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PaddleDetection
提交 41a64f6a 编写于 作者: Y Yancey1989
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Merge branch 'develop' of github.com:PaddlePaddle/Paddle into parallel_graph_mode

上级 06936a2f bc16bcda
展开全部 隐藏空白更改
内联 并排
Showing with 18697 addition and 6644 deletion
CMakeLists.txt
浏览文件 @ 41a64f6a
......@@ -65,6 +65,7 @@ option(WITH_GOLANG "Compile PaddlePaddle with GOLANG" OFF)
option(GLIDE_INSTALL "Download and install go dependencies " ON)
option(USE_NNPACK "Compile PaddlePaddle with NNPACK library" OFF)
option(WITH_DISTRIBUTE "Compile with distributed support" OFF)
option(WITH_PSLIB "Compile with pslib support" OFF)
option(USE_EIGEN_FOR_BLAS "Use matrix multiplication in Eigen" OFF)
option(EIGEN_USE_THREADS "Compile with multi-threaded Eigen" OFF)
option(WITH_ARM_FP16 "Use half precision support on armv8.2-a cpu" OFF)
......@@ -125,18 +126,12 @@ if(ANDROID OR IOS)
add_definitions(-DPADDLE_MOBILE_INFERENCE)
endif()
if (APPLE OR WIN32)
if (APPLE)
set(WITH_MKL OFF CACHE STRING
"Disable MKL for building on mac and windows" FORCE)
"Disable MKL for building on mac" FORCE)
endif()
if (WIN32)
set(WITH_AVX OFF CACHE STRING
"Disable AVX when compiling for Windows" FORCE)
set(WITH_DSO OFF CACHE STRING
"Disable DSO when compiling for Windows" FORCE)
set(WITH_MKL OFF CACHE STRING
"Disable MKL when compiling for Windows" FORCE)
set(WITH_DISTRIBUTE OFF CACHE STRING
"Disable DISTRIBUTE when compiling for Windows" FORCE)
set(WITH_C_API OFF CACHE STRING
......@@ -209,14 +204,20 @@ include(external/xxhash) # download xxhash
include(external/dlpack)
include(external/snappy) # download snappy
include(external/snappystream) # download snappystream
include(external/warpctc) # download, build, install warpctc
if (NOT WIN32)
# there is no official support of warpctc, nccl, cupti in windows
include(external/warpctc) # download, build, install warpctc
# there is no official support of nccl, cupti in windows
include(cupti)
include(external/gzstream)
endif (NOT WIN32)
if(WITH_PSLIB)
include(external/libmct)
include(external/pslib_brpc)
include(external/pslib)
endif(WITH_PSLIB)
if(WITH_DISTRIBUTE)
if(WITH_GRPC)
include(external/grpc)
......@@ -284,6 +285,12 @@ set(EXTERNAL_LIBS
${PYTHON_LIBRARIES}
)
if(WITH_PSLIB)
list(APPEND EXTERNAL_LIBS pslib)
list(APPEND EXTERNAL_LIBS pslib_brpc)
list(APPEND EXTERNAL_LIBS libmct)
endif(WITH_PSLIB)
if(WITH_AMD_GPU)
find_package(HIP)
include(hip)
......
cmake/configure.cmake
浏览文件 @ 41a64f6a
......@@ -84,6 +84,10 @@ if(NOT WITH_GOLANG)
add_definitions(-DPADDLE_WITHOUT_GOLANG)
endif(NOT WITH_GOLANG)
if(WITH_PSLIB)
add_definitions(-DPADDLE_WITH_PSLIB)
endif()
if(WITH_GPU)
add_definitions(-DPADDLE_WITH_CUDA)
add_definitions(-DEIGEN_USE_GPU)
......
cmake/cudnn.cmake
浏览文件 @ 41a64f6a
......@@ -44,9 +44,9 @@ if(WIN32)
set(CUDNN_LIB_NAME "cudnn.lib" "cudnn64_7.dll")
endif(WIN32)
if(Apple)
if(APPLE)
set(CUDNN_LIB_NAME "libcudnn.dylib" "libcudnn.so")
endif(Apple)
endif(APPLE)
find_library(CUDNN_LIBRARY NAMES ${CUDNN_LIB_NAME} # libcudnn_static.a
PATHS ${CUDNN_CHECK_LIBRARY_DIRS} ${CUDNN_INCLUDE_DIR} ${__libpath_hist}
......
cmake/external/libmct.cmake 0 → 100644
浏览文件 @ 41a64f6a
# Copyright (c) 2017 PaddlePaddle Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
IF(NOT ${WITH_LIBMCT})
return()
ENDIF(NOT ${WITH_LIBMCT})
IF(WIN32 OR APPLE)
MESSAGE(WARNING
"Windows or Mac is not supported with LIBMCT in Paddle yet."
"Force WITH_LIBMCT=OFF")
SET(WITH_LIBMCT OFF CACHE STRING "Disable LIBMCT package in Windows and MacOS" FORCE)
return()
ENDIF()
INCLUDE(ExternalProject)
SET(LIBMCT_PROJECT "extern_libmct")
IF((NOT DEFINED LIBMCT_VER) OR (NOT DEFINED LIBMCT_URL))
MESSAGE(STATUS "use pre defined download url")
SET(LIBMCT_VER "0.1.0" CACHE STRING "" FORCE)
SET(LIBMCT_NAME "libmct" CACHE STRING "" FORCE)
SET(LIBMCT_URL "https://raw.githubusercontent.com/PaddlePaddle/Fleet/release/${LIBMCT_VER}/${LIBMCT_NAME}.tar.gz" CACHE STRING "" FORCE)
ENDIF()
MESSAGE(STATUS "LIBMCT_NAME: ${LIBMCT_NAME}, LIBMCT_URL: ${LIBMCT_URL}")
SET(LIBMCT_SOURCE_DIR "${THIRD_PARTY_PATH}/libmct")
SET(LIBMCT_DOWNLOAD_DIR "${LIBMCT_SOURCE_DIR}/src/${LIBMCT_PROJECT}")
SET(LIBMCT_DST_DIR "libmct")
SET(LIBMCT_INSTALL_ROOT "${THIRD_PARTY_PATH}/install")
SET(LIBMCT_INSTALL_DIR ${LIBMCT_INSTALL_ROOT}/${LIBMCT_DST_DIR})
SET(LIBMCT_ROOT ${LIBMCT_INSTALL_DIR})
SET(LIBMCT_INC_DIR ${LIBMCT_ROOT}/include)
SET(CMAKE_INSTALL_RPATH "${CMAKE_INSTALL_RPATH}" "${LIBMCT_ROOT}/lib")
INCLUDE_DIRECTORIES(${LIBMCT_INC_DIR})
FILE(WRITE ${LIBMCT_DOWNLOAD_DIR}/CMakeLists.txt
"PROJECT(LIBMCT)\n"
"cmake_minimum_required(VERSION 3.0)\n"
"install(DIRECTORY ${LIBMCT_NAME}/include ${LIBMCT_NAME}/lib \n"
" DESTINATION ${LIBMCT_DST_DIR})\n")
ExternalProject_Add(
${LIBMCT_PROJECT}
${EXTERNAL_PROJECT_LOG_ARGS}
PREFIX ${LIBMCT_SOURCE_DIR}
DOWNLOAD_DIR ${LIBMCT_DOWNLOAD_DIR}
DOWNLOAD_COMMAND wget --no-check-certificate ${LIBMCT_URL} -c -q -O ${LIBMCT_NAME}.tar.gz
&& tar zxvf ${LIBMCT_NAME}.tar.gz
DOWNLOAD_NO_PROGRESS 1
UPDATE_COMMAND ""
CMAKE_ARGS -DCMAKE_INSTALL_PREFIX=${LIBMCT_INSTALL_ROOT}
CMAKE_CACHE_ARGS -DCMAKE_INSTALL_PREFIX:PATH=${LIBMCT_INSTALL_ROOT}
)
if (${CMAKE_VERSION} VERSION_LESS "3.3.0" OR NOT WIN32)
set(dummyfile ${CMAKE_CURRENT_BINARY_DIR}/boost_dummy.c)
file(WRITE ${dummyfile} "const char *dummy = \"${dummyfile}\";")
add_library(libmct STATIC ${dummyfile})
else()
add_library(libmct INTERFACE)
endif()
#ADD_LIBRARY(libmct SHARED IMPORTED GLOBAL)
ADD_DEPENDENCIES(libmct ${LIBMCT_PROJECT})
LIST(APPEND external_project_dependencies libmct)
cmake/external/mkldnn.cmake
浏览文件 @ 41a64f6a
......@@ -23,15 +23,14 @@ SET(MKLDNN_SOURCES_DIR ${THIRD_PARTY_PATH}/mkldnn)
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)
IF(APPLE)
MESSAGE(WARNING
"Windows or Mac is not supported with MKLDNN in Paddle yet."
"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)
SET(WITH_MKLDNN OFF CACHE STRING "Disable MKLDNN in MacOS" FORCE)
return()
ENDIF()
SET(MKLDNN_LIB "${MKLDNN_INSTALL_DIR}/lib/libmkldnn.so" CACHE FILEPATH "mkldnn library." FORCE)
MESSAGE(STATUS "Set ${MKLDNN_INSTALL_DIR}/lib to runtime path")
SET(CMAKE_INSTALL_RPATH_USE_LINK_PATH TRUE)
SET(CMAKE_INSTALL_RPATH "${CMAKE_INSTALL_RPATH}" "${MKLDNN_INSTALL_DIR}/lib")
......@@ -44,10 +43,14 @@ IF(${CBLAS_PROVIDER} STREQUAL "MKLML")
ELSE()
MESSAGE(FATAL_ERROR "Should enable MKLML when build MKLDNN")
ENDIF()
SET(MKLDNN_FLAG "-Wno-error=strict-overflow -Wno-error=unused-result -Wno-error=array-bounds")
SET(MKLDNN_FLAG "${MKLDNN_FLAG} -Wno-unused-result -Wno-unused-value")
SET(MKLDNN_CFLAG "${CMAKE_C_FLAGS} ${MKLDNN_FLAG}")
SET(MKLDNN_CXXFLAG "${CMAKE_CXX_FLAGS} ${MKLDNN_FLAG}")
IF(NOT WIN32)
SET(MKLDNN_FLAG "-Wno-error=strict-overflow -Wno-error=unused-result -Wno-error=array-bounds")
SET(MKLDNN_FLAG "${MKLDNN_FLAG} -Wno-unused-result -Wno-unused-value")
SET(MKLDNN_CFLAG "${CMAKE_C_FLAGS} ${MKLDNN_FLAG}")
SET(MKLDNN_CXXFLAG "${CMAKE_CXX_FLAGS} ${MKLDNN_FLAG}")
ENDIF(NOT WIN32)
ExternalProject_Add(
${MKLDNN_PROJECT}
${EXTERNAL_PROJECT_LOG_ARGS}
......@@ -58,8 +61,15 @@ ExternalProject_Add(
UPDATE_COMMAND ""
CMAKE_ARGS -DCMAKE_CXX_COMPILER=${CMAKE_CXX_COMPILER}
CMAKE_ARGS -DCMAKE_C_COMPILER=${CMAKE_C_COMPILER}
CMAKE_ARGS -DCMAKE_CXX_FLAGS=${CMAKE_CXX_FLAGS}
CMAKE_ARGS -DCMAKE_CXX_FLAGS_RELEASE=${CMAKE_CXX_FLAGS_RELEASE}
CMAKE_ARGS -DCMAKE_CXX_FLAGS_DEBUG=${CMAKE_CXX_FLAGS_DEBUG}
CMAKE_ARGS -DCMAKE_C_FLAGS=${CMAKE_C_FLAGS}
CMAKE_ARGS -DCMAKE_C_FLAGS_DEBUG=${CMAKE_C_FLAGS_DEBUG}
CMAKE_ARGS -DCMAKE_C_FLAGS_RELEASE=${CMAKE_C_FLAGS_RELEASE}
CMAKE_ARGS -DCMAKE_INSTALL_PREFIX=${MKLDNN_INSTALL_DIR}
CMAKE_ARGS -DCMAKE_BUILD_TYPE=${CMAKE_BUILD_TYPE}
CMAKE_ARGS -DCMAKE_POSITION_INDEPENDENT_CODE=ON
CMAKE_ARGS -DMKLROOT=${MKLML_ROOT}
CMAKE_ARGS -DCMAKE_C_FLAGS=${MKLDNN_CFLAG}
CMAKE_ARGS -DCMAKE_CXX_FLAGS=${MKLDNN_CXXFLAG}
......@@ -67,6 +77,11 @@ ExternalProject_Add(
CMAKE_CACHE_ARGS -DCMAKE_INSTALL_PREFIX:PATH=${MKLDNN_INSTALL_DIR}
-DMKLROOT:PATH=${MKLML_ROOT}
)
if(WIN32)
SET(MKLDNN_LIB "${MKLDNN_INSTALL_DIR}/lib/mkldnn.lib" CACHE FILEPATH "mkldnn library." FORCE)
else(WIN32)
SET(MKLDNN_LIB "${MKLDNN_INSTALL_DIR}/lib/libmkldnn.so" CACHE FILEPATH "mkldnn library." FORCE)
endif(WIN32)
ADD_LIBRARY(shared_mkldnn SHARED IMPORTED GLOBAL)
SET_PROPERTY(TARGET shared_mkldnn PROPERTY IMPORTED_LOCATION ${MKLDNN_LIB})
......@@ -85,10 +100,14 @@ ADD_DEPENDENCIES(mkldnn ${MKLDNN_PROJECT})
# copy the real so.0 lib to install dir
# it can be directly contained in wheel or capi
SET(MKLDNN_SHARED_LIB ${MKLDNN_INSTALL_DIR}/libmkldnn.so.0)
ADD_CUSTOM_COMMAND(OUTPUT ${MKLDNN_SHARED_LIB}
COMMAND cp ${MKLDNN_LIB} ${MKLDNN_SHARED_LIB}
DEPENDS mkldnn)
if(WIN32)
SET(MKLDNN_SHARED_LIB ${MKLDNN_INSTALL_DIR}/lib/mkldnn.dll)
else(WIN32)
SET(MKLDNN_SHARED_LIB ${MKLDNN_INSTALL_DIR}/libmkldnn.so.0)
ADD_CUSTOM_COMMAND(OUTPUT ${MKLDNN_SHARED_LIB}
COMMAND ${CMAKE_COMMAND} -E copy ${MKLDNN_LIB} ${MKLDNN_SHARED_LIB}
DEPENDS mkldnn)
endif(WIN32)
ADD_CUSTOM_TARGET(mkldnn_shared_lib ALL DEPENDS ${MKLDNN_SHARED_LIB})
IF(WITH_C_API)
......
cmake/external/mklml.cmake
浏览文件 @ 41a64f6a
......@@ -16,56 +16,67 @@ IF(NOT ${WITH_MKLML})
return()
ENDIF(NOT ${WITH_MKLML})
IF(WIN32 OR APPLE)
IF(APPLE)
MESSAGE(WARNING
"Windows or Mac is not supported with MKLML in Paddle yet."
"Mac is not supported with MKLML in Paddle yet."
"Force WITH_MKLML=OFF")
SET(WITH_MKLML OFF CACHE STRING "Disable MKLML package in Windows and MacOS" FORCE)
return()
ENDIF()
INCLUDE(ExternalProject)
SET(MKLML_PROJECT "extern_mklml")
IF((NOT DEFINED MKLML_VER) OR (NOT DEFINED MKLML_URL))
MESSAGE(STATUS "use pre defined download url")
SET(MKLML_VER "mklml_lnx_2019.0.20180710" CACHE STRING "" FORCE)
SET(MKLML_URL "http://paddlepaddledeps.cdn.bcebos.com/${MKLML_VER}.tgz" CACHE STRING "" FORCE)
ENDIF()
MESSAGE(STATUS "MKLML_VER: ${MKLML_VER}, MKLML_URL: ${MKLML_URL}")
SET(MKLML_SOURCE_DIR "${THIRD_PARTY_PATH}/mklml")
SET(MKLML_DOWNLOAD_DIR "${MKLML_SOURCE_DIR}/src/${MKLML_PROJECT}")
SET(MKLML_DST_DIR "mklml")
SET(MKLML_INSTALL_ROOT "${THIRD_PARTY_PATH}/install")
SET(MKLML_INSTALL_DIR ${MKLML_INSTALL_ROOT}/${MKLML_DST_DIR})
SET(MKLML_ROOT ${MKLML_INSTALL_DIR})
SET(MKLML_INC_DIR ${MKLML_ROOT}/include)
SET(MKLML_LIB_DIR ${MKLML_ROOT}/lib)
SET(MKLML_LIB ${MKLML_LIB_DIR}/libmklml_intel.so)
SET(MKLML_IOMP_LIB ${MKLML_LIB_DIR}/libiomp5.so)
if(WIN32)
SET(MKLML_LIB ${MKLML_LIB_DIR}/mklml.lib)
SET(MKLML_IOMP_LIB ${MKLML_LIB_DIR}/libiomp5md.lib)
SET(MKLML_SHARED_LIB ${MKLML_LIB_DIR}/mklml.dll)
SET(MKLML_SHARED_IOMP_LIB ${MKLML_LIB_DIR}/libiomp5md.dll)
else()
SET(MKLML_LIB ${MKLML_LIB_DIR}/libmklml_intel.so)
SET(MKLML_IOMP_LIB ${MKLML_LIB_DIR}/libiomp5.so)
SET(MKLML_SHARED_LIB ${MKLML_LIB_DIR}/libmklml_intel.so)
SET(MKLML_SHARED_IOMP_LIB ${MKLML_LIB_DIR}/libiomp5.so)
endif()
SET(CMAKE_INSTALL_RPATH "${CMAKE_INSTALL_RPATH}" "${MKLML_ROOT}/lib")
INCLUDE_DIRECTORIES(${MKLML_INC_DIR})
IF((NOT DEFINED MKLML_VER) OR (NOT DEFINED MKLML_URL))
MESSAGE(STATUS "use pre defined download url")
if(WIN32)
SET(MKLML_VER "mklml_win_2019.0.20180710" CACHE STRING "" FORCE)
SET(MKLML_URL "https://paddlepaddledeps.cdn.bcebos.com/${MKLML_VER}.zip" CACHE STRING "" FORCE)
else()
SET(MKLML_VER "mklml_lnx_2019.0.20180710" CACHE STRING "" FORCE)
SET(MKLML_URL "http://paddlepaddledeps.cdn.bcebos.com/${MKLML_VER}.tgz" CACHE STRING "" FORCE)
ENDIF()
endif()
FILE(WRITE ${MKLML_DOWNLOAD_DIR}/CMakeLists.txt
"PROJECT(MKLML)\n"
"cmake_minimum_required(VERSION 3.0)\n"
"install(DIRECTORY ${MKLML_VER}/include ${MKLML_VER}/lib \n"
" DESTINATION ${MKLML_DST_DIR})\n")
SET(MKLML_PROJECT "extern_mklml")
MESSAGE(STATUS "MKLML_VER: ${MKLML_VER}, MKLML_URL: ${MKLML_URL}")
SET(MKLML_SOURCE_DIR "${THIRD_PARTY_PATH}/mklml")
SET(MKLML_DOWNLOAD_DIR "${MKLML_SOURCE_DIR}/src/${MKLML_PROJECT}")
ExternalProject_Add(
${MKLML_PROJECT}
${EXTERNAL_PROJECT_LOG_ARGS}
PREFIX ${MKLML_SOURCE_DIR}
PREFIX ${MKLML_SOURCE_DIR}
URL ${MKLML_URL}
DOWNLOAD_DIR ${MKLML_DOWNLOAD_DIR}
DOWNLOAD_COMMAND wget --no-check-certificate ${MKLML_URL} -c -q -O ${MKLML_VER}.tgz
&& tar zxf ${MKLML_VER}.tgz
DOWNLOAD_NO_PROGRESS 1
UPDATE_COMMAND ""
CMAKE_ARGS -DCMAKE_INSTALL_PREFIX=${MKLML_INSTALL_ROOT}
CMAKE_CACHE_ARGS -DCMAKE_INSTALL_PREFIX:PATH=${MKLML_INSTALL_ROOT}
CONFIGURE_COMMAND ""
BUILD_COMMAND ""
UPDATE_COMMAND ""
INSTALL_COMMAND
${CMAKE_COMMAND} -E copy_directory ${MKLML_DOWNLOAD_DIR}/include ${MKLML_INC_DIR} &&
${CMAKE_COMMAND} -E copy_directory ${MKLML_DOWNLOAD_DIR}/lib ${MKLML_LIB_DIR}
)
INCLUDE_DIRECTORIES(${MKLML_INC_DIR})
ADD_LIBRARY(mklml SHARED IMPORTED GLOBAL)
SET_PROPERTY(TARGET mklml PROPERTY IMPORTED_LOCATION ${MKLML_LIB})
ADD_DEPENDENCIES(mklml ${MKLML_PROJECT})
......
cmake/external/pslib.cmake 0 → 100644
浏览文件 @ 41a64f6a
# Copyright (c) 2017 PaddlePaddle Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
IF(NOT ${WITH_PSLIB})
return()
ENDIF(NOT ${WITH_PSLIB})
IF(WIN32 OR APPLE)
MESSAGE(WARNING
"Windows or Mac is not supported with PSLIB in Paddle yet."
"Force WITH_PSLIB=OFF")
SET(WITH_PSLIB OFF CACHE STRING "Disable PSLIB package in Windows and MacOS" FORCE)
return()
ENDIF()
INCLUDE(ExternalProject)
SET(PSLIB_PROJECT "extern_pslib")
IF((NOT DEFINED PSLIB_VER) OR (NOT DEFINED PSLIB_URL))
MESSAGE(STATUS "use pre defined download url")
SET(PSLIB_VER "0.1.0" CACHE STRING "" FORCE)
SET(PSLIB_NAME "pslib" CACHE STRING "" FORCE)
SET(PSLIB_URL "https://raw.githubusercontent.com/PaddlePaddle/Fleet/release/${PSLIB_VER}/${PSLIB_NAME}.tar.gz" CACHE STRING "" FORCE)
ENDIF()
MESSAGE(STATUS "PSLIB_NAME: ${PSLIB_NAME}, PSLIB_URL: ${PSLIB_URL}")
SET(PSLIB_SOURCE_DIR "${THIRD_PARTY_PATH}/pslib")
SET(PSLIB_DOWNLOAD_DIR "${PSLIB_SOURCE_DIR}/src/${PSLIB_PROJECT}")
SET(PSLIB_DST_DIR "pslib")
SET(PSLIB_INSTALL_ROOT "${THIRD_PARTY_PATH}/install")
SET(PSLIB_INSTALL_DIR ${PSLIB_INSTALL_ROOT}/${PSLIB_DST_DIR})
SET(PSLIB_ROOT ${PSLIB_INSTALL_DIR})
SET(PSLIB_INC_DIR ${PSLIB_ROOT}/include)
SET(PSLIB_LIB_DIR ${PSLIB_ROOT}/lib)
SET(PSLIB_LIB ${PSLIB_LIB_DIR}/libps.so)
SET(PSLIB_IOMP_LIB ${PSLIB_LIB_DIR}/libiomp5.so) #todo what is this
SET(CMAKE_INSTALL_RPATH "${CMAKE_INSTALL_RPATH}" "${PSLIB_ROOT}/lib")
INCLUDE_DIRECTORIES(${PSLIB_INC_DIR})
FILE(WRITE ${PSLIB_DOWNLOAD_DIR}/CMakeLists.txt
"PROJECT(PSLIB)\n"
"cmake_minimum_required(VERSION 3.0)\n"
"install(DIRECTORY ${PSLIB_NAME}/include ${PSLIB_NAME}/lib \n"
" DESTINATION ${PSLIB_DST_DIR})\n")
ExternalProject_Add(
${PSLIB_PROJECT}
${EXTERNAL_PROJECT_LOG_ARGS}
PREFIX ${PSLIB_SOURCE_DIR}
DOWNLOAD_DIR ${PSLIB_DOWNLOAD_DIR}
DOWNLOAD_COMMAND wget --no-check-certificate ${PSLIB_URL} -c -q -O ${PSLIB_NAME}.tar.gz
&& tar zxvf ${PSLIB_NAME}.tar.gz
DOWNLOAD_NO_PROGRESS 1
UPDATE_COMMAND ""
CMAKE_ARGS -DCMAKE_INSTALL_PREFIX=${PSLIB_INSTALL_ROOT}
CMAKE_CACHE_ARGS -DCMAKE_INSTALL_PREFIX:PATH=${PSLIB_INSTALL_ROOT}
)
ADD_LIBRARY(pslib SHARED IMPORTED GLOBAL)
SET_PROPERTY(TARGET pslib PROPERTY IMPORTED_LOCATION ${PSLIB_LIB})
ADD_DEPENDENCIES(pslib ${PSLIB_PROJECT})
LIST(APPEND external_project_dependencies pslib)
IF(WITH_C_API)
INSTALL(FILES ${PSLIB_LIB} ${PSLIB_IOMP_LIB} DESTINATION lib)
ENDIF()
cmake/external/pslib_brpc.cmake 0 → 100644
浏览文件 @ 41a64f6a
# Copyright (c) 2017 PaddlePaddle Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
IF(NOT ${WITH_PSLIB_BRPC})
return()
ENDIF(NOT ${WITH_PSLIB_BRPC})
IF(WIN32 OR APPLE)
MESSAGE(WARNING
"Windows or Mac is not supported with PSLIB_BRPC in Paddle yet."
