Merge branch 'develop' of https://github.com/PaddlePaddle/Paddle into multithread-sparse-adam

README.md

@@ -19,6 +19,15 @@ Our vision is to enable deep learning for everyone via PaddlePaddle.
 Please refer to our [release announcement](https://github.com/PaddlePaddle/Paddle/releases) to track the latest feature of PaddlePaddle.
 
 
+欢迎来到 PaddlePaddle GitHub
+
+PaddlePaddle (PArallel Distributed Deep LEarning) 是一个简单易用、高效灵活、可扩展的深度学习平台，最初由百度科学家和工程师共同开发，目的是将深度学习技术应用到百度的众多产品中。
+
+我们的愿景是让每个人都能通过PaddlePaddle接触深度学习
+
+跟进PaddlePaddle最新特性请参考我们的[版本说明](https://github.com/PaddlePaddle/Paddle/releases)
+
+
 ### Latest PaddlePaddle Release: [Fluid 1.2.0](https://github.com/PaddlePaddle/Paddle/tree/release/1.2)
 ### Install Latest Stable Release:
 ```
@@ -34,6 +43,23 @@ pip install paddlepaddle-gpu==1.2.0.post85
 # For installation on other platform, refer to http://paddlepaddle.org/
 ```
 
+
+### PaddlePaddle最新版本: [Fluid 1.2.0](https://github.com/PaddlePaddle/Paddle/tree/release/1.2)
+### 安装最新稳定版本:
+```
+# Linux CPU
+pip install paddlepaddle
+# Linux GPU cuda9cudnn7
+pip install paddlepaddle-gpu
+# Linux GPU cuda8cudnn7
+pip install paddlepaddle-gpu==1.2.0.post87
+# Linux GPU cuda8cudnn5
+pip install paddlepaddle-gpu==1.2.0.post85
+
+# 其他平台上的安装指引请参考 http://paddlepaddle.org/
+```
+
+
 ## Features
 
 - **Flexibility**
@@ -74,10 +100,38 @@ pip install paddlepaddle-gpu==1.2.0.post85
     Baidu and it has achieved a significant impact. We hope you can also explore
     the capability of PaddlePaddle to make an impact on your product.
 
+## 特点
+
+- **灵活性**
+
+    PaddlePaddle支持丰富的神经网络架构和优化算法。易于配置复杂模型，例如带有注意力机制或复杂记忆连接的神经网络机器翻译模型。
+
+-  **高效性**
+
+    为了高效使用异步计算资源，PaddlePaddle对框架的不同层进行优化，包括计算、存储、架构和通信。下面是一些样例：
+    
+    - 通过SSE/AVX 内置函数、BLAS库(例如MKL、OpenBLAS、cuBLAS)或定制的CPU/GPU内核优化数学操作。
+    - 通过MKL-DNN库优化CNN网络
+    - 高度优化循环网络，无需执行 `padding` 操作即可处理 **变长** 序列
+    - 针对高维稀疏数据模型，优化了局部和分布式训练。
+     
+
+- **稳定性**
+
+    有了 PaddlePaddle，使得利用各种CPU/GPU和机器来加速训练变得简单。PaddlePaddle 通过优化通信可以实现巨大吞吐量和快速执行。
+
+- **连接产品**
+
+    另外，PaddlePaddle 的设计也易于部署。在百度，PaddlePaddle 已经部署到含有巨大用户量的产品和服务上，包括广告点击率（CTR）预测、大规模图像分类、光学字符识别（OCR）、搜索排序，计算机病毒检测、推荐系统等等。PaddlePaddle广泛应用于百度产品中，产生了非常重要的影响。我们希望您也能探索 PaddlePaddle 的能力，为您的产品创造新的影响力和效果。
+
 ## Installation
 
 It is recommended to read [this doc](http://paddlepaddle.org/documentation/docs/zh/1.2/beginners_guide/install/index_cn.html) on our website.
 
+## 安装
+
+推荐阅读官网上的[安装说明](http://paddlepaddle.org/documentation/docs/zh/1.2/beginners_guide/install/index_cn.html) 
+
 ## Documentation
 
 We provide [English](http://paddlepaddle.org/documentation/docs/en/1.2/getstarted/index_en.html) and
@@ -99,10 +153,37 @@ We provide [English](http://paddlepaddle.org/documentation/docs/en/1.2/getstarte
 
    We appreciate your contributions!
 
+## 文档
+
+我们提供[英文](http://paddlepaddle.org/documentation/docs/en/1.2/getstarted/index_en.html)和
+[中文](http://paddlepaddle.org/documentation/docs/zh/1.2/beginners_guide/index.html) 文档
+
+- [深度学习101](https://github.com/PaddlePaddle/book)
+
+  或许您想从这个在线交互式书籍开始，可以在Jupyter Notebook中运行
+
+- [分布式训练](http://paddlepaddle.org/documentation/docs/zh/1.2/user_guides/howto/training/cluster_howto.html)
+
+  可以在MPI集群上运行分布式训练任务
+
+- [Python API](http://paddlepaddle.org/documentation/docs/zh/1.2/api_cn/index_cn.html)
+
+   新的API支持代码更少更简洁的程序
+
+- [贡献方式](http://paddlepaddle.org/documentation/docs/zh/1.2/advanced_usage/development/contribute_to_paddle/index_cn.html)
+
+   欢迎您的贡献!
 
 ## Ask Questions
 
 You are welcome to submit questions and bug reports as [Github Issues](https://github.com/PaddlePaddle/Paddle/issues).
 
+## 答疑
+
+欢迎您将问题和bug报告以[Github Issues](https://github.com/PaddlePaddle/Paddle/issues)的形式提交
+
 ## Copyright and License
 PaddlePaddle is provided under the [Apache-2.0 license](LICENSE).
+
+## 版权和许可证
+PaddlePaddle由[Apache-2.0 license](LICENSE)提供

benchmark/fluid/fluid_benchmark.py

@@ -81,9 +81,11 @@ def dist_transpile(trainer_id, args, train_prog, startup_prog):
     # the role, should be either PSERVER or TRAINER
     training_role = os.getenv("PADDLE_TRAINING_ROLE")
 
-    config = distribute_transpiler.DistributeTranspilerConfig()
+    config = fluid.DistributeTranspilerConfig()
     config.slice_var_up = not args.no_split_var
+    config.min_block_size = 1048576
     t = distribute_transpiler.DistributeTranspiler(config=config)
+
     t.transpile(
         trainer_id,
         # NOTE: *MUST* use train_prog, for we are using with guard to

cmake/external/brpc.cmake

@@ -14,14 +14,16 @@
 
 INCLUDE(ExternalProject)
 
-find_library(SSL_LIBRARY NAMES ssl)
+find_package(OpenSSL REQUIRED) 
+
+message(STATUS "ssl:" ${OPENSSL_SSL_LIBRARY})
+message(STATUS "crypto:" ${OPENSSL_CRYPTO_LIBRARY})
+
 ADD_LIBRARY(ssl SHARED IMPORTED GLOBAL)
-SET_PROPERTY(TARGET ssl PROPERTY IMPORTED_LOCATION ${SSL_LIBRARY})
+SET_PROPERTY(TARGET ssl PROPERTY IMPORTED_LOCATION ${OPENSSL_SSL_LIBRARY})
 
-find_library(CRYPTO_LIBRARY NAMES crypto)
 ADD_LIBRARY(crypto SHARED IMPORTED GLOBAL)
-SET_PROPERTY(TARGET crypto PROPERTY IMPORTED_LOCATION ${CRYPTO_LIBRARY})
-
+SET_PROPERTY(TARGET crypto PROPERTY IMPORTED_LOCATION ${OPENSSL_CRYPTO_LIBRARY})
 
 SET(BRPC_SOURCES_DIR ${THIRD_PARTY_PATH}/brpc)
 SET(BRPC_INSTALL_DIR ${THIRD_PARTY_PATH}/install/brpc)
@@ -31,14 +33,15 @@ SET(BRPC_LIBRARIES "${BRPC_INSTALL_DIR}/lib/libbrpc.a" CACHE FILEPATH "brpc libr
 INCLUDE_DIRECTORIES(${BRPC_INCLUDE_DIR})
 
 # Reference https://stackoverflow.com/questions/45414507/pass-a-list-of-prefix-paths-to-externalproject-add-in-cmake-args
-set(prefix_path "${THIRD_PARTY_PATH}/install/gflags|${THIRD_PARTY_PATH}/install/leveldb|${THIRD_PARTY_PATH}/install/snappy|${THIRD_PARTY_PATH}/install/gtest|${THIRD_PARTY_PATH}/install/protobuf|${THIRD_PARTY_PATH}/install/zlib")
+set(prefix_path "${THIRD_PARTY_PATH}/install/gflags|${THIRD_PARTY_PATH}/install/leveldb|${THIRD_PARTY_PATH}/install/snappy|${THIRD_PARTY_PATH}/install/gtest|${THIRD_PARTY_PATH}/install/protobuf|${THIRD_PARTY_PATH}/install/zlib|${THIRD_PARTY_PATH}/install/glog")
 
 # If minimal .a is need, you can set  WITH_DEBUG_SYMBOLS=OFF
 ExternalProject_Add(
     extern_brpc
     ${EXTERNAL_PROJECT_LOG_ARGS}
+    # TODO(gongwb): change to de newst repo when they changed.
     GIT_REPOSITORY  "https://github.com/gongweibao/brpc"
-    GIT_TAG         "7dc04defad1fd4173aae170c3fcbde131b65155a"
+    GIT_TAG         "e9b67ec1b7458f2af5fae76451afe1e27e01b4b4"
     PREFIX          ${BRPC_SOURCES_DIR}
     UPDATE_COMMAND  ""
     CMAKE_ARGS      -DCMAKE_CXX_COMPILER=${CMAKE_CXX_COMPILER}
@@ -50,7 +53,7 @@ ExternalProject_Add(
                     -DCMAKE_POSITION_INDEPENDENT_CODE=ON
                     -DCMAKE_BUILD_TYPE=${THIRD_PARTY_BUILD_TYPE}
                     -DCMAKE_PREFIX_PATH=${prefix_path}
-                    -DBRPC_WITH_GLOG=ON
+                    -DWITH_GLOG=ON
                     -DIOBUF_WITH_HUGE_BLOCK=ON
                     -DBRPC_WITH_RDMA=${WITH_BRPC_RDMA}
                     ${EXTERNAL_OPTIONAL_ARGS}
@@ -65,5 +68,6 @@ ADD_LIBRARY(brpc STATIC IMPORTED GLOBAL)
 SET_PROPERTY(TARGET brpc PROPERTY IMPORTED_LOCATION ${BRPC_LIBRARIES})
 ADD_DEPENDENCIES(brpc extern_brpc)
 
+add_definitions(-DBRPC_WITH_GLOG)
 
 LIST(APPEND external_project_dependencies brpc)

cmake/external/gtest.cmake

@@ -12,8 +12,12 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 
-IF(WITH_TESTING)
-    ENABLE_TESTING()
+#FIXME:(gongwb) Move brpc's gtest dependency.
+IF(WITH_TESTING OR (WITH_DISTRIBUTE AND NOT WITH_GRPC))
+    IF(WITH_TESTING)
+        ENABLE_TESTING()
+    ENDIF(WITH_TESTING)
+
     INCLUDE(ExternalProject)
 
     SET(GTEST_SOURCES_DIR ${THIRD_PARTY_PATH}/gtest)
@@ -76,4 +80,4 @@ IF(WITH_TESTING)
     ADD_DEPENDENCIES(gtest_main extern_gtest)
 
     LIST(APPEND external_project_dependencies gtest gtest_main)
-ENDIF(WITH_TESTING)
+ENDIF(WITH_TESTING OR (WITH_DISTRIBUTE AND NOT WITH_GRPC))

cmake/external/leveldb.cmake

@@ -24,8 +24,8 @@ ExternalProject_Add(
     extern_leveldb
     ${EXTERNAL_PROJECT_LOG_ARGS}
     PREFIX ${LEVELDB_SOURCES_DIR}
-    URL "https://github.com/google/leveldb/archive/v1.18.tar.gz"
-    URL_MD5 "73770de34a2a5ab34498d2e05b2b7fa0"
+    GIT_REPOSITORY "https://github.com/google/leveldb"
+    GIT_TAG v1.18
     CONFIGURE_COMMAND ""
     BUILD_COMMAND CXXFLAGS=-fPIC make -j ${NUM_OF_PROCESSOR} libleveldb.a
     INSTALL_COMMAND mkdir -p ${LEVELDB_INSTALL_DIR}/lib/ 

paddle/fluid/API.spec

@@ -77,6 +77,8 @@ paddle.fluid.layers.sequence_softmax ArgSpec(args=['input', 'use_cudnn', 'name']
 paddle.fluid.layers.softmax ArgSpec(args=['input', 'use_cudnn', 'name'], varargs=None, keywords=None, defaults=(True, None))
 paddle.fluid.layers.pool2d ArgSpec(args=['input', 'pool_size', 'pool_type', 'pool_stride', 'pool_padding', 'global_pooling', 'use_cudnn', 'ceil_mode', 'name', 'exclusive'], varargs=None, keywords=None, defaults=(-1, 'max', 1, 0, False, True, False, None, True))
 paddle.fluid.layers.pool3d ArgSpec(args=['input', 'pool_size', 'pool_type', 'pool_stride', 'pool_padding', 'global_pooling', 'use_cudnn', 'ceil_mode', 'name', 'exclusive'], varargs=None, keywords=None, defaults=(-1, 'max', 1, 0, False, True, False, None, True))
+paddle.fluid.layers.adaptive_pool2d ArgSpec(args=['input', 'pool_size', 'pool_type', 'require_index', 'name'], varargs=None, keywords=None, defaults=('max', False, None))
+paddle.fluid.layers.adaptive_pool3d ArgSpec(args=['input', 'pool_size', 'pool_type', 'require_index', 'name'], varargs=None, keywords=None, defaults=('max', False, None))
 paddle.fluid.layers.batch_norm ArgSpec(args=['input', 'act', 'is_test', 'momentum', 'epsilon', 'param_attr', 'bias_attr', 'data_layout', 'in_place', 'name', 'moving_mean_name', 'moving_variance_name', 'do_model_average_for_mean_and_var', 'fuse_with_relu', 'use_global_stats'], varargs=None, keywords=None, defaults=(None, False, 0.9, 1e-05, None, None, 'NCHW', False, None, None, None, False, False, False))
 paddle.fluid.layers.beam_search_decode ArgSpec(args=['ids', 'scores', 'beam_size', 'end_id', 'name'], varargs=None, keywords=None, defaults=(None,))
 paddle.fluid.layers.conv2d_transpose ArgSpec(args=['input', 'num_filters', 'output_size', 'filter_size', 'padding', 'stride', 'dilation', 'groups', 'param_attr', 'bias_attr', 'use_cudnn', 'act', 'name'], varargs=None, keywords=None, defaults=(None, None, 0, 1, 1, None, None, None, True, None, None))

paddle/fluid/framework/CMakeLists.txt

@@ -169,9 +169,12 @@ cc_library(variable_helper SRCS variable_helper.cc DEPS lod_tensor)
 cc_library(naive_executor SRCS naive_executor.cc DEPS op_registry device_context scope framework_proto glog lod_rank_table feed_fetch_method graph_to_program_pass variable_helper)
 
 if(WITH_DISTRIBUTE)
-  cc_library(executor SRCS executor.cc DEPS op_registry device_context scope framework_proto glog lod_rank_table feed_fetch_method sendrecvop_grpc cares grpc++_unsecure grpc_unsecure gpr graph_to_program_pass variable_helper)
-  set(DISTRIBUTE_COMPILE_FLAGS "-Wno-non-virtual-dtor -Wno-error=non-virtual-dtor -Wno-error=delete-non-virtual-dtor")
-  set_source_files_properties(executor.cc PROPERTIES COMPILE_FLAGS ${DISTRIBUTE_COMPILE_FLAGS})
+    cc_library(executor SRCS executor.cc DEPS op_registry device_context scope framework_proto glog
+        lod_rank_table feed_fetch_method sendrecvop_rpc  ${GLOB_DISTRIBUTE_DEPS} graph_to_program_pass variable_helper)
+
+   set(DISTRIBUTE_COMPILE_FLAGS "-Wno-non-virtual-dtor -Wno-error=non-virtual-dtor -Wno-error=delete-non-virtual-dtor")
+   set_source_files_properties(executor.cc PROPERTIES COMPILE_FLAGS ${DISTRIBUTE_COMPILE_FLAGS})
+
 else()
   if(WITH_NGRAPH)
     if(NOT WIN32)

paddle/fluid/framework/data_layout_transform.cc

@@ -85,7 +85,7 @@ void TransDataLayout(const OpKernelType& kernel_type_for_var,
   out->mutable_data(expected_kernel_type.place_, in.type());
 
   framework::VisitDataType(
-      framework::ToDataType(in.type()),
+      in.type(),
       CastDataLayout(pool.Get(expected_kernel_type.place_), axis, in, out));
 
   out->set_layout(expected_kernel_type.data_layout_);
@@ -101,7 +101,7 @@ void* GetDataFromTensor(const Tensor& tensor, mkldnn::memory::data_type type) {
     case mkldnn::memory::data_type::f32:
       return platform::to_void_cast(tensor.data<float>());
     case mkldnn::memory::data_type::s8:
-      return platform::to_void_cast(tensor.data<char>());
+      return platform::to_void_cast(tensor.data<int8_t>());
     case mkldnn::memory::data_type::u8:
       return platform::to_void_cast(tensor.data<unsigned char>());
     case mkldnn::memory::data_type::s16:
@@ -144,7 +144,7 @@ void TransDataLayoutFromMKLDNN(const OpKernelType& kernel_type_for_var,
 
   memory::data_type in_type = ToMKLDNNDataType(in.type());
   PADDLE_ENFORCE(in_type != memory::data_type::data_undef,
-                 "Input tensor type is not supported: ", in.type().name());
+                 "Input tensor type is not supported: %s", in.type());
   memory::data_type out_type = in_type;
 
   auto in_format = platform::MKLDNNFormatForSize(in_tz.size(), in.format());

paddle/fluid/framework/data_layout_transform.h

@@ -50,14 +50,14 @@ inline DataLayout ToPaddleLayout(const MKLDNNFormat& format) {
   }
 }
 
-inline MKLDNNDataType ToMKLDNNDataType(const std::type_index type) {
-  static const std::map<std::type_index, MKLDNNDataType> dict{
-      {std::type_index(typeid(float)), MKLDNNDataType::f32},  // NOLINT
-      {std::type_index(typeid(char)), MKLDNNDataType::s8},    // NOLINT
-      {std::type_index(typeid(unsigned char)), MKLDNNDataType::u8},
-      {std::type_index(typeid(int16_t)), MKLDNNDataType::s16},
-      {std::type_index(typeid(int32_t)), MKLDNNDataType::s32}};
-  auto iter = dict.find(type);
+inline MKLDNNDataType ToMKLDNNDataType(proto::VarType::Type type) {
+  static std::unordered_map<int, MKLDNNDataType> dict{
+      {DataTypeTrait<float>::DataType, MKLDNNDataType::f32},
+      {DataTypeTrait<int8_t>::DataType, MKLDNNDataType::s8},
+      {DataTypeTrait<uint8_t>::DataType, MKLDNNDataType::u8},
+      {DataTypeTrait<int16_t>::DataType, MKLDNNDataType::s16},
+      {DataTypeTrait<int32_t>::DataType, MKLDNNDataType::s32}};
+  auto iter = dict.find(static_cast<int>(type));
   if (iter != dict.end()) return iter->second;
   return MKLDNNDataType::data_undef;
 }

paddle/fluid/framework/data_type.cc

@@ -26,7 +26,7 @@ struct DataTypeMap {
   std::unordered_map<std::type_index, proto::VarType::Type> cpp_to_proto_;
   std::unordered_map<int, std::type_index> proto_to_cpp_;
   std::unordered_map<int, std::string> proto_to_str_;
-  std::unordered_map<std::type_index, size_t> cpp_to_size_;
+  std::unordered_map<int, size_t> proto_to_size_;
 };
 
 static DataTypeMap* InitDataTypeMap();
@@ -45,7 +45,7 @@ static inline void RegisterType(DataTypeMap* map,
   map->proto_to_cpp_.emplace(static_cast<int>(proto_type), typeid(T));
   map->cpp_to_proto_.emplace(typeid(T), proto_type);
   map->proto_to_str_.emplace(static_cast<int>(proto_type), name);
-  map->cpp_to_size_.emplace(typeid(T), sizeof(T));
+  map->proto_to_size_.emplace(static_cast<int>(proto_type), sizeof(T));
 }
 
 static DataTypeMap* InitDataTypeMap() {
@@ -54,17 +54,7 @@ static DataTypeMap* InitDataTypeMap() {
 #define RegType(cc_type, proto_type) \
   RegisterType<cc_type>(retv, proto_type, #cc_type)
 
-  // NOTE: Add your customize type here.
-  RegType(float16, proto::VarType::FP16);
-  RegType(float, proto::VarType::FP32);
-  RegType(double, proto::VarType::FP64);
-  RegType(int, proto::VarType::INT32);
-  RegType(int64_t, proto::VarType::INT64);
-  RegType(bool, proto::VarType::BOOL);
-  RegType(size_t, proto::VarType::SIZE_T);
-  RegType(int16_t, proto::VarType::INT16);
-  RegType(uint8_t, proto::VarType::UINT8);
-  RegType(int8_t, proto::VarType::INT8);
+  _ForEachDataType_(RegType);
 
 #undef RegType
   return retv;
@@ -96,12 +86,12 @@ std::string DataTypeToString(const proto::VarType::Type type) {
                static_cast<int>(type));
 }
 
-size_t SizeOfType(std::type_index type) {
-  auto it = gDataTypeMap().cpp_to_size_.find(type);
-  if (it != gDataTypeMap().cpp_to_size_.end()) {
+size_t SizeOfType(proto::VarType::Type type) {
+  auto it = gDataTypeMap().proto_to_size_.find(static_cast<int>(type));
+  if (it != gDataTypeMap().proto_to_size_.end()) {
     return it->second;
   }
-  PADDLE_THROW("Not support %s as tensor type", type.name());
+  PADDLE_THROW("Not support %s as tensor type", DataTypeToString(type));
 }
 
 }  // namespace framework

paddle/fluid/framework/data_type.h

@@ -22,46 +22,59 @@ limitations under the License. */
 namespace paddle {
 namespace framework {
 
