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

CMakeLists.txt

@@ -73,6 +73,7 @@ option(PY_VERSION       "Compile PaddlePaddle with python3 support"     ${PY_VER
 if(NOT PY_VERSION)
   set(PY_VERSION 2.7)
 endif()
+set(PYBIND11_PYTHON_VERSION ${PY_VERSION})
 
 # CMAKE_BUILD_TYPE
 if(NOT CMAKE_BUILD_TYPE)
@@ -159,6 +160,7 @@ endif()
 ########################################################################################
 
 include(external/mklml)     # download mklml package
+include(external/xbyak)     # download xbyak package
 include(external/libxsmm)   # download, build, install libxsmm
 include(external/zlib)      # download, build, install zlib
 include(external/gflags)    # download, build, install gflags
@@ -175,6 +177,7 @@ include(external/any)       # download libn::any
 include(external/eigen)     # download eigen3
 include(external/pybind11)  # download pybind11
 include(external/cares)
+include(external/cub)
 
 if(WITH_DISTRIBUTE)
     if(WITH_GRPC)
@@ -201,12 +204,11 @@ include(external/snappy)    # download snappy
 include(external/snappystream)
 include(external/threadpool)
 
+set(WITH_ANAKIN OFF CACHE STRING "Disable Anakin first, will add it later." FORCE)
 if(WITH_GPU)
     include(cuda)
     include(tensorrt)
     include(external/anakin)
-else()
-  set(WITH_ANAKIN OFF CACHE STRING "Anakin is valid only when GPU is set." FORCE)
 endif()
 
 include(cudnn)              # set cudnn libraries, must before configure

cmake/configure.cmake

@@ -97,6 +97,14 @@ if(WITH_GPU)
         endif()
         include_directories(${TENSORRT_INCLUDE_DIR})
     endif()
+    if(WITH_ANAKIN)
+        if(${CUDA_VERSION_MAJOR} VERSION_LESS 8)
+            message(FATAL_ERROR "Anakin needs CUDA >= 8.0 to compile")
+        endif()
+        if(${CUDNN_MAJOR_VERSION} VERSION_LESS 7)
+            message(FATAL_ERROR "Anakin needs CUDNN >= 7.0 to compile")
+        endif()
+    endif()
 elseif(WITH_AMD_GPU)
     add_definitions(-DPADDLE_WITH_HIP)
     set(CMAKE_C_FLAGS "${CMAKE_C_FLAGS} -D__HIP_PLATFORM_HCC__")

cmake/external/anakin.cmake

@@ -2,10 +2,22 @@ if (NOT WITH_ANAKIN)
   return()
 endif()
 
-set(ANAKIN_INSTALL_DIR "${THIRD_PARTY_PATH}/install/anakin" CACHE PATH
-  "Anakin install path." FORCE)
-set(ANAKIN_INCLUDE "${ANAKIN_INSTALL_DIR}" CACHE STRING "root of Anakin header files")
-set(ANAKIN_LIBRARY "${ANAKIN_INSTALL_DIR}" CACHE STRING "path of Anakin library")
+INCLUDE(ExternalProject)
+set(ANAKIN_SOURCE_DIR  ${THIRD_PARTY_PATH}/anakin)
+# the anakin install dir is only default one now
+set(ANAKIN_INSTALL_DIR ${THIRD_PARTY_PATH}/anakin/src/extern_anakin/output)
+set(ANAKIN_INCLUDE     ${ANAKIN_INSTALL_DIR})
+set(ANAKIN_LIBRARY     ${ANAKIN_INSTALL_DIR})
+set(ANAKIN_SHARED_LIB  ${ANAKIN_LIBRARY}/libanakin.so)
+set(ANAKIN_SABER_LIB   ${ANAKIN_LIBRARY}/libanakin_saber_common.so)
+
+# TODO(luotao): ANAKIN_MODLE_URL will move to demo ci later.
+set(ANAKIN_MODLE_URL "http://paddle-inference-dist.bj.bcebos.com/mobilenet_v2.anakin.bin")
+execute_process(COMMAND bash -c "mkdir -p ${ANAKIN_SOURCE_DIR}")
+execute_process(COMMAND bash -c "cd ${ANAKIN_SOURCE_DIR}; wget -q --no-check-certificate ${ANAKIN_MODLE_URL}")
+
+include_directories(${ANAKIN_INCLUDE})
+include_directories(${ANAKIN_INCLUDE}/saber/)
 
 set(ANAKIN_COMPILE_EXTRA_FLAGS 
     -Wno-error=unused-but-set-variable -Wno-unused-but-set-variable
@@ -20,36 +32,33 @@ set(ANAKIN_COMPILE_EXTRA_FLAGS
     -Wno-reorder 
     -Wno-error=cpp)
 
-set(ANAKIN_LIBRARY_URL "https://github.com/pangge/Anakin/releases/download/Version0.1.0/anakin.tar.gz")
-
-# A helper function used in Anakin, currently, to use it, one need to recursively include
-# nearly all the header files.
-function(fetch_include_recursively root_dir)
-    if (IS_DIRECTORY ${root_dir})
-        include_directories(${root_dir})
-    endif()
-
-    file(GLOB ALL_SUB RELATIVE ${root_dir} ${root_dir}/*)
-    foreach(sub ${ALL_SUB})
-        if (IS_DIRECTORY ${root_dir}/${sub})
-            fetch_include_recursively(${root_dir}/${sub})
-        endif()
-    endforeach()
-endfunction()
-
-if (NOT EXISTS "${ANAKIN_INSTALL_DIR}")
-    # download library
-    message(STATUS "Download Anakin library from ${ANAKIN_LIBRARY_URL}")
-    execute_process(COMMAND bash -c "mkdir -p ${ANAKIN_INSTALL_DIR}")
-    execute_process(COMMAND bash -c "rm -rf ${ANAKIN_INSTALL_DIR}/*")
-    execute_process(COMMAND bash -c "cd ${ANAKIN_INSTALL_DIR}; wget --no-check-certificate -q ${ANAKIN_LIBRARY_URL}")
-    execute_process(COMMAND bash -c "mkdir -p ${ANAKIN_INSTALL_DIR}")
-    execute_process(COMMAND bash -c "cd ${ANAKIN_INSTALL_DIR}; tar xzf anakin.tar.gz")
-endif()
+ExternalProject_Add(
+    extern_anakin
+    ${EXTERNAL_PROJECT_LOG_ARGS}
+    # TODO(luotao): use PaddlePaddle/Anakin later
+    GIT_REPOSITORY      "https://github.com/luotao1/Anakin"
+    GIT_TAG             "3957ae9263eaa0b1986758dac60a88852afb09be"
+    PREFIX              ${ANAKIN_SOURCE_DIR}
+    UPDATE_COMMAND      ""
+    CMAKE_ARGS          -DUSE_GPU_PLACE=YES
+                        -DUSE_X86_PLACE=YES
+                        -DBUILD_WITH_UNIT_TEST=NO
+                        -DPROTOBUF_ROOT=${THIRD_PARTY_PATH}/install/protobuf
+                        -DMKLML_ROOT=${THIRD_PARTY_PATH}/install/mklml
+                        -DCUDNN_ROOT=${CUDNN_ROOT}
+                        ${EXTERNAL_OPTIONAL_ARGS}
+    CMAKE_CACHE_ARGS    -DCMAKE_INSTALL_PREFIX:PATH=${ANAKIN_INSTALL_DIR}
+)
 
-if (WITH_ANAKIN)
-    message(STATUS "Anakin for inference is enabled")
-    message(STATUS "Anakin is set INCLUDE:${ANAKIN_INCLUDE} LIBRARY:${ANAKIN_LIBRARY}")
-    fetch_include_recursively(${ANAKIN_INCLUDE})
-    link_directories(${ANAKIN_LIBRARY})
-endif()
+message(STATUS "Anakin for inference is enabled")
+message(STATUS "Anakin is set INCLUDE:${ANAKIN_INCLUDE} LIBRARY:${ANAKIN_LIBRARY}")
+
+add_library(anakin_shared SHARED IMPORTED GLOBAL)
+set_property(TARGET anakin_shared PROPERTY IMPORTED_LOCATION ${ANAKIN_SHARED_LIB})
+add_dependencies(anakin_shared extern_anakin protobuf mklml)
+
+add_library(anakin_saber SHARED IMPORTED GLOBAL)
+set_property(TARGET anakin_saber PROPERTY IMPORTED_LOCATION ${ANAKIN_SABER_LIB})
+add_dependencies(anakin_saber extern_anakin protobuf mklml)
+
+list(APPEND external_project_dependencies anakin_shared anakin_saber)

cmake/external/cub.cmake

+if(NOT WITH_GPU)
+  return()
+endif()
+
+include(ExternalProject)
+
+set(CUB_SOURCE_DIR ${THIRD_PARTY_PATH}/cub)
+set(CUB_INCLUDE_DIR ${CUB_SOURCE_DIR}/src/extern_cub)
+
+include_directories(${CUB_INCLUDE_DIR})
+
+ExternalProject_Add(
+  extern_cub
+  ${EXTERNAL_PROJECT_LOG_ARGS}
+  GIT_REPOSITORY "https://github.com/NVlabs/cub.git"
+  GIT_TAG        "v1.8.0"
+  PREFIX         ${CUB_SOURCE_DIR}
+  UPDATE_COMMAND ""
+  CONFIGURE_COMMAND ""
+  BUILD_COMMAND     ""
+  INSTALL_COMMAND   ""
+  TEST_COMMAND      ""
+)
+
+if(${CMAKE_VERSION} VERSION_LESS "3.3.0")
+  set(dummyfile ${CMAKE_CURRENT_BINARY_DIR}/cub_dummy.c)
+  file(WRITE ${dummyfile} "const char *dummy = \"${dummyfile}\";")
+  add_library(cub STATIC ${dummyfile})
+else()
+  add_library(cub INTERFACE)
+endif()
+
+add_dependencies(cub extern_cub)
+
+LIST(APPEND externl_project_dependencies cub)

cmake/external/mkldnn.cmake

@@ -24,7 +24,7 @@ SET(MKLDNN_INSTALL_DIR    ${THIRD_PARTY_PATH}/install/mkldnn)
 SET(MKLDNN_INC_DIR        "${MKLDNN_INSTALL_DIR}/include" CACHE PATH "mkldnn include directory." FORCE)
 
 IF(WIN32 OR APPLE)
-    MESSAGE(WARNING 
+    MESSAGE(WARNING
         "Windows or Mac is not supported with MKLDNN in Paddle yet."
         "Force WITH_MKLDNN=OFF")
     SET(WITH_MKLDNN OFF CACHE STRING "Disable MKLDNN in Windows and MacOS" FORCE)
@@ -57,8 +57,10 @@ ExternalProject_Add(
     GIT_TAG             "a29d8487a63afca3d5b8c5bbdbb473cf8ccc6e51"
     PREFIX              ${MKLDNN_SOURCES_DIR}
     UPDATE_COMMAND      ""
+    CMAKE_ARGS          -DCMAKE_CXX_COMPILER=${CMAKE_CXX_COMPILER}
+    CMAKE_ARGS          -DCMAKE_C_COMPILER=${CMAKE_C_COMPILER}
     CMAKE_ARGS          -DCMAKE_INSTALL_PREFIX=${MKLDNN_INSTALL_DIR}
-    CMAKE_ARGS          -DCMAKE_BUILD_TYPE=${CMAKE_BUILD_TYPE} 
+    CMAKE_ARGS          -DCMAKE_BUILD_TYPE=${CMAKE_BUILD_TYPE}
     CMAKE_ARGS          -DMKLROOT=${MKLML_ROOT}
     CMAKE_ARGS          -DCMAKE_C_FLAGS=${MKLDNN_CFLAG}
     CMAKE_ARGS          -DCMAKE_CXX_FLAGS=${MKLDNN_CXXFLAG}

cmake/external/xbyak.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.
+
+set(WITH_XBYAK ON)
+if(WIN32 OR APPLE)
+    SET(WITH_XBYAK OFF CACHE STRING "Disable XBYAK in Windows and MacOS" FORCE)
+    return()
+endif()
+
+include(ExternalProject)
+
+set(XBYAK_PROJECT       extern_xbyak)
+set(XBYAK_PREFIX_DIR    ${THIRD_PARTY_PATH}/xbyak)
+set(XBYAK_INSTALL_ROOT  ${THIRD_PARTY_PATH}/install/xbyak)
+set(XBYAK_INC_DIR       ${XBYAK_INSTALL_ROOT}/include)
+
+include_directories(${XBYAK_INC_DIR})
+include_directories(${XBYAK_INC_DIR}/xbyak)
+
+add_definitions(-DPADDLE_WITH_XBYAK)
+
+# xbyak options
+add_definitions(-DXBYAK64)
+add_definitions(-DXBYAK_NO_OP_NAMES)
+
+ExternalProject_Add(
+    ${XBYAK_PROJECT}
+    ${EXTERNAL_PROJECT_LOG_ARGS}
+    DEPENDS             ""
+    GIT_REPOSITORY      "https://github.com/herumi/xbyak.git"
+    GIT_TAG             "v5.661"  # Jul 26th
+    PREFIX              ${XBYAK_PREFIX_DIR}
+    UPDATE_COMMAND      ""
+    CMAKE_ARGS          -DCMAKE_INSTALL_PREFIX=${XBYAK_INSTALL_ROOT}
+    CMAKE_CACHE_ARGS    -DCMAKE_INSTALL_PREFIX:PATH=${XBYAK_INSTALL_ROOT}
+)
+
+if (${CMAKE_VERSION} VERSION_LESS "3.3.0")
+    set(dummyfile ${CMAKE_CURRENT_BINARY_DIR}/xbyak_dummy.c)
+    file(WRITE ${dummyfile} "const char *dummy_xbyak = \"${dummyfile}\";")
+    add_library(xbyak STATIC ${dummyfile})
+else()
+    add_library(xbyak INTERFACE)
+endif()
+
+add_dependencies(xbyak ${XBYAK_PROJECT})
+list(APPEND external_project_dependencies xbyak)

cmake/inference_lib.cmake

@@ -143,7 +143,7 @@ if (WITH_ANAKIN AND WITH_GPU)
     copy(anakin_inference_lib DEPS paddle_inference_api inference_anakin_api
         SRCS
         ${PADDLE_BINARY_DIR}/paddle/fluid/inference/api/libinference_anakin_api* # compiled anakin api
-        ${PADDLE_BINARY_DIR}/third_party/install/anakin/*.tar.gz # anakin release
+        ${ANAKIN_INSTALL_DIR} # anakin release
         DSTS ${dst_dir}/inference/anakin ${dst_dir}/inference/anakin)
      list(APPEND inference_deps anakin_inference_lib)
 endif()

doc/fluid/api/executor.rst

@@ -38,11 +38,3 @@ _switch_scope
 ..  autofunction:: paddle.fluid.executor._switch_scope
     :noindex:
 
-.. _api_fluid_executor_fetch_var:
-
-fetch_var
----------
-
-..  autofunction:: paddle.fluid.executor.fetch_var
-    :noindex:
-

doc/fluid/api/fluid.rst

@@ -106,22 +106,6 @@ _switch_scope
 ..  autofunction:: paddle.fluid._switch_scope
     :noindex:
 
-.. _api_fluid_fetch_var:
-
-fetch_var
----------
-
-..  autofunction:: paddle.fluid.fetch_var
-    :noindex:
-
-.. _api_fluid_Go:
-
-Go
---
-
-..  autoclass:: paddle.fluid.Go
-    :members:
-    :noindex:
 
 .. _api_fluid_make_channel:
 

doc/fluid/design/ir/draft.md → doc/fluid/design/ir/overview.md

@@ -177,8 +177,8 @@ graph = PassRegistry::Instance().Get("op_fuse_pass").Apply(std::move(grah));
 auto mem_opt_pass = PassRegistry::Instance().Get("memory_optimization_pass");
 mem_opt_pass.SetNotOwned<int>("optimize_level", 1);
 mem_opt_pass->Apply(std::move(graph));
-graph = PassRegistry::Instance().Get("multi_device_pass").Apply(std::move(grah));
-graph = PassRegistry::Instance().Get("multi_device_check_pass").Apply(std::move(grah));
+graph = PassRegistry::Instance().Get("multi_devices_pass").Apply(std::move(grah));
+graph = PassRegistry::Instance().Get("multi_devices_check_pass").Apply(std::move(grah));
 Executor exe;
 exe.Run(graph);
 

paddle/fluid/API.spec

@@ -6,7 +6,7 @@ paddle.fluid.Program.create_block ArgSpec(args=['self', 'parent_idx'], varargs=N
 paddle.fluid.Program.current_block ArgSpec(args=['self'], varargs=None, keywords=None, defaults=None)
 paddle.fluid.Program.get_desc ArgSpec(args=['self'], varargs=None, keywords=None, defaults=None)
 paddle.fluid.Program.global_block ArgSpec(args=['self'], varargs=None, keywords=None, defaults=None)
-paddle.fluid.Program.inference_optimize ArgSpec(args=['self'], varargs=None, keywords=None, defaults=None)
+paddle.fluid.Program.inference_optimize ArgSpec(args=['self', 'export_for_deployment'], varargs=None, keywords=None, defaults=(True,))
 paddle.fluid.Program.list_vars ArgSpec(args=['self'], varargs=None, keywords=None, defaults=None)
 paddle.fluid.Program.optimized_guard ArgSpec(args=[], varargs='args', keywords='kwds', defaults=None)
 paddle.fluid.Program.parse_from_string ArgSpec(args=['binary_str'], varargs=None, keywords=None, defaults=None)
@@ -18,6 +18,9 @@ paddle.fluid.Operator.all_attrs ArgSpec(args=['self'], varargs=None, keywords=No
 paddle.fluid.Operator.attr ArgSpec(args=['self', 'name'], varargs=None, keywords=None, defaults=None)
 paddle.fluid.Operator.attr_type ArgSpec(args=['self', 'name'], varargs=None, keywords=None, defaults=None)
 paddle.fluid.Operator.block_attr ArgSpec(args=['self', 'name'], varargs=None, keywords=None, defaults=None)
+paddle.fluid.Operator.block_attr_id ArgSpec(args=['self', 'name'], varargs=None, keywords=None, defaults=None)
+paddle.fluid.Operator.blocks_attr ArgSpec(args=['self', 'name'], varargs=None, keywords=None, defaults=None)
+paddle.fluid.Operator.blocks_attr_ids ArgSpec(args=['self', 'name'], varargs=None, keywords=None, defaults=None)
 paddle.fluid.Operator.has_attr ArgSpec(args=['self', 'name'], varargs=None, keywords=None, defaults=None)
 paddle.fluid.Operator.has_kernel ArgSpec(args=['self', 'op_type'], varargs=None, keywords=None, defaults=None)
 paddle.fluid.Operator.input ArgSpec(args=['self', 'name'], varargs=None, keywords=None, defaults=None)
@@ -34,21 +37,10 @@ paddle.fluid.default_main_program ArgSpec(args=[], varargs=None, keywords=None,
 paddle.fluid.program_guard ArgSpec(args=[], varargs='args', keywords='kwds', defaults=None)
 paddle.fluid.get_var ArgSpec(args=['name', 'program'], varargs=None, keywords=None, defaults=(None,))
 paddle.fluid.Executor.__init__ ArgSpec(args=['self', 'place'], varargs=None, keywords=None, defaults=None)
-paddle.fluid.Executor.as_lodtensor ArgSpec(args=['self', 'data'], varargs=None, keywords=None, defaults=None)
 paddle.fluid.Executor.close ArgSpec(args=['self'], varargs=None, keywords=None, defaults=None)
 paddle.fluid.Executor.run ArgSpec(args=['self', 'program', 'feed', 'fetch_list', 'feed_var_name', 'fetch_var_name', 'scope', 'return_numpy', 'use_program_cache'], varargs=None, keywords=None, defaults=(None, None, None, 'feed', 'fetch', None, True, False))
 paddle.fluid.global_scope ArgSpec(args=[], varargs=None, keywords=None, defaults=None)
 paddle.fluid.scope_guard ArgSpec(args=[], varargs='args', keywords='kwds', defaults=None)
-paddle.fluid.fetch_var ArgSpec(args=['name', 'scope', 'return_numpy'], varargs=None, keywords=None, defaults=(None, True))
-paddle.fluid.Go.__init__ ArgSpec(args=['self', 'name'], varargs=None, keywords=None, defaults=(None,))
-paddle.fluid.Go.construct_go_op ArgSpec(args=['self'], varargs=None, keywords=None, defaults=None)
-paddle.fluid.make_channel ArgSpec(args=['dtype', 'capacity'], varargs=None, keywords=None, defaults=(0,))
-paddle.fluid.channel_send ArgSpec(args=['channel', 'value', 'is_copy'], varargs=None, keywords=None, defaults=(False,))
-paddle.fluid.channel_recv ArgSpec(args=['channel', 'return_value'], varargs=None, keywords=None, defaults=None)
-paddle.fluid.channel_close ArgSpec(args=['channel'], varargs=None, keywords=None, defaults=None)
-paddle.fluid.Select.__init__ ArgSpec(args=['self', 'name'], varargs=None, keywords=None, defaults=(None,))
-paddle.fluid.Select.case ArgSpec(args=['self', 'channel_action_fn', 'channel', 'value', 'is_copy'], varargs=None, keywords=None, defaults=(False,))
-paddle.fluid.Select.default ArgSpec(args=['self'], varargs=None, keywords=None, defaults=None)
 paddle.fluid.Trainer.__init__ ArgSpec(args=['self', 'train_func', 'optimizer_func', 'param_path', 'place', 'parallel', 'checkpoint_config'], varargs=None, keywords=None, defaults=(None, None, False, None))
 paddle.fluid.Trainer.save_params ArgSpec(args=['self', 'param_path'], varargs=None, keywords=None, defaults=None)
 paddle.fluid.Trainer.stop ArgSpec(args=['self'], varargs=None, keywords=None, defaults=None)
@@ -62,20 +54,16 @@ paddle.fluid.CheckpointConfig.__init__ ArgSpec(args=['self', 'checkpoint_dir', '
 paddle.fluid.Inferencer.__init__ ArgSpec(args=['self', 'infer_func', 'param_path', 'place', 'parallel'], varargs=None, keywords=None, defaults=(None, False))
 paddle.fluid.Inferencer.infer ArgSpec(args=['self', 'inputs', 'return_numpy'], varargs=None, keywords=None, defaults=(True,))
 paddle.fluid.DistributeTranspiler.__init__ ArgSpec(args=['self', 'config'], varargs=None, keywords=None, defaults=(None,))
-paddle.fluid.DistributeTranspiler.create_splited_vars ArgSpec(args=['self', 'source_var', 'block', 'tag'], varargs=None, keywords=None, defaults=None)
 paddle.fluid.DistributeTranspiler.get_pserver_program ArgSpec(args=['self', 'endpoint'], varargs=None, keywords=None, defaults=None)
 paddle.fluid.DistributeTranspiler.get_startup_program ArgSpec(args=['self', 'endpoint', 'pserver_program'], varargs=None, keywords=None, defaults=None)
 paddle.fluid.DistributeTranspiler.get_trainer_program ArgSpec(args=['self'], varargs=None, keywords=None, defaults=None)
 paddle.fluid.DistributeTranspiler.transpile ArgSpec(args=['self', 'trainer_id', 'program', 'pservers', 'trainers', 'sync_mode'], varargs=None, keywords=None, defaults=(None, '127.0.0.1:6174', 1, True))
 paddle.fluid.InferenceTranspiler.__init__ 
-paddle.fluid.InferenceTranspiler.fuse_batch_norm ArgSpec(args=['self', 'program', 'place', 'scope'], varargs=None, keywords=None, defaults=None)
-paddle.fluid.InferenceTranspiler.fuse_relu_mkldnn ArgSpec(args=['self', 'program'], varargs=None, keywords=None, defaults=None)
 paddle.fluid.InferenceTranspiler.transpile ArgSpec(args=['self', 'program', 'place', 'scope'], varargs=None, keywords=None, defaults=(None,))
 paddle.fluid.memory_optimize ArgSpec(args=['input_program', 'skip_opt_set', 'print_log', 'level'], varargs=None, keywords=None, defaults=(None, False, 0))
 paddle.fluid.release_memory ArgSpec(args=['input_program', 'skip_opt_set'], varargs=None, keywords=None, defaults=(None,))
 paddle.fluid.DistributeTranspilerConfig.__init__ 
 paddle.fluid.ParallelExecutor.__init__ ArgSpec(args=['self', 'use_cuda', 'loss_name', 'main_program', 'share_vars_from', 'exec_strategy', 'build_strategy', 'num_trainers', 'trainer_id'], varargs=None, keywords='kwargs', defaults=(None, None, None, None, None, 1, 0))
-paddle.fluid.ParallelExecutor.bcast_params ArgSpec(args=['self'], varargs=None, keywords=None, defaults=None)
 paddle.fluid.ParallelExecutor.run ArgSpec(args=['self', 'fetch_list', 'feed', 'feed_dict', 'return_numpy'], varargs=None, keywords=None, defaults=(None, None, True))
 paddle.fluid.ExecutionStrategy.__init__ __init__(self: paddle.fluid.core.ExecutionStrategy) -> None
 paddle.fluid.BuildStrategy.GradientScaleStrategy.__init__ __init__(self: paddle.fluid.core.GradientScaleStrategy, arg0: int) -> None
@@ -89,7 +77,7 @@ paddle.fluid.io.save_persistables ArgSpec(args=['executor', 'dirname', 'main_pro
 paddle.fluid.io.load_vars ArgSpec(args=['executor', 'dirname', 'main_program', 'vars', 'predicate', 'filename'], varargs=None, keywords=None, defaults=(None, None, None, None))
 paddle.fluid.io.load_params ArgSpec(args=['executor', 'dirname', 'main_program', 'filename'], varargs=None, keywords=None, defaults=(None, None))
 paddle.fluid.io.load_persistables ArgSpec(args=['executor', 'dirname', 'main_program', 'filename'], varargs=None, keywords=None, defaults=(None, None))
-paddle.fluid.io.save_inference_model ArgSpec(args=['dirname', 'feeded_var_names', 'target_vars', 'executor', 'main_program', 'model_filename', 'params_filename'], varargs=None, keywords=None, defaults=(None, None, None))
+paddle.fluid.io.save_inference_model ArgSpec(args=['dirname', 'feeded_var_names', 'target_vars', 'executor', 'main_program', 'model_filename', 'params_filename', 'export_for_deployment'], varargs=None, keywords=None, defaults=(None, None, None, True))
 paddle.fluid.io.load_inference_model ArgSpec(args=['dirname', 'executor', 'model_filename', 'params_filename'], varargs=None, keywords=None, defaults=(None, None))
 paddle.fluid.io.get_inference_program ArgSpec(args=['target_vars', 'main_program'], varargs=None, keywords=None, defaults=(None,))
 paddle.fluid.initializer.ConstantInitializer.__init__ ArgSpec(args=['self', 'value', 'force_cpu'], varargs=None, keywords=None, defaults=(0.0, False))
@@ -338,14 +326,11 @@ paddle.fluid.contrib.BeamSearchDecoder.read_array ArgSpec(args=['self', 'init',
 paddle.fluid.contrib.BeamSearchDecoder.update_array ArgSpec(args=['self', 'array', 'value'], varargs=None, keywords=None, defaults=None)
 paddle.fluid.contrib.memory_usage ArgSpec(args=['program', 'batch_size'], varargs=None, keywords=None, defaults=None)
 paddle.fluid.transpiler.DistributeTranspiler.__init__ ArgSpec(args=['self', 'config'], varargs=None, keywords=None, defaults=(None,))
-paddle.fluid.transpiler.DistributeTranspiler.create_splited_vars ArgSpec(args=['self', 'source_var', 'block', 'tag'], varargs=None, keywords=None, defaults=None)
 paddle.fluid.transpiler.DistributeTranspiler.get_pserver_program ArgSpec(args=['self', 'endpoint'], varargs=None, keywords=None, defaults=None)
 paddle.fluid.transpiler.DistributeTranspiler.get_startup_program ArgSpec(args=['self', 'endpoint', 'pserver_program'], varargs=None, keywords=None, defaults=None)
 paddle.fluid.transpiler.DistributeTranspiler.get_trainer_program ArgSpec(args=['self'], varargs=None, keywords=None, defaults=None)
 paddle.fluid.transpiler.DistributeTranspiler.transpile ArgSpec(args=['self', 'trainer_id', 'program', 'pservers', 'trainers', 'sync_mode'], varargs=None, keywords=None, defaults=(None, '127.0.0.1:6174', 1, True))
 paddle.fluid.transpiler.InferenceTranspiler.__init__ 
-paddle.fluid.transpiler.InferenceTranspiler.fuse_batch_norm ArgSpec(args=['self', 'program', 'place', 'scope'], varargs=None, keywords=None, defaults=None)
-paddle.fluid.transpiler.InferenceTranspiler.fuse_relu_mkldnn ArgSpec(args=['self', 'program'], varargs=None, keywords=None, defaults=None)
 paddle.fluid.transpiler.InferenceTranspiler.transpile ArgSpec(args=['self', 'program', 'place', 'scope'], varargs=None, keywords=None, defaults=(None,))
 paddle.fluid.transpiler.memory_optimize ArgSpec(args=['input_program', 'skip_opt_set', 'print_log', 'level'], varargs=None, keywords=None, defaults=(None, False, 0))
 paddle.fluid.transpiler.release_memory ArgSpec(args=['input_program', 'skip_opt_set'], varargs=None, keywords=None, defaults=(None,))

