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

test=develop

CMakeLists.txt

@@ -75,6 +75,7 @@ option(WITH_INFERENCE_API_TEST   "Test fluid inference high-level api interface"
 option(WITH_SYSTEM_BLAS   "Use system blas library"           OFF)
 option(PY_VERSION       "Compile PaddlePaddle with python3 support"     ${PY_VERSION})
 option(WITH_FAST_MATH   "Make use of fast math library, might affect the precision to some extent" ON)
+option(WITH_WBAES       "Compile PaddlePaddle with WBAES support"       ON)
 
 # PY_VERSION
 if(NOT PY_VERSION)
@@ -148,6 +149,7 @@ include(external/dlpack)
 include(external/snappy)    # download snappy
 include(external/snappystream) # download snappystream
 include(external/warpctc)   # download, build, install warpctc
+include(external/wbaes)     # download wbaes
 
 if (NOT WIN32)
 # there is no official support of nccl, cupti in windows

cmake/configure.cmake

@@ -157,3 +157,7 @@ endif(WITH_BRPC_RDMA)
 if(ON_INFER)
     add_definitions(-DPADDLE_ON_INFERENCE)
 endif(ON_INFER)
+
+if(WITH_WBAES)
+    add_definitions(-DPADDLE_WITH_WBAES)
+endif(WITH_WBAES)

cmake/external/protobuf.cmake

@@ -201,7 +201,7 @@ FUNCTION(build_protobuf TARGET_NAME BUILD_FOR_HOST)
         SET(OPTIONAL_ARGS ${OPTIONAL_ARGS} "-DCMAKE_GENERATOR_PLATFORM=x64")
     ENDIF()
 
-    SET(PROTOBUF_REPO "https://github.com/google/protobuf.git")
+    SET(PROTOBUF_REPO "https://github.com/protocolbuffers/protobuf.git")
     SET(PROTOBUF_TAG "9f75c5aa851cd877fb0d93ccc31b8567a6706546")
 
     ExternalProject_Add(

cmake/external/wbaes.cmake

+# Copyright (c) 2019 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.
+
+IF(NOT ${WITH_WBAES})
+    return()
+ENDIF(NOT ${WITH_WBAES})
+
+INCLUDE(ExternalProject)
+SET(WBAES_DST_DIR       "wbaes")
+SET(WBAES_INSTALL_ROOT  "${THIRD_PARTY_PATH}/install")
+SET(WBAES_INSTALL_DIR   ${WBAES_INSTALL_ROOT}/${WBAES_DST_DIR})
+SET(WBAES_ROOT          ${WBAES_INSTALL_DIR})
+SET(WBAES_INC_DIR       ${WBAES_ROOT}/include)
+SET(WBAES_LIB_DIR       ${WBAES_ROOT}/lib)
+
+SET(CMAKE_INSTALL_RPATH "${CMAKE_INSTALL_RPATH}" "${WBAES_ROOT}/lib")
+SET(CMAKE_INSTALL_RPATH_USE_LINK_PATH TRUE)
+
+IF(APPLE)
+    SET(WBAES_TAG   "v1.0.0" CACHE STRING "" FORCE)
+    SET(WBAES_URL   "http://paddlepaddledeps.bj.bcebos.com/wbaes-sdk.mac.${WBAES_TAG}.tgz" CACHE STRING "" FORCE)
+    SET(WBAES_LIB   ${WBAES_LIB_DIR}/libwbaes.dylib)
+    SET(WBAES_SHARED_LIB   ${WBAES_LIB_DIR}/libwbaes.dylib)
+ELSEIF(WIN32)
+    SET(WBAES_TAG   "v1.0.0" CACHE STRING "" FORCE)
+    SET(WBAES_URL   "http://paddlepaddledeps.bj.bcebos.com/wbaes-sdk.windows-x64.${WBAES_TAG}.tgz" CACHE STRING "" FORCE)
+    SET(WBAES_LIB   ${WBAES_LIB_DIR}/libwbaes.lib)
+    SET(WBAES_SHARED_LIB   ${WBAES_LIB_DIR}/libwbaes.dll)
+ELSE()
+    SET(WBAES_TAG   "v1.0.2" CACHE STRING "" FORCE)
+    SET(WBAES_URL   "http://paddlepaddledeps.bj.bcebos.com/wbaes-sdk.linux-x86_64.${WBAES_TAG}.tgz" CACHE STRING "" FORCE)
+    SET(WBAES_LIB   ${WBAES_LIB_DIR}/libwbaes.so)
+    SET(WBAES_SHARED_LIB   ${WBAES_LIB_DIR}/libwbaes.so)
+ENDIF()
+
+SET(WBAES_PROJECT       "extern_wbaes")
+MESSAGE(STATUS "WBAES_URL: ${WBAES_URL}, WBAES_LIB: ${WBAES_LIB}")
+SET(WBAES_SOURCE_DIR    "${THIRD_PARTY_PATH}/wbaes") 
+SET(WBAES_DOWNLOAD_DIR  "${WBAES_SOURCE_DIR}/src/${WBAES_PROJECT}")
+
+ExternalProject_Add(
+    ${WBAES_PROJECT}
+    ${EXTERNAL_PROJECT_LOG_ARGS}
+    PREFIX                  ${WBAES_SOURCE_DIR}
+    URL                     ${WBAES_URL}
+    DOWNLOAD_DIR            ${WBAES_DOWNLOAD_DIR}
+    DOWNLOAD_NO_PROGRESS    1
+    CONFIGURE_COMMAND       ""
+    BUILD_COMMAND           ""
+    INSTALL_COMMAND         ""
+        ${CMAKE_COMMAND} -E copy_directory ${WBAES_DOWNLOAD_DIR}/include ${WBAES_INC_DIR} &&
+        ${CMAKE_COMMAND} -E copy_directory ${WBAES_DOWNLOAD_DIR}/lib ${WBAES_LIB_DIR}
+)
+
+INCLUDE_DIRECTORIES(${WBAES_INC_DIR})
+
+ADD_LIBRARY(wbaes SHARED IMPORTED GLOBAL)
+SET_PROPERTY(TARGET wbaes PROPERTY IMPORTED_LOCATION ${WBAES_LIB})
+SET_PROPERTY(TARGET wbaes PROPERTY IMPORTED_NO_SONAME 1)
+ADD_DEPENDENCIES(wbaes ${WBAES_PROJECT})

cmake/generic.cmake

@@ -264,6 +264,14 @@ function(cc_library TARGET_NAME)
         list(REMOVE_ITEM cc_library_DEPS warpctc)
         add_dependencies(${TARGET_NAME} warpctc)
       endif()
+      # Only deps libwbaes.so, not link
+      if("${cc_library_DEPS};" MATCHES "wbaes;")
+        list(REMOVE_ITEM cc_library_DEPS wbaes)
+        if(NOT "${TARGET_NAME}" MATCHES "dynload_wbaes")
+          list(APPEND cc_library_DEPS dynload_wbaes)
+        endif()
+        add_dependencies(${TARGET_NAME} wbaes)
+      endif()
       # Only deps libmklml.so, not link
       if("${cc_library_DEPS};" MATCHES "mklml;")
         list(REMOVE_ITEM cc_library_DEPS mklml)

cmake/inference_lib.cmake

@@ -170,6 +170,14 @@ copy(snappystream_lib
         DSTS ${dst_dir} ${dst_dir}/lib
         DEPS snappystream)
 
+if (WITH_WBAES)
+    set(dst_dir "${FLUID_INSTALL_DIR}/third_party/install/wbaes")
+    copy(wbaes_lib
+            SRCS ${WBAES_INC_DIR} ${WBAES_LIB}
+            DSTS ${dst_dir} ${dst_dir}/lib
+            DEPS wbaes)
+endif ()
+
 set(dst_dir "${FLUID_INSTALL_DIR}/third_party/install/zlib")
 copy(zlib_lib
         SRCS ${ZLIB_INCLUDE_DIR} ${ZLIB_LIBRARIES}

paddle/fluid/API.spec

@@ -15,7 +15,9 @@ paddle.fluid.cpu_places (ArgSpec(args=['device_count'], varargs=None, keywords=N
 paddle.fluid.cuda_pinned_places (ArgSpec(args=['device_count'], varargs=None, keywords=None, defaults=(None,)), ('document', 'd0c3ebd813c39958c92b78e3eef7e912'))
 paddle.fluid.Executor.__init__ (ArgSpec(args=['self', 'place'], varargs=None, keywords=None, defaults=None), ('document', '6adf97f83acf6453d4a6a4b1070f3754'))
 paddle.fluid.Executor.close (ArgSpec(args=['self'], varargs=None, keywords=None, defaults=None), ('document', 'f5369953dd0c443961cf79f7a00e1a03'))
+paddle.fluid.Executor.infer_from_dataset (ArgSpec(args=['self', 'program', 'dataset', 'scope', 'thread', 'debug', 'fetch_list', 'fetch_info', 'print_period'], varargs=None, keywords=None, defaults=(None, None, None, 0, False, None, None, 100)), ('document', '9c7decb955b9c4f718114179c8985581'))
 paddle.fluid.Executor.run (ArgSpec(args=['self', 'program', 'feed', 'fetch_list', 'feed_var_name', 'fetch_var_name', 'scope', 'return_numpy', 'use_program_cache'], varargs=None, keywords=None, defaults=(None, None, None, 'feed', 'fetch', None, True, False)), ('document', 'f482e93b38b4018796969a2e1dde479d'))
+paddle.fluid.Executor.train_from_dataset (ArgSpec(args=['self', 'program', 'dataset', 'scope', 'thread', 'debug', 'fetch_list', 'fetch_info', 'print_period'], varargs=None, keywords=None, defaults=(None, None, None, 0, False, None, None, 100)), ('document', 'd521011d79e71080fe9b5bb179b43518'))
 paddle.fluid.global_scope (ArgSpec(args=[], varargs=None, keywords=None, defaults=None), ('document', 'e148d3ab1ed8edf3e928212a375959c0'))
 paddle.fluid.scope_guard (ArgSpec(args=['scope'], varargs=None, keywords=None, defaults=None), ('document', 'b94d1f6bcc29c4fb58fc0058561250c2'))
 paddle.fluid.DistributeTranspiler.__init__ (ArgSpec(args=['self', 'config'], varargs=None, keywords=None, defaults=(None,)), ('document', '6adf97f83acf6453d4a6a4b1070f3754'))
@@ -36,15 +38,15 @@ paddle.fluid.DataFeedDesc.desc (ArgSpec(args=['self'], varargs=None, keywords=No
 paddle.fluid.DataFeedDesc.set_batch_size (ArgSpec(args=['self', 'batch_size'], varargs=None, keywords=None, defaults=None), ('document', '8d9f44601e0a99dd431f14fd9250cd21'))
 paddle.fluid.DataFeedDesc.set_dense_slots (ArgSpec(args=['self', 'dense_slots_name'], varargs=None, keywords=None, defaults=None), ('document', 'eb894b464bbcd1b4bc8038398954f766'))
 paddle.fluid.DataFeedDesc.set_use_slots (ArgSpec(args=['self', 'use_slots_name'], varargs=None, keywords=None, defaults=None), ('document', '415c56600ce4e198c071cad01409a690'))
-paddle.fluid.AsyncExecutor.__init__ (ArgSpec(args=['self', 'place', 'run_mode'], varargs=None, keywords=None, defaults=(None, '')), ('document', '6adf97f83acf6453d4a6a4b1070f3754'))
-paddle.fluid.AsyncExecutor.config_distributed_nodes (ArgSpec(args=['self'], varargs=None, keywords=None, defaults=None), ('document', '4810dbe1870452f16b3c60b6c5fd1459'))
-paddle.fluid.AsyncExecutor.download_data (ArgSpec(args=['self', 'afs_path', 'local_path', 'fs_default_name', 'ugi', 'file_cnt', 'hadoop_home', 'process_num'], varargs=None, keywords=None, defaults=('$HADOOP_HOME', 12)), ('document', '799a2066cc26819f1ed31f47c15ad083'))
+paddle.fluid.AsyncExecutor.__init__ (ArgSpec(args=['self', 'place', 'run_mode'], varargs=None, keywords=None, defaults=(None, '')), ('document', '4e85874dddcd06c38f5717992d741589'))
+paddle.fluid.AsyncExecutor.config_distributed_nodes (ArgSpec(args=['self'], varargs=None, keywords=None, defaults=None), ('document', '762980fe0181eb41e3d1081b26ed76b1'))
+paddle.fluid.AsyncExecutor.download_data (ArgSpec(args=['self', 'afs_path', 'local_path', 'fs_default_name', 'ugi', 'file_cnt', 'hadoop_home', 'process_num'], varargs=None, keywords=None, defaults=('$HADOOP_HOME', 12)), ('document', '39e3ccddf8ea8db75ea85287c9147c3b'))
 paddle.fluid.AsyncExecutor.get_instance (ArgSpec(args=['self'], varargs=None, keywords=None, defaults=None), ('document', 'f8688f76a2db1243c7097a60c507b182'))
 paddle.fluid.AsyncExecutor.init_model (ArgSpec(args=['self'], varargs=None, keywords=None, defaults=None), ('document', '504f39be2007404a17e5cabea1256c7d'))
-paddle.fluid.AsyncExecutor.init_server (ArgSpec(args=['self', 'dist_desc'], varargs=None, keywords=None, defaults=None), ('document', 'c403ab46c5d3ef25c0f7e94ae75dcb68'))
-paddle.fluid.AsyncExecutor.init_worker (ArgSpec(args=['self', 'dist_desc', 'startup_program'], varargs=None, keywords=None, defaults=None), ('document', 'dcf08f4bf2f3282acf11391f5d39c536'))
+paddle.fluid.AsyncExecutor.init_server (ArgSpec(args=['self', 'dist_desc'], varargs=None, keywords=None, defaults=None), ('document', '384fa5fbb99912db1baf7ef7784bd312'))
+paddle.fluid.AsyncExecutor.init_worker (ArgSpec(args=['self', 'dist_desc', 'startup_program'], varargs=None, keywords=None, defaults=None), ('document', 'f0a36d7c8561039f60a6f6555c7fee0b'))
 paddle.fluid.AsyncExecutor.run (ArgSpec(args=['self', 'program', 'data_feed', 'filelist', 'thread_num', 'fetch', 'mode', 'debug'], varargs=None, keywords=None, defaults=('', False)), ('document', '848fc53484e8326f6325feea87fe955c'))
-paddle.fluid.AsyncExecutor.save_model (ArgSpec(args=['self', 'save_path'], varargs=None, keywords=None, defaults=None), ('document', 'c8ac0dfcb3b187aba25d03af7fea56b2'))
+paddle.fluid.AsyncExecutor.save_model (ArgSpec(args=['self', 'save_path'], varargs=None, keywords=None, defaults=None), ('document', '145b5c0da01bfff397142e51361f4b75'))
 paddle.fluid.AsyncExecutor.stop (ArgSpec(args=['self'], varargs=None, keywords=None, defaults=None), ('document', '5f23d043607bb5d55e466ec3f578e093'))
 paddle.fluid.CompiledProgram.__init__ (ArgSpec(args=['self', 'program_or_graph'], varargs=None, keywords=None, defaults=None), ('document', '6adf97f83acf6453d4a6a4b1070f3754'))
 paddle.fluid.CompiledProgram.with_data_parallel (ArgSpec(args=['self', 'loss_name', 'build_strategy', 'exec_strategy', 'share_vars_from', 'places'], varargs=None, keywords=None, defaults=(None, None, None, None, None)), ('document', 'a8c7793803cf976680d9478e378fa356'))

paddle/fluid/framework/CMakeLists.txt

+
 #windows treat symbolic file as a real file, which is different with unix
 #We create a hidden file and compile it instead of origin source file.
 function(windows_symbolic TARGET)
@@ -22,9 +23,13 @@ endfunction()
 
 add_subdirectory(ir)
 add_subdirectory(details)
+add_subdirectory(fleet)
+add_subdirectory(io)
 #ddim lib
 proto_library(framework_proto SRCS framework.proto)
+proto_library(data_feed_proto SRCS data_feed.proto)
 proto_library(async_executor_proto SRCS data_feed.proto)
+proto_library(trainer_desc_proto SRCS trainer_desc.proto data_feed.proto)
 
 cc_library(ddim SRCS ddim.cc DEPS eigen3 boost enforce)
 cc_test(ddim_test SRCS ddim_test.cc DEPS ddim)
@@ -129,9 +134,11 @@ cc_test(version_test SRCS version_test.cc DEPS version)
 cc_library(proto_desc SRCS var_desc.cc op_desc.cc block_desc.cc program_desc.cc DEPS shape_inference op_info operator glog version)
 
 cc_library(op_registry SRCS op_registry.cc DEPS op_proto_maker op_info operator glog proto_desc memory_optimize_helper)
+
 nv_test(op_registry_test SRCS op_registry_test.cc DEPS op_registry)
 
 py_proto_compile(framework_py_proto SRCS framework.proto data_feed.proto)
+py_proto_compile(trainer_py_proto SRCS trainer_desc.proto data_feed.proto)
 #Generate an empty \
     #__init__.py to make framework_py_proto as a valid python module.
 add_custom_target(framework_py_proto_init ALL COMMAND ${CMAKE_COMMAND} -E touch __init__.py)
@@ -165,29 +172,43 @@ else()
 endif()
 
 cc_library(executor_gc_helper SRCS executor_gc_helper.cc DEPS scope proto_desc operator garbage_collector)
-
 if(WITH_DISTRIBUTE)
-  cc_library(executor SRCS executor.cc DEPS op_registry device_context scope framework_proto glog
-    lod_rank_table feed_fetch_method sendrecvop_rpc  ${GLOB_DISTRIBUTE_DEPS} graph_to_program_pass variable_helper ${NGRAPH_EXE_DEPS})
-  set(DISTRIBUTE_COMPILE_FLAGS "-Wno-non-virtual-dtor -Wno-error=non-virtual-dtor -Wno-error=delete-non-virtual-dtor")
-  set_source_files_properties(executor.cc PROPERTIES COMPILE_FLAGS ${DISTRIBUTE_COMPILE_FLAGS})
+  cc_library(executor SRCS executor.cc multi_trainer.cc dataset_factory.cc
+  dist_multi_trainer.cc trainer_factory.cc trainer.cc data_feed_factory.cc
+  data_feed.cc device_worker.cc hogwild_worker.cc downpour_worker.cc
+  pull_dense_worker.cc device_worker_factory.cc data_set.cc DEPS op_registry
+  device_context scope framework_proto trainer_desc_proto glog fs shell fleet_wrapper lodtensor_printer
+  lod_rank_table feed_fetch_method sendrecvop_rpc  ${GLOB_DISTRIBUTE_DEPS}
+  graph_to_program_pass variable_helper data_feed_proto ${NGRAPH_EXE_DEPS} timer)
+set(DISTRIBUTE_COMPILE_FLAGS "-Wno-non-virtual-dtor -Wno-error=non-virtual-dtor -Wno-error=delete-non-virtual-dtor")
+set_source_files_properties(executor.cc PROPERTIES COMPILE_FLAGS ${DISTRIBUTE_COMPILE_FLAGS})
 else()
-  cc_library(executor SRCS executor.cc DEPS op_registry device_context scope framework_proto glog lod_rank_table feed_fetch_method graph_to_program_pass variable_helper ${NGRAPH_EXE_DEPS})
+  cc_library(executor SRCS executor.cc multi_trainer.cc dataset_factory.cc
+  dist_multi_trainer.cc trainer_factory.cc trainer.cc data_feed_factory.cc
+  data_feed.cc device_worker.cc hogwild_worker.cc downpour_worker.cc
+  pull_dense_worker.cc device_worker_factory.cc data_set.cc DEPS op_registry
+  device_context scope framework_proto data_feed_proto trainer_desc_proto glog
+  lod_rank_table fs shell fleet_wrapper lodtensor_printer feed_fetch_method
+  graph_to_program_pass variable_helper ${NGRAPH_EXE_DEPS} timer data_feed_proto)
   cc_test(test_naive_executor SRCS naive_executor_test.cc DEPS naive_executor elementwise_add_op)
 endif()
 
 target_link_libraries(executor while_op_helper executor_gc_helper)
 
 cc_library(parallel_executor SRCS parallel_executor.cc DEPS
-        threaded_ssa_graph_executor scope_buffered_ssa_graph_executor parallel_ssa_graph_executor
+        threaded_ssa_graph_executor scope_buffered_ssa_graph_executor parallel_ssa_graph_executor async_ssa_graph_executor
         graph build_strategy
         fast_threaded_ssa_graph_executor variable_helper)
 
-if(WITH_PSLIB)
-    cc_library(async_executor SRCS async_executor.cc data_feed.cc data_feed_factory.cc executor_thread_worker.cc DEPS op_registry device_context scope framework_proto glog lod_rank_table feed_fetch_method graph_to_program_pass async_executor_proto variable_helper pslib_brpc pslib timer)
-else()
-    cc_library(async_executor SRCS async_executor.cc data_feed.cc data_feed_factory.cc executor_thread_worker.cc DEPS op_registry device_context scope framework_proto glog lod_rank_table feed_fetch_method graph_to_program_pass async_executor_proto variable_helper timer)
-endif(WITH_PSLIB)
+cc_library(async_executor SRCS async_executor.cc data_feed.cc data_feed_factory.cc
+           executor_thread_worker.cc multi_trainer.cc dist_multi_trainer.cc
+           trainer_factory.cc trainer.cc device_worker.cc hogwild_worker.cc
+           downpour_worker.cc pull_dense_worker.cc device_worker_factory.cc
+           data_set.cc dataset_factory.cc
+           DEPS op_registry device_context scope framework_proto
+           trainer_desc_proto glog lod_rank_table fleet_wrapper lodtensor_printer
+           feed_fetch_method graph_to_program_pass data_feed_proto
+           variable_helper timer fs shell)
 
 
 cc_test(data_feed_test SRCS data_feed_test.cc DEPS async_executor)
@@ -214,18 +235,18 @@ cc_test(dlpack_tensor_test SRCS dlpack_tensor_test.cc DEPS dlpack_tensor glog)
 # Get the current working branch
 execute_process(
   COMMAND git rev-parse --abbrev-ref HEAD
-  WORKING_DIRECTORY ${CMAKE_SOURCE_DIR}
-  OUTPUT_VARIABLE PADDLE_BRANCH
-  OUTPUT_STRIP_TRAILING_WHITESPACE
-)
+    WORKING_DIRECTORY ${CMAKE_SOURCE_DIR}
+      OUTPUT_VARIABLE PADDLE_BRANCH
+        OUTPUT_STRIP_TRAILING_WHITESPACE
+	)
 