+template <typename T>
+struct DataTypeTrait {};
+
+// Stub handle for void
+template <>
+struct DataTypeTrait<void> {
+  constexpr static auto DataType = proto::VarType::RAW;
+};
+
+#define _ForEachDataTypeHelper_(callback, cpp_type, proto_type) \
+  callback(cpp_type, ::paddle::framework::proto::VarType::proto_type);
+
+#define _ForEachDataType_(callback)                                     \
+  _ForEachDataTypeHelper_(callback, float, FP32);                       \
+  _ForEachDataTypeHelper_(callback, ::paddle::platform::float16, FP16); \
+  _ForEachDataTypeHelper_(callback, double, FP64);                      \
+  _ForEachDataTypeHelper_(callback, int, INT32);                        \
+  _ForEachDataTypeHelper_(callback, int64_t, INT64);                    \
+  _ForEachDataTypeHelper_(callback, bool, BOOL);                        \
+  _ForEachDataTypeHelper_(callback, uint8_t, UINT8);                    \
+  _ForEachDataTypeHelper_(callback, int16_t, INT16);                    \
+  _ForEachDataTypeHelper_(callback, int8_t, INT8)
+
+#define DefineDataTypeTrait(cpp_type, proto_type) \
+  template <>                                     \
+  struct DataTypeTrait<cpp_type> {                \
+    constexpr static auto DataType = proto_type;  \
+  }
+
+_ForEachDataType_(DefineDataTypeTrait);
+
+#undef DefineDataTypeTrait
+
 extern proto::VarType::Type ToDataType(std::type_index type);
 extern std::type_index ToTypeIndex(proto::VarType::Type type);
 
 template <typename Visitor>
 inline void VisitDataType(proto::VarType::Type type, Visitor visitor) {
-  switch (type) {
-    case proto::VarType::FP16:
-      visitor.template apply<platform::float16>();
-      break;
-    case proto::VarType::FP32:
-      visitor.template apply<float>();
-      break;
-    case proto::VarType::FP64:
-      visitor.template apply<double>();
-      break;
-    case proto::VarType::INT32:
-      visitor.template apply<int>();
-      break;
-    case proto::VarType::INT64:
-      visitor.template apply<int64_t>();
-      break;
-    case proto::VarType::BOOL:
-      visitor.template apply<bool>();
-      break;
-    case proto::VarType::UINT8:
-      visitor.template apply<uint8_t>();
-      break;
-    case proto::VarType::INT16:
-      visitor.template apply<int16_t>();
-      break;
-    case proto::VarType::INT8:
-      visitor.template apply<int8_t>();
-      break;
-    default:
-      PADDLE_THROW("Not supported %d", type);
-  }
+#define VisitDataTypeCallback(cpp_type, proto_type) \
+  do {                                              \
+    if (type == proto_type) {                       \
+      visitor.template apply<cpp_type>();           \
+      return;                                       \
+    }                                               \
+  } while (0)
+
+  _ForEachDataType_(VisitDataTypeCallback);
+#undef VisitDataTypeCallback
+  PADDLE_THROW("Not supported %d", type);
 }
 
 extern std::string DataTypeToString(const proto::VarType::Type type);
-extern size_t SizeOfType(std::type_index type);
+extern size_t SizeOfType(proto::VarType::Type type);
 inline std::ostream& operator<<(std::ostream& out,
                                 const proto::VarType::Type& type) {
   out << DataTypeToString(type);

paddle/fluid/framework/data_type_test.cc

@@ -26,15 +26,15 @@ TEST(DataType, float16) {
 
   Tensor tensor;
   CPUPlace cpu;
-  tensor.mutable_data(cpu, f::ToTypeIndex(dtype));
+  tensor.mutable_data(cpu, dtype);
 
   // test fp16 tensor
-  EXPECT_EQ(tensor.type(), std::type_index(typeid(float16)));
+  EXPECT_EQ(tensor.type(), f::ToDataType(typeid(float16)));
 
   // test fp16 size
-  EXPECT_EQ(f::SizeOfType(f::ToTypeIndex(dtype)), 2u);
+  EXPECT_EQ(f::SizeOfType(dtype), 2u);
 
   // test debug info
-  std::string type = "float16";
+  std::string type = "::paddle::platform::float16";
   EXPECT_STREQ(f::DataTypeToString(dtype).c_str(), type.c_str());
 }

paddle/fluid/framework/details/CMakeLists.txt

@@ -12,12 +12,19 @@ cc_library(multi_devices_graph_check_pass SRCS multi_devices_graph_check_pass.cc
 
 cc_library(variable_visitor SRCS variable_visitor.cc DEPS lod_tensor selected_rows)
 
+if(WITH_DISTRIBUTE)
+    if(NOT WITH_GRPC)
+        set(DISTRIBUTE_COMPILE_FLAGS "-Wno-non-virtual-dtor -Wno-error=non-virtual-dtor -Wno-error=delete-non-virtual-dtor")
+        set_source_files_properties(reduce_op_handle.cc PROPERTIES COMPILE_FLAGS ${DISTRIBUTE_COMPILE_FLAGS})
+    endif()
+endif()
+
 if(WITH_GPU)
     nv_library(all_reduce_op_handle SRCS all_reduce_op_handle.cc DEPS op_handle_base scope lod_tensor ddim memory
             dynload_cuda variable_visitor)
     if(WITH_DISTRIBUTE)
         nv_library(reduce_op_handle SRCS reduce_op_handle.cc DEPS op_handle_base variable_visitor scope
-            ddim dynload_cuda selected_rows_functor sendrecvop_grpc)
+            ddim dynload_cuda selected_rows_functor sendrecvop_rpc)
     else()
         nv_library(reduce_op_handle SRCS reduce_op_handle.cc DEPS op_handle_base variable_visitor scope
             ddim dynload_cuda selected_rows_functor)
@@ -30,7 +37,7 @@ else()
              variable_visitor)
     if(WITH_DISTRIBUTE)
         cc_library(reduce_op_handle SRCS reduce_op_handle.cc DEPS op_handle_base variable_visitor scope
-            ddim selected_rows_functor sendrecvop_grpc)
+            ddim selected_rows_functor sendrecvop_rpc)
     else()
         cc_library(reduce_op_handle SRCS reduce_op_handle.cc DEPS op_handle_base variable_visitor scope
             ddim selected_rows_functor)

paddle/fluid/framework/details/all_reduce_op_handle.cc

@@ -127,7 +127,7 @@ void AllReduceOpHandle::RunImpl() {
 
       // Reduce All Tensor to trg in CPU
       ReduceLoDTensor func(lod_tensors, &trg);
-      VisitDataType(ToDataType(lod_tensors[0]->type()), func);
+      VisitDataType(lod_tensors[0]->type(), func);
 
       for (size_t i = 1; i < local_scopes_.size(); ++i) {
         auto &scope =

paddle/fluid/framework/details/fuse_vars_op_handle.h

@@ -33,7 +33,7 @@ struct FuseVarsOpHandle : public OpHandleBase {
   FuseVarsOpHandle(ir::Node *node, Scope *local_scope,
                    const platform::Place &place,
                    const std::unordered_map<std::string, int64_t> &inputs_numel,
-                   const std::type_index &var_type)
+                   const proto::VarType::Type var_type)
       : OpHandleBase(node),
         local_scope_(local_scope),
         place_(place),
@@ -57,7 +57,7 @@ struct FuseVarsOpHandle : public OpHandleBase {
   Scope *local_scope_;
   const platform::Place place_;
   const std::unordered_map<std::string, int64_t> inputs_numel_;
-  const std::type_index type_;
+  const proto::VarType::Type type_;
   int64_t total_numel_;
 };
 }  // namespace details

paddle/fluid/framework/details/reduce_op_handle.cc

@@ -218,18 +218,18 @@ void ReduceOpHandle::RunImpl() {
       }
 
 #if defined PADDLE_WITH_CUDA && defined PADDLE_WITH_DISTRIBUTE
-      if (framework::IsType<const float>(in_selected_rows[0]->value().type())) {
+      if (in_selected_rows[0]->value().type() ==
+          framework::proto::VarType::FP32) {
         GatherSelectedRows<platform::CUDADeviceContext, float>(
             in_selected_rows, in_places, dev_ctxes_, out_var_handle, t_out_p,
             out_var->GetMutable<framework::SelectedRows>());
-      } else if (framework::IsType<const double>(
-                     in_selected_rows[0]->value().type())) {
+      } else if (in_selected_rows[0]->value().type() ==
+                 framework::proto::VarType::FP64) {
         GatherSelectedRows<platform::CUDADeviceContext, double>(
             in_selected_rows, in_places, dev_ctxes_, out_var_handle, t_out_p,
             out_var->GetMutable<framework::SelectedRows>());
       } else {
-        PADDLE_ENFORCE(false,
-                       "only support double or float when gahter SelectedRows");
+        PADDLE_THROW("only support double or float when gather SelectedRows");
       }
 #endif
     });
@@ -246,7 +246,7 @@ void ReduceOpHandle::RunImpl() {
         if (!FLAGS_cpu_deterministic) {
           ReduceLoDTensor func(lod_tensors,
                                out_var->GetMutable<framework::LoDTensor>());
-          VisitDataType(ToDataType(lod_tensors[0]->type()), func);
+          VisitDataType(lod_tensors[0]->type(), func);
         } else {
           // We sum lod_tensors to reduce_sum_trg which is in local_scopes_0
           // here, but it doesn't mean reduce_sum_trg must be in local_scopes_0.
@@ -256,7 +256,7 @@ void ReduceOpHandle::RunImpl() {
                                       ->FindVar(out_var_handle->name_)
                                       ->GetMutable<framework::LoDTensor>();
           ReduceLoDTensor func(lod_tensors, &reduce_sum_trg);
-          VisitDataType(ToDataType(lod_tensors[0]->type()), func);
+          VisitDataType(lod_tensors[0]->type(), func);
 
           auto trg = out_var->GetMutable<framework::LoDTensor>();
           if (reduce_sum_trg.data<void>() != trg->data<void>()) {

paddle/fluid/framework/dlpack_tensor.cc

@@ -13,7 +13,7 @@
 // limitations under the License.
 
 #include "paddle/fluid/framework/dlpack_tensor.h"
-
+#include "paddle/fluid/framework/data_type.h"
 namespace paddle {
 namespace framework {
 
@@ -36,26 +36,23 @@ static ::DLDataType GetDLDataTypeCode() {
   return dtype;
 }
 
-static DLDataType GetDLDataTypeFromTypeIndex(const std::type_index &type) {
-#define REG_DL_DATA_TYPE(type) \
-  { std::type_index(typeid(type)), GetDLDataTypeCode<type>() }
-  static const std::unordered_map<std::type_index, ::DLDataType>
-      type_to_dtype_map({
-          REG_DL_DATA_TYPE(platform::float16),  // NOLINT
-          REG_DL_DATA_TYPE(float),              // NOLINT
-          REG_DL_DATA_TYPE(double),             // NOLINT
-          REG_DL_DATA_TYPE(int),                // NOLINT
-          REG_DL_DATA_TYPE(int64_t),            // NOLINT
-          REG_DL_DATA_TYPE(bool),               // NOLINT
-          REG_DL_DATA_TYPE(size_t),             // NOLINT
-          REG_DL_DATA_TYPE(int16_t),            // NOLINT
-          REG_DL_DATA_TYPE(uint8_t),            // NOLINT
-          REG_DL_DATA_TYPE(int8_t)              // NOLINT
-      });
+static std::unordered_map<int, ::DLDataType> CreateDLDataTypeMap() {
+  static std::unordered_map<int, ::DLDataType> result;
+
+#define REG_DL_DATA_TYPE(cpp_type, proto_type) \
+  result[static_cast<int>(proto_type)] = GetDLDataTypeCode<cpp_type>()
+
+  _ForEachDataType_(REG_DL_DATA_TYPE);
+#undef REG_DL_DATA_TYPE
+  return result;
+}
+
+static DLDataType GetDLDataTypeFromTypeIndex(proto::VarType::Type type) {
+  static auto type_to_dtype_map = CreateDLDataTypeMap();
   static auto type_to_dtype_map_end_it = type_to_dtype_map.end();
-  auto it = type_to_dtype_map.find(type);
-  PADDLE_ENFORCE(it != type_to_dtype_map_end_it, "Unsupported data type %s",
-                 type.name());
+  auto it = type_to_dtype_map.find(static_cast<int>(type));
+  PADDLE_ENFORCE(it != type_to_dtype_map_end_it, "Unsupported data type %d",
+                 type);
   return it->second;
 #undef REG_DL_DATA_TYPE
 }

paddle/fluid/framework/dlpack_tensor_test.cc

@@ -91,23 +91,11 @@ void TestMainLoop() {
     }
   }
 }
+TEST(dlpack, test_all) {
+#define TestCallback(cpp_type, proto_type) TestMainLoop<cpp_type>()
 
-#define PADDLE_DLPACK_TEST(type) \
-  TEST(dlpack, test_##type) { TestMainLoop<type>(); }
-
-using float16 = platform::float16;
-PADDLE_DLPACK_TEST(float16);
-PADDLE_DLPACK_TEST(float);
-PADDLE_DLPACK_TEST(double);
-PADDLE_DLPACK_TEST(int);
-PADDLE_DLPACK_TEST(int64_t);
-PADDLE_DLPACK_TEST(bool);
-PADDLE_DLPACK_TEST(size_t);
-PADDLE_DLPACK_TEST(int16_t);
-PADDLE_DLPACK_TEST(uint8_t);
-PADDLE_DLPACK_TEST(int8_t);
-
-#undef PADDLE_DLPACK_TEST
+  _ForEachDataType_(TestCallback);
+}
 
 }  // namespace framework
 }  // namespace paddle

paddle/fluid/framework/executor.cc

@@ -157,9 +157,9 @@ void Executor::Close() {
 #ifdef PADDLE_WITH_DISTRIBUTE
   // TODO(typhoonzero): complete message will need to use real trainer_id,
   // except 0.
-  ::paddle::operators::distributed::RPCClient::GetInstance<
-      ::paddle::operators::distributed::GRPCClient>(0)
-      ->SendComplete();
+  auto client =
+      paddle::operators::distributed::RPCClient::GetInstance<RPCCLIENT_T>(0);
+  client->SendComplete();
 #endif
 }
 

paddle/fluid/framework/executor_thread_worker.cc

@@ -139,39 +139,19 @@ void print_lod_tensor(std::string var_name, const LoDTensor& lod_tensor) {
   std::cout << sstream.str() << std::endl;
 }
 
-void print_fetch_var(Scope* scope, std::string var_name) {
-  const LoDTensor& tensor = scope->FindVar(var_name)->Get<LoDTensor>();
-
-  if (std::type_index(tensor.type()) ==
-      std::type_index(typeid(platform::float16))) {
-    print_lod_tensor<platform::float16>(var_name, tensor);
-  } else if (std::type_index(tensor.type()) == std::type_index(typeid(float))) {
-    print_lod_tensor<float>(var_name, tensor);
-  } else if (std::type_index(tensor.type()) ==
-             std::type_index(typeid(double))) {
-    print_lod_tensor<double>(var_name, tensor);
-  } else if (std::type_index(tensor.type()) == std::type_index(typeid(int))) {
-    print_lod_tensor<int>(var_name, tensor);
-  } else if (std::type_index(tensor.type()) ==
-             std::type_index(typeid(int64_t))) {
-    print_lod_tensor<int64_t>(var_name, tensor);
-  } else if (std::type_index(tensor.type()) == std::type_index(typeid(bool))) {
-    print_lod_tensor<bool>(var_name, tensor);
-  } else if (std::type_index(tensor.type()) ==
-             std::type_index(typeid(uint8_t))) {
-    print_lod_tensor<uint8_t>(var_name, tensor);
-  } else if (std::type_index(tensor.type()) ==
-             std::type_index(typeid(int16_t))) {
-    print_lod_tensor<int16_t>(var_name, tensor);
-  } else if (std::type_index(tensor.type()) ==
-             std::type_index(typeid(int8_t))) {
-    print_lod_tensor<int8_t>(var_name, tensor);
-  } else {
-    VLOG(1) << "print_fetch_var: unrecognized data type:"
-            << tensor.type().name();
-  }
-
-  return;
+static void print_fetch_var(Scope* scope, const std::string& var_name) {
+  auto& tensor = scope->FindVar(var_name)->Get<LoDTensor>();
+
+#define PrintLoDTensorCallback(cpp_type, proto_type) \
+  do {                                               \
+    if (tensor.type() == proto_type) {               \
+      print_lod_tensor<cpp_type>(var_name, tensor);  \
+      return;                                        \
+    }                                                \
+  } while (0)
+
+  _ForEachDataType_(PrintLoDTensorCallback);
+  VLOG(1) << "print_fetch_var: unrecognized data type:" << tensor.type();
 }
 
 void ExecutorThreadWorker::TrainFiles() {

paddle/fluid/framework/ir/CMakeLists.txt

@@ -42,6 +42,8 @@ pass_library(multi_batch_merge_pass base)
 pass_library(conv_bn_fuse_pass inference)
 pass_library(seqconv_eltadd_relu_fuse_pass inference)
 pass_library(is_test_pass base)
+pass_library(conv_elementwise_add_act_fuse_pass inference)
+pass_library(conv_elementwise_add2_act_fuse_pass inference)
 if(WITH_MKLDNN)
     pass_library(mkldnn_placement_pass base)
     pass_library(depthwise_conv_mkldnn_pass base)

paddle/fluid/framework/ir/conv_elementwise_add2_act_fuse.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 <string>
+#include "paddle/fluid/framework/ir/conv_elementwise_add2_act_fuse_pass.h"
+
+namespace paddle {
+namespace framework {
+namespace ir {
+
+#define GET_IR_NODE(node__) GET_IR_NODE_FROM_SUBGRAPH(node__, node__, pattern);
+#define GET_NODES                      \
+  GET_IR_NODE(conv_op);                \
+  GET_IR_NODE(conv_out);               \
+  GET_IR_NODE(conv_filter);            \
+  GET_IR_NODE(elementwise_add_op);     \
+  GET_IR_NODE(elementwise_add_in_y);   \
+  GET_IR_NODE(elementwise_add_out);    \
+  GET_IR_NODE(elementwise_add_op_1);   \
+  GET_IR_NODE(elementwise_add_in_y_1); \
+  GET_IR_NODE(elementwise_add_out_1);  \
+  GET_IR_NODE(act_op);                 \
+  GET_IR_NODE(act_out);
+
+// Inherient the basic infomation from `base_desc`, and modify some fields.
+framework::proto::OpDesc PrepareOpDesc(
+    const framework::proto::OpDesc& base_desc, const std::string& bias,
+    const std::string& bias1, const std::string& activation,
+    const std::string& output) {
+  auto proto = base_desc;
+  framework::OpDesc desc(proto, nullptr);
+  desc.SetInput("Bias", {bias});
+  desc.SetInput("ResidualData", {bias1});
+  desc.SetAttr("activation", activation);
+  desc.SetOutput("Output", {output});
+  desc.SetAttr("is_test", true);
+  desc.SetAttr("use_cudnn", false);
+
+  return *desc.Proto();
+}
+
+std::unique_ptr<ir::Graph> ConvElementwiseAddActFusePass::ApplyImpl(
+    std::unique_ptr<ir::Graph> graph) const {
+  const std::string pattern_name = "conv_elementwise_add_act_fuse";
+  FusePassBase::Init(pattern_name, graph.get());
+
+  GraphPatternDetector gpd;
+  auto* x = gpd.mutable_pattern()->NewNode("x")->AsInput()->assert_is_op_input(
+      "conv2d", "Input");
+
+  patterns::ConvElementwiseaddAct pattern(gpd.mutable_pattern(), pattern_name);
+  pattern(x);
+
+  auto handler = [&](const GraphPatternDetector::subgraph_t& subgraph,
+                     Graph* g) {
+    GET_NODES;
+
+    auto base_op_desc = *conv_op->Op()->Proto();
+    std::string bias_name = elementwise_add_in_y->Name();
+    std::string bias1_name = elementwise_add_in_y_1->Name();
+    std::string act_op_type = act_op->Op()->Type();
+    std::string act_op_out = act_out->Name();
+
+    auto new_op_proto = PrepareOpDesc(base_op_desc, bias_name, bias1_name,
+                                      act_op_type, act_op_out);
+    framework::OpDesc new_op_desc(new_op_proto, nullptr);
+
+    // Create a new node for the fused op.
+    auto new_conv_op = graph->CreateOpNode(&new_op_desc);
+
+    // Link inputs and outputs.
+    PADDLE_ENFORCE(subgraph.count(x));
+    auto* conv_in_node = subgraph.at(x);
+
+    IR_NODE_LINK_TO(conv_in_node, new_conv_op);            // Input
+    IR_NODE_LINK_TO(conv_filter, new_conv_op);             // Filter
+    IR_NODE_LINK_TO(elementwise_add_in_y, new_conv_op);    // Bias
+    IR_NODE_LINK_TO(elementwise_add_in_y_1, new_conv_op);  // ResidualData
+    IR_NODE_LINK_TO(new_conv_op, act_out);                 // Output
+
+    // Delete the unneeded nodes.
+    GraphSafeRemoveNodes(graph.get(),
+                         {conv_op, elementwise_add_op, elementwise_add_op_1,
+                          elementwise_add_out});
+  };
+  gpd(graph.get(), handler);
+  return graph;
+}
+
+}  // namespace ir
+}  // namespace framework
+}  // namespace paddle
+
+REGISTER_PASS(conv_elementwise_add2_act_fuse_pass,
+              paddle::framework::ir::ConvElementwiseAdd2ActFusePass);

paddle/fluid/framework/ir/conv_elementwise_add2_act_fuse_pass.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/ir/conv_elementwise_add2_act_fuse_pass.h"
+#include <string>
+
+namespace paddle {
+namespace framework {
+namespace ir {
+
+#define GET_IR_NODE(node__) GET_IR_NODE_FROM_SUBGRAPH(node__, node__, pattern);
+#define GET_NODES                      \
+  GET_IR_NODE(conv_op);                \
+  GET_IR_NODE(conv_out);               \
+  GET_IR_NODE(conv_filter);            \
+  GET_IR_NODE(elementwise_add_op);     \
+  GET_IR_NODE(elementwise_add_in_y);   \
+  GET_IR_NODE(elementwise_add_out);    \
+  GET_IR_NODE(elementwise_add_op_1);   \
+  GET_IR_NODE(elementwise_add_in_y_1); \
+  GET_IR_NODE(elementwise_add_out_1);  \
+  GET_IR_NODE(act_op);                 \
+  GET_IR_NODE(act_out);
+
+// Inherient the basic infomation from `base_desc`, and modify some fields.
+framework::proto::OpDesc PrepareOpDesc(
+    const framework::proto::OpDesc& base_desc, const std::string& bias,
+    const std::string& bias1, const std::string& activation,
+    const std::string& output) {
+  auto proto = base_desc;
+  framework::OpDesc desc(proto, nullptr);
+  desc.SetInput("Bias", {bias});
+  desc.SetInput("ResidualData", {bias1});
+  desc.SetAttr("activation", activation);
+  desc.SetOutput("Output", {output});
+  desc.SetAttr("is_test", true);
+
+  return *desc.Proto();
+}
+
+std::unique_ptr<ir::Graph> ConvElementwiseAdd2ActFusePass::ApplyImpl(
+    std::unique_ptr<ir::Graph> graph) const {
+  const std::string pattern_name = "conv_elementwise_add_act_fuse";
+  FusePassBase::Init(pattern_name, graph.get());
+
+  GraphPatternDetector gpd;
+  auto* x = gpd.mutable_pattern()->NewNode("x")->AsInput()->assert_is_op_input(
+      "conv2d", "Input");
+
+  patterns::ConvElementwiseadd2Act pattern(gpd.mutable_pattern(), pattern_name);
+  pattern(x);
+
+  auto handler = [&](const GraphPatternDetector::subgraph_t& subgraph,
+                     Graph* g) {
+    GET_NODES;
+
+    auto base_op_desc = *conv_op->Op()->Proto();
+    std::string bias_name = elementwise_add_in_y->Name();
+    std::string bias1_name = elementwise_add_in_y_1->Name();
+    std::string act_op_type = act_op->Op()->Type();
+    std::string act_op_out = act_out->Name();
+
+    auto new_op_proto = PrepareOpDesc(base_op_desc, bias_name, bias1_name,
+                                      act_op_type, act_op_out);
+    framework::OpDesc new_op_desc(new_op_proto, nullptr);
+
+    // Create a new node for the fused op.
+    graph->CreateOpNode(&new_op_desc);
+
+    // Link inputs and outputs.
+    PADDLE_ENFORCE(subgraph.count(x));
+    auto* conv_in_node = subgraph.at(x);
+
+    IR_NODE_LINK_TO(conv_in_node, conv_op);            // Input
+    IR_NODE_LINK_TO(conv_filter, conv_op);             // Filter
+    IR_NODE_LINK_TO(conv_op, conv_out);                // Output
+    IR_NODE_LINK_TO(elementwise_add_in_y, conv_op);    // Bias
+    IR_NODE_LINK_TO(elementwise_add_in_y_1, conv_op);  // Bias
+
+    // Delete the unneeded nodes.
+    GraphSafeRemoveNodes(graph.get(),
+                         {conv_op, elementwise_add_op, elementwise_add_op_1,
+                          elementwise_add_out});
+  };
+  gpd(graph.get(), handler);
+  return graph;
+}
+
+}  // namespace ir
+}  // namespace framework
+}  // namespace paddle
+
+REGISTER_PASS(conv_elementwise_add2_act_fuse_pass,
+              paddle::framework::ir::ConvElementwiseAdd2ActFusePass);