"Force WITH_PSLIB_BRPC=OFF")
SET(WITH_PSLIB_BRPC OFF CACHE STRING "Disable PSLIB_BRPC package in Windows and MacOS" FORCE)
return()
ENDIF()
INCLUDE(ExternalProject)
SET(PSLIB_BRPC_PROJECT "extern_pslib_brpc")
IF((NOT DEFINED PSLIB_BRPC_NAME) OR (NOT DEFINED PSLIB_BRPC_URL))
MESSAGE(STATUS "use pre defined download url")
SET(PSLIB_BRPC_VER "0.1.0" CACHE STRING "" FORCE)
SET(PSLIB_BRPC_NAME "pslib_brpc" CACHE STRING "" FORCE)
SET(PSLIB_BRPC_URL "https://raw.githubusercontent.com/PaddlePaddle/Fleet/release/${PSLIB_BRPC_VER}/${PSLIB_BRPC_NAME}.tar.gz" CACHE STRING "" FORCE)
ENDIF()
MESSAGE(STATUS "PSLIB_BRPC_NAME: ${PSLIB_BRPC_NAME}, PSLIB_BRPC_URL: ${PSLIB_BRPC_URL}")
SET(PSLIB_BRPC_SOURCE_DIR "${THIRD_PARTY_PATH}/pslib_brpc")
SET(PSLIB_BRPC_DOWNLOAD_DIR "${PSLIB_BRPC_SOURCE_DIR}/src/${PSLIB_BRPC_PROJECT}")
SET(PSLIB_BRPC_DST_DIR "pslib_brpc")
SET(PSLIB_BRPC_INSTALL_ROOT "${THIRD_PARTY_PATH}/install")
SET(PSLIB_BRPC_INSTALL_DIR ${PSLIB_BRPC_INSTALL_ROOT}/${PSLIB_BRPC_DST_DIR})
SET(PSLIB_BRPC_ROOT ${PSLIB_BRPC_INSTALL_DIR})
SET(PSLIB_BRPC_INC_DIR ${PSLIB_BRPC_ROOT}/include)
SET(PSLIB_BRPC_LIB_DIR ${PSLIB_BRPC_ROOT}/lib)
SET(PSLIB_BRPC_LIB ${PSLIB_BRPC_LIB_DIR}/libbrpc.a)
SET(PSLIB_BRPC_IOMP_LIB ${PSLIB_BRPC_LIB_DIR}/libiomp5.so) #todo what is this
SET(CMAKE_INSTALL_RPATH "${CMAKE_INSTALL_RPATH}" "${PSLIB_BRPC_ROOT}/lib")
INCLUDE_DIRECTORIES(${PSLIB_BRPC_INC_DIR})
FILE(WRITE ${PSLIB_BRPC_DOWNLOAD_DIR}/CMakeLists.txt
"PROJECT(PSLIB_BRPC)\n"
"cmake_minimum_required(VERSION 3.0)\n"
"install(DIRECTORY ${PSLIB_BRPC_NAME}/include ${PSLIB_BRPC_NAME}/lib \n"
" DESTINATION ${PSLIB_BRPC_DST_DIR})\n")
ExternalProject_Add(
${PSLIB_BRPC_PROJECT}
${EXTERNAL_PROJECT_LOG_ARGS}
PREFIX ${PSLIB_BRPC_SOURCE_DIR}
DOWNLOAD_DIR ${PSLIB_BRPC_DOWNLOAD_DIR}
DOWNLOAD_COMMAND wget --no-check-certificate ${PSLIB_BRPC_URL} -c -q -O ${PSLIB_BRPC_NAME}.tar.gz
&& tar zxvf ${PSLIB_BRPC_NAME}.tar.gz
DOWNLOAD_NO_PROGRESS 1
UPDATE_COMMAND ""
CMAKE_ARGS -DCMAKE_INSTALL_PREFIX=${PSLIB_BRPC_INSTALL_ROOT}
CMAKE_CACHE_ARGS -DCMAKE_INSTALL_PREFIX:PATH=${PSLIB_BRPC_INSTALL_ROOT}
)
ADD_LIBRARY(pslib_brpc SHARED IMPORTED GLOBAL)
SET_PROPERTY(TARGET pslib_brpc PROPERTY IMPORTED_LOCATION ${PSLIB_BRPC_LIB})
ADD_DEPENDENCIES(pslib_brpc ${PSLIB_BRPC_PROJECT})
LIST(APPEND external_project_dependencies pslib_brpc)
IF(WITH_C_API)
INSTALL(FILES ${PSLIB_BRPC_LIB} ${PSLIB_BRPC_IOMP_LIB} DESTINATION lib)
ENDIF()
cmake/external/python.cmake
浏览文件 @ 41a64f6a
......@@ -18,8 +18,8 @@ ENDIF()
INCLUDE(python_module)
FIND_PACKAGE(PythonInterp ${PY_VERSION})
FIND_PACKAGE(PythonLibs ${PY_VERSION})
FIND_PACKAGE(PythonInterp ${PY_VERSION} REQUIRED)
FIND_PACKAGE(PythonLibs ${PY_VERSION} REQUIRED)
if(WIN32)
execute_process(COMMAND "${PYTHON_EXECUTABLE}" "-c"
......@@ -79,6 +79,5 @@ IF(PYTHONINTERP_FOUND)
"please use pip to upgrade protobuf. pip install -U protobuf")
ENDIF()
ENDIF(PYTHONINTERP_FOUND)
INCLUDE_DIRECTORIES(${PYTHON_INCLUDE_DIR})
INCLUDE_DIRECTORIES(${PYTHON_NUMPY_INCLUDE_DIR})
cmake/external/snappy.cmake
浏览文件 @ 41a64f6a
......@@ -24,12 +24,6 @@ set(SNAPPY_SOURCES_DIR ${THIRD_PARTY_PATH}/snappy)
set(SNAPPY_INSTALL_DIR ${THIRD_PARTY_PATH}/install/snappy)
set(SNAPPY_INCLUDE_DIR "${SNAPPY_INSTALL_DIR}/include" CACHE PATH "snappy include directory." FORCE)
if (WIN32)
set(SNAPPY_LIBRARIES "${SNAPPY_INSTALL_DIR}/lib/snappy.lib")
else(WIN32)
set(SNAPPY_LIBRARIES "${SNAPPY_INSTALL_DIR}/lib/libsnappy.a")
endif (WIN32)
ExternalProject_Add(
extern_snappy
GIT_REPOSITORY "https://github.com/google/snappy"
......@@ -56,6 +50,16 @@ ExternalProject_Add(
-DCMAKE_POSITION_INDEPENDENT_CODE:BOOL=ON
-DCMAKE_BUILD_TYPE:STRING=${THIRD_PARTY_BUILD_TYPE}
)
IF(WIN32)
IF(NOT EXISTS "${SNAPPY_INSTALL_DIR}/lib/libsnappy.lib")
add_custom_command(TARGET extern_snappy POST_BUILD
COMMAND cmake -E copy ${SNAPPY_INSTALL_DIR}/lib/snappy.lib ${SNAPPY_INSTALL_DIR}/lib/libsnappy.lib
)
ENDIF()
set(SNAPPY_LIBRARIES "${SNAPPY_INSTALL_DIR}/lib/libsnappy.lib")
else(WIN32)
set(SNAPPY_LIBRARIES "${SNAPPY_INSTALL_DIR}/lib/libsnappy.a")
endif (WIN32)
add_library(snappy STATIC IMPORTED GLOBAL)
set_property(TARGET snappy PROPERTY IMPORTED_LOCATION ${SNAPPY_LIBRARIES})
......
cmake/external/warpctc.cmake
浏览文件 @ 41a64f6a
......@@ -26,25 +26,33 @@ SET(WARPCTC_INCLUDE_DIR "${WARPCTC_INSTALL_DIR}/include"
# Used in unit test test_WarpCTCLayer
SET(WARPCTC_LIB_DIR "${WARPCTC_INSTALL_DIR}/lib"
CACHE PATH "Warp-ctc Library Directory" FORCE)
SET(WARPCTC_LIBRARIES "${WARPCTC_INSTALL_DIR}/lib/libwarpctc${CMAKE_SHARED_LIBRARY_SUFFIX}"
CACHE FILEPATH "Warp-ctc Library" FORCE)
IF(CMAKE_CXX_COMPILER_ID STREQUAL "Clang" OR CMAKE_CXX_COMPILER_ID STREQUAL "AppleClang" )
IF(CMAKE_CXX_COMPILER_ID STREQUAL "Clang" OR CMAKE_CXX_COMPILER_ID STREQUAL "AppleClang" OR WIN32)
SET(USE_OMP OFF)
ELSE()
SET(USE_OMP ON)
ENDIF()
IF(WIN32)
SET(WARPCTC_REPOSITORY "https://github.com/wopeizl/warp-ctc.git")
ELSE()
SET(WARPCTC_REPOSITORY "https://github.com/dzhwinter/warp-ctc.git")
ENDIF()
ExternalProject_Add(
extern_warpctc
${EXTERNAL_PROJECT_LOG_ARGS}
GIT_REPOSITORY "https://github.com/dzhwinter/warp-ctc.git"
GIT_REPOSITORY ${WARPCTC_REPOSITORY}
PREFIX ${WARPCTC_SOURCES_DIR}
UPDATE_COMMAND ""
CMAKE_ARGS -DCMAKE_CXX_COMPILER=${CMAKE_CXX_COMPILER}
-DCMAKE_C_COMPILER=${CMAKE_C_COMPILER}
-DCMAKE_CXX_FLAGS=${CMAKE_CXX_FLAGS}
-DCMAKE_C_FLAGS=${CMAKE_C_FLAGS}
-DCMAKE_C_FLAGS_DEBUG=${CMAKE_C_FLAGS_DEBUG}
-DCMAKE_C_FLAGS_RELEASE=${CMAKE_C_FLAGS_RELEASE}
-DCMAKE_CXX_FLAGS=${CMAKE_CXX_FLAGS}
-DCMAKE_CXX_FLAGS_RELEASE=${CMAKE_CXX_FLAGS_RELEASE}
-DCMAKE_CXX_FLAGS_DEBUG=${CMAKE_CXX_FLAGS_DEBUG}
-DCMAKE_INSTALL_PREFIX=${WARPCTC_INSTALL_DIR}
-DWITH_GPU=${WITH_GPU}
-DWITH_OMP=${USE_OMP}
......@@ -59,6 +67,18 @@ ExternalProject_Add(
-DCMAKE_POSITION_INDEPENDENT_CODE:BOOL=ON
-DCMAKE_INSTALL_PREFIX:PATH=${WARPCTC_INSTALL_DIR}
)
IF(WIN32)
IF(NOT EXISTS "${WARPCTC_INSTALL_DIR}/lib/warpctc${CMAKE_SHARED_LIBRARY_SUFFIX}")
add_custom_command(TARGET extern_warpctc POST_BUILD
COMMAND cmake -E copy ${WARPCTC_INSTALL_DIR}/bin/warpctc${CMAKE_SHARED_LIBRARY_SUFFIX} ${WARPCTC_INSTALL_DIR}/lib/warpctc${CMAKE_SHARED_LIBRARY_SUFFIX}
)
ENDIF()
SET(WARPCTC_LIBRARIES "${WARPCTC_INSTALL_DIR}/lib/warpctc${CMAKE_SHARED_LIBRARY_SUFFIX}"
CACHE FILEPATH "Warp-ctc Library" FORCE)
else(WIN32)
SET(WARPCTC_LIBRARIES "${WARPCTC_INSTALL_DIR}/lib/libwarpctc${CMAKE_SHARED_LIBRARY_SUFFIX}"
CACHE FILEPATH "Warp-ctc Library" FORCE)
ENDIF(WIN32)
MESSAGE(STATUS "warp-ctc library: ${WARPCTC_LIBRARIES}")
INCLUDE_DIRECTORIES(${WARPCTC_INCLUDE_DIR}) # For warpctc code to include its headers.
......
cmake/external/xxhash.cmake
浏览文件 @ 41a64f6a
......@@ -56,7 +56,12 @@ else()
endif()
if (WIN32)
set(XXHASH_LIBRARIES "${XXHASH_INSTALL_DIR}/lib/xxhash.lib")
IF(NOT EXISTS "${XXHASH_INSTALL_DIR}/lib/libxxhash.lib")
add_custom_command(TARGET extern_xxhash POST_BUILD
COMMAND cmake -E copy ${XXHASH_INSTALL_DIR}/lib/xxhash.lib ${XXHASH_INSTALL_DIR}/lib/libxxhash.lib
)
ENDIF()
set(XXHASH_LIBRARIES "${XXHASH_INSTALL_DIR}/lib/libxxhash.lib")
else()
set(XXHASH_LIBRARIES "${XXHASH_INSTALL_DIR}/lib/libxxhash.a")
endif ()
......
cmake/external/zlib.cmake
浏览文件 @ 41a64f6a
......@@ -19,12 +19,6 @@ SET(ZLIB_INSTALL_DIR ${THIRD_PARTY_PATH}/install/zlib)
SET(ZLIB_ROOT ${ZLIB_INSTALL_DIR} CACHE FILEPATH "zlib root directory." FORCE)
SET(ZLIB_INCLUDE_DIR "${ZLIB_INSTALL_DIR}/include" CACHE PATH "zlib include directory." FORCE)
IF(WIN32)
SET(ZLIB_LIBRARIES "${ZLIB_INSTALL_DIR}/lib/zlibstatic.lib" CACHE FILEPATH "zlib library." FORCE)
ELSE(WIN32)
SET(ZLIB_LIBRARIES "${ZLIB_INSTALL_DIR}/lib/libz.a" CACHE FILEPATH "zlib library." FORCE)
ENDIF(WIN32)
INCLUDE_DIRECTORIES(${ZLIB_INCLUDE_DIR}) # For zlib code to include its own headers.
INCLUDE_DIRECTORIES(${THIRD_PARTY_PATH}/install) # For Paddle code to include zlib.h.
......@@ -49,6 +43,16 @@ ExternalProject_Add(
-DCMAKE_POSITION_INDEPENDENT_CODE:BOOL=ON
-DCMAKE_BUILD_TYPE:STRING=${THIRD_PARTY_BUILD_TYPE}
)
IF(WIN32)
IF(NOT EXISTS "${ZLIB_INSTALL_DIR}/lib/libz.lib")
add_custom_command(TARGET extern_zlib POST_BUILD
COMMAND cmake -E copy ${ZLIB_INSTALL_DIR}/lib/zlibstatic.lib ${ZLIB_INSTALL_DIR}/lib/libz.lib
)
ENDIF()
SET(ZLIB_LIBRARIES "${ZLIB_INSTALL_DIR}/lib/libz.lib" CACHE FILEPATH "zlib library." FORCE)
ELSE(WIN32)
SET(ZLIB_LIBRARIES "${ZLIB_INSTALL_DIR}/lib/libz.a" CACHE FILEPATH "zlib library." FORCE)
ENDIF(WIN32)
ADD_LIBRARY(zlib STATIC IMPORTED GLOBAL)
SET_PROPERTY(TARGET zlib PROPERTY IMPORTED_LOCATION ${ZLIB_LIBRARIES})
......
cmake/generic.cmake
浏览文件 @ 41a64f6a
......@@ -267,7 +267,11 @@ function(cc_library TARGET_NAME)
list(APPEND cc_library_DEPS dynload_mklml)
endif()
add_dependencies(${TARGET_NAME} mklml)
target_link_libraries(${TARGET_NAME} "-L${MKLML_LIB_DIR} -liomp5 -Wl,--as-needed")
if(WIN32)
target_link_libraries(${TARGET_NAME} ${MKLML_IOMP_LIB})
else(WIN32)
target_link_libraries(${TARGET_NAME} "-L${MKLML_LIB_DIR} -liomp5 -Wl,--as-needed")
endif(WIN32)
endif()
# remove link to python, see notes at:
# https://github.com/pybind/pybind11/blob/master/docs/compiling.rst#building-manually
......
cmake/inference_lib.cmake
浏览文件 @ 41a64f6a
......@@ -32,24 +32,35 @@ function(copy TARGET)
list(GET copy_lib_SRCS ${index} src)
list(GET copy_lib_DSTS ${index} dst)
if (WIN32)
# windows cmd shell will not expand wildcard automatically.
# below expand the files,libs and copy them by rules.
file(GLOB header_files ${src} "*.h")
file(GLOB static_lib_files ${src} "*.lib")
file(GLOB dll_lib_files ${src} "*.dll")
set(src_files ${header_files} ${static_lib_files} ${dll_lib_files})
if (NOT "${src_files}" STREQUAL "")
list(REMOVE_DUPLICATES src_files)
endif ()
add_custom_command(TARGET ${TARGET} PRE_BUILD
COMMAND ${CMAKE_COMMAND} -E make_directory "${dst}"
)
foreach (src_file ${src_files})
if(IS_DIRECTORY ${src})
get_filename_component(last_path ${src} NAME)
string(APPEND dst "/" ${last_path})
add_custom_command(TARGET ${TARGET} PRE_BUILD
COMMAND ${CMAKE_COMMAND} -E copy "${src_file}" "${dst}"
COMMENT "copying ${src_file} -> ${dst}")
endforeach ()
COMMAND ${CMAKE_COMMAND} -E make_directory "${dst}"
)
if(EXISTS ${src})
add_custom_command(TARGET ${TARGET} PRE_BUILD
COMMAND cmake -E copy_directory "${src}" "${dst}"
COMMENT "copying ${src} -> ${dst}")
else()
message(WARNING "${src} not exist!")
endif()
else()
# windows cmd shell will not expand wildcard automatically.
# below expand the files, and copy them by rules.
file(GLOB src_files ${src})
if (NOT "${src_files}" STREQUAL "")
list(REMOVE_DUPLICATES src_files)
endif ()
add_custom_command(TARGET ${TARGET} PRE_BUILD
COMMAND ${CMAKE_COMMAND} -E make_directory "${dst}"
)
foreach (src_file ${src_files})
add_custom_command(TARGET ${TARGET} PRE_BUILD
COMMAND ${CMAKE_COMMAND} -E copy "${src_file}" "${dst}"
COMMENT "copying ${src_file} -> ${dst}")
endforeach ()
endif()
else (WIN32) # not windows
add_custom_command(TARGET ${TARGET} PRE_BUILD
COMMAND mkdir -p "${dst}"
......@@ -95,7 +106,7 @@ copy(xxhash_lib
DEPS xxhash
)
if (NOT PROTOBUF_FOUND)
if (NOT PROTOBUF_FOUND OR WIN32)
set(dst_dir "${FLUID_INSTALL_DIR}/third_party/install/protobuf")
copy(protobuf_lib
SRCS ${PROTOBUF_INCLUDE_DIR} ${PROTOBUF_LIBRARY}
......@@ -104,20 +115,20 @@ if (NOT PROTOBUF_FOUND)
)
endif ()
if (NOT CBLAS_FOUND)
set(dst_dir "${FLUID_INSTALL_DIR}/third_party/install/openblas")
copy(openblas_lib
SRCS ${CBLAS_INSTALL_DIR}/lib ${CBLAS_INSTALL_DIR}/include
DSTS ${dst_dir} ${dst_dir}
DEPS extern_openblas
)
elseif (WITH_MKLML)
if (WITH_MKLML)
set(dst_dir "${FLUID_INSTALL_DIR}/third_party/install/mklml")
copy(mklml_lib
SRCS ${MKLML_LIB} ${MKLML_IOMP_LIB} ${MKLML_INC_DIR}
DSTS ${dst_dir}/lib ${dst_dir}/lib ${dst_dir}
DEPS mklml
)
elseif (NOT CBLAS_FOUND OR WIN32)
set(dst_dir "${FLUID_INSTALL_DIR}/third_party/install/openblas")
copy(openblas_lib
SRCS ${CBLAS_INSTALL_DIR}/lib ${CBLAS_INSTALL_DIR}/include
DSTS ${dst_dir} ${dst_dir}
DEPS extern_openblas
)
endif ()
if (WITH_MKLDNN)
......@@ -138,27 +149,25 @@ if (WITH_NGRAPH)
)
endif ()
if (NOT WIN32)
if (NOT MOBILE_INFERENCE AND NOT RPI)
set(dst_dir "${FLUID_INSTALL_DIR}/third_party/install/snappy")
copy(snappy_lib
SRCS ${SNAPPY_INCLUDE_DIR} ${SNAPPY_LIBRARIES}
DSTS ${dst_dir} ${dst_dir}/lib
DEPS snappy)
if (NOT MOBILE_INFERENCE AND NOT RPI)
set(dst_dir "${FLUID_INSTALL_DIR}/third_party/install/snappy")
copy(snappy_lib
SRCS ${SNAPPY_INCLUDE_DIR} ${SNAPPY_LIBRARIES}
DSTS ${dst_dir} ${dst_dir}/lib
DEPS snappy)
set(dst_dir "${FLUID_INSTALL_DIR}/third_party/install/snappystream")
copy(snappystream_lib
SRCS ${SNAPPYSTREAM_INCLUDE_DIR} ${SNAPPYSTREAM_LIBRARIES}
DSTS ${dst_dir} ${dst_dir}/lib
DEPS snappystream)
set(dst_dir "${FLUID_INSTALL_DIR}/third_party/install/snappystream")
copy(snappystream_lib
SRCS ${SNAPPYSTREAM_INCLUDE_DIR} ${SNAPPYSTREAM_LIBRARIES}
DSTS ${dst_dir} ${dst_dir}/lib
DEPS snappystream)
set(dst_dir "${FLUID_INSTALL_DIR}/third_party/install/zlib")
copy(zlib_lib
SRCS ${ZLIB_INCLUDE_DIR} ${ZLIB_LIBRARIES}
DSTS ${dst_dir} ${dst_dir}/lib
DEPS zlib)
endif ()
endif (NOT WIN32)
set(dst_dir "${FLUID_INSTALL_DIR}/third_party/install/zlib")
copy(zlib_lib
SRCS ${ZLIB_INCLUDE_DIR} ${ZLIB_LIBRARIES}
DSTS ${dst_dir} ${dst_dir}/lib
DEPS zlib)
endif ()
# paddle fluid module
set(src_dir "${PADDLE_SOURCE_DIR}/paddle/fluid")
......@@ -191,9 +200,21 @@ if (WITH_ANAKIN AND WITH_MKL)
list(APPEND inference_deps anakin_inference_lib)
endif ()
if (TENSORRT_FOUND)
copy(tensorrt_lib DEPS ${inference_deps}
SRCS ${TENSORRT_ROOT}/include/Nv*.h ${TENSORRT_ROOT}/lib/libnvinfer*
DSTS ${FLUID_INSTALL_DIR}/third_party/install/tensorrt/include ${FLUID_INSTALL_DIR}/third_party/install/tensorrt/lib)
endif ()
set(module "inference")
if(WIN32)
set(paddle_fluid_lib ${PADDLE_BINARY_DIR}/paddle/fluid/inference/${CMAKE_BUILD_TYPE}/libpaddle_fluid.*)
else(WIN32)
set(paddle_fluid_lib ${PADDLE_BINARY_DIR}/paddle/fluid/inference/libpaddle_fluid.*)
endif(WIN32)
copy(inference_lib DEPS ${inference_deps}
SRCS ${src_dir}/${module}/*.h ${PADDLE_BINARY_DIR}/paddle/fluid/inference/libpaddle_fluid.*
SRCS ${src_dir}/${module}/*.h ${paddle_fluid_lib}
${src_dir}/${module}/api/paddle_*.h
DSTS ${dst_dir}/${module} ${dst_dir}/${module} ${dst_dir}/${module}
)
......@@ -233,7 +254,7 @@ copy(third_party DEPS fluid_lib_dist
# only need libpaddle_fluid.so/a and paddle_*.h for inference-only library
copy(inference_api_lib DEPS fluid_lib_dist
SRCS ${FLUID_INSTALL_DIR}/paddle/fluid/inference/libpaddle_fluid.*
SRCS ${paddle_fluid_lib}
${FLUID_INSTALL_DIR}/paddle/fluid/inference/paddle_*.h
DSTS ${FLUID_INFERENCE_INSTALL_DIR}/paddle/lib ${FLUID_INFERENCE_INSTALL_DIR}/paddle/include
)
......
cmake/operators.cmake
浏览文件 @ 41a64f6a
......@@ -84,7 +84,7 @@ function(op_library TARGET)
endif()
if (WIN32)
# remove windows unsupported op, because windows has no nccl, no warpctc such ops.
foreach(windows_unsupport_op "nccl_op" "gen_nccl_id_op" "warpctc_op")
foreach(windows_unsupport_op "nccl_op" "gen_nccl_id_op")
if ("${TARGET}" STREQUAL "${windows_unsupport_op}")
return()
endif()
......
