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PaddleDetection
提交 b16e832d 编写于 作者: Q Qiao Longfei
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Merge branch 'develop' of https://github.com/PaddlePaddle/Paddle into fix-dist-sparse-decay

上级 877289c4 d09d6ead
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Showing with 10335 addition and 4238 deletion
CMakeLists.txt
浏览文件 @ b16e832d
......@@ -55,6 +55,7 @@ option(WITH_DOUBLE "Compile PaddlePaddle with double precision" OFF)
option(WITH_RDMA "Compile PaddlePaddle with RDMA support" OFF)
option(WITH_TIMER "Compile PaddlePaddle with stats timer" OFF)
option(WITH_PROFILER "Compile PaddlePaddle with GPU profiler and gperftools" OFF)
option(WITH_JEMALLOC "Compile PaddlePaddle with jemalloc" OFF)
option(WITH_DOC "Compile PaddlePaddle with documentation" OFF)
option(WITH_COVERAGE "Compile PaddlePaddle with code coverage" OFF)
option(COVERALLS_UPLOAD "Package code coverage data to coveralls" OFF)
......@@ -261,6 +262,12 @@ if (WITH_PROFILER)
add_definitions(-DWITH_GPERFTOOLS)
endif()
if (WITH_JEMALLOC)
find_package(JeMalloc REQUIRED)
include_directories(${JEMALLOC_INCLUDE_DIR})
add_definitions(-DWITH_JEMALLOC)
endif()
include(generic) # simplify cmake module
include(package) # set paddle packages
include(ccache) # set ccache for compilation
......@@ -290,7 +297,7 @@ if(WITH_PSLIB)
list(APPEND EXTERNAL_LIBS pslib_brpc)
list(APPEND EXTERNAL_LIBS libmct)
endif(WITH_PSLIB)
if(WITH_AMD_GPU)
find_package(HIP)
include(hip)
......
Dockerfile
浏览文件 @ b16e832d
......@@ -94,52 +94,52 @@ RUN localedef -i en_US -f UTF-8 en_US.UTF-8
# specify sphinx version as 1.5.6 and remove -U option for [pip install -U
# sphinx-rtd-theme] since -U option will cause sphinx being updated to newest
# version(1.7.1 for now), which causes building documentation failed.
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 && \
RUN pip3 --no-cache-dir install -U wheel && \
pip3 --no-cache-dir install -U docopt PyYAML sphinx==1.5.6 && \
pip3 --no-cache-dir install sphinx-rtd-theme==0.1.9 recommonmark && \
pip3.6 --no-cache-dir install -U wheel && \
pip3.6 --no-cache-dir install -U docopt PyYAML sphinx==1.5.6 && \
pip3.6 --no-cache-dir install sphinx-rtd-theme==0.1.9 recommonmark && \
pip3.7 --no-cache-dir install -U wheel && \
pip3.7 --no-cache-dir install -U docopt PyYAML sphinx==1.5.6 && \
pip3.7 --no-cache-dir 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 && \
pip install sphinx-rtd-theme==0.1.9 recommonmark
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
pip --no-cache-dir install -U pip setuptools wheel && \
pip --no-cache-dir install -U docopt PyYAML sphinx==1.5.6 && \
pip --no-cache-dir install sphinx-rtd-theme==0.1.9 recommonmark
RUN pip3 --no-cache-dir install 'pre-commit==1.10.4' 'ipython==5.3.0' && \
pip3 --no-cache-dir install 'ipykernel==4.6.0' 'jupyter==1.0.0' && \
pip3 --no-cache-dir install opencv-python && \
pip3.6 --no-cache-dir install 'pre-commit==1.10.4' 'ipython==5.3.0' && \
pip3.6 --no-cache-dir install 'ipykernel==4.6.0' 'jupyter==1.0.0' && \
pip3.6 --no-cache-dir install opencv-python && \
pip3.7 --no-cache-dir install 'pre-commit==1.10.4' 'ipython==5.3.0' && \
pip3.7 --no-cache-dir install 'ipykernel==4.6.0' 'jupyter==1.0.0' && \
pip3.7 --no-cache-dir install opencv-python && \
pip --no-cache-dir install 'pre-commit==1.10.4' 'ipython==5.3.0' && \
pip --no-cache-dir install 'ipykernel==4.6.0' 'jupyter==1.0.0' && \
pip --no-cache-dir 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
RUN pip3 --no-cache-dir install pylint pytest astroid isort
RUN pip3.6 --no-cache-dir install pylint pytest astroid isort
RUN pip3.7 --no-cache-dir install pylint pytest astroid isort
RUN pip --no-cache-dir 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
RUN pip3 --no-cache-dir install -r /root/requirements.txt
RUN pip3.6 --no-cache-dir install -r /root/requirements.txt
RUN pip3.7 --no-cache-dir install -r /root/requirements.txt
RUN pip --no-cache-dir 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]
RUN apt-get install -y libssl-dev libffi-dev && apt-get clean -y
RUN pip3 --no-cache-dir install certifi urllib3[secure]
RUN pip3.6 --no-cache-dir install certifi urllib3[secure]
RUN pip3.7 --no-cache-dir install certifi urllib3[secure]
RUN pip --no-cache-dir install certifi urllib3[secure]
# Install woboq_codebrowser to /woboq
......@@ -149,6 +149,14 @@ RUN git clone https://github.com/woboq/woboq_codebrowser /woboq && \
-DCMAKE_BUILD_TYPE=Release . \
make)
# ar mishandles 4GB files
# https://sourceware.org/bugzilla/show_bug.cgi?id=14625
# remove them when apt-get support 2.27 and higher version
RUN wget -q https://launchpad.net/ubuntu/+archive/primary/+sourcefiles/binutils/2.27-9ubuntu1/binutils_2.27.orig.tar.gz && \
tar -xzf binutils_2.27.orig.tar.gz && \
cd binutils-2.27 && \
./configure && make -j && make install && cd .. && rm -rf binutils-2.27 binutils_2.27.orig.tar.gz
# Configure OpenSSH server. c.f. https://docs.docker.com/engine/examples/running_ssh_service
RUN mkdir /var/run/sshd
RUN echo 'root:root' | chpasswd
......
cmake/FindJeMalloc.cmake 0 → 100644
浏览文件 @ b16e832d
# - Find JeMalloc library
# Find the native JeMalloc includes and library
#
# JEMALLOC_INCLUDE_DIR - where to find jemalloc.h, etc.
# JEMALLOC_LIBRARIES - List of libraries when using jemalloc.
# JEMALLOC_FOUND - True if jemalloc found.
find_path(JEMALLOC_INCLUDE_DIR
NAMES jemalloc/jemalloc.h
HINTS ${JEMALLOC_ROOT_DIR}/include)
find_library(JEMALLOC_LIBRARIES
NAMES jemalloc
HINTS ${JEMALLOC_ROOT_DIR}/lib)
include(FindPackageHandleStandardArgs)
find_package_handle_standard_args(jemalloc DEFAULT_MSG JEMALLOC_LIBRARIES JEMALLOC_INCLUDE_DIR)
mark_as_advanced(
JEMALLOC_LIBRARIES
JEMALLOC_INCLUDE_DIR)
cmake/configure.cmake
浏览文件 @ b16e832d
......@@ -134,6 +134,7 @@ if(WITH_GPU)
message(WARNING "Anakin needs CUDNN >= 7.0 to compile. Force WITH_ANAKIN=OFF")
set(WITH_ANAKIN OFF CACHE STRING "Anakin is valid only when CUDNN >= 7.0." FORCE)
endif()
add_definitions(-DWITH_ANAKIN)
endif()
if(WITH_ANAKIN)
# NOTICE(minqiyang): the end slash is important because $CUDNN_INCLUDE_DIR
......
cmake/cuda.cmake
浏览文件 @ b16e832d
......@@ -2,7 +2,7 @@ if(NOT WITH_GPU)
return()
endif()
set(paddle_known_gpu_archs "30 35 50 52 60 61 70")
set(paddle_known_gpu_archs "30 35 50 52 60 61 70 75")
set(paddle_known_gpu_archs7 "30 35 50 52")
set(paddle_known_gpu_archs8 "30 35 50 52 60 61")
......@@ -59,7 +59,7 @@ endfunction()
# select_nvcc_arch_flags(out_variable)
function(select_nvcc_arch_flags out_variable)
# List of arch names
set(archs_names "Kepler" "Maxwell" "Pascal" "All" "Manual")
set(archs_names "Kepler" "Maxwell" "Pascal" "Volta" "Turing" "All" "Manual")
set(archs_name_default "All")
if(NOT CMAKE_CROSSCOMPILING)
list(APPEND archs_names "Auto")
......@@ -93,6 +93,8 @@ function(select_nvcc_arch_flags out_variable)
set(cuda_arch_bin "60 61")
elseif(${CUDA_ARCH_NAME} STREQUAL "Volta")
set(cuda_arch_bin "70")
elseif(${CUDA_ARCH_NAME} STREQUAL "Turing")
set(cuda_arch_bin "75")
elseif(${CUDA_ARCH_NAME} STREQUAL "All")
set(cuda_arch_bin ${paddle_known_gpu_archs})
elseif(${CUDA_ARCH_NAME} STREQUAL "Auto")
......@@ -139,10 +141,12 @@ endfunction()
message(STATUS "CUDA detected: " ${CUDA_VERSION})
if (${CUDA_VERSION} LESS 7.0)
set(paddle_known_gpu_archs ${paddle_known_gpu_archs})
add_definitions("-DPADDLE_CUDA_BINVER=\"60\"")
elseif (${CUDA_VERSION} LESS 8.0) # CUDA 7.x
set(paddle_known_gpu_archs ${paddle_known_gpu_archs7})
list(APPEND CUDA_NVCC_FLAGS "-D_MWAITXINTRIN_H_INCLUDED")
list(APPEND CUDA_NVCC_FLAGS "-D__STRICT_ANSI__")
add_definitions("-DPADDLE_CUDA_BINVER=\"70\"")
elseif (${CUDA_VERSION} LESS 9.0) # CUDA 8.x
set(paddle_known_gpu_archs ${paddle_known_gpu_archs8})
list(APPEND CUDA_NVCC_FLAGS "-D_MWAITXINTRIN_H_INCLUDED")
......@@ -150,6 +154,7 @@ elseif (${CUDA_VERSION} LESS 9.0) # CUDA 8.x
# CUDA 8 may complain that sm_20 is no longer supported. Suppress the
# warning for now.
list(APPEND CUDA_NVCC_FLAGS "-Wno-deprecated-gpu-targets")
add_definitions("-DPADDLE_CUDA_BINVER=\"80\"")
endif()
include_directories(${CUDA_INCLUDE_DIRS})
......
cmake/cudnn.cmake
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......@@ -89,6 +89,7 @@ if(CUDNN_FOUND)
if(NOT CUDNN_MAJOR_VERSION)
set(CUDNN_VERSION "???")
else()
add_definitions("-DPADDLE_CUDNN_BINVER=\"${CUDNN_MAJOR_VERSION}\"")
math(EXPR CUDNN_VERSION
"${CUDNN_MAJOR_VERSION} * 1000 +
${CUDNN_MINOR_VERSION} * 100 + ${CUDNN_PATCHLEVEL_VERSION}")
......
cmake/external/cub.cmake
浏览文件 @ b16e832d
......@@ -32,4 +32,4 @@ endif()
add_dependencies(cub extern_cub)
LIST(APPEND externl_project_dependencies cub)
LIST(APPEND external_project_dependencies cub)
cmake/external/dlpack.cmake
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......@@ -28,4 +28,4 @@ endif()
add_dependencies(dlpack extern_dlpack)
LIST(APPEND externl_project_dependencies dlpack)
LIST(APPEND external_project_dependencies dlpack)
cmake/external/mkldnn.cmake
浏览文件 @ b16e832d
......@@ -106,10 +106,10 @@ else(WIN32)
SET(MKLDNN_SHARED_LIB ${MKLDNN_INSTALL_DIR}/libmkldnn.so.0)
ADD_CUSTOM_COMMAND(OUTPUT ${MKLDNN_SHARED_LIB}
COMMAND ${CMAKE_COMMAND} -E copy ${MKLDNN_LIB} ${MKLDNN_SHARED_LIB}
DEPENDS mkldnn)
DEPENDS mkldnn shared_mkldnn)
endif(WIN32)
ADD_CUSTOM_TARGET(mkldnn_shared_lib ALL DEPENDS ${MKLDNN_SHARED_LIB})
ADD_DEPENDENCIES(mkldnn_shared_lib ${MKLDNN_PROJECT} mkldnn)
IF(WITH_C_API)
INSTALL(FILES ${MKLDNN_SHARED_LIB} DESTINATION lib)
ENDIF()
......
cmake/external/mklml.cmake
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......@@ -16,14 +16,6 @@ IF(NOT ${WITH_MKLML})
return()
ENDIF(NOT ${WITH_MKLML})
IF(APPLE)
MESSAGE(WARNING
"Mac is not supported with MKLML in Paddle yet."
"Force WITH_MKLML=OFF")
SET(WITH_MKLML OFF CACHE STRING "Disable MKLML package in Windows and MacOS" FORCE)
return()
ENDIF()
INCLUDE(ExternalProject)
SET(MKLML_DST_DIR "mklml")
SET(MKLML_INSTALL_ROOT "${THIRD_PARTY_PATH}/install")
......@@ -47,10 +39,13 @@ SET(CMAKE_INSTALL_RPATH "${CMAKE_INSTALL_RPATH}" "${MKLML_ROOT}/lib")
IF((NOT DEFINED MKLML_VER) OR (NOT DEFINED MKLML_URL))
MESSAGE(STATUS "use pre defined download url")
if(WIN32)
SET(MKLML_VER "mklml_win_2019.0.20180710" CACHE STRING "" FORCE)
SET(MKLML_VER "mklml_win_2019.0.1.20180928" CACHE STRING "" FORCE)
SET(MKLML_URL "https://paddlepaddledeps.cdn.bcebos.com/${MKLML_VER}.zip" CACHE STRING "" FORCE)
elseif(APPLE)
SET(MKLML_VER "mklml_mac_2019.0.1.20180928" CACHE STRING "" FORCE)
SET(MKLML_URL "http://paddlepaddledeps.cdn.bcebos.com/${MKLML_VER}.tgz" CACHE STRING "" FORCE)
else()
SET(MKLML_VER "mklml_lnx_2019.0.20180710" CACHE STRING "" FORCE)
SET(MKLML_VER "mklml_lnx_2019.0.1.20180928" CACHE STRING "" FORCE)
SET(MKLML_URL "http://paddlepaddledeps.cdn.bcebos.com/${MKLML_VER}.tgz" CACHE STRING "" FORCE)
ENDIF()
endif()
......
cmake/external/ngraph.cmake
浏览文件 @ b16e832d
......@@ -37,13 +37,12 @@ INCLUDE(GNUInstallDirs)
INCLUDE(ExternalProject)
SET(NGRAPH_PROJECT "extern_ngraph")
SET(NGRAPH_VERSION "0.9")
SET(NGRAPH_GIT_TAG "f9fd9d4cc318dc59dd4b68448e7fbb5f67a28bd0")
SET(NGRAPH_GIT_TAG "08851c2c45fcf9fa9c74871dd3dbc3fe38f37cc9")
SET(NGRAPH_SOURCES_DIR ${THIRD_PARTY_PATH}/ngraph)
SET(NGRAPH_INSTALL_DIR ${THIRD_PARTY_PATH}/install/ngraph)
SET(NGRAPH_INC_DIR ${NGRAPH_INSTALL_DIR}/include)
SET(NGRAPH_LIB_DIR ${NGRAPH_INSTALL_DIR}/${CMAKE_INSTALL_LIBDIR})
SET(NGRAPH_SHARED_LIB_NAME libngraph.so.${NGRAPH_VERSION})
SET(NGRAPH_SHARED_LIB_NAME libngraph.so)
SET(NGRAPH_CPU_LIB_NAME libcpu_backend.so)
SET(NGRAPH_TBB_LIB_NAME libtbb.so.2)
SET(NGRAPH_GIT_REPO "https://github.com/NervanaSystems/ngraph.git")
......
cmake/generic.cmake
浏览文件 @ b16e832d
......@@ -115,6 +115,10 @@ function(common_link TARGET_NAME)
if (WITH_PROFILER)
target_link_libraries(${TARGET_NAME} gperftools::profiler)
endif()
if (WITH_JEMALLOC)
target_link_libraries(${TARGET_NAME} ${JEMALLOC_LIBRARIES})
endif()
endfunction()
......@@ -228,7 +232,7 @@ function(merge_static_libs TARGET_NAME)
# Get the file names of the libraries to be merged
set(libfiles ${libfiles} $<TARGET_FILE:${lib}>)
endforeach()
# msvc will put libarary in directory of "/Release/xxxlib" by default
# msvc will put libarary in directory of "/Release/xxxlib" by default
# COMMAND cmake -E remove "${CMAKE_CURRENT_BINARY_DIR}/${CMAKE_BUILD_TYPE}/${TARGET_NAME}.lib"
add_custom_command(TARGET ${TARGET_NAME} POST_BUILD
COMMAND cmake -E make_directory "${CMAKE_CURRENT_BINARY_DIR}/${CMAKE_BUILD_TYPE}"
......
cmake/inference_lib.cmake
浏览文件 @ b16e832d
......@@ -136,7 +136,7 @@ if (WITH_MKLDNN)
copy(mkldnn_lib
SRCS ${MKLDNN_INC_DIR} ${MKLDNN_SHARED_LIB}
DSTS ${dst_dir} ${dst_dir}/lib
DEPS mkldnn
DEPS mkldnn_shared_lib
)
endif ()
......
cmake/operators.cmake
浏览文件 @ b16e832d
......@@ -110,7 +110,7 @@ function(op_library TARGET)
# Define operators that don't need pybind here.
foreach(manual_pybind_op "compare_op" "logical_op" "nccl_op"
"tensor_array_read_write_op" "tensorrt_engine_op" "conv_fusion_op"
"fusion_transpose_flatten_concat_op")
"fusion_transpose_flatten_concat_op" "fusion_conv_inception_op")
if ("${TARGET}" STREQUAL "${manual_pybind_op}")
set(pybind_flag 1)
endif()
......
cmake/simd.cmake
浏览文件 @ b16e832d
......@@ -57,46 +57,43 @@ int main()
return 0;
}" SSE3_FOUND)
# disable AVX by default on windows
if(NOT WIN32)
# Check AVX
set(CMAKE_REQUIRED_FLAGS ${AVX_FLAG})
set(AVX_FOUND_EXITCODE 1 CACHE STRING "Result from TRY_RUN" FORCE)
CHECK_CXX_SOURCE_RUNS("
#include <immintrin.h>
int main()
{
__m256 a = _mm256_set_ps (-1.0f, 2.0f, -3.0f, 4.0f, -1.0f, 2.0f, -3.0f, 4.0f);
__m256 b = _mm256_set_ps (1.0f, 2.0f, 3.0f, 4.0f, 1.0f, 2.0f, 3.0f, 4.0f);
__m256 result = _mm256_add_ps (a, b);
return 0;
}" AVX_FOUND)
# Check AVX
set(CMAKE_REQUIRED_FLAGS ${AVX_FLAG})
set(AVX_FOUND_EXITCODE 1 CACHE STRING "Result from TRY_RUN" FORCE)
CHECK_CXX_SOURCE_RUNS("
#include <immintrin.h>
int main()
{
__m256 a = _mm256_set_ps (-1.0f, 2.0f, -3.0f, 4.0f, -1.0f, 2.0f, -3.0f, 4.0f);
__m256 b = _mm256_set_ps (1.0f, 2.0f, 3.0f, 4.0f, 1.0f, 2.0f, 3.0f, 4.0f);
__m256 result = _mm256_add_ps (a, b);
return 0;
}" AVX_FOUND)
# Check AVX 2
set(CMAKE_REQUIRED_FLAGS ${AVX2_FLAG})
set(AVX2_FOUND_EXITCODE 1 CACHE STRING "Result from TRY_RUN" FORCE)
CHECK_CXX_SOURCE_RUNS("
#include <immintrin.h>
int main()
{
__m256i a = _mm256_set_epi32 (-1, 2, -3, 4, -1, 2, -3, 4);
__m256i result = _mm256_abs_epi32 (a);
return 0;
}" AVX2_FOUND)
# Check AVX 2
set(CMAKE_REQUIRED_FLAGS ${AVX2_FLAG})
set(AVX2_FOUND_EXITCODE 1 CACHE STRING "Result from TRY_RUN" FORCE)
CHECK_CXX_SOURCE_RUNS("
#include <immintrin.h>
int main()
{
__m256i a = _mm256_set_epi32 (-1, 2, -3, 4, -1, 2, -3, 4);
__m256i result = _mm256_abs_epi32 (a);
return 0;
}" AVX2_FOUND)
# Check AVX512F
set(CMAKE_REQUIRED_FLAGS ${AVX512F_FLAG})
set(AVX512F_FOUND_EXITCODE 1 CACHE STRING "Result from TRY_RUN" FORCE)
CHECK_CXX_SOURCE_RUNS("
#include <immintrin.h>
int main()
{
__m512i a = _mm512_set_epi32 (-1, 2, -3, 4, -1, 2, -3, 4,
13, -5, 6, -7, 9, 2, -6, 3);
__m512i result = _mm512_abs_epi32 (a);
return 0;
}" AVX512F_FOUND)
endif(NOT WIN32)
# Check AVX512F
set(CMAKE_REQUIRED_FLAGS ${AVX512F_FLAG})
set(AVX512F_FOUND_EXITCODE 1 CACHE STRING "Result from TRY_RUN" FORCE)
CHECK_CXX_SOURCE_RUNS("
#include <immintrin.h>
int main()
{
__m512i a = _mm512_set_epi32 (-1, 2, -3, 4, -1, 2, -3, 4,
13, -5, 6, -7, 9, 2, -6, 3);
__m512i result = _mm512_abs_epi32 (a);
return 0;
}" AVX512F_FOUND)
set(CMAKE_REQUIRED_FLAGS ${CMAKE_REQUIRED_FLAGS_RETAINED})
mark_as_advanced(MMX_FOUND SSE2_FOUND SSE3_FOUND AVX_FOUND AVX2_FOUND AVX512F_FOUND)
paddle/contrib/float16/float16_transpiler.py
浏览文件 @ b16e832d
......@@ -60,7 +60,7 @@ class Float16Transpiler:
raise TypeError("place should be as CPUPlace/CUDAPlace type")
if scope is None:
scope = global_scope()
if not isinstance(scope, core.Scope):
if not isinstance(scope, core._Scope):
raise TypeError("scope should be as Scope type or None")
self.scope = scope
......
