespace inference
+}  // namespace paddle
+
+#define PADDLE_DISALLOW_COPY_AND_ASSIGN(type__) \
+                                                \
+  type__(const type__ &) = delete;              \
+                                                \
+  void operator=(const type__ &) = delete;

paddle/fluid/inference/analysis/node.cc

+/* Copyright (c) 2018 PaddlePaddle Authors. All Rights Reserved.
+
+Licensed under the Apache License, Version 2.0 (the "License");
+you may not use this file except in compliance with the License.
+You may obtain a copy of the License at
+
+http://www.apache.org/licenses/LICENSE-2.0
+
+Unless required by applicable law or agreed to in writing, software
+distributed under the License is distributed on an "AS IS" BASIS,
+WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+See the License for the specific language governing permissions and
+limitations under the License. */
+
+#include "paddle/fluid/inference/analysis/node.h"
+#include "glog/logging.h"
+#include "paddle/fluid/platform/enforce.h"
+
+namespace paddle {
+namespace inference {
+namespace analysis {
+
+std::vector<Dot::Attr> Value::dot_attrs() const {
+  return std::vector<Dot::Attr>({Dot::Attr("style", "filled,rounded"),
+                                 Dot::Attr("shape", "box"),
+                                 Dot::Attr("fillcolor", "red")});
+}
+
+std::vector<Dot::Attr> Function::dot_attrs() const {
+  return std::vector<Dot::Attr>({Dot::Attr("style", "filled,rounded"),
+                                 Dot::Attr("shape", "diamond"),
+                                 Dot::Attr("fillcolor", "yellow")});
+}
+
+Node *NodeMap::Create(Node::Type type) {
+  switch (type) {
+    case Node::Type::kFunction:
+      nodes_.emplace_back(new Function);
+      break;
+    case Node::Type::kValue:
+      nodes_.emplace_back(new Value);
+      break;
+    default:
+      PADDLE_THROW("Not supported node type.");
+  }
+  nodes_.back()->id_ = size() - 1;
+  return nodes_.back().get();
+}
+
+Node *NodeMap::GetMutable(size_t id) {
+  PADDLE_ENFORCE_GT(size(), id);
+  return nodes_[id].get();
+}
+
+const Node &NodeMap::Get(size_t id) const {
+  PADDLE_ENFORCE_GT(size(), id);
+  return *nodes_[id].get();
+}
+
+void NodeMap::Delete(size_t id) {
+  PADDLE_ENFORCE_LT(id, size());
+  nodes_[id]->SetDeleted();
+}
+
+}  // namespace analysis
+}  // namespace inference
+}  // namespace paddle

paddle/fluid/inference/analysis/node.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. */
+
+/*
+ * This file defines the Node class and its subclasses. A Node is the basis
+ * analysis element in a computation graph.
+ * There are basically two kinds of nodes, the function node and value node.
+ */
+#pragma once
+
+#include <memory>
+#include <string>
+#include <unordered_map>
+#include <vector>
+
+#include "paddle/fluid/inference/analysis/device.h"
+#include "paddle/fluid/inference/analysis/dot.h"
+#include "paddle/fluid/inference/analysis/helper.h"
+
+namespace paddle {
+namespace inference {
+namespace analysis {
+
+class NodeMap;
+
+/*
+ * Node Representation.
+ *
+ * This is a very important class for analysis. It is the base class of all
+ * nodes computed by a program that may be used as operands to other nodes.
+ * Node is the super class of other important classes such as Function and
+ * Value, some nodes can have a name.
+ */
+class Node {
+ public:
+  // Node type. NOTE the new node types should add here.
+  enum class Type { kNone = -1, kFunction, kValue, kFunctionBlock };
+
+  Node() = default;
+
+  struct Attr;
+
+  // Cast to a subclass type, Function for example.
+  template <typename Subclass>
+  Subclass &As() {
+    return *dynamic_cast<Subclass *>(this);
+  }
+
+  // Formatted representation of this Node.
+  virtual std::string repr() const {
+    return name() + "(" + std::to_string(id()) + ")";
+  }
+
+  // DOT node representation. One Node type can customize its own node
+  // representation.
+  virtual std::vector<Dot::Attr> dot_attrs() const {
+    return std::vector<Dot::Attr>({Dot::Attr("style", "filled")});
+  }
+
+  // Get an additional attribute and convert it to T data type. NOTE this will
+  // silently create a new attribute if not exists.
+  Attr &attr(const std::string &name) { return attrs_[name]; }
+
+  int id() const { return id_; }
+
+  bool deleted() const { return deleted_; }
+  void SetDeleted() { deleted_ = true; }
+
+  void SetName(const std::string &name) { name_ = name; }
+  const std::string &name() const { return name_; }
+
+  void SetType(Type type) { type_ = type; }
+  Type type() const { return type_; }
+
+  void *extra_info() const { return extra_info_; }
+  void SetExtraInfo(void *extra_info) { extra_info_ = extra_info; }
+
+  // Input links.
+  std::vector<Node *> inlinks;
+  // Output links.
+  std::vector<Node *> outlinks;
+
+  // A helper class to maintain the status from Pass.
+  // TODO(superjomn) add a checker here to ensure the T is primary.
+  struct Attr {
+    // NOTE T should be a primary type or a struct combined by several primary
+    // types.
+    // NOTE the STL containers should not use here.
+    // Some usages
+    // Attr attr;
+    // T data;
+    // attr.data.assign((char*)data, sizeof(data));
+
+    bool &Bool() { return As<bool>(); }
+    float &Float() { return As<float>(); }
+    int32_t &Int32() { return As<int32_t>(); }
+    int64_t &Int64() { return As<int64_t>(); }
+
+   private:
+    template <typename T>
+    T &As() {
+      // init storage in the first usage.
+      if (data_.empty()) {
+        VLOG(4) << "resize data to " << sizeof(T);
+        type_hash_ = typeid(T).hash_code();
+        data_.resize(sizeof(T));
+      }
+      PADDLE_ENFORCE(type_hash_ == typeid(T).hash_code(), "type not matched");
+      PADDLE_ENFORCE_EQ(data_.size(), sizeof(T), "Node attr type recast error");
+      return *reinterpret_cast<T *>(&data_[0]);
+    }
+
+   private:
+    std::string data_;
+    size_t type_hash_{std::numeric_limits<size_t>::max()};
+  };
+
+  virtual ~Node() {}
+
+  friend class NodeMap;
+
+  PADDLE_DISALLOW_COPY_AND_ASSIGN(Node);
+
+ protected:
+  // The id number not the name is a node's unique identifier in the computation
+  // graph.
+  int id_{-1};
+  std::string name_;
+  Type type_{Type::kNone};
+  // Mark this node is deleted by some pass.
+  bool deleted_{false};
+
+  void *extra_info_;
+
+  mutable std::unordered_map<std::string, Attr> attrs_;
+};
+
+class Function;
+/*
+ * Value represents a value node, it has some attributes including dims, data
+ * type and so on.
+ */
+class Value : public Node {
+ public:
+  enum class DataType { kInt32, kInt64, kFloat32, kFloat64 };
+  using Dims = std::vector<int>;
+
+  void SetDataType(DataType data_type) { data_type_ = data_type; }
+  DataType data_type() const { return data_type_; }
+
+  void SetDims(const Dims &dims) { dims_ = dims; }
+  const Dims &dims() const { return dims_; }
+
+  Device device() const { return device_; }
+  void SetDevice(Device device) { device_ = device; }
+
+  std::vector<Dot::Attr> dot_attrs() const override;
+
+  PADDLE_DISALLOW_COPY_AND_ASSIGN(Value);
+
+ protected:
+  Value() { SetType(Node::Type::kValue); }
+  friend class NodeMap;
+
+ private:
+  DataType data_type_;
+  Dims dims_;
+  Device device_;
+};
+
+/*
+ * Function represents any kind of executable concepts that takes several Values
+ * as input, and outputs several Values.
+ */
+class Function : public Node {
+ public:
+  std::vector<Dot::Attr> dot_attrs() const override;
+
+  // Get the operator's type from Desc.
+  const std::string &func_type() const { return func_type_; }
+  // Set the operator's type.
+  void SetFuncType(const std::string &func_type) { func_type_ = func_type; }
+
+  PADDLE_DISALLOW_COPY_AND_ASSIGN(Function);
+
+ protected:
+  std::string func_type_;
+  Function() { SetType(Node::Type::kFunction); }
+  friend class NodeMap;
+};
+
+/*
+ * FunctionBlock is a Node that contains a sub-graph multiple Node.
+ */
+struct FunctionBlock : public Node {
+  std::string repr() const override { return "block-" + std::to_string(id()); }
+  std::vector<Node *> subgraph;
+};
+
+class NodeMap {
+ public:
+  // Create a new node with type.
+  Node *Create(Node::Type type);
+
+  // Get a node by its id.
+  Node *GetMutable(size_t id);
+
+  const Node &Get(size_t id) const;
+
+  void Delete(size_t id);
+
+  const std::vector<std::unique_ptr<Node>> &nodes() { return nodes_; }
+
+  size_t size() const { return nodes_.size(); }
+
+ private:
+  std::vector<std::unique_ptr<Node>> nodes_;
+  std::unordered_map<std::string, Node *> map_;
+};
+
+}  // namespace analysis
+}  // namespace inference
+}  // namespace paddle

paddle/fluid/inference/analysis/node_tester.cc

+/* Copyright (c) 2018 PaddlePaddle Authors. All Rights Reserved.
+
+   Licensed under the Apache License, Version 2.0 (the "License");
+   you may not use this file except in compliance with the License.
+   You may obtain a copy of the License at
+
+   http://www.apache.org/licenses/LICENSE-2.0
+
+   Unless required by applicable law or agreed to in writing, software
+   distributed under the License is distributed on an "AS IS" BASIS,
+   WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+   See the License for the specific language governing permissions and
+   limitations under the License. */
+
+#include "paddle/fluid/inference/analysis/node.h"
+
+#include <gtest/gtest.h>
+
+namespace paddle {
+namespace inference {
+namespace analysis {
+
+TEST(Node, Attr) {
+  // Node is an abstract class, use Value instead for they share the same Attr
+  // logic.
+  NodeMap nodes;
+  auto* node = nodes.Create(Node::Type::kValue);
+  node->attr("v0").Int32() = 2008;
+  ASSERT_EQ(node->attr("v0").Int32(), 2008);
+}
+
+}  // namespace analysis
+}  // namespace inference
+}  // namespace paddle

