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[feat] [assistant] [I3T96X] add new Dataset operator LibriSpeechDataset

This commit is contained in:
djc 2021-08-22 13:39:37 +08:00
parent 9f08cdc4ab
commit 4e6f7dc97d
2817 changed files with 31970 additions and 42022 deletions
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0
1.txt Normal file
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3
CMakeLists.txt
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@ -24,9 +24,6 @@ if(${CMAKE_SYSTEM_NAME} MATCHES "Darwin")
-Wno-return-std-move -Wno-unused-private-field -Wno-unused-lambda-capture -Wno-sign-compare \
-Wno-overloaded-virtual -Wno-unneeded-internal-declaration -Wno-unused-variable -Wno-pessimizing-move \
-Wno-inconsistent-missing-override -DHALF_ENABLE_CPP11_USER_LITERALS=0 -D_FORTIFY_SOURCE=2")
elseif(ENABLE_SYM_FILE)
set(CMAKE_CXX_FLAGS_RELEASE "$ENV{CXXFLAGS} -O2 -g -ggdb -Wl,--allow-shlib-undefined \
-DHALF_ENABLE_CPP11_USER_LITERALS=0 -D_FORTIFY_SOURCE=2")
else()
set(CMAKE_CXX_FLAGS_RELEASE "$ENV{CXXFLAGS} -O2 -Wl,--allow-shlib-undefined \
-DHALF_ENABLE_CPP11_USER_LITERALS=0 -D_FORTIFY_SOURCE=2")

2
build.bat
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@ -18,7 +18,7 @@
SET BASE_PATH=%CD%
SET BUILD_PATH=%BASE_PATH%/build
SET threads=8
SET threads=6
SET ENABLE_GITEE=OFF
set VERSION_MAJOR=''

14
build.sh
View File

@ -27,7 +27,7 @@ usage()
echo " [-P on|off] [-z [on|off]] [-M on|off] [-V 10.1|11.1|310|910] [-I arm64|arm32|x86_64] [-K] \\"
echo " [-B on|off] [-E] [-l on|off] [-n full|lite|off] [-H on|off] \\"
echo " [-A on|off] [-S on|off] [-k on|off] [-W sse|neon|avx|avx512|off] \\"
echo " [-L Tensor-RT path] [-y on|off] \\"
echo " [-L Tensor-RT path] \\"
echo ""
echo "Options:"
echo " -d Debug mode"
@ -61,10 +61,9 @@ usage()
echo " -l Compile with python dependency, default on"
echo " -S Enable enable download cmake compile dependency from gitee , default off"
echo " -k Enable make clean, clean up compilation generated cache "
echo " -W Enable SIMD instruction set, use [sse|neon|avx|avx512|off], default avx for cloud CPU backend"
echo " -W Enable x86_64 SSE or AVX instruction set, use [sse|neon|avx|avx512|off], default off for lite and avx for CPU"
echo " -H Enable hidden"
echo " -L Link and specify Tensor-RT library path, default disable Tensor-RT lib linking"
echo " -y Compile the symbol table switch and save the symbol table to the directory output"
}
# check value of input is 'on' or 'off'
@ -123,9 +122,8 @@ checkopts()
TENSORRT_HOME=""
USER_ENABLE_DUMP_IR=false
USER_ENABLE_DEBUGGER=false
ENABLE_SYM_FILE="off"
# Process the options
while getopts 'drvj:c:t:hb:s:a:g:p:ie:m:l:I:RP:D:zM:V:K:B:En:A:S:k:W:H:L:y' opt
while getopts 'drvj:c:t:hb:s:a:g:p:ie:m:l:I:RP:D:zM:V:K:B:En:A:S:k:W:H:L:' opt
do
CASE_SENSIVE_ARG=${OPTARG}
OPTARG=$(echo ${OPTARG} | tr '[A-Z]' '[a-z]')
@ -142,9 +140,6 @@ checkopts()
exit 1
fi
;;
y)
ENABLE_SYM_FILE="on"
;;
r)
DEBUG_MODE="off"
;;
@ -447,9 +442,6 @@ build_mindspore()
if [[ -n "$TRAIN_MODE" ]]; then
CMAKE_ARGS="${CMAKE_ARGS} -DENABLE_${TRAIN_MODE}=ON"
fi
if [[ "X$ENABLE_SYM_FILE" = "Xon" ]]; then
CMAKE_ARGS="${CMAKE_ARGS} -DENABLE_SYM_FILE=ON"
fi
if [[ "X$ENABLE_ASAN" = "Xon" ]]; then
CMAKE_ARGS="${CMAKE_ARGS} -DENABLE_ASAN=ON"
fi

44
cmake/external_libs/ffmpeg.cmake Normal file
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@ -0,0 +1,44 @@
set(FFMPEG_FLAGS
--disable-programs
--disable-doc
--disable-debug
--disable-avdevice
--disable-postproc
--disable-avfilter
--disable-network
--disable-encoders
--disable-hwaccels
--disable-muxers
--disable-bsfs
--disable-protocols
--enable-protocol=file
--enable-protocol=pipe
--disable-indevs
--disable-outdevs
--disable-devices
--disable-filters
--disable-bzlib
--disable-iconv
--disable-libxcb
--disable-lzma
--disable-sdl2
--disable-xlib
--disable-zlib)
set(REQ_URL "https://github.com/FFmpeg/FFmpeg/archive/n4.3.1.tar.gz")
set(MD5 "426ca412ca61634a248c787e29507206")
mindspore_add_pkg(ffmpeg
VER 4.3.1
LIBS avcodec avformat avutil swresample swscale
URL ${REQ_URL}
MD5 ${MD5}
CONFIGURE_COMMAND ./configure --disable-static --enable-shared --disable-x86asm ${FFMPEG_FLAGS}
)
include_directories(${ffmpeg_INC})
add_library(mindspore::avcodec ALIAS ffmpeg::avcodec)
add_library(mindspore::avformat ALIAS ffmpeg::avformat)
add_library(mindspore::avutil ALIAS ffmpeg::avutil)
add_library(mindspore::swresample ALIAS ffmpeg::swresample)
add_library(mindspore::swscale ALIAS ffmpeg::swscale)

6
cmake/external_libs/flatbuffers.cmake
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@ -1,10 +1,10 @@
if(MSVC)
set(flatbuffers_CXXFLAGS "${CMAKE_CXX_FLAGS}")
set(flatbuffers_CFLAGS "${CMAKE_C_FLAGS}")
set(flatbuffers_CFLAGS "${CMAKE_CXX_FLAGS}")
set(flatbuffers_LDFLAGS "${CMAKE_SHARED_LINKER_FLAGS}")
else()
set(flatbuffers_CXXFLAGS "-fPIC -fPIE -D_FORTIFY_SOURCE=2 -O2 -fstack-protector-strong")
set(flatbuffers_CFLAGS "-fPIC -fPIE -D_FORTIFY_SOURCE=2 -O2 -fstack-protector-strong")
set(flatbuffers_CXXFLAGS "-D_FORTIFY_SOURCE=2 -O2")
set(flatbuffers_CFLAGS "-D_FORTIFY_SOURCE=2 -O2")
endif()
if(WIN32)

14
cmake/external_libs/glog.cmake
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@ -1,15 +1,13 @@
set(glog_CXXFLAGS "-D_FORTIFY_SOURCE=2 -O2 ${SECURE_CXX_FLAGS} -Dgoogle=mindspore_private")
set(glog_CFLAGS "-D_FORTIFY_SOURCE=2 -O2")
if(NOT ENABLE_GLIBCXX)
set(glog_CXXFLAGS "${glog_CXXFLAGS} -D_GLIBCXX_USE_CXX11_ABI=0")
endif()
if(BUILD_LITE)
set(glog_CXXFLAGS "-D_FORTIFY_SOURCE=2 -O2 ${SECURE_CXX_FLAGS} -Dgoogle=mindspore_private")
set(glog_CFLAGS "-D_FORTIFY_SOURCE=2 -O2 ${SECURE_C_FLAGS}")
set(glog_LDFLAGS "${SECURE_SHARED_LINKER_FLAGS}")
set(glog_patch "")
set(glog_lib glog)
else()
set(glog_CXXFLAGS "-D_FORTIFY_SOURCE=2 -O2 ${SECURE_CXX_FLAGS} -Dgoogle=mindspore_private")
set(glog_CFLAGS "-D_FORTIFY_SOURCE=2 -O2")
if(NOT ENABLE_GLIBCXX)
set(glog_CXXFLAGS "${glog_CXXFLAGS} -D_GLIBCXX_USE_CXX11_ABI=0")
endif()
set(glog_patch ${CMAKE_SOURCE_DIR}/third_party/patch/glog/glog.patch001)
set(glog_lib mindspore_glog)
endif()

2
cmake/external_libs/json.cmake
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@ -9,7 +9,7 @@ endif()
if(ENABLE_GITEE)
set(REQ_URL "https://gitee.com/mirrors/JSON-for-Modern-CPP/repository/archive/v3.6.1.zip")
set(MD5 "36ea0d9a709c6667b2798a62f6b197ae")
set(MD5 "5bda78ce308e6cfcf614dcf1d5ff27a7")
set(INCLUDE "./include")
else()
set(REQ_URL "https://github.com/nlohmann/json/releases/download/v3.6.1/include.zip")

1
cmake/mind_expression.cmake
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@ -89,6 +89,7 @@ if(ENABLE_MINDDATA)
include(${CMAKE_SOURCE_DIR}/cmake/external_libs/tinyxml2.cmake)
include(${CMAKE_SOURCE_DIR}/cmake/external_libs/cppjieba.cmake)
include(${CMAKE_SOURCE_DIR}/cmake/external_libs/sentencepiece.cmake)
include(${CMAKE_SOURCE_DIR}/cmake/external_libs/ffmpeg.cmake)
endif()
if(ENABLE_MINDDATA)

1
cmake/options.cmake
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@ -25,7 +25,6 @@ option(ENABLE_ACL "enable acl" OFF)
option(ENABLE_GLIBCXX "enable_glibcxx" OFF)
option(MODE_ASCEND_ALL "supports all ascend platform" OFF)
option(MODE_ASCEND_ACL "supports ascend acl mode only" OFF)
option(ENABLE_SYM_FILE "enable sym file" OFF)
if(NOT ENABLE_D AND NOT ENABLE_TESTCASES AND NOT ENABLE_ACL AND NOT ENABLE_GE)
set(ENABLE_GLIBCXX ON)

19
cmake/package.cmake
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@ -12,8 +12,6 @@ set(CPACK_TEMPORARY_PACKAGE_FILE_NAME ${BUILD_PATH}/package/mindspore)
set(CPACK_TEMPORARY_INSTALL_DIRECTORY ${BUILD_PATH}/package/mindspore)
set(CPACK_PACK_ROOT_DIR ${BUILD_PATH}/package/)
set(CPACK_CMAKE_SOURCE_DIR ${CMAKE_SOURCE_DIR})
set(CPACK_ENABLE_SYM_FILE ${ENABLE_SYM_FILE})
set(CPACK_CMAKE_BUILD_TYPE ${CMAKE_BUILD_TYPE})
if(ENABLE_GE)
set(CPACK_MS_BACKEND "ge")
set(CPACK_MS_TARGET "ascend or cpu")
@ -127,6 +125,17 @@ if(ENABLE_MINDDATA)
DESTINATION ${INSTALL_LIB_DIR} RENAME libicudata.so.67 COMPONENT mindspore)
install(FILES ${icu4c_LIBPATH}/libicui18n.so.67.1
DESTINATION ${INSTALL_LIB_DIR} RENAME libicui18n.so.67 COMPONENT mindspore)
install(FILES ${ffmpeg_LIBPATH}/libavcodec.so.58.91.100
DESTINATION ${INSTALL_LIB_DIR} RENAME libavcodec.so.58 COMPONENT mindspore)
install(FILES ${ffmpeg_LIBPATH}/libavformat.so.58.45.100
DESTINATION ${INSTALL_LIB_DIR} RENAME libavformat.so.58 COMPONENT mindspore)
install(FILES ${ffmpeg_LIBPATH}/libavutil.so.56.51.100
DESTINATION ${INSTALL_LIB_DIR} RENAME libavutil.so.56 COMPONENT mindspore)
install(FILES ${ffmpeg_LIBPATH}/libswresample.so.3.7.100
DESTINATION ${INSTALL_LIB_DIR} RENAME libswresample.so.3 COMPONENT mindspore)
install(FILES ${ffmpeg_LIBPATH}/libswscale.so.5.7.100
DESTINATION ${INSTALL_LIB_DIR} RENAME libswscale.so.5 COMPONENT mindspore)
endif()
if(ENABLE_CPU)
@ -198,6 +207,12 @@ if(NOT ENABLE_GE)
set(ASCEND_DRIVER_PATH ${ASCEND_PATH}/driver/lib64/common)
if(ENABLE_D)
install(
TARGETS ms_profile
DESTINATION ${INSTALL_LIB_DIR}
COMPONENT mindspore
)
install(
TARGETS hccl_plugin
DESTINATION ${INSTALL_LIB_DIR}

4
cmake/package_lite.cmake
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@ -330,6 +330,8 @@ elseif(WIN32)
DESTINATION ${CONVERTER_ROOT_DIR}/include COMPONENT ${RUNTIME_COMPONENT_NAME})
install(FILES ${TOP_DIR}/mindspore/lite/tools/converter/model_parser.h
DESTINATION ${CONVERTER_ROOT_DIR}/include COMPONENT ${RUNTIME_COMPONENT_NAME})
install(FILES ${TOP_DIR}/mindspore/lite/tools/converter/dump_graph.h
DESTINATION ${CONVERTER_ROOT_DIR}/include COMPONENT ${RUNTIME_COMPONENT_NAME})
install(FILES ${TOP_DIR}/mindspore/lite/tools/converter/ops/ops_def.h
DESTINATION ${CONVERTER_ROOT_DIR}/include COMPONENT ${RUNTIME_COMPONENT_NAME})
install(DIRECTORY ${TOP_DIR}/build/mindspore/schema/ DESTINATION ${CONVERTER_ROOT_DIR}/include/schema
@ -460,6 +462,8 @@ else()
DESTINATION ${CONVERTER_ROOT_DIR}/include COMPONENT ${RUNTIME_COMPONENT_NAME})
install(FILES ${TOP_DIR}/mindspore/lite/tools/converter/model_parser.h
DESTINATION ${CONVERTER_ROOT_DIR}/include COMPONENT ${RUNTIME_COMPONENT_NAME})
install(FILES ${TOP_DIR}/mindspore/lite/tools/converter/dump_graph.h
DESTINATION ${CONVERTER_ROOT_DIR}/include COMPONENT ${RUNTIME_COMPONENT_NAME})
install(FILES ${TOP_DIR}/mindspore/lite/tools/converter/ops/ops_def.h
DESTINATION ${CONVERTER_ROOT_DIR}/include COMPONENT ${RUNTIME_COMPONENT_NAME})
install(DIRECTORY ${TOP_DIR}/mindspore/lite/build/schema/ DESTINATION ${CONVERTER_ROOT_DIR}/include/schema

55
cmake/package_script.cmake
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@ -77,48 +77,6 @@ set(ENV{BACKEND_TARGET} ${CPACK_MS_TARGET})
set(ENV{MS_PACKAGE_NAME} ${CPACK_MS_PACKAGE_NAME})
set(ENV{COMMIT_ID} ${GIT_COMMIT_ID})
file(GLOB DEBUG_SYM
${MS_PACK_ROOT_DIR}/mindspore/*.so
${MS_PACK_ROOT_DIR}/mindspore/lib/*.so
)
file(GLOB DEBUG_STRIP_SYM
${MS_PACK_ROOT_DIR}/mindspore/*.so
${MS_PACK_ROOT_DIR}/mindspore/lib/*.so*
)
set(CMAKE_OBJCOPY $ENV{CROSS_COMPILE}objcopy)
set(CMAKE_STRIP $ENV{CROSS_COMPILE}strip)
if(CPACK_ENABLE_SYM_FILE)
foreach(schema ${DEBUG_SYM})
execute_process(
COMMAND ${CMAKE_OBJCOPY} "--only-keep-debug" ${schema} ${schema}.sym
WORKING_DIRECTORY ${MS_PACK_ROOT_DIR}
)
endforeach()
endif()
if("${CPACK_CMAKE_BUILD_TYPE}" STREQUAL "Release")
foreach(schema ${DEBUG_STRIP_SYM})
execute_process(
COMMAND ${CMAKE_STRIP} ${schema}
WORKING_DIRECTORY ${MS_PACK_ROOT_DIR}
)
endforeach()
endif()
file(GLOB DEBUG_SYM_FILE
${MS_PACK_ROOT_DIR}/mindspore/*.sym
${MS_PACK_ROOT_DIR}/mindspore/lib/*.sym
)
if(CPACK_ENABLE_SYM_FILE)
file(MAKE_DIRECTORY ${MS_ROOT_DIR}/debug_info)
file(COPY ${DEBUG_SYM_FILE} DESTINATION ${MS_ROOT_DIR}/debug_info/)
file(REMOVE_RECURSE ${DEBUG_SYM_FILE})
endif()
execute_process(
COMMAND ${PYTHON} ${MS_ROOT_DIR}/setup.py "bdist_wheel"
WORKING_DIRECTORY ${MS_PACK_ROOT_DIR}
@ -146,16 +104,3 @@ file(COPY ${MS_PACK_ROOT_DIR}/${NEW_FILE_NAME} DESTINATION ${MS_ROOT_DIR}/output
file(SHA256 ${MS_ROOT_DIR}/output/${NEW_FILE_NAME} SHA256_VAR)
file(WRITE ${MS_ROOT_DIR}/output/${NEW_FILE_NAME}.sha256 ${SHA256_VAR} " " ${NEW_FILE_NAME})
set(CMAKE_TAR $ENV{CROSS_COMPILE}tar)
if(CPACK_ENABLE_SYM_FILE)
file(MAKE_DIRECTORY ${MS_ROOT_DIR}/output/${PACKAGE_NAME}-${VERSION}-${PY_TAGS}-${PLATFORM_TAG})
file(COPY ${MS_ROOT_DIR}/debug_info/ DESTINATION
${MS_ROOT_DIR}/output/${PACKAGE_NAME}-${VERSION}-${PY_TAGS}-${PLATFORM_TAG}/)
execute_process(COMMAND
${CMAKE_COMMAND} -E ${CMAKE_TAR} cfv
${MS_ROOT_DIR}/output/${PACKAGE_NAME}-${VERSION}-${PY_TAGS}-${PLATFORM_TAG}.zip
${MS_ROOT_DIR}/output/${PACKAGE_NAME}-${VERSION}-${PY_TAGS}-${PLATFORM_TAG}/ --format=zip
WORKING_DIRECTORY ${MS_ROOT_DIR})
file(REMOVE_RECURSE ${MS_ROOT_DIR}/debug_info)
file(REMOVE_RECURSE ${MS_ROOT_DIR}/output/${PACKAGE_NAME}-${VERSION}-${PY_TAGS}-${PLATFORM_TAG})
endif()

12
cmake/package_tar.cmake
View File

@ -91,6 +91,18 @@ if(ENABLE_MINDDATA)
DESTINATION ${INSTALL_LIB_DIR}
COMPONENT mindspore
)
file(GLOB_RECURSE FFMPEG_LIB_LIST
${ffmpeg_LIBPATH}/libavcodec*
${ffmpeg_LIBPATH}/libavformat*
${ffmpeg_LIBPATH}/libavutil*
${ffmpeg_LIBPATH}/libswresample*
${ffmpeg_LIBPATH}/libswscale*
)
install(
FILES ${FFMPEG_LIB_LIST}
DESTINATION ${INSTALL_LIB_DIR}
COMPONENT mindspore
)
endif()
# CPU mode

13
cmake/package_win.cmake
View File

@ -42,6 +42,7 @@ set(opencv_LIBPATH ${opencv_LIBPATH}/../bin/)
set(jpeg_turbo_LIBPATH ${jpeg_turbo_LIBPATH}/../bin/)
set(sqlite_LIBPATH ${sqlite_LIBPATH}/../bin/)
set(tinyxml2_LIBPATH ${tinyxml2_LIBPATH}/../bin/)
set(ffmpeg_LIBPATH ${ffmpeg_LIBPATH}/../bin/)
message("offline debugger does not support windows system temporarily")
@ -97,6 +98,18 @@ if(ENABLE_MINDDATA)
DESTINATION ${INSTALL_LIB_DIR}
COMPONENT mindspore
)
file(GLOB_RECURSE FFMPEG_LIB_LIST
${ffmpeg_LIBPATH}/libavcodec*
${ffmpeg_LIBPATH}/libavformat*
${ffmpeg_LIBPATH}/libavutil*
${ffmpeg_LIBPATH}/libswresample*
${ffmpeg_LIBPATH}/libswscale*
)
install(
FILES ${FFMPEG_LIB_LIST}
DESTINATION ${INSTALL_LIB_DIR}
COMPONENT mindspore
)
endif()
if(ENABLE_CPU)

2
docker/OWNERS
View File

@ -1,4 +1,2 @@
approvers:
- zhoufeng54
reviewers:
- HW_KK

3
docker/mindspore-cpu/devel/Dockerfile
View File

@ -58,11 +58,8 @@ RUN apt install -y libffi-dev libssl-dev zlib1g-dev libbz2-dev libncurses5-dev \
&& make install -j4 \
&& rm -f /usr/local/bin/python \
&& rm -f /usr/local/bin/pip \
&& rm -f /usr/local/lib/libpython3.7m.so.1.0 \
&& ln -s ${PYTHON_ROOT_PATH}/bin/python3.7 /usr/local/bin/python \
&& ln -s ${PYTHON_ROOT_PATH}/bin/pip3.7 /usr/local/bin/pip \
&& ln -s ${PYTHON_ROOT_PATH}/lib/libpython3.7m.so.1.0 /usr/local/lib/libpython3.7m.so.1.0 \
&& ldconfig \
&& rm -rf /tmp/cpython-3.7.5 \
&& rm -f /tmp/v3.7.5.tar.gz

5
docker/mindspore-cpu/runtime/Dockerfile
View File

@ -51,16 +51,13 @@ RUN apt install -y libffi-dev libssl-dev zlib1g-dev libbz2-dev libncurses5-dev \
&& tar -xvf v3.7.5.tar.gz \
&& cd /tmp/cpython-3.7.5 \
&& mkdir -p ${PYTHON_ROOT_PATH} \
&& ./configure --prefix=${PYTHON_ROOT_PATH} --enable-shared \
&& ./configure --prefix=${PYTHON_ROOT_PATH} \
&& make -j4 \
&& make install -j4 \
&& rm -f /usr/local/bin/python \
&& rm -f /usr/local/bin/pip \
&& rm -f /usr/local/lib/libpython3.7m.so.1.0 \
&& ln -s ${PYTHON_ROOT_PATH}/bin/python3.7 /usr/local/bin/python \
&& ln -s ${PYTHON_ROOT_PATH}/bin/pip3.7 /usr/local/bin/pip \
&& ln -s ${PYTHON_ROOT_PATH}/lib/libpython3.7m.so.1.0 /usr/local/lib/libpython3.7m.so.1.0 \
&& ldconfig \
&& rm -rf /tmp/cpython-3.7.5 \
&& rm -f /tmp/v3.7.5.tar.gz

7
docker/mindspore-gpu/devel/Dockerfile
View File

@ -1,4 +1,4 @@
FROM nvidia/cuda:11.1-cudnn8-devel-ubuntu18.04
FROM nvidia/cuda:10.1-cudnn7-devel-ubuntu18.04
MAINTAINER leonwanghui <leon.wanghui@huawei.com>
@ -43,7 +43,7 @@ RUN DEBIAN_FRONTEND=noninteractive apt install -y \
libnuma-dev
# Configure cuDNN (v7.6.5)
RUN ln -s /usr/lib/x86_64-linux-gnu/libcudnn.so.8.0.5 /usr/local/cuda/lib64/libcudnn.so
RUN ln -s /usr/lib/x86_64-linux-gnu/libcudnn.so.7.6.5 /usr/local/cuda/lib64/libcudnn.so
# Set bash
RUN echo "dash dash/sh boolean false" | debconf-set-selections
@ -62,11 +62,8 @@ RUN apt install -y libffi-dev libssl-dev zlib1g-dev libbz2-dev libncurses5-dev \
&& make install -j4 \
&& rm -f /usr/local/bin/python \
&& rm -f /usr/local/bin/pip \
&& rm -f /usr/local/lib/libpython3.7m.so.1.0 \
&& ln -s ${PYTHON_ROOT_PATH}/bin/python3.7 /usr/local/bin/python \
&& ln -s ${PYTHON_ROOT_PATH}/bin/pip3.7 /usr/local/bin/pip \
&& ln -s ${PYTHON_ROOT_PATH}/lib/libpython3.7m.so.1.0 /usr/local/lib/libpython3.7m.so.1.0 \
&& ldconfig \
&& rm -rf /tmp/cpython-3.7.5 \
&& rm -f /tmp/v3.7.5.tar.gz

7
docker/mindspore-gpu/runtime/Dockerfile
View File

@ -1,4 +1,4 @@
FROM nvidia/cuda:11.1-cudnn8-devel-ubuntu18.04
FROM nvidia/cuda:10.1-cudnn7-devel-ubuntu18.04
MAINTAINER leonwanghui <leon.wanghui@huawei.com>
@ -53,16 +53,13 @@ RUN apt install -y libffi-dev libssl-dev zlib1g-dev libbz2-dev libncurses5-dev \
&& tar -xvf v3.7.5.tar.gz \
&& cd /tmp/cpython-3.7.5 \
&& mkdir -p ${PYTHON_ROOT_PATH} \
&& ./configure --prefix=${PYTHON_ROOT_PATH} --enable-shared \
&& ./configure --prefix=${PYTHON_ROOT_PATH} \
&& make -j4 \
&& make install -j4 \
&& rm -f /usr/local/bin/python \
&& rm -f /usr/local/bin/pip \
&& rm -f /usr/local/lib/libpython3.7m.so.1.0 \
&& ln -s ${PYTHON_ROOT_PATH}/bin/python3.7 /usr/local/bin/python \
&& ln -s ${PYTHON_ROOT_PATH}/bin/pip3.7 /usr/local/bin/pip \
&& ln -s ${PYTHON_ROOT_PATH}/lib/libpython3.7m.so.1.0 /usr/local/lib/libpython3.7m.so.1.0 \
&& ldconfig \
&& rm -rf /tmp/cpython-3.7.5 \
&& rm -f /tmp/v3.7.5.tar.gz

6
include/api/callback/callback.h
View File

@ -23,6 +23,12 @@
#include "include/api/data_type.h"
#include "include/api/dual_abi_helper.h"
#ifdef _WIN32
#define MS_API __declspec(dllexport)
#else
#define MS_API __attribute__((visibility("default")))
#endif
namespace mindspore {
class Model;
class ModelImpl;

6
include/api/callback/ckpt_saver.h
View File

@ -22,6 +22,12 @@
#include <memory>
#include "include/api/callback/callback.h"
#ifdef _WIN32
#define MS_API __declspec(dllexport)
#else
#define MS_API __attribute__((visibility("default")))
#endif
namespace mindspore {
class CkptSaver: public TrainCallBack {

6
include/api/callback/loss_monitor.h
View File

@ -21,6 +21,12 @@
#include <utility>
#include "include/api/callback/callback.h"
#ifdef _WIN32
#define MS_API __declspec(dllexport)
#else
#define MS_API __attribute__((visibility("default")))
#endif
using GraphPoint = std::pair<int, float>;
namespace mindspore {

6
include/api/callback/lr_scheduler.h
View File

@ -22,6 +22,12 @@
#include <memory>
#include "include/api/callback/callback.h"
#ifdef _WIN32
#define MS_API __declspec(dllexport)
#else
#define MS_API __attribute__((visibility("default")))
#endif
namespace mindspore {
constexpr int DONT_UPDATE_LR = 0;

6
include/api/callback/time_monitor.h
View File

@ -22,6 +22,12 @@
#include <memory>
#include "include/api/callback/callback.h"
#ifdef _WIN32
#define MS_API __declspec(dllexport)
#else
#define MS_API __attribute__((visibility("default")))
#endif
namespace mindspore {
class TimeMonitor: public TrainCallBack {

6
include/api/callback/train_accuracy.h
View File

@ -24,6 +24,12 @@
#include "include/api/callback/callback.h"
#include "include/api/metrics/accuracy.h"
#ifdef _WIN32
#define MS_API __declspec(dllexport)
#else
#define MS_API __attribute__((visibility("default")))
#endif
using GraphPoint = std::pair<int, float>;
namespace mindspore {

6
include/api/cfg.h
View File

@ -23,6 +23,12 @@
#include "include/api/data_type.h"
#include "include/api/dual_abi_helper.h"
#ifdef _WIN32
#define MS_API __declspec(dllexport)
#else
#define MS_API __attribute__((visibility("default")))
#endif
namespace mindspore {
class MixPrecisionCfg {