paddle/fluid/API.spec
浏览文件 @ b16e832d
......@@ -464,11 +464,7 @@ paddle.fluid.unique_name.switch ArgSpec(args=['new_generator'], varargs=None, ke
paddle.fluid.unique_name.guard ArgSpec(args=[], varargs='args', keywords='kwds', defaults=None)
paddle.fluid.recordio_writer.convert_reader_to_recordio_file ArgSpec(args=['filename', 'reader_creator', 'feeder', 'compressor', 'max_num_records', 'feed_order'], varargs=None, keywords=None, defaults=(Compressor.Snappy, 1000, None))
paddle.fluid.recordio_writer.convert_reader_to_recordio_files ArgSpec(args=['filename', 'batch_per_file', 'reader_creator', 'feeder', 'compressor', 'max_num_records', 'feed_order'], varargs=None, keywords=None, defaults=(Compressor.Snappy, 1000, None))
paddle.fluid.Scope.__init__ __init__(self: paddle.fluid.core.Scope) -> None
paddle.fluid.Scope.drop_kids drop_kids(self: paddle.fluid.core.Scope) -> None
paddle.fluid.Scope.find_var find_var(self: paddle.fluid.core.Scope, arg0: unicode) -> paddle.fluid.core.Variable
paddle.fluid.Scope.new_scope new_scope(self: paddle.fluid.core.Scope) -> paddle.fluid.core.Scope
paddle.fluid.Scope.var var(self: paddle.fluid.core.Scope, arg0: unicode) -> paddle.fluid.core.Variable
paddle.fluid.Scope Scope() -> paddle.fluid.core._Scope
paddle.reader.map_readers ArgSpec(args=['func'], varargs='readers', keywords=None, defaults=None)
paddle.reader.buffered ArgSpec(args=['reader', 'size'], varargs=None, keywords=None, defaults=None)
paddle.reader.compose ArgSpec(args=[], varargs='readers', keywords='kwargs', defaults=None)
......
paddle/fluid/framework/CMakeLists.txt
浏览文件 @ b16e832d
......@@ -7,27 +7,17 @@ function(windows_symbolic TARGET)
cmake_parse_arguments(windows_symbolic "${options}" "${oneValueArgs}" "${multiValueArgs}" ${ARGN})
set(final_path ${CMAKE_CURRENT_SOURCE_DIR}/${windows_symbolic_PATH})
foreach(src ${windows_symbolic_SRCS})
get_filename_component(src ${src} NAME_WE)
if (NOT EXISTS ${CMAKE_CURRENT_SOURCE_DIR}/${src}.cc OR NOT EXISTS ${CMAKE_CURRENT_SOURCE_DIR}/${src}.cu)
message(FATAL " ${src}.cc and ${src}.cu must exsits, and ${src}.cu must be symbolic file.")
endif()
#only copy the xx.cu to.xx.cu when the content are modified
set(copy_flag 1)
if (EXISTS ${CMAKE_CURRENT_SOURCE_DIR}/.${src}.cu)
file(READ ${CMAKE_CURRENT_SOURCE_DIR}/${src}.cc SOURCE_STR)
file(READ ${CMAKE_CURRENT_SOURCE_DIR}/.${src}.cu TARGET_STR)
if (SOURCE_STR STREQUAL TARGET_STR)
set(copy_flag 0)
endif()
endif()
if (copy_flag)
add_custom_command(OUTPUT .${src}.cu
COMMAND ${CMAKE_COMMAND} -E remove ${CMAKE_CURRENT_SOURCE_DIR}/.${src}.cu
COMMAND ${CMAKE_COMMAND} -E copy "${CMAKE_CURRENT_SOURCE_DIR}/${src}.cc" "${CMAKE_CURRENT_SOURCE_DIR}/.${src}.cu"
COMMENT "create hidden file of ${src}.cu")
endif(copy_flag)
add_custom_target(${TARGET} ALL DEPENDS .${src}.cu)
get_filename_component(src ${src} NAME_WE)
if (NOT EXISTS ${final_path}/${src}.cc OR NOT EXISTS ${final_path}/${src}.cu)
message(FATAL " ${src}.cc and ${src}.cu must exsits, and ${src}.cu must be symbolic file.")
endif()
file(GENERATE OUTPUT ${final_path}/.${src}.cu INPUT ${final_path}/${src}.cc)
add_custom_command(OUTPUT ${final_path}/.${src}.cu
COMMAND ${CMAKE_COMMAND} -E copy_if_different "${final_path}/${src}.cc" "${final_path}/.${src}.cu"
COMMENT "create hidden file of ${src}.cu")
add_custom_target(${TARGET} ALL DEPENDS .${src}.cu)
endforeach()
endfunction()
......@@ -37,9 +27,10 @@ add_subdirectory(details)
proto_library(framework_proto SRCS framework.proto)
proto_library(async_executor_proto SRCS data_feed.proto)
cc_library(ddim SRCS ddim.cc DEPS eigen3 boost)
cc_library(ddim SRCS ddim.cc DEPS eigen3 boost enforce)
cc_test(ddim_test SRCS ddim_test.cc DEPS ddim)
nv_test(dim_test SRCS dim_test.cu DEPS ddim)
cc_test(unroll_array_ops_test SRCS unroll_array_ops_test.cc)
cc_library(data_type SRCS data_type.cc DEPS framework_proto ddim device_context)
cc_test(data_type_test SRCS data_type_test.cc DEPS data_type place tensor)
if(WITH_GPU)
......@@ -78,17 +69,23 @@ cc_library(garbage_collector SRCS garbage_collector.cc DEPS device_context memor
cc_library(reader SRCS reader.cc DEPS lod_tensor ddim)
cc_test(reader_test SRCS reader_test.cc DEPS reader)
cc_test(variable_test SRCS variable_test.cc)
cc_library(threadpool SRCS threadpool.cc DEPS enforce)
cc_test(threadpool_test SRCS threadpool_test.cc DEPS threadpool)
cc_library(scope SRCS scope.cc DEPS glog threadpool)
cc_library(var_type_traits SRCS var_type_traits DEPS lod_tensor selected_rows framework_proto)
if (WITH_GPU)
target_link_libraries(var_type_traits dynload_cuda)
endif()
cc_test(var_type_traits_test SRCS var_type_traits_test.cc DEPS var_type_traits)
cc_library(scope SRCS scope.cc DEPS glog threadpool xxhash var_type_traits)
cc_library(scope_pool SRCS scope_pool.cc DEPS scope)
cc_test(scope_test SRCS scope_test.cc DEPS scope)
cc_test(variable_test SRCS variable_test.cc DEPS tensor var_type_traits)
cc_library(data_device_transform SRCS data_device_transform.cc DEPS tensor)
nv_test(data_device_transform_test SRCS data_device_transform_test.cu
DEPS operator op_registry device_context math_function)
DEPS operator op_registry device_context math_function scope)
if(WITH_GPU)
if (WIN32)
......@@ -133,11 +130,9 @@ cc_test(version_test SRCS version_test.cc DEPS version)
cc_library(proto_desc SRCS var_desc.cc op_desc.cc block_desc.cc program_desc.cc DEPS shape_inference op_info operator glog version)
if(WITH_NGRAPH)
if(NOT WIN32)
cc_library(ngraph_bridge SRCS ngraph_bridge.cc DEPS operator framework_proto ngraph)
cc_library(ngraph_operator SRCS ngraph_operator.cc DEPS ngraph_bridge operator op_info device_context tensor scope glog
shape_inference data_transform lod_tensor profiler ngraph)
endif(NOT WIN32)
cc_library(ngraph_bridge SRCS ngraph_bridge.cc DEPS operator framework_proto ngraph)
cc_library(ngraph_operator SRCS ngraph_operator.cc DEPS ngraph_bridge operator op_info device_context tensor scope glog
shape_inference data_transform lod_tensor profiler)
endif(WITH_NGRAPH)
cc_library(op_registry SRCS op_registry.cc DEPS op_proto_maker op_info operator glog proto_desc)
......@@ -179,11 +174,7 @@ if(WITH_DISTRIBUTE)
else()
if(WITH_NGRAPH)
if(NOT WIN32)
cc_library(executor SRCS executor.cc DEPS op_registry device_context scope framework_proto glog lod_rank_table feed_fetch_method graph_to_program_pass ngraph ngraph_operator variable_helper)
else(NOT WIN32)
cc_library(executor SRCS executor.cc DEPS op_registry device_context scope framework_proto glog lod_rank_table feed_fetch_method graph_to_program_pass variable_helper)
endif(NOT WIN32)
cc_library(executor SRCS executor.cc DEPS op_registry device_context scope framework_proto glog lod_rank_table feed_fetch_method graph_to_program_pass ngraph_operator variable_helper)
else(WITH_NGRAPH)
cc_library(executor SRCS executor.cc DEPS op_registry device_context scope framework_proto glog lod_rank_table feed_fetch_method graph_to_program_pass variable_helper)
endif(WITH_NGRAPH)
......@@ -193,14 +184,14 @@ endif()
target_link_libraries(executor garbage_collector)
cc_library(parallel_executor SRCS parallel_executor.cc DEPS
threaded_ssa_graph_executor scope_buffered_ssa_graph_executor
threaded_ssa_graph_executor scope_buffered_ssa_graph_executor parallel_ssa_graph_executor
graph build_strategy
fast_threaded_ssa_graph_executor variable_helper)
if(WITH_PSLIB)
cc_library(async_executor SRCS async_executor.cc data_feed.cc data_feed_factory.cc executor_thread_worker.cc DEPS op_registry device_context scope framework_proto glog lod_rank_table feed_fetch_method graph_to_program_pass async_executor_proto variable_helper pslib_brpc pslib)
cc_library(async_executor SRCS async_executor.cc data_feed.cc data_feed_factory.cc executor_thread_worker.cc DEPS op_registry device_context scope framework_proto glog lod_rank_table feed_fetch_method graph_to_program_pass async_executor_proto variable_helper pslib_brpc pslib timer)
else()
cc_library(async_executor SRCS async_executor.cc data_feed.cc data_feed_factory.cc executor_thread_worker.cc DEPS op_registry device_context scope framework_proto glog lod_rank_table feed_fetch_method graph_to_program_pass async_executor_proto variable_helper)
cc_library(async_executor SRCS async_executor.cc data_feed.cc data_feed_factory.cc executor_thread_worker.cc DEPS op_registry device_context scope framework_proto glog lod_rank_table feed_fetch_method graph_to_program_pass async_executor_proto variable_helper timer)
endif(WITH_PSLIB)
......
paddle/fluid/framework/array.h
浏览文件 @ b16e832d
......@@ -15,34 +15,123 @@
#pragma once
#include <cstdint>
#include "paddle/fluid/platform/hostdevice.h"
#include "paddle/fluid/framework/unroll_array_ops.h"
#include "paddle/fluid/platform/enforce.h"
namespace paddle {
namespace framework {
template <typename T, size_t N>
class Array {
static_assert(N > 0, "The size of array must be larger than 0");
public:
HOSTDEVICE Array() {}
static constexpr size_t kSize = N;
HOSTDEVICE inline Array() {}
HOSTDEVICE explicit Array(const T &val) {
for (size_t i = 0; i < N; ++i) data_[i] = val;
template <typename... Args>
HOSTDEVICE inline explicit Array(const T &val, Args... args) {
static_assert(N == sizeof...(Args) + 1, "Invalid argument");
UnrollVarArgsAssign<T>::Run(data_, val, args...);
}
HOSTDEVICE const T *Get() const { return data_; }
HOSTDEVICE inline void Fill(const T &val) {
UnrollFillConstant<N>::Run(data_, val);
}
HOSTDEVICE T *GetMutable() { return data_; }
HOSTDEVICE inline const T *Get() const { return data_; }
HOSTDEVICE T &operator[](size_t index) { return data_[index]; }
HOSTDEVICE inline T *GetMutable() { return data_; }
HOSTDEVICE const T &operator[](size_t index) const { return data_[index]; }
HOSTDEVICE inline T &operator[](size_t i) { return *advance(data_, i); }
// Writing "return data_[i]" would cause compilation warning/error:
// "array subscript is above array bound" in Python 35 CI.
// It seems that it is a false warning of GCC if we do not check the bounds
// of array index. But for better performance, we do not check in operator[]
// like what is in STL. If users want to check the bounds, use at() instead
HOSTDEVICE inline const T &operator[](size_t i) const {
return *advance(data_, i);
}
HOSTDEVICE inline T &at(size_t i) {
#ifndef __CUDA_ARCH__
PADDLE_ENFORCE_LT(i, N, "Array index out of bounds");
#endif
return (*this)[i];
}
HOSTDEVICE inline const T &at(size_t i) const {
#ifndef __CUDA_ARCH__
PADDLE_ENFORCE_LT(i, N, "Array index out of bounds");
#endif
return (*this)[i];
}
HOSTDEVICE constexpr size_t size() const { return N; }
HOSTDEVICE inline bool operator==(const Array<T, N> &other) const {
return UnrollCompare<N>::Run(data_, other.data_);
}
HOSTDEVICE inline bool operator!=(const Array<T, N> &other) const {
return !(*this == other);
}
private:
template <typename U>
HOSTDEVICE static inline U *advance(U *ptr, size_t i) {
return ptr + i;
}
T data_[N];
};
template <typename T>
class Array<T, 0> {
public:
static constexpr size_t kSize = 0;
HOSTDEVICE inline Array() {}
HOSTDEVICE inline void Fill(const T &val) {}
HOSTDEVICE inline constexpr T *Get() const { return nullptr; }
// Add constexpr to GetMutable() cause warning in MAC
HOSTDEVICE inline T *GetMutable() { return nullptr; }
HOSTDEVICE inline T &operator[](size_t) {
#ifdef __CUDA_ARCH__
static T obj();
return obj;
#else
PADDLE_THROW("Array<T, 0> has no element");
#endif
}
HOSTDEVICE inline const T &operator[](size_t) const {
#ifdef __CUDA_ARCH__
static const T obj();
return obj;
#else
PADDLE_THROW("Array<T, 0> has no element");
#endif
}
HOSTDEVICE inline T &at(size_t i) { return (*this)[i]; }
HOSTDEVICE inline const T &at(size_t i) const { return (*this)[i]; }
HOSTDEVICE constexpr size_t size() const { return 0; }
HOSTDEVICE constexpr bool operator==(const Array<T, 0> &other) const {
return true;
}
HOSTDEVICE constexpr bool operator!=(const Array<T, 0> &other) const {
return false;
}
};
} // namespace framework
} // namespace paddle
paddle/fluid/framework/async_executor.cc
浏览文件 @ b16e832d
......@@ -304,8 +304,13 @@ void AsyncExecutor::RunFromFile(const ProgramDesc& main_program,
// start executing ops in multiple threads
for (int thidx = 0; thidx < actual_thread_num; ++thidx) {
threads.push_back(
std::thread(&ExecutorThreadWorker::TrainFiles, workers[thidx].get()));
if (debug) {
threads.push_back(std::thread(&ExecutorThreadWorker::TrainFilesWithTimer,
workers[thidx].get()));
} else {
threads.push_back(
std::thread(&ExecutorThreadWorker::TrainFiles, workers[thidx].get()));
}
}
for (auto& th : threads) {
......
paddle/fluid/framework/data_device_transform_test.cu
浏览文件 @ b16e832d
......@@ -17,6 +17,7 @@ limitations under the License. */
#include "paddle/fluid/framework/lod_tensor.h"
#include "paddle/fluid/framework/op_info.h"
#include "paddle/fluid/framework/op_registry.h"
#include "paddle/fluid/framework/scope.h"
#include "paddle/fluid/operators/elementwise/elementwise_op_function.h"
#include "paddle/fluid/operators/math/math_function.h"
#include "paddle/fluid/platform/device_context.h"
......