paddle/fluid/framework/CMakeLists.txt

@@ -100,7 +100,7 @@ else()
 endif()
 
 
-cc_library(parallel_executor SRCS parallel_executor.cc DEPS threaded_ssa_graph_executor scope_buffered_ssa_graph_executor graph graph_viz_pass multi_devices_graph_builder ssa_graph_printer ssa_graph_checker)
+cc_library(parallel_executor SRCS parallel_executor.cc DEPS threaded_ssa_graph_executor scope_buffered_ssa_graph_executor graph graph_viz_pass multi_devices_graph_pass multi_devices_graph_print_pass multi_devices_graph_check_pass)
 
 cc_library(prune SRCS prune.cc DEPS framework_proto)
 cc_test(prune_test SRCS prune_test.cc DEPS op_info prune recurrent_op device_context)

paddle/fluid/framework/details/CMakeLists.txt

@@ -5,9 +5,9 @@ cc_library(fetch_op_handle SRCS fetch_op_handle.cc DEPS op_handle_base scope lod
 cc_library(computation_op_handle SRCS computation_op_handle.cc DEPS framework_proto scope place operator op_registry)
 cc_library(rpc_op_handle SRCS rpc_op_handle.cc DEPS framework_proto scope place operator op_registry)
 
-cc_library(ssa_graph_builder SRCS ssa_graph_builder.cc DEPS graph graph_helper)
-cc_library(ssa_graph_printer SRCS ssa_graph_printer.cc DEPS ssa_graph_builder)
-cc_library(ssa_graph_checker SRCS ssa_graph_checker.cc DEPS ssa_graph_builder)
+cc_library(multi_devices_helper SRCS multi_devices_helper.cc DEPS graph graph_helper)
+cc_library(multi_devices_graph_print_pass SRCS multi_devices_graph_print_pass.cc DEPS multi_devices_helper)
+cc_library(multi_devices_graph_check_pass SRCS multi_devices_graph_check_pass.cc DEPS multi_devices_helper)
 
 cc_library(variable_visitor SRCS variable_visitor.cc DEPS lod_tensor selected_rows)
 
@@ -28,7 +28,7 @@ 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(multi_devices_graph_builder SRCS multi_devices_graph_builder.cc DEPS ssa_graph_builder computation_op_handle
+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)
 
 cc_library(ssa_graph_executor SRCS ssa_graph_executor.cc DEPS graph framework_proto)

paddle/fluid/framework/details/ssa_graph_checker.cc → paddle/fluid/framework/details/multi_devices_graph_check_pass.cc

@@ -12,7 +12,7 @@
 // See the License for the specific language governing permissions and
 // limitations under the License.
 
-#include "paddle/fluid/framework/details/ssa_graph_checker.h"
+#include "paddle/fluid/framework/details/multi_devices_graph_check_pass.h"
 #include <string>
 #include "paddle/fluid/framework/ir/graph.h"
 
@@ -86,7 +86,7 @@ bool SSAGraghBuilderWithChecker::IsValidGraph(const ir::Graph *graph) const {
 }  // namespace framework
 }  // namespace paddle
 
-REGISTER_PASS(multi_device_check_pass,
+REGISTER_PASS(multi_devices_check_pass,
               paddle::framework::details::SSAGraghBuilderWithChecker)
     .RequireGraphAttr(paddle::framework::details::kGraphVars)
     .RequireGraphAttr(paddle::framework::details::kGraphDepVars)

paddle/fluid/framework/details/ssa_graph_checker.h → paddle/fluid/framework/details/multi_devices_graph_check_pass.h

@@ -14,7 +14,7 @@
 
 #pragma once
 
-#include "paddle/fluid/framework/details/ssa_graph_builder.h"
+#include "paddle/fluid/framework/details/multi_devices_helper.h"
 
 #include <string>
 
@@ -22,7 +22,7 @@ namespace paddle {
 namespace framework {
 namespace details {
 
-class SSAGraghBuilderWithChecker : public SSAGraphBuilder {
+class SSAGraghBuilderWithChecker : public ir::Pass {
  protected:
   std::unique_ptr<ir::Graph> ApplyImpl(
       std::unique_ptr<ir::Graph> graph) const override {

paddle/fluid/framework/details/multi_devices_graph_builder.cc → paddle/fluid/framework/details/multi_devices_graph_pass.cc

@@ -21,7 +21,7 @@
 #include "paddle/fluid/framework/details/broadcast_op_handle.h"
 #include "paddle/fluid/framework/details/computation_op_handle.h"
 #include "paddle/fluid/framework/details/data_balance_op_handle.h"
-#include "paddle/fluid/framework/details/multi_devices_graph_builder.h"
+#include "paddle/fluid/framework/details/multi_devices_graph_pass.h"
 #include "paddle/fluid/framework/details/reduce_op_handle.h"
 #include "paddle/fluid/framework/details/rpc_op_handle.h"
 #include "paddle/fluid/framework/details/scale_loss_grad_op_handle.h"
@@ -33,6 +33,92 @@
 namespace paddle {
 namespace framework {
 namespace details {
+namespace {
+void PolishGraphToSupportDataHazards(ir::Graph *graph) {
+  for (auto &var_map : graph->Get<GraphVars>(kGraphVars)) {
+    for (auto &name_pair : var_map) {
+      if (name_pair.second.size() <= 1) {
+        continue;
+      }
+      auto it_new = name_pair.second.rbegin();
+      auto it_old = name_pair.second.rbegin();
+      ++it_old;
+      for (; it_old != name_pair.second.rend(); it_new = it_old, ++it_old) {
+        OpHandleBase *write_op = (*it_new)->GeneratedOp();
+        const auto &read_ops = (*it_old)->PendingOps();
+
+        for (auto *read_op : read_ops) {
+          // Manually add a dependency var from read_op to write_op;
+          if (read_op == write_op) {
+            // Read Write is the same op.
+            continue;
+          }
+          bool has_dep = false;
+          for (auto *r_out : read_op->Outputs()) {
+            for (auto *w_in : write_op->Inputs()) {
+              if (r_out->Node() == w_in->Node()) {
+                has_dep = true;
+                break;
+              }
+            }
+          }
+          if (has_dep) continue;
+
+          auto *dep_var = new DummyVarHandle(graph->CreateControlDepVar());
+          read_op->AddOutput(dep_var);
+          write_op->AddInput(dep_var);
+          graph->Get<GraphDepVars>(kGraphDepVars).emplace(dep_var);
+        }
+      }
+    }
+  }
+}
+
+VarHandle *CreateOrGetLatestVarHandle(ir::Graph *graph, ir::Node *node,
+                                      const platform::Place &place,
+                                      size_t place_offset) {
+  auto &var_holders = graph->Get<GraphVars>(kGraphVars)[place_offset];
+  auto &var_holder = var_holders[node->Name()];
+  VarHandle *var = nullptr;
+  if (var_holder.empty()) {
+    if (node->Var()) {
+      var = new VarHandle(graph->CreateVarNode(node->Var()), 0, place_offset,
+                          node->Name(), place);
+    } else {
+      var = new VarHandle(
+          graph->CreateEmptyNode(node->Name(), ir::Node::Type::kVariable), 0,
+          place_offset, node->Name(), place);
+    }
+    var_holder.emplace_back(var);
+  } else {
+    var = var_holder.rbegin()->get();
+  }
+  return var;
+}
+
+void CreateOpOutput(ir::Graph *graph, OpHandleBase *op_handle,
+                    ir::Node *new_node, const platform::Place &place,
+                    size_t place_offset) {
+  auto &vars =
+      graph->Get<GraphVars>(kGraphVars)[place_offset][new_node->Name()];
+  size_t version = vars.size();
+  auto var =
+      new VarHandle(new_node, version, place_offset, new_node->Name(), place);
+  vars.emplace_back(var);
+  op_handle->AddOutput(var);
+}
+
+void AddOutputToLeafOps(ir::Graph *graph) {
+  for (auto &op : graph->Get<GraphOps>(kGraphOps)) {
+    if (!op->Outputs().empty()) {
+      continue;
+    }
+    auto *dummy_leaf = new DummyVarHandle(graph->CreateControlDepVar());
+    graph->Get<GraphDepVars>(kGraphDepVars).emplace(dummy_leaf);
+    op->AddOutput(dummy_leaf);
+  }
+}
+}  // namespace
 
 static const char kLossVarName[] = "loss_var_name";
 static const char kPlaces[] = "places";
@@ -751,7 +837,7 @@ bool MultiDevSSAGraphBuilder::IsScaleLossOp(ir::Node *node) const {
 }  // namespace framework
 }  // namespace paddle
 
-REGISTER_PASS(multi_device_pass,
+REGISTER_PASS(multi_devices_pass,
               paddle::framework::details::MultiDevSSAGraphBuilder)
     .RequirePassAttr(paddle::framework::details::kLossVarName)
     .RequirePassAttr(paddle::framework::details::kPlaces)

paddle/fluid/framework/details/multi_devices_graph_builder.h → paddle/fluid/framework/details/multi_devices_graph_pass.h

@@ -18,7 +18,7 @@
 #include <vector>
 
 #include "paddle/fluid/framework/details/build_strategy.h"
-#include "paddle/fluid/framework/details/ssa_graph_builder.h"
+#include "paddle/fluid/framework/details/multi_devices_helper.h"
 #include "paddle/fluid/framework/ir/graph.h"
 
 namespace paddle {
@@ -30,7 +30,7 @@ namespace framework {
 class Scope;
 namespace details {
 
-class MultiDevSSAGraphBuilder : public SSAGraphBuilder {
+class MultiDevSSAGraphBuilder : public ir::Pass {
  protected:
   std::unique_ptr<ir::Graph> ApplyImpl(
       std::unique_ptr<ir::Graph> graph) const override;

paddle/fluid/framework/details/ssa_graph_printer.cc → paddle/fluid/framework/details/multi_devices_graph_print_pass.cc

@@ -12,7 +12,7 @@
 // See the License for the specific language governing permissions and
 // limitations under the License.
 
-#include "paddle/fluid/framework/details/ssa_graph_printer.h"
+#include "paddle/fluid/framework/details/multi_devices_graph_print_pass.h"
 #include <string>
 #include "paddle/fluid/framework/ir/graph.h"
 
@@ -82,5 +82,5 @@ void GraphvizSSAGraphPrinter::Print(const ir::Graph &graph,
 }  // namespace framework
 }  // namespace paddle
 
-REGISTER_PASS(multi_device_print_pass,
+REGISTER_PASS(multi_devices_print_pass,
               paddle::framework::details::SSAGraghBuilderWithPrinter);

paddle/fluid/framework/details/ssa_graph_printer.h → paddle/fluid/framework/details/multi_devices_graph_print_pass.h

@@ -18,7 +18,7 @@
 #include <iosfwd>
 #include <ostream>
 #include <string>
-#include "paddle/fluid/framework/details/ssa_graph_builder.h"
+#include "paddle/fluid/framework/details/multi_devices_helper.h"
 
 namespace paddle {
 namespace framework {
@@ -35,7 +35,7 @@ class GraphvizSSAGraphPrinter : public SSAGraphPrinter {
   void Print(const ir::Graph& graph, std::ostream& sout) const override;
 };
 
-class SSAGraghBuilderWithPrinter : public SSAGraphBuilder {
+class SSAGraghBuilderWithPrinter : public ir::Pass {
  protected:
   std::unique_ptr<ir::Graph> ApplyImpl(
       std::unique_ptr<ir::Graph> graph) const override {

paddle/fluid/framework/details/multi_devices_helper.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/multi_devices_helper.h"
+
+namespace paddle {
+namespace framework {
+namespace details {}  // namespace details
+}  // namespace framework
+}  // namespace paddle

paddle/fluid/framework/details/ssa_graph_builder.h → paddle/fluid/framework/details/multi_devices_helper.h

@@ -52,33 +52,6 @@ const char kGraphOps[] = "ops";
 
 typedef std::unordered_map<std::string, int> ShardedVarDevice;
 const char kShardedVarDevice[] = "sharded_var_device";
-
-class SSAGraphBuilder : public ir::Pass {
- public:
-  SSAGraphBuilder() {}
-  virtual ~SSAGraphBuilder() {}
-
-  DISABLE_COPY_AND_ASSIGN(SSAGraphBuilder);
-
- protected:
-  /*
-    Dependency graph has been constructed. However, there are still data
-    hazards need to be handled.
-  */
-  static void PolishGraphToSupportDataHazards(ir::Graph *graph);
-
-  static VarHandle *CreateOrGetLatestVarHandle(ir::Graph *graph, ir::Node *node,
-                                               const platform::Place &place,
-                                               size_t place_offset);
-
-  // Add an output variable (each_var_name, place, place_offset) to op_handle,
-  // which belongs to graph
-  static void CreateOpOutput(ir::Graph *graph, OpHandleBase *op_handle,
-                             ir::Node *new_node, const platform::Place &place,
-                             size_t place_offset);
-
-  static void AddOutputToLeafOps(ir::Graph *graph);
-};
 }  // namespace details
 }  // namespace framework
 }  // namespace paddle

paddle/fluid/framework/details/ssa_graph_builder.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/ssa_graph_builder.h"
-#include <utility>
-
-namespace paddle {
-namespace framework {
-namespace details {
-void SSAGraphBuilder::PolishGraphToSupportDataHazards(ir::Graph *graph) {
-  for (auto &var_map : graph->Get<GraphVars>(kGraphVars)) {
-    for (auto &name_pair : var_map) {
-      if (name_pair.second.size() <= 1) {
-        continue;
-      }
-      auto it_new = name_pair.second.rbegin();
-      auto it_old = name_pair.second.rbegin();
-      ++it_old;
-      for (; it_old != name_pair.second.rend(); it_new = it_old, ++it_old) {
-        OpHandleBase *write_op = (*it_new)->GeneratedOp();
-        const auto &read_ops = (*it_old)->PendingOps();
-
-        for (auto *read_op : read_ops) {
-          // Manually add a dependency var from read_op to write_op;
-          if (read_op == write_op) {
-            // Read Write is the same op.
-            continue;
-          }
-          bool has_dep = false;
-          for (auto *r_out : read_op->Outputs()) {
-            for (auto *w_in : write_op->Inputs()) {
-              if (r_out->Node() == w_in->Node()) {
-                has_dep = true;
-                break;
-              }
-            }
-          }
-          if (has_dep) continue;
-
-          auto *dep_var = new DummyVarHandle(graph->CreateControlDepVar());
-          read_op->AddOutput(dep_var);
-          write_op->AddInput(dep_var);
-          graph->Get<GraphDepVars>(kGraphDepVars).emplace(dep_var);
-        }
-      }
-    }
-  }
-}
-
-VarHandle *SSAGraphBuilder::CreateOrGetLatestVarHandle(
-    ir::Graph *graph, ir::Node *node, const platform::Place &place,
-    size_t place_offset) {
-  auto &var_holders = graph->Get<GraphVars>(kGraphVars)[place_offset];
-  auto &var_holder = var_holders[node->Name()];
-  VarHandle *var = nullptr;
-  if (var_holder.empty()) {
-    if (node->Var()) {
-      var = new VarHandle(graph->CreateVarNode(node->Var()), 0, place_offset,
-                          node->Name(), place);
-    } else {
-      var = new VarHandle(
-          graph->CreateEmptyNode(node->Name(), ir::Node::Type::kVariable), 0,
-          place_offset, node->Name(), place);
-    }
-    var_holder.emplace_back(var);
-  } else {
-    var = var_holder.rbegin()->get();
-  }
-  return var;
-}
-
-void SSAGraphBuilder::CreateOpOutput(ir::Graph *graph, OpHandleBase *op_handle,
-                                     ir::Node *new_node,
-                                     const platform::Place &place,
-                                     size_t place_offset) {
-  auto &vars =
-      graph->Get<GraphVars>(kGraphVars)[place_offset][new_node->Name()];
-  size_t version = vars.size();
-  auto var =
-      new VarHandle(new_node, version, place_offset, new_node->Name(), place);
-  vars.emplace_back(var);
-  op_handle->AddOutput(var);
-}
-
-void SSAGraphBuilder::AddOutputToLeafOps(ir::Graph *graph) {
-  for (auto &op : graph->Get<GraphOps>(kGraphOps)) {
-    if (!op->Outputs().empty()) {
-      continue;
-    }
-    auto *dummy_leaf = new DummyVarHandle(graph->CreateControlDepVar());
-    graph->Get<GraphDepVars>(kGraphDepVars).emplace(dummy_leaf);
-    op->AddOutput(dummy_leaf);
-  }
-}
-}  // namespace details
-}  // namespace framework
-}  // namespace paddle

paddle/fluid/framework/details/threaded_ssa_graph_executor.cc

@@ -14,7 +14,7 @@
 
 #include "paddle/fluid/framework/details/threaded_ssa_graph_executor.h"
 
-#include "paddle/fluid/framework/details/ssa_graph_builder.h"
+#include "paddle/fluid/framework/details/multi_devices_helper.h"
 #include "paddle/fluid/platform/profiler.h"
 
 namespace paddle {

paddle/fluid/framework/executor.cc

@@ -330,12 +330,7 @@ void Executor::RunPreparedContext(ExecutorPrepareContext* ctx, Scope* scope,
   }
 
   for (auto& op : ctx->ops_) {
-    VLOG(4) << place_ << " " << op->DebugStringEx(local_scope);
     op->Run(*local_scope, place_);
-    // NOTE! Please do not delete this line, it's usefull because the debug
-    // string before and after op.run are different, after run the output
-    // will have right shape which is usefull for debug.
-    VLOG(3) << place_ << " " << op->DebugStringEx(local_scope);
 
     if (FLAGS_benchmark) {
       VLOG(2) << "Memory used after operator " + op->Type() + " running: "

paddle/fluid/framework/ir/graph.cc

@@ -182,9 +182,11 @@ Graph::Graph(const ProgramDesc &program) : program_(program) {
   }
 
   /**
-   * We only handle write after read(WAR), since it should not have a write
-   * after write in program. If there are write after write operators, we need
-   * prune them.
+   * We should handle write after read(WAR) and write after write(WAW) here.
+   * Because some of the operators of the program can be executed parallelly.
+   * So, to make the program running in the right order, we should add the
+   * dependence of WAR and WAW.
+   *
    *
    * https://en.wikipedia.org/wiki/Hazard_(computer_architecture)#Write_after_read_(WAR)
    */
@@ -201,6 +203,19 @@ Graph::Graph(const ProgramDesc &program) : program_(program) {
           (*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.");
+
+      // Add write after write dependence
+      ir::Node *upstream_op =
+          (*it_old)->inputs.empty() ? nullptr : (*it_old)->inputs[0];
+      if (upstream_op) {
+        ir::Node *dep_var = CreateControlDepVar();
+        write_op->inputs.push_back(dep_var);
+        upstream_op->outputs.push_back(dep_var);
+        dep_var->outputs.push_back(write_op);
+        dep_var->inputs.push_back(upstream_op);
+      }
+
       for (auto *read_op : read_ops) {
         // Manually add a dependency var from read_op to write_op;
         if (read_op == write_op) {

paddle/fluid/framework/op_desc.cc

@@ -238,7 +238,20 @@ Attribute OpDesc::GetNullableAttr(const std::string &name) const {
   }
 }
 
-int OpDesc::GetBlockAttr(const std::string &name) const {
+std::vector<int> OpDesc::GetBlocksAttrIds(const std::string &name) const {
+  auto it = attrs_.find(name);
+  PADDLE_ENFORCE(it != attrs_.end(), "Attribute %s is not found", name);
+  auto blocks = boost::get<std::vector<BlockDesc *>>(it->second);
+
+  std::vector<int> ids;
+  for (auto n : blocks) {
+    ids.push_back(n->ID());
+  }
+
+  return ids;
+}
+
+int OpDesc::GetBlockAttrId(const std::string &name) const {
   auto it = attrs_.find(name);
   PADDLE_ENFORCE(it != attrs_.end(), "Attribute %s is not found", name);
   return boost::get<BlockDesc *>(it->second)->ID();

paddle/fluid/framework/op_desc.h

@@ -83,7 +83,9 @@ class OpDesc {
 
   Attribute GetNullableAttr(const std::string &name) const;
 