paddle/fluid/framework/ir/conv_elementwise_add2_act_fuse_pass.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 "paddle/fluid/framework/ir/fuse_pass_base.h"
+#include "paddle/fluid/framework/ir/graph_pattern_detector.h"
+
+namespace paddle {
+namespace framework {
+namespace ir {
+
+class ConvElementwiseAdd2ActFusePass : public FusePassBase {
+ public:
+  virtual ~ConvElementwiseAdd2ActFusePass() {}
+
+ protected:
+  std::unique_ptr<ir::Graph> ApplyImpl(std::unique_ptr<ir::Graph> graph) const;
+};
+
+}  // namespace ir
+}  // namespace framework
+}  // namespace paddle

paddle/fluid/framework/ir/conv_elementwise_add_act_fuse_pass.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/ir/conv_elementwise_add_act_fuse_pass.h"
+#include <string>
+#include "paddle/fluid/framework/ir/graph_viz_pass.h"
+
+namespace paddle {
+namespace framework {
+namespace ir {
+
+#define GET_IR_NODE(node__) GET_IR_NODE_FROM_SUBGRAPH(node__, node__, pattern);
+#define GET_NODES                    \
+  GET_IR_NODE(conv_op);              \
+  GET_IR_NODE(conv_out);             \
+  GET_IR_NODE(conv_filter);          \
+  GET_IR_NODE(elementwise_add_op);   \
+  GET_IR_NODE(elementwise_add_in_y); \
+  GET_IR_NODE(elementwise_add_out);  \
+  GET_IR_NODE(act_op);               \
+  GET_IR_NODE(act_out);
+
+// Inherient the basic infomation from `base_desc`, and modify some fields.
+framework::proto::OpDesc PrepareOpDesc(
+    const framework::proto::OpDesc& base_desc, const std::string& bias,
+    const std::string& activation, const std::string& output) {
+  auto proto = base_desc;
+  framework::OpDesc desc(proto, nullptr);
+  desc.SetType("conv2d_fusion");
+  desc.SetInput("Bias", {bias});
+  desc.SetInput("ResidualData", {});
+  desc.SetAttr("activation", activation);
+  desc.SetOutput("Output", {output});
+  desc.SetAttr("is_test", true);
+  desc.SetAttr("use_cudnn", false);
+  desc.Flush();
+  return *desc.Proto();
+}
+
+std::unique_ptr<ir::Graph> ConvElementwiseAddActFusePass::ApplyImpl(
+    std::unique_ptr<ir::Graph> graph) const {
+  const std::string pattern_name = "conv_elementwise_add_act_fuse";
+  FusePassBase::Init(pattern_name, graph.get());
+
+  GraphPatternDetector gpd;
+  auto* x = gpd.mutable_pattern()
+                ->NewNode("x")
+                ->assert_is_op_input("conv2d", "Input")
+                ->AsInput();
+
+  patterns::ConvElementwiseaddAct pattern(gpd.mutable_pattern(), pattern_name);
+  pattern(x);
+
+  auto handler = [&](const GraphPatternDetector::subgraph_t& subgraph,
+                     Graph* g) {
+    GET_NODES;
+
+    auto base_op_desc = *conv_op->Op()->Proto();
+    std::string bias_name = elementwise_add_in_y->Name();
+    std::string act_op_type = act_op->Op()->Type();
+    std::string act_op_out = act_out->Name();
+
+    auto new_op_proto =
+        PrepareOpDesc(base_op_desc, bias_name, act_op_type, act_op_out);
+    framework::OpDesc new_op_desc(new_op_proto, nullptr);
+
+    // Create a new node for the fused op.
+    auto* new_conv_op = graph->CreateOpNode(&new_op_desc);
+
+    // Link inputs and outputs.
+    PADDLE_ENFORCE(subgraph.count(x));
+    auto* conv_in_node = subgraph.at(x);
+
+    IR_NODE_LINK_TO(conv_in_node, new_conv_op);          // Input
+    IR_NODE_LINK_TO(conv_filter, new_conv_op);           // Filter
+    IR_NODE_LINK_TO(elementwise_add_in_y, new_conv_op);  // Bias
+    IR_NODE_LINK_TO(new_conv_op, act_out);               // Output
+
+    // Delete the unneeded nodes.
+    GraphSafeRemoveNodes(graph.get(), {conv_op, conv_out, elementwise_add_op,
+                                       elementwise_add_out, act_op});
+  };
+
+  gpd(graph.get(), handler);
+  return graph;
+}
+
+}  // namespace ir
+}  // namespace framework
+}  // namespace paddle
+
+REGISTER_PASS(conv_elementwise_add_act_fuse_pass,
+              paddle::framework::ir::ConvElementwiseAddActFusePass);

paddle/fluid/framework/ir/conv_elementwise_add_act_fuse_pass.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 "paddle/fluid/framework/ir/fuse_pass_base.h"
+#include "paddle/fluid/framework/ir/graph_pattern_detector.h"
+
+namespace paddle {
+namespace framework {
+namespace ir {
+
+class ConvElementwiseAddActFusePass : public FusePassBase {
+ public:
+  virtual ~ConvElementwiseAddActFusePass() {}
+
+ protected:
+  std::unique_ptr<ir::Graph> ApplyImpl(std::unique_ptr<ir::Graph> graph) const;
+};
+
+}  // namespace ir
+}  // namespace framework
+}  // namespace paddle

paddle/fluid/framework/ir/graph_pattern_detector.cc

@@ -17,6 +17,7 @@
 #include <string>
 #include <vector>
 
+#include "graph_pattern_detector.h"
 #include "paddle/fluid/framework/ir/graph_helper.h"
 #include "paddle/fluid/framework/ir/graph_pattern_detector.h"
 #include "paddle/fluid/framework/ir/graph_traits.h"
@@ -25,6 +26,7 @@
 #include "paddle/fluid/platform/enforce.h"
 #include "paddle/fluid/string/pretty_log.h"
 #include "paddle/fluid/string/printf.h"
+
 namespace paddle {
 namespace framework {
 namespace ir {
@@ -104,7 +106,7 @@ bool GraphPatternDetector::MarkPDNodesInGraph(const ir::Graph &graph) {
   for (auto &node : GraphTraits::DFS(graph)) {
     for (const auto &pdnode : pattern_.nodes()) {
       if (pdnode->Tell(&node)) {
-        VLOG(4) << "pdnode " << pdnode->name() << " marked";
+        VLOG(4) << "Node " << node.Name() << " marked as " << pdnode->name();
         pdnodes2nodes_[pdnode.get()].insert(&node);
       }
     }
@@ -1099,6 +1101,115 @@ PDNode *patterns::ElementwiseAdd::operator()(PDNode *x_var, PDNode *y_var) {
 
   return out_var;
 }
+
+std::unordered_set<std::string> conv_act_set({"identity", "sigmoid", "relu",
+                                              "relu6", "relux", "tanh",
+                                              "band_pass"});
+
+PDNode *patterns::ConvElementwiseaddAct::operator()(PDNode *conv_in) {
+  conv_in->AsInput();
+  auto conv_op = pattern->NewNode(conv_op_repr())->assert_is_op("conv2d");
+  auto conv_out = pattern->NewNode(conv_out_repr())
+                      ->assert_is_op_output("conv2d")
+                      ->assert_is_op_input("elementwise_add", "X")
+                      ->AsIntermediate();
+  auto conv_filter = pattern->NewNode(conv_filter_repr())
+                         ->assert_is_op_input("conv2d", "Filter")
+                         ->AsInput();
+  auto elementwise_add_op = pattern->NewNode(elementwise_add_op_repr())
+                                ->assert_is_op("elementwise_add");
+  auto elementwise_add_in_y = pattern->NewNode(elementwise_add_in_y_repr())
+                                  ->assert_is_op_input("elementwise_add", "Y")
+                                  ->AsInput();
+  auto elementwise_add_out = pattern->NewNode(elementwise_add_out_repr())
+                                 ->assert_is_op_output("elementwise_add")
+                                 ->AsIntermediate();
+
+  auto act_op = pattern->NewNode(act_op_repr())
+                    ->assert_is_op()
+                    ->assert_more([&](Node *node) {
+                      auto op_type = node->Name();
+                      return conv_act_set.count(op_type);
+                    });
+
+  auto act_out = pattern->NewNode(act_out_repr())
+                     ->assert_is_var()
+                     // is activation op's output.
+                     ->assert_more([&](Node *node) {
+                       for (auto *in_op : node->inputs) {
+                         if (conv_act_set.count(in_op->Name())) {
+                           return true;
+                         }
+                       }
+                       return false;
+                     })
+                     ->AsOutput();
+
+  conv_op->LinksFrom({conv_in, conv_filter});
+  conv_out->LinksFrom({conv_op});
+  elementwise_add_op->LinksFrom({conv_out, elementwise_add_in_y})
+      .LinksTo({elementwise_add_out});
+  act_op->LinksFrom({elementwise_add_out}).LinksTo({act_out});
+
+  return act_out;
+}
+
+PDNode *patterns::ConvElementwiseadd2Act::operator()(PDNode *conv_in) {
+  auto conv_op = pattern->NewNode(conv_op_repr())->assert_is_op("conv2d");
+  auto conv_filter = pattern->NewNode(conv_filter_repr())
+                         ->assert_is_op_input("conv2d", "Filter")
+                         ->AsInput();
+  auto conv_out = pattern->NewNode(conv_out_repr())
+                      ->assert_is_op_output("conv2d")
+                      ->assert_is_op_input("elementwise_add", "X")
+                      ->AsIntermediate();
+  auto elementwise_add_op = pattern->NewNode(elementwise_add_op_repr())
+                                ->assert_is_op("elementwise_add");
+  auto elementwise_add_in_y = pattern->NewNode(elementwise_add_in_y_repr())
+                                  ->assert_is_op_input("elementwise_add", "Y")
+                                  ->AsInput();
+  auto elementwise_add_out = pattern->NewNode(elementwise_add_out_repr())
+                                 ->assert_is_op_output("elementwise_add")
+                                 ->assert_is_op_input("elementwise_add", "X")
+                                 ->AsIntermediate();
+
+  auto elementwise_add_op_1 = pattern->NewNode(elementwise_add_op_1_repr())
+                                  ->assert_is_op("elementwise_add");
+  auto elementwise_add_in_y_1 = pattern->NewNode(elementwise_add_in_y_1_repr())
+                                    ->assert_is_op_input("elementwise_add", "Y")
+                                    ->AsInput();
+  auto elementwise_add_out_1 = pattern->NewNode(elementwise_add_out_1_repr())
+                                   ->assert_is_op_output("elementwise_add")
+                                   ->AsIntermediate();
+
+  auto act_op = pattern->NewNode(act_op_repr())
+                    ->assert_is_op()
+                    ->assert_more([&](Node *node) {
+                      auto op_type = node->Name();
+                      return conv_act_set.count(op_type);
+                    });
+  auto act_out = pattern->NewNode(act_out_repr())
+                     ->assert_is_var()
+                     // is activation op's output.
+                     ->assert_more([&](Node *node) {
+                       for (auto *in_op : node->inputs) {
+                         if (conv_act_set.count(in_op->Name())) {
+                           return true;
+                         }
+                       }
+                       return false;
+                     })
+                     ->AsOutput();
+
+  conv_op->LinksFrom({conv_in, conv_filter}).LinksTo({conv_out});
+  elementwise_add_op->LinksFrom({conv_out, elementwise_add_in_y})
+      .LinksTo({elementwise_add_out});
+  elementwise_add_op_1->LinksFrom(
+      {elementwise_add_out, elementwise_add_in_y_1});
+  act_op->LinksFrom({elementwise_add_out_1}).LinksTo({act_out});
+  return act_out;
+}
+
 }  // namespace ir
 }  // namespace framework
 }  // namespace paddle

paddle/fluid/framework/ir/graph_pattern_detector.h

@@ -671,6 +671,51 @@ struct ElementwiseAdd : public PatternBase {
   PATTERN_DECL_NODE(elementwise_add_y);
   PATTERN_DECL_NODE(elementwise_add_out);
 };
+
+// Conv + ElementwiseAdd + an activation
+// This pattern can futher fuse the conv related ops after the conv+bn fusion.
+struct ConvElementwiseaddAct : public PatternBase {
+  ConvElementwiseaddAct(PDPattern* pattern, const std::string& name_scope)
+      : PatternBase(pattern, name_scope, "conv_elementwiseadd_act") {}
+
+  PDNode* operator()(PDNode* conv_in);
+
+  PATTERN_DECL_NODE(conv_op);
+  PATTERN_DECL_NODE(conv_out);
+  PATTERN_DECL_NODE(conv_filter);
+
+  PATTERN_DECL_NODE(elementwise_add_op);
+  PATTERN_DECL_NODE(elementwise_add_in_y);  // input
+  PATTERN_DECL_NODE(elementwise_add_out);
+
+  PATTERN_DECL_NODE(act_op);
+  PATTERN_DECL_NODE(act_out);
+};
+
+// Conv + ElementwiseAdd + ElementwiseAdd + Activation
+struct ConvElementwiseadd2Act : public PatternBase {
+  ConvElementwiseadd2Act(PDPattern* pattern, const std::string& name_scope)
+      : PatternBase(pattern, name_scope,
+                    "conv_elementwiseadd2_elementwiseadd_act") {}
+
+  PDNode* operator()(PDNode* conv_in);
+
+  PATTERN_DECL_NODE(conv_op);
+  PATTERN_DECL_NODE(conv_filter);
+  PATTERN_DECL_NODE(conv_out);
+
+  PATTERN_DECL_NODE(elementwise_add_op);
+  PATTERN_DECL_NODE(elementwise_add_in_y);  // input
+  PATTERN_DECL_NODE(elementwise_add_out);
+
+  PATTERN_DECL_NODE(elementwise_add_op_1);
+  PATTERN_DECL_NODE(elementwise_add_in_y_1);  // input
+  PATTERN_DECL_NODE(elementwise_add_out_1);
+
+  PATTERN_DECL_NODE(act_op);
+  PATTERN_DECL_NODE(act_out);
+};
+
 }  // namespace patterns
 
 // Link two ir::Nodes from each other.

paddle/fluid/framework/lod_tensor.cc

@@ -70,9 +70,9 @@ std::ostream &operator<<(std::ostream &os, const LoDTensor &t) {
   // only print first ten elements
   int64_t size = t.numel() < 10 ? t.numel() : 10;
   for (int64_t i = 0; i < size; ++i) {
-    if (IsType<float>(t.type())) {
+    if (t.type() == proto::VarType::FP32) {
       os << t.data<float>()[i] << " ";
-    } else if (IsType<int64_t>(t.type())) {
+    } else if (t.type() == proto::VarType::INT64) {
       os << t.data<int64_t>()[i] << " ";
     } else {
       PADDLE_THROW("LoDTensor data type not in [float, int64_t]");
@@ -387,7 +387,7 @@ void LoDTensor::MergeLoDTensor(
   PADDLE_ENFORCE(!lod_tensors.empty());
 
   framework::DDim new_dim = lod_tensors[0]->dims();
-  std::type_index new_type = lod_tensors[0]->type();
+  auto new_type = lod_tensors[0]->type();
   framework::DataLayout new_layout = lod_tensors[0]->layout();
   LoD new_lod = lod_tensors[0]->lod();
   for (size_t i = 1; i < lod_tensors.size(); ++i) {

paddle/fluid/framework/ngraph_operator.cc

@@ -471,27 +471,23 @@ void NgraphEngine::Run(const Scope& scope, const platform::Place& place) const {
       auto* tensor_pd = GetLoDTensorOrSelectedRowsValueFromVar(*var);
       PADDLE_ENFORCE(sp == Ddim2Shape(tensor_pd->dims()),
                      "Ensure ngraph tensor layout align with paddle tensor");
-      if (tensor_pd->type().hash_code() ==
-          typeid(float).hash_code()) {  // NOLINT
+      if (tensor_pd->type() == proto::VarType::FP32) {
         const float* arr = tensor_pd->data<float>();
         ti = backend_->create_tensor(ngraph::element::f32, sp,
                                      const_cast<float*>(arr));
-      } else if (tensor_pd->type().hash_code() ==
-                 typeid(int).hash_code()) {  // NOLINT
+      } else if (tensor_pd->type() == proto::VarType::INT32) {
         const int* arr = tensor_pd->data<int>();
         ti = backend_->create_tensor(ngraph::element::i32, sp,
                                      const_cast<int*>(arr));
-      } else if (tensor_pd->type().hash_code() == typeid(int64_t).hash_code()) {
+      } else if (tensor_pd->type() == proto::VarType::INT64) {
         const int64_t* arr = tensor_pd->data<int64_t>();
         ti = backend_->create_tensor(ngraph::element::i64, sp,
                                      const_cast<int64_t*>(arr));
-      } else if (tensor_pd->type().hash_code() ==
-                 typeid(double).hash_code()) {  // NOLINT
+      } else if (tensor_pd->type() == proto::VarType::FP64) {
         const double* arr = tensor_pd->data<double>();
         ti = backend_->create_tensor(ngraph::element::f64, sp,
                                      const_cast<double*>(arr));
-      } else if (tensor_pd->type().hash_code() ==
-                 typeid(bool).hash_code()) {  // NOLINT
+      } else if (tensor_pd->type() == proto::VarType::BOOL) {
         const bool* arr = tensor_pd->data<bool>();
         ti = backend_->create_tensor(ngraph::element::boolean, sp,
                                      const_cast<bool*>(arr));

paddle/fluid/framework/op_kernel_type_test.cc

@@ -34,7 +34,8 @@ TEST(OpKernelType, ToString) {
   OpKernelType op_kernel_type2(DataType::FP16, CUDAPlace(0), DataLayout::kNCHW,
                                LibraryType::kCUDNN);
   ASSERT_EQ(paddle::framework::KernelTypeToString(op_kernel_type2),
-            "data_type[float16]:data_layout[NCHW]:place[CUDAPlace(0)]:library_"
+            "data_type[::paddle::platform::float16]:data_layout[NCHW]:place["
+            "CUDAPlace(0)]:library_"
             "type[CUDNN]");
 }
 

paddle/fluid/framework/operator.cc

@@ -45,10 +45,9 @@ std::vector<std::tuple<platform::Place, LibraryType>> kKernelPriority = {
 
 proto::VarType::Type GetDataTypeOfVar(const Variable* var) {
   if (var->IsType<framework::LoDTensor>()) {
-    return framework::ToDataType(var->Get<framework::LoDTensor>().type());
+    return var->Get<framework::LoDTensor>().type();
   } else if (var->IsType<framework::SelectedRows>()) {
-    return framework::ToDataType(
-        var->Get<framework::SelectedRows>().value().type());
+    return var->Get<framework::SelectedRows>().value().type();
   } else {
     PADDLE_THROW("Var should be LoDTensor or SelectedRows");
   }
@@ -95,13 +94,13 @@ static std::string GetDtype(const Scope& scope, const std::string& name) {
     if (UNLIKELY(!tensor.IsInitialized())) {
       return "";
     }
-    return DataTypeToString(ToDataType(tensor.type()));
+    return DataTypeToString(tensor.type());
   } else if (var->IsType<SelectedRows>()) {
     auto tensor = var->Get<SelectedRows>().value();
     if (UNLIKELY(!tensor.IsInitialized())) {
       return "uninited";
     } else {
-      return DataTypeToString(ToDataType(tensor.type()));
+      return DataTypeToString(tensor.type());
     }
   } else {
     return "";
@@ -688,7 +687,8 @@ static void CheckTensorNANOrInf(const std::string& name,
   if (tensor.memory_size() == 0) {
     return;
   }
-  if (!IsType<float>(tensor.type()) && !IsType<double>(tensor.type())) {
+  if (tensor.type() != proto::VarType::FP32 &&
+      tensor.type() != proto::VarType::FP64) {
     return;
   }
   PADDLE_ENFORCE(!framework::TensorContainsInf(tensor),
@@ -883,7 +883,7 @@ proto::VarType::Type OperatorWithKernel::IndicateDataType(
         if (t != nullptr) {
           PADDLE_ENFORCE(t->IsInitialized(), "Input %s is not initialized: %s",
                          ipt_name, DebugString());
-          int tmp = static_cast<int>(ToDataType(t->type()));
+          int tmp = static_cast<int>(t->type());
           PADDLE_ENFORCE(
               tmp == data_type || data_type == -1,
               "DataType of Paddle Op %s must be the same. Get %s(%d) != %s(%d)",

paddle/fluid/framework/selected_rows.cc

@@ -218,11 +218,11 @@ void SelectedRows::Get(const framework::Tensor& ids, framework::Tensor* value,
       if (index < 0) {
         VLOG(5) << "id " << id << " not in the table, return 0";
         framework::VisitDataType(
-            framework::ToDataType(value_->type()),
+            value_->type(),
             TensorFillVisitor(value, i * value_width, value_width, 0.0));
       } else {
         framework::VisitDataType(
-            framework::ToDataType(value_->type()),
+            value_->type(),
             TensorCopyVisitor(value, i * value_width, *value_.get(),
                               index * value_width, value_width));
       }

paddle/fluid/framework/tensor.cc

@@ -16,7 +16,7 @@ limitations under the License. */
 
 namespace paddle {
 namespace framework {
-extern size_t SizeOfType(std::type_index type);
+extern size_t SizeOfType(proto::VarType::Type type);
 void Tensor::check_memory_size() const {
   PADDLE_ENFORCE_NOT_NULL(
       holder_, "Tensor holds no memory. Call Tensor::mutable_data first.");
@@ -31,7 +31,7 @@ size_t Tensor::memory_size() const {
   return holder_ == nullptr ? 0UL : holder_->size() - offset_;
 }
 
-void* Tensor::mutable_data(platform::Place place, std::type_index type,
+void* Tensor::mutable_data(platform::Place place, proto::VarType::Type type,
                            memory::Allocator::Attr attr,
                            size_t requested_size) {
   type_ = type;

paddle/fluid/framework/tensor.h

@@ -19,9 +19,9 @@ limitations under the License. */
 #include <memory>
 #include <typeindex>
 #include <vector>
-
 #include "paddle/fluid/framework/data_layout.h"
 #include "paddle/fluid/framework/ddim.h"
+#include "paddle/fluid/framework/framework.pb.h"
 #include "paddle/fluid/memory/memory.h"
 #include "paddle/fluid/platform/device_context.h"
 #include "paddle/fluid/platform/enforce.h"
@@ -67,7 +67,7 @@ class Tensor {
   friend struct EigenVector;
 
  public:
-  Tensor() : type_(typeid(float)), offset_(0) {}
+  Tensor() : type_(proto::VarType::FP32), offset_(0) {}
 
