Merge remote-tracking branch 'upstream/develop' into windows/mkl

test=develop

CMakeLists.txt

@@ -65,6 +65,7 @@ option(WITH_GOLANG      "Compile PaddlePaddle with GOLANG"              OFF)
 option(GLIDE_INSTALL    "Download and install go dependencies "         ON)
 option(USE_NNPACK       "Compile PaddlePaddle with NNPACK library"      OFF)
 option(WITH_DISTRIBUTE  "Compile with distributed support"              OFF)
+option(WITH_PSLIB       "Compile with pslib support"                    OFF)
 option(USE_EIGEN_FOR_BLAS   "Use matrix multiplication in Eigen"        OFF)
 option(EIGEN_USE_THREADS "Compile with multi-threaded Eigen"            OFF)
 option(WITH_ARM_FP16    "Use half precision support on armv8.2-a cpu"   OFF)
@@ -203,14 +204,20 @@ include(external/xxhash)    # download xxhash
 include(external/dlpack)
 include(external/snappy)    # download snappy
 include(external/snappystream) # download snappystream
+include(external/warpctc)   # download, build, install warpctc
 
 if (NOT WIN32)
-# there is no official support of warpctc, nccl, cupti in windows
-include(external/warpctc)   # download, build, install warpctc
+# there is no official support of nccl, cupti in windows
 include(cupti)
 include(external/gzstream)
 endif (NOT WIN32)
 
+if(WITH_PSLIB)
+    include(external/libmct)
+    include(external/pslib_brpc)
+    include(external/pslib)
+endif(WITH_PSLIB)
+
 if(WITH_DISTRIBUTE)
     if(WITH_GRPC)
         include(external/grpc)
@@ -278,6 +285,12 @@ set(EXTERNAL_LIBS
     ${PYTHON_LIBRARIES}
 )
 
+if(WITH_PSLIB)
+    list(APPEND EXTERNAL_LIBS pslib)
+    list(APPEND EXTERNAL_LIBS pslib_brpc)
+    list(APPEND EXTERNAL_LIBS libmct)
+endif(WITH_PSLIB)
+    
 if(WITH_AMD_GPU)
     find_package(HIP)
     include(hip)

cmake/configure.cmake

@@ -84,6 +84,10 @@ if(NOT WITH_GOLANG)
     add_definitions(-DPADDLE_WITHOUT_GOLANG)
 endif(NOT WITH_GOLANG)
 
+if(WITH_PSLIB)
+    add_definitions(-DPADDLE_WITH_PSLIB)
+endif()
+
 if(WITH_GPU)
     add_definitions(-DPADDLE_WITH_CUDA)
     add_definitions(-DEIGEN_USE_GPU)

cmake/external/libmct.cmake

+# Copyright (c) 2017 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_LIBMCT})
+  return()
+ENDIF(NOT ${WITH_LIBMCT})
+
+IF(WIN32 OR APPLE)
+    MESSAGE(WARNING
+        "Windows or Mac is not supported with LIBMCT in Paddle yet."
+        "Force WITH_LIBMCT=OFF")
+    SET(WITH_LIBMCT OFF CACHE STRING "Disable LIBMCT package in Windows and MacOS" FORCE)
+    return()
+ENDIF()
+
+INCLUDE(ExternalProject)
+
+SET(LIBMCT_PROJECT       "extern_libmct")
+IF((NOT DEFINED LIBMCT_VER) OR (NOT DEFINED LIBMCT_URL))
+  MESSAGE(STATUS "use pre defined download url")
+  SET(LIBMCT_VER "0.1.0" CACHE STRING "" FORCE)
+  SET(LIBMCT_NAME "libmct" CACHE STRING "" FORCE)
+  SET(LIBMCT_URL "https://raw.githubusercontent.com/PaddlePaddle/Fleet/release/${LIBMCT_VER}/${LIBMCT_NAME}.tar.gz" CACHE STRING "" FORCE) 
+ENDIF()
+MESSAGE(STATUS "LIBMCT_NAME: ${LIBMCT_NAME}, LIBMCT_URL: ${LIBMCT_URL}")
+SET(LIBMCT_SOURCE_DIR    "${THIRD_PARTY_PATH}/libmct")
+SET(LIBMCT_DOWNLOAD_DIR  "${LIBMCT_SOURCE_DIR}/src/${LIBMCT_PROJECT}")
+SET(LIBMCT_DST_DIR       "libmct")
+SET(LIBMCT_INSTALL_ROOT  "${THIRD_PARTY_PATH}/install")
+SET(LIBMCT_INSTALL_DIR   ${LIBMCT_INSTALL_ROOT}/${LIBMCT_DST_DIR})
+SET(LIBMCT_ROOT          ${LIBMCT_INSTALL_DIR})
+SET(LIBMCT_INC_DIR       ${LIBMCT_ROOT}/include)
+SET(CMAKE_INSTALL_RPATH "${CMAKE_INSTALL_RPATH}" "${LIBMCT_ROOT}/lib")
+
+INCLUDE_DIRECTORIES(${LIBMCT_INC_DIR})
+
+FILE(WRITE ${LIBMCT_DOWNLOAD_DIR}/CMakeLists.txt
+  "PROJECT(LIBMCT)\n"
+  "cmake_minimum_required(VERSION 3.0)\n"
+  "install(DIRECTORY ${LIBMCT_NAME}/include ${LIBMCT_NAME}/lib \n"
+  "        DESTINATION ${LIBMCT_DST_DIR})\n")
+
+ExternalProject_Add(
+    ${LIBMCT_PROJECT}
+    ${EXTERNAL_PROJECT_LOG_ARGS}
+    PREFIX                ${LIBMCT_SOURCE_DIR}
+    DOWNLOAD_DIR          ${LIBMCT_DOWNLOAD_DIR}
+    DOWNLOAD_COMMAND      wget --no-check-certificate ${LIBMCT_URL} -c -q -O ${LIBMCT_NAME}.tar.gz
+                          && tar zxvf ${LIBMCT_NAME}.tar.gz
+    DOWNLOAD_NO_PROGRESS  1
+    UPDATE_COMMAND        ""
+    CMAKE_ARGS            -DCMAKE_INSTALL_PREFIX=${LIBMCT_INSTALL_ROOT}
+    CMAKE_CACHE_ARGS      -DCMAKE_INSTALL_PREFIX:PATH=${LIBMCT_INSTALL_ROOT}
+)
+
+if (${CMAKE_VERSION} VERSION_LESS "3.3.0" OR NOT WIN32)
+    set(dummyfile ${CMAKE_CURRENT_BINARY_DIR}/boost_dummy.c)
+    file(WRITE ${dummyfile} "const char *dummy = \"${dummyfile}\";")
+    add_library(libmct STATIC ${dummyfile})
+else()
+    add_library(libmct INTERFACE)
+endif()
+
+#ADD_LIBRARY(libmct SHARED IMPORTED GLOBAL)
+ADD_DEPENDENCIES(libmct ${LIBMCT_PROJECT})
+LIST(APPEND external_project_dependencies libmct)
+

cmake/external/pslib.cmake

+# Copyright (c) 2017 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_PSLIB})
+  return()
+ENDIF(NOT ${WITH_PSLIB})
+
+IF(WIN32 OR APPLE)
+    MESSAGE(WARNING
+        "Windows or Mac is not supported with PSLIB in Paddle yet."
+        "Force WITH_PSLIB=OFF")
+    SET(WITH_PSLIB OFF CACHE STRING "Disable PSLIB package in Windows and MacOS" FORCE)
+    return()
+ENDIF()
+
+INCLUDE(ExternalProject)
+
+SET(PSLIB_PROJECT       "extern_pslib")
+IF((NOT DEFINED PSLIB_VER) OR (NOT DEFINED PSLIB_URL))
+  MESSAGE(STATUS "use pre defined download url")
+  SET(PSLIB_VER "0.1.0" CACHE STRING "" FORCE) 
+  SET(PSLIB_NAME "pslib" CACHE STRING "" FORCE) 
+  SET(PSLIB_URL "https://raw.githubusercontent.com/PaddlePaddle/Fleet/release/${PSLIB_VER}/${PSLIB_NAME}.tar.gz" CACHE STRING "" FORCE) 
+ENDIF()
+MESSAGE(STATUS "PSLIB_NAME: ${PSLIB_NAME}, PSLIB_URL: ${PSLIB_URL}")
+SET(PSLIB_SOURCE_DIR    "${THIRD_PARTY_PATH}/pslib")
+SET(PSLIB_DOWNLOAD_DIR  "${PSLIB_SOURCE_DIR}/src/${PSLIB_PROJECT}")
+SET(PSLIB_DST_DIR       "pslib")
+SET(PSLIB_INSTALL_ROOT  "${THIRD_PARTY_PATH}/install")
+SET(PSLIB_INSTALL_DIR   ${PSLIB_INSTALL_ROOT}/${PSLIB_DST_DIR})
+SET(PSLIB_ROOT          ${PSLIB_INSTALL_DIR})
+SET(PSLIB_INC_DIR       ${PSLIB_ROOT}/include)
+SET(PSLIB_LIB_DIR       ${PSLIB_ROOT}/lib)
+SET(PSLIB_LIB           ${PSLIB_LIB_DIR}/libps.so)
+SET(PSLIB_IOMP_LIB      ${PSLIB_LIB_DIR}/libiomp5.so) #todo what is this
+SET(CMAKE_INSTALL_RPATH "${CMAKE_INSTALL_RPATH}" "${PSLIB_ROOT}/lib")
+
+INCLUDE_DIRECTORIES(${PSLIB_INC_DIR})
+
+FILE(WRITE ${PSLIB_DOWNLOAD_DIR}/CMakeLists.txt
+  "PROJECT(PSLIB)\n"
+  "cmake_minimum_required(VERSION 3.0)\n"
+  "install(DIRECTORY ${PSLIB_NAME}/include ${PSLIB_NAME}/lib \n"
+  "        DESTINATION ${PSLIB_DST_DIR})\n")
+
+ExternalProject_Add(
+    ${PSLIB_PROJECT}
+    ${EXTERNAL_PROJECT_LOG_ARGS}
+    PREFIX                ${PSLIB_SOURCE_DIR}
+    DOWNLOAD_DIR          ${PSLIB_DOWNLOAD_DIR}
+    DOWNLOAD_COMMAND      wget --no-check-certificate ${PSLIB_URL} -c -q -O ${PSLIB_NAME}.tar.gz
+                          && tar zxvf ${PSLIB_NAME}.tar.gz
+    DOWNLOAD_NO_PROGRESS  1
+    UPDATE_COMMAND        ""
+    CMAKE_ARGS            -DCMAKE_INSTALL_PREFIX=${PSLIB_INSTALL_ROOT}
+    CMAKE_CACHE_ARGS      -DCMAKE_INSTALL_PREFIX:PATH=${PSLIB_INSTALL_ROOT}
+)
+
+ADD_LIBRARY(pslib SHARED IMPORTED GLOBAL)
+SET_PROPERTY(TARGET pslib PROPERTY IMPORTED_LOCATION ${PSLIB_LIB})
+ADD_DEPENDENCIES(pslib ${PSLIB_PROJECT})
+LIST(APPEND external_project_dependencies pslib)
+
+IF(WITH_C_API)
+  INSTALL(FILES ${PSLIB_LIB} ${PSLIB_IOMP_LIB} DESTINATION lib)
+ENDIF()

cmake/external/pslib_brpc.cmake

+# Copyright (c) 2017 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_PSLIB_BRPC})
+  return()
+ENDIF(NOT ${WITH_PSLIB_BRPC})
+
+IF(WIN32 OR APPLE)
+    MESSAGE(WARNING
+        "Windows or Mac is not supported with PSLIB_BRPC in Paddle yet."
+        "Force WITH_PSLIB_BRPC=OFF")
+    SET(WITH_PSLIB_BRPC OFF CACHE STRING "Disable PSLIB_BRPC package in Windows and MacOS" FORCE)
+    return()
+ENDIF()
+
+INCLUDE(ExternalProject)
+
+SET(PSLIB_BRPC_PROJECT       "extern_pslib_brpc")
+IF((NOT DEFINED PSLIB_BRPC_NAME) OR (NOT DEFINED PSLIB_BRPC_URL))
+  MESSAGE(STATUS "use pre defined download url")
+  SET(PSLIB_BRPC_VER "0.1.0" CACHE STRING "" FORCE)
+  SET(PSLIB_BRPC_NAME "pslib_brpc" CACHE STRING "" FORCE)
+  SET(PSLIB_BRPC_URL "https://raw.githubusercontent.com/PaddlePaddle/Fleet/release/${PSLIB_BRPC_VER}/${PSLIB_BRPC_NAME}.tar.gz" CACHE STRING "" FORCE)
+ENDIF()
+MESSAGE(STATUS "PSLIB_BRPC_NAME: ${PSLIB_BRPC_NAME}, PSLIB_BRPC_URL: ${PSLIB_BRPC_URL}")
+SET(PSLIB_BRPC_SOURCE_DIR    "${THIRD_PARTY_PATH}/pslib_brpc")
+SET(PSLIB_BRPC_DOWNLOAD_DIR  "${PSLIB_BRPC_SOURCE_DIR}/src/${PSLIB_BRPC_PROJECT}")
+SET(PSLIB_BRPC_DST_DIR       "pslib_brpc")
+SET(PSLIB_BRPC_INSTALL_ROOT  "${THIRD_PARTY_PATH}/install")
+SET(PSLIB_BRPC_INSTALL_DIR   ${PSLIB_BRPC_INSTALL_ROOT}/${PSLIB_BRPC_DST_DIR})
+SET(PSLIB_BRPC_ROOT          ${PSLIB_BRPC_INSTALL_DIR})
+SET(PSLIB_BRPC_INC_DIR       ${PSLIB_BRPC_ROOT}/include)
+SET(PSLIB_BRPC_LIB_DIR       ${PSLIB_BRPC_ROOT}/lib)
+SET(PSLIB_BRPC_LIB           ${PSLIB_BRPC_LIB_DIR}/libbrpc.a)
+SET(PSLIB_BRPC_IOMP_LIB      ${PSLIB_BRPC_LIB_DIR}/libiomp5.so) #todo what is this
+SET(CMAKE_INSTALL_RPATH "${CMAKE_INSTALL_RPATH}" "${PSLIB_BRPC_ROOT}/lib")
+
+INCLUDE_DIRECTORIES(${PSLIB_BRPC_INC_DIR})
+
+FILE(WRITE ${PSLIB_BRPC_DOWNLOAD_DIR}/CMakeLists.txt
+  "PROJECT(PSLIB_BRPC)\n"
+  "cmake_minimum_required(VERSION 3.0)\n"
+  "install(DIRECTORY ${PSLIB_BRPC_NAME}/include ${PSLIB_BRPC_NAME}/lib \n"
+  "        DESTINATION ${PSLIB_BRPC_DST_DIR})\n")
+
+ExternalProject_Add(
+    ${PSLIB_BRPC_PROJECT}
+    ${EXTERNAL_PROJECT_LOG_ARGS}
+    PREFIX                ${PSLIB_BRPC_SOURCE_DIR}
+    DOWNLOAD_DIR          ${PSLIB_BRPC_DOWNLOAD_DIR}
+    DOWNLOAD_COMMAND      wget --no-check-certificate ${PSLIB_BRPC_URL} -c -q -O ${PSLIB_BRPC_NAME}.tar.gz
+                          && tar zxvf ${PSLIB_BRPC_NAME}.tar.gz
+    DOWNLOAD_NO_PROGRESS  1
+    UPDATE_COMMAND        ""
+    CMAKE_ARGS            -DCMAKE_INSTALL_PREFIX=${PSLIB_BRPC_INSTALL_ROOT}
+    CMAKE_CACHE_ARGS      -DCMAKE_INSTALL_PREFIX:PATH=${PSLIB_BRPC_INSTALL_ROOT}
+)
+
+ADD_LIBRARY(pslib_brpc SHARED IMPORTED GLOBAL)
+SET_PROPERTY(TARGET pslib_brpc PROPERTY IMPORTED_LOCATION ${PSLIB_BRPC_LIB})
+ADD_DEPENDENCIES(pslib_brpc ${PSLIB_BRPC_PROJECT})
+LIST(APPEND external_project_dependencies pslib_brpc)
+
+IF(WITH_C_API)
+  INSTALL(FILES ${PSLIB_BRPC_LIB} ${PSLIB_BRPC_IOMP_LIB} DESTINATION lib)
+ENDIF()

cmake/external/python.cmake

@@ -18,8 +18,8 @@ ENDIF()
 
 INCLUDE(python_module)
 
-FIND_PACKAGE(PythonInterp ${PY_VERSION})
-FIND_PACKAGE(PythonLibs ${PY_VERSION})
+FIND_PACKAGE(PythonInterp ${PY_VERSION} REQUIRED)
+FIND_PACKAGE(PythonLibs ${PY_VERSION} REQUIRED)
 
 if(WIN32)
     execute_process(COMMAND "${PYTHON_EXECUTABLE}" "-c"
@@ -85,6 +85,5 @@ IF(PYTHONINTERP_FOUND)
         "please use pip to upgrade protobuf. pip install -U protobuf")
     ENDIF()
 ENDIF(PYTHONINTERP_FOUND)
-
 INCLUDE_DIRECTORIES(${PYTHON_INCLUDE_DIR})
 INCLUDE_DIRECTORIES(${PYTHON_NUMPY_INCLUDE_DIR})

cmake/inference_lib.cmake

@@ -200,6 +200,13 @@ if (WITH_ANAKIN AND WITH_MKL)
     list(APPEND inference_deps anakin_inference_lib)
 endif ()
 
+if (TENSORRT_FOUND)
+    copy(tensorrt_lib DEPS ${inference_deps} 
+        SRCS ${TENSORRT_ROOT}/include/Nv*.h ${TENSORRT_ROOT}/lib/libnvinfer*
+        DSTS ${FLUID_INSTALL_DIR}/third_party/install/tensorrt/include ${FLUID_INSTALL_DIR}/third_party/install/tensorrt/lib)
+endif ()
+
+
 set(module "inference")
 if(WIN32)
     set(paddle_fluid_lib ${PADDLE_BINARY_DIR}/paddle/fluid/inference/${CMAKE_BUILD_TYPE}/libpaddle_fluid.*)

cmake/operators.cmake

@@ -84,7 +84,7 @@ function(op_library TARGET)
     endif()
     if (WIN32)
     # remove windows unsupported op, because windows has no nccl, no warpctc such ops.
-    foreach(windows_unsupport_op "nccl_op" "gen_nccl_id_op" "warpctc_op")
+    foreach(windows_unsupport_op "nccl_op" "gen_nccl_id_op")
         if ("${TARGET}" STREQUAL "${windows_unsupport_op}")
           return()
         endif()

paddle/fluid/API.spec

@@ -37,8 +37,16 @@ paddle.fluid.DataFeedDesc.desc ArgSpec(args=['self'], varargs=None, keywords=Non
 paddle.fluid.DataFeedDesc.set_batch_size ArgSpec(args=['self', 'batch_size'], varargs=None, keywords=None, defaults=None)
 paddle.fluid.DataFeedDesc.set_dense_slots ArgSpec(args=['self', 'dense_slots_name'], varargs=None, keywords=None, defaults=None)
 paddle.fluid.DataFeedDesc.set_use_slots ArgSpec(args=['self', 'use_slots_name'], varargs=None, keywords=None, defaults=None)
-paddle.fluid.AsyncExecutor.__init__ ArgSpec(args=['self', 'place'], varargs=None, keywords=None, defaults=(None,))
-paddle.fluid.AsyncExecutor.run ArgSpec(args=['self', 'program', 'data_feed', 'filelist', 'thread_num', 'fetch', 'debug'], varargs=None, keywords=None, defaults=(False,))
+paddle.fluid.AsyncExecutor.__init__ ArgSpec(args=['self', 'place', 'run_mode'], varargs=None, keywords=None, defaults=(None, ''))
+paddle.fluid.AsyncExecutor.config_distributed_nodes ArgSpec(args=['self'], varargs=None, keywords=None, defaults=None)
+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))
+paddle.fluid.AsyncExecutor.get_instance ArgSpec(args=['self'], varargs=None, keywords=None, defaults=None)
+paddle.fluid.AsyncExecutor.init_model ArgSpec(args=['self'], varargs=None, keywords=None, defaults=None)
+paddle.fluid.AsyncExecutor.init_server ArgSpec(args=['self', 'dist_desc'], varargs=None, keywords=None, defaults=None)
+paddle.fluid.AsyncExecutor.init_worker ArgSpec(args=['self', 'dist_desc', 'startup_program'], varargs=None, keywords=None, defaults=None)
+paddle.fluid.AsyncExecutor.run ArgSpec(args=['self', 'program', 'data_feed', 'filelist', 'thread_num', 'fetch', 'mode', 'debug'], varargs=None, keywords=None, defaults=('', False))
+paddle.fluid.AsyncExecutor.save_model ArgSpec(args=['self', 'save_path'], varargs=None, keywords=None, defaults=None)
+paddle.fluid.AsyncExecutor.stop ArgSpec(args=['self'], varargs=None, keywords=None, defaults=None)
 paddle.fluid.io.save_vars ArgSpec(args=['executor', 'dirname', 'main_program', 'vars', 'predicate', 'filename'], varargs=None, keywords=None, defaults=(None, None, None, None))
 paddle.fluid.io.save_params ArgSpec(args=['executor', 'dirname', 'main_program', 'filename'], varargs=None, keywords=None, defaults=(None, None))
 paddle.fluid.io.save_persistables ArgSpec(args=['executor', 'dirname', 'main_program', 'filename'], varargs=None, keywords=None, defaults=(None, None))
@@ -201,6 +209,7 @@ paddle.fluid.layers.merge_selected_rows ArgSpec(args=['x', 'name'], varargs=None
 paddle.fluid.layers.get_tensor_from_selected_rows ArgSpec(args=['x', 'name'], varargs=None, keywords=None, defaults=(None,))
 paddle.fluid.layers.lstm ArgSpec(args=['input', 'init_h', 'init_c', 'max_len', 'hidden_size', 'num_layers', 'dropout_prob', 'is_bidirec', 'is_test', 'name', 'default_initializer', 'seed'], varargs=None, keywords=None, defaults=(0.0, False, False, None, None, -1))
 paddle.fluid.layers.psroi_pool ArgSpec(args=['input', 'rois', 'output_channels', 'spatial_scale', 'pooled_height', 'pooled_width', 'name'], varargs=None, keywords=None, defaults=(None,))
+paddle.fluid.layers.huber_loss ArgSpec(args=['input', 'label', 'delta'], varargs=None, keywords=None, defaults=None)
 paddle.fluid.layers.data ArgSpec(args=['name', 'shape', 'append_batch_size', 'dtype', 'lod_level', 'type', 'stop_gradient'], varargs=None, keywords=None, defaults=(True, 'float32', 0, VarType.LOD_TENSOR, True))
 paddle.fluid.layers.open_files ArgSpec(args=['filenames', 'shapes', 'lod_levels', 'dtypes', 'thread_num', 'buffer_size', 'pass_num', 'is_test'], varargs=None, keywords=None, defaults=(None, None, 1, None))
 paddle.fluid.layers.read_file ArgSpec(args=['reader'], varargs=None, keywords=None, defaults=None)
@@ -367,7 +376,7 @@ paddle.fluid.optimizer.MomentumOptimizer.__init__ ArgSpec(args=['self', 'learnin
 paddle.fluid.optimizer.MomentumOptimizer.minimize ArgSpec(args=['self', 'loss', 'startup_program', 'parameter_list', 'no_grad_set'], varargs=None, keywords=None, defaults=(None, None, None))
 paddle.fluid.optimizer.AdagradOptimizer.__init__ ArgSpec(args=['self', 'learning_rate', 'epsilon', 'regularization', 'name'], varargs=None, keywords=None, defaults=(1e-06, None, None))
 paddle.fluid.optimizer.AdagradOptimizer.minimize ArgSpec(args=['self', 'loss', 'startup_program', 'parameter_list', 'no_grad_set'], varargs=None, keywords=None, defaults=(None, None, None))
-paddle.fluid.optimizer.AdamOptimizer.__init__ ArgSpec(args=['self', 'learning_rate', 'beta1', 'beta2', 'epsilon', 'regularization', 'name'], varargs=None, keywords=None, defaults=(0.001, 0.9, 0.999, 1e-08, None, None))
+paddle.fluid.optimizer.AdamOptimizer.__init__ ArgSpec(args=['self', 'learning_rate', 'beta1', 'beta2', 'epsilon', 'regularization', 'name', 'lazy_mode'], varargs=None, keywords=None, defaults=(0.001, 0.9, 0.999, 1e-08, None, None, False))
 paddle.fluid.optimizer.AdamOptimizer.minimize ArgSpec(args=['self', 'loss', 'startup_program', 'parameter_list', 'no_grad_set'], varargs=None, keywords=None, defaults=(None, None, None))
 paddle.fluid.optimizer.AdamaxOptimizer.__init__ ArgSpec(args=['self', 'learning_rate', 'beta1', 'beta2', 'epsilon', 'regularization', 'name'], varargs=None, keywords=None, defaults=(0.001, 0.9, 0.999, 1e-08, None, None))
 paddle.fluid.optimizer.AdamaxOptimizer.minimize ArgSpec(args=['self', 'loss', 'startup_program', 'parameter_list', 'no_grad_set'], varargs=None, keywords=None, defaults=(None, None, None))