 # Get the latest abbreviated commit hash of the working branch
 execute_process(
   COMMAND git log -1 --format=%h
-  WORKING_DIRECTORY ${CMAKE_SOURCE_DIR}
-  OUTPUT_VARIABLE PADDLE_COMMIT
-  OUTPUT_STRIP_TRAILING_WHITESPACE
-)
+    WORKING_DIRECTORY ${CMAKE_SOURCE_DIR}
+      OUTPUT_VARIABLE PADDLE_COMMIT
+        OUTPUT_STRIP_TRAILING_WHITESPACE
+	)
 
 message(STATUS "commit: ${PADDLE_COMMIT}")
 message(STATUS "branch: ${PADDLE_BRANCH}")

paddle/fluid/framework/async_executor.cc

@@ -26,212 +26,44 @@ limitations under the License. */
 #include "paddle/fluid/framework/lod_tensor_array.h"
 #include "paddle/fluid/framework/op_registry.h"
 #include "paddle/fluid/framework/reader.h"
+#include "paddle/fluid/framework/trainer.h"
+#include "paddle/fluid/framework/trainer_desc.pb.h"
+#include "paddle/fluid/framework/trainer_factory.h"
 #include "paddle/fluid/inference/io.h"
 #include "paddle/fluid/platform/place.h"
 #include "paddle/fluid/pybind/pybind.h"
-#ifdef PADDLE_WITH_PSLIB
-#include <pslib.h>
-#endif
 
 namespace paddle {
 namespace framework {
 AsyncExecutor::AsyncExecutor(Scope* scope, const platform::Place& place)
     : root_scope_(scope), place_(place) {}
 
-void AsyncExecutor::CreateThreads(
-    ExecutorThreadWorker* worker, const ProgramDesc& main_program,
-    const std::shared_ptr<DataFeed>& reader,
-    const std::vector<std::string>& fetch_var_names, Scope* root_scope,
-    const int thread_index, const bool debug) {
-  worker->SetThreadId(thread_index);
-  worker->SetDebug(debug);
-  worker->SetRootScope(root_scope);
-  worker->CreateThreadResource(main_program, place_);
-  worker->SetDataFeed(reader);
-  worker->SetFetchVarNames(fetch_var_names);
-  worker->BindingDataFeedMemory();
-#ifdef PADDLE_WITH_PSLIB
-  worker->SetPSlibPtr(_pslib_ptr);
-  worker->SetPullDenseThread(_pull_dense_thread);
-  worker->SetParamConfig(&_param_config);
-#endif
-}
-
-void PrepareReaders(std::vector<std::shared_ptr<DataFeed>>& readers,  // NOLINT
-                    const int thread_num, const DataFeedDesc& data_feed_desc,
-                    const std::vector<std::string>& filelist) {
-  readers.resize(thread_num);
-  for (size_t i = 0; i < readers.size(); ++i) {
-    readers[i] = DataFeedFactory::CreateDataFeed(data_feed_desc.name());
-    readers[i]->Init(data_feed_desc);  // set batch_size and queue_size here
-  }
-  readers[0]->SetFileList(filelist);
-}
-
-#ifdef PADDLE_WITH_PSLIB
 void AsyncExecutor::InitServer(const std::string& dist_desc, int index) {
-  _pslib_ptr = std::shared_ptr<paddle::distributed::PSlib>(
-      new paddle::distributed::PSlib());
-  _pslib_ptr->init_server(dist_desc, index);
-  InitParamConfig();
+  fleet_ptr_ = FleetWrapper::GetInstance();
+  fleet_ptr_->InitServer(dist_desc, index);
 }
 
 void AsyncExecutor::InitWorker(const std::string& dist_desc,
                                const std::vector<uint64_t>& host_sign_list,
                                int node_num, int index) {
-  _pslib_ptr = std::shared_ptr<paddle::distributed::PSlib>(
-      new paddle::distributed::PSlib());
-  _pslib_ptr->init_worker(
-      dist_desc, const_cast<uint64_t*>(host_sign_list.data()), node_num, index);
-
-  InitParamConfig();
+  fleet_ptr_ = FleetWrapper::GetInstance();
+  fleet_ptr_->InitWorker(dist_desc, host_sign_list, node_num, index);
 }
 
-uint64_t AsyncExecutor::StartServer() { return _pslib_ptr->run_server(); }
+uint64_t AsyncExecutor::StartServer() { return fleet_ptr_->RunServer(); }
 
-void AsyncExecutor::StopServer() { _pslib_ptr->stop_server(); }
+void AsyncExecutor::StopServer() { fleet_ptr_->StopServer(); }
 
 void AsyncExecutor::GatherServers(const std::vector<uint64_t>& host_sign_list,
                                   int node_num) {
-  _pslib_ptr->gather_servers(const_cast<uint64_t*>(host_sign_list.data()),
-                             node_num);
-}
-
-void AsyncExecutor::InitParamConfig() {
-  for (int i = 0; i < _pslib_ptr->get_param()
-                          ->server_param()
-                          .downpour_server_param()
-                          .downpour_table_param_size();
-       ++i) {
-    if (_pslib_ptr->get_param()
-            ->server_param()
-            .downpour_server_param()
-            .downpour_table_param(i)
-            .table_class()
-            .find("SparseTable") != -1) {
-      _param_config.fea_dim = _pslib_ptr->get_param()
-                                  ->server_param()
-                                  .downpour_server_param()
-                                  .downpour_table_param(i)
-                                  .accessor()
-                                  .fea_dim();
-      break;
-    }
-  }
-  _param_config.slot_dim = _param_config.fea_dim - 2;
-  _param_config.tmp_push_dense_wait_times = static_cast<int32_t>(
-      _pslib_ptr->get_param()->trainer_param().push_dense_per_batch());
-  _param_config.tmp_push_sparse_wait_times = static_cast<int32_t>(
-      _pslib_ptr->get_param()->trainer_param().push_sparse_per_batch());
-
-  for (auto t = 0u; t < _pslib_ptr->get_param()->trainer_param().skip_op_size();
-       ++t) {
-    _param_config.skip_op.push_back(
-        _pslib_ptr->get_param()->trainer_param().skip_op(t));
-  }
-
-  for (auto t = 0u;
-       t < _pslib_ptr->get_param()->trainer_param().sparse_table_size(); ++t) {
-    auto& table = _pslib_ptr->get_param()->trainer_param().sparse_table(t);
-    std::vector<std::string> tmp_sparse_variable_name;
-    for (int i = 0u; i < table.slot_value_size(); ++i) {
-      tmp_sparse_variable_name.push_back(table.slot_value(i));
-      _param_config.slot_alias_to_table[table.slot_key(i)] = table.table_id();
-    }
-    std::vector<std::string> tmp_sparse_gradient_variable_name;
-    for (auto i = 0u; i < table.slot_gradient_size(); ++i) {
-      tmp_sparse_gradient_variable_name.push_back(table.slot_gradient(i));
-    }
-    _param_config.slot_input_vec[table.table_id()] =
-        std::move(tmp_sparse_variable_name);
-    _param_config.gradient_var[table.table_id()] =
-        std::move(tmp_sparse_gradient_variable_name);
-    _param_config.sparse_table_id.push_back(table.table_id());
-  }
-
-  for (auto t = 0u;
-       t < _pslib_ptr->get_param()->trainer_param().dense_table_size(); ++t) {
-    auto& table = _pslib_ptr->get_param()->trainer_param().dense_table(t);
-    std::vector<std::string> tmp_dense_variable_name;
-    for (int i = 0u; i < table.dense_variable_name_size(); ++i) {
-      tmp_dense_variable_name.push_back(table.dense_variable_name(i));
-    }
-    std::vector<std::string> tmp_dense_gradient_variable_name;
-    for (auto i = 0u; i < table.dense_gradient_variable_name_size(); ++i) {
-      tmp_dense_gradient_variable_name.push_back(
-          table.dense_gradient_variable_name(i));
-    }
-    _param_config.dense_variable_name[table.table_id()] =
-        std::move(tmp_dense_variable_name);
-    _param_config.dense_gradient_variable_name[table.table_id()] =
-        std::move(tmp_dense_gradient_variable_name);
-    _param_config.dense_table_id.push_back(table.table_id());
-    _param_config.dense_table_size.push_back(table.fea_dim());
-  }
+  fleet_ptr_->GatherServers(host_sign_list, node_num);
 }
 
-void AsyncExecutor::InitModel() {
-  for (auto table_id : _param_config.dense_table_id) {
-    std::vector<paddle::ps::Region> regions;
-    for (auto& t : _param_config.dense_variable_name[table_id]) {
-      Variable* var = root_scope_->FindVar(t);
-      CHECK(var != nullptr) << "var[" << t << "] not found";
-      LoDTensor* tensor = var->GetMutable<LoDTensor>();
-
-      float* g = tensor->data<float>();
-      CHECK(g != nullptr) << "var[" << t << "] value not initialized";
-
-      float init_range = 0.2;
-      int rown = tensor->dims()[0];
-      init_range /= sqrt(rown);
-
-      std::normal_distribution<float> ndistr(0.0, 1.0);
-      for (auto i = 0u; i < tensor->numel(); ++i) {
-        g[i] = ndistr(local_random_engine()) * init_range;
-      }
-
-      paddle::ps::Region reg(g, tensor->numel());
-      regions.emplace_back(std::move(reg));
-    }
+// todo InitModel
+void AsyncExecutor::InitModel() {}
 
-    auto push_status = _pslib_ptr->_worker_ptr->push_dense_param(
-        regions.data(), regions.size(), table_id);
-    push_status.wait();
-    auto status = push_status.get();
-    if (status != 0) {
-      LOG(FATAL) << "push dense param failed, status[" << status << "]";
-      exit(-1);
-    }
-  }
-}
-
-void AsyncExecutor::SaveModel(const std::string& path) {
-  auto ret = _pslib_ptr->_worker_ptr->flush();
-  ret.wait();
-  ret = _pslib_ptr->_worker_ptr->save(path, 0);
-  ret.wait();
-  int32_t feasign_cnt = ret.get();
-  if (feasign_cnt == -1) {  // (colourful-tree) TODO should be feasign_cnt < 0
-    LOG(FATAL) << "save model failed";
-    exit(-1);
-  }
-}
-
-void AsyncExecutor::PrepareDenseThread(const std::string& mode) {
-  if (mode == "mpi") {
-    DensePullThreadParam param;
-    param.ps_client = _pslib_ptr->_worker_ptr;
-    param.threshold = 1;
-    param.training_thread_num = actual_thread_num;
-    param.root_scope = root_scope_;
-    param.dense_params = &_param_config.dense_variable_name;
-
-    _pull_dense_thread =
-        std::shared_ptr<DensePullThread>(new DensePullThread(param));
-    _pull_dense_thread->start();
-  }
-}
-#endif
+// todo SaveModel
+void AsyncExecutor::SaveModel(const std::string& path) {}
 
 void AsyncExecutor::RunFromFile(const ProgramDesc& main_program,
                                 const std::string& data_feed_desc_str,
@@ -256,14 +88,14 @@ void AsyncExecutor::RunFromFile(const ProgramDesc& main_program,
   google::protobuf::TextFormat::ParseFromString(data_feed_desc_str,
                                                 &data_feed_desc);
 
-  actual_thread_num = thread_num;
+  actual_thread_num_ = thread_num;
   int file_cnt = filelist.size();
   PADDLE_ENFORCE(file_cnt > 0, "File list cannot be empty");
 
-  if (actual_thread_num > file_cnt) {
+  if (actual_thread_num_ > file_cnt) {
     VLOG(1) << "Thread num = " << thread_num << ", file num = " << file_cnt
             << ". Changing thread_num = " << file_cnt;
-    actual_thread_num = file_cnt;
+    actual_thread_num_ = file_cnt;
   }
 
   /*
@@ -279,12 +111,14 @@ void AsyncExecutor::RunFromFile(const ProgramDesc& main_program,
    */
   // todo: should be factory method for creating datafeed
   std::vector<std::shared_ptr<DataFeed>> readers;
-  PrepareReaders(readers, actual_thread_num, data_feed_desc, filelist);
+  /*
+  PrepareReaders(readers, actual_thread_num_, data_feed_desc, filelist);
 #ifdef PADDLE_WITH_PSLIB
   PrepareDenseThread(mode);
 #endif
+  */
   std::vector<std::shared_ptr<ExecutorThreadWorker>> workers;
-  workers.resize(actual_thread_num);
+  workers.resize(actual_thread_num_);
   for (auto& worker : workers) {
 #ifdef PADDLE_WITH_PSLIB
     if (mode == "mpi") {
@@ -298,13 +132,15 @@ void AsyncExecutor::RunFromFile(const ProgramDesc& main_program,
   }
 
   // prepare thread resource here
-  for (int thidx = 0; thidx < actual_thread_num; ++thidx) {
+  /*
+  for (int thidx = 0; thidx < actual_thread_num_; ++thidx) {
     CreateThreads(workers[thidx].get(), main_program, readers[thidx],
                   fetch_var_names, root_scope_, thidx, debug);
   }
+  */
 
   // start executing ops in multiple threads
-  for (int thidx = 0; thidx < actual_thread_num; ++thidx) {
+  for (int thidx = 0; thidx < actual_thread_num_; ++thidx) {
     if (debug) {
       threads.push_back(std::thread(&ExecutorThreadWorker::TrainFilesWithTimer,
                                     workers[thidx].get()));
@@ -317,15 +153,19 @@ void AsyncExecutor::RunFromFile(const ProgramDesc& main_program,
   for (auto& th : threads) {
     th.join();
   }
+  // TODO(guru4elephant): we don't need this
+  /*
 #ifdef PADDLE_WITH_PSLIB
   if (mode == "mpi") {
     _pull_dense_thread->stop();
   }
 #endif
+  */
+  VLOG(3) << "start to run from files in async_executor";
+  VLOG(3) << "Drop current scope kids";
   root_scope_->DropKids();
-
   return;
 }
 
-}  // einit_modelnd namespace framework
+}  // end namespace framework
 }  // end namespace paddle

paddle/fluid/framework/async_executor.h

@@ -25,8 +25,10 @@ limitations under the License. */
 #include <typeinfo>
 #include <vector>
 #include "paddle/fluid/framework/data_feed.pb.h"
+#include "paddle/fluid/framework/data_set.h"
 #include "paddle/fluid/framework/executor.h"
 #include "paddle/fluid/framework/executor_thread_worker.h"
+#include "paddle/fluid/framework/fleet/fleet_wrapper.h"
 #include "paddle/fluid/framework/program_desc.h"
 #include "paddle/fluid/framework/scope.h"
 
@@ -65,9 +67,10 @@ class AsyncExecutor {
                    const std::string& data_feed_desc_str,
                    const std::vector<std::string>& filelist,
                    const int thread_num,
-                   const std::vector<std::string>& fetch_names,
-                   const std::string& mode, const bool debug = false);
-#ifdef PADDLE_WITH_PSLIB
+                   const std::vector<std::string>& fetch_var_names,
+                   const std::string& mode, const bool debug);
+
+  // TODO(guru4elephant): make init server decoupled from executor
   void InitServer(const std::string& dist_desc, int index);
   void InitWorker(const std::string& dist_desc,
                   const std::vector<uint64_t>& host_sign_list, int node_num,
@@ -77,31 +80,14 @@ class AsyncExecutor {
   void GatherServers(const std::vector<uint64_t>& host_sign_list, int node_num);
   void InitModel();
   void SaveModel(const std::string& path);
-  void InitParamConfig();
-#endif
-
- private:
-  void CreateThreads(ExecutorThreadWorker* worker,
-                     const ProgramDesc& main_program,
-                     const std::shared_ptr<DataFeed>& reader,
-                     const std::vector<std::string>& fetch_var_names,
-                     Scope* root_scope, const int thread_index,
-                     const bool debug);
-#ifdef PADDLE_WITH_PSLIB
-  void PrepareDenseThread(const std::string& mode);
-#endif
 
  public:
-#ifdef PADDLE_WITH_PSLIB
-  std::shared_ptr<paddle::distributed::PSlib> _pslib_ptr;
-  std::shared_ptr<DensePullThread> _pull_dense_thread;
-  AsyncWorkerParamConfig _param_config;
-#endif
+  std::shared_ptr<paddle::framework::FleetWrapper> fleet_ptr_;
   Scope* root_scope_;
   platform::Place place_;
 
  private:
-  int actual_thread_num;
+  int actual_thread_num_;
 };
 
 }  // namespace framework

paddle/fluid/framework/blocking_queue.h

@@ -33,6 +33,14 @@ class BlockingQueue {
     cv_.notify_one();
   }
 
+  void Push(T &&item) {
+    {
+      std::lock_guard<std::mutex> g(mutex_);
+      q_.emplace_back(std::move(item));
+    }
+    cv_.notify_one();
+  }
+
   template <typename U>
   void Extend(const U &items) {
     {
@@ -44,6 +52,17 @@ class BlockingQueue {
     cv_.notify_all();
   }
 
+  template <typename U>
+  void Extend(U &&items) {
+    {
+      std::lock_guard<std::mutex> g(mutex_);
+      for (auto &item : items) {
+        q_.emplace_back(std::move(item));
+      }
+    }
+    cv_.notify_all();
+  }
+
   std::deque<T> PopAll(size_t ms, bool *timeout) {
     auto time =
         std::chrono::system_clock::now() + std::chrono::milliseconds(ms);
@@ -64,6 +83,18 @@ class BlockingQueue {
     return rc;
   }
 
+  void Pop(T *t) {
+    std::unique_lock<std::mutex> lock(mutex_);
+    cv_.wait(lock, [=] { return !q_.empty(); });
+    *t = std::move(q_.front());
+    q_.pop_front();
+  }
+
+  size_t Size() {
+    std::lock_guard<std::mutex> lock(mutex_);
+    return q_.size();
+  }
+
  private:
   std::mutex mutex_;
   std::condition_variable cv_;

paddle/fluid/framework/data_feed.h

@@ -15,17 +15,23 @@ limitations under the License. */
 #pragma once
 
 #include <fstream>
+#include <future>  // NOLINT
 #include <memory>
 #include <mutex>  // NOLINT
+#include <sstream>
 #include <string>
 #include <thread>  // NOLINT
+#include <utility>
 #include <vector>
 
+#include "paddle/fluid/framework/blocking_queue.h"
 #include "paddle/fluid/framework/data_feed.pb.h"
+#include "paddle/fluid/framework/fleet/fleet_wrapper.h"
 #include "paddle/fluid/framework/lod_tensor.h"
 #include "paddle/fluid/framework/reader.h"
 #include "paddle/fluid/framework/variable.h"
 #include "paddle/fluid/operators/reader/blocking_queue.h"
+#include "paddle/fluid/string/string_helper.h"
 
 namespace paddle {
 namespace framework {
@@ -48,7 +54,10 @@ namespace framework {
 //   }
 class DataFeed {
  public:
-  DataFeed() {}
+  DataFeed() {
+    mutex_for_pick_file_ = nullptr;
+    file_idx_ = nullptr;
+  }
   virtual ~DataFeed() {}
   virtual void Init(const paddle::framework::DataFeedDesc& data_feed_desc) = 0;
   virtual bool CheckFile(const char* filename) {
@@ -59,6 +68,7 @@ class DataFeed {
   // Otherwise, Init() function will init finish_set_filelist_ flag.
   virtual bool SetFileList(const std::vector<std::string>& files);
   virtual bool Start() = 0;
+
   // The trainer calls the Next() function, and the DataFeed will load a new
   // batch to the feed_vec. The return value of this function is the batch
   // size of the current batch.
@@ -74,6 +84,36 @@ class DataFeed {
   // This function is used for binding feed_vec memory
   virtual void AddFeedVar(Variable* var, const std::string& name);
 
+  // This function will do nothing at default
+  virtual void SetMemoryData(void* memory_data) {}
+  // This function will do nothing at default
+  virtual void SetMemoryDataMutex(std::mutex* mutex) {}
+  // This function will do nothing at default
+  virtual void SetThreadId(int thread_id) {}
+  // This function will do nothing at default
+  virtual void SetThreadNum(int thread_num) {}
+  // This function will do nothing at default
+  virtual void SetTrainerNum(int trainer_num) {}
+  virtual void SetFileListMutex(std::mutex* mutex) {
+    mutex_for_pick_file_ = mutex;
+  }
+  virtual void SetFileListIndex(size_t* file_index) { file_idx_ = file_index; }
+  virtual void LoadIntoMemory() {
+    PADDLE_THROW("This function(LoadIntoMemory) is not implemented.");
+  }
+  virtual void LocalShuffle() {
+    PADDLE_THROW("This function(LocalShuffle) is not implemented.");
+  }
+  virtual void GlobalShuffle() {
+    PADDLE_THROW("This function(GlobalShuffle) is not implemented.");
+  }
+  // This function will do nothing at default
+  virtual void FillMemoryDataToChannel() {}
+  // This function will do nothing at default
+  virtual void FillChannelToMemoryData() {}
+  // This function will do nothing at default
+  virtual void PutInsToChannel(const std::string& ins_str) {}
+
  protected:
   // The following three functions are used to check if it is executed in this
   // order:
@@ -87,9 +127,9 @@ class DataFeed {
   // safe).
   virtual bool PickOneFile(std::string* filename);
 
-  static std::vector<std::string> filelist_;
-  static size_t file_idx_;
-  static std::mutex mutex_for_pick_file_;
+  std::vector<std::string> filelist_;
+  size_t* file_idx_;
+  std::mutex* mutex_for_pick_file_;
 
   // the alias of used slots, and its order is determined by
   // data_feed_desc(proto object)
@@ -112,8 +152,9 @@ class DataFeed {
   int batch_size_;
 
   bool finish_init_;
-  static bool finish_set_filelist_;
+  bool finish_set_filelist_;
   bool finish_start_;
+  std::string pipe_command_;
 };
 
 // PrivateQueueDataFeed is the base virtual class for ohther DataFeeds.
@@ -136,6 +177,7 @@ class PrivateQueueDataFeed : public DataFeed {
   virtual void SetQueueSize(int queue_size);
   // The reading and parsing method called in the ReadThread.
   virtual bool ParseOneInstance(T* instance) = 0;
+  virtual bool ParseOneInstanceFromPipe(T* instance) = 0;
   // This function is used to put instance to vec_ins
   virtual void AddInstanceToInsVec(T* vec_ins, const T& instance,
                                    int index) = 0;
@@ -150,11 +192,58 @@ class PrivateQueueDataFeed : public DataFeed {
   //     ifstream one line and one line parse: 6034 ms
   //     fread one buffer and one buffer parse: 7097 ms
   std::ifstream file_;
+  std::shared_ptr<FILE> fp_;
   size_t queue_size_;
+  string::LineFileReader reader_;
   // The queue for store parsed data
   std::unique_ptr<paddle::operators::reader::BlockingQueue<T>> queue_;
 };
 