   /*! Return a pointer to mutable memory block. */
   template <typename T>
@@ -88,7 +88,7 @@ class Tensor {
                   memory::Allocator::Attr attr = memory::Allocator::kDefault,
                   size_t requested_size = 0);
 
-  void* mutable_data(platform::Place place, std::type_index type,
+  void* mutable_data(platform::Place place, proto::VarType::Type type,
                      memory::Allocator::Attr attr = memory::Allocator::kDefault,
                      size_t requested_size = 0);
 
@@ -138,7 +138,7 @@ class Tensor {
     return holder_->place();
   }
 
-  std::type_index type() const {
+  proto::VarType::Type type() const {
     PADDLE_ENFORCE_NOT_NULL(
         holder_, "Tensor not initialized yet when Tensor::type() is called.");
     return type_;
@@ -165,7 +165,7 @@ class Tensor {
  private:
   /*! holds the memory block if allocated. */
   std::shared_ptr<memory::Allocation> holder_;
-  std::type_index type_;
+  proto::VarType::Type type_;
   /**
    * @brief points to elements dimensions.
    *

paddle/fluid/framework/tensor_impl.h

@@ -24,9 +24,8 @@ template <typename T>
 inline const T* Tensor::data() const {
   check_memory_size();
   bool valid =
-      std::is_same<T, void>::value || type_ == std::type_index(typeid(T));
-  PADDLE_ENFORCE(valid, "Tensor holds the wrong type, it holds %s",
-                 type_.name());
+      std::is_same<T, void>::value || type_ == DataTypeTrait<T>::DataType;
+  PADDLE_ENFORCE(valid, "Tensor holds the wrong type, it holds %d", type_);
 
   return reinterpret_cast<const T*>(
       reinterpret_cast<uintptr_t>(holder_->ptr()) + offset_);
@@ -38,9 +37,8 @@ template <typename T>
 inline T* Tensor::data() {
   check_memory_size();
   bool valid =
-      std::is_same<T, void>::value || type_ == std::type_index(typeid(T));
-  PADDLE_ENFORCE(valid, "Tensor holds the wrong type, it holds %s",
-                 type_.name());
+      std::is_same<T, void>::value || type_ == DataTypeTrait<T>::DataType;
+  PADDLE_ENFORCE(valid, "Tensor holds the wrong type, it holds %s", type_);
   return reinterpret_cast<T*>(reinterpret_cast<uintptr_t>(holder_->ptr()) +
                               offset_);
 }
@@ -60,7 +58,7 @@ inline T* Tensor::mutable_data(platform::Place place,
                                size_t requested_size) {
   static_assert(std::is_pod<T>::value, "T must be POD");
   return reinterpret_cast<T*>(
-      mutable_data(place, typeid(T), attr, requested_size));
+      mutable_data(place, DataTypeTrait<T>::DataType, attr, requested_size));
 }
 
 inline Tensor ReshapeToMatrix(const Tensor& src, int num_col_dims) {

paddle/fluid/framework/tensor_util.cc

@@ -186,8 +186,8 @@ struct AnyDTypeVisitor {
 template <typename Predicate, typename DevCtx>
 inline void AnyImpl(Predicate predicate, const framework::Tensor& tensor,
                     const DevCtx& ctx, framework::Tensor* out) {
-  VisitDataType(ToDataType(tensor.type()), AnyDTypeVisitor<Predicate, DevCtx>(
-                                               predicate, tensor, ctx, out));
+  VisitDataType(tensor.type(), AnyDTypeVisitor<Predicate, DevCtx>(
+                                   predicate, tensor, ctx, out));
 }
 
 template <typename Predicate>
@@ -379,7 +379,7 @@ void TensorToStream(std::ostream& os, const Tensor& tensor,
      // int32_t  size
      // void*    protobuf message
     proto::VarType::TensorDesc desc;
-    desc.set_data_type(framework::ToDataType(tensor.type()));
+    desc.set_data_type(tensor.type());
     auto dims = framework::vectorize(tensor.dims());
     auto* pb_dims = desc.mutable_dims();
     pb_dims->Resize(static_cast<int>(dims.size()), 0);
@@ -461,9 +461,7 @@ void TensorFromStream(std::istream& is, Tensor* tensor,
     tensor->Resize(framework::make_ddim(dims));
     void* buf;
     auto ctx = platform::CPUDeviceContext();
-    size_t size =
-        tensor->numel() *
-        framework::SizeOfType(framework::ToTypeIndex(desc.data_type()));
+    size_t size = tensor->numel() * framework::SizeOfType(desc.data_type());
     if (platform::is_gpu_place(dev_ctx.GetPlace())) {
 #ifdef PADDLE_WITH_CUDA
       Tensor cpu_tensor;

paddle/fluid/inference/api/analysis_predictor.cc

@@ -289,10 +289,10 @@ bool AnalysisPredictor::GetFetch(std::vector<PaddleTensor> *outputs,
     auto type = fetch.type();
     auto output = &(outputs->at(i));
     output->name = fetchs_[idx]->Input("X")[0];
-    if (type == typeid(float)) {
+    if (type == framework::proto::VarType::FP32) {
       GetFetchOne<float>(fetch, output);
       output->dtype = PaddleDType::FLOAT32;
-    } else if (type == typeid(int64_t)) {
+    } else if (type == framework::proto::VarType::INT64) {
       GetFetchOne<int64_t>(fetch, output);
       output->dtype = PaddleDType::INT64;
     } else {

paddle/fluid/inference/api/analysis_predictor_tester.cc

@@ -55,7 +55,12 @@ TEST(AnalysisPredictor, analysis_off) {
 }
 
 TEST(AnalysisPredictor, analysis_on) {
-  AnalysisConfig config(false);
+#ifdef PADDLE_WITH_CUDA
+  AnalysisConfig config(true);
+  config.fraction_of_gpu_memory = 0.15;
+#else
+  AnalysisConfig config;
+#endif
   config.model_dir = FLAGS_dirname;
   config.enable_ir_optim = true;
 

paddle/fluid/inference/api/api_impl.cc

@@ -266,10 +266,10 @@ bool NativePaddlePredictor::GetFetch(std::vector<PaddleTensor> *outputs,
     auto type = fetch.type();
     auto output = &(outputs->at(i));
     output->name = fetchs_[idx]->Input("X")[0];
-    if (type == typeid(float)) {
+    if (type == framework::DataTypeTrait<float>::DataType) {
       GetFetchOne<float>(fetch, output);
       output->dtype = PaddleDType::FLOAT32;
-    } else if (type == typeid(int64_t)) {
+    } else if (type == framework::DataTypeTrait<int64_t>::DataType) {
       GetFetchOne<int64_t>(fetch, output);
       output->dtype = PaddleDType::INT64;
     } else {

paddle/fluid/inference/api/api_impl_tester.cc

@@ -36,10 +36,10 @@ namespace paddle {
 PaddleTensor LodTensorToPaddleTensor(framework::LoDTensor* t) {
   PaddleTensor pt;
 
-  if (t->type() == typeid(int64_t)) {
+  if (t->type() == framework::proto::VarType::INT64) {
     pt.data.Reset(t->data<void>(), t->numel() * sizeof(int64_t));
     pt.dtype = PaddleDType::INT64;
-  } else if (t->type() == typeid(float)) {
+  } else if (t->type() == framework::proto::VarType::FP32) {
     pt.data.Reset(t->data<void>(), t->numel() * sizeof(float));
     pt.dtype = PaddleDType::FLOAT32;
   } else {

paddle/fluid/inference/api/paddle_pass_builder.h

@@ -118,7 +118,10 @@ class GpuPassStrategy : public PassStrategy {
  public:
   GpuPassStrategy() : PassStrategy({}) {
     passes_.assign({
-        "infer_clean_graph_pass", "conv_bn_fuse_pass",
+        "infer_clean_graph_pass",               //
+        "conv_bn_fuse_pass",                    //
+        "conv_elementwise_add_act_fuse_pass",   //
+        "conv_elementwise_add2_act_fuse_pass",  //
     });
   }
 

paddle/fluid/inference/io.cc

@@ -79,7 +79,7 @@ void LoadPersistables(framework::Executor* executor, framework::Scope* scope,
 
   for (auto* var : global_block.AllVars()) {
     if (IsPersistable(var)) {
-      VLOG(3) << "persistable variable's name: " << var->Name();
+      VLOG(4) << "persistable variable's name: " << var->Name();
 
       framework::VarDesc* new_var = load_block->Var(var->Name());
       new_var->SetShape(var->GetShape());

paddle/fluid/inference/tests/api/tester_helper.h

@@ -373,7 +373,7 @@ static bool CompareTensorData(const framework::LoDTensor &a,
   }
 
   for (size_t i = 0; i < a_size; i++) {
-    if (a.type() == typeid(float)) {
+    if (a.type() == framework::proto::VarType::FP32) {
       const auto *a_data = a.data<float>();
       const auto *b_data = b.data<float>();
       if (std::abs(a_data[i] - b_data[i]) > 1e-3) {
@@ -382,7 +382,7 @@ static bool CompareTensorData(const framework::LoDTensor &a,
             b_data[i]);
         return false;
       }
-    } else if (a.type() == typeid(int64_t)) {
+    } else if (a.type() == framework::proto::VarType::INT64) {
       const auto *a_data = a.data<int64_t>();
       const auto *b_data = b.data<int64_t>();
       if (std::abs(a_data[i] - b_data[i]) > 1e-3) {

paddle/fluid/inference/tests/api/trt_models_tester.cc

@@ -78,6 +78,7 @@ void profile(std::string model_dir, bool use_analysis, bool use_tensorrt) {
   std::vector<PaddleTensor> outputs;
   if (use_analysis || use_tensorrt) {
     contrib::AnalysisConfig config(true);
+    config.pass_builder()->TurnOnDebug();
     SetConfig<contrib::AnalysisConfig>(&config, model_dir, true, use_tensorrt,
                                        FLAGS_batch_size);
     TestPrediction(reinterpret_cast<PaddlePredictor::Config*>(&config),
@@ -141,9 +142,31 @@ TEST(TensorRT_resnext50, profile) {
   profile(model_dir, /* use_analysis */ true, FLAGS_use_tensorrt);
 }
 
+TEST(resnext50, compare_analysis_native) {
+  std::string model_dir = FLAGS_infer_model + "/resnext50";
+  compare(model_dir, false /*use tensorrt*/);
+}
+
 TEST(TensorRT_mobilenet, analysis) {
   std::string model_dir = FLAGS_infer_model + "/" + "mobilenet";
-  compare(model_dir, /* use_tensorrt */ false);
+  compare(model_dir, false /* use_tensorrt */);
+}
+
+TEST(AnalysisPredictor, use_gpu) {
+  std::string model_dir = FLAGS_infer_model + "/" + "mobilenet";
+  AnalysisConfig config(true);
+  config.model_dir = model_dir;
+  config.fraction_of_gpu_memory = 0.15;
+  config.pass_builder()->TurnOnDebug();
+
+  std::vector<std::vector<PaddleTensor>> inputs_all;
+  auto predictor = CreatePaddlePredictor(config);
+  SetFakeImageInput(&inputs_all, model_dir, false, "__model__", "");
+
+  std::vector<PaddleTensor> outputs;
+  for (auto& input : inputs_all) {
+    ASSERT_TRUE(predictor->Run(input, &outputs));
+  }
 }
 
 }  // namespace inference

paddle/fluid/operators/affine_grid_op.cc

@@ -78,7 +78,7 @@ class AffineGridOp : public framework::OperatorWithKernel {
       library = framework::LibraryType::kCUDNN;
     }
 #endif
-    auto data_type = framework::ToDataType(ctx.Input<Tensor>("Theta")->type());
+    auto data_type = ctx.Input<Tensor>("Theta")->type();
     return framework::OpKernelType(data_type, ctx.GetPlace(),
                                    framework::DataLayout::kAnyLayout, library);
   }
@@ -188,9 +188,9 @@ class AffineGridOpGrad : public framework::OperatorWithKernel {
       library_ = framework::LibraryType::kCUDNN;
     }
 #endif
-    return framework::OpKernelType(
-        framework::ToDataType(ctx.Input<Tensor>("Theta")->type()),
-        ctx.GetPlace(), framework::DataLayout::kAnyLayout, library_);
+    return framework::OpKernelType(ctx.Input<Tensor>("Theta")->type(),
+                                   ctx.GetPlace(),
+                                   framework::DataLayout::kAnyLayout, library_);
   }
 };
 

paddle/fluid/operators/arg_max_op.cc

@@ -28,6 +28,5 @@ REGISTER_OP_CPU_KERNEL(
                                     int32_t>,
     paddle::operators::ArgMaxKernel<paddle::platform::CPUDeviceContext,
                                     int16_t>,
-    paddle::operators::ArgMaxKernel<paddle::platform::CPUDeviceContext, size_t>,
     paddle::operators::ArgMaxKernel<paddle::platform::CPUDeviceContext,
                                     uint8_t>);

paddle/fluid/operators/arg_max_op.cu

@@ -25,7 +25,5 @@ REGISTER_OP_CUDA_KERNEL(
                                     int32_t>,
     paddle::operators::ArgMaxKernel<paddle::platform::CUDADeviceContext,
                                     int16_t>,
-    paddle::operators::ArgMaxKernel<paddle::platform::CUDADeviceContext,
-                                    size_t>,
     paddle::operators::ArgMaxKernel<paddle::platform::CUDADeviceContext,
                                     uint8_t>);

paddle/fluid/operators/arg_min_op.cc

@@ -28,6 +28,5 @@ REGISTER_OP_CPU_KERNEL(
                                     int32_t>,
     paddle::operators::ArgMinKernel<paddle::platform::CPUDeviceContext,
                                     int16_t>,
-    paddle::operators::ArgMinKernel<paddle::platform::CPUDeviceContext, size_t>,
     paddle::operators::ArgMinKernel<paddle::platform::CPUDeviceContext,
                                     uint8_t>);

paddle/fluid/operators/arg_min_op.cu

@@ -25,7 +25,5 @@ REGISTER_OP_CUDA_KERNEL(
                                     int32_t>,
     paddle::operators::ArgMinKernel<paddle::platform::CUDADeviceContext,
                                     int16_t>,
-    paddle::operators::ArgMinKernel<paddle::platform::CUDADeviceContext,
-                                    size_t>,
     paddle::operators::ArgMinKernel<paddle::platform::CUDADeviceContext,
                                     uint8_t>);

paddle/fluid/operators/array_to_lod_tensor_op.cc

@@ -58,7 +58,7 @@ struct ArrayToLoDFunctor : public boost::static_visitor<void> {
     ArrayToLoDFunctorImpl<DeviceContext> functor;
     functor.dev_ctx_ = dev_ctx;
     functor.prev_functor_ = this;
-    framework::VisitDataType(framework::ToDataType(out->type()), functor);
+    framework::VisitDataType(out->type(), functor);
   }
 };
 
@@ -91,7 +91,7 @@ class ArrayToLoDTensorOp : public framework::OperatorBase {
     PADDLE_ENFORCE(!x.empty(), "There's no element in the input array.");
     int rank = x[0].dims().size();
     platform::Place place = x[0].place();
-    std::type_index data_type = x[0].type();
+    auto data_type = x[0].type();
     int64_t batch_size = x[0].dims()[0];
     framework::DDim ins_dims = rank > 1
                                    ? framework::slice_ddim(x[0].dims(), 1, rank)

paddle/fluid/operators/attention_lstm_op.cc

@@ -121,9 +121,8 @@ void AttentionLSTMOp::InferShape(framework::InferShapeContext* ctx) const {
 
 framework::OpKernelType AttentionLSTMOp::GetExpectedKernelType(
     const framework::ExecutionContext& ctx) const {
-  return framework::OpKernelType(
-      framework::ToDataType(ctx.Input<framework::LoDTensor>("X")->type()),
-      ctx.device_context());
+  return framework::OpKernelType(ctx.Input<framework::LoDTensor>("X")->type(),
+                                 ctx.device_context());
 }
 
 void AttentionLSTMOpMaker::Make() {

paddle/fluid/operators/average_accumulates_op.cc

@@ -103,9 +103,8 @@ class AverageAccumulatesOp : public framework::OperatorWithKernel {
  protected:
   framework::OpKernelType GetExpectedKernelType(
       const framework::ExecutionContext& ctx) const override {
-    return framework::OpKernelType(
-        framework::ToDataType(ctx.Input<Tensor>("param")->type()),
-        ctx.GetPlace());
+    return framework::OpKernelType(ctx.Input<Tensor>("param")->type(),
+                                   ctx.GetPlace());
   }
 };
 

paddle/fluid/operators/batch_norm_op.cc

@@ -72,8 +72,7 @@ class BatchNormOp : public framework::OperatorWithKernel {
  protected:
   framework::OpKernelType GetExpectedKernelType(
       const framework::ExecutionContext &ctx) const override {
-    auto input_data_type =
-        framework::ToDataType(ctx.Input<Tensor>("X")->type());
+    auto input_data_type = ctx.Input<Tensor>("X")->type();
     // By default, the type of the scale, bias, mean,
     // and var tensors should both be float. (For float or float16 input tensor)
     // or double (For double input tensor).
@@ -81,17 +80,13 @@ class BatchNormOp : public framework::OperatorWithKernel {
     if (input_data_type == framework::proto::VarType::FP64) {
       bn_param_type = framework::proto::VarType::FP64;
     }
-    PADDLE_ENFORCE_EQ(bn_param_type,
-                      framework::ToDataType(ctx.Input<Tensor>("Scale")->type()),
+    PADDLE_ENFORCE_EQ(bn_param_type, ctx.Input<Tensor>("Scale")->type(),
                       "Scale input should be of float type");
-    PADDLE_ENFORCE_EQ(bn_param_type,
-                      framework::ToDataType(ctx.Input<Tensor>("Bias")->type()),
+    PADDLE_ENFORCE_EQ(bn_param_type, ctx.Input<Tensor>("Bias")->type(),
                       "Bias input should be of float type");
-    PADDLE_ENFORCE_EQ(bn_param_type,
-                      framework::ToDataType(ctx.Input<Tensor>("Mean")->type()),
+    PADDLE_ENFORCE_EQ(bn_param_type, ctx.Input<Tensor>("Mean")->type(),
                       "Mean input should be of float type");
-    PADDLE_ENFORCE_EQ(bn_param_type, framework::ToDataType(
-                                         ctx.Input<Tensor>("Variance")->type()),
+    PADDLE_ENFORCE_EQ(bn_param_type, ctx.Input<Tensor>("Variance")->type(),
                       "Variance input should be of float type");
 
     // TODO(pzelazko-intel): enable MKLDNN layout when it's ready
@@ -413,9 +408,8 @@ class BatchNormGradOp : public framework::OperatorWithKernel {
     }
 #endif
 
-    return framework::OpKernelType(
-        framework::ToDataType(ctx.Input<Tensor>("X")->type()), ctx.GetPlace(),
-        layout, library);
+    return framework::OpKernelType(ctx.Input<Tensor>("X")->type(),
+                                   ctx.GetPlace(), layout, library);
   }
 };
 

paddle/fluid/operators/beam_search_decode_op.cc

@@ -145,7 +145,7 @@ class BeamSearchDecodeOp : public framework::OperatorBase {
     LoDTensor* sentenceScores = ctx.Output<LoDTensor>("SentenceScores");
 
     framework::VisitDataType(
-        framework::ToDataType(scores->at(0).type()),
+        scores->at(0).type(),
         BeamSearchDecodeFunctor(*ids, *scores, sentenceIds, sentenceScores,
                                 beam_size, end_id));
   }

paddle/fluid/operators/beam_search_op.cc

@@ -282,8 +282,7 @@ class BeamSearchOp : public framework::OperatorWithKernel {
   framework::OpKernelType GetExpectedKernelType(
       const framework::ExecutionContext &ctx) const override {
     framework::OpKernelType kt = framework::OpKernelType(
-        framework::ToDataType(
-            ctx.Input<framework::LoDTensor>("pre_ids")->type()),
+        ctx.Input<framework::LoDTensor>("pre_ids")->type(),
         platform::CPUPlace());
     return kt;
   }

paddle/fluid/operators/bpr_loss_op.cc

@@ -47,9 +47,8 @@ class BprLossOp : public framework::OperatorWithKernel {
   // is determined by its input "X".
   framework::OpKernelType GetExpectedKernelType(
       const framework::ExecutionContext& ctx) const override {
-    return framework::OpKernelType(
-        framework::ToDataType(ctx.Input<Tensor>("X")->type()),
-        platform::CPUPlace());
+    return framework::OpKernelType(ctx.Input<Tensor>("X")->type(),
+                                   platform::CPUPlace());
   }
 };
 
@@ -94,9 +93,8 @@ class BprLossGradientOp : public framework::OperatorWithKernel {
   // is determined by its input "X".
   framework::OpKernelType GetExpectedKernelType(
       const framework::ExecutionContext& ctx) const override {
-    return framework::OpKernelType(
-        framework::ToDataType(ctx.Input<Tensor>("X")->type()),
-        platform::CPUPlace());
+    return framework::OpKernelType(ctx.Input<Tensor>("X")->type(),
+                                   platform::CPUPlace());
   }
 };
 

paddle/fluid/operators/controlflow/CMakeLists.txt

 include(operators)
-register_operators()
+register_operators(DEPS naive_executor)
 
 file(APPEND ${pybind_file} "USE_OP(less_than);\nUSE_OP(logical_and);\nUSE_NO_KERNEL_OP(read_from_array);\n")

paddle/fluid/operators/controlflow/conditional_block_op.cc

@@ -48,13 +48,12 @@ class ConditionalOp : public framework::OperatorBase {
     if (!(ips.size() == 1UL && ips[0]->IsInitialized())) {
       PADDLE_THROW("should have one initialized input as condition");
     }
-    if (!(framework::IsType<bool>(ips[0]->type()) &&  // NOLINT
-          ips[0]->numel() == 1)) {
-      PADDLE_THROW(
-          "condition input's data type should be bool, "
-          "numel should be 1, actual numel is %d",
-          ips[0]->numel());
-    }
+
+    PADDLE_ENFORCE(ips[0]->type() == framework::proto::VarType::BOOL &&
+                       ips[0]->numel() == 1,
+                   "condition input's data type should be bool, "
+                   "numel should be 1, actual numel is %d",
+                   ips[0]->numel());
     bool res = false;
     if (platform::is_gpu_place(ips[0]->place())) {
 #ifdef PADDLE_WITH_CUDA

paddle/fluid/operators/controlflow/while_op.cc

@@ -261,7 +261,7 @@ class WhileGradOp : public framework::OperatorBase {
           if (var->IsType<LoDTensor>()) {
             auto &inside_tensor = var->Get<framework::LoDTensor>();
             framework::AttributeMap attrs;
-            attrs["dtype"] = framework::ToDataType(inside_tensor.type());
+            attrs["dtype"] = inside_tensor.type();
             attrs["shape"] = framework::vectorize2int(inside_tensor.dims());
             attrs["value"] = 0.0f;
 

paddle/fluid/operators/conv_op.cc

@@ -44,7 +44,9 @@ void ConvOp::InferShape(framework::InferShapeContext* ctx) const {
   std::vector<int> dilations = ctx->Attrs().Get<std::vector<int>>("dilations");
 
