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PaddleDetection

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PaddleDetection
提交 79d4a99d 编写于 作者: L luotao1
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Merge branch 'develop' into anakin_v4

上级 e89936fc 1f09bc32
隐藏空白更改
内联 并排
Showing with 2126 addition and 351 deletion
CMakeLists.txt
浏览文件 @ 79d4a99d
......@@ -24,6 +24,9 @@ message(STATUS "CXX compiler: ${CMAKE_CXX_COMPILER}, version: "
"${CMAKE_CXX_COMPILER_ID} ${CMAKE_CXX_COMPILER_VERSION}")
message(STATUS "C compiler: ${CMAKE_C_COMPILER}, version: "
"${CMAKE_C_COMPILER_ID} ${CMAKE_C_COMPILER_VERSION}")
if(WIN32)
set(CMAKE_STATIC_LIBRARY_PREFIX lib)
endif(WIN32)
if(NOT CMAKE_CROSSCOMPILING)
find_package(CUDA QUIET)
......@@ -165,7 +168,6 @@ include(external/python) # download, build, install python
include(external/openblas) # download, build, install openblas
include(external/mkldnn) # download, build, install mkldnn
include(external/swig) # download, build, install swig
include(external/warpctc) # download, build, install warpctc
include(external/boost) # download boost
include(external/any) # download libn::any
include(external/eigen) # download eigen3
......@@ -173,6 +175,14 @@ include(external/pybind11) # download pybind11
include(external/cares)
include(external/cub)
if (NOT WIN32)
# there is no official support of snappystream, warpctc, nccl, cupti in windows
include(external/snappy) # download snappy
include(external/snappystream) # download snappystream
include(external/warpctc) # download, build, install warpctc
include(cupti)
endif (NOT WIN32)
if(WITH_DISTRIBUTE)
if(WITH_GRPC)
include(external/grpc)
......@@ -194,13 +204,10 @@ if(WITH_BRPC_RDMA)
endif()
endif()
include(external/snappy) # download snappy
include(external/snappystream)
include(external/threadpool)
include(external/threadpool)
include(flags) # set paddle compile flags
include(cudnn) # set cudnn libraries, must before configure
include(cupti)
include(configure) # add paddle env configuration
if(WITH_GPU)
......
cmake/configure.cmake
浏览文件 @ 79d4a99d
......@@ -61,6 +61,11 @@ if(NOT CMAKE_CROSSCOMPILING)
endif()
endif()
if(WIN32)
# windows stupid compile option for all targets.
add_definitions(-D_XKEYCHECK_H)
endif(WIN32)
if(NOT WITH_GOLANG)
add_definitions(-DPADDLE_WITHOUT_GOLANG)
endif(NOT WITH_GOLANG)
......
cmake/external/boost.cmake
浏览文件 @ 79d4a99d
......@@ -28,7 +28,12 @@ if((NOT DEFINED BOOST_TAR) OR (NOT DEFINED BOOST_URL))
set(BOOST_TAR "boost_1_41_0" CACHE STRING "" FORCE)
set(BOOST_URL "http://paddlepaddledeps.cdn.bcebos.com/${BOOST_TAR}.tar.gz" CACHE STRING "" FORCE)
endif()
MESSAGE(STATUS "BOOST_TAR: ${BOOST_TAR}, BOOST_URL: ${BOOST_URL}")
IF (WIN32)
MESSAGE(WARNING, "In windows, boost can not be downloaded automaticlly, please build it manually and put it at " ${THIRD_PARTY_PATH}install/boost)
else()
MESSAGE(STATUS "BOOST_TAR: ${BOOST_TAR}, BOOST_URL: ${BOOST_URL}")
ENDIF(WIN32)
set(BOOST_SOURCES_DIR ${THIRD_PARTY_PATH}/boost)
set(BOOST_DOWNLOAD_DIR "${BOOST_SOURCES_DIR}/src/${BOOST_PROJECT}")
set(BOOST_INCLUDE_DIR "${BOOST_DOWNLOAD_DIR}/${BOOST_TAR}" CACHE PATH "boost include directory." FORCE)
......@@ -36,12 +41,13 @@ set_directory_properties(PROPERTIES CLEAN_NO_CUSTOM 1)
include_directories(${BOOST_INCLUDE_DIR})
if (NOT WIN32)
ExternalProject_Add(
${BOOST_PROJECT}
${EXTERNAL_PROJECT_LOG_ARGS}
DOWNLOAD_DIR ${BOOST_DOWNLOAD_DIR}
DOWNLOAD_COMMAND wget --no-check-certificate ${BOOST_URL} -c -q -O ${BOOST_TAR}.tar.gz
&& tar zxf ${BOOST_TAR}.tar.gz
&& tar zxf ${BOOST_TAR}.tar.gz
DOWNLOAD_NO_PROGRESS 1
PREFIX ${BOOST_SOURCES_DIR}
CONFIGURE_COMMAND ""
......@@ -49,8 +55,9 @@ ExternalProject_Add(
INSTALL_COMMAND ""
UPDATE_COMMAND ""
)
endif(NOT WIN32)
if (${CMAKE_VERSION} VERSION_LESS "3.3.0")
if (${CMAKE_VERSION} VERSION_LESS "3.3.0" OR NOT WIN32)
set(dummyfile ${CMAKE_CURRENT_BINARY_DIR}/boost_dummy.c)
file(WRITE ${dummyfile} "const char *dummy = \"${dummyfile}\";")
add_library(boost STATIC ${dummyfile})
......