-  int GetBlockAttr(const std::string &name) const;
+  int GetBlockAttrId(const std::string &name) const;
+
+  std::vector<int> GetBlocksAttrIds(const std::string &name) const;
 
   void Rename(const std::string &old_name, const std::string &new_name);
 

paddle/fluid/framework/operator.cc

@@ -127,7 +127,7 @@ static LoD GetLoD(const Scope& scope, const std::string& name) {
 }
 
 void OperatorBase::Run(const Scope& scope, const platform::Place& place) {
-  VLOG(10) << "- " << DebugStringEx(&scope);
+  VLOG(4) << place << " " << DebugStringEx(&scope);
   if (platform::is_gpu_place(place)) {
 #ifndef PADDLE_WITH_CUDA
     PADDLE_THROW("Cannot run operator on place %s", place);
@@ -139,7 +139,7 @@ void OperatorBase::Run(const Scope& scope, const platform::Place& place) {
   platform::DeviceContextPool& pool = platform::DeviceContextPool::Instance();
   platform::RecordEvent record_event(Type(), pool.Get(place));
   RunImpl(scope, place);
-  VLOG(10) << "+ " << DebugStringEx(&scope);
+  VLOG(3) << place << " " << DebugStringEx(&scope);
 }
 
 bool OperatorBase::HasInputs(const std::string& name) const {

paddle/fluid/framework/parallel_executor.cc

@@ -25,9 +25,9 @@ limitations under the License. */
 #include "paddle/fluid/platform/nccl_helper.h"
 #endif
 
+#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/scope_buffered_ssa_graph_executor.h"
-#include "paddle/fluid/framework/details/ssa_graph_checker.h"
-#include "paddle/fluid/framework/details/ssa_graph_printer.h"
 #include "paddle/fluid/framework/details/threaded_ssa_graph_executor.h"
 #include "paddle/fluid/platform/profiler.h"
 
@@ -57,39 +57,39 @@ std::unique_ptr<ir::Graph> ApplyParallelExecutorPass(
   }
 
   // Convert graph to run on multi-devices.
-  auto multi_device_pass =
-      ir::PassRegistry::Instance().Get("multi_device_pass");
-  multi_device_pass->SetNotOwned<const std::vector<platform::Place>>("places",
-                                                                     &places);
-  multi_device_pass->SetNotOwned<const std::string>("loss_var_name",
-                                                    &loss_var_name);
-  multi_device_pass->SetNotOwned<const std::unordered_set<std::string>>(
+  auto multi_devices_pass =
+      ir::PassRegistry::Instance().Get("multi_devices_pass");
+  multi_devices_pass->SetNotOwned<const std::vector<platform::Place>>("places",
+                                                                      &places);
+  multi_devices_pass->SetNotOwned<const std::string>("loss_var_name",
+                                                     &loss_var_name);
+  multi_devices_pass->SetNotOwned<const std::unordered_set<std::string>>(
       "params", &param_names);
-  multi_device_pass->SetNotOwned<const std::vector<Scope *>>("local_scopes",
-                                                             &local_scopes);
-  multi_device_pass->SetNotOwned<const BuildStrategy>("strategy", &strategy);
+  multi_devices_pass->SetNotOwned<const std::vector<Scope *>>("local_scopes",
+                                                              &local_scopes);
+  multi_devices_pass->SetNotOwned<const BuildStrategy>("strategy", &strategy);
 
 #ifdef PADDLE_WITH_CUDA
   platform::NCCLContextMap *nctx = use_cuda ? nccl_ctxs : nullptr;
-  multi_device_pass->SetNotOwned<platform::NCCLContextMap>("nccl_ctxs", nctx);
+  multi_devices_pass->SetNotOwned<platform::NCCLContextMap>("nccl_ctxs", nctx);
 #endif
-  graph = multi_device_pass->Apply(std::move(graph));
+  graph = multi_devices_pass->Apply(std::move(graph));
 
   // Apply a graph print pass to record a graph with device info.
   if (!strategy.debug_graphviz_path_.empty()) {
-    auto multi_device_print_pass =
-        ir::PassRegistry::Instance().Get("multi_device_print_pass");
-    multi_device_print_pass->SetNotOwned<const std::string>(
+    auto multi_devices_print_pass =
+        ir::PassRegistry::Instance().Get("multi_devices_print_pass");
+    multi_devices_print_pass->SetNotOwned<const std::string>(
         "debug_graphviz_path", &strategy.debug_graphviz_path_);
-    multi_device_print_pass->Set<details::GraphvizSSAGraphPrinter>(
+    multi_devices_print_pass->Set<details::GraphvizSSAGraphPrinter>(
         "graph_printer", new details::GraphvizSSAGraphPrinter);
-    graph = multi_device_print_pass->Apply(std::move(graph));
+    graph = multi_devices_print_pass->Apply(std::move(graph));
   }
 
   // Verify that the graph is correct for multi-device executor.
-  auto multi_device_check_pass =
-      ir::PassRegistry::Instance().Get("multi_device_check_pass");
-  graph = multi_device_check_pass->Apply(std::move(graph));
+  auto multi_devices_check_pass =
+      ir::PassRegistry::Instance().Get("multi_devices_check_pass");
+  graph = multi_devices_check_pass->Apply(std::move(graph));
   return graph;
 }
 
@@ -354,6 +354,6 @@ ParallelExecutor::~ParallelExecutor() {
 }  // namespace paddle
 
 USE_PASS(graph_viz_pass);
-USE_PASS(multi_device_pass);
-USE_PASS(multi_device_check_pass);
-USE_PASS(multi_device_print_pass);
+USE_PASS(multi_devices_pass);
+USE_PASS(multi_devices_check_pass);
+USE_PASS(multi_devices_print_pass);

paddle/fluid/framework/parallel_executor.h

@@ -19,7 +19,7 @@ limitations under the License. */
 #include <unordered_set>
 #include <vector>
 #include "paddle/fluid/framework/details/execution_strategy.h"
-#include "paddle/fluid/framework/details/multi_devices_graph_builder.h"
+#include "paddle/fluid/framework/details/multi_devices_graph_pass.h"
 #include "paddle/fluid/framework/executor.h"
 #include "paddle/fluid/framework/op_info.h"
 #include "paddle/fluid/framework/program_desc.h"

paddle/fluid/framework/program_desc.cc

@@ -58,7 +58,7 @@ ProgramDesc::ProgramDesc(const ProgramDesc &o) {
       for (const std::string &attr_name : op->AttrNames()) {
         if (op->GetAttrType(attr_name) == proto::AttrType::BLOCK) {
           int sub_block_id =
-              o.Block(block_id).Op(op_id)->GetBlockAttr(attr_name);
+              o.Block(block_id).Op(op_id)->GetBlockAttrId(attr_name);
           op->SetBlockAttr(attr_name, MutableBlock(sub_block_id));
         }
       }

paddle/fluid/inference/analysis/analyzer.cc

@@ -44,13 +44,13 @@ class DfgPassManagerImpl final : public DfgPassManager {
     if (FLAGS_inference_analysis_enable_tensorrt_subgraph_engine) {
       auto trt_teller = [&](const Node* node) {
         std::unordered_set<std::string> teller_set(
-            {"elementwise_add", "mul", "conv2d", "pool2d", "relu"});
+            {"elementwise_add", "mul", "conv2d", "pool2d", "relu", "softmax"});
         if (!node->IsFunction()) return false;
 
         const auto* func = static_cast<const Function*>(node);
-        if (teller_set.count(func->func_type()))
+        if (teller_set.count(func->func_type())) {
           return true;
-        else {
+        } else {
           return false;
         }
       };

paddle/fluid/inference/analysis/data_flow_graph.cc

@@ -337,6 +337,34 @@ ExtractInputAndOutputOfSubGraph(std::vector<Node *> &graph) {  // NOLINT
                         std::vector<Node *>(outputs.begin(), outputs.end()));
 }
 
+void FilterRedundantOutputOfSubGraph(DataFlowGraph *graph) {
+  std::vector<Node *> op_nodes;
+  for (auto &node : GraphTraits<DataFlowGraph>(graph).nodes_in_TS()) {
+    if (node.type() == Node::Type::kValue || node.deleted()) {
+      continue;
+    }
+    op_nodes.push_back(&node);
+  }
+  size_t op_num = op_nodes.size();
+  for (size_t i = 0; i < op_num; i++) {
+    if (op_nodes[i]->type() == Node::Type::kFunction) continue;
+    std::unordered_set<std::string> follow_up_input_names;
+    for (size_t j = i + 1; j < op_num; j++) {
+      for (auto *in : op_nodes[j]->inlinks) {
+        follow_up_input_names.insert(in->name());
+      }
+    }
+    std::vector<Node *> filtered_subgraph_outlinks;
+    for (auto *out : op_nodes[i]->outlinks) {
+      if (follow_up_input_names.count(out->name())) {
+        filtered_subgraph_outlinks.push_back(out);
+      }
+    }
+    PADDLE_ENFORCE_GE(filtered_subgraph_outlinks.size(), 1UL);
+    op_nodes[i]->outlinks = filtered_subgraph_outlinks;
+  }
+}
+
 }  // namespace analysis
 }  // namespace inference
 }  // namespace paddle

paddle/fluid/inference/analysis/data_flow_graph.h

@@ -178,6 +178,7 @@ struct GraphTraits<DataFlowGraph> {
 std::pair<std::vector<Node *>, std::vector<Node *>>
 ExtractInputAndOutputOfSubGraph(std::vector<Node *> &graph);  // NOLINT
 
+void FilterRedundantOutputOfSubGraph(DataFlowGraph *graph);
 }  // namespace analysis
 }  // namespace inference
 }  // namespace paddle

paddle/fluid/inference/analysis/data_flow_graph_to_fluid_pass.cc

@@ -52,6 +52,7 @@ bool DataFlowGraphToFluidPass::Initialize(Argument *argument) {
 bool DataFlowGraphToFluidPass::Finalize() { return true; }
 
 void DataFlowGraphToFluidPass::Run(DataFlowGraph *graph) {
+  FilterRedundantOutputOfSubGraph(graph);
   LOG(INFO) << "graph.inputs " << graph->inputs.size();
   for (auto &node : GraphTraits<DataFlowGraph>(graph).nodes_in_TS()) {
     if (node.deleted()) continue;

paddle/fluid/inference/analysis/dfg_graphviz_draw_pass.cc

@@ -46,9 +46,9 @@ std::string DFG_GraphvizDrawPass::Draw(DataFlowGraph *graph) {
   for (size_t i = 0; i < graph->nodes.size(); i++) {
     const Node &node = graph->nodes.Get(i);
     if (!config_.display_deleted_node && node.deleted()) continue;
-    for (auto &in : node.inlinks) {
-      if (!config_.display_deleted_node && in->deleted()) continue;
-      dot.AddEdge(in->repr(), node.repr(), {});
+    for (auto &out : node.outlinks) {
+      if (!config_.display_deleted_node && out->deleted()) continue;
+      dot.AddEdge(node.repr(), out->repr(), {});
     }
   }
   return dot.Build();

paddle/fluid/inference/api/CMakeLists.txt

@@ -45,7 +45,6 @@ endfunction(inference_api_test)
 
 cc_library(paddle_inference_api SRCS api.cc api_impl.cc DEPS lod_tensor)
 
-
 cc_test(test_paddle_inference_api
         SRCS api_tester.cc
         DEPS paddle_inference_api)
@@ -62,22 +61,18 @@ inference_api_test(test_api_tensorrt_subgraph_engine SRC api_tensorrt_subgraph_e
 endif()
 
 if (WITH_ANAKIN) # only needed in CI
-    # Due to Anakin do not have official library releases and the versions of protobuf and cuda do not match Paddle's,
-    # so anakin library will not be merged to our official inference library. To use anakin prediction API, one need to
-    # compile the libinference_anakin_api.a and compile with anakin.so.
-    fetch_include_recursively(${ANAKIN_INCLUDE})
     # compile the libinference_anakin_api.a and anakin.so.
-    nv_library(inference_anakin_api SRCS api.cc api_anakin_engine.cc)
-    nv_library(inference_anakin_api_shared SHARED SRCS api.cc api_anakin_engine.cc)
-    target_compile_options(inference_anakin_api BEFORE PUBLIC ${ANAKIN_COMPILE_EXTRA_FLAGS})
-    target_compile_options(inference_anakin_api_shared BEFORE PUBLIC ${ANAKIN_COMPILE_EXTRA_FLAGS})
-    target_link_libraries(inference_anakin_api anakin anakin_saber_common)
-    target_link_libraries(inference_anakin_api_shared anakin anakin_saber_common)
+    nv_library(inference_anakin_api SRCS api.cc api_anakin_engine.cc DEPS anakin_shared anakin_saber)
+    #nv_library(inference_anakin_api_shared SHARED SRCS api.cc api_anakin_engine.cc DEPS anakin)
+    function(anakin_target target_name)
+      target_compile_options(${target_name} BEFORE PUBLIC ${ANAKIN_COMPILE_EXTRA_FLAGS})
+    endfunction()
+    anakin_target(inference_anakin_api)
+    #anakin_target(inference_anakin_api_shared)
     if (WITH_TESTING)
-        # this test is unstable, disable it first.
-        #cc_test(inference_anakin_test SRCS api_anakin_engine_tester.cc
-                                  #ARGS --model=${ANAKIN_INSTALL_DIR}/mobilenet_v2.anakin.bin
-                                  #DEPS inference_anakin_api_shared)
-        #target_compile_options(inference_anakin_test BEFORE PUBLIC ${ANAKIN_COMPILE_EXTRA_FLAGS})
-     endif(WITH_TESTING)
+        cc_test(inference_anakin_test SRCS api_anakin_engine_tester.cc
+                ARGS --model=${ANAKIN_SOURCE_DIR}/mobilenet_v2.anakin.bin
+                DEPS inference_anakin_api dynload_cuda SERIAL)
+        target_compile_options(inference_anakin_test BEFORE PUBLIC ${ANAKIN_COMPILE_EXTRA_FLAGS})
+    endif(WITH_TESTING)
 endif()

paddle/fluid/inference/api/api.cc

@@ -12,6 +12,7 @@ WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 See the License for the specific language governing permissions and
 limitations under the License. */
 
+#include <glog/logging.h>
 #include "paddle/fluid/inference/api/paddle_inference_api.h"
 
 namespace paddle {
@@ -40,19 +41,36 @@ PaddleBuf::PaddleBuf(PaddleBuf&& other)
 PaddleBuf::PaddleBuf(const PaddleBuf& other) { *this = other; }
 
 PaddleBuf& PaddleBuf::operator=(const PaddleBuf& other) {
+  if (!other.memory_owned_) {
+    data_ = other.data_;
+    length_ = other.length_;
+    memory_owned_ = other.memory_owned_;
+  } else {
+    Resize(other.length());
+    memcpy(data_, other.data(), other.length());
+    length_ = other.length();
+    memory_owned_ = true;
+  }
+  return *this;
+}
+
+PaddleBuf& PaddleBuf::operator=(PaddleBuf&& other) {
   // only the buffer with external memory can be copied
-  assert(!other.memory_owned_);
   data_ = other.data_;
   length_ = other.length_;
   memory_owned_ = other.memory_owned_;
+  other.data_ = nullptr;
+  other.length_ = 0;
+  other.memory_owned_ = false;
   return *this;
 }
 
 void PaddleBuf::Resize(size_t length) {
   // Only the owned memory can be reset, the external memory can't be changed.
   if (length_ == length) return;
-  assert(memory_owned_);
-  Free();
+  if (memory_owned_) {
+    Free();
+  }
   data_ = new char[length];
   length_ = length;
   memory_owned_ = true;
@@ -68,7 +86,7 @@ void PaddleBuf::Reset(void* data, size_t length) {
 void PaddleBuf::Free() {
   if (memory_owned_ && data_) {
     assert(length_ > 0);
-    delete static_cast<char*>(data_);
+    delete[] static_cast<char*>(data_);
     data_ = nullptr;
     length_ = 0;
   }

paddle/fluid/inference/api/paddle_inference_api.h

@@ -40,6 +40,7 @@ class PaddleBuf {
   // Copy only available when memory is managed externally.
   explicit PaddleBuf(const PaddleBuf&);
   PaddleBuf& operator=(const PaddleBuf&);
+  PaddleBuf& operator=(PaddleBuf&&);
   // Do not own the memory.
   PaddleBuf(void* data, size_t length)
       : data_(data), length_(length), memory_owned_{false} {}

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

 # Add TRT tests
 nv_library(tensorrt_converter
   SRCS mul_op.cc conv2d_op.cc fc_op.cc pool2d_op.cc elementwise_op.cc
-activation_op.cc
+activation_op.cc softmax_op.cc
   DEPS tensorrt_engine operator scope framework_proto op_registry)
 
 nv_test(test_op_converter SRCS test_op_converter.cc DEPS
@@ -21,3 +21,6 @@ nv_test(test_trt_pool2d_op SRCS test_pool2d_op.cc pool2d_op.cc
 
 nv_test(test_trt_elementwise_op SRCS test_elementwise_op.cc elementwise_op.cc
         DEPS ${FLUID_CORE_MODULES} tensorrt_engine elementwise_add_op SERIAL)
+
+nv_test(test_trt_softmax_op SRCS test_softmax_op.cc softmax_op.cc
+        DEPS ${FLUID_CORE_MODULES} tensorrt_engine softmax_op SERIAL)

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

+/* Copyright (c) 2018 PaddlePaddle Authors. All Rights Reserved.
+
+Licensed under the Apache License, Version 2.0 (the "License");
+you may not use this file except in compliance with the License.
+You may obtain a copy of the License at
+
+http://www.apache.org/licenses/LICENSE-2.0
+
+Unless required by applicable law or agreed to in writing, software
+distributed under the License is distributed on an "AS IS" BASIS,
+WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+See the License for the specific language governing permissions and
+limitations under the License. */
+
+#include "paddle/fluid/inference/tensorrt/convert/op_converter.h"
+
+namespace paddle {
+namespace inference {
+namespace tensorrt {
+
+/*
+ * SoftMaxOp, ISoftMaxLayer in TRT. This Layer doesn't has weights.
+ */
+class SoftMaxOpConverter : public OpConverter {
+ public:
+  void operator()(const framework::proto::OpDesc& op,
+                  const framework::Scope& scope, bool test_mode) override {
+    VLOG(4)
+        << "convert a fluid softmax op to tensorrt softmax layer without bias";
+    framework::OpDesc op_desc(op, nullptr);
+    // Declare inputs
+    auto* input1 = engine_->GetITensor(op_desc.Input("X")[0]);
+    auto* layer = TRT_ENGINE_ADD_LAYER(engine_, SoftMax,
+                                       *const_cast<nvinfer1::ITensor*>(input1));
+
+    auto output_name = op_desc.Output("Out")[0];
+    engine_->SetITensor(output_name, layer->getOutput(0));
+    if (test_mode) {
+      engine_->DeclareOutput(output_name);
+    }
+  }
+};
+
+}  // namespace tensorrt
+}  // namespace inference
+}  // namespace paddle
+
+USE_OP(softmax);
+REGISTER_TRT_OP_CONVERTER(softmax, SoftMaxOpConverter);

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

+/* Copyright (c) 2018 PaddlePaddle Authors. All Rights Reserved.
+
+   Licensed under the Apache License, Version 2.0 (the "License");
+   you may not use this file except in compliance with the License.
+   You may obtain a copy of the License at
+
+   http://www.apache.org/licenses/LICENSE-2.0
+
+   Unless required by applicable law or agreed to in writing, software
+   distributed under the License is distributed on an "AS IS" BASIS,
+   WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+   See the License for the specific language governing permissions and
+   limitations under the License. */
+#include <gtest/gtest.h>
+#include "paddle/fluid/framework/op_registry.h"
+#include "paddle/fluid/inference/tensorrt/convert/ut_helper.h"
+
+namespace paddle {
+namespace inference {
+namespace tensorrt {
+
+TEST(SoftMaxOpConverter, main) {
+  framework::Scope scope;
+  std::unordered_set<std::string> parameters;
+  TRTConvertValidation validator(8, parameters, scope, 1000);
+
+  std::vector<int> tensor_shape{8, 10};
+  validator.DeclInputVar("softmax-X", tensor_shape,
+                         nvinfer1::DimsCHW(10, 1, 1));
+  validator.DeclOutputVar("softmax-Out", nvinfer1::DimsCHW(10, 1, 1));
+
+  // Prepare Op description
+  framework::OpDesc desc;
+  desc.SetType("softmax");
+  desc.SetInput("X", {"softmax-X"});
+  desc.SetOutput("Out", {"softmax-Out"});
+
+  LOG(INFO) << "set OP";
+  validator.SetOp(*desc.Proto());
+  LOG(INFO) << "execute";
+
+  validator.Execute(3);
+}
+
+}  // namespace tensorrt
+}  // namespace inference
+}  // namespace paddle
+
+USE_OP(softmax);

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

@@ -79,6 +79,12 @@ class TRTConvertValidation {
   }
 
   // Declare a Variable as input with random initialization.
+  void DeclInputVar(const std::string& name, const std::vector<int> tensor_dims,
+                    const nvinfer1::Dims& trt_dims) {
+    DeclVar(name, tensor_dims);
+    engine_->DeclareInput(name, nvinfer1::DataType::kFLOAT, trt_dims);
+  }
+
   void DeclInputVar(const std::string& name, const nvinfer1::Dims& dims) {
     DeclVar(name, dims);
     // Declare TRT inputs.
@@ -94,12 +100,18 @@ class TRTConvertValidation {
     DeclVar(name, dims);
   }
 
-  // Declare a variable in a fluid Scope.
-  void DeclVar(const std::string& name, const nvinfer1::Dims& dims,
-               bool is_param = false) {
+  void DeclVar(const std::string& name, const std::vector<int> dim_vec) {
     platform::CPUPlace place;
     platform::CPUDeviceContext ctx(place);
 
+    auto* x = scope_.Var(name);
+    auto* x_tensor = x->GetMutable<framework::LoDTensor>();
+    x_tensor->Resize(framework::make_ddim(dim_vec));
+    RandomizeTensor(x_tensor, place, ctx);
+  }
+  // Declare a variable in a fluid Scope.
+  void DeclVar(const std::string& name, const nvinfer1::Dims& dims,
+               bool is_param = false) {
     // Init Fluid tensor.
     std::vector<int> dim_vec(dims.d, dims.d + dims.nbDims);
     // There is no batchsize in ITensor's shape, but We should add it to
@@ -107,10 +119,8 @@ class TRTConvertValidation {
     // if_add_batch_ flag is true, add the max batchsize to dim_vec.
     if (is_param != true && if_add_batch_ == true)
       dim_vec.insert(dim_vec.begin(), max_batch_size_);
-    auto* x = scope_.Var(name);
-    auto* x_tensor = x->GetMutable<framework::LoDTensor>();
-    x_tensor->Resize(framework::make_ddim(dim_vec));
-    RandomizeTensor(x_tensor, place, ctx);
+
+    DeclVar(name, dim_vec);
   }
 
   void SetOp(const framework::proto::OpDesc& desc) {

paddle/fluid/operators/CMakeLists.txt

@@ -235,7 +235,12 @@ else()
 endif()
 
 op_library(cross_entropy_op DEPS cross_entropy)
-op_library(softmax_with_cross_entropy_op DEPS cross_entropy softmax)
+if(WITH_GPU)
+  op_library(softmax_with_cross_entropy_op DEPS cross_entropy softmax cub)
+else()
+  op_library(softmax_with_cross_entropy_op DEPS cross_entropy softmax)
+endif()
+
 op_library(softmax_op DEPS softmax)
 op_library(sequence_softmax_op DEPS softmax)
 if (WITH_GPU AND TENSORRT_FOUND)
@@ -273,9 +278,9 @@ op_library(squeeze_op DEPS reshape_op)
 op_library(extract_rows_op DEPS memory)
 op_library(flatten_op DEPS reshape_op)
 
-
 if (WITH_GPU)
     op_library(conv_op DEPS vol2col depthwise_conv im2col)
+    op_library(layer_norm_op DEPS cub)
 else()
     op_library(conv_op DEPS vol2col im2col)
 endif()

paddle/fluid/operators/conv_mkldnn_op.cc

@@ -280,12 +280,16 @@ class ConvMKLDNNOpKernel : public paddle::framework::OpKernel<T> {
      * ('any') which lets a primitive (convolution in this case) choose
      * the memory format preferred for best performance
      */
+    std::string data_format = ctx.Attr<std::string>("data_format");
+    auto chosen_memory_format =
+        platform::data_format_to_memory_format(data_format);
+
     auto src_md = platform::MKLDNNMemDesc(
-        src_tz, platform::MKLDNNGetDataType<T>(), memory::format::any);
+        src_tz, platform::MKLDNNGetDataType<T>(), chosen_memory_format);
     auto weights_md = platform::MKLDNNMemDesc(
-        weights_tz, platform::MKLDNNGetDataType<T>(), memory::format::any);
+        weights_tz, platform::MKLDNNGetDataType<T>(), chosen_memory_format);
     auto dst_md = platform::MKLDNNMemDesc(
-        dst_tz, platform::MKLDNNGetDataType<T>(), memory::format::any);
+        dst_tz, platform::MKLDNNGetDataType<T>(), chosen_memory_format);
 
