Skip to content

P
PaddleDetection

PaddlePaddle / PaddleDetection
大约 1 年 前同步成功

通知 695
Star 11112
Fork 2696
P
PaddleDetection
提交 0016b6a2 编写于 作者: L liuwei1031
浏览文件
操作

Merge branch 'develop' of https://github.com/PaddlePaddle/Paddle into develop

上级 ab5442d9 e043ea96
展开全部 隐藏空白更改
内联 并排
Showing with 8537 addition and 3811 deletion
CMakeLists.txt
浏览文件 @ 0016b6a2
......@@ -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 @ ab5442d9
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 @ 0016b6a2
......@@ -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 @ 0016b6a2
......@@ -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 @ ab5442d9
# 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 @ ab5442d9
# 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 @ ab5442d9
# 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 @ ab5442d9
# 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 @ 0016b6a2
......@@ -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 @ 0016b6a2
......@@ -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 @ 0016b6a2
......@@ -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 ${GFLAGS_LIBRARIES} DESTINATION third_party/gflags/lib)
ENDIF()
ENDIF()
cmake/external/glog.cmake
浏览文件 @ 0016b6a2
......@@ -26,14 +26,8 @@ ENDIF(WIN32)
INCLUDE_DIRECTORIES(${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 @ 0016b6a2
......@@ -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 @ 0016b6a2
......@@ -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 @ 0016b6a2
......@@ -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 @ 0016b6a2
......@@ -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 @ 0016b6a2
......@@ -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 @ ab5442d9
# 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 @ 0016b6a2
......@@ -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 @ 0016b6a2
......@@ -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 @ 0016b6a2
......@@ -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 @ 0016b6a2
......@@ -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 @ 0016b6a2
......@@ -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 @ 0016b6a2
......@@ -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 @ ab5442d9
# 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 @ 0016b6a2
......@@ -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 ${THIRD_PARTY_PATH}/warpctc)
......
cmake/external/xxhash.cmake
浏览文件 @ 0016b6a2
......@@ -73,12 +73,3 @@ include_directories(${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 @ 0016b6a2
......@@ -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 ${ZLIB_LIBRARIES} DESTINATION third_party/zlib/lib)
ENDIF()
ENDIF()
cmake/flags.cmake
浏览文件 @ 0016b6a2
......@@ -156,10 +156,8 @@ set(GPU_COMMON_FLAGS
endif(NOT WIN32)
if (APPLE)
if(NOT CMAKE_CROSSCOMPILING)
# On Mac OS X build fat binaries with x86_64 architectures by default.
set (CMAKE_OSX_ARCHITECTURES "x86_64" CACHE STRING "Build architectures for OSX" FORCE)
endif()
# On Mac OS X build fat binaries with x86_64 architectures by default.
set (CMAKE_OSX_ARCHITECTURES "x86_64" CACHE STRING "Build architectures for OSX" FORCE)
# On Mac OS X register class specifier is deprecated and will cause warning error on latest clang 10.0
set (COMMON_FLAGS -Wno-deprecated-register)
endif(APPLE)
......
cmake/generic.cmake
浏览文件 @ 0016b6a2
......@@ -90,11 +90,11 @@
# including binary directory for generated headers.
include_directories(${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
......@@ -388,6 +388,7 @@ function(cc_test TARGET_NAME)
endif()
set_property(TEST ${TARGET_NAME} PROPERTY ENVIRONMENT FLAGS_cpu_deterministic=true)
set_property(TEST ${TARGET_NAME} PROPERTY ENVIRONMENT FLAGS_init_allocated_mem=true)
set_property(TEST ${TARGET_NAME} PROPERTY ENVIRONMENT FLAGS_limit_of_tmp_allocation=4294967296) # 4G
set_property(TEST ${TARGET_NAME} PROPERTY ENVIRONMENT FLAGS_cudnn_deterministic=true)
# No unit test should exceed 10 minutes.
set_tests_properties(${TARGET_NAME} PROPERTIES TIMEOUT 600)
......@@ -460,6 +461,7 @@ function(nv_test TARGET_NAME)
endif()
set_property(TEST ${TARGET_NAME} PROPERTY ENVIRONMENT FLAGS_cpu_deterministic=true)
set_property(TEST ${TARGET_NAME} PROPERTY ENVIRONMENT FLAGS_init_allocated_mem=true)
set_property(TEST ${TARGET_NAME} PROPERTY ENVIRONMENT FLAGS_limit_of_tmp_allocation=4294967296) # 4G
set_property(TEST ${TARGET_NAME} PROPERTY ENVIRONMENT FLAGS_cudnn_deterministic=true)
endif()
endfunction(nv_test)
......@@ -655,12 +657,6 @@ function(paddle_protobuf_generate_cpp SRCS HDRS)
set(${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 +673,7 @@ 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 ${FIL}"
VERBATIM )
......@@ -714,9 +710,10 @@ function(py_test TARGET_NAME)
set(oneValueArgs "")
set(multiValueArgs SRCS DEPS ARGS ENVS)
cmake_parse_arguments(py_test "${options}" "${oneValueArgs}" "${multiValueArgs}" ${ARGN})
add_test(NAME ${TARGET_NAME}
COMMAND ${CMAKE_COMMAND} -E env FLAGS_init_allocated_mem=true FLAGS_cudnn_deterministic=true
FLAGS_cpu_deterministic=true
FLAGS_cpu_deterministic=true FLAGS_limit_of_tmp_allocation=4294967296 # 4G
PYTHONPATH=${PADDLE_BINARY_DIR}/python ${py_test_ENVS}
${PYTHON_EXECUTABLE} -u ${py_test_SRCS} ${py_test_ARGS}
WORKING_DIRECTORY ${CMAKE_CURRENT_BINARY_DIR})
......
cmake/inference_lib.cmake
浏览文件 @ 0016b6a2
......@@ -149,25 +149,23 @@ if (WITH_NGRAPH)
)
endif ()
if (NOT MOBILE_INFERENCE AND NOT RPI)
set(dst_dir "${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 @ 0016b6a2
......@@ -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 @ 0016b6a2
......@@ -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/API.spec
浏览文件 @ 0016b6a2
......@@ -67,6 +67,7 @@ paddle.fluid.initializer.BilinearInitializer.__init__ ArgSpec(args=['self'], var
paddle.fluid.initializer.MSRAInitializer.__init__ ArgSpec(args=['self', 'uniform', 'fan_in', 'seed'], varargs=None, keywords=None, defaults=(True, None, 0))
paddle.fluid.initializer.force_init_on_cpu ArgSpec(args=[], varargs=None, keywords=None, defaults=None)
paddle.fluid.initializer.init_on_cpu ArgSpec(args=[], varargs='args', keywords='kwds', defaults=None)
paddle.fluid.initializer.NumpyArrayInitializer.__init__ ArgSpec(args=['self', 'value'], varargs=None, keywords=None, defaults=None)
paddle.fluid.layers.fc ArgSpec(args=['input', 'size', 'num_flatten_dims', 'param_attr', 'bias_attr', 'act', 'is_test', 'name'], varargs=None, keywords=None, defaults=(1, None, None, None, False, None))
paddle.fluid.layers.embedding ArgSpec(args=['input', 'size', 'is_sparse', 'is_distributed', 'padding_idx', 'param_attr', 'dtype'], varargs=None, keywords=None, defaults=(False, False, None, None, 'float32'))
paddle.fluid.layers.dynamic_lstm ArgSpec(args=['input', 'size', 'h_0', 'c_0', 'param_attr', 'bias_attr', 'use_peepholes', 'is_reverse', 'gate_activation', 'cell_activation', 'candidate_activation', 'dtype', 'name'], varargs=None, keywords=None, defaults=(None, None, None, None, True, False, 'sigmoid', 'tanh', 'tanh', 'float32', None))
......@@ -121,7 +122,7 @@ paddle.fluid.layers.transpose ArgSpec(args=['x', 'perm', 'name'], varargs=None,
paddle.fluid.layers.im2sequence ArgSpec(args=['input', 'filter_size', 'stride', 'padding', 'input_image_size', 'out_stride', 'name'], varargs=None, keywords=None, defaults=(1, 1, 0, None, 1, None))
paddle.fluid.layers.nce ArgSpec(args=['input', 'label', 'num_total_classes', 'sample_weight', 'param_attr', 'bias_attr', 'num_neg_samples', 'name', 'sampler', 'custom_dist', 'seed', 'is_sparse'], varargs=None, keywords=None, defaults=(None, None, None, None, None, 'uniform', None, 0, False))
paddle.fluid.layers.hsigmoid ArgSpec(args=['input', 'label', 'num_classes', 'param_attr', 'bias_attr', 'name', 'path_table', 'path_code', 'is_custom', 'is_sparse'], varargs=None, keywords=None, defaults=(None, None, None, None, None, False, False))
paddle.fluid.layers.beam_search ArgSpec(args=['pre_ids', 'pre_scores', 'ids', 'scores', 'beam_size', 'end_id', 'level', 'name'], varargs=None, keywords=None, defaults=(0, None))
paddle.fluid.layers.beam_search ArgSpec(args=['pre_ids', 'pre_scores', 'ids', 'scores', 'beam_size', 'end_id', 'level', 'is_accumulated', 'name'], varargs=None, keywords=None, defaults=(0, True, None))
paddle.fluid.layers.row_conv ArgSpec(args=['input', 'future_context_size', 'param_attr', 'act'], varargs=None, keywords=None, defaults=(None, None))
paddle.fluid.layers.multiplex ArgSpec(args=['inputs', 'index'], varargs=None, keywords=None, defaults=None)
paddle.fluid.layers.layer_norm ArgSpec(args=['input', 'scale', 'shift', 'begin_norm_axis', 'epsilon', 'param_attr', 'bias_attr', 'act', 'name'], varargs=None, keywords=None, defaults=(True, True, 1, 1e-05, None, None, None, None))
......@@ -197,7 +198,7 @@ paddle.fluid.layers.clip ArgSpec(args=['x', 'min', 'max', 'name'], varargs=None,
paddle.fluid.layers.clip_by_norm ArgSpec(args=['x', 'max_norm', 'name'], varargs=None, keywords=None, defaults=(None,))
paddle.fluid.layers.mean ArgSpec(args=['x', 'name'], varargs=None, keywords=None, defaults=(None,))
paddle.fluid.layers.mul ArgSpec(args=['x', 'y', 'x_num_col_dims', 'y_num_col_dims', 'name'], varargs=None, keywords=None, defaults=(1, 1, None))
paddle.fluid.layers.sigmoid_cross_entropy_with_logits ArgSpec(args=['x', 'label', 'ignore_index', 'name'], varargs=None, keywords=None, defaults=(-100, None))
paddle.fluid.layers.sigmoid_cross_entropy_with_logits ArgSpec(args=['x', 'label', 'ignore_index', 'name', 'normalize'], varargs=None, keywords=None, defaults=(-100, None, False))
paddle.fluid.layers.maxout ArgSpec(args=['x', 'groups', 'name'], varargs=None, keywords=None, defaults=(None,))
paddle.fluid.layers.space_to_depth ArgSpec(args=['x', 'blocksize', 'name'], varargs=None, keywords=None, defaults=(None,))
paddle.fluid.layers.affine_grid ArgSpec(args=['theta', 'out_shape', 'name'], varargs=None, keywords=None, defaults=(None,))
......@@ -318,6 +319,7 @@ paddle.fluid.layers.anchor_generator ArgSpec(args=['input', 'anchor_sizes', 'asp
paddle.fluid.layers.roi_perspective_transform ArgSpec(args=['input', 'rois', 'transformed_height', 'transformed_width', 'spatial_scale'], varargs=None, keywords=None, defaults=(1.0,))
paddle.fluid.layers.generate_proposal_labels ArgSpec(args=['rpn_rois', 'gt_classes', 'is_crowd', 'gt_boxes', 'im_info', 'batch_size_per_im', 'fg_fraction', 'fg_thresh', 'bg_thresh_hi', 'bg_thresh_lo', 'bbox_reg_weights', 'class_nums', 'use_random'], varargs=None, keywords=None, defaults=(256, 0.25, 0.25, 0.5, 0.0, [0.1, 0.1, 0.2, 0.2], None, True))
paddle.fluid.layers.generate_proposals ArgSpec(args=['scores', 'bbox_deltas', 'im_info', 'anchors', 'variances', 'pre_nms_top_n', 'post_nms_top_n', 'nms_thresh', 'min_size', 'eta', 'name'], varargs=None, keywords=None, defaults=(6000, 1000, 0.5, 0.1, 1.0, None))
paddle.fluid.layers.generate_mask_labels ArgSpec(args=['im_info', 'gt_classes', 'is_crowd', 'gt_segms', 'rois', 'labels_int32', 'num_classes', 'resolution'], varargs=None, keywords=None, defaults=None)
paddle.fluid.layers.iou_similarity ArgSpec(args=['x', 'y', 'name'], varargs=None, keywords=None, defaults=(None,))
paddle.fluid.layers.box_coder ArgSpec(args=['prior_box', 'prior_box_var', 'target_box', 'code_type', 'box_normalized', 'name'], varargs=None, keywords=None, defaults=('encode_center_size', True, None))
paddle.fluid.layers.polygon_box_transform ArgSpec(args=['input', 'name'], varargs=None, keywords=None, defaults=(None,))
......@@ -357,6 +359,7 @@ paddle.fluid.contrib.QuantizeTranspiler.__init__ ArgSpec(args=['self', 'weight_b
paddle.fluid.contrib.QuantizeTranspiler.convert_to_int8 ArgSpec(args=['self', 'program', 'place', 'scope'], varargs=None, keywords=None, defaults=(None,))
paddle.fluid.contrib.QuantizeTranspiler.freeze_program ArgSpec(args=['self', 'program', 'place', 'fuse_bn', 'scope'], varargs=None, keywords=None, defaults=(False, None))
paddle.fluid.contrib.QuantizeTranspiler.training_transpile ArgSpec(args=['self', 'program', 'startup_program'], varargs=None, keywords=None, defaults=(None, None))
paddle.fluid.contrib.reader.ctr_reader.ctr_reader ArgSpec(args=['feed_dict', 'file_type', 'file_format', 'dense_slot_index', 'sparse_slot_index', 'capacity', 'thread_num', 'batch_size', 'file_list', 'slots', 'name'], varargs=None, keywords=None, defaults=(None,))
paddle.fluid.contrib.build_compressor ArgSpec(args=['place', 'data_reader', 'data_feeder', 'scope', 'metrics', 'epoch', 'config'], varargs=None, keywords=None, defaults=(None, None, None, None, None, None, None))
paddle.fluid.contrib.CompressPass.__init__ ArgSpec(args=['self', 'place', 'data_reader', 'data_feeder', 'scope', 'metrics', 'epoch', 'program_exe'], varargs=None, keywords=None, defaults=(None, None, None, None, None, None, None))
paddle.fluid.contrib.CompressPass.add_strategy ArgSpec(args=['self', 'strategy'], varargs=None, keywords=None, defaults=None)
......
paddle/fluid/framework/CMakeLists.txt
浏览文件 @ 0016b6a2
......@@ -131,8 +131,6 @@ cc_library(proto_desc SRCS var_desc.cc op_desc.cc block_desc.cc program_desc.cc
if(WITH_NGRAPH)
cc_library(ngraph_bridge SRCS ngraph_bridge.cc DEPS operator framework_proto ngraph)
cc_library(ngraph_operator SRCS ngraph_operator.cc DEPS ngraph_bridge operator op_info device_context tensor scope glog
shape_inference data_transform lod_tensor profiler)
endif(WITH_NGRAPH)
cc_library(op_registry SRCS op_registry.cc DEPS op_proto_maker op_info operator glog proto_desc)
......@@ -171,13 +169,12 @@ if(WITH_DISTRIBUTE)
set(DISTRIBUTE_COMPILE_FLAGS "-Wno-non-virtual-dtor -Wno-error=non-virtual-dtor -Wno-error=delete-non-virtual-dtor")
set_source_files_properties(executor.cc PROPERTIES COMPILE_FLAGS ${DISTRIBUTE_COMPILE_FLAGS})
else()
if(WITH_NGRAPH)
cc_library(executor SRCS executor.cc DEPS op_registry device_context scope framework_proto glog lod_rank_table feed_fetch_method graph_to_program_pass ngraph_operator variable_helper)
else(WITH_NGRAPH)
if (WITH_NGRAPH)
cc_library(executor SRCS executor.cc DEPS op_registry device_context scope framework_proto glog lod_rank_table feed_fetch_method graph_to_program_pass variable_helper ngraph_engine)
else ()
cc_library(executor SRCS executor.cc DEPS op_registry device_context scope framework_proto glog lod_rank_table feed_fetch_method graph_to_program_pass variable_helper)
endif(WITH_NGRAPH)
endif()
cc_test(test_naive_executor SRCS naive_executor_test.cc DEPS naive_executor elementwise_add_op)
endif()
......
paddle/fluid/framework/details/build_strategy.cc
浏览文件 @ 0016b6a2
......@@ -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/details/build_strategy.h
浏览文件 @ 0016b6a2
......@@ -91,7 +91,7 @@ struct BuildStrategy {
int num_trainers_{1};
int trainer_id_{0};
std::vector<std::string> trainers_endpoints_;
bool remove_unnecessary_lock_{false};
bool remove_unnecessary_lock_{true};
// NOTE:
// Before you add new options, think if it's a general strategy that works
......
paddle/fluid/framework/details/execution_strategy.h
浏览文件 @ 0016b6a2
......@@ -25,7 +25,10 @@ struct ExecutionStrategy {
size_t num_threads_{0};
bool use_cuda_{true};
bool allow_op_delay_{false};
size_t num_iteration_per_drop_scope_{1};
// If we set this to 1, we will delete all variables when finish a batch. and
// this will loss 15%+ performance.
// Please be aware about this parameters.
size_t num_iteration_per_drop_scope_{100};
ExecutorType type_{kDefault};
bool dry_run_{false};
};
......
paddle/fluid/framework/executor.cc
浏览文件 @ 0016b6a2
......@@ -27,7 +27,7 @@ limitations under the License. */
#include "paddle/fluid/platform/profiler.h"
#ifdef PADDLE_WITH_NGRAPH
#include "paddle/fluid/framework/ngraph_operator.h"
#include "paddle/fluid/operators/ngraph/ngraph_engine.h"
#endif
DECLARE_bool(benchmark);
......@@ -133,24 +133,6 @@ static void DeleteUnusedTensors(
}
}
static void EnableFusedOp(ExecutorPrepareContext* ctx) {
#ifdef PADDLE_WITH_NGRAPH
VLOG(3) << "use_ngraph=True";
auto intervals = NgraphOperator::NgraphOpIntervals(&ctx->ops_);
for (auto& interval : intervals) {
auto* ng_op = new NgraphOperator(ctx->prog_, ctx->block_id_, interval.at(0),
interval.at(1));
*interval[0] = std::unique_ptr<OperatorBase>(ng_op);
}
for (auto it = intervals.rbegin(); it != intervals.rend(); ++it) {
ctx->ops_.erase(it->at(0) + 1, it->at(1));
}
#else
LOG(WARNING)
<< "'NGRAPH' is not supported, Please re-compile with WITH_NGRAPH option";
#endif
}
Executor::Executor(const platform::Place& place) : place_(place) {}
void Executor::Close() {
......@@ -204,6 +186,9 @@ void Executor::Run(const ProgramDesc& pdesc, Scope* scope, int block_id,
bool create_local_scope, bool create_vars) {
platform::RecordBlock b(block_id);
if (FLAGS_use_mkldnn) EnableMKLDNN(pdesc);
#ifdef PADDLE_WITH_NGRAPH
if (FLAGS_use_ngraph) operators::NgraphEngine::EnableNgraph(pdesc);
#endif
auto ctx = Prepare(pdesc, block_id);
RunPreparedContext(ctx.get(), scope, create_local_scope, create_vars);
}
......@@ -379,7 +364,6 @@ std::unique_ptr<ExecutorPrepareContext> Executor::Prepare(
for (auto& op_desc : block.AllOps()) {
ctx->ops_.push_back(OpRegistry::CreateOp(*op_desc));
}
if (FLAGS_use_ngraph) EnableFusedOp(ctx.get());
return ctx;
}
......
paddle/fluid/framework/ir/pass.cc
浏览文件 @ 0016b6a2
......@@ -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/framework/lod_tensor.cc
浏览文件 @ 0016b6a2
......@@ -54,13 +54,14 @@ std::ostream &operator<<(std::ostream &os, const LoD &lod) {
std::ostream &operator<<(std::ostream &os, const LoDTensor &t) {
if (!platform::is_cpu_place(t.place())) {
LoDTensor tt;
framework::TensorCopy(t, platform::CPUPlace(), &tt);
LoDTensor cpu_tensor;
cpu_tensor.set_lod(t.lod());
framework::TensorCopy(t, platform::CPUPlace(), &cpu_tensor);
platform::DeviceContextPool &pool = platform::DeviceContextPool::Instance();
auto &dev_ctx = *pool.Get(t.place());
dev_ctx.Wait();
os << tt;
os << cpu_tensor;
return os;
}
......
paddle/fluid/framework/mixed_vector.h
浏览文件 @ 0016b6a2
/* 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
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
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. */
Unless required by applicable law or agreed to in writing, software
distributed under the License is distributed on an "AS IS" BASIS,
WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
See the License for the specific language governing permissions and
limitations under the License. */
#pragma once
......
paddle/fluid/framework/ngraph_operator.h 已删除 100644 → 0
浏览文件 @ ab5442d9
/* Copyright (c) 2018 PaddlePaddle Authors. All Rights Reserved.