155
include/api/context.h
View File

@ -38,19 +38,12 @@ class Allocator;
class Delegate;
class DeviceInfoContext;
/// \brief Context is used to store environment variables during execution.
class MS_API Context {
public:
Context();
~Context() = default;
/// \brief Set the number of threads at runtime. This option is only valid for MindSpore Lite.
///
/// \param[in] thread_num the number of threads at runtime.
void SetThreadNum(int32_t thread_num);
/// \brief Get the current thread number setting.
///
/// \return The current thread number setting.
int32_t GetThreadNum() const;
/// \brief Set the thread affinity to CPU cores.
@ -67,10 +60,6 @@ class MS_API Context {
void SetDelegate(const std::shared_ptr<Delegate> &delegate);
std::shared_ptr<Delegate> GetDelegate() const;
/// \brief Get a mutable reference of DeviceInfoContext vector in this context. Only MindSpore Lite supports
/// heterogeneous scenarios with multiple members in the vector.
///
/// \return Mutable reference of DeviceInfoContext vector in this context.
std::vector<std::shared_ptr<DeviceInfoContext>> &MutableDeviceInfo();
private:
@ -78,24 +67,14 @@ class MS_API Context {
std::shared_ptr<Data> data_;
};
/// \brief DeviceInfoContext defines different device contexts.
class MS_API DeviceInfoContext : public std::enable_shared_from_this<DeviceInfoContext> {
public:
struct Data;
DeviceInfoContext();
virtual ~DeviceInfoContext() = default;
/// \brief Get the type of this DeviceInfoContext.
///
/// \return Type of this DeviceInfoContext.
virtual enum DeviceType GetDeviceType() const = 0;
/// \brief A similar function to RTTI is provided when the -fno-rtti compilation option is turned on, which converts
/// DeviceInfoContext to a shared pointer of type T, and returns nullptr if the conversion fails.
///
/// \param T Type
/// \return A pointer of type T after conversion. If the conversion fails, it will be nullptr.
template <class T>
std::shared_ptr<T> Cast() {
static_assert(std::is_base_of<DeviceInfoContext, T>::value, "Wrong cast type.");
@ -105,89 +84,41 @@ class MS_API DeviceInfoContext : public std::enable_shared_from_this<DeviceInfoC
return std::static_pointer_cast<T>(shared_from_this());
}
/// \brief obtain provider's name
///
/// \return provider's name.
std::string GetProvider() const;
/// \brief set provider's name.
///
/// \param[in] provider define the provider's name.
void SetProvider(const std::string &provider);
/// \brief obtain provider's device type.
///
/// \return provider's device type.
std::string GetProviderDevice() const;
/// \brief set provider's device type.
///
/// \param[in] device define the provider's device type.EG: CPU.
void SetProviderDevice(const std::string &device);
/// \brief set memory allocator.
///
/// \param[in] allocator define the memory allocator which can be defined by user.
void SetAllocator(const std::shared_ptr<Allocator> &allocator);
/// \brief obtain memory allocator.
///
/// \return memory allocator.
std::shared_ptr<Allocator> GetAllocator() const;
protected:
std::shared_ptr<Data> data_;
};
/// \brief Derived from DeviceInfoContext, The configuration of the model running on the CPU. This option is only valid
/// for MindSpore Lite.
class MS_API CPUDeviceInfo : public DeviceInfoContext {
public:
/// \brief Get the type of this DeviceInfoContext.
///
/// \return Type of this DeviceInfoContext.
enum DeviceType GetDeviceType() const override { return DeviceType::kCPU; };
/// \brief Set enables to perform the float16 inference
///
/// \param[in] is_fp16 Enable float16 inference or not.
void SetEnableFP16(bool is_fp16);
/// \brief Get enables to perform the float16 inference
///
/// \return Whether enable float16 inference.
bool GetEnableFP16() const;
};
/// \brief Derived from DeviceInfoContext, The configuration of the model running on the NPU. This option is only valid
/// for MindSpore Lite.
class MS_API KirinNPUDeviceInfo : public DeviceInfoContext {
public:
/// \brief Get the type of this DeviceInfoContext.
///
/// \return Type of this DeviceInfoContext.
enum DeviceType GetDeviceType() const override { return DeviceType::kKirinNPU; };
/// \brief Set the NPU frequency.
///
/// \param[in] frequency Can be set to 1 (low power consumption), 2 (balanced), 3 (high performance), 4 (extreme
/// performance), default as 3.
void SetFrequency(int frequency);
/// \brief Get the NPU frequency.
///
/// \return NPU frequency
int GetFrequency() const;
};
/// \brief Derived from DeviceInfoContext, The configuration of the model running on the GPU.
class MS_API GPUDeviceInfo : public DeviceInfoContext {
public:
/// \brief Get the type of this DeviceInfoContext.
///
/// \return Type of this DeviceInfoContext.
enum DeviceType GetDeviceType() const override { return DeviceType::kGPU; };
/// \brief Set device id.
///
/// \param[in] device_id The device id.
void SetDeviceID(uint32_t device_id);
/// \brief Get the device id.
///
/// \return The device id.
uint32_t GetDeviceID() const;
void SetGpuTrtInferMode(bool gpu_trt_infer_mode);
@ -196,15 +127,8 @@ class MS_API GPUDeviceInfo : public DeviceInfoContext {
inline void SetPrecisionMode(const std::string &precison_mode);
inline std::string GetPrecisionMode() const;
/// \brief Set enables to perform the float16 inference
///
/// \param[in] is_fp16 Enable float16 inference or not.
void SetEnableFP16(bool is_fp16);
/// \brief Get enables to perform the float16 inference
///
/// \return Whether enable float16 inference.
bool GetEnableFP16() const;
private:
void SetPrecisionMode(const std::vector<char> &precision_mode);
std::vector<char> GetPrecisionModeChar() const;
@ -215,113 +139,52 @@ void GPUDeviceInfo::SetPrecisionMode(const std::string &precision_mode) {
}
std::string GPUDeviceInfo::GetPrecisionMode() const { return CharToString(GetPrecisionModeChar()); }
/// \brief Derived from DeviceInfoContext, The configuration of the model running on the Ascend910. This option is
/// invalid for MindSpore Lite.
class MS_API Ascend910DeviceInfo : public DeviceInfoContext {
public:
/// \brief Get the type of this DeviceInfoContext.
///
/// \return Type of this DeviceInfoContext.
enum DeviceType GetDeviceType() const override { return DeviceType::kAscend910; };
/// \brief Set device id.
///
/// \param[in] device_id The device id.
void SetDeviceID(uint32_t device_id);
/// \brief Get the device id.
///
/// \return The device id.
uint32_t GetDeviceID() const;
};
/// \brief Derived from DeviceInfoContext, The configuration of the model running on the Ascend310. This option is
/// invalid for MindSpore Lite.
class MS_API Ascend310DeviceInfo : public DeviceInfoContext {
public:
/// \brief Get the type of this DeviceInfoContext.
///
/// \return Type of this DeviceInfoContext.
enum DeviceType GetDeviceType() const override { return DeviceType::kAscend310; };
/// \brief Set device id.
///
/// \param[in] device_id The device id.
void SetDeviceID(uint32_t device_id);
/// \brief Get the device id.
///
/// \return The device id.
uint32_t GetDeviceID() const;
inline void SetDumpConfigPath(const std::string &cfg_path);
inline std::string GetDumpConfigPath() const;
/// \brief Set AIPP configuration file path.
///
/// \param[in] cfg_path AIPP configuration file path.
// aipp config file
inline void SetInsertOpConfigPath(const std::string &cfg_path);
/// \brief Get AIPP configuration file path.
///
/// \return AIPP configuration file path.
inline std::string GetInsertOpConfigPath() const;
/// \brief Set format of model inputs.
///
/// \param[in] format Optional "NCHW", "NHWC", etc.
// nchw or nhwc
inline void SetInputFormat(const std::string &format);
/// \brief Get format of model inputs.
///
/// \return The format of model inputs.
inline std::string GetInputFormat() const;
/// \brief Set shape of model inputs.
///
/// \param[in] shape e.g. "input_op_name1: 1,2,3,4;input_op_name2: 4,3,2,1".
// Mandatory while dynamic batch: e.g. "input_op_name1: 1,2,3,4;input_op_name2: 4,3,2,1"
inline void SetInputShape(const std::string &shape);
/// \brief Get shape of model inputs.
///
/// \return The shape of model inputs.
inline std::string GetInputShape() const;
/// \brief Set shape of model inputs.
///
/// \param[in] shape e.g. {{1, {1,2,3,4}}, {2, {4,3,2,1}}} means the first input shape 1,2,3,4 and the second input
/// shape 4,3,2,1.
void SetInputShapeMap(const std::map<int, std::vector<int>> &shape);
/// \brief Get shape of model inputs.
///
/// \return The shape of model inputs.
std::map<int, std::vector<int>> GetInputShapeMap() const;
void SetDynamicBatchSize(const std::vector<size_t> &dynamic_batch_size);
inline std::string GetDynamicBatchSize() const;
/// \brief Set type of model outputs.
///
/// \param[in] output_type FP32, UINT8 or FP16, default as FP32.
// FP32, UINT8 or FP16, default as FP32
void SetOutputType(enum DataType output_type);
/// \brief Get type of model outputs.
///
/// \return The set type of model outputs.
enum DataType GetOutputType() const;
/// \brief Set precision mode of model.
///
/// \param[in] precision_mode Optional "force_fp16", "allow_fp32_to_fp16", "must_keep_origin_dtype" and
/// "allow_mix_precision", "force_fp16" is set as default
// "force_fp16", "allow_fp32_to_fp16", "must_keep_origin_dtype" or "allow_mix_precision", default as "force_fp16"
inline void SetPrecisionMode(const std::string &precision_mode);
/// \brief Get precision mode of model.
///
/// \return The set type of model outputs
inline std::string GetPrecisionMode() const;
/// \brief Set op select implementation mode.
///
/// \param[in] op_select_impl_mode Optional "high_performance" and "high_precision", "high_performance" is set as
/// default.
// Optional "high_performance" and "high_precision", "high_performance" is set as default
inline void SetOpSelectImplMode(const std::string &op_select_impl_mode);
/// \brief Get op select implementation mode.
///
/// \return The set op select implementation mode.
inline std::string GetOpSelectImplMode() const;
inline void SetFusionSwitchConfigPath(const std::string &cfg_path);

64
include/api/kernel.h
View File

@ -24,16 +24,9 @@
#include "include/api/context.h"
namespace mindspore::kernel {
/// \brief The Kernel class is used to define a MindSpore Kernel.
class Kernel {
public:
Kernel() = default;
/// \brief Constructor.
///
/// \param[in] inputs define the input tensors for kernel.
/// \param[in] outputs define the output tensors for kernel.
/// \param[in] primitive define the primitive of kernel generated by flatbuffers.
/// \param[in] ctx define the context for kernel.
Kernel(const std::vector<mindspore::MSTensor> &inputs, const std::vector<mindspore::MSTensor> &outputs,
const schema::Primitive *primitive, const mindspore::Context *ctx)
: context_(ctx), inputs_(std::move(inputs)), outputs_(std::move(outputs)), primitive_(primitive) {
@ -41,65 +34,32 @@ class Kernel {
type_ = primitive->value_type();
}
}
/// \brief Destructor.
virtual ~Kernel() = default;
/// \brief prepare for executing kernel.
///
/// \return result code.
virtual int Prepare() = 0;
/// \brief execute the kernel.
///
/// \return result code.
virtual int Execute() = 0;
/// \brief resize the kernel input shape, memory need to refresh.
///
/// \return result code.
virtual int ReSize() = 0;
/// \brief set kernel's input tensors.
///
/// \param[in] in_tensors define the input tensors.
virtual void set_inputs(const std::vector<mindspore::MSTensor> &in_tensors) { this->inputs_ = in_tensors; }
/// \brief set kernel's input tensor.
///
/// \param[in] in_tensor define the input tensor.
/// \param[in] index define the index of the input tensor.
virtual void set_input(mindspore::MSTensor in_tensor, int index) { this->inputs_[index] = in_tensor; }
/// \brief set kernel's output tensors.
///
/// \param[in] out_tensors define the output tensors.
virtual void set_outputs(const std::vector<mindspore::MSTensor> &out_tensors) { this->outputs_ = out_tensors; }
/// \brief set kernel's output tensor.
///
/// \param[in] out_tensor define the output tensor.
/// \param[in] index define the index of the output tensor.
virtual void set_output(mindspore::MSTensor out_tensor, int index) { this->outputs_[index] = out_tensor; }
/// \brief obtain kernel's input tensors.
///
/// \return input tensors.
virtual const std::vector<mindspore::MSTensor> &inputs() { return this->inputs_; }
/// \brief obtain kernel's output tensors.
///
/// \return output tensors.
virtual const std::vector<mindspore::MSTensor> &outputs() { return this->outputs_; }
/// \brief obtain kernel's name.
///
/// \return kernel's name.
std::string name() const { return this->name_; }
/// \brief set kernel's name.
///
/// \param[in] name define the kernel's name.
void set_name(const std::string &name) { this->name_ = name; }
/// \brief obtain kernel's context.
///
/// \return kernel's context.
const mindspore::Context *context() const { return this->context_; }
/// \brief obtain kernel's type.
///
/// \return kernel's type.
virtual schema::PrimitiveType type() const { return type_; }
/// \brief obtain the primitive of kernel generated by flatbuffers.
///
/// \return the primitive of kernel generated by flatbuffers.
const schema::Primitive *primitive() const { return this->primitive_; }
protected:

47
include/api/model.h
View File

@ -37,75 +37,32 @@ class Metrics;
namespace dataset {
class Dataset;
} // namespace dataset
/// \brief The Model class is used to define a MindSpore model, facilitating computational graph management.
class MS_API Model {
public:
Model();
~Model();
Model(const Model &) = delete;
void operator=(const Model &) = delete;
/// \brief Builds a model so that it can run on a device.
///
/// \param[in] graph GraphCell is a derivative of Cell. Cell is not available currently. GraphCell can be constructed
/// from Graph, for example, model.Build(GraphCell(graph), context).
/// \param[in] model_context A context used to store options during execution.
/// \param[in] train_cfg A config used by training.
///
/// \return Status.
Status Build(GraphCell graph, const std::shared_ptr<Context> &model_context = nullptr,
const std::shared_ptr<TrainCfg> &train_cfg = nullptr);
/// \brief Resizes the shapes of inputs.
///
/// \param[in] inputs A vector that includes all input tensors in order.
/// \param[in] dims Defines the new shapes of inputs, should be consistent with inputs.
///
/// \return Status.
Status Resize(const std::vector<MSTensor> &inputs, const std::vector<std::vector<int64_t>> &dims);
/// \brief Inference model.
///
/// \param[in] inputs A vector where model inputs are arranged in sequence.
/// \param[out] outputs Which is a pointer to a vector. The model outputs are filled in the container in sequence.
/// \param[in] before CallBack before predict.
/// \param[in] after CallBack after predict.
///
/// \return Status.
Status Predict(const std::vector<MSTensor> &inputs, std::vector<MSTensor> *outputs,
const MSKernelCallBack &before = nullptr, const MSKernelCallBack &after = nullptr);
/// \brief Obtains all input tensors of the model.
///
/// \return The vector that includes all input tensors.
std::vector<MSTensor> GetInputs();
/// \brief Obtains the input tensor of the model by name.
///
/// \return The input tensor with the given name, if the name is not found, an invalid tensor is returned.
inline MSTensor GetInputByTensorName(const std::string &tensor_name);
Status InitMetrics(std::vector<Metrics *> metrics);
std::vector<Metrics *> GetMetrics();
/// \brief Obtains all output tensors of the model.
///
/// \return The vector that includes all output tensors.
std::vector<MSTensor> GetOutputs();
/// \brief Obtains names of all output tensors of the model.
///
/// \return A vector that includes names of all output tensors.
inline std::vector<std::string> GetOutputTensorNames();
/// \brief Obtains the output tensor of the model by name.
///
/// \return The output tensor with the given name, if the name is not found, an invalid tensor is returned.
inline MSTensor GetOutputByTensorName(const std::string &tensor_name);
inline std::vector<MSTensor> GetOutputsByNodeName(const std::string &tensor_name);
/// \brief Inference model.
///
/// \param[in] device_type Device type，options are kGPU, kAscend910, etc.
/// \param[in] model_type The type of model file, options are ModelType::kMindIR, ModelType::kOM.
///
/// \return Is supported or not.
static bool CheckModelSupport(enum DeviceType device_type, ModelType model_type);
Status SetTrainMode(bool train);

32
include/api/serialization.h
View File

@ -27,43 +27,13 @@
#include "include/api/dual_abi_helper.h"
namespace mindspore {
/// \brief The Serialization class is used to summarize methods for reading and writing model files.
class MS_API Serialization {
public:
/// \brief Loads a model file from memory buffer.
///
/// \param[in] model_data A buffer filled by model file.
/// \param[in] data_size The size of the buffer.
/// \param[in] model_type The Type of model file, options are ModelType::kMindIR, ModelType::kOM.
/// \param[out] graph The output parameter, an object saves graph data.
/// \param[in] dec_key The decryption key, key length is 16, 24, or 32.
/// \param[in] dec_mode The decryption mode, optional options are AES-GCM, AES-CBC.
///
/// \return Status.
inline static Status Load(const void *model_data, size_t data_size, ModelType model_type, Graph *graph,
const Key &dec_key = {}, const std::string &dec_mode = kDecModeAesGcm);
/// \brief Loads a model file from path, is not supported on MindSpore Lite.
///
/// \param[in] file The path of model file.
/// \param[in] model_type The Type of model file, options are ModelType::kMindIR, ModelType::kOM.
/// \param[out] graph The output parameter, an object saves graph data.
/// \param[in] dec_key The decryption key, key length is 16, 24, or 32.
/// \param[in] dec_mode The decryption mode, optional options are AES-GCM, AES-CBC.
///
/// \return Status.
inline static Status Load(const std::string &file, ModelType model_type, Graph *graph, const Key &dec_key = {},
const std::string &dec_mode = kDecModeAesGcm);
/// \brief Load multiple models from multiple files, MindSpore Lite does not provide this feature.
///
/// \param[in] files The path of model files.
/// \param[in] model_type The Type of model file, options are ModelType::kMindIR, ModelType::kOM.
/// \param[out] graph The output parameter, an object saves graph data.
/// \param[in] dec_key The decryption key, key length is 16, 24, or 32.
/// \param[in] dec_mode The decryption mode, optional options are AES-GCM, AES-CBC.
///
/// \return Status.
inline static Status Load(const std::vector<std::string> &files, ModelType model_type, std::vector<Graph> *graphs,
const Key &dec_key = {}, const std::string &dec_mode = kDecModeAesGcm);
static Status SetParameters(const std::map<std::string, Buffer> &parameters, Model *model);

94
include/api/types.h
View File

@ -25,21 +25,11 @@
#include "include/api/dual_abi_helper.h"
#include "include/api/format.h"
#ifndef MS_API
#ifdef _WIN32
#ifdef _MSC_VER
#ifdef BUILDING_DLL
#define MS_API __declspec(dllexport)
#else
#define MS_API __declspec(dllimport)
#endif
#else
#define MS_API __declspec(dllexport)
#endif
#else
#define MS_API __attribute__((visibility("default")))
#endif
#endif
namespace mindspore {
enum ModelType : uint32_t {
@ -74,64 +64,18 @@ struct QuantParam {
};
class Allocator;
/// \brief The MSTensor class defines a tensor in MindSpore.
class MS_API MSTensor {
public:
class Impl;
/// \brief Creates a MSTensor object, whose data need to be copied before accessed by Model, must be used in pairs
/// with DestroyTensorPtr.
///
/// \param[in] name The name of the MSTensor.
/// \param[in] type The data type of the MSTensor.
/// \param[in] shape The shape of the MSTensor.
/// \param[in] data The data pointer that points to allocated memory.
/// \param[in] data_len The length of the memory, in bytes.
///
/// \return A pointer of MSTensor.
static inline MSTensor *CreateTensor(const std::string &name, DataType type, const std::vector<int64_t> &shape,
const void *data, size_t data_len) noexcept;
/// \brief Creates a MSTensor object, whose data can be directly accessed by Model, must be used in pairs with
/// DestroyTensorPtr.
///
/// \param[in] name The name of the MSTensor.
/// \param[in] type The data type of the MSTensor.
/// \param[in] shape The shape of the MSTensor.
/// \param[in] data The data pointer that points to allocated memory.
/// \param[in] data_len The length of the memory, in bytes.
///
/// \return A pointer of MSTensor.
static inline MSTensor *CreateRefTensor(const std::string &name, DataType type, const std::vector<int64_t> &shape,
const void *data, size_t data_len) noexcept;
/// \brief Creates a MSTensor object, whose device data can be directly accessed by Model, must be used in pairs with
/// DestroyTensorPtr.
///
/// \param[in] name The name of the MSTensor.
/// \param[in] type The data type of the MSTensor.
/// \param[in] shape The shape of the MSTensor.
/// \param[in] data The data pointer that points to device memory.
/// \param[in] data_len The length of the memory, in bytes.
///
/// \return A pointer of MSTensor.
static inline MSTensor *CreateDevTensor(const std::string &name, DataType type, const std::vector<int64_t> &shape,
const void *data, size_t data_len) noexcept;
/// \brief Create a string type MSTensor object whose data can be accessed by Model only after being copied, must be
/// used in pair with DestroyTensorPtr.
///
/// \param[in] name The name of the MSTensor.
/// \param[in] str A vector container containing several strings.
///
/// \return A pointer of MSTensor.
static inline MSTensor *StringsToTensor(const std::string &name, const std::vector<std::string> &str);
/// \brief Parse the string type MSTensor object into strings.
///
/// \param[in] tensor A MSTensor object.
///
/// \return A vector container containing several strings.
static inline std::vector<std::string> TensorToStrings(const MSTensor &tensor);
/// \brief Destroy an object created by Clone, StringsToTensor, CreateRefTensor, CreateDevTensor or CreateTensor. Do
/// not use it to destroy MSTensor from other sources.
///
/// \param[in] tensor A MSTensor object.
static void DestroyTensorPtr(MSTensor *tensor) noexcept;
MSTensor();
@ -141,51 +85,19 @@ class MS_API MSTensor {
explicit MSTensor(std::nullptr_t);
~MSTensor();
/// \brief Obtains the name of the MSTensor.
///
/// \return The name of the MSTensor.
inline std::string Name() const;
/// \brief Obtains the data type of the MSTensor.
///
/// \return The data type of the MSTensor.
enum DataType DataType() const;
/// \brief Obtains the shape of the MSTensor.
///
/// \return The shape of the MSTensor.
const std::vector<int64_t> &Shape() const;
/// \brief Obtains the number of elements of the MSTensor.
///
/// \return The number of elements of the MSTensor.
int64_t ElementNum() const;
/// \brief Obtains a shared pointer to the copy of data of the MSTensor. The data can be read on host.
///
/// \return A shared pointer to the copy of data of the MSTensor.
std::shared_ptr<const void> Data() const;
/// \brief Obtains the pointer to the data of the MSTensor. If the MSTensor is a device tensor, the data cannot be
/// accessed directly on host.
///
/// \return A pointer to the data of the MSTensor.
void *MutableData();
/// \brief Obtains the length of the data of the MSTensor, in bytes.
///
/// \return The length of the data of the MSTensor, in bytes.
size_t DataSize() const;
/// \brief Gets the boolean value that indicates whether the memory of MSTensor is on device.
///
/// \return The boolean value that indicates whether the memory of MSTensor is on device.
bool IsDevice() const;
/// \brief Gets a deep copy of the MSTensor, must be used in pair with DestroyTensorPtr.
///
/// \return A pointer points to a deep copy of the MSTensor.
MSTensor *Clone() const;
/// \brief Gets the boolean value that indicates whether the MSTensor is valid.
///
/// \return The boolean value that indicates whether the MSTensor is valid.
bool operator==(std::nullptr_t) const;
/// \brief Gets the boolean value that indicates whether the MSTensor is valid.
///
/// \return The boolean value that indicates whether the MSTensor is valid.
bool operator!=(std::nullptr_t) const;
bool operator==(const MSTensor &tensor) const;

7
mindspore/_checkparam.py
View File

@ -23,7 +23,6 @@ from itertools import repeat, zip_longest
from collections import deque
from collections.abc import Iterable
import numpy as np
from mindspore import context
from mindspore import log as logger
from mindspore.common import dtype as mstype
from mindspore._c_expression import Tensor as Tensor_
@ -148,7 +147,7 @@ def check_number(arg_value, value, rel, arg_type=int, arg_name=None, prim_name=N
Check argument integer.
Example:
- number = check_number(number, 0, Rel.GE, "number", None) # number >= 0
- number = check_int(number, 0, Rel.GE, "number", None) # number >= 0
"""
rel_fn = Rel.get_fns(rel)
prim_name = f'in `{prim_name}`' if prim_name else ''
@ -847,10 +846,6 @@ class Validator:
"""Returns an empty Tensor."""
return Tensor_(dtype, shape)
@staticmethod
def check_type_support(dtype, device, supported_dtypes):
return dtype in supported_dtypes or not context.get_context('device_target') == device
def check_input_format(input_param):
"""Judge input format."""