paddle/fluid/API.spec
浏览文件 @ 41a64f6a
......@@ -37,8 +37,16 @@ paddle.fluid.DataFeedDesc.desc ArgSpec(args=['self'], varargs=None, keywords=Non
paddle.fluid.DataFeedDesc.set_batch_size ArgSpec(args=['self', 'batch_size'], varargs=None, keywords=None, defaults=None)
paddle.fluid.DataFeedDesc.set_dense_slots ArgSpec(args=['self', 'dense_slots_name'], varargs=None, keywords=None, defaults=None)
paddle.fluid.DataFeedDesc.set_use_slots ArgSpec(args=['self', 'use_slots_name'], varargs=None, keywords=None, defaults=None)
paddle.fluid.AsyncExecutor.__init__ ArgSpec(args=['self', 'place'], varargs=None, keywords=None, defaults=(None,))
paddle.fluid.AsyncExecutor.run ArgSpec(args=['self', 'program', 'data_feed', 'filelist', 'thread_num', 'fetch', 'debug'], varargs=None, keywords=None, defaults=(False,))
paddle.fluid.AsyncExecutor.__init__ ArgSpec(args=['self', 'place', 'run_mode'], varargs=None, keywords=None, defaults=(None, ''))
paddle.fluid.AsyncExecutor.config_distributed_nodes ArgSpec(args=['self'], varargs=None, keywords=None, defaults=None)
paddle.fluid.AsyncExecutor.download_data ArgSpec(args=['self', 'afs_path', 'local_path', 'fs_default_name', 'ugi', 'file_cnt', 'hadoop_home', 'process_num'], varargs=None, keywords=None, defaults=('$HADOOP_HOME', 12))
paddle.fluid.AsyncExecutor.get_instance ArgSpec(args=['self'], varargs=None, keywords=None, defaults=None)
paddle.fluid.AsyncExecutor.init_model ArgSpec(args=['self'], varargs=None, keywords=None, defaults=None)
paddle.fluid.AsyncExecutor.init_server ArgSpec(args=['self', 'dist_desc'], varargs=None, keywords=None, defaults=None)
paddle.fluid.AsyncExecutor.init_worker ArgSpec(args=['self', 'dist_desc', 'startup_program'], varargs=None, keywords=None, defaults=None)
paddle.fluid.AsyncExecutor.run ArgSpec(args=['self', 'program', 'data_feed', 'filelist', 'thread_num', 'fetch', 'mode', 'debug'], varargs=None, keywords=None, defaults=('', False))
paddle.fluid.AsyncExecutor.save_model ArgSpec(args=['self', 'save_path'], varargs=None, keywords=None, defaults=None)
paddle.fluid.AsyncExecutor.stop ArgSpec(args=['self'], varargs=None, keywords=None, defaults=None)
paddle.fluid.io.save_vars ArgSpec(args=['executor', 'dirname', 'main_program', 'vars', 'predicate', 'filename'], varargs=None, keywords=None, defaults=(None, None, None, None))
paddle.fluid.io.save_params ArgSpec(args=['executor', 'dirname', 'main_program', 'filename'], varargs=None, keywords=None, defaults=(None, None))
paddle.fluid.io.save_persistables ArgSpec(args=['executor', 'dirname', 'main_program', 'filename'], varargs=None, keywords=None, defaults=(None, None))
......@@ -200,7 +208,9 @@ paddle.fluid.layers.bilinear_tensor_product ArgSpec(args=['x', 'y', 'size', 'act
paddle.fluid.layers.merge_selected_rows ArgSpec(args=['x', 'name'], varargs=None, keywords=None, defaults=(None,))
paddle.fluid.layers.get_tensor_from_selected_rows ArgSpec(args=['x', 'name'], varargs=None, keywords=None, defaults=(None,))
paddle.fluid.layers.lstm ArgSpec(args=['input', 'init_h', 'init_c', 'max_len', 'hidden_size', 'num_layers', 'dropout_prob', 'is_bidirec', 'is_test', 'name', 'default_initializer', 'seed'], varargs=None, keywords=None, defaults=(0.0, False, False, None, None, -1))
paddle.fluid.layers.py_func ArgSpec(args=['func', 'x', 'out', 'backward_func', 'skip_vars_in_backward_input'], varargs=None, keywords=None, defaults=(None, None))
paddle.fluid.layers.psroi_pool ArgSpec(args=['input', 'rois', 'output_channels', 'spatial_scale', 'pooled_height', 'pooled_width', 'name'], varargs=None, keywords=None, defaults=(None,))
paddle.fluid.layers.huber_loss ArgSpec(args=['input', 'label', 'delta'], varargs=None, keywords=None, defaults=None)
paddle.fluid.layers.data ArgSpec(args=['name', 'shape', 'append_batch_size', 'dtype', 'lod_level', 'type', 'stop_gradient'], varargs=None, keywords=None, defaults=(True, 'float32', 0, VarType.LOD_TENSOR, True))
paddle.fluid.layers.open_files ArgSpec(args=['filenames', 'shapes', 'lod_levels', 'dtypes', 'thread_num', 'buffer_size', 'pass_num', 'is_test'], varargs=None, keywords=None, defaults=(None, None, 1, None))
paddle.fluid.layers.read_file ArgSpec(args=['reader'], varargs=None, keywords=None, defaults=None)
......@@ -341,6 +351,39 @@ paddle.fluid.contrib.QuantizeTranspiler.__init__ ArgSpec(args=['self', 'weight_b
paddle.fluid.contrib.QuantizeTranspiler.convert_to_int8 ArgSpec(args=['self', 'program', 'place', 'scope'], varargs=None, keywords=None, defaults=(None,))
paddle.fluid.contrib.QuantizeTranspiler.freeze_program ArgSpec(args=['self', 'program', 'place', 'fuse_bn', 'scope'], varargs=None, keywords=None, defaults=(False, None))
paddle.fluid.contrib.QuantizeTranspiler.training_transpile ArgSpec(args=['self', 'program', 'startup_program'], varargs=None, keywords=None, defaults=(None, None))
paddle.fluid.contrib.build_compressor ArgSpec(args=['place', 'data_reader', 'data_feeder', 'scope', 'metrics', 'epoch', 'config'], varargs=None, keywords=None, defaults=(None, None, None, None, None, None, None))
paddle.fluid.contrib.CompressPass.__init__ ArgSpec(args=['self', 'place', 'data_reader', 'data_feeder', 'scope', 'metrics', 'epoch', 'program_exe'], varargs=None, keywords=None, defaults=(None, None, None, None, None, None, None))
paddle.fluid.contrib.CompressPass.add_strategy ArgSpec(args=['self', 'strategy'], varargs=None, keywords=None, defaults=None)
paddle.fluid.contrib.CompressPass.apply ArgSpec(args=['self', 'graph'], varargs=None, keywords=None, defaults=None)
paddle.fluid.contrib.ImitationGraph.__init__ ArgSpec(args=['self', 'program'], varargs=None, keywords=None, defaults=(None,))
paddle.fluid.contrib.ImitationGraph.all_parameters ArgSpec(args=['self'], varargs=None, keywords=None, defaults=None)
paddle.fluid.contrib.SensitivePruneStrategy.__init__ ArgSpec(args=['self', 'pruner', 'start_epoch', 'end_epoch', 'delta_rate', 'acc_loss_threshold', 'sensitivities'], varargs=None, keywords=None, defaults=(None, 0, 10, 0.2, 0.2, None))
paddle.fluid.contrib.SensitivePruneStrategy.on_batch_begin ArgSpec(args=['self', 'context'], varargs=None, keywords=None, defaults=None)
paddle.fluid.contrib.SensitivePruneStrategy.on_batch_end ArgSpec(args=['self', 'context'], varargs=None, keywords=None, defaults=None)
paddle.fluid.contrib.SensitivePruneStrategy.on_compress_begin ArgSpec(args=['self', 'context'], varargs=None, keywords=None, defaults=None)
paddle.fluid.contrib.SensitivePruneStrategy.on_compress_end ArgSpec(args=['self', 'context'], varargs=None, keywords=None, defaults=None)
paddle.fluid.contrib.SensitivePruneStrategy.on_epoch_begin ArgSpec(args=['self', 'context'], varargs=None, keywords=None, defaults=None)
paddle.fluid.contrib.SensitivePruneStrategy.on_epoch_end ArgSpec(args=['self', 'context'], varargs=None, keywords=None, defaults=None)
paddle.fluid.contrib.MagnitudePruner.__init__ ArgSpec(args=['self', 'threshold'], varargs=None, keywords=None, defaults=None)
paddle.fluid.contrib.MagnitudePruner.prune ArgSpec(args=['self', 'param', 'threshold'], varargs=None, keywords=None, defaults=(None,))
paddle.fluid.contrib.RatioPruner.__init__ ArgSpec(args=['self', 'ratios'], varargs=None, keywords=None, defaults=(None,))
paddle.fluid.contrib.RatioPruner.prune ArgSpec(args=['self', 'param', 'ratio'], varargs=None, keywords=None, defaults=(None,))
paddle.fluid.contrib.load_persistables_for_increment ArgSpec(args=['dirname', 'executor', 'program', 'lookup_table_var', 'lookup_table_var_path'], varargs=None, keywords=None, defaults=None)
paddle.fluid.contrib.load_persistables_for_inference ArgSpec(args=['dirname', 'executor', 'program', 'lookup_table_var_name'], varargs=None, keywords=None, defaults=None)
paddle.fluid.contrib.convert_dist_to_sparse_program ArgSpec(args=['program'], varargs=None, keywords=None, defaults=None)
paddle.fluid.contrib.HDFSClient.__init__ ArgSpec(args=['self', 'hadoop_home', 'configs'], varargs=None, keywords=None, defaults=None)
paddle.fluid.contrib.HDFSClient.delete ArgSpec(args=['self', 'hdfs_path'], varargs=None, keywords=None, defaults=None)
paddle.fluid.contrib.HDFSClient.download ArgSpec(args=['self', 'hdfs_path', 'local_path', 'overwrite', 'unzip'], varargs=None, keywords=None, defaults=(False, False))
paddle.fluid.contrib.HDFSClient.is_dir ArgSpec(args=['self', 'hdfs_path'], varargs=None, keywords=None, defaults=(None,))
paddle.fluid.contrib.HDFSClient.is_exist ArgSpec(args=['self', 'hdfs_path'], varargs=None, keywords=None, defaults=(None,))
paddle.fluid.contrib.HDFSClient.ls ArgSpec(args=['self', 'hdfs_path'], varargs=None, keywords=None, defaults=None)
paddle.fluid.contrib.HDFSClient.lsr ArgSpec(args=['self', 'hdfs_path', 'only_file', 'sort'], varargs=None, keywords=None, defaults=(True, True))
paddle.fluid.contrib.HDFSClient.make_local_dirs ArgSpec(args=['local_path'], varargs=None, keywords=None, defaults=None)
paddle.fluid.contrib.HDFSClient.makedirs ArgSpec(args=['self', 'hdfs_path'], varargs=None, keywords=None, defaults=None)
paddle.fluid.contrib.HDFSClient.rename ArgSpec(args=['self', 'hdfs_src_path', 'hdfs_dst_path', 'overwrite'], varargs=None, keywords=None, defaults=(False,))
paddle.fluid.contrib.HDFSClient.upload ArgSpec(args=['self', 'hdfs_path', 'local_path', 'overwrite', 'retry_times'], varargs=None, keywords=None, defaults=(False, 5))
paddle.fluid.contrib.multi_download ArgSpec(args=['client', 'hdfs_path', 'local_path', 'trainer_id', 'trainers', 'multi_processes'], varargs=None, keywords=None, defaults=(5,))
paddle.fluid.contrib.multi_upload ArgSpec(args=['client', 'hdfs_path', 'local_path', 'multi_processes', 'overwrite', 'sync'], varargs=None, keywords=None, defaults=(5, False, True))
paddle.fluid.transpiler.DistributeTranspiler.__init__ ArgSpec(args=['self', 'config'], 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_pserver_programs ArgSpec(args=['self', 'endpoint'], varargs=None, keywords=None, defaults=None)
......@@ -367,7 +410,7 @@ paddle.fluid.optimizer.MomentumOptimizer.__init__ ArgSpec(args=['self', 'learnin
paddle.fluid.optimizer.MomentumOptimizer.minimize ArgSpec(args=['self', 'loss', 'startup_program', 'parameter_list', 'no_grad_set'], varargs=None, keywords=None, defaults=(None, None, None))
paddle.fluid.optimizer.AdagradOptimizer.__init__ ArgSpec(args=['self', 'learning_rate', 'epsilon', 'regularization', 'name'], varargs=None, keywords=None, defaults=(1e-06, None, None))
paddle.fluid.optimizer.AdagradOptimizer.minimize ArgSpec(args=['self', 'loss', 'startup_program', 'parameter_list', 'no_grad_set'], varargs=None, keywords=None, defaults=(None, None, None))
paddle.fluid.optimizer.AdamOptimizer.__init__ ArgSpec(args=['self', 'learning_rate', 'beta1', 'beta2', 'epsilon', 'regularization', 'name'], varargs=None, keywords=None, defaults=(0.001, 0.9, 0.999, 1e-08, None, None))
paddle.fluid.optimizer.AdamOptimizer.__init__ ArgSpec(args=['self', 'learning_rate', 'beta1', 'beta2', 'epsilon', 'regularization', 'name', 'lazy_mode'], varargs=None, keywords=None, defaults=(0.001, 0.9, 0.999, 1e-08, None, None, False))
paddle.fluid.optimizer.AdamOptimizer.minimize ArgSpec(args=['self', 'loss', 'startup_program', 'parameter_list', 'no_grad_set'], varargs=None, keywords=None, defaults=(None, None, None))
paddle.fluid.optimizer.AdamaxOptimizer.__init__ ArgSpec(args=['self', 'learning_rate', 'beta1', 'beta2', 'epsilon', 'regularization', 'name'], varargs=None, keywords=None, defaults=(0.001, 0.9, 0.999, 1e-08, None, None))
paddle.fluid.optimizer.AdamaxOptimizer.minimize ArgSpec(args=['self', 'loss', 'startup_program', 'parameter_list', 'no_grad_set'], varargs=None, keywords=None, defaults=(None, None, None))
......
paddle/fluid/framework/CMakeLists.txt
浏览文件 @ 41a64f6a
# windows treat symbolic file as a real file, which is different with unix
# We create a hidden file and compile it instead of origin source file.
#windows treat symbolic file as a real file, which is different with unix
#We create a hidden file and compile it instead of origin source file.
function(windows_symbolic TARGET)
set(oneValueArgs "")
set(multiValueArgs SRCS DEPS)
set(multiValueArgs SRCS PATH)
cmake_parse_arguments(windows_symbolic "${options}" "${oneValueArgs}" "${multiValueArgs}" ${ARGN})
set(final_path ${CMAKE_CURRENT_SOURCE_DIR}/${windows_symbolic_PATH})
foreach(src ${windows_symbolic_SRCS})
get_filename_component(src ${src} NAME_WE)
if (NOT EXISTS ${CMAKE_CURRENT_SOURCE_DIR}/${src}.cc OR NOT EXISTS ${CMAKE_CURRENT_SOURCE_DIR}/${src}.cu)
message(FATAL " ${src}.cc and ${src}.cu must exsits, and ${src}.cu must be symbolic file.")
endif()
# only copy the xx.cu to .xx.cu when the content are modified
#only copy the xx.cu to.xx.cu when the content are modified
set(copy_flag 1)
if (EXISTS ${CMAKE_CURRENT_SOURCE_DIR}/.${src}.cu)
file(READ ${CMAKE_CURRENT_SOURCE_DIR}/${src}.cc SOURCE_STR)
......@@ -32,7 +33,7 @@ endfunction()
add_subdirectory(ir)
add_subdirectory(details)
# ddim lib
#ddim lib
proto_library(framework_proto SRCS framework.proto)
proto_library(async_executor_proto SRCS data_feed.proto)
......@@ -91,8 +92,8 @@ nv_test(data_device_transform_test SRCS data_device_transform_test.cu
if(WITH_GPU)
if (WIN32)
# windows treat symbolic file as a real file, which is different with unix
# We create a hidden file and compile it instead of origin source file.
#windows treat symbolic file as a real file, which is different with unix
#We create a hidden file and compile it instead of origin source file.
windows_symbolic(hidden_file SRCS data_type_transform.cu)
nv_library(data_type_transform SRCS .data_type_transform.cu DEPS tensor)
add_dependencies(data_type_transform hidden_file)
......@@ -143,7 +144,8 @@ cc_library(op_registry SRCS op_registry.cc DEPS op_proto_maker op_info operator
nv_test(op_registry_test SRCS op_registry_test.cc DEPS op_registry)
py_proto_compile(framework_py_proto SRCS framework.proto data_feed.proto)
# Generate an empty __init__.py to make framework_py_proto as a valid python module.
#Generate an empty \
#__init__.py to make framework_py_proto as a valid python module.
add_custom_target(framework_py_proto_init ALL COMMAND ${CMAKE_COMMAND} -E touch __init__.py)
add_dependencies(framework_py_proto framework_py_proto_init)
if (NOT WIN32)
......@@ -195,7 +197,12 @@ cc_library(parallel_executor SRCS parallel_executor.cc DEPS
graph build_strategy
fast_threaded_ssa_graph_executor variable_helper)
cc_library(async_executor SRCS async_executor.cc data_feed.cc data_feed_factory.cc executor_thread_worker.cc DEPS op_registry device_context scope framework_proto glog lod_rank_table feed_fetch_method graph_to_program_pass async_executor_proto variable_helper)
if(WITH_PSLIB)
cc_library(async_executor SRCS async_executor.cc data_feed.cc data_feed_factory.cc executor_thread_worker.cc DEPS op_registry device_context scope framework_proto glog lod_rank_table feed_fetch_method graph_to_program_pass async_executor_proto variable_helper pslib_brpc pslib)
else()
cc_library(async_executor SRCS async_executor.cc data_feed.cc data_feed_factory.cc executor_thread_worker.cc DEPS op_registry device_context scope framework_proto glog lod_rank_table feed_fetch_method graph_to_program_pass async_executor_proto variable_helper)
endif(WITH_PSLIB)
cc_test(data_feed_test SRCS data_feed_test.cc DEPS async_executor)
cc_library(prune SRCS prune.cc DEPS framework_proto)
......
paddle/fluid/framework/async_executor.cc
浏览文件 @ 41a64f6a
......@@ -29,6 +29,9 @@ limitations under the License. */
#include "paddle/fluid/inference/io.h"
#include "paddle/fluid/platform/place.h"
#include "paddle/fluid/pybind/pybind.h"
#ifdef PADDLE_WITH_PSLIB
#include <pslib.h>
#endif
namespace paddle {
namespace framework {
......@@ -47,6 +50,11 @@ void AsyncExecutor::CreateThreads(
worker->SetDataFeed(reader);
worker->SetFetchVarNames(fetch_var_names);
worker->BindingDataFeedMemory();
#ifdef PADDLE_WITH_PSLIB
worker->SetPSlibPtr(_pslib_ptr);
worker->SetPullDenseThread(_pull_dense_thread);
worker->SetParamConfig(&_param_config);
#endif
}
void PrepareReaders(std::vector<std::shared_ptr<DataFeed>>& readers, // NOLINT
......@@ -60,12 +68,177 @@ void PrepareReaders(std::vector<std::shared_ptr<DataFeed>>& readers, // NOLINT
readers[0]->SetFileList(filelist);
}
#ifdef PADDLE_WITH_PSLIB
void AsyncExecutor::InitServer(const std::string& dist_desc, int index) {
_pslib_ptr = std::shared_ptr<paddle::distributed::PSlib>(
new paddle::distributed::PSlib());
_pslib_ptr->init_server(dist_desc, index);
InitParamConfig();
}
void AsyncExecutor::InitWorker(const std::string& dist_desc,
const std::vector<uint64_t>& host_sign_list,
int node_num, int index) {
_pslib_ptr = std::shared_ptr<paddle::distributed::PSlib>(
new paddle::distributed::PSlib());
_pslib_ptr->init_worker(
dist_desc, const_cast<uint64_t*>(host_sign_list.data()), node_num, index);
InitParamConfig();
}
uint64_t AsyncExecutor::StartServer() { return _pslib_ptr->run_server(); }
void AsyncExecutor::StopServer() { _pslib_ptr->stop_server(); }
void AsyncExecutor::GatherServers(const std::vector<uint64_t>& host_sign_list,
int node_num) {
_pslib_ptr->gather_servers(const_cast<uint64_t*>(host_sign_list.data()),
node_num);
}
void AsyncExecutor::InitParamConfig() {
for (int i = 0; i < _pslib_ptr->get_param()
->server_param()
.downpour_server_param()
.downpour_table_param_size();
++i) {
if (_pslib_ptr->get_param()
->server_param()
.downpour_server_param()
.downpour_table_param(i)
.table_class()
.find("SparseTable") != -1) {
_param_config.fea_dim = _pslib_ptr->get_param()
->server_param()
.downpour_server_param()
.downpour_table_param(i)
.accessor()
.fea_dim();
break;
}
}
_param_config.slot_dim = _param_config.fea_dim - 2;
_param_config.tmp_push_dense_wait_times = static_cast<int32_t>(
_pslib_ptr->get_param()->trainer_param().push_dense_per_batch());
_param_config.tmp_push_sparse_wait_times = static_cast<int32_t>(
_pslib_ptr->get_param()->trainer_param().push_sparse_per_batch());
for (auto t = 0u; t < _pslib_ptr->get_param()->trainer_param().skip_op_size();
++t) {
_param_config.skip_op.push_back(
_pslib_ptr->get_param()->trainer_param().skip_op(t));
}
for (auto t = 0u;
t < _pslib_ptr->get_param()->trainer_param().sparse_table_size(); ++t) {
auto& table = _pslib_ptr->get_param()->trainer_param().sparse_table(t);
std::vector<std::string> tmp_sparse_variable_name;
for (int i = 0u; i < table.slot_value_size(); ++i) {
tmp_sparse_variable_name.push_back(table.slot_value(i));
_param_config.slot_alias_to_table[table.slot_key(i)] = table.table_id();
}
std::vector<std::string> tmp_sparse_gradient_variable_name;
for (auto i = 0u; i < table.slot_gradient_size(); ++i) {
tmp_sparse_gradient_variable_name.push_back(table.slot_gradient(i));
}
_param_config.slot_input_vec[table.table_id()] =
std::move(tmp_sparse_variable_name);
_param_config.gradient_var[table.table_id()] =
std::move(tmp_sparse_gradient_variable_name);
_param_config.sparse_table_id.push_back(table.table_id());
}
for (auto t = 0u;
t < _pslib_ptr->get_param()->trainer_param().dense_table_size(); ++t) {
auto& table = _pslib_ptr->get_param()->trainer_param().dense_table(t);
std::vector<std::string> tmp_dense_variable_name;
for (int i = 0u; i < table.dense_variable_name_size(); ++i) {
tmp_dense_variable_name.push_back(table.dense_variable_name(i));
}
std::vector<std::string> tmp_dense_gradient_variable_name;
for (auto i = 0u; i < table.dense_gradient_variable_name_size(); ++i) {
tmp_dense_gradient_variable_name.push_back(
table.dense_gradient_variable_name(i));
}
_param_config.dense_variable_name[table.table_id()] =
std::move(tmp_dense_variable_name);
_param_config.dense_gradient_variable_name[table.table_id()] =
std::move(tmp_dense_gradient_variable_name);
_param_config.dense_table_id.push_back(table.table_id());
_param_config.dense_table_size.push_back(table.fea_dim());
}
}
void AsyncExecutor::InitModel() {
for (auto table_id : _param_config.dense_table_id) {
std::vector<paddle::ps::Region> regions;
for (auto& t : _param_config.dense_variable_name[table_id]) {
Variable* var = root_scope_->FindVar(t);
CHECK(var != nullptr) << "var[" << t << "] not found";
LoDTensor* tensor = var->GetMutable<LoDTensor>();
float* g = tensor->data<float>();
CHECK(g != nullptr) << "var[" << t << "] value not initialized";
float init_range = 0.2;
int rown = tensor->dims()[0];
init_range /= sqrt(rown);
std::normal_distribution<float> ndistr(0.0, 1.0);
for (auto i = 0u; i < tensor->numel(); ++i) {
g[i] = ndistr(local_random_engine()) * init_range;
}
paddle::ps::Region reg(g, tensor->numel());
regions.emplace_back(std::move(reg));
}
auto push_status = _pslib_ptr->_worker_ptr->push_dense_param(
regions.data(), regions.size(), table_id);
push_status.wait();
auto status = push_status.get();
if (status != 0) {
LOG(FATAL) << "push dense param failed, status[" << status << "]";
exit(-1);
}
}
}
void AsyncExecutor::SaveModel(const std::string& path) {
auto ret = _pslib_ptr->_worker_ptr->flush();
ret.wait();
ret = _pslib_ptr->_worker_ptr->save(path, 0);
ret.wait();
int32_t feasign_cnt = ret.get();
if (feasign_cnt == -1) { // (colourful-tree) TODO should be feasign_cnt < 0
LOG(FATAL) << "save model failed";
exit(-1);
}
}
void AsyncExecutor::PrepareDenseThread(const std::string& mode) {
if (mode == "mpi") {
DensePullThreadParam param;
param.ps_client = _pslib_ptr->_worker_ptr;
param.threshold = 1;
param.training_thread_num = actual_thread_num;
param.root_scope = root_scope_;
param.dense_params = &_param_config.dense_variable_name;
_pull_dense_thread =
std::shared_ptr<DensePullThread>(new DensePullThread(param));
_pull_dense_thread->start();
}
}
#endif
void AsyncExecutor::RunFromFile(const ProgramDesc& main_program,
const std::string& data_feed_desc_str,
const std::vector<std::string>& filelist,
const int thread_num,
const std::vector<std::string>& fetch_var_names,
const bool debug) {
const std::string& mode, const bool debug) {
std::vector<std::thread> threads;
auto& block = main_program.Block(0);
......@@ -82,7 +255,7 @@ void AsyncExecutor::RunFromFile(const ProgramDesc& main_program,
google::protobuf::TextFormat::ParseFromString(data_feed_desc_str,
&data_feed_desc);
int actual_thread_num = thread_num;
actual_thread_num = thread_num;
int file_cnt = filelist.size();
PADDLE_ENFORCE(file_cnt > 0, "File list cannot be empty");
......@@ -106,11 +279,21 @@ void AsyncExecutor::RunFromFile(const ProgramDesc& main_program,
// todo: should be factory method for creating datafeed
std::vector<std::shared_ptr<DataFeed>> readers;
PrepareReaders(readers, actual_thread_num, data_feed_desc, filelist);
#ifdef PADDLE_WITH_PSLIB
PrepareDenseThread(mode);
#endif
std::vector<std::shared_ptr<ExecutorThreadWorker>> workers;
workers.resize(actual_thread_num);
for (auto& worker : workers) {
#ifdef PADDLE_WITH_PSLIB
if (mode == "mpi") {
worker.reset(new AsyncExecutorThreadWorker);
} else {
worker.reset(new ExecutorThreadWorker);
}
#else
worker.reset(new ExecutorThreadWorker);
#endif
}
// prepare thread resource here
......@@ -128,7 +311,11 @@ void AsyncExecutor::RunFromFile(const ProgramDesc& main_program,
for (auto& th : threads) {
th.join();
}
#ifdef PADDLE_WITH_PSLIB
if (mode == "mpi") {
_pull_dense_thread->stop();
}
#endif
root_scope_->DropKids();
return;
......