paddle/fluid/inference/engine.h

@@ -14,6 +14,7 @@ limitations under the License. */
 
 #pragma once
 
+#include <string>
 #include "paddle/fluid/framework/framework.pb.h"
 
 namespace paddle {
@@ -58,8 +59,8 @@ class EngineBase {
 
 struct Buffer {
   void* buffer{nullptr};               // buffer should be allocated only once.
-  int max_size;                        // buffer allocated space.
-  int size;                            // data size.
+  size_t max_size;                     // buffer allocated space.
+  size_t size;                         // data size.
   DeviceType device{DeviceType::UNK};  // tells which device this buffer is on.
 };
 

paddle/fluid/inference/tensorrt/CMakeLists.txt

 nv_library(tensorrt_engine SRCS engine.cc DEPS framework_proto)
 nv_test(test_tensorrt SRCS test_tensorrt.cc DEPS dynload_cuda device_context dynamic_loader)
 nv_test(test_tensorrt_engine SRCS test_engine.cc DEPS dynload_cuda tensorrt_engine)
-
 add_subdirectory(convert)

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

 nv_test(test_op_converter SRCS test_op_converter.cc mul_op.cc conv2d_op.cc DEPS ${FLUID_CORE_MODULES})
-nv_test(test_trt_activation_op SRCS test_activation_op.cc activation_op.cc
+nv_test(test_trt_activation_op SRCS test_activation_op.cc activation_op.cc io_converter.cc
   DEPS ${FLUID_CORE_MODULES} activation_op tensorrt_engine)
 nv_test(test_io_converter SRCS test_io_converter.cc io_converter.cc DEPS dynload_cuda dynamic_loader lod_tensor)

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

@@ -21,15 +21,18 @@ namespace tensorrt {
 class ReluOpConverter : public OpConverter {
  public:
   ReluOpConverter() {}
-  void operator()(const framework::OpDesc& op) override {
+  void operator()(const framework::proto::OpDesc& op) override {
+    // Here the two nullptr looks strange, that's because the
+    // framework::OpDesc's constructor is strange.
+    framework::OpDesc op_desc(op, nullptr, nullptr);
     LOG(INFO) << "convert a fluid relu op to tensorrt activation layer whose "
                  "type is Relu";
     const nvinfer1::ITensor* input_tensor =
-        engine_->GetITensor(op.Input("X")[0]);
+        engine_->GetITensor(op_desc.Input("X")[0]);
     nvinfer1::IActivationLayer* layer = TRT_ENGINE_ADD_LAYER(
         engine_, Activation, *const_cast<nvinfer1::ITensor*>(input_tensor),
         nvinfer1::ActivationType::kRELU);
-    engine_->SetITensor(op.Output("Out")[0], layer->getOutput(0));
+    engine_->SetITensor(op_desc.Output("Out")[0], layer->getOutput(0));
   }
 };
 

paddle/fluid/inference/tensorrt/convert/conv2d_op.cc

@@ -21,7 +21,7 @@ namespace tensorrt {
 class Conv2dOpConverter : public OpConverter {
  public:
   Conv2dOpConverter() {}
-  void operator()(const framework::OpDesc& op) override {
+  void operator()(const framework::proto::OpDesc& op) override {
     LOG(INFO)
         << "convert a fluid conv2d op to tensorrt conv layer without bias";
   }

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

@@ -23,26 +23,42 @@ namespace tensorrt {
 using platform::is_gpu_place;
 using platform::is_cpu_place;
 
-class DefaultInputConverter : public EngineInputConverter {
+class DefaultIOConverter : public EngineIOConverter {
  public:
-  DefaultInputConverter() {}
+  DefaultIOConverter() {}
   // NOTE out is GPU memory.
   virtual void operator()(const LoDTensor& in, void* out,
                           size_t max_size) override {
     PADDLE_ENFORCE(out != nullptr);
-    PADDLE_ENFORCE_LE(in.memory_size(), max_size);
+    PADDLE_ENFORCE(stream_ != nullptr);
     const auto& place = in.place();
+    size_t size = in.memory_size();
+    PADDLE_ENFORCE_LE(size, max_size);
     if (is_cpu_place(place)) {
-      PADDLE_ENFORCE(stream_ != nullptr);
-      PADDLE_ENFORCE_EQ(0,
-                        cudaMemcpyAsync(out, in.data<float>(), in.memory_size(),
-                                        cudaMemcpyHostToDevice, *stream_));
-
+      PADDLE_ENFORCE_EQ(0, cudaMemcpyAsync(out, in.data<float>(), size,
+                                           cudaMemcpyHostToDevice, *stream_));
     } else if (is_gpu_place(place)) {
-      PADDLE_ENFORCE_EQ(0,
-                        cudaMemcpyAsync(out, in.data<float>(), in.memory_size(),
-                                        cudaMemcpyHostToHost, *stream_));
-
+      PADDLE_ENFORCE_EQ(0, cudaMemcpyAsync(out, in.data<float>(), size,
+                                           cudaMemcpyDeviceToDevice, *stream_));
+    } else {
+      PADDLE_THROW("Unknown device for converter");
+    }
+    cudaStreamSynchronize(*stream_);
+  }
+  // NOTE in is GPU memory.
+  virtual void operator()(const void* in, LoDTensor* out,
+                          size_t max_size) override {
+    PADDLE_ENFORCE(in != nullptr);
+    PADDLE_ENFORCE(stream_ != nullptr);
+    const auto& place = out->place();
+    size_t size = out->memory_size();
+    PADDLE_ENFORCE_LE(size, max_size);
+    if (is_cpu_place(place)) {
+      PADDLE_ENFORCE_EQ(0, cudaMemcpyAsync(out->data<float>(), in, size,
+                                           cudaMemcpyDeviceToHost, *stream_));
+    } else if (is_gpu_place(place)) {
+      PADDLE_ENFORCE_EQ(0, cudaMemcpyAsync(out->data<float>(), in, size,
+                                           cudaMemcpyDeviceToDevice, *stream_));
     } else {
       PADDLE_THROW("Unknown device for converter");
     }
@@ -50,7 +66,8 @@ class DefaultInputConverter : public EngineInputConverter {
   }
 };
 
-REGISTER_TENSORRT_INPUT_CONVERTER(default, DefaultInputConverter);
+// fluid LodTensor <-> tensorrt ITensor
+REGISTER_TENSORRT_IO_CONVERTER(default, DefaultIOConverter);
 
 }  // namespace tensorrt
 }  // namespace inference

paddle/fluid/inference/tensorrt/convert/io_converter.h

@@ -14,6 +14,7 @@ limitations under the License. */
 
 #pragma once
 
+#include <string>
 #include <unordered_map>
 #include "paddle/fluid/framework/lod_tensor.h"
 #include "paddle/fluid/inference/utils/singleton.h"
@@ -25,43 +26,57 @@ namespace tensorrt {
 using framework::LoDTensor;
 
 /*
- * Convert Input from Fluid to an Engine.
- * TensorRT's ITensor follows row major, NCHW. Fluid is also row major, so in
- * most cases just need to copy the data.
+ * Convert Input from Fluid to TensorRT Engine.
+ * Convert Output from TensorRT Engine to Fluid.
+ *
+ * Note that TensorRT's ITensor follows row major, NCHW. Fluid is also row
+ * major,
+ * so in the default case just need to copy the data.
  */
-class EngineInputConverter {
+class EngineIOConverter {
  public:
-  EngineInputConverter() {}
+  EngineIOConverter() {}
 
   virtual void operator()(const LoDTensor& in, void* out, size_t max_size) {}
+  virtual void operator()(const void* in, LoDTensor* out, size_t max_size) {}
 
   void SetStream(cudaStream_t* stream) { stream_ = stream; }
 
-  static void Run(const std::string& in_op_type, const LoDTensor& in, void* out,
-                  size_t max_size, cudaStream_t* stream) {
+  static void ConvertInput(const std::string& op_type, const LoDTensor& in,
+                           void* out, size_t max_size, cudaStream_t* stream) {
     PADDLE_ENFORCE(stream != nullptr);
-    auto* converter = Registry<EngineInputConverter>::Lookup(
-        in_op_type, "default" /* default_type */);
+    auto* converter = Registry<EngineIOConverter>::Lookup(
+        op_type, "default" /* default_type */);
     PADDLE_ENFORCE_NOT_NULL(converter);
     converter->SetStream(stream);
     (*converter)(in, out, max_size);
   }
 
-  virtual ~EngineInputConverter() {}
+  static void ConvertOutput(const std::string& op_type, const void* in,
+                            LoDTensor* out, size_t max_size,
+                            cudaStream_t* stream) {
+    PADDLE_ENFORCE(stream != nullptr);
+    auto* converter = Registry<EngineIOConverter>::Lookup(
+        op_type, "default" /* default_type */);
+    PADDLE_ENFORCE_NOT_NULL(converter);
+    converter->SetStream(stream);
+    (*converter)(in, out, max_size);
+  }
+
+  virtual ~EngineIOConverter() {}
 
  protected:
   cudaStream_t* stream_{nullptr};
 };
 
+#define REGISTER_TENSORRT_IO_CONVERTER(op_type__, Converter__)        \
+  struct trt_io_##op_type__##_converter {                             \
+    trt_io_##op_type__##_converter() {                                \
+      Registry<EngineIOConverter>::Register<Converter__>(#op_type__); \
+    }                                                                 \
+  };                                                                  \
+  trt_io_##op_type__##_converter trt_io_##op_type__##_converter__;
+
 }  // namespace tensorrt
 }  // namespace inference
 }  // namespace paddle
-
-#define REGISTER_TENSORRT_INPUT_CONVERTER(in_op_type__, Converter__) \
-  struct trt_input_##in_op_type__##_converter {                      \
-    trt_input_##in_op_type__##_converter() {                         \
-      ::paddle::inference::Registry<EngineInputConverter>::Register< \
-          Converter__>(#in_op_type__);                               \
-    }                                                                \
-  };                                                                 \
-  trt_input_##in_op_type__##_converter trt_input_##in_op_type__##_converter__;