3
mindspore/_extends/graph_kernel/expanders/__init__.py
View File

@ -18,6 +18,7 @@ from .addn import AddN
from .assign_add import AssignAdd
from .batchnorm import BatchNorm
from .batchnorm_grad import BatchNormGrad
from .bias_add import BiasAdd
from .bias_add_grad import BiasAddGrad
from .clip_by_norm_no_div_sum import ClipByNormNoDivSum
from .conv2d import Conv2D
@ -25,6 +26,7 @@ from .complex import CAbs, CAdd, CDiv, CMul, CSub
from .dropout_grad import DropoutGrad
from .equal_count import EqualCount
from .erfc import Erfc
from .expand_dims import ExpandDims
from .fused_adam import FusedAdam
from .fused_adam_weight_decay import FusedAdamWeightDecay
from .fused_mul_add import FusedMulAdd
@ -49,7 +51,6 @@ from .sigmoid import Sigmoid
from .sigmoid_cross_entropy_with_logits import SigmoidCrossEntropyWithLogits
from .sigmoid_cross_entropy_with_logits_grad import SigmoidCrossEntropyWithLogitsGrad
from .sigmoid_grad import SigmoidGrad
from .slice import Slice
from .softmax import Softmax
from .softmax_cross_entropy_with_logits import SoftmaxCrossEntropyWithLogits
from .softmax_grad_ext import SoftmaxGradExt

7
mindspore/_extends/graph_kernel/expanders/_utils.py
View File

@ -80,9 +80,6 @@ class Expander:
class ExpanderInfoValidator:
"""ExpanderInfoValidator is the utility class which defines the validator decorator for expanders"""
def __init__(self):
"""Init"""
@staticmethod
def _add_check_function(kls, func):
"""
@ -201,8 +198,8 @@ def to_frac_z_axis(ori_shape, ori_axis):
return frac_z_axis
def infer_shape_from_fractalnz(fractal):
"get original shape from fractalnz shape"
def infer_shape_from_fractalNz(fractal):
"get original shape from fractalNz shape"
shape = []
dims = len(fractal)
batch = dims - 4

163
mindspore/_extends/graph_kernel/expanders/batchnorm.py
View File

@ -24,7 +24,6 @@ from .expand_dims import ExpandDims
@VLD.check_attrs('is_training', 'momentum', 'epsilon')
class BatchNorm(Expander):
"""BatchNorm expander"""
def _expand(self, graph_builder):
# get op info
input_x = self.inputs[0]
@ -43,8 +42,81 @@ class BatchNorm(Expander):
input_x = graph_builder.emit('Cast', [input_x], attrs={'dst_type': input_x_new_type})
if self.attrs['is_training']:
self.inputs[0] = input_x
res_y, mean_res, variance_res, mean_muls, y_sqrt_rec = self._bn_train(graph_builder)
reduce_axis = ()
shape_x = input_x.shape
if input_x.data_format == DF.NHWC:
reduce_axis = (0, 1, 2)
num = shape_x[0] * shape_x[1] * shape_x[2]
else:
reduce_axis = (0, 2, 3)
num = shape_x[0] * shape_x[2] * shape_x[3]
num_rec = 1.0 / num
num_rec_v = graph_builder.value(input_scale.dtype, num_rec)
# compute mean value of input_x
mean_sum = graph_builder.emit(
'ReduceSum', [input_x], attrs={'reduce_axis': reduce_axis, 'keep_dims': False})
mean_muls = graph_builder.emit('Mul', [mean_sum, num_rec_v])
# compute variance of input_x
if input_x.data_format in (DF.DEFAULT, DF.NCHW):
mean_muls_expand = graph_builder.emit(
'Reshape', [mean_muls], attrs={'shape': ExpandDims.infer_shape(mean_muls.shape, [-1, -1])})
else:
mean_muls_expand = mean_muls
var_sub = graph_builder.emit('Sub', [input_x, mean_muls_expand])
var_mul = graph_builder.emit('Mul', [var_sub, var_sub])
var_sum = graph_builder.emit('ReduceSum', [var_mul], attrs={'reduce_axis': reduce_axis, 'keep_dims': False})
var_mul = graph_builder.emit('Mul', [var_sum, num_rec_v])
# y_sqrt_rec means 1 / sqrt(variance + epsilon), which is calculated in backward pass
scalar_one = 1.0
scalar_one_v = graph_builder.value(input_scale.dtype, scalar_one)
y_add = graph_builder.emit('Add', [var_mul, epsilon_v])
y_sqrt = graph_builder.emit('Sqrt', [y_add])
y_sqrt_rec = graph_builder.emit('RealDiv', [scalar_one_v, y_sqrt])
# compute res_y
tmp_sub = graph_builder.emit('Sub', [input_x, mean_muls_expand])
if input_x.data_format in (DF.DEFAULT, DF.NCHW):
y_sqrt_rec_expand = graph_builder.emit(
'Reshape', [y_sqrt_rec], attrs={'shape': ExpandDims.infer_shape(y_sqrt_rec.shape, [-1, -1])})
else:
y_sqrt_rec_expand = y_sqrt_rec
y_norm = graph_builder.emit('Mul', [tmp_sub, y_sqrt_rec_expand])
if input_x.data_format in (DF.DEFAULT, DF.NCHW):
input_scale_expand = graph_builder.emit(
'Reshape', [input_scale], attrs={'shape': ExpandDims.infer_shape(input_scale.shape, [-1, -1])})
else:
input_scale_expand = input_scale
res_y_mul = graph_builder.emit('Mul', [input_scale_expand, y_norm])
if input_x.data_format in (DF.DEFAULT, DF.NCHW):
input_offset_expand = graph_builder.emit(
'Reshape', [input_offset], attrs={'shape': ExpandDims.infer_shape(input_offset.shape, [-1, -1])})
else:
input_offset_expand = input_offset
res_y = graph_builder.emit('Add', [res_y_mul, input_offset_expand])
# compute mean_res
momentum_sub = scalar_one - self.attrs['momentum']
momentum_v_sub = graph_builder.value(input_scale.dtype, momentum_sub)
new_running_mean_tmp = graph_builder.emit('Mul', [momentum_v_sub, input_mean])
momentum_v = graph_builder.value(input_scale.dtype, self.attrs['momentum'])
current_mean_tmp = graph_builder.emit('Mul', [momentum_v, mean_muls])
updated_moving_mean = graph_builder.emit('Add', [new_running_mean_tmp, current_mean_tmp])
mean_res = graph_builder.emit(
'InplaceAssign', [input_mean, updated_moving_mean, updated_moving_mean], attrs={'fake_output': True})
# variance_res is calculated by sample variance, and need to multiply by num / (num - 1)
var_num = float(num) / (num - 1)
var_num_v = graph_builder.value(input_scale.dtype, var_num)
var_mul_update = graph_builder.emit('Mul', [var_num_v, var_mul])
new_running_var_tmp = graph_builder.emit('Mul', [momentum_v_sub, input_variance])
current_var_tmp = graph_builder.emit('Mul', [momentum_v, var_mul_update])
updated_moving_variance = graph_builder.emit('Add', [new_running_var_tmp, current_var_tmp])
variance_res = graph_builder.emit(
'InplaceAssign', [input_variance, updated_moving_variance, updated_moving_variance],
attrs={'fake_output': True})
if input_x_new_type != input_x_ori_type:
res_y = graph_builder.emit('Cast', [res_y], attrs={'dst_type': input_x_ori_type})
return res_y, mean_res, variance_res, mean_muls, y_sqrt_rec
@ -68,88 +140,3 @@ class BatchNorm(Expander):
if input_x_new_type != input_x_ori_type:
res_y = graph_builder.emit('Cast', [res_y], attrs={'dst_type': input_x_ori_type})
return res_y, var_add, var_add, var_add, var_add
def _bn_train(self, graph_builder):
"""expand BatchNorm for training mode"""
input_x = self.inputs[0]
input_scale = self.inputs[1]
input_offset = self.inputs[2]
input_mean = self.inputs[3]
input_variance = self.inputs[4]
epsilon_v = graph_builder.value(input_scale.dtype, self.attrs['epsilon'])
reduce_axis = ()
shape_x = input_x.shape
if input_x.data_format == DF.NHWC:
reduce_axis = (0, 1, 2)
num = shape_x[0] * shape_x[1] * shape_x[2]
else:
reduce_axis = (0, 2, 3)
num = shape_x[0] * shape_x[2] * shape_x[3]
num_rec = 1.0 / num
num_rec_v = graph_builder.value(input_scale.dtype, num_rec)
# compute mean value of input_x
mean_sum = graph_builder.emit(
'ReduceSum', [input_x], attrs={'reduce_axis': reduce_axis, 'keep_dims': False})
mean_muls = graph_builder.emit('Mul', [mean_sum, num_rec_v])
# compute variance of input_x
if input_x.data_format in (DF.DEFAULT, DF.NCHW):
mean_muls_expand = graph_builder.emit(
'Reshape', [mean_muls], attrs={'shape': ExpandDims.infer_shape(mean_muls.shape, [-1, -1])})
else:
mean_muls_expand = mean_muls
var_sub = graph_builder.emit('Sub', [input_x, mean_muls_expand])
var_mul = graph_builder.emit('Mul', [var_sub, var_sub])
var_sum = graph_builder.emit('ReduceSum', [var_mul], attrs={'reduce_axis': reduce_axis, 'keep_dims': False})
var_mul = graph_builder.emit('Mul', [var_sum, num_rec_v])
# y_sqrt_rec means 1 / sqrt(variance + epsilon), which is calculated in backward pass
scalar_one = 1.0
scalar_one_v = graph_builder.value(input_scale.dtype, scalar_one)
y_add = graph_builder.emit('Add', [var_mul, epsilon_v])
y_sqrt = graph_builder.emit('Sqrt', [y_add])
y_sqrt_rec = graph_builder.emit('RealDiv', [scalar_one_v, y_sqrt])
# compute res_y
tmp_sub = graph_builder.emit('Sub', [input_x, mean_muls_expand])
if input_x.data_format in (DF.DEFAULT, DF.NCHW):
y_sqrt_rec_expand = graph_builder.emit(
'Reshape', [y_sqrt_rec], attrs={'shape': ExpandDims.infer_shape(y_sqrt_rec.shape, [-1, -1])})
else:
y_sqrt_rec_expand = y_sqrt_rec
y_norm = graph_builder.emit('Mul', [tmp_sub, y_sqrt_rec_expand])
if input_x.data_format in (DF.DEFAULT, DF.NCHW):
input_scale_expand = graph_builder.emit(
'Reshape', [input_scale], attrs={'shape': ExpandDims.infer_shape(input_scale.shape, [-1, -1])})
else:
input_scale_expand = input_scale
res_y_mul = graph_builder.emit('Mul', [input_scale_expand, y_norm])
if input_x.data_format in (DF.DEFAULT, DF.NCHW):
input_offset_expand = graph_builder.emit(
'Reshape', [input_offset], attrs={'shape': ExpandDims.infer_shape(input_offset.shape, [-1, -1])})
else:
input_offset_expand = input_offset
res_y = graph_builder.emit('Add', [res_y_mul, input_offset_expand])
# compute mean_res
momentum_sub = scalar_one - self.attrs['momentum']
momentum_v_sub = graph_builder.value(input_scale.dtype, momentum_sub)
new_running_mean_tmp = graph_builder.emit('Mul', [momentum_v_sub, input_mean])
momentum_v = graph_builder.value(input_scale.dtype, self.attrs['momentum'])
current_mean_tmp = graph_builder.emit('Mul', [momentum_v, mean_muls])
updated_moving_mean = graph_builder.emit('Add', [new_running_mean_tmp, current_mean_tmp])
mean_res = graph_builder.emit(
'InplaceAssign', [input_mean, updated_moving_mean, updated_moving_mean], attrs={'fake_output': True})
# variance_res is calculated by sample variance, and need to multiply by num / (num - 1)
var_num = float(num) / (num - 1)
var_num_v = graph_builder.value(input_scale.dtype, var_num)
var_mul_update = graph_builder.emit('Mul', [var_num_v, var_mul])
new_running_var_tmp = graph_builder.emit('Mul', [momentum_v_sub, input_variance])
current_var_tmp = graph_builder.emit('Mul', [momentum_v, var_mul_update])
updated_moving_variance = graph_builder.emit('Add', [new_running_var_tmp, current_var_tmp])
variance_res = graph_builder.emit(
'InplaceAssign', [input_variance, updated_moving_variance, updated_moving_variance],
attrs={'fake_output': True})
return res_y, mean_res, variance_res, mean_muls, y_sqrt_rec

2
mindspore/_extends/graph_kernel/expanders/batchnorm_grad.py
View File

@ -17,14 +17,12 @@ from mindspore._extends.graph_kernel.model.model import DataFormat as DF
from ._utils import Expander, ExpanderInfoValidator as VLD
from .expand_dims import ExpandDims
@VLD.add_format(DF.NHWC, DF.NHWC, DF.DEFAULT, DF.DEFAULT, DF.DEFAULT, DF.DEFAULT)
@VLD.add_format(DF.NCHW, DF.NCHW, DF.DEFAULT, DF.DEFAULT, DF.DEFAULT, DF.DEFAULT)
@VLD.add_format(DF.DEFAULT, DF.DEFAULT, DF.DEFAULT, DF.DEFAULT, DF.DEFAULT, DF.DEFAULT)
@VLD.check_attrs('is_training', 'epsilon')
class BatchNormGrad(Expander):
"""BatchNormGrad expander"""
def _expand(self, graph_builder):
# get op info
input_dy = self.inputs[0]

48
mindspore/_extends/graph_kernel/expanders/bias_add.py Normal file
View File

@ -0,0 +1,48 @@
# Copyright 2020-2021 Huawei Technologies Co., Ltd
#
# 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.
# ===========================================================================
"""generate json desc for bias_add"""
from mindspore._extends.graph_kernel.model.model import DataFormat as DF
from ._utils import Expander, ExpanderInfoValidator as VLD
from .expand_dims import ExpandDims
@VLD.add_format(DF.DEFAULT, DF.DEFAULT)
@VLD.add_format(DF.NCHW, DF.DEFAULT)
@VLD.add_format(DF.NHWC, DF.DEFAULT)
class BiasAdd(Expander):
"""BiasAdd expander"""
def _expand(self, graph_builder):
input_x, input_y = self.inputs
if input_x.data_format == DF.NCHW:
input_y_expand = graph_builder.emit(
'Reshape', [input_y], attrs={'shape': ExpandDims.infer_shape(input_y.shape, [1, 2])})
result = graph_builder.emit('Add', [input_x, input_y_expand])
elif input_x.data_format == DF.DEFAULT:
if len(input_x.shape) == 2:
result = graph_builder.emit('Add', [input_x, input_y])
elif len(input_x.shape) == 3:
input_y_expand = graph_builder.emit(
'Reshape', [input_y], attrs={'shape': ExpandDims.infer_shape(input_y.shape, 1)})
result = graph_builder.emit('Add', [input_x, input_y_expand])
else: # len == 4
input_y_expand = graph_builder.emit(
'Reshape', [input_y], attrs={'shape': ExpandDims.infer_shape(input_y.shape, [1, 2])})
result = graph_builder.emit('Add', [input_x, input_y_expand])
else: # NHWC
result = graph_builder.emit('Add', [input_x, input_y])
return result

1
mindspore/_extends/graph_kernel/expanders/fused_mul_add.py
View File

@ -15,7 +15,6 @@
"""generate json desc for FusedMulAdd"""
from ._utils import Expander
class FusedMulAdd(Expander):
"""FusedMulAdd expander"""

6
mindspore/_extends/graph_kernel/expanders/layernorm.py
View File

@ -15,15 +15,13 @@
"""generate json desc for LayerNorm"""
from mindspore._extends.graph_kernel.model.model import DataFormat as DF
from ._utils import Expander, ExpanderInfoValidator as VLD
from ._utils import infer_shape_from_fractalnz, get_reduced_ori_shape, to_frac_z_axis
from ._utils import infer_shape_from_fractalNz, get_reduced_ori_shape, to_frac_z_axis
@VLD.add_format(DF.FRAC_NZ, DF.DEFAULT, DF.DEFAULT)
@VLD.add_format(DF.DEFAULT, DF.DEFAULT, DF.DEFAULT)
@VLD.check_attrs('begin_norm_axis', 'begin_params_axis', 'epsilon')
class LayerNorm(Expander):
"""LayerNorm expander"""
def _expand(self, graph_builder):
input_x, input_gamma, input_beta = self.inputs
processor = self.processor
@ -38,7 +36,7 @@ class LayerNorm(Expander):
ori_shape_x = input_x.shape
if input_x.data_format == DF.FRAC_NZ:
ori_shape_x = infer_shape_from_fractalnz(ori_shape_x)
ori_shape_x = infer_shape_from_fractalNz(ori_shape_x)
# Calculate the scaling ratio of the average
if begin_norm_axis < 0:

21
mindspore/_extends/graph_kernel/expanders/matmul.py
View File

@ -17,7 +17,6 @@ from mindspore._extends.graph_kernel.model.model import DataFormat as DF
from mindspore._extends.graph_kernel.model.model import GraphKernelUnsupportedException as GKException
from ._utils import Expander, ExpanderInfoValidator as VLD
@VLD.check_attrs('transpose_a', 'transpose_b', 'left_format', 'right_format')
class MatMul(Expander):
"""
@ -25,7 +24,7 @@ class MatMul(Expander):
"""
def __init__(self, expand_info):
super(MatMul, self).__init__(expand_info)
super().__init__(expand_info)
self.transpose_a = self.attrs['transpose_a']
self.transpose_b = self.attrs['transpose_b']
self.left_format = self.attrs['left_format']
@ -48,28 +47,28 @@ class MatMul(Expander):
if input_num < 2:
raise GKException("matul inputs number should bigger than 1, but got {}.".format(input_num))
def _trans_shape(self, shape):
trans_shape = list(shape)
trans_shape[-2] = shape[-1]
trans_shape[-1] = shape[-2]
return trans_shape
def _expand(self, graph_builder):
def transpose(shape):
trans_shape = list(shape)
trans_shape[-2] = shape[-1]
trans_shape[-1] = shape[-2]
return trans_shape
if not self._optimize_to_mul():
raise GKException("MatMul/BatchMatMul do not need to be replaced by Mul")
# Matmul is replaced by Mul([b m k], [b k n]) when k==1
#Matmul is replaced by Mul([b m k], [b k n]) when k==1
input_a = self.inputs[0]
input_b = self.inputs[1]
if self.transpose_a:
shape_a_trans = transpose(self.shape_a)
shape_a_trans = self._trans_shape(self.shape_a)
input_a = graph_builder.emit('Reshape', [input_a], attrs={'shape': shape_a_trans})
if self.transpose_b:
shape_b_trans = transpose(self.shape_b)
shape_b_trans = self._trans_shape(self.shape_b)
input_b = graph_builder.emit('Reshape', [input_b], attrs={'shape': shape_b_trans})
result = graph_builder.emit('Mul', [input_a, input_b])
if 'dst_type' in self.attrs and self.inputs[0].dtype != self.attrs['dst_type']:
result = graph_builder.emit('Cast', [result], attrs={'dst_type': self.attrs['dst_type']})
return result
class BatchMatMul(MatMul):
"""BatchMatMul expander"""

5
mindspore/_extends/graph_kernel/expanders/minimum_grad.py
View File

@ -24,7 +24,7 @@ class MinimumGrad(Expander):
def _check(self):
if not self.attrs.get('grad_x', True) and not self.attrs.get('grad_y', True):
raise GKException("both grad_x and grad_y are False.")
return super(MinimumGrad, self)._check()
return super()._check()
def _expand(self, graph_builder):
input_x, input_y, input_dout = self.inputs
@ -34,8 +34,7 @@ class MinimumGrad(Expander):
dx = graph_builder.emit('Mul', [le_result, input_dout])
dy = graph_builder.emit('Sub', [input_dout, dx])
# for minimumgrad op, output_shape should be equal to input_shape,
# but some elementwise operating may broadcast input_shape
# for minimumgrad op, output_shape should be equal to input_shape, but some elementwise operating may broadcast input_shape
# then output_shape not equal to original input_shape, so need to reduce output to let them equal
reduce_axis_x = self.get_reduce_axis(input_x.shape, dx.shape)
reduce_axis_y = self.get_reduce_axis(input_y.shape, dy.shape)

5
mindspore/_extends/graph_kernel/expanders/softmax.py
View File

@ -15,8 +15,7 @@
"""generate json desc for softmax"""
from mindspore._extends.graph_kernel.model.model import DataFormat as DF
from ._utils import Expander, ExpanderInfoValidator as VLD
from ._utils import infer_shape_from_fractalnz, get_reduced_ori_shape, to_frac_z_axis
from ._utils import infer_shape_from_fractalNz, get_reduced_ori_shape, to_frac_z_axis
@VLD.add_format(DF.FRAC_NZ)
@VLD.add_format(DF.DEFAULT)
@ -31,7 +30,7 @@ class Softmax(Expander):
ori_shape = input_x.shape
if input_x.data_format == DF.FRAC_NZ:
ori_shape = infer_shape_from_fractalnz(input_x.shape)
ori_shape = infer_shape_from_fractalNz(input_x.shape)
for i, _ in enumerate(list(axis)):
if axis[i] < 0:

5
mindspore/_extends/graph_kernel/expanders/softmax_grad_ext.py
View File

@ -15,8 +15,7 @@
"""generate json desc for SoftmaxGradExt"""
from mindspore._extends.graph_kernel.model.model import DataFormat as DF
from ._utils import Expander, ExpanderInfoValidator as VLD
from ._utils import infer_shape_from_fractalnz, get_reduced_ori_shape, to_frac_z_axis
from ._utils import infer_shape_from_fractalNz, get_reduced_ori_shape, to_frac_z_axis
@VLD.add_format(DF.FRAC_NZ, DF.FRAC_NZ, DF.DEFAULT)
@VLD.add_format(DF.DEFAULT, DF.DEFAULT, DF.DEFAULT)
@ -30,7 +29,7 @@ class SoftmaxGradExt(Expander):
ori_shape = x.shape
if x.data_format == DF.FRAC_NZ:
ori_shape = infer_shape_from_fractalnz(ori_shape)
ori_shape = infer_shape_from_fractalNz(ori_shape)
if not axis:
axis = []
for i, _ in enumerate(ori_shape):

4
mindspore/_extends/graph_kernel/expanders/square_sum_v1.py
View File

@ -15,7 +15,7 @@
"""generate json desc for SquareSumV1"""
from mindspore._extends.graph_kernel.model.model import DataFormat as DF
from ._utils import Expander, ExpanderInfoValidator as VLD
from ._utils import infer_shape_from_fractalnz, get_reduced_ori_shape, to_frac_z_axis
from ._utils import infer_shape_from_fractalNz, get_reduced_ori_shape, to_frac_z_axis
@VLD.add_format(DF.FRAC_NZ)
@ -30,7 +30,7 @@ class SquareSumV1(Expander):
ori_shape = x.shape
if x.data_format == DF.FRAC_NZ:
ori_shape = infer_shape_from_fractalnz(ori_shape)
ori_shape = infer_shape_from_fractalNz(ori_shape)
if not axis:
axis = []
for i, _ in enumerate(ori_shape):

9
mindspore/_extends/graph_kernel/model/graph_parallel.py
View File

@ -17,8 +17,6 @@ from .model import PrimLib
class ParalGain:
"""Paral Gain"""
def __init__(self, fusion_type, bottleneck, gain, block_assign, type_info):
self.fusion_type = fusion_type
self.bottleneck = bottleneck
@ -43,9 +41,7 @@ class ScheduleAnalyzer:
self.ops = graph.ops
self.dom_op = [out.op for out in outputs]
@staticmethod
def prod(shape):
"""Compute shape product"""
def prod(self, shape):
res = shape[0]
for i in range(1, len(shape)):
res = res * shape[i]
@ -258,7 +254,7 @@ class ScheduleAnalyzer:
fusion_type = "block_fusion"
type_info = None
activate_pipeline_optimization = False # Disable pipeline optimization for now.
activate_pipeline_optimization = False # Disable pipeline optimization for now.
if activate_pipeline_optimization:
pipeline_info = ScheduleAnalyzer.pipeline_fusion_analyze(
blocks, op_sizes, exclude_gid)
@ -291,5 +287,4 @@ def block_parallel_estimate(graphs):
def parallel_estimate(graphs):
"""Estimate parallel gain"""
return block_parallel_estimate(graphs)

251
mindspore/_extends/graph_kernel/model/graph_split.py
View File

@ -13,6 +13,7 @@
# limitations under the License.
# ===========================================================================
"""Cost model splitter"""
import os
from functools import reduce as prod_reduce
from mindspore import log as logger
from .model import PrimLib, Graph, Tensor, Operator
@ -38,24 +39,20 @@ class GraphSplitByPattern:
def sync(self, x, y):
"""sync from y to x"""
for i in self.alive:
self._link(self.map[y][i], x, i)
def _link(self, cond, f, t):
"""link from `f` to `t`"""
if cond:
self.map[f][t] = True
if self.map[y][i] and not self.map[x][i]:
self.map[x][i] = True
def fuse(self, x, y):
"""fuse y to x"""
for i in self.alive:
# i is the succeeding node of y, links the x's previous nodes to i
if self.map[y][i] and not self.map[x][i]:
for pre in self.alive:
self._link(self.map[pre][x], pre, i)
# i is the previous node of y, link i to x's succeeding nodes
if self.map[pre][x] and not self.map[pre][i]:
self.map[pre][i] = True
if self.map[i][y] and not self.map[i][x]:
for suc in self.alive:
self._link(self.map[x][suc], i, suc)
if self.map[x][suc] and not self.map[i][suc]:
self.map[i][suc] = True
self.alive.remove(y)
class Area:
@ -70,10 +67,6 @@ class GraphSplitByPattern:
self.stitch_ops = set()
self.stitch_atomic_ops = set()
def has_stitch_op(self):
"""check stitch_op exists"""
return self.stitch_ops or self.stitch_atomic_ops
def __init__(self, init_op, is_output, unique_id, reach_tab, recompute_ops=None):
self.pattern = PrimLib.iter_type(init_op) if init_op is not None else PrimLib.UNKNOWN
self.ops = [] if init_op is None else [init_op]
@ -293,35 +286,31 @@ class GraphSplitByPattern:
def fuse(self, selector):
"""Fuse areas"""
def _fuse_area():
changed = False
while True:
for dominant in self.areas:
result = selector(dominant)
if result is None or not result[0]:
continue
fuse_areas, is_forward = result
fuse_areas = self.limit_area_size(dominant, fuse_areas)
if not fuse_areas:
continue
if is_forward:
for area in fuse_areas:
dominant.fuse(area)
self.set_area_map(area.ops, dominant)
self.areas.remove(area)
else:
forward_area = dominant
for area in fuse_areas:
area.fuse(forward_area)
self.set_area_map(forward_area.ops, area)
self.areas.remove(forward_area)
forward_area = area
return True
return False
changed, do_again = False, True
while do_again:
do_again = _fuse_area()
changed = changed or do_again
return changed
if result is not None and result[0]:
fuse_areas, is_forward = result
fuse_areas = self.limit_area_size(dominant, fuse_areas)
if not fuse_areas:
continue
if is_forward:
for area in fuse_areas:
dominant.fuse(area)
self.set_area_map(area.ops, dominant)
self.areas.remove(area)
else:
forward_area = dominant
for area in fuse_areas:
area.fuse(forward_area)
self.set_area_map(forward_area.ops, area)
self.areas.remove(forward_area)
forward_area = area
changed = True
break
else:
return changed
def fuse_recom(self, selector):
"""Fuse recompute area to its user"""
@ -359,6 +348,21 @@ class GraphSplitByPattern:
graphmodes.append("basic" if area.mode == self.Area.MODE_BASIC else "composite")
return subgraphs, graphmodes
def dump_subgraphs(self, subgraphs):
"""Dump subgraphs"""
if os.environ.get("ENABLE_SUBGRAPHS", "off") == "on":
subgraphs_str = "subgraphs:\nlen: " + str(len(subgraphs)) + "\n"
for i, sub in enumerate(subgraphs):
subgraphs_str += str("============") + str(i) + "\n"
subgraphs_str += str(sub)
dirname = 'subgraphs'
if not os.path.exists(dirname):
os.makedirs(dirname)
graphname = self.graph.name
filename = dirname + '/' + graphname + '.log'
with os.fdopen(os.open(filename, os.O_RDWR | os.O_CREAT), 'w+') as f:
f.write(subgraphs_str)
def pattern_fuse(self, fuse_func=None):
"""fuse Areas by pattern repeatedly"""
del fuse_func
@ -372,38 +376,34 @@ class GraphSplitByPattern:
# Note: after this function, the input output relation is not maintained.
self.split_output_reshapes()
subgraphs, graphmodes = self.to_subgraphs()
self.dump_subgraphs(subgraphs)
return subgraphs, graphmodes
def split_output_reshapes(self):
"""Force split the output Reshapes into other new area"""
def _remove_output_reshape(reshape_ops, other_ops):
def _run():
for op in reshape_ops:
if any([to_op in other_ops for to_op in op.output.to_ops]):
reshape_ops.remove(op)
other_ops.append(op)
return True
return False
while _run():
pass
"""Force split the output reshapes into other new """
new_areas = []
for area in self.areas:
reshape_ops = [op for op in area.ops if PrimLib.iter_type(op) == PrimLib.RESHAPE]
other_ops = [op for op in area.ops if op not in reshape_ops]
if not other_ops or not reshape_ops:
out_reshape_ops = [op for op in area.ops if PrimLib.iter_type(op) == PrimLib.RESHAPE]
remain_ops = [op for op in area.ops if op not in out_reshape_ops]
if not remain_ops or not out_reshape_ops:
continue
# remove the output reshape from "reshape_ops" and add it into "other_ops"
_remove_output_reshape(reshape_ops, other_ops)
if not reshape_ops:
continue
for op in reshape_ops:
a = self.Area(op, False, 0, self.reach_tab)
self.set_default_mode(a)
new_areas.append(a)
area.ops = other_ops
if len(other_ops) == 1:
self.set_default_mode(area)
changed = True
while changed:
changed = False
for op in out_reshape_ops:
if any([to_op in remain_ops for to_op in op.output.to_ops]):
out_reshape_ops.remove(op)
remain_ops.append(op)
changed = True
break
if out_reshape_ops:
for op in out_reshape_ops:
a = self.Area(op, False, 0, self.reach_tab)
self.set_default_mode(a)
new_areas.append(a)
area.ops = remain_ops
if len(remain_ops) == 1:
self.set_default_mode(area)
if new_areas:
self.areas += new_areas
@ -472,8 +472,8 @@ class GraphSplitByPattern:
region_ops.append(op)
return False, None, weight, True
# region fails to grow
max_weight = 20
if weight > max_weight or len(op.inputs) > 1 or PrimLib.iter_type(op) > PrimLib.BROADCAST:
MAX_WEIGHT = 20
if weight > MAX_WEIGHT or len(op.inputs) > 1 or PrimLib.iter_type(op) > PrimLib.BROADCAST:
return False, None, weight, False
# region grows successfully
weight = weight + 1
@ -486,7 +486,7 @@ class GraphSplitByPattern:
cheap_regions = []
for output in outputs:
# tensor should have user other than user_area to be fused
if len(output.to_ops) < 2:
if output.para_type != Tensor.PARA_OUTPUT and len(output.to_ops) < 2:
continue
region_ops = []
grow = True
@ -533,7 +533,14 @@ class GraphSplitByPattern:
"""find recompute regions and copy them out to new Areas"""
def do_recompute_fuse():
"""split the unfusing pattern by add recompute area"""
def recompute_cheap_region(dom):
recompute_suc = False
orig_areas = []
orig_areas.extend(self.areas)
for dom in orig_areas:
if dom not in self.areas or not dom.out_relations:
continue
cheap_regions = self.find_cheap_regions(dom)
dom_changed = False
for cheap_region in cheap_regions:
user_areas = self.select_user_area(cheap_region[-1].output)
if not user_areas:
@ -543,17 +550,12 @@ class GraphSplitByPattern:
self.pattern_fuse(self.fuse_recom)
self.clear_recompute()
if self.recom_res:
return True
return False
recompute_suc = False
orig_areas = []
orig_areas.extend(self.areas)
for dom in orig_areas:
if dom not in self.areas or not dom.out_relations:
continue
cheap_regions = self.find_cheap_regions(dom)
if recompute_cheap_region(dom):
recompute_suc = True
recompute_suc = True
# Copy region at most once for this dom
dom_changed = True
break
if dom_changed:
break
return recompute_suc
if self.enable_recompute:
@ -561,6 +563,9 @@ class GraphSplitByPattern:
self.pattern_fuse()
use_poly_reduce = True
class GraphSplitGpu(GraphSplitByPattern):
"""Graph splitter"""
BORADCAST_FUSE_DEPTH = 20
@ -611,7 +616,7 @@ class GraphSplitGpu(GraphSplitByPattern):
return fused, True
def _broadcast_pat_exclude(dom, a, r):
if a.pattern == PrimLib.REDUCE:
if use_poly_reduce and a.pattern == PrimLib.REDUCE:
return dom.pattern > PrimLib.ELEMWISE or r > PrimLib.ELEMWISE
return a.pattern > PrimLib.REDUCE or r > PrimLib.BROADCAST
@ -636,14 +641,34 @@ class GraphSplitGpu(GraphSplitByPattern):
fused.append(a)
return fused, False
def _check_reduce_exclude(dom):
if use_poly_reduce:
return False
# exclude large all-reduce
if len(dom.ops[0].inputs[0].shape) == len(dom.ops[0].attrs["reduce_axis"]) and \
dom.ops[0].inputs[0].get_size() > 10000:
return True
# exclude multi output
for a in dom.in_relations.keys():
if len(a.out_relations) > 1:
return True
if any([op.output.para_type == Tensor.PARA_OUTPUT for op in a.ops]):
return True
return False
def _reduce_pat_exclude(_, a, r):
if len(a.ops) > self.REDUCE_FUSE_DEPTH:
return True
return a.pattern > PrimLib.ELEMWISE or r > PrimLib.REDUCE or r == PrimLib.BROADCAST
if use_poly_reduce:
return a.pattern > PrimLib.ELEMWISE or r > PrimLib.REDUCE or r == PrimLib.BROADCAST
return a.pattern > PrimLib.BROADCAST or r > PrimLib.REDUCE
def _reduce_depth(dom):
if dom.pattern != PrimLib.REDUCE or len(dom.in_relations) != 1:
return None
if _check_reduce_exclude(dom):
return None
a, r = list(dom.in_relations.items())[0]
if dom.ops[0].inputs[0].dtype == "float16" and a.is_output and len(a.ops) >= 10 and \
_is_atomic_add_available(dom):
@ -656,6 +681,8 @@ class GraphSplitGpu(GraphSplitByPattern):
def _reduce_width(dom):
if dom.pattern != PrimLib.REDUCE:
return None
if _check_reduce_exclude(dom):
return None
fused = []
for a, r in dom.in_relations.items():
if dom.ops[0].inputs[0].dtype == "float16" and a.is_output and len(a.ops) >= 10 and \
@ -736,16 +763,16 @@ class GraphSplitGpu(GraphSplitByPattern):
def _may_stitch(dom, a, r):
if a.pattern <= PrimLib.REDUCE and r <= PrimLib.BROADCAST and dom.check_acyclic(a):
if _reduce_nums(a.ops) >= 2:
return False
dom_outs = [op.output for op in dom.ops]
a_ins = [op_input for op in a.ops for op_input in op.inputs]
a_outs = [op.output for op in a.ops]
a_final_outs = [tensor for tensor in a_outs if tensor not in a_ins]
stitch_tensors = [tensor for tensor in dom_outs if tensor in a_ins]
if not _same_stitch_axis(stitch_tensors, a_final_outs):
return False
return any([_tensor_size(tensor) >= 1024 * 1024 for tensor in stitch_tensors])
if _reduce_nums(a.ops) < 2:
dom_outs = [op.output for op in dom.ops]
a_ins = [op_input for op in a.ops for op_input in op.inputs]
a_outs = [op.output for op in a.ops]
a_final_outs = [tensor for tensor in a_outs if tensor not in a_ins]
stitch_tensors = [tensor for tensor in dom_outs if tensor in a_ins]
if _same_stitch_axis(stitch_tensors, a_final_outs):
for tensor in stitch_tensors:
if _tensor_size(tensor) >= 1024 * 1024:
return True
return False
def _reduce_stitch(dom):
@ -758,15 +785,14 @@ class GraphSplitGpu(GraphSplitByPattern):
fused = []
for a, r in dom.out_relations.items():
if not _may_stitch(dom, a, r):
continue
if a.pattern == PrimLib.REDUCE:
if a.ops[0].attrs['reduce_axis'] == dom.ops[0].attrs['reduce_axis']:
if _may_stitch(dom, a, r):
if a.pattern == PrimLib.REDUCE:
if a.ops[0].attrs['reduce_axis'] == dom.ops[0].attrs['reduce_axis']:
dom.stitch_info.stitch_ops.add(dom.ops[0].output.name)
fused.append(a)
elif a.pattern == PrimLib.BROADCAST:
dom.stitch_info.stitch_ops.add(dom.ops[0].output.name)
fused.append(a)
elif a.pattern == PrimLib.BROADCAST:
dom.stitch_info.stitch_ops.add(dom.ops[0].output.name)
fused.append(a)
return fused, False
def _transpose(dom):
@ -778,16 +804,6 @@ class GraphSplitGpu(GraphSplitByPattern):
fused.append(a)
return fused, True
def _strided_slice(dom):
if dom.dom_op().prim != "StridedSlice":
return None
fused = []
for a, _ in dom.in_relations.items():
if a.pattern <= PrimLib.BROADCAST and a.check_acyclic(dom) and \
len(a.out_relations) == 1 and not a.is_output:
fused.append(a)
return fused, True
def _fuse_loop():
changed = True
while changed:
@ -798,10 +814,10 @@ class GraphSplitGpu(GraphSplitByPattern):
changed = self.fuse(_reduce_width) or changed
changed = self.fuse(_broadcast_depth) or changed
changed = self.fuse(_broadcast_width) or changed
changed = self.fuse(_strided_slice) or changed
changed = self.fuse(_reduce_output) or changed
if enable_stitch_fusion:
changed = self.fuse(_reduce_stitch) or changed
if use_poly_reduce:
changed = self.fuse(_reduce_output) or changed
if enable_stitch_fusion:
changed = self.fuse(_reduce_stitch) or changed
self.fuse(_transpose)
def _fuse_once(fuse_func):
@ -809,8 +825,9 @@ class GraphSplitGpu(GraphSplitByPattern):
fuse_func(_reduce_depth) or fuse_func(_reduce_width) or fuse_func(_broadcast_depth) or \
fuse_func(_broadcast_width):
return
if fuse_func(_reduce_output) or (enable_stitch_fusion and fuse_func(_reduce_stitch)):
return
if use_poly_reduce:
if fuse_func(_reduce_output) or (enable_stitch_fusion and fuse_func(_reduce_stitch)):
return
fuse_func(_transpose)
return

