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提交 36cd18b5 编写于 作者: P peizhilin
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Merge remote-tracking branch 'upstream/develop' into windows/build

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.github/ISSUE_TEMPLATE/---feature-request-.md 0 → 100644
浏览文件 @ 36cd18b5
---
name: 建议(Feature request)
about: 您可以提出您的建议。 You could use this template for reporting a suggestion  issue.
---
欢迎您对PaddlePaddle提出建议，非常感谢您对PaddlePaddle的贡献！
在留下您的建议时，辛苦您同步提供如下信息：
- 版本、环境信息
1）PaddlePaddle版本：请提供您的PaddlePaddle版本号，例如1.1
2）CPU/GPU：您是否使用GPU进行训练，如是，请提供您的CUDA和cuDNN版本号
3）系统环境：请您描述系统类型、版本，例如Mac OS 10.14
- 复现信息：如为报错，请给出复现环境、复现步骤
- 建议描述：请您详细描述，您认为需优化的功能
Thank you for contributing to PaddlePaddle.
Before submitting the issue, you could search issue in the github in case that there was a similar issue submitted or resolved before.
Please make sure that this is a feature request.
**System information**
-PaddlePaddle version （eg.1.1）or CommitID
-CPU: including CPUMKL/OpenBlas/MKLDNN version
-GPU: including CUDA/CUDNN version
-OS Platform (eg.Mac OS 10.14)
**To Reproduce**
Steps to reproduce the behavior
**Describe the feature and the current behavior/state.**
**Any Other info.**
.github/ISSUE_TEMPLATE/---inference-issue-.md 0 → 100644
浏览文件 @ 36cd18b5
---
name: 预测（Inference Issue）
about: 您可以提问预测中报错、应用等问题。 You could use this template for reporting an inference issue.
---
为使您的问题得到快速解决，在建立Issue前，请您先通过如下方式搜索是否有相似问题:【搜索issue关键字】【使用labels筛选】【官方文档】
如果您没有查询到相似问题，为快速解决您的提问，建立issue时请提供如下细节信息：
- 标题：简洁、精准描述您的问题，例如“最新预测库的API文档在哪儿 ”
- 版本、环境信息：
   1）PaddlePaddle版本：请提供您的PaddlePaddle版本号（如1.1）或CommitID
   2）CPU：预测若用CPU，请提供CPU型号，MKL/OpenBlas/MKLDNN/等数学库使用情况
   3）GPU：预测若用GPU，请提供GPU型号、CUDA和CUDNN版本号
   4）系统环境：请您描述系统类型、版本（如Mac OS 10.14），Python版本
-预测信息
   1）C++预测：请您提供预测库安装包的版本信息，及其中的version.txt文件
   2）CMake包含路径的完整命令
   3）API信息（如调用请提供）
   4）预测库来源：官网下载/特殊环境（如BCLOUD编译）
- 复现信息：如为报错，请给出复现环境、复现步骤
- 问题描述：请详细描述您的问题，同步贴出报错信息、日志/代码关键片段
Thank you for contributing to PaddlePaddle.
Before submitting the issue, you could search issue in the github in case that th
If there is no solution,please make sure that this is an inference issue including the following details :
**System information**
-PaddlePaddle version （eg.1.1）or CommitID
-CPU: including CPUMKL/OpenBlas/MKLDNN version
-GPU: including CUDA/CUDNN version
-OS Platform (eg.Mac OS 10.14)
-Python version
-Cmake orders
-C++version.txt
-API information
**To Reproduce**
Steps to reproduce the behavior
**Describe your current behavior**
**Code to reproduce the issue**
**Other info / logs**
.github/ISSUE_TEMPLATE/---installation-issue-.md 0 → 100644
浏览文件 @ 36cd18b5
---
name: 安装（Installation Issue）
about: 您可以提问安装、编译出现报错等问题。 You could use this template for reporting an installation
 issue.
---
为使您的问题得到快速解决，在建立Issue前，请您先通过如下方式搜索是否有相似问题:【搜索issue关键字】【使用labels筛选】【官方文档】
建立issue时，为快速解决问题，请您根据使用情况给出如下信息：
- 标题：请包含关键词“安装错误”/“编译错误”，例如“Mac编译错误”
- 版本、环境信息：
   1）PaddlePaddle版本：请提供您的PaddlePaddle版本号（如1.1）或CommitID
   2）CPU：请提供CPU型号，MKL/OpenBlas/MKLDNN/等数学库的使用情况
   3）GPU：请提供GPU型号，CUDA和CUDNN版本号
   4）系统环境：请说明系统类型、版本（如Mac OS 10.14）、Python版本
- 安装方式信息：
1）pip安装/docker安装
2）本地编译：请提供cmake命令，编译命令
3）docker编译：请提供docker镜像，编译命令           
 特殊环境请注明：如离线安装等
- 复现信息：如为报错，请给出复现环境、复现步骤
- 问题描述：请详细描述您的问题，同步贴出报错信息、日志/代码关键片段
Thank you for contributing to PaddlePaddle.
Before submitting the issue, you could search issue in Github in case that there was a similar issue submitted or resolved before.
If there is no solution,please make sure that this is an installation issue including the following details:
**System information**
-PaddlePaddle version （eg.1.1）or CommitID
-CPU: including CPUMKL/OpenBlas/MKLDNN version
-GPU: including CUDA/CUDNN version
-OS Platform (eg. Mac OS 10.14)
-Python version
- Install method: pip install/install with docker/build from source(without docker)/build within docker
- Other special cases that you think may be related to this problem, eg. offline install, special internet condition  
**To Reproduce**
Steps to reproduce the behavior
**Describe your current behavior**
**Code to reproduce the issue**
**Other info / logs**
.github/ISSUE_TEMPLATE/---model-issue-.md 0 → 100644
浏览文件 @ 36cd18b5
---
name: 模型（Model Issue）
about: 您可以提问模型、算法、数据集方向的使用报错等问题。You could use this template for reporting a model/
algorithm/dataset  issue.
---
为使您的问题得到快速解决，在建立Issue前，请您先通过如下方式搜索是否有相似问题:【搜索issue关键字】【使用labels筛选】【官方文档】
建立issue时，为快速解决问题，请您根据使用情况给出如下信息：
- 标题：简洁、精准描述您的问题，例如“ssd 模型前置lstm报错  ”
- 版本、环境信息：
   1）PaddlePaddle版本：请提供PaddlePaddle版本号，例如1.1或CommitID
   2）CPU：请提供CPU型号，MKL/OpenBlas/MKLDNN/等数学库的使用情况
   3）GPU：请提供GPU型号，CUDA和CUDNN版本号
   4）系统环境：请说明系统类型、版本（例如Mac OS 10.14），Python版本
- 模型信息
   1）模型名称 2）使用数据集名称 3）使用算法名称 4）模型链接
- 复现信息：如为报错，请给出复现环境、复现步骤
- 问题描述：请详细描述您的问题，同步贴出报错信息、日志/代码关键片段
Thank you for contributing to PaddlePaddle.
Before submitting the issue, you could search issue in the github.Probably there was a similar issue submitted or resolved before.
If there is no solution,please make sure that this is a issue of models including the following details:
**System information**
-PaddlePaddle version （eg.1.1）or CommitID
-CPU: including CPUMKL/OpenBlas/MKLDNN version
-GPU: including CUDA/CUDNN version
-OS Platform (eg.Mac OS 10.14)
-Python version
-Name of Models&Dataset/details of operator
**To Reproduce**
Steps to reproduce the behavior
**Describe your current behavior**
**Code to reproduce the issue**
**Other info / logs**
.github/ISSUE_TEMPLATE/---others-.md 0 → 100644
浏览文件 @ 36cd18b5
---
name: 其他（Others）
about: 如上述分类未包含您的问题，可在此提出。 You could use this template for reporting other issues
---
为使您的问题得到快速解决，在建立Issues前，请您先通过如下方式搜索是否有相似问题:【搜索issue关键字】【使用labels筛选】【官方文档】
如果您没有查询到相似问题，为快速解决您的提问，建立issue时请提供如下细节信息：
- 标题：简洁、精准概括您的问题
- 版本、环境信息：
   1）PaddlePaddle版本：请提供您的PaddlePaddle版本号，例如1.1或CommitID
   2）CPU/GPU：如果您使用GPU训练，请提供GPU驱动版本、CUDA和cuDNN版本号
   3）系统环境：请您描述系统类型、版本，例如Mac OS 10.14
   4）Python版本号
   5）显存信息
- 复现信息：如为报错，请给出复现环境、复现步骤
- 问题描述：请详细描述您的问题，同步贴出报错信息、日志/代码关键片段
Thank you for contributing to PaddlePaddle.
Before submitting the issue, you could search issue in the github in case that there was a similar issue submitted or resolved before.
If there is no solution,please provide us with the following details :
**System information**
-PaddlePaddle version （eg.1.1）or CommitID
-CPU: including CPUMKL/OpenBlas/MKLDNN version
-GPU: including CUDA/cuDNN version
-OS Platform and Distribution(eg.Mac OS 10.14)
-Python version
**To Reproduce**
Steps to reproduce the behavior
**Describe your current behavior**
**Code to reproduce the issue**
**Other info / logs**
.github/ISSUE_TEMPLATE/---training-issue-.md 0 → 100644
浏览文件 @ 36cd18b5
---
name: 训练（Training issue）
about: 您可以提问训练中报错、应用、出core等问题。 You could use this template for reporting an training
 issue.
---
为使您的问题得到快速解决，在建立Issues前，请您先通过如下方式搜索是否有相似问题:【搜索issue关键字】【使用labels筛选】【官方文档】
如果您没有查询到相似问题，为快速解决您的提问，建立issue时请提供如下细节信息：
- 标题：简洁、精准概括您的问题，例如“Insufficient Memory xxx" ”
- 版本、环境信息：
   1）PaddlePaddle版本：请提供您的PaddlePaddle版本号，例如1.1或CommitID
   2）CPU：预测若用CPU，请提供CPU型号，MKL/OpenBlas/MKLDNN/等数学库使用情况
   3）GPU：预测若用GPU，请提供GPU型号、CUDA和CUDNN版本号
   4）系统环境：请您描述系统类型、版本，例如Mac OS 10.14，Python版本
- 训练信息
   1）单机/多机，单卡/多卡
   2）显存信息
   3）Operator信息
- 复现信息：如为报错，请给出复现环境、复现步骤
- 问题描述：请详细描述您的问题，同步贴出报错信息、日志、可复现的代码片段
Thank you for contributing to PaddlePaddle.
Before submitting the issue, you could search issue in the github in case that there was a similar issue submitted or resolved before.
If there is no solution,please make sure that this is a training issue including the following details:
**System information**
-PaddlePaddle version （eg.1.1）or CommitID
-CPU: including CPUMKL/OpenBlas/MKLDNN version
-GPU: including CUDA/CUDNN version
-OS Platform (eg.Mac OS 10.14)
-Other imformation: Distriuted training/informantion of operator/
Graphics card storage
**To Reproduce**
Steps to reproduce the behavior
**Describe your current behavior**
**Code to reproduce the issue**
**Other info / logs**
CMakeLists.txt
浏览文件 @ 36cd18b5
......@@ -204,7 +204,9 @@ include(external/eigen) # download eigen3
include(external/pybind11) # download pybind11
include(external/cares)
include(external/cub)
include(external/rocprim)
include(external/xxhash) # download xxhash
include(external/dlpack)
include(external/snappy) # download snappy
include(external/snappystream) # download snappystream
......
Dockerfile
浏览文件 @ 36cd18b5
......@@ -22,6 +22,27 @@ ENV HOME /root
# Add bash enhancements
COPY ./paddle/scripts/docker/root/ /root/
# Prepare packages for Python
RUN apt-get update && \
apt-get install -y make build-essential libssl-dev zlib1g-dev libbz2-dev \
libreadline-dev libsqlite3-dev wget curl llvm libncurses5-dev libncursesw5-dev \
xz-utils tk-dev libffi-dev liblzma-dev
# Install Python3.6
RUN mkdir -p /root/python_build/ && wget -q https://www.sqlite.org/2018/sqlite-autoconf-3250300.tar.gz && \
tar -zxf sqlite-autoconf-3250300.tar.gz && cd sqlite-autoconf-3250300 && \
./configure -prefix=/usr/local && make -j8 && make install && cd ../ && rm sqlite-autoconf-3250300.tar.gz && \
wget -q https://www.python.org/ftp/python/3.6.0/Python-3.6.0.tgz && \
tar -xzf Python-3.6.0.tgz && cd Python-3.6.0 && \
CFLAGS="-Wformat" ./configure --prefix=/usr/local/ --enable-shared > /dev/null && \
make -j8 > /dev/null && make altinstall > /dev/null
# Install Python3.7
RUN wget -q https://www.python.org/ftp/python/3.7.0/Python-3.7.0.tgz && \
tar -xzf Python-3.7.0.tgz && cd Python-3.7.0 && \
CFLAGS="-Wformat" ./configure --prefix=/usr/local/ --enable-shared > /dev/null && \
make -j8 > /dev/null && make altinstall > /dev/null
RUN apt-get update && \
apt-get install -y --allow-downgrades patchelf \
python3 python3-dev python3-pip \
......@@ -74,6 +95,12 @@ RUN localedef -i en_US -f UTF-8 en_US.UTF-8
RUN pip3 install -U wheel && \
pip3 install -U docopt PyYAML sphinx==1.5.6 && \
pip3 install sphinx-rtd-theme==0.1.9 recommonmark && \
pip3.6 install -U wheel && \
pip3.6 install -U docopt PyYAML sphinx==1.5.6 && \
pip3.6 install sphinx-rtd-theme==0.1.9 recommonmark && \
pip3.7 install -U wheel && \
pip3.7 install -U docopt PyYAML sphinx==1.5.6 && \
pip3.7 install sphinx-rtd-theme==0.1.9 recommonmark && \
easy_install -U pip && \
pip install -U pip setuptools wheel && \
pip install -U docopt PyYAML sphinx==1.5.6 && \
......@@ -82,22 +109,34 @@ RUN pip3 install -U wheel && \
RUN pip3 install 'pre-commit==1.10.4' 'ipython==5.3.0' && \
pip3 install 'ipykernel==4.6.0' 'jupyter==1.0.0' && \
pip3 install opencv-python && \
pip3.6 install 'pre-commit==1.10.4' 'ipython==5.3.0' && \
pip3.6 install 'ipykernel==4.6.0' 'jupyter==1.0.0' && \
pip3.6 install opencv-python && \
pip3.7 install 'pre-commit==1.10.4' 'ipython==5.3.0' && \
pip3.7 install 'ipykernel==4.6.0' 'jupyter==1.0.0' && \
pip3.7 install opencv-python && \
pip install 'pre-commit==1.10.4' 'ipython==5.3.0' && \
pip install 'ipykernel==4.6.0' 'jupyter==1.0.0' && \
pip install opencv-python
#For docstring checker
RUN pip3 install pylint pytest astroid isort
RUN pip3.6 install pylint pytest astroid isort
RUN pip3.7 install pylint pytest astroid isort
RUN pip install pylint pytest astroid isort LinkChecker
COPY ./python/requirements.txt /root/
RUN pip3 install -r /root/requirements.txt
RUN pip3.6 install -r /root/requirements.txt
RUN pip3.7 install -r /root/requirements.txt
RUN pip install -r /root/requirements.txt
# To fix https://github.com/PaddlePaddle/Paddle/issues/1954, we use
# the solution in https://urllib3.readthedocs.io/en/latest/user-guide.html#ssl-py2
RUN apt-get install -y libssl-dev libffi-dev
RUN pip3 install certifi urllib3[secure]
RUN pip3.6 install certifi urllib3[secure]
RUN pip3.7 install certifi urllib3[secure]
RUN pip install certifi urllib3[secure]
......
cmake/external/dlpack.cmake 0 → 100644
浏览文件 @ 36cd18b5
include(ExternalProject)
set(DLPACK_SOURCE_DIR ${THIRD_PARTY_PATH}/dlpack)
set(DLPACK_INCLUDE_DIR ${DLPACK_SOURCE_DIR}/src/extern_dlpack/include)
include_directories(${DLPACK_INCLUDE_DIR})
ExternalProject_Add(
extern_dlpack
${EXTERNAL_PROJECT_LOG_ARGS}
GIT_REPOSITORY "https://github.com/dmlc/dlpack.git"
GIT_TAG "v0.2"
PREFIX ${DLPACK_SOURCE_DIR}
UPDATE_COMMAND ""
CONFIGURE_COMMAND ""
BUILD_COMMAND ""
INSTALL_COMMAND ""
TEST_COMMAND ""
)
if(${CMAKE_VERSION} VERSION_LESS "3.3.0")
set(dummyfile ${CMAKE_CURRENT_BINARY_DIR}/dlpack_dummy.c)
file(WRITE ${dummyfile} "const char *dummy = \"${dummyfile}\";")
add_library(dlpack STATIC ${dummyfile})
else()
add_library(dlpack INTERFACE)
endif()
add_dependencies(dlpack extern_dlpack)
LIST(APPEND externl_project_dependencies dlpack)
cmake/external/mkldnn.cmake
浏览文件 @ 36cd18b5
......@@ -53,7 +53,7 @@ ExternalProject_Add(
${EXTERNAL_PROJECT_LOG_ARGS}
DEPENDS ${MKLDNN_DEPENDS}
GIT_REPOSITORY "https://github.com/01org/mkl-dnn.git"
GIT_TAG "21fb5f2af1dd14e132af4f1b79160977ee487818"
GIT_TAG "830a10059a018cd2634d94195140cf2d8790a75a"
PREFIX ${MKLDNN_SOURCES_DIR}
UPDATE_COMMAND ""
CMAKE_ARGS -DCMAKE_CXX_COMPILER=${CMAKE_CXX_COMPILER}
......
cmake/external/rocprim.cmake 0 → 100644
浏览文件 @ 36cd18b5
if (NOT WITH_AMD_GPU)
return()
endif()
# rocprim is "ROCm Parallel Primitives" for short.
# It is a header-only library providing HIP and HC parallel primitives
# for developing performant GPU-accelerated code on AMD ROCm platform.
if("x${HCC_HOME}" STREQUAL "x")
set(HCC_HOME "/opt/rocm/hcc")
endif()
INCLUDE(ExternalProject)
SET(ROCPRIM_SOURCE_DIR ${THIRD_PARTY_PATH}/rocprim)
SET(ROCPRIM_INSTALL_DIR ${THIRD_PARTY_PATH}/install/rocprim)
SET(ROCPRIM_INCLUDE_DIR ${ROCPRIM_INSTALL_DIR}/include)
ExternalProject_Add(
extern_rocprim
GIT_REPOSITORY "https://github.com/ROCmSoftwarePlatform/rocPRIM.git"
GIT_TAG 5bd41b96ab8d8343330fb2c3e1b96775bde3b3fc
PREFIX ${ROCPRIM_SOURCE_DIR}
UPDATE_COMMAND ""
CMAKE_ARGS -DCMAKE_CXX_COMPILER=${HCC_HOME}/bin/hcc
CMAKE_ARGS -DONLY_INSTALL=ON
CMAKE_ARGS -DBUILD_TEST=OFF
CMAKE_ARGS -DCMAKE_INSTALL_PREFIX=${ROCPRIM_INSTALL_DIR}
INSTALL_DIR ${ROCPRIM_INSTALL_DIR}
${EXTERNAL_PROJECT_LOG_ARGS}
)
INCLUDE_DIRECTORIES(${ROCPRIM_INCLUDE_DIR})
if (${CMAKE_VERSION} VERSION_LESS "3.3.0")
set(dummyfile ${CMAKE_CURRENT_BINARY_DIR}/rocprim_dummy.c)
file(WRITE ${dummyfile} "const char *dummy_rocprim = \"${dummyfile}\";")
add_library(rocprim STATIC ${dummyfile})
else()
add_library(rocprim INTERFACE)
endif()
add_dependencies(rocprim extern_rocprim)
cmake/flags.cmake
浏览文件 @ 36cd18b5
......@@ -129,6 +129,9 @@ set(COMMON_FLAGS
-Wno-error=parentheses-equality # Warnings in pybind11
-Wno-error=ignored-attributes # Warnings in Eigen, gcc 6.3
-Wno-error=terminate # Warning in PADDLE_ENFORCE
-Wno-error=int-in-bool-context # Warning in Eigen gcc 7.2
-Wimplicit-fallthrough=0 # Warning in tinyformat.h
-Wno-error=maybe-uninitialized # Warning in boost gcc 7.2
)
set(GPU_COMMON_FLAGS
......