paddle/fluid/framework/ddim.cc
浏览文件 @ b16e832d
......@@ -18,312 +18,159 @@ limitations under the License. */
namespace paddle {
namespace framework {
/// @cond HIDDEN
template <int i>
Dim<i> make_dim(const int64_t* d) {
return Dim<i>(*d, make_dim<i - 1>(d + 1));
}
template <>
Dim<0> make_dim<0>(const int64_t* d) {
return Dim<0>(*d);
}
void make_ddim(DDim& ddim, const int64_t* dims, int n) {
switch (n) {
case 0:
ddim = make_dim<0>(dims);
break;
case 1:
ddim = make_dim<1>(dims);
break;
case 2:
ddim = make_dim<2>(dims);
break;
case 3:
ddim = make_dim<3>(dims);
break;
case 4:
ddim = make_dim<4>(dims);
break;
case 5:
ddim = make_dim<5>(dims);
break;
case 6:
ddim = make_dim<6>(dims);
break;
case 7:
ddim = make_dim<7>(dims);
break;
case 8:
ddim = make_dim<8>(dims);
break;
case 9:
ddim = make_dim<9>(dims);
break;
default:
PADDLE_THROW("Dynamic dimensions must have between [1, 9] dimensions.");
}
}
/// @endcond
DDim make_ddim(std::initializer_list<int64_t> dims) {
DDim result(make_dim(0));
make_ddim(result, dims.begin(), dims.size());
return result;
return DDim(dims.begin(), dims.size());
}
DDim make_ddim(const std::vector<int64_t>& dims) {
DDim result(make_dim(0));
make_ddim(result, &dims[0], dims.size());
return result;
return DDim(dims.data(), dims.size());
}
DDim make_ddim(const std::vector<int>& dims) {
std::vector<int64_t> res(dims.size());
std::transform(dims.begin(), dims.end(), res.begin(),
[](int d) { return static_cast<int64_t>(d); });
return make_ddim(res);
return DDim(dims.data(), dims.size());
}
/// @cond HIDDEN
// XXX For some reason, putting this in an anonymous namespace causes errors
class DynamicMutableIndexer : public boost::static_visitor<int64_t&> {
public:
explicit DynamicMutableIndexer(int idx) : idx_(idx) {}
struct DDimEqualityVisitor {
explicit DDimEqualityVisitor(const int64_t* d) : d_(d) {}
template <int D>
int64_t& operator()(Dim<D>& dim) const {
return dim[idx_];
inline bool operator()(const Dim<D>& self) const {
return UnrollCompare<D>::Run(self.Get(), d_);
}
private:
int idx_;
const int64_t* d_;
};
class DynamicConstIndexer : public boost::static_visitor<int64_t> {
public:
explicit DynamicConstIndexer(int idx) : idx_(idx) {}
template <int D>
int64_t operator()(const Dim<D>& dim) const {
return dim[idx_];
}
private:
int idx_;
};
/// @endcond
int64_t& DDim::operator[](int idx) {
return boost::apply_visitor(DynamicMutableIndexer(idx), var);
bool DDim::operator==(const DDim& d) const {
return size() == d.size() &&
this->apply_visitor(DDimEqualityVisitor(d.Get()));
}
int64_t DDim::operator[](int idx) const {
return boost::apply_visitor(DynamicConstIndexer(idx), var);
}
bool DDim::operator!=(const DDim& d) const { return !(*this == d); }
int DDim::size() const { return arity(*this); }
struct DDimPlusVisitor {
explicit DDimPlusVisitor(const int64_t* d1, const int64_t* d2)
: d1_(d1), d2_(d2) {}
bool DDim::operator==(DDim d) const {
if (var.which() != d.getVar().which()) {
return false;
} else {
std::vector<int64_t> v1 = vectorize(*this);
std::vector<int64_t> v2 = vectorize(d);
for (unsigned int i = 0; i < v1.size(); i++) {
if (v1[i] != v2[i]) {
return false;
}
}
return true;
template <int D>
inline void operator()(Dim<D>& self) const {
UnrollAdd<D>::Run(d1_, d2_, self.GetMutable());
}
}
bool DDim::operator!=(DDim d) const { return !(*this == d); }
DDim DDim::operator+(DDim d) const {
std::vector<int64_t> v1 = vectorize(*this);
std::vector<int64_t> v2 = vectorize(d);
std::vector<int64_t> v3;
assert(v1.size() == v2.size());
for (unsigned int i = 0; i < v1.size(); i++) {
v3.push_back(v1[i] + v2[i]);
}
const int64_t* d1_;
const int64_t* d2_;
};
return make_ddim(v3);
DDim DDim::operator+(const DDim& d) const {
PADDLE_ENFORCE(size() == d.size());
DDim ret;
ret.rank_ = rank_;
ret.apply_visitor(DDimPlusVisitor(Get(), d.Get()));
return ret;
}
DDim DDim::operator*(DDim d) const {
std::vector<int64_t> v1 = vectorize(*this);
std::vector<int64_t> v2 = vectorize(d);
struct DDimMulVisitor {
explicit DDimMulVisitor(const int64_t* d1, const int64_t* d2)
: d1_(d1), d2_(d2) {}
std::vector<int64_t> v3;
assert(v1.size() == v2.size());
for (unsigned int i = 0; i < v1.size(); i++) {
v3.push_back(v1[i] * v2[i]);
template <int D>
inline void operator()(Dim<D>& self) const {
UnrollMul<D>::Run(d1_, d2_, self.GetMutable());
}
return make_ddim(v3);
const int64_t* d1_;
const int64_t* d2_;
};
DDim DDim::operator*(const DDim& d) const {
PADDLE_ENFORCE(size() == d.size());
DDim ret;
ret.rank_ = rank_;
ret.apply_visitor(DDimMulVisitor(Get(), d.Get()));
return ret;
}
int64_t get(const DDim& ddim, int idx) { return ddim[idx]; }
void set(DDim& ddim, int idx, int value) { ddim[idx] = value; }
/// @cond HIDDEN
struct VectorizeVisitor : public boost::static_visitor<> {
std::vector<int64_t>& vector;
explicit VectorizeVisitor(std::vector<int64_t>& v) : vector(v) {}
template <typename T>
void operator()(const T& t) {
vector.push_back(t.head);
this->operator()(t.tail);
}
void operator()(const Dim<0>& t) {}
};
/// @endcond
void set(DDim& ddim, int idx, int value) { ddim[idx] = value; } // NOLINT
std::vector<int64_t> vectorize(const DDim& ddim) {
std::vector<int64_t> result;
VectorizeVisitor visitor(result);
boost::apply_visitor(visitor, ddim);
std::vector<int64_t> result(DDim::kMaxRank);
dynamic_dim_assign(ddim.Get(), result.data(), ddim.size());
result.resize(ddim.size());
return result;
}
// NOTE: framework::vectorize converts to type int64_t
// which does not fit cudnn inputs.
std::vector<int> vectorize2int(const DDim& ddim) {
std::vector<int64_t> temp = vectorize(ddim);
std::vector<int> result(temp.begin(), temp.end());
std::vector<int> result(DDim::kMaxRank);
dynamic_dim_assign(ddim.Get(), result.data(), ddim.size());
result.resize(ddim.size());
return result;
}
struct ProductVisitor : public boost::static_visitor<int64_t> {
struct ProductVisitor {
template <int D>
int64_t operator()(const Dim<D>& dim) {
inline int64_t operator()(const Dim<D>& dim) {
return product(dim);
}
};
int64_t product(const DDim& ddim) {
ProductVisitor visitor;
return boost::apply_visitor(visitor, ddim);
return ddim.apply_visitor(ProductVisitor());
}
struct SliceVectorizeVisitor : public boost::static_visitor<> {
std::vector<int64_t>& vector;
int begin;
int end;
SliceVectorizeVisitor(std::vector<int64_t>& v, int b, int e)
: vector(v), begin(b), end(e) {
PADDLE_ENFORCE(begin < end,
"Begin index must be less than end index in ddim slice.");
PADDLE_ENFORCE(begin >= 0,
"Begin index can't be less than zero in ddim slice.");
}
template <int S>
void operator()(const Dim<S>& dim) {
if (begin == 0) {
vector.push_back(dim.head);
} else {
--begin;
}
--end;
if (end > 0) {
this->operator()(dim.tail);
}
}
void operator()(const Dim<0>& dim) {
PADDLE_ENFORCE(end == 0, "End index in ddim slice is out of bound.");
}
};
DDim slice_ddim(const DDim& dim, int begin, int end) {
std::vector<int64_t> vec;
vec.reserve(end - begin);
SliceVectorizeVisitor visitor(vec, begin, end);
boost::apply_visitor(visitor, dim);
return make_ddim(vec);
PADDLE_ENFORCE(begin >= 0 && end <= dim.size(),
"[begin(%d), end(%d)) must be inside [0, %d) in ddim slice.",
begin, end, dim.size());
// Constructor of DDim would check whether end - begin is valid
return DDim(dim.Get() + begin, end - begin);
}
/// \cond HIDDEN
struct ArityVisitor : boost::static_visitor<int> {
template <int D>
int operator()(Dim<D>) const {
return D;
}
};
/// \endcond
int arity(const DDim& d) { return boost::apply_visitor(ArityVisitor(), d); }
int arity(const DDim& d) { return d.size(); }
/// \cond HIDDEN
struct DDimPrinter : boost::static_visitor<void> {
struct DDimPrinter {
std::ostream& os;
explicit DDimPrinter(std::ostream& os_) : os(os_) {}
template <typename T>
void operator()(const T& t) {
template <int D>
void operator()(const Dim<D>& t) {
os << t;
}
};
/// \endcond
std::ostream& operator<<(std::ostream& os, const DDim& ddim) {
DDimPrinter printer(os);
boost::apply_visitor(printer, ddim);
ddim.apply_visitor(DDimPrinter(os));
return os;
}
DDim::DDim(std::initializer_list<int64_t> init_list) {
*this = make_ddim(init_list);
}
DDim flatten_to_2d(const DDim& src, int num_col_dims) {
int rank = src.size();
return make_ddim({product(slice_ddim(src, 0, num_col_dims)),
product(slice_ddim(src, num_col_dims, rank))});
return DDim({product(slice_ddim(src, 0, num_col_dims)),
product(slice_ddim(src, num_col_dims, src.size()))});
}
DDim flatten_to_1d(const DDim& src) { return make_ddim({product(src)}); }
DDim flatten_to_1d(const DDim& src) { return DDim({product(src)}); }
DDim stride(const DDim& ddim) {
std::vector<int64_t> strides(ddim.size());
DDim strides;
strides.rank_ = ddim.size();
strides[ddim.size() - 1] = 1;
for (int i = ddim.size() - 2; i >= 0; --i) {
strides[i] = strides[i + 1] * ddim[i + 1];
}
return framework::make_ddim(strides);
return strides;
}
DDim stride_numel(const framework::DDim& ddim) {
std::vector<int64_t> strides(ddim.size());
DDim stride_numel(const DDim& ddim) {
DDim strides;
strides.rank_ = ddim.size();
strides[ddim.size() - 1] = ddim[ddim.size() - 1];
for (int i = ddim.size() - 2; i >= 0; --i) {
strides[i] = strides[i + 1] * ddim[i];
}
return framework::make_ddim(strides);
return strides;
}
} // namespace framework
......
paddle/fluid/framework/ddim.h
浏览文件 @ b16e832d
......@@ -18,62 +18,145 @@ limitations under the License. */
#include <stdexcept>
#include <vector>
#include "paddle/fluid/framework/dim.h"
#include "paddle/fluid/platform/enforce.h"
#include "paddle/fluid/platform/variant.h"
namespace paddle {
namespace framework {
#define PADDLE_VISIT_DDIM_BASE(rank, callback) \
case (rank): { \
constexpr auto kRank = (rank); \
return (callback); \
}
#define PADDLE_VISIT_DDIM(rank, callback) \
switch (rank) { \
PADDLE_VISIT_DDIM_BASE(0, callback); \
PADDLE_VISIT_DDIM_BASE(1, callback); \
PADDLE_VISIT_DDIM_BASE(2, callback); \
PADDLE_VISIT_DDIM_BASE(3, callback); \
PADDLE_VISIT_DDIM_BASE(4, callback); \
PADDLE_VISIT_DDIM_BASE(5, callback); \
PADDLE_VISIT_DDIM_BASE(6, callback); \
PADDLE_VISIT_DDIM_BASE(7, callback); \
PADDLE_VISIT_DDIM_BASE(8, callback); \
PADDLE_VISIT_DDIM_BASE(9, callback); \
default: \
PADDLE_THROW("Invalid rank %d", rank); \
}
template <typename T1, typename T2>
inline void dynamic_dim_assign(const T1* in, T2* out, int n) {
PADDLE_VISIT_DDIM(n, (static_dim_assign<kRank, T1, T2>(in, out)));
}
/**
* \brief A dynamically sized dimension.
*
* The number of dimensions must be between [1, 9].
*/
struct DDim {
typedef boost::variant<Dim<0>, Dim<1>, Dim<2>, Dim<3>, Dim<4>, Dim<5>, Dim<6>,
Dim<7>, Dim<8>, Dim<9>>
DDimVar;
DDimVar var;
class DDim {
public:
constexpr static int kMaxRank = 9;
DDim() : rank_(1) { dim_[0] = 0; }
DDim() : var(Dim<1>()) {}
DDim(const DDim& ddim) : dim_() { CopyFrom(ddim); }
DDim(const int* d, int n) : rank_(n) {
dynamic_dim_assign(d, dim_.GetMutable(), n);
}
DDim(const int64_t* d, int n) : rank_(n) {
dynamic_dim_assign(d, dim_.GetMutable(), n);
}
template <int D>
explicit DDim(const Dim<D>& in) : var(in) {}
/*implicit*/ DDim(const Dim<D>& in) : rank_(D) { // NOLINT
UnsafeCast<D>() = in;
}
/*implicit*/ DDim(std::initializer_list<int64_t> init_list)
: DDim(init_list.begin(), init_list.size()) {}
/*implicit*/ DDim(std::initializer_list<int64_t> init_list);
inline DDim& operator=(const DDim& ddim) { return CopyFrom(ddim); }
template <int D>
DDim& operator=(const Dim<D>& in) {
var = in;
inline DDim& operator=(const Dim<D>& dim) {
rank_ = D;
UnsafeCast<D>() = dim;
return *this;
}
int64_t& operator[](int idx);
int64_t operator[](int idx) const;
inline int64_t& operator[](int idx) { return dim_[idx]; }
inline int64_t operator[](int idx) const { return dim_[idx]; }
inline int64_t& at(int idx) {
PADDLE_ENFORCE(idx >= 0 && idx < rank_, "Invalid idx %d", idx);
return dim_[idx];
}
inline int64_t at(int idx) const {
PADDLE_ENFORCE(idx >= 0 && idx < rank_, "Invalid idx %d", idx);
return dim_[idx];
}
template <typename Visitor>
typename Visitor::result_type apply_visitor(Visitor& visitor) {
return var.apply_visitor(visitor);
typename std::result_of<Visitor(Dim<0>&)>::type apply_visitor(
Visitor&& visitor) {
PADDLE_VISIT_DDIM(rank_, visitor(UnsafeCast<kRank>()));
}
template <typename Visitor>
typename Visitor::result_type apply_visitor(Visitor& visitor) const {
return var.apply_visitor(visitor);
typename std::result_of<Visitor(const Dim<0>&)>::type apply_visitor(
Visitor&& visitor) const {
PADDLE_VISIT_DDIM(rank_, visitor(UnsafeCast<kRank>()));
}
DDimVar getVar() { return var; }
bool operator==(const DDim& d) const;
bool operator!=(const DDim& d) const;
DDim operator+(const DDim& d) const;
bool operator==(DDim d) const;
DDim operator*(const DDim& d) const;
bool operator!=(DDim d) const;
inline const int64_t* Get() const { return dim_.Get(); }
DDim operator+(DDim d) const;
inline int64_t* GetMutable() { return dim_.GetMutable(); }
DDim operator*(DDim d) const;
inline int size() const { return rank_; }
private:
template <int D>
inline Dim<D>& UnsafeCast() {
static_assert(D >= 0 && D <= kMaxRank, "Invalid rank");
auto* p = static_cast<void*>(&dim_);
return *reinterpret_cast<Dim<D>*>(p);
}
template <int D>
inline const Dim<D>& UnsafeCast() const {
static_assert(D >= 0 && D <= kMaxRank, "Invalid rank");
auto* p = static_cast<const void*>(&dim_);
return *reinterpret_cast<const Dim<D>*>(p);
}
int size() const;
inline DDim& CopyFrom(const DDim& ddim) {
PADDLE_VISIT_DDIM(ddim.rank_, (*this = ddim.UnsafeCast<kRank>()));
}
friend DDim stride(const DDim& ddim);
friend DDim stride_numel(const DDim& ddim);
private:
Dim<kMaxRank> dim_;
int rank_;
};
#undef PADDLE_VISIT_DDIM_BASE
#undef PADDLE_VISIT_DDIM
/**
* \brief Make a DDim from std::vector<int64_t>
*
......@@ -92,7 +175,7 @@ DDim make_ddim(const std::vector<int>& dims);
DDim make_ddim(std::initializer_list<int64_t> dims);
int64_t get(const DDim& dim, int idx);
void set(DDim& dim, int idx, int val);
void set(DDim& dim, int idx, int val); // NOLINT
std::vector<int64_t> vectorize(const DDim& ddim);
std::vector<int> vectorize2int(const DDim& ddim);
......@@ -129,12 +212,3 @@ DDim stride(const DDim& ddim);
DDim stride_numel(const DDim& ddim);
} // namespace framework
} // namespace paddle
namespace boost {
template <typename T>
T get(const paddle::framework::DDim& in) {
return boost::get<T>(in.var);
}
} // namespace boost
paddle/fluid/framework/details/CMakeLists.txt
浏览文件 @ b16e832d
......@@ -77,6 +77,8 @@ cc_library(ssa_graph_executor SRCS ssa_graph_executor.cc DEPS ${SSA_GRAPH_EXECUT
cc_library(threaded_ssa_graph_executor SRCS threaded_ssa_graph_executor.cc DEPS fetch_op_handle ssa_graph_executor scope
simple_threadpool device_context)
cc_library(parallel_ssa_graph_executor SRCS parallel_ssa_graph_executor.cc DEPS threaded_ssa_graph_executor)
cc_test(broadcast_op_test SRCS broadcast_op_handle_test.cc DEPS var_handle op_handle_base scope ddim memory
device_context broadcast_op_handle)
cc_test(gather_op_test SRCS gather_op_handle_test.cc DEPS var_handle op_handle_base scope ddim memory
......
paddle/fluid/framework/details/all_reduce_op_handle.cc
浏览文件 @ b16e832d
......@@ -19,6 +19,13 @@
#include "paddle/fluid/framework/details/variable_visitor.h"
#include "paddle/fluid/platform/profiler.h"
// asynchronous nccl allreduce or synchronous issue:
// https://github.com/PaddlePaddle/Paddle/issues/15049
DEFINE_bool(
sync_nccl_allreduce, false,
"If set true, will call `cudaStreamSynchronize(nccl_stream)`"
"after allreduce, this mode can get better performance in some scenarios.");
namespace paddle {
namespace framework {
namespace details {
......@@ -48,100 +55,104 @@ AllReduceOpHandle::AllReduceOpHandle(ir::Node *node,
void AllReduceOpHandle::RunImpl() {
platform::RecordEvent record_event(Name(), dev_ctxes_.cbegin()->second);
// FIXME(typhoonzero): If scope0(global scope) have NCCL_ID_VAR,
// this is a distributed or inter-process call, find a better way.