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

@@ -21,7 +21,7 @@ namespace tensorrt {
 class MulOpConverter : public OpConverter {
  public:
   MulOpConverter() {}
-  void operator()(const framework::OpDesc& op) override {
+  void operator()(const framework::proto::OpDesc& op) override {
     LOG(INFO) << "convert a fluid mul op to tensorrt fc layer without bias";
   }
 };

paddle/fluid/inference/tensorrt/convert/op_converter.h

@@ -31,10 +31,10 @@ namespace tensorrt {
 class OpConverter {
  public:
   OpConverter() {}
-  virtual void operator()(const framework::OpDesc& op) {}
+  virtual void operator()(const framework::proto::OpDesc& op) {}
 
-  void Run(const framework::OpDesc& op, TensorRTEngine* engine) {
-    std::string type = op.Type();
+  void Run(const framework::proto::OpDesc& op, TensorRTEngine* engine) {
+    std::string type = op.type();
     auto* it = Registry<OpConverter>::Lookup(type);
     PADDLE_ENFORCE_NOT_NULL(it, "no OpConverter for optype [%s]", type);
     it->SetEngine(engine);
@@ -42,14 +42,16 @@ class OpConverter {
   }
 
   // convert fluid op to tensorrt layer
-  void ConvertOp(const framework::OpDesc& op, TensorRTEngine* engine) {
+  void ConvertOp(const framework::proto::OpDesc& op, TensorRTEngine* engine) {
     OpConverter::Run(op, engine);
   }
 
   // convert fluid block to tensorrt network
-  void ConvertBlock(const framework::BlockDesc& block, TensorRTEngine* engine) {
-    for (auto op : block.AllOps()) {
-      OpConverter::Run(*op, engine);
+  void ConvertBlock(const framework::proto::BlockDesc& block,
+                    TensorRTEngine* engine) {
+    for (size_t i = 0; i < block.ops_size(); i++) {
+      const auto& op = block.ops(i);
+      OpConverter::Run(op, engine);
     }
   }
 

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

@@ -16,6 +16,7 @@ limitations under the License. */
 #include "paddle/fluid/framework/lod_tensor.h"
 #include "paddle/fluid/framework/op_registry.h"
 #include "paddle/fluid/framework/program_desc.h"
+#include "paddle/fluid/inference/tensorrt/convert/io_converter.h"
 #include "paddle/fluid/inference/tensorrt/convert/op_converter.h"
 #include "paddle/fluid/platform/device_context.h"
 #include "paddle/fluid/platform/place.h"
@@ -26,7 +27,7 @@ namespace paddle {
 namespace inference {
 namespace tensorrt {
 
-void Compare(float input, float expect) {
+void Compare(const std::string op_type, float input, float expect) {
   framework::Scope scope;
   platform::CUDAPlace place;
   platform::CUDADeviceContext ctx(place);
@@ -35,6 +36,7 @@ void Compare(float input, float expect) {
   auto x_var = scope.Var("X");
   auto x_tensor = x_var->GetMutable<framework::LoDTensor>();
   x_tensor->Resize({1, 1});
+  x_tensor->mutable_data<float>(place);
   std::vector<float> init;
   init.push_back(input);
   framework::TensorFromVector(init, ctx, x_tensor);
@@ -45,14 +47,15 @@ void Compare(float input, float expect) {
   out_tensor->mutable_data<float>(place);
 
   framework::OpDesc op_desc;
-  op_desc.SetType("relu");
+  op_desc.SetType(op_type);
   op_desc.SetInput("X", {"X"});
   op_desc.SetOutput("Out", {"Out"});
 
-  auto relu_op = framework::OpRegistry::CreateOp(op_desc);
+  auto op = framework::OpRegistry::CreateOp(*op_desc.Proto());
 
   // run fluid op
-  relu_op->Run(scope, place);
+  op->Run(scope, place);
+  // get fluid output
   std::vector<float> out1;
   framework::TensorToVector(*out_tensor, ctx, &out1);
 
@@ -63,21 +66,28 @@ void Compare(float input, float expect) {
   engine->InitNetwork();
   engine->DeclareInput("X", nvinfer1::DataType::kFLOAT,
                        nvinfer1::DimsCHW{1, 1, 1});
-
+  // convert op
   OpConverter op_converter;
-  op_converter.ConvertOp(op_desc, engine);
+  op_converter.ConvertOp(*op_desc.Proto(), engine);
 
   engine->DeclareOutput("Out");
   engine->FreezeNetwork();
-  engine->SetInputFromCPU("X", &input, 1 * sizeof(float));
 
-  // run tensorrt op
+  // convert LoDTensor to ITensor
+  size_t size = x_tensor->memory_size();
+  EngineIOConverter::ConvertInput(op_type, *x_tensor,
+                                  engine->buffer("X").buffer, size, &stream);
+  // run tensorrt Outp
   engine->Execute(1);
-
-  float out2;
-  engine->GetOutputInCPU("Out", &out2, 1 * sizeof(float));
-
-  ASSERT_EQ(out1[0], out2);
+  // convert ITensor to LoDTensor
+  EngineIOConverter::ConvertOutput(op_type, engine->buffer("Out").buffer,
+                                   out_tensor, size, &stream);
+  // get tensorrt output
+  std::vector<float> out2;
+  framework::TensorToVector(*out_tensor, ctx, &out2);
+
+  // compare
+  ASSERT_EQ(out1[0], out2[0]);
   ASSERT_EQ(out1[0], expect);
 
   delete engine;
@@ -85,8 +95,8 @@ void Compare(float input, float expect) {
 }
 
 TEST(OpConverter, ConvertRelu) {
-  Compare(1, 1);   // relu(1) = 1
-  Compare(-5, 0);  // relu(-5) = 0
+  Compare("relu", 1, 1);   // relu(1) = 1
+  Compare("relu", -5, 0);  // relu(-5) = 0
 }
 
 }  // namespace tensorrt

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

@@ -12,40 +12,63 @@ WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 See the License for the specific language governing permissions and
 limitations under the License. */
 
+#include <gtest/gtest.h>
 #include "paddle/fluid/framework/lod_tensor.h"
 #include "paddle/fluid/inference/tensorrt/convert/io_converter.h"
 
-#include <gtest/gtest.h>
-
 namespace paddle {
 namespace inference {
 namespace tensorrt {
 
-class EngineInputConverterTester : public ::testing::Test {
- public:
-  void SetUp() override { tensor.Resize({10, 10}); }
+void IOConverterTester(const platform::DeviceContext& ctx) {
+  cudaStream_t stream;
+  ASSERT_EQ(0, cudaStreamCreate(&stream));
 
-  framework::LoDTensor tensor;
-};
+  // init fluid in_tensor
+  framework::LoDTensor in_tensor;
+  in_tensor.Resize({10, 10});
+  auto place = ctx.GetPlace();
+  in_tensor.mutable_data<float>(place);
+  std::vector<float> init;
+  for (int64_t i = 0; i < 10 * 10; ++i) {
+    init.push_back(i);
+  }
+  framework::TensorFromVector(init, ctx, &in_tensor);
 
-TEST_F(EngineInputConverterTester, DefaultCPU) {
+  // init tensorrt buffer
   void* buffer;
-  tensor.mutable_data<float>(platform::CPUPlace());
-  ASSERT_EQ(cudaMalloc(&buffer, tensor.memory_size()), 0);
+  size_t size = in_tensor.memory_size();
+  ASSERT_EQ(cudaMalloc(&buffer, size), 0);
 
-  cudaStream_t stream;
-  EngineInputConverter::Run("test", tensor, buffer, tensor.memory_size(),
-                            &stream);
+  // convert fluid in_tensor to tensorrt buffer
+  EngineIOConverter::ConvertInput("test", in_tensor, buffer, size, &stream);
+
+  // convert tensorrt buffer to fluid out_tensor
+  framework::LoDTensor out_tensor;
+  out_tensor.Resize({10, 10});
+  out_tensor.mutable_data<float>(place);
+  EngineIOConverter::ConvertOutput("test", buffer, &out_tensor, size, &stream);
+
+  // compare in_tensor and out_tensor
+  std::vector<float> result;
+  framework::TensorToVector(out_tensor, ctx, &result);
+  EXPECT_EQ(init.size(), result.size());
+  for (size_t i = 0; i < init.size(); i++) {
+    EXPECT_EQ(init[i], result[i]);
+  }
+  cudaStreamDestroy(stream);
 }
 
-TEST_F(EngineInputConverterTester, DefaultGPU) {
-  void* buffer;
-  tensor.mutable_data<float>(platform::CUDAPlace());
-  ASSERT_EQ(cudaMalloc(&buffer, tensor.memory_size()), 0);
+TEST(EngineIOConverterTester, DefaultCPU) {
+  platform::CPUPlace place;
+  platform::CPUDeviceContext ctx(place);
+  IOConverterTester(ctx);
+}
 
-  cudaStream_t stream;
-  EngineInputConverter::Run("test", tensor, buffer, tensor.memory_size(),
-                            &stream);
+TEST(EngineIOConverterTester, DefaultGPU) {
+  platform::CUDAPlace place;
+  platform::CUDADeviceContext ctx(place);
+  IOConverterTester(ctx);
 }
 
 }  // namespace tensorrt

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

@@ -29,7 +29,7 @@ TEST(OpConverter, ConvertBlock) {
   conv2d_op->SetType("conv2d");
 
   OpConverter converter;
-  converter.ConvertBlock(*block, nullptr /*TensorRTEngine*/);
+  converter.ConvertBlock(*block->Proto(), nullptr /*TensorRTEngine*/);
 }
 
