merge release/1.1

CMakeLists.txt

@@ -69,6 +69,7 @@ option(WITH_ANAKIN      "Compile with Anakin library"                   OFF)
 option(WITH_GRPC     "Use grpc as the default rpc framework"            ${WITH_DISTRIBUTE})
 option(WITH_BRPC_RDMA     "Use brpc rdma as the rpc protocal"           OFF)
 option(WITH_INFERENCE    "Compile fluid inference library"              ON)
+option(ON_INFER         "Turn on inference optimization."               OFF)
 option(WITH_INFERENCE_API_TEST   "Test fluid inference high-level api interface"  OFF)
 option(WITH_SYSTEM_BLAS   "Use system blas library"           OFF)
 option(PY_VERSION       "Compile PaddlePaddle with python3 support"     ${PY_VERSION})
@@ -179,6 +180,7 @@ include(external/eigen)     # download eigen3
 include(external/pybind11)  # download pybind11
 include(external/cares)
 include(external/cub)
+include(external/xxhash)    # download xxhash
 
 if (NOT WIN32)
 # there is no official support of snappystream, warpctc, nccl, cupti in windows
@@ -301,3 +303,11 @@ if(WITH_DOC)
     find_python_module(recommonmark REQUIRED)
     add_subdirectory(doc)
 endif()
+
+if (ON_INFER)
+    message(STATUS "On inference mode, will take place some specific optimization.")
+    add_definitions(-DPADDLE_ON_INFERENCE)
+else()
+    #TODO(luotao), combine this warning with `make inference_lib_dist` command.
+    message(WARNING "On inference mode, will take place some specific optimization. Turn on the ON_INFER flag when building inference_lib only.")
+endif()

README.md

@@ -2,8 +2,8 @@
 
 
 [![Build Status](https://travis-ci.org/PaddlePaddle/Paddle.svg?branch=develop)](https://travis-ci.org/PaddlePaddle/Paddle)
-[![Documentation Status](https://img.shields.io/badge/docs-latest-brightgreen.svg?style=flat)](http://paddlepaddle.org/documentation/docs/en/1.0/getstarted/index_en.html)
-[![Documentation Status](https://img.shields.io/badge/中文文档-最新-brightgreen.svg)](http://paddlepaddle.org/documentation/docs/zh/1.0/beginners_guide/index.html)
+[![Documentation Status](https://img.shields.io/badge/docs-latest-brightgreen.svg?style=flat)](http://paddlepaddle.org/documentation/docs/en/1.1/getstarted/index_en.html)
+[![Documentation Status](https://img.shields.io/badge/中文文档-最新-brightgreen.svg)](http://paddlepaddle.org/documentation/docs/zh/1.1/beginners_guide/index.html)
 [![Release](https://img.shields.io/github/release/PaddlePaddle/Paddle.svg)](https://github.com/PaddlePaddle/Paddle/releases)
 [![License](https://img.shields.io/badge/license-Apache%202-blue.svg)](LICENSE)
 
@@ -19,7 +19,7 @@ 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.
 
 
-### Latest PaddlePaddle Release: [Fluid 1.0.1](https://github.com/PaddlePaddle/Paddle/tree/release/1.0.0)
+### Latest PaddlePaddle Release: [Fluid 1.1.0](https://github.com/PaddlePaddle/Paddle/tree/release/1.1)
 ### Install Latest Stable Release:
 ```
 # Linux CPU
@@ -27,9 +27,9 @@ pip install paddlepaddle
 # Linux GPU cuda9cudnn7
 pip install paddlepaddle-gpu
 # Linux GPU cuda8cudnn7
-pip install paddlepaddle-gpu==1.0.1.post87
+pip install paddlepaddle-gpu==1.1.0.post87
 # Linux GPU cuda8cudnn5
-pip install paddlepaddle-gpu==1.0.1.post85
+pip install paddlepaddle-gpu==1.1.0.post85
 
 # For installation on other platform, refer to http://paddlepaddle.org/
 ```
@@ -76,26 +76,26 @@ pip install paddlepaddle-gpu==1.0.1.post85
 
 ## Installation
 
-It is recommended to read [this doc](http://paddlepaddle.org/documentation/docs/zh/1.0/beginners_guide/index.html) on our website.
+It is recommended to read [this doc](http://paddlepaddle.org/documentation/docs/zh/1.1/beginners_guide/index.html) on our website.
 
 ## Documentation
 
-We provide [English](http://paddlepaddle.org/documentation/docs/en/1.0.0/getstarted/index_en.html) and
-[Chinese](http://paddlepaddle.org/documentation/docs/zh/1.0/beginners_guide/index.html) documentation.
+We provide [English](http://paddlepaddle.org/documentation/docs/en/1.1/getstarted/index_en.html) and
+[Chinese](http://paddlepaddle.org/documentation/docs/zh/1.1/beginners_guide/index.html) documentation.
 
 - [Deep Learning 101](https://github.com/PaddlePaddle/book)
 
   You might want to start from this online interactive book that can run in a Jupyter Notebook.
 
-- [Distributed Training](http://paddlepaddle.org/documentation/docs/zh/1.0/user_guides/howto/training/cluster_howto.html)
+- [Distributed Training](http://paddlepaddle.org/documentation/docs/zh/1.1/user_guides/howto/training/cluster_howto.html)
 
   You can run distributed training jobs on MPI clusters.
 
-- [Python API](http://paddlepaddle.org/documentation/api/zh/1.0/fluid.html)
+- [Python API](http://paddlepaddle.org/documentation/api/zh/1.1/fluid.html)
 
    Our new API enables much shorter programs.
 
-- [How to Contribute](http://paddlepaddle.org/documentation/docs/zh/1.0/advanced_usage/development/contribute_to_paddle.html)
+- [How to Contribute](http://paddlepaddle.org/documentation/docs/zh/1.1/advanced_usage/development/contribute_to_paddle.html)
 
    We appreciate your contributions!
 

benchmark/fluid/args.py

@@ -142,5 +142,10 @@ def parse_args():
         choices=['reduce', 'all_reduce'],
         default='all_reduce',
         help='Specify the reduce strategy, can be reduce, all_reduce')
+    parser.add_argument(
+        '--fuse_broadcast_op',
+        action='store_true',
+        help='If set, would fuse multiple broadcast operators into one fused_broadcast operator.'
+    )
     args = parser.parse_args()
     return args

benchmark/fluid/fluid_benchmark.py

@@ -177,6 +177,7 @@ def train_parallel(train_args, test_args, args, train_prog, test_prog,
     else:
         build_strategy.reduce_strategy = fluid.BuildStrategy(
         ).ReduceStrategy.AllReduce
+    build_strategy.fuse_broadcast_op = args.fuse_broadcast_op
 
     avg_loss = train_args[0]
 
@@ -240,7 +241,6 @@ def train_parallel(train_args, test_args, args, train_prog, test_prog,
 
             if args.use_fake_data or args.use_reader_op:
                 try:
-
                     fetch_ret = exe.run(fetch_list)
                 except fluid.core.EOFException as eof:
                     break

cmake/configure.cmake

@@ -50,11 +50,7 @@ if(NOT WITH_PROFILER)
 endif(NOT WITH_PROFILER)
 
 if(NOT CMAKE_CROSSCOMPILING)
-    if(WITH_AVX AND AVX512F_FOUND)
-        set(SIMD_FLAG ${AVX512F_FLAG})
-    elseif(WITH_AVX AND AVX2_FOUND)
-        set(SIMD_FLAG ${AVX2_FLAG})
-    elseif(WITH_AVX AND AVX_FOUND)
+    if(WITH_AVX AND AVX_FOUND)
         set(SIMD_FLAG ${AVX_FLAG})
     elseif(SSE3_FOUND)
         set(SIMD_FLAG ${SSE3_FLAG})

cmake/external/xxhash.cmake

+INCLUDE(ExternalProject)
+
+set(XXHASH_SOURCE_DIR ${THIRD_PARTY_PATH}/xxhash)
+set(XXHASH_INSTALL_DIR ${THIRD_PARTY_PATH}/install/xxhash)
+set(XXHASH_INCLUDE_DIR "${XXHASH_INSTALL_DIR}/include")
+
+IF(WITH_STATIC_LIB)
+  SET(BUILD_CMD make lib)
+ELSE()
+  IF(APPLE)
+    SET(BUILD_CMD sed -i \"\" "s/-Wstrict-prototypes -Wundef/-Wstrict-prototypes -Wundef -fPIC/g" ${XXHASH_SOURCE_DIR}/src/extern_xxhash/Makefile && make lib)
+  ELSE(APPLE)
+    SET(BUILD_CMD sed -i "s/-Wstrict-prototypes -Wundef/-Wstrict-prototypes -Wundef -fPIC/g" ${XXHASH_SOURCE_DIR}/src/extern_xxhash/Makefile && make lib)
+  ENDIF(APPLE)
+ENDIF()
+
+ExternalProject_Add(
+    extern_xxhash
+    ${EXTERNAL_PROJECT_LOG_ARGS}
+    GIT_REPOSITORY  "https://github.com/Cyan4973/xxHash"
+    GIT_TAG         "v0.6.5"
+    PREFIX          ${XXHASH_SOURCE_DIR}
+    DOWNLOAD_NAME   "xxhash"
+    UPDATE_COMMAND  ""
+    CONFIGURE_COMMAND ""
+    BUILD_IN_SOURCE 1
+    PATCH_COMMAND
+    BUILD_COMMAND     ${BUILD_CMD}
+    INSTALL_COMMAND   export PREFIX=${XXHASH_INSTALL_DIR}/ && make install
+    TEST_COMMAND      ""
+)
+
+set(XXHASH_LIBRARIES "${XXHASH_INSTALL_DIR}/lib/libxxhash.a")
+INCLUDE_DIRECTORIES(${XXHASH_INCLUDE_DIR})
+
+add_library(xxhash STATIC IMPORTED GLOBAL)
+set_property(TARGET xxhash PROPERTY IMPORTED_LOCATION ${XXHASH_LIBRARIES})
+include_directories(${XXHASH_INCLUDE_DIR})
+add_dependencies(xxhash extern_xxhash)
+
+LIST(APPEND external_project_dependencies xxhash)
+
+IF(WITH_C_API)
+  INSTALL(DIRECTORY ${XXHASH_INCLUDE_DIR} DESTINATION third_party/xxhash)
+  IF(ANDROID)
+    INSTALL(FILES ${XXHASH_LIBRARIES} DESTINATION third_party/xxhash/lib/${ANDROID_ABI})
+  ELSE()
+    INSTALL(FILES ${XXHASH_LIBRARIES} DESTINATION third_party/xxhash/lib)
+  ENDIF()
+ENDIF()

cmake/inference_lib.cmake

@@ -31,7 +31,7 @@ function(copy TARGET)
     foreach(index RANGE ${len})
         list(GET copy_lib_SRCS ${index} src)
         list(GET copy_lib_DSTS ${index} dst)
-        add_custom_command(TARGET ${TARGET} PRE_BUILD 
+        add_custom_command(TARGET ${TARGET} PRE_BUILD
           COMMAND mkdir -p "${dst}"
           COMMAND cp -r "${src}" "${dst}"
           COMMENT "copying ${src} -> ${dst}")
@@ -67,6 +67,13 @@ copy(boost_lib
   DEPS boost
 )
 
+set(dst_dir "${FLUID_INSTALL_DIR}/third_party/install/xxhash")
+copy(xxhash_lib
+  SRCS ${XXHASH_INCLUDE_DIR} ${XXHASH_LIBRARIES}
+  DSTS ${dst_dir} ${dst_dir}/lib
+  DEPS xxhash
+)
+
 if(NOT PROTOBUF_FOUND)
     set(dst_dir "${FLUID_INSTALL_DIR}/third_party/install/protobuf")
     copy(protobuf_lib
@@ -186,7 +193,7 @@ copy(cmake_cache
   DSTS ${FLUID_INSTALL_DIR})
 
 # This command generates a complete fluid library for both train and inference
-add_custom_target(fluid_lib_dist DEPENDS ${fluid_lib_dist_dep}) 
+add_custom_target(fluid_lib_dist DEPENDS ${fluid_lib_dist_dep})
 
 # Following commands generate a inference-only fluid library
 # third_party, version.txt and CMakeCache.txt are the same position with ${FLUID_INSTALL_DIR}

paddle/CMakeLists.txt

@@ -24,6 +24,7 @@ if(NOT WITH_FLUID_ONLY)
 endif()
 
 add_subdirectory(testing)
+set(PYTHON_TESTS_DIR ${PADDLE_BINARY_DIR}/python/paddle/fluid/tests CACHE INTERNAL "python tests directory")
 if(NOT MOBILE_INFERENCE AND NOT RPI AND NOT WITH_C_API)
   add_subdirectory(fluid)
 endif()

paddle/fluid/API.spec

@@ -176,6 +176,7 @@ paddle.fluid.layers.sigmoid_cross_entropy_with_logits ArgSpec(args=['x', 'label'
 paddle.fluid.layers.maxout ArgSpec(args=['x', 'groups', 'name'], varargs=None, keywords=None, defaults=(None,))
 paddle.fluid.layers.sequence_reverse ArgSpec(args=['x', 'name'], varargs=None, keywords=None, defaults=(None,))
 paddle.fluid.layers.affine_channel ArgSpec(args=['x', 'scale', 'bias', 'data_layout', 'name'], varargs=None, keywords=None, defaults=(None, None, 'NCHW', None))
+paddle.fluid.layers.hash ArgSpec(args=['input', 'hash_size', 'num_hash', 'name'], varargs=None, keywords=None, defaults=(1, None))
 paddle.fluid.layers.data ArgSpec(args=['name', 'shape', 'append_batch_size', 'dtype', 'lod_level', 'type', 'stop_gradient'], varargs=None, keywords=None, defaults=(True, 'float32', 0, VarType.LOD_TENSOR, True))
 paddle.fluid.layers.open_files ArgSpec(args=['filenames', 'shapes', 'lod_levels', 'dtypes', 'thread_num', 'buffer_size', 'pass_num', 'is_test'], varargs=None, keywords=None, defaults=(None, None, 1, None))
 paddle.fluid.layers.read_file ArgSpec(args=['reader'], varargs=None, keywords=None, defaults=None)
@@ -354,6 +355,8 @@ paddle.fluid.optimizer.ModelAverage.__init__ ArgSpec(args=['self', 'average_wind
 paddle.fluid.optimizer.ModelAverage.apply ArgSpec(args=[], varargs='args', keywords='kwds', defaults=None)
 paddle.fluid.optimizer.ModelAverage.minimize ArgSpec(args=['self', 'loss', 'startup_program', 'parameter_list', 'no_grad_set'], varargs=None, keywords=None, defaults=(None, None, None))
 paddle.fluid.optimizer.ModelAverage.restore ArgSpec(args=['self', 'executor'], varargs=None, keywords=None, defaults=None)
+paddle.fluid.optimizer.LarsMomentumOptimizer.__init__ ArgSpec(args=['self', 'learning_rate', 'momentum', 'lars_coeff', 'lars_weight_decay', 'regularization', 'name'], varargs=None, keywords=None, defaults=(0.001, 0.0005, None, None))
+paddle.fluid.optimizer.LarsMomentumOptimizer.minimize ArgSpec(args=['self', 'loss', 'startup_program', 'parameter_list', 'no_grad_set'], varargs=None, keywords=None, defaults=(None, None, None))
 paddle.fluid.backward.append_backward ArgSpec(args=['loss', 'parameter_list', 'no_grad_set', 'callbacks'], varargs=None, keywords=None, defaults=(None, None, None))
 paddle.fluid.regularizer.L1DecayRegularizer.__init__ ArgSpec(args=['self', 'regularization_coeff'], varargs=None, keywords=None, defaults=(0.0,))
 paddle.fluid.regularizer.L2DecayRegularizer.__init__ ArgSpec(args=['self', 'regularization_coeff'], varargs=None, keywords=None, defaults=(0.0,))

paddle/fluid/framework/attribute.cc

@@ -64,6 +64,13 @@ Attribute GetAttrValue(const proto::OpDesc::Attr& attr_desc) {
     case proto::AttrType::LONG: {
       return attr_desc.l();
     }
+    case proto::AttrType::LONGS: {
+      std::vector<int64_t> val(attr_desc.longs_size());
+      for (int i = 0; i < attr_desc.longs_size(); ++i) {
+        val[i] = attr_desc.longs(i);
+      }
+      return val;
+    }
     default:
       PADDLE_THROW("Unsupport attr type %d", attr_desc.type());
   }

paddle/fluid/framework/attribute.h

@@ -26,6 +26,113 @@ limitations under the License. */
 
 namespace paddle {
 namespace framework {
+
+template <typename T>
+struct ExtractAttribute {
+  explicit ExtractAttribute(const std::string& attr_name)
+      : attr_name_(attr_name) {}
+
+  T* operator()(Attribute& attr) const {
+    T* attr_value = nullptr;
+    try {
+      attr_value = &boost::get<T>(attr);
+    } catch (boost::bad_get& bad_get) {
+      PADDLE_THROW("Cannot get attribute %s by type %s, its type is %s",
+                   attr_name_, paddle::platform::demangle(typeid(T).name()),
+                   paddle::platform::demangle(attr.type().name()));
+    }
+    return attr_value;
+  }
+
+  const std::string& attr_name_;
+};
+
+// special handle bool
+// FIXME(yuyang18): Currently we cast bool into int in python binding. It is
+// hard to change the logic there. In another way, we should correct handle
+// if the user set `some_flag=1`.
+//
+// FIX ME anytime if there is a better solution.
+template <>
+struct ExtractAttribute<bool> {
+  explicit ExtractAttribute(const std::string& attr_name)
+      : attr_name_(attr_name) {}
+
+  bool* operator()(Attribute& attr) const {
+    if (attr.type() == typeid(int)) {  // NOLINT
+      int val = boost::get<int>(attr);
+      attr = static_cast<bool>(val);
+    } else if (attr.type() == typeid(float)) {  // NOLINT
+      float val = boost::get<float>(attr);
+      attr = static_cast<bool>(val);
+    }
+    bool* attr_value = nullptr;
+    try {
+      attr_value = &boost::get<bool>(attr);
+    } catch (boost::bad_get& bad_get) {
+      PADDLE_THROW("Cannot get attribute %s by type bool, its type is %s",
+                   attr_name_, paddle::platform::demangle(attr.type().name()));
+    }
+    return attr_value;
+  }
+
+  const std::string& attr_name_;
+};
+
+template <>
+struct ExtractAttribute<int64_t> {
+  explicit ExtractAttribute(const std::string& attr_name)
+      : attr_name_(attr_name) {}
+
+  int64_t* operator()(Attribute& attr) const {
+    if (attr.type() == typeid(int)) {  // NOLINT
+      int val = boost::get<int>(attr);
+      attr = static_cast<int64_t>(val);
+    } else if (attr.type() == typeid(float)) {  // NOLINT
+      int val = boost::get<float>(attr);
+      attr = static_cast<int64_t>(val);
+    }
+    int64_t* attr_value = nullptr;
+    try {
+      attr_value = &boost::get<int64_t>(attr);
+    } catch (boost::bad_get& bad_get) {
+      PADDLE_THROW("Cannot get attribute %s by type int64_t, its type is %s",
+                   attr_name_, paddle::platform::demangle(attr.type().name()));
+    }
+    return attr_value;
+  }
+
+  const std::string& attr_name_;
+};
+
+template <>
+struct ExtractAttribute<std::vector<int64_t>> {
+  explicit ExtractAttribute(const std::string& attr_name)
+      : attr_name_(attr_name) {}
+
+  std::vector<int64_t>* operator()(Attribute& attr) const {
+    if (attr.type() == typeid(std::vector<int>)) {  // NOLINT
+      std::vector<int> val = boost::get<std::vector<int>>(attr);
+      std::vector<int64_t> vec(val.begin(), val.end());
+      attr = vec;
+    } else if (attr.type() == typeid(std::vector<float>)) {  // NOLINT
+      std::vector<float> val = boost::get<std::vector<float>>(attr);
+      std::vector<int64_t> vec(val.begin(), val.end());
+      attr = vec;
+    }
+    std::vector<int64_t>* attr_value = nullptr;
+    try {
+      attr_value = &boost::get<std::vector<int64_t>>(attr);
+    } catch (boost::bad_get& bad_get) {
+      PADDLE_THROW("Cannot get attribute %s by type int64_t, its type is %s",
+                   attr_name_, paddle::platform::demangle(attr.type().name()));
+    }
+    return attr_value;
+  }
+
+  const std::string& attr_name_;
+};
+
 template <typename T>
 inline proto::AttrType AttrTypeID() {
   Attribute tmp = T();
@@ -42,7 +149,11 @@ class AttrReader {
   inline const T& Get(const std::string& name) const {
     PADDLE_ENFORCE(attrs_.count(name) != 0, "%s should be in AttributeMap",
                    name);
-    return boost::get<T>(attrs_.at(name));
+
+    Attribute& attr = const_cast<Attribute&>(attrs_.at(name));
+    ExtractAttribute<T> extract_attr(name);
+    T* attr_value = extract_attr(attr);
+    return *attr_value;
   }
 
  private:
@@ -82,7 +193,7 @@ class DefaultValueSetter {
  public:
   explicit DefaultValueSetter(T default_value)
       : default_value_(default_value) {}
-  void operator()(T& value) const { value = default_value_; }
+  void operator()(T& value) const { value = default_value_; }  // NOLINT
 
  private:
   T default_value_;
@@ -117,84 +228,6 @@ class EnumInContainer {
   std::unordered_set<T> container_;
 };
 
-template <typename T>
-struct ExtractAttribute {
-  explicit ExtractAttribute(const std::string& attr_name)
-      : attr_name_(attr_name) {}
-
-  T* operator()(Attribute& attr) const {
-    T* attr_value = nullptr;
-    try {
-      attr_value = &boost::get<T>(attr);
-    } catch (boost::bad_get& bad_get) {
-      PADDLE_THROW("Cannot get attribute %s by type %s, its type is %s",
-                   attr_name_, paddle::platform::demangle(typeid(T).name()),
-                   paddle::platform::demangle(attr.type().name()));
-    }
-    return attr_value;
-  }
-
-  const std::string& attr_name_;
-};
-
-// special handle bool
-// FIXME(yuyang18): Currently we cast bool into int in python binding. It is
-// hard to change the logic there. In another way, we should correct handle
-// if the user set `some_flag=1`.
-//
-// FIX ME anytime if there is a better solution.
-template <>
-struct ExtractAttribute<bool> {
-  explicit ExtractAttribute(const std::string& attr_name)
-      : attr_name_(attr_name) {}
-
-  bool* operator()(Attribute& attr) const {
-    if (attr.type() == typeid(int)) {  // NOLINT
-      int val = boost::get<int>(attr);
-      attr = static_cast<bool>(val);
-    } else if (attr.type() == typeid(float)) {  // NOLINT
-      float val = boost::get<float>(attr);
-      attr = static_cast<bool>(val);
-    }
-    bool* attr_value = nullptr;
-    try {
-      attr_value = &boost::get<bool>(attr);
-    } catch (boost::bad_get& bad_get) {
-      PADDLE_THROW("Cannot get attribute %s by type bool, its type is %s",
-                   attr_name_, paddle::platform::demangle(attr.type().name()));
-    }
-    return attr_value;
-  }
-
-  const std::string& attr_name_;
-};
-
-template <>
-struct ExtractAttribute<int64_t> {
-  explicit ExtractAttribute(const std::string& attr_name)
-      : attr_name_(attr_name) {}
-
-  int64_t* operator()(Attribute& attr) const {
-    if (attr.type() == typeid(int)) {  // NOLINT
-      int val = boost::get<int>(attr);
-      attr = static_cast<int64_t>(val);
-    } else if (attr.type() == typeid(float)) {  // NOLINT
-      int val = boost::get<float>(attr);
-      attr = static_cast<int64_t>(val);
-    }
-    int64_t* attr_value = nullptr;
-    try {
-      attr_value = &boost::get<int64_t>(attr);
-    } catch (boost::bad_get& bad_get) {
-      PADDLE_THROW("Cannot get attribute %s by type int64_t, its type is %s",
-                   attr_name_, paddle::platform::demangle(attr.type().name()));
-    }
-    return attr_value;
-  }
-
-  const std::string& attr_name_;
-};
-
 // check whether a certain attribute fit its limits
 // an attribute can have more than one limits
 template <typename T>
@@ -235,7 +268,7 @@ class TypedAttrChecker {
     return *this;
   }
 
-  void operator()(AttributeMap& attr_map) const {
+  void operator()(AttributeMap& attr_map) const {  // NOLINT
     if (!attr_map.count(attr_name_)) {
       // user do not set this attr
       PADDLE_ENFORCE(!default_value_setter_.empty(),
@@ -271,7 +304,7 @@ class OpAttrChecker {
     return *(checker.target<TypedAttrChecker<T>>());
   }
 
-  void Check(AttributeMap& attr_map) const {
+  void Check(AttributeMap& attr_map) const {  // NOLINT
     for (const auto& checker : attr_checkers_) {
       checker(attr_map);
     }

paddle/fluid/framework/details/CMakeLists.txt

@@ -16,12 +16,14 @@ if(WITH_GPU)
             dynload_cuda variable_visitor)
     nv_library(reduce_op_handle SRCS reduce_op_handle.cc DEPS op_handle_base variable_visitor scope ddim dynload_cuda)
     nv_library(broadcast_op_handle SRCS broadcast_op_handle.cc DEPS op_handle_base scope ddim memory variable_visitor dynload_cuda)
+    nv_library(fused_broadcast_op_handle SRCS fused_broadcast_op_handle.cc DEPS broadcast_op_handle)
 
 else()
     cc_library(all_reduce_op_handle SRCS all_reduce_op_handle.cc DEPS op_handle_base scope lod_tensor ddim memory
              variable_visitor)
     cc_library(reduce_op_handle SRCS reduce_op_handle.cc DEPS op_handle_base variable_visitor scope ddim)
     cc_library(broadcast_op_handle SRCS broadcast_op_handle.cc DEPS op_handle_base scope ddim memory variable_visitor)
+    cc_library(fused_broadcast_op_handle SRCS fused_broadcast_op_handle.cc DEPS broadcast_op_handle)
 endif()
 
 cc_library(data_balance_op_handle SRCS data_balance_op_handle.cc DEPS op_handle_base scope lod_tensor)
@@ -34,7 +36,7 @@ if(WITH_GPU)
 endif()
 
 cc_library(multi_devices_graph_pass SRCS multi_devices_graph_pass.cc DEPS multi_devices_helper computation_op_handle
-        scale_loss_grad_op_handle rpc_op_handle all_reduce_op_handle reduce_op_handle broadcast_op_handle data_balance_op_handle)
+        scale_loss_grad_op_handle rpc_op_handle all_reduce_op_handle reduce_op_handle broadcast_op_handle data_balance_op_handle fused_broadcast_op_handle)
 
 if(WITH_GPU)
   cc_library(ssa_graph_executor SRCS ssa_graph_executor.cc DEPS graph framework_proto reference_count_pass)
@@ -58,4 +60,4 @@ cc_library(fast_threaded_ssa_graph_executor SRCS fast_threaded_ssa_graph_executo
 cc_library(build_strategy SRCS build_strategy.cc DEPS
         graph_viz_pass multi_devices_graph_pass
         multi_devices_graph_print_pass multi_devices_graph_check_pass
-        fuse_elewise_add_act_pass)
+        fuse_elewise_add_act_pass multi_batch_merge_pass)

paddle/fluid/framework/details/broadcast_op_handle.cc

@@ -48,16 +48,27 @@ void BroadcastOpHandle::RunImpl() {
     var_scopes.emplace_back(s->FindVar(kLocalExecScopeName)->Get<Scope *>());
   }
 
+  BroadcastOneVar(*in_var_handle, out_var_handles, var_scopes);
+}
+
+void BroadcastOpHandle::BroadcastOneVar(
+    const VarHandle &in_var_handle,
+    const std::vector<VarHandle *> &out_var_handles,
+    const std::vector<const Scope *> &var_scopes) {
   auto *in_var =
-      var_scopes.at(in_var_handle->scope_idx_)->FindVar(in_var_handle->name_);
+      var_scopes.at(in_var_handle.scope_idx_)->FindVar(in_var_handle.name_);
   PADDLE_ENFORCE_NOT_NULL(in_var);
   Tensor &in_tensor = VariableVisitor::GetMutableTensor(in_var);
+  if (UNLIKELY(!in_tensor.IsInitialized())) {
+    VLOG(3) << "in var " << in_var_handle.name_ << "not inited, return!";
+    return;
+  }
 
-  InitOutputValue(*in_var_handle, out_var_handles);
+  InitOutputValue(in_var_handle, out_var_handles);
 
   if (platform::is_cpu_place(in_tensor.place())) {
     for (auto *out_var_handle : out_var_handles) {
-      if (out_var_handle->IsTheSameVar(*in_var_handle)) {
+      if (out_var_handle->IsTheSameVar(in_var_handle)) {
         continue;
       }
       auto &out_p = out_var_handle->place_;
@@ -114,12 +125,12 @@ void BroadcastOpHandle::RunImpl() {
         }
       }
 
-      if (!out_handle->IsTheSameVar(*in_var_handle)) {
-        auto out_var = var_scopes.at(in_var_handle->scope_idx_)
+      if (!out_handle->IsTheSameVar(in_var_handle)) {
+        auto out_var = var_scopes.at(in_var_handle.scope_idx_)
                            ->FindVar(out_var_handles[0]->name_);
         paddle::framework::TensorCopy(
-            in_tensor, in_var_handle->place_,
-            *(dev_ctxes_.at(in_var_handle->place_)),
+            in_tensor, in_var_handle.place_,
+            *(dev_ctxes_.at(in_var_handle.place_)),
             &VariableVisitor::GetMutableTensor(out_var));
       }
     });

paddle/fluid/framework/details/broadcast_op_handle.h

@@ -61,7 +61,10 @@ struct BroadcastOpHandle : public OpHandleBase {
  protected:
   void RunImpl() override;
 
- private:
+  void BroadcastOneVar(const VarHandle &in_var_handle,
+                       const std::vector<VarHandle *> &out_var_handles,
+                       const std::vector<const Scope *> &var_scopes);
+
   std::vector<Scope *> local_scopes_;
   std::vector<platform::Place> places_;
 #ifdef PADDLE_WITH_CUDA

paddle/fluid/framework/details/build_strategy.cc

@@ -121,6 +121,7 @@ std::unique_ptr<ir::Graph> BuildStrategy::Apply(
 
 USE_PASS(fuse_elewise_add_act_pass);
 USE_PASS(graph_viz_pass);
+USE_PASS(multi_batch_merge_pass);
 USE_PASS(multi_devices_pass);
 USE_PASS(multi_devices_check_pass);
 USE_PASS(multi_devices_print_pass);

paddle/fluid/framework/details/build_strategy.h

@@ -69,6 +69,8 @@ struct BuildStrategy {
 
   bool enable_data_balance_{false};
 
+  bool fuse_broadcast_op_{false};
+
   // User normally doesn't need to call this API.
   // The PassBuilder allows for more customized insert, remove of passes
   // from python side.

