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PaddleDetection
提交 7e0c9f50 编写于 作者: F fengjiayi
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Merge branch 'develop' of https://github.com/PaddlePaddle/Paddle into dev_sequence_padding_op

上级 9cb455fa 599a3264
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Showing with 14842 addition and 2453 deletion
.gitignore
浏览文件 @ 7e0c9f50
......@@ -5,6 +5,7 @@ python/paddle/v2/fluid/tests/book/image_classification_resnet.inference.model/
python/paddle/v2/fluid/tests/book/image_classification_vgg.inference.model/
python/paddle/v2/fluid/tests/book/label_semantic_roles.inference.model/
*.DS_Store
*.vs
build/
build_doc/
*.user
......@@ -15,6 +16,7 @@ build_doc/
.cproject
.pydevproject
.settings/
CMakeSettings.json
Makefile
.test_env/
third_party/
......
CMakeLists.txt
浏览文件 @ 7e0c9f50
......@@ -24,6 +24,9 @@ message(STATUS "CXX compiler: ${CMAKE_CXX_COMPILER}, version: "
"${CMAKE_CXX_COMPILER_ID} ${CMAKE_CXX_COMPILER_VERSION}")
message(STATUS "C compiler: ${CMAKE_C_COMPILER}, version: "
"${CMAKE_C_COMPILER_ID} ${CMAKE_C_COMPILER_VERSION}")
if(WIN32)
set(CMAKE_STATIC_LIBRARY_PREFIX lib)
endif(WIN32)
if(NOT CMAKE_CROSSCOMPILING)
find_package(CUDA QUIET)
......@@ -138,12 +141,6 @@ else()
set(THIRD_PARTY_BUILD_TYPE Release)
endif()
if(WITH_MKL)
option(MKL_SPLIT_GEMM "PaddlePaddle MKL gemm would split to small ones" OFF)
if (MKL_SPLIT_GEMM)
add_definitions(-DPADDLE_MKL_SPLIT_GEMM)
endif()
endif()
set(WITH_MKLML ${WITH_MKL})
if (NOT DEFINED WITH_MKLDNN)
if (WITH_MKL AND AVX2_FOUND)
......@@ -171,7 +168,6 @@ include(external/python) # download, build, install python
include(external/openblas) # download, build, install openblas
include(external/mkldnn) # download, build, install mkldnn
include(external/swig) # download, build, install swig
include(external/warpctc) # download, build, install warpctc
include(external/boost) # download boost
include(external/any) # download libn::any
include(external/eigen) # download eigen3
......@@ -179,6 +175,14 @@ include(external/pybind11) # download pybind11
include(external/cares)
include(external/cub)
if (NOT WIN32)
# there is no official support of snappystream, warpctc, nccl, cupti in windows
include(external/snappy) # download snappy
include(external/snappystream) # download snappystream
include(external/warpctc) # download, build, install warpctc
include(cupti)
endif (NOT WIN32)
if(WITH_DISTRIBUTE)
if(WITH_GRPC)
include(external/grpc)
......@@ -200,26 +204,25 @@ if(WITH_BRPC_RDMA)
endif()
endif()
include(external/snappy) # download snappy
include(external/snappystream)
include(external/threadpool)
include(flags) # set paddle compile flags
include(cudnn) # set cudnn libraries, must before configure
include(configure) # add paddle env configuration
set(WITH_ANAKIN OFF CACHE STRING "Disable Anakin first, will add it later." FORCE)
if(WITH_GPU)
include(cuda)
include(tensorrt)
include(external/anakin)
elseif()
set(WITH_ANAKIN OFF CACHE STRING "Anakin is used in GPU only now." FORCE)
endif()
include(cudnn) # set cudnn libraries, must before configure
include(cupti)
include(configure) # add paddle env configuration
include(generic) # simplify cmake module
include(package) # set paddle packages
include(ccache) # set ccache for compilation
include(util) # set unittest and link libs
include(rdma) # set rdma libraries
include(flags) # set paddle compile flags
include(version) # set PADDLE_VERSION
include(coveralls) # set code coverage
include(inference_lib) # add paddle fluid inference libraries
......
cmake/configure.cmake
浏览文件 @ 7e0c9f50
......@@ -50,13 +50,22 @@ if(NOT WITH_PROFILER)
endif(NOT WITH_PROFILER)
if(NOT CMAKE_CROSSCOMPILING)
if(WITH_AVX AND AVX_FOUND)
if(WITH_AVX AND AVX512F_FOUND)
set(SIMD_FLAG ${AVX512F_FLAG})
elseif(WITH_AVX AND AVX2_FOUND)
set(SIMD_FLAG ${AVX2_FLAG})
elseif(WITH_AVX AND AVX_FOUND)
set(SIMD_FLAG ${AVX_FLAG})
elseif(SSE3_FOUND)
set(SIMD_FLAG ${SSE3_FLAG})
endif()
endif()
if(WIN32)
# windows stupid compile option for all targets.
add_definitions(-D_XKEYCHECK_H)
endif(WIN32)
if(NOT WITH_GOLANG)
add_definitions(-DPADDLE_WITHOUT_GOLANG)
endif(NOT WITH_GOLANG)
......@@ -99,12 +108,21 @@ if(WITH_GPU)
endif()
if(WITH_ANAKIN)
if(${CUDA_VERSION_MAJOR} VERSION_LESS 8)
message(FATAL_ERROR "Anakin needs CUDA >= 8.0 to compile")
message(WARNING "Anakin needs CUDA >= 8.0 to compile. Force WITH_ANAKIN=OFF")
set(WITH_ANAKIN OFF CACHE STRING "Anakin is valid only when CUDA >= 8.0." FORCE)
endif()
if(${CUDNN_MAJOR_VERSION} VERSION_LESS 7)
message(FATAL_ERROR "Anakin needs CUDNN >= 7.0 to compile")
message(WARNING "Anakin needs CUDNN >= 7.0 to compile. Force WITH_ANAKIN=OFF")
set(WITH_ANAKIN OFF CACHE STRING "Anakin is valid only when CUDNN >= 7.0." FORCE)
endif()
endif()
if(WITH_ANAKIN)
# NOTICE(minqiyang): the end slash is important because $CUDNN_INCLUDE_DIR
# is a softlink to real cudnn.h directory
set(ENV{CUDNN_INCLUDE_DIR} "${CUDNN_INCLUDE_DIR}/")
get_filename_component(CUDNN_LIBRARY_DIR ${CUDNN_LIBRARY} DIRECTORY)
set(ENV{CUDNN_LIBRARY} ${CUDNN_LIBRARY_DIR})
endif()
elseif(WITH_AMD_GPU)
add_definitions(-DPADDLE_WITH_HIP)
set(CMAKE_C_FLAGS "${CMAKE_C_FLAGS} -D__HIP_PLATFORM_HCC__")
......
cmake/cudnn.cmake
浏览文件 @ 7e0c9f50
......@@ -25,8 +25,25 @@ list(APPEND CUDNN_CHECK_LIBRARY_DIRS
$ENV{CUDNN_ROOT}
$ENV{CUDNN_ROOT}/lib64
$ENV{CUDNN_ROOT}/lib
/usr/lib)
find_library(CUDNN_LIBRARY NAMES libcudnn.so libcudnn.dylib # libcudnn_static.a
/usr/lib
${CUDA_TOOLKIT_ROOT_DIR}
${CUDA_TOOLKIT_ROOT_DIR}/lib/x64
)
set(CUDNN_LIB_NAME "")
if (LINUX)
set(CUDNN_LIB_NAME "libcudnn.so")
endif(LINUX)
if(WIN32)
# only support cudnn7
set(CUDNN_LIB_NAME "cudnn.lib" "cudnn64_7.dll")
endif(WIN32)
if(Apple)
set(CUDNN_LIB_NAME "libcudnn.dylib" "libcudnn.so")
endif(Apple)
find_library(CUDNN_LIBRARY NAMES ${CUDNN_LIB_NAME} # libcudnn_static.a
PATHS ${CUDNN_CHECK_LIBRARY_DIRS} ${CUDNN_INCLUDE_DIR} ${__libpath_hist}
NO_DEFAULT_PATH
DOC "Path to cuDNN library.")
......
cmake/external/anakin.cmake
浏览文件 @ 7e0c9f50
......@@ -2,6 +2,11 @@ if (NOT WITH_ANAKIN)
return()
endif()
option(ANAKIN_ENABLE_OP_TIMER "Get more detailed information with Anakin op time" OFF)
if(ANAKIN_ENABLE_OP_TIMER)
add_definitions(-DPADDLE_ANAKIN_ENABLE_OP_TIMER)
endif()
INCLUDE(ExternalProject)
set(ANAKIN_SOURCE_DIR ${THIRD_PARTY_PATH}/anakin)
# the anakin install dir is only default one now
......@@ -11,33 +16,45 @@ set(ANAKIN_LIBRARY ${ANAKIN_INSTALL_DIR})
set(ANAKIN_SHARED_LIB ${ANAKIN_LIBRARY}/libanakin.so)
set(ANAKIN_SABER_LIB ${ANAKIN_LIBRARY}/libanakin_saber_common.so)
# TODO(luotao): ANAKIN_MODLE_URL will move to demo ci later.
set(ANAKIN_MODLE_URL "http://paddle-inference-dist.bj.bcebos.com/mobilenet_v2.anakin.bin")
# TODO(luotao): ANAKIN_MODLE_URL etc will move to demo ci later.
set(INFERENCE_URL "http://paddle-inference-dist.bj.bcebos.com")
set(ANAKIN_MODLE_URL "${INFERENCE_URL}/mobilenet_v2.anakin.bin")
set(ANAKIN_RNN_MODLE_URL "${INFERENCE_URL}/anakin_test%2Fditu_rnn.anakin2.model.bin")
set(ANAKIN_RNN_DATA_URL "${INFERENCE_URL}/anakin_test%2Fditu_rnn_data.txt")
execute_process(COMMAND bash -c "mkdir -p ${ANAKIN_SOURCE_DIR}")
execute_process(COMMAND bash -c "cd ${ANAKIN_SOURCE_DIR}; wget -q --no-check-certificate ${ANAKIN_MODLE_URL}")
execute_process(COMMAND bash -c "cd ${ANAKIN_SOURCE_DIR}; wget -q --no-check-certificate ${ANAKIN_MODLE_URL} -N")
execute_process(COMMAND bash -c "cd ${ANAKIN_SOURCE_DIR}; wget -q --no-check-certificate ${ANAKIN_RNN_MODLE_URL} -N")
execute_process(COMMAND bash -c "cd ${ANAKIN_SOURCE_DIR}; wget -q --no-check-certificate ${ANAKIN_RNN_DATA_URL} -N")
include_directories(${ANAKIN_INCLUDE})
include_directories(${ANAKIN_INCLUDE}/saber/)
include_directories(${ANAKIN_INCLUDE}/saber/core/)
include_directories(${ANAKIN_INCLUDE}/saber/funcs/impl/x86/)
include_directories(${ANAKIN_INCLUDE}/saber/funcs/impl/cuda/base/cuda_c/)
set(ANAKIN_COMPILE_EXTRA_FLAGS
set(ANAKIN_COMPILE_EXTRA_FLAGS
-Wno-error=unused-but-set-variable -Wno-unused-but-set-variable
-Wno-error=unused-variable -Wno-unused-variable
-Wno-error=unused-variable -Wno-unused-variable
-Wno-error=format-extra-args -Wno-format-extra-args
-Wno-error=comment -Wno-comment
-Wno-error=format -Wno-format
-Wno-error=maybe-uninitialized -Wno-maybe-uninitialized
-Wno-error=switch -Wno-switch
-Wno-error=return-type -Wno-return-type
-Wno-error=return-type -Wno-return-type
-Wno-error=non-virtual-dtor -Wno-non-virtual-dtor
-Wno-error=ignored-qualifiers
-Wno-ignored-qualifiers
-Wno-sign-compare
-Wno-reorder
-Wno-reorder
-Wno-error=cpp)
ExternalProject_Add(
extern_anakin
${EXTERNAL_PROJECT_LOG_ARGS}
# TODO(luotao): use PaddlePaddle/Anakin later
DEPENDS ${MKLML_PROJECT}
# Anakin codes error on Intel(R) Xeon(R) Gold 5117 CPU, temporary do not compile avx512 related code.
GIT_REPOSITORY "https://github.com/luotao1/Anakin"
GIT_TAG "3957ae9263eaa0b1986758dac60a88852afb09be"
GIT_TAG "211d1fc5d813d70c0c14072f9083cf25f40940ea"
PREFIX ${ANAKIN_SOURCE_DIR}
UPDATE_COMMAND ""
CMAKE_ARGS -DUSE_GPU_PLACE=YES
......@@ -46,6 +63,8 @@ ExternalProject_Add(
-DPROTOBUF_ROOT=${THIRD_PARTY_PATH}/install/protobuf
-DMKLML_ROOT=${THIRD_PARTY_PATH}/install/mklml
-DCUDNN_ROOT=${CUDNN_ROOT}
-DCUDNN_INCLUDE_DIR=${CUDNN_INCLUDE_DIR}
-DENABLE_OP_TIMER=${ANAKIN_ENABLE_OP_TIMER}
${EXTERNAL_OPTIONAL_ARGS}
CMAKE_CACHE_ARGS -DCMAKE_INSTALL_PREFIX:PATH=${ANAKIN_INSTALL_DIR}
)
......
cmake/external/boost.cmake
浏览文件 @ 7e0c9f50
......@@ -28,7 +28,12 @@ if((NOT DEFINED BOOST_TAR) OR (NOT DEFINED BOOST_URL))
set(BOOST_TAR "boost_1_41_0" CACHE STRING "" FORCE)
set(BOOST_URL "http://paddlepaddledeps.cdn.bcebos.com/${BOOST_TAR}.tar.gz" CACHE STRING "" FORCE)
endif()
MESSAGE(STATUS "BOOST_TAR: ${BOOST_TAR}, BOOST_URL: ${BOOST_URL}")
IF (WIN32)
MESSAGE(WARNING, "In windows, boost can not be downloaded automaticlly, please build it manually and put it at " ${THIRD_PARTY_PATH}install/boost)
else()
MESSAGE(STATUS "BOOST_TAR: ${BOOST_TAR}, BOOST_URL: ${BOOST_URL}")
ENDIF(WIN32)
set(BOOST_SOURCES_DIR ${THIRD_PARTY_PATH}/boost)
set(BOOST_DOWNLOAD_DIR "${BOOST_SOURCES_DIR}/src/${BOOST_PROJECT}")
set(BOOST_INCLUDE_DIR "${BOOST_DOWNLOAD_DIR}/${BOOST_TAR}" CACHE PATH "boost include directory." FORCE)
......@@ -36,12 +41,13 @@ set_directory_properties(PROPERTIES CLEAN_NO_CUSTOM 1)
include_directories(${BOOST_INCLUDE_DIR})
if (NOT WIN32)
ExternalProject_Add(
${BOOST_PROJECT}
${EXTERNAL_PROJECT_LOG_ARGS}
DOWNLOAD_DIR ${BOOST_DOWNLOAD_DIR}
DOWNLOAD_COMMAND wget --no-check-certificate ${BOOST_URL} -c -q -O ${BOOST_TAR}.tar.gz
&& tar zxf ${BOOST_TAR}.tar.gz
&& tar zxf ${BOOST_TAR}.tar.gz
DOWNLOAD_NO_PROGRESS 1
PREFIX ${BOOST_SOURCES_DIR}
CONFIGURE_COMMAND ""
......@@ -49,8 +55,9 @@ ExternalProject_Add(
INSTALL_COMMAND ""
UPDATE_COMMAND ""
)
endif(NOT WIN32)
if (${CMAKE_VERSION} VERSION_LESS "3.3.0")
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(boost STATIC ${dummyfile})
......
cmake/external/gflags.cmake
浏览文件 @ 7e0c9f50
......@@ -18,7 +18,7 @@ SET(GFLAGS_SOURCES_DIR ${THIRD_PARTY_PATH}/gflags)
SET(GFLAGS_INSTALL_DIR ${THIRD_PARTY_PATH}/install/gflags)
SET(GFLAGS_INCLUDE_DIR "${GFLAGS_INSTALL_DIR}/include" CACHE PATH "gflags include directory." FORCE)
IF(WIN32)
set(GFLAGS_LIBRARIES "${GFLAGS_INSTALL_DIR}/lib/gflags.lib" CACHE FILEPATH "GFLAGS_LIBRARIES" FORCE)
set(GFLAGS_LIBRARIES "${GFLAGS_INSTALL_DIR}/lib/libgflags.lib" CACHE FILEPATH "GFLAGS_LIBRARIES" FORCE)
ELSE(WIN32)
set(GFLAGS_LIBRARIES "${GFLAGS_INSTALL_DIR}/lib/libgflags.a" CACHE FILEPATH "GFLAGS_LIBRARIES" FORCE)
ENDIF(WIN32)
......@@ -45,7 +45,13 @@ ExternalProject_Add(
-DCMAKE_POSITION_INDEPENDENT_CODE:BOOL=ON
-DCMAKE_BUILD_TYPE:STRING=${THIRD_PARTY_BUILD_TYPE}
)
IF(WIN32)
IF(NOT EXISTS "${GFLAGS_INSTALL_DIR}/lib/libgflags.lib")
add_custom_command(TARGET extern_gflags POST_BUILD
COMMAND cmake -E rename ${GFLAGS_INSTALL_DIR}/lib/gflags_static.lib ${GFLAGS_INSTALL_DIR}/lib/libgflags.lib
)
ENDIF()
ENDIF(WIN32)
ADD_LIBRARY(gflags STATIC IMPORTED GLOBAL)
SET_PROPERTY(TARGET gflags PROPERTY IMPORTED_LOCATION ${GFLAGS_LIBRARIES})
ADD_DEPENDENCIES(gflags extern_gflags)
......@@ -60,3 +66,4 @@ IF(WITH_C_API)
INSTALL(FILES ${GFLAGS_LIBRARIES} DESTINATION third_party/gflags/lib)
ENDIF()
ENDIF()
cmake/external/glog.cmake
浏览文件 @ 7e0c9f50
......@@ -60,6 +60,13 @@ ExternalProject_Add(
-DCMAKE_POSITION_INDEPENDENT_CODE:BOOL=ON
-DCMAKE_BUILD_TYPE:STRING=${THIRD_PARTY_BUILD_TYPE}
)
IF(WIN32)
IF(NOT EXISTS "${GLOG_INSTALL_DIR}/lib/libglog.lib")
add_custom_command(TARGET extern_glog POST_BUILD
COMMAND cmake -E rename ${GLOG_INSTALL_DIR}/lib/glog.lib ${GLOG_INSTALL_DIR}/lib/libglog.lib
)
ENDIF()
ENDIF(WIN32)
ADD_LIBRARY(glog STATIC IMPORTED GLOBAL)
SET_PROPERTY(TARGET glog PROPERTY IMPORTED_LOCATION ${GLOG_LIBRARIES})
......
cmake/external/mkldnn.cmake
浏览文件 @ 7e0c9f50
......@@ -54,7 +54,7 @@ ExternalProject_Add(
${EXTERNAL_PROJECT_LOG_ARGS}
DEPENDS ${MKLDNN_DEPENDS}
GIT_REPOSITORY "https://github.com/01org/mkl-dnn.git"
GIT_TAG "a29d8487a63afca3d5b8c5bbdbb473cf8ccc6e51"
GIT_TAG "64e03a1939e0d526aa8e9f2e3f7dc0ad8d372944"
PREFIX ${MKLDNN_SOURCES_DIR}
UPDATE_COMMAND ""
CMAKE_ARGS -DCMAKE_CXX_COMPILER=${CMAKE_CXX_COMPILER}
......
cmake/external/openblas.cmake
浏览文件 @ 7e0c9f50
......@@ -17,20 +17,29 @@ IF(USE_EIGEN_FOR_BLAS)
ENDIF(USE_EIGEN_FOR_BLAS)
INCLUDE(cblas)
# IF(WIN32 AND NOT ${CBLAS_FOUND})
IF(NOT ${CBLAS_FOUND})
INCLUDE(ExternalProject)
SET(CBLAS_SOURCES_DIR ${THIRD_PARTY_PATH}/openblas)
SET(CBLAS_INSTALL_DIR ${THIRD_PARTY_PATH}/install/openblas)
SET(CBLAS_INC_DIR "${CBLAS_INSTALL_DIR}/include" CACHE PATH "openblas include directory." FORCE)
SET(CBLAS_INCLUDE_DIR "${CBLAS_INSTALL_DIR}/include" CACHE PATH "openblas include directory." FORCE)
SET(CBLAS_LIBRARIES
"${CBLAS_INSTALL_DIR}/lib/${CMAKE_STATIC_LIBRARY_PREFIX}openblas${CMAKE_STATIC_LIBRARY_SUFFIX}"
CACHE FILEPATH "openblas library." FORCE)
ADD_DEFINITIONS(-DPADDLE_USE_OPENBLAS)
IF (WIN32)
SET(CBLAS_FOUND true)
MESSAGE(WARNING, "In windows, openblas only support msvc build, please build it manually and put it at " ${CBLAS_INSTALL_DIR})
ENDIF(WIN32)
IF (NOT WIN32)
SET(OPENBLAS_CC "${CMAKE_C_COMPILER} -Wno-unused-but-set-variable -Wno-unused-variable")
SET(OPENBLAS_COMMIT "v0.2.20")
......@@ -69,7 +78,6 @@ IF(NOT ${CBLAS_FOUND})
ENDIF()
SET(COMMON_ARGS CC=${OPENBLAS_CC} NO_SHARED=1 NO_LAPACK=1 libs)
ExternalProject_Add(
extern_openblas
${EXTERNAL_PROJECT_LOG_ARGS}
......@@ -84,9 +92,11 @@ IF(NOT ${CBLAS_FOUND})
UPDATE_COMMAND ""
CONFIGURE_COMMAND ""
)
ELSE()
ENDIF(NOT WIN32)
SET(CBLAS_PROVIDER openblas)
IF(WITH_C_API)
INSTALL(DIRECTORY ${CBLAS_INC_DIR} DESTINATION third_party/openblas)
INSTALL(DIRECTORY ${CBLAS_INCLUDE_DIR} DESTINATION third_party/openblas)
# Because libopenblas.a is a symbolic link of another library, thus need to
# install the whole directory.
IF(ANDROID)
......@@ -107,7 +117,8 @@ IF(NOT ${CBLAS_FOUND})
ENDIF(NOT ${CBLAS_FOUND})
MESSAGE(STATUS "BLAS library: ${CBLAS_LIBRARIES}")
INCLUDE_DIRECTORIES(${CBLAS_INC_DIR})
MESSAGE(STATUS "BLAS Include: ${CBLAS_INCLUDE_DIR}")
INCLUDE_DIRECTORIES(${CBLAS_INCLUDE_DIR})
# FIXME(gangliao): generate cblas target to track all high performance
# linear algebra libraries for cc_library(xxx SRCS xxx.c DEPS cblas)
......
cmake/external/protobuf.cmake
浏览文件 @ 7e0c9f50
......@@ -14,11 +14,14 @@
INCLUDE(ExternalProject)
# Always invoke `FIND_PACKAGE(Protobuf)` for importing function protobuf_generate_cpp
IF(NOT WIN32)
FIND_PACKAGE(Protobuf QUIET)
ENDIF(NOT WIN32)
macro(UNSET_VAR VAR_NAME)
UNSET(${VAR_NAME} CACHE)
UNSET(${VAR_NAME})
endmacro()
UNSET_VAR(PROTOBUF_INCLUDE_DIR)
UNSET_VAR(PROTOBUF_FOUND)
UNSET_VAR(PROTOBUF_PROTOC_EXECUTABLE)
......@@ -94,12 +97,14 @@ macro(PROMPT_PROTOBUF_LIB)
SET(protobuf_DEPS ${ARGN})
MESSAGE(STATUS "Protobuf protoc executable: ${PROTOBUF_PROTOC_EXECUTABLE}")
MESSAGE(STATUS "Protobuf-lite library: ${PROTOBUF_LITE_LIBRARY}")
MESSAGE(STATUS "Protobuf library: ${PROTOBUF_LIBRARY}")
MESSAGE(STATUS "Protoc library: ${PROTOBUF_PROTOC_LIBRARY}")
MESSAGE(STATUS "Protobuf version: ${PROTOBUF_VERSION}")
INCLUDE_DIRECTORIES(${PROTOBUF_INCLUDE_DIR})
# Assuming that all the protobuf libraries are of the same type.
IF(${PROTOBUF_LIBRARY} MATCHES "${CMAKE_STATIC_LIBRARY_SUFFIX}$")
IF(${PROTOBUF_LIBRARY} MATCHES ${CMAKE_STATIC_LIBRARY_SUFFIX})
SET(protobuf_LIBTYPE STATIC)
ELSEIF(${PROTOBUF_LIBRARY} MATCHES "${CMAKE_SHARED_LIBRARY_SUFFIX}$")
SET(protobuf_LIBTYPE SHARED)
......@@ -137,18 +142,25 @@ macro(SET_PROTOBUF_VERSION)
endmacro()
set(PROTOBUF_ROOT "" CACHE PATH "Folder contains protobuf")
IF (WIN32)
SET(PROTOBUF_ROOT ${THIRD_PARTY_PATH}/install/protobuf)
MESSAGE(WARNING, "In windows, protobuf only support msvc build, please build it manually and put it at " ${PROTOBUF_ROOT})
ENDIF(WIN32)
if (NOT "${PROTOBUF_ROOT}" STREQUAL "")
find_path(PROTOBUF_INCLUDE_DIR google/protobuf/message.h PATHS ${PROTOBUF_ROOT}/include NO_DEFAULT_PATH)
find_library(PROTOBUF_LIBRARY protobuf PATHS ${PROTOBUF_ROOT}/lib NO_DEFAULT_PATH)
find_library(PROTOBUF_LITE_LIBRARY protobuf-lite PATHS ${PROTOBUF_ROOT}/lib NO_DEFAULT_PATH)
find_library(PROTOBUF_PROTOC_LIBRARY protoc PATHS ${PROTOBUF_ROOT}/lib NO_DEFAULT_PATH)
find_library(PROTOBUF_LIBRARY protobuf libprotobuf.lib PATHS ${PROTOBUF_ROOT}/lib NO_DEFAULT_PATH)
find_library(PROTOBUF_LITE_LIBRARY protobuf-lite libprotobuf-lite.lib PATHS ${PROTOBUF_ROOT}/lib NO_DEFAULT_PATH)
find_library(PROTOBUF_PROTOC_LIBRARY protoc libprotoc.lib PATHS ${PROTOBUF_ROOT}/lib NO_DEFAULT_PATH)
find_program(PROTOBUF_PROTOC_EXECUTABLE protoc PATHS ${PROTOBUF_ROOT}/bin NO_DEFAULT_PATH)
if (PROTOBUF_INCLUDE_DIR AND PROTOBUF_LIBRARY AND PROTOBUF_LITE_LIBRARY AND PROTOBUF_PROTOC_LIBRARY AND PROTOBUF_PROTOC_EXECUTABLE)
message(STATUS "Using custom protobuf library in ${PROTOBUF_ROOT}.")
