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P
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“2454298b0b2050d10e1d1e4543438237febc6542”上不存在“source/dnode/snode/git@gitcode.net:taosdata/tdengine.git”
提交 ef788603 编写于 作者: S sneaxiy
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merge develop 

test=develop
上级 d8568acd f4dec5cd
隐藏空白更改
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Showing with 954 addition and 2338 deletion
CMakeLists.txt
浏览文件 @ ef788603
......@@ -33,9 +33,7 @@ if(WIN32)
set(CMAKE_CXX_FLAGS_RELEASE "${CMAKE_CXX_FLAGS_RELEASE} /bigobj /MT")
endif(WIN32)
if(NOT CMAKE_CROSSCOMPILING)
find_package(CUDA QUIET)
endif(NOT CMAKE_CROSSCOMPILING)
find_package(CUDA QUIET)
find_package(Git REQUIRED)
find_package(Threads REQUIRED)
......@@ -49,7 +47,6 @@ option(WITH_MKL "Compile PaddlePaddle with MKL support." ${AVX_FO
option(WITH_NGRAPH "Compile PaddlePaddle with nGraph support." OFF)
option(WITH_DSO "Compile PaddlePaddle with dynamic linked CUDA" ON)
option(WITH_TESTING "Compile PaddlePaddle with unit testing" OFF)
option(WITH_SWIG_PY "Compile PaddlePaddle with inference api" ON)
option(WITH_PYTHON "Compile PaddlePaddle with python interpreter" ON)
option(WITH_DOUBLE "Compile PaddlePaddle with double precision" OFF)
option(WITH_RDMA "Compile PaddlePaddle with RDMA support" OFF)
......@@ -60,11 +57,9 @@ option(WITH_DOC "Compile PaddlePaddle with documentation" OFF)
option(WITH_COVERAGE "Compile PaddlePaddle with code coverage" OFF)
option(COVERALLS_UPLOAD "Package code coverage data to coveralls" OFF)
option(ON_TRAVIS "Exclude special unit test on Travis CI" OFF)
option(WITH_C_API "Compile PaddlePaddle with C-API(Prediction)" OFF)
option(WITH_FLUID_ONLY "Compile PaddlePaddle fluid only" OFF)
option(WITH_GOLANG "Compile PaddlePaddle with GOLANG" OFF)
option(GLIDE_INSTALL "Download and install go dependencies " ON)
option(USE_NNPACK "Compile PaddlePaddle with NNPACK library" OFF)
option(WITH_DISTRIBUTE "Compile with distributed support" OFF)
option(WITH_PSLIB "Compile with pslib support" OFF)
option(USE_EIGEN_FOR_BLAS "Use matrix multiplication in Eigen" OFF)
......@@ -96,37 +91,6 @@ if(NOT CMAKE_BUILD_TYPE)
FORCE)
endif()
if(ANDROID OR IOS)
if(ANDROID)
if(${CMAKE_SYSTEM_VERSION} VERSION_LESS "16")
message(FATAL_ERROR "Unsupport standalone toolchains with Android API level lower than 16")
endif()
endif()
set(WITH_GPU OFF CACHE STRING
"Disable GPU when cross-compiling for Android and iOS" FORCE)
set(WITH_AVX OFF CACHE STRING
"Disable AVX when cross-compiling for Android and iOS" FORCE)
set(WITH_PYTHON OFF CACHE STRING
"Disable PYTHON when cross-compiling for Android and iOS" FORCE)
set(WITH_RDMA OFF CACHE STRING
"Disable RDMA when cross-compiling for Android and iOS" FORCE)
set(WITH_MKL OFF CACHE STRING
"Disable MKL when cross-compiling for Android and iOS" FORCE)
set(WITH_NGRAPH OFF CACHE STRING
"Disable nGraph when cross-compiling for Android and iOS" FORCE)
set(WITH_GOLANG OFF CACHE STRING
"Disable golang when cross-compiling for Android and iOS" FORCE)
# Compile PaddlePaddle mobile inference library
if (NOT WITH_C_API)
set(WITH_C_API ON CACHE STRING
"Always compile the C_API when cross-compiling for Android and iOS" FORCE)
endif()
set(MOBILE_INFERENCE ON)
add_definitions(-DPADDLE_MOBILE_INFERENCE)
endif()
if (APPLE)
set(WITH_MKL OFF CACHE STRING
"Disable MKL for building on mac" FORCE)
......@@ -135,8 +99,6 @@ endif()
if (WIN32)
set(WITH_DISTRIBUTE OFF CACHE STRING
"Disable DISTRIBUTE when compiling for Windows" FORCE)
set(WITH_C_API OFF CACHE STRING
"Disable C_API when compiling for Windows" FORCE)
set(WITH_FLUID_ONLY ON CACHE STRING
"Enable FLUID_ONLY when compiling for Windows" FORCE)
endif()
......@@ -150,21 +112,7 @@ set(FLUID_INSTALL_DIR "${CMAKE_BINARY_DIR}/fluid_install_dir" CACHE STRING
set(FLUID_INFERENCE_INSTALL_DIR "${CMAKE_BINARY_DIR}/fluid_inference_install_dir" CACHE STRING
"A path setting fluid inference shared and static libraries")
if (WITH_C_API AND WITH_PYTHON)
message(WARNING "It is suggest not embedded a python interpreter in Paddle "
"when using C-API. It will give an unpredictable behavior when using a "
"different Python interpreter from compiling.")
endif()
if (WITH_C_API)
set(WITH_FLUID_ONLY OFF CACHE STRING "Disable install fluid when compile the C_API" FORCE)
endif()
if(MOBILE_INFERENCE)
set(THIRD_PARTY_BUILD_TYPE MinSizeRel)
else()
set(THIRD_PARTY_BUILD_TYPE Release)
endif()
set(THIRD_PARTY_BUILD_TYPE Release)
set(WITH_MKLML ${WITH_MKL})
if (NOT DEFINED WITH_MKLDNN)
......@@ -193,7 +141,6 @@ include(external/python) # download, build, install python
include(external/openblas) # download, build, install openblas
include(external/mkldnn) # download, build, install mkldnn
include(external/ngraph) # download, build, install nGraph
include(external/swig) # download, build, install swig
include(external/boost) # download boost
include(external/any) # download libn::any
include(external/eigen) # download eigen3
......@@ -312,11 +259,6 @@ if(WITH_MKLDNN)
list(APPEND EXTERNAL_LIBS ${MKLDNN_LIB})
endif()
if(USE_NNPACK)
include(external/nnpack)
list(APPEND EXTERNAL_LIBS ${NNPACK_LIBS})
endif(USE_NNPACK)
set(PADDLE_PYTHON_BUILD_DIR "${CMAKE_CURRENT_BINARY_DIR}/python/build")
set(CMAKE_CXX_FLAGS_RELWITHDEBINFO "-O3 -g -DNDEBUG")
......
Dockerfile.android 已删除 100644 → 0
浏览文件 @ d8568acd
FROM ubuntu:16.04
MAINTAINER PaddlePaddle Authors <paddle-dev@baidu.com>
ARG UBUNTU_MIRROR
RUN /bin/bash -c 'if [[ -n ${UBUNTU_MIRROR} ]]; then sed -i 's#http://archive.ubuntu.com/ubuntu#${UBUNTU_MIRROR}#g' /etc/apt/sources.list; fi'
# ENV variables
ARG ANDROID_ABI
ARG ANDROID_API
ENV ANDROID_ABI=${ANDROID_ABI:-"armeabi-v7a"}
ENV ANDROID_API=${ANDROID_API:-21}
ENV HOME=/root \
ANDROID_NDK_HOME=/opt/android-ndk-linux \
ANDROID_TOOLCHAINS_DIR=/opt/toolchains
RUN apt-get update && \
apt-get install -y \
git python-dev python-pip python-numpy \
wget curl tar unzip gcc g++ locales clang-format-3.8 swig cmake && \
apt-get clean -y
# git credential to skip password typing
RUN git config --global credential.helper store
# Fix locales to en_US.UTF-8
RUN localedef -i en_US -f UTF-8 en_US.UTF-8
RUN pip install --upgrade pip==9.0.3 && \
pip install -U 'protobuf==3.1.0' && \
pip install -U wheel sphinx && \
pip install pre-commit
# Android NDK
RUN mkdir -p ${ANDROID_TOOLCHAINS_DIR} && \
mkdir -p /opt/android-ndk-tmp && \
cd /opt/android-ndk-tmp && \
wget -q https://dl.google.com/android/repository/android-ndk-r14b-linux-x86_64.zip && \
unzip -q android-ndk-r14b-linux-x86_64.zip && \
mv android-ndk-r14b ${ANDROID_NDK_HOME} && \
rm -rf /opt/android-ndk-tmp
cmake/cblas.cmake
浏览文件 @ ef788603
......@@ -64,24 +64,18 @@ endif()
## Then find the reference-cblas. www.netlib.org/blas/
set(REFERENCE_CBLAS_ROOT $ENV{REFERENCE_CBLAS_ROOT} CACHE PATH
"Folder contains reference-cblas")
if(NOT CMAKE_CROSSCOMPILING)
set(REFERENCE_CBLAS_INCLUDE_SEARCH_PATHS
${REFERENCE_CBLAS_ROOT}/include
/usr/include
/usr/include/cblas
)
set(REFERENCE_CBLAS_INCLUDE_SEARCH_PATHS
${REFERENCE_CBLAS_ROOT}/include
/usr/include
/usr/include/cblas
)
set(REFERENCE_CBLAS_LIB_SEARCH_PATHS
${REFERENCE_CBLAS_ROOT}/lib
/usr/lib
/usr/lib/blas/reference/
/usr/lib/reference/
)
else()
# Disable the finding of reference cblas under host's system path
set(REFERENCE_CBLAS_INCLUDE_SEARCH_PATHS ${REFERENCE_CBLAS_ROOT}/include)
set(REFERENCE_CBLAS_LIB_SEARCH_PATHS ${REFERENCE_CBLAS_ROOT}/lib)
endif()
set(REFERENCE_CBLAS_LIB_SEARCH_PATHS
${REFERENCE_CBLAS_ROOT}/lib
/usr/lib
/usr/lib/blas/reference/
/usr/lib/reference/
)
if(WITH_SYSTEM_BLAS)
find_path(REFERENCE_CBLAS_INCLUDE_DIR NAMES cblas.h PATHS
......@@ -98,10 +92,3 @@ if(WITH_SYSTEM_BLAS)
message(STATUS "Found reference-cblas (include: ${CBLAS_INC_DIR}, library: ${CBLAS_LIBRARIES})")
endif()
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/configure.cmake
浏览文件 @ ef788603
......@@ -49,12 +49,10 @@ if(NOT WITH_PROFILER)
add_definitions(-DPADDLE_DISABLE_PROFILER)
endif(NOT WITH_PROFILER)
if(NOT CMAKE_CROSSCOMPILING)
if(WITH_AVX AND AVX_FOUND)
set(SIMD_FLAG ${AVX_FLAG})
elseif(SSE3_FOUND)
set(SIMD_FLAG ${SSE3_FLAG})
endif()
if(WITH_AVX AND AVX_FOUND)
set(SIMD_FLAG ${AVX_FLAG})
elseif(SSE3_FOUND)
set(SIMD_FLAG ${SSE3_FLAG})
endif()
if(WIN32)
......
cmake/cross_compiling/android.cmake 已删除 100644 → 0
浏览文件 @ d8568acd
# Copyright (c) 2016 PaddlePaddle Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
# This is a toolchain file for cross-compiling for Android, and the
# configuration refers to the open-source resposity:
# https://github.com/taka-no-me/android-cmake
# Most of the variables are compatible with that used in
# https://developer.android.com/ndk/guides/cmake.html
# The supported variables are listed belows:
#
# ANDROID_STANDALONE_TOOLCHAIN
# ANDROID_TOOLCHAIN
# ANDROID_ABI
# ANDROID_NATIVE_API_LEVEL
# ANDROID_ARM_MODE
# ANDROID_ARM_NEON
#
# For CMake >= 3.7.0, all the settings will be delivered to CMake system
# variables to let CMake do the cross-compiling configurations itself.
# More detail of cross-compiling settings
# https://cmake.org/cmake/help/v3.7/manual/cmake-toolchains.7.html
IF(NOT ANDROID)
return()
ENDIF()
# check the exist of android standalone toolchain
IF(NOT DEFINED ANDROID_STANDALONE_TOOLCHAIN)
SET(ANDROID_STANDALONE_TOOLCHAIN $ENV{ANDROID_STANDALONE_TOOLCHAIN}
CACHE PATH "Folder holds the standalone toolchain of Android NDK")
ENDIF()
IF(NOT ANDROID_STANDALONE_TOOLCHAIN)
MESSAGE(WARNING "It is recommended to set ANDROID_STANDALONE_TOOLCHAIN to "
"use a standalone toolchain.\n"
"To cross-compile for Android, you need to:\n"
"1. Download an Android NDK from"
" https://developer.android.com/ndk/downloads/index.html\n"
"2. Setup a standalone toolchain"
"https://developer.android.google.cn/ndk/guides/standalone_toolchain.html?hl=zh-cn\n")
ENDIF()
IF(NOT DEFINED CMAKE_SYSTEM_VERSION AND ANDROID_NATIVE_API_LEVEL)
IF(ANDROID_NATIVE_API_LEVEL MATCHES "^android-[0-9]+$")
STRING(REPLACE "android-" "" CMAKE_SYSTEM_VERSION "${CMAKE_MATCH_0}")
ELSEIF(ANDROID_NATIVE_API_LEVEL MATCHES "^[0-9]+$")
SET(CMAKE_SYSTEM_VERSION ${ANDROID_NATIVE_API_LEVEL})
ENDIF()
ENDIF()
IF(NOT DEFINED ANDROID_TOOLCHAIN)
SET(ANDROID_TOOLCHAIN clang)
ENDIF()
IF(NOT DEFINED ANDROID_ABI)
SET(ANDROID_ABI "armeabi-v7a")
ENDIF()
IF(NOT DEFINED ANDROID_ARM_MODE)
SET(ANDROID_ARM_MODE ON)
ENDIF()
IF(ANDROID_ARM_MODE)
SET(ANDROID_ARM_MODE_NAME "arm")
ELSE(ANDROID_ARM_MODE)
SET(ANDROID_ARM_MODE_NAME "thumb")
ENDIF(ANDROID_ARM_MODE)
IF(NOT DEFINED ANDROID_ARM_NEON)
SET(ANDROID_ARM_NEON ON)
ENDIF()
IF("${CMAKE_VERSION}" VERSION_LESS "3.7.0")
IF("${CMAKE_VERSION}" VERSION_LESS "3.1.0")
SET(CMAKE_SYSTEM_NAME "Linux")
ENDIF()
MESSAGE(WARNING "It is recommended to use CMake >= 3.7.0 (current version: "
"${CMAKE_VERSION}), when cross-compiling for Android.")
IF(ANDROID_STANDALONE_TOOLCHAIN)
# Use standalone toolchain
SET(CMAKE_SYSROOT "${ANDROID_STANDALONE_TOOLCHAIN}/sysroot")
IF(NOT CMAKE_SYSTEM_VERSION)
SET(ANDROID_STANDALONE_TOOLCHAIN_API "")
SET(ANDROID_API_LEVEL_H_REGEX "^[\t ]*#[\t ]*define[\t ]+__ANDROID_API__[\t ]+([0-9]+)")
FILE(STRINGS "${ANDROID_STANDALONE_TOOLCHAIN}/sysroot/usr/include/android/api-level.h"
ANDROID_API_LEVEL_H_CONTENT REGEX "${ANDROID_API_LEVEL_H_REGEX}")
IF(ANDROID_API_LEVEL_H_CONTENT MATCHES "${ANDROID_API_LEVEL_H_REGEX}")
SET(ANDROID_STANDALONE_TOOLCHAIN_API "${CMAKE_MATCH_1}")
ENDIF()
SET(CMAKE_SYSTEM_VERSION ${ANDROID_STANDALONE_TOOLCHAIN_API})
ENDIF()
# Toolchain
SET(ANDROID_TOOLCHAIN_ROOT ${ANDROID_STANDALONE_TOOLCHAIN})
ELSE(ANDROID_NDK)
# TODO: use android ndk
ENDIF()
IF(ANDROID_ABI MATCHES "^armeabi(-v7a)?$")
SET(ANDROID_TOOLCHAIN_NAME arm-linux-androideabi)
IF(ANDROID_ABI STREQUAL "armeabi")
SET(CMAKE_SYSTEM_PROCESSOR armv5te)
SET(ANDROID_CLANG_TRIPLE armv5te-none-linux-androideabi)
ELSEIF(ANDROID_ABI STREQUAL "armeabi-v7a")
SET(CMAKE_SYSTEM_PROCESSOR armv7-a)
SET(ANDROID_CLANG_TRIPLE armv7-none-linux-androideabi)
ENDIF()
ELSEIF(ANDROID_ABI STREQUAL "arm64-v8a")
SET(ANDROID_TOOLCHAIN_NAME aarch64-linux-android)
SET(CMAKE_SYSTEM_PROCESSOR aarch64)
SET(ANDROID_CLANG_TRIPLE aarch64-none-linux-android)
ELSE()
MESSAGE(FATAL_ERROR "Invalid Android ABI: ${ANDROID_ABI}.")