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.
+#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)
   set(oneValueArgs "")
   set(multiValueArgs SRCS PATH)
   cmake_parse_arguments(windows_symbolic "${options}" "${oneValueArgs}" "${multiValueArgs}" ${ARGN})
   set(final_path ${CMAKE_CURRENT_SOURCE_DIR}/${windows_symbolic_PATH})
   foreach(src ${windows_symbolic_SRCS})
-    get_filename_component(src ${src} NAME_WE)
-    if (NOT EXISTS ${final_path}/${src}.cc OR NOT EXISTS ${final_path}/${src}.cu)
-        message(FATAL " ${src}.cc and ${src}.cu must exsits, and ${src}.cu must be symbolic file.")
-    endif()
-
-    file(GENERATE OUTPUT ${final_path}/.${src}.cu INPUT ${final_path}/${src}.cc)
-
-    add_custom_command(OUTPUT ${final_path}/.${src}.cu
-            COMMAND ${CMAKE_COMMAND} -E remove ${final_path}/.${src}.cu
-            COMMAND ${CMAKE_COMMAND} -E copy "${final_path}/${src}.cc" "${final_path}/.${src}.cu"
-            COMMENT "create hidden file of ${src}.cu")
-    add_custom_target(${TARGET} ALL DEPENDS .${src}.cu)
+  get_filename_component(src ${src} NAME_WE)
+  if (NOT EXISTS ${CMAKE_CURRENT_SOURCE_DIR}/${src}.cc OR NOT EXISTS ${CMAKE_CURRENT_SOURCE_DIR}/${src}.cu)
+      message(FATAL " ${src}.cc and ${src}.cu must exsits, and ${src}.cu must be symbolic file.")
+  endif()
+
+#only copy the xx.cu to.xx.cu when the content are modified
+  set(copy_flag 1)
+  if (EXISTS ${CMAKE_CURRENT_SOURCE_DIR}/.${src}.cu)
+  file(READ ${CMAKE_CURRENT_SOURCE_DIR}/${src}.cc SOURCE_STR)
+  file(READ ${CMAKE_CURRENT_SOURCE_DIR}/.${src}.cu TARGET_STR)
+  if (SOURCE_STR STREQUAL TARGET_STR)
+    set(copy_flag 0)
+  endif()
+  endif()
+  if (copy_flag)
+  add_custom_command(OUTPUT .${src}.cu
+          COMMAND ${CMAKE_COMMAND} -E remove ${CMAKE_CURRENT_SOURCE_DIR}/.${src}.cu
+          COMMAND ${CMAKE_COMMAND} -E copy "${CMAKE_CURRENT_SOURCE_DIR}/${src}.cc" "${CMAKE_CURRENT_SOURCE_DIR}/.${src}.cu"
+          COMMENT "create hidden file of ${src}.cu")
+  endif(copy_flag)
+  add_custom_target(${TARGET} ALL DEPENDS .${src}.cu)
   endforeach()
 endfunction()
 
 add_subdirectory(ir)
 add_subdirectory(details)
-# ddim lib
+#ddim lib
 proto_library(framework_proto SRCS framework.proto)
 proto_library(async_executor_proto SRCS data_feed.proto)
 
@@ -83,8 +92,8 @@ nv_test(data_device_transform_test SRCS data_device_transform_test.cu
 
 if(WITH_GPU)
   if (WIN32)
-    # 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.
+#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.
       windows_symbolic(hidden_file SRCS data_type_transform.cu)
       nv_library(data_type_transform SRCS .data_type_transform.cu DEPS tensor)
       add_dependencies(data_type_transform hidden_file)
@@ -135,7 +144,8 @@ cc_library(op_registry SRCS op_registry.cc DEPS op_proto_maker op_info operator
 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)
-# Generate an empty __init__.py to make framework_py_proto as a valid python module.
+#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)
 add_dependencies(framework_py_proto framework_py_proto_init)
 if (NOT WIN32)
@@ -187,7 +197,12 @@ cc_library(parallel_executor SRCS parallel_executor.cc DEPS
         graph build_strategy
         fast_threaded_ssa_graph_executor variable_helper)
 
-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)
+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)
+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)
+endif(WITH_PSLIB)
+
 
 cc_test(data_feed_test SRCS data_feed_test.cc DEPS async_executor)
 cc_library(prune SRCS prune.cc DEPS framework_proto)

paddle/fluid/framework/async_executor.cc

@@ -29,6 +29,9 @@ limitations under the License. */
 #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 {
@@ -47,6 +50,11 @@ void AsyncExecutor::CreateThreads(
   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
@@ -60,12 +68,177 @@ void PrepareReaders(std::vector<std::shared_ptr<DataFeed>>& readers,  // NOLINT
   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();
+}
+
+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();
+}
+
+uint64_t AsyncExecutor::StartServer() { return _pslib_ptr->run_server(); }
+
+void AsyncExecutor::StopServer() { _pslib_ptr->stop_server(); }
+
+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());
+  }
+}
+
+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));
+    }
+
+    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
+
 void AsyncExecutor::RunFromFile(const ProgramDesc& main_program,
                                 const std::string& data_feed_desc_str,
                                 const std::vector<std::string>& filelist,
                                 const int thread_num,
                                 const std::vector<std::string>& fetch_var_names,
-                                const bool debug) {
+                                const std::string& mode, const bool debug) {
   std::vector<std::thread> threads;
 
   auto& block = main_program.Block(0);
@@ -82,7 +255,7 @@ void AsyncExecutor::RunFromFile(const ProgramDesc& main_program,
   google::protobuf::TextFormat::ParseFromString(data_feed_desc_str,
                                                 &data_feed_desc);
 
-  int 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");
 
@@ -106,11 +279,21 @@ 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);
-
+#ifdef PADDLE_WITH_PSLIB
+  PrepareDenseThread(mode);
+#endif
   std::vector<std::shared_ptr<ExecutorThreadWorker>> workers;
   workers.resize(actual_thread_num);
   for (auto& worker : workers) {
+#ifdef PADDLE_WITH_PSLIB
+    if (mode == "mpi") {
+      worker.reset(new AsyncExecutorThreadWorker);
+    } else {
+      worker.reset(new ExecutorThreadWorker);
+    }
+#else
     worker.reset(new ExecutorThreadWorker);
+#endif
   }
 
   // prepare thread resource here
@@ -128,7 +311,11 @@ void AsyncExecutor::RunFromFile(const ProgramDesc& main_program,
   for (auto& th : threads) {
     th.join();
   }
-
+#ifdef PADDLE_WITH_PSLIB
+  if (mode == "mpi") {
+    _pull_dense_thread->stop();
+  }
+#endif
   root_scope_->DropKids();
 
   return;

paddle/fluid/framework/async_executor.h

@@ -14,9 +14,11 @@ limitations under the License. */
 
 #pragma once
 
+#include <time.h>
 #include <map>
 #include <memory>
-#include <mutex>  // NOLINT
+#include <mutex>   // NOLINT
+#include <random>  // local_random_engine
 #include <set>
 #include <string>
 #include <thread>  // NOLINT
@@ -30,6 +32,31 @@ limitations under the License. */
 
 namespace paddle {
 namespace framework {
+
+inline double current_realtime() {
+#if !defined(_WIN32)
+  struct timespec tp;
+  clock_gettime(CLOCK_REALTIME, &tp);
+  return tp.tv_sec + tp.tv_nsec * 1e-9;
+#else
+  return 0.0;
+#endif
+}
+
+inline std::default_random_engine& local_random_engine() {
+  struct engine_wrapper_t {
+    std::default_random_engine engine;
+    engine_wrapper_t() {
+      static std::atomic<uint64_t> x(0);
+      std::seed_seq sseq = {x++, x++, x++,
+                            static_cast<uint64_t>(current_realtime() * 1000)};
+      engine.seed(sseq);
+    }
+  };
+  thread_local engine_wrapper_t r;
+  return r.engine;
+}
+
 class AsyncExecutor {
  public:
   AsyncExecutor(Scope* scope, const platform::Place& place);
@@ -39,7 +66,19 @@ class AsyncExecutor {
                    const std::vector<std::string>& filelist,
                    const int thread_num,
                    const std::vector<std::string>& fetch_names,
-                   const bool debug = false);
+                   const std::string& mode, const bool debug = false);
+#ifdef PADDLE_WITH_PSLIB
+  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);
+  uint64_t StartServer();
+  void StopServer();
+  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,
@@ -48,10 +87,21 @@ class AsyncExecutor {
                      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
   Scope* root_scope_;
   platform::Place place_;
+
+ private:
+  int actual_thread_num;
 };
 
 }  // namespace framework

paddle/fluid/framework/data_feed.cc

@@ -64,6 +64,7 @@ bool DataFeed::PickOneFile(std::string* filename) {
     return false;
   }
   *filename = filelist_[file_idx_++];
+  LOG(ERROR) << "pick file:" << *filename;
   return true;
 }
 

paddle/fluid/framework/details/CMakeLists.txt

@@ -50,8 +50,10 @@ cc_library(data_balance_op_handle SRCS data_balance_op_handle.cc DEPS op_handle_
 cc_library(gather_op_handle SRCS gather_op_handle.cc DEPS op_handle_base scope ddim memory variable_visitor)
 cc_library(fuse_vars_op_handle SRCS fuse_vars_op_handle.cc DEPS op_handle_base scope)
 
+cc_library(memory_optimize_pass SRCS analysis_var_pass.cc memory_reuse_types.cc DEPS graph graph_helper pass)
 cc_library(modify_op_lock_and_record_event_pass SRCS modify_op_lock_and_record_event_pass.cc DEPS computation_op_handle op_graph_view multi_devices_helper)
-
+cc_library(memory_early_delete_pass SRCS memory_early_delete_pass.cc DEPS memory_optimize_pass computation_op_handle scale_loss_grad_op_handle rpc_op_handle
+        all_reduce_op_handle reduce_op_handle broadcast_op_handle data_balance_op_handle graph graph_helper pass)
 cc_library(reference_count_pass_helper SRCS reference_count_pass_helper.cc DEPS garbage_collector computation_op_handle)
 cc_library(eager_deletion_op_handle SRCS eager_deletion_op_handle.cc DEPS lod_tensor selected_rows reference_count_pass_helper)
 cc_library(eager_deletion_pass SRCS eager_deletion_pass.cc DEPS computation_op_handle eager_deletion_op_handle graph graph_helper pass)
@@ -63,7 +65,12 @@ cc_library(all_reduce_deps_pass SRCS all_reduce_deps_pass.cc DEPS graph graph_he
 cc_library(multi_devices_graph_pass SRCS multi_devices_graph_pass.cc DEPS multi_devices_helper computation_op_handle
         scale_loss_grad_op_handle rpc_op_handle all_reduce_op_handle reduce_op_handle broadcast_op_handle data_balance_op_handle fused_broadcast_op_handle)
 
-set(SSA_GRAPH_EXECUTOR_DEPS graph framework_proto sequential_execution_pass modify_op_lock_and_record_event_pass all_reduce_deps_pass reference_count_pass eager_deletion_pass) 
+set(SSA_GRAPH_EXECUTOR_DEPS graph framework_proto sequential_execution_pass modify_op_lock_and_record_event_pass all_reduce_deps_pass reference_count_pass eager_deletion_pass memory_optimize_pass memory_early_delete_pass)
+if (WITH_GPU)
+  list(APPEND SSA_GRAPH_EXECUTOR_DEPS reference_count_pass)
+endif()
+cc_test(memory_reuse_types_test SRCS memory_reuse_types_test.cc memory_reuse_types.cc DEPS framework_proto graph)
+cc_test(analysis_var_pass_test SRCS analysis_var_pass_test.cc analysis_var_pass.cc memory_reuse_types.cc DEPS framework_proto graph graph_helper op_registry pass)
 
 cc_library(ssa_graph_executor SRCS ssa_graph_executor.cc DEPS ${SSA_GRAPH_EXECUTOR_DEPS})
 
@@ -84,4 +91,5 @@ cc_test(fused_broadcast_op_test SRCS fused_broadcast_op_handle_test.cc DEPS fuse
 cc_library(build_strategy SRCS build_strategy.cc DEPS
         graph_viz_pass multi_devices_graph_pass
         multi_devices_graph_print_pass multi_devices_graph_check_pass
-        fuse_elewise_add_act_pass multi_batch_merge_pass)
+        fuse_elewise_add_act_pass multi_batch_merge_pass
+        memory_optimize_pass)

paddle/fluid/framework/details/analysis_var_pass.h

+// Copyright (c) 2018 PaddlePaddle Authors. All Rights Reserved.
+//
+// Licensed under the Apache License, Version 2.0 (the "License");
+// you may not use this file except in compliance with the License.
+// You may obtain a copy of the License at
+//
+//     http://www.apache.org/licenses/LICENSE-2.0
+//
+// Unless required by applicable law or agreed to in writing, software
+// distributed under the License is distributed on an "AS IS" BASIS,
+// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+// See the License for the specific language governing permissions and
+// limitations under the License.
+
+#pragma once
+
+#include <algorithm>
+#include <list>
+#include <map>
+#include <memory>
+#include <set>
+#include <string>
+#include <unordered_map>
+#include <utility>
+#include <vector>
+
+#include "paddle/fluid/framework/data_type.h"
+#include "paddle/fluid/framework/details/memory_reuse_types.h"
+#include "paddle/fluid/framework/ir/graph.h"
+#include "paddle/fluid/framework/ir/pass.h"
+
+namespace paddle {
+namespace framework {
+namespace details {
+constexpr char kAllOpDescs[] = "all_op_descs";
+
+std::vector<ir::Node*> SortOpLikeDescOrder(const ir::Graph& graph);
+// sort op in bfs order
+std::vector<ir::Node*> BFSSortGraphOps(const ir::Graph& graph);
+
+class ControlFlowGraph;
+
+class AnalysisVarPass : public ir::Pass {
+ protected:
+  std::unique_ptr<ir::Graph> ApplyImpl(
+      std::unique_ptr<ir::Graph> graph) const override;
+
+ private:
+  // fill the variable map(var_nodes) by version.
+  void InitSSAGraphNodes() const;
+  // update program descs
+  void RenameVarInGraphDesc(const std::string& var,
+                            const std::string& cache_var, size_t idx) const;
+  // update ir nodes
+  void RenameVarInGraphNode(const std::string& var,
+                            const std::string& cache_var, size_t idx,
+                            ir::Graph* graph) const;
+
+  void SubGraphOptimize(OpDesc* op_desc) const;
+  // valid a tensor can be reuse or not
+  bool NodeCanReused(ir::Node* node) const;
+  // scan subblock and collect the output/input variables.
+  std::unordered_set<std::string> GetSubBlockVars(
+      const std::unordered_set<ir::Node*>&) const;
+  // check op has subblock or not
+  bool OpHasSubBlock(OpDesc* desc) const;
+
+ private:
+  // Reuse Node Pool, Owned.
+  mutable OrderedNodePairPool pool_;
+  // controlflow Graph
+  mutable std::unique_ptr<ControlFlowGraph> cfg_;
+  // skip set
+  mutable std::unordered_set<std::string> skip_set_;
+  // var nodes
+  mutable std::map<std::string, std::vector<ir::Node*>> var_nodes_;
+};
+
+class ControlFlowGraph {
+ public:
+  ControlFlowGraph() = default;
+  // For IR Graph in parallelexecutor
+  explicit ControlFlowGraph(const ir::Graph& graph);
+
+  void LiveVariableAnalysis();
+
+  void RenameVarInCFGGraph(const std::string& old_node,
+                           const std::string& new_node, int begin_idx);
+
+  const std::set<std::string> LiveIn(ir::Node* op) const;
+  const std::set<std::string> LiveOut(ir::Node* op) const;
+  const std::set<std::string> Use(ir::Node* op) const;
+  const std::vector<ir::Node*> Ops() const;
+  std::vector<ir::Node*>& Ops();
+
+  // for ssa-graph nodes
+  ir::Node* GetNodeFromVarName(const std::string& name, ir::Node* op) const;
+
+ private:
+  void BuildCFGGraph();
+  void ConnectNodes();
+  using NodeListMap = std::unordered_map<ir::Node*, std::set<ir::Node*>>;
+  using VarSetMap = std::map<ir::Node*, std::set<std::string>>;
+  // successors ops use the output variables.
+  NodeListMap successors_;
+  // predecessors ops generated input variables.
+  NodeListMap predecessors_;
+  // variables lived before run current op.
+  VarSetMap live_in_;
+  // variables lived after run current op.
+  VarSetMap live_out_;
+  VarSetMap uses_;  // op inputs
+  VarSetMap defs_;  // op outputs
+
+  std::vector<ir::Node*> ops_;  // op sequence by topology sort
+};
+
+}  // namespace details
+}  // namespace framework
+}  // namespace paddle

