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提交 37faf495 编写于 作者: R ranqiu
浏览文件
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Merge branch 'develop' of https://github.com/PaddlePaddle/Paddle into r-doc

上级 e635e3fd d865b047
隐藏空白更改
内联 并排
Showing with 1539 addition and 357 deletion
.travis.yml
浏览文件 @ 37faf495
......@@ -36,10 +36,6 @@ before_install:
# protobuf version.
- sudo pip install -r $TRAVIS_BUILD_DIR/python/requirements.txt
- sudo pip install wheel sphinx==1.5.6 recommonmark sphinx-rtd-theme==0.1.9 virtualenv pre-commit LinkChecker
- curl https://glide.sh/get | bash
- eval "$(GIMME_GO_VERSION=1.8.3 gimme)"
- go get -u github.com/alecthomas/gometalinter
- gometalinter --install
- |
function timeout() { perl -e 'alarm shift; exec @ARGV' "$@"; }
script:
......
CMakeLists.txt
浏览文件 @ 37faf495
......@@ -27,7 +27,7 @@ if(NOT CMAKE_CROSSCOMPILING)
endif(NOT CMAKE_CROSSCOMPILING)
find_package(Git REQUIRED)
find_package(Threads REQUIRED)
if(NOT ANDROID)
if(NOT ANDROID AND NOT IOS)
find_package(Boost QUIET)
endif()
......@@ -64,27 +64,29 @@ if(NOT CMAKE_BUILD_TYPE)
FORCE)
endif()
if(ANDROID)
if(${CMAKE_SYSTEM_VERSION} VERSION_LESS "16")
message(FATAL_ERROR "Unsupport standalone toolchains with Android API level lower than 16")
elseif(${CMAKE_SYSTEM_VERSION} VERSION_LESS "21")
# TODO: support glog for Android api 16 ~ 19 in the future
message(WARNING "Using the unofficial git repository <https://github.com/Xreki/glog.git> instead")
if(ANDROID OR IOS)
if(ANDROID)
if(AND ${CMAKE_SYSTEM_VERSION} VERSION_LESS "16")
message(FATAL_ERROR "Unsupport standalone toolchains with Android API level lower than 16")
elseif(${CMAKE_SYSTEM_VERSION} VERSION_LESS "21")
# TODO: support glog for Android api 16 ~ 19 in the future
message(WARNING "Using the unofficial git repository <https://github.com/Xreki/glog.git> instead")
endif()
endif()
set(WITH_GPU OFF CACHE STRING
"Disable GPU when cross-compiling for Android" FORCE)
"Disable GPU when cross-compiling for Android and iOS" FORCE)
set(WITH_AVX OFF CACHE STRING
"Disable AVX when cross-compiling for Android" FORCE)
"Disable AVX when cross-compiling for Android and iOS" FORCE)
set(WITH_PYTHON OFF CACHE STRING
"Disable PYTHON when cross-compiling for Android" FORCE)
"Disable PYTHON when cross-compiling for Android and iOS" FORCE)
set(WITH_RDMA OFF CACHE STRING
"Disable RDMA when cross-compiling for Android" FORCE)
"Disable RDMA when cross-compiling for Android and iOS" FORCE)
set(WITH_MKLDNN OFF CACHE STRING
"Disable MKLDNN when cross-compiling for Android" FORCE)
"Disable MKLDNN when cross-compiling for Android and iOS" FORCE)
set(WITH_MKLML OFF CACHE STRING
"Disable MKLML package when cross-compiling for Android" FORCE)
endif(ANDROID)
"Disable MKLML package when cross-compiling for Android and iOS" FORCE)
endif()
set(THIRD_PARTY_PATH "${CMAKE_BINARY_DIR}/third_party" CACHE STRING
"A path setting third party libraries download & build directories.")
......
cmake/cblas.cmake
浏览文件 @ 37faf495
......@@ -171,3 +171,10 @@ if (REFERENCE_CBLAS_INCLUDE_DIR AND REFERENCE_CBLAS_LIBRARY)
add_definitions(-DPADDLE_USE_REFERENCE_CBLAS)
message(STATUS "Found reference-cblas (include: ${CBLAS_INC_DIR}, library: ${CBLAS_LIBRARIES})")
endif()
if(IOS_USE_VECLIB_FOR_BLAS AND VECLIB_FOUND)
set(CBLAS_FOUND ON)
set(CBLAS_PROVIDER vecLib)
set(CBLAS_INC_DIR ${VECLIB_INC_DIR})
add_definitions(-DPADDLE_USE_VECLIB)
endif()
cmake/cross_compiling/ios.cmake 0 → 100644
浏览文件 @ 37faf495
# Copyright (c) 2016 PaddlePaddle Authors. All Rights Reserve.
#
# 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.
# This is a toolchain file for cross-compiling for iOS, and the
# configuration largely refers to public toolchain file:
# https://raw.githubusercontent.com/leetal/ios-cmake/master/ios.toolchain.cmake
# and
# https://github.com/cristeab/ios-cmake
#
# Supports options:
# IOS_PLATFORM = OS (default) or SIMULATOR
# This decides if SDKS will be selected from the iPhoneOS.platform or iPhoneSimulator.platform folders
# OS - the default, used to build for iPhone and iPad physical devices, which have an arm arch.
# SIMULATOR - used to build for the Simulator platforms, which have an x86 arch.
# IOS_ARCH
# The archectures wanted to support, such "arm64", "armv7;arm64"
# IOS_DEPLOYMENT_TARGET
# The minimum iOS deployment version, such as "7.0"
# IOS_ENABLE_BITCODE = ON (default) or OFF
# IOS_USE_VECLIB_FOR_BLAS = OFF (default) or ON
# IOS_DEVELOPER_ROOT = automatic(default) or /path/to/platform/Developer folder
# By default this location is automatcially chosen based on the IOS_PLATFORM value above.
# If set manually, it will override the default location and force the user of a particular Developer Platform
# IOS_SDK_ROOT = automatic(default) or /path/to/platform/Developer/SDKs/SDK folder
# By default this location is automatcially chosen based on the IOS_DEVELOPER_ROOT value.
# In this case it will always be the most up-to-date SDK found in the IOS_DEVELOPER_ROOT path.
# If set manually, this will force the use of a specific SDK version
# Macros:
# set_xcode_property (TARGET XCODE_PROPERTY XCODE_VALUE)
# A convenience macro for setting xcode specific properties on targets
# example: set_xcode_property (myioslib IPHONEOS_DEPLOYMENT_TARGET "3.1")
# find_host_package (PROGRAM ARGS)
# A macro used to find executable programs on the host system, not within the iOS environment.
# Thanks to the android-cmake project for providing the command
if(NOT IOS)
return()
endif()
set(CMAKE_SYSTEM_NAME Darwin)
# Get the Xcode version being used.
execute_process(COMMAND xcodebuild -version
OUTPUT_VARIABLE XCODE_VERSION
RESULT_VARIABLE XCODE_VERSION_RESULT
ERROR_QUIET OUTPUT_STRIP_TRAILING_WHITESPACE)
if(NOT ${XCODE_VERSION_RESULT})
string(REGEX MATCH "Xcode [0-9\\.]+" XCODE_VERSION "${XCODE_VERSION}")
string(REGEX REPLACE "Xcode ([0-9\\.]+)" "\\1" XCODE_VERSION "${XCODE_VERSION}")
message(STATUS "Building with Xcode version: ${XCODE_VERSION}")
else()
message(FATAL_ERROR "Cannot execute xcodebuild, please check whether xcode is installed.")
endif()
# Required as of cmake 2.8.10
set(CMAKE_OSX_DEPLOYMENT_TARGET "" CACHE STRING "Force unset of the deployment target for iOS" FORCE)
# Setup iOS platform unless specified manually with IOS_PLATFORM
if(NOT DEFINED IOS_PLATFORM)
set(IOS_PLATFORM "OS")
endif()
set(IOS_PLATFORM ${IOS_PLATFORM} CACHE STRING "Type of iOS Platform")
# Set the architecture for iOS
if(NOT DEFINED IOS_ARCH)
if(IOS_PLATFORM STREQUAL "OS")
# FIXME(liuyiqun): support "armv7;armv7s;arm64" future
set(IOS_ARCH "arm64")
elseif(IOS_PLATFORM STREQUAL "SIMULATOR")
set(IOS_ARCH "i386;x86_64")
elseif(IOS_PLATFORM STREQUAL "WATCHOS")
set(IOS_ARCH armv7k)
endif()
endif()
set(CMAKE_OSX_ARCHITECTURES ${IOS_ARCH} CACHE string "Build architecture for iOS")
# Specify minimum iOS deployment version
if(NOT DEFINED IOS_DEPLOYMENT_TARGET)
set(IOS_DEPLOYMENT_TARGET "7.0")
endif()
set(IOS_DEPLOYMENT_TARGET ${IOS_DEPLOYMENT_TARGET} CACHE STRING "Minimum iOS version")
# Whether to enable bitcode
if(NOT DEFINED IOS_ENABLE_BITCODE)
set(IOS_ENABLE_BITCODE ON)
endif()
set(IOS_ENABLE_BITCODE ${IOS_ENABLE_BITCODE} CACHE BOOL "Whether to enable bitcode")
if(NOT DEFINED IOS_USE_VECLIB_FOR_BLAS)
set(IOS_USE_VECLIB_FOR_BLAS OFF)
endif()
set(IOS_USE_VECLIB_FOR_BLAS ${IOS_UES_VECLIB_FOR_BLAS} CACHE BOOL "Whether to use veclib")
# Check the platform selection and setup for developer root
if(${IOS_PLATFORM} STREQUAL "OS")
set(IOS_PLATFORM_LOCATION "iPhoneOS.platform")
set(XCODE_IOS_PLATFORM iphoneos)
# This causes the installers to properly locate the output libraries
set(CMAKE_XCODE_EFFECTIVE_PLATFORMS "-iphoneos")
elseif(${IOS_PLATFORM} STREQUAL "SIMULATOR")
set(IOS_PLATFORM_LOCATION "iPhoneSimulator.platform")
set(XCODE_IOS_PLATFORM iphonesimulator)
# This causes the installers to properly locate the output libraries
set(CMAKE_XCODE_EFFECTIVE_PLATFORMS "-iphonesimulator")
elseif(${IOS_PLATFORM} STREQUAL "WATCHOS")
set(IOS_PLATFORM_LOCATION "WatchOS.platform")
set(XCODE_IOS_PLATFORM watchos)
# This causes the installers to properly locate the output libraries
set(CMAKE_XCODE_EFFECTIVE_PLATFORMS "-watchos")
else(${IOS_PLATFORM} STREQUAL "OS")
message(FATAL_ERROR "Unsupported IOS_PLATFORM value selected. Please set to\n"
"\t OS, SIMULATOR, or WATCHOS.")
endif()
# Check iOS developer toolchain
if(NOT DEFINED IOS_DEVELOPER_ROOT)
# Setup iOS developer location
execute_process(COMMAND xcode-select -print-path
OUTPUT_VARIABLE XCODE_DEVELOPER_DIR
RESULT_VARIABLE XCODE_DEVELOPER_DIR_RESULT
ERROR_QUIET OUTPUT_STRIP_TRAILING_WHITESPACE)
# Xcode 4.3 changed the installation location, choose the most recent one available
if(${XCODE_VERSION} VERSION_LESS "4.3.0")
set(IOS_DEVELOPER_ROOT "/Developer/Platforms/${IOS_PLATFORM_LOCATION}/Developer")
else()
set(IOS_DEVELOPER_ROOT "${XCODE_DEVELOPER_DIR}/Platforms/${IOS_PLATFORM_LOCATION}/Developer")
endif()
endif()
if(EXISTS ${IOS_DEVELOPER_ROOT})
set(IOS_DEVELOPER_ROOT ${IOS_DEVELOPER_ROOT} CACHE PATH "Location of iOS Platform")
else()
message(FATAL_ERROR "Invalid IOS_DEVELOPER_ROOT: ${IOS_DEVELOPER_ROOT} does not exist.")
endif()
# Check iOS SDK
if(NOT DEFINED IOS_SDK_ROOT)
# Find and use the most recent iOS sdk
file(GLOB IOS_SDK_LISTS "${IOS_DEVELOPER_ROOT}/SDKs/*")
if(IOS_SDK_LISTS)
list(SORT IOS_SDK_LISTS)
list(REVERSE IOS_SDK_LISTS)
list(GET IOS_SDK_LISTS 0 IOS_SDK_ROOT)
else(IOS_SDK_LISTS)
message(FATAL_ERROR "No iOS SDK's found in default search path ${IOS_DEVELOPER_ROOT}."
" Please manually set IOS_SDK_ROOT or install the iOS SDK.")
endif(IOS_SDK_LISTS)
endif()
if(EXISTS ${IOS_SDK_ROOT})
set(IOS_SDK_ROOT ${IOS_SDK_ROOT} CACHE PATH "Location of the selected iOS SDK")
message(STATUS "iOS toolchain: ${IOS_SDK_ROOT}")
else()
message(FATAL_ERROR "Invalid IOS_SDK_ROOT: ${IOS_SDK_ROOT} does not exist.")
endif()
# Set the sysroot default to the most recent SDK
set(CMAKE_OSX_SYSROOT ${IOS_SDK_ROOT} CACHE PATH "Sysroot used for iOS support")
# Get version of iOS SDK
execute_process(COMMAND xcodebuild -sdk ${CMAKE_OSX_SYSROOT} -version SDKVersion
OUTPUT_VARIABLE IOS_SDK_VERSION
RESULT_VARIABLE IOS_SDK_VERSION_RESULT
ERROR_QUIET OUTPUT_STRIP_TRAILING_WHITESPACE)
if(${IOS_SDK_VERSION_RESULT})
string(REGEX MATCH "(([0-9]+)\\.)+([0-9]+)" IOS_SDK_VERSION "${IOS_SDK_ROOT}")
endif()
if(NOT IOS_SDK_VERSION)
message(WARNING "Cannot get SDK's version.")