paddle/fluid/framework/details/fused_broadcast_op_handle.cc

+//   Copyright (c) 2018 PaddlePaddle Authors. All Rights Reserved.
+//
+// Licensed under the Apache License, Version 2.0 (the "License");
+// you may not use this file except in compliance with the License.
+// You may obtain a copy of the License at
+//
+//     http://www.apache.org/licenses/LICENSE-2.0
+//
+// Unless required by applicable law or agreed to in writing, software
+// distributed under the License is distributed on an "AS IS" BASIS,
+// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+// See the License for the specific language governing permissions and
+// limitations under the License.
+
+#include "paddle/fluid/framework/details/fused_broadcast_op_handle.h"
+#include "paddle/fluid/framework/details/container_cast.h"
+#include "paddle/fluid/framework/details/variable_visitor.h"
+#include "paddle/fluid/platform/profiler.h"
+
+namespace paddle {
+namespace framework {
+namespace details {
+
+void FusedBroadcastOpHandle::RunImpl() {
+  platform::RecordEvent record_event(Name(), dev_ctxes_.begin()->second);
+
+  if (places_.size() == 1UL) return;
+
+  auto in_var_handles = DynamicCast<VarHandle>(inputs_);
+  auto out_var_handles = DynamicCast<VarHandle>(outputs_);
+
+  WaitInputVarGenerated();
+
+  std::vector<const Scope *> var_scopes;
+  for (auto *s : local_scopes_) {
+    var_scopes.emplace_back(s->FindVar(kLocalExecScopeName)->Get<Scope *>());
+  }
+
+  size_t place_num = places_.size();
+  PADDLE_ENFORCE_EQ(in_var_handles.size() * place_num, out_var_handles.size());
+
+  for (size_t i = 0; i < in_var_handles.size(); ++i) {
+    BroadcastOneVar(
+        *in_var_handles[i],
+        std::vector<VarHandle *>(out_var_handles.begin() + i * place_num,
+                                 out_var_handles.begin() + (i + 1) * place_num),
+        var_scopes);
+  }
+}
+
+std::string FusedBroadcastOpHandle::Name() const { return "fused_broadcast"; }
+
+}  // namespace details
+}  // namespace framework
+}  // namespace paddle

paddle/fluid/framework/details/fused_broadcast_op_handle.h

+//   Copyright (c) 2018 PaddlePaddle Authors. All Rights Reserved.
+//
+// Licensed under the Apache License, Version 2.0 (the "License");
+// you may not use this file except in compliance with the License.
+// You may obtain a copy of the License at
+//
+//     http://www.apache.org/licenses/LICENSE-2.0
+//
+// Unless required by applicable law or agreed to in writing, software
+// distributed under the License is distributed on an "AS IS" BASIS,
+// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+// See the License for the specific language governing permissions and
+// limitations under the License.
+
+#pragma once
+
+#include <map>
+#include <string>
+#include <vector>
+
+#include "paddle/fluid/framework/details/broadcast_op_handle.h"
+#include "paddle/fluid/framework/details/multi_devices_helper.h"
+#include "paddle/fluid/framework/lod_tensor.h"
+#include "paddle/fluid/framework/scope.h"
+#include "paddle/fluid/framework/selected_rows.h"
+#include "paddle/fluid/platform/device_context.h"
+
+#ifdef PADDLE_WITH_CUDA
+#include "paddle/fluid/platform/nccl_helper.h"
+#endif
+
+namespace paddle {
+namespace framework {
+namespace details {
+
+struct FusedBroadcastOpHandle : public BroadcastOpHandle {
+ public:
+#ifdef PADDLE_WITH_CUDA
+  FusedBroadcastOpHandle(ir::Node *node,
+                         const std::vector<Scope *> local_scopes,
+                         const std::vector<platform::Place> &places,
+                         const platform::NCCLContextMap *nccl_ctx)
+      : BroadcastOpHandle(node, local_scopes, places, nccl_ctx) {}
+#else
+  FusedBroadcastOpHandle(ir::Node* node, const std::vector<Scope*> local_scopes,
+                         const std::vector<platform::Place>& places)
+      : BroadcastOpHandle(node, local_scopes, places) {}
+#endif
+  std::string Name() const override;
+
+ protected:
+  void RunImpl() override;
+};
+
+}  // namespace details
+}  // namespace framework
+}  // namespace paddle

paddle/fluid/framework/details/multi_devices_graph_pass.cc

@@ -21,6 +21,7 @@
 #include "paddle/fluid/framework/details/broadcast_op_handle.h"
 #include "paddle/fluid/framework/details/computation_op_handle.h"
 #include "paddle/fluid/framework/details/data_balance_op_handle.h"
+#include "paddle/fluid/framework/details/fused_broadcast_op_handle.h"
 #include "paddle/fluid/framework/details/multi_devices_graph_pass.h"
 #include "paddle/fluid/framework/details/reduce_op_handle.h"
 #include "paddle/fluid/framework/details/rpc_op_handle.h"
@@ -347,7 +348,7 @@ std::unique_ptr<ir::Graph> MultiDevSSAGraphBuilder::ApplyImpl(
           BuildStrategy::GradientScaleStrategy::kCustomized) {
         // TODO(paddle-dev): Why is there no input for this op_handle?
         auto loss_grad_name = node->Op()->OutputArgumentNames()[0];
-        CreateScaleLossGradOp(&result, loss_grad_name);
+        CreateScaleLossGradOp(&result, loss_grad_name, node->outputs[0]);
       }
       // This assumes the backward generating code will ensure IsScaleLossOp
       // is true only for the op that scale the final scalar loss.
@@ -436,10 +437,14 @@ std::unique_ptr<ir::Graph> MultiDevSSAGraphBuilder::ApplyImpl(
   if ((use_gpu &&
        strategy_.reduce_ == BuildStrategy::ReduceStrategy::kReduce) ||
       is_dist_train) {
-    for (size_t dev_id = 0; dev_id < bcast_var_name_set.size(); ++dev_id) {
-      auto &to_bcast_set = bcast_var_name_set[dev_id];
-      for (auto &bcast_name : to_bcast_set) {
-        CreateBroadcastOp(&result, bcast_name, dev_id);
+    if (strategy_.fuse_broadcast_op_) {
+      CreateFusedBroadcastOp(&result, bcast_var_name_set);
+    } else {
+      for (size_t dev_id = 0; dev_id < bcast_var_name_set.size(); ++dev_id) {
+        auto &to_bcast_set = bcast_var_name_set[dev_id];
+        for (auto &bcast_name : to_bcast_set) {
+          CreateBroadcastOp(&result, bcast_name, dev_id);
+        }
       }
     }
   }
@@ -508,6 +513,44 @@ void MultiDevSSAGraphBuilder::CreateBroadcastOp(ir::Graph *result,
   }
 }
 
+void MultiDevSSAGraphBuilder::CreateFusedBroadcastOp(
+    ir::Graph *result,
+    const std::vector<std::unordered_set<std::string>> &bcast_varnames) const {
+#ifdef PADDLE_WITH_CUDA
+  auto *op_handle = new FusedBroadcastOpHandle(
+      result->CreateEmptyNode("fused_broadcast", ir::Node::Type::kOperation),
+      local_scopes_, places_, nccl_ctxs_);
+#else
+  auto *op_handle = new FusedBroadcastOpHandle(
+      result->CreateEmptyNode("fused_broadcast", ir::Node::Type::kOperation),
+      local_scopes_, places_);
+#endif
+  result->Get<GraphOps>(kGraphOps).emplace_back(op_handle);
+
+  for (size_t i = 0; i < places_.size(); ++i) {
+    auto &p = places_[i];
+    SetCommunicationContext(op_handle, p);
+  }
+
+  for (size_t dev_id = 0; dev_id < bcast_varnames.size(); ++dev_id) {
+    for (auto &p_name : bcast_varnames[dev_id]) {
+      auto *in =
+          result->Get<GraphVars>(kGraphVars).at(dev_id).at(p_name).back().get();
+      op_handle->AddInput(in);
+      for (size_t out_dev_id = 0; out_dev_id < places_.size(); ++out_dev_id) {
+        auto &p = places_[out_dev_id];
+        auto &vars =
+            result->Get<GraphVars>(kGraphVars).at(out_dev_id).at(p_name);
+        auto *out_var = new VarHandle(
+            result->CreateEmptyNode(p_name, ir::Node::Type::kVariable),
+            vars.size(), out_dev_id, p_name, p);
+        vars.emplace_back(out_var);
+        op_handle->AddOutput(out_var);
+      }
+    }
+  }
+}
+
 void MultiDevSSAGraphBuilder::CreateComputationalOp(ir::Graph *result,
                                                     ir::Node *node,
                                                     int dev_id) const {
@@ -602,7 +645,8 @@ int MultiDevSSAGraphBuilder::GetVarDeviceID(const ir::Graph &graph,
 }
 
 void MultiDevSSAGraphBuilder::CreateScaleLossGradOp(
-    ir::Graph *result, const std::string &loss_grad_name) const {
+    ir::Graph *result, const std::string &loss_grad_name,
+    ir::Node *out_var_node) const {
   for (size_t i = 0; i < places_.size(); ++i) {
     // Insert ScaleCost OpHandle
     auto *dev_ctx = platform::DeviceContextPool::Instance().Get(places_[i]);
@@ -617,10 +661,8 @@ void MultiDevSSAGraphBuilder::CreateScaleLossGradOp(
     // loss->pending_ops_.emplace_back(op_handle);
     // op_handle->inputs_.emplace_back(loss);
 
-    CreateOpOutput(
-        result, op_handle,
-        result->CreateEmptyNode(loss_grad_name, ir::Node::Type::kVariable),
-        places_[i], i);
+    CreateOpOutput(result, op_handle,
+                   result->CreateVarNode(out_var_node->Var()), places_[i], i);
   }
 }
 
@@ -680,7 +722,8 @@ int MultiDevSSAGraphBuilder::CreateDistTrainOp(ir::Graph *result,
   }
 
   if (node->Op()->Type() == "split_byref" ||
-      node->Op()->Type() == "split_selected_rows") {
+      node->Op()->Type() == "split_selected_rows" ||
+      node->Op()->Type() == "split_ids") {
     // TODO(paddle-dev): getting the first var is not safe.
     op_dev_id = GetVarDeviceID(*result, input_var_names[0]);
     if (strategy_.reduce_ == BuildStrategy::ReduceStrategy::kAllReduce) {

paddle/fluid/framework/details/multi_devices_graph_pass.h

@@ -61,7 +61,8 @@ class MultiDevSSAGraphBuilder : public ir::Pass {
                               size_t num_places) const;
 
   void CreateScaleLossGradOp(ir::Graph *result,
-                             const std::string &loss_grad_name) const;
+                             const std::string &loss_grad_name,
+                             ir::Node *out_var_node) const;
 
   VarHandle *CreateReduceOp(ir::Graph *result, const std::string &og,
                             int dst_dev_id) const;
@@ -78,6 +79,10 @@ class MultiDevSSAGraphBuilder : public ir::Pass {
   void CreateBroadcastOp(ir::Graph *result, const std::string &p_name,
                          size_t src_dev_id) const;
 
+  void CreateFusedBroadcastOp(
+      ir::Graph *result,
+      const std::vector<std::unordered_set<std::string>> &bcast_varnames) const;
+
   bool IsSparseGradient(const std::string &og) const;
 
   size_t GetAppropriateDeviceID(

paddle/fluid/framework/framework.proto

@@ -35,6 +35,7 @@ enum AttrType {
   BLOCK = 8;
   LONG = 9;
   BLOCKS = 10;
+  LONGS = 11;
 }
 
 // OpDesc describes an instance of a C++ framework::OperatorBase
@@ -55,6 +56,7 @@ message OpDesc {
     optional int32 block_idx = 12;
     optional int64 l = 13;
     repeated int32 blocks_idx = 14;
+    repeated int64 longs = 15;
   };
 
   message Var {

paddle/fluid/framework/ir/CMakeLists.txt

@@ -36,6 +36,7 @@ pass_library(fc_lstm_fuse_pass inference)
 pass_library(embedding_fc_lstm_fuse_pass inference)
 pass_library(fc_gru_fuse_pass inference)
 pass_library(seq_concat_fc_fuse_pass inference)
+pass_library(multi_batch_merge_pass base)
 pass_library(conv_bn_fuse_pass inference)
 pass_library(seqconv_eltadd_relu_fuse_pass inference)
 if(WITH_MKLDNN)

paddle/fluid/framework/ir/graph.cc

@@ -89,14 +89,20 @@ bool IsDistTrainOp(ir::Node *node, const std::vector<std::string> &send_vars,
 Graph::Graph(const ProgramDesc &program) : program_(program) {
   // Make the nodes id start from 0.
   Node::ResetId();
+  auto var_nodes = InitFromProgram(program_);
+  ResolveHazard(var_nodes);
+}
 
+std::map<std::string, std::vector<ir::Node *>> Graph::InitFromProgram(
+    const ProgramDesc &program) {
   VLOG(3) << "block in program:" << program_.Size();
   std::unordered_map<std::string, VarDesc *> all_vars;
+  // var nodes for each var name, will have multiple versions in SSA
+  std::map<std::string, std::vector<ir::Node *>> var_nodes;
   for (auto *var : program.Block(0).AllVars()) {
     all_vars.emplace(var->Name(), var);
   }
 
-  std::map<std::string, std::vector<ir::Node *>> var_nodes;
   for (auto *op : program.Block(0).AllOps()) {
     ir::Node *node = CreateOpNode(op);
     // For input args, reuse the same var name if it was created before.
@@ -134,7 +140,11 @@ Graph::Graph(const ProgramDesc &program) : program_(program) {
       var->inputs.push_back(node);
     }
   }
+  return std::move(var_nodes);
+}
 
+void Graph::ResolveHazard(
+    const std::map<std::string, std::vector<ir::Node *>> &var_nodes) {
   /**
    * We should handle write after read(WAR) and write after write(WAW) here.
    * Because some of the operators of the program can be executed parallelly.
@@ -153,6 +163,7 @@ Graph::Graph(const ProgramDesc &program) : program_(program) {
     auto it_old = versions.rbegin();
     ++it_old;
     for (; it_old != versions.rend(); it_new = it_old, ++it_old) {
+      VLOG(3) << "deal with var: " << (*it_new)->Name();
       ir::Node *write_op =
           (*it_new)->inputs.empty() ? nullptr : (*it_new)->inputs[0];
       const auto &read_ops = (*it_old)->outputs;

paddle/fluid/framework/ir/graph.h

@@ -160,6 +160,12 @@ class Graph {
     return nullptr;
   }
 
+  std::map<std::string, std::vector<ir::Node *>> InitFromProgram(
+      const ProgramDesc &program);
+
+  void ResolveHazard(
+      const std::map<std::string, std::vector<ir::Node *>> &var_nodes);
+
  private:
   // This method takes ownership of `node`.
   ir::Node *AddNode(ir::Node *node) {

paddle/fluid/framework/ir/graph_helper.cc

@@ -120,19 +120,25 @@ size_t GraphNum(const Graph &graph) {
   std::deque<ir::Node *> q_nodes;
   std::vector<std::unordered_set<ir::Node *>> graph_nodes;
   std::unordered_set<ir::Node *> g_nodes;
+  // q_set used to record records in the queue.
+  std::unordered_set<ir::Node *> q_set;
   size_t graph_count = 0;
 
-  auto traverse_nodes = [&visited_nodes,
-                         &q_nodes](const std::vector<ir::Node *> &nodes) {
-    std::copy_if(
-        nodes.begin(), nodes.end(), std::back_inserter(q_nodes),
-        [&visited_nodes](Node *node) { return !visited_nodes.count(node); });
+  auto traverse_nodes = [&visited_nodes, &q_nodes,
+                         &q_set](const std::vector<ir::Node *> &nodes) {
+    for (auto n : nodes) {
+      if (visited_nodes.count(n) == 0 && q_set.count(n) == 0) {
+        q_nodes.push_back(n);
+        q_set.insert(n);
+      }
+    }
   };
 
   while (visited_nodes.size() != nodes.size()) {
     if (!q_nodes.empty()) {
       auto cur_node = q_nodes.front();
       q_nodes.pop_front();
+      q_set.erase(cur_node);
       visited_nodes.insert(cur_node);
       g_nodes.insert(cur_node);
       traverse_nodes(cur_node->inputs);
@@ -146,6 +152,7 @@ size_t GraphNum(const Graph &graph) {
       for (auto &n : nodes) {
         if (visited_nodes.count(n) == 0) {
           q_nodes.push_back(n);
+          q_set.insert(n);
           break;
         }
       }

paddle/fluid/framework/ir/multi_batch_merge_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/multi_batch_merge_pass.h"
+
+#include <map>
+#include <string>
+#include <vector>
+
+#include "paddle/fluid/framework/ir/graph_helper.h"
+#include "paddle/fluid/framework/op_proto_maker.h"
+
+namespace paddle {
+namespace framework {
+namespace ir {
+
+static const char kNumRepeats[] = "num_repeats";
+typedef std::unordered_map<std::string, std::vector<ir::Node*>> SSAVarList;
+
+ir::Node* SameNameVar(std::unordered_set<ir::Node*> all, ir::Node* target) {
+  for (auto n : all) {
+    if (target->IsVar() && target->Name() == n->Name()) {
+      return n;
+    }
+  }
+  return nullptr;
+}
+
+VarDesc CopyVarDesc(VarDesc* var_desc) {
+  VarDesc repeated_var(var_desc->Name());
+  // copy other variable attributes
+  if (var_desc->GetType() != proto::VarType::READER) {
+    repeated_var.SetType(var_desc->GetType());
+    repeated_var.SetShape(var_desc->GetShape());
+    repeated_var.SetDataType(var_desc->GetDataType());
+    repeated_var.SetLoDLevel(var_desc->GetLoDLevel());
+    repeated_var.SetPersistable(var_desc->Persistable());
+  } else {
+    // TODO(typhoonzero): copy reader var
+  }
+  return repeated_var;
+}
+
+VarDesc UpdateGradVarDesc(
+    VarDesc* var_desc, int repeat,
+    const std::unordered_set<std::string>& grad_names,
+    const std::unordered_set<std::string>& bn_vars_need_rename) {
+  if (grad_names.find(var_desc->Name()) != grad_names.end() ||
+      bn_vars_need_rename.find(var_desc->Name()) != bn_vars_need_rename.end()) {
+    std::string new_gname =
+        string::Sprintf("%s.repeat.%d", var_desc->Name(), repeat);
+    VarDesc repeated_var = CopyVarDesc(var_desc);
+    repeated_var.SetName(new_gname);
+    VLOG(3) << "update " << var_desc->Name() << " to repeat " << repeat;
+    return repeated_var;
+  }
+  return *var_desc;
+}
+
+std::unique_ptr<Graph> BatchMergePass::ApplyImpl(
+    std::unique_ptr<Graph> graph) const {
+  int num_repeats = Get<const int>(kNumRepeats);
+  std::vector<Node*> forward_backward_ops;
+  std::vector<Node*> optimize_ops;
+  std::vector<Node*> lr_ops;  // ops other than forward/backward/optimize
+  std::unordered_set<std::string> grad_names;
+
+  std::vector<ir::Node*> nodes = TopologySortOperations(*graph);
+  auto origin_nodes = graph->ReleaseNodes();
+  VLOG(3) << "origin nodes count: " << origin_nodes.size();
+  ir::Graph& result = *graph;
+
+  // 1. record op nodes of different roles
+  for (auto node : nodes) {
+    if (node->IsVar()) continue;
+    int op_role = boost::get<int>(node->Op()->GetAttr(
+        framework::OpProtoAndCheckerMaker::OpRoleAttrName()));
+    if ((op_role == static_cast<int>(framework::OpRole::kForward)) ||
+        (op_role & static_cast<int>(framework::OpRole::kBackward)) ||
+        (op_role & static_cast<int>(framework::OpRole::kLoss))) {
+      forward_backward_ops.push_back(node);
+    } else if ((op_role & static_cast<int>(framework::OpRole::kOptimize)) ||
+               (op_role & static_cast<int>(framework::OpRole::kDist)) ||
+               (op_role & static_cast<int>(framework::OpRole::kRPC))) {
+      optimize_ops.push_back(node);
+      auto op_role_var = node->Op()->GetNullableAttr(
+          OpProtoAndCheckerMaker::OpRoleVarAttrName());
+      auto op_role_vars = boost::get<std::vector<std::string>>(op_role_var);
+      for (size_t i = 0; i < op_role_vars.size(); i += 2) {
+        grad_names.insert(op_role_vars[i + 1]);
+      }
+    } else if (op_role & static_cast<int>(framework::OpRole::kLRSched)) {
+      lr_ops.push_back(node);
+    } else {  // NOLINT
+      PADDLE_THROW("Invalid op_role: %d", static_cast<int>(op_role));
+    }
+  }
+
+  // 2. copy forward backward
+  ir::Node* prev_repeat_last_op_node = nullptr;
+  // record origin_grad -> repeated grad list map.
+  std::map<ir::Node*, std::vector<ir::Node*>> grad_repeated_map;
+  std::map<std::string, std::vector<ir::Node*>> created;
+  std::unordered_set<std::string> bn_vars_need_rename;
+  for (int i = 0; i < num_repeats; ++i) {
+    std::unordered_set<ir::Node*> copied;
+    for (size_t node_idx = 0; node_idx < forward_backward_ops.size();
+         ++node_idx) {
+      auto node = forward_backward_ops[node_idx];
+      OpDesc repeated_op(*(node->Op()), node->Op()->Block());
+      // 3. rename grad outputs to current repeat.
+      for (auto outname : repeated_op.OutputArgumentNames()) {
+        if (grad_names.find(outname) != grad_names.end()) {
+          std::string new_gname = string::Sprintf("%s.repeat.%d", outname, i);
+          repeated_op.RenameOutput(outname, new_gname);
+        }
+      }
+      // 3.5 let batch_norm ops use independent vars, note batch_norm_grad do
+      // not need this update
+      if (node->Name() == "batch_norm") {
+        // NOTE: assume bn op created by layers use save var as output mean and
+        // variance
+        std::string new_mean_name =
+            string::Sprintf("%s.repeat.%d", repeated_op.Input("Mean")[0], i);
+        std::string new_var_name = string::Sprintf(
+            "%s.repeat.%d", repeated_op.Input("Variance")[0], i);
+        bn_vars_need_rename.insert(repeated_op.Input("Mean")[0]);
+        bn_vars_need_rename.insert(repeated_op.Input("Variance")[0]);
+        VLOG(3) << "renaming " << repeated_op.Input("Mean")[0] << " to "
+                << new_mean_name;
+        repeated_op.RenameInput(repeated_op.Input("Mean")[0], new_mean_name);
+        repeated_op.RenameInput(repeated_op.Input("Variance")[0], new_var_name);
+        repeated_op.RenameOutput(repeated_op.Output("MeanOut")[0],
+                                 new_mean_name);
+        repeated_op.RenameOutput(repeated_op.Output("VarianceOut")[0],
+                                 new_var_name);
+      }
+
+      // 3.9 do copy
+      auto repeated_node = result.CreateOpNode(&repeated_op);
+      copied.insert(node);
+
+      // 4. add deps between repeats
+      if (node_idx == forward_backward_ops.size() - 1) {
+        prev_repeat_last_op_node = repeated_node;
+      }
+      if (node_idx == 0 && prev_repeat_last_op_node) {
+        auto* depvar = result.CreateControlDepVar();
+        prev_repeat_last_op_node->outputs.push_back(depvar);
+        depvar->inputs.push_back(prev_repeat_last_op_node);
+        repeated_node->inputs.push_back(depvar);
+        depvar->outputs.push_back(repeated_node);
+      }
+
+      for (auto in_node : node->inputs) {
+        if (in_node->IsCtrlVar()) {
+          continue;
+        }
+        ir::Node* var = nullptr;
+        auto updated_var = UpdateGradVarDesc(in_node->Var(), i, grad_names,
+                                             bn_vars_need_rename);
+        // should be initialized by startup, how to initilize tensor in the
+        // scope?
+        if (node->Name() == "batch_norm" &&
+            bn_vars_need_rename.find(in_node->Name()) !=
+                bn_vars_need_rename.end()) {
+          // Create bn mean/variance for each repeat
+          var = result.CreateVarNode(&updated_var);
+          created[updated_var.Name()].push_back(var);
+          copied.insert(in_node);
+          repeated_node->inputs.push_back(var);
+          var->outputs.push_back(repeated_node);
+          continue;
+        }
+
+        // for other ops
+        if (in_node->inputs.empty() && i > 0) {
+          // do not copy head vars (inputs, params) in repeats > 0
+          var = created.at(in_node->Name()).back();
+        } else {
+          if (copied.find(in_node) == copied.end()) {
+            var = result.CreateVarNode(&updated_var);
+            if (grad_names.find(in_node->Var()->Name()) != grad_names.end()) {
+              grad_repeated_map[in_node].push_back(var);
+            }
+            copied.insert(in_node);
+            created[updated_var.Name()].push_back(var);
+          } else {
+            var = created.at(updated_var.Name()).back();
+          }
+        }
+        repeated_node->inputs.push_back(var);
+        var->outputs.push_back(repeated_node);
+      }
+      for (auto out_node : node->outputs) {
+        if (out_node->IsCtrlVar()) {
+          continue;
+        }
+        ir::Node* var = nullptr;
+        auto updated_var = UpdateGradVarDesc(out_node->Var(), i, grad_names,
+                                             bn_vars_need_rename);
+        if (copied.find(out_node) == copied.end()) {
+          var = result.CreateVarNode(&updated_var);
+          if (grad_names.find(out_node->Var()->Name()) != grad_names.end()) {
+            grad_repeated_map[out_node].push_back(var);
+          }
+          copied.insert(out_node);
+          created[updated_var.Name()].push_back(var);
+        } else {
+          var = created.at(updated_var.Name()).back();
+        }
+        repeated_node->outputs.push_back(var);
+        var->inputs.push_back(repeated_node);
+      }
+    }
+  }
+
+  // 5. create GRAD merge op node
+  for (auto kv : grad_repeated_map) {
+    OpDesc sum_op;
+    sum_op.SetType("sum");
+    std::vector<std::string> repeated_grad_names;
+    for (auto r : kv.second) {
+      repeated_grad_names.push_back(r->Var()->Name());
+    }
+    sum_op.SetInput("X", repeated_grad_names);
+    sum_op.SetOutput("Out", {kv.first->Var()->Name()});
+    sum_op.SetAttr(OpProtoAndCheckerMaker::OpRoleAttrName(),
+                   static_cast<int>(OpRole::kBackward));
+    auto sum_op_node = result.CreateOpNode(&sum_op);
+    for (auto r : kv.second) {
+      sum_op_node->inputs.push_back(r);
+      r->outputs.push_back(sum_op_node);
+    }
+    auto sum_out_var_node = result.CreateVarNode(kv.first->Var());
+    sum_op_node->outputs.push_back(sum_out_var_node);
+    sum_out_var_node->inputs.push_back(sum_op_node);
+    created[sum_out_var_node->Name()].push_back(sum_out_var_node);
+
+    OpDesc scale_op;
+    scale_op.SetType("scale");
+    scale_op.SetInput("X", {sum_out_var_node->Var()->Name()});
+    // NOTE: inplace scale.
+    scale_op.SetOutput("Out", {sum_out_var_node->Var()->Name()});
+    scale_op.SetAttr("scale", static_cast<float>(1.0f / num_repeats));
+    scale_op.SetAttr(OpProtoAndCheckerMaker::OpRoleAttrName(),
+                     static_cast<int>(OpRole::kBackward));
+    auto scale_op_node = result.CreateOpNode(&scale_op);
+    scale_op_node->inputs.push_back(sum_out_var_node);
+    sum_out_var_node->outputs.push_back(scale_op_node);
+    auto scale_out_var_node = result.CreateVarNode(sum_out_var_node->Var());
+    scale_op_node->outputs.push_back(scale_out_var_node);
+    scale_out_var_node->inputs.push_back(scale_op_node);
+    created[scale_out_var_node->Name()].push_back(scale_out_var_node);
+  }
+  // 6. add optimize ops
+  {
+    auto copy_node = [&result, &created](ir::Node* node) {
+      auto op_node = result.CreateOpNode(node->Op());
+      // copy op ins/outs
+      // NOTE: for send/recv ops, the OpDesc uses ctrldepvar to describe
+      // dependencies, so create those depvars if OpDesc have in/outs.
+      for (auto in_node : node->inputs) {
+        if (in_node->IsCtrlVar() && !in_node->Var()) {
+          continue;
+        }
+        ir::Node* var = nullptr;
+        if (created.find(in_node->Name()) == created.end()) {
+          var = result.CreateVarNode(in_node->Var());
+          created[in_node->Name()].push_back(var);
+        } else {
+          var = created.at(in_node->Name()).back();
+        }
+        op_node->inputs.push_back(var);
+        var->outputs.push_back(op_node);
+      }
+      for (auto out_node : node->outputs) {
+        if (out_node->IsCtrlVar() && !out_node->Var()) {
+          continue;
+        }
+        auto var = result.CreateVarNode(out_node->Var());
+        created[out_node->Name()].push_back(var);
+        op_node->outputs.push_back(var);
+        var->inputs.push_back(op_node);
+      }
+    };
+    for (auto node : lr_ops) {
+      copy_node(node);
+    }
+    for (auto node : optimize_ops) {
+      copy_node(node);
+    }
+  }
+
+  result.ResolveHazard(created);
+  return graph;
+}
+
+}  // namespace ir
+}  // namespace framework
+}  // namespace paddle
+
+REGISTER_PASS(multi_batch_merge_pass, paddle::framework::ir::BatchMergePass)
+    .RequirePassAttr(paddle::framework::ir::kNumRepeats);

paddle/fluid/framework/ir/multi_batch_merge_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.h"
+#include "paddle/fluid/framework/ir/pass.h"
+
+namespace paddle {
+namespace framework {
+namespace ir {
+
+// BatchMergePass is used to copy forward and backward ops for several
+// times to run several batches to simulate large batch size training
+// as if we have more than 1 GPUs.
+// User can define how many batches to run, gradients will be merged
+// through those repeats, and then do optimization using merged gradients.
+// This pass is extremely useful when doing large batch-size distributed
+// sync training, we can simulate even large batch size as if we have more
+// GPUs.
+
+class BatchMergePass : public Pass {
+ public:
+  virtual ~BatchMergePass() {}
+
+ protected:
+  std::unique_ptr<Graph> ApplyImpl(std::unique_ptr<Graph> graph) const override;
+};
+
+}  // namespace ir
+}  // namespace framework
+}  // namespace paddle

paddle/fluid/framework/lod_tensor.cc

@@ -418,7 +418,7 @@ void LoDTensor::MergeLoDTensor(
     PADDLE_ENFORCE_EQ(new_lod.size(), lod.size());
     for (size_t j = 0; j < lod.size(); ++j) {
       auto &sub_lod = new_lod[j];
-      auto &offset = sub_lod.back();
+      size_t offset = sub_lod.back();
       for (size_t k = 1; k < lod[j].size(); ++k) {
         sub_lod.push_back(lod[j][k] + offset);
       }

paddle/fluid/framework/lod_tensor_array.h

@@ -18,6 +18,8 @@ limitations under the License. */
 
 namespace paddle {
 namespace framework {
+
 using LoDTensorArray = std::vector<LoDTensor>;
-}
+
+}  // namespace framework
 }  // namespace paddle

paddle/fluid/framework/op_desc.cc

@@ -419,8 +419,15 @@ struct SetAttrDescVisitor : public boost::static_visitor<void> {
     }
     VectorToRepeated(blocks_idx, attr_->mutable_blocks_idx());
   }
+
   void operator()(BlockDesc *desc) const { attr_->set_block_idx(desc->ID()); }
+
   void operator()(int64_t v) const { attr_->set_l(v); }
+
+  void operator()(const std::vector<int64_t> &v) const {
+    VectorToRepeated(v, attr_->mutable_longs());
+  }
+
   void operator()(boost::blank) const { PADDLE_THROW("Unexpected branch"); }
 };
 

paddle/fluid/framework/op_proto_maker.h

@@ -28,12 +28,12 @@ enum class OpRole {
   kBackward = 0x0001,
   kOptimize = 0x0002,
   // RPC role is for send/recv releated op
-  kRPC = 0x0003,
+  kRPC = 0x0004,
   // Dist role is for split_byref/split_selected_rows/concat
   // used for distributed training.
-  kDist = 0x0004,
+  kDist = 0x0008,
   // Tag all learning rate scheduler operators.
-  kLRSched = 0x0005,
+  kLRSched = 0x0010,
 
   kLoss = 0x0100,
   // The default value of op's role. This should be only used for unittests and

paddle/fluid/framework/operator.cc

@@ -358,11 +358,11 @@ static bool VarIsTensor(const Variable* var) {
   return var->IsType<LoDTensor>() || var->IsType<SelectedRows>();
 }
 
-static const Tensor* GetTensorFromVar(Variable* var) {
+const Tensor* GetTensorFromVar(const Variable* var) {
   if (var->IsType<LoDTensor>()) {
-    return var->GetMutable<LoDTensor>();
+    return static_cast<const Tensor*>(&(var->Get<LoDTensor>()));
   } else if (var->IsType<SelectedRows>()) {
-    return var->GetMutable<SelectedRows>()->mutable_value();
+    return &(var->Get<SelectedRows>().value());
   } else {
     PADDLE_THROW("Variable type_id %s, expect LoDTensor/SelectedRows.",
                  var->Type().name());
@@ -415,8 +415,7 @@ bool ExecutionContext::HasOutput(const std::string& name) const {
 template <>
 const Tensor* ExecutionContext::Input<Tensor>(const std::string& name) const {
   auto* var = InputVar(name);
-  return var == nullptr ? nullptr
-                        : GetTensorFromVar(const_cast<Variable*>(var));
+  return var == nullptr ? nullptr : GetTensorFromVar(var);
 }
 
 template <>

paddle/fluid/framework/operator.h

@@ -63,6 +63,7 @@ inline std::string GradVarName(const std::string& var_name) {
 }
 
 proto::VarType::Type GetDataTypeOfVar(const Variable* var);
+const Tensor* GetTensorFromVar(const Variable* var);
 
 class OperatorBase;
 class ExecutionContext;

paddle/fluid/framework/parallel_executor.cc

@@ -109,18 +109,9 @@ ParallelExecutor::ParallelExecutor(
   if (member_->local_scopes_.size() != 1 && local_scopes.empty()) {
     BCastParamsToDevices(bcast_vars);
   }
-  // Startup Program has been run. All local scopes has correct parameters.
+// Startup Program has been run. All local scopes has correct parameters.
 