cmake/generic.cmake
浏览文件 @ 36cd18b5
......@@ -461,25 +461,29 @@ function(hip_library TARGET_NAME)
else()
add_library(${TARGET_NAME} STATIC ${_cmake_options} ${_generated_files} ${_sources})
set_target_properties(${TARGET_NAME} PROPERTIES LINKER_LANGUAGE CXX)
target_link_libraries(${TARGET_NAME} /opt/rocm/hip/lib/libhip_hcc.so /opt/rocm/hip/lib/libhip_device.a)
find_fluid_modules(${TARGET_NAME})
target_link_libraries(${TARGET_NAME} /opt/rocm/hip/lib/libhip_hcc.so /opt/rocm/hip/lib/libhip_device.a /opt/rocm/rccl/lib/librccl.so /opt/rocm/hiprand/lib/libhiprand.so)
find_fluid_modules(${TARGET_NAME})
endif()
if (hip_library_DEPS)
add_dependencies(${TARGET_NAME} ${hip_library_DEPS})
target_link_libraries(${TARGET_NAME} ${hip_library_DEPS})
if("${hip_library_DEPS}" MATCHES "ARCHIVE_START")
# Support linking flags: --whole-archive (Linux) / -force_load (MacOS).
# WARNING: Please don't use ARCHIVE_START&ARCHIVE_END if TARGET_NAME will be linked by other libraries.
target_circle_link_libraries(${TARGET_NAME} ${hip_library_DEPS})
list(REMOVE_ITEM hip_library_DEPS ARCHIVE_START ARCHIVE_END)
else()
target_link_libraries(${TARGET_NAME} ${hip_library_DEPS})
endif()
# cpplint code style
foreach(source_file ${hip_library_SRCS})
string(REGEX REPLACE "\\.[^.]*$" "" source ${source_file})
if(EXISTS ${CMAKE_CURRENT_SOURCE_DIR}/${source}.h)
list(APPEND hip_library_HEADERS ${CMAKE_CURRENT_SOURCE_DIR}/${source}.h)
endif()
string(REGEX REPLACE "\\.[^.]*$" "" source ${source_file})
if(EXISTS ${CMAKE_CURRENT_SOURCE_DIR}/${source}.h)
list(APPEND hip_library_HEADERS ${CMAKE_CURRENT_SOURCE_DIR}/${source}.h)
endif()
endforeach()
else(hip_library_SRCS)
if (hip_library_DEPS)
merge_static_libs(${TARGET_NAME} ${hip_library_DEPS})
merge_static_libs(${TARGET_NAME} ${hip_library_DEPS})
else()
message(FATAL "Please specify source file or library in nv_library.")
message(FATAL "Please specify source file or library in nv_library.")
endif()
endif(hip_library_SRCS)
endif()
......
cmake/hip.cmake
浏览文件 @ 36cd18b5
......@@ -3,6 +3,8 @@ if(NOT WITH_AMD_GPU)
endif()
include_directories("/opt/rocm/include")
include_directories("/opt/rocm/hip/include")
include_directories("/opt/rocm/miopen/include")
include_directories("/opt/rocm/hipblas/include")
include_directories("/opt/rocm/hiprand/include")
include_directories("/opt/rocm/rocrand/include")
......@@ -11,20 +13,40 @@ include_directories("/opt/rocm/thrust")
list(APPEND EXTERNAL_LIBS "-L/opt/rocm/lib/ -lhip_hcc")
set(HIP_HCC_FLAGS "${HIP_HCC_FLAGS} -fPIC -DPADDLE_WITH_HIP -std=c++14" )
set(HIP_HCC_FLAGS "${HIP_HCC_FLAGS} -fPIC -DPADDLE_WITH_HIP -std=c++11" )
if(WITH_DSO)
set(HIP_HCC_FLAGS "${HIP_HCC_FLAGS} -DPADDLE_USE_DSO")
endif(WITH_DSO)
if(WITH_DOUBLE)
set(HIP_HCC_FLAGS "${HIP_HCC_FLAGS} -DPADDLE_TYPE_DOUBLE")
endif(WITH_DOUBLE)
if(WITH_TESTING)
set(HIP_HCC_FLAGS "${HIP_HCC_FLAGS} -DPADDLE_WITH_TESTING")
endif(WITH_TESTING)
if(WITH_DISTRIBUTE)
set(HIP_HCC_FLAGS "${HIP_HCC_FLAGS} -DPADDLE_WITH_DISTRIBUTE")
endif(WITH_DISTRIBUTE)
if(WITH_GRPC)
set(HIP_HCC_FLAGS "${HIP_HCC_FLAGS} -DPADDLE_WITH_GRPC")
endif(WITH_GRPC)
if(NOT WITH_GOLANG)
set(HIP_HCC_FLAGS "${HIP_HCC_FLAGS} -DPADDLE_WITHOUT_GOLANG")
endif(NOT WITH_GOLANG)
if(WITH_MKLDNN)
set(HIP_HCC_FLAGS "${HIP_HCC_FLAGS} -DPADDLE_WITH_MKLDNN")
endif(WITH_MKLDNN)
set(HIP_HCC_FLAGS "${HIP_HCC_FLAGS} -DANY_IMPL_ANY_CAST_MOVEABLE")
if(NOT WITH_RDMA)
set(HIP_HCC_FLAGS "${HIP_HCC_FLAGS} -DPADDLE_DISABLE_RDMA")
endif(NOT WITH_RDMA)
if(CMAKE_BUILD_TYPE STREQUAL "Debug")
list(APPEND HIP_HCC_FLAGS ${CMAKE_CXX_FLAGS_DEBUG})
elseif(CMAKE_BUILD_TYPE STREQUAL "RelWithDebInfo")
......
paddle/fluid/API.spec
浏览文件 @ 36cd18b5
......@@ -276,7 +276,7 @@ paddle.fluid.layers.hard_shrink ArgSpec(args=['x', 'threshold'], varargs=None, k
paddle.fluid.layers.cumsum ArgSpec(args=['x', 'axis', 'exclusive', 'reverse'], varargs=None, keywords=None, defaults=(None, None, None))
paddle.fluid.layers.thresholded_relu ArgSpec(args=['x', 'threshold'], varargs=None, keywords=None, defaults=(None,))
paddle.fluid.layers.prior_box ArgSpec(args=['input', 'image', 'min_sizes', 'max_sizes', 'aspect_ratios', 'variance', 'flip', 'clip', 'steps', 'offset', 'name', 'min_max_aspect_ratios_order'], varargs=None, keywords=None, defaults=(None, [1.0], [0.1, 0.1, 0.2, 0.2], False, False, [0.0, 0.0], 0.5, None, False))
paddle.fluid.layers.density_prior_box ArgSpec(args=['input', 'image', 'densities', 'fixed_sizes', 'fixed_ratios', 'variance', 'clip', 'steps', 'offset', 'name'], varargs=None, keywords=None, defaults=(None, None, None, [0.1, 0.1, 0.2, 0.2], False, [0.0, 0.0], 0.5, None))
paddle.fluid.layers.density_prior_box ArgSpec(args=['input', 'image', 'densities', 'fixed_sizes', 'fixed_ratios', 'variance', 'clip', 'steps', 'offset', 'flatten_to_2d', 'name'], varargs=None, keywords=None, defaults=(None, None, None, [0.1, 0.1, 0.2, 0.2], False, [0.0, 0.0], 0.5, False, None))
paddle.fluid.layers.multi_box_head ArgSpec(args=['inputs', 'image', 'base_size', 'num_classes', 'aspect_ratios', 'min_ratio', 'max_ratio', 'min_sizes', 'max_sizes', 'steps', 'step_w', 'step_h', 'offset', 'variance', 'flip', 'clip', 'kernel_size', 'pad', 'stride', 'name', 'min_max_aspect_ratios_order'], varargs=None, keywords=None, defaults=(None, None, None, None, None, None, None, 0.5, [0.1, 0.1, 0.2, 0.2], True, False, 1, 0, 1, None, False))
paddle.fluid.layers.bipartite_match ArgSpec(args=['dist_matrix', 'match_type', 'dist_threshold', 'name'], varargs=None, keywords=None, defaults=(None, None, None))
paddle.fluid.layers.target_assign ArgSpec(args=['input', 'matched_indices', 'negative_indices', 'mismatch_value', 'name'], varargs=None, keywords=None, defaults=(None, None, None))
......@@ -342,7 +342,7 @@ paddle.fluid.transpiler.RoundRobin.dispatch ArgSpec(args=['self', 'varlist'], va
paddle.fluid.transpiler.RoundRobin.reset ArgSpec(args=['self'], varargs=None, keywords=None, defaults=None)
paddle.fluid.transpiler.DistributeTranspilerConfig.__init__
paddle.fluid.nets.simple_img_conv_pool ArgSpec(args=['input', 'num_filters', 'filter_size', 'pool_size', 'pool_stride', 'pool_padding', 'pool_type', 'global_pooling', 'conv_stride', 'conv_padding', 'conv_dilation', 'conv_groups', 'param_attr', 'bias_attr', 'act', 'use_cudnn'], varargs=None, keywords=None, defaults=(0, 'max', False, 1, 0, 1, 1, None, None, None, True))
paddle.fluid.nets.sequence_conv_pool ArgSpec(args=['input', 'num_filters', 'filter_size', 'param_attr', 'act', 'pool_type'], varargs=None, keywords=None, defaults=(None, 'sigmoid', 'max'))
paddle.fluid.nets.sequence_conv_pool ArgSpec(args=['input', 'num_filters', 'filter_size', 'param_attr', 'act', 'pool_type', 'bias_attr'], varargs=None, keywords=None, defaults=(None, 'sigmoid', 'max', None))
paddle.fluid.nets.glu ArgSpec(args=['input', 'dim'], varargs=None, keywords=None, defaults=(-1,))
paddle.fluid.nets.scaled_dot_product_attention ArgSpec(args=['queries', 'keys', 'values', 'num_heads', 'dropout_rate'], varargs=None, keywords=None, defaults=(1, 0.0))
paddle.fluid.nets.img_conv_group ArgSpec(args=['input', 'conv_num_filter', 'pool_size', 'conv_padding', 'conv_filter_size', 'conv_act', 'param_attr', 'conv_with_batchnorm', 'conv_batchnorm_drop_rate', 'pool_stride', 'pool_type', 'use_cudnn'], varargs=None, keywords=None, defaults=(1, 3, None, None, False, 0.0, 1, 'max', True))
......
paddle/fluid/framework/CMakeLists.txt
浏览文件 @ 36cd18b5
......@@ -116,8 +116,9 @@ cc_test(op_proto_maker_test SRCS op_proto_maker_test.cc DEPS op_proto_maker)
cc_library(op_info SRCS op_info.cc DEPS attribute framework_proto)
cc_library(shape_inference SRCS shape_inference.cc DEPS ddim attribute device_context)
cc_library(transfer_scope_cache SRCS transfer_scope_cache.cc DEPS scope framework_proto)
cc_library(operator SRCS operator.cc DEPS op_info device_context tensor scope glog
shape_inference data_transform lod_tensor profiler)
shape_inference data_transform lod_tensor profiler transfer_scope_cache)
cc_test(operator_test SRCS operator_test.cc DEPS operator op_registry device_context)
......@@ -192,3 +193,6 @@ cc_test(tuple_test SRCS tuple_test.cc )
if (NOT WIN32)
cc_test(rw_lock_test SRCS rw_lock_test.cc)
endif (NOT WIN32)
cc_library(dlpack_tensor SRCS dlpack_tensor.cc DEPS tensor dlpack)
cc_test(dlpack_tensor_test SRCS dlpack_tensor_test.cc DEPS dlpack_tensor glog)
paddle/fluid/framework/dlpack_tensor.cc 0 → 100644
浏览文件 @ 36cd18b5
// Copyright (c) 2018 PaddlePaddle Authors. All Rights Reserved.
//
// Licensed under the Apache License, Version 2.0 (the "License");
// you may not use this file except in compliance with the License.
// You may obtain a copy of the License at
//
// http://www.apache.org/licenses/LICENSE-2.0
//
// Unless required by applicable law or agreed to in writing, software
// distributed under the License is distributed on an "AS IS" BASIS,
// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
// See the License for the specific language governing permissions and
// limitations under the License.
#include "paddle/fluid/framework/dlpack_tensor.h"
namespace paddle {
namespace framework {
namespace internal {
template <typename T>
static ::DLDataType GetDLDataTypeCode() {
::DLDataType dtype;
if (std::is_same<T, platform::float16>::value ||
std::is_floating_point<T>::value) {
dtype.code = kDLFloat;
} else if (std::is_unsigned<T>::value) {
dtype.code = kDLUInt;
} else if (std::is_integral<T>::value) {
dtype.code = kDLInt;
} else {
PADDLE_THROW("Unsupported data type %s", typeid(T).name());
}
dtype.bits = 8 * sizeof(T);
dtype.lanes = 1;
return dtype;
}
static DLDataType GetDLDataTypeFromTypeIndex(const std::type_index &type) {
#define REG_DL_DATA_TYPE(type) \
{ std::type_index(typeid(type)), GetDLDataTypeCode<type>() }
static const std::unordered_map<std::type_index, ::DLDataType>
type_to_dtype_map({
REG_DL_DATA_TYPE(platform::float16), // NOLINT
REG_DL_DATA_TYPE(float), // NOLINT
REG_DL_DATA_TYPE(double), // NOLINT
REG_DL_DATA_TYPE(int), // NOLINT
REG_DL_DATA_TYPE(int64_t), // NOLINT
REG_DL_DATA_TYPE(bool), // NOLINT
REG_DL_DATA_TYPE(size_t), // NOLINT
REG_DL_DATA_TYPE(int16_t), // NOLINT
REG_DL_DATA_TYPE(uint8_t), // NOLINT
REG_DL_DATA_TYPE(int8_t) // NOLINT
});
static auto type_to_dtype_map_end_it = type_to_dtype_map.end();
auto it = type_to_dtype_map.find(type);
PADDLE_ENFORCE(it != type_to_dtype_map_end_it, "Unsupported data type %s",
type.name());
return it->second;
#undef REG_DL_DATA_TYPE
}
struct DLContextVisitor : public boost::static_visitor<::DLContext> {
inline ::DLContext operator()(const platform::CPUPlace &place) const {
DLContext ctx;
ctx.device_type = kDLCPU;
ctx.device_id = 0;
return ctx;
}
inline ::DLContext operator()(const platform::CUDAPlace &place) const {
#ifdef PADDLE_WITH_CUDA
DLContext ctx;
ctx.device_type = kDLGPU;
ctx.device_id = place.device;
return ctx;
#else
PADDLE_THROW("platform::CUDAPlace is not supported in CPU only version");
#endif
}
inline ::DLContext operator()(const platform::CUDAPinnedPlace &place) const {
#ifdef PADDLE_WITH_CUDA
DLContext ctx;
ctx.device_type = kDLCPUPinned;
ctx.device_id = 0;
return ctx;
#else
PADDLE_THROW(
"platform::CUDAPinnedPlace is not supported in CPU only version");
#endif
}
};
} // namespace internal
DLPackTensor::DLPackTensor(const Tensor &tensor, LaneType lanes) {
// init data, data buffer
t_.data = const_cast<void *>(tensor.data<void>());
// init ctx, DLContext type with device_type and device_id
auto place = tensor.place();
t_.ctx = boost::apply_visitor(internal::DLContextVisitor(), place);
// init dtype
t_.dtype = internal::GetDLDataTypeFromTypeIndex(tensor.type());
t_.dtype.lanes = lanes;
// init ndim, tensor rank
auto &dims = tensor.dims();
using DimType = decltype(t_.ndim); // int
t_.ndim = static_cast<DimType>(dims.size());
// init shape, tensor dims
t_.shape = shape_;
for (DimType i = 0; i < t_.ndim; ++i) {
t_.shape[i] = dims[i];
}
// init strides, nullptr means the tensor is compact
t_.strides = nullptr;
// init byte_offset
t_.byte_offset = 0;
}
} // namespace framework
} // namespace paddle
paddle/fluid/framework/dlpack_tensor.h 0 → 100644
浏览文件 @ 36cd18b5
// 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 <dlpack/dlpack.h>
#include "paddle/fluid/framework/tensor.h"
namespace paddle {
namespace framework {
class DLPackTensor {
public:
using LaneType = decltype(::DLTensor::dtype.lanes); // uint16_t
using ShapeType =
std::remove_reference<decltype(::DLTensor::shape[0])>::type; // int64_t
// lanes is only used in CPU to enable vectorization
explicit DLPackTensor(const Tensor& tensor, LaneType lanes = 1);
inline operator const ::DLTensor&() const { return t_; }
inline operator ::DLTensor&() { return t_; }
private:
::DLTensor t_;
// The shape in DLTensor is defined as int64_t*
// Add this member to make TVMTensor init without heap allocation
ShapeType shape_[9];
};
} // namespace framework
} // namespace paddle
paddle/fluid/framework/dlpack_tensor_test.cc 0 → 100644
浏览文件 @ 36cd18b5
// Copyright (c) 2018 PaddlePaddle Authors. All Rights Reserved.
//
// Licensed under the Apache License, Version 2.0 (the "License");
// you may not use this file except in compliance with the License.
// You may obtain a copy of the License at
//
// http://www.apache.org/licenses/LICENSE-2.0
//
// Unless required by applicable law or agreed to in writing, software
// distributed under the License is distributed on an "AS IS" BASIS,
// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
// See the License for the specific language governing permissions and
// limitations under the License.