+template <typename T>
+class InMemoryDataFeed : public PrivateQueueDataFeed<T> {
+ public:
+  InMemoryDataFeed();
+  virtual ~InMemoryDataFeed() {}
+  virtual void Init(const paddle::framework::DataFeedDesc& data_feed_desc) = 0;
+  virtual bool Start();
+  virtual int Next();
+  virtual void SetMemoryData(void* memory_data);
+  virtual void SetMemoryDataMutex(std::mutex* mutex);
+  virtual void SetThreadId(int thread_id);
+  virtual void SetThreadNum(int thread_num);
+  virtual void SetTrainerNum(int trainer_num);
+  virtual void PutInsToChannel(const std::string& ins_str);
+  virtual void FillMemoryDataToChannel();
+  virtual void FillChannelToMemoryData();
+  virtual void LoadIntoMemory();
+  virtual void LocalShuffle();
+  virtual void GlobalShuffle();
+
+ protected:
+  virtual void AddInstanceToInsVec(T* vec_ins, const T& instance,
+                                   int index) = 0;
+  virtual bool ParseOneInstance(T* instance) = 0;
+  virtual bool ParseOneInstanceFromPipe(T* instance) = 0;
+  virtual void PutToFeedVec(const T& ins_vec) = 0;
+  virtual void SerializeIns(const std::vector<T*>& ins, std::string* str) = 0;
+  virtual void DeserializeIns(std::vector<T>* ins, const std::string& str) = 0;
+  virtual std::pair<int64_t, int64_t> GetMemoryDataInterval();
+
+  int thread_id_;
+  int thread_num_;
+  int trainer_num_;
+  uint32_t rand_seed;
+  std::vector<T>* memory_data_;
+  std::mutex* mutex_for_update_memory_data_;
+  // when read ins, we put ins from one channel to the other,
+  // and when finish reading, we set cur_channel = 1 - cur_channel,
+  // so if cur_channel=0, all data are in shuffled_ins_, else shuffled_ins_out_
+  int cur_channel_;
+  std::shared_ptr<paddle::framework::BlockingQueue<T>> shuffled_ins_;
+  std::shared_ptr<paddle::framework::BlockingQueue<T>> shuffled_ins_out_;
+  int64_t fleet_send_batch_size_;
+};
+
 // This class define the data type of instance(ins_vec) in MultiSlotDataFeed
 class MultiSlotType {
  public:
@@ -176,6 +265,7 @@ class MultiSlotType {
     offset_[0] = 0;
   }
   const std::vector<size_t>& GetOffset() const { return offset_; }
+  std::vector<size_t>& MutableOffset() { return offset_; }
   void AddValue(const float v) {
     CheckFloat();
     float_feasign_.push_back(v);
@@ -198,8 +288,33 @@ class MultiSlotType {
     }
   }
   const std::vector<float>& GetFloatData() const { return float_feasign_; }
+  std::vector<float>& MutableFloatData() { return float_feasign_; }
   const std::vector<uint64_t>& GetUint64Data() const { return uint64_feasign_; }
+  std::vector<uint64_t>& MutableUint64Data() { return uint64_feasign_; }
   const std::string& GetType() const { return type_; }
+  std::string& MutableType() { return type_; }
+
+  std::string DebugString() {
+    std::stringstream ss;
+    ss << "\ntype: " << type_ << "\n";
+    ss << "offset: ";
+    ss << "[";
+    for (const size_t& i : offset_) {
+      ss << offset_[i] << ",";
+    }
+    ss << "]\ndata: [";
+    if (type_[0] == 'f') {
+      for (const float& i : float_feasign_) {
+        ss << i << ",";
+      }
+    } else {
+      for (const uint64_t& i : uint64_feasign_) {
+        ss << i << ",";
+      }
+    }
+    ss << "]\n";
+    return ss.str();
+  }
 
  private:
   void CheckType(const std::string& type) const {
@@ -228,13 +343,37 @@ class MultiSlotDataFeed
   virtual ~MultiSlotDataFeed() {}
   virtual void Init(const paddle::framework::DataFeedDesc& data_feed_desc);
   virtual bool CheckFile(const char* filename);
+  // virtual void ReadThread();
 
  protected:
+  virtual void ReadThread();
   virtual void AddInstanceToInsVec(std::vector<MultiSlotType>* vec_ins,
                                    const std::vector<MultiSlotType>& instance,
                                    int index);
   virtual bool ParseOneInstance(std::vector<MultiSlotType>* instance);
+  virtual bool ParseOneInstanceFromPipe(std::vector<MultiSlotType>* instance);
   virtual void PutToFeedVec(const std::vector<MultiSlotType>& ins_vec);
 };
+
+class MultiSlotInMemoryDataFeed
+    : public InMemoryDataFeed<std::vector<MultiSlotType>> {
+ public:
+  MultiSlotInMemoryDataFeed() {}
+  virtual ~MultiSlotInMemoryDataFeed() {}
+  virtual void Init(const paddle::framework::DataFeedDesc& data_feed_desc);
+
+ protected:
+  virtual void AddInstanceToInsVec(std::vector<MultiSlotType>* vec_ins,
+                                   const std::vector<MultiSlotType>& instance,
+                                   int index);
+  virtual bool ParseOneInstance(std::vector<MultiSlotType>* instance);
+  virtual bool ParseOneInstanceFromPipe(std::vector<MultiSlotType>* instance);
+  virtual void PutToFeedVec(const std::vector<MultiSlotType>& ins_vec);
+  virtual void SerializeIns(const std::vector<std::vector<MultiSlotType>*>& ins,
+                            std::string* str);
+  virtual void DeserializeIns(std::vector<std::vector<MultiSlotType>>* ins,
+                              const std::string& str);
+};
+
 }  // namespace framework
 }  // namespace paddle

paddle/fluid/framework/data_feed.proto

@@ -27,4 +27,6 @@ message DataFeedDesc {
   optional string name = 1;
   optional int32 batch_size = 2 [ default = 32 ];
   optional MultiSlotDesc multi_slot_desc = 3;
+  optional string pipe_command = 4;
+  optional int32 thread_num = 5;
 }

paddle/fluid/framework/data_feed_factory.cc

@@ -54,11 +54,15 @@ std::string DataFeedFactory::DataFeedTypeList() {
 std::shared_ptr<DataFeed> DataFeedFactory::CreateDataFeed(
     std::string data_feed_class) {
   if (g_data_feed_map.count(data_feed_class) < 1) {
+    LOG(WARNING) << "Your DataFeed " << data_feed_class
+                 << "is not supported currently";
+    LOG(WARNING) << "Supported DataFeed: " << DataFeedTypeList();
     exit(-1);
   }
   return g_data_feed_map[data_feed_class]();
 }
 
 REGISTER_DATAFEED_CLASS(MultiSlotDataFeed);
+REGISTER_DATAFEED_CLASS(MultiSlotInMemoryDataFeed);
 }  // namespace framework
 }  // namespace paddle

paddle/fluid/framework/data_feed_test.cc

@@ -324,7 +324,7 @@ TEST(DataFeed, MultiSlotUnitTest) {
       load_datafeed_param_from_file(protofile);
   std::vector<MultiTypeSet> reader_elem_set;
   std::vector<MultiTypeSet> file_elem_set;
-  GetElemSetFromReader(&reader_elem_set, data_feed_desc, filelist, 4);
-  GetElemSetFromFile(&file_elem_set, data_feed_desc, filelist);
-  CheckIsUnorderedSame(reader_elem_set, file_elem_set);
+  // GetElemSetFromReader(&reader_elem_set, data_feed_desc, filelist, 4);
+  // GetElemSetFromFile(&file_elem_set, data_feed_desc, filelist);
+  // CheckIsUnorderedSame(reader_elem_set, file_elem_set);
 }

paddle/fluid/framework/data_set.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_set.h"
+#include <random>
+#include "google/protobuf/io/zero_copy_stream_impl.h"
+#include "google/protobuf/message.h"
+#include "google/protobuf/text_format.h"
+#include "paddle/fluid/framework/data_feed_factory.h"
+#include "paddle/fluid/framework/io/fs.h"
+#include "paddle/fluid/platform/timer.h"
+
+#if defined _WIN32 || defined __APPLE__
+#else
+#define _LINUX
+#endif
+
+namespace paddle {
+namespace framework {
+
+// constructor
+template <typename T>
+DatasetImpl<T>::DatasetImpl() {
+  thread_num_ = 1;
+  trainer_num_ = 1;
+  file_idx_ = 0;
+}
+
+// set filelist, file_idx_ will reset to zero.
+template <typename T>
+void DatasetImpl<T>::SetFileList(const std::vector<std::string>& filelist) {
+  VLOG(3) << "filelist size: " << filelist.size();
+  filelist_ = filelist;
+  file_idx_ = 0;
+}
+
+// set expect thread num. actually it may change
+template <typename T>
+void DatasetImpl<T>::SetThreadNum(int thread_num) {
+  VLOG(3) << "SetThreadNum thread_num=" << thread_num;
+  thread_num_ = thread_num;
+}
+
+// if you run distributed, and want to do global shuffle,
+// set this before global shuffle.
+// be sure you call CreateReaders before SetTrainerNum
+template <typename T>
+void DatasetImpl<T>::SetTrainerNum(int trainer_num) {
+  trainer_num_ = trainer_num;
+  // should inform reader of trainer_num directly
+  for (auto reader : readers_) {
+    reader->SetTrainerNum(trainer_num);
+  }
+}
+
+template <typename T>
+void DatasetImpl<T>::SetHdfsConfig(const std::string& fs_name,
+                                   const std::string& fs_ugi) {
+  fs_name_ = fs_name;
+  fs_ugi_ = fs_ugi;
+  std::string cmd = std::string("hadoop fs");
+  cmd += " -D fs.default.name=" + fs_name;
+  cmd += " -D hadoop.job.ugi=" + fs_ugi;
+  paddle::framework::hdfs_set_command(cmd);
+}
+
+template <typename T>
+void DatasetImpl<T>::SetDataFeedDesc(const std::string& data_feed_desc_str) {
+  google::protobuf::TextFormat::ParseFromString(data_feed_desc_str,
+                                                &data_feed_desc_);
+}
+
+// readers_.size() may not be equal to thread_num_,
+// it changes when filelist_.size() < thread_num_
+template <typename T>
+std::vector<std::shared_ptr<paddle::framework::DataFeed>>&
+DatasetImpl<T>::GetReaders() {
+  return readers_;
+}
+
+// if sent message between workers, should first call this function
+template <typename T>
+void DatasetImpl<T>::RegisterClientToClientMsgHandler() {
+  auto fleet_ptr = FleetWrapper::GetInstance();
+  VLOG(3) << "RegisterClientToClientMsgHandler";
+  fleet_ptr->RegisterClientToClientMsgHandler(
+      0, [this](int msg_type, int client_id, const std::string& msg) -> int {
+        return this->ReceiveFromClient(msg_type, client_id, msg);
+      });
+  VLOG(3) << "RegisterClientToClientMsgHandler done";
+}
+
+// load data into memory, Dataset hold this memory,
+// which will later be fed into readers' channel
+template <typename T>
+void DatasetImpl<T>::LoadIntoMemory() {
+  VLOG(3) << "DatasetImpl<T>::LoadIntoMemory() begin";
+  platform::Timer timeline;
+  timeline.Start();
+  if (readers_.size() == 0) {
+    CreateReaders();
+  }
+  std::vector<std::thread> load_threads;
+  for (int64_t i = 0; i < thread_num_; ++i) {
+    load_threads.push_back(std::thread(
+        &paddle::framework::DataFeed::LoadIntoMemory, readers_[i].get()));
+  }
+  for (std::thread& t : load_threads) {
+    t.join();
+  }
+  timeline.Pause();
+  VLOG(3) << "DatasetImpl<T>::LoadIntoMemory() end"
+          << ", memory data size=" << memory_data_.size()
+          << ", cost time=" << timeline.ElapsedSec() << " seconds";
+}
+
+// release memory data
+template <typename T>
+void DatasetImpl<T>::ReleaseMemory() {
+  VLOG(3) << "DatasetImpl<T>::ReleaseMemory() begin";
+  std::vector<T>().swap(memory_data_);
+  VLOG(3) << "DatasetImpl<T>::ReleaseMemory() end";
+}
+
+// do local shuffle
+template <typename T>
+void DatasetImpl<T>::LocalShuffle() {
+  VLOG(3) << "DatasetImpl<T>::LocalShuffle() begin";
+  platform::Timer timeline;
+  timeline.Start();
+  if (readers_.size() == 0) {
+    CreateReaders();
+  }
+  // if it is not InMemory, memory_data_ is empty
+  std::random_shuffle(memory_data_.begin(), memory_data_.end());
+
+  std::vector<std::thread> local_shuffle_threads;
+  for (int64_t i = 0; i < thread_num_; ++i) {
+    local_shuffle_threads.push_back(std::thread(
+        &paddle::framework::DataFeed::LocalShuffle, readers_[i].get()));
+  }
+  for (std::thread& t : local_shuffle_threads) {
+    t.join();
+  }
+  std::vector<T>().swap(memory_data_);
+  timeline.Pause();
+  VLOG(3) << "DatasetImpl<T>::LocalShuffle() end, cost time="
+          << timeline.ElapsedSec() << " seconds";
+}
+
+template <typename T>
+void DatasetImpl<T>::GlobalShuffle() {
+  VLOG(3) << "DatasetImpl<T>::GlobalShuffle() begin";
+  platform::Timer timeline;
+  timeline.Start();
+  if (readers_.size() == 0) {
+    CreateReaders();
+  }
+  // if it is not InMemory, memory_data_ is empty
+  std::random_shuffle(memory_data_.begin(), memory_data_.end());
+  VLOG(3) << "start global shuffle threads";
+  std::vector<std::thread> global_shuffle_threads;
+  for (int i = 0; i < thread_num_; ++i) {
+    global_shuffle_threads.push_back(std::thread(
+        &paddle::framework::DataFeed::GlobalShuffle, readers_[i].get()));
+  }
+  for (std::thread& t : global_shuffle_threads) {
+    t.join();
+  }
+  std::vector<T>().swap(memory_data_);
+  timeline.Pause();
+  VLOG(3) << "DatasetImpl<T>::GlobalShuffle() end, cost time="
+          << timeline.ElapsedSec() << " seconds";
+}
+
+template <typename T>
+void DatasetImpl<T>::CreateReaders() {
+  VLOG(3) << "Calling CreateReaders()";
+  CHECK(thread_num_ > 0) << "thread_num should > 0";
+  int file_cnt = filelist_.size();
+  int memory_data_size = memory_data_.size();
+  if (memory_data_size != 0 && thread_num_ > memory_data_size) {
+    VLOG(3) << "Dataset thread num = " << thread_num_
+            << ", memory data size = " << memory_data_size
+            << ". Changing Dataset thread num = " << memory_data_size;
+    thread_num_ = memory_data_size;
+  } else if (file_cnt != 0 && thread_num_ > file_cnt) {
+    VLOG(3) << "Dataset thread num = " << thread_num_
+            << ", file num = " << file_cnt
+            << ". Changing Dataset thread num = " << file_cnt;
+    thread_num_ = file_cnt;
+  }
+  VLOG(3) << "thread_num in Readers: " << thread_num_;
+  VLOG(3) << "readers size: " << readers_.size();
+  VLOG(3) << "Filelist size in readers: " << filelist_.size();
+  if (readers_.size() != 0) {
+    return;
+  }
+  VLOG(3) << "data feed class name: " << data_feed_desc_.name();
+  for (int i = 0; i < thread_num_; ++i) {
+    readers_.push_back(DataFeedFactory::CreateDataFeed(data_feed_desc_.name()));
+    readers_.back()->Init(data_feed_desc_);
+    readers_.back()->SetMemoryData(&memory_data_);
+    readers_.back()->SetMemoryDataMutex(&mutex_for_update_memory_data_);
+    readers_.back()->SetThreadId(i);
+    readers_.back()->SetThreadNum(thread_num_);
+    readers_.back()->SetTrainerNum(trainer_num_);
+    readers_.back()->SetFileListMutex(&mutex_for_pick_file_);
+    readers_.back()->SetFileListIndex(&file_idx_);
+    readers_.back()->SetFileList(filelist_);
+  }
+}
+
+template <typename T>
+void DatasetImpl<T>::DestroyReaders() {
+  VLOG(3) << "Calling DestroyReaders()";
+  // clear memory_data_ before fill it
+  // because if LoadIntoMemory but no Shuffle,
+  // memory_data_ has empty data which has been std::move to channel
+  if (memory_data_.size() != 0) {
+    std::vector<T>().swap(memory_data_);
+  }
+  std::vector<std::thread> fill_threads;
+  for (int i = 0; i < thread_num_; ++i) {
+    fill_threads.push_back(
+        std::thread(&paddle::framework::DataFeed::FillChannelToMemoryData,
+                    readers_[i].get()));
+  }
+  for (std::thread& t : fill_threads) {
+    t.join();
+  }
+  std::vector<std::shared_ptr<paddle::framework::DataFeed>>().swap(readers_);
+  VLOG(3) << "readers size: " << readers_.size();
+  // if memory_data_ is empty, which means it's not InMemory mode,
+  // so the next epoch should read all data again
+  if (memory_data_.size() == 0) {
+    file_idx_ = 0;
+  }
+}
+
+template <typename T>
+int DatasetImpl<T>::ReceiveFromClient(int msg_type, int client_id,
+                                      const std::string& msg) {
+#ifdef _LINUX
+  VLOG(3) << "ReceiveFromClient msg_type=" << msg_type
+          << ", client_id=" << client_id << ", msg length=" << msg.length();
+  auto fleet_ptr = FleetWrapper::GetInstance();
+  int64_t index = rand_r(&rand_seed) % thread_num_;
+  VLOG(3) << "ramdom index=" << index;
+  readers_[index]->PutInsToChannel(msg);
+#endif
+  return 0;
+}
+
+// explicit instantiation
+template class DatasetImpl<std::vector<MultiSlotType>>;
+
+}  // end namespace framework
+}  // end namespace paddle

paddle/fluid/framework/data_set.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 <fstream>
+#include <memory>
+#include <mutex>  // NOLINT
+#include <string>
+#include <thread>  // NOLINT
+#include <utility>
+#include <vector>
+
+#include "paddle/fluid/framework/data_feed.h"
+
+namespace paddle {
+namespace framework {
+
+// Dataset is a abstract class, which defines user interfaces
+// Example Usage:
+//    Dataset* dataset = DatasetFactory::CreateDataset("InMemoryDataset")
+//    dataset->SetFileList(std::vector<std::string>{"a.txt", "b.txt"})
+//    dataset->SetThreadNum(1)
+//    dataset->CreateReaders();
+//    dataset->SetDataFeedDesc(your_data_feed_desc);
+//    dataset->LoadIntoMemory();
+//    dataset->SetTrainerNum(2);
+//    dataset->GlobalShuffle();
+class Dataset {
+ public:
+  Dataset() {}
+  virtual ~Dataset() {}
+  // set file list
+  virtual void SetFileList(const std::vector<std::string>& filelist) = 0;
+  // set readers' num
+  virtual void SetThreadNum(int thread_num) = 0;
+  // set workers' num
+  virtual void SetTrainerNum(int trainer_num) = 0;
+  // set fs name and ugi
+  virtual void SetHdfsConfig(const std::string& fs_name,
+                             const std::string& fs_ugi) = 0;
+  // set data fedd desc, which contains:
+  //   data feed name, batch size, slots
+  virtual void SetDataFeedDesc(const std::string& data_feed_desc_str) = 0;
+  // get file list
+  virtual const std::vector<std::string>& GetFileList() = 0;
+  // get thread num
+  virtual int GetThreadNum() = 0;
+  // get worker num
+  virtual int GetTrainerNum() = 0;
+  // get hdfs config
+  virtual std::pair<std::string, std::string> GetHdfsConfig() = 0;
+  // get data fedd desc
+  virtual const paddle::framework::DataFeedDesc& GetDataFeedDesc() = 0;
+  // get readers, the reader num depend both on thread num
+  // and filelist size
+  virtual std::vector<std::shared_ptr<paddle::framework::DataFeed>>&
+  GetReaders() = 0;
+  // register message handler between workers
+  virtual void RegisterClientToClientMsgHandler() = 0;
+  // load all data into memory
+  virtual void LoadIntoMemory() = 0;
+  // release all memory data
+  virtual void ReleaseMemory() = 0;
+  // local shuffle data
+  virtual void LocalShuffle() = 0;
+  // global shuffle data
+  virtual void GlobalShuffle() = 0;
+  // create readers
+  virtual void CreateReaders() = 0;
+  // destroy readers
+  virtual void DestroyReaders() = 0;
+
+ protected:
+  virtual int ReceiveFromClient(int msg_type, int client_id,
+                                const std::string& msg) = 0;
+};
+
+// DatasetImpl is the implementation of Dataset,
+// it holds memory data if user calls load_into_memory
+template <typename T>
+class DatasetImpl : public Dataset {
+ public:
+  DatasetImpl();
+  virtual ~DatasetImpl() {}
+
+  virtual void SetFileList(const std::vector<std::string>& filelist);
+  virtual void SetThreadNum(int thread_num);
+  virtual void SetTrainerNum(int trainer_num);
+  virtual void SetHdfsConfig(const std::string& fs_name,
+                             const std::string& fs_ugi);
+  virtual void SetDataFeedDesc(const std::string& data_feed_desc_str);
+
+  virtual const std::vector<std::string>& GetFileList() { return filelist_; }
+  virtual int GetThreadNum() { return thread_num_; }
+  virtual int GetTrainerNum() { return trainer_num_; }
+  virtual std::pair<std::string, std::string> GetHdfsConfig() {
+    return std::make_pair(fs_name_, fs_ugi_);
+  }
+  virtual const paddle::framework::DataFeedDesc& GetDataFeedDesc() {
+    return data_feed_desc_;
+  }
+  virtual std::vector<std::shared_ptr<paddle::framework::DataFeed>>&
+  GetReaders();
+
+  virtual void RegisterClientToClientMsgHandler();
+  virtual void LoadIntoMemory();
+  virtual void ReleaseMemory();
+  virtual void LocalShuffle();
+  virtual void GlobalShuffle();
+  virtual void CreateReaders();
+  virtual void DestroyReaders();
+
+ protected:
+  virtual int ReceiveFromClient(int msg_type, int client_id,
+                                const std::string& msg);
+  std::vector<std::shared_ptr<paddle::framework::DataFeed>> readers_;
+  std::vector<T> memory_data_;
+  std::mutex mutex_for_update_memory_data_;
+  int thread_num_;
+  paddle::framework::DataFeedDesc data_feed_desc_;
+  int trainer_num_;
+  std::vector<std::string> filelist_;
+  size_t file_idx_;
+  std::mutex mutex_for_pick_file_;
+  std::string fs_name_;
+  std::string fs_ugi_;
+  unsigned int rand_seed;
+};
+
+// use std::vector<MultiSlotType> as data type
+class MultiSlotDataset : public DatasetImpl<std::vector<MultiSlotType>> {
+ public:
+  MultiSlotDataset() {}
+  virtual ~MultiSlotDataset() {}
+};
+
+}  // end namespace framework
+}  // end namespace paddle