ENDIF()
SET(ANDROID_TOOLCHAIN_PREFIX "${ANDROID_TOOLCHAIN_ROOT}/bin/${ANDROID_TOOLCHAIN_NAME}-")
IF(ANDROID_TOOLCHAIN STREQUAL clang)
SET(ANDROID_C_COMPILER_NAME clang)
SET(ANDROID_CXX_COMPILER_NAME clang++)
SET(CMAKE_C_COMPILER_TARGET ${ANDROID_CLANG_TRIPLE})
SET(CMAKE_CXX_COMPILER_TARGET ${ANDROID_CLANG_TRIPLE})
ELSEIF(ANDROID_TOOLCHAIN STREQUAL gcc)
SET(ANDROID_C_COMPILER_NAME gcc)
SET(ANDROID_CXX_COMPILER_NAME g++)
ELSE()
MESSAGE(FATAL_ERROR "Invalid Android toolchain: ${ANDROID_TOOLCHAIN}")
ENDIF()
# C compiler
IF(NOT CMAKE_C_COMPILER)
SET(ANDROID_C_COMPILER "${ANDROID_TOOLCHAIN_PREFIX}${ANDROID_C_COMPILER_NAME}")
ELSE()
GET_FILENAME_COMPONENT(ANDROID_C_COMPILER ${CMAKE_C_COMPILER} PROGRAM)
ENDIF()
IF(NOT EXISTS ${ANDROID_C_COMPILER})
MESSAGE(FATAL_ERROR "Cannot find C compiler: ${ANDROID_C_COMPILER}")
ENDIF()
# CXX compiler
IF(NOT CMAKE_CXX_COMPILER)
SET(ANDROID_CXX_COMPILER "${ANDROID_TOOLCHAIN_PREFIX}${ANDROID_CXX_COMPILER_NAME}")
ELSE()
GET_FILENAME_COMPONENT(ANDROID_CXX_COMPILER ${CMAKE_CXX_COMPILER} PROGRAM)
ENDIF()
IF(NOT EXISTS ${ANDROID_CXX_COMPILER})
MESSAGE(FATAL_ERROR "Cannot find CXX compiler: ${ANDROID_CXX_COMPILER}")
ENDIF()
SET(CMAKE_C_COMPILER ${ANDROID_C_COMPILER} CACHE PATH "C compiler" FORCE)
SET(CMAKE_CXX_COMPILER ${ANDROID_CXX_COMPILER} CACHE PATH "CXX compiler" FORCE)
# Toolchain and ABI specific flags.
SET(ANDROID_COMPILER_FLAGS "-ffunction-sections -fdata-sections")
SET(ANDROID_LINKER_FLAGS "-Wl,--gc-sections")
IF(ANDROID_ABI STREQUAL "armeabi")
LIST(APPEND ANDROID_COMPILER_FLAGS
-march=armv5te
-mtune=xscale
-msoft-float)
ELSEIF(ANDROID_ABI STREQUAL "armeabi-v7a")
LIST(APPEND ANDROID_COMPILER_FLAGS
-march=armv7-a
-mfloat-abi=softfp)
IF(ANDROID_ARM_NEON)
LIST(APPEND ANDROID_COMPILER_FLAGS -mfpu=neon)
ELSE()
LIST(APPEND ANDROID_COMPILER_FLAGS -mfpu=vfpv3-d16)
ENDIF()
LIST(APPEND ANDROID_LINKER_FLAGS -Wl,--fix-cortex-a8)
ELSEIF(ANDROID_ABI STREQUAL "arm64-v8a")
LIST(APPEND ANDROID_COMPILER_FLAGS -march=armv8-a)
ENDIF()
IF(ANDROID_ABI MATCHES "^armeabi(-v7a)?$")
IF(ANDROID_ARM_MODE)
LIST(APPEND ANDROID_COMPILER_FLAGS -marm)
ELSE()
LIST(APPEND ANDROID_COMPILER_FLAGS -mthumb)
ENDIF()
IF(ANDROID_TOOLCHAIN STREQUAL clang)
# Disable integrated-as for better compatibility.
LIST(APPEND ANDROID_COMPILER_FLAGS -fno-integrated-as)
ENDIF()
ENDIF()
IF(ANDROID_TOOLCHAIN STREQUAL clang)
# CMake automatically forwards all compiler flags to the linker,
# and clang doesn't like having -Wa flags being used for linking.
# To prevent CMake from doing this would require meddling with
# the CMAKE_<LANG>_COMPILE_OBJECT rules, which would get quite messy.
LIST(APPEND ANDROID_LINKER_FLAGS -Qunused-arguments)
ENDIF()
STRING(REPLACE ";" " " ANDROID_COMPILER_FLAGS "${ANDROID_COMPILER_FLAGS}")
STRING(REPLACE ";" " " ANDROID_LINKER_FLAGS "${ANDROID_LINKER_FLAGS}")
SET(CMAKE_C_FLAGS "${ANDROID_COMPILER_FLAGS} ${CMAKE_C_FLAGS}"
CACHE STRING "C flags")
SET(CMAKE_CXX_FLAGS "${ANDROID_COMPILER_FLAGS} ${CMAKE_CXX_FLAGS}"
CACHE STRING "CXX flags")
SET(CMAKE_SHARED_LINKER_FLAGS "${ANDROID_LINKER_FLAGS} ${CMAKE_SHARED_LINKER_FLAGS}"
CACHE STRING "shared linker flags")
SET(CMAKE_POSITION_INDEPENDENT_CODE TRUE)
SET(CMAKE_EXE_LINKER_FLAGS "-pie -fPIE ${ANDROID_LINKER_FLAGS} ${CMAKE_EXE_LINKER_FLAGS}"
CACHE STRING "executable linker flags")
MESSAGE(STATUS "Android: Targeting API '${CMAKE_SYSTEM_VERSION}' "
"with architecture '${ANDROID_ARM_MODE_NAME}', "
"ABI '${ANDROID_ABI}', and processor '${CMAKE_SYSTEM_PROCESSOR}'")
MESSAGE(STATUS "System CMAKE_C_FLAGS: " ${CMAKE_C_FLAGS})
MESSAGE(STATUS "System CMAKE_CXX_FLAGS: " ${CMAKE_CXX_FLAGS})
ELSE()
IF(ANDROID_STANDALONE_TOOLCHAIN)
SET(CMAKE_ANDROID_STANDALONE_TOOLCHAIN ${ANDROID_STANDALONE_TOOLCHAIN})
ENDIF()
SET(CMAKE_ANDROID_ARCH_ABI ${ANDROID_ABI})
IF(ANDROID_ABI MATCHES "^armeabi(-v7a)?$")
SET(CMAKE_ANDROID_ARM_MODE ${ANDROID_ARM_MODE})
IF(ANDROID_ABI STREQUAL "armeabi-v7a")
SET(CMAKE_ANDROID_ARM_NEON ${ANDROID_ARM_NEON})
ENDIF()
ENDIF()
ENDIF()
cmake/cross_compiling/host.cmake 已删除 100644 → 0
浏览文件 @ d8568acd
# Copyright (c) 2016 PaddlePaddle Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
# find host C compiler
IF(HOST_C_COMPILER)
SET(HOST_C_COMPILER_NAME ${HOST_C_COMPILER})
ELSEIF(NOT $ENV{CC} STREQUAL "")
SET(HOST_C_COMPILER_NAME $ENV{CC})
ELSE()
SET(HOST_C_COMPILER_NAME cc)
ENDIF()
GET_FILENAME_COMPONENT(HOST_C_COMPILER_PATH ${HOST_C_COMPILER_NAME} PROGRAM)
IF(NOT HOST_C_COMPILER_PATH OR NOT EXISTS ${HOST_C_COMPILER_PATH})
MESSAGE(FATAL_ERROR "Cannot find host C compiler, set host C compiler:\n"
"\tcmake .. -DHOST_C_COMPILER=...")
ENDIF()
# find host CXX compiler
IF(HOST_CXX_COMPILER)
SET(HOST_CXX_COMPILER_NAME ${HOST_CXX_COMPILER})
ELSEIF(NOT $ENV{CXX} STREQUAL "")
SET(HOST_CXX_COMPILER_NAME $ENV{CXX})
ELSE()
SET(HOST_CXX_COMPILER_NAME c++)
ENDIF()
GET_FILENAME_COMPONENT(HOST_CXX_COMPILER_PATH ${HOST_CXX_COMPILER_NAME} PROGRAM)
IF(NOT HOST_CXX_COMPILER_PATH OR NOT EXISTS ${HOST_CXX_COMPILER_PATH})
MESSAGE(FATAL_ERROR "Cannot find host CXX compiler, set host CXX compiler:\n"
"\tcmake .. -DHOST_CXX_COMPILER=...")
ENDIF()
SET(HOST_C_COMPILER ${HOST_C_COMPILER_PATH} CACHE PATH "Host C compiler")
SET(HOST_CXX_COMPILER ${HOST_CXX_COMPILER_PATH} CACHE PATH "Host CXX compiler")
MESSAGE(STATUS "Found host C compiler: " ${HOST_C_COMPILER})
MESSAGE(STATUS "Found host CXX compiler: " ${HOST_CXX_COMPILER})
cmake/cross_compiling/ios.cmake 已删除 100644 → 0
浏览文件 @ d8568acd
# Copyright (c) 2016 PaddlePaddle Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
# 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")
set(IOS_ARCH "armv7;armv7s;arm64")
elseif(IOS_PLATFORM STREQUAL "SIMULATOR")
set(IOS_ARCH "i386;x86_64")
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=hidden -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/cross_compiling/raspberry_pi.cmake 已删除 100644 → 0
浏览文件 @ d8568acd
# Copyright (c) 2016 PaddlePaddle Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
# This is a toolchain file for cross-compiling for Raspberry Pi.
# The supported variables are listed belows:
#
# RPI_TOOLCHAIN
# RPI_ARM_NEON
#
# Also you can set CMAKE_C/CXX_COMPILER yourself, through cmake arguments.
IF(NOT RPI)
return()
ENDIF()
SET(CMAKE_SYSTEM_NAME Linux)
SET(CMAKE_SYSTEM_VERSION 1)
SET(CMAKE_SYSTEM_PROCESSOR arm)
# check the exist of raspberry pi toolchain
IF(NOT DEFINED RPI_TOOLCHAIN)
SET(RPI_TOOLCHAIN $ENV{RPI_TOOLCHAIN}
CACHE PATH "Folder holds the toolchain of Raspberr Pi")
ENDIF()
IF(NOT RPI_TOOLCHAIN)
MESSAGE(WARNING "It is recommended to set RPI_TOOLCHAIN to use toolchain.\n"
"To cross-compile for Raspberry Pi, you need to download the tools using:\n"
" git clone https://github.com/raspberrypi/tools\n")
ENDIF()
IF(NOT DEFINED RPI_ARM_NEON)
SET(RPI_ARM_NEON ON)
ENDIF()
IF(RPI_TOOLCHAIN)
SET(RPI_TOOLCHAIN_ROOT ${RPI_TOOLCHAIN})
IF(RPI_TOOLCHAIN_ROOT MATCHES "gcc-linaro-arm-linux-gnueabihf-raspbian(-x64)?$")
# gcc-linaro-arm-linux-gnueabihf-raspbian
# gcc-linaro-arm-linux-gnueabihf-raspbian-x64
SET(RPI_TOOLCHAIN_NAME arm-linux-gnueabihf)
ENDIF()
SET(RPI_TOOLCHAIN_PREFIX "${RPI_TOOLCHAIN_ROOT}/bin/${RPI_TOOLCHAIN_NAME}-")
ENDIF()
# C compiler
IF(NOT CMAKE_C_COMPILER)
SET(RPI_C_COMPILER "${RPI_TOOLCHAIN_PREFIX}gcc")
ELSE()
GET_FILENAME_COMPONENT(RPI_C_COMPILER ${CMAKE_C_COMPILER} PROGRAM)
ENDIF()
IF(NOT EXISTS ${RPI_C_COMPILER})
MESSAGE(FATAL_ERROR "Cannot find C compiler: ${RPI_C_COMPILER}")
ENDIF()
# CXX compiler
IF(NOT CMAKE_CXX_COMPILER)
SET(RPI_CXX_COMPILER "${RPI_TOOLCHAIN_PREFIX}g++")
ELSE()
GET_FILENAME_COMPONENT(RPI_CXX_COMPILER ${CMAKE_CXX_COMPILER} PROGRAM)
ENDIF()
IF(NOT EXISTS ${RPI_CXX_COMPILER})
MESSAGE(FATAL_ERROR "Cannot find CXX compiler: ${RPI_CXX_COMPILER}")
ENDIF()
SET(CMAKE_C_COMPILER ${RPI_C_COMPILER} CACHE PATH "C compiler" FORCE)
SET(CMAKE_CXX_COMPILER ${RPI_CXX_COMPILER} CACHE PATH "CXX compiler" FORCE)
IF(RPI_ARM_NEON)
SET(RPI_C_FLAGS "${RPI_C_FLAGS} -mfpu=neon")
ENDIF()
SET(CMAKE_C_FLAGS "${RPI_C_FLAGS} ${CMAKE_C_FLAGS}" CACHE STRING "C flags")
SET(CMAKE_CXX_FLAGS "${RPI_C_FLAGS} ${CMAKE_CXX_FLAGS}" CACHE STRING "CXX flags")
cmake/cuda.cmake
浏览文件 @ ef788603
......@@ -63,9 +63,7 @@ function(select_nvcc_arch_flags out_variable)
# List of arch names
set(archs_names "Kepler" "Maxwell" "Pascal" "Volta" "Turing" "All" "Manual")
set(archs_name_default "All")
if(NOT CMAKE_CROSSCOMPILING)
list(APPEND archs_names "Auto")
endif()
list(APPEND archs_names "Auto")
# set CUDA_ARCH_NAME strings (so it will be seen as dropbox in CMake-Gui)
set(CUDA_ARCH_NAME ${archs_name_default} CACHE STRING "Select target NVIDIA GPU achitecture.")
......
cmake/external/cares.cmake
浏览文件 @ ef788603
......@@ -13,7 +13,7 @@
# limitations under the License.
#
IF(MOBILE_INFERENCE OR NOT WITH_DISTRIBUTE)
IF(NOT WITH_DISTRIBUTE)
return()
ENDIF()
......
cmake/external/gflags.cmake
浏览文件 @ ef788603
......@@ -71,13 +71,3 @@ if (WIN32)
set_property(GLOBAL PROPERTY OS_DEPENDENCY_MODULES shlwapi.lib)
endif(HAVE_SHLWAPI)
endif (WIN32)
IF(WITH_C_API)
INSTALL(DIRECTORY ${GFLAGS_INCLUDE_DIR} DESTINATION third_party/gflags)
IF(ANDROID)
INSTALL(FILES ${GFLAGS_LIBRARIES} DESTINATION third_party/gflags/lib/${ANDROID_ABI})
ELSE()
INSTALL(FILES ${GFLAGS_LIBRARIES} DESTINATION third_party/gflags/lib)
ENDIF()
ENDIF()
cmake/external/glog.cmake
浏览文件 @ ef788603
......@@ -26,14 +26,8 @@ ENDIF(WIN32)
INCLUDE_DIRECTORIES(${GLOG_INCLUDE_DIR})
IF(ANDROID AND ${CMAKE_SYSTEM_VERSION} VERSION_LESS "21")
# Using the unofficial glog for Android API < 21
SET(GLOG_REPOSITORY "https://github.com/Xreki/glog.git")
SET(GLOG_TAG "8a547150548b284382ccb6582408e9140ff2bea8")
ELSE()
SET(GLOG_REPOSITORY "https://github.com/google/glog.git")
SET(GLOG_TAG "v0.3.5")
ENDIF()
SET(GLOG_REPOSITORY "https://github.com/google/glog.git")
SET(GLOG_TAG "v0.3.5")
ExternalProject_Add(
extern_glog
......@@ -78,12 +72,3 @@ ADD_DEPENDENCIES(glog extern_glog gflags)
LINK_LIBRARIES(glog gflags)
LIST(APPEND external_project_dependencies glog)
IF(WITH_C_API)
INSTALL(DIRECTORY ${GLOG_INCLUDE_DIR} DESTINATION third_party/glog)
IF(ANDROID)
INSTALL(FILES ${GLOG_LIBRARIES} DESTINATION third_party/glog/lib/${ANDROID_ABI})
ELSE()
INSTALL(FILES ${GLOG_LIBRARIES} DESTINATION third_party/glog/lib)
ENDIF()
ENDIF()
cmake/external/grpc.cmake
浏览文件 @ ef788603
......@@ -13,7 +13,7 @@
# limitations under the License.
#
IF(MOBILE_INFERENCE OR NOT WITH_DISTRIBUTE)
IF(NOT WITH_DISTRIBUTE)
return()
ENDIF()
......
cmake/external/gzstream.cmake
浏览文件 @ ef788603
......@@ -13,10 +13,6 @@
# limitations under the License.
#
IF(MOBILE_INFERENCE)
return()
ENDIF()
include (ExternalProject)
# NOTE: gzstream is needed when linking with ctr reader.
......
cmake/external/libxsmm.cmake
浏览文件 @ ef788603
......@@ -19,8 +19,8 @@ IF(NOT WITH_LIBXSMM)
return()
ENDIF()
IF(WIN32 OR APPLE OR ANDROID OR IOS)
MESSAGE(WARNING "Windows, Mac or Mobile are not supported with libxsmm in Paddle yet.")
IF(WIN32 OR APPLE)
MESSAGE(WARNING "Windows, Mac are not supported with libxsmm in Paddle yet.")
SET(WITH_LIBXSMM OFF CACHE STRING "Disable LIBXSMM" FORCE)
return()
ENDIF()
......
cmake/external/mkldnn.cmake
浏览文件 @ ef788603
......@@ -110,7 +110,3 @@ else(WIN32)
endif(WIN32)
ADD_CUSTOM_TARGET(mkldnn_shared_lib ALL DEPENDS ${MKLDNN_SHARED_LIB})
ADD_DEPENDENCIES(mkldnn_shared_lib ${MKLDNN_PROJECT} mkldnn)
IF(WITH_C_API)
INSTALL(FILES ${MKLDNN_SHARED_LIB} DESTINATION lib)
ENDIF()
cmake/external/mklml.cmake
浏览文件 @ ef788603
......@@ -74,7 +74,3 @@ ADD_LIBRARY(mklml SHARED IMPORTED GLOBAL)
SET_PROPERTY(TARGET mklml PROPERTY IMPORTED_LOCATION ${MKLML_LIB})
ADD_DEPENDENCIES(mklml ${MKLML_PROJECT})
LIST(APPEND external_project_dependencies mklml)
IF(WITH_C_API)
INSTALL(FILES ${MKLML_LIB} ${MKLML_IOMP_LIB} DESTINATION lib)
ENDIF()
cmake/external/nnpack.cmake 已删除 100644 → 0
浏览文件 @ d8568acd
# Find the NNPACK library
# NNPACK_ROOT - where to find NNPACK include and library.
#
set(NNPACK_FOUND OFF)
set(NNPACK_ROOT $ENV{NNPACK_ROOT} CACHE PATH "Folder contains NNPACK")
find_path(NNPACK_INC_DIR nnpack.h PATHS ${NNPACK_ROOT}/include)
find_library(NNPACK_LIB NAMES nnpack PATHS ${NNPACK_ROOT}/lib)
find_library(PTHREADPOOL_LIB NAMES pthreadpool PATHS ${NNPACK_ROOT}/lib)
find_library(NNPACK_UKERNELS_LIB NAMES nnpack_ukernels PATHS ${NNPACK_ROOT}/lib)
find_library(NNPACK_CPUFEATURES_LIB NAMES cpufeatures PATHS ${NNPACK_ROOT}/lib)
if(NNPACK_INC_DIR AND NNPACK_LIB AND PTHREADPOOL_LIB)
set(NNPACK_FOUND ON)
INCLUDE_DIRECTORIES(${NNPACK_INC_DIR})
set(NNPACK_LIBS)
list(APPEND NNPACK_LIBS ${NNPACK_LIB} ${PTHREADPOOL_LIB})
if (NNPACK_UKERNELS_LIB)
list(APPEND NNPACK_LIBS ${NNPACK_UKERNELS_LIB})
endif()
if (NNPACK_CPUFEATURES_LIB)
list(APPEND NNPACK_LIBS ${NNPACK_CPUFEATURES_LIB})
endif()
if(NOT ANDROID)
list(APPEND NNPACK_LIBS "rt")
endif()
else()
message(FATAL_ERROR "Cannot find NNPACK in (${NNPACK_ROOT})")
endif()
cmake/external/openblas.cmake
浏览文件 @ ef788603
......@@ -40,38 +40,12 @@ IF(NOT ${CBLAS_FOUND})
SET(OPENBLAS_CC "${CMAKE_C_COMPILER} -Wno-unused-but-set-variable -Wno-unused-variable")
SET(OPENBLAS_COMMIT "v0.2.20")
IF(CMAKE_CROSSCOMPILING)
SET(OPTIONAL_ARGS HOSTCC=${HOST_C_COMPILER})
GET_FILENAME_COMPONENT(CROSS_SUFFIX ${CMAKE_C_COMPILER} DIRECTORY)
SET(CROSS_SUFFIX ${CROSS_SUFFIX}/)
IF(ANDROID)
IF(ANDROID_ABI MATCHES "^armeabi(-v7a)?$")
# use softfp
SET(OPTIONAL_ARGS ${OPTIONAL_ARGS} TARGET=ARMV7 ARM_SOFTFP_ABI=1 USE_THREAD=0)
ELSEIF(ANDROID_ABI STREQUAL "arm64-v8a")
SET(OPTIONAL_ARGS ${OPTIONAL_ARGS} TARGET=ARMV8 BINARY=64 USE_THREAD=0)
ENDIF()
ELSEIF(IOS)
IF(CMAKE_OSX_ARCHITECTURES MATCHES "arm64")
SET(OPENBLAS_CC "${OPENBLAS_CC} ${CMAKE_C_FLAGS} -isysroot ${CMAKE_OSX_SYSROOT}")
SET(OPENBLAS_CC "${OPENBLAS_CC} -arch arm64")
SET(OPTIONAL_ARGS ${OPTIONAL_ARGS} TARGET=ARMV8 BINARY=64 USE_THREAD=0 CROSS_SUFFIX=${CROSS_SUFFIX})
ELSE()
MESSAGE(FATAL_ERROR "OpenBLAS only support arm64 architectures on iOS. "
"You can set IOS_USE_VECLIB_FOR_BLAS=ON or USE_EIGEN_FOR_BLAS=ON to use other blas library instead.")