Licensed under the Apache License, Version 2.0 (the "License");
you may not use this file except in compliance with the License.
You may obtain a copy of the License at
http://www.apache.org/licenses/LICENSE-2.0
Unless required by applicable law or agreed to in writing, software
distributed under the License is distributed on an "AS IS" BASIS,
WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
See the License for the specific language governing permissions and
limitations under the License. */
#pragma once
#include <algorithm>
#include <string>
#include <unordered_map>
#include <vector>
#include "paddle/fluid/framework/attribute.h"
#include "paddle/fluid/framework/op_info.h"
#include "paddle/fluid/framework/op_kernel_type.h"
#include "paddle/fluid/framework/operator.h"
#include "paddle/fluid/framework/program_desc.h"
#include "paddle/fluid/framework/scope.h"
#include "paddle/fluid/platform/variant.h"
#include "ngraph/type/element_type.hpp"
namespace paddle {
namespace framework {
class NgraphOperator : public OperatorBase {
public:
static std::vector<
std::vector<std::vector<std::unique_ptr<OperatorBase>>::iterator>>
NgraphOpIntervals(
std::vector<std::unique_ptr<paddle::framework::OperatorBase>>* ops);
explicit NgraphOperator(
const ProgramDesc& prog, size_t block_id,
std::vector<std::unique_ptr<OperatorBase>>::iterator start,
std::vector<std::unique_ptr<OperatorBase>>::iterator end,
const std::string& type = "fused_op", const VariableNameMap& inputs = {},
const VariableNameMap& outputs = {}, const AttributeMap& attrs = {});
void RunImpl(const Scope& scope, const platform::Place& place) const final;
private:
const ProgramDesc pdesc_;
size_t block_;
std::vector<std::shared_ptr<OperatorBase>> fused_ops_;
std::unordered_map<std::string, ngraph::element::Type> var_type_map_;
std::unordered_set<std::string> persistables_;
std::unordered_set<std::string> fetches_;
std::unordered_set<std::string> post_op_inputs_;
bool is_full_ = false;
void Process();
};
} // namespace framework
} // namespace paddle
paddle/fluid/framework/operator.cc
浏览文件 @ 0016b6a2
......@@ -19,8 +19,6 @@ limitations under the License. */
#include <sstream>
#include <string>
#include <vector>
#include "gflags/gflags.h"
#include "glog/logging.h"
#include "paddle/fluid/framework/data_transform.h"
#include "paddle/fluid/framework/executor.h"
#include "paddle/fluid/framework/lod_tensor.h"
......
paddle/fluid/imperative/CMakeLists.txt
浏览文件 @ 0016b6a2
if(WITH_PYTHON)
cc_library(layer SRCS layer.cc DEPS proto_desc operator)
cc_library(tracer SRCS tracer.cc DEPS proto_desc)
cc_library(layer SRCS layer.cc DEPS proto_desc operator device_context blas)
cc_library(tracer SRCS tracer.cc DEPS proto_desc device_context)
cc_library(engine SRCS engine.cc)
endif()
paddle/fluid/imperative/layer.cc
浏览文件 @ 0016b6a2
......@@ -13,6 +13,7 @@
// limitations under the License.
#include "paddle/fluid/imperative/layer.h"
#include <deque>
#include <limits>
#include <map>
......@@ -22,6 +23,9 @@
#include "paddle/fluid/framework/lod_tensor.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/math/blas.h"
#include "paddle/fluid/platform/device_context.h"
#include "paddle/fluid/string/printf.h"
namespace paddle {
......@@ -34,22 +38,66 @@ std::map<int, py::object> py_funcs_;
using framework::Variable;
void AddTo(Variable* src, Variable* dst) {
framework::LoDTensor* dst_tensor = dst->GetMutable<framework::LoDTensor>();
framework::LoDTensor* src_tensor = src->GetMutable<framework::LoDTensor>();
namespace detail {
template <typename T>
class TensorAddToFunctor : public boost::static_visitor<> {
public:
TensorAddToFunctor(int64_t numel, const T* x, T* y)
: numel_(numel), x_(x), y_(y) {}
void operator()(const platform::CPUPlace& place) {
platform::CPUDeviceContext* ctx = dynamic_cast<platform::CPUDeviceContext*>(
platform::DeviceContextPool::Instance().Get(place));
auto blas = operators::math::GetBlas<platform::CPUDeviceContext, T>(*ctx);
blas.AXPY(numel_, 1., x_, y_);
}
#ifdef PADDLE_WITH_CUDA
void operator()(const platform::CUDAPlace& place) {
platform::CUDADeviceContext* ctx =
dynamic_cast<platform::CUDADeviceContext*>(
platform::DeviceContextPool::Instance().Get(place));
auto blas = operators::math::GetBlas<platform::CUDADeviceContext, T>(*ctx);
blas.AXPY(numel_, 1., x_, y_);
}
#else
void operator()(const platform::CUDAPlace& place) {
PADDLE_THROW("Do NOT support gradient merge in place %s", place);
}
#endif
// there is NO blas in CUDAPinnedPlace
void operator()(const platform::CUDAPinnedPlace& place) {
PADDLE_THROW("Do NOT support gradient merge in place %s", place);
}
private:
int64_t numel_;
const T* x_;
T* y_;
};
} // namespace detail
void AddTo(Variable* src, Variable* dst, platform::Place place) {
framework::Tensor* dst_tensor = dst->GetMutable<framework::LoDTensor>();
framework::Tensor* src_tensor = src->GetMutable<framework::LoDTensor>();
// FIXME(minqiyang): loss_grad op will pass a zero grad of label
// ugly fix for it
if (src_tensor->numel() == 0) {
return;
}
PADDLE_ENFORCE(dst_tensor->numel() == src_tensor->numel(),
"dst_numel %lld vs. src_numel %lld", dst_tensor->numel(),
src_tensor->numel());
float* dst_data = dst_tensor->mutable_data<float>(platform::CPUPlace());
const float* src_data = src_tensor->data<float>();
for (int64_t i = 0; i < src_tensor->numel(); ++i) {
dst_data[i] += src_data[i];
}
detail::TensorAddToFunctor<float> func(
src_tensor->numel(), src_tensor->data<float>(),
dst_tensor->mutable_data<float>(place));
boost::apply_visitor(func, place);
}
class Autograd {
......@@ -120,6 +168,36 @@ class Autograd {
}
};
std::unique_ptr<VarBase> VarBase::NewVarBase(const platform::Place& dst_place,
const bool blocking) const {
PADDLE_ENFORCE(var_->IsInitialized(),
"Variable must be initialized when getting numpy tensor");
std::unique_ptr<VarBase> new_var(new VarBase());
framework::LoDTensor* tensor =
new_var->var_->GetMutable<framework::LoDTensor>();
tensor->Resize(var_->Get<framework::LoDTensor>().dims());
tensor->set_lod(var_->Get<framework::LoDTensor>().lod());
if (blocking) {
platform::DeviceContext* dev_ctx =
platform::DeviceContextPool::Instance().Get(dst_place);
framework::TensorCopySync(var_->Get<framework::LoDTensor>(), dst_place,
tensor);
dev_ctx->Wait();
} else {
framework::TensorCopy(var_->Get<framework::LoDTensor>(), dst_place, tensor);
}
if (platform::is_gpu_place(dst_place)) {
VLOG(3) << "copy tensor " << var_desc_->Name() << " from gpu";
}
return new_var;
}
framework::LoDTensor& VarBase::GradValue() {
VLOG(3) << "get var grad " << var_desc_->Name();
return *(grads_->var_->GetMutable<framework::LoDTensor>());
......@@ -162,9 +240,8 @@ std::map<std::string, std::vector<VarBase*>> OpBase::ApplyGrad() {
PADDLE_ENFORCE_NOT_NULL(op_kernel, "only support op with kernel");
framework::Scope scope;
platform::CPUPlace place;
PreparedOp p = PreparedOp::Prepare(ctx, *op_kernel, place);
p.op.RuntimeInferShape(scope, place, ctx);
PreparedOp p = PreparedOp::Prepare(ctx, *op_kernel, place_);
p.op.RuntimeInferShape(scope, place_, ctx);
p.func(framework::ExecutionContext(p.op, scope, *p.dev_ctx, p.ctx));
}
......@@ -176,7 +253,7 @@ std::map<std::string, std::vector<VarBase*>> OpBase::ApplyGrad() {
for (size_t i = 0; i < outputs.size(); ++i) {
framework::Variable* grad = outputs[i];
framework::Variable* orig_grad = origin_outputs[i];
AddTo(grad, orig_grad);
AddTo(grad, orig_grad, place_);
delete grad;
}
}
......@@ -188,8 +265,10 @@ void VarBase::RunBackward() {
VLOG(3) << "start backward";
auto grads_t = grads_->var_->GetMutable<framework::LoDTensor>();
float* data = grads_t->mutable_data<float>(platform::CPUPlace());
std::fill(data, data + grads_t->numel(), 1.0);
operators::math::set_constant(
*(platform::DeviceContextPool::Instance().Get(
var_->GetMutable<framework::LoDTensor>()->place())),
grads_t, 1.0);
PADDLE_ENFORCE(
grads_ ==
......
paddle/fluid/imperative/layer.h
浏览文件 @ 0016b6a2
......@@ -21,17 +21,21 @@
#include <map> // NOLINT
#include <string> // NOLINT
#include <vector> // NOLINT
#include <memory> // NOLINT
#include "paddle/fluid/framework/op_desc.h"
#include "paddle/fluid/framework/operator.h"
#include "paddle/fluid/framework/var_desc.h"
#include "paddle/fluid/platform/enforce.h"
#include "paddle/fluid/platform/device_context.h"
#include "paddle/fluid/imperative/type_defs.h"
namespace paddle {
namespace imperative {
class VarBase;
namespace py = ::pybind11;
class PreparedOp {
......@@ -81,6 +85,8 @@ class PreparedOp {
return PreparedOp(op, ctx, kernel_iter->second, dev_ctx);
}
inline platform::DeviceContext* GetDeviceContext() const { return dev_ctx; }
const framework::OperatorBase& op;
const framework::RuntimeContext& ctx;
framework::OperatorWithKernel::OpKernelFunc func;
......@@ -148,6 +154,9 @@ class VarBase {
framework::LoDTensor& GradValue();
std::unique_ptr<VarBase> NewVarBase(const platform::Place& dst_place,
const bool blocking) const;
inline std::string GradName() const {
PADDLE_ENFORCE(
var_desc_,
......@@ -176,7 +185,8 @@ class OpBase {
: op_desc_(nullptr),
forward_id_(-1),
grad_op_desc_(nullptr),
backward_id_(-1) {}
backward_id_(-1),
place_(platform::CPUPlace()) {}
virtual ~OpBase() {
if (grad_op_desc_) delete grad_op_desc_;
......@@ -193,6 +203,8 @@ class OpBase {
framework::OpDesc* grad_op_desc_;
int backward_id_;
platform::Place place_;
VarBasePtrMap input_vars_;
VarBasePtrMap output_vars_;
OpBasePtrMap pre_ops_;
......
paddle/fluid/imperative/tracer.cc
浏览文件 @ 0016b6a2
......@@ -14,6 +14,10 @@
#include "paddle/fluid/imperative/tracer.h"
#include "paddle/fluid/operators/math/math_function.h"
#include "paddle/fluid/platform/device_context.h"
#include "paddle/fluid/platform/enforce.h"
namespace paddle {
namespace imperative {
......@@ -31,16 +35,38 @@ void CreateGradOp(const framework::OpDesc& op_desc,
*grad_op_desc = grad_op_descs[0].release();
}
void InitVar(framework::Variable* var, framework::Variable* grad_var) {
void InitVar(framework::Variable* var, framework::Variable* grad_var,
platform::DeviceContext* dev_ctx) {
PADDLE_ENFORCE_NOT_NULL(dev_ctx,
"Could not get valid device from forward op");
auto& var_t = var->Get<framework::LoDTensor>();
float* data =
grad_var->GetMutable<framework::LoDTensor>()->mutable_data<float>(
var_t.dims(), platform::CPUPlace());
std::fill(data, data + var_t.numel(), 0.0);
grad_var->GetMutable<framework::LoDTensor>()->mutable_data<float>(
var_t.dims(), dev_ctx->GetPlace());
operators::math::set_constant(
*dev_ctx, grad_var->GetMutable<framework::LoDTensor>(), 0.0);
}
platform::Place GetExpectedPlace(platform::Place place, VarBasePtrMap inputs) {
platform::Place result = place;
for (auto it : inputs) {
for (VarBase* var : it.second) {
platform::Place tmp_place =
var->var_->Get<framework::LoDTensor>().place();
if (!platform::is_same_place(tmp_place, result)) {
PADDLE_THROW(
"Input variable should keep in the same place: %s, but get place: "
"%s of input %s instead",
result, tmp_place, it.first);
}
}
}
return result;
}
void Tracer::Trace(OpBase* op, const VarBasePtrMap& inputs,
const VarBasePtrMap& outputs, framework::BlockDesc* block,
const platform::Place expected_place,
const bool stop_gradient) {
std::map<std::string, VarBase*> vars;
......@@ -105,10 +131,11 @@ void Tracer::Trace(OpBase* op, const VarBasePtrMap& inputs,
PADDLE_ENFORCE_NOT_NULL(op_kernel, "only support op with kernel");
framework::Scope scope;
platform::CPUPlace place;
PreparedOp p = PreparedOp::Prepare(ctx, *op_kernel, place);
p.op.RuntimeInferShape(scope, place, ctx);
p.func(framework::ExecutionContext(p.op, scope, *p.dev_ctx, p.ctx));
op->place_ = GetExpectedPlace(expected_place, inputs);
PreparedOp prepared_op = PreparedOp::Prepare(ctx, *op_kernel, op->place_);
prepared_op.op.RuntimeInferShape(scope, op->place_, ctx);
prepared_op.func(framework::ExecutionContext(
prepared_op.op, scope, *prepared_op.dev_ctx, prepared_op.ctx));
if (!stop_gradient) {
framework::OpDesc* grad_op_desc;
......@@ -131,7 +158,8 @@ void Tracer::Trace(OpBase* op, const VarBasePtrMap& inputs,
} else {
VarBase* var = vars[var_it->second];
if (!var->grads_->var_->IsInitialized()) {
InitVar(var->var_, var->grads_->var_);
InitVar(var->var_, var->grads_->var_,
prepared_op.GetDeviceContext());
}
// Douts.
grad_in_vars.push_back(var->grads_->var_);
......@@ -144,10 +172,13 @@ void Tracer::Trace(OpBase* op, const VarBasePtrMap& inputs,
for (const std::string& grad_outvar : it.second) {
block->FindRecursiveOrCreateVar(grad_outvar);
auto var_it = grad_to_var->find(grad_outvar);
PADDLE_ENFORCE(var_it != grad_to_var->end());
PADDLE_ENFORCE(var_it != grad_to_var->end(),
"Could not found the grad op output var, should this "
"operator %s's stop gradient be True",
op_desc->Type());
VarBase* var = vars[var_it->second];
if (!var->grads_->var_->IsInitialized()) {
InitVar(var->var_, var->grads_->var_);
InitVar(var->var_, var->grads_->var_, prepared_op.GetDeviceContext());
}
grad_out_vars.push_back(var->grads_->var_);
}
......@@ -189,16 +220,23 @@ std::vector<VarBase*> Tracer::PyTrace(OpBase* op,
for (VarBase* out : outputs) {
grad_input_vars.push_back(out->var_);
}
platform::CPUPlace place;
for (VarBase* out : outputs) {
grad_input_vars.push_back(out->grads_->var_);
if (!grad_input_vars.back()->IsInitialized()) {
InitVar(out->var_, grad_input_vars.back());
// TODO(minqiyang): Add GPU support for PyLayer, only support CPU now
InitVar(out->var_, grad_input_vars.back(),
platform::DeviceContextPool::Instance().Get(place));
}
}
for (const VarBase* inp : inputs) {
grad_output_vars.push_back(inp->grads_->var_);
if (!grad_output_vars.back()->IsInitialized()) {
InitVar(inp->var_, grad_output_vars.back());
// TODO(minqiyang): Add GPU support for PyLayer, only support CPU now
InitVar(inp->var_, grad_output_vars.back(),
platform::DeviceContextPool::Instance().Get(place));
}
}
}
......
paddle/fluid/imperative/tracer.h
浏览文件 @ 0016b6a2
......@@ -22,6 +22,7 @@
#include "paddle/fluid/framework/op_registry.h"
#include "paddle/fluid/imperative/engine.h"
#include "paddle/fluid/imperative/layer.h"
#include "paddle/fluid/platform/place.h"
namespace paddle {
namespace imperative {
......@@ -34,21 +35,25 @@ void CreateGradOp(const framework::OpDesc& op_desc,
void InitVar(framework::Variable* var, framework::Variable* grad_var);
platform::Place GetExpectedPlace(platform::Place place, VarBasePtrMap inputs);
class Tracer {
public:
explicit Tracer(framework::BlockDesc* root_block) : root_block_(root_block) {}
virtual ~Tracer() {}
void Trace(OpBase* op,
const std::map<std::string, std::vector<VarBase*>>& inputs,
const std::map<std::string, std::vector<VarBase*>>& outputs,
framework::BlockDesc* block, const bool stop_gradient = false);
void Trace(OpBase* op, const VarBasePtrMap& inputs,
const VarBasePtrMap& outputs, framework::BlockDesc* block,
const platform::Place expected_place,
const bool stop_gradient = false);
std::vector<VarBase*> PyTrace(OpBase* op, const std::vector<VarBase*>& inputs,
bool stop_gradient = false);
private:
platform::Place GetPlace(const VarBasePtrMap& inputs);
framework::BlockDesc* root_block_;
};
......
paddle/fluid/inference/analysis/passes/CMakeLists.txt
浏览文件 @ 0016b6a2
cc_library(ir_graph_build_pass SRCS ir_graph_build_pass.cc DEPS analysis_pass argument ir_pass_manager)
cc_library(ir_analysis_pass SRCS ir_analysis_pass.cc DEPS analysis_pass argument ir_pass_manager)
cc_library(memory_optim_pass SRCS memory_optimize_pass.cc DEPS analysis_pass)
cc_library(memory_optim_pass SRCS memory_optimize_pass.cc DEPS analysis_pass zero_copy_tensor)
cc_library(ir_params_sync_among_devices_pass SRCS ir_params_sync_among_devices_pass.cc DEPS analysis_pass argument ir_pass_manager)
cc_library(ir_graph_to_program_pass SRCS ir_graph_to_program_pass.cc DEPS analysis_pass graph_to_program_pass)
......
paddle/fluid/inference/api/paddle_pass_builder.h
浏览文件 @ 0016b6a2
......@@ -154,13 +154,16 @@ class GpuPassStrategy : public PassStrategy {
public:
GpuPassStrategy() : PassStrategy({}) {
passes_.assign({
"infer_clean_graph_pass", //
"conv_affine_channel_fuse_pass", //
"conv_eltwiseadd_affine_channel_fuse_pass", //
"conv_bn_fuse_pass", //
"conv_elementwise_add_act_fuse_pass", //
"conv_elementwise_add2_act_fuse_pass", //
"conv_elementwise_add_fuse_pass", //
"infer_clean_graph_pass", //
"conv_affine_channel_fuse_pass", //
"conv_eltwiseadd_affine_channel_fuse_pass", //
"conv_bn_fuse_pass", //
#if CUDNN_VERSION >= 7100 // To run conv_fusion, the version of cudnn must be
// guaranteed at least v7
"conv_elementwise_add_act_fuse_pass", //
"conv_elementwise_add2_act_fuse_pass", //
"conv_elementwise_add_fuse_pass", //
#endif
});
for (int i = 6; i >= 3; i--) {
......
paddle/fluid/inference/tensorrt/convert/test_op_converter.cc
浏览文件 @ 0016b6a2
......@@ -29,9 +29,9 @@ TEST(OpConverter, ConvertBlock) {
// init trt engine
cudaStream_t stream_;
std::unique_ptr<TensorRTEngine> engine_;
engine_.reset(new TensorRTEngine(5, 1 << 15, &stream_));
engine_->InitNetwork();
PADDLE_ENFORCE_EQ(cudaStreamCreate(&stream_), 0);
engine_.reset(new TensorRTEngine(5, 1 << 15, stream_));
engine_->InitNetwork();
engine_->DeclareInput("conv2d-X", nvinfer1::DataType::kFLOAT,
nvinfer1::Dims3(2, 5, 5));
......
paddle/fluid/inference/tensorrt/convert/ut_helper.h
浏览文件 @ 0016b6a2
......@@ -78,11 +78,9 @@ class TRTConvertValidation {
scope_(scope),
if_add_batch_(if_add_batch),
max_batch_size_(max_batch_size) {
// create engine.
engine_.reset(new TensorRTEngine(max_batch_size, workspace_size, &stream_));
engine_->InitNetwork();
PADDLE_ENFORCE_EQ(cudaStreamCreate(&stream_), 0);
engine_.reset(new TensorRTEngine(max_batch_size, workspace_size, stream_));
engine_->InitNetwork();
}
// Declare a Variable as input with random initialization.