paddle/fluid/framework/dataset_factory.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/dataset_factory.h"
+#include <memory>
+#include <string>
+#include <unordered_map>
+
+#include "paddle/fluid/framework/data_set.h"
+
+namespace paddle {
+namespace framework {
+typedef std::shared_ptr<Dataset> (*CreateDatasetFunction)();
+typedef std::unordered_map<std::string, CreateDatasetFunction> datasetMap;
+datasetMap g_dataset_map;
+
+#define REGISTER_DATASET_CLASS(dataset_class)                   \
+  namespace {                                                   \
+  std::shared_ptr<Dataset> Creator_##dataset_class() {          \
+    return std::shared_ptr<Dataset>(new dataset_class);         \
+  }                                                             \
+  class __Registerer_##dataset_class {                          \
+   public:                                                      \
+    __Registerer_##dataset_class() {                            \
+      g_dataset_map[#dataset_class] = &Creator_##dataset_class; \
+    }                                                           \
+  };                                                            \
+  __Registerer_##dataset_class g_registerer_##dataset_class;    \
+  }  // namespace
+
+std::string DatasetFactory::DatasetTypeList() {
+  std::string dataset_types;
+  for (auto iter = g_dataset_map.begin(); iter != g_dataset_map.end(); ++iter) {
+    if (iter != g_dataset_map.begin()) {
+      dataset_types += ", ";
+    }
+    dataset_types += iter->first;
+  }
+  return dataset_types;
+}
+
+std::shared_ptr<Dataset> DatasetFactory::CreateDataset(
+    std::string dataset_class) {
+  if (g_dataset_map.count(dataset_class) < 1) {
+    LOG(WARNING) << "Your Dataset " << dataset_class
+                 << "is not supported currently";
+    LOG(WARNING) << "Supported Dataset: " << DatasetTypeList();
+    exit(-1);
+  }
+  return g_dataset_map[dataset_class]();
+}
+
+REGISTER_DATASET_CLASS(MultiSlotDataset);
+}  // namespace framework
+}  // namespace paddle

paddle/fluid/framework/dataset_factory.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 <memory>
+#include <string>
+#include "paddle/fluid/framework/data_set.h"
+
+namespace paddle {
+namespace framework {
+class DatasetFactory {
+ public:
+  static std::string DatasetTypeList();
+  static std::shared_ptr<Dataset> CreateDataset(std::string dataset_class);
+};
+}  // namespace framework
+}  // namespace paddle

paddle/fluid/framework/details/CMakeLists.txt

@@ -96,6 +96,12 @@ cc_library(threaded_ssa_graph_executor SRCS threaded_ssa_graph_executor.cc DEPS
 
 cc_library(parallel_ssa_graph_executor SRCS parallel_ssa_graph_executor.cc DEPS threaded_ssa_graph_executor)
 
+set(ASYNC_SSA_GRAPH_EXECUTOR_DEPS threaded_ssa_graph_executor)
+if(WITH_DISTRIBUTE)
+    list(APPEND ASYNC_SSA_GRAPH_EXECUTOR_DEPS communicator)
+endif()
+cc_library(async_ssa_graph_executor SRCS async_ssa_graph_executor.cc DEPS ${ASYNC_SSA_GRAPH_EXECUTOR_DEPS})
+
 cc_test(broadcast_op_test SRCS broadcast_op_handle_test.cc DEPS var_handle op_handle_base scope ddim memory
         device_context broadcast_op_handle)
 cc_test(gather_op_test SRCS gather_op_handle_test.cc DEPS var_handle op_handle_base scope ddim memory

paddle/fluid/framework/details/async_ssa_graph_executor.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/async_ssa_graph_executor.h"
+
+#include "paddle/fluid/framework/variable_helper.h"
+
+#ifdef PADDLE_WITH_DISTRIBUTE
+#include "paddle/fluid/operators/distributed/communicator.h"
+#endif
+
+namespace paddle {
+namespace framework {
+namespace details {
+
+inline void NewTempScopeAndInitVars(const std::vector<VarInfo> &var_infos,
+                                    Scope *scope) {
+  VLOG(3) << "NewTempScopeAndInitVars";
+  Scope &local_scope = scope->NewScope();
+  *scope->Var(details::kLocalExecScopeName)->GetMutable<Scope *>() =
+      &local_scope;
+
+  for (auto &info : var_infos) {
+    if (scope->FindVar(info.name_) != nullptr) {
+      continue;
+    }
+
+    if (info.persistable_) {  // Persistable
+      InitializeVariable(scope->Var(info.name_), info.type_);
+    } else {
+      InitializeVariable(local_scope.Var(info.name_), info.type_);
+    }
+  }
+}
+
+// get RpcContext and remote send and recv op
+void ProcessGraph(std::vector<ir::Graph *> graphs, Scope *scope) {
+#ifdef PADDLE_WITH_DISTRIBUTE
+  using RpcCtxMap = operators::distributed::RpcCtxMap;
+  VLOG(3) << "ProcessGraph";
+  RpcCtxMap send_varname_to_ctx;
+  RpcCtxMap recv_varname_to_ctx;
+  for (auto i = 0; i < graphs.size(); ++i) {
+    std::vector<ir::Node *> nodes_to_delete;
+    for (auto &node : graphs[i]->Nodes()) {
+      VLOG(3) << "node name " << node->Name();
+      if (node && node->IsOp()) {
+        if (node->Name() == "send") {
+          auto send_var_name = node->Op()->Input("X")[0];
+          auto send_varnames = boost::get<std::vector<std::string>>(
+              node->Op()->GetNullableAttr("send_varnames"));
+          auto epmap = boost::get<std::vector<std::string>>(
+              node->Op()->GetNullableAttr("epmap"));
+          auto height_section = boost::get<std::vector<int64_t>>(
+              node->Op()->GetNullableAttr("sections"));
+          send_varname_to_ctx[send_var_name] =
+              operators::distributed::RpcContext(send_var_name, send_varnames,
+                                                 epmap, height_section);
+          VLOG(3) << "find and init an send op: "
+                  << send_varname_to_ctx[send_var_name];
+        } else if (node->Name() == "recv") {
+          auto recv_var_name = node->Op()->Output("Out")[0];
+          auto recv_varnames = boost::get<std::vector<std::string>>(
+              node->Op()->GetNullableAttr("recv_varnames"));
+          auto epmap = boost::get<std::vector<std::string>>(
+              node->Op()->GetNullableAttr("epmap"));
+          recv_varname_to_ctx[recv_var_name] =
+              operators::distributed::RpcContext(recv_var_name, recv_varnames,
+                                                 epmap, {});
+          nodes_to_delete.push_back(node);
+          VLOG(3) << "find and remove an recv op: "
+                  << recv_varname_to_ctx[recv_var_name];
+        }
+      }
+    }
+  }
+  // init communicator here
+  if (send_varname_to_ctx.size() > 0) {
+    VLOG(3) << "this is distribute mode, will use communicator";
+    operators::distributed::Communicator::Init(send_varname_to_ctx,
+                                               recv_varname_to_ctx, scope);
+    operators::distributed::Communicator::GetInstance()->Start();
+  }
+#endif
+}
+
+AsyncSSAGraphExecutor::AsyncSSAGraphExecutor(
+    const ExecutionStrategy &strategy, const std::vector<Scope *> &local_scopes,
+    const std::vector<platform::Place> &places, std::vector<ir::Graph *> graphs)
+    : strategy_(std::move(strategy)),
+      local_scopes_(std::move(local_scopes)),
+      pool_(places.size() >= 2 ? new ::ThreadPool(places.size()) : nullptr),
+      places_(std::move(places)),
+      graphs_(std::move(graphs)) {
+  VLOG(3) << "build AsyncSSAGraphExecutor";
+  PADDLE_ENFORCE_EQ(places_.size(), local_scopes_.size());
+
+  // set the correct size of thread pool to each device.
+  strategy_.num_threads_ = strategy_.num_threads_ < places_.size()
+                               ? 1UL
+                               : strategy_.num_threads_ / places_.size();
+  VLOG(1) << "set num_threads: " << strategy_.num_threads_
+          << " to run the operators of the graph on each device.";
+  for (size_t i = 0; i < places.size(); ++i) {
+    executors_.emplace_back(new details::ThreadedSSAGraphExecutor(
+        strategy_, {local_scopes_[i]}, {places_[i]}, graphs_[i]));
+  }
+
+  for (auto &node : graphs_[0]->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();
+    }
+  }
+  for (auto *scope : local_scopes_) {
+    NewTempScopeAndInitVars(var_infos_, scope);
+  }
+  ProcessGraph(graphs_, local_scopes_[0]);
+}
+
+void AsyncSSAGraphExecutor::StartOffPythonTrainLoop() {
+  VLOG(3) << "StartOffPythonTrainLoop size = " << places_.size();
+  for (size_t i = 1; i < places_.size(); ++i) {
+    auto call = [this, i]() -> void {
+      VLOG(3) << "start off python thread " << i;
+      try {
+        while (true) {
+          executors_[i]->Run({});
+        }
+      } catch (...) {
+        exception_holder_.Catch(std::current_exception());
+        VLOG(3) << "get exception type = " << exception_holder_.Type();
+      }
+      VLOG(3) << "thread " << i << " exited!";
+    };
+    run_futures_.emplace_back(pool_->enqueue(std::move(call)));
+  }
+}
+
+void AsyncSSAGraphExecutor::HandleException() {
+  if (exception_holder_.IsCaught()) {
+    for (auto &f : run_futures_) {
+      VLOG(3) << "wait future";
+      f.wait();
+    }
+    VLOG(3) << "caught exception " << exception_holder_.Type()
+            << ", rethrow it";
+    run_futures_.clear();
+    exception_holder_.ReThrow();
+  }
+}
+
+FeedFetchList AsyncSSAGraphExecutor::Run(
+    const std::vector<std::string> &fetch_tensors) {
+  // init once
+  if (run_futures_.size() == 0 && places_.size() > 1) {
+    exception_holder_.Clear();
+    StartOffPythonTrainLoop();
+  }
+
+  if (places_.size() == 1) {
+    exception_holder_.Clear();
+  } else {
+    HandleException();
+  }
+
+  FeedFetchList fetch_data;
+  fetch_data.reserve(fetch_tensors.size());
+
+  try {
+    fetch_data = executors_[0]->Run(fetch_tensors);
+  } catch (...) {
+    exception_holder_.Catch(std::current_exception());
+  }
+
+  HandleException();
+
+  FeedFetchList ret;
+  for (size_t fetch_idx = 0; fetch_idx < fetch_tensors.size(); ++fetch_idx) {
+    std::vector<const LoDTensor *> lodtensor_ptrs;
+    lodtensor_ptrs.push_back(&fetch_data.at(fetch_idx));
+    ret.emplace_back();
+    ret.back().MergeLoDTensor(lodtensor_ptrs, platform::CPUPlace());
+  }
+  return ret;
+}
+
+}  // namespace details
+}  // namespace framework
+}  // namespace paddle

paddle/fluid/framework/details/async_ssa_graph_executor.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 <memory>
+#include <string>
+#include <utility>
+#include <vector>
+
+#include "ThreadPool.h"
+#include "paddle/fluid/framework/details/threaded_ssa_graph_executor.h"
+
+namespace paddle {
+namespace framework {
+namespace details {
+
+struct VarInfo {
+  std::string name_;
+  proto::VarType::Type type_;
+  bool persistable_;
+};
+
+class AsyncSSAGraphExecutor : public SSAGraphExecutor {
+ public:
+  AsyncSSAGraphExecutor(const ExecutionStrategy &strategy,
+                        const std::vector<Scope *> &local_scopes,
+                        const std::vector<platform::Place> &places,
+                        std::vector<ir::Graph *> graphs);
+  ~AsyncSSAGraphExecutor() final = default;
+  const ir::Graph &Graph() const override { return *graphs_[0]; }
+
+  FeedFetchList Run(const std::vector<std::string> &fetch_tensors) override;
+
+ private:
+  void StartOffPythonTrainLoop();
+  void HandleException();
+
+ private:
+  ExecutionStrategy strategy_;
+  std::vector<Scope *> local_scopes_;
+  std::unique_ptr<::ThreadPool> pool_{nullptr};
+  std::vector<platform::Place> places_;
+  std::vector<ir::Graph *> graphs_;
+
+  std::vector<std::unique_ptr<details::ThreadedSSAGraphExecutor>> executors_;
+  ExceptionHolder exception_holder_;
+  std::vector<std::future<void>> run_futures_;
+  std::vector<VarInfo> var_infos_;
+};
+
+}  // namespace details
+}  // namespace framework
+}  // namespace paddle

paddle/fluid/framework/details/build_strategy.cc

@@ -184,8 +184,12 @@ class ParallelExecutorPassBuilder : public ir::PassBuilder {
   // Convert graph to run on multi-devices.
   void AppendMultiDevPass(const BuildStrategy &strategy) {
     ir::Pass *multi_devices_pass = nullptr;
-    if (strategy.is_distribution_) {
-      VLOG(10) << "Add dist_multi_devices_pass";
+
+    if (strategy_.async_mode_) {
+      multi_devices_pass = AppendPass("async_multi_devices_pass").get();
+    } else if (strategy_.is_distribution_) {
+      VLOG(10)
+          << "Add dist_multi_devices_pass, multi device parameter server mode";
       multi_devices_pass = AppendPass("dist_multi_devices_pass").get();
     } else {
       if (strategy.reduce_ == BuildStrategy::ReduceStrategy::kAllReduce) {
@@ -234,10 +238,12 @@ ir::Graph *BuildStrategy::Apply(ir::Graph *graph,
 #else
                                 const bool use_cuda) const {
 #endif
+  VLOG(3) << "apply all passes";
   // Create a default one if not finalized by user.
   CreatePassesFromStrategy(false);
 
   for (std::shared_ptr<ir::Pass> &pass : pass_builder_->AllPasses()) {
+    VLOG(3) << "apply " << pass->Type();
     if (IsMultiDevPass(pass->Type())) {
       pass->Erase(kPlaces);
       pass->SetNotOwned<const std::vector<platform::Place>>(kPlaces, &places);
@@ -293,6 +299,7 @@ ir::Graph *BuildStrategy::Apply(ir::Graph *graph,
     graph = pass->Apply(graph);
     VLOG(3) << "Finish Apply Pass " << pass->Type();
   }
+  VLOG(3) << "All Passes Applied";
   return graph;
 }
 

paddle/fluid/framework/details/build_strategy.h

@@ -91,12 +91,17 @@ struct BuildStrategy {
 
   bool enable_sequential_execution_{false};
 
-  bool fuse_broadcast_op_{false};
+  // NOTE(zcd): In reduce mode, fusing broadcast ops may make the program
+  // faster. Because fusing broadcast OP equals delaying the execution of all
+  // broadcast Ops, in this case, all nccl streams are used only for reduce
+  // operations for a period of time.
+  bool fuse_broadcast_ops_{false};
 
   // FIXME(zcd): is_distribution_ is a temporary field, because in pserver mode,
   // num_trainers is 1, so the current fields of build_strategy doesn't tell if
   // it's distributed model.
   bool is_distribution_{false};
+  bool async_mode_{false};
   int num_trainers_{1};
   int trainer_id_{0};
   std::vector<std::string> trainers_endpoints_;

paddle/fluid/framework/details/exception_holder.h

@@ -14,6 +14,9 @@
 
 #pragma once
 
+#include <memory>
+#include <string>
+
 #include "glog/logging.h"
 #include "paddle/fluid/platform/enforce.h"
 
@@ -64,6 +67,21 @@ class ExceptionHolder {
     ClearImpl();
   }
 
+  std::string Type() {
+    std::lock_guard<std::mutex> lock(mu_);
+    switch (type_) {
+      case kNone:
+        return "None";
+      case kEnforceNotMet: {
+        return "EnforceNotMet";
+      }
+      case kEOF: {
+        return "EOF";
+      }
+    }
+    return "unknown";
+  }
+
  private:
   void ClearImpl() {
     exception_.reset();

paddle/fluid/framework/details/execution_strategy.h

@@ -31,6 +31,8 @@ struct ExecutionStrategy {
   size_t num_iteration_per_drop_scope_{1};
   ExecutorType type_{kDefault};
   bool dry_run_{false};
+  size_t num_iteration_per_run_{1};  // only use with async_ssa_graph_executor
+                                     // and pyreader with data queue
 };
 
 }  //  namespace details

paddle/fluid/framework/details/fast_threaded_ssa_graph_executor.cc

@@ -56,6 +56,7 @@ FeedFetchList FastThreadedSSAGraphExecutor::Run(
   fetches.resize(fetch_tensors.size());
   std::unordered_map<std::string, std::vector<VarHandleBase *>> fetched_vars;
   std::vector<FetchOpHandle *> fetch_ops;
+  std::vector<OpHandleBase *> ready_fetch_ops;
 
   for (auto &fetch_var_name : fetch_tensors) {
     for (auto &var_map : graph_->Get<details::GraphVars>(details::kGraphVars)) {
@@ -70,8 +71,9 @@ FeedFetchList FastThreadedSSAGraphExecutor::Run(
     auto &var_name = fetch_tensors[i];
     auto fetched_var_it = fetched_vars.find(var_name);
     PADDLE_ENFORCE(fetched_var_it != fetched_vars.end(),
-                   "Cannot find fetched variable.(Perhaps the main_program "
-                   "is not set to ParallelExecutor)");
+                   "Cannot find fetched variable(%s).(Perhaps the main_program "
+                   "is not set to ParallelExecutor)",
+                   var_name);
 
     auto &vars = fetched_var_it->second;
 
@@ -88,7 +90,11 @@ FeedFetchList FastThreadedSSAGraphExecutor::Run(
       op->AddInput(var);
     }
 
-    (*op_deps)[op] = static_cast<int>(op->NotReadyInputSize());
+    int dep = static_cast<int>(op->NotReadyInputSize());
+    (*op_deps)[op] = dep;
+    if (dep == 0) {
+      ready_fetch_ops.emplace_back(op);
+    }
   }
 
   size_t num_complete = 0;
@@ -97,7 +103,9 @@ FeedFetchList FastThreadedSSAGraphExecutor::Run(
   for (auto op : bootstrap_ops_) {
     RunOpAsync(op_deps.get(), op, complete_q);
   }
-
+  for (auto op : ready_fetch_ops) {
+    RunOpAsync(op_deps.get(), op, complete_q);
+  }
   while (num_complete != op_deps->size()) {
     size_t num_comp = complete_q->Pop();
     if (num_comp == -1UL) {

paddle/fluid/framework/details/fetch_op_handle.cc

@@ -13,9 +13,9 @@
 // limitations under the License.
 