paddle/fluid/framework/details/analysis_var_pass_test.cc

+// Copyright (c) 2018 PaddlePaddle Authors. All Rights Reserved.
+//
+// Licensed under the Apache License, Version 2.0 (the "License");
+// you may not use this file except in compliance with the License.
+// You may obtain a copy of the License at
+//
+//     http://www.apache.org/licenses/LICENSE-2.0
+//
+// Unless required by applicable law or agreed to in writing, software
+// distributed under the License is distributed on an "AS IS" BASIS,
+// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+// See the License for the specific language governing permissions and
+// limitations under the License.
+
+#include "paddle/fluid/framework/details/analysis_var_pass.h"
+#include <algorithm>
+#include <iostream>
+#include <iterator>
+#include "glog/logging.h"
+#include "gtest/gtest.h"
+#include "paddle/fluid/framework/ir/graph.h"
+#include "paddle/fluid/framework/ir/graph_helper.h"
+#include "paddle/fluid/framework/op_registry.h"
+#include "paddle/fluid/framework/operator.h"
+#include "paddle/fluid/framework/program_desc.h"
+
+namespace paddle {
+namespace framework {
+
+class DummyOp : public OperatorBase {
+ public:
+  DummyOp(const std::string& type, const VariableNameMap& inputs,
+          const VariableNameMap& outputs, const AttributeMap& attrs)
+      : OperatorBase(type, inputs, outputs, attrs) {}
+
+ private:
+  void RunImpl(const Scope& scope,
+               const platform::Place& place) const override {}
+};
+
+class SumOpMaker : public OpProtoAndCheckerMaker {
+ public:
+  void Make() {
+    AddInput("X", "").AsDuplicable();
+    AddOutput("Out", "");
+    AddComment("");
+  }
+};
+
+class AssignOpMaker : public OpProtoAndCheckerMaker {
+ public:
+  void Make() {
+    AddInput("X", "").AsDuplicable();
+    AddOutput("Out", "");
+    AddComment("");
+  }
+};
+
+class DummyVarTypeInference : public VarTypeInference {
+ public:
+  void operator()(const OpDesc& op_desc, BlockDesc* block) const override {
+    auto& inputs = op_desc.Input("X");
+    auto type = block->Var(inputs.front())->GetType();
+    auto out_var_name = op_desc.Output("Out").front();
+    block->Var(out_var_name)->SetType(type);
+  }
+};
+
+}  // namespace framework
+}  // namespace paddle
+
+REGISTER_OPERATOR(sum, paddle::framework::DummyOp,
+                  paddle::framework::SumOpMaker,
+                  paddle::framework::DummyVarTypeInference);
+REGISTER_OPERATOR(assign, paddle::framework::DummyOp,
+                  paddle::framework::AssignOpMaker,
+                  paddle::framework::DummyVarTypeInference);
+REGISTER_OPERATOR(dummy, paddle::framework::DummyOp,
+                  paddle::framework::SumOpMaker,
+                  paddle::framework::DummyVarTypeInference);
+/*
+  https://en.wikipedia.org/wiki/Live_variable_analysis
+  Create a customed classical dependency graph, left row is the instruction
+  number.
+  1. a = 1
+  2. b = a
+  3. c = a
+  4. d = b + c
+  5. e = d
+
+  a--------+
+  |        |
+  b        c
+  |        |
+  d--------+
+  |
+  e
+  Then analysis these variable's liveness range
+ */
+
+namespace paddle {
+namespace framework {
+namespace details {
+
+static inline bool IsSameDesc(OpDesc* op1, OpDesc* op2) {
+  return op1->Type() == op2->Type() && op1->Inputs() == op2->Inputs() &&
+         op1->Outputs() == op2->Outputs();
+}
+
+inline static ProgramDesc FillProgramDesc() {
+  ProgramDesc prog;
+  prog.MutableBlock(0)->Var("a")->SetType(proto::VarType::LOD_TENSOR);
+  prog.MutableBlock(0)->Var("b")->SetType(proto::VarType::LOD_TENSOR);
+  prog.MutableBlock(0)->Var("c")->SetType(proto::VarType::LOD_TENSOR);
+  prog.MutableBlock(0)->Var("d")->SetType(proto::VarType::LOD_TENSOR);
+  prog.MutableBlock(0)->Var("e")->SetType(proto::VarType::LOD_TENSOR);
+  {
+    auto* op = prog.MutableBlock(0)->AppendOp();
+    op->SetType("assign");
+    op->SetInput("X", {"a"});
+    op->SetOutput("Out", {"b"});
+  }
+  {
+    auto* op = prog.MutableBlock(0)->AppendOp();
+    op->SetType("assign");
+    op->SetInput("X", {"a"});
+    op->SetOutput("Out", {"c"});
+  }
+  {
+    auto* op = prog.MutableBlock(0)->AppendOp();
+    op->SetType("sum");
+    op->SetInput("X", {"b", "c"});
+    op->SetOutput("Out", {"d"});
+  }
+  {
+    auto* op = prog.MutableBlock(0)->AppendOp();
+    op->SetType("assign");
+    op->SetInput("X", {"d"});
+    op->SetOutput("Out", {"e"});
+  }
+  return prog;
+}
+
+template <typename Container>
+inline static std::string DebugString(const Container& c) {
+  std::stringstream ss;
+  for (auto& item : c) {
+    ss << item << " ";
+  }
+  return ss.str();
+}
+
+TEST(CFGGraph, IRGraph) {
+  // prepare ir graph
+  auto prog = FillProgramDesc();
+  ir::Graph graph(prog);
+  const std::vector<OpDesc*>* all_op_descs =
+      new std::vector<OpDesc*>(prog.Block(0).AllOps());
+  graph.Set(details::kAllOpDescs, all_op_descs);  // take ownership
+
+  ControlFlowGraph cfg(graph);
+  cfg.LiveVariableAnalysis();
+
+  // test assign op
+  ASSERT_TRUE((std::set<std::string>{"a"} == cfg.LiveIn(cfg.Ops()[0])));
+  ASSERT_TRUE((std::set<std::string>{"a", "b"} == cfg.LiveOut(cfg.Ops()[0])));
+
+  // test assign op
+  ASSERT_TRUE((std::set<std::string>{"a", "b"} == cfg.LiveIn(cfg.Ops()[1])));
+  ASSERT_TRUE((std::set<std::string>{"b", "c"} == cfg.LiveOut(cfg.Ops()[1])));
+
+  // test sum op
+  ASSERT_TRUE((std::set<std::string>{"b", "c"} == cfg.LiveIn(cfg.Ops()[2])));
+  ASSERT_TRUE((std::set<std::string>{"d"} == cfg.LiveOut(cfg.Ops()[2])));
+
+  // test assign op
+  ASSERT_TRUE((std::set<std::string>{"d"} == cfg.LiveIn(cfg.Ops()[3])));
+  ASSERT_TRUE((std::set<std::string>{} == cfg.LiveOut(cfg.Ops()[3])));
+}
+
+// 1. normal test
+TEST(SortOpLikeDescOrder, NormalTest) {
+  auto prog = FillProgramDesc();
+  ir::Graph graph(prog);
+  const std::vector<OpDesc*>* all_op_descs =
+      new std::vector<OpDesc*>(prog.Block(0).AllOps());
+  graph.Set(details::kAllOpDescs, all_op_descs);  // take ownership
+
+  auto nodes = SortOpLikeDescOrder(graph);
+  auto op_descs = prog.Block(0).AllOps();
+  for (size_t i = 0; i < nodes.size(); ++i) {
+    auto node = nodes[i];
+    auto op_desc = op_descs[i];
+    ASSERT_TRUE(IsSameDesc(node->Op(), op_desc));
+  }
+}
+
+// 2. remove some op_desc
+TEST(SortOpLikeDescOrder, RemoveOpDesc) {
+  auto prog = FillProgramDesc();
+  ir::Graph graph(prog);
+  const std::vector<OpDesc*>* all_op_descs =
+      new std::vector<OpDesc*>(prog.Block(0).AllOps());
+  graph.Set(details::kAllOpDescs, all_op_descs);  // take ownership
+  auto nodes = graph.Nodes();
+  auto op_descs = prog.Block(0).AllOps();
+  ir::Node* found_node = nullptr;
+  for (auto node : nodes) {
+    if (node->IsOp() && node->outputs.back()->Name() == "e") {
+      found_node = node;
+      break;
+    }
+  }
+  PADDLE_ENFORCE(found_node != nullptr);
+  for (auto it = op_descs.begin(); it != op_descs.end();) {
+    if (IsSameDesc(*it, found_node->Op())) {
+      it = op_descs.erase(it);
+    } else {
+      ++it;
+    }
+  }
+
+  auto find_node_in_graph = [&](std::string s) {
+    ir::Node* ret = nullptr;
+    for (auto n : graph.Nodes()) {
+      if (n->Name() == s) {
+        ret = n;
+        break;
+      }
+    }
+    PADDLE_ENFORCE(ret != nullptr);
+    return ret;
+  };
+
+  ir::Node* e = find_node_in_graph("e");
+  ir::Node* d = find_node_in_graph("d");
+  std::remove(d->outputs.begin(), d->outputs.end(), found_node);
+  graph.RemoveNode(found_node);
+  graph.RemoveNode(e);
+
+  // other node keeps the same order
+  auto remain_nodes = SortOpLikeDescOrder(graph);
+  for (size_t i = 0; i < remain_nodes.size(); ++i) {
+    auto node = remain_nodes[i];
+    auto op_desc = op_descs[i];
+    ASSERT_TRUE(IsSameDesc(node->Op(), op_desc));
+  }
+}
+
+// 3. add some op_desc
+TEST(SortOpLikeDescOrder, AddOpDesc) {
+  auto prog = FillProgramDesc();
+  const std::vector<OpDesc*>* all_op_descs =
+      new std::vector<OpDesc*>(prog.Block(0).AllOps());
+  ir::Graph graph(prog);
+
+  auto find_node_in_graph = [&](std::string s) {
+    ir::Node* ret = nullptr;
+    for (auto n : graph.Nodes()) {
+      if (n->Name() == s) {
+        ret = n;
+        break;
+      }
+    }
+    PADDLE_ENFORCE(ret != nullptr);
+    return ret;
+  };
+
+  // cached desc different with real one
+  // mimic the intermidiete pass modify the programdesc.
+  graph.Set(details::kAllOpDescs, all_op_descs);  // take ownership
+
+  auto op_descs = prog.Block(0).AllOps();
+
+  auto op = prog.MutableBlock(0)->AppendOp();
+  prog.MutableBlock(0)->Var("d1")->SetType(proto::VarType::LOD_TENSOR);
+  op->SetType("sum");
+  op->SetInput("X", {"b", "c"});
+  op->SetOutput("Out", {"d1"});
+  ir::Node* node = graph.CreateOpNode(op);
+  ir::Node* d1 = graph.CreateVarNode(prog.MutableBlock(0)->Var("d1"));
+  ir::Node* b = find_node_in_graph("b");
+  ir::Node* c = find_node_in_graph("c");
+  node->outputs.emplace_back(d1);
+  node->inputs.emplace_back(b);
+  node->inputs.emplace_back(c);
+  d1->inputs.emplace_back(node);
+  b->outputs.emplace_back(node);
+  c->outputs.emplace_back(node);
+  op_descs.insert(op_descs.begin() + 4, op);
+
+  auto nodes = SortOpLikeDescOrder(graph);
+
+  for (size_t i = 0; i < nodes.size(); ++i) {
+    auto node = nodes[i];
+    auto op_desc = op_descs[i];
+    ASSERT_TRUE(IsSameDesc(node->Op(), op_desc));
+  }
+}
+
+// 4. add and delete some op_desc
+TEST(SortOpLikeDescOrder, AddAndDeleteOpDesc) {
+  auto prog = FillProgramDesc();
+  ir::Graph graph(prog);
+  const std::vector<OpDesc*>* all_op_descs =
+      new std::vector<OpDesc*>(prog.Block(0).AllOps());
+  graph.Set(details::kAllOpDescs, all_op_descs);  // take ownership
+
+  auto find_node_in_graph = [&](std::string s) {
+    ir::Node* ret = nullptr;
+    for (auto n : graph.Nodes()) {
+      if (n->Name() == s) {
+        ret = n;
+        break;
+      }
+    }
+    PADDLE_ENFORCE(ret != nullptr);
+    return ret;
+  };
+
+  // remove sum node
+  auto op_descs = prog.Block(0).AllOps();
+  ir::Node* found_node = nullptr;
+  auto nodes = graph.Nodes();
+  for (auto node : nodes) {
+    if (node->Name() == "sum") {
+      found_node = node;
+      break;
+    }
+  }
+  PADDLE_ENFORCE(found_node != nullptr);
+  for (auto it = op_descs.begin(); it != op_descs.end();) {
+    if (IsSameDesc(*it, found_node->Op())) {
+      it = op_descs.erase(it);
+    } else {
+      ++it;
+    }
+  }
+  {
+    ir::Node* d = find_node_in_graph("d");
+    ir::Node* c = find_node_in_graph("c");
+    ir::Node* e = find_node_in_graph("e");
+    std::remove(d->outputs.begin(), d->outputs.end(), found_node);
+    std::remove(c->outputs.begin(), c->outputs.end(), found_node);
+    ir::Node* pending_op = found_node->outputs[0]->outputs[0];
+    graph.RemoveNode(e);
+    graph.RemoveNode(pending_op);
+    graph.RemoveNode(found_node);
+  }
+
+  // add node
+  auto op = prog.MutableBlock(0)->AppendOp();
+  prog.MutableBlock(0)->Var("d1")->SetType(proto::VarType::LOD_TENSOR);
+  op->SetType("sum");
+  op->SetInput("X", {"b", "c"});
+  op->SetOutput("Out", {"d1"});
+  {
+    ir::Node* node = graph.CreateOpNode(op);
+    ir::Node* d1 = graph.CreateVarNode(prog.MutableBlock(0)->Var("d1"));
+    ir::Node* b = find_node_in_graph("b");
+    ir::Node* c = find_node_in_graph("c");
+    node->outputs.emplace_back(d1);
+    node->inputs.emplace_back(b);
+    node->inputs.emplace_back(c);
+    b->outputs.emplace_back(node);
+    c->outputs.emplace_back(node);
+  }
+  op_descs.insert(op_descs.begin() + 2, op);
+
+  // check the order
+  auto mynodes = SortOpLikeDescOrder(graph);
+  for (size_t i = 0; i < mynodes.size(); ++i) {
+    auto node = mynodes[i];
+    auto op_desc = op_descs[i];
+    ASSERT_TRUE(IsSameDesc(node->Op(), op_desc));
+  }
+}
+
+// 5. add and replace some op_desc inplace.
+TEST(SortOpLikeDescOrder, AddAndReplaceOpDescInplace) {
+  auto prog = FillProgramDesc();
+  ir::Graph graph(prog);
+  const std::vector<OpDesc*>* all_op_descs =
+      new std::vector<OpDesc*>(prog.Block(0).AllOps());
+  graph.Set(details::kAllOpDescs, all_op_descs);  // take ownership
+
+  auto find_node_in_graph = [&](std::string s) {
+    ir::Node* ret = nullptr;
+    for (auto n : graph.Nodes()) {
+      if (n->Name() == s) {
+        ret = n;
+        break;
+      }
+    }
+    PADDLE_ENFORCE(ret != nullptr);
+    return ret;
+  };
+
+  auto op_descs = prog.Block(0).AllOps();
+  // add node
+  auto op = prog.MutableBlock(0)->AppendOp();
+  prog.MutableBlock(0)->Var("d1")->SetType(proto::VarType::LOD_TENSOR);
+  op->SetType("sum");
+  op->SetInput("X", {"b", "c"});
+  op->SetOutput("Out", {"d1"});
+  {
+    ir::Node* node = graph.CreateOpNode(op);
+    ir::Node* d1 = graph.CreateVarNode(prog.MutableBlock(0)->Var("d1"));
+    ir::Node* b = find_node_in_graph("b");
+    ir::Node* c = find_node_in_graph("c");
+    node->outputs.emplace_back(d1);
+    node->inputs.emplace_back(b);
+    node->inputs.emplace_back(c);
+    d1->inputs.emplace_back(node);
+    b->outputs.emplace_back(node);
+    c->outputs.emplace_back(node);
+  }
+
+  op_descs.emplace_back(op);
+
+  // replace op_desc inplace
+  auto nodes = graph.Nodes();
+  ir::Node* found_node = nullptr;
+  for (auto node : nodes) {
+    if (node->IsOp() && node->Op() && node->Name() == "assign") {
+      if (node->outputs.size() == 1 && node->outputs[0]->Name() == "e") {
+        found_node = node;
+        break;
+      }
+    }
+  }
+  {
+    ir::Node* d = find_node_in_graph("d");
+    ir::Node* e = find_node_in_graph("e");
+    std::remove(d->outputs.begin(), d->outputs.end(), found_node);
+    std::remove(e->inputs.begin(), e->inputs.end(), found_node);
+    graph.RemoveNode(found_node);
+  }
+  op_descs.erase(op_descs.begin() + 3);
+
+  auto replace_op = prog.MutableBlock(0)->AppendOp();
+  replace_op->SetType("sum");
+  replace_op->SetInput("X", {"d", "d1"});
+  replace_op->SetOutput("Out", {"e"});
+  {
+    ir::Node* sum2 = graph.CreateOpNode(replace_op);
+    ir::Node* e = find_node_in_graph("e");
+    ir::Node* d = find_node_in_graph("d");
+    ir::Node* d1 = find_node_in_graph("d1");
+    sum2->inputs.emplace_back(d);
+    sum2->inputs.emplace_back(d1);
+    sum2->outputs.emplace_back(e);
+    e->inputs.emplace_back(sum2);
+    d->outputs.emplace_back(sum2);
+    d1->outputs.emplace_back(sum2);
+  }
+
+  op_descs.emplace_back(replace_op);
+  // compare op order
+  auto graph_nodes = SortOpLikeDescOrder(graph);
+  for (size_t i = 0; i < graph_nodes.size(); ++i) {
+    auto node = graph_nodes[i];
+    auto op_desc = op_descs[i];
+    ASSERT_TRUE(IsSameDesc(node->Op(), op_desc));
+  }
+}
+
+}  // namespace details
+}  // namespace framework
+}  // namespace paddle

paddle/fluid/framework/details/build_strategy.cc

@@ -14,11 +14,16 @@ limitations under the License. */
 
 #include "paddle/fluid/framework/details/build_strategy.h"
 
+#include <glog/logging.h>
+#include <memory>
+
+#include "paddle/fluid/framework/details/memory_reuse_types.h"
 #include "paddle/fluid/framework/details/multi_devices_graph_check_pass.h"
 #include "paddle/fluid/framework/details/multi_devices_graph_print_pass.h"
 #include "paddle/fluid/framework/details/reduce_op_handle.h"
 #include "paddle/fluid/framework/details/sequential_execution_pass.h"
 #include "paddle/fluid/framework/ir/graph.h"
+#include "paddle/fluid/framework/ir/graph_helper.h"
 #include "paddle/fluid/framework/ir/graph_viz_pass.h"
 
 namespace paddle {
@@ -69,6 +74,14 @@ class ParallelExecutorPassBuilder : public ir::PassBuilder {
     }
     VLOG(1) << "CollectiveContext:" << context->String();
 
+    // NOTE(dzh): memory optimize should be a runtime pass.
+    // However, after multi_devices_pass, VarHandle, OpHandle is
+    // the de-fact IR, any reuse on Graph is meaningless.
+    // A side-effect of that, memory optimize cannot forsee the fetched vars
+    // , so fetchlist should be set persistable before call the Run interface.
+    if (strategy.memory_optimize_) {
+      auto analysis_var_pass = AppendPass("analysis_var_pass");
+    }
     // Convert graph to run on multi-devices.
     auto multi_devices_pass = AppendPass("multi_devices_pass");
     multi_devices_pass->SetNotOwned<const BuildStrategy>("strategy",
@@ -79,8 +92,11 @@ class ParallelExecutorPassBuilder : public ir::PassBuilder {
     // Add a graph print pass to record a graph with device info.
     if (!strategy_.debug_graphviz_path_.empty()) {
       auto multi_devices_print_pass = AppendPass("multi_devices_print_pass");
-      multi_devices_print_pass->SetNotOwned<const std::string>(
-          "debug_graphviz_path", &strategy_.debug_graphviz_path_);
+      const std::string graph_path =
+          string::Sprintf("%s%s", strategy_.debug_graphviz_path_.c_str(),
+                          "_multi_devices_graph");
+      multi_devices_print_pass->Set<std::string>(kGraphvizPath,
+                                                 new std::string(graph_path));
       multi_devices_print_pass->Set<details::GraphvizSSAGraphPrinter>(
           "graph_printer", new details::GraphvizSSAGraphPrinter);
     }
@@ -127,7 +143,6 @@ std::unique_ptr<ir::Graph> BuildStrategy::Apply(
   CreatePassesFromStrategy(false);
 
   std::unique_ptr<ir::Graph> graph(new ir::Graph(main_program));
-
   for (std::shared_ptr<ir::Pass> &pass : pass_builder_->AllPasses()) {
     if (pass->Type() == "multi_devices_pass") {
       pass->Erase("places");
@@ -145,6 +160,17 @@ std::unique_ptr<ir::Graph> BuildStrategy::Apply(
       pass->Erase("nccl_ctxs");
       pass->SetNotOwned<platform::NCCLContextMap>("nccl_ctxs", nctx);
 #endif
+    } else if (pass->Type() == "analysis_var_pass") {
+      const std::vector<OpDesc *> *all_op_descs =
+          new std::vector<OpDesc *>(main_program.Block(0).AllOps());
+      graph->Set<const std::vector<OpDesc *>>(kAllOpDescs,
+                                              all_op_descs);  // take ownership
+      graph->Set<GraphNodePool>(kGraphNodePool,
+                                new GraphNodePool);  // take ownership
+
+      pass->Erase(kAllOpDescs);
+      pass->SetNotOwned<const std::vector<OpDesc *>>(kAllOpDescs, all_op_descs);
+
     } else if (pass->Type() == "sequential_execution_pass") {
       LOG(INFO) << "set enable_sequential_execution:"
                 << enable_sequential_execution_;
@@ -166,6 +192,7 @@ std::unique_ptr<ir::Graph> BuildStrategy::Apply(
   }
   return graph;
 }
+
 }  // namespace details
 }  // namespace framework
 }  // namespace paddle
@@ -176,6 +203,7 @@ USE_PASS(multi_batch_merge_pass);
 USE_PASS(multi_devices_pass);
 USE_PASS(multi_devices_check_pass);
 USE_PASS(multi_devices_print_pass);
+USE_PASS(analysis_var_pass);
 USE_PASS(sequential_execution_pass);
 USE_PASS(all_reduce_deps_pass);
 USE_PASS(modify_op_lock_and_record_event_pass);

paddle/fluid/framework/details/build_strategy.h

@@ -60,8 +60,15 @@ struct BuildStrategy {
     kCustomized = 2,
   };
 
+  enum class OptimizeStrategy {
+    // To be Implemented,bruteforce, recursive compute unused var names.
+    kBruteForce = 0,
+    kControlFlowGraph = 1,  // use cfg_graph algorithm, faster speed.
+  };
+
   ReduceStrategy reduce_{ReduceStrategy::kAllReduce};
   GradientScaleStrategy gradient_scale_{GradientScaleStrategy::kCoeffNumDevice};
+  OptimizeStrategy strategy_{OptimizeStrategy::kControlFlowGraph};
 
   std::string debug_graphviz_path_{""};
 
@@ -69,6 +76,10 @@ struct BuildStrategy {
 
   bool enable_data_balance_{false};
 
+  bool memory_optimize_{false};
+
+  bool memory_early_delete_{false};
+
   bool enable_sequential_execution_{false};
 
   bool fuse_broadcast_op_{false};

paddle/fluid/framework/details/early_delete_op_handle.h

+// Copyright (c) 2018 PaddlePaddle Authors. All Rights Reserved.
+//
+// Licensed under the Apache License, Version 2.0 (the "License");
+// you may not use this file except in compliance with the License.
+// You may obtain a copy of the License at
+//
+//     http://www.apache.org/licenses/LICENSE-2.0
+//
+// Unless required by applicable law or agreed to in writing, software
+// distributed under the License is distributed on an "AS IS" BASIS,
+// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+// See the License for the specific language governing permissions and
+// limitations under the License.
+
+#pragma once
+#include <string>
+#include <vector>
+#include "paddle/fluid/framework/details/computation_op_handle.h"
+#include "paddle/fluid/framework/details/op_handle_base.h"
+#include "paddle/fluid/framework/details/var_handle.h"
+#include "paddle/fluid/framework/garbage_collector.h"
+#include "paddle/fluid/framework/lod_tensor_array.h"
+#include "paddle/fluid/framework/scope.h"
+#include "paddle/fluid/framework/selected_rows.h"
+#include "paddle/fluid/framework/tensor.h"
+
+namespace paddle {
+namespace framework {
+namespace details {
+
+class EarlyDeleteOpHandle : public OpHandleBase {
+ public:
+  EarlyDeleteOpHandle(ir::Node* node, const Scope* scope,
+                      const platform::Place& place,
+                      const std::vector<std::string>& names,
+                      GarbageCollector* gc)
+      : OpHandleBase(node),
+        scope_(scope),
+        place_(place),
+        names_(names),
+        gc_(gc) {
+#ifdef PADDLE_WITH_CUDA
+    if (IsStreamGarabageCollector()) {
+      auto gpu_place = boost::get<platform::CUDAPlace>(place);
+      PADDLE_ENFORCE(cudaSetDevice(gpu_place.device));
+      PADDLE_ENFORCE(cudaEventCreateWithFlags(&event_, cudaEventDisableTiming));
+    }
+#endif
+  }
+  ~EarlyDeleteOpHandle() {
+#ifdef PADDLE_WITH_CUDA
+    if (IsStreamGarabageCollector()) {
+      auto gpu_place = boost::get<platform::CUDAPlace>(dev_ctx_->GetPlace());
+      PADDLE_ENFORCE(cudaSetDevice(gpu_place.device));
+      PADDLE_ENFORCE(cudaEventDestroy(event_));
+    }
+#endif
+  }
+
+  std::string Name() const override { return "early_delete"; }
+
+ protected:
+  void RunImpl() override {
+    std::vector<std::shared_ptr<memory::Allocation>> tensors;
+    auto* local_scope = scope_->FindVar(kLocalExecScopeName)->Get<Scope*>();
+    for (auto& var_name : names_) {
+      auto* var = local_scope->FindVar(var_name);
+      PADDLE_ENFORCE(var != nullptr,
+                     string::Sprintf("Local Scope not has var %s", var_name));
+      if (var->IsType<LoDTensor>()) {
+        tensors.emplace_back(var->GetMutable<LoDTensor>()->MoveMemoryHolder());
+      } else if (var->IsType<SelectedRows>()) {
+        tensors.emplace_back(var->GetMutable<SelectedRows>()
+                                 ->mutable_value()
+                                 ->MoveMemoryHolder());
+      } else if (var->IsType<LoDTensorArray>()) {
+        LoDTensorArray* tensor_array = var->GetMutable<LoDTensorArray>();
+        for (auto& tensor : *tensor_array) {
+          tensors.emplace_back(tensor.MoveMemoryHolder());
+        }
+      }
+    }
+    if (!tensors.empty()) {
+      ClearTensors(tensors);
+    }
+  }
+
+ private:
+  void ClearTensors(
+      const std::vector<std::shared_ptr<memory::Allocation>>& tensors) {
+    if (platform::is_cpu_place(place_)) {
+      ClearCPUTensors(tensors);
+    } else {
+      ClearGPUTensors(tensors);
+    }
+  }
+
+  void ClearCPUTensors(
+      const std::vector<std::shared_ptr<memory::Allocation>>& tensors) {
+    auto* gc = dynamic_cast<CPUGarbageCollector*>(gc_);
+    if (gc != nullptr) {
+      gc->Add(tensors);
+    }
+  }
+
+  void ClearGPUTensors(
+      const std::vector<std::shared_ptr<memory::Allocation>>& tensors) {
+#ifdef PADDLE_WITH_CUDA
+    auto* gc = dynamic_cast<StreamGarbageCollector*>(gc_);
+    if (gc != nullptr) {
+      auto compute_stream = dev_ctx_->stream();
+      auto callback_stream = gc->stream();
+      auto callback_func = [=]() {
+        PADDLE_ENFORCE(cudaEventRecord(event_, compute_stream));
+        PADDLE_ENFORCE(cudaStreamWaitEvent(callback_stream, event_, 0));
+      };
+      gc_->Add(tensors, callback_func);
+    } else {
+      gc_->Add(tensors);
+    }
+  }
+
+  bool IsStreamGarabageCollector() const {
+    return dynamic_cast<const StreamGarbageCollector*>(gc_) != nullptr;
+#endif
+  }
+
+  const Scope* scope_;
+  const platform::Place place_;
+  std::vector<std::string> names_;
+  GarbageCollector* gc_;
+#ifdef PADDLE_WITH_CUDA
+  platform::CUDADeviceContext* dev_ctx_;
+  cudaEvent_t event_;
+#endif
+};
+
+}  // namespace details
+}  // namespace framework
+}  // namespace paddle