ENDIF()
ELSEIF(RPI)
# use hardfp
SET(OPTIONAL_ARGS ${OPTIONAL_ARGS} TARGET=ARMV7 USE_THREAD=0)
ENDIF()
ELSE()
IF(APPLE)
SET(OPENBLAS_CC "${CMAKE_C_COMPILER} -isysroot ${CMAKE_OSX_SYSROOT}")
ENDIF()
SET(OPTIONAL_ARGS "")
IF(CMAKE_SYSTEM_PROCESSOR MATCHES "^x86(_64)?$")
SET(OPTIONAL_ARGS DYNAMIC_ARCH=1 NUM_THREADS=64)
ENDIF()
IF(APPLE)
SET(OPENBLAS_CC "${CMAKE_C_COMPILER} -isysroot ${CMAKE_OSX_SYSROOT}")
ENDIF()
SET(OPTIONAL_ARGS "")
IF(CMAKE_SYSTEM_PROCESSOR MATCHES "^x86(_64)?$")
SET(OPTIONAL_ARGS DYNAMIC_ARCH=1 NUM_THREADS=64)
ENDIF()
SET(COMMON_ARGS CC=${OPENBLAS_CC} NO_SHARED=1 NO_LAPACK=1 libs)
......@@ -92,25 +66,6 @@ IF(NOT ${CBLAS_FOUND})
ELSE()
ENDIF(NOT WIN32)
SET(CBLAS_PROVIDER openblas)
IF(WITH_C_API)
INSTALL(DIRECTORY ${CBLAS_INC_DIR} DESTINATION third_party/openblas)
# Because libopenblas.a is a symbolic link of another library, thus need to
# install the whole directory.
IF(ANDROID)
SET(TMP_INSTALL_DIR third_party/openblas/lib/${ANDROID_ABI})
ELSE()
SET(TMP_INSTALL_DIR third_party/openblas/lib)
ENDIF()
INSTALL(CODE "execute_process(
COMMAND ${CMAKE_COMMAND} -E copy_directory ${CBLAS_INSTALL_DIR}/lib
${CMAKE_INSTALL_PREFIX}/${TMP_INSTALL_DIR}
)"
)
INSTALL(CODE "MESSAGE(STATUS \"Installing: \"
\"${CBLAS_INSTALL_DIR}/lib -> ${CMAKE_INSTALL_PREFIX}/${TMP_INSTALL_DIR}\"
)"
)
ENDIF()
ENDIF(NOT ${CBLAS_FOUND})
MESSAGE(STATUS "BLAS library: ${CBLAS_LIBRARIES}")
......
cmake/external/protobuf.cmake
浏览文件 @ ef788603
......@@ -204,15 +204,6 @@ FUNCTION(build_protobuf TARGET_NAME BUILD_FOR_HOST)
SET(PROTOBUF_REPO "https://github.com/google/protobuf.git")
SET(PROTOBUF_TAG "9f75c5aa851cd877fb0d93ccc31b8567a6706546")
IF(MOBILE_INFERENCE)
# The reason why the official version is not used is described in
# https://github.com/PaddlePaddle/Paddle/issues/6114
SET(PROTOBUF_REPO "https://github.com/qingqing01/protobuf.git")
SET(PROTOBUF_TAG "v3.2.0")
IF(NOT BUILD_FOR_HOST)
SET(OPTIONAL_ARGS ${OPTIONAL_ARGS} "-Dprotobuf_BUILD_PROTOC_BINARIES=OFF")
ENDIF()
ENDIF()
ExternalProject_Add(
${TARGET_NAME}
......@@ -240,19 +231,7 @@ FUNCTION(build_protobuf TARGET_NAME BUILD_FOR_HOST)
)
ENDFUNCTION()
IF(NOT MOBILE_INFERENCE)
SET(PROTOBUF_VERSION 3.1)
ELSE()
SET(PROTOBUF_VERSION 3.2)
ENDIF()
IF(CMAKE_CROSSCOMPILING)
build_protobuf(protobuf_host TRUE)
LIST(APPEND external_project_dependencies protobuf_host)
SET(PROTOBUF_PROTOC_EXECUTABLE ${protobuf_host_PROTOC_EXECUTABLE}
CACHE FILEPATH "protobuf executable." FORCE)
ENDIF()
SET(PROTOBUF_VERSION 3.1)
IF(NOT PROTOBUF_FOUND)
build_protobuf(extern_protobuf FALSE)
......@@ -266,20 +245,7 @@ IF(NOT PROTOBUF_FOUND)
SET(PROTOBUF_PROTOC_LIBRARY ${extern_protobuf_PROTOC_LIBRARY}
CACHE FILEPATH "protoc library." FORCE)
IF(WITH_C_API)
INSTALL(DIRECTORY ${PROTOBUF_INCLUDE_DIR} DESTINATION third_party/protobuf)
IF(ANDROID)
INSTALL(FILES ${PROTOBUF_LITE_LIBRARY} DESTINATION third_party/protobuf/lib/${ANDROID_ABI})
ELSE()
INSTALL(FILES ${PROTOBUF_LITE_LIBRARY} DESTINATION third_party/protobuf/lib)
ENDIF()
ENDIF()
IF(CMAKE_CROSSCOMPILING)
PROMPT_PROTOBUF_LIB(protobuf_host extern_protobuf)
ELSE()
SET(PROTOBUF_PROTOC_EXECUTABLE ${extern_protobuf_PROTOC_EXECUTABLE}
CACHE FILEPATH "protobuf executable." FORCE)
PROMPT_PROTOBUF_LIB(extern_protobuf)
ENDIF()
SET(PROTOBUF_PROTOC_EXECUTABLE ${extern_protobuf_PROTOC_EXECUTABLE}
CACHE FILEPATH "protobuf executable." FORCE)
PROMPT_PROTOBUF_LIB(extern_protobuf)
ENDIF(NOT PROTOBUF_FOUND)
cmake/external/pslib.cmake
浏览文件 @ ef788603
......@@ -71,7 +71,3 @@ ADD_LIBRARY(pslib SHARED IMPORTED GLOBAL)
SET_PROPERTY(TARGET pslib PROPERTY IMPORTED_LOCATION ${PSLIB_LIB})
ADD_DEPENDENCIES(pslib ${PSLIB_PROJECT})
LIST(APPEND external_project_dependencies pslib)
IF(WITH_C_API)
INSTALL(FILES ${PSLIB_LIB} ${PSLIB_IOMP_LIB} DESTINATION lib)
ENDIF()
cmake/external/pslib_brpc.cmake
浏览文件 @ ef788603
......@@ -71,7 +71,3 @@ ADD_LIBRARY(pslib_brpc SHARED IMPORTED GLOBAL)
SET_PROPERTY(TARGET pslib_brpc PROPERTY IMPORTED_LOCATION ${PSLIB_BRPC_LIB})
ADD_DEPENDENCIES(pslib_brpc ${PSLIB_BRPC_PROJECT})
LIST(APPEND external_project_dependencies pslib_brpc)
IF(WITH_C_API)
INSTALL(FILES ${PSLIB_BRPC_LIB} ${PSLIB_BRPC_IOMP_LIB} DESTINATION lib)
ENDIF()
cmake/external/snappy.cmake
浏览文件 @ ef788603
......@@ -12,10 +12,6 @@
# See the License for the specific language governing permissions and
# limitations under the License.
if(MOBILE_INFERENCE OR RPI)
return()
endif()
include (ExternalProject)
# NOTE: snappy is needed when linking with recordio
......
cmake/external/snappystream.cmake
浏览文件 @ ef788603
......@@ -12,10 +12,6 @@
# See the License for the specific language governing permissions and
# limitations under the License.
IF(MOBILE_INFERENCE OR RPI)
return()
ENDIF()
include (ExternalProject)
set(SNAPPYSTREAM_SOURCES_DIR ${THIRD_PARTY_PATH}/snappy_stream)
......
cmake/external/swig.cmake 已删除 100644 → 0
浏览文件 @ d8568acd
# Copyright (c) 2016 PaddlePaddle Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
IF(NOT WITH_SWIG_PY)
return()
ENDIF()
FIND_PACKAGE(SWIG)
IF(NOT SWIG_FOUND)
# build swig as an external project
INCLUDE(ExternalProject)
SET(SWIG_SOURCES_DIR ${THIRD_PARTY_PATH}/swig)
SET(SWIG_INSTALL_DIR ${THIRD_PARTY_PATH}/install/swig)
SET(SWIG_TARGET_VERSION "3.0.2")
SET(SWIG_DOWNLOAD_SRC_MD5 "62f9b0d010cef36a13a010dc530d0d41")
SET(SWIG_DOWNLOAD_WIN_MD5 "3f18de4fc09ab9abb0d3be37c11fbc8f")
IF(WIN32)
# swig.exe available as pre-built binary on Windows:
ExternalProject_Add(swig
URL http://prdownloads.sourceforge.net/swig/swigwin-${SWIG_TARGET_VERSION}.zip
URL_MD5 ${SWIG_DOWNLOAD_WIN_MD5}
SOURCE_DIR ${SWIG_SOURCES_DIR}
CONFIGURE_COMMAND ""
BUILD_COMMAND ""
INSTALL_COMMAND ""
UPDATE_COMMAND ""
)
SET(SWIG_DIR ${SWIG_SOURCES_DIR} CACHE FILEPATH "SWIG Directory" FORCE)
SET(SWIG_EXECUTABLE ${SWIG_SOURCES_DIR}/swig.exe CACHE FILEPATH "SWIG Executable" FORCE)
ELSE(WIN32)
# swig uses bison find it by cmake and pass it down
FIND_PACKAGE(BISON)
# From SWIG configure
ExternalProject_Add(swig
GIT_REPOSITORY https://github.com/swig/swig.git
GIT_TAG rel-3.0.10
PREFIX ${SWIG_SOURCES_DIR}
CONFIGURE_COMMAND cd <SOURCE_DIR> && ./autogen.sh && ./configure
--prefix=${SWIG_INSTALL_DIR} --without-pcre
BUILD_COMMAND cd <SOURCE_DIR> && make
INSTALL_COMMAND cd <SOURCE_DIR> && make install
UPDATE_COMMAND ""
)
SET(SWIG_DIR ${SWIG_INSTALL_DIR}/share/swig/${SWIG_TARGET_VERSION})
SET(SWIG_EXECUTABLE ${SWIG_INSTALL_DIR}/bin/swig)
ENDIF(WIN32)
LIST(APPEND external_project_dependencies swig)
ENDIF(NOT SWIG_FOUND)
cmake/external/warpctc.cmake
浏览文件 @ ef788603
......@@ -12,10 +12,6 @@
# See the License for the specific language governing permissions and
# limitations under the License.
IF(MOBILE_INFERENCE)
return()
ENDIF()
INCLUDE(ExternalProject)
SET(WARPCTC_SOURCES_DIR ${THIRD_PARTY_PATH}/warpctc)
......
cmake/external/xxhash.cmake
浏览文件 @ ef788603
......@@ -73,12 +73,3 @@ include_directories(${XXHASH_INCLUDE_DIR})
add_dependencies(xxhash extern_xxhash)
LIST(APPEND external_project_dependencies xxhash)
IF(WITH_C_API)
INSTALL(DIRECTORY ${XXHASH_INCLUDE_DIR} DESTINATION third_party/xxhash)
IF(ANDROID)
INSTALL(FILES ${XXHASH_LIBRARIES} DESTINATION third_party/xxhash/lib/${ANDROID_ABI})
ELSE()
INSTALL(FILES ${XXHASH_LIBRARIES} DESTINATION third_party/xxhash/lib)
ENDIF()
ENDIF()
cmake/external/zlib.cmake
浏览文件 @ ef788603
......@@ -59,12 +59,3 @@ SET_PROPERTY(TARGET zlib PROPERTY IMPORTED_LOCATION ${ZLIB_LIBRARIES})
ADD_DEPENDENCIES(zlib extern_zlib)
LIST(APPEND external_project_dependencies zlib)
IF(WITH_C_API)
INSTALL(DIRECTORY ${ZLIB_INCLUDE_DIR} DESTINATION third_party/zlib)
IF(ANDROID)
INSTALL(FILES ${ZLIB_LIBRARIES} DESTINATION third_party/zlib/lib/${ANDROID_ABI})
ELSE()
INSTALL(FILES ${ZLIB_LIBRARIES} DESTINATION third_party/zlib/lib)
ENDIF()
ENDIF()
cmake/flags.cmake
浏览文件 @ ef788603
......@@ -156,10 +156,8 @@ set(GPU_COMMON_FLAGS
endif(NOT WIN32)
if (APPLE)
if(NOT CMAKE_CROSSCOMPILING)
# On Mac OS X build fat binaries with x86_64 architectures by default.
set (CMAKE_OSX_ARCHITECTURES "x86_64" CACHE STRING "Build architectures for OSX" FORCE)
endif()
# On Mac OS X build fat binaries with x86_64 architectures by default.
set (CMAKE_OSX_ARCHITECTURES "x86_64" CACHE STRING "Build architectures for OSX" FORCE)
# On Mac OS X register class specifier is deprecated and will cause warning error on latest clang 10.0
set (COMMON_FLAGS -Wno-deprecated-register)
endif(APPLE)
......
cmake/generic.cmake
浏览文件 @ ef788603
......@@ -90,11 +90,11 @@
# including binary directory for generated headers.
include_directories(${CMAKE_CURRENT_BINARY_DIR})
if(NOT APPLE AND NOT ANDROID)
if(NOT APPLE)
find_package(Threads REQUIRED)
link_libraries(${CMAKE_THREAD_LIBS_INIT})
set(CMAKE_CXX_LINK_EXECUTABLE "${CMAKE_CXX_LINK_EXECUTABLE} -pthread -ldl -lrt")
endif(NOT APPLE AND NOT ANDROID)
endif(NOT APPLE)
set_property(GLOBAL PROPERTY FLUID_MODULES "")
# find all fluid modules is used for paddle fluid static library
......@@ -655,12 +655,6 @@ function(paddle_protobuf_generate_cpp SRCS HDRS)
set(${SRCS})
set(${HDRS})
if (MOBILE_INFERENCE)
set(EXTRA_FLAG "lite:")
else()
set(EXTRA_FLAG "")
endif()
foreach(FIL ${ARGN})
get_filename_component(ABS_FIL ${FIL} ABSOLUTE)
get_filename_component(FIL_WE ${FIL} NAME_WE)
......@@ -677,7 +671,7 @@ function(paddle_protobuf_generate_cpp SRCS HDRS)
COMMAND ${CMAKE_COMMAND} -E make_directory "${CMAKE_CURRENT_BINARY_DIR}"
COMMAND ${PROTOBUF_PROTOC_EXECUTABLE}
-I${CMAKE_CURRENT_SOURCE_DIR}
--cpp_out "${EXTRA_FLAG}${CMAKE_CURRENT_BINARY_DIR}" ${ABS_FIL}
--cpp_out "${CMAKE_CURRENT_BINARY_DIR}" ${ABS_FIL}
DEPENDS ${ABS_FIL} protoc
COMMENT "Running C++ protocol buffer compiler on ${FIL}"
VERBATIM )
......
cmake/inference_lib.cmake
浏览文件 @ ef788603
......@@ -149,25 +149,23 @@ if (WITH_NGRAPH)
)
endif ()
if (NOT MOBILE_INFERENCE AND NOT RPI)
set(dst_dir "${FLUID_INSTALL_DIR}/third_party/install/snappy")
copy(snappy_lib
SRCS ${SNAPPY_INCLUDE_DIR} ${SNAPPY_LIBRARIES}
DSTS ${dst_dir} ${dst_dir}/lib
DEPS snappy)
set(dst_dir "${FLUID_INSTALL_DIR}/third_party/install/snappy")
copy(snappy_lib
SRCS ${SNAPPY_INCLUDE_DIR} ${SNAPPY_LIBRARIES}
DSTS ${dst_dir} ${dst_dir}/lib
DEPS snappy)
set(dst_dir "${FLUID_INSTALL_DIR}/third_party/install/snappystream")
copy(snappystream_lib
SRCS ${SNAPPYSTREAM_INCLUDE_DIR} ${SNAPPYSTREAM_LIBRARIES}
DSTS ${dst_dir} ${dst_dir}/lib
DEPS snappystream)
set(dst_dir "${FLUID_INSTALL_DIR}/third_party/install/snappystream")
copy(snappystream_lib
SRCS ${SNAPPYSTREAM_INCLUDE_DIR} ${SNAPPYSTREAM_LIBRARIES}
DSTS ${dst_dir} ${dst_dir}/lib
DEPS snappystream)
set(dst_dir "${FLUID_INSTALL_DIR}/third_party/install/zlib")
copy(zlib_lib
SRCS ${ZLIB_INCLUDE_DIR} ${ZLIB_LIBRARIES}
DSTS ${dst_dir} ${dst_dir}/lib
DEPS zlib)
endif ()
set(dst_dir "${FLUID_INSTALL_DIR}/third_party/install/zlib")
copy(zlib_lib
SRCS ${ZLIB_INCLUDE_DIR} ${ZLIB_LIBRARIES}
DSTS ${dst_dir} ${dst_dir}/lib
DEPS zlib)
# paddle fluid module
set(src_dir "${PADDLE_SOURCE_DIR}/paddle/fluid")
......
cmake/system.cmake
浏览文件 @ ef788603
......@@ -74,21 +74,6 @@ MARK_AS_ADVANCED(HOST_SYSTEM CPU_CORES)
MESSAGE(STATUS "Found Paddle host system: ${HOST_SYSTEM}, version: ${HOST_SYSTEM_VERSION}")
MESSAGE(STATUS "Found Paddle host system's CPU: ${CPU_CORES} cores")
# configuration for cross-compiling
IF(DEFINED CMAKE_SYSTEM_NAME)
INCLUDE(cross_compiling/host)
IF(${CMAKE_SYSTEM_NAME} STREQUAL "Android")
SET(ANDROID TRUE)
INCLUDE(cross_compiling/android)
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()
# external dependencies log output
SET(EXTERNAL_PROJECT_LOG_ARGS
LOG_DOWNLOAD 0 # Wrap download in script to log output
......