 }  // namespace tensorrt

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

@@ -16,7 +16,6 @@ limitations under the License. */
 #include "gtest/gtest.h"
 #include "paddle/fluid/inference/tests/test_helper.h"
 
-DEFINE_string(data_set, "cifar10", "Data set to test");
 DEFINE_string(dirname, "", "Directory of the inference model.");
 DEFINE_string(fp16_dirname, "", "Directory of the float16 inference model.");
 DEFINE_int32(batch_size, 1, "Batch size of input data");
@@ -35,19 +34,19 @@ TEST(inference, image_classification) {
   // 0. Call `paddle::framework::InitDevices()` initialize all the devices
   // In unittests, this is done in paddle/testing/paddle_gtest_main.cc
 
+  const bool is_combined = false;
+  std::vector<std::vector<int64_t>> feed_target_shapes =
+      GetFeedTargetShapes(dirname, is_combined);
+
   paddle::framework::LoDTensor input;
   // Use normilized image pixels as input data,
   // which should be in the range [0.0, 1.0].
-  if (FLAGS_data_set == "cifar10") {
-    SetupTensor<float>(&input, {FLAGS_batch_size, 3, 32, 32},
-                       static_cast<float>(0), static_cast<float>(1));
-  } else if (FLAGS_data_set == "imagenet") {
-    SetupTensor<float>(&input, {FLAGS_batch_size, 3, 224, 224},
-                       static_cast<float>(0), static_cast<float>(1));
-  } else {
-    LOG(FATAL) << "Only cifar10 or imagenet is supported.";
-  }
-
+  feed_target_shapes[0][0] = FLAGS_batch_size;
+  paddle::framework::DDim input_dims =
+      paddle::framework::make_ddim(feed_target_shapes[0]);
+  LOG(INFO) << input_dims;
+  SetupTensor<float>(&input, input_dims, static_cast<float>(0),
+                     static_cast<float>(1));
   std::vector<paddle::framework::LoDTensor*> cpu_feeds;
   cpu_feeds.push_back(&input);
 
@@ -60,7 +59,7 @@ TEST(inference, image_classification) {
     LOG(INFO) << "--- CPU Runs: ---";
     LOG(INFO) << "Batch size is " << FLAGS_batch_size;
     TestInference<paddle::platform::CPUPlace, false, true>(
-        dirname, cpu_feeds, cpu_fetchs1, FLAGS_repeat);
+        dirname, cpu_feeds, cpu_fetchs1, FLAGS_repeat, is_combined);
     LOG(INFO) << output1.dims();
   }
 
@@ -73,7 +72,7 @@ TEST(inference, image_classification) {
   LOG(INFO) << "--- GPU Runs: ---";
   LOG(INFO) << "Batch size is " << FLAGS_batch_size;
   TestInference<paddle::platform::CUDAPlace, false, true>(
-      dirname, cpu_feeds, cpu_fetchs2, FLAGS_repeat);
+      dirname, cpu_feeds, cpu_fetchs2, FLAGS_repeat, is_combined);
   LOG(INFO) << output2.dims();
 
   if (!FLAGS_skip_cpu) {

paddle/fluid/inference/tests/test_helper.h

@@ -89,6 +89,50 @@ void CheckError(const paddle::framework::LoDTensor& output1,
   EXPECT_EQ(count, 0U) << "There are " << count << " different elements.";
 }
 
+std::unique_ptr<paddle::framework::ProgramDesc> InitProgram(
+    paddle::framework::Executor* executor, paddle::framework::Scope* scope,
+    const std::string& dirname, const bool is_combined = false) {
+  std::unique_ptr<paddle::framework::ProgramDesc> inference_program;
+  if (is_combined) {
+    // All parameters are saved in a single file.
+    // Hard-coding the file names of program and parameters in unittest.
+    // The file names should be consistent with that used in Python API
+    //  `fluid.io.save_inference_model`.
+    std::string prog_filename = "__model_combined__";
+    std::string param_filename = "__params_combined__";
+    inference_program =
+        paddle::inference::Load(executor, scope, dirname + "/" + prog_filename,
+                                dirname + "/" + param_filename);
+  } else {
+    // Parameters are saved in separate files sited in the specified
+    // `dirname`.
+    inference_program = paddle::inference::Load(executor, scope, dirname);
+  }
+  return inference_program;
+}
+
+std::vector<std::vector<int64_t>> GetFeedTargetShapes(
+    const std::string& dirname, const bool is_combined = false) {
+  auto place = paddle::platform::CPUPlace();
+  auto executor = paddle::framework::Executor(place);
+  auto* scope = new paddle::framework::Scope();
+
+  auto inference_program = InitProgram(&executor, scope, dirname, is_combined);
+  auto& global_block = inference_program->Block(0);
+
+  const std::vector<std::string>& feed_target_names =
+      inference_program->GetFeedTargetNames();
+  std::vector<std::vector<int64_t>> feed_target_shapes;
+  for (size_t i = 0; i < feed_target_names.size(); ++i) {
+    auto* var = global_block.FindVar(feed_target_names[i]);
+    std::vector<int64_t> var_shape = var->GetShape();
+    feed_target_shapes.push_back(var_shape);
+  }
+
+  delete scope;
+  return feed_target_shapes;
+}
+
 template <typename Place, bool CreateVars = true, bool PrepareContext = false>
 void TestInference(const std::string& dirname,
                    const std::vector<paddle::framework::LoDTensor*>& cpu_feeds,
@@ -124,22 +168,7 @@ void TestInference(const std::string& dirname,
     paddle::platform::RecordEvent record_event(
         "init_program",
         paddle::platform::DeviceContextPool::Instance().Get(place));
-
-    if (is_combined) {
-      // All parameters are saved in a single file.
-      // Hard-coding the file names of program and parameters in unittest.
-      // The file names should be consistent with that used in Python API
-      //  `fluid.io.save_inference_model`.
-      std::string prog_filename = "__model_combined__";
-      std::string param_filename = "__params_combined__";
-      inference_program = paddle::inference::Load(
-          &executor, scope, dirname + "/" + prog_filename,
-          dirname + "/" + param_filename);
-    } else {
-      // Parameters are saved in separate files sited in the specified
-      // `dirname`.
-      inference_program = paddle::inference::Load(&executor, scope, dirname);
-    }
+    inference_program = InitProgram(&executor, scope, dirname, is_combined);
   }
   // Disable the profiler and print the timing information
   paddle::platform::DisableProfiler(paddle::platform::EventSortingKey::kDefault,

paddle/fluid/operators/CMakeLists.txt

@@ -186,6 +186,11 @@ endif()
 
 add_subdirectory(detail)
 if(WITH_DISTRIBUTE)
+    if(WITH_GPU)
+        op_library(gen_nccl_id_op DEPS nccl_common)
+    else()
+        set(DEPS_OPS ${DEPS_OPS} gen_nccl_id_op)
+    endif()
     set(DISTRIBUTE_DEPS sendrecvop_grpc grpc++_unsecure grpc_unsecure gpr cares zlib protobuf)
     set(DISTRIBUTE_COMPILE_FLAGS "-Wno-non-virtual-dtor -Wno-error=non-virtual-dtor -Wno-error=delete-non-virtual-dtor")
     op_library(send_op DEPS ${DISTRIBUTE_DEPS})
@@ -202,8 +207,9 @@ if(WITH_DISTRIBUTE)
     set_source_files_properties(send_barrier_op.cc PROPERTIES COMPILE_FLAGS ${DISTRIBUTE_COMPILE_FLAGS})
     set_source_files_properties(send_recv_op_test.cc PROPERTIES COMPILE_FLAGS ${DISTRIBUTE_COMPILE_FLAGS})
     cc_test(test_send_recv SRCS send_recv_op_test.cc DEPS prefetch_op send_op listen_and_serv_op sum_op executor)
+    cc_test(test_send_nccl_id SRCS test_send_nccl_id.cc DEPS send_op listen_and_serv_op executor)
 else()
-    set(DEPS_OPS ${DEPS_OPS} send_op prefetch_op recv_op listen_and_serv_op send_vars_op send_barrier_op)
+    set(DEPS_OPS ${DEPS_OPS} send_op prefetch_op recv_op listen_and_serv_op send_vars_op send_barrier_op gen_nccl_id_op)
 endif()
 
 op_library(cross_entropy_op DEPS cross_entropy)

paddle/fluid/operators/detail/grpc_client.cc

@@ -52,7 +52,7 @@ bool RPCClient::AsyncSendVariable(const std::string& ep,
     // stub context
     SendProcessor* s = new SendProcessor(ch);
     s->Prepare(var_h, time_out);
-    s->response_call_back_ = NULL;
+    s->response_call_back_ = nullptr;
 
     auto call = s->stub_g_.PrepareUnaryCall(
         s->context_.get(), "/sendrecv.SendRecvService/SendVariable", req, &cq_);

paddle/fluid/operators/detail/grpc_client.h

@@ -57,7 +57,9 @@ void ProcGetResponse(const VarHandle& var_h, const grpc::ByteBuffer& msg);
 
 class BaseProcessor {
  public:
-  explicit BaseProcessor(std::shared_ptr<grpc::Channel> ch) { context_ = NULL; }
+  explicit BaseProcessor(std::shared_ptr<grpc::Channel> ch) {
+    context_ = nullptr;
+  }
 
   virtual ~BaseProcessor() {}
 
@@ -105,7 +107,7 @@ class SendProcessor : public BaseProcessor {
 
   ::grpc::GenericStub stub_g_;
   ::grpc::ByteBuffer reply_;
-  RequestSendCallBack response_call_back_ = NULL;
+  RequestSendCallBack response_call_back_ = nullptr;
 };
 
 typedef std::function<void(const VarHandle&, const ::grpc::ByteBuffer&)>

paddle/fluid/operators/detail/grpc_server.h

@@ -47,6 +47,7 @@ class AsyncGRPCServer final {
   explicit AsyncGRPCServer(const std::string &address, bool sync_mode)
       : address_(address), sync_mode_(sync_mode), ready_(0) {}
 