16
mindspore/_extends/graph_kernel/model/model.py
View File

@ -216,7 +216,6 @@ class PrimLib:
'Transpose': Prim(OPAQUE),
'Tile': Prim(BROADCAST),
'BroadcastTo': Prim(BROADCAST),
'StridedSlice': Prim(OPAQUE),
'MatMul': Prim(OPAQUE),
'TransData': Prim(OPAQUE),
'BatchMatMul': Prim(OPAQUE),
@ -422,13 +421,14 @@ class Graph:
for t in op.inputs:
if t not in inputs and t.op not in self.ops:
inputs.append(t)
if op.output in outputs:
continue
if op.output.para_type == Tensor.PARA_OUTPUT or not op.output.to_ops:
outputs.append(op.output)
continue
if any([succ not in self.ops for succ in op.output.to_ops]):
outputs.append(op.output)
if op.output not in outputs:
if op.output.para_type == Tensor.PARA_OUTPUT or not op.output.to_ops:
outputs.append(op.output)
else:
for d in op.output.to_ops:
if d not in self.ops:
outputs.append(op.output)
break
if self.inputs:
inputs = self.inputs

129
mindspore/_extends/graph_kernel/model/model_builder.py
View File

@ -28,13 +28,11 @@ class GraphBuilder:
self.graph = Graph(name, [])
def set_input(self, *para):
"""set input to graph inputs"""
for t in para:
t.para_type = Tensor.PARA_INPUT
self.graph.inputs.append(t)
def set_output(self, *para):
"""set output to graph inputs"""
for t in para:
t.para_type = Tensor.PARA_OUTPUT
self.graph.outputs.append(t)
@ -52,8 +50,6 @@ class GraphBuilder:
def graph_scope(self, name):
"""The graph scope to be processed"""
class GraphScope:
"""Graph Scope"""
def __init__(self, gb):
self.gb = gb
@ -81,6 +77,7 @@ class GraphBuilder:
"""Create a new Value"""
if name in (None, ''):
name = self._alloc_tensor_name()
v = Value(name, dtype, value)
return v
@ -108,7 +105,6 @@ class GraphBuilder:
return output
def get(self):
"""Get graphs"""
return self.graphs
@ -127,14 +123,34 @@ class CompositeGraph:
def load(self, desc):
"""Load Graph from json"""
def _attr_of(op):
if not op['attr']:
return dict()
attr = {}
for a in op['attr']:
if a['name'] == 'axis' and op['name'] in ('ReduceSum', 'ReduceMax', 'ReduceMin'):
attr['reduce_axis'] = a['value']
def _attr_of(op, inputs, output):
def _get_axis_while_none(input_shape, output_shape):
red_axis = []
if len(output_shape) == len(input_shape):
for i, s in enumerate(output_shape):
if s == 1 and input_shape[i] > 1:
red_axis.append(i)
else:
red_axis = list(range(len(output_shape)))
return red_axis
attr = {}
if op['name'] in ('ReduceSum', 'ReduceMax', 'ReduceMin'):
for a in op['attr']:
if a['name'] == 'axis':
red_axis, dim_size = [], len(inputs[0].shape)
if not a['value']:
red_axis = _get_axis_while_none(inputs[0].shape, output.shape)
else:
if isinstance(a['value'], int):
a['value'] = [a['value']]
for i in a['value']:
red_axis.append(i if i >= 0 else dim_size + i)
attr['reduce_axis'] = red_axis
if a['name'] == "reduce_output_fuse":
attr['reduce_output_fuse'] = a['value']
elif op['attr']:
for a in op['attr']:
attr[a['name']] = a['value']
return attr
@ -150,6 +166,7 @@ class CompositeGraph:
'shape'], out_desc['data_type'], out_desc['format']
self.tensors[name] = builder.tensor(
shape, dtype, data_format, name=name, para_type=Tensor.PARA_OUTPUT)
cur_fusion = None
for op in desc['op_desc']:
inputs = [self.tensors[d['tensor_name']] for x in op['input_desc'] for d in x if 'value' not in d]
out_desc = op['output_desc']
@ -160,17 +177,25 @@ class CompositeGraph:
inputs[1].para_type = Tensor.PARA_OUTPUT
output = inputs[2]
self.tensors[name] = output
continue
output = self.tensors.get(name, None)
if not output:
output = builder.tensor(shape, dtype, data_format, name=name)
self.tensors[name] = output
builder.op(op['name'], output, inputs, attrs=_attr_of(op))
else:
output = self.tensors.get(name, None)
if not output:
output = builder.tensor(
shape, dtype, data_format, name=name)
self.tensors[name] = output
builder.op(op['name'], output, inputs,
attrs=_attr_of(op, inputs, output))
if 'fusion' in op:
if cur_fusion is None:
cur_fusion = output
else:
cur_fusion.add_buddy(output)
if op['fusion'].endswith('_end'):
cur_fusion = None
self.graph = builder.get()[0]
self.desc = desc
def add_stitch_info(self, subgraph, desc):
"""add stitch info to desc"""
if subgraph.stitch_info and subgraph.stitch_info.stitch_ops:
buffer_stitch = {'stitch_op': list(subgraph.stitch_info.stitch_ops)}
if subgraph.stitch_info.stitch_atomic_ops:
@ -179,7 +204,6 @@ class CompositeGraph:
return desc
def add_recompute_ops(self, subgraph, desc):
"""add recompute ops to desc"""
if subgraph.recompute_ops:
desc['recompute_ops'] = [op.output.name for op in subgraph.recompute_ops]
return desc
@ -203,40 +227,43 @@ class CompositeGraph:
inputs, outputs = subgraph.deduce_parameters()
graph_ops = set(subgraph.ops)
inplace_assign, inplace_assign_z = self._pre_dump(outputs)
def dump_output(t):
if t.name in inplace_assign:
z = inplace_assign_z if inplace_assign_z is not None else self.tensors[t.name]
return {'data_type': z.dtype, 'shape': z.shape, 'tensor_name': inplace_assign[t.name]}
return {'data_type': t.dtype, 'shape': t.shape, 'tensor_name': t.name}
def dump_op_desc(d):
if d['name'] == 'InplaceAssign':
y = d['input_desc'][1][0]['tensor_name']
if self.tensors[y].op in graph_ops:
z, fake = (inplace_assign_z, False) if inplace_assign_z is not None else (self.tensors[y], True)
inplace_desc = copy.deepcopy(d)
inplace_desc['attr'] = {'name': 'fake_output', 'value': fake}
z_desc, out_desc = inplace_desc['input_desc'][2][0], inplace_desc['output_desc'][0]
z_desc['shape'] = z.shape
z_desc['data_type'] = z.dtype
z_desc['tensor_name'] = z.name
out_desc['shape'] = z.shape
out_desc['data_type'] = z.dtype
return inplace_desc
op = self.tensors[d['output_desc'][0]['tensor_name']].op
if op in graph_ops or op in subgraph.recompute_ops:
return d
return None
for key in self.desc.keys():
for key in self.desc:
if key == 'input_desc':
desc[key] = [[{'data_type': t.dtype, 'shape': t.shape, 'tensor_name': t.name}] for t in inputs]
desc[key] = [
[{'data_type': t.dtype, 'shape': t.shape, 'tensor_name': t.name}] for t in inputs]
elif key == 'output_desc':
desc[key] = list(map(dump_output, outputs))
out_desc = []
for t in outputs:
if t.name in inplace_assign:
z = inplace_assign_z if inplace_assign_z is not None else self.tensors[t.name]
out_desc.append(
{'data_type': z.dtype, 'shape': z.shape, 'tensor_name': inplace_assign[t.name]})
else:
out_desc.append(
{'data_type': t.dtype, 'shape': t.shape, 'tensor_name': t.name})
desc[key] = out_desc
elif key == 'op_desc':
op_desc = map(dump_op_desc, self.desc[key])
desc[key] = [d for d in op_desc if d is not None]
op_desc = []
for d in self.desc[key]:
if d['name'] == 'InplaceAssign':
y = d['input_desc'][1][0]['tensor_name']
if self.tensors[y].op in graph_ops:
z, fake = (inplace_assign_z, False) if inplace_assign_z is not None else (
self.tensors[y], True)
inplace_desc = copy.deepcopy(d)
inplace_desc['attr'] = {'name': 'fake_output', 'value': fake}
z_desc, out_desc = inplace_desc['input_desc'][2][0], inplace_desc['output_desc'][0]
z_desc['shape'] = z.shape
z_desc['data_type'] = z.dtype
z_desc['tensor_name'] = z.name
out_desc['shape'] = z.shape
out_desc['data_type'] = z.dtype
op_desc.append(inplace_desc)
else:
op = self.tensors[d['output_desc'][0]['tensor_name']].op
if op in graph_ops or op in subgraph.recompute_ops:
op_desc.append(d)
desc[key] = op_desc
elif key == 'op':
desc[key] = subgraph.name
else:

67
mindspore/_extends/graph_kernel/model/op_infer.py
View File

@ -16,7 +16,7 @@
import copy
import sys
from functools import reduce as prod_reduce
from functools import reduce
from .model import GraphKernelUnsupportedException as GKException
from .model import PrimLib, DataFormat as DF
@ -101,24 +101,22 @@ class OpInfer:
class _Elemwise(OpInfer):
"""Common infer for elementwise operators"""
@staticmethod
def broadcast_shape(shapes):
def _broadcast_shape(self, shapes):
"""deduce broadcast shape using same rules as numpy"""
dim_size = max([len(shape) for shape in shapes])
align_shapes = [[1] * (dim_size - len(shape)) + shape for shape in shapes]
out_shape = [1] * dim_size
for i in range(dim_size):
for align_shape in align_shapes:
if align_shape[i] == 1:
continue
if out_shape[i] == 1:
out_shape[i] = align_shape[i]
elif out_shape[i] != align_shape[i]:
raise GKException("shape broadcast failed!")
if align_shape[i] > 1:
if out_shape[i] == 1:
out_shape[i] = align_shape[i]
if out_shape[i] != align_shape[i]:
raise GKException("shape broadcast failed!")
return out_shape
@staticmethod
def defaultformat_to_nz(default_shape):
def _to_nz(self, default_shape):
"""default format shape to fractal_Nz format shape"""
if len(default_shape) not in (1, 2):
raise GKException("shape is too long!")
@ -144,17 +142,17 @@ class _Elemwise(OpInfer):
"""returns the output shape with broadcast"""
# in case all inputs are default format/NHWC/NCHW
is_default = [op_input.data_format in (DF.DEFAULT, DF.NHWC, DF.NCHW) for op_input in self.inputs]
is_default = [input.data_format in (DF.DEFAULT, DF.NHWC, DF.NCHW) for input in self.inputs]
if all(is_default):
return self.broadcast_shape([op_input.shape for op_input in self.inputs])
return self._broadcast_shape([input.shape for input in self.inputs])
# in case formats are fractal_nz, default_fromat/NHWC/HCHW(optional)
is_default_frac_nz = [op_input.data_format in (DF.DEFAULT, DF.NHWC, DF.NCHW, DF.FRAC_NZ)
for op_input in self.inputs]
is_default_frac_nz = [input.data_format in (DF.DEFAULT, DF.NHWC, DF.NCHW, DF.FRAC_NZ)
for input in self.inputs]
if all(is_default_frac_nz):
nz_shapes = [self.defaultformat_to_nz(op_input.shape) if op_input.data_format != DF.FRAC_NZ
else op_input.shape for op_input in self.inputs]
return self.broadcast_shape(nz_shapes)
nz_shapes = [self._to_nz(input.shape) if input.data_format != DF.FRAC_NZ else input.shape
for input in self.inputs]
return self._broadcast_shape(nz_shapes)
raise GKException("Only support default and fractal_nz")
@ -216,11 +214,9 @@ class _Reshape(OpInfer):
class Reshape(_Reshape):
"""Reshape op infer"""
def _check_shape(self):
size_before_reshape = prod_reduce(lambda x, y: x * y, self.inputs[0].shape)
size_after_reshape = prod_reduce(lambda x, y: x * y, self.attrs["shape"])
size_before_reshape = reduce(lambda x, y: x * y, self.inputs[0].shape)
size_after_reshape = reduce(lambda x, y: x * y, self.attrs["shape"])
if size_before_reshape != size_after_reshape:
raise GKException("The shape product before and after reshaping should be equal")
@ -229,15 +225,11 @@ class Reshape(_Reshape):
class Cast(_Elemwise):
"""Cast op infer"""
def _infer_type(self):
return self.attrs["dst_type"]
class InplaceAssign(_Elemwise):
"""InplaceAssign op infer"""
def _infer_shape(self):
return self.inputs[2].shape
@ -249,8 +241,6 @@ class InplaceAssign(_Elemwise):
class BroadcastTo(OpInfer):
"""BroadcastTo op infer"""
def _infer_shape(self):
return self.attrs["shape"]
@ -266,8 +256,6 @@ class _CompareOp(_Elemwise):
class CImag(OpInfer):
"""CImag op infer"""
def _check_type(self):
if self.inputs[0].dtype != "complex64":
raise GKException(
@ -278,8 +266,6 @@ class CImag(OpInfer):
class CReal(OpInfer):
"""CReal op infer"""
def _check_type(self):
if self.inputs[0].dtype != "complex64":
raise GKException(
@ -290,8 +276,6 @@ class CReal(OpInfer):
class Complex(OpInfer):
"""Complex op infer"""
def _check_type(self):
if self.inputs[0].dtype != "float32":
raise GKException(
@ -304,28 +288,26 @@ class Complex(OpInfer):
class Less(_CompareOp):
"""Less op infer"""
pass
class LessEqual(_CompareOp):
"""LessEqual op infer"""
pass
class Equal(_CompareOp):
"""Equal op infer"""
pass
class Greater(_CompareOp):
"""Greater op infer"""
pass
class GreaterEqual(_CompareOp):
"""GreaterEqual op infer"""
pass
class Select(_Elemwise):
"""Select op infer"""
def _check_type(self):
if self.inputs[0].dtype != "bool":
raise GKException("Select's input[0] should be a bool condition but got {}".format(self.inputs[0].dtype))
@ -337,7 +319,6 @@ class Select(_Elemwise):
def check_format_any(formats, checked_format):
"""Check whether input format in formats list"""
if not isinstance(formats, (list, tuple)):
raise GKException("formats {} should be list or tuple, but got {}.".format(formats, type(formats)))
if checked_format not in formats:
@ -345,13 +326,11 @@ def check_format_any(formats, checked_format):
def check_nd(data, nd):
"""Check whether data are nd format"""
if not isinstance(data, (list, tuple)) or len(data) != nd:
raise GKException("input should be {}D list or tuple, but got {}.".format(nd, data))
def conv_had_pad(pad_list, pad_mode):
"""Check whether conv need to add pad"""
if not isinstance(pad_list, (list, tuple)) or len(pad_list) != 4:
raise GKException("pad_list should be 4D list or tuple, but got {}".format(pad_list))
if pad_list[0] != pad_list[1] or pad_list[2] != pad_list[3]:

4
mindspore/_extends/graph_kernel/parallel_estimate.py
View File

@ -21,7 +21,7 @@ from . import model
def estimate_ops(json_str: str):
"""Call cost model to estimate ops."""
"""Call costmodel to estimate ops."""
try:
json_obj = json.loads(json_str)
graph_descs = json_obj["graph_desc"]
@ -38,7 +38,7 @@ def estimate_ops(json_str: str):
def estimate_calulation_amount(json_str: str):
"""Call cost model to estimate calculation amount of op."""
"""Call costmodel to estimate calculation amount of op."""
try:
graph_desc = json.loads(json_str)
comp = model.load_composite(graph_desc)

6
mindspore/_extends/graph_kernel/splitter.py
View File

@ -24,7 +24,7 @@ from . import utils
def split_with_json(json_str, flags_str):
"""Call cost model to split GraphKernel"""
"""Call costmodel to split GraphKernel"""
try:
graph_desc = json.loads(json_str)
flags = json.loads(flags_str)
@ -57,11 +57,11 @@ def _dump_split_info(flags, graph_json, graph_desc, subgraphs, graph_mode):
return
utils.create_dir(utils.GRAPH_KERNEL_DUMP_PATH)
filename = os.path.join(utils.GRAPH_KERNEL_DUMP_PATH, "graph_kernel_split_mode.txt")
with os.fdopen(os.open(filename, os.O_WRONLY | os.O_CREAT), "a+") as f:
with open(filename, "a+") as f:
f.write("********** main graph: {} **********\n".format(graph_desc.name))
f.write("input json:\n{}\n".format(graph_json))
f.write("graph desc:\n{}\n".format(str(graph_desc)))
if len(subgraphs) > 1 or subgraphs[0].stitch_info.has_stitch_op():
if len(subgraphs) > 1:
for i, g in enumerate(subgraphs):
f.write("-------- subgraph {}, mode: {} --------\n".format(i, graph_mode[i]))
f.write("{}\n".format(str(g)))

2
mindspore/_extends/graph_kernel/utils.py
View File

@ -26,5 +26,3 @@ def create_dir(pathname):
os.mkdir(pathname)
except OSError:
pass
finally:
pass

5
mindspore/_extends/parallel_compile/akg_compiler/akg_process.py
View File

@ -50,6 +50,11 @@ def _compile_akg_task_gpu(json_strs, attrs):
if not res:
raise ValueError("Compile error, args: {}! build attrs: {}".format(json_str, attrs))
pid_path = os.path.realpath("./cuda_meta_" + str(os.getpid()))
if os.path.exists(pid_path):
copy_json(pid_path, os.path.realpath("./cuda_meta_" + str(os.getppid())))
shutil.rmtree(pid_path)
def _compile_akg_task_ascend(json_strs, attrs):
"""

26
mindspore/_extends/parallel_compile/tbe_compiler/tbe_adapter.py
View File

@ -32,7 +32,7 @@ from te_fusion.parallel_compilation import init_multi_process_env, start_ga_mult
get_finished_compilation_task
from .tbe_helper import get_soc_info, assemble_op_args, get_compute_op_list, get_options_info, get_fuzz_build_info, \
BuildType, adjust_custom_op_info, pack_op_args, get_module_name
BuildType, adjust_custom_op_info, pack_op_args
from .tbe_job import TbeJob, JobStatus
PLATFORM_FLAG = ["Ascend310", "Ascend910", "Hi3796CV300ES", "Ascend710", "Ascend610", "Hi3796CV300CS", "SD3403"]
@ -242,7 +242,7 @@ def check_support(job: TbeJob):
op_func_name = compute_op_info["func_name"]
if op_func_name in ("resize_nearest_neighbor_v2_grad_d", "resize_bilinear_v2_grad"):
attrs.pop(-2)
op_module_name = get_module_name(compute_op_info)
op_module_name = compute_op_info["module_name"]
py_module_path = compute_op_info["py_module_path"]
_normalize_module_name(op_module_name, py_module_path)
func_name = "check_supported"
@ -281,7 +281,7 @@ def select_op_format(job: TbeJob):
compute_op_info = compute_op_info_list[0]
adjust_custom_op_info(compute_op_info)
inputs, outputs, attrs = assemble_op_args(compute_op_info)
op_module_name = get_module_name(compute_op_info)
op_module_name = compute_op_info["module_name"]
py_module_path = compute_op_info["py_module_path"]
_normalize_module_name(op_module_name, py_module_path)
op_func_name = "op_select_format"
@ -317,7 +317,7 @@ def _pre_build_compute_op_info(compute_op, job):
if l1_size != -1:
set_L1_info("op_L1_space", -1)
inputs, outputs, attrs = assemble_op_args(compute_op)
op_module_name = get_module_name(compute_op)
op_module_name = compute_op["module_name"]
py_module_path = compute_op["py_module_path"]
op_func_name = compute_op["func_name"]
op_type = compute_op["type"]
@ -340,8 +340,8 @@ def _pre_build_compute_op_info(compute_op, job):
job.info("OpType {} support op_impl_mode, current op_impl_mode:{}".format(op_type, op_impl_mode))
options = get_options_info(job.content)
dispatch_prebuild_task(job.source_id, job.id, l1_size, op_module_name, op_type, op_func_name, unknown_shape,
(inputs, outputs, attrs, options), int64_mode, dynamic_compile_static, unknown_shape,
job.rl_tune_switch, job.rl_tune_list, job.pass_list, job.op_tune_switch, job.op_tune_list)
(inputs, outputs, attrs, options), int64_mode, dynamic_compile_static, job.rl_tune_switch,
job.rl_tune_list, job.pass_list, job.op_tune_switch, job.op_tune_list)
def get_prebuild_output(op_name):
@ -391,7 +391,7 @@ def build_single_pre_op(job: TbeJob):
inputs, outputs, attrs = assemble_op_args(compute_op_info)
op_type = compute_op_info["type"]
l1_size = job.content["l1_size"]
op_module_name = get_module_name(compute_op_info)
op_module_name = compute_op_info["module_name"]
op_kernel_name = compute_op_info["op_name"]
py_module_path = compute_op_info["py_module_path"]
op_func_name = compute_op_info["func_name"]
@ -404,9 +404,9 @@ def build_single_pre_op(job: TbeJob):
fuzz_build_info = get_fuzz_build_info(job.content)
dispatch_single_op_compile_task(job.source_id, job.id, l1_size, op_module_name, op_type, op_func_name,
op_kernel_name, unknown_shape, (inputs, outputs, attrs, options), int64_mode,
None, None, dynamic_compile_static, unknown_shape, op_pattern,
json.dumps(fuzz_build_info), job.rl_tune_switch, job.rl_tune_list, job.pass_list,
job.op_tune_switch, job.op_tune_list)
None, None, dynamic_compile_static, op_pattern, json.dumps(fuzz_build_info),
job.rl_tune_switch, job.rl_tune_list, job.pass_list, job.op_tune_switch,
job.op_tune_list)
return True
@ -487,7 +487,7 @@ def rl_tune_single_op(job: TbeJob):
inputs, outputs, attrs = assemble_op_args(compute_op_info)
op_type = compute_op_info["type"]
l1_size = job.content["l1_size"]
op_module_name = get_module_name(compute_op_info)
op_module_name = compute_op_info["module_name"]
op_kernel_name = compute_op_info["op_name"]
full_name = compute_op_info["name"]
py_module_path = compute_op_info["py_module_path"]
@ -503,7 +503,7 @@ def rl_tune_single_op(job: TbeJob):
device_id = job.content["SocInfo"]["deviceId"]
try:
build_single_op_from_c(op_module_name, op_func_name, op_type, "build", unknown_shape,
(inputs, outputs, attrs), int64_mode, dynamic_compile_static, unknown_shape, op_pattern,
(inputs, outputs, attrs), int64_mode, dynamic_compile_static, op_pattern,
auto_tiling_mode, device_id, json.dumps(fuzz_build_info))
# pylint: disable=broad-except
except Exception:
@ -547,7 +547,7 @@ def rl_tune_fusion_op(job: TbeJob):
compute_op_list = get_compute_op_list(job.content)
op_module_names_str = ""
for op in compute_op_list:
op_module_names_str = op_module_names_str + "," + get_module_name(op)
op_module_names_str = op_module_names_str + "," + op["module_name"]
op_module_names_str = op_module_names_str[1:]
from schedule_search.rl_online_tune import dispatch_fusion_tune_task
res = dispatch_fusion_tune_task(job.source_id, job.id, l1_size, base_kernel, op_kernel_name, op_module_names_str,