paddle/fluid/framework/async_executor.h
浏览文件 @ 41a64f6a
......@@ -14,9 +14,11 @@ limitations under the License. */
#pragma once
#include <time.h>
#include <map>
#include <memory>
#include <mutex> // NOLINT
#include <mutex> // NOLINT
#include <random> // local_random_engine
#include <set>
#include <string>
#include <thread> // NOLINT
......@@ -30,6 +32,31 @@ limitations under the License. */
namespace paddle {
namespace framework {
inline double current_realtime() {
#if !defined(_WIN32)
struct timespec tp;
clock_gettime(CLOCK_REALTIME, &tp);
return tp.tv_sec + tp.tv_nsec * 1e-9;
#else
return 0.0;
#endif
}
inline std::default_random_engine& local_random_engine() {
struct engine_wrapper_t {
std::default_random_engine engine;
engine_wrapper_t() {
static std::atomic<uint64_t> x(0);
std::seed_seq sseq = {x++, x++, x++,
static_cast<uint64_t>(current_realtime() * 1000)};
engine.seed(sseq);
}
};
thread_local engine_wrapper_t r;
return r.engine;
}
class AsyncExecutor {
public:
AsyncExecutor(Scope* scope, const platform::Place& place);
......@@ -39,7 +66,19 @@ class AsyncExecutor {
const std::vector<std::string>& filelist,
const int thread_num,
const std::vector<std::string>& fetch_names,
const bool debug = false);
const std::string& mode, const bool debug = false);
#ifdef PADDLE_WITH_PSLIB
void InitServer(const std::string& dist_desc, int index);
void InitWorker(const std::string& dist_desc,
const std::vector<uint64_t>& host_sign_list, int node_num,
int index);
uint64_t StartServer();
void StopServer();
void GatherServers(const std::vector<uint64_t>& host_sign_list, int node_num);
void InitModel();
void SaveModel(const std::string& path);
void InitParamConfig();
#endif
private:
void CreateThreads(ExecutorThreadWorker* worker,
......@@ -48,10 +87,21 @@ class AsyncExecutor {
const std::vector<std::string>& fetch_var_names,
Scope* root_scope, const int thread_index,
const bool debug);
#ifdef PADDLE_WITH_PSLIB
void PrepareDenseThread(const std::string& mode);
#endif
public:
#ifdef PADDLE_WITH_PSLIB
std::shared_ptr<paddle::distributed::PSlib> _pslib_ptr;
std::shared_ptr<DensePullThread> _pull_dense_thread;
AsyncWorkerParamConfig _param_config;
#endif
Scope* root_scope_;
platform::Place place_;
private:
int actual_thread_num;
};
} // namespace framework
......
paddle/fluid/framework/data_feed.cc
浏览文件 @ 41a64f6a
......@@ -64,6 +64,7 @@ bool DataFeed::PickOneFile(std::string* filename) {
return false;
}
*filename = filelist_[file_idx_++];
LOG(ERROR) << "pick file:" << *filename;
return true;
}
......
paddle/fluid/framework/data_layout_transform.cc
浏览文件 @ 41a64f6a
......@@ -85,7 +85,7 @@ void TransDataLayout(const OpKernelType& kernel_type_for_var,
out->mutable_data(expected_kernel_type.place_, in.type());
framework::VisitDataType(
framework::ToDataType(in.type()),
in.type(),
CastDataLayout(pool.Get(expected_kernel_type.place_), axis, in, out));
out->set_layout(expected_kernel_type.data_layout_);
......@@ -101,7 +101,7 @@ void* GetDataFromTensor(const Tensor& tensor, mkldnn::memory::data_type type) {
case mkldnn::memory::data_type::f32:
return platform::to_void_cast(tensor.data<float>());
case mkldnn::memory::data_type::s8:
return platform::to_void_cast(tensor.data<char>());
return platform::to_void_cast(tensor.data<int8_t>());
case mkldnn::memory::data_type::u8:
return platform::to_void_cast(tensor.data<unsigned char>());
case mkldnn::memory::data_type::s16:
......@@ -144,7 +144,7 @@ void TransDataLayoutFromMKLDNN(const OpKernelType& kernel_type_for_var,
memory::data_type in_type = ToMKLDNNDataType(in.type());
PADDLE_ENFORCE(in_type != memory::data_type::data_undef,
"Input tensor type is not supported: ", in.type().name());
"Input tensor type is not supported: %s", in.type());
memory::data_type out_type = in_type;
auto in_format = platform::MKLDNNFormatForSize(in_tz.size(), in.format());
......
paddle/fluid/framework/data_layout_transform.h
浏览文件 @ 41a64f6a
......@@ -50,14 +50,14 @@ inline DataLayout ToPaddleLayout(const MKLDNNFormat& format) {
}
}
inline MKLDNNDataType ToMKLDNNDataType(const std::type_index type) {
static const std::map<std::type_index, MKLDNNDataType> dict{
{std::type_index(typeid(float)), MKLDNNDataType::f32}, // NOLINT
{std::type_index(typeid(char)), MKLDNNDataType::s8}, // NOLINT
{std::type_index(typeid(unsigned char)), MKLDNNDataType::u8},
{std::type_index(typeid(int16_t)), MKLDNNDataType::s16},
{std::type_index(typeid(int32_t)), MKLDNNDataType::s32}};
auto iter = dict.find(type);
inline MKLDNNDataType ToMKLDNNDataType(proto::VarType::Type type) {
static std::unordered_map<int, MKLDNNDataType> dict{
{DataTypeTrait<float>::DataType, MKLDNNDataType::f32},
{DataTypeTrait<int8_t>::DataType, MKLDNNDataType::s8},
{DataTypeTrait<uint8_t>::DataType, MKLDNNDataType::u8},
{DataTypeTrait<int16_t>::DataType, MKLDNNDataType::s16},
{DataTypeTrait<int32_t>::DataType, MKLDNNDataType::s32}};
auto iter = dict.find(static_cast<int>(type));
if (iter != dict.end()) return iter->second;
return MKLDNNDataType::data_undef;
}
......
paddle/fluid/framework/data_type.cc
浏览文件 @ 41a64f6a
......@@ -26,7 +26,7 @@ struct DataTypeMap {
std::unordered_map<std::type_index, proto::VarType::Type> cpp_to_proto_;
std::unordered_map<int, std::type_index> proto_to_cpp_;
std::unordered_map<int, std::string> proto_to_str_;
std::unordered_map<std::type_index, size_t> cpp_to_size_;
std::unordered_map<int, size_t> proto_to_size_;
};
static DataTypeMap* InitDataTypeMap();
......@@ -45,7 +45,7 @@ static inline void RegisterType(DataTypeMap* map,
map->proto_to_cpp_.emplace(static_cast<int>(proto_type), typeid(T));
map->cpp_to_proto_.emplace(typeid(T), proto_type);
map->proto_to_str_.emplace(static_cast<int>(proto_type), name);
map->cpp_to_size_.emplace(typeid(T), sizeof(T));
map->proto_to_size_.emplace(static_cast<int>(proto_type), sizeof(T));
}
static DataTypeMap* InitDataTypeMap() {
......@@ -54,17 +54,7 @@ static DataTypeMap* InitDataTypeMap() {
#define RegType(cc_type, proto_type) \
RegisterType<cc_type>(retv, proto_type, #cc_type)
// NOTE: Add your customize type here.
RegType(float16, proto::VarType::FP16);
RegType(float, proto::VarType::FP32);
RegType(double, proto::VarType::FP64);
RegType(int, proto::VarType::INT32);
RegType(int64_t, proto::VarType::INT64);
RegType(bool, proto::VarType::BOOL);
RegType(size_t, proto::VarType::SIZE_T);
RegType(int16_t, proto::VarType::INT16);
RegType(uint8_t, proto::VarType::UINT8);
RegType(int8_t, proto::VarType::INT8);
_ForEachDataType_(RegType);
#undef RegType
return retv;
......@@ -96,12 +86,12 @@ std::string DataTypeToString(const proto::VarType::Type type) {
static_cast<int>(type));
}
size_t SizeOfType(std::type_index type) {
auto it = gDataTypeMap().cpp_to_size_.find(type);
if (it != gDataTypeMap().cpp_to_size_.end()) {
size_t SizeOfType(proto::VarType::Type type) {
auto it = gDataTypeMap().proto_to_size_.find(static_cast<int>(type));
if (it != gDataTypeMap().proto_to_size_.end()) {
return it->second;
}
PADDLE_THROW("Not support %s as tensor type", type.name());
PADDLE_THROW("Not support %s as tensor type", DataTypeToString(type));
}
} // namespace framework
......
paddle/fluid/framework/data_type.h
浏览文件 @ 41a64f6a
......@@ -22,46 +22,59 @@ limitations under the License. */
namespace paddle {
namespace framework {
template <typename T>
struct DataTypeTrait {};
// Stub handle for void
template <>
struct DataTypeTrait<void> {
constexpr static auto DataType = proto::VarType::RAW;
};
#define _ForEachDataTypeHelper_(callback, cpp_type, proto_type) \
callback(cpp_type, ::paddle::framework::proto::VarType::proto_type);
#define _ForEachDataType_(callback) \
_ForEachDataTypeHelper_(callback, float, FP32); \
_ForEachDataTypeHelper_(callback, ::paddle::platform::float16, FP16); \
_ForEachDataTypeHelper_(callback, double, FP64); \
_ForEachDataTypeHelper_(callback, int, INT32); \
_ForEachDataTypeHelper_(callback, int64_t, INT64); \
_ForEachDataTypeHelper_(callback, bool, BOOL); \
_ForEachDataTypeHelper_(callback, uint8_t, UINT8); \
_ForEachDataTypeHelper_(callback, int16_t, INT16); \
_ForEachDataTypeHelper_(callback, int8_t, INT8)
#define DefineDataTypeTrait(cpp_type, proto_type) \
template <> \
struct DataTypeTrait<cpp_type> { \
constexpr static auto DataType = proto_type; \
}
_ForEachDataType_(DefineDataTypeTrait);
#undef DefineDataTypeTrait
extern proto::VarType::Type ToDataType(std::type_index type);
extern std::type_index ToTypeIndex(proto::VarType::Type type);
template <typename Visitor>
inline void VisitDataType(proto::VarType::Type type, Visitor visitor) {
switch (type) {
case proto::VarType::FP16:
visitor.template apply<platform::float16>();
break;
case proto::VarType::FP32:
visitor.template apply<float>();
break;
case proto::VarType::FP64:
visitor.template apply<double>();
break;
case proto::VarType::INT32:
visitor.template apply<int>();
break;
case proto::VarType::INT64:
visitor.template apply<int64_t>();
break;
case proto::VarType::BOOL:
visitor.template apply<bool>();
break;
case proto::VarType::UINT8:
visitor.template apply<uint8_t>();
break;
case proto::VarType::INT16:
visitor.template apply<int16_t>();
break;
case proto::VarType::INT8:
visitor.template apply<int8_t>();
break;
default:
PADDLE_THROW("Not supported %d", type);
}
#define VisitDataTypeCallback(cpp_type, proto_type) \
do { \
if (type == proto_type) { \
visitor.template apply<cpp_type>(); \
return; \
} \
} while (0)
_ForEachDataType_(VisitDataTypeCallback);
#undef VisitDataTypeCallback
PADDLE_THROW("Not supported %d", type);
}
extern std::string DataTypeToString(const proto::VarType::Type type);
extern size_t SizeOfType(std::type_index type);
extern size_t SizeOfType(proto::VarType::Type type);
inline std::ostream& operator<<(std::ostream& out,
const proto::VarType::Type& type) {
out << DataTypeToString(type);
......
paddle/fluid/framework/data_type_test.cc
浏览文件 @ 41a64f6a
......@@ -26,15 +26,15 @@ TEST(DataType, float16) {
Tensor tensor;
CPUPlace cpu;
tensor.mutable_data(cpu, f::ToTypeIndex(dtype));
tensor.mutable_data(cpu, dtype);
// test fp16 tensor
EXPECT_EQ(tensor.type(), std::type_index(typeid(float16)));
EXPECT_EQ(tensor.type(), f::ToDataType(typeid(float16)));
// test fp16 size
EXPECT_EQ(f::SizeOfType(f::ToTypeIndex(dtype)), 2u);
EXPECT_EQ(f::SizeOfType(dtype), 2u);
// test debug info
std::string type = "float16";
std::string type = "::paddle::platform::float16";
EXPECT_STREQ(f::DataTypeToString(dtype).c_str(), type.c_str());
}
paddle/fluid/framework/details/CMakeLists.txt
浏览文件 @ 41a64f6a
......@@ -50,8 +50,10 @@ cc_library(data_balance_op_handle SRCS data_balance_op_handle.cc DEPS op_handle_
cc_library(gather_op_handle SRCS gather_op_handle.cc DEPS op_handle_base scope ddim memory variable_visitor)
cc_library(fuse_vars_op_handle SRCS fuse_vars_op_handle.cc DEPS op_handle_base scope)
cc_library(memory_optimize_pass SRCS analysis_var_pass.cc memory_reuse_types.cc DEPS graph graph_helper pass)
cc_library(modify_op_lock_and_record_event_pass SRCS modify_op_lock_and_record_event_pass.cc DEPS computation_op_handle op_graph_view multi_devices_helper)
cc_library(memory_early_delete_pass SRCS memory_early_delete_pass.cc DEPS memory_optimize_pass computation_op_handle scale_loss_grad_op_handle rpc_op_handle
all_reduce_op_handle reduce_op_handle broadcast_op_handle data_balance_op_handle graph graph_helper pass)
cc_library(reference_count_pass_helper SRCS reference_count_pass_helper.cc DEPS garbage_collector computation_op_handle)
cc_library(eager_deletion_op_handle SRCS eager_deletion_op_handle.cc DEPS lod_tensor selected_rows reference_count_pass_helper)
cc_library(eager_deletion_pass SRCS eager_deletion_pass.cc DEPS computation_op_handle eager_deletion_op_handle graph graph_helper pass)
......@@ -63,7 +65,12 @@ cc_library(all_reduce_deps_pass SRCS all_reduce_deps_pass.cc DEPS graph graph_he
cc_library(multi_devices_graph_pass SRCS multi_devices_graph_pass.cc DEPS multi_devices_helper computation_op_handle
scale_loss_grad_op_handle rpc_op_handle all_reduce_op_handle reduce_op_handle broadcast_op_handle data_balance_op_handle fused_broadcast_op_handle)
set(SSA_GRAPH_EXECUTOR_DEPS graph framework_proto sequential_execution_pass modify_op_lock_and_record_event_pass all_reduce_deps_pass reference_count_pass eager_deletion_pass)
set(SSA_GRAPH_EXECUTOR_DEPS graph framework_proto sequential_execution_pass modify_op_lock_and_record_event_pass all_reduce_deps_pass reference_count_pass eager_deletion_pass memory_optimize_pass memory_early_delete_pass)
if (WITH_GPU)
list(APPEND SSA_GRAPH_EXECUTOR_DEPS reference_count_pass)
endif()
cc_test(memory_reuse_types_test SRCS memory_reuse_types_test.cc memory_reuse_types.cc DEPS framework_proto graph)
cc_test(analysis_var_pass_test SRCS analysis_var_pass_test.cc analysis_var_pass.cc memory_reuse_types.cc DEPS framework_proto graph graph_helper op_registry pass)
cc_library(ssa_graph_executor SRCS ssa_graph_executor.cc DEPS ${SSA_GRAPH_EXECUTOR_DEPS})
......@@ -86,4 +93,5 @@ cc_test(fused_broadcast_op_test SRCS fused_broadcast_op_handle_test.cc DEPS fuse
cc_library(build_strategy SRCS build_strategy.cc DEPS
graph_viz_pass multi_devices_graph_pass
multi_devices_graph_print_pass multi_devices_graph_check_pass
fuse_elewise_add_act_pass multi_batch_merge_pass)
fuse_elewise_add_act_pass multi_batch_merge_pass
memory_optimize_pass)
paddle/fluid/framework/details/all_reduce_op_handle.cc
浏览文件 @ 41a64f6a
......@@ -138,7 +138,7 @@ void AllReduceOpHandle::RunImpl() {
// Reduce All Tensor to trg in CPU
ReduceLoDTensor func(lod_tensors, &trg);
VisitDataType(ToDataType(lod_tensors[0]->type()), func);
VisitDataType(lod_tensors[0]->type(), func);
for (size_t i = 1; i < local_scopes_.size(); ++i) {
auto &scope =
......
paddle/fluid/framework/details/analysis_var_pass.cc 0 → 100644
浏览文件 @ 41a64f6a
此差异已折叠。
paddle/fluid/framework/details/analysis_var_pass.h 0 → 100644
浏览文件 @ 41a64f6a
// Copyright (c) 2018 PaddlePaddle Authors. All Rights Reserved.
//
// Licensed under the Apache License, Version 2.0 (the "License");
// you may not use this file except in compliance with the License.
// You may obtain a copy of the License at
//
// http://www.apache.org/licenses/LICENSE-2.0
//
// Unless required by applicable law or agreed to in writing, software
// distributed under the License is distributed on an "AS IS" BASIS,
// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
// See the License for the specific language governing permissions and
// limitations under the License.
#pragma once
#include <algorithm>
#include <list>
#include <map>
#include <memory>
#include <set>
#include <string>
#include <unordered_map>
#include <utility>
#include <vector>
#include "paddle/fluid/framework/data_type.h"
#include "paddle/fluid/framework/details/memory_reuse_types.h"
#include "paddle/fluid/framework/ir/graph.h"
#include "paddle/fluid/framework/ir/pass.h"
namespace paddle {
namespace framework {
namespace details {
constexpr char kAllOpDescs[] = "all_op_descs";
std::vector<ir::Node*> SortOpLikeDescOrder(const ir::Graph& graph);
// sort op in bfs order
std::vector<ir::Node*> BFSSortGraphOps(const ir::Graph& graph);
class ControlFlowGraph;
class AnalysisVarPass : public ir::Pass {
protected:
std::unique_ptr<ir::Graph> ApplyImpl(
std::unique_ptr<ir::Graph> graph) const override;
private:
// fill the variable map(var_nodes) by version.
void InitSSAGraphNodes() const;
// update program descs
void RenameVarInGraphDesc(const std::string& var,
const std::string& cache_var, size_t idx) const;
// update ir nodes
void RenameVarInGraphNode(const std::string& var,
const std::string& cache_var, size_t idx,
ir::Graph* graph) const;
void SubGraphOptimize(OpDesc* op_desc) const;
// valid a tensor can be reuse or not
bool NodeCanReused(ir::Node* node) const;
// scan subblock and collect the output/input variables.
std::unordered_set<std::string> GetSubBlockVars(
const std::unordered_set<ir::Node*>&) const;
// check op has subblock or not
bool OpHasSubBlock(OpDesc* desc) const;
private:
// Reuse Node Pool, Owned.
mutable OrderedNodePairPool pool_;
// controlflow Graph
mutable std::unique_ptr<ControlFlowGraph> cfg_;
// skip set
mutable std::unordered_set<std::string> skip_set_;
// var nodes
mutable std::map<std::string, std::vector<ir::Node*>> var_nodes_;
};
class ControlFlowGraph {
public:
ControlFlowGraph() = default;
// For IR Graph in parallelexecutor
explicit ControlFlowGraph(const ir::Graph& graph);
void LiveVariableAnalysis();
void RenameVarInCFGGraph(const std::string& old_node,
const std::string& new_node, int begin_idx);
const std::set<std::string> LiveIn(ir::Node* op) const;
const std::set<std::string> LiveOut(ir::Node* op) const;
const std::set<std::string> Use(ir::Node* op) const;
const std::vector<ir::Node*> Ops() const;
std::vector<ir::Node*>& Ops();
// for ssa-graph nodes
ir::Node* GetNodeFromVarName(const std::string& name, ir::Node* op) const;
private:
void BuildCFGGraph();
void ConnectNodes();
using NodeListMap = std::unordered_map<ir::Node*, std::set<ir::Node*>>;
using VarSetMap = std::map<ir::Node*, std::set<std::string>>;
// successors ops use the output variables.
NodeListMap successors_;
// predecessors ops generated input variables.
NodeListMap predecessors_;
// variables lived before run current op.
VarSetMap live_in_;
// variables lived after run current op.
VarSetMap live_out_;
VarSetMap uses_; // op inputs
VarSetMap defs_; // op outputs
std::vector<ir::Node*> ops_; // op sequence by topology sort
};
} // namespace details
} // namespace framework
} // namespace paddle
paddle/fluid/framework/details/analysis_var_pass_test.cc 0 → 100644
浏览文件 @ 41a64f6a
// Copyright (c) 2018 PaddlePaddle Authors. All Rights Reserved.
//
// Licensed under the Apache License, Version 2.0 (the "License");
// you may not use this file except in compliance with the License.
// You may obtain a copy of the License at
//
// http://www.apache.org/licenses/LICENSE-2.0
//
// Unless required by applicable law or agreed to in writing, software
// distributed under the License is distributed on an "AS IS" BASIS,
// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
// See the License for the specific language governing permissions and
// limitations under the License.
#include "paddle/fluid/framework/details/analysis_var_pass.h"
#include <algorithm>
#include <iostream>
#include <iterator>
#include "glog/logging.h"
#include "gtest/gtest.h"
#include "paddle/fluid/framework/ir/graph.h"
#include "paddle/fluid/framework/ir/graph_helper.h"
#include "paddle/fluid/framework/op_registry.h"
#include "paddle/fluid/framework/operator.h"
#include "paddle/fluid/framework/program_desc.h"
namespace paddle {
namespace framework {
class DummyOp : public OperatorBase {
public:
DummyOp(const std::string& type, const VariableNameMap& inputs,
const VariableNameMap& outputs, const AttributeMap& attrs)
: OperatorBase(type, inputs, outputs, attrs) {}
private:
void RunImpl(const Scope& scope,
const platform::Place& place) const override {}
};
class SumOpMaker : public OpProtoAndCheckerMaker {
public:
void Make() {
AddInput("X", "").AsDuplicable();
AddOutput("Out", "");
AddComment("");
}
};
class AssignOpMaker : public OpProtoAndCheckerMaker {
public:
void Make() {
AddInput("X", "").AsDuplicable();
AddOutput("Out", "");
AddComment("");
}
};
class DummyVarTypeInference : public VarTypeInference {
public:
void operator()(const OpDesc& op_desc, BlockDesc* block) const override {
auto& inputs = op_desc.Input("X");
auto type = block->Var(inputs.front())->GetType();
auto out_var_name = op_desc.Output("Out").front();
block->Var(out_var_name)->SetType(type);
}
};
} // namespace framework
} // namespace paddle
REGISTER_OPERATOR(sum, paddle::framework::DummyOp,
paddle::framework::SumOpMaker,
paddle::framework::DummyVarTypeInference);
REGISTER_OPERATOR(assign, paddle::framework::DummyOp,
paddle::framework::AssignOpMaker,
paddle::framework::DummyVarTypeInference);
REGISTER_OPERATOR(dummy, paddle::framework::DummyOp,
paddle::framework::SumOpMaker,
paddle::framework::DummyVarTypeInference);
/*
https://en.wikipedia.org/wiki/Live_variable_analysis
Create a customed classical dependency graph, left row is the instruction
number.