cmake/util.cmake
浏览文件 @ ef788603
......@@ -53,118 +53,3 @@ function(target_circle_link_libraries TARGET_NAME)
"-Wl,--end-group")
endif()
endfunction()
# compile_cu_as_cpp
# Make a cu file compiled as C++
# Arguments: Source files
macro(compile_cu_as_cpp)
foreach(s ${ARGN})
set_source_files_properties(${s} PROPERTIES LANGUAGE CXX)
set_source_files_properties(${s} PROPERTIES COMPILE_FLAGS "-x c++")
endforeach()
endmacro()
# link_paddle_exe
# add paddle library for a paddle executable, such as trainer, pserver.
#
# It will handle WITH_PYTHON etc.
function(link_paddle_exe TARGET_NAME)
if(WITH_RDMA)
generate_rdma_links()
endif()
if(MOBILE_INFERENCE)
target_circle_link_libraries(${TARGET_NAME}
ARCHIVE_START
paddle_gserver
paddle_function
ARCHIVE_END
paddle_math
paddle_utils
paddle_parameter
paddle_proto
paddle_cuda
${EXTERNAL_LIBS}
${CMAKE_THREAD_LIBS_INIT}
${CMAKE_DL_LIBS}
${RDMA_LD_FLAGS}
${RDMA_LIBS})
else()
target_circle_link_libraries(${TARGET_NAME}
ARCHIVE_START
paddle_gserver
paddle_function
ARCHIVE_END
paddle_pserver
paddle_trainer_lib
paddle_network
paddle_math
paddle_utils
paddle_parameter
paddle_proto
paddle_cuda
paddle_optimizer
${EXTERNAL_LIBS}
${CMAKE_THREAD_LIBS_INIT}
${CMAKE_DL_LIBS}
${RDMA_LD_FLAGS}
${RDMA_LIBS})
endif()
if(ANDROID)
target_link_libraries(${TARGET_NAME} log)
endif(ANDROID)
if(WITH_MKLML AND MKLML_LIB_DIR AND MKLML_IOMP_LIB)
target_link_libraries(${TARGET_NAME} "-L${MKLML_LIB_DIR} -liomp5 -Wl,--as-needed")
endif()
add_dependencies(${TARGET_NAME} ${external_project_dependencies})
endfunction()
# link_paddle_test
# Link a paddle unittest for target
# TARGET_NAME: the unittest target name
# Rest Arguemnts: not used.
function(link_paddle_test TARGET_NAME)
link_paddle_exe(${TARGET_NAME})
target_link_libraries(${TARGET_NAME}
paddle_test_main
paddle_test_util
${GTEST_LIBRARIES})
endfunction()
# add_unittest_without_exec
#
# create a paddle unittest. not specifically define how to run this unittest.
# TARGET_NAME: the unittest target name, same as executable file name
# Rest Arguments: the source files to compile this unittest.
macro(add_unittest_without_exec TARGET_NAME)
add_executable(${TARGET_NAME} ${ARGN})
link_paddle_test(${TARGET_NAME})
endmacro()
# add_unittest
# create a paddle unittest and just to execute this binary to make unittest.
#
# TARGET_NAME: the unittest target name, same as executable file name
# Rest Arguments: the source files to compile this unittest.
macro(add_unittest TARGET_NAME)
add_unittest_without_exec(${TARGET_NAME} ${ARGN})
add_test(${TARGET_NAME} ${TARGET_NAME})
endmacro()
# add_simple_unittest
# create a paddle unittest with file name. It just compile ${TARGET_NAME}.cpp to
# ${TARGET_NAME} and then execute it.
macro(add_simple_unittest TARGET_NAME)
add_unittest(${TARGET_NAME} ${TARGET_NAME}.cpp)
endmacro()
# Creates C resources file from files in given resource file
function(create_resources res_file output_file)
add_custom_command(
OUTPUT ${output_file}
COMMAND python ARGS ${PADDLE_SOURCE_DIR}/cmake/make_resource.py ${res_file} ${output_file}
DEPENDS ${res_file} ${PADDLE_SOURCE_DIR}/cmake/make_resource.py)
endfunction()
paddle/fluid/framework/details/build_strategy.cc
浏览文件 @ ef788603
......@@ -24,6 +24,7 @@ limitations under the License. */
#include "paddle/fluid/framework/details/sequential_execution_pass.h"
#include "paddle/fluid/framework/ir/graph.h"
#include "paddle/fluid/framework/ir/graph_helper.h"
#include "paddle/fluid/framework/ir/graph_to_program_pass.h"
#include "paddle/fluid/framework/ir/graph_viz_pass.h"
namespace paddle {
......@@ -243,3 +244,4 @@ USE_PASS(sequential_execution_pass);
USE_PASS(all_reduce_deps_pass);
USE_PASS(modify_op_lock_and_record_event_pass);
USE_PASS(lock_free_optimize_pass);
USE_PASS(graph_to_program_pass);
paddle/fluid/framework/ir/pass.cc
浏览文件 @ ef788603
......@@ -28,10 +28,14 @@ std::unique_ptr<Graph> Pass::Apply(std::unique_ptr<Graph> graph) const {
PADDLE_ENFORCE(graph->Has(attr), "Required graph atrribute %s not set.",
attr);
}
auto* native_graph = graph.get();
auto applied_graph = ApplyImpl(std::move(graph));
// TODO(panyx0718): Add more verifications.
PADDLE_ENFORCE(!HasCircle(*applied_graph),
"Illegal Pass. Generated graph shouldn't has cycle.");
PADDLE_ENFORCE(applied_graph.get() == native_graph,
"Pass::Apply() cannot delete the passed graph and shouldn't "
"return a new graph.(For the need of pybind11)");
applied_ = true;
return applied_graph;
}
......
paddle/fluid/operators/conv_fusion_op.cu.cc
浏览文件 @ ef788603
......@@ -104,7 +104,9 @@ class CUDNNConvFusionOpKernel : public framework::OpKernel<T> {
// ------------------- cudnn conv algorithm ---------------------
cudnnConvolutionFwdAlgo_t algo;
auto handle = dev_ctx.cudnn_handle();
auto workspace_handle = dev_ctx.cudnn_workspace_handle();
Tensor cudnn_workspace;
void* cudnn_workspace_ptr = nullptr;
CUDNN_ENFORCE(platform::dynload::cudnnSetConvolutionMathType(
cudnn_conv_desc, CUDNN_DEFAULT_MATH));
......@@ -118,19 +120,24 @@ class CUDNNConvFusionOpKernel : public framework::OpKernel<T> {
workspace_size_limit, &algo));
VLOG(3) << "cuDNN forward algo " << algo;
} else {
cudnn_workspace =
ctx.AllocateTmpTensor<int8_t, platform::CUDADeviceContext>(
framework::make_ddim(
{static_cast<int64_t>(workspace_size_limit)}),
dev_ctx);
cudnn_workspace_ptr = static_cast<void*>(cudnn_workspace.data<int8_t>());
auto search_func = [&]() {
int returned_algo_count;
std::array<cudnnConvolutionFwdAlgoPerf_t, kNUM_CUDNN_FWD_ALGS>
fwd_perf_stat;
auto cudnn_find_func = [&](void* cudnn_workspace) {
CUDNN_ENFORCE(
platform::dynload::cudnnFindConvolutionForwardAlgorithmEx(
handle, cudnn_input_desc, input_data, cudnn_filter_desc,
filter_data, cudnn_conv_desc, cudnn_output_desc, output_data,
kNUM_CUDNN_FWD_ALGS, &returned_algo_count,
fwd_perf_stat.data(), cudnn_workspace, workspace_size_limit));
};
workspace_handle.RunFunc(cudnn_find_func, workspace_size_limit);
CUDNN_ENFORCE(platform::dynload::cudnnFindConvolutionForwardAlgorithmEx(
handle, cudnn_input_desc, input_data, cudnn_filter_desc,
filter_data, cudnn_conv_desc, cudnn_output_desc, output_data,
kNUM_CUDNN_FWD_ALGS, &returned_algo_count, fwd_perf_stat.data(),
cudnn_workspace_ptr, workspace_size_limit));
VLOG(3) << "Perf result: (algo: stat, time, memory)";
for (int i = 0; i < returned_algo_count; ++i) {
const auto& stat = fwd_perf_stat[i];
......@@ -181,6 +188,15 @@ class CUDNNConvFusionOpKernel : public framework::OpKernel<T> {
PADDLE_ENFORCE_LE(workspace_size_in_bytes, workspace_size_limit,
"workspace_size to be allocated exceeds the limit");
if (!cudnn_workspace_ptr) {
cudnn_workspace =
ctx.AllocateTmpTensor<int8_t, platform::CUDADeviceContext>(
framework::make_ddim(
{static_cast<int64_t>(workspace_size_in_bytes)}),
dev_ctx);
cudnn_workspace_ptr = static_cast<void*>(cudnn_workspace.data<int8_t>());
}
if ((activation == "identity") && (!residual)) {
// Only the CUDNN_CONVOLUTION_FWD_ALGO_IMPLICIT_PRECOMP_GEMM algo is
// enabled with CUDNN_ACTIVATION_IDENTITY in cuDNN lib.
......@@ -188,13 +204,12 @@ class CUDNNConvFusionOpKernel : public framework::OpKernel<T> {
// cudnnConvolutionForward and cudnnAddTensor
// ------------- cudnn conv forward and bias add ---------------------
ScalingParamType<T> alpha = 1.0f, beta = 0.0f;
auto cudnn_func = [&](void* cudnn_workspace) {
CUDNN_ENFORCE(platform::dynload::cudnnConvolutionForward(
handle, &alpha, cudnn_input_desc, input_data, cudnn_filter_desc,
filter_data, cudnn_conv_desc, algo, cudnn_workspace,
workspace_size_in_bytes, &beta, cudnn_output_desc, output_data));
};
workspace_handle.RunFunc(cudnn_func, workspace_size_in_bytes);
CUDNN_ENFORCE(platform::dynload::cudnnConvolutionForward(
handle, &alpha, cudnn_input_desc, input_data, cudnn_filter_desc,
filter_data, cudnn_conv_desc, algo, cudnn_workspace_ptr,
workspace_size_in_bytes, &beta, cudnn_output_desc, output_data));
CUDNN_ENFORCE(platform::dynload::cudnnAddTensor(
handle, &alpha, cudnn_bias_desc, bias_data, &alpha, cudnn_output_desc,
output_data));
......@@ -205,15 +220,13 @@ class CUDNNConvFusionOpKernel : public framework::OpKernel<T> {
// ------------------- cudnn conv+bias+act forward --------------------
ScalingParamType<T> alpha1 = 1.0f;
ScalingParamType<T> alpha2 = residual ? 1.0f : 0.0f;
auto cudnn_func = [&](void* cudnn_workspace) {
CUDNN_ENFORCE(platform::dynload::cudnnConvolutionBiasActivationForward(
handle, &alpha1, cudnn_input_desc, input_data, cudnn_filter_desc,
filter_data, cudnn_conv_desc, algo, cudnn_workspace,
workspace_size_in_bytes, &alpha2, cudnn_output_desc, residual_data,
cudnn_bias_desc, bias_data, cudnn_act_desc, cudnn_output_desc,
output_data));
};
workspace_handle.RunFunc(cudnn_func, workspace_size_in_bytes);
CUDNN_ENFORCE(platform::dynload::cudnnConvolutionBiasActivationForward(
handle, &alpha1, cudnn_input_desc, input_data, cudnn_filter_desc,
filter_data, cudnn_conv_desc, algo, cudnn_workspace_ptr,
workspace_size_in_bytes, &alpha2, cudnn_output_desc, residual_data,
cudnn_bias_desc, bias_data, cudnn_act_desc, cudnn_output_desc,
output_data));
}
std::vector<int> channels = ctx.Attr<std::vector<int>>("split_channels");
if (channels.size()) {
......
paddle/fluid/operators/conv_transpose_cudnn_op.cu.cc
浏览文件 @ ef788603
......@@ -104,16 +104,18 @@ class CUDNNConvTransposeOpKernel : public framework::OpKernel<T> {
int output_offset = output->numel() / output->dims()[0] / groups;
int filter_offset = filter->numel() / groups;
T alpha = 1.0f, beta = 0.0f;
auto workspace_handle = dev_ctx.cudnn_workspace_handle();
auto temp_allocation =
platform::DeviceTemporaryAllocator::Instance().Get(dev_ctx).Allocate(
workspace_size_in_bytes);
void* cudnn_workspace = temp_allocation->ptr();
for (int g = 0; g < groups; g++) {
auto cudnn_func = [&](void* cudnn_workspace) {
CUDNN_ENFORCE(platform::dynload::cudnnConvolutionBackwardData(
handle, &alpha, cudnn_filter_desc, filter_data + filter_offset * g,
cudnn_input_desc, input_data + input_offset * g, cudnn_conv_desc,
algo, cudnn_workspace, workspace_size_in_bytes, &beta,
cudnn_output_desc, output_data + output_offset * g));
};
workspace_handle.RunFunc(cudnn_func, workspace_size_in_bytes);
CUDNN_ENFORCE(platform::dynload::cudnnConvolutionBackwardData(
handle, &alpha, cudnn_filter_desc, filter_data + filter_offset * g,
cudnn_input_desc, input_data + input_offset * g, cudnn_conv_desc,
algo, cudnn_workspace, workspace_size_in_bytes, &beta,
cudnn_output_desc, output_data + output_offset * g));
}
}
};
......@@ -209,20 +211,22 @@ class CUDNNConvTransposeGradOpKernel : public framework::OpKernel<T> {
output_grad->numel() / output_grad->dims()[0] / groups;
int filter_offset = filter->numel() / groups;
T alpha = 1.0f, beta = 0.0f;
auto workspace_handle = dev_ctx.cudnn_workspace_handle();
auto temp_allocation =
platform::DeviceTemporaryAllocator::Instance().Get(dev_ctx).Allocate(
workspace_size_in_bytes);
void* cudnn_workspace = temp_allocation->ptr();
if (input_grad) {
T* input_grad_data = input_grad->mutable_data<T>(ctx.GetPlace());
// Because beta is zero, it is unnecessary to reset input_grad.
for (int g = 0; g < groups; g++) {
auto cudnn_func = [&](void* cudnn_workspace) {
CUDNN_ENFORCE(platform::dynload::cudnnConvolutionForward(
handle, &alpha, cudnn_output_desc,
output_grad_data + output_grad_offset * g, cudnn_filter_desc,
filter_data + filter_offset * g, cudnn_conv_desc, data_algo,
cudnn_workspace, workspace_size_in_bytes, &beta, cudnn_input_desc,
input_grad_data + input_offset * g));
};
workspace_handle.RunFunc(cudnn_func, workspace_size_in_bytes);
CUDNN_ENFORCE(platform::dynload::cudnnConvolutionForward(
handle, &alpha, cudnn_output_desc,
output_grad_data + output_grad_offset * g, cudnn_filter_desc,
filter_data + filter_offset * g, cudnn_conv_desc, data_algo,
cudnn_workspace, workspace_size_in_bytes, &beta, cudnn_input_desc,
input_grad_data + input_offset * g));
}
}
......@@ -232,15 +236,12 @@ class CUDNNConvTransposeGradOpKernel : public framework::OpKernel<T> {
// Because beta is zero, it is unnecessary to reset filter_grad.
// Gradient with respect to the filter
for (int g = 0; g < groups; g++) {
auto cudnn_func = [&](void* cudnn_workspace) {
CUDNN_ENFORCE(platform::dynload::cudnnConvolutionBackwardFilter(
handle, &alpha, cudnn_output_desc,
output_grad_data + output_grad_offset * g, cudnn_input_desc,
input_data + input_offset * g, cudnn_conv_desc, filter_algo,
cudnn_workspace, workspace_size_in_bytes, &beta,
cudnn_filter_desc, filter_grad_data + filter_offset * g));
};
workspace_handle.RunFunc(cudnn_func, workspace_size_in_bytes);
CUDNN_ENFORCE(platform::dynload::cudnnConvolutionBackwardFilter(
handle, &alpha, cudnn_output_desc,
output_grad_data + output_grad_offset * g, cudnn_input_desc,
input_data + input_offset * g, cudnn_conv_desc, filter_algo,
cudnn_workspace, workspace_size_in_bytes, &beta, cudnn_filter_desc,
filter_grad_data + filter_offset * g));
}
}
}
......
paddle/fluid/operators/distributed/collective_server_test.cc
浏览文件 @ ef788603
......@@ -20,6 +20,7 @@ limitations under the License. */
#include "paddle/fluid/framework/block_desc.h"
#include "paddle/fluid/framework/op_registry.h"
#include "paddle/fluid/framework/operator.h"
#include "paddle/fluid/framework/tensor_util.h"
#include "paddle/fluid/operators/distributed/collective_client.h"
#include "paddle/fluid/operators/distributed/collective_server.h"
......@@ -57,7 +58,7 @@ std::unique_ptr<framework::Scope> GenerateVars(platform::Place place) {
auto* tensor = slr->mutable_value();
auto* rows = slr->mutable_rows();
tensor->Resize(framework::make_ddim({20000, 1024}));
tensor->Resize(framework::make_ddim({3, 1024}));
tensor->mutable_data<float>(place);
paddle::operators::math::set_constant(ctx, tensor, 32.7);
......@@ -80,6 +81,20 @@ void Gather(const std::vector<distributed::RemoteVar>& vars,
std::vector<const framework::SelectedRows*> dst;
client->Gather(vars, &dst, *dev_ctx, scope);
std::cout << "dst:" << distributed::GetSelectedRowsInfo(*dst[0]);
dev_ctx->Wait();
ASSERT_EQ(dst[0]->value().dims(), framework::make_ddim({3, 1024}));
ASSERT_EQ(dst[0]->height(), 20000);
ASSERT_EQ(dst[0]->rows().size(), static_cast<size_t>(3));
for (int i = 0; i < 3; i++) {
ASSERT_EQ(dst[0]->rows()[i], i);
}
std::vector<float> vec;
TensorToVector(dst[0]->value(), *dev_ctx, &vec);
for (size_t i = 0; i < 3 * 1024; i++) {
ASSERT_FLOAT_EQ(vec[i], 32.7);
}
}
TEST(CollectiveServer, GPU) {
......