SET(PROTOBUF_FOUND true)
SET_PROTOBUF_VERSION()
PROMPT_PROTOBUF_LIB()
else()
message(WARNING "Cannot find protobuf library in ${PROTOBUF_ROOT}.")
message(WARNING "Cannot find protobuf library in ${PROTOBUF_ROOT}")
endif()
endif()
......@@ -239,6 +251,7 @@ IF(CMAKE_CROSSCOMPILING)
CACHE FILEPATH "protobuf executable." FORCE)
ENDIF()
IF(NOT PROTOBUF_FOUND)
build_protobuf(extern_protobuf FALSE)
......
cmake/flags.cmake
浏览文件 @ 7e0c9f50
......@@ -102,7 +102,6 @@ set(COMMON_FLAGS
-fno-omit-frame-pointer
-Wall
-Wextra
-Werror
-Wnon-virtual-dtor
-Wdelete-non-virtual-dtor
-Wno-unused-parameter
......@@ -115,6 +114,11 @@ set(COMMON_FLAGS
-Wno-error=terminate # Warning in PADDLE_ENFORCE
)
# https://github.com/PaddlePaddle/Paddle/issues/12773
if (NOT WIN32)
list(APPEND COMMON_FLAGS -Werror)
endif()
set(GPU_COMMON_FLAGS
-fPIC
-fno-omit-frame-pointer
......@@ -142,6 +146,11 @@ else()
${GPU_COMMON_FLAGS})
endif()
if(UNIX AND NOT APPLE)
# except apple from nix*Os family
set(LINUX TRUE)
endif(UNIX AND NOT APPLE)
foreach(flag ${COMMON_FLAGS})
safe_set_cflag(CMAKE_C_FLAGS ${flag})
......
cmake/generic.cmake
浏览文件 @ 7e0c9f50
......@@ -148,7 +148,8 @@ function(merge_static_libs TARGET_NAME)
COMMAND rm "${CMAKE_CURRENT_BINARY_DIR}/lib${TARGET_NAME}.a"
COMMAND /usr/bin/libtool -static -o "${CMAKE_CURRENT_BINARY_DIR}/lib${TARGET_NAME}.a" ${libfiles}
)
else() # general UNIX: use "ar" to extract objects and re-add to a common lib
endif(APPLE)
if(LINUX) # general UNIX: use "ar" to extract objects and re-add to a common lib
set(target_DIR ${CMAKE_CURRENT_BINARY_DIR}/${TARGET_NAME}.dir)
foreach(lib ${libs})
......@@ -187,7 +188,36 @@ function(merge_static_libs TARGET_NAME)
COMMAND ${CMAKE_AR} crs ${target_LIBNAME} `find ${target_DIR} -name '*.o'`
COMMAND ${CMAKE_RANLIB} ${target_LIBNAME}
WORKING_DIRECTORY ${target_DIR})
endif()
endif(LINUX)
if(WIN32) # windows do not support gcc/nvcc combined compiling. Use msvc lib.exe to merge libs.
# Make the generated dummy source file depended on all static input
# libs. If input lib changes,the source file is touched
# which causes the desired effect (relink).
add_custom_command(OUTPUT ${target_SRCS}
COMMAND ${CMAKE_COMMAND} -E touch ${target_SRCS}
DEPENDS ${libs})
# Generate dummy staic lib
file(WRITE ${target_SRCS} "const char *dummy_${TARGET_NAME} = \"${target_SRCS}\";")
add_library(${TARGET_NAME} STATIC ${target_SRCS})
target_link_libraries(${TARGET_NAME} ${libs_deps})
foreach(lib ${libs})
# Get the file names of the libraries to be merged
#if(NOT $<TARGET_FILE:${lib}> MATCHES "lib.*\\.lib")
# message("library" ${lib})
# set(libfiles ${libfiles} lib$<TARGET_FILE:${lib}>)
#else()
set(libfiles ${libfiles} $<TARGET_FILE:${lib}>)
#endif()
endforeach()
# windows cmd return error in clean env.
# COMMAND del "${CMAKE_CURRENT_BINARY_DIR}/${CMAKE_BUILD_TYPE}/${TARGET_NAME}.lib"
add_custom_command(TARGET ${TARGET_NAME} POST_BUILD
COMMAND lib /OUT:${CMAKE_CURRENT_BINARY_DIR}/lib${TARGET_NAME}.lib ${libfiles}
)
endif(WIN32)
endfunction(merge_static_libs)
function(cc_library TARGET_NAME)
......@@ -195,6 +225,10 @@ function(cc_library TARGET_NAME)
set(oneValueArgs "")
set(multiValueArgs SRCS DEPS)
cmake_parse_arguments(cc_library "${options}" "${oneValueArgs}" "${multiValueArgs}" ${ARGN})
if(WIN32)
# add libxxx.lib prefix in windows
set(${TARGET_NAME}_LIB_NAME "${CMAKE_STATIC_LIBRARY_PREFIX}${TARGET_NAME}${CMAKE_STATIC_LIBRARY_SUFFIX}" CACHE STRING "output library name for target ${TARGET_NAME}")
endif(WIN32)
if(cc_library_SRCS)
if(cc_library_SHARED OR cc_library_shared) # build *.so
add_library(${TARGET_NAME} SHARED ${cc_library_SRCS})
......
cmake/inference_lib.cmake
浏览文件 @ 7e0c9f50
......@@ -101,6 +101,7 @@ if(WITH_MKLDNN)
)
endif()
if (NOT WIN32)
if(NOT MOBILE_INFERENCE AND NOT RPI)
set(dst_dir "${FLUID_INSTALL_DIR}/third_party/install/snappy")
copy(snappy_lib
......@@ -120,15 +121,23 @@ if(NOT MOBILE_INFERENCE AND NOT RPI)
DSTS ${dst_dir} ${dst_dir}/lib
DEPS zlib)
endif()
endif(NOT WIN32)
# paddle fluid module
set(src_dir "${PADDLE_SOURCE_DIR}/paddle/fluid")
set(dst_dir "${FLUID_INSTALL_DIR}/paddle/fluid")
set(module "framework")
if (NOT WIN32)
copy(framework_lib DEPS framework_py_proto
SRCS ${src_dir}/${module}/*.h ${src_dir}/${module}/details/*.h ${PADDLE_BINARY_DIR}/paddle/fluid/framework/framework.pb.h
DSTS ${dst_dir}/${module} ${dst_dir}/${module}/details ${dst_dir}/${module}
)
else()
copy(framework_lib
SRCS ${src_dir}/${module}/*.h ${src_dir}/${module}/details/*.h ${PADDLE_BINARY_DIR}/paddle/fluid/framework/framework.pb.h
DSTS ${dst_dir}/${module} ${dst_dir}/${module}/details ${dst_dir}/${module}
)
endif(NOT WIN32)
set(module "memory")
copy(memory_lib
......
cmake/simd.cmake
浏览文件 @ 7e0c9f50
......@@ -10,6 +10,7 @@ if(CMAKE_COMPILER_IS_GNUCC OR CMAKE_COMPILER_IS_GNUCXX OR CMAKE_CXX_COMPILER_ID
set(SSE3_FLAG "-msse3")
set(AVX_FLAG "-mavx")
set(AVX2_FLAG "-mavx2")
set(AVX512F_FLAG "-mavx512f")
elseif(MSVC)
set(MMX_FLAG "/arch:MMX")
set(SSE2_FLAG "/arch:SSE2")
......@@ -81,5 +82,16 @@ int main()
return 0;
}" AVX2_FOUND)
# Check AVX512F
set(CMAKE_REQUIRED_FLAGS ${AVX512F_FLAG})
set(AVX512F_FOUND_EXITCODE 1 CACHE STRING "Result from TRY_RUN" FORCE)
CHECK_CXX_SOURCE_RUNS("
#include <immintrin.h>
int main()
{
__m512i a = _mm512_undefined_epi32();
return 0;
}" AVX512F_FOUND)
set(CMAKE_REQUIRED_FLAGS ${CMAKE_REQUIRED_FLAGS_RETAINED})
mark_as_advanced(MMX_FOUND SSE2_FOUND SSE3_FOUND AVX_FOUND AVX2_FOUND)
mark_as_advanced(MMX_FOUND SSE2_FOUND SSE3_FOUND AVX_FOUND AVX2_FOUND AVX512F_FOUND)
doc/fluid/design/dist_train/dist_train_nccl2.md
浏览文件 @ 7e0c9f50
# Distributed Training with NCCL2
We design a pattern that can enable training with `ParallelExecutor` and
using [NCCL2](https://developer.nvidia.com/nccl) as it's collective
use [NCCL2](https://developer.nvidia.com/nccl) as it's collective
communication library.
In `ParallelExecutor` we can use `AllReduce` or `Reduce` and `Broadcast`
......@@ -9,14 +9,14 @@ to do multi GPU training. And if we initialize NCCL2 communicators as
ranks in a distributed environment, we can simply run the `ParallelExecutor`
as a distributed program! The only thing that may be different than in
the single node version is that we need to broadcast the NCCL unique ID
to all the nodes, and initialize communicators using that ID, so NCCL2
will know each other as ranks.
to all the nodes and initialize communicators using that ID, so NCCL2
can know each other as ranks.
To achieve this feature, we introduce a new operator: `gen_nccl_id` op,
so we are ***not*** "bind to" running NCCL2 with MPI, we can run it in
what ever platform you like.
whatever platform you like.
It have two running modes:
It has two running modes:
1. Generate and broadcast mode, which should be used on trainer 0;
1. Listen and fetch mode, which should be used on trainers other than 0.
......@@ -29,7 +29,7 @@ initialize NCCL communicator objects.
<img src="src/ncc2_design.png">
The above figure indicates the general process when training with NCCL2
distributed. Each trainer have the number of communicators equal to the
distributed. Each trainer has the number of communicators equal to the
number of GPUs, but the ranks should match the global ranks number: here
we have total 8 GPUs, so `nranks==8`, for each trainer, the ranks should
be from 0 ~ 3 on trainer 0 and 4 ~ 7 on trainer 1.
doc/fluid/design/others/graph_survey.md
浏览文件 @ 7e0c9f50
......@@ -28,7 +28,7 @@ def get_symbol(num_classes=10, **kwargs):
Varible here is actually a Symbol. Every basic Symbol will correspond to one Node, and every Node has its own NodeAttr. There is a op field in NodeAttr class, when a Symbol represents Variable(often input data), the op field is null.
Varible here is actually a Symbol. Every basic Symbol will correspond to one Node, and every Node has its own AnyAttr. There is a op field in AnyAttr class, when a Symbol represents Variable(often input data), the op field is null.
Symbol contains a data member, std::vector<NodeEntry> outputs, and NodeEntry cantains a poniter to Node. We can follow the Node pointer to get all the Graph.
......
doc/fluid/dev/new_op_cn.md
浏览文件 @ 7e0c9f50
......@@ -36,19 +36,19 @@
<tbody>
<tr>
<td>OpProtoMake定义 </td>
<td>`.cc`文件，Backward Op不需要定义OpProtoMake </td>
<td>.cc 文件，Backward Op不需要定义OpProtoMake </td>
</tr>
<tr>
<td>Op定义 </td>
<td> `.cc`文件</td>
<td> .cc 文件</td>
</tr>
<tr>
<td>Kernel实现 </td>
<td> CPU、CUDA共享Kernel实现在`.h`文件中，否则，CPU 实现在`.cc`文件中，CUDA 实现在`.cu`文件中。</td>
<td> CPU、CUDA共享Kernel实现在.h 文件中，否则，CPU 实现在.cc 文件中，CUDA 实现在.cu 文件中。</td>
</tr>
<tr>
<td>注册Op </td>
<td> Op注册实现在`.cc`文件；Kernel注册CPU实现在`.cc`文件中，CUDA实现在`.cu`文件中</td>
<td> Op注册实现在.cc 文件；Kernel注册CPU实现在.cc 文件中，CUDA实现在.cu 文件中</td>
</tr>
</tbody>
</table>
......@@ -119,10 +119,29 @@ $$Out = scale*X$$
这个例子有`AddAttr<AttrType>("scale", "...").SetDefault(1.0);` : 增加`scale`系数，作为参数属性，并且设置默认值为1.0。
### 定义GradProtoMaker类
每个Op的必须有一个对应的GraProtoMaker，若未定制对应前向Op的GradProtoMaker，fluid提供了DefaultGradProtoMaker，默认注册会使用全部输入输出，包括Input, Output, Output@Grad等，使用不需要的变量的会造成显存浪费。
下面示例定义了ScaleOp的GradProtoMaker。
```cpp
class ScaleGradMaker : public framework::SingleGradOpDescMaker {
public:
using framework::SingleGradOpDescMaker::SingleGradOpDescMaker;
std::unique_ptr<framework::OpDesc> Apply() const override {
auto *grad_op = new framework::OpDesc();
grad_op->SetType("scale");
grad_op->SetInput("X", OutputGrad("Out"));
grad_op->SetOutput("Out", InputGrad("X"));
grad_op->SetAttr("scale", GetAttr("scale"));
return std::unique_ptr<framework::OpDesc>(grad_op);
}
};
```
### 定义Operator类
下面的点实现了MulOp的定义：
下面实现了MulOp的定义：
```cpp
class MulOp : public framework::OperatorWithKernel {
......@@ -334,3 +353,83 @@ ctest -R test_mul_op
- 注册Op时的类型名，需要和该Op的名字一样。即不允许在`A_op.cc`里面，注册`REGISTER_OPERATOR(B, ...)`等，这将会导致单元测试出错。
- 如果Op没有实现CUDA Kernel，请不要创建空的`*_op.cu`，这将会导致单元测试出错。
- 如果多个Op依赖一些共用的函数，可以创建非`*_op.*`格式的文件来存放，如`gather.h`文件。
### PADDLE_ENFORCE使用注意
实现Op时检查数据的合法性需要使用PADDLE_ENFORCE以及PADDLE_ENFORCE_EQ等宏定义，基本格式如下：
```
PADDLE_ENFORCE(表达式, 错误提示信息)
PADDLE_ENFORCE_EQ(比较对象A, 比较对象B, 错误提示信息)
```
如果表达式为真，或者比较对象A=B，则检查通过，否则会终止程序运行，向用户反馈相应的错误提示信息。
为了确保提示友好易懂，开发者需要注意其使用方法。
#### 总体原则
任何使用了PADDLE_ENFORCE与PADDLE_ENFORCE_**检查的地方，必须有详略得当的备注解释！**错误提示信息**不能为空！
#### 提示信息书写标准
1. [required] 哪里错了？为什么错了？
- 例如：`ValueError: Mismatched label shape`
2. [optional] 期望的输入是什么样的？实际的输入是怎样的？
- 例如：`Expected labels dimension=1. Received 4.`
3. [optional] 能否给出修改意见？
- 例如：`Suggested Fix:If your classifier expects one-hot encoding label,check your n_classes argument to the estimatorand/or the shape of your label.Otherwise, check the shape of your label.`
如果并非必要或者简洁的描述即可表达清楚以上要点，根据情况书写亦可。
##### FAQ 典型问题
1. 无报错信息或报错信息过于简单，不能给用户提供有效的提示！
问题示例1 ：未写提示信息
```
PADDLE_ENFORCE(ctx->HasInput("X"), "");
```
问题示例2 ：提示信息过于简单
```
PADDLE_ENFORCE(i != nullptr, "i must be set"); // i是什么？
```
2. 在报错信息中使用开发人员定义的变量缩写，不易理解！
问题示例：
```
PADDLE_ENFORCE(forward_pd != nullptr,
"Fail to find eltwise_fwd_pd in device context"); //eltwise_fwd_pd用户可能看不懂
```
3. OP内部调用非法接口：Op内部如果出现Output = ShareDataWith(Input)
问题示例：
```cpp
auto *out = ctx.Output<framework::LoDTensor>("Out");
auto *in = ctx.Input<framework::LoDTensor>("X");
out->ShareDataWith(*in);
```
Op内部如果出现Output = ShareDataWith(Input)，相当于operator图的中有一条隐藏边，连接了Input和Output，这条边无法在图分析中表达，引发基于图优化的错误。
4. OP实现的性能实践
调用了eigen的broadcast, chop等操作，性能会比手写cuda kernel差几倍以上。此时cpu的实现可以复用eigen，gpu实现可以实现cuda kernel.
#### OP InferShape检查提示信息特别说明
- 检查输入输出变量，请统一遵循以下格式
`Input(变量名) of OP名 operator should not be null.`
正确示例：
```
PADDLE_ENFORCE(ctx->HasInput("Input"),
"Input(Input) of LSTMP operator should not be null.");
```
- 反向Op的输入输出检查，要写明反向Op的名字
正确示例：
```
PADDLE_ENFORCE(ctx->HasInput("X"),
"Input(X) of LoDResetGrad opreator should not be null.");
```
doc/fluid/dev/releasing_process_en.md
浏览文件 @ 7e0c9f50
......@@ -50,6 +50,33 @@ pop-up box, choose the current release branch and click "Run Build" button. You
* pypi does not allow overwrite the already uploaded version of wheel package, even if you delete the
old version. you must change the version number before upload a new one.
### Publish wheel Packages for MacOS
You need to build the binary wheel package for MacOS before publishing, to
make sure that the package can be used by many versions of MacOS
(10.11, 10.12, 10.13) and different python installs (python.org, homebrew, etc.),
you must build the package ***exactly*** following below steps:
Build steps:
1. install python from python.org downloads, and make sure it's currently in use
in your system.
1. `export MACOSX_DEPLOYMENT_TARGET=10.11`, use `10.11` is enough for recent versions.
1. `git clone https://github.com/PaddlePaddle/Paddle.git && cd Paddle && mkdir build && cd build`
1. `cmake -DWITH_GPU=OFF -DWITH_MKL=OFF -DWITH_SYSTEM_BLAS=OFF ..`, make sure the output of `cmake` command is using the correct python interpreter installed from python.org
1. `make -j`
1. `pip install delocate`
1. `mkdir fixed_wheel && delocate-wheel -w fixed_wheel python/dist/*.whl`
Then the whl under `fixed_wheel` is ready to upload.
Install steps:
1. run `pip install paddlepaddle...whl`
1. find the `libpython.dylib` that are currently in use:
- for python.org package installs, do nothing.
- for other python installs, find the path of `libpython*.dylib` and `export LD_LIBRARY_PATH=you path && DYLD_LIBRARY_PATH=your path`
## Publish Docker Images
Our CI tool will push latest images to DockerHub, so we only need to push a version tag like:
......
doc/fluid/dev/use_eigen_cn.md
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......@@ -7,7 +7,7 @@
Eigen Tensor模块对element-wise计算提供了强大的支持，并且书写一份代码，可以同时在CPU、GPU执行。但Eigen Tensor是一个正在开发中的模块，因此可能测试不够完备，文档较少。
关于Eigen Tensor模块的详细介绍请参考[文档1](https://github.com/RLovelett/eigen/blob/master/unsupported/Eigen/CXX11/src/Tensor/README.md) 和[文档2](https://bitbucket.org/eigen/eigen/src/default/unsupported/Eigen/CXX11/src/Tensor/README.md)
关于Eigen Tensor模块的详细介绍请参考[Eigen文档](https://bitbucket.org/eigen/eigen/src/default/unsupported/Eigen/CXX11/src/Tensor/README.md)
## paddle::framework::Tensor
......
doc/fluid/howto/cluster/nccl2_rdma_training.md
浏览文件 @ 7e0c9f50
# Distributed Training with NCCL2 and RDMA
When doing distributed multi-GPU training, network bandwith often becomes the
bottle neck. We introduce a way to use NCCL2 to do such training job to
achieve best performace.
When doing distributed multi-GPU training, network bandwidth often becomes the
bottleneck. We introduce a way to use NCCL2 to do such training job to
achieve best performance.
## Prepare Hardwares with RDMA and Multiple GPUs
## Prepare Hardware with RDMA and Multiple GPUs
I'm using two Linux servers each of them is installed with 8 GPUs and
I'm using two Linux servers each of them installed with 8 GPUs and
one 100Gb RDMA card.
Base environment is:
......@@ -25,7 +25,7 @@ In general, the steps including:
1. Use docker to run tests and make sure GPUs and RDMA can work inside
the container.
I'll ommit section "Install GPU drivers" because we can find it easily
I'll omit the section "Install GPU drivers" because we can find it easily
somewhere else.
### Install RDMA drivers
......@@ -33,7 +33,7 @@ somewhere else.
For my case, I've got two machines with device
"Mellanox Technologies MT27700 Family [ConnectX-4]" installed. The OS was
"CentOS 7.4" and I updated the kernel to version 4.4 so that docker can
work with latest overlay2 filesystem.
work with the latest overlay2 filesystem.
***NOTE: before you start, make sure you have a way to get a console
of the server other than ssh because we may need to re-configure the
......@@ -45,14 +45,14 @@ network device.***
1. Run `./mlnxofedinstall --add-kernel-support` in the software package.
1. Run `/etc/init.d/openibd restart` to make everything work, note that
this operation may cause the network goes down if you are using this
RDMA device as default network device and use ssh to login the server.
RDMA device as default network device and use ssh to log in the server.
1. Re-configure the network interface, for example:
`ifconfig eth2 192.168.16.30/20 up`, then add routes if needed:
`ip route add default via 192.168.16.1 dev eth2`.
1. Do the same thing on the other node.
1. Use `ping` to test if the two nodes have typical ICMP connection.
1. Use either `udaddy` or `ib_write_bw` to test the network connection is
ready and have the desired bandwith.
ready and have the desired bandwidth.
### Prepare Docker Image to Run RDMA Programs
......@@ -60,7 +60,7 @@ network device.***
package in it.
1. Start a docker container and mount GPU driver libs into it (you can
skip this step if you are using nvidia-docker).
1. Mount RDMA dirvers and libs into the docker image (see below section),
1. Mount RDMA drivers and libs into the docker image (see below section),
also `udaddy` and `ib_write_bw` if needed.
1. Mount GPU devices and RDMA devices into the container using `--device`
or just use privileged mode `--privileged`.
......
doc/fluid/new_docs/advanced_usage/deploy/native_infer.rst
浏览文件 @ 7e0c9f50
......@@ -4,13 +4,11 @@ Paddle 预测 API
为了更简单方便的预测部署，Fluid 提供了一套高层 API
用来隐藏底层不同的优化实现。
`预测库相关代码 <https://github.com/PaddlePaddle/Paddle/tree/develop/paddle/contrib/inference>`__
`预测库相关代码 <https://github.com/PaddlePaddle/Paddle/tree/develop/paddle/fluid/inference/api>`__
包括
- 头文件 ``paddle_inference_api.h`` 定义了所有的接口
- 库文件\ ``libpaddle_fluid.so`` 或 ``libpaddle_fluid.a``
- 库文件 ``libpaddle_inference_api.so`` 或
``libpaddle_inference_api.a``
编译和依赖可以参考 :ref:`install_or_build_cpp_inference_lib` 。
......@@ -97,12 +95,11 @@ engine
CHECK(predictor->Run(slots, &outputs));
// 获取 outputs ...