-  // Step 2. Create vars in each scope;
-  std::vector<details::VariableInfo> var_infos;
-  for (auto *var : main_program.Block(0).AllVars()) {
-    var_infos.emplace_back();
-    var_infos.back().name_ = var->Name();
-    var_infos.back().type_ = var->GetType();
-    var_infos.back().persistable_ = var->Persistable();
-  }
-
-// Step 3. Convert main_program to SSA form and dependency graph. Also, insert
+// Step 2. Convert main_program to SSA form and dependency graph. Also, insert
 // ncclOp
 #ifdef PADDLE_WITH_CUDA
   std::unique_ptr<ir::Graph> graph = build_strategy.Apply(
@@ -156,6 +147,26 @@ ParallelExecutor::ParallelExecutor(
                            params, member_->local_scopes_, member_->use_cuda_);
 #endif
 
+  // Step 3. Create vars in each scope. Passes may also create new vars.
+  //         skip control vars and empty vars
+  std::vector<details::VariableInfo> var_infos;
+  for (auto &node : graph->Nodes()) {
+    if (node->IsVar() && !node->IsCtrlVar() && node->Var()) {
+      var_infos.emplace_back();
+      var_infos.back().name_ = node->Var()->Name();
+      var_infos.back().type_ = node->Var()->GetType();
+      var_infos.back().persistable_ = node->Var()->Persistable();
+    }
+  }
+
+  if (VLOG_IS_ON(5)) {
+    // If the loss_var_name is given, the number of graph should be only one.
+    if (loss_var_name.size()) {
+      PADDLE_ENFORCE_EQ(ir::GraphNum(*graph), 1,
+                        "The number of graph should be only one");
+    }
+  }
+
   if (exec_strategy.type_ == ExecutionStrategy::kDefault) {
     member_->executor_.reset(new details::ThreadedSSAGraphExecutor(
         exec_strategy, member_->local_scopes_, places, std::move(graph)));
@@ -179,6 +190,10 @@ void ParallelExecutor::BCastParamsToDevices(
     }
 
     auto &main_tensor = main_var->Get<LoDTensor>();
+    if (!main_tensor.IsInitialized()) {
+      VLOG(3) << "one in var not inited, return!";
+      continue;
+    }
     auto &dims = main_tensor.dims();
     if (paddle::platform::is_gpu_place(main_tensor.place())) {
 #ifdef PADDLE_WITH_CUDA

paddle/fluid/framework/scope.h

@@ -78,6 +78,8 @@ class Scope {
   /// Drop all kids scopes belonged to this scope.
   void DropKids();
 
+  std::list<Scope*>& kids() const { return kids_; }
+
   /// Find if a scope exists in the kid scopes
   bool HasKid(const Scope* scope) const;
 

paddle/fluid/framework/type_defs.h

@@ -36,7 +36,7 @@ using Attribute =
     boost::variant<boost::blank, int, float, std::string, std::vector<int>,
                    std::vector<float>, std::vector<std::string>, bool,
                    std::vector<bool>, BlockDesc*, int64_t,
-                   std::vector<BlockDesc*>>;
+                   std::vector<BlockDesc*>, std::vector<int64_t>>;
 
 using AttributeMap = std::unordered_map<std::string, Attribute>;
 

paddle/fluid/inference/CMakeLists.txt

@@ -30,7 +30,7 @@ if (WITH_GPU AND TENSORRT_FOUND)
 endif()
 
 # Create static library
-cc_library(paddle_fluid DEPS ${fluid_modules} ${STATIC_INFERENCE_APIS} zero_copy_tensor)
+cc_library(paddle_fluid DEPS ${fluid_modules} ${STATIC_INFERENCE_APIS} zero_copy_tensor reset_tensor_array)
 
 if(NOT APPLE)
   # TODO(liuyiqu: Temporarily disable the link flag because it is not support on Mac.
@@ -40,7 +40,7 @@ endif()
 
 # Create shared library
 cc_library(paddle_fluid_shared SHARED SRCS ${SHARED_INFERENCE_SRCS}
-    DEPS ${fluid_modules} paddle_fluid_api)
+    DEPS ${fluid_modules} paddle_fluid_api reset_tensor_array)
 
 set_target_properties(paddle_fluid_shared PROPERTIES OUTPUT_NAME paddle_fluid)
 if(NOT APPLE)

paddle/fluid/inference/analysis/analyzer.cc

@@ -107,6 +107,9 @@ void Analyzer::Run(Argument* argument) {
     passes.push_back("mkldnn_placement_pass");
   }
 #endif
+  // infer_clean_graph_pass should be the first default pass
+  // after mkldnn_placement_pass.
+  passes.push_back("infer_clean_graph_pass");
   for (auto& pass : ir_passes_) {
     if (!disabled_ir_passes_.count(pass)) {
       passes.push_back(pass);

paddle/fluid/inference/analysis/analyzer.h

@@ -67,7 +67,6 @@ class Analyzer : public OrderedRegistry<PassManager> {
   // larger fusion.
   const std::vector<std::string> all_ir_passes_{{
       // Manual update the passes here.
-      "infer_clean_graph_pass",         //
       "attention_lstm_fuse_pass",       //
       "seqconv_eltadd_relu_fuse_pass",  //
       "embedding_fc_lstm_fuse_pass",    //

paddle/fluid/inference/api/CMakeLists.txt

@@ -18,7 +18,8 @@ if(APPLE)
 endif(APPLE)
 
 
-set(inference_deps paddle_inference_api paddle_fluid_api analysis pass ir_pass_manager naive_executor ${GLOB_PASS_LIB})
+set(inference_deps paddle_inference_api paddle_fluid_api analysis pass ir_pass_manager naive_executor ${GLOB_PASS_LIB}
+        )
 
 if(WITH_GPU AND TENSORRT_FOUND)
     set(inference_deps ${inference_deps} paddle_inference_tensorrt_subgraph_engine analysis_predictor)
@@ -31,10 +32,17 @@ function(inference_api_test TARGET_NAME)
         set(multiValueArgs ARGS)
         cmake_parse_arguments(inference_test "${options}" "${oneValueArgs}" "${multiValueArgs}" ${ARGN})
 
-        cc_test(${TARGET_NAME}
-                SRCS ${inference_test_SRC}
-                DEPS "${inference_deps}"
-                ARGS --dirname=${PYTHON_TESTS_DIR}/book/)
+	if (WITH_GPU)
+		cc_test(${TARGET_NAME}
+			SRCS ${inference_test_SRC}
+			DEPS "${inference_deps}"
+			ARGS --dirname=${PYTHON_TESTS_DIR}/book/ --fraction_of_gpu_memory_to_use=0.15)
+        else()
+		cc_test(${TARGET_NAME}
+			SRCS ${inference_test_SRC}
+			DEPS "${inference_deps}"
+			ARGS --dirname=${PYTHON_TESTS_DIR}/book/)
+	endif()
         if(inference_test_ARGS)
             set_tests_properties(${TARGET_NAME}
                     PROPERTIES DEPENDS "${inference_test_ARGS}")
@@ -42,7 +50,8 @@ function(inference_api_test TARGET_NAME)
     endif(WITH_TESTING)
 endfunction(inference_api_test)
 
-cc_library(paddle_inference_api SRCS api.cc api_impl.cc helper.cc DEPS lod_tensor scope)
+cc_library(reset_tensor_array SRCS details/reset_tensor_array.cc DEPS lod_tensor scope)
+cc_library(paddle_inference_api SRCS api.cc api_impl.cc helper.cc DEPS reset_tensor_array lod_tensor scope)
 cc_library(analysis_predictor SRCS analysis_predictor.cc DEPS paddle_inference_api analysis naive_executor zero_copy_tensor)
 cc_library(zero_copy_tensor SRCS details/zero_copy_tensor.cc DEPS paddle_inference_api)
 cc_library(zero_copy_tensor_dummy SRCS details/zero_copy_tensor_dummy.cc DEPS paddle_inference_api)
@@ -52,8 +61,6 @@ cc_test(test_paddle_inference_api
 
 inference_api_test(test_api_impl SRC api_impl_tester.cc
                     ARGS test_word2vec test_image_classification)
-
-set(PYTHON_TESTS_DIR ${PADDLE_BINARY_DIR}/python/paddle/fluid/tests)
 cc_test(test_analysis_predictor SRCS analysis_predictor_tester.cc DEPS analysis_predictor ${inference_deps} paddle_inference_api
         ARGS --dirname=${PYTHON_TESTS_DIR}/book)
 

paddle/fluid/inference/api/analysis_predictor.cc

@@ -82,6 +82,7 @@ bool AnalysisPredictor::Init(
 
   // Get the feed_target_names and fetch_target_names
   PrepareFeedFetch();
+
   return true;
 }
 
@@ -109,6 +110,10 @@ bool AnalysisPredictor::Run(const std::vector<PaddleTensor> &inputs,
     return false;
   }
   VLOG(3) << "predict cost: " << timer.toc() << "ms";
+
+  // Fix TensorArray reuse not cleaned bug.
+  tensor_array_batch_cleaner_.CollectTensorArrays(scope_.get());
+  tensor_array_batch_cleaner_.ResetTensorArray();
   return true;
 }
 
@@ -322,6 +327,9 @@ std::unique_ptr<ZeroCopyTensor> AnalysisPredictor::GetOutputTensor(
 
 bool AnalysisPredictor::ZeroCopyRun() {
   executor_->Run();
+  // Fix TensorArray reuse not cleaned bug.
+  tensor_array_batch_cleaner_.CollectTensorArrays(scope_.get());
+  tensor_array_batch_cleaner_.ResetTensorArray();
   return true;
 }
 

paddle/fluid/inference/api/analysis_predictor.h

@@ -18,6 +18,7 @@
 #include "paddle/fluid/framework/naive_executor.h"
 #include "paddle/fluid/inference/analysis/analyzer.h"
 #include "paddle/fluid/inference/api/api_impl.h"
+#include "paddle/fluid/inference/api/details/reset_tensor_array.h"
 #include "paddle/fluid/inference/api/paddle_inference_api.h"
 #include "paddle/fluid/string/printf.h"
 
@@ -88,6 +89,7 @@ class AnalysisPredictor : public PaddlePredictor {
   // Memory buffer for feed inputs. The temporary LoDTensor will cause serious
   // concurrency problems, so cache them.
   std::vector<framework::LoDTensor> feed_tensors_;
+  details::TensorArrayBatchCleaner tensor_array_batch_cleaner_;
 };
 
 }  // namespace paddle

paddle/fluid/inference/api/api_impl.cc

@@ -22,6 +22,7 @@ limitations under the License. */
 
 #include "paddle/fluid/framework/feed_fetch_method.h"
 #include "paddle/fluid/inference/api/api_impl.h"
+#include "paddle/fluid/inference/api/details/reset_tensor_array.h"
 #include "paddle/fluid/inference/api/helper.h"
 #include "paddle/fluid/platform/cpu_helper.h"
 #include "paddle/fluid/platform/profiler.h"
@@ -157,6 +158,10 @@ bool NativePaddlePredictor::Run(const std::vector<PaddleTensor> &inputs,
     return false;
   }
   VLOG(3) << "predict cost: " << timer.toc() << "ms";
+
+  // Fix TensorArray reuse not cleaned bug.
+  tensor_array_batch_cleaner_.CollectTensorArrays(scope_.get());
+  tensor_array_batch_cleaner_.ResetTensorArray();
   return true;
 }
 

paddle/fluid/inference/api/api_impl.h

@@ -26,11 +26,11 @@ limitations under the License. */
 #include <string>
 #include <vector>
 
-#include "paddle/fluid/inference/api/paddle_inference_api.h"
-
 #include "paddle/fluid/framework/ddim.h"
 #include "paddle/fluid/framework/lod_tensor.h"
+#include "paddle/fluid/framework/lod_tensor_array.h"
 #include "paddle/fluid/framework/naive_executor.h"
+#include "paddle/fluid/inference/api/details/reset_tensor_array.h"
 #include "paddle/fluid/inference/api/paddle_inference_api.h"
 #include "paddle/fluid/inference/io.h"
 #include "paddle/fluid/platform/init.h"
@@ -77,6 +77,7 @@ class NativePaddlePredictor : public PaddlePredictor {
   std::vector<framework::OpDesc *> fetchs_;
   // Do not use unique_ptr, use parent scope to delete
   framework::Scope *sub_scope_{nullptr};
+  details::TensorArrayBatchCleaner tensor_array_batch_cleaner_;
 };
 
 }  // namespace paddle

paddle/fluid/inference/api/demo_ci/CMakeLists.txt

@@ -52,6 +52,7 @@ include_directories("${PADDLE_LIB}")
 include_directories("${PADDLE_LIB}/third_party/install/protobuf/include")
 include_directories("${PADDLE_LIB}/third_party/install/glog/include")
 include_directories("${PADDLE_LIB}/third_party/install/gflags/include")
+include_directories("${PADDLE_LIB}/third_party/install/xxhash/include")
 if (NOT WIN32)
 include_directories("${PADDLE_LIB}/third_party/install/snappy/include")
 include_directories("${PADDLE_LIB}/third_party/install/snappystream/include")
@@ -61,8 +62,8 @@ endif(NOT WIN32)
 include_directories("${PADDLE_LIB}/third_party/boost")
 include_directories("${PADDLE_LIB}/third_party/eigen3")
 
-if (NOT WIN32) 
-  if (USE_TENSORRT AND WITH_GPU) 
+if (NOT WIN32)
+  if (USE_TENSORRT AND WITH_GPU)
       include_directories("${TENSORRT_INCLUDE_DIR}")
       link_directories("${TENSORRT_LIB_DIR}")
   endif()
@@ -77,13 +78,14 @@ endif(NOT WIN32)
 link_directories("${PADDLE_LIB}/third_party/install/protobuf/lib")
 link_directories("${PADDLE_LIB}/third_party/install/glog/lib")
 link_directories("${PADDLE_LIB}/third_party/install/gflags/lib")
+link_directories("${PADDLE_LIB}/third_party/install/xxhash/lib")
 link_directories("${PADDLE_LIB}/paddle/lib")
 
 add_executable(${DEMO_NAME} ${DEMO_NAME}.cc)
 
 if(WITH_MKL)
   include_directories("${PADDLE_LIB}/third_party/install/mklml/include")
-  set(MATH_LIB ${PADDLE_LIB}/third_party/install/mklml/lib/libmklml_intel${CMAKE_SHARED_LIBRARY_SUFFIX} 
+  set(MATH_LIB ${PADDLE_LIB}/third_party/install/mklml/lib/libmklml_intel${CMAKE_SHARED_LIBRARY_SUFFIX}
                ${PADDLE_LIB}/third_party/install/mklml/lib/libiomp5${CMAKE_SHARED_LIBRARY_SUFFIX})
   set(MKLDNN_PATH "${PADDLE_LIB}/third_party/install/mkldnn")
   if(EXISTS ${MKLDNN_PATH})
@@ -107,7 +109,7 @@ if (NOT WIN32)
 set(EXTERNAL_LIB "-lrt -ldl -lpthread")
 set(DEPS ${DEPS}
     ${MATH_LIB} ${MKLDNN_LIB}
-    glog gflags protobuf snappystream snappy z
+    glog gflags protobuf snappystream snappy z xxhash
     ${EXTERNAL_LIB})
 else()
 set(DEPS ${DEPS}
@@ -120,7 +122,7 @@ endif(NOT WIN32)
 
 if(WITH_GPU)
   if(NOT WIN32)
-    if (USE_TENSORRT) 
+    if (USE_TENSORRT)
       set(DEPS ${DEPS} ${TENSORRT_LIB_DIR}/libnvinfer${CMAKE_STATIC_LIBRARY_SUFFIX})
       set(DEPS ${DEPS} ${TENSORRT_LIB_DIR}/libnvinfer_plugin${CMAKE_STATIC_LIBRARY_SUFFIX})
     endif()

paddle/fluid/inference/api/demo_ci/run.sh

@@ -16,7 +16,7 @@ if [ $2 == ON ]; then
 fi
 if [ $3 == ON ]; then
   use_gpu_list='true false'
-else    
+else
   use_gpu_list='false'
 fi
 
@@ -83,7 +83,7 @@ for WITH_STATIC_LIB in ON OFF; do
     -DWITH_STATIC_LIB=$WITH_STATIC_LIB
   make -j
   for use_gpu in $use_gpu_list; do
-    for vis_demo_name in $vis_demo_list; do 
+    for vis_demo_name in $vis_demo_list; do
       ./vis_demo \
         --modeldir=$DATA_DIR/$vis_demo_name/model \
         --data=$DATA_DIR/$vis_demo_name/data.txt \
@@ -95,7 +95,7 @@ for WITH_STATIC_LIB in ON OFF; do
       fi
     done
   done
-  
+
   # --------tensorrt mobilenet------
   if [ $USE_TENSORRT == ON -a $TEST_GPU_CPU == ON ]; then
     rm -rf *
@@ -107,7 +107,7 @@ for WITH_STATIC_LIB in ON OFF; do
       -DUSE_TENSORRT=$USE_TENSORRT \
       -DTENSORRT_INCLUDE_DIR=$TENSORRT_INCLUDE_DIR \
       -DTENSORRT_LIB_DIR=$TENSORRT_LIB_DIR
-    make -j 
+    make -j
     ./trt_mobilenet_demo \
       --modeldir=$DATA_DIR/mobilenet/model \
       --data=$DATA_DIR/mobilenet/data.txt \

paddle/fluid/inference/api/details/reset_tensor_array.cc

+// Copyright (c) 2018 PaddlePaddle Authors. All Rights Reserved.
+//
+// Licensed under the Apache License, Version 2.0 (the "License");
+// you may not use this file except in compliance with the License.
+// You may obtain a copy of the License at
+//
+//     http://www.apache.org/licenses/LICENSE-2.0
+//
+// Unless required by applicable law or agreed to in writing, software
+// distributed under the License is distributed on an "AS IS" BASIS,
+// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+// See the License for the specific language governing permissions and
+// limitations under the License.
+
+#include "paddle/fluid/inference/api/details/reset_tensor_array.h"
+
+namespace paddle {
+namespace details {
+
+// Should be called after the parameters are loaded.
+void TensorArrayBatchCleaner::CollectTensorArrays(framework::Scope *scope) {
+  if (flag_) {
+    for (auto &var_name : scope->LocalVarNames()) {
+      auto *var = scope->FindVar(var_name);
+      // TODO(Superjomn) should avoid the case when a TensorArray is a
+      // parameter.
+      if (var_name == "feed" || var_name == "fetch") continue;
+      if (var->Type() == typeid(framework::LoDTensorArray)) {
+        VLOG(4) << "collect " << var_name;
+        arrays_.push_back(var->GetMutable<framework::LoDTensorArray>());
+      }
+    }
+    for (auto *kid : scope->kids()) {
+      CollectTensorArrays(kid);
+    }
+
+    VLOG(3) << "Collect " << arrays_.size() << " arrays";
+    flag_ = false;
+  }
+}
+
+// Should be called when `Run` finished.
+void TensorArrayBatchCleaner::ResetTensorArray() {
+  for (auto *arr : arrays_) {
+    arr->clear();
+  }
+}
+
+}  // namespace details
+}  // namespace paddle

paddle/fluid/inference/api/details/reset_tensor_array.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 <vector>
+#include "paddle/fluid/framework/lod_tensor_array.h"
+#include "paddle/fluid/framework/scope.h"
+
+namespace paddle {
+namespace details {
+
+// Clean the TensorArray each batch to make the behavior the same with the
+// training phase.
+struct TensorArrayBatchCleaner {
+  // Fix the tensor array not clear in the inference scenarios.
+  void CollectTensorArrays(framework::Scope *scope);
+  void ResetTensorArray();
+
+ private:
+  bool flag_{true};
+  std::vector<framework::LoDTensorArray *> arrays_;
+};
+
+}  // namespace details
+}  // namespace paddle

paddle/fluid/inference/api/paddle_inference_api.h

@@ -124,7 +124,7 @@ class ZeroCopyTensor {
   std::vector<std::vector<size_t>> lod() const;
 
  protected:
-  ZeroCopyTensor(void* scope) : scope_{scope} {}
+  explicit ZeroCopyTensor(void* scope) : scope_{scope} {}
   void SetName(const std::string& name) { name_ = name; }
   void* FindTensor() const;
 
@@ -259,12 +259,6 @@ struct AnalysisConfig : public NativeConfig {
     kExclude   // Specify the disabled passes in `ir_passes`.
   };
 
-  void SetIncludeMode() {
-    ir_mode = IrPassMode::kInclude;
-    // this pass has to be run at the beginning of all fuse passes
-    ir_passes = {"infer_clean_graph_pass"};
-  }
-
   // Determine whether to perform graph optimization.
   bool enable_ir_optim = true;
   // Manually determine the IR passes to run.

paddle/fluid/inference/tests/api/analyzer_rnn1_tester.cc

@@ -228,6 +228,7 @@ void SetInput(std::vector<std::vector<PaddleTensor>> *inputs) {
 TEST(Analyzer_rnn1, profile) {
   contrib::AnalysisConfig cfg;
   SetConfig(&cfg);
+  cfg.use_gpu = false;
   std::vector<PaddleTensor> outputs;
 
   std::vector<std::vector<PaddleTensor>> input_slots_all;

paddle/fluid/operators/CMakeLists.txt

@@ -268,6 +268,7 @@ if (WITH_GPU AND TENSORRT_FOUND)
 else()
     set(DEPS_OPS ${DEPS_OPS} tensorrt_engine_op)
 endif()
+op_library(hash_op DEPS xxhash)
 op_library(clip_by_norm_op DEPS selected_rows_functor selected_rows)
 op_library(sum_op DEPS selected_rows_functor)
 op_library(sgd_op DEPS selected_rows_functor)

paddle/fluid/operators/beam_search_decode_op.cc

@@ -79,6 +79,9 @@ struct BeamSearchDecodeFunctor {
   bool tensor_on_gpu_;
   size_t beam_size_;
   int end_id_;
+  // TODO(Superjomn) Here might result serious performance issue in the
+  // concurrency
+  // scenarios.
   const LoDTensorArray& step_ids_origin_;
   const LoDTensorArray& step_scores_origin_;
   LoDTensorArray step_ids_ = LoDTensorArray();

paddle/fluid/operators/detection/generate_proposals_op.cc

@@ -284,7 +284,7 @@ static inline Tensor NMS(const platform::DeviceContext &ctx, Tensor *bbox,
       selected_indices.push_back(idx);
       ++selected_num;
     }
-    sorted_indices.erase(sorted_indices.end());
+    sorted_indices.erase(sorted_indices.end() - 1);
     if (flag && eta < 1 && adaptive_threshold > 0.5) {
       adaptive_threshold *= eta;
     }

paddle/fluid/operators/fake_init_op.cc

+/* Copyright (c) 2018 PaddlePaddle Authors. All Rights Reserved.
+
+Licensed under the Apache License, Version 2.0 (the "License");
+you may not use this file except in compliance with the License.
+You may obtain a copy of the License at
+
+    http://www.apache.org/licenses/LICENSE-2.0
+
+Unless required by applicable law or agreed to in writing, software
+distributed under the License is distributed on an "AS IS" BASIS,
+WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+See the License for the specific language governing permissions and
+limitations under the License. */
+
+#include "paddle/fluid/framework/data_type.h"
+#include "paddle/fluid/framework/op_registry.h"
+#include "paddle/fluid/operators/math/math_function.h"
+
+namespace paddle {
+namespace operators {
+
+class FakeInitInferShape : public framework::InferShapeBase {
+ public:
+  void operator()(framework::InferShapeContext *ctx) const override {
+    PADDLE_ENFORCE(ctx->HasOutput("Out"),
+                   "Output(Out) of FakeInitOp should not be null.");
+    auto &shape = ctx->Attrs().Get<std::vector<int64_t>>("shape");
+    ctx->SetOutputDim("Out", framework::make_ddim(shape));
+  }
+};
+
+class FakeInitOp : public framework::OperatorBase {
+ public:
+  using framework::OperatorBase::OperatorBase;
+
+ private:
+  void RunImpl(const framework::Scope &scope,
+               const platform::Place &dev_place) const override {
+    framework::Tensor *tensor = nullptr;
+
+    auto &out_var = *scope.FindVar(Output("Out"));
+
+    if (out_var.IsType<framework::LoDTensor>()) {
+      tensor = out_var.GetMutable<framework::LoDTensor>();
+      tensor->Resize(framework::make_ddim(Attr<std::vector<int64_t>>("shape")));
+    } else if (out_var.IsType<framework::SelectedRows>()) {
+      tensor = out_var.GetMutable<framework::SelectedRows>()->mutable_value();
+      tensor->Resize(framework::make_ddim(Attr<std::vector<int64_t>>("shape")));
+    } else {
+      PADDLE_THROW(
+          "fake init op's output only"
+          "supports SelectedRows and LoDTensor");
+    }
+  }
+};
+
+class FakeInitOpVarTypeInference : public framework::VarTypeInference {
+ public:
+  void operator()(const framework::OpDesc &op_desc,
+                  framework::BlockDesc *block) const override {}
+};
+
+class FakeInitOpMaker : public framework::OpProtoAndCheckerMaker {
+ public:
+  void Make() override {
+    AddAttr<std::vector<int64_t>>("shape",
+                                  "(vector<int64_t>) The shape of the output");
+    AddOutput("Out",
+              "(Tensor) Tensor of specified shape will be filled "
+              "with the specified value");
+    AddComment(R"DOC(
+FakeInit Operator.
+
+Init an variable but not alloc memory for it, it is used for init the
+table parameter at trainer side in distributed lookup table.
+
+)DOC");
+  }
+};
+}  // namespace operators
+}  // namespace paddle
+
+namespace ops = paddle::operators;
+REGISTER_OPERATOR(fake_init, ops::FakeInitOp, ops::FakeInitInferShape,
+                  ops::FakeInitOpMaker, paddle::framework::EmptyGradOpMaker,
+                  ops::FakeInitOpVarTypeInference);

paddle/fluid/operators/fill_constant_op.cc

@@ -24,7 +24,7 @@ class FillConstantInferShape : public framework::InferShapeBase {
   void operator()(framework::InferShapeContext *ctx) const override {
     PADDLE_ENFORCE(ctx->HasOutput("Out"),
                    "Output(Out) of FillConstantOp should not be null.");
-    auto &shape = ctx->Attrs().Get<std::vector<int>>("shape");
+    auto &shape = ctx->Attrs().Get<std::vector<int64_t>>("shape");
     ctx->SetOutputDim("Out", framework::make_ddim(shape));
   }
 };
@@ -47,10 +47,10 @@ class FillConstantOp : public framework::OperatorBase {
 
     if (out_var.IsType<framework::LoDTensor>()) {
       tensor = out_var.GetMutable<framework::LoDTensor>();
-      tensor->Resize(framework::make_ddim(Attr<std::vector<int>>("shape")));
+      tensor->Resize(framework::make_ddim(Attr<std::vector<int64_t>>("shape")));
     } else if (out_var.IsType<framework::SelectedRows>()) {
       tensor = out_var.GetMutable<framework::SelectedRows>()->mutable_value();
-      tensor->Resize(framework::make_ddim(Attr<std::vector<int>>("shape")));
+      tensor->Resize(framework::make_ddim(Attr<std::vector<int64_t>>("shape")));
     } else {
       PADDLE_THROW(
           "fill constant op's output only"
@@ -83,7 +83,8 @@ class FillConstantOpMaker : public framework::OpProtoAndCheckerMaker {
                  "(int, default 5 (FP32)) "
                  "Output data type")
         .SetDefault(framework::proto::VarType::FP32);
-    AddAttr<std::vector<int>>("shape", "(vector<int>) The shape of the output");
+    AddAttr<std::vector<int64_t>>("shape",
+                                  "(vector<int64_t>) The shape of the output");
     AddAttr<float>("value", "(float, default 0) The value to be filled")
         .SetDefault(0.0f);
     AddAttr<bool>("force_cpu",

paddle/fluid/operators/gaussian_random_op.cc

@@ -52,7 +52,7 @@ class GaussianRandomOp : public framework::OperatorWithKernel {
   void InferShape(framework::InferShapeContext* ctx) const override {
     PADDLE_ENFORCE(ctx->HasOutput("Out"),
                    "Output(Out) of GaussianRandomOp should not be null.");
-    auto shape = ctx->Attrs().Get<std::vector<int>>("shape");
+    auto shape = ctx->Attrs().Get<std::vector<int64_t>>("shape");
     std::vector<int64_t> temp;
     temp.reserve(shape.size());
     for (auto dim : shape) {
@@ -88,9 +88,9 @@ class GaussianRandomOpMaker : public framework::OpProtoAndCheckerMaker {
   void Make() override {
     AddOutput("Out", "Output matrix of gaussian random op");
 