   PADDLE_ENFORCE(in_dims.size() == 4 || in_dims.size() == 5,
-                 "Conv intput should be 4-D or 5-D tensor.");
+                 "Conv intput should be 4-D or 5-D tensor, get %u",
+                 in_dims.size());
+
   PADDLE_ENFORCE_EQ(
       in_dims.size(), filter_dims.size(),
       "Conv input dimension and filter dimension should be the same.");
@@ -95,10 +97,8 @@ framework::OpKernelType ConvOp::GetExpectedKernelType(
   }
 #endif
 
-  auto input_data_type =
-      framework::ToDataType(ctx.Input<Tensor>("Input")->type());
-  auto filter_data_type =
-      framework::ToDataType(ctx.Input<Tensor>("Filter")->type());
+  auto input_data_type = ctx.Input<Tensor>("Input")->type();
+  auto filter_data_type = ctx.Input<Tensor>("Filter")->type();
   PADDLE_ENFORCE_EQ(input_data_type, filter_data_type,
                     "input and filter data type should be consistent");
 
@@ -382,9 +382,9 @@ framework::OpKernelType ConvOpGrad::GetExpectedKernelType(
   }
 #endif
 
-  return framework::OpKernelType(
-      framework::ToDataType(ctx.Input<Tensor>("Input")->type()), ctx.GetPlace(),
-      layout_, library_, customized_type_value);
+  return framework::OpKernelType(ctx.Input<Tensor>("Input")->type(),
+                                 ctx.GetPlace(), layout_, library_,
+                                 customized_type_value);
 }
 
 }  // namespace operators

paddle/fluid/operators/conv_transpose_op.cc

@@ -104,9 +104,8 @@ framework::OpKernelType ConvTransposeOp::GetExpectedKernelType(
   }
 #endif
 
-  return framework::OpKernelType(
-      framework::ToDataType(ctx.Input<Tensor>("Input")->type()), ctx.GetPlace(),
-      layout_, library_);
+  return framework::OpKernelType(ctx.Input<Tensor>("Input")->type(),
+                                 ctx.GetPlace(), layout_, library_);
 }
 
 void Conv2DTransposeOpMaker::Make() {
@@ -335,9 +334,8 @@ framework::OpKernelType ConvTransposeOpGrad::GetExpectedKernelType(
 
   std::string data_format = ctx.Attr<std::string>("data_format");
   framework::DataLayout layout_ = framework::StringToDataLayout(data_format);
-  return framework::OpKernelType(
-      framework::ToDataType(ctx.Input<Tensor>("Input")->type()), ctx.GetPlace(),
-      layout_, library_);
+  return framework::OpKernelType(ctx.Input<Tensor>("Input")->type(),
+                                 ctx.GetPlace(), layout_, library_);
 }
 
 }  // namespace operators

paddle/fluid/operators/crf_decoding_op.cc

@@ -118,9 +118,8 @@ class CRFDecodingOp : public framework::OperatorWithKernel {
  protected:
   framework::OpKernelType GetExpectedKernelType(
       const framework::ExecutionContext& ctx) const override {
-    return framework::OpKernelType(
-        framework::ToDataType(ctx.Input<LoDTensor>("Emission")->type()),
-        platform::CPUPlace());
+    return framework::OpKernelType(ctx.Input<LoDTensor>("Emission")->type(),
+                                   platform::CPUPlace());
   }
 };
 }  // namespace operators

paddle/fluid/operators/crop_op.cc

@@ -51,9 +51,8 @@ class CropOp : public framework::OperatorWithKernel {
 
   framework::OpKernelType GetExpectedKernelType(
       const framework::ExecutionContext& ctx) const override {
-    return framework::OpKernelType(
-        framework::ToDataType(ctx.Input<framework::LoDTensor>("X")->type()),
-        ctx.device_context());
+    return framework::OpKernelType(ctx.Input<framework::LoDTensor>("X")->type(),
+                                   ctx.device_context());
   }
 };
 
@@ -174,9 +173,7 @@ class CropOpGrad : public framework::OperatorWithKernel {
   framework::OpKernelType GetExpectedKernelType(
       const framework::ExecutionContext& ctx) const override {
     return framework::OpKernelType(
-        framework::ToDataType(
-            ctx.Input<framework::LoDTensor>(framework::GradVarName("Out"))
-                ->type()),
+        ctx.Input<framework::LoDTensor>(framework::GradVarName("Out"))->type(),
         ctx.device_context());
   }
 };

paddle/fluid/operators/cross_entropy_op.cc

@@ -57,9 +57,8 @@ class CrossEntropyOp : public framework::OperatorWithKernel {
   // is determined by its input "X".
   framework::OpKernelType GetExpectedKernelType(
       const framework::ExecutionContext& ctx) const override {
-    return framework::OpKernelType(
-        framework::ToDataType(ctx.Input<Tensor>("X")->type()),
-        ctx.device_context());
+    return framework::OpKernelType(ctx.Input<Tensor>("X")->type(),
+                                   ctx.device_context());
   }
 };
 
@@ -111,9 +110,8 @@ class CrossEntropyGradientOp : public framework::OperatorWithKernel {
   // is determined by its input "X".
   framework::OpKernelType GetExpectedKernelType(
       const framework::ExecutionContext& ctx) const override {
-    return framework::OpKernelType(
-        framework::ToDataType(ctx.Input<Tensor>("X")->type()),
-        ctx.device_context());
+    return framework::OpKernelType(ctx.Input<Tensor>("X")->type(),
+                                   ctx.device_context());
   }
 };
 

paddle/fluid/operators/ctc_align_op.cc

@@ -36,9 +36,8 @@ class CTCAlignOp : public framework::OperatorWithKernel {
  protected:
   framework::OpKernelType GetExpectedKernelType(
       const framework::ExecutionContext& ctx) const override {
-    return framework::OpKernelType(
-        framework::ToDataType(ctx.Input<Tensor>("Input")->type()),
-        ctx.device_context());
+    return framework::OpKernelType(ctx.Input<Tensor>("Input")->type(),
+                                   ctx.device_context());
   }
 };
 

paddle/fluid/operators/cudnn_lstm_op.cu.cc

@@ -300,9 +300,11 @@ class CudnnLSTMGPUKernel : public framework::OpKernel<T> {
     }
     CudnnRNNCache *cudnn_rnn_cache = nullptr;
     if (cache_var->IsInitialized()) {
+      // const_cast is usually bad.
       cudnn_rnn_cache = const_cast<framework::Variable *>(cache_var)
                             ->GetMutable<CudnnRNNCache>();
     } else {
+      // const_cast is usually bad.
       cudnn_rnn_cache = const_cast<framework::Variable *>(cache_var)
                             ->GetMutable<CudnnRNNCache>();
       std::random_device rnd;

paddle/fluid/operators/detection/anchor_generator_op.cc

@@ -53,8 +53,7 @@ class AnchorGeneratorOp : public framework::OperatorWithKernel {
   framework::OpKernelType GetExpectedKernelType(
       const framework::ExecutionContext& ctx) const override {
     return framework::OpKernelType(
-        framework::ToDataType(ctx.Input<framework::Tensor>("Input")->type()),
-        ctx.device_context());
+        ctx.Input<framework::Tensor>("Input")->type(), ctx.device_context());
   }
 };
 

paddle/fluid/operators/detection/bipartite_match_op.cc

@@ -45,9 +45,8 @@ class BipartiteMatchOp : public framework::OperatorWithKernel {
  protected:
   framework::OpKernelType GetExpectedKernelType(
       const framework::ExecutionContext& ctx) const override {
-    return framework::OpKernelType(
-        framework::ToDataType(ctx.Input<LoDTensor>("DistMat")->type()),
-        platform::CPUPlace());
+    return framework::OpKernelType(ctx.Input<LoDTensor>("DistMat")->type(),
+                                   platform::CPUPlace());
   }
 };
 

paddle/fluid/operators/detection/density_prior_box_op.cc

@@ -66,8 +66,7 @@ class DensityPriorBoxOp : public framework::OperatorWithKernel {
   framework::OpKernelType GetExpectedKernelType(
       const framework::ExecutionContext& ctx) const override {
     return framework::OpKernelType(
-        framework::ToDataType(ctx.Input<framework::Tensor>("Input")->type()),
-        ctx.GetPlace());
+        ctx.Input<framework::Tensor>("Input")->type(), ctx.GetPlace());
   }
 };
 

paddle/fluid/operators/detection/generate_proposals_op.cc

@@ -66,9 +66,8 @@ class GenerateProposalsOp : public framework::OperatorWithKernel {
  protected:
   framework::OpKernelType GetExpectedKernelType(
       const framework::ExecutionContext &ctx) const override {
-    return framework::OpKernelType(
-        framework::ToDataType(ctx.Input<Tensor>("Anchors")->type()),
-        ctx.device_context());
+    return framework::OpKernelType(ctx.Input<Tensor>("Anchors")->type(),
+                                   ctx.device_context());
   }
 };
 

paddle/fluid/operators/detection/mine_hard_examples_op.cc

@@ -249,8 +249,7 @@ class MineHardExamplesOp : public framework::OperatorWithKernel {
   framework::OpKernelType GetExpectedKernelType(
       const framework::ExecutionContext& ctx) const override {
     return framework::OpKernelType(
-        framework::ToDataType(ctx.Input<framework::Tensor>("ClsLoss")->type()),
-        platform::CPUPlace());
+        ctx.Input<framework::Tensor>("ClsLoss")->type(), platform::CPUPlace());
   }
 };
 

paddle/fluid/operators/detection/multiclass_nms_op.cc

@@ -65,8 +65,7 @@ class MultiClassNMSOp : public framework::OperatorWithKernel {
   framework::OpKernelType GetExpectedKernelType(
       const framework::ExecutionContext& ctx) const override {
     return framework::OpKernelType(
-        framework::ToDataType(
-            ctx.Input<framework::LoDTensor>("Scores")->type()),
+        ctx.Input<framework::LoDTensor>("Scores")->type(),
         platform::CPUPlace());
   }
 };

paddle/fluid/operators/detection/prior_box_op.cc

@@ -72,8 +72,7 @@ class PriorBoxOp : public framework::OperatorWithKernel {
   framework::OpKernelType GetExpectedKernelType(
       const framework::ExecutionContext& ctx) const override {
     return framework::OpKernelType(
-        framework::ToDataType(ctx.Input<framework::Tensor>("Input")->type()),
-        ctx.device_context());
+        ctx.Input<framework::Tensor>("Input")->type(), ctx.device_context());
   }
 };
 

paddle/fluid/operators/detection/roi_perspective_transform_op.cc

@@ -498,9 +498,8 @@ class ROIPerspectiveTransformOp : public framework::OperatorWithKernel {
  protected:
   framework::OpKernelType GetExpectedKernelType(
       const framework::ExecutionContext& ctx) const override {
-    return framework::OpKernelType(
-        framework::ToDataType(ctx.Input<framework::Tensor>("X")->type()),
-        ctx.device_context());
+    return framework::OpKernelType(ctx.Input<framework::Tensor>("X")->type(),
+                                   ctx.device_context());
   }
 };
 
@@ -519,9 +518,8 @@ class ROIPerspectiveTransformGradOp : public framework::OperatorWithKernel {
  protected:
   framework::OpKernelType GetExpectedKernelType(
       const framework::ExecutionContext& ctx) const override {
-    return framework::OpKernelType(
-        framework::ToDataType(ctx.Input<framework::Tensor>("X")->type()),
-        ctx.device_context());
+    return framework::OpKernelType(ctx.Input<framework::Tensor>("X")->type(),
+                                   ctx.device_context());
   }
 };
 

paddle/fluid/operators/detection/rpn_target_assign_op.cc

@@ -78,8 +78,7 @@ class RpnTargetAssignOp : public framework::OperatorWithKernel {
   framework::OpKernelType GetExpectedKernelType(
       const framework::ExecutionContext& ctx) const override {
     return framework::OpKernelType(
-        framework::ToDataType(
-            ctx.Input<framework::LoDTensor>("Anchor")->type()),
+        ctx.Input<framework::LoDTensor>("Anchor")->type(),
         platform::CPUPlace());
   }
 };

paddle/fluid/operators/detection/target_assign_op.cc

@@ -57,9 +57,8 @@ class TargetAssignOp : public framework::OperatorWithKernel {
  protected:
   framework::OpKernelType GetExpectedKernelType(
       const framework::ExecutionContext& ctx) const override {
-    return framework::OpKernelType(
-        framework::ToDataType(ctx.Input<framework::LoDTensor>("X")->type()),
-        ctx.device_context());
+    return framework::OpKernelType(ctx.Input<framework::LoDTensor>("X")->type(),
+                                   ctx.device_context());
   }
 };
 

paddle/fluid/operators/detection_map_op.cc

@@ -71,8 +71,7 @@ class DetectionMAPOp : public framework::OperatorWithKernel {
   framework::OpKernelType GetExpectedKernelType(
       const framework::ExecutionContext& ctx) const override {
     return framework::OpKernelType(
-        framework::ToDataType(
-            ctx.Input<framework::Tensor>("DetectRes")->type()),
+        ctx.Input<framework::Tensor>("DetectRes")->type(),
         platform::CPUPlace());
   }
 };

paddle/fluid/operators/distributed/CMakeLists.txt

@@ -12,7 +12,7 @@ configure_file(send_recv.proto.in ${CMAKE_CURRENT_SOURCE_DIR}/send_recv.proto @O
 set(DISTRIBUTE_COMPILE_FLAGS "-Wno-non-virtual-dtor -Wno-error=non-virtual-dtor -Wno-error=delete-non-virtual-dtor")
 
 if(WITH_GRPC)
-  grpc_library(sendrecvop_grpc SRCS grpc_bytebuffer_stream.cc sendrecvop_utils.cc grpc_client.cc
+  grpc_library(sendrecvop_rpc SRCS grpc_bytebuffer_stream.cc sendrecvop_utils.cc grpc_client.cc
         request_handler_impl.cc rpc_client.cc rpc_server.cc grpc_server.cc variable_response.cc grpc_variable_response.cc grpc_serde.cc collective_client.cc collective_server.cc
       PROTO send_recv.proto 
       DEPS lod_tensor selected_rows_functor memory)
@@ -20,36 +20,43 @@ if(WITH_GRPC)
   set_source_files_properties(grpc_serde_test.cc rpc_server_test.cc PROPERTIES COMPILE_FLAGS ${DISTRIBUTE_COMPILE_FLAGS})
 
   cc_test(grpc_serde_test SRCS grpc_serde_test.cc 
-    DEPS grpc++_unsecure grpc_unsecure gpr cares zlib protobuf sendrecvop_grpc scope profiler math_function SERIAL)
+    DEPS grpc++_unsecure grpc_unsecure gpr cares zlib protobuf sendrecvop_rpc scope profiler math_function SERIAL)
 
   cc_test(rpc_server_test SRCS rpc_server_test.cc
-    DEPS sendrecvop_grpc grpc++_unsecure grpc_unsecure gpr cares zlib protobuf executor  proto_desc lookup_sparse_table_op SERIAL)
+    DEPS sendrecvop_rpc grpc++_unsecure grpc_unsecure gpr cares zlib protobuf executor  proto_desc lookup_sparse_table_op SERIAL)
 
   cc_test(varhandle_test SRCS varhandle_test.cc DEPS profiler)
 
   if(WITH_GPU)
   cc_test(collective_server_test SRCS collective_server_test.cc 
-      DEPS sendrecvop_grpc grpc++_unsecure grpc_unsecure gpr cares zlib protobuf executor
+      DEPS sendrecvop_rpc grpc++_unsecure grpc_unsecure gpr cares zlib protobuf executor
       selected_rows_functor  scope math_function SERIAL)
   endif()
 
-  cc_library(parameter_prefetch SRCS parameter_prefetch.cc DEPS sendrecvop_grpc memory)
+  cc_library(parameter_prefetch SRCS parameter_prefetch.cc DEPS sendrecvop_rpc memory)
 else()
-  set_source_files_properties(brpc_server.cc brpc_client.cc rpc_server_test.cc brpc_serde_test.cc
-      brpc_variable_response.cc brpc_sendrecvop_utils.cc brpc_rdma_pool.cc PROPERTIES COMPILE_FLAGS ${DISTRIBUTE_COMPILE_FLAGS})
+  set_source_files_properties(brpc_server.cc parameter_prefetch.cc brpc_client.cc rpc_server_test.cc brpc_serde_test.cc
+      brpc_variable_response.cc brpc_sendrecvop_utils.cc brpc_rdma_pool.cc collective_server.cc collective_server_test.cc
+      collective_client.cc PROPERTIES COMPILE_FLAGS ${DISTRIBUTE_COMPILE_FLAGS})
 
-  brpc_library(sendrecvop_brpc SRCS brpc_client.cc brpc_server.cc rpc_server.cc rpc_client.cc request_handler_impl.cc brpc_sendrecvop_utils.cc
-      brpc_variable_response.cc variable_response.cc sendrecvop_utils.cc brpc_rdma_pool.cc
+  brpc_library(sendrecvop_rpc SRCS brpc_client.cc brpc_server.cc rpc_server.cc rpc_client.cc request_handler_impl.cc brpc_sendrecvop_utils.cc
+      brpc_variable_response.cc variable_response.cc sendrecvop_utils.cc brpc_rdma_pool.cc collective_client.cc collective_server.cc
     PROTO send_recv.proto
     DEPS lod_tensor selected_rows memory)
 
-  cc_library(parameter_prefetch SRCS parameter_prefetch.cc DEPS sendrecvop_brpc memory)
+  cc_library(parameter_prefetch SRCS parameter_prefetch.cc DEPS sendrecvop_rpc memory)
 
-  set(brpc_test_depends sendrecvop_brpc brpc ssl crypto protobuf leveldb gflags glog executor proto_desc lookup_table_op snappystream snappy)
+  set(brpc_test_depends sendrecvop_rpc brpc ssl crypto protobuf leveldb gflags glog executor
+      proto_desc lookup_sparse_table_op snappystream snappy zlib)
 
-  cc_test(brpc_server_test SRCS rpc_server_test.cc
+  cc_test(rpc_server_test SRCS rpc_server_test.cc
       DEPS ${brpc_test_depends} SERIAL)
 
   cc_test(brpc_serde_test SRCS brpc_serde_test.cc
       DEPS ${brpc_test_depends} SERIAL)
+
+  if(WITH_GPU)
+  cc_test(collective_server_test SRCS collective_server_test.cc 
+      DEPS ${brpc_test_depends} selected_rows_functor  scope math_function SERIAL)
+  endif()
 endif()

paddle/fluid/operators/distributed/brpc_client.cc

@@ -14,135 +14,316 @@
 
 #include "paddle/fluid/operators/distributed/brpc_client.h"
 #include "paddle/fluid/framework/threadpool.h"
+#include "paddle/fluid/operators/distributed/brpc_sendrecvop_utils.h"
+#include "paddle/fluid/platform/profiler.h"
 
 namespace paddle {
 namespace operators {
 namespace distributed {
 
-DEFINE_int32(brpc_channel_num, 24,
-             "Number of channels to send requests connected to one server");
 DEFINE_int32(timeout_ms, 30000, "RPC timeout in milliseconds");
 DEFINE_int32(max_retry, 3, "Max retries(not including the first RPC)");
 
 BRPCClient::~BRPCClient() { Wait(); }
 
-void HandleSendResponse(brpc::Controller* cntl,
-                        sendrecv::VoidMessage* response) {
+void HandleSendResponse(brpc::Controller* cntl, sendrecv::VoidMessage* response,
+                        VarHandlePtr var_h, ChannelQueuePtr ch_ptr,
+                        ChannelContextPtr ch_ctx, BRPCClient* cls) {
   // std::unique_ptr makes sure cntl/response will be deleted before returning.
   std::unique_ptr<brpc::Controller> cntl_guard(cntl);
   std::unique_ptr<sendrecv::VoidMessage> response_guard(response);
 
+  // this channel can be used by other now.
+  ch_ptr->Push(ch_ctx);
+
   if (cntl->Failed()) {
-    LOG(WARNING) << "Fail to send EchoRequest, " << cntl->ErrorText();
+    LOG(FATAL) << "Fail to send SendVar: " << var_h->name()
+               << ", error text: " << cntl->ErrorText();
+    var_h->Finish(false);
+    cls->DecreaseReqCount();
     return;
   }
-  LOG(INFO) << "Received response from " << cntl->remote_side()
-            << " latency=" << cntl->latency_us() << "us";
+  var_h->Finish(true);
+  cls->DecreaseReqCount();
+
+  VLOG(4) << "HandleSendResponse from: " << cntl->remote_side()
+          << ", varname: " << var_h->name()
+          << ", latency: " << cntl->latency_us() << "us";
+  VLOG(4) << "Finish HandleSendResponse";
 }
 
-bool BRPCClient::AsyncSendVar(const std::string& ep,
-                              const platform::DeviceContext& ctx,
-                              const framework::Scope& scope,
-                              const std::string& var_name, int64_t time_out) {
+VarHandlePtr BRPCClient::AsyncSendVar(const std::string& ep,
+                                      const platform::DeviceContext& ctx,
+                                      const framework::Scope& scope,
+                                      const std::string& var_name,
+                                      int64_t time_out) {
   const platform::DeviceContext* p_ctx = &ctx;
   const std::string ep_val = ep;
   const std::string var_name_val = var_name;
   const framework::Scope* p_scope = &scope;
   const auto ch_ptr = GetChannel(ep_val);
+  const std::string method = "SendRPC";
+  VarHandlePtr var_h(new VarHandle(ep, method, var_name_val, p_ctx, p_scope));
+
+  framework::AsyncIO([=] {
+    auto ch_ctx = ch_ptr->Pop();
+    brpc::Controller* cntl = new brpc::Controller();
+    sendrecv::VoidMessage* response = new sendrecv::VoidMessage();
+    cntl->set_timeout_ms(time_out);
 
-  framework::AsyncIO(
-      [var_name_val, p_ctx, ep_val, p_scope, time_out, ch_ptr, this] {
-        auto ch_ctx = ch_ptr->Pop();
-        brpc::Controller* cntl = new brpc::Controller();
-        sendrecv::VoidMessage* response = new sendrecv::VoidMessage();
-        cntl->set_timeout_ms(time_out);
+    auto* var = p_scope->FindVar(var_name_val);
+    sendrecv::VariableMessage request;
+    distributed::SerializeToIOBuf(var_name_val, var, *p_ctx, &request,
+                                  &cntl->request_attachment(), "", false,
+                                  trainer_id_);
 
-        google::protobuf::Closure* done =
-            brpc::NewCallback(&HandleSendResponse, cntl, response);
+    google::protobuf::Closure* done = brpc::NewCallback(
+        &HandleSendResponse, cntl, response, var_h, ch_ptr, ch_ctx, this);
 
-        sendrecv::VariableMessage request;
-        ch_ctx->stub->SendVariable(cntl, &request, response, done);
-      });
+    platform::RecordRPCEvent record_event(method, p_ctx);
+
+    ch_ctx->stub->SendVariable(cntl, &request, response, done);
+
+    if (UNLIKELY(platform::IsProfileEnabled())) {
+      var_h->Wait();
+    }
+  });
   req_count_++;
 
-  return true;
+  return var_h;
 }
+void HandleFetchBarrierResponse(brpc::Controller* cntl,
+                                sendrecv::VariableMessage* response,
+                                VarHandlePtr var_h, ChannelQueuePtr ch_ptr,
+                                ChannelContextPtr ch_ctx, BRPCClient* cls) {
+  // std::unique_ptr makes sure cntl/response will be deleted before returning.
+  std::unique_ptr<brpc::Controller> cntl_guard(cntl);
+  std::unique_ptr<sendrecv::VariableMessage> response_guard(response);
+
+  // this channel can be used other now.
+  ch_ptr->Push(ch_ctx);
 