编译时，联编 ``libpaddle_fluid.a/.so`` 和
``libpaddle_inference_api.a/.so`` 便可。
编译时，联编 ``libpaddle_fluid.a/.so`` 即可。
详细代码参考
------------
- `inference
demos <https://github.com/PaddlePaddle/Paddle/tree/develop/paddle/contrib/inference/demo>`__
- `复杂单线程/多线程例子 <https://github.com/PaddlePaddle/Paddle/blob/develop/paddle/contrib/inference/test_paddle_inference_api_impl.cc>`__
demos <https://github.com/PaddlePaddle/Paddle/tree/develop/paddle/fluid/inference/api/demo_ci>`__
- `复杂单线程/多线程例子 <https://github.com/PaddlePaddle/Paddle/blob/develop/paddle/fluid/inference/api/api_impl_tester.cc>`__
paddle/fluid/API.spec
浏览文件 @ 7e0c9f50
......@@ -55,7 +55,7 @@ paddle.fluid.Inferencer.__init__ ArgSpec(args=['self', 'infer_func', 'param_path
paddle.fluid.Inferencer.infer ArgSpec(args=['self', 'inputs', 'return_numpy'], varargs=None, keywords=None, defaults=(True,))
paddle.fluid.DistributeTranspiler.__init__ ArgSpec(args=['self', 'config'], varargs=None, keywords=None, defaults=(None,))
paddle.fluid.DistributeTranspiler.get_pserver_program ArgSpec(args=['self', 'endpoint'], varargs=None, keywords=None, defaults=None)
paddle.fluid.DistributeTranspiler.get_startup_program ArgSpec(args=['self', 'endpoint', 'pserver_program'], varargs=None, keywords=None, defaults=None)
paddle.fluid.DistributeTranspiler.get_startup_program ArgSpec(args=['self', 'endpoint', 'pserver_program', 'startup_program'], varargs=None, keywords=None, defaults=(None,))
paddle.fluid.DistributeTranspiler.get_trainer_program ArgSpec(args=['self'], varargs=None, keywords=None, defaults=None)
paddle.fluid.DistributeTranspiler.transpile ArgSpec(args=['self', 'trainer_id', 'program', 'pservers', 'trainers', 'sync_mode'], varargs=None, keywords=None, defaults=(None, '127.0.0.1:6174', 1, True))
paddle.fluid.InferenceTranspiler.__init__
......@@ -78,7 +78,7 @@ paddle.fluid.io.load_vars ArgSpec(args=['executor', 'dirname', 'main_program', '
paddle.fluid.io.load_params ArgSpec(args=['executor', 'dirname', 'main_program', 'filename'], varargs=None, keywords=None, defaults=(None, None))
paddle.fluid.io.load_persistables ArgSpec(args=['executor', 'dirname', 'main_program', 'filename'], varargs=None, keywords=None, defaults=(None, None))
paddle.fluid.io.save_inference_model ArgSpec(args=['dirname', 'feeded_var_names', 'target_vars', 'executor', 'main_program', 'model_filename', 'params_filename', 'export_for_deployment'], varargs=None, keywords=None, defaults=(None, None, None, True))
paddle.fluid.io.load_inference_model ArgSpec(args=['dirname', 'executor', 'model_filename', 'params_filename'], varargs=None, keywords=None, defaults=(None, None))
paddle.fluid.io.load_inference_model ArgSpec(args=['dirname', 'executor', 'model_filename', 'params_filename', 'pserver_endpoints'], varargs=None, keywords=None, defaults=(None, None, None))
paddle.fluid.io.get_inference_program ArgSpec(args=['target_vars', 'main_program'], varargs=None, keywords=None, defaults=(None,))
paddle.fluid.initializer.ConstantInitializer.__init__ ArgSpec(args=['self', 'value', 'force_cpu'], varargs=None, keywords=None, defaults=(0.0, False))
paddle.fluid.initializer.UniformInitializer.__init__ ArgSpec(args=['self', 'low', 'high', 'seed'], varargs=None, keywords=None, defaults=(-1.0, 1.0, 0))
......@@ -154,12 +154,18 @@ paddle.fluid.layers.image_resize ArgSpec(args=['input', 'out_shape', 'scale', 'n
paddle.fluid.layers.image_resize_short ArgSpec(args=['input', 'out_short_len', 'resample'], varargs=None, keywords=None, defaults=('BILINEAR',))
paddle.fluid.layers.resize_bilinear ArgSpec(args=['input', 'out_shape', 'scale', 'name'], varargs=None, keywords=None, defaults=(None, None, None))
paddle.fluid.layers.gather ArgSpec(args=['input', 'index'], varargs=None, keywords=None, defaults=None)
paddle.fluid.layers.scatter ArgSpec(args=['input', 'index', 'updates', 'name'], varargs=None, keywords=None, defaults=(None,))
paddle.fluid.layers.random_crop ArgSpec(args=['x', 'shape', 'seed'], varargs=None, keywords=None, defaults=(None,))
paddle.fluid.layers.mean_iou ArgSpec(args=['input', 'label', 'num_classes'], varargs=None, keywords=None, defaults=None)
paddle.fluid.layers.relu ArgSpec(args=['x'], varargs=None, keywords=None, defaults=None)
paddle.fluid.layers.log ArgSpec(args=['x'], varargs=None, keywords=None, defaults=None)
paddle.fluid.layers.relu ArgSpec(args=['x', 'name'], varargs=None, keywords=None, defaults=(None,))
paddle.fluid.layers.log ArgSpec(args=['x', 'name'], varargs=None, keywords=None, defaults=(None,))
paddle.fluid.layers.crop ArgSpec(args=['x', 'shape', 'offsets', 'name'], varargs=None, keywords=None, defaults=(None, None, None))
paddle.fluid.layers.rank_loss ArgSpec(args=['label', 'left', 'right', 'name'], varargs=None, keywords=None, defaults=(None,))
paddle.fluid.layers.prelu ArgSpec(args=['x', 'mode', 'param_attr', 'name'], varargs=None, keywords=None, defaults=(None, None))
paddle.fluid.layers.flatten ArgSpec(args=['x', 'axis', 'name'], varargs=None, keywords=None, defaults=(1, None))
paddle.fluid.layers.sequence_mask ArgSpec(args=['x', 'maxlen', 'dtype', 'name'], varargs=None, keywords=None, defaults=(None, 'int64', None))
paddle.fluid.layers.stack ArgSpec(args=['x', 'axis'], varargs=None, keywords=None, defaults=(0,))
paddle.fluid.layers.unstack ArgSpec(args=['x', 'axis', 'num'], varargs=None, keywords=None, defaults=(0, 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_recordio_file ArgSpec(args=['filename', 'shapes', 'lod_levels', 'dtypes', 'pass_num', 'for_parallel'], varargs=None, keywords=None, defaults=(1, 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))
......@@ -189,7 +195,7 @@ paddle.fluid.layers.argsort ArgSpec(args=['input', 'axis', 'name'], varargs=None
paddle.fluid.layers.ones ArgSpec(args=['shape', 'dtype', 'force_cpu'], varargs=None, keywords=None, defaults=(False,))
paddle.fluid.layers.zeros ArgSpec(args=['shape', 'dtype', 'force_cpu'], varargs=None, keywords=None, defaults=(False,))
paddle.fluid.layers.reverse ArgSpec(args=['x', 'axis'], varargs=None, keywords=None, defaults=None)
paddle.fluid.layers.While.__init__ ArgSpec(args=['self', 'cond', 'name'], varargs=None, keywords=None, defaults=(None,))
paddle.fluid.layers.While.__init__ ArgSpec(args=['self', 'cond', 'is_test', 'name'], varargs=None, keywords=None, defaults=(False, None))
paddle.fluid.layers.While.block ArgSpec(args=['self'], varargs=None, keywords=None, defaults=None)
paddle.fluid.layers.Switch.__init__ ArgSpec(args=['self', 'name'], varargs=None, keywords=None, defaults=(None,))
paddle.fluid.layers.Switch.case ArgSpec(args=['self', 'condition'], varargs=None, keywords=None, defaults=None)
......@@ -249,7 +255,6 @@ paddle.fluid.layers.logical_not ArgSpec(args=[], varargs='args', keywords='kwarg
paddle.fluid.layers.uniform_random_batch_size_like ArgSpec(args=[], varargs='args', keywords='kwargs', defaults=None)
paddle.fluid.layers.gaussian_random ArgSpec(args=[], varargs='args', keywords='kwargs', defaults=None)
paddle.fluid.layers.gaussian_random_batch_size_like ArgSpec(args=[], varargs='args', keywords='kwargs', defaults=None)
paddle.fluid.layers.scatter ArgSpec(args=[], varargs='args', keywords='kwargs', defaults=None)
paddle.fluid.layers.sum ArgSpec(args=[], varargs='args', keywords='kwargs', defaults=None)
paddle.fluid.layers.slice ArgSpec(args=[], varargs='args', keywords='kwargs', defaults=None)
paddle.fluid.layers.shape ArgSpec(args=[], varargs='args', keywords='kwargs', defaults=None)
......@@ -328,7 +333,7 @@ paddle.fluid.contrib.BeamSearchDecoder.update_array ArgSpec(args=['self', 'array
paddle.fluid.contrib.memory_usage ArgSpec(args=['program', 'batch_size'], varargs=None, keywords=None, defaults=None)
paddle.fluid.transpiler.DistributeTranspiler.__init__ ArgSpec(args=['self', 'config'], varargs=None, keywords=None, defaults=(None,))
paddle.fluid.transpiler.DistributeTranspiler.get_pserver_program ArgSpec(args=['self', 'endpoint'], varargs=None, keywords=None, defaults=None)
paddle.fluid.transpiler.DistributeTranspiler.get_startup_program ArgSpec(args=['self', 'endpoint', 'pserver_program'], varargs=None, keywords=None, defaults=None)
paddle.fluid.transpiler.DistributeTranspiler.get_startup_program ArgSpec(args=['self', 'endpoint', 'pserver_program', 'startup_program'], varargs=None, keywords=None, defaults=(None,))
paddle.fluid.transpiler.DistributeTranspiler.get_trainer_program ArgSpec(args=['self'], varargs=None, keywords=None, defaults=None)
paddle.fluid.transpiler.DistributeTranspiler.transpile ArgSpec(args=['self', 'trainer_id', 'program', 'pservers', 'trainers', 'sync_mode'], varargs=None, keywords=None, defaults=(None, '127.0.0.1:6174', 1, True))
paddle.fluid.transpiler.InferenceTranspiler.__init__
......@@ -375,7 +380,7 @@ paddle.fluid.LoDTensor.__init__ 1. __init__(self: paddle.fluid.core.LoDTensor, a
paddle.fluid.LoDTensor.has_valid_recursive_sequence_lengths has_valid_recursive_sequence_lengths(self: paddle.fluid.core.LoDTensor) -> bool
paddle.fluid.LoDTensor.lod lod(self: paddle.fluid.core.LoDTensor) -> List[List[int]]
paddle.fluid.LoDTensor.recursive_sequence_lengths recursive_sequence_lengths(self: paddle.fluid.core.LoDTensor) -> List[List[int]]
paddle.fluid.LoDTensor.set 1. set(self: paddle.fluid.core.Tensor, arg0: numpy.ndarray[float32], arg1: paddle::platform::CPUPlace) -> None 2. set(self: paddle.fluid.core.Tensor, arg0: numpy.ndarray[int32], arg1: paddle::platform::CPUPlace) -> None 3. set(self: paddle.fluid.core.Tensor, arg0: numpy.ndarray[float64], arg1: paddle::platform::CPUPlace) -> None 4. set(self: paddle.fluid.core.Tensor, arg0: numpy.ndarray[int64], arg1: paddle::platform::CPUPlace) -> None 5. set(self: paddle.fluid.core.Tensor, arg0: numpy.ndarray[bool], arg1: paddle::platform::CPUPlace) -> None 6. set(self: paddle.fluid.core.Tensor, arg0: numpy.ndarray[uint16], arg1: paddle::platform::CPUPlace) -> None 7. set(self: paddle.fluid.core.Tensor, arg0: numpy.ndarray[uint8], arg1: paddle::platform::CPUPlace) -> None 8. set(self: paddle.fluid.core.Tensor, arg0: numpy.ndarray[float32], arg1: paddle::platform::CUDAPlace) -> None 9. set(self: paddle.fluid.core.Tensor, arg0: numpy.ndarray[int32], arg1: paddle::platform::CUDAPlace) -> None 10. set(self: paddle.fluid.core.Tensor, arg0: numpy.ndarray[float64], arg1: paddle::platform::CUDAPlace) -> None 11. set(self: paddle.fluid.core.Tensor, arg0: numpy.ndarray[int64], arg1: paddle::platform::CUDAPlace) -> None 12. set(self: paddle.fluid.core.Tensor, arg0: numpy.ndarray[bool], arg1: paddle::platform::CUDAPlace) -> None 13. set(self: paddle.fluid.core.Tensor, arg0: numpy.ndarray[uint16], arg1: paddle::platform::CUDAPlace) -> None 14. set(self: paddle.fluid.core.Tensor, arg0: numpy.ndarray[uint8], arg1: paddle::platform::CUDAPlace) -> None 15. set(self: paddle.fluid.core.Tensor, arg0: numpy.ndarray[float32], arg1: paddle::platform::CUDAPinnedPlace) -> None 16. set(self: paddle.fluid.core.Tensor, arg0: numpy.ndarray[int32], arg1: paddle::platform::CUDAPinnedPlace) -> None 17. set(self: paddle.fluid.core.Tensor, arg0: numpy.ndarray[float64], arg1: paddle::platform::CUDAPinnedPlace) -> None 18. set(self: paddle.fluid.core.Tensor, arg0: numpy.ndarray[int64], arg1: paddle::platform::CUDAPinnedPlace) -> None 19. set(self: paddle.fluid.core.Tensor, arg0: numpy.ndarray[bool], arg1: paddle::platform::CUDAPinnedPlace) -> None 20. set(self: paddle.fluid.core.Tensor, arg0: numpy.ndarray[uint16], arg1: paddle::platform::CUDAPinnedPlace) -> None 21. set(self: paddle.fluid.core.Tensor, arg0: numpy.ndarray[uint8], arg1: paddle::platform::CUDAPinnedPlace) -> None
paddle.fluid.LoDTensor.set 1. set(self: paddle.fluid.core.Tensor, arg0: numpy.ndarray[float32], arg1: paddle::platform::CPUPlace) -> None 2. set(self: paddle.fluid.core.Tensor, arg0: numpy.ndarray[int32], arg1: paddle::platform::CPUPlace) -> None 3. set(self: paddle.fluid.core.Tensor, arg0: numpy.ndarray[float64], arg1: paddle::platform::CPUPlace) -> None 4. set(self: paddle.fluid.core.Tensor, arg0: numpy.ndarray[int64], arg1: paddle::platform::CPUPlace) -> None 5. set(self: paddle.fluid.core.Tensor, arg0: numpy.ndarray[bool], arg1: paddle::platform::CPUPlace) -> None 6. set(self: paddle.fluid.core.Tensor, arg0: numpy.ndarray[uint16], arg1: paddle::platform::CPUPlace) -> None 7. set(self: paddle.fluid.core.Tensor, arg0: numpy.ndarray[uint8], arg1: paddle::platform::CPUPlace) -> None 8. set(self: paddle.fluid.core.Tensor, arg0: numpy.ndarray[int8], arg1: paddle::platform::CPUPlace) -> None 9. set(self: paddle.fluid.core.Tensor, arg0: numpy.ndarray[float32], arg1: paddle::platform::CUDAPlace) -> None 10. set(self: paddle.fluid.core.Tensor, arg0: numpy.ndarray[int32], arg1: paddle::platform::CUDAPlace) -> None 11. set(self: paddle.fluid.core.Tensor, arg0: numpy.ndarray[float64], arg1: paddle::platform::CUDAPlace) -> None 12. set(self: paddle.fluid.core.Tensor, arg0: numpy.ndarray[int64], arg1: paddle::platform::CUDAPlace) -> None 13. set(self: paddle.fluid.core.Tensor, arg0: numpy.ndarray[bool], arg1: paddle::platform::CUDAPlace) -> None 14. set(self: paddle.fluid.core.Tensor, arg0: numpy.ndarray[uint16], arg1: paddle::platform::CUDAPlace) -> None 15. set(self: paddle.fluid.core.Tensor, arg0: numpy.ndarray[uint8], arg1: paddle::platform::CUDAPlace) -> None 16. set(self: paddle.fluid.core.Tensor, arg0: numpy.ndarray[int8], arg1: paddle::platform::CUDAPlace) -> None 17. set(self: paddle.fluid.core.Tensor, arg0: numpy.ndarray[float32], arg1: paddle::platform::CUDAPinnedPlace) -> None 18. set(self: paddle.fluid.core.Tensor, arg0: numpy.ndarray[int32], arg1: paddle::platform::CUDAPinnedPlace) -> None 19. set(self: paddle.fluid.core.Tensor, arg0: numpy.ndarray[float64], arg1: paddle::platform::CUDAPinnedPlace) -> None 20. set(self: paddle.fluid.core.Tensor, arg0: numpy.ndarray[int64], arg1: paddle::platform::CUDAPinnedPlace) -> None 21. set(self: paddle.fluid.core.Tensor, arg0: numpy.ndarray[bool], arg1: paddle::platform::CUDAPinnedPlace) -> None 22. set(self: paddle.fluid.core.Tensor, arg0: numpy.ndarray[uint16], arg1: paddle::platform::CUDAPinnedPlace) -> None 23. set(self: paddle.fluid.core.Tensor, arg0: numpy.ndarray[uint8], arg1: paddle::platform::CUDAPinnedPlace) -> None 24. set(self: paddle.fluid.core.Tensor, arg0: numpy.ndarray[int8], arg1: paddle::platform::CUDAPinnedPlace) -> None
paddle.fluid.LoDTensor.set_lod set_lod(self: paddle.fluid.core.LoDTensor, arg0: List[List[int]]) -> None
paddle.fluid.LoDTensor.set_recursive_sequence_lengths set_recursive_sequence_lengths(self: paddle.fluid.core.LoDTensor, arg0: List[List[int]]) -> None
paddle.fluid.LoDTensor.shape shape(self: paddle.fluid.core.Tensor) -> List[int]
......
paddle/fluid/CMakeLists.txt
浏览文件 @ 7e0c9f50
......@@ -2,9 +2,13 @@ add_subdirectory(memory)
add_subdirectory(platform)
add_subdirectory(framework)
add_subdirectory(operators)
add_subdirectory(pybind)
add_subdirectory(string)
if (NOT WIN32)
add_subdirectory(pybind)
add_subdirectory(recordio)
endif(NOT WIN32)
if(WITH_INFERENCE)
# NOTE: please add subdirectory inference at last.
add_subdirectory(inference)
......
paddle/fluid/framework/CMakeLists.txt
浏览文件 @ 7e0c9f50
add_subdirectory(details)
add_subdirectory(ir)
if (NOT WIN32)
add_subdirectory(details)
endif (NOT WIN32)
# ddim lib
proto_library(framework_proto SRCS framework.proto)
......@@ -28,8 +30,12 @@ if(WITH_GPU)
else()
cc_test(mixed_vector_test SRCS mixed_vector_test.cc DEPS place memory device_context tensor)
endif()
if (NOT WIN32)
cc_library(lod_tensor SRCS lod_tensor.cc DEPS ddim place tensor framework_proto recordio)
else()
cc_library(lod_tensor SRCS lod_tensor.cc DEPS ddim place tensor framework_proto)
endif (NOT WIN32)
cc_test(lod_tensor_test SRCS lod_tensor_test.cc DEPS lod_tensor memory)
nv_test(lod_tensor_gpu_test SRCS lod_tensor_test.cu DEPS lod_tensor)
......@@ -69,14 +75,22 @@ cc_library(op_proto_maker SRCS op_proto_maker.cc DEPS framework_proto attribute
cc_test(op_proto_maker_test SRCS op_proto_maker_test.cc DEPS op_proto_maker)
cc_library(op_info SRCS op_info.cc DEPS attribute framework_proto)
cc_library(shape_inference SRCS shape_inference.cc DEPS ddim attribute device_context)
if (NOT WIN32)
cc_library(operator SRCS operator.cc DEPS op_info device_context tensor scope glog
shape_inference data_transform lod_tensor profiler)
else()
cc_library(operator SRCS operator.cc DEPS op_info device_context tensor scope glog
shape_inference data_transform lod_tensor)
endif(NOT WIN32)
cc_test(operator_test SRCS operator_test.cc DEPS operator op_registry device_context)
cc_library(proto_desc SRCS var_desc.cc op_desc.cc block_desc.cc program_desc.cc DEPS shape_inference op_info operator glog)
cc_library(op_registry SRCS op_registry.cc DEPS op_proto_maker op_info operator glog proto_desc)
nv_test(op_registry_test SRCS op_registry_test.cc DEPS op_registry)
if (NOT WIN32)
py_proto_compile(framework_py_proto SRCS framework.proto)
# Generate an empty __init__.py to make framework_py_proto as a valid python module.
add_custom_target(framework_py_proto_init ALL COMMAND ${CMAKE_COMMAND} -E touch __init__.py)
......@@ -86,6 +100,7 @@ add_custom_command(TARGET framework_py_proto POST_BUILD
COMMAND cp *.py ${PADDLE_BINARY_DIR}/python/paddle/fluid/proto/
COMMENT "Copy generated python proto into directory paddle/fluid/proto."
WORKING_DIRECTORY ${CMAKE_CURRENT_BINARY_DIR})
endif(NOT WIN32)
cc_library(lod_rank_table SRCS lod_rank_table.cc DEPS lod_tensor)
......@@ -99,8 +114,13 @@ else()
cc_library(executor SRCS executor.cc DEPS op_registry device_context scope framework_proto glog lod_rank_table feed_fetch_method)
endif()
cc_library(parallel_executor SRCS parallel_executor.cc DEPS threaded_ssa_graph_executor scope_buffered_ssa_graph_executor graph graph_viz_pass multi_devices_graph_pass multi_devices_graph_print_pass multi_devices_graph_check_pass)
if (NOT WIN32)
cc_library(parallel_executor SRCS parallel_executor.cc DEPS
threaded_ssa_graph_executor scope_buffered_ssa_graph_executor
graph graph_viz_pass multi_devices_graph_pass
multi_devices_graph_print_pass multi_devices_graph_check_pass
fast_threaded_ssa_graph_executor)
endif() # NOT WIN32
cc_library(prune SRCS prune.cc DEPS framework_proto)
cc_test(prune_test SRCS prune_test.cc DEPS op_info prune recurrent_op device_context)
......@@ -115,6 +135,10 @@ cc_test(cow_ptr_tests SRCS details/cow_ptr_test.cc)
# cc_test(channel_test SRCS channel_test.cc)
cc_test(tuple_test SRCS tuple_test.cc )
if (NOT WIN32)
cc_test(rw_lock_test SRCS rw_lock_test.cc)
endif (NOT WIN32)
# disable test temporarily.
# TODO https://github.com/PaddlePaddle/Paddle/issues/11971
# cc_test(concurrency_test SRCS concurrency_test.cc DEPS go_op channel_close_op channel_create_op
......
paddle/fluid/framework/array.h 0 → 100644
浏览文件 @ 7e0c9f50
// 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 <cstdint>
#include "paddle/fluid/platform/hostdevice.h"
namespace paddle {
namespace framework {
template <typename T, size_t N>
class Array {
static_assert(N > 0, "The size of array must be larger than 0");
public:
HOSTDEVICE Array() {}
HOSTDEVICE explicit Array(const T &val) {
for (size_t i = 0; i < N; ++i) data_[i] = val;
}
HOSTDEVICE const T *Get() const { return data_; }
HOSTDEVICE T *GetMutable() { return data_; }
HOSTDEVICE T &operator[](size_t index) { return data_[index]; }
HOSTDEVICE const T &operator[](size_t index) const { return data_[index]; }
HOSTDEVICE constexpr size_t size() const { return N; }
private:
T data_[N];
};
} // namespace framework
} // namespace paddle
paddle/fluid/framework/attribute.h
浏览文件 @ 7e0c9f50
......@@ -128,7 +128,8 @@ struct ExtractAttribute {
attr_value = &boost::get<T>(attr);
} catch (boost::bad_get& bad_get) {
PADDLE_THROW("Cannot get attribute %s by type %s, its type is %s",
attr_name_, typeid(T).name(), attr.type().name());
attr_name_, paddle::platform::demangle(typeid(T).name()),
paddle::platform::demangle(attr.type().name()));
}
return attr_value;
}
......@@ -160,7 +161,7 @@ struct ExtractAttribute<bool> {
attr_value = &boost::get<bool>(attr);
} catch (boost::bad_get& bad_get) {
PADDLE_THROW("Cannot get attribute %s by type bool, its type is %s",
attr_name_, attr.type().name());
attr_name_, paddle::platform::demangle(attr.type().name()));
}
return attr_value;
}
......@@ -186,7 +187,7 @@ struct ExtractAttribute<int64_t> {
attr_value = &boost::get<int64_t>(attr);
} catch (boost::bad_get& bad_get) {
PADDLE_THROW("Cannot get attribute %s by type int64_t, its type is %s",
attr_name_, attr.type().name());
attr_name_, paddle::platform::demangle(attr.type().name()));
}
return attr_value;
}
......
paddle/fluid/framework/data_type.cc
浏览文件 @ 7e0c9f50
......@@ -64,6 +64,7 @@ static DataTypeMap* InitDataTypeMap() {
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);
#undef RegType
return retv;
......
paddle/fluid/framework/data_type.h
浏览文件 @ 7e0c9f50
......@@ -26,6 +26,7 @@ namespace framework {
extern proto::VarType::Type ToDataType(std::type_index type);
extern std::type_index ToTypeIndex(proto::VarType::Type type);
#if !defined(_WIN32)
template <typename Visitor>
inline void VisitDataType(proto::VarType::Type type, Visitor visitor) {
switch (type) {
......@@ -53,10 +54,47 @@ inline void VisitDataType(proto::VarType::Type type, Visitor visitor) {
case proto::VarType::INT16:
visitor.template operator()<int16_t>();
break;
case proto::VarType::INT8:
visitor.template operator()<int8_t>();
break;
default:
PADDLE_THROW("Not supported %d", type);
}
}
#else
// the msvc compiler do not implement two-stage name lookup correctly.
template <typename Visitor>
inline void VisitDataType(proto::VarType::Type type, Visitor visitor) {
switch (type) {
case proto::VarType::FP16:
visitor.operator()<platform::float16>();
break;
case proto::VarType::FP32:
visitor.operator()<float>();
break;
case proto::VarType::FP64:
visitor.operator()<double>();
break;
case proto::VarType::INT32:
visitor.operator()<int>();
break;
case proto::VarType::INT64:
visitor.operator()<int64_t>();
break;
case proto::VarType::BOOL:
visitor.operator()<bool>();
break;
case proto::VarType::UINT8:
visitor.operator()<uint8_t>();
break;
case proto::VarType::INT16:
visitor.operator()<int16_t>();
break;
default:
PADDLE_THROW("Not supported %d", type);
}
}
#endif // _WIN32
extern std::string DataTypeToString(const proto::VarType::Type type);
extern size_t SizeOfType(std::type_index type);
......
paddle/fluid/framework/details/CMakeLists.txt
浏览文件 @ 7e0c9f50
......@@ -42,3 +42,5 @@ cc_test(gather_op_test SRCS gather_op_handle_test.cc DEPS var_handle op_handle_b
cc_library(scope_buffered_ssa_graph_executor SRCS scope_buffered_ssa_graph_executor.cc DEPS ssa_graph_executor)
#cc_test(reduce_op_handle_test SRCS reduce_op_handle_test.cc DEPS var_handle op_handle_base scope ddim memory
# device_context reduce_op_handle )
cc_library(fast_threaded_ssa_graph_executor SRCS fast_threaded_ssa_graph_executor.cc
DEPS fetch_op_handle ssa_graph_executor scope simple_threadpool device_context)
paddle/fluid/framework/details/exception_holder.h
浏览文件 @ 7e0c9f50
......@@ -14,6 +14,7 @@
#pragma once
#include "glog/logging.h"
#include "paddle/fluid/platform/enforce.h"
namespace paddle {
......@@ -22,27 +23,24 @@ namespace details {
class ExceptionHolder {
public:
void Catch(const platform::EnforceNotMet& exp) {
std::lock_guard<std::mutex> lock(mu_);
exception_.reset(new platform::EnforceNotMet(exp));
type_ = kEnforceNotMet;
}
void Catch(const platform::EOFException& exp) {
std::lock_guard<std::mutex> lock(mu_);
// EOFException will not cover up existing EnforceNotMet.
if (exception_.get() == nullptr) {
exception_.reset(new platform::EOFException(exp));
type_ = kEOF;
void Catch(std::exception_ptr eptr) {
try {
std::rethrow_exception(eptr);
} catch (platform::EOFException exp) {
Catch(exp);
} catch (platform::EnforceNotMet exp) {
Catch(exp);
} catch (...) {
LOG(FATAL) << "Unknown exception caught";
}
}
bool ExceptionCatched() const {
bool IsCaught() const {
std::lock_guard<std::mutex> lock(mu_);
return exception_.get() != nullptr;
}
void Throw() {
void ReThrow() {
std::lock_guard<std::mutex> lock(mu_);
switch (type_) {
case kNone:
......@@ -50,27 +48,41 @@ class ExceptionHolder {
case kEnforceNotMet: {
auto e = *static_cast<platform::EnforceNotMet*>(exception_.get());
throw e;
break;
}
case kEOF: {
auto e = *static_cast<platform::EOFException*>(exception_.get());
throw e;
break;
}
default:
LOG(FATAL) << "Unknown exception.";
}
exception_.reset();
type_ = kNone;
ClearImpl();
}
void Clear() {
std::lock_guard<std::mutex> lock(mu_);
ClearImpl();
}
private:
void ClearImpl() {
exception_.reset();
type_ = kNone;
}
private:
void Catch(const platform::EnforceNotMet& exp) {
std::lock_guard<std::mutex> lock(mu_);
exception_.reset(new platform::EnforceNotMet(exp));
type_ = kEnforceNotMet;
}
void Catch(const platform::EOFException& exp) {
std::lock_guard<std::mutex> lock(mu_);
// EOFException will not cover up existing EnforceNotMet.
if (exception_.get() == nullptr) {
exception_.reset(new platform::EOFException(exp));
type_ = kEOF;
}
}
enum ExceptionType { kNone, kEnforceNotMet, kEOF };
ExceptionType type_{kNone};
......
paddle/fluid/framework/details/execution_strategy.h
浏览文件 @ 7e0c9f50
......@@ -19,10 +19,13 @@ namespace framework {
namespace details {
struct ExecutionStrategy {
enum ExecutorType { kDefault = 0, kExperimental = 1 };
size_t num_threads_{0};
bool use_cuda_{true};
bool allow_op_delay_{false};
size_t num_iteration_per_drop_scope_{100};
ExecutorType type_{kDefault};
};
} // namespace details
......
paddle/fluid/framework/details/fast_threaded_ssa_graph_executor.cc 0 → 100644
浏览文件 @ 7e0c9f50
// 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/fast_threaded_ssa_graph_executor.h"
#include <string>
#include <vector>
#include "paddle/fluid/framework/details/fetch_op_handle.h"
#include "paddle/fluid/framework/details/multi_devices_helper.h"
namespace paddle {
namespace framework {
namespace details {
FastThreadedSSAGraphExecutor::FastThreadedSSAGraphExecutor(
const ExecutionStrategy &strategy, const std::vector<Scope *> &local_scopes,
const std::vector<platform::Place> &places,
std::unique_ptr<ir::Graph> &&graph)
: strategy_(strategy),
local_scopes_(local_scopes),
places_(places),
graph_(std::move(graph)),
pool_(strategy.num_threads_ +
1), // add one more thread for generate op_deps
fetch_ctxs_(places) {
auto &ops = graph_->Get<details::GraphOps>("ops");
for (auto &op : ops) {
int dep = static_cast<int>(op->NotReadyInputSize());
op_deps_.emplace(op.get(), dep);
if (dep == 0) {
bootstrap_ops_.emplace_back(op.get());
}
}
PrepareAtomicOpDeps();
}
FeedFetchList FastThreadedSSAGraphExecutor::Run(
const std::vector<std::string> &fetch_tensors) {
std::unique_ptr<std::unordered_map<OpHandleBase *, std::atomic<int>>>
op_deps = atomic_op_deps_.get();
PrepareAtomicOpDeps();
paddle::framework::FeedFetchList fetches;
fetches.resize(fetch_tensors.size());
std::unordered_map<std::string, std::vector<VarHandleBase *>> fetched_vars;
std::vector<std::unique_ptr<ir::Node>> fetch_nodes;
std::vector<std::unique_ptr<FetchOpHandle>> fetch_ops;
for (auto &fetch_var_name : fetch_tensors) {
for (auto &var_map : graph_->Get<details::GraphVars>("vars")) {
auto it = var_map.find(fetch_var_name);
if (it != var_map.end()) {
fetched_vars[fetch_var_name].push_back(it->second.rbegin()->get());
}
}
}
for (size_t i = 0; i < fetch_tensors.size(); ++i) {
auto &var_name = fetch_tensors[i];
auto fetched_var_it = fetched_vars.find(var_name);
PADDLE_ENFORCE(fetched_var_it != fetched_vars.end(),
"Cannot find fetched variable.(Perhaps the main_program "
"is not set to ParallelExecutor)");
auto &vars = fetched_var_it->second;
fetch_nodes.emplace_back(new ir::Node("fetch", ir::Node::Type::kOperation));
auto *op = new FetchOpHandle(fetch_nodes.back().get(), &fetches, i,
&local_scopes_);
fetch_ops.emplace_back(op);
for (auto &p : places_) {
op->SetDeviceContext(p, fetch_ctxs_.Get(p));
}
for (auto *var : vars) {
op->AddInput(var);
}
(*op_deps)[op] = static_cast<int>(op->NotReadyInputSize());
}
size_t num_complete = 0;
remaining_ = 0;
BlockingQueue<size_t> complete_q;
for (auto op : bootstrap_ops_) {
RunOpAsync(op_deps.get(), op, &complete_q);
}
while (num_complete != op_deps->size()) {
size_t num_comp = complete_q.Pop();
if (num_comp == -1UL) {
int remaining = 0;
while (true) {
remaining = remaining_;
if (remaining == 0) {
break;
}
for (int i = 0; i < remaining; ++i) {
complete_q.Pop();
}
}
exception_.ReThrow();
}
num_complete += num_comp;
}
// Wait FetchOps.