cmake/external/gflags.cmake
浏览文件 @ 79d4a99d
......@@ -18,7 +18,7 @@ SET(GFLAGS_SOURCES_DIR ${THIRD_PARTY_PATH}/gflags)
SET(GFLAGS_INSTALL_DIR ${THIRD_PARTY_PATH}/install/gflags)
SET(GFLAGS_INCLUDE_DIR "${GFLAGS_INSTALL_DIR}/include" CACHE PATH "gflags include directory." FORCE)
IF(WIN32)
set(GFLAGS_LIBRARIES "${GFLAGS_INSTALL_DIR}/lib/gflags.lib" CACHE FILEPATH "GFLAGS_LIBRARIES" FORCE)
set(GFLAGS_LIBRARIES "${GFLAGS_INSTALL_DIR}/lib/libgflags.lib" CACHE FILEPATH "GFLAGS_LIBRARIES" FORCE)
ELSE(WIN32)
set(GFLAGS_LIBRARIES "${GFLAGS_INSTALL_DIR}/lib/libgflags.a" CACHE FILEPATH "GFLAGS_LIBRARIES" FORCE)
ENDIF(WIN32)
......@@ -45,7 +45,13 @@ ExternalProject_Add(
-DCMAKE_POSITION_INDEPENDENT_CODE:BOOL=ON
-DCMAKE_BUILD_TYPE:STRING=${THIRD_PARTY_BUILD_TYPE}
)
IF(WIN32)
IF(NOT EXISTS "${GFLAGS_INSTALL_DIR}/lib/libgflags.lib")
add_custom_command(TARGET extern_gflags POST_BUILD
COMMAND cmake -E rename ${GFLAGS_INSTALL_DIR}/lib/gflags_static.lib ${GFLAGS_INSTALL_DIR}/lib/libgflags.lib
)
ENDIF()
ENDIF(WIN32)
ADD_LIBRARY(gflags STATIC IMPORTED GLOBAL)
SET_PROPERTY(TARGET gflags PROPERTY IMPORTED_LOCATION ${GFLAGS_LIBRARIES})
ADD_DEPENDENCIES(gflags extern_gflags)
......@@ -60,3 +66,4 @@ IF(WITH_C_API)
INSTALL(FILES ${GFLAGS_LIBRARIES} DESTINATION third_party/gflags/lib)
ENDIF()
ENDIF()
cmake/external/glog.cmake
浏览文件 @ 79d4a99d
......@@ -60,6 +60,13 @@ ExternalProject_Add(
-DCMAKE_POSITION_INDEPENDENT_CODE:BOOL=ON
-DCMAKE_BUILD_TYPE:STRING=${THIRD_PARTY_BUILD_TYPE}
)
IF(WIN32)
IF(NOT EXISTS "${GLOG_INSTALL_DIR}/lib/libglog.lib")
add_custom_command(TARGET extern_glog POST_BUILD
COMMAND cmake -E rename ${GLOG_INSTALL_DIR}/lib/glog.lib ${GLOG_INSTALL_DIR}/lib/libglog.lib
)
ENDIF()
ENDIF(WIN32)
ADD_LIBRARY(glog STATIC IMPORTED GLOBAL)
SET_PROPERTY(TARGET glog PROPERTY IMPORTED_LOCATION ${GLOG_LIBRARIES})
......
cmake/external/openblas.cmake
浏览文件 @ 79d4a99d
......@@ -17,20 +17,29 @@ IF(USE_EIGEN_FOR_BLAS)
ENDIF(USE_EIGEN_FOR_BLAS)
INCLUDE(cblas)
# IF(WIN32 AND NOT ${CBLAS_FOUND})
IF(NOT ${CBLAS_FOUND})
INCLUDE(ExternalProject)
SET(CBLAS_SOURCES_DIR ${THIRD_PARTY_PATH}/openblas)
SET(CBLAS_INSTALL_DIR ${THIRD_PARTY_PATH}/install/openblas)
SET(CBLAS_INC_DIR "${CBLAS_INSTALL_DIR}/include" CACHE PATH "openblas include directory." FORCE)
SET(CBLAS_INCLUDE_DIR "${CBLAS_INSTALL_DIR}/include" CACHE PATH "openblas include directory." FORCE)
SET(CBLAS_LIBRARIES
"${CBLAS_INSTALL_DIR}/lib/${CMAKE_STATIC_LIBRARY_PREFIX}openblas${CMAKE_STATIC_LIBRARY_SUFFIX}"
CACHE FILEPATH "openblas library." FORCE)
ADD_DEFINITIONS(-DPADDLE_USE_OPENBLAS)
IF (WIN32)
SET(CBLAS_FOUND true)
MESSAGE(WARNING, "In windows, openblas only support msvc build, please build it manually and put it at " ${CBLAS_INSTALL_DIR})
ENDIF(WIN32)
IF (NOT WIN32)
SET(OPENBLAS_CC "${CMAKE_C_COMPILER} -Wno-unused-but-set-variable -Wno-unused-variable")
SET(OPENBLAS_COMMIT "v0.2.20")
......@@ -69,7 +78,6 @@ IF(NOT ${CBLAS_FOUND})
ENDIF()
SET(COMMON_ARGS CC=${OPENBLAS_CC} NO_SHARED=1 NO_LAPACK=1 libs)
ExternalProject_Add(
extern_openblas
${EXTERNAL_PROJECT_LOG_ARGS}
......@@ -84,9 +92,11 @@ IF(NOT ${CBLAS_FOUND})
UPDATE_COMMAND ""
CONFIGURE_COMMAND ""
)
ELSE()
ENDIF(NOT WIN32)
SET(CBLAS_PROVIDER openblas)
IF(WITH_C_API)
INSTALL(DIRECTORY ${CBLAS_INC_DIR} DESTINATION third_party/openblas)
INSTALL(DIRECTORY ${CBLAS_INCLUDE_DIR} DESTINATION third_party/openblas)