 #include "paddle/fluid/framework/details/fetch_op_handle.h"
-
 #include <string>
 #include <vector>
+#include "paddle/fluid/platform/profiler.h"
 
 namespace paddle {
 namespace framework {
@@ -44,6 +44,7 @@ void FetchOpHandle::WaitAndMergeCPUTensors() const {
 }
 
 void FetchOpHandle::RunImpl() {
+  platform::RecordEvent record_event(Name());
   WaitInputVarGenerated(platform::CPUPlace());
 
   tensors_.resize(inputs_.size());
@@ -62,7 +63,8 @@ void FetchOpHandle::RunImpl() {
     auto &t = var->Get<framework::LoDTensor>();
     if (platform::is_gpu_place(t.place())) {
 #ifdef PADDLE_WITH_CUDA
-      TensorCopySync(t, cpu, &tensors_[i]);
+      TensorCopy(t, cpu, *dev_ctxes_.at(t.place()), &tensors_[i]);
+      dev_ctxes_.at(t.place())->Wait();
 #endif
     } else {
       tensors_[i].ShareDataWith(t);

paddle/fluid/framework/details/memory_optimize_helper.cc

@@ -131,16 +131,7 @@ size_t NodeSize(const VarDesc& node) {
   return type_size * std::abs(size);
 }
 
-size_t NodeSize(ir::Node* n) {
-  VarDesc* desc = nullptr;
-  // some op do not have block pointer
-  if (n->inputs[0]->Op() != nullptr) {
-    desc = FindVarDescInBlock(n);
-  } else {
-    desc = n->Var();
-  }
-  return NodeSize(*desc);
-}
+size_t NodeSize(ir::Node* n) { return NodeSize(*(n->Var())); }
 
 std::string DebugStringImpl(VarDesc* var) {
   std::stringstream ss;
@@ -163,24 +154,22 @@ std::string DebugStringImpl(VarDesc* var) {
 }
 
 std::string DebugString(ir::Node* var) {
-  return DebugStringImpl(FindVarDescInBlock(var));
+  return DebugStringImpl(GetVarDesc(var));
 }
 
 // NOTE(dzh): based ir node, if a large node has been reused
 // by a small size node, then next time it appear in pool, it will
 // have the small size. Find the original node shap from blockdesc.
-VarDesc* FindVarDescInBlock(ir::Node* n) {
+VarDesc* GetVarDesc(ir::Node* n) {
   PADDLE_ENFORCE(n->IsVar() && !n->IsCtrlVar() && n->inputs.size() == 1);
-  BlockDesc* block = n->inputs[0]->Op()->Block();
-  PADDLE_ENFORCE(block->HasVar(n->Name()),
-                 string::Sprintf("Block do not has var %s", n->Name()));
-  return block->FindVar(n->Name());
+  return n->Var();
 }
 
 struct NodeComparator {
   bool operator()(ir::Node* lhs, ir::Node* rhs) const {
-    auto* lhs_desc = FindVarDescInBlock(lhs);
-    auto* rhs_desc = FindVarDescInBlock(rhs);
+    if (lhs->Var()->GetType() != rhs->Var()->GetType()) return false;
+    auto* lhs_desc = GetVarDesc(lhs);
+    auto* rhs_desc = GetVarDesc(rhs);
     // match data type
     if (lhs_desc->GetDataType() != rhs_desc->GetDataType()) {
       return false;
@@ -204,7 +193,7 @@ void OrderedSet::Insert(ir::Node* var) {
     return;
   }
 
-  auto* var_desc = FindVarDescInBlock(var);
+  auto* var_desc = var->Var();
   auto var_shape = var_desc->GetShape();
   int batch_size = static_cast<int>(var_shape[0]);
 
@@ -212,7 +201,7 @@ void OrderedSet::Insert(ir::Node* var) {
   Iter it = nodes_.begin();
   while (it != nodes_.end()) {
     auto& prev = it->front();
-    auto* cache_desc = FindVarDescInBlock(prev);
+    auto* cache_desc = GetVarDesc(prev);
     int cache_batch_size = cache_desc->GetShape()[0];
     if ((cache_batch_size == -1 && batch_size == -1) ||
         (cache_batch_size != -1 && batch_size != -1)) {
@@ -336,10 +325,16 @@ int MinChunkSize() {
 bool NodeCanReused(const VarDesc& node) {
   auto type = node.GetType();
   // only these types holds bulk of gpu memory
-  if (!(type == proto::VarType::LOD_TENSOR ||
-        type == proto::VarType::LOD_TENSOR_ARRAY)) {
-    return false;
-  }
+  // FIXME(liuwei1031) did not find good ways to test SELECTED_ROWS and
+  // LOD_TENSOR_ARRAY re-use logic,
+  // disable them in version 1.4
+  // if (!(type == proto::VarType::LOD_TENSOR ||
+  //       type == proto::VarType::SELECTED_ROWS ||
+  //       type == proto::VarType::LOD_TENSOR_ARRAY)) {
+  //   return false;
+  // }
+  if (type != proto::VarType::LOD_TENSOR) return false;
+
   // persistable variable is parameter
   if (node.Persistable()) {
     return false;

paddle/fluid/framework/details/memory_optimize_helper.h

@@ -20,6 +20,7 @@
 #include <map>
 #include <set>
 #include <string>
+#include <unordered_map>
 #include <utility>
 #include <vector>
 #include "paddle/fluid/framework/data_type.h"
@@ -140,11 +141,7 @@ size_t NodeSize(const VarDesc&);
 
 std::string DebugString(ir::Node* var);
 
-// NOTE(dzhwinter)
-// after node reuse, the replaced node shape is
-// different with its VarDesc. So need to find the
-// correct VarDesc in Block.
-VarDesc* FindVarDescInBlock(ir::Node* n);
+VarDesc* GetVarDesc(ir::Node* n);
 
 static inline bool IsSameDesc(OpDesc* op1, OpDesc* op2) {
   return op1->Type() == op2->Type() && op1->Inputs() == op2->Inputs() &&

paddle/fluid/framework/details/multi_devices_graph_pass.cc

@@ -198,8 +198,22 @@ void MultiDevSSAGraphBuilderBase::ApplyImpl(ir::Graph *graph) const {
               static_cast<bool>(boost::get<int>(node->Op()->GetAttr(
                                     OpProtoAndCheckerMaker::OpRoleAttrName())) &
                                 static_cast<int>(OpRole::kBackward));
+          // optimize op is already processed in DealWithSpecialOp,
+          // here we only consider backward op
           if (!is_bk_op) continue;
 
+          /*
+           * the op that will generate the gradient of on parameter will have
+           one attr op_role_var
+           * to record the parameter and gradient, like:
+            attrs {
+              name: "op_role_var"
+              type: STRINGS
+              strings: "fc_1.b_0"
+              strings: "fc_1.b_0@GRAD"
+            }
+           */
+
           // Currently, we assume that once gradient is generated, it can be
           // broadcast, and each gradient is only broadcast once.
           auto backward_vars =
@@ -256,6 +270,8 @@ void MultiDevSSAGraphBuilderBase::InsertScaleLossGradOp(
       break;
   }
 
+  VLOG(3) << "loss_scale: " << loss_scale;
+
   if (loss_scale) {
     // TODO(paddle-dev): Why is there no input for this op_handle?
     auto loss_grad_name = node->Op()->OutputArgumentNames()[0];
@@ -407,7 +423,7 @@ void MultiDevSSAGraphBuilderBase::CreateFusedBroadcastOp(
 
 void MultiDevSSAGraphBuilderBase::CreateComputationalOp(ir::Graph *result,
                                                         ir::Node *node,
-                                                        int dev_id) const {
+                                                        size_t dev_id) const {
   result->Get<GraphOps>(kGraphOps).emplace_back(
       new ComputationOpHandle(result->CreateOpNode(node->Op()),
                               local_scopes_[dev_id], places_[dev_id], dev_id));
@@ -494,9 +510,8 @@ void MultiDevSSAGraphBuilderBase::CreateComputationalOps(
   }
 }
 
-VarHandle *MultiDevSSAGraphBuilderBase::CreateReduceOp(ir::Graph *result,
-                                                       const std::string &og,
-                                                       int dst_dev_id) const {
+VarHandle *MultiDevSSAGraphBuilderBase::CreateReduceOp(
+    ir::Graph *result, const std::string &og, size_t dst_dev_id) const {
 #if defined(PADDLE_WITH_CUDA) && !defined(_WIN32)
   result->Get<GraphOps>(kGraphOps).emplace_back(new ReduceOpHandle(
       result->CreateEmptyNode("reduce", ir::Node::Type::kOperation),
@@ -643,7 +658,7 @@ bool ReduceSSAGraphBuilder::DealWithSpecialOp(ir::Graph *result,
 
 void ReduceSSAGraphBuilder::InsertPostprocessOps(ir::Graph *result) const {
   if (UseGPU()) {
-    if (strategy_.fuse_broadcast_op_) {
+    if (strategy_.fuse_broadcast_ops_) {
       CreateFusedBroadcastOp(result, bcast_var_name_set_);
     } else {
       for (size_t dev_id = 0; dev_id < bcast_var_name_set_.size(); ++dev_id) {
@@ -774,6 +789,8 @@ bool DistSSAGraphBuilder::DealWithSpecialOp(ir::Graph *result,
   } else if (OpHaveRole(*node, OpRole::kDist)) {
     int op_dev_id = CreateDistTrainOp(result, node);
     if (node->Op()->Type() == "concat") {
+      // the input(block of parameter) of concat is on different device,
+      // the output(parameter) will on one device.
       auto origin_param_name = node->Op()->OutputArgumentNames()[0];
       bcast_var_name_set_[op_dev_id].emplace(origin_param_name);
     }
@@ -781,6 +798,7 @@ bool DistSSAGraphBuilder::DealWithSpecialOp(ir::Graph *result,
   } else {
     int op_dev_id = GetOpDeviceID(node);
     if (op_dev_id != -1) {  // This op only runs on one specific device.
+      // optimize op will be processed here.
       CreateComputationalOp(result, node, op_dev_id);
       for (ir::Node *n : node->outputs) {
         sharded_var_device_.emplace(n->Name(), op_dev_id);
@@ -961,6 +979,7 @@ bool DistSSAGraphBuilder::IsEncoded(const std::string &p_name) const {
 void DistSSAGraphBuilder::InsertCollectiveOp(ir::Graph *result,
                                              const std::string &p_name,
                                              const std::string &g_name) const {
+  // collective gradient to each device
   size_t cur_device_id = 0;
   switch (strategy_.reduce_) {
     case BuildStrategy::ReduceStrategy::kReduce:
@@ -1002,7 +1021,7 @@ void DistSSAGraphBuilder::InsertPostprocessOps(ir::Graph *result) const {
         strategy_.reduce_ == BuildStrategy::ReduceStrategy::kReduce) {
       return;
     }
-    if (strategy_.fuse_broadcast_op_) {
+    if (strategy_.fuse_broadcast_ops_) {
       CreateFusedBroadcastOp(result, bcast_var_name_set_);
     } else {
       for (size_t dev_id = 0; dev_id < bcast_var_name_set_.size(); ++dev_id) {
@@ -1049,3 +1068,5 @@ REGISTER_MULTI_DEVICES_PASS(
     paddle::framework::details::AllReduceSSAGraphBuilder);
 REGISTER_MULTI_DEVICES_PASS(dist_multi_devices_pass,
                             paddle::framework::details::DistSSAGraphBuilder);
+REGISTER_MULTI_DEVICES_PASS(async_multi_devices_pass,
+                            paddle::framework::details::AsyncSSAGraphBuilder);

paddle/fluid/framework/details/multi_devices_graph_pass.h

@@ -56,8 +56,8 @@ class MultiDevSSAGraphBuilderBase : public ir::Pass {
 
   bool UseGPU() const;
 
-  bool NeedCollectiveForGrad(const std::string &grad_name,
-                             std::vector<ir::Node *> ops) const;
+  virtual bool NeedCollectiveForGrad(const std::string &grad_name,
+                                     std::vector<ir::Node *> ops) const;
 
   bool IsScaleLossOp(ir::Node *node) const;
 
@@ -70,10 +70,10 @@ class MultiDevSSAGraphBuilderBase : public ir::Pass {
                              proto::VarType::Type dtype) const;
 
   VarHandle *CreateReduceOp(ir::Graph *result, const std::string &og,
-                            int dst_dev_id) const;
+                            size_t dst_dev_id) const;
 
   void CreateComputationalOp(ir::Graph *result, ir::Node *node,
-                             int dev_id) const;
+                             size_t dev_id) const;
 
   bool IsSparseGradient(const std::string &og) const;
 
@@ -115,6 +115,35 @@ class AllReduceSSAGraphBuilder : public MultiDevSSAGraphBuilderBase {
   virtual void InsertPostprocessOps(ir::Graph *result) const {}
 };
 
+class AsyncSSAGraphBuilder : public MultiDevSSAGraphBuilderBase {
+ protected:
+  void InsertCollectiveOp(ir::Graph *result, const std::string &p_name,
+                          const std::string &g_name) const override {}
+
+  bool NeedCollectiveForGrad(const std::string &grad_name,
+                             std::vector<ir::Node *> ops) const {
+    return false;
+  }
+
+  bool DealWithSpecialOp(ir::Graph *result, ir::Node *node) const override {
+    if (node->Op()->Type() == "recv") {
+      VLOG(1) << "set recv op do_not_run to true";
+      node->Op()->SetAttr("do_not_run", true);
+      node->Op()->Flush();
+    } else if (node->Name() == "lookup_table" || node->Name() == "nce" ||
+               node->Name() == "hierarchical_sigmoid") {
+      // in async_mode, we do not need remote prefetch, because communicator
+      // will do async parameter recv.
+      VLOG(1) << "set " << node->Name() << " op remote_prefetch to false";
+      node->Op()->SetAttr("remote_prefetch", false);
+      node->Op()->Flush();
+    }
+    return false;
+  }
+
+  void InsertPostprocessOps(ir::Graph *result) const override {}
+};
+
 class BalanceVarSSAGraphBuilder : public MultiDevSSAGraphBuilderBase {
  protected:
   int GetVarDeviceID(const std::string &varname) const;

paddle/fluid/framework/details/op_registry.h

@@ -16,6 +16,7 @@ limitations under the License. */
 
 #include <string>
 #include <tuple>
+#include <type_traits>
 #include <unordered_map>
 #include <unordered_set>
 #include <vector>
@@ -183,6 +184,10 @@ struct OpInfoFiller<T, kGradOpDescMaker> {
       T maker(fwd_op, no_grad_set, grad_to_var, grad_block);
       return maker();
     };
+
+    info->use_default_grad_op_desc_maker_ =
+        std::is_base_of<DefaultGradOpDescMaker<true>, T>::value ||
+        std::is_base_of<DefaultGradOpDescMaker<false>, T>::value;
   }
 };
 

paddle/fluid/framework/details/threaded_ssa_graph_executor.cc

@@ -31,11 +31,23 @@ ThreadedSSAGraphExecutor::ThreadedSSAGraphExecutor(
       prepare_pool_(1),
       pool_(strategy.num_threads_ >= 2 ? new ::ThreadPool(strategy.num_threads_)
                                        : nullptr) {
+  if (strategy_.num_iteration_per_run_ > 1) {
+    int read_op_num = 0;
+    for (auto *node : graph_->Nodes()) {
+      if (node->IsOp() && node->Name() == "read") {
+        read_op_num++;
+      }
+    }
+    if (read_op_num == 0) {
+      LOG(WARNING) << "when num_iteration_per_run_ is larger then 1, the model "
+                      "should use pyreader to feed data!";
+    }
+  }
   PrepareOpDeps();
   CopyOpDeps();
 }
 
-FeedFetchList ThreadedSSAGraphExecutor::Run(
+inline FeedFetchList ThreadedSSAGraphExecutor::RunImpl(
     const std::vector<std::string> &fetch_tensors) {
   std::unique_ptr<platform::RecordEvent> event(
       new platform::RecordEvent("ThreadedSSAGraphExecutorPrepare"));
@@ -68,7 +80,6 @@ FeedFetchList ThreadedSSAGraphExecutor::Run(
     }
     set.clear();
   };
-  auto run_all_op = [&](OpHandleBase *op) { RunOp(ready_vars, op); };
   // Clean run context
   run_op_futures_.clear();
   exception_holder_.Clear();
@@ -84,6 +95,8 @@ FeedFetchList ThreadedSSAGraphExecutor::Run(
     auto cur_ready_vars = ready_vars->PopAll(1, &timeout);
     if (timeout) {
       if (exception_holder_.IsCaught()) {
+        VLOG(3) << "caught exception " << exception_holder_.Type()
+                << ", rethrow it";
         for (auto &run_op_future : run_op_futures_) {
           run_op_future.wait();
         }
@@ -102,7 +115,7 @@ FeedFetchList ThreadedSSAGraphExecutor::Run(
         auto &deps = pending_ops[op];
         --deps;
         if (deps == 0) {
-          run_all_op(op);
+          ready_ops.insert(op);
         }
       }
     }
@@ -114,6 +127,14 @@ FeedFetchList ThreadedSSAGraphExecutor::Run(
   return fetch_data;
 }
 
+FeedFetchList ThreadedSSAGraphExecutor::Run(
+    const std::vector<std::string> &fetch_tensors) {
+  for (size_t j = 0; j < strategy_.num_iteration_per_run_ - 1; ++j) {
+    RunImpl({});
+  }
+  return RunImpl(fetch_tensors);
+}
+
 void ThreadedSSAGraphExecutor::InsertFetchOps(
     const std::vector<std::string> &fetch_tensors,
     std::vector<FetchOpHandle *> *fetch_ops,

paddle/fluid/framework/details/threaded_ssa_graph_executor.h

@@ -23,7 +23,9 @@
 #include <unordered_set>
 #include <utility>
 #include <vector>
-#include "ThreadPool.h"  // ThreadPool in thrird party
+
+#include <ThreadPool.h>  // ThreadPool in thrird party
+
 #include "paddle/fluid/framework/blocking_queue.h"
 #include "paddle/fluid/framework/details/exception_holder.h"
 #include "paddle/fluid/framework/details/execution_strategy.h"
@@ -59,6 +61,7 @@ class ThreadedSSAGraphExecutor : public SSAGraphExecutor {
   ~ThreadedSSAGraphExecutor() final = default;
 
  private:
+  inline FeedFetchList RunImpl(const std::vector<std::string> &fetch_tensors);
   void RunOp(const std::shared_ptr<BlockingQueue<VarHandleBase *>> &ready_var_q,
              details::OpHandleBase *op);
 

paddle/fluid/framework/device_worker.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/device_worker.h"
+
+namespace paddle {
+namespace framework {
+
+void DeviceWorker::SetRootScope(Scope* root_scope) { root_scope_ = root_scope; }
+
+void DeviceWorker::SetDataFeed(const std::shared_ptr<DataFeed>& data_feed) {
+  device_reader_ = data_feed;
+}
+
+}  // namespace framework
+}  // namespace paddle

paddle/fluid/framework/device_worker.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 <fstream>
+#include <map>
+#include <memory>
+#include <mutex>  // NOLINT
+#include <string>
+#include <thread>  // NOLINT
+#include <vector>
+
+#include "paddle/fluid/framework/data_feed.h"
+#include "paddle/fluid/framework/fleet/fleet_wrapper.h"
+#include "paddle/fluid/framework/lod_tensor.h"
+#include "paddle/fluid/framework/op_registry.h"
+#include "paddle/fluid/framework/program_desc.h"
+#include "paddle/fluid/framework/reader.h"
+#include "paddle/fluid/framework/trainer_desc.pb.h"
+#include "paddle/fluid/framework/variable_helper.h"
+#include "paddle/fluid/operators/reader/blocking_queue.h"
+#include "paddle/fluid/platform/place.h"
+#include "paddle/fluid/platform/port.h"
+#include "paddle/fluid/platform/timer.h"
+
+namespace paddle {
+namespace framework {
+
+class PullDenseWorker {
+ public:
+  virtual ~PullDenseWorker() {}
+  virtual void Initialize(const TrainerDesc& param);
+  int Start();
+  void Stop();
+  void SetRootScope(Scope* scope) { root_scope_ = scope; }
+  void IncreaseThreadVersion(int thread_id, uint64_t table_id);
+  void ResetThreadVersion(uint64_t table_id);
+  void Wait(std::vector<::std::future<int32_t>>* status_vec);
+  static std::shared_ptr<PullDenseWorker> GetInstance() {
+    if (NULL == s_instance_) {
+      s_instance_.reset(new paddle::framework::PullDenseWorker());
+    }
+    return s_instance_;
+  }
+
+ private:
+  PullDenseWorker() : root_scope_(NULL) {}
+  void Run();
+  bool CheckUpdateParam(uint64_t table_id);
+
+ private:
+  static std::shared_ptr<PullDenseWorker> s_instance_;
+  std::shared_ptr<paddle::framework::FleetWrapper> fleet_ptr_;
+  PullDenseWorkerParameter param_;
+  DownpourWorkerParameter dwp_param_;
+  Scope* root_scope_;
+  bool running_;
+
+  static std::map<uint64_t, uint64_t> last_versions_;
+  static std::map<uint64_t, uint64_t> current_version_;
+  static std::mutex mutex_for_version_;
+  static std::map<uint64_t, std::vector<uint64_t>> training_versions_;
+  static std::map<uint64_t, std::vector<std::string>> dense_value_names_;
+
+  std::thread t_;
+  int thread_num_;
+  int sleep_time_ms_;
+  int threshold_;
+
+  std::vector<::std::future<int32_t>> pull_dense_status_;
+  uint32_t pull_dense_fail_times_ = 0;
+  std::vector<float> base_norm_param_;
+  std::vector<float> mean_;
+  std::vector<float> scale_;
+  float squared_sum_epsilon_ = 1e-4;
+  std::mutex mutex_for_mean_scale_;
+  float total_batch_num_ = 0;
+};
+
+// should incorporate different type of device
+class DeviceWorker {
+ public:
+  DeviceWorker() {}
+  virtual ~DeviceWorker() {}
+  virtual void Initialize(const TrainerDesc& desc) = 0;
+  virtual void SetDeviceIndex(int tid) = 0;
+  virtual void TrainFiles() = 0;
+  virtual void PrintFetchVars() = 0;
+  virtual void TrainFilesWithProfiler() = 0;
+  virtual void CreateDeviceResource(const ProgramDesc& main_prog) = 0;
+  // will make this zero copy in the future
+  virtual void BindingDataFeedMemory() = 0;
+  virtual void SetRootScope(Scope* root_scope);
+  virtual void SetDataFeed(const std::shared_ptr<DataFeed>& data_feed);
+  virtual void SetPlace(const paddle::platform::Place& place) {
+    place_ = place;
+  }
+
+ protected:
+  Scope* root_scope_;
+  paddle::platform::Place place_;
+  std::shared_ptr<DataFeed> device_reader_;
+  int64_t batch_num_;
+  FetchConfig fetch_config_;
+};
+
+class CPUWorkerBase : public DeviceWorker {
+ public:
+  CPUWorkerBase() {}
+  virtual ~CPUWorkerBase() {}
+  virtual void SetDeviceIndex(int tid) { thread_id_ = tid; }
+  virtual void TrainFiles() = 0;
+  virtual void TrainFilesWithProfiler() {}
+  virtual void PrintFetchVars() {}
+  virtual void CreateDeviceResource(const ProgramDesc& main_prog) {}
+
+ protected:
+  int thread_id_;
+};
+
+class HogwildWorker : public CPUWorkerBase {
+ public:
+  HogwildWorker() {}
+  virtual ~HogwildWorker() {}
+  virtual void Initialize(const TrainerDesc& desc);
+  virtual void TrainFiles();
+  virtual void TrainFilesWithProfiler();
+  virtual void PrintFetchVars();
+  virtual void CreateDeviceResource(const ProgramDesc& main_prog);
+  virtual void BindingDataFeedMemory();
+
+ protected:
+  void CreateThreadOperators(const ProgramDesc& program);
+  void CreateThreadScope(const ProgramDesc& program);
+  std::vector<std::string> op_names_;
+  std::vector<OperatorBase*> ops_;
+  Scope* thread_scope_;
+  HogwildWorkerParameter param_;
+  std::vector<std::string> skip_ops_;
+};
+
+class DownpourWorker : public HogwildWorker {
+ public:
+  DownpourWorker() {}
+  virtual ~DownpourWorker() {}
+  virtual void Initialize(const TrainerDesc& desc);
+  virtual void TrainFiles();
+  virtual void TrainFilesWithProfiler();
+
+ protected:
+  std::shared_ptr<paddle::framework::FleetWrapper> fleet_ptr_;
+  std::shared_ptr<paddle::framework::PullDenseWorker> pull_dense_worker_;
+  void FillSparseValue(size_t table_id);
+  void PushGradients();
+  void CollectLabelInfo(size_t table_id);
+
+ private:
+  bool need_to_push_dense_;
+  bool need_to_push_sparse_;
+  DownpourWorkerParameter param_;
+  // just save the value in param_ for easy access
+  std::map<uint64_t, std::string> label_var_name_;
+  std::map<uint64_t, std::vector<std::string>> sparse_key_names_;
+  std::map<uint64_t, std::vector<std::string>> sparse_value_names_;
+  std::map<uint64_t, std::vector<std::string>> sparse_grad_names_;
+  std::map<uint64_t, std::vector<std::string>> dense_value_names_;
+  std::map<uint64_t, std::vector<std::string>> dense_grad_names_;
+
+  // feasign
+  std::map<uint64_t, std::vector<uint64_t>> features_;
+  // feasign stats
+  std::map<uint64_t, std::vector<float>> feature_labels_;
+  // feasign embedding
+  std::map<uint64_t, std::vector<std::vector<float>>> feature_values_;
+  // feasign embedding gradient
+  std::map<uint64_t, std::vector<std::vector<float>>> feature_grads_;
+  // skipped ops
+  std::vector<std::string> skip_ops_;
+
+  std::shared_ptr<PullDenseWorker> _pull_dense_worker;
+  std::vector<::std::future<int32_t>> push_sparse_status_;
+  std::vector<::std::future<int32_t>> push_dense_status_;
+};
+
+}  // namespace framework
+}  // namespace paddle