if (!fetch_ops.empty()) {
fetch_ops.clear();
}
return fetches;
}
void FastThreadedSSAGraphExecutor::RunOpAsync(
std::unordered_map<OpHandleBase *, std::atomic<int>> *op_deps,
OpHandleBase *op, BlockingQueue<size_t> *complete_q) {
++remaining_;
this->pool_.enqueue([=] {
OpHandleBase *op_to_run = op;
size_t complete = 0;
while (op_to_run != nullptr) {
try {
op_to_run->Run(strategy_.use_cuda_);
++complete;
} catch (...) {
exception_.Catch(std::current_exception());
--remaining_;
complete_q->Push(-1UL);
return;
}
auto &outputs = op_to_run->Outputs();
op_to_run = nullptr;
for (auto &output : outputs) {
for (auto &pending_op : output->PendingOps()) {
std::atomic<int> &deps = op_deps->at(pending_op);
if (deps.fetch_sub(1) == 1) { // pending_op ready
if (op_to_run == nullptr) {
op_to_run = pending_op;
} else {
this->RunOpAsync(op_deps, pending_op, complete_q);
}
}
}
}
}
--remaining_;
complete_q->Push(complete);
});
}
void FastThreadedSSAGraphExecutor::PrepareAtomicOpDeps() {
atomic_op_deps_ = pool_.enqueue([&] {
std::unordered_map<OpHandleBase *, std::atomic<int>> *op_deps =
new std::unordered_map<OpHandleBase *, std::atomic<int>>;
for (auto &pair : op_deps_) {
(*op_deps)[pair.first] = pair.second;
}
return std::unique_ptr<
std::unordered_map<OpHandleBase *, std::atomic<int>>>(op_deps);
});
}
const ir::Graph &FastThreadedSSAGraphExecutor::Graph() const { return *graph_; }
} // namespace details
} // namespace framework
} // namespace paddle
paddle/fluid/framework/details/fast_threaded_ssa_graph_executor.h 0 → 100644
浏览文件 @ 7e0c9f50
// 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 "ThreadPool.h"
#include "paddle/fluid/framework/blocking_queue.h"
#include "paddle/fluid/framework/details/exception_holder.h"
#include "paddle/fluid/framework/details/execution_strategy.h"
#include "paddle/fluid/framework/details/ssa_graph_executor.h"
namespace paddle {
namespace framework {
class Scope;
namespace details {
class OpHandleBase;
class FastThreadedSSAGraphExecutor : public SSAGraphExecutor {
public:
FastThreadedSSAGraphExecutor(const ExecutionStrategy &strategy,
const std::vector<Scope *> &local_scopes,
const std::vector<platform::Place> &places,
std::unique_ptr<ir::Graph> &&graph);
FeedFetchList Run(const std::vector<std::string> &fetch_tensors) override;
const ir::Graph &Graph() const override;
private:
ExecutionStrategy strategy_;
std::vector<Scope *> local_scopes_;
std::vector<platform::Place> places_;
std::unique_ptr<ir::Graph> graph_;
std::unordered_map<OpHandleBase *, int> op_deps_;
std::vector<OpHandleBase *> bootstrap_ops_;
::ThreadPool pool_;
platform::DeviceContextPool fetch_ctxs_;
std::atomic<int> remaining_;
void RunOpAsync(std::unordered_map<OpHandleBase *, std::atomic<int>> *op_deps,
OpHandleBase *op, BlockingQueue<size_t> *complete_q);
void PrepareAtomicOpDeps();
std::future<
std::unique_ptr<std::unordered_map<OpHandleBase *, std::atomic<int>>>>
atomic_op_deps_;
ExceptionHolder exception_;
};
} // namespace details
} // namespace framework
} // namespace paddle
paddle/fluid/framework/details/multi_devices_graph_pass.cc
浏览文件 @ 7e0c9f50
......@@ -763,6 +763,8 @@ void MultiDevSSAGraphBuilder::CreateDistTrainOp(ir::Graph *result,
// Create RPC related op handles that connects its in ops and out ops.
void MultiDevSSAGraphBuilder::CreateRPCOp(ir::Graph *result,
ir::Node *node) const {
// FIXME(typhoonzero): Cleanup this deps for both sync mode and async mode
// put them into transpiler.
int op_dev_id = -1;
if (node->Op()->Type() == "send") {
// TODO(paddle-dev): getting the first var is not safe.
......@@ -771,26 +773,42 @@ void MultiDevSSAGraphBuilder::CreateRPCOp(ir::Graph *result,
"This hack no longer holds, please fix.");
// the variable name which contains .block means it was splited by
// split_byref op
// so that we can balance the variable blocks to all the pserver
// instances.
if (strategy_.reduce_ == BuildStrategy::ReduceStrategy::kAllReduce &&
node->inputs[0]->Name().find(".block") == std::string::npos) {
std::vector<std::string> input_var_names;
for (ir::Node *n : node->inputs) {
input_var_names.push_back(n->Name());
}
op_dev_id = GetAppropriateDeviceID(input_var_names);
auto send_param_grad = boost::get<std::vector<std::string>>(
node->Op()->GetAttr(OpProtoAndCheckerMaker::OpRoleVarAttrName()));
PADDLE_ENFORCE_EQ(send_param_grad.size(), 2U);
op_dev_id = GetAppropriateDeviceID({send_param_grad[1]});
VLOG(10) << "send grad " << input_var_names[0] << " origin "
<< send_param_grad[1] << " place: " << op_dev_id;
for (auto &varname : input_var_names) {
result->Get<ShardedVarDevice>(kShardedVarDevice)
.emplace(varname, op_dev_id);
}
result->Get<ShardedVarDevice>(kShardedVarDevice)
.emplace(send_param_grad[1], op_dev_id);
}
} else if (node->Op()->Type() == "recv") {
std::vector<std::string> output_var_names;
for (ir::Node *n : node->outputs) {
output_var_names.push_back(n->Name());
}
op_dev_id = GetAppropriateDeviceID(output_var_names);
auto recv_param_grad = boost::get<std::vector<std::string>>(
node->Op()->GetAttr(OpProtoAndCheckerMaker::OpRoleVarAttrName()));
// FIXME(typhoonzero): assume each recv op output one param
// Use the same place as send.
if (recv_param_grad.size() == 2U) {
op_dev_id = GetVarDeviceID(*result, recv_param_grad[1]);
VLOG(10) << "recv param " << recv_param_grad[0]
<< " get grad place: " << recv_param_grad[1]
<< " place: " << op_dev_id;
} else {
op_dev_id = GetAppropriateDeviceID(output_var_names);
}
for (auto &varname : output_var_names) {
result->Get<ShardedVarDevice>(kShardedVarDevice)
.emplace(varname, op_dev_id);
......
paddle/fluid/framework/details/multi_devices_graph_print_pass.cc
浏览文件 @ 7e0c9f50
......@@ -54,7 +54,8 @@ void GraphvizSSAGraphPrinter::Print(const ir::Graph &graph,
sout << "var_" << cur_var_id << " [label=\"" << var_handle_ptr->name_
<< "\\n"
<< var_handle_ptr->place_ << "\\n"
<< var_handle_ptr->version_ << "\"]" << std::endl;
<< "scope: " << var_handle_ptr->scope_idx_ << "\\n"
<< "v" << var_handle_ptr->version_ << "\"]" << std::endl;
} else if (dummy_ptr) {
sout << "var_" << cur_var_id << " [label=\"dummy\"]" << std::endl;
}
......
paddle/fluid/framework/details/op_handle_base.cc
浏览文件 @ 7e0c9f50
......@@ -158,6 +158,16 @@ void OpHandleBase::RunAndRecordEvent(platform::Place p,
#endif
}
size_t OpHandleBase::NotReadyInputSize() const {
std::unordered_set<VarHandleBase *> res;
for (auto *var : inputs_) {
if (var->GeneratedOp() != nullptr) {
res.emplace(var);
}
}
return res.size();
}
} // namespace details
} // namespace framework
} // namespace paddle
paddle/fluid/framework/details/op_handle_base.h
浏览文件 @ 7e0c9f50
......@@ -81,6 +81,8 @@ class OpHandleBase {
return res.size();
}
size_t NotReadyInputSize() const;
const std::vector<VarHandleBase *> &Outputs() const { return outputs_; }
size_t NoDummyInputSize() const;
......
paddle/fluid/framework/details/threaded_ssa_graph_executor.cc
浏览文件 @ 7e0c9f50
......@@ -107,11 +107,11 @@ FeedFetchList ThreadedSSAGraphExecutor::Run(
auto cur_ready_vars = ready_vars.PopAll(1, &timeout);
if (timeout) {
if (exception_holder_.ExceptionCatched()) {
if (exception_holder_.IsCaught()) {
for (auto &run_op_future : run_op_futures_) {
run_op_future.wait();
}
exception_holder_.Throw();
exception_holder_.ReThrow();
} else {
continue;
}
......@@ -220,12 +220,8 @@ void ThreadedSSAGraphExecutor::RunOp(
running_ops_--;
ready_var_q->Extend(op->Outputs());
VLOG(10) << op << " " << op->Name() << "Signal posted";
} catch (platform::EOFException ex) {
exception_holder_.Catch(ex);
} catch (platform::EnforceNotMet ex) {
exception_holder_.Catch(ex);
} catch (...) {
LOG(FATAL) << "Unknown exception catched";
exception_holder_.Catch(std::current_exception());
}
};
if (pool_) {
......
paddle/fluid/framework/framework.proto
浏览文件 @ 7e0c9f50
......@@ -107,6 +107,7 @@ message VarType {
// Tensor<size_t> is used in C++.
SIZE_T = 19;
UINT8 = 20;
INT8 = 21;
// Other types that may need additional descriptions
LOD_TENSOR = 7;
......
paddle/fluid/framework/ir/CMakeLists.txt
浏览文件 @ 7e0c9f50
......@@ -3,7 +3,14 @@ cc_library(graph SRCS graph.cc DEPS node)
cc_library(graph_helper SRCS graph_helper.cc DEPS graph)
cc_library(pass SRCS pass.cc DEPS graph node graph_helper)
cc_library(graph_viz_pass SRCS graph_viz_pass.cc DEPS graph pass graph_helper)
cc_library(graph_traits SRCS graph_traits.cc DEPS graph)
cc_library(graph_pattern_detecter SRCS graph_pattern_detecter.cc DEPS graph graph_helper graph_traits)
cc_library(fc_fuse_pass SRCS fc_fuse_pass.cc DEPS graph graph_pattern_detecter)
cc_library(infer_clean_graph_pass SRCS infer_clean_graph_pass.cc DEPS graph pass)
cc_test(pass_test SRCS pass_test.cc DEPS graph pass graph_helper)
cc_test(graph_test SRCS graph_test.cc DEPS graph graph_helper op_registry)
cc_test(graph_helper_test SRCS graph_helper_test.cc DEPS graph graph_helper op_registry)
cc_test(test_graph_pattern_detecter SRCS graph_pattern_detecter_tester.cc DEPS graph_pattern_detecter)
cc_test(test_fc_fuse_pass SRCS fc_fuse_pass_tester.cc DEPS fc_fuse_pass graph_pattern_detecter graph pass graph_traits framework_proto)
paddle/fluid/framework/ir/fc_fuse_pass.cc 0 → 100644
浏览文件 @ 7e0c9f50
// Copyright (c) 2018 PaddlePaddle Authors. All Rights Reserved.
//
// Licensed under the Apache License, Version 2.0 (the "License");
// you may not use this file except in compliance with the License.
// You may obtain a copy of the License at
//
// http://www.apache.org/licenses/LICENSE-2.0
//
// Unless required by applicable law or agreed to in writing, software
// distributed under the License is distributed on an "AS IS" BASIS,
// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
// See the License for the specific language governing permissions and
// limitations under the License.
#include "paddle/fluid/framework/ir/fc_fuse_pass.h"
#include <string>
#include <vector>
#include "paddle/fluid/platform/enforce.h"
namespace paddle {
namespace framework {
namespace ir {
bool VarOutLinksToOp(Node* node, const std::string& op_type) {
for (auto* out : node->outputs) {
if (out->IsOp() && out->Op()->Type() == op_type) {
return true;
}
}
return false;
}
void BuildFCPattern(PDPattern* pattern) {
// make sure the selected MUL op has one input argument is a parameter.
auto* mul_parameter_var = pattern->NewNode(
[](Node* node) {
return node->IsVar() && node->outputs.size() == 1UL &&
node->outputs.front()->Op()->Type() == "mul" && node->Var() &&
node->Var()->Persistable(); // check is a parameter
},
"mul_weight" /*name*/);
auto* mul_tmp_input_var = pattern->NewNode(
[](Node* node) {
bool result =
node->IsVar() && node->outputs.size() >= 1UL && node->Var() &&
!node->Var()->Persistable(); // this input is not an parameter.
if (!result) return false;
// check whether one output is MUL op.
for (auto* op : node->outputs) {
if (op->IsOp() && op->Op()->Type() == "mul") return true;
}
return false;
},
"mul_tmp_var" /*name*/);
// select a MUL op
auto* mul_op = pattern->NewNode(
[](Node* node) {
return node->IsOp() && // start from an Op
node->Op()->Type() == "mul"; // type is mul
// the output should be consumed only by one element_add, that check
// leaves in a Var PDNode.
},
"mul" /*name*/);
// make sure the MUL op's output has only one consumer and links to an
// ELEMENTWISE_ADD op.
auto* mul_out_var = pattern->NewNode(
[](Node* node) {
return node->IsVar() && // starts from a Var
node->outputs.size() == 1UL && // only has one consumer
node->outputs.front()->IsOp() && // check basic logic
node->Var() && // not a ControlDepVar
node->outputs.front()->Op()->Type() ==
"elementwise_add"; // a very strong validation
},
"mul_out");
// this check is not essential, just to make the corresponding variable Node
// retrival easier.
auto* elementwise_add_tmp_var = pattern->NewNode(
[](Node* node) {
return node->IsVar() && node->outputs.size() >= 1UL && node->Var() &&
VarOutLinksToOp(node, "elementwise_add");
},
"elementwise_add_tmpvar");
// select an ELEMENTWISE_ADD op
auto* elementwise_add_op = pattern->NewNode(
[](Node* node) {
return node->IsOp() && node->Op()->Type() == "elementwise_add";
},
"elementwise_add" /*name*/);
// get the ELEMENTWISE_ADD op's output
auto* elementwise_add_out_var = pattern->NewNode(
[](Node* node) {
return node->IsVar() && node->inputs.size() == 1UL && node->Var() &&
node->inputs.front()->Op()->Type() == "elementwise_add";
},
"elementwise_add_out");
pattern->AddEdge(mul_parameter_var, mul_op);
pattern->AddEdge(mul_tmp_input_var, mul_op);
pattern->AddEdge(mul_op, mul_out_var);
pattern->AddEdge(mul_out_var, elementwise_add_op);
pattern->AddEdge(elementwise_add_tmp_var, elementwise_add_op);
pattern->AddEdge(elementwise_add_op, elementwise_add_out_var);
}
// Replace the node `from` in the links to `to`
bool LinksReplace(std::vector<Node*>* links, Node* from, Node* to) {
for (auto*& n : *links) {
if (n == from) {
n = to;
return true;
}
}
return false;
}
std::unique_ptr<ir::Graph> FCFusePass::ApplyImpl(
std::unique_ptr<ir::Graph> graph) const {
PADDLE_ENFORCE(graph.get());
std::unordered_set<Node*> nodes2delete;
GraphPatternDetecter gpd;
BuildFCPattern(gpd.mutable_pattern());
#define GET_NODE(id) \
PADDLE_ENFORCE(subgraph.count(gpd.pattern().RetriveNode(#id)), \
"pattern has no Node called %s", #id); \
auto* id = subgraph.at(gpd.pattern().RetriveNode(#id)); \
PADDLE_ENFORCE_NOT_NULL(id, "subgraph has no node %s", #id);
auto handler = [&](const GraphPatternDetecter::subgraph_t& subgraph,
Graph* g) {
VLOG(4) << "handle FC fuse";
// Currently, there is no FC op available, so I will just simulate the
// scenerio.
// FC's fusion is simple, just op fuse, no need to process the
// parameters.
GET_NODE(mul_tmp_var); // x
GET_NODE(mul_weight); // Y
GET_NODE(elementwise_add_tmpvar); // bias
GET_NODE(elementwise_add_out); // Out
GET_NODE(mul); // MUL op
GET_NODE(elementwise_add); // ELEMENT_ADD op
GET_NODE(mul_out); // tmp
#undef GET_NODE
// Create an FC Node.
OpDesc desc;
std::string fc_x_in = mul_tmp_var->Name();
std::string fc_Y_in = mul_weight->Name();
std::string fc_bias_in = elementwise_add_tmpvar->Name();
std::string fc_out = elementwise_add_out->Name();
desc.SetInput("Input", std::vector<std::string>({fc_x_in}));
desc.SetInput("W", std::vector<std::string>({fc_Y_in}));
desc.SetInput("Bias", std::vector<std::string>({fc_bias_in}));
desc.SetOutput("Out", std::vector<std::string>({fc_out}));
desc.SetType("fc");
auto fc_node = g->CreateOpNode(&desc); // OpDesc will be copied.
fc_node->inputs =
std::vector<Node*>({mul_tmp_var, mul_weight, elementwise_add_tmpvar});
fc_node->outputs.push_back(elementwise_add_out);
// Update link relatons
PADDLE_ENFORCE(LinksReplace(&mul_tmp_var->outputs, mul, fc_node));
PADDLE_ENFORCE(LinksReplace(&mul_weight->outputs, mul, fc_node));
PADDLE_ENFORCE(LinksReplace(&elementwise_add_tmpvar->outputs,
elementwise_add, fc_node));
PADDLE_ENFORCE(
LinksReplace(&elementwise_add_out->inputs, elementwise_add, fc_node));
// Drop old nodes
graph->RemoveNode(mul);
graph->RemoveNode(elementwise_add);
graph->RemoveNode(mul_out); // tmp variable
};
gpd(graph.get(), handler);
return graph;
}
} // namespace ir
} // namespace framework
} // namespace paddle
REGISTER_PASS(fc_fuse_pass, paddle::framework::ir::FCFusePass);
paddle/fluid/framework/ir/fc_fuse_pass.h 0 → 100644
浏览文件 @ 7e0c9f50
// Copyright (c) 2018 PaddlePaddle Authors. All Rights Reserved.
//
// Licensed under the Apache License, Version 2.0 (the "License");
// you may not use this file except in compliance with the License.
// You may obtain a copy of the License at
//
// http://www.apache.org/licenses/LICENSE-2.0
//
// Unless required by applicable law or agreed to in writing, software
// distributed under the License is distributed on an "AS IS" BASIS,
// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
// See the License for the specific language governing permissions and
// limitations under the License.
#include "paddle/fluid/framework/ir/graph.h"
#include "paddle/fluid/framework/ir/graph_pattern_detecter.h"
#include "paddle/fluid/framework/ir/pass.h"
namespace paddle {
namespace framework {
namespace ir {
/*
* Fuse the MUL and ELEMENTWISE_ADD to a FCOp.
*/
class FCFusePass : public Pass {
public:
virtual ~FCFusePass() {}
protected:
std::unique_ptr<ir::Graph> ApplyImpl(std::unique_ptr<ir::Graph> graph) const;
};
} // namespace ir
} // namespace framework
} // namespace paddle
paddle/fluid/framework/ir/fc_fuse_pass_tester.cc 0 → 100644
浏览文件 @ 7e0c9f50
// Copyright (c) 2018 PaddlePaddle Authors. All Rights Reserved.
//
// Licensed under the Apache License, Version 2.0 (the "License");
// you may not use this file except in compliance with the License.
// You may obtain a copy of the License at
//
// http://www.apache.org/licenses/LICENSE-2.0
//
// Unless required by applicable law or agreed to in writing, software
// distributed under the License is distributed on an "AS IS" BASIS,
// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
// See the License for the specific language governing permissions and
// limitations under the License.
#include "paddle/fluid/framework/ir/fc_fuse_pass.h"
#include <gtest/gtest.h>
namespace paddle {
namespace framework {
namespace ir {
void SetOp(ProgramDesc* prog, const std::string& type,
const std::vector<std::string>& inputs,
const std::vector<std::string>& outputs) {
auto* op = prog->MutableBlock(0)->AppendOp();
op->SetType(type);
op->SetInput("Xs", inputs);
op->SetOutput("Ys", outputs);
}
// a->OP0->b
// a->OP1->c
// (b, c)->mul->d
// (d, e)->elementwise_add->f
ProgramDesc BuildProgramDesc() {
ProgramDesc prog;
for (auto& v : std::vector<std::string>({"a", "b", "c", "d", "e", "f"})) {
auto* var = prog.MutableBlock(0)->Var(v);
var->SetType(proto::VarType::SELECTED_ROWS);
if (v == "c") {
var->SetPersistable(true);
}
}
SetOp(&prog, "OP0", std::vector<std::string>({"a"}),
std::vector<std::string>({"b"}));
SetOp(&prog, "OP1", std::vector<std::string>({"a"}),
std::vector<std::string>({"c"}));
SetOp(&prog, "mul", std::vector<std::string>({"b", "c"}),
std::vector<std::string>({"d"}));
SetOp(&prog, "elementwise_add", std::vector<std::string>({"d", "e"}),
std::vector<std::string>({"f"}));
return prog;
}
TEST(FCFusePass, basic) {
auto prog = BuildProgramDesc();
std::unique_ptr<ir::Graph> graph(new ir::Graph(prog));
auto pass = PassRegistry::Instance().Get("fc_fuse_pass");
int pre_nodes = graph->Nodes().size();
graph = pass->Apply(std::move(graph));
int after_nodes = graph->Nodes().size();
// Remove 3 Nodes: MUL,ELEMENTWISE_ADD, mul_out
// Add 1 Node: FC
EXPECT_EQ(pre_nodes - 2, after_nodes);
// Assert fc op in newly generated graph
int fc_count = 0;
for (auto* node : graph->Nodes()) {
if (node->IsOp() && node->Op()->Type() == "fc") {
++fc_count;
}
}
EXPECT_EQ(fc_count, 1);
}
} // namespace ir
} // namespace framework
} // namespace paddle
USE_PASS(fc_fuse_pass);
paddle/fluid/framework/ir/graph.cc
浏览文件 @ 7e0c9f50
......@@ -117,7 +117,15 @@ Graph::Graph(const ProgramDesc &program) : program_(program) {
}
// For output args, always create a new var.
for (auto &each_var_name : op->OutputArgumentNames()) {
ir::Node *var = CreateVarNode(all_vars.at(each_var_name));
ir::Node *var = nullptr;
if (all_vars.count(each_var_name) != 0) {
var = CreateVarNode(all_vars.at(each_var_name));
} else {
// Operation output vars can be @EMPTY@. For example, while_grad
// can have multi @EMPTY@ outputs with no VarDesc.
// TODO(panyx0718): Add a test.
var = CreateEmptyNode(each_var_name, ir::Node::Type::kVariable);
}
var_nodes[each_var_name].push_back(var);
node->outputs.push_back(var);
var->inputs.push_back(node);
......@@ -208,7 +216,8 @@ Graph::Graph(const ProgramDesc &program) : program_(program) {
// Add write after write dependence
ir::Node *upstream_op =
(*it_old)->inputs.empty() ? nullptr : (*it_old)->inputs[0];
if (upstream_op) {
// TODO(zcd): Add a test.
if (upstream_op && upstream_op != write_op) {
ir::Node *dep_var = CreateControlDepVar();
write_op->inputs.push_back(dep_var);
upstream_op->outputs.push_back(dep_var);
......
paddle/fluid/framework/ir/graph.h
浏览文件 @ 7e0c9f50
......@@ -28,6 +28,38 @@ namespace paddle {
namespace framework {
namespace ir {
/*
* The graph is a Directed Acyclic Single Static Assignment Graph.
*
* In more detail, the following properties must hold:
*
* The graph shouldn't contain cycle. Each node is a black-box to the graph
* so the node itself could be a loop operator.
*
* Each Variable-type node has only one input (thus single static assignment).
*
* The output/input of operator is variable and the output/input of variable
* is operator.
*
* The following data harzards in Program are addressed in the Graph:
*
* Write-After-Read
* a = op1(x)
* x = op2(b)
* A control-dependency connection is created bettwen op1 and op2 such that
* op1->op2, so as to ensure correct order.
*
* Write-After-Write
* x = op1(a)
* x = op2(b)
* A control-dependency connection is created between op1 and op2 such that
* op1->op2, so as to ensure correct order.
*
* Other properties currently hold, but is not enforced yet:
*
* Variable-type node (not control dep) with the same variable name share
* the same underlying VarDesc.