# Because libopenblas.a is a symbolic link of another library, thus need to
# install the whole directory.
IF(ANDROID)
......@@ -107,7 +117,8 @@ IF(NOT ${CBLAS_FOUND})
ENDIF(NOT ${CBLAS_FOUND})
MESSAGE(STATUS "BLAS library: ${CBLAS_LIBRARIES}")
INCLUDE_DIRECTORIES(${CBLAS_INC_DIR})
MESSAGE(STATUS "BLAS Include: ${CBLAS_INCLUDE_DIR}")
INCLUDE_DIRECTORIES(${CBLAS_INCLUDE_DIR})
# FIXME(gangliao): generate cblas target to track all high performance
# linear algebra libraries for cc_library(xxx SRCS xxx.c DEPS cblas)
......
cmake/external/protobuf.cmake
浏览文件 @ 79d4a99d
......@@ -14,11 +14,14 @@
INCLUDE(ExternalProject)
# Always invoke `FIND_PACKAGE(Protobuf)` for importing function protobuf_generate_cpp
IF(NOT WIN32)
FIND_PACKAGE(Protobuf QUIET)
ENDIF(NOT WIN32)
macro(UNSET_VAR VAR_NAME)
UNSET(${VAR_NAME} CACHE)
UNSET(${VAR_NAME})
endmacro()
UNSET_VAR(PROTOBUF_INCLUDE_DIR)
UNSET_VAR(PROTOBUF_FOUND)
UNSET_VAR(PROTOBUF_PROTOC_EXECUTABLE)
......@@ -94,12 +97,14 @@ macro(PROMPT_PROTOBUF_LIB)
SET(protobuf_DEPS ${ARGN})
MESSAGE(STATUS "Protobuf protoc executable: ${PROTOBUF_PROTOC_EXECUTABLE}")
MESSAGE(STATUS "Protobuf-lite library: ${PROTOBUF_LITE_LIBRARY}")
MESSAGE(STATUS "Protobuf library: ${PROTOBUF_LIBRARY}")
MESSAGE(STATUS "Protoc library: ${PROTOBUF_PROTOC_LIBRARY}")
MESSAGE(STATUS "Protobuf version: ${PROTOBUF_VERSION}")
INCLUDE_DIRECTORIES(${PROTOBUF_INCLUDE_DIR})
# Assuming that all the protobuf libraries are of the same type.
IF(${PROTOBUF_LIBRARY} MATCHES "${CMAKE_STATIC_LIBRARY_SUFFIX}$")
IF(${PROTOBUF_LIBRARY} MATCHES ${CMAKE_STATIC_LIBRARY_SUFFIX})
SET(protobuf_LIBTYPE STATIC)
ELSEIF(${PROTOBUF_LIBRARY} MATCHES "${CMAKE_SHARED_LIBRARY_SUFFIX}$")
SET(protobuf_LIBTYPE SHARED)
......@@ -137,18 +142,25 @@ macro(SET_PROTOBUF_VERSION)
endmacro()
set(PROTOBUF_ROOT "" CACHE PATH "Folder contains protobuf")
IF (WIN32)
SET(PROTOBUF_ROOT ${THIRD_PARTY_PATH}/install/protobuf)
MESSAGE(WARNING, "In windows, protobuf only support msvc build, please build it manually and put it at " ${PROTOBUF_ROOT})
ENDIF(WIN32)
if (NOT "${PROTOBUF_ROOT}" STREQUAL "")
find_path(PROTOBUF_INCLUDE_DIR google/protobuf/message.h PATHS ${PROTOBUF_ROOT}/include NO_DEFAULT_PATH)
find_library(PROTOBUF_LIBRARY protobuf PATHS ${PROTOBUF_ROOT}/lib NO_DEFAULT_PATH)
find_library(PROTOBUF_LITE_LIBRARY protobuf-lite PATHS ${PROTOBUF_ROOT}/lib NO_DEFAULT_PATH)
find_library(PROTOBUF_PROTOC_LIBRARY protoc PATHS ${PROTOBUF_ROOT}/lib NO_DEFAULT_PATH)
find_library(PROTOBUF_LIBRARY protobuf libprotobuf.lib PATHS ${PROTOBUF_ROOT}/lib NO_DEFAULT_PATH)
find_library(PROTOBUF_LITE_LIBRARY protobuf-lite libprotobuf-lite.lib PATHS ${PROTOBUF_ROOT}/lib NO_DEFAULT_PATH)
find_library(PROTOBUF_PROTOC_LIBRARY protoc libprotoc.lib PATHS ${PROTOBUF_ROOT}/lib NO_DEFAULT_PATH)
find_program(PROTOBUF_PROTOC_EXECUTABLE protoc PATHS ${PROTOBUF_ROOT}/bin NO_DEFAULT_PATH)
if (PROTOBUF_INCLUDE_DIR AND PROTOBUF_LIBRARY AND PROTOBUF_LITE_LIBRARY AND PROTOBUF_PROTOC_LIBRARY AND PROTOBUF_PROTOC_EXECUTABLE)
message(STATUS "Using custom protobuf library in ${PROTOBUF_ROOT}.")