-    AddAttr<std::vector<int>>("shape",
-                              "(vector<int>) "
-                              "The dimension of random tensor.");
+    AddAttr<std::vector<int64_t>>("shape",
+                                  "(vector<int64_t>) "
+                                  "The dimension of random tensor.");
     AddAttr<float>("mean",
                    "(float, default 0.0) "
                    "mean of random tensor.")

paddle/fluid/operators/hash_op.cc

+/* Copyright (c) 2016 PaddlePaddle Authors. All Rights Reserved.
+
+Licensed under the Apache License, Version 2.0 (the "License");
+you may not use this file except in compliance with the License.
+You may obtain a copy of the License at
+
+    http://www.apache.org/licenses/LICENSE-2.0
+
+Unless required by applicable law or agreed to in writing, software
+distributed under the License is distributed on an "AS IS" BASIS,
+WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+See the License for the specific language governing permissions and
+limitations under the License. */
+
+#include "paddle/fluid/operators/hash_op.h"
+#include <string>
+#include <vector>
+
+namespace paddle {
+namespace operators {
+
+class HashOp : public framework::OperatorWithKernel {
+ public:
+  HashOp(const std::string &type, const framework::VariableNameMap &inputs,
+         const framework::VariableNameMap &outputs,
+         const framework::AttributeMap &attrs)
+      : OperatorWithKernel(type, inputs, outputs, attrs) {}
+
+  void InferShape(framework::InferShapeContext *ctx) const override {
+    PADDLE_ENFORCE(ctx->HasInput("X"),
+                   "Input(X) of HashOp should not be null.");
+    PADDLE_ENFORCE(ctx->HasOutput("Out"),
+                   "Output(Out) of HashOp should not be null.");
+
+    auto dims = ctx->GetInputDim("X");
+    PADDLE_ENFORCE_EQ(dims.size(), 2UL,
+                      "The input of hash_op's dimensions must be 2");
+    std::vector<int64_t> out_dims;
+    out_dims.reserve(dims.size() + 1);
+    // copy all dims except the last one
+    for (size_t i = 0u; i != dims.size() - 1; ++i) {
+      out_dims.emplace_back(dims[i]);
+    }
+    int num_hash = ctx->Attrs().Get<int>("num_hash");
+    out_dims.emplace_back(num_hash);
+    // keep the last dim to 1
+    out_dims.emplace_back(1);
+
+    ctx->SetOutputDim("Out", framework::make_ddim(out_dims));
+    ctx->ShareLoD("X", /*->*/ "Out");
+  }
+};
+
+class HashOpMaker : public framework::OpProtoAndCheckerMaker {
+ public:
+  void Make() override {
+    AddInput("X", "(Tensor) Input tensor of scale operator.");
+    AddOutput("Out", "(Tensor) Output tensor of scale operator.");
+    AddComment(R"DOC(
+**Hash Operator**
+$$Out = scale * X$$
+)DOC");
+    AddAttr<int>("num_hash", "").SetDefault(1);
+    AddAttr<int>("mod_by", "").SetDefault(100000);
+  }
+};
+
+}  // namespace operators
+}  // namespace paddle
+
+namespace ops = paddle::operators;
+
+REGISTER_OP_WITHOUT_GRADIENT(hash, ops::HashOp, ops::HashOpMaker);
+REGISTER_OP_CPU_KERNEL(hash, ops::HashKerel<int>, ops::HashKerel<int64_t>);

paddle/fluid/operators/hash_op.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
+
+extern "C" {
+#include <xxhash.h>
+}
+#include "paddle/fluid/framework/eigen.h"
+#include "paddle/fluid/framework/op_registry.h"
+
+namespace paddle {
+namespace operators {
+// template <typename DeviceContext, typename T>
+template <typename T>
+class HashKerel : public framework::OpKernel<T> {
+ public:
+  virtual void Compute(const framework::ExecutionContext& context) const {
+    auto* out_t = context.Output<framework::LoDTensor>("Out");
+    auto* in_t = context.Input<framework::LoDTensor>("X");
+    int mod_by = context.Attr<int>("mod_by");
+    int num_hash = context.Attr<int>("num_hash");
+    auto* output = out_t->mutable_data<T>(context.GetPlace());
+
+    auto in_dims = in_t->dims();
+    auto in_lod = in_t->lod();
+    PADDLE_ENFORCE_EQ(
+        static_cast<uint64_t>(in_dims[0]), in_lod[0].back(),
+        "The actual input data's size mismatched with LoD information.");
+
+    auto seq_length = in_dims[0];
+    auto last_dim = in_dims[in_dims.size() - 1];
+    auto* input = in_t->data<T>();
+    for (int idx = 0; idx < seq_length; ++idx) {
+      for (int ihash = 0; ihash != num_hash; ++ihash) {
+        output[idx * num_hash + ihash] =
+            XXH64(input, sizeof(int) * last_dim, ihash) % mod_by;
+      }
+      input += last_dim;
+    }
+  }
+};
+
+}  // namespace operators
+}  // namespace paddle

paddle/fluid/operators/lars_momentum_op.cc

+/* Copyright (c) 2018 PaddlePaddle Authors. All Rights Reserved.
+
+Licensed under the Apache License, Version 2.0 (the "License");
+you may not use this file except in compliance with the License.
+You may obtain a copy of the License at
+
+    http://www.apache.org/licenses/LICENSE-2.0
+
+Unless required by applicable law or agreed to in writing, software
+distributed under the License is distributed on an "AS IS" BASIS,
+WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+See the License for the specific language governing permissions and
+limitations under the License. */
+
+#include "paddle/fluid/operators/lars_momentum_op.h"
+#include "paddle/fluid/operators/momentum_op.h"
+
+namespace paddle {
+namespace operators {
+
+class LarsMomentumOpMaker : public framework::OpProtoAndCheckerMaker {
+ public:
+  void Make() override {
+    AddInput("Param",
+             "(LoDTensor, default LoDTensor<float>) "
+             "Input parameter that has to be updated");
+    AddInput("Grad",
+             "(LoDTensor, default LoDTensor<float>) "
+             "Input gradient of the parameter");
+    AddInput("Velocity",
+             "(LoDTensor, default LoDTensor<float>) "
+             "Input velocity (corresponding to the parameter) "
+             "that has to be updated");
+    AddInput("LearningRate",
+             "(LoDTensor, default LoDTensor<float>) "
+             "Input learning rate");
+
+    AddOutput("ParamOut",
+              "(LoDTensor) This output is updated parameter. "
+              "It shared memory with Input(Param).");
+    AddOutput("VelocityOut",
+              "(LoDTensor) This output is updated velocity. "
+              "It shared memory with Input(Velocity).");
+
+    AddAttr<float>("mu", "(float) Momentum coefficient");
+    AddAttr<float>("lars_coeff", "(float, default 0.001) LARS coefficient.")
+        .SetDefault(0.001);
+    AddAttr<float>("lars_weight_decay",
+                   "(float, default 0.0005) LARS weight decay")
+        .SetDefault(0.0005);
+
+    AddComment(R"DOC(
+Lars Momentum Optimizer.
+
+This optimizer use LARS (https://arxiv.org/abs/1708.03888) to optimize each
+weight using a local learning rate:
+
+$$
+local\_lr = \eta  * 
+    \frac{\left \| param \right \|}{\left \| grad \right \| + \beta *\left \| param \right \|} \\
+velocity = mu * velocity + 
+    local\_lr * (grad + \beta * param) \\
+param = param - velocity. \\
+$$
+
+Note that we use lars_weight_decay here to decay weights, you may need not to
+use L2 regularizers in case of using LARS.
+
+)DOC");
+  }
+};
+
+class LarsMomentumOpVarTypeInference : public framework::VarTypeInference {
+ public:
+  void operator()(const framework::OpDesc &op_desc,
+                  framework::BlockDesc *block) const override {}
+};
+}  // namespace operators
+}  // namespace paddle
+
+namespace ops = paddle::operators;
+REGISTER_OPERATOR(lars_momentum, ops::MomentumOp, ops::LarsMomentumOpMaker,
+                  paddle::framework::EmptyGradOpMaker,
+                  ops::LarsMomentumOpVarTypeInference);
+REGISTER_OP_CPU_KERNEL(lars_momentum, ops::LarsMomentumOpKernel<float>,
+                       ops::LarsMomentumOpKernel<double>);

paddle/fluid/operators/lars_momentum_op.cu

+/* 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 "paddle/fluid/framework/op_registry.h"
+#include "paddle/fluid/operators/lars_momentum_op.h"
+
+namespace paddle {
+namespace operators {
+
+template <typename T>
+__global__ void MomentumLarsKernel(const T* p, const T* g, const T* v,
+                                   const T* learning_rate, const T mu,
+                                   const int64_t num, const T lars_coeff,
+                                   const T lars_weight_decay, const T* p_norm,
+                                   const T* g_norm, T* p_out, T* v_out) {
+  T lr = learning_rate[0];
+  T local_lr = learning_rate[0];
+  for (int i = blockIdx.x * blockDim.x + threadIdx.x; i < num;
+       i += blockDim.x * gridDim.x) {
+    if (p_norm[0] > 0 && g_norm[0] > 0) {
+      local_lr = lr * lars_coeff * p_norm[0] /
+                 (g_norm[0] + lars_weight_decay * p_norm[0]);
+    }
+    T v_new = v[i] * mu + local_lr * (g[i] + lars_weight_decay * p[i]);
+    v_out[i] = v_new;
+    p_out[i] = p[i] - v_new;
+  }
+}
+
+template <typename DeviceContext, typename T>
+class LarsMomentumOpCUDAKernel : public framework::OpKernel<T> {
+ public:
+  void Compute(const framework::ExecutionContext& ctx) const override {
+    auto param_out = ctx.Output<framework::LoDTensor>("ParamOut");
+    auto velocity_out = ctx.Output<framework::LoDTensor>("VelocityOut");
+    auto param = ctx.Input<framework::LoDTensor>("Param");
+    auto velocity = ctx.Input<framework::LoDTensor>("Velocity");
+    auto grad = ctx.Input<framework::LoDTensor>("Grad");
+    auto learning_rate = ctx.Input<framework::LoDTensor>("LearningRate");
+
+    T* p_out = param_out->mutable_data<T>(ctx.GetPlace());
+    T* v_out = velocity_out->mutable_data<T>(ctx.GetPlace());
+
+    T mu = static_cast<T>(ctx.Attr<float>("mu"));
+    T lars_coeff = ctx.Attr<float>("lars_coeff");
+    T lars_weight_decay = ctx.Attr<float>("lars_weight_decay");
+
+    auto* p = param->data<T>();
+    auto* v = velocity->data<T>();
+    auto* g = grad->data<T>();
+    auto* lr = learning_rate->data<T>();
+
+    int block = 512;
+    int grid = (param->numel() + block - 1) / block;
+
+    auto eigen_p = framework::EigenVector<T>::Flatten(*param);
+    auto eigen_g = framework::EigenVector<T>::Flatten(*grad);
+    // calculate norms using eigein and launch the kernel.
+    framework::Tensor p_norm_t, g_norm_t;
+    p_norm_t.Resize({1});
+    g_norm_t.Resize({1});
+    auto* p_norm_data = p_norm_t.mutable_data<T>(ctx.GetPlace());
+    auto* g_norm_data = g_norm_t.mutable_data<T>(ctx.GetPlace());
+    auto ep_norm = framework::EigenScalar<T>::From(p_norm_t);
+    auto eg_norm = framework::EigenScalar<T>::From(g_norm_t);
+
+    auto* place = ctx.template device_context<DeviceContext>().eigen_device();
+    ep_norm.device(*place) = eigen_p.square().sum().sqrt();
+    eg_norm.device(*place) = eigen_g.square().sum().sqrt();
+    MomentumLarsKernel<<<grid, block, 0, ctx.cuda_device_context().stream()>>>(
+        p, g, v, lr, mu, param->numel(), lars_coeff, lars_weight_decay,
+        p_norm_data, g_norm_data, p_out, v_out);
+  }
+};
+
+}  // namespace operators
+}  // namespace paddle
+
+namespace ops = paddle::operators;
+REGISTER_OP_CUDA_KERNEL(
+    lars_momentum,
+    ops::LarsMomentumOpCUDAKernel<paddle::platform::CUDADeviceContext, float>,
+    ops::LarsMomentumOpCUDAKernel<paddle::platform::CUDADeviceContext, double>);

paddle/fluid/operators/lars_momentum_op.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 "paddle/fluid/framework/eigen.h"
+#include "paddle/fluid/framework/op_registry.h"
+
+namespace paddle {
+namespace operators {
+
+template <typename T>
+class LarsMomentumOpKernel : public framework::OpKernel<T> {
+ public:
+  void Compute(const framework::ExecutionContext& ctx) const override {
+    auto param_out = ctx.Output<framework::LoDTensor>("ParamOut");
+    auto velocity_out = ctx.Output<framework::LoDTensor>("VelocityOut");
+    auto param = ctx.Input<framework::LoDTensor>("Param");
+    auto velocity = ctx.Input<framework::LoDTensor>("Velocity");
+    auto learning_rate = ctx.Input<framework::LoDTensor>("LearningRate");
+    auto* grad_var = ctx.InputVar("Grad");
+    // only support dense for now.
+    PADDLE_ENFORCE(grad_var->IsType<framework::LoDTensor>());
+    auto grad = ctx.Input<framework::LoDTensor>("Grad");
+
+    param_out->mutable_data<T>(ctx.GetPlace());
+    velocity_out->mutable_data<T>(ctx.GetPlace());
+
+    T mu = static_cast<T>(ctx.Attr<float>("mu"));
+    T lars_coeff = ctx.Attr<float>("lars_coeff");
+    T lars_weight_decay = ctx.Attr<float>("lars_weight_decay");
+
+    auto p_out = framework::EigenVector<T>::Flatten(*param_out);
+    auto v_out = framework::EigenVector<T>::Flatten(*velocity_out);
+
+    auto p = framework::EigenVector<T>::Flatten(*param);
+    auto v = framework::EigenVector<T>::Flatten(*velocity);
+    auto g = framework::EigenVector<T>::Flatten(*grad);
+    auto* lr = learning_rate->data<T>();
+
+    framework::Tensor p_norm_t, g_norm_t;
+    p_norm_t.Resize({1});
+    g_norm_t.Resize({1});
+    p_norm_t.mutable_data<T>(ctx.GetPlace());
+    g_norm_t.mutable_data<T>(ctx.GetPlace());
+    auto ep_norm = framework::EigenScalar<T>::From(p_norm_t);
+    auto eg_norm = framework::EigenScalar<T>::From(g_norm_t);
+
+    ep_norm = p.square().sum().sqrt();
+    eg_norm = g.square().sum().sqrt();
+    T local_lr = lr[0];
+    if (ep_norm(0) > 0 && eg_norm(0) > 0) {
+      local_lr = lr[0] * lars_coeff * ep_norm(0) /
+                 (eg_norm(0) + lars_weight_decay * ep_norm(0));
+    }
+    v_out = v * mu + local_lr * (g + lars_weight_decay * p);
+    p_out = p - v_out;
+  }
+};
+
+}  // namespace operators
+}  // namespace paddle

paddle/fluid/operators/listen_and_serv_op.cc

@@ -27,6 +27,10 @@ limitations under the License. */
 #include "paddle/fluid/operators/distributed/request_handler_impl.h"
 #include "paddle/fluid/operators/listen_and_serv_op.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");
+
 namespace paddle {
 namespace operators {
 
@@ -332,11 +336,14 @@ void ListenAndServOp::RunImpl(const framework::Scope &scope,
       sync_mode, checkpoint_block_id));
 
   rpc_service_->RegisterRPC(distributed::kRequestSend,
-                            request_send_handler_.get());
+                            request_send_handler_.get(),
+                            FLAGS_rpc_send_thread_num);
   rpc_service_->RegisterRPC(distributed::kRequestGet,
-                            request_get_handler_.get());
+                            request_get_handler_.get(),
+                            FLAGS_rpc_get_thread_num);
   rpc_service_->RegisterRPC(distributed::kRequestPrefetch,
-                            request_prefetch_handler_.get());
+                            request_prefetch_handler_.get(),
+                            FLAGS_rpc_prefetch_thread_num);
   rpc_service_->RegisterRPC(distributed::kRequestCheckpoint,
                             request_checkpoint_handler_.get());
 

paddle/fluid/operators/lookup_table_op.cc

@@ -81,6 +81,12 @@ class LookupTableOpMaker : public framework::OpProtoAndCheckerMaker {
                      "Otherwise the given value indicates padding the output "
                      "with zeros whenever lookup encounters it in Ids.")
         .SetDefault(kNoPadding);
+    // NOTE(minqiyang): grad_inplace is an temporal attribute,
+    // please do NOT set this attribute in python layer.
+    AddAttr<bool>("grad_inplace",
+                  "(boolean, default false) "
+                  "If the grad op reuse the input's variable.")
+        .SetDefault(false);
     AddComment(R"DOC(
 Lookup Table Operator.
 
@@ -115,7 +121,7 @@ class LookupTableOpGrad : public framework::OperatorWithKernel {
  protected:
   framework::OpKernelType GetExpectedKernelType(
       const framework::ExecutionContext& ctx) const override {
-    auto data_type = framework::GetDataTypeOfVar(ctx.InputVar("W"));
+    auto data_type = framework::GetDataTypeOfVar(ctx.InputVar("Out"));
     return framework::OpKernelType(data_type, ctx.device_context());
   }
 };

paddle/fluid/operators/lookup_table_op.h

@@ -21,6 +21,7 @@ limitations under the License. */
 #include "paddle/fluid/framework/lod_tensor.h"
 #include "paddle/fluid/framework/op_registry.h"
 #include "paddle/fluid/framework/selected_rows.h"
+#include "paddle/fluid/operators/math/blas.h"
 
 namespace paddle {
 namespace operators {
@@ -68,6 +69,7 @@ class LookupTableKernel : public framework::OpKernel<T> {
       const auto *table = table_t.value().data<T>();
       auto *output = output_t->mutable_data<T>(context.GetPlace());
 
+      auto blas = math::GetBlas<platform::CPUDeviceContext, T>(context);
       for (int64_t i = 0; i < ids_numel; ++i) {
         if (padding_idx != kNoPadding && ids[i] == padding_idx) {
           memset(output + i * row_width, 0, row_width * sizeof(T));
@@ -75,8 +77,8 @@ class LookupTableKernel : public framework::OpKernel<T> {
           PADDLE_ENFORCE_GE(ids[i], 0);
           auto id_index = table_t.Index(ids[i]);
           PADDLE_ENFORCE_GE(id_index, 0, "the input key should be exists.");
-          memcpy(output + i * row_width, table + id_index * row_width,
-                 row_width * sizeof(T));
+          blas.VCOPY(row_width, table + id_index * row_width,
+                     output + i * row_width);
         }
       }
     }
@@ -111,27 +113,37 @@ class LookupTableGradKernel : public framework::OpKernel<T> {
       auto *ids_data = ids->data<int64_t>();
       int64_t ids_num = ids->numel();
 
-      framework::Vector<int64_t> new_rows;
-      new_rows.reserve(ids_num);
-      for (int64_t i = 0; i < ids_num; i++) {
-        new_rows.push_back(ids_data[i]);
-      }
+      std::vector<int64_t> new_rows;
+      new_rows.resize(ids_num);
+      std::memcpy(&new_rows[0], ids_data, ids_num * sizeof(int64_t));
       d_table->set_rows(new_rows);
 
       auto *d_table_value = d_table->mutable_value();
       d_table_value->Resize({ids_num, table_dim[1]});
-      d_table_value->mutable_data<T>(context.GetPlace());
-
-      d_table->set_height(table_dim[0]);
-
-      auto *d_output_data = d_output->data<T>();
-      auto *d_table_data = d_table_value->data<T>();
-
-      auto d_output_dims = d_output->dims();
-      PADDLE_ENFORCE_EQ(
-          d_table_value->dims(),
-          framework::flatten_to_2d(d_output_dims, d_output_dims.size() - 1));
-      memcpy(d_table_data, d_output_data, sizeof(T) * d_output->numel());
+      // FIXME(minqiyang):
+      // memory optimization will NOT reuse Tensor with SelectedRows
+      // so we could just share the tensor here directly.
+      // However, the InferVarType method will infer the output SelectedRows
+      // to Tensor sometimes, which is a bug, so we will add an attribute
+      // here to indicate the inplace and remove this attribute after
+      // the InferVarType's bug was fixed
+      bool grad_inplace = context.Attr<bool>("grad_inplace");
+      if (grad_inplace) {
+        d_table_value->ShareDataWith(*d_output);
+      } else {
+        d_table_value->mutable_data<T>(context.GetPlace());
+
+        d_table->set_height(table_dim[0]);
+
+        auto *d_output_data = d_output->data<T>();
+        auto *d_table_data = d_table_value->data<T>();
+
+        auto d_output_dims = d_output->dims();
+        PADDLE_ENFORCE_EQ(
+            d_table_value->dims(),
+            framework::flatten_to_2d(d_output_dims, d_output_dims.size() - 1));
+        memcpy(d_table_data, d_output_data, sizeof(T) * d_output->numel());
+      }
     } else {
       auto *ids = context.Input<LoDTensor>("Ids");
       auto *d_output = context.Input<LoDTensor>(framework::GradVarName("Out"));

paddle/fluid/operators/math/selected_rows_functor.cc

@@ -12,9 +12,8 @@ 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 <map>
 #include <set>
-#include <vector>
+#include <unordered_map>
 
 #include "paddle/fluid/operators/math/blas.h"
 #include "paddle/fluid/operators/math/selected_rows_functor.h"
@@ -230,8 +229,24 @@ template struct SelectedRowsAddToTensor<platform::CPUDeviceContext, int64_t>;
 // add or mul.
 namespace scatter {
 
-size_t FindPos(const std::vector<int64_t>& rows, int64_t value) {
-  return std::find(rows.begin(), rows.end(), value) - rows.begin();
+template <typename DeviceContext, typename T>
+typename std::enable_if<
+    std::is_floating_point<T>::value &&
+    std::is_same<DeviceContext, platform::CPUDeviceContext>::value>::type
+elementwise_add_to(const DeviceContext& ctx, BlasT<DeviceContext, T>* blas,
+                   size_t data_len, const T* in, T* out) {
+  blas->AXPY(data_len, 1., in, out);
+}
+
+template <typename DeviceContext, typename T>
+typename std::enable_if<
+    !std::is_floating_point<T>::value &&
+    std::is_same<DeviceContext, platform::CPUDeviceContext>::value>::type
+elementwise_add_to(const DeviceContext& ctx, BlasT<DeviceContext, T>* blas,
+                   size_t data_len, const T* in, T* out) {
+  for (int64_t i = 0; i < data_len; i++) {
+    out[i] += in[i];
+  }
 }
 
 template <typename T>
@@ -246,48 +261,84 @@ struct MergeAdd<platform::CPUDeviceContext, T> {
   void operator()(const platform::CPUDeviceContext& context,
                   const framework::SelectedRows& input,
                   framework::SelectedRows* output) {
-    framework::SelectedRows& out = *output;
-    std::vector<int64_t> input_rows(input.rows());
+    std::vector<const framework::SelectedRows*> inputs;
+    inputs.push_back(&input);
+    (*this)(context, inputs, output);
+  }
 
-    std::map<int64_t, std::vector<int64_t>> merge_row_map;
-    for (size_t i = 0; i < input_rows.size(); ++i) {
-      merge_row_map[input_rows[i]].push_back(i);
+  void operator()(const platform::CPUDeviceContext& context,
+                  const std::vector<const framework::SelectedRows*>& inputs,
+                  framework::SelectedRows* output) {
+    if (inputs.size() == 0) {
+      VLOG(3) << "no input! return";
+      return;
     }
-
-    std::vector<int64_t> merge_rows(merge_row_map.size());
-    size_t idx = 0;
-    int64_t input_width = input.value().dims()[1];
-    out.set_height(input.height());
-
-    T* out_data = out.mutable_value()->mutable_data<T>(
+    const framework::SelectedRows* has_value_input = nullptr;
+    for (auto* in : inputs) {
+      if (in->rows().size() > 0) {
+        has_value_input = in;
+        break;
+      }
+    }
+    if (has_value_input == nullptr) {
+      VLOG(3) << "no input has value! just return" << std::endl;
+      return;
+    }
+    auto input_width = has_value_input->value().dims()[1];
+    auto input_height = has_value_input->height();
+    framework::SelectedRows& out = *output;
+    std::set<int64_t> merged_row_set;
+    for (auto* input : inputs) {
+      if (input->rows().size() == 0) {
+        continue;
+      }
+      PADDLE_ENFORCE_EQ(input_width, input->value().dims()[1],
+                        "all input should have same "
+                        "dimension except for the first one");
+      PADDLE_ENFORCE_EQ(input_height, input->height(),
+                        "all input should have same height");
+      merged_row_set.insert(input->rows().begin(), input->rows().end());
+    }
+    std::vector<int64_t> merge_rows(merged_row_set.begin(),
+                                    merged_row_set.end());
+    std::unordered_map<int64_t, size_t> rows_to_id;
+    for (size_t i = 0; i < merge_rows.size(); ++i) {
+      rows_to_id[merge_rows[i]] = i;
+    }
+    out.set_rows(merge_rows);
+    out.set_height(input_height);
+    out.mutable_value()->mutable_data<T>(
         framework::make_ddim(
             {static_cast<int64_t>(merge_rows.size()), input_width}),
         context.GetPlace());
-    const T* in_data = input.value().data<T>();
-
-    for (auto& row_pair : merge_row_map) {
-      auto* out_ptr = out_data + idx * input_width;
-      auto& rows = row_pair.second;
-      merge_rows[idx] = row_pair.first;
-      ++idx;
-      // rows.size() is always larger than 0
-      std::memcpy(out_ptr, in_data + rows[0] * input_width,
-                  sizeof(T) * input_width);
-
-      for (size_t i = 1; i < rows.size(); ++i) {
-        auto* in_ptr = in_data + rows[i] * input_width;
-        for (int64_t j = 0; j < input_width; ++j) {
-          out_ptr[j] += in_ptr[j];
-        }
+
+    math::SetConstant<platform::CPUDeviceContext, T> constant_functor;
+    constant_functor(context, out.mutable_value(), 0.0);
+
+    auto* out_data = out.mutable_value()->data<T>();
+
+    auto blas = math::GetBlas<platform::CPUDeviceContext, T>(context);
+    for (auto* input : inputs) {
+      if (input->rows().size() == 0) {
+        continue;
+      }
+      auto* input_data = input->value().data<T>();
+      auto& input_rows = input->rows();
+
+      for (size_t i = 0; i < input_rows.size(); i++) {
+        size_t out_i = rows_to_id[input_rows[i]];
+        elementwise_add_to<platform::CPUDeviceContext, T>(
+            context, &blas, static_cast<size_t>(input_width),
+            &input_data[i * input_width], &out_data[out_i * input_width]);
       }
     }
-
-    out.set_rows(merge_rows);
   }
 };
 
 template struct MergeAdd<platform::CPUDeviceContext, int>;
 template struct MergeAdd<platform::CPUDeviceContext, int64_t>;
+template struct MergeAdd<platform::CPUDeviceContext, float>;
+template struct MergeAdd<platform::CPUDeviceContext, double>;
 
 template <typename T>
 struct UpdateToTensor<platform::CPUDeviceContext, T> {

paddle/fluid/operators/math/selected_rows_functor.cu

@@ -267,10 +267,15 @@ struct MergeAdd<platform::CUDADeviceContext, T> {
   void operator()(const platform::CUDADeviceContext& context,
                   const framework::SelectedRows& input,
                   framework::SelectedRows* output) {
-    framework::SelectedRows& out = *output;
     framework::Vector<int64_t> input_rows(input.rows());
+    if (input_rows.size() == 0) {
+      return;
+    }
+
+    framework::SelectedRows& out = *output;
     std::set<int64_t> row_set(input_rows.begin(), input_rows.end());
-    std::vector<int64_t> merge_rows(row_set.begin(), row_set.end());
+    std::vector<int64_t> merge_rows_cpu(row_set.begin(), row_set.end());
+    framework::Vector<int64_t> merge_rows(merge_rows_cpu);
 
     auto input_width = input.value().dims()[1];
 