*/
class Graph {
public:
explicit Graph(const ProgramDesc &program);
......@@ -66,11 +98,13 @@ class Graph {
// Create a normal variable with non-null VarDesc.
ir::Node *CreateVarNode(VarDesc *var_desc) {
PADDLE_ENFORCE(var_desc);
return AddNode(new ir::Node(var_desc));
}
// Create a normal runnable operator with OpDesc.
ir::Node *CreateOpNode(OpDesc *op_desc) {
PADDLE_ENFORCE(op_desc);
return AddNode(new ir::Node(op_desc));
}
......@@ -102,6 +136,12 @@ class Graph {
return ret;
}
void RemoveNode(ir::Node *node) {
PADDLE_ENFORCE(node_set_.find(node) != node_set_.end());
node_set_.erase(node);
nodes_.erase(node);
}
private:
// This method takes ownership of `node`.
ir::Node *AddNode(ir::Node *node) {
......@@ -111,14 +151,8 @@ class Graph {
return node;
}
void RemoveNode(ir::Node *node) {
PADDLE_ENFORCE(node_set_.find(node) != node_set_.end());
node_set_.erase(node);
nodes_.erase(node);
}
// NOTE: program_ shouldn't be exposed to user.
const ProgramDesc &program_;
const ProgramDesc program_;
std::map<std::string, boost::any> attrs_;
std::map<std::string, std::function<void(void)>> attr_dels_;
std::map<ir::Node *, std::unique_ptr<ir::Node>> nodes_;
......
paddle/fluid/framework/ir/graph_helper.cc
浏览文件 @ 7e0c9f50
......@@ -104,7 +104,7 @@ std::map<ir::Node *, std::unordered_set<ir::Node *>> BuildOperationAdjList(
for (auto &adj_n : var->inputs) {
PADDLE_ENFORCE(adj_n->NodeType() == ir::Node::Type::kOperation);
adj_list[n].insert(adj_n);
VLOG(3) << "adj " << adj_n->Name() << reinterpret_cast<void *>(adj_n)
VLOG(4) << "adj " << adj_n->Name() << reinterpret_cast<void *>(adj_n)
<< " -> " << n->Name() << reinterpret_cast<void *>(n)
<< " via " << var->Name() << reinterpret_cast<void *>(var);
}
......
paddle/fluid/framework/ir/graph_pattern_detecter.cc 0 → 100644
浏览文件 @ 7e0c9f50
// 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 <array>
#include <string>
#include <vector>
#include "paddle/fluid/framework/ir/graph_helper.h"
#include "paddle/fluid/framework/ir/graph_pattern_detecter.h"
#include "paddle/fluid/framework/ir/graph_traits.h"
#include "paddle/fluid/platform/enforce.h"
namespace paddle {
namespace framework {
namespace ir {
size_t PDPattern::id_ = 0UL;
PDNode* PDPattern::NewNode(PDNode::teller_t&& teller, const std::string& name) {
if (!name.empty()) {
PADDLE_ENFORCE_EQ(node_map_.count(name), 0,
"PDNode's name should be unique, get duplicate [%s]",
name);
}
nodes_.emplace_back(new PDNode(std::move(teller), name));
auto* cur = nodes_.back().get();
node_map_[name] = cur;
return cur;
}
PDNode* PDPattern::RetriveNode(const std::string& id) const {
auto it = node_map_.find(id);
if (it == node_map_.end()) {
return nullptr;
}
return it->second;
}
void PDPattern::AddEdge(PDNode* a, PDNode* b) {
PADDLE_ENFORCE(a);
PADDLE_ENFORCE(b);
PADDLE_ENFORCE(a != b, "can't connect to the same nodes.");
edges_.emplace_back(a, b);
}
void GraphPatternDetecter::operator()(Graph* graph,
GraphPatternDetecter::handle_t handler) {
if (!MarkPDNodesInGraph(*graph)) return;
auto subgraphs = DetectPatterns();
UniquePatterns(&subgraphs);
RemoveOverlappedMatch(&subgraphs);
for (auto& g : subgraphs) {
handler(g, graph);
}
}
bool GraphPatternDetecter::MarkPDNodesInGraph(const ir::Graph& graph) {
VLOG(4) << "mark pdnodes in graph";
if (graph.Nodes().empty()) return false;
for (auto& node : GraphTraits::DFS(graph)) {
for (const auto& pdnode : pattern_.nodes()) {
if (pdnode->Tell(&node)) {
VLOG(4) << "pdnode " << pdnode->name() << " marked";
pdnodes2nodes_[pdnode.get()].insert(&node);
}
}
}
VLOG(3) << pdnodes2nodes_.size() << " nodes marked";
return !pdnodes2nodes_.empty();
}
struct HitGroup {
std::unordered_map<PDNode*, Node*> roles;
bool Match(Node* node, PDNode* pat) {
if (nodes_.count(node)) {
if (!roles.count(pat)) return false;
return roles[pat] == node;
}
return !roles.count(pat) || roles.at(pat) == node;
}
void Register(Node* node, PDNode* pat) {
roles[pat] = node;
nodes_.insert(node);
}
private:
std::unordered_set<Node*> nodes_;
};
// Tell whether Node a links to b.
bool IsNodesLink(Node* a, Node* b) {
for (auto* node : a->outputs) {
if (b == node) {
return true;
}
}
return false;
}
std::vector<GraphPatternDetecter::subgraph_t>
GraphPatternDetecter::DetectPatterns() {
// Init empty subgraphs.
std::vector<GraphPatternDetecter::subgraph_t> result;
std::vector<HitGroup> init_groups;
PADDLE_ENFORCE(!pattern_.edges().empty(), "At least one edge is needed");
auto* first_pnode = pattern_.edges().front().first;
if (!pdnodes2nodes_.count(first_pnode)) return result;
for (auto* node : pdnodes2nodes_[first_pnode]) {
HitGroup group;
group.roles[first_pnode] = node;
init_groups.emplace_back(group);
}
int step = 0;
std::array<std::vector<HitGroup>, 2> bi_records;
bi_records[0] = std::move(init_groups);
// Extend a PDNode to subgraphs by deducing the connection relations defined
// in edges of PDNodes.
for (const auto& edge : pattern_.edges()) {
VLOG(4) << "check " << edge.first->name() << " -> " << edge.second->name();
// Each role has two PDNodes, which indicates two roles.
// Detect two Nodes that can match these two roles and they are connected.
auto& pre_groups = bi_records[step % 2];
auto& cur_groups = bi_records[1 - (step++ % 2)];
cur_groups.clear();
// source -> target
for (Node* source : pdnodes2nodes_[edge.first]) {
for (Node* target : pdnodes2nodes_[edge.second]) {
// TODO(Superjomn) add some prune strategies.
for (const auto& group : pre_groups) {
HitGroup new_group = group;
if (IsNodesLink(source, target) &&
new_group.Match(source, edge.first)) {
new_group.Register(source, edge.first);
if (new_group.Match(target, edge.second)) {
new_group.Register(target, edge.second);
cur_groups.push_back(new_group);
// TODO(Superjomn) need to unique
}
}
}
}
}
VLOG(3) << "step " << step << " get records: " << cur_groups.size();
}
for (auto& group : bi_records[step % 2]) {
GraphPatternDetecter::subgraph_t subgraph;
for (auto& role : group.roles) {
subgraph.emplace(role.first, role.second);
}
result.emplace_back(subgraph);
}
return result;
}
void GraphPatternDetecter::UniquePatterns(
std::vector<GraphPatternDetecter::subgraph_t>* subgraphs) {
if (subgraphs->empty()) return;
std::vector<GraphPatternDetecter::subgraph_t> result;
std::unordered_set<size_t> set;
for (auto& g : *subgraphs) {
size_t key = 0;
for (auto& item : g) {
key ^= std::hash<void*>{}(item.first);
key ^= std::hash<void*>{}(item.second);
}
if (!set.count(key)) {
result.emplace_back(g);
set.insert(key);
}
}
*subgraphs = result;
}
void GraphPatternDetecter::RemoveOverlappedMatch(
std::vector<subgraph_t>* subgraphs) {
std::vector<subgraph_t> result;
std::unordered_set<Node*> node_set;
for (const auto& subgraph : *subgraphs) {
bool valid = true;
for (auto& item : subgraph) {
if (node_set.count(item.second)) {
valid = false;
break;
}
}
if (valid) {
for (auto& item : subgraph) {
node_set.insert(item.second);
}
result.push_back(subgraph);
}
}
*subgraphs = result;
}
} // namespace ir
} // namespace framework
} // namespace paddle
paddle/fluid/framework/ir/graph_pattern_detecter.h 0 → 100644
浏览文件 @ 7e0c9f50
// 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
#ifdef PADDLE_WITH_TESTING
#include <gtest/gtest_prod.h>
#endif
#include <numeric>
#include "paddle/fluid/framework/ir/graph.h"
#include "paddle/fluid/framework/ir/node.h"
namespace paddle {
namespace framework {
namespace ir {
// Some basic torminolygies:
// - PDPattern: a pattern defined as a data flow graph.
// - PDNode: the node in the pattern, each PDNode represents an `ir::Node`
// that meets some conditions defined in `PDNode.teller`.
// - A pattern is defined with PDNodes with edges.
// Pattern detector node. This node helps to build a pattern.
struct PDNode {
// tell whether an ir::Node* is a candidation for a PDNode.
using teller_t = std::function<bool(Node*)>;
PDNode(teller_t&& teller, const std::string& name = "")
: teller_(teller), name_(name) {
PADDLE_ENFORCE(teller_ != nullptr, "invalid teller functer is set.");
}
PDNode(PDNode&& other) = default;
std::vector<PDNode*> inlinks;
std::vector<PDNode*> outlinks;
bool Tell(Node* node) const {
PADDLE_ENFORCE(teller_ != nullptr, "teller should be set for a PDNode");
return teller_(node);
}
const std::string& name() const { return name_; }
PDNode(const PDNode&) = delete;
PDNode& operator=(const PDNode&) = delete;
private:
teller_t teller_;
std::string name_;
};
/*
* A pattern in a graph, which defined with PDNode and edges. Most graph
* patterns can be divided into PDNodes and link relations between them.
*
* For example, the FC fusion need to filter the MUL and ELEMENTWISE_ADD
* operators from the computation graph, the MUL's output should have only one
* consumer which is the ELEMENTWISE_ADD.
* This pattern can be defined as with the following pseudo codes
*
* // Create two operator PDNodes.
* MUL = PDPattern.NewNode()
* ELE = PDPattern.NewNode()
* // Create the variable PDNodes.
* MUL_out = PDPattern.NewNode()
* // Add teller to define some rules that help to filter the target Nodes.
* MUL.teller = lambda(node): node->IsOp() && node->Op()->Type == "mul";
* ELE.teller = lambda(node): \
* node->IsOp() && node->Op()->Type == "elementwise_add";
* MUL_out.teller = lambda(node): node->IsVar() && (MUL in node->inputs)
* && (ELE in node->outputs)
*
* One can add more specific tellers for PDNodes or edges, both the Operator
* and Variable Nodes can be ruled in PDNode.teller.
*
* PDPattern can record the general patterns, such as the pattern represents
* - Op in CPU -> Op in GPU -> Op in CPU, to findout the IO abnormal place.
* - Ops whose inputs and outputs share the same variables
*/
class PDPattern {
public:
using edge_t = std::pair<PDNode*, PDNode*>;
void AddEdge(PDNode* a, PDNode* b);
PDNode* NewNode(PDNode::teller_t&& teller, const std::string& name = NewID());
PDNode* RetriveNode(const std::string& id) const;
const std::vector<std::unique_ptr<PDNode>>& nodes() const { return nodes_; }
const std::vector<edge_t>& edges() const { return edges_; }
private:
#ifdef PADDLE_WITH_TESTING
FRIEND_TEST(PDPattern, AddEdge);
FRIEND_TEST(PDPattern, NewNode);
#endif
static std::string NewID() { return "pdnode-" + std::to_string(id_++); }
std::vector<std::unique_ptr<PDNode>> nodes_;
std::vector<edge_t> edges_;
std::unordered_map<std::string, PDNode*> node_map_;
static size_t id_;
};
/*
* GraphPatternDetecter helps to detect the specific patterns in the graph.
* Input a pattern, output a list of the matched subgraphs/nodes.
* This helper can be used to support fuse(conv+batchnorm => batchnorm e.g.).
*
* The algorithm has three phases:
* 1. Mark the nodes that match the defined PDNodes in a PDPattern,
* 2. Extend a PDNode to subgraphs by deducing the connection relation defined
* in PAPattern(the edges),
* 3. Get the filtered subgraphs and treat them with a pre-defined handler.
*
* Usage:
* // Create a detector
* GraphPatternDetecter detector;
* // Define the detector's pattern, by adding PDNode and define the edges.
* auto* node0 = detector.mutable_pattern().AddNode(...)
* auto* node1 = detector.mutable_pattern().AddNode(...)
* node0->teller = some lambda.
* node1->teller = some lambda.
* detector.mutable_pattern().AddEdge(node0, node1);
* // Create an handler, to define the behavior of treating the filtered
* // subgraphs that comply with the patterns.
* GraphPatternDetecter::handle_t handler = some labmda
* // Execute the detector.
* detector(&graph, handler);
*/
class GraphPatternDetecter {
public:
using subgraph_t = std::unordered_map<PDNode*, Node*>;
// Operate on the detected pattern.
using handle_t =
std::function<void(const subgraph_t& /*hitted pattern*/, Graph*)>;
void operator()(Graph* graph, handle_t handler);
const PDPattern& pattern() const { return pattern_; }
PDPattern* mutable_pattern() { return &pattern_; }
private:
// Mark the nodes that fits the pattern.
bool MarkPDNodesInGraph(const ir::Graph& graph);
// Detect all the pattern and output the hit records.
std::vector<subgraph_t> DetectPatterns();
// Remove duplicate patterns.
void UniquePatterns(std::vector<subgraph_t>* subgraphs);
// Remove overlapped match subgraphs, when overlapped, keep the previous one.
void RemoveOverlappedMatch(std::vector<subgraph_t>* subgraphs);
#ifdef PADDLE_WITH_TESTING
FRIEND_TEST(GraphPatternDetecter, MarkPDNodesInGraph);
FRIEND_TEST(GraphPatternDetecter, DetectPatterns);
#endif
private:
using hit_rcd_t =
std::pair<Node* /*node in graph*/, PDNode* /*node in pattern*/>;
PDPattern pattern_;
std::vector<hit_rcd_t> marked_records_;
std::unordered_map<const PDNode*, std::unordered_set<Node*>> pdnodes2nodes_;
};
} // namespace ir
} // namespace framework
} // namespace paddle
paddle/fluid/framework/ir/graph_pattern_detecter_tester.cc 0 → 100644
浏览文件 @ 7e0c9f50
// Copyright (c) 2018 PaddlePaddle Authors. All Rights Reserved.
//
// Licensed under the Apache License, Version 2.0 (the "License");
// you may not use this file except in compliance with the License.
// You may obtain a copy of the License at
//
// http://www.apache.org/licenses/LICENSE-2.0
//
// Unless required by applicable law or agreed to in writing, software
// distributed under the License is distributed on an "AS IS" BASIS,
// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
// See the License for the specific language governing permissions and
// limitations under the License.
#include "paddle/fluid/framework/ir/graph_pattern_detecter.h"
#include <gtest/gtest.h>
namespace paddle {
namespace framework {
namespace ir {
void BuildGraph(Graph* g) {
ir::Node* o1 = g->CreateEmptyNode("op1", Node::Type::kOperation);
ir::Node* o2 = g->CreateEmptyNode("op2", Node::Type::kOperation);
ir::Node* o3 = g->CreateEmptyNode("op3", Node::Type::kOperation);
ir::Node* o4 = g->CreateEmptyNode("op4", Node::Type::kOperation);
ir::Node* o5 = g->CreateEmptyNode("op5", Node::Type::kOperation);
ir::Node* v1 = g->CreateEmptyNode("var1", Node::Type::kVariable);
ir::Node* v2 = g->CreateEmptyNode("var2", Node::Type::kVariable);
ir::Node* v3 = g->CreateEmptyNode("var3", Node::Type::kVariable);
ir::Node* v4 = g->CreateEmptyNode("var4", Node::Type::kVariable);
// o1->v1->o2
o1->outputs.push_back(v1);
o2->inputs.push_back(v1);
v1->inputs.push_back(o1);
v1->outputs.push_back(o2);
// o2->v2->o3
// o2->v2->o4
o2->outputs.push_back(v2);
o3->inputs.push_back(v2);
o4->inputs.push_back(v2);
v2->inputs.push_back(o2);
v2->outputs.push_back(o3);
v2->outputs.push_back(o4);
// o2->v3->o5
o2->outputs.push_back(v3);
o5->inputs.push_back(v3);
v3->inputs.push_back(o2);
v3->outputs.push_back(o5);
// o3-v4->o5
o3->outputs.push_back(v4);
o5->inputs.push_back(v4);
v4->inputs.push_back(o3);
v4->outputs.push_back(o5);
}
TEST(PDPattern, NewNode) {
PDPattern x;
auto* n = x.NewNode([](Node* x) { return true; });
ASSERT_TRUE(n);
ASSERT_EQ(x.nodes_.size(), 1UL);
}
TEST(PDPattern, AddEdge) {
PDPattern x;
auto* a = x.NewNode([](Node* x) { return true; });
auto* b = x.NewNode([](Node* x) { return true; });
ASSERT_TRUE(a);
ASSERT_TRUE(b);
x.AddEdge(a, b);
ASSERT_EQ(x.nodes_.size(), 2UL);
ASSERT_EQ(x.edges_.size(), 1UL);
ASSERT_EQ(x.edges_.front().first, a);
ASSERT_EQ(x.edges_.front().second, b);
ASSERT_EQ(x.nodes().size(), 2UL);
ASSERT_EQ(x.edges().size(), 1UL);
ASSERT_EQ(x.edges().front().first, a);
ASSERT_EQ(x.edges().front().second, b);
}
TEST(GraphPatternDetecter, MarkPDNodesInGraph) {
GraphPatternDetecter x;
// mark o2, o3, v2
// The pattern is a graph:
// o2(a node named o2) -> v2(a node named v2)
// v2 -> o3(a node named o3)
auto* o2 = x.pattern_.NewNode([](Node* node) {
// The teller can be any condition, such as op type, or variable's shape.
return node && node->Name() == "op2" && node->IsOp();
});
auto* o3 = x.pattern_.NewNode([](Node* node) {
// The teller can be any condition, such as op type, or variable's shape.
return node && node->Name() == "op3" && node->IsOp();
});
auto* v2 = x.pattern_.NewNode([](Node* node) {
// The teller can be any condition, such as op type, or variable's shape.
return node && node->Name() == "var2" && node->IsVar();
});
ASSERT_FALSE(o2->Tell(nullptr));
ASSERT_FALSE(o3->Tell(nullptr));
ASSERT_FALSE(v2->Tell(nullptr));
x.pattern_.AddEdge(o2, v2);
x.pattern_.AddEdge(v2, o3);
ASSERT_EQ(x.pattern_.edges().size(), 2UL);
ASSERT_EQ(x.pattern_.edges()[0].first, o2);
ASSERT_EQ(x.pattern_.edges()[0].second, v2);
ASSERT_EQ(x.pattern_.edges()[1].first, v2);
ASSERT_EQ(x.pattern_.edges()[1].second, o3);
ProgramDesc program;
Graph graph(program);
BuildGraph(&graph);
x.MarkPDNodesInGraph(graph);
ASSERT_EQ(x.pdnodes2nodes_.size(), 3UL);
auto subgraphs = x.DetectPatterns();
ASSERT_EQ(subgraphs.size(), 1UL);
}
TEST(GraphPatternDetecter, MultiSubgraph) {
ProgramDesc program;
Graph graph(program);
BuildGraph(&graph);
GraphPatternDetecter x;
// The pattern is a graph:
// op -> var
auto* any_op = x.mutable_pattern()->NewNode(
[](Node* node) {
return node->IsOp() && (node->Name() == "op2" || node->Name() == "op3");
},
"OP0");
auto* any_var = x.mutable_pattern()->NewNode(
[](Node* node) { return node->IsVar(); }, "VAR");
auto* any_op1 = x.mutable_pattern()->NewNode(
[](Node* node) { return node->IsOp(); }, "OP1");
x.mutable_pattern()->AddEdge(any_op, any_var);
x.mutable_pattern()->AddEdge(any_var, any_op1);
int count = 0;
GraphPatternDetecter::handle_t handle = [&](
const GraphPatternDetecter::subgraph_t& s, Graph* g) {
LOG(INFO) << "Detect " << s.at(any_op)->Name() << " -> "
<< s.at(any_var)->Name() << " -> " << s.at(any_op1)->Name();
count++;
};
x(&graph, handle);
// 1. Detect op3 -> var4 -> op5
// 2. Detect op2 -> var2 -> op3
// 3. Detect op2 -> var2 -> op4
// 4. Detect op2 -> var3 -> op5
// But 2 and 3 and 4 overlapped, so keep 2, so the final choices are 1 and 2
ASSERT_GE(count, 1);
ASSERT_LE(count, 2);
}
} // namespace ir
} // namespace framework
} // namespace paddle
paddle/fluid/framework/ir/graph_test.cc
浏览文件 @ 7e0c9f50
......@@ -36,7 +36,7 @@ class SumOpMaker : public OpProtoAndCheckerMaker {
public:
void Make() {
AddInput("X", "").AsDuplicable();
AddOutput("Out", "");
AddOutput("Out", "").AsDuplicable();
AddComment("");
}
};
......@@ -59,11 +59,27 @@ class SumOpVarTypeInference : public VarTypeInference {
block->Var(out_var_name)->SetType(default_var_type);
}
};
class DummyOpMaker : public OpProtoAndCheckerMaker {
public:
void Make() {
AddInput("X", "").AsDuplicable();
AddOutput("Out", "").AsDuplicable();
AddComment("");
}
};
class DummyOpVarTypeInference : public VarTypeInference {
public:
void operator()(const OpDesc &op_desc, BlockDesc *block) const override {}
};
} // namespace framework
} // namespace paddle
REGISTER_OPERATOR(sum, paddle::framework::NOP, paddle::framework::SumOpMaker,
paddle::framework::SumOpVarTypeInference);
REGISTER_OPERATOR(dummy, paddle::framework::NOP, paddle::framework::SumOpMaker,
paddle::framework::SumOpVarTypeInference);
REGISTER_OPERATOR(sum_without_infer_var_type, paddle::framework::NOP,
paddle::framework::SumOpMaker);
......@@ -110,5 +126,85 @@ TEST(GraphTest, Basic) {
}
ASSERT_EQ(nodes.size(), 5);
}
TEST(GraphTest, WriteAfterRead) {
// void Test() {
ProgramDesc prog;
auto *op = prog.MutableBlock(0)->AppendOp();
op->SetType("sum");
op->SetInput("X", {"a"});
op->SetOutput("Out", {"b"});
op->SetAttr("op_role", 1);
op = prog.MutableBlock(0)->AppendOp();
op->SetType("dummy");
op->SetInput("X", {"c"});
op->SetOutput("Out", {"a"});
op->SetAttr("op_role", 1);
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);
std::unique_ptr<ir::Graph> g(new ir::Graph(prog));
ir::Node *control_dep1 = nullptr;
ir::Node *control_dep2 = nullptr;
for (ir::Node *n : g->Nodes()) {
if (n->Name() == "sum") {
ASSERT_EQ(n->outputs[0]->Name(), "b");
ASSERT_TRUE(ir::IsControlDepVar(*n->outputs[1]));
control_dep1 = n->outputs[1];
ASSERT_EQ(n->outputs.size(), 2);
}
if (n->Name() == "dummy") {
ASSERT_EQ(n->inputs[0]->Name(), "c");
ASSERT_TRUE(ir::IsControlDepVar(*n->inputs[1]));
control_dep2 = n->inputs[1];
ASSERT_EQ(n->inputs.size(), 2);
}
}
ASSERT_EQ(control_dep1, control_dep2);
}
TEST(GraphTest, WriteAfterWrite) {
// void Test() {
ProgramDesc prog;
auto *op = prog.MutableBlock(0)->AppendOp();
op->SetType("sum");
op->SetInput("X", {"a"});
op->SetOutput("Out", {"b"});
op->SetAttr("op_role", 1);
op = prog.MutableBlock(0)->AppendOp();
op->SetType("dummy");
op->SetInput("X", {"c"});
op->SetOutput("Out", {"b"});
op->SetAttr("op_role", 1);
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);
std::unique_ptr<ir::Graph> g(new ir::Graph(prog));
ir::Node *control_dep1 = nullptr;
ir::Node *control_dep2 = nullptr;
for (ir::Node *n : g->Nodes()) {
if (n->Name() == "sum") {
ASSERT_EQ(n->outputs[0]->Name(), "b");
ASSERT_TRUE(ir::IsControlDepVar(*n->outputs[1]));
ASSERT_EQ(n->outputs.size(), 2);
control_dep1 = n->outputs[1];
}
if (n->Name() == "dummy") {
ASSERT_EQ(n->inputs[0]->Name(), "c");
ASSERT_TRUE(ir::IsControlDepVar(*n->inputs[1]));
control_dep2 = n->inputs[1];
ASSERT_EQ(n->inputs.size(), 2);
}
}
ASSERT_NE(control_dep1, nullptr);
ASSERT_NE(control_dep2, nullptr);
ASSERT_EQ(control_dep1, control_dep2);
}
} // namespace framework
} // namespace paddle
paddle/fluid/framework/ir/graph_traits.cc 0 → 100644
浏览文件 @ 7e0c9f50
// Copyright (c) 2018 PaddlePaddle Authors. All Rights Reserved.
//
// Licensed under the Apache License, Version 2.0 (the "License");
// you may not use this file except in compliance with the License.
// You may obtain a copy of the License at
//
// http://www.apache.org/licenses/LICENSE-2.0
//
// Unless required by applicable law or agreed to in writing, software
// distributed under the License is distributed on an "AS IS" BASIS,
// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
// See the License for the specific language governing permissions and
// limitations under the License.
#include "paddle/fluid/framework/ir/graph_traits.h"
namespace paddle {
namespace framework {
namespace ir {
//
// NodesDFSIterator
//
NodesDFSIterator::NodesDFSIterator(const std::vector<Node *> &source) {
for (auto *x : source) stack_.push(x);
}
NodesDFSIterator::NodesDFSIterator(NodesDFSIterator &&other) noexcept
: stack_(std::move(other.stack_)),
visited_(std::move(other.visited_)) {}
NodesDFSIterator::NodesDFSIterator(const NodesDFSIterator &other)
: stack_(other.stack_), visited_(other.visited_) {}
Node &NodesDFSIterator::operator*() {
PADDLE_ENFORCE(!stack_.empty());
return *stack_.top();
}
NodesDFSIterator &NodesDFSIterator::operator++() {
PADDLE_ENFORCE(!stack_.empty(), "the iterator exceeds range");
visited_.insert(stack_.top());
auto *cur = stack_.top();
stack_.pop();
for (auto *x : cur->outputs) {
if (!visited_.count(x)) {
stack_.push(x);
}
}
return *this;
}
bool NodesDFSIterator::operator==(const NodesDFSIterator &other) {
if (stack_.empty()) return other.stack_.empty();
if ((!stack_.empty()) && (!other.stack_.empty())) {
return stack_.top() == other.stack_.top();
}
return false;
}
NodesDFSIterator &NodesDFSIterator::operator=(const NodesDFSIterator &other) {
stack_ = other.stack_;
visited_ = other.visited_;
return *this;
}
Node *NodesDFSIterator::operator->() { return stack_.top(); }
} // namespace ir
} // namespace framework
} // namespace paddle
paddle/fluid/framework/ir/graph_traits.h 0 → 100644
浏览文件 @ 7e0c9f50
// 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 <stack>
#include <vector>
#include "paddle/fluid/framework/ir/graph.h"
#include "paddle/fluid/framework/ir/node.h"
namespace paddle {
namespace framework {
namespace ir {
template <typename IteratorT>
class iterator_range {
IteratorT begin_, end_;
public:
template <typename Container>
explicit iterator_range(Container &&c) : begin_(c.begin()), end_(c.end()) {}
iterator_range(const IteratorT &begin, const IteratorT &end)
: begin_(begin), end_(end) {}
const IteratorT &begin() const { return begin_; }
const IteratorT &end() const { return end_; }
};
// DFS iterator on nodes.
struct NodesDFSIterator
: public std::iterator<std::forward_iterator_tag, Node *> {
NodesDFSIterator() = default;
explicit NodesDFSIterator(const std::vector<Node *> &source);
NodesDFSIterator(NodesDFSIterator &&other) noexcept;
NodesDFSIterator(const NodesDFSIterator &other);
Node &operator*();
NodesDFSIterator &operator++();
// TODO(Superjomn) current implementation just compare the first
// element, need to compare the graph and all the elements in the queue and
// set.