1. a = 1
2. b = a
3. c = a
4. d = b + c
5. e = d
a--------+
| |
b c
| |
d--------+
|
e
Then analysis these variable's liveness range
*/
namespace paddle {
namespace framework {
namespace details {
static inline bool IsSameDesc(OpDesc* op1, OpDesc* op2) {
return op1->Type() == op2->Type() && op1->Inputs() == op2->Inputs() &&
op1->Outputs() == op2->Outputs();
}
inline static ProgramDesc FillProgramDesc() {
ProgramDesc prog;
prog.MutableBlock(0)->Var("a")->SetType(proto::VarType::LOD_TENSOR);
prog.MutableBlock(0)->Var("b")->SetType(proto::VarType::LOD_TENSOR);
prog.MutableBlock(0)->Var("c")->SetType(proto::VarType::LOD_TENSOR);
prog.MutableBlock(0)->Var("d")->SetType(proto::VarType::LOD_TENSOR);
prog.MutableBlock(0)->Var("e")->SetType(proto::VarType::LOD_TENSOR);
{
auto* op = prog.MutableBlock(0)->AppendOp();
op->SetType("assign");
op->SetInput("X", {"a"});
op->SetOutput("Out", {"b"});
}
{
auto* op = prog.MutableBlock(0)->AppendOp();
op->SetType("assign");
op->SetInput("X", {"a"});
op->SetOutput("Out", {"c"});
}
{
auto* op = prog.MutableBlock(0)->AppendOp();
op->SetType("sum");
op->SetInput("X", {"b", "c"});
op->SetOutput("Out", {"d"});
}
{
auto* op = prog.MutableBlock(0)->AppendOp();
op->SetType("assign");
op->SetInput("X", {"d"});
op->SetOutput("Out", {"e"});
}
return prog;
}
template <typename Container>
inline static std::string DebugString(const Container& c) {
std::stringstream ss;
for (auto& item : c) {
ss << item << " ";
}
return ss.str();
}
TEST(CFGGraph, IRGraph) {
// prepare ir graph
auto prog = FillProgramDesc();
ir::Graph graph(prog);
const std::vector<OpDesc*>* all_op_descs =
new std::vector<OpDesc*>(prog.Block(0).AllOps());
graph.Set(details::kAllOpDescs, all_op_descs); // take ownership
ControlFlowGraph cfg(graph);
cfg.LiveVariableAnalysis();
// test assign op
ASSERT_TRUE((std::set<std::string>{"a"} == cfg.LiveIn(cfg.Ops()[0])));
ASSERT_TRUE((std::set<std::string>{"a", "b"} == cfg.LiveOut(cfg.Ops()[0])));
// test assign op
ASSERT_TRUE((std::set<std::string>{"a", "b"} == cfg.LiveIn(cfg.Ops()[1])));
ASSERT_TRUE((std::set<std::string>{"b", "c"} == cfg.LiveOut(cfg.Ops()[1])));
// test sum op
ASSERT_TRUE((std::set<std::string>{"b", "c"} == cfg.LiveIn(cfg.Ops()[2])));
ASSERT_TRUE((std::set<std::string>{"d"} == cfg.LiveOut(cfg.Ops()[2])));
// test assign op
ASSERT_TRUE((std::set<std::string>{"d"} == cfg.LiveIn(cfg.Ops()[3])));
ASSERT_TRUE((std::set<std::string>{} == cfg.LiveOut(cfg.Ops()[3])));
}
// 1. normal test
TEST(SortOpLikeDescOrder, NormalTest) {
auto prog = FillProgramDesc();
ir::Graph graph(prog);
const std::vector<OpDesc*>* all_op_descs =
new std::vector<OpDesc*>(prog.Block(0).AllOps());
graph.Set(details::kAllOpDescs, all_op_descs); // take ownership
auto nodes = SortOpLikeDescOrder(graph);
auto op_descs = prog.Block(0).AllOps();
for (size_t i = 0; i < nodes.size(); ++i) {
auto node = nodes[i];
auto op_desc = op_descs[i];
ASSERT_TRUE(IsSameDesc(node->Op(), op_desc));
}
}
// 2. remove some op_desc
TEST(SortOpLikeDescOrder, RemoveOpDesc) {
auto prog = FillProgramDesc();
ir::Graph graph(prog);
const std::vector<OpDesc*>* all_op_descs =
new std::vector<OpDesc*>(prog.Block(0).AllOps());
graph.Set(details::kAllOpDescs, all_op_descs); // take ownership
auto nodes = graph.Nodes();
auto op_descs = prog.Block(0).AllOps();
ir::Node* found_node = nullptr;
for (auto node : nodes) {
if (node->IsOp() && node->outputs.back()->Name() == "e") {
found_node = node;
break;
}
}
PADDLE_ENFORCE(found_node != nullptr);
for (auto it = op_descs.begin(); it != op_descs.end();) {
if (IsSameDesc(*it, found_node->Op())) {
it = op_descs.erase(it);
} else {
++it;
}
}
auto find_node_in_graph = [&](std::string s) {
ir::Node* ret = nullptr;
for (auto n : graph.Nodes()) {
if (n->Name() == s) {
ret = n;
break;
}
}
PADDLE_ENFORCE(ret != nullptr);
return ret;
};
ir::Node* e = find_node_in_graph("e");
ir::Node* d = find_node_in_graph("d");
std::remove(d->outputs.begin(), d->outputs.end(), found_node);
graph.RemoveNode(found_node);
graph.RemoveNode(e);
// other node keeps the same order
auto remain_nodes = SortOpLikeDescOrder(graph);
for (size_t i = 0; i < remain_nodes.size(); ++i) {
auto node = remain_nodes[i];
auto op_desc = op_descs[i];
ASSERT_TRUE(IsSameDesc(node->Op(), op_desc));
}
}
// 3. add some op_desc
TEST(SortOpLikeDescOrder, AddOpDesc) {
auto prog = FillProgramDesc();
const std::vector<OpDesc*>* all_op_descs =
new std::vector<OpDesc*>(prog.Block(0).AllOps());
ir::Graph graph(prog);
auto find_node_in_graph = [&](std::string s) {
ir::Node* ret = nullptr;
for (auto n : graph.Nodes()) {
if (n->Name() == s) {
ret = n;
break;
}
}
PADDLE_ENFORCE(ret != nullptr);
return ret;
};
// cached desc different with real one
// mimic the intermidiete pass modify the programdesc.
graph.Set(details::kAllOpDescs, all_op_descs); // take ownership
auto op_descs = prog.Block(0).AllOps();
auto op = prog.MutableBlock(0)->AppendOp();
prog.MutableBlock(0)->Var("d1")->SetType(proto::VarType::LOD_TENSOR);
op->SetType("sum");
op->SetInput("X", {"b", "c"});
op->SetOutput("Out", {"d1"});
ir::Node* node = graph.CreateOpNode(op);
ir::Node* d1 = graph.CreateVarNode(prog.MutableBlock(0)->Var("d1"));
ir::Node* b = find_node_in_graph("b");
ir::Node* c = find_node_in_graph("c");
node->outputs.emplace_back(d1);
node->inputs.emplace_back(b);
node->inputs.emplace_back(c);
d1->inputs.emplace_back(node);
b->outputs.emplace_back(node);
c->outputs.emplace_back(node);
op_descs.insert(op_descs.begin() + 4, op);
auto nodes = SortOpLikeDescOrder(graph);
for (size_t i = 0; i < nodes.size(); ++i) {
auto node = nodes[i];
auto op_desc = op_descs[i];
ASSERT_TRUE(IsSameDesc(node->Op(), op_desc));
}
}
// 4. add and delete some op_desc
TEST(SortOpLikeDescOrder, AddAndDeleteOpDesc) {
auto prog = FillProgramDesc();
ir::Graph graph(prog);
const std::vector<OpDesc*>* all_op_descs =
new std::vector<OpDesc*>(prog.Block(0).AllOps());
graph.Set(details::kAllOpDescs, all_op_descs); // take ownership
auto find_node_in_graph = [&](std::string s) {
ir::Node* ret = nullptr;
for (auto n : graph.Nodes()) {
if (n->Name() == s) {
ret = n;
break;
}
}
PADDLE_ENFORCE(ret != nullptr);
return ret;
};
// remove sum node
auto op_descs = prog.Block(0).AllOps();
ir::Node* found_node = nullptr;
auto nodes = graph.Nodes();
for (auto node : nodes) {
if (node->Name() == "sum") {
found_node = node;
break;
}
}
PADDLE_ENFORCE(found_node != nullptr);
for (auto it = op_descs.begin(); it != op_descs.end();) {
if (IsSameDesc(*it, found_node->Op())) {
it = op_descs.erase(it);
} else {
++it;
}
}
{
ir::Node* d = find_node_in_graph("d");
ir::Node* c = find_node_in_graph("c");
ir::Node* e = find_node_in_graph("e");
std::remove(d->outputs.begin(), d->outputs.end(), found_node);
std::remove(c->outputs.begin(), c->outputs.end(), found_node);
ir::Node* pending_op = found_node->outputs[0]->outputs[0];
graph.RemoveNode(e);
graph.RemoveNode(pending_op);
graph.RemoveNode(found_node);
}
// add node
auto op = prog.MutableBlock(0)->AppendOp();
prog.MutableBlock(0)->Var("d1")->SetType(proto::VarType::LOD_TENSOR);
op->SetType("sum");
op->SetInput("X", {"b", "c"});
op->SetOutput("Out", {"d1"});
{
ir::Node* node = graph.CreateOpNode(op);
ir::Node* d1 = graph.CreateVarNode(prog.MutableBlock(0)->Var("d1"));
ir::Node* b = find_node_in_graph("b");
ir::Node* c = find_node_in_graph("c");
node->outputs.emplace_back(d1);
node->inputs.emplace_back(b);
node->inputs.emplace_back(c);
b->outputs.emplace_back(node);
c->outputs.emplace_back(node);
}
op_descs.insert(op_descs.begin() + 2, op);
// check the order
auto mynodes = SortOpLikeDescOrder(graph);
for (size_t i = 0; i < mynodes.size(); ++i) {
auto node = mynodes[i];
auto op_desc = op_descs[i];
ASSERT_TRUE(IsSameDesc(node->Op(), op_desc));
}
}
// 5. add and replace some op_desc inplace.
TEST(SortOpLikeDescOrder, AddAndReplaceOpDescInplace) {
auto prog = FillProgramDesc();
ir::Graph graph(prog);
const std::vector<OpDesc*>* all_op_descs =
new std::vector<OpDesc*>(prog.Block(0).AllOps());
graph.Set(details::kAllOpDescs, all_op_descs); // take ownership
auto find_node_in_graph = [&](std::string s) {
ir::Node* ret = nullptr;
for (auto n : graph.Nodes()) {
if (n->Name() == s) {
ret = n;
break;
}
}
PADDLE_ENFORCE(ret != nullptr);
return ret;
};
auto op_descs = prog.Block(0).AllOps();
// add node
auto op = prog.MutableBlock(0)->AppendOp();
prog.MutableBlock(0)->Var("d1")->SetType(proto::VarType::LOD_TENSOR);
op->SetType("sum");
op->SetInput("X", {"b", "c"});
op->SetOutput("Out", {"d1"});
{
ir::Node* node = graph.CreateOpNode(op);
ir::Node* d1 = graph.CreateVarNode(prog.MutableBlock(0)->Var("d1"));
ir::Node* b = find_node_in_graph("b");
ir::Node* c = find_node_in_graph("c");
node->outputs.emplace_back(d1);
node->inputs.emplace_back(b);
node->inputs.emplace_back(c);
d1->inputs.emplace_back(node);
b->outputs.emplace_back(node);
c->outputs.emplace_back(node);
}
op_descs.emplace_back(op);
// replace op_desc inplace
auto nodes = graph.Nodes();
ir::Node* found_node = nullptr;
for (auto node : nodes) {
if (node->IsOp() && node->Op() && node->Name() == "assign") {
if (node->outputs.size() == 1 && node->outputs[0]->Name() == "e") {
found_node = node;
break;
}
}
}
{
ir::Node* d = find_node_in_graph("d");
ir::Node* e = find_node_in_graph("e");
std::remove(d->outputs.begin(), d->outputs.end(), found_node);
std::remove(e->inputs.begin(), e->inputs.end(), found_node);
graph.RemoveNode(found_node);
}
op_descs.erase(op_descs.begin() + 3);
auto replace_op = prog.MutableBlock(0)->AppendOp();
replace_op->SetType("sum");
replace_op->SetInput("X", {"d", "d1"});
replace_op->SetOutput("Out", {"e"});
{
ir::Node* sum2 = graph.CreateOpNode(replace_op);
ir::Node* e = find_node_in_graph("e");
ir::Node* d = find_node_in_graph("d");
ir::Node* d1 = find_node_in_graph("d1");
sum2->inputs.emplace_back(d);
sum2->inputs.emplace_back(d1);
sum2->outputs.emplace_back(e);
e->inputs.emplace_back(sum2);
d->outputs.emplace_back(sum2);
d1->outputs.emplace_back(sum2);
}
op_descs.emplace_back(replace_op);
// compare op order
auto graph_nodes = SortOpLikeDescOrder(graph);
for (size_t i = 0; i < graph_nodes.size(); ++i) {
auto node = graph_nodes[i];
auto op_desc = op_descs[i];
ASSERT_TRUE(IsSameDesc(node->Op(), op_desc));
}
}
} // namespace details
} // namespace framework
} // namespace paddle
paddle/fluid/framework/details/build_strategy.cc
浏览文件 @ 41a64f6a
......@@ -14,11 +14,16 @@ limitations under the License. */
#include "paddle/fluid/framework/details/build_strategy.h"
#include <glog/logging.h>
#include <memory>
#include "paddle/fluid/framework/details/memory_reuse_types.h"
#include "paddle/fluid/framework/details/multi_devices_graph_check_pass.h"
#include "paddle/fluid/framework/details/multi_devices_graph_print_pass.h"
#include "paddle/fluid/framework/details/reduce_op_handle.h"
#include "paddle/fluid/framework/details/sequential_execution_pass.h"
#include "paddle/fluid/framework/ir/graph.h"
#include "paddle/fluid/framework/ir/graph_helper.h"
#include "paddle/fluid/framework/ir/graph_viz_pass.h"
namespace paddle {
......@@ -71,6 +76,14 @@ class ParallelExecutorPassBuilder : public ir::PassBuilder {
}
VLOG(1) << "CollectiveContext:" << context->String();
// NOTE(dzh): memory optimize should be a runtime pass.
// However, after multi_devices_pass, VarHandle, OpHandle is
// the de-fact IR, any reuse on Graph is meaningless.
// A side-effect of that, memory optimize cannot forsee the fetched vars
// , so fetchlist should be set persistable before call the Run interface.
if (strategy.memory_optimize_) {
auto analysis_var_pass = AppendPass("analysis_var_pass");
}
// Convert graph to run on multi-devices.
auto multi_devices_pass = AppendPass("multi_devices_pass");
multi_devices_pass->SetNotOwned<const BuildStrategy>("strategy",
......@@ -81,8 +94,11 @@ class ParallelExecutorPassBuilder : public ir::PassBuilder {
// Add a graph print pass to record a graph with device info.
if (!strategy_.debug_graphviz_path_.empty()) {
auto multi_devices_print_pass = AppendPass("multi_devices_print_pass");
multi_devices_print_pass->SetNotOwned<const std::string>(
"debug_graphviz_path", &strategy_.debug_graphviz_path_);
const std::string graph_path =
string::Sprintf("%s%s", strategy_.debug_graphviz_path_.c_str(),
"_multi_devices_graph");
multi_devices_print_pass->Set<std::string>(kGraphvizPath,
new std::string(graph_path));
multi_devices_print_pass->Set<details::GraphvizSSAGraphPrinter>(
"graph_printer", new details::GraphvizSSAGraphPrinter);
}
......@@ -117,9 +133,7 @@ std::shared_ptr<ir::PassBuilder> BuildStrategy::CreatePassesFromStrategy(
std::unique_ptr<ir::Graph> BuildStrategy::Apply(
const ProgramDesc &main_program, const std::vector<platform::Place> &places,
const std::string &loss_var_name,
const std::unordered_set<std::string> &param_names,
const std::vector<Scope *> &local_scopes,
const std::string &loss_var_name, const std::vector<Scope *> &local_scopes,
#if defined(PADDLE_WITH_CUDA) && !defined(_WIN32)
const bool use_cuda, platform::NCCLContextMap *nccl_ctxs) const {
#else
......@@ -129,16 +143,12 @@ std::unique_ptr<ir::Graph> BuildStrategy::Apply(
CreatePassesFromStrategy(false);
std::unique_ptr<ir::Graph> graph(new ir::Graph(main_program));
for (std::shared_ptr<ir::Pass> &pass : pass_builder_->AllPasses()) {
if (pass->Type() == "multi_devices_pass") {
pass->Erase("places");
pass->SetNotOwned<const std::vector<platform::Place>>("places", &places);
pass->Erase("loss_var_name");
pass->SetNotOwned<const std::string>("loss_var_name", &loss_var_name);
pass->Erase("params");
pass->SetNotOwned<const std::unordered_set<std::string>>("params",
&param_names);
pass->Erase("local_scopes");
pass->SetNotOwned<const std::vector<Scope *>>("local_scopes",
&local_scopes);
......@@ -147,6 +157,17 @@ std::unique_ptr<ir::Graph> BuildStrategy::Apply(
pass->Erase("nccl_ctxs");
pass->SetNotOwned<platform::NCCLContextMap>("nccl_ctxs", nctx);
#endif
} else if (pass->Type() == "analysis_var_pass") {
const std::vector<OpDesc *> *all_op_descs =
new std::vector<OpDesc *>(main_program.Block(0).AllOps());
graph->Set<const std::vector<OpDesc *>>(kAllOpDescs,
all_op_descs); // take ownership
graph->Set<GraphNodePool>(kGraphNodePool,
new GraphNodePool); // take ownership
pass->Erase(kAllOpDescs);
pass->SetNotOwned<const std::vector<OpDesc *>>(kAllOpDescs, all_op_descs);
} else if (pass->Type() == "sequential_execution_pass") {
LOG(INFO) << "set enable_sequential_execution:"
<< enable_sequential_execution_;
......@@ -168,6 +189,7 @@ std::unique_ptr<ir::Graph> BuildStrategy::Apply(
}
return graph;
}
} // namespace details
} // namespace framework
} // namespace paddle
......@@ -178,6 +200,7 @@ USE_PASS(multi_batch_merge_pass);
USE_PASS(multi_devices_pass);
USE_PASS(multi_devices_check_pass);
USE_PASS(multi_devices_print_pass);
USE_PASS(analysis_var_pass);
USE_PASS(sequential_execution_pass);
USE_PASS(all_reduce_deps_pass);
USE_PASS(modify_op_lock_and_record_event_pass);
paddle/fluid/framework/details/build_strategy.h
浏览文件 @ 41a64f6a
......@@ -60,8 +60,15 @@ struct BuildStrategy {
kCustomized = 2,
};
enum class OptimizeStrategy {
// To be Implemented,bruteforce, recursive compute unused var names.
kBruteForce = 0,
kControlFlowGraph = 1, // use cfg_graph algorithm, faster speed.
};
ReduceStrategy reduce_{ReduceStrategy::kAllReduce};
GradientScaleStrategy gradient_scale_{GradientScaleStrategy::kCoeffNumDevice};
OptimizeStrategy strategy_{OptimizeStrategy::kControlFlowGraph};
std::string debug_graphviz_path_{""};
......@@ -69,6 +76,10 @@ struct BuildStrategy {
bool enable_data_balance_{false};
bool memory_optimize_{false};
bool memory_early_delete_{false};
bool enable_sequential_execution_{false};
bool fuse_broadcast_op_{false};
......@@ -97,16 +108,15 @@ struct BuildStrategy {
// Apply the passes built by the pass_builder_. The passes will be
// applied to the Program and output an ir::Graph.
std::unique_ptr<ir::Graph> Apply(
const ProgramDesc &main_program,
const std::vector<platform::Place> &places,
const std::string &loss_var_name,
const std::unordered_set<std::string> &param_names,
const std::vector<Scope *> &local_scopes,
std::unique_ptr<ir::Graph> Apply(const ProgramDesc &main_program,
const std::vector<platform::Place> &places,
const std::string &loss_var_name,
const std::vector<Scope *> &local_scopes,
#if defined(PADDLE_WITH_CUDA) && !defined(_WIN32)
const bool use_cuda, platform::NCCLContextMap *nccl_ctxs) const;
const bool use_cuda,
platform::NCCLContextMap *nccl_ctxs) const;
#else
const bool use_cuda) const;
const bool use_cuda) const;
#endif
private:
......
paddle/fluid/framework/details/early_delete_op_handle.h 0 → 100644
浏览文件 @ 41a64f6a
// Copyright (c) 2018 PaddlePaddle Authors. All Rights Reserved.
//
// Licensed under the Apache License, Version 2.0 (the "License");
// you may not use this file except in compliance with the License.
// You may obtain a copy of the License at
//
// http://www.apache.org/licenses/LICENSE-2.0
//
// Unless required by applicable law or agreed to in writing, software
// distributed under the License is distributed on an "AS IS" BASIS,
// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
// See the License for the specific language governing permissions and
// limitations under the License.
#pragma once
#include <string>
#include <vector>
#include "paddle/fluid/framework/details/computation_op_handle.h"
#include "paddle/fluid/framework/details/op_handle_base.h"
#include "paddle/fluid/framework/details/var_handle.h"
#include "paddle/fluid/framework/garbage_collector.h"
#include "paddle/fluid/framework/lod_tensor_array.h"
#include "paddle/fluid/framework/scope.h"
#include "paddle/fluid/framework/selected_rows.h"
#include "paddle/fluid/framework/tensor.h"
namespace paddle {
namespace framework {
namespace details {
class EarlyDeleteOpHandle : public OpHandleBase {
public:
EarlyDeleteOpHandle(ir::Node* node, const Scope* scope,
const platform::Place& place,
const std::vector<std::string>& names,
GarbageCollector* gc)
: OpHandleBase(node),
scope_(scope),
place_(place),
names_(names),
gc_(gc) {
#ifdef PADDLE_WITH_CUDA
if (IsStreamGarabageCollector()) {
auto gpu_place = boost::get<platform::CUDAPlace>(place);
PADDLE_ENFORCE(cudaSetDevice(gpu_place.device));
PADDLE_ENFORCE(cudaEventCreateWithFlags(&event_, cudaEventDisableTiming));
}
#endif
}
~EarlyDeleteOpHandle() {
#ifdef PADDLE_WITH_CUDA
if (IsStreamGarabageCollector()) {
auto gpu_place = boost::get<platform::CUDAPlace>(dev_ctx_->GetPlace());
PADDLE_ENFORCE(cudaSetDevice(gpu_place.device));
PADDLE_ENFORCE(cudaEventDestroy(event_));
}
#endif
}
std::string Name() const override { return "early_delete"; }
protected:
void RunImpl() override {
std::vector<std::shared_ptr<memory::Allocation>> tensors;
auto* local_scope = scope_->FindVar(kLocalExecScopeName)->Get<Scope*>();
for (auto& var_name : names_) {
auto* var = local_scope->FindVar(var_name);
PADDLE_ENFORCE(var != nullptr,
string::Sprintf("Local Scope not has var %s", var_name));
if (var->IsType<LoDTensor>()) {
tensors.emplace_back(var->GetMutable<LoDTensor>()->MoveMemoryHolder());
} else if (var->IsType<SelectedRows>()) {
tensors.emplace_back(var->GetMutable<SelectedRows>()
->mutable_value()
->MoveMemoryHolder());
} else if (var->IsType<LoDTensorArray>()) {
LoDTensorArray* tensor_array = var->GetMutable<LoDTensorArray>();
for (auto& tensor : *tensor_array) {
tensors.emplace_back(tensor.MoveMemoryHolder());
}
}
}
if (!tensors.empty()) {
ClearTensors(tensors);
}
}
private:
void ClearTensors(
const std::vector<std::shared_ptr<memory::Allocation>>& tensors) {
if (platform::is_cpu_place(place_)) {
ClearCPUTensors(tensors);
} else {
ClearGPUTensors(tensors);
}
}
void ClearCPUTensors(
const std::vector<std::shared_ptr<memory::Allocation>>& tensors) {
auto* gc = dynamic_cast<CPUGarbageCollector*>(gc_);
if (gc != nullptr) {
gc->Add(tensors);
}
}
void ClearGPUTensors(
const std::vector<std::shared_ptr<memory::Allocation>>& tensors) {
#ifdef PADDLE_WITH_CUDA
auto* gc = dynamic_cast<StreamGarbageCollector*>(gc_);
if (gc != nullptr) {
auto compute_stream = dev_ctx_->stream();
auto callback_stream = gc->stream();
auto callback_func = [=]() {
PADDLE_ENFORCE(cudaEventRecord(event_, compute_stream));
PADDLE_ENFORCE(cudaStreamWaitEvent(callback_stream, event_, 0));
};
gc_->Add(tensors, callback_func);
} else {
gc_->Add(tensors);
}
}
bool IsStreamGarabageCollector() const {
return dynamic_cast<const StreamGarbageCollector*>(gc_) != nullptr;
#endif
}
const Scope* scope_;
const platform::Place place_;
std::vector<std::string> names_;
GarbageCollector* gc_;
#ifdef PADDLE_WITH_CUDA
platform::CUDADeviceContext* dev_ctx_;
cudaEvent_t event_;
#endif
};
} // namespace details
} // namespace framework
} // namespace paddle
paddle/fluid/framework/details/fuse_vars_op_handle.h
浏览文件 @ 41a64f6a
......@@ -33,7 +33,7 @@ struct FuseVarsOpHandle : public OpHandleBase {
FuseVarsOpHandle(ir::Node *node, Scope *local_scope,
const platform::Place &place,
const std::unordered_map<std::string, int64_t> &inputs_numel,
const std::type_index &var_type)
const proto::VarType::Type var_type)
: OpHandleBase(node),
local_scope_(local_scope),
place_(place),
......@@ -57,7 +57,7 @@ struct FuseVarsOpHandle : public OpHandleBase {
Scope *local_scope_;
const platform::Place place_;
const std::unordered_map<std::string, int64_t> inputs_numel_;
const std::type_index type_;
const proto::VarType::Type type_;
int64_t total_numel_;
};
} // namespace details
......
paddle/fluid/framework/details/memory_early_delete_pass.cc 0 → 100644
浏览文件 @ 41a64f6a
// Copyright (c) 2018 PaddlePaddle Authors. All Rights Reserved.