SET(PROTOBUF_FOUND true)
SET_PROTOBUF_VERSION()
PROMPT_PROTOBUF_LIB()
else()
message(WARNING "Cannot find protobuf library in ${PROTOBUF_ROOT}.")
message(WARNING "Cannot find protobuf library in ${PROTOBUF_ROOT}")
endif()
endif()
......@@ -239,6 +251,7 @@ IF(CMAKE_CROSSCOMPILING)
CACHE FILEPATH "protobuf executable." FORCE)
ENDIF()
IF(NOT PROTOBUF_FOUND)
build_protobuf(extern_protobuf FALSE)
......
cmake/generic.cmake
浏览文件 @ 79d4a99d
......@@ -148,7 +148,8 @@ function(merge_static_libs TARGET_NAME)
COMMAND rm "${CMAKE_CURRENT_BINARY_DIR}/lib${TARGET_NAME}.a"
COMMAND /usr/bin/libtool -static -o "${CMAKE_CURRENT_BINARY_DIR}/lib${TARGET_NAME}.a" ${libfiles}
)
else() # general UNIX: use "ar" to extract objects and re-add to a common lib
endif(APPLE)
if(LINUX) # general UNIX: use "ar" to extract objects and re-add to a common lib
set(target_DIR ${CMAKE_CURRENT_BINARY_DIR}/${TARGET_NAME}.dir)
foreach(lib ${libs})
......@@ -187,7 +188,36 @@ function(merge_static_libs TARGET_NAME)
COMMAND ${CMAKE_AR} crs ${target_LIBNAME} `find ${target_DIR} -name '*.o'`
COMMAND ${CMAKE_RANLIB} ${target_LIBNAME}
WORKING_DIRECTORY ${target_DIR})
endif()
endif(LINUX)
if(WIN32) # windows do not support gcc/nvcc combined compiling. Use msvc lib.exe to merge libs.
# Make the generated dummy source file depended on all static input
# libs. If input lib changes,the source file is touched
# which causes the desired effect (relink).
add_custom_command(OUTPUT ${target_SRCS}
COMMAND ${CMAKE_COMMAND} -E touch ${target_SRCS}
DEPENDS ${libs})
# Generate dummy staic lib
file(WRITE ${target_SRCS} "const char *dummy_${TARGET_NAME} = \"${target_SRCS}\";")
add_library(${TARGET_NAME} STATIC ${target_SRCS})
target_link_libraries(${TARGET_NAME} ${libs_deps})
foreach(lib ${libs})
# Get the file names of the libraries to be merged
#if(NOT $<TARGET_FILE:${lib}> MATCHES "lib.*\\.lib")
# message("library" ${lib})
# set(libfiles ${libfiles} lib$<TARGET_FILE:${lib}>)
#else()
set(libfiles ${libfiles} $<TARGET_FILE:${lib}>)
#endif()
endforeach()
# windows cmd return error in clean env.
# COMMAND del "${CMAKE_CURRENT_BINARY_DIR}/${CMAKE_BUILD_TYPE}/${TARGET_NAME}.lib"
add_custom_command(TARGET ${TARGET_NAME} POST_BUILD
COMMAND lib /OUT:${CMAKE_CURRENT_BINARY_DIR}/lib${TARGET_NAME}.lib ${libfiles}
)
endif(WIN32)
endfunction(merge_static_libs)
function(cc_library TARGET_NAME)
......@@ -195,6 +225,10 @@ function(cc_library TARGET_NAME)
set(oneValueArgs "")
set(multiValueArgs SRCS DEPS)
cmake_parse_arguments(cc_library "${options}" "${oneValueArgs}" "${multiValueArgs}" ${ARGN})
if(WIN32)
# add libxxx.lib prefix in windows
set(${TARGET_NAME}_LIB_NAME "${CMAKE_STATIC_LIBRARY_PREFIX}${TARGET_NAME}${CMAKE_STATIC_LIBRARY_SUFFIX}" CACHE STRING "output library name for target ${TARGET_NAME}")
endif(WIN32)
if(cc_library_SRCS)
if(cc_library_SHARED OR cc_library_shared) # build *.so
add_library(${TARGET_NAME} SHARED ${cc_library_SRCS})
......
cmake/inference_lib.cmake
浏览文件 @ 79d4a99d
......@@ -101,6 +101,7 @@ if(WITH_MKLDNN)
)
endif()
if (NOT WIN32)
if(NOT MOBILE_INFERENCE AND NOT RPI)
set(dst_dir "${FLUID_INSTALL_DIR}/third_party/install/snappy")
copy(snappy_lib
......@@ -120,15 +121,23 @@ if(NOT MOBILE_INFERENCE AND NOT RPI)
DSTS ${dst_dir} ${dst_dir}/lib
DEPS zlib)
endif()
endif(NOT WIN32)
# paddle fluid module
set(src_dir "${PADDLE_SOURCE_DIR}/paddle/fluid")
set(dst_dir "${FLUID_INSTALL_DIR}/paddle/fluid")
set(module "framework")
if (NOT WIN32)
copy(framework_lib DEPS framework_py_proto
SRCS ${src_dir}/${module}/*.h ${src_dir}/${module}/details/*.h ${PADDLE_BINARY_DIR}/paddle/fluid/framework/framework.pb.h
DSTS ${dst_dir}/${module} ${dst_dir}/${module}/details ${dst_dir}/${module}
)
else()
copy(framework_lib
SRCS ${src_dir}/${module}/*.h ${src_dir}/${module}/details/*.h ${PADDLE_BINARY_DIR}/paddle/fluid/framework/framework.pb.h
DSTS ${dst_dir}/${module} ${dst_dir}/${module}/details ${dst_dir}/${module}
)
endif(NOT WIN32)
set(module "memory")
copy(memory_lib
......