15
mindspore/_extends/parallel_compile/tbe_compiler/tbe_helper.py
View File

@ -179,6 +179,8 @@ def get_options_info(job_content):
options["op_debug_level"] = job_content["SocInfo"]["op_debug_level"]
options["op_impl_mode"] = job_content["SocInfo"]["op_impl_mode"]
options["op_debug_dir"] = job_content["SocInfo"]["op_debug_dir"]
options["op_compiler_cache_dir"] = job_content["SocInfo"]["op_compiler_cache_dir"]
options["op_compiler_cache_mode"] = job_content["SocInfo"]["op_compiler_cache_mode"]
options["mdl_bank_path"] = job_content["SocInfo"]["op_debug_level"]
options["op_bank_path"] = job_content["SocInfo"]["op_bank_path"]
options["deviceId"] = job_content["SocInfo"]["deviceId"]
@ -218,19 +220,6 @@ def get_func_names(job_content):
return func_names
def get_module_name(compute_op_info):
"""
get compute_op_info
:param compute_op_info:
:return:
"""
unknown_shape = compute_op_info["unknown_shape"]
op_module_name = compute_op_info["module_name"]
if unknown_shape:
op_module_name = op_module_name.split(".")[0] + ".dynamic." + op_module_name.split(".")[-1]
return op_module_name
def adjust_custom_op_info(compute_op_info):
"""
adjust custom op info

5
mindspore/_extends/parallel_compile/tbe_compiler/tbe_job.py
View File

@ -71,13 +71,12 @@ def _get_message(msg, args):
class TbeJob:
""" Tbe compilation job """
def __init__(self, source_id, job_id, job_type, content, fusion_op_name, json_str, sys_info):
def __init__(self, source_id, job_id, job_type, content, json_str, sys_info):
self.source_id = source_id
self.id = job_id
self.type = JobType(job_type)
self.status = JobStatus.JOB_INITIAL
self.content = content
self.fusion_op_name = fusion_op_name
self.result = ""
self.process_info = []
self.json_string = json_str
@ -150,8 +149,8 @@ class TbeJob:
result["source_id"] = self.source_id
result["job_id"] = self.id
result["job_type"] = self.type.value
result["fusion_op_name"] = self.fusion_op_name
result["result"] = self.result
self.debug("Resp result:{}".format(json.dumps(result)))
process_info = []
for info in self.process_info:
msg = {"index": info.index, "level": info.level.value, "message": info.info}

9
mindspore/_extends/parallel_compile/tbe_compiler/tbe_job_manager.py
View File

@ -102,9 +102,8 @@ class TbeJobManager:
source_id = job_json["source_id"]
job_type = job_json["job_type"]
sys_info = self._get_job_sys_info()
fusion_op_name = "NA" if "fusion_op_name" not in job_json["job_content"] else job_json["job_content"][
"fusion_op_name"]
job = TbeJob(source_id, job_id, job_type, job_json["job_content"], fusion_op_name, job_str, sys_info)
job = TbeJob(source_id, job_id, job_type, job_json["job_content"], job_str, sys_info)
job.debug("Req job string: {}".format(job_str))
post_job(self._all_jobs, job)
if not self.tbe_initialize and job.type != JobType.INITIALIZE_JOB:
job.error(
@ -116,7 +115,6 @@ class TbeJobManager:
return res
# pylint: disable=broad-except
except Exception:
# pylint: disable=no-value-for-parameter
sys_info = self._get_job_sys_info()
job = TbeJob(-1, -1, "", None, job_str, sys_info) if job is None else job
job.status = JobStatus.JOB_FAILED
@ -263,6 +261,9 @@ class TbeJobManager:
return self.add_to_finished_jobs(query_job, JobStatus.JOB_SUCCESS)
target_job = get_job(self._running_jobs, target_source_id, target_job_id)
if target_job:
query_job.debug("Found job in Running jobs, source_id:{}, job_id:{}".format(target_source_id,
target_job_id))
target_job.debug("Be Queried")
query_job.result = target_job.get_result()
return self.add_to_finished_jobs(query_job, JobStatus.JOB_SUCCESS)
target_job = get_job(self._all_jobs, target_source_id, target_job_id)

7
mindspore/_extends/parse/parser.py
View File

@ -159,17 +159,12 @@ def resolve_symbol(namespace, symbol):
if getattr(resolve_, "__hash__") is None:
return resolve_
# Raise NotImplementedError when parsing the numpy methods, but not the numpy constant.
if namespace.name == "numpy" and isinstance(resolve_, (types.FunctionType, types.MethodType, types.ModuleType)):
raise NotImplementedError(
f"MindSpore does not support to use the numpy methods in the function construct with the graph mode.")
# If need trope the obj
if resolve_ in convert_object_map:
resolve_ = convert_object_map.get(resolve_)
logger.debug("convert resolve = %r", resolve_)
if resolve_ == NO_IMPLEMENT:
raise NotImplementedError(f"Not support for `{symbol}`.")
raise NotImplementedError(f"Not support for `{symbol}`")
except Exception as e:
if isinstance(e, NotImplementedError):
raise e

12
mindspore/_extends/parse/standard_method.py
View File

@ -1312,8 +1312,7 @@ def sum(x, axis=None, dtype=None, keepdims=False, initial=None): # pylint: disab
>>> print(input_x.sum(axis=1))
[10. 35.]
"""
input_x = x.astype(mstype.int32) if x.dtype == mstype.bool_ else x
dtype = input_x.dtype if dtype is None else dtype
dtype = x.dtype if dtype is None else dtype
if not isinstance(keepdims, int):
const_utils.raise_type_error("integer argument expected")
if initial is not None and not isinstance(initial, (int, float, bool)):
@ -1323,14 +1322,14 @@ def sum(x, axis=None, dtype=None, keepdims=False, initial=None): # pylint: disab
else:
axis = check_and_canonicalize_axes(axis, x.ndim)
if not check_type_support(input_x.dtype, 'GPU', (mstype.float64, mstype.float32, mstype.float16)):
input_x = input_x.astype(mstype.float32)
if x.dtype == mstype.bool_:
x = x.astype("int32")
if 0 in x.shape:
x = const_utils.make_tensor([0], x.dtype)
if keepdims:
res = _reduce_sum_keepdims(input_x, axis)
res = _reduce_sum_keepdims(x, axis)
else:
res = _reduce_sum_default(input_x, axis)
res = _reduce_sum_default(x, axis)
if initial is not None:
res += initial
return res.astype(dtype)
@ -1649,7 +1648,6 @@ get_log2_size = constexpr(validator.get_log2_size)
check_axis_type = constexpr(validator.check_axis_type)
check_and_canonicalize_axes = constexpr(validator.check_and_canonicalize_axes)
empty_compile = constexpr(validator.empty_compile)
check_type_support = constexpr(validator.check_type_support)
def tensor_bool(x):

18
mindspore/_extends/remote/kernel_build_server.py
View File

@ -16,6 +16,7 @@
import os
from mindspore import log as logger
from mindspore._extends.parallel_compile.akg_compiler.akg_process import create_akg_parallel_process
from mindspore._extends.parallel_compile.akg_compiler.compiler import run_compiler as akg_compile_single
class Messager:
@ -145,7 +146,9 @@ class AkgBuilder():
def handle(self, messager, arg):
"""Handle message about akg"""
if arg == 'AKG/START':
if arg == 'AKG/PID':
messager.send_res(os.getpid())
elif arg == 'AKG/START':
messager.send_ack()
process_num_str = messager.get_message()
messager.send_ack()
@ -170,8 +173,17 @@ class AkgBuilder():
else:
messager.send_ack(False)
break
else:
raise RuntimeError("Unknown message type: %s" % arg)
elif arg == 'AKG/COMPILE':
messager.send_ack()
json = messager.get_message()
try:
akg_compile_single(json, self.attrs)
except ValueError:
messager.send_ack(False)
messager.exit()
finally:
pass
messager.send_ack()
def get_logger():

15
mindspore/ccsrc/CMakeLists.txt
View File

@ -297,14 +297,20 @@ if(MODE_ASCEND_ALL)
${ASCEND_DRIVER_BACK_PATH})
find_library(DATATRANSFER datatransfer HINTS ${ASCEND_RUNTIME_PATH} ${ASCEND_TOOLKIT_RUNTIME_PATH}
${ASCEND_DRIVER_BACK_PATH})
find_library(PROFILING msprofiler ${ASCEND_RUNTIME_PATH} ${ASCEND_TOOLKIT_RUNTIME_PATH})
find_library(PROFILING msprofiler_fwkacl ${ASCEND_RUNTIME_PATH} ${ASCEND_TOOLKIT_RUNTIME_PATH})
find_library(ACL ascendcl ${ASCEND_RUNTIME_PATH} ${ASCEND_TOOLKIT_RUNTIME_PATH})
find_library(PLATFORM platform ${ASCEND_RUNTIME_PATH} ${ASCEND_TOOLKIT_RUNTIME_PATH})
find_library(OPTILING optiling ${ASCEND_OPP_PATH} ${ASCEND_TOOLKIT_OPP_PATH})
find_library(OPT_FEATURE opt_feature ${ASCEND_RUNTIME_PATH} ${ASCEND_TOOLKIT_RUNTIME_PATH})
add_library(ms_profile SHARED
${CMAKE_CURRENT_SOURCE_DIR}/runtime/device/ascend/profiling/profiling_callback_register.cc)
set_target_properties(ms_profile PROPERTIES LINKER_LANGUAGE CXX)
target_link_options(ms_profile PRIVATE -Wl,-init,common_log_init)
target_link_libraries(ms_profile -Wl,--start-group -Wl,--whole-archive ${PROFILING} -Wl,--no-whole-archive
mindspore::protobuf -Wl,--end-group)
target_link_libraries(mindspore ${RUNTIME_LIB} ${TSDCLIENT} ${DATATRANSFER} ${ERROR_MANAGER} -Wl,--no-as-needed
${OPTILING} ${PLATFORM} ${ACL} ${OPT_FEATURE} ${PROFILING})
${OPTILING} ${PLATFORM} ${ACL} ${OPT_FEATURE})
target_link_libraries(mindspore -Wl,--start-group proto_input mindspore::protobuf -Wl,--end-group)
elseif(CMAKE_SYSTEM_NAME MATCHES "Windows")
target_link_libraries(mindspore -Wl,--start-group proto_input mindspore::protobuf mindspore::sentencepiece
@ -319,7 +325,7 @@ endif()
set(CMAKE_BUILD_WITH_INSTALL_RPATH TRUE)
set_property(SOURCE "pipeline/jit/init.cc" PROPERTY
COMPILE_DEFINITIONS SUBMODULE_ID=mindspore::SubModuleId::SM_PIPELINE)
pybind11_add_module(_c_expression NO_EXTRAS "pipeline/jit/init.cc" NO_EXTRAS)
pybind11_add_module(_c_expression "pipeline/jit/init.cc")
MESSAGE(STATUS "operation system is ${CMAKE_SYSTEM}")
if(CMAKE_SYSTEM_NAME MATCHES "Linux")
@ -369,6 +375,9 @@ else()
proto_input -Wl,--no-whole-archive)
target_link_libraries(_c_expression PRIVATE mindspore::pybind11_module)
target_link_libraries(_c_expression PRIVATE mindspore_gvar)
if(MODE_ASCEND_ALL)
target_link_libraries(_c_expression PRIVATE -Wl,--no-as-needed ms_profile)
endif()
endif()
if(USE_GLOG)

3
mindspore/ccsrc/backend/kernel_compiler/CMakeLists.txt
View File

@ -35,8 +35,6 @@ if(ENABLE_CPU)
"cpu/fl/*.cc"
"cpu/ps/*.cc"
"cpu/quantum/*.cc"
"cpu/pyfunc/*.cc"
"cpu/rl/*.cc"
)
if(NOT ENABLE_MPI)
@ -85,7 +83,6 @@ if(NOT ENABLE_CPU OR WIN32)
list(REMOVE_ITEM CPU_SRC_LIST "cpu/fl/get_model_kernel.cc")
list(REMOVE_ITEM CPU_SRC_LIST "cpu/fl/start_fl_job_kernel.cc")
list(REMOVE_ITEM CPU_SRC_LIST "cpu/fl/update_model_kernel.cc")
list(REMOVE_ITEM CPU_SRC_LIST "cpu/fl/push_metrics_kernel.cc")
endif()
if(ENABLE_GPU)

430
mindspore/ccsrc/backend/kernel_compiler/akg/akg_kernel_build.cc
View File

@ -16,11 +16,6 @@
#include "backend/kernel_compiler/akg/akg_kernel_build.h"
#include <stdio.h>
#include <errno.h>
#include <fcntl.h>
#include <unistd.h>
#include <algorithm>
#include <map>
#include <memory>
@ -28,7 +23,6 @@
#include <unordered_set>
#include <utility>
#include <vector>
#include <iostream>
#include "nlohmann/json.hpp"
#include "ir/dtype.h"
#include "ir/func_graph.h"
@ -40,346 +34,17 @@
namespace mindspore {
namespace kernel {
#define INIT_SET_FROM_2D_ARRAY(set_var, list_idx) \
std::set<size_t> set_var(kernel_lists_[list_idx], kernel_lists_[list_idx] + kernel_lists_[list_idx][kMaxKernelNum_]);
#define LIST_BEGIN(list_idx) kernel_lists_[list_idx]
#define LIST_END(list_idx) (kernel_lists_[list_idx] + kernel_lists_[list_idx][kMaxKernelNum_])
#define RESET_LIST_SIZE(list_idx, val) kernel_lists_[list_idx][kMaxKernelNum_] = val
#define INCREASE_LIST_SIZE(list_idx, val) kernel_lists_[list_idx][kMaxKernelNum_] += val
constexpr int32_t PROCESS_NUM = 16;
constexpr int32_t TIME_OUT = 300;
bool AkgKernelPool::LockMng::TryLock() {
// Try to lock 100 times. Return errno if lock unsuccessfully
uint32_t trial = 100;
int32_t ret = -1;
while (trial > 0) {
ret = lockf(fd_, F_TLOCK, 0);
if (ret == 0 || (errno != EACCES && errno != EAGAIN)) {
break;
}
trial--;
usleep(5000);
}
if (ret == -1) {
MS_LOG(ERROR) << "Failed to acquire the lock, errno:" << strerror(errno) << ".";
return false;
}
return true;
}
void AkgKernelPool::LockMng::Unlock() {
auto ret = lockf(fd_, F_ULOCK, 0);
if (ret == -1) {
MS_LOG(ERROR) << "Failed to release the lock, errno:" << strerror(errno);
}
}
std::string AkgKernelPool::GetCurrentPath() {
char cwd[PATH_MAX];
char *ret = getcwd(cwd, sizeof(cwd));
if (ret == nullptr) {
MS_LOG(ERROR) << "Get current work directory failed, errno:" << strerror(errno);
return "";
}
char abspath[PATH_MAX];
char *res = realpath(cwd, abspath);
if (res == nullptr) {
MS_LOG(ERROR) << "Change to realpath failed, errno:" << strerror(errno);
return "";
}
return std::string(abspath);
}
void *AkgKernelPool::CreateSharedMem(const std::string &path) {
is_creator_ = false;
auto hash_id = std::hash<std::string>()(path);
auto key_id = static_cast<key_t>(hash_id);
auto mem_size = sizeof(size_t) * kListNum_ * (kMaxKernelNum_ + 1) + 512;
{
LockMng lock(fd_);
if (!lock.locked_) {
MS_LOG(ERROR) << "Failed to acquire lock.";
return nullptr;
}
// check if the shared memory exists or not.
// remove shared memory if exists and the nattach is 0
struct shmid_ds buf;
auto id = shmget(key_id, mem_size, 0);
if (id != -1) {
auto ret = shmctl(id, IPC_STAT, &buf);
if (ret == -1) {
MS_LOG(ERROR) << "Failed to get the info of shared memory, errno:" << strerror(errno);
return nullptr;
}
if (buf.shm_nattch == 0) {
ret = shmctl(id, IPC_RMID, nullptr);
if (ret < 0) {
MS_LOG(EXCEPTION) << "Realse shared_mem failed, errno:" << strerror(errno);
}
}
}
}
LockMng lock(fd_);
if (!lock.locked_) {
MS_LOG(ERROR) << "Failed to acquire lock.";
return nullptr;
}
shm_id_ = shmget(key_id, mem_size, IPC_CREAT | IPC_EXCL | 0600);
if (shm_id_ == -1) {
if (errno == EEXIST) {
shm_id_ = shmget(key_id, mem_size, 0);
}
if (shm_id_ == -1) {
MS_LOG(ERROR) << "Create shared_mem failed, error no:" << strerror(errno);
return nullptr;
}
} else {
is_creator_ = true;
}
auto local_addr = shmat(shm_id_, nullptr, 0);
if (local_addr == reinterpret_cast<void *>(-1)) {
MS_LOG(ERROR) << "Attach to shared_mem failed, error no:" << strerror(errno);
return nullptr;
}
if (is_creator_) {
(void)memset(local_addr, 0, mem_size);
}
return local_addr;
}
int32_t AkgKernelPool::Init(const std::vector<JsonNodePair> &build_args) {
auto cp = GetCurrentPath();
if (cp.empty()) {
return -1;
}
fd_ = open(kKeyName_, O_CREAT | O_RDWR, S_IRUSR | S_IWUSR);
if (fd_ == -1) {
MS_LOG(ERROR) << "open file <" << kKeyName_ << "> failed, errno:" << strerror(errno);
return -1;
}
auto addr = CreateSharedMem(cp);
if (addr == nullptr) {
return -1;
}
InitKernelLists(addr);
auto ret = AddKernels(build_args);
if (ret != 0) {
MS_LOG(ERROR) << "AkgKernelPool AddKernels failed.";
return false;
}
return 0;
}
AkgKernelPool::~AkgKernelPool() {
{
LockMng lock(fd_);
if (!lock.locked_) {
MS_LOG(EXCEPTION) << "Failed to acquire lock.";
}
struct shmid_ds buf;
auto ret = shmctl(shm_id_, IPC_STAT, &buf);
if (ret == -1) {
MS_LOG(EXCEPTION) << "Failed to get the info of shared memory, errno:" << strerror(errno);
}
bool need_delete_by_last = false;
// if the creator exits unexpectedly and fails to delete the shm, the last process will try to delete the shm
if (((buf.shm_perm.mode & SHM_DEST) == 0) && (buf.shm_nattch == 1)) {
need_delete_by_last = true;
}
// Detach shared memory
ret = shmdt(reinterpret_cast<void *>(kernel_lists_[0]));
if (ret < 0) {
MS_LOG(EXCEPTION) << "Shared_mem detach failed, errno:" << strerror(errno);
}
// Realse shared_memroy
if (is_creator_ || need_delete_by_last) {
ret = shmctl(shm_id_, IPC_RMID, nullptr);
if (ret < 0) {
MS_LOG(EXCEPTION) << "Realse shared_mem failed, errno:" << strerror(errno);
}
}
}
// Close key file
if (fd_ != -1) {
(void)close(fd_);
}
}
int32_t AkgKernelPool::AddKernels(const std::vector<JsonNodePair> &build_args) {
LockMng lock(fd_);
if (!lock.locked_) {
MS_LOG(ERROR) << "Failed to acquire lock.";
return -1;
}
INIT_SET_FROM_2D_ARRAY(todo_list, kToDoIdx_);
INIT_SET_FROM_2D_ARRAY(doing_list, kDoingIdx_);
INIT_SET_FROM_2D_ARRAY(done_list, kDoneIdx_);
for (const auto &[json_generator, anf_node] : build_args) {
MS_EXCEPTION_IF_NULL(anf_node);
auto kernel_name = json_generator.kernel_name();
auto hash_id = std::hash<std::string>()(kernel_name);
if (self_kernel_ids_.count(hash_id) != 0) {
MS_LOG(ERROR) << "Duplicated hash_id in list.";
return -1;
}
self_kernel_ids_.emplace(hash_id);
}
std::set<size_t> diff_from_todo;
std::set<size_t> diff_from_doing;
std::set<size_t> diff_from_done;
// add the unique kernel only once, so need to check if it exists in todo_list, doing_list, or done_list
std::set_difference(self_kernel_ids_.begin(), self_kernel_ids_.end(), todo_list.begin(), todo_list.end(),
std::inserter(diff_from_todo, diff_from_todo.begin()));
std::set_difference(diff_from_todo.begin(), diff_from_todo.end(), doing_list.begin(), doing_list.end(),
std::inserter(diff_from_doing, diff_from_doing.begin()));
std::set_difference(diff_from_doing.begin(), diff_from_doing.end(), done_list.begin(), done_list.end(),
std::inserter(diff_from_done, diff_from_done.begin()));
auto new_kernel_size = diff_from_done.size();
if (new_kernel_size + todo_list.size() > static_cast<size_t>(kMaxKernelNum_)) {
MS_LOG(ERROR) << "The size of kernels is " << new_kernel_size << ", while the left space of the pool is "
<< kMaxKernelNum_ - todo_list.size();
return -1;
}
std::copy(diff_from_done.begin(), diff_from_done.end(), LIST_END(kToDoIdx_));
INCREASE_LIST_SIZE(kToDoIdx_, new_kernel_size);
return 0;
}
int32_t AkgKernelPool::FetchKernels(std::set<size_t> *out) {
LockMng lock(fd_);
if (!lock.locked_) {
MS_LOG(ERROR) << "Failed to acquire lock.";
return -1;
}
std::set<size_t> left_in_todo_list;
// filter out kernels which belongs to other processes
auto FilterBySelfList = [&left_in_todo_list, &out, this](size_t id) {
if (this->self_kernel_ids_.count(id) != 0) {
out->emplace(id);
} else {
left_in_todo_list.emplace(id);
}
};
std::for_each(LIST_BEGIN(kToDoIdx_), LIST_END(kToDoIdx_), FilterBySelfList);
std::copy(out->begin(), out->end(), LIST_END(kDoingIdx_));
INCREASE_LIST_SIZE(kDoingIdx_, out->size());
std::copy(left_in_todo_list.begin(), left_in_todo_list.end(), LIST_BEGIN(kToDoIdx_));
RESET_LIST_SIZE(kToDoIdx_, left_in_todo_list.size());
return 0;
}
int32_t AkgKernelPool::UpdateAndWait(const std::set<size_t> &ids) {
if (!ids.empty()) {
LockMng lock(fd_);
if (!lock.locked_) {
MS_LOG(ERROR) << "Failed to acquire lock.";
return -1;
}
// update the state of finished kernels to `done`
std::copy(ids.begin(), ids.end(), LIST_END(kDoneIdx_));
INCREASE_LIST_SIZE(kDoneIdx_, ids.size());
// delete the finished kernels from doing_list
std::vector<size_t> left_in_doing_list;
INIT_SET_FROM_2D_ARRAY(doing_list, kDoingIdx_);
std::set_difference(doing_list.begin(), doing_list.end(), ids.begin(), ids.end(),
std::inserter(left_in_doing_list, left_in_doing_list.begin()));
std::copy(left_in_doing_list.begin(), left_in_doing_list.end(), LIST_BEGIN(kDoingIdx_));
RESET_LIST_SIZE(kDoingIdx_, left_in_doing_list.size());
}
auto ret = Wait();
if (ret != 0) {
MS_LOG(ERROR) << "AkgKernelPool Wait failed.";
return -1;
}
return 0;
}
int32_t AkgKernelPool::Wait() {
// wait until all the kernels which belong to this process finish compiling
uint32_t trials = 1000;
while (trials > 0) {
{
LockMng lock(fd_);
if (!lock.locked_) {
MS_LOG(ERROR) << "Failed to acquire lock.";
return -1;
}
INIT_SET_FROM_2D_ARRAY(done_list, kDoneIdx_);
if (std::all_of(self_kernel_ids_.begin(), self_kernel_ids_.end(),
[&done_list](size_t id) { return done_list.count(id) != 0; })) {
return 0;
}
}
usleep(1000000);
trials--;
}
MS_LOG(ERROR) << "Time out while wait kernel compiling";
return -1;
}
std::vector<JsonNodePair> AkgKernelBuilder::GetNotCachedKernels(const std::vector<JsonNodePair> &build_args) {
std::vector<std::string> AkgKernelBuilder::GetNotCachedKernelJsons(const std::vector<JsonNodePair> &build_args) {
// Remove cached nodes, gether unique nodes, and collect repeated nodes which need postprecess.
std::vector<std::string> jsons;
std::unordered_set<std::string> kernel_name_set;
std::vector<JsonNodePair> new_build_args;
for (const auto &[json_generator, anf_node] : build_args) {
MS_EXCEPTION_IF_NULL(anf_node);
auto kernel_name = json_generator.kernel_name();
MS_LOG(DEBUG) << "Akg start compile op: " << kernel_name;
auto cached_kernel_pack = AkgSearchCache(kernel_name);
if (cached_kernel_pack != nullptr) {
@ -394,9 +59,11 @@ std::vector<JsonNodePair> AkgKernelBuilder::GetNotCachedKernels(const std::vecto
continue;
}
kernel_name_set.insert(kernel_name);
new_build_args.push_back({json_generator, anf_node});
auto kernel_json = json_generator.kernel_json_str();
AkgSaveJsonInfo(kernel_name, kernel_json);
jsons.push_back(kernel_json);
}
return new_build_args;
return jsons;
}
bool AkgKernelBuilder::InsertToCache(const std::vector<JsonNodePair> &build_args) {
@ -423,84 +90,39 @@ bool AkgKernelBuilder::HandleRepeatNodes() {
<< anf_node->fullname_with_scope() << "].";
return false;
}
MS_LOG(DEBUG) << "Use just compiled kernel, kernel_name[" << kernel_name << "], fullname_with_scope["
<< anf_node->fullname_with_scope() << "].";
MS_LOG(INFO) << "Use just compiled kernel, kernel_name[" << kernel_name << "], fullname_with_scope["
<< anf_node->fullname_with_scope() << "].";
AkgSetKernelMod(cached_kernel_pack, json_generator, anf_node);
}
return true;
}
std::vector<std::string> AkgKernelBuilder::GetKernelJsonsByHashId(const std::vector<JsonNodePair> &build_args,
std::set<size_t> fetched_ids) {
std::vector<std::string> jsons;
for (const auto &[json_generator, anf_node] : build_args) {
MS_EXCEPTION_IF_NULL(anf_node);
auto kernel_name = json_generator.kernel_name();
auto hash_id = std::hash<std::string>()(kernel_name);
if (fetched_ids.count(hash_id) == 0) {
continue;
}
auto kernel_json = json_generator.kernel_json_str();
AkgSaveJsonInfo(kernel_name, kernel_json);
jsons.push_back(kernel_json);
}
return jsons;
}
bool AkgKernelBuilder::AkgOpParallelBuild(const std::vector<JsonNodePair> &build_args) {
repeat_nodes_.clear();
auto new_build_args = GetNotCachedKernels(build_args);
if (new_build_args.empty()) {
auto jsons = GetNotCachedKernelJsons(build_args);
if (jsons.empty()) {
return true;
}
AkgKernelPool kp;
auto ret = kp.Init(new_build_args);
if (ret != 0) {
MS_LOG(ERROR) << "AkgKernelPool init failed.";
auto client = GetClient();
MS_EXCEPTION_IF_NULL(client);
if (!client->AkgStart(PROCESS_NUM, TIME_OUT)) {
MS_LOG(ERROR) << "Akg start failed.";
return false;
}
std::set<size_t> fetched_ids;
ret = kp.FetchKernels(&fetched_ids);
if (ret != 0) {
MS_LOG(ERROR) << "AkgKernelPool FetchKernels failed.";
auto attrs = CollectBuildAttrs();
if (!attrs.empty() && !client->AkgSendAttr(attrs)) {
MS_LOG(ERROR) << "Akg send attr failed.";
return false;
}
if (!fetched_ids.empty()) {
auto jsons = GetKernelJsonsByHashId(new_build_args, fetched_ids);
auto client = GetClient();
MS_EXCEPTION_IF_NULL(client);
if (!client->AkgStart(PROCESS_NUM, TIME_OUT)) {
MS_LOG(ERROR) << "Akg start failed.";
return false;
}
auto attrs = CollectBuildAttrs();
if (!attrs.empty() && !client->AkgSendAttr(attrs)) {
MS_LOG(ERROR) << "Akg send attr failed.";
return false;
}
if (!client->AkgSendData(jsons)) {
MS_LOG(ERROR) << "Akg send data failed.";
return false;
}
if (!client->AkgWait()) {
MS_LOG(ERROR) << "Akg compile failed.";
return false;
}
}
ret = kp.UpdateAndWait(fetched_ids);
if (ret != 0) {
MS_LOG(ERROR) << "AkgKernelPool UpdateAndWait failed.";
if (!client->AkgSendData(jsons)) {
MS_LOG(ERROR) << "Akg send data failed.";
return false;
}
if (!client->AkgWait()) {
MS_LOG(ERROR) << "Akg compile failed.";
return false;
}
// All unique done here, cache them and set kernel.
if (!InsertToCache(build_args)) {
MS_LOG(ERROR) << "Insert cache failed.";
@ -546,7 +168,7 @@ bool AkgKernelBuilder::AkgKernelParallelBuild(const std::vector<AnfNodePtr> &anf
}
if (json_and_node.empty()) {
MS_LOG(INFO) << "There is no akg kernel to be compiled.";
MS_LOG(DEBUG) << "There is no kernel needed to be compiled.";
return true;
}