#ifdef PADDLE_WITH_CUDA
if (NoDummyInputSize() == 1 &&
local_scopes_[0]->FindLocalVar(NCCL_ID_VARNAME) == nullptr) {
#else
if (NoDummyInputSize() == 1) {
#endif
return; // No need to all reduce when GPU count = 1;
} else {
// Wait input done
WaitInputVarGenerated();
auto in_var_handles = DynamicCast<VarHandle>(this->Inputs());
auto out_var_handles = DynamicCast<VarHandle>(this->Outputs());
PADDLE_ENFORCE_EQ(
in_var_handles.size(), places_.size(),
"The NoDummyInputSize should be equal to the number of places.");
PADDLE_ENFORCE_EQ(
in_var_handles.size(), out_var_handles.size(),
"The NoDummyInputSize and NoDummyOutputSize should be equal.");
std::vector<const LoDTensor *> lod_tensors;
for (size_t i = 0; i < local_scopes_.size(); ++i) {
auto *s = local_scopes_[i];
auto &local_scope = *s->FindVar(kLocalExecScopeName)->Get<Scope *>();
auto &lod_tensor =
local_scope.FindVar(in_var_handles[i]->name_)->Get<LoDTensor>();
lod_tensors.emplace_back(&lod_tensor);
PADDLE_ENFORCE_EQ(in_var_handles[i]->name_, out_var_handles[i]->name_,
"The name of input and output should be equal.");
}
WaitInputVarGenerated();
auto in_var_handles = DynamicCast<VarHandle>(this->Inputs());
auto out_var_handles = DynamicCast<VarHandle>(this->Outputs());
PADDLE_ENFORCE_EQ(
in_var_handles.size(), places_.size(),
"The NoDummyInputSize should be equal to the number of places.");
PADDLE_ENFORCE_EQ(
in_var_handles.size(), out_var_handles.size(),
"The NoDummyInputSize and NoDummyOutputSize should be equal.");
std::vector<const LoDTensor *> lod_tensors;
for (size_t i = 0; i < local_scopes_.size(); ++i) {
auto *s = local_scopes_[i];
auto &local_scope = *s->FindVar(kLocalExecScopeName)->Get<Scope *>();
auto &lod_tensor =
local_scope.FindVar(in_var_handles[i]->name_)->Get<LoDTensor>();
lod_tensors.emplace_back(&lod_tensor);
PADDLE_ENFORCE_EQ(in_var_handles[i]->name_, out_var_handles[i]->name_,
"The name of input and output should be equal.");
}
if (platform::is_gpu_place(lod_tensors[0]->place())) {
if (platform::is_gpu_place(lod_tensors[0]->place())) {
#if defined(PADDLE_WITH_CUDA) && !defined(_WIN32)
PADDLE_ENFORCE(nccl_ctxs_, "nccl_ctxs should not be nullptr.");
int dtype = -1;
size_t numel = 0;
std::vector<std::function<void()>> all_reduce_calls;
for (size_t i = 0; i < local_scopes_.size(); ++i) {
auto &p = places_[i];
auto &lod_tensor = *lod_tensors[i];
void *buffer = const_cast<void *>(lod_tensor.data<void>());
if (dtype == -1) {
dtype = platform::ToNCCLDataType(lod_tensor.type());
}
PADDLE_ENFORCE(nccl_ctxs_, "nccl_ctxs should not be nullptr.");
int dtype = -1;
size_t numel = 0;
std::vector<std::function<void()>> all_reduce_calls;
for (size_t i = 0; i < local_scopes_.size(); ++i) {
auto &p = places_[i];
auto &lod_tensor = *lod_tensors[i];
void *buffer = const_cast<void *>(lod_tensor.data<void>());
if (numel == 0) {
numel = static_cast<size_t>(lod_tensor.numel());
}
if (dtype == -1) {
dtype = platform::ToNCCLDataType(lod_tensor.type());
}
int dev_id = boost::get<platform::CUDAPlace>(p).device;
auto &nccl_ctx = nccl_ctxs_->at(dev_id);
auto stream = nccl_ctx.stream();
auto comm = nccl_ctx.comm_;
all_reduce_calls.emplace_back([=] {
PADDLE_ENFORCE(platform::dynload::ncclAllReduce(
buffer, buffer, numel, static_cast<ncclDataType_t>(dtype),
ncclSum, comm, stream));
});
if (numel == 0) {
numel = static_cast<size_t>(lod_tensor.numel());
}
this->RunAndRecordEvent([&] {
int dev_id = boost::get<platform::CUDAPlace>(p).device;
auto &nccl_ctx = nccl_ctxs_->at(dev_id);
auto stream = nccl_ctx.stream();
auto comm = nccl_ctx.comm_;
all_reduce_calls.emplace_back([=] {
PADDLE_ENFORCE(platform::dynload::ncclAllReduce(
buffer, buffer, numel, static_cast<ncclDataType_t>(dtype), ncclSum,
comm, stream));
});
}
this->RunAndRecordEvent([&] {
if (all_reduce_calls.size() == 1UL) {
// Do not use NCCLGroup when manage NCCL by per thread per device
all_reduce_calls[0]();
} else {
platform::NCCLGroupGuard guard;
for (auto &call : all_reduce_calls) {
call();
}
});
}
});
if (FLAGS_sync_nccl_allreduce) {
for (auto &p : places_) {
int dev_id = boost::get<platform::CUDAPlace>(p).device;
auto &nccl_ctx = nccl_ctxs_->at(dev_id);
auto stream = nccl_ctx.stream();
cudaStreamSynchronize(stream);
}
}
#else
PADDLE_THROW("Not compiled with CUDA");
PADDLE_THROW("Not compiled with CUDA");
#endif
} else { // Special handle CPU only Operator's gradient. Like CRF
auto &trg = *this->local_scopes_[0]
->FindVar(kLocalExecScopeName)
->Get<Scope *>()
->FindVar(out_var_handles[0]->name_)
->GetMutable<framework::LoDTensor>();
// Reduce All Tensor to trg in CPU
ReduceLoDTensor func(lod_tensors, &trg);
VisitDataType(lod_tensors[0]->type(), func);
for (size_t i = 1; i < local_scopes_.size(); ++i) {
auto &scope =
*local_scopes_[i]->FindVar(kLocalExecScopeName)->Get<Scope *>();
auto &p = places_[i];
auto *var = scope.FindVar(out_var_handles[i]->name_);
auto *dev_ctx = dev_ctxes_.at(p);
RunAndRecordEvent(p, [&trg, var, dev_ctx, p] {
auto &tensor_gpu = *var->GetMutable<framework::LoDTensor>();
auto &tensor_cpu = trg;
TensorCopy(tensor_cpu, p, *dev_ctx, &tensor_gpu);
});
}
} else { // Special handle CPU only Operator's gradient. Like CRF
auto &trg = *this->local_scopes_[0]
->FindVar(kLocalExecScopeName)
->Get<Scope *>()
->FindVar(out_var_handles[0]->name_)
->GetMutable<framework::LoDTensor>();
// Reduce All Tensor to trg in CPU
ReduceLoDTensor func(lod_tensors, &trg);
VisitDataType(lod_tensors[0]->type(), func);
for (size_t i = 1; i < local_scopes_.size(); ++i) {
auto &scope =
*local_scopes_[i]->FindVar(kLocalExecScopeName)->Get<Scope *>();
auto &p = places_[i];
auto *var = scope.FindVar(out_var_handles[i]->name_);
auto *dev_ctx = dev_ctxes_.at(p);
RunAndRecordEvent(p, [&trg, var, dev_ctx, p] {
auto &tensor_gpu = *var->GetMutable<framework::LoDTensor>();
auto &tensor_cpu = trg;
TensorCopy(tensor_cpu, p, *dev_ctx, &tensor_gpu);
});
}
}
}
......
paddle/fluid/framework/details/build_strategy.cc
浏览文件 @ b16e832d
......@@ -18,7 +18,7 @@ limitations under the License. */
#include <memory>
#include "paddle/fluid/framework/details/memory_reuse_types.h"
#include "paddle/fluid/framework/details/multi_devices_graph_check_pass.h"
#include "paddle/fluid/framework/details/multi_devices_graph_pass.h"
#include "paddle/fluid/framework/details/multi_devices_graph_print_pass.h"
#include "paddle/fluid/framework/details/reduce_op_handle.h"
#include "paddle/fluid/framework/details/sequential_execution_pass.h"
......@@ -31,7 +31,11 @@ namespace framework {
namespace details {
static inline bool SeqOnlyAllReduceOps(const BuildStrategy &strategy) {
return (!strategy.enable_sequential_execution_ && strategy.num_trainers_ > 1);
// Should fix the allreduce op order if scheduling
// them in multiple threads or processes to avoid hang.
return (!strategy.enable_sequential_execution_ &&
strategy.num_trainers_ > 1) ||
strategy.enable_parallel_graph_;
}
class ParallelExecutorPassBuilder : public ir::PassBuilder {
......@@ -67,7 +71,7 @@ class ParallelExecutorPassBuilder : public ir::PassBuilder {
context->endpoints_ = strategy_.trainers_endpoints_;
context->trainer_id_ = strategy_.trainer_id_;
PADDLE_ENFORCE(strategy_.trainer_id_ >= 0, "trainer_id_ >= 0");
if (strategy_.trainer_id_ > 0) {
if (strategy_.trainer_id_ > 0 && strategy_.trainers_endpoints_.size() > 0) {
PADDLE_ENFORCE((unsigned)(strategy_.trainer_id_) <
strategy_.trainers_endpoints_.size(),
"trainer_id_ < endpoints_ size");
......@@ -82,12 +86,8 @@ class ParallelExecutorPassBuilder : public ir::PassBuilder {
if (strategy.memory_optimize_) {
auto analysis_var_pass = AppendPass("analysis_var_pass");
}
// Convert graph to run on multi-devices.
auto multi_devices_pass = AppendPass("multi_devices_pass");
multi_devices_pass->SetNotOwned<const BuildStrategy>("strategy",
&strategy_);
multi_devices_pass->Set<int>("num_trainers",
new int(strategy_.num_trainers_));
AppendMultiDevPass(strategy);
// Add a graph print pass to record a graph with device info.
if (!strategy_.debug_graphviz_path_.empty()) {
......@@ -113,6 +113,25 @@ class ParallelExecutorPassBuilder : public ir::PassBuilder {
}
}
// Convert graph to run on multi-devices.
void AppendMultiDevPass(const BuildStrategy &strategy) {
ir::Pass *multi_devices_pass;
if (strategy_.is_distribution_) {
multi_devices_pass = AppendPass("dist_multi_devices_pass").get();
} else {
if (strategy.reduce_ == BuildStrategy::ReduceStrategy::kAllReduce) {
multi_devices_pass =
AppendPass("allreduce_mode_multi_devices_pass").get();
} else if (strategy.reduce_ == BuildStrategy::ReduceStrategy::kReduce) {
multi_devices_pass = AppendPass("reduce_mode_multi_devices_pass").get();
} else {
PADDLE_THROW("Unknown reduce strategy.");
}
}
multi_devices_pass->SetNotOwned<const BuildStrategy>("strategy",
&strategy_);
}
private:
BuildStrategy strategy_;
};
......@@ -129,9 +148,14 @@ std::shared_ptr<ir::PassBuilder> BuildStrategy::CreatePassesFromStrategy(
return pass_builder_;
}
bool BuildStrategy::IsMultiDevPass(const std::string &pass_name) const {
return framework::details::MultiDevSSAGraphBuilder().count(pass_name) > 0;
}
std::unique_ptr<ir::Graph> BuildStrategy::Apply(
const ProgramDesc &main_program, const std::vector<platform::Place> &places,
const std::string &loss_var_name, const std::vector<Scope *> &local_scopes,
const size_t &nranks,
#if defined(PADDLE_WITH_CUDA) && !defined(_WIN32)
const bool use_cuda, platform::NCCLContextMap *nccl_ctxs) const {
#else
......@@ -142,19 +166,23 @@ std::unique_ptr<ir::Graph> BuildStrategy::Apply(
std::unique_ptr<ir::Graph> graph(new ir::Graph(main_program));
for (std::shared_ptr<ir::Pass> &pass : pass_builder_->AllPasses()) {
if (pass->Type() == "multi_devices_pass") {
pass->Erase("places");
pass->SetNotOwned<const std::vector<platform::Place>>("places", &places);
pass->Erase("loss_var_name");
pass->SetNotOwned<const std::string>("loss_var_name", &loss_var_name);
pass->Erase("local_scopes");
pass->SetNotOwned<const std::vector<Scope *>>("local_scopes",
if (IsMultiDevPass(pass->Type())) {
pass->Erase(kPlaces);
pass->SetNotOwned<const std::vector<platform::Place>>(kPlaces, &places);
pass->Erase(kLossVarName);
pass->SetNotOwned<const std::string>(kLossVarName, &loss_var_name);
pass->Erase(kLocalScopes);
pass->SetNotOwned<const std::vector<Scope *>>(kLocalScopes,
&local_scopes);
pass->Erase(kNRanks);
pass->Set<size_t>(kNRanks, new size_t(nranks));
#if defined(PADDLE_WITH_CUDA) && !defined(_WIN32)
platform::NCCLContextMap *nctx = use_cuda ? nccl_ctxs : nullptr;
pass->Erase("nccl_ctxs");
pass->SetNotOwned<platform::NCCLContextMap>("nccl_ctxs", nctx);
#endif
} else if (pass->Type() == "analysis_var_pass") {
const std::vector<OpDesc *> *all_op_descs =
new std::vector<OpDesc *>(main_program.Block(0).AllOps());
......@@ -195,7 +223,9 @@ std::unique_ptr<ir::Graph> BuildStrategy::Apply(
USE_PASS(fuse_elewise_add_act_pass);
USE_PASS(graph_viz_pass);
USE_PASS(multi_batch_merge_pass);
USE_PASS(multi_devices_pass);
USE_PASS(reduce_mode_multi_devices_pass);
USE_PASS(allreduce_mode_multi_devices_pass);
USE_PASS(dist_multi_devices_pass);
USE_PASS(multi_devices_check_pass);
USE_PASS(multi_devices_print_pass);
USE_PASS(analysis_var_pass);
......
paddle/fluid/framework/details/build_strategy.h
浏览文件 @ b16e832d
......@@ -74,8 +74,6 @@ struct BuildStrategy {
bool fuse_elewise_add_act_ops_{false};
bool enable_data_balance_{false};
bool memory_optimize_{false};
bool memory_early_delete_{false};
......@@ -84,6 +82,10 @@ struct BuildStrategy {
bool fuse_broadcast_op_{false};
// FIXME(zcd): is_distribution_ is a temporary field, because in pserver mode,
// num_trainers is 1, so the current fields of build_strategy doesn't tell if
// it's distributed model.
bool is_distribution_{false};
int num_trainers_{1};
int trainer_id_{0};
std::vector<std::string> trainers_endpoints_;
......@@ -104,12 +106,15 @@ struct BuildStrategy {
bool IsFinalized() const { return is_finalized_; }
bool IsMultiDevPass(const std::string &pass_name) const;
// Apply the passes built by the pass_builder_. The passes will be
// applied to the Program and output an ir::Graph.
std::unique_ptr<ir::Graph> Apply(const ProgramDesc &main_program,
const std::vector<platform::Place> &places,
const std::string &loss_var_name,
const std::vector<Scope *> &local_scopes,
const size_t &nranks,
#if defined(PADDLE_WITH_CUDA) && !defined(_WIN32)
const bool use_cuda,
platform::NCCLContextMap *nccl_ctxs) const;
......@@ -117,6 +122,13 @@ struct BuildStrategy {
const bool use_cuda) const;
#endif
// If set true, ParallelExecutor would build the main_program into multiple
// graphs,
// each of the graphs would run with one device. This approach can achieve
// better performance
// on some scenarios.
mutable bool enable_parallel_graph_ = false;
private:
mutable bool is_finalized_ = false;
mutable std::shared_ptr<ir::PassBuilder> pass_builder_;
......
paddle/fluid/framework/details/eager_deletion_op_handle.cc
浏览文件 @ b16e832d
......@@ -88,7 +88,7 @@ void EagerDeletionOpHandle::RunImpl() {
}
} else {
PADDLE_THROW("Type %s of %s is not supported eager deletion",
var->Type().name(), name);
framework::ToTypeName(var->Type()), name);
}
}
......
paddle/fluid/framework/details/execution_strategy.h
浏览文件 @ b16e832d
......@@ -25,7 +25,7 @@ struct ExecutionStrategy {
size_t num_threads_{0};
bool use_cuda_{true};
bool allow_op_delay_{false};
size_t num_iteration_per_drop_scope_{100};
size_t num_iteration_per_drop_scope_{1};
ExecutorType type_{kDefault};
bool dry_run_{false};
};
......
paddle/fluid/framework/details/fast_threaded_ssa_graph_executor.cc
浏览文件 @ b16e832d
......@@ -120,6 +120,7 @@ FeedFetchList FastThreadedSSAGraphExecutor::Run(
ClearFetchOp(graph_.get(), &fetch_ops);
return fetches;
}
void FastThreadedSSAGraphExecutor::RunOpAsync(
std::unordered_map<OpHandleBase *, std::atomic<int>> *op_deps,
OpHandleBase *op,
......
paddle/fluid/framework/details/multi_devices_graph_check_pass.cc
浏览文件 @ b16e832d
......@@ -12,8 +12,8 @@
// See the License for the specific language governing permissions and
// limitations under the License.
#include "paddle/fluid/framework/details/multi_devices_graph_check_pass.h"
#include <string>
#include "paddle/fluid/framework/details/multi_devices_helper.h"
#include "paddle/fluid/framework/ir/graph.h"
#include "paddle/fluid/framework/ir/graph_helper.h"
......@@ -21,68 +21,78 @@ namespace paddle {
namespace framework {
namespace details {
bool SSAGraghBuilderWithChecker::IsValidGraph(const ir::Graph *graph) const {
std::unordered_map<OpHandleBase *, size_t> pending_ops;
std::unordered_set<VarHandleBase *> pending_vars;
std::unordered_set<VarHandleBase *> ready_vars;
std::unordered_set<OpHandleBase *> ready_ops;
class SSAGraghBuilderWithChecker : public ir::Pass {
protected:
std::unique_ptr<ir::Graph> ApplyImpl(
std::unique_ptr<ir::Graph> graph) const override {
PADDLE_ENFORCE(IsValidGraph(graph.get()));
return graph;
}
auto insert_pending_var = [&](VarHandleBase *var) {
pending_vars.insert(var);
if (var->GeneratedOp() == nullptr) {
ready_vars.emplace(var);
}
};
bool IsValidGraph(const ir::Graph *graph) const {
std::unordered_map<OpHandleBase *, size_t> pending_ops;
std::unordered_set<VarHandleBase *> pending_vars;
std::unordered_set<VarHandleBase *> ready_vars;
std::unordered_set<OpHandleBase *> ready_ops;
for (auto &var_map : graph->Get<GraphVars>(kGraphVars)) {
for (auto &name_pair : var_map) {
for (auto &version_pair : name_pair.second) {
insert_pending_var(version_pair);
auto insert_pending_var = [&](VarHandleBase *var) {
pending_vars.insert(var);
if (var->GeneratedOp() == nullptr) {
ready_vars.emplace(var);
}
}
}
};
for (auto &var : graph->Get<GraphDepVars>(kGraphDepVars)) {
insert_pending_var(var);
}
for (auto &var_map : graph->Get<GraphVars>(kGraphVars)) {
for (auto &name_pair : var_map) {
for (auto &version_pair : name_pair.second) {
insert_pending_var(version_pair);
}
}
}
for (OpHandleBase *op : ir::FilterByNodeWrapper<OpHandleBase>(*graph)) {
if (op->Inputs().empty()) {
ready_ops.insert(op);
} else {
pending_ops.insert({op, op->NoDupInputSize()});
for (auto &var : graph->Get<GraphDepVars>(kGraphDepVars)) {
insert_pending_var(var);
}
}
auto run_all_ops = [&](std::unordered_set<OpHandleBase *> &set) {
for (auto *op : set) {
for (auto out : op->Outputs()) {
ready_vars.emplace(out);
for (OpHandleBase *op : ir::FilterByNodeWrapper<OpHandleBase>(*graph)) {
if (op->Inputs().empty()) {
ready_ops.insert(op);
} else {
pending_ops.insert({op, op->NoDupInputSize()});
}
}
set.clear();
};
while (!pending_vars.empty()) {
run_all_ops(ready_ops);
auto run_all_ops = [&](std::unordered_set<OpHandleBase *> &set) {
for (auto *op : set) {
for (auto out : op->Outputs()) {
ready_vars.emplace(out);
}
}
set.clear();
};
if (ready_vars.empty()) {
return false;
}
while (!pending_vars.empty()) {
run_all_ops(ready_ops);
for (auto ready_var : ready_vars) {
pending_vars.erase(ready_var);
for (auto *op : ready_var->PendingOps()) {
auto &deps = --pending_ops[op];
if (deps == 0) {
ready_ops.insert(op);
if (ready_vars.empty()) {
return false;
}
for (auto ready_var : ready_vars) {
pending_vars.erase(ready_var);
for (auto *op : ready_var->PendingOps()) {
auto &deps = --pending_ops[op];
if (deps == 0) {
ready_ops.insert(op);
}
}
}
ready_vars.clear();
}
ready_vars.clear();
return true;
}
return true;
}
};
} // namespace details
} // namespace framework
} // namespace paddle
......
paddle/fluid/framework/details/multi_devices_graph_pass.h
浏览文件 @ b16e832d
......@@ -13,6 +13,7 @@
// limitations under the License.