set(IOS_SDK_VERSION 1)
endif()
set(CMAKE_SYSTEM_VERSION ${IOS_SDK_VERSION})
# Find the C & C++ compilers for the specified SDK.
if(NOT CMAKE_C_COMPILER)
# Default to use clang
execute_process(COMMAND xcrun -sdk ${CMAKE_OSX_SYSROOT} -find clang
OUTPUT_VARIABLE IOS_C_COMPILER
RESULT_VARIABLE IOS_C_COMPILER_RESULT
ERROR_QUIET OUTPUT_STRIP_TRAILING_WHITESPACE)
if(${IOS_C_COMPILER_RESULT})
get_filename_component(IOS_C_COMPILER clang PROGRAM)
endif()
else(NOT CMAKE_C_COMPILER)
# User can set it in cmake command
get_filename_component(IOS_C_COMPILER ${CMAKE_C_COMPILER} PROGRAM)
endif(NOT CMAKE_C_COMPILER)
if(NOT EXISTS ${IOS_C_COMPILER})
message(FATAL_ERROR "Cannot find C compiler: ${IOS_C_COMPILER}")
endif()
if(NOT CMAKE_CXX_COMPILER)
# Default to use clang++
execute_process(COMMAND xcrun -sdk ${CMAKE_OSX_SYSROOT} -find clang++
OUTPUT_VARIABLE IOS_CXX_COMPILER
RESULT_VARIABLE IOS_CXX_COMPILER_RESULT
ERROR_QUIET OUTPUT_STRIP_TRAILING_WHITESPACE)
if(${IOS_CXX_COMPILER_RESULT})
get_filename_component(IOS_CXX_COMPILER clang++ PROGRAM)
endif()
else(NOT CMAKE_CXX_COMPILER)
# User can set it in cmake command
get_filename_component(IOS_CXX_COMPILER ${CMAKE_CXX_COMPILER} PROGRAM)
endif(NOT CMAKE_CXX_COMPILER)
if(NOT EXISTS ${IOS_CXX_COMPILER})
message(FATAL_ERROR "Cannot find CXX compiler: ${IOS_CXX_COMPILER}")
endif()
set(CMAKE_C_COMPILER ${IOS_C_COMPILER} CACHE PATH "C compiler" FORCE)
set(CMAKE_CXX_COMPILER ${IOS_CXX_COMPILER} CACHE PATH "CXX compiler" FORCE)
set(CMAKE_C_OSX_COMPATIBILITY_VERSION_FLAG "-compatibility_version ")
set(CMAKE_C_OSX_CURRENT_VERSION_FLAG "-current_version ")
set(CMAKE_CXX_OSX_COMPATIBILITY_VERSION_FLAG "${CMAKE_C_OSX_COMPATIBILITY_VERSION_FLAG}")
set(CMAKE_CXX_OSX_CURRENT_VERSION_FLAG "${CMAKE_C_OSX_CURRENT_VERSION_FLAG}")
# Set iOS specific C/C++ flags
if(IOS_PLATFORM STREQUAL "OS")
if(XCODE_VERSION VERSION_LESS "7.0")
set(XCODE_IOS_PLATFORM_VERSION_FLAGS "-mios-version-min=${IOS_DEPLOYMENT_TARGET}")
else()
# Xcode 7.0+ uses flags we can build directly from XCODE_IOS_PLATFORM.
set(XCODE_IOS_PLATFORM_VERSION_FLAGS "-m${XCODE_IOS_PLATFORM}-version-min=${IOS_DEPLOYMENT_TARGET}")
endif()
else()
set(XCODE_IOS_FLATFORM_VERSION_FLAGS "-mios-simulator-version-min=${IOS_DEPLOYMENT_TARGET}")
endif()
if(IOS_ENABLE_BITCODE)
set(XCODE_IOS_BITCODE_FLAGS "${IOS_COMPILER_FLAGS} -fembed-bitcode")
else()
set(XCODE_IOS_BITCODE_FLAGS "")
endif()
set(IOS_COMPILER_FLAGS "${XCODE_IOS_PLATFORM_VERSION_FLAGS} ${XCODE_IOS_BITCODE_FLAGS}")
# Hidden visibilty is required for cxx on iOS
set(CMAKE_C_FLAGS "${IOS_COMPILER_FLAGS} ${CMAKE_C_FLAGS}" CACHE STRING "C flags")
set(CMAKE_CXX_FLAGS "${IOS_COMPILER_FLAGS} -fvisibility-inlines-hidden ${CMAKE_CXX_FLAGS}" CACHE STRING "CXX flags")
set(IOS_LINK_FLAGS "${XCODE_IOS_PLATFORM_VERSION_FLAGS} -Wl,-search_paths_first")
if(IOS_USE_VECLIB_FOR_BLAS)
# Find vecLib for iOS
set(VECLIB_SEARCH_DIRS
${IOS_SDK_ROOT}/System/Library/Frameworks/Accelerate.framework/Versions/Current/Frameworks
${IOS_SDK_ROOT}/System/Library/Frameworks/Accelerate.framework/Frameworks
)
find_path(VECLIB_INC_DIR vecLib.h PATHS ${VECLIB_SEARCH_DIRS}/vecLib.framework/Headers)
include(FindPackageHandleStandardArgs)
find_package_handle_standard_args(vecLib DEFAULT_MSG VECLIB_INC_DIR)
if(VECLIB_FOUND)
if(VECLIB_INC_DIR MATCHES "^/System/Library/Frameworks/vecLib.framework.*")
set(IOS_LINK_FLAGS ${IOS_LINK_FLAGS} -lcblas "-framework vecLib")
message(STATUS "Found standalone vecLib.framework")
else()
set(IOS_LINK_FLAGS ${IOS_LINK_FLAGS} -lcblas "-framework Accelerate")
message(STATUS "Found vecLib as part of Accelerate.framework")
endif()
endif()
endif()
set(CMAKE_C_LINK_FLAGS "${IOS_LINK_FLAGS} ${CMAKE_C_LINK_FLAGS}")
set(CMAKE_CXX_LINK_FLAGS "${IOS_LINK_FLAGS} ${CMAKE_CXX_LINK_FLAGS}")
set(CMAKE_PLATFORM_HAS_INSTALLNAME 1)
if(NOT IOS_ENABLE_BITCODE)
set(CMAKE_SHARED_LIBRARY_CREATE_C_FLAGS "-dynamiclib -headerpad_max_install_names")
set(CMAKE_SHARED_MODULE_CREATE_C_FLAGS "-bundle -headerpad_max_install_names")
else()
set(CMAKE_SHARED_LIBRARY_CREATE_C_FLAGS "-dynamiclib")
set(CMAKE_SHARED_MODULE_CREATE_C_FLAGS "-bundle")
endif()
set(CMAKE_SHARED_MODULE_LOADER_C_FLAG "-Wl,-bundle_loader,")
set(CMAKE_SHARED_MODULE_LOADER_CXX_FLAG "-Wl,-bundle_loader,")
set(CMAKE_FIND_LIBRARY_SUFFIXES ".dylib" ".so" ".a")
# hack: if a new cmake (which uses CMAKE_INSTALL_NAME_TOOL) runs on an old build tree
# (where install_name_tool was hardcoded) and where CMAKE_INSTALL_NAME_TOOL isn't in the cache
# and still cmake didn't fail in CMakeFindBinUtils.cmake (because it isn't rerun)
# hardcode CMAKE_INSTALL_NAME_TOOL here to install_name_tool, so it behaves as it did before, Alex
if(NOT DEFINED CMAKE_INSTALL_NAME_TOOL)
find_program(CMAKE_INSTALL_NAME_TOOL install_name_tool)
endif()
# Set the find root to the iOS developer roots and to user defined paths
set(CMAKE_FIND_ROOT_PATH ${IOS_DEVELOPER_ROOT} ${IOS_SDK_ROOT} ${CMAKE_PREFIX_PATH}
CACHE string "iOS find search path root")
# default to searching for frameworks first
set(CMAKE_FIND_FRAMEWORK FIRST)
# set up the default search directories for frameworks
set(CMAKE_SYSTEM_FRAMEWORK_PATH
${IOS_SDK_ROOT}/System/Library/Frameworks
${IOS_SDK_ROOT}/System/Library/PrivateFrameworks
${IOS_SDK_ROOT}/Developer/Library/Frameworks
)
# only search the iOS sdks, not the remainder of the host filesystem
set(CMAKE_FIND_ROOT_PATH_MODE_PROGRAM NEVER)
set(CMAKE_FIND_ROOT_PATH_MODE_LIBRARY ONLY)
set(CMAKE_FIND_ROOT_PATH_MODE_INCLUDE ONLY)
message(STATUS "iOS: Targeting iOS '${CMAKE_SYSTEM_VERSION}', "
"building for '${IOS_PLATFORM}' platform, with architecture '${CMAKE_OSX_ARCHITECTURES}'")
message(STATUS "System CMAKE_C_FLAGS: ${CMAKE_C_FLAGS}")
message(STATUS "System CMAKE_CXX_FLAGS: ${CMAKE_CXX_FLAGS}")
# Used in ExternalProject command
string(REPLACE ";" "\\$<SEMICOLON>" EXTERNAL_IOS_ARCHITECTURES "${CMAKE_OSX_ARCHITECTURES}")
set(EXTERNAL_OPTIONAL_ARGS
-DCMAKE_OSX_SYSROOT=${CMAKE_OSX_SYSROOT}
-DCMAKE_OSX_ARCHITECTURES=${EXTERNAL_IOS_ARCHITECTURES})
# This little macro lets you set any XCode specific property
macro(set_xcode_property TARGET XCODE_PROPERTY XCODE_VALUE)
set_property (TARGET ${TARGET} PROPERTY XCODE_ATTRIBUTE_${XCODE_PROPERTY} ${XCODE_VALUE})
endmacro(set_xcode_property)
# This macro lets you find executable programs on the host system
macro(find_host_package)
set(CMAKE_FIND_ROOT_PATH_MODE_PROGRAM NEVER)
set(CMAKE_FIND_ROOT_PATH_MODE_LIBRARY NEVER)
set(CMAKE_FIND_ROOT_PATH_MODE_INCLUDE NEVER)
set(IOS FALSE)
find_package(${ARGN})
set(IOS TRUE)
set(CMAKE_FIND_ROOT_PATH_MODE_PROGRAM ONLY)
set(CMAKE_FIND_ROOT_PATH_MODE_LIBRARY ONLY)
set(CMAKE_FIND_ROOT_PATH_MODE_INCLUDE ONLY)
endmacro(find_host_package)
cmake/external/gflags.cmake
浏览文件 @ 37faf495
......@@ -39,13 +39,14 @@ ExternalProject_Add(
PREFIX ${GFLAGS_SOURCES_DIR}
UPDATE_COMMAND ""
CMAKE_ARGS -DCMAKE_CXX_COMPILER=${CMAKE_CXX_COMPILER}
CMAKE_ARGS -DCMAKE_C_COMPILER=${CMAKE_C_COMPILER}
CMAKE_ARGS -DCMAKE_CXX_FLAGS=${CMAKE_CXX_FLAGS}
CMAKE_ARGS -DCMAKE_C_FLAGS=${CMAKE_C_FLAGS}
CMAKE_ARGS -DCMAKE_INSTALL_PREFIX=${GFLAGS_INSTALL_DIR}
CMAKE_ARGS -DCMAKE_POSITION_INDEPENDENT_CODE=ON
CMAKE_ARGS -DBUILD_TESTING=OFF
CMAKE_ARGS -DCMAKE_BUILD_TYPE=Release
-DCMAKE_C_COMPILER=${CMAKE_C_COMPILER}
-DCMAKE_CXX_FLAGS=${CMAKE_CXX_FLAGS}
-DCMAKE_C_FLAGS=${CMAKE_C_FLAGS}
-DCMAKE_INSTALL_PREFIX=${GFLAGS_INSTALL_DIR}
-DCMAKE_POSITION_INDEPENDENT_CODE=ON
-DBUILD_TESTING=OFF
-DCMAKE_BUILD_TYPE=Release
${EXTERNAL_OPTIONAL_ARGS}
CMAKE_CACHE_ARGS -DCMAKE_INSTALL_PREFIX:PATH=${GFLAGS_INSTALL_DIR}
-DCMAKE_POSITION_INDEPENDENT_CODE:BOOL=ON
-DCMAKE_BUILD_TYPE:STRING=Release
......
cmake/external/glog.cmake
浏览文件 @ 37faf495
......@@ -34,16 +34,17 @@ ExternalProject_Add(
PREFIX ${GLOG_SOURCES_DIR}
UPDATE_COMMAND ""
CMAKE_ARGS -DCMAKE_CXX_COMPILER=${CMAKE_CXX_COMPILER}
CMAKE_ARGS -DCMAKE_C_COMPILER=${CMAKE_C_COMPILER}
CMAKE_ARGS -DCMAKE_CXX_FLAGS=${CMAKE_CXX_FLAGS}
CMAKE_ARGS -DCMAKE_C_FLAGS=${CMAKE_C_FLAGS}
CMAKE_ARGS -DCMAKE_INSTALL_PREFIX=${GLOG_INSTALL_DIR}
CMAKE_ARGS -DCMAKE_INSTALL_LIBDIR=${GLOG_INSTALL_DIR}/lib
CMAKE_ARGS -DCMAKE_POSITION_INDEPENDENT_CODE=ON
CMAKE_ARGS -DWITH_GFLAGS=ON
CMAKE_ARGS -Dgflags_DIR=${GFLAGS_INSTALL_DIR}/lib/cmake/gflags
CMAKE_ARGS -DBUILD_TESTING=OFF
CMAKE_ARGS -DCMAKE_BUILD_TYPE=Release
-DCMAKE_C_COMPILER=${CMAKE_C_COMPILER}
-DCMAKE_CXX_FLAGS=${CMAKE_CXX_FLAGS}
-DCMAKE_C_FLAGS=${CMAKE_C_FLAGS}
-DCMAKE_INSTALL_PREFIX=${GLOG_INSTALL_DIR}
-DCMAKE_INSTALL_LIBDIR=${GLOG_INSTALL_DIR}/lib
-DCMAKE_POSITION_INDEPENDENT_CODE=ON
-DWITH_GFLAGS=ON
-Dgflags_DIR=${GFLAGS_INSTALL_DIR}/lib/cmake/gflags
-DBUILD_TESTING=OFF
-DCMAKE_BUILD_TYPE=Release
${EXTERNAL_OPTIONAL_ARGS}
CMAKE_CACHE_ARGS -DCMAKE_INSTALL_PREFIX:PATH=${GLOG_INSTALL_DIR}
-DCMAKE_INSTALL_LIBDIR:PATH=${GLOG_INSTALL_DIR}/lib
-DCMAKE_POSITION_INDEPENDENT_CODE:BOOL=ON
......
cmake/external/gtest.cmake
浏览文件 @ 37faf495
......@@ -48,15 +48,16 @@ IF(WITH_TESTING)
PREFIX ${GTEST_SOURCES_DIR}
UPDATE_COMMAND ""
CMAKE_ARGS -DCMAKE_CXX_COMPILER=${CMAKE_CXX_COMPILER}
CMAKE_ARGS -DCMAKE_C_COMPILER=${CMAKE_C_COMPILER}
CMAKE_ARGS -DCMAKE_CXX_FLAGS=${CMAKE_CXX_FLAGS}
CMAKE_ARGS -DCMAKE_C_FLAGS=${CMAKE_C_FLAGS}
CMAKE_ARGS -DCMAKE_INSTALL_PREFIX=${GTEST_INSTALL_DIR}
CMAKE_ARGS -DCMAKE_POSITION_INDEPENDENT_CODE=ON
CMAKE_ARGS -DBUILD_GMOCK=ON
CMAKE_ARGS -Dgtest_disable_pthreads=ON
CMAKE_ARGS -Dgtest_force_shared_crt=ON
CMAKE_ARGS -DCMAKE_BUILD_TYPE=Release
-DCMAKE_C_COMPILER=${CMAKE_C_COMPILER}
-DCMAKE_CXX_FLAGS=${CMAKE_CXX_FLAGS}
-DCMAKE_C_FLAGS=${CMAKE_C_FLAGS}
-DCMAKE_INSTALL_PREFIX=${GTEST_INSTALL_DIR}
-DCMAKE_POSITION_INDEPENDENT_CODE=ON
-DBUILD_GMOCK=ON
-Dgtest_disable_pthreads=ON
-Dgtest_force_shared_crt=ON
-DCMAKE_BUILD_TYPE=Release
${EXTERNAL_OPTIONAL_ARGS}
CMAKE_CACHE_ARGS -DCMAKE_INSTALL_PREFIX:PATH=${GTEST_INSTALL_DIR}
-DCMAKE_POSITION_INDEPENDENT_CODE:BOOL=ON
-DCMAKE_BUILD_TYPE:STRING=Release
......