75
mindspore/ccsrc/backend/kernel_compiler/akg/akg_kernel_build.h
View File

@ -17,13 +17,10 @@
#ifndef MINDSPORE_CCSRC_BACKEND_KERNEL_COMPILER_AKG_AKG_KERNEL_BUILD_H_
#define MINDSPORE_CCSRC_BACKEND_KERNEL_COMPILER_AKG_AKG_KERNEL_BUILD_H_
#include <sys/shm.h>
#include <string>
#include <utility>
#include <vector>
#include <map>
#include <set>
#include "ir/anf.h"
#include "backend/kernel_compiler/kernel.h"
#include "backend/session/kernel_build_client.h"
@ -47,83 +44,13 @@ class AkgKernelBuilder {
bool AkgKernelParallelBuild(const std::vector<AnfNodePtr> &anf_nodes);
private:
std::vector<JsonNodePair> GetNotCachedKernels(const std::vector<JsonNodePair> &build_args);
std::vector<std::string> GetKernelJsonsByHashId(const std::vector<JsonNodePair> &build_args,
std::set<size_t> fetched_ids);
std::vector<std::string> GetNotCachedKernelJsons(const std::vector<JsonNodePair> &build_args);
bool InsertToCache(const std::vector<JsonNodePair> &build_args);
bool HandleRepeatNodes();
bool AkgOpParallelBuild(const std::vector<JsonNodePair> &build_args);
std::vector<JsonNodePair> repeat_nodes_;
std::string CollectBuildAttrs();
};
class AkgKernelPool {
public:
class LockMng {
public:
explicit LockMng(int32_t fd) {
fd_ = fd;
locked_ = TryLock();
}
virtual ~LockMng() {
if (locked_) {
Unlock();
}
}
bool locked_{false};
private:
bool TryLock();
void Unlock();
int32_t fd_{-1};
};
public:
AkgKernelPool() = default;
virtual ~AkgKernelPool();
int32_t Init(const std::vector<JsonNodePair> &build_args);
int32_t FetchKernels(std::set<size_t> *out);
int32_t UpdateAndWait(const std::set<size_t> &ids);
constexpr inline static size_t kMaxKernelNum_{1000};
// allocate memory for todo_list, doing_list, done_list
constexpr inline static size_t kListNum_{3};
constexpr inline static auto kKeyName_ = "./akg_build_tmp.key";
constexpr inline static int32_t kToDoIdx_ = 0;
constexpr inline static int32_t kDoingIdx_ = 1;
constexpr inline static int32_t kDoneIdx_ = 2;
private:
void *CreateSharedMem(const std::string &path);
std::string GetCurrentPath();
inline void InitKernelLists(void *addr) {
kernel_lists_[kToDoIdx_] = reinterpret_cast<size_t *>(addr);
kernel_lists_[kDoingIdx_] = kernel_lists_[kToDoIdx_] + kMaxKernelNum_ + 1;
kernel_lists_[kDoneIdx_] = kernel_lists_[kDoingIdx_] + kMaxKernelNum_ + 1;
}
int32_t AddKernels(const std::vector<JsonNodePair> &kernel_jsons);
int32_t Wait();
int32_t shm_id_{-1};
bool is_creator_{false};
int32_t fd_{-1};
// includes 3 lists: todo_list, doing_list, done_list.
// each list has kMaxKernelNum_ + 1 elements and, the count of elements in each list
// is stored in kernel_lists_[xx][kMaxKernelNum_]
size_t *kernel_lists_[kListNum_]{nullptr, nullptr, nullptr};
std::set<size_t> self_kernel_ids_;
};
} // namespace kernel
} // namespace mindspore

6
mindspore/ccsrc/backend/kernel_compiler/akg/akg_kernel_json_decoder.cc
View File

@ -15,6 +15,12 @@
*/
#include "backend/kernel_compiler/akg/akg_kernel_json_decoder.h"
#include <algorithm>
#include <memory>
#include <sstream>
#include <string>
#include <map>
#include <vector>
#include "backend/kernel_compiler/akg/akg_kernel_json_generator.h"
#include "backend/kernel_compiler/common_utils.h"
#include "backend/session/anf_runtime_algorithm.h"

6
mindspore/ccsrc/backend/kernel_compiler/akg/akg_kernel_json_generator.cc
View File

@ -16,6 +16,12 @@
#include "backend/kernel_compiler/akg/akg_kernel_json_generator.h"
#include <algorithm>
#include <functional>
#include <map>
#include <set>
#include <sstream>
#include <tuple>
#if ENABLE_GPU
#include <cuda.h>
#endif

1
mindspore/ccsrc/backend/kernel_compiler/akg/akg_kernel_metadata.cc
View File

@ -15,6 +15,7 @@
*/
#include "backend/kernel_compiler/akg/akg_kernel_metadata.h"
#include <memory>
#include "backend/session/anf_runtime_algorithm.h"
#include "backend/kernel_compiler/oplib/oplib.h"
#include "backend/kernel_compiler/common_utils.h"

13
mindspore/ccsrc/backend/kernel_compiler/akg/ascend/akg_ascend_kernel_build.cc
View File

@ -16,6 +16,13 @@
#include "backend/kernel_compiler/akg/ascend/akg_ascend_kernel_build.h"
#include <algorithm>
#include <map>
#include <memory>
#include <string>
#include <unordered_set>
#include <utility>
#include <vector>
#include "ir/dtype.h"
#include "ir/func_graph.h"
#include "backend/kernel_compiler/common_utils.h"
@ -27,20 +34,18 @@
namespace mindspore {
namespace kernel {
KernelPackPtr AkgAscendKernelBuilder::AkgSearchCache(const std::string &kernel_name) {
return tbe::TbeUtils::SearchCache(kernel_name, true);
return tbe::TbeUtils::SearchCache(kernel_name, kProcessorAiCore);
}
KernelPackPtr AkgAscendKernelBuilder::AkgInsertCache(const std::string &kernel_name) {
return tbe::TbeUtils::InsertCache(kernel_name, kProcessorAiCore, true);
return tbe::TbeUtils::InsertCache(kernel_name, kProcessorAiCore);
}
void AkgAscendKernelBuilder::AkgSetKernelMod(const KernelPackPtr &kernel_pack,
const AkgKernelJsonGenerator &json_generator, const AnfNodePtr &anf_node) {
auto kernel_mod_ptr = std::make_shared<AkgKernelMod>(kernel_pack);
auto kernel_json_info = kernel_pack->kernel_json_info();
kernel_mod_ptr->SetInputSizeList(json_generator.input_size_list());
kernel_mod_ptr->SetOutputSizeList(json_generator.output_size_list());
kernel_mod_ptr->SetWorkspaceSizeList(kernel_json_info.workspaces);
AnfAlgo::SetKernelMod(kernel_mod_ptr, anf_node.get());
}

13
mindspore/ccsrc/backend/kernel_compiler/akg/ascend/akg_ascend_kernel_mod.cc
View File

@ -49,7 +49,7 @@ const std::vector<size_t> &AkgKernelMod::GetOutputSizeList() const { return outp
const std::vector<size_t> &AkgKernelMod::GetWorkspaceSizeList() const { return workspace_size_list_; }
bool AkgKernelMod::Launch(const std::vector<AddressPtr> &inputs, const std::vector<AddressPtr> &workspace,
bool AkgKernelMod::Launch(const std::vector<AddressPtr> &inputs, const std::vector<AddressPtr> &,
const std::vector<AddressPtr> &outputs, void *stream_ptr) {
if (stream_ptr == nullptr) {
MS_LOG(ERROR) << "stream_ptr should not be nullptr.";
@ -74,10 +74,6 @@ bool AkgKernelMod::Launch(const std::vector<AddressPtr> &inputs, const std::vect
[](const AddressPtr &input) -> void * { return input->addr; });
(void)std::transform(std::begin(outputs), std::end(outputs), std::back_inserter(runtime_args),
[](const AddressPtr &output) -> void * { return output->addr; });
if (!workspace.empty()) {
(void)std::transform(std::begin(workspace), std::end(workspace), std::back_inserter(runtime_args),
[](const AddressPtr &addr) -> void * { return addr->addr; });
}
rtL2Ctrl_t *l2ctrl = nullptr;
auto stream = static_cast<rtStream_t *>(stream_ptr);
@ -90,8 +86,7 @@ bool AkgKernelMod::Launch(const std::vector<AddressPtr> &inputs, const std::vect
return true;
}
std::vector<TaskInfoPtr> AkgKernelMod::GenTask(const std::vector<AddressPtr> &inputs,
const std::vector<AddressPtr> &workspace,
std::vector<TaskInfoPtr> AkgKernelMod::GenTask(const std::vector<AddressPtr> &inputs, const std::vector<AddressPtr> &,
const std::vector<AddressPtr> &outputs, uint32_t stream_id) {
if (kernel_pack_ == nullptr) {
MS_LOG(EXCEPTION) << "kernel pack should not be nullptr.";
@ -112,10 +107,6 @@ std::vector<TaskInfoPtr> AkgKernelMod::GenTask(const std::vector<AddressPtr> &in
[](const AddressPtr &input) -> void * { return input->addr; });
(void)std::transform(std::begin(outputs), std::end(outputs), std::back_inserter(output_data_addrs),
[](const AddressPtr &output) -> void * { return output->addr; });
if (!workspace.empty()) {
(void)std::transform(std::begin(workspace), std::end(workspace), std::back_inserter(workspace_addrs),
[](const AddressPtr &workspace) -> void * { return workspace->addr; });
}
uint32_t block_dim = DEFAULT_BLOCK_DIM; // default blockdim equal to 1.
auto func_stub = KernelManager::GenFuncStub(*kernel_pack_, false, &block_dim);

3
mindspore/ccsrc/backend/kernel_compiler/akg/gpu/akg_gpu_kernel_build.cc
View File

@ -39,15 +39,14 @@ KernelPackPtr AkgGpuKernelBuilder::AkgInsertCache(const std::string &kernel_name
void AkgGpuKernelBuilder::AkgSetKernelMod(const KernelPackPtr &kernel_pack,
const AkgKernelJsonGenerator &json_generator, const AnfNodePtr &anf_node) {
auto kernel_mod_ptr = std::make_shared<GpuKernelMod>(kernel_pack);
auto kernel_json_info = kernel_pack->kernel_json_info();
kernel_mod_ptr->SetInputSizeList(json_generator.input_size_list());
kernel_mod_ptr->SetOutputSizeList(json_generator.output_size_list());
kernel_mod_ptr->SetWorkspaceSizeList(kernel_json_info.workspaces);
AnfAlgo::SetKernelMod(kernel_mod_ptr, anf_node.get());
}
void AkgGpuKernelBuilder::AkgSaveJsonInfo(const string &kernel_name, const string &kernel_json) {
kernel::SaveJsonInfo(kernel_name, kernel_json, kernel::KernelMeta::GetInstance()->kernel_meta_path());
}
} // namespace kernel
} // namespace mindspore

11
mindspore/ccsrc/backend/kernel_compiler/akg/gpu/akg_gpu_kernel_mod.cc
View File

@ -15,7 +15,8 @@
*/
#include "backend/kernel_compiler/akg/gpu/akg_gpu_kernel_mod.h"
#include <fstream>
#include <algorithm>
#include "nlohmann/json.hpp"
#include "utils/ms_utils.h"
@ -91,15 +92,13 @@ void GpuKernelMod::SetInputSizeList(const std::vector<size_t> &size_list) { inpu
void GpuKernelMod::SetOutputSizeList(const std::vector<size_t> &size_list) { output_size_list_ = size_list; }
void GpuKernelMod::SetWorkspaceSizeList(const std::vector<size_t> &size_list) { workspace_size_list_ = size_list; }
const std::vector<size_t> &GpuKernelMod::GetInputSizeList() const { return input_size_list_; }
const std::vector<size_t> &GpuKernelMod::GetOutputSizeList() const { return output_size_list_; }
const std::vector<size_t> &GpuKernelMod::GetWorkspaceSizeList() const { return workspace_size_list_; }
bool GpuKernelMod::Launch(const std::vector<AddressPtr> &inputs, const std::vector<AddressPtr> &workspace,
bool GpuKernelMod::Launch(const std::vector<AddressPtr> &inputs, const std::vector<AddressPtr> &,
const std::vector<AddressPtr> &outputs, void *stream_ptr) {
if (stream_ptr == 0) {
MS_LOG(ERROR) << "stream_ptr should not be nullptr.";
@ -123,10 +122,6 @@ bool GpuKernelMod::Launch(const std::vector<AddressPtr> &inputs, const std::vect
[](const AddressPtr &input) -> void * { return reinterpret_cast<void *>(&(input->addr)); });
(void)std::transform(std::begin(outputs), std::end(outputs), std::back_inserter(runtimeargs),
[](const AddressPtr &output) -> void * { return reinterpret_cast<void *>(&(output->addr)); });
if (!workspace.empty()) {
(void)std::transform(std::begin(workspace), std::end(workspace), std::back_inserter(runtimeargs),
[](const AddressPtr &addr) -> void * { return reinterpret_cast<void *>(&(addr->addr)); });
}
result = cuLaunchKernel(kernel_addr, thread_info[0], thread_info[1], thread_info[2], thread_info[3], thread_info[4],
thread_info[5], 0, reinterpret_cast<CUstream>(stream_ptr),
reinterpret_cast<void **>(&runtimeargs[0]), 0);

1
mindspore/ccsrc/backend/kernel_compiler/akg/gpu/akg_gpu_kernel_mod.h
View File

@ -60,7 +60,6 @@ class GpuKernelMod : public KernelMod {
void SetInputSizeList(const std::vector<size_t> &size_list);
void SetOutputSizeList(const std::vector<size_t> &size_list);
void SetWorkspaceSizeList(const std::vector<size_t> &size_list);
const std::vector<size_t> &GetInputSizeList() const override;
const std::vector<size_t> &GetOutputSizeList() const override;
const std::vector<size_t> &GetWorkspaceSizeList() const override;

66
mindspore/ccsrc/backend/kernel_compiler/common_utils.cc
View File

@ -141,8 +141,14 @@ FusionType GetFusionTypeByName(const std::string &name) {
return iter->first;
}
void KernelMeta::Initialize() {
kernel_meta_path_ = std::string(kGpuKernelMeta) + "/";
void KernelMeta::Initialize(int pid) {
if (pid == -1) {
kernel_meta_path_ = std::string(kGpuKernelMeta) + "_" + std::to_string(getpid()) + "/";
} else {
kernel_meta_path_ = std::string(kGpuKernelMeta) + "_" + std::to_string(pid) + "/";
}
// remove old kernel cache
RemoveKernelCache();
#if defined(_WIN32) || defined(_WIN64)
auto ret = mkdir(kernel_meta_path_.c_str());
@ -155,6 +161,21 @@ void KernelMeta::Initialize() {
initialized_ = true;
}
void KernelMeta::RemoveKernelCache() {
DIR *dir = opendir(kernel_meta_path_.c_str());
if (dir == nullptr) {
return;
}
struct dirent *entry;
while ((entry = readdir(dir)) != nullptr) {
std::string kernel_file = entry->d_name;
std::string kernel_file_realpath = kernel_meta_path_ + kernel_file;
(void)remove(kernel_file_realpath.c_str());
}
(void)closedir(dir);
(void)rmdir(kernel_meta_path_.c_str());
}
std::string KernelMeta::Search(const std::string &kernel_name) const {
if (!initialized_) {
return "";
@ -206,7 +227,7 @@ KernelPackPtr SearchCache(const std::string &kernel_name, const std::string &pro
KernelPackPtr kernel_pack = std::make_shared<KernelPack>();
// just a tmp solution.
if (!kernel_pack->ReadFromJsonFile(kernel_json, processor)) {
MS_LOG(ERROR) << "Read cache json and bin file failed[" << kernel_json << "].";
MS_LOG(DEBUG) << "Read cache json and bin file failed[" << kernel_json << "].";
return nullptr;
} else {
return kernel_pack;
@ -229,7 +250,7 @@ KernelPackPtr InsertCache(const std::string &kernel_name, const std::string &pro
(void)kernel_json.append(kernel_name).append(kJsonSuffix);
KernelPackPtr kernel_pack = std::make_shared<KernelPack>();
if (!kernel_pack->ReadFromJsonFile(kernel_json, processor)) {
MS_LOG(ERROR) << "Read json and bin file failed[" << kernel_json << "].";
MS_LOG(DEBUG) << "Read json and bin file failed[" << kernel_json << "].";
return nullptr;
}
@ -693,9 +714,6 @@ void GetFuncGraphOutputNodes(const FuncGraphPtr &func_graph, std::vector<AnfNode
for (size_t input_idx = 1; input_idx < cnode->inputs().size(); ++input_idx) {
auto input_node = cnode->input(input_idx);
MS_EXCEPTION_IF_NULL(input_node);
if (input_node->isa<CNode>() && AnfAlgo::GetInputTensorNum(input_node) == 0) {
continue;
}
output_list->push_back(AnfAlgo::VisitKernel(input_node, 0).first);
}
} else {
@ -970,39 +988,5 @@ size_t CalOffset(const std::vector<int64_t> &start, const std::vector<int64_t> &
}
return offset;
}
size_t UnitSizeInBytes(const mindspore::TypeId &t) {
size_t bytes = 0;
switch (t) {
case kNumberTypeBool:
case kNumberTypeInt8:
case kNumberTypeUInt8:
bytes = sizeof(int8_t);
break;
case kNumberTypeInt16:
case kNumberTypeUInt16:
case kNumberTypeFloat16:
bytes = sizeof(int16_t);
break;
case kNumberTypeInt:
case kNumberTypeUInt:
case kNumberTypeInt32:
case kNumberTypeUInt32:
case kNumberTypeFloat:
case kNumberTypeFloat32:
bytes = sizeof(int32_t);
break;
case kNumberTypeUInt64:
case kNumberTypeInt64:
case kNumberTypeFloat64:
bytes = sizeof(int64_t);
break;
default:
MS_LOG(EXCEPTION) << "Invalid types " << t;
break;
}
return bytes;
}
} // namespace kernel
} // namespace mindspore

4
mindspore/ccsrc/backend/kernel_compiler/common_utils.h
View File

@ -55,7 +55,8 @@ using KernelMetaPtr = std::shared_ptr<KernelMetaInfo>;
class KernelMeta {
public:
KernelMeta() = default;
void Initialize();
void Initialize(int pid);
void RemoveKernelCache();
std::string Search(const std::string &kernel_name) const;
bool Insert(const std::string &kernel_name, const std::string &kernel_json);
std::string kernel_meta_path() const { return kernel_meta_path_; }
@ -143,7 +144,6 @@ size_t CalOffset(const std::vector<int64_t> &start, const std::vector<int64_t> &
std::vector<int64_t> CalDimOffset(const std::vector<int64_t> &input_shape);
size_t GetCopySize(const std::vector<int64_t> &dim_offset, const std::vector<int64_t> &start,
const std::vector<int64_t> &stop);
size_t UnitSizeInBytes(const mindspore::TypeId &t);
} // namespace kernel
} // namespace mindspore

2
mindspore/ccsrc/backend/kernel_compiler/cpu/adam_cpu_kernel.cc
View File

@ -83,7 +83,7 @@ void AdamCPUKernel::LaunchAdamNnacl(const std::vector<kernel::AddressPtr> &input
MS_LOG(EXCEPTION) << "AdamFp32 failed.";
}
};
ParallelLaunchAutoSearch(task, lens, this, &parallel_search_info_);
CPUKernelUtils::ParallelForAutoSearch(task, lens, &parallel_search_info_);
}
void AdamCPUKernel::InitKernel(const CNodePtr &kernel_node) {

85
mindspore/ccsrc/backend/kernel_compiler/cpu/adam_weight_decay_cpu_kernel.cc
View File

@ -26,26 +26,46 @@ namespace mindspore {
namespace kernel {
constexpr size_t kSizeFloat16 = sizeof(float16);
constexpr size_t kSizeFloat32 = sizeof(float);
constexpr size_t kScalarIndex = 0;
constexpr size_t kAdamWeightDecayInputSize = 9;
constexpr size_t kAdamWeightDecayOutputSize = 3;
void AdamWeightDecayCPUKernel::ParallelForAdam(const CTask &task, size_t count) {
auto max_thread_num = common::ThreadPool::GetInstance().GetSyncRunThreadNum();
const float block_size = 128.0;
const float align_size = 16.0;
size_t thread_num = count < block_size * max_thread_num ? std::ceil(count / block_size) : max_thread_num;
std::vector<common::Task> tasks;
size_t start = 0;
size_t once_compute_size = align_size * std::ceil(count / (align_size * thread_num));
while (start < count) {
size_t end = (start + once_compute_size) > count ? count : (start + once_compute_size);
auto block = [&, start, end]() {
task(start, end);
return common::SUCCESS;
};
tasks.emplace_back(block);
start += once_compute_size;
}
common::ThreadPool::GetInstance().SyncRun(tasks);
}
template <typename T, typename S>
void AdamWeightDecayCPUKernel::LaunchFusedAdam(const std::vector<AddressPtr> &inputs, const std::vector<AddressPtr> &) {
auto var = reinterpret_cast<T *>(inputs[VAR]->addr);
auto m = reinterpret_cast<T *>(inputs[M]->addr);
auto v = reinterpret_cast<T *>(inputs[V]->addr);
auto lr = reinterpret_cast<T *>(inputs[LR]->addr)[kScalarIndex];
auto beta1 = reinterpret_cast<T *>(inputs[BETA1]->addr)[kScalarIndex];
auto beta2 = reinterpret_cast<T *>(inputs[BETA2]->addr)[kScalarIndex];
auto epsilon = reinterpret_cast<T *>(inputs[EPSILON]->addr)[kScalarIndex];
auto decay = reinterpret_cast<T *>(inputs[DECAY]->addr)[kScalarIndex];
auto gradient16 = reinterpret_cast<S *>(inputs[GRAD]->addr);
void AdamWeightDecayCPUKernel::LaunchFusedAdam(const std::vector<AddressPtr> &inputs,
const std::vector<AddressPtr> &outputs) {
auto var = reinterpret_cast<T *>(inputs[0]->addr);
auto m = reinterpret_cast<T *>(inputs[1]->addr);
auto v = reinterpret_cast<T *>(inputs[2]->addr);
auto lr = reinterpret_cast<T *>(inputs[3]->addr)[0];
auto beta1 = reinterpret_cast<T *>(inputs[4]->addr)[0];
auto beta2 = reinterpret_cast<T *>(inputs[5]->addr)[0];
auto epsilon = reinterpret_cast<T *>(inputs[6]->addr)[0];
auto decay = reinterpret_cast<T *>(inputs[7]->addr)[0];
auto gradient16 = reinterpret_cast<S *>(inputs[8]->addr);
const auto beta1_minus = 1 - beta1;
const auto beta2_minus = 1 - beta2;
// multithreading
size_t lens = inputs[VAR]->size > 0 ? static_cast<size_t>(inputs[VAR]->size / sizeof(float)) : 1;
size_t lens = inputs[0]->size > 0 ? static_cast<size_t>(inputs[0]->size / sizeof(float)) : 1;
std::function<void(size_t, size_t)> task;
task = [&](size_t start, size_t end) {
@ -61,27 +81,28 @@ void AdamWeightDecayCPUKernel::LaunchFusedAdam(const std::vector<AddressPtr> &in
var[i] -= lr * update;
}
};
CPUKernelUtils::ParallelFor(task, lens);
ParallelForAdam(task, lens);
}
template <typename T>
void AdamWeightDecayCPUKernel::LaunchAdamWeightDecay(const std::vector<AddressPtr> &inputs,
const std::vector<AddressPtr> &) {
auto var = reinterpret_cast<T *>(inputs[VAR]->addr);
auto m = reinterpret_cast<T *>(inputs[M]->addr);
auto v = reinterpret_cast<T *>(inputs[V]->addr);
auto lr = reinterpret_cast<T *>(inputs[LR]->addr)[kScalarIndex];
auto beta1 = reinterpret_cast<T *>(inputs[BETA1]->addr)[kScalarIndex];
auto beta2 = reinterpret_cast<T *>(inputs[BETA2]->addr)[kScalarIndex];
auto epsilon = reinterpret_cast<T *>(inputs[EPSILON]->addr)[kScalarIndex];
auto decay = reinterpret_cast<T *>(inputs[DECAY]->addr)[kScalarIndex];
auto gradient = reinterpret_cast<T *>(inputs[GRAD]->addr);
const std::vector<AddressPtr> &outputs) {
auto var = reinterpret_cast<T *>(inputs[0]->addr);
auto m = reinterpret_cast<T *>(inputs[1]->addr);
auto v = reinterpret_cast<T *>(inputs[2]->addr);
auto lr = reinterpret_cast<T *>(inputs[3]->addr)[0];
auto beta1 = reinterpret_cast<T *>(inputs[4]->addr)[0];
auto beta2 = reinterpret_cast<T *>(inputs[5]->addr)[0];
auto epsilon = reinterpret_cast<T *>(inputs[6]->addr)[0];
auto decay = reinterpret_cast<T *>(inputs[7]->addr)[0];
auto gradient = reinterpret_cast<T *>(inputs[8]->addr);
const auto beta1_minus = 1 - beta1;
const auto beta2_minus = 1 - beta2;
// multithreading
size_t lens = inputs[VAR]->size > 0 ? static_cast<size_t>(inputs[VAR]->size / sizeof(float)) : 1;
size_t lens = inputs[0]->size > 0 ? static_cast<size_t>(inputs[0]->size / sizeof(float)) : 1;
std::function<void(size_t, size_t)> task;
task = [&](size_t start, size_t end) {
size_t i = AdamWeightDecayFp32(var, m, v, lr, beta1, beta2, epsilon, decay, gradient, start, end);
// remaining
@ -93,14 +114,14 @@ void AdamWeightDecayCPUKernel::LaunchAdamWeightDecay(const std::vector<AddressPt
var[i] -= lr * update;
}
};
CPUKernelUtils::ParallelFor(task, lens);
ParallelForAdam(task, lens);
}
void AdamWeightDecayCPUKernel::InitKernel(const CNodePtr &kernel_node) {
MS_EXCEPTION_IF_NULL(kernel_node);
std::vector<size_t> var_shape = AnfAlgo::GetInputDeviceShape(kernel_node, VAR);
dtype_ = AnfAlgo::GetInputDeviceDataType(kernel_node, VAR);
gradient_dtype_ = AnfAlgo::GetInputDeviceDataType(kernel_node, GRAD);
std::vector<size_t> var_shape = AnfAlgo::GetInputDeviceShape(kernel_node, 0);
dtype_ = AnfAlgo::GetInputDeviceDataType(kernel_node, 0);
gradient_dtype_ = AnfAlgo::GetInputDeviceDataType(kernel_node, 8);
size_t input_num = AnfAlgo::GetInputTensorNum(kernel_node);
if (input_num != kAdamWeightDecayInputSize) {
MS_LOG(EXCEPTION) << "Input number is " << input_num << ", but AdamWeightDecay needs 9 inputs.";
@ -134,12 +155,12 @@ void AdamWeightDecayCPUKernel::CheckParam(const std::vector<kernel::AddressPtr>
}
size_t elem1_size = elem_num_ * kSizeFloat32;
size_t elem2_size = gradient_dtype_ == kNumberTypeFloat16 ? elem_num_ * kSizeFloat16 : elem1_size;
if (inputs[VAR]->size != elem1_size || inputs[M]->size != elem1_size || inputs[V]->size != elem1_size ||
inputs[GRAD]->size != elem2_size) {
if (inputs[0]->size != elem1_size || inputs[1]->size != elem1_size || inputs[2]->size != elem1_size ||
inputs[8]->size != elem2_size) {
MS_LOG(EXCEPTION) << "Error input data size!";
}
if (inputs[LR]->size != kSizeFloat32 || inputs[BETA1]->size != kSizeFloat32 || inputs[BETA2]->size != kSizeFloat32 ||
inputs[EPSILON]->size != kSizeFloat32 || inputs[DECAY]->size != kSizeFloat32) {
if (inputs[3]->size != kSizeFloat32 || inputs[4]->size != kSizeFloat32 || inputs[5]->size != kSizeFloat32 ||
inputs[6]->size != kSizeFloat32 || inputs[7]->size != kSizeFloat32) {
MS_LOG(EXCEPTION) << "The attribute beta, lr, epsilon and weight decay must be float!";
}
}