paddle/fluid/operators/fused/fusion_conv_inception_op.cu
浏览文件 @ ef788603
......@@ -216,18 +216,19 @@ class CUDNNConvInceptionFusionOpKernel : public framework::OpKernel<T> {
out_datas.push_back(
static_cast<void*>(output_data + (oc0 + oc1 + oc2) * h * w));
auto temp_allocation =
platform::DeviceTemporaryAllocator::Instance().Get(dev_ctx).Allocate(
workspace_size_in_bytes);
void* cudnn_workspace = temp_allocation->ptr();
for (int i = 0; i < 4; ++i) {
auto func = [&](void* cudnn_workspace) {
CUDNN_ENFORCE(platform::dynload::cudnnConvolutionBiasActivationForward(
handle, &alpha, in_desc[i], in_datas[i], filter_desc[i],
static_cast<const void*>(filters[i]->data<T>()), conv_desc[i],
algo[i], cudnn_workspace, workspace_size_in_bytes, &beta,
out_desc[i], out_datas[i], bias_desc[i],
static_cast<const void*>(bias[i]->data<T>()), cudnn_act_desc,
out_desc[i], out_datas[i]));
};
auto workspace_handle = dev_ctx.cudnn_workspace_handle();
workspace_handle.RunFunc(func, workspace_size_in_bytes);
CUDNN_ENFORCE(platform::dynload::cudnnConvolutionBiasActivationForward(
handle, &alpha, in_desc[i], in_datas[i], filter_desc[i],
static_cast<const void*>(filters[i]->data<T>()), conv_desc[i],
algo[i], cudnn_workspace, workspace_size_in_bytes, &beta, out_desc[i],
out_datas[i], bias_desc[i],
static_cast<const void*>(bias[i]->data<T>()), cudnn_act_desc,
out_desc[i], out_datas[i]));
}
cudnnTensorDescriptor_t x_desc;
......
paddle/fluid/operators/warpctc_cudnn_op.cu.cc
浏览文件 @ ef788603
......@@ -144,17 +144,19 @@ class CudnnCTCKernel : public framework::OpKernel<T> {
CUDNN_CTC_LOSS_ALGO_DETERMINISTIC, cu_ctcloss_desc, &workspace_size));
T* loss_data = loss->mutable_data<T>(loss_dims, ctx.GetPlace());
auto workspace_handle = dev_ctx.cudnn_workspace_handle();
auto cudnn_func = [&](void* cudnn_workspace) {
CUDNN_ENFORCE(platform::dynload::cudnnCTCLoss(
handle, cu_logits_desc, warpctc_logits_data, warpctc_label_data,
warpctc_label_lengths.data(), warpctc_logits_lengths.data(),
loss_data, cu_grad_desc, warpctc_grad_data,
CUDNN_CTC_LOSS_ALGO_DETERMINISTIC, cu_ctcloss_desc, cudnn_workspace,
workspace_size));
};
workspace_handle.RunFunc(cudnn_func, workspace_size);
math::SetConstant<DeviceContext, T>()(
ctx.template device_context<DeviceContext>(), loss, static_cast<T>(0));
auto temp_allocation =
platform::DeviceTemporaryAllocator::Instance().Get(dev_ctx).Allocate(
workspace_size);
void* cudnn_workspace = temp_allocation->ptr();
CUDNN_ENFORCE(platform::dynload::cudnnCTCLoss(
handle, cu_logits_desc, warpctc_logits_data, warpctc_label_data,
warpctc_label_lengths.data(), warpctc_logits_lengths.data(), loss_data,
cu_grad_desc, warpctc_grad_data, CUDNN_CTC_LOSS_ALGO_DETERMINISTIC,
cu_ctcloss_desc, cudnn_workspace, workspace_size));
}
};
......
paddle/fluid/pybind/CMakeLists.txt
浏览文件 @ ef788603
......@@ -18,9 +18,9 @@ if(WITH_PYTHON)
SRCS ${PYBIND_SRCS}
DEPS ${PYBIND_DEPS}
${GLOB_OP_LIB} ${GLOB_OPERATOR_DEPS})
if(NOT APPLE AND NOT ANDROID AND NOT WIN32)
if(NOT APPLE AND NOT WIN32)
target_link_libraries(paddle_pybind rt)
endif(NOT APPLE AND NOT ANDROID AND NOT WIN32)
endif(NOT APPLE AND NOT WIN32)
endif(WITH_AMD_GPU)
get_property (os_dependency_modules GLOBAL PROPERTY OS_DEPENDENCY_MODULES)
......
paddle/fluid/pybind/ir.cc
浏览文件 @ ef788603
......@@ -15,7 +15,9 @@
#include "paddle/fluid/pybind/ir.h"
#include <string>
#include <unordered_map>
#include <unordered_set>
#include "paddle/fluid/framework/ir/graph.h"
#include "paddle/fluid/framework/ir/graph_pattern_detector.h"
#include "paddle/fluid/framework/ir/node.h"
#include "paddle/fluid/framework/op_desc.h"
#include "paddle/fluid/framework/var_desc.h"
......@@ -24,6 +26,7 @@
namespace py = pybind11;
using paddle::framework::ir::Graph;
using paddle::framework::ir::Node;
using paddle::framework::ir::GraphSafeRemoveNodes;
using paddle::framework::OpDesc;
using paddle::framework::ProgramDesc;
using paddle::framework::VarDesc;
......@@ -32,6 +35,7 @@ using pybind11::return_value_policy;
namespace paddle {
namespace pybind {
void BindGraph(py::module *m) {
m->def("graph_safe_remove_nodes", GraphSafeRemoveNodes);
py::class_<Graph, std::shared_ptr<Graph>>(
*m, "Graph",
"The graph is a Directed Acyclic Single Static Assignment Graph, see "
......@@ -42,6 +46,8 @@ void BindGraph(py::module *m) {
.def("get_float", &Graph::Get<float>)
.def("get_double", &Graph::Get<double>)
.def("get_string", &Graph::Get<std::string>)
.def("get_program", &Graph::Get<ProgramDesc>)
.def("get_marked_nodes", &Graph::Get<std::unordered_set<const Node *>>)
.def("set", [](Graph &self, const std::string &attr_name,
int attr) { return self.Set(attr_name, new int(attr)); })
.def("set",
......@@ -57,6 +63,17 @@ void BindGraph(py::module *m) {
[](Graph &self, const std::string &attr_name, double attr) {
return self.Set(attr_name, new double(attr));
})
.def("set",
[](Graph &self, const std::string &attr_name,
const ProgramDesc &attr) {
return self.Set(attr_name, new ProgramDesc(attr));
})
.def("set",
[](Graph &self, const std::string &attr_name,
const std::unordered_set<const Node *> &attr) {
return self.Set(attr_name,
new std::unordered_set<const Node *>(attr));
})
.def("erase", &Graph::Erase)
.def("nodes", &Graph::Nodes, return_value_policy::reference)
.def("create_var_node",
......@@ -85,12 +102,52 @@ void BindNode(py::module *m) {
py::class_<Node> node(*m, "Node");
node.def("name", &Node::Name)
.def("node_type", &Node::NodeType)
.def("var", &Node::Var)
.def("op", &Node::Op)
.def("var", &Node::Var, return_value_policy::reference)
.def("op", &Node::Op, return_value_policy::reference)
.def("id", &Node::id)
.def("is_op", &Node::IsOp)
.def("is_var", &Node::IsVar)
.def("is_ctrl_var", &Node::IsCtrlVar)
.def("inputs_remove",
[](Node &self, int node_id) {
for (auto it = self.inputs.begin(); it != self.inputs.end();
it++) {
if ((*it)->id() == node_id) {
self.inputs.erase(it);
}
}
})
.def("inputs_remove",
[](Node &self, Node &node) {
for (auto it = self.inputs.begin(); it != self.inputs.end();
it++) {
if (*it == &node) {
self.inputs.erase(it);
}
}
})
.def("inputs_append",
[](Node &self, Node &node) { self.inputs.push_back(&node); })
.def("outputs_remove",
[](Node &self, int node_id) {
for (auto it = self.outputs.begin(); it != self.outputs.end();
it++) {
if ((*it)->id() == node_id) {
self.outputs.erase(it);
}
}
})
.def("outputs_remove",
[](Node &self, Node &node) {
for (auto it = self.outputs.begin(); it != self.outputs.end();
it++) {
if (*it == &node) {
self.outputs.erase(it);
}
}
})
.def("outputs_append",
[](Node &self, Node &node) { self.outputs.push_back(&node); })
.def_readwrite("inputs", &Node::inputs)
.def_readwrite("outputs", &Node::outputs);
......
paddle/fluid/pybind/protobuf.cc
浏览文件 @ ef788603
......@@ -228,7 +228,7 @@ void BindBlockDesc(pybind11::module *m) {
void BindVarDsec(pybind11::module *m) {
pybind11::class_<pd::VarDesc> var_desc(*m, "VarDesc", "");
var_desc
var_desc.def(pybind11::init<const std::string &>())
.def("name", &pd::VarDesc::Name, pybind11::return_value_policy::reference)
.def("set_name", &pd::VarDesc::SetName)
.def("set_shape", &pd::VarDesc::SetShape)
......
paddle/fluid/pybind/pybind.cc
浏览文件 @ ef788603
......@@ -788,21 +788,33 @@ All parameter, weight, gradient are variables in Paddle.
m.def("disable_profiler", platform::DisableProfiler);
m.def("is_profiler_enabled", platform::IsProfileEnabled);
m.def("reset_profiler", platform::ResetProfiler);
m.def("get_pass", [](const py::bytes &binary_str) {
std::string pass_type(binary_str);
auto pass = framework::ir::PassRegistry::Instance().Get(pass_type);
return std::shared_ptr<framework::ir::Pass>(std::move(pass));
});
py::class_<ir::Pass, std::shared_ptr<ir::Pass>> pass(m, "Pass");
pass.def(py::init())
.def("has", &ir::Pass::Has)
.def("set",
[](ir::Pass &self, const std::string &attr_name,
const ProgramDesc &attr) {
return self.Set(attr_name, new ProgramDesc(attr));
})
.def(
"set_str",
"set",
[](ir::Pass &self, const std::string &name, const std::string &attr) {
self.Set<std::string>(name, new std::string(attr));
})
.def("set_int", [](ir::Pass &self, const std::string &name,
int val) { self.Set<const int>(name, new int(val)); })
.def("set", [](ir::Pass &self, const std::string &name,
int val) { self.Set<const int>(name, new int(val)); })
.def("get_program", &ir::Pass::Get<ProgramDesc>)
.def("type", &ir::Pass::Type)
.def("apply", [](ir::Pass &self, std::shared_ptr<ir::Graph> graph) {
std::unique_ptr<ir::Graph> origin_graph(graph.get());
auto optim_graph = self.Apply(std::move(origin_graph));
graph.reset(optim_graph.release());
optim_graph.release();
});
py::class_<ir::PassBuilder, std::shared_ptr<ir::PassBuilder>> pb(
......
paddle/py_paddle/.gitignore 已删除 100644 → 0
浏览文件 @ d8568acd
swig_paddle.py
_swig_paddle.so
paddle/py_paddle/dataprovider_converter.py 已删除 100644 → 0
浏览文件 @ d8568acd
# Copyright (c) 2016 PaddlePaddle Authors. All Rights Reserved
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import paddle.trainer.PyDataProvider2 as dp2
import collections
import swig_paddle
import numpy
import itertools
from functools import reduce
__all__ = ['DataProviderConverter']
class IScanner(object):
"""
The scanner will scan Python object two passes, then convert it to Paddle's
argument.
In the first pass, `pre_scan` will be invoked by every data instance, and
then invoke `finish_pre_scan` to arguments. And the second pass do the same
thing except the functions changed to `scan`, `finish_scan`.
During the first pass, a scanner may count the shape of input matrix and
allocate memory for this argument. Then fill the data into this argument
in second pass.
"""
def __init__(self, input_type, pos):
self.input_type = input_type
if not isinstance(self.input_type, dp2.InputType):
raise ValueError("input type should be dataprovider2.InputType")
self.pos = pos
# data_in_gpu is used to indicate whether to create argument on GPU
# or not in GPU mode. Now if using one thread (trainer_count=1),
# trainer uses NeuralNetwork which needs to create argument on GPU
# before calling forward function. So, set data_in_gpu to True.
# Otherwise, trainer uses MultiGradientMachine which will transfer
# data from CPU to GPU in the forward function, set data_in_gpu to
# False in this case.
self.data_in_gpu = swig_paddle.isUsingGpu(
) and swig_paddle.getTrainerCount() == 1
def pre_scan(self, dat):
"""
First pass scan method. During this method, the scanner could count the
data number, and get the total memory size this batch would use.
:param dat: The python object.
"""
pass
def finish_pre_scan(self, argument):
"""
Finish first scan pass. Allocate the memory.
:param argument: Output arguments object.
:type argument: swig_paddle.Arguments
:param dat: Output arguments object.
:type dat: The Python object, numpy.array or List.
:return:
"""
pass
def scan(self, dat):
"""
Second pass scan method. Copy the data to arguments.
:param dat: The python object.
"""
pass
def finish_scan(self, argument):
"""
Finish second pass. Finalize the resources, etc.
:param argument: Output arguments object.
:type argument: swig_paddle.Arguments
"""
pass
class DenseScanner(IScanner):
"""
:type __mat__: numpy.ndarray
"""
def __init__(self, input_type, pos):
IScanner.__init__(self, input_type, pos)
self.__mat__ = None
self.__shape__ = None
self.__height__ = 0
self.__dim__ = 0
def pre_scan(self, dat):
self.__height__ += 1
if self.__shape__ is None:
self.__shape__ = numpy.array(dat).shape
if len(self.__shape__) > 3:
raise ValueError(
"The dimension of input cannot be greater than 3.")
if len(self.__shape__) == 0:
raise ValueError(
"The input should be a vector, please check your input data."
)
self.__dim__ = reduce(lambda x, y: x * y, self.__shape__)
if len(self.__shape__) == 1 and self.__dim__ != self.input_type.dim:
raise ValueError(
"The data size must be equal to it in data layer.")
else:
if self.__shape__ != numpy.array(dat).shape:
raise ValueError(
"The data shape must be same in one mini-batch.")
def finish_pre_scan(self, argument):
self.__mat__ = numpy.ndarray(
shape=(self.__height__, self.__dim__), dtype=numpy.float32)
self.__height__ = 0
def scan(self, dat):
# It's better to use NumPy array for speed.
dat = numpy.array(dat)
dat = dat.flatten()
self.__mat__[self.__height__] = dat
self.__height__ += 1
def finish_scan(self, argument):
assert isinstance(argument, swig_paddle.Arguments)
if self.__mat__.dtype != numpy.float32:
self.__mat__ = self.__mat__.astype(numpy.float32)
m = swig_paddle.Matrix.createDenseFromNumpy(self.__mat__, True,
self.data_in_gpu)
argument.setSlotValue(self.pos, m)
if len(self.__shape__) > 1:
# The last-two dimenstions are the frame height and width.
# For example, the layout is CHW for 3-D feature of image.
# The H and W are the frame height and width.
h, w = self.__shape__[-2:]
argument.setSlotFrameHeight(self.pos, h)
argument.setSlotFrameWidth(self.pos, w)
self.__shape__ = None
class SparseBinaryScanner(IScanner):
def __init__(self, input_type, pos):
IScanner.__init__(self, input_type, pos)
self.__rows__ = [0]
self.__cols__ = []
self.__height__ = 0
self.__value__ = []
def scan(self, dat):
self.extend_cols(dat)
self.__rows__.append(len(self.__cols__))
self.__height__ += 1
def extend_cols(self, dat):
self.__cols__.extend(dat)
def finish_scan(self, argument):
assert isinstance(argument, swig_paddle.Arguments)
m = swig_paddle.Matrix.createSparse(
self.__height__,
self.input_type.dim,
len(self.__cols__),
len(self.__value__) == 0,
False, # trans
False) # TODO supoort GPU
assert isinstance(m, swig_paddle.Matrix)
m.sparseCopyFrom(self.__rows__, self.__cols__, self.__value__)
argument.setSlotValue(self.pos, m)
class SparseFloatScanner(SparseBinaryScanner):
def __init__(self, input_type, pos):
SparseBinaryScanner.__init__(self, input_type, pos)
def extend_cols(self, dat):
self.__cols__.extend((x[0] for x in dat))
self.__value__.extend((x[1] for x in dat))
class IndexScanner(IScanner):
def __init__(self, input_type, pos):
IScanner.__init__(self, input_type, pos)
self.__ids__ = None
self.__idx__ = 0
def pre_scan(self, dat):
self.__idx__ += 1
def finish_pre_scan(self, argument):
self.__ids__ = [0] * self.__idx__
self.__idx__ = 0
def scan(self, dat):
self.__ids__[self.__idx__] = dat
self.__idx__ += 1
def finish_scan(self, argument):
ids = swig_paddle.IVector.create(self.__ids__, self.data_in_gpu)
assert isinstance(argument, swig_paddle.Arguments)
argument.setSlotIds(self.pos, ids)
class SequenceScanner(IScanner):
def __init__(self, input_type, pos, inner_scanner, setter):
IScanner.__init__(self, input_type, pos)
self.__seq__ = [0]
self.__inner_scanner__ = inner_scanner
self.__setter__ = setter
def pre_scan(self, dat):
for each in dat:
self.__inner_scanner__.pre_scan(each)
def finish_pre_scan(self, argument):
self.__inner_scanner__.finish_pre_scan(argument)
def scan(self, dat):
self.__seq__.append(self.__seq__[-1] + self.get_size(dat))
for each in dat:
self.__inner_scanner__.scan(each)
def finish_scan(self, argument):
seq = swig_paddle.IVector.create(self.__seq__, False)
self.__setter__(argument, self.pos, seq)
self.__inner_scanner__.finish_scan(argument)
def get_size(self, dat):
if isinstance(self.__inner_scanner__, SequenceScanner):
return sum(self.__inner_scanner__.get_size(item) for item in dat)
else:
return len(dat)
class DataProviderConverter(object):
def __init__(self, input_types):
self.input_types = input_types
assert isinstance(self.input_types, collections.Sequence)
for each in self.input_types:
assert isinstance(each, dp2.InputType)
def convert(self, dat, argument=None):
if argument is None:
argument = swig_paddle.Arguments.createArguments(0)
assert isinstance(argument, swig_paddle.Arguments)
argument.resize(len(self.input_types))
scanners = [
DataProviderConverter.create_scanner(i, each_type)
for i, each_type in enumerate(self.input_types)
]
for each_sample in dat:
for each_step, scanner in itertools.izip(each_sample, scanners):
scanner.pre_scan(each_step)
for scanner in scanners:
scanner.finish_pre_scan(argument)
for each_sample in dat:
for each_step, scanner in itertools.izip(each_sample, scanners):
scanner.scan(each_step)
for scanner in scanners:
scanner.finish_scan(argument)
return argument
def __call__(self, dat, argument=None):
return self.convert(dat, argument)
@staticmethod
def create_scanner(i, each):
assert isinstance(each, dp2.InputType)
retv = None
if each.type == dp2.DataType.Dense:
retv = DenseScanner(each, i)
elif each.type == dp2.DataType.Index:
retv = IndexScanner(each, i)
elif each.type == dp2.DataType.SparseNonValue:
retv = SparseBinaryScanner(each, i)
elif each.type == dp2.DataType.SparseValue:
retv = SparseFloatScanner(each, i)
assert retv is not None
if each.seq_type == dp2.SequenceType.SUB_SEQUENCE:
retv = SequenceScanner(
each, i, retv,
lambda a, p, seq: a.setSlotSubSequenceStartPositions(p, seq))
if each.seq_type in [
dp2.SequenceType.SUB_SEQUENCE, dp2.SequenceType.SEQUENCE
]:
retv = SequenceScanner(
each, i, retv,
lambda a, p, seq: a.setSlotSequenceStartPositions(p, seq))
return retv
paddle/py_paddle/util.py 已删除 100644 → 0
浏览文件 @ d8568acd
# Copyright (c) 2016 PaddlePaddle Authors. All Rights Reserved
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
"""
Some Useful method for py_paddle.