......@@ -175,7 +173,7 @@ class TRTConvertValidation {
op_->Run(scope_, place);
// Execute TRT.
engine_->Execute(batch_size);
cudaStreamSynchronize(*engine_->stream());
cudaStreamSynchronize(engine_->stream());
ASSERT_FALSE(op_desc_->OutputArgumentNames().empty());
const size_t output_space_size = 3000;
......@@ -184,7 +182,7 @@ class TRTConvertValidation {
std::vector<float> fluid_out;
std::vector<float> trt_out(output_space_size);
engine_->GetOutputInCPU(output, &trt_out[0], output_space_size);
cudaStreamSynchronize(*engine_->stream());
cudaStreamSynchronize(engine_->stream());
auto* var = scope_.FindVar(output);
auto tensor = var->GetMutable<framework::LoDTensor>();
......
paddle/fluid/inference/tensorrt/engine.cc
浏览文件 @ 0016b6a2
......@@ -42,14 +42,13 @@ void TensorRTEngine::Execute(int batch_size) {
PADDLE_ENFORCE(buf.device == DeviceType::GPU);
buffers.push_back(buf.buffer);
}
PADDLE_ENFORCE_NOT_NULL(stream_);
infer_context_->enqueue(batch_size, buffers.data(), *stream_, nullptr);
cudaStreamSynchronize(*stream_);
infer_context_->enqueue(batch_size, buffers.data(), stream_, nullptr);
cudaStreamSynchronize(stream_);
SetRuntimeBatch(batch_size);
}
TensorRTEngine::~TensorRTEngine() {
cudaStreamSynchronize(*stream_);
cudaStreamSynchronize(stream_);
// clean buffer
for (auto &buf : buffers_) {
if (buf.device == DeviceType::GPU && buf.buffer != nullptr) {
......@@ -173,7 +172,7 @@ void TensorRTEngine::GetOutputInGPU(const std::string &name, void *dst,
auto &buf = buffer(name);
PADDLE_ENFORCE_NOT_NULL(buf.buffer, "buffer should be allocated before");
PADDLE_ENFORCE_EQ(cudaMemcpyAsync(dst, buf.buffer, dst_size,
cudaMemcpyDeviceToDevice, *stream_),
cudaMemcpyDeviceToDevice, stream_),
0);
}
......@@ -194,7 +193,7 @@ void TensorRTEngine::GetOutputInCPU(const std::string &name, void *dst,
auto &buf = buffer(name);
PADDLE_ENFORCE_NOT_NULL(buf.buffer, "buffer should be allocated before");
PADDLE_ENFORCE_EQ(0, cudaMemcpyAsync(dst, buf.buffer, dst_size,
cudaMemcpyDeviceToHost, *stream_));
cudaMemcpyDeviceToHost, stream_));
}
Buffer &TensorRTEngine::buffer(const std::string &name) {
......@@ -211,12 +210,11 @@ void TensorRTEngine::SetInputFromCPU(const std::string &name, const void *data,
auto &buf = buffer(name);
PADDLE_ENFORCE_NOT_NULL(buf.buffer);
PADDLE_ENFORCE_NOT_NULL(data);
PADDLE_ENFORCE_NOT_NULL(stream_);
PADDLE_ENFORCE_LE(size, buf.max_size, "buffer is too small");
PADDLE_ENFORCE(buf.device == DeviceType::GPU);
buf.size = size;
PADDLE_ENFORCE_EQ(0, cudaMemcpyAsync(buf.buffer, data, size,
cudaMemcpyHostToDevice, *stream_));
cudaMemcpyHostToDevice, stream_));
}
void TensorRTEngine::SetInputFromGPU(const std::string &name, const void *data,
......@@ -227,7 +225,7 @@ void TensorRTEngine::SetInputFromGPU(const std::string &name, const void *data,
PADDLE_ENFORCE_LE(size, buf.max_size, "buffer is too small");
PADDLE_ENFORCE(buf.device == DeviceType::GPU);
PADDLE_ENFORCE_EQ(0, cudaMemcpyAsync(buf.buffer, data, size,
cudaMemcpyDeviceToDevice, *stream_));
cudaMemcpyDeviceToDevice, stream_));
}
void TensorRTEngine::SetITensor(const std::string &name,
......
paddle/fluid/inference/tensorrt/engine.h
浏览文件 @ 0016b6a2
......@@ -54,17 +54,14 @@ class TensorRTEngine : public EngineBase {
nvinfer1::Weights w_;
};
TensorRTEngine(int max_batch, int max_workspace,
cudaStream_t* stream = nullptr, int device = 0,
TensorRTEngine(int max_batch, int max_workspace, cudaStream_t stream,
int device = 0,
nvinfer1::ILogger& logger = NaiveLogger::Global())
: max_batch_(max_batch),
max_workspace_(max_workspace),
stream_(stream ? stream : &default_stream_),
stream_(stream),
logger_(logger),
device_(device) {
freshDeviceId();
cudaStreamCreate(stream_);
}
device_(device) {}
virtual ~TensorRTEngine();
......@@ -102,7 +99,7 @@ class TensorRTEngine : public EngineBase {
// NOTE this should be used after calling `FreezeNetwork`.
Buffer& buffer(const std::string& name) override;
cudaStream_t* stream() { return stream_; }
cudaStream_t stream() { return stream_; }
// Fill an input from CPU memory with name and size.
void SetInputFromCPU(const std::string& name, const void* data, size_t size);
......@@ -158,9 +155,8 @@ class TensorRTEngine : public EngineBase {
// batch size of the current data, will be updated each Executation.
int batch_size_{-1};
cudaStream_t* stream_;
// If stream_ is not set from outside, hold its own stream.
cudaStream_t default_stream_;
cudaStream_t stream_;
nvinfer1::ILogger& logger_;
std::vector<Buffer> buffers_;
......@@ -208,38 +204,6 @@ class TensorRTEngine : public EngineBase {
#define TRT_ENGINE_ADD_LAYER(engine__, layer__, ARGS...) \
engine__->network()->add##layer__(ARGS);
/*
* Helper to control the TensorRT engine's creation and deletion.
*/
class TRT_EngineManager {
public:
bool HasEngine(const std::string& name) const {
return engines_.count(name) != 0;
}
// Get an engine called `name`.
TensorRTEngine* Get(const std::string& name) const {
return engines_.at(name).get();
}
// Create or get an engine called `name`
TensorRTEngine* Create(int max_batch, int max_workspace, cudaStream_t* stream,
const std::string& name, int gpu_device = 0) {
auto* p = new TensorRTEngine(max_batch, max_workspace, stream, gpu_device);
engines_[name].reset(p);
return p;
}
void DeleteALl() {
for (auto& item : engines_) {
item.second.reset(nullptr);
}
}
private:
std::unordered_map<std::string, std::unique_ptr<TensorRTEngine>> engines_;
};
} // namespace tensorrt
} // namespace inference
} // namespace paddle
paddle/fluid/inference/tensorrt/test_engine.cc
浏览文件 @ 0016b6a2
......@@ -27,8 +27,8 @@ namespace tensorrt {
class TensorRTEngineTest : public ::testing::Test {
protected:
void SetUp() override {
// ASSERT_EQ(0, cudaStreamCreate(&stream_));
engine_ = new TensorRTEngine(10, 1 << 10, &stream_);
ASSERT_EQ(0, cudaStreamCreate(&stream_));
engine_ = new TensorRTEngine(10, 1 << 10, stream_);
engine_->InitNetwork();
}
......
paddle/fluid/inference/tests/api/tester_helper.h
浏览文件 @ 0016b6a2
......@@ -56,6 +56,13 @@ DECLARE_int32(paddle_num_threads);
namespace paddle {
namespace inference {
float Random(float low, float high) {
static std::random_device rd;
static std::mt19937 mt(rd());
std::uniform_real_distribution<double> dist(low, high);
return dist(mt);
}
void PrintConfig(const PaddlePredictor::Config *config, bool use_analysis) {
const auto *analysis_config =
reinterpret_cast<const contrib::AnalysisConfig *>(config);
......@@ -176,7 +183,7 @@ void SetFakeImageInput(std::vector<std::vector<PaddleTensor>> *inputs,
float *input_data = static_cast<float *>(input.data.data());
// fill input data, for profile easily, do not use random data here.
for (size_t j = 0; j < len; ++j) {
*(input_data + j) = static_cast<float>(j) / len;
*(input_data + j) = Random(0.0, 1.0) / 10.;
}
}
(*inputs).emplace_back(input_slots);
......@@ -344,6 +351,16 @@ void CompareNativeAndAnalysis(
CompareResult(analysis_outputs, native_outputs);
}
void CompareNativeAndAnalysis(
PaddlePredictor *native_pred, PaddlePredictor *analysis_pred,
const std::vector<std::vector<PaddleTensor>> &inputs) {
int batch_size = FLAGS_batch_size;
std::vector<PaddleTensor> native_outputs, analysis_outputs;
native_pred->Run(inputs[0], &native_outputs, batch_size);
analysis_pred->Run(inputs[0], &analysis_outputs, batch_size);
CompareResult(analysis_outputs, native_outputs);
}
template <typename T>
std::string LoDTensorSummary(const framework::LoDTensor &tensor) {
std::stringstream ss;
......
paddle/fluid/inference/tests/api/trt_models_tester.cc
浏览文件 @ 0016b6a2
......@@ -107,6 +107,27 @@ void compare(std::string model_dir, bool use_tensorrt) {
inputs_all);
}
void compare_continuous_input(std::string model_dir, bool use_tensorrt) {
contrib::AnalysisConfig analysis_config;
SetConfig<contrib::AnalysisConfig>(&analysis_config, model_dir, true,
use_tensorrt, FLAGS_batch_size);
auto config =
reinterpret_cast<const PaddlePredictor::Config*>(&analysis_config);
auto native_pred = CreateTestPredictor(config, false);
auto analysis_pred = CreateTestPredictor(config, true);
for (int i = 0; i < 100; i++) {
std::vector<std::vector<PaddleTensor>> inputs_all;
if (!FLAGS_prog_filename.empty() && !FLAGS_param_filename.empty()) {
SetFakeImageInput(&inputs_all, model_dir, true, FLAGS_prog_filename,
FLAGS_param_filename);
} else {
SetFakeImageInput(&inputs_all, model_dir, false, "__model__", "");
}
CompareNativeAndAnalysis(native_pred.get(), analysis_pred.get(),
inputs_all);
}
}
TEST(TensorRT_mobilenet, compare) {
std::string model_dir = FLAGS_infer_model + "/mobilenet";
compare(model_dir, /* use_tensorrt */ true);
......@@ -162,5 +183,15 @@ TEST(TensorRT_mobilenet, profile) {
profile(model_dir, true, false);
}
TEST(resnet50, compare_continuous_input) {
std::string model_dir = FLAGS_infer_model + "/resnet50";
compare_continuous_input(model_dir, true);
}
TEST(resnet50, compare_continuous_input_native) {
std::string model_dir = FLAGS_infer_model + "/resnet50";
compare_continuous_input(model_dir, false);
}
} // namespace inference
} // namespace paddle
paddle/fluid/memory/allocation/legacy_allocator.cc
浏览文件 @ 0016b6a2
......@@ -14,6 +14,7 @@
#include "paddle/fluid/memory/allocation/legacy_allocator.h"
#include <string>
#include <utility>
#include <vector>
#include "glog/logging.h"
#include "paddle/fluid/memory/detail/buddy_allocator.h"
......@@ -37,7 +38,7 @@ template <typename Place>
void *Alloc(const Place &place, size_t size);
template <typename Place>
void Free(const Place &place, void *p);
void Free(const Place &place, void *p, size_t size);
template <typename Place>
size_t Used(const Place &place);
......@@ -52,6 +53,11 @@ size_t memory_usage(const platform::Place &p);
using BuddyAllocator = detail::BuddyAllocator;
std::unordered_map</*device id*/ int,
std::pair</*current memory usage*/ uint64_t,
/*peak memory usage*/ uint64_t>>
gpu_mem_info;
BuddyAllocator *GetCPUBuddyAllocator() {
// We tried thread_local for inference::RNN1 model, but that not works much
// for multi-thread test.
......@@ -98,7 +104,8 @@ void *Alloc<platform::CPUPlace>(const platform::CPUPlace &place, size_t size) {
}
template <>
void Free<platform::CPUPlace>(const platform::CPUPlace &place, void *p) {
void Free<platform::CPUPlace>(const platform::CPUPlace &place, void *p,
size_t size) {
VLOG(10) << "Free pointer=" << p << " on " << platform::Place(place);
GetCPUBuddyAllocator()->Free(p);
}
......@@ -177,9 +184,16 @@ void *Alloc<platform::CUDAPlace>(const platform::CUDAPlace &place,
LOG(WARNING) << "GPU memory used: "
<< string::HumanReadableSize(Used<platform::CUDAPlace>(place));
platform::SetDeviceId(cur_dev);
}
if (FLAGS_init_allocated_mem) {
cudaMemset(ptr, 0xEF, size);
} else {
gpu_mem_info[place.device].first += size;
if (gpu_mem_info[place.device].first > gpu_mem_info[place.device].second) {
gpu_mem_info[place.device].second = gpu_mem_info[place.device].first;
VLOG(3) << "device: " << place.device << " peak memory usage : "
<< (gpu_mem_info[place.device].second >> 20) << " MiB";
}
if (FLAGS_init_allocated_mem) {
cudaMemset(ptr, 0xEF, size);
}
}
return ptr;
#else
......@@ -188,9 +202,11 @@ void *Alloc<platform::CUDAPlace>(const platform::CUDAPlace &place,
}
template <>
void Free<platform::CUDAPlace>(const platform::CUDAPlace &place, void *p) {
void Free<platform::CUDAPlace>(const platform::CUDAPlace &place, void *p,
size_t size) {
#ifdef PADDLE_WITH_CUDA
GetGPUBuddyAllocator(place.device)->Free(p);
gpu_mem_info[place.device].first -= size;
#else
PADDLE_THROW("'CUDAPlace' is not supported in CPU only device.");
#endif
......@@ -243,7 +259,7 @@ void *Alloc<platform::CUDAPinnedPlace>(const platform::CUDAPinnedPlace &place,
template <>
void Free<platform::CUDAPinnedPlace>(const platform::CUDAPinnedPlace &place,
void *p) {
void *p, size_t size) {
#ifdef PADDLE_WITH_CUDA
GetCUDAPinnedBuddyAllocator()->Free(p);
#else
......@@ -264,15 +280,17 @@ struct AllocVisitor : public boost::static_visitor<void *> {
};
struct FreeVisitor : public boost::static_visitor<void> {
inline explicit FreeVisitor(void *ptr) : ptr_(ptr) {}
inline explicit FreeVisitor(void *ptr, size_t size)
: ptr_(ptr), size_(size) {}
template <typename Place>
inline void operator()(const Place &place) const {
Free<Place>(place, ptr_);
Free<Place>(place, ptr_, size_);
}
private:
void *ptr_;
size_t size_;
};
size_t Usage::operator()(const platform::CPUPlace &cpu) const {
......@@ -304,8 +322,9 @@ Allocation *LegacyAllocator::AllocateImpl(size_t size, Allocator::Attr attr) {
}
void LegacyAllocator::Free(Allocation *allocation) {
boost::apply_visitor(legacy::FreeVisitor(allocation->ptr()),
allocation->place());
boost::apply_visitor(
legacy::FreeVisitor(allocation->ptr(), allocation->size()),
allocation->place());
delete allocation;
}
} // namespace allocation
......
paddle/fluid/operators/CMakeLists.txt
浏览文件 @ 0016b6a2
......@@ -13,6 +13,7 @@ add_subdirectory(detection)
add_subdirectory(elementwise)
add_subdirectory(fused)
add_subdirectory(metrics)
add_subdirectory(ngraph)
add_subdirectory(optimizers)
add_subdirectory(reduce_ops)
add_subdirectory(sequence_ops)
......@@ -66,7 +67,7 @@ set(COMMON_OP_DEPS ${OP_HEADER_DEPS})
set(COMMON_OP_DEPS ${COMMON_OP_DEPS} selected_rows_functor selected_rows lod_tensor maxouting unpooling pooling lod_rank_table context_project sequence_pooling executor)
set(COMMON_OP_DEPS ${COMMON_OP_DEPS} dynload_warpctc)
set(COMMON_OP_DEPS ${COMMON_OP_DEPS} sequence_padding sequence_scale cos_sim_functor memory jit_kernel_helper concat_and_split cross_entropy softmax vol2col im2col sampler tree2col)
set(COMMON_OP_DEPS ${COMMON_OP_DEPS} sequence2batch lstm_compute matrix_bit_code gru_compute activation_functions)
set(COMMON_OP_DEPS ${COMMON_OP_DEPS} sequence2batch lstm_compute matrix_bit_code gru_compute activation_functions beam_search)
if (WITH_GPU)
set(COMMON_OP_DEPS ${COMMON_OP_DEPS} depthwise_conv prelu)
endif()
......@@ -86,7 +87,6 @@ set(GLOB_OPERATOR_DEPS ${OPERATOR_DEPS} CACHE INTERNAL "Global Op dependencies")
cc_test(gather_test SRCS gather_test.cc DEPS tensor)
cc_test(scatter_test SRCS scatter_test.cc DEPS tensor math_function)
cc_test(beam_search_decode_op_test SRCS beam_search_decode_op_test.cc DEPS lod_tensor)
cc_test(beam_search_op_test SRCS beam_search_op_test.cc DEPS lod_tensor beam_search_op)
cc_test(strided_memcpy_test SRCS strided_memcpy_test.cc DEPS tensor memory)
cc_test(save_load_op_test SRCS save_load_op_test.cc DEPS save_op load_op)
cc_test(save_load_combine_op_test SRCS save_load_combine_op_test.cc DEPS save_combine_op load_combine_op)
......
paddle/fluid/operators/affine_channel_op.cu
浏览文件 @ 0016b6a2
......@@ -83,7 +83,7 @@ __global__ void AffineChannelScaleBiasGradientCUDAKernel(
T* dbias) {
const int outer_size = C;
const int inner_size = N * HxW;
typedef cub::BlockReduce<T, BlockDim> BlockReduce;
typedef cub::BlockReduce<double, BlockDim> BlockReduce;
__shared__ typename BlockReduce::TempStorage ds_storage;
__shared__ typename BlockReduce::TempStorage db_storage;
......@@ -97,13 +97,16 @@ __global__ void AffineChannelScaleBiasGradientCUDAKernel(
ds_sum += dy[index] * x[index];
db_sum += dy[index];
}
ds_sum = BlockReduce(ds_storage).Reduce(ds_sum, cub::Sum());
db_sum = BlockReduce(db_storage).Reduce(db_sum, cub::Sum());
__syncthreads();
auto ds_out =
BlockReduce(ds_storage).Reduce(static_cast<double>(ds_sum), cub::Sum());
auto db_out =
BlockReduce(db_storage).Reduce(static_cast<double>(db_sum), cub::Sum());
__syncthreads();
if (threadIdx.x == 0) {
dscale[i] = ds_sum;
dbias[i] = db_sum;
dscale[i] = ds_out;
dbias[i] = db_out;
}
__syncthreads();
}
}
......