+  if (cntl->Failed()) {
+    LOG(FATAL) << "Fail to get HandleFetchBarrierResponse: " << var_h->name()
+               << ", error text: " << cntl->ErrorText();
+    var_h->Finish(false);
+    cls->DecreaseReqCount();
+    return;
+  }
+
+  var_h->Finish(true);
+  cls->DecreaseReqCount();
+
+  VLOG(4) << "HandleFetchBarrierResponse from: " << cntl->remote_side()
+          << ", varname: " << var_h->name()
+          << ", latency: " << cntl->latency_us() << "us";
+  VLOG(4) << "Finish HandleFetchBarrierResponse";
+}
 void HandleGetResponse(brpc::Controller* cntl,
-                       sendrecv::VariableMessage* response) {
+                       sendrecv::VariableMessage* response, VarHandlePtr var_h,
+                       ChannelQueuePtr ch_ptr, ChannelContextPtr ch_ctx,
+                       BRPCClient* cls) {
   // std::unique_ptr makes sure cntl/response will be deleted before returning.
   std::unique_ptr<brpc::Controller> cntl_guard(cntl);
   std::unique_ptr<sendrecv::VariableMessage> response_guard(response);
 
+  // this channel can be used other now.
+  ch_ptr->Push(ch_ctx);
+
   if (cntl->Failed()) {
-    LOG(WARNING) << "Fail to send EchoRequest, " << cntl->ErrorText();
+    LOG(FATAL) << "Fail to GetVar: " << var_h->name()
+               << ", error text: " << cntl->ErrorText();
+    cls->DecreaseReqCount();
+    var_h->Finish(false);
     return;
   }
-  LOG(INFO) << "Received response from " << cntl->remote_side()
-            << " latency=" << cntl->latency_us() << "us";
 
-  // framework::Variable* outvar = nullptr;
-  // DeserializeFromByteBuffer(ret_msg, *var_h.ctx, var_h.scope, &outvar);
+  VLOG(4) << "HandleGetResponse from: " << cntl->remote_side()
+          << ", varname: " << var_h->name()
+          << ", latency: " << cntl->latency_us() << "us";
+
+  framework::Variable* outvar = nullptr;
+  int trainer_id;
+  distributed::DeserializeFromIOBuf(*response, cntl->response_attachment(),
+                                    *var_h->ctx(), var_h->scope(), &outvar,
+                                    &trainer_id);
+  VLOG(4) << "Finish HandleGetResponse";
+  cls->DecreaseReqCount();
+  var_h->Finish(true);
 }
 
-bool BRPCClient::AsyncGetVar(const std::string& ep,
-                             const platform::DeviceContext& ctx,
-                             const framework::Scope& scope,
-                             const std::string& var_name, int64_t time_out) {
+VarHandlePtr BRPCClient::_AsyncGetVar(const std::string& ep,
+                                      const platform::DeviceContext& ctx,
+                                      const framework::Scope& scope,
+                                      const std::string& var_name,
+                                      const std::string& method_name,
+                                      int64_t time_out) {
   const platform::DeviceContext* p_ctx = &ctx;
   const std::string ep_val = ep;
   const std::string var_name_val = var_name;
   const framework::Scope* p_scope = &scope;
-  const auto ch = GetChannel(ep_val);
+  const auto ch_ptr = GetChannel(ep_val);
+  const std::string method = "GetRPC";
+  VarHandlePtr var_h(new VarHandle(ep, method, var_name_val, p_ctx, p_scope));
+
+  framework::AsyncIO([=] {
+    auto ch_ctx = ch_ptr->Pop();
+
+    brpc::Controller* cntl = new brpc::Controller();
+    sendrecv::VariableMessage* response = new sendrecv::VariableMessage();
+    cntl->set_timeout_ms(time_out);
 
-  framework::AsyncIO(
-      [var_name_val, ep_val, p_scope, p_ctx, time_out, ch, this] {});
+    sendrecv::VariableMessage req;
+    req.set_varname(var_name_val);
+    req.set_trainer_id(trainer_id_);
+
+    google::protobuf::Closure* done = brpc::NewCallback(
+        &HandleGetResponse, cntl, response, var_h, ch_ptr, ch_ctx, this);
+
+    platform::RecordRPCEvent record_event(method, p_ctx);
+
+    if (method_name == "GetMonomerVariable") {
+      ch_ctx->stub->GetMonomerVariable(cntl, &req, response, done);
+    } else {
+      ch_ctx->stub->GetVariable(cntl, &req, response, done);
+    }
+
+    if (UNLIKELY(platform::IsProfileEnabled())) {
+      var_h->Wait();
+    }
+  });
 
   req_count_++;
 
-  return true;
+  return var_h;
+}
+
+VarHandlePtr BRPCClient::AsyncGetMonomerVariable(
+    const std::string& ep, const platform::DeviceContext& ctx,
+    const framework::Scope& scope, const std::string& var_name,
+    int64_t time_out) {
+  return _AsyncGetVar(ep, ctx, scope, var_name, "GetMonomerVariable", time_out);
+}
+
+VarHandlePtr BRPCClient::AsyncGetMonomerBarrier(const std::string& ep,
+                                                const std::string& var_name,
+                                                int64_t time_out) {
+  return AsyncSendMessage(ep, "GetMonomerBarrier", var_name, time_out);
 }
 
-bool BRPCClient::AsyncPrefetchVar(const std::string& ep,
-                                  const platform::DeviceContext& ctx,
-                                  const framework::Scope& scope,
-                                  const std::string& in_var_name,
-                                  const std::string& out_var_name,
-                                  int64_t time_out) {
+VarHandlePtr BRPCClient::AsyncGetVar(const std::string& ep,
+                                     const platform::DeviceContext& ctx,
+                                     const framework::Scope& scope,
+                                     const std::string& var_name,
+                                     int64_t time_out) {
+  return _AsyncGetVar(ep, ctx, scope, var_name, "GetVariable", time_out);
+}
+
+VarHandlePtr BRPCClient::AsyncPrefetchVar(const std::string& ep,
+                                          const platform::DeviceContext& ctx,
+                                          const framework::Scope& scope,
+                                          const std::string& in_var_name,
+                                          const std::string& out_var_name,
+                                          const std::string& table_name,
+                                          int64_t time_out) {
   const platform::DeviceContext* p_ctx = &ctx;
   const std::string ep_val = ep;
   const std::string in_var_name_val = in_var_name;
   const std::string out_var_name_val = out_var_name;
+  const std::string table_name_val = table_name;
   const framework::Scope* p_scope = &scope;
-  const auto ch = GetChannel(ep_val);
+  const auto ch_ptr = GetChannel(ep_val);
+
+  const std::string method = "PrefetchRPC";
+
+  VarHandlePtr var_h(
+      new VarHandle(ep, method, out_var_name_val, p_ctx, p_scope));
+
+  framework::AsyncIO([=] {
+    auto ch_ctx = ch_ptr->Pop();
+
+    brpc::Controller* cntl = new brpc::Controller();
+    sendrecv::VariableMessage* response = new sendrecv::VariableMessage();
+    cntl->set_timeout_ms(time_out);
+
+    auto* var = p_scope->FindVar(in_var_name_val);
+    sendrecv::VariableMessage req;
+    distributed::SerializeToIOBuf(in_var_name_val, var, *p_ctx, &req,
+                                  &cntl->request_attachment(), out_var_name_val,
+                                  false, 0, table_name_val);
+
+    platform::RecordRPCEvent record_event(method, p_ctx);
+
+    google::protobuf::Closure* done = brpc::NewCallback(
+        &HandleGetResponse, cntl, response, var_h, ch_ptr, ch_ctx, this);
 
-  framework::AsyncIO([in_var_name_val, out_var_name_val, ep_val, p_scope, p_ctx,
-                      time_out, ch, this] {});
+    ch_ctx->stub->PrefetchVariable(cntl, &req, response, done);
+
+    if (UNLIKELY(platform::IsProfileEnabled())) {
+      var_h->Wait();
+    }
+  });
 
   req_count_++;
-  return true;
+  return var_h;
 }
 
-void BRPCClient::AsyncSendBatchBarrier(const std::string& ep,
-                                       int64_t time_out) {
-  req_count_++;
+VarHandlePtr BRPCClient::AsyncSendBatchBarrier(const std::string& ep,
+                                               int64_t time_out) {
+  return AsyncSendMessage(ep, "BatchBarrierRPC", BATCH_BARRIER_MESSAGE,
+                          time_out);
 }
 
-void BRPCClient::AsyncSendFetchBarrier(const std::string& ep,
-                                       int64_t time_out) {
+VarHandlePtr BRPCClient::AsyncSendFetchBarrier(const std::string& ep,
+                                               int64_t time_out) {
+  auto ch_ptr = GetChannel(ep);
+  auto ch_ctx = ch_ptr->Pop();
+
+  brpc::Controller* cntl = new brpc::Controller();
+  sendrecv::VariableMessage* response = new sendrecv::VariableMessage();
+  cntl->set_timeout_ms(time_out);
+
+  sendrecv::VariableMessage req;
+  req.set_varname(FETCH_BARRIER_MESSAGE);
+
+  const std::string method = "FetchBarrierRPC";
+  // var handle
+  VarHandlePtr var_h(
+      new VarHandle(ep, method, FETCH_BARRIER_MESSAGE, nullptr, nullptr));
+
+  platform::RecordRPCEvent record_event(method, nullptr);
+
+  google::protobuf::Closure* done = brpc::NewCallback(
+      &HandleFetchBarrierResponse, cntl, response, var_h, ch_ptr, ch_ctx, this);
+
+  ch_ctx->stub->GetVariable(cntl, &req, response, done);
+
   req_count_++;
+
+  if (UNLIKELY(platform::IsProfileEnabled())) {
+    var_h->Wait();
+  }
+
+  return var_h;
 }
 
-void BRPCClient::Wait() {
-  std::unique_lock<std::mutex> lk(sync_mutex_);
-  sync_cond_.wait(lk, [this] { return req_count_ == 0; });
+bool BRPCClient::Wait() {
+  VLOG(9) << "begin to brpcclient wait";
+  {
+    std::unique_lock<std::mutex> lk(sync_mutex_);
+    sync_cond_.wait(lk, [this] { return req_count_ == 0; });
+  }
+  VLOG(9) << "end to brpcclient wait";
+  return true;
 }
 
 ChannelQueuePtr BRPCClient::GetChannel(const std::string& ep) {
+  VLOG(4) << "begin to GetChannel:" << ep;
   {
     std::lock_guard<std::mutex> guard(chan_mutex_);
     auto it = channels_.find(ep);
     if (it != channels_.end()) {
+      VLOG(4) << "end to GetChannel:" << ep;
       return it->second;
     }
   }
@@ -150,12 +331,20 @@ ChannelQueuePtr BRPCClient::GetChannel(const std::string& ep) {
   ChannelQueuePtr q(new framework::BlockingQueue<ChannelContextPtr>());
 
   brpc::ChannelOptions options;
+#ifdef PADDLE_WITH_BRPC_RDMA
+  options.use_rdma = true;
+#endif
   options.protocol = "baidu_std";
-  options.connection_type = "pooled";
-  options.connect_timeout_ms = 100;
+  // don't use pooled type. the server can't afford that.
+  options.connection_type = "single";
+  options.connect_timeout_ms = 1000;
   options.timeout_ms = FLAGS_timeout_ms /*milliseconds*/;
   options.max_retry = FLAGS_max_retry;
-  for (int i = 0; i < FLAGS_brpc_channel_num; ++i) {
+
+  VLOG(1) << "create " << brpc_channel_num_per_server_
+          << " brpc channels to pserver:" << ep;
+
+  for (int i = 0; i < brpc_channel_num_per_server_; ++i) {
     std::shared_ptr<ChannelContext> c(new ChannelContext());
     if (c->channel.Init(ep.c_str(), &options) != 0) {
       LOG(FATAL) << "Fail to initialize channel";
@@ -172,9 +361,75 @@ ChannelQueuePtr BRPCClient::GetChannel(const std::string& ep) {
     channels_[ep] = q;
   }
 
+  VLOG(4) << "end to GetChannel:" << ep;
   return q;
 }
 
+VarHandlePtr BRPCClient::AsyncSendComplete(const std::string& ep,
+                                           int64_t time_out) {
+  return AsyncSendMessage(ep, "SendCompleteRPC", COMPLETE_MESSAGE, time_out);
+}
+
+void BRPCClient::SendComplete() {
+  for (auto& kv : channels_) {
+    AsyncSendComplete(kv.first);
+  }
+}
+
+VarHandlePtr BRPCClient::AsyncSendVarMessage(
+    const std::string& ep, const std::string& method_name,
+    const sendrecv::VariableMessage& req, int64_t time_out) {
+  auto ch_ptr = GetChannel(ep);
+  auto ch_ctx = ch_ptr->Pop();
+
+  brpc::Controller* cntl = new brpc::Controller();
+  sendrecv::VoidMessage* response = new sendrecv::VoidMessage();
+  cntl->set_timeout_ms(time_out);
+
+  platform::RecordRPCEvent record_event(method_name, nullptr);
+
+  VarHandlePtr var_h(
+      new VarHandle(ep, method_name, req.varname(), nullptr, nullptr));
+
+  google::protobuf::Closure* done = brpc::NewCallback(
+      &HandleSendResponse, cntl, response, var_h, ch_ptr, ch_ctx, this);
+
+  if (method_name == "CheckPointNotifyRPC") {
+    ch_ctx->stub->CheckpointNotify(cntl, &req, response, done);
+  } else if (method_name == "GetMonomerBarrier") {
+    ch_ctx->stub->GetMonomerBarrier(cntl, &req, response, done);
+  } else {
+    ch_ctx->stub->SendVariable(cntl, &req, response, done);
+  }
+  req_count_++;
+
+  if (UNLIKELY(platform::IsProfileEnabled())) {
+    var_h->Wait();
+  }
+
+  return var_h;
+}
+
+VarHandlePtr BRPCClient::AsyncSendMessage(const std::string& ep,
+                                          const std::string& method_name,
+                                          const std::string& message,
+                                          int64_t time_out) {
+  sendrecv::VariableMessage req;
+  req.set_varname(message);
+
+  return AsyncSendVarMessage(ep, method_name, req, time_out);
+}
+
+VarHandlePtr BRPCClient::AsyncCheckpointNotify(const std::string& ep,
+                                               const std::string& dir,
+                                               int64_t time_out) {
+  sendrecv::VariableMessage req;
+  req.set_varname(CHECKPOINT_SAVE_MESSAGE);
+  req.set_out_varname(dir);
+
+  return AsyncSendVarMessage(ep, "CheckPointNotifyRPC", req, time_out);
+}
+
 }  // namespace distributed
 }  // namespace operators
 }  // namespace paddle

paddle/fluid/operators/distributed/brpc_client.h

@@ -31,6 +31,8 @@ limitations under the License. */
 #include "paddle/fluid/framework/lod_tensor.h"
 #include "paddle/fluid/framework/scope.h"
 #include "paddle/fluid/framework/selected_rows.h"
+#include "paddle/fluid/operators/distributed/brpc_sendrecvop_utils.h"
+#include "paddle/fluid/operators/distributed/request_handler.h"
 #include "paddle/fluid/operators/distributed/rpc_client.h"
 #include "paddle/fluid/operators/distributed/send_recv.pb.h"
 #include "paddle/fluid/platform/macros.h"  // for DISABLE_COPY_AND_ASSIGN
@@ -53,33 +55,94 @@ class BRPCClient : public RPCClient {
   BRPCClient() {}
   virtual ~BRPCClient();
 
-  bool AsyncSendVar(const std::string& ep, const platform::DeviceContext& ctx,
-                    const framework::Scope& scope, const std::string& var_name,
-                    int64_t time_out = FLAGS_rpc_deadline) override;
+  VarHandlePtr AsyncSendVar(const std::string& ep,
+                            const platform::DeviceContext& ctx,
+                            const framework::Scope& scope,
+                            const std::string& var_name,
+                            int64_t time_out = FLAGS_rpc_deadline) override;
 
-  bool AsyncGetVar(const std::string& ep, const platform::DeviceContext& ctx,
-                   const framework::Scope& scope, const std::string& var_name,
-                   int64_t time_out = FLAGS_rpc_deadline) override;
+  VarHandlePtr AsyncGetVar(const std::string& ep,
+                           const platform::DeviceContext& ctx,
+                           const framework::Scope& scope,
+                           const std::string& var_name,
+                           int64_t time_out = FLAGS_rpc_deadline) override;
 
-  bool AsyncPrefetchVar(const std::string& ep,
-                        const platform::DeviceContext& ctx,
-                        const framework::Scope& scope,
-                        const std::string& in_var_name,
-                        const std::string& out_var_name,
-                        int64_t time_out = FLAGS_rpc_deadline) override;
+  VarHandlePtr AsyncGetMonomerBarrier(
+      const std::string& ep, const std::string& var_name,
+      int64_t time_out = FLAGS_rpc_deadline) override;
 
-  void AsyncSendBatchBarrier(const std::string& ep,
-                             int64_t time_out = FLAGS_rpc_deadline) override;
+  VarHandlePtr AsyncGetMonomerVariable(
+      const std::string& ep, const platform::DeviceContext& ctx,
+      const framework::Scope& scope, const std::string& var_name,
+      int64_t time_out = FLAGS_rpc_deadline) override;
 
-  void AsyncSendFetchBarrier(const std::string& ep,
-                             int64_t time_out = FLAGS_rpc_deadline) override;
+  VarHandlePtr AsyncPrefetchVar(const std::string& ep,
+                                const platform::DeviceContext& ctx,
+                                const framework::Scope& scope,
+                                const std::string& in_var_name,
+                                const std::string& out_var_name,
+                                const std::string& table_name = "",
+                                int64_t time_out = FLAGS_rpc_deadline) override;
 
-  void Wait() override;
+  VarHandlePtr AsyncSendBatchBarrier(
+      const std::string& ep, int64_t time_out = FLAGS_rpc_deadline) override;
+
+  VarHandlePtr AsyncSendFetchBarrier(
+      const std::string& ep, int64_t time_out = FLAGS_rpc_deadline) override;
+
+  VarHandlePtr AsyncCheckpointNotify(
+      const std::string& ep, const std::string& dir,
+      int64_t time_out = FLAGS_rpc_deadline) override;
+
+  bool Wait() override;
+
+  void SendComplete() override;
 
  private:
+  VarHandlePtr _AsyncGetVar(const std::string& ep,
+                            const platform::DeviceContext& ctx,
+                            const framework::Scope& scope,
+                            const std::string& var_name,
+                            const std::string& method_name,
+                            int64_t time_out = FLAGS_rpc_deadline);
+
   void Proceed();
   ChannelQueuePtr GetChannel(const std::string& ep);
 
+  VarHandlePtr AsyncSendComplete(const std::string& ep,
+                                 int64_t time_out = FLAGS_rpc_deadline);
+
+  VarHandlePtr AsyncSendMessage(const std::string& ep,
+                                const std::string& method_name,
+                                const std::string& message, int64_t time_out);
+
+  VarHandlePtr AsyncSendVarMessage(const std::string& ep,
+                                   const std::string& method_name,
+                                   const sendrecv::VariableMessage& req,
+                                   int64_t time_out);
+
+  friend void HandleSendResponse(brpc::Controller* cntl,
+                                 sendrecv::VoidMessage* response,
+                                 VarHandlePtr var_h, ChannelQueuePtr ch_ptr,
+                                 ChannelContextPtr ch_ctx, BRPCClient* cls);
+
+  friend void HandleGetResponse(brpc::Controller* cntl,
+                                sendrecv::VariableMessage* response,
+                                VarHandlePtr var_h, ChannelQueuePtr ch_ptr,
+                                ChannelContextPtr ch_ctx, BRPCClient* cls);
+
+  friend void HandleFetchBarrierResponse(brpc::Controller* cntl,
+                                         sendrecv::VariableMessage* response,
+                                         VarHandlePtr var_h,
+                                         ChannelQueuePtr ch_ptr,
+                                         ChannelContextPtr ch_ctx,
+                                         BRPCClient* cls);
+  void DecreaseReqCount() {
+    if (--req_count_ <= 0) {
+      sync_cond_.notify_all();
+    }
+  }
+
  private:
   std::unordered_map<std::string, ChannelQueuePtr> channels_;
 
@@ -88,6 +151,8 @@ class BRPCClient : public RPCClient {
   std::condition_variable sync_cond_;
   std::atomic<int64_t> req_count_{0};
 