#pragma once
#include <string>
#include <utility>
#include <vector>
......@@ -30,84 +31,154 @@ namespace framework {
class Scope;
namespace details {
class MultiDevSSAGraphBuilder : public ir::Pass {
constexpr char kLossVarName[] = "loss_var_name";
constexpr char kPlaces[] = "places";
constexpr char kLocalScopes[] = "local_scopes";
constexpr char kStrategy[] = "strategy";
constexpr char kNRanks[] = "nranks";
class MultiDevSSAGraphBuilderBase : public ir::Pass {
protected:
std::unique_ptr<ir::Graph> ApplyImpl(
std::unique_ptr<ir::Graph> graph) const override;
private:
void CreateOpHandleIOs(ir::Graph *result, ir::Node *node,
size_t device_id) const;
void Init() const;
virtual void Init() const;
#if defined(PADDLE_WITH_CUDA) && !defined(_WIN32)
mutable platform::NCCLContextMap *nccl_ctxs_;
#endif
virtual std::vector<ir::Node *> SortOperations(const ir::Graph &graph) const;
int GetVarDeviceID(
const ir::Graph &graph, const std::string &varname,
const std::unordered_map<std::string, int> &sharded_var_device) const;
virtual void InsertCollectiveOp(ir::Graph *result, const std::string &p_name,
const std::string &g_name) const = 0;
bool IsScaleLossOp(ir::Node *node) const;
virtual bool DealWithSpecialOp(ir::Graph *result, ir::Node *node) const = 0;
int CreateRPCOp(
ir::Graph *result, ir::Node *node,
std::unordered_map<std::string, int> *sharded_var_device) const;
int CreateDistTrainOp(
ir::Graph *result, ir::Node *node,
std::unordered_map<std::string, int> *sharded_var_device) const;
virtual void InsertPostprocessOps(ir::Graph *result) const = 0;
std::vector<std::string> FindDistTrainSendVars(
const std::vector<ir::Node *> &nodes) const;
bool UseGPU() const;
std::vector<std::string> FindDistTrainRecvVars(
const std::vector<ir::Node *> &nodes) const;
bool NeedCollectiveOps() const;
bool IsScaleLossOp(ir::Node *node) const;
void CreateComputationalOps(ir::Graph *result, ir::Node *node,
size_t num_places) const;
void CreateScaleLossGradOp(ir::Graph *result,
const std::string &loss_grad_name,
ir::Node *out_var_node,
ir::Node *out_var_node, size_t loss_scale,
proto::VarType::Type dtype) const;
VarHandle *CreateReduceOp(ir::Graph *result, const std::string &og,
int dst_dev_id) const;
void CreateComputationalOp(ir::Graph *result, ir::Node *node,
int dev_id) const;
int GetOpDeviceID(
const ir::Graph &graph, ir::Node *node,
const std::unordered_map<std::string, int> &sharded_var_device) const;
void InsertAllReduceOp(ir::Graph *result, const std::string &og) const;
bool IsSparseGradient(const std::string &og) const;
void InsertDataBalanceOp(ir::Graph *result,
const std::vector<std::string> &datas) const;
void CreateAllReduceOp(ir::Graph *result, const std::string &og) const;
void CreateBroadcastOp(ir::Graph *result, const std::string &p_name,
size_t src_dev_id) const;
void InsertScaleLossGradOp(ir::Graph *result, const ir::Node *node) const;
void CreateFusedBroadcastOp(
ir::Graph *result,
const std::vector<std::unordered_set<std::string>> &bcast_varnames) const;
bool IsSparseGradient(const std::string &og) const;
size_t GetAppropriateDeviceID(
const std::vector<std::string> &var_names) const;
void SetCommunicationContext(OpHandleBase *op_handle,
const platform::Place &p) const;
void CreateOpHandleIOs(ir::Graph *result, ir::Node *node,
size_t device_id) const;
#if defined(PADDLE_WITH_CUDA) && !defined(_WIN32)
mutable platform::NCCLContextMap *nccl_ctxs_;
#endif
mutable std::string loss_var_name_;
mutable std::vector<platform::Place> places_;
mutable std::vector<Scope *> local_scopes_;
mutable BuildStrategy strategy_;
mutable std::unordered_map<std::string, VarDesc *> all_vars_;
};
class AllReduceSSAGraphBuilder : public MultiDevSSAGraphBuilderBase {
protected:
virtual void InsertCollectiveOp(ir::Graph *result, const std::string &p_name,
const std::string &g_name) const;
virtual bool DealWithSpecialOp(ir::Graph *result, ir::Node *node) const {
return false;
}
virtual void InsertPostprocessOps(ir::Graph *result) const {}
};
class BalanceVarSSAGraphBuilder : public MultiDevSSAGraphBuilderBase {
protected:
int GetVarDeviceID(const std::string &varname) const;
int GetOpDeviceID(ir::Node *node) const;
size_t GetAppropriateDeviceID(
const std::vector<std::string> &var_names) const;
virtual void ResetState() const;
mutable std::unordered_map<std::string, int> sharded_var_device_;
mutable std::vector<int64_t> balance_vars_;
};
class ReduceSSAGraphBuilder : public BalanceVarSSAGraphBuilder {
protected:
virtual void Init() const;
virtual void InsertCollectiveOp(ir::Graph *result, const std::string &p_name,
const std::string &g_name) const;
virtual bool DealWithSpecialOp(ir::Graph *result, ir::Node *node) const;
virtual void InsertPostprocessOps(ir::Graph *result) const;
virtual std::vector<ir::Node *> SortOperations(const ir::Graph &graph) const;
virtual void ResetState() const;
int GetOpDeviceID(ir::Node *node,
std::unordered_map<std::string, std::vector<ir::Node *>>
*delay_ops) const;
std::vector<ir::Node *> SortForReduceMode(
const std::vector<ir::Node *> &topo_ops) const;
mutable std::vector<std::unordered_set<std::string>> bcast_var_name_set_;
};
class DistSSAGraphBuilder : public BalanceVarSSAGraphBuilder {
protected:
virtual void Init() const;
virtual bool DealWithSpecialOp(ir::Graph *result, ir::Node *node) const;
virtual void InsertPostprocessOps(ir::Graph *result) const;
virtual void InsertCollectiveOp(ir::Graph *result, const std::string &p_name,
const std::string &g_name) const;
virtual void ResetState() const;
int CreateRPCOp(ir::Graph *result, ir::Node *node) const;
int CreateDistTrainOp(ir::Graph *result, ir::Node *node) const;
mutable std::vector<std::unordered_set<std::string>> bcast_var_name_set_;
mutable bool need_broadcast_var_{false};
};
std::unordered_set<std::string> &MultiDevSSAGraphBuilder();
} // namespace details
} // namespace framework
} // namespace paddle
paddle/fluid/framework/details/parallel_ssa_graph_executor.cc 0 → 100644
浏览文件 @ b16e832d
// 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/details/parallel_ssa_graph_executor.h"
namespace paddle {
namespace framework {
namespace details {
ParallelSSAGraphExecutor::ParallelSSAGraphExecutor(
const ExecutionStrategy &strategy, const std::vector<Scope *> &local_scopes,
const std::vector<platform::Place> &places,
std::vector<std::unique_ptr<ir::Graph>> &&graphs)
: strategy_(std::move(strategy)),
local_scopes_(std::move(local_scopes)),
pool_(places.size() >= 2 ? new ::ThreadPool(places.size()) : nullptr),
places_(std::move(places)),
graphs_(std::move(graphs)) {
PADDLE_ENFORCE_EQ(places_.size(), local_scopes_.size());
// set the correct size of thread pool to each device.
strategy_.num_threads_ = strategy_.num_threads_ < places_.size()
? 1UL
: strategy_.num_threads_ / places_.size();
VLOG(1) << "set num_threads: " << strategy_.num_threads_
<< " to run the operators of the graph on each device.";
for (size_t i = 0; i < places.size(); ++i) {
executors_.emplace_back(new details::ThreadedSSAGraphExecutor(
strategy_, {local_scopes_[i]}, {places_[i]}, std::move(graphs_[i])));
}
}
FeedFetchList ParallelSSAGraphExecutor::Run(
const std::vector<std::string> &fetch_tensors) {
std::vector<std::future<FeedFetchList>> run_futures;
std::vector<FeedFetchList> fetch_data;
FeedFetchList ret;
fetch_data.reserve(places_.size());
ret.reserve(fetch_tensors.size());
exception_holder_.Clear();
for (size_t i = 0; i < places_.size(); ++i) {
auto call = [this, i, &fetch_tensors]() -> FeedFetchList {
try {
return executors_[i]->Run(fetch_tensors);
} catch (...) {
exception_holder_.Catch(std::current_exception());
}
return FeedFetchList();
};
if (pool_) {
run_futures.emplace_back(pool_->enqueue(std::move(call)));
} else {
fetch_data.emplace_back(std::move(call()));
}
}
if (pool_) {
for (auto &f : run_futures) {
if (exception_holder_.IsCaught()) {
f.wait();
} else {
fetch_data.emplace_back(std::move(f.get()));
}
}
}
if (exception_holder_.IsCaught()) {
exception_holder_.ReThrow();
}
for (size_t fetch_idx = 0; fetch_idx < fetch_tensors.size(); ++fetch_idx) {
std::vector<const LoDTensor *> lodtensor_ptrs;
lodtensor_ptrs.reserve(local_scopes_.size());
for (size_t scope_idx = 0; scope_idx < local_scopes_.size(); ++scope_idx) {
lodtensor_ptrs.push_back(&fetch_data.at(scope_idx).at(fetch_idx));
}
ret.emplace_back();
ret.back().MergeLoDTensor(lodtensor_ptrs, platform::CPUPlace());
}
return ret;
}
} // namespace details
} // namespace framework
} // namespace paddle
paddle/fluid/framework/details/parallel_ssa_graph_executor.h 0 → 100644
浏览文件 @ b16e832d
// 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 <string>
#include <vector>
#include "ThreadPool.h"
#include "paddle/fluid/framework/details/threaded_ssa_graph_executor.h"
namespace paddle {
namespace framework {
namespace details {
class ParallelSSAGraphExecutor : public SSAGraphExecutor {
public:
ParallelSSAGraphExecutor(const ExecutionStrategy &strategy,
const std::vector<Scope *> &local_scopes,
const std::vector<platform::Place> &places,
std::vector<std::unique_ptr<ir::Graph>> &&graphs);
~ParallelSSAGraphExecutor() final = default;
const ir::Graph &Graph() const override { return *graphs_[0]; }
FeedFetchList Run(const std::vector<std::string> &fetch_tensors) override;
private:
ExecutionStrategy strategy_;
std::vector<Scope *> local_scopes_;
std::unique_ptr<::ThreadPool> pool_{nullptr};
std::vector<platform::Place> places_;
std::vector<std::unique_ptr<ir::Graph>> graphs_;
std::vector<std::unique_ptr<details::ThreadedSSAGraphExecutor>> executors_;
ExceptionHolder exception_holder_;
};
} // namespace details
} // namespace framework
} // namespace paddle
paddle/fluid/framework/details/scope_buffered_ssa_graph_executor.cc
浏览文件 @ b16e832d
......@@ -56,7 +56,7 @@ FeedFetchList ScopeBufferedSSAGraphExecutor::Run(
}
}
std::vector<framework::LoDTensor> fetch_data;
std::exception_ptr eptr;
std::exception_ptr eptr = nullptr;
try {
fetch_data = underlying_executor_->Run(fetch_tensors);
} catch (...) {
......@@ -64,20 +64,26 @@ FeedFetchList ScopeBufferedSSAGraphExecutor::Run(
}
platform::RecordEvent e("ScopeBufferedSSAGraphExecutorAfterRun", nullptr);
drop_scope_counter_ += 1;
++drop_scope_counter_;
if (!fetch_tensors.empty() ||
drop_scope_counter_ == strategy_.num_iteration_per_drop_scope_) {
drop_scope_counter_ = 0;
// Wait All computational streams
for (auto p : places_) {
platform::DeviceContextPool::Instance().Get(p)->Wait();
bool stream_end = false;
if (!fetch_tensors.empty()) {
WaitComputationalStreams();
stream_end = true;
}
if (drop_scope_counter_ == strategy_.num_iteration_per_drop_scope_) {
if (!stream_end) {
WaitComputationalStreams();
}
for (auto &scope : local_scopes_) {
auto &local_scope =
*scope->Var(details::kLocalExecScopeName)->GetMutable<Scope *>();
scope->DeleteScope(local_scope);
}
drop_scope_counter_ = 0;
}
if (eptr) {
std::rethrow_exception(eptr);
......
paddle/fluid/framework/details/scope_buffered_ssa_graph_executor.h
浏览文件 @ b16e832d
......@@ -47,6 +47,14 @@ class ScopeBufferedSSAGraphExecutor : public SSAGraphExecutor {
FeedFetchList Run(const std::vector<std::string>& fetch_tensors) override;
private:
inline void WaitComputationalStreams() {
// Wait All computational streams
for (auto p : places_) {
platform::DeviceContextPool::Instance().Get(p)->Wait();
}
}
private:
size_t drop_scope_counter_{0};
......
paddle/fluid/framework/details/variable_visitor.cc
浏览文件 @ b16e832d
......@@ -24,7 +24,7 @@ static void VisitVariable(Variable* var, Func* func) {
} else if (var->IsType<SelectedRows>()) {
(*func)(var->GetMutable<SelectedRows>());
} else {
PADDLE_THROW("Not supported type %s", var->Type().name());
PADDLE_THROW("Not supported type %s", ToTypeName(var->Type()));
}
}
......@@ -35,7 +35,7 @@ static void VisitVariable(const Variable& var, Func* func) {
} else if (var.IsType<SelectedRows>()) {
(*func)(var.Get<SelectedRows>());
} else {
PADDLE_THROW("Not supported type %s", var.Type().name());
PADDLE_THROW("Not supported type %s", ToTypeName(var.Type()));
}
}
......
paddle/fluid/framework/dim.h
浏览文件 @ b16e832d
......@@ -16,332 +16,184 @@
#include <iostream>
#include <sstream>
#include <stdexcept>
#include <string>
#include <type_traits>
#include "paddle/fluid/framework/array.h"
#include "paddle/fluid/platform/assert.h"
#include "paddle/fluid/platform/enforce.h"
#include "paddle/fluid/platform/hostdevice.h"
namespace paddle {
namespace framework {
// Statically sized, statically indexed dimension
template <int i>
struct Dim {
static constexpr int dimensions = i;
template <int D>
class Dim : public Array<int64_t, D> {
public:
static_assert(D >= 0, "D must be not less than 0");
template <typename... Args>
HOSTDEVICE Dim(int64_t _head, Args... _tail) : head(_head), tail(_tail...) {
static_assert(sizeof...(_tail) == i - 1,
"Dim initialized with the wrong number of parameters");
}
static constexpr int kRank = D;
using BaseClass = Array<int64_t, D>;
HOSTDEVICE
Dim(int64_t _head, const Dim<i - 1>& _tail) : head(_head), tail(_tail) {}
inline Dim(int64_t head, const Dim<D - 1>& tail) {
(*this)[0] = head;
new (this->GetMutable() + 1) Dim<D - 1>(tail);
}
HOSTDEVICE
Dim() : head(0), tail() {}
template <typename... Args>
HOSTDEVICE explicit Dim(int64_t head, Args... args)
: BaseClass(head, args...) {}
/** Construct a Dim from a linear index and size. Uses Fortran order
* indexing. */
HOSTDEVICE
Dim(int64_t idx, const Dim<i>& size)
: head(idx % size.head), tail(idx / size.head, size.tail) {}
HOSTDEVICE Dim(int64_t idx, const Dim<D>& size);
/** Construct a Dim with each dimension set to the given index */
HOSTDEVICE
Dim(int64_t idx) : head(idx), tail(idx) {}
HOSTDEVICE explicit Dim(int64_t idx) { this->Fill(idx); }
HOSTDEVICE
bool operator==(const Dim<i>& o) const {
return (head == o.head) && (tail == o.tail);
}
HOSTDEVICE
bool operator!=(const Dim<i>& o) const { return !(*this == o); }
HOSTDEVICE
int64_t& operator[](int idx);
HOSTDEVICE
int64_t operator[](int idx) const;
HOSTDEVICE Dim() = default;
HOST std::string to_string() const;
int64_t head;
Dim<i - 1> tail;
};
// Base case specialization
template <>
struct Dim<0> {
static constexpr int dimensions = 0;
HOSTDEVICE
Dim(int64_t _head) {}
HOSTDEVICE
Dim() {}
HOSTDEVICE
Dim(int idx, const Dim<0>& size) {
#ifndef __CUDA_ARCH__
if (idx > 0) {
throw std::invalid_argument("Index out of range.");
}
#else
PADDLE_ASSERT(idx == 0);
#endif
}
HOSTDEVICE
bool operator==(const Dim<0>& o) const { return true; }
HOSTDEVICE
bool operator!=(const Dim<0>& o) const { return false; }
HOSTDEVICE
int64_t& operator[](int idx);
HOSTDEVICE
int64_t operator[](int idx) const;
};
namespace {
// Helper for accessing Dim classes
template <int i>
struct DimGetter {
// Return a copy if Dim is const
template <typename D>
HOSTDEVICE static int64_t impl(const D& d) {
return DimGetter<i - 1>::impl(d.tail);
}
// Return a reference if Dim is mutable
template <typename D>
HOSTDEVICE static int64_t& impl(D& d) {
return DimGetter<i - 1>::impl(d.tail);
namespace detail {
template <int kStart, int kEnd, bool kStop>
struct FortranOrderIndexingConstructorFunctor {
HOSTDEVICE inline static void Run(const int64_t* in, int64_t* idx,
int64_t* out) {
out[kStart] = (*idx) % in[kStart];
(*idx) /= in[kStart];
FortranOrderIndexingConstructorFunctor<kStart + 1, kEnd,
kStart + 1 == kEnd>::Run(in, idx,
out);
}
};
// Eureka! We found the element!
template <>
struct DimGetter<0> {
// Return a copy if Dim is const
template <typename D>
HOSTDEVICE static int64_t impl(const D& d) {
return d.head;
}
// Return a reference if Dim is mutable
template <typename D>
HOSTDEVICE static int64_t& impl(D& d) {
return d.head;
}
template <int kStart, int kEnd>
struct FortranOrderIndexingConstructorFunctor<kStart, kEnd, true> {
HOSTDEVICE inline static void Run(const int64_t* in, int64_t* idx,
int64_t* out) {}
};
} // namespace detail
template <int D>
HOSTDEVICE int64_t& indexer(Dim<D>& dim, int idx) {
#ifndef __CUDA_ARCH__
if (idx < 0) {
throw std::invalid_argument("Tried to access a negative dimension");
}
#else
PADDLE_ASSERT(idx >= 0);
#endif
if (idx == 0) {
return dim.head;
}
return indexer(dim.tail, idx - 1);
}
template <>
HOSTDEVICE int64_t& indexer<0>(Dim<0>& dim, int idx) {
#ifndef __CUDA_ARCH__
throw std::invalid_argument("Invalid index");
#else
PADDLE_ASSERT(false);
#if CUDA_VERSION < 8000
// On CUDA versions previous to 8.0, only __shared__ variables
// could be declared as static in the device code.
int64_t head = 0;
#else
static int64_t head = 0;
#endif
return head;
#endif
}
template <int D>
HOSTDEVICE int64_t indexer(const Dim<D>& dim, int idx) {
#ifndef __CUDA_ARCH__
if (idx < 0) {
throw std::invalid_argument("Tried to access a negative dimension");
}
#else
PADDLE_ASSERT(idx >= 0);
#endif
if (idx == 0) {
return dim.head;
}
return indexer(dim.tail, idx - 1);
}
template <>
HOSTDEVICE int64_t indexer<0>(const Dim<0>& dim, int idx) {
#ifndef __CUDA_ARCH__
throw std::invalid_argument("Invalid index");
#else
PADDLE_ASSERT(false);
#if CUDA_VERSION < 8000
// On CUDA versions previous to 8.0, only __shared__ variables
// could be declared as static in the device code.
int64_t head = 0;
#else
static int64_t head = 0;
#endif
return head;
#endif
}
} // namespace
// Static access to constant Dim
template <int i, int l>
HOSTDEVICE int64_t get(const Dim<l>& d) {
return DimGetter<i>::impl(d);
HOSTDEVICE Dim<D>::Dim(int64_t idx, const Dim<D>& size) {
detail::FortranOrderIndexingConstructorFunctor<0, D, D == 0>::Run(
size.Get(), &idx, this->GetMutable());
}
// Static access to mutable Dim
template <int i, int l>
HOSTDEVICE int64_t& get(Dim<l>& d) {
return DimGetter<i>::impl(d);
template <int idx, int D>
HOSTDEVICE inline int64_t get(const Dim<D>& dim) {
return dim[idx];
}
// Dynamic access to constant Dim
template <int l>
HOSTDEVICE int64_t Dim<l>::operator[](int i) const {
return indexer(*this, i);
template <int idx, int D>
HOSTDEVICE inline int64_t& get(Dim<D>& dim) { // NOLINT
return dim[idx];
}
// Dynamic access to mutable Dim
template <int l>
HOSTDEVICE int64_t& Dim<l>::operator[](int i) {
return indexer(*this, i);
}
// Dynamic access to constant Dim
inline HOSTDEVICE int64_t Dim<0>::operator[](int i) const {
return indexer(*this, i);
}
// Dynamic access to mutable Dim
inline HOSTDEVICE int64_t& Dim<0>::operator[](int i) {
return indexer(*this, i);
}
// Dynamic access to constant Dim
// without std::enable_if will try to instantiate this on get<0>(d)
template <int l>
HOSTDEVICE typename std::enable_if<(l > 0), int64_t>::type get(const Dim<l>& d,
int i) {
return d[i];
template <int D>
HOSTDEVICE inline int64_t get(const Dim<D>& dim, int idx) {
return dim[idx];
}
// Dynamic access to mutable Dim
template <int l>
HOSTDEVICE typename std::enable_if<(l > 0), int64_t&>::type get(Dim<l>& d,
int i) {
return d[i];
template <int D>
HOSTDEVICE inline int64_t& get(Dim<D>& dim, int idx) { // NOLINT
return dim[idx];
}
// Dot product of two dims
template <int i>
HOSTDEVICE int64_t linearize(const Dim<i>& a, const Dim<i>& b) {
return a.head * b.head + linearize(a.tail, b.tail);
}
// Base case dot product of two Dims
// Notice it is inline because it is no longer a template
template <>
HOSTDEVICE inline int64_t linearize(const Dim<0>& a, const Dim<0>& b) {
return 0;
template <int D>
HOSTDEVICE inline int64_t linearize(const Dim<D>& a, const Dim<D>& b) {
return UnrollProduct<D>::Run(a.Get(), b.Get());
}
// Product of a Dim
template <int i>
HOSTDEVICE int64_t product(const Dim<i>& a, int prod = 1) {
return prod * a.head * product(a.tail);
}
// Base case product of a Dim
// Notice it is inline because it is no longer a template
template <>
HOSTDEVICE inline int64_t product(const Dim<0>& a, int prod) {
return prod;
template <int D>
HOSTDEVICE inline int64_t product(const Dim<D>& a) {
return UnrollProduct<D>::Run(a.Get());
}
// Is 0 <= idx_i < size_i for all i?
template <int i>
HOSTDEVICE bool contained(const Dim<i>& idx, const Dim<i>& size) {
return ((0 <= idx.head) && (idx.head < size.head) &&
contained(idx.tail, size.tail));
}
namespace detail {
template <int kStart, int kEnd, bool kStop>
struct ContainedFunctor {
HOSTDEVICE static inline bool Run(const int64_t* idx, const int64_t* size) {
return (idx[kStart] >= 0 && idx[kStart] < size[kStart]) &&
ContainedFunctor<kStart + 1, kEnd, kStart + 1 == kEnd>::Run(idx,
size);
}
};
// Base case of is 0 <= idx_i < size_i ?