paddle/fluid/operators/beam_search_op.cc
浏览文件 @ 0016b6a2
......@@ -12,205 +12,15 @@ WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
See the License for the specific language governing permissions and
limitations under the License. */
#include <algorithm>
#include <map>
#include "paddle/fluid/operators/beam_search_op.h"
#include <string>
#include <vector>
#include "paddle/fluid/framework/lod_tensor.h"
#include "paddle/fluid/framework/op_registry.h"
#include "paddle/fluid/operators/beam_search_op.h"
namespace paddle {
namespace operators {
void BeamSearch::operator()(const framework::LoDTensor &pre_ids,
const framework::LoDTensor &pre_scores,
framework::LoDTensor *selected_ids,
framework::LoDTensor *selected_scores) {
auto abs_lod = framework::ToAbsOffset(ids_->lod());
auto &high_level = abs_lod[lod_level_];
auto items = SelectTopBeamSizeItems(pre_ids, pre_scores);
auto selected_items = ToMap(items, high_level.back());
VLOG(3) << "selected_items:";
for (size_t i = 0; i < selected_items.size(); ++i) {
VLOG(3) << "offset:" << i;
for (auto &item : selected_items[i]) {
VLOG(3) << ItemToString(item);
}
}
PruneEndBeams(pre_ids, &selected_items);
// calculate the output tensor's height
size_t num_instances = std::accumulate(
std::begin(selected_items), std::end(selected_items), 0,
[](size_t a, std::vector<Item> &b) { return a + b.size(); });
// the output tensor shape should be [num_instances, 1]
auto dims = framework::make_ddim(
std::vector<int64_t>({static_cast<int>(num_instances), 1}));
selected_ids->Resize(dims);
selected_scores->Resize(dims);
std::map<size_t /*offset*/, std::vector<Item>> hash;
framework::LoD new_lod;
auto *ids_data = selected_ids->mutable_data<int64_t>(platform::CPUPlace());
auto *scores_data =
selected_scores->mutable_data<float>(platform::CPUPlace());
// fill in data
std::vector<size_t> low_level;
size_t low_offset = 0;
for (auto &items : selected_items) {
low_level.push_back(low_offset);
for (auto &item : items) {
ids_data[low_offset] = item.id;
scores_data[low_offset] = item.score;
low_offset++;
}
}
low_level.push_back(low_offset);
// fill lod
framework::LoD lod(2);
lod[0].assign(high_level.begin(), high_level.end());
lod[1].assign(low_level.begin(), low_level.end());
if (!framework::CheckLoD(lod)) {
PADDLE_THROW("lod %s is not right", framework::LoDToString(lod));
}
selected_ids->set_lod(lod);
selected_scores->set_lod(lod);
}
void BeamSearch::PruneEndBeams(const framework::LoDTensor &pre_ids,
std::vector<std::vector<Item>> *items) {
auto *pre_ids_data = pre_ids.data<int64_t>();
auto abs_lod = framework::ToAbsOffset(ids_->lod());
auto &high_level = abs_lod[lod_level_];
for (size_t src_idx = 0; src_idx < high_level.size() - 1; ++src_idx) {
size_t src_prefix_start = high_level[src_idx];
size_t src_prefix_end = high_level[src_idx + 1];
bool finish_flag = true;
for (size_t offset = src_prefix_start; offset < src_prefix_end; offset++) {
for (auto &item : items->at(offset)) {
if (item.id != static_cast<size_t>(end_id_) ||
pre_ids_data[offset] != end_id_) {
finish_flag = false;
break;
}
}
if (!finish_flag) break;
}
if (finish_flag) { // all branchs of the beam (source sentence) end and
// prune this beam
for (size_t offset = src_prefix_start; offset < src_prefix_end; offset++)
items->at(offset).clear();
}
}
}
std::vector<std::vector<BeamSearch::Item>> BeamSearch::ToMap(
const std::vector<std::vector<Item>> &items, size_t element_num) {
std::vector<std::vector<Item>> result;
result.resize(element_num);
for (auto &entries : items) {
for (const auto &item : entries) {
result[item.offset].push_back(item);
}
}
return result;
}
std::vector<std::vector<BeamSearch::Item>> BeamSearch::SelectTopBeamSizeItems(
const framework::LoDTensor &pre_ids,
const framework::LoDTensor &pre_scores) {
std::vector<std::vector<Item>> result;
std::vector<Item> items;
// for each source sentence, select the top beam_size items across all
// candidate sets.
while (NextItemSet(pre_ids, pre_scores, &items)) {
std::nth_element(
std::begin(items), std::begin(items) + beam_size_, std::end(items),
[](const Item &a, const Item &b) { return a.score > b.score; });
// prune the top beam_size items.
if (items.size() > beam_size_) {
items.resize(beam_size_);
}
result.emplace_back(items);
}
VLOG(3) << "SelectTopBeamSizeItems result size " << result.size();
for (auto &items : result) {
VLOG(3) << "item set:";
for (auto &item : items) {
VLOG(3) << ItemToString(item);
}
}
return result;
}
// the candidates of a source
bool BeamSearch::NextItemSet(const framework::LoDTensor &pre_ids,
const framework::LoDTensor &pre_scores,
std::vector<BeamSearch::Item> *items) {
if (sent_offset_ >= ids_->NumElements(lod_level_)) {
return false;
}
// find the current candidates
auto ids = *ids_;
auto scores = *scores_;
auto abs_lod = framework::ToAbsOffset(ids.lod());
auto *ids_data = ids.data<int64_t>();
auto *scores_data = scores.data<float>();
size_t instance_dim = 1;
for (int i = 1; i < ids.dims().size(); i++) {
instance_dim *= ids.dims()[i];
}
auto *pre_ids_data = pre_ids.data<int64_t>();
auto *pre_scores_data = pre_scores.data<float>();
items->clear();
items->reserve(framework::product(ids.dims()));
for (size_t offset = abs_lod[lod_level_][sent_offset_];
offset < abs_lod[lod_level_][sent_offset_ + 1]; offset++) {
auto pre_id = pre_ids_data[offset];
auto pre_score = pre_scores_data[offset];
if (pre_id == end_id_) {
// Allocate all probability mass to eos_id for finished branchs and the
// other candidate ids can be ignored.
items->emplace_back(offset, end_id_, pre_score);
} else {
for (size_t d = 0; d < instance_dim; d++) {
const size_t dim_offset = offset * instance_dim + d;
items->emplace_back(offset, ids_data[dim_offset],
scores_data[dim_offset]);
}
}
}
sent_offset_++;
return true;
}
std::ostream &operator<<(std::ostream &os, const BeamSearch::Item &item) {
os << "{";
os << "offset: " << item.offset << ", ";
os << "id: " << item.id << ", ";
os << "score: " << item.score << "";
os << "}";
return os;
}
std::string ItemToString(const BeamSearch::Item &item) {
std::ostringstream stream;
stream << item;
return stream.str();
}
class BeamSearchOpMaker : public framework::OpProtoAndCheckerMaker {
public:
void Make() override {
......@@ -219,18 +29,23 @@ class BeamSearchOpMaker : public framework::OpProtoAndCheckerMaker {
"(LoDTensor) The LoDTensor containing the selected ids at the "
"previous step. It should be a tensor with shape (batch_size, 1) "
"and lod `[[0, 1, ... , batch_size], [0, 1, ..., batch_size]]` at "
"thefirst step.");
"the first step.");
AddInput("pre_scores",
"(LoDTensor) The LoDTensor containing the accumulated "
"scores corresponding to the selected ids at the previous step.");
AddInput("ids",
"(LoDTensor) The LoDTensor containing the candidates ids. Its "
"shape should be (batch_size * beam_size, K), where K supposed to "
"be beam_size.");
"shape should be (batch_size * beam_size, W). If not set, it will "
"be calculated out according to Input(scores) in this operator.")
.AsDispensable();
AddInput("scores",
"(LoDTensor) The LodTensor containing the accumulated scores "
"corresponding to Input(ids) and its shape is the same as the "
"shape of Input(ids).");
"(LoDTensor) The LoDTensor containing the current scores "
"corresponding to Input(ids). If Input(ids) is not nullptr, its "
"shape is the same as that of Input(ids)."
"If is_accumulated is true, Input(scores) is accumulated scores "
"and will be used derectedly. Else, each score will be "
"transformed to the log field and accumulate Input(pre_sores) "
"first.");
AddOutput("selected_ids",
"A LodTensor that stores the IDs selected by beam search.");
AddOutput("selected_scores",
......@@ -242,6 +57,9 @@ class BeamSearchOpMaker : public framework::OpProtoAndCheckerMaker {
AddAttr<int>("beam_size", "beam size for beam search");
AddAttr<int>("end_id",
"the token id which indicates the end of a sequence");
AddAttr<bool>("is_accumulated",
"Whether the Input(scores) is accumulated scores.")
.SetDefault(true);
AddComment(R"DOC(
This operator does the search in beams for one time step.
......@@ -265,10 +83,9 @@ class BeamSearchOp : public framework::OperatorWithKernel {
public:
using framework::OperatorWithKernel::OperatorWithKernel;
protected:
void InferShape(framework::InferShapeContext *ctx) const override {
for (const std::string &arg :
std::vector<std::string>({"pre_ids", "ids", "scores"})) {
std::vector<std::string>({"pre_ids", "scores"})) {
PADDLE_ENFORCE(ctx->HasInput(arg), "BeamSearch need input argument '%s'",
arg);
}
......@@ -279,12 +96,22 @@ class BeamSearchOp : public framework::OperatorWithKernel {
}
}
protected:
framework::OpKernelType GetExpectedKernelType(
const framework::ExecutionContext &ctx) const override {
framework::OpKernelType kt = framework::OpKernelType(
ctx.Input<framework::LoDTensor>("pre_ids")->type(),
platform::CPUPlace());
return kt;
auto *scores = ctx.Input<framework::LoDTensor>("scores");
size_t level = ctx.Attr<int>("level");
size_t batch_size = scores->lod()[level].size() - 1;
// The current CUDA kernel only support cases with batch_size < 4.
// Compute on CPU for cases with batch_size > 4.
if (batch_size <= 4) {
return framework::OpKernelType(
ctx.Input<framework::LoDTensor>("pre_ids")->type(), ctx.GetPlace());
} else {
return framework::OpKernelType(
ctx.Input<framework::LoDTensor>("pre_ids")->type(),
platform::CPUPlace());
}
}
};
......
paddle/fluid/operators/beam_search_op.cu.cc 0 → 100644
浏览文件 @ 0016b6a2
/* 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. */
#include "paddle/fluid/operators/beam_search_op.h"
#include "paddle/fluid/framework/op_registry.h"
namespace ops = paddle::operators;
REGISTER_OP_CUDA_KERNEL(
beam_search,
ops::BeamSearchOpKernel<paddle::platform::CUDADeviceContext, float>,
ops::BeamSearchOpKernel<paddle::platform::CUDADeviceContext, double>,
ops::BeamSearchOpKernel<paddle::platform::CUDADeviceContext, int>,
ops::BeamSearchOpKernel<paddle::platform::CUDADeviceContext, int64_t>);
paddle/fluid/operators/beam_search_op.h
浏览文件 @ 0016b6a2
......@@ -4,7 +4,7 @@ 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
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,
......@@ -14,187 +14,12 @@ limitations under the License. */
#pragma once
#include <string>
#include <vector>
#include "paddle/fluid/framework/lod_tensor.h"
#include "paddle/fluid/framework/operator.h"
#include "paddle/fluid/operators/math/beam_search.h"
namespace paddle {
namespace operators {
/*
* This is an implementation of beam search.
*
* To explain the details, lets take machine translation task for example, in
* this task, one source sentence is translated to multiple target sentences,
* during this period, one sentence will be translated to multiple translation
* prefixes(target sentence that have not ended), in each time step a prefix
* will have some candidates, input the candidate ids and their corresponding
* scores (probabilities), it will sort and select the top beam_size candidates
* for each source sentence, and store the selected candidates's score and their
* corresponding ids to LoDTensors.
*
* A detailed example:
*
* Input
*
* ids:
* LoD (should have 2 levels)
* first level: [0, 1, 4]
* second level: [0, 1, 2, 3, 4]
*
* tensor's data
* [
* [4, 2, 5]
* [2, 1, 3]
* [3, 5, 2]
* [8, 2, 1]
* ]
*
* scores:
* LoD same as `ids`
* tensor's data
* [
* [0.5, 0.3, 0.2]
* [0.6, 0.3, 0.1]
* [0.9, 0.5, 0.1]
* [0.7, 0.5, 0.1]
* ]
*
* the inputs means that there are 2 source sentences to translate, and the
* first source has 1 prefix, the second source has 2 prefix.
*
* lets assume beam size is 2, and the beam search's output should be
* LoD
* first level:
* [0, 1, 2]
* second level:
* [0, 2, 4]
*
* id tensor's data
* [[
* 4,
* 1,
* 3,
* 8,
* ]]
*
* score tensor's data
* [[
* 0.5,
* 0.3,
* 0.9,
* 0.7
* ]]
*
* TODO all the prune operations should be in the beam search, so it is better
* to split the beam search algorithm into a sequence of smaller operators, and
* the prune operators can be inserted in this sequence.
*/
class BeamSearch {
public:
// TODO(superjom) make type customizable
using id_t = size_t;
using score_t = float;
/*
* Input the arguments that needed by this class.
*/
BeamSearch(const framework::LoDTensor& ids,
const framework::LoDTensor& scores, size_t level, size_t beam_size,
int end_id)
: beam_size_(beam_size),
ids_(&ids),
scores_(&scores),
lod_level_(level),
end_id_(end_id) {}
/*
* The main function of beam search.
*
* @selected_ids: a [None, 1]-shaped tensor with LoD.
* In a machine translation model, it might be the candidate term id sets,
* each set stored as a varience-length sequence.
* The format might be described with a two-level LoD
* - [[0 1]
* - [0 1 2]]
* - [[]
* - [0 1]]
* the first level of LoD tells that there are two source sentences. The
* second level describes the details of the candidate id set's offsets in
* the
* source sentences.
*
* @selected_scores: a LoD tensor with the same shape and LoD with
* selected_ids.
* It stores the corresponding scores of candidate ids in selected_ids.
*
* Return false if all the input tensor is empty, in machine translation task
* that means no candidates is provided, and the task will stop running.
*/
void operator()(const framework::LoDTensor& pre_ids,
const framework::LoDTensor& pre_scores,
framework::LoDTensor* selected_ids,
framework::LoDTensor* selected_scores);
/*
* The basic items help to sort.
*/
struct Item {
Item() {}
Item(size_t offset, size_t id, float score)
: offset(offset), id(id), score(score) {}
// offset in the higher lod level.
size_t offset;
// // prefix id in the lower lod level.
// size_t prefix;
// the candidate id
id_t id;
// the corresponding score
score_t score;
};
protected:
/*
* Prune the source sentences all branchs finished, and it is optional.
* Pruning must one step later than finishing (thus pre_ids is needed here),
* since the end tokens must be writed out.
*/
void PruneEndBeams(const framework::LoDTensor& pre_ids,
std::vector<std::vector<Item>>* items);
/*
* Transform the items into a map whose key is offset, value is the items.
* NOTE low performance.
*/
std::vector<std::vector<Item>> ToMap(
const std::vector<std::vector<Item>>& inputs, size_t element_num);
/*
* For each source, select top beam_size records.
*/
std::vector<std::vector<Item>> SelectTopBeamSizeItems(
const framework::LoDTensor& pre_ids,
const framework::LoDTensor& pre_scores);
/*
* Get the items of next source sequence, return false if no remaining items.
*/
bool NextItemSet(const framework::LoDTensor& pre_ids,
const framework::LoDTensor& pre_scores,
std::vector<Item>* items);
private:
size_t beam_size_;
const framework::LoDTensor* ids_;
const framework::LoDTensor* scores_;
size_t lod_level_{0};
size_t sent_offset_{0};
int end_id_{0};
};
std::ostream& operator<<(std::ostream& os, const BeamSearch::Item& item);
std::string ItemToString(const BeamSearch::Item& item);
template <typename DeviceContext, typename T>
class BeamSearchOpKernel : public framework::OpKernel<T> {
public:
......@@ -203,7 +28,7 @@ class BeamSearchOpKernel : public framework::OpKernel<T> {
auto* scores = context.Input<framework::LoDTensor>("scores");
auto* pre_ids = context.Input<framework::LoDTensor>("pre_ids");
auto* pre_scores = context.Input<framework::LoDTensor>("pre_scores");
PADDLE_ENFORCE_NOT_NULL(ids);
PADDLE_ENFORCE_NOT_NULL(scores);
PADDLE_ENFORCE_NOT_NULL(pre_ids);
PADDLE_ENFORCE_NOT_NULL(pre_scores);
......@@ -211,14 +36,20 @@ class BeamSearchOpKernel : public framework::OpKernel<T> {
size_t level = context.Attr<int>("level");
size_t beam_size = context.Attr<int>("beam_size");
int end_id = context.Attr<int>("end_id");
BeamSearch alg(*ids, *scores, level, beam_size, end_id);
bool is_accumulated = context.Attr<bool>("is_accumulated");
auto selected_ids = context.Output<framework::LoDTensor>("selected_ids");
auto selected_scores =
context.Output<framework::LoDTensor>("selected_scores");
PADDLE_ENFORCE_NOT_NULL(selected_ids);
PADDLE_ENFORCE_NOT_NULL(selected_scores);
alg(*pre_ids, *pre_scores, selected_ids, selected_scores);
math::BeamSearchFunctor<DeviceContext, T> alg;
alg(context.template device_context<DeviceContext>(), pre_ids, pre_scores,
ids, scores, selected_ids, selected_scores, level, beam_size, end_id,
is_accumulated);
}
};
} // namespace operators
} // namespace paddle
paddle/fluid/operators/beam_search_op_test.cc 已删除 100644 → 0
浏览文件 @ ab5442d9
/* 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. */
#include "paddle/fluid/operators/beam_search_op.h"
#include <gtest/gtest.h>
#include <vector>
namespace paddle {
namespace test {
using std::vector;
using framework::LoDTensor;
using framework::LoD;
using operators::BeamSearch;
using paddle::platform::CPUPlace;
using std::cout;
using std::endl;
void CreateInput(LoDTensor* ids, LoDTensor* scores) {
LoD lod;
vector<size_t> level0({0, 2, 4});
vector<size_t> level1({0, 1, 2, 3, 4});
lod.push_back(level0);
lod.push_back(level1);
ids->set_lod(lod);
scores->set_lod(lod);
auto dims = framework::make_ddim(vector<int64_t>({4, 3}));
ids->Resize(dims);
scores->Resize(dims);
CPUPlace place;
auto* ids_data = ids->mutable_data<int64_t>(place);
auto* scores_data = scores->mutable_data<float>(place);
vector<int64_t> _ids({4, 2, 5, 2, 1, 3, 3, 5, 2, 8, 2, 1});
vector<float> _scores(
{0.5f, 0.3f, 0.2f, 0.6f, 0.3f, 0.1f, 0.9f, 0.5f, 0.1f, 0.7f, 0.5f, 0.1f});
for (int i = 0; i < 12; i++) {
ids_data[i] = _ids[i];
scores_data[i] = _scores[i];
}
}
// It seems that beam_search_op has bugs.
TEST(DISABLED_beam_search_op, run) {
CPUPlace place;
LoDTensor ids, scores;
CreateInput(&ids, &scores);
LoDTensor pre_ids;
pre_ids.Resize(framework::make_ddim(vector<int64_t>(4, 1)));
for (int i = 0; i < 4; i++) {
pre_ids.mutable_data<int64_t>(place)[i] = i + 1;
}
LoDTensor pre_scores;
pre_scores.Resize(framework::make_ddim(vector<int64_t>(4, 1)));
for (int i = 0; i < 4; i++) {
pre_scores.mutable_data<float>(place)[i] = 0.1 * (i + 1);
}
BeamSearch beamsearch(ids, scores, (size_t)0, (size_t)2, 0);
LoDTensor sids, sscores;
beamsearch(pre_ids, pre_scores, &sids, &sscores);
LOG(INFO) << "score: " << sscores << endl;
ASSERT_EQ(sids.lod(), sscores.lod());
vector<int> tids({4, 2, 3, 8});
vector<float> tscores({0.5f, 0.6f, 0.9f, 0.7f});
for (int i = 0; i < 4; i++) {
ASSERT_EQ(tids[i], sids.data<int64_t>()[i]);
ASSERT_EQ(tscores[i], sscores.data<float>()[i]);
}
}
} // namespace test
} // namespace paddle
paddle/fluid/operators/bpr_loss_op.h
浏览文件 @ 0016b6a2
......@@ -87,8 +87,8 @@ class BprLossGradientOpKernel : public framework::OpKernel<T> {
auto* label = ctx.Input<Tensor>("Label");
auto* dx = ctx.Output<Tensor>(framework::GradVarName("X"));
const int step_size = x->dims()[0];
const int num_classes = x->dims()[1];
const size_t step_size = static_cast<size_t>(x->dims()[0]);
const size_t num_classes = static_cast<size_t>(x->dims()[1]);
T* dx_data = dx->mutable_data<T>(ctx.GetPlace());
const T* dy_data = dy->data<T>();
const T* x_data = x->data<T>();
......
paddle/fluid/operators/detection/CMakeLists.txt
浏览文件 @ 0016b6a2
......@@ -45,3 +45,7 @@ detection_library(roi_perspective_transform_op SRCS roi_perspective_transform_op
foreach(src ${LOCAL_DETECTION_LIBS})
set(OP_LIBRARY ${src} ${OP_LIBRARY} CACHE INTERNAL "op libs")
endforeach()
cc_library(mask_util SRCS mask_util.cc DEPS memory)
cc_test(mask_util_test SRCS mask_util_test.cc DEPS memory mask_util)
detection_library(generate_mask_labels_op SRCS generate_mask_labels_op.cc DEPS mask_util)
paddle/fluid/operators/detection/bbox_util.h
浏览文件 @ 0016b6a2
/* Copyright (c) 2018 PaddlePaddle Authors. All Rights Reserved.