+  static constexpr int brpc_channel_num_per_server_ = 4;
+
   // mutex for GetChannel thread safety
   std::mutex chan_mutex_;
   DISABLE_COPY_AND_ASSIGN(BRPCClient);

paddle/fluid/operators/distributed/brpc_rdma_pool.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.
+
+#ifdef PADDLE_WITH_BRPC_RDMA
+
+#include "paddle/fluid/operators/distributed/brpc_rdma_pool.h"
+#include "brpc/channel.h"
+#include "brpc/rdma/rdma_helper.h"
+#include "paddle/fluid/platform/enforce.h"
+
+namespace paddle {
+namespace operators {
+namespace distributed {
+
+RdmaMemPool& RdmaMemPool::Instance() {
+  static RdmaMemPool* g_rdma_mem_pool = new RdmaMemPool();
+  return *g_rdma_mem_pool;
+}
+
+void* RdmaMemPool::Find(const std::string& varname, int64_t size) {
+  pthread_rwlock_rdlock(&access_);
+  auto it = pool_.find(varname);
+  if (it == pool_.end()) {
+    pthread_rwlock_unlock(&access_);
+    return nullptr;
+  }
+
+  auto info = it->second;
+  if (info.data_size != size) {
+    pthread_rwlock_unlock(&access_);
+    PADDLE_ENFORCE(false, "var:%s size:%ld != %ld", varname, size,
+                   info.data_size);
+    return nullptr;
+  }
+
+  pthread_rwlock_unlock(&access_);
+  return info.data;
+}
+
+void RdmaMemPool::Register(const std::string& varname, void* data,
+                           int64_t data_size) {
+  void* old = Find(varname, data_size);
+  if (old != nullptr) {
+    if (data != old) {
+      PADDLE_ENFORCE(false, "var:%s data:%ld != %ld", varname, data, old);
+    }
+    VLOG(7) << "Find on rdma:" << varname << " data:" << data
+            << " data_size:" << data_size;
+    return;
+  }
+
+  VarInfo info;
+  info.data = data;
+  info.data_size = data_size;
+
+  pthread_rwlock_wrlock(&access_);
+  pool_[varname] = info;
+  pthread_rwlock_unlock(&access_);
+
+  if (brpc::rdma::RegisterMemoryForRdma(data, data_size)) {
+    LOG(FATAL) << "register " << varname << " data:" << data
+               << " data_size:" << data_size << " error";
+  }
+
+  VLOG(4) << "register on rdma:" << varname << " data:" << data
+          << " data_size:" << data_size;
+}
+
+}  // namespace distributed
+}  // namespace operators
+}  // namespace paddle
+
+#endif

paddle/fluid/operators/distributed/brpc_rdma_pool.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
+#ifdef PADDLE_WITH_BRPC_RDMA
+
+#include <pthread.h>  // NOLINT
+#include <string>
+#include <unordered_map>
+
+namespace paddle {
+namespace operators {
+namespace distributed {
+
+/*
+ * This class is used to avoid duplicated registion of brpc::rdma.
+ */
+class RdmaMemPool {
+ public:
+  static RdmaMemPool& Instance();
+  RdmaMemPool() : access_(PTHREAD_RWLOCK_INITIALIZER) {}
+
+  virtual ~RdmaMemPool() { pthread_rwlock_destroy(&access_); }
+
+  void Register(const std::string& varname, void* data, int64_t size);
+  void* Find(const std::string& varname, int64_t size);
+
+ private:
+  struct VarInfo {
+    void* data;
+    int64_t data_size;
+
+    VarInfo() : data(nullptr), data_size(0) {}
+  };
+
+ private:
+  std::unordered_map<std::string, VarInfo> pool_;
+  pthread_rwlock_t access_;
+};
+
+}  // namespace distributed
+}  // namespace operators
+}  // namespace paddle
+
+#endif

paddle/fluid/operators/distributed/brpc_sendrecvop_utils.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. */
+
+#ifdef PADDLE_WITH_CUDA
+#include <nccl.h>
+#endif
+#include <sys/time.h>
+#include <thread>  // NOLINT
+
+#include "paddle/fluid/framework/data_type.h"
+#include "paddle/fluid/operators/distributed/brpc_rdma_pool.h"
+#include "paddle/fluid/operators/distributed/brpc_sendrecvop_utils.h"
+#include "paddle/fluid/operators/distributed/brpc_variable_response.h"
+#include "paddle/fluid/operators/distributed/send_recv.pb.h"
+#include "paddle/fluid/platform/profiler.h"
+
+namespace paddle {
+namespace operators {
+namespace distributed {
+
+class IOBufWriter {
+ public:
+  static void Append(butil::IOBuf* iobuf, int k, const char* v, int64_t vlen) {
+    iobuf->append(reinterpret_cast<char*>(&k), 4);
+    iobuf->append(reinterpret_cast<char*>(&vlen), 8);
+    iobuf->append(v, vlen);
+  }
+
+  static void AppendTCPZeroCopy(butil::IOBuf* iobuf, int k, const char* v,
+                                int64_t vlen, bool in_cuda_pinned,
+                                void (*destroy)(void*), void* user_data) {
+    VLOG(7) << "AppendTCPZeroCopy "
+            << " k:" << k
+            << " data:" << static_cast<void*>(const_cast<char*>(v))
+            << " data_size:" << vlen << " in_cuda_pinned:" << in_cuda_pinned;
+
+    iobuf->append(reinterpret_cast<char*>(&k), 4);
+    iobuf->append(reinterpret_cast<char*>(&vlen), 8);
+
+    // FIXME(gongwb): use append_zerocopy
+    /*
+    if (in_cuda_pinned) {
+      iobuf->append_zerocopy(v, vlen, IOBufWriter::FreeMemory);
+    } else {
+      iobuf->append_zerocopy(v, vlen, nullptr);
+    }
+    */
+    iobuf->append(v, vlen);
+    destroy(user_data);
+  }
+
+#ifdef PADDLE_WITH_BRPC_RDMA
+  static void AppendRdmaZeroCopy(const std::string varname, butil::IOBuf* iobuf,
+                                 int k, const char* v, int64_t vlen,
+                                 bool in_cuda_pinned, void (*destroy)(void*),
+                                 void* user_data) {
+    VLOG(7) << "AppendRdmaZeroCopy varname:" << varname << " k:" << k
+            << " data:" << static_cast<void*>(const_cast<char*>(v))
+            << " data_size:" << vlen << " in_cuda_pinned:" << in_cuda_pinned;
+
+    iobuf->append(reinterpret_cast<char*>(&k), 4);
+    iobuf->append(reinterpret_cast<char*>(&vlen), 8);
+
+    RdmaMemPool::Instance().Register(
+        varname, static_cast<void*>(const_cast<char*>(v)), vlen);
+
+    // FIXME(gongwb): use append_zerocopy
+    // iobuf->append_zerocopy(v, vlen, nullptr);
+    iobuf->append(v, vlen);
+    destroy(user_data);
+    return;
+  }
+#endif
+
+  static void AppendZeroCopy(const std::string varname, butil::IOBuf* iobuf,
+                             int k, const char* v, int64_t vlen,
+                             bool in_cuda_pinned, void (*destroy)(void*),
+                             void* user_data) {
+#ifdef PADDLE_WITH_BRPC_RDMA
+    IOBufWriter::AppendRdmaZeroCopy(varname, iobuf, k, v, vlen, in_cuda_pinned,
+                                    destroy, user_data);
+#else
+    IOBufWriter::AppendTCPZeroCopy(iobuf, k, v, vlen, in_cuda_pinned, destroy,
+                                   user_data);
+#endif
+  }
+};
+
+void SerializeToIOBuf(const std::string& name, framework::Variable* var,
+                      const platform::DeviceContext& ctx, VarMsg* request,
+                      butil::IOBuf* iobuf, const std::string& out_varname,
+                      bool var_is_not_stable, int trainer_id,
+                      const std::string& table_name) {
+  std::unique_ptr<TensorPayload> payload;
+
+  request->set_varname(name);
+  request->set_trainer_id(trainer_id);
+  // Note: normally the profiler is enabled in 1 trainer, hence only
+  // 1 trainer returns true for ShouldSendProfileState(). It tells PS
+  // servers the trainer's profiling state so that PS can follow the
+  // trainer.
+  if (platform::ShouldSendProfileState()) {
+    if (platform::IsProfileEnabled()) {
+      request->set_profile(platform::kEnableProfiler);
+    } else {
+      request->set_profile(platform::kDisableProfiler);
+    }
+  }
+  if (!out_varname.empty()) {
+    request->set_out_varname(out_varname);
+  }
+  if (!table_name.empty()) {
+    request->set_table_name(table_name);
+  }
+  if (var->IsType<framework::LoDTensor>()) {
+    request->set_type(::sendrecv::LOD_TENSOR);
+    payload.reset(new TensorPayload(GetTensorPayload(var, ctx, request)));
+  } else if (var->IsType<framework::SelectedRows>()) {
+    request->set_type(::sendrecv::SELECTED_ROWS);
+    payload.reset(new TensorPayload(GetSelectedRowsPayload(var, ctx, request)));
+#ifdef PADDLE_WITH_CUDA
+  } else if (var->IsType<ncclUniqueId>()) {
+    request->set_type(::sendrecv::NCCL_ID);
+    const ncclUniqueId& uid = var->Get<ncclUniqueId>();
+    // TODO(gongwb): use append_zero to avoid data copy.
+    IOBufWriter::Append(iobuf,
+                        sendrecv::VariableMessage::kSerializedFieldNumber,
+                        uid.internal, NCCL_UNIQUE_ID_BYTES);
+    return;
+#endif
+  } else {
+    PADDLE_THROW("Serialize does not support type: %s",
+                 typeid(var->Type()).name());
+  }
+
+  PADDLE_ENFORCE_NOT_NULL(payload);
+
+  // FIXME(gongwb): it seems that can use zero copy.
+  if (var_is_not_stable) {
+    IOBufWriter::Append(
+        iobuf, ::sendrecv::VariableMessage::kSerializedFieldNumber,
+        static_cast<const char*>(payload->ptr()), payload->memory_size());
+  } else {
+    if (platform::is_gpu_place(ctx.GetPlace())) {
+#ifdef PADDLE_WITH_CUDA
+      IOBufWriter::AppendZeroCopy(
+          name, iobuf, ::sendrecv::VariableMessage::kSerializedFieldNumber,
+          static_cast<const char*>(payload->ptr()), payload->memory_size(),
+          true, SerializeDestroyCallback, static_cast<void*>(payload.get()));
+      payload.release();
+#endif
+    } else {
+      IOBufWriter::AppendZeroCopy(
+          name, iobuf, ::sendrecv::VariableMessage::kSerializedFieldNumber,
+          static_cast<const char*>(payload->ptr()), payload->memory_size(),
+          false, SerializeDestroyCallback, static_cast<void*>(payload.get()));
+      payload.release();
+    }
+  }
+
+  if (var->IsType<framework::SelectedRows>()) {
+    auto* slr = var->GetMutable<framework::SelectedRows>();
+    size_t rows_memory_size =
+        slr->rows().size() * framework::SizeOfType(typeid(int64_t));
+
+    IOBufWriter::Append(iobuf, ::sendrecv::VariableMessage::kRowsFieldNumber,
+                        reinterpret_cast<const char*>(slr->rows().data()),
+                        static_cast<int64_t>(rows_memory_size));
+  }
+}
+
+void DeserializeFromIOBuf(const ::sendrecv::VariableMessage& meta,
+                          const butil::IOBuf& iobuf,
+                          const platform::DeviceContext& ctx,
+                          const framework::Scope* scope,
+                          framework::Variable** var, int* trainer_id) {
+  operators::distributed::BRPCVariableResponse resp(scope, &ctx);
+  PADDLE_ENFORCE(resp.Parse(iobuf, meta) == 0, "parse iobuf to tensor error!");
+  *var = resp.GetVar();
+  *trainer_id = resp.GetTrainerId();
+}
+
+}  // namespace distributed
+}  // namespace operators
+}  // namespace paddle

paddle/fluid/operators/distributed/brpc_sendrecvop_utils.h

+/* 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. */
+
+#pragma once
+
+#include <sys/time.h>
+#include <iostream>
+#include <string>
+#include <vector>
+
+#include "brpc/channel.h"
+#include "paddle/fluid/framework/data_type.h"
+#include "paddle/fluid/framework/lod_tensor.h"
+#include "paddle/fluid/framework/scope.h"
+#include "paddle/fluid/framework/selected_rows.h"
+#include "paddle/fluid/framework/tensor_util.h"
+#include "paddle/fluid/framework/var_type.h"
+#include "paddle/fluid/operators/distributed/send_recv.pb.h"
+#include "paddle/fluid/operators/distributed/sendrecvop_utils.h"
+
+namespace paddle {
+namespace operators {
+namespace distributed {
+
+void SerializeToIOBuf(const std::string& name, framework::Variable* var,
+                      const platform::DeviceContext& ctx, VarMsg* request,
+                      butil::IOBuf* iobuf, const std::string& out_varname,
+                      bool var_is_not_stable, const int trainer_id = 0,
+                      const std::string& table_name = std::string());
+
+void DeserializeFromIOBuf(const VarMsg& meta, const butil::IOBuf& iobuf,
+                          const platform::DeviceContext& ctx,
+                          const framework::Scope* scope,
+                          framework::Variable** var, int* trainer_id);
+
+}  // namespace distributed
+}  // namespace operators
+}  // namespace paddle

paddle/fluid/operators/distributed/brpc_serde_test.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 "brpc/channel.h"
+#include "google/protobuf/text_format.h"
+#include "gtest/gtest.h"
+#include "paddle/fluid/framework/lod_tensor.h"
+#include "paddle/fluid/framework/tensor_util.h"
+#include "paddle/fluid/framework/variable.h"
+#include "paddle/fluid/operators/distributed/brpc_sendrecvop_utils.h"
+#include "paddle/fluid/operators/distributed/brpc_variable_response.h"
+#include "paddle/fluid/operators/distributed/sendrecvop_utils.h"
+#include "paddle/fluid/operators/distributed/variable_response.h"
+#include "paddle/fluid/operators/math/math_function.h"
+#include "paddle/fluid/platform/place.h"
+#include "paddle/fluid/string/printf.h"
+
+namespace framework = paddle::framework;
+namespace platform = paddle::platform;
+namespace operators = paddle::operators;
+namespace math = paddle::operators::math;
+namespace memory = paddle::memory;
+
+void RunSerdeTestSelectedRows(platform::Place place) {
+  platform::DeviceContextPool& pool = platform::DeviceContextPool::Instance();
+  auto& ctx = *pool.Get(place);
+
+  butil::IOBuf iobuf;
+  sendrecv::VariableMessage msg;
+  int tensor_numel = 564 * 128;
+
+  // serialize var to IOBuf
+  {
+    framework::Variable var;
+    auto* slr = var.GetMutable<framework::SelectedRows>();
+    slr->set_height(1000);
+    auto* tensor = slr->mutable_value();
+    auto* rows = slr->mutable_rows();
+    tensor->Resize(framework::make_ddim({564, 128}));
+    tensor->mutable_data<float>(place);
+    math::set_constant(ctx, tensor, 32.7);
+    for (int i = 0; i < 564; ++i) rows->push_back(i);
+
+    operators::distributed::SerializeToIOBuf("myvar", &var, ctx, &msg, &iobuf,
+                                             "", false);
+  }
+
+  // desrialize
+  {
+    framework::Scope scope;
+    scope.Var("myvar");
+    operators::distributed::BRPCVariableResponse resp(&scope, &ctx);
+    EXPECT_EQ(resp.Parse(iobuf, msg), 0);
+
+    framework::Variable* var2 = resp.GetVar();
+
+    auto* slr2 = var2->GetMutable<framework::SelectedRows>();
+    auto* tensor2 = slr2->mutable_value();
+    auto* rows2 = slr2->mutable_rows();
+    float* tensor_data2 = nullptr;
+    framework::Tensor tmp_tensor;
+
+    if (platform::is_gpu_place(ctx.GetPlace())) {
+      platform::CPUPlace cpu;
+      framework::TensorCopy(*tensor2, cpu, &tmp_tensor);
+      tensor_data2 = tmp_tensor.data<float>();
+    } else {
+      tensor_data2 = const_cast<float*>(tensor2->data<float>());
+    }
+    const int64_t* rows_data2 = rows2->data();
+
+    for (int i = 0; i < tensor_numel; ++i) {
+      EXPECT_FLOAT_EQ(tensor_data2[i], 32.7);
+    }
+    for (size_t i = 0; i < rows2->size(); ++i) {
+      EXPECT_EQ(rows_data2[i], static_cast<int64_t>(i));
+    }
+    EXPECT_EQ(slr2->height(), 1000);
+  }
+}
+
+void RunTestLodTensor(platform::Place place) {
+  platform::DeviceContextPool& pool = platform::DeviceContextPool::Instance();
+  auto& ctx = *pool.Get(place);
+
+  // serialize var to ByteBuffer
+  butil::IOBuf iobuf;
+  sendrecv::VariableMessage msg;
+  int tensor_numel = 512 * 8 * 4 * 2;
+  {
+    framework::Variable var;
+    auto* tensor = var.GetMutable<framework::LoDTensor>();
+    tensor->Resize(framework::make_ddim({512, 8, 4, 2}));
+    framework::LoD lod;
+    lod.push_back(framework::Vector<size_t>({1, 3, 8}));
+    tensor->set_lod(lod);
+    tensor->mutable_data<float>(place);
+    math::set_constant(ctx, tensor, 31.9);
+
+    operators::distributed::SerializeToIOBuf("myvar", &var, ctx, &msg, &iobuf,
+                                             "", false);
+  }
+
+  // check sendrecv::VariableMessage meta data
+  {
+    EXPECT_EQ(msg.varname(), "myvar");
+    EXPECT_EQ(msg.type(), 0);
+    EXPECT_EQ(msg.dims()[0], 512);
+    EXPECT_EQ(msg.dims()[1], 8);
+    EXPECT_EQ(msg.dims()[2], 4);
+    EXPECT_EQ(msg.dims()[3], 2);
+    EXPECT_EQ(msg.lod_level(), 1);
+    EXPECT_EQ(msg.lod(0).lod_data(0), 1);
+    EXPECT_EQ(msg.lod(0).lod_data(1), 3);
+    EXPECT_EQ(msg.lod(0).lod_data(2), 8);
+  }
+
+  // deserialize
+  {
+    framework::Scope scope;
+    scope.Var("myvar");
+    operators::distributed::BRPCVariableResponse resp(&scope, &ctx);
+    EXPECT_EQ(resp.Parse(iobuf, msg), 0);
+
+    framework::Variable* var2 = resp.GetVar();
+
+    auto tensor2 = var2->Get<framework::LoDTensor>();
+    float* tensor_data2 = nullptr;
+    framework::Tensor tmp_tensor;
+
+    if (platform::is_gpu_place(ctx.GetPlace())) {
+      platform::CPUPlace cpu;
+      framework::TensorCopy(tensor2, cpu, &tmp_tensor);
+      tensor_data2 = tmp_tensor.data<float>();
+    } else {
+      tensor_data2 = const_cast<float*>(tensor2.data<float>());
+    }
+
+    for (int i = 0; i < tensor_numel; ++i)
+      EXPECT_FLOAT_EQ(tensor_data2[i], 31.9);
+  }
+}
+
+TEST(LodTensor, Run) {
+  platform::CPUPlace place;
+  RunTestLodTensor(place);
+#ifdef PADDLE_WITH_CUDA
+  platform::CUDAPlace gpu(0);
+  RunTestLodTensor(gpu);
+#endif
+}
+
+TEST(SelectedRows, Run) {
+  platform::CPUPlace place;
+  RunSerdeTestSelectedRows(place);
+#ifdef PADDLE_WITH_CUDA
+  platform::CUDAPlace gpu;
+  RunSerdeTestSelectedRows(gpu);
+#endif
+}

paddle/fluid/operators/distributed/brpc_server.cc

@@ -13,84 +13,287 @@
 // limitations under the License.
 
 #include "paddle/fluid/operators/distributed/brpc_server.h"
+#include "paddle/fluid/framework/threadpool.h"
+#include "paddle/fluid/operators/distributed/brpc_sendrecvop_utils.h"
+#include "paddle/fluid/operators/distributed/brpc_variable_response.h"
 #include "paddle/fluid/operators/distributed/request_handler.h"
 
 namespace sendrecv {
 
-typedef std::unordered_map<std::string,
-                           paddle::operators::distributed::RequestHandler*>
+namespace distributed = paddle::operators::distributed;
+
+typedef std::unordered_map<std::string, distributed::RequestHandler*>
     HandlerMap;
 
 class BRPCServiceImpl : public SendRecvService {
  public:
-  explicit BRPCServiceImpl(const HandlerMap& rpc_call_map)
-      : request_send_h_(nullptr),
-        request_get_h_(nullptr),
-        request_prefetch_h_(nullptr) {
-    auto it = rpc_call_map.find(paddle::operators::distributed::kRequestSend);
+  explicit BRPCServiceImpl(const HandlerMap& rpc_call_map,
+                           distributed::RPCServer* rpc_server)
+      : rpc_server_(rpc_server) {
+    VLOG(3) << "BRPCServiceImpl size: " << rpc_call_map.size();
+    auto it = rpc_call_map.find(distributed::kRequestSend);
     if (it != rpc_call_map.end()) {
       request_send_h_ = it->second;
+      send_threads_.reset(new paddle::framework::ThreadPool(
+          rpc_server_->GetThreadNum(distributed::kRequestSend)));
     }
 
-    it = rpc_call_map.find(paddle::operators::distributed::kRequestSend);
+    it = rpc_call_map.find(distributed::kRequestGet);
     if (it != rpc_call_map.end()) {
       request_get_h_ = it->second;
+      get_threads_.reset(new paddle::framework::ThreadPool(
+          rpc_server_->GetThreadNum(distributed::kRequestGet)));
     }
 
-    it = rpc_call_map.find(paddle::operators::distributed::kRequestPrefetch);
+    it = rpc_call_map.find(distributed::kRequestPrefetch);
     if (it != rpc_call_map.end()) {
       request_prefetch_h_ = it->second;
+      prefetch_threads_.reset(new paddle::framework::ThreadPool(
+          rpc_server_->GetThreadNum(distributed::kRequestPrefetch)));
+    }
+
+    it = rpc_call_map.find(distributed::kRequestCheckpoint);
+    if (it != rpc_call_map.end()) {
+      request_checkpoint_h_ = it->second;
+      checkpoint_notify_threads_.reset(new paddle::framework::ThreadPool(
+          rpc_server_->GetThreadNum(distributed::kRequestPrefetch)));
+    }
+
+    it = rpc_call_map.find(distributed::kRequestGetMonomerVariable);
+    if (it != rpc_call_map.end()) {
+      request_get_monomer_handler_h_ = it->second;
+    }
+
+    it = rpc_call_map.find(distributed::kRequestGetMonomerBarrier);
+    if (it != rpc_call_map.end()) {
+      request_get_monomer_barrier_handler_h_ = it->second;
     }
   }
 
   virtual ~BRPCServiceImpl() {}
-
   void SendVariable(google::protobuf::RpcController* cntl_butil,
                     const VariableMessage* request, VoidMessage* response,
                     google::protobuf::Closure* done) override {
+    send_threads_->Run(
+        [=] { _SendVariable(cntl_butil, request, response, done); });
+  }
+
+  void _SendVariable(google::protobuf::RpcController* cntl_butil,
+                     const VariableMessage* request, VoidMessage* response,
+                     google::protobuf::Closure* done) {
     PADDLE_ENFORCE(request_send_h_ != nullptr,
                    "RequestSend handler should be registed first!");
     brpc::ClosureGuard done_guard(done);
-
-    paddle::framework::Scope* local_scope = request_send_h_->scope();
-    paddle::framework::Variable* outvar = nullptr;
-    paddle::framework::Variable* invar = nullptr;
+    brpc::Controller* cntl = static_cast<brpc::Controller*>(cntl_butil);
 
     std::string varname = request->varname();
+    VLOG(3) << "RequestSend var_name:" << varname
+            << ", trainer_id:" << request->trainer_id()
+            << ", from:" << cntl->remote_side();
 
-    if (!request_send_h_->sync_mode()) {
-      local_scope = &request_send_h_->scope()->NewScope();
-      invar = local_scope->Var(varname);
-    } else {
-      invar = local_scope->FindVar(varname);
-    }
+    distributed::BRPCVariableResponse resp(request_send_h_->scope(),
+                                           request_send_h_->dev_ctx(),
+                                           !request_send_h_->sync_mode());
+    PADDLE_ENFORCE(resp.Parse(cntl->request_attachment(), *request) == 0,
+                   "parse iobuf to tensor error!");
 
-    request_send_h_->Handle(varname, local_scope, invar, &outvar);
+    auto scope = resp.GetMutableLocalScope();
+    auto invar = resp.GetVar();
+    int trainer_id = request->trainer_id();
+    paddle::framework::Variable* outvar = nullptr;
 
-    if (!request_send_h_->sync_mode()) {
-      request_send_h_->scope()->DeleteScope(local_scope);
-    }
+    request_send_h_->Handle(varname, scope, invar, &outvar, trainer_id);
   }
 
   void GetVariable(google::protobuf::RpcController* cntl_butil,
                    const VariableMessage* request, VariableMessage* response,
                    google::protobuf::Closure* done) override {
+    get_threads_->Run(
+        [=] { _GetVariable(cntl_butil, request, response, done); });
+  }
+
+  void _GetVariable(google::protobuf::RpcController* cntl_butil,
+                    const VariableMessage* request, VariableMessage* response,
+                    google::protobuf::Closure* done) {
     PADDLE_ENFORCE(request_get_h_ != nullptr,
                    "RequestGet handler should be registed first!");
-  }
 