"""
import swig_paddle
import os
import paddle.trainer.PyDataProviderWrapper
import paddle.proto.ParameterConfig_pb2
import paddle.proto.ModelConfig_pb2
import paddle.proto.TrainerConfig_pb2
import weakref
import numpy
import struct
import sys
import copy
def initializePaddle(*args):
"""
To initialize paddle process.
:param args: Command line options, such as --use_gpu=0, etc.
:return: Nothing.
"""
old_argv = copy.deepcopy(sys.argv)
old_pypath = os.getenv("PYTHONPATH")
pypath = os.path.abspath(os.path.join(os.path.dirname(__file__), '..'))
if old_pypath is not None:
pypath = os.pathsep.join([pypath, old_pypath])
os.putenv("PYTHONPATH", pypath)
args = [""] + list(args) # argv[0] is command name, it is not important.
swig_paddle.__initPaddle__(args)
sys.argv = old_argv
def __monkeypatch_init_paddle__():
swig_paddle.__initPaddle__ = swig_paddle.initPaddle
swig_paddle.initPaddle = initializePaddle
class __ParameterCallbackWrapper__(swig_paddle.UpdateCallback):
"""
Wrap the python callable object to paddle.UpdateCallback.
INTERNAL USE ONLY.
"""
def __init__(self, callback):
swig_paddle.UpdateCallback.__init__(self)
self.callback = callback
def apply(self, param):
self.callback(param)
@staticmethod
def wrap(callback):
"""
Cast the python callable object/paddle.UpdateCallback to
swig_paddle.UpdateCallback.__disown__
:param callback: callable or swig_paddle.UpdateCallback object.
"""
if isinstance(callback, swig_paddle.UpdateCallback):
return callback.__disown__()
elif isinstance(callback, weakref.ProxyType):
raise RuntimeError("Should not pass __disown__ object")
else:
return __ParameterCallbackWrapper__(callback).__disown__()
def __arguments_to_numpy__(i, arg):
assert isinstance(arg, swig_paddle.Arguments)
value = arg.getSlotValue(i)
ids = arg.getSlotIds(i)
prob = arg.getSlotIn(i)
if value is not None:
assert isinstance(value, swig_paddle.Matrix)
value = value.copyToNumpyMat()
if ids is not None:
assert isinstance(ids, swig_paddle.IVector)
ids = ids.copyToNumpyArray()
if prob is not None:
assert isinstance(prob, swig_paddle.Matrix)
prob = prob.copyToNumpyMat()
return {"value": value, "id": ids, "prob": prob}
def __monkeypatch_gradient_machine__():
"""
Add some class methods to GradientMachine.
This method should be only used internally.
"""
swig_paddle.GradientMachine.loadFromConfigFile = \
staticmethod(loadGradientMachine)
def __matrix_to_numpy__(m):
if isinstance(m, swig_paddle.Matrix):
return m.copyToNumpyMat()
elif isinstance(m, swig_paddle.IVector):
return m.copyToNumpyArra()
else:
raise RuntimeError("Input arg should be matrix or vecotr.")
def createFromConfigProto(protoObj,
createMode=swig_paddle.CREATE_MODE_NORMAL,
paramTypes=[
swig_paddle.PARAMETER_VALUE,
swig_paddle.PARAMETER_GRADIENT,
swig_paddle.PARAMETER_MOMENTUM
]):
"""
Create Gradient Machine From Proto object.
:param protoObj: Model config
:type protoObj: proto.ModelConfig_pb2.ModelConfig
:param createMode: Create Mode, default is normal.
:type createMode: int
:param paramTypes: the gradient machine parameter type.
:type paramTypes: list of int
:return: paddle.GradientMachine
"""
assert isinstance(protoObj, paddle.proto.ModelConfig)
return swig_paddle.GradientMachine.createByConfigProtoStr(
protoObj.SerializeToString(), createMode, paramTypes)
swig_paddle.GradientMachine.createFromConfigProto = \
staticmethod(createFromConfigProto)
def forwardTest(self, inArgs):
"""
forwardTest. forward gradient machine in test mode, and return a numpy
matrix dict.
:param inArgs: The input arguments
:type inArgs: paddle.Arguments
:return: A dictionary with keys ['id', 'value'], each value is a
numpy.ndarray.
"""
outArgs = swig_paddle.Arguments.createArguments(0)
self.forward(inArgs, outArgs, swig_paddle.PASS_TEST)
return [
__arguments_to_numpy__(i, outArgs)
for i in xrange(outArgs.getSlotNum())
]
swig_paddle.GradientMachine.forwardTest = forwardTest
# Monkey patching backward
swig_paddle.GradientMachine.__backward__ = swig_paddle.GradientMachine.backward
def backward(self, callback):
"""
GradientMachine Backward
:param callback: a callback which parameter is (paddle.Parameter) or
a paddle.UpdateCallback object.
"""
self.__backward__(__ParameterCallbackWrapper__.wrap(callback))
swig_paddle.GradientMachine.backward = backward
# Monkey patching forwardBackward.
swig_paddle.GradientMachine.__forwardBackward__ = \
swig_paddle.GradientMachine.forwardBackward
def forwardBackward(self,
inArgs,
outArgs,
passType,
callback=swig_paddle.UpdateCallback()):
"""
GradientMachine forward backward.
:param inArgs: Input Arguments for GradientMachine.
:type inArgs: paddle.Arguments
:param outArgs: Output Arguments for GradientMachine.
:type outArgs: paddle.Arguments
:param passType: gradient machine's pass type.
:type passType: paddle.PassType
:param callback: a callable object with arguments (paddle.Parameter) or
a paddle.UpdateCallback it will be called when
backward
"""
self.__forwardBackward__(inArgs, outArgs, passType,
__ParameterCallbackWrapper__.wrap(callback))
swig_paddle.GradientMachine.forwardBackward = forwardBackward
def getParameters(self):
return (self.getParameter(i) for i in xrange(self.getParameterSize()))
swig_paddle.GradientMachine.getParameters = getParameters
def getNonStaticParameters(self):
return (self.getNonStaticParameter(i)
for i in xrange(self.getNonStaticParameterSize()))
swig_paddle.GradientMachine.getNonStaticParameters = getNonStaticParameters
def getLayerOutputs(self, layerNames):
"""
getLayerOutputs. get outputs of layers and return a numpy matrix dict.
:param layerNames: layer names.
:type layerNames: string or list.
"""
if isinstance(layerNames, basestring):
layerNames = [layerNames]
elif not isinstance(layerNames, list):
raise RuntimeError("Input args shuld be string or a sting list.")
output = dict()
for name in layerNames:
output[name] = __arguments_to_numpy__(0, self.getLayerOutput(name))
return output
swig_paddle.GradientMachine.getLayerOutputs = getLayerOutputs
def loadGradientMachine(config_filename, model_dir=None):
"""
Load a gradient machine from config file name/path.
:param config_filename: The trainer config file name/path
:param model_dir: The model parameter directory. None if same as the
directory of config_filename
:return: GradientMachine with some enhance methods.
:rtype: paddle.GradientMachine
"""
trainer_config = swig_paddle.TrainerConfig.createFromTrainerConfigFile(
config_filename)
assert isinstance(trainer_config, swig_paddle.TrainerConfig)
model_conf = trainer_config.getModelConfig()
network = swig_paddle.GradientMachine.createByModelConfig(model_conf)
assert isinstance(network, swig_paddle.GradientMachine)
if model_dir is None:
model_dir = os.path.dirname(config_filename)
network.loadParameters(model_dir)
return network
def loadParameterFile(fn):
"""
Load Paddle Parameter file to numpy.ndarray
:param fn: file name or file like object.
:type fn: str or file like object.
:return: numpy array
:rtype: numpy.ndarray
:raise: paddle.UnsupportError when parameter format is wrong.
"""
if isinstance(fn, str):
with open(fn, 'rb') as f:
return loadParameterFile(f)
elif hasattr(fn, 'read'): # File like object
version, = struct.unpack('i', fn.read(4))
if version != 0:
raise swig_paddle.UnsupportError()
value_length, = struct.unpack("I", fn.read(4))
if value_length != 4 and value_length != 8:
raise swig_paddle.UnsupportError()
dtype = 'float32' if value_length == 4 else 'float64'
param_size, = struct.unpack("L", fn.read(8))
value = numpy.fromfile(fn, dtype)
if len(value) != param_size:
raise swig_paddle.UnsupportError()
return value
else:
raise swig_paddle.UnsupportError()
class DataProviderWrapperConverter(object):
"""
A class convert DataFormat from PyDataProvider Wrapper to
py_paddle.paddle.Arguemnts.
"""
class DenseValueConverter(object):
"""
Internal class
"""
def __init__(self, header_def):
self.__dim__ = header_def.dim
self.buf = []
def append(self, other):
assert len(other) == self.__dim__
self.buf += other
def __call__(self, slot_idx, arg):
mat = swig_paddle.Matrix.createDense(self.buf,
len(self.buf) / self.__dim__,
self.__dim__)
arg.setSlotValue(slot_idx, mat)
class IdValueConverter(object):
"""
Internal class
"""
def __init__(self, *args):
self.buf = []
def append(self, other):
assert isinstance(other, int)
self.buf.append(other)
def __call__(self, slot_idx, arg):
arg.setSlotIds(slot_idx, swig_paddle.IVector.create(self.buf))
class SparseNonValueConverter(object):
"""
Internal class
"""
def __init__(self, slot_def):
self.indices = [0]
self.cols = []
self.dim = slot_def.dim
def append(self, other):
self.indices.append(self.indices[-1] + len(other))
self.cols += other
def __call__(self, slot_idx, arg):
mat = swig_paddle.Matrix.createSparse(
len(self.indices) - 1, self.dim, len(self.cols), True)
assert isinstance(mat, swig_paddle.Matrix)
mat.sparseCopyFrom(self.indices, self.cols)
self.putIntoArg(slot_idx, arg, mat)
def putIntoArg(self, slot_idx, arg, mat):
arg.setSlotValue(slot_idx, mat)
class SparseValueConverter(SparseNonValueConverter):
"""
Internal class
"""
def __init__(self, slot_def):
super(DataProviderWrapperConverter.SparseValueConverter,
self).__init__(slot_def)
self.values = []
def append(self, other):
super(DataProviderWrapperConverter.SparseValueConverter,
self).append(map(lambda x: x[0], other))
self.values += map(lambda x: x[1], other)
def __call__(self, slot_idx, arg):
mat = swig_paddle.Matrix.createSparse(
len(self.indices) - 1, self.dim, len(self.cols), False)
assert isinstance(mat, swig_paddle.Matrix)
mat.sparseCopyFrom(self.indices, self.cols, self.values)
self.putIntoArg(slot_idx, arg, mat)
__SLOT_VALUE_CONVERTER_MAP__ = {
paddle.trainer.PyDataProviderWrapper.DenseSlot: DenseValueConverter,
paddle.trainer.PyDataProviderWrapper.IndexSlot: IdValueConverter,
paddle.trainer.PyDataProviderWrapper.SparseNonValueSlot:
SparseNonValueConverter,
paddle.trainer.PyDataProviderWrapper.SparseValueSlot:
SparseValueConverter
}
def __init__(self, use_seq, header):
"""
Ctor
:param use_seq: True if use sequence.
:param header: List of slots type,
trainer.PyDataProviderWrapper.SlotType
"""
self.__use_seq__ = use_seq
self.__header__ = header
def convert(self, wrapper_data, argument=None):
"""
Convert PyDataProviderWrapper format to paddle.Argument
:param wrapper_data: PyDataProviderWrapper yield's data list.
:param argument: The output paddle.Arguments.
If it is not None, it will assign data in this
arguments, else it will create new arguments.
:return: arguments that contains data.
:rtype: paddle.Arguments
"""
if argument is None:
argument = swig_paddle.Arguments.createArguments(0)
assert isinstance(argument, swig_paddle.Arguments)
argument.resize(len(self.__header__))
values = map(
lambda x: DataProviderWrapperConverter.__SLOT_VALUE_CONVERTER_MAP__[x.__class__](x),
self.__header__)
if self.__use_seq__:
seq_dim = [[] for _ in xrange(self.__header__.__len__())]
seq_start_pos = [[0] for _ in xrange(self.__header__.__len__())]
for each_sample in wrapper_data:
for slot_idx, sequence in enumerate(each_sample):
for raw_data in sequence:
values[slot_idx].append(raw_data)
seq_start_pos[slot_idx].append(seq_start_pos[slot_idx][-1] +
len(sequence))
seq_dim[slot_idx].append(len(sequence))
for slot_idx in xrange(len(self.__header__)):
argument.setSlotSequenceDim(
slot_idx, swig_paddle.IVector.create(seq_dim[slot_idx]))
argument.setSlotSequenceStartPositions(
slot_idx,
swig_paddle.IVector.create(seq_start_pos[slot_idx]))
else:
for each_sample in wrapper_data:
for raw_data, value in zip(each_sample, values):
value.append(raw_data)
for i, v in enumerate(values):
v(i, argument)
return argument
def __call__(self, wrapper_data, argument=None):
"""
Invoke self.convert. See documents in self.convert.
"""
return self.convert(wrapper_data, argument)
def __monkey_patch_protobuf_objects__():
def ParameterConfig_toProto(self):
"""
Convert paddle.ParameterConfig to
proto.ParameterConfig_pb2.ParameterConfig
:return: proto.ParameterConfig_pb2.ParameterConfig object.
"""
param_conf = paddle.proto.ParameterConfig_pb2.ParameterConfig()
param_conf.ParseFromString(self.toProtoString())
return param_conf
swig_paddle.ParameterConfig.toProto = ParameterConfig_toProto
def OptimizationConfig_toProto(self):
"""
Convert paddle.OptimizationConfig to
proto.TrainerConfig_pb2.OptimizationConfig
:return: proto.TrainerConfig_pb2.OptimizationConfig
"""
opt_conf = proto.TrainerConfig_pb2.OptimizationConfig()
opt_conf.ParseFromString(self.toProtoString())
return opt_conf
swig_paddle.OptimizationConfig.toProto = OptimizationConfig_toProto
def OptimizationConfig_createFromProto(protoObj):
"""
Create a new paddle.OptimizationConfig from
proto.TrainerConfig_pb2.OptimizationConfig
:param protoObj: proto.TrainerConfig_pb2.OptimizationConfig
:return: paddle.OptimizationConfig
"""
assert isinstance(protoObj, paddle.proto.OptimizationConfig)
return swig_paddle.OptimizationConfig.createFromProtoString(
protoObj.SerializeToString())
swig_paddle.OptimizationConfig.createFromProto = staticmethod(
OptimizationConfig_createFromProto)
def TrainerConfig_createFromProto(protoObj):
"""
Create a new paddle.TrainerConfig from
proto.OptimizationConfig
:param protoObj: proto.TrainerConfig
:return: paddle.TrainerConfig
"""
assert isinstance(protoObj, paddle.proto.TrainerConfig)
return swig_paddle.TrainerConfig.createFromProtoString(
protoObj.SerializeToString())
swig_paddle.TrainerConfig.createFromProto = staticmethod(
TrainerConfig_createFromProto)
def __monkey_patch_parameter__():
def getBufs(self):
"""
get all parameter vectors.
NOTE: the return value is a generator. Maybe you need to cast to
list or tuple or something else.
:return: generator of all parameter vectors.
:rtype: generator
"""
return (self.getBuf(i) for i in xrange(swig_paddle.NUM_PARAMETER_TYPES))
swig_paddle.Parameter.getBufs = getBufs
def __monkey_patch_trainer__():
swig_paddle.Trainer.__create__ = staticmethod(swig_paddle.Trainer.create)
def Trainer_create(config, model=None):
"""
Create a trainer for model with TrainerCOnfig trainer_config
trainer_config.model_config will be ignored when model is supplied.
Trainer.trainOneBatch() and Trainer.forwardOneBatch() can be used only
when trainer_config.data_config is set.
A typical usage for Trainer is:
.. code-block:: python
trainer = Trainer.create(trainer_config, model)
for p in xrange(num_passes)
while True:
data = get_next_batch(batch_size)
if not data:
break
trainer.trainOneDataBatch(batch_size, data)
trainer.finishTrainPass()
trainer.finishTrain()
The trainer will take care of logging, model saving, distributed
training, etc.
:param config: trainer configuration
:type config: paddle.proto.TrainerConfig
:param model: the model to be trained
:type model: swig_paddle.GradientMachine
:return: a trainer
:rtype swig_paddle.Trainer
"""
assert isinstance(config, paddle.proto.TrainerConfig)
if model is not None:
assert isinstance(model, swig_paddle.GradientMachine)
return swig_paddle.Trainer.__create__(
swig_paddle.TrainerConfig.createFromProto(config), model)
swig_paddle.Trainer.create = staticmethod(Trainer_create)
swig_paddle.Trainer.__getForwardOutput__ = \
swig_paddle.Trainer.getForwardOutput
def getForwardOutput(self):
"""
Get the netword outputs from the previous trainOneBatch(),
trainOneDataBatch(), testOneDataPatch(), or forwardOneBatch() call.
:return: list of dictionary with keys ['id', 'value'], each value is a
numpy.ndarray.