Licensed under the Apache License, Version 2.0 (the "License");
you may not use this file except in compliance with the License.
You may obtain a copy of the License at
http://www.apache.org/licenses/LICENSE-2.0
Unless required by applicable law or agreed to in writing, software
distributed under the License is distributed on an "AS IS" BASIS,
WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
See the License for the specific language governing permissions and
limitations under the License. */
#pragma once
#include <algorithm>
#include "paddle/fluid/framework/eigen.h"
......@@ -88,7 +92,9 @@ void BboxOverlaps(const framework::Tensor& r_boxes,
inter_w = std::max(x_max - x_min + 1, zero);
inter_h = std::max(y_max - y_min + 1, zero);
inter_area = inter_w * inter_h;
overlaps_et(i, j) = inter_area / (r_box_area + c_box_area - inter_area);
overlaps_et(i, j) =
(inter_area == 0.) ? 0 : inter_area /
(r_box_area + c_box_area - inter_area);
}
}
}
......
paddle/fluid/operators/detection/generate_mask_labels_op.cc 0 → 100644
浏览文件 @ 0016b6a2
/* Copyright (c) 2018 PaddlePaddle Authors. All Rights Reserved.
Licensed under the Apache License, Version 2.0 (the "License");
you may not use this file except in compliance with the License.
You may obtain a copy of the License at
http://www.apache.org/licenses/LICENSE-2.0
Unless required by applicable law or agreed to in writing, software
distributed under the License is distributed on an "AS IS" BASIS,
WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
See the License for the specific language governing permissions and
limitations under the License. */
#include <math.h>
#include <algorithm>
#include <string>
#include <vector>
#include "paddle/fluid/framework/lod_tensor.h"
#include "paddle/fluid/framework/op_registry.h"
#include "paddle/fluid/operators/detection/bbox_util.h"
#include "paddle/fluid/operators/detection/mask_util.h"
#include "paddle/fluid/operators/gather.h"
#include "paddle/fluid/operators/math/concat_and_split.h"
#include "paddle/fluid/operators/math/math_function.h"
namespace paddle {
namespace operators {
using Tensor = framework::Tensor;
using LoDTensor = framework::LoDTensor;
const int kBoxDim = 4;
template <typename T>
void AppendMask(LoDTensor* out, int64_t offset, Tensor* to_add) {
auto* out_data = out->data<T>();
auto* to_add_data = to_add->data<T>();
memcpy(out_data + offset, to_add_data, to_add->numel() * sizeof(T));
}
class GenerateMaskLabelsOp : public framework::OperatorWithKernel {
public:
using framework::OperatorWithKernel::OperatorWithKernel;
void InferShape(framework::InferShapeContext* ctx) const override {
PADDLE_ENFORCE(ctx->HasInput("ImInfo"), "Input(ImInfo) shouldn't be null.");
PADDLE_ENFORCE(ctx->HasInput("GtClasses"),
"Input(GtClasses) shouldn't be null.");
PADDLE_ENFORCE(ctx->HasInput("IsCrowd"),
"Input(IsCrowd) shouldn't be null.");
PADDLE_ENFORCE(ctx->HasInput("GtSegms"),
"Input(GtSegms) shouldn't be null.");
PADDLE_ENFORCE(ctx->HasInput("Rois"), "Input(Rois) shouldn't be null.");
PADDLE_ENFORCE(ctx->HasInput("LabelsInt32"),
"Input(LabelsInt32) shouldn't be null.");
PADDLE_ENFORCE(
ctx->HasOutput("MaskRois"),
"Output(MaskRois) of GenerateMaskLabelsOp should not be null");
PADDLE_ENFORCE(
ctx->HasOutput("RoiHasMaskInt32"),
"Output(RoiHasMaskInt32) of GenerateMaskLabelsOp should not be null");
PADDLE_ENFORCE(
ctx->HasOutput("MaskInt32"),
"Output(MaskInt32) of GenerateMaskLabelsOp should not be null");
auto im_info_dims = ctx->GetInputDim("ImInfo");
auto gt_segms_dims = ctx->GetInputDim("GtSegms");
PADDLE_ENFORCE_EQ(im_info_dims.size(), 2,
"The rank of Input(ImInfo) must be 2.");
PADDLE_ENFORCE_EQ(gt_segms_dims.size(), 2,
"The rank of Input(GtSegms) must be 2.");
PADDLE_ENFORCE_EQ(gt_segms_dims[1], 2,
"The second dim of Input(GtSegms) must be 2.");
int num_classes = ctx->Attrs().Get<int>("num_classes");
int resolution = ctx->Attrs().Get<int>("resolution");
ctx->SetOutputDim("MaskRois", {-1, 4});
ctx->SetOutputDim("RoiHasMaskInt32", {-1, 1});
ctx->SetOutputDim("MaskInt32", {-1, num_classes * resolution * resolution});
}
protected:
framework::OpKernelType GetExpectedKernelType(
const framework::ExecutionContext& ctx) const override {
auto data_type = framework::GetDataTypeOfVar(ctx.InputVar("Rois"));
return framework::OpKernelType(data_type, platform::CPUPlace());
}
};
/*
* Expand masks from shape (#masks, M ** 2) to (#masks, #classes * M ** 2)
* to encode class specific mask targets.
*/
template <typename T>
static inline void ExpandMaskTarget(const platform::CPUDeviceContext& ctx,
const Tensor& masks,
const Tensor& mask_class_labels,
const int resolution, const int num_classes,
Tensor* mask_targets) {
const uint8_t* masks_data = masks.data<uint8_t>();
int64_t num_mask = masks.dims()[0];
const int* mask_class_labels_data = mask_class_labels.data<int>();
const int M = resolution * resolution;
const int mask_dim = M * num_classes;
int* mask_targets_data =
mask_targets->mutable_data<int>({num_mask, mask_dim}, ctx.GetPlace());
math::set_constant(ctx, mask_targets, -1);
for (int64_t mask_id = 0; mask_id < num_mask; ++mask_id) {
int cls = mask_class_labels_data[mask_id];
int start = M * cls;
if (cls > 0) {
for (int i = 0; i < M; ++i) {
mask_targets_data[mask_id * mask_dim + start + i] =
static_cast<int>(masks_data[mask_id * M + i]);
}
}
}
}
template <typename T>
std::vector<Tensor> SampleMaskForOneImage(
const platform::CPUDeviceContext& ctx, const Tensor& im_info,
const Tensor& gt_classes, const Tensor& is_crowd, const Tensor& gt_segms,
const Tensor& rois, const Tensor& label_int32, const int num_classes,
const int resolution, const framework::LoD& segm_length) {
// Prepare the mask targets by associating one gt mask to each training roi
// that has a fg (non-bg) class label.
const int64_t gt_size = static_cast<int64_t>(gt_classes.dims()[0]);
const int64_t roi_size = static_cast<int64_t>(rois.dims()[0]);
const int* gt_classes_data = gt_classes.data<int>();
const int* is_crowd_data = is_crowd.data<int>();
const int* label_int32_data = label_int32.data<int>();
PADDLE_ENFORCE_EQ(roi_size, label_int32.dims()[0]);
std::vector<int> mask_gt_inds, fg_inds;
std::vector<std::vector<std::vector<T>>> gt_polys;
auto polys_num = segm_length[1];
auto segm_lod_offset = framework::ConvertToOffsetBasedLoD(segm_length);
auto lod1 = segm_lod_offset[1];
auto lod2 = segm_lod_offset[2];
const T* polys_data = gt_segms.data<T>();
for (int64_t i = 0; i < gt_size; ++i) {
if ((gt_classes_data[i] > 0) && (is_crowd_data[i] == 0)) {
mask_gt_inds.emplace_back(i);
// slice fg segmentation polys
int poly_num = polys_num[i];
std::vector<std::vector<T>> polys;
int s_idx = lod1[i];
for (int j = 0; j < poly_num; ++j) {
int s = lod2[s_idx + j];
int e = lod2[s_idx + j + 1];
PADDLE_ENFORCE_NE(s, e);
std::vector<T> plts(polys_data + s * 2, polys_data + e * 2);
polys.push_back(plts);
}
gt_polys.push_back(polys);
}
}
for (int64_t i = 0; i < roi_size; ++i) {
if (label_int32_data[i] > 0) {
fg_inds.emplace_back(i);
}
}
int gt_num = mask_gt_inds.size();
int fg_num = fg_inds.size();
Tensor boxes_from_polys;
boxes_from_polys.mutable_data<T>({gt_num, 4}, platform::CPUPlace());
Poly2Boxes(gt_polys, boxes_from_polys.data<T>());
std::vector<int> roi_has_mask =
std::vector<int>(fg_inds.begin(), fg_inds.end());
Tensor mask_class_labels;
Tensor masks;
Tensor rois_fg;
auto im_scale = im_info.data<T>()[2];
if (fg_num > 0) {
// Class labels for the foreground rois
mask_class_labels.mutable_data<int>({fg_num, 1}, ctx.GetPlace());
Gather<int>(label_int32_data, 1, fg_inds.data(), fg_inds.size(),
mask_class_labels.data<int>());
uint8_t* masks_data = masks.mutable_data<uint8_t>(
{fg_num, resolution * resolution}, ctx.GetPlace());
// Find overlap between all foreground rois and the bounding boxes
// enclosing each segmentation
T* rois_fg_data = rois_fg.mutable_data<T>({fg_num, 4}, ctx.GetPlace());
Gather<T>(rois.data<T>(), 4, fg_inds.data(), fg_inds.size(),
rois_fg.data<T>());
for (int k = 0; k < rois_fg.numel(); ++k) {
rois_fg_data[k] = rois_fg_data[k] / im_scale;
}
Tensor overlaps_bbfg_bbpolys;
overlaps_bbfg_bbpolys.mutable_data<T>({fg_num, gt_num}, ctx.GetPlace());
BboxOverlaps<T>(rois_fg, boxes_from_polys, &overlaps_bbfg_bbpolys);
// Map from each fg rois to the index of the mask with highest overlap
// (measured by bbox overlap)
T* overlaps_bbfg_bbpolys_data = overlaps_bbfg_bbpolys.data<T>();
std::vector<int> fg_masks_inds;
for (int64_t i = 0; i < fg_num; ++i) {
const T* v = overlaps_bbfg_bbpolys_data + i * gt_num;
T max_overlap = std::numeric_limits<T>::min();
int id = 0;
for (int64_t j = 0; j < gt_num; ++j) {
if (v[j] > max_overlap) {
max_overlap = v[j];
id = j;
}
}
fg_masks_inds.push_back(id);
}
// add fg targets
for (int64_t i = 0; i < fg_num; ++i) {
int fg_polys_ind = fg_masks_inds[i];
T* roi_fg = rois_fg_data + i * 4;
uint8_t* mask = masks_data + i * resolution * resolution;
Polys2MaskWrtBox(gt_polys[fg_polys_ind], roi_fg, resolution, mask);
}
} else {
// The network cannot handle empty blobs, so we must provide a mask
// We simply take the first bg roi, given it an all -1's mask (ignore
// label), and label it with class zero (bg).
int bg_num = 1;
T* rois_fg_data = rois_fg.mutable_data<T>({bg_num, 4}, ctx.GetPlace());
const T* rois_data = rois.data<T>();
std::vector<int> bg_inds;
for (int64_t i = 0; i < roi_size; ++i) {
if (label_int32_data[i] == 0) {
bg_inds.emplace_back(i);
rois_fg_data[0] = rois_data[0] / im_scale;
rois_fg_data[1] = rois_data[1] / im_scale;
rois_fg_data[2] = rois_data[2] / im_scale;
rois_fg_data[3] = rois_data[3] / im_scale;
break;
}
}
masks.mutable_data<uint8_t>({bg_num, resolution * resolution},
ctx.GetPlace());
math::set_constant(ctx, &masks, -1);
int* mask_class_labels_data =
mask_class_labels.mutable_data<int>({bg_num, 1}, ctx.GetPlace());
mask_class_labels_data[0] = 0;
roi_has_mask = std::vector<int>(bg_inds.begin(), bg_inds.end());
}
Tensor masks_expand;
ExpandMaskTarget<T>(ctx, masks, mask_class_labels, resolution, num_classes,
&masks_expand);
T* rois_fg_data = rois_fg.data<T>();
for (int k = 0; k < rois_fg.numel(); ++k) {
rois_fg_data[k] = rois_fg_data[k] * im_scale;
}
Tensor roi_has_mask_t;
int roi_has_mask_size = roi_has_mask.size();
int* roi_has_mask_data =
roi_has_mask_t.mutable_data<int>({roi_has_mask_size, 1}, ctx.GetPlace());
std::copy(roi_has_mask.begin(), roi_has_mask.end(), roi_has_mask_data);
std::vector<Tensor> res;
res.emplace_back(rois_fg);
res.emplace_back(roi_has_mask_t);
res.emplace_back(masks_expand);
return res;
}
template <typename T>
class GenerateMaskLabelsKernel : public framework::OpKernel<T> {
public:
void Compute(const framework::ExecutionContext& ctx) const override {
auto* im_info = ctx.Input<LoDTensor>("ImInfo");
auto* gt_classes = ctx.Input<LoDTensor>("GtClasses");
auto* is_crowd = ctx.Input<LoDTensor>("IsCrowd");
auto* gt_segms = ctx.Input<LoDTensor>("GtSegms");
auto* rois = ctx.Input<LoDTensor>("Rois");
auto* label_int32 = ctx.Input<LoDTensor>("LabelsInt32");
auto* mask_rois = ctx.Output<LoDTensor>("MaskRois");
auto* roi_has_mask_int32 = ctx.Output<LoDTensor>("RoiHasMaskInt32");
auto* mask_int32 = ctx.Output<LoDTensor>("MaskInt32");
int num_classes = ctx.Attr<int>("num_classes");
int resolution = ctx.Attr<int>("resolution");
PADDLE_ENFORCE_EQ(gt_classes->lod().size(), 1UL,
"GenerateMaskLabelsOp gt_classes needs 1 level of LoD");
PADDLE_ENFORCE_EQ(is_crowd->lod().size(), 1UL,
"GenerateMaskLabelsOp is_crowd needs 1 level of LoD");
PADDLE_ENFORCE_EQ(rois->lod().size(), 1UL,
"GenerateMaskLabelsOp rois needs 1 level of LoD");
PADDLE_ENFORCE_EQ(label_int32->lod().size(), 1UL,
"GenerateMaskLabelsOp label_int32 needs 1 level of LoD");
PADDLE_ENFORCE_EQ(gt_segms->lod().size(), 3UL);
int64_t n = static_cast<int64_t>(gt_classes->lod().back().size() - 1);
PADDLE_ENFORCE_EQ(gt_segms->lod()[0].size() - 1, n);
int mask_dim = num_classes * resolution * resolution;
mask_rois->mutable_data<T>({rois->numel(), kBoxDim}, ctx.GetPlace());
roi_has_mask_int32->mutable_data<int>({rois->numel(), 1}, ctx.GetPlace());
mask_int32->mutable_data<int>({rois->numel(), mask_dim}, ctx.GetPlace());
framework::LoD lod;
std::vector<size_t> lod0(1, 0);
int64_t num_mask = 0;
auto& dev_ctx = ctx.device_context<platform::CPUDeviceContext>();
auto gt_classes_lod = gt_classes->lod().back();
auto is_crowd_lod = is_crowd->lod().back();
auto rois_lod = rois->lod().back();
auto label_int32_lod = label_int32->lod().back();
auto gt_segms_lod = gt_segms->lod();
for (int i = 0; i < n; ++i) {
Tensor im_info_slice = im_info->Slice(i, i + 1);
Tensor gt_classes_slice =
gt_classes->Slice(gt_classes_lod[i], gt_classes_lod[i + 1]);
Tensor is_crowd_slice =
is_crowd->Slice(is_crowd_lod[i], is_crowd_lod[i + 1]);
Tensor label_int32_slice =
label_int32->Slice(label_int32_lod[i], label_int32_lod[i + 1]);
Tensor rois_slice = rois->Slice(rois_lod[i], rois_lod[i + 1]);
auto sub_lod_and_offset =
framework::GetSubLoDAndAbsoluteOffset(gt_segms_lod, i, i + 1, 0);
auto lod_length = sub_lod_and_offset.first;
size_t s = sub_lod_and_offset.second.first;
size_t e = sub_lod_and_offset.second.second;
Tensor gt_segms_slice = gt_segms->Slice(s, e);
std::vector<Tensor> tensor_output = SampleMaskForOneImage<T>(
dev_ctx, im_info_slice, gt_classes_slice, is_crowd_slice,
gt_segms_slice, rois_slice, label_int32_slice, num_classes,
resolution, lod_length);
Tensor sampled_mask_rois = tensor_output[0];
Tensor sampled_roi_has_mask_int32 = tensor_output[1];
Tensor sampled_mask_int32 = tensor_output[2];
AppendMask<T>(mask_rois, kBoxDim * num_mask, &sampled_mask_rois);
AppendMask<int>(roi_has_mask_int32, num_mask,
&sampled_roi_has_mask_int32);
AppendMask<int>(mask_int32, mask_dim * num_mask, &sampled_mask_int32);
num_mask += sampled_mask_rois.dims()[0];
lod0.emplace_back(num_mask);
}
lod.emplace_back(lod0);
mask_rois->set_lod(lod);
roi_has_mask_int32->set_lod(lod);
mask_int32->set_lod(lod);
mask_rois->Resize({num_mask, kBoxDim});
roi_has_mask_int32->Resize({num_mask, 1});
mask_int32->Resize({num_mask, mask_dim});
}
};
class GenerateMaskLabelsOpMaker : public framework::OpProtoAndCheckerMaker {
public:
void Make() override {
AddInput("ImInfo",
"(Tensor), This input is a 2D Tensor with shape [B, 3]. "
"B is the number of input images, "
"each element consists of im_height, im_width, im_scale.");
AddInput("GtClasses",
"(LoDTensor), This input is a 2D LoDTensor with shape [M, 1]. "
"M is the number of groundtruth, "
"each element is a class label of groundtruth.");
AddInput(
"IsCrowd",
"(LoDTensor), This input is a 2D LoDTensor with shape [M, 1]. "
"M is the number of groundtruth, "
"each element is a flag indicates whether a groundtruth is crowd.");
AddInput(
"GtSegms",
"(LoDTensor), This input is a 2D LoDTensor with shape [S, 2], it's LoD "
"level is 3. The LoD[0] represents the gt objects number of each "
"instance. LoD[1] represents the segmentation counts of each objects. "
"LoD[2] represents the polygons number of each segmentation. S the "
"total number of polygons coordinate points. Each element is (x, y) "
"coordinate points.");
AddInput(
"Rois",
"(LoDTensor), This input is a 2D LoDTensor with shape [R, 4]. "
"R is the number of rois which is the output of "
"generate_proposal_labels, "
"each element is a bounding box with (xmin, ymin, xmax, ymax) format.");
AddInput("LabelsInt32",
"(LoDTensor), This intput is a 2D LoDTensor with shape [R, 1], "
"each element repersents a class label of a roi");
AddOutput(
"MaskRois",
"(LoDTensor), This output is a 2D LoDTensor with shape [P, 4]. "
"P is the number of mask, "
"each element is a bounding box with [xmin, ymin, xmax, ymax] format.");
AddOutput("RoiHasMaskInt32",
"(LoDTensor), This output is a 2D LoDTensor with shape [P, 1], "
"each element repersents the output mask rois index with regard "
"to input rois");
AddOutput("MaskInt32",
"(LoDTensor), This output is a 4D LoDTensor with shape [P, Q], "
"Q equal to num_classes * resolution * resolution");
AddAttr<int>("num_classes", "Class number.");
AddAttr<int>("resolution", "Resolution of mask.");
AddComment(R"DOC(
This operator can be, for given the RoIs and corresponding labels,
to sample foreground RoIs. This mask branch also has
a :math: `K \\times M^{2}` dimensional output targets for each foreground
RoI, which encodes K binary masks of resolution M x M, one for each of the
K classes. This mask targets are used to compute loss of mask branch.