     // create a conv primitive descriptor and save it for usage in backward
     std::shared_ptr<mkldnn::convolution_forward::primitive_desc> conv_pd =
@@ -423,16 +427,20 @@ class ConvMKLDNNGradOpKernel : public paddle::framework::OpKernel<T> {
      * ('any') which lets a primitive (conv backward in this case) choose
      * the memory format preferred for best performance
      */
+    std::string data_format = ctx.Attr<std::string>("data_format");
+    auto chosen_memory_format =
+        platform::data_format_to_memory_format(data_format);
+
     auto src_md = platform::MKLDNNMemDesc(
-        src_tz, platform::MKLDNNGetDataType<T>(), memory::format::any);
+        src_tz, platform::MKLDNNGetDataType<T>(), chosen_memory_format);
     auto diff_src_md = platform::MKLDNNMemDesc(
-        src_tz, platform::MKLDNNGetDataType<T>(), memory::format::any);
+        src_tz, platform::MKLDNNGetDataType<T>(), chosen_memory_format);
     auto weights_md = platform::MKLDNNMemDesc(
-        weights_tz, platform::MKLDNNGetDataType<T>(), memory::format::any);
+        weights_tz, platform::MKLDNNGetDataType<T>(), chosen_memory_format);
     auto diff_weights_md = platform::MKLDNNMemDesc(
-        weights_tz, platform::MKLDNNGetDataType<T>(), memory::format::any);
+        weights_tz, platform::MKLDNNGetDataType<T>(), chosen_memory_format);
     auto diff_dst_md = platform::MKLDNNMemDesc(
-        dst_tz, platform::MKLDNNGetDataType<T>(), memory::format::any);
+        dst_tz, platform::MKLDNNGetDataType<T>(), chosen_memory_format);
 
     // Retrieve conv_pd from device context
     auto conv_pd =

paddle/fluid/operators/crop_op.cc

-/* Copyright (c) 2016 PaddlePaddle Authors. All Rights Reserved.
+/* 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.
@@ -188,6 +188,7 @@ namespace ops = paddle::operators;
 REGISTER_OPERATOR(crop, ops::CropOp, ops::CropOpMaker,
                   paddle::framework::DefaultGradOpDescMaker<true>);
 REGISTER_OPERATOR(crop_grad, ops::CropOpGrad);
-REGISTER_OP_CPU_KERNEL(crop, ops::CropKernel<float>);
+REGISTER_OP_CPU_KERNEL(
+    crop, ops::CropKernel<paddle::platform::CPUDeviceContext, float>);
 REGISTER_OP_CPU_KERNEL(
     crop_grad, ops::CropGradKernel<paddle::platform::CPUDeviceContext, float>);

paddle/fluid/operators/crop_op.cu

-/* Copyright (c) 2016 PaddlePaddle Authors. All Rights Reserved.
+/* 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.
@@ -16,6 +16,7 @@ limitations under the License. */
 #include "paddle/fluid/operators/crop_op.h"
 
 namespace ops = paddle::operators;
-REGISTER_OP_CUDA_KERNEL(crop, ops::CropKernel<float>);
+REGISTER_OP_CUDA_KERNEL(
+    crop, ops::CropKernel<paddle::platform::CUDADeviceContext, float>);
 REGISTER_OP_CUDA_KERNEL(
     crop_grad, ops::CropGradKernel<paddle::platform::CUDADeviceContext, float>);

paddle/fluid/operators/crop_op.h

-/* Copyright (c) 2016 PaddlePaddle Authors. All Rights Reserved.
+/* 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.
@@ -58,32 +58,74 @@ static std::vector<int> GetOffsets(const framework::ExecutionContext& ctx) {
   return res;
 }
 
-template <typename T>
+template <typename DeviceContext, typename T, size_t D>
+void CropFunction(const framework::ExecutionContext& context) {
+  auto* x = context.Input<Tensor>("X");
+  auto* out = context.Output<Tensor>("Out");
+  auto out_dims = out->dims();
+  if (out_dims[0] == -1) {
+    out_dims[0] = x->dims()[0];
+  }
+  out->mutable_data<T>(out_dims, context.GetPlace());
+  auto x_stride = framework::stride(x->dims());
+  auto out_stride = framework::stride(out->dims());
+  auto offsets = GetOffsets(context);
+  int64_t offset = 0;
+  for (size_t i = 0; i < offsets.size(); ++i) {
+    offset += (x_stride[i] * offsets[i]);
+  }
+
+  auto x_tensor = EigenTensor<T, D>::From(*x);
+  auto out_tensor = EigenTensor<T, D>::From(*out);
+  Eigen::array<int, D> e_offsets;
+  Eigen::array<int, D> e_shape;
+  for (size_t i = 0; i < D; ++i) {
+    e_offsets[i] = offsets[i];
+    e_shape[i] = out->dims()[i];
+  }
+  auto& place =
+      *context.template device_context<DeviceContext>().eigen_device();
+  out_tensor.device(place) = x_tensor.slice(e_offsets, e_shape);
+}
+
+template <typename DeviceContext, typename T>
 class CropKernel : public framework::OpKernel<T> {
  public:
   void Compute(const framework::ExecutionContext& context) const override {
-    auto* x = context.Input<Tensor>("X");
-    auto* out = context.Output<Tensor>("Out");
-    const T* x_data = x->data<T>();
-    T* out_data = out->mutable_data<T>(context.GetPlace());
-    auto x_stride = framework::stride(x->dims());
-    auto out_stride = framework::stride(out->dims());
-    auto offsets = GetOffsets(context);
-    int64_t offset = 0;
-    for (size_t i = 0; i < offsets.size(); ++i) {
-      offset += (x_stride[i] * offsets[i]);
+    int rank = context.Input<Tensor>("X")->dims().size();
+    switch (rank) {
+      case 1:
+        CropFunction<DeviceContext, T, 1>(context);
+        break;
+      case 2:
+        CropFunction<DeviceContext, T, 2>(context);
+        break;
+      case 3:
+        CropFunction<DeviceContext, T, 3>(context);
+        break;
+      case 4:
+        CropFunction<DeviceContext, T, 4>(context);
+        break;
+      case 5:
+        CropFunction<DeviceContext, T, 5>(context);
+        break;
+      case 6:
+        CropFunction<DeviceContext, T, 6>(context);
+        break;
+      default:
+        PADDLE_THROW(
+            "CropOp only support tensors with no more than 6 dimensions.");
     }
-    StridedMemcpy<T>(context.device_context(), x_data + offset, x_stride,
-                     out->dims(), out_stride, out_data);
   }
 };
 
 template <typename DeviceContext, typename T, size_t D>
 void CropGradFunction(const framework::ExecutionContext& context) {
   auto* d_x = context.Output<Tensor>(framework::GradVarName("X"));
+  auto* x = context.Input<Tensor>("X");
   if (d_x != nullptr) {
     auto* d_out = context.Input<Tensor>(framework::GradVarName("Out"));
-    d_x->mutable_data<T>(context.GetPlace());
+    d_x->mutable_data<T>(x->dims(), context.GetPlace());
     auto offsets = GetOffsets(context);
     Eigen::array<std::pair<int, int>, D> paddings;
     for (size_t i = 0; i < D; ++i) {

paddle/fluid/operators/detection/mine_hard_examples_op.cc

@@ -227,6 +227,9 @@ class MineHardExamplesOp : public framework::OperatorWithKernel {
       PADDLE_ENFORCE_GT(
           neg_pos_ratio, 0.0f,
           "neg_pos_ratio must greater than zero in max_negative mode");
+      PADDLE_ENFORCE_LT(
+          neg_dist_threshold, 1.0f,
+          "neg_dist_threshold must less than one in max_negative mode");
       PADDLE_ENFORCE_GT(
           neg_dist_threshold, 0.0f,
           "neg_dist_threshold must greater than zero in max_negative mode");

paddle/fluid/operators/distributed/request_handler_impl.cc

@@ -41,6 +41,7 @@ bool RequestSendHandler::Handle(const std::string& varname,
 
   // Async
   if (!sync_mode_) {
+    rpc_server_->Profiler().OneStep();
     try {
       executor_->RunPreparedContext((*grad_to_prepared_ctx_)[varname].get(),
                                     scope);

paddle/fluid/operators/distributed/rpc_server.cc

@@ -18,11 +18,44 @@
 #include <string>
 
 #include "paddle/fluid/operators/distributed/rpc_server.h"
+#include "paddle/fluid/platform/profiler.h"
+
+DEFINE_int32(rpc_server_profile_period, 0,
+             "the period of listen_and_serv to do profile");
+DEFINE_string(rpc_server_profile_path, "/dev/null",
+              "the profile log file path");
 
 namespace paddle {
 namespace operators {
 namespace distributed {
 
+RPCServerProfiler::RPCServerProfiler(int profile_period,
+                                     const std::string& profile_log_path)
+    : profile_period_(profile_period), profile_log_path_(profile_log_path) {
+  step_ = 0;
+}
+
+void RPCServerProfiler::OneStep() {
+  PADDLE_ENFORCE_LE(step_, profile_period_,
+                    "step_ should not be larger then "
+                    "profile_period_");
+  if (profile_period_ <= 0) {
+    return;
+  }
+
+  if (step_ == 0) {
+    auto pf_state = paddle::platform::ProfilerState::kCPU;
+    paddle::platform::EnableProfiler(pf_state);
+  }
+  if (step_ == profile_period_) {
+    paddle::platform::DisableProfiler(paddle::platform::EventSortingKey::kTotal,
+                                      profile_log_path_);
+    step_ = 0;
+  } else {
+    step_++;
+  }
+}
+
 void RPCServer::ShutDown() {
   LOG(INFO) << "RPCServer ShutDown ";
   ShutDownImpl();

paddle/fluid/operators/distributed/rpc_server.h

@@ -19,16 +19,33 @@
 #include <thread>  // NOLINT
 #include <utility>
 #include <vector>
+
 #include "paddle/fluid/operators/distributed/request_handler.h"
 
+DECLARE_int32(rpc_server_profile_period);
+DECLARE_string(rpc_server_profile_path);
+
 namespace paddle {
 namespace operators {
 namespace distributed {
 
+class RPCServerProfiler {
+ public:
+  RPCServerProfiler(int profile_period, const std::string& profile_log_path);
+  void OneStep();
+
+ private:
+  const int profile_period_;
+  std::string profile_log_path_;
+  int step_;
+};
+
 class RPCServer {
  public:
   explicit RPCServer(const std::string& address, int client_num)
       : cur_cond_(0),
+        profiler_(FLAGS_rpc_server_profile_period,
+                  FLAGS_rpc_server_profile_path),
         bind_address_(address),
         exit_flag_(false),
         selected_port_(0),
@@ -67,6 +84,7 @@ class RPCServer {
   void Complete();
 
   void ResetBarrierCounter();
+  RPCServerProfiler& Profiler() { return profiler_; }
 
  protected:
   virtual void ShutDownImpl() = 0;
@@ -79,6 +97,7 @@ class RPCServer {
   std::unordered_map<std::string, int> rpc_cond_map_;
   std::atomic<int> cur_cond_;
   std::condition_variable rpc_cond_;
+  RPCServerProfiler profiler_;
 
  protected:
   std::string bind_address_;

paddle/fluid/operators/elementwise_op_function.h

@@ -534,8 +534,8 @@ void ElemwiseGradCompute(const framework::ExecutionContext& ctx,
                          const framework::Tensor& dout, int axis,
                          framework::Tensor* dx, framework::Tensor* dy,
                          DX_OP dx_op, DY_OP dy_op) {
-  const framework::DDim x_dim = x.dims();
-  const framework::DDim y_dim = y.dims();
+  const framework::DDim& x_dim = x.dims();
+  const framework::DDim& y_dim = y.dims();
   if (x.dims() == y.dims()) {
     ElemwiseGradComputeNoBroadcast<DeviceContext, T, DX_OP, DY_OP>(
         ctx, x_dim, y_dim, x, y, out, dout, axis, dx, dy, dx_op, dy_op);
@@ -558,19 +558,19 @@ void ElemwiseExplicitGradCompute(const framework::ExecutionContext& ctx,
                                  framework::Tensor* dx, framework::Tensor* dy,
                                  DX_OP dx_op, DY_OP dy_op) {
   if (dy == nullptr) {
-    const framework::DDim dx_dims = dout.dims();
+    const framework::DDim& dx_dims = dout.dims();
     auto dy_dims = dx_dims;
     ElemwiseGradComputeNoBroadcast<DeviceContext, T, DX_OP, DY_OP>(
         ctx, dx_dims, dy_dims, x, y, out, dout, axis, dx, dy, dx_op, dy_op);
   } else {
     if (dout.dims() == dy->dims()) {
-      const framework::DDim dx_dims = dout.dims();
-      const framework::DDim dy_dims = dy->dims();
+      const framework::DDim& dx_dims = dout.dims();
+      const framework::DDim& dy_dims = dy->dims();
       ElemwiseGradComputeNoBroadcast<DeviceContext, T, DX_OP, DY_OP>(
           ctx, dx_dims, dy_dims, x, y, out, dout, axis, dx, dy, dx_op, dy_op);
     } else {  // Y is a scalar
       auto dx_dims = dout.dims();
-      const framework::DDim dy_dims = dy->dims();
+      const framework::DDim& dy_dims = dy->dims();
       ElemwiseGradComputeWithBroadcast<DeviceContext, T, DX_OP, DY_OP>(
           ctx, dx_dims, dy_dims, x, y, out, dout, axis, dx, dy, dx_op, dy_op);
     }

paddle/fluid/operators/layer_norm_op.cu

-/* Copyright (c) 2016 PaddlePaddle Authors. All Rights Reserved.
+/* 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.
@@ -12,8 +12,512 @@ 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 <cub/cub.cuh>
 #include "paddle/fluid/operators/layer_norm_op.h"
 
+namespace paddle {
+namespace operators {
+
+inline static int GetDesiredBlockDim(int block_dim) {
+  const int kMaxBlockDim = 512;
+  return block_dim >= kMaxBlockDim
+             ? kMaxBlockDim
+             : (1 << (static_cast<int>(std::log2f(block_dim))));
+}
+
+#define FIXED_BLOCK_DIM_CASE_BASE(log2_block_dim, ...)  \
+  case (1 << (log2_block_dim)): {                       \
+    constexpr auto kBlockDim = (1 << (log2_block_dim)); \
+    __VA_ARGS__;                                        \
+  } break
+
+#define FIXED_BLOCK_DIM_CASE(...)              \
+  FIXED_BLOCK_DIM_CASE_BASE(9, ##__VA_ARGS__); \
+  FIXED_BLOCK_DIM_CASE_BASE(8, ##__VA_ARGS__); \
+  FIXED_BLOCK_DIM_CASE_BASE(7, ##__VA_ARGS__); \
+  FIXED_BLOCK_DIM_CASE_BASE(6, ##__VA_ARGS__); \
+  FIXED_BLOCK_DIM_CASE_BASE(5, ##__VA_ARGS__); \
+  FIXED_BLOCK_DIM_CASE_BASE(4, ##__VA_ARGS__); \
+  FIXED_BLOCK_DIM_CASE_BASE(3, ##__VA_ARGS__); \
+  FIXED_BLOCK_DIM_CASE_BASE(2, ##__VA_ARGS__); \
+  FIXED_BLOCK_DIM_CASE_BASE(1, ##__VA_ARGS__)
+
+static __device__ __forceinline__ float real_sqrt(float x) { return sqrtf(x); }
+static __device__ __forceinline__ double real_sqrt(double x) { return sqrt(x); }
+
+template <typename T>
+struct PairForLayerNorm {
+  __device__ __forceinline__ PairForLayerNorm() {}
+  __device__ __forceinline__ PairForLayerNorm(const T &first, const T &second)
+      : first_(first), second_(second) {}
+
+  T first_;
+  T second_;
+};
+
+template <typename T>
+struct PairForLayerNormAddFunctor {
+  __device__ __forceinline__ PairForLayerNorm<T> operator()(
+      const PairForLayerNorm<T> &p1, const PairForLayerNorm<T> &p2) {
+    return PairForLayerNorm<T>(p1.first_ + p2.first_, p1.second_ + p2.second_);
+  }
+};
+
+template <typename T, int BlockDim>
+__global__ void LayerNormForward(const T *x, const T *scale, const T *bias,
+                                 T *y, T *mean, T *var, float epsilon,
+                                 int feature_size) {
+  using BlockReduce = cub::BlockReduce<PairForLayerNorm<T>, BlockDim>;
+  __shared__ typename BlockReduce::TempStorage temp_storage;
+
+  int beg_idx = blockIdx.x * feature_size + threadIdx.x;
+  int end_idx = (blockIdx.x + 1) * feature_size;
+
+  // Step 1: Reduce to calculate mean and var
+  T mean_val = static_cast<T>(0);
+  T var_val = static_cast<T>(0);
+  for (int i = beg_idx; i < end_idx; i += BlockDim) {
+    T tmp = x[i];
+    mean_val += tmp;
+    var_val += (tmp * tmp);
+  }
+  auto pair = BlockReduce(temp_storage)
+                  .Reduce(PairForLayerNorm<T>(mean_val, var_val),
+                          PairForLayerNormAddFunctor<T>());
+  if (threadIdx.x == 0) {
+    auto tmp = pair.first_ / feature_size;
+    mean[blockIdx.x] = tmp;
+    var[blockIdx.x] = pair.second_ / feature_size - tmp * tmp;
+  }
+  __syncthreads();
+  mean_val = mean[blockIdx.x];
+  var_val = static_cast<T>(real_sqrt(var[blockIdx.x] + epsilon));
+
+  // Step 2: Calculate y
+  if (scale != nullptr) {
+    if (bias != nullptr) {
+      for (int i = beg_idx, j = threadIdx.x; i < end_idx;
+           i += BlockDim, j += BlockDim) {
+        y[i] = scale[j] * (x[i] - mean_val) / var_val + bias[j];
+      }
+    } else {
+      for (int i = beg_idx, j = threadIdx.x; i < end_idx;
+           i += BlockDim, j += BlockDim) {
+        y[i] = scale[j] * (x[i] - mean_val) / var_val;
+      }
+    }
+  } else {  // scale == nullptr
+    if (bias != nullptr) {
+      for (int i = beg_idx, j = threadIdx.x; i < end_idx;
+           i += BlockDim, j += BlockDim) {
+        y[i] = (x[i] - mean_val) / var_val + bias[j];
+      }
+    } else {
+      for (int i = beg_idx, j = threadIdx.x; i < end_idx;
+           i += BlockDim, j += BlockDim) {
+        y[i] = (x[i] - mean_val) / var_val;
+      }
+    }
+  }
+}
+
+// Make sure that d_scale != nullptr && d_bias != nullptr
+// Since d_scale != nullptr, scale would not be nullptr
+template <typename T, int BlockDim, bool HasDx>
+__global__ void LayerNormBackwardGradientAll(const T *x, const T *d_y,
+                                             T *d_scale, T *d_bias, T *d_x,
+                                             const T *mean, const T *var,
+                                             const T *scale, float epsilon,
+                                             int batch_size, int feature_size) {
+  using BlockReduce = cub::BlockReduce<PairForLayerNorm<T>, BlockDim>;
+  __shared__ typename BlockReduce::TempStorage temp_storage;
+
+  int beg_idx = threadIdx.x * feature_size + blockIdx.x;
+  int end_idx = batch_size * feature_size + blockIdx.x;
+  int stride = BlockDim * feature_size;
+
+  T d_scale_partial = 0, d_bias_partial = 0;
+
+  for (int i = beg_idx; i < end_idx; i += stride) {
+    int row_idx = i / feature_size;
+    auto var_val = static_cast<T>(real_sqrt(var[row_idx] + epsilon));
+    d_scale_partial += d_y[i] * (x[i] - mean[row_idx]) / var_val;
+    d_bias_partial += d_y[i];
+    if (HasDx) {
+      d_x[i] = d_y[i] * scale[blockIdx.x] / var_val;
+    }
+  }
+
+  auto pair = BlockReduce(temp_storage)
+                  .Reduce(PairForLayerNorm<T>(d_scale_partial, d_bias_partial),
+                          PairForLayerNormAddFunctor<T>());
+
+  if (threadIdx.x == 0) {
+    d_scale[blockIdx.x] = pair.first_;
+    d_bias[blockIdx.x] = pair.second_;
+  }
+}
+
+// Make sure that there is only one true expression: d_scale != nullptr
+// or d_bias != nullptr
+// Notice: scale may be nullptr
+template <typename T, int BlockDim, bool HasDx, bool HasDScale>
+__global__ void LayerNormBackwardGradientScaleOrBias(
+    const T *x, const T *d_y, T *d_scale, T *d_bias, T *d_x, const T *mean,
+    const T *var, const T *scale, float epsilon, int batch_size,
+    int feature_size) {
+  using BlockReduce = cub::BlockReduce<T, BlockDim>;
+  __shared__ typename BlockReduce::TempStorage temp_storage;
+  int beg_idx = threadIdx.x * feature_size + blockIdx.x;
+  int end_idx = batch_size * feature_size + blockIdx.x;
+  int stride = BlockDim * feature_size;
+  T d_scale_or_d_bias_partial = 0;
+
+  for (int i = beg_idx; i < end_idx; i += stride) {
+    int row_idx = i / feature_size;
+    auto var_val = static_cast<T>(real_sqrt(var[row_idx] + epsilon));
+    if (HasDScale) {
+      d_scale_or_d_bias_partial += d_y[i] * (x[i] - mean[row_idx]) / var_val;
+    } else {  // d_bias != nullptr
+      d_scale_or_d_bias_partial += d_y[i];
+    }
+
+    if (HasDx) {
+      if (scale != nullptr) {
+        d_x[i] = d_y[i] * scale[blockIdx.x] / var_val;
+      } else {
+        d_x[i] = d_y[i] / var_val;
+      }
+    }
+  }
+
+  d_scale_or_d_bias_partial =
+      BlockReduce(temp_storage).Reduce(d_scale_or_d_bias_partial, cub::Sum());
+
+  if (threadIdx.x == 0) {
+    if (HasDScale) {
+      d_scale[blockIdx.x] = d_scale_or_d_bias_partial;
+    } else {
+      d_bias[blockIdx.x] = d_scale_or_d_bias_partial;
+    }
+  }
+}
+
+template <typename T, int BlockDim>
+__global__ void LayerNormBackwardPostProcessToCalculateDX(const T *x, T *d_x,
+                                                          const T *mean,
+                                                          const T *var,
+                                                          float epsilon,
+                                                          int feature_size) {
+  using BlockReduce = cub::BlockReduce<PairForLayerNorm<T>, BlockDim>;
+  __shared__ typename BlockReduce::TempStorage temp_storage;
+  __shared__ T d_x_reduce_tmp[2];
+
+  int beg_idx = blockIdx.x * feature_size + threadIdx.x;
+  int end_idx = (blockIdx.x + 1) * feature_size;
+
+  T block_mean = mean[blockIdx.x];
+  T block_var = var[blockIdx.x];
+  T d_x_mean_partial = 0, d_x_var_partial = 0;
+  for (int i = beg_idx; i < end_idx; i += BlockDim) {
+    d_x_mean_partial += d_x[i];
+    d_x_var_partial += d_x[i] * (x[i] - block_mean);
+  }
+
+  auto pair =
+      BlockReduce(temp_storage)
+          .Reduce(PairForLayerNorm<T>(d_x_mean_partial, d_x_var_partial),
+                  PairForLayerNormAddFunctor<T>());
+
+  if (threadIdx.x == 0) {
+    d_x_reduce_tmp[0] = pair.first_ / feature_size;
+    d_x_reduce_tmp[1] = pair.second_ / (feature_size * (block_var + epsilon));
+  }
+  __syncthreads();
+
+  d_x_mean_partial = d_x_reduce_tmp[0];
+  d_x_var_partial = d_x_reduce_tmp[1];
+  for (int i = beg_idx; i < end_idx; i += BlockDim) {
+    d_x[i] -= d_x_mean_partial;
+    d_x[i] -= (x[i] - block_mean) * d_x_var_partial;
+  }
+}
+
+// Here, we only calculate d_x
+template <typename T, int BlockDim>
+__global__ void LayerNormBackwardGradientOnlyDX(const T *x, const T *d_y,
+                                                T *d_x, const T *mean,
+                                                const T *var, const T *scale,
+                                                float epsilon,
+                                                int feature_size) {
+  using BlockReduce = cub::BlockReduce<PairForLayerNorm<T>, BlockDim>;
+  __shared__ typename BlockReduce::TempStorage temp_storage;
+  __shared__ T d_x_reduce_tmp[2];
+
+  int beg_idx = blockIdx.x * feature_size + threadIdx.x;
+  int end_idx = (blockIdx.x + 1) * feature_size;
+
+  T block_mean = mean[blockIdx.x], block_var = var[blockIdx.x];
+  T d_x_mean_partial = 0, d_x_var_partial = 0;
+  for (int i = beg_idx; i < end_idx; i += BlockDim) {
+    auto var_val = static_cast<T>(real_sqrt(block_var + epsilon));
+    if (scale != nullptr) {
+      int col_idx = i % feature_size;
+      d_x[i] = d_y[i] * scale[col_idx] / var_val;
+    } else {
+      d_x[i] = d_y[i] / var_val;
+    }
+    d_x_mean_partial += d_x[i];
+    d_x_var_partial += d_x[i] * (x[i] - block_mean);
+  }
+
+  auto pair =
+      BlockReduce(temp_storage)
+          .Reduce(PairForLayerNorm<T>(d_x_mean_partial, d_x_var_partial),
+                  PairForLayerNormAddFunctor<T>());
+
+  if (threadIdx.x == 0) {
+    d_x_reduce_tmp[0] = pair.first_ / feature_size;
+    d_x_reduce_tmp[1] = pair.second_ / (feature_size * (block_var + epsilon));
+  }
+  __syncthreads();
+
+  d_x_mean_partial = d_x_reduce_tmp[0];
+  d_x_var_partial = d_x_reduce_tmp[1];
+  for (int i = beg_idx; i < end_idx; i += BlockDim) {
+    d_x[i] -= d_x_mean_partial;
+    d_x[i] -= (x[i] - block_mean) * d_x_var_partial;
+  }
+}
+
+template <typename T>
+__global__ void LayerNormBackwardWhenBatchSizeIsOne(
+    const T *x, const T *d_y, T *d_x, T *d_scale, T *d_bias, const T *mean,
+    const T *var, const T *scale, float epsilon, int feature_size) {
+  int idx = threadIdx.x + blockIdx.x * blockDim.x;
+  if (idx < feature_size) {
+    auto var_val = static_cast<T>(real_sqrt(var[idx] + epsilon));
+    if (d_x != nullptr) {
+      if (d_scale == nullptr) {
+        d_x[idx] = d_y[idx] / var_val;
+      } else {
+        d_x[idx] = d_y[idx] * scale[idx] / var_val;
+      }
+    }
+
+    if (d_scale != nullptr) {
+      d_scale[idx] = d_y[idx] * (x[idx] - mean[idx]) / var_val;
+    }
+
+    if (d_bias != nullptr) d_bias[idx] = d_y[idx];
+  }
+}
+
+template <typename T>
+static void LayerNormBackward(const T *x, const T *d_y, const T *scale,
+                              const T *mean, const T *var, T *d_x, T *d_scale,
+                              T *d_bias, float epsilon, int batch_size,
+                              int feature_size, cudaStream_t stream) {
+  const int kMaxBlockDim = 512;
+  int gradient_flag = ((d_x != nullptr ? 1 : 0) << 2) |
+                      ((d_scale != nullptr ? 1 : 0) << 1) |
+                      ((d_bias != nullptr ? 1 : 0));
+  if (gradient_flag == 0) return;
+
+  if (batch_size == 1) {
+    LayerNormBackwardWhenBatchSizeIsOne<
+        T><<<(feature_size + kMaxBlockDim - 1) / kMaxBlockDim, kMaxBlockDim, 0,
+             stream>>>(x, d_y, d_x, d_scale, d_bias, mean, var, scale, epsilon,
+                       feature_size);
+
+    if (d_x != nullptr) {
+      switch (GetDesiredBlockDim(feature_size)) {
+        FIXED_BLOCK_DIM_CASE(LayerNormBackwardPostProcessToCalculateDX<
+                             T, kBlockDim><<<1, kBlockDim, 0, stream>>>(
+            x, d_x, mean, var, epsilon, feature_size));
+      }
+    }
+    return;
+  }
+
+  auto block_dim = GetDesiredBlockDim(batch_size);
+  switch (gradient_flag) {
+    case 1:  // d_x == nulptr, d_scale == nullptr, d_bias != nullptr
+      switch (block_dim) {
+        FIXED_BLOCK_DIM_CASE(LayerNormBackwardGradientScaleOrBias<
+                             T, kBlockDim, false,
+                             false><<<feature_size, kBlockDim, 0, stream>>>(
+            x, d_y, d_scale, d_bias, d_x, mean, var, scale, epsilon, batch_size,
+            feature_size));
+      }
+      break;
+    case 2:  // d_x == nullptr, d_scale != nullptr, d_bias == nullptr
+      switch (block_dim) {
+        FIXED_BLOCK_DIM_CASE(LayerNormBackwardGradientScaleOrBias<
+                             T, kBlockDim, false,
+                             true><<<feature_size, kBlockDim, 0, stream>>>(
+            x, d_y, d_scale, d_bias, d_x, mean, var, scale, epsilon, batch_size,
+            feature_size));
+      }
+      break;
+    case 3:  // d_x == nullptr, d_scale != nulptr, d_bias != nullptr
+      switch (block_dim) {
+        FIXED_BLOCK_DIM_CASE(
+            LayerNormBackwardGradientAll<
+                T, kBlockDim, false><<<feature_size, kBlockDim, 0, stream>>>(
+                x, d_y, d_scale, d_bias, d_x, mean, var, scale, epsilon,
+                batch_size, feature_size));
+      }
+      break;
+    case 4:  // d_x != nullptr, d_scale == nullptr, d_bias == nullptr
+      switch (GetDesiredBlockDim(feature_size)) {
+        FIXED_BLOCK_DIM_CASE(
+            LayerNormBackwardGradientOnlyDX<
+                T, kBlockDim><<<batch_size, kBlockDim, 0, stream>>>(
+                x, d_y, d_x, mean, var, scale, epsilon, feature_size));
+      }
+      break;
+    case 5:  // d_x != nulptr, d_scale == nullptr, d_bias != nullptr
+      switch (block_dim) {
+        FIXED_BLOCK_DIM_CASE(LayerNormBackwardGradientScaleOrBias<
+                             T, kBlockDim, true,
+                             false><<<feature_size, kBlockDim, 0, stream>>>(
+            x, d_y, d_scale, d_bias, d_x, mean, var, scale, epsilon, batch_size,
+            feature_size));
+      }
+      switch (GetDesiredBlockDim(feature_size)) {
+        FIXED_BLOCK_DIM_CASE(
+            LayerNormBackwardPostProcessToCalculateDX<
+                T, kBlockDim><<<batch_size, kBlockDim, 0, stream>>>(
+                x, d_x, mean, var, epsilon, feature_size));
+      }
+      break;
+    case 6:  // d_x != nullptr, d_scale != nullptr, d_bias == nullptr
+      switch (block_dim) {
+        FIXED_BLOCK_DIM_CASE(LayerNormBackwardGradientScaleOrBias<
+                             T, kBlockDim, true,
+                             true><<<feature_size, kBlockDim, 0, stream>>>(
+            x, d_y, d_scale, d_bias, d_x, mean, var, scale, epsilon, batch_size,
+            feature_size));
+      }
+      switch (GetDesiredBlockDim(feature_size)) {
+        FIXED_BLOCK_DIM_CASE(
+            LayerNormBackwardPostProcessToCalculateDX<
+                T, kBlockDim><<<batch_size, kBlockDim, 0, stream>>>(
+                x, d_x, mean, var, epsilon, feature_size));
+      }
+      break;
+    case 7:  // d_x != nullptr, d_scale != nullptr, d_bias != nullptr
+      switch (block_dim) {
+        FIXED_BLOCK_DIM_CASE(
+            LayerNormBackwardGradientAll<
+                T, kBlockDim, true><<<feature_size, kBlockDim, 0, stream>>>(
+                x, d_y, d_scale, d_bias, d_x, mean, var, scale, epsilon,
+                batch_size, feature_size));
+      }
+      switch (GetDesiredBlockDim(feature_size)) {
+        FIXED_BLOCK_DIM_CASE(
+            LayerNormBackwardPostProcessToCalculateDX<
+                T, kBlockDim><<<batch_size, kBlockDim, 0, stream>>>(
+                x, d_x, mean, var, epsilon, feature_size));
+      }
+      break;
+    default:
+      break;
+  }
+}
+
+template <typename T>
+class LayerNormKernel<platform::CUDADeviceContext, T>
+    : public framework::OpKernel<T> {
+ public:
+  void Compute(const framework::ExecutionContext &ctx) const override {
+    const float epsilon = ctx.Attr<float>("epsilon");
+    auto *scale = ctx.Input<Tensor>("Scale");
+    auto *bias = ctx.Input<Tensor>("Bias");
+    auto *x = ctx.Input<Tensor>("X");
+
+    auto *y = ctx.Output<Tensor>("Y");
+    auto *mean = ctx.Output<Tensor>("Mean");
+    auto *var = ctx.Output<Tensor>("Variance");
+    const auto begin_norm_axis = ctx.Attr<int>("begin_norm_axis");
+
+    const auto x_dims = x->dims();
+    auto *x_data = x->data<T>();
+    auto *y_data = y->mutable_data<T>(ctx.GetPlace());
+    auto *mean_data = mean->mutable_data<T>(ctx.GetPlace());
+    auto *var_data = var->mutable_data<T>(ctx.GetPlace());
+    auto *scale_data = (scale == nullptr ? nullptr : scale->data<T>());
+    auto *bias_data = (bias == nullptr ? nullptr : bias->data<T>());
+
+    auto matrix_dim = framework::flatten_to_2d(x_dims, begin_norm_axis);
+    int batch_size = static_cast<int>(matrix_dim[0]);
+    int feature_size = static_cast<int>(matrix_dim[1]);
+
+    auto stream = ctx.cuda_device_context().stream();
+
+    switch (GetDesiredBlockDim(feature_size)) {
+      FIXED_BLOCK_DIM_CASE(
+          LayerNormForward<T, kBlockDim><<<batch_size, kBlockDim, 0, stream>>>(
+              x_data, scale_data, bias_data, y_data, mean_data, var_data,
+              epsilon, feature_size));
+      default:
+        PADDLE_THROW(
+            "Product from begin_norm_axis to end must be larger than 1");
+        break;
+    }
+  }
+};
+
+template <typename T>
+class LayerNormGradKernel<platform::CUDADeviceContext, T>
+    : public framework::OpKernel<T> {
+ public:
+  void Compute(const framework::ExecutionContext &ctx) const override {
+    const float epsilon = ctx.Attr<float>("epsilon");
+    // d_x, d_scale, d_bias may be nullptr
+    auto *d_x = ctx.Output<Tensor>(framework::GradVarName("X"));
+    auto *d_scale = ctx.Output<Tensor>(framework::GradVarName("Scale"));
+    auto *d_bias = ctx.Output<Tensor>(framework::GradVarName("Bias"));
+
+    auto *x = ctx.Input<Tensor>("X");
+    auto *mean = ctx.Input<Tensor>("Mean");
+    auto *var = ctx.Input<Tensor>("Variance");
+    auto *scale = ctx.Input<Tensor>("Scale");
+    auto *d_y = ctx.Input<Tensor>(framework::GradVarName("Y"));
+
+    auto *x_data = x->data<T>();
+    auto *d_y_data = d_y->data<T>();
+    auto *mean_data = mean->data<T>();
+    auto *var_data = var->data<T>();
+    auto *scale_data = (scale == nullptr ? nullptr : scale->data<T>());
+    auto *d_scale_data =
+        (d_scale == nullptr ? nullptr
+                            : d_scale->mutable_data<T>(ctx.GetPlace()));
+    auto *d_bias_data =
+        (d_bias == nullptr ? nullptr : d_bias->mutable_data<T>(ctx.GetPlace()));
+    auto *d_x_data =
+        (d_x == nullptr ? nullptr : d_x->mutable_data<T>(ctx.GetPlace()));
+
+    const auto &x_dims = x->dims();
+    const auto begin_norm_axis = ctx.Attr<int>("begin_norm_axis");
+    auto matrix_dim = framework::flatten_to_2d(x_dims, begin_norm_axis);
+    int batch_size = static_cast<int>(matrix_dim[0]);
+    int feature_size = static_cast<int>(matrix_dim[1]);
+
+    auto stream = ctx.cuda_device_context().stream();
+
+    LayerNormBackward<T>(x_data, d_y_data, scale_data, mean_data, var_data,
+                         d_x_data, d_scale_data, d_bias_data, epsilon,
+                         batch_size, feature_size, stream);
+  }
+};
+
+#undef FIXED_BLOCK_DIM_CASE_BASE
+#undef FIXED_BLOCK_DIM_CASE
+}  // namespace operators
+}  // namespace paddle
+
 namespace ops = paddle::operators;
 REGISTER_OP_CUDA_KERNEL(
     layer_norm,