"""
outArgs = self.__getForwardOutput__()
return [
__arguments_to_numpy__(i, outArgs)
for i in xrange(outArgs.getSlotNum())
]
swig_paddle.Trainer.getForwardOutput = getForwardOutput
def monkeypatches():
patches = [
__monkeypatch_init_paddle__, __monkeypatch_gradient_machine__,
__monkey_patch_protobuf_objects__, __monkey_patch_parameter__,
__monkey_patch_trainer__
]
for patch in patches:
patch()
paddle/scripts/README.md
浏览文件 @ ef788603
......@@ -40,7 +40,6 @@ The lastest pre-built build environment images are:
| Image | Tag |
| ----- | --- |
| paddlepaddle/paddle | latest-dev |
| paddlepaddle/paddle | latest-dev-android |
### Start Build
......@@ -68,8 +67,6 @@ Users can specify the following Docker build arguments with either "ON" or "OFF"
| `WITH_TESTING` | OFF | Build unit tests binaries. |
| `WITH_MKL` | ON | Build with [Intel® MKL](https://software.intel.com/en-us/mkl) and [Intel® MKL-DNN](https://github.com/01org/mkl-dnn) support. |
| `WITH_GOLANG` | OFF | Build fault-tolerant parameter server written in go. |
| `WITH_SWIG_PY` | ON | Build with SWIG python API support. |
| `WITH_C_API` | OFF | Build capi libraries for inference. |
| `WITH_PYTHON` | ON | Build with python support. Turn this off if build is only for capi. |
| `WITH_STYLE_CHECK` | ON | Check the code style when building. |
| `PYTHON_ABI` | "" | Build for different python ABI support, can be cp27-cp27m or cp27-cp27mu |
......
paddle/scripts/paddle_build.sh
浏览文件 @ ef788603
......@@ -33,7 +33,6 @@ function print_usage() {
${BLUE}gen_doc_lib${NONE}: generate paddle documents library
${BLUE}html${NONE}: convert C++ source code into HTML
${BLUE}dockerfile${NONE}: generate paddle release dockerfile
${BLUE}capi${NONE}: generate paddle CAPI package
${BLUE}fluid_inference_lib${NONE}: deploy fluid inference library
${BLUE}check_style${NONE}: run code style check
${BLUE}cicheck${NONE}: run CI tasks
......@@ -180,9 +179,7 @@ function cmake_gen() {
-DWITH_AVX=${WITH_AVX:-OFF}
-DWITH_GOLANG=${WITH_GOLANG:-OFF}
-DCUDA_ARCH_NAME=${CUDA_ARCH_NAME:-All}
-DWITH_C_API=${WITH_C_API:-OFF}
-DWITH_PYTHON=${WITH_PYTHON:-ON}
-DWITH_SWIG_PY=${WITH_SWIG_PY:-ON}
-DCUDNN_ROOT=/usr/
-DWITH_TESTING=${WITH_TESTING:-ON}
-DCMAKE_MODULE_PATH=/opt/rocm/hip/cmake
......@@ -216,8 +213,6 @@ EOF
-DWITH_AVX=${WITH_AVX:-OFF} \
-DWITH_GOLANG=${WITH_GOLANG:-OFF} \
-DCUDA_ARCH_NAME=${CUDA_ARCH_NAME:-All} \
-DWITH_SWIG_PY=${WITH_SWIG_PY:-ON} \
-DWITH_C_API=${WITH_C_API:-OFF} \
-DWITH_PYTHON=${WITH_PYTHON:-ON} \
-DCUDNN_ROOT=/usr/ \
-DWITH_TESTING=${WITH_TESTING:-ON} \
......@@ -706,59 +701,43 @@ EOF
EOF
}
function gen_capi_package() {
if [[ ${WITH_C_API} == "ON" ]]; then
capi_install_prefix=${INSTALL_PREFIX:-/paddle/build}/capi_output
rm -rf $capi_install_prefix
make DESTDIR="$capi_install_prefix" install
cd $capi_install_prefix/
ls | egrep -v "^Found.*item$" | xargs tar -czf ${PADDLE_ROOT}/build/paddle.tgz
fi
}
function gen_fluid_lib() {
mkdir -p ${PADDLE_ROOT}/build
cd ${PADDLE_ROOT}/build
if [[ ${WITH_C_API:-OFF} == "OFF" ]] ; then
cat <<EOF
cat <<EOF
========================================
Generating fluid library for train and inference ...
========================================
EOF
cmake .. -DWITH_DISTRIBUTE=OFF -DON_INFER=ON
make -j `nproc` fluid_lib_dist
make -j `nproc` inference_lib_dist
fi
cmake .. -DWITH_DISTRIBUTE=OFF -DON_INFER=ON
make -j `nproc` fluid_lib_dist
make -j `nproc` inference_lib_dist
}
function tar_fluid_lib() {
if [[ ${WITH_C_API:-OFF} == "OFF" ]] ; then
cat <<EOF
cat <<EOF
========================================
Taring fluid library for train and inference ...
========================================
EOF
cd ${PADDLE_ROOT}/build
cp -r fluid_install_dir fluid
tar -czf fluid.tgz fluid
cp -r fluid_inference_install_dir fluid_inference
tar -czf fluid_inference.tgz fluid_inference
fi
cd ${PADDLE_ROOT}/build
cp -r fluid_install_dir fluid
tar -czf fluid.tgz fluid
cp -r fluid_inference_install_dir fluid_inference
tar -czf fluid_inference.tgz fluid_inference
}
function test_fluid_lib() {
if [[ ${WITH_C_API:-OFF} == "OFF" ]] ; then
cat <<EOF
cat <<EOF
========================================
Testing fluid library for inference ...
========================================
EOF
cd ${PADDLE_ROOT}/paddle/fluid/inference/api/demo_ci
./run.sh ${PADDLE_ROOT} ${WITH_MKL:-ON} ${WITH_GPU:-OFF} ${INFERENCE_DEMO_INSTALL_DIR} \
${TENSORRT_INCLUDE_DIR:-/usr/local/TensorRT/include} \
${TENSORRT_LIB_DIR:-/usr/local/TensorRT/lib}
./clean.sh
fi
cd ${PADDLE_ROOT}/paddle/fluid/inference/api/demo_ci
./run.sh ${PADDLE_ROOT} ${WITH_MKL:-ON} ${WITH_GPU:-OFF} ${INFERENCE_DEMO_INSTALL_DIR} \
${TENSORRT_INCLUDE_DIR:-/usr/local/TensorRT/include} \
${TENSORRT_LIB_DIR:-/usr/local/TensorRT/lib}
./clean.sh
}
function main() {
......@@ -791,11 +770,6 @@ function main() {
dockerfile)
gen_dockerfile ${PYTHON_ABI:-""}
;;
capi)
cmake_gen ${PYTHON_ABI:-""}
build
gen_capi_package
;;
fluid_inference_lib)
cmake_gen ${PYTHON_ABI:-""}
gen_fluid_lib
......@@ -810,7 +784,6 @@ function main() {
build
assert_api_not_changed ${PYTHON_ABI:-""}
run_test
gen_capi_package
gen_fluid_lib
test_fluid_lib
assert_api_spec_approvals
......@@ -820,7 +793,6 @@ function main() {
assert_api_spec_approvals
;;
test_inference)
gen_capi_package
gen_fluid_lib
test_fluid_lib
;;
......
paddle/scripts/paddle_docker_build.sh
浏览文件 @ ef788603
......@@ -28,7 +28,6 @@ function start_build_docker() {
-e WITH_AVX=ON \
-e WITH_GOLANG=OFF \
-e WITH_TESTING=ON \
-e WITH_C_API=OFF \
-e WITH_COVERAGE=ON \
-e COVERALLS_UPLOAD=ON \
-e WITH_DEB=OFF \
......@@ -67,9 +66,6 @@ function main() {
DOCKER_REPO="paddlepaddle/paddle"
VERSION="latest-dev"
PADDLE_ROOT="$( cd "$( dirname "${BASH_SOURCE[0]}")/../../" && pwd )"
if [ "$1" == "build_android" ]; then
VERSION="latest-dev-android"
fi
IMG=${DOCKER_REPO}:${VERSION}
start_build_docker $@
}
......
python/CMakeLists.txt
浏览文件 @ ef788603
......@@ -39,7 +39,6 @@ add_custom_target(copy_paddle_pybind ALL DEPENDS ${FLUID_CORE})
IF(WIN32)
add_custom_command(OUTPUT ${PADDLE_PYTHON_BUILD_DIR}/.timestamp
COMMAND ${CMAKE_COMMAND} -E copy_directory ${PADDLE_SOURCE_DIR}/python/paddle ${PADDLE_BINARY_DIR}/python/paddle/
COMMAND ${CMAKE_COMMAND} -E copy_directory ${PADDLE_SOURCE_DIR}/paddle/py_paddle ${PADDLE_BINARY_DIR}/python/
COMMAND ${CMAKE_COMMAND} -E env ${py_env} ${PYTHON_EXECUTABLE} setup.py bdist_wheel
COMMAND ${CMAKE_COMMAND} -E touch ${PADDLE_PYTHON_BUILD_DIR}/.timestamp
COMMAND ${CMAKE_COMMAND} -E remove_directory ${PADDLE_PYTHON_BUILD_DIR}/lib-python
......@@ -48,7 +47,6 @@ ELSE(WIN32)
add_custom_command(OUTPUT ${PADDLE_PYTHON_BUILD_DIR}/.timestamp
COMMAND touch stub.cc
COMMAND cp -r ${PADDLE_SOURCE_DIR}/python/paddle ${PADDLE_BINARY_DIR}/python
COMMAND cp -r ${PADDLE_SOURCE_DIR}/paddle/py_paddle ${PADDLE_BINARY_DIR}/python/
COMMAND env ${py_env} ${PYTHON_EXECUTABLE} setup.py bdist_wheel
COMMAND ${CMAKE_COMMAND} -E touch ${PADDLE_PYTHON_BUILD_DIR}/.timestamp
COMMAND ${CMAKE_COMMAND} -E remove_directory ${PADDLE_PYTHON_BUILD_DIR}/lib-python
......
python/paddle/fluid/contrib/slim/graph/graph.py
浏览文件 @ ef788603
......@@ -11,8 +11,12 @@
# 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.
from __future__ import print_function
import os
import subprocess
from ....framework import Program
from ....framework import Block
from .... import core
__all__ = ['Graph', 'ImitationGraph', 'IRGraph']
......
paddle/py_paddle/__init__.py → python/paddle/fluid/contrib/slim/quantization/__init__.py
浏览文件 @ ef788603
# Copyright (c) 2016 PaddlePaddle Authors. All Rights Reserved
# Copyright (c) 2019 PaddlePaddle Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
......@@ -12,13 +12,9 @@
# See the License for the specific language governing permissions and
# limitations under the License.
from util import DataProviderWrapperConverter
from dataprovider_converter import DataProviderConverter
from __future__ import print_function
__all__ = [
'paddle',
'DataProviderConverter',
'DataProviderWrapperConverter', # for deprecated usage.
'loadParameterFile'
]
util.monkeypatches()
from . import quantization_pass
from .quantization_pass import *
__all__ = quantization_pass.__all__
python/paddle/fluid/contrib/slim/quantization/quantization_pass.py 0 → 100644
浏览文件 @ ef788603
# Copyright (c) 2018 PaddlePaddle Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import collections
from .... import core
from ....framework import IrGraph
from ....framework import Program
from ....framework import Variable
from ....initializer import Constant
from .... import unique_name
__all__ = ['QuantizationTransformPass']
class QuantizationTransformPass(object):
def __init__(self,
scope=None,
program_exe=None,
weight_bits=8,
activation_bits=8,
activation_quantize_type='abs_max',
weight_quantize_type='abs_max',
window_size=10000):
"""
Convert and rewrite the IrGraph according to weight and
activation quantization type.
Args:
weight_bits (int): quantization bit number for weights,
the bias is not quantized.
activation_bits (int): quantization bit number for activation.
activation_quantize_type (str): quantization type for activation,
now support 'abs_max', 'range_abs_max'. If use 'abs_max' mode,
the quantization scale will be calculated dynamically each step
in both training and testing period. If use 'range_abs_max',
a static quantization scale will be calculated during training
and used in inference.
weight_quantize_type (str): quantization type for weights,
support 'abs_max'. The 'range_abs_max' usually is not used for
weight, since weights are fixed once the model is well trained.
window_size (int): the window size for 'range_abs_max' quantization.
Examples:
.. code-block:: python
# The original graph will be rewrite.
import paddle.fluid as fluid
from paddle.fluid.contrib.slim.quantization \
import QuantizationTransformPass
from paddle.fluid.contrib.slim.graph import IrGraph
from paddle.fluid import core
graph = IrGraph(core.Graph(program.desc), for_test=False)
exe = fluid.Executor(fluid.CPUPlace())
transform_pass = QuantizationTransformPass(fluid.global_scope(),
exe)
transform_pass.apply(graph)
"""
self._scope = scope
self._program_exe = program_exe
self._weight_bits = weight_bits
self._activation_bits = activation_bits
quant_type = ['abs_max', 'range_abs_max']
if activation_quantize_type not in quant_type:
raise ValueError(
"Unknown activation_quantize_type : '%s'. It can only be ",
"'abs_max' or 'range_abs_max'.", str(activation_quantize_type))
if weight_quantize_type not in quant_type:
raise ValueError(
"Unknown weight_quantize_type: '%s'. It can only be ",
"'abs_max' or 'range_abs_max'.", str(weight_quantize_type))
self._activation_quantize_type = activation_quantize_type
self._weight_quantize_type = weight_quantize_type
self._window_size = window_size
self._need_initialized = collections.OrderedDict()
self._quantizable_ops = ['conv2d', 'depthwise_conv2d', 'mul']
self._quantizable_grad_ops = [
'%s_grad' % (op) for op in self._quantizable_ops
]
self._fake_quant_op_types = [
'fake_quantize_abs_max', 'fake_quantize_range_abs_max'
]
self._fake_dequant_op_types = ['fake_dequantize_max_abs']
self._is_test = None
self._global_step = None
def apply(self, graph):
assert isinstance(graph,
IrGraph), 'graph must be the instance of IrGraph.'
self._need_initialized.clear()
self._is_test = graph.is_test()
# marked the variable which has been dequantized.
dequantized_vars = collections.OrderedDict()
params = [p.name() for p in graph.all_parameters()]
def _transform_forward(graph, op):
for var_node in op.inputs:
if var_node.name() in dequantized_vars:
dequant_var_node = dequantized_vars[var_node.name()]
else:
quant_bits = self._weight_bits if var_node.name() in params \
else self._activation_bits
quant_type = self._weight_quantize_type if var_node.name() \
in params else self._activation_quantize_type
quant_var_node, scale_var_node = self._insert_quant_op(
graph, var_node, quant_bits, quant_type)
dequant_var_node = self._insert_dequant_op(
graph, quant_var_node, scale_var_node, quant_bits)
dequantized_vars[var_node.name()] = dequant_var_node
graph.update_input_link(var_node, dequant_var_node, op)
def _transform_backward(graph, op):
no_dequanted_input_vars = True
for var_node in op.inputs:
if var_node.name() in dequantized_vars:
dequant_var_node = dequantized_vars[var_node.name()]
graph.update_input_link(var_node, dequant_var_node, op)
no_dequanted_input_vars = False
if no_dequanted_input_vars:
raise ValueError("There is no dequanted inputs for op %s." %
(op.name()))
if not self._is_test:
self._create_global_step(graph)
ops = graph.all_ops()
# The process of _transform_forward and _transform_backward is needed in two for loops.
# The loop for transforming the forward graph:
for op in ops:
if op.name() in self._quantizable_ops:
_transform_forward(graph, op)
# The loop for renaming the inputs of backward op.
for op in ops:
if op.name() in self._quantizable_grad_ops:
_transform_backward(graph, op)
if len(self._need_initialized) > 0:
assert self._scope is not None, \
'The scope cannot be set None when activation_quantize_type equals to range_abs_max.'
assert self._program_exe is not None, \
'The program_exe cannot be set None when activation_quantize_type equals to range_abs_max.'
init_program = Program()
for var_desc, initializer in self._need_initialized.iteritems():
var = Variable(init_program.global_block())
var._set_desc(var_desc)
initializer(var, init_program.global_block())
self._program_exe.run(program=init_program, scope=self._scope)
return graph
def _create_global_step(self, graph):
if self._weight_quantize_type == 'range_abs_max' or \
self._activation_quantize_type == 'range_abs_max':
counter_name = '@STEP_COUNTER@'
for node in graph.all_vars():
if node.name() == counter_name:
self._global_step = node
if self._global_step is None:
global_step_in = graph.create_param_node(
name=counter_name,
var_type=core.VarDesc.VarType.LOD_TENSOR,
shape=[1],
var_dtype=core.VarDesc.VarType.INT64)
self._need_initialized[global_step_in.var()] = \
Constant(value=0, force_cpu=True)
global_step_out = graph.create_var_node_from_desc(
global_step_in.var())
increment_op = graph.create_op_node(
op_type='increment',
attrs={'step': 1.0},
inputs={'X': global_step_in},
outputs={'Out': global_step_out})
graph.link_to(global_step_in, increment_op)
graph.link_to(increment_op, global_step_out)
self._global_step = global_step_out
def _insert_quant_op(self, graph, var_node, quant_bits, quant_type):
"""
Insert fake_quantize_op in the graph.
"""
if quant_type == 'abs_max':
return self._insert_quant_abs_max_op(graph, var_node, quant_bits)
elif quant_type == 'range_abs_max':
return self._insert_quant_range_abs_max_op(graph, var_node,
quant_bits)
def _insert_quant_abs_max_op(self, graph, var_node, quant_bits):
"""
Insert fake_quantize_abs_max op in the graph.
"""
assert var_node.is_var(), '{} is not a var'.format(var_node.name())
quant_var_node = graph.create_var_node(
name=self._quantized_var_name(var_node.name()),
var_type=var_node.var().type(),
shape=var_node.var().shape(),
var_dtype=var_node.var().dtype())
scale_var_node = graph.create_var_node(
name=self._quantized_scale_name(var_node.name()),
var_type=var_node.var().type(),
shape=var_node.var().shape(),
var_dtype=var_node.var().dtype())
quant_op_node = graph.create_op_node(
op_type='fake_quantize_abs_max',
attrs={'bit_length': quant_bits},
inputs={'X': var_node},
outputs={'Out': quant_var_node,
'OutScale': scale_var_node})
graph.link_to(var_node, quant_op_node)
graph.link_to(quant_op_node, quant_var_node)
graph.link_to(quant_op_node, scale_var_node)
return quant_var_node, scale_var_node
def _insert_quant_range_abs_max_op(self, graph, var_node, quant_bits):
"""
Insert fake_quantize_range_abs_max on the graph.
"""
assert var_node.is_var(), '{} is not a var'.format(var_node.name())
quant_var_node = graph.create_var_node(
name=self._quantized_var_name(var_node.name()),
var_type=var_node.var().type(),
shape=var_node.var().shape(),
var_dtype=var_node.var().dtype())
scale_in_node = graph.create_param_node(
name=self._quantized_scale_name(var_node.name()),
var_type=core.VarDesc.VarType.LOD_TENSOR,
shape=[1],
var_dtype=var_node.var().dtype())
self._need_initialized[scale_in_node.var()] = Constant(value=0.001)
scale_out_node = graph.create_var_node_from_desc(scale_in_node.var())
inputs = {'X': var_node, 'InScale': scale_in_node}
outputs = {'Out': quant_var_node, 'OutScale': scale_out_node}
if not self._is_test:
# The name of scales_var_node maybe 'scales_0', 'scales_1', etc.
scales_node = graph.create_param_node(
name=unique_name.generate('scales'),
var_type=core.VarDesc.VarType.LOD_TENSOR,
shape=[self._window_size],
var_dtype=var_node.var().dtype())
self._need_initialized[scales_node.var()] = Constant(value=0)
inputs['Iter'] = self._global_step
outputs['OutScales'] = scales_node
attrs = {
'window_size': self._window_size,
'bit_length': quant_bits,
'is_test': self._is_test
}
quant_op_node = graph.create_op_node(
op_type='fake_quantize_range_abs_max',
attrs=attrs,
inputs=inputs,
outputs=outputs)
graph.link_to(var_node, quant_op_node)
graph.link_to(scale_in_node, quant_op_node)
graph.link_to(quant_op_node, quant_var_node)
graph.link_to(quant_op_node, scale_out_node)
if not self._is_test:
graph.link_to(self._global_step, quant_op_node)
graph.link_to(quant_op_node, scales_node)
return quant_var_node, scale_out_node
def _insert_dequant_op(self, graph, var_node, scale_var_node, quant_bits):
"""
Insert fake_dequantize_op in the graph.