)DOC");
}
};
} // namespace operators
} // namespace paddle
namespace ops = paddle::operators;
REGISTER_OPERATOR(generate_mask_labels, ops::GenerateMaskLabelsOp,
ops::GenerateMaskLabelsOpMaker,
paddle::framework::EmptyGradOpMaker);
REGISTER_OP_CPU_KERNEL(generate_mask_labels,
ops::GenerateMaskLabelsKernel<float>);
paddle/fluid/operators/detection/generate_proposal_labels_op.cc
浏览文件 @ 0016b6a2
......@@ -48,20 +48,21 @@ class GenerateProposalLabelsOp : public framework::OperatorWithKernel {
"Input(GtBoxes) shouldn't be null.");
PADDLE_ENFORCE(ctx->HasInput("ImInfo"), "Input(ImInfo) shouldn't be null.");
PADDLE_ENFORCE(ctx->HasOutput("Rois"),
"Output(Rois) of RpnTargetAssignOp should not be null");
PADDLE_ENFORCE(
ctx->HasOutput("Rois"),
"Output(Rois) of GenerateProposalLabelsOp should not be null");
PADDLE_ENFORCE(
ctx->HasOutput("LabelsInt32"),
"Output(LabelsInt32) of RpnTargetAssignOp should not be null");
"Output(LabelsInt32) of GenerateProposalLabelsOp should not be null");
PADDLE_ENFORCE(
ctx->HasOutput("BboxTargets"),
"Output(BboxTargets) of RpnTargetAssignOp should not be null");
PADDLE_ENFORCE(
ctx->HasOutput("BboxInsideWeights"),
"Output(BboxInsideWeights) of RpnTargetAssignOp should not be null");
PADDLE_ENFORCE(
ctx->HasOutput("BboxOutsideWeights"),
"Output(BboxOutsideWeights) of RpnTargetAssignOp should not be null");
"Output(BboxTargets) of GenerateProposalLabelsOp should not be null");
PADDLE_ENFORCE(ctx->HasOutput("BboxInsideWeights"),
"Output(BboxInsideWeights) of GenerateProposalLabelsOp "
"should not be null");
PADDLE_ENFORCE(ctx->HasOutput("BboxOutsideWeights"),
"Output(BboxOutsideWeights) of GenerateProposalLabelsOp "
"should not be null");
auto rpn_rois_dims = ctx->GetInputDim("RpnRois");
auto gt_boxes_dims = ctx->GetInputDim("GtBoxes");
......@@ -225,30 +226,36 @@ void GatherBoxesLabels(const platform::CPUDeviceContext& context,
template <typename T>
std::vector<Tensor> SampleRoisForOneImage(
const platform::CPUDeviceContext& context, Tensor* rpn_rois,
Tensor* gt_classes, Tensor* is_crowd, Tensor* gt_boxes, Tensor* im_info,
const int batch_size_per_im, const float fg_fraction, const float fg_thresh,
const float bg_thresh_hi, const float bg_thresh_lo,
const platform::CPUDeviceContext& context, const Tensor& rpn_rois_in,
const Tensor& gt_classes, const Tensor& is_crowd, const Tensor& gt_boxes,
const Tensor& im_info, const int batch_size_per_im, const float fg_fraction,
const float fg_thresh, const float bg_thresh_hi, const float bg_thresh_lo,
const std::vector<float>& bbox_reg_weights, const int class_nums,
std::minstd_rand engine, bool use_random) {
auto rpn_rois_et = framework::EigenTensor<T, 2>::From(*rpn_rois);
auto im_scale = im_info->data<T>()[2];
rpn_rois_et = rpn_rois_et / im_scale;
auto im_scale = im_info.data<T>()[2];
Tensor rpn_rois;
rpn_rois.mutable_data<T>(rpn_rois_in.dims(), context.GetPlace());
T* rpn_rois_dt = rpn_rois.data<T>();
const T* rpn_rois_in_dt = rpn_rois_in.data<T>();
for (int i = 0; i < rpn_rois.numel(); ++i) {
rpn_rois_dt[i] = rpn_rois_in_dt[i] / im_scale;
}
Tensor boxes;
int proposals_num = gt_boxes->dims()[0] + rpn_rois->dims()[0];
int proposals_num = gt_boxes.dims()[0] + rpn_rois.dims()[0];
boxes.mutable_data<T>({proposals_num, kBoxDim}, context.GetPlace());
Concat<T>(context, *gt_boxes, *rpn_rois, &boxes);
Concat<T>(context, gt_boxes, rpn_rois, &boxes);
// Overlaps
Tensor proposal_to_gt_overlaps;
proposal_to_gt_overlaps.mutable_data<T>({proposals_num, gt_boxes->dims()[0]},
proposal_to_gt_overlaps.mutable_data<T>({proposals_num, gt_boxes.dims()[0]},
context.GetPlace());
BboxOverlaps<T>(boxes, *gt_boxes, &proposal_to_gt_overlaps);
BboxOverlaps<T>(boxes, gt_boxes, &proposal_to_gt_overlaps);
// Generate proposal index
std::vector<std::vector<int>> fg_bg_gt = SampleFgBgGt<T>(
context, &proposal_to_gt_overlaps, *is_crowd, batch_size_per_im,
context, &proposal_to_gt_overlaps, is_crowd, batch_size_per_im,
fg_fraction, fg_thresh, bg_thresh_hi, bg_thresh_lo, engine, use_random);
std::vector<int> fg_inds = fg_bg_gt[0];
std::vector<int> bg_inds = fg_bg_gt[1];
......@@ -263,7 +270,7 @@ std::vector<Tensor> SampleRoisForOneImage(
sampled_boxes.mutable_data<T>(bbox_dim, context.GetPlace());
sampled_labels.mutable_data<int>({boxes_num}, context.GetPlace());
sampled_gts.mutable_data<T>({fg_num, kBoxDim}, context.GetPlace());
GatherBoxesLabels<T>(context, boxes, *gt_boxes, *gt_classes, fg_inds, bg_inds,
GatherBoxesLabels<T>(context, boxes, gt_boxes, gt_classes, fg_inds, bg_inds,
gt_inds, &sampled_boxes, &sampled_labels, &sampled_gts);
// Compute targets
......@@ -397,8 +404,8 @@ class GenerateProposalLabelsKernel : public framework::OpKernel<T> {
gt_boxes->Slice(gt_boxes_lod[i], gt_boxes_lod[i + 1]);
Tensor im_info_slice = im_info->Slice(i, i + 1);
std::vector<Tensor> tensor_output = SampleRoisForOneImage<T>(
dev_ctx, &rpn_rois_slice, &gt_classes_slice, &is_crowd_slice,
&gt_boxes_slice, &im_info_slice, batch_size_per_im, fg_fraction,
dev_ctx, rpn_rois_slice, gt_classes_slice, is_crowd_slice,
gt_boxes_slice, im_info_slice, batch_size_per_im, fg_fraction,
fg_thresh, bg_thresh_hi, bg_thresh_lo, bbox_reg_weights, class_nums,
engine, use_random);
Tensor sampled_rois = tensor_output[0];
......@@ -467,7 +474,7 @@ class GenerateProposalLabelsOpMaker : public framework::OpProtoAndCheckerMaker {
"P usuall equal to batch_size_per_im * batch_size, "
"each element is a bounding box with [xmin, ymin, xmax, ymax] format.");
AddOutput("LabelsInt32",
"(LoDTensor), This output is a 2D LoDTensor with shape [P], "
"(LoDTensor), This output is a 2D LoDTensor with shape [P, 1], "
"each element repersents a class label of a roi");
AddOutput("BboxTargets",
"(LoDTensor), This output is a 2D LoDTensor with shape [P, 4 * "
......
paddle/fluid/operators/detection/mask_util.cc 0 → 100644
浏览文件 @ 0016b6a2
/* 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. */
#include "paddle/fluid/operators/detection/mask_util.h"
#include <math.h>
#include <stdlib.h>
#include <algorithm>
#include <limits>
#include <utility>
#include "paddle/fluid/memory/memory.h"
namespace paddle {
namespace operators {
uint32_t UMax(uint32_t a, uint32_t b) { return (a > b) ? a : b; }
static inline int Compare(const void* a, const void* b) {
uint32_t c = *(reinterpret_cast<const uint32_t*>(a));
uint32_t d = *(reinterpret_cast<const uint32_t*>(b));
return c > d ? 1 : c < d ? -1 : 0;
}
void Decode(const uint32_t* cnts, int m, uint8_t* mask) {
uint8_t v = 0;
for (int j = 0; j < m; j++) {
for (uint32_t k = 0; k < cnts[j]; k++) {
*(mask++) = v;
}
v = !v;
}
}
typedef uint32_t uint;
void Poly2Mask(const float* xy, int k, int h, int w, uint8_t* mask) {
int j, m = 0;
double scale = 5;
int *x, *y, *u, *v;
uint *a, *b;
platform::CPUPlace cpu;
auto xptr = memory::Alloc(cpu, sizeof(int) * (k + 1) * 2);
x = reinterpret_cast<int*>(xptr->ptr());
y = x + (k + 1);
for (j = 0; j < k; j++) x[j] = static_cast<int>(scale * xy[j * 2 + 0] + .5);
x[k] = x[0];
for (j = 0; j < k; j++) y[j] = static_cast<int>(scale * xy[j * 2 + 1] + .5);
y[k] = y[0];
for (j = 0; j < k; j++) {
m += UMax(abs(x[j] - x[j + 1]), abs(y[j] - y[j + 1])) + 1;
}
auto vptr = memory::Alloc(cpu, sizeof(int) * m * 2);
u = reinterpret_cast<int*>(vptr->ptr());
v = u + m;
m = 0;
for (j = 0; j < k; j++) {
int xs = x[j], xe = x[j + 1], ys = y[j], ye = y[j + 1], dx, dy, t, d;
int flip;
double s;
dx = abs(xe - xs);
dy = abs(ys - ye);
flip = (dx >= dy && xs > xe) || (dx < dy && ys > ye);
if (flip) {
t = xs;
xs = xe;
xe = t;
t = ys;
ys = ye;
ye = t;
}
if (dx >= dy) {
s = dx == 0 ? 0 : static_cast<double>(ye - ys) / dx;
for (d = 0; d <= dx; d++) {
t = flip ? dx - d : d;
u[m] = t + xs;
v[m] = static_cast<int>(ys + s * t + .5);
m++;
}
} else {
s = dy == 0 ? 0 : static_cast<double>(xe - xs) / dy;
for (d = 0; d <= dy; d++) {
t = flip ? dy - d : d;
v[m] = t + ys;
u[m] = static_cast<int>(xs + s * t + .5);
m++;
}
}
}
/* get points along y-boundary and downsample */
k = m;
m = 0;
double xd, yd;
auto xyptr = memory::Alloc(cpu, sizeof(int) * k * 2);
x = reinterpret_cast<int*>(xyptr->ptr());
y = x + k;
for (j = 1; j < k; j++) {
if (u[j] != u[j - 1]) {
xd = static_cast<double>(u[j] < u[j - 1] ? u[j] : u[j] - 1);
xd = (xd + .5) / scale - .5;
if (floor(xd) != xd || xd < 0 || xd > w - 1) continue;
yd = static_cast<double>(v[j] < v[j - 1] ? v[j] : v[j - 1]);
yd = (yd + .5) / scale - .5;
if (yd < 0)
yd = 0;
else if (yd > h)
yd = h;
yd = ceil(yd);
x[m] = static_cast<int>(xd);
y[m] = static_cast<int>(yd);
m++;
}
}
/* compute rle encoding given y-boundary points */
k = m;
auto aptr = memory::Alloc(cpu, sizeof(uint) * (k + 1));
a = reinterpret_cast<uint*>(aptr->ptr());
for (j = 0; j < k; j++) a[j] = static_cast<uint>(x[j] * h + y[j]);
a[k++] = static_cast<uint>(h * w);
qsort(a, k, sizeof(uint), Compare);
uint p = 0;
for (j = 0; j < k; j++) {
uint t = a[j];
a[j] -= p;
p = t;
}
auto bptr = memory::Alloc(cpu, sizeof(uint32_t) * k);
b = reinterpret_cast<uint32_t*>(bptr->ptr());
j = m = 0;
b[m++] = a[j++];
while (j < k) {
if (a[j] > 0) {
b[m++] = a[j++];
} else {
j++;
if (j < k) b[m - 1] += a[j++];
}
}
// convert to mask
auto mskptr = memory::Alloc(cpu, sizeof(uint8_t) * h * w);
uint8_t* msk = reinterpret_cast<uint8_t*>(mskptr->ptr());
Decode(b, m, msk);
for (int ii = 0; ii < h; ++ii) {
for (int jj = 0; jj < w; ++jj) {
mask[ii * w + jj] = msk[jj * h + ii];
}
}
}
void Poly2Boxes(const std::vector<std::vector<std::vector<float>>>& polys,
float* boxes) {
// lists
for (size_t i = 0; i < polys.size(); ++i) {
float x0 = std::numeric_limits<float>::max();
float x1 = std::numeric_limits<float>::min();
float y0 = std::numeric_limits<float>::max();
float y1 = std::numeric_limits<float>::min();
// each list may have more than one polys
for (size_t j = 0; j < polys[i].size(); ++j) {
for (size_t k = 0; k < polys[i][j].size() / 2; ++k) {
x0 = std::min(x0, polys[i][j][2 * k]);
x1 = std::max(x1, polys[i][j][2 * k]);
y0 = std::min(y0, polys[i][j][2 * k + 1]);
y1 = std::max(y1, polys[i][j][2 * k + 1]);
}
}
boxes[i * 4] = x0;
boxes[i * 4 + 1] = y0;
boxes[i * 4 + 2] = x1;
boxes[i * 4 + 3] = y1;
}
}
void Polys2MaskWrtBox(const std::vector<std::vector<float>>& polygons,
const float* box, int M, uint8_t* mask) {
float w = box[2] - box[0];
float h = box[3] - box[1];
w = std::max(w, static_cast<float>(1.));
h = std::max(h, static_cast<float>(1.));
uint8_t* msk = nullptr;
if (polygons.size() == 1UL) {
msk = mask;
} else {
msk = reinterpret_cast<uint8_t*>(
malloc(M * M * polygons.size() * sizeof(uint8_t)));
}
for (size_t i = 0; i < polygons.size(); ++i) {
int k = polygons[i].size() / 2;
std::vector<float> p;
for (int j = 0; j < k; ++j) {
float pw = (polygons[i][2 * j] - box[0]) * M / w;
float ph = (polygons[i][2 * j + 1] - box[1]) * M / h;
p.push_back(pw);
p.push_back(ph);
}
uint8_t* msk_i = msk + i * M * M;
Poly2Mask(p.data(), k, M, M, msk_i);
}
if (polygons.size() > 1UL) {
for (size_t i = 0; i < polygons.size(); ++i) {
uint8_t* msk_i = msk + i * M * M;
for (int j = 0; j < M * M; ++j) {
if (i == 0) {
mask[j] = msk_i[j];
} else {
mask[j] = (mask[j] + msk_i[j]) > 0 ? 1 : 0;
}
}
}
free(msk);
}
}
} // namespace operators
} // namespace paddle
paddle/fluid/operators/detection/mask_util.h 0 → 100644
浏览文件 @ 0016b6a2
/* 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. */
#pragma once
#include <stdint.h>
#include <vector>
namespace paddle {
namespace operators {
void Poly2Mask(const float* ploy, int k, int h, int w, uint8_t* mask);
void Poly2Boxes(const std::vector<std::vector<std::vector<float>>>& polys,
float* boxes);
void Polys2MaskWrtBox(const std::vector<std::vector<float>>& polygons,
const float* box, int M, uint8_t* mask);
} // namespace operators
} // namespace paddle
paddle/fluid/operators/detection/mask_util_test.cc 0 → 100644
浏览文件 @ 0016b6a2
/* 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. */
#include "paddle/fluid/operators/detection/mask_util.h"
#include <gtest/gtest.h>
#include "paddle/fluid/memory/memory.h"
namespace paddle {
namespace operators {
template <typename T>
void Compare(const T* a, const T* b, const int n) {
for (int i = 0; i < n; i++) {
EXPECT_EQ(a[i], b[i]);
}
}
TEST(MaskUtil, Poly2MaskTest) {
float polys[] = {1.97f, 1.88f, 5.81f, 1.88f, 1.69f,
6.53f, 5.94f, 6.38f, 1.97f, 1.88f};
int h = 8, w = 8;
int k = 5; // length(polys) / 2
// clang-format off
uint8_t expect_mask[] = {
0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 1, 1, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 1, 0, 0, 0, 0,
0, 0, 1, 1, 1, 0, 0, 0,
0, 0, 1, 1, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0
};
// clang-format on
// the groud-truth mask is computed by coco API:
//
// import pycocotools.mask as mask_util
// import numpy as np
// segm = [1.97, 1.88, 5.81, 1.88, 1.69, 6.53, 5.94, 6.38, 1.97, 1.88]
// rles = mask_util.frPyObjects([segm], im_h, im_w)
// mask = mask_util.decode(rles)
// print mask
platform::CPUPlace cpu;
auto allocation = memory::Alloc(cpu, sizeof(expect_mask));
uint8_t* mask = reinterpret_cast<uint8_t*>(allocation->ptr());
Poly2Mask(polys, k, h, w, mask);
Compare<uint8_t>(expect_mask, mask, h * w);
}
TEST(MaskUtil, Poly2BoxesTest) {
// clang-format off
std::vector<std::vector<std::vector<float>>> polys = {
{{1.97f, 1.88f, 5.81f, 1.88f, 1.69f, 6.53f, 5.94f, 6.38f, 1.97f, 1.88f}},
{{2.97f, 1.88f, 3.81f, 1.68f, 1.69f, 6.63f, 6.94f, 6.58f, 2.97f, 0.88f}}
};
float expect_boxes[] = {
1.69f, 1.88f, 5.94f, 6.53f,
1.69f, 0.88f, 6.94f, 6.63f
};
// clang-format on
platform::CPUPlace cpu;
auto allocation = memory::Alloc(cpu, sizeof(expect_boxes));
float* boxes = reinterpret_cast<float*>(allocation->ptr());
Poly2Boxes(polys, boxes);
Compare<float>(expect_boxes, boxes, 8);
}
TEST(MaskUtil, Polys2MaskWrtBoxTest) {
// clang-format off
std::vector<std::vector<std::vector<float>>> polys = {{
{1.97f, 1.88f, 5.81f, 1.88f, 1.69f, 6.53f, 5.94f, 6.38f, 1.97f, 1.88f},
{2.97f, 1.88f, 3.81f, 1.68f, 1.69f, 6.63f, 6.94f, 6.58f, 2.97f, 0.88f}}};
float expect_boxes[] = {
1.69f, 0.88f, 6.94f, 6.63f
};
uint8_t expect_mask[] = {
0, 0, 0, 0, 0, 0, 0, 0,
0, 1, 1, 1, 1, 1, 0, 0,
0, 0, 1, 1, 1, 0, 0, 0,
0, 0, 1, 1, 1, 0, 0, 0,
0, 0, 1, 1, 1, 0, 0, 0,
0, 1, 1, 1, 1, 1, 0, 0,
0, 1, 1, 1, 1, 1, 1, 0,
1, 1, 1, 1, 1, 1, 1, 1
};
// clang-format on
platform::CPUPlace cpu;
auto allocation = memory::Alloc(cpu, sizeof(expect_boxes));
float* boxes = reinterpret_cast<float*>(allocation->ptr());
Poly2Boxes(polys, boxes);
Compare<float>(expect_boxes, boxes, 4);
auto allocat_mask = memory::Alloc(cpu, sizeof(expect_mask));
uint8_t* mask = reinterpret_cast<uint8_t*>(allocat_mask->ptr());
int M = 8;
Polys2MaskWrtBox(polys[0], expect_boxes, M, mask);
Compare<uint8_t>(expect_mask, mask, M * M);
}
} // namespace operators
} // namespace paddle
paddle/fluid/operators/distributed/CMakeLists.txt
浏览文件 @ 0016b6a2
......@@ -20,7 +20,7 @@ if(WITH_GRPC)
collective_client.cc collective_server.cc
${GRPC_SRCS}
PROTO send_recv.proto
DEPS lod_tensor selected_rows_functor memory)
DEPS lod_tensor selected_rows_functor memory scope ${GRPC_DEPS})
set_source_files_properties(grpc_serde_test.cc rpc_server_test.cc PROPERTIES COMPILE_FLAGS ${DISTRIBUTE_COMPILE_FLAGS})
set(RPC_DEPS sendrecvop_rpc ${GRPC_DEPS})
......@@ -32,15 +32,17 @@ else()
set(BRPC_SRCS brpc/brpc_client.cc brpc/brpc_server.cc brpc/brpc_sendrecvop_utils.cc brpc/brpc_variable_response.cc brpc/brpc_rdma_pool.cc)
set_source_files_properties(${BRPC_SRCS} parameter_prefetch.cc rpc_server_test.cc brpc/brpc_serde_test.cc collective_server.cc collective_server_test.cc collective_client.cc PROPERTIES COMPILE_FLAGS ${DISTRIBUTE_COMPILE_FLAGS})
set(BRPC_DEPS brpc ssl crypto protobuf leveldb snappystream snappy zlib)
brpc_library(sendrecvop_rpc SRCS sendrecvop_utils.cc
request_handler_impl.cc rpc_client.cc rpc_server.cc
variable_response.cc
collective_client.cc collective_server.cc
${BRPC_SRCS}
PROTO send_recv.proto
DEPS lod_tensor selected_rows memory)
DEPS lod_tensor selected_rows memory scope ${BRPC_DEPS})
set(RPC_DEPS sendrecvop_rpc brpc ssl crypto protobuf leveldb snappystream snappy zlib)
set(RPC_DEPS sendrecvop_rpc ${BRPC_DEPS})
cc_test(brpc_serde_test SRCS brpc/brpc_serde_test.cc
DEPS ${RPC_DEPS} gflags glog executor proto_desc lookup_sparse_table_op SERIAL)
endif()
......