NodesDFSIterator &operator=(const NodesDFSIterator &other);
bool operator==(const NodesDFSIterator &other);
bool operator!=(const NodesDFSIterator &other) { return !(*this == other); }
Node *operator->();
private:
std::stack<Node *> stack_;
std::unordered_set<Node *> visited_;
};
/*
* GraphTraits contains some graph traversal algorithms.
*
* Usage:
*
*/
struct GraphTraits {
static iterator_range<NodesDFSIterator> DFS(const Graph &g) {
auto start_points = ExtractStartPoints(g);
NodesDFSIterator x(start_points);
return iterator_range<NodesDFSIterator>(NodesDFSIterator(start_points),
NodesDFSIterator());
}
private:
// The nodes those have no input will be treated as start points.
static std::vector<Node *> ExtractStartPoints(const Graph &g) {
std::vector<Node *> result;
for (auto *node : g.Nodes()) {
if (node->inputs.empty()) {
result.push_back(node);
}
}
return result;
}
};
} // namespace ir
} // namespace framework
} // namespace paddle
paddle/fluid/framework/ir/graph_viz_pass.cc
浏览文件 @ 7e0c9f50
......@@ -25,6 +25,7 @@ static const char kGraphVizPath[] = "graph_viz_path";
std::unique_ptr<ir::Graph> GraphVizPass::ApplyImpl(
std::unique_ptr<ir::Graph> graph) const {
const std::string graph_viz_path = Get<std::string>(kGraphVizPath);
VLOG(3) << "draw IR graph viz to " << graph_viz_path;
std::unique_ptr<std::ostream> fout(new std::ofstream(graph_viz_path));
PADDLE_ENFORCE(fout->good());
std::ostream& sout = *fout;
......
paddle/fluid/framework/ir/infer_clean_graph_pass.cc 0 → 100644
浏览文件 @ 7e0c9f50
// 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 <algorithm>
#include "paddle/fluid/framework/ir/graph.h"
#include "paddle/fluid/framework/ir/pass.h"
namespace paddle {
namespace framework {
namespace ir {
class InferCleanGraphPass : public Pass {
public:
virtual ~InferCleanGraphPass() {}
protected:
std::unique_ptr<ir::Graph> ApplyImpl(std::unique_ptr<ir::Graph> graph) const {
PADDLE_ENFORCE(graph.get());
auto is_valid_node = [](Node* x) {
return x && IsControlDepVar(*x) && x->IsVar() && !x->Var();
};
std::unordered_set<Node*> invalid_nodes;
for (auto* node : graph->Nodes()) {
if (is_valid_node(node)) {
invalid_nodes.insert(node);
}
}
// remove nodes from the graph.
for (auto* node : invalid_nodes) {
graph->RemoveNode(node);
}
// clean edges.
for (auto* node : graph->Nodes()) {
CleanEdges(&node->inputs, invalid_nodes);
CleanEdges(&node->outputs, invalid_nodes);
}
return graph;
}
void CleanEdges(std::vector<Node*>* nodes,
const std::unordered_set<Node*>& to_remove) const {
auto it = std::remove_if(nodes->begin(), nodes->end(),
[&](Node* x) { return to_remove.count(x); });
nodes->erase(it, nodes->end());
}
};
} // namespace ir
} // namespace framework
} // namespace paddle
REGISTER_PASS(infer_clean_graph_pass,
paddle::framework::ir::InferCleanGraphPass);
paddle/fluid/framework/ir/node.cc
浏览文件 @ 7e0c9f50
......@@ -17,7 +17,7 @@ limitations under the License. */
namespace paddle {
namespace framework {
namespace ir {
const char Node::kControlDepVarName[] = "__control_var";
constexpr char Node::kControlDepVarName[];
} // namespace ir
} // namespace framework
} // namespace paddle
paddle/fluid/framework/ir/node.h
浏览文件 @ 7e0c9f50
......@@ -27,21 +27,21 @@ namespace ir {
class Node {
public:
enum class Type { kOperation, kVariable };
static const char kControlDepVarName[];
static constexpr char kControlDepVarName[] = "__control_var";
explicit Node(const std::string& name, Type type)
: name_(name), var_desc_(nullptr), op_desc_(nullptr), type_(type) {}
explicit Node(VarDesc* var_desc)
: name_(var_desc->Name()),
var_desc_(var_desc),
var_desc_(new VarDesc(*var_desc)),
op_desc_(nullptr),
type_(Type::kVariable) {}
explicit Node(OpDesc* op_desc)
: name_(op_desc->Type()),
var_desc_(nullptr),
op_desc_(op_desc),
op_desc_(new OpDesc(*op_desc, op_desc->Block())),
type_(Type::kOperation) {}
Type NodeType() const { return type_; }
......@@ -50,21 +50,24 @@ class Node {
VarDesc* Var() {
PADDLE_ENFORCE(type_ == Type::kVariable);
return var_desc_;
return var_desc_.get();
}
OpDesc* Op() {
PADDLE_ENFORCE(type_ == Type::kOperation);
return op_desc_;
PADDLE_ENFORCE(IsOp());
return op_desc_.get();
}
bool IsOp() const { return type_ == Type::kOperation; }
bool IsVar() const { return type_ == Type::kVariable; }
std::vector<Node*> inputs;
std::vector<Node*> outputs;
protected:
const std::string name_;
VarDesc* var_desc_;
OpDesc* op_desc_;
std::unique_ptr<VarDesc> var_desc_;
std::unique_ptr<OpDesc> op_desc_;
Type type_;
private:
......
paddle/fluid/framework/lod_tensor.cc
浏览文件 @ 7e0c9f50
......@@ -25,8 +25,10 @@ limitations under the License. */
#include "paddle/fluid/memory/memcpy.h"
#include "paddle/fluid/memory/memory.h"
#if !defined(_WIN32)
#include "paddle/fluid/recordio/scanner.h"
#include "paddle/fluid/recordio/writer.h"
#endif // _WIN32
namespace paddle {
namespace framework {
......@@ -300,6 +302,7 @@ void DeserializeFromStream(std::istream &is, LoDTensor *tensor,
TensorFromStream(is, static_cast<Tensor *>(tensor), dev_ctx);
}
#if !defined(_WIN32)
void WriteToRecordIO(recordio::Writer *writer,
const std::vector<LoDTensor> &tensor,
const platform::DeviceContext &dev_ctx) {
......@@ -329,7 +332,19 @@ bool ReadFromRecordIO(recordio::Scanner *scanner,
return true;
}
#else
class Writer {};
class Scanner {};
void WriteToRecordIO(recordio::Writer *writer,
const std::vector<LoDTensor> &tensor,
const platform::DeviceContext &dev_ctx) {}
bool ReadFromRecordIO(recordio::Scanner *scanner,
const platform::DeviceContext &dev_ctx,
std::vector<LoDTensor> *result_ptr) {
PADDLE_ENFORCE("windows didn't supported recordio!.");
return true;
}
#endif // _WIN32
std::vector<LoDTensor> LoDTensor::SplitLoDTensor(
const std::vector<platform::Place> places) const {
check_memory_size();
......
paddle/fluid/framework/lod_tensor_test.cc
浏览文件 @ 7e0c9f50
......@@ -274,6 +274,7 @@ TEST(LoD, ConvertToOffsetBasedLoD) {
EXPECT_EQ(offset_lod, expected);
}
#if !defined(_WIN32)
template <typename T>
static void TestRecordIO() {
LoDTensor tensor;
......@@ -320,6 +321,7 @@ TEST(LoDTensor, RecordIO) {
TestRecordIO<float>();
TestRecordIO<double>();
}
#endif // !defined(_WIN32)
} // namespace framework
} // namespace paddle
paddle/fluid/framework/op_desc.cc
浏览文件 @ 7e0c9f50
......@@ -202,6 +202,52 @@ std::vector<std::string> OpDesc::AttrNames() const {
}
void OpDesc::SetAttr(const std::string &name, const Attribute &v) {
// NOTICE(minqiyang): pybind11 will take the empty list in python as
// the std::vector<int> type in C++; so we have to change the attr's type
// here if we meet this issue
proto::AttrType attr_type = static_cast<proto::AttrType>(v.which() - 1);
if (attr_type == proto::AttrType::INTS &&
boost::get<std::vector<int>>(v).size() == 0u) {
// Find current attr via attr name and set the correct attribute value
const proto::OpProto::Attr &attr = GetProtoAttr(name);
switch (attr.type()) {
case proto::AttrType::BOOLEANS: {
VLOG(11) << "SetAttr: " << Type() << ", " << name
<< " from INTS to BOOLEANS";
this->attrs_[name] = std::vector<bool>();
break;
}
case proto::AttrType::INTS: {
VLOG(11) << "SetAttr: " << Type() << ", " << name
<< " from INTS to INTS";
this->attrs_[name] = std::vector<int>();
break;
}
case proto::AttrType::FLOATS: {
VLOG(11) << "SetAttr: " << Type() << ", " << name
<< " from INTS to FLOATS";
this->attrs_[name] = std::vector<float>();
break;
}
case proto::AttrType::STRINGS: {
VLOG(11) << "SetAttr: " << Type() << ", " << name
<< " from INTS to STRINGS";
this->attrs_[name] = std::vector<std::string>();
break;
}
case proto::AttrType::BLOCKS: {
VLOG(11) << "SetAttr: " << Type() << ", " << name
<< " from INTS to BLOCKS";
this->SetBlocksAttr(name, std::vector<BlockDesc *>());
return;
}
default:
PADDLE_THROW("Wrong attr type %d", attr.type());
}
need_update_ = true;
return;
}
this->attrs_[name] = v;
need_update_ = true;
}
......@@ -229,6 +275,19 @@ Attribute OpDesc::GetAttr(const std::string &name) const {
return it->second;
}
const proto::OpProto::Attr &OpDesc::GetProtoAttr(
const std::string &name) const {
const proto::OpProto &proto = OpInfoMap::Instance().Get(Type()).Proto();
for (int i = 0; i != proto.attrs_size(); ++i) {
const proto::OpProto::Attr &attr = proto.attrs(i);
if (attr.name() == name) {
return attr;
}
}
PADDLE_THROW("Attribute %s is not found in proto %s", name, proto.type());
}
Attribute OpDesc::GetNullableAttr(const std::string &name) const {
auto it = attrs_.find(name);
if (it != attrs_.end()) {
......
paddle/fluid/framework/op_desc.h
浏览文件 @ 7e0c9f50
......@@ -81,6 +81,8 @@ class OpDesc {
Attribute GetAttr(const std::string &name) const;
const proto::OpProto::Attr &GetProtoAttr(const std::string &name) const;
Attribute GetNullableAttr(const std::string &name) const;
int GetBlockAttrId(const std::string &name) const;
......
paddle/fluid/framework/operator.cc
浏览文件 @ 7e0c9f50
......@@ -74,6 +74,12 @@ static DDim GetDims(const Scope& scope, const std::string& name,
}
}
static bool VarInited(const Scope& scope, const std::string& name) {
Variable* var = scope.FindVar(name);
if (var == nullptr) return false;
return var->IsInitialized();
}
static std::string GetDtype(const Scope& scope, const std::string& name) {
Variable* var = scope.FindVar(name);
if (var == nullptr) {
......@@ -87,8 +93,12 @@ static std::string GetDtype(const Scope& scope, const std::string& name) {
}
return DataTypeToString(ToDataType(tensor.type()));
} else if (var->IsType<SelectedRows>()) {
return DataTypeToString(
ToDataType(var->Get<SelectedRows>().value().type()));
auto tensor = var->Get<SelectedRows>().value();
if (UNLIKELY(!tensor.IsInitialized())) {
return "uninited";
} else {
return DataTypeToString(ToDataType(tensor.type()));
}
} else {
return "";
}
......@@ -197,16 +207,21 @@ std::string OperatorBase::DebugStringEx(const Scope* scope) const {
auto& input = *it;
ss << input.first << "[";
for (size_t i = 0; i < input.second.size(); ++i) {
ss << input.second[i];
auto var_name = input.second[i];
ss << var_name;
if (scope) {
int row_size = GetRowSize(*scope, input.second[i]);
if (row_size >= 0) {
ss << "[row_size=" << row_size << "]";
if (!VarInited(*scope, var_name)) {
ss << "[uninited]";
} else {
int row_size = GetRowSize(*scope, var_name);
if (row_size >= 0) {
ss << "[row_size=" << row_size << "]";
}
std::string dtype = GetDtype(*scope, var_name);
ss << ":" << dtype;
ss << "[" << GetDims(*scope, var_name, true) << "]";
ss << "(" << GetLoD(*scope, var_name) << ")";
}
std::string dtype = GetDtype(*scope, input.second[i]);
ss << ":" << dtype;
ss << "[" << GetDims(*scope, input.second[i], true) << "]";
ss << "(" << GetLoD(*scope, input.second[i]) << ")";
}
if (i != input.second.size() - 1) {
ss << ", ";
......@@ -223,14 +238,19 @@ std::string OperatorBase::DebugStringEx(const Scope* scope) const {
auto& output = *it;
ss << output.first << "[";
for (size_t i = 0; i < output.second.size(); ++i) {
ss << output.second[i];
auto var_name = output.second[i];
ss << var_name;
if (scope) {
int row_size = GetRowSize(*scope, output.second[i]);
if (row_size >= 0) {
ss << "[row_size=" << row_size << "]";
if (!VarInited(*scope, var_name)) {
ss << "[uninited]";
} else {
int row_size = GetRowSize(*scope, output.second[i]);
if (row_size >= 0) {
ss << "[row_size=" << row_size << "]";
}
ss << "[" << GetDims(*scope, var_name, true) << "]";
ss << "(" << GetLoD(*scope, var_name) << ")";
}
ss << "[" << GetDims(*scope, output.second[i], true) << "]";
ss << "(" << GetLoD(*scope, output.second[i]) << ")";
}
if (i != output.second.size() - 1) {
ss << ", ";
......
paddle/fluid/framework/parallel_executor.cc
浏览文件 @ 7e0c9f50
......@@ -25,6 +25,7 @@ limitations under the License. */
#include "paddle/fluid/platform/nccl_helper.h"
#endif
#include "paddle/fluid/framework/details/fast_threaded_ssa_graph_executor.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/scope_buffered_ssa_graph_executor.h"
......@@ -193,8 +194,14 @@ ParallelExecutor::ParallelExecutor(
member_->local_scopes_, member_->use_cuda_, build_strategy);
#endif
member_->executor_.reset(new details::ThreadedSSAGraphExecutor(
exec_strategy, member_->local_scopes_, places, std::move(graph)));
if (exec_strategy.type_ == ExecutionStrategy::kDefault) {
member_->executor_.reset(new details::ThreadedSSAGraphExecutor(
exec_strategy, member_->local_scopes_, places, std::move(graph)));
} else {
member_->executor_.reset(new details::FastThreadedSSAGraphExecutor(
exec_strategy, member_->local_scopes_, places, std::move(graph)));
}
member_->executor_.reset(new details::ScopeBufferedSSAGraphExecutor(
exec_strategy, member_->local_scopes_, std::move(var_infos),
member_->places_, std::move(member_->executor_)));
......
paddle/fluid/framework/program_desc.cc
浏览文件 @ 7e0c9f50
......@@ -55,11 +55,20 @@ ProgramDesc::ProgramDesc(const ProgramDesc &o) {
auto all_ops = blocks_[block_id]->AllOps();
for (size_t op_id = 0; op_id < all_ops.size(); ++op_id) {
auto &op = all_ops[op_id];
for (const std::string &attr_name : op->AttrNames()) {
if (op->GetAttrType(attr_name) == proto::AttrType::BLOCK) {
int sub_block_id =
o.Block(block_id).Op(op_id)->GetBlockAttrId(attr_name);
op->SetBlockAttr(attr_name, MutableBlock(sub_block_id));
} else if (op->GetAttrType(attr_name) == proto::AttrType::BLOCKS) {
std::vector<int> sub_block_ids =
o.Block(block_id).Op(op_id)->GetBlocksAttrIds(attr_name);
std::vector<BlockDesc *> block_descs;
for (int block_id : sub_block_ids) {
block_descs.push_back(MutableBlock(block_id));
}
op->SetBlocksAttr(attr_name, block_descs);
}
}
}
......@@ -68,24 +77,16 @@ ProgramDesc::ProgramDesc(const ProgramDesc &o) {
ProgramDesc::ProgramDesc(const proto::ProgramDesc &desc) {
desc_ = desc;
for (auto &block_desc : *desc_.mutable_blocks()) {
blocks_.emplace_back(new BlockDesc(this, &block_desc));
}
for (auto &block : blocks_) {
for (auto *op : block->AllOps()) {
for (const auto &attr : op->Proto()->attrs()) {
if (attr.type() == proto::AttrType::BLOCK) {
size_t blk_idx = attr.block_idx();
op->SetBlockAttr(attr.name(), this->MutableBlock(blk_idx));
}
}
}
}
InitFromProto();
}
ProgramDesc::ProgramDesc(const std::string &binary_str) {
PADDLE_ENFORCE(desc_.ParseFromString(binary_str),
"Fail to parse program_desc from binary string.");
InitFromProto();
}
void ProgramDesc::InitFromProto() {
for (auto &block_desc : *desc_.mutable_blocks()) {
blocks_.emplace_back(new BlockDesc(this, &block_desc));
}
......@@ -95,6 +96,13 @@ ProgramDesc::ProgramDesc(const std::string &binary_str) {
if (attr.type() == proto::AttrType::BLOCK) {
size_t blk_idx = attr.block_idx();
op->SetBlockAttr(attr.name(), this->MutableBlock(blk_idx));
} else if (attr.type() == proto::AttrType::BLOCKS) {
auto blks_idx = attr.blocks_idx();
std::vector<BlockDesc *> block_descs;
for (int blk_idx : blks_idx) {
block_descs.push_back(this->MutableBlock(blk_idx));
}
op->SetBlocksAttr(attr.name(), block_descs);
}
}
}
......
paddle/fluid/framework/program_desc.h
浏览文件 @ 7e0c9f50
......@@ -76,6 +76,8 @@ class ProgramDesc {
void SetFetchHolderName(const std::string &fetch_holder_name);
private:
void InitFromProto();
proto::ProgramDesc desc_;
std::vector<std::unique_ptr<BlockDesc>> blocks_;
......
paddle/fluid/framework/program_desc_test.cc
浏览文件 @ 7e0c9f50
......@@ -42,6 +42,19 @@ TEST(ProgramDesc, copy_ctor) {
out->SetType(proto::VarType::LOD_TENSOR);
op->SetOutput("Y", {out->Name()});
BlockDesc* new_block = program.AppendBlock(*global_block);
op = new_block->AppendOp();
op->SetType("mul");
op = global_block->AppendOp();
op->SetType("op_with_subblock");
op->SetAttr("sub_block", new_block);
std::vector<BlockDesc*> sub_blocks;
sub_blocks.push_back(program.AppendBlock(*global_block));
sub_blocks.push_back(program.AppendBlock(*global_block));
op->SetAttr("sub_blocks", sub_blocks);
ProgramDesc program_copy(program);
auto* global_block_copy = program_copy.MutableBlock(0);
......@@ -64,6 +77,8 @@ TEST(ProgramDesc, copy_ctor) {
assert_same_var("Y", y);
assert_same_var("Out", out);
bool found_sub_block = false;
bool found_sub_blocks = false;
for (size_t i = 0; i < global_block->OpSize(); ++i) {
auto op_origin = global_block->Op(i);
auto op_copy = global_block_copy->Op(i);
......@@ -74,8 +89,17 @@ TEST(ProgramDesc, copy_ctor) {
ASSERT_EQ(op_copy->Proto()->SerializeAsString(),
op_origin->Proto()->SerializeAsString());
}
if (op->Type() == "op_with_subblock") {
ASSERT_EQ(1, op->GetBlockAttrId("sub_block"));
found_sub_block = true;
ASSERT_EQ(2, op->GetBlocksAttrIds("sub_blocks").size());
found_sub_blocks = true;
}
}
ASSERT_TRUE(found_sub_block);
ASSERT_TRUE(found_sub_blocks);
// Not check block's protostr are same it because the order of vars could be
// different and it is correct.
}
......
paddle/fluid/framework/rw_lock.h 0 → 100644
浏览文件 @ 7e0c9f50
/* 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
#if !defined(_WIN32)
#include <pthread.h>
#endif // !_WIN32
#include "paddle/fluid/platform/enforce.h"
namespace paddle {
namespace framework {
#if !defined(_WIN32)
struct RWLock {
RWLock() { pthread_rwlock_init(&lock_, nullptr); }
~RWLock() { pthread_rwlock_destroy(&lock_); }
void RDLock() {
PADDLE_ENFORCE_EQ(pthread_rwlock_rdlock(&lock_), 0,
"acquire read lock failed");
}
void WRLock() {
PADDLE_ENFORCE_EQ(pthread_rwlock_wrlock(&lock_), 0,
"acquire write lock failed");
}
void UNLock() {
PADDLE_ENFORCE_EQ(pthread_rwlock_unlock(&lock_), 0, "unlock failed");
}
private:
pthread_rwlock_t lock_;
};
#else
// https://stackoverflow.com/questions/7125250/making-pthread-rwlock-wrlock-recursive
// In windows, rw_lock seems like a hack. Use empty object and do nothing.
struct RWLock {
void RDLock() {}
void WRLock() {}
void UNLock() {}
};
#endif
} // namespace framework
} // namespace paddle
paddle/fluid/framework/rw_lock_test.cc 0 → 100644
浏览文件 @ 7e0c9f50
/* 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/rw_lock.h"
#include <gtest/gtest.h>
#include <chrono> // NOLINT
#include <thread> // NOLINT
#include <vector>
namespace f = paddle::framework;
void f1(f::RWLock *lock) {
lock->RDLock();
lock->UNLock();
}
TEST(RWLOCK, read_read) {
f::RWLock lock;
lock.RDLock();
std::thread t1(f1, &lock);
std::thread t2(f1, &lock);
t1.join();
t2.join();
lock.UNLock();
}
void f2(f::RWLock *lock, std::vector<int> *result) {
lock->RDLock();
ASSERT_EQ(result->size(), 0UL);
lock->UNLock();
}
void f3(f::RWLock *lock, std::vector<int> *result) {
lock->WRLock();
result->push_back(1);
lock->UNLock();
}
TEST(RWLOCK, read_write) {
f::RWLock lock;
std::vector<int> result;
lock.RDLock();
std::thread t1(f2, &lock, &result);
t1.join();
std::thread t2(f3, &lock, &result);
std::this_thread::sleep_for(std::chrono::seconds(1));
ASSERT_EQ(result.size(), 0UL);
lock.UNLock();
t2.join();
ASSERT_EQ(result.size(), 1UL);
}
void f4(f::RWLock *lock, std::vector<int> *result) {
lock->RDLock();
ASSERT_EQ(result->size(), 1UL);
lock->UNLock();
}
TEST(RWLOCK, write_read) {
f::RWLock lock;
std::vector<int> result;
lock.WRLock();
std::thread t1(f4, &lock, &result);
std::this_thread::sleep_for(std::chrono::seconds(1));
result.push_back(1);
lock.UNLock();
t1.join();
}
paddle/fluid/framework/selected_rows.cc
浏览文件 @ 7e0c9f50
......@@ -120,66 +120,76 @@ bool SelectedRows::HasKey(int64_t key) const {
: true;
}
std::vector<std::pair<int64_t, int64_t>> SelectedRows::Get(
const std::vector<int64_t>& keys, framework::Tensor* value) const {
int64_t SelectedRows::AutoGrownIndex(int64_t key, bool auto_grown) {
rwlock_->RDLock();
auto iter = id_to_index_.find(key);
if (iter == id_to_index_.end()) {
rwlock_->UNLock();
if (!auto_grown) {
PADDLE_THROW("key %d not found", key);
}
rwlock_->WRLock();
auto map_size = id_to_index_.size();
auto vector_size = rows_.size();
if (map_size != vector_size) {
rwlock_->UNLock();
PADDLE_THROW(
"id_to_index_ size %d should have the same size with rows_ %d",
map_size, vector_size);
}
auto write_iter = id_to_index_.find(key);
if (write_iter == id_to_index_.end()) {
int row_num = rows_.size();
if (row_num == value_->dims()[0]) {
rwlock_->UNLock();
PADDLE_THROW("selected rows is full, then length exceed %d", row_num);
}
// key logic to put a key into id_to_index_
rows_.push_back(key);
auto index = static_cast<int64_t>(rows_.size() - 1);
id_to_index_[key] = index;
rwlock_->UNLock();
return index;
} else {
auto index = write_iter->second;
rwlock_->UNLock();
return index;
}
} else {
auto index = iter->second;
rwlock_->UNLock();
return index;
}
}
void SelectedRows::SyncIndex() {
rwlock_->WRLock();
id_to_index_.clear();
for (size_t i = 0; i < rows_.size(); ++i) {
id_to_index_[rows_[i]] = i;
}
rwlock_->UNLock();
}
void SelectedRows::Get(const framework::Tensor& ids, framework::Tensor* value,
bool auto_grown) {
PADDLE_ENFORCE(value->IsInitialized(),
"The value tensor should be initialized.");
std::vector<std::pair<int64_t, int64_t>> non_keys_pair;
if (keys.empty()) {
if (ids.numel() == 0) {
VLOG(3) << "keys is empty, please check data!";
} else {
int64_t value_width = value_->numel() / value_->dims()[0];
PADDLE_ENFORCE_EQ(value_width, value->numel() / value->dims()[0],
"output tensor should have the same shape with table "
"except the dims[0].");
for (size_t i = 0; i < keys.size(); ++i) {
int64_t index = Index(keys[i]);
if (index == -1) {
non_keys_pair.push_back(
std::make_pair(keys[i], static_cast<int64_t>(i)));
} else {
framework::VisitDataType(
framework::ToDataType(value_->type()),
TensorCopyVisitor(value, i * value_width, *value_.get(),
index * value_width, value_width));
}
for (int i = 0; i < ids.numel(); ++i) {
int64_t index = AutoGrownIndex(ids.data<int64_t>()[i], auto_grown);
framework::VisitDataType(
framework::ToDataType(value_->type()),
TensorCopyVisitor(value, i * value_width, *value_.get(),
index * value_width, value_width));
}
}
return non_keys_pair;
}
bool SelectedRows::Set(int64_t key, const framework::Tensor& value) {
PADDLE_ENFORCE(value.IsInitialized(), "The value should be initialized.");
if (value_->IsInitialized()) {
PADDLE_ENFORCE_EQ(
value.type(), value_->type(),
"The type of the value should be same with the original value");
}
PADDLE_ENFORCE_EQ(value.dims()[0], static_cast<size_t>(1),
"The first dim of value should be 1.");
std::lock_guard<std::mutex> lock(*auto_grown_mutex_.get());
auto index = Index(key);
bool is_new_key = false;
if (index == -1) {
rows_.push_back(key);
index = rows_.size() - 1;
is_new_key = true;
// whether need to resize the table
if (static_cast<int64_t>(rows_.size()) > value_->dims()[0]) {
auto dims = value_->dims();
dims[0] = (dims[0] + 1) << 1;
framework::VisitDataType(framework::ToDataType(value.type()),
ReAllocateVisitor(dims, value_.get()));
}
}
framework::VisitDataType(
framework::ToDataType(value.type()),
TensorCopyVisitor(value_.get(),
index * value_->numel() / value_->dims()[0], value,
static_cast<int64_t>(0), value.numel()));
return is_new_key;
}
} // namespace framework
......
paddle/fluid/framework/selected_rows.h
浏览文件 @ 7e0c9f50
......@@ -17,10 +17,12 @@ limitations under the License. */
#include <algorithm>
#include <memory>
#include <mutex> // NOLINT
#include <unordered_map>
#include <utility>
#include <vector>
#include "paddle/fluid/framework/lod_tensor.h"
#include "paddle/fluid/framework/rw_lock.h"
#include "paddle/fluid/framework/tensor.h"
#include "paddle/fluid/memory/memcpy.h"
......@@ -48,13 +50,13 @@ class SelectedRows {
SelectedRows(const std::vector<int64_t>& rows, const int64_t& height)
: rows_(rows), height_(height) {
value_.reset(new Tensor());
auto_grown_mutex_.reset(new std::mutex);
rwlock_.reset(new RWLock);
}
SelectedRows() {
height_ = 0;
value_.reset(new Tensor());
auto_grown_mutex_.reset(new std::mutex);
rwlock_.reset(new RWLock);
}
platform::Place place() const { return value_->place(); }
......@@ -74,47 +76,51 @@ class SelectedRows {
void set_rows(const Vector<int64_t>& rows) { rows_ = rows; }
/*
* @brief wheter has the specified key in the table.