paddle/fluid/framework/details/memory_early_delete_pass.cc

+// Copyright (c) 2018 PaddlePaddle Authors. All Rights Reserved.
+//
+// Licensed under the Apache License, Version 2.0 (the "License");
+// you may not use this file except in compliance with the License.
+// You may obtain a copy of the License at
+//
+//     http://www.apache.org/licenses/LICENSE-2.0
+//
+// Unless required by applicable law or agreed to in writing, software
+// distributed under the License is distributed on an "AS IS" BASIS,
+// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+// See the License for the specific language governing permissions and
+// limitations under the License.
+
+#include "paddle/fluid/framework/details/memory_early_delete_pass.h"
+#include <queue>
+#include <string>
+#include <vector>
+#include "paddle/fluid/framework/details/memory_reuse_types.h"
+#include "paddle/fluid/framework/details/multi_devices_helper.h"
+#include "paddle/fluid/framework/details/reference_count_pass_helper.h"
+#include "paddle/fluid/framework/ir/graph_helper.h"
+
+namespace paddle {
+namespace framework {
+namespace details {
+
+static ComputationOpHandle* FindNextComputationOpHandle(VarHandle* var_in) {
+  std::queue<VarHandleBase*> queue;
+  queue.push(var_in);
+  do {
+    auto* var = queue.front();
+    queue.pop();
+    for (auto* op : var->PendingOps()) {
+      auto* compute_op = dynamic_cast<ComputationOpHandle*>(op);
+      if (compute_op != nullptr && compute_op->GetPlace() == var_in->place_) {
+        return compute_op;
+      }
+      for (auto* out_var : op->Outputs()) {
+        queue.push(out_var);
+      }
+    }
+  } while (!queue.empty());
+  return nullptr;
+}
+
+std::unique_ptr<ir::Graph> MemoryEarlyDeletePass::ApplyImpl(
+    std::unique_ptr<ir::Graph> graph) const {
+  auto& graph_pool = Get<GraphNodePool>(kGraphNodePool);
+  auto& gcs = Get<GarbageCollectorMap>(kGarbageCollector);
+
+  std::unordered_map<std::string, std::unordered_set<OpDesc*>> unlived_vars;
+  unlived_vars.reserve(graph_pool.size());
+  for (auto& pair : graph_pool) {
+    unlived_vars.insert(std::make_pair(pair.first, pair.second));
+  }
+
+  auto compare_and_insert_early_delete_op = [&](
+      OpHandleBase* op, const std::vector<VarHandleBase*>& vars) {
+    if (unlived_vars.empty()) return;
+    // unlived vars can be deleted after the last used op has finished.
+    auto* compute_op = dynamic_cast<ComputationOpHandle*>(op);
+    const auto& places = Get<std::vector<platform::Place>>(kAllPlaces);
+    for (auto& var : vars) {
+      auto* var_handle = dynamic_cast<VarHandle*>(var);
+      auto var_name = var->Node()->Name();
+      auto& var_place = var_handle->place_;
+      if (unlived_vars.count(var_name) == 0) continue;
+      if (!unlived_vars[var_name].empty()) {
+        if (compute_op != nullptr &&
+            unlived_vars[var_name].count(compute_op->Node()->Op()) != 0) {
+          unlived_vars[var_name].erase(compute_op->Node()->Op());
+        }
+        continue;
+      }
+
+      if (var_handle == nullptr || !var_handle->Node()->IsVar() ||
+          var_handle->Node()->IsCtrlVar())
+        continue;
+
+      // shameless copyed from reference count pass.
+      if (compute_op == nullptr) {
+        // use next computation op scope
+        compute_op = FindNextComputationOpHandle(var_handle);
+      }
+      auto* early_delete_node =
+          graph->CreateEmptyNode("early_delete", ir::Node::Type::kOperation);
+      GarbageCollector* gc = gcs.at(places[compute_op->GetScopeIdx()]).get();
+      auto* early_delete_handle = new EarlyDeleteOpHandle(
+          early_delete_node, compute_op->GetScope(), var_place, {var_name}, gc);
+      if (compute_op->Outputs().empty()) {
+        auto* dep_var = new DummyVarHandle(graph->CreateControlDepVar());
+        compute_op->AddOutput(dep_var);
+        graph->Get<GraphDepVars>(kGraphDepVars).emplace(dep_var);
+      }
+      early_delete_handle->AddInput(compute_op->Outputs().front());
+      VLOG(5) << "Add early delete op " << var_name << " to Operator"
+              << compute_op->Name();
+    }
+  };
+
+  auto all_ops = ir::FilterByNodeWrapper<OpHandleBase>(*graph);
+  for (auto& op : all_ops) {
+    compare_and_insert_early_delete_op(op, op->Inputs());
+    compare_and_insert_early_delete_op(op, op->Outputs());
+  }
+  return graph;
+}
+
+}  // namespace details
+}  // namespace framework
+}  // namespace paddle
+
+REGISTER_PASS(memory_early_delete_pass,
+              paddle::framework::details::MemoryEarlyDeletePass)
+    .RequireGraphAttr(paddle::framework::details::kGraphNodePool)
+    .RequireGraphAttr(paddle::framework::details::kGarbageCollector);

paddle/fluid/framework/details/memory_early_delete_pass.h

+// Copyright (c) 2018 PaddlePaddle Authors. All Rights Reserved.
+//
+// Licensed under the Apache License, Version 2.0 (the "License");
+// you may not use this file except in compliance with the License.
+// You may obtain a copy of the License at
+//
+//     http://www.apache.org/licenses/LICENSE-2.0
+//
+// Unless required by applicable law or agreed to in writing, software
+// distributed under the License is distributed on an "AS IS" BASIS,
+// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+// See the License for the specific language governing permissions and
+// limitations under the License.
+
+#pragma once
+#include "paddle/fluid/framework/details/early_delete_op_handle.h"
+#include "paddle/fluid/framework/ir/graph.h"
+#include "paddle/fluid/framework/ir/pass.h"
+
+namespace paddle {
+namespace framework {
+namespace details {
+
+class MemoryEarlyDeletePass : public ir::Pass {
+ protected:
+  std::unique_ptr<ir::Graph> ApplyImpl(
+      std::unique_ptr<ir::Graph> graph) const override;
+};
+
+}  // namespace details
+}  // namespace framework
+}  // namespace paddle

paddle/fluid/framework/details/memory_reuse_types.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/memory_reuse_types.h"
+#include <iostream>
+#include <sstream>
+#include <string>
+
+namespace paddle {
+namespace framework {
+namespace details {
+
+size_t NodeSizeInBytes(ir::Node* n) {
+  auto* desc = FindVarDescInBlock(n);
+  auto shape = desc->GetShape();
+  size_t type_size = SizeOfType(desc->GetDataType());
+  int size = 1;
+  for (auto& s : shape) {
+    size *= s;
+  }
+  return type_size * std::abs(size);
+}
+
+std::string DebugStringImpl(VarDesc* var) {
+  std::stringstream ss;
+  ss << var->Name();
+  ss << "[";
+  try {
+    auto shape = var->GetShape();
+    for (size_t i = 0; i < shape.size(); ++i) {
+      if (i != shape.size() - 1) {
+        ss << shape[i] << ",";
+      } else {
+        ss << shape[i];
+      }
+    }
+    ss << "]";
+  } catch (...) {
+    ss << "Var has no VarDesc !!! Name:" << var->Name();
+  }
+  return ss.str();
+}
+
+std::string DebugString(ir::Node* var) {
+  return DebugStringImpl(FindVarDescInBlock(var));
+}
+// return DebugString(var->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) {
+  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());
+}
+
+struct NodeComparator {
+  bool operator()(ir::Node* lhs, ir::Node* rhs) const {
+    auto* lhs_desc = FindVarDescInBlock(lhs);
+    auto* rhs_desc = FindVarDescInBlock(rhs);
+    auto lhs_shape = lhs_desc->GetShape();
+    auto rhs_shape = rhs_desc->GetShape();
+    if ((lhs_shape[0] == -1 && rhs_shape[0] == -1) ||
+        (lhs_shape[0] != -1 && rhs_shape[0] != -1)) {
+      return NodeSizeInBytes(lhs) <= NodeSizeInBytes(rhs);
+    } else {
+      return false;
+    }
+  }
+};
+
+void OrderedNodePairPool::Insert(ir::Node* var, ir::Node* op) {
+  PADDLE_ENFORCE(var->IsVar() && !var->IsCtrlVar());
+  PADDLE_ENFORCE(op->IsOp());
+  if (mark_table_.count(var->Name()) != 0) {
+    mark_table_[var->Name()]->second.insert(op);
+    return;
+  }
+
+  auto* var_desc = FindVarDescInBlock(var);
+  auto var_shape = var_desc->GetShape();
+  int batch_size = static_cast<int>(var_shape[0]);
+
+  NodeComparator compare_node;
+  Iter it = nodes_.begin();
+  while (it != nodes_.end()) {
+    auto* cache_desc = FindVarDescInBlock(it->first);
+    int cache_batch_size = cache_desc->GetShape()[0];
+    if ((cache_batch_size == -1 && batch_size == -1) ||
+        (cache_batch_size != -1 && batch_size != -1)) {
+      if (compare_node(it->first, var)) {
+        ++it;
+      } else {
+        break;
+      }
+    } else if (cache_batch_size == -1 && batch_size != -1) {
+      ++it;
+    } else if (cache_batch_size != -1 && batch_size == -1) {
+      break;
+    }
+  }
+
+  it =
+      nodes_.insert(it, std::make_pair(var, std::unordered_set<ir::Node*>{op}));
+  mark_table_[var->Name()] = it;
+}
+
+int OrderedNodePairPool::GetIndex(ir::Node* var) {
+  return std::distance(nodes_.begin(), mark_table_[var->Name()]);
+}
+
+ir::Node* OrderedNodePairPool::NodeMatch(ir::Node* var) const {
+  ir::Node* found_node = nullptr;
+  NodeComparator compare_node;
+
+  for (auto it = nodes_.begin(); it != nodes_.end(); ++it) {
+    if (compare_node(var, it->first)) {
+      found_node = it->first;
+      break;
+    }
+  }
+  return found_node;
+}
+
+void OrderedNodePairPool::Erase(ir::Node* var) {
+  PADDLE_ENFORCE(mark_table_.count(var->Name()));
+  nodes_.erase(mark_table_[var->Name()]);
+  mark_table_.erase(var->Name());
+}
+
+std::string OrderedNodePairPool::ToString() const {
+  std::stringstream ss;
+  for (auto it = nodes_.begin(); it != nodes_.end(); ++it) {
+    ss << DebugString(it->first) << " ";
+  }
+  return ss.str();
+}
+
+}  // namespace details
+}  // namespace framework
+}  // namespace paddle

paddle/fluid/framework/details/memory_reuse_types.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 <algorithm>
+#include <iostream>
+#include <iterator>
+#include <list>
+#include <string>
+#include <utility>
+#include <vector>
+#include "paddle/fluid/framework/data_type.h"
+#include "paddle/fluid/framework/ir/graph.h"
+
+namespace paddle {
+namespace framework {
+namespace details {
+
+constexpr char kFetchedVars[] = "fetched_vars";
+constexpr char kGraphNodePool[] = "graph_node_pool";
+
+// NOTE(dzh): Variable and the operators use the var.
+// for early delete pass.
+// Because analysis var pass build base on ir::Node, which maybe released
+// or modified between passes, so we use OpDesc* to mark ops.
+using GraphNodePool = std::vector<
+    std::pair<std::string /*var node*/, std::unordered_set<OpDesc*> /* ops */>>;
+
+// NOTE(dzh): by default, it sort node in ascend order(by node bytes size).
+// in fluid, -1 means the batch_size is determined in runtime.
+// the node batch_size equal -1 always ranking in the front than the node not.
+// For example,
+// node0[-1, 1] node1[-1, 1, 1], node2[1,1], node3[1,1024], ..
+// O(1) insert, delete
+class OrderedNodePairPool {
+ public:
+  using NodePair = std::pair<ir::Node*, std::unordered_set<ir::Node*>>;
+  using Iter = typename std::list<NodePair>::iterator;
+  using ConstIter = typename std::list<NodePair>::const_iterator;
+
+  void Insert(ir::Node* var, ir::Node* op);
+
+  void Erase(ir::Node* var);
+
+  bool Has(ir::Node* var) { return mark_table_.count(var->Name()); }
+
+  ir::Node* NodeMatch(ir::Node* var) const;
+  // map store non-const iterator, can not promise const
+  int GetIndex(ir::Node* var);
+  // pool all node to string
+  std::string ToString() const;
+
+  Iter begin() { return nodes_.begin(); }
+  Iter end() { return nodes_.end(); }
+  ConstIter begin() const { return nodes_.begin(); }
+  ConstIter end() const { return nodes_.end(); }
+  size_t size() const { return nodes_.size(); }
+
+ private:
+  // for searching.
+  std::unordered_map<std::string, Iter> mark_table_;
+  // node swap pairs. var -> ops dep var
+  std::list<NodePair> nodes_;
+};
+
+// node memory size in bytes
+size_t NodeSizeInBytes(ir::Node* n);
+
+std::string DebugString(ir::Node* var);
+
+// std::string DebugString(VarDesc* var);
+VarDesc* FindVarDescInBlock(ir::Node* n);
+
+}  // namespace details
+}  // namespace framework
+}  // namespace paddle

paddle/fluid/framework/details/memory_reuse_types_test.cc

+// Copyright (c) 2018 PaddlePaddle Authors. All Rights Reserved.
+//
+// Licensed under the Apache License, Version 2.0 (the "License");
+// you may not use this file except in compliance with the License.
+// You may obtain a copy of the License at
+//
+//     http://www.apache.org/licenses/LICENSE-2.0
+//
+// Unless required by applicable law or agreed to in writing, software
+// distributed under the License is distributed on an "AS IS" BASIS,
+// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+// See the License for the specific language governing permissions and
+// limitations under the License.
+
+#include "paddle/fluid/framework/details/memory_reuse_types.h"
+#include <algorithm>
+#include <iostream>
+#include <memory>
+#include <sstream>
+#include <string>
+#include <utility>
+#include <vector>
+#include "glog/logging.h"
+#include "gtest/gtest.h"
+
+namespace paddle {
+namespace framework {
+namespace details {
+
+TEST(OrderedNodePairPool, Normal) {
+  OrderedNodePairPool pool;
+  std::vector<std::unique_ptr<ir::Node>> nodes;
+
+  // clang-format off
+  std::vector<std::vector<int64_t>> shapes = {{-1, 10},
+                                              {-1, 20},
+                                              {1, 2},
+                                              {5, 2},
+                                              {10, 20},
+                                              {-1, 2, 5},
+                                              {-1, 1, 5},
+                                              {-1, 1}};
+  // clang-format on
+  const int COUNT = shapes.size();
+  ProgramDesc prog;
+  BlockDesc* block_desc = prog.MutableBlock(0);
+  auto* op_desc = block_desc->AppendOp();
+  op_desc->SetType("dummy");
+  std::unique_ptr<ir::Node> op = ir::CreateNodeForTest(op_desc);
+
+  for (int i = 0; i < COUNT; ++i) {
+    auto desc = block_desc->Var(std::to_string(i));
+    desc->SetShape(shapes[i]);
+    std::unique_ptr<ir::Node> node = ir::CreateNodeForTest(desc);
+    node->inputs.emplace_back(op.get());
+    nodes.emplace_back(std::move(node));
+  }
+
+  for (auto& node : nodes) {
+    pool.Insert(node.get(), op.get());
+  }
+
+  // assert its order and interface.
+  std::cout << pool.ToString() << std::endl;
+  pool.Erase(nodes.front().get());
+  std::cout << pool.ToString() << std::endl;
+
+  ASSERT_EQ(pool.size(), static_cast<size_t>(COUNT - 1));
+  ASSERT_EQ(pool.GetIndex(nodes.back().get()), 0);
+
+  {
+    auto v1 = block_desc->Var("11");
+    v1->SetShape({-1, 256, 56, 56});
+    std::unique_ptr<ir::Node> node1 = ir::CreateNodeForTest(v1);
+    node1->inputs.emplace_back(op.get());
+    auto* cache = pool.NodeMatch(node1.get());
+    ASSERT_EQ(cache, nullptr);
+  }
+  {
+    auto v2 = block_desc->Var("12");
+    v2->SetShape({-1, 2, 5});
+    std::unique_ptr<ir::Node> node1 = ir::CreateNodeForTest(v2);
+    node1->inputs.emplace_back(op.get());
+    auto* cache = pool.NodeMatch(node1.get());
+    ASSERT_EQ(pool.GetIndex(cache), 2);  // match 6:[-1,2,5]
+  }
+  {
+    auto v3 = block_desc->Var("13");
+    v3->SetShape({2, 5});
+    std::unique_ptr<ir::Node> node1 = ir::CreateNodeForTest(v3);
+    node1->inputs.emplace_back(op.get());
+    auto* cache = pool.NodeMatch(node1.get());
+    ASSERT_EQ(pool.GetIndex(cache), 5);  // match  4:[5,2]
+  }
+}
+
+}  // namespace details
+}  // namespace framework
+}  // namespace paddle

paddle/fluid/framework/details/multi_devices_graph_print_pass.cc

@@ -85,4 +85,5 @@ void GraphvizSSAGraphPrinter::Print(const ir::Graph &graph,
 }  // namespace paddle
 
 REGISTER_PASS(multi_devices_print_pass,
-              paddle::framework::details::SSAGraghBuilderWithPrinter);
+              paddle::framework::details::SSAGraghBuilderWithPrinter)
+    .RequirePassAttr(paddle::framework::details::kGraphvizPath);

paddle/fluid/framework/details/multi_devices_graph_print_pass.h

@@ -14,6 +14,7 @@
 
 #pragma once
 
+#include <glog/logging.h>
 #include <fstream>
 #include <iosfwd>
 #include <ostream>
@@ -24,6 +25,8 @@ namespace paddle {
 namespace framework {
 namespace details {
 
+constexpr char kGraphvizPath[] = "debug_graphviz_path";
+
 class SSAGraphPrinter {
  public:
   virtual ~SSAGraphPrinter() {}
@@ -40,7 +43,7 @@ class SSAGraghBuilderWithPrinter : public ir::Pass {
   std::unique_ptr<ir::Graph> ApplyImpl(
       std::unique_ptr<ir::Graph> graph) const override {
     std::unique_ptr<std::ostream> fout(
-        new std::ofstream(Get<const std::string>("debug_graphviz_path")));
+        new std::ofstream(Get<std::string>(kGraphvizPath)));
     PADDLE_ENFORCE(fout->good());
     Get<GraphvizSSAGraphPrinter>("graph_printer").Print(*graph, *fout);
     return graph;

paddle/fluid/framework/details/op_handle_base.h

@@ -25,7 +25,7 @@ namespace paddle {
 namespace framework {
 namespace details {
 
-constexpr char kLocalExecScopeName[] = "@LCOAL_SCOPE@";
+constexpr char kLocalExecScopeName[] = "@LOCAL_SCOPE@";
 
 // Wraps ir::Node and provide helper utilities.
 // It's responsible for populating necessary fields of ir::Node.

paddle/fluid/framework/executor.cc

@@ -100,7 +100,7 @@ static void DeleteUnusedTensors(
           continue;
         }
         auto* var = scope.FindVar(name);
-        if (var != nullptr) {
+        if (var == nullptr) {
           continue;
         }
 

paddle/fluid/framework/executor_thread_worker.cc

@@ -13,6 +13,7 @@ See the License for the specific language governing permissions and
 limitations under the License. */
 
 #include "paddle/fluid/framework/executor_thread_worker.h"
+#include <algorithm>
 #include "google/protobuf/io/zero_copy_stream_impl.h"
 #include "google/protobuf/message.h"
 #include "google/protobuf/text_format.h"
@@ -32,6 +33,89 @@ limitations under the License. */
 namespace paddle {
 namespace framework {
 
+#ifdef PADDLE_WITH_PSLIB
+int DensePullThread::start() {
+  _running = true;
+  _t = std::thread(&DensePullThread::run, this);
+  return 0;
+}
+
+void DensePullThread::run() {
+  while (_running) {
+    _pull_dense_status.resize(0);
+    for (auto& t : _dense_variable_name) {
+      if (check_update_param(t.first)) {
+        auto status = pull_dense(t.first);
+        _pull_dense_status.emplace_back(std::move(status));
+        reset_thread_version(t.first);
+      }
+    }
+    if (_pull_dense_status.size() != 0) {
+      wait_all();
+    }
+
+    usleep(_sleep_time_ms * 1000);
+  }
+}
+bool DensePullThread::check_update_param(uint64_t table_id) {
+  {
+    std::lock_guard<std::mutex> lock(_mutex_for_version);
+    auto& version = _training_versions[table_id];
+    _current_version[table_id] =
+        *(std::min_element(version.begin(), version.end()));
+  }
+  if (_current_version[table_id] - _last_versions[table_id] < _threshold) {
+    return false;
+  }
+  return true;
+}
+
+void DensePullThread::reset_thread_version(uint64_t table_id) {
+  std::lock_guard<std::mutex> lock(_mutex_for_version);
+  _last_versions[table_id] = _current_version[table_id];
+}
+std::future<int32_t> DensePullThread::pull_dense(uint64_t table_id) {
+  auto& regions = _regions[table_id];
+  regions.clear();
+  auto& variables = _dense_variable_name[table_id];
+  regions.resize(variables.size());
+
+  for (auto i = 0u; i < variables.size(); ++i) {
+    auto& t = variables[i];
+    Variable* var = _root_scope->FindVar(t);
+    LoDTensor* tensor = var->GetMutable<LoDTensor>();
+
+    float* w = tensor->data<float>();
+    paddle::ps::Region reg(w, tensor->numel());
+    regions[i] = std::move(reg);
+  }
+  return _ps_client->pull_dense(regions.data(), regions.size(), table_id);
+}
+
+void DensePullThread::wait_all() {
+  for (auto& t : _pull_dense_status) {
+    t.wait();
+    auto status = t.get();
+    if (status != 0) {
+      LOG(WARNING) << "pull dense failed times:" << ++_pull_dense_fail_times;
+    }
+  }
+
+  if (_pull_dense_fail_times > 20) {
+    LOG(FATAL) << "pull dense failed times more than 20 times";
+    exit(-1);
+  }
+
+  _pull_dense_status.resize(0);
+}
+
+void DensePullThread::increase_thread_version(int thread_id,
+                                              uint64_t table_id) {
+  std::lock_guard<std::mutex> lock(_mutex_for_version);
+  _training_versions[table_id][thread_id]++;
+}
+#endif
+
 void ExecutorThreadWorker::CreateThreadOperators(const ProgramDesc& program) {
   auto& block = program.Block(0);
   op_names_.clear();
@@ -202,5 +286,358 @@ void ExecutorThreadWorker::SetRootScope(Scope* g_scope) {
   root_scope_ = g_scope;
 }
 