paddle/fluid/operators/distributed/brpc/brpc_client.cc
浏览文件 @ 0016b6a2
......@@ -62,7 +62,7 @@ VarHandlePtr BRPCClient::AsyncSendVar(const std::string& ep,
const std::string var_name_val = var_name;
const framework::Scope* p_scope = &scope;
const auto ch_ptr = GetChannel(ep_val);
const std::string method = "SendRPC";
const std::string method = kSendRPC;
VarHandlePtr var_h(new VarHandle(ep, method, var_name_val, p_ctx, p_scope));
framework::AsyncIO([=] {
......@@ -156,15 +156,18 @@ VarHandlePtr BRPCClient::_AsyncGetVar(const std::string& ep,
const platform::DeviceContext& ctx,
const framework::Scope& scope,
const std::string& var_name,
const std::string& out_var_name,
const std::string& method_name,
int64_t time_out) {
const platform::DeviceContext* p_ctx = &ctx;
const std::string ep_val = ep;
const std::string var_name_val = var_name;
const std::string out_varname_val = out_var_name;
const framework::Scope* p_scope = &scope;
const auto ch_ptr = GetChannel(ep_val);
const std::string method = "GetRPC";
VarHandlePtr var_h(new VarHandle(ep, method, var_name_val, p_ctx, p_scope));
const std::string method = kGetRPC;
VarHandlePtr var_h(
new VarHandle(ep, method, out_varname_val, p_ctx, p_scope));
framework::AsyncIO([=] {
auto ch_ctx = ch_ptr->Pop();
......@@ -175,6 +178,7 @@ VarHandlePtr BRPCClient::_AsyncGetVar(const std::string& ep,
sendrecv::VariableMessage req;
req.set_varname(var_name_val);
req.set_out_varname(out_varname_val);
req.set_trainer_id(trainer_id_);
google::protobuf::Closure* done = brpc::NewCallback(
......@@ -182,8 +186,10 @@ VarHandlePtr BRPCClient::_AsyncGetVar(const std::string& ep,
platform::RecordRPCEvent record_event(method, p_ctx);
if (method_name == "GetMonomerVariable") {
if (method_name == kGetMonomerRPC) {
ch_ctx->stub->GetMonomerVariable(cntl, &req, response, done);
} else if (method_name == kGetNoBarrierRPC) {
ch_ctx->stub->GetVariableNoBarrier(cntl, &req, response, done);
} else {
ch_ctx->stub->GetVariable(cntl, &req, response, done);
}
......@@ -198,25 +204,39 @@ VarHandlePtr BRPCClient::_AsyncGetVar(const std::string& ep,
return var_h;
}
VarHandlePtr BRPCClient::AsyncGetVarNoBarrier(
const std::string& ep, const platform::DeviceContext& ctx,
const framework::Scope& scope, const std::string& var_name,
const std::string& out_var_name, int64_t time_out) {
std::string var_name_no_barrier =
string::Sprintf("%s%s", var_name, WITHOUT_BARRIER_MESSAGE);
return _AsyncGetVar(ep, ctx, scope, var_name_no_barrier, out_var_name,
kGetNoBarrierRPC, time_out);
}
VarHandlePtr BRPCClient::AsyncGetMonomerVariable(
const std::string& ep, const platform::DeviceContext& ctx,
const framework::Scope& scope, const std::string& var_name,
int64_t time_out) {
return _AsyncGetVar(ep, ctx, scope, var_name, "GetMonomerVariable", time_out);
return _AsyncGetVar(ep, ctx, scope, var_name, var_name, kGetMonomerRPC,
time_out);
}
VarHandlePtr BRPCClient::AsyncGetMonomerBarrier(const std::string& ep,
const std::string& var_name,
int64_t time_out) {
return AsyncSendMessage(ep, "GetMonomerBarrier", var_name, time_out);
return AsyncSendMessage(ep, kSendMonomerFetchBarrierRPC, var_name, time_out);
}
VarHandlePtr BRPCClient::AsyncGetVar(const std::string& ep,
const platform::DeviceContext& ctx,
const framework::Scope& scope,
const std::string& var_name,
const std::string& out_var_name,
int64_t time_out) {
return _AsyncGetVar(ep, ctx, scope, var_name, "GetVariable", time_out);
return _AsyncGetVar(ep, ctx, scope, var_name, out_var_name, kGetRPC,
time_out);
}
VarHandlePtr BRPCClient::AsyncPrefetchVar(const std::string& ep,
......@@ -234,7 +254,7 @@ VarHandlePtr BRPCClient::AsyncPrefetchVar(const std::string& ep,
const framework::Scope* p_scope = &scope;
const auto ch_ptr = GetChannel(ep_val);
const std::string method = "PrefetchRPC";
const std::string method = kPrefetchRPC;
VarHandlePtr var_h(
new VarHandle(ep, method, out_var_name_val, p_ctx, p_scope));
......@@ -270,7 +290,7 @@ VarHandlePtr BRPCClient::AsyncPrefetchVar(const std::string& ep,
VarHandlePtr BRPCClient::AsyncSendBatchBarrier(const std::string& ep,
int64_t time_out) {
return AsyncSendMessage(ep, "BatchBarrierRPC", BATCH_BARRIER_MESSAGE,
return AsyncSendMessage(ep, kBatchBarrierRPC, BATCH_BARRIER_MESSAGE,
time_out);
}
......@@ -286,7 +306,7 @@ VarHandlePtr BRPCClient::AsyncSendFetchBarrier(const std::string& ep,
sendrecv::VariableMessage req;
req.set_varname(FETCH_BARRIER_MESSAGE);
const std::string method = "FetchBarrierRPC";
const std::string method = kFetchBarrierRPC;
// var handle
VarHandlePtr var_h(
new VarHandle(ep, method, FETCH_BARRIER_MESSAGE, nullptr, nullptr));
......@@ -367,7 +387,7 @@ ChannelQueuePtr BRPCClient::GetChannel(const std::string& ep) {
VarHandlePtr BRPCClient::AsyncSendComplete(const std::string& ep,
int64_t time_out) {
return AsyncSendMessage(ep, "SendCompleteRPC", COMPLETE_MESSAGE, time_out);
return AsyncSendMessage(ep, kSendCompleteRPC, COMPLETE_MESSAGE, time_out);
}
void BRPCClient::SendComplete() {
......@@ -394,9 +414,9 @@ VarHandlePtr BRPCClient::AsyncSendVarMessage(
google::protobuf::Closure* done = brpc::NewCallback(
&HandleSendResponse, cntl, response, var_h, ch_ptr, ch_ctx, this);
if (method_name == "CheckPointNotifyRPC") {
if (method_name == kCheckPointNotifyRPC) {
ch_ctx->stub->CheckpointNotify(cntl, &req, response, done);
} else if (method_name == "GetMonomerBarrier") {
} else if (method_name == kSendMonomerFetchBarrierRPC) {
ch_ctx->stub->GetMonomerBarrier(cntl, &req, response, done);
} else {
ch_ctx->stub->SendVariable(cntl, &req, response, done);
......
paddle/fluid/operators/distributed/brpc/brpc_client.h
浏览文件 @ 0016b6a2
......@@ -65,6 +65,7 @@ class BRPCClient : public RPCClient {
const platform::DeviceContext& ctx,
const framework::Scope& scope,
const std::string& var_name,
const std::string& out_var_name,
int64_t time_out = FLAGS_rpc_deadline) override;
VarHandlePtr AsyncGetMonomerBarrier(
......@@ -76,6 +77,13 @@ class BRPCClient : public RPCClient {
const framework::Scope& scope, const std::string& var_name,
int64_t time_out = FLAGS_rpc_deadline) override;
VarHandlePtr AsyncGetVarNoBarrier(const std::string& ep,
const platform::DeviceContext& ctx,
const framework::Scope& scope,
const std::string& var_name,
const std::string& out_varname,
int64_t time_out = FLAGS_rpc_deadline);
VarHandlePtr AsyncPrefetchVar(const std::string& ep,
const platform::DeviceContext& ctx,
const framework::Scope& scope,
......@@ -103,6 +111,7 @@ class BRPCClient : public RPCClient {
const platform::DeviceContext& ctx,
const framework::Scope& scope,
const std::string& var_name,
const std::string& out_var_name,
const std::string& method_name,
int64_t time_out = FLAGS_rpc_deadline);
......
paddle/fluid/operators/distributed/brpc/brpc_server.cc
浏览文件 @ 0016b6a2
......@@ -45,6 +45,13 @@ class BRPCServiceImpl : public SendRecvService {
rpc_server_->GetThreadNum(distributed::kRequestGet)));
}
it = rpc_call_map.find(distributed::kRequestGetNoBarrier);
if (it != rpc_call_map.end()) {
request_getnobarrier_h_ = it->second;
getnobarrier_threads_.reset(new paddle::framework::ThreadPool(
rpc_server_->GetThreadNum(distributed::kRequestGetNoBarrier)));
}
it = rpc_call_map.find(distributed::kRequestPrefetch);
if (it != rpc_call_map.end()) {
request_prefetch_h_ = it->second;
......@@ -112,6 +119,14 @@ class BRPCServiceImpl : public SendRecvService {
[=] { _GetVariable(cntl_butil, request, response, done); });
}
void GetVariableNoBarrier(google::protobuf::RpcController* cntl_butil,
const VariableMessage* request,
VariableMessage* response,
google::protobuf::Closure* done) override {
getnobarrier_threads_->Run(
[=] { _GetVariableNoBarrier(cntl_butil, request, response, done); });
}
void _GetVariable(google::protobuf::RpcController* cntl_butil,
const VariableMessage* request, VariableMessage* response,
google::protobuf::Closure* done) {
......@@ -122,23 +137,59 @@ class BRPCServiceImpl : public SendRecvService {
brpc::Controller* cntl = static_cast<brpc::Controller*>(cntl_butil);
std::string varname = request->varname();
std::string out_varname = request->out_varname();
VLOG(3) << "RequestGet varname:" << varname
<< ", out_varname:" << out_varname
<< ", trainer_id:" << request->trainer_id()
<< ", from:" << cntl->remote_side();
auto scope = request_get_h_->scope();
auto invar = scope->FindVar(varname);
paddle::framework::Variable* invar = nullptr;
int trainer_id = request->trainer_id();
paddle::framework::Variable* outvar = nullptr;
request_get_h_->Handle(varname, scope, invar, &outvar, trainer_id,
out_varname);
if (outvar) {
distributed::SerializeToIOBuf(out_varname, outvar,
*request_get_h_->dev_ctx(), response,
&cntl->response_attachment(), "", false);
}
}
void _GetVariableNoBarrier(google::protobuf::RpcController* cntl_butil,
const VariableMessage* request,
VariableMessage* response,
google::protobuf::Closure* done) {
PADDLE_ENFORCE(request_getnobarrier_h_ != nullptr,
"RequestGetNoBarrier handler should be registed first!");
brpc::ClosureGuard done_guard(done);
brpc::Controller* cntl = static_cast<brpc::Controller*>(cntl_butil);
std::string varname = request->varname();
std::string out_varname = request->out_varname();
int trainer_id = request->trainer_id();
VLOG(3) << "RequestGetNoBarrier varname:" << varname
<< ", out_varname:" << out_varname << ", trainer_id:" << trainer_id
<< ", from:" << cntl->remote_side();
auto scope = request_getnobarrier_h_->scope();
paddle::framework::Variable* invar = nullptr;
paddle::framework::Variable* outvar = nullptr;
request_get_h_->Handle(varname, scope, invar, &outvar, trainer_id);
request_getnobarrier_h_->Handle(varname, scope, invar, &outvar, trainer_id,
out_varname);
if (outvar) {
distributed::SerializeToIOBuf(varname, outvar, *request_get_h_->dev_ctx(),
response, &cntl->response_attachment(), "",
false);
distributed::SerializeToIOBuf(
out_varname, outvar, *request_getnobarrier_h_->dev_ctx(), response,
&cntl->response_attachment(), "", false);
}
}
void PrefetchVariable(google::protobuf::RpcController* cntl_butil,
const VariableMessage* request,
VariableMessage* response,
......@@ -282,6 +333,7 @@ class BRPCServiceImpl : public SendRecvService {
private:
distributed::RequestHandler* request_send_h_{nullptr};
distributed::RequestHandler* request_get_h_{nullptr};
distributed::RequestHandler* request_getnobarrier_h_{nullptr};
distributed::RequestHandler* request_prefetch_h_{nullptr};
distributed::RequestHandler* request_checkpoint_h_{nullptr};
distributed::RequestHandler* request_get_monomer_handler_h_{nullptr};
......@@ -289,9 +341,10 @@ class BRPCServiceImpl : public SendRecvService {
distributed::RPCServer* rpc_server_{nullptr};
// FIXME(gongwb): brpc should support process one rpce use one threadpool.
// FIXME(gongwb): brpc should support process one rpc use one threadpool.
std::unique_ptr<paddle::framework::ThreadPool> send_threads_;
std::unique_ptr<paddle::framework::ThreadPool> get_threads_;
std::unique_ptr<paddle::framework::ThreadPool> getnobarrier_threads_;
std::unique_ptr<paddle::framework::ThreadPool> prefetch_threads_;
std::unique_ptr<paddle::framework::ThreadPool> checkpoint_notify_threads_;
};
......
paddle/fluid/operators/distributed/collective_server_test.cc
浏览文件 @ 0016b6a2
......@@ -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/distributed/grpc/grpc_client.cc
浏览文件 @ 0016b6a2
......@@ -74,7 +74,7 @@ VarHandlePtr GRPCClient::AsyncSendVar(const std::string& ep,
const framework::Scope* p_scope = &scope;
const auto ch = GetChannel(ep_val);
SendProcessor* s = new SendProcessor(ch);
const std::string method = "SendRPC";
const std::string method = kSendRPC;
VarHandlePtr h(new VarHandle(ep, method, var_name_val, p_ctx, p_scope));
s->Prepare(h, time_out);
......@@ -107,7 +107,7 @@ VarHandlePtr GRPCClient::AsyncSendVar(const std::string& ep,
void ProcGetResponse(const VarHandle& var_h,
const ::grpc::ByteBuffer& ret_msg) {
VLOG(100) << "ProcGetResponse";
VLOG(4) << "ProcGetResponse";
framework::Variable* outvar = nullptr;
// get response's trainer_id is not used
int trainer_id;
......@@ -127,59 +127,74 @@ VarHandlePtr GRPCClient::AsyncGetVar(const std::string& ep,
const platform::DeviceContext& ctx,
const framework::Scope& scope,
const std::string& var_name,
const std::string& out_varname,
int64_t time_out) {
return _AsyncGetVar(ep, ctx, scope, var_name,
return _AsyncGetVar(ep, ctx, scope, kGetRPC, var_name, out_varname,
"/sendrecv.SendRecvService/GetVariable", time_out);
}
VarHandlePtr GRPCClient::AsyncGetVarNoBarrier(
const std::string& ep, const platform::DeviceContext& ctx,
const framework::Scope& scope, const std::string& var_name,
const std::string& out_varname, int64_t time_out) {
std::string var_name_no_barrier =
string::Sprintf("%s%s", var_name, WITHOUT_BARRIER_MESSAGE);
return _AsyncGetVar(
ep, ctx, scope, kGetNoBarrierRPC, var_name_no_barrier, out_varname,
"/sendrecv.SendRecvService/GetVariableNoBarrier", time_out);
}
VarHandlePtr GRPCClient::AsyncGetMonomerVariable(
const std::string& ep, const platform::DeviceContext& ctx,
const framework::Scope& scope, const std::string& var_name,
int64_t time_out) {
return _AsyncGetVar(ep, ctx, scope, var_name,
return _AsyncGetVar(ep, ctx, scope, kGetMonomerRPC, var_name, var_name,
"/sendrecv.SendRecvService/GetMonomerVariable", time_out);
}
VarHandlePtr GRPCClient::_AsyncGetVar(const std::string& ep,
const platform::DeviceContext& ctx,
const framework::Scope& scope,
const std::string& var_name,
const std::string& rpc_path,
int64_t time_out) {
VarHandlePtr GRPCClient::_AsyncGetVar(
const std::string& ep, const platform::DeviceContext& ctx,
const framework::Scope& scope, const std::string& method,
const std::string& var_name, const std::string& out_varname,
const std::string& rpc_path, int64_t time_out) {
const platform::DeviceContext* p_ctx = &ctx;
const std::string ep_val = ep;
const std::string var_name_val = var_name;
const std::string out_varname_val = out_varname;
const framework::Scope* p_scope = &scope;
const auto ch = GetChannel(ep_val);
GetProcessor* s = new GetProcessor(ch);
const std::string method = "GetRPC";
VarHandlePtr h(new VarHandle(ep, method, var_name_val, p_ctx, p_scope));
VarHandlePtr h(new VarHandle(ep, method, out_varname_val, p_ctx, p_scope));
s->Prepare(h, time_out);
framework::AsyncIO([var_name_val, s, method, p_ctx, h, rpc_path, this] {
// prepare input
sendrecv::VariableMessage req;
req.set_varname(var_name_val);
req.set_trainer_id(trainer_id_);
::grpc::ByteBuffer buf;
RequestToByteBuffer<sendrecv::VariableMessage>(req, &buf);
framework::AsyncIO(
[var_name_val, out_varname_val, s, method, p_ctx, h, rpc_path, this] {
// prepare input
sendrecv::VariableMessage req;
req.set_varname(var_name_val);
req.set_out_varname(out_varname_val);
req.set_trainer_id(trainer_id_);
::grpc::ByteBuffer buf;
RequestToByteBuffer<sendrecv::VariableMessage>(req, &buf);
VLOG(3) << s->GetVarHandlePtr()->String() << " begin";
VLOG(3) << s->GetVarHandlePtr()->String() << " begin";
// stub context
s->response_call_back_ = ProcGetResponse;
// stub context
s->response_call_back_ = ProcGetResponse;
platform::RecordRPCEvent record_event(method, p_ctx);
platform::RecordRPCEvent record_event(method, p_ctx);
auto call =
s->stub_g_.PrepareUnaryCall(s->context_.get(), rpc_path, buf, &cq_);
call->StartCall();
call->Finish(&s->reply_, &s->status_, reinterpret_cast<void*>(s));
auto call =
s->stub_g_.PrepareUnaryCall(s->context_.get(), rpc_path, buf, &cq_);
call->StartCall();
call->Finish(&s->reply_, &s->status_, reinterpret_cast<void*>(s));
if (UNLIKELY(platform::IsProfileEnabled())) {
h->Wait();
}
});
if (UNLIKELY(platform::IsProfileEnabled())) {
h->Wait();
}
});
req_count_++;
......@@ -202,7 +217,7 @@ VarHandlePtr GRPCClient::AsyncPrefetchVar(const std::string& ep,
const auto ch = GetChannel(ep_val);
GetProcessor* s = new GetProcessor(ch);
const std::string method = "PrefetchRPC";
const std::string method = kPrefetchRPC;
VarHandlePtr h(new VarHandle(ep, method, out_var_name_val, p_ctx, p_scope));
s->Prepare(h, time_out);
......@@ -242,7 +257,7 @@ VarHandlePtr GRPCClient::AsyncSendBatchBarrier(const std::string& ep,
const auto ch = GetChannel(ep);
BatchBarrierProcessor* s = new BatchBarrierProcessor(ch);
const std::string method = "BatchBarrierRPC";
const std::string method = kBatchBarrierRPC;
VarHandlePtr h(
new VarHandle(ep, method, BATCH_BARRIER_MESSAGE, nullptr, nullptr));
s->Prepare(h, time_out);
......@@ -267,7 +282,7 @@ VarHandlePtr GRPCClient::AsyncSendFetchBarrier(const std::string& ep,
int64_t time_out) {
const auto ch = GetChannel(ep);
FetchBarrierProcessor* s = new FetchBarrierProcessor(ch);
const std::string method = "FetchBarrierRPC";
const std::string method = kFetchBarrierRPC;
VarHandlePtr h(
new VarHandle(ep, method, FETCH_BARRIER_MESSAGE, nullptr, nullptr));
s->Prepare(h, time_out);
......@@ -293,7 +308,7 @@ VarHandlePtr GRPCClient::AsyncGetMonomerBarrier(const std::string& ep,
int64_t time_out) {
const auto ch = GetChannel(ep);
BatchBarrierProcessor* s = new BatchBarrierProcessor(ch);
const std::string method = "SendMonomerFetchBarrierRPC";
const std::string method = kSendMonomerFetchBarrierRPC;
VarHandlePtr h(new VarHandle(ep, method, var_name, nullptr, nullptr));
s->Prepare(h, time_out);
......@@ -320,7 +335,7 @@ VarHandlePtr GRPCClient::AsyncSendComplete(const std::string& ep,
const auto ch = GetChannel(ep);
BatchBarrierProcessor* s = new BatchBarrierProcessor(ch);
const std::string method = "SendCompleteRPC";
const std::string method = kSendCompleteRPC;
VarHandlePtr h(new VarHandle(ep, method, COMPLETE_MESSAGE, nullptr, nullptr));
s->Prepare(h, time_out);
......@@ -347,7 +362,7 @@ VarHandlePtr GRPCClient::AsyncCheckpointNotify(const std::string& ep,
CheckpointNotifyProcessor* s = new CheckpointNotifyProcessor(ch);
const std::string method = "CheckPointNotifyRPC";
const std::string method = kCheckPointNotifyRPC;
VarHandlePtr h(
new VarHandle(ep, method, CHECKPOINT_SAVE_MESSAGE, nullptr, nullptr));
......