+  ~AsyncGRPCServer() {}
   void WaitServerReady();
   void RunSyncUpdate();
 

paddle/fluid/operators/detail/send_recv.proto

@@ -32,6 +32,7 @@ service SendRecvService {
 enum VarType {
   LOD_TENSOR = 0;
   SELECTED_ROWS = 1;
+  NCCL_ID = 2;
 }
 
 // NOTICE(gongwb):don't modify this proto if you are not

paddle/fluid/operators/detail/sendrecvop_utils.cc

@@ -14,6 +14,9 @@ limitations under the License. */
 
 #include "paddle/fluid/operators/detail/sendrecvop_utils.h"
 
+#ifdef PADDLE_WITH_CUDA
+#include <nccl.h>
+#endif
 #include <sys/time.h>
 #include <thread>  // NOLINT
 
@@ -129,6 +132,10 @@ void SerializeToByteBuffer(const std::string& name, framework::Variable* var,
   } else if (var->IsType<framework::SelectedRows>()) {
     request.set_type(::sendrecv::SELECTED_ROWS);
     GetSelectedRowsPayload(var, ctx, &request, &payload, &payload_size);
+#ifdef PADDLE_WITH_CUDA
+  } else if (var->IsType<ncclUniqueId>()) {
+    request.set_type(::sendrecv::NCCL_ID);
+#endif
   } else {
     PADDLE_THROW("Serialize does not support type: %s",
                  typeid(var->Type()).name());
@@ -149,6 +156,24 @@ void SerializeToByteBuffer(const std::string& name, framework::Variable* var,
   void* buf = buffer.get();
   ProtoEncodeHelper e(static_cast<char*>(buf), 1024);
   e.WriteRawBytes(std::string(header.data(), header.size()));
+// NCCLID is copied directly to the message, return bytebuffer
+// with only one slice if serializing NCCLID.
+#ifdef PADDLE_WITH_CUDA
+  if (var->IsType<ncclUniqueId>()) {
+    e.WriteVarlengthBeginning(VarMsg::kSerializedFieldNumber,
+                              NCCL_UNIQUE_ID_BYTES);
+    const ncclUniqueId& uid = var->Get<ncclUniqueId>();
+    e.WriteRawBytes(std::string(uid.internal, NCCL_UNIQUE_ID_BYTES));
+
+    // for serialize NCCL_ID
+    ::grpc::Slice slices(e.size());
+    memcpy(const_cast<uint8_t*>(slices.begin()), e.data(), e.size());
+    ::grpc::ByteBuffer tmp(&slices, 1);
+    msg->Swap(&tmp);
+    return;
+  }
+#endif
+
   e.WriteVarlengthBeginning(VarMsg::kSerializedFieldNumber, payload_size);
   // steal reference of tensor data
   ::grpc::Slice slices[4];  // metadata, tensor, rows meta, rows

paddle/fluid/operators/detail/variable_response.cc

@@ -17,6 +17,9 @@
 #include <string>
 #include <utility>
 #include <vector>
+#ifdef PADDLE_WITH_CUDA
+#include <nccl.h>
+#endif
 #include "paddle/fluid/platform/profiler.h"
 
 #include "paddle/fluid/operators/detail/send_recv.pb.h"
@@ -368,7 +371,8 @@ int VariableResponse::Parse(Source* source) {
       }
       case sendrecv::VariableMessage::kSerializedFieldNumber: {
         PADDLE_ENFORCE((meta_.type() == sendrecv::SELECTED_ROWS ||
-                        meta_.type() == sendrecv::LOD_TENSOR) &&
+                        meta_.type() == sendrecv::LOD_TENSOR ||
+                        meta_.type() == sendrecv::NCCL_ID) &&
                            meta_.varname() != "",
                        "meta info should be got first!");
 
@@ -378,6 +382,22 @@ int VariableResponse::Parse(Source* source) {
           return tag;
         }
 
+        if (meta_.type() == sendrecv::NCCL_ID) {
+#ifdef PADDLE_WITH_CUDA
+          auto* var = scope_->FindVar(meta_.varname());
+          if (var != nullptr) {
+            ncclUniqueId* id = var->GetMutable<ncclUniqueId>();
+            if (!ReadRaw(&input, *dev_ctx_, platform::CPUPlace(), id->internal,
+                         num_bytes)) {
+              return tag;
+            }
+          }
+          break;
+#else
+          PADDLE_THROW("Not compiled with CUDA!");
+#endif
+        }
+
         framework::DDim dims = GetDims(meta_.dims());
         if (meta_.type() == sendrecv::LOD_TENSOR) {
           PADDLE_ENFORCE(meta_.lod_size() >= 0,

paddle/fluid/operators/gen_nccl_id_op.cc

+/* Copyright (c) 2016 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 <nccl.h>
+#include <stdint.h>
+#include <ostream>
+#include <string>
+
+#include "paddle/fluid/framework/executor.h"
+#include "paddle/fluid/framework/lod_tensor.h"
+#include "paddle/fluid/framework/op_registry.h"
+#include "paddle/fluid/framework/threadpool.h"
+#include "paddle/fluid/operators/detail/grpc_client.h"
+#include "paddle/fluid/operators/detail/grpc_server.h"
+#include "paddle/fluid/platform/nccl_helper.h"
+
+namespace paddle {
+namespace operators {
+
+class GenNCCLIdOp : public framework::OperatorBase {
+ public:
+  GenNCCLIdOp(const std::string& type, const framework::VariableNameMap& inputs,
+              const framework::VariableNameMap& outputs,
+              const framework::AttributeMap& attrs)
+      : OperatorBase(type, inputs, outputs, attrs) {}
+
+  void RunImpl(const framework::Scope& scope,
+               const platform::Place& dev_place) const override {
+    platform::DeviceContextPool& pool = platform::DeviceContextPool::Instance();
+    // put nccl id in CPUPlace
+    auto& dev_ctx = *pool.Get(platform::CPUPlace());
+    int trainer_id = Attr<int>("trainer_id");
+    framework::Scope& local_scope = scope.NewScope();
+
+    if (trainer_id == 0) {
+      GenerateAndSend(&local_scope, dev_ctx);
+    } else {
+      GetIdByServer(&local_scope, dev_ctx);
+    }
+  }
+
+ private:
+  void GenerateAndSend(framework::Scope* scope,
+                       const platform::DeviceContext& dev_ctx) const {
+    auto var = scope->FindVar(NCCL_ID_VARNAME);
+    PADDLE_ENFORCE_NOT_NULL(var);
+    auto id = var->GetMutable<ncclUniqueId>();
+    PADDLE_ENFORCE(platform::dynload::ncclGetUniqueId(id));
+
+    std::vector<std::string> endpoint_list =
+        Attr<std::vector<std::string>>("endpoint_list");
+    detail::RPCClient client;
+    for (auto& ep : endpoint_list) {
+      VLOG(3) << "sending nccl id to " << ep;
+      client.AsyncSendVariable(ep, dev_ctx, *scope, NCCL_ID_VARNAME);
+    }
+    client.Wait();
+    VLOG(3) << "sending completed...";
+  }
+
+  void GetIdByServer(framework::Scope* scope,
+                     const platform::DeviceContext& dev_ctx) const {
+    std::string endpoint = Attr<std::string>("endpoint");
+    // NOTE: Can not use unique_ptr here because the default
+    // deleter will call GRPC Server's base class's dtor and
+    // that will cause a wired crash.
+    detail::AsyncGRPCServer rpc_service(endpoint, true);
+    framework::ProgramDesc empty_program;
+    framework::Executor executor(dev_ctx.GetPlace());
+    rpc_service.SetScope(scope);
+    rpc_service.SetDevCtx(&dev_ctx);
+    rpc_service.SetProgram(&empty_program);
+    rpc_service.SetExecutor(&executor);
+
+    std::thread server_thread(
+        std::bind(&detail::AsyncGRPCServer::RunSyncUpdate, &rpc_service));
+    rpc_service.SetCond(0);
+    VLOG(3) << "start getting nccl id from trainer 0...";
+    auto recv = rpc_service.Get();
+    VLOG(3) << "got nccl id and stop server...";
+    rpc_service.ShutDown();
+    VLOG(3) << "rpc server stopped";
+    server_thread.join();
+  }
+};
+
+class GenNCCLIdOpMaker : public framework::OpProtoAndCheckerMaker {
+ public:
+  void Make() override {
+    AddOutput("NCCLID", "Raw variable contains a NCCL UniqueId instaces.");
+    AddComment(R"DOC(
+GenNCCLId operator
+
+For trainer 0: generate a new UniqueId and send it to all the other trainers.
+For trainer 1~n: start a gRPC server to get the UniqueId, once got, stop the server.
+)DOC");
+    AddAttr<std::string>("endpoint",
+                         "(string), e.g. 127.0.0.1:6175 "
+                         "current listen endpoint");
+    AddAttr<std::vector<std::string>>(
+        "endpoint_list",
+        "['trainer1_ip:port', 'trainer2_ip:port', ...] "
+        "list of trainer endpoints start from trainer 1")
+        .SetDefault({});
+    AddAttr<int>("trainer_id",
+                 "(int default 0) "
+                 "The index of the trainer in distributed training.")
+        .SetDefault(0);
+  }
+};
+
+}  // namespace operators
+}  // namespace paddle
+
+namespace ops = paddle::operators;
+
+REGISTER_OPERATOR(gen_nccl_id, ops::GenNCCLIdOp, ops::GenNCCLIdOpMaker);

paddle/fluid/operators/math/sequence2batch.h

@@ -64,18 +64,22 @@ class LoDTensor2BatchFunctor {
                   bool is_reverse = false) const {
     if (!is_cal_batch_lod) {
       auto lods = batch->lod();
-      PADDLE_ENFORCE_GT(lods.size(), 2UL);
-      PADDLE_ENFORCE_EQ(lods[1].size(),
-                        static_cast<size_t>(lod_tensor.dims()[0]));
+      PADDLE_ENFORCE_GT(lods.size(), 2UL,
+                        "The LoD of LoDTensor should inlcude at least 2-level "
+                        "sequence information.");
+      PADDLE_ENFORCE_EQ(
+          lods[1].size(), static_cast<size_t>(lod_tensor.dims()[0]),
+          "The LoD information should be consistent with the dims.");
       CopyMatrixRowsFunctor<DeviceContext, T> to_batch;
       to_batch(context, lod_tensor, lods[1], batch, true);
       return;
     }
 
     auto lods = lod_tensor.lod();
-    auto lod = lods[0];
     PADDLE_ENFORCE_EQ(lods.size(), 1UL, "Only support one level sequence now.");
 