@@ -296,6 +301,73 @@ struct MergeAdd<platform::CUDADeviceContext, T> {
         out.mutable_rows()->CUDAMutableData(context.GetPlace()),
         out.rows().size(), input_width);
   }
+
+  void operator()(const platform::CUDADeviceContext& context,
+                  const std::vector<const framework::SelectedRows*>& inputs,
+                  framework::SelectedRows* output) {
+    if (inputs.size() == 0) {
+      VLOG(3) << "no input! return";
+      return;
+    }
+    const framework::SelectedRows* has_value_input = nullptr;
+    for (auto* in : inputs) {
+      if (in->rows().size() > 0) {
+        has_value_input = in;
+        break;
+      }
+    }
+    if (has_value_input == nullptr) {
+      VLOG(3) << "no input has value! just return" << std::endl;
+      return;
+    }
+    auto input_width = has_value_input->value().dims()[1];
+    auto input_height = has_value_input->height();
+    framework::SelectedRows& out = *output;
+    std::set<int64_t> merged_row_set;
+    for (auto* input : inputs) {
+      if (input->rows().size() == 0) {
+        continue;
+      }
+      PADDLE_ENFORCE_EQ(input_width, input->value().dims()[1],
+                        "all input should have same "
+                        "dimension except for the first one");
+      PADDLE_ENFORCE_EQ(input_height, input->height(),
+                        "all input should have same height");
+      merged_row_set.insert(input->rows().begin(), input->rows().end());
+    }
+    std::vector<int64_t> merge_rows_cpu(merged_row_set.begin(),
+                                        merged_row_set.end());
+    framework::Vector<int64_t> merge_rows(merge_rows_cpu);
+
+    out.set_rows(merge_rows);
+    out.set_height(input_height);
+    out.mutable_value()->mutable_data<T>(
+        framework::make_ddim(
+            {static_cast<int64_t>(merge_rows.size()), input_width}),
+        context.GetPlace());
+
+    math::SetConstant<platform::CUDADeviceContext, T> constant_functor;
+    constant_functor(context, out.mutable_value(), 0.0);
+
+    auto* out_data = out.mutable_value()->data<T>();
+
+    const int block_size = 256;
+    dim3 threads(block_size, 1);
+
+    for (auto* input : inputs) {
+      if (input->rows().size() == 0) {
+        continue;
+      }
+      auto* input_data = input->value().data<T>();
+      auto& input_rows = input->rows();
+      dim3 grid1(input_rows.size(), 1);
+
+      MergeAddKernel<T, 256><<<grid1, threads, 0, context.stream()>>>(
+          input_data, input_rows.CUDAData(context.GetPlace()), out_data,
+          out.mutable_rows()->CUDAMutableData(context.GetPlace()),
+          out.rows().size(), input_width);
+    }
+  }
 };
 
 template struct MergeAdd<platform::CUDADeviceContext, float>;

paddle/fluid/operators/math/selected_rows_functor.h

@@ -83,104 +83,9 @@ struct MergeAdd {
   void operator()(const DeviceContext& context,
                   const framework::SelectedRows& input,
                   framework::SelectedRows* output);
-};
-
-template <>
-struct MergeAdd<platform::CPUDeviceContext, float> {
-  framework::SelectedRows operator()(const platform::CPUDeviceContext& context,
-                                     const framework::SelectedRows& input) {
-    framework::SelectedRows out;
-    (*this)(context, input, &out);
-    return out;
-  }
-
-  void operator()(const platform::CPUDeviceContext& context,
-                  const framework::SelectedRows& input,
-                  framework::SelectedRows* output) {
-    framework::SelectedRows& out = *output;
-    std::vector<int64_t> input_rows(input.rows());
-
-    std::map<int64_t, std::vector<int64_t>> merge_row_map;
-    for (size_t i = 0; i < input_rows.size(); ++i) {
-      merge_row_map[input_rows[i]].push_back(i);
-    }
-
-    std::vector<int64_t> merge_rows(merge_row_map.size());
-    size_t idx = 0;
-    int64_t input_width = input.value().dims()[1];
-    out.set_height(input.height());
-
-    auto* out_data = out.mutable_value()->mutable_data<float>(
-        framework::make_ddim(
-            {static_cast<int64_t>(merge_rows.size()), input_width}),
-        context.GetPlace());
-    auto* in_data = input.value().data<float>();
-
-    auto blas = GetBlas<platform::CPUDeviceContext, float>(context);
-    for (auto& row_pair : merge_row_map) {
-      auto* out_ptr = out_data + idx * input_width;
-      auto& rows = row_pair.second;
-      merge_rows[idx] = row_pair.first;
-      ++idx;
-      // rows.size() is always larger than 0
-      blas.VCOPY(input_width, in_data + rows[0] * input_width, out_ptr);
-
-      for (size_t i = 1; i < rows.size(); ++i) {
-        blas.AXPY(input_width, 1., in_data + rows[i] * input_width, out_ptr);
-      }
-    }
-
-    out.set_rows(merge_rows);
-  }
-};
-
-template <>
-struct MergeAdd<platform::CPUDeviceContext, double> {
-  framework::SelectedRows operator()(const platform::CPUDeviceContext& context,
-                                     const framework::SelectedRows& input) {
-    framework::SelectedRows out;
-    (*this)(context, input, &out);
-    return out;
-  }
-
-  void operator()(const platform::CPUDeviceContext& context,
-                  const framework::SelectedRows& input,
-                  framework::SelectedRows* output) {
-    framework::SelectedRows& out = *output;
-    std::vector<int64_t> input_rows(input.rows());
-
-    std::map<int64_t, std::vector<int64_t>> merge_row_map;
-    for (size_t i = 0; i < input_rows.size(); ++i) {
-      merge_row_map[input_rows[i]].push_back(i);
-    }
-
-    std::vector<int64_t> merge_rows(merge_row_map.size());
-    size_t idx = 0;
-    int64_t input_width = input.value().dims()[1];
-    out.set_height(input.height());
-
-    auto* out_data = out.mutable_value()->mutable_data<double>(
-        framework::make_ddim(
-            {static_cast<int64_t>(merge_rows.size()), input_width}),
-        context.GetPlace());
-    auto* in_data = input.value().data<double>();
-
-    auto blas = GetBlas<platform::CPUDeviceContext, double>(context);
-    for (auto& row_pair : merge_row_map) {
-      auto* out_ptr = out_data + idx * input_width;
-      auto& rows = row_pair.second;
-      merge_rows[idx] = row_pair.first;
-      ++idx;
-      // rows.size() is always larger than 0
-      blas.VCOPY(input_width, in_data + rows[0] * input_width, out_ptr);
-
-      for (size_t i = 1; i < rows.size(); ++i) {
-        blas.AXPY(input_width, 1., in_data + rows[i] * input_width, out_ptr);
-      }
-    }
-
-    out.set_rows(merge_rows);
-  }
+  void operator()(const DeviceContext& context,
+                  const std::vector<const framework::SelectedRows*>& inputs,
+                  framework::SelectedRows* output);
 };
 
 template <typename DeviceContext, typename T>

paddle/fluid/operators/math/selected_rows_functor_test.cc

@@ -302,6 +302,64 @@ TEST(selected_rows_functor, cpu_merge_add_int) {
   EXPECT_EQ(out_data[1 * row_numel], 2);
   EXPECT_EQ(out_data[2 * row_numel], 1);
 }
+
+TEST(selected_rows_functor, cpu_merge_add_multi) {
+  paddle::platform::CPUPlace cpu_place;
+  paddle::platform::CPUDeviceContext ctx(cpu_place);
+  paddle::operators::math::SetConstant<paddle::platform::CPUDeviceContext,
+                                       float>
+      set_const;
+
+  int64_t height = 10;
+  int64_t row_numel = 8;
+
+  std::vector<int64_t> rows1{5, 2, 5, 3, 5};
+  std::unique_ptr<paddle::framework::SelectedRows> selected_rows1{
+      new paddle::framework::SelectedRows(rows1, height)};
+  auto* in1_value = selected_rows1->mutable_value();
+  in1_value->mutable_data<float>(
+      paddle::framework::make_ddim(
+          {static_cast<int64_t>(rows1.size()), row_numel}),
+      cpu_place);
+  set_const(ctx, in1_value, 1.0);
+
+  std::vector<int64_t> rows2{2, 5, 3, 5, 3};
+  std::unique_ptr<paddle::framework::SelectedRows> selected_rows2{
+      new paddle::framework::SelectedRows(rows2, height)};
+  auto* in2_value = selected_rows2->mutable_value();
+  in2_value->mutable_data<float>(
+      paddle::framework::make_ddim(
+          {static_cast<int64_t>(rows2.size()), row_numel}),
+      cpu_place);
+  set_const(ctx, in2_value, 1.0);
+
+  std::unique_ptr<paddle::framework::SelectedRows> output{
+      new paddle::framework::SelectedRows()};
+  output->set_height(height);
+  paddle::operators::math::scatter::MergeAdd<paddle::platform::CPUDeviceContext,
+                                             float>
+      merge_add_functor;
+
+  std::vector<const paddle::framework::SelectedRows*> inputs;
+  inputs.push_back(selected_rows1.get());
+  inputs.push_back(selected_rows2.get());
+  merge_add_functor(ctx, inputs, output.get());
+
+  EXPECT_EQ(output->height(), height);
+  EXPECT_EQ(output->value().dims(),
+            paddle::framework::make_ddim({3, row_numel}));
+
+  std::vector<int64_t> ret_rows{2, 3, 5};
+  EXPECT_EQ(output->rows(), ret_rows);
+
+  auto* out_data = output->value().data<float>();
+  for (size_t i = 0; i < ret_rows.size(); ++i) {
+    for (size_t j = 0; j < row_numel; ++j) {
+      EXPECT_EQ(out_data[i * row_numel + j], ret_rows[i]);
+    }
+  }
+}
+
 TEST(selected_rows_functor, cpu_sum_to) {
   paddle::platform::CPUPlace cpu_place;
   paddle::platform::CPUDeviceContext ctx(cpu_place);
@@ -318,6 +376,7 @@ TEST(selected_rows_functor, cpu_sum_to) {
       paddle::framework::make_ddim(
           {static_cast<int64_t>(rows1.size()), row_numel}),
       cpu_place);
+
   functor(ctx, in1_value, 1.0);
   std::vector<int64_t> rows2{0, 5, 7, 9};
   std::unique_ptr<paddle::framework::SelectedRows> selected_rows2{
@@ -327,6 +386,7 @@ TEST(selected_rows_functor, cpu_sum_to) {
       paddle::framework::make_ddim(
           {static_cast<int64_t>(rows2.size()), row_numel}),
       cpu_place);
+
   functor(ctx, in2_value, 2.0);
   std::unique_ptr<paddle::framework::SelectedRows> output{
       new paddle::framework::SelectedRows()};

paddle/fluid/operators/math/selected_rows_functor_test.cu

@@ -241,3 +241,67 @@ TEST(selected_rows_functor, gpu_add_to) {
   // row9: 2.0 + 3.0
   EXPECT_EQ(tensor1_cpu_data[9 * row_numel + 6], 5.0);
 }
+
+TEST(selected_rows_functor, gpu_merge_add) {
+  paddle::platform::CUDAPlace gpu_place(0);
+  paddle::platform::CPUPlace cpu_place;
+  paddle::platform::CUDADeviceContext& ctx =
+      *reinterpret_cast<paddle::platform::CUDADeviceContext*>(
+          paddle::platform::DeviceContextPool::Instance().Get(gpu_place));
+  paddle::operators::math::SetConstant<paddle::platform::CUDADeviceContext,
+                                       float>
+      set_const;
+
+  int64_t height = 10;
+  int64_t row_numel = 8;
+
+  std::vector<int64_t> rows1{5, 2, 5, 3, 5};
+  std::unique_ptr<paddle::framework::SelectedRows> selected_rows1{
+      new paddle::framework::SelectedRows(rows1, height)};
+  auto* in1_value = selected_rows1->mutable_value();
+  in1_value->mutable_data<float>(
+      paddle::framework::make_ddim(
+          {static_cast<int64_t>(rows1.size()), row_numel}),
+      gpu_place);
+  set_const(ctx, in1_value, 1.0);
+
+  std::vector<int64_t> rows2{2, 5, 3, 5, 3};
+  std::unique_ptr<paddle::framework::SelectedRows> selected_rows2{
+      new paddle::framework::SelectedRows(rows2, height)};
+  auto* in2_value = selected_rows2->mutable_value();
+  in2_value->mutable_data<float>(
+      paddle::framework::make_ddim(
+          {static_cast<int64_t>(rows2.size()), row_numel}),
+      gpu_place);
+  set_const(ctx, in2_value, 1.0);
+
+  std::unique_ptr<paddle::framework::SelectedRows> output{
+      new paddle::framework::SelectedRows()};
+  output->set_height(height);
+  paddle::operators::math::scatter::MergeAdd<
+      paddle::platform::CUDADeviceContext, float>
+      merge_add_functor;
+
+  std::vector<const paddle::framework::SelectedRows*> inputs;
+  inputs.push_back(selected_rows1.get());
+  inputs.push_back(selected_rows2.get());
+  merge_add_functor(ctx, inputs, output.get());
+
+  paddle::framework::Tensor output_cpu;
+  paddle::framework::TensorCopy(output->value(), cpu_place, ctx, &output_cpu);
+  ctx.Wait();
+
+  EXPECT_EQ(output->height(), height);
+  EXPECT_EQ(output->value().dims(),
+            paddle::framework::make_ddim({3, row_numel}));
+
+  std::vector<int64_t> ret_rows{2, 3, 5};
+  EXPECT_EQ(output->rows(), ret_rows);
+
+  auto* out_data = output_cpu.data<float>();
+  for (size_t i = 0; i < ret_rows.size(); ++i) {
+    for (size_t j = 0; j < row_numel; ++j) {
+      EXPECT_EQ(out_data[i * row_numel + j], ret_rows[i]);
+    }
+  }
+}

paddle/fluid/operators/merge_ids_op.cc

@@ -20,13 +20,16 @@ namespace operators {
 class MergeIdsOpMaker : public framework::OpProtoAndCheckerMaker {
  public:
   void Make() override {
-    AddInput("Ids", "(LoDTensor) the input ids with shape{batch_num, 1}");
-    AddInput(
-        "X",
-        "(LoDTensors) multi input tensor with shape{batch_num, N}, N is the "
-        "size of embedding table")
+    AddInput("Ids", "(LoDTensor) the input ids with shape{batch_num, 1}")
+        .AsDuplicable();
+    AddInput("Rows", "(LoDTensor) the input ids with shape{row_size, 1}, ")
+        .AsDuplicable();
+    AddInput("X",
+             "(LoDTensors) multi input tensor with shape{Rows, N}, N is the "
+             "size of embedding table")
+        .AsDuplicable();
+    AddOutput("Out", "(LoDTensor) The merged outputs of the input tensors.")
         .AsDuplicable();
-    AddOutput("Out", "(LoDTensor) The merged outputs of the input tensors.");
 
     AddComment(R"DOC(
 Merge multi LoDTensor's into one according to Ids's shard num.
@@ -79,15 +82,19 @@ class MergeIdsOp : public framework::OperatorWithKernel {
   using framework::OperatorWithKernel::OperatorWithKernel;
 
   void InferShape(framework::InferShapeContext *ctx) const override {
-    PADDLE_ENFORCE(ctx->HasInput("Ids"), "MergeIdsOp must has input Ids.");
-    PADDLE_ENFORCE(ctx->HasInputs("X"), "MergeIdsOp must has input X.");
-    PADDLE_ENFORCE(ctx->HasOutput("Out"), "MergeIdsOp must has output Out.");
+    PADDLE_ENFORCE(ctx->HasInputs("Ids"),
+                   "MergeIdsOp must has multi input Ids.");
+    PADDLE_ENFORCE(ctx->HasInputs("Rows"),
+                   "MergeIdsOp must has multi input Rows.");
+    PADDLE_ENFORCE(ctx->HasInputs("X"), "MergeIdsOp must has multi input X.");
+    PADDLE_ENFORCE(ctx->HasOutputs("Out"),
+                   "MergeIdsOp must has multi output Out.");
 
     auto ids_var_type = ctx->GetInputsVarType("Ids").front();
-    auto ids_dims = ctx->GetInputDim("Ids");
+    auto ids_dims = ctx->GetInputsDim("Ids");
     if (ids_var_type == framework::proto::VarType::LOD_TENSOR) {
-      PADDLE_ENFORCE_EQ(ids_dims.size(), 2);
-      PADDLE_ENFORCE_EQ(ids_dims[1], 1);
+      PADDLE_ENFORCE_EQ(ids_dims[0].size(), 2);
+      PADDLE_ENFORCE_EQ(ids_dims[0][1], 1);
     }
     auto x_var_type = ctx->GetInputsVarType("X");
     for (auto &var_type : x_var_type) {

paddle/fluid/operators/merge_ids_op.h

@@ -14,6 +14,8 @@ limitations under the License. */
 
 #pragma once
 
+#include <tuple>
+#include <unordered_map>
 #include <vector>
 #include "paddle/fluid/framework/op_registry.h"
 #include "paddle/fluid/framework/tensor_util.h"
@@ -30,59 +32,70 @@ class MergeIdsOpKernel : public framework::OpKernel<T> {
     if (!platform::is_cpu_place(place)) {
       PADDLE_THROW("MergeIds do not support GPU kernel");
     }
-    VLOG(3) << "run in MergeIdsOpKernel";
 
-    const auto *ids_var = ctx.InputVar("Ids");
-    PADDLE_ENFORCE(ids_var->IsType<framework::LoDTensor>(),
-                   "only support to merge Ids of LoDTensor");
+    const auto ids = ctx.MultiInput<framework::LoDTensor>("Ids");
+    const auto row_ids = ctx.MultiInput<framework::LoDTensor>("Rows");
+    const auto x_tensors = ctx.MultiInput<framework::LoDTensor>("X");
+    auto outs = ctx.MultiOutput<framework::LoDTensor>("Out");
 
-    const auto &ids_tensor = ids_var->Get<framework::LoDTensor>();
-    const auto &ids_dims = ids_tensor.dims();
-    const int64_t *ids = ids_tensor.data<int64_t>();
+    PADDLE_ENFORCE_EQ(row_ids.size(), x_tensors.size(),
+                      "the number of Rows and X should be the same");
+    PADDLE_ENFORCE_EQ(ids.size(), outs.size(),
+                      "the number of Ids and Out should be the same");
 
-    auto x_tensors = ctx.MultiInput<framework::LoDTensor>("X");
+    int row_ids_size = 0;
+    int row_size = 0;
+    int embedding_size = 0;
 
-    auto *out = ctx.Output<framework::LoDTensor>("Out");
+    for (int i = 0; i < x_tensors.size(); ++i) {
+      const auto *x_tensor = x_tensors[i];
+      const auto *row_id = row_ids[i];
 
-    int batch_size = 0;
-    int embedding_size = 0;
-    for (auto &input : x_tensors) {
-      if (framework::product(input->dims()) != 0) {
-        if (embedding_size == 0) {
-          embedding_size = input->dims()[1];
-        }
-        PADDLE_ENFORCE_EQ(embedding_size, input->dims()[1],
-                          "embedding size of all input should be the same");
-        batch_size += input->dims()[0];
+      if (embedding_size == 0) {
+        embedding_size = x_tensor->dims()[1];
       }
+      PADDLE_ENFORCE_EQ(embedding_size, x_tensor->dims()[1],
+                        "embedding size of all input should be the same");
+      row_size += x_tensor->dims()[0];
+      row_ids_size += row_id->dims()[0];
     }
+
     PADDLE_ENFORCE_EQ(
-        batch_size, ids_dims[0],
-        "the batch size of ids and merged embedding value should be the same");
+        row_size, row_ids_size,
+        "the merged X dim[0] and merged Rows dim[0] should be the same");
+
+    std::unordered_map<int64_t, std::tuple<int64_t, int64_t>>
+        selected_rows_idx_map;
+    for (int i = 0; i < x_tensors.size(); ++i) {
+      const auto *row_id = row_ids[i];
+
+      for (int j = 0; j < row_id->numel(); ++j) {
+        int64_t key = row_id->data<int64_t>()[j];
+        std::tuple<int64_t, int64_t> val = std::make_tuple(i, j);
+        selected_rows_idx_map.insert(std::make_pair(key, val));
+      }
+    }
+    PADDLE_ENFORCE_EQ(row_ids_size, selected_rows_idx_map.size(),
+                      "the rows and tensor map size should be the same");
+
+    for (int i = 0; i < outs.size(); ++i) {
+      auto *out_ids = ids[i];
+      auto *out = outs[i];
 
-    const size_t shard_num = x_tensors.size();
+      out->set_lod(out_ids->lod());
 
-    if (shard_num == 1) {
-      VLOG(3) << "only one shard, we can copy the data directly";
-      TensorCopy(*x_tensors[0], place, out);
-    } else {
-      std::vector<int> in_indexs(shard_num, 0);
+      int nums = static_cast<int>(out_ids->dims()[0]);
       auto *out_data = out->mutable_data<T>(
-          framework::make_ddim({batch_size, embedding_size}), place);
-      // copy data from ins[shard_num] to out.
-      for (int i = 0; i < ids_dims[0]; ++i) {
-        int64_t id = ids[i];
-        size_t shard_id = static_cast<size_t>(id) % shard_num;
-        int index = in_indexs[shard_id];
-        memcpy(out_data + embedding_size * i,
-               x_tensors[shard_id]->data<T>() + index * embedding_size,
+          framework::make_ddim({nums, embedding_size}), place);
+      for (int j = 0; j < nums; ++j) {
+        int id = out_ids->data<int64_t>()[j];
+        auto row_tuple = selected_rows_idx_map[id];
+        int64_t row_idx = std::get<1>(row_tuple);
+        const auto *x_tensor = x_tensors[std::get<0>(row_tuple)];
+
+        memcpy(out_data + embedding_size * j,
+               x_tensor->data<T>() + row_idx * embedding_size,
                sizeof(T) * embedding_size);
-        in_indexs[shard_id] += 1;
-      }
-
-      for (size_t i = 0; i < shard_num; ++i) {
-        PADDLE_ENFORCE_EQ(in_indexs[i], x_tensors[i]->dims()[0],
-                          "after merge, all data in x_tensor should be used");
       }
     }
   }

paddle/fluid/operators/momentum_op.cc

@@ -19,54 +19,6 @@ namespace operators {
 
 using Tensor = framework::Tensor;
 
-class MomentumOp : public framework::OperatorWithKernel {
- public:
-  using framework::OperatorWithKernel::OperatorWithKernel;
-
- protected:
-  void InferShape(framework::InferShapeContext* ctx) const override {
-    PADDLE_ENFORCE(ctx->HasInput("Param"),
-                   "Input(param) of Momentum should not be null.");
-    PADDLE_ENFORCE(ctx->HasInput("Grad"),
-                   "Input(grad) of Momentum should not be null.");
-    PADDLE_ENFORCE(ctx->HasInput("Velocity"),
-                   "Input(velocity) of Momentum should not be null.");
-    PADDLE_ENFORCE(ctx->HasInput("LearningRate"),
-                   "Input(LearningRate) of Momentum should not be null.");
-    PADDLE_ENFORCE(
-        ctx->GetInputsVarType("Param").front() ==
-            framework::proto::VarType::LOD_TENSOR,
-        "The input var's type should be LoDTensor, but the received is %s",
-        ctx->Inputs("Param").front(), ctx->GetInputsVarType("Param").front());
-
-    PADDLE_ENFORCE(ctx->HasOutput("ParamOut"),
-                   "Output(ParamOut) of Momentum should not be null.");
-    PADDLE_ENFORCE(ctx->HasOutput("VelocityOut"),
-                   "Output(VelocityOut) of Momentum should not be null.");
-
-    auto param_dim = ctx->GetInputDim("Param");
-    if (ctx->GetInputsVarType("Grad")[0] ==
-        framework::proto::VarType::LOD_TENSOR) {
-      PADDLE_ENFORCE_EQ(
-          param_dim, ctx->GetInputDim("Grad"),
-          "Param and Grad input of MomentumOp should have the same dimension.");
-      PADDLE_ENFORCE_EQ(
-          param_dim, ctx->GetInputDim("Velocity"),
-          "Param and Velocity of MomentumOp should have the same dimension.");
-    }
-    PADDLE_ENFORCE_EQ(framework::product(ctx->GetInputDim("LearningRate")), 1,
-                      "Learning_rate should be a scalar");
-
-    ctx->SetOutputDim("ParamOut", param_dim);
-    ctx->SetOutputDim("VelocityOut", param_dim);
-  }
-  framework::OpKernelType GetExpectedKernelType(
-      const framework::ExecutionContext& ctx) const override {
-    auto input_data_type = framework::GetDataTypeOfVar(ctx.InputVar("Param"));
-    return framework::OpKernelType(input_data_type, ctx.GetPlace());
-  }
-};
-
 class MomentumOpInferVarType : public framework::VarTypeInference {
  public:
   void operator()(const framework::OpDesc& op_desc,

paddle/fluid/operators/momentum_op.h

@@ -28,6 +28,54 @@ using framework::SelectedRows;
 struct NoNesterov;
 struct UseNesterov;
 
+class MomentumOp : public framework::OperatorWithKernel {
+ public:
+  using framework::OperatorWithKernel::OperatorWithKernel;
+
+ protected:
+  void InferShape(framework::InferShapeContext* ctx) const override {
+    PADDLE_ENFORCE(ctx->HasInput("Param"),
+                   "Input(param) of Momentum should not be null.");
+    PADDLE_ENFORCE(ctx->HasInput("Grad"),
+                   "Input(grad) of Momentum should not be null.");
+    PADDLE_ENFORCE(ctx->HasInput("Velocity"),
+                   "Input(velocity) of Momentum should not be null.");
+    PADDLE_ENFORCE(ctx->HasInput("LearningRate"),
+                   "Input(LearningRate) of Momentum should not be null.");
+    PADDLE_ENFORCE(
+        ctx->GetInputsVarType("Param").front() ==
+            framework::proto::VarType::LOD_TENSOR,
+        "The input var's type should be LoDTensor, but the received is %s",
+        ctx->Inputs("Param").front(), ctx->GetInputsVarType("Param").front());
+
+    PADDLE_ENFORCE(ctx->HasOutput("ParamOut"),
+                   "Output(ParamOut) of Momentum should not be null.");
+    PADDLE_ENFORCE(ctx->HasOutput("VelocityOut"),
+                   "Output(VelocityOut) of Momentum should not be null.");
+
+    auto param_dim = ctx->GetInputDim("Param");
+    if (ctx->GetInputsVarType("Grad")[0] ==
+        framework::proto::VarType::LOD_TENSOR) {
+      PADDLE_ENFORCE_EQ(
+          param_dim, ctx->GetInputDim("Grad"),
+          "Param and Grad input of MomentumOp should have the same dimension.");
+      PADDLE_ENFORCE_EQ(
+          param_dim, ctx->GetInputDim("Velocity"),
+          "Param and Velocity of MomentumOp should have the same dimension.");
+    }
+    PADDLE_ENFORCE_EQ(framework::product(ctx->GetInputDim("LearningRate")), 1,
+                      "Learning_rate should be a scalar");
+
+    ctx->SetOutputDim("ParamOut", param_dim);
+    ctx->SetOutputDim("VelocityOut", param_dim);
+  }
+  framework::OpKernelType GetExpectedKernelType(
+      const framework::ExecutionContext& ctx) const override {
+    auto input_data_type = framework::GetDataTypeOfVar(ctx.InputVar("Param"));
+    return framework::OpKernelType(input_data_type, ctx.GetPlace());
+  }
+};
+
 template <typename T>
 class CPUDenseMomentumFunctor {
  private:

paddle/fluid/operators/split_ids_op.cc

@@ -20,20 +20,27 @@ namespace operators {
 class SplitIdsOpMaker : public framework::OpProtoAndCheckerMaker {
  public:
   void Make() override {
-    AddInput("Ids", "(LoDTensor) the input ids with shape{batch_num, 1}");
-    AddOutput("Out", "(LoDTensor) The outputs of the input Ids.")
+    AddInput("Ids", "(LoDTensor) the input ids with shape{batch_num, 1}")
+        .AsDuplicable();
+
+    AddOutput("Out", "(LoDTensors) The outputs of the input Ids.")
         .AsDuplicable();
 
     AddComment(R"DOC(
 Split a LoDTensor of Ids into multi LoDTensors, the number is pserver's number
 Example:
   Input:
-    X = [1,2,3,4,5,6]
+    X = [[1,2,3,4,5,6],[2,3]]
 
   Out(3 output):
-    out0 = [3, 6]
-    out1 = [1, 4]
-    out2 = [2, 5]
+    if compress is True:
+        out0 = [3, 3, 6]
+        out1 = [1, 4]
+        out2 = [2, 2, 5]
+    else:
+        out0 = [3, 6]
+        out1 = [1, 4]
+        out2 = [2, 5]
 )DOC");
   }
 };
@@ -43,16 +50,24 @@ class SplitIdsOp : public framework::OperatorWithKernel {
   using framework::OperatorWithKernel::OperatorWithKernel;
 
   void InferShape(framework::InferShapeContext *ctx) const override {
-    PADDLE_ENFORCE(ctx->HasInput("Ids"), "SplitIdsOp must has input Ids.");
+    PADDLE_ENFORCE(ctx->HasInputs("Ids"), "SplitIdsOp must has input Ids.");
     PADDLE_ENFORCE(ctx->HasOutputs("Out"), "SplitIdsOp must has output Out.");
 
     auto ids_var_type = ctx->GetInputsVarType("Ids").front();
-    auto ids_dims = ctx->GetInputDim("Ids");
+    auto ids_dims = ctx->GetInputsDim("Ids");
     if (ids_var_type == framework::proto::VarType::LOD_TENSOR) {
-      PADDLE_ENFORCE_EQ(ids_dims.size(), 2);
-      PADDLE_ENFORCE_EQ(ids_dims[1], 1);
+      PADDLE_ENFORCE_EQ(ids_dims[0].size(), 2);
     }
   }
+
+ protected:
+  framework::OpKernelType GetExpectedKernelType(
+      const framework::ExecutionContext &ctx) const override {
+    return framework::OpKernelType(
+        framework::ToDataType(
+            ctx.MultiInput<framework::Tensor>("Ids").front()->type()),
+        ctx.GetPlace());
+  }
 };
 
 class SplitIdsOpInferVarType : public framework::VarTypeInference {
@@ -66,12 +81,28 @@ class SplitIdsOpInferVarType : public framework::VarTypeInference {
   }
 };
 
+class SplitIdsOpGradMaker : public framework::SingleGradOpDescMaker {
+ public:
+  using framework::SingleGradOpDescMaker::SingleGradOpDescMaker;
+
+ protected:
+  std::unique_ptr<framework::OpDesc> Apply() const override {
+    auto grad = new framework::OpDesc();
+    grad->SetType("concat");
+    grad->SetInput("X", OutputGrad("Out"));
+    grad->SetOutput("Out", InputGrad("Ids"));
+    grad->SetAttr("axis", 0);
+    return std::unique_ptr<framework::OpDesc>(grad);
+  }
+};
+
 }  // namespace operators
 }  // namespace paddle
 
 namespace ops = paddle::operators;
 REGISTER_OPERATOR(split_ids, ops::SplitIdsOp, ops::SplitIdsOpMaker,
-                  ops::SplitIdsOpInferVarType);
+                  ops::SplitIdsOpGradMaker, ops::SplitIdsOpInferVarType);
+
 REGISTER_OP_CPU_KERNEL(
     split_ids, ops::SplitIdsOpKernel<paddle::platform::CPUPlace, int64_t>,
     ops::SplitIdsOpKernel<paddle::platform::CPUPlace, float>);

paddle/fluid/operators/split_ids_op.h

@@ -14,6 +14,8 @@ limitations under the License. */
 
 #pragma once
 
+#include <iterator>
+#include <set>
 #include <unordered_map>
 #include <vector>
 #include "paddle/fluid/framework/op_registry.h"
@@ -31,19 +33,39 @@ class SplitIdsOpKernel : public framework::OpKernel<T> {
       PADDLE_THROW("SplitIds do not support GPU kernel");
     }
 
-    const auto *ids_var = ctx.InputVar("Ids");
+    const auto ids_vars = ctx.MultiInputVar("Ids");
+
+    PADDLE_ENFORCE_GT(ids_vars.size(), 0, "The number of Ids should > 0");
+    auto *ids_var = ids_vars[0];
+
     if (ids_var->IsType<framework::LoDTensor>()) {
-      const auto &ids_dims = ctx.Input<framework::LoDTensor>("Ids")->dims();
-      const T *ids = ctx.Input<framework::LoDTensor>("Ids")->data<T>();
+      int batch_size = 0;
+      const auto ids_tensors = ctx.MultiInput<framework::LoDTensor>("Ids");
+      for (size_t i = 0; i < ids_tensors.size(); ++i) {
+        batch_size += ids_tensors[i]->dims()[0];
+      }
+      VLOG(4) << "Get Total BatchSize is: " << batch_size;
+
+      std::vector<T> all_ids(batch_size);
+      int offset = 0;
+      for (size_t i = 0; i < ids_tensors.size(); ++i) {
+        const auto *ids = ids_tensors[i];
+        std::memcpy(all_ids.data() + offset, ids->data<T>(),
+                    ids->numel() * sizeof(T));
+        offset += ids->numel();
+      }
+
+      std::set<T> st(all_ids.begin(), all_ids.end());
+      all_ids.assign(st.begin(), st.end());
+
       auto outs = ctx.MultiOutput<framework::LoDTensor>("Out");
       const size_t shard_num = outs.size();
-
       std::vector<std::vector<T>> out_ids;
       out_ids.resize(outs.size());
 