* @brief Get the index of key in rows
*
* @return -1 if the key does not exists.
*/
int64_t Index(int64_t key) const {
auto it = std::find(rows_.begin(), rows_.end(), key);
if (it == rows_.end()) {
PADDLE_THROW("id %s not in table", key);
}
return static_cast<int64_t>(std::distance(rows_.begin(), it));
}
/*
* @brief whether has the specified key in the table.
*
* @return true if the key is exists.
*/
bool HasKey(int64_t key) const;
/*
* @brief Get value by the key list, if the
* @brief Get value by the key list.
* Note!!! this interface is only used when selected_rows is used as
* parameters
* for distribute lookup table.
*
* @return a list of pair which contains the non-exists key and the index in
* the value
*/
std::vector<std::pair<int64_t, int64_t>> Get(const std::vector<int64_t>& keys,
framework::Tensor* value) const;
void Get(const framework::Tensor& ids, framework::Tensor* value,
bool auto_grown = false);
/*
* @brief Set a key-value pair into the table.
* This function will double the value memory if it's not engouth.
* @brief Get the index of the key from id_to_index_ map. If the key not
* exist,
* add the key into id_to_index_.
*
* @note:
* 1. The first dim of the value should be 1
* 2. The value should be initialized and the data type
* should be the same with the table.
*
* @return true if the key is a new one, otherwise false
* Note!!! this interface is only used when selected_rows is used as
* parameters
* for distribute lookup table.
*
* @return index of the key.
*/
bool Set(int64_t key, const Tensor& value);
int64_t AutoGrownIndex(int64_t key, bool auto_grown);
/*
* @brief Get the index of key in rows
*
* @return -1 if the key does not exists.
*/
int64_t Index(int64_t key) const {
auto it = std::find(rows_.begin(), rows_.end(), key);
if (it == rows_.end()) {
return static_cast<int64_t>(-1);
}
return static_cast<int64_t>(std::distance(rows_.begin(), it));
}
void SyncIndex();
DDim GetCompleteDims() const {
std::vector<int64_t> dims = vectorize(value_->dims());
......@@ -127,9 +133,10 @@ class SelectedRows {
// SelectedRows are simply concated when adding together. Until a
// SelectedRows add a Tensor, will the duplicate rows be handled.
Vector<int64_t> rows_;
std::unordered_map<int64_t, int64_t> id_to_index_;
std::unique_ptr<Tensor> value_{nullptr};
int64_t height_;
std::unique_ptr<std::mutex> auto_grown_mutex_{nullptr};
std::unique_ptr<RWLock> rwlock_{nullptr};
};
/*
......
paddle/fluid/framework/selected_rows_test.cc
浏览文件 @ 7e0c9f50
......@@ -9,8 +9,11 @@ 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/selected_rows.h"
#include <time.h>
#include <thread> // NOLINT
#include "gtest/gtest.h"
#include "paddle/fluid/framework/selected_rows.h"
namespace paddle {
namespace framework {
......@@ -59,39 +62,129 @@ TEST_F(SelectedRowsTester, SerializeAndDeseralize) {
ASSERT_EQ(selected_rows_->GetCompleteDims(), dst_tensor.GetCompleteDims());
}
TEST_F(SelectedRowsTester, SparseTable) {
TEST(SelectedRows, SparseTable) {
platform::CPUPlace cpu;
SelectedRows table;
int64_t table_size = 100;
int64_t embedding_width = 8;
// initialize a sparse table
table.mutable_value()->Resize(framework::make_ddim({1, 100}));
table.mutable_value()->mutable_data<float>(cpu);
table.mutable_rows()->push_back(1);
table.mutable_value()->Resize(
framework::make_ddim({table_size, embedding_width}));
auto* data = table.mutable_value()->mutable_data<float>(cpu);
for (int64_t i = 0; i < table_size; ++i) {
for (int64_t j = 0; j < embedding_width; ++j) {
data[i * embedding_width + j] = static_cast<float>(i);
}
}
ASSERT_EQ(table.AutoGrownIndex(10, true), 0);
ASSERT_EQ(table.AutoGrownIndex(8, true), 1);
ASSERT_EQ(table.AutoGrownIndex(8, true), 1);
ASSERT_EQ(table.AutoGrownIndex(6, true), 2);
ASSERT_TRUE(table.HasKey(10));
ASSERT_TRUE(table.HasKey(8));
ASSERT_TRUE(table.HasKey(6));
ASSERT_EQ(table.rows().size(), 3);
framework::Tensor ids;
ids.Resize(framework::make_ddim({4}));
auto* ids_data = ids.mutable_data<int64_t>(cpu);
ids_data[0] = static_cast<int64_t>(6);
ids_data[1] = static_cast<int64_t>(6);
ids_data[2] = static_cast<int64_t>(8);
ids_data[3] = static_cast<int64_t>(10);
int64_t key = 10000;
int64_t non_key = 999;
framework::Tensor value;
value.Resize(framework::make_ddim({1, 100}));
auto ptr = value.mutable_data<float>(cpu);
ptr[0] = static_cast<float>(10);
framework::Tensor get_value;
auto* value_data = get_value.mutable_data<float>(
framework::make_ddim({4, embedding_width}), cpu);
table.Get(ids, &get_value);
ASSERT_EQ(table.rows().size(), static_cast<size_t>(1));
ASSERT_EQ(table.HasKey(key), false);
for (int j = 0; j < embedding_width; ++j) {
ASSERT_EQ(value_data[0 * embedding_width + j], 2);
}
for (int j = 0; j < embedding_width; ++j) {
ASSERT_EQ(value_data[1 * embedding_width + j], 2);
}
for (int j = 0; j < embedding_width; ++j) {
ASSERT_EQ(value_data[2 * embedding_width + j], 1);
}
for (int j = 0; j < embedding_width; ++j) {
ASSERT_EQ(value_data[3 * embedding_width + j], 0);
}
}
table.Set(key, value);
void f1(SelectedRows* table, int table_size) {
for (int i = 1000000; i > 0; --i) {
auto id = i % table_size;
int64_t index1 = table->AutoGrownIndex(id, true);
int64_t index2 = table->AutoGrownIndex(id, false);
int64_t index3 = table->AutoGrownIndex(id, true);
ASSERT_EQ(index1, index2);
ASSERT_EQ(index2, index3);
}
}
ASSERT_EQ(table.rows().size(), static_cast<size_t>(2));
ASSERT_EQ(table.HasKey(key), true);
// check re-allocate
ASSERT_EQ(table.value().dims()[0], static_cast<int64_t>(4));
void f2(SelectedRows* table, int table_size) {
for (int i = 0; i < 1000000; ++i) {
auto id = i % table_size;
int64_t index1 = table->AutoGrownIndex(id, true);
int64_t index2 = table->AutoGrownIndex(id, false);
int64_t index3 = table->AutoGrownIndex(id, true);
ASSERT_EQ(index1, index2);
ASSERT_EQ(index2, index3);
}
}
framework::Tensor get_value;
get_value.mutable_data<float>(framework::make_ddim({2, 100}), cpu);
std::vector<int64_t> keys({non_key, key});
auto non_key_pairs = table.Get(keys, &get_value);
void f3(SelectedRows* table, int table_size) {
clock_t t1 = clock();
for (int i = 100000; i > 0; --i) {
auto id1 = table->AutoGrownIndex(i % table_size, true);
auto id2 = table->Index(i % table_size);
ASSERT_EQ(id1, id2);
}
clock_t t2 = clock();
std::cout << "f3 run time:" << t2 - t1 << std::endl;
}
void f4(SelectedRows* table, int table_size) {
clock_t t1 = clock();
for (int i = 0; i < 100000; ++i) {
auto id1 = table->AutoGrownIndex(i % table_size, true);
auto id2 = table->Index(i % table_size);
ASSERT_EQ(id1, id2);
}
clock_t t2 = clock();
std::cout << "f4 run time:" << t2 - t1 << std::endl;
}
TEST(SelectedRows, MultiThreadAutoIndex) {
platform::CPUPlace cpu;
SelectedRows table;
int64_t table_size = 100000;
int64_t embedding_width = 8;
// initialize a sparse table
table.mutable_value()->Resize(
framework::make_ddim({table_size, embedding_width}));
auto* data = table.mutable_value()->mutable_data<float>(cpu);
for (int64_t i = 0; i < table_size; ++i) {
for (int64_t j = 0; j < embedding_width; ++j) {
data[i * embedding_width + j] = static_cast<float>(i);
}
}
ASSERT_EQ(get_value.data<float>()[100], static_cast<float>(10));
ASSERT_EQ(non_key_pairs.size(), static_cast<size_t>(1));
ASSERT_EQ(non_key_pairs[0].first, non_key);
std::thread t1(f1, &table, table_size);
std::thread t11(f1, &table, table_size);
std::thread t2(f2, &table, table_size);
std::thread t22(f2, &table, table_size);
t1.join();
t11.join();
t2.join();
t22.join();
std::thread t3(f3, &table, table_size);
std::thread t4(f4, &table, table_size);
t3.join();
t4.join();
}
} // namespace framework
......
paddle/fluid/framework/tensor.cc
浏览文件 @ 7e0c9f50
......@@ -31,7 +31,8 @@ size_t Tensor::memory_size() const {
return holder_ == nullptr ? 0UL : holder_->size() - offset_;
}
void* Tensor::mutable_data(platform::Place place, std::type_index type) {
void* Tensor::mutable_data(platform::Place place, std::type_index type,
size_t requested_size) {
if (holder_ != nullptr) {
holder_->set_type(type);
}
......@@ -39,7 +40,11 @@ void* Tensor::mutable_data(platform::Place place, std::type_index type) {
"When calling this method, the Tensor's numel must be "
"equal or larger than zero. "
"Please check Tensor::Resize has been called first.");
int64_t size = numel() * SizeOfType(type);
size_t size = numel() * SizeOfType(type);
if (requested_size) {
PADDLE_ENFORCE_GE(requested_size, size);
size = requested_size;
}
/* some versions of boost::variant don't have operator!= */
if (holder_ == nullptr || !(holder_->place() == place) ||
holder_->size() < size + offset_) {
......@@ -68,10 +73,10 @@ void* Tensor::mutable_data(platform::Place place, std::type_index type) {
offset_);
}
void* Tensor::mutable_data(platform::Place place) {
void* Tensor::mutable_data(platform::Place place, size_t requested_size) {
PADDLE_ENFORCE(this->holder_ != nullptr,
"Cannot invoke mutable data if current hold nothing.");
return mutable_data(place, holder_->type());
return mutable_data(place, holder_->type(), requested_size);
}
Tensor& Tensor::ShareDataWith(const Tensor& src) {
......@@ -112,5 +117,6 @@ Tensor& Tensor::Resize(const DDim& dims) {
const DDim& Tensor::dims() const { return dims_; }
int64_t Tensor::numel() const { return product(dims_); }
} // namespace framework
} // namespace paddle
paddle/fluid/framework/tensor.h
浏览文件 @ 7e0c9f50
......@@ -89,22 +89,24 @@ class Tensor {
* @note If not exist, then allocation.
*/
template <typename T>
T* mutable_data(platform::Place place);
T* mutable_data(platform::Place place, size_t requested_size = 0);
void* mutable_data(platform::Place place, std::type_index type);
void* mutable_data(platform::Place place, std::type_index type,
size_t requested_size = 0);
void* mutable_data(platform::Place place);
void* mutable_data(platform::Place place, size_t requested_size = 0);
/**
* @brief Return a pointer to mutable memory block.
*
* @param[in] dims The dimensions of the memory block.
* @param[in] place The place of the memory block.
* @param[in] dims The dimensions of the memory block.
* @param[in] place The place of the memory block.
* @param[in] requested_size The size of the block in bytes.
*
* @note If not exist, then allocation.
*/
template <typename T>
T* mutable_data(DDim dims, platform::Place place);
T* mutable_data(DDim dims, platform::Place place, size_t requested_size = 0);
/*! Return the dimensions of the memory block. */
const DDim& dims() const;
......
paddle/fluid/framework/tensor_impl.h
浏览文件 @ 7e0c9f50
......@@ -46,19 +46,28 @@ inline T* Tensor::data() {
}
template <typename T>
inline T* Tensor::mutable_data(DDim dims, platform::Place place) {
inline T* Tensor::mutable_data(DDim dims, platform::Place place,
size_t requested_size) {
static_assert(std::is_pod<T>::value, "T must be POD");
Resize(dims);
return mutable_data<T>(place);
return mutable_data<T>(place, requested_size);
}
template <typename T>
inline T* Tensor::mutable_data(platform::Place place) {
inline T* Tensor::mutable_data(platform::Place place, size_t requested_size) {
static_assert(std::is_pod<T>::value, "T must be POD");
return reinterpret_cast<T*>(mutable_data(place, typeid(T)));
return reinterpret_cast<T*>(mutable_data(place, typeid(T), requested_size));
}
inline Tensor ReshapeToMatrix(const Tensor& src, int num_col_dims) {
int rank = src.dims().size();
PADDLE_ENFORCE_GE(
rank, 2,
"'ReshapeToMatrix()' is only used for flatten high rank "
"tensors to matrixs. Can not be used in reshaping vectors.");
if (rank == 2) {
return src;
}
Tensor res;
res.ShareDataWith(src);
res.Resize(flatten_to_2d(src.dims(), num_col_dims));
......
paddle/fluid/framework/threadpool.cc
浏览文件 @ 7e0c9f50
......@@ -20,6 +20,9 @@
DEFINE_int32(io_threadpool_size, 100,
"number of threads used for doing IO, default 100");
DEFINE_int32(dist_threadpool_size, 0,
"number of threads used for distributed executed.");
namespace paddle {
namespace framework {
......@@ -35,6 +38,10 @@ void ThreadPool::Init() {
if (threadpool_.get() == nullptr) {
// TODO(Yancey1989): specify the max threads number
int num_threads = std::thread::hardware_concurrency();
if (FLAGS_dist_threadpool_size > 0) {
num_threads = FLAGS_dist_threadpool_size;
VLOG(1) << "set dist_threadpool_size to " << num_threads;
}
PADDLE_ENFORCE_GT(num_threads, 0);
threadpool_.reset(new ThreadPool(num_threads));
}
......
paddle/fluid/inference/analysis/CMakeLists.txt
浏览文件 @ 7e0c9f50
cc_library(ir_pass_manager SRCS ir_pass_manager.cc DEPS graph pass)
cc_library(analysis SRCS pass_manager.cc dot.cc node.cc data_flow_graph.cc graph_traits.cc subgraph_splitter.cc
analyzer.cc
helper.cc
# passes
fluid_to_data_flow_graph_pass.cc
data_flow_graph_to_fluid_pass.cc
dfg_graphviz_draw_pass.cc
tensorrt_subgraph_pass.cc
tensorrt_subgraph_node_mark_pass.cc
analyzer.cc
helper.cc
model_store_pass.cc
DEPS framework_proto proto_desc)
fluid_to_ir_pass.cc
model_store_pass.cc
DEPS framework_proto proto_desc ir_pass_manager graph pass)
cc_test(test_node SRCS node_tester.cc DEPS analysis)
cc_test(test_dot SRCS dot_tester.cc DEPS analysis)
cc_binary(inference_analyzer SRCS analyzer_main.cc DEPS analysis)
......@@ -18,7 +22,7 @@ function (inference_analysis_test TARGET)
if(WITH_TESTING)
set(options "")
set(oneValueArgs "")
set(multiValueArgs SRCS)
set(multiValueArgs SRCS EXTRA_DEPS)
cmake_parse_arguments(analysis_test "${options}" "${oneValueArgs}" "${multiValueArgs}" ${ARGN})
set(mem_opt "")
......@@ -27,19 +31,51 @@ function (inference_analysis_test TARGET)
endif()
cc_test(${TARGET}
SRCS "${analysis_test_SRCS}"
DEPS analysis
DEPS analysis graph fc_fuse_pass graph_viz_pass infer_clean_graph_pass graph_pattern_detecter pass ${analysis_test_EXTRA_DEPS}
ARGS --inference_model_dir=${PYTHON_TESTS_DIR}/book/word2vec.inference.model ${mem_opt})
set_tests_properties(${TARGET} PROPERTIES DEPENDS test_word2vec)
endif(WITH_TESTING)
endfunction(inference_analysis_test)
set(DITU_RNN_MODEL_URL "http://paddle-inference-dist.bj.bcebos.com/ditu_rnn_fluid%2Fmodel.tar.gz")
set(DITU_RNN_DATA_URL "http://paddle-inference-dist.bj.bcebos.com/ditu_rnn_fluid%2Fdata.txt.tar.gz")
set(DITU_INSTALL_DIR "${THIRD_PARTY_PATH}/install/ditu_rnn" CACHE PATH "Ditu RNN model and data root." FORCE)
set(DITU_RNN_MODEL ${DITU_INSTALL_DIR}/model)
set(DITU_RNN_DATA ${DITU_INSTALL_DIR}/data.txt)
function (inference_download_and_uncompress target url gz_filename)
message(STATUS "Download inference test stuff ${gz_filename} from ${url}")
execute_process(COMMAND bash -c "mkdir -p ${DITU_INSTALL_DIR}")
execute_process(COMMAND bash -c "cd ${DITU_INSTALL_DIR} && wget -q ${url}")
execute_process(COMMAND bash -c "cd ${DITU_INSTALL_DIR} && tar xzf ${gz_filename}")
message(STATUS "finish downloading ${gz_filename}")
endfunction(inference_download_and_uncompress)
if (NOT EXISTS ${DITU_INSTALL_DIR})
inference_download_and_uncompress(ditu_rnn_model ${DITU_RNN_MODEL_URL} "ditu_rnn_fluid%2Fmodel.tar.gz")
inference_download_and_uncompress(ditu_rnn_data ${DITU_RNN_DATA_URL} "ditu_rnn_fluid%2Fdata.txt.tar.gz")
endif()
inference_analysis_test(test_analyzer SRCS analyzer_tester.cc
EXTRA_DEPS paddle_inference_api paddle_fluid_api ir_pass_manager analysis
# ir
fc_fuse_pass
graph_viz_pass
infer_clean_graph_pass
graph_pattern_detecter
infer_clean_graph_pass
pass
ARGS --inference_model_dir=${PYTHON_TESTS_DIR}/book/word2vec.inference.model
--infer_ditu_rnn_model=${DITU_INSTALL_DIR}/model
--infer_ditu_rnn_data=${DITU_INSTALL_DIR}/data.txt)
inference_analysis_test(test_data_flow_graph SRCS data_flow_graph_tester.cc)
inference_analysis_test(test_data_flow_graph_to_fluid_pass SRCS data_flow_graph_to_fluid_pass_tester.cc)
inference_analysis_test(test_fluid_to_ir_pass SRCS fluid_to_ir_pass_tester.cc)
inference_analysis_test(test_fluid_to_data_flow_graph_pass SRCS fluid_to_data_flow_graph_pass_tester.cc)
inference_analysis_test(test_subgraph_splitter SRCS subgraph_splitter_tester.cc)
inference_analysis_test(test_dfg_graphviz_draw_pass SRCS dfg_graphviz_draw_pass_tester.cc)
inference_analysis_test(test_tensorrt_subgraph_pass SRCS tensorrt_subgraph_pass_tester.cc)
inference_analysis_test(test_pass_manager SRCS pass_manager_tester.cc)
inference_analysis_test(test_tensorrt_subgraph_node_mark_pass SRCS tensorrt_subgraph_node_mark_pass_tester.cc)
inference_analysis_test(test_analyzer SRCS analyzer_tester.cc)
inference_analysis_test(test_model_store_pass SRCS model_store_pass_tester.cc)
paddle/fluid/inference/analysis/analyzer.cc
浏览文件 @ 7e0c9f50
......@@ -17,22 +17,23 @@
#include "paddle/fluid/inference/analysis/data_flow_graph_to_fluid_pass.h"
#include "paddle/fluid/inference/analysis/dfg_graphviz_draw_pass.h"
#include "paddle/fluid/inference/analysis/fluid_to_data_flow_graph_pass.h"
#include "paddle/fluid/inference/analysis/fluid_to_ir_pass.h"
#include "paddle/fluid/inference/analysis/model_store_pass.h"
#include "paddle/fluid/inference/analysis/pass_manager.h"
#include "paddle/fluid/inference/analysis/tensorrt_subgraph_node_mark_pass.h"
#include "paddle/fluid/inference/analysis/tensorrt_subgraph_pass.h"
namespace paddle {
DEFINE_bool(inference_analysis_enable_tensorrt_subgraph_engine, true,
DEFINE_bool(IA_enable_tensorrt_subgraph_engine, false,
"Enable subgraph to TensorRT engine for acceleration");
DEFINE_string(inference_analysis_graphviz_log_root, "./",
DEFINE_bool(IA_enable_ir, false, "Turn on IR support");
DEFINE_string(IA_graphviz_log_root, "./",
"Graphviz debuger for data flow graphs.");
DEFINE_string(inference_analysis_output_storage_path, "",
"optimized model output path");
DEFINE_string(IA_output_storage_path, "", "optimized model output path");
namespace paddle {
namespace inference {
namespace analysis {
......@@ -40,11 +41,38 @@ class DfgPassManagerImpl final : public DfgPassManager {
public:
DfgPassManagerImpl() {
// TODO(Superjomn) set the key with pass reprs.
AddPass("fluid-to-data-flow-graph", new FluidToDataFlowGraphPass);
if (FLAGS_inference_analysis_enable_tensorrt_subgraph_engine) {
LOG(INFO)
<< "-----------------------------------------------------------------";
if (FLAGS_IA_enable_ir) {
AddPass("fluid-to-ir-pass", new FluidToIrPass);
} else {
AddPass("fluid-to-data-flow-graph", new FluidToDataFlowGraphPass);
}
TryAddTensorRtPass();
AddPass("data-flow-graph-to-fluid", new DataFlowGraphToFluidPass);
if (!FLAGS_IA_output_storage_path.empty()) {
AddPass("model-store-pass", new ModelStorePass);
}
LOG(INFO)
<< "-----------------------------------------------------------------";
}
std::string repr() const override { return "dfg-pass-manager"; }
std::string description() const override { return "DFG pass manager."; }
private:
void AddPass(const std::string& name, Pass* pass) {
VLOG(3) << "Adding pass " << name;
Register(name, pass);
AddGraphvizDebugerPass(pass);
}
void TryAddTensorRtPass() {
if (FLAGS_IA_enable_tensorrt_subgraph_engine) {
auto trt_teller = [&](const Node* node) {
std::unordered_set<std::string> teller_set(
{"elementwise_add", "mul", "conv2d", "pool2d", "relu", "softmax"});
{"elementwise_add", "mul", "conv2d", "pool2d", "relu", "softmax",
"depthwise_conv2d", "batch_norm"});
if (!node->IsFunction()) return false;
const auto* func = static_cast<const Function*>(node);
......@@ -59,20 +87,6 @@ class DfgPassManagerImpl final : public DfgPassManager {
new TensorRTSubgraphNodeMarkPass(trt_teller));
AddPass("tensorrt-subgraph", new TensorRTSubGraphPass(trt_teller));
}
AddPass("data-flow-graph-to-fluid", new DataFlowGraphToFluidPass);
if (!FLAGS_inference_analysis_output_storage_path.empty()) {
AddPass("model-store-pass", new ModelStorePass);
}
}
std::string repr() const override { return "dfg-pass-manager"; }
std::string description() const override { return "DFG pass manager."; }
private:
void AddPass(const std::string& name, Pass* pass) {
LOG(INFO) << "Adding pass " << name;
Register(name, pass);
AddGraphvizDebugerPass(pass);
}
// Add the graphviz debuger pass if the parent pass has one.
......
paddle/fluid/inference/analysis/analyzer.h
浏览文件 @ 7e0c9f50
......@@ -39,14 +39,14 @@ limitations under the License. */
#include "paddle/fluid/inference/analysis/pass.h"
#include "paddle/fluid/inference/analysis/pass_manager.h"
namespace paddle {
// TODO(Superjomn) add a definition flag like PADDLE_WITH_TENSORRT and hide this
// flag if not available.
DECLARE_bool(inference_analysis_enable_tensorrt_subgraph_engine);
DECLARE_string(inference_analysis_graphviz_log_root);
DECLARE_string(inference_analysis_output_storage_path);
DECLARE_bool(IA_enable_tensorrt_subgraph_engine);
DECLARE_string(IA_graphviz_log_root);
DECLARE_string(IA_output_storage_path);
DECLARE_bool(IA_enable_ir);
namespace paddle {
namespace inference {
namespace analysis {
......
paddle/fluid/inference/analysis/analyzer_tester.cc
浏览文件 @ 7e0c9f50
......@@ -13,15 +13,25 @@
// limitations under the License.
#include "paddle/fluid/inference/analysis/analyzer.h"
#include <google/protobuf/text_format.h>
#include <gtest/gtest.h>
#include "paddle/fluid/framework/ir/pass.h"
#include "paddle/fluid/inference/analysis/ut_helper.h"
#include "paddle/fluid/inference/api/helper.h"
#include "paddle/fluid/inference/api/paddle_inference_api.h"
DEFINE_string(infer_ditu_rnn_model, "", "model path for ditu RNN");
DEFINE_string(infer_ditu_rnn_data, "", "data path for ditu RNN");
DEFINE_int32(batch_size, 10, "batch size.");
DEFINE_int32(repeat, 1, "Running the inference program repeat times.");
namespace paddle {
namespace inference {
namespace analysis {
TEST(Analyzer, analysis_without_tensorrt) {
FLAGS_inference_analysis_enable_tensorrt_subgraph_engine = false;
FLAGS_IA_enable_tensorrt_subgraph_engine = false;
Argument argument;
argument.fluid_model_dir.reset(new std::string(FLAGS_inference_model_dir));
Analyzer analyser;
......@@ -29,13 +39,331 @@ TEST(Analyzer, analysis_without_tensorrt) {
}
TEST(Analyzer, analysis_with_tensorrt) {
FLAGS_inference_analysis_enable_tensorrt_subgraph_engine = true;
FLAGS_IA_enable_tensorrt_subgraph_engine = true;
Argument argument;
argument.fluid_model_dir.reset(new std::string(FLAGS_inference_model_dir));
Analyzer analyser;
analyser.Run(&argument);
}
void TestWord2vecPrediction(const std::string &model_path) {
NativeConfig config;
config.model_dir = model_path;
config.use_gpu = false;
config.device = 0;
auto predictor =
::paddle::CreatePaddlePredictor<NativeConfig, PaddleEngineKind::kNative>(
config);
// One single batch
int64_t data[4] = {1, 2, 3, 4};
PaddleTensor tensor;
tensor.shape = std::vector<int>({4, 1});
tensor.data = PaddleBuf(data, sizeof(data));
tensor.dtype = PaddleDType::INT64;
// For simplicity, we set all the slots with the same data.
std::vector<PaddleTensor> slots(4, tensor);
std::vector<PaddleTensor> outputs;
CHECK(predictor->Run(slots, &outputs));
PADDLE_ENFORCE(outputs.size(), 1UL);
// Check the output buffer size and result of each tid.