paddle/fluid/operators/listen_and_serv_op.cc

@@ -25,10 +25,6 @@ limitations under the License. */
 
 #include "paddle/fluid/operators/distributed/request_handler_impl.h"
 #include "paddle/fluid/operators/listen_and_serv_op.h"
-#include "paddle/fluid/platform/profiler.h"
-
-DEFINE_int32(listen_and_serv_profile_period, 0,
-             "the period of listen_and_serv to do profile");
 
 namespace paddle {
 namespace operators {
@@ -108,6 +104,7 @@ void ListenAndServOp::RunSyncLoop(
     framework::Scope *recv_scope,
     const std::vector<int> &prefetch_block_id_list,
     const int checkpoint_point_block_id) const {
+  VLOG(2) << "RunSyncLoop";
   size_t num_blocks = program->Size();
   auto optimize_blocks =
       Attr<std::vector<framework::BlockDesc *>>(kOptimizeBlocks);
@@ -128,17 +125,8 @@ void ListenAndServOp::RunSyncLoop(
 
   rpc_service_->ResetBarrierCounter();
 
-  int32_t profile_step = 0;
   while (true) {
-    PADDLE_ENFORCE_LE(profile_step, FLAGS_listen_and_serv_profile_period,
-                      "profile_step should not be larger then "
-                      "FLAGS_listen_and_serv_profile_period");
-    if (FLAGS_listen_and_serv_profile_period > 0) {
-      if (profile_step == 0) {
-        auto pf_state = paddle::platform::ProfilerState::kCPU;
-        paddle::platform::EnableProfiler(pf_state);
-      }
-    }
+    rpc_service_->Profiler().OneStep();
     // Get from multiple trainers, we don't care about the order in which
     // the gradients arrives, just add suffix 0~n and merge the gradient.
     rpc_service_->SetCond(distributed::kRequestSend);
@@ -180,21 +168,13 @@ void ListenAndServOp::RunSyncLoop(
     // reset received sparse vars to avoid reuse it in the next mini-batch
     dynamic_cast<distributed::RequestSendHandler *>(request_send_handler_.get())
         ->ResetSparseVarRecorder();
-    if (FLAGS_listen_and_serv_profile_period > 0) {
-      if (profile_step == FLAGS_listen_and_serv_profile_period) {
-        paddle::platform::DisableProfiler(
-            paddle::platform::EventSortingKey::kTotal, "/dev/null");
-        profile_step = 0;
-      } else {
-        profile_step++;
-      }
-    }
   }  // while(true)
 }
 
 void ListenAndServOp::RunAsyncLoop(framework::Executor *executor,
                                    framework::ProgramDesc *program,
                                    framework::Scope *recv_scope) const {
+  VLOG(2) << "RunAsyncLoop";
   // grad name to block id
   std::unordered_map<std::string, int32_t> grad_to_block_id;
   std::unordered_map<int32_t, std::string> id_to_grad;

paddle/fluid/operators/softmax_with_cross_entropy_op.cu

-/* Copyright (c) 2016 PaddlePaddle Authors. All Rights Reserved.
+/* 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.
@@ -14,6 +14,8 @@ limitations under the License. */
 
 #define EIGEN_USE_GPU
 
+#include <cub/cub.cuh>
+#include "paddle/fluid/operators/math/cross_entropy.h"
 #include "paddle/fluid/operators/softmax_with_cross_entropy_op.h"
 
 namespace paddle {
@@ -53,8 +55,196 @@ __global__ void SoftCrossEntropyGradientKernel(T* logit_grad,
     logit_grad[ids] = loss_grad[row_ids] * (logit_grad[ids] - labels[ids]);
   }
 }
+
 }  // namespace
 
+static __device__ __forceinline__ float real_exp(float x) { return expf(x); }
+static __device__ __forceinline__ double real_exp(double x) { return exp(x); }
+static __device__ __forceinline__ float real_log(float x) {
+  return math::TolerableValue<float>()(logf(x));
+}
+static __device__ __forceinline__ double real_log(double x) {
+  return math::TolerableValue<double>()(log(x));
+}
+
+/** In the following codes, 3 CUDA kernels are implemented to calculate softmax
+ * and loss **/
+/*
+  Supposing the x is `logits` and y is `labels`, the equations are as
+followings:
+
+  cross\_entropy_i = \sum_{j}[- y_i_j * log({e^{x_i_j}/\sum_{j}e^{x_i_j}})]
+        = \sum_{j}[- y_i_j * log({e^{x_i_j - max_i}/\sum_{j}e^{x_i_j-max_i}})]
+        = \sum_{j}[-y_i_j * (x_i_j - max_i - log\sum_{j}e^{x_i_j - max_i})]
+        = \sum_{j}[-y_i_j * (x_i_j - max_i - logDiffMaxSum_i)]
+        = \sum_{j}(-y_i_j * tmp_i_j)
+
+  softmax_i_j = e^{tmp_i_j}
+
+where:
+  max_i = \max_{j}{x_i_j}
+  logDiffMaxSum_i = log\sum_{j}e^{x_i_j - max_i}
+  tmp_i_j = x_i_j - max_i - logDiffMaxSum_i
+
+Therefore, the calculation can be separated into 3 steps:
+Step 1: row-wise operation to calculate max_i
+Step 2: row-wise operation to calculate logDiffMaxSum_i
+Step 3: caculate tmp_i_j, and finally get softmax_i_j and cross\_entropy_i
+
+To save memory, we can share memory among max_i, logDiffMaxSum_i and
+cross\_entropy_i.
+In this way, the 3 steps should be changed to:
+Step 1 (RowReductionForMax): row-wise operation to calculate max_i
+Step 2 (RowReductionForDiffMaxSum): calculate immediate result of softmax'_i_j =
+x_i_j - max_i, and row-wise operation to calculate logDiffMaxSum_i
+Step 3 (RowReductionForSoftmaxAndCrossEntropy): calculate tmp_i_j = softmax'_i_j
+- logDiffMaxSum_i, and finally get softmax_i_j and cross\_entropy_i
+*/
+
+// There are 3 kinds of reduce algorithms in cub:
+// BLOCK_REDUCE_RAKING_COMMUTATIVE_ONLY
+// BLOCK_REDUCE_RAKING
+// BLOCK_REDUCE_WARP_REDUCTIONS (default)
+template <typename T, int BlockDim>
+using BlockReduce =
+    cub::BlockReduce<T, BlockDim /*, cub::BLOCK_REDUCE_WARP_REDUCTIONS*/>;
+
+template <typename T, int BlockDim>
+using BlockReduceTempStorage = typename BlockReduce<T, BlockDim>::TempStorage;
+
+// Make sure that BlockDim <= feature_size
+// This kernel is used to calculate the max element of each row
+template <typename T, int BlockDim>
+__global__ void RowReductionForMax(const T* logits_data, T* max_data,
+                                   int feature_size) {
+  __shared__ BlockReduceTempStorage<T, BlockDim> temp_storage;
+
+  auto beg_idx = feature_size * blockIdx.x + threadIdx.x;
+  auto end_idx = feature_size * (blockIdx.x + 1);
+
+  T cur_max = logits_data[beg_idx];
+  beg_idx += BlockDim;
+  while (beg_idx < end_idx) {
+    if (cur_max < logits_data[beg_idx]) {
+      cur_max = logits_data[beg_idx];
+    }
+    beg_idx += BlockDim;
+  }
+
+  cur_max = BlockReduce<T, BlockDim>(temp_storage).Reduce(cur_max, cub::Max());
+
+  if (threadIdx.x == 0) {
+    max_data[blockIdx.x] = cur_max < -64 ? -64 : cur_max;
+  }
+}
+
+// Make sure that BlockDim <= feature_size
+template <typename T, int BlockDim>
+__global__ void RowReductionForDiffMaxSum(const T* logits_data, T* max_data,
+                                          T* softmax, int feature_size) {
+  __shared__ BlockReduceTempStorage<T, BlockDim> temp_storage;
+
+  auto beg_idx = feature_size * blockIdx.x + threadIdx.x;
+  auto end_idx = feature_size * (blockIdx.x + 1);
+
+  auto block_max = max_data[blockIdx.x];
+
+  softmax[beg_idx] = logits_data[beg_idx] - block_max;
+  T diff_max_sum = real_exp(softmax[beg_idx]);
+  beg_idx += BlockDim;
+  while (beg_idx < end_idx) {
+    softmax[beg_idx] = logits_data[beg_idx] - block_max;
+    diff_max_sum += real_exp(softmax[beg_idx]);
+    beg_idx += BlockDim;
+  }
+
+  diff_max_sum =
+      BlockReduce<T, BlockDim>(temp_storage).Reduce(diff_max_sum, cub::Sum());
+  if (threadIdx.x == 0) max_data[blockIdx.x] = real_log(diff_max_sum);
+}
+
+// Make sure that BlockDim <= feature_size
+template <typename T, int BlockDim>
+__global__ void RowReductionForSoftmaxAndCrossEntropy(const T* logits_data,
+                                                      const T* labels_data,
+                                                      T* loss_data, T* softmax,
+                                                      int feature_size) {
+  __shared__ BlockReduceTempStorage<T, BlockDim> temp_storage;
+
+  auto beg_idx = feature_size * blockIdx.x + threadIdx.x;
+  auto end_idx = feature_size * (blockIdx.x + 1);
+
+  // log_diff_max_sum shares memory with loss
+  auto block_log_diff_max_sum = loss_data[blockIdx.x];
+  auto tmp = softmax[beg_idx] - block_log_diff_max_sum;
+  softmax[beg_idx] = real_exp(tmp);
+  auto loss = -labels_data[beg_idx] * tmp;
+  beg_idx += BlockDim;
+  while (beg_idx < end_idx) {
+    tmp = softmax[beg_idx] - block_log_diff_max_sum;
+    softmax[beg_idx] = real_exp(tmp);
+    loss -= (labels_data[beg_idx] * tmp);
+    beg_idx += BlockDim;
+  }
+
+  loss = BlockReduce<T, BlockDim>(temp_storage).Reduce(loss, cub::Sum());
+  if (threadIdx.x == 0) loss_data[blockIdx.x] = loss;
+}
+
+template <typename T>
+__global__ void SetSoftmaxToOneWhenFeatureSizeIsOne(T* out, int batch_size) {
+  auto idx = threadIdx.x + blockIdx.x * blockDim.x;
+  if (idx < batch_size) out[idx] = static_cast<T>(1);
+}
+
+template <typename T>
+static void SoftmaxWithCrossEntropyFusedKernel(const T* logits_data,
+                                               const T* labels_data,
+                                               T* softmax_data, T* loss_data,
+                                               int batch_size, int feature_size,
+                                               cudaStream_t stream) {
+  constexpr int kMaxBlockDim = 512;
+  int block_dim = feature_size >= kMaxBlockDim
+                      ? kMaxBlockDim
+                      : (1 << static_cast<int>(std::log2(feature_size)));
+
+#define CALL_SOFTMAX_WITH_CROSS_ENTROPY_FUSED_KERNEL(BlockDim)                \
+  case BlockDim:                                                              \
+    RowReductionForMax<T, BlockDim><<<batch_size, BlockDim, 0, stream>>>(     \
+        logits_data, loss_data, feature_size);                                \
+    RowReductionForDiffMaxSum<T,                                              \
+                              BlockDim><<<batch_size, BlockDim, 0, stream>>>( \
+        logits_data, loss_data, softmax_data, feature_size);                  \
+    RowReductionForSoftmaxAndCrossEntropy<                                    \
+        T, BlockDim><<<batch_size, BlockDim, 0, stream>>>(                    \
+        logits_data, labels_data, loss_data, softmax_data, feature_size);     \
+    break
+
+  switch (block_dim) {
+    CALL_SOFTMAX_WITH_CROSS_ENTROPY_FUSED_KERNEL(512);
+    CALL_SOFTMAX_WITH_CROSS_ENTROPY_FUSED_KERNEL(256);
+    CALL_SOFTMAX_WITH_CROSS_ENTROPY_FUSED_KERNEL(128);
+    CALL_SOFTMAX_WITH_CROSS_ENTROPY_FUSED_KERNEL(64);
+    CALL_SOFTMAX_WITH_CROSS_ENTROPY_FUSED_KERNEL(32);
+    CALL_SOFTMAX_WITH_CROSS_ENTROPY_FUSED_KERNEL(16);
+    CALL_SOFTMAX_WITH_CROSS_ENTROPY_FUSED_KERNEL(8);
+    CALL_SOFTMAX_WITH_CROSS_ENTROPY_FUSED_KERNEL(4);
+    CALL_SOFTMAX_WITH_CROSS_ENTROPY_FUSED_KERNEL(2);
+    case 1:
+      SetSoftmaxToOneWhenFeatureSizeIsOne<<<(batch_size + kMaxBlockDim - 1) /
+                                                kMaxBlockDim,
+                                            kMaxBlockDim, 0, stream>>>(
+          softmax_data, batch_size);
+      cudaMemsetAsync(loss_data, 0, batch_size, stream);
+      break;
+    default:
+      PADDLE_THROW("BlockDim must be 2^n in softmax_with_cross_entropy_op");
+      break;
+  }
+
+#undef CALL_SOFTMAX_WITH_CROSS_ENTROPY_FUSED_KERNEL
+}
+
 template <typename T>
 class SoftmaxWithCrossEntropyCUDAKernel : public framework::OpKernel<T> {
  public:
@@ -66,14 +256,24 @@ class SoftmaxWithCrossEntropyCUDAKernel : public framework::OpKernel<T> {
     Tensor* softmax = context.Output<Tensor>("Softmax");
 