       // split id by their shard_num.
-      for (int i = 0; i < ids_dims[0]; ++i) {
-        T id = ids[i];
+      for (int i = 0; i < all_ids.size(); ++i) {
+        T id = all_ids[i];
         size_t shard_id = static_cast<size_t>(id) % shard_num;
         out_ids[shard_id].push_back(id);
       }
@@ -64,7 +86,7 @@ class SplitIdsOpKernel : public framework::OpKernel<T> {
       PADDLE_ENFORCE_EQ(ids_dims[0],
                         static_cast<int64_t>(ids_selected_rows->rows().size()),
                         "");
-      const T *ids = ids_selected_rows->value().data<T>();
+      const T *ids_data = ids_selected_rows->value().data<T>();
       const auto &ids_rows = ids_selected_rows->rows();
       auto outs = ctx.MultiOutput<framework::SelectedRows>("Out");
       const size_t shard_num = outs.size();
@@ -87,7 +109,7 @@ class SplitIdsOpKernel : public framework::OpKernel<T> {
         T *output = out->mutable_value()->mutable_data<T>(ddim, place);
         for (int64_t i = 0; i < ddim[0]; ++i) {
           memcpy(output + i * row_width,
-                 ids + id_to_index[out->rows()[i]] * row_width,
+                 ids_data + id_to_index[out->rows()[i]] * row_width,
                  row_width * sizeof(T));
         }
       }

paddle/fluid/operators/split_selected_rows_op.cc

@@ -22,9 +22,9 @@ class SplitSelectedRowsOpMaker : public framework::OpProtoAndCheckerMaker {
   void Make() override {
     AddInput("X", "The input SelectedRows.");
     AddOutput("Out", "The outputs of the input SelectedRows.").AsDuplicable();
-    AddAttr<std::vector<int>>("height_sections",
-                              "Height for each output SelectedRows.")
-        .SetDefault(std::vector<int>({}));
+    AddAttr<std::vector<int64_t>>("height_sections",
+                                  "Height for each output SelectedRows.")
+        .SetDefault(std::vector<int64_t>({}));
 
     AddComment(R"DOC(
 Split a SelectedRows with a specified rows section.

paddle/fluid/operators/split_selected_rows_op.h

@@ -21,7 +21,7 @@ limitations under the License. */
 namespace paddle {
 namespace operators {
 
-static int FindOutIdx(int row, const std::vector<int>& abs_sections) {
+static int FindOutIdx(int row, const std::vector<int64_t>& abs_sections) {
   for (size_t i = 1; i < abs_sections.size(); ++i) {
     if (row < abs_sections[i]) {
       return i - 1;
@@ -30,9 +30,9 @@ static int FindOutIdx(int row, const std::vector<int>& abs_sections) {
   return abs_sections.size() - 1;
 }
 
-static std::vector<int> ToAbsoluteSection(
-    const std::vector<int>& height_sections) {
-  std::vector<int> abs_sections;
+static std::vector<int64_t> ToAbsoluteSection(
+    const std::vector<int64_t>& height_sections) {
+  std::vector<int64_t> abs_sections;
   abs_sections.resize(height_sections.size());
   abs_sections[0] = 0;
   for (size_t i = 1; i < height_sections.size(); ++i) {
@@ -47,7 +47,7 @@ class SplitSelectedRowsOpKernel : public framework::OpKernel<T> {
   void Compute(const framework::ExecutionContext& ctx) const override {
     auto* x = ctx.Input<framework::SelectedRows>("X");
     auto outs = ctx.MultiOutput<framework::SelectedRows>("Out");
-    auto height_sections = ctx.Attr<std::vector<int>>("height_sections");
+    auto height_sections = ctx.Attr<std::vector<int64_t>>("height_sections");
 
     auto abs_sections = ToAbsoluteSection(height_sections);
 

paddle/fluid/operators/sum_op.cc

@@ -82,14 +82,15 @@ class SumOp : public framework::OperatorWithKernel {
     if (x_vars[0]->IsType<framework::LoDTensor>()) {
       int dtype = -1;
       for (auto& x_var : x_vars) {
-        auto& lod_tensor = x_var->Get<framework::LoDTensor>();
-        if (lod_tensor.numel() == 0) {
+        // FIXME(zcd): The input x_var may be SelectedRows or LoDTensor.
+        auto tensor = framework::GetTensorFromVar(x_var);
+        if (tensor->numel() == 0) {
           continue;
         }
         if (dtype == -1) {
-          dtype = framework::ToDataType(lod_tensor.type());
+          dtype = framework::ToDataType(tensor->type());
         } else {
-          PADDLE_ENFORCE_EQ(dtype, framework::ToDataType(lod_tensor.type()));
+          PADDLE_ENFORCE_EQ(dtype, framework::ToDataType(tensor->type()));
         }
       }
       PADDLE_ENFORCE_NE(dtype, -1,

paddle/fluid/operators/sum_op.h

@@ -83,79 +83,54 @@ class SumKernel : public framework::OpKernel<T> {
         }
       }
     } else if (out_var->IsType<framework::SelectedRows>()) {
-      std::unique_ptr<framework::SelectedRows> in0;
-      if (in_place) {
-        // If is in_place, we store the input[0] to in0
-        auto &in_sel0 = in_vars[0]->Get<SelectedRows>();
-        auto &rows = in_sel0.rows();
-#ifdef PADDLE_WITH_CUDA
-        std::vector<int64_t> rows_in_cpu;
-        rows_in_cpu.reserve(rows.size());
-        for (auto item : rows) {
-          rows_in_cpu.push_back(item);
-        }
-        in0.reset(new framework::SelectedRows(rows_in_cpu, in_sel0.height()));
-#else
-        in0.reset(new framework::SelectedRows(rows, in_sel0.height()));
-#endif
-        in0->mutable_value()->ShareDataWith(in_sel0.value());
+      if (in_place && in_vars.size() < 2) {
+        return;
       }
 
-      auto get_selected_row = [&](size_t i) -> const SelectedRows & {
-        if (i == 0 && in0) {
-          return *in0.get();
-        } else {
-          return in_vars[i]->Get<SelectedRows>();
+      std::vector<const paddle::framework::SelectedRows *> inputs;
+      SelectedRows temp_in0;
+
+      if (in_place) {
+        auto &in0 = in_vars[0]->Get<SelectedRows>();
+        temp_in0.set_height(in0.height());
+        temp_in0.set_rows(in0.rows());
+        framework::TensorCopy(in0.value(), in0.place(),
+                              context.device_context(),
+                              temp_in0.mutable_value());
+        inputs.push_back(&temp_in0);
+        for (size_t i = 1; i < in_vars.size(); ++i) {
+          auto &in = in_vars[i]->Get<SelectedRows>();
+          if (in.rows().size() > 0) {
+            inputs.push_back(&in);
+          }
+        }
+      } else {
+        for (auto &in_var : in_vars) {
+          auto &in = in_var->Get<SelectedRows>();
+          if (in.rows().size() > 0) {
+            inputs.push_back(&in_var->Get<SelectedRows>());
+          }
         }
-      };
+      }
 
       auto *out = context.Output<SelectedRows>("Out");
       out->mutable_rows()->clear();
-      auto *out_value = out->mutable_value();
-
-      // Runtime InferShape
-      size_t first_dim = 0;
-      for (size_t i = 0; i < in_num; i++) {
-        auto &sel_row = get_selected_row(i);
-        first_dim += sel_row.rows().size();
-      }
 
-      std::vector<int64_t> in_dim;
-      for (size_t i = 0; i < in_num; i++) {
-        auto &sel_row = get_selected_row(i);
-        if (sel_row.rows().size() > 0) {
-          in_dim = framework::vectorize(sel_row.value().dims());
+      bool has_data = false;
+      for (auto &in : inputs) {
+        if (in->rows().size() > 0) {
+          has_data = true;
           break;
         }
       }
-      if (in_dim.empty()) {
-        VLOG(3) << "WARNING: all the inputs are empty";
-        in_dim =
-            framework::vectorize(get_selected_row(in_num - 1).value().dims());
+      if (has_data) {
+        math::scatter::MergeAdd<DeviceContext, T> merge_add;
+        merge_add(context.template device_context<DeviceContext>(), inputs,
+                  out);
       } else {
-        in_dim[0] = static_cast<int64_t>(first_dim);
-      }
-
-      out_value->Resize(framework::make_ddim(in_dim));
-      out_value->mutable_data<T>(context.GetPlace());
-      // if all the input sparse vars are empty, no need to
-      // merge these vars.
-      if (first_dim == 0UL) {
-        return;
-      }
-
-      math::SelectedRowsAddTo<DeviceContext, T> functor;
-
-      int64_t offset = 0;
-      for (size_t i = 0; i < in_num; i++) {
-        auto &sel_row = get_selected_row(i);
-        if (sel_row.rows().size() == 0) {
-          continue;
-        }
-        PADDLE_ENFORCE_EQ(out->height(), sel_row.height());
-        functor(context.template device_context<DeviceContext>(), sel_row,
-                offset, out);
-        offset += sel_row.value().numel();
+        // no data, just set a empty out tensor.
+        out->mutable_value()->mutable_data<T>(framework::make_ddim({0}),
+                                              context.GetPlace());
       }
     } else if (out_var->IsType<framework::LoDTensorArray>()) {
       auto &out_array = *out_var->GetMutable<framework::LoDTensorArray>();

paddle/fluid/operators/uniform_random_op.cc

@@ -29,7 +29,7 @@ class CPUUniformRandomKernel : public framework::OpKernel<T> {
     if (out_var->IsType<framework::LoDTensor>()) {
       tensor = out_var->GetMutable<framework::LoDTensor>();
     } else if (out_var->IsType<framework::SelectedRows>()) {
-      auto shape = ctx.Attr<std::vector<int>>("shape");
+      auto shape = ctx.Attr<std::vector<int64_t>>("shape");
       auto *selected_rows = out_var->GetMutable<framework::SelectedRows>();
       tensor = selected_rows->mutable_value();
       tensor->Resize(framework::make_ddim(shape));
@@ -67,7 +67,7 @@ class UniformRandomOp : public framework::OperatorWithKernel {
     PADDLE_ENFORCE(
         ctx->Attrs().Get<float>("min") < ctx->Attrs().Get<float>("max"),
         "uniform_random's min must less then max");
-    auto &shape = ctx->Attrs().Get<std::vector<int>>("shape");
+    auto &shape = ctx->Attrs().Get<std::vector<int64_t>>("shape");
     std::vector<int64_t> temp;
     temp.reserve(shape.size());
     for (auto dim : shape) {
@@ -94,7 +94,7 @@ This operator initializes a tensor with random values sampled from a
 uniform distribution. The random result is in set [min, max].
 
 )DOC");
-    AddAttr<std::vector<int>>("shape", "The shape of the output tensor");
+    AddAttr<std::vector<int64_t>>("shape", "The shape of the output tensor");
     AddAttr<float>("min", "Minimum value of uniform random. [default -1.0].")
         .SetDefault(-1.0f);
     AddAttr<float>("max", "Maximun value of uniform random. [default 1.0].")

paddle/fluid/operators/uniform_random_op.cu

@@ -48,7 +48,7 @@ class GPUUniformRandomKernel : public framework::OpKernel<T> {
     if (out_var->IsType<framework::LoDTensor>()) {
       tensor = out_var->GetMutable<framework::LoDTensor>();
     } else if (out_var->IsType<framework::SelectedRows>()) {
-      auto shape = context.Attr<std::vector<int>>("shape");
+      auto shape = context.Attr<std::vector<int64_t>>("shape");
       tensor = out_var->GetMutable<framework::SelectedRows>()->mutable_value();
       tensor->Resize(framework::make_ddim(shape));
     } else {

paddle/fluid/platform/init.cc

@@ -116,21 +116,51 @@ void InitDevices(bool init_p2p, const std::vector<int> devices) {
   platform::SetNumThreads(FLAGS_paddle_num_threads);
 #endif
 
-  if (platform::jit::MayIUse(platform::jit::avx512f)) {
-#ifndef __AVX512F__
-    LOG(WARNING) << "AVX512F is available, Please re-compile on local machine";
+#if !defined(_WIN32) && !defined(__APPLE__) && !defined(__OSX__)
+  if (platform::jit::MayIUse(platform::jit::avx)) {
+#ifndef __AVX__
+    LOG(WARNING) << "AVX is available, Please re-compile on local machine";
 #endif
   }
-  if (platform::jit::MayIUse(platform::jit::avx2)) {
-#ifndef __AVX2__
-    LOG(WARNING) << "AVX2 is available, Please re-compile on local machine";
+
+// Throw some informations when CPU instructions mismatch.
+#define AVX_GUIDE(compiletime, runtime)                                     \
+  LOG(FATAL)                                                                \
+      << "This version is compiled on higher instruction(" #compiletime     \
+         ") system, you may encounter illegal instruction error running on" \
+         " your local CPU machine. Please reinstall the " #runtime          \
+         " version or compile from source code."
+
+#ifdef __AVX512F__
+  if (!platform::jit::MayIUse(platform::jit::avx512f)) {
+    if (platform::jit::MayIUse(platform::jit::avx2)) {
+      AVX_GUIDE(AVX512, AVX2);
+    } else if (platform::jit::MayIUse(platform::jit::avx)) {
+      AVX_GUIDE(AVX512, AVX);
+    } else {
+      AVX_GUIDE(AVX512, NonAVX);
+    }
+  }
 #endif
+
+#ifdef __AVX2__
+  if (!platform::jit::MayIUse(platform::jit::avx2)) {
+    if (platform::jit::MayIUse(platform::jit::avx)) {
+      AVX_GUIDE(AVX2, AVX);
+    } else {
+      AVX_GUIDE(AVX2, NonAVX);
+    }
   }
-  if (platform::jit::MayIUse(platform::jit::avx)) {
-#ifndef __AVX__
-    LOG(WARNING) << "AVX is available, Please re-compile on local machine";
 #endif
+
+#ifdef __AVX__
+  if (!platform::jit::MayIUse(platform::jit::avx)) {
+    AVX_GUIDE(AVX, NonAVX);
   }
+#endif
+#undef AVX_GUIDE
+
+#endif
 }
 
 void InitGLOG(const std::string &prog_name) {

paddle/fluid/pybind/protobuf.cc

@@ -57,6 +57,18 @@ struct variant_caster<V<Ts...>> {
     auto caster = make_caster<T>();
     if (!load_success_ && caster.load(src, convert)) {
       load_success_ = true;
+
+      if (std::is_same<T, std::vector<float>>::value) {
+        auto caster_ints = make_caster<std::vector<int64_t>>();
+        if (caster_ints.load(src, convert)) {
+          VLOG(4) << "This value are floats and int64_ts satisfy "
+                     "simultaneously, will set it's type to "
+                     "std::vector<int64_t>";
+          value = cast_op<std::vector<int64_t>>(caster_ints);
+          return true;
+        }
+      }
+
       value = cast_op<T>(caster);
       return true;
     }
@@ -259,6 +271,8 @@ void BindOpDesc(pybind11::module *m) {
   pybind11::enum_<pd::proto::AttrType>(*m, "AttrType", "")
       .value("INT", pd::proto::AttrType::INT)
       .value("INTS", pd::proto::AttrType::INTS)
+      .value("LONG", pd::proto::AttrType::LONG)
+      .value("LONGS", pd::proto::AttrType::LONGS)
       .value("FLOAT", pd::proto::AttrType::FLOAT)
       .value("FLOATS", pd::proto::AttrType::FLOATS)
       .value("STRING", pd::proto::AttrType::STRING)

paddle/fluid/pybind/pybind.cc

@@ -645,9 +645,13 @@ All parameter, weight, gradient are variables in Paddle.
 
   py::class_<ir::Pass, std::shared_ptr<ir::Pass>> pass(m, "Pass");
   pass.def(py::init())
-      .def("set_str", [](ir::Pass &self, const std::string &name,
-                         const std::string &attr) {
-        self.Set<std::string>(name, new std::string(attr));
+      .def(
+          "set_str",
+          [](ir::Pass &self, const std::string &name, const std::string &attr) {
+            self.Set<std::string>(name, new std::string(attr));
+          })
+      .def("set_int", [](ir::Pass &self, const std::string &name, int val) {
+        self.Set<const int>(name, new int(val));
       });
 
   py::class_<ir::PassBuilder, std::shared_ptr<ir::PassBuilder>> pb(

paddle/fluid/train/demo/CMakeLists.txt

@@ -15,6 +15,7 @@ include_directories("${PADDLE_LIB}")
 include_directories("${PADDLE_LIB}/third_party/install/protobuf/include")
 include_directories("${PADDLE_LIB}/third_party/install/glog/include")
 include_directories("${PADDLE_LIB}/third_party/install/gflags/include")
+include_directories("${PADDLE_LIB}/third_party/install/xxhash/include")
 include_directories("${PADDLE_LIB}/third_party/install/snappy/include")
 include_directories("${PADDLE_LIB}/third_party/install/snappystream/include")
 include_directories("${PADDLE_LIB}/third_party/install/zlib/include")
@@ -27,6 +28,7 @@ link_directories("${PADDLE_LIB}/third_party/install/snappystream/lib")
 link_directories("${PADDLE_LIB}/third_party/install/protobuf/lib")
 link_directories("${PADDLE_LIB}/third_party/install/glog/lib")
 link_directories("${PADDLE_LIB}/third_party/install/gflags/lib")
+link_directories("${PADDLE_LIB}/third_party/install/xxhash/lib")
 link_directories("${PADDLE_LIB}/third_party/install/zlib/lib")
 
 add_executable(demo_trainer demo_trainer.cc)
@@ -62,5 +64,5 @@ target_link_libraries(demo_trainer
         ${ARCHIVE_END}
         ${MATH_LIB}
         ${MKLDNN_LIB}
-        glog gflags protobuf snappystream snappy z
+        glog gflags protobuf snappystream snappy z xxhash
         ${EXTERNAL_LIB})

paddle/scripts/paddle_build.sh

@@ -95,9 +95,9 @@ function cmake_gen() {
                 exit 1
             fi
         fi
-    else 
+    else
         if [ "$1" != "" ]; then
-            echo "using python abi: $1"     
+            echo "using python abi: $1"
             if [ "$1" == "cp27-cp27m" ]; then
                 export LD_LIBRARY_PATH=/opt/_internal/cpython-2.7.11-ucs2/lib:${LD_LIBRARY_PATH#/opt/_internal/cpython-2.7.11-ucs4/lib:}
                 export PATH=/opt/python/cp27-cp27m/bin/:${PATH}
@@ -119,7 +119,7 @@ function cmake_gen() {
            fi
         fi
     fi
-    
+
     if [ "$SYSTEM" == "Darwin" ]; then
         WITH_DISTRIBUTE=${WITH_DISTRIBUTE:-ON}
         WITH_AVX=${WITH_AVX:-ON}
@@ -127,7 +127,7 @@ function cmake_gen() {
     else
         INFERENCE_DEMO_INSTALL_DIR=${INFERENCE_DEMO_INSTALL_DIR:-/root/.cache/inference_demo}
     fi
-    
+
     cat <<EOF
     ========================================
     Configuring cmake in /paddle/build ...
@@ -394,8 +394,8 @@ EOF
         export http_proxy=
         export https_proxy=
         # TODO: jiabin need to refine this part when these tests fixed on mac
-        ctest --output-on-failure -j $1     
-        # make install should also be test when unittest 
+        ctest --output-on-failure -j $1
+        # make install should also be test when unittest
         make install -j 8
         pip install --user ${INSTALL_PREFIX:-/paddle/build}/opt/paddle/share/wheels/*.whl
         if [[ ${WITH_FLUID_ONLY:-OFF} == "OFF" ]] ; then
@@ -659,7 +659,7 @@ function gen_fluid_lib() {
     Generating fluid library for train and inference ...
     ========================================
 EOF
-        cmake .. -DWITH_DISTRIBUTE=OFF
+        cmake .. -DWITH_DISTRIBUTE=OFF -DON_INFER=ON
         make -j `nproc` fluid_lib_dist
         make -j `nproc` inference_lib_dist
       fi

python/paddle/dataset/wmt16.py

@@ -78,7 +78,7 @@ def __build_dict(tar_file, dict_size, save_path, lang):
                     six.iteritems(word_dict), key=lambda x: x[1],
                     reverse=True)):
             if idx + 3 == dict_size: break
-            fout.write("%s\n" % (word[0]))
+            fout.write("%s\n" % (cpt.to_bytes(word[0])))
 
 
 def __load_dict(tar_file, dict_size, lang, reverse=False):

python/paddle/fluid/__init__.py

@@ -121,6 +121,9 @@ def __bootstrap__():
         read_env_flags.append('rpc_server_profile_period')
         read_env_flags.append('rpc_server_profile_path')
         read_env_flags.append('enable_rpc_profiler')
+        read_env_flags.append('rpc_send_thread_num')
+        read_env_flags.append('rpc_get_thread_num')
+        read_env_flags.append('rpc_prefetch_thread_num')
 
     if core.is_compiled_with_cuda():
         read_env_flags += [

python/paddle/fluid/evaluator.py

@@ -316,7 +316,7 @@ class DetectionMAP(Evaluator):
         gt_label (Variable): The ground truth label index, which is a LoDTensor
             with shape [N, 1].
         gt_box (Variable): The ground truth bounding box (bbox), which is a
-            LoDTensor with shape [N, 6]. The layout is [xmin, ymin, xmax, ymax].
+            LoDTensor with shape [N, 4]. The layout is [xmin, ymin, xmax, ymax].
         gt_difficult (Variable|None): Whether this ground truth is a difficult
             bounding bbox, which can be a LoDTensor [N, 1] or not set. If None,
             it means all the ground truth labels are not difficult bbox.

python/paddle/fluid/layers/learning_rate_scheduler.py

@@ -27,7 +27,7 @@ from . import nn
 from . import ops
 from . import tensor
 from ..initializer import init_on_cpu
-from ..framework import default_main_program, Parameter, unique_name
+from ..framework import default_main_program, Parameter, unique_name, name_scope
 
 __all__ = [
     'exponential_decay', 'natural_exp_decay', 'inverse_time_decay',
@@ -332,14 +332,16 @@ def append_LARS(params_grads, learning_rate, weight_decay):
             return grad_norm + weight_decay * param_norm
 
     for param, grad in params_grads:
-        param_lr = param.optimize_attr['learning_rate']
-        param_norm = ops.sqrt(nn.reduce_sum(input=ops.square(param)))
-        grad_norm = ops.sqrt(nn.reduce_sum(input=ops.square(grad)))
-        if type(param_lr) == float and param_lr == 1.0:
-            decayed_lr = learning_rate * param_norm \
-                / _balanced_weight(param_norm, grad_norm)
-        else:
-            decayed_lr = learning_rate * param_lr * param_norm \
-                / _balanced_weight(param_norm, grad_norm)
-        # set back param local learning rate
-        param.optimize_attr['learning_rate'] = decayed_lr
+        with param.block.program.optimized_guard(
+            [param, grad]), name_scope("optimizer"):
+            param_lr = param.optimize_attr['learning_rate']
+            param_norm = ops.sqrt(nn.reduce_sum(input=ops.square(param)))
+            grad_norm = ops.sqrt(nn.reduce_sum(input=ops.square(grad)))
+            if type(param_lr) == float and param_lr == 1.0:
+                decayed_lr = learning_rate * param_norm \
+                    / _balanced_weight(param_norm, grad_norm)
+            else:
+                decayed_lr = learning_rate * param_lr * param_norm \
+                    / _balanced_weight(param_norm, grad_norm)
+            # set back param local learning rate
+            param.optimize_attr['learning_rate'] = decayed_lr

python/paddle/fluid/layers/nn.py

@@ -156,6 +156,7 @@ __all__ = [
     'maxout',
     'sequence_reverse',
     'affine_channel',
+    'hash',
 ]
 
 
@@ -1991,17 +1992,17 @@ def sequence_slice(input, offset, length, name=None):
     """
     **Sequence Slice Layer**
 
-    The layer crops a subsequence from given sequence with given start 
+    The layer crops a subsequence from given sequence with given start
     offset and subsequence length.
 
     It only supports sequence data (LoDTensor with lod_level equal to 1).
 
     .. code-block:: text
-    
+
 	- Case:
 
             Given the input Variable **input**:
-                
+
                 input.data = [[a1, a2], [b1, b2], [c1, c2], [d1, d2], [e1, e2]],
                 input.lod = [[3, 2]],
                 input.dims = (5, 2),
@@ -2009,16 +2010,16 @@ def sequence_slice(input, offset, length, name=None):
             with offset.data = [[0], [1]] and length.data = [[2], [1]],
 
             the output Variable will be
-                
+
                 out.data = [[a1, a2], [b1, b2], [e1, e2]],
                 out.lod = [[2, 1]],
                 out.dims = (3, 2).
-	
-    NOTE: The first dimension size of **input**, **offset** and **length** 
+
+    NOTE: The first dimension size of **input**, **offset** and **length**
           should be equal. The **offset** should start from 0.
-    
+
     Args:
-        input(Variable): The input Variable which consists of the complete 
+        input(Variable): The input Variable which consists of the complete
                          sequences.
         offset(Variable): The offset to slice each sequence.
         length(Variable): The length of each subsequence.
@@ -2037,7 +2038,7 @@ def sequence_slice(input, offset, length, name=None):
                               dtype='float32', lod_level=1)
              offset = fluid.layers.assign(input=np.array([[0, 1]]).astype("int32"))
              length = fluid.layers.assign(input=np.array([[2, 1]]).astype("int32"))
-             subseqs = fluid.layers.sequence_slice(input=seqs, offset=offset, 
+             subseqs = fluid.layers.sequence_slice(input=seqs, offset=offset,
                                                    length=length)
     """
     helper = LayerHelper("sequence_slice", **locals())
@@ -2420,12 +2421,12 @@ def layer_norm(input,
         param_attr(ParamAttr|None): The parameter attribute for the learnable
             gain :math:`g`. If :attr:`scale` is False, :attr:`param_attr` is
             omitted. If :attr:`scale` is True and :attr:`param_attr` is None,
-            a default :code:`ParamAttr` would be added as scale. The 
-            :attr:`param_attr` is initialized as 1 if it is added. Default None. 
+            a default :code:`ParamAttr` would be added as scale. The
+            :attr:`param_attr` is initialized as 1 if it is added. Default None.
         bias_attr(ParamAttr|None): The parameter attribute for the learnable
             bias :math:`b`. If :attr:`shift` is False, :attr:`bias_attr` is
             omitted. If :attr:`shift` is True and :attr:`param_attr` is None,
-            a default :code:`ParamAttr` would be added as bias. The 
+            a default :code:`ParamAttr` would be added as bias. The
             :attr:`bias_attr` is initialized as 0 if it is added. Default None.
         act(str): Activation to be applied to the output of layer normalizaiton.
                   Default None.
@@ -3043,8 +3044,8 @@ def sequence_unpad(x, length, name=None):
     """
     **Sequence Unpad Layer**
 
-    This layer removes the padding data in the input sequences and convert 
-    them into sequences with actual length as output, identitied by lod 
+    This layer removes the padding data in the input sequences and convert
+    them into sequences with actual length as output, identitied by lod
     information.
 