2
mindspore/ccsrc/backend/kernel_compiler/cpu/adam_weight_decay_cpu_kernel.h
View File

@ -32,6 +32,7 @@ class AdamWeightDecayCPUKernel : public CPUKernel {
const std::vector<AddressPtr> &outputs) override;
private:
void ParallelForAdam(const CTask &task, size_t count);
void CheckParam(const std::vector<AddressPtr> &inputs, const std::vector<AddressPtr> &outputs);
template <typename T, typename S>
void LaunchFusedAdam(const std::vector<AddressPtr> &inputs, const std::vector<AddressPtr> &outputs);
@ -40,7 +41,6 @@ class AdamWeightDecayCPUKernel : public CPUKernel {
size_t elem_num_{0};
TypeId dtype_{kTypeUnknown};
TypeId gradient_dtype_{kTypeUnknown};
enum input_list_ { VAR, M, V, LR, BETA1, BETA2, EPSILON, DECAY, GRAD };
};
MS_REG_CPU_KERNEL(AdamWeightDecay,

25
mindspore/ccsrc/backend/kernel_compiler/cpu/apply_adagrad_cpu_kernel.cc
View File

@ -76,10 +76,27 @@ void ApplyAdagradCPUKernel::LaunchKernel(const std::vector<AddressPtr> &inputs,
// multithreading
size_t length = inputs[0]->size / sizeof(T);
auto task = [this, &var, &accum, lr, gradient](size_t start, size_t end) {
LaunchApplyAdagrad(var, accum, lr, gradient, start, end);
};
CPUKernelUtils::ParallelForAutoSearch(task, length, &parallel_search_info_);
size_t max_thread_num = std::thread::hardware_concurrency();
size_t use_thread_num = length < 128 * max_thread_num ? std::ceil(length / 128.0) : max_thread_num;
std::vector<std::thread> threads;
threads.reserve(use_thread_num);
size_t start = 0;
const size_t batch_size = (length + use_thread_num - 1) / use_thread_num;
if (batch_size == 0) {
MS_LOG(EXCEPTION) << "Error occur in launch kernel";
return;
}
while (start < length) {
size_t end = (start + batch_size) > length ? length : (start + batch_size);
threads.emplace_back(
std::thread(&ApplyAdagradCPUKernel::LaunchApplyAdagrad<T *>, this, var, accum, lr, gradient, start, end));
start += batch_size;
}
for (auto &it : threads) {
it.join();
}
// Copy result to output tensor
auto output_var = reinterpret_cast<T *>(outputs[0]->addr);

33
mindspore/ccsrc/backend/kernel_compiler/cpu/arithmetic_cpu_kernel.cc
View File

@ -19,7 +19,6 @@
#include "runtime/device/cpu/cpu_device_address.h"
#include "nnacl/fp32/power_fp32.h"
#include "nnacl/fp32/sub_fp32.h"
#include "nnacl/fp32/mul_fp32.h"
namespace mindspore {
namespace kernel {
@ -55,7 +54,7 @@ void ArithmeticCPUKernel<T>::Sub(const T *input1, const T *input2, T *out) {
auto task = [&](size_t start, size_t end) {
ElementSub(input1 + start, input2 + start, out + start, end - start);
};
ParallelLaunchAutoSearch(task, output_size_, this, &parallel_search_info_);
CPUKernelUtils::ParallelFor(task, output_size_, MAX_SUB_SERIAL_SIZE);
return;
}
if (op_para.in_elements_num0_ == 1 || op_para.in_elements_num1_ == 1) {
@ -66,7 +65,7 @@ void ArithmeticCPUKernel<T>::Sub(const T *input1, const T *input2, T *out) {
ElementOptSub(input1 + start, input2, out + start, end - start, &op_para);
}
};
ParallelLaunchAutoSearch(task, output_size_, this, &parallel_search_info_);
CPUKernelUtils::ParallelFor(task, output_size_, MAX_SUB_SERIAL_SIZE);
return;
}
}
@ -85,26 +84,6 @@ void ArithmeticCPUKernel<T>::Sub(const T *input1, const T *input2, T *out) {
template <typename T>
void ArithmeticCPUKernel<T>::Mul(const T *input1, const T *input2, T *out) {
if constexpr (std::is_same_v<T, float>) {
if (input_shape1_ == input_shape2_) {
auto task = [&](size_t start, size_t end) {
ElementMul(input1 + start, input2 + start, out + start, end - start);
};
ParallelLaunchAutoSearch(task, output_size_, this, &parallel_search_info_);
return;
}
if (op_para.in_elements_num0_ == 1 || op_para.in_elements_num1_ == 1) {
auto task = [&](size_t start, size_t end) {
if (op_para.in_elements_num0_ == 1) {
ElementOptMul(input1, input2 + start, out + start, end - start, &op_para);
} else {
ElementOptMul(input1 + start, input2, out + start, end - start, &op_para);
}
};
ParallelLaunchAutoSearch(task, output_size_, this, &parallel_search_info_);
return;
}
}
BroadcastIterator base_iter(input_shape1_, input_shape2_, output_shape_);
auto task = [&input1, &input2, &out, &base_iter](size_t start, size_t end) {
auto iter = base_iter;
@ -149,21 +128,21 @@ void ArithmeticCPUKernel<T>::RealDiv(const T *input1, const T *input2, T *out) {
auto task = [&](size_t start, size_t end) {
ElementRealDiv<T>(input1 + start, input2 + start, out + start, end - start, 1, 1);
};
ParallelLaunchAutoSearch(task, output_size_, this, &parallel_search_info_);
CPUKernelUtils::ParallelFor(task, output_size_, MAX_DIV_SERIAL_SIZE);
return;
}
if (op_para.in_elements_num0_ == 1) {
auto task = [&](size_t start, size_t end) {
ElementRealDiv<T>(input1, input2 + start, out + start, end - start, 0, 1);
};
ParallelLaunchAutoSearch(task, output_size_, this, &parallel_search_info_);
CPUKernelUtils::ParallelFor(task, output_size_, MAX_DIV_SERIAL_SIZE);
return;
}
if (op_para.in_elements_num1_ == 1) {
auto task = [&](size_t start, size_t end) {
ElementRealDiv<T>(input1 + start, input2, out + start, end - start, 1, 0);
};
ParallelLaunchAutoSearch(task, output_size_, this, &parallel_search_info_);
CPUKernelUtils::ParallelFor(task, output_size_, MAX_DIV_SERIAL_SIZE);
return;
}
@ -360,7 +339,7 @@ void ArithmeticCPUKernel<T>::SquaredDifference(const T *input1, const T *input2,
iter.GenNextPos();
}
};
ParallelLaunchAutoSearch(task, output_size_, this, &parallel_search_info_);
CPUKernelUtils::ParallelFor(task, output_size_);
}
template <typename T>

2
mindspore/ccsrc/backend/kernel_compiler/cpu/arithmetic_cpu_kernel.h
View File

@ -77,8 +77,6 @@ MS_REG_CPU_KERNEL_T(RealDiv, KernelAttr(), ArithmeticCPUKernel, int64_t);
MS_REG_CPU_KERNEL_T(Div, KernelAttr(), ArithmeticCPUKernel, int32_t);
MS_REG_CPU_KERNEL_T(Div, KernelAttr(), ArithmeticCPUKernel, float);
MS_REG_CPU_KERNEL_T(Div, KernelAttr(), ArithmeticCPUKernel, int64_t);
MS_REG_CPU_KERNEL_T(Mul, KernelAttr(), ArithmeticCPUKernel, float);
MS_REG_CPU_KERNEL_T(Mul, KernelAttr(), ArithmeticCPUKernel, int32_t);
MS_REG_CPU_KERNEL_T(
FloorDiv, KernelAttr().AddInputAttr(kNumberTypeInt64).AddInputAttr(kNumberTypeInt64).AddOutputAttr(kNumberTypeInt64),
ArithmeticCPUKernel, int64_t);

32
mindspore/ccsrc/backend/kernel_compiler/cpu/arithmetic_logic_cpu_kernel.cc
View File

@ -13,12 +13,10 @@
* See the License for the specific language governing permissions and
* limitations under the License.
*/
#include "backend/kernel_compiler/cpu/arithmetic_logic_cpu_kernel.h"
#include <cmath>
#include <string>
#include <map>
#include <functional>
#include "backend/kernel_compiler/cpu/arithmetic_logic_cpu_kernel.h"
#include "runtime/device/cpu/cpu_device_address.h"
namespace mindspore {
@ -31,9 +29,7 @@ void ArithmeticLogicCPUKernel<T>::Less(const T *input1, const T *input2, bool *o
auto iter = base_iter;
iter.SetPos(start);
for (size_t i = start; i < end; i++) {
auto x = input1[iter.GetInputPosA()];
auto y = input2[iter.GetInputPosB()];
out[i] = std::less<T>()(x, y);
out[i] = input1[iter.GetInputPosA()] < input2[iter.GetInputPosB()];
iter.GenNextPos();
}
};
@ -41,9 +37,7 @@ void ArithmeticLogicCPUKernel<T>::Less(const T *input1, const T *input2, bool *o
} else {
base_iter.SetPos(0);
for (size_t i = 0; i < output_size_; i++) {
auto x = input1[base_iter.GetInputPosA()];
auto y = input2[base_iter.GetInputPosB()];
out[i] = std::less<T>()(x, y);
out[i] = input1[base_iter.GetInputPosA()] < input2[base_iter.GetInputPosB()];
base_iter.GenNextPos();
}
}
@ -56,9 +50,7 @@ void ArithmeticLogicCPUKernel<T>::Equal(const T *input1, const T *input2, bool *
auto iter = base_iter;
iter.SetPos(start);
for (size_t i = start; i < end; i++) {
auto x = input1[iter.GetInputPosA()];
auto y = input2[iter.GetInputPosB()];
out[i] = std::equal_to<T>()(x, y);
out[i] = input1[iter.GetInputPosA()] == input2[iter.GetInputPosB()];
iter.GenNextPos();
}
};
@ -72,9 +64,7 @@ void ArithmeticLogicCPUKernel<T>::NotEqual(const T *input1, const T *input2, boo
auto iter = base_iter;
iter.SetPos(start);
for (size_t i = start; i < end; i++) {
auto x = input1[iter.GetInputPosA()];
auto y = input2[iter.GetInputPosB()];
out[i] = std::not_equal_to<T>()(x, y);
out[i] = input1[iter.GetInputPosA()] != input2[iter.GetInputPosB()];
iter.GenNextPos();
}
};
@ -116,9 +106,7 @@ void ArithmeticLogicCPUKernel<T>::Greater(const T *input1, const T *input2, bool
auto iter = base_iter;
iter.SetPos(start);
for (size_t i = start; i < end; i++) {
auto x = input1[iter.GetInputPosA()];
auto y = input2[iter.GetInputPosB()];
out[i] = std::greater<T>()(x, y);
out[i] = input1[iter.GetInputPosA()] > input2[iter.GetInputPosB()];
iter.GenNextPos();
}
};
@ -132,9 +120,7 @@ void ArithmeticLogicCPUKernel<T>::GreaterEqual(const T *input1, const T *input2,
auto iter = base_iter;
iter.SetPos(start);
for (size_t i = start; i < end; i++) {
auto x = input1[iter.GetInputPosA()];
auto y = input2[iter.GetInputPosB()];
out[i] = std::greater_equal<T>()(x, y);
out[i] = input1[iter.GetInputPosA()] >= input2[iter.GetInputPosB()];
iter.GenNextPos();
}
};
@ -148,9 +134,7 @@ void ArithmeticLogicCPUKernel<T>::LessEqual(const T *input1, const T *input2, bo
auto iter = base_iter;
iter.SetPos(start);
for (size_t i = start; i < end; i++) {
auto x = input1[iter.GetInputPosA()];
auto y = input2[iter.GetInputPosB()];
out[i] = std::less_equal<T>()(x, y);
out[i] = input1[iter.GetInputPosA()] <= input2[iter.GetInputPosB()];
iter.GenNextPos();
}
};

48
mindspore/ccsrc/backend/kernel_compiler/cpu/arithmetic_self_cpu_kernel.cc
View File

@ -20,7 +20,6 @@
#include <map>
#include "backend/kernel_compiler/cpu/arithmetic_self_cpu_kernel.h"
#include "runtime/device/cpu/cpu_device_address.h"
#include "nnacl/fp32/exp_fp32.h"
namespace mindspore {
namespace kernel {
@ -32,15 +31,7 @@ void Square(const T *in, T *out, size_t size) {
out[i] = in[i] * in[i];
}
};
ParallelLaunch(task, size, MAX_SQUARE_SERIAL_SIZE);
}
template <typename T>
void Exp(const T *in, T *out, size_t size) {
if constexpr (std::is_same_v<T, float>) {
auto task = [&in, &out](size_t start, size_t end) { ExpFp32(in + start, out + start, end - start); };
ParallelLaunch(task, size, MAX_EXP_SERIAL_SIZE);
}
CPUKernelUtils::ParallelFor(task, size, MAX_SQUARE_SERIAL_SIZE);
}
template <typename T>
@ -66,7 +57,7 @@ void Neg(const T *in, T *out, size_t size) {
out[i] = -in[i];
}
};
ParallelLaunch(task, size, MAX_NEG_SERIAL_SIZE);
CPUKernelUtils::ParallelFor(task, size, MAX_NEG_SERIAL_SIZE);
}
template <typename T>
@ -271,7 +262,6 @@ void Identity(const T *in, T *out, size_t size) {
static const std::map<std::string, OperateType> kArithmeticOpTypeMap = {{prim::kPrimNeg->name(), NEG},
{prim::kPrimSquare->name(), SQUARE},
{prim::kPrimOnesLike->name(), ONESLIKE},
{prim::kPrimExp->name(), EXP},
{prim::kPrimZerosLike->name(), ZEROSLIKE},
{prim::kPrimLogicalNot->name(), LOGICALNOT},
{prim::kPrimSign->name(), SIGN},
@ -334,29 +324,17 @@ void ArithmeticSelfCPUKernel::LaunchKernel(const std::vector<AddressPtr> &inputs
T *output = reinterpret_cast<T *>(outputs[0]->addr);
size_t lens = outputs[0]->size > 0 ? static_cast<size_t>(outputs[0]->size / sizeof(T)) : 1;
static const std::map<OperateType, std::function<void(const T *in, T *out, size_t size)>> kArithmeticOpFuncMap = {
{SQUARE, Square<T>},
{SIGN, Sign<T>},
{NEG, Neg<T>},
{LOGICALNOT, LogicalNot<T>},
{ONESLIKE, OnesLike<T>},
{ZEROSLIKE, ZerosLike<T>},
{FLOOR, Floor<T>},
{RECIPROCAL, Reciprocal<T>},
{GELU, Gelu<T>},
{SIN, Sin<T>},
{COS, Cos<T>},
{TAN, Tan<T>},
{ASIN, Asin<T>},
{ACOS, ACos<T>},
{ATAN, Atan<T>},
{SINH, Sinh<T>},
{COSH, Cosh<T>},
{ASINH, Asinh<T>},
{ACOSH, Acosh<T>},
{ATANH, Atanh<T>},
{RINT, Rint<T>},
{ROUND, Round<T>},
{EXP, Exp<T>}};
{SQUARE, Square<T>}, {SIGN, Sign<T>},
{NEG, Neg<T>}, {LOGICALNOT, LogicalNot<T>},
{ONESLIKE, OnesLike<T>}, {ZEROSLIKE, ZerosLike<T>},
{FLOOR, Floor<T>}, {RECIPROCAL, Reciprocal<T>},
{GELU, Gelu<T>}, {SIN, Sin<T>},
{COS, Cos<T>}, {TAN, Tan<T>},
{ASIN, Asin<T>}, {ACOS, ACos<T>},
{ATAN, Atan<T>}, {SINH, Sinh<T>},
{COSH, Cosh<T>}, {ASINH, Asinh<T>},
{ACOSH, Acosh<T>}, {ATANH, Atanh<T>},
{RINT, Rint<T>}, {ROUND, Round<T>}};
if (kArithmeticOpFuncMap.find(operate_type_) != kArithmeticOpFuncMap.end()) {
kArithmeticOpFuncMap.at(operate_type_)(input, output, lens);
} else {

9
mindspore/ccsrc/backend/kernel_compiler/cpu/arithmetic_self_cpu_kernel.h
View File

@ -20,9 +20,8 @@
#include "backend/kernel_compiler/cpu/cpu_kernel.h"
#include "backend/kernel_compiler/cpu/cpu_kernel_factory.h"
const float MAX_NEG_SERIAL_SIZE = 5000;
const float MAX_SQUARE_SERIAL_SIZE = 5000;
const float MAX_EXP_SERIAL_SIZE = 15000;
const float MAX_NEG_SERIAL_SIZE = 20000;
const float MAX_SQUARE_SERIAL_SIZE = 20000;
namespace mindspore {
namespace kernel {
@ -59,10 +58,6 @@ class IdentityCPUKernel : public ArithmeticSelfCPUKernel {
MS_REG_CPU_KERNEL(Square, KernelAttr().AddInputAttr(kNumberTypeInt32).AddOutputAttr(kNumberTypeInt32),
ArithmeticSelfCPUKernel);
MS_REG_CPU_KERNEL(Square, KernelAttr().AddInputAttr(kNumberTypeFloat32).AddOutputAttr(kNumberTypeFloat32),
ArithmeticSelfCPUKernel);
MS_REG_CPU_KERNEL(Exp, KernelAttr().AddInputAttr(kNumberTypeFloat32).AddOutputAttr(kNumberTypeFloat32),
ArithmeticSelfCPUKernel);
MS_REG_CPU_KERNEL(Neg, KernelAttr().AddInputAttr(kNumberTypeFloat32).AddOutputAttr(kNumberTypeFloat32),
ArithmeticSelfCPUKernel);
MS_REG_CPU_KERNEL(Neg, KernelAttr().AddInputAttr(kNumberTypeInt32).AddOutputAttr(kNumberTypeInt32),

2
mindspore/ccsrc/backend/kernel_compiler/cpu/bias_add_cpu_kernel.cc
View File

@ -90,7 +90,7 @@ bool BiasAddCPUKernel::Launch(const std::vector<AddressPtr> &inputs, const std::
ElementAdd(src_addr + n_offset, bias_addr, output_addr + n_offset, input_shape_[1]);
}
};
ParallelLaunchAutoSearch(task, input_shape_[0], this, &parallel_search_info_);
CPUKernelUtils::ParallelForAutoSearch(task, input_shape_[0], &parallel_search_info_);
}
return true;
}

2
mindspore/ccsrc/backend/kernel_compiler/cpu/bias_add_grad_cpu_kernel.cc
View File

@ -55,7 +55,7 @@ bool BiasAddGradCPUKernel::Launch(const std::vector<AddressPtr> &inputs, const s
auto task = [&](size_t start, size_t end) {
ReduceSumDim2Axis0(end - start, input_shape_[1], input_shape_[0], input_addr + start, output_addr + start);
};
ParallelLaunchAutoSearch(task, input_shape_[1], this, &parallel_search_info_);
CPUKernelUtils::ParallelForAutoSearch(task, input_shape_[1], &parallel_search_info_);
}
return true;
}

2
mindspore/ccsrc/backend/kernel_compiler/cpu/concat_cpu_kernel.cc
View File

@ -74,7 +74,7 @@ bool ConcatCPUKernel<T>::Launch(const std::vector<kernel::AddressPtr> &inputs, c
}
}
};
ParallelLaunchAutoSearch(task, before_axis, this, &parallel_search_info_);
CPUKernelUtils::ParallelForAutoSearch(task, before_axis, &parallel_search_info_);
return true;
}

71
mindspore/ccsrc/backend/kernel_compiler/cpu/cpu_kernel.cc
View File

@ -138,77 +138,6 @@ void CPUKernelUtils::ParallelForAutoSearch(const CTask &task, size_t count, Para
}
}
ActorThreadPool *GetActorMgrInnerThreadPool() {
auto actor_manager = ActorMgr::GetActorMgrRef();
auto thread_pool = actor_manager->GetActorThreadPool();
// Init thread_pool if env is windows or ascend, in case that it won't be init in graph_scheduler.
if (thread_pool == nullptr) {
const size_t kMaxThreadNum = 23;
size_t max_thread_num = std::thread::hardware_concurrency() - 1;
if (max_thread_num < 1) {
max_thread_num = 1;
}
max_thread_num = max_thread_num < kMaxThreadNum ? max_thread_num : kMaxThreadNum;
actor_manager->Initialize(true, 0, max_thread_num);
thread_pool = actor_manager->GetActorThreadPool();
MS_EXCEPTION_IF_NULL(thread_pool);
}
return thread_pool;
}
// Use threadpool of mindrt
void ParallelLaunch(const CTask &task, size_t count, float block_size, Content content) {
auto thread_pool = GetActorMgrInnerThreadPool();
size_t kernel_thread_num = thread_pool->GetKernelThreadNum();
if (kernel_thread_num == 0) {
MS_LOG(EXCEPTION) << "Actor inner pool has been init, but kernel thread is 0!";
}
size_t thread_num = count < block_size * kernel_thread_num ? std::ceil(count / block_size) : kernel_thread_num;
size_t once_compute_size = (count + thread_num - 1) / thread_num;
size_t task_num = count / once_compute_size;
if (count % once_compute_size != 0) {
task_num += 1;
}
auto func = [&](void *, int task_id, float, float) {
size_t start = task_id * once_compute_size;
size_t end = (start + once_compute_size) > count ? count : (start + once_compute_size);
task(start, end);
return common::SUCCESS;
};
thread_pool->ParallelLaunch(func, content, task_num);
}
void ParallelLaunchAutoSearch(const CTask &task, size_t count, Content content,
ParallelSearchInfo *parallel_search_info) {
const size_t MAX_POW = 6;
const size_t AVG_COUNT = 5;
size_t current_pow = parallel_search_info->search_count / AVG_COUNT;
if (current_pow < MAX_POW) {
if (parallel_search_info->search_count % AVG_COUNT == 0) {
parallel_search_info->tmp_sum_cost_time = 0;
}
float block_size = static_cast<float>(count) / std::pow(2.0f, current_pow);
double start_time = GetTime();
ParallelLaunch(task, count, block_size, content);
double cost_time = GetTime() - start_time;
parallel_search_info->tmp_sum_cost_time += cost_time;
parallel_search_info->search_count++;
if (parallel_search_info->search_count % AVG_COUNT == 0) {
double avg_time = parallel_search_info->tmp_sum_cost_time / AVG_COUNT;
if (parallel_search_info->min_cost_time > avg_time) {
parallel_search_info->min_cost_time = avg_time;
parallel_search_info->best_block_size = block_size;
parallel_search_info->best_pow = current_pow;
} else if (current_pow - parallel_search_info->best_pow >= 2) {
parallel_search_info->search_count = AVG_COUNT * MAX_POW;
}
}
} else {
ParallelLaunch(task, count, parallel_search_info->best_block_size, content);
}
}
std::vector<size_t> CPUKernelUtils::FlatShapeByAxis(const std::vector<size_t> &shape, int axis) {
if (axis < 0) {
axis = axis + SizeToInt(shape.size());

25
mindspore/ccsrc/backend/kernel_compiler/cpu/cpu_kernel.h
View File

@ -1,5 +1,5 @@
/**
* Copyright 2019-2021 Huawei Technologies Co., Ltd
* Copyright 2019 Huawei Technologies Co., Ltd
*
* Licensed under the Apache License, Version 2.0 (the "License");
* you may not use this file except in compliance with the License.
@ -25,8 +25,6 @@
#include "backend/session/anf_runtime_algorithm.h"
#include "backend/kernel_compiler/common_utils.h"
#include "ir/anf.h"
#include "runtime/framework/graph_scheduler.h"
#include "actor/actormgr.h"
using mindspore::kernel::Address;
using mindspore::kernel::AddressPtr;
@ -64,7 +62,6 @@ const char DELTA[] = "delta";
const char SORTED[] = "sorted";
const char ADJ_ST[] = "adjoint_st";
const char ADJ_dT[] = "adjoint_dt";
const char PERIODS[] = "periods";
enum OperateType {
ADD = 0,
@ -122,7 +119,6 @@ enum OperateType {
ATAN2,
RINT,
ROUND,
EXP,
IDENTITY,
};
@ -156,19 +152,6 @@ class CPUKernel : public kernel::KernelMod {
std::vector<size_t> output_size_list_;
std::vector<size_t> workspace_size_list_;
ParallelSearchInfo parallel_search_info_;
template <typename T>
inline T *GetDeviceAddress(const std::vector<AddressPtr> &addr_list, size_t index) {
if (index >= addr_list.size()) {
MS_LOG(EXCEPTION) << "Address index(" << index << ") out of range(" << addr_list.size() << ")";
}
if ((addr_list[index] == nullptr) || (addr_list[index]->addr == nullptr) || (addr_list[index]->size == 0)) {
MS_LOG(EXCEPTION) << "The device address is empty, address index: " << index;
}
return reinterpret_cast<T *>(addr_list[index]->addr);
}
};
class CPUKernelUtils {
@ -226,12 +209,6 @@ class TransposeIterator {
std::vector<size_t> axes_;
size_t pos_{0};
};
ActorThreadPool *GetActorMgrInnerThreadPool();
void ParallelLaunch(const CTask &task, size_t count, float block_size = 128.0, Content content = nullptr);
void ParallelLaunchAutoSearch(const CTask &task, size_t count, Content content,
ParallelSearchInfo *parallel_search_info);
} // namespace kernel
} // namespace mindspore

6
mindspore/ccsrc/backend/kernel_compiler/cpu/cpu_kernel_factory.cc
View File

@ -21,7 +21,6 @@
#include <string>
#include "runtime/device/kernel_info.h"
#include "runtime/device/cpu/kernel_select_cpu.h"
namespace mindspore {
namespace kernel {
@ -112,11 +111,6 @@ std::pair<bool, size_t> CPUKernelFactory::CPUKernelAttrCheck(const std::string &
MS_LOG(INFO) << "Not registered CPU kernel: op[" << kernel_name << "]!";
return std::make_pair(false, 0);
}
if (device::cpu::IsDynamicParamKernel(kernel_name)) {
return std::make_pair(true, 0);
}
auto kernel_attrs = GetSupportedKernelAttrList(kernel_name);
if (kernel_attrs[0].GetInputSize() == 0 && kernel_attrs[0].GetOutputSize() == 0) {
auto op_info_ptr = mindspore::kernel::OpLib::FindOp(kernel_name, kernel::OpImplyType::kCPU);

52
mindspore/ccsrc/backend/kernel_compiler/cpu/crop_and_resize_cpu_kernel.cc
View File