+#ifdef PADDLE_WITH_PSLIB
+//  AsyncExecutor
+void AsyncExecutorThreadWorker::TrainFiles() {
+  SetDevice();
+
+  int fetch_var_num = fetch_var_names_.size();
+  fetch_values_.clear();
+  fetch_values_.resize(fetch_var_num);
+
+  thread_reader_->Start();
+
+  int cur_batch;
+  int batch_cnt = 0;
+  while ((cur_batch = thread_reader_->Next()) > 0) {
+    // executor run here
+    TrainOneNetwork();
+
+    ++batch_cnt;
+    thread_scope_->DropKids();
+
+    if (debug_ == false || thread_id_ != 0) {
+      continue;
+    }
+
+    for (int i = 0; i < fetch_var_num; ++i) {
+      print_fetch_var(thread_scope_, fetch_var_names_[i]);
+    }  // end for (int i = 0...)
+  }    // end while ()
+}
+
+void AsyncExecutorThreadWorker::SetPSlibPtr(
+    std::shared_ptr<paddle::distributed::PSlib> pslib_ptr) {
+  _pslib_ptr = pslib_ptr;
+}
+void AsyncExecutorThreadWorker::SetPullDenseThread(
+    std::shared_ptr<DensePullThread> dpt) {
+  _pull_dense_thread = dpt;
+}
+void AsyncExecutorThreadWorker::TrainOneNetwork() {
+  PrepareParams();
+
+  for (auto& op : ops_) {
+    if (op->Type().find("sgd") != std::string::npos) {
+      continue;
+    }
+    bool need_skip = false;
+    for (auto t = 0u; t < _param_config->skip_op.size(); ++t) {
+      if (op->Type().find(_param_config->skip_op[t]) != std::string::npos) {
+        need_skip = true;
+        break;
+      }
+    }
+    if (!need_skip) {
+      op->Run(*thread_scope_, place_);
+    }
+  }
+  UpdateParams();
+}
+
+void AsyncExecutorThreadWorker::SetParamConfig(
+    AsyncWorkerParamConfig* param_config) {
+  _param_config = param_config;
+}
+
+void AsyncExecutorThreadWorker::PrepareParams() {
+  for (auto table_id : _param_config->sparse_table_id) {
+    PullSparse(table_id);
+    for (auto& t : _pull_sparse_status) {
+      t.wait();
+      auto status = t.get();
+      if (status != 0) {
+        LOG(ERROR) << "pull sparse failed, status[" << status << "]";
+        exit(-1);
+      }
+    }
+  }
+  _pull_sparse_status.resize(0);
+
+  for (auto table_id : _param_config->sparse_table_id) {
+    FillSparse(table_id);
+  }
+}
+
+void AsyncExecutorThreadWorker::UpdateParams() {
+  for (auto i : _param_config->sparse_table_id) {
+    PushSparse(i);
+  }
+  for (auto i : _param_config->dense_table_id) {
+    PushDense(i);
+  }
+  int32_t tmp_push_dense_wait_times = -1;
+  int32_t tmp_push_sparse_wait_times = -1;
+  static uint32_t push_dense_wait_times =
+      static_cast<uint32_t>(tmp_push_dense_wait_times);
+  static uint32_t push_sparse_wait_times =
+      static_cast<uint32_t>(tmp_push_sparse_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 (_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);
+  }
+  for (auto dense_table_id : _param_config->dense_table_id) {
+    _pull_dense_thread->increase_thread_version(thread_id_, dense_table_id);
+  }
+}
+
+void AsyncExecutorThreadWorker::PushDense(int table_id) {
+  std::vector<paddle::ps::Region> regions;
+  for (auto& t : _param_config->dense_gradient_variable_name[table_id]) {
+    Variable* var = thread_scope_->FindVar(t);
+    CHECK(var != nullptr) << "var[" << t << "] not found";
+    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_dense_status.push_back(std::move(status));
+}
+
+void AsyncExecutorThreadWorker::PullSparse(int table_id) {
+  auto& features = _features[table_id];
+  auto& feature_value = _feature_value[table_id];
+  auto fea_dim = _param_config->fea_dim;
+  // slot id starts from 1
+  features.clear();
+  features.resize(0);
+  features.reserve(MAX_FEASIGN_NUM);
+  const std::vector<std::string>& feed_vec = thread_reader_->GetUseSlotAlias();
+  // slot_idx = 0 is label TODO
+  for (auto slot_idx = 1u; slot_idx < feed_vec.size(); ++slot_idx) {
+    Variable* var = thread_scope_->FindVar(feed_vec[slot_idx]);
+    LoDTensor* tensor = var->GetMutable<LoDTensor>();
+    int64_t* ids = tensor->data<int64_t>();
+    int len = tensor->numel();
+    for (auto i = 0u; i < len; ++i) {
+      // todo(colourful-tree): current trick - filter feasign=use_slot_mod(
+      // bug: datafeed fill use_slot_mod for empty slot)
+      if (ids[i] == 0u) {
+        continue;
+      }
+      features.push_back(static_cast<uint64_t>(ids[i]));
+    }
+  }
+  check_pull_push_memory(features, &feature_value, fea_dim);
+
+  std::vector<float*> pull_feature_value;
+  for (auto i = 0u; i < features.size(); ++i) {
+    pull_feature_value.push_back(feature_value[i].data());
+  }
+
+  auto status = _pslib_ptr->_worker_ptr->pull_sparse(
+      pull_feature_value.data(), table_id, features.data(), features.size());
+  _pull_sparse_status.push_back(std::move(status));
+
+  auto& push_g = _feature_push_value[table_id];
+  check_pull_push_memory(features, &push_g, fea_dim);
+
+  collect_feasign_info(table_id);
+}
+
+void AsyncExecutorThreadWorker::FillSparse(int table_id) {
+  auto slot_dim = _param_config->slot_dim;
+  auto fea_dim = _param_config->fea_dim;
+  auto& features = _features[table_id];
+  auto& fea_value = _feature_value[table_id];
+
+  CHECK(features.size() > 0) << "feature size check failed";
+
+  auto fea_idx = 0u;
+
+  std::vector<float> init_value(fea_dim);
+
+  const std::vector<std::string>& feed_vec = thread_reader_->GetUseSlotAlias();
+  // slot_idx = 0 is label TODO
+  for (auto slot_idx = 1u; slot_idx < feed_vec.size(); ++slot_idx) {
+    Variable* var = thread_scope_->FindVar(feed_vec[slot_idx]);
+    LoDTensor* tensor = var->GetMutable<LoDTensor>();
+    int64_t* ids = tensor->data<int64_t>();
+    int len = tensor->numel();
+    Variable* var_emb = thread_scope_->FindVar(
+        _param_config->slot_input_vec[table_id][slot_idx - 1]);
+    LoDTensor* tensor_emb = var_emb->GetMutable<LoDTensor>();
+    float* ptr =
+        tensor_emb->mutable_data<float>({len, slot_dim}, platform::CPUPlace());
+    memset(ptr, 0, sizeof(float) * len * slot_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 + slot_dim * index, init_value.data() + 2,
+               sizeof(float) * slot_dim);
+        continue;
+      }
+      memcpy(ptr + slot_dim * index, fea_value[fea_idx].data() + 2,
+             sizeof(float) * slot_dim);
+      fea_idx++;
+    }
+  }
+}
+
+void AsyncExecutorThreadWorker::PushSparse(int table_id) {
+  auto slot_dim = _param_config->slot_dim;
+  auto fea_dim = _param_config->fea_dim;
+  auto& features = _features[table_id];
+  auto& push_g = _feature_push_value[table_id];
+  check_pull_push_memory(features, &push_g, fea_dim);
+  CHECK(push_g.size() == features.size() + 1)
+      << "push_g size:" << push_g.size()
+      << " features size:" << features.size();
+  uint64_t fea_idx = 0u;
+  auto& fea_info = _fea_info[table_id];
+  int offset = 2;
+  const std::vector<std::string>& feed_vec = thread_reader_->GetUseSlotAlias();
+  // slot_idx = 0 is label
+  for (auto slot_idx = 1u; slot_idx < feed_vec.size(); ++slot_idx) {
+    if (_param_config->slot_alias_to_table.find(feed_vec[slot_idx]) ==
+        _param_config->slot_alias_to_table.end()) {
+      LOG(ERROR) << "ERROR slot_idx:" << slot_idx
+                 << " name:" << feed_vec[slot_idx];
+    } else if (_param_config->slot_alias_to_table[feed_vec[slot_idx]] !=
+               table_id) {
+      continue;
+    }
+    Variable* g_var = thread_scope_->FindVar(
+        _param_config->gradient_var[table_id][slot_idx - 1]);
+    CHECK(g_var != nullptr)
+        << "var[" << _param_config->gradient_var[table_id][slot_idx - 1]
+        << "] not found";
+    LoDTensor* g_tensor = g_var->GetMutable<LoDTensor>();
+    if (g_tensor == NULL) {
+      LOG(ERROR) << "var["
+                 << _param_config->gradient_var[table_id][slot_idx - 1]
+                 << "] not found";
+      exit(-1);
+    }
+    float* g = g_tensor->data<float>();
+
+    Variable* var = thread_scope_->FindVar(feed_vec[slot_idx]);
+    CHECK(var != nullptr) << "var[" << feed_vec[slot_idx] << "] not found";
+    LoDTensor* tensor = var->GetMutable<LoDTensor>();
+    if (tensor == NULL) {
+      LOG(ERROR) << "var[" << feed_vec[slot_idx] << "] not found";
+      exit(-1);
+    }
+    int len = tensor->numel();
+    CHECK(slot_dim * len == g_tensor->numel())
+        << "len:" << len << " g_numel:" << g_tensor->numel();
+    CHECK(len == tensor->numel()) << "len:" << len
+                                  << "t_numel:" << tensor->numel();
+    int64_t* ids = tensor->data<int64_t>();
+    for (auto id_idx = 0u; id_idx < len; ++id_idx) {
+      if (ids[id_idx] == 0) {
+        g += slot_dim;
+        continue;
+      }
+      memcpy(push_g[fea_idx].data() + offset, g, sizeof(float) * slot_dim);
+      push_g[fea_idx][0] = 1.0f;
+      CHECK(fea_idx < fea_info.size()) << "fea_idx:" << fea_idx
+                                       << " size:" << fea_info.size();
+      push_g[fea_idx][1] = static_cast<float>(fea_info[fea_idx].label);
+      g += slot_dim;
+      fea_idx++;
+    }
+  }
+  CHECK(fea_idx == features.size()) << "fea_idx:" << fea_idx
+                                    << " features size:" << features.size();
+  CHECK_GT(features.size(), 0);
+
+  std::vector<float*> push_g_vec;
+  for (auto i = 0u; i < features.size(); ++i) {
+    push_g_vec.push_back(push_g[i].data());
+  }
+  auto status = _pslib_ptr->_worker_ptr->push_sparse(
+      table_id, features.data(), (const float**)push_g_vec.data(),
+      features.size());
+  _push_sparse_status.push_back(std::move(status));
+}
+
+void AsyncExecutorThreadWorker::collect_feasign_info(int table_id) {
+  auto& fea_info = _fea_info[table_id];
+  auto& feature = _features[table_id];
+  fea_info.resize(feature.size());
+  const std::vector<std::string>& feed_vec = thread_reader_->GetUseSlotAlias();
+  Variable* var = thread_scope_->FindVar(feed_vec[0]);
+  LoDTensor* tensor = var->GetMutable<LoDTensor>();
+  int64_t* label = tensor->data<int64_t>();
+
+  int global_index = 0;
+  for (auto slot_idx = 1u; slot_idx < feed_vec.size(); ++slot_idx) {
+    Variable* var = thread_scope_->FindVar(feed_vec[slot_idx]);
+    LoDTensor* tensor = var->GetMutable<LoDTensor>();
+    int64_t* ids = tensor->data<int64_t>();
+
+    int fea_idx = 0;
+    for (auto ins_idx = 1u; ins_idx < tensor->lod()[0].size(); ++ins_idx) {
+      for (; fea_idx < tensor->lod()[0][ins_idx]; ++fea_idx) {
+        if (ids[fea_idx] == 0u) {
+          continue;
+        }
+        FeasignInfo info{slot_idx, ins_idx, label[ins_idx - 1]};
+
+        fea_info[global_index++] = std::move(info);
+      }
+    }
+  }
+  CHECK(global_index == feature.size())
+      << "expect fea info size:" << feature.size() << " real:" << global_index;
+}
+
+void AsyncExecutorThreadWorker::check_pull_push_memory(
+    const std::vector<uint64_t>& features,
+    std::vector<std::vector<float>>* push_g, int dim) {
+  push_g->resize(features.size() + 1);
+  for (auto& t : *push_g) {
+    t.resize(dim);
+  }
+}
+
+void AsyncExecutorThreadWorker::check_pull_push_memory(
+    const std::vector<uint64_t>& features, std::vector<float*>* push_g,
+    int dim) {
+  if (features.size() > push_g->size()) {
+    push_g->reserve(features.size() + 1);
+    auto size = features.size() - push_g->size() + 1;
+    for (auto i = 0u; i < size; ++i) {
+      float* ptr = new float[dim];
+      push_g->push_back(ptr);
+    }
+  }
+}
+#endif
+
 }  // einit_modelnd namespace framework
 }  // end namespace paddle

paddle/fluid/framework/executor_thread_worker.h

@@ -25,16 +25,119 @@ limitations under the License. */
 #include "paddle/fluid/framework/executor.h"
 #include "paddle/fluid/framework/program_desc.h"
 #include "paddle/fluid/framework/scope.h"
+#ifdef PADDLE_WITH_PSLIB
+#include <pslib.h>
+#endif
 
 namespace paddle {
 namespace framework {
+
 void CreateTensor(Variable* var, proto::VarType::Type var_type);
+#ifdef PADDLE_WITH_PSLIB
+static const uint32_t MAX_FEASIGN_NUM = 1000 * 100 * 100;
+
+struct AsyncWorkerParamConfig {
+  int slot_dim;
+  int fea_dim;
+  int32_t tmp_push_dense_wait_times;
+  int32_t tmp_push_sparse_wait_times;
+
+  std::vector<std::string> skip_op;
+
+  std::map<uint64_t, std::vector<std::string>> dense_variable_name;
+  std::map<uint64_t, std::vector<std::string>> dense_gradient_variable_name;
+  std::vector<int> dense_table_id;
+  // fea_dim for each dense table
+  std::vector<uint32_t> dense_table_size;
+  std::vector<int> sparse_table_id;
+  std::map<uint64_t, std::vector<std::string>> slot_input_vec;
+  std::map<uint64_t, std::vector<std::string>> gradient_var;
+  std::map<std::string, uint64_t> slot_alias_to_table;
+};
+
+struct DensePullThreadParam {
+  std::shared_ptr<paddle::ps::PSClient> ps_client;
+  int threshold;
+  int training_thread_num;
+  Scope* root_scope;
+  std::map<uint64_t, std::vector<std::string>>* dense_params;
+  int sleep_time_ms = 2;
+};
+
+class DensePullThread {
+ public:
+  explicit DensePullThread(const DensePullThreadParam& param)
+      : _running(false) {
+    _ps_client = param.ps_client;
+    _threshold = param.threshold;
+    _thread_num = param.training_thread_num;
+    _root_scope = param.root_scope;
+    _sleep_time_ms = param.sleep_time_ms;
+
+    for (auto& t : *param.dense_params) {
+      _dense_variable_name[t.first].insert(_dense_variable_name[t.first].end(),
+                                           t.second.begin(), t.second.end());
+      _training_versions[t.first].resize(_thread_num, 0);
+      _last_versions[t.first] = 0;
+      _current_version[t.first] = 0;
+    }
+  }
+
+  int start();
+
+  void stop() {
+    if (_running) {
+      _running = false;
+      _t.join();
+    }
+  }
+
+  void increase_thread_version(int thread_id, uint64_t table_id);
+  void reset_thread_version(uint64_t table_id);
+  std::future<int32_t> pull_dense(uint64_t table_id);
+  void pull_dense2(uint64_t table_id);
+  void wait_all();
+
+ private:
+  void run();
+  bool check_update_param(uint64_t table_id);
+
+ private:
+  std::shared_ptr<paddle::ps::PSClient> _ps_client;
+  int _thread_num;
+  int _threshold;
+  int _sleep_time_ms;
+  Scope* _root_scope;
+  bool _running;
+
+  std::map<uint64_t, uint64_t> _last_versions;
+  std::map<uint64_t, uint64_t> _current_version;
+  std::mutex _mutex_for_version;
+  std::map<uint64_t, std::vector<uint64_t>> _training_versions;
+  std::map<uint64_t, std::vector<std::string>> _dense_variable_name;
+
+  std::thread _t;
+
+  std::vector<::std::future<int32_t>> _pull_dense_status;
+
+  std::map<uint64_t, std::vector<paddle::ps::Region>> _regions;
+  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;
+};
+#endif
 
 class ExecutorThreadWorker {
  public:
   ExecutorThreadWorker()
       : thread_id_(-1), root_scope_(NULL), thread_scope_(NULL), debug_(false) {}
-  ~ExecutorThreadWorker() {}
+  virtual ~ExecutorThreadWorker() {}
 
   void CreateThreadResource(const framework::ProgramDesc& program,
                             const paddle::platform::Place& place);
@@ -51,9 +154,15 @@ class ExecutorThreadWorker {
   // set data feed declared in executor
   void SetDataFeed(const std::shared_ptr<DataFeed>& datafeed);
   // A multi-thread training function
-  void TrainFiles();
+  virtual void TrainFiles();
   // set fetch variable names from python interface assigned by users
   void SetFetchVarNames(const std::vector<std::string>& fetch_var_names);
+#ifdef PADDLE_WITH_PSLIB
+  virtual void SetPSlibPtr(
+      std::shared_ptr<paddle::distributed::PSlib> pslib_ptr) {}
+  virtual void SetPullDenseThread(std::shared_ptr<DensePullThread> dpt) {}
+  virtual void SetParamConfig(AsyncWorkerParamConfig* param_config) {}
+#endif
 
  private:
   void CreateThreadScope(const framework::ProgramDesc& program);
@@ -77,12 +186,58 @@ class ExecutorThreadWorker {
   Scope* root_scope_;
   // a thread scope, father scope is global score which is shared
   Scope* thread_scope_;
-
- private:
   std::vector<std::string> fetch_var_names_;
   std::vector<std::vector<float>> fetch_values_;
   bool debug_;
 };
 
+#ifdef PADDLE_WITH_PSLIB
+class AsyncExecutorThreadWorker : public ExecutorThreadWorker {
+ public:
+  AsyncExecutorThreadWorker() {}
+  virtual ~AsyncExecutorThreadWorker() {}
+  void SetPSlibPtr(std::shared_ptr<paddle::distributed::PSlib> pslib_ptr);
+  void SetPullDenseThread(std::shared_ptr<DensePullThread> dpt);
+  void SetParamConfig(AsyncWorkerParamConfig* param_config);
+  void TrainFiles();
+  void TrainOneNetwork();
+  void PrepareParams();
+  void UpdateParams();
+  void PullSparse(int table_id);
+  void FillSparse(int table_id);
+  void PushSparse(int table_id);
+  void PushDense(int table_id);
+
+  void check_pull_push_memory(const std::vector<uint64_t>& features,
+                              std::vector<float*>* push_g, int dim);
+  void check_pull_push_memory(const std::vector<uint64_t>& features,
+                              std::vector<std::vector<float>>* push_g, int dim);
+  void collect_feasign_info(int table_id);
+
+ private:
+  struct FeasignInfo {
+    uint32_t slot;
+    uint32_t ins;
+    int64_t label;
+  };
+
+  std::map<uint64_t, std::vector<uint64_t>> _features;
+  std::map<uint64_t, std::vector<FeasignInfo>> _fea_info;
+  std::map<uint64_t, std::vector<std::vector<float>>> _feature_value;
+  std::map<uint64_t, std::vector<std::vector<float>>> _feature_push_value;
+
+  std::shared_ptr<paddle::distributed::PSlib> _pslib_ptr;
+
+  std::shared_ptr<DensePullThread> _pull_dense_thread;
+
+  std::vector<::std::future<int32_t>> _pull_sparse_status;
+  std::vector<::std::future<int32_t>> _pull_dense_status;
+  std::vector<::std::future<int32_t>> _push_sparse_status;
+  std::vector<::std::future<int32_t>> _push_dense_status;
+
+  AsyncWorkerParamConfig* _param_config;
+};
+#endif
+
 }  // namespace framework
 }  // namespace paddle

paddle/fluid/framework/ir/CMakeLists.txt

@@ -44,6 +44,7 @@ pass_library(seqconv_eltadd_relu_fuse_pass inference)
 pass_library(is_test_pass base)
 pass_library(conv_elementwise_add_act_fuse_pass inference)
 pass_library(conv_elementwise_add2_act_fuse_pass inference)
+pass_library(conv_elementwise_add_fuse_pass inference)
 if(WITH_MKLDNN)
     pass_library(mkldnn_placement_pass base)
     pass_library(depthwise_conv_mkldnn_pass base)