cmake/external/openblas.cmake
浏览文件 @ 37faf495
......@@ -29,30 +29,41 @@ IF(NOT ${CBLAS_FOUND})
"${CBLAS_INSTALL_DIR}/lib/${CMAKE_STATIC_LIBRARY_PREFIX}openblas${CMAKE_STATIC_LIBRARY_SUFFIX}"
CACHE FILEPATH "openblas library." FORCE)
IF(APPLE)
SET(OPENBLAS_CC "${CMAKE_C_COMPILER} -isysroot ${CMAKE_OSX_SYSROOT}")
SET(COMMON_ARGS CC=${OPENBLAS_CC} NO_SHARED=1 NO_LAPACK=1 libs)
ELSE()
SET(COMMON_ARGS CC=${CMAKE_C_COMPILER} NO_SHARED=1 NO_LAPACK=1 libs)
ENDIF()
SET(OPENBLAS_CC "${CMAKE_C_COMPILER}")
IF(CMAKE_CROSSCOMPILING)
SET(OPTIONAL_ARGS HOSTCC=${HOST_C_COMPILER})
GET_FILENAME_COMPONENT(CROSS_SUFFIX ${CMAKE_C_COMPILER} DIRECTORY)
SET(CROSS_SUFFIX ${CROSS_SUFFIX}/)
IF(ANDROID)
# arm_soft_fp_abi branch of OpenBLAS to support softfp
# https://github.com/xianyi/OpenBLAS/tree/arm_soft_fp_abi
SET(OPENBLAS_COMMIT "b5c96fcfcdc82945502a2303116a64d89985daf5")
IF(ANDROID_ABI MATCHES "^armeabi(-v7a)?$")
SET(TARGET "ARMV7")
SET(OPTIONAL_ARGS ${OPTIONAL_ARGS} TARGET=ARMV7 ARM_SOFTFP_ABI=1 USE_THREAD=0)
ELSEIF(ANDROID_ABI STREQUAL "arm64-v8a")
SET(TARGET "ARMV8")
SET(OPTIONAL_ARGS ${OPTIONAL_ARGS} TARGET=ARMV8 BINARY=64 USE_THREAD=0)
ENDIF()
ELSEIF(IOS)
# FIXME(liuyiqun): support multiple architectures
SET(OPENBLAS_COMMIT "b5c96fcfcdc82945502a2303116a64d89985daf5")
SET(OPENBLAS_CC "${OPENBLAS_CC} ${CMAKE_C_FLAGS} -isysroot ${CMAKE_OSX_SYSROOT}")
IF(CMAKE_OSX_ARCHITECTURES MATCHES "armv7")
SET(OPENBLAS_CC "${OPENBLAS_CC} -arch armv7")
SET(OPTIONAL_ARGS ${OPTIONAL_ARGS} TARGET=ARMV7 ARM_SOFTFP_ABI=1 USE_THREAD=0)
ELSEIF(CMAKE_OSX_ARCHITECTURES MATCHES "arm64")
SET(OPENBLAS_CC "${OPENBLAS_CC} -arch arm64")
SET(OPTIONAL_ARGS ${OPTIONAL_ARGS} TARGET=ARMV8 BINARY=64 USE_THREAD=0 CROSS_SUFFIX=${CROSS_SUFFIX})
ENDIF()
SET(OPTIONAL_ARGS HOSTCC=${HOST_C_COMPILER} TARGET=${TARGET} ARM_SOFTFP_ABI=1 USE_THREAD=0)
ELSEIF(RPI)
# use hardfp
SET(OPENBLAS_COMMIT "v0.2.20")
SET(OPTIONAL_ARGS HOSTCC=${HOST_C_COMPILER} TARGET=ARMV7 USE_THREAD=0)
SET(OPTIONAL_ARGS ${OPTIONAL_ARGS} TARGET=ARMV7 USE_THREAD=0)
ENDIF()
ELSE()
IF(APPLE)
SET(OPENBLAS_CC "${CMAKE_C_COMPILER} -isysroot ${CMAKE_OSX_SYSROOT}")
ENDIF()
SET(OPENBLAS_COMMIT "v0.2.20")
SET(OPTIONAL_ARGS "")
IF(CMAKE_SYSTEM_PROCESSOR MATCHES "^x86(_64)?$")
......@@ -60,6 +71,8 @@ IF(NOT ${CBLAS_FOUND})
ENDIF()
ENDIF()
SET(COMMON_ARGS CC=${OPENBLAS_CC} NO_SHARED=1 NO_LAPACK=1 libs)
ExternalProject_Add(
extern_openblas
${EXTERNAL_PROJECT_LOG_ARGS}
......
cmake/external/protobuf.cmake
浏览文件 @ 37faf495
......@@ -173,7 +173,8 @@ FUNCTION(build_protobuf TARGET_NAME BUILD_FOR_HOST)
"-DCMAKE_CXX_FLAGS=${CMAKE_CXX_FLAGS}"
"-DCMAKE_C_FLAGS=${CMAKE_C_FLAGS}"
"-Dprotobuf_WITH_ZLIB=ON"
"-DZLIB_ROOT:FILEPATH=${ZLIB_ROOT}")
"-DZLIB_ROOT:FILEPATH=${ZLIB_ROOT}"
${EXTERNAL_OPTIONAL_ARGS})
SET(OPTIONAL_CACHE_ARGS "-DZLIB_ROOT:STRING=${ZLIB_ROOT}")
ENDIF()
......
cmake/external/python.cmake
浏览文件 @ 37faf495
......@@ -12,16 +12,17 @@
# See the License for the specific language governing permissions and
# limitations under the License.
INCLUDE(ExternalProject)
IF(NOT WITH_PYTHON)
return()
ENDIF()
INCLUDE(python_module)
FIND_PACKAGE(PythonInterp 2.7)
IF(WITH_PYTHON)
FIND_PACKAGE(PythonLibs 2.7)
# Fixme: Maybe find a static library. Get SHARED/STATIC by FIND_PACKAGE.
ADD_LIBRARY(python SHARED IMPORTED GLOBAL)
SET_PROPERTY(TARGET python PROPERTY IMPORTED_LOCATION ${PYTHON_LIBRARIES})
ENDIF(WITH_PYTHON)
FIND_PACKAGE(PythonLibs 2.7)
# Fixme: Maybe find a static library. Get SHARED/STATIC by FIND_PACKAGE.
ADD_LIBRARY(python SHARED IMPORTED GLOBAL)
SET_PROPERTY(TARGET python PROPERTY IMPORTED_LOCATION ${PYTHON_LIBRARIES})
SET(py_env "")
IF(PYTHONINTERP_FOUND)
......@@ -36,9 +37,5 @@ IF(PYTHONINTERP_FOUND)
ENDIF()
ENDIF(PYTHONINTERP_FOUND)
IF(WITH_PYTHON)
INCLUDE_DIRECTORIES(${PYTHON_INCLUDE_DIR})
INCLUDE_DIRECTORIES(${PYTHON_NUMPY_INCLUDE_DIR})
ELSE()
SET(PYTHON_LIBRARIES "")
ENDIF()
INCLUDE_DIRECTORIES(${PYTHON_INCLUDE_DIR})
INCLUDE_DIRECTORIES(${PYTHON_NUMPY_INCLUDE_DIR})
cmake/external/swig.cmake
浏览文件 @ 37faf495
......@@ -12,6 +12,10 @@
# See the License for the specific language governing permissions and
# limitations under the License.
IF(NOT WITH_SWIG_PY)
return()
ENDIF()
FIND_PACKAGE(SWIG)
IF(NOT SWIG_FOUND)
......
cmake/external/warpctc.cmake
浏览文件 @ 37faf495
......@@ -16,25 +16,14 @@ INCLUDE(ExternalProject)
SET(WARPCTC_SOURCES_DIR ${THIRD_PARTY_PATH}/warpctc)
SET(WARPCTC_INSTALL_DIR ${THIRD_PARTY_PATH}/install/warpctc)
SET(WARPCTC_INCLUDE_DIR "${WARPCTC_INSTALL_DIR}/include" CACHE PATH "Warp-ctc Directory" FORCE)
INCLUDE_DIRECTORIES(${WARPCTC_INCLUDE_DIR})
SET(WARPCTC_LIB_DIR "${WARPCTC_INSTALL_DIR}/lib" CACHE PATH "Warp-ctc Library Directory" FORCE)
IF(WIN32)
SET(WARPCTC_LIBRARIES
"${WARPCTC_INSTALL_DIR}/lib/warpctc.dll" CACHE FILEPATH "Warp-ctc Library" FORCE)
ELSE(WIN32)
IF(APPLE)
SET(_warpctc_SHARED_SUFFIX dylib)
ELSE(APPLE)
SET(_warpctc_SHARED_SUFFIX so)
ENDIF(APPLE)
SET(WARPCTC_LIBRARIES
"${WARPCTC_INSTALL_DIR}/lib/libwarpctc.${_warpctc_SHARED_SUFFIX}" CACHE FILEPATH "Warp-ctc Library" FORCE)
ENDIF(WIN32)
SET(WARPCTC_INCLUDE_DIR "${WARPCTC_INSTALL_DIR}/include"
CACHE PATH "Warp-ctc Directory" FORCE)
# Used in unit test test_WarpCTCLayer
SET(WARPCTC_LIB_DIR "${WARPCTC_INSTALL_DIR}/lib"
CACHE PATH "Warp-ctc Library Directory" FORCE)
SET(WARPCTC_LIBRARIES "${WARPCTC_INSTALL_DIR}/lib/libwarpctc${CMAKE_SHARED_LIBRARY_SUFFIX}"
CACHE FILEPATH "Warp-ctc Library" FORCE)
IF(CMAKE_CXX_COMPILER_ID STREQUAL "Clang" OR CMAKE_CXX_COMPILER_ID STREQUAL "AppleClang" )
SET(USE_OMP OFF)
......@@ -49,22 +38,26 @@ ExternalProject_Add(
PREFIX ${WARPCTC_SOURCES_DIR}
UPDATE_COMMAND ""
CMAKE_ARGS -DCMAKE_CXX_COMPILER=${CMAKE_CXX_COMPILER}
CMAKE_ARGS -DCMAKE_C_COMPILER=${CMAKE_C_COMPILER}
CMAKE_ARGS -DCMAKE_CXX_FLAGS=${CMAKE_CXX_FLAGS}
CMAKE_ARGS -DCMAKE_C_FLAGS=${CMAKE_C_FLAGS}
CMAKE_ARGS -DCMAKE_INSTALL_PREFIX=${WARPCTC_INSTALL_DIR}
CMAKE_ARGS -DWITH_GPU=${WITH_GPU}
CMAKE_ARGS -DWITH_OMP=${USE_OMP}
CMAKE_ARGS -DWITH_TORCH=OFF
CMAKE_ARGS -DCMAKE_DISABLE_FIND_PACKAGE_Torch=ON
CMAKE_ARGS -DBUILD_SHARED=ON
CMAKE_ARGS -DCMAKE_POSITION_INDEPENDENT_CODE=ON
CMAKE_ARGS -DCMAKE_BUILD_TYPE=Release
-DCMAKE_C_COMPILER=${CMAKE_C_COMPILER}
-DCMAKE_CXX_FLAGS=${CMAKE_CXX_FLAGS}
-DCMAKE_C_FLAGS=${CMAKE_C_FLAGS}
-DCMAKE_INSTALL_PREFIX=${WARPCTC_INSTALL_DIR}
-DWITH_GPU=${WITH_GPU}
-DWITH_OMP=${USE_OMP}
-DWITH_TORCH=OFF
-DCMAKE_DISABLE_FIND_PACKAGE_Torch=ON
-DBUILD_SHARED=ON
-DCMAKE_POSITION_INDEPENDENT_CODE=ON
-DCMAKE_BUILD_TYPE=Release
${EXTERNAL_OPTIONAL_ARGS}
CMAKE_CACHE_ARGS -DCMAKE_BUILD_TYPE:STRING=Release
-DCMAKE_POSITION_INDEPENDENT_CODE:BOOL=ON
-DCMAKE_INSTALL_PREFIX:PATH=${WARPCTC_INSTALL_DIR}
)
MESSAGE(STATUS "warp-ctc library: ${WARPCTC_LIBRARIES}")
INCLUDE_DIRECTORIES(${WARPCTC_INCLUDE_DIR})
ADD_LIBRARY(warpctc STATIC IMPORTED GLOBAL)
SET_PROPERTY(TARGET warpctc PROPERTY IMPORTED_LOCATION ${WARPCTC_LIBRARIES})
ADD_DEPENDENCIES(warpctc extern_warpctc)
......
cmake/external/zlib.cmake
浏览文件 @ 37faf495
......@@ -34,15 +34,16 @@ ExternalProject_Add(
GIT_TAG "v1.2.8"
PREFIX ${ZLIB_SOURCES_DIR}
UPDATE_COMMAND ""
CMAKE_ARGS -DCMAKE_CXX_COMPILER=${CMAKE_CXX_COMPILER}
CMAKE_ARGS -DCMAKE_C_COMPILER=${CMAKE_C_COMPILER}
CMAKE_ARGS -DCMAKE_CXX_FLAGS=${CMAKE_CXX_FLAGS}
CMAKE_ARGS -DCMAKE_C_FLAGS=${CMAKE_C_FLAGS}
CMAKE_ARGS -DCMAKE_INSTALL_PREFIX=${ZLIB_INSTALL_DIR}
CMAKE_ARGS -DBUILD_SHARED_LIBS=OFF
CMAKE_ARGS -DCMAKE_POSITION_INDEPENDENT_CODE=ON
CMAKE_ARGS -DCMAKE_MACOSX_RPATH=ON
CMAKE_ARGS -DCMAKE_BUILD_TYPE=Release
-DCMAKE_CXX_COMPILER=${CMAKE_CXX_COMPILER}
-DCMAKE_C_FLAGS=${CMAKE_C_FLAGS}
-DCMAKE_CXX_FLAGS=${CMAKE_CXX_FLAGS}
-DCMAKE_INSTALL_PREFIX=${ZLIB_INSTALL_DIR}
-DBUILD_SHARED_LIBS=OFF
-DCMAKE_POSITION_INDEPENDENT_CODE=ON
-DCMAKE_MACOSX_RPATH=ON
-DCMAKE_BUILD_TYPE=Release
${EXTERNAL_OPTIONAL_ARGS}
CMAKE_CACHE_ARGS -DCMAKE_INSTALL_PREFIX:PATH=${ZLIB_INSTALL_DIR}
-DCMAKE_POSITION_INDEPENDENT_CODE:BOOL=ON
-DCMAKE_BUILD_TYPE:STRING=Release
......
cmake/flags.cmake
浏览文件 @ 37faf495
......@@ -128,8 +128,10 @@ set(GPU_COMMON_FLAGS
)
if (APPLE)
# On Mac OS X build fat binaries with x86_64 architectures by default.
set (CMAKE_OSX_ARCHITECTURES "x86_64" CACHE STRING "Build architectures for OSX" FORCE)
if(NOT CMAKE_CROSSCOMPILING)
# On Mac OS X build fat binaries with x86_64 architectures by default.
set (CMAKE_OSX_ARCHITECTURES "x86_64" CACHE STRING "Build architectures for OSX" FORCE)
endif()
else()
set(GPU_COMMON_FLAGS
-Wall
......
cmake/system.cmake
浏览文件 @ 37faf495
......@@ -24,11 +24,10 @@ IF(WIN32)
SET(HOST_SYSTEM "win32")
ELSE(WIN32)
IF(APPLE)
EXEC_PROGRAM (sw_vers ARGS -productVersion OUTPUT_VARIABLE MACOSX_VERSION)
STRING(REGEX MATCH "[0-9]+.[0-9]+" VERSION "${MACOSX_VERSION}")
SET(MACOS_VERSION ${VERSION})
SET(HOST_SYSTEM "macosx")
IF(NOT DEFINED ENV{MACOSX_DEPLOYMENT_TARGET})
EXEC_PROGRAM(sw_vers ARGS -productVersion OUTPUT_VARIABLE HOST_SYSTEM_VERSION)
STRING(REGEX MATCH "[0-9]+.[0-9]+" MACOS_VERSION "${HOST_SYSTEM_VERSION}")
IF(NOT DEFINED $ENV{MACOSX_DEPLOYMENT_TARGET})
# Set cache variable - end user may change this during ccmake or cmake-gui configure.
SET(CMAKE_OSX_DEPLOYMENT_TARGET ${MACOS_VERSION} CACHE STRING
"Minimum OS X version to target for deployment (at runtime); newer APIs weak linked. Set to empty string for default value.")