// Notice it is inline because it is no longer a template
template <>
HOSTDEVICE inline bool contained(const Dim<0>& idx, const Dim<0>& size) {
return true;
template <int kStart, int kEnd>
struct ContainedFunctor<kStart, kEnd, true> {
HOSTDEVICE static constexpr inline bool Run(const int64_t* idx,
const int64_t* size) {
return true;
}
};
} // namespace detail
template <int D>
HOSTDEVICE inline bool contained(const Dim<D>& idx, const Dim<D>& size) {
return detail::ContainedFunctor<0, D, D == 0>::Run(idx.Get(), size.Get());
}
/**
* \brief Compute exclusive prefix-multiply of a Dim.
*/
template <int i>
HOSTDEVICE Dim<i> ex_prefix_mul(const Dim<i>& src, int mul = 1) {
return Dim<i>(mul, ex_prefix_mul(src.tail, mul * src.head));
}
namespace detail {
template <int kStart, int kEnd, bool kStop>
struct ExPrefixMulFunctor {
HOSTDEVICE static inline void Run(const int64_t* in, int64_t* out) {
kStart == 0 ? out[kStart] = 1 : out[kStart] =
out[kStart - 1] * in[kStart - 1];
detail::ExPrefixMulFunctor<kStart + 1, kEnd, kStart + 1 == kEnd>::Run(in,
out);
}
};
template <int kStart, int kEnd>
struct ExPrefixMulFunctor<kStart, kEnd, true> {
HOSTDEVICE static inline void Run(const int64_t* in, int64_t* out) {}
};
} // namespace detail
///\cond HIDDEN
// Base case of ex_prefix_mul
// Notice it is inline because it is no longer a template
template <>
HOSTDEVICE inline Dim<0> ex_prefix_mul(const Dim<0>& src, int mul) {
return Dim<0>();
template <int D>
HOSTDEVICE inline Dim<D> ex_prefix_mul(const Dim<D>& src) {
Dim<D> ret;
detail::ExPrefixMulFunctor<0, D, D == 0>::Run(src.Get(), ret.GetMutable());
return ret;
}
///\endcond
/**
* Add two dimensions together
*/
template <int i>
HOSTDEVICE Dim<i> dim_plus(const Dim<i>& a, const Dim<i>& b) {
return Dim<i>(a.head + b.head, dim_plus(a.tail, b.tail));
}
// Base case
template <>
HOSTDEVICE inline Dim<0> dim_plus(const Dim<0>& a, const Dim<0>& b) {
return Dim<0>();
template <int D>
HOSTDEVICE inline Dim<D> dim_plus(const Dim<D>& a, const Dim<D>& b) {
Dim<D> ret;
UnrollAdd<D>::Run(a.Get(), b.Get(), ret.GetMutable());
return ret;
}
template <int i>
HOSTDEVICE Dim<i> operator+(const Dim<i>& lhs, const Dim<i>& rhs) {
template <int D>
HOSTDEVICE inline Dim<D> operator+(const Dim<D>& lhs, const Dim<D>& rhs) {
return dim_plus(lhs, rhs);
}
/**
* Multiply two dimensions together
*/
template <int i>
HOSTDEVICE Dim<i> dim_mult(const Dim<i>& a, const Dim<i>& b) {
return Dim<i>(a.head * b.head, dim_mult(a.tail, b.tail));
}
// Base case
template <>
HOSTDEVICE inline Dim<0> dim_mult(const Dim<0>& a, const Dim<0>& b) {
return Dim<0>();
template <int D>
HOSTDEVICE inline Dim<D> dim_mult(const Dim<D>& a, const Dim<D>& b) {
Dim<D> ret;
UnrollMul<D>::Run(a.Get(), b.Get(), ret.GetMutable());
return ret;
}
template <int i>
HOSTDEVICE Dim<i> operator*(const Dim<i>& lhs, const Dim<i>& rhs) {
template <int D>
HOSTDEVICE Dim<D> operator*(const Dim<D>& lhs, const Dim<D>& rhs) {
return dim_mult(lhs, rhs);
}
......@@ -354,23 +206,32 @@ HOSTDEVICE Dim<i> operator*(const Dim<i>& lhs, const Dim<i>& rhs) {
* \return Dim object the same size as \p size with normalized strides
*
*/
namespace detail {
template <int kStart, int kEnd, bool kStop>
struct NormalizeStridesFunctor {
HOSTDEVICE static void Run(const int64_t* size, const int64_t* stride,
int64_t* ret) {
ret[kStart] = (size[kStart] == 1 ? 0 : stride[kStart]);
NormalizeStridesFunctor<kStart + 1, kEnd, kStart + 1 == kEnd>::Run(
size, stride, ret);
}
};
template <int i>
HOSTDEVICE Dim<i> normalize_strides(const Dim<i>& size, const Dim<i>& stride) {
int norm_stride = size.head == 1 ? 0 : stride.head;
return Dim<i>(norm_stride, normalize_strides(size.tail, stride.tail));
}
///\cond HIDDEN
template <int kStart, int kEnd>
struct NormalizeStridesFunctor<kStart, kEnd, true> {
HOSTDEVICE static void Run(const int64_t* size, const int64_t* stride,
int64_t* ret) {}
};
} // namespace detail
template <>
HOSTDEVICE inline Dim<0> normalize_strides(const Dim<0>& size,
const Dim<0>& stride) {
return Dim<0>();
template <int D>
HOSTDEVICE Dim<D> normalize_strides(const Dim<D>& size, const Dim<D>& stride) {
Dim<D> ret;
detail::NormalizeStridesFunctor<0, D, D == 0>::Run(size.Get(), stride.Get(),
ret.GetMutable());
return ret;
}
///\endcond
/**
* Helper function to create a Dim
*
......@@ -379,25 +240,17 @@ HOSTDEVICE inline Dim<0> normalize_strides(const Dim<0>& size,
*/
template <typename... Args>
HOSTDEVICE Dim<sizeof...(Args)> make_dim(Args... idxes) {
HOSTDEVICE inline Dim<sizeof...(Args)> make_dim(Args... idxes) {
return Dim<sizeof...(Args)>(idxes...);
}
// Allows us to output a Dim
// XXX For some reason, overloading fails to resolve this correctly
template <int i>
typename std::enable_if<(i > 1), std::ostream&>::type operator<<(
std::ostream& os, const Dim<i>& d) {
os << d.head << ", " << d.tail;
return os;
}
// Base case that allows us to output a Dim
// XXX I wish this could be an overload instead of a template
template <int i>
typename std::enable_if<(i == 1), std::ostream&>::type operator<<(
std::ostream& os, const Dim<i>& d) {
os << d.head;
template <int D>
inline std::ostream& operator<<(std::ostream& os, const Dim<D>& d) {
os << d[0];
for (int i = 1; i < D; ++i) {
os << ", " << d[i];
}
return os;
}
......@@ -405,17 +258,15 @@ inline std::ostream& operator<<(std::ostream& os, const Dim<0>& d) {
return os;
}
template <int i>
HOST std::string Dim<i>::to_string() const {
template <int D>
HOST std::string Dim<D>::to_string() const {
std::stringstream stream;
stream << *this;
return stream.str();
}
template <int D>
HOSTDEVICE Dim<D> linear_to_dimension(int linear_index, Dim<D> extents) {
HOSTDEVICE Dim<D> linear_to_dimension(int linear_index, const Dim<D>& extents) {
Dim<D> result;
for (int i = 0; i < D - 1; ++i) {
......@@ -428,5 +279,10 @@ HOSTDEVICE Dim<D> linear_to_dimension(int linear_index, Dim<D> extents) {
return result;
}
template <int D, typename T1, typename T2>
inline void static_dim_assign(const T1* in, T2* out) {
UnrollAssign<D>::Run(in, out);
}
} // namespace framework
} // namespace paddle
paddle/fluid/framework/dlpack_tensor.cc
浏览文件 @ b16e832d
......@@ -59,7 +59,7 @@ static DLDataType GetDLDataTypeFromTypeIndex(proto::VarType::Type type) {
struct DLContextVisitor : public boost::static_visitor<::DLContext> {
inline ::DLContext operator()(const platform::CPUPlace &place) const {
DLContext ctx;
::DLContext ctx;
ctx.device_type = kDLCPU;
ctx.device_id = 0;
return ctx;
......@@ -67,7 +67,7 @@ struct DLContextVisitor : public boost::static_visitor<::DLContext> {
inline ::DLContext operator()(const platform::CUDAPlace &place) const {
#ifdef PADDLE_WITH_CUDA
DLContext ctx;
::DLContext ctx;
ctx.device_type = kDLGPU;
ctx.device_id = place.device;
return ctx;
......@@ -78,7 +78,7 @@ struct DLContextVisitor : public boost::static_visitor<::DLContext> {
inline ::DLContext operator()(const platform::CUDAPinnedPlace &place) const {
#ifdef PADDLE_WITH_CUDA
DLContext ctx;
::DLContext ctx;
ctx.device_type = kDLCPUPinned;
ctx.device_id = 0;
return ctx;
......
paddle/fluid/framework/dlpack_tensor.h
浏览文件 @ b16e832d
......@@ -38,7 +38,7 @@ class DLPackTensor {
// The shape in DLTensor is defined as int64_t*
// Add this member to make TVMTensor init without heap allocation
ShapeType shape_[9];
ShapeType shape_[DDim::kMaxRank];
};
} // namespace framework
......
paddle/fluid/framework/executor.cc
浏览文件 @ b16e832d
......@@ -22,7 +22,7 @@ limitations under the License. */
#include "paddle/fluid/framework/reader.h"
#include "paddle/fluid/framework/transfer_scope_cache.h"
#include "paddle/fluid/framework/variable_helper.h"
#include "paddle/fluid/operators/detail/macros.h"
#include "paddle/fluid/operators/distributed/distributed.h"
#include "paddle/fluid/platform/place.h"
#include "paddle/fluid/platform/profiler.h"
......@@ -119,7 +119,7 @@ static void DeleteUnusedTensors(
}
} else {
PADDLE_THROW("Type %s of %s is not supported eager deletion",
var->Type().name(), name);
framework::ToTypeName(var->Type()), name);
}
}
}
......
paddle/fluid/framework/executor_thread_worker.cc
浏览文件 @ b16e832d
......@@ -29,6 +29,7 @@ limitations under the License. */
#include "paddle/fluid/inference/io.h"
#include "paddle/fluid/platform/cpu_helper.h"
#include "paddle/fluid/platform/place.h"
#include "paddle/fluid/platform/timer.h"
#include "paddle/fluid/pybind/pybind.h"
namespace paddle {
namespace framework {
......@@ -180,6 +181,7 @@ void ExecutorThreadWorker::SetDevice() {
return;
#else
static unsigned concurrency_cap = std::thread::hardware_concurrency();
LOG(WARNING) << "concurrency capacity " << concurrency_cap;
int thread_id = this->thread_id_;
if (static_cast<unsigned>(thread_id) < concurrency_cap) {
......@@ -238,6 +240,55 @@ static void print_fetch_var(Scope* scope, const std::string& var_name) {
VLOG(1) << "print_fetch_var: unrecognized data type:" << tensor.type();
}
void ExecutorThreadWorker::TrainFilesWithTimer() {
platform::SetNumThreads(1);
SetDevice();
thread_reader_->Start();
std::vector<double> op_total_time;
std::vector<std::string> op_name;
for (auto& op : ops_) {
op_name.push_back(op->Type());
}
op_total_time.resize(ops_.size());
for (size_t i = 0; i < op_total_time.size(); ++i) {
op_total_time[i] = 0.0;
}
platform::Timer timeline;
double total_time = 0.0;
double read_time = 0.0;
int cur_batch;
int batch_cnt = 0;
timeline.Start();
while ((cur_batch = thread_reader_->Next()) > 0) {
timeline.Pause();
read_time += timeline.ElapsedSec();
total_time += timeline.ElapsedSec();
for (size_t i = 0; i < ops_.size(); ++i) {
timeline.Start();
ops_[i]->Run(*thread_scope_, place_);
timeline.Pause();
op_total_time[i] += timeline.ElapsedSec();
total_time += timeline.ElapsedSec();
}
++batch_cnt;
thread_scope_->DropKids();
if (thread_id_ == 0) {
if (batch_cnt > 0 && batch_cnt % 1000 == 0) {
for (size_t i = 0; i < ops_.size(); ++i) {
fprintf(stderr, "op_name:[%zu][%s], op_mean_time:[%fs]\n", i,
op_name[i].c_str(), op_total_time[i] / batch_cnt);
}
fprintf(stderr, "mean read time: %fs\n", read_time / batch_cnt);
int fetch_var_num = fetch_var_names_.size();
for (int i = 0; i < fetch_var_num; ++i) {
print_fetch_var(thread_scope_, fetch_var_names_[i]);
}
}
}
timeline.Start();
}
}
void ExecutorThreadWorker::TrainFiles() {
platform::SetNumThreads(1);
......@@ -320,10 +371,12 @@ void AsyncExecutorThreadWorker::SetPSlibPtr(
std::shared_ptr<paddle::distributed::PSlib> pslib_ptr) {
_pslib_ptr = pslib_ptr;
}
void AsyncExecutorThreadWorker::SetPullDenseThread(
std::shared_ptr<DensePullThread> dpt) {
_pull_dense_thread = dpt;
}
void AsyncExecutorThreadWorker::TrainOneNetwork() {
PrepareParams();
......
paddle/fluid/framework/executor_thread_worker.h
浏览文件 @ b16e832d
......@@ -155,6 +155,8 @@ class ExecutorThreadWorker {
void SetDataFeed(const std::shared_ptr<DataFeed>& datafeed);
// A multi-thread training function
virtual void TrainFiles();
// with timer log
virtual void TrainFilesWithTimer();
// set fetch variable names from python interface assigned by users
void SetFetchVarNames(const std::vector<std::string>& fetch_var_names);
#ifdef PADDLE_WITH_PSLIB
......
paddle/fluid/framework/ir/CMakeLists.txt
浏览文件 @ b16e832d
......@@ -45,6 +45,7 @@ pass_library(is_test_pass base)
pass_library(conv_elementwise_add_act_fuse_pass inference)
pass_library(conv_elementwise_add2_act_fuse_pass inference)
pass_library(conv_elementwise_add_fuse_pass inference)
pass_library(conv_affine_channel_fuse_pass inference)
if(WITH_MKLDNN)
pass_library(mkldnn_placement_pass base)
pass_library(depthwise_conv_mkldnn_pass base)
......
paddle/fluid/framework/ir/conv_affine_channel_fuse_pass.cc 0 → 100644
浏览文件 @ b16e832d
// 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/ir/conv_affine_channel_fuse_pass.h"
#include <functional>
#include <string>
#include <vector>
#include "paddle/fluid/framework/lod_tensor.h"
#include "paddle/fluid/operators/math/cpu_vec.h"
#include "paddle/fluid/platform/enforce.h"
namespace paddle {
namespace framework {
namespace ir {
#define GET_CONV_BN_NODES(pattern_name) \
/* OPERATORS */ \
GET_IR_NODE_FROM_SUBGRAPH(conv, conv, pattern_name); \
GET_IR_NODE_FROM_SUBGRAPH(affine_channel, affine_channel, pattern_name); \
/* CONV inputs */ \
GET_IR_NODE_FROM_SUBGRAPH(conv_weight, conv_weight, pattern_name); \
/* CONV outputs */ \
GET_IR_NODE_FROM_SUBGRAPH(conv_out, conv_out, pattern_name); \
/* Affine Channel inputs */ \
GET_IR_NODE_FROM_SUBGRAPH(ac_scale, ac_scale, pattern_name); \
GET_IR_NODE_FROM_SUBGRAPH(ac_bias, ac_bias, pattern_name); \
/* Affine channel outputs */ \
GET_IR_NODE_FROM_SUBGRAPH(ac_out, ac_out, pattern_name); /* Out */
void recompute_bias_and_weights(const Scope* scope, ir::Node* conv_weight,
const ir::Node& ac_scale,
const LoDTensor& ac_bias_tensor,
LoDTensor* eltwise_y_in_tensor) {
using EigenVectorArrayMap =
Eigen::Map<Eigen::Array<float, Eigen::Dynamic, 1>>;
using ConstEigenVectorArrayMap =
Eigen::Map<const Eigen::Array<float, Eigen::Dynamic, 1>>;
using EigenMatrixArrayMap = Eigen::Map<
Eigen::Array<float, Eigen::Dynamic, Eigen::Dynamic, Eigen::RowMajor>>;
// Re-compute bias of conv2d from AffineChannel
PADDLE_ENFORCE_EQ(eltwise_y_in_tensor->dims(), ac_bias_tensor.dims());
auto* scale_tensor = scope->FindVar(ac_scale.Name())->GetMutable<LoDTensor>();
ConstEigenVectorArrayMap scale_array(scale_tensor->data<float>(),
scale_tensor->numel(), 1);
ConstEigenVectorArrayMap ac_bias_array(ac_bias_tensor.data<float>(),
ac_bias_tensor.numel(), 1);
EigenVectorArrayMap eltwise_y_in_array(
eltwise_y_in_tensor->mutable_data<float>(platform::CPUPlace()),
eltwise_y_in_tensor->numel(), 1);
eltwise_y_in_array = (eltwise_y_in_array * scale_array) + ac_bias_array;
// Re-compute weight of conv2d from AffineChannel
auto* weights = scope->FindVar(conv_weight->Name())->GetMutable<LoDTensor>();
auto weights_shape = weights->dims();
auto weights_shape_2d = flatten_to_2d(weights_shape, 1);
EigenMatrixArrayMap weights_array_2d(
weights->mutable_data<float>(platform::CPUPlace()), weights_shape_2d[0],
weights_shape_2d[1]);
weights_array_2d.colwise() *= scale_array;
}
std::unique_ptr<ir::Graph> ConvAffineChannelFusePass::ApplyImpl(
std::unique_ptr<ir::Graph> graph) const {
PADDLE_ENFORCE(graph.get());
FusePassBase::Init(name_scope_, graph.get());
auto* scope = param_scope();
PADDLE_ENFORCE(scope);
GraphPatternDetector gpd;
auto* conv_input =
gpd.mutable_pattern()
->NewNode(patterns::PDNodeName(name_scope_, "conv_input"))
->AsInput()
->assert_is_op_input("conv2d", "Input");
patterns::ConvAffineChannel conv_ac_pattern(gpd.mutable_pattern(),
name_scope_);
conv_ac_pattern(conv_input, false /*with_eltwise_add*/);
int found_conv_ac_count = 0;
auto handler = [&](const GraphPatternDetector::subgraph_t& subgraph,
Graph* g) {
VLOG(4) << "handle ConvAffineChannel fuse";
GET_CONV_BN_NODES(conv_ac_pattern);
// check if fuse can be done and if MKL-DNN should be used
FuseOptions fuse_option = FindFuseOption(*conv, *affine_channel);
if (fuse_option == DO_NOT_FUSE) {
VLOG(3) << "do not perform conv+affinechannel fuse";
return;
}
// Create eltwise_y (conv bias) variable
VarDesc eltwise_y_in_desc(
patterns::PDNodeName(name_scope_, "eltwise_y_in"));
eltwise_y_in_desc.SetPersistable(true);
auto* eltwise_y_in_node = g->CreateVarNode(&eltwise_y_in_desc);
auto* eltwise_y_in_tensor =
scope->Var(eltwise_y_in_node->Name())->GetMutable<LoDTensor>();
// Get affine_channel bias
auto* ac_bias_tensor =
scope->FindVar(ac_bias->Name())->GetMutable<LoDTensor>();
// Initialize eltwise_y
eltwise_y_in_tensor->Resize(ac_bias_tensor->dims());
std::fill_n(eltwise_y_in_tensor->mutable_data<float>(platform::CPUPlace()),
eltwise_y_in_tensor->numel(), 0.0f);
// update weights and biases
recompute_bias_and_weights(scope, conv_weight, *ac_scale, *ac_bias_tensor,
eltwise_y_in_tensor);
// create an elementwise add node.