     Tensor* loss = context.Output<Tensor>("Loss");
-    softmax->mutable_data<T>(context.GetPlace());
-    loss->mutable_data<T>(context.GetPlace());
-
-    math::SoftmaxFunctor<platform::CUDADeviceContext, T>()(
-        context.cuda_device_context(), logits, softmax);
-    math::CrossEntropyFunctor<platform::CUDADeviceContext, T>()(
-        context.cuda_device_context(), loss, softmax, labels,
-        context.Attr<bool>("soft_label"));
+    auto* softmax_data = softmax->mutable_data<T>(context.GetPlace());
+    auto* loss_data = loss->mutable_data<T>(context.GetPlace());
+
+    auto soft_label = context.Attr<bool>("soft_label");
+    if (soft_label) {
+      int batch_size = logits->dims()[0];
+      int feature_size = logits->dims()[1];
+      auto* logits_data = logits->data<T>();
+      auto* labels_data = labels->data<T>();
+      SoftmaxWithCrossEntropyFusedKernel(
+          logits_data, labels_data, softmax_data, loss_data, batch_size,
+          feature_size, context.cuda_device_context().stream());
+    } else {
+      math::SoftmaxCUDNNFunctor<T>()(context.cuda_device_context(), logits,
+                                     softmax);
+      math::CrossEntropyFunctor<platform::CUDADeviceContext, T>()(
+          context.cuda_device_context(), loss, softmax, labels, false);
+    }
   }
 };
 

paddle/fluid/platform/CMakeLists.txt

@@ -18,7 +18,11 @@ else()
 endif()
 cc_test(enforce_test SRCS enforce_test.cc DEPS stringpiece enforce)
 
-cc_library(cpu_info SRCS cpu_info.cc DEPS gflags glog enforce)
+set(CPU_INFO_DEPS gflags glog enforce)
+IF(WITH_XBYAK)
+    list(APPEND CPU_INFO_DEPS xbyak)
+ENDIF()
+cc_library(cpu_info SRCS cpu_info.cc DEPS ${CPU_INFO_DEPS})
 cc_test(cpu_info_test SRCS cpu_info_test.cc DEPS cpu_info)
 
 nv_library(gpu_info SRCS gpu_info.cc DEPS gflags glog enforce)

paddle/fluid/platform/cpu_info.cc

@@ -14,6 +14,11 @@ limitations under the License. */
 
 #include "paddle/fluid/platform/cpu_info.h"
 
+#ifdef PADDLE_WITH_XBYAK
+#include "xbyak/xbyak.h"
+#include "xbyak/xbyak_util.h"
+#endif
+
 #ifdef __APPLE__
 #include <sys/sysctl.h>
 #include <sys/types.h>
@@ -98,5 +103,39 @@ size_t CUDAPinnedMaxChunkSize() {
   return CUDAPinnedMaxAllocSize() / 256;
 }
 
+#ifdef PADDLE_WITH_XBYAK
+namespace jit {
+
+static Xbyak::util::Cpu cpu;
+bool MayIUse(const cpu_isa_t cpu_isa) {
+  using namespace Xbyak::util;  // NOLINT
+  switch (cpu_isa) {
+    case sse42:
+      return cpu.has(Cpu::tSSE42);
+    case avx2:
+      return cpu.has(Cpu::tAVX2);
+    case avx512_common:
+      return cpu.has(Cpu::tAVX512F);
+    case avx512_core:
+      return true && cpu.has(Cpu::tAVX512F) && cpu.has(Cpu::tAVX512BW) &&
+             cpu.has(Cpu::tAVX512VL) && cpu.has(Cpu::tAVX512DQ);
+    case avx512_core_vnni:
+      return true && cpu.has(Cpu::tAVX512F) && cpu.has(Cpu::tAVX512BW) &&
+             cpu.has(Cpu::tAVX512VL) && cpu.has(Cpu::tAVX512DQ) &&
+             cpu.has(Cpu::tAVX512_VNNI);
+    case avx512_mic:
+      return true && cpu.has(Cpu::tAVX512F) && cpu.has(Cpu::tAVX512CD) &&
+             cpu.has(Cpu::tAVX512ER) && cpu.has(Cpu::tAVX512PF);
+    case avx512_mic_4ops:
+      return true && MayIUse(avx512_mic) && cpu.has(Cpu::tAVX512_4FMAPS) &&
+             cpu.has(Cpu::tAVX512_4VNNIW);
+    case isa_any:
+      return true;
+  }
+  return false;
+}
+
+}  // namespace jit
+#endif
 }  // namespace platform
 }  // namespace paddle

paddle/fluid/platform/cpu_info.h

@@ -37,5 +37,25 @@ size_t CUDAPinnedMinChunkSize();
 //! Get the maximum chunk size for buddy allocator.
 size_t CUDAPinnedMaxChunkSize();
 
+#ifdef PADDLE_WITH_XBYAK
+namespace jit {
+
+typedef enum {
+  isa_any,
+  sse42,
+  avx2,
+  avx512_common,
+  avx512_core,
+  avx512_core_vnni,
+  avx512_mic,
+  avx512_mic_4ops,
+} cpu_isa_t;  // Instruction set architecture
+
+// May I use some instruction
+inline bool MayIUse(const cpu_isa_t cpu_isa);
+
+}  // namespace jit
+#endif
+
 }  // namespace platform
 }  // namespace paddle

paddle/fluid/platform/device_tracer.cc

@@ -189,6 +189,8 @@ void CUPTIAPI bufferCompleted(CUcontext ctx, uint32_t streamId, uint8_t *buffer,
 }
 }  // namespace
 
+#endif  // PADDLE_WITH_CUPTI
+
 class DeviceTracerImpl : public DeviceTracer {
  public:
   DeviceTracerImpl() : enabled_(false) {}
@@ -244,6 +246,8 @@ class DeviceTracerImpl : public DeviceTracer {
     if (enabled_) {
       return;
     }
+
+#ifdef PADDLE_WITH_CUPTI
     EnableActivity();
 
     // Register callbacks for buffer requests and completed by CUPTI.
@@ -262,6 +266,7 @@ class DeviceTracerImpl : public DeviceTracer {
         dynload::cuptiEnableCallback(1, subscriber_, CUPTI_CB_DOMAIN_DRIVER_API,
                                      CUPTI_DRIVER_TRACE_CBID_cuLaunchKernel));
     CUPTI_CALL(dynload::cuptiGetTimestamp(&start_ns_));
+#endif  // PADDLE_WITH_CUPTI
     enabled_ = true;
   }
 
@@ -313,16 +318,21 @@ class DeviceTracerImpl : public DeviceTracer {
   }
 
   void Disable() {
+#ifdef PADDLE_WITH_CUPTI
     // flush might cause additional calls to DeviceTracker.
     dynload::cuptiActivityFlushAll(CUPTI_ACTIVITY_FLAG_FLUSH_FORCED);
+#endif  // PADDLE_WITH_CUPTI
     std::lock_guard<std::mutex> l(trace_mu_);
+#ifdef PADDLE_WITH_CUPTI
     DisableActivity();
     dynload::cuptiUnsubscribe(subscriber_);
     CUPTI_CALL(dynload::cuptiGetTimestamp(&end_ns_));
+#endif  // PADDLE_WITH_CUPTI
     enabled_ = false;
   }
 
  private:
+#ifdef PADDLE_WITH_CUPTI
   static void CUPTIAPI ApiCallback(void *userdata, CUpti_CallbackDomain domain,
                                    CUpti_CallbackId cbid, const void *cbdata) {
     auto *cbInfo = reinterpret_cast<const CUpti_CallbackData *>(cbdata);
@@ -340,7 +350,8 @@ class DeviceTracerImpl : public DeviceTracer {
       VLOG(1) << "Unhandled API Callback for " << domain << " " << cbid;
     }
   }
-
+  CUpti_SubscriberHandle subscriber_;
+#endif  // PADDLE_WITH_CUPTI
   std::mutex trace_mu_;
   bool enabled_;
   uint64_t start_ns_;
@@ -349,45 +360,9 @@ class DeviceTracerImpl : public DeviceTracer {
   std::vector<MemRecord> mem_records_;
   std::vector<CPURecord> cpu_records_;
   std::unordered_map<uint32_t, std::string> correlations_;
-  CUpti_SubscriberHandle subscriber_;
-};
-
-#endif  // PADDLE_WITH_CUPTI
-
-class DeviceTracerDummy : public DeviceTracer {
- public:
-  DeviceTracerDummy() {}
-
-  void AddAnnotation(uint64_t id, const std::string &anno) {}
-
-  void AddCPURecords(const std::string &anno, uint64_t start_ns,
-                     uint64_t end_ns, int64_t device_id, int64_t thread_id) {}
-
-  void AddMemRecords(const std::string &name, uint64_t start_ns,
-                     uint64_t end_ns, int64_t device_id, int64_t stream_id,
-                     uint32_t correlation_id, uint64_t bytes) {}
-
-  void AddKernelRecords(uint64_t start, uint64_t end, int64_t device_id,
-                        int64_t stream_id, uint32_t correlation_id) {}
-
-  bool IsEnabled() { return false; }
-
-  void Enable() {}
-
-  proto::Profile GenProfile(const std::string &profile_path) {
-    return proto::Profile();
-  }
-
-  void Disable() {}
 };
 
-void CreateTracer(DeviceTracer **t) {
-#ifdef PADDLE_WITH_CUPTI
-  *t = new DeviceTracerImpl();
-#else
-  *t = new DeviceTracerDummy();
-#endif  // PADDLE_WITH_CUPTI
-}
+void CreateTracer(DeviceTracer **t) { *t = new DeviceTracerImpl(); }
 
 DeviceTracer *GetDeviceTracer() {
   std::call_once(tracer_once_flag, CreateTracer, &tracer);

paddle/fluid/platform/device_tracer.h

@@ -13,6 +13,9 @@ See the License for the specific language governing permissions and
 limitations under the License. */
 #pragma once
 
+#include <sys/time.h>
+#include <time.h>
+#include <chrono>  // NOLINT
 #include <string>
 
 #include "paddle/fluid/platform/dynload/cupti.h"
@@ -25,6 +28,12 @@ namespace platform {
 // WARN: Under Development. Don't depend on it yet.
 //////////////////////
 
+inline uint64_t PosixInNsec() {
+  struct timeval tv;
+  gettimeofday(&tv, nullptr);
+  return 1000 * (static_cast<uint64_t>(tv.tv_sec) * 1000000 + tv.tv_usec);
+}
+
 // DeviceTracer performs the following tasks:
 // 1. Register cuda callbacks for various events: kernel, memcpy, etc.
 // 2. Collect cuda statistics: start/end ts, memory, etc.

paddle/fluid/platform/mkldnn_helper.h

@@ -223,7 +223,7 @@ class MKLDNNHandler {
   static std::string GetHash(mkldnn::memory::dims& operand_dims,  // NOLINT
                              const std::string& suffix) {
     return dims2str(operand_dims) + suffix;
-  };
+  }
 
  protected:
   static std::string dims2str(const mkldnn::memory::dims& operand_dims) {
@@ -251,5 +251,17 @@ inline mkldnn::memory::format MKLDNNFormatForSize(
   return data_format;
 }
 
+inline mkldnn::memory::format data_format_to_memory_format(
+    const std::string& data_format) {
+  switch (framework::StringToDataLayout(data_format)) {
+    case framework::DataLayout::kNHWC:
+      return mkldnn::memory::format::nhwc;
+    case framework::DataLayout::kNCHW:
+      return mkldnn::memory::format::nchw;
+    default:
+      return mkldnn::memory::format::any;
+  }
+}
+
 }  // namespace platform
 }  // namespace paddle

paddle/fluid/platform/profiler.cc

@@ -15,7 +15,6 @@ limitations under the License. */
 #include "paddle/fluid/platform/profiler.h"
 
 #include <sys/time.h>
-#include <time.h>
 #include <algorithm>
 #include <iomanip>
 #include <limits>
@@ -97,12 +96,6 @@ inline uint64_t GetTimeInNsec() {
       .count();
 }
 
-inline uint64_t PosixInNsec() {
-  struct timeval tv;
-  gettimeofday(&tv, nullptr);
-  return 1000 * (static_cast<uint64_t>(tv.tv_sec) * 1000000 + tv.tv_usec);
-}
-
 Event::Event(EventType type, std::string name, uint32_t thread_id,
              const DeviceContext* dev_ctx)
     : type_(type), name_(name), thread_id_(thread_id), has_cuda_(false) {
@@ -277,12 +270,13 @@ struct EventItem {
   double min_time;
   double max_time;
   double ave_time;
+  float ratio;
 };
 
 // Print results
 void PrintProfiler(const std::vector<std::vector<EventItem>>& events_table,
                    const std::string& sorted_domain, const size_t name_width,
-                   const size_t data_width) {
+                   const size_t data_width, double total) {
   // Output header information
   std::cout << "\n------------------------->"
             << "     Profiling Report     "
@@ -307,7 +301,8 @@ void PrintProfiler(const std::vector<std::vector<EventItem>>& events_table,
   std::cout << std::setw(name_width) << "Event" << std::setw(data_width)
             << "Calls" << std::setw(data_width) << "Total"
             << std::setw(data_width) << "Min." << std::setw(data_width)
-            << "Max." << std::setw(data_width) << "Ave." << std::endl;
+            << "Max." << std::setw(data_width) << "Ave."
+            << std::setw(data_width) << "Ratio." << std::endl;
   for (size_t i = 0; i < events_table.size(); ++i) {
     for (size_t j = 0; j < events_table[i].size(); ++j) {
       const EventItem& event_item = events_table[i][j];
@@ -316,7 +311,9 @@ void PrintProfiler(const std::vector<std::vector<EventItem>>& events_table,
                 << std::setw(data_width) << event_item.total_time
                 << std::setw(data_width) << event_item.min_time
                 << std::setw(data_width) << event_item.max_time
-                << std::setw(data_width) << event_item.ave_time << std::endl;
+                << std::setw(data_width) << event_item.ave_time
+                << std::setw(data_width) << event_item.total_time / total
+                << std::endl;
     }
   }
   std::cout << std::endl;
@@ -366,6 +363,7 @@ void ParseEvents(const std::vector<std::vector<Event>>& events,
 
   std::vector<std::vector<EventItem>> events_table;
   size_t max_name_width = 0;
+  double total = 0.;  // the total time
   for (size_t i = 0; i < events.size(); i++) {
     std::list<Event> pushed_events;
     std::vector<EventItem> event_items;
@@ -386,6 +384,7 @@ void ParseEvents(const std::vector<std::vector<Event>>& events,
                                g_state == ProfilerState::kAll)
                                   ? rit->CudaElapsedMs(events[i][j])
                                   : rit->CpuElapsedMs(events[i][j]);
+          total += event_time;
 
           std::string event_name =
               "thread" + std::to_string(rit->thread_id()) + "::" + rit->name();
@@ -394,7 +393,8 @@ void ParseEvents(const std::vector<std::vector<Event>>& events,
           if (event_idx.find(event_name) == event_idx.end()) {
             event_idx[event_name] = event_items.size();
             EventItem event_item = {event_name, 1,          event_time,
-                                    event_time, event_time, event_time};
+                                    event_time, event_time, event_time,
+                                    0.};
             event_items.push_back(event_item);
           } else {
             int index = event_idx[event_name];
@@ -438,7 +438,7 @@ void ParseEvents(const std::vector<std::vector<Event>>& events,
   }
 
   // Print report
-  PrintProfiler(events_table, sorted_domain, max_name_width + 4, 12);
+  PrintProfiler(events_table, sorted_domain, max_name_width + 4, 12, total);
 }
 
 void DisableProfiler(EventSortingKey sorted_key,

paddle/fluid/pybind/protobuf.cc

@@ -301,7 +301,8 @@ void BindOpDesc(pybind11::module *m) {
              std::string ser(seriralized);
              self.SetAttr(name, ser);
            })
-      .def("block_attr", &pd::OpDesc::GetBlockAttr)
+      .def("block_attr_id", &pd::OpDesc::GetBlockAttrId)
+      .def("blocks_attr_ids", &pd::OpDesc::GetBlocksAttrIds)
       .def("check_attrs", &pd::OpDesc::CheckAttrs)
       .def("infer_shape", &pd::OpDesc::InferShape)
       .def("infer_var_type", &pd::OpDesc::InferVarType)

paddle/fluid/pybind/pybind.cc

@@ -394,8 +394,10 @@ All parameter, weight, gradient are variables in Paddle.
     InferenceOptimize(*(origin.Proto()), &pruned_desc);
     return new ProgramDesc(pruned_desc);
   });
-  m.def("empty_var_name", []() { return framework::kEmptyVarName; });
-  m.def("grad_var_suffix", []() { return framework::kGradVarSuffix; });
+  m.def("empty_var_name",
+        []() { return std::string(framework::kEmptyVarName); });
+  m.def("grad_var_suffix",
+        []() { return std::string(framework::kGradVarSuffix); });
   m.def_submodule(
        "var_names",
        "The module will return special predefined variable name in Paddle")
@@ -662,7 +664,7 @@ All parameter, weight, gradient are variables in Paddle.
                   const std::string &, Scope *, std::vector<Scope *> &,
                   const ExecutionStrategy &, const BuildStrategy &, size_t,
                   size_t>())
-      .def("bcast_params", &ParallelExecutor::BCastParamsToDevices)
+      .def("_bcast_params", &ParallelExecutor::BCastParamsToDevices)
       // NOTE: even we return a vec<Scope*>* to Python use reference policy.
       // We still cannot get local_scope from this vector, since the element
       // of vec<Scope*> will be freed by Python GC. We can only return Scope*

python/paddle/dataset/cifar.py

@@ -28,11 +28,12 @@ images per class.
 
 """
 
-import cPickle
 import itertools
 import numpy
 import paddle.dataset.common
 import tarfile
+from six.moves import zip
+from six.moves import cPickle as pickle
 
 __all__ = ['train100', 'test100', 'train10', 'test10', 'convert']
 
@@ -48,7 +49,7 @@ def reader_creator(filename, sub_name, cycle=False):
         data = batch['data']
         labels = batch.get('labels', batch.get('fine_labels', None))
         assert labels is not None
-        for sample, label in itertools.izip(data, labels):
+        for sample, label in zip(data, labels):
             yield (sample / 255.0).astype(numpy.float32), int(label)
 
     def reader():
@@ -58,7 +59,7 @@ def reader_creator(filename, sub_name, cycle=False):
 
             while True:
                 for name in names:
-                    batch = cPickle.load(f.extractfile(name))
+                    batch = pickle.load(f.extractfile(name))
                     for item in read_batch(batch):
                         yield item
                 if not cycle:

python/paddle/dataset/common.py

@@ -20,9 +20,8 @@ import shutil
 import sys
 import importlib
 import paddle.dataset
-import cPickle
+import six.moves.cPickle as pickle
 import glob
-import cPickle as pickle
 
 __all__ = [
     'DATA_HOME',
@@ -75,13 +74,13 @@ def download(url, module_name, md5sum, save_name=None):
     retry_limit = 3
     while not (os.path.exists(filename) and md5file(filename) == md5sum):
         if os.path.exists(filename):
-            print "file md5", md5file(filename), md5sum
+            print("file md5", md5file(filename), md5sum)
         if retry < retry_limit:
             retry += 1
         else:
             raise RuntimeError("Cannot download {0} within retry limit {1}".
                                format(url, retry_limit))
-        print "Cache file %s not found, downloading %s" % (filename, url)
+        print("Cache file %s not found, downloading %s" % (filename, url))
         r = requests.get(url, stream=True)
         total_length = r.headers.get('content-length')
 
@@ -104,8 +103,9 @@ def download(url, module_name, md5sum, save_name=None):
 
 
 def fetch_all():
-    for module_name in filter(lambda x: not x.startswith("__"),
-                              dir(paddle.dataset)):
+    for module_name in [
+            x for x in dir(paddle.dataset) if not x.startswith("__")
+    ]:
         if "fetch" in dir(
                 importlib.import_module("paddle.dataset.%s" % module_name)):
             getattr(
@@ -114,8 +114,9 @@ def fetch_all():
 
 
 def fetch_all_recordio(path):
-    for module_name in filter(lambda x: not x.startswith("__"),
-                              dir(paddle.dataset)):
+    for module_name in [
+            x for x in dir(paddle.dataset) if not x.startswith("__")
+    ]:
         if "convert" in dir(
                 importlib.import_module("paddle.dataset.%s" % module_name)) and \
                 not module_name == "common":
@@ -126,7 +127,7 @@ def fetch_all_recordio(path):
                 "convert")(ds_path)
 
 
-def split(reader, line_count, suffix="%05d.pickle", dumper=cPickle.dump):
+def split(reader, line_count, suffix="%05d.pickle", dumper=pickle.dump):
     """
     you can call the function as:
 
@@ -167,7 +168,7 @@ def split(reader, line_count, suffix="%05d.pickle", dumper=cPickle.dump):
 def cluster_files_reader(files_pattern,
                          trainer_count,
                          trainer_id,
-                         loader=cPickle.load):
+                         loader=pickle.load):
     """
     Create a reader that yield element from the given files, select
     a file set according trainer count and trainer_id
@@ -188,7 +189,7 @@ def cluster_files_reader(files_pattern,
         my_file_list = []
         for idx, fn in enumerate(file_list):
             if idx % trainer_count == trainer_id:
-                print "append file: %s" % fn
+                print("append file: %s" % fn)
                 my_file_list.append(fn)
         for fn in my_file_list:
             with open(fn, "r") as f:
@@ -221,7 +222,7 @@ def convert(output_path, reader, line_count, name_prefix):
         for l in lines:
             # FIXME(Yancey1989):
             # dumps with protocol: pickle.HIGHEST_PROTOCOL
-            writer.write(cPickle.dumps(l))
+            writer.write(pickle.dumps(l))
         writer.close()
 
     lines = []

python/paddle/dataset/conll05.py

@@ -24,18 +24,19 @@ import tarfile
 import gzip
 import itertools
 import paddle.dataset.common
+from six.moves import zip
 
 __all__ = ['test, get_dict', 'get_embedding', 'convert']
 
 DATA_URL = 'http://www.cs.upc.edu/~srlconll/conll05st-tests.tar.gz'
 DATA_MD5 = '387719152ae52d60422c016e92a742fc'
-WORDDICT_URL = 'http://paddlepaddle.bj.bcebos.com/demo/srl_dict_and_embedding/wordDict.txt'
+WORDDICT_URL = 'http://paddlemodels.bj.bcebos.com/conll05st%2FwordDict.txt'
 WORDDICT_MD5 = 'ea7fb7d4c75cc6254716f0177a506baa'
-VERBDICT_URL = 'http://paddlepaddle.bj.bcebos.com/demo/srl_dict_and_embedding/verbDict.txt'
+VERBDICT_URL = 'http://paddlemodels.bj.bcebos.com/conll05st%2FverbDict.txt'
 VERBDICT_MD5 = '0d2977293bbb6cbefab5b0f97db1e77c'
-TRGDICT_URL = 'http://paddlepaddle.bj.bcebos.com/demo/srl_dict_and_embedding/targetDict.txt'
+TRGDICT_URL = 'http://paddlemodels.bj.bcebos.com/conll05st%2FtargetDict.txt'
 TRGDICT_MD5 = 'd8c7f03ceb5fc2e5a0fa7503a4353751'
-EMB_URL = 'http://paddlepaddle.bj.bcebos.com/demo/srl_dict_and_embedding/emb'
+EMB_URL = 'http://paddlemodels.bj.bcebos.com/conll05st%2Femb'
 EMB_MD5 = 'bf436eb0faa1f6f9103017f8be57cdb7'
 
 UNK_IDX = 0
@@ -87,12 +88,12 @@ def corpus_reader(data_path, words_name, props_name):
             sentences = []
             labels = []
             one_seg = []
-            for word, label in itertools.izip(words_file, props_file):
+            for word, label in zip(words_file, props_file):
                 word = word.strip()
                 label = label.strip().split()
 
                 if len(label) == 0:  # end of sentence
-                    for i in xrange(len(one_seg[0])):
+                    for i in range(len(one_seg[0])):
                         a_kind_lable = [x[i] for x in one_seg]
                         labels.append(a_kind_lable)
 

python/paddle/dataset/flowers.py

@@ -28,10 +28,9 @@ Graphics and Image Processing (2008)
 http://www.robots.ox.ac.uk/~vgg/publications/papers/nilsback08.{pdf,ps.gz}.
 