......@@ -49,6 +48,8 @@ ELSE(WIN32)
ELSEIF(LINUX_ISSUE MATCHES "Fedora")
SET(HOST_SYSTEM "fedora")
ENDIF()
STRING(REGEX MATCH "(([0-9]+)\\.)+([0-9]+)" HOST_SYSTEM_VERSION "${LINUX_ISSUE}")
ENDIF(EXISTS "/etc/issue")
IF(EXISTS "/etc/redhat-release")
......@@ -70,7 +71,7 @@ CMAKE_HOST_SYSTEM_INFORMATION(RESULT CPU_CORES QUERY NUMBER_OF_LOGICAL_CORES)
MARK_AS_ADVANCED(HOST_SYSTEM CPU_CORES)
MESSAGE(STATUS "Found Paddle host system: ${HOST_SYSTEM}")
MESSAGE(STATUS "Found Paddle host system: ${HOST_SYSTEM}, version: ${HOST_SYSTEM_VERSION}")
MESSAGE(STATUS "Found Paddle host system's CPU: ${CPU_CORES} cores")
# configuration for cross-compiling
......@@ -82,6 +83,9 @@ IF(DEFINED CMAKE_SYSTEM_NAME)
ELSEIF(${CMAKE_SYSTEM_NAME} STREQUAL "RPi")
SET(RPI TRUE)
INCLUDE(cross_compiling/raspberry_pi)
ELSEIF(${CMAKE_SYSTEM_NAME} STREQUAL "iOS")
SET(IOS TRUE)
INCLUDE(cross_compiling/ios)
ENDIF()
ENDIF()
......
cmake/util.cmake
浏览文件 @ 37faf495
......@@ -25,7 +25,9 @@ function(target_circle_link_libraries TARGET_NAME)
endif()
endforeach()
if("${CMAKE_CXX_COMPILER_ID}" STREQUAL "Clang" OR "${CMAKE_CXX_COMPILER_ID}" STREQUAL "AppleClang")
list(APPEND LIBS "-undefined dynamic_lookup")
if(IOS AND NOT IOS_ENABLE_BITCODE)
list(APPEND LIBS "-undefined dynamic_lookup")
endif()
endif()
list(REVERSE libsInArgn)
target_link_libraries(${TARGET_NAME}
......
paddle/CMakeLists.txt
浏览文件 @ 37faf495
......@@ -19,7 +19,7 @@ if(Boost_FOUND)
endif()
if(WITH_C_API)
add_subdirectory(capi)
add_subdirectory(capi)
endif()
if(WITH_SWIG_PY)
......
paddle/capi/CMakeLists.txt
浏览文件 @ 37faf495
......@@ -28,42 +28,38 @@ add_style_check_target(paddle_capi ${CAPI_SOURCES} ${CAPI_HEADER}
add_dependencies(paddle_capi paddle_proto)
# combine all paddle static libraries together, into libpaddle_capi_whole.a
# user should use PaddleCAPI as -lpaddle_capi_whole
set(capi_whole_library libpaddle_capi_whole.a)
add_custom_target(paddle_capi_whole ALL
COMMAND mkdir -p o_files/capi && cd o_files/capi/ && ar -x $<TARGET_FILE:paddle_capi>
COMMAND mkdir -p o_files/utils && cd o_files/utils/ && ar -x $<TARGET_FILE:paddle_utils>
COMMAND mkdir -p o_files/parameter && cd o_files/parameter/ && ar -x $<TARGET_FILE:paddle_parameter>
COMMAND mkdir -p o_files/math && cd o_files/math/ && ar -x $<TARGET_FILE:paddle_math>
COMMAND mkdir -p o_files/cuda && cd o_files/cuda/ && ar -x $<TARGET_FILE:paddle_cuda>
COMMAND mkdir -p o_files/function && cd o_files/function/ && ar -x $<TARGET_FILE:paddle_function>
COMMAND mkdir -p o_files/gserver && cd o_files/gserver/ && ar -x $<TARGET_FILE:paddle_gserver>
COMMAND mkdir -p o_files/proto && cd o_files/proto/ && ar -x $<TARGET_FILE:paddle_proto>
COMMAND mkdir -p o_files/network && cd o_files/network/ && ar -x $<TARGET_FILE:paddle_network>
COMMAND mkdir -p o_files/pserver && cd o_files/pserver/ && ar -x $<TARGET_FILE:paddle_pserver>
COMMAND ar crs ${capi_whole_library} `find ./o_files -name '*.o'`
COMMAND rm -rf o_files
WORKING_DIRECTORY ${CMAKE_CURRENT_BINARY_DIR}
DEPENDS paddle_capi paddle_utils paddle_parameter paddle_math
paddle_cuda paddle_function paddle_gserver
paddle_proto paddle_pserver paddle_network
)
set_target_properties(paddle_capi_whole
PROPERTIES IMPORTED_LOCATION ${CMAKE_CURRENT_BINARY_DIR}/${capi_whole_library})
set(PADDLE_CAPI_INFER_LIBS
paddle_utils
paddle_parameter
paddle_math
paddle_cuda
paddle_function
paddle_gserver
paddle_proto
paddle_pserver
paddle_network)
cc_library(paddle_capi_whole DEPS paddle_capi ${PADDLE_CAPI_INFER_LIBS})
set(LINK_FLAGS " -Wl,--retain-symbols-file ${CMAKE_CURRENT_SOURCE_DIR}/export.sym -Wl,--version-script ${CMAKE_CURRENT_SOURCE_DIR}/export.map")
# TODO: merge mkl into paddle_capi_shared
add_library(paddle_capi_shared SHARED ${CAPI_SOURCES})
set_target_properties(paddle_capi_shared PROPERTIES LINK_FLAGS "${LINK_FLAGS}")
target_include_directories(paddle_capi_shared PUBLIC ${CMAKE_CURRENT_BINARY_DIR})
link_paddle_exe(paddle_capi_shared)
# No shared library for iOS
if(NOT IOS)
set(LINK_FLAGS " -Wl,--retain-symbols-file ${CMAKE_CURRENT_SOURCE_DIR}/export.sym -Wl,--version-script ${CMAKE_CURRENT_SOURCE_DIR}/export.map")
# TODO: merge mkl into paddle_capi_shared
add_library(paddle_capi_shared SHARED ${CAPI_SOURCES})
set_target_properties(paddle_capi_shared PROPERTIES LINK_FLAGS "${LINK_FLAGS}")
target_include_directories(paddle_capi_shared PUBLIC ${CMAKE_CURRENT_BINARY_DIR})
link_paddle_exe(paddle_capi_shared)
endif()
# install library & headers.
install(FILES ${CAPI_HEADERS} DESTINATION include/paddle)
install(FILES ${CMAKE_CURRENT_BINARY_DIR}/config.h DESTINATION include/paddle)
if(ANDROID)
install(TARGETS paddle_capi_whole paddle_capi_shared
ARCHIVE DESTINATION lib/${ANDROID_ABI}
LIBRARY DESTINATION lib/${ANDROID_ABI})
execute_process(
COMMAND ${GIT_EXECUTABLE} log --pretty=oneline -1
OUTPUT_VARIABLE GIT_COMMITS_LIST
......@@ -72,9 +68,6 @@ if(ANDROID)
if(${GIT_COMMITS_LIST_RESULT})
set(GIT_COMMITS_LIST "No commits.")
endif()
install(FILES ${CMAKE_CURRENT_BINARY_DIR}/${capi_whole_library}
DESTINATION lib/${ANDROID_ABI})
install(TARGETS paddle_capi_shared DESTINATION lib/${ANDROID_ABI})
install(CODE "FILE(WRITE ${CMAKE_INSTALL_PREFIX}/lib/${ANDROID_ABI}/BUILD.txt
\"Compiler:\n\"
\"\\t${CMAKE_C_COMPILER}\\n\"
......@@ -88,8 +81,11 @@ if(ANDROID)
)"
)
else(ANDROID)
install(FILES ${CMAKE_CURRENT_BINARY_DIR}/${capi_whole_library} DESTINATION lib)
install(TARGETS paddle_capi_shared DESTINATION lib)
install(TARGETS paddle_capi_whole
ARCHIVE DESTINATION lib)
if(NOT IOS)
install(TARGETS paddle_capi_shared DESTINATION lib)
endif()
endif(ANDROID)
# this variable used for unittest
......
paddle/function/neon/NeonDepthwiseConv.cpp
浏览文件 @ 37faf495
......@@ -52,7 +52,7 @@ public:
int outputHeight = output[2];
int outputWidth = output[3];
int filterMultiplier = outputChannels / groups_;
CHECK_EQ(inputChannels, groups_);
CHECK_EQ(static_cast<size_t>(inputChannels), groups_);
// only support strideH() == strideW() and filterHeight == filterWidth.
CHECK_EQ(strideH(), strideW());
......
paddle/gserver/activations/ActivationFunction.cpp
浏览文件 @ 37faf495
......@@ -22,9 +22,12 @@ limitations under the License. */
#include <type_traits>
#include "paddle/parameter/Argument.h"
#include "paddle/utils/ClassRegistrar.h"
#include "paddle/utils/Logging.h"
#ifdef PADDLE_USE_MKLDNN
#include "MKLDNNActivation.h"
#endif
namespace paddle {
static ClassRegistrar<ActivationFunction> gActivationRegistrar;
......@@ -456,6 +459,12 @@ Error __must_check backward(Argument& act) {
END_DEFINE_ACTIVATION(log)
ActivationFunction* ActivationFunction::create(const std::string& type) {
#ifdef PADDLE_USE_MKLDNN
if (!type.empty() && type.compare(0, 7, "mkldnn_") == 0) {
return MKLDNNActivation::create(type);
}
#endif
return gActivationRegistrar.createByType(type);
}
......
paddle/gserver/activations/MKLDNNActivation.cpp 0 → 100644
浏览文件 @ 37faf495
/* Copyright (c) 2017 PaddlePaddle Authors. All Rights Reserve.
Licensed under the Apache License, Version 2.0 (the "License");
you may not use this file except in compliance with the License.
You may obtain a copy of the License at
http://www.apache.org/licenses/LICENSE-2.0
Unless required by applicable law or agreed to in writing, software
distributed under the License is distributed on an "AS IS" BASIS,
WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
See the License for the specific language governing permissions and
limitations under the License. */
#include "MKLDNNActivation.h"
#include "mkldnn.hpp"
#include "paddle/utils/ClassRegistrar.h"
namespace paddle {
static ClassRegistrar<ActivationFunction> gMKLDNNActivationRegistrar;
/**
* @def MKLDNN_ACTIVATION_CLASS_NAME
* @note MKLDNN_ACTIVATION_CLASS_NAME(relu) relu_;
* means mkldnn_reluActivation relu_;
*/
#define MKLDNN_ACTIVATION_CLASS_NAME(ACT_TYPE) mkldnn_##ACT_TYPE##Activation
/**
* @def DEFINE_MKLDNN_ELTWISE_ACTIVATION
*/
#define DEFINE_MKLDNN_ELTWISE_ACTIVATION(ACT_TYPE, ALPHA, BWD_ALPHA) \
class MKLDNN_ACTIVATION_CLASS_NAME(ACT_TYPE) \
: public MKLDNNEltwiseActivation { \
private: \
static const std::string name; \
static const float alpha; \
static const float bwdAlpha; \
\
public: \
const std::string& getName() const { return name; } \
float getAlpha() const { return alpha; } \
float getBwdAlpha() const { return bwdAlpha; } \
}; \
const std::string MKLDNN_ACTIVATION_CLASS_NAME(ACT_TYPE)::name = \
"mkldnn_" #ACT_TYPE; \
const float MKLDNN_ACTIVATION_CLASS_NAME(ACT_TYPE)::alpha = ALPHA; \
const float MKLDNN_ACTIVATION_CLASS_NAME(ACT_TYPE)::bwdAlpha = BWD_ALPHA; \
static InitFunction __reg_activation__mkldnn_##ACT_TYPE([] { \
gMKLDNNActivationRegistrar \
.registerClass<MKLDNN_ACTIVATION_CLASS_NAME(ACT_TYPE)>( \
"mkldnn_" #ACT_TYPE); \
});
/**
* @brief MKLDNN Relu Activation.
* Actually mkldnn_relu is Leaky Relu.
* f(x) = x (x >= 0)
* f(x) = negative_slope * x (x < 0)
* @note the negative_slope should be -0.f in forward
*/
DEFINE_MKLDNN_ELTWISE_ACTIVATION(relu, -0.f, 0.f)
/**
* @brief MKLDNN Tanh Activation.
*/
DEFINE_MKLDNN_ELTWISE_ACTIVATION(tanh, 0.f, 0.f)
/**
* @brief MKLDNN ELU(Exponential Linear Unit) Activation.
* f(x) = x (x >= 0)
* f(x) = negative_slope * (exp(x) - 1) (x < 0)
*/
DEFINE_MKLDNN_ELTWISE_ACTIVATION(elu, 0.f, 0.f)
ActivationFunction* MKLDNNActivation::create(const std::string& type) {
return gMKLDNNActivationRegistrar.createByType(type);
}
std::vector<std::string> MKLDNNActivation::getAllRegisteredTypes() {
std::vector<std::string> types;
gMKLDNNActivationRegistrar.forEachType(
[&](const std::string& type) { types.push_back(type); });
return types;
}
} // namespace paddle
paddle/gserver/activations/MKLDNNActivation.h 0 → 100644
浏览文件 @ 37faf495
/* Copyright (c) 2017 PaddlePaddle Authors. All Rights Reserve.
Licensed under the Apache License, Version 2.0 (the "License");
you may not use this file except in compliance with the License.
You may obtain a copy of the License at
http://www.apache.org/licenses/LICENSE-2.0
Unless required by applicable law or agreed to in writing, software
distributed under the License is distributed on an "AS IS" BASIS,
WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
See the License for the specific language governing permissions and
limitations under the License. */
#pragma once
#include "ActivationFunction.h"
#include "mkldnn.hpp"
#include "paddle/gserver/layers/MKLDNNBase.h"
#include "paddle/math/MKLDNNMatrix.h"
#include "paddle/parameter/Argument.h"
namespace paddle {
/**
* @brief Base class of MKLDNN Activation.