//
// Licensed under the Apache License, Version 2.0 (the "License");
// you may not use this file except in compliance with the License.
// You may obtain a copy of the License at
//
// http://www.apache.org/licenses/LICENSE-2.0
//
// Unless required by applicable law or agreed to in writing, software
// distributed under the License is distributed on an "AS IS" BASIS,
// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
// See the License for the specific language governing permissions and
// limitations under the License.
#include "paddle/fluid/framework/details/memory_early_delete_pass.h"
#include <queue>
#include <string>
#include <vector>
#include "paddle/fluid/framework/details/memory_reuse_types.h"
#include "paddle/fluid/framework/details/multi_devices_helper.h"
#include "paddle/fluid/framework/details/reference_count_pass_helper.h"
#include "paddle/fluid/framework/ir/graph_helper.h"
namespace paddle {
namespace framework {
namespace details {
static ComputationOpHandle* FindNextComputationOpHandle(VarHandle* var_in) {
std::queue<VarHandleBase*> queue;
queue.push(var_in);
do {
auto* var = queue.front();
queue.pop();
for (auto* op : var->PendingOps()) {
auto* compute_op = dynamic_cast<ComputationOpHandle*>(op);
if (compute_op != nullptr && compute_op->GetPlace() == var_in->place_) {
return compute_op;
}
for (auto* out_var : op->Outputs()) {
queue.push(out_var);
}
}
} while (!queue.empty());
return nullptr;
}
std::unique_ptr<ir::Graph> MemoryEarlyDeletePass::ApplyImpl(
std::unique_ptr<ir::Graph> graph) const {
auto& graph_pool = Get<GraphNodePool>(kGraphNodePool);
auto& gcs = Get<GarbageCollectorMap>(kGarbageCollector);
std::unordered_map<std::string, std::unordered_set<OpDesc*>> unlived_vars;
unlived_vars.reserve(graph_pool.size());
for (auto& pair : graph_pool) {
unlived_vars.insert(std::make_pair(pair.first, pair.second));
}
auto compare_and_insert_early_delete_op = [&](
OpHandleBase* op, const std::vector<VarHandleBase*>& vars) {
if (unlived_vars.empty()) return;
// unlived vars can be deleted after the last used op has finished.
auto* compute_op = dynamic_cast<ComputationOpHandle*>(op);
const auto& places = Get<std::vector<platform::Place>>(kAllPlaces);
for (auto& var : vars) {
auto* var_handle = dynamic_cast<VarHandle*>(var);
auto var_name = var->Node()->Name();
auto& var_place = var_handle->place_;
if (unlived_vars.count(var_name) == 0) continue;
if (!unlived_vars[var_name].empty()) {
if (compute_op != nullptr &&
unlived_vars[var_name].count(compute_op->Node()->Op()) != 0) {
unlived_vars[var_name].erase(compute_op->Node()->Op());
}
continue;
}
if (var_handle == nullptr || !var_handle->Node()->IsVar() ||
var_handle->Node()->IsCtrlVar())
continue;
// shameless copyed from reference count pass.
if (compute_op == nullptr) {
// use next computation op scope
compute_op = FindNextComputationOpHandle(var_handle);
}
auto* early_delete_node =
graph->CreateEmptyNode("early_delete", ir::Node::Type::kOperation);
GarbageCollector* gc = gcs.at(places[compute_op->GetScopeIdx()]).get();
auto* early_delete_handle = new EarlyDeleteOpHandle(
early_delete_node, compute_op->GetScope(), var_place, {var_name}, gc);
if (compute_op->Outputs().empty()) {
auto* dep_var = new DummyVarHandle(graph->CreateControlDepVar());
compute_op->AddOutput(dep_var);
graph->Get<GraphDepVars>(kGraphDepVars).emplace(dep_var);
}
early_delete_handle->AddInput(compute_op->Outputs().front());
VLOG(5) << "Add early delete op " << var_name << " to Operator"
<< compute_op->Name();
}
};
auto all_ops = ir::FilterByNodeWrapper<OpHandleBase>(*graph);
for (auto& op : all_ops) {
compare_and_insert_early_delete_op(op, op->Inputs());
compare_and_insert_early_delete_op(op, op->Outputs());
}
return graph;
}
} // namespace details
} // namespace framework
} // namespace paddle
REGISTER_PASS(memory_early_delete_pass,
paddle::framework::details::MemoryEarlyDeletePass)
.RequireGraphAttr(paddle::framework::details::kGraphNodePool)
.RequireGraphAttr(paddle::framework::details::kGarbageCollector);
paddle/fluid/framework/details/memory_early_delete_pass.h 0 → 100644
浏览文件 @ 41a64f6a
// Copyright (c) 2018 PaddlePaddle Authors. All Rights Reserved.
//
// Licensed under the Apache License, Version 2.0 (the "License");
// you may not use this file except in compliance with the License.
// You may obtain a copy of the License at
//
// http://www.apache.org/licenses/LICENSE-2.0
//
// Unless required by applicable law or agreed to in writing, software
// distributed under the License is distributed on an "AS IS" BASIS,
// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
// See the License for the specific language governing permissions and
// limitations under the License.
#pragma once
#include "paddle/fluid/framework/details/early_delete_op_handle.h"
#include "paddle/fluid/framework/ir/graph.h"
#include "paddle/fluid/framework/ir/pass.h"
namespace paddle {
namespace framework {
namespace details {
class MemoryEarlyDeletePass : public ir::Pass {
protected:
std::unique_ptr<ir::Graph> ApplyImpl(
std::unique_ptr<ir::Graph> graph) const override;
};
} // namespace details
} // namespace framework
} // namespace paddle
paddle/fluid/framework/details/memory_reuse_types.cc 0 → 100644
浏览文件 @ 41a64f6a
// Copyright (c) 2018 PaddlePaddle Authors. All Rights Reserved.
//
// Licensed under the Apache License, Version 2.0 (the "License");
// you may not use this file except in compliance with the License.
// You may obtain a copy of the License at
//
// http://www.apache.org/licenses/LICENSE-2.0
//
// Unless required by applicable law or agreed to in writing, software
// distributed under the License is distributed on an "AS IS" BASIS,
// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
// See the License for the specific language governing permissions and
// limitations under the License.
#include "paddle/fluid/framework/details/memory_reuse_types.h"
#include <iostream>
#include <sstream>
#include <string>
namespace paddle {
namespace framework {
namespace details {
size_t NodeSizeInBytes(ir::Node* n) {
auto* desc = FindVarDescInBlock(n);
auto shape = desc->GetShape();
size_t type_size = SizeOfType(desc->GetDataType());
int size = 1;
for (auto& s : shape) {
size *= s;
}
return type_size * std::abs(size);
}
std::string DebugStringImpl(VarDesc* var) {
std::stringstream ss;
ss << var->Name();
ss << "[";
try {
auto shape = var->GetShape();
for (size_t i = 0; i < shape.size(); ++i) {
if (i != shape.size() - 1) {
ss << shape[i] << ",";
} else {
ss << shape[i];
}
}
ss << "]";
} catch (...) {
ss << "Var has no VarDesc !!! Name:" << var->Name();
}
return ss.str();
}
std::string DebugString(ir::Node* var) {
return DebugStringImpl(FindVarDescInBlock(var));
}
// return DebugString(var->Var()); }
// NOTE(dzh): based ir node, if a large node has been reused
// by a small size node, then next time it appear in pool, it will
// have the small size. Find the original node shap from blockdesc.
VarDesc* FindVarDescInBlock(ir::Node* n) {
PADDLE_ENFORCE(n->IsVar() && !n->IsCtrlVar() && n->inputs.size() == 1);
BlockDesc* block = n->inputs[0]->Op()->Block();
PADDLE_ENFORCE(block->HasVar(n->Name()),
string::Sprintf("Block do not has var %s", n->Name()));
return block->FindVar(n->Name());
}
struct NodeComparator {
bool operator()(ir::Node* lhs, ir::Node* rhs) const {
auto* lhs_desc = FindVarDescInBlock(lhs);
auto* rhs_desc = FindVarDescInBlock(rhs);
auto lhs_shape = lhs_desc->GetShape();
auto rhs_shape = rhs_desc->GetShape();
if ((lhs_shape[0] == -1 && rhs_shape[0] == -1) ||
(lhs_shape[0] != -1 && rhs_shape[0] != -1)) {
return NodeSizeInBytes(lhs) <= NodeSizeInBytes(rhs);
} else {
return false;
}
}
};
void OrderedNodePairPool::Insert(ir::Node* var, ir::Node* op) {
PADDLE_ENFORCE(var->IsVar() && !var->IsCtrlVar());
PADDLE_ENFORCE(op->IsOp());
if (mark_table_.count(var->Name()) != 0) {
mark_table_[var->Name()]->second.insert(op);
return;
}
auto* var_desc = FindVarDescInBlock(var);
auto var_shape = var_desc->GetShape();
int batch_size = static_cast<int>(var_shape[0]);
NodeComparator compare_node;
Iter it = nodes_.begin();
while (it != nodes_.end()) {
auto* cache_desc = FindVarDescInBlock(it->first);
int cache_batch_size = cache_desc->GetShape()[0];
if ((cache_batch_size == -1 && batch_size == -1) ||
(cache_batch_size != -1 && batch_size != -1)) {
if (compare_node(it->first, var)) {
++it;
} else {
break;
}
} else if (cache_batch_size == -1 && batch_size != -1) {
++it;
} else if (cache_batch_size != -1 && batch_size == -1) {
break;
}
}
it =
nodes_.insert(it, std::make_pair(var, std::unordered_set<ir::Node*>{op}));
mark_table_[var->Name()] = it;
}
int OrderedNodePairPool::GetIndex(ir::Node* var) {
return std::distance(nodes_.begin(), mark_table_[var->Name()]);
}
ir::Node* OrderedNodePairPool::NodeMatch(ir::Node* var) const {
ir::Node* found_node = nullptr;
NodeComparator compare_node;
for (auto it = nodes_.begin(); it != nodes_.end(); ++it) {
if (compare_node(var, it->first)) {
found_node = it->first;
break;
}
}
return found_node;
}
void OrderedNodePairPool::Erase(ir::Node* var) {
PADDLE_ENFORCE(mark_table_.count(var->Name()));
nodes_.erase(mark_table_[var->Name()]);
mark_table_.erase(var->Name());
}
std::string OrderedNodePairPool::ToString() const {
std::stringstream ss;
for (auto it = nodes_.begin(); it != nodes_.end(); ++it) {
ss << DebugString(it->first) << " ";
}
return ss.str();
}
} // namespace details
} // namespace framework
} // namespace paddle
paddle/fluid/framework/details/memory_reuse_types.h 0 → 100644
浏览文件 @ 41a64f6a
// Copyright (c) 2018 PaddlePaddle Authors. All Rights Reserved.
//
// Licensed under the Apache License, Version 2.0 (the "License");
// you may not use this file except in compliance with the License.
// You may obtain a copy of the License at
//
// http://www.apache.org/licenses/LICENSE-2.0
//
// Unless required by applicable law or agreed to in writing, software
// distributed under the License is distributed on an "AS IS" BASIS,
// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
// See the License for the specific language governing permissions and
// limitations under the License.
#pragma once
#include <algorithm>
#include <iostream>
#include <iterator>
#include <list>
#include <string>
#include <utility>
#include <vector>
#include "paddle/fluid/framework/data_type.h"
#include "paddle/fluid/framework/ir/graph.h"
namespace paddle {
namespace framework {
namespace details {
constexpr char kFetchedVars[] = "fetched_vars";
constexpr char kGraphNodePool[] = "graph_node_pool";
// NOTE(dzh): Variable and the operators use the var.
// for early delete pass.
// Because analysis var pass build base on ir::Node, which maybe released
// or modified between passes, so we use OpDesc* to mark ops.
using GraphNodePool = std::vector<
std::pair<std::string /*var node*/, std::unordered_set<OpDesc*> /* ops */>>;
// NOTE(dzh): by default, it sort node in ascend order(by node bytes size).
// in fluid, -1 means the batch_size is determined in runtime.
// the node batch_size equal -1 always ranking in the front than the node not.
// For example,
// node0[-1, 1] node1[-1, 1, 1], node2[1,1], node3[1,1024], ..
// O(1) insert, delete
class OrderedNodePairPool {
public:
using NodePair = std::pair<ir::Node*, std::unordered_set<ir::Node*>>;
using Iter = typename std::list<NodePair>::iterator;
using ConstIter = typename std::list<NodePair>::const_iterator;
void Insert(ir::Node* var, ir::Node* op);
void Erase(ir::Node* var);
bool Has(ir::Node* var) { return mark_table_.count(var->Name()); }
ir::Node* NodeMatch(ir::Node* var) const;
// map store non-const iterator, can not promise const
int GetIndex(ir::Node* var);
// pool all node to string
std::string ToString() const;
Iter begin() { return nodes_.begin(); }
Iter end() { return nodes_.end(); }
ConstIter begin() const { return nodes_.begin(); }
ConstIter end() const { return nodes_.end(); }
size_t size() const { return nodes_.size(); }
private:
// for searching.
std::unordered_map<std::string, Iter> mark_table_;
// node swap pairs. var -> ops dep var
std::list<NodePair> nodes_;
};
// node memory size in bytes
size_t NodeSizeInBytes(ir::Node* n);
std::string DebugString(ir::Node* var);
// std::string DebugString(VarDesc* var);
VarDesc* FindVarDescInBlock(ir::Node* n);
} // namespace details
} // namespace framework
} // namespace paddle
paddle/fluid/framework/details/memory_reuse_types_test.cc 0 → 100644
浏览文件 @ 41a64f6a
// Copyright (c) 2018 PaddlePaddle Authors. All Rights Reserved.
//
// Licensed under the Apache License, Version 2.0 (the "License");
// you may not use this file except in compliance with the License.
// You may obtain a copy of the License at
//
// http://www.apache.org/licenses/LICENSE-2.0
//
// Unless required by applicable law or agreed to in writing, software
// distributed under the License is distributed on an "AS IS" BASIS,
// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
// See the License for the specific language governing permissions and
// limitations under the License.
#include "paddle/fluid/framework/details/memory_reuse_types.h"
#include <algorithm>
#include <iostream>
#include <memory>
#include <sstream>
#include <string>
#include <utility>
#include <vector>
#include "glog/logging.h"
#include "gtest/gtest.h"
namespace paddle {
namespace framework {
namespace details {
TEST(OrderedNodePairPool, Normal) {
OrderedNodePairPool pool;
std::vector<std::unique_ptr<ir::Node>> nodes;
// clang-format off
std::vector<std::vector<int64_t>> shapes = {{-1, 10},
{-1, 20},
{1, 2},
{5, 2},
{10, 20},
{-1, 2, 5},
{-1, 1, 5},
{-1, 1}};
// clang-format on
const int COUNT = shapes.size();
ProgramDesc prog;
BlockDesc* block_desc = prog.MutableBlock(0);
auto* op_desc = block_desc->AppendOp();
op_desc->SetType("dummy");
std::unique_ptr<ir::Node> op = ir::CreateNodeForTest(op_desc);
for (int i = 0; i < COUNT; ++i) {
auto desc = block_desc->Var(std::to_string(i));
desc->SetShape(shapes[i]);
std::unique_ptr<ir::Node> node = ir::CreateNodeForTest(desc);
node->inputs.emplace_back(op.get());
nodes.emplace_back(std::move(node));
}
for (auto& node : nodes) {
pool.Insert(node.get(), op.get());
}
// assert its order and interface.
std::cout << pool.ToString() << std::endl;
pool.Erase(nodes.front().get());
std::cout << pool.ToString() << std::endl;
ASSERT_EQ(pool.size(), static_cast<size_t>(COUNT - 1));
ASSERT_EQ(pool.GetIndex(nodes.back().get()), 0);
{
auto v1 = block_desc->Var("11");
v1->SetShape({-1, 256, 56, 56});
std::unique_ptr<ir::Node> node1 = ir::CreateNodeForTest(v1);
node1->inputs.emplace_back(op.get());
auto* cache = pool.NodeMatch(node1.get());
ASSERT_EQ(cache, nullptr);
}
{
auto v2 = block_desc->Var("12");
v2->SetShape({-1, 2, 5});
std::unique_ptr<ir::Node> node1 = ir::CreateNodeForTest(v2);
node1->inputs.emplace_back(op.get());
auto* cache = pool.NodeMatch(node1.get());
ASSERT_EQ(pool.GetIndex(cache), 2); // match 6:[-1,2,5]
}
{
auto v3 = block_desc->Var("13");
v3->SetShape({2, 5});
std::unique_ptr<ir::Node> node1 = ir::CreateNodeForTest(v3);
node1->inputs.emplace_back(op.get());
auto* cache = pool.NodeMatch(node1.get());
ASSERT_EQ(pool.GetIndex(cache), 5); // match 4:[5,2]
}
}
} // namespace details
} // namespace framework
} // namespace paddle
paddle/fluid/framework/details/multi_devices_graph_pass.cc
浏览文件 @ 41a64f6a
......@@ -130,7 +130,6 @@ void AddOutputToLeafOps(ir::Graph *graph) {
static const char kLossVarName[] = "loss_var_name";
static const char kPlaces[] = "places";
static const char kParams[] = "params";
static const char kLocalScopes[] = "local_scopes";
static const char kStrategy[] = "strategy";
static const char kNumTrainers[] = "num_trainers";
......@@ -147,9 +146,6 @@ void MultiDevSSAGraphBuilder::Init() const {
nccl_ctxs_ = &Get<platform::NCCLContextMap>("nccl_ctxs");
#endif
for (auto &p : Get<const std::unordered_set<std::string>>(kParams)) {
grad_names_.insert(GradVarName(p));
}
balance_vars_.resize(places_.size(), 0);
if (strategy_.enable_data_balance_ && places_.size() == 1) {
LOG(WARNING) << "It is no need to enable data balance when there is only "
......@@ -905,7 +901,6 @@ REGISTER_PASS(multi_devices_pass,
paddle::framework::details::MultiDevSSAGraphBuilder)
.RequirePassAttr(paddle::framework::details::kLossVarName)
.RequirePassAttr(paddle::framework::details::kPlaces)
.RequirePassAttr(paddle::framework::details::kParams)
.RequirePassAttr(paddle::framework::details::kLocalScopes)
.RequirePassAttr(paddle::framework::details::kStrategy)
.RequirePassAttr(paddle::framework::details::kNumTrainers);
paddle/fluid/framework/details/multi_devices_graph_pass.h
浏览文件 @ 41a64f6a
......@@ -102,7 +102,6 @@ class MultiDevSSAGraphBuilder : public ir::Pass {
mutable std::string loss_var_name_;
mutable std::vector<platform::Place> places_;
mutable std::vector<Scope *> local_scopes_;
mutable std::unordered_set<std::string> grad_names_;
mutable BuildStrategy strategy_;
mutable std::unordered_map<std::string, VarDesc *> all_vars_;
......
paddle/fluid/framework/details/multi_devices_graph_print_pass.cc
浏览文件 @ 41a64f6a
......@@ -85,4 +85,5 @@ void GraphvizSSAGraphPrinter::Print(const ir::Graph &graph,
} // namespace paddle
REGISTER_PASS(multi_devices_print_pass,
paddle::framework::details::SSAGraghBuilderWithPrinter);
paddle::framework::details::SSAGraghBuilderWithPrinter)
.RequirePassAttr(paddle::framework::details::kGraphvizPath);
paddle/fluid/framework/details/multi_devices_graph_print_pass.h
浏览文件 @ 41a64f6a
......@@ -14,6 +14,7 @@
#pragma once
#include <glog/logging.h>
#include <fstream>
#include <iosfwd>
#include <ostream>
......@@ -24,6 +25,8 @@ namespace paddle {
namespace framework {
namespace details {
constexpr char kGraphvizPath[] = "debug_graphviz_path";
class SSAGraphPrinter {
public:
virtual ~SSAGraphPrinter() {}
......@@ -40,7 +43,7 @@ class SSAGraghBuilderWithPrinter : public ir::Pass {
std::unique_ptr<ir::Graph> ApplyImpl(
std::unique_ptr<ir::Graph> graph) const override {
std::unique_ptr<std::ostream> fout(
new std::ofstream(Get<const std::string>("debug_graphviz_path")));
new std::ofstream(Get<std::string>(kGraphvizPath)));
PADDLE_ENFORCE(fout->good());
Get<GraphvizSSAGraphPrinter>("graph_printer").Print(*graph, *fout);
return graph;
......
paddle/fluid/framework/details/op_handle_base.h
浏览文件 @ 41a64f6a
......@@ -25,7 +25,8 @@ namespace paddle {
namespace framework {
namespace details {
constexpr char kLocalExecScopeName[] = "@LCOAL_SCOPE@";
constexpr char kLocalExecScopeName[] = "@LOCAL_SCOPE@";
// Wraps ir::Node and provide helper utilities.
// It's responsible for populating necessary fields of ir::Node.
class OpHandleBase {
......
paddle/fluid/framework/details/reduce_op_handle.cc
浏览文件 @ 41a64f6a
......@@ -218,18 +218,18 @@ void ReduceOpHandle::RunImpl() {
}
#if defined PADDLE_WITH_CUDA && defined PADDLE_WITH_DISTRIBUTE
if (framework::IsType<const float>(in_selected_rows[0]->value().type())) {
if (in_selected_rows[0]->value().type() ==
framework::proto::VarType::FP32) {
GatherSelectedRows<platform::CUDADeviceContext, float>(
in_selected_rows, in_places, dev_ctxes_, out_var_handle, t_out_p,
out_var->GetMutable<framework::SelectedRows>());
} else if (framework::IsType<const double>(
in_selected_rows[0]->value().type())) {
} else if (in_selected_rows[0]->value().type() ==
framework::proto::VarType::FP64) {
GatherSelectedRows<platform::CUDADeviceContext, double>(
in_selected_rows, in_places, dev_ctxes_, out_var_handle, t_out_p,
out_var->GetMutable<framework::SelectedRows>());
} else {
PADDLE_ENFORCE(false,
"only support double or float when gahter SelectedRows");
PADDLE_THROW("only support double or float when gather SelectedRows");
}
#endif
});
......@@ -246,7 +246,7 @@ void ReduceOpHandle::RunImpl() {
if (!FLAGS_cpu_deterministic) {
ReduceLoDTensor func(lod_tensors,
out_var->GetMutable<framework::LoDTensor>());
VisitDataType(ToDataType(lod_tensors[0]->type()), func);
VisitDataType(lod_tensors[0]->type(), func);
} else {
// We sum lod_tensors to reduce_sum_trg which is in local_scopes_0
// here, but it doesn't mean reduce_sum_trg must be in local_scopes_0.
......@@ -256,7 +256,7 @@ void ReduceOpHandle::RunImpl() {
->FindVar(out_var_handle->name_)
->GetMutable<framework::LoDTensor>();
ReduceLoDTensor func(lod_tensors, &reduce_sum_trg);
VisitDataType(ToDataType(lod_tensors[0]->type()), func);
VisitDataType(lod_tensors[0]->type(), func);
auto trg = out_var->GetMutable<framework::LoDTensor>();
if (reduce_sum_trg.data<void>() != trg->data<void>()) {
......
paddle/fluid/framework/dlpack_tensor.cc
浏览文件 @ 41a64f6a
......@@ -13,7 +13,7 @@
// limitations under the License.