doc/fluid/new_docs/advanced_usage/deploy/native_infer.rst
浏览文件 @ 79d4a99d
......@@ -9,8 +9,6 @@ Paddle 预测 API
- 头文件 ``paddle_inference_api.h`` 定义了所有的接口
- 库文件\ ``libpaddle_fluid.so`` 或 ``libpaddle_fluid.a``
- 库文件 ``libpaddle_inference_api.so`` 或
``libpaddle_inference_api.a``
编译和依赖可以参考 :ref:`install_or_build_cpp_inference_lib` 。
......@@ -97,8 +95,7 @@ engine
CHECK(predictor->Run(slots, &outputs));
// 获取 outputs ...
编译时，联编 ``libpaddle_fluid.a/.so`` 和
``libpaddle_inference_api.a/.so`` 便可。
编译时，联编 ``libpaddle_fluid.a/.so`` 即可。
详细代码参考
------------
......
paddle/fluid/API.spec
浏览文件 @ 79d4a99d
......@@ -162,6 +162,7 @@ paddle.fluid.layers.crop ArgSpec(args=['x', 'shape', 'offsets', 'name'], varargs
paddle.fluid.layers.rank_loss ArgSpec(args=['label', 'left', 'right', 'name'], varargs=None, keywords=None, defaults=(None,))
paddle.fluid.layers.prelu ArgSpec(args=['x', 'mode', 'param_attr', 'name'], varargs=None, keywords=None, defaults=(None, None))
paddle.fluid.layers.flatten ArgSpec(args=['x', 'axis', 'name'], varargs=None, keywords=None, defaults=(1, None))
paddle.fluid.layers.sequence_mask ArgSpec(args=['x', 'maxlen', 'dtype', 'name'], varargs=None, keywords=None, defaults=(None, 'int64', None))
paddle.fluid.layers.stack ArgSpec(args=['x', 'axis'], varargs=None, keywords=None, defaults=(0,))
paddle.fluid.layers.data ArgSpec(args=['name', 'shape', 'append_batch_size', 'dtype', 'lod_level', 'type', 'stop_gradient'], varargs=None, keywords=None, defaults=(True, 'float32', 0, VarType.LOD_TENSOR, True))
paddle.fluid.layers.open_recordio_file ArgSpec(args=['filename', 'shapes', 'lod_levels', 'dtypes', 'pass_num', 'for_parallel'], varargs=None, keywords=None, defaults=(1, True))
......@@ -377,7 +378,7 @@ paddle.fluid.LoDTensor.__init__ 1. __init__(self: paddle.fluid.core.LoDTensor, a
paddle.fluid.LoDTensor.has_valid_recursive_sequence_lengths has_valid_recursive_sequence_lengths(self: paddle.fluid.core.LoDTensor) -> bool
paddle.fluid.LoDTensor.lod lod(self: paddle.fluid.core.LoDTensor) -> List[List[int]]
paddle.fluid.LoDTensor.recursive_sequence_lengths recursive_sequence_lengths(self: paddle.fluid.core.LoDTensor) -> List[List[int]]
paddle.fluid.LoDTensor.set 1. set(self: paddle.fluid.core.Tensor, arg0: numpy.ndarray[float32], arg1: paddle::platform::CPUPlace) -> None 2. set(self: paddle.fluid.core.Tensor, arg0: numpy.ndarray[int32], arg1: paddle::platform::CPUPlace) -> None 3. set(self: paddle.fluid.core.Tensor, arg0: numpy.ndarray[float64], arg1: paddle::platform::CPUPlace) -> None 4. set(self: paddle.fluid.core.Tensor, arg0: numpy.ndarray[int64], arg1: paddle::platform::CPUPlace) -> None 5. set(self: paddle.fluid.core.Tensor, arg0: numpy.ndarray[bool], arg1: paddle::platform::CPUPlace) -> None 6. set(self: paddle.fluid.core.Tensor, arg0: numpy.ndarray[uint16], arg1: paddle::platform::CPUPlace) -> None 7. set(self: paddle.fluid.core.Tensor, arg0: numpy.ndarray[uint8], arg1: paddle::platform::CPUPlace) -> None 8. set(self: paddle.fluid.core.Tensor, arg0: numpy.ndarray[float32], arg1: paddle::platform::CUDAPlace) -> None 9. set(self: paddle.fluid.core.Tensor, arg0: numpy.ndarray[int32], arg1: paddle::platform::CUDAPlace) -> None 10. set(self: paddle.fluid.core.Tensor, arg0: numpy.ndarray[float64], arg1: paddle::platform::CUDAPlace) -> None 11. set(self: paddle.fluid.core.Tensor, arg0: numpy.ndarray[int64], arg1: paddle::platform::CUDAPlace) -> None 12. set(self: paddle.fluid.core.Tensor, arg0: numpy.ndarray[bool], arg1: paddle::platform::CUDAPlace) -> None 13. set(self: paddle.fluid.core.Tensor, arg0: numpy.ndarray[uint16], arg1: paddle::platform::CUDAPlace) -> None 14. set(self: paddle.fluid.core.Tensor, arg0: numpy.ndarray[uint8], arg1: paddle::platform::CUDAPlace) -> None 15. set(self: paddle.fluid.core.Tensor, arg0: numpy.ndarray[float32], arg1: paddle::platform::CUDAPinnedPlace) -> None 16. set(self: paddle.fluid.core.Tensor, arg0: numpy.ndarray[int32], arg1: paddle::platform::CUDAPinnedPlace) -> None 17. set(self: paddle.fluid.core.Tensor, arg0: numpy.ndarray[float64], arg1: paddle::platform::CUDAPinnedPlace) -> None 18. set(self: paddle.fluid.core.Tensor, arg0: numpy.ndarray[int64], arg1: paddle::platform::CUDAPinnedPlace) -> None 19. set(self: paddle.fluid.core.Tensor, arg0: numpy.ndarray[bool], arg1: paddle::platform::CUDAPinnedPlace) -> None 20. set(self: paddle.fluid.core.Tensor, arg0: numpy.ndarray[uint16], arg1: paddle::platform::CUDAPinnedPlace) -> None 21. set(self: paddle.fluid.core.Tensor, arg0: numpy.ndarray[uint8], arg1: paddle::platform::CUDAPinnedPlace) -> None
paddle.fluid.LoDTensor.set 1. set(self: paddle.fluid.core.Tensor, arg0: numpy.ndarray[float32], arg1: paddle::platform::CPUPlace) -> None 2. set(self: paddle.fluid.core.Tensor, arg0: numpy.ndarray[int32], arg1: paddle::platform::CPUPlace) -> None 3. set(self: paddle.fluid.core.Tensor, arg0: numpy.ndarray[float64], arg1: paddle::platform::CPUPlace) -> None 4. set(self: paddle.fluid.core.Tensor, arg0: numpy.ndarray[int64], arg1: paddle::platform::CPUPlace) -> None 5. set(self: paddle.fluid.core.Tensor, arg0: numpy.ndarray[bool], arg1: paddle::platform::CPUPlace) -> None 6. set(self: paddle.fluid.core.Tensor, arg0: numpy.ndarray[uint16], arg1: paddle::platform::CPUPlace) -> None 7. set(self: paddle.fluid.core.Tensor, arg0: numpy.ndarray[uint8], arg1: paddle::platform::CPUPlace) -> None 8. set(self: paddle.fluid.core.Tensor, arg0: numpy.ndarray[int8], arg1: paddle::platform::CPUPlace) -> None 9. set(self: paddle.fluid.core.Tensor, arg0: numpy.ndarray[float32], arg1: paddle::platform::CUDAPlace) -> None 10. set(self: paddle.fluid.core.Tensor, arg0: numpy.ndarray[int32], arg1: paddle::platform::CUDAPlace) -> None 11. set(self: paddle.fluid.core.Tensor, arg0: numpy.ndarray[float64], arg1: paddle::platform::CUDAPlace) -> None 12. set(self: paddle.fluid.core.Tensor, arg0: numpy.ndarray[int64], arg1: paddle::platform::CUDAPlace) -> None 13. set(self: paddle.fluid.core.Tensor, arg0: numpy.ndarray[bool], arg1: paddle::platform::CUDAPlace) -> None 14. set(self: paddle.fluid.core.Tensor, arg0: numpy.ndarray[uint16], arg1: paddle::platform::CUDAPlace) -> None 15. set(self: paddle.fluid.core.Tensor, arg0: numpy.ndarray[uint8], arg1: paddle::platform::CUDAPlace) -> None 16. set(self: paddle.fluid.core.Tensor, arg0: numpy.ndarray[int8], arg1: paddle::platform::CUDAPlace) -> None 17. set(self: paddle.fluid.core.Tensor, arg0: numpy.ndarray[float32], arg1: paddle::platform::CUDAPinnedPlace) -> None 18. set(self: paddle.fluid.core.Tensor, arg0: numpy.ndarray[int32], arg1: paddle::platform::CUDAPinnedPlace) -> None 19. set(self: paddle.fluid.core.Tensor, arg0: numpy.ndarray[float64], arg1: paddle::platform::CUDAPinnedPlace) -> None 20. set(self: paddle.fluid.core.Tensor, arg0: numpy.ndarray[int64], arg1: paddle::platform::CUDAPinnedPlace) -> None 21. set(self: paddle.fluid.core.Tensor, arg0: numpy.ndarray[bool], arg1: paddle::platform::CUDAPinnedPlace) -> None 22. set(self: paddle.fluid.core.Tensor, arg0: numpy.ndarray[uint16], arg1: paddle::platform::CUDAPinnedPlace) -> None 23. set(self: paddle.fluid.core.Tensor, arg0: numpy.ndarray[uint8], arg1: paddle::platform::CUDAPinnedPlace) -> None 24. set(self: paddle.fluid.core.Tensor, arg0: numpy.ndarray[int8], arg1: paddle::platform::CUDAPinnedPlace) -> None
paddle.fluid.LoDTensor.set_lod set_lod(self: paddle.fluid.core.LoDTensor, arg0: List[List[int]]) -> None
paddle.fluid.LoDTensor.set_recursive_sequence_lengths set_recursive_sequence_lengths(self: paddle.fluid.core.LoDTensor, arg0: List[List[int]]) -> None
paddle.fluid.LoDTensor.shape shape(self: paddle.fluid.core.Tensor) -> List[int]
......