* Common activation function are provieded,
* including mkldnn_relu, mkldnn_elu, mkldnn_tanh, mkldnn_softmax
*/
class MKLDNNActivation : public ActivationFunction {
protected:
// input value element count
size_t cnt_;
// should not merge the resetBwd into resetFwd,
// because the grad data would be changing before backward.
bool needResetBwd_;
// mkldnn matrix, primitive, stream and pipeline
MKLDNNMatrixPtr val_;
MKLDNNMatrixPtr grad_;
std::shared_ptr<MKLDNNStream> stream_;
std::shared_ptr<mkldnn::primitive> fwd_;
std::shared_ptr<mkldnn::primitive> bwd_;
std::vector<mkldnn::primitive> pipelineFwd_;
std::vector<mkldnn::primitive> pipelineBwd_;
public:
MKLDNNActivation() : cnt_(0), needResetBwd_(true) {}
~MKLDNNActivation() {}
static ActivationFunction* create(const std::string& type);
static std::vector<std::string> getAllRegisteredTypes();
virtual const std::string& getName() const = 0;
virtual Error __must_check forward(Argument& act) = 0;
virtual Error __must_check backward(Argument& act) = 0;
};
/**
* @brief Base class of MKLDNN Eltwise Activation,
* includes mkldnn_relu, mkldnn_elu and mkldnn_tanh.
*/
class MKLDNNEltwiseActivation : public MKLDNNActivation {
typedef mkldnn::eltwise_forward eltwise_fwd;
typedef mkldnn::eltwise_backward eltwise_bwd;
protected:
// save the forward primitive desc, which can be used backward
std::shared_ptr<eltwise_fwd::primitive_desc> fwdPD_;
// eltwise_bwd need src input value
MKLDNNMatrixPtr inVal_;
// use for copy data
std::shared_ptr<mkldnn::reorder> copyInVal_;
public:
MKLDNNEltwiseActivation() {}
~MKLDNNEltwiseActivation() {}
virtual const std::string& getName() const = 0;
// in common, the alpha of forward and backward should be equal.
// but for relu, to avoid negative value, they should be opposite
virtual float getAlpha() const = 0;
virtual float getBwdAlpha() const = 0;
virtual float getBeta() const { return 0.f; }
virtual mkldnn::algorithm getAlgo(const std::string& type) const {
if (type == "mkldnn_relu") {
return mkldnn::algorithm::eltwise_relu;
} else if (type == "mkldnn_tanh") {
return mkldnn::algorithm::eltwise_tanh;
} else if (type == "mkldnn_elu") {
return mkldnn::algorithm::eltwise_elu;
} else {
LOG(FATAL) << "Unkown eltwise activation type: " << type;
}
return (mkldnn::algorithm)0;
}
/**
* reshape and reset the forward primitives
*/
void resetFwd(Argument& act) {
if (cnt_ == act.value->getElementCnt()) {
return;
}
cnt_ = act.value->getElementCnt();
stream_.reset(new MKLDNNStream());
auto eng = CPUEngine::Instance().getEngine();
// get algo setting
mkldnn::algorithm algo = getAlgo(this->getName());
// note: alpha represents the NegativeSlope when used in relu.
float alpha = getAlpha();
float beta = getBeta();
/// forward
pipelineFwd_.clear();
val_ = std::dynamic_pointer_cast<MKLDNNMatrix>(act.value);
if (val_ == nullptr) {
int bs = act.getBatchSize();
int ih = act.getFrameHeight() > 0 ? act.getFrameHeight() : 1;
int iw = act.getFrameWidth() > 0 ? act.getFrameWidth() : 1;
int ic = cnt_ / bs / ih / iw;
CHECK_EQ(cnt_, (size_t)bs * ic * ih * iw);
val_ = MKLDNNMatrix::create(
act.value, {bs, ic, ih, iw}, mkldnn::memory::format::nchw, eng);
CHECK(val_);
}
auto fwdDesc = eltwise_fwd::desc(mkldnn::prop_kind::forward_training,
algo,
val_->getMemoryDesc(),
alpha,
beta);
fwdPD_.reset(new eltwise_fwd::primitive_desc(fwdDesc, eng));
// use inplace for forward but save input value before submit
inVal_ = val_;
if (act.grad) {
// only copy when need do backward
inVal_ = MKLDNNMatrix::create(nullptr, val_->getPrimitiveDesc());
copyInVal_ = std::make_shared<mkldnn::reorder>(*val_, *inVal_);
CHECK(copyInVal_) << "should not be emptry";
pipelineFwd_.push_back(*copyInVal_);
}
fwd_.reset(new eltwise_fwd(*fwdPD_, *val_, *val_));
pipelineFwd_.push_back(*fwd_);
needResetBwd_ = true;
}
/**
* reset the backward primitives, can not merge into resetFwd as the grad data
* would be changing before backward.
*/
void resetBwd(Argument& act) {
if (!needResetBwd_) {
return;
}
needResetBwd_ = false;
mkldnn::algorithm algo = getAlgo(this->getName());
float alpha = getBwdAlpha();
float beta = getBeta();
grad_ = MKLDNNMatrix::create(act.grad, val_->getPrimitiveDesc());
auto eng = CPUEngine::Instance().getEngine();
auto bwdDesc = eltwise_bwd::desc(
algo, grad_->getMemoryDesc(), val_->getMemoryDesc(), alpha, beta);
auto bwdPD = eltwise_bwd::primitive_desc(bwdDesc, eng, *fwdPD_);
CHECK(inVal_);
bwd_.reset(new eltwise_bwd(bwdPD, *inVal_, *grad_, *grad_));
pipelineBwd_.clear();
pipelineBwd_.push_back(*bwd_);
}
Error __must_check forward(Argument& act) {
resetFwd(act);
stream_->submit(pipelineFwd_);
return Error();
}
Error __must_check backward(Argument& act) {
resetBwd(act);
stream_->submit(pipelineBwd_);
return Error();
}
};
} // namespace paddle
paddle/gserver/layers/MKLDNNConvLayer.cpp
浏览文件 @ 37faf495
......@@ -294,12 +294,9 @@ void MKLDNNConvLayer::resetOutValue(
std::shared_ptr<conv_fwd::primitive_desc>& pd, MKLDNNMatrixPtr& out) {
out = MKLDNNMatrix::create(output_.value, pd->dst_primitive_desc());
// change original output value from cpu matrix to mkldnn matrix
output_.value = std::dynamic_pointer_cast<Matrix>(out);
// create reorder if output value has cpu device and pd do not match
cpuOutVal_ = nullptr;
cpuOutVal_ = nullptr;
cvtOutVal_ = nullptr;
if (!outputIsOnlyMKLDNN()) {
const MatrixPtr& cpuOut = getOutput(CPU_DEVICE).value;
memory::dims outDims = memory::dims{bs_, oc_, oh_, ow_};
......
paddle/gserver/layers/MKLDNNFcLayer.cpp
浏览文件 @ 37faf495
......@@ -172,12 +172,10 @@ void MKLDNNFcLayer::resetWgtBiasValue(MKLDNNMatrixPtr& wgt,
void MKLDNNFcLayer::resetOutValue(MKLDNNMatrixPtr& out) {
out = MKLDNNMatrix::create(output_.value, {bs_, oc_}, format::nc, engine_);
// change original output value to mkldnn output value
output_.value = std::dynamic_pointer_cast<Matrix>(out);
if (!outputIsOnlyMKLDNN()) {
// fc cpu output value do not need create convert
// just share point
getOutput(CPU_DEVICE).value->setData(output_.value->getData());
getOutput(CPU_DEVICE).value->setData(out->getData());
}
}
......
paddle/gserver/layers/MKLDNNLayer.h
浏览文件 @ 37faf495
......@@ -119,6 +119,10 @@ public:
inputElemenCnt_ = elemenCnt;
reshape(bs_, ic_, ih_, iw_, oc_, oh_, ow_);
resetFwd(pipelineFwd_, inVal_, wgtVal_, biasVal_, outVal_);
if (outVal_) {
// change original output value to mkldnn output value
output_.value = std::dynamic_pointer_cast<Matrix>(outVal_);
}
convertWeightsFromPaddle();
needResetBwd_ = true;
}
......
paddle/gserver/layers/MKLDNNPoolLayer.cpp
浏览文件 @ 37faf495
......@@ -134,7 +134,6 @@ void MKLDNNPoolLayer::resetOutValue(MKLDNNMatrixPtr& out) {
memory::dims outDims = memory::dims{bs_, oc_, oh_, ow_};
out = MKLDNNMatrix::create(
output_.value, outDims, inVal_->getFormat(), engine_);
output_.value = std::dynamic_pointer_cast<Matrix>(out);
// create reorder if output value has cpu device and pd do not match
cpuOutVal_ = nullptr;
......
paddle/gserver/layers/SequenceSliceLayer.cpp
浏览文件 @ 37faf495
......@@ -73,9 +73,10 @@ void SequenceSliceLayer::checkInputs() {
CHECK(inputSeq.hasSeq()) << "The first input of sequence slice layer "
<< "must be a sequence.";
const MatrixPtr indices1 = getInputValue(1);
CHECK_EQ(static_cast<size_t>(indices1->getHeight()),
inputSeq.hasSubseq() ? inputSeq.getNumSubSequences()
: inputSeq.getNumSequences())
CHECK_EQ(
indices1->getHeight(),
static_cast<size_t>(inputSeq.hasSubseq() ? inputSeq.getNumSubSequences()
: inputSeq.getNumSequences()))
<< "Height of the second input should be equal to number of sequence "
<< "in the first input.";
if (inputLayers_.size() == 3) {
......@@ -151,7 +152,7 @@ void SequenceSliceLayer::calSelectedRows(const MatrixPtr starts,
if (ends) endPos = inputSeqInfoVec_[i][j] + ends->getElement(rowIdx, k);
int seqLen = endPos - begPos + 1;
CHECK_GT(seqLen, 0U);
CHECK_GT(seqLen, 0);
for (int m = begPos; m <= endPos; ++m) selectedRows_.push_back(m);
hasSubseq
? outSubSeqStartPos_.push_back(outSubSeqStartPos_.back() + seqLen)
......
paddle/gserver/tests/MKLDNNTester.cpp
浏览文件 @ 37faf495
......@@ -64,15 +64,17 @@ void MKLDNNTester::reset(const TestConfig& dnn,
configs_[i], &(layerMaps_[i]), &(parameters_[i]), &(testLayers_[i]));
}
refLayer_ = testLayers_[REF];
dnnLayer_ = std::dynamic_pointer_cast<MKLDNNLayer>(testLayers_[DNN]);
CHECK(dnnLayer_);
// for comparison with Paddle reference results,
// need manually add cpu device output for test
dnnLayer_->addOutputArgument(CPU_DEVICE);
dnnLayer_ = testLayers_[DNN];
EXPECT_EQ(dataLayers_[DNN].size(), dataLayers_[REF].size());
EXPECT_EQ(parameters_[DNN].size(), parameters_[REF].size());
setInputImgSize();
// for comparison with Paddle reference results,
// need manually add cpu device output for test
MKLDNNLayerPtr dnnLayer = std::dynamic_pointer_cast<MKLDNNLayer>(dnnLayer_);
if (dnnLayer) {
dnnLayer->addOutputArgument(CPU_DEVICE);
}
}
void MKLDNNTester::setInputImgSize() {
......@@ -122,7 +124,7 @@ void MKLDNNTester::randomTopDiffs() {
void MKLDNNTester::checkForward() {
VLOG(MKLDNN_ALL) << "Check Forward";
printTopDatas();
double delta = compareMatrix(dnnLayer_->getOutput(-1).value,
double delta = compareMatrix(dnnLayer_->getOutput(CPU_DEVICE).value,
refLayer_->getOutputValue());
EXPECT_LE(fabs(delta), eps_);
}
......@@ -155,7 +157,10 @@ void MKLDNNTester::checkBackwardWgts() {
vector<VectorPtr> dnnWgts; // used to temply save mkldnn weights
saveWgt(parameters_[DNN], dnnWgts);
dnnLayer_->convertWeightsToPaddle();
MKLDNNLayerPtr dnnLayer = std::dynamic_pointer_cast<MKLDNNLayer>(dnnLayer_);
if (dnnLayer) {
dnnLayer->convertWeightsToPaddle();
}
for (size_t i = 0; i < parameters_[DNN].size(); ++i) {
const VectorPtr& dnn = parameters_[DNN][i]->getBuf(PARAMETER_VALUE);
const VectorPtr& ref = parameters_[REF][i]->getBuf(PARAMETER_VALUE);
......@@ -322,6 +327,10 @@ void MKLDNNTester::runOnce() {
// and clearTopDatas(REF) should be coverd by ref layers
clearBotDiffs(REF);
clearWgtDiffs(REF);
// it is necessary to clear bottom diffs when only activation is dnn type
if (configs_[DNN].layerConfig.active_type().compare(0, 7, "mkldnn_") == 0) {
clearBotDiffs(DNN);
}
}
void MKLDNNTester::run(const TestConfig& dnn,
......@@ -333,8 +342,19 @@ void MKLDNNTester::run(const TestConfig& dnn,
float epsilon,
bool log,
int level) {
VLOG(MKLDNN_TESTS) << "Test MKLDNN functionality: " << dnn.layerConfig.type()
<< " vs " << ref.layerConfig.type();
CHECK(dnn.layerConfig.type().compare(0, 7, "mkldnn_") == 0 ||
dnn.layerConfig.active_type().compare(0, 7, "mkldnn_") == 0)
<< "should be MKLDNN layer or MKLDNN activation";
if (dnn.layerConfig.type() == ref.layerConfig.type()) {
VLOG(MKLDNN_TESTS) << "Test MKLDNN functionality: "
<< dnn.layerConfig.active_type() << " vs "
<< ref.layerConfig.active_type();
} else {
VLOG(MKLDNN_TESTS) << "Test MKLDNN functionality: "
<< dnn.layerConfig.type() << " vs "
<< ref.layerConfig.type();
}
ih_ = inputImgH;
iw_ = inputImgW;
iter_ = iter;
......
paddle/gserver/tests/MKLDNNTester.h
浏览文件 @ 37faf495
......@@ -41,8 +41,7 @@ protected:
vector<LayerMap> layerMaps_;
vector<vector<ParameterPtr>> parameters_;
vector<LayerPtr> testLayers_;
LayerPtr refLayer_;
MKLDNNLayerPtr dnnLayer_;
LayerPtr refLayer_, dnnLayer_;
/// run some iterations, all the result should pass
size_t iter_;
......