#include "paddle/fluid/framework/dlpack_tensor.h"
#include <glog/logging.h>
#include <gtest/gtest.h>
#include <vector>
namespace paddle {
namespace framework {
namespace { // NOLINT
template <typename T>
constexpr uint8_t GetDLDataTypeCode() {
return std::is_same<platform::float16, T>::value ||
std::is_floating_point<T>::value
? static_cast<uint8_t>(kDLFloat)
: (std::is_unsigned<T>::value
? static_cast<uint8_t>(kDLUInt)
: (std::is_integral<T>::value ? static_cast<uint8_t>(kDLInt)
: static_cast<uint8_t>(-1)));
}
} // NOLINT
template <typename T>
void TestMain(const platform::Place &place, uint16_t lanes) {
DDim dims{4, 5, 6, 7};
Tensor tensor;
tensor.Resize(dims);
void *p = tensor.mutable_data<T>(place);
DLPackTensor dlpack_tensor(tensor, lanes);
::DLTensor &dl_tensor = dlpack_tensor;
CHECK_EQ(p, dl_tensor.data);
if (platform::is_cpu_place(place)) {
CHECK_EQ(kDLCPU, dl_tensor.ctx.device_type);
CHECK_EQ(0, dl_tensor.ctx.device_id);
} else if (platform::is_gpu_place(place)) {
CHECK_EQ(kDLGPU, dl_tensor.ctx.device_type);
CHECK_EQ(boost::get<platform::CUDAPlace>(place).device,
dl_tensor.ctx.device_id);
} else if (platform::is_cuda_pinned_place(place)) {
CHECK_EQ(kDLCPUPinned, dl_tensor.ctx.device_type);
CHECK_EQ(0, dl_tensor.ctx.device_id);
} else {
CHECK_EQ(false, true);
}
CHECK_EQ(dims.size(), dl_tensor.ndim);
for (auto i = 0; i < dims.size(); ++i) {
CHECK_EQ(dims[i], dl_tensor.shape[i]);
}
CHECK_EQ(dl_tensor.strides == nullptr, true);
CHECK_EQ(static_cast<uint64_t>(0), dl_tensor.byte_offset);
CHECK_EQ(lanes, dl_tensor.dtype.lanes);
CHECK_EQ(sizeof(T) * 8, dl_tensor.dtype.bits);
CHECK_EQ(GetDLDataTypeCode<T>(), dl_tensor.dtype.code);
}
template <typename T>
void TestMainLoop() {
#ifdef PADDLE_WITH_CUDA
std::vector<platform::Place> places{platform::CPUPlace(),
platform::CUDAPlace(0),
platform::CUDAPinnedPlace()};
if (platform::GetCUDADeviceCount() > 1) {
places.emplace_back(platform::CUDAPlace(1));
}
#else
std::vector<platform::Place> places{platform::CPUPlace()};
#endif
std::vector<uint16_t> lanes{1, 2};
for (auto &p : places) {
for (auto &l : lanes) {
TestMain<T>(p, l);
}
}
}
#define PADDLE_DLPACK_TEST(type) \
TEST(dlpack, test_##type) { TestMainLoop<type>(); }
using float16 = platform::float16;
PADDLE_DLPACK_TEST(float16);
PADDLE_DLPACK_TEST(float);
PADDLE_DLPACK_TEST(double);
PADDLE_DLPACK_TEST(int);
PADDLE_DLPACK_TEST(int64_t);
PADDLE_DLPACK_TEST(bool);
PADDLE_DLPACK_TEST(size_t);
PADDLE_DLPACK_TEST(int16_t);
PADDLE_DLPACK_TEST(uint8_t);
PADDLE_DLPACK_TEST(int8_t);
#undef PADDLE_DLPACK_TEST
} // namespace framework
} // namespace paddle
paddle/fluid/framework/executor.cc
浏览文件 @ 36cd18b5
......@@ -20,6 +20,7 @@ limitations under the License. */
#include "paddle/fluid/framework/ngraph_operator.h"
#include "paddle/fluid/framework/op_registry.h"
#include "paddle/fluid/framework/reader.h"
#include "paddle/fluid/framework/transfer_scope_cache.h"
#include "paddle/fluid/operators/detail/macros.h"
#include "paddle/fluid/platform/place.h"
#include "paddle/fluid/platform/profiler.h"
......@@ -391,8 +392,8 @@ void Executor::RunPreparedContext(ExecutorPrepareContext* ctx, Scope* scope,
int64_t max_memory_size = GetEagerDeletionThreshold();
std::unique_ptr<GarbageCollector<Tensor>> gc;
// WhileOp would set keep_kids to false
// WhileGradOp would need the scopes created in WhileOp
// WhileOp would set keep_kids to true,
// because WhileGradOp needs the scopes created in WhileOp.
// Perhaps, we should not perform eager deletion in WhileOp
// The scopes and variables created by WhileOp would be deleted
// in WhileGradOp.
......
paddle/fluid/framework/naive_executor.cc
浏览文件 @ 36cd18b5
......@@ -83,6 +83,7 @@ void NaiveExecutor::Run() {
for (auto &op : ops_) {
VLOG(3) << std::this_thread::get_id() << " run " << op->Type()
<< " on scope " << scope_;
op->SetIsCalledByExecutor(false);
op->Run(*scope_, place_);
}
}
......
paddle/fluid/framework/op_desc.cc
浏览文件 @ 36cd18b5
......@@ -252,6 +252,12 @@ void OpDesc::SetAttr(const std::string &name, const Attribute &v) {
this->attrs_[name] = std::vector<int>();
break;
}
case proto::AttrType::LONGS: {
VLOG(110) << "SetAttr: " << Type() << ", " << name
<< " from LONGS to LONGS";
this->attrs_[name] = std::vector<int64_t>();
break;
}
case proto::AttrType::FLOATS: {
VLOG(110) << "SetAttr: " << Type() << ", " << name
<< " from INTS to FLOATS";
......
paddle/fluid/framework/operator.cc
浏览文件 @ 36cd18b5
......@@ -22,6 +22,7 @@ limitations under the License. */
#include "paddle/fluid/framework/lod_tensor.h"
#include "paddle/fluid/framework/operator.h"
#include "paddle/fluid/framework/shape_inference.h"
#include "paddle/fluid/framework/transfer_scope_cache.h"
#include "paddle/fluid/framework/var_type.h"
#include "paddle/fluid/platform/profiler.h"
......@@ -33,11 +34,6 @@ DEFINE_bool(check_nan_inf, false,
namespace paddle {
namespace framework {
// Combine two hash values to a single hash.
inline size_t CombineHash(size_t seed, size_t a) {
return (seed ^ a) + 0x9e3779b9 + (seed << 6) + (seed >> 2);
}
std::vector<std::tuple<platform::Place, LibraryType>> kKernelPriority = {
std::make_tuple(platform::CUDAPlace(0), LibraryType::kCUDNN),
std::make_tuple(platform::CUDAPlace(0), LibraryType::kPlain),
......@@ -794,17 +790,6 @@ void OperatorWithKernel::TransferInplaceVarsBack(
Scope* OperatorWithKernel::TryTransferData(
const Scope& scope, const OpKernelType& expected_kernel_key,
std::vector<std::string>* transfered_inplace_vars) const {
// In the inference scenerio, the scopes will be reused across the batches, so
// the `new_scope` here will result in GPU memroy explosion over the running of
// operators.
// We use a thread_local cache to fix that issue, the key in the cache is the
// combination of the `scope` argument, from_kernel_type, target_kernel_type.
// Have a discussion with @Superjomn or the inference developers if some changes
// on this logic for this macro might not tested on the other scenerios.
#ifdef PADDLE_ON_INFERENCE
thread_local std::unordered_map<size_t, Scope*> infer_transfer_scope_cache;
#endif
Scope* new_scope = nullptr;
for (auto& var_name_item : Inputs()) {
for (auto& var_name : var_name_item.second) {
......@@ -835,23 +820,23 @@ Scope* OperatorWithKernel::TryTransferData(
VLOG(30) << "Transform Variable " << var_name << " from "
<< kernel_type_for_var << " to " << expected_kernel_key;
#ifdef PADDLE_ON_INFERENCE
size_t infer_cache_key =
CombineHash(OpKernelType::Hash()(kernel_type_for_var),
OpKernelType::Hash()(expected_kernel_key));
infer_cache_key =
CombineHash(infer_cache_key, std::hash<const Scope*>()(&scope));
auto it = infer_transfer_scope_cache.find(infer_cache_key);
if (it != infer_transfer_scope_cache.end()) {
new_scope = infer_transfer_scope_cache[infer_cache_key];
} else {
new_scope = &scope.NewScope();
infer_transfer_scope_cache[infer_cache_key] = new_scope;
// In the inference scenerio, the scopes will be reused across the
// batches, so the `new_scope` here will result in GPU memroy explosion
// over the running of operators.
// We use a thread_local cache to fix that issue, the key in the cache is
// the combination of the `scope` argument, from_kernel_type,
// target_kernel_type.
// Have a discussion with @Superjomn or the inference developers if some
// changes on this logic for this macro might not tested on the other
// scenerios.
// If this op is not called by an Executor or ParallelExecutor, it should
// called by a NaiveExecutor, the NaiveExecutor will cache the scopes and
// variables, that behavior a lot different.
if (!run_by_executor_) {
new_scope = TryCreateTransferScope(kernel_type_for_var,
expected_kernel_key, &scope);
}
#endif
if (new_scope == nullptr) {
if (!new_scope) {
new_scope = &scope.NewScope();
}
......
paddle/fluid/framework/operator.h
浏览文件 @ 36cd18b5
......@@ -127,6 +127,8 @@ class OperatorBase {
//! Get all outputs variable names
virtual std::vector<std::string> OutputVars(bool has_intermediate) const;
void SetIsCalledByExecutor(bool x) { run_by_executor_ = x; }
protected:
std::string type_;
// NOTE: in case of OpGrad, inputs_ contains:
......@@ -139,6 +141,8 @@ class OperatorBase {
// IG (Inputs Gradients)
VariableNameMap outputs_;
AttributeMap attrs_;
// Whether this operator executes in an Executor.
bool run_by_executor_{true};
private:
void GenerateTemporaryNames();
......
paddle/fluid/framework/transfer_scope_cache.cc 0 → 100644
浏览文件 @ 36cd18b5
// Copyright (c) 2018 PaddlePaddle Authors. All Rights Reserved.
//
// Licensed under the Apache License, Version 2.0 (the "License");
// you may not use this file except in compliance with the License.
// You may obtain a copy of the License at
//
// http://www.apache.org/licenses/LICENSE-2.0
//
// Unless required by applicable law or agreed to in writing, software
// distributed under the License is distributed on an "AS IS" BASIS,
// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
// See the License for the specific language governing permissions and
// limitations under the License.
#include "paddle/fluid/framework/transfer_scope_cache.h"
namespace paddle {
namespace framework {
std::unordered_map<size_t, Scope*>& global_transfer_data_cache() {
thread_local auto* x = new std::unordered_map<size_t, Scope*>;
return *x;
}
std::unordered_set<Scope*>& global_transfer_scope_cache() {
thread_local auto* x = new std::unordered_set<Scope*>;
return *x;
}
Scope* TryCreateTransferScope(OpKernelType type0, OpKernelType type1,
const Scope* scope) {
Scope* new_scope{nullptr};
size_t infer_cache_key =
CombineHash(OpKernelType::Hash()(type0), OpKernelType::Hash()(type1));
infer_cache_key =
CombineHash(infer_cache_key, std::hash<const Scope*>()(scope));
auto it = global_transfer_data_cache().find(infer_cache_key);
if (it != global_transfer_data_cache().end()) {
new_scope = global_transfer_data_cache()[infer_cache_key];
} else {
new_scope = &scope->NewScope();
global_transfer_data_cache()[infer_cache_key] = new_scope;
}
global_transfer_scope_cache().insert(new_scope);
return new_scope;
}
void RemoveKidsFromTransferScopeCache(Scope* scope) {
auto it = global_transfer_scope_cache().find(scope);
if (it != global_transfer_scope_cache().end()) {
global_transfer_scope_cache().erase(it);
}
for (auto* s : scope->kids()) {
auto it = global_transfer_scope_cache().find(s);
if (it != global_transfer_scope_cache().end()) {
global_transfer_scope_cache().erase(it);
}
}
// remove global transfer data cache
auto& cache = global_transfer_data_cache();
for (auto it = cache.begin(); it != cache.end();) {
if (it->second == scope)
it = cache.erase(it);
else
it++;
}
}
} // namespace framework
} // namespace paddle
paddle/fluid/framework/transfer_scope_cache.h 0 → 100644
浏览文件 @ 36cd18b5
// 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 <thread> // NOLINT
#include <unordered_map>
#include <unordered_set>
#include "paddle/fluid/framework/op_kernel_type.h"
#include "paddle/fluid/framework/scope.h"
namespace paddle {
namespace framework {
std::unordered_map<size_t, Scope*>& global_transfer_data_cache();
std::unordered_set<Scope*>& global_transfer_scope_cache();
// Combine two hash values to a single hash.
static size_t CombineHash(size_t seed, size_t a) {
return (seed ^ a) + 0x9e3779b9 + (seed << 6) + (seed >> 2);
}
Scope* TryCreateTransferScope(OpKernelType type0, OpKernelType type1,
const Scope* scope);
void RemoveKidsFromTransferScopeCache(Scope* scope);
} // namespace framework
} // namespace paddle
paddle/fluid/inference/CMakeLists.txt
浏览文件 @ 36cd18b5
......@@ -4,6 +4,7 @@ endif()
# analysis and tensorrt must be added before creating static library,
# otherwise, there would be undefined reference to them in static library.
add_subdirectory(analysis)
add_subdirectory(utils)
if (TENSORRT_FOUND)
add_subdirectory(tensorrt)
endif()
......
paddle/fluid/inference/api/CMakeLists.txt
浏览文件 @ 36cd18b5
......@@ -30,7 +30,9 @@ cc_library(paddle_pass_builder SRCS paddle_pass_builder.cc)
cc_library(analysis_predictor SRCS analysis_predictor.cc DEPS paddle_inference_api analysis naive_executor zero_copy_tensor reset_tensor_array analysis_config paddle_pass_builder ir_pass_manager)
cc_library(zero_copy_tensor SRCS details/zero_copy_tensor.cc DEPS scope lod_tensor enforce)
cc_library(zero_copy_tensor_dummy SRCS details/zero_copy_tensor_dummy.cc)
cc_library(paddle_inference_api SRCS api.cc api_impl.cc helper.cc DEPS lod_tensor scope paddle_pass_builder reset_tensor_array analysis_config analysis_config paddle_pass_builder DEPS zero_copy_tensor)
cc_library(paddle_inference_api SRCS api.cc api_impl.cc helper.cc DEPS
lod_tensor scope paddle_pass_builder reset_tensor_array analysis_config
analysis_config paddle_pass_builder zero_copy_tensor reset_tensor_array)
cc_test(test_paddle_inference_api
SRCS api_tester.cc
......
paddle/fluid/inference/api/analysis_config.cc
浏览文件 @ 36cd18b5
......@@ -46,6 +46,7 @@ contrib::AnalysisConfig::AnalysisConfig(const contrib::AnalysisConfig &other) {
prog_file = other.prog_file;
param_file = other.param_file;
specify_input_name = other.specify_input_name;
cpu_math_library_num_threads_ = other.cpu_math_library_num_threads_;
// fields from this.
enable_ir_optim = other.enable_ir_optim;
use_feed_fetch_ops = other.use_feed_fetch_ops;
......@@ -72,6 +73,7 @@ contrib::AnalysisConfig::AnalysisConfig(contrib::AnalysisConfig &&other) {
prog_file = other.prog_file;
param_file = other.param_file;
specify_input_name = other.specify_input_name;
cpu_math_library_num_threads_ = other.cpu_math_library_num_threads_;
// fields from this.
enable_ir_optim = other.enable_ir_optim;
use_feed_fetch_ops = other.use_feed_fetch_ops;
......
paddle/fluid/inference/api/analysis_predictor.cc
浏览文件 @ 36cd18b5
......@@ -31,11 +31,11 @@
#include "paddle/fluid/inference/tensorrt/convert/op_converter.h"
#endif
#include "paddle/fluid/inference/utils/singleton.h"
#include "paddle/fluid/memory/memcpy.h"
#include "paddle/fluid/platform/cpu_helper.h"
#include "paddle/fluid/platform/profiler.h"
DECLARE_bool(profile);
DECLARE_int32(paddle_num_threads);
namespace paddle {
......@@ -65,7 +65,7 @@ bool AnalysisPredictor::Init(
}
// no matter with or without MKLDNN
paddle::platform::SetNumThreads(FLAGS_paddle_num_threads);
paddle::platform::SetNumThreads(config_.cpu_math_library_num_threads());
if (!PrepareScope(parent_scope)) {
return false;
......@@ -158,6 +158,14 @@ bool AnalysisPredictor::PrepareExecutor() {
return true;
}
void AnalysisPredictor::SetMkldnnThreadID(int tid) {
#ifdef PADDLE_WITH_MKLDNN
platform::set_cur_thread_id(tid);
#else
LOG(ERROR) << "Please compile with MKLDNN first to use MKLDNN";
#endif
}
bool AnalysisPredictor::Run(const std::vector<PaddleTensor> &inputs,
std::vector<PaddleTensor> *output_data,
int batch_size) {
......@@ -165,7 +173,6 @@ bool AnalysisPredictor::Run(const std::vector<PaddleTensor> &inputs,
inference::Timer timer;
timer.tic();
// set feed variable
std::vector<framework::LoDTensor> feeds;
framework::Scope *scope = sub_scope_ ? sub_scope_ : scope_.get();
if (!SetFeed(inputs, scope)) {
LOG(ERROR) << "fail to set feed";
......@@ -206,17 +213,29 @@ bool AnalysisPredictor::SetFeed(const std::vector<PaddleTensor> &inputs,
framework::DDim ddim = framework::make_ddim(inputs[i].shape);
void *input_ptr;
if (inputs[i].dtype == PaddleDType::INT64) {
input_ptr = input.mutable_data<int64_t>(ddim, platform::CPUPlace());
input_ptr = input.mutable_data<int64_t>(ddim, place_);
} else if (inputs[i].dtype == PaddleDType::FLOAT32) {
input_ptr = input.mutable_data<float>(ddim, platform::CPUPlace());
input_ptr = input.mutable_data<float>(ddim, place_);
} else {
LOG(ERROR) << "unsupported feed type " << inputs[i].dtype;
return false;
}
// TODO(panyx0718): Init LoDTensor from existing memcpy to save a copy.
std::memcpy(static_cast<void *>(input_ptr), inputs[i].data.data(),
inputs[i].data.length());
if (platform::is_cpu_place(place_)) {
// TODO(panyx0718): Init LoDTensor from existing memcpy to save a copy.
std::memcpy(static_cast<void *>(input_ptr), inputs[i].data.data(),
inputs[i].data.length());
} else {
#ifdef PADDLE_WITH_CUDA
auto dst_gpu_place = boost::get<platform::CUDAPlace>(place_);
memory::Copy(dst_gpu_place, static_cast<void *>(input_ptr),
platform::CPUPlace(), inputs[i].data.data(),
inputs[i].data.length(),
0); // stream 0 for sync copy
#else
PADDLE_THROW("Not compile with CUDA, should not reach here.");
#endif
}
// TODO(Superjomn) Low performance, need optimization for heavy LoD copy.
framework::LoD lod;
for (auto &level : inputs[i].lod) {
......