+    brpc::ClosureGuard done_guard(done);
+    brpc::Controller* cntl = static_cast<brpc::Controller*>(cntl_butil);
+
+    std::string varname = request->varname();
+    VLOG(3) << "RequestGet varname:" << varname
+            << ", trainer_id:" << request->trainer_id()
+            << ", from:" << cntl->remote_side();
+
+    auto scope = request_get_h_->scope();
+    auto invar = scope->FindVar(varname);
+    int trainer_id = request->trainer_id();
+    paddle::framework::Variable* outvar = nullptr;
+
+    request_get_h_->Handle(varname, scope, invar, &outvar, trainer_id);
+
+    if (outvar) {
+      distributed::SerializeToIOBuf(varname, outvar, *request_get_h_->dev_ctx(),
+                                    response, &cntl->response_attachment(), "",
+                                    false);
+    }
+  }
   void PrefetchVariable(google::protobuf::RpcController* cntl_butil,
                         const VariableMessage* request,
                         VariableMessage* response,
                         google::protobuf::Closure* done) override {
+    prefetch_threads_->Run(
+        [=] { _PrefetchVariable(cntl_butil, request, response, done); });
+  }
+
+  void _PrefetchVariable(google::protobuf::RpcController* cntl_butil,
+                         const VariableMessage* request,
+                         VariableMessage* response,
+                         google::protobuf::Closure* done) {
     PADDLE_ENFORCE(request_prefetch_h_ != nullptr,
                    "kRequestPrefetch handler should be registed first!");
+
+    brpc::ClosureGuard done_guard(done);
+    brpc::Controller* cntl = static_cast<brpc::Controller*>(cntl_butil);
+
+    // prefetch process...
+    std::string in_var_name = request->varname();
+    std::string out_var_name = request->out_varname();
+    VLOG(3) << "RequestPrefetch, in_var_name: " << in_var_name
+            << ", out_var_name: " << out_var_name
+            << ", trainer_id:" << request->trainer_id()
+            << ", from:" << cntl->remote_side();
+
+    distributed::BRPCVariableResponse resp(
+        request_prefetch_h_->scope(), request_prefetch_h_->dev_ctx(), true);
+
+    PADDLE_ENFORCE(resp.Parse(cntl->request_attachment(), *request) == 0,
+                   "parse iobuf to tensor error!");
+
+    auto scope = resp.GetMutableLocalScope();
+    auto invar = scope->FindVar(in_var_name);
+    std::string table_name = request->table_name();
+    int trainer_id = request->trainer_id();
+    paddle::framework::Variable* outvar = scope->Var(out_var_name);
+
+    request_prefetch_h_->Handle(in_var_name, scope, invar, &outvar, trainer_id,
+                                out_var_name, table_name);
+
+    distributed::SerializeToIOBuf(out_var_name, outvar,
+                                  *request_prefetch_h_->dev_ctx(), response,
+                                  &cntl->response_attachment(), "", true);
+  }
+
+  void CheckpointNotify(google::protobuf::RpcController* cntl_butil,
+                        const VariableMessage* request, VoidMessage* response,
+                        google::protobuf::Closure* done) override {
+    checkpoint_notify_threads_->Run(
+        [=] { _CheckpointNotify(cntl_butil, request, response, done); });
+  }
+
+  void _CheckpointNotify(google::protobuf::RpcController* cntl_butil,
+                         const VariableMessage* request, VoidMessage* response,
+                         google::protobuf::Closure* done) {
+    PADDLE_ENFORCE(
+        request_checkpoint_h_ != nullptr,
+        "kRequestCheckpointNotify handler should be registed first!");
+
+    brpc::ClosureGuard done_guard(done);
+    brpc::Controller* cntl = static_cast<brpc::Controller*>(cntl_butil);
+
+    distributed::BRPCVariableResponse resp(request_checkpoint_h_->scope(),
+                                           request_checkpoint_h_->dev_ctx());
+
+    auto scope = resp.GetMutableLocalScope();
+
+    std::string checkpoint_notify = request->varname();
+    std::string checkpoint_dir = request->out_varname();
+    int trainer_id = request->trainer_id();
+
+    VLOG(4) << "RequestCheckpointNotify notify: " << checkpoint_notify
+            << ", dir: " << checkpoint_dir
+            << ", trainer_id:" << request->trainer_id()
+            << ", from:" << cntl->remote_side();
+
+    request_checkpoint_h_->Handle(checkpoint_notify, scope, nullptr, nullptr,
+                                  trainer_id, checkpoint_dir);
+  }
+
+  void GetMonomerVariable(google::protobuf::RpcController* cntl_butil,
+                          const VariableMessage* request,
+                          VariableMessage* response,
+                          google::protobuf::Closure* done) override {
+    PADDLE_ENFORCE(
+        request_get_monomer_handler_h_ != nullptr,
+        "kRequestGetMonomerVariable handler should be registed first!");
+
+    brpc::ClosureGuard done_guard(done);
+    brpc::Controller* cntl = static_cast<brpc::Controller*>(cntl_butil);
+
+    // proc request.
+    std::string varname = request->varname();
+    VLOG(3) << "GetMonomerVariable " << varname
+            << ", trainer_id:" << request->trainer_id()
+            << ", from:" << cntl->remote_side();
+
+    rpc_server_->WaitVarCond(varname);
+    distributed::MonomerHandle h = rpc_server_->GetMonomer(varname);
+
+    auto scope = h.scope_;
+    auto invar = scope->FindVar(varname);
+    paddle::framework::Variable* outvar = nullptr;
+
+    request_get_monomer_handler_h_->Handle(varname, scope, invar, &outvar,
+                                           request->trainer_id());
+
+    if (outvar) {
+      distributed::SerializeToIOBuf(varname, outvar, *h.dev_ctx_, response,
+                                    &cntl->response_attachment(), "", false);
+    }
+  }
+
+  void GetMonomerBarrier(google::protobuf::RpcController* cntl_butil,
+                         const VariableMessage* request, VoidMessage* response,
+                         google::protobuf::Closure* done) override {
+    PADDLE_ENFORCE(
+        request_get_monomer_barrier_handler_h_ != nullptr,
+        "RequestGetMonomerBarrier handler should be registed first!");
+
+    brpc::ClosureGuard done_guard(done);
+    brpc::Controller* cntl = static_cast<brpc::Controller*>(cntl_butil);
+
+    std::string varname = request->varname();
+    VLOG(3) << "RequestGetMonomerBarrier var_name:" << varname
+            << ", trainer_id:" << request->trainer_id()
+            << ", from:" << cntl->remote_side();
+
+    rpc_server_->WaitVarCond(varname);
+    distributed::MonomerHandle h = rpc_server_->GetMonomer(varname);
+
+    paddle::framework::Scope* scope = nullptr;
+    paddle::framework::Variable* invar = nullptr;
+    paddle::framework::Variable* outvar = nullptr;
+
+    request_get_monomer_barrier_handler_h_->Handle(
+        varname, scope, invar, &outvar, request->trainer_id());
   }
 
  private:
-  paddle::operators::distributed::RequestHandler* request_send_h_;
-  paddle::operators::distributed::RequestHandler* request_get_h_;
-  paddle::operators::distributed::RequestHandler* request_prefetch_h_;
+  distributed::RequestHandler* request_send_h_{nullptr};
+  distributed::RequestHandler* request_get_h_{nullptr};
+  distributed::RequestHandler* request_prefetch_h_{nullptr};
+  distributed::RequestHandler* request_checkpoint_h_{nullptr};
+  distributed::RequestHandler* request_get_monomer_handler_h_{nullptr};
+  distributed::RequestHandler* request_get_monomer_barrier_handler_h_{nullptr};
+
+  distributed::RPCServer* rpc_server_{nullptr};
+
+  // FIXME(gongwb): brpc should support process one rpce use one threadpool.
+  std::unique_ptr<paddle::framework::ThreadPool> send_threads_;
+  std::unique_ptr<paddle::framework::ThreadPool> get_threads_;
+  std::unique_ptr<paddle::framework::ThreadPool> prefetch_threads_;
+  std::unique_ptr<paddle::framework::ThreadPool> checkpoint_notify_threads_;
 };
 }  // namespace sendrecv
 
@@ -100,7 +303,7 @@ namespace distributed {
 
 void AsyncBRPCServer::StartServer() {
   // Instance of your service.
-  sendrecv::BRPCServiceImpl service_impl(rpc_call_map_);
+  sendrecv::BRPCServiceImpl service_impl(rpc_call_map_, this);
 
   // Add the service into server. Notice the second parameter, because the
   // service is put on stack, we don't want server to delete it, otherwise
@@ -111,6 +314,9 @@ void AsyncBRPCServer::StartServer() {
   }
 
   brpc::ServerOptions options;
+#ifdef PADDLE_WITH_BRPC_RDMA
+  options.use_rdma = true;
+#endif
   options.idle_timeout_sec = idle_timeout_s_;
   options.max_concurrency = max_concurrency_;
   if (server_.Start(bind_address_.c_str(), &options) != 0) {

paddle/fluid/operators/distributed/brpc_variable_response.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/operators/distributed/brpc_variable_response.h"
+#include "paddle/fluid/operators/distributed/send_recv.pb.h"
+
+namespace paddle {
+namespace operators {
+namespace distributed {
+
+namespace pb = ::google::protobuf;
+using vr = ::sendrecv::VariableMessage;
+
+int BRPCVariableResponse::Parse(Source* source) {
+  pb::io::ZeroCopyInputStream* input_stream = source->contents();
+  pb::io::CodedInputStream input(input_stream);
+  input.SetTotalBytesLimit(INT_MAX, INT_MAX);
+
+  while (1) {
+    unsigned int tag = 0;
+    if (!input.ReadLittleEndian32(&tag)) {
+      break;
+    }
+
+    uint64_t num_bytes = 0;
+    if (!input.ReadLittleEndian64(&num_bytes)) {
+      break;
+    }
+
+    int field = static_cast<int>(tag);
+    int ret = field == 0 ? -1 : field;
+    switch (field) {
+      case vr::kSerializedFieldNumber: {
+        if (!ProcSerializedField(field, &input, num_bytes)) {
+          return ret;
+        }
+        break;
+      }
+      case vr::kRowsFieldNumber: {
+        PADDLE_ENFORCE((meta_.type() == sendrecv::SELECTED_ROWS ||
+                        meta_.type() == sendrecv::LOD_TENSOR) &&
+                           meta_.varname() != "",
+                       "meta info should be got first!");
+
+        if (!CopySelectRowsData(&input, *dev_ctx_, num_bytes)) {
+          return ret;
+        }
+        break;
+      }
+      default: {
+        PADDLE_ENFORCE(false, "not surpported %u fieldnumber", field);
+        return ret;
+      }
+    }
+  }
+
+  return 0;
+}
+}  // namespace distributed
+}  // namespace operators
+}  // namespace paddle

paddle/fluid/operators/distributed/brpc_variable_response.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 "brpc/channel.h"
+#include "paddle/fluid/framework/data_type.h"
+#include "paddle/fluid/framework/lod_tensor.h"
+#include "paddle/fluid/framework/scope.h"
+#include "paddle/fluid/framework/selected_rows.h"
+#include "paddle/fluid/framework/var_type.h"
+
+#include "paddle/fluid/operators/distributed/send_recv.pb.h"
+
+#include "google/protobuf/io/coded_stream.h"
+#include "google/protobuf/io/zero_copy_stream.h"
+#include "paddle/fluid/framework/tensor.h"
+#include "paddle/fluid/operators/distributed/variable_response.h"
+
+namespace paddle {
+namespace operators {
+namespace distributed {
+
+class BRPCSourceWrapper : public Source {
+ public:
+  explicit BRPCSourceWrapper(const butil::IOBuf& iobuf) : source_(iobuf) {}
+  ::google::protobuf::io::ZeroCopyInputStream* contents() override {
+    return &source_;
+  }
+
+ private:
+  butil::IOBufAsZeroCopyInputStream source_;
+};
+
+class BRPCVariableResponse : public VariableResponse {
+ public:
+  BRPCVariableResponse(const framework::Scope* scope,
+                       const platform::DeviceContext* dev_ctx,
+                       bool create_scope = false)
+      : VariableResponse(scope, dev_ctx, create_scope) {}
+
+  virtual ~BRPCVariableResponse() {}
+
+  // parse attachment from iobuf
+  int Parse(Source* source) override;
+  int Parse(const butil::IOBuf& iobuf, const sendrecv::VariableMessage& meta) {
+    BRPCSourceWrapper wrapper(iobuf);
+    return VariableResponse::Parse(&wrapper, meta);
+  }
+};
+
+};  // namespace distributed
+};  // namespace operators
+};  // namespace paddle

paddle/fluid/operators/distributed/grpc_client.cc

@@ -293,8 +293,7 @@ VarHandlePtr GRPCClient::AsyncGetMonomerBarrier(const std::string& ep,
   const auto ch = GetChannel(ep);
   BatchBarrierProcessor* s = new BatchBarrierProcessor(ch);
   const std::string method = "SendMonomerFetchBarrierRPC";
-  VarHandlePtr h(
-      new VarHandle(ep, method, FETCH_BARRIER_MESSAGE, nullptr, nullptr));
+  VarHandlePtr h(new VarHandle(ep, method, var_name, nullptr, nullptr));
   s->Prepare(h, time_out);
 
   VLOG(30) << s->GetVarHandlePtr()->String() << " begin";

paddle/fluid/operators/distributed/grpc_serde.cc

@@ -32,13 +32,6 @@ namespace paddle {
 namespace operators {
 namespace distributed {
 
-static void SerializeDestroyCallback(void* payload) {
-  if (payload != nullptr) {
-    auto* shared_payload = reinterpret_cast<TensorPayload*>(payload);
-    delete shared_payload;
-  }
-}
-
 void SerializeToByteBuffer(const std::string& name, framework::Variable* var,
                            const platform::DeviceContext& ctx,
                            ::grpc::ByteBuffer* msg, const std::string& out_name,
@@ -122,8 +115,7 @@ void SerializeToByteBuffer(const std::string& name, framework::Variable* var,
   if (var->IsType<framework::SelectedRows>()) {
     auto* slr = var->GetMutable<framework::SelectedRows>();
     ProtoEncodeHelper e2(static_cast<char*>(buf), 128);
-    size_t rows_memory_size =
-        slr->rows().size() * framework::SizeOfType(typeid(int64_t));
+    size_t rows_memory_size = slr->rows().size() * sizeof(int64_t);
     e2.WriteVarlengthBeginning(VarMsg::kRowsFieldNumber, rows_memory_size);
     slices[2] = ::grpc::Slice(e2.size());
     memcpy(const_cast<uint8_t*>(slices[2].begin()), e2.data(), e2.size());

paddle/fluid/operators/distributed/rpc_server.h

@@ -75,6 +75,10 @@ class RPCServer {
   void RegisterRPC(const std::string& rpc_name, RequestHandler* handler,
                    int thread_num = 5);
 
+  int GetThreadNum(const std::string& rpc_name) {
+    return rpc_thread_num_[rpc_name];
+  }
+
   // Wait util all the clients have reached the barrier for one
   // rpc method. This function should be called in the
   // RequestHandler if you want to run the server/client in a

paddle/fluid/operators/distributed/sendrecvop_utils.cc

@@ -18,6 +18,7 @@ limitations under the License. */
 #include <thread>  // NOLINT
 
 #include "paddle/fluid/framework/data_type.h"
+#include "paddle/fluid/operators/distributed/brpc_rdma_pool.h"
 #include "paddle/fluid/operators/distributed/sendrecvop_utils.h"
 #include "paddle/fluid/operators/distributed/variable_response.h"
 #include "paddle/fluid/platform/port.h"
@@ -45,7 +46,6 @@ static TensorPayload GetCommunicationAllocationFromTensor(
     memory::Copy(cuda_pinned, result->ptr(),
                  boost::get<platform::CUDAPlace>(tensor.place()),
                  tensor.data<void>(), copy_size, gpu_dev_ctx.stream());
-
     ctx.Wait();
     return TensorPayload(result);
 #else
@@ -61,8 +61,7 @@ TensorPayload GetTensorPayload(framework::Variable* var,
   auto tensor = var->Get<framework::LoDTensor>();
   // FIXME(wuyi): data types in send_recv.proto is copied from
   // framework.proto
-  request->set_data_type(
-      static_cast<VarMsg::Type>(framework::ToDataType(tensor.type())));
+  request->set_data_type(static_cast<VarMsg::Type>(tensor.type()));
   for (auto& dim : framework::vectorize(tensor.dims())) {
     request->add_dims(dim);
   }
@@ -83,8 +82,7 @@ TensorPayload GetSelectedRowsPayload(framework::Variable* var,
                                      const platform::DeviceContext& ctx,
                                      VarMsg* request) {
   auto* slr = var->GetMutable<framework::SelectedRows>();
-  request->set_data_type(
-      static_cast<VarMsg::Type>(framework::ToDataType(slr->value().type())));
+  request->set_data_type(static_cast<VarMsg::Type>(slr->value().type()));
   request->set_lod_level(0);
   request->set_slr_height(slr->height());
 

paddle/fluid/operators/distributed/sendrecvop_utils.h

@@ -50,6 +50,13 @@ class TensorPayload final {
   size_t memory_size_;
 };
 
+inline void SerializeDestroyCallback(void* payload) {
+  if (payload != nullptr) {
+    auto* shared_payload = reinterpret_cast<TensorPayload*>(payload);
+    delete shared_payload;
+  }
+}
+
 TensorPayload GetTensorPayload(framework::Variable* var,
                                const platform::DeviceContext& ctx,
                                VarMsg* request);
@@ -58,18 +65,19 @@ TensorPayload GetSelectedRowsPayload(framework::Variable* var,
                                      const platform::DeviceContext& ctx,
                                      VarMsg* request);
 
-inline std::type_index ToTypeIndex(sendrecv::VariableMessage::Type type) {
+inline framework::proto::VarType::Type ToVarType(
+    sendrecv::VariableMessage::Type type) {
   switch (type) {
     case sendrecv::VariableMessage::FP32:
-      return typeid(float);  // NOLINT
+      return framework::proto::VarType::FP32;  // NOLINT
     case sendrecv::VariableMessage::FP64:
-      return typeid(double);  // NOLINT
+      return framework::proto::VarType::FP64;  // NOLINT
     case sendrecv::VariableMessage::INT32:
-      return typeid(int);  // NOLINT
+      return framework::proto::VarType::INT32;  // NOLINT
     case sendrecv::VariableMessage::INT64:
-      return typeid(int64_t);  // NOLINT
+      return framework::proto::VarType::INT64;  // NOLINT
     case sendrecv::VariableMessage::BOOL:
-      return typeid(bool);  // NOLINT
+      return framework::proto::VarType::BOOL;  // NOLINT
     default:
       PADDLE_THROW("Not support type %d", type);
   }

paddle/fluid/operators/distributed/variable_response.cc

@@ -114,7 +114,7 @@ bool VariableResponse::CopyLodTensorData(
   tensor->set_lod(lod);
 
   void* tensor_data =
-      tensor->mutable_data(ctx.GetPlace(), ToTypeIndex(meta_.data_type()));
+      tensor->mutable_data(ctx.GetPlace(), ToVarType(meta_.data_type()));
 
   VLOG(6) << "Tensor.memory_size = " << tensor->memory_size()
           << ", Buffer Size = " << length;
@@ -139,13 +139,13 @@ bool VariableResponse::CopySelectRowsTensorData(
   slr->set_height(meta_.slr_height());
   auto* tensor = slr->mutable_value();
   tensor->Resize(dims);
-  PADDLE_ENFORCE_EQ(static_cast<size_t>(tensor->numel()),
-                    length / framework::SizeOfType(
-                                 paddle::operators::distributed::ToTypeIndex(
-                                     meta_.data_type())));
+  PADDLE_ENFORCE_EQ(
+      static_cast<size_t>(tensor->numel()),
+      length / framework::SizeOfType(paddle::operators::distributed::ToVarType(
+                   meta_.data_type())));
   void* tensor_data = tensor->mutable_data(
       ctx.GetPlace(),
-      paddle::operators::distributed::ToTypeIndex(meta_.data_type()));
+      paddle::operators::distributed::ToVarType(meta_.data_type()));
 
   if (!ReadRaw(input, ctx, tensor->place(), tensor_data, length)) {
     return false;
@@ -159,8 +159,7 @@ bool VariableResponse::CopySelectRowsData(
     const platform::DeviceContext& ctx, int length) {
   auto* slr = GetVar()->GetMutable<framework::SelectedRows>();
   slr->mutable_rows()->clear();
-  slr->mutable_rows()->resize(length /
-                              framework::SizeOfType(typeid(int64_t)));  // int64
+  slr->mutable_rows()->resize(length / sizeof(int64_t));  // int64
   int64_t* rows_data = slr->mutable_rows()->data();
 
   // copy rows CPU data, GPU data will be copied lazily.

paddle/fluid/operators/distributed_ops/CMakeLists.txt

@@ -2,9 +2,9 @@ include(operators)
 
 set(DISTRIBUTE_DEPS "")
 if(WITH_GRPC)
-    set(DISTRIBUTE_DEPS sendrecvop_grpc grpc++_unsecure grpc_unsecure gpr cares zlib protobuf node)
+    set(DISTRIBUTE_DEPS sendrecvop_rpc grpc++_unsecure grpc_unsecure gpr cares zlib protobuf node)
 else()
-    set(DISTRIBUTE_DEPS sendrecvop_brpc brpc leveldb snappystream snappy protobuf ssl crypto zlib node)
+    set(DISTRIBUTE_DEPS sendrecvop_rpc brpc leveldb snappystream snappy protobuf ssl crypto zlib node)
     if(WITH_BRPC_RDMA)
         find_library(IBVERBS_LIBRARY NAMES ibverbs)
         ADD_LIBRARY(ibverbs SHARED IMPORTED GLOBAL)

paddle/fluid/operators/distributed_ops/listen_and_serv_op.cc

@@ -26,10 +26,11 @@ limitations under the License. */
 
 #include "paddle/fluid/operators/distributed/request_handler_impl.h"
 #include "paddle/fluid/operators/distributed_ops/listen_and_serv_op.h"
+#include "paddle/fluid/platform/profiler.h"
 
-DEFINE_int32(rpc_send_thread_num, 5, "number of threads for rpc send");
-DEFINE_int32(rpc_get_thread_num, 5, "number of threads for rpc get");
-DEFINE_int32(rpc_prefetch_thread_num, 5, "number of threads for rpc prefetch");
+DEFINE_int32(rpc_send_thread_num, 12, "number of threads for rpc send");
+DEFINE_int32(rpc_get_thread_num, 12, "number of threads for rpc get");
+DEFINE_int32(rpc_prefetch_thread_num, 12, "number of threads for rpc prefetch");
 
 namespace paddle {
 namespace operators {

paddle/fluid/operators/distributed_ops/merge_ids_op.cc

@@ -108,9 +108,7 @@ class MergeIdsOp : public framework::OperatorWithKernel {
   framework::OpKernelType GetExpectedKernelType(
       const framework::ExecutionContext &ctx) const override {
     return framework::OpKernelType(
-        framework::ToDataType(
-            ctx.MultiInput<framework::Tensor>("X").front()->type()),
-        ctx.GetPlace());
+        ctx.MultiInput<framework::Tensor>("X").front()->type(), ctx.GetPlace());
   }
 };
 

paddle/fluid/operators/distributed_ops/ref_by_trainer_id_op.cc

@@ -42,9 +42,7 @@ class RefByTrainerIdOp : public framework::OperatorWithKernel {
   framework::OpKernelType GetExpectedKernelType(
       const framework::ExecutionContext &ctx) const override {
     return framework::OpKernelType(
-        framework::ToDataType(
-            ctx.MultiInput<framework::Tensor>("X")[0]->type()),
-        ctx.GetPlace());
+        ctx.MultiInput<framework::Tensor>("X")[0]->type(), ctx.GetPlace());
   }
 };
 

paddle/fluid/operators/distributed_ops/send_op.cc

@@ -58,7 +58,9 @@ class SendOp : public framework::OperatorBase {
     }
     if (sync_send) {
       for (size_t i = 0; i < rets.size(); i++) {
+        VLOG(7) << "before sync_send " << ins[i] << "from " << epmap[i];
         PADDLE_ENFORCE(rets[i]->Wait(), "internal error in RPCClient");
+        VLOG(7) << "after sync_send " << ins[i] << "from " << epmap[i];
       }
     }
   }