paddle/fluid/framework/ir/conv_elementwise_add_fuse_pass.cc

+// Copyright (c) 2018 PaddlePaddle Authors. All Rights Reserved.
+//
+// Licensed under the Apache License, Version 2.0 (the "License");
+// you may not use this file except in compliance with the License.
+// You may obtain a copy of the License at
+//
+//     http://www.apache.org/licenses/LICENSE-2.0
+//
+// Unless required by applicable law or agreed to in writing, software
+// distributed under the License is distributed on an "AS IS" BASIS,
+// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+// See the License for the specific language governing permissions and
+// limitations under the License.
+
+#include <string>
+
+#include "paddle/fluid/framework/ir/conv_elementwise_add_fuse_pass.h"
+#include "paddle/fluid/framework/ir/graph_viz_pass.h"
+
+namespace paddle {
+namespace framework {
+namespace ir {
+
+#define GET_IR_NODE(node__) GET_IR_NODE_FROM_SUBGRAPH(node__, node__, pattern);
+#define GET_NODES                    \
+  GET_IR_NODE(conv_op);              \
+  GET_IR_NODE(conv_out);             \
+  GET_IR_NODE(conv_filter);          \
+  GET_IR_NODE(elementwise_add_op);   \
+  GET_IR_NODE(elementwise_add_in_y); \
+  GET_IR_NODE(elementwise_add_out);
+
+std::unique_ptr<ir::Graph> ConvElementwiseAddFusePass::ApplyImpl(
+    std::unique_ptr<ir::Graph> graph) const {
+  const std::string pattern_name = "conv_elementwise_add_fuse";
+  FusePassBase::Init(pattern_name, graph.get());
+
+  GraphPatternDetector gpd;
+  auto* x = gpd.mutable_pattern()
+                ->NewNode("x")
+                ->assert_is_op_input("conv2d", "Input")
+                ->AsInput();
+
+  patterns::ConvElementwiseadd pattern(gpd.mutable_pattern(), pattern_name);
+  pattern(x);
+
+  auto handler = [&](const GraphPatternDetector::subgraph_t& subgraph,
+                     Graph* g) {
+    GET_NODES;
+
+    auto base_op_desc = *conv_op->Op()->Proto();
+    std::string bias_name = elementwise_add_in_y->Name();
+    std::string output_name = elementwise_add_out->Name();
+
+    std::string act_type = "identity";
+    framework::OpDesc new_op_desc(base_op_desc, nullptr);
+    new_op_desc.SetType("conv2d_fusion");
+    new_op_desc.SetInput("Bias", {bias_name});
+    new_op_desc.SetInput("ResidualData", {});
+    new_op_desc.SetAttr("activation", act_type);
+    new_op_desc.SetOutput("Output", {output_name});
+    new_op_desc.SetAttr("is_test", true);
+    new_op_desc.SetAttr("use_cudnn", false);
+    new_op_desc.Flush();
+
+    // Create a new node for the fused op.
+    auto* new_conv_op = graph->CreateOpNode(&new_op_desc);
+
+    // Link inputs and outputs.
+    PADDLE_ENFORCE(subgraph.count(x));
+    auto* conv_in_node = subgraph.at(x);
+
+    IR_NODE_LINK_TO(conv_in_node, new_conv_op);          // Input
+    IR_NODE_LINK_TO(conv_filter, new_conv_op);           // Filter
+    IR_NODE_LINK_TO(elementwise_add_in_y, new_conv_op);  // Bias
+    IR_NODE_LINK_TO(new_conv_op, elementwise_add_out);   // Output
+
+    // Delete the unneeded nodes.
+    GraphSafeRemoveNodes(graph.get(), {conv_op, conv_out, elementwise_add_op});
+  };
+
+  gpd(graph.get(), handler);
+  return graph;
+}
+
+}  // namespace ir
+}  // namespace framework
+}  // namespace paddle
+
+REGISTER_PASS(conv_elementwise_add_fuse_pass,
+              paddle::framework::ir::ConvElementwiseAddFusePass);

paddle/fluid/framework/ir/conv_elementwise_add_fuse_pass.h

+// Copyright (c) 2018 PaddlePaddle Authors. All Rights Reserved.
+//
+// Licensed under the Apache License, Version 2.0 (the "License");
+// you may not use this file except in compliance with the License.
+// You may obtain a copy of the License at
+//
+//     http://www.apache.org/licenses/LICENSE-2.0
+//
+// Unless required by applicable law or agreed to in writing, software
+// distributed under the License is distributed on an "AS IS" BASIS,
+// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+// See the License for the specific language governing permissions and
+// limitations under the License.
+
+#pragma once
+#include "paddle/fluid/framework/ir/fuse_pass_base.h"
+#include "paddle/fluid/framework/ir/graph_pattern_detector.h"
+
+namespace paddle {
+namespace framework {
+namespace ir {
+
+class ConvElementwiseAddFusePass : public FusePassBase {
+ public:
+  virtual ~ConvElementwiseAddFusePass() {}
+
+ protected:
+  std::unique_ptr<ir::Graph> ApplyImpl(std::unique_ptr<ir::Graph> graph) const;
+};
+
+}  // namespace ir
+}  // namespace framework
+}  // namespace paddle

paddle/fluid/framework/ir/graph.cc

@@ -162,7 +162,10 @@ void Graph::ResolveHazard(
           (*it_new)->inputs.empty() ? nullptr : (*it_new)->inputs[0];
       const auto &read_ops = (*it_old)->outputs;
 
-      PADDLE_ENFORCE(write_op, "The write_op should not be empty.");
+      PADDLE_ENFORCE(
+          write_op,
+          string::Sprintf("The write_op of var %s should not be empty.",
+                          (*it_new)->Name()));
 
       // Add write after write dependence
       ir::Node *upstream_op =

paddle/fluid/framework/ir/graph_helper.cc

@@ -18,6 +18,7 @@ limitations under the License. */
 #include <fstream>
 #include <iosfwd>
 #include <ostream>
+#include <unordered_map>
 #include <unordered_set>
 
 DEFINE_string(print_sub_graph_dir, "",
@@ -121,7 +122,7 @@ std::map<ir::Node *, std::unordered_set<ir::Node *>> BuildOperationAdjList(
 }
 
 size_t GraphNum(const Graph &graph) {
-  std::unordered_set<ir::Node *> nodes = graph.Nodes();
+  std::unordered_set<ir::Node *> nodes(graph.Nodes());
   std::unordered_set<ir::Node *> visited_nodes;
   visited_nodes.reserve(nodes.size());
   std::deque<ir::Node *> q_nodes;

paddle/fluid/framework/ir/graph_helper.h

@@ -24,6 +24,7 @@ limitations under the License. */
 namespace paddle {
 namespace framework {
 namespace ir {
+
 // Test if the graph contains circle.
 bool HasCircle(const Graph &graph);
 

paddle/fluid/framework/ir/graph_pattern_detector.cc

@@ -17,7 +17,6 @@
 #include <string>
 #include <vector>
 
-#include "graph_pattern_detector.h"
 #include "paddle/fluid/framework/ir/graph_helper.h"
 #include "paddle/fluid/framework/ir/graph_pattern_detector.h"
 #include "paddle/fluid/framework/ir/graph_traits.h"
@@ -1210,6 +1209,33 @@ PDNode *patterns::ConvElementwiseadd2Act::operator()(PDNode *conv_in) {
   return act_out;
 }
 
+PDNode *patterns::ConvElementwiseadd::operator()(PDNode *conv_in) {
+  conv_in->AsInput();
+  auto conv_op = pattern->NewNode(conv_op_repr())->assert_is_op("conv2d");
+  auto conv_out = pattern->NewNode(conv_out_repr())
+                      ->assert_is_op_output("conv2d")
+                      ->assert_is_op_input("elementwise_add", "X")
+                      ->AsIntermediate();
+  auto conv_filter = pattern->NewNode(conv_filter_repr())
+                         ->assert_is_op_input("conv2d", "Filter")
+                         ->AsInput();
+  auto elementwise_add_op = pattern->NewNode(elementwise_add_op_repr())
+                                ->assert_is_op("elementwise_add");
+  auto elementwise_add_in_y = pattern->NewNode(elementwise_add_in_y_repr())
+                                  ->assert_is_op_input("elementwise_add", "Y")
+                                  ->AsInput();
+  auto elementwise_add_out = pattern->NewNode(elementwise_add_out_repr())
+                                 ->assert_is_op_output("elementwise_add")
+                                 ->AsOutput();
+
+  conv_op->LinksFrom({conv_in, conv_filter});
+  conv_out->LinksFrom({conv_op});
+  elementwise_add_op->LinksFrom({conv_out, elementwise_add_in_y})
+      .LinksTo({elementwise_add_out});
+
+  return elementwise_add_out;
+}
+
 }  // namespace ir
 }  // namespace framework
 }  // namespace paddle

paddle/fluid/framework/ir/graph_pattern_detector.h

@@ -716,6 +716,24 @@ struct ConvElementwiseadd2Act : public PatternBase {
   PATTERN_DECL_NODE(act_out);
 };
 
+// Conv + ElementwiseAdd
+// This pattern should be used after ConvElementwiseadd2Act or
+// ConvElementwiseadd pass
+struct ConvElementwiseadd : public PatternBase {
+  ConvElementwiseadd(PDPattern* pattern, const std::string& name_scope)
+      : PatternBase(pattern, name_scope, "conv_elementwiseadd") {}
+
+  PDNode* operator()(PDNode* conv_in);
+
+  PATTERN_DECL_NODE(conv_op);
+  PATTERN_DECL_NODE(conv_out);
+  PATTERN_DECL_NODE(conv_filter);
+
+  PATTERN_DECL_NODE(elementwise_add_op);
+  PATTERN_DECL_NODE(elementwise_add_in_y);
+  PATTERN_DECL_NODE(elementwise_add_out);
+};
+
 }  // namespace patterns
 
 // Link two ir::Nodes from each other.

paddle/fluid/framework/ir/node.cc

@@ -30,6 +30,14 @@ std::unique_ptr<Node> CreateNodeForTest(const std::string &name,
   return std::unique_ptr<Node>(new Node(name, type));
 }
 
+std::unique_ptr<Node> CreateNodeForTest(VarDesc *var_desc) {
+  return std::unique_ptr<Node>(new Node(var_desc));
+}
+
+std::unique_ptr<Node> CreateNodeForTest(OpDesc *op_desc) {
+  return std::unique_ptr<Node>(new Node(op_desc));
+}
+
 }  // namespace ir
 }  // namespace framework
 }  // namespace paddle

paddle/fluid/framework/ir/node.h

@@ -18,7 +18,6 @@ limitations under the License. */
 #include <typeindex>
 #include <typeinfo>
 #include <vector>
-
 #include "paddle/fluid/framework/op_desc.h"
 #include "paddle/fluid/framework/var_desc.h"
 #include "paddle/fluid/platform/macros.h"
@@ -125,6 +124,8 @@ class Node {
   friend class Graph;
   friend std::unique_ptr<Node> CreateNodeForTest(const std::string& name,
                                                  Node::Type type);
+  friend std::unique_ptr<Node> CreateNodeForTest(VarDesc* var_desc);
+  friend std::unique_ptr<Node> CreateNodeForTest(OpDesc* op_desc);
 
   explicit Node(const std::string& name, Type type)
       : name_(name), var_desc_(nullptr), op_desc_(nullptr), type_(type) {}
@@ -152,7 +153,9 @@ class Node {
 
 std::unique_ptr<Node> CreateNodeForTest(const std::string& name,
                                         Node::Type type);
+std::unique_ptr<Node> CreateNodeForTest(VarDesc* var_desc);
 
+std::unique_ptr<Node> CreateNodeForTest(OpDesc* op_desc);
 }  // namespace ir
 }  // namespace framework
 }  // namespace paddle

paddle/fluid/framework/ngraph_operator.cc

@@ -278,7 +278,8 @@ std::shared_ptr<ngraph::runtime::Backend> NgraphEngine::backend_ =
     ngraph::runtime::Backend::create("CPU");
 
 void NgraphEngine::GetNgInputShape(std::shared_ptr<OperatorBase> op) {
-  op->RuntimeInferShape(scope_, place_);
+  RuntimeContext ctx(op->Inputs(), op->Outputs(), scope_);
+  op->RuntimeInferShape(scope_, place_, ctx);
   for (auto& var_name_item : op->Inputs()) {
     for (auto& var_name : var_name_item.second) {
       auto* var = scope_.FindVar(var_name);

paddle/fluid/framework/operator.cc

@@ -137,6 +137,23 @@ static LoD GetLoD(const Scope& scope, const std::string& name) {
   }
 }
 
+RuntimeContext::RuntimeContext(const VariableNameMap& innames,
+                               const VariableNameMap& outnames,
+                               const Scope& scope) {
+  for (auto& var_name_item : innames) {
+    std::vector<Variable*>& input_vars = inputs[var_name_item.first];
+    for (auto& var_name : var_name_item.second) {
+      input_vars.push_back(scope.FindVar(var_name));
+    }
+  }
+  for (auto& var_name_item : outnames) {
+    std::vector<Variable*>& output_vars = outputs[var_name_item.first];
+    for (auto& var_name : var_name_item.second) {
+      output_vars.push_back(scope.FindVar(var_name));
+    }
+  }
+}
+
 void OperatorBase::Run(const Scope& scope, const platform::Place& place) {
   VLOG(4) << place << " " << DebugStringEx(&scope);
   if (platform::is_gpu_place(place)) {
@@ -412,11 +429,48 @@ bool ExecutionContext::HasOutput(const std::string& name) const {
   return var != nullptr;
 }
 
+const Variable* ExecutionContext::InputVar(const std::string& name) const {
+  auto it = ctx_.inputs.find(name);
+  if (it == ctx_.inputs.end()) return nullptr;
+
+  PADDLE_ENFORCE_LE(it->second.size(), 1UL,
+                    "Operator %s's input %s should contain only one variable.",
+                    op_.Type(), name);
+  return it->second.empty() ? nullptr : it->second[0];
+}
+
+const Variable* ExecutionContext::LegacyInputVar(
+    const std::string& name) const {
+  auto ipt = op_.Input(name);
+  return ipt == kEmptyVarName ? nullptr : scope_.FindVar(ipt);
+}
+
+Variable* ExecutionContext::OutputVar(const std::string& name) const {
+  auto it = ctx_.outputs.find(name);
+  if (it == ctx_.outputs.end()) return nullptr;
+
+  PADDLE_ENFORCE_LE(it->second.size(), 1UL,
+                    "Operator %s's output %s should contain only one variable.",
+                    op_.Type(), name);
+  return it->second.empty() ? nullptr : it->second[0];
+}
+
+Variable* ExecutionContext::LegacyOutputVar(const std::string& name) const {
+  auto opt = op_.Output(name);
+  return opt == kEmptyVarName ? nullptr : scope_.FindVar(opt);
+}
+
 template <>
 const Tensor* ExecutionContext::Input<Tensor>(const std::string& name) const {
   return Input<LoDTensor>(name);
 }
 
+template <>
+const Tensor* ExecutionContext::LegacyInput<Tensor>(
+    const std::string& name) const {
+  return LegacyInput<LoDTensor>(name);
+}
+
 template <>
 const std::vector<const Tensor*> ExecutionContext::MultiInput<Tensor>(
     const std::string& name) const {
@@ -441,6 +495,11 @@ Tensor* ExecutionContext::Output<Tensor>(const std::string& name) const {
   return Output<LoDTensor>(name);
 }
 
+template <>
+Tensor* ExecutionContext::LegacyOutput<Tensor>(const std::string& name) const {
+  return LegacyOutput<LoDTensor>(name);
+}
+
 template <>
 std::vector<Tensor*> ExecutionContext::MultiOutput<Tensor>(
     const std::string& name) const {
@@ -477,23 +536,22 @@ bool OpSupportGPU(const std::string& op_type) {
 
 class RuntimeInferShapeContext : public InferShapeContext {
  public:
-  RuntimeInferShapeContext(const OperatorBase& op, const Scope& scope)
-      : op_(op), scope_(scope) {}
+  RuntimeInferShapeContext(const OperatorBase& op, const Scope& scope,
+                           const RuntimeContext& ctx)
+      : op_(op), scope_(scope), ctx_(ctx) {}
 
   bool HasInput(const std::string& name) const override {
     // has only one input
-    const auto& ins = op_.Inputs();
+    const auto& ins = ctx_.inputs;
     auto it = ins.find(name);
     if (it == ins.end()) {
       return false;
     }
     const auto& in = it->second;
-    if (in.size() == 0 || in[0] == kEmptyVarName) {
-      return false;
-    }
+    if (in.size() == 0) return false;
     PADDLE_ENFORCE_EQ(in.size(), 1UL,
                       "Input %s should not have more than one inputs", name);
-    return scope_.FindVar(in[0]) != nullptr;
+    return in[0] != nullptr;
   }
 
   bool HasOutput(const std::string& name) const override {
@@ -678,6 +736,7 @@ class RuntimeInferShapeContext : public InferShapeContext {
  private:
   const OperatorBase& op_;
   const Scope& scope_;
+  const RuntimeContext& ctx_;
 };
 
 static void CheckTensorNANOrInf(const std::string& name,
@@ -696,15 +755,15 @@ static void CheckTensorNANOrInf(const std::string& name,
 }
 
 void OperatorWithKernel::RuntimeInferShape(const Scope& scope,
-                                           const platform::Place& place) const {
-  RuntimeInferShapeContext infer_shape_ctx(*this, scope);
+                                           const platform::Place& place,
+                                           const RuntimeContext& ctx) const {
+  RuntimeInferShapeContext infer_shape_ctx(*this, scope, ctx);
   this->InferShape(&infer_shape_ctx);
 }
 
 void OperatorWithKernel::RunImpl(const Scope& scope,
                                  const platform::Place& place) const {
-  RuntimeInferShapeContext infer_shape_ctx(*this, scope);
-  this->InferShape(&infer_shape_ctx);
+  RuntimeContext ctx(Inputs(), Outputs(), scope);
   platform::DeviceContextPool& pool = platform::DeviceContextPool::Instance();
   auto* dev_ctx = pool.Get(place);
 
@@ -718,15 +777,8 @@ void OperatorWithKernel::RunImpl(const Scope& scope,
 
   OpKernelMap& kernels = kernels_iter->second;
 
-  // TODO(dzhwinter) : kernel fallback mechanism will be added when all the
-  // transform functions are ready.
-
-  // for (auto& candidate : kKernelPriority) {
-  //   Do selection
-  // }
-
-  auto expected_kernel_key =
-      this->GetExpectedKernelType(ExecutionContext(*this, scope, *dev_ctx));
+  auto expected_kernel_key = this->GetExpectedKernelType(
+      ExecutionContext(*this, scope, *dev_ctx, ctx));
   VLOG(3) << "expected_kernel_key:" << expected_kernel_key;
 
   auto kernel_iter = kernels.find(expected_kernel_key);
@@ -748,7 +800,7 @@ void OperatorWithKernel::RunImpl(const Scope& scope,
   // do data transformScope &transfer_scope;
   std::vector<std::string> transfered_inplace_vars;
   auto* transfer_scope =
-      TryTransferData(scope, expected_kernel_key, &transfered_inplace_vars);
+      PrepareData(scope, expected_kernel_key, &transfered_inplace_vars, &ctx);
 
   // exec scope is the scope that kernel actually executed on.
   const Scope& exec_scope =
@@ -758,7 +810,11 @@ void OperatorWithKernel::RunImpl(const Scope& scope,
     dev_ctx = pool.Get(expected_kernel_key.place_);
   }
 
-  kernel_iter->second(ExecutionContext(*this, exec_scope, *dev_ctx));
+  RuntimeInferShapeContext infer_shape_ctx(*this, exec_scope, ctx);
+  this->InferShape(&infer_shape_ctx);
+  // TODO(panyx0718): ExecutionContext should only depend on RuntimeContext
+  // not Scope. Imperative mode only pass inputs and get outputs.
+  kernel_iter->second(ExecutionContext(*this, exec_scope, *dev_ctx, ctx));
 
   if (!transfered_inplace_vars.empty()) {
     // there is inplace variable has been transfered.
@@ -782,6 +838,7 @@ void OperatorWithKernel::RunImpl(const Scope& scope,
     }
   }
 }
+
 void OperatorWithKernel::TransferInplaceVarsBack(
     const Scope& scope, const std::vector<std::string>& inplace_vars,
     const Scope& transfer_scope) const {
@@ -797,13 +854,19 @@ void OperatorWithKernel::TransferInplaceVarsBack(
   }
 }
 
-Scope* OperatorWithKernel::TryTransferData(
+Scope* OperatorWithKernel::PrepareData(
     const Scope& scope, const OpKernelType& expected_kernel_key,
-    std::vector<std::string>* transfered_inplace_vars) const {
+    std::vector<std::string>* transfered_inplace_vars,
+    RuntimeContext* ctx) const {
   Scope* new_scope = nullptr;
   for (auto& var_name_item : Inputs()) {
-    for (auto& var_name : var_name_item.second) {
+    std::vector<Variable*>& input_vars = ctx->inputs[var_name_item.first];
+
+    for (size_t i = 0; i < var_name_item.second.size(); ++i) {
+      auto& var_name = var_name_item.second[i];
       auto* var = scope.FindVar(var_name);
+      input_vars[i] = var;
+
       // Only tensor can be tranfer to another device.
       if (var == nullptr || !VarIsTensor(*var)) {
         continue;
@@ -851,6 +914,7 @@ Scope* OperatorWithKernel::TryTransferData(
       }
 
       auto* trans_var = new_scope->Var(var_name);
+      input_vars[i] = trans_var;
 
       Tensor out;
       TransformData(expected_kernel_key, kernel_type_for_var, *tensor_in, &out);

paddle/fluid/framework/operator.h

@@ -70,6 +70,15 @@ Tensor* GetMutableLoDTensorOrSelectedRowsValueFromVar(Variable* var);
 class OperatorBase;
 class ExecutionContext;
 
+class RuntimeContext {
+ public:
+  RuntimeContext(const VariableNameMap& innames,
+                 const VariableNameMap& outnames, const Scope& scope);
+
+  VariableValueMap inputs;
+  VariableValueMap outputs;
+};
+
 /**
  * OperatorBase has the basic elements that Net will call to do computation.
  * Only CreateOperator from OpRegistry will new Operator directly. User
@@ -129,7 +138,8 @@ class OperatorBase {
 
   void SetIsCalledByExecutor(bool x) { run_by_executor_ = x; }
   virtual void RuntimeInferShape(const Scope& scope,
-                                 const platform::Place& place) const {}
+                                 const platform::Place& place,
+                                 const RuntimeContext& ctx) const {}
 
  protected:
   std::string type_;
@@ -156,8 +166,9 @@ class OperatorBase {
 class ExecutionContext {
  public:
   ExecutionContext(const OperatorBase& op, const Scope& scope,
-                   const platform::DeviceContext& device_context)
-      : op_(op), scope_(scope), device_context_(device_context) {}
+                   const platform::DeviceContext& device_context,
+                   const RuntimeContext& ctx)
+      : op_(op), scope_(scope), device_context_(device_context), ctx_(ctx) {}
 
   const OperatorBase& op() const { return op_; }
 
@@ -180,15 +191,9 @@ class ExecutionContext {
     return op_.Outputs(name).size();
   }
 
-  const Variable* InputVar(const std::string& name) const {
-    auto ipt = op_.Input(name);
-    return ipt == kEmptyVarName ? nullptr : scope_.FindVar(ipt);
-  }
+  const Variable* InputVar(const std::string& name) const;
 
-  Variable* OutputVar(const std::string& name) const {
-    auto opt = op_.Output(name);
-    return opt == kEmptyVarName ? nullptr : scope_.FindVar(opt);
-  }
+  Variable* OutputVar(const std::string& name) const;
 
   const std::vector<const Variable*> MultiInputVar(
       const std::string& name) const {
@@ -227,6 +232,22 @@ class ExecutionContext {
     return var == nullptr ? nullptr : var->GetMutable<T>();
   }
 