paddle/fluid/inference/api/analysis_predictor.h
浏览文件 @ 36cd18b5
......@@ -69,6 +69,8 @@ class AnalysisPredictor : public PaddlePredictor {
framework::Scope *scope() { return scope_.get(); }
framework::ProgramDesc &program() { return *inference_program_; }
void SetMkldnnThreadID(int tid);
protected:
bool PrepareProgram(const std::shared_ptr<framework::ProgramDesc> &program);
bool PrepareScope(const std::shared_ptr<framework::Scope> &parent_scope);
......
paddle/fluid/inference/api/api_impl.cc
浏览文件 @ 36cd18b5
......@@ -24,11 +24,11 @@ limitations under the License. */
#include "paddle/fluid/inference/api/api_impl.h"
#include "paddle/fluid/inference/api/details/reset_tensor_array.h"
#include "paddle/fluid/inference/api/helper.h"
#include "paddle/fluid/memory/memcpy.h"
#include "paddle/fluid/platform/cpu_helper.h"
#include "paddle/fluid/platform/profiler.h"
DEFINE_bool(profile, false, "Turn on profiler for fluid");
DECLARE_int32(paddle_num_threads);
namespace paddle {
namespace {
......@@ -74,7 +74,7 @@ bool NativePaddlePredictor::Init(
}
// no matter with or without MKLDNN
paddle::platform::SetNumThreads(FLAGS_paddle_num_threads);
paddle::platform::SetNumThreads(config_.cpu_math_library_num_threads());
if (config_.use_gpu) {
place_ = paddle::platform::CUDAPlace(config_.device);
......@@ -135,7 +135,6 @@ bool NativePaddlePredictor::Run(const std::vector<PaddleTensor> &inputs,
Timer timer;
timer.tic();
// set feed variable
std::vector<framework::LoDTensor> feeds;
framework::Scope *scope = sub_scope_ != nullptr ? sub_scope_ : scope_.get();
if (!SetFeed(inputs, scope)) {
LOG(ERROR) << "fail to set feed";
......@@ -191,17 +190,30 @@ bool NativePaddlePredictor::SetFeed(const std::vector<PaddleTensor> &inputs,
framework::DDim ddim = framework::make_ddim(inputs[i].shape);
void *input_ptr;
if (inputs[i].dtype == PaddleDType::INT64) {
input_ptr = input.mutable_data<int64_t>(ddim, platform::CPUPlace());
input_ptr = input.mutable_data<int64_t>(ddim, place_);
} else if (inputs[i].dtype == PaddleDType::FLOAT32) {
input_ptr = input.mutable_data<float>(ddim, platform::CPUPlace());
input_ptr = input.mutable_data<float>(ddim, place_);
} else {
LOG(ERROR) << "unsupported feed type " << inputs[i].dtype;
return false;
}
// TODO(panyx0718): Init LoDTensor from existing memcpy to save a copy.
std::memcpy(static_cast<void *>(input_ptr), inputs[i].data.data(),
inputs[i].data.length());
if (platform::is_cpu_place(place_)) {
// TODO(panyx0718): Init LoDTensor from existing memcpy to save a copy.
std::memcpy(static_cast<void *>(input_ptr), inputs[i].data.data(),
inputs[i].data.length());
} else {
#ifdef PADDLE_WITH_CUDA
auto dst_gpu_place = boost::get<platform::CUDAPlace>(place_);
memory::Copy(dst_gpu_place, static_cast<void *>(input_ptr),
platform::CPUPlace(), inputs[i].data.data(),
inputs[i].data.length(),
0); // stream 0 for sync copy
#else
PADDLE_THROW("Not compile with CUDA, should not reach here.");
#endif
}
// TODO(Superjomn) Low performance, need optimization for heavy LoD copy.
framework::LoD lod;
for (auto &level : inputs[i].lod) {
......
paddle/fluid/inference/api/demo_ci/CMakeLists.txt
浏览文件 @ 36cd18b5
......@@ -46,8 +46,6 @@ if(WITH_GPU)
endif()
endif(NOT WIN32)
endif()
include_directories("D:/Paddle/")
include_directories("${PADDLE_LIB}")
include_directories("${PADDLE_LIB}/third_party/install/protobuf/include")
include_directories("${PADDLE_LIB}/third_party/install/glog/include")
......
paddle/fluid/inference/api/paddle_analysis_config.h
浏览文件 @ 36cd18b5
......@@ -51,9 +51,9 @@ struct AnalysisConfig : public NativeConfig {
int max_batch_size = 1);
bool use_tensorrt() const { return use_tensorrt_; }
void EnableMKLDNN();
// NOTE this is just for internal development, please not use it.
// NOT stable yet.
void EnableMKLDNN();
bool use_mkldnn() const { return use_mkldnn_; }
friend class ::paddle::AnalysisPredictor;
......
paddle/fluid/inference/api/paddle_api.h
浏览文件 @ 36cd18b5
......@@ -186,6 +186,19 @@ struct NativeConfig : public PaddlePredictor::Config {
// Specify the variable's name of each input if input tensors don't follow the
// `feeds` and `fetches` of the phase `save_inference_model`.
bool specify_input_name{false};
// Set and get the number of cpu math library threads.
void SetCpuMathLibraryNumThreads(int cpu_math_library_num_threads) {
cpu_math_library_num_threads_ = cpu_math_library_num_threads;
}
int cpu_math_library_num_threads() const {
return cpu_math_library_num_threads_;
}
protected:
// number of cpu math library (such as MKL, OpenBlas) threads for each
// instance.
int cpu_math_library_num_threads_{1};
};
// A factory to help create different predictors.
......
paddle/fluid/inference/tensorrt/convert/split_op.cc
浏览文件 @ 36cd18b5
......@@ -19,9 +19,6 @@ namespace paddle {
namespace inference {
namespace tensorrt {
/*
* SplitOp.
*/
class SplitOpConverter : public OpConverter {
public:
void operator()(const framework::proto::OpDesc& op,
......@@ -40,16 +37,11 @@ class SplitOpConverter : public OpConverter {
int axis = boost::get<int>(op_desc.GetAttr("axis"));
std::vector<int> output_lengths =
boost::get<std::vector<int>>(op_desc.GetAttr("sections"));
// split on batch is not supported in TensorRT
PADDLE_ENFORCE(axis != 0);
if (axis < 0) {
axis += input_dims.nbDims;
} else {
axis -= 1;
}
axis += (axis < 0) ? input_dims.nbDims : -1;
PADDLE_ENFORCE(output_lengths.size() == output_num);
//
plugin::SplitPlugin* plugin = new plugin::SplitPlugin(axis, output_lengths);
nvinfer1::IPluginLayer* layer =
engine_->AddPlugin(&input, input_num, plugin);
......
paddle/fluid/inference/tensorrt/convert/test_split_op.cc
浏览文件 @ 36cd18b5
......@@ -20,30 +20,92 @@ namespace paddle {
namespace inference {
namespace tensorrt {
TEST(split_op, test) {
template <int BatchSize, int Axis>
void TensorRTSplitTest(const std::vector<int> &in_shape,
const std::vector<int> &sections) {
std::unordered_set<std::string> parameters({""});
framework::Scope scope;
TRTConvertValidation validator(10, parameters, scope, 1000);
validator.DeclInputVar("split_input", nvinfer1::DimsCHW(3, 2, 2));
validator.DeclOutputVar("split_out1", nvinfer1::DimsCHW(2, 2, 2));
validator.DeclOutputVar("split_out2", nvinfer1::DimsCHW(1, 2, 2));
TRTConvertValidation validator(BatchSize + 1, parameters, scope, 10000);
auto make_dim = [](const std::vector<int> &shape) {
nvinfer1::DimsCHW dim;
dim.c() = shape[0];
dim.h() = shape[1];
dim.w() = shape[2];
return dim;
};
validator.DeclInputVar("split_input", make_dim(in_shape));
std::vector<std::string> output_vars;
for (size_t i = 0; i < sections.size(); ++i) {
auto out_shape = in_shape;
out_shape[Axis - 1] = sections[i];
std::string output_name = "split_out" + std::to_string(i);
validator.DeclOutputVar(output_name, make_dim(out_shape));
output_vars.push_back(output_name);
}
// Prepare Op description
framework::OpDesc desc;
desc.SetType("split");
desc.SetInput("X", {"split_input"});
desc.SetOutput("Out", {"split_out1", "split_out2"});
desc.SetOutput("Out", output_vars);
int num = 0;
int axis = 1;
std::vector<int> output_lengths = {2, 1};
desc.SetAttr("axis", axis);
desc.SetAttr("num", num);
desc.SetAttr("sections", output_lengths);
desc.SetAttr("axis", Axis);
desc.SetAttr("num", 0);
desc.SetAttr("sections", sections);
validator.SetOp(*desc.Proto());
validator.Execute(1);
validator.Execute(BatchSize);
}
// batch = 0, axis = 1, same shape
TEST(split_op, test_same_shape_axis1_batch1) {
TensorRTSplitTest<1, 1>({4, 2, 2}, {2, 2});
}
// batch = 0, axis = 1, different shape
TEST(split_op, test_different_shape_axis1_batch1) {
TensorRTSplitTest<1, 1>({3, 2, 2}, {2, 1});
}
// batch = 10, axis = 1, same shape
TEST(split_op, test_same_shape_axis1_batch10) {
TensorRTSplitTest<10, 1>({4, 2, 2}, {2, 2});
}
// batch = 10, axis = 1, different shape
TEST(split_op, test_different_shape_axis1_batch10) {
TensorRTSplitTest<10, 1>({3, 2, 2}, {2, 1});
}
// batch = 0, axis = 2, same shape
TEST(split_op, test_same_shape_axis2_batch1) {
TensorRTSplitTest<1, 2>({3, 4, 2}, {2, 2});
}
// batch = 0, axis = 2, different shape
TEST(split_op, test_different_shape_axis2_batch1) {
TensorRTSplitTest<1, 2>({3, 3, 2}, {2, 1});
}
// batch = 10, axis = 2, same shape
TEST(split_op, test_same_shape_axis2_batch10) {
TensorRTSplitTest<10, 2>({3, 4, 2}, {2, 2});
}
// batch = 10, axis = 2, different shape
TEST(split_op, test_different_shape_axis2_batch10) {
TensorRTSplitTest<10, 2>({3, 3, 2}, {2, 1});
}
// batch = 0, axis = 3, same shape
TEST(split_op, test_same_shape_axis3_batch1) {
TensorRTSplitTest<1, 3>({3, 2, 4}, {2, 2});
}
// batch = 0, axis = 3, different shape
TEST(split_op, test_different_shape_axis3_batch1) {
TensorRTSplitTest<1, 3>({3, 2, 3}, {2, 1});
}
// batch = 10, axis = 3, same shape
TEST(split_op, test_same_shape_axis3_batch10) {
TensorRTSplitTest<10, 3>({3, 2, 4}, {2, 2});
}
// batch = 10, axis = 3, different shape
TEST(split_op, test_different_shape_axis3_batch10) {
TensorRTSplitTest<10, 3>({3, 2, 3}, {2, 1});
}
} // namespace tensorrt
......
paddle/fluid/inference/tensorrt/plugin/split_op_plugin.cu
浏览文件 @ 36cd18b5
......@@ -12,6 +12,8 @@
// See the License for the specific language governing permissions and
// limitations under the License.
#include <cuda_fp16.h>
#include <algorithm>
#include "paddle/fluid/inference/tensorrt/plugin/split_op_plugin.h"
namespace paddle {
......@@ -19,6 +21,52 @@ namespace inference {
namespace tensorrt {
namespace plugin {
// copied from operators::math::SplitFunctor
template <typename T>
__global__ void SplitKernel(const T* input_data, const int in_row,
const int in_col, const int* out_cols,
int out_cols_size, T** outputs_data) {
int tid_x = blockIdx.x * blockDim.x + threadIdx.x;
int curr_segment = 0;
int curr_offset = out_cols[0];
for (; tid_x < in_col; tid_x += blockDim.x * gridDim.x) {
int curr_col_offset = out_cols[curr_segment + 1];
while (curr_col_offset <= tid_x) {
curr_offset = curr_col_offset;
++curr_segment;
curr_col_offset = out_cols[curr_segment + 1];
}
int local_col = tid_x - curr_offset;
int segment_width = curr_col_offset - curr_offset;
T* output_ptr = outputs_data[curr_segment];
if (output_ptr != nullptr) {
int tid_y = blockIdx.y * blockDim.y + threadIdx.y;
for (; tid_y < in_row; tid_y += blockDim.y * gridDim.y)
output_ptr[tid_y * segment_width + local_col] =
input_data[tid_y * in_col + tid_x];
}
}
}
template <typename T>
__global__ void SplitKernel(const T* input_data, const int in_row,
const int in_col, const int fixed_out_col,
T** outputs_data) {
int tid_x = blockIdx.x * blockDim.x + threadIdx.x;
for (; tid_x < in_col; tid_x += blockDim.x * gridDim.x) {
int split = tid_x / fixed_out_col;
int in_offset = tid_x - split * fixed_out_col;
T* output_ptr = outputs_data[split];
if (output_ptr != nullptr) {
int tid_y = blockIdx.y * blockDim.y + threadIdx.y;
for (; tid_y < in_row; tid_y += blockDim.y * gridDim.y)
output_ptr[tid_y * fixed_out_col + in_offset] =
input_data[tid_y * in_col + tid_x];
}
}
}
nvinfer1::Dims SplitPlugin::getOutputDimensions(
int index, const nvinfer1::Dims* input_dims, int num_inputs) {
PADDLE_ENFORCE_EQ(num_inputs, 1);
......@@ -31,48 +79,96 @@ nvinfer1::Dims SplitPlugin::getOutputDimensions(
int SplitPlugin::initialize() {
PADDLE_ENFORCE_LE(axis_, nvinfer1::Dims::MAX_DIMS);
// notice input dims is [C, H, W]
nvinfer1::Dims dims = this->getInputDims(0);
outer_rows_ = 1;
inner_cols_ = 1;
for (int i = 0; i < axis_; ++i) {
outer_rows_ *= dims.d[i];
}
for (int i = axis_ + 1; i < dims.nbDims; ++i) {
inner_cols_ *= dims.d[i];
}
same_shape_ = true;
std::vector<int> segment_offsets(1, 0);
for (int i = 0; i < this->getNbOutputs(); ++i) {
segment_offsets.push_back(segment_offsets.back() + output_length_[i]);
if (output_length_[i] != output_length_[0]) {
same_shape_ = false;
}
segment_offsets.push_back(segment_offsets.back() +
output_length_[i] * inner_cols_);
}
segment_offsets_ = segment_offsets;
nvinfer1::Dims dims = this->getInputDims(0);
nx_ = 1;
for (int i = dims.nbDims - 1; i > axis_; --i) {
nx_ *= dims.d[i];
inner_cols_ *= dims.d[axis_];
d_segment_offsets_ = segment_offsets;
segment_offsets_ = std::move(segment_offsets);
d_output_ptrs_.resize(this->getNbOutputs(), nullptr);
return 0;
}
template <typename T>
inline void Split(cudaStream_t stream, const bool same_shape,
const int outer_rows, const int inner_cols,
const std::vector<int>& segment_offsets,
const int* d_segment_offsets, const T* input, T** outputs) {
const int kThreadsPerBlock = 1024;
const int kMaxBlocks = 65535;
int block_cols = kThreadsPerBlock;
if (inner_cols < kThreadsPerBlock) { // block_cols is aligned by 32.
block_cols = ((inner_cols + 31) >> 5) << 5;
}
ny_ = dims.d[axis_];
nz_ = 1;
for (int i = axis_ - 1; i >= 0; --i) {
nz_ *= dims.d[i];
int block_rows = kThreadsPerBlock / block_cols;
dim3 block_size = dim3(block_cols, block_rows, 1);
int grid_cols =
std::min((inner_cols + block_cols - 1) / block_cols, kMaxBlocks);
int grid_rows =
std::min(kMaxBlocks / grid_cols, std::max(outer_rows / block_rows, 1));
dim3 grid_size = dim3(grid_cols, grid_rows, 1);
if (same_shape) {
SplitKernel<<<grid_size, block_size, 0, stream>>>(
input, outer_rows, inner_cols, segment_offsets[1], outputs);
} else {
SplitKernel<<<grid_size, block_size, 0, stream>>>(
input, outer_rows, inner_cols, d_segment_offsets,
static_cast<int>(segment_offsets.size()), outputs);
}
return 0;
}
int SplitPlugin::enqueue(int batchSize, const void* const* inputs,
void** outputs, void* workspace, cudaStream_t stream) {
auto const& input_dims = this->getInputDims(0);
int input_size = 0;
float const* idata = reinterpret_cast<float const*>(inputs[0]);
float** odatas = reinterpret_cast<float**>(outputs);
// kernel impl here.
int inputBatchOffset = nx_ * ny_ * nz_;
for (size_t i = 0; i < this->getNbOutputs(); i++) {
for (size_t j = 0; j < batchSize; j++) {
cudaMemcpyAsync(
odatas[i] +
j * (segment_offsets_[i + 1] - segment_offsets_[i]) * nx_ *
sizeof(float),
inputs[0] +
(inputBatchOffset * j + segment_offsets_[i] * nx_) *
sizeof(float),
(segment_offsets_[i + 1] - segment_offsets_[i]) * nx_ * sizeof(float),
cudaMemcpyDeviceToDevice, stream);
float const* input_ptr = reinterpret_cast<float const*>(inputs[0]);
if (((batchSize == 1 && axis_ == 0) || axis_ == -1) &&
this->getNbOutputs() < 10) {
float** output_ptrs = reinterpret_cast<float**>(outputs);
int data_type_size = (this->getDataType() == nvinfer1::DataType::kFLOAT)
? sizeof(float)
: sizeof(__half);
for (int i = 0; i < this->getNbOutputs(); ++i) {
PADDLE_ENFORCE(
cudaMemcpyAsync(
output_ptrs[i], input_ptr + segment_offsets_[i],
(segment_offsets_[i + 1] - segment_offsets_[i]) * data_type_size,
cudaMemcpyDeviceToDevice, stream) == cudaSuccess);
}
} else {
outer_rows_ *= batchSize;
const int* d_segment_offsets_ptr =
thrust::raw_pointer_cast(&d_segment_offsets_[0]);
float** output_ptrs = thrust::raw_pointer_cast(&d_output_ptrs_[0]);
PADDLE_ENFORCE(cudaMemcpyAsync(output_ptrs, outputs,
this->getNbOutputs() * sizeof(float*),
cudaMemcpyHostToDevice,
stream) == cudaSuccess);
if (this->getDataType() == nvinfer1::DataType::kFLOAT) {
Split(stream, same_shape_, outer_rows_, inner_cols_, segment_offsets_,
d_segment_offsets_ptr, input_ptr, output_ptrs);
} else {
Split(stream, same_shape_, outer_rows_, inner_cols_, segment_offsets_,
d_segment_offsets_ptr, (__half*)input_ptr, // NOLINT
(__half**)output_ptrs); // NOLINT
}
}
return cudaGetLastError() != cudaSuccess;
}
......