PADDLE_ENFORCE(outputs.front().data.length(), 33168UL);
float result[5] = {0.00129761, 0.00151112, 0.000423564, 0.00108815,
0.000932706};
const size_t num_elements = outputs.front().data.length() / sizeof(float);
// The outputs' buffers are in CPU memory.
for (size_t i = 0; i < std::min(5UL, num_elements); i++) {
LOG(INFO) << "data: "
<< static_cast<float *>(outputs.front().data.data())[i];
PADDLE_ENFORCE(static_cast<float *>(outputs.front().data.data())[i],
result[i]);
}
}
namespace {
struct DataRecord {
std::vector<std::vector<std::vector<float>>> link_step_data_all;
std::vector<std::vector<float>> week_data_all, minute_data_all;
std::vector<size_t> lod1, lod2, lod3;
std::vector<std::vector<float>> rnn_link_data, rnn_week_datas,
rnn_minute_datas;
size_t batch_iter{0};
size_t batch_size{1};
DataRecord() = default;
explicit DataRecord(const std::string &path, int batch_size = 1)
: batch_size(batch_size) {
Load(path);
}
DataRecord NextBatch() {
DataRecord data;
size_t batch_end = batch_iter + batch_size;
// NOTE skip the final batch, if no enough data is provided.
if (batch_end <= link_step_data_all.size()) {
data.link_step_data_all.assign(link_step_data_all.begin() + batch_iter,
link_step_data_all.begin() + batch_end);
data.week_data_all.assign(week_data_all.begin() + batch_iter,
week_data_all.begin() + batch_end);
data.minute_data_all.assign(minute_data_all.begin() + batch_iter,
minute_data_all.begin() + batch_end);
// Prepare LoDs
data.lod1.push_back(0);
data.lod2.push_back(0);
data.lod3.push_back(0);
CHECK(!data.link_step_data_all.empty()) << "empty";
CHECK(!data.week_data_all.empty());
CHECK(!data.minute_data_all.empty());
CHECK_EQ(data.link_step_data_all.size(), data.week_data_all.size());
CHECK_EQ(data.minute_data_all.size(), data.link_step_data_all.size());
for (size_t j = 0; j < data.link_step_data_all.size(); j++) {
for (const auto &d : data.link_step_data_all[j]) {
data.rnn_link_data.push_back(d);
}
data.rnn_week_datas.push_back(data.week_data_all[j]);
data.rnn_minute_datas.push_back(data.minute_data_all[j]);
// calculate lod
data.lod1.push_back(data.lod1.back() +
data.link_step_data_all[j].size());
data.lod3.push_back(data.lod3.back() + 1);
for (size_t i = 1; i < data.link_step_data_all[j].size() + 1; i++) {
data.lod2.push_back(data.lod2.back() +
data.link_step_data_all[j].size());
}
}
}
batch_iter += batch_size;
return data;
}
void Load(const std::string &path) {
std::ifstream file(path);
std::string line;
int num_lines = 0;
while (std::getline(file, line)) {
num_lines++;
std::vector<std::string> data;
split(line, ':', &data);
std::vector<std::vector<float>> link_step_data;
std::vector<std::string> link_datas;
split(data[0], '|', &link_datas);
for (auto &step_data : link_datas) {
std::vector<float> tmp;
split_to_float(step_data, ',', &tmp);
link_step_data.push_back(tmp);
}
// load week data
std::vector<float> week_data;
split_to_float(data[2], ',', &week_data);
// load minute data
std::vector<float> minute_data;
split_to_float(data[1], ',', &minute_data);
link_step_data_all.push_back(std::move(link_step_data));
week_data_all.push_back(std::move(week_data));
minute_data_all.push_back(std::move(minute_data));
}
}
};
void PrepareInputs(std::vector<PaddleTensor> *input_slots, DataRecord *data,
int batch_size) {
PaddleTensor lod_attention_tensor, init_zero_tensor, lod_tensor_tensor,
week_tensor, minute_tensor;
lod_attention_tensor.name = "data_lod_attention";
init_zero_tensor.name = "cell_init";
lod_tensor_tensor.name = "data";
week_tensor.name = "week";
minute_tensor.name = "minute";
auto one_batch = data->NextBatch();
std::vector<int> rnn_link_data_shape(
{static_cast<int>(one_batch.rnn_link_data.size()),
static_cast<int>(one_batch.rnn_link_data.front().size())});
lod_attention_tensor.shape.assign({1, 2});
lod_attention_tensor.lod.assign({one_batch.lod1, one_batch.lod2});
init_zero_tensor.shape.assign({batch_size, 15});
init_zero_tensor.lod.assign({one_batch.lod3});
lod_tensor_tensor.shape = rnn_link_data_shape;
lod_tensor_tensor.lod.assign({one_batch.lod1});
// clang-format off
week_tensor.shape.assign(
{static_cast<int>(one_batch.rnn_week_datas.size()),
static_cast<int>(one_batch.rnn_week_datas.front().size())});
week_tensor.lod.assign({one_batch.lod3});
minute_tensor.shape.assign(
{static_cast<int>(one_batch.rnn_minute_datas.size()),
static_cast<int>(one_batch.rnn_minute_datas.front().size())});
minute_tensor.lod.assign({one_batch.lod3});
// clang-format on
// assign data
TensorAssignData(&lod_attention_tensor,
std::vector<std::vector<float>>({{0, 0}}));
std::vector<float> tmp_zeros(batch_size * 15, 0.);
TensorAssignData(&init_zero_tensor, {tmp_zeros});
TensorAssignData(&lod_tensor_tensor, one_batch.rnn_link_data);
TensorAssignData(&week_tensor, one_batch.rnn_week_datas);
TensorAssignData(&minute_tensor, one_batch.rnn_minute_datas);
// Set inputs.
auto init_zero_tensor1 = init_zero_tensor;
init_zero_tensor1.name = "hidden_init";
input_slots->assign({week_tensor, init_zero_tensor, minute_tensor,
init_zero_tensor1, lod_attention_tensor,
lod_tensor_tensor});
for (auto &tensor : *input_slots) {
tensor.dtype = PaddleDType::FLOAT32;
}
}
std::string DescribeTensor(const PaddleTensor &tensor) {
std::stringstream os;
os << "Tensor [" << tensor.name << "]\n";
os << " - type: ";
switch (tensor.dtype) {
case PaddleDType::FLOAT32:
os << "float32";
break;
case PaddleDType::INT64:
os << "int64";
break;
default:
os << "unset";
}
os << '\n';
os << " - shape: " << to_string(tensor.shape) << '\n';
os << " - lod: ";
for (auto &l : tensor.lod) {
os << to_string(l) << "; ";
}
os << "\n";
os << " - data: ";
int dim = std::accumulate(tensor.shape.begin(), tensor.shape.end(), 1,
[](int a, int b) { return a * b; });
for (int i = 0; i < dim; i++) {
os << static_cast<float *>(tensor.data.data())[i] << " ";
}
os << '\n';
return os.str();
}
} // namespace
const float ditu_rnn_target_data[] = {
104.711, 11.2431, 1.35422, 0, 0, 0, 0, 0,
27.7039, 1.41486, 7.09526, 0, 0, 0, 0, 0,
7.6481, 6.5324, 56.383, 2.88018, 8.92918, 132.007, 4.27429, 2.02934,
14.1727, 10.7461, 25.0616, 16.0197, 14.4163, 16.9199, 6.75517, 0,
80.0249, 4.77739, 0, 0, 0, 0, 0, 0,
47.5643, 2.67029, 8.76252, 0, 0, 0, 0, 0,
51.8822, 4.4411, 0, 0, 0, 0, 0, 0,
10.7286, 12.0595, 10.6672, 0, 0, 0, 0, 0,
93.5771, 3.84641, 0, 0, 0, 0, 0, 0,
169.426, 0, 0, 0, 0, 0, 0, 0};
// Test with a really complicate model.
void TestDituRNNPrediction(const std::string &model_path,
const std::string &data_path, int batch_size,
bool use_analysis, bool activate_ir,
int num_times = 1) {
FLAGS_IA_enable_ir = activate_ir;
FLAGS_IA_enable_tensorrt_subgraph_engine = false;
FLAGS_IA_output_storage_path = "./analysis.out";
std::string model_out;
if (use_analysis) {
Argument argument(model_path);
argument.model_output_store_path.reset(new std::string("./analysis.out"));
Analyzer analyzer;
analyzer.Run(&argument);
// Should get the transformed model stored to ./analysis.out
model_out = "./analysis.out";
ASSERT_TRUE(PathExists(model_out));
} else {
model_out = FLAGS_infer_ditu_rnn_model;
}
NativeConfig config;
config.prog_file = model_out + "/__model__";
config.param_file = model_out + "/param";
config.use_gpu = false;
config.device = 0;
config.specify_input_name = true;
auto predictor =
CreatePaddlePredictor<NativeConfig, PaddleEngineKind::kNative>(config);
std::vector<PaddleTensor> input_slots;
DataRecord data(data_path, batch_size);
// Prepare inputs.
PrepareInputs(&input_slots, &data, batch_size);
std::vector<PaddleTensor> outputs;
Timer timer;
timer.tic();
for (int i = 0; i < num_times; i++) {
predictor->Run(input_slots, &outputs);
}
LOG(INFO) << "===========profile result===========";
LOG(INFO) << "batch_size: " << batch_size << ", repeat: " << num_times
<< ", latency: " << timer.toc() / num_times << "ms";
LOG(INFO) << "=====================================";
for (auto &out : outputs) {
size_t size = std::accumulate(out.shape.begin(), out.shape.end(), 1,
[](int a, int b) { return a * b; });
float *data = static_cast<float *>(out.data.data());
for (size_t i = 0;
i < std::min(sizeof(ditu_rnn_target_data) / sizeof(float), size);
i++) {
EXPECT_NEAR(data[i], ditu_rnn_target_data[i], 1e-3);
}
}
}
// Turn on the IR pass supportion, run a real inference and check the result.
TEST(Analyzer, SupportIRPass) {
FLAGS_IA_enable_ir = true;
FLAGS_IA_enable_tensorrt_subgraph_engine = false;
FLAGS_IA_output_storage_path = "./analysis.out";
Argument argument(FLAGS_inference_model_dir);
argument.model_output_store_path.reset(new std::string("./analysis.out"));
Analyzer analyzer;
analyzer.Run(&argument);
// Should get the transformed model stored to ./analysis.out
ASSERT_TRUE(PathExists("./analysis.out"));
// Inference from this path.
TestWord2vecPrediction("./analysis.out");
}
// Directly infer with the original model.
TEST(Analyzer, DituRNN_without_analysis) {
TestDituRNNPrediction(FLAGS_infer_ditu_rnn_model, FLAGS_infer_ditu_rnn_data,
FLAGS_batch_size, false, false, FLAGS_repeat);
}
// Inference with the original model with the analysis turned on, the analysis
// module will transform the program to a data flow graph.
TEST(Analyzer, DituRNN_with_analysis) {
LOG(INFO) << "ditu rnn with analysis";
TestDituRNNPrediction(FLAGS_infer_ditu_rnn_model, FLAGS_infer_ditu_rnn_data,
FLAGS_batch_size, true, false, FLAGS_repeat);
}
// Inference with analysis and IR. The IR module will fuse some large kernels.
TEST(Analyzer, DituRNN_with_analysis_with_IR) {
LOG(INFO) << "ditu rnn with analysis and IR fuse";
TestDituRNNPrediction(FLAGS_infer_ditu_rnn_model, FLAGS_infer_ditu_rnn_data,
FLAGS_batch_size, true, true, FLAGS_repeat);
}
} // namespace analysis
} // namespace inference
} // namespace paddle
USE_PASS(fc_fuse_pass);
USE_PASS(graph_viz_pass);
USE_PASS(infer_clean_graph_pass);
paddle/fluid/inference/analysis/data_flow_graph.cc
浏览文件 @ 7e0c9f50
......@@ -19,14 +19,16 @@ limitations under the License. */
namespace paddle {
namespace inference {
namespace analysis {
using ir_node_t = framework::ir::Node;
using ir_graph_t = framework::ir::Graph;
// It is a better idea that the inputs and outputs of this graph is set manually
// before, but there must be a Pass that helps to prune the unnecessary ops that
// do not contribute to the given targets, so in this pass, analysis and get the
// inputs and outputs is OK.
void DataFlowGraph::Build() {
inputs.clear();
outputs.clear();
inputs_.clear();
outputs_.clear();
std::unordered_set<Node *> ins;
std::unordered_set<Node *> outs;
for (auto &node : nodes.nodes()) {
......@@ -42,18 +44,140 @@ void DataFlowGraph::Build() {
// similarly, the nodes that in outs but not in ins is the graphs' outputs
for (auto *in : ins) {
if (!outs.count(in)) {
inputs.push_back(in);
inputs_.push_back(in);
}
}
for (auto *out : outs) {
if (!outs.count(out)) {
outputs.push_back(out);
if (!ins.count(out)) {
outputs_.push_back(out);
}
}
Clean();
}
void DataFlowGraph::Build(const framework::proto::ProgramDesc &prog) {
// insert vars
// The `var2id` keeps a map from a variable's name to its Node-id, the Node-id
// will keep updating to its latest alias during the graph-building.
std::unordered_map<std::string, size_t> var2id;
auto &main_block = prog.blocks(framework::kRootBlockIndex);
for (int i = 0; i < main_block.vars_size(); i++) {
const auto &var = main_block.vars(i);
auto *v = nodes.Create(Node::Type::kValue);
v->SetName(var.name());
v->SetPbDesc(const_cast<void *>(static_cast<const void *>(&var)));
v->SetPbMsg(var.SerializeAsString());
var2id[var.name()] = v->id();
}
// The variables in a SSA can only write once, so if a variable is written
// multiple times(quite common in our ProgramDesc design), multiple alias
// Nodes of this variable will be created, and each will just write once.
// An set that keep all the names of the variables(the original, not alias)
// that have been written(as outputs). Once an Op's output variable hit the
// set, it should create a new alias and update the global alias for this
// variable. And that make a Data Flow Graph a SSA.
std::unordered_set<Node *> unique_written_vars;
for (int i = 0; i < main_block.ops_size(); i++) {
const auto &op = main_block.ops(i);
auto *o = nodes.Create(Node::Type::kFunction);
o->SetName(op.type());
static_cast<Function *>(o)->SetFuncType(op.type());
// Link to the original protobuf message's memory, make it easier to
// generate from a data flow graph to fluid ProgramDesc.
o->SetPbDesc(const_cast<void *>(static_cast<const void *>(&op)));
o->SetPbMsg(op.SerializeAsString());
// set inputs and outputs
for (int j = 0; j < op.inputs_size(); j++) {
auto &in_var = op.inputs(j);
for (int k = 0; k < in_var.arguments_size(); k++) {
auto *in = nodes.GetMutable(var2id.at(in_var.arguments(k)));
in->outlinks.push_back(o);
o->inlinks.push_back(in);
unique_written_vars.insert(in);
}
}
for (int j = 0; j < op.outputs_size(); j++) {
auto &out_var = op.outputs(j);
for (int k = 0; k < out_var.arguments_size(); k++) {
auto *out = nodes.GetMutable(var2id[out_var.arguments(k)]);
if (unique_written_vars.count(out)) {
// Loop found, for example, a = op(a), use SSA, change to a1 = op(a).
auto *out_alias = nodes.Create(Node::Type::kValue);
out_alias->SetName(out->name());
out_alias->SetPbDesc(out->pb_desc());
out_alias->SetPbMsg(out->pb_msg());
var2id[out_alias->name()] =
out_alias->id(); // update variable's alias Node
LOG(INFO) << "loop found in graph, create SSA alias node ["
<< out_alias->repr() << "] for [" << out->repr() << "]";
out = out_alias;
}
out->inlinks.push_back(o);
o->outlinks.push_back(out);
}
}
}
// Analysis and extract the inputs and outputs of this graph.
Build();
}
void DataFlowGraph::Build(const framework::ir::Graph &graph) {
// Create nodes
std::unordered_map<ir_node_t *, Node *> ir_node_map;
for (auto *ir_node : graph.Nodes()) {
Node *x{nullptr};
if (ir_node->IsOp()) {
PADDLE_ENFORCE(ir_node->Op());
VLOG(4) << "get op " << ir_node << " " << ir_node->Name();
x = nodes.Create(Node::Type::kFunction);
x->attr("ir_node").Pointer() = ir_node;
PADDLE_ENFORCE(ir_node->Op()->Proto());
x->SetName(ir_node->Op()->Proto()->type());
x->SetPbMsg(ir_node->Op()->Proto()->SerializeAsString());
} else if (ir_node->IsVar()) {
// Not create a Node for IR ControlDepVar, considering Inference currently
// just used in single thread scenerio.
VLOG(4) << "get var " << ir_node->Name();
x = nodes.Create(Node::Type::kValue);
x->attr("ir_node").Pointer() = ir_node;
x->SetName(ir_node->Name());
// x->SetPbMsg(ir_node->Var()->Proto()->SerializeAsString());
} else {
PADDLE_THROW("Failed to create an Node from IR, unknown type");
}
ir_node_map.emplace(ir_node, x);
}
VLOG(4) << "finish creating Nodes";
VLOG(4) << "to create edge";
// Create links
for (auto *ir_node : graph.Nodes()) {
auto it = ir_node_map.find(ir_node);
// Skip ControlDepVar.
if (it == ir_node_map.end()) continue;
auto *node = it->second;
for (auto *x : ir_node->inputs) {
if (!ir_node_map.count(x)) continue;
node->inlinks.push_back(ir_node_map.at(x));
}
for (auto *x : ir_node->outputs) {
if (!ir_node_map.count(x)) continue;
node->outlinks.push_back(ir_node_map.at(x));
}
}
Build();
PADDLE_ENFORCE(!inputs_.empty(),
"Can't deduce any inputs from the graph, Is the graph empty?");
ir_graph = &graph;
VLOG(3) << "finished build from IR";
}
void DataFlowGraph::Clean() {
for (auto &node : nodes.nodes()) {
std::unordered_set<Node *> inlinks_set(node->inlinks.begin(),
......@@ -61,11 +185,9 @@ void DataFlowGraph::Clean() {
std::unordered_set<Node *> outlinks_set(node->outlinks.begin(),
node->outlinks.end());
if (inlinks_set.size() < node->inlinks.size()) {
LOG(INFO) << "Clean: node " << node->repr() << " prune duplicate inputs";
node->inlinks.assign(inlinks_set.begin(), inlinks_set.end());
}
if (outlinks_set.size() < node->outlinks.size()) {
LOG(INFO) << "Clean: node " << node->repr() << " prune duplicate inputs";
node->outlinks.assign(outlinks_set.begin(), outlinks_set.end());
}
}
......@@ -112,10 +234,10 @@ GraphTraits<DataFlowGraph>::NodesBFSIterator::NodesBFSIterator(
const std::vector<Node *> &source)
: queue_(source.begin(), source.end()) {}
// GraphTraits<DataFlowGraph>::NodesBFSIterator::NodesBFSIterator(
// GraphTraits<DataFlowGraph>::NodesBFSIterator &&other) noexcept
// : queue_(std::move(other.queue_)),
// visited_(std::move(other.visited_)) {}
GraphTraits<DataFlowGraph>::NodesBFSIterator::NodesBFSIterator(
GraphTraits<DataFlowGraph>::NodesBFSIterator &&other) noexcept
: queue_(std::move(other.queue_)),
visited_(std::move(other.visited_)) {}
GraphTraits<DataFlowGraph>::NodesBFSIterator::NodesBFSIterator(
const GraphTraits<DataFlowGraph>::NodesBFSIterator &other)
......@@ -159,7 +281,7 @@ bool GraphTraits<DataFlowGraph>::NodesBFSIterator::operator==(
if (queue_.empty()) return other.queue_.empty();
if ((!queue_.empty()) && (!other.queue_.empty())) {
return queue_.front() == other.queue_.front() &&
visited_.size() == other.visited_.size(); // here need to check the
visited_.size() == other.visited_.size();
// equality of queue and
// visited. Just a light but week implementation.
}
......@@ -174,10 +296,10 @@ GraphTraits<DataFlowGraph>::NodesDFSIterator::NodesDFSIterator(
for (auto *x : source) stack_.push(x);
}
// GraphTraits<DataFlowGraph>::NodesDFSIterator::NodesDFSIterator(
// GraphTraits<DataFlowGraph>::NodesDFSIterator &&other) noexcept
// : stack_(std::move(other.stack_)),
// visited_(std::move(other.visited_)) {}
GraphTraits<DataFlowGraph>::NodesDFSIterator::NodesDFSIterator(
GraphTraits<DataFlowGraph>::NodesDFSIterator &&other) noexcept
: stack_(std::move(other.stack_)),
visited_(std::move(other.visited_)) {}
GraphTraits<DataFlowGraph>::NodesDFSIterator::NodesDFSIterator(
const GraphTraits<DataFlowGraph>::NodesDFSIterator &other)
......@@ -339,7 +461,7 @@ ExtractInputAndOutputOfSubGraph(std::vector<Node *> &graph) { // NOLINT
void FilterRedundantOutputOfSubGraph(DataFlowGraph *graph) {
std::vector<Node *> op_nodes;
for (auto &node : GraphTraits<DataFlowGraph>(graph).nodes_in_TS()) {
for (auto &node : GraphTraits<DataFlowGraph>(*graph).nodes_in_TS()) {
if (node.type() == Node::Type::kValue || node.deleted()) {
continue;
}
......
paddle/fluid/inference/analysis/data_flow_graph.h
浏览文件 @ 7e0c9f50
......@@ -26,6 +26,7 @@ limitations under the License. */
#include <utility>
#include <vector>
#include "paddle/fluid/framework/ir/graph.h"
#include "paddle/fluid/inference/analysis/graph_traits.h"
#include "paddle/fluid/inference/analysis/node.h"
#include "paddle/fluid/platform/enforce.h"
......@@ -41,19 +42,43 @@ namespace analysis {
*/
struct DataFlowGraph {
NodeMap nodes;
std::vector<Node *> inputs;
std::vector<Node *> outputs;
// inputs and outputs are deduced from the graph.
// Used to interact with IR.
const framework::ir::Graph *ir_graph{nullptr};
// Extract inputs and outputs of the graph.
void Build();
void Build(const framework::proto::ProgramDesc &prog);
// Build a graph from ir::Graph.
void Build(const framework::ir::Graph &graph);
// Get an attribute.
AnyAttr &Attr(const std::string &key) { return attrs_[key]; }
// Output a DOT graph file for debug.
std::string DotString() const;
std::string HumanReadableInfo(bool show_values = true,
bool show_functions = true) const;
const std::vector<Node *> &inputs() const {
PADDLE_ENFORCE(!inputs_.empty(),
"No inputs are deduced, need to Build() first.");
return inputs_;
}
const std::vector<Node *> &outputs() const {
PADDLE_ENFORCE(!outputs_.empty(),
"No outputs are deduced, need to Build() first.");
return outputs_;
}
private:
mutable std::vector<Node *> inputs_;
mutable std::vector<Node *> outputs_;
std::unordered_map<std::string, AnyAttr> attrs_;
// Remove duplicate edges and so on.
void Clean();
};
......@@ -70,7 +95,7 @@ struct GraphTraits<DataFlowGraph> {
: public std::iterator<std::forward_iterator_tag, Node *> {
NodesBFSIterator() = default;
explicit NodesBFSIterator(const std::vector<Node *> &source);
// NodesBFSIterator(NodesBFSIterator &&other) noexcept;
NodesBFSIterator(NodesBFSIterator &&other) noexcept;
// NOTE Heavy to use.
NodesBFSIterator(const NodesBFSIterator &other);
......@@ -93,8 +118,8 @@ struct GraphTraits<DataFlowGraph> {
struct NodesDFSIterator
: public std::iterator<std::forward_iterator_tag, Node *> {
NodesDFSIterator() = default;
explicit NodesDFSIterator(const std::vector<Node *> &source);
// NodesDFSIterator(NodesDFSIterator &&other) noexcept;
NodesDFSIterator(const std::vector<Node *> &source);
NodesDFSIterator(NodesDFSIterator &&other) noexcept;
NodesDFSIterator(const NodesDFSIterator &other);
Node &operator*();
......@@ -116,7 +141,7 @@ struct GraphTraits<DataFlowGraph> {
struct NodesTSIterator
: public std::iterator<std::forward_iterator_tag, Node *> {
NodesTSIterator() = default;
explicit NodesTSIterator(const std::vector<Node *> &source);
NodesTSIterator(const std::vector<Node *> &source);
NodesTSIterator(NodesTSIterator &&other)
: sorted_(std::move(other.sorted_)), cursor_(other.cursor_) {
other.cursor_ = 0;
......@@ -138,7 +163,7 @@ struct GraphTraits<DataFlowGraph> {
size_t cursor_{0};
};
explicit GraphTraits(DataFlowGraph *graph) : graph_(graph) {}
explicit GraphTraits(const DataFlowGraph &graph) : graph_(graph) {}
// default use BFS to visit the nodes.
iterator_range<NodesBFSIterator> nodes() {
......@@ -156,20 +181,20 @@ struct GraphTraits<DataFlowGraph> {
private:
NodesBFSIterator nodes_bfs_begin() {
return NodesBFSIterator(graph_->inputs);
return NodesBFSIterator(graph_.inputs());
}
NodesBFSIterator nodes_bfs_end() { return NodesBFSIterator(); }
NodesDFSIterator nodes_dfs_begin() {
return NodesDFSIterator(graph_->inputs);
return NodesDFSIterator(graph_.inputs());
}
NodesDFSIterator nodes_dfs_end() { return NodesDFSIterator(); }
NodesTSIterator nodes_ts_begin() { return NodesTSIterator(graph_->inputs); }
NodesTSIterator nodes_ts_begin() { return NodesTSIterator(graph_.inputs()); }
NodesTSIterator nodes_ts_end() { return NodesTSIterator(); }
private:
DataFlowGraph *graph_;
const DataFlowGraph &graph_;
};
// Extract the inputs and outputs of a graph. The inputs and outputs of a
......
paddle/fluid/inference/analysis/data_flow_graph_to_fluid_pass.h
浏览文件 @ 7e0c9f50
......@@ -27,9 +27,6 @@
namespace paddle {
namespace inference {
DECLARE_int32(tensorrt_max_batchsize);
DECLARE_int32(tensorrt_workspace_size);
namespace analysis {
class DataFlowGraphToFluidPass final : public DataFlowGraphPass {
public:
......
paddle/fluid/inference/analysis/dfg_graphviz_draw_pass.cc
浏览文件 @ 7e0c9f50
......@@ -29,7 +29,7 @@ void DFG_GraphvizDrawPass::Run(DataFlowGraph *graph) {
auto png_path = dot_path.substr(0, dot_path.size() - 4) + ".png";
std::string message;
LOG(INFO) << "draw to " << png_path;
VLOG(3) << "draw to " << png_path;
ExecShellCommand("dot -Tpng " + dot_path + " -o " + png_path, &message);
}
......
paddle/fluid/inference/analysis/fluid_to_data_flow_graph_pass_tester.cc
浏览文件 @ 7e0c9f50
......@@ -30,7 +30,7 @@ TEST(FluidToDataFlowGraphPass, Test) {
ASSERT_EQ(argument.main_dfg->nodes.size(), 38UL);
pass.Finalize();
ASSERT_FALSE(argument.main_dfg->DotString().empty());
EXPECT_FALSE(argument.main_dfg->inputs.empty());
EXPECT_FALSE(argument.main_dfg->inputs().empty());
}
} // namespace analysis
......
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