#include "paddle/fluid/framework/dlpack_tensor.h"
#include "paddle/fluid/framework/data_type.h"
namespace paddle {
namespace framework {
......@@ -36,26 +36,23 @@ static ::DLDataType GetDLDataTypeCode() {
return dtype;
}
static DLDataType GetDLDataTypeFromTypeIndex(const std::type_index &type) {
#define REG_DL_DATA_TYPE(type) \
{ std::type_index(typeid(type)), GetDLDataTypeCode<type>() }
static const std::unordered_map<std::type_index, ::DLDataType>
type_to_dtype_map({
REG_DL_DATA_TYPE(platform::float16), // NOLINT
REG_DL_DATA_TYPE(float), // NOLINT
REG_DL_DATA_TYPE(double), // NOLINT
REG_DL_DATA_TYPE(int), // NOLINT
REG_DL_DATA_TYPE(int64_t), // NOLINT
REG_DL_DATA_TYPE(bool), // NOLINT
REG_DL_DATA_TYPE(size_t), // NOLINT
REG_DL_DATA_TYPE(int16_t), // NOLINT
REG_DL_DATA_TYPE(uint8_t), // NOLINT
REG_DL_DATA_TYPE(int8_t) // NOLINT
});
static std::unordered_map<int, ::DLDataType> CreateDLDataTypeMap() {
static std::unordered_map<int, ::DLDataType> result;
#define REG_DL_DATA_TYPE(cpp_type, proto_type) \
result[static_cast<int>(proto_type)] = GetDLDataTypeCode<cpp_type>()
_ForEachDataType_(REG_DL_DATA_TYPE);
#undef REG_DL_DATA_TYPE
return result;
}
static DLDataType GetDLDataTypeFromTypeIndex(proto::VarType::Type type) {
static auto type_to_dtype_map = CreateDLDataTypeMap();
static auto type_to_dtype_map_end_it = type_to_dtype_map.end();
auto it = type_to_dtype_map.find(type);
PADDLE_ENFORCE(it != type_to_dtype_map_end_it, "Unsupported data type %s",
type.name());
auto it = type_to_dtype_map.find(static_cast<int>(type));
PADDLE_ENFORCE(it != type_to_dtype_map_end_it, "Unsupported data type %d",
type);
return it->second;
#undef REG_DL_DATA_TYPE
}
......
paddle/fluid/framework/dlpack_tensor_test.cc
浏览文件 @ 41a64f6a
......@@ -91,23 +91,11 @@ void TestMainLoop() {
}
}
}
TEST(dlpack, test_all) {
#define TestCallback(cpp_type, proto_type) TestMainLoop<cpp_type>()
#define PADDLE_DLPACK_TEST(type) \
TEST(dlpack, test_##type) { TestMainLoop<type>(); }
using float16 = platform::float16;
PADDLE_DLPACK_TEST(float16);
PADDLE_DLPACK_TEST(float);
PADDLE_DLPACK_TEST(double);
PADDLE_DLPACK_TEST(int);
PADDLE_DLPACK_TEST(int64_t);
PADDLE_DLPACK_TEST(bool);
PADDLE_DLPACK_TEST(size_t);
PADDLE_DLPACK_TEST(int16_t);
PADDLE_DLPACK_TEST(uint8_t);
PADDLE_DLPACK_TEST(int8_t);
#undef PADDLE_DLPACK_TEST
_ForEachDataType_(TestCallback);
}
} // namespace framework
} // namespace paddle
paddle/fluid/framework/executor.cc
浏览文件 @ 41a64f6a
......@@ -100,7 +100,7 @@ static void DeleteUnusedTensors(
continue;
}
auto* var = scope.FindVar(name);
if (var != nullptr) {
if (var == nullptr) {
continue;
}
......
paddle/fluid/framework/executor_thread_worker.cc
浏览文件 @ 41a64f6a
......@@ -13,6 +13,7 @@ See the License for the specific language governing permissions and
limitations under the License. */
#include "paddle/fluid/framework/executor_thread_worker.h"
#include <algorithm>
#include "google/protobuf/io/zero_copy_stream_impl.h"
#include "google/protobuf/message.h"
#include "google/protobuf/text_format.h"
......@@ -32,6 +33,89 @@ limitations under the License. */
namespace paddle {
namespace framework {
#ifdef PADDLE_WITH_PSLIB
int DensePullThread::start() {
_running = true;
_t = std::thread(&DensePullThread::run, this);
return 0;
}
void DensePullThread::run() {
while (_running) {
_pull_dense_status.resize(0);
for (auto& t : _dense_variable_name) {
if (check_update_param(t.first)) {
auto status = pull_dense(t.first);
_pull_dense_status.emplace_back(std::move(status));
reset_thread_version(t.first);
}
}
if (_pull_dense_status.size() != 0) {
wait_all();
}
usleep(_sleep_time_ms * 1000);
}
}
bool DensePullThread::check_update_param(uint64_t table_id) {
{
std::lock_guard<std::mutex> lock(_mutex_for_version);
auto& version = _training_versions[table_id];
_current_version[table_id] =
*(std::min_element(version.begin(), version.end()));
}
if (_current_version[table_id] - _last_versions[table_id] < _threshold) {
return false;
}
return true;
}
void DensePullThread::reset_thread_version(uint64_t table_id) {
std::lock_guard<std::mutex> lock(_mutex_for_version);
_last_versions[table_id] = _current_version[table_id];
}
std::future<int32_t> DensePullThread::pull_dense(uint64_t table_id) {
auto& regions = _regions[table_id];
regions.clear();
auto& variables = _dense_variable_name[table_id];
regions.resize(variables.size());
for (auto i = 0u; i < variables.size(); ++i) {
auto& t = variables[i];
Variable* var = _root_scope->FindVar(t);
LoDTensor* tensor = var->GetMutable<LoDTensor>();
float* w = tensor->data<float>();
paddle::ps::Region reg(w, tensor->numel());
regions[i] = std::move(reg);
}
return _ps_client->pull_dense(regions.data(), regions.size(), table_id);
}
void DensePullThread::wait_all() {
for (auto& t : _pull_dense_status) {
t.wait();
auto status = t.get();
if (status != 0) {
LOG(WARNING) << "pull dense failed times:" << ++_pull_dense_fail_times;
}
}
if (_pull_dense_fail_times > 20) {
LOG(FATAL) << "pull dense failed times more than 20 times";
exit(-1);
}
_pull_dense_status.resize(0);
}
void DensePullThread::increase_thread_version(int thread_id,
uint64_t table_id) {
std::lock_guard<std::mutex> lock(_mutex_for_version);
_training_versions[table_id][thread_id]++;
}
#endif
void ExecutorThreadWorker::CreateThreadOperators(const ProgramDesc& program) {
auto& block = program.Block(0);
op_names_.clear();
......@@ -139,39 +223,19 @@ void print_lod_tensor(std::string var_name, const LoDTensor& lod_tensor) {
std::cout << sstream.str() << std::endl;
}
void print_fetch_var(Scope* scope, std::string var_name) {
const LoDTensor& tensor = scope->FindVar(var_name)->Get<LoDTensor>();
if (std::type_index(tensor.type()) ==
std::type_index(typeid(platform::float16))) {
print_lod_tensor<platform::float16>(var_name, tensor);
} else if (std::type_index(tensor.type()) == std::type_index(typeid(float))) {
print_lod_tensor<float>(var_name, tensor);
} else if (std::type_index(tensor.type()) ==
std::type_index(typeid(double))) {
print_lod_tensor<double>(var_name, tensor);
} else if (std::type_index(tensor.type()) == std::type_index(typeid(int))) {
print_lod_tensor<int>(var_name, tensor);
} else if (std::type_index(tensor.type()) ==
std::type_index(typeid(int64_t))) {
print_lod_tensor<int64_t>(var_name, tensor);
} else if (std::type_index(tensor.type()) == std::type_index(typeid(bool))) {
print_lod_tensor<bool>(var_name, tensor);
} else if (std::type_index(tensor.type()) ==
std::type_index(typeid(uint8_t))) {
print_lod_tensor<uint8_t>(var_name, tensor);
} else if (std::type_index(tensor.type()) ==
std::type_index(typeid(int16_t))) {
print_lod_tensor<int16_t>(var_name, tensor);
} else if (std::type_index(tensor.type()) ==
std::type_index(typeid(int8_t))) {
print_lod_tensor<int8_t>(var_name, tensor);
} else {
VLOG(1) << "print_fetch_var: unrecognized data type:"
<< tensor.type().name();
}
static void print_fetch_var(Scope* scope, const std::string& var_name) {
auto& tensor = scope->FindVar(var_name)->Get<LoDTensor>();
return;
#define PrintLoDTensorCallback(cpp_type, proto_type) \
do { \
if (tensor.type() == proto_type) { \
print_lod_tensor<cpp_type>(var_name, tensor); \
return; \
} \
} while (0)
_ForEachDataType_(PrintLoDTensorCallback);
VLOG(1) << "print_fetch_var: unrecognized data type:" << tensor.type();
}
void ExecutorThreadWorker::TrainFiles() {
......@@ -222,5 +286,358 @@ void ExecutorThreadWorker::SetRootScope(Scope* g_scope) {
root_scope_ = g_scope;
}
#ifdef PADDLE_WITH_PSLIB
// AsyncExecutor
void AsyncExecutorThreadWorker::TrainFiles() {
SetDevice();
int fetch_var_num = fetch_var_names_.size();
fetch_values_.clear();
fetch_values_.resize(fetch_var_num);
thread_reader_->Start();
int cur_batch;
int batch_cnt = 0;
while ((cur_batch = thread_reader_->Next()) > 0) {
// executor run here
TrainOneNetwork();
++batch_cnt;
thread_scope_->DropKids();
if (debug_ == false || thread_id_ != 0) {
continue;
}
for (int i = 0; i < fetch_var_num; ++i) {
print_fetch_var(thread_scope_, fetch_var_names_[i]);
} // end for (int i = 0...)
} // end while ()
}
void AsyncExecutorThreadWorker::SetPSlibPtr(
std::shared_ptr<paddle::distributed::PSlib> pslib_ptr) {
_pslib_ptr = pslib_ptr;
}
void AsyncExecutorThreadWorker::SetPullDenseThread(
std::shared_ptr<DensePullThread> dpt) {
_pull_dense_thread = dpt;
}
void AsyncExecutorThreadWorker::TrainOneNetwork() {
PrepareParams();
for (auto& op : ops_) {
if (op->Type().find("sgd") != std::string::npos) {
continue;
}
bool need_skip = false;
for (auto t = 0u; t < _param_config->skip_op.size(); ++t) {
if (op->Type().find(_param_config->skip_op[t]) != std::string::npos) {
need_skip = true;
break;
}
}
if (!need_skip) {
op->Run(*thread_scope_, place_);
}
}
UpdateParams();
}
void AsyncExecutorThreadWorker::SetParamConfig(
AsyncWorkerParamConfig* param_config) {
_param_config = param_config;
}
void AsyncExecutorThreadWorker::PrepareParams() {
for (auto table_id : _param_config->sparse_table_id) {
PullSparse(table_id);
for (auto& t : _pull_sparse_status) {
t.wait();
auto status = t.get();
if (status != 0) {
LOG(ERROR) << "pull sparse failed, status[" << status << "]";
exit(-1);
}
}
}
_pull_sparse_status.resize(0);
for (auto table_id : _param_config->sparse_table_id) {
FillSparse(table_id);
}
}
void AsyncExecutorThreadWorker::UpdateParams() {
for (auto i : _param_config->sparse_table_id) {
PushSparse(i);
}
for (auto i : _param_config->dense_table_id) {
PushDense(i);
}
int32_t tmp_push_dense_wait_times = -1;
int32_t tmp_push_sparse_wait_times = -1;
static uint32_t push_dense_wait_times =
static_cast<uint32_t>(tmp_push_dense_wait_times);
static uint32_t push_sparse_wait_times =
static_cast<uint32_t>(tmp_push_sparse_wait_times);
if (_push_dense_status.size() >= push_dense_wait_times) {
for (auto& t : _push_dense_status) {
t.wait();
}
_push_dense_status.resize(0);
}
if (tmp_push_dense_wait_times == -1) {
_push_dense_status.resize(0);
}
if (_push_sparse_status.size() >= push_sparse_wait_times) {
for (auto& t : _push_sparse_status) {
t.wait();
}
_push_sparse_status.resize(0);
}
if (tmp_push_sparse_wait_times == -1) {
_push_sparse_status.resize(0);
}
for (auto dense_table_id : _param_config->dense_table_id) {
_pull_dense_thread->increase_thread_version(thread_id_, dense_table_id);
}
}
void AsyncExecutorThreadWorker::PushDense(int table_id) {
std::vector<paddle::ps::Region> regions;
for (auto& t : _param_config->dense_gradient_variable_name[table_id]) {
Variable* var = thread_scope_->FindVar(t);
CHECK(var != nullptr) << "var[" << t << "] not found";
LoDTensor* tensor = var->GetMutable<LoDTensor>();
int count = tensor->numel();
float* g = tensor->data<float>();
paddle::ps::Region reg(g, count);
regions.emplace_back(std::move(reg));
}
auto status = _pslib_ptr->_worker_ptr->push_dense(regions.data(),
regions.size(), table_id);
_push_dense_status.push_back(std::move(status));
}
void AsyncExecutorThreadWorker::PullSparse(int table_id) {
auto& features = _features[table_id];
auto& feature_value = _feature_value[table_id];
auto fea_dim = _param_config->fea_dim;
// slot id starts from 1
features.clear();
features.resize(0);
features.reserve(MAX_FEASIGN_NUM);
const std::vector<std::string>& feed_vec = thread_reader_->GetUseSlotAlias();
// slot_idx = 0 is label TODO
for (auto slot_idx = 1u; slot_idx < feed_vec.size(); ++slot_idx) {
Variable* var = thread_scope_->FindVar(feed_vec[slot_idx]);
LoDTensor* tensor = var->GetMutable<LoDTensor>();
int64_t* ids = tensor->data<int64_t>();
int len = tensor->numel();
for (auto i = 0u; i < len; ++i) {
// todo(colourful-tree): current trick - filter feasign=use_slot_mod(
// bug: datafeed fill use_slot_mod for empty slot)
if (ids[i] == 0u) {
continue;
}
features.push_back(static_cast<uint64_t>(ids[i]));
}
}
check_pull_push_memory(features, &feature_value, fea_dim);
std::vector<float*> pull_feature_value;
for (auto i = 0u; i < features.size(); ++i) {
pull_feature_value.push_back(feature_value[i].data());
}
auto status = _pslib_ptr->_worker_ptr->pull_sparse(
pull_feature_value.data(), table_id, features.data(), features.size());
_pull_sparse_status.push_back(std::move(status));
auto& push_g = _feature_push_value[table_id];
check_pull_push_memory(features, &push_g, fea_dim);
collect_feasign_info(table_id);
}
void AsyncExecutorThreadWorker::FillSparse(int table_id) {
auto slot_dim = _param_config->slot_dim;
auto fea_dim = _param_config->fea_dim;
auto& features = _features[table_id];
auto& fea_value = _feature_value[table_id];
CHECK(features.size() > 0) << "feature size check failed";
auto fea_idx = 0u;
std::vector<float> init_value(fea_dim);
const std::vector<std::string>& feed_vec = thread_reader_->GetUseSlotAlias();
// slot_idx = 0 is label TODO
for (auto slot_idx = 1u; slot_idx < feed_vec.size(); ++slot_idx) {
Variable* var = thread_scope_->FindVar(feed_vec[slot_idx]);
LoDTensor* tensor = var->GetMutable<LoDTensor>();
int64_t* ids = tensor->data<int64_t>();
int len = tensor->numel();
Variable* var_emb = thread_scope_->FindVar(
_param_config->slot_input_vec[table_id][slot_idx - 1]);
LoDTensor* tensor_emb = var_emb->GetMutable<LoDTensor>();
float* ptr =
tensor_emb->mutable_data<float>({len, slot_dim}, platform::CPUPlace());
memset(ptr, 0, sizeof(float) * len * slot_dim);
auto& tensor_lod = tensor->lod()[0];
LoD data_lod{tensor_lod};
tensor_emb->set_lod(data_lod);
for (auto index = 0u; index < len; ++index) {
if (ids[index] == 0u) {
memcpy(ptr + slot_dim * index, init_value.data() + 2,
sizeof(float) * slot_dim);
continue;
}
memcpy(ptr + slot_dim * index, fea_value[fea_idx].data() + 2,
sizeof(float) * slot_dim);
fea_idx++;
}
}
}
void AsyncExecutorThreadWorker::PushSparse(int table_id) {
auto slot_dim = _param_config->slot_dim;
auto fea_dim = _param_config->fea_dim;
auto& features = _features[table_id];
auto& push_g = _feature_push_value[table_id];
check_pull_push_memory(features, &push_g, fea_dim);
CHECK(push_g.size() == features.size() + 1)
<< "push_g size:" << push_g.size()
<< " features size:" << features.size();
uint64_t fea_idx = 0u;
auto& fea_info = _fea_info[table_id];
int offset = 2;
const std::vector<std::string>& feed_vec = thread_reader_->GetUseSlotAlias();
// slot_idx = 0 is label
for (auto slot_idx = 1u; slot_idx < feed_vec.size(); ++slot_idx) {
if (_param_config->slot_alias_to_table.find(feed_vec[slot_idx]) ==
_param_config->slot_alias_to_table.end()) {
LOG(ERROR) << "ERROR slot_idx:" << slot_idx
<< " name:" << feed_vec[slot_idx];
} else if (_param_config->slot_alias_to_table[feed_vec[slot_idx]] !=
table_id) {
continue;
}
Variable* g_var = thread_scope_->FindVar(
_param_config->gradient_var[table_id][slot_idx - 1]);
CHECK(g_var != nullptr)
<< "var[" << _param_config->gradient_var[table_id][slot_idx - 1]
<< "] not found";
LoDTensor* g_tensor = g_var->GetMutable<LoDTensor>();
if (g_tensor == NULL) {
LOG(ERROR) << "var["
<< _param_config->gradient_var[table_id][slot_idx - 1]
<< "] not found";
exit(-1);
}
float* g = g_tensor->data<float>();
Variable* var = thread_scope_->FindVar(feed_vec[slot_idx]);
CHECK(var != nullptr) << "var[" << feed_vec[slot_idx] << "] not found";
LoDTensor* tensor = var->GetMutable<LoDTensor>();
if (tensor == NULL) {
LOG(ERROR) << "var[" << feed_vec[slot_idx] << "] not found";
exit(-1);
}
int len = tensor->numel();
CHECK(slot_dim * len == g_tensor->numel())
<< "len:" << len << " g_numel:" << g_tensor->numel();
CHECK(len == tensor->numel()) << "len:" << len
<< "t_numel:" << tensor->numel();
int64_t* ids = tensor->data<int64_t>();
for (auto id_idx = 0u; id_idx < len; ++id_idx) {
if (ids[id_idx] == 0) {
g += slot_dim;
continue;
}
memcpy(push_g[fea_idx].data() + offset, g, sizeof(float) * slot_dim);
push_g[fea_idx][0] = 1.0f;
CHECK(fea_idx < fea_info.size()) << "fea_idx:" << fea_idx
<< " size:" << fea_info.size();
push_g[fea_idx][1] = static_cast<float>(fea_info[fea_idx].label);
g += slot_dim;
fea_idx++;
}
}
CHECK(fea_idx == features.size()) << "fea_idx:" << fea_idx
<< " features size:" << features.size();
CHECK_GT(features.size(), 0);
std::vector<float*> push_g_vec;
for (auto i = 0u; i < features.size(); ++i) {
push_g_vec.push_back(push_g[i].data());
}
auto status = _pslib_ptr->_worker_ptr->push_sparse(
table_id, features.data(), (const float**)push_g_vec.data(),
features.size());
_push_sparse_status.push_back(std::move(status));
}
void AsyncExecutorThreadWorker::collect_feasign_info(int table_id) {
auto& fea_info = _fea_info[table_id];
auto& feature = _features[table_id];
fea_info.resize(feature.size());
const std::vector<std::string>& feed_vec = thread_reader_->GetUseSlotAlias();
Variable* var = thread_scope_->FindVar(feed_vec[0]);
LoDTensor* tensor = var->GetMutable<LoDTensor>();
int64_t* label = tensor->data<int64_t>();
int global_index = 0;
for (auto slot_idx = 1u; slot_idx < feed_vec.size(); ++slot_idx) {
Variable* var = thread_scope_->FindVar(feed_vec[slot_idx]);
LoDTensor* tensor = var->GetMutable<LoDTensor>();
int64_t* ids = tensor->data<int64_t>();
int fea_idx = 0;
for (auto ins_idx = 1u; ins_idx < tensor->lod()[0].size(); ++ins_idx) {
for (; fea_idx < tensor->lod()[0][ins_idx]; ++fea_idx) {
if (ids[fea_idx] == 0u) {
continue;
}
FeasignInfo info{slot_idx, ins_idx, label[ins_idx - 1]};
fea_info[global_index++] = std::move(info);
}
}
}
CHECK(global_index == feature.size())
<< "expect fea info size:" << feature.size() << " real:" << global_index;
}
void AsyncExecutorThreadWorker::check_pull_push_memory(
const std::vector<uint64_t>& features,
std::vector<std::vector<float>>* push_g, int dim) {
push_g->resize(features.size() + 1);
for (auto& t : *push_g) {
t.resize(dim);
}
}
void AsyncExecutorThreadWorker::check_pull_push_memory(
const std::vector<uint64_t>& features, std::vector<float*>* push_g,
int dim) {
if (features.size() > push_g->size()) {
push_g->reserve(features.size() + 1);
auto size = features.size() - push_g->size() + 1;
for (auto i = 0u; i < size; ++i) {
float* ptr = new float[dim];
push_g->push_back(ptr);
}
}
}
#endif
} // einit_modelnd namespace framework
} // end namespace paddle
paddle/fluid/framework/executor_thread_worker.h
浏览文件 @ 41a64f6a
......@@ -25,16 +25,119 @@ limitations under the License. */
#include "paddle/fluid/framework/executor.h"
#include "paddle/fluid/framework/program_desc.h"
#include "paddle/fluid/framework/scope.h"
#ifdef PADDLE_WITH_PSLIB
#include <pslib.h>
#endif
namespace paddle {
namespace framework {
void CreateTensor(Variable* var, proto::VarType::Type var_type);
#ifdef PADDLE_WITH_PSLIB
static const uint32_t MAX_FEASIGN_NUM = 1000 * 100 * 100;
struct AsyncWorkerParamConfig {
int slot_dim;
int fea_dim;
int32_t tmp_push_dense_wait_times;
int32_t tmp_push_sparse_wait_times;
std::vector<std::string> skip_op;
std::map<uint64_t, std::vector<std::string>> dense_variable_name;
std::map<uint64_t, std::vector<std::string>> dense_gradient_variable_name;
std::vector<int> dense_table_id;
// fea_dim for each dense table
std::vector<uint32_t> dense_table_size;
std::vector<int> sparse_table_id;
std::map<uint64_t, std::vector<std::string>> slot_input_vec;
std::map<uint64_t, std::vector<std::string>> gradient_var;
std::map<std::string, uint64_t> slot_alias_to_table;
};
struct DensePullThreadParam {
std::shared_ptr<paddle::ps::PSClient> ps_client;
int threshold;
int training_thread_num;
Scope* root_scope;
std::map<uint64_t, std::vector<std::string>>* dense_params;
int sleep_time_ms = 2;
};
class DensePullThread {
public:
explicit DensePullThread(const DensePullThreadParam& param)
: _running(false) {
_ps_client = param.ps_client;
_threshold = param.threshold;
_thread_num = param.training_thread_num;
_root_scope = param.root_scope;
_sleep_time_ms = param.sleep_time_ms;
for (auto& t : *param.dense_params) {
_dense_variable_name[t.first].insert(_dense_variable_name[t.first].end(),
t.second.begin(), t.second.end());
_training_versions[t.first].resize(_thread_num, 0);
_last_versions[t.first] = 0;
_current_version[t.first] = 0;
}
}
int start();
void stop() {
if (_running) {
_running = false;
_t.join();
}
}
void increase_thread_version(int thread_id, uint64_t table_id);
void reset_thread_version(uint64_t table_id);
std::future<int32_t> pull_dense(uint64_t table_id);
void pull_dense2(uint64_t table_id);
void wait_all();
private:
void run();
bool check_update_param(uint64_t table_id);
private:
std::shared_ptr<paddle::ps::PSClient> _ps_client;
int _thread_num;
int _threshold;
int _sleep_time_ms;
Scope* _root_scope;
bool _running;
std::map<uint64_t, uint64_t> _last_versions;
std::map<uint64_t, uint64_t> _current_version;
std::mutex _mutex_for_version;
std::map<uint64_t, std::vector<uint64_t>> _training_versions;
std::map<uint64_t, std::vector<std::string>> _dense_variable_name;
std::thread _t;
std::vector<::std::future<int32_t>> _pull_dense_status;
std::map<uint64_t, std::vector<paddle::ps::Region>> _regions;
uint32_t _pull_dense_fail_times = 0;
std::vector<float> _base_norm_param;
std::vector<float> _mean;
std::vector<float> _scale;
float _squared_sum_epsilon = 1e-4;
std::mutex _mutex_for_mean_scale;
float _total_batch_num = 0;
};
#endif
class ExecutorThreadWorker {
public:
ExecutorThreadWorker()
: thread_id_(-1), root_scope_(NULL), thread_scope_(NULL), debug_(false) {}
~ExecutorThreadWorker() {}
virtual ~ExecutorThreadWorker() {}
void CreateThreadResource(const framework::ProgramDesc& program,
const paddle::platform::Place& place);
......@@ -51,9 +154,15 @@ class ExecutorThreadWorker {
// set data feed declared in executor
void SetDataFeed(const std::shared_ptr<DataFeed>& datafeed);
// A multi-thread training function
void TrainFiles();
virtual void TrainFiles();
// set fetch variable names from python interface assigned by users
void SetFetchVarNames(const std::vector<std::string>& fetch_var_names);
#ifdef PADDLE_WITH_PSLIB
virtual void SetPSlibPtr(
std::shared_ptr<paddle::distributed::PSlib> pslib_ptr) {}
virtual void SetPullDenseThread(std::shared_ptr<DensePullThread> dpt) {}
virtual void SetParamConfig(AsyncWorkerParamConfig* param_config) {}
#endif
private:
void CreateThreadScope(const framework::ProgramDesc& program);
......@@ -77,12 +186,58 @@ class ExecutorThreadWorker {
Scope* root_scope_;
// a thread scope, father scope is global score which is shared
Scope* thread_scope_;
private:
std::vector<std::string> fetch_var_names_;
std::vector<std::vector<float>> fetch_values_;
bool debug_;
};
#ifdef PADDLE_WITH_PSLIB
class AsyncExecutorThreadWorker : public ExecutorThreadWorker {
public:
AsyncExecutorThreadWorker() {}
virtual ~AsyncExecutorThreadWorker() {}
void SetPSlibPtr(std::shared_ptr<paddle::distributed::PSlib> pslib_ptr);
void SetPullDenseThread(std::shared_ptr<DensePullThread> dpt);
void SetParamConfig(AsyncWorkerParamConfig* param_config);
void TrainFiles();
void TrainOneNetwork();
void PrepareParams();
void UpdateParams();
void PullSparse(int table_id);
void FillSparse(int table_id);
void PushSparse(int table_id);
void PushDense(int table_id);
void check_pull_push_memory(const std::vector<uint64_t>& features,
std::vector<float*>* push_g, int dim);
void check_pull_push_memory(const std::vector<uint64_t>& features,
std::vector<std::vector<float>>* push_g, int dim);
void collect_feasign_info(int table_id);
private:
struct FeasignInfo {
uint32_t slot;
uint32_t ins;
int64_t label;
};
std::map<uint64_t, std::vector<uint64_t>> _features;
std::map<uint64_t, std::vector<FeasignInfo>> _fea_info;
std::map<uint64_t, std::vector<std::vector<float>>> _feature_value;
std::map<uint64_t, std::vector<std::vector<float>>> _feature_push_value;
std::shared_ptr<paddle::distributed::PSlib> _pslib_ptr;
std::shared_ptr<DensePullThread> _pull_dense_thread;
std::vector<::std::future<int32_t>> _pull_sparse_status;
std::vector<::std::future<int32_t>> _pull_dense_status;
std::vector<::std::future<int32_t>> _push_sparse_status;
std::vector<::std::future<int32_t>> _push_dense_status;
AsyncWorkerParamConfig* _param_config;
};
#endif
} // namespace framework
} // namespace paddle
paddle/fluid/framework/ir/CMakeLists.txt
浏览文件 @ 41a64f6a
......@@ -44,6 +44,7 @@ pass_library(seqconv_eltadd_relu_fuse_pass inference)
pass_library(is_test_pass base)
pass_library(conv_elementwise_add_act_fuse_pass inference)
pass_library(conv_elementwise_add2_act_fuse_pass inference)
pass_library(conv_elementwise_add_fuse_pass inference)
if(WITH_MKLDNN)
pass_library(mkldnn_placement_pass base)
pass_library(depthwise_conv_mkldnn_pass base)
......
paddle/fluid/framework/ir/conv_elementwise_add_fuse_pass.cc 0 → 100644
浏览文件 @ 41a64f6a
// Copyright (c) 2018 PaddlePaddle Authors. All Rights Reserved.