+    auto lod = lods[0];
+
     std::vector<SeqInfo> seq_info;
     for (size_t seq_id = 0; seq_id < lod.size() - 1; ++seq_id) {
       int length = lod[seq_id + 1] - lod[seq_id];
@@ -157,9 +161,12 @@ class Batch2LoDTensorFunctor {
                   const framework::LoDTensor& batch,
                   framework::LoDTensor* lod_tensor) const {
     auto in_lod = batch.lod();
-    PADDLE_ENFORCE_GT(in_lod.size(), 2UL);
-    PADDLE_ENFORCE_EQ(in_lod[1].size(),
-                      static_cast<size_t>(lod_tensor->dims()[0]));
+    PADDLE_ENFORCE_GT(in_lod.size(), 2UL,
+                      "The LoD of LoDTensor should inlcude at least 2-level "
+                      "sequence information.");
+    PADDLE_ENFORCE_EQ(
+        in_lod[1].size(), static_cast<size_t>(lod_tensor->dims()[0]),
+        "The LoD information should be consistent with the dims.");
     CopyMatrixRowsFunctor<DeviceContext, T> to_seq;
     to_seq(context, batch, in_lod[1], lod_tensor, false);
   }

paddle/fluid/operators/reshape_op.h

@@ -92,14 +92,16 @@ class ReshapeOp : public framework::OperatorWithKernel {
     }
 
     if (unk_dim_idx != -1) {
-      output_shape[unk_dim_idx] = -in_size / capacity;
-      // in_size < 0 and is un-determinate in compile time, skip the check,
-      // for example, in_dims = [-1, 8, 1, 1], shape = [-1, 3, 8],
-      // capacity = -24, in_size = -8, output_shape[0] = 0
-      // the following check will fail.
       if (in_size > 0) {
+        // in_size < 0 and is un-determinate in compile time, skip the check,
+        // for example, in_dims = [-1, 8, 1, 1], shape = [-1, 3, 8],
+        // capacity = -24, in_size = -8, output_shape[0] = 0
+        // the following check will fail.
+        output_shape[unk_dim_idx] = -in_size / capacity;
         PADDLE_ENFORCE_EQ(output_shape[unk_dim_idx] * capacity, -in_size,
                           "Invalid shape is given.");
+      } else {
+        output_shape[unk_dim_idx] = -1;
       }
     } else {
       PADDLE_ENFORCE_EQ(capacity, in_size, "Invalid shape is given.");
@@ -122,7 +124,10 @@ class ReshapeKernel : public framework::OpKernel<T> {
   void Compute(const framework::ExecutionContext &ctx) const {
     auto *out = ctx.Output<framework::LoDTensor>("Out");
     auto *in = ctx.Input<framework::LoDTensor>("X");
-    auto *shape_tensor = ctx.Input<framework::LoDTensor>("Shape");
+
+    auto *shape_tensor = ctx.HasInput("Shape")
+                             ? ctx.Input<framework::LoDTensor>("Shape")
+                             : nullptr;
 
     framework::DDim out_dims = out->dims();
 

paddle/fluid/operators/test_send_nccl_id.cc

+/* Copyright (c) 2016 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 <unistd.h>
+#include <string>
+#include <thread>  // NOLINT
+
+#include "gtest/gtest.h"
+#include "paddle/fluid/framework/op_registry.h"
+#include "paddle/fluid/framework/operator.h"
+#include "paddle/fluid/framework/program_desc.h"
+#include "paddle/fluid/operators/detail/grpc_client.h"
+#include "paddle/fluid/operators/listen_and_serv_op.h"
+#include "paddle/fluid/operators/math/math_function.h"
+#include "paddle/fluid/operators/math/selected_rows_functor.h"
+#include "paddle/fluid/platform/nccl_helper.h"
+#include "paddle/fluid/string/printf.h"
+
+USE_NO_KERNEL_OP(listen_and_serv);
+
+namespace f = paddle::framework;
+namespace p = paddle::platform;
+namespace m = paddle::operators::math;
+namespace detail = paddle::operators::detail;
+namespace string = paddle::string;
+
+std::unique_ptr<detail::AsyncGRPCServer> rpc_service;
+
+void StartServer(std::atomic<bool>* initialized) {
+  f::Scope scope;
+  p::CPUPlace place;
+  scope.Var(NCCL_ID_VARNAME);
+  p::DeviceContextPool& pool = p::DeviceContextPool::Instance();
+  auto& dev_ctx = *pool.Get(p::CPUPlace());
+
+  rpc_service.reset(new detail::AsyncGRPCServer("127.0.0.1:0", true));
+
+  f::ProgramDesc empty_program;
+  f::Executor executor(dev_ctx.GetPlace());
+  rpc_service->SetScope(&scope);
+  rpc_service->SetDevCtx(&dev_ctx);
+  rpc_service->SetProgram(&empty_program);
+  rpc_service->SetExecutor(&executor);
+
+  std::thread server_thread(
+      std::bind(&detail::AsyncGRPCServer::RunSyncUpdate, rpc_service.get()));
+  *initialized = true;
+  rpc_service->SetCond(0);
+  auto recv = rpc_service->Get();
+  LOG(INFO) << "got nccl id and stop server...";
+  rpc_service->ShutDown();
+  server_thread.join();
+}
+
+TEST(SendNcclId, Normal) {
+  std::atomic<bool> initialized{false};
+  std::thread server_thread(StartServer, &initialized);
+  while (!initialized) {
+  }
+  // wait server to start
+  // sleep(2);
+  rpc_service->WaitServerReady();
+
+  f::Scope scope;
+  p::CPUPlace place;
+  p::DeviceContextPool& pool = p::DeviceContextPool::Instance();
+  auto& dev_ctx = *pool.Get(p::CPUPlace());
+
+  auto var = scope.Var(NCCL_ID_VARNAME);
+  // var->SetType(f::proto::VarType_Type_RAW);
+  auto id = var->GetMutable<ncclUniqueId>();
+  p::dynload::ncclGetUniqueId(id);
+
+  int port = rpc_service->GetSelectedPort();
+  std::string ep = string::Sprintf("127.0.0.1:%d", port);
+  detail::RPCClient client;
+
+  client.AsyncSendVariable(ep, dev_ctx, scope, NCCL_ID_VARNAME);
+  client.Wait();
+  server_thread.join();
+  auto* ptr = rpc_service.release();
+  delete ptr;
+}

paddle/fluid/platform/nccl_helper.h

@@ -14,12 +14,15 @@
 
 #pragma once
 
+#include <stdio.h>
 #include <thread>  // NOLINT
 #include <typeindex>
 #include <vector>
 #include "paddle/fluid/platform/dynload/nccl.h"
 #include "paddle/fluid/platform/enforce.h"
 
+#define NCCL_ID_VARNAME "NCCLID"
+
 namespace paddle {
 namespace platform {
 
@@ -73,7 +76,9 @@ struct NCCLContextMap {
   std::unordered_map<int, NCCLContext> contexts_;
   std::vector<int> order_;
 
-  explicit NCCLContextMap(const std::vector<platform::Place> &places) {
+  explicit NCCLContextMap(const std::vector<platform::Place> &places,
+                          ncclUniqueId *nccl_id = nullptr,
+                          size_t num_trainers = 1, size_t trainer_id = 0) {
     PADDLE_ENFORCE(!places.empty());
     order_.reserve(places.size());
     for (auto &p : places) {
@@ -85,18 +90,34 @@ struct NCCLContextMap {
         order_.size(), contexts_.size(),
         "NCCL Context Map does not support contain two or more same device");
 
-    if (places.size() > 1) {
-      std::unique_ptr<ncclComm_t[]> comms(new ncclComm_t[order_.size()]);
+    if (places.size() <= 1) {
+      return;
+    }
+    std::unique_ptr<ncclComm_t[]> comms(new ncclComm_t[order_.size()]);
+    // if pass nccl_id here, can assume we are doing multi node training
+    if (nccl_id == nullptr) {
+      std::lock_guard<std::mutex> guard(NCCLGroupGuard::NCCLMutex());
+      PADDLE_ENFORCE(platform::dynload::ncclCommInitAll(
+          comms.get(), static_cast<int>(order_.size()), order_.data()));
+    } else {
+      PADDLE_ENFORCE_GT(num_trainers, 1);
+      // TODO(wuyi): need to ensure each node have same number of GPUs
       {
-        std::lock_guard<std::mutex> guard(NCCLGroupGuard::NCCLMutex());
-        PADDLE_ENFORCE(platform::dynload::ncclCommInitAll(
-            comms.get(), static_cast<int>(order_.size()), order_.data()));
-      }
-      int i = 0;
-      for (auto &dev_id : order_) {
-        contexts_.at(dev_id).comm_ = comms[i++];
+        int nranks = num_trainers * order_.size();
+        NCCLGroupGuard gurad;
+        for (auto &gpu_id : order_) {
+          int rank = trainer_id * order_.size() + gpu_id;
+          VLOG(3) << "init nccl rank: " << rank << " nranks: " << nranks;
+          PADDLE_ENFORCE(cudaSetDevice(gpu_id));
+          PADDLE_ENFORCE(platform::dynload::ncclCommInitRank(
+              comms.get() + gpu_id, nranks, *nccl_id, rank));
+        }
       }
     }
+    int i = 0;
+    for (auto &dev_id : order_) {
+      contexts_.at(dev_id).comm_ = comms[i++];
+    }
   }
 