     .. code-block:: text
@@ -3054,9 +3055,9 @@ def sequence_unpad(x, length, name=None):
 	Given input Variable **x**:
 	    x.data = [[ 1.0,  2.0,  3.0,  4.0,  5.0],
 		      [ 6.0,  7.0,  8.0,  9.0, 10.0],
-		      [11.0, 12.0, 13.0, 14.0, 15.0]], 
-     
-	in which there are 3 sequences padded to length 5, and the acutal length 
+		      [11.0, 12.0, 13.0, 14.0, 15.0]],
+
+	in which there are 3 sequences padded to length 5, and the acutal length
 	specified by input Variable **length**:
 
 	    length.data = [[2], [3], [4]],
@@ -3064,7 +3065,7 @@ def sequence_unpad(x, length, name=None):
 	after unpadding, the output Variable will be:
 
 	    out.data = [[1.0, 2.0, 6.0, 7.0, 8.0, 11.0, 12.0, 13.0, 14.0]]
-	    out.lod = [[2, 3, 4]]      
+	    out.lod = [[2, 3, 4]]
 
     Args:
         x(Variable): Input Variable which contains the padded sequences with
@@ -5499,9 +5500,9 @@ def roi_align(input,
     Examples:
         .. code-block:: python
 
-            align_out = fluid.layers.roi_align(input=x, 
-                                               rois=rois, 
-                                               pooled_height=7, 
+            align_out = fluid.layers.roi_align(input=x,
+                                               rois=rois,
+                                               pooled_height=7,
                                                pooled_width=7,
                                                spatial_scale=0.5,
                                                sampling_ratio=-1)
@@ -7498,7 +7499,6 @@ def sequence_reverse(x, name=None):
         out(${y_type}): ${y_comment}
     """
     helper = LayerHelper("sequence_reverse", **locals())
-
     if name is None:
         out = helper.create_variable_for_type_inference(dtype=x.dtype)
     else:
@@ -7519,7 +7519,7 @@ def affine_channel(x, scale=None, bias=None, data_layout='NCHW', name=None):
     Useful for replacing spatial batch norm with its equivalent fixed
     transformation. The input also can be 2D tensor and applies a affine
     transformation in second dimension.
-    
+
     Args:
         x (Variable): Feature map input can be a 4D tensor with order NCHW
             or NHWC. It also can be a 2D tensor and the affine transformation
@@ -7552,3 +7552,31 @@ def affine_channel(x, scale=None, bias=None, data_layout='NCHW', name=None):
         attrs={"data_layout": data_layout},
         outputs={"Out": out})
     return out
+
+
+def hash(input, hash_size, num_hash=1, name=None):
+    """
+    hash the input
+     Args:
+        input (Variable): The input variable which is a one-hot word.
+        hash_size (int): The space size for hash algorithm.
+        num_hash (int): The times of hash, default 1.
+        name (str, default None): The name of this layer.
+     Returns:
+        Variable: The hash result variable which is a LoDTensor.
+     Examples:
+        .. code-block:: python
+            word_dict = paddle.dataset.imdb.word_dict()
+            x = fluid.layers.data(shape[1], dtype='int32', lod_level=1)
+            out = fluid.layers.hash(input=x, len(word_dict))
+    """
+    helper = LayerHelper('hash', **locals())
+    out = helper.create_variable_for_type_inference(
+        helper.input_dtype(), stop_gradient=True)
+    helper.append_op(
+        type='hash',
+        inputs={'X': input},
+        outputs={'Out': out},
+        attrs={'num_hash': num_hash,
+               'mod_by': hash_size})
+    return out

python/paddle/fluid/metrics.py

@@ -13,8 +13,6 @@
 # limitations under the License.
 """
 Fluid Metrics
-
-The metrics are accomplished via Python natively.
 """
 
 from __future__ import print_function
@@ -24,6 +22,12 @@ import copy
 import warnings
 import six
 
+from .layer_helper import LayerHelper
+from .initializer import Constant
+from . import unique_name
+from .framework import Program, Variable, program_guard
+from . import layers
+
 __all__ = [
     'MetricBase',
     'CompositeMetric',
@@ -478,67 +482,6 @@ class EditDistance(MetricBase):
         return avg_distance, avg_instance_error
 
 
-class DetectionMAP(MetricBase):
-    """
-    Calculate the detection mean average precision (mAP).
-    mAP is the metric to measure the accuracy of object detectors
-    like Faster R-CNN, SSD, etc.
-    It is the average of the maximum precisions at different recall values.
-    Please get more information from the following articles:
-      https://sanchom.wordpress.com/tag/average-precision/
-
-      https://arxiv.org/abs/1512.02325
-
-    The general steps are as follows:
-
-        1. calculate the true positive and false positive according to the input
-            of detection and labels.
-        2. calculate mAP value, support two versions: '11 point' and 'integral'.
-
-    Examples:
-        .. code-block:: python
-
-            pred = fluid.layers.fc(input=data, size=1000, act="tanh")
-            batch_map = layers.detection_map(
-                input,
-                label,
-                class_num,
-                background_label,
-                overlap_threshold=overlap_threshold,
-                evaluate_difficult=evaluate_difficult,
-                ap_version=ap_version)
-            metric = fluid.metrics.DetectionMAP()
-            for data in train_reader():
-                loss, preds, labels = exe.run(fetch_list=[cost, batch_map])
-                batch_size = data[0]
-                metric.update(value=batch_map, weight=batch_size)
-                numpy_map = metric.eval()
-    """
-
-    def __init__(self, name=None):
-        super(DetectionMAP, self).__init__(name)
-        # the current map value
-        self.value = .0
-        self.weight = .0
-
-    def update(self, value, weight):
-        if not _is_number_or_matrix_(value):
-            raise ValueError(
-                "The 'value' must be a number(int, float) or a numpy ndarray.")
-        if not _is_number_(weight):
-            raise ValueError("The 'weight' must be a number(int, float).")
-        self.value += value
-        self.weight += weight
-
-    def eval(self):
-        if self.weight == 0:
-            raise ValueError(
-                "There is no data in DetectionMAP Metrics. "
-                "Please check layers.detection_map output has added to DetectionMAP."
-            )
-        return self.value / self.weight
-
-
 class Auc(MetricBase):
     """
     Auc metric adapts to the binary classification.
@@ -616,3 +559,179 @@ class Auc(MetricBase):
             idx -= 1
 
         return auc / tot_pos / tot_neg if tot_pos > 0.0 and tot_neg > 0.0 else 0.0
+
+
+class DetectionMAP(object):
+    """
+    Calculate the detection mean average precision (mAP).
+
+    The general steps are as follows:
+    1. calculate the true positive and false positive according to the input
+        of detection and labels.
+    2. calculate mAP value, support two versions: '11 point' and 'integral'.
+
+    Please get more information from the following articles:
+      https://sanchom.wordpress.com/tag/average-precision/
+      https://arxiv.org/abs/1512.02325
+
+    Args:
+        input (Variable): The detection results, which is a LoDTensor with shape
+            [M, 6]. The layout is [label, confidence, xmin, ymin, xmax, ymax].
+        gt_label (Variable): The ground truth label index, which is a LoDTensor
+            with shape [N, 1].
+        gt_box (Variable): The ground truth bounding box (bbox), which is a
+            LoDTensor with shape [N, 4]. The layout is [xmin, ymin, xmax, ymax].
+        gt_difficult (Variable|None): Whether this ground truth is a difficult
+            bounding bbox, which can be a LoDTensor [N, 1] or not set. If None,
+            it means all the ground truth labels are not difficult bbox.
+        class_num (int): The class number.
+        background_label (int): The index of background label, the background
+            label will be ignored. If set to -1, then all categories will be
+            considered, 0 by defalut.
+        overlap_threshold (float): The threshold for deciding true/false
+            positive, 0.5 by defalut.
+        evaluate_difficult (bool): Whether to consider difficult ground truth
+            for evaluation, True by defalut. This argument does not work when
+            gt_difficult is None.
+        ap_version (string): The average precision calculation ways, it must be
+            'integral' or '11point'. Please check
+            https://sanchom.wordpress.com/tag/average-precision/ for details.
+            - 11point: the 11-point interpolated average precision.
+            - integral: the natural integral of the precision-recall curve.
+
+    Examples:
+        .. code-block:: python
+
+            exe = fluid.Executor(place)
+            map_evaluator = fluid.Evaluator.DetectionMAP(input,
+                gt_label, gt_box, gt_difficult)
+            cur_map, accum_map = map_evaluator.get_map_var()
+            fetch = [cost, cur_map, accum_map]
+            for epoch in PASS_NUM:
+                map_evaluator.reset(exe)
+                for data in batches:
+                    loss, cur_map_v, accum_map_v = exe.run(fetch_list=fetch)
+
+        In the above example:
+
+            'cur_map_v' is the mAP of current mini-batch.
+            'accum_map_v' is the accumulative mAP of one pass.
+    """
+
+    def __init__(self,
+                 input,
+                 gt_label,
+                 gt_box,
+                 gt_difficult=None,
+                 class_num=None,
+                 background_label=0,
+                 overlap_threshold=0.5,
+                 evaluate_difficult=True,
+                 ap_version='integral'):
+
+        self.helper = LayerHelper('map_eval')
+        gt_label = layers.cast(x=gt_label, dtype=gt_box.dtype)
+        if gt_difficult:
+            gt_difficult = layers.cast(x=gt_difficult, dtype=gt_box.dtype)
+            label = layers.concat([gt_label, gt_difficult, gt_box], axis=1)
+        else:
+            label = layers.concat([gt_label, gt_box], axis=1)
+
+        # calculate mean average precision (mAP) of current mini-batch
+        map = layers.detection_map(
+            input,
+            label,
+            class_num,
+            background_label,
+            overlap_threshold=overlap_threshold,
+            evaluate_difficult=evaluate_difficult,
+            ap_version=ap_version)
+
+        states = []
+        states.append(
+            self._create_state(
+                dtype='int32', shape=None, suffix='accum_pos_count'))
+        states.append(
+            self._create_state(
+                dtype='float32', shape=None, suffix='accum_true_pos'))
+        states.append(
+            self._create_state(
+                dtype='float32', shape=None, suffix='accum_false_pos'))
+        var = self._create_state(dtype='int32', shape=[1], suffix='has_state')
+        self.helper.set_variable_initializer(
+            var, initializer=Constant(value=int(0)))
+        self.has_state = var
+
+        # calculate accumulative mAP
+        accum_map = layers.detection_map(
+            input,
+            label,
+            class_num,
+            background_label,
+            overlap_threshold=overlap_threshold,
+            evaluate_difficult=evaluate_difficult,
+            has_state=self.has_state,
+            input_states=states,
+            out_states=states,
+            ap_version=ap_version)
+
+        layers.fill_constant(
+            shape=self.has_state.shape,
+            value=1,
+            dtype=self.has_state.dtype,
+            out=self.has_state)
+
+        self.cur_map = map
+        self.accum_map = accum_map
+
+    def _create_state(self, suffix, dtype, shape):
+        """
+        Create state variable.
+        Args:
+            suffix(str): the state suffix.
+            dtype(str|core.VarDesc.VarType): the state data type
+            shape(tuple|list): the shape of state
+        Returns: State variable
+        """
+        state = self.helper.create_variable(
+            name="_".join([unique_name.generate(self.helper.name), suffix]),
+            persistable=True,
+            dtype=dtype,
+            shape=shape)
+        return state
+
+    def get_map_var(self):
+        """
+        Returns: mAP variable of current mini-batch and
+            accumulative mAP variable cross mini-batches.
+        """
+        return self.cur_map, self.accum_map
+
+    def reset(self, executor, reset_program=None):
+        """
+        Reset metric states at the begin of each pass/user specified batch.
+
+        Args:
+            executor(Executor): a executor for executing
+                the reset_program.
+            reset_program(Program|None): a single Program for reset process.
+                If None, will create a Program.
+        """
+
+        def _clone_var_(block, var):
+            assert isinstance(var, Variable)
+            return block.create_var(
+                name=var.name,
+                shape=var.shape,
+                dtype=var.dtype,
+                type=var.type,
+                lod_level=var.lod_level,
+                persistable=var.persistable)
+
+        if reset_program is None:
+            reset_program = Program()
+        with program_guard(main_program=reset_program):
+            var = _clone_var_(reset_program.current_block(), self.has_state)
+            layers.fill_constant(
+                shape=var.shape, value=0, dtype=var.dtype, out=var)
+        executor.run(reset_program)

python/paddle/fluid/op.py

@@ -120,6 +120,8 @@ class OpDescCreationMethod(object):
                     new_attr.strings.extend(user_defined_attr)
                 elif attr.type == framework_pb2.BOOLEANS:
                     new_attr.bools.extend(user_defined_attr)
+                elif attr.type == framework_pb2.LONGS:
+                    new_attr.longs.extend(user_defined_attr)
                 elif attr.type == framework_pb2.INT_PAIRS:
                     for p in user_defined_attr:
                         pair = new_attr.int_pairs.add()

python/paddle/fluid/optimizer.py

@@ -14,6 +14,7 @@
 
 from __future__ import print_function
 import re
+import sys
 from collections import defaultdict
 from paddle.fluid.framework import Program, Variable, name_scope, default_main_program
 from . import framework
@@ -32,7 +33,8 @@ __all__ = [
     'SGD', 'Momentum', 'Adagrad', 'Adam', 'Adamax', 'DecayedAdagrad', 'Ftrl',
     'SGDOptimizer', 'MomentumOptimizer', 'AdagradOptimizer', 'AdamOptimizer',
     'AdamaxOptimizer', 'DecayedAdagradOptimizer', 'RMSPropOptimizer',
-    'FtrlOptimizer', 'Adadelta', 'ModelAverage', 'RMSPropOptimizer'
+    'FtrlOptimizer', 'Adadelta', 'ModelAverage', 'LarsMomentum',
+    'LarsMomentumOptimizer'
 ]
 
 
@@ -105,7 +107,6 @@ class Optimizer(object):
         param = param_and_grad[0]
         param_lr = param.optimize_attr['learning_rate']
         if type(param_lr) == Variable:
-            print("returns updated param lr ", param_lr)
             return param_lr
         else:
             if param_lr == 1.0:
@@ -400,6 +401,91 @@ class MomentumOptimizer(Optimizer):
         return momentum_op
 
 
+class LarsMomentumOptimizer(Optimizer):
+    """
+    Momentum optimizer with LARS support
+
+    The update equations are as follows:
+
+    .. math::
+
+        & local\_learning\_rate = learning\_rate * lars\_coeff * \\
+          \\frac{||param||}{||gradient|| + lars\_weight\_decay * ||param||}
+
+        & velocity = mu * velocity + local\_learning\_rate * (gradient + lars\_weight\_decay * param)
+
+        & param = param - velocity
+
+    Args:
+        learning_rate (float|Variable): the learning rate used to update parameters. \
+        Can be a float value or a Variable with one float value as data element.
+        momentum (float): momentum factor
+        lars_coeff (float): defines how much we trust the layer to change its weights.
+        lars_weight_decay (float): weight decay coefficient for decaying using LARS.
+        regularization: A Regularizer, such as
+                        fluid.regularizer.L2DecayRegularizer.
+        name: A optional name prefix.
+        
+
+    Examples:
+        .. code-block:: python
+
+            optimizer = fluid.optimizer.LarsMomentum(learning_rate=0.2, momentum=0.1, lars_weight_decay=0.001)
+            optimizer.minimize(cost)
+    """
+    _velocity_acc_str = "velocity"
+
+    def __init__(self,
+                 learning_rate,
+                 momentum,
+                 lars_coeff=0.001,
+                 lars_weight_decay=0.0005,
+                 regularization=None,
+                 name=None):
+        assert learning_rate is not None
+        assert momentum is not None
+        super(LarsMomentumOptimizer, self).__init__(
+            learning_rate=learning_rate,
+            regularization=regularization,
+            name=name)
+        self.type = "lars_momentum"
+        self._momentum = momentum
+        self._lars_coeff = float(lars_coeff)
+        self._lars_weight_decay = float(lars_weight_decay)
+
+    def _create_accumulators(self, block, parameters):
+        assert isinstance(block, framework.Block)
+
+        for p in parameters:
+            self._add_accumulator(self._velocity_acc_str, p)
+
+    def _append_optimize_op(self, block, param_and_grad):
+        assert isinstance(block, framework.Block)
+
+        velocity_acc = self._get_accumulator(self._velocity_acc_str,
+                                             param_and_grad[0])
+        # create the momentum optimize op
+        momentum_op = block.append_op(
+            type=self.type,
+            inputs={
+                "Param": param_and_grad[0],
+                "Grad": param_and_grad[1],
+                "Velocity": velocity_acc,
+                "LearningRate": self._create_param_lr(param_and_grad)
+            },
+            outputs={
+                "ParamOut": param_and_grad[0],
+                "VelocityOut": velocity_acc
+            },
+            attrs={
+                "mu": self._momentum,
+                "lars_coeff": self._lars_coeff,
+                "lars_weight_decay": self._lars_weight_decay
+            })
+
+        return momentum_op
+
+
 class AdagradOptimizer(Optimizer):
     """
     **Adaptive Gradient Algorithm (Adagrad)**
@@ -1221,6 +1307,7 @@ DecayedAdagrad = DecayedAdagradOptimizer
 Adadelta = AdadeltaOptimizer
 RMSProp = RMSPropOptimizer
 Ftrl = FtrlOptimizer
+LarsMomentum = LarsMomentumOptimizer
 
 
 class ModelAverage(Optimizer):

python/paddle/fluid/tests/CMakeLists.txt

-set(PYTHON_TESTS_DIR ${PADDLE_BINARY_DIR}/python/paddle/fluid/tests CACHE INTERNAL "python tests directory")
-
 file(GLOB TEST_OPS RELATIVE "${CMAKE_CURRENT_SOURCE_DIR}" "test_*.py")
 string(REPLACE ".py" "" TEST_OPS "${TEST_OPS}")
 

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

 file(GLOB TEST_OPS RELATIVE "${CMAKE_CURRENT_SOURCE_DIR}" "test_*.py")
 string(REPLACE ".py" "" TEST_OPS "${TEST_OPS}")
 
-# default test
-foreach(src ${TEST_OPS})
-    py_test(${src} SRCS ${src}.py)
-endforeach()
+if(NOT APPLE)
+    # default test
+    foreach(src ${TEST_OPS})
+        py_test(${src} SRCS ${src}.py)
+    endforeach()
+else()
+    foreach(src ${TEST_OPS})
+        if(${src} STREQUAL "test_image_classification_vgg")
+            message(WARNING "These tests has been disabled in OSX for random fail: \n" ${src})
+        elseif(${src} STREQUAL "test_image_classification_resnet")
+            message(WARNING "These tests has been disabled in OSX for random fail: \n" ${src})
+        elseif()
+            py_test(${src} SRCS ${src}.py)
+        endif()
+    endforeach()
+endif()

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

@@ -17,6 +17,10 @@ if(NOT WITH_DISTRIBUTE)
     list(REMOVE_ITEM TEST_OPS test_listen_and_serv_op)
     LIST(REMOVE_ITEM TEST_OPS test_dist_mnist)
     LIST(REMOVE_ITEM TEST_OPS test_dist_word2vec)
+    LIST(REMOVE_ITEM TEST_OPS test_dist_ctr)
+    LIST(REMOVE_ITEM TEST_OPS test_dist_simnet_bow)
+    LIST(REMOVE_ITEM TEST_OPS test_dist_mnist_batch_merge)
+    LIST(REMOVE_ITEM TEST_OPS test_dist_text_classification)
 endif(NOT WITH_DISTRIBUTE)
 
 list(REMOVE_ITEM TEST_OPS test_seq_concat_op) # FIXME(helin): https://github.com/PaddlePaddle/Paddle/issues/8290
@@ -88,4 +92,6 @@ py_test_modules(test_parallel_executor_crf MODULES test_parallel_executor_crf SE
 py_test_modules(test_parallel_executor_fetch_feed MODULES test_parallel_executor_fetch_feed SERIAL)
 set_tests_properties(test_parallel_executor_fetch_feed PROPERTIES TIMEOUT 150)
 py_test_modules(test_parallel_executor_transformer MODULES test_parallel_executor_transformer SERIAL)
-py_test_modules(test_image_classification_resnet MODULES test_image_classification_resnet SERIAL)
+if(NOT APPLE)
+    py_test_modules(test_image_classification_resnet MODULES test_image_classification_resnet SERIAL)
+endif()

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

@@ -95,7 +95,7 @@ class TestDistMnist2x2(TestDistRunnerBase):
 
         # Reader
         train_reader = paddle.batch(
-            paddle.dataset.mnist.train(), batch_size=batch_size)
+            paddle.dataset.mnist.test(), batch_size=batch_size)
         test_reader = paddle.batch(
             paddle.dataset.mnist.test(), batch_size=batch_size)
         opt.minimize(avg_cost)

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

+#   Copyright (c) 2018 PaddlePaddle Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+from __future__ import print_function
+
+import numpy as np
+import argparse
+import time
+import math
+
+import paddle
+import paddle.fluid as fluid
+import paddle.fluid.profiler as profiler
+from paddle.fluid import core
+import unittest
+from multiprocessing import Process
+import os
+import signal
+from functools import reduce
+from test_dist_base import TestDistRunnerBase, runtime_main
+from dist_mnist import cnn_model
+
+DTYPE = "float32"
+
+
+def test_merge_reader(repeat_batch_size=8):
+    orig_reader = paddle.dataset.mnist.test()
+    record_batch = []
+    b = 0
+    for d in orig_reader():
+        if b >= repeat_batch_size:
+            break
+        record_batch.append(d)
+        b += 1
+    while True:
+        for d in record_batch:
+            yield d
+
+
+class TestDistMnist2x2(TestDistRunnerBase):
+    def get_model(self, batch_size=2):
+        # Input data
+        images = fluid.layers.data(name='pixel', shape=[1, 28, 28], dtype=DTYPE)
+        label = fluid.layers.data(name='label', shape=[1], dtype='int64')
+
+        # Train program
+        predict = cnn_model(images)
+        cost = fluid.layers.cross_entropy(input=predict, label=label)
+        avg_cost = fluid.layers.mean(x=cost)
+
+        # Evaluator
+        batch_size_tensor = fluid.layers.create_tensor(dtype='int64')
+        batch_acc = fluid.layers.accuracy(
+            input=predict, label=label, total=batch_size_tensor)
+
+        inference_program = fluid.default_main_program().clone()
+        # Optimization
+        opt = fluid.optimizer.Momentum(learning_rate=0.001, momentum=0.9)
+
+        # Reader
+        train_reader = paddle.batch(test_merge_reader, batch_size=batch_size)
+        test_reader = paddle.batch(
+            paddle.dataset.mnist.test(), batch_size=batch_size)
+        opt.minimize(avg_cost)
+        return inference_program, avg_cost, train_reader, test_reader, batch_acc, predict
+
+
+if __name__ == "__main__":
+    runtime_main(TestDistMnist2x2)

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

+#   Copyright (c) 2018 PaddlePaddle Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+from __future__ import print_function
+
+import numpy as np
+import argparse
+import time
+import math
+
+import paddle
+import paddle.fluid as fluid
+import paddle.fluid.profiler as profiler
+from paddle.fluid import core
+import unittest
+from multiprocessing import Process
+import os
+import signal
+from functools import reduce
+from test_dist_base import TestDistRunnerBase, runtime_main
+from dist_mnist import cnn_model
+
+DTYPE = "float32"
+paddle.dataset.mnist.fetch()
+
+# Fix seed for test
+fluid.default_startup_program().random_seed = 1
+fluid.default_main_program().random_seed = 1
+
+
+class TestDistMnist2x2(TestDistRunnerBase):
+    def get_model(self, batch_size=2):
+        # Input data
+        images = fluid.layers.data(name='pixel', shape=[1, 28, 28], dtype=DTYPE)
+        label = fluid.layers.data(name='label', shape=[1], dtype='int64')
+
+        # Train program
+        predict = cnn_model(images)
+        cost = fluid.layers.cross_entropy(input=predict, label=label)
+        avg_cost = fluid.layers.mean(x=cost)
+
+        # Evaluator
+        batch_size_tensor = fluid.layers.create_tensor(dtype='int64')
+        batch_acc = fluid.layers.accuracy(
+            input=predict, label=label, total=batch_size_tensor)
+
+        inference_program = fluid.default_main_program().clone()
+        # Optimization
+        opt = fluid.optimizer.LarsMomentumOptimizer(
+            learning_rate=0.001, momentum=0.9)
+
+        # Reader
+        train_reader = paddle.batch(
+            paddle.dataset.mnist.test(), batch_size=batch_size)
+        test_reader = paddle.batch(
+            paddle.dataset.mnist.test(), batch_size=batch_size)
+        opt.minimize(avg_cost)
+        return inference_program, avg_cost, train_reader, test_reader, batch_acc, predict
+
+
+if __name__ == "__main__":
+    runtime_main(TestDistMnist2x2)

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

@@ -1159,6 +1159,7 @@ def prepare_encoder(src_word,
             name=pos_enc_param_name,
             trainable=False,
             initializer=fluid.initializer.ConstantInitializer(0.001)))
+    src_pos_enc.stop_gradient = True
     enc_input = src_word_emb + src_pos_enc
     return layers.dropout(
         enc_input,

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

@@ -26,10 +26,11 @@ import argparse
 import paddle.fluid as fluid
 
 RUN_STEP = 10
+DEFAULT_BATCH_SIZE = 2
 
 
 class TestDistRunnerBase(object):
-    def get_model(self, batch_size=2):
+    def get_model(self, batch_size=DEFAULT_BATCH_SIZE):
         raise NotImplementedError(
             "get_model should be implemented by child classes.")
 
@@ -48,8 +49,7 @@ class TestDistRunnerBase(object):
         return t
 
     def run_pserver(self, args):
-
-        self.get_model(batch_size=2)
+        self.get_model(batch_size=args.batch_size)
         # NOTE: pserver should not call memory optimize
         t = self.get_transpiler(args.trainer_id,
                                 fluid.default_main_program(), args.endpoints,
@@ -65,7 +65,7 @@ class TestDistRunnerBase(object):
 
     def run_trainer(self, args):
         test_program, avg_cost, train_reader, test_reader, batch_acc, predict = \
-            self.get_model(batch_size=2)
+            self.get_model(batch_size=args.batch_size)
 
         if args.mem_opt:
             fluid.memory_optimize(fluid.default_main_program(), skip_grads=True)
@@ -92,6 +92,11 @@ class TestDistRunnerBase(object):
         strategy.allow_op_delay = False
 
         build_stra = fluid.BuildStrategy()
+        if args.batch_merge_repeat > 1:
+            pass_builder = build_stra._create_passes_from_strategy()
+            mypass = pass_builder.insert_pass(
+                len(pass_builder.all_passes()) - 2, "multi_batch_merge_pass")
+            mypass.set_int("num_repeats", args.batch_merge_repeat)
 
         if args.use_reduce:
             build_stra.reduce_strategy = fluid.BuildStrategy.ReduceStrategy.Reduce
@@ -145,6 +150,9 @@ def runtime_main(test_class):
     parser.add_argument('--use_reduce', action='store_true')
     parser.add_argument(
         '--use_reader_alloc', action='store_true', required=False, default=True)
+    parser.add_argument('--batch_size', required=False, type=int, default=2)
+    parser.add_argument(
+        '--batch_merge_repeat', required=False, type=int, default=1)
 
     args = parser.parse_args()
 
@@ -180,7 +188,7 @@ class TestDistBase(unittest.TestCase):
         self._pservers = 2
         self._ps_endpoints = "127.0.0.1:%s,127.0.0.1:%s" % (
             self._find_free_port(), self._find_free_port())
-        self._python_interp = "python"
+        self._python_interp = sys.executable
         self._sync_mode = True
         self._enforce_place = None
         self._mem_opt = False
@@ -244,9 +252,18 @@ class TestDistBase(unittest.TestCase):
                                  (e, retry_times))
                 retry_times -= 1
 
-    def _run_local(self, model, envs, check_error_log):
+    def _run_local(self,
+                   model,
+                   envs,
+                   check_error_log=False,
+                   batch_size=DEFAULT_BATCH_SIZE,
+                   batch_merge_repeat=1):
 
         cmd = "%s %s --role trainer" % (self._python_interp, model)
+        if batch_size != DEFAULT_BATCH_SIZE:
+            cmd += " --batch_size %d" % batch_size
+        if batch_merge_repeat > 1:
+            cmd += " --batch_merge_repeat %d" % batch_merge_repeat
 
         if self.__use_cuda:
             cmd += " --use_cuda"

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

@@ -18,14 +18,14 @@ import unittest
 from test_dist_base import TestDistBase
 
 
+# FIXME(tangwei): sum op can not handle when inputs is empty.
 class TestDistCTR2x2(TestDistBase):
     def _setup_config(self):
         self._sync_mode = True
         self._enforce_place = "CPU"
 
-
-def test_dist_ctr(self):
-    self.check_with_place("dist_ctr.py", delta=1e-7, check_error_log=False)
+    def test_dist_ctr(self):
+        self.check_with_place("dist_ctr.py", delta=1e-7, check_error_log=False)
 
 
 if __name__ == "__main__":

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

@@ -26,6 +26,15 @@ class TestDistMnist2x2(TestDistBase):
         self.check_with_place("dist_mnist.py", delta=1e-5)
 
 
+class TestDistMnist2x2Lars(TestDistBase):
+    def _setup_config(self):
+        self._sync_mode = True
+        self._use_reduce = False
+
+    def test_se_resnext(self):
+        self.check_with_place("dist_mnist_lars.py", delta=1e-5)
+
+
 class TestDistMnist2x2WithMemopt(TestDistBase):
     def _setup_config(self):
         self._sync_mode = True
@@ -40,8 +49,7 @@ class TestDistMnistAsync(TestDistBase):
         self._sync_mode = False
         self._use_reduce = False
 
-    # FIXME(typhoonzero): fix async mode test later
-    def no_test_dist_train(self):
+    def test_dist_train(self):
         self.check_with_place("dist_mnist.py", delta=200)
 
 

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

+#   Copyright (c) 2018 PaddlePaddle Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+from __future__ import print_function
+import unittest
+from test_dist_base import TestDistBase
+import os
+
+
+class TestDistMnist2x2(TestDistBase):
+    def _setup_config(self):
+        self._sync_mode = True
+        self._use_reduce = False
+
+    def test_dist_train(self):
+        self.check_with_place("dist_mnist_batch_merge.py", delta=1e-5)
+
+    def check_with_place(self,
+                         model_file,
+                         delta=1e-3,
+                         check_error_log=False,
+                         need_envs={}):
+        # TODO(typhoonzero): should auto adapt GPU count on the machine.
+        required_envs = {
+            "PATH": os.getenv("PATH", ""),
+            "PYTHONPATH": os.getenv("PYTHONPATH", ""),
+            "LD_LIBRARY_PATH": os.getenv("LD_LIBRARY_PATH", ""),
+            "FLAGS_fraction_of_gpu_memory_to_use": "0.15",
+            "FLAGS_cudnn_deterministic": "1",
+        }
+
+        required_envs.update(need_envs)
+
+        if check_error_log:
+            required_envs["GLOG_v"] = "7"
+            required_envs["GLOG_logtostderr"] = "1"
+
+        no_merge_losses = self._run_local(
+            model_file,
+            required_envs,
+            check_error_log=check_error_log,
+            batch_size=4)
+
+        batch_merge_losses = self._run_local(
+            model_file,
+            required_envs,
+            check_error_log=check_error_log,
+            batch_size=2,
+            batch_merge_repeat=2)
+        # Ensure both result have values.
+        self.assertGreater(len(no_merge_losses), 1)
+        self.assertEqual(len(no_merge_losses), len(batch_merge_losses))
+
+
+if __name__ == "__main__":
+    unittest.main()

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

@@ -40,8 +40,7 @@ class TestDistSeResneXt2x2Async(TestDistBase):
         self._sync_mode = False
         self._use_reader_alloc = False
 
-    #FIXME(typhoonzero): fix async mode later
-    def no_test_dist_train(self):
+    def test_dist_train(self):
         self.check_with_place("dist_se_resnext.py", delta=100)
 
 

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

@@ -42,7 +42,6 @@ class TestDistSimnetBow2x2DenseAsync(TestDistBase):
         self._sync_mode = False
         self._enforce_place = "CPU"
 
-    #FIXME(typhoonzero): fix async tests later
     def no_test_simnet_bow(self):
         need_envs = {
             "IS_DISTRIBUTED": '0',
@@ -79,8 +78,7 @@ class TestDistSimnetBow2x2SparseAsync(TestDistBase):
         self._sync_mode = False
         self._enforce_place = "CPU"
 
-    #FIXME(typhoonzero): fix async tests later
-    def no_test_simnet_bow(self):
+    def test_simnet_bow(self):
         need_envs = {
             "IS_DISTRIBUTED": '0',
             "IS_SPARSE": '1',
@@ -94,7 +92,6 @@ class TestDistSimnetBow2x2SparseAsync(TestDistBase):
 
 
 # FIXME(tangwei): Learningrate variable is not created on pserver.
-"""
 class TestDistSimnetBow2x2LookupTableSync(TestDistBase):
     def _setup_config(self):
         self._sync_mode = True
@@ -147,7 +144,7 @@ class TestDistSimnetBow2x2LookupTableNotContainLRSync(TestDistBase):
             delta=1e-5,
             check_error_log=False,
             need_envs=need_envs)
-"""
+
 
 if __name__ == "__main__":
     unittest.main()

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

@@ -480,7 +480,7 @@ class TestDistLookupTable(TestDistLookupTableBase):
     def transpiler_test_impl(self):
         pserver1, startup1 = self.get_pserver(self.pserver1_ep)
 
-        self.assertEqual(len(pserver1.blocks), 6)
+        self.assertEqual(len(pserver1.blocks), 5)
         # 0 listen_and_serv
         # 1 optimize for fc_w or fc_b adam
         self.assertEqual([op.type for op in pserver1.blocks[1].ops],
@@ -491,26 +491,32 @@ class TestDistLookupTable(TestDistLookupTableBase):
         # 3 prefetch -> lookup_sparse_table for data0
         self.assertEqual([op.type for op in pserver1.blocks[3].ops],
                          ["lookup_sparse_table"])
-        # 4 prefetch -> lookup_sparse_table for data1
-        self.assertEqual([op.type for op in pserver1.blocks[4].ops],
-                         ["lookup_sparse_table"])
-        # 5 save table
-        self.assertEqual([op.type for op in pserver1.blocks[5].ops], ["save"])
+        # 4 save table
+        self.assertEqual([op.type for op in pserver1.blocks[4].ops], ["save"])
 
-        trainer, _ = self.get_trainer()
+        trainer, trainer_startup = self.get_trainer()
         self.assertEqual(len(trainer.blocks), 1)
         ops = [
-            'split_ids', 'prefetch', 'merge_ids', 'sequence_pool', 'split_ids',
-            'prefetch', 'merge_ids', 'sequence_pool', 'concat', 'mul',
-            'elementwise_add', 'cross_entropy', 'mean', 'fill_constant',
-            'mean_grad', 'cross_entropy_grad', 'elementwise_add_grad', 'send',
-            'mul_grad', 'send', 'concat_grad', 'sequence_pool_grad',
-            'lookup_table_grad', 'sequence_pool_grad', 'lookup_table_grad',
-            'sum', 'split_ids', 'send', 'send_barrier', 'recv', 'recv',
-            'fetch_barrier'
+            'split_ids', 'prefetch', 'merge_ids', 'sequence_pool',
+            'sequence_pool', 'concat', 'mul', 'elementwise_add',
+            'cross_entropy', 'mean', 'fill_constant', 'mean_grad',
+            'cross_entropy_grad', 'elementwise_add_grad', 'send', 'mul_grad',
+            'send', 'concat_grad', 'sequence_pool_grad', 'lookup_table_grad',
+            'sequence_pool_grad', 'lookup_table_grad', 'sum', 'split_ids',
+            'send', 'send_barrier', 'recv', 'recv', 'fetch_barrier'
         ]
         self.assertEqual([op.type for op in trainer.blocks[0].ops], ops)
 