OpDesc desc;
desc.SetInput("X", std::vector<std::string>({conv_out->Name()}));
desc.SetInput("Y", std::vector<std::string>({eltwise_y_in_node->Name()}));
desc.SetOutput("Out", std::vector<std::string>({ac_out->Name()}));
desc.SetType("elementwise_add");
desc.SetAttr("axis", 1);
auto eltwise_op = g->CreateOpNode(&desc); // OpDesc will be copied.
GraphSafeRemoveNodes(graph.get(), {ac_scale, ac_bias, affine_channel});
IR_NODE_LINK_TO(conv_out, eltwise_op);
IR_NODE_LINK_TO(eltwise_y_in_node, eltwise_op);
IR_NODE_LINK_TO(eltwise_op, ac_out);
found_conv_ac_count++;
};
gpd(graph.get(), handler);
AddStatis(found_conv_ac_count);
return graph;
}
std::unique_ptr<ir::Graph> ConvEltwiseAddAffineChannelFusePass::ApplyImpl(
std::unique_ptr<ir::Graph> graph) const {
PADDLE_ENFORCE(graph.get());
FusePassBase::Init(name_scope_, graph.get());
auto* scope = param_scope();
PADDLE_ENFORCE(scope);
GraphPatternDetector gpd;
auto* conv_input =
gpd.mutable_pattern()
->NewNode(patterns::PDNodeName(name_scope_, "conv_input"))
->AsInput()
->assert_is_op_input("conv2d", "Input");
patterns::ConvAffineChannel conv_ac_pattern(gpd.mutable_pattern(),
name_scope_);
conv_ac_pattern(conv_input, true /*with_eltwise_add*/);
int found_conv_ac_count = 0;
auto handler = [&](const GraphPatternDetector::subgraph_t& subgraph,
Graph* g) {
VLOG(4) << "handle ConvBN fuse";
GET_CONV_BN_NODES(conv_ac_pattern);
// OPERATORS
GET_IR_NODE_FROM_SUBGRAPH(eltwise, eltwise, conv_ac_pattern);
// BIAS inputs
GET_IR_NODE_FROM_SUBGRAPH(eltwise_y_in, eltwise_y_in, conv_ac_pattern);
// BIAS outputs
GET_IR_NODE_FROM_SUBGRAPH(eltwise_out, eltwise_out, conv_ac_pattern);
// Get eltwise_y (conv bias) variable
auto* eltwise_y_in_tensor =
scope->FindVar(eltwise_y_in->Name())->GetMutable<LoDTensor>();
// Get batch norm bias
auto* ac_bias_tensor =
scope->FindVar(ac_bias->Name())->GetMutable<LoDTensor>();
recompute_bias_and_weights(scope, conv_weight, *ac_scale, *ac_bias_tensor,
eltwise_y_in_tensor);
// Update the elementwise_add node
eltwise->Op()->SetAttr("axis", 1);
eltwise->Op()->SetOutput("Out", std::vector<std::string>({ac_out->Name()}));
GraphSafeRemoveNodes(graph.get(),
{ac_scale, ac_bias, affine_channel, eltwise_out});
IR_NODE_LINK_TO(eltwise, ac_out);
found_conv_ac_count++;
};
gpd(graph.get(), handler);
AddStatis(found_conv_ac_count);
return graph;
}
} // namespace ir
} // namespace framework
} // namespace paddle
REGISTER_PASS(conv_affine_channel_fuse_pass,
paddle::framework::ir::ConvAffineChannelFusePass);
REGISTER_PASS(conv_eltwiseadd_affine_channel_fuse_pass,
paddle::framework::ir::ConvEltwiseAddAffineChannelFusePass);
paddle/fluid/framework/ir/conv_affine_channel_fuse_pass.h 0 → 100644
浏览文件 @ b16e832d
// 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 <string>
#include "paddle/fluid/framework/ir/fuse_pass_base.h"
#include "paddle/fluid/framework/ir/graph.h"
#include "paddle/fluid/framework/ir/graph_pattern_detector.h"
namespace paddle {
namespace framework {
namespace ir {
/*
* Fuse the Conv and ConvAffineChannel.
*/
class ConvAffineChannelFusePass : public FusePassBase {
public:
virtual ~ConvAffineChannelFusePass() {}
protected:
std::unique_ptr<ir::Graph> ApplyImpl(std::unique_ptr<ir::Graph> graph) const;
const std::string name_scope_{"conv_affine_channel_fuse"};
};
class ConvEltwiseAddAffineChannelFusePass : public FusePassBase {
public:
virtual ~ConvEltwiseAddAffineChannelFusePass() {}
protected:
std::unique_ptr<ir::Graph> ApplyImpl(std::unique_ptr<ir::Graph> graph) const;
const std::string name_scope_{"conv_eltwiseadd_affine_channel_fuse"};
};
} // namespace ir
} // namespace framework
} // namespace paddle
paddle/fluid/framework/ir/conv_elementwise_add2_act_fuse_pass.cc
浏览文件 @ b16e832d
......@@ -40,18 +40,20 @@ framework::proto::OpDesc PrepareOpDesc(
const std::string& output) {
auto proto = base_desc;
framework::OpDesc desc(proto, nullptr);
desc.SetType("conv2d_fusion");
desc.SetInput("Bias", {bias});
desc.SetInput("ResidualData", {bias1});
desc.SetAttr("activation", activation);
desc.SetOutput("Output", {output});
desc.SetAttr("is_test", true);
desc.SetAttr("use_cudnn", false);
desc.Flush();
return *desc.Proto();
}
std::unique_ptr<ir::Graph> ConvElementwiseAdd2ActFusePass::ApplyImpl(
std::unique_ptr<ir::Graph> graph) const {
const std::string pattern_name = "conv_elementwise_add_act_fuse";
const std::string pattern_name = "conv_elementwise_add2_act_fuse";
FusePassBase::Init(pattern_name, graph.get());
GraphPatternDetector gpd;
......@@ -76,22 +78,23 @@ std::unique_ptr<ir::Graph> ConvElementwiseAdd2ActFusePass::ApplyImpl(
framework::OpDesc new_op_desc(new_op_proto, nullptr);
// Create a new node for the fused op.
graph->CreateOpNode(&new_op_desc);
auto* new_conv_op = graph->CreateOpNode(&new_op_desc);
// Link inputs and outputs.
PADDLE_ENFORCE(subgraph.count(x));
auto* conv_in_node = subgraph.at(x);
IR_NODE_LINK_TO(conv_in_node, conv_op); // Input
IR_NODE_LINK_TO(conv_filter, conv_op); // Filter
IR_NODE_LINK_TO(conv_op, conv_out); // Output
IR_NODE_LINK_TO(elementwise_add_in_y, conv_op); // Bias
IR_NODE_LINK_TO(elementwise_add_in_y_1, conv_op); // Bias
IR_NODE_LINK_TO(conv_in_node, new_conv_op); // Input
IR_NODE_LINK_TO(conv_filter, new_conv_op); // Filter
IR_NODE_LINK_TO(elementwise_add_in_y, new_conv_op); // Bias
IR_NODE_LINK_TO(elementwise_add_in_y_1, new_conv_op); // Bias
IR_NODE_LINK_TO(new_conv_op, act_out); // Output
// Delete the unneeded nodes.
GraphSafeRemoveNodes(graph.get(),
{conv_op, elementwise_add_op, elementwise_add_op_1,
elementwise_add_out});
GraphSafeRemoveNodes(
graph.get(),
{conv_op, conv_out, elementwise_add_op, elementwise_add_op_1,
elementwise_add_out, elementwise_add_out_1, act_op});
};
gpd(graph.get(), handler);
return graph;
......
paddle/fluid/framework/ir/graph.cc
浏览文件 @ b16e832d
......@@ -23,66 +23,8 @@ limitations under the License. */
namespace paddle {
namespace framework {
namespace ir {
namespace {
void CheckProgram(const ProgramDesc &program) {
#define _INT(role) static_cast<int>(role)
std::map<int, bool> visit;
for (OpDesc *op : program.Block(0).AllOps()) {
// For backward compatibility, some program doesn't have role added.
if (!op->HasAttr(OpProtoAndCheckerMaker::OpRoleAttrName())) continue;
int role_id =
boost::get<int>(op->GetAttr(OpProtoAndCheckerMaker::OpRoleAttrName()));
visit[role_id] = true;
switch (role_id) {
case _INT(OpRole::kForward):
if (visit.find(_INT(OpRole::kBackward)) != visit.end()) {
LOG(ERROR) << "Cannot add backward operator before forward operator "
<< op->Type();
}
break;
case _INT(OpRole::kBackward):
case _INT(OpRole::kBackward) | _INT(OpRole::kLoss):
PADDLE_ENFORCE(
visit.find(_INT(OpRole::kOptimize)) == visit.end(),
"Cannot add backward operator %s after optimize operator.",
op->Type());
break;
case _INT(OpRole::kForward) | _INT(OpRole::kLoss):
PADDLE_ENFORCE(visit.find(_INT(OpRole::kBackward) |
_INT(OpRole::kLoss)) == visit.end(),
"Cannot add backward|loss operator before "
"forward|loss operator %s.",
op->Type());
PADDLE_ENFORCE(
visit.find(_INT(OpRole::kOptimize)) == visit.end(),
"Cannot add forward|loss operator %s after optimize operator.",
op->Type());
break;
case _INT(OpRole::kOptimize):
case _INT(OpRole::kOptimize) | _INT(OpRole::kLRSched):
PADDLE_ENFORCE(visit.find(_INT(OpRole::kBackward)) != visit.end(),
"Optimize operators %s must follow backward operator.",
op->Type());
break;
case _INT(OpRole::kLRSched):
case _INT(OpRole::kDist):
case _INT(OpRole::kRPC):
case _INT(OpRole::kNotSpecified):
break;
default:
LOG(FATAL) << "Unknown operator role. Don't add new role because "
"you don't know what you are doing.";
}
}
#undef _INT
}
} // namespace
Graph::Graph(const ProgramDesc &program) : program_(program) {
CheckProgram(program_);
auto var_nodes = InitFromProgram(program_);
ResolveHazard(var_nodes);
}
......
paddle/fluid/framework/ir/graph_pattern_detector.cc
浏览文件 @ b16e832d
......@@ -1101,9 +1101,7 @@ PDNode *patterns::ElementwiseAdd::operator()(PDNode *x_var, PDNode *y_var) {
return out_var;
}
std::unordered_set<std::string> conv_act_set({"identity", "sigmoid", "relu",
"relu6", "relux", "tanh",
"band_pass"});
std::unordered_set<std::string> conv_act_set({"identity", "relu"});
PDNode *patterns::ConvElementwiseaddAct::operator()(PDNode *conv_in) {
conv_in->AsInput();
......@@ -1169,13 +1167,13 @@ PDNode *patterns::ConvElementwiseadd2Act::operator()(PDNode *conv_in) {
->AsInput();
auto elementwise_add_out = pattern->NewNode(elementwise_add_out_repr())
->assert_is_op_output("elementwise_add")
->assert_is_op_input("elementwise_add", "X")
->assert_is_op_input("elementwise_add", "Y")
->AsIntermediate();
auto elementwise_add_op_1 = pattern->NewNode(elementwise_add_op_1_repr())
->assert_is_op("elementwise_add");
auto elementwise_add_in_y_1 = pattern->NewNode(elementwise_add_in_y_1_repr())
->assert_is_op_input("elementwise_add", "Y")
->assert_is_op_input("elementwise_add", "X")
->AsInput();
auto elementwise_add_out_1 = pattern->NewNode(elementwise_add_out_1_repr())
->assert_is_op_output("elementwise_add")
......@@ -1203,8 +1201,8 @@ PDNode *patterns::ConvElementwiseadd2Act::operator()(PDNode *conv_in) {
conv_op->LinksFrom({conv_in, conv_filter}).LinksTo({conv_out});
elementwise_add_op->LinksFrom({conv_out, elementwise_add_in_y})
.LinksTo({elementwise_add_out});
elementwise_add_op_1->LinksFrom(
{elementwise_add_out, elementwise_add_in_y_1});
elementwise_add_op_1->LinksFrom({elementwise_add_out, elementwise_add_in_y_1})
.LinksTo({elementwise_add_out_1});
act_op->LinksFrom({elementwise_add_out_1}).LinksTo({act_out});
return act_out;
}
......@@ -1236,6 +1234,78 @@ PDNode *patterns::ConvElementwiseadd::operator()(PDNode *conv_in) {
return elementwise_add_out;
}
PDNode *patterns::ConvAffineChannel::operator()(
paddle::framework::ir::PDNode *conv_input, bool with_eltwise_add) {
// Create Operators
conv_input->assert_is_op_input("conv2d", "Input");
auto *conv_op = pattern->NewNode(conv_repr())->assert_is_op("conv2d");
PDNode *eltwise_op = nullptr;
if (with_eltwise_add) {
eltwise_op =
pattern->NewNode(eltwise_repr())->assert_is_op("elementwise_add");
}
auto *affine_channel_op =
pattern->NewNode(affine_channel_repr())->assert_is_op("affine_channel");
// Create variables
// Conv Filter
auto *conv_weight_var = pattern->NewNode(conv_weight_repr())
->AsInput()
->assert_is_persistable_var()
->assert_is_op_input("conv2d", "Filter");
auto *conv_out_var = pattern->NewNode(conv_out_repr())
->AsIntermediate()
->assert_is_only_output_of_op("conv2d");
PDNode *eltwise_y_in_var = nullptr;
PDNode *eltwise_out_var = nullptr;
if (with_eltwise_add) {
// Conv output as Bias input
conv_out_var->assert_is_op_input("elementwise_add", "X");
// Bias
eltwise_y_in_var = pattern->NewNode(eltwise_y_in_repr())
->assert_is_op_input("elementwise_add", "Y")
->AsInput();
eltwise_out_var = pattern->NewNode(eltwise_out_repr())
->AsIntermediate()
->assert_is_only_output_of_op("elementwise_add");
} else {
// Conv output as AffineChannel input
conv_out_var->assert_is_op_input("affine_channel", "X");
}
// AC Scale
auto *ac_scale_var = pattern->NewNode(ac_scale_repr())
->AsInput()
->assert_is_persistable_var()
->assert_is_op_input("affine_channel", "Scale");
// AC Bias
auto *ac_bias_var = pattern->NewNode(ac_bias_repr())
->AsInput()
->assert_is_persistable_var()
->assert_is_op_input("affine_channel", "Bias");
// AC output
auto *ac_out_var = pattern->NewNode(ac_out_repr())
->AsOutput()
->assert_is_op_output("affine_channel");
conv_op->LinksFrom({conv_input, conv_weight_var}).LinksTo({conv_out_var});
if (with_eltwise_add) {
eltwise_op->LinksFrom({conv_out_var, eltwise_y_in_var})
.LinksTo({eltwise_out_var});
affine_channel_op->LinksFrom({eltwise_out_var, ac_scale_var, ac_bias_var})
.LinksTo({ac_out_var});
} else {
affine_channel_op->LinksFrom({conv_out_var, ac_scale_var, ac_bias_var})
.LinksTo({ac_out_var});
}
return ac_out_var;
}
} // namespace ir
} // namespace framework
} // namespace paddle
paddle/fluid/framework/ir/graph_pattern_detector.h
浏览文件 @ b16e832d
......@@ -734,6 +734,38 @@ struct ConvElementwiseadd : public PatternBase {
PATTERN_DECL_NODE(elementwise_add_out);
};
// Conv with affine_channel
// op: conv + (elementwise_add +) affine_channel
// named nodes:
// conv_weight, conv_out, conv,
// ac_x, ac_scale, ac_bias
// affine_channel, ac_out
struct ConvAffineChannel : public PatternBase {
ConvAffineChannel(PDPattern* pattern, const std::string& name_scope)
: PatternBase(pattern, name_scope, "conv_affine_channel") {}
PDNode* operator()(PDNode* conv_input, bool with_eltwise_add);
// declare operator node's name
PATTERN_DECL_NODE(conv);
PATTERN_DECL_NODE(affine_channel);
PATTERN_DECL_NODE(eltwise); // ELEMENTWISE_ADD
// CONV inputs
PATTERN_DECL_NODE(conv_weight); // Filter
// CONV outputs
PATTERN_DECL_NODE(conv_out); // tmp
// ELTWISE inputs
PATTERN_DECL_NODE(eltwise_y_in);
// ELTWISE outputs
PATTERN_DECL_NODE(eltwise_out); // tmp
// AC(Affine_Channel) inputs
PATTERN_DECL_NODE(ac_scale);
PATTERN_DECL_NODE(ac_bias);
// AC outputs
PATTERN_DECL_NODE(ac_out); // Out
};
} // namespace patterns
// Link two ir::Nodes from each other.
......
paddle/fluid/framework/ir/multi_batch_merge_pass.cc
浏览文件 @ b16e832d
......@@ -75,6 +75,7 @@ std::unique_ptr<Graph> BatchMergePass::ApplyImpl(
std::vector<Node*> optimize_ops;
std::vector<Node*> lr_ops; // ops other than forward/backward/optimize
std::unordered_set<std::string> grad_names;
std::unordered_map<std::string, std::string> gradname2paramname;
std::vector<ir::Node*> nodes = TopologySortOperations(*graph);
auto origin_nodes = graph->ReleaseNodes();
......@@ -99,6 +100,7 @@ std::unique_ptr<Graph> BatchMergePass::ApplyImpl(
auto op_role_vars = boost::get<std::vector<std::string>>(op_role_var);
for (size_t i = 0; i < op_role_vars.size(); i += 2) {
grad_names.insert(op_role_vars[i + 1]);
gradname2paramname[op_role_vars[i + 1]] = op_role_vars[i];
}
} else if (op_role & static_cast<int>(framework::OpRole::kLRSched)) {
lr_ops.push_back(node);
......@@ -109,7 +111,7 @@ std::unique_ptr<Graph> BatchMergePass::ApplyImpl(
// 2. copy forward backward
ir::Node* prev_repeat_last_op_node = nullptr;
// record origin_grad -> repeated grad list map.