paddle/fluid/inference/tensorrt/plugin/split_op_plugin.h
浏览文件 @ 36cd18b5
......@@ -14,6 +14,7 @@
#pragma once
#include <thrust/device_vector.h>
#include <vector>
#include "paddle/fluid/inference/tensorrt/plugin/trt_plugin.h"
......@@ -25,7 +26,7 @@ namespace plugin {
class SplitPlugin : public PluginTensorRT {
public:
SplitPlugin(int axis, std::vector<int> const &output_lengths)
: axis_(axis), output_length_(output_lengths) {}
: axis_(axis), same_shape_(true), output_length_(output_lengths) {}
SplitPlugin(void const *serial_data, size_t serial_length) {
deserializeBase(serial_data, serial_length);
......@@ -60,9 +61,13 @@ class SplitPlugin : public PluginTensorRT {
}
int axis_;
int outer_rows_;
int inner_cols_;
bool same_shape_;
std::vector<int> output_length_;
int nx_, ny_, nz_;
std::vector<int> segment_offsets_;
thrust::device_vector<int> d_segment_offsets_;
thrust::device_vector<float *> d_output_ptrs_;
};
} // namespace plugin
......
paddle/fluid/inference/tests/api/CMakeLists.txt
浏览文件 @ 36cd18b5
......@@ -74,7 +74,7 @@ inference_analysis_api_test(test_analyzer_seq_conv1 ${SEQ_CONV1_INSTALL_DIR} ana
# ocr
set(OCR_INSTALL_DIR "${INFERENCE_DEMO_INSTALL_DIR}/ocr")
if (NOT EXISTS ${OCR_INSTALL_DIR})
inference_download_and_uncompress(${OCR_INSTALL_DIR} "http://paddlemodels.cdn.bcebos.com/" "inference-vis-demos%2Focr.tar.gz")
inference_download_and_uncompress(${OCR_INSTALL_DIR} "http://paddlemodels.cdn.bcebos.com/" "inference-vis-demos%2Focr.tar.gz")
endif()
inference_analysis_api_test(test_analyzer_ocr ${OCR_INSTALL_DIR} analyzer_vis_tester.cc)
......@@ -88,31 +88,31 @@ inference_analysis_api_test_with_fake_data(test_analyzer_mobilenet
# anakin
if (WITH_ANAKIN AND WITH_MKL) # only needed in CI
# anakin rnn1
set(ANAKIN_INSTALL_DIR "${INFERENCE_DEMO_INSTALL_DIR}/anakin")
set(ANAKIN_RNN1_INSTALL_DIR "${ANAKIN_INSTALL_DIR}/rnn1")
inference_download(${ANAKIN_RNN1_INSTALL_DIR} ${INFERENCE_URL} "anakin_test%2Fditu_rnn.anakin2.model.bin")
inference_download(${ANAKIN_RNN1_INSTALL_DIR} ${INFERENCE_URL} "anakin_test%2Fditu_rnn_data.txt")
cc_test(test_anakin_rnn1 SRCS anakin_rnn1_tester.cc
ARGS --model=${ANAKIN_RNN1_INSTALL_DIR}/anakin_test%2Fditu_rnn.anakin2.model.bin
--datapath=${ANAKIN_RNN1_INSTALL_DIR}/anakin_test%2Fditu_rnn_data.txt
DEPS inference_anakin_api_shared SERIAL)
# anakin mobilenet
if(WITH_GPU)
set(ANAKIN_MOBILENET_INSTALL_DIR "${ANAKIN_INSTALL_DIR}/mobilenet")
inference_download(${ANAKIN_MOBILENET_INSTALL_DIR} ${INFERENCE_URL} "mobilenet_v2.anakin.bin")
cc_test(test_anakin_mobilenet SRCS anakin_mobilenet_tester.cc
ARGS --model=${ANAKIN_MOBILENET_INSTALL_DIR}/mobilenet_v2.anakin.bin
DEPS inference_anakin_api_shared dynload_cuda SERIAL)
endif()
# anakin rnn1
set(ANAKIN_INSTALL_DIR "${INFERENCE_DEMO_INSTALL_DIR}/anakin")
set(ANAKIN_RNN1_INSTALL_DIR "${ANAKIN_INSTALL_DIR}/rnn1")
inference_download(${ANAKIN_RNN1_INSTALL_DIR} ${INFERENCE_URL} "anakin_test%2Fditu_rnn.anakin2.model.bin")
inference_download(${ANAKIN_RNN1_INSTALL_DIR} ${INFERENCE_URL} "anakin_test%2Fditu_rnn_data.txt")
cc_test(test_anakin_rnn1 SRCS anakin_rnn1_tester.cc
ARGS --model=${ANAKIN_RNN1_INSTALL_DIR}/anakin_test%2Fditu_rnn.anakin2.model.bin
--datapath=${ANAKIN_RNN1_INSTALL_DIR}/anakin_test%2Fditu_rnn_data.txt
DEPS inference_anakin_api_shared SERIAL)
# anakin mobilenet
if(WITH_GPU)
set(ANAKIN_MOBILENET_INSTALL_DIR "${ANAKIN_INSTALL_DIR}/mobilenet")
inference_download(${ANAKIN_MOBILENET_INSTALL_DIR} ${INFERENCE_URL} "mobilenet_v2.anakin.bin")
cc_test(test_anakin_mobilenet SRCS anakin_mobilenet_tester.cc
ARGS --model=${ANAKIN_MOBILENET_INSTALL_DIR}/mobilenet_v2.anakin.bin
DEPS inference_anakin_api_shared dynload_cuda SERIAL)
endif()
endif()
if(WITH_GPU AND TENSORRT_FOUND)
set(TRT_MODEL_INSTALL_DIR "${INFERENCE_DEMO_INSTALL_DIR}/trt")
if (NOT EXISTS ${TRT_MODEL_INSTALL_DIR})
inference_download_and_uncompress(${TRT_MODEL_INSTALL_DIR} ${INFERENCE_URL}/tensorrt_test "trt_test_models.tar.gz")
endif()
inference_analysis_test(test_trt_models SRCS trt_models_tester.cc
EXTRA_DEPS ${INFERENCE_EXTRA_DEPS}
ARGS --infer_model=${TRT_MODEL_INSTALL_DIR}/trt_test_models SERIAL)
set(TRT_MODEL_INSTALL_DIR "${INFERENCE_DEMO_INSTALL_DIR}/trt")
if (NOT EXISTS ${TRT_MODEL_INSTALL_DIR})
inference_download_and_uncompress(${TRT_MODEL_INSTALL_DIR} ${INFERENCE_URL}/tensorrt_test "trt_test_models.tar.gz")
endif()
inference_analysis_test(test_trt_models SRCS trt_models_tester.cc
EXTRA_DEPS ${INFERENCE_EXTRA_DEPS}
ARGS --infer_model=${TRT_MODEL_INSTALL_DIR}/trt_test_models SERIAL)
endif()
paddle/fluid/inference/tests/api/analyzer_resnet50_tester.cc
浏览文件 @ 36cd18b5
......@@ -27,6 +27,7 @@ void SetConfig(AnalysisConfig *cfg) {
cfg->device = 0;
cfg->enable_ir_optim = true;
cfg->specify_input_name = true;
cfg->SetCpuMathLibraryNumThreads(FLAGS_paddle_num_threads);
}
void SetInput(std::vector<std::vector<PaddleTensor>> *inputs) {
......
paddle/fluid/inference/tests/api/config_printer.h
浏览文件 @ 36cd18b5
......@@ -53,6 +53,8 @@ std::ostream &operator<<(std::ostream &os, const NativeConfig &config) {
os << GenSpaces(num_spaces) << "param_file: " << config.param_file << "\n";
os << GenSpaces(num_spaces)
<< "specify_input_name: " << config.specify_input_name << "\n";
os << GenSpaces(num_spaces)
<< "cpu_num_threads: " << config.cpu_math_library_num_threads() << "\n";
num_spaces--;
os << GenSpaces(num_spaces) << "}\n";
return os;
......
paddle/fluid/inference/tests/api/tester_helper.h
浏览文件 @ 36cd18b5
......@@ -42,6 +42,7 @@ DEFINE_bool(use_analysis, true,
"Running the inference program in analysis mode.");
DECLARE_bool(profile);
DECLARE_int32(paddle_num_threads);
namespace paddle {
namespace inference {
......@@ -204,22 +205,23 @@ void TestMultiThreadPrediction(
int batch_size = FLAGS_batch_size;
int num_times = FLAGS_repeat;
std::vector<std::thread> threads;
std::vector<std::unique_ptr<PaddlePredictor>> predictors;
predictors.emplace_back(CreateTestPredictor(config, use_analysis));
for (int tid = 1; tid < num_threads; ++tid) {
predictors.emplace_back(predictors.front()->Clone());
}
auto main_predictor = CreateTestPredictor(config, use_analysis);
size_t total_time{0};
for (int tid = 0; tid < num_threads; ++tid) {
threads.emplace_back([&, tid]() {
#ifdef PADDLE_WITH_MKLDNN
platform::set_cur_thread_id(static_cast<int>(tid) + 1);
#endif
// Each thread should have local inputs and outputs.
// The inputs of each thread are all the same.
std::vector<PaddleTensor> outputs_tid;
auto &predictor = predictors[tid];
// To ensure the thread binding correctly,
// please clone inside the threadpool.
auto predictor = main_predictor->Clone();
#ifdef PADDLE_WITH_MKLDNN
if (use_analysis) {
static_cast<AnalysisPredictor *>(predictor.get())
->SetMkldnnThreadID(static_cast<int>(tid) + 1);
}
#endif
// warmup run
LOG(INFO) << "Running thread " << tid << ", warm up run...";
......
paddle/fluid/inference/utils/CMakeLists.txt 0 → 100644
浏览文件 @ 36cd18b5
cc_library(benchmark SRCS benchmark.cc DEPS enforce)
cc_test(test_benchmark SRCS benchmark_tester.cc DEPS benchmark)
paddle/fluid/inference/utils/benchmark.cc 0 → 100644
浏览文件 @ 36cd18b5
// Copyright (c) 2018 PaddlePaddle Authors. All Rights Reserved.
//
// Licensed under the Apache License, Version 2.0 (the "License");
// you may not use this file except in compliance with the License.
// You may obtain a copy of the License at
//
// http://www.apache.org/licenses/LICENSE-2.0
//
// Unless required by applicable law or agreed to in writing, software
// distributed under the License is distributed on an "AS IS" BASIS,
// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
// See the License for the specific language governing permissions and
// limitations under the License.
#include "paddle/fluid/inference/utils/benchmark.h"
#include <sstream>
#include "paddle/fluid/platform/enforce.h"
namespace paddle {
namespace inference {
std::string Benchmark::SerializeToString() const {
std::stringstream ss;
ss << "-----------------------------------------------------\n";
ss << "name\t";
ss << "batch_size\t";
ss << "num_threads\t";
ss << "latency\t";
ss << "qps";
ss << '\n';
ss << name_ << "\t";
ss << batch_size_ << "\t";
ss << num_threads_ << "\t";
ss << latency_ << "\t";
ss << 1000 / latency_;
ss << '\n';
return ss.str();
}
void Benchmark::PersistToFile(const std::string &path) const {
std::ofstream file(path, std::ios::app);
PADDLE_ENFORCE(file.is_open(), "Can not open %s to add benchmark", path);
file << SerializeToString();
file.flush();
file.close();
}
} // namespace inference
} // namespace paddle
paddle/fluid/inference/utils/benchmark.h 0 → 100644
浏览文件 @ 36cd18b5
// 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 <fstream>
#include <iostream>
namespace paddle {
namespace inference {
/*
* Helper class to calculate the performance.
*/
struct Benchmark {
int batch_size() const { return batch_size_; }
void SetBatchSize(int x) { batch_size_ = x; }
int num_threads() const { return num_threads_; }
void SetNumThreads(int x) { num_threads_ = x; }
bool use_gpu() const { return use_gpu_; }
void SetUseGpu() { use_gpu_ = true; }
int latency() const { return latency_; }
void SetLatency(int x) { latency_ = x; }
const std::string& name() const { return name_; }
void SetName(const std::string& name) { name_ = name; }
std::string SerializeToString() const;
void PersistToFile(const std::string& path) const;
private:
bool use_gpu_{false};
int batch_size_{0};
int latency_;
int num_threads_{1};
std::string name_;
};
} // namespace inference
} // namespace paddle
paddle/fluid/inference/utils/benchmark_tester.cc 0 → 100644
浏览文件 @ 36cd18b5
// Copyright (c) 2018 PaddlePaddle Authors. All Rights Reserved.
//
// Licensed under the Apache License, Version 2.0 (the "License");
// you may not use this file except in compliance with the License.
// You may obtain a copy of the License at
//
// http://www.apache.org/licenses/LICENSE-2.0
//
// Unless required by applicable law or agreed to in writing, software
// distributed under the License is distributed on an "AS IS" BASIS,
// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
// See the License for the specific language governing permissions and
// limitations under the License.
#include "paddle/fluid/inference/utils/benchmark.h"
#include <glog/logging.h>
#include <gtest/gtest.h>
using namespace paddle::inference;
TEST(Benchmark, basic) {
Benchmark benchmark;
benchmark.SetName("key0");
benchmark.SetBatchSize(10);
benchmark.SetUseGpu();
benchmark.SetLatency(220);
LOG(INFO) << "benchmark:\n" << benchmark.SerializeToString();
}
TEST(Benchmark, PersistToFile) {
Benchmark benchmark;
benchmark.SetName("key0");
benchmark.SetBatchSize(10);
benchmark.SetUseGpu();
benchmark.SetLatency(220);
benchmark.PersistToFile("1.log");
benchmark.PersistToFile("1.log");
benchmark.PersistToFile("1.log");
}
\ No newline at end of file
paddle/fluid/operators/detection/CMakeLists.txt
浏览文件 @ 36cd18b5
......@@ -22,7 +22,7 @@ iou_similarity_op.cu)
detection_library(mine_hard_examples_op SRCS mine_hard_examples_op.cc)
detection_library(multiclass_nms_op SRCS multiclass_nms_op.cc poly_util.cc gpc.cc)
detection_library(prior_box_op SRCS prior_box_op.cc prior_box_op.cu)
detection_library(density_prior_box_op SRCS density_prior_box_op.cc)
detection_library(density_prior_box_op SRCS density_prior_box_op.cc density_prior_box_op.cu)
detection_library(anchor_generator_op SRCS anchor_generator_op.cc
anchor_generator_op.cu)
detection_library(target_assign_op SRCS target_assign_op.cc
......
paddle/fluid/operators/detection/density_prior_box_op.cc
浏览文件 @ 36cd18b5
......@@ -39,24 +39,27 @@ class DensityPriorBoxOp : public framework::OperatorWithKernel {
auto fixed_sizes = ctx->Attrs().Get<std::vector<float>>("fixed_sizes");
auto fixed_ratios = ctx->Attrs().Get<std::vector<float>>("fixed_ratios");
auto densities = ctx->Attrs().Get<std::vector<int>>("densities");
bool flatten = ctx->Attrs().Get<bool>("flatten_to_2d");
PADDLE_ENFORCE_EQ(fixed_sizes.size(), densities.size(),
"The number of fixed_sizes and densities must be equal.");
size_t num_priors = 0;
if ((fixed_sizes.size() > 0) && (densities.size() > 0)) {
for (size_t i = 0; i < densities.size(); ++i) {
if (fixed_ratios.size() > 0) {
num_priors += (fixed_ratios.size()) * (pow(densities[i], 2));
}
}
for (size_t i = 0; i < densities.size(); ++i) {
num_priors += (fixed_ratios.size()) * (pow(densities[i], 2));
}
if (!flatten) {
std::vector<int64_t> dim_vec(4);
dim_vec[0] = input_dims[2];
dim_vec[1] = input_dims[3];
dim_vec[2] = num_priors;
dim_vec[3] = 4;
ctx->SetOutputDim("Boxes", framework::make_ddim(dim_vec));
ctx->SetOutputDim("Variances", framework::make_ddim(dim_vec));
} else {
int64_t dim0 = input_dims[2] * input_dims[3] * num_priors;
ctx->SetOutputDim("Boxes", {dim0, 4});
ctx->SetOutputDim("Variances", {dim0, 4});
}
std::vector<int64_t> dim_vec(4);
dim_vec[0] = input_dims[2];
dim_vec[1] = input_dims[3];
dim_vec[2] = num_priors;
dim_vec[3] = 4;
ctx->SetOutputDim("Boxes", framework::make_ddim(dim_vec));
ctx->SetOutputDim("Variances", framework::make_ddim(dim_vec));
}
protected:
......@@ -64,7 +67,7 @@ class DensityPriorBoxOp : public framework::OperatorWithKernel {
const framework::ExecutionContext& ctx) const override {
return framework::OpKernelType(
framework::ToDataType(ctx.Input<framework::Tensor>("Input")->type()),
platform::CPUPlace());
ctx.GetPlace());
}
};
......@@ -101,7 +104,10 @@ class DensityPriorBoxOpMaker : public framework::OpProtoAndCheckerMaker {
});
AddAttr<bool>("clip", "(bool) Whether to clip out-of-boundary boxes.")
.SetDefault(true);
AddAttr<bool>("flatten_to_2d",
"(bool) Whether to flatten to 2D and "
"the second dim is 4.")
.SetDefault(false);
AddAttr<float>(
"step_w",
"Density prior boxes step across width, 0.0 for auto calculation.")
......
paddle/fluid/operators/detection/density_prior_box_op.cu 0 → 100644
浏览文件 @ 36cd18b5
/* Copyright (c) 2018 PaddlePaddle Authors. All Rights Reserved.
Licensed under the Apache License, Version 2.0 (the "License");
you may not use this file except in compliance with the License.