+  template <typename T>
+  const T* LegacyInput(const std::string& name) const {
+    auto* var = LegacyInputVar(name);
+    return var == nullptr ? nullptr : &var->Get<T>();
+  }
+
+  template <typename T>
+  T* LegacyOutput(const std::string& name) const {
+    auto var = LegacyOutputVar(name);
+    return var == nullptr ? nullptr : var->GetMutable<T>();
+  }
+
+  const Variable* LegacyInputVar(const std::string& name) const;
+
+  Variable* LegacyOutputVar(const std::string& name) const;
+
   template <typename T>
   const std::vector<const T*> MultiInput(const std::string& name) const {
     auto names = op_.Inputs(name);
@@ -286,11 +307,16 @@ class ExecutionContext {
   const OperatorBase& op_;
   const Scope& scope_;
   const platform::DeviceContext& device_context_;
+  const RuntimeContext& ctx_;
 };
 
 template <>
 const Tensor* ExecutionContext::Input<Tensor>(const std::string& name) const;
 
+template <>
+const Tensor* ExecutionContext::LegacyInput<Tensor>(
+    const std::string& name) const;
+
 template <>
 const std::vector<const Tensor*> ExecutionContext::MultiInput<Tensor>(
     const std::string& name) const;
@@ -298,6 +324,9 @@ const std::vector<const Tensor*> ExecutionContext::MultiInput<Tensor>(
 template <>
 Tensor* ExecutionContext::Output<Tensor>(const std::string& name) const;
 
+template <>
+Tensor* ExecutionContext::LegacyOutput<Tensor>(const std::string& name) const;
+
 template <>
 std::vector<Tensor*> ExecutionContext::MultiOutput<Tensor>(
     const std::string& name) const;
@@ -350,8 +379,8 @@ class OperatorWithKernel : public OperatorBase {
     OpInfoMap::Instance().Get(Type()).infer_shape_(ctx);
   }
 
-  void RuntimeInferShape(const Scope& scope,
-                         const platform::Place& place) const override;
+  void RuntimeInferShape(const Scope& scope, const platform::Place& place,
+                         const RuntimeContext& ctx) const override;
 
  protected:
   virtual OpKernelType GetExpectedKernelType(const ExecutionContext& ctx) const;
@@ -371,9 +400,10 @@ class OperatorWithKernel : public OperatorBase {
    *
    * * transfered_inplace_vars is a output vector.
    */
-  Scope* TryTransferData(
-      const Scope& scope, const OpKernelType& expected_kernel_key,
-      std::vector<std::string>* transfered_inplace_vars) const;
+  Scope* PrepareData(const Scope& scope,
+                     const OpKernelType& expected_kernel_key,
+                     std::vector<std::string>* transfered_inplace_vars,
+                     RuntimeContext* ctx) const;
 
   void TransferInplaceVarsBack(const Scope& scope,
                                const std::vector<std::string>& inplace_vars,

paddle/fluid/framework/parallel_executor.cc

@@ -13,6 +13,7 @@ See the License for the specific language governing permissions and
 limitations under the License. */
 
 #include "paddle/fluid/framework/parallel_executor.h"
+#include <algorithm>
 #include <string>
 #include <tuple>
 #include <vector>
@@ -93,6 +94,7 @@ class ParallelExecutorPrivate {
     }
   }
 
+  BuildStrategy build_strategy_;
   std::vector<platform::Place> places_;
   std::vector<Scope *> local_scopes_;
   Scope *global_scope_;  // not owned
@@ -169,6 +171,14 @@ std::unique_ptr<ir::Graph> ParallelExecutorPrivate::PrepareGCAndRefCnts(
     eager_deletion_pass->SetNotOwned(details::kAllPlaces, &places_);
     graph = eager_deletion_pass->Apply(std::move(graph));
     VLOG(10) << "EagerDeletionPass Applied";
+
+    if (build_strategy_.memory_early_delete_) {
+      auto early_delete_pass =
+          ir::PassRegistry::Instance().Get("memory_early_delete_pass");
+      early_delete_pass->SetNotOwned(details::kGarbageCollector, &gcs_);
+      graph = early_delete_pass->Apply(std::move(graph));
+    }
+    VLOG(10) << "MemoryEarlyDeletePass Applied.";
   }
 
   return graph;
@@ -189,6 +199,7 @@ ParallelExecutor::ParallelExecutor(
     : member_(new ParallelExecutorPrivate(places)) {
   member_->global_scope_ = scope;
   member_->use_cuda_ = exec_strategy.use_cuda_;
+  member_->build_strategy_ = build_strategy;
   member_->use_all_reduce_ =
       build_strategy.reduce_ == BuildStrategy::ReduceStrategy::kAllReduce;
 
@@ -245,7 +256,6 @@ ParallelExecutor::ParallelExecutor(
       build_strategy.Apply(main_program, member_->places_, loss_var_name,
                            params, member_->local_scopes_, member_->use_cuda_);
 #endif
-
   auto max_memory_size = GetEagerDeletionThreshold();
   if (max_memory_size >= 0) {
     graph = member_->PrepareGCAndRefCnts(std::move(graph),
@@ -280,10 +290,12 @@ ParallelExecutor::ParallelExecutor(
 
   if (exec_strategy.type_ == ExecutionStrategy::kDefault) {
     member_->executor_.reset(new details::ThreadedSSAGraphExecutor(
-        exec_strategy, member_->local_scopes_, places, std::move(graph)));
+        exec_strategy, member_->local_scopes_, member_->places_,
+        std::move(graph)));
   } else {
     member_->executor_.reset(new details::FastThreadedSSAGraphExecutor(
-        exec_strategy, member_->local_scopes_, places, std::move(graph)));
+        exec_strategy, member_->local_scopes_, member_->places_,
+        std::move(graph)));
   }
 
   member_->executor_.reset(new details::ScopeBufferedSSAGraphExecutor(
@@ -423,5 +435,6 @@ ParallelExecutor::~ParallelExecutor() {
 }  // namespace framework
 }  // namespace paddle
 
+USE_PASS(memory_early_delete_pass);
 USE_PASS(reference_count_pass);
 USE_PASS(eager_deletion_pass);

paddle/fluid/framework/tensor_test.cc

@@ -74,6 +74,22 @@ TEST(Tensor, MutableData) {
     p2 = src_tensor.mutable_data<float>(framework::make_ddim({2, 2}),
                                         platform::CPUPlace());
     EXPECT_EQ(p1, p2);
+
+    float* p3 = nullptr;
+    float* p4 = nullptr;
+    // set src_tensor a different type but smaller size.
+    // memory block is supposed to be unchanged.
+    auto* tmp = src_tensor.mutable_data<uint8_t>(framework::make_ddim({2, 2}),
+                                                 platform::CPUPlace());
+    p3 = reinterpret_cast<float*>(tmp);
+    EXPECT_EQ(p1, p3);
+
+    // set src_tensor a different type but bigger size.
+    // memory block is supposed to be changed.
+    auto* tmp2 = src_tensor.mutable_data<double>(
+        framework::make_ddim({2, 2, 3}), platform::CPUPlace());
+    p4 = reinterpret_cast<float*>(tmp2);
+    EXPECT_NE(p1, p4);
   }
   // Not sure if it's desired, but currently, Tensor type can be changed.
   {

paddle/fluid/framework/type_defs.h

@@ -28,8 +28,11 @@ class OperatorBase;
 class OpDesc;
 class InferShapeContext;
 class BlockDesc;
+class Variable;
 
 using VariableNameMap = std::map<std::string, std::vector<std::string>>;
+// TODO(panyx0718): Replace vector with something like gtl::Vector.
+using VariableValueMap = std::map<std::string, std::vector<Variable*>>;
 
 // The order should be as same as framework.proto
 using Attribute =

paddle/fluid/inference/analysis/ir_passes/tensorrt_subgraph_pass.cc

@@ -63,7 +63,6 @@ std::unique_ptr<framework::ir::Graph> analysis::TensorRtSubgraphPass::ApplyImpl(
 void TensorRtSubgraphPass::CreateTensorRTOp(framework::ir::Node *node,
                                             Graph *graph) const {
   auto *op_desc = node->Op();
-  static int counter{0};
   auto &subgraph = *Agent(node).subgraph();
   PADDLE_ENFORCE(!subgraph.empty());
 
@@ -192,8 +191,6 @@ void TensorRtSubgraphPass::CreateTensorRTOp(framework::ir::Node *node,
           block_desc.Proto()->SerializeAsString());
   SetAttr(op_desc->Proto(), "max_batch_size", Get<int>("max_batch_size"));
   SetAttr(op_desc->Proto(), "workspace_size", Get<int>("workspace_size"));
-  SetAttr(op_desc->Proto(), "engine_uniq_key",
-          "trt-" + std::to_string(counter++));
   SetAttr(op_desc->Proto(), "parameters", ExtractParameters(graph->Nodes()));
   SetAttr(op_desc->Proto(), "output_name_mapping", output_mapping);
 }

paddle/fluid/inference/api/paddle_pass_builder.h

@@ -122,6 +122,7 @@ class GpuPassStrategy : public PassStrategy {
         "conv_bn_fuse_pass",                    //
         "conv_elementwise_add_act_fuse_pass",   //
         "conv_elementwise_add2_act_fuse_pass",  //
+        "conv_elementwise_add_fuse_pass",       //
     });
   }
 

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

@@ -103,6 +103,7 @@ class OpConverter {
   void ConvertBlock(const framework::proto::BlockDesc& block,
                     const std::unordered_set<std::string>& parameters,
                     const framework::Scope& scope, TensorRTEngine* engine) {
+    std::unique_lock<std::mutex> lk(mut_);
     for (int i = 0; i < block.ops_size(); i++) {
       const auto& op = block.ops(i);
       ConvertOp(op, parameters, scope, engine);
@@ -125,6 +126,7 @@ class OpConverter {
   std::unordered_map<std::string, OpConverter*> converters_;
   // fluid inference scope
   framework::Scope* scope_{nullptr};
+  std::mutex mut_;
 };
 
 }  // namespace tensorrt

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

@@ -30,6 +30,13 @@ function(inference_analysis_api_test_with_fake_data target install_dir filename
         ARGS --infer_model=${install_dir}/model)
 endfunction()
 
+function(inference_analysis_api_test_with_refer_result target install_dir filename)
+    inference_analysis_test(${target} SRCS ${filename}
+        EXTRA_DEPS ${INFERENCE_EXTRA_DEPS}
+        ARGS --infer_model=${install_dir}/model --infer_data=${install_dir}/data.txt
+             --refer_result=${install_dir}/result.txt)
+endfunction()
+
 # RNN1
 if(NOT APPLE AND WITH_MKLML)
     set(RNN1_INSTALL_DIR "${INFERENCE_DEMO_INSTALL_DIR}/rnn1")
@@ -83,14 +90,21 @@ set(OCR_INSTALL_DIR "${INFERENCE_DEMO_INSTALL_DIR}/ocr")
 if (NOT EXISTS ${OCR_INSTALL_DIR})
     inference_download_and_uncompress(${OCR_INSTALL_DIR} "http://paddlemodels.cdn.bcebos.com/" "inference-vis-demos%2Focr.tar.gz")
 endif()
-inference_analysis_api_test(test_analyzer_ocr ${OCR_INSTALL_DIR} analyzer_vis_tester.cc)
+inference_analysis_api_test_with_refer_result(test_analyzer_ocr ${OCR_INSTALL_DIR} analyzer_vis_tester.cc)
+
+# mobilenet with transpose op
+set(MOBILENET_INSTALL_DIR "${INFERENCE_DEMO_INSTALL_DIR}/mobilenet")
+if (NOT EXISTS ${MOBILENET_INSTALL_DIR})
+    inference_download_and_uncompress(${MOBILENET_INSTALL_DIR} "http://paddlemodels.cdn.bcebos.com/" "inference-vis-demos%2Fmobilenet.tar.gz")
+endif()
+inference_analysis_api_test_with_refer_result(test_analyzer_mobilenet_transpose ${MOBILENET_INSTALL_DIR} analyzer_vis_tester.cc)
 
 # resnet50
 inference_analysis_api_test_with_fake_data(test_analyzer_resnet50
   "${INFERENCE_DEMO_INSTALL_DIR}/resnet50" analyzer_resnet50_tester.cc "resnet50_model.tar.gz")
 
 # mobilenet with depthwise_conv op
-inference_analysis_api_test_with_fake_data(test_analyzer_mobilenet
+inference_analysis_api_test_with_fake_data(test_analyzer_mobilenet_depthwise_conv
   "${INFERENCE_DEMO_INSTALL_DIR}/mobilenet_depthwise_conv" analyzer_resnet50_tester.cc "mobilenet_model.tar.gz")
 
 # anakin

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

@@ -93,18 +93,20 @@ void profile(bool use_mkldnn = false) {
   SetInput(&input_slots_all);
   TestPrediction(reinterpret_cast<const PaddlePredictor::Config *>(&cfg),
                  input_slots_all, &outputs, FLAGS_num_threads);
-
   if (FLAGS_num_threads == 1 && !FLAGS_test_all_data) {
-    const float ocr_result_data[] = {
-        5.273636460856323538e-08, 3.296741795111302054e-07,
-        1.873261190610264748e-08, 3.403730275408634043e-08,
-        3.383312474625199684e-08};
-    PADDLE_ENFORCE_EQ(outputs.size(), 1UL);
-    size_t size = GetSize(outputs[0]);
-    PADDLE_ENFORCE_GT(size, 0);
-    float *result = static_cast<float *>(outputs[0].data.data());
-    for (size_t i = 0; i < std::min(5UL, size); i++) {
-      EXPECT_NEAR(result[i], ocr_result_data[i], 1e-3);
+    std::string line;
+    std::ifstream file(FLAGS_refer_result);
+    std::getline(file, line);
+    auto refer = ProcessALine(line);
+    file.close();
+
+    auto &output = outputs.front();
+    size_t numel = output.data.length() / PaddleDtypeSize(output.dtype);
+    CHECK_EQ(numel, refer.data.size());
+    for (size_t i = 0; i < numel; ++i) {
+      CHECK_LT(
+          fabs(static_cast<float *>(output.data.data())[i] - refer.data[i]),
+          1e-5);
     }
   }
 }

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

@@ -36,6 +36,7 @@
 DEFINE_string(model_name, "", "model name");
 DEFINE_string(infer_model, "", "model path");
 DEFINE_string(infer_data, "", "data file");
+DEFINE_string(refer_result, "", "reference result for comparison");
 DEFINE_int32(batch_size, 1, "batch size.");
 DEFINE_int32(repeat, 1, "Running the inference program repeat times.");
 DEFINE_bool(test_all_data, false, "Test the all dataset in data file.");

paddle/fluid/operators/CMakeLists.txt

@@ -64,9 +64,7 @@ endif()
 set(COMMON_OP_DEPS ${OP_HEADER_DEPS})
 
 set(COMMON_OP_DEPS ${COMMON_OP_DEPS} selected_rows_functor selected_rows lod_tensor maxouting unpooling pooling lod_rank_table context_project sequence_pooling executor)
-if (NOT WIN32)
-    set(COMMON_OP_DEPS ${COMMON_OP_DEPS} dynload_warpctc)
-endif()
+set(COMMON_OP_DEPS ${COMMON_OP_DEPS} dynload_warpctc)
 set(COMMON_OP_DEPS ${COMMON_OP_DEPS} sequence_padding sequence_scale cos_sim_functor memory jit_kernel concat_and_split cross_entropy softmax vol2col im2col sampler)
 set(COMMON_OP_DEPS ${COMMON_OP_DEPS} sequence2batch lstm_compute matrix_bit_code gru_compute activation_functions)
 if (WITH_GPU)

paddle/fluid/operators/beam_search_decode_op.cc

@@ -122,7 +122,8 @@ class BeamSearchDecodeOp : public framework::OperatorBase {
     platform::DeviceContextPool& pool = platform::DeviceContextPool::Instance();
     auto& dev_ctx = *pool.Get(dev_place);
 
-    framework::ExecutionContext ctx(*this, scope, dev_ctx);
+    framework::RuntimeContext run_ctx(Inputs(), Outputs(), scope);
+    framework::ExecutionContext ctx(*this, scope, dev_ctx, run_ctx);
 
     const LoDTensorArray* ids = ctx.Input<LoDTensorArray>("Ids");
     const LoDTensorArray* scores = ctx.Input<LoDTensorArray>("Scores");