@ -144,7 +144,8 @@ bool CropAndResizeCPUKernel<T>::Launch(const std::vector<kernel::AddressPtr> &in
const int bottom_y_index = ceilf(target_y);
const int left_x_index = floorf(target_x);
const int right_x_index = ceilf(target_x);
const float y_lerp = target_y - top_y_index;
const float x_lerp = target_x - left_x_index;
const float top_left = static_cast<float>(
input_image[((box_index * input_height_ + top_y_index) * input_width_ + left_x_index) * channel_ +
pos_channel]);
@ -157,9 +158,9 @@ bool CropAndResizeCPUKernel<T>::Launch(const std::vector<kernel::AddressPtr> &in
const float bottom_right = static_cast<float>(
input_image[((box_index * input_height_ + bottom_y_index) * input_width_ + right_x_index) * channel_ +
pos_channel]);
const float top = top_left + (top_right - top_left) * (target_x - left_x_index);
const float bottom = bottom_left + (bottom_right - bottom_left) * (target_x - left_x_index);
output[pos] = top + (bottom - top) * (target_y - top_y_index);
const float top = top_left + (top_right - top_left) * x_lerp;
const float bottom = bottom_left + (bottom_right - bottom_left) * x_lerp;
output[pos] = top + (bottom - top) * y_lerp;
} else if (method_ == 3) {
int y1h = static_cast<int>(y1 * input_height_);
int x1w = static_cast<int>(x1 * input_width_);
@ -169,37 +170,36 @@ bool CropAndResizeCPUKernel<T>::Launch(const std::vector<kernel::AddressPtr> &in
int h = ((y2h - y1h + 1) > 1) ? y2h - y1h + 1 : 1;
float y_point = (pos_y + 0.5) * (h / static_cast<float>(final_height_)) - 0.5;
int top_y_index = std::min(std::max(0, static_cast<int>(floorf(y_point))), h - 1);
int bottom_y_index = std::min(std::max(0, static_cast<int>(ceilf(y_point))), h - 1);
int top_y_index = floorf(y_point);
top_y_index = std::min(std::max(0, top_y_index), h - 1);
int bottom_y_index = ceilf(y_point);
bottom_y_index = std::min(std::max(0, bottom_y_index), h - 1);
float x_point = (pos_x + 0.5) * (w / static_cast<float>(final_width_)) - 0.5;
int left_x_index = std::min(std::max(0, static_cast<int>(floorf(x_point))), w - 1);
int right_x_index = std::min(std::max(0, static_cast<int>(ceilf(x_point))), w - 1);
int left_x_index = floorf(x_point);
left_x_index = std::min(std::max(0, left_x_index), w - 1);
int right_x_index = ceilf(x_point);
right_x_index = std::min(std::max(0, right_x_index), w - 1);
const float y_lerp = y_point - top_y_index;
const float x_lerp = x_point - left_x_index;
const int y_top_index = box_index * input_height_ + y1h + top_y_index;
const int y_bottom_index = box_index * input_height_ + y1h + bottom_y_index;
const int y_top_index = std::max(0, y1h + top_y_index);
const int y_bottom_index = std::max(0, y1h + bottom_y_index);
const int x_left_index = std::max(0, x1w + left_x_index);
const int x_right_index = std::max(0, x1w + right_x_index);
const float top_left = static_cast<float>(
input_image[((box_index * input_height_ + y_top_index) * input_width_ + x_left_index) * channel_ +
pos_channel]);
const float top_right = static_cast<float>(
input_image[((box_index * input_height_ + y_top_index) * input_width_ + x_right_index) * channel_ +
pos_channel]);
const float top_left =
static_cast<float>(input_image[(y_top_index * input_width_ + x1w + left_x_index) * channel_ + pos_channel]);
const float top_right =
static_cast<float>(input_image[(y_top_index * input_width_ + x1w + right_x_index) * channel_ + pos_channel]);
const float bottom_left = static_cast<float>(
input_image[((box_index * input_height_ + y_bottom_index) * input_width_ + x_left_index) * channel_ +
pos_channel]);
input_image[(y_bottom_index * input_width_ + x1w + left_x_index) * channel_ + pos_channel]);
const float bottom_right = static_cast<float>(
input_image[((box_index * input_height_ + y_bottom_index) * input_width_ + x_right_index) * channel_ +
pos_channel]);
output[pos] = top_left * (1 - y_lerp) * (1 - x_lerp) + bottom_right * y_lerp * x_lerp +
top_right * (1 - y_lerp) * x_lerp + bottom_left * y_lerp * (1 - x_lerp);
input_image[(y_bottom_index * input_width_ + x1w + right_x_index) * channel_ + pos_channel]);
float ret = top_left * (1 - y_lerp) * (1 - x_lerp) + bottom_right * y_lerp * x_lerp +
top_right * (1 - y_lerp) * x_lerp + bottom_left * y_lerp * (1 - x_lerp);
output[pos] = ret;
} else {
// Nearest Neighbour
const int closest_x_index = roundf(target_x);

17
mindspore/ccsrc/backend/kernel_compiler/cpu/crop_and_resize_cpu_kernel.h
View File

@ -35,14 +35,15 @@ class CropAndResizeCPUKernel : public CPUKernel {
const std::vector<AddressPtr> &outputs) override;
private:
int method_{1};
float extrapolation_value_{0.0};
int output_size_{0};
int input_height_{0};
int input_width_{0};
int final_height_{0};
int final_width_{0};
int channel_{0};
int method_;
float extrapolation_value_;
int input_crop_size_;
int output_size_;
int input_height_;
int input_width_;
int final_height_;
int final_width_;
int channel_;
};
MS_REG_CPU_KERNEL_T(CropAndResize,

11
mindspore/ccsrc/backend/kernel_compiler/cpu/dropout_grad_kernel.cc
View File

@ -43,9 +43,9 @@ void DropoutGradCpuBwdKernel::InitKernel(const CNodePtr &kernel_node) {
bool DropoutGradCpuBwdKernel::Launch(const std::vector<AddressPtr> &inputs, const std::vector<AddressPtr> &,
const std::vector<AddressPtr> &outputs) {
if (dtype_ == kNumberTypeFloat16) {
DropoutBackwardKernel<float16>(inputs, outputs, keep_prob_);
DropoutBackwardKernel<float16>(inputs, outputs, num_count_, keep_prob_);
} else if (dtype_ == kNumberTypeFloat32) {
DropoutBackwardKernel<float>(inputs, outputs, keep_prob_);
DropoutBackwardKernel<float>(inputs, outputs, num_count_, keep_prob_);
} else {
MS_LOG(ERROR) << "Input data type: " << dtype_ << " is not supported for DropoutGrad kernel for CPU.";
}
@ -55,7 +55,8 @@ bool DropoutGradCpuBwdKernel::Launch(const std::vector<AddressPtr> &inputs, cons
template <typename T>
void DropoutGradCpuBwdKernel::DropoutBackwardKernel(const std::vector<AddressPtr> &inputs,
const std::vector<AddressPtr> &outputs, float keep_prob) {
const std::vector<AddressPtr> &outputs, size_t num_count,
float keep_prob) {
auto *output = reinterpret_cast<T *>(outputs[0]->addr);
const auto *input = reinterpret_cast<T *>(inputs[0]->addr);
const auto *mask = reinterpret_cast<T *>(inputs[1]->addr);
@ -69,7 +70,7 @@ void DropoutGradCpuBwdKernel::DropoutBackwardKernel(const std::vector<AddressPtr
input_tmp[i] = static_cast<float>(input[i]);
mask_tmp[i] = static_cast<float>(mask[i]);
}
DropoutGrad(input_tmp, mask_tmp, output_tmp, SizeToInt(num_count_), scale);
DropoutGrad(input_tmp, mask_tmp, output_tmp, num_count_, scale);
for (size_t i = 0; i < num_count_; ++i) {
output[i] = static_cast<float16>(output_tmp[i]);
}
@ -77,7 +78,7 @@ void DropoutGradCpuBwdKernel::DropoutBackwardKernel(const std::vector<AddressPtr
delete[] output_tmp;
delete[] mask_tmp;
} else if constexpr (std::is_same_v<T, float>) {
DropoutGrad(input, mask, output, SizeToInt(num_count_), scale);
DropoutGrad(input, mask, output, num_count_, scale);
}
}
} // namespace kernel

2
mindspore/ccsrc/backend/kernel_compiler/cpu/dropout_grad_kernel.h
View File

@ -40,7 +40,7 @@ class DropoutGradCpuBwdKernel : public CPUKernel {
TypeId dtype_{kTypeUnknown};
template <typename T>
void DropoutBackwardKernel(const std::vector<AddressPtr> &inputs, const std::vector<AddressPtr> &outputs,
float keep_prob);
size_t num_count, float keep_prob);
};
MS_REG_CPU_KERNEL(DropoutGrad, KernelAttr(), DropoutGradCpuBwdKernel);

101
mindspore/ccsrc/backend/kernel_compiler/cpu/eltwise_grad_cpu_kernel.cc
View File

@ -1,5 +1,5 @@
/**
* Copyright 2020-2021 Huawei Technologies Co., Ltd
* Copyright 2020 Huawei Technologies Co., Ltd
*
* Licensed under the Apache License, Version 2.0 (the "License");
* you may not use this file except in compliance with the License.
@ -13,10 +13,8 @@
* See the License for the specific language governing permissions and
* limitations under the License.
*/
#include "backend/kernel_compiler/cpu/eltwise_grad_cpu_kernel.h"
#include <string>
#include <map>
#include "backend/kernel_compiler/cpu/eltwise_grad_cpu_kernel.h"
#include "common/thread_pool.h"
#include "runtime/device/cpu/cpu_device_address.h"
#include "nnacl/fp32_grad/activation_grad.h"
@ -27,50 +25,50 @@ namespace mindspore {
namespace kernel {
template <typename T>
void EltWiseGradCPUKernel<T>::ReluGrad(const T *input1, const T *input2, T *out, size_t start, size_t end) const {
if constexpr (!std::is_same<T, float>::value) {
if constexpr (std::is_same_v<T, float>) {
int ret = ::ReluGrad(input1 + start, input2 + start, end - start, out + start);
if (ret == NNACL_ERR) {
MS_LOG(EXCEPTION) << "ReLUGrad failed.";
}
} else {
MS_LOG(EXCEPTION) << "ReLUGrad only support float";
}
int ret = ::ReluGrad(input1 + start, input2 + start, end - start, out + start);
if (ret == NNACL_ERR) {
MS_LOG(EXCEPTION) << "ReLUGrad execute failed.";
}
}
template <typename T>
void EltWiseGradCPUKernel<T>::ReLU6Grad(const T *input1, const T *input2, T *out, size_t start, size_t end) const {
if constexpr (!std::is_same<T, float>::value) {
if constexpr (std::is_same_v<T, float>) {
int ret = ::Relu6Grad(input1 + start, input2 + start, end - start, out + start);
if (ret == NNACL_ERR) {
MS_LOG(EXCEPTION) << "ReLU6Grad failed.";
}
} else {
MS_LOG(EXCEPTION) << "ReLU6Grad only support float";
}
int ret = ::Relu6Grad(input1 + start, input2 + start, end - start, out + start);
if (ret == NNACL_ERR) {
MS_LOG(EXCEPTION) << "ReLU6Grad execute failed.";
}
}
template <typename T>
void EltWiseGradCPUKernel<T>::AbsGrad(const T *input1, const T *input2, T *out, size_t start, size_t end) const {
if constexpr (!std::is_same<T, float>::value) {
if constexpr (std::is_same_v<T, float>) {
int ret = ::ElementAbsGrad(input1 + start, input2 + start, out + start, end - start);
if (ret == NNACL_ERR) {
MS_LOG(EXCEPTION) << "AbsGrad failed.";
}
} else {
MS_LOG(EXCEPTION) << "AbsGrad only support float";
}
int ret = ::ElementAbsGrad(input1 + start, input2 + start, out + start, end - start);
if (ret == NNACL_ERR) {
MS_LOG(EXCEPTION) << "AbsGrad execute failed.";
}
}
template <typename T>
void EltWiseGradCPUKernel<T>::SigmoidGrad(const T *input1, const T *input2, T *out, size_t start, size_t end) const {
if constexpr (!std::is_same<T, float>::value) {
if constexpr (std::is_same_v<T, float>) {
int ret = ::SigmoidGrad(input2 + start, input1 + start, end - start, out + start);
if (ret == NNACL_ERR) {
MS_LOG(EXCEPTION) << "SigmoidGrad failed.";
}
} else {
MS_LOG(EXCEPTION) << "SigmoidGrad only support float";
}
int ret = ::SigmoidGrad(input2 + start, input1 + start, end - start, out + start);
if (ret == NNACL_ERR) {
MS_LOG(EXCEPTION) << "SigmoidGrad execute failed.";
}
}
template <typename T>
@ -82,14 +80,14 @@ void EltWiseGradCPUKernel<T>::SqrtGrad(const T *input1, const T *input2, T *out,
template <typename T>
void EltWiseGradCPUKernel<T>::TanhGrad(const T *input1, const T *input2, T *out, size_t start, size_t end) const {
if constexpr (!std::is_same<T, float>::value) {
if constexpr (std::is_same_v<T, float>) {
int ret = ::TanhGrad(input2 + start, input1 + start, end - start, out + start);
if (ret == NNACL_ERR) {
MS_LOG(EXCEPTION) << "TanhGrad failed.";
}
} else {
MS_LOG(EXCEPTION) << "TanhGrad only support float";
}
int ret = ::TanhGrad(input2 + start, input1 + start, end - start, out + start);
if (ret == NNACL_ERR) {
MS_LOG(EXCEPTION) << "TanhGrad execute failed.";
}
}
template <typename T>
@ -209,18 +207,6 @@ void EltWiseGradCPUKernel<T>::AcoshGrad(const T *input1, const T *input2, T *out
}
}
template <typename T>
void EltWiseGradCPUKernel<T>::SoftplusGrad(const T *input1, const T *input2, T *out, size_t start, size_t end) const {
if constexpr (!std::is_same<T, float>::value) {
MS_LOG(EXCEPTION) << "SoftplusGrad only support float";
}
int ret = ::SoftplusGrad(input1 + start, input2 + start, end - start, out + start);
if (ret == NNACL_ERR) {
MS_LOG(EXCEPTION) << "SoftplusGrad execute failed.";
}
}
template <typename T>
void EltWiseGradCPUKernel<T>::InitKernel(const CNodePtr &kernel_node) {
MS_EXCEPTION_IF_NULL(kernel_node);
@ -233,19 +219,12 @@ bool EltWiseGradCPUKernel<T>::Launch(const std::vector<kernel::AddressPtr> &inpu
const std::vector<kernel::AddressPtr> &outputs) {
static const std::map<std::string,
std::function<void(EltWiseGradCPUKernel *, const T *, const T *, T *, size_t, size_t)>>
elt_map{{prim::kPrimReluGrad->name(), &EltWiseGradCPUKernel<T>::ReluGrad},
{prim::kPrimRelu6Grad->name(), &EltWiseGradCPUKernel<T>::ReLU6Grad},
{prim::kPrimSigmoidGrad->name(), &EltWiseGradCPUKernel<T>::SigmoidGrad},
{prim::kPrimAbsGrad->name(), &EltWiseGradCPUKernel<T>::AbsGrad},
{prim::kPrimTanhGrad->name(), &EltWiseGradCPUKernel<T>::TanhGrad},
{prim::kPrimSqrtGrad->name(), &EltWiseGradCPUKernel<T>::SqrtGrad},
{prim::kPrimGeLUGrad->name(), &EltWiseGradCPUKernel<T>::GeluGrad},
{prim::kPrimAsinGrad->name(), &EltWiseGradCPUKernel<T>::AsinGrad},
{prim::kPrimACosGrad->name(), &EltWiseGradCPUKernel<T>::ACosGrad},
{prim::kPrimAtanGrad->name(), &EltWiseGradCPUKernel<T>::AtanGrad},
{prim::kPrimAsinhGrad->name(), &EltWiseGradCPUKernel<T>::AsinhGrad},
{prim::kPrimAcoshGrad->name(), &EltWiseGradCPUKernel<T>::AcoshGrad},
{prim::kPrimSoftplusGrad->name(), &EltWiseGradCPUKernel<T>::SoftplusGrad}};
elt_map{{"ReluGrad", &EltWiseGradCPUKernel<T>::ReluGrad}, {"ReLU6Grad", &EltWiseGradCPUKernel<T>::ReLU6Grad},
{"SigmoidGrad", &EltWiseGradCPUKernel<T>::SigmoidGrad}, {"AbsGrad", &EltWiseGradCPUKernel<T>::AbsGrad},
{"TanhGrad", &EltWiseGradCPUKernel<T>::TanhGrad}, {"SqrtGrad", &EltWiseGradCPUKernel<T>::SqrtGrad},
{"GeLUGrad", &EltWiseGradCPUKernel<T>::GeluGrad}, {"AsinGrad", &EltWiseGradCPUKernel<T>::AsinGrad},
{"ACosGrad", &EltWiseGradCPUKernel<T>::ACosGrad}, {"AtanGrad", &EltWiseGradCPUKernel<T>::AtanGrad},
{"AsinhGrad", &EltWiseGradCPUKernel<T>::AsinhGrad}, {"AcoshGrad", &EltWiseGradCPUKernel<T>::AcoshGrad}};
if (inputs.size() < 2 || outputs.size() != 1) {
MS_LOG(ERROR) << kernel_name_ << " requires at least 2 inputs and 1 output, but got " << inputs.size()
<< " inputs and " << outputs.size() << " output.";
@ -259,9 +238,9 @@ bool EltWiseGradCPUKernel<T>::Launch(const std::vector<kernel::AddressPtr> &inpu
const auto input1 = reinterpret_cast<T *>(inputs[1]->addr);
auto output = reinterpret_cast<T *>(outputs[0]->addr);
ParallelLaunchAutoSearch(
CPUKernelUtils::ParallelForAutoSearch(
std::bind(elt_map.at(kernel_name_), this, input0, input1, output, std::placeholders::_1, std::placeholders::_2),
outputs[0]->size / sizeof(T), this, &parallel_search_info_);
outputs[0]->size / sizeof(T), &parallel_search_info_);
return true;
}
} // namespace kernel

7
mindspore/ccsrc/backend/kernel_compiler/cpu/eltwise_grad_cpu_kernel.h
View File

@ -1,5 +1,5 @@
/**
* Copyright 2020-2021 Huawei Technologies Co., Ltd
* Copyright 2020 Huawei Technologies Co., Ltd
*
* Licensed under the Apache License, Version 2.0 (the "License");
* you may not use this file except in compliance with the License.
@ -48,7 +48,6 @@ class EltWiseGradCPUKernel : public CPUKernel {
void AtanGrad(const T *input1, const T *input2, T *out, size_t start, size_t end) const;
void AsinhGrad(const T *input1, const T *input2, T *out, size_t start, size_t end) const;
void AcoshGrad(const T *input1, const T *input2, T *out, size_t start, size_t end) const;
void SoftplusGrad(const T *input1, const T *input2, T *out, size_t start, size_t end) const;
std::string kernel_name_ = "";
};
@ -104,10 +103,6 @@ MS_REG_CPU_KERNEL_T(
AcoshGrad,
KernelAttr().AddInputAttr(kNumberTypeFloat32).AddInputAttr(kNumberTypeFloat32).AddOutputAttr(kNumberTypeFloat32),
EltWiseGradCPUKernel, float);
MS_REG_CPU_KERNEL_T(
SoftplusGrad,
KernelAttr().AddInputAttr(kNumberTypeFloat32).AddInputAttr(kNumberTypeFloat32).AddOutputAttr(kNumberTypeFloat32),
EltWiseGradCPUKernel, float);
} // namespace kernel
} // namespace mindspore

31
mindspore/ccsrc/backend/kernel_compiler/cpu/fl/fused_pull_weight_kernel.h
View File

@ -30,7 +30,7 @@
namespace mindspore {
namespace kernel {
// The duration between two PullWeights requests when return code is ResponseCode_SucNotReady.
// The duration between two downloading requests when return code is ResponseCode_SucNotReady.
constexpr int kRetryDurationOfPullWeights = 200;
template <typename T>
class FusedPullWeightKernel : public CPUKernel {
@ -51,17 +51,19 @@ class FusedPullWeightKernel : public CPUKernel {
MS_EXCEPTION_IF_NULL(fbb);
total_iteration_++;
uint64_t step_num_per_iteration = fl::worker::FLWorker::GetInstance().worker_step_num_per_iteration();
// The worker has to train kWorkerTrainStepNum standalone iterations before it communicates with server.
MS_LOG(INFO) << "Try to pull weights. Local step number: " << total_iteration_
<< ", step number needs to run per iteration: " << step_num_per_iteration;
if (step_num_per_iteration != fl::kOneStepPerIteration &&
total_iteration_ % step_num_per_iteration != fl::kTrainBeginStepNum) {
if (total_iteration_ % fl::worker::FLWorker::GetInstance().worker_step_num_per_iteration() !=
fl::kTrainBeginStepNum) {
return true;
}
fl_iteration_++;
MS_LOG(INFO) << "Launching pulling weight for federated learning iteration " << fl_iteration_;
if (fl_iteration_ > ps::PSContext::instance()->fl_iteration_num()) {
MS_LOG(INFO) << ps::PSContext::instance()->fl_iteration_num() << " iterations are completed.";
fl_iteration_ = 1;
}
MS_LOG(INFO) << "Start pulling weight for federated learning iteration " << fl_iteration_;
if (!BuildPullWeightReq(fbb)) {
MS_LOG(EXCEPTION) << "Building request for FusedPullWeight failed.";
return false;
@ -71,16 +73,11 @@ class FusedPullWeightKernel : public CPUKernel {
const schema::ResponsePullWeight *pull_weight_rsp = nullptr;
int retcode = schema::ResponseCode_SucNotReady;
while (retcode == schema::ResponseCode_SucNotReady) {
if (!fl::worker::FLWorker::GetInstance().running()) {
MS_LOG(WARNING) << "Worker has finished.";
return true;
}
if (!fl::worker::FLWorker::GetInstance().SendToServer(
0, fbb->GetBufferPointer(), fbb->GetSize(), ps::core::TcpUserCommand::kPullWeight, &pull_weight_rsp_msg)) {
MS_LOG(WARNING) << "Sending request for FusedPullWeight to server 0 failed. Retry later.";
retcode = schema::ResponseCode_SucNotReady;
std::this_thread::sleep_for(std::chrono::milliseconds(kRetryDurationOfPullWeights));
continue;
MS_LOG(WARNING) << "Sending request for FusedPullWeight to server 0 failed. This iteration is dropped.";
fl::worker::FLWorker::GetInstance().SetIterationRunning();
return true;
}
MS_EXCEPTION_IF_NULL(pull_weight_rsp_msg);
@ -91,8 +88,6 @@ class FusedPullWeightKernel : public CPUKernel {
fl_iteration_ = pull_weight_rsp->iteration();
MS_LOG(DEBUG) << "Server is not ready for downloading yet. Reason: " << pull_weight_rsp->reason()->str()
<< ". Retry later.";
// Recreate fbb to avoid memory leak of FlatBuffers.
fbb = std::make_shared<fl::FBBuilder>();
if (!BuildPullWeightReq(fbb)) {
MS_LOG(EXCEPTION) << "Building request for FusedDownloadWeightsByKeys failed.";
return false;
@ -121,7 +116,7 @@ class FusedPullWeightKernel : public CPUKernel {
return false;
}
}
MS_LOG(INFO) << "Pull weights for " << weight_full_names_ << " success. Iteration: " << fl_iteration_;
MS_LOG(INFO) << "Pull weights for " << weight_full_names_ << " succeed. Iteration: " << fl_iteration_;
fl::worker::FLWorker::GetInstance().SetIterationRunning();
return true;
}

31
mindspore/ccsrc/backend/kernel_compiler/cpu/fl/fused_push_weight_kernel.h
View File

@ -28,7 +28,7 @@
namespace mindspore {
namespace kernel {
// The duration between two PushWeights requests when return code is ResponseCode_SucNotReady.
// The duration between two uploading requests when return code is ResponseCode_SucNotReady.
constexpr int kRetryDurationOfPushWeights = 200;
template <typename T>
class FusedPushWeightKernel : public CPUKernel {
@ -49,17 +49,19 @@ class FusedPushWeightKernel : public CPUKernel {
MS_EXCEPTION_IF_NULL(fbb);
total_iteration_++;
uint64_t step_num_per_iteration = fl::worker::FLWorker::GetInstance().worker_step_num_per_iteration();
// The worker has to train kWorkerTrainStepNum standalone iterations before it communicates with server.
MS_LOG(INFO) << "Try to push weights. Local step number: " << total_iteration_
<< ", step number needs to run per iteration: " << step_num_per_iteration;
if (step_num_per_iteration != fl::kOneStepPerIteration &&
total_iteration_ % step_num_per_iteration != fl::kTrainEndStepNum) {
if (total_iteration_ % fl::worker::FLWorker::GetInstance().worker_step_num_per_iteration() !=
fl::kTrainBeginStepNum) {
return true;
}
fl_iteration_++;
MS_LOG(INFO) << "Launching pushing weight for federated learning iteration " << fl_iteration_;
if (fl_iteration_ > ps::PSContext::instance()->fl_iteration_num()) {
MS_LOG(INFO) << ps::PSContext::instance()->fl_iteration_num() << " iterations are completed.";
fl_iteration_ = 1;
}
MS_LOG(INFO) << "Start pushing weight for federated learning iteration " << fl_iteration_;
if (!BuildPushWeightReq(fbb, inputs)) {
MS_LOG(EXCEPTION) << "Building request for FusedPushWeight failed.";
return false;
@ -71,17 +73,13 @@ class FusedPushWeightKernel : public CPUKernel {
const schema::ResponsePushWeight *push_weight_rsp = nullptr;
int retcode = schema::ResponseCode_SucNotReady;
while (retcode == schema::ResponseCode_SucNotReady) {
if (!fl::worker::FLWorker::GetInstance().running()) {
MS_LOG(WARNING) << "Worker has finished.";
return true;
}
if (!fl::worker::FLWorker::GetInstance().SendToServer(i, fbb->GetBufferPointer(), fbb->GetSize(),
ps::core::TcpUserCommand::kPushWeight,
&push_weight_rsp_msg)) {
MS_LOG(WARNING) << "Sending request for FusedPushWeight to server " << i << " failed.";
retcode = schema::ResponseCode_SucNotReady;
std::this_thread::sleep_for(std::chrono::milliseconds(kRetryDurationOfPushWeights));
continue;
MS_LOG(WARNING) << "Sending request for FusedPushWeight to server " << i
<< " failed. This iteration is dropped.";
fl::worker::FLWorker::GetInstance().SetIterationCompleted();
return true;
}
MS_EXCEPTION_IF_NULL(push_weight_rsp_msg);
@ -107,7 +105,8 @@ class FusedPushWeightKernel : public CPUKernel {
}
}
MS_LOG(INFO) << "Push weights for " << weight_full_names_ << " success. Iteration: " << fl_iteration_;
MS_LOG(INFO) << "Push weights for " << weight_full_names_ << " succeed. Iteration: " << fl_iteration_;
fl::worker::FLWorker::GetInstance().SetIterationCompleted();
return true;
}

20
mindspore/ccsrc/backend/kernel_compiler/cpu/masked_select_cpu_kernel.h
View File

@ -52,26 +52,6 @@ MS_REG_CPU_KERNEL_T(
MaskedSelect,
KernelAttr().AddInputAttr(kNumberTypeInt32).AddInputAttr(kNumberTypeBool).AddOutputAttr(kNumberTypeInt32),
MaskedSelectCPUKernel, int);
MS_REG_CPU_KERNEL_T(
MaskedSelect,
KernelAttr().AddInputAttr(kNumberTypeInt16).AddInputAttr(kNumberTypeBool).AddOutputAttr(kNumberTypeInt16),
MaskedSelectCPUKernel, int16_t);
MS_REG_CPU_KERNEL_T(
MaskedSelect,
KernelAttr().AddInputAttr(kNumberTypeInt64).AddInputAttr(kNumberTypeBool).AddOutputAttr(kNumberTypeInt64),
MaskedSelectCPUKernel, int64_t);
MS_REG_CPU_KERNEL_T(
MaskedSelect,
KernelAttr().AddInputAttr(kNumberTypeFloat16).AddInputAttr(kNumberTypeBool).AddOutputAttr(kNumberTypeFloat16),
MaskedSelectCPUKernel, float16);
MS_REG_CPU_KERNEL_T(
MaskedSelect,
KernelAttr().AddInputAttr(kNumberTypeFloat64).AddInputAttr(kNumberTypeBool).AddOutputAttr(kNumberTypeFloat64),
MaskedSelectCPUKernel, double);
} // namespace kernel
} // namespace mindspore
#endif // MINDSPORE_CCSRC_BACKEND_KERNEL_COMPILER_CPU_MASKED_SELECTED_CPU_KERNEL_H_

32
mindspore/ccsrc/backend/kernel_compiler/cpu/masked_select_grad_cpu_kernel.h
View File

@ -58,38 +58,6 @@ MS_REG_CPU_KERNEL_T(MaskedSelectGrad,
.AddInputAttr(kNumberTypeInt32)
.AddOutputAttr(kNumberTypeInt32),
MaskedSelectGradCPUKernel, int);
MS_REG_CPU_KERNEL_T(MaskedSelectGrad,
KernelAttr()
.AddInputAttr(kNumberTypeFloat16)
.AddInputAttr(kNumberTypeBool)
.AddInputAttr(kNumberTypeFloat16)
.AddOutputAttr(kNumberTypeFloat16),
MaskedSelectGradCPUKernel, float16);
MS_REG_CPU_KERNEL_T(MaskedSelectGrad,
KernelAttr()
.AddInputAttr(kNumberTypeFloat64)
.AddInputAttr(kNumberTypeBool)
.AddInputAttr(kNumberTypeFloat64)
.AddOutputAttr(kNumberTypeFloat64),
MaskedSelectGradCPUKernel, double);
MS_REG_CPU_KERNEL_T(MaskedSelectGrad,
KernelAttr()
.AddInputAttr(kNumberTypeInt16)
.AddInputAttr(kNumberTypeBool)
.AddInputAttr(kNumberTypeInt16)
.AddOutputAttr(kNumberTypeInt16),
MaskedSelectGradCPUKernel, int16_t);
MS_REG_CPU_KERNEL_T(MaskedSelectGrad,
KernelAttr()
.AddInputAttr(kNumberTypeInt64)
.AddInputAttr(kNumberTypeBool)
.AddInputAttr(kNumberTypeInt64)
.AddOutputAttr(kNumberTypeInt64),
MaskedSelectGradCPUKernel, int64_t);
} // namespace kernel
} // namespace mindspore
#endif // MINDSPORE_CCSRC_BACKEND_KERNEL_COMPILER_CPU_MASKED_SELECTED_GRAD_CPU_KERNEL_H_

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