paddle/fluid/framework/device_worker_factory.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/device_worker_factory.h"
+#include <memory>
+#include <string>
+#include <unordered_map>
+
+namespace paddle {
+namespace framework {
+
+typedef std::shared_ptr<DeviceWorker> (*Createdevice_workerFunction)();
+typedef std::unordered_map<std::string, Createdevice_workerFunction>
+    device_workerMap;
+device_workerMap g_device_worker_map;
+#define REGISTER_DEVICE_WORKER_CLASS(device_worker_class)                \
+  namespace {                                                            \
+  std::shared_ptr<DeviceWorker> Creator_##device_worker_class() {        \
+    return std::shared_ptr<DeviceWorker>(new device_worker_class);       \
+  }                                                                      \
+  class __Registerer_##device_worker_class {                             \
+   public:                                                               \
+    __Registerer_##device_worker_class() {                               \
+      g_device_worker_map[#device_worker_class] =                        \
+          &Creator_##device_worker_class;                                \
+    }                                                                    \
+  };                                                                     \
+  __Registerer_##device_worker_class g_registerer_##device_worker_class; \
+  }  // namespace
+
+std::string DeviceWorkerFactory::DeviceWorkerTypeList() {
+  std::string device_worker_types;
+  for (auto iter = g_device_worker_map.begin();
+       iter != g_device_worker_map.end(); ++iter) {
+    if (iter != g_device_worker_map.begin()) {
+      device_worker_types += ", ";
+    }
+    device_worker_types += iter->first;
+  }
+  return device_worker_types;
+}
+
+std::shared_ptr<DeviceWorker> DeviceWorkerFactory::CreateDeviceWorker(
+    std::string device_worker_class) {
+  if (g_device_worker_map.count(device_worker_class) < 1) {
+    exit(-1);
+  }
+  return g_device_worker_map[device_worker_class]();
+}
+
+REGISTER_DEVICE_WORKER_CLASS(HogwildWorker);
+REGISTER_DEVICE_WORKER_CLASS(DownpourWorker);
+}  // namespace framework
+}  // namespace paddle

paddle/fluid/framework/device_worker_factory.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 <memory>
+#include <string>
+#include "paddle/fluid/framework/device_worker.h"
+
+namespace paddle {
+namespace framework {
+
+class DeviceWorkerFactory {
+ public:
+  static std::string DeviceWorkerTypeList();
+  static std::shared_ptr<DeviceWorker> CreateDeviceWorker(
+      std::string device_worker_class);
+};
+}  // namespace framework
+}  // namespace paddle

paddle/fluid/framework/device_worker_test.cc

+// Copyright (c) 2019 PaddlePaddle Authors. All Rights Reserved.
+//
+// Licensed under the Apache License, Version 2.0 (the "License");
+// you may not use this file except in compliance with the License.
+// You may obtain a copy of the License at
+//
+//     http://www.apache.org/licenses/LICENSE-2.0
+//
+// Unless required by applicable law or agreed to in writing, software
+// distributed under the License is distributed on an "AS IS" BASIS,
+// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+// See the License for the specific language governing permissions and
+// limitations under the License.
+
+#include <gtest/gtest.h>
+#include "paddle/fluid/framework/trainer.h"
+
+namespace paddle {
+namespace framework {
+TEST() {
+  // create hogwild device worker
+}
+}
+}

paddle/fluid/framework/dist_multi_trainer.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 <string>
+#include <vector>
+#include "paddle/fluid/framework/data_feed_factory.h"
+#include "paddle/fluid/framework/data_set.h"
+#include "paddle/fluid/framework/device_worker_factory.h"
+#include "paddle/fluid/framework/trainer.h"
+
+namespace paddle {
+namespace framework {
+
+void DistMultiTrainer::Initialize(const TrainerDesc& trainer_desc,
+                                  Dataset* dataset) {
+  thread_num_ = trainer_desc.thread_num();
+  SetDataset(dataset);
+
+  dataset->CreateReaders();
+  const std::vector<std::shared_ptr<paddle::framework::DataFeed>> readers =
+      dataset->GetReaders();
+
+  thread_num_ = readers.size();
+  workers_.resize(thread_num_);
+
+  for (int i = 0; i < thread_num_; ++i) {
+    workers_[i] = DeviceWorkerFactory::CreateDeviceWorker(
+        trainer_desc.device_worker_name());
+    workers_[i]->SetDeviceIndex(i);
+    workers_[i]->SetDataFeed(readers[i]);
+    workers_[i]->Initialize(trainer_desc);
+  }
+
+  VLOG(3) << "going to initialize pull dense worker";
+  pull_dense_worker_ = PullDenseWorker::GetInstance();
+  pull_dense_worker_->Initialize(trainer_desc);
+  VLOG(3) << "initialize pull dense worker";
+  SetDebug(trainer_desc.debug());
+}
+
+void DistMultiTrainer::InitOtherEnv(const ProgramDesc& main_program) {
+  pull_dense_worker_->SetRootScope(root_scope_);
+  pull_dense_worker_->Start();
+  VLOG(3) << "init other env done.";
+}
+
+void DistMultiTrainer::Run() {
+  for (int thidx = 0; thidx < thread_num_; ++thidx) {
+    if (!debug_) {
+      threads_.push_back(
+          std::thread(&DeviceWorker::TrainFiles, workers_[thidx].get()));
+    } else {
+      threads_.push_back(std::thread(&DeviceWorker::TrainFilesWithProfiler,
+                                     workers_[thidx].get()));
+    }
+  }
+}
+
+void DistMultiTrainer::Finalize() {
+  for (auto& th : threads_) {
+    th.join();
+  }
+  pull_dense_worker_->Stop();
+  dataset_ptr_->DestroyReaders();
+  root_scope_->DropKids();
+}
+
+}  // end namespace framework
+}  // end namespace paddle

paddle/fluid/framework/downpour_worker.cc

+/* Copyright (c) 2019 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/device_worker.h"
+#include "paddle/fluid/framework/device_worker_factory.h"
+#include "paddle/fluid/platform/cpu_helper.h"
+
+namespace paddle {
+namespace framework {
+
+void DownpourWorker::Initialize(const TrainerDesc& desc) {
+  param_ = desc.downpour_param();
+  for (size_t i = 0; i < param_.sparse_table_size(); ++i) {
+    uint64_t table_id =
+        static_cast<uint64_t>(param_.sparse_table(i).table_id());
+    TableParameter table = param_.sparse_table(i);
+    sparse_key_names_[table_id].resize(table.sparse_key_name_size());
+    for (size_t j = 0; j < table.sparse_key_name_size(); ++j) {
+      sparse_key_names_[table_id][j] = table.sparse_key_name(j);
+    }
+    sparse_value_names_[table_id].resize(table.sparse_value_name_size());
+    for (size_t j = 0; j < table.sparse_value_name_size(); ++j) {
+      sparse_value_names_[table_id][j] = table.sparse_value_name(j);
+    }
+    sparse_grad_names_[table_id].resize(table.sparse_grad_name_size());
+    for (size_t j = 0; j < table.sparse_grad_name_size(); ++j) {
+      sparse_grad_names_[table_id][j] = table.sparse_grad_name(j);
+    }
+    label_var_name_[table_id] = table.label_var_name();
+  }
+
+  for (size_t i = 0; i < param_.dense_table_size(); ++i) {
+    uint64_t table_id = static_cast<uint64_t>(param_.dense_table(i).table_id());
+    auto table = param_.dense_table(i);
+    dense_value_names_[table_id].resize(table.dense_value_name_size());
+    for (size_t j = 0; j < table.dense_value_name_size(); ++j) {
+      dense_value_names_[table_id][j] = table.dense_value_name(j);
+    }
+    dense_grad_names_[table_id].resize(table.dense_grad_name_size());
+    for (size_t j = 0; j < table.dense_grad_name_size(); ++j) {
+      dense_grad_names_[table_id][j] = table.dense_grad_name(j);
+    }
+  }
+
+  skip_ops_.resize(param_.skip_ops_size());
+  for (size_t i = 0; i < param_.skip_ops_size(); ++i) {
+    skip_ops_[i] = param_.skip_ops(i);
+  }
+
+  need_to_push_sparse_ = param_.push_sparse();
+  need_to_push_dense_ = param_.push_dense();
+
+  fleet_ptr_ = FleetWrapper::GetInstance();
+  fetch_config_ = desc.fetch_config();
+}
+
+void DownpourWorker::CollectLabelInfo(size_t table_idx) {
+  uint64_t table_id = static_cast<uint64_t>(
+      param_.program_config(0).pull_sparse_table_id(table_idx));
+
+  TableParameter table;
+  for (auto i : param_.sparse_table()) {
+    if (i.table_id() == table_id) {
+      table = i;
+      break;
+    }
+  }
+  auto& feature = features_[table_id];
+  auto& feature_label = feature_labels_[table_id];
+  feature_label.resize(feature.size());
+  Variable* var = thread_scope_->FindVar(label_var_name_[table_id]);
+  LoDTensor* tensor = var->GetMutable<LoDTensor>();
+  int64_t* label_ptr = tensor->data<int64_t>();
+
+  int global_index = 0;
+  for (size_t i = 0; i < sparse_key_names_[table_id].size(); ++i) {
+    VLOG(3) << "sparse_key_names_[" << i
+            << "]: " << sparse_key_names_[table_id][i];
+    Variable* fea_var = thread_scope_->FindVar(sparse_key_names_[table_id][i]);
+    LoDTensor* tensor = fea_var->GetMutable<LoDTensor>();
+    int64_t* ids = tensor->data<int64_t>();
+    int fea_idx = 0;
+    // tensor->lod()[0].size() == batch_size + 1
+    for (auto lod_idx = 1u; lod_idx < tensor->lod()[0].size(); ++lod_idx) {
+      for (; fea_idx < tensor->lod()[0][lod_idx]; ++fea_idx) {
+        // should be skipped feasign defined in protobuf
+        if (ids[fea_idx] == 0u) {
+          continue;
+        }
+        feature_label[global_index++] =
+            static_cast<float>(label_ptr[lod_idx - 1]);
+      }
+    }
+  }
+  CHECK(global_index == feature.size())
+      << "expect fea info size:" << feature.size() << " real:" << global_index;
+}
+
+void DownpourWorker::FillSparseValue(size_t table_idx) {
+  uint64_t table_id = static_cast<uint64_t>(
+      param_.program_config(0).pull_sparse_table_id(table_idx));
+
+  TableParameter table;
+  for (auto i : param_.sparse_table()) {
+    if (i.table_id() == table_id) {
+      table = i;
+      break;
+    }
+  }
+
+  auto& fea_value = feature_values_[table_id];
+  auto fea_idx = 0u;
+
+  std::vector<float> init_value(table.fea_dim());
+  for (size_t i = 0; i < sparse_key_names_[table_id].size(); ++i) {
+    std::string slot_name = sparse_key_names_[table_id][i];
+    std::string emb_slot_name = sparse_value_names_[table_id][i];
+    Variable* var = thread_scope_->FindVar(slot_name);
+    LoDTensor* tensor = var->GetMutable<LoDTensor>();
+    int64_t* ids = tensor->data<int64_t>();
+    int len = tensor->numel();
+    Variable* var_emb = thread_scope_->FindVar(emb_slot_name);
+    LoDTensor* tensor_emb = var_emb->GetMutable<LoDTensor>();
+    float* ptr = tensor_emb->mutable_data<float>({len, table.emb_dim()},
+                                                 platform::CPUPlace());
+    memset(ptr, 0, sizeof(float) * len * table.emb_dim());
+    auto& tensor_lod = tensor->lod()[0];
+    LoD data_lod{tensor_lod};
+    tensor_emb->set_lod(data_lod);
+    for (auto index = 0u; index < len; ++index) {
+      if (ids[index] == 0u) {
+        memcpy(ptr + table.emb_dim() * index, init_value.data() + 2,
+               sizeof(float) * table.emb_dim());
+        continue;
+      }
+      memcpy(ptr + table.emb_dim() * index, fea_value[fea_idx].data() + 2,
+             sizeof(float) * table.emb_dim());
+      fea_idx++;
+    }
+  }
+}
+
+void DownpourWorker::TrainFilesWithProfiler() {
+  VLOG(3) << "Begin to train files with profiler";
+  platform::SetNumThreads(1);
+  device_reader_->Start();
+  std::vector<double> op_total_time;
+  std::vector<std::string> op_name;
+  for (auto& op : ops_) {
+    bool need_skip = false;
+    for (auto t = 0u; t < skip_ops_.size(); ++t) {
+      if (op->Type().find(skip_ops_[t]) != std::string::npos) {
+        need_skip = true;
+        break;
+      }
+    }
+    if (!need_skip) {
+      op_name.push_back(op->Type());
+    }
+  }
+
+  VLOG(3) << "op name size: " << op_name.size();
+  op_total_time.resize(op_name.size());
+  for (size_t i = 0; i < op_total_time.size(); ++i) {
+    op_total_time[i] = 0.0;
+  }
+  platform::Timer timeline;
+  double total_time = 0.0;
+  double read_time = 0.0;
+  double pull_sparse_time = 0.0;
+  double collect_label_time = 0.0;
+  double fill_sparse_time = 0.0;
+  double push_sparse_time = 0.0;
+  double push_dense_time = 0.0;
+  int cur_batch;
+  int batch_cnt = 0;
+  uint64_t total_inst = 0;
+  timeline.Start();
+  while ((cur_batch = device_reader_->Next()) > 0) {
+    timeline.Pause();
+    read_time += timeline.ElapsedSec();
+    total_time += timeline.ElapsedSec();
+    VLOG(3) << "program config size: " << param_.program_config_size();
+    for (size_t i = 0; i < param_.program_config(0).pull_sparse_table_id_size();
+         ++i) {
+      uint64_t tid = static_cast<uint64_t>(
+          param_.program_config(0).pull_sparse_table_id(i));
+      TableParameter table;
+      for (auto i : param_.sparse_table()) {
+        if (i.table_id() == tid) {
+          table = i;
+          break;
+        }
+      }
+      timeline.Start();
+      fleet_ptr_->PullSparseVarsSync(*thread_scope_, tid,
+                                     sparse_key_names_[tid], &features_[tid],
+                                     &feature_values_[tid], table.fea_dim());
+      timeline.Pause();
+      pull_sparse_time += timeline.ElapsedSec();
+      total_time += timeline.ElapsedSec();
+      timeline.Start();
+      CollectLabelInfo(i);
+      timeline.Pause();
+      collect_label_time += timeline.ElapsedSec();
+      total_time += timeline.ElapsedSec();
+      timeline.Start();
+      FillSparseValue(i);
+      timeline.Pause();
+      fill_sparse_time += timeline.ElapsedSec();
+      total_time += timeline.ElapsedSec();
+    }
+    VLOG(3) << "Fill sparse value for all sparse table done.";
+
+    int run_op_idx = 0;
+    for (auto& op : ops_) {
+      bool need_skip = false;
+      for (auto t = 0u; t < skip_ops_.size(); ++t) {
+        if (op->Type().find(skip_ops_[t]) != std::string::npos) {
+          need_skip = true;
+          break;
+        }
+      }
+      if (!need_skip) {
+        timeline.Start();
+        VLOG(3) << "Going to run op " << op_name[run_op_idx];
+        op->Run(*thread_scope_, place_);
+        VLOG(3) << "Op " << op_name[run_op_idx] << " Finished";
+        timeline.Pause();
+        op_total_time[run_op_idx++] += timeline.ElapsedSec();
+        total_time += timeline.ElapsedSec();
+      }
+    }
+
+    if (need_to_push_sparse_) {
+      for (size_t i = 0;
+           i < param_.program_config(0).push_sparse_table_id_size(); ++i) {
+        uint64_t tid = static_cast<uint64_t>(
+            param_.program_config(0).push_sparse_table_id(i));
+        TableParameter table;
+        for (auto i : param_.sparse_table()) {
+          if (i.table_id() == tid) {
+            table = i;
+            break;
+          }
+        }
+        timeline.Start();
+        fleet_ptr_->PushSparseVarsWithLabelAsync(
+            *thread_scope_, tid, features_[tid], feature_labels_[tid],
+            sparse_key_names_[tid], sparse_grad_names_[tid], table.emb_dim(),
+            &feature_grads_[tid], &push_sparse_status_);
+        timeline.Pause();
+        push_sparse_time += timeline.ElapsedSec();
+        total_time += timeline.ElapsedSec();
+      }
+    }
+
+    if (need_to_push_dense_) {
+      timeline.Start();
+      for (size_t i = 0;
+           i < param_.program_config(0).push_dense_table_id_size(); ++i) {
+        uint64_t tid = static_cast<uint64_t>(
+            param_.program_config(0).push_dense_table_id(i));
+        fleet_ptr_->PushDenseVarsAsync(
+            *thread_scope_, tid, dense_grad_names_[tid], &push_sparse_status_);
+      }
+      timeline.Pause();
+      push_dense_time += timeline.ElapsedSec();
+      total_time += timeline.ElapsedSec();
+      VLOG(3) << "push sparse and dense gradient done.";
+      int32_t tmp_push_dense_wait_times = -1;
+      static uint32_t push_dense_wait_times =
+          static_cast<uint32_t>(tmp_push_dense_wait_times);
+      if (push_dense_status_.size() >= push_dense_wait_times) {
+        for (auto& t : push_dense_status_) {
+          t.wait();
+        }
+        push_dense_status_.resize(0);
+      }
+
+      if (tmp_push_dense_wait_times == -1) {
+        push_dense_status_.resize(0);
+      }
+    }
+
+    if (need_to_push_sparse_) {
+      int32_t tmp_push_sparse_wait_times = -1;
+      static uint32_t push_sparse_wait_times =
+          static_cast<uint32_t>(tmp_push_sparse_wait_times);
+      if (push_sparse_status_.size() >= push_sparse_wait_times) {
+        for (auto& t : push_sparse_status_) {
+          t.wait();
+        }
+        push_sparse_status_.resize(0);
+      }
+
+      if (tmp_push_sparse_wait_times == -1) {
+        push_sparse_status_.resize(0);
+      }
+
+      VLOG(3) << "going to increase thread version";
+      VLOG(3) << "push dense table id size: "
+              << param_.program_config(0).push_dense_table_id_size();
+    }
+
+    if (need_to_push_dense_) {
+      for (size_t i = 0;
+           i < param_.program_config(0).push_dense_table_id_size(); ++i) {
+        uint64_t tid = static_cast<uint64_t>(
+            param_.program_config(0).push_dense_table_id(i));
+        pull_dense_worker_->IncreaseThreadVersion(thread_id_, tid);
+      }
+    }
+
+    PrintFetchVars();
+    thread_scope_->DropKids();
+    total_inst += cur_batch;
+    ++batch_cnt;
+
+    if (thread_id_ == 0) {
+      // should be configured here
+      if (batch_cnt > 0 && batch_cnt % 100 == 0) {
+        for (size_t i = 0; i < op_total_time.size(); ++i) {
+          fprintf(stderr, "op_name:[%zu][%s], op_mean_time:[%fs]\n", i,
+                  op_name[i].c_str(), op_total_time[i] / batch_cnt);
+        }
+        fprintf(stderr, "mean read time: %fs\n", read_time / batch_cnt);
+        fprintf(stderr, "IO percent: %f\n", read_time / total_time * 100);
+        fprintf(stderr, "pull sparse time percent: %f\n",
+                pull_sparse_time / total_time * 100);
+        fprintf(stderr, "collect label time percent: %f\n",
+                collect_label_time / total_time * 100);
+        fprintf(stderr, "fill sparse time percent: %f\n",
+                fill_sparse_time / total_time * 100);
+        fprintf(stderr, "push sparse time percent: %f\n",
+                push_sparse_time / total_time * 100);
+        fprintf(stderr, "push dense time percent: %f\n",
+                push_dense_time / total_time * 100);
+        fprintf(stderr, "%6.2f instances/s\n", total_inst / total_time);
+      }
+    }
+    timeline.Start();
+  }
+}
+
+void DownpourWorker::TrainFiles() {
+  VLOG(3) << "Begin to train files";
+  platform::SetNumThreads(1);
+  device_reader_->Start();
+  int batch_cnt = 0;
+  int cur_batch;
+  while ((cur_batch = device_reader_->Next()) > 0) {
+    // pull sparse here
+    for (size_t i = 0; i < param_.program_config(0).pull_sparse_table_id_size();
+         ++i) {
+      uint64_t tid = static_cast<uint64_t>(
+          param_.program_config(0).pull_sparse_table_id(i));
+      TableParameter table;
+      for (auto i : param_.sparse_table()) {
+        if (i.table_id() == tid) {
+          table = i;
+          break;
+        }
+      }
+      fleet_ptr_->PullSparseVarsSync(*thread_scope_, tid,
+                                     sparse_key_names_[tid], &features_[tid],
+                                     &feature_values_[tid], table.fea_dim());
+      CollectLabelInfo(i);
+      FillSparseValue(i);
+    }
+    VLOG(3) << "fill sparse value for all sparse table done.";
+
+    // do computation here
+    for (auto& op : ops_) {
+      bool need_skip = false;
+      for (auto t = 0u; t < skip_ops_.size(); ++t) {
+        if (op->Type().find(skip_ops_[t]) != std::string::npos) {
+          need_skip = true;
+          break;
+        }
+      }
+      if (!need_skip) {
+        op->Run(*thread_scope_, place_);
+      }
+    }
+
+    if (need_to_push_sparse_) {
+      // push gradients here
+      for (size_t i = 0;
+           i < param_.program_config(0).push_sparse_table_id_size(); ++i) {
+        uint64_t tid = static_cast<uint64_t>(
+            param_.program_config(0).push_sparse_table_id(i));
+        TableParameter table;
+        for (auto i : param_.sparse_table()) {
+          if (i.table_id() == tid) {
+            table = i;
+            break;
+          }
+        }
+        fleet_ptr_->PushSparseVarsWithLabelAsync(
+            *thread_scope_, tid, features_[tid], feature_labels_[tid],
+            sparse_key_names_[tid], sparse_grad_names_[tid], table.emb_dim(),
+            &feature_grads_[tid], &push_sparse_status_);
+      }
+    }
+
+    if (need_to_push_dense_) {
+      for (size_t i = 0;
+           i < param_.program_config(0).push_dense_table_id_size(); ++i) {
+        uint64_t tid = static_cast<uint64_t>(
+            param_.program_config(0).push_dense_table_id(i));
+        fleet_ptr_->PushDenseVarsAsync(
+            *thread_scope_, tid, dense_grad_names_[tid], &push_sparse_status_);
+      }
+
+      VLOG(3) << "push dense gradient done.";
+      // the following code should be more precise and clean
+      // TODO(guru4elephant)
+      int32_t tmp_push_dense_wait_times = -1;
+      static uint32_t push_dense_wait_times =
+          static_cast<uint32_t>(tmp_push_dense_wait_times);
+
+      if (push_dense_status_.size() >= push_dense_wait_times) {
+        for (auto& t : push_dense_status_) {
+          t.wait();
+        }
+        push_dense_status_.resize(0);
+      }
+
+      if (tmp_push_dense_wait_times == -1) {
+        push_dense_status_.resize(0);
+      }
+    }
+
+    if (need_to_push_sparse_) {
+      VLOG(3) << "push sparse gradient done.";
+      int32_t tmp_push_sparse_wait_times = -1;
+      static uint32_t push_sparse_wait_times =
+          static_cast<uint32_t>(tmp_push_sparse_wait_times);
+      if (push_sparse_status_.size() >= push_sparse_wait_times) {
+        for (auto& t : push_sparse_status_) {
+          t.wait();
+        }
+        push_sparse_status_.resize(0);
+      }
+
+      if (tmp_push_sparse_wait_times == -1) {
+        push_sparse_status_.resize(0);
+      }
+    }
+
+    if (need_to_push_dense_) {
+      for (size_t i = 0;
+           i < param_.program_config(0).push_dense_table_id_size(); ++i) {
+        uint64_t tid = static_cast<uint64_t>(
+            param_.program_config(0).push_dense_table_id(i));
+        pull_dense_worker_->IncreaseThreadVersion(thread_id_, tid);
+      }
+    }
+
+    PrintFetchVars();
+    thread_scope_->DropKids();
+    ++batch_cnt;
+  }
+}
+
+}  // end namespace framework
+}  // end namespace paddle

paddle/fluid/framework/executor.cc

@@ -18,14 +18,16 @@ limitations under the License. */
 #include <unordered_map>
 #include <unordered_set>
 #include <utility>
-
-#include "paddle/fluid/framework/executor_gc_helper.h"
+#include "google/protobuf/io/zero_copy_stream_impl.h"
+#include "google/protobuf/message.h"
+#include "google/protobuf/text_format.h"
 #include "paddle/fluid/framework/feed_fetch_method.h"
 #include "paddle/fluid/framework/lod_rank_table.h"
 #include "paddle/fluid/framework/lod_tensor_array.h"
 #include "paddle/fluid/framework/op_registry.h"
 #include "paddle/fluid/framework/reader.h"
-#include "paddle/fluid/framework/threadpool.h"
+#include "paddle/fluid/framework/trainer_desc.pb.h"
+#include "paddle/fluid/framework/trainer_factory.h"
 #include "paddle/fluid/framework/transfer_scope_cache.h"
 #include "paddle/fluid/framework/variable_helper.h"
 #include "paddle/fluid/operators/controlflow/while_op_helper.h"
@@ -115,6 +117,35 @@ void Executor::CreateVariables(const ProgramDesc& pdesc, Scope* scope,
   }
 }
 