   NCCLContextMap(const NCCLContextMap &other) = delete;

paddle/fluid/pybind/pybind.cc

@@ -503,12 +503,13 @@ All parameter, weight, gradient are variables in Paddle.
               const ProgramDesc &main_program, const std::string &loss_var_name,
               Scope *scope, std::vector<Scope *> &local_scopes,
               bool allow_op_delay, bool use_default_grad_scale,
-              bool balance_parameter_opt_between_cards) {
+              bool balance_parameter_opt_between_cards, size_t num_trainers,
+              size_t trainer_id) {
              new (&self) ParallelExecutor(
                  num_threads, use_event, places, params, bcast_vars,
                  main_program, loss_var_name, scope, local_scopes,
                  allow_op_delay, use_default_grad_scale,
-                 balance_parameter_opt_between_cards);
+                 balance_parameter_opt_between_cards, num_trainers, trainer_id);
            })
       .def("bcast_params", &ParallelExecutor::BCastParamsToGPUs)
       // NOTE: even we return a vec<Scope*>* to Python use reference policy.

python/paddle/fluid/backward.py

@@ -480,6 +480,8 @@ def append_backward(loss, parameter_list=None, no_grad_set=None,
 
     program.current_block_idx = current_block_idx
     program.sync_with_cpp()
+    # FIXME(zcd): prevent loss.grad optimized by mem_opt.
+    loss.block.var(_append_grad_suffix_(loss.name)).persistable = True
 
     if parameter_list is not None:
         parameters = parameter_list

python/paddle/fluid/framework.py

@@ -489,7 +489,7 @@ class Operator(object):
             'rnn_memory_helper_grad', 'conditional_block', 'while', 'send',
             'recv', 'listen_and_serv', 'parallel_do', 'save_combine',
             'load_combine', 'ncclInit', 'channel_create', 'channel_close',
-            'channel_send', 'channel_recv', 'select'
+            'channel_send', 'channel_recv', 'select', 'gen_nccl_id'
         }
         if type not in no_kernel_op_set:
             self.desc.infer_var_type(self.block.desc)

python/paddle/fluid/parallel_executor.py

@@ -31,7 +31,9 @@ class ParallelExecutor(object):
                  allow_op_delay=False,
                  share_vars_from=None,
                  use_default_grad_scale=True,
-                 balance_parameter_opt_between_cards=False):
+                 balance_parameter_opt_between_cards=False,
+                 num_trainers=1,
+                 trainer_id=0):
         """
         ParallelExecutor can run program in parallel.
 
@@ -55,6 +57,11 @@ class ParallelExecutor(object):
             balance_parameter_opt_between_cards(bool, default True): Whether
                 updating different gradients on different cards. Currently, it
                 is not recommended.
+            num_trainers(int, default 1): If greater than 1, NCCL will be
+                initialized with multpile rank of nodes, each node should have
+                same number of GPUs. Distributed training will be enabled then.
+            trainer_id(int, default 0): Must use together with num_trainers.
+                trainer_id is the "rank" of current node starts from 0.
 
         Returns:
             A ParallelExecutor object.
@@ -134,8 +141,9 @@ class ParallelExecutor(object):
             local_scopes,
             allow_op_delay,
             use_default_grad_scale,
-            balance_parameter_opt_between_cards)
-
+            balance_parameter_opt_between_cards,
+            num_trainers,
+            trainer_id)
         self.scope = scope
 
     def run(self, fetch_list, feed=None, feed_dict=None):

python/paddle/fluid/tests/book/high-level-api/CMakeLists.txt

@@ -6,4 +6,5 @@ foreach(src ${TEST_OPS})
     py_test(${src} SRCS ${src}.py)
 endforeach()
 
+add_subdirectory(fit_a_line)
 add_subdirectory(recognize_digits)

python/paddle/fluid/tests/book/high-level-api/fit_a_line/CMakeLists.txt

+file(GLOB TEST_OPS RELATIVE "${CMAKE_CURRENT_SOURCE_DIR}" "test_*.py")
+string(REPLACE ".py" "" TEST_OPS "${TEST_OPS}")
+
+# default test
+foreach(src ${TEST_OPS})
+    py_test(${src} SRCS ${src}.py)
+endforeach()

python/paddle/fluid/tests/book/high-level-api/fit_a_line/test_fit_a_line.py

+#   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.
+
+import paddle
+import paddle.fluid as fluid
+import contextlib
+import numpy
+import unittest
+
+# train reader
+BATCH_SIZE = 20
+
+train_reader = paddle.batch(
+    paddle.reader.shuffle(
+        paddle.dataset.uci_housing.train(), buf_size=500),
+    batch_size=BATCH_SIZE)
+
+test_reader = paddle.batch(
+    paddle.reader.shuffle(
+        paddle.dataset.uci_housing.test(), buf_size=500),
+    batch_size=BATCH_SIZE)
+
+
+def inference_program():
+    x = fluid.layers.data(name='x', shape=[13], dtype='float32')
+    y_predict = fluid.layers.fc(input=x, size=1, act=None)
+    return y_predict
+
+
+def linear():
+    y = fluid.layers.data(name='y', shape=[1], dtype='float32')
+    y_predict = inference_program()
+
+    loss = fluid.layers.square_error_cost(input=y_predict, label=y)
+    avg_loss = fluid.layers.mean(loss)
+
+    return avg_loss
+
+
+def train(use_cuda, save_dirname):
+    place = fluid.CUDAPlace(0) if use_cuda else fluid.CPUPlace()
+
+    trainer = fluid.Trainer(
+        train_func=linear,
+        infer_func=inference_program,
+        place=place,
+        optimizer=fluid.optimizer.SGD(learning_rate=0.001))
+
+    def event_handler(event):
+        if isinstance(event, fluid.EndEpochEvent):
+            test_metrics = trainer.test(
+                reader=test_reader, feed_order=['x', 'y'])
+            print test_metrics
+            '''
+            
+            ...
+            ['25.768919467926025']
+            ['15.343549569447836']
+            ...
+            
+            '''
+            if float(test_metrics[0]) < 20.0:
+                if save_dirname is not None:
+                    # NOT clear yet
+                    # fluid.io.save_inference_model(save_dirname, ['x'], [y_predict])
+                    # trainer.save_params(save_dirname)
+                    # https://github.com/PaddlePaddle/Paddle/pull/10445
+                    trainer.save_inference_model(save_dirname)
+            return
+
+    trainer.train(
+        reader=train_reader,
+        num_epochs=100,
+        event_handler=event_handler,
+        feed_order=['x', 'y'])
+
+
+# infer
+def infer(use_cuda, save_dirname=None):
+    if save_dirname is None:
+        return
+
+    place = fluid.CUDAPlace(0) if use_cuda else fluid.CPUPlace()
+    inferencer = fluid.Inferencer(param_path=save_dirname, place=place)
+
+    batch_size = 10
+    tensor_x = numpy.random.uniform(0, 10, [batch_size, 13]).astype("float32")
+
+    results = inferencer.infer({'x': tensor_x})
+    print("infer results: ", results[0])
+
+
+def main(use_cuda):
+    if use_cuda and not fluid.core.is_compiled_with_cuda():
+        return
+
+    # Directory for saving the trained model
+    save_dirname = "fit_a_line.inference.model"
+
+    train(use_cuda, save_dirname)
+    infer(use_cuda, save_dirname)
+
+
+class TestFitALine(unittest.TestCase):
+    def test_cpu(self):
+        with self.program_scope_guard():
+            with fluid.unique_name.guard():
+                main(use_cuda=False)
+
+    def test_cuda(self):
+        with self.program_scope_guard():
+            with fluid.unique_name.guard():
+                main(use_cuda=True)
+
+    @contextlib.contextmanager
+    def program_scope_guard(self):
+        prog = fluid.Program()
+        startup_prog = fluid.Program()
+        scope = fluid.core.Scope()
+        with fluid.scope_guard(scope):
+            with fluid.program_guard(prog, startup_prog):
+                yield
+
+
+if __name__ == '__main__':
+    unittest.main()

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

@@ -170,7 +170,7 @@ def train(word_dict,
         assert save_dirname is None
 
     adagrad = fluid.optimizer.Adagrad(learning_rate=0.002)
-    optimize_ops, params_grads = adagrad.minimize(cost)
+    adagrad.minimize(cost)
 
     train_data = paddle.batch(
         paddle.reader.shuffle(

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

@@ -33,7 +33,7 @@ def train(use_cuda, save_dirname, is_local):
     avg_cost = fluid.layers.mean(cost)
 
     sgd_optimizer = fluid.optimizer.SGD(learning_rate=0.001)
-    optimize_ops, params_grads = sgd_optimizer.minimize(avg_cost)
+    sgd_optimizer.minimize(avg_cost)
 
     BATCH_SIZE = 20
 

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

@@ -125,7 +125,7 @@ def train(net_type, use_cuda, save_dirname, is_local):
     test_program = fluid.default_main_program().clone(for_test=True)
 
     optimizer = fluid.optimizer.Adam(learning_rate=0.001)
-    optimize_ops, params_grads = optimizer.minimize(avg_cost)
+    optimizer.minimize(avg_cost)
 
     BATCH_SIZE = 128
     PASS_NUM = 1

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

@@ -175,7 +175,7 @@ def train(use_cuda, save_dirname=None, is_local=True):
             decay_steps=100000,
             decay_rate=0.5,
             staircase=True))
-    optimize_ops, params_grads = sgd_optimizer.minimize(avg_cost)
+    sgd_optimizer.minimize(avg_cost)
 
     # TODO(qiao)
     # add dependency track and move this config before optimizer

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

@@ -185,7 +185,7 @@ def train_main(use_cuda, is_sparse, is_local=True):
         learning_rate=1e-4,
         regularization=fluid.regularizer.L2DecayRegularizer(
             regularization_coeff=0.1))
-    optimize_ops, params_grads = optimizer.minimize(avg_cost)
+    optimizer.minimize(avg_cost)
 
     train_data = paddle.batch(
         paddle.reader.shuffle(

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

@@ -95,7 +95,7 @@ def train(nn_type,
     test_program = fluid.default_main_program().clone(for_test=True)
 
     optimizer = fluid.optimizer.Adam(learning_rate=0.001)
-    optimize_ops, params_grads = optimizer.minimize(avg_loss)
+    optimizer.minimize(avg_loss)
 
     place = fluid.CUDAPlace(0) if use_cuda else fluid.CPUPlace()
 