paddle/gserver/tests/test_MKLDNN.cpp
浏览文件 @ 37faf495
......@@ -17,6 +17,7 @@ limitations under the License. */
#include <vector>
#include "MKLDNNTester.h"
#include "ModelConfig.pb.h"
#include "paddle/gserver/activations/MKLDNNActivation.h"
#include "paddle/math/MathUtils.h"
using namespace paddle; // NOLINT
......@@ -162,7 +163,6 @@ void testPoolLayer(const testPoolDesc& pm) {
0});
LayerInputConfig* input = cfg.layerConfig.add_inputs();
PoolConfig* pool = input->mutable_pool_conf();
// pool->set_pool_type(poolType);
pool->set_channels(pm.ch);
pool->set_img_size(pm.iw);
pool->set_img_size_y(pm.ih);
......@@ -191,7 +191,7 @@ void testPoolLayer(const testPoolDesc& pm) {
}
}
TEST(MkldnnLayer, PoolLayer) {
TEST(MKLDNNLayer, PoolLayer) {
/* bs, ch, ih, iw, oh, ow, fh, fw, ph, pw, sh, sw*/
testPoolLayer({2, 1, 4, 4, 2, 2, 3, 3, 0, 0, 2, 2});
testPoolLayer({10, 8, 16, 16, 8, 8, 2, 2, 0, 0, 2, 2});
......@@ -203,6 +203,49 @@ TEST(MkldnnLayer, PoolLayer) {
testPoolLayer({2, 8, 56, 56, 29, 29, 3, 3, 1, 1, 2, 2});
}
struct testActDesc {
int bs, ch;
int ih, iw;
};
static void getAddtoConfig(TestConfig& cfg, const testActDesc& pm) {
cfg.biasSize = 0;
cfg.layerConfig.set_type("addto");
cfg.layerConfig.set_size(pm.ch * pm.ih * pm.iw);
cfg.inputDefs.push_back(
{INPUT_DATA,
"layer_0",
/* size of input layer= */ size_t(pm.ch * pm.ih * pm.iw),
0});
cfg.layerConfig.add_inputs();
}
void testActivation(std::string& type, const testActDesc& pm) {
const std::string compareTypes[] = {type, type.erase(0, 7)};
TestConfig cfg;
getAddtoConfig(cfg, pm);
TestConfig ref = cfg;
cfg.layerConfig.set_active_type(compareTypes[0]);
ref.layerConfig.set_active_type(compareTypes[1]);
MKLDNNTester tester;
for (auto bs : {pm.bs, 1}) {
tester.run(cfg, ref, bs, pm.ih, pm.iw);
}
}
TEST(MKLDNNActivation, Activations) {
auto types = MKLDNNActivation::getAllRegisteredTypes();
// TODO(TJ): mkldnn_softmax not implemented, paddle do not have elu activation
std::set<string> excluded{"mkldnn_softmax", "mkldnn_elu"};
for (auto type : types) {
if (excluded.count(type)) {
continue;
}
testActivation(type, {16, 64, 32, 32});
}
}
// TODO(TJ): add branch test
int main(int argc, char** argv) {
......
paddle/math/MathFunctions.h
浏览文件 @ 37faf495
......@@ -26,7 +26,7 @@ limitations under the License. */
#include <mkl_lapacke.h>
#endif
#ifdef PADDLE_USE_ATLAS
#if defined(PADDLE_USE_ATLAS) || defined(PADDLE_USE_VECLIB)
extern "C" {
#include <cblas.h>
#include <clapack.h>
......
paddle/operators/cross_entropy_op.cc 0 → 100644
浏览文件 @ 37faf495
/* Copyright (c) 2016 PaddlePaddle Authors. All Rights Reserve.
Licensed under the Apache License, Version 2.0 (the "License");
you may not use this file except in compliance with the License.
You may obtain a copy of the License at
http://www.apache.org/licenses/LICENSE-2.0
Unless required by applicable law or agreed to in writing, software
distributed under the License is distributed on an "AS IS" BASIS,
WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
See the License for the specific language governing permissions and
limitations under the License. */
#include "paddle/operators/cross_entropy_op.h"
namespace paddle {
namespace operators {
using framework::LoDTensor;
class CrossEntropyOp : public framework::OperatorWithKernel {
public:
using framework::OperatorWithKernel::OperatorWithKernel;
protected:
void InferShape(const framework::InferShapeContext &ctx) const override {
PADDLE_ENFORCE_NOT_NULL(ctx.InputVar("X"), "Input(X) must not be null.");
PADDLE_ENFORCE_NOT_NULL(ctx.InputVar("Label"),
"Input(Label) must not be null.");
PADDLE_ENFORCE_NOT_NULL(ctx.OutputVar("Y"), "Output(Y) must not be null.");
auto x = ctx.Input<Tensor>("X");
auto label = ctx.Input<Tensor>("Label");
PADDLE_ENFORCE_EQ(x->dims().size(), 2, "Input(X)'s rank must be 2.");
PADDLE_ENFORCE_EQ(label->dims().size(), 2,
"Input(Label)'s rank must be 2.");
// TODO(xinghai-sun): remove this check after swtiching to bool
PADDLE_ENFORCE(ctx.Attr<int>("soft_label") == 0 ||
ctx.Attr<int>("soft_label") == 1);
PADDLE_ENFORCE_EQ(x->dims()[0], label->dims()[0],
"The 1st dimension of Input(X) and Input(Label) must "
"be equal.");
if (ctx.Attr<int>("soft_label") == 1) {
PADDLE_ENFORCE_EQ(x->dims()[1], label->dims()[1],
"If Attr(soft_label) == 1, The 2nd dimension of "
"Input(X) and Input(Label) must be equal.");
} else {
PADDLE_ENFORCE_EQ(label->dims()[1], 1,
"If Attr(soft_label) == 0, The 2nd dimension of "
"Input(Label) must be 1.");
}
ctx.Output<LoDTensor>("Y")->Resize({x->dims()[0], 1});
}
};
class CrossEntropyGradientOp : public framework::OperatorWithKernel {
public:
using framework::OperatorWithKernel::OperatorWithKernel;
protected:
void InferShape(const framework::InferShapeContext &ctx) const override {
PADDLE_ENFORCE_NOT_NULL(ctx.InputVar("X"), "Input(X) must not be null.");
PADDLE_ENFORCE_NOT_NULL(ctx.InputVar("Label"),
"Input(Label) must not be null.");
PADDLE_ENFORCE_NOT_NULL(ctx.InputVar(framework::GradVarName("Y")),
"Input(Y@GRAD) must not be null.");
auto x = ctx.Input<Tensor>("X");
auto label = ctx.Input<Tensor>("Label");
auto dy = ctx.Input<Tensor>(framework::GradVarName("Y"));
PADDLE_ENFORCE_EQ(x->dims().size(), 2, "Input(X)'s rank must be 2.");
PADDLE_ENFORCE_EQ(dy->dims().size(), 2, "Input(Y@Grad)'s rank must be 2.");
PADDLE_ENFORCE_EQ(label->dims().size(), 2,
"Input(Label)'s rank must be 2.");
// TODO(xinghai-sun): remove this check after swtiching to bool
PADDLE_ENFORCE(ctx.Attr<int>("soft_label") == 0 ||
ctx.Attr<int>("soft_label") == 1);
PADDLE_ENFORCE_EQ(x->dims()[0], label->dims()[0],
"The 1st dimension of Input(X) and Input(Label) must "
"be equal.");
PADDLE_ENFORCE_EQ(x->dims()[0], dy->dims()[0],
"The 1st dimension of Input(X) and Input(Y@Grad) must "
"be equal.");
PADDLE_ENFORCE_EQ(dy->dims()[1], 1,
"The 2nd dimension of Input(Y@Grad) must be 1.");
if (ctx.Attr<int>("soft_label") == 1) {
PADDLE_ENFORCE_EQ(x->dims()[1], label->dims()[1],
"If Attr(soft_label) == 1, The 2nd dimension of "
"Input(X) and Input(Label) must be equal.");
} else {
PADDLE_ENFORCE_EQ(label->dims()[1], 1,
"If Attr(soft_label) == 0, The 2nd dimension of "
"Input(Label) must be 1.");
}
auto dx = ctx.Output<LoDTensor>(framework::GradVarName("X"));
dx->Resize(x->dims());
}
};
class CrossEntropyOpMaker : public framework::OpProtoAndCheckerMaker {
public:
CrossEntropyOpMaker(framework::OpProto *proto,
framework::OpAttrChecker *op_checker)
: OpProtoAndCheckerMaker(proto, op_checker) {
AddInput("X", "The first input of CrossEntropyOp");
AddInput("Label", "The second input of CrossEntropyOp");
AddOutput("Y", "The output of CrossEntropyOp");
AddAttr<int>("soft_label", "Is soft label. Default zero.").SetDefault(0);
AddComment(R"DOC(
CrossEntropy Operator.
It supports both standard cross-entropy and soft-label cross-entropy loss
computation.
1) One-hot cross-entropy:
soft_label = 0, Label[i, 0] indicates the class index for sample i:
Y[i] = -log(X[i, Label[i]])
2) Soft-label cross-entropy:
soft_label = 1, Label[i, j] indicates the soft label of class j
for sample i:
Y[i] = \sum_j{-Label[i, j] * log(X[i, j])}
Please make sure that in this case the summuation of each row of Label
equals one.
3) One-hot cross-entropy with vecterized Input(Label):
As a special case of 2), when each row of Input(Label) has only one
non-zero element (equals 1), soft-label cross-entropy degenerates to a
one-hot cross-entropy with one-hot label representation.
)DOC");
}
};
} // namespace operators
} // namespace paddle
namespace ops = paddle::operators;
REGISTER_OP(cross_entropy, ops::CrossEntropyOp, ops::CrossEntropyOpMaker,
cross_entropy_grad, ops::CrossEntropyGradientOp);
REGISTER_OP_CPU_KERNEL(cross_entropy, ops::CrossEntropyOpKernel<float>);
REGISTER_OP_CPU_KERNEL(cross_entropy_grad,
ops::CrossEntropyGradientOpKernel<float>);
paddle/operators/onehot_cross_entropy_op.cu → paddle/operators/cross_entropy_op.cu
浏览文件 @ 37faf495
......@@ -13,27 +13,13 @@
limitations under the License. */
#include "paddle/framework/op_registry.h"
#include "paddle/operators/cross_entropy_op.h"
#include "paddle/platform/assert.h"
#include "paddle/platform/hostdevice.h"
namespace paddle {
namespace operators {
using Tensor = framework::Tensor;
template <typename T>
__host__ __device__ T clipping_log(const T x) {
PADDLE_ASSERT(std::is_floating_point<T>::value);
const T kApproInf = 1e20;
T v = log(x);
if (v == INFINITY) {
return kApproInf;
}
if (v == -INFINITY) {
return -kApproInf;
}
return v;
}
template <typename T>
__global__ void CrossEntropyKernel(T* Y, const T* X, const int* label,
const int N, const int D) {
......@@ -42,7 +28,20 @@ __global__ void CrossEntropyKernel(T* Y, const T* X, const int* label,
for (int i = blockIdx.x * blockDim.x + threadIdx.x; i < N;
i += blockDim.x * gridDim.x) {
PADDLE_ASSERT(label[i] >= 0 && label[i] < D);
Y[i] = -clipping_log(X[i * D + label[i]]);
Y[i] = -tolerable_value(log(X[i * D + label[i]]));
}
}
template <typename T>
__global__ void SoftCrossEntropyKernel(T* Y, const T* X, const T* label,
const int N, const int D) {
for (int i = blockIdx.x * blockDim.x + threadIdx.x; i < N;
i += blockDim.x * gridDim.x) {
T sum = static_cast<T>(0);
for (int j = 0; j < D; j++) {
sum += label[i * D + j] * tolerable_value(log(X[i * D + j]));
}
Y[i] = -sum;
}
}
......@@ -69,57 +68,84 @@ __global__ void CrossEntropyGradientKernel(T* dX, const T* dY, const T* X,
}
template <typename T>
class OnehotCrossEntropyOpCUDAKernel : public framework::OpKernel {
__global__ void SoftCrossEntropyGradientKernel(T* dX, const T* dY, const T* X,
const T* label, const int N,
const int D) {
// TOOD(qingqing): optimize for this kernel
for (int i = blockIdx.x * blockDim.x + threadIdx.x; i < N;
i += blockDim.x * gridDim.x) {
for (int j = 0; j < D; ++j) {
int idx = i * D + j;
dX[idx] = -label[idx] * dY[i] / X[idx];
}
}
}
template <typename T>
class CrossEntropyOpCUDAKernel : public framework::OpKernel {
public:
void Compute(const framework::ExecutionContext& ctx) const override {
PADDLE_ENFORCE(platform::is_gpu_place(ctx.GetPlace()),
"It must use GPUPlace.");
auto X = ctx.Input<Tensor>("X");
const T* Xdata = X->data<T>();
const int* label_data = ctx.Input<Tensor>("label")->data<int>();
auto Y = ctx.Output<Tensor>("Y");
Y->mutable_data<T>(ctx.GetPlace());
T* Ydata = Y->data<T>();
auto x = ctx.Input<Tensor>("X");
auto y = ctx.Output<Tensor>("Y");
auto label = ctx.Input<Tensor>("Label");
int N = X->dims()[0];
int D = X->dims()[1];
auto* x_data = x->data<T>();
y->mutable_data<T>(ctx.GetPlace());
auto* y_data = y->data<T>();
int n = x->dims()[0];
int d = x->dims()[1];
int block = 512;
int grid = (N + block - 1) / block;
int grid = (n + block - 1) / block;
// TODO(qingqing) launch kernel on specified stream
// base on ExecutionContext.
CrossEntropyKernel<T><<<grid, block>>>(Ydata, Xdata, label_data, N, D);
if (ctx.Attr<int>("soft_label") == 1) {
auto* label_data = ctx.Input<Tensor>("Label")->data<T>();
SoftCrossEntropyKernel<T><<<grid, block>>>(y_data, x_data, label_data, n,
d);
} else {
auto* label_data = ctx.Input<Tensor>("Label")->data<int>();
CrossEntropyKernel<T><<<grid, block>>>(y_data, x_data, label_data, n, d);
}
}
};
template <typename T>
class OnehotCrossEntropyGradientOpCUDAKernel : public framework::OpKernel {
class CrossEntropyGradientOpCUDAKernel : public framework::OpKernel {
public:
void Compute(const framework::ExecutionContext& ctx) const override {
PADDLE_ENFORCE(platform::is_gpu_place(ctx.GetPlace()),
"It must use GPUPlace.");
auto X = ctx.Input<Tensor>("X");
auto dX = ctx.Output<Tensor>(framework::GradVarName("X"));
auto dY = ctx.Input<Tensor>(framework::GradVarName("Y"));
auto label = ctx.Input<Tensor>("label");
auto x = ctx.Input<Tensor>("X");
auto dx = ctx.Output<Tensor>(framework::GradVarName("X"));
auto dy = ctx.Input<Tensor>(framework::GradVarName("Y"));
auto label = ctx.Input<Tensor>("Label");
auto* dXdata = dX->template mutable_data<T>(ctx.GetPlace());
auto* dYdata = dY->template data<T>();
auto* Xdata = X->template data<T>();
auto* label_data = label->data<int>();
auto* dx_data = dx->mutable_data<T>(ctx.GetPlace());
auto* dy_data = dy->data<T>();
auto* x_data = x->data<T>();
int N = X->dims()[0];
int D = X->dims()[1];
int n = x->dims()[0];
int d = x->dims()[1];
int block = 512;
int grid = (N * D + block - 1) / block;
zero<T><<<grid, block>>>(dXdata, N * D);
grid = (N + block - 1) / block;
int grid = (n * d + block - 1) / block;
zero<T><<<grid, block>>>(dx_data, n * d);
grid = (n + block - 1) / block;
// TODO(qingqing): launch kernel on specified stream
// base on ExecutionContext.