paddle/fluid/CMakeLists.txt
浏览文件 @ 79d4a99d
......@@ -2,9 +2,13 @@ add_subdirectory(memory)
add_subdirectory(platform)
add_subdirectory(framework)
add_subdirectory(operators)
add_subdirectory(pybind)
add_subdirectory(string)
if (NOT WIN32)
add_subdirectory(pybind)
add_subdirectory(recordio)
endif(NOT WIN32)
if(WITH_INFERENCE)
# NOTE: please add subdirectory inference at last.
add_subdirectory(inference)
......
paddle/fluid/framework/CMakeLists.txt
浏览文件 @ 79d4a99d
add_subdirectory(details)
add_subdirectory(ir)
if (NOT WIN32)
add_subdirectory(details)
endif (NOT WIN32)
# ddim lib
proto_library(framework_proto SRCS framework.proto)
......@@ -28,8 +30,12 @@ if(WITH_GPU)
else()
cc_test(mixed_vector_test SRCS mixed_vector_test.cc DEPS place memory device_context tensor)
endif()
if (NOT WIN32)
cc_library(lod_tensor SRCS lod_tensor.cc DEPS ddim place tensor framework_proto recordio)
else()
cc_library(lod_tensor SRCS lod_tensor.cc DEPS ddim place tensor framework_proto)
endif (NOT WIN32)
cc_test(lod_tensor_test SRCS lod_tensor_test.cc DEPS lod_tensor memory)
nv_test(lod_tensor_gpu_test SRCS lod_tensor_test.cu DEPS lod_tensor)
......@@ -69,14 +75,22 @@ cc_library(op_proto_maker SRCS op_proto_maker.cc DEPS framework_proto attribute
cc_test(op_proto_maker_test SRCS op_proto_maker_test.cc DEPS op_proto_maker)
cc_library(op_info SRCS op_info.cc DEPS attribute framework_proto)
cc_library(shape_inference SRCS shape_inference.cc DEPS ddim attribute device_context)
if (NOT WIN32)
cc_library(operator SRCS operator.cc DEPS op_info device_context tensor scope glog
shape_inference data_transform lod_tensor profiler)
else()
cc_library(operator SRCS operator.cc DEPS op_info device_context tensor scope glog
shape_inference data_transform lod_tensor)
endif(NOT WIN32)
cc_test(operator_test SRCS operator_test.cc DEPS operator op_registry device_context)
cc_library(proto_desc SRCS var_desc.cc op_desc.cc block_desc.cc program_desc.cc DEPS shape_inference op_info operator glog)
cc_library(op_registry SRCS op_registry.cc DEPS op_proto_maker op_info operator glog proto_desc)
nv_test(op_registry_test SRCS op_registry_test.cc DEPS op_registry)
if (NOT WIN32)
py_proto_compile(framework_py_proto SRCS framework.proto)
# Generate an empty __init__.py to make framework_py_proto as a valid python module.
add_custom_target(framework_py_proto_init ALL COMMAND ${CMAKE_COMMAND} -E touch __init__.py)
......@@ -86,6 +100,7 @@ add_custom_command(TARGET framework_py_proto POST_BUILD
COMMAND cp *.py ${PADDLE_BINARY_DIR}/python/paddle/fluid/proto/
COMMENT "Copy generated python proto into directory paddle/fluid/proto."
WORKING_DIRECTORY ${CMAKE_CURRENT_BINARY_DIR})
endif(NOT WIN32)
cc_library(lod_rank_table SRCS lod_rank_table.cc DEPS lod_tensor)
......@@ -120,7 +135,9 @@ cc_test(cow_ptr_tests SRCS details/cow_ptr_test.cc)
# cc_test(channel_test SRCS channel_test.cc)
cc_test(tuple_test SRCS tuple_test.cc )
if (NOT WIN32)
cc_test(rw_lock_test SRCS rw_lock_test.cc)
endif (NOT WIN32)
# disable test temporarily.
# TODO https://github.com/PaddlePaddle/Paddle/issues/11971
......
paddle/fluid/framework/data_type.cc
浏览文件 @ 79d4a99d
......@@ -64,6 +64,7 @@ static DataTypeMap* InitDataTypeMap() {
RegType(size_t, proto::VarType::SIZE_T);
RegType(int16_t, proto::VarType::INT16);
RegType(uint8_t, proto::VarType::UINT8);
RegType(int8_t, proto::VarType::INT8);
#undef RegType
return retv;
......
paddle/fluid/framework/data_type.h
浏览文件 @ 79d4a99d
......@@ -26,6 +26,7 @@ namespace framework {
extern proto::VarType::Type ToDataType(std::type_index type);
extern std::type_index ToTypeIndex(proto::VarType::Type type);
#if !defined(_WIN32)
template <typename Visitor>
inline void VisitDataType(proto::VarType::Type type, Visitor visitor) {
switch (type) {
......@@ -53,10 +54,47 @@ inline void VisitDataType(proto::VarType::Type type, Visitor visitor) {
case proto::VarType::INT16:
visitor.template operator()<int16_t>();
break;
case proto::VarType::INT8:
visitor.template operator()<int8_t>();
break;
default:
PADDLE_THROW("Not supported %d", type);
}
}
#else
// the msvc compiler do not implement two-stage name lookup correctly.