"""
assert var_node.is_var(), '{} is not a var'.format(var_node.name())
dequant_var_node = graph.create_var_node(
name=self._dequantized_var_name(var_node.name()),
var_type=var_node.var().type(),
shape=var_node.var().shape(),
var_dtype=var_node.var().dtype())
max_range = (1 << (quant_bits - 1)) - 1
dequant_op_node = graph.create_op_node(
op_type='fake_dequantize_max_abs',
attrs={'max_range': float(max_range)},
inputs={'X': var_node,
'Scale': scale_var_node},
outputs={'Out': dequant_var_node})
graph.link_to(var_node, dequant_op_node)
graph.link_to(scale_var_node, dequant_op_node)
graph.link_to(dequant_op_node, dequant_var_node)
return dequant_var_node
def _quantized_var_name(self, var_name):
"""
Return quantized variable name for the input `var_name`.
"""
return "%s.quantized" % (var_name)
def _dequantized_var_name(self, var_name):
"""
Return dequantized variable name for the input `var_name`.
"""
return "%s.dequantized" % (var_name)
def _quantized_scale_name(self, var_name):
"""
Return the scale name of quantized variable for the input `var_name`.
"""
return "%s.scale" % (var_name)
python/paddle/fluid/contrib/slim/unitest/test_quantization_pass.py 0 → 100644
浏览文件 @ ef788603
# copyright (c) 2018 paddlepaddle authors. all rights reserved.
#
# licensed under the apache license, version 2.0 (the "license");
# you may not use this file except in compliance with the license.
# you may obtain a copy of the license at
#
# http://www.apache.org/licenses/license-2.0
#
# unless required by applicable law or agreed to in writing, software
# distributed under the license is distributed on an "as is" basis,
# without warranties or conditions of any kind, either express or implied.
# see the license for the specific language governing permissions and
# limitations under the license.
import unittest
import random
import numpy as np
import paddle.fluid as fluid
import six
from paddle.fluid.framework import Program
from paddle.fluid.framework import IrGraph
from paddle.fluid.contrib.slim.quantization import QuantizationTransformPass
from paddle.fluid import core
def linear_fc(num):
data = fluid.layers.data(name='image', shape=[1, 32, 32], dtype='float32')
label = fluid.layers.data(name='label', shape=[1], dtype='int64')
hidden = data
for _ in six.moves.xrange(num):
hidden = fluid.layers.fc(hidden, size=128, act='relu')
loss = fluid.layers.cross_entropy(input=hidden, label=label)
loss = fluid.layers.mean(loss)
return loss
def residual_block(num):
def conv_bn_layer(input,
ch_out,
filter_size,
stride,
padding,
act='relu',
bias_attr=False):
tmp = fluid.layers.conv2d(
input=input,
filter_size=filter_size,
num_filters=ch_out,
stride=stride,
padding=padding,
act=None,
bias_attr=bias_attr)
return fluid.layers.batch_norm(input=tmp, act=act)
data = fluid.layers.data(name='image', shape=[1, 32, 32], dtype='float32')
label = fluid.layers.data(name='label', shape=[1], dtype='int64')
hidden = data
for _ in six.moves.xrange(num):
conv = conv_bn_layer(hidden, 16, 3, 1, 1, act=None, bias_attr=True)
short = conv_bn_layer(hidden, 16, 1, 1, 0, act=None)
hidden = fluid.layers.elementwise_add(x=conv, y=short, act='relu')
fc = fluid.layers.fc(input=hidden, size=10)
loss = fluid.layers.cross_entropy(input=fc, label=label)
loss = fluid.layers.mean(loss)
return loss
class TestQuantizationTransformPass(unittest.TestCase):
def setUp(self):
self.quantizable_op_and_inputs = {
'conv2d': ['Input', 'Filter'],
'depthwise_conv2d': ['Input', 'Filter'],
'mul': ['X', 'Y']
}
self.quantizable_grad_op_inputs = {
'conv2d_grad': ['Input', 'Filter'],
'depthwise_conv2d_grad': ['Input', 'Filter'],
'mul_grad': ['X', 'Y']
}
def check_program(self, transform_pass, program):
quantized_ops = set()
for block in program.blocks:
for op in block.ops:
# check forward
if op.type in self.quantizable_op_and_inputs:
for arg_name in op.input_arg_names:
self.assertTrue(
arg_name.endswith('.quantized.dequantized'))
quantized_ops.add(arg_name)
for op in block.ops:
# check backward
if op.type in self.quantizable_grad_op_inputs:
for pname in self.quantizable_grad_op_inputs[op.type]:
arg_name = op.input(pname)[0]
self.assertTrue(
arg_name.endswith('.quantized.dequantized'))
self.assertTrue(arg_name in quantized_ops)
def linear_fc_quant(self, quant_type):
main = fluid.Program()
startup = fluid.Program()
with fluid.program_guard(main, startup):
loss = linear_fc(3)
opt = fluid.optimizer.Adam(learning_rate=0.001)
opt.minimize(loss)
exe = fluid.Executor(fluid.CPUPlace())
graph = IrGraph(core.Graph(main.desc), for_test=False)
transform_pass = QuantizationTransformPass(
scope=fluid.global_scope(),
program_exe=exe,
activation_quantize_type=quant_type)
transform_pass.apply(graph)
marked_nodes = set()
for op in graph.all_ops():
if op.name().find('quantize') > -1:
marked_nodes.add(op)
graph.draw('.', 'quantize_fc_' + quant_type, marked_nodes)
program = graph.to_program()
self.check_program(transform_pass, program)
val_graph = IrGraph(core.Graph(program.desc), for_test=False)
val_marked_nodes = set()
for op in val_graph.all_ops():
if op.name().find('quantize') > -1:
val_marked_nodes.add(op)
val_graph.draw('.', 'val_fc_' + quant_type, val_marked_nodes)
def test_linear_fc_quant_abs_max(self):
self.act_quant_op_type = 'fake_quantize_abs_max'
self.linear_fc_quant('abs_max')
def test_linear_fc_quant_range_abs_max(self):
self.act_quant_op_type = 'fake_quantize_range_abs_max'
self.linear_fc_quant('range_abs_max')
def residual_block_quant(self, quant_type):
main = fluid.Program()
startup = fluid.Program()
with fluid.program_guard(main, startup):
loss = residual_block(2)
opt = fluid.optimizer.Adam(learning_rate=0.001)
opt.minimize(loss)
exe = fluid.Executor(fluid.CPUPlace())
graph = IrGraph(core.Graph(main.desc), for_test=False)
transform_pass = QuantizationTransformPass(
scope=fluid.global_scope(),
program_exe=exe,
activation_quantize_type=quant_type)
transform_pass.apply(graph)
marked_nodes = set()
for op in graph.all_ops():
if op.name().find('quantize') > -1:
marked_nodes.add(op)
graph.draw('.', 'quantize_residual_' + quant_type, marked_nodes)
program = graph.to_program()
self.check_program(transform_pass, program)
val_graph = IrGraph(core.Graph(program.desc), for_test=False)
val_marked_nodes = set()
for op in val_graph.all_ops():
if op.name().find('quantize') > -1:
val_marked_nodes.add(op)
val_graph.draw('.', 'val_residual_' + quant_type, val_marked_nodes)
def test_residual_block_abs_max(self):
self.act_quant_op_type = 'fake_quantize_abs_max'
self.residual_block_quant('abs_max')
def test_residual_block_range_abs_max(self):
self.act_quant_op_type = 'fake_quantize_range_abs_max'
self.residual_block_quant('range_abs_max')
if __name__ == '__main__':
unittest.main()
python/paddle/fluid/executor.py
浏览文件 @ ef788603
......@@ -305,7 +305,9 @@ class Executor(object):
def __init__(self, place):
self.place = place
self.program_caches = dict()
self.executor = None
p = core.Place()
p.set_place(self.place)
self._default_executor = core.Executor(p)
self._closed = False
def _get_program_cache(self, program_cache_key):
......@@ -397,12 +399,13 @@ class Executor(object):
>>> ...
>>> exe.close()
"""
if not self._closed and self.executor:
self.executor.close()
if not self._closed:
self._default_executor.close()
self._closed = True
def _run_parallel(self, program, scope, feed, fetch_list, fetch_var_name,
return_numpy):
exe = program._executor
if isinstance(feed, dict):
feed_tensor_dict = dict()
for feed_name in feed:
......@@ -414,8 +417,7 @@ class Executor(object):
feed_tensor.set(feed[feed_name], core.CPUPlace())
feed_tensor_dict[feed_name] = feed_tensor
self.executor.feed_and_split_tensor_into_local_scopes(
feed_tensor_dict)
exe.feed_and_split_tensor_into_local_scopes(feed_tensor_dict)
elif isinstance(feed, list) or isinstance(feed, tuple):
if len(feed) != len(program._places):
raise ValueError(
......@@ -436,10 +438,10 @@ class Executor(object):
tensor = tmp
res_dict[feed_name] = tensor
res.append(res_dict)
self.executor.feed_tensors_into_local_scopes(res)
exe.feed_tensors_into_local_scopes(res)
fetch_var_names = list(map(_to_name_str, fetch_list))
self.executor.run(fetch_var_names, fetch_var_name)
exe.run(fetch_var_names, fetch_var_name)
arr = scope.find_var(fetch_var_name).get_lod_tensor_array()
if return_numpy:
......@@ -511,12 +513,9 @@ class Executor(object):
compiled = isinstance(program, compiler.CompiledProgram)
# For backward compatibility, run directly.
if not compiled:
if not self.executor:
p = core.Place()
p.set_place(self.place)
self.executor = core.Executor(p)
return self._run(
program,
self._default_executor,
feed=feed,
fetch_list=fetch_list,
feed_var_name=feed_var_name,
......@@ -526,7 +525,6 @@ class Executor(object):
use_program_cache=use_program_cache)
program._compile(scope, self.place)
self.executor = program._executor
if program._is_data_parallel:
return self._run_parallel(
program,
......@@ -536,12 +534,13 @@ class Executor(object):
fetch_var_name=fetch_var_name,
return_numpy=return_numpy)
elif program._is_inference:
return self._run_inference(program, feed)
return self._run_inference(program._executor, feed)
else:
# TODO(panyx0718): Can compile program to optimize executor
# performance.
return self._run(
program._program,
self._default_executor,
feed=feed,
fetch_list=fetch_list,
feed_var_name=feed_var_name,
......@@ -550,8 +549,8 @@ class Executor(object):
return_numpy=return_numpy,
use_program_cache=use_program_cache)
def _run(self, program, feed, fetch_list, feed_var_name, fetch_var_name,
scope, return_numpy, use_program_cache):
def _run(self, program, exe, feed, fetch_list, feed_var_name,
fetch_var_name, scope, return_numpy, use_program_cache):
if feed is None:
feed = {}
......@@ -589,11 +588,11 @@ class Executor(object):
fetch_var_name=fetch_var_name)
self._feed_data(program, feed, feed_var_name, scope)
self.executor.run(program.desc, scope, 0, True, True)
exe.run(program.desc, scope, 0, True, True)
outs = self._fetch_data(fetch_list, fetch_var_name, scope)
if return_numpy:
outs = as_numpy(outs)
return outs
def _run_inference(self, program, feed):
return self.executor.run(feed)
def _run_inference(self, exe, feed):
return exe.run(feed)
python/paddle/fluid/framework.py
浏览文件 @ ef788603
......@@ -23,6 +23,7 @@ import traceback
import six
import numpy as np
import subprocess
from .. import compat as cpt
from .proto import framework_pb2
......@@ -1512,6 +1513,154 @@ class Block(object):
return ret_var
class IrGraph(object):
"""
IrGraph uses core.Graph as the delegation to accomplish the manipulation.
"""
def __init__(self, graph, for_test=False):
"""
Construct the IrGraph using core.Graph.
Args:
graph(core.Graph): C++ Graph.
for_test(bool): True for the test graph and false for the train graph.
"""
assert isinstance(
graph, core.Graph), 'graph must be the instance of core.Graph.'
self.graph = graph
self._for_test = for_test
def is_test(self):
return self._for_test
def all_parameters(self):
param_nodes = set()
for node in self.graph.nodes():
if node.is_var() and node.var() is not None and node.var(
).persistable():
param_nodes.add(node)
return param_nodes
def all_vars(self):
return {node for node in self.graph.nodes() if node.is_var()}
def all_ops(self):
return {node for node in self.graph.nodes() if node.is_op()}
def create_param_node(self, name, var_type, shape, var_dtype):
var_desc = core.VarDesc(name)
var_desc.set_type(var_type)
var_desc.set_shape(shape)
var_desc.set_dtype(var_dtype)
var_desc.set_persistable(True)
return self.graph.create_var_node(var_desc)
def create_var_node(self, name, var_type, shape, var_dtype):
var_desc = core.VarDesc(name)
var_desc.set_type(var_type)
var_desc.set_shape(shape)
var_desc.set_dtype(var_dtype)
return self.graph.create_var_node(var_desc)
def create_var_node_from_desc(self, var_desc):
return self.graph.create_var_node(var_desc)
def create_op_node(self, op_type, attrs, inputs, outputs):
op_desc = core.OpDesc()
op_desc.set_type(op_type)
for attr, value in attrs.iteritems():
self._update_desc_attr(op_desc, attr, value)
for input_name, var_nodes in inputs.iteritems():
if not isinstance(var_nodes, list):
var_nodes = [var_nodes]
op_desc.set_input(input_name,
[var_node.name() for var_node in var_nodes])
for output_name, var_nodes in outputs.iteritems():
if not isinstance(var_nodes, list):
var_nodes = [var_nodes]
op_desc.set_output(output_name,
[var_node.name() for var_node in var_nodes])
return self.graph.create_op_node(op_desc)
def create_op_node_from_desc(self, op_desc):
return self.graph.create_op_node(op_desc)
def update_input_link(self, old_input_node, new_input_node, op_node):
assert old_input_node in self.graph.nodes() and new_input_node in self.graph.nodes() and \
op_node in self.graph.nodes(), 'Th three arguments must be in the graph nodes.'
old_input_node.outputs_remove(op_node)
op_node.inputs_remove(old_input_node)
new_input_node.outputs_append(op_node)
op_node.inputs_append(new_input_node)
op_node.op()._rename_input(old_input_node.name(), new_input_node.name())
def link_to(self, node_in, node_out):
assert node_in in self.graph.nodes() and node_out in self.graph.nodes(), \
'Th two arguments must be in the graph nodes.'
node_in.outputs_append(node_out)
node_out.inputs_append(node_in)
def safe_remove_nodes(self, remove_nodes):
if not isinstance(remove_nodes, set):
remove_nodes = set(remove_nodes)
core.graph_safe_remove_nodes(self.graph, remove_nodes)
def draw(self, save_path, name, marked_nodes=None):
def _convert_to_pdf(dot_file_path):
pdf_save_path = os.path.splitext(dot_file_path)[0] + '.pdf'
exited_code = subprocess.call('dot -Tpdf ' + dot_file_path \
+ ' -o ' + pdf_save_path, shell=True)
if exited_code != 0:
print('The dot command is needed for creating pdf files.')
print('The {} is saved as the dot filetype.'.format(
dot_file_path))
remove_ctr_vars = set()
ops_num = 0
for node in self.graph.nodes():
if node.is_ctrl_var():
remove_ctr_vars.add(node)
elif node.is_op():
ops_num += 1
print('Total ops num = {}.'.format(ops_num))
self.safe_remove_nodes(remove_ctr_vars)
if marked_nodes is not None:
if not isinstance(marked_nodes, set):
marked_nodes = set(marked_nodes)
marked_nodes = marked_nodes - remove_ctr_vars
if self.graph.has('__graphviz__marked_node__'):
self.graph.erase('__graphviz__marked_node__')
self.graph.set('__graphviz__marked_node__', marked_nodes)
viz_dot_path = os.path.join(save_path, name) + '.dot'
viz_pass = core.get_pass('graph_viz_pass')
viz_pass.set('graph_viz_path', viz_dot_path)
viz_pass.apply(self.graph)
_convert_to_pdf(viz_dot_path)
def to_program(self):
convert_pass = core.get_pass('graph_to_program_pass')
convert_pass.set('program', Program().desc)
convert_pass.apply(self.graph)
desc = convert_pass.get_program('program')
program = Program._construct_from_desc(desc)
return program
def _update_desc_attr(self, desc, name, val):
"""
Update the value of desc's attribute by attribute's name.
"""
if isinstance(val, Block):
desc.set_block_attr(name, val.desc)
elif isinstance(val, list) and val and all(
isinstance(v, Block) for v in val):
desc.set_blocks_attr(name, [v.desc for v in val])
elif isinstance(val, core.BlockDesc) or \
isinstance(val, core.ProgramDesc):
desc.set_serialized_attr(name, val.serialize_to_string())
else:
desc._set_attr(name, val)
class Program(object):
"""
Python Program. Beneath it is a ProgramDesc, which is used for
......@@ -1936,6 +2085,23 @@ class Program(object):
p._sync_with_cpp()
return p
@staticmethod
def _construct_from_desc(desc):
"""
Construct a program from program desc.
Args:
desc(core.ProgramDesc): The program desc for constructing.
Returns:
Program: A program.
"""
p = Program()
p.desc = desc
p.blocks = [Block(p, i) for i in six.moves.range(p.desc.num_blocks())]
p._sync_with_cpp()
return p
@property
def random_seed(self):
"""
......
python/paddle/fluid/tests/book/test_word2vec.py
浏览文件 @ ef788603
......@@ -220,9 +220,7 @@ def infer(use_cuda, save_dirname=None):
np_data = np.array(results[0])
infer_out = infer_outputs[0].data.float_data()
for a, b in zip(np_data[0], infer_out):
g_a = float("{:.6g}".format(a))
g_b = float("{:.6g}".format(b))
assert g_a == g_b
assert np.isclose(a, b), "a: {}, b: {}".format(a, b)
def main(use_cuda, is_sparse, is_parallel):
......
python/paddle/fluid/tests/unittests/test_dist_base.py
浏览文件 @ ef788603
......@@ -123,7 +123,7 @@ class TestDistRunnerBase(object):
pass_builder = build_stra._finalize_strategy_and_create_passes()
mypass = pass_builder.insert_pass(
len(pass_builder.all_passes()) - 3, "multi_batch_merge_pass")
mypass.set_int("num_repeats", args.batch_merge_repeat)
mypass.set("num_repeats", args.batch_merge_repeat)
if args.update_method == "nccl2":
build_stra.num_trainers = len(args.endpoints.split(","))
......
python/paddle/fluid/tests/unittests/test_pass_builder.py
浏览文件 @ ef788603
......@@ -111,7 +111,7 @@ class TestPassBuilder(unittest.TestCase):
pass_builder.remove_pass(len(pass_builder.all_passes()) - 1)
self.assertEqual(origin_len + 1, len(pass_builder.all_passes()))
viz_pass.set_str("graph_viz_path", "/tmp/test_viz_pass")
viz_pass.set("graph_viz_path", "/tmp/test_viz_pass")
self.check_network_convergence(
use_cuda=core.is_compiled_with_cuda(),
......
python/setup.py.in
浏览文件 @ ef788603
......@@ -113,6 +113,7 @@ packages=['paddle',
'paddle.fluid.contrib.slim.core',
'paddle.fluid.contrib.slim.graph',
'paddle.fluid.contrib.slim.prune',
'paddle.fluid.contrib.slim.quantization',
'paddle.fluid.contrib.utils',
'paddle.fluid.transpiler',
'paddle.fluid.transpiler.details']
......
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