+void Executor::RunFromDataset(const ProgramDesc& main_program, Scope* scope,
+                              Dataset* dataset,
+                              const std::string& trainer_desc_str) {
+  VLOG(3) << "Start to RunFromDataset in executor";
+  TrainerDesc trainer_desc;
+  google::protobuf::TextFormat::ParseFromString(trainer_desc_str,
+                                                &trainer_desc);
+  VLOG(3) << "Going to create trainer, trainer class is "
+          << trainer_desc.class_name();
+  std::shared_ptr<TrainerBase> trainer;
+  trainer = TrainerFactory::CreateTrainer(trainer_desc.class_name());
+  // initialize trainer
+  VLOG(3) << "Going to initialize trainer";
+  trainer->Initialize(trainer_desc, dataset);
+  VLOG(3) << "Set root scope here";
+  trainer->SetScope(scope);
+  // prepare training environment and helper environment
+  VLOG(3) << "Try to init train environment";
+  trainer->InitTrainerEnv(main_program, place_);
+  VLOG(3) << "Try to init other environment";
+  trainer->InitOtherEnv(main_program);
+  // training and finalize training
+  VLOG(3) << "Trainer starts to run";
+  trainer->Run();
+  VLOG(3) << "Trainer going to finalize";
+  trainer->Finalize();
+  return;
+}
+
 void Executor::Run(const ProgramDesc& pdesc, Scope* scope, int block_id,
                    bool create_local_scope, bool create_vars,
                    const std::vector<std::string>& skip_ref_cnt_vars,

paddle/fluid/framework/executor.h

@@ -19,6 +19,8 @@ limitations under the License. */
 #include <string>
 #include <unordered_map>
 #include <vector>
+#include "paddle/fluid/framework/data_set.h"
+#include "paddle/fluid/framework/executor_gc_helper.h"
 #include "paddle/fluid/framework/garbage_collector.h"
 #include "paddle/fluid/framework/op_info.h"
 #include "paddle/fluid/framework/program_desc.h"
@@ -110,6 +112,9 @@ class Executor {
 
   void EnableMKLDNN(const ProgramDesc& program);
 
+  void RunFromDataset(const ProgramDesc& main_program, Scope* scope,
+                      Dataset* dataset, const std::string& trainer_desc_str);
+
  private:
   const platform::Place place_;
 };

paddle/fluid/framework/executor_thread_worker.cc

@@ -14,6 +14,7 @@ limitations under the License. */
 
 #include "paddle/fluid/framework/executor_thread_worker.h"
 #include <algorithm>
+#include <utility>
 #include "google/protobuf/io/zero_copy_stream_impl.h"
 #include "google/protobuf/message.h"
 #include "google/protobuf/text_format.h"
@@ -244,6 +245,7 @@ void ExecutorThreadWorker::TrainFilesWithTimer() {
   platform::SetNumThreads(1);
   SetDevice();
   thread_reader_->Start();
+
   std::vector<double> op_total_time;
   std::vector<std::string> op_name;
   for (auto& op : ops_) {
@@ -273,7 +275,7 @@ void ExecutorThreadWorker::TrainFilesWithTimer() {
     ++batch_cnt;
     thread_scope_->DropKids();
     if (thread_id_ == 0) {
-      if (batch_cnt > 0 && batch_cnt % 1000 == 0) {
+      if (batch_cnt > 0 && batch_cnt % 100 == 0) {
         for (size_t i = 0; i < ops_.size(); ++i) {
           fprintf(stderr, "op_name:[%zu][%s], op_mean_time:[%fs]\n", i,
                   op_name[i].c_str(), op_total_time[i] / batch_cnt);
@@ -283,6 +285,7 @@ void ExecutorThreadWorker::TrainFilesWithTimer() {
         for (int i = 0; i < fetch_var_num; ++i) {
           print_fetch_var(thread_scope_, fetch_var_names_[i]);
         }
+        fprintf(stderr, "IO percent: %f\n", read_time / total_time);
       }
     }
     timeline.Start();
@@ -293,7 +296,7 @@ void ExecutorThreadWorker::TrainFiles() {
   platform::SetNumThreads(1);
 
   // todo: configurable
-  SetDevice();
+  // SetDevice();
 
   int fetch_var_num = fetch_var_names_.size();
   fetch_values_.clear();
@@ -513,7 +516,6 @@ void AsyncExecutorThreadWorker::PullSparse(int table_id) {
 
   auto& push_g = _feature_push_value[table_id];
   check_pull_push_memory(features, &push_g, fea_dim);
-
   collect_feasign_info(table_id);
 }
 

paddle/fluid/framework/fleet/CMakeLists.txt

+if(WITH_PSLIB)
+    cc_library(fleet_wrapper SRCS fleet_wrapper.cc DEPS framework_proto variable_helper scope pslib_brpc pslib)
+else()
+    cc_library(fleet_wrapper SRCS fleet_wrapper.cc DEPS framework_proto variable_helper scope)
+endif(WITH_PSLIB)

paddle/fluid/framework/fleet/fleet_wrapper.cc

+// Copyright (c) 2019 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.
+
+/* 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/fleet/fleet_wrapper.h"
+#include <utility>
+#include "paddle/fluid/framework/data_feed.h"
+#include "paddle/fluid/framework/scope.h"
+
+namespace paddle {
+namespace framework {
+
+const uint32_t MAX_FEASIGN_NUM = 1024 * 100 * 100;
+std::shared_ptr<FleetWrapper> FleetWrapper::s_instance_ = NULL;
+bool FleetWrapper::is_initialized_ = false;
+
+#ifdef PADDLE_WITH_PSLIB
+template <class AR>
+paddle::ps::Archive<AR>& operator<<(paddle::ps::Archive<AR>& ar,
+                                    const MultiSlotType& ins) {
+  ar << ins.GetType();
+  ar << ins.GetOffset();
+  ar << ins.GetFloatData();
+  ar << ins.GetUint64Data();
+  return ar;
+}
+
+template <class AR>
+paddle::ps::Archive<AR>& operator>>(paddle::ps::Archive<AR>& ar,
+                                    MultiSlotType& ins) {
+  ar >> ins.MutableType();
+  ar >> ins.MutableOffset();
+  ar >> ins.MutableFloatData();
+  ar >> ins.MutableUint64Data();
+  return ar;
+}
+#endif
+
+#ifdef PADDLE_WITH_PSLIB
+std::shared_ptr<paddle::distributed::PSlib> FleetWrapper::pslib_ptr_ = NULL;
+#endif
+
+void FleetWrapper::InitServer(const std::string& dist_desc, int index) {
+#ifdef PADDLE_WITH_PSLIB
+  if (!is_initialized_) {
+    VLOG(3) << "Going to init server";
+    pslib_ptr_ = std::shared_ptr<paddle::distributed::PSlib>(
+        new paddle::distributed::PSlib());
+    pslib_ptr_->init_server(dist_desc, index);
+    is_initialized_ = true;
+  } else {
+    VLOG(3) << "Server can be initialized only once";
+  }
+#endif
+}
+
+void FleetWrapper::InitWorker(const std::string& dist_desc,
+                              const std::vector<uint64_t>& host_sign_list,
+                              int node_num, int index) {
+#ifdef PADDLE_WITH_PSLIB
+  if (!is_initialized_) {
+    VLOG(3) << "Going to init worker";
+    pslib_ptr_ = std::shared_ptr<paddle::distributed::PSlib>(
+        new paddle::distributed::PSlib());
+    pslib_ptr_->init_worker(dist_desc,
+                            const_cast<uint64_t*>(host_sign_list.data()),
+                            node_num, index);
+    is_initialized_ = true;
+  } else {
+    VLOG(3) << "Worker can be initialized only once";
+  }
+#endif
+}
+
+void FleetWrapper::StopServer() {
+#ifdef PADDLE_WITH_PSLIB
+  VLOG(3) << "Going to stop server";
+  pslib_ptr_->stop_server();
+#endif
+}
+
+uint64_t FleetWrapper::RunServer() {
+#ifdef PADDLE_WITH_PSLIB
+  VLOG(3) << "Going to run server";
+  return pslib_ptr_->run_server();
+#else
+  return 0;
+#endif
+}
+
+void FleetWrapper::GatherServers(const std::vector<uint64_t>& host_sign_list,
+                                 int node_num) {
+#ifdef PADDLE_WITH_PSLIB
+  VLOG(3) << "Going to gather server ips";
+  pslib_ptr_->gather_servers(const_cast<uint64_t*>(host_sign_list.data()),
+                             node_num);
+#endif
+}
+
+void FleetWrapper::GatherClients(const std::vector<uint64_t>& host_sign_list) {
+#ifdef PADDLE_WITH_PSLIB
+  VLOG(3) << "Going to gather client ips";
+  size_t len = host_sign_list.size();
+  pslib_ptr_->gather_clients(const_cast<uint64_t*>(host_sign_list.data()), len);
+#endif
+}
+
+std::vector<uint64_t> FleetWrapper::GetClientsInfo() {
+#ifdef PADDLE_WITH_PSLIB
+  VLOG(3) << "Going to get client info";
+  return pslib_ptr_->get_client_info();
+#endif
+  return std::vector<uint64_t>();
+}
+
+void FleetWrapper::CreateClient2ClientConnection() {
+#ifdef PADDLE_WITH_PSLIB
+  VLOG(3) << "Going to create client2client connection";
+  pslib_ptr_->create_client2client_connection();
+#endif
+}
+
+void FleetWrapper::PullSparseVarsSync(
+    const Scope& scope, const uint64_t table_id,
+    const std::vector<std::string>& var_names, std::vector<uint64_t>* fea_keys,
+    std::vector<std::vector<float>>* fea_values, int fea_value_dim) {
+#ifdef PADDLE_WITH_PSLIB
+  std::vector<::std::future<int32_t>> pull_sparse_status;
+  pull_sparse_status.resize(0);
+  fea_keys->clear();
+  fea_keys->resize(0);
+  fea_keys->reserve(MAX_FEASIGN_NUM);
+  for (auto name : var_names) {
+    Variable* var = scope.FindVar(name);
+    LoDTensor* tensor = var->GetMutable<LoDTensor>();
+    int64_t* ids = tensor->data<int64_t>();
+    int len = tensor->numel();
+    for (auto i = 0u; i < len; ++i) {
+      if (ids[i] == 0u) {
+        continue;
+      }
+      fea_keys->push_back(static_cast<uint64_t>(ids[i]));
+    }
+  }
+  fea_values->resize(fea_keys->size() + 1);
+  for (auto& t : *fea_values) {
+    t.resize(fea_value_dim);
+  }
+  std::vector<float*> pull_result_ptr;
+  for (auto& t : *fea_values) {
+    pull_result_ptr.push_back(t.data());
+  }
+  auto status = pslib_ptr_->_worker_ptr->pull_sparse(
+      pull_result_ptr.data(), table_id, fea_keys->data(), fea_keys->size());
+  pull_sparse_status.push_back(std::move(status));
+  for (auto& t : pull_sparse_status) {
+    t.wait();
+    auto status = t.get();
+    if (status != 0) {
+      LOG(ERROR) << "fleet pull sparse failed, status[" << status << "]";
+      exit(-1);
+    }
+  }
+#endif
+}
+
+void FleetWrapper::PullDenseVarsAsync(
+    const Scope& scope, const uint64_t tid,
+    const std::vector<std::string>& var_names,
+    std::vector<::std::future<int32_t>>* pull_dense_status) {
+#ifdef PADDLE_WITH_PSLIB
+  auto& regions = _regions[tid];
+  regions.clear();
+  regions.resize(var_names.size());
+  for (auto i = 0u; i < var_names.size(); ++i) {
+    Variable* var = scope.FindVar(var_names[i]);
+    LoDTensor* tensor = var->GetMutable<LoDTensor>();
+    float* w = tensor->data<float>();
+    paddle::ps::Region reg(w, tensor->numel());
+    regions[i] = std::move(reg);
+  }
+  auto status =
+      pslib_ptr_->_worker_ptr->pull_dense(regions.data(), regions.size(), tid);
+  pull_dense_status->push_back(std::move(status));
+#endif
+}
+
+void FleetWrapper::PullDenseVarsSync(
+    const Scope& scope, const uint64_t tid,
+    const std::vector<std::string>& var_names) {
+#ifdef PADDLE_WITH_PSLIB
+  auto& regions = _regions[tid];
+  regions.clear();
+  regions.reserve(var_names.size());
+  for (auto& t : var_names) {
+    Variable* var = scope.FindVar(t);
+    LoDTensor* tensor = var->GetMutable<LoDTensor>();
+    float* w = tensor->data<float>();
+    paddle::ps::Region reg(w, tensor->numel());
+    regions.emplace_back(std::move(reg));
+  }
+  auto status =
+      pslib_ptr_->_worker_ptr->pull_dense(regions.data(), regions.size(), tid);
+  status.wait();
+#endif
+}
+
+void FleetWrapper::PushDenseParamSync(
+    const Scope& scope, const uint64_t table_id,
+    const std::vector<std::string>& var_names) {
+#ifdef PADDLE_WITH_PSLIB
+  auto place = platform::CPUPlace();
+  std::vector<paddle::ps::Region> regions;
+  for (auto& t : var_names) {
+    Variable* var = scope.FindVar(t);
+    LoDTensor* tensor = var->GetMutable<LoDTensor>();
+    float* g = tensor->mutable_data<float>(place);
+    paddle::ps::Region reg(g, tensor->numel());
+    regions.emplace_back(std::move(reg));
+  }
+  auto push_status = pslib_ptr_->_worker_ptr->push_dense_param(
+      regions.data(), regions.size(), table_id);
+  push_status.wait();
+  auto status = push_status.get();
+  CHECK(status == 0) << "push dense param failed, status[" << status << "]";
+#endif
+}
+
+void FleetWrapper::PushDenseVarsSync(
+    Scope* scope, const uint64_t table_id,
+    const std::vector<std::string>& var_names) {}
+
+void FleetWrapper::PushDenseVarsAsync(
+    const Scope& scope, const uint64_t table_id,
+    const std::vector<std::string>& var_names,
+    std::vector<::std::future<int32_t>>* push_sparse_status) {
+#ifdef PADDLE_WITH_PSLIB
+  std::vector<paddle::ps::Region> regions;
+  for (auto& t : var_names) {
+    Variable* var = scope.FindVar(t);
+    LoDTensor* tensor = var->GetMutable<LoDTensor>();
+    int count = tensor->numel();
+    float* g = tensor->data<float>();
+    paddle::ps::Region reg(g, count);
+    regions.emplace_back(std::move(reg));
+  }
+  auto status = pslib_ptr_->_worker_ptr->push_dense(regions.data(),
+                                                    regions.size(), table_id);
+  push_sparse_status->push_back(std::move(status));
+#endif
+}
+
+void FleetWrapper::PushSparseVarsWithLabelAsync(
+    const Scope& scope, const uint64_t table_id,
+    const std::vector<uint64_t>& fea_keys, const std::vector<float>& fea_labels,
+    const std::vector<std::string>& sparse_key_names,
+    const std::vector<std::string>& sparse_grad_names, const int emb_dim,
+    std::vector<std::vector<float>>* push_values,
+    std::vector<::std::future<int32_t>>* push_sparse_status) {
+#ifdef PADDLE_WITH_PSLIB
+  int offset = 2;
+  uint64_t fea_idx = 0u;
+  for (size_t i = 0; i < sparse_key_names.size(); ++i) {
+    Variable* g_var = scope.FindVar(sparse_grad_names[i]);
+    CHECK(g_var != nullptr) << "var[" << sparse_grad_names[i] << "] not found";
+    LoDTensor* g_tensor = g_var->GetMutable<LoDTensor>();
+    if (g_tensor == NULL) {
+      LOG(ERROR) << "var[" << sparse_key_names[i] << "] not found";
+      exit(-1);
+    }
+    float* g = g_tensor->data<float>();
+    Variable* var = scope.FindVar(sparse_key_names[i]);
+    CHECK(var != nullptr) << "var[" << sparse_key_names[i] << "] not found";
+    LoDTensor* tensor = var->GetMutable<LoDTensor>();
+    if (tensor == NULL) {
+      LOG(ERROR) << "var[" << sparse_key_names[i] << "] not found";
+      exit(-1);
+    }
+    int len = tensor->numel();
+    int64_t* ids = tensor->data<int64_t>();
+    push_values->resize(fea_keys.size() + 1);
+    for (auto& t : *push_values) {
+      t.resize(emb_dim + offset);
+    }
+
+    for (auto id_idx = 0u; id_idx < len; ++id_idx) {
+      if (ids[id_idx] == 0) {
+        g += emb_dim;
+        continue;
+      }
+      CHECK(fea_idx < (*push_values).size());
+      CHECK(fea_idx < fea_labels.size());
+      memcpy((*push_values)[fea_idx].data() + offset, g,
+             sizeof(float) * emb_dim);
+      (*push_values)[fea_idx][0] = 1.0f;
+      (*push_values)[fea_idx][1] = static_cast<float>(fea_labels[fea_idx]);
+      g += emb_dim;
+      fea_idx++;
+    }
+  }
+  CHECK(fea_idx == fea_keys.size()) << "fea_idx: " << fea_idx
+                                    << "features size: " << fea_keys.size();
+  std::vector<float*> push_g_vec;
+  for (auto i = 0u; i < fea_keys.size(); ++i) {
+    push_g_vec.push_back((*push_values)[i].data());
+  }
+  auto status = pslib_ptr_->_worker_ptr->push_sparse(
+      table_id, fea_keys.data(), (const float**)push_g_vec.data(),
+      fea_keys.size());
+  push_sparse_status->push_back(std::move(status));
+
+#endif
+}
+
+int FleetWrapper::RegisterClientToClientMsgHandler(int msg_type,
+                                                   MsgHandlerFunc handler) {
+#ifdef PADDLE_WITH_PSLIB
+  VLOG(3) << "calling FleetWrapper::RegisterClientToClientMsgHandler";
+  VLOG(3) << "pslib_ptr_=" << pslib_ptr_;
+  VLOG(3) << "_worker_ptr=" << pslib_ptr_->_worker_ptr;
+  return pslib_ptr_->_worker_ptr->registe_client2client_msg_handler(msg_type,
+                                                                    handler);
+#else
+  VLOG(0) << "FleetWrapper::RegisterClientToClientMsgHandler"
+          << " does nothing when no pslib";
+#endif
+  return 0;
+}
+
+std::future<int32_t> FleetWrapper::SendClientToClientMsg(
+    int msg_type, int to_client_id, const std::string& msg) {
+#ifdef PADDLE_WITH_PSLIB
+  return pslib_ptr_->_worker_ptr->send_client2client_msg(msg_type, to_client_id,
+                                                         msg);
+#else
+  VLOG(0) << "FleetWrapper::SendClientToClientMsg"
+          << " does nothing when no pslib";
+#endif
+  return std::future<int32_t>();
+}
+
+template <typename T>
+void FleetWrapper::Serialize(const std::vector<T*>& t, std::string* str) {
+#ifdef PADDLE_WITH_PSLIB
+  paddle::ps::BinaryArchive ar;
+  for (size_t i = 0; i < t.size(); ++i) {
+    ar << *(t[i]);
+  }
+  *str = std::string(ar.buffer(), ar.length());
+#else
+  VLOG(0) << "FleetWrapper::Serialize does nothing when no pslib";
+#endif
+}
+
+template <typename T>
+void FleetWrapper::Deserialize(std::vector<T>* t, const std::string& str) {
+#ifdef PADDLE_WITH_PSLIB
+  if (str.length() == 0) {
+    return;
+  }
+  paddle::ps::BinaryArchive ar;
+  ar.set_read_buffer(const_cast<char*>(str.c_str()), str.length(), nullptr);
+  if (ar.cursor() == ar.finish()) {
+    return;
+  }
+  while (ar.cursor() < ar.finish()) {
+    t->push_back(ar.get<T>());
+  }
+  CHECK(ar.cursor() == ar.finish());
+  VLOG(3) << "Deserialize size " << t->size();
+#else
+  VLOG(0) << "FleetWrapper::Deserialize does nothing when no pslib";
+#endif
+}
+
+template void FleetWrapper::Serialize<std::vector<MultiSlotType>>(
+    const std::vector<std::vector<MultiSlotType>*>&, std::string*);
+template void FleetWrapper::Deserialize<std::vector<MultiSlotType>>(
+    std::vector<std::vector<MultiSlotType>>*, const std::string&);
+
+}  // end namespace framework
+}  // end namespace paddle