 """
-import cPickle
 import itertools
 import functools
-from common import download
+from .common import download
 import tarfile
 import scipy.io as scio
 from paddle.dataset.image import *
@@ -39,6 +38,8 @@ from paddle.reader import *
 import os
 import numpy as np
 from multiprocessing import cpu_count
+from six.moves import cPickle as pickle
+from six.moves import zip
 __all__ = ['train', 'test', 'valid']
 
 DATA_URL = 'http://www.robots.ox.ac.uk/~vgg/data/flowers/102/102flowers.tgz'
@@ -116,10 +117,10 @@ def reader_creator(data_file,
                 file = file.strip()
                 batch = None
                 with open(file, 'r') as f:
-                    batch = cPickle.load(f)
+                    batch = pickle.load(f)
                 data = batch['data']
                 labels = batch['label']
-                for sample, label in itertools.izip(data, batch['label']):
+                for sample, label in zip(data, batch['label']):
                     yield sample, int(label) - 1
             if not cycle:
                 break

python/paddle/dataset/image.py

@@ -36,7 +36,7 @@ except ImportError:
     cv2 = None
 import os
 import tarfile
-import cPickle
+import six.moves.cPickle as pickle
 
 __all__ = [
     "load_image_bytes", "load_image", "resize_short", "to_chw", "center_crop",
@@ -86,10 +86,10 @@ def batch_images_from_tar(data_file,
                 output = {}
                 output['label'] = labels
                 output['data'] = data
-                cPickle.dump(
+                pickle.dump(
                     output,
                     open('%s/batch_%d' % (out_path, file_id), 'w'),
-                    protocol=cPickle.HIGHEST_PROTOCOL)
+                    protocol=pickle.HIGHEST_PROTOCOL)
                 file_id += 1
                 data = []
                 labels = []
@@ -97,10 +97,10 @@ def batch_images_from_tar(data_file,
         output = {}
         output['label'] = labels
         output['data'] = data
-        cPickle.dump(
+        pickle.dump(
             output,
             open('%s/batch_%d' % (out_path, file_id), 'w'),
-            protocol=cPickle.HIGHEST_PROTOCOL)
+            protocol=pickle.HIGHEST_PROTOCOL)
 
     with open(meta_file, 'a') as meta:
         for file in os.listdir(out_path):

python/paddle/dataset/imdb.py

@@ -42,13 +42,13 @@ def tokenize(pattern):
         # sequential access of member files, other than
         # tarfile.extractfile, which does random access and might
         # destroy hard disks.
-        tf = tarf.next()
+        tf = next(tarf)
         while tf != None:
             if bool(pattern.match(tf.name)):
                 # newline and punctuations removal and ad-hoc tokenization.
                 yield tarf.extractfile(tf).read().rstrip("\n\r").translate(
                     None, string.punctuation).lower().split()
-            tf = tarf.next()
+            tf = next(tarf)
 
 
 def build_dict(pattern, cutoff):
@@ -62,11 +62,11 @@ def build_dict(pattern, cutoff):
             word_freq[word] += 1
 
     # Not sure if we should prune less-frequent words here.
-    word_freq = filter(lambda x: x[1] > cutoff, word_freq.items())
+    word_freq = [x for x in list(word_freq.items()) if x[1] > cutoff]
 
     dictionary = sorted(word_freq, key=lambda x: (-x[1], x[0]))
     words, _ = list(zip(*dictionary))
-    word_idx = dict(zip(words, xrange(len(words))))
+    word_idx = dict(list(zip(words, list(range(len(words))))))
     word_idx['<unk>'] = len(words)
     return word_idx
 

python/paddle/dataset/imikolov.py

@@ -64,11 +64,11 @@ def build_dict(min_word_freq=50):
             # remove <unk> for now, since we will set it as last index
             del word_freq['<unk>']
 
-        word_freq = filter(lambda x: x[1] > min_word_freq, word_freq.items())
+        word_freq = [x for x in list(word_freq.items()) if x[1] > min_word_freq]
 
         word_freq_sorted = sorted(word_freq, key=lambda x: (-x[1], x[0]))
         words, _ = list(zip(*word_freq_sorted))
-        word_idx = dict(zip(words, xrange(len(words))))
+        word_idx = dict(list(zip(words, list(range(len(words))))))
         word_idx['<unk>'] = len(words)
 
     return word_idx

python/paddle/dataset/mnist.py

@@ -65,7 +65,7 @@ def reader_creator(image_filename, label_filename, buffer_size):
 
                 images = images / 255.0 * 2.0 - 1.0
 
-                for i in xrange(buffer_size):
+                for i in range(buffer_size):
                     yield images[i, :], int(labels[i])
         finally:
             try:

python/paddle/dataset/movielens.py

@@ -16,7 +16,7 @@ Movielens 1-M dataset.
 
 Movielens 1-M dataset contains 1 million ratings from 6000 users on 4000
 movies, which was collected by GroupLens Research. This module will download
-Movielens 1-M dataset from 
+Movielens 1-M dataset from
 http://files.grouplens.org/datasets/movielens/ml-1m.zip and parse training
 set and test set into paddle reader creators.
 
@@ -187,7 +187,7 @@ def max_movie_id():
     Get the maximum value of movie id.
     """
     __initialize_meta_info__()
-    return reduce(__max_index_info__, MOVIE_INFO.viewvalues()).index
+    return reduce(__max_index_info__, list(MOVIE_INFO.values())).index
 
 
 def max_user_id():
@@ -195,7 +195,7 @@ def max_user_id():
     Get the maximum value of user id.
     """
     __initialize_meta_info__()
-    return reduce(__max_index_info__, USER_INFO.viewvalues()).index
+    return reduce(__max_index_info__, list(USER_INFO.values())).index
 
 
 def __max_job_id_impl__(a, b):
@@ -210,7 +210,7 @@ def max_job_id():
     Get the maximum value of job id.
     """
     __initialize_meta_info__()
-    return reduce(__max_job_id_impl__, USER_INFO.viewvalues()).job_id
+    return reduce(__max_job_id_impl__, list(USER_INFO.values())).job_id
 
 
 def movie_categories():
@@ -243,7 +243,7 @@ def unittest():
     for test_count, _ in enumerate(test()()):
         pass
 
-    print train_count, test_count
+    print(train_count, test_count)
 
 
 def fetch():

python/paddle/dataset/mq2007.py

@@ -26,7 +26,7 @@ http://research.microsoft.com/en-us/um/beijing/projects/letor/LETOR4.0/Data/MQ20
 import os
 import functools
 import rarfile
-from common import download
+from .common import download
 import numpy as np
 
 # URL = "http://research.microsoft.com/en-us/um/beijing/projects/letor/LETOR4.0/Data/MQ2007.rar"
@@ -53,7 +53,7 @@ class Query(object):
   ----------
   query_id : int
     query_id in dataset, mapping from query to relevance documents
-  relevance_score : int 
+  relevance_score : int
     relevance score of query and document pair
   feature_vector : array, dense feature
     feature in vector format
@@ -92,7 +92,7 @@ class Query(object):
             sys.stdout.write("expect 48 space split parts, get %d" %
                              (len(parts)))
             return None
-        # format : 0 qid:10 1:0.000272 2:0.000000 .... 
+        # format : 0 qid:10 1:0.000272 2:0.000000 ....
         self.relevance_score = int(parts[0])
         self.query_id = int(parts[1].split(':')[1])
         for p in parts[2:]:
@@ -295,7 +295,7 @@ def __reader__(filepath, format="pairwise", shuffle=False, fill_missing=-1):
   --------
   filename : string
   fill_missing : fill the missing value. default in MQ2007 is -1
-  
+
   Returns
   ------
   yield
@@ -330,4 +330,4 @@ if __name__ == "__main__":
     mytest = functools.partial(
         __reader__, filepath="MQ2007/MQ2007/Fold1/sample", format="listwise")
     for label, query in mytest():
-        print label, query
+        print(label, query)

python/paddle/dataset/sentiment.py

@@ -43,11 +43,11 @@ def download_data_if_not_yet():
             nltk.data.path.append(paddle.dataset.common.DATA_HOME)
         movie_reviews.categories()
     except LookupError:
-        print "Downloading movie_reviews data set, please wait....."
+        print("Downloading movie_reviews data set, please wait.....")
         nltk.download(
             'movie_reviews', download_dir=paddle.dataset.common.DATA_HOME)
-        print "Download data set success....."
-        print "Path is " + nltk.data.find('corpora/movie_reviews').path
+        print("Download data set success.....")
+        print("Path is " + nltk.data.find('corpora/movie_reviews').path)
 
 
 def get_word_dict():
@@ -64,7 +64,7 @@ def get_word_dict():
         for field in movie_reviews.fileids(category):
             for words in movie_reviews.words(field):
                 word_freq_dict[words] += 1
-    words_sort_list = word_freq_dict.items()
+    words_sort_list = list(word_freq_dict.items())
     words_sort_list.sort(cmp=lambda a, b: b[1] - a[1])
     for index, word in enumerate(words_sort_list):
         words_freq_sorted.append((word[0], index))
@@ -80,7 +80,8 @@ def sort_files():
     files_list = list()
     neg_file_list = movie_reviews.fileids('neg')
     pos_file_list = movie_reviews.fileids('pos')
-    files_list = list(chain.from_iterable(zip(neg_file_list, pos_file_list)))
+    files_list = list(
+        chain.from_iterable(list(zip(neg_file_list, pos_file_list))))
     return files_list
 
 

python/paddle/dataset/tests/common_test.py

@@ -36,7 +36,7 @@ class TestCommon(unittest.TestCase):
     def test_split(self):
         def test_reader():
             def reader():
-                for x in xrange(10):
+                for x in range(10):
                     yield x
 
             return reader
@@ -49,7 +49,7 @@ class TestCommon(unittest.TestCase):
 
     def test_cluster_file_reader(self):
         _, temp_path = tempfile.mkstemp()
-        for x in xrange(5):
+        for x in range(5):
             with open(temp_path + '/%05d.test' % x) as f:
                 f.write('%d\n' % x)
         reader = paddle.dataset.common.cluster_files_reader(
@@ -63,7 +63,7 @@ class TestCommon(unittest.TestCase):
 
         def test_reader():
             def reader():
-                for x in xrange(record_num):
+                for x in range(record_num):
                     yield x
 
             return reader

python/paddle/dataset/tests/imikolov_test.py

@@ -59,7 +59,7 @@ class TestMikolov(unittest.TestCase):
         self.assertEqual(first_line, read_line)
 
     def test_total(self):
-        _, idx = zip(*WORD_DICT.items())
+        _, idx = list(zip(*list(WORD_DICT.items())))
         self.assertEqual(sorted(idx)[-1], len(WORD_DICT) - 1)
 
 

python/paddle/dataset/tests/test_sentiment.py

@@ -24,9 +24,8 @@ from nltk.corpus import movie_reviews
 class TestSentimentMethods(unittest.TestCase):
     def test_get_word_dict(self):
         word_dict = st.get_word_dict()[0:10]
-        test_word_list = [(u',', 0), (u'the', 1), (u'.', 2), (u'a', 3),
-                          (u'and', 4), (u'of', 5), (u'to', 6), (u"'", 7),
-                          (u'is', 8), (u'in', 9)]
+        test_word_list = [(',', 0), ('the', 1), ('.', 2), ('a', 3), ('and', 4),
+                          ('of', 5), ('to', 6), ("'", 7), ('is', 8), ('in', 9)]
         for idx, each in enumerate(word_dict):
             self.assertEqual(each, test_word_list[idx])
         self.assertTrue("/root/.cache/paddle/dataset" in nltk.data.path)

python/paddle/dataset/uci_housing.py

@@ -49,9 +49,12 @@ def feature_range(maximums, minimums):
     import matplotlib.pyplot as plt
     fig, ax = plt.subplots()
     feature_num = len(maximums)
-    ax.bar(range(feature_num), maximums - minimums, color='r', align='center')
+    ax.bar(list(range(feature_num)),
+           maximums - minimums,
+           color='r',
+           align='center')
     ax.set_title('feature scale')
-    plt.xticks(range(feature_num), feature_names)
+    plt.xticks(list(range(feature_num)), feature_names)
     plt.xlim([-1, feature_num])
     fig.set_figheight(6)
     fig.set_figwidth(10)
@@ -71,7 +74,7 @@ def load_data(filename, feature_num=14, ratio=0.8):
     maximums, minimums, avgs = data.max(axis=0), data.min(axis=0), data.sum(
         axis=0) / data.shape[0]
     feature_range(maximums[:-1], minimums[:-1])
-    for i in xrange(feature_num - 1):
+    for i in range(feature_num - 1):
         data[:, i] = (data[:, i] - avgs[i]) / (maximums[i] - minimums[i])
     offset = int(data.shape[0] * ratio)
     UCI_TRAIN_DATA = data[:offset]

python/paddle/dataset/wmt14.py

@@ -36,11 +36,10 @@ URL_DEV_TEST = ('http://www-lium.univ-lemans.fr/~schwenk/'
 MD5_DEV_TEST = '7d7897317ddd8ba0ae5c5fa7248d3ff5'
 # this is a small set of data for test. The original data is too large and
 # will be add later.
-URL_TRAIN = ('http://paddlepaddle.cdn.bcebos.com/demo/'
-             'wmt_shrinked_data/wmt14.tgz')
+URL_TRAIN = ('http://paddlemodels.bj.bcebos.com/wmt/wmt14.tgz')
 MD5_TRAIN = '0791583d57d5beb693b9414c5b36798c'
 # BLEU of this trained model is 26.92
-URL_MODEL = 'http://paddlepaddle.bj.bcebos.com/demo/wmt_14/wmt14_model.tar.gz'
+URL_MODEL = 'http://paddlemodels.bj.bcebos.com/wmt%2Fwmt14.tgz'
 MD5_MODEL = '0cb4a5366189b6acba876491c8724fa3'
 
 START = "<s>"
@@ -154,8 +153,8 @@ def get_dict(dict_size, reverse=True):
     tar_file = paddle.dataset.common.download(URL_TRAIN, 'wmt14', MD5_TRAIN)
     src_dict, trg_dict = __read_to_dict(tar_file, dict_size)
     if reverse:
-        src_dict = {v: k for k, v in src_dict.items()}
-        trg_dict = {v: k for k, v in trg_dict.items()}
+        src_dict = {v: k for k, v in list(src_dict.items())}
+        trg_dict = {v: k for k, v in list(trg_dict.items())}
     return src_dict, trg_dict
 
 

python/paddle/dataset/wmt16.py

@@ -70,7 +70,9 @@ def __build_dict(tar_file, dict_size, save_path, lang):
         fout.write("%s\n%s\n%s\n" % (START_MARK, END_MARK, UNK_MARK))
         for idx, word in enumerate(
                 sorted(
-                    word_dict.iteritems(), key=lambda x: x[1], reverse=True)):
+                    iter(list(word_dict.items())),
+                    key=lambda x: x[1],
+                    reverse=True)):
             if idx + 3 == dict_size: break
             fout.write("%s\n" % (word[0]))
 

python/paddle/fluid/__init__.py

@@ -14,54 +14,52 @@
 
 from __future__ import print_function
 # import all class inside framework into fluid module
-import framework
-from framework import *
+from . import framework
+from .framework import *
 # import all class inside executor into fluid module
-import executor
-from executor import *
-
-import trainer
-from trainer import Trainer
-from trainer import BeginEpochEvent
-from trainer import EndEpochEvent
-from trainer import BeginStepEvent
-from trainer import EndStepEvent
-from trainer import CheckpointConfig
-
-import inferencer
-from inferencer import Inferencer
-
-import io
-import evaluator
-import initializer
-import layers
-import contrib
-import nets
-import optimizer
-import backward
-import regularizer
-import average
-import metrics
-import transpiler
-from param_attr import ParamAttr, WeightNormParamAttr
-from data_feeder import DataFeeder
-from core import LoDTensor, LoDTensorArray, CPUPlace, CUDAPlace, CUDAPinnedPlace, Scope
-from transpiler import DistributeTranspiler, InferenceTranspiler, \
+from . import executor
+from .executor import *
+
+from . import trainer
+from .trainer import Trainer
+from .trainer import BeginEpochEvent
+from .trainer import EndEpochEvent
+from .trainer import BeginStepEvent
+from .trainer import EndStepEvent
+from .trainer import CheckpointConfig
+
+from . import inferencer
+from .inferencer import Inferencer
+
+from . import io
+from . import evaluator
+from . import initializer
+from . import layers
+from . import contrib
+from . import nets
+from . import optimizer
+from . import backward
+from . import regularizer
+from . import average
+from . import metrics
+from . import transpiler
+from .param_attr import ParamAttr, WeightNormParamAttr
+from .data_feeder import DataFeeder
+from .core import LoDTensor, LoDTensorArray, CPUPlace, CUDAPlace, CUDAPinnedPlace, Scope
+from .transpiler import DistributeTranspiler, InferenceTranspiler, \
     memory_optimize, release_memory, DistributeTranspilerConfig
-from concurrency import (Go, make_channel, channel_send, channel_recv,
-                         channel_close, Select)
-from lod_tensor import create_lod_tensor, create_random_int_lodtensor
-import clip
-import profiler
-import unique_name
-import recordio_writer
-import parallel_executor
-from parallel_executor import *
+from .lod_tensor import create_lod_tensor, create_random_int_lodtensor
+from . import clip
+from . import profiler
+from . import unique_name
+from . import recordio_writer
+from . import parallel_executor
+from .parallel_executor import *
 from paddle.fluid.layers.math_op_patch import monkey_patch_variable
 
 Tensor = LoDTensor
 
-__all__ = framework.__all__ + executor.__all__ + concurrency.__all__ + \
+__all__ = framework.__all__ + executor.__all__ + \
     trainer.__all__ + inferencer.__all__ + transpiler.__all__ + \
     parallel_executor.__all__ + lod_tensor.__all__ + [
         'io',
@@ -99,8 +97,8 @@ def __bootstrap__():
         None
     """
     import sys
-    import core
     import os
+    from . import core
 
     in_test = 'unittest' in sys.modules
 
@@ -128,7 +126,8 @@ def __bootstrap__():
     ]
     if core.is_compiled_with_dist():
         read_env_flags.append('rpc_deadline')
-        read_env_flags.append('listen_and_serv_profile_period')
+        read_env_flags.append('rpc_server_profile_period')
+        read_env_flags.append('rpc_server_profile_path')
 
     if core.is_compiled_with_cuda():
         read_env_flags += [

python/paddle/fluid/annotations.py

@@ -12,6 +12,7 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 
+from __future__ import print_function
 import functools
 import sys
 
@@ -28,7 +29,7 @@ def deprecated(since, instead, extra_message=""):
 
         @functools.wraps(func)
         def wrapper(*args, **kwargs):
-            print >> sys.stderr, err_msg
+            print(err_msg, file=sys.stderr)
             return func(*args, **kwargs)
 
         wrapper.__doc__ += "\n    "

python/paddle/fluid/backward.py

@@ -16,7 +16,8 @@ from paddle.fluid import framework as framework
 from . import core
 import collections
 import copy
-import unique_name
+import six
+from . import unique_name
 
 __all__ = ['append_backward']
 
@@ -44,17 +45,25 @@ def _create_op_desc_(op_type, inputs, outputs, attrs):
     """
     op_desc = core.OpDesc()
     op_desc.set_type(op_type)
-    for para, args in inputs.iteritems():
-        op_desc.set_input(para, args)
-    for para, args in outputs.iteritems():
-        op_desc.set_output(para, args)
+    for para, args in list(inputs.items()):
+        op_desc.set_input(
+            para,
+            list(
+                map(lambda arg: arg.decode() if isinstance(arg, six.binary_type) else arg,
+                    args)))
+    for para, args in list(outputs.items()):
+        op_desc.set_output(
+            para,
+            list(
+                map(lambda arg: arg.decode() if isinstance(arg, six.binary_type) else arg,
+                    args)))
 
     op_role_attr_name = core.op_proto_and_checker_maker.kOpRoleAttrName()
 
     if op_role_attr_name not in attrs:
         attrs[
             op_role_attr_name] = core.op_proto_and_checker_maker.OpRole.Backward
-    for name, val in attrs.iteritems():
+    for name, val in list(attrs.items()):
         if isinstance(val, framework.Block):
             op_desc.set_block_attr(name, val.desc)
         else:
@@ -105,7 +114,9 @@ def _strip_grad_suffix_(name):
     e.g. x@GRAD ==> x
          y@GRAD@RENAME@1 ==> y
     """
-    pos = name.find(core.grad_var_suffix())
+    if isinstance(name, six.text_type):
+        name = name.encode()
+    pos = name.find(six.b(core.grad_var_suffix()))
     return name[:pos] if pos != -1 else name
 
 
@@ -114,7 +125,9 @@ def _append_grad_suffix_(name):
     Append grad suffix to the given variable name
     e.g. x ==> x@GRAD
     """
-    return name + core.grad_var_suffix()
+    if isinstance(name, six.text_type):
+        name = name.encode()
+    return name + six.b(core.grad_var_suffix())
 
 
 def _addup_repetitive_outputs_(op_descs):
@@ -174,7 +187,7 @@ def _addup_repetitive_outputs_(op_descs):
                     op_desc.set_output(param_name, arg_names)
                     renamed_vars[var_name].append(new_name)
 
-    for var_name, inputs in renamed_vars.iteritems():
+    for var_name, inputs in list(renamed_vars.items()):
         if len(inputs) > 1:
             pending_sum_ops.append(
                 (_create_op_desc_("sum", {"X": inputs}, {"Out": [var_name]},
@@ -198,16 +211,19 @@ def _remove_no_grad_branch_(op_descs, no_grad_set):
         out_arg_names = op_desc.output_arg_names()
         if len(out_arg_names) == 0 or _all_in_set_(out_arg_names, no_grad_set):
             return True
-        if _all_in_set_(
-                filter(lambda name: name.find(core.grad_var_suffix()) != -1,
-                       op_desc.input_arg_names()), no_grad_set):
+        if _all_in_set_([
+                name for name in op_desc.input_arg_names()
+                if name.find(core.grad_var_suffix()) != -1
+        ], no_grad_set):
             no_grad_set.update(out_arg_names)
             return True
         return False
 
     # Remove ops whose outputs are all in no_grad_dict
-    op_descs = filter(
-        lambda op_desc: not _op_can_be_removed_(op_desc, no_grad_set), op_descs)
+    op_descs = [
+        op_desc for op_desc in op_descs
+        if not _op_can_be_removed_(op_desc, no_grad_set)
+    ]
     # Insert fill_zeros_like_op
     to_insert = []
     for idx, op_desc in enumerate(op_descs):
@@ -217,12 +233,12 @@ def _remove_no_grad_branch_(op_descs, no_grad_set):
                     "X": [_strip_grad_suffix_(arg)]
                 }, {"Out": [arg]}, {}), idx))
 
-    map(lambda p: op_descs.insert(p[1], p[0]), reversed(to_insert))
+    list([op_descs.insert(p[1], p[0]) for p in reversed(to_insert)])
 
     return op_descs
 
 
-import proto.framework_pb2 as framework_pb2
+from .proto import framework_pb2
 
 
 def serialize_op_decs(op_desc):
@@ -244,8 +260,10 @@ def _callback_lookup_(op):
     if op.type == 'parallel_do' and op.attr('use_nccl'):
         all_vars = op.block.vars
         param_names = set(op.input('parameters'))
-        param_names = filter(lambda name: all_vars[name].stop_gradient is False,
-                             param_names)
+        param_names = [
+            name for name in param_names
+            if all_vars[name].stop_gradient is False
+        ]
         param_grad_names = [n + "@GRAD" for n in param_names]
 
         class ParallelDoCallBack(object):
@@ -326,7 +344,7 @@ def _append_backward_ops_(block,
         grad_sub_block_list = []
         # If the op has its own sub-block, deal with the sub-block first
         if op.has_attr("sub_block"):
-            sub_block = program.block(op.block_attr("sub_block"))
+            sub_block = program.block(op.block_attr_id("sub_block"))
             grad_sub_block = program.create_block()
             grad_sub_block._set_forward_block_idx(sub_block.idx)
             cb = _callback_lookup_(op)
@@ -388,7 +406,7 @@ def _append_backward_vars_(block, start_op_idx, grad_to_var, grad_info_map):
     for op_idx in range(start_op_idx, block.desc.op_size()):
         op_desc = block.desc.op(op_idx)
         if op_desc.has_attr("sub_block"):
-            sub_block = block.program.block(op_desc.block_attr("sub_block"))
+            sub_block = block.program.block(op_desc.block_attr_id("sub_block"))
             _append_backward_vars_(sub_block, 0, grad_to_var, grad_info_map)
         new_vars = set()
         # create new gradient variables
@@ -399,7 +417,7 @@ def _append_backward_vars_(block, start_op_idx, grad_to_var, grad_info_map):
                 continue
             block.desc.var(grad_var_name)
             new_vars.add(grad_var_name)
-            if not grad_to_var.has_key(grad_var_name):
+            if grad_var_name not in grad_to_var:
                 continue
             grad_info_map[grad_to_var[grad_var_name]] = (grad_var_name, block)
         # infer_shape and infer_type
@@ -427,7 +445,7 @@ def _rename_grad_(block, start_op_idx, grad_to_var, target_grad_map):
                 op_desc.rename_output(name, new_name)
                 var_map[name] = new_name
 
-    for g, ng in var_map.iteritems():
+    for g, ng in list(var_map.items()):
         if g in grad_to_var:
             grad_to_var[ng] = grad_to_var[g]
             grad_to_var.pop(g)
@@ -439,7 +457,7 @@ def _get_stop_gradients_(program):
     for block in program.blocks:
         assert isinstance(block, framework.Block)
         block_no_grad_set = set()
-        for var in block.vars.itervalues():
+        for var in list(block.vars.values()):
             assert isinstance(var, framework.Variable)
             if var.stop_gradient:
                 block_no_grad_set.add(_append_grad_suffix_(var.name))
@@ -452,51 +470,51 @@ def append_backward(loss, parameter_list=None, no_grad_set=None,
     """
     Append backward part to main_program.
 