You may obtain a copy of the License at
http://www.apache.org/licenses/LICENSE-2.0
Unless required by applicable law or agreed to in writing, software
distributed under the License is distributed on an "AS IS" BASIS,
WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
See the License for the specific language governing permissions and
limitations under the License. */
#include "paddle/fluid/operators/detection/density_prior_box_op.h"
namespace paddle {
namespace operators {
template <typename T>
static __device__ inline T Clip(T in) {
return min(max(in, 0.), 1.);
}
template <typename T>
static __global__ void GenDensityPriorBox(
const int height, const int width, const int im_height, const int im_width,
const T offset, const T step_width, const T step_height,
const int num_priors, const T* ratios_shift, bool is_clip, const T var_xmin,
const T var_ymin, const T var_xmax, const T var_ymax, T* out, T* var) {
int gidx = blockIdx.x * blockDim.x + threadIdx.x;
int gidy = blockIdx.y * blockDim.y + threadIdx.y;
int step_x = blockDim.x * gridDim.x;
int step_y = blockDim.y * gridDim.y;
const T* width_ratio = ratios_shift;
const T* height_ratio = ratios_shift + num_priors;
const T* width_shift = ratios_shift + 2 * num_priors;
const T* height_shift = ratios_shift + 3 * num_priors;
for (int j = gidy; j < height; j += step_y) {
for (int i = gidx; i < width * num_priors; i += step_x) {
int h = j;
int w = i / num_priors;
int k = i % num_priors;
T center_x = (w + offset) * step_width;
T center_y = (h + offset) * step_height;
T center_x_temp = center_x + width_shift[k];
T center_y_temp = center_y + height_shift[k];
T box_width_ratio = width_ratio[k] / 2.;
T box_height_ratio = height_ratio[k] / 2.;
T xmin = max((center_x_temp - box_width_ratio) / im_width, 0.);
T ymin = max((center_y_temp - box_height_ratio) / im_height, 0.);
T xmax = min((center_x_temp + box_width_ratio) / im_width, 1.);
T ymax = min((center_y_temp + box_height_ratio) / im_height, 1.);
int out_offset = (j * width * num_priors + i) * 4;
out[out_offset] = is_clip ? Clip<T>(xmin) : xmin;
out[out_offset + 1] = is_clip ? Clip<T>(ymin) : ymin;
out[out_offset + 2] = is_clip ? Clip<T>(xmax) : xmax;
out[out_offset + 3] = is_clip ? Clip<T>(ymax) : ymax;
var[out_offset] = var_xmin;
var[out_offset + 1] = var_ymin;
var[out_offset + 2] = var_xmax;
var[out_offset + 3] = var_ymax;
}
}
}
template <typename T>
class DensityPriorBoxOpCUDAKernel : public framework::OpKernel<T> {
public:
void Compute(const framework::ExecutionContext& ctx) const override {
auto* input = ctx.Input<paddle::framework::Tensor>("Input");
auto* image = ctx.Input<paddle::framework::Tensor>("Image");
auto* boxes = ctx.Output<paddle::framework::Tensor>("Boxes");
auto* vars = ctx.Output<paddle::framework::Tensor>("Variances");
auto variances = ctx.Attr<std::vector<float>>("variances");
auto is_clip = ctx.Attr<bool>("clip");
auto fixed_sizes = ctx.Attr<std::vector<float>>("fixed_sizes");
auto fixed_ratios = ctx.Attr<std::vector<float>>("fixed_ratios");
auto densities = ctx.Attr<std::vector<int>>("densities");
T step_w = static_cast<T>(ctx.Attr<float>("step_w"));
T step_h = static_cast<T>(ctx.Attr<float>("step_h"));
T offset = static_cast<T>(ctx.Attr<float>("offset"));
auto img_width = image->dims()[3];
auto img_height = image->dims()[2];
auto feature_width = input->dims()[3];
auto feature_height = input->dims()[2];
T step_width, step_height;
if (step_w == 0 || step_h == 0) {
step_width = static_cast<T>(img_width) / feature_width;
step_height = static_cast<T>(img_height) / feature_height;
} else {
step_width = step_w;
step_height = step_h;
}
int num_priors = 0;
for (size_t i = 0; i < densities.size(); ++i) {
num_priors += (fixed_ratios.size()) * (pow(densities[i], 2));
}
int step_average = static_cast<int>((step_width + step_height) * 0.5);
framework::Tensor h_temp;
T* tdata = h_temp.mutable_data<T>({num_priors * 4}, platform::CPUPlace());
int idx = 0;
for (size_t s = 0; s < fixed_sizes.size(); ++s) {
auto fixed_size = fixed_sizes[s];
int density = densities[s];
for (size_t r = 0; r < fixed_ratios.size(); ++r) {
float ar = fixed_ratios[r];
int shift = step_average / density;
float box_width_ratio = fixed_size * sqrt(ar);
float box_height_ratio = fixed_size / sqrt(ar);
for (int di = 0; di < density; ++di) {
for (int dj = 0; dj < density; ++dj) {
float center_x_temp = shift / 2. + dj * shift - step_average / 2.;
float center_y_temp = shift / 2. + di * shift - step_average / 2.;
tdata[idx] = box_width_ratio;
tdata[num_priors + idx] = box_height_ratio;
tdata[2 * num_priors + idx] = center_x_temp;
tdata[3 * num_priors + idx] = center_y_temp;
idx++;
}
}
}
}
boxes->mutable_data<T>(ctx.GetPlace());
vars->mutable_data<T>(ctx.GetPlace());
framework::Tensor d_temp;
framework::TensorCopySync(h_temp, ctx.GetPlace(), &d_temp);
// At least use 32 threads, at most 512 threads.
// blockx is multiple of 32.
int blockx = std::min(((feature_width * num_priors + 31) >> 5) << 5, 512L);
int gridx = (feature_width * num_priors + blockx - 1) / blockx;
dim3 threads(blockx, 1);
dim3 grids(gridx, feature_height);
auto stream =
ctx.template device_context<platform::CUDADeviceContext>().stream();
GenDensityPriorBox<T><<<grids, threads, 0, stream>>>(
feature_height, feature_width, img_height, img_width, offset,
step_width, step_height, num_priors, d_temp.data<T>(), is_clip,
variances[0], variances[1], variances[2], variances[3],
boxes->data<T>(), vars->data<T>());
}
}; // namespace operators
} // namespace operators
} // namespace paddle
namespace ops = paddle::operators;
REGISTER_OP_CUDA_KERNEL(density_prior_box,
ops::DensityPriorBoxOpCUDAKernel<float>,
ops::DensityPriorBoxOpCUDAKernel<double>);
paddle/fluid/operators/detection/density_prior_box_op.h
浏览文件 @ 36cd18b5
/* Copyright (c) 2016 PaddlePaddle Authors. All Rights Reserved.
/* 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
......@@ -52,18 +52,16 @@ class DensityPriorBoxOpKernel : public framework::OpKernel<T> {
step_height = step_h;
}
int num_priors = 0;
if (fixed_sizes.size() > 0 && densities.size() > 0) {
for (size_t i = 0; i < densities.size(); ++i) {
if (fixed_ratios.size() > 0) {
num_priors += (fixed_ratios.size()) * (pow(densities[i], 2));
}
}
for (size_t i = 0; i < densities.size(); ++i) {
num_priors += (fixed_ratios.size()) * (pow(densities[i], 2));
}
boxes->mutable_data<T>(ctx.GetPlace());
vars->mutable_data<T>(ctx.GetPlace());
auto e_boxes = framework::EigenTensor<T, 4>::From(*boxes).setConstant(0.0);
auto box_dim = vars->dims();
boxes->Resize({feature_height, feature_width, num_priors, 4});
auto e_boxes = framework::EigenTensor<T, 4>::From(*boxes).setConstant(0.0);
int step_average = static_cast<int>((step_width + step_height) * 0.5);
for (int h = 0; h < feature_height; ++h) {
......@@ -76,36 +74,34 @@ class DensityPriorBoxOpKernel : public framework::OpKernel<T> {
auto fixed_size = fixed_sizes[s];
int density = densities[s];
// Generate density prior boxes with fixed ratios.
if (fixed_ratios.size() > 0) {
for (size_t r = 0; r < fixed_ratios.size(); ++r) {
float ar = fixed_ratios[r];
int shift = step_average / density;
float box_width_ratio = fixed_size * sqrt(ar);
float box_height_ratio = fixed_size / sqrt(ar);
for (int di = 0; di < density; ++di) {
for (int dj = 0; dj < density; ++dj) {
float center_x_temp =
center_x - step_average / 2. + shift / 2. + dj * shift;
float center_y_temp =
center_y - step_average / 2. + shift / 2. + di * shift;
e_boxes(h, w, idx, 0) =
(center_x_temp - box_width_ratio / 2.) / img_width >= 0
? (center_x_temp - box_width_ratio / 2.) / img_width
: 0;
e_boxes(h, w, idx, 1) =
(center_y_temp - box_height_ratio / 2.) / img_height >= 0
? (center_y_temp - box_height_ratio / 2.) / img_height
: 0;
e_boxes(h, w, idx, 2) =
(center_x_temp + box_width_ratio / 2.) / img_width <= 1
? (center_x_temp + box_width_ratio / 2.) / img_width
: 1;
e_boxes(h, w, idx, 3) =
(center_y_temp + box_height_ratio / 2.) / img_height <= 1
? (center_y_temp + box_height_ratio / 2.) / img_height
: 1;
idx++;
}
for (size_t r = 0; r < fixed_ratios.size(); ++r) {
float ar = fixed_ratios[r];
int shift = step_average / density;
float box_width_ratio = fixed_size * sqrt(ar);
float box_height_ratio = fixed_size / sqrt(ar);
for (int di = 0; di < density; ++di) {
for (int dj = 0; dj < density; ++dj) {
float center_x_temp =
center_x - step_average / 2. + shift / 2. + dj * shift;
float center_y_temp =
center_y - step_average / 2. + shift / 2. + di * shift;
e_boxes(h, w, idx, 0) =
(center_x_temp - box_width_ratio / 2.) / img_width >= 0
? (center_x_temp - box_width_ratio / 2.) / img_width
: 0;
e_boxes(h, w, idx, 1) =
(center_y_temp - box_height_ratio / 2.) / img_height >= 0
? (center_y_temp - box_height_ratio / 2.) / img_height
: 0;
e_boxes(h, w, idx, 2) =
(center_x_temp + box_width_ratio / 2.) / img_width <= 1
? (center_x_temp + box_width_ratio / 2.) / img_width
: 1;
e_boxes(h, w, idx, 3) =
(center_y_temp + box_height_ratio / 2.) / img_height <= 1
? (center_y_temp + box_height_ratio / 2.) / img_height
: 1;
idx++;
}
}
}
......@@ -139,6 +135,7 @@ class DensityPriorBoxOpKernel : public framework::OpKernel<T> {
e_vars = var_et.broadcast(Eigen::DSizes<int, 2>(box_num, 1));
vars->Resize(var_dim);
boxes->Resize(box_dim);
}
}; // namespace operators
......
paddle/fluid/operators/distributed/grpc_client.cc
浏览文件 @ 36cd18b5
......@@ -22,6 +22,8 @@ limitations under the License. */
#include "paddle/fluid/platform/port.h"
#include "paddle/fluid/platform/profiler.h"
DECLARE_bool(rpc_disable_reuse_port);
namespace paddle {
namespace operators {
namespace distributed {
......@@ -383,6 +385,9 @@ std::shared_ptr<grpc::Channel> GRPCClient::GetChannel(const std::string& ep) {
// Channel configurations:
grpc::ChannelArguments args;
args.SetInt(GRPC_ARG_MAX_RECONNECT_BACKOFF_MS, 2000);
if (FLAGS_rpc_disable_reuse_port) {
args.SetInt(GRPC_ARG_ALLOW_REUSEPORT, 0);
}
args.SetCompressionAlgorithm(GRPC_COMPRESS_NONE);
args.SetMaxSendMessageSize(std::numeric_limits<int>::max());
args.SetMaxReceiveMessageSize(std::numeric_limits<int>::max());
......
paddle/fluid/operators/distributed/grpc_server.cc
浏览文件 @ 36cd18b5
......@@ -20,6 +20,8 @@ limitations under the License. */
using ::grpc::ServerAsyncResponseWriter;
DECLARE_bool(rpc_disable_reuse_port);
namespace paddle {
namespace operators {
namespace distributed {
......@@ -252,6 +254,20 @@ void AsyncGRPCServer::WaitServerReady() {
VLOG(40) << "AsyncGRPCServer WaitSeverReady";
}
// Define an option subclass in order to disable SO_REUSEPORT for the
// server socket.
// Come from:
// https://github.com/tensorflow/tensorflow/blob/master/tensorflow/core/distributed_runtime/rpc/grpc_server_lib.cc
class NoReusePortOption : public ::grpc::ServerBuilderOption {
public:
void UpdateArguments(::grpc::ChannelArguments* args) override {
args->SetInt(GRPC_ARG_ALLOW_REUSEPORT, 0);
}
void UpdatePlugins(std::vector<std::unique_ptr<::grpc::ServerBuilderPlugin>>*
plugins) override {}
};
void AsyncGRPCServer::StartServer() {
::grpc::ServerBuilder builder;
builder.AddListeningPort(bind_address_, ::grpc::InsecureServerCredentials(),
......@@ -259,6 +275,10 @@ void AsyncGRPCServer::StartServer() {
builder.SetMaxSendMessageSize(std::numeric_limits<int>::max());
builder.SetMaxReceiveMessageSize(std::numeric_limits<int>::max());
if (FLAGS_rpc_disable_reuse_port) {
builder.SetOption(
std::unique_ptr<::grpc::ServerBuilderOption>(new NoReusePortOption));
}
builder.RegisterService(&service_);
for (auto t : rpc_call_map_) {
......
paddle/fluid/operators/distributed/sendrecvop_utils.cc
浏览文件 @ 36cd18b5
......@@ -22,6 +22,8 @@ limitations under the License. */
#include "paddle/fluid/operators/distributed/variable_response.h"
#include "paddle/fluid/platform/port.h"
DEFINE_bool(rpc_disable_reuse_port, false, "Disable SO_REUSEPORT or not.");
namespace paddle {
namespace operators {
namespace distributed {
......
paddle/fluid/operators/math/blas_impl.cu.h
浏览文件 @ 36cd18b5
......@@ -16,6 +16,9 @@
#include "paddle/fluid/operators/math/math_function.h"
#include "paddle/fluid/platform/dynload/cublas.h"
#include "paddle/fluid/platform/gpu_info.h"
DECLARE_bool(enable_cublas_tensor_op_math);
namespace paddle {
namespace operators {
......@@ -42,11 +45,44 @@ struct CUBlas<float> {
}
template <typename... ARGS>
static void GEMM_BATCH(ARGS... args) {
static void GEMM_STRIDED_BATCH(ARGS... args) {
#if CUDA_VERSION >= 8000
PADDLE_ENFORCE(platform::dynload::cublasSgemmStridedBatched(args...));
#else
PADDLE_THROW("SgemmStridedBatched is not supported on cuda <= 7.5");
#endif
}
// NOTES: GEMM_EX can use Tensor Core to accelerate matrix multiply.
// https://docs.nvidia.com/cuda/cublas/index.html#cublassetmathmode
template <typename... ARGS>
static void GEMM_EX(platform::CUDADeviceContext *dev_ctx,
cublasOperation_t transa, cublasOperation_t transb, int m,
int n, int k, const float *alpha, const void *A,
cudaDataType_t Atype, int lda, const void *B,
cudaDataType_t Btype, int ldb, const float *beta, void *C,
cudaDataType_t Ctype, int ldc) {
// Because the gcc 4.8 doesn't expand template parameter pack that
// appears in a lambda-expression, I can not use template parameter pack
// here.
auto cublas_call = [&]() {
#if CUDA_VERSION >= 8000
VLOG(5) << "use_tensor_op_math: "
<< (platform::TensorCoreAvailable() ? "True" : "False");
PADDLE_ENFORCE(platform::dynload::cublasSgemmEx(
dev_ctx->cublas_handle(), transa, transb, m, n, k, alpha, A, Atype,
lda, B, Btype, ldb, beta, C, Ctype, ldc));
#else
PADDLE_THROW("cublasSgemmEx is supported on cuda >= 8.0");
#endif
};
#if CUDA_VERSION >= 9000
// NOTES: To use Tensor Core, we should change the cublas config,
// but the cublas may be hold by multi-thread.
dev_ctx->CublasCall(cublas_call, CUBLAS_TENSOR_OP_MATH);
#else
cublas_call();
#endif
}
};
......@@ -69,13 +105,18 @@ struct CUBlas<double> {
}
template <typename... ARGS>
static void GEMM_BATCH(ARGS... args) {
static void GEMM_STRIDED_BATCH(ARGS... args) {
#if CUDA_VERSION >= 8000
PADDLE_ENFORCE(platform::dynload::cublasDgemmStridedBatched(args...));
#else
PADDLE_THROW("DgemmStridedBatched is not supported on cuda <= 7.5");
#endif
}
template <typename... ARGS>
static void GEMM_EX(ARGS... args) {
PADDLE_THROW("Currently there are not cublasDgemmEx.");
}
};
template <>
......@@ -96,14 +137,16 @@ struct CUBlas<platform::float16> {
reinterpret_cast<__half *>(C), ldc));
}
static void GEMM_BATCH(cublasHandle_t handle, cublasOperation_t transa,
cublasOperation_t transb, int m, int n, int k,
const float16 *alpha, const float16 *A, int lda,
long long int strideA, const float16 *B, // NOLINT
int ldb, long long int strideB, // NOLINT
const float16 *beta, float16 *C, int ldc,
long long int strideC, // NOLINT
int batchCount) {
static void GEMM_STRIDED_BATCH(cublasHandle_t handle,
cublasOperation_t transa,
cublasOperation_t transb, int m, int n, int k,
const float16 *alpha, const float16 *A,
int lda, long long int strideA, // NOLINT
const float16 *B, // NOLINT
int ldb, long long int strideB, // NOLINT
const float16 *beta, float16 *C, int ldc,
long long int strideC, // NOLINT
int batchCount) {
#if CUDA_VERSION >= 8000
PADDLE_ENFORCE(platform::dynload::cublasHgemmStridedBatched(
handle, transa, transb, m, n, k,
......@@ -114,6 +157,45 @@ struct CUBlas<platform::float16> {
ldc, strideC, batchCount));
#else
PADDLE_THROW("HgemmStridedBatched is not supported on cuda <= 7.5");
#endif
}
// NOTES: GEMM_EX can use Tensor Core to accelerate matrix multiply.
// https://docs.nvidia.com/cuda/cublas/index.html#cublassetmathmode
template <typename... ARGS>
static void GEMM_EX(platform::CUDADeviceContext *dev_ctx,
cublasOperation_t transa, cublasOperation_t transb, int m,
int n, int k, const void *alpha, const void *A,
cudaDataType_t Atype, int lda, const void *B,
cudaDataType_t Btype, int ldb, const void *beta, void *C,
cudaDataType_t Ctype, int ldc,
cudaDataType_t computeType) {
auto cublas_call = [&]() {
#if CUDA_VERSION >= 8000
cublasGemmAlgo_t algo = CUBLAS_GEMM_DFALT;
#if CUDA_VERSION >= 9000
bool use_tensor_op_math = platform::TensorCoreAvailable();
if (use_tensor_op_math) {
algo = CUBLAS_GEMM_DFALT_TENSOR_OP;
}
VLOG(5) << "use_tensor_op_math: "
<< (use_tensor_op_math ? "True" : "False");
#endif // CUDA_VERSION >= 9000
PADDLE_ENFORCE(platform::dynload::cublasGemmEx(
dev_ctx->cublas_handle(), transa, transb, m, n, k, alpha, A, Atype,
lda, B, Btype, ldb, beta, C, Ctype, ldc, computeType, algo));
#else
PADDLE_THROW("cublasGemmEx is supported on cuda >= 8.0");
#endif
};
#if CUDA_VERSION >= 9000
// NOTES: To use Tensor Core, we should change the cublas config,
// but the cublas may be hold by multi-thread.
dev_ctx->CublasCall(cublas_call, CUBLAS_TENSOR_OP_MATH);
#else
cublas_call();
#endif
}
};
......@@ -133,8 +215,21 @@ void Blas<platform::CUDADeviceContext>::GEMM(CBLAS_TRANSPOSE transA,
cublasOperation_t cuTransB =
(transB == CblasNoTrans) ? CUBLAS_OP_N : CUBLAS_OP_T;
CUBlas<T>::GEMM(context_.cublas_handle(), cuTransB, cuTransA, N, M, K, &alpha,
B, ldb, A, lda, &beta, C, N);
#if CUDA_VERSION >= 8000
if (FLAGS_enable_cublas_tensor_op_math && std::is_same<T, float>::value) {
auto &cuda_ctx = const_cast<platform::CUDADeviceContext &>(context_);
CUBlas<T>::GEMM_EX(&cuda_ctx, cuTransB, cuTransA, N, M, K, &alpha, B,
CUDA_R_32F, ldb, A, CUDA_R_32F, lda, &beta, C,
CUDA_R_32F, N);
} else {
#endif // CUDA_VERSION >= 8000
CUBlas<T>::GEMM(context_.cublas_handle(), cuTransB, cuTransA, N, M, K,
&alpha, B, ldb, A, lda, &beta, C, N);
#if CUDA_VERSION >= 8000
}
#endif // CUDA_VERSION >= 8000
}
template <>
......@@ -157,30 +252,18 @@ inline void Blas<platform::CUDADeviceContext>::GEMM(
PADDLE_ENFORCE_GE(context_.GetComputeCapability(), 53,
"cublas fp16 gemm requires GPU compute capability >= 53");
#if CUDA_VERSION >= 8000
float h_alpha = static_cast<float>(alpha);
float h_beta = static_cast<float>(beta);
cublasGemmAlgo_t algo = CUBLAS_GEMM_DFALT;
#if CUDA_VERSION >= 9000
if (context_.GetComputeCapability() >= 70) {
PADDLE_ENFORCE(platform::dynload::cublasSetMathMode(
context_.cublas_handle(), CUBLAS_TENSOR_OP_MATH));
algo = CUBLAS_GEMM_DFALT_TENSOR_OP;
} else {
PADDLE_ENFORCE(platform::dynload::cublasSetMathMode(
context_.cublas_handle(), CUBLAS_DEFAULT_MATH));
}
#endif // CUDA_VERSION >= 9000
#if CUDA_VERSION >= 8000
// cublasHgemm does true FP16 computation which is slow for non-Volta
// GPUs. So use cublasGemmEx instead which does pesudo FP16 computation:
// input/output in fp16, computation in fp32, which can also be accelerated
// using tensor cores in volta GPUs.