//
// Licensed under the Apache License, Version 2.0 (the "License");
// you may not use this file except in compliance with the License.
// You may obtain a copy of the License at
//
// http://www.apache.org/licenses/LICENSE-2.0
//
// Unless required by applicable law or agreed to in writing, software
// distributed under the License is distributed on an "AS IS" BASIS,
// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
// See the License for the specific language governing permissions and
// limitations under the License.
#include <string>
#include "paddle/fluid/framework/ir/conv_elementwise_add_fuse_pass.h"
#include "paddle/fluid/framework/ir/graph_viz_pass.h"
namespace paddle {
namespace framework {
namespace ir {
#define GET_IR_NODE(node__) GET_IR_NODE_FROM_SUBGRAPH(node__, node__, pattern);
#define GET_NODES \
GET_IR_NODE(conv_op); \
GET_IR_NODE(conv_out); \
GET_IR_NODE(conv_filter); \
GET_IR_NODE(elementwise_add_op); \
GET_IR_NODE(elementwise_add_in_y); \
GET_IR_NODE(elementwise_add_out);
std::unique_ptr<ir::Graph> ConvElementwiseAddFusePass::ApplyImpl(
std::unique_ptr<ir::Graph> graph) const {
const std::string pattern_name = "conv_elementwise_add_fuse";
FusePassBase::Init(pattern_name, graph.get());
GraphPatternDetector gpd;
auto* x = gpd.mutable_pattern()
->NewNode("x")
->assert_is_op_input("conv2d", "Input")
->AsInput();
patterns::ConvElementwiseadd pattern(gpd.mutable_pattern(), pattern_name);
pattern(x);
auto handler = [&](const GraphPatternDetector::subgraph_t& subgraph,
Graph* g) {
GET_NODES;
auto base_op_desc = *conv_op->Op()->Proto();
std::string bias_name = elementwise_add_in_y->Name();
std::string output_name = elementwise_add_out->Name();
std::string act_type = "identity";
framework::OpDesc new_op_desc(base_op_desc, nullptr);
new_op_desc.SetType("conv2d_fusion");
new_op_desc.SetInput("Bias", {bias_name});
new_op_desc.SetInput("ResidualData", {});
new_op_desc.SetAttr("activation", act_type);
new_op_desc.SetOutput("Output", {output_name});
new_op_desc.SetAttr("is_test", true);
new_op_desc.SetAttr("use_cudnn", false);
new_op_desc.Flush();
// Create a new node for the fused op.
auto* new_conv_op = graph->CreateOpNode(&new_op_desc);
// Link inputs and outputs.
PADDLE_ENFORCE(subgraph.count(x));
auto* conv_in_node = subgraph.at(x);
IR_NODE_LINK_TO(conv_in_node, new_conv_op); // Input
IR_NODE_LINK_TO(conv_filter, new_conv_op); // Filter
IR_NODE_LINK_TO(elementwise_add_in_y, new_conv_op); // Bias
IR_NODE_LINK_TO(new_conv_op, elementwise_add_out); // Output
// Delete the unneeded nodes.
GraphSafeRemoveNodes(graph.get(), {conv_op, conv_out, elementwise_add_op});
};
gpd(graph.get(), handler);
return graph;
}
} // namespace ir
} // namespace framework
} // namespace paddle
REGISTER_PASS(conv_elementwise_add_fuse_pass,
paddle::framework::ir::ConvElementwiseAddFusePass);
paddle/fluid/framework/ir/conv_elementwise_add_fuse_pass.h 0 → 100644
浏览文件 @ 41a64f6a
// Copyright (c) 2018 PaddlePaddle Authors. All Rights Reserved.
//
// Licensed under the Apache License, Version 2.0 (the "License");
// you may not use this file except in compliance with the License.
// You may obtain a copy of the License at
//
// http://www.apache.org/licenses/LICENSE-2.0
//
// Unless required by applicable law or agreed to in writing, software
// distributed under the License is distributed on an "AS IS" BASIS,
// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
// See the License for the specific language governing permissions and
// limitations under the License.
#pragma once
#include "paddle/fluid/framework/ir/fuse_pass_base.h"
#include "paddle/fluid/framework/ir/graph_pattern_detector.h"
namespace paddle {
namespace framework {
namespace ir {
class ConvElementwiseAddFusePass : public FusePassBase {
public:
virtual ~ConvElementwiseAddFusePass() {}
protected:
std::unique_ptr<ir::Graph> ApplyImpl(std::unique_ptr<ir::Graph> graph) const;
};
} // namespace ir
} // namespace framework
} // namespace paddle
paddle/fluid/framework/ir/conv_elementwise_add_mkldnn_fuse_pass.cc
浏览文件 @ 41a64f6a
......@@ -24,35 +24,6 @@ namespace paddle {
namespace framework {
namespace ir {
// The function keeps the graph consistent by replacing
// a node 'from' in the set of inputs nodes
// of the visited node by a node 'to'.
void CorrectGraphEdges(Graph* graph, Node* from, Node* to) {
for (auto& node : GraphTraits::DFS(*graph)) {
auto from_in_inputs =
std::find(std::begin(node.inputs), std::end(node.inputs), from);
if (from_in_inputs != std::end(node.inputs)) {
IR_NODE_LINK_TO(to, (&node));
auto inputs = node.Op()->Inputs();
using input_type = VariableNameMap::value_type;
std::for_each(std::begin(inputs), std::end(inputs),
[from, to, &node](const input_type& i) -> void {
auto param_names = i.second;
auto pi = std::find(std::begin(param_names),
std::end(param_names), from->Name());
if (pi != std::end(param_names)) {
node.Op()->SetInput(i.first, {to->Name()});
}
});
}
}
}
bool IsReachable(ir::Graph* graph, Node* from, Node* to) {
auto find_node = [](ir::Graph* graph, const Node* node) -> Node* {
for (auto n : graph->Nodes()) {
......@@ -99,25 +70,12 @@ bool IsReachable(ir::Graph* graph, Node* from, Node* to) {
return false;
}
boost::optional<Node*> HasBias(const Node& op, const std::string& bias_name) {
auto bias_input_names = op.Op()->Inputs();
auto bias_it = bias_input_names.find(bias_name);
if (bias_it != std::end(bias_input_names)) {
bool has_bias = !bias_it->second.empty();
if (has_bias) {
auto bias_names = bias_it->second;
auto bias_names_it =
std::find_if(std::begin(op.inputs), std::end(op.inputs),
[&bias_names](Node* n) -> bool {
return n->Name() == bias_names[0];
});
return *bias_names_it;
}
}
return boost::none;
template <typename T>
boost::optional<T> HasAttribute(const Node& op, const std::string& attr) {
if (op.Op()->HasAttr(attr))
return boost::get<T>(op.Op()->GetAttr(attr));
else
return boost::none;
}
ResidualConnectionMKLDNNFusePass::IdentityFuseHandle::IdentityFuseHandle(
......@@ -151,40 +109,18 @@ void ResidualConnectionMKLDNNFusePass::IdentityFuseHandle::operator()(
if (!IsReachable(graph, elementwise_add_identity, conv_output)) return;
OpDesc op_desc;
op_desc.SetType("conv2d");
op_desc.SetInput("Input", {conv_input->Name()});
op_desc.SetInput("Filter", {conv_filter->Name()});
op_desc.SetInput("ResidualData", {elementwise_add_identity->Name()});
op_desc.SetOutput("Output", {conv_output->Name()});
auto fuse_relu = HasAttribute<bool>(*conv_op, "fuse_relu");
if (fuse_relu && *fuse_relu) return;
auto conv_bias = HasBias(*conv_op, "Bias");
conv_op->Op()->SetInput("ResidualData", {elementwise_add_identity->Name()});
conv_op->Op()->SetOutput("Output", {elementwise_add_out->Name()});
conv_op->Op()->SetAttr("fuse_residual_connection", true);
if (conv_bias) {
op_desc.SetInput("Bias", {(*conv_bias)->Name()});
}
for (const auto& attr : conv_op->Op()->GetAttrMap()) {
op_desc.SetAttr(attr.first, attr.second);
}
op_desc.SetAttr("fuse_residual_connection", true);
GraphSafeRemoveNodes(graph, {conv_output, elementwise_add_op});
auto fused_conv_op = graph->CreateOpNode(&op_desc);
IR_NODE_LINK_TO(conv_input, fused_conv_op);
IR_NODE_LINK_TO(conv_filter, fused_conv_op);
IR_NODE_LINK_TO(elementwise_add_identity, fused_conv_op);
IR_NODE_LINK_TO(fused_conv_op, conv_output);
if (conv_bias) {
IR_NODE_LINK_TO((*conv_bias), fused_conv_op);
}
IR_NODE_LINK_TO(elementwise_add_identity, conv_op);
IR_NODE_LINK_TO(conv_op, elementwise_add_out);
CorrectGraphEdges(graph, elementwise_add_out, conv_output);
GraphSafeRemoveNodes(graph,
{elementwise_add_out, conv_op, elementwise_add_op});
(*fusion_stats)++;
}
......@@ -229,60 +165,33 @@ void ResidualConnectionMKLDNNFusePass::ProjectionFuseHandle::operator()(
Node* projection_node;
Node* residual_conv_op;
Node* residual_conv_input;
Node* residual_conv_filter;
Node* residual_conv_output;
if (IsReachable(graph, conv_x_input, conv_y_output)) {
projection_node = conv_x_output;
residual_conv_op = conv_y_op;
residual_conv_input = conv_y_input;
residual_conv_filter = conv_y_filter;
residual_conv_output = conv_y_output;
} else if (IsReachable(graph, conv_y_input, conv_x_output)) {
projection_node = conv_y_output;
residual_conv_op = conv_x_op;
residual_conv_input = conv_x_input;
residual_conv_filter = conv_x_filter;
residual_conv_output = conv_x_output;
} else {
return;
}
OpDesc op_desc;
op_desc.SetType("conv2d");
auto fuse_relu = HasAttribute<bool>(*residual_conv_op, "fuse_relu");
if (fuse_relu && *fuse_relu) return;
op_desc.SetInput("Input", {residual_conv_input->Name()});
op_desc.SetInput("Filter", {residual_conv_filter->Name()});
op_desc.SetInput("ResidualData", {projection_node->Name()});
op_desc.SetOutput("Output", {residual_conv_output->Name()});
residual_conv_op->Op()->SetInput("ResidualData", {projection_node->Name()});
residual_conv_op->Op()->SetOutput("Output", {elementwise_add_out->Name()});
auto residual_conv_bias = HasBias(*residual_conv_op, "Bias");
residual_conv_op->Op()->SetAttr("fuse_residual_connection", true);
if (residual_conv_bias) {
op_desc.SetInput("Bias", {(*residual_conv_bias)->Name()});
}
for (const auto& attr : residual_conv_op->Op()->GetAttrMap()) {
op_desc.SetAttr(attr.first, attr.second);
}
op_desc.SetAttr("fuse_residual_connection", true);
GraphSafeRemoveNodes(graph, {residual_conv_output, elementwise_add_op});
auto fused_conv_op = graph->CreateOpNode(&op_desc);
IR_NODE_LINK_TO(residual_conv_input, fused_conv_op);
IR_NODE_LINK_TO(residual_conv_filter, fused_conv_op);
IR_NODE_LINK_TO(projection_node, fused_conv_op);
IR_NODE_LINK_TO(fused_conv_op, residual_conv_output);
if (residual_conv_bias) {
IR_NODE_LINK_TO((*residual_conv_bias), fused_conv_op);
}
IR_NODE_LINK_TO(projection_node, residual_conv_op);
IR_NODE_LINK_TO(residual_conv_op, elementwise_add_out);
CorrectGraphEdges(graph, elementwise_add_out, residual_conv_output);
GraphSafeRemoveNodes(
graph, {elementwise_add_out, residual_conv_op, elementwise_add_op});
(*fusion_stats)++;
}
......
paddle/fluid/framework/ir/graph.cc
浏览文件 @ 41a64f6a
......@@ -162,7 +162,10 @@ void Graph::ResolveHazard(
(*it_new)->inputs.empty() ? nullptr : (*it_new)->inputs[0];
const auto &read_ops = (*it_old)->outputs;
PADDLE_ENFORCE(write_op, "The write_op should not be empty.");
PADDLE_ENFORCE(
write_op,
string::Sprintf("The write_op of var %s should not be empty.",
(*it_new)->Name()));
// Add write after write dependence
ir::Node *upstream_op =
......
paddle/fluid/framework/ir/graph_helper.cc
浏览文件 @ 41a64f6a
......@@ -18,6 +18,7 @@ limitations under the License. */
#include <fstream>
#include <iosfwd>
#include <ostream>
#include <unordered_map>
#include <unordered_set>
DEFINE_string(print_sub_graph_dir, "",
......@@ -121,7 +122,7 @@ std::map<ir::Node *, std::unordered_set<ir::Node *>> BuildOperationAdjList(
}
size_t GraphNum(const Graph &graph) {
std::unordered_set<ir::Node *> nodes = graph.Nodes();
std::unordered_set<ir::Node *> nodes(graph.Nodes());
std::unordered_set<ir::Node *> visited_nodes;
visited_nodes.reserve(nodes.size());
std::deque<ir::Node *> q_nodes;
......
paddle/fluid/framework/ir/graph_helper.h
浏览文件 @ 41a64f6a
......@@ -24,6 +24,7 @@ limitations under the License. */
namespace paddle {
namespace framework {
namespace ir {
// Test if the graph contains circle.
bool HasCircle(const Graph &graph);
......
paddle/fluid/framework/ir/graph_pattern_detector.cc
浏览文件 @ 41a64f6a
......@@ -17,7 +17,6 @@
#include <string>
#include <vector>
#include "graph_pattern_detector.h"
#include "paddle/fluid/framework/ir/graph_helper.h"
#include "paddle/fluid/framework/ir/graph_pattern_detector.h"
#include "paddle/fluid/framework/ir/graph_traits.h"
......@@ -1210,6 +1209,33 @@ PDNode *patterns::ConvElementwiseadd2Act::operator()(PDNode *conv_in) {
return act_out;
}
PDNode *patterns::ConvElementwiseadd::operator()(PDNode *conv_in) {
conv_in->AsInput();
auto conv_op = pattern->NewNode(conv_op_repr())->assert_is_op("conv2d");
auto conv_out = pattern->NewNode(conv_out_repr())
->assert_is_op_output("conv2d")
->assert_is_op_input("elementwise_add", "X")
->AsIntermediate();
auto conv_filter = pattern->NewNode(conv_filter_repr())
->assert_is_op_input("conv2d", "Filter")
->AsInput();
auto elementwise_add_op = pattern->NewNode(elementwise_add_op_repr())
->assert_is_op("elementwise_add");
auto elementwise_add_in_y = pattern->NewNode(elementwise_add_in_y_repr())
->assert_is_op_input("elementwise_add", "Y")
->AsInput();
auto elementwise_add_out = pattern->NewNode(elementwise_add_out_repr())
->assert_is_op_output("elementwise_add")
->AsOutput();
conv_op->LinksFrom({conv_in, conv_filter});
conv_out->LinksFrom({conv_op});
elementwise_add_op->LinksFrom({conv_out, elementwise_add_in_y})
.LinksTo({elementwise_add_out});
return elementwise_add_out;
}
} // namespace ir
} // namespace framework
} // namespace paddle
paddle/fluid/framework/ir/graph_pattern_detector.h
浏览文件 @ 41a64f6a
......@@ -716,6 +716,24 @@ struct ConvElementwiseadd2Act : public PatternBase {
PATTERN_DECL_NODE(act_out);
};
// Conv + ElementwiseAdd
// This pattern should be used after ConvElementwiseadd2Act or
// ConvElementwiseadd pass
struct ConvElementwiseadd : public PatternBase {
ConvElementwiseadd(PDPattern* pattern, const std::string& name_scope)
: PatternBase(pattern, name_scope, "conv_elementwiseadd") {}
PDNode* operator()(PDNode* conv_in);
PATTERN_DECL_NODE(conv_op);
PATTERN_DECL_NODE(conv_out);
PATTERN_DECL_NODE(conv_filter);
PATTERN_DECL_NODE(elementwise_add_op);
PATTERN_DECL_NODE(elementwise_add_in_y);
PATTERN_DECL_NODE(elementwise_add_out);
};
} // namespace patterns
// Link two ir::Nodes from each other.
......
paddle/fluid/framework/ir/node.cc
浏览文件 @ 41a64f6a
......@@ -30,6 +30,14 @@ std::unique_ptr<Node> CreateNodeForTest(const std::string &name,
return std::unique_ptr<Node>(new Node(name, type));
}
std::unique_ptr<Node> CreateNodeForTest(VarDesc *var_desc) {
return std::unique_ptr<Node>(new Node(var_desc));
}
std::unique_ptr<Node> CreateNodeForTest(OpDesc *op_desc) {
return std::unique_ptr<Node>(new Node(op_desc));
}
} // namespace ir
} // namespace framework
} // namespace paddle
paddle/fluid/framework/ir/node.h
浏览文件 @ 41a64f6a
......@@ -18,7 +18,6 @@ limitations under the License. */
#include <typeindex>
#include <typeinfo>
#include <vector>
#include "paddle/fluid/framework/op_desc.h"
#include "paddle/fluid/framework/var_desc.h"
#include "paddle/fluid/platform/macros.h"
......@@ -125,6 +124,8 @@ class Node {
friend class Graph;
friend std::unique_ptr<Node> CreateNodeForTest(const std::string& name,
Node::Type type);
friend std::unique_ptr<Node> CreateNodeForTest(VarDesc* var_desc);
friend std::unique_ptr<Node> CreateNodeForTest(OpDesc* op_desc);
explicit Node(const std::string& name, Type type)
: name_(name), var_desc_(nullptr), op_desc_(nullptr), type_(type) {}
......@@ -152,7 +153,9 @@ class Node {
std::unique_ptr<Node> CreateNodeForTest(const std::string& name,
Node::Type type);
std::unique_ptr<Node> CreateNodeForTest(VarDesc* var_desc);
std::unique_ptr<Node> CreateNodeForTest(OpDesc* op_desc);
} // namespace ir
} // namespace framework
} // namespace paddle
paddle/fluid/framework/lod_tensor.cc
浏览文件 @ 41a64f6a
......@@ -70,9 +70,9 @@ std::ostream &operator<<(std::ostream &os, const LoDTensor &t) {
// only print first ten elements
int64_t size = t.numel() < 10 ? t.numel() : 10;
for (int64_t i = 0; i < size; ++i) {
if (IsType<float>(t.type())) {
if (t.type() == proto::VarType::FP32) {
os << t.data<float>()[i] << " ";
} else if (IsType<int64_t>(t.type())) {
} else if (t.type() == proto::VarType::INT64) {
os << t.data<int64_t>()[i] << " ";
} else {
PADDLE_THROW("LoDTensor data type not in [float, int64_t]");
......@@ -157,13 +157,8 @@ bool CheckLoD(const LoD &in, int tensor_height) {
if (level.size() < 2) return false;
// check: the first offset(the begin offset) of each level should be 0.
if (level.front() != 0) return false;
// check: all the offsets in a level should be ascending(no same items
// allows).
if (!std::is_sorted(level.begin(), level.begin(), [](size_t a, size_t b) {
if (a < b) return true;
return false;
})) {
LOG(INFO) << "ascending error";
// check: all the offsets in a level should be ascending(allow same items)
if (!std::is_sorted(level.begin(), level.end())) {
return false;
}
}
......@@ -387,7 +382,7 @@ void LoDTensor::MergeLoDTensor(
PADDLE_ENFORCE(!lod_tensors.empty());
framework::DDim new_dim = lod_tensors[0]->dims();
std::type_index new_type = lod_tensors[0]->type();
auto new_type = lod_tensors[0]->type();
framework::DataLayout new_layout = lod_tensors[0]->layout();
LoD new_lod = lod_tensors[0]->lod();
for (size_t i = 1; i < lod_tensors.size(); ++i) {
......
paddle/fluid/framework/lod_tensor_test.cc
浏览文件 @ 41a64f6a
......@@ -217,6 +217,11 @@ TEST(LoD, CheckLoD) {
// check with underlying tensor storage.
ASSERT_TRUE(CheckLoD(relative_lod, 5));
ASSERT_FALSE(CheckLoD(relative_lod, 9));
// check whether lod is ascending-sorted (allow same items)
ASSERT_TRUE(CheckLoD({{0, 1, 2, 3, 4, 5}}, 5));
ASSERT_TRUE(CheckLoD({{0, 1, 3, 3, 4, 5}}, 5));
ASSERT_FALSE(CheckLoD({{0, 1, 3, 2, 5}}, 5));
}
TEST(LoD, CheckAbsLoD) {
......
paddle/fluid/framework/ngraph_operator.cc
浏览文件 @ 41a64f6a
......@@ -278,7 +278,8 @@ std::shared_ptr<ngraph::runtime::Backend> NgraphEngine::backend_ =
ngraph::runtime::Backend::create("CPU");
void NgraphEngine::GetNgInputShape(std::shared_ptr<OperatorBase> op) {
op->RuntimeInferShape(scope_, place_);
RuntimeContext ctx(op->Inputs(), op->Outputs(), scope_);
op->RuntimeInferShape(scope_, place_, ctx);
for (auto& var_name_item : op->Inputs()) {
for (auto& var_name : var_name_item.second) {
auto* var = scope_.FindVar(var_name);
......@@ -471,27 +472,23 @@ void NgraphEngine::Run(const Scope& scope, const platform::Place& place) const {
auto* tensor_pd = GetLoDTensorOrSelectedRowsValueFromVar(*var);
PADDLE_ENFORCE(sp == Ddim2Shape(tensor_pd->dims()),
"Ensure ngraph tensor layout align with paddle tensor");
if (tensor_pd->type().hash_code() ==
typeid(float).hash_code()) { // NOLINT
if (tensor_pd->type() == proto::VarType::FP32) {
const float* arr = tensor_pd->data<float>();
ti = backend_->create_tensor(ngraph::element::f32, sp,
const_cast<float*>(arr));
} else if (tensor_pd->type().hash_code() ==
typeid(int).hash_code()) { // NOLINT
} else if (tensor_pd->type() == proto::VarType::INT32) {
const int* arr = tensor_pd->data<int>();
ti = backend_->create_tensor(ngraph::element::i32, sp,
const_cast<int*>(arr));
} else if (tensor_pd->type().hash_code() == typeid(int64_t).hash_code()) {
} else if (tensor_pd->type() == proto::VarType::INT64) {
const int64_t* arr = tensor_pd->data<int64_t>();
ti = backend_->create_tensor(ngraph::element::i64, sp,
const_cast<int64_t*>(arr));
} else if (tensor_pd->type().hash_code() ==
typeid(double).hash_code()) { // NOLINT
} else if (tensor_pd->type() == proto::VarType::FP64) {
const double* arr = tensor_pd->data<double>();
ti = backend_->create_tensor(ngraph::element::f64, sp,
const_cast<double*>(arr));
} else if (tensor_pd->type().hash_code() ==
typeid(bool).hash_code()) { // NOLINT
} else if (tensor_pd->type() == proto::VarType::BOOL) {
const bool* arr = tensor_pd->data<bool>();
ti = backend_->create_tensor(ngraph::element::boolean, sp,
const_cast<bool*>(arr));
......
paddle/fluid/framework/op_desc.cc
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paddle/fluid/framework/op_desc.h
浏览文件 @ 41a64f6a
......@@ -123,6 +123,8 @@ class OpDesc {
BlockDesc *Block() { return this->block_; }
const BlockDesc *Block() const { return this->block_; }
private:
template <typename MapType>
static std::vector<typename MapType::key_type> MapKeys(const MapType &map) {
......
paddle/fluid/framework/operator.cc
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paddle/fluid/framework/operator.h
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paddle/fluid/framework/type_defs.h
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