-    A complete neural network training is made up of forward and backward 
-    propagation. However, when we configure a network, we only need to 
-    specify its forwrd part. The backward part is generated automatically 
+    A complete neural network training is made up of forward and backward
+    propagation. However, when we configure a network, we only need to
+    specify its forwrd part. The backward part is generated automatically
     according to the forward part by this function.
 
-    In most cases, users do not need to invoke this function manually. It 
+    In most cases, users do not need to invoke this function manually. It
     will be automatically invoked by the optimizer's `minimize` function.
 
     Args:
         loss(Variable): The loss variable of the network.
-        parameter_list(list[string]|None): Names of parameters that need 
-                                           to be updated by optimizers. 
-                                           If it is None, all parameters 
+        parameter_list(list[string]|None): Names of parameters that need
+                                           to be updated by optimizers.
+                                           If it is None, all parameters
                                            will be updated.
                                            Default: None
-        no_grad_set(set|None): Variables in the Block 0 whose gradients 
-                               should be ignored. All variables with 
-                               `step_gradient=True` from all blocks will 
+        no_grad_set(set|None): Variables in the Block 0 whose gradients
+                               should be ignored. All variables with
+                               `step_gradient=True` from all blocks will
                                be automatically added into this set.
                                Default: None
-        callbacks(list[callable object]|None): The callbacks are used for 
-                                               doing some custom jobs during 
-                                               backward part building. All 
-                                               callable objects in it will 
-                                               be invoked once each time a 
-                                               new gradient operator is added 
-                                               into the program. The callable 
-                                               object must has two input 
-                                               parameters: 'block' and 'context'. 
-                                               The 'block' is the block which 
-                                               the new gradient operator will 
-                                               be added to. The 'context' is a 
-                                               map, whose keys are gradient 
-                                               variable names and values are 
+        callbacks(list[callable object]|None): The callbacks are used for
+                                               doing some custom jobs during
+                                               backward part building. All
+                                               callable objects in it will
+                                               be invoked once each time a
+                                               new gradient operator is added
+                                               into the program. The callable
+                                               object must has two input
+                                               parameters: 'block' and 'context'.
+                                               The 'block' is the block which
+                                               the new gradient operator will
+                                               be added to. The 'context' is a
+                                               map, whose keys are gradient
+                                               variable names and values are
                                                corresponding original variables.
-                                               In addition to this, the 'context' 
-                                               has another special key-value pair: 
-                                               the key is string '__current_op_desc__' 
-                                               and the value is the op_desc of the 
-                                               gradient operator who has just 
-                                               triggered the callable object. 
+                                               In addition to this, the 'context'
+                                               has another special key-value pair:
+                                               the key is string '__current_op_desc__'
+                                               and the value is the op_desc of the
+                                               gradient operator who has just
+                                               triggered the callable object.
 
     Returns:
-        list[(Variable,Variable)]: Pairs of parameter and its 
-        corresponding gradients. The key is the parameter and the 
+        list[(Variable,Variable)]: Pairs of parameter and its
+        corresponding gradients. The key is the parameter and the
         value is gradient variable.
 
     Raises:
@@ -535,7 +553,7 @@ def append_backward(loss, parameter_list=None, no_grad_set=None,
         no_grad_set = set()
     no_grad_set = copy.copy(no_grad_set)
     no_grad_dict = _get_stop_gradients_(program)
-    no_grad_dict[0].update(map(_append_grad_suffix_, no_grad_set))
+    no_grad_dict[0].update(list(map(_append_grad_suffix_, no_grad_set)))
 
     grad_info_map = dict()
     root_block = program.block(0)
@@ -558,7 +576,7 @@ def append_backward(loss, parameter_list=None, no_grad_set=None,
 
     block_no_grad_set = set(map(_strip_grad_suffix_, no_grad_dict[0]))
     op_path = _find_op_path_(root_block, [loss], [], block_no_grad_set)
-    no_grad_dict[0].update(map(_append_grad_suffix_, block_no_grad_set))
+    no_grad_dict[0].update(list(map(_append_grad_suffix_, block_no_grad_set)))
 
     _append_backward_ops_(root_block, op_path, root_block, no_grad_dict,
                           grad_to_var, callbacks)
@@ -697,7 +715,7 @@ def calc_gradient(targets, inputs, target_gradients=None, no_grad_set=None):
         no_grad_set = set()
     no_grad_set = copy.copy(no_grad_set)
     no_grad_dict = _get_stop_gradients_(prog)
-    no_grad_dict[0].update(map(_append_grad_suffix_, no_grad_set))
+    no_grad_dict[0].update(list(map(_append_grad_suffix_, no_grad_set)))
 
     fwd_op_num = block.desc.op_size()
 
@@ -731,7 +749,7 @@ def calc_gradient(targets, inputs, target_gradients=None, no_grad_set=None):
 
     block_no_grad_set = set(map(_strip_grad_suffix_, no_grad_dict[0]))
     op_path = _find_op_path_(block, targets, inputs, block_no_grad_set)
-    no_grad_dict[0].update(map(_append_grad_suffix_, block_no_grad_set))
+    no_grad_dict[0].update(list(map(_append_grad_suffix_, block_no_grad_set)))
     grad_to_var = dict()
     grad_info_map = dict()
     _append_backward_ops_(block, op_path, block, no_grad_dict, grad_to_var)

python/paddle/fluid/clip.py

@@ -13,10 +13,11 @@
 # limitations under the License.
 
 import copy
+import six
 
 import functools
-import layers
-import framework
+from . import layers
+from . import framework
 from . import core
 
 __all__ = [
@@ -80,8 +81,7 @@ def error_clip_callback(block, context):
     # the context is a grad_to_var map
     grad_to_var = context
     op_desc = block.desc.op(block.desc.op_size() - 1)
-    for grad_n in filter(lambda n: grad_to_var.has_key(n),
-                         op_desc.output_arg_names()):
+    for grad_n in [n for n in op_desc.output_arg_names() if n in grad_to_var]:
         fwd_var = block._var_recursive(grad_to_var[grad_n])
         error_clip = getattr(fwd_var, "error_clip", None)
         if not (error_clip is None or isinstance(error_clip,
@@ -247,8 +247,8 @@ class GradientClipByGlobalNorm(BaseGradientClipAttr):
     """
 
     def __init__(self, clip_norm, group_name="default_group"):
-        if not isinstance(group_name, basestring):
-            raise TypeError("'group_name' must be a basestring.")
+        if not isinstance(group_name, six.string_types):
+            raise TypeError("'group_name' must be a %s." % (six.string_types))
 
         self.clip_norm = clip_norm
         self.group_name = group_name
@@ -284,7 +284,7 @@ class GradientClipByGlobalNorm(BaseGradientClipAttr):
                 x=clip_var,
                 y=layers.elementwise_max(
                     x=clip_var, y=group_norm_var))
-            assert group_scale_var.shape == (1L, )
+            assert group_scale_var.shape == (1, )
             self.context[group_scale_name] = group_scale_var
 
         new_grad = layers.elementwise_mul(
@@ -313,7 +313,7 @@ def set_gradient_clip(clip, param_list=None, program=None):
         program = framework.default_main_program()
     if param_list is None:
         param_list = program.block(0).all_parameters()
-    if all(isinstance(elem, basestring) for elem in param_list):
+    if all(isinstance(elem, six.string_types) for elem in param_list):
         param_list = [program.block(0).var(elem) for elem in param_list]
     if not all(isinstance(elem, framework.Parameter) for elem in param_list):
         raise TypeError(

python/paddle/fluid/concurrency.py

@@ -12,15 +12,14 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 
-from layers.control_flow import BlockGuard, equal
+from .layers.control_flow import BlockGuard, equal
 from .framework import Operator
-from layer_helper import LayerHelper, unique_name
-from layers import fill_constant
-import core
+from .layer_helper import LayerHelper, unique_name
+from .layers import fill_constant
+from . import core
 
 __all__ = [
-    'Go', 'make_channel', 'channel_send', 'channel_recv', 'channel_close',
-    'Select'
+    'make_channel', 'channel_send', 'channel_recv', 'channel_close', 'Select'
 ]
 
 
@@ -35,10 +34,10 @@ class Go(BlockGuard):
     def __exit__(self, exc_type, exc_val, exc_tb):
         if exc_type is not None:
             return False
-        self.construct_go_op()
+        self._construct_go_op()
         return super(Go, self).__exit__(exc_type, exc_val, exc_tb)
 
-    def construct_go_op(self):
+    def _construct_go_op(self):
         main_program = self.helper.main_program
         go_block = main_program.current_block()
         parent_block = main_program.block(main_program.current_block()

python/paddle/fluid/contrib/__init__.py

@@ -12,9 +12,9 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 
-import decoder
-from decoder import *
-import memory_usage_calc
-from memory_usage_calc import *
+from . import decoder
+from .decoder import *
+from . import memory_usage_calc
+from .memory_usage_calc import *
 
 __all__ = decoder.__all__ + memory_usage_calc.__all__

python/paddle/fluid/contrib/decoder/__init__.py

@@ -12,7 +12,7 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 
-import beam_search_decoder
-from beam_search_decoder import *
+from . import beam_search_decoder
+from .beam_search_decoder import *
 
 __all__ = beam_search_decoder.__all__

python/paddle/fluid/contrib/decoder/beam_search_decoder.py

@@ -22,6 +22,7 @@ This API is still under active development and may change drastically.
 
 import contextlib
 import numpy as np
+import six
 
 from ... import layers
 from ...framework import Variable
@@ -191,7 +192,7 @@ class StateCell(object):
         self._helper = LayerHelper('state_cell', name=name)
         self._cur_states = {}
         self._state_names = []
-        for state_name, state in states.items():
+        for state_name, state in six.iteritems(states):
             if not isinstance(state, InitState):
                 raise ValueError('state must be an InitState object.')
             self._cur_states[state_name] = state
@@ -346,7 +347,7 @@ class StateCell(object):
         if self._in_decoder and not self._switched_decoder:
             self._switch_decoder()
 
-        for input_name, input_value in inputs.items():
+        for input_name, input_value in six.iteritems(inputs):
             if input_name not in self._inputs:
                 raise ValueError('Unknown input %s. '
                                  'Please make sure %s in input '
@@ -361,7 +362,7 @@ class StateCell(object):
         if self._in_decoder and not self._switched_decoder:
             self._switched_decoder()
 
-        for state_name, decoder_state in self._states_holder.items():
+        for state_name, decoder_state in six.iteritems(self._states_holder):
             if id(self._cur_decoder_obj) not in decoder_state:
                 raise ValueError('Unknown decoder object, please make sure '
                                  'switch_decoder been invoked.')
@@ -671,7 +672,7 @@ class BeamSearchDecoder(object):
             feed_dict = {}
             update_dict = {}
 
-            for init_var_name, init_var in self._input_var_dict.items():
+            for init_var_name, init_var in six.iteritems(self._input_var_dict):
                 if init_var_name not in self.state_cell._inputs:
                     raise ValueError('Variable ' + init_var_name +
                                      ' not found in StateCell!\n')
@@ -721,7 +722,8 @@ class BeamSearchDecoder(object):
                     self.state_cell.update_states()
                     self.update_array(prev_ids, selected_ids)
                     self.update_array(prev_scores, selected_scores)
-                    for update_name, var_to_update in update_dict.items():
+                    for update_name, var_to_update in six.iteritems(
+                            update_dict):
                         self.update_array(var_to_update, feed_dict[update_name])
 
     def read_array(self, init, is_ids=False, is_scores=False):

python/paddle/fluid/data_feeder.py

@@ -12,14 +12,14 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 
-from __future__ import print_function
-import core
+from . import core
 import numpy
 import os
-import six.moves as six
+import six
+from six.moves import zip, range, xrange
 import multiprocessing
 
-from framework import Variable, default_main_program
+from .framework import Variable, default_main_program
 
 __all__ = ['DataFeeder']
 
@@ -53,7 +53,7 @@ class DataToLoDTensorConverter(object):
         self.data = []
         self.lod = []
 
-        for i in six.range(lod_level):
+        for i in six.moves.range(lod_level):
             self.lod.append([])
 
     def feed(self, data):
@@ -142,7 +142,7 @@ class DataFeeder(object):
         if program is None:
             program = default_main_program()
         for each_var in feed_list:
-            if isinstance(each_var, basestring):
+            if isinstance(each_var, six.string_types):
                 each_var = program.block(0).var(each_var)
             if not isinstance(each_var, Variable):
                 raise TypeError("Feed list should contain a list of variable")
@@ -174,7 +174,7 @@ class DataFeeder(object):
             dict: the result of conversion.
         """
         converter = []
-        for lod_level, shape, dtype in six.zip(
+        for lod_level, shape, dtype in six.moves.zip(
                 self.feed_lod_level, self.feed_shapes, self.feed_dtypes):
             converter.append(
                 DataToLoDTensorConverter(
@@ -187,10 +187,12 @@ class DataFeeder(object):
             assert len(each_sample) == len(converter), (
                 "The number of fields in data (%s) does not match " +
                 "len(feed_list) (%s)") % (len(each_sample), len(converter))
-            for each_converter, each_slot in six.zip(converter, each_sample):
+            for each_converter, each_slot in six.moves.zip(converter,
+                                                           each_sample):
                 each_converter.feed(each_slot)
         ret_dict = {}
-        for each_name, each_converter in six.zip(self.feed_names, converter):
+        for each_name, each_converter in six.moves.zip(self.feed_names,
+                                                       converter):
             ret_dict[each_name] = each_converter.done()
         return ret_dict
 
@@ -212,12 +214,14 @@ class DataFeeder(object):
         if isinstance(self.place, core.CUDAPlace):
             places = [
                 core.CUDAPlace(i)
-                for i in six.xrange(self._get_number_of_places_(num_places))
+                for i in six.moves.xrange(
+                    self._get_number_of_places_(num_places))
             ]
         else:
             places = [
                 core.CPUPlace()
-                for _ in six.xrange(self._get_number_of_places_(num_places))
+                for _ in six.moves.xrange(
+                    self._get_number_of_places_(num_places))
             ]
 
         if len(iterable) != len(places):
@@ -227,7 +231,7 @@ class DataFeeder(object):
                              "must be same.")
 
         place = self.place
-        for p, batch in six.zip(places, iterable):
+        for p, batch in six.moves.zip(places, iterable):
             self.place = p
             yield self.feed(batch)
         self.place = place

python/paddle/fluid/debugger.py

@@ -14,8 +14,8 @@
 
 import sys
 import re
-from graphviz import GraphPreviewGenerator
-import proto.framework_pb2 as framework_pb2
+from .graphviz import GraphPreviewGenerator
+from .proto import framework_pb2
 from google.protobuf import text_format
 
 _vartype2str_ = [

python/paddle/fluid/evaluator.py

@@ -15,11 +15,11 @@
 import warnings
 import numpy as np
 
-import layers
-from framework import Program, Variable, program_guard
-import unique_name
-from layer_helper import LayerHelper
-from initializer import Constant
+from . import layers
+from .framework import Program, Variable, program_guard
+from . import unique_name
+from .layer_helper import LayerHelper
+from .initializer import Constant
 
 __all__ = [
     'ChunkEvaluator',

python/paddle/fluid/executor.py

@@ -14,12 +14,11 @@
 
 import numpy as np
 import contextlib
-from framework import Program, default_main_program, Variable
+import six
+from .framework import Program, default_main_program, Variable
 from . import core
 
-__all__ = [
-    'Executor', 'global_scope', 'scope_guard', '_switch_scope', 'fetch_var'
-]
+__all__ = ['Executor', 'global_scope', 'scope_guard', '_switch_scope']
 
 g_scope = core.Scope()
 
@@ -170,7 +169,7 @@ def has_fetch_operators(block, fetch_targets, fetch_holder_name):
     return fetch_count > 0
 
 
-def fetch_var(name, scope=None, return_numpy=True):
+def _fetch_var(name, scope=None, return_numpy=True):
     """
     Fetch the value of the variable with the given name from the
     given scope.
@@ -204,23 +203,54 @@ def fetch_var(name, scope=None, return_numpy=True):
 
 
 def _get_program_cache_key(feed, fetch_list):
-    feed_var_names = feed.keys()
+    feed_var_names = list(feed.keys())
 
     def to_name_str(var):
         if isinstance(var, Variable):
             return var.desc.name()
         elif isinstance(var, str):
             return var
-        elif isinstance(var, basestring):
+        elif isinstance(var, six.string_types):
             return str(var)
         else:
             raise TypeError(str(var) + " should be Variable or str")
 
-    fetch_var_names = map(to_name_str, fetch_list)
+    fetch_var_names = list(map(to_name_str, fetch_list))
 
     return str(feed_var_names + fetch_var_names)
 
 
+def _as_lodtensor(data, place):
+    """
+        Convert numpy.ndarray to Tensor, its only support Tensor without LoD information.
+        For higher dimensional sequence data, please use LoDTensor directly.
+
+        Examples:
+            >>> import paddle.fluid as fluid
+            >>> place = fluid.CPUPlace()
+            >>> exe = fluid.executor(place)
+            >>> data = np.array(size=(100, 200, 300))
+            >>> np_outs = map(lambda x: fluid.executor._as_lodtensor(x, place), data)
+            >>>     ...
+
+        Args:
+            data(numpy.ndarray): a instance of array
+
+        Returns:
+            LoDTensor
+        """
+    if isinstance(data, list):
+        raise RuntimeError("Some of your feed data hold LoD information. \
+                They can not be completely cast from a list of Python \
+                ndarray to LoDTensor. Please convert data to LoDTensor \
+                directly before feeding the data.\
+                ")
+    # single tensor case
+    tensor = core.LoDTensor()
+    tensor.set(data, place)
+    return tensor
+
+
 class Executor(object):
     """
     An Executor in Python, only support the single-GPU running. For multi-cards, please refer to
@@ -229,8 +259,8 @@ class Executor(object):
     to feed map and fetch_list. Feed map provides input data for the program. fetch_list provides
     the variables(or names) that user want to get after program run. Note: the executor will run all
     operators in the program but not only the operators dependent by the fetch_list.
-    It store the global variables into the global scope, and create a local scope for the temporary 
-    variables. The local scope contents will be discarded after every minibatch forward/backward finished. 
+    It store the global variables into the global scope, and create a local scope for the temporary
+    variables. The local scope contents will be discarded after every minibatch forward/backward finished.
     But the global scope variables will be persistent through different runs.
     All of ops in program will be running in sequence.
 
@@ -249,35 +279,6 @@ class Executor(object):
         self.program_caches = dict()
         self._closed = False
 
-    def as_lodtensor(self, data):
-        """
-        Convert numpy.ndarray to Tensor, its only support Tensor without LoD information.
-        For higher dimensional sequence data, please use LoDTensor directly.
-
-        Examples:
-            >>> import paddle.fluid as fluid
-            >>> exe = fluid.executor(fluid.CPUPlace())
-            >>> data = np.array(size=(100, 200, 300))
-            >>> np_outs = map(lambda x: exe.as_lodtensor(x), data)
-            >>>     ...
-
-        Args:
-            data(numpy.ndarray): a instance of array
-
-        Returns:
-            LoDTensor
-        """
-        if isinstance(data, list):
-            raise RuntimeError("Some of your feed data hold LoD information. \
-                They can not be completely cast from a list of Python \
-                ndarray to LoDTensor. Please convert data to LoDTensor \
-                directly before feeding the data.\
-                ")
-        # single tensor case
-        tensor = core.LoDTensor()
-        tensor.set(data, self.place)
-        return tensor
-
     def _get_program_cache(self, program_cache_key):
         return self.program_caches.get(program_cache_key, None)
 
@@ -336,7 +337,7 @@ class Executor(object):
                 feed_target_name = op.desc.output('Out')[0]
                 cur_feed = feed[feed_target_name]
                 if not isinstance(cur_feed, core.LoDTensor):
-                    cur_feed = self.as_lodtensor(cur_feed)
+                    cur_feed = _as_lodtensor(cur_feed, self.place)
                 idx = op.desc.attr('col')
                 core.set_feed_variable(scope, cur_feed, feed_var_name, idx)
             else:
@@ -345,7 +346,7 @@ class Executor(object):
     def _fetch_data(self, fetch_list, fetch_var_name, scope):
         outs = [
             core.get_fetch_variable(scope, fetch_var_name, i)
-            for i in xrange(len(fetch_list))
+            for i in range(len(fetch_list))
         ]
         return outs
 

python/paddle/fluid/graphviz.py

@@ -14,12 +14,13 @@
 
 import os
 import random
+import six
 import subprocess
 import logging
 
 
 def crepr(v):
-    if type(v) is str or type(v) is unicode:
+    if isinstance(v, six.string_types):
         return '"%s"' % v
     return str(v)
 
@@ -104,7 +105,7 @@ class Graph(object):
 
     def _rank_repr(self):
         ranks = sorted(
-            self.rank_groups.items(),
+            list(self.rank_groups.items()),
             cmp=lambda a, b: a[1].priority > b[1].priority)
         repr = []
         for x in ranks:
@@ -148,7 +149,7 @@ class Node(object):
             name=self.name,
             label=self.label,
             extra=',' + ','.join("%s=%s" % (key, crepr(value))
-                                 for key, value in self.attrs.items())
+                                 for key, value in list(self.attrs.items()))
             if self.attrs else "")
         return reprs
 
@@ -172,7 +173,7 @@ class Edge(object):
             target=self.target.name,
             extra="" if not self.attrs else
             "[" + ','.join("{}={}".format(attr[0], crepr(attr[1]))
-                           for attr in self.attrs.items()) + "]")
+                           for attr in list(self.attrs.items())) + "]")
         return repr
 
 

python/paddle/fluid/inferencer.py

@@ -14,14 +14,14 @@
 
 import contextlib
 
-import core
-
-import executor
-import framework
-import io
-import parallel_executor
-import unique_name
-from trainer import check_and_get_place
+from . import core
+
+from . import executor
+from . import framework
+from . import io
+from . import parallel_executor
+from . import unique_name
+from .trainer import check_and_get_place
 
 __all__ = ['Inferencer', ]
 

python/paddle/fluid/initializer.py

@@ -12,11 +12,11 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 
-import framework
+from . import framework
 import numpy as np
 import contextlib
-from framework import convert_np_dtype_to_dtype_
-from core import VarDesc
+from .framework import convert_np_dtype_to_dtype_
+from .core import VarDesc
 
 __all__ = [
     'Constant', 'Uniform', 'Normal', 'Xavier', 'Bilinear', 'MSRA',
@@ -264,7 +264,8 @@ class NormalInitializer(Initializer):
                 "dtype": int(var.dtype),
                 "mean": self._mean,
                 "std": self._std_dev,
-                "seed": self._seed
+                "seed": self._seed,
+                "use_mkldnn": False
             })
         var.op = op
         return op

python/paddle/fluid/layer_helper.py

@@ -14,12 +14,14 @@
 
 import copy
 import itertools
+import six
 
-from framework import Variable, Parameter, default_main_program, default_startup_program, dtype_is_floating
-import unique_name
+from .framework import Variable, Parameter, default_main_program, default_startup_program, dtype_is_floating
+from . import unique_name
 from paddle.fluid.initializer import Constant, Xavier
-from param_attr import ParamAttr, WeightNormParamAttr
-import core
+from .param_attr import ParamAttr, WeightNormParamAttr
+from . import core
+from six.moves import zip
 
 
 class LayerHelper(object):
@@ -83,7 +85,7 @@ class LayerHelper(object):
             raise ValueError("parameter number mismatch")
         elif len(param_attr) == 1 and length != 1:
             tmp = [None] * length
-            for i in xrange(length):
+            for i in range(length):
                 tmp[i] = copy.deepcopy(param_attr[0])
             param_attr = tmp
         return param_attr
@@ -91,7 +93,7 @@ class LayerHelper(object):
     def iter_inputs_and_params(self, input_param_name='input'):
         inputs = self.multiple_input(input_param_name)
         param_attrs = self.multiple_param_attr(len(inputs))
-        for ipt, param_attr in itertools.izip(inputs, param_attrs):
+        for ipt, param_attr in zip(inputs, param_attrs):
             yield ipt, param_attr
 
     def input_dtype(self, input_param_name='input'):
@@ -218,7 +220,7 @@ class LayerHelper(object):
                 norm = __norm_op(reshape, dim=0, block=block)
                 __reshape_op(norm, out=out, shape=out_shape, block=block)
             else:
-                perm = range(len(x.shape))
+                perm = list(range(len(x.shape)))
                 perm[0], perm[dim] = dim, 0
                 transpose = __transpose_op(x, perm, block=block)
                 norm = __norm_op(transpose, dim=0, block=block)
@@ -397,8 +399,10 @@ class LayerHelper(object):
         act = self.kwargs.get('act', None)
         if act is None:
             return input_var
-        if isinstance(act, basestring):
+        if isinstance(act, six.string_types):
             act = {'type': act}
+        else:
+            raise TypeError(str(act) + " should be unicode or str")
 
         if 'use_cudnn' in self.kwargs and self.kwargs.get('use_cudnn'):
             act['use_cudnn'] = self.kwargs.get('use_cudnn')