PADDLE_ENFORCE(platform::dynload::cublasGemmEx(
context_.cublas_handle(), cuTransB, cuTransA, N, M, K, &h_alpha, B,
CUDA_R_16F, ldb, A, CUDA_R_16F, lda, &h_beta, C, CUDA_R_16F, N,
CUDA_R_32F, algo));
auto &cuda_ctx = const_cast<platform::CUDADeviceContext &>(context_);
CUBlas<platform::float16>::GEMM_EX(
&cuda_ctx, cuTransB, cuTransA, N, M, K, &h_alpha, B, CUDA_R_16F, ldb, A,
CUDA_R_16F, lda, &h_beta, C, CUDA_R_16F, N, CUDA_R_32F);
#else
// CUDA 7.5 does not support cublasGemmEx, hence we fall back to use hgemm
CUBlas<platform::float16>::GEMM(context_.cublas_handle(), cuTransB, cuTransA,
......@@ -199,8 +282,38 @@ void Blas<platform::CUDADeviceContext>::GEMM(bool transA, bool transB, int M,
// the cblas convention.
cublasOperation_t cuTransA = transA ? CUBLAS_OP_T : CUBLAS_OP_N;
cublasOperation_t cuTransB = transB ? CUBLAS_OP_T : CUBLAS_OP_N;
CUBlas<T>::GEMM(context_.cublas_handle(), cuTransB, cuTransA, N, M, K, &alpha,
B, ldb, A, lda, &beta, C, ldc);
#if CUDA_VERSION >= 8000
if (FLAGS_enable_cublas_tensor_op_math && std::is_same<T, float>::value) {
auto &cuda_ctx = const_cast<platform::CUDADeviceContext &>(context_);
CUBlas<T>::GEMM_EX(&cuda_ctx, cuTransB, cuTransA, N, M, K, &alpha, B,
CUDA_R_32F, ldb, A, CUDA_R_32F, lda, &beta, C,
CUDA_R_32F, ldc);
} else {
#endif // CUDA_VERSION >= 8000
CUBlas<T>::GEMM(context_.cublas_handle(), cuTransB, cuTransA, N, M, K,
&alpha, B, ldb, A, lda, &beta, C, ldc);
#if CUDA_VERSION >= 8000
}
#endif // CUDA_VERSION >= 8000
}
template <>
template <>
inline void Blas<platform::CUDADeviceContext>::GEMM(
bool transA, bool transB, int M, int N, int K, platform::float16 alpha,
const platform::float16 *A, int lda, const platform::float16 *B, int ldb,
platform::float16 beta, platform::float16 *C, int ldc) const {
// Note that cublas follows fortran order, so the order is different from
// the cblas convention.
cublasOperation_t cuTransA = transA ? CUBLAS_OP_T : CUBLAS_OP_N;
cublasOperation_t cuTransB = transB ? CUBLAS_OP_T : CUBLAS_OP_N;
CUBlas<platform::float16>::GEMM(context_.cublas_handle(), cuTransB, cuTransA,
N, M, K, &alpha, B, ldb, A, lda, &beta, C,
ldc);
}
template <>
......@@ -238,9 +351,34 @@ void Blas<platform::CUDADeviceContext>::BatchedGEMM(
(transB == CblasNoTrans) ? CUBLAS_OP_N : CUBLAS_OP_T;
const int64_t strideC = M * N;
CUBlas<T>::GEMM_BATCH(context_.cublas_handle(), cuTransB, cuTransA, N, M, K,
&alpha, B, ldb, strideB, A, lda, strideA, &beta, C, ldc,
strideC, batchCount);
#if CUDA_VERSION >= 9010
if (FLAGS_enable_cublas_tensor_op_math && std::is_same<T, float>::value) {
auto cublas_call = [&]() {
cublasGemmAlgo_t algo = CUBLAS_GEMM_DFALT;
bool use_tensor_op_math = platform::TensorCoreAvailable();
if (use_tensor_op_math) {
algo = CUBLAS_GEMM_DFALT_TENSOR_OP;
}
VLOG(5) << "use_tensor_op_math: "
<< (use_tensor_op_math ? "True" : "False");
PADDLE_ENFORCE(platform::dynload::cublasGemmStridedBatchedEx(
context_.cublas_handle(), cuTransB, cuTransA, N, M, K, &alpha, B,
CUDA_R_32F, ldb, strideB, A, CUDA_R_32F, lda, strideA, &beta, C,
CUDA_R_32F, ldc, strideC, batchCount, CUDA_R_32F, algo));
};
auto &dev_ctx = const_cast<platform::CUDADeviceContext &>(context_);
dev_ctx.CublasCall(cublas_call, CUBLAS_TENSOR_OP_MATH);
} else {
#endif // CUDA_VERSION >= 9010
CUBlas<T>::GEMM_STRIDED_BATCH(context_.cublas_handle(), cuTransB, cuTransA,
N, M, K, &alpha, B, ldb, strideB, A, lda,
strideA, &beta, C, ldc, strideC, batchCount);
#if CUDA_VERSION >= 9010
}
#endif // CUDA_VERSION >= 9010
}
} // namespace math
......
paddle/fluid/operators/math/fc_compute.h
浏览文件 @ 36cd18b5
......@@ -17,8 +17,6 @@ limitations under the License. */
#include "paddle/fluid/operators/math/blas.h"
#include "paddle/fluid/operators/math/jit_kernel.h"
DECLARE_int32(paddle_num_threads);
namespace paddle {
namespace operators {
namespace math {
......@@ -43,7 +41,7 @@ inline void FCCompute(const BlasT<DeviceContext, T>& blas, const int M,
.template Get<jitkernel::VAddKernel<T>>(N);
#ifdef PADDLE_WITH_MKLML
#pragma omp parallel for if (FLAGS_paddle_num_threads > 1)
#pragma omp parallel for
#endif
for (int i = 0; i < M; i++) {
T* dst = Y + i * N;
......
paddle/fluid/operators/tensor_array_to_tensor_op.cc
浏览文件 @ 36cd18b5
......@@ -106,9 +106,9 @@ class LoDTensorArray2TensorOp : public framework::OperatorBase {
out_inx_dim[0] = inx.size();
out_inx.Resize(out_inx_dim);
auto &local_scope = scope.NewScope();
std::string var_name = "out_index";
framework::Variable *tmp_index_var =
const_cast<framework::Scope &>(scope).Var(var_name);
framework::Variable *tmp_index_var = local_scope.Var(var_name);
auto &tmp_index_tensor =
*(tmp_index_var->GetMutable<paddle::framework::LoDTensor>());
tmp_index_tensor.Resize(out_inx_dim);
......@@ -128,12 +128,12 @@ class LoDTensorArray2TensorOp : public framework::OperatorBase {
out_dims[axis] = out_dim_sum;
out.Resize(out_dims);
LodTensorArray2LodTensorVector(scope, base_name, Input("X"), &names);
// Invoke Reshape Op
LodTensorArray2LodTensorVector(local_scope, base_name, Input("X"), &names);
// Invoke concat Op
auto concat_op = framework::OpRegistry::CreateOp(
"concat", {{"X", names}}, {{"Out", {Output("Out")}}}, attrs);
concat_op->Run(scope, place);
concat_op->Run(local_scope, place);
}
};
......
paddle/fluid/platform/cpu_helper.cc
浏览文件 @ 36cd18b5
......@@ -41,7 +41,7 @@ void SetNumThreads(int num_threads) {
#elif defined(PADDLE_WITH_MKLML)
int real_num_threads = num_threads > 1 ? num_threads : 1;
platform::dynload::MKL_Set_Num_Threads(real_num_threads);
omp_set_num_threads(num_threads);
omp_set_num_threads(real_num_threads);
#else
PADDLE_ENFORCE(false, "To be implemented.");
#endif
......
paddle/fluid/platform/device_context.h
浏览文件 @ 36cd18b5
......@@ -143,6 +143,39 @@ class CudnnWorkspaceHandle {
std::unique_ptr<std::lock_guard<std::mutex>> guard_;
};
#if CUDA_VERSION >= 9000
class ScopedCublasMathMode {
public:
ScopedCublasMathMode(cublasHandle_t handle, cublasMath_t new_math_mode)
: handle_(handle) {
need_reset = false;
PADDLE_ENFORCE(
platform::dynload::cublasGetMathMode(handle_, &old_math_mode_),
"Failed to get old cublas math mode");
if (old_math_mode_ != new_math_mode) {
PADDLE_ENFORCE(
platform::dynload::cublasSetMathMode(handle_, new_math_mode),
"Failed to set old cublas math mode");
need_reset = true;
}
}
~ScopedCublasMathMode() {
if (need_reset) {
PADDLE_ENFORCE(
platform::dynload::cublasSetMathMode(handle_, old_math_mode_),
"Failed to set old cublas math mode");
}
}
private:
cublasHandle_t handle_;
cublasMath_t old_math_mode_;
bool need_reset;
};
#endif
class CUDADeviceContext : public DeviceContext {
public:
explicit CUDADeviceContext(CUDAPlace place);
......@@ -199,6 +232,18 @@ class CUDADeviceContext : public DeviceContext {
callback_manager_->Wait();
}
#if CUDA_VERSION >= 9000
/*! \brief CublasCall may need to change cublas's config,
* but the cublas may be hold by multi-thread, so we should
* add lock here. */
template <typename Callback>
void CublasCall(Callback callback, cublasMath_t new_math) {
std::lock_guard<std::mutex> guard(cublas_mtx_);
ScopedCublasMathMode scoped_cublas_math(cublas_handle_, new_math);
callback();
}
#endif
private:
CUDAPlace place_;
......@@ -220,6 +265,8 @@ class CUDADeviceContext : public DeviceContext {
// If we use mtx_ for StreamCallbackManager, deadlock may occur sometimes
mutable std::mutex callback_mtx_;
std::unique_ptr<StreamCallbackManager> callback_manager_;
mutable std::mutex cublas_mtx_;
};
template <>
......
paddle/fluid/platform/dynload/cublas.cc
浏览文件 @ 36cd18b5
......@@ -32,6 +32,9 @@ CUBLAS_BLAS_ROUTINE_EACH_R2(DEFINE_WRAP);
CUBLAS_BLAS_ROUTINE_EACH_R3(DEFINE_WRAP);
#endif
#ifdef CUBLAS_BLAS_ROUTINE_EACH_R4
CUBLAS_BLAS_ROUTINE_EACH_R4(DEFINE_WRAP);
#endif
} // namespace dynload
} // namespace platform
} // namespace paddle
paddle/fluid/platform/dynload/cublas.h
浏览文件 @ 36cd18b5
......@@ -61,9 +61,6 @@ extern void *cublas_dso_handle;
extern DynLoad__##__name __name
#endif
#define DECLARE_DYNAMIC_LOAD_CUBLAS_V2_WRAP(__name) \
DECLARE_DYNAMIC_LOAD_CUBLAS_WRAP(__name)
#define CUBLAS_BLAS_ROUTINE_EACH(__macro) \
__macro(cublasSaxpy_v2); \
__macro(cublasDaxpy_v2); \
......@@ -106,11 +103,22 @@ CUBLAS_BLAS_ROUTINE_EACH_R2(DECLARE_DYNAMIC_LOAD_CUBLAS_WRAP)
// APIs available after CUDA 9.0
#if CUDA_VERSION >= 9000
#define CUBLAS_BLAS_ROUTINE_EACH_R3(__macro) __macro(cublasSetMathMode);
#define CUBLAS_BLAS_ROUTINE_EACH_R3(__macro) \
__macro(cublasSetMathMode); \
__macro(cublasGetMathMode);
CUBLAS_BLAS_ROUTINE_EACH_R3(DECLARE_DYNAMIC_LOAD_CUBLAS_WRAP)
#endif
// APIs available after CUDA 9.1
#if CUDA_VERSION >= 9010
#define CUBLAS_BLAS_ROUTINE_EACH_R4(__macro) \
__macro(cublasGemmBatchedEx); \
__macro(cublasGemmStridedBatchedEx);
CUBLAS_BLAS_ROUTINE_EACH_R4(DECLARE_DYNAMIC_LOAD_CUBLAS_WRAP)
#endif
#undef DECLARE_DYNAMIC_LOAD_CUBLAS_WRAP
} // namespace dynload
} // namespace platform
......
paddle/fluid/platform/gpu_info.cc
浏览文件 @ 36cd18b5
......@@ -35,6 +35,16 @@ DEFINE_double(fraction_of_gpu_memory_to_use, fraction_of_gpu_memory_to_use,
"additional trunks of the same size will be requested from gpu "
"until the gpu has no memory left for another trunk.");
DEFINE_bool(
enable_cublas_tensor_op_math, false,
"The enable_cublas_tensor_op_math indicate whether to use Tensor Core, "
"but it may loss precision. Currently, There are two CUDA libraries that"
" use Tensor Cores, cuBLAS and cuDNN. cuBLAS uses Tensor Cores to speed up"
" GEMM computations(the matrices must be either half precision or single "
"precision); cuDNN uses Tensor Cores to speed up both convolutions(the "
"input and output must be half precision) and recurrent neural networks "
"(RNNs).");
namespace paddle {
namespace platform {
......@@ -73,6 +83,16 @@ int GetCUDADriverVersion(int id) {
return driver_version;
}
bool TensorCoreAvailable() {
#if CUDA_VERSION >= 9000
int device = GetCurrentDeviceId();
int driver_version = GetCUDAComputeCapability(device);
return driver_version >= 70;
#else
return false;
#endif
}
int GetCUDAMultiProcessors(int id) {
PADDLE_ENFORCE_LT(id, GetCUDADeviceCount(), "id must less than GPU count");
int count;
......
paddle/fluid/platform/gpu_info.h
浏览文件 @ 36cd18b5
......@@ -35,6 +35,9 @@ int GetCUDARuntimeVersion(int id);
//! Get the driver version of the ith GPU
int GetCUDADriverVersion(int id);
//! Wheter the current device support TensorCore
bool TensorCoreAvailable();
//! Get the MultiProcessors of the ith GPU.
int GetCUDAMultiProcessors(int i);
......
paddle/fluid/pybind/CMakeLists.txt
浏览文件 @ 36cd18b5
......@@ -5,8 +5,8 @@ if(WITH_PYTHON)
if(WITH_AMD_GPU)
hip_library(paddle_pybind SHARED
SRCS ${PYBIND_SRCS}
DEPS ${PYBIND_DEPS}
${GLOB_OP_LIB} ${GLOB_OPERATOR_DEPS})
DEPS ARCHIVE_START ${PYBIND_DEPS}
${GLOB_OP_LIB} ${GLOB_OPERATOR_DEPS} ARCHIVE_END)
else()
cc_library(paddle_pybind SHARED
SRCS ${PYBIND_SRCS}
......
paddle/scripts/paddle_build.sh
浏览文件 @ 36cd18b5
......@@ -94,6 +94,30 @@ function cmake_gen() {
else
exit 1
fi
elif [ "$1" == "cp36-cp36m" ]; then
if [ -d "/Library/Frameworks/Python.framework/Versions/3.6" ]; then
export LD_LIBRARY_PATH=/Library/Frameworks/Python.framework/Versions/3.6/lib/
export DYLD_LIBRARY_PATH=/Library/Frameworks/Python.framework/Versions/3.6/lib/
export PATH=/Library/Frameworks/Python.framework/Versions/3.6/bin/:${PATH}
PYTHON_FLAGS="-DPYTHON_EXECUTABLE:FILEPATH=/Library/Frameworks/Python.framework/Versions/3.6/bin/python3
-DPYTHON_INCLUDE_DIR:PATH=/Library/Frameworks/Python.framework/Versions/3.6/include/python3.6m/
-DPYTHON_LIBRARY:FILEPATH=/Library/Frameworks/Python.framework/Versions/3.6/lib/libpython3.6m.dylib"
WITH_FLUID_ONLY=${WITH_FLUID_ONLY:-ON}
else
exit 1
fi
elif [ "$1" == "cp37-cp37m" ]; then
if [ -d "/Library/Frameworks/Python.framework/Versions/3.7" ]; then
export LD_LIBRARY_PATH=/Library/Frameworks/Python.framework/Versions/3.7/lib/
export DYLD_LIBRARY_PATH=/Library/Frameworks/Python.framework/Versions/3.7/lib/
export PATH=/Library/Frameworks/Python.framework/Versions/3.7/bin/:${PATH}
PYTHON_FLAGS="-DPYTHON_EXECUTABLE:FILEPATH=/Library/Frameworks/Python.framework/Versions/3.7/bin/python3
-DPYTHON_INCLUDE_DIR:PATH=/Library/Frameworks/Python.framework/Versions/3.7/include/python3.7m/
-DPYTHON_LIBRARY:FILEPATH=/Library/Frameworks/Python.framework/Versions/3.7/lib/libpython3.7m.dylib"
WITH_FLUID_ONLY=${WITH_FLUID_ONLY:-ON}
else
exit 1
fi
fi
else
if [ "$1" != "" ]; then
......@@ -116,6 +140,18 @@ function cmake_gen() {
export PYTHON_FLAGS="-DPYTHON_EXECUTABLE:FILEPATH=/opt/_internal/cpython-3.5.1/bin/python3
-DPYTHON_INCLUDE_DIR:PATH=/opt/_internal/cpython-3.5.1/include/python3.5m
-DPYTHON_LIBRARIES:FILEPATH=/opt/_internal/cpython-3.5.1/lib/libpython3.so"
elif [ "$1" == "cp36-cp36m" ]; then
export LD_LIBRARY_PATH=/opt/_internal/cpython-3.6.0/lib/:${LD_LIBRARY_PATH}
export PATH=/opt/_internal/cpython-3.6.0/bin/:${PATH}
export PYTHON_FLAGS="-DPYTHON_EXECUTABLE:FILEPATH=/opt/_internal/cpython-3.6.0/bin/python3
-DPYTHON_INCLUDE_DIR:PATH=/opt/_internal/cpython-3.6.0/include/python3.6m
-DPYTHON_LIBRARIES:FILEPATH=/opt/_internal/cpython-3.6.0/lib/libpython3.so"
elif [ "$1" == "cp37-cp37m" ]; then
export LD_LIBRARY_PATH=/opt/_internal/cpython-3.7.0/lib/:${LD_LIBRARY_PATH}
export PATH=/opt/_internal/cpython-3.7.0/bin/:${PATH}
export PYTHON_FLAGS="-DPYTHON_EXECUTABLE:FILEPATH=/opt/_internal/cpython-3.7.0/bin/python3
-DPYTHON_INCLUDE_DIR:PATH=/opt/_internal/cpython-3.7.0/include/python3.7m
-DPYTHON_LIBRARIES:FILEPATH=/opt/_internal/cpython-3.7.0/lib/libpython3.so"
fi
fi
fi
......@@ -419,7 +455,7 @@ function assert_api_not_changed() {
source .env/bin/activate
pip install ${PADDLE_ROOT}/build/python/dist/*whl
python ${PADDLE_ROOT}/tools/print_signatures.py paddle.fluid > new.spec
if [ "$1" == "cp35-cp35m" ]; then
if [ "$1" == "cp35-cp35m" ] || [ "$1" == "cp36-cp36m" ] || [ "$1" == "cp37-cp37m" ]; then
# Use sed to make python2 and python3 sepc keeps the same
sed -i 's/arg0: str/arg0: unicode/g' new.spec
sed -i "s/\(.*Transpiler.*\).__init__ ArgSpec(args=\['self'].*/\1.__init__ /g" new.spec
......