paddle/fluid/operators/controlflow/parallel_do_op.cc

-/* Copyright (c) 2016 PaddlePaddle Authors. All Rights Reserved.
-
-Licensed under the Apache License, Version 2.0 (the "License");
-you may not use this file except in compliance with the License.
-You may obtain a copy of the License at
-
-    http://www.apache.org/licenses/LICENSE-2.0
-
-Unless required by applicable law or agreed to in writing, software
-distributed under the License is distributed on an "AS IS" BASIS,
-WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-See the License for the specific language governing permissions and
-limitations under the License. */
-
-#include <vector>
-
-#include "paddle/fluid/framework/executor.h"
-#include "paddle/fluid/framework/op_registry.h"
-#include "paddle/fluid/framework/threadpool.h"
-#include "paddle/fluid/operators/detail/safe_ref.h"
-
-namespace paddle {
-namespace operators {
-
-static constexpr char kInputs[] = "inputs";
-static constexpr char kParameters[] = "parameters";
-static constexpr char kPlaces[] = "places";
-
-static constexpr char kOutputs[] = "outputs";
-static constexpr char kParallelScopes[] = "parallel_scopes";
-
-static constexpr char kParallelBlock[] = "sub_block";
-static constexpr char kUseNCCL[] = "use_nccl";
-
-using LoDTensor = framework::LoDTensor;
-using SelectedRows = framework::SelectedRows;
-
-static void SplitTensorAndMoveTensorToScopes(
-    const framework::Scope &scope, std::vector<framework::Scope *> *sub_scopes,
-    const std::vector<platform::Place> &places,
-    const std::vector<std::string> &names) {
-  size_t num_sub_scopes = 0;
-  for (auto &argu : names) {
-    const auto &tensor =
-        detail::Ref(scope.FindVar(argu),
-                    "Cannot find variable %s in the parent scope", argu)
-            .Get<LoDTensor>();
-    auto lod_tensors = tensor.SplitLoDTensor(places);
-
-    for (auto &lod : lod_tensors) {
-      VLOG(3) << lod.dims();
-    }
-    if (num_sub_scopes == 0) {
-      num_sub_scopes = lod_tensors.size();
-    } else {
-      PADDLE_ENFORCE_EQ(num_sub_scopes, lod_tensors.size());
-    }
-    PADDLE_ENFORCE_NE(num_sub_scopes, 0);
-    if (sub_scopes->size() == 0) {
-      sub_scopes->reserve(num_sub_scopes);
-      for (size_t i = 0; i < num_sub_scopes; ++i) {
-        sub_scopes->emplace_back(&scope.NewScope());
-      }
-    }
-
-    for (size_t i = 0; i < lod_tensors.size(); ++i) {
-      *detail::Ref(sub_scopes->at(i)->Var(argu),
-                   "Cannot find variable in the sub-scope", argu)
-           .GetMutable<LoDTensor>() = lod_tensors[i];
-    }
-  }
-}
-
-inline void CopyOrShare(const framework::Variable &src,
-                        const platform::Place &dst_place,
-                        framework::Variable *dst) {
-  if (src.IsType<LoDTensor>()) {
-    if (src.Get<LoDTensor>().place() == dst_place) {
-      dst->GetMutable<LoDTensor>()->ShareDataWith(src.Get<LoDTensor>());
-      dst->GetMutable<LoDTensor>()->set_lod(src.Get<LoDTensor>().lod());
-    } else {
-      TensorCopy(src.Get<LoDTensor>(), dst_place, dst->GetMutable<LoDTensor>());
-    }
-  } else if (src.IsType<SelectedRows>()) {
-    auto &src_sr = src.Get<SelectedRows>();
-    auto *dst_sr = dst->GetMutable<SelectedRows>();
-    dst_sr->set_height(src_sr.height());
-    if (src_sr.value().place() == dst_place) {
-      dst_sr->mutable_value()->ShareDataWith(src_sr.value());
-      dst_sr->set_rows(src_sr.rows());
-    } else {
-      TensorCopy(src_sr.value(), dst_place, dst_sr->mutable_value());
-    }
-  } else {
-    PADDLE_THROW("Expect LoDTensor/SelectedRows, get %s", src.Type().name());
-  }
-}
-
-void WaitOnPlace(const platform::Place place) {
-  platform::DeviceContextPool &pool = platform::DeviceContextPool::Instance();
-  auto &dev_ctx = *pool.Get(place);
-  dev_ctx.Wait();
-}
-
-void WaitOnPlaces(const std::vector<platform::Place> places) {
-  platform::DeviceContextPool &pool = platform::DeviceContextPool::Instance();
-
-  for (auto &place : places) {
-    auto &dev_ctx = *pool.Get(place);
-    dev_ctx.Wait();
-  }
-}
-
-class ParallelDoOp : public framework::OperatorBase {
- public:
-  ParallelDoOp(const std::string &type,
-               const framework::VariableNameMap &inputs,
-               const framework::VariableNameMap &outputs,
-               const framework::AttributeMap &attrs)
-      : framework::OperatorBase(type, inputs, outputs, attrs) {}
-
- private:
-  void RunImpl(const framework::Scope &scope,
-               const platform::Place &place) const override {
-    // get device context from pool
-    platform::DeviceContextPool &pool = platform::DeviceContextPool::Instance();
-    auto &dev_ctx = *pool.Get(place);
-
-    auto *block = Attr<framework::BlockDesc *>(kParallelBlock);
-    auto *program = block->Program();
-
-    auto &places = scope.FindVar(Input(kPlaces))->Get<platform::PlaceList>();
-
-    auto &sub_scopes = *scope.FindVar(Output(kParallelScopes))
-                            ->GetMutable<std::vector<framework::Scope *>>();
-
-    // split input
-    SplitTensorAndMoveTensorToScopes(scope, &sub_scopes, places,
-                                     Inputs(kInputs));
-
-    // copy parameter
-    for (auto &param : Inputs(kParameters)) {
-      PADDLE_ENFORCE(scope.FindVar(param)->IsType<LoDTensor>(),
-                     "Only support parameter type as LoDTensor");
-      auto &src = scope.FindVar(param)->Get<LoDTensor>();
-
-      auto *sub_scope0 = sub_scopes[0];
-      auto *dst0 = sub_scope0->Var(param)->GetMutable<LoDTensor>();
-      dst0->ShareDataWith(src);
-
-      for (size_t i = 1; i < sub_scopes.size(); ++i) {
-        auto &place = places[i];
-        auto *sub_scope = sub_scopes[i];
-        auto *dst = sub_scope->Var(param)->GetMutable<LoDTensor>();
-        framework::TensorCopy(src, place, dst);
-      }
-    }
-    WaitOnPlaces(places);
-
-    std::vector<std::future<void>> workers;
-    workers.reserve(places.size());
-    for (size_t place_idx = 0; place_idx < sub_scopes.size(); ++place_idx) {
-      auto &place = places[place_idx];
-      auto *cur_scope = sub_scopes[place_idx];
-
-      workers.emplace_back(framework::Async([program, cur_scope, place, block] {
-        framework::Executor executor(place);
-        executor.Run(*program, cur_scope, block->ID(),
-                     false /*create_local_scope*/);
-      }));
-    }
-    for (auto &worker : workers) {
-      worker.wait();
-    }
-    WaitOnPlaces(places);
-
-    // merge output
-    for (auto &o_name : Outputs(kOutputs)) {
-      std::vector<const framework::LoDTensor *> lod_tensors;
-      lod_tensors.reserve(sub_scopes.size());
-      for (auto *sub_scope : sub_scopes) {
-        lod_tensors.emplace_back(&sub_scope->FindVar(o_name)->Get<LoDTensor>());
-      }
-
-      auto *lod_tensor_to_be_merged =
-          scope.FindVar(o_name)->GetMutable<LoDTensor>();
-      lod_tensor_to_be_merged->MergeLoDTensor(lod_tensors, dev_ctx.GetPlace());
-    }
-    WaitOnPlaces(places);
-  }
-};
-
-class ParallelDoOpProtoMaker : public framework::OpProtoAndCheckerMaker {
- public:
-  void Make() override {
-    AddInput(kInputs, "").AsDuplicable();
-    AddInput(kParameters, "").AsDuplicable();
-    AddInput(kPlaces, "");
-    AddOutput(kOutputs, "").AsDuplicable();
-    AddOutput(kParallelScopes, "");
-    AddAttr<framework::BlockDesc *>(kParallelBlock, "");
-    AddAttr<bool>(kUseNCCL, "true if we use nccl on backward")
-        .SetDefault(false);
-    AddComment(R"DOC(
-ParallelDo Operator.
-)DOC");
-  }
-};
-
-class ParallelDoGradOp : public framework::OperatorBase {
- public:
-  ParallelDoGradOp(const std::string &type,
-                   const framework::VariableNameMap &inputs,
-                   const framework::VariableNameMap &outputs,
-                   const framework::AttributeMap &attrs)
-      : framework::OperatorBase(type, inputs, outputs, attrs) {}
-
- private:
-  void RunImpl(const framework::Scope &scope,
-               const platform::Place &place) const override {
-    auto *block = Attr<framework::BlockDesc *>(kParallelBlock);
-    auto *program = block->Program();
-
-    auto &sub_scopes = scope.FindVar(Input(kParallelScopes))
-                           ->Get<std::vector<framework::Scope *>>();
-    auto &places = scope.FindVar(Input(kPlaces))->Get<platform::PlaceList>();
-
-    // feed output@grad
-    SplitTensorAndMoveTensorToScopes(
-        scope, const_cast<std::vector<framework::Scope *> *>(&sub_scopes),
-        places, Inputs(framework::GradVarName(kOutputs)));
-    WaitOnPlaces(places);
-
-    // exe run
-    std::vector<std::future<void>> workers;
-    for (size_t i = 0; i < sub_scopes.size(); ++i) {
-      auto &place = places[i];
-      auto *cur_scope = sub_scopes[i];
-
-      // execute
-      workers.emplace_back(framework::Async([program, cur_scope, place, block] {
-        framework::Executor executor(place);
-        executor.Run(*program, cur_scope, block->ID(),
-                     false /*create_local_scope*/);
-      }));
-    }
-    for (auto &worker : workers) {
-      worker.wait();
-    }
-    WaitOnPlaces(places);
-
-    // NCCL allreduce op will be added by backward,
-    // so no need to explicitly accumulate grad
-    if (!(Attr<bool>(kUseNCCL))) {
-      AccumulateGrad(scope, place, sub_scopes, places);
-    } else {
-      for (auto &place : places) {
-        PADDLE_ENFORCE(platform::is_gpu_place(place),
-                       "NCCL only supports cuda place");
-      }
-    }
-    for (auto &s : Outputs(framework::GradVarName(kParameters))) {
-      if (s == framework::kEmptyVarName) {
-        continue;
-      }
-      VLOG(3) << "Moving " << s;
-      CopyOrShare(*sub_scopes[0]->FindVar(s), place, scope.FindVar(s));
-    }
-    WaitOnPlaces(places);
-  }
-
-  void AccumulateGrad(const framework::Scope &scope,
-                      const platform::Place &place,
-                      const std::vector<framework::Scope *> &sub_scopes,
-                      const platform::PlaceList &places) const {
-    for (auto &s : Outputs(framework::GradVarName(kParameters))) {
-      if (s == framework::kEmptyVarName) {
-        continue;
-      }
-      VLOG(3) << "Accumulating " << s;
-      if (s == framework::kEmptyVarName) continue;
-      std::string tmp_name;
-      auto *tmp = sub_scopes[0]->Var(&tmp_name);
-
-      for (size_t i = 1; i < sub_scopes.size(); ++i) {
-        CopyOrShare(*sub_scopes[i]->FindVar(s), places[0], tmp);
-        WaitOnPlaces(places);
-
-        auto sum_op = framework::OpRegistry::CreateOp(
-            "sum", {{"X", {s, tmp_name}}}, {{"Out", {s}}},
-            framework::AttributeMap{{"use_mkldnn", {false}}});
-        VLOG(10) << sum_op->DebugStringEx(sub_scopes[0]);
-        sum_op->Run(*sub_scopes[0], places[0]);
-        WaitOnPlace(places[0]);
-      }
-
-      CopyOrShare(*sub_scopes[0]->FindVar(s), place, scope.FindVar(s));
-    }
-    WaitOnPlaces(places);
-  }
-};
-
-std::ostream &operator<<(std::ostream &sout,
-                         const std::vector<std::string> &strs) {
-  std::copy(strs.begin(), strs.end(),
-            std::ostream_iterator<std::string>(sout, ","));
-  return sout;
-}
-
-class ParallelDoGradOpDescMaker : public framework::SingleGradOpDescMaker {
- public:
-  using framework::SingleGradOpDescMaker::SingleGradOpDescMaker;
-
- protected:
-  virtual std::unique_ptr<framework::OpDesc> Apply() const {
-    auto *grad = new framework::OpDesc();
-    grad->SetType("parallel_do_grad");
-    for (auto &input_param : this->InputNames()) {
-      VLOG(3) << input_param;
-      grad->SetInput(input_param, this->Input(input_param));
-      if (input_param != kPlaces) {
-        grad->SetOutput(framework::GradVarName(input_param),
-                        this->InputGrad(input_param, false));
-      }
-    }
-    auto *g_block = this->grad_block_[0];
-
-    // All variable name that needed by gradient operators
-    std::unordered_set<std::string> all_inputs_in_grad_blocks;
-
-    for (size_t i = 0; i < g_block->OpSize(); ++i) {
-      auto *op = g_block->Op(i);
-      for (auto &var_name : op->InputArgumentNames()) {
-        all_inputs_in_grad_blocks.insert(var_name);
-      }
-    }
-
-    for (auto &output_param : this->OutputNames()) {
-      if (output_param == kParallelScopes) {
-        grad->SetInput(output_param, this->Output(output_param));
-        grad->SetInput(framework::GradVarName(output_param),
-                       this->Output(output_param));
-      } else {
-        grad->SetInput(output_param, this->Output(output_param));
-        std::vector<std::string> og_names;
-        for (auto &og_name : this->OutputGrad(output_param)) {
-          if (all_inputs_in_grad_blocks.count(og_name) != 0) {
-            // there are some gradient operators who need the OG. So make this
-            // OG as an input of parallel.do
-            og_names.push_back(og_name);
-          }
-          // else, there is no operator who need the OG. Do not use this OG as
-          // an input
-        }
-        grad->SetInput(framework::GradVarName(output_param), og_names);
-      }
-    }
-    grad->SetInput("Communicator", {"nccl_com__do_not_change_"});
-    grad->SetAttrMap(this->Attrs());
-    grad->SetBlockAttr(kParallelBlock, grad_block_[0]);
-
-    return std::unique_ptr<framework::OpDesc>(grad);
-  }
-};
-
-class ParallelDoGradOpShapeInference : public framework::InferShapeBase {
- public:
-  void operator()(framework::InferShapeContext *ctx) const override {
-    PADDLE_ENFORCE(ctx->HasInputs(kParameters));
-    PADDLE_ENFORCE(ctx->HasInputs(kInputs));
-    PADDLE_ENFORCE(ctx->HasInputs(kOutputs));
-
-    ctx->SetOutputsDim(framework::GradVarName(kParameters),
-                       ctx->GetInputsDim(kParameters));
-
-    auto i_dims = ctx->GetInputsDim(kInputs);
-    auto ig_names = ctx->Outputs(framework::GradVarName(kInputs));
-
-    for (size_t i = 0; i < ig_names.size(); ++i) {
-      auto &ig_name = ig_names[i];
-      if (ig_name == framework::kEmptyVarName) {
-        continue;
-      }
-
-      ctx->SetDims({ig_name}, {i_dims[i]});
-    }
-
-    auto p_dims = ctx->GetInputsDim(kParameters);
-    auto pg_names = ctx->Outputs(framework::GradVarName(kParameters));
-    for (size_t i = 0; i < pg_names.size(); ++i) {
-      auto &pg_name = pg_names[i];
-      if (pg_name == framework::kEmptyVarName) {
-        continue;
-      }
-      ctx->SetDims({pg_name}, {p_dims[i]});
-    }
-  }
-};
-
-class ParallelDoGradOpVarTypeInference : public framework::VarTypeInference {
- public:
-  void operator()(const framework::OpDesc &op_desc,
-                  framework::BlockDesc *block) const override {
-    framework::BlockDesc *sub_block =
-        boost::get<framework::BlockDesc *>(op_desc.GetAttr(kParallelBlock));
-    for (auto &out_vars : op_desc.Outputs()) {
-      for (auto &out_var : out_vars.second) {
-        auto &var = block->FindRecursiveOrCreateVar(out_var);
-        auto sub_var = sub_block->FindRecursiveOrCreateVar(out_var);
-        if (sub_var.GetType() != var.GetType()) {
-          var.SetType(sub_var.GetType());
-        }
-      }
-    }
-  }
-};
-
-}  // namespace operators
-}  // namespace paddle
-
-REGISTER_OPERATOR(parallel_do, paddle::operators::ParallelDoOp,
-                  paddle::operators::ParallelDoOpProtoMaker,
-                  paddle::operators::ParallelDoGradOpDescMaker);
-REGISTER_OPERATOR(parallel_do_grad, paddle::operators::ParallelDoGradOp,
-                  paddle::operators::ParallelDoGradOpShapeInference,
-                  paddle::operators::ParallelDoGradOpVarTypeInference);

paddle/fluid/operators/hierarchical_sigmoid_op.h

@@ -150,19 +150,27 @@ class HierarchicalSigmoidGradOpKernel : public framework::OpKernel<T> {
                                                        label.data<int64_t>()));
     }
 
-    auto& place = *ctx.template device_context<DeviceContext>().eigen_device();
-    auto pre_out_mat = EigenMatrix<T>::From(pre_out);
-    auto pre_out_grad_mat = EigenMatrix<T>::From(pre_out_grad);
-    auto out_grad_mat = EigenMatrix<T>::From(out_grad);
+    // softrelu derivative
 
-    Eigen::array<int, 2> bcast{1, static_cast<int>(pre_out_grad.dims()[1])};
+    auto blas = math::GetBlas<DeviceContext, T>(ctx);
 
-    // softrelu derivative
-    pre_out_grad_mat.device(place) =
-        static_cast<T>(1.0) - static_cast<T>(1.0) / pre_out_mat.exp();
+    auto* pre_out_grad_data = pre_out_grad.data<T>();
+    auto* pre_out_data = pre_out.data<T>();
+    auto n = pre_out.numel();
+    blas.VEXP(n, pre_out_data, pre_out_grad_data);
+    blas.VINV(n, pre_out_grad_data, pre_out_grad_data);
+    for (int64_t i = 0; i < n; ++i) {
+      pre_out_grad_data[i] = 1.0 - pre_out_grad_data[i];
+    }
     bit_code->Sub(&pre_out_grad);  // the gradient of clip(w * x + b)
-    pre_out_grad_mat.device(place) =
-        pre_out_grad_mat * out_grad_mat.broadcast(bcast);
+    auto* out_grad_data = out_grad.data<T>();
+
+    int64_t dim0 = pre_out_grad.dims()[0];
+    int64_t dim1 = pre_out_grad.dims()[1];
+    for (int64_t i = 0; i < dim0; ++i) {
+      T tmp = out_grad_data[i];
+      blas.SCAL(dim1, tmp, pre_out_grad_data + i * dim1);
+    }
     // TODO(guosheng): multiply pre_out_grad with subgradient of clipping to
     // be consistent with the clipping in forward.
 

paddle/fluid/operators/huber_loss_op.cc

@@ -124,8 +124,9 @@ REGISTER_OPERATOR(huber_loss, ops::HuberLossOp, ops::HuberLossOpMaker<float>,
                   paddle::framework::DefaultGradOpDescMaker<true>);
 REGISTER_OPERATOR(huber_loss_grad, ops::HuberLossGradOp);
 REGISTER_OP_CPU_KERNEL(
-    huber_loss,
-    ops::HuberLossKernel<paddle::platform::CPUDeviceContext, float>);
+    huber_loss, ops::HuberLossKernel<paddle::platform::CPUDeviceContext, float>,
+    ops::HuberLossKernel<paddle::platform::CPUDeviceContext, double>);
 REGISTER_OP_CPU_KERNEL(
     huber_loss_grad,
-    ops::HuberLossGradKernel<paddle::platform::CPUDeviceContext, float>);
+    ops::HuberLossGradKernel<paddle::platform::CPUDeviceContext, float>,
+    ops::HuberLossGradKernel<paddle::platform::CPUDeviceContext, double>);

paddle/fluid/operators/math/blas.h

@@ -181,6 +181,9 @@ class Blas {
               const framework::Tensor& mat_b, const MatDescriptor& dim_b,
               T alpha, framework::Tensor* mat_out, T beta) const;
 
+  template <typename T>
+  void VINV(int n, const T* a, T* y) const;
+
  private:
   const DeviceContext& context_;
 };
@@ -282,6 +285,11 @@ class BlasT : private Blas<DeviceContext> {
     Base()->template BatchedGEMM<T>(args...);
   }
 
+  template <typename... ARGS>
+  void VINV(ARGS... args) const {
+    Base()->template VINV<T>(args...);
+  }
+
  private:
   const Blas<DeviceContext>* Base() const {
     return static_cast<const Blas<DeviceContext>*>(this);

paddle/fluid/operators/math/blas_impl.h

@@ -118,6 +118,11 @@ struct CBlas<float> {
   static void VPOW(ARGS... args) {
     platform::dynload::vsPowx(args...);
   }
+
+  template <typename... ARGS>
+  static void VINV(ARGS... args) {
+    platform::dynload::vsInv(args...);
+  }
 };
 
 template <>
@@ -213,6 +218,11 @@ struct CBlas<double> {
   static void VPOW(ARGS... args) {
     platform::dynload::vdPowx(args...);
   }
+
+  template <typename... ARGS>
+  static void VINV(ARGS... args) {
+    platform::dynload::vdInv(args...);
+  }
 };
 
 #else
@@ -603,6 +613,17 @@ void Blas<DeviceContext>::MatMul(const framework::Tensor &mat_a,
         dim_a.stride_, dim_b.stride_);
   }
 }
+template <typename DeviceContext>
+template <typename T>
+void Blas<DeviceContext>::VINV(int n, const T *a, T *y) const {
+#ifdef PADDLE_WITH_MKLML
+  CBlas<T>::VINV(n, a, y);
+#else
+  for (int i = 0; i < n; ++i) {
+    y[i] = 1.0 / a[i];
+  }
+#endif
+}
 
 }  // namespace math
 }  // namespace operators

paddle/fluid/operators/optimizers/adam_op.cc

@@ -109,6 +109,11 @@ class AdamOpMaker : public framework::OpProtoAndCheckerMaker {
                    "(float, default 1.0e-8) "
                    "Constant for numerical stability")
         .SetDefault(1.0e-8f);
+    AddAttr<bool>(
+        "lazy_mode",
+        "(bool, default false) "
+        "only update the parameter that has gradient in sparse update")
+        .SetDefault(false);
 
     AddComment(R"DOC(
 Adam Optimizer.

paddle/fluid/operators/optimizers/adam_op.h

@@ -177,12 +177,13 @@ struct SparseAdamFunctor {
   const int64_t* rows_;
   int64_t row_numel_;
   int64_t row_count_;
+  bool lazy_mode_;
 
   SparseAdamFunctor(T beta1, T beta2, T epsilon, const T* beta1_pow,
                     const T* beta2_pow, const T* mom1, T* mom1_out,
                     const T* mom2, T* mom2_out, const T* lr, const T* grad,
                     const T* param, T* param_out, const int64_t* rows,
-                    int64_t row_numel, int64_t row_count)
+                    int64_t row_numel, int64_t row_count, bool lazy_mode)
       : beta1_(beta1),
         beta2_(beta2),
         epsilon_(epsilon),
@@ -198,13 +199,10 @@ struct SparseAdamFunctor {
         param_out_(param_out),
         rows_(rows),
         row_numel_(row_numel),
-        row_count_(row_count) {}
-
-  inline HOSTDEVICE void operator()(size_t i) const {
-    auto row_idx =
-        math::BinarySearch<int64_t>(rows_, row_count_, i / row_numel_);
-    T g = row_idx >= 0 ? grad_[row_idx * row_numel_ + i % row_numel_] : 0;
+        row_count_(row_count),
+        lazy_mode_(lazy_mode) {}
 
+  inline HOSTDEVICE void adam_update(size_t i, T g) const {
     // The following code is the same as dense
     T mom1 = moment1_[i];
     T mom2 = moment2_[i];
@@ -225,6 +223,17 @@ struct SparseAdamFunctor {
     moment2_out_[i] = mom2;
     param_out_[i] = p;
   }
+
+  inline HOSTDEVICE void operator()(size_t i) const {
+    auto row_idx =
+        math::BinarySearch<int64_t>(rows_, row_count_, i / row_numel_);
+    if (lazy_mode_ && row_idx < 0) {
+      return;
+    } else {
+      T g = row_idx >= 0 ? grad_[row_idx * row_numel_ + i % row_numel_] : 0;
+      adam_update(i, g);
+    }
+  }
 };
 
 template <typename DeviceContext, typename T>
@@ -240,6 +249,7 @@ class AdamOpKernel : public framework::OpKernel<T> {
     using paddle::framework::LoDTensor;
     using paddle::operators::detail::Ref;
 
+    bool lazy_mode = ctx.Attr<bool>("lazy_mode");
     T beta1 = static_cast<T>(ctx.Attr<float>("beta1"));
     T beta2 = static_cast<T>(ctx.Attr<float>("beta2"));
     T epsilon = static_cast<T>(ctx.Attr<float>("epsilon"));
@@ -351,11 +361,23 @@ class AdamOpKernel : public framework::OpKernel<T> {
           mom2_out.template mutable_data<T>(ctx.GetPlace()),
           lr.template data<T>(), grad_data, param.template data<T>(),
           param_out.template mutable_data<T>(ctx.GetPlace()), rows, row_numel,
-          grad_merge.rows().size());
-      platform::ForRange<DeviceContext> for_range(
-          static_cast<const DeviceContext&>(ctx.device_context()),
-          param.numel());
-      for_range(functor);
+          grad_merge.rows().size(), lazy_mode);
+      VLOG(3) << "lazy_mode :" << lazy_mode;
+      if (lazy_mode && platform::is_cpu_place(ctx.GetPlace())) {
+        size_t row_count = grad_merge.rows().size();
+        std::vector<int64_t> cpu_rows(grad_merge.rows());
+        for (size_t row_index = 0; row_index < row_count; ++row_index) {
+          for (size_t offset = 0; offset < row_numel; ++offset) {
+            size_t i = cpu_rows[row_index] * row_numel + offset;
+            functor.adam_update(i, grad_data[row_index * row_numel + offset]);
+          }
+        }
+      } else {
+        platform::ForRange<DeviceContext> for_range(
+            static_cast<const DeviceContext&>(ctx.device_context()),
+            param.numel());
+        for_range(functor);
+      }
     } else {
       PADDLE_THROW("Variable type not supported by adam_op");
     }

paddle/fluid/operators/tensorrt/CMakeLists.txt

 op_library(tensorrt_engine_op DEPS tensorrt_engine tensorrt_converter)
-file(APPEND ${pybind_file} "USE_CUDA_ONLY_OP(tensorrt_engine);\n")
+file(APPEND ${pybind_file} "USE_NO_KERNEL_OP(tensorrt_engine);\n")
 nv_test(test_tensorrt_engine_op SRCS tensorrt_engine_op_test.cc
   DEPS tensorrt_engine_op
   analysis)

paddle/fluid/operators/tensorrt/tensorrt_engine_op.cc

@@ -21,8 +21,6 @@
 
 namespace paddle {
 
-DEFINE_int32(tensorrt_engine_batch_size, 1, "the batch_size of TensorRT");
-
 namespace operators {
 
 class TensorRTEngineOpMaker : public framework::OpProtoAndCheckerMaker {
@@ -31,7 +29,6 @@ class TensorRTEngineOpMaker : public framework::OpProtoAndCheckerMaker {
     AddInput("Xs", "A list of inputs.").AsDuplicable();
     AddOutput("Ys", "A list of outputs").AsDuplicable();
     AddAttr<std::string>("subgraph", "the subgraph.");
-    AddAttr<std::string>("engine_uniq_key", "unique key for the TRT engine.");
     AddAttr<int>("max_batch_size", "the maximum batch size.");
     AddAttr<int>("workspace_size", "the workspace size.");
     AddComment("TensorRT engine operator.");
@@ -50,6 +47,6 @@ class TensorRTEngineInferVarType : public framework::VarTypeInference {
 namespace ops = paddle::operators;
 
 REGISTER_OPERATOR(tensorrt_engine, ops::TensorRTEngineOp,
-                  ops::TensorRTEngineOpMaker, ops::TensorRTEngineOpMaker);
+                  ops::TensorRTEngineOpMaker);
 
 #endif  // PADDLE_WITH_CUDA

paddle/fluid/operators/tensorrt/tensorrt_engine_op.cu.cc

-/* Copyright (c) 2016 PaddlePaddle Authors. All Rights Reserved.
-
-Licensed under the Apache License, Version 2.0 (the "License");
-you may not use this file except in compliance with the License.
-You may obtain a copy of the License at
-
-    http://www.apache.org/licenses/LICENSE-2.0
-
-Unless required by applicable law or agreed to in writing, software
-distributed under the License is distributed on an "AS IS" BASIS,
-WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-See the License for the specific language governing permissions and
-limitations under the License. */
-
-#include "paddle/fluid/operators/tensorrt/tensorrt_engine_op.h"
-
-namespace ops = paddle::operators;
-
-REGISTER_OP_CUDA_KERNEL(
-    tensorrt_engine,
-    ops::TensorRTEngineKernel<paddle::platform::CUDADeviceContext, float>,
-    ops::TensorRTEngineKernel<paddle::platform::CUDADeviceContext, double>,
-    ops::TensorRTEngineKernel<paddle::platform::CUDADeviceContext, int>,
-    ops::TensorRTEngineKernel<paddle::platform::CUDADeviceContext, int64_t>);

paddle/fluid/operators/tensorrt/tensorrt_engine_op_test.cc

@@ -24,8 +24,7 @@ limitations under the License. */
 #include "paddle/fluid/inference/tensorrt/convert/op_converter.h"
 #include "paddle/fluid/inference/tensorrt/convert/ut_helper.h"
 
-USE_CUDA_ONLY_OP(tensorrt_engine);
-
+USE_NO_KERNEL_OP(tensorrt_engine);
 namespace paddle {
 namespace operators {