paddle/fluid/framework/fleet/fleet_wrapper.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 <memory>
+#ifdef PADDLE_WITH_PSLIB
+#include <archive.h>
+#include <pslib.h>
+#endif
+#include <atomic>
+#include <ctime>
+#include <map>
+#include <random>
+#include <string>
+#include <vector>
+#include "paddle/fluid/framework/program_desc.h"
+#include "paddle/fluid/framework/scope.h"
+#include "paddle/fluid/framework/variable_helper.h"
+#include "paddle/fluid/platform/macros.h"  // for DISABLE_COPY_AND_ASSIGN
+
+namespace paddle {
+namespace framework {
+
+// A wrapper class for pslib.h, this class follows Singleton pattern
+// i.e. only initialized once in the current process
+// Example:
+//    std::shared_ptr<FleetWrapper> fleet_ptr =
+//         FleetWrapper::GetInstance();
+//    string dist_desc;
+//    fleet_ptr->InitServer(dist_desc, 0);
+// interface design principles:
+// Pull
+//   Sync: PullSparseVarsSync
+//   Async: PullSparseVarsAsync(not implemented currently)
+// Push
+//   Sync: PushSparseVarsSync
+//   Async: PushSparseVarsAsync(not implemented currently)
+//   Async: PushSparseVarsWithLabelAsync(with special usage)
+// Push dense variables to server in Async mode
+// Param<in>: scope, table_id, var_names
+// Param<out>: push_sparse_status
+
+class FleetWrapper {
+ public:
+  virtual ~FleetWrapper() {}
+  FleetWrapper() {}
+  // Pull sparse variables from server in Sync mode
+  // Param<in>: scope, table_id, var_names, fea_keys
+  // Param<out>: fea_values
+  void PullSparseVarsSync(const Scope& scope, const uint64_t table_id,
+                          const std::vector<std::string>& var_names,
+                          std::vector<uint64_t>* fea_keys,
+                          std::vector<std::vector<float>>* fea_values,
+                          int fea_dim);
+
+  void PullDenseVarsSync(const Scope& scope, const uint64_t table_id,
+                         const std::vector<std::string>& var_names);
+
+  void PullDenseVarsAsync(
+      const Scope& scope, const uint64_t table_id,
+      const std::vector<std::string>& var_names,
+      std::vector<::std::future<int32_t>>* pull_dense_status);
+
+  void PushDenseParamSync(const Scope& scope, const uint64_t table_id,
+                          const std::vector<std::string>& var_names);
+
+  // Push dense variables to server in async mode
+  // Param<in>: scope, table_id, var_names,
+  // Param<out>: push_sparse_status
+  void PushDenseVarsAsync(
+      const Scope& scope, const uint64_t table_id,
+      const std::vector<std::string>& var_names,
+      std::vector<::std::future<int32_t>>* push_sparse_status);
+
+  void PushDenseVarsSync(Scope* scope, const uint64_t table_id,
+                         const std::vector<std::string>& var_names);
+
+  // Push sparse variables with labels to server in Async mode
+  // This is specially designed for click/show stats in server
+  // Param<in>: scope, table_id, var_grad_names,
+  //            fea_keys, fea_labels, sparse_grad_names
+  // Param<out>: push_values, push_sparse_status
+  void PushSparseVarsWithLabelAsync(
+      const Scope& scope, const uint64_t table_id,
+      const std::vector<uint64_t>& fea_keys,
+      const std::vector<float>& fea_labels,
+      const std::vector<std::string>& sparse_key_names,
+      const std::vector<std::string>& sparse_grad_names, const int emb_dim,
+      std::vector<std::vector<float>>* push_values,
+      std::vector<::std::future<int32_t>>* push_sparse_status);
+
+  // Push sparse variables to server in Async mode
+  // Param<In>: scope, table_id, fea_keys, sparse_grad_names
+  // Param<Out>: push_values, push_sparse_status
+  /*
+  void PushSparseVarsAsync(
+          const Scope& scope,
+          const uint64_t table_id,
+          const std::vector<uint64_t>& fea_keys,
+          const std::vector<std::string>& sparse_grad_names,
+          std::vector<std::vector<float>>* push_values,
+          std::vector<::std::future<int32_t>>* push_sparse_status);
+  */
+
+  void InitServer(const std::string& dist_desc, int index);
+  void InitWorker(const std::string& dist_desc,
+                  const std::vector<uint64_t>& host_sign_list, int node_num,
+                  int index);
+  void StopServer();
+  uint64_t RunServer();
+  void GatherServers(const std::vector<uint64_t>& host_sign_list, int node_num);
+  // gather client ip
+  void GatherClients(const std::vector<uint64_t>& host_sign_list);
+  // get client info
+  std::vector<uint64_t> GetClientsInfo();
+  // create client to client connection
+  void CreateClient2ClientConnection();
+
+  // register client to client communication
+  typedef std::function<int32_t(int, int, const std::string&)> MsgHandlerFunc;
+  int RegisterClientToClientMsgHandler(int msg_type, MsgHandlerFunc handler);
+  // send client to client message
+  std::future<int32_t> SendClientToClientMsg(int msg_type, int to_client_id,
+                                             const std::string& msg);
+
+  template <typename T>
+  void Serialize(const std::vector<T*>& t, std::string* str);
+  template <typename T>
+  void Deserialize(std::vector<T>* t, const std::string& str);
+  static std::shared_ptr<FleetWrapper> GetInstance() {
+    if (NULL == s_instance_) {
+      s_instance_.reset(new paddle::framework::FleetWrapper());
+    }
+    return s_instance_;
+  }
+
+#ifdef PADDLE_WITH_PSLIB
+  static std::shared_ptr<paddle::distributed::PSlib> pslib_ptr_;
+#endif
+
+ private:
+  static std::shared_ptr<FleetWrapper> s_instance_;
+#ifdef PADDLE_WITH_PSLIB
+  std::map<uint64_t, std::vector<paddle::ps::Region>> _regions;
+#endif
+
+ protected:
+  static bool is_initialized_;
+  DISABLE_COPY_AND_ASSIGN(FleetWrapper);
+};
+
+}  // end namespace framework
+}  // end namespace paddle

paddle/fluid/framework/grad_op_desc_maker.h

@@ -147,7 +147,7 @@ class SingleGradOpDescMaker : public GradOpDescMakerBase {
  public:
   using GradOpDescMakerBase::GradOpDescMakerBase;
 
-  std::vector<std::unique_ptr<OpDesc>> operator()() const {
+  std::vector<std::unique_ptr<OpDesc>> operator()() const final {
     std::vector<std::unique_ptr<OpDesc>> retv;
     retv.emplace_back(this->Apply());
     return retv;
@@ -158,14 +158,14 @@ class SingleGradOpDescMaker : public GradOpDescMakerBase {
 };
 
 template <bool DropEmptyIG = true>
-class DefaultGradOpDescMaker : public SingleGradOpDescMaker {
+class DefaultGradOpDescMaker final : public SingleGradOpDescMaker {
  public:
   using SingleGradOpDescMaker::SingleGradOpDescMaker;
 
  protected:
-  virtual std::unique_ptr<OpDesc> Apply() const {
+  std::unique_ptr<OpDesc> Apply() const final {
     auto* grad = new OpDesc();
-    grad->SetType(this->GradOpType());
+    grad->SetType(this->ForwardOpType() + "_grad");
 
     for (auto& input_param : this->InputNames()) {
       grad->SetInput(input_param, this->Input(input_param));
@@ -182,18 +182,12 @@ class DefaultGradOpDescMaker : public SingleGradOpDescMaker {
 
     return std::unique_ptr<OpDesc>(grad);
   }
-
-  virtual std::string GradOpType() const {
-    return this->ForwardOpType() + "_grad";
-  }
 };
 
-class EmptyGradOpMaker : public GradOpDescMakerBase {
+class EmptyGradOpMaker final : public GradOpDescMakerBase {
  public:
   using GradOpDescMakerBase::GradOpDescMakerBase;
-  std::vector<std::unique_ptr<OpDesc>> operator()() const override {
-    return {};
-  }
+  std::vector<std::unique_ptr<OpDesc>> operator()() const final { return {}; }
 };
 
 }  // namespace framework

paddle/fluid/framework/hogwild_worker.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/device_worker.h"
+#include "paddle/fluid/framework/device_worker_factory.h"
+#include "paddle/fluid/platform/cpu_helper.h"
+#include "paddle/fluid/platform/lodtensor_printer.h"
+
+namespace paddle {
+namespace framework {
+
+void HogwildWorker::Initialize(const TrainerDesc& desc) {
+  fetch_config_ = desc.fetch_config();
+  param_ = desc.hogwild_param();
+  skip_ops_.resize(param_.skip_ops_size());
+  for (size_t i = 0; i < param_.skip_ops_size(); ++i) {
+    skip_ops_[i] = param_.skip_ops(i);
+  }
+}
+
+void HogwildWorker::CreateThreadOperators(const ProgramDesc& program) {
+  auto& block = program.Block(0);
+  op_names_.clear();
+  for (auto& op_desc : block.AllOps()) {
+    std::unique_ptr<OperatorBase> local_op = OpRegistry::CreateOp(*op_desc);
+    op_names_.push_back(op_desc->Type());
+    OperatorBase* local_op_ptr = local_op.release();
+    ops_.push_back(local_op_ptr);
+    continue;
+  }
+}
+
+void HogwildWorker::CreateThreadScope(const ProgramDesc& program) {
+  auto& block = program.Block(0);
+
+  PADDLE_ENFORCE_NOT_NULL(
+      root_scope_, "root_scope should be set before creating thread scope");
+
+  thread_scope_ = &root_scope_->NewScope();
+  for (auto& var : block.AllVars()) {
+    if (var->Persistable()) {
+      auto* ptr = root_scope_->Var(var->Name());
+      InitializeVariable(ptr, var->GetType());
+    } else {
+      auto* ptr = thread_scope_->Var(var->Name());
+      InitializeVariable(ptr, var->GetType());
+    }
+  }
+}
+
+void HogwildWorker::BindingDataFeedMemory() {
+  const std::vector<std::string>& input_feed =
+      device_reader_->GetUseSlotAlias();
+  for (auto name : input_feed) {
+    device_reader_->AddFeedVar(thread_scope_->Var(name), name);
+  }
+}
+
+void HogwildWorker::CreateDeviceResource(const ProgramDesc& main_prog) {
+  CreateThreadScope(main_prog);
+  CreateThreadOperators(main_prog);
+}
+
+void HogwildWorker::TrainFilesWithProfiler() {
+  platform::SetNumThreads(1);
+  device_reader_->Start();
+  std::vector<double> op_total_time;
+  std::vector<std::string> op_name;
+  for (auto& op : ops_) {
+    op_name.push_back(op->Type());
+  }
+  op_total_time.resize(ops_.size());
+  for (size_t i = 0; i < op_total_time.size(); ++i) {
+    op_total_time[i] = 0.0;
+  }
+  platform::Timer timeline;
+  double total_time = 0.0;
+  double read_time = 0.0;
+  int cur_batch;
+  int batch_cnt = 0;
+  timeline.Start();
+  uint64_t total_inst = 0;
+  while ((cur_batch = device_reader_->Next()) > 0) {
+    VLOG(3) << "read a batch in thread " << thread_id_;
+    timeline.Pause();
+    read_time += timeline.ElapsedSec();
+    total_time += timeline.ElapsedSec();
+    for (size_t i = 0; i < ops_.size(); ++i) {
+      bool need_skip = false;
+      for (auto t = 0u; t < skip_ops_.size(); ++t) {
+        if (ops_[i]->Type().find(skip_ops_[t]) != std::string::npos) {
+          need_skip = true;
+          break;
+        }
+      }
+      timeline.Start();
+      VLOG(3) << "Going to run op " << op_name[i];
+      if (!need_skip) {
+        ops_[i]->Run(*thread_scope_, place_);
+      }
+      VLOG(3) << "Op " << op_name[i] << " Finished";
+      timeline.Pause();
+      op_total_time[i] += timeline.ElapsedSec();
+      total_time += timeline.ElapsedSec();
+    }
+    total_inst += cur_batch;
+    ++batch_cnt;
+    PrintFetchVars();
+    if (thread_id_ == 0) {
+      if (batch_cnt > 0 && batch_cnt % 100 == 0) {
+        for (size_t i = 0; i < ops_.size(); ++i) {
+          fprintf(stderr, "op_name:[%zu][%s], op_mean_time:[%fs]\n", i,
+                  op_name[i].c_str(), op_total_time[i] / batch_cnt);
+        }
+        fprintf(stderr, "mean read time: %fs\n", read_time / batch_cnt);
+        fprintf(stderr, "IO percent: %f\n", read_time / total_time * 100);
+        fprintf(stderr, "%6.2f instances/s\n", total_inst / total_time);
+      }
+    }
+    thread_scope_->DropKids();
+    timeline.Start();
+  }
+}
+
+void HogwildWorker::TrainFiles() {
+  platform::SetNumThreads(1);
+
+  // how to accumulate fetched values here
+  device_reader_->Start();
+  int cur_batch;
+  while ((cur_batch = device_reader_->Next()) > 0) {
+    for (auto& op : ops_) {
+      bool need_skip = false;
+      for (auto t = 0u; t < skip_ops_.size(); ++t) {
+        if (op->Type().find(skip_ops_[t]) != std::string::npos) {
+          need_skip = true;
+          break;
+        }
+      }
+      if (!need_skip) {
+        op->Run(*thread_scope_, place_);
+      }
+    }
+
+    PrintFetchVars();
+    thread_scope_->DropKids();
+  }
+}
+
+void HogwildWorker::PrintFetchVars() {
+  // call count
+  batch_num_++;
+  int batch_per_print = fetch_config_.print_period();
+  if (thread_id_ == 0) {
+    if (batch_num_ % batch_per_print == 0) {
+      int fetch_var_num = fetch_config_.fetch_var_names_size();
+      for (int i = 0; i < fetch_var_num; ++i) {
+        platform::PrintVar(thread_scope_, fetch_config_.fetch_var_names(i),
+                           fetch_config_.fetch_var_str_format(i));
+      }
+    }
+  }
+}
+
+}  // end namespace framework
+}  // end namespace paddle

paddle/fluid/framework/io/CMakeLists.txt

+cc_library(fs SRCS fs.cc DEPS string_helper glog boost)
+cc_library(shell SRCS shell.cc DEPS string_helper glog)

paddle/fluid/framework/io/fs.h

+// Copyright (c) 2019 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 <stdio.h>
+#include <memory>
+#include <string>
+#include <vector>
+#include "glog/logging.h"
+#include "paddle/fluid/framework/io/shell.h"
+#include "paddle/fluid/string/string_helper.h"
+
+namespace paddle {
+namespace framework {
+
+int fs_select_internal(const std::string& path);
+
+// localfs
+extern size_t localfs_buffer_size();
+
+extern void localfs_set_buffer_size(size_t x);
+
+extern std::shared_ptr<FILE> localfs_open_read(std::string path,
+                                               const std::string& converter);
+
+extern std::shared_ptr<FILE> localfs_open_write(std::string path,
+                                                const std::string& converter);
+
+extern int64_t localfs_file_size(const std::string& path);
+
+extern void localfs_remove(const std::string& path);
+
+extern std::vector<std::string> localfs_list(const std::string& path);
+
+extern std::string localfs_tail(const std::string& path);
+
+extern bool localfs_exists(const std::string& path);
+
+extern void localfs_mkdir(const std::string& path);
+
+// hdfs
+extern size_t hdfs_buffer_size();
+
+extern void hdfs_set_buffer_size(size_t x);
+
+extern const std::string& hdfs_command();
+
+extern void hdfs_set_command(const std::string& x);
+
+extern std::shared_ptr<FILE> hdfs_open_read(std::string path, int* err_no,
+                                            const std::string& converter);
+
+extern std::shared_ptr<FILE> hdfs_open_write(std::string path, int* err_no,
+                                             const std::string& converter);
+
+extern void hdfs_remove(const std::string& path);
+
+extern std::vector<std::string> hdfs_list(const std::string& path);
+
+extern std::string hdfs_tail(const std::string& path);
+
+extern bool hdfs_exists(const std::string& path);
+
+extern void hdfs_mkdir(const std::string& path);
+
+// aut-detect fs
+extern std::shared_ptr<FILE> fs_open_read(const std::string& path, int* err_no,
+                                          const std::string& converter);
+
+extern std::shared_ptr<FILE> fs_open_write(const std::string& path, int* err_no,
+                                           const std::string& converter);
+
+extern std::shared_ptr<FILE> fs_open(const std::string& path,
+                                     const std::string& mode, int* err_no,
+                                     const std::string& converter = "");
+
+extern int64_t fs_file_size(const std::string& path);
+
+extern void fs_remove(const std::string& path);
+
+extern std::vector<std::string> fs_list(const std::string& path);
+
+extern std::string fs_tail(const std::string& path);
+
+extern bool fs_exists(const std::string& path);
+
+extern void fs_mkdir(const std::string& path);
+}  // namespace framework
+}  // namespace paddle

paddle/fluid/framework/io/shell.h

+// Copyright (c) 2019 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 <fcntl.h>
+#include <sys/stat.h>
+#ifdef _WIN32
+#include <windows.h>
+#else
+#include <sys/syscall.h>
+#endif
+#include <sys/types.h>
+#ifndef _WIN32
+#include <sys/wait.h>
+#endif
+#include <memory>
+#include <string>
+#include <utility>
+#include "paddle/fluid/platform/port.h"
+#include "paddle/fluid/string/string_helper.h"
+
+namespace paddle {
+namespace framework {
+
+inline bool& shell_verbose_internal() {
+  static bool x = false;
+  return x;
+}
+
+inline bool shell_verbose() { return shell_verbose_internal(); }
+
+inline void shell_set_verbose(bool x) { shell_verbose_internal() = x; }
+
+extern std::shared_ptr<FILE> shell_fopen(const std::string& path,
+                                         const std::string& mode);
+
+extern std::shared_ptr<FILE> shell_popen(const std::string& cmd,
+                                         const std::string& mode, int* err_no);
+
+extern std::pair<std::shared_ptr<FILE>, std::shared_ptr<FILE>> shell_p2open(
+    const std::string& cmd);
+
+inline void shell_execute(const std::string& cmd) {
+  int err_no = 0;
+  do {
+    err_no = 0;
+    shell_popen(cmd, "w", &err_no);
+  } while (err_no == -1);
+}
+
+extern std::string shell_get_command_output(const std::string& cmd);
+
+}  // namespace framework
+}  // namespace paddle

paddle/fluid/framework/ir/multi_batch_merge_pass.cc

@@ -84,7 +84,8 @@ void BatchMergePass::ApplyImpl(ir::Graph* graph) const {
 
   // 1. record op nodes of different roles
   for (auto node : nodes) {
-    if (node->IsVar()) continue;
+    if (!node->IsOp()) continue;
+    PADDLE_ENFORCE(node->Op(), "must find opdesc");
     int op_role = boost::get<int>(node->Op()->GetAttr(
         framework::OpProtoAndCheckerMaker::OpRoleAttrName()));
     if ((op_role == static_cast<int>(framework::OpRole::kForward)) ||

paddle/fluid/framework/ir/pass.cc

@@ -13,11 +13,16 @@ See the License for the specific language governing permissions and
 limitations under the License. */
 
 #include "paddle/fluid/framework/ir/pass.h"
+
+#include <memory>
+#include <utility>
+
 #include "paddle/fluid/framework/ir/graph_helper.h"
 
 namespace paddle {
 namespace framework {
 namespace ir {
+
 Graph* Pass::Apply(Graph* graph) const {
   PADDLE_ENFORCE(graph, "graph passed to Pass::Apply() cannot be empty.");
   for (const std::string& attr : required_pass_attrs_) {

paddle/fluid/framework/parallel_executor.h

@@ -81,6 +81,7 @@ class ParallelExecutor {
                                     const BuildStrategy &build_strategy) const;
 
   ParallelExecutorPrivate *member_;
+  std::vector<std::unique_ptr<ir::Graph>> async_graphs_;
 #if defined(PADDLE_WITH_CUDA) && !defined(_WIN32)
   std::unique_ptr<ncclUniqueId> local_nccl_id_;
 #endif

paddle/fluid/framework/reader.cc

@@ -69,6 +69,9 @@ void ReaderBase::Start() {
 
 ReaderBase::~ReaderBase() {}
 
-DecoratedReader::~DecoratedReader() { reader_->Shutdown(); }
+DecoratedReader::~DecoratedReader() {
+  VLOG(1) << "~DecoratedReader";
+  reader_->Shutdown();
+}
 }  // namespace framework
 }  // namespace paddle

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

-cc_library(anakin_op_converter SRCS fc.cc conv2d.cc conv2d_fusion.cc
- elementwise.cc activation.cc pool2d.cc concat.cc split.cc relu.cc  softmax.cc batch_norm.cc reshape.cc flatten.cc transpose.cc density_prior_box.cc detection_out.cc scale.cc dropout.cc im2sequence.cc sum.cc DEPS anakin_engine framework_proto scope op_registry)
+cc_library(anakin_op_converter SRCS fc.cc conv2d.cc conv2d_fusion.cc elementwise.cc activation.cc pool2d.cc concat.cc split.cc relu.cc  softmax.cc batch_norm.cc reshape.cc flatten.cc transpose.cc density_prior_box.cc detection_out.cc scale.cc dropout.cc im2sequence.cc sum.cc DEPS anakin_engine framework_proto scope op_registry)
 
 cc_test(test_anakin_fc SRCS test_fc_op.cc DEPS anakin_op_converter mul_op SERIAL)
 cc_test(test_anakin_conv2d SRCS test_conv2d_op.cc DEPS anakin_op_converter conv_op im2col vol2col depthwise_conv SERIAL)