CrossEntropyGradientKernel<T><<<grid, block>>>(dXdata, dYdata, Xdata,
label_data, N, D);
if (ctx.Attr<int>("soft_label") == 1) {
auto* label_data = label->data<T>();
SoftCrossEntropyGradientKernel<T><<<grid, block>>>(
dx_data, dy_data, x_data, label_data, n, d);
} else {
auto* label_data = label->data<int>();
CrossEntropyGradientKernel<T><<<grid, block>>>(dx_data, dy_data, x_data,
label_data, n, d);
}
}
};
......@@ -127,7 +153,6 @@ class OnehotCrossEntropyGradientOpCUDAKernel : public framework::OpKernel {
} // namespace paddle
namespace ops = paddle::operators;
REGISTER_OP_GPU_KERNEL(onehot_cross_entropy,
ops::OnehotCrossEntropyOpCUDAKernel<float>);
REGISTER_OP_GPU_KERNEL(onehot_cross_entropy_grad,
ops::OnehotCrossEntropyGradientOpCUDAKernel<float>);
REGISTER_OP_GPU_KERNEL(cross_entropy, ops::CrossEntropyOpCUDAKernel<float>);
REGISTER_OP_GPU_KERNEL(cross_entropy_grad,
ops::CrossEntropyGradientOpCUDAKernel<float>);
paddle/operators/onehot_cross_entropy_op.h → paddle/operators/cross_entropy_op.h
浏览文件 @ 37faf495
......@@ -14,6 +14,7 @@ limitations under the License. */
#pragma once
#include "paddle/framework/op_registry.h"
#include "paddle/platform/hostdevice.h"
namespace paddle {
namespace operators {
......@@ -21,75 +22,93 @@ namespace operators {
using Tensor = framework::Tensor;
template <typename T>
inline T tolerable_value(const T x) {
static_assert(std::is_floating_point<T>::value,
"tolerable_value works only on float, "
"double and double double.");
HOSTDEVICE T tolerable_value(const T x) {
PADDLE_ASSERT(std::is_floating_point<T>::value);
const T kApproInf = 1e20;
if (x == INFINITY) {
return kApproInf;
}
if (x == -INFINITY) {
return -kApproInf;
}
return x;
}
template <typename T>
class OnehotCrossEntropyOpKernel : public framework::OpKernel {
class CrossEntropyOpKernel : public framework::OpKernel {
public:
void Compute(const framework::ExecutionContext& ctx) const override {
PADDLE_ENFORCE(platform::is_cpu_place(ctx.GetPlace()),
"It must use CPUPlace.");
auto X = ctx.Input<Tensor>("X");
const T* Xdata = X->data<T>();
const int* label_data = ctx.Input<Tensor>("label")->data<int>();
auto Y = ctx.Output<Tensor>("Y");
Y->mutable_data<T>(ctx.GetPlace());
T* Ydata = Y->data<T>();
int batch_size = X->dims()[0];
int class_num = X->dims()[1];
for (int i = 0; i < batch_size; ++i) {
int index = i * class_num + label_data[i];
Ydata[i] = -tolerable_value(std::log(Xdata[index]));
auto x = ctx.Input<Tensor>("X");
auto y = ctx.Output<Tensor>("Y");
auto* x_data = x->data<T>();
y->mutable_data<T>(ctx.GetPlace());
auto* y_data = y->data<T>();
int batch_size = x->dims()[0];
int class_num = x->dims()[1];
if (ctx.Attr<int>("soft_label") == 1) {
auto* label_data = ctx.Input<Tensor>("Label")->data<T>();
int index = 0;
for (int i = 0; i < batch_size; ++i) {
T sum = static_cast<T>(0);
for (int j = 0; j < class_num; ++j) {
sum += label_data[index] * tolerable_value(std::log(x_data[index]));
y_data[i] = -sum;
index++;
}
}
} else {
auto* label_data = ctx.Input<Tensor>("Label")->data<int>();
for (int i = 0; i < batch_size; ++i) {
int index = i * class_num + label_data[i];
y_data[i] = -tolerable_value(std::log(x_data[index]));
}
}
}
};
template <typename T>
class OnehotCrossEntropyGradientOpKernel : public framework::OpKernel {
class CrossEntropyGradientOpKernel : public framework::OpKernel {
public:
void Compute(const framework::ExecutionContext& ctx) const override {
PADDLE_ENFORCE(platform::is_cpu_place(ctx.GetPlace()),
"It must use CPUPlace.");
auto X = ctx.Input<Tensor>("X");
auto dX = ctx.Output<Tensor>(framework::GradVarName("X"));
auto dY = ctx.Input<Tensor>(framework::GradVarName("Y"));
auto label = ctx.Input<Tensor>("label");
auto x = ctx.Input<Tensor>("X");
auto dx = ctx.Output<Tensor>(framework::GradVarName("X"));
auto dy = ctx.Input<Tensor>(framework::GradVarName("Y"));
auto label = ctx.Input<Tensor>("Label");
auto* dXdata = dX->template mutable_data<T>(ctx.GetPlace());
auto* dYdata = dY->template data<T>();
auto* Xdata = X->template data<T>();
auto* label_data = label->data<int>();
auto* dx_data = dx->mutable_data<T>(ctx.GetPlace());
auto* dy_data = dy->data<T>();
auto* x_data = x->data<T>();
const int batch_size = X->dims()[0];
const int class_num = X->dims()[1];
int batch_size = x->dims()[0];
int class_num = x->dims()[1];
// TODO(qingqing): make zero setting an common function.
memset(dXdata, 0, sizeof(T) * batch_size * class_num);
for (int i = 0; i < batch_size; ++i) {
int index = i * class_num + label_data[i];
dXdata[index] = -tolerable_value(dYdata[i] / Xdata[index]);
if (ctx.Attr<int>("soft_label") == 1) {
auto* label_data = ctx.Input<Tensor>("Label")->data<T>();
int index = 0;
for (int i = 0; i < batch_size; ++i) {
for (int j = 0; j < class_num; ++j) {
dx_data[index] = -label_data[index] * dy_data[i] / x_data[index];
index++;
}
}
} else {
auto* label_data = label->data<int>();
memset(dx_data, 0, sizeof(T) * batch_size * class_num);
for (int i = 0; i < batch_size; ++i) {
PADDLE_ASSERT(label_data[i] >= 0 || label_data[i] < class_num);
int index = i * class_num + label_data[i];
dx_data[index] = -dy_data[i] / x_data[index];
}
}
}
};
......
paddle/operators/onehot_cross_entropy_op.cc 已删除 100644 → 0
浏览文件 @ e635e3fd
/* Copyright (c) 2016 PaddlePaddle Authors. All Rights Reserve.
Licensed under the Apache License, Version 2.0 (the "License");
you may not use this file except in compliance with the License.
You may obtain a copy of the License at
http://www.apache.org/licenses/LICENSE-2.0
Unless required by applicable law or agreed to in writing, software
distributed under the License is distributed on an "AS IS" BASIS,
WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
See the License for the specific language governing permissions and
limitations under the License. */
#include "paddle/operators/onehot_cross_entropy_op.h"
namespace paddle {
namespace operators {
class OnehotCrossEntropyOp : public framework::OperatorWithKernel {
public:
using framework::OperatorWithKernel::OperatorWithKernel;
protected:
void InferShape(const framework::InferShapeContext &ctx) const override {
PADDLE_ENFORCE_NOT_NULL(
ctx.InputVar("X"),
"Input(X) of OnehotCrossEntropyOp should not be null.");
PADDLE_ENFORCE_NOT_NULL(
ctx.InputVar("label"),
"Input(label) of OnehotCrossEntropyOp should not be null.");
PADDLE_ENFORCE_NOT_NULL(
ctx.OutputVar("Y"),
"Output(Y) of OnehotCrossEntropyOp should not be null.");
auto *X = ctx.Input<Tensor>("X");
auto *label = ctx.Input<Tensor>("label");
PADDLE_ENFORCE_EQ(X->dims().size(), 2, "X's dimension must be 2.");
PADDLE_ENFORCE_EQ(label->dims().size(), 1, "label's dimension must be 1.");
PADDLE_ENFORCE_EQ(X->dims()[0], label->dims()[0]);
ctx.Output<framework::LoDTensor>("Y")->Resize({X->dims()[0], 1});
}
};
class OnehotCrossEntropyGradientOp : public framework::OperatorWithKernel {
public:
using framework::OperatorWithKernel::OperatorWithKernel;
protected:
void InferShape(const framework::InferShapeContext &ctx) const override {
auto dX = ctx.Output<framework::LoDTensor>(framework::GradVarName("X"));
auto X = ctx.Input<Tensor>("X");
dX->Resize(X->dims());
}
};
class OnehotCrossEntropyOpMaker : public framework::OpProtoAndCheckerMaker {
public:
OnehotCrossEntropyOpMaker(framework::OpProto *proto,
framework::OpAttrChecker *op_checker)
: OpProtoAndCheckerMaker(proto, op_checker) {
AddInput("X", "The first input of OnehotCrossEntropyOp");
AddInput("label", "The second input of OnehotCrossEntropyOp");
AddOutput("Y", "The output of OnehotCrossEntropyOp");
AddComment(R"DOC(
OnehotCrossEntropy Operator.
Y[i] = -log(X[i][j])
)DOC");
}
};
} // namespace operators
} // namespace paddle
namespace ops = paddle::operators;
REGISTER_OP(onehot_cross_entropy, ops::OnehotCrossEntropyOp,
ops::OnehotCrossEntropyOpMaker, onehot_cross_entropy_grad,
ops::OnehotCrossEntropyGradientOp);
REGISTER_OP_CPU_KERNEL(onehot_cross_entropy,
ops::OnehotCrossEntropyOpKernel<float>);
REGISTER_OP_CPU_KERNEL(onehot_cross_entropy_grad,
ops::OnehotCrossEntropyGradientOpKernel<float>);
paddle/operators/prelu_op.h
浏览文件 @ 37faf495
......@@ -54,8 +54,9 @@ class PReluKernel : public framework::OpKernel {
int numel = x->numel();
Transform(context.device_context(), x_ptr, x_ptr + numel, o_ptr,
PReluFunctor<T>(alpha_ptr));
Transform<Place> trans;
trans(context.device_context(), x_ptr, x_ptr + numel, o_ptr,
PReluFunctor<T>(alpha_ptr));
}
};
......@@ -91,8 +92,9 @@ class PReluGradKernel : public framework::OpKernel {
const T* out_ptr = out->data<T>();
int numel = dx->numel();
Transform(context.device_context(), out_ptr, out_ptr + numel, dout_ptr,
dx_ptr, PReluGradFunctor<T>(alpha_ptr));
Transform<Place> trans;
trans(context.device_context(), out_ptr, out_ptr + numel, dout_ptr, dx_ptr,
PReluGradFunctor<T>(alpha_ptr));
// TODO (Zhuoyuan): add dalpha upgrade when GPU kernels ready
}
......
paddle/platform/transform.h
浏览文件 @ 37faf495
......@@ -29,45 +29,71 @@
namespace paddle {
namespace platform {
// Transform on host or device. It provides the same API in std library.
template <typename InputIter, typename OutputIter, typename UnaryOperation>
void Transform(const DeviceContext& context, InputIter first, InputIter last,
OutputIter result, UnaryOperation op) {
auto place = context.GetPlace();
if (is_cpu_place(place)) {
template <typename Place>
struct Transform {
template <typename InputIter, typename OutputIter, typename UnaryOperation>
void operator()(const DeviceContext& context, InputIter first, InputIter last,
OutputIter result, UnaryOperation op);
template <typename InputIter1, typename InputIter2, typename OutputIter,
typename BinaryOperation>
void operator()(const DeviceContext& context, InputIter1 first1,
InputIter1 last1, InputIter2 first2, OutputIter result,
BinaryOperation op);
};
template <>
struct Transform<platform::CPUPlace> {
template <typename InputIter, typename OutputIter, typename UnaryOperation>
void operator()(const DeviceContext& context, InputIter first, InputIter last,
OutputIter result, UnaryOperation op) {
auto place = context.GetPlace();
PADDLE_ENFORCE(is_cpu_place(place), "It must use CPU place.");
std::transform(first, last, result, op);
} else {
#ifdef __NVCC__
auto& ctx = reinterpret_cast<const CUDADeviceContext&>(context);
using namespace details;
thrust::transform(thrust::cuda::par.on(ctx.stream()), DevPtrCast(first),
DevPtrCast(last), DevPtrCast(result), op);
#else
PADDLE_THROW("Do not invoke `Transform<GPUPlace>` in .cc file");
#endif
}
}
template <typename InputIter1, typename InputIter2, typename OutputIter,
typename BinaryOperation>
void Transform(const DeviceContext& context, InputIter1 first1,
InputIter1 last1, InputIter2 first2, OutputIter result,
BinaryOperation op) {
auto place = context.GetPlace();
if (is_cpu_place(place)) {
template <typename InputIter1, typename InputIter2, typename OutputIter,
typename BinaryOperation>
void operator()(const DeviceContext& context, InputIter1 first1,
InputIter1 last1, InputIter2 first2, OutputIter result,
BinaryOperation op) {
auto place = context.GetPlace();
PADDLE_ENFORCE(is_cpu_place(place), "It must use CPU place.");
std::transform(first1, last1, first2, result, op);
} else {
}
};
#ifdef __NVCC__
template <>
struct Transform<platform::GPUPlace> {
template <typename InputIter, typename OutputIter, typename UnaryOperation>
void operator()(const DeviceContext& context, InputIter first, InputIter last,
OutputIter result, UnaryOperation op) {
auto place = context.GetPlace();
PADDLE_ENFORCE(is_gpu_place(place), "It must use GPU place.");
auto& ctx = reinterpret_cast<const CUDADeviceContext&>(context);
using namespace details;
thrust::transform(thrust::cuda::par.on(ctx.stream()), DevPtrCast(first1),
DevPtrCast(last1), DevPtrCast(first2), DevPtrCast(result),
thrust::transform(thrust::cuda::par.on(ctx.stream()),
details::DevPtrCast(first), details::DevPtrCast(last),
details::DevPtrCast(result), op);
}
template <typename InputIter1, typename InputIter2, typename OutputIter,
typename BinaryOperation>
void operator()(const DeviceContext& context, InputIter1 first1,
InputIter1 last1, InputIter2 first2, OutputIter result,
BinaryOperation op) {
auto place = context.GetPlace();
PADDLE_ENFORCE(is_gpu_place(place), "It must use GPU place.");
auto& ctx = reinterpret_cast<const CUDADeviceContext&>(context);
thrust::transform(thrust::cuda::par.on(ctx.stream()),
details::DevPtrCast(first1), details::DevPtrCast(last1),
details::DevPtrCast(first2), details::DevPtrCast(result),
op);
#else
PADDLE_THROW("Do not invoke `Transform<GPUPlace>` in .cc file");
#endif
}
};
#endif
} // namespace platform
} // namespace paddle
paddle/platform/transform_test.cu
浏览文件 @ 37faf495
......@@ -15,6 +15,7 @@
#include <gtest/gtest.h>
#include "paddle/memory/memcpy.h"
#include "paddle/memory/memory.h"
#include "paddle/platform/hostdevice.h"
#include "paddle/platform/transform.h"
template <typename T>
......@@ -38,7 +39,8 @@ TEST(Transform, CPUUnary) {
using namespace paddle::platform;
CPUDeviceContext ctx;
float buf[4] = {0.1, 0.2, 0.3, 0.4};
Transform(ctx, buf, buf + 4, buf, Scale<float>(10));
Transform<paddle::platform::CPUPlace> trans;
trans(ctx, buf, buf + 4, buf, Scale<float>(10));
for (int i = 0; i < 4; ++i) {
ASSERT_NEAR(buf[i], static_cast<float>(i + 1), 1e-5);
}
......@@ -52,7 +54,8 @@ TEST(Transform, GPUUnary) {
float cpu_buf[4] = {0.1, 0.2, 0.3, 0.4};
float* gpu_buf = static_cast<float*>(Alloc(gpu0, sizeof(float) * 4));
Copy(gpu0, gpu_buf, CPUPlace(), cpu_buf, sizeof(cpu_buf));
Transform(ctx, gpu_buf, gpu_buf + 4, gpu_buf, Scale<float>(10));
Transform<paddle::platform::GPUPlace> trans;
trans(ctx, gpu_buf, gpu_buf + 4, gpu_buf, Scale<float>(10));
ctx.Wait();
Copy(CPUPlace(), cpu_buf, gpu0, gpu_buf, sizeof(cpu_buf));
Free(gpu0, gpu_buf);
......@@ -65,7 +68,9 @@ TEST(Transform, CPUBinary) {
using namespace paddle::platform;
using namespace paddle::memory;
int buf[4] = {1, 2, 3, 4};
Transform(CPUDeviceContext(), buf, buf + 4, buf, buf, Multiply<int>());
Transform<paddle::platform::CPUPlace> trans;
CPUDeviceContext ctx;
trans(ctx, buf, buf + 4, buf, buf, Multiply<int>());
for (int i = 0; i < 4; ++i) {
ASSERT_EQ((i + 1) * (i + 1), buf[i]);
}
......@@ -79,11 +84,12 @@ TEST(Transform, GPUBinary) {
CUDADeviceContext ctx(gpu0);
int* gpu_buf = static_cast<int*>(Alloc(gpu0, sizeof(buf)));
Copy(gpu0, gpu_buf, CPUPlace(), buf, sizeof(buf));
Transform(ctx, gpu_buf, gpu_buf + 4, gpu_buf, gpu_buf, Multiply<int>());
Transform<paddle::platform::GPUPlace> trans;
trans(ctx, gpu_buf, gpu_buf + 4, gpu_buf, gpu_buf, Multiply<int>());
ctx.Wait();
Copy(CPUPlace(), buf, gpu0, gpu_buf, sizeof(buf));
Free(gpu0, gpu_buf);
for (int i = 0; i < 4; ++i) {
ASSERT_EQ((i + 1) * (i + 1), buf[i]);
}
}
\ No newline at end of file
}
paddle/pserver/CMakeLists.txt
浏览文件 @ 37faf495
......@@ -45,14 +45,18 @@ add_dependencies(paddle_pserver paddle_proto ${external_project_dependencies})
set(PSERVER_MAIN_SOURCES
ParameterServer2Main.cpp)
add_executable(paddle_pserver_main
${PSERVER_MAIN_SOURCES})
link_paddle_exe(paddle_pserver_main)
if(WITH_TESTING)
add_subdirectory(test)
endif()
install(TARGETS paddle_pserver_main
RUNTIME DESTINATION opt/paddle/bin
PERMISSIONS OWNER_EXECUTE OWNER_WRITE OWNER_READ
GROUP_EXECUTE GROUP_READ WORLD_EXECUTE WORLD_READ)
set_target_properties(paddle_pserver_main PROPERTIES INSTALL_RPATH_USE_LINK_PATH TRUE)
if(NOT WITH_C_API)
add_executable(paddle_pserver_main ${PSERVER_MAIN_SOURCES})
link_paddle_exe(paddle_pserver_main)
install(TARGETS paddle_pserver_main
RUNTIME DESTINATION opt/paddle/bin
PERMISSIONS OWNER_EXECUTE OWNER_WRITE OWNER_READ
GROUP_EXECUTE GROUP_READ WORLD_EXECUTE WORLD_READ)
set_target_properties(paddle_pserver_main PROPERTIES INSTALL_RPATH_USE_LINK_PATH TRUE)
endif()