paddle/fluid/operators/distributed/grpc/grpc_client.h
浏览文件 @ 0016b6a2
......@@ -186,8 +186,15 @@ class GRPCClient : public RPCClient {
const platform::DeviceContext& ctx,
const framework::Scope& scope,
const std::string& var_name,
const std::string& out_varname,
int64_t time_out = FLAGS_rpc_deadline) override;
VarHandlePtr AsyncGetVarNoBarrier(
const std::string& ep, const platform::DeviceContext& ctx,
const framework::Scope& scope, const std::string& var_name,
const std::string& out_varname,
int64_t time_out = FLAGS_rpc_deadline) override;
VarHandlePtr AsyncGetMonomerVariable(
const std::string& ep, const platform::DeviceContext& ctx,
const framework::Scope& scope, const std::string& var_name,
......@@ -228,11 +235,11 @@ class GRPCClient : public RPCClient {
void Proceed();
std::shared_ptr<grpc::Channel> GetChannel(const std::string& ep);
VarHandlePtr _AsyncGetVar(const std::string& ep,
const platform::DeviceContext& ctx,
const framework::Scope& scope,
const std::string& var_name, const std::string& rpc,
int64_t time_out);
VarHandlePtr _AsyncGetVar(
const std::string& ep, const platform::DeviceContext& ctx,
const framework::Scope& scope, const std::string& method,
const std::string& var_name, const std::string& out_varname,
const std::string& rpc_path, int64_t time_out = FLAGS_rpc_deadline);
private:
grpc::CompletionQueue cq_;
......
paddle/fluid/operators/distributed/grpc/grpc_server.cc
浏览文件 @ 0016b6a2
......@@ -136,17 +136,65 @@ class RequestGet final : public RequestBase {
void Process() override {
// proc request.
std::string varname = request_.varname();
std::string out_varname = request_.out_varname();
int trainer_id = request_.trainer_id();
VLOG(4) << "RequestGet " << varname;
VLOG(4) << "RequestGet " << out_varname << " from " << varname;
auto scope = request_handler_->scope();
auto invar = scope->FindVar(varname);
framework::Variable* invar = nullptr;
framework::Variable* outvar = nullptr;
request_handler_->Handle(varname, scope, invar, &outvar, trainer_id);
request_handler_->Handle(varname, scope, invar, &outvar, trainer_id,
out_varname);
if (outvar) {
SerializeToByteBuffer(varname, outvar, *request_handler_->dev_ctx(),
SerializeToByteBuffer(out_varname, outvar, *request_handler_->dev_ctx(),
&reply_);
}
Finish(reply_, &responder_);
}
protected:
sendrecv::VariableMessage request_;
::grpc::ByteBuffer reply_;
ServerAsyncResponseWriter<::grpc::ByteBuffer> responder_;
};
class RequestGetNoBarrier final : public RequestBase {
public:
explicit RequestGetNoBarrier(GrpcService::AsyncService* service,
::grpc::ServerCompletionQueue* cq,
RequestHandler* request_handler, int req_id)
: RequestBase(service, cq, request_handler, req_id), responder_(&ctx_) {
auto method_id =
static_cast<int>(distributed::GrpcMethod::kGetVariableNoBarrier);
service_->RequestAsyncUnary(
method_id, &ctx_, &request_, &responder_, cq_, cq_,
reinterpret_cast<void*>(static_cast<intptr_t>(req_id)));
}
virtual ~RequestGetNoBarrier() {}
std::string GetReqName() override { return request_.varname(); }
void Process() override {
// proc request.
std::string varname = request_.varname();
std::string out_varname = request_.out_varname();
int trainer_id = request_.trainer_id();
VLOG(4) << "RequestGetNoBarrier " << out_varname << " from " << varname;
auto scope = request_handler_->scope();
framework::Variable* invar = nullptr;
framework::Variable* outvar = nullptr;
request_handler_->Handle(varname, scope, invar, &outvar, trainer_id,
out_varname);
if (outvar) {
SerializeToByteBuffer(out_varname, outvar, *request_handler_->dev_ctx(),
&reply_);
}
Finish(reply_, &responder_);
......@@ -460,6 +508,9 @@ void AsyncGRPCServer::TryToRegisterNewOne(const std::string& rpc_name,
b = new RequestSend(&service_, cq.get(), handler, req_id);
} else if (rpc_name == kRequestGet) {
b = new RequestGet(&service_, cq.get(), handler, req_id);
} else if (rpc_name == kRequestGetNoBarrier) {
b = new RequestGetNoBarrier(&service_, cq.get(), handler, req_id);
} else if (rpc_name == kRequestGetMonomerVariable) {
b = new RequestGetMonomerVariable(&service_, cq.get(), handler, req_id,
this);
......
paddle/fluid/operators/distributed/grpc/grpc_service.h
浏览文件 @ 0016b6a2
......@@ -81,6 +81,7 @@ enum class GrpcMethod {
kGetVariable,
kPrefetchVariable,
kCheckpointNotify,
kGetVariableNoBarrier,
kGetMonomerVariable,
kGetMonomerBarrier,
};
......@@ -94,6 +95,8 @@ inline const char* GrpcMethodName(GrpcMethod id) {
return "/sendrecv.SendRecvService/SendVariable";
case GrpcMethod::kGetVariable:
return "/sendrecv.SendRecvService/GetVariable";
case GrpcMethod::kGetVariableNoBarrier:
return "/sendrecv.SendRecvService/GetVariableNoBarrier";
case GrpcMethod::kGetMonomerVariable:
return "/sendrecv.SendRecvService/GetMonomerVariable";
case GrpcMethod::kGetMonomerBarrier:
......
paddle/fluid/operators/distributed/request_handler.h
浏览文件 @ 0016b6a2
......@@ -42,11 +42,24 @@ constexpr char kRequestGetMonomerBarrier[] = "RequestGetMonomerBarrier";
constexpr char kRequestPrefetch[] = "RequestPrefetch";
constexpr char kRequestCheckpoint[] = "RequestCheckpoint";
constexpr char kRequestPassBarrier[] = "RequestPassBarrier";
constexpr char kRequestGetNoBarrier[] = "GetVariableNoBarrier";
constexpr char kSendRPC[] = "SendRPC";
constexpr char kGetRPC[] = "GetRPC";
constexpr char kGetNoBarrierRPC[] = "GetNoBarrierRPC";
constexpr char kGetMonomerRPC[] = "GetMonomerRPC";
constexpr char kPrefetchRPC[] = "PrefetchRPC";
constexpr char kBatchBarrierRPC[] = "BatchBarrierRPC";
constexpr char kFetchBarrierRPC[] = "FetchBarrierRPC";
constexpr char kSendMonomerFetchBarrierRPC[] = "SendMonomerFetchBarrierRPC";
constexpr char kSendCompleteRPC[] = "SendCompleteRPC";
constexpr char kCheckPointNotifyRPC[] = "CheckPointNotifyRPC";
#define LISTEN_TERMINATE_MESSAGE "TERMINATE@RECV"
#define BATCH_BARRIER_MESSAGE "BATCH_BARRIER@RECV"
#define FETCH_BARRIER_MESSAGE "FETCH_BARRIER@RECV"
#define COMPLETE_MESSAGE "COMPLETE@RECV"
#define WITHOUT_BARRIER_MESSAGE "@WITHOUT_BARRIER@RECV"
#define CHECKPOINT_SAVE_MESSAGE "SAVE@CHECKPOINTNOTIFY"
#define CHECKPOINT_LOAD_MESSAGE "LOAD@CHECKPOINTNOTIFY"
......
paddle/fluid/operators/distributed/request_handler_impl.cc
浏览文件 @ 0016b6a2
......@@ -23,6 +23,7 @@
#include "paddle/fluid/framework/selected_rows.h"
#include "paddle/fluid/framework/variable_helper.h"
#include "paddle/fluid/operators/distributed/rpc_server.h"
#include "paddle/fluid/string/piece.h"
#include "paddle/fluid/string/printf.h"
namespace paddle {
......@@ -81,7 +82,8 @@ bool RequestGetHandler::Handle(const std::string& varname,
const int trainer_id,
const std::string& out_var_name,
const std::string& table_name) {
VLOG(4) << "RequestGetHandler:" << varname;
VLOG(4) << "RequestGetHandler:" << varname
<< " out_var_name: " << out_var_name;
if (sync_mode_) {
if (varname == FETCH_BARRIER_MESSAGE) {
......@@ -112,6 +114,32 @@ bool RequestGetHandler::Handle(const std::string& varname,
return true;
}
bool RequestGetNoBarrierHandler::Handle(const std::string& varname,
framework::Scope* scope,
framework::Variable* invar,
framework::Variable** outvar,
const int trainer_id,
const std::string& out_var_name,
const std::string& table_name) {
VLOG(4) << "RequestGetNoBarrierHandler:" << varname
<< " out_var_name: " << out_var_name;
// get var from pserver immediately without barriers
string::Piece without_barrier_piece(WITHOUT_BARRIER_MESSAGE);
string::Piece var_name_piece = string::Piece(varname);
if (string::Contains(var_name_piece, without_barrier_piece)) {
var_name_piece = string::TrimSuffix(var_name_piece, without_barrier_piece);
VLOG(4) << "Get var " << var_name_piece << " with "
<< WITHOUT_BARRIER_MESSAGE;
*outvar = scope_->FindVar(var_name_piece.ToString());
return true;
} else {
PADDLE_THROW("GetNoBarrier must contain %s", WITHOUT_BARRIER_MESSAGE);
}
return true;
}
bool RequestPrefetchHandler::Handle(const std::string& varname,
framework::Scope* scope,
framework::Variable* invar,
......
paddle/fluid/operators/distributed/request_handler_impl.h
浏览文件 @ 0016b6a2
......@@ -67,6 +67,16 @@ class RequestGetHandler final : public RequestHandler {
bool enable_dc_asgd_;
};
class RequestGetNoBarrierHandler final : public RequestHandler {
public:
RequestGetNoBarrierHandler() : RequestHandler(false) {}
virtual ~RequestGetNoBarrierHandler() {}
bool Handle(const std::string& varname, framework::Scope* scope,
framework::Variable* var, framework::Variable** outvar,
const int trainer_id, const std::string& out_var_name = "",
const std::string& table_name = "") override;
};
static inline void BuildVar(const std::string& param_name,
std::initializer_list<const char*> arguments,
paddle::framework::proto::OpDesc::Var* var) {
......
paddle/fluid/operators/distributed/rpc_client.h
浏览文件 @ 0016b6a2
......@@ -43,8 +43,15 @@ class RPCClient {
const platform::DeviceContext& ctx,
const framework::Scope& scope,
const std::string& var_name,
const std::string& out_varname,
int64_t time_out = FLAGS_rpc_deadline) = 0;
virtual VarHandlePtr AsyncGetVarNoBarrier(
const std::string& ep, const platform::DeviceContext& ctx,
const framework::Scope& scope, const std::string& var_name,
const std::string& out_varname,
int64_t time_out = FLAGS_rpc_deadline) = 0;
virtual VarHandlePtr AsyncGetMonomerVariable(
const std::string& ep, const platform::DeviceContext& ctx,
const framework::Scope& scope, const std::string& var_name,
......
paddle/fluid/operators/distributed/variable_response.cc
浏览文件 @ 0016b6a2
......@@ -117,8 +117,9 @@ bool VariableResponse::CopyLodTensorData(
tensor->mutable_data(ctx.GetPlace(), ToVarType(meta_.data_type()));
VLOG(6) << "Tensor.memory_size = " << tensor->memory_size()
<< ", Buffer Size = " << length;
PADDLE_ENFORCE_EQ(tensor->memory_size(), static_cast<unsigned int>(length));
<< ", Buffer Size = " << length << ", dims:" << dims
<< ", numel:" << tensor->numel();
PADDLE_ENFORCE_GE(tensor->memory_size(), static_cast<unsigned int>(length));
return ReadRaw(input, ctx, tensor->place(), tensor_data, length);
}
......
paddle/fluid/operators/distributed_ops/listen_and_serv_op.cc
浏览文件 @ 0016b6a2
......@@ -347,6 +347,8 @@ void ListenAndServOp::RunImpl(const framework::Scope &scope,
new distributed::RequestPrefetchHandler(sync_mode));
request_checkpoint_handler_.reset(new distributed::RequestCheckpointHandler(
sync_mode, checkpoint_block_id));
request_get_no_barrier_handler_.reset(
new distributed::RequestGetNoBarrierHandler());
rpc_service_->RegisterRPC(distributed::kRequestSend,
request_send_handler_.get(),
......@@ -359,6 +361,8 @@ void ListenAndServOp::RunImpl(const framework::Scope &scope,
FLAGS_rpc_prefetch_thread_num);
rpc_service_->RegisterRPC(distributed::kRequestCheckpoint,
request_checkpoint_handler_.get());
rpc_service_->RegisterRPC(distributed::kRequestGetNoBarrier,
request_get_no_barrier_handler_.get());
auto optimize_blocks =
Attr<std::vector<framework::BlockDesc *>>(kOptimizeBlocks);
......@@ -413,6 +417,7 @@ void ListenAndServOp::RunImpl(const framework::Scope &scope,
f(request_get_handler_.get());
f(request_prefetch_handler_.get());
f(request_checkpoint_handler_.get());
f(request_get_no_barrier_handler_.get());
// start the server listening after all member initialized.
server_thread_.reset(new std::thread(RunServer, rpc_service_));
......
paddle/fluid/operators/gather_op.cc
浏览文件 @ 0016b6a2
此差异已折叠。
paddle/fluid/operators/math/beam_search.cc 0 → 100644
浏览文件 @ 0016b6a2
此差异已折叠。
paddle/fluid/operators/math/beam_search.cu 0 → 100644
浏览文件 @ 0016b6a2
此差异已折叠。
paddle/fluid/operators/math/beam_search.h 0 → 100644
浏览文件 @ 0016b6a2
此差异已折叠。
paddle/fluid/operators/math/beam_search_test.cc 0 → 100644
浏览文件 @ 0016b6a2
此差异已折叠。
paddle/fluid/operators/nce_op.h
浏览文件 @ 0016b6a2
此差异已折叠。
paddle/fluid/operators/ngraph/CMakeLists.txt 0 → 100644
浏览文件 @ 0016b6a2
此差异已折叠。
paddle/fluid/operators/ngraph/ngraph_engine.cc 0 → 100644
浏览文件 @ 0016b6a2
此差异已折叠。
paddle/fluid/operators/ngraph/ngraph_engine.h 0 → 100644
浏览文件 @ 0016b6a2
此差异已折叠。
paddle/fluid/operators/ngraph/ngraph_engine_op.cc 0 → 100644
浏览文件 @ 0016b6a2
此差异已折叠。
paddle/fluid/operators/ngraph/ngraph_engine_op.h 0 → 100644
浏览文件 @ 0016b6a2
此差异已折叠。
paddle/fluid/platform/gpu_info.cc
浏览文件 @ 0016b6a2
此差异已折叠。
paddle/fluid/pybind/CMakeLists.txt
浏览文件 @ 0016b6a2
此差异已折叠。
paddle/fluid/pybind/imperative.cc
浏览文件 @ 0016b6a2
此差异已折叠。
paddle/fluid/pybind/ir.cc
浏览文件 @ 0016b6a2
此差异已折叠。
paddle/fluid/pybind/protobuf.cc
浏览文件 @ 0016b6a2
此差异已折叠。
paddle/fluid/pybind/pybind.cc
浏览文件 @ 0016b6a2
此差异已折叠。
paddle/py_paddle/.gitignore 已删除 100644 → 0
浏览文件 @ ab5442d9
此差异已折叠。
paddle/py_paddle/dataprovider_converter.py 已删除 100644 → 0
浏览文件 @ ab5442d9
此差异已折叠。
paddle/py_paddle/util.py 已删除 100644 → 0
浏览文件 @ ab5442d9
此差异已折叠。
paddle/scripts/README.md
浏览文件 @ 0016b6a2
此差异已折叠。
paddle/scripts/paddle_build.sh
浏览文件 @ 0016b6a2
此差异已折叠。
python/CMakeLists.txt
浏览文件 @ 0016b6a2
此差异已折叠。
python/paddle/fluid/contrib/int8_inference/__init__.py 0 → 100644
浏览文件 @ 0016b6a2
此差异已折叠。
python/paddle/fluid/contrib/int8_inference/utility.py 0 → 100644
浏览文件 @ 0016b6a2
此差异已折叠。
python/paddle/fluid/contrib/reader/README.md 0 → 100644
浏览文件 @ 0016b6a2
此差异已折叠。
python/paddle/fluid/contrib/tests/test_calibration.py 0 → 100644
浏览文件 @ 0016b6a2
此差异已折叠。
python/paddle/fluid/data_feeder.py
浏览文件 @ 0016b6a2
此差异已折叠。
python/paddle/fluid/framework.py
浏览文件 @ 0016b6a2
此差异已折叠。
python/paddle/fluid/initializer.py
浏览文件 @ 0016b6a2
此差异已折叠。
python/paddle/fluid/io.py
浏览文件 @ 0016b6a2
此差异已折叠。
python/paddle/fluid/layer_helper.py
浏览文件 @ 0016b6a2
此差异已折叠。
python/paddle/fluid/layers/io.py
浏览文件 @ 0016b6a2
此差异已折叠。
python/paddle/fluid/layers/nn.py
浏览文件 @ 0016b6a2
此差异已折叠。
python/paddle/fluid/optimizer.py
浏览文件 @ 0016b6a2
此差异已折叠。
python/setup.py.in
浏览文件 @ 0016b6a2
此差异已折叠。
Markdown is supported
0% 或 .
You are about to add 0 people to the discussion. Proceed with caution.
先完成此消息的编辑！
想要评论请 注册 或