+        startup_ops = [
+            'fill_constant', 'fill_constant', 'fill_constant', 'fill_constant',
+            'fill_constant', 'fill_constant', 'fill_constant', 'fill_constant',
+            'fill_constant', 'fill_constant', 'fill_constant', 'fill_constant',
+            'fill_constant', 'fill_constant', 'uniform_random', 'recv', 'recv',
+            'fetch_barrier', 'fake_init'
+        ]
+        self.assertEqual([op.type for op in trainer_startup.blocks[0].ops],
+                         startup_ops)
+
 
 class TestAsyncLocalLookupTable(TestDistLookupTableBase):
     def net_conf(self):
@@ -553,7 +559,7 @@ class TestAsyncDistLookupTable(TestDistLookupTableBase):
 
         pserver1, startup1 = self.get_pserver(self.pserver1_ep, config, False)
 
-        self.assertEqual(len(pserver1.blocks), 6)
+        self.assertEqual(len(pserver1.blocks), 5)
         # 0 listen_and_serv
         # 1 optimize for fc_w or fc_b adam
         self.assertEqual([op.type for op in pserver1.blocks[1].ops],
@@ -563,22 +569,19 @@ class TestAsyncDistLookupTable(TestDistLookupTableBase):
         # 3 prefetch -> lookup_sparse_table for data0
         self.assertEqual([op.type for op in pserver1.blocks[3].ops],
                          ["lookup_sparse_table"])
-        # 4 prefetch -> lookup_sparse_table for data1
-        self.assertEqual([op.type for op in pserver1.blocks[4].ops],
-                         ["lookup_sparse_table"])
-        # 5 save table
-        self.assertEqual([op.type for op in pserver1.blocks[5].ops], ["save"])
+        # 4 save table
+        self.assertEqual([op.type for op in pserver1.blocks[4].ops], ["save"])
 
         trainer, _ = self.get_trainer(config)
         self.assertEqual(len(trainer.blocks), 1)
         ops = [
-            'split_ids', 'prefetch', 'merge_ids', 'sequence_pool', 'split_ids',
-            'prefetch', 'merge_ids', 'sequence_pool', 'concat', 'mul',
-            'elementwise_add', 'cross_entropy', 'mean', 'fill_constant',
-            'mean_grad', 'cross_entropy_grad', 'elementwise_add_grad', 'send',
-            'mul_grad', 'send', 'concat_grad', 'sequence_pool_grad',
-            'lookup_table_grad', 'sequence_pool_grad', 'lookup_table_grad',
-            'sum', 'split_ids', 'send', 'recv', 'recv'
+            'split_ids', 'prefetch', 'merge_ids', 'sequence_pool',
+            'sequence_pool', 'concat', 'mul', 'elementwise_add',
+            'cross_entropy', 'mean', 'fill_constant', 'mean_grad',
+            'cross_entropy_grad', 'elementwise_add_grad', 'send', 'mul_grad',
+            'send', 'concat_grad', 'sequence_pool_grad', 'lookup_table_grad',
+            'sequence_pool_grad', 'lookup_table_grad', 'sum', 'split_ids',
+            'send', 'recv', 'recv'
         ]
         self.assertEqual([op.type for op in trainer.blocks[0].ops], ops)
 

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

+#   Copyright (c) 2018 PaddlePaddle Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+from __future__ import print_function
+
+import unittest
+
+import paddle.fluid.core as core
+from paddle.fluid.op import Operator
+
+
+class TestFakeInitOpSelectedRows(unittest.TestCase):
+    def check_with_place(self, place, is_selected_rows):
+        scope = core.Scope()
+
+        out_var_name = 'Out'
+        if is_selected_rows:
+            out_tensor = scope.var(out_var_name).get_selected_rows().get_tensor(
+            )
+        else:
+            out_tensor = scope.var(out_var_name).get_tensor()
+
+        var_shape = [4, 784]
+
+        # create and run fake_init_op
+        fake_init_op = Operator("fake_init", Out=out_var_name, shape=var_shape)
+        fake_init_op.run(scope, place)
+
+        self.assertEqual(var_shape, out_tensor._get_dims())
+
+    def test_fake_init_selected_rows(self):
+        places = [core.CPUPlace()]
+        if core.is_compiled_with_cuda():
+            places.append(core.CUDAPlace(0))
+        for place in places:
+            for is_selected_rows in [True, False]:
+                self.check_with_place(place, is_selected_rows)
+
+
+if __name__ == "__main__":
+    unittest.main()

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

+#   Copyright (c) 2018 PaddlePaddle Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+import unittest
+import numpy as np
+from op_test import OpTest
+
+
+class TestScaleOp(OpTest):
+    def setUp(self):
+        self.op_type = "hash"
+        self.init_test_case()
+        self.inputs = {'X': (self.in_seq, self.lod)}
+        self.attrs = {'num_hash': 4, 'mod_by': 10000}
+        self.outputs = {'Out': (self.out_seq, self.lod)}
+
+    def init_test_case(self):
+        np.random.seed = 1
+        self.in_seq = np.random.randint(0, 10, (30, 1)).astype("int32")
+        self.lod = [[9, 4, 11, 6]]
+        #  self.out_seq = np.ones([30, 4, 1], dtype=np.int32)
+        self.out_seq = [
+            [[9662], [9217], [1129], [8487]], [[9662], [9217], [1129], [8487]],
+            [[8310], [1327], [1654], [4567]], [[6897], [3218], [2013], [1241]],
+            [[9407], [6715], [6949], [8094]], [[8473], [694], [5142], [2479]],
+            [[8310], [1327], [1654], [4567]], [[6897], [3218], [2013], [1241]],
+            [[4372], [9456], [8204], [6695]], [[6897], [3218], [2013], [1241]],
+            [[8473], [694], [5142], [2479]], [[4372], [9456], [8204], [6695]],
+            [[4372], [9456], [8204], [6695]], [[8473], [694], [5142], [2479]],
+            [[9407], [6715], [6949], [8094]], [[9369], [4525], [8935], [9210]],
+            [[4372], [9456], [8204], [6695]], [[4372], [9456], [8204], [6695]],
+            [[9369], [4525], [8935], [9210]], [[6897], [3218], [2013], [1241]],
+            [[9038], [7951], [5953], [8657]], [[9407], [6715], [6949], [8094]],
+            [[9662], [9217], [1129], [8487]], [[9369], [4525], [8935], [9210]],
+            [[9038], [7951], [5953], [8657]], [[9662], [9217], [1129], [8487]],
+            [[9369], [4525], [8935], [9210]], [[1719], [5986], [9919], [3421]],
+            [[4372], [9456], [8204], [6695]], [[9038], [7951], [5953], [8657]]
+        ]
+        self.out_seq = np.array(self.out_seq)
+
+    def test_check_output(self):
+        self.check_output()
+
+
+if __name__ == "__main__":
+    unittest.main()

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

@@ -22,15 +22,28 @@ from op_test import OpTest
 class TestMergeIdsOp(OpTest):
     def setUp(self):
         self.op_type = "merge_ids"
-        ids = np.array([[0], [2], [2], [3], [5], [5], [6]]).astype('int64')
-        x0 = np.array([[0.1, 0.2], [0.2, 0.3], [0.3, 0.4]]).astype('float32')
-        x1 = np.array([]).astype('float32')
-        x2 = np.array([[0.4, 0.5], [0.4, 0.5], [0.5, 0.6],
-                       [0.5, 0.6]]).astype('float32')
-        out = np.array([[0.1, 0.2], [0.4, 0.5], [0.4, 0.5], [0.2, 0.3],
-                        [0.5, 0.6], [0.5, 0.6], [0.3, 0.4]]).astype('float32')
-        self.inputs = {'Ids': ids, "X": [('x0', x0), ('x1', x1), ('x2', x2)]}
-        self.outputs = {'Out': out}
+        ids1 = np.array([[0], [2], [5], [6]]).astype('int64')
+        ids2 = np.array([[0], [2], [2], [3]]).astype('int64')
+
+        rows1 = np.array([[0], [2]]).astype('int64')
+        rows2 = np.array([[3], [5]]).astype('int64')
+        rows3 = np.array([[6]]).astype('int64')
+
+        x0 = np.array([[0.1, 0.2], [0.2, 0.3]]).astype('float32')
+        x1 = np.array([[0.3, 0.4], [0.4, 0.5]]).astype('float32')
+        x2 = np.array([[0.5, 0.6]]).astype('float32')
+
+        out1 = np.array(
+            [[0.1, 0.2], [0.2, 0.3], [0.4, 0.5], [0.5, 0.6]]).astype('float32')
+        out2 = np.array(
+            [[0.1, 0.2], [0.2, 0.3], [0.2, 0.3], [0.3, 0.4]]).astype('float32')
+
+        self.inputs = {
+            'Ids': [('ids1', ids1), ('ids2', ids2)],
+            "Rows": [('rows1', rows1), ('rows2', rows2), ('rows3', rows3)],
+            "X": [('x0', x0), ('x1', x1), ('x2', x2)]
+        }
+        self.outputs = {'Out': [('out1', out1), ('out2', out2)]}
 
     def test_check_output(self):
         self.check_output()

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

+#   Copyright (c) 2018 PaddlePaddle Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+import unittest
+
+import paddle.fluid as fluid
+from paddle.fluid.framework import Program, program_guard
+
+
+class TestMetricsDetectionMap(unittest.TestCase):
+    def test_detection_map(self):
+        program = fluid.Program()
+        with program_guard(program):
+            detect_res = fluid.layers.data(
+                name='detect_res',
+                shape=[10, 6],
+                append_batch_size=False,
+                dtype='float32')
+            label = fluid.layers.data(
+                name='label',
+                shape=[10, 1],
+                append_batch_size=False,
+                dtype='float32')
+            box = fluid.layers.data(
+                name='bbox',
+                shape=[10, 4],
+                append_batch_size=False,
+                dtype='float32')
+            map_eval = fluid.metrics.DetectionMAP(
+                detect_res, label, box, class_num=21)
+            cur_map, accm_map = map_eval.get_map_var()
+            self.assertIsNotNone(cur_map)
+            self.assertIsNotNone(accm_map)
+        print(str(program))
+
+
+if __name__ == '__main__':
+    unittest.main()

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

@@ -90,6 +90,45 @@ class TestMomentumOp2(OpTest):
         self.check_output()
 
 
+class TestLarsMomentumOp(OpTest):
+    def setUp(self):
+        self.op_type = "lars_momentum"
+
+        param = np.random.random((123, 321)).astype("float32")
+        grad = np.random.random((123, 321)).astype("float32")
+        velocity = np.zeros((123, 321)).astype("float32")
+        learning_rate = np.array([0.001]).astype("float32")
+        mu = 0.0001
+        lars_coeff = 0.001
+        lars_weight_decay = 0.0005
+
+        self.inputs = {
+            'Param': param,
+            'Grad': grad,
+            'Velocity': velocity,
+            'LearningRate': learning_rate
+        }
+
+        self.attrs = {
+            'mu': mu,
+            'lars_coeff': lars_coeff,
+            'lars_weight_decay': lars_weight_decay
+        }
+
+        pnorm = np.sqrt(np.square(param).sum())
+        gnorm = np.sqrt(np.square(grad).sum())
+        local_lr = learning_rate * lars_coeff * pnorm / (
+            gnorm + lars_weight_decay * param)
+        velocity_out = mu * velocity + local_lr * (grad + lars_weight_decay *
+                                                   param)
+        param_out = param - velocity_out
+
+        self.outputs = {'ParamOut': param_out, 'VelocityOut': velocity_out}
+
+    def test_check_output(self):
+        self.check_output()
+
+
 class TestSparseMomentumOp(unittest.TestCase):
     def setUp(self):
         self.use_nesterov = False

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

@@ -25,18 +25,21 @@ from paddle.fluid.op import Operator
 class TestSplitIdsOp(OpTest):
     def setUp(self):
         self.op_type = "split_ids"
-        ids = np.array([[0], [2], [2], [3], [5], [5], [6]]).astype('int64')
+        ids1 = np.array([[0], [2], [2], [3], [5], [5], [6]]).astype('int64')
+        ids2 = np.array([[6], [2], [3], [3], [5], [2], [6]]).astype('int64')
+        ids3 = np.array([[2], [2], [2], [3], [5], [5], [6]]).astype('int64')
+
         out0 = np.array([[0], [3], [6]]).astype('int64')
         out1 = np.array([[]]).astype('int64')
-        out2 = np.array([[2], [2], [5], [5]]).astype('int64')
-        self.inputs = {'Ids': ids}
+        out2 = np.array([[2], [5]]).astype('int64')
+        self.inputs = {'Ids': [('ids1', ids1), ('ids2', ids2), ('ids3', ids3)]}
         self.outputs = {'Out': [('out0', out0), ('out1', out1), ('out2', out2)]}
 
     def test_check_output(self):
         self.check_output()
 
 
-class TestSpliteIds(unittest.TestCase):
+class TestSplitSelectedRows(unittest.TestCase):
     def get_places(self):
         places = [core.CPUPlace()]
         return places

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

@@ -99,7 +99,6 @@ class TestSpliteSelectedRows(unittest.TestCase):
         out0_grad.set_height(height)
         out0_grad_tensor = out0_grad.get_tensor()
         np_array = np.ones((len(rows0), row_numel)).astype("float32")
-        np_array[0, 0] = 2.0
         out0_grad_tensor.set(np_array, place)
 
         out1_grad = scope.var("out1@GRAD").get_selected_rows()
@@ -108,7 +107,6 @@ class TestSpliteSelectedRows(unittest.TestCase):
         out1_grad.set_height(height)
         out1_grad_tensor = out1_grad.get_tensor()
         np_array = np.ones((len(rows1), row_numel)).astype("float32")
-        np_array[0, 1] = 4.0
         out1_grad_tensor.set(np_array, place)
 
         x_grad = scope.var("X@GRAD").get_selected_rows()
@@ -121,11 +119,13 @@ class TestSpliteSelectedRows(unittest.TestCase):
 
         grad_op.run(scope, place)
 
-        self.assertEqual(x_grad.rows(), rows0 + rows1)
+        merged_rows = set(rows0 + rows1)
+        self.assertEqual(set(x_grad.rows()), set(rows0 + rows1))
         self.assertEqual(x_grad.height(), height)
 
+        print(np.array(x_grad.get_tensor()))
         self.assertAlmostEqual(2.0, np.array(x_grad.get_tensor())[0, 0])
-        self.assertAlmostEqual(4.0, np.array(x_grad.get_tensor())[2, 1])
+        self.assertAlmostEqual(1.0, np.array(x_grad.get_tensor())[2, 1])
 
 
 if __name__ == "__main__":

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

@@ -45,16 +45,30 @@ class TestSumOp(OpTest):
 
 
 class TestSelectedRowsSumOp(OpTest):
-    def check_with_place(self, place):
-        scope = core.Scope()
-        self.check_input_and_optput(scope, place, True, True, True)
-        self.check_input_and_optput(scope, place, False, True, True)
-        self.check_input_and_optput(scope, place, False, False, True)
-        self.check_input_and_optput(scope, place, False, False, False)
+    def check_with_place(self, place, inplace):
+        self.height = 10
+        self.row_numel = 12
+        self.rows = [0, 1, 2, 3, 4, 5, 6]
+
+        self.check_input_and_optput(core.Scope(), place, inplace, True, True,
+                                    True)
+        self.check_input_and_optput(core.Scope(), place, inplace, False, True,
+                                    True)
+        self.check_input_and_optput(core.Scope(), place, inplace, False, False,
+                                    True)
+        self.check_input_and_optput(core.Scope(), place, inplace, False, False,
+                                    False)
+
+    def _get_array(self, row_num, row_numel):
+        array = np.ones((row_num, row_numel)).astype("float32")
+        for i in range(row_num):
+            array[i] *= i
+        return array
 
     def check_input_and_optput(self,
                                scope,
                                place,
+                               inplace,
                                w1_has_data=False,
                                w2_has_data=False,
                                w3_has_data=False):
@@ -64,35 +78,43 @@ class TestSelectedRowsSumOp(OpTest):
         self.create_selected_rows(scope, place, "W3", w3_has_data)
 
         # create Out Variable
-        out = scope.var('Out').get_selected_rows()
+        if inplace:
+            out_var_name = "W1"
+        else:
+            out_var_name = "Out"
+        out = scope.var(out_var_name).get_selected_rows()
 
         # create and run sum operator
-        sum_op = Operator("sum", X=["W1", "W2", "W3"], Out='Out')
+        sum_op = Operator("sum", X=["W1", "W2", "W3"], Out=out_var_name)
         sum_op.run(scope, place)
 
         has_data_w_num = 0
-        for w in [w1_has_data, w2_has_data, w3_has_data]:
-            if not w:
+        for has_data in [w1_has_data, w2_has_data, w3_has_data]:
+            if has_data:
                 has_data_w_num += 1
 
-        self.assertEqual(7 * has_data_w_num, len(out.rows()))
+        if has_data_w_num > 0:
+            self.assertEqual(len(out.rows()), 7)
+            self.assertTrue(
+                np.array_equal(
+                    np.array(out.get_tensor()),
+                    self._get_array(len(self.rows), self.row_numel) *
+                    has_data_w_num))
+        else:
+            self.assertEqual(len(out.rows()), 0)
 
-    def create_selected_rows(self, scope, place, var_name, isEmpty):
+    def create_selected_rows(self, scope, place, var_name, has_data):
         # create and initialize W Variable
-        if not isEmpty:
-            rows = [0, 1, 2, 3, 4, 5, 6]
-            row_numel = 12
+        if has_data:
+            rows = self.rows
         else:
             rows = []
-            row_numel = 12
 
         var = scope.var(var_name)
         w_selected_rows = var.get_selected_rows()
-        w_selected_rows.set_height(len(rows))
+        w_selected_rows.set_height(self.height)
         w_selected_rows.set_rows(rows)
-        w_array = np.ones((len(rows), row_numel)).astype("float32")
-        for i in range(len(rows)):
-            w_array[i] *= i
+        w_array = self._get_array(len(rows), self.row_numel)
         w_tensor = w_selected_rows.get_tensor()
         w_tensor.set(w_array, place)
 
@@ -100,9 +122,11 @@ class TestSelectedRowsSumOp(OpTest):
 
     def test_w_is_selected_rows(self):
         places = [core.CPUPlace()]
-        # currently only support CPU
+        if core.is_compiled_with_cuda():
+            places.append(core.CUDAPlace(0))
         for place in places:
-            self.check_with_place(place)
+            for inplace in [True, False]:
+                self.check_with_place(place, inplace)
 
 
 if __name__ == "__main__":

python/paddle/fluid/transpiler/distribute_transpiler.py

@@ -475,6 +475,26 @@ class DistributeTranspiler(object):
         delete_ops(self.origin_program.global_block(), self.optimize_ops)
         delete_ops(self.origin_program.global_block(), lr_ops)
 
+        # delete table init op
+        if self.has_distributed_lookup_table:
+            table_var = self.startup_program.global_block().vars[
+                self.table_name]
+            table_param_init_op = []
+            for op in self.startup_program.global_block().ops:
+                if self.table_name in op.output_arg_names:
+                    table_param_init_op.append(op)
+            init_op_num = len(table_param_init_op)
+            if init_op_num != 1:
+                raise ValueError("table init op num should be 1, now is " + str(
+                    init_op_num))
+            table_init_op = table_param_init_op[0]
+            self.startup_program.global_block().append_op(
+                type="fake_init",
+                inputs={},
+                outputs={"Out": table_var},
+                attrs={"shape": table_init_op.attr('shape')})
+            delete_ops(self.startup_program.global_block(), table_param_init_op)
+
         self.origin_program.__str__()
 
         if wait_port:
@@ -713,7 +733,7 @@ in a single call.")
                 for _, op in enumerate(self.optimize_ops):
                     # optimizer is connected to itself
                     if op.attr(OP_ROLE_VAR_ATTR_NAME)[0] == optimize_target_param_name and \
-                        op not in global_ops:
+                            op not in global_ops:
                         log("append opt op: ", op.type, op.input_arg_names,
                             merged_var)
                         __append_optimize_op__(op, per_opt_block,
@@ -1034,15 +1054,11 @@ to transpile() call.")
     def _replace_lookup_table_op_with_prefetch(self, program,
                                                pserver_endpoints):
         # 1. replace lookup_table_op with split_ids_op -> prefetch_op -> sum_op
-        # self.all_prefetch_input_vars =
-        #       [[var0_prefetch_in_pserver0, var0_prefetch_in_pserver1]
-        #        [var1_prefetch_in_pserver0, var1_prefetch_in_pserver1]]
+        self.all_in_ids_vars = []
         self.all_prefetch_input_vars = []
-
-        # self.all_prefetch_input_vars =
-        #       [[var0_prefetch_in_pserver0, var0_prefetch_in_pserver1]
-        #        [var1_prefetch_in_pserver0, var1_prefetch_in_pserver1]]
         self.all_prefetch_output_vars = []
+        self.all_out_emb_vars = []
+        lookup_table_op_index = -1
 
         continue_search_lookup_table_op = True
         while continue_search_lookup_table_op:
@@ -1052,72 +1068,68 @@ to transpile() call.")
                 if op.type == LOOKUP_TABLE_TYPE:
                     continue_search_lookup_table_op = True
 
-                    lookup_table_op_index = list(all_ops).index(op)
+                    lookup_table_op_index = lookup_table_op_index if lookup_table_op_index != -1 else list(
+                        all_ops).index(op)
                     ids_name = op.input("Ids")
                     out_name = op.output("Out")
 
                     ids_var = program.global_block().vars[ids_name[0]]
-                    prefetch_input_vars = self._create_splited_vars(
-                        source_var=ids_var,
-                        block=program.global_block(),
-                        tag="_prefetch_in_")
-                    self.all_prefetch_input_vars.append(prefetch_input_vars)
+                    self.all_in_ids_vars.append(ids_var)
 
                     out_var = program.global_block().vars[out_name[0]]
-                    prefetch_output_vars = self._create_splited_vars(
-                        source_var=out_var,
-                        block=program.global_block(),
-                        tag="_prefetch_out_")
-                    self.all_prefetch_output_vars.append(prefetch_output_vars)
-
-                    # insert split_ids_op
-                    program.global_block()._insert_op(
-                        index=lookup_table_op_index,
-                        type="split_ids",
-                        inputs={
-                            'Ids': [
-                                program.global_block().vars[varname]
-                                for varname in ids_name
-                            ]
-                        },
-                        outputs={"Out": prefetch_input_vars})
-
-                    # insert prefetch_op
-                    program.global_block()._insert_op(
-                        index=lookup_table_op_index + 1,
-                        type="prefetch",
-                        inputs={'X': prefetch_input_vars},
-                        outputs={"Out": prefetch_output_vars},
-                        attrs={
-                            "epmap": pserver_endpoints,
-                            # FIXME(qiao) temporarily disable this config because prefetch
-                            # is not act as other rpc op, it's more like a forward op
-                            # RPC_OP_ROLE_ATTR_NAME: RPC_OP_ROLE_ATTR_VALUE
-                        })
-
-                    # insert concat_op
-                    program.global_block()._insert_op(
-                        index=lookup_table_op_index + 2,
-                        type="merge_ids",
-                        inputs={
-                            'Ids': [
-                                program.global_block().vars[varname]
-                                for varname in ids_name
-                            ],
-                            'X': prefetch_output_vars
-                        },
-                        outputs={
-                            "Out": [
-                                program.global_block().vars[varname]
-                                for varname in out_name
-                            ]
-                        })
+                    self.all_out_emb_vars.append(out_var)
 
                     # delete lookup_table_op
                     delete_ops(program.global_block(), [op])
                     # break for loop
                     break
 
+        for index in range(len(self.pserver_endpoints)):
+            in_var = program.global_block().create_var(
+                name=str("prefetch_compress_in_tmp_" + str(index)),
+                type=self.all_in_ids_vars[0].type,
+                shape=self.all_in_ids_vars[0].shape,
+                dtype=self.all_in_ids_vars[0].dtype)
+            self.all_prefetch_input_vars.append(in_var)
+
+            out_var = program.global_block().create_var(
+                name=str("prefetch_compress_out_tmp_" + str(index)),
+                type=self.all_out_emb_vars[0].type,
+                shape=self.all_out_emb_vars[0].shape,
+                dtype=self.all_out_emb_vars[0].dtype)
+            self.all_prefetch_output_vars.append(out_var)
+
+        # insert split_ids_op
+        program.global_block()._insert_op(
+            index=lookup_table_op_index,
+            type="split_ids",
+            inputs={'Ids': self.all_in_ids_vars},
+            outputs={"Out": self.all_prefetch_input_vars})
+
+        # insert prefetch_op
+        program.global_block()._insert_op(
+            index=lookup_table_op_index + 1,
+            type="prefetch",
+            inputs={'X': self.all_prefetch_input_vars},
+            outputs={"Out": self.all_prefetch_output_vars},
+            attrs={
+                "epmap": pserver_endpoints,
+                # FIXME(qiao) temporarily disable this config because prefetch
+                # is not act as other rpc op, it's more like a forward op
+                # RPC_OP_ROLE_ATTR_NAME: RPC_OP_ROLE_ATTR_VALUE
+            })
+
+        # insert concat_op
+        program.global_block()._insert_op(
+            index=lookup_table_op_index + 2,
+            type="merge_ids",
+            inputs={
+                'Ids': self.all_in_ids_vars,
+                'Rows': self.all_prefetch_input_vars,
+                'X': self.all_prefetch_output_vars
+            },
+            outputs={"Out": self.all_out_emb_vars})
+
     def _split_table_grad_and_add_send_vars(self, program, pserver_endpoints):
         # 2. add split_ids_op and send_op to send gradient to pservers
 
@@ -1134,7 +1146,8 @@ to transpile() call.")
                     inputs={
                         'Ids': [program.global_block().vars[table_grad_name]]
                     },
-                    outputs={"Out": self.trainer_side_table_grad_list})
+                    outputs={"Out": self.trainer_side_table_grad_list},
+                    attrs={RPC_OP_ROLE_ATTR_NAME: DIST_OP_ROLE_ATTR_VALUE})
                 program.global_block()._insert_op(
                     index=op_index + 2,
                     type="send",
@@ -1160,32 +1173,31 @@ to transpile() call.")
         # STEP: create prefetch block
         table_var = pserver_program.global_block().vars[self.table_name]
         prefetch_var_name_to_block_id = []
-        for index in range(len(self.all_prefetch_input_vars)):
-            prefetch_block = pserver_program._create_block(optimize_block.idx)
-            trainer_ids = self.all_prefetch_input_vars[index][pserver_index]
-            pserver_ids = pserver_program.global_block().create_var(
-                name=trainer_ids.name,
-                type=trainer_ids.type,
-                shape=trainer_ids.shape,
-                dtype=trainer_ids.dtype)
-            trainer_out = self.all_prefetch_output_vars[index][pserver_index]
-            pserver_out = pserver_program.global_block().create_var(
-                name=trainer_out.name,
-                type=trainer_out.type,
-                shape=trainer_out.shape,
-                dtype=trainer_out.dtype)
-            prefetch_block.append_op(
-                type="lookup_sparse_table",
-                inputs={'Ids': pserver_ids,
-                        "W": table_var},
-                outputs={"Out": pserver_out},
-                attrs={
-                    "is_sparse": True,  # has no effect on lookup_table op
-                    "is_distributed": True,
-                    "padding_idx": -1
-                })
-            prefetch_var_name_to_block_id.append(trainer_ids.name + ":" + str(
-                prefetch_block.idx))
+        prefetch_block = pserver_program._create_block(optimize_block.idx)
+        trainer_ids = self.all_prefetch_input_vars[pserver_index]
+        pserver_ids = pserver_program.global_block().create_var(
+            name=trainer_ids.name,
+            type=trainer_ids.type,
+            shape=trainer_ids.shape,
+            dtype=trainer_ids.dtype)
+        trainer_out = self.all_prefetch_output_vars[pserver_index]
+        pserver_out = pserver_program.global_block().create_var(
+            name=trainer_out.name,
+            type=trainer_out.type,
+            shape=trainer_out.shape,
+            dtype=trainer_out.dtype)
+        prefetch_block.append_op(
+            type="lookup_sparse_table",
+            inputs={'Ids': pserver_ids,
+                    "W": table_var},
+            outputs={"Out": pserver_out},
+            attrs={
+                "is_sparse": True,  # has no effect on lookup_table op
+                "is_distributed": True,
+                "padding_idx": -1
+            })
+        prefetch_var_name_to_block_id.append(trainer_ids.name + ":" + str(
+            prefetch_block.idx))
         return prefetch_var_name_to_block_id
 
     def _create_table_optimize_block(self, pserver_index, pserver_program,
@@ -1364,16 +1376,6 @@ to transpile() call.")
             program.global_block()._sync_with_cpp()
         return var_mapping
 
-    def _create_splited_vars(self, source_var, block, tag):
-        return [
-            block.create_var(
-                name=str(source_var.name + tag + str(index)),
-                type=source_var.type,
-                shape=source_var.shape,
-                dtype=source_var.dtype)
-            for index in range(len(self.pserver_endpoints))
-        ]
-
     def _clone_var(self, block, var, persistable=True):
         return block.create_var(
             name=var.name,
@@ -1431,7 +1433,7 @@ to transpile() call.")
         elif op_type == "adamax":
             if varkey in ["Moment", "InfNorm"]:
                 return param_shape
-        elif op_type == "momentum":
+        elif op_type in ["momentum", "lars_momentum"]:
             if varkey == "Velocity":
                 return param_shape
         elif op_type == "rmsprop":
@@ -1442,6 +1444,10 @@ to transpile() call.")
                 return param_shape
         elif op_type == "sgd":
             pass
+        else:
+            raise ValueError(
+                "Not supported optimizer for distributed training: %s" %
+                op_type)
         return orig_shape
 
     def _get_varname_parts(self, varname):

python/paddle/fluid/transpiler/memory_optimization_transpiler.py

@@ -171,7 +171,7 @@ class ControlFlowGraph(object):
                 self._live_out[i] |= self._live_in[s]
             self._live_in[i] = self._uses[i] | (
                 self._live_out[i] - self._defs[i])
-            if live_in[i] != self._live_in[i]:
+            if live_in[i] != set(self._live_in[i]):
                 for d in self._presuccessors[i]:
                     worklist.append(d)
 
@@ -321,8 +321,7 @@ class ControlFlowGraph(object):
 
                         if not compare_shape(x_shape, cache_shape, level):
                             continue
-                        # TODO(qijun): actually, we should compare
-                        # dtype_to_size[x_dtype] and dtype_to_size[cache_dtype]
+                        # TODO(qijun): dtype_to_size[x_dtype] and dtype_to_size[cache_dtype]
                         if x_dtype != cache_dtype:
                             continue
 
@@ -487,7 +486,6 @@ def memory_optimize(input_program,
             skip_opt_set = grad_set
         else:
             skip_opt_set.update(grad_set)
-
     cfgs = _get_cfgs(input_program)
     for cfg in cfgs:
         cfg.memory_optimize(skip_opt_set=skip_opt_set, level=level)

python/paddle/utils/__init__.py

@@ -12,5 +12,5 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 
-from plot import Ploter
+from .plot import Ploter
 __all__ = ['dump_config', 'Ploter']