paddle/scripts/travis/build_ios.sh 0 → 100755
浏览文件 @ 37faf495
#!/bin/bash
set -e
# Create the build directory for CMake.
mkdir -p $TRAVIS_BUILD_DIR/build_ios
cd $TRAVIS_BUILD_DIR/build_ios
# Compile paddle binaries
cmake -DCMAKE_SYSTEM_NAME=iOS \
-DIOS_PLATFORM=OS \
-DCMAKE_OSX_ARCHITECTURES="arm64" \
-DWITH_C_API=ON \
-DUSE_EIGEN_FOR_BLAS=ON \
-DWITH_TESTING=OFF \
-DWITH_SWIG_PY=OFF \
-DWITH_STYLE_CHECK=OFF \
-DCMAKE_BUILD_TYPE=Release \
..
make -j 2
paddle/scripts/travis/check_style.sh
浏览文件 @ 37faf495
......@@ -8,6 +8,12 @@ function abort(){
trap 'abort' 0
set -e
# install glide
curl https://glide.sh/get | bash
eval "$(GIMME_GO_VERSION=1.8.3 gimme)"
go get -u github.com/alecthomas/gometalinter
gometalinter --install
cd $TRAVIS_BUILD_DIR
export PATH=/usr/bin:$PATH
pre-commit install
......
paddle/trainer/CMakeLists.txt
浏览文件 @ 37faf495
......@@ -50,22 +50,22 @@ macro(add_paddle_exe TARGET_NAME)
link_paddle_exe(${TARGET_NAME})
endmacro()
add_paddle_exe(paddle_trainer
TrainerMain.cpp)
add_paddle_exe(paddle_merge_model
MergeModel.cpp)
if(WITH_TESTING)
add_subdirectory(tests)
add_subdirectory(tests)
endif()
install(TARGETS paddle_trainer paddle_merge_model
RUNTIME DESTINATION opt/paddle/bin
PERMISSIONS OWNER_EXECUTE OWNER_WRITE OWNER_READ
GROUP_EXECUTE GROUP_READ WORLD_EXECUTE WORLD_READ)
set_target_properties(paddle_trainer PROPERTIES INSTALL_RPATH_USE_LINK_PATH TRUE)
set_target_properties(paddle_merge_model PROPERTIES INSTALL_RPATH_USE_LINK_PATH TRUE)
if(NOT WITH_C_API)
add_paddle_exe(paddle_trainer TrainerMain.cpp)
add_paddle_exe(paddle_merge_model MergeModel.cpp)
install(TARGETS paddle_trainer paddle_merge_model
RUNTIME DESTINATION opt/paddle/bin
PERMISSIONS OWNER_EXECUTE OWNER_WRITE OWNER_READ
GROUP_EXECUTE GROUP_READ WORLD_EXECUTE WORLD_READ)
set_target_properties(paddle_trainer PROPERTIES INSTALL_RPATH_USE_LINK_PATH TRUE)
set_target_properties(paddle_merge_model PROPERTIES INSTALL_RPATH_USE_LINK_PATH TRUE)
endif()
if(APPLE)
set(CMAKE_EXE_LINKER_FLAGS "-framework CoreFoundation -framework Security")
......@@ -73,6 +73,8 @@ endif()
if(WITH_GOLANG)
add_dependencies(paddle_trainer_lib paddle_pserver_cclient)
target_link_libraries(paddle_trainer paddle_pserver_cclient)
target_link_libraries(paddle_trainer_lib paddle_pserver_cclient)
if(NOT WITH_C_API)
target_link_libraries(paddle_trainer paddle_pserver_cclient)
endif()
endif(WITH_GOLANG)
paddle/utils/Excepts.h
浏览文件 @ 37faf495
......@@ -17,7 +17,8 @@ limitations under the License. */
#include <fenv.h>
#if defined(__APPLE__) || defined(__OSX__)
#if (defined(__APPLE__) || defined(__OSX__)) && !defined(__arm__) && \
!defined(__aarch64__)
int fegetexcept(void);
int feenableexcept(unsigned int excepts);
......
paddle/utils/arch/linux/Locks.cpp
浏览文件 @ 37faf495
......@@ -40,6 +40,8 @@ void Semaphore::wait() { sem_wait(&m->sem); }
void Semaphore::post() { sem_post(&m->sem); }
/// SpinLockPrivate
#ifdef PADDLE_USE_PTHREAD_SPINLOCK
class SpinLockPrivate {
......@@ -79,6 +81,8 @@ SpinLock::~SpinLock() { delete m; }
void SpinLock::lock() { m->lock(); }
void SpinLock::unlock() { m->unlock(); }
/// ThreadBarrierPrivate
#ifdef PADDLE_USE_PTHREAD_BARRIER
class ThreadBarrierPrivate {
......@@ -136,6 +140,8 @@ public:
#endif
/// ThreadBarrier
ThreadBarrier::ThreadBarrier(int count) : m(new ThreadBarrierPrivate(count)) {}
ThreadBarrier::~ThreadBarrier() { delete m; }
void ThreadBarrier::wait() { m->wait(); }
......
paddle/utils/arch/osx/Excepts.cpp
浏览文件 @ 37faf495
......@@ -14,7 +14,8 @@ limitations under the License. */
#include "paddle/utils/Excepts.h"
#if defined(__APPLE__) || defined(__OSX__)
#if (defined(__APPLE__) || defined(__OSX__)) && !defined(__arm__) && \
!defined(__aarch64__)
int fegetexcept(void) {
static fenv_t fenv;
......
python/paddle/v2/framework/tests/test_cross_entropy_op.py 0 → 100644
浏览文件 @ 37faf495
import unittest
import numpy as np
from op_test import OpTest
class TestCrossEntropyOp1(OpTest):
"""Test standard cross-entropy, with index representation of labels.
"""
def setUp(self):
self.op_type = "cross_entropy"
batch_size = 30
class_num = 10
X = np.random.uniform(0.1, 1.0,
[batch_size, class_num]).astype("float32")
label = np.random.randint(0, class_num, (batch_size, 1), dtype="int32")
cross_entropy = np.asmatrix(
[[-np.log(X[i][label[i][0]])] for i in range(X.shape[0])],
dtype="float32")
self.inputs = {"X": X, "Label": label}
self.outputs = {"Y": cross_entropy}
self.attrs = {'soft_label': 0}
def test_check_output(self):
self.check_output()
def test_check_grad(self):
self.check_grad(["X"], "Y")
class TestCrossEntropyOp2(OpTest):
"""Test soft-label cross-entropy, with vecterized soft labels.
"""
def setUp(self):
self.op_type = "cross_entropy"
batch_size = 10
class_num = 5
X = np.random.uniform(0.1, 1.0,
[batch_size, class_num]).astype("float32")
label = np.random.uniform(0.1, 1.0,
[batch_size, class_num]).astype("float32")
label /= label.sum(axis=1, keepdims=True)
cross_entropy = (-label * np.log(X)).sum(
axis=1, keepdims=True).astype("float32")
self.inputs = {'X': X, 'Label': label}
self.outputs = {'Y': cross_entropy}
self.attrs = {'soft_label': 1}
def test_check_output(self):
self.check_output()
def test_check_grad(self):
self.check_grad(['X'], 'Y')
class TestCrossEntropyOp3(OpTest):
"""Test one-hot cross-entropy, with vecterized one-hot representation of
labels.
"""
def setUp(self):
self.op_type = "cross_entropy"
batch_size = 30
class_num = 10
X = np.random.uniform(0.1, 1.0,
[batch_size, class_num]).astype("float32")
label_index = np.random.randint(
0, class_num, (batch_size), dtype="int32")
label = np.zeros(X.shape)
label[np.arange(batch_size), label_index] = 1
cross_entropy = np.asmatrix(
[[-np.log(X[i][label_index[i]])] for i in range(X.shape[0])],
dtype="float32")
cross_entropy2 = (-label * np.log(X)).sum(
axis=1, keepdims=True).astype("float32")
self.inputs = {'X': X, 'Label': label}
self.outputs = {'Y': cross_entropy}
self.attrs = {'soft_label': 1}
def test_check_output(self):
self.check_output()
def test_check_grad(self):
self.check_grad(['X'], 'Y')
if __name__ == "__main__":
unittest.main()
python/paddle/v2/framework/tests/test_mnist.py
浏览文件 @ 37faf495
......@@ -128,7 +128,7 @@ def fc_layer(net, input, size, act="softmax", bias=True, param=None, name=None):
def cross_entropy_layer(net, input, label):
cost_name = "cross_entropy_%d" % uniq_id()
cross_entropy_op = Operator(
"onehot_cross_entropy", X=input, label=label, Y=cost_name)
"cross_entropy", X=input, Label=label, Y=cost_name)
net.append_op(cross_entropy_op)
scope.new_var(cost_name)
net.infer_shape(scope)
......@@ -181,7 +181,7 @@ def error_rate(predict, label):
images = data_layer(name="pixel", dims=[BATCH_SIZE, 784])
labels = data_layer(name="label", dims=[BATCH_SIZE])
labels = data_layer(name="label", dims=[BATCH_SIZE, 1])
fc1 = fc_layer(net=forward_net, input=images, size=100, act="sigmoid")
fc2 = fc_layer(net=forward_net, input=fc1, size=100, act="sigmoid")
predict = fc_layer(net=forward_net, input=fc2, size=10, act="softmax")
......@@ -215,6 +215,7 @@ def test(cost_name):
for data in test_reader():
image_data = numpy.array(map(lambda x: x[0], data)).astype("float32")
label_data = numpy.array(map(lambda x: x[1], data)).astype("int32")
label_data = numpy.expand_dims(label_data, axis=1)
feed_data(images, image_data)
feed_data(labels, label_data)
......@@ -235,6 +236,7 @@ for pass_id in range(PASS_NUM):
for data in train_reader():
image_data = numpy.array(map(lambda x: x[0], data)).astype("float32")
label_data = numpy.array(map(lambda x: x[1], data)).astype("int32")
label_data = numpy.expand_dims(label_data, axis=1)
feed_data(images, image_data)
feed_data(labels, label_data)
......
python/paddle/v2/framework/tests/test_onehot_cross_entropy_op.py 已删除 100644 → 0
浏览文件 @ e635e3fd
import unittest
import numpy
from op_test import OpTest
class TestOnehotCrossEntropyOp(OpTest):
def setUp(self):
self.op_type = "onehot_cross_entropy"
batch_size = 30
class_num = 10
X = numpy.random.uniform(0.1, 1.0,
[batch_size, class_num]).astype("float32")
labels = numpy.random.randint(0, class_num, batch_size, dtype="int32")
cross_entropy = numpy.asmatrix(
[[-numpy.log(X[i][labels[i]])] for i in range(X.shape[0])],
dtype="float32")
self.inputs = {"X": X, "label": labels}
self.outputs = {"Y": cross_entropy}
def test_check_output(self):
self.check_output()
def test_check_grad(self):
self.check_grad(["X"], "Y")
if __name__ == "__main__":
unittest.main()
python/paddle/v2/framework/tests/test_prelu_op.py
浏览文件 @ 37faf495
......@@ -17,10 +17,10 @@ class PReluTest(OpTest):
assert out_np is not self.inputs['X']
self.outputs = {'Out': out_np}
def not_test_check_output(self):
def test_check_output(self):
self.check_output()
def not_test_check_grad(self):
def test_check_grad(self):
self.check_grad(['X'], 'Out')
......
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