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

@@ -160,7 +160,7 @@ def train(use_cuda, save_dirname, is_local=True):
     test_program = fluid.default_main_program().clone(for_test=True)
 
     sgd_optimizer = SGDOptimizer(learning_rate=0.2)
-    optimize_ops, params_grads = sgd_optimizer.minimize(avg_cost)
+    sgd_optimizer.minimize(avg_cost)
 
     place = fluid.CUDAPlace(0) if use_cuda else fluid.CPUPlace()
 

python/paddle/fluid/tests/book/test_word2vec.py

@@ -101,7 +101,7 @@ def train(use_cuda, is_sparse, is_parallel, save_dirname, is_local=True):
         avg_cost = fluid.layers.mean(pd())
 
     sgd_optimizer = fluid.optimizer.SGD(learning_rate=0.001)
-    optimize_ops, params_grads = sgd_optimizer.minimize(avg_cost)
+    sgd_optimizer.minimize(avg_cost)
 
     train_reader = paddle.batch(
         paddle.dataset.imikolov.train(word_dict, N), BATCH_SIZE)

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

+#   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.
+
+import unittest
+
+import paddle.fluid as fluid
+import paddle.fluid.core as core
+import paddle.fluid.layers as layers
+from paddle.fluid.transpiler.distribute_transpiler import delete_ops
+import numpy
+
+
+class TestDistTranspiler(unittest.TestCase):
+    def setUp(self):
+        self.trainer_id = 0
+        self.trainers = 2
+        self.pservers = 2
+        self.pserver_eps = "127.0.0.1:6174,127.0.0.1:6175"
+        self.current_pserver_ep = "127.0.0.1:6174"
+
+    def net_conf(self):
+        x = fluid.layers.data(name='x', shape=[1000], dtype='float32')
+
+        y_predict = fluid.layers.fc(input=x,
+                                    size=1000,
+                                    act=None,
+                                    param_attr=fluid.ParamAttr(name='fc_w'))
+
+        y = fluid.layers.data(name='y', shape=[1], dtype='float32')
+
+        cost = fluid.layers.square_error_cost(input=y_predict, label=y)
+        avg_cost = fluid.layers.mean(cost)
+        sgd_optimizer = fluid.optimizer.SGD(learning_rate=0.1)
+
+        optimize_ops, params_grads = sgd_optimizer.minimize(avg_cost)
+        return optimize_ops, params_grads
+
+    def test_transpiler(self):
+        trainer = self.get_trainer()
+        pserver, startup = self.get_pserver(self.current_pserver_ep)
+
+        self.assertEqual([op.type for op in trainer.global_block().ops],
+                         self.get_expect_trainer_ops())
+
+        self.assertEqual(len(pserver.blocks), 3)
+        # block0: listen_and_serv
+        self.assertEqual([op.type for op in pserver.blocks[0].ops],
+                         ["listen_and_serv"])
+        # block2: optimize pass
+        self.assertEqual([op.type for op in pserver.blocks[1].ops],
+                         ["sum", "scale", "sgd"])
+
+        # confirm startup program
+
+        self.assertEqual([op.type for op in startup.global_block().ops], [
+            "fill_constant", "fill_constant", "uniform_random", "uniform_random"
+        ])
+
+        # the variable #fc_w will be split into two blocks 
+        fc_w_var = startup.global_block().var("fc_w.block1")
+        self.assertEqual(fc_w_var.shape, (500, 1000))
+
+    def get_main_program(self):
+        main = fluid.Program()
+
+        with fluid.program_guard(main):
+            self.net_conf()
+
+        return main
+
+    def get_expect_trainer_ops(self):
+        trainer = fluid.Program()
+
+        with fluid.program_guard(trainer):
+            optimize_ops, params_grads = self.net_conf()
+
+        delete_ops(trainer.global_block(), optimize_ops)
+        return [op.type for op in trainer.global_block().ops
+                ] + ["split_byref", "send", "concat"]
+
+    def get_trainer(self):
+        return self._transpiler_instance().get_trainer_program()
+
+    def get_pserver(self, ep):
+        t = self._transpiler_instance()
+        pserver = t.get_pserver_program(ep)
+        startup = t.get_startup_program(ep, pserver)
+        return pserver, startup
+
+    def _transpiler_instance(self):
+        main = self.get_main_program()
+        t = fluid.DistributeTranspiler()
+        t.transpile(
+            self.trainer_id,
+            program=main,
+            pservers=self.pserver_eps,
+            trainers=self.trainers)
+        return t
+
+
+if __name__ == "__main__":
+    unittest.main()

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

@@ -27,12 +27,15 @@ class TestNetWithDtype(unittest.TestCase):
     def set_network(self):
         self.dtype = "float64"
         self.init_dtype()
-        self.x = fluid.layers.data(name='x', shape=[13], dtype=self.dtype)
-        self.y = fluid.layers.data(name='y', shape=[1], dtype=self.dtype)
-        y_predict = fluid.layers.fc(input=self.x, size=1, act=None)
+        main = fluid.Program()
+        with fluid.program_guard(main):
+            self.x = fluid.layers.data(name='x', shape=[13], dtype=self.dtype)
+            self.y = fluid.layers.data(name='y', shape=[1], dtype=self.dtype)
+            y_predict = fluid.layers.fc(input=self.x, size=1, act=None)
 
-        cost = fluid.layers.square_error_cost(input=y_predict, label=self.y)
-        avg_cost = fluid.layers.mean(cost)
+            cost = fluid.layers.square_error_cost(input=y_predict, label=self.y)
+            avg_cost = fluid.layers.mean(cost)
+        self.program = main
         self.fetch_list = [avg_cost]
 
         sgd_optimizer = fluid.optimizer.SGD(learning_rate=0.001)
@@ -45,7 +48,7 @@ class TestNetWithDtype(unittest.TestCase):
         exe = fluid.Executor(place)
         exe.run(fluid.default_startup_program())
         for data in train_reader():
-            exe.run(fluid.default_main_program(),
+            exe.run(self.program,
                     feed=feeder.feed(data),
                     fetch_list=self.fetch_list)
             # the main program is runable, the datatype is fully supported
@@ -68,7 +71,7 @@ class TestNetWithDtype(unittest.TestCase):
 
 
 # TODO(dzhwinter): make sure the fp16 is runable
-# class TestFloat16(SimpleNet):
+# class TestFloat16(TestNetWithDtype):
 #     def init_dtype(self):
 #         self.dtype = "float16"
 

python/paddle/fluid/transpiler/__init__.py

@@ -11,6 +11,7 @@
 # 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.
+
 from distribute_transpiler import DistributeTranspiler
 from inference_transpiler import InferenceTranspiler
 from memory_optimization_transpiler import memory_optimize, release_memory

python/paddle/fluid/transpiler/distribute_transpiler.py

@@ -17,7 +17,7 @@ from __future__ import print_function
 import math
 
 import distributed_splitter as splitter
-from .. import core
+from .. import core, framework
 from ..framework import Program, default_main_program, \
                         default_startup_program, \
                         Variable, Parameter, grad_var_name
@@ -417,7 +417,7 @@ class DistributeTranspiler:
         def __append_optimize_op__(op, block, grad_to_block_id):
             if self._is_opt_op(op):
                 self._append_pserver_ops(block, op, endpoint, grad_to_block_id,
-                                         default_main_program())
+                                         self.origin_program)
             else:
                 self._append_pserver_non_opt_ops(block, op)
 

tools/manylinux1/README.md

@@ -28,3 +28,38 @@ git clone https://github.com/paddlepaddle/paddle
 cd paddle/tools/manylinux1
 REPO=[yourrepo] ./build_all.sh
 ```
+
+## Build PaddlePaddle for the different Python ABIs
+
+Choose one of the following Python ABI and set the correct environment variables.
+
+- cp27-cp27m
+
+  ```bash
+  export LD_LIBRARY_PATH=/opt/_internal/cpython-2.7.11-ucs2/lib:${LD_LIBRARY_PATH#/opt/_internal/cpython-2.7.11-ucs4/lib:}
+  export PATH=/opt/python/cp27-cp27m/bin/:${PATH}
+  export PYTHON_FLAGS="-DPYTHON_EXECUTABLE:FILEPATH=/opt/python/cp27-cp27m/bin/python
+        -DPYTHON_INCLUDE_DIR:PATH=/opt/python/cp27-cp27m/include/python2.7
+        -DPYTHON_LIBRARIES:FILEPATH=/opt/_internal/cpython-2.7.11-ucs2/lib/libpython2.7.so"
+  ```
+
+- cp27-cp27mu
+
+  ```bash
+  export LD_LIBRARY_PATH=/opt/_internal/cpython-2.7.11-ucs4/lib:${LD_LIBRARY_PATH#/opt/_internal/cpython-2.7.11-ucs2/lib:}
+  export PATH=/opt/python/cp27-cp27mu/bin/:${PATH}
+  export PYTHON_FLAGS="-DPYTHON_EXECUTABLE:FILEPATH=/opt/python/cp27-cp27mu/bin/python
+        -DPYTHON_INCLUDE_DIR:PATH=/opt/python/cp27-cp27mu/include/python2.7
+        -DPYTHON_LIBRARIES:FILEPATH=/opt/_internal/cpython-2.7.11-ucs4/lib/libpython2.7.so"
+  ```
+
+And then add the `PYTHON_FLAGS` as your cmake flags:
+
+```bash
+cmake ..
+  ${PYTHON_FLAGS} \
+  -DWITH_GPU=OFF \
+  ...
+```
+
+You can find more details about cmake flags at [here](http://www.paddlepaddle.org/docs/develop/documentation/fluid/en/build_and_install/build_from_source_en.html#appendix-build-options)