// record origin_grad -> repeated_grad_list map.
std::map<ir::Node*, std::vector<ir::Node*>> grad_repeated_map;
std::map<std::string, std::vector<ir::Node*>> created;
std::unordered_set<std::string> bn_vars_need_rename;
......@@ -124,10 +126,16 @@ std::unique_ptr<Graph> BatchMergePass::ApplyImpl(
if (grad_names.find(outname) != grad_names.end()) {
std::string new_gname = string::Sprintf("%s.repeat.%d", outname, i);
repeated_op.RenameOutput(outname, new_gname);
// remove op_role_var for backward ops that outputs grad for a
// parameter.
repeated_op.SetAttr(OpProtoAndCheckerMaker::OpRoleVarAttrName(),
std::vector<std::string>());
}
}
// 3.5 let batch_norm ops use independent vars, note batch_norm_grad do
// not need this update
// not need this update, because only moving mean and variance should be
// differ, trainable parameter scale and bias is the same as other
// parameters.
if (node->Name() == "batch_norm") {
// NOTE: assume bn op created by layers use save var as output mean and
// variance
......@@ -224,16 +232,25 @@ std::unique_ptr<Graph> BatchMergePass::ApplyImpl(
var->inputs.push_back(repeated_node);
}
}
}
} // end copy forward backward
// 5. create GRAD merge op node
// 5. create GRAD merge op node: sum(repeat.0...repeat.n) ->
// scale(1/num_repeats)
for (auto kv : grad_repeated_map) {
OpDesc sum_op;
sum_op.SetType("sum");
std::vector<std::string> repeated_grad_names;
std::vector<std::string> param_grad_op_role_var;
for (auto r : kv.second) {
repeated_grad_names.push_back(r->Var()->Name());
}
// NOTE: use op_role_var to control allreduce op appending in
// multi_devices_graph_pass, we want to append op_role_var
// only once for the merged gradient, so break after first call.
param_grad_op_role_var.push_back(
gradname2paramname.at(kv.first->Var()->Name())); // param
param_grad_op_role_var.push_back(kv.first->Var()->Name()); // grad
sum_op.SetInput("X", repeated_grad_names);
sum_op.SetOutput("Out", {kv.first->Var()->Name()});
sum_op.SetAttr(OpProtoAndCheckerMaker::OpRoleAttrName(),
......@@ -256,6 +273,10 @@ std::unique_ptr<Graph> BatchMergePass::ApplyImpl(
scale_op.SetAttr("scale", static_cast<float>(1.0f / num_repeats));
scale_op.SetAttr(OpProtoAndCheckerMaker::OpRoleAttrName(),
static_cast<int>(OpRole::kBackward));
scale_op.SetAttr(OpProtoAndCheckerMaker::OpRoleVarAttrName(),
param_grad_op_role_var);
auto scale_op_node = result.CreateOpNode(&scale_op);
scale_op_node->inputs.push_back(sum_out_var_node);
sum_out_var_node->outputs.push_back(scale_op_node);
......
paddle/fluid/framework/mixed_vector.h
浏览文件 @ b16e832d
......@@ -215,8 +215,8 @@ class Vector {
auto stream = dev_ctx->stream();
void *src = gpu_->ptr();
void *dst = cpu_.data();
memory::Copy(platform::CPUPlace(), dst, CUDAPlace().get(), src,
gpu_->size(), stream);
paddle::memory::Copy(platform::CPUPlace(), dst, CUDAPlace().get(), src,
gpu_->size(), stream);
dev_ctx->Wait();
}
......@@ -261,8 +261,8 @@ class Vector {
auto *dev_ctx = static_cast<platform::CUDADeviceContext *>(
platform::DeviceContextPool::Instance().Get(place));
auto stream = dev_ctx->stream();
memory::Copy(CUDAPlace().get(), dst, platform::CPUPlace(), src,
gpu_->size(), stream);
paddle::memory::Copy(CUDAPlace().get(), dst, platform::CPUPlace(), src,
gpu_->size(), stream);
}
void ImmutableCPU() const {
......@@ -284,7 +284,7 @@ class Vector {
bool IsInCPU() const { return flag_ & kDataInCPU; }
mutable std::vector<T> cpu_;
mutable memory::AllocationPtr gpu_;
mutable paddle::memory::AllocationPtr gpu_;
mutable int flag_;
mutable std::mutex mtx_;
......
paddle/fluid/framework/naive_executor.cc
浏览文件 @ b16e832d
......@@ -40,14 +40,14 @@ void NaiveExecutor::Prepare(Scope *scope, const ProgramDesc &program_desc,
void NaiveExecutor::Run() {
#ifndef PADDLE_ON_INFERENCE
LOG_FIRST_N(WARNING, 15) << "The NaiveExecutor can not work properly if the "
"cmake flag ON_INFER is not set.";
LOG_FIRST_N(WARNING, 15) << "Unlike the training phase, all the scopes and "
"variables will be reused to save the allocation "
"overhead.";
LOG_FIRST_N(WARNING, 15) << "Please re-compile the inference library by "
"setting the cmake flag ON_INFER=ON if you are "
"running Paddle Inference";
LOG_FIRST_N(WARNING, 5) << "The NaiveExecutor can not work properly if the "
"cmake flag ON_INFER is not set.";
LOG_FIRST_N(WARNING, 5) << "Unlike the training phase, all the scopes and "
"variables will be reused to save the allocation "
"overhead.";
LOG_FIRST_N(WARNING, 5) << "Please re-compile the inference library by "
"setting the cmake flag ON_INFER=ON if you are "
"running Paddle Inference";
#endif // PADDLE_ON_INFERENCE
for (auto &op : ops_) {
VLOG(3) << std::this_thread::get_id() << " run " << op->Type()
......
paddle/fluid/framework/ngraph_bridge.cc
浏览文件 @ b16e832d
......@@ -32,8 +32,11 @@ std::map<std::string,
std::string, std::shared_ptr<ngraph::Node>>>)>>
NgraphBridge::NG_NODE_MAP = {
{"fill_constant", paddle::operators::ngraphs::BuildFillConstantNode},
{"mean", paddle::operators::ngraphs::BuildMeanNode},
{"mean_grad", paddle::operators::ngraphs::BuildMeanGradNode},
{"mul", paddle::operators::ngraphs::BuildMulNode},
{"mul_grad", paddle::operators::ngraphs::BuildMulGradNode},
{"scale", paddle::operators::ngraphs::BuildScaleNode},
{"relu", paddle::operators::ngraphs::BuildUnaryNode<ngraph::op::Relu>},
{"tanh", paddle::operators::ngraphs::BuildUnaryNode<ngraph::op::Tanh>},
{"top_k", paddle::operators::ngraphs::BuildTopKNode}};
......
paddle/fluid/framework/ngraph_operator.cc
浏览文件 @ b16e832d
......@@ -399,7 +399,7 @@ void NgraphEngine::BuildNgFunction() {
BuildNgNodes();
ngraph_function_ = nullptr;
ngraph::NodeVector func_outputs;
ngraph::op::ParameterVector func_inputs;
ngraph::ParameterVector func_inputs;
for (auto& vo : var_out_) {
func_outputs.push_back(var_node_map_->at(vo));
......@@ -539,7 +539,7 @@ void NgraphEngine::Run(const Scope& scope, const platform::Place& place) const {
}
}
backend_->call(ngraph_function_, t_out, t_in);
backend_->call(backend_->compile(ngraph_function_), t_out, t_in);
} // NgraphEngine::RunImpl
} // namespace framework
} // namespace paddle
paddle/fluid/framework/op_proto_maker.cc
浏览文件 @ b16e832d
......@@ -82,10 +82,6 @@ void OpProtoAndCheckerMaker::operator()(proto::OpProto* proto,
AddAttr<std::string>(OpNamescopeAttrName(), "Operator name with namesope.")
.SetDefault("");
AddAttr<std::vector<std::string>>(OpCreationCallstackAttrName(),
"Callstack for Op Creatation.")
.SetDefault({});
Validate();
}
......
paddle/fluid/framework/op_proto_maker.h
浏览文件 @ b16e832d
......@@ -47,7 +47,6 @@ class OpProtoAndCheckerMaker {
static const char *OpRoleAttrName() { return "op_role"; }
static const char *OpRoleVarAttrName() { return "op_role_var"; }
static const char *OpNamescopeAttrName() { return "op_namescope"; }
static const char *OpCreationCallstackAttrName() { return "op_callstack"; }
void operator()(proto::OpProto *proto, OpAttrChecker *attr_checker);
......
paddle/fluid/framework/op_registry.h
浏览文件 @ b16e832d
......@@ -23,7 +23,8 @@ limitations under the License. */
#include <unordered_map>
#include <unordered_set>
#include "glog/logging.h" // For VLOG()
#define GLOG_NO_ABBREVIATED_SEVERITIES // msvc conflict logging with windows.h
#include "glog/logging.h" // For VLOG()
#include "paddle/fluid/framework/attribute.h"
#include "paddle/fluid/framework/details/op_registry.h"
#include "paddle/fluid/framework/framework.pb.h"
......
paddle/fluid/framework/operator.cc
浏览文件 @ b16e832d
......@@ -16,15 +16,9 @@ limitations under the License. */
#include <glog/logging.h>
#include <algorithm>
#include <sstream>
#include <string>
#include <vector>
#include "gflags/gflags.h"
#include "glog/logging.h"
#include "paddle/fluid/framework/data_transform.h"
#include "paddle/fluid/framework/executor.h"
#include "paddle/fluid/framework/lod_tensor.h"
#include "paddle/fluid/framework/op_proto_maker.h"
#include "paddle/fluid/framework/operator.h"
#include "paddle/fluid/framework/shape_inference.h"
#include "paddle/fluid/framework/transfer_scope_cache.h"
......@@ -162,67 +156,31 @@ RuntimeContext::RuntimeContext(const VariableNameMap& innames,
}
void OperatorBase::Run(const Scope& scope, const platform::Place& place) {
try {
if (VLOG_IS_ON(4)) {
VLOG(4) << place << " " << DebugStringEx(&scope);
}
if (platform::is_gpu_place(place)) {
VLOG(4) << place << " " << DebugStringEx(&scope);
if (platform::is_gpu_place(place)) {
#ifndef PADDLE_WITH_CUDA
PADDLE_THROW("Cannot run operator on place %s", place);
PADDLE_THROW("Cannot run operator on place %s", place);
#else
auto dev_id = boost::get<platform::CUDAPlace>(place).device;
platform::SetDeviceId(dev_id);
auto dev_id = boost::get<platform::CUDAPlace>(place).device;
platform::SetDeviceId(dev_id);
#endif
}
// The profile has a process-wide mutex, results in serious performance
// issue
// in concurrency scenerio. Here use an `if` to fix this issue.
// Please not remove the `if`, ask @Superjomn if there are any concern.
if (platform::IsProfileEnabled()) {
platform::DeviceContextPool& pool =
platform::DeviceContextPool::Instance();
platform::RecordEvent record_event(Type(), pool.Get(place));
RunImpl(scope, place);
} else {
RunImpl(scope, place);
}
if (VLOG_IS_ON(3)) {
VLOG(3) << place << " " << DebugStringEx(&scope);
}
} catch (platform::EnforceNotMet exception) {
if (Attrs().count("sub_block") != 0) {
throw exception;
}
auto& callstack = Attr<std::vector<std::string>>(
OpProtoAndCheckerMaker::OpCreationCallstackAttrName());
}
if (callstack.empty()) {
throw exception;
}
std::ostringstream sout;
sout << "Invoke operator " << Type() << " error.\n";
sout << "Python Callstacks: \n";
for (auto& line : callstack) {
sout << line;
}
sout << "C++ Callstacks: \n";
sout << exception.err_str_;
exception.err_str_ = sout.str();
throw exception;
} catch (...) {
std::rethrow_exception(std::current_exception());
// The profile has a process-wide mutex, results in serious performance issue
// in concurrency scenerio. Here use an `if` to fix this issue.
// Please not remove the `if`, ask @Superjomn if there are any concern.
if (platform::IsProfileEnabled()) {
platform::DeviceContextPool& pool = platform::DeviceContextPool::Instance();
platform::RecordEvent record_event(Type(), pool.Get(place));
RunImpl(scope, place);
} else {
RunImpl(scope, place);
}
VLOG(3) << place << " " << DebugStringEx(&scope);
}
bool OperatorBase::HasInputs(const std::string& name) const {
if (inputs_.find(name) != inputs_.end()) {
return true;
} else {
return false;
}
return inputs_.find(name) != inputs_.end();
}
std::string OperatorBase::Input(const std::string& name) const {
......@@ -421,7 +379,7 @@ const Tensor* GetLoDTensorOrSelectedRowsValueFromVar(const Variable& var) {
return &(var.Get<SelectedRows>().value());
} else {
PADDLE_THROW("Variable type_id %s, expect LoDTensor/SelectedRows.",
var.Type().name());
ToTypeName(var.Type()));
}
}
......@@ -432,7 +390,7 @@ Tensor* GetMutableLoDTensorOrSelectedRowsValueFromVar(Variable* var) {
return var->GetMutable<SelectedRows>()->mutable_value();
} else {
PADDLE_THROW("Variable type_id %s, expect LoDTensor/SelectedRows.",
var->Type().name());
ToTypeName(var->Type()));
}
}
......@@ -526,7 +484,7 @@ const std::vector<const Tensor*> ExecutionContext::MultiInput<Tensor>(
PADDLE_ENFORCE(
var->IsType<LoDTensor>(),
"should be LoDTensor, but the received type is %s",
var->Type().name());
ToTypeName(var->Type()));
return &(var->Get<LoDTensor>());
});
return res;
......@@ -545,7 +503,7 @@ const std::vector<const Tensor*> ExecutionContext::LegacyMultiInput<Tensor>(
PADDLE_ENFORCE(
var->IsType<LoDTensor>(),
"%s should be LoDTensor, but the received type is %s",
sub_name, var->Type().name());
sub_name, ToTypeName(var->Type()));
return &(var->Get<LoDTensor>());
});
return res;
......@@ -574,7 +532,7 @@ std::vector<Tensor*> ExecutionContext::MultiOutput<Tensor>(
PADDLE_ENFORCE(
var->IsType<LoDTensor>(),
"%s should be LoDTensor, but the received type is %s",
sub_name, var->Type().name());
sub_name, ToTypeName(var->Type()));
return var->GetMutable<LoDTensor>();
});
return res;
......@@ -816,7 +774,7 @@ class RuntimeInferShapeContext : public InferShapeContext {
PADDLE_THROW(
"Only LoDTensor/SelectedRows support 'GetDim', but Variables "
"type_id is %s.",
var->Type().name());
ToTypeName(var->Type()));
}
}
......@@ -839,7 +797,7 @@ class RuntimeInferShapeContext : public InferShapeContext {
var->GetMutable<SelectedRows>()->set_height(dim[0]);
} else {
PADDLE_THROW("Variable type_id %s, expect LoDTensor/SelectedRows.",
var->Type().name());
ToTypeName(var->Type()));
}
}
......@@ -1082,12 +1040,11 @@ Scope* OperatorWithKernel::PrepareData(
proto::VarType::Type OperatorWithKernel::IndicateDataType(
const ExecutionContext& ctx) const {
auto& scope = ctx.scope();
int data_type = -1;
std::string last_input_name;
for (auto& input : this->inputs_) {
for (auto& ipt_name : input.second) {
auto* var = scope.FindVar(ipt_name);
const std::vector<const Variable*> vars = ctx.MultiInputVar(input.first);
for (size_t i = 0; i < vars.size(); ++i) {
const Variable* var = vars[i];
if (var != nullptr) {
const Tensor* t = nullptr;
if (var->IsType<Tensor>()) {
......@@ -1098,15 +1055,14 @@ proto::VarType::Type OperatorWithKernel::IndicateDataType(
t = &(var->Get<SelectedRows>().value());
}
if (t != nullptr) {
PADDLE_ENFORCE(t->IsInitialized(), "Input %s is not initialized",
ipt_name);
PADDLE_ENFORCE(t->IsInitialized(), "Input %s(%lu)is not initialized",
input.first, i);
int tmp = static_cast<int>(t->type());
PADDLE_ENFORCE(
tmp == data_type || data_type == -1,
"DataType of Paddle Op %s must be the same. Get %s(%d) != %s(%d)",
Type(), last_input_name, data_type, ipt_name, tmp);
"DataType of Paddle Op %s must be the same. Get (%d) != (%d)",
Type(), data_type, tmp);
data_type = tmp;
last_input_name = ipt_name;
}
}
}
......
paddle/fluid/framework/operator.h
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paddle/fluid/framework/operator_test.cc
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......@@ -288,3 +288,30 @@ TEST(OpKernel, multi_inputs) {
auto op = paddle::framework::OpRegistry::CreateOp(op_desc);
op->Run(scope, cpu_place);
}
TEST(VarNameTest, all) {
std::string var_name("X");
std::string grad_var_name = paddle::framework::GradVarName(var_name);
ASSERT_EQ(grad_var_name, "X@GRAD");
std::string original_var_name =
paddle::framework::GradOriginalVarName(grad_var_name);
ASSERT_EQ(original_var_name, "X");
original_var_name = paddle::framework::GradOriginalVarName(original_var_name);
ASSERT_EQ(original_var_name, "X");
std::string var_name_2("XYZ");
grad_var_name = paddle::framework::GradVarName(var_name_2);
ASSERT_EQ(grad_var_name, "XYZ@GRAD");
original_var_name = paddle::framework::GradOriginalVarName(grad_var_name);
ASSERT_EQ(original_var_name, "XYZ");
original_var_name = paddle::framework::GradOriginalVarName(original_var_name);
ASSERT_EQ(original_var_name, "XYZ");
std::string var_name_3("");
grad_var_name = paddle::framework::GradVarName(var_name_3);
ASSERT_EQ(grad_var_name, "@GRAD");
original_var_name = paddle::framework::GradOriginalVarName(grad_var_name);
ASSERT_EQ(original_var_name, "");
original_var_name = paddle::framework::GradOriginalVarName(original_var_name);
ASSERT_EQ(original_var_name, "");
}
paddle/fluid/framework/parallel_executor.h
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......@@ -28,6 +28,10 @@ limitations under the License. */
#include "paddle/fluid/framework/tensor.h"
#include "paddle/fluid/platform/device_context.h"
#if defined(PADDLE_WITH_CUDA) && !defined(_WIN32)
#include "paddle/fluid/platform/nccl_helper.h"
#endif
namespace paddle {
namespace framework {
......@@ -68,8 +72,14 @@ class ParallelExecutor {
private:
void BCastParamsToDevices(const std::unordered_set<std::string> &vars) const;
bool EnableParallelGraphExecution(const ProgramDesc &main_program,
const ExecutionStrategy &exec_strategy,
const BuildStrategy &build_strategy) const;
ParallelExecutorPrivate *member_;
#if defined(PADDLE_WITH_CUDA) && !defined(_WIN32)
std::unique_ptr<ncclUniqueId> local_nccl_id_;
#endif
};
} // namespace framework
......
paddle/fluid/framework/rw_lock.h
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paddle/fluid/framework/scope.cc
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paddle/fluid/framework/scope.h
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paddle/fluid/framework/scope_pool.cc 0 → 100644
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paddle/fluid/framework/threadpool.cc
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......@@ -89,7 +89,6 @@ void ThreadPool::TaskLoop() {
task = std::move(tasks_.front());
tasks_.pop();
}
// run the task
task();
}
......
paddle/fluid/framework/unroll_array_ops.h 0 → 100644
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paddle/fluid/framework/unroll_array_ops_test.cc 0 → 100644
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paddle/fluid/framework/var_type.h
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paddle/fluid/framework/var_type_inference_test.cc
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......@@ -108,7 +108,7 @@ TEST(InferVarType, sum_op_without_infer_var_type) {
op->InferVarType(prog.MutableBlock(0));
ASSERT_EQ(proto::VarType_Type_LOD_TENSOR,
ASSERT_EQ(proto::VarType::LOD_TENSOR,
prog.MutableBlock(0)->Var("test2_out")->GetType());
}
......
paddle/fluid/framework/var_type_traits.cc 0 → 100644
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paddle/fluid/framework/var_type_traits.h 0 → 100644
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paddle/fluid/framework/var_type_traits_test.cc 0 → 100644
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paddle/fluid/framework/variable.h
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paddle/fluid/imperative/layer.cc
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paddle/fluid/imperative/layer.h
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paddle/fluid/imperative/tracer.h
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paddle/fluid/inference/api/helper.h
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paddle/fluid/inference/tensorrt/op_teller.cc 0 → 100644
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paddle/fluid/inference/tensorrt/op_teller.h 0 → 100644
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paddle/fluid/operators/conv_op.cc
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paddle/fluid/operators/conv_op.h
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paddle/fluid/operators/crop_op.h
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paddle/fluid/operators/cudnn_rnn_cache.h 0 → 100644
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paddle/fluid/operators/cum_op.h
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paddle/fluid/operators/distributed/distributed_pb.h 0 → 100644
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paddle/fluid/operators/expand_op.h
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paddle/fluid/operators/fc_op.cc
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paddle/fluid/operators/fill_constant_op.cu.cc 0 → 100644
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paddle/fluid/operators/fill_constant_op.h 0 → 100644
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paddle/fluid/operators/load_op.cc
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paddle/fluid/operators/mul_op.cc
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paddle/fluid/operators/nce_op.cc
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paddle/fluid/operators/nce_op.h
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paddle/fluid/operators/ngraph/ops/mean_op.h 0 → 100644
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paddle/fluid/operators/ngraph/ops/scale_op.h 0 → 100644
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paddle/fluid/operators/norm_op.h
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paddle/fluid/operators/save_op.cc
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paddle/fluid/operators/sum_op.cc
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paddle/fluid/operators/sum_op.h
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paddle/fluid/platform/enforce.h
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paddle/fluid/platform/nccl_helper.h
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paddle/fluid/platform/profiler.cc
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paddle/fluid/platform/timer.cc 0 → 100644
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paddle/fluid/platform/timer.h 0 → 100644
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paddle/fluid/platform/timer_test.cc 0 → 100644
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paddle/fluid/pybind/CMakeLists.txt
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paddle/fluid/pybind/const_value.cc
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paddle/fluid/pybind/imperative.cc
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paddle/fluid/pybind/pybind.cc
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paddle/fluid/string/printf.h
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paddle/scripts/paddle_build.sh
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paddle/testing/paddle_gtest_main.cc
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python/paddle/fluid/__init__.py
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python/paddle/fluid/data_feeder.py
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python/paddle/fluid/executor.py
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python/paddle/fluid/framework.py
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python/paddle/fluid/imperative/nn.py 0 → 100644
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python/paddle/fluid/initializer.py
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python/paddle/fluid/layer_helper.py
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python/paddle/fluid/layers/io.py
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python/paddle/fluid/layers/nn.py
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python/paddle/fluid/metrics.py
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python/paddle/fluid/optimizer.py
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tools/check_doc_approval.py 0 → 100644
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