paddle/testing/paddle_gtest_main.cc
浏览文件 @ 36cd18b5
......@@ -28,7 +28,7 @@ int main(int argc, char** argv) {
for (int i = 0; i < argc; ++i) {
new_argv.push_back(argv[i]);
}
#ifdef PADDLE_WITH_CUDA
#if defined(PADDLE_WITH_CUDA) || defined(PADDLE_WITH_HIP)
new_argv.push_back(
strdup("--tryfromenv=fraction_of_gpu_memory_to_use,allocator_strategy"));
#else
......
python/paddle/fluid/__init__.py
浏览文件 @ 36cd18b5
......@@ -129,11 +129,13 @@ def __bootstrap__():
read_env_flags.append('rpc_send_thread_num')
read_env_flags.append('rpc_get_thread_num')
read_env_flags.append('rpc_prefetch_thread_num')
read_env_flags.append('rpc_disable_reuse_port')
if core.is_compiled_with_cuda():
read_env_flags += [
'fraction_of_gpu_memory_to_use', 'cudnn_deterministic',
'conv_workspace_size_limit', 'cudnn_exhaustive_search'
'enable_cublas_tensor_op_math', 'conv_workspace_size_limit',
'cudnn_exhaustive_search'
]
core.init_gflags([sys.argv[0]] +
["--tryfromenv=" + ",".join(read_env_flags)])
......
python/paddle/fluid/layers/control_flow.py
浏览文件 @ 36cd18b5
......@@ -896,9 +896,10 @@ def array_to_lod_tensor(x, table):
def increment(x, value=1.0, in_place=True):
"""
This function performs an operation that increments each value in the
This function performs an operation that increments the value in the
input :math:`x` by an amount: :math:`value` as mentioned in the input
parameter. This operation is performed in-place by default.
parameter. This operation is performed in-place by default. Notice that
the number of elements in :math:`x` must be equal to 1.
Args:
x (Variable|list): The tensor that has the input values.
......@@ -911,7 +912,8 @@ def increment(x, value=1.0, in_place=True):
Examples:
.. code-block:: python
data = fluid.layers.data(name='data', shape=[32, 32], dtype='float32')
data = fluid.layers.data(name='data', shape=[1], dtype='float32',
append_batch_size=False)
data = fluid.layers.increment(x=data, value=3.0, in_place=True)
"""
helper = LayerHelper("increment", **locals())
......
python/paddle/fluid/layers/detection.py
浏览文件 @ 36cd18b5
......@@ -1029,6 +1029,7 @@ def density_prior_box(input,
clip=False,
steps=[0.0, 0.0],
offset=0.5,
flatten_to_2d=False,
name=None):
"""
**Density Prior Box Operator**
......@@ -1065,22 +1066,24 @@ def density_prior_box(input,
height/weight of the input will be automatically calculated.
Default: [0., 0.]
offset(float): Prior boxes center offset. Default: 0.5
flatten_to_2d(bool): Whether to flatten output prior boxes and variance
to 2D shape, the second dim is 4. Default: False.
name(str): Name of the density prior box op. Default: None.
Returns:
tuple: A tuple with two Variable (boxes, variances)
boxes: the output density prior boxes of PriorBox.
The layout is [H, W, num_priors, 4].
H is the height of input, W is the width of input,
num_priors is the total
box count of each position of input.
The layout is [H, W, num_priors, 4] when flatten_to_2d is False.
The layout is [H * W * num_priors, 4] when flatten_to_2d is True.
H is the height of input, W is the width of input,
num_priors is the total box count of each position of input.
variances: the expanded variances of PriorBox.
The layout is [H, W, num_priors, 4].
H is the height of input, W is the width of input
num_priors is the total
box count of each position of input
The layout is [H, W, num_priors, 4] when flatten_to_2d is False.
The layout is [H * W * num_priors, 4] when flatten_to_2d is True.
H is the height of input, W is the width of input
num_priors is the total box count of each position of input.
Examples:
......@@ -1089,14 +1092,11 @@ def density_prior_box(input,
box, var = fluid.layers.density_prior_box(
input=conv1,
image=images,
min_sizes=[100.],
max_sizes=[200.],
aspect_ratios=[1.0, 1.0 / 2.0, 2.0],
densities=[3, 4],
fixed_sizes=[50., 60.],
fixed_ratios=[1.0, 3.0, 1.0 / 3.0],
flip=True,
clip=True)
densities=[4, 2, 1],
fixed_sizes=[32.0, 64.0, 128.0],
fixed_ratios=[1.],
clip=True,
flatten_to_2d=True)
"""
helper = LayerHelper("density_prior_box", **locals())
dtype = helper.input_dtype()
......@@ -1127,14 +1127,11 @@ def density_prior_box(input,
'step_w': steps[0],
'step_h': steps[1],
'offset': offset,
'densities': densities,
'fixed_sizes': fixed_sizes,
'fixed_ratios': fixed_ratios,
'flatten_to_2d': flatten_to_2d,
}
if densities is not None and len(densities) > 0:
attrs['densities'] = densities
if fixed_sizes is not None and len(fixed_sizes) > 0:
attrs['fixed_sizes'] = fixed_sizes
if fixed_ratios is not None and len(fixed_ratios) > 0:
attrs['fixed_ratios'] = fixed_ratios
box = helper.create_variable_for_type_inference(dtype)
var = helper.create_variable_for_type_inference(dtype)
helper.append_op(
......
python/paddle/fluid/layers/nn.py
浏览文件 @ 36cd18b5
......@@ -2134,11 +2134,16 @@ def pool2d(input,
input tensor is NCHW, where N is batch size, C is
the number of channels, H is the height of the
feature, and W is the width of the feature.
pool_size (int): The side length of pooling windows. All pooling
windows are squares with pool_size on a side.
pool_size (int|list|tuple): The pool kernel size. If pool kernel size is a tuple or list,
it must contain two integers, (pool_size_Height, pool_size_Width).
Otherwise, the pool kernel size will be a square of an int.
pool_type: ${pooling_type_comment}
pool_stride (int): stride of the pooling layer.
pool_padding (int): padding size.
pool_stride (int|list|tuple): The pool stride size. If pool stride size is a tuple or list,
it must contain two integers, (pool_stride_Height, pool_stride_Width).
Otherwise, the pool stride size will be a square of an int.
pool_padding (int|list|tuple): The pool padding size. If pool padding size is a tuple,
it must contain two integers, (pool_padding_on_Height, pool_padding_on_Width).
Otherwise, the pool padding size will be a square of an int.
global_pooling (bool): ${global_pooling_comment}
use_cudnn (bool): ${use_cudnn_comment}
ceil_mode (bool): ${ceil_mode_comment}
......
python/paddle/fluid/nets.py
浏览文件 @ 36cd18b5
......@@ -250,7 +250,8 @@ def sequence_conv_pool(input,
filter_size,
param_attr=None,
act="sigmoid",
pool_type="max"):
pool_type="max",
bias_attr=None):
"""
The sequence_conv_pool is composed with Sequence Convolution and Pooling.
......@@ -266,6 +267,11 @@ def sequence_conv_pool(input,
pool_type (str): Pooling type can be :math:`max` for max-pooling, :math:`average` for
average-pooling, :math:`sum` for sum-pooling, :math:`sqrt` for sqrt-pooling.
Default :math:`max`.
bias_attr (ParamAttr|bool|None): The parameter attribute for the bias of sequence_conv.
If it is set to False, no bias will be added to the output units.
If it is set to None or one attribute of ParamAttr, sequence_conv
will create ParamAttr as bias_attr. If the Initializer of the bias_attr
is not set, the bias is initialized zero. Default: None.
Return:
Variable: The final result after Sequence Convolution and Pooling.
......@@ -289,6 +295,7 @@ def sequence_conv_pool(input,
num_filters=num_filters,
filter_size=filter_size,
param_attr=param_attr,
bias_attr=bias_attr,
act=act)
pool_out = layers.sequence_pool(input=conv_out, pool_type=pool_type)
......
python/paddle/fluid/tests/book/test_image_classification.py
浏览文件 @ 36cd18b5
......@@ -239,7 +239,7 @@ def infer(use_cuda, save_dirname=None):
assert len(results[0]) == len(transpiler_results[0])
for i in range(len(results[0])):
np.testing.assert_almost_equal(
results[0][i], transpiler_results[0][i], decimal=5)
results[0][i], transpiler_results[0][i], decimal=4)
print("infer results: ", results[0])
......
python/paddle/fluid/tests/test_detection.py
浏览文件 @ 36cd18b5
......@@ -112,38 +112,42 @@ class TestDetection(unittest.TestCase):
class TestPriorBox(unittest.TestCase):
def test_prior_box(self):
data_shape = [3, 224, 224]
images = fluid.layers.data(
name='pixel', shape=data_shape, dtype='float32')
conv1 = fluid.layers.conv2d(images, 3, 3, 2)
box, var = layers.prior_box(
input=conv1,
image=images,
min_sizes=[100.0],
aspect_ratios=[1.],
flip=True,
clip=True)
assert len(box.shape) == 4
assert box.shape == var.shape
assert box.shape[3] == 4
program = Program()
with program_guard(program):
data_shape = [3, 224, 224]
images = fluid.layers.data(
name='pixel', shape=data_shape, dtype='float32')
conv1 = fluid.layers.conv2d(images, 3, 3, 2)
box, var = layers.prior_box(
input=conv1,
image=images,
min_sizes=[100.0],
aspect_ratios=[1.],
flip=True,
clip=True)
assert len(box.shape) == 4
assert box.shape == var.shape
assert box.shape[3] == 4
class TestDensityPriorBox(unittest.TestCase):
def test_density_prior_box(self):
data_shape = [3, 224, 224]
images = fluid.layers.data(
name='pixel', shape=data_shape, dtype='float32')
conv1 = fluid.layers.conv2d(images, 3, 3, 2)
box, var = layers.density_prior_box(
input=conv1,
image=images,
densities=[3, 4],
fixed_sizes=[50., 60.],
fixed_ratios=[1.0],
clip=True)
assert len(box.shape) == 4
assert box.shape == var.shape
assert box.shape[3] == 4
program = Program()
with program_guard(program):
data_shape = [3, 224, 224]
images = fluid.layers.data(
name='pixel', shape=data_shape, dtype='float32')
conv1 = fluid.layers.conv2d(images, 3, 3, 2)
box, var = layers.density_prior_box(
input=conv1,
image=images,
densities=[3, 4],
fixed_sizes=[50., 60.],
fixed_ratios=[1.0],
clip=True)
assert len(box.shape) == 4
assert box.shape == var.shape
assert box.shape[-1] == 4
class TestAnchorGenerator(unittest.TestCase):
......
python/paddle/fluid/tests/unittests/test_density_prior_box_op.py
浏览文件 @ 36cd18b5
......@@ -36,7 +36,8 @@ class TestDensityPriorBoxOp(OpTest):
'offset': self.offset,
'densities': self.densities,
'fixed_sizes': self.fixed_sizes,
'fixed_ratios': self.fixed_ratios
'fixed_ratios': self.fixed_ratios,
'flatten_to_2d': self.flatten_to_2d
}
self.outputs = {'Boxes': self.out_boxes, 'Variances': self.out_var}
......@@ -48,16 +49,17 @@ class TestDensityPriorBoxOp(OpTest):
self.set_data()
def set_density(self):
self.densities = []
self.fixed_sizes = []
self.fixed_ratios = []
self.densities = [4, 2, 1]
self.fixed_sizes = [32.0, 64.0, 128.0]
self.fixed_ratios = [1.0]
self.layer_w = 17
self.layer_h = 17
self.image_w = 533
self.image_h = 533
self.flatten_to_2d = False
def init_test_params(self):
self.layer_w = 32
self.layer_h = 32
self.image_w = 40
self.image_h = 40
self.set_density()
self.step_w = float(self.image_w) / float(self.layer_w)
self.step_h = float(self.image_h) / float(self.layer_h)
......@@ -69,8 +71,6 @@ class TestDensityPriorBoxOp(OpTest):
self.variances = [0.1, 0.1, 0.2, 0.2]
self.variances = np.array(self.variances, dtype=np.float).flatten()
self.set_density()
self.clip = True
self.num_priors = 0
if len(self.fixed_sizes) > 0 and len(self.densities) > 0:
......@@ -129,6 +129,9 @@ class TestDensityPriorBoxOp(OpTest):
(self.layer_h, self.layer_w, self.num_priors, 1))
self.out_boxes = out_boxes.astype('float32')
self.out_var = out_var.astype('float32')
if self.flatten_to_2d:
self.out_boxes = self.out_boxes.reshape((-1, 4))
self.out_var = self.out_var.reshape((-1, 4))
class TestDensityPriorBox(TestDensityPriorBoxOp):
......@@ -136,6 +139,11 @@ class TestDensityPriorBox(TestDensityPriorBoxOp):
self.densities = [3, 4]
self.fixed_sizes = [1.0, 2.0]
self.fixed_ratios = [1.0]
self.layer_w = 32
self.layer_h = 32
self.image_w = 40
self.image_h = 40
self.flatten_to_2d = True
if __name__ == '__main__':
......
python/paddle/fluid/tests/unittests/test_layers.py
浏览文件 @ 36cd18b5
......@@ -202,6 +202,17 @@ class TestBook(unittest.TestCase):
self.assertIsNotNone(layers.sequence_unpad(x=x, length=length))
print(str(program))
def test_pool2d(self):
program = Program()
with program_guard(program):
x = layers.data(name='x', shape=[3, 224, 224], dtype='float32')
self.assertIsNotNone(
layers.pool2d(
x,
pool_size=[5, 3],
pool_stride=[1, 2],
pool_padding=(2, 1)))
def test_lstm_unit(self):
program = Program()
with program_guard(program):
......
python/paddle/fluid/tests/unittests/test_multiclass_nms_op.py
浏览文件 @ 36cd18b5
......@@ -145,10 +145,15 @@ def batched_multiclass_nms(boxes, scores, background, score_threshold,
lod.append(nmsed_num)
if nmsed_num == 0: continue
tmp_det_out = []
for c, indices in nmsed_outs.items():
for idx in indices:
xmin, ymin, xmax, ymax = boxes[n][idx][:]
det_outs.append([c, scores[n][c][idx], xmin, ymin, xmax, ymax])
tmp_det_out.append(
[c, scores[n][c][idx], xmin, ymin, xmax, ymax])
sorted_det_out = sorted(
tmp_det_out, key=lambda tup: tup[0], reverse=False)
det_outs.extend(sorted_det_out)
return det_outs, lod
......@@ -210,7 +215,7 @@ class TestMulticlassNMSOp(OpTest):
class TestMulticlassNMSOpNoOutput(TestMulticlassNMSOp):
def set_argument(self):
# Here set 2.0 to test the case there is no outputs.
# In practical use, 0.0 < score_threshold < 1.0
# In practical use, 0.0 < score_threshold < 1.0
self.score_threshold = 2.0
......
tools/manylinux1/Dockerfile.x64
浏览文件 @ 36cd18b5
......@@ -16,7 +16,7 @@ ENV PKG_CONFIG_PATH=/usr/local/lib/pkgconfig
RUN yum install -y sqlite-devel zlib-devel openssl-devel pcre-devel vim tk-devel tkinter libtool xz graphviz
COPY build_scripts /build_scripts
RUN bash build_scripts/build.sh && \
bash build_scripts/install_nccl2.sh && rm -r build_scripts
bash build_scripts/install_nccl2.sh && rm -rf build_scripts
ENV SSL_CERT_FILE=/opt/_internal/certs.pem
......
tools/manylinux1/build_scripts/build_utils.sh
浏览文件 @ 36cd18b5
......@@ -50,6 +50,15 @@ function do_cpython_build {
mkdir -p ${prefix}/lib
# -Wformat added for https://bugs.python.org/issue17547 on Python 2.6
if [ $(lex_pyver $py_ver) -eq $(lex_pyver 3.6) ]; then
wget https://www.sqlite.org/2018/sqlite-autoconf-3250300.tar.gz
tar -zxf sqlite-autoconf-3250300.tar.gz
cd sqlite-autoconf-3250300
./configure --prefix=/usr/local
make -j8 && make install
cd ../ && rm sqlite-autoconf-3250300.tar.gz
fi
# NOTE --enable-shared for generating libpython shared library needed for
# linking of some of the nupic.core test executables.
if [ $(lex_pyver $py_ver) -ge $(lex_pyver 3.7) ]; then
......@@ -59,9 +68,9 @@ function do_cpython_build {
make -j8 > /dev/null
make altinstall > /dev/null
else
CFLAGS="-Wformat" ./configure --prefix=${prefix} --enable-shared $unicode_flags > /dev/null
make -j8 > /dev/null
make install > /dev/null
LD_LIBRARY_PATH=/usr/local/lib:${LD_LIBRARY_PATH} CFLAGS="-Wformat" ./configure --prefix=${prefix} --enable-shared $unicode_flags > /dev/null
LD_LIBRARY_PATH=/usr/local/lib:${LD_LIBRARY_PATH} make -j8 > /dev/null
LD_LIBRARY_PATH=/usr/local/lib:${LD_LIBRARY_PATH} make install > /dev/null
fi
popd
echo "ZZZ looking for libpython"
......
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