template <typename Visitor>
inline void VisitDataType(proto::VarType::Type type, Visitor visitor) {
switch (type) {
case proto::VarType::FP16:
visitor.operator()<platform::float16>();
break;
case proto::VarType::FP32:
visitor.operator()<float>();
break;
case proto::VarType::FP64:
visitor.operator()<double>();
break;
case proto::VarType::INT32:
visitor.operator()<int>();
break;
case proto::VarType::INT64:
visitor.operator()<int64_t>();
break;
case proto::VarType::BOOL:
visitor.operator()<bool>();
break;
case proto::VarType::UINT8:
visitor.operator()<uint8_t>();
break;
case proto::VarType::INT16:
visitor.operator()<int16_t>();
break;
default:
PADDLE_THROW("Not supported %d", type);
}
}
#endif // _WIN32
extern std::string DataTypeToString(const proto::VarType::Type type);
extern size_t SizeOfType(std::type_index type);
......
paddle/fluid/framework/framework.proto
浏览文件 @ 79d4a99d
......@@ -107,6 +107,7 @@ message VarType {
// Tensor<size_t> is used in C++.
SIZE_T = 19;
UINT8 = 20;
INT8 = 21;
// Other types that may need additional descriptions
LOD_TENSOR = 7;
......
paddle/fluid/framework/lod_tensor.cc
浏览文件 @ 79d4a99d
......@@ -25,8 +25,10 @@ limitations under the License. */
#include "paddle/fluid/memory/memcpy.h"
#include "paddle/fluid/memory/memory.h"
#if !defined(_WIN32)
#include "paddle/fluid/recordio/scanner.h"
#include "paddle/fluid/recordio/writer.h"
#endif // _WIN32
namespace paddle {
namespace framework {
......@@ -300,6 +302,7 @@ void DeserializeFromStream(std::istream &is, LoDTensor *tensor,
TensorFromStream(is, static_cast<Tensor *>(tensor), dev_ctx);
}
#if !defined(_WIN32)
void WriteToRecordIO(recordio::Writer *writer,
const std::vector<LoDTensor> &tensor,
const platform::DeviceContext &dev_ctx) {
......@@ -329,7 +332,19 @@ bool ReadFromRecordIO(recordio::Scanner *scanner,
return true;
}
#else
class Writer {};
class Scanner {};
void WriteToRecordIO(recordio::Writer *writer,
const std::vector<LoDTensor> &tensor,
const platform::DeviceContext &dev_ctx) {}
bool ReadFromRecordIO(recordio::Scanner *scanner,
const platform::DeviceContext &dev_ctx,
std::vector<LoDTensor> *result_ptr) {
PADDLE_ENFORCE("windows didn't supported recordio!.");
return true;
}
#endif // _WIN32
std::vector<LoDTensor> LoDTensor::SplitLoDTensor(
const std::vector<platform::Place> places) const {
check_memory_size();
......
paddle/fluid/framework/lod_tensor_test.cc
浏览文件 @ 79d4a99d
......@@ -274,6 +274,7 @@ TEST(LoD, ConvertToOffsetBasedLoD) {
EXPECT_EQ(offset_lod, expected);
}
#if !defined(_WIN32)
template <typename T>
static void TestRecordIO() {
LoDTensor tensor;
......@@ -320,6 +321,7 @@ TEST(LoDTensor, RecordIO) {
TestRecordIO<float>();
TestRecordIO<double>();
}
#endif // !defined(_WIN32)
} // namespace framework
} // namespace paddle
paddle/fluid/framework/operator.cc
浏览文件 @ 79d4a99d
......@@ -74,6 +74,12 @@ static DDim GetDims(const Scope& scope, const std::string& name,
}
}
static bool VarInited(const Scope& scope, const std::string& name) {
Variable* var = scope.FindVar(name);
if (var == nullptr) return false;
return var->IsInitialized();
}
static std::string GetDtype(const Scope& scope, const std::string& name) {
Variable* var = scope.FindVar(name);
if (var == nullptr) {
......@@ -87,8 +93,12 @@ static std::string GetDtype(const Scope& scope, const std::string& name) {
}
return DataTypeToString(ToDataType(tensor.type()));
} else if (var->IsType<SelectedRows>()) {
return DataTypeToString(
ToDataType(var->Get<SelectedRows>().value().type()));
auto tensor = var->Get<SelectedRows>().value();
if (UNLIKELY(!tensor.IsInitialized())) {
return "uninited";
} else {
return DataTypeToString(ToDataType(tensor.type()));
}
} else {
return "";
}
......@@ -197,16 +207,21 @@ std::string OperatorBase::DebugStringEx(const Scope* scope) const {
auto& input = *it;
ss << input.first << "[";
for (size_t i = 0; i < input.second.size(); ++i) {
ss << input.second[i];
auto var_name = input.second[i];
ss << var_name;
if (scope) {
int row_size = GetRowSize(*scope, input.second[i]);
if (row_size >= 0) {
ss << "[row_size=" << row_size << "]";
if (!VarInited(*scope, var_name)) {
ss << "[uninited]";
} else {
int row_size = GetRowSize(*scope, var_name);
if (row_size >= 0) {
ss << "[row_size=" << row_size << "]";
}
std::string dtype = GetDtype(*scope, var_name);
ss << ":" << dtype;
ss << "[" << GetDims(*scope, var_name, true) << "]";
ss << "(" << GetLoD(*scope, var_name) << ")";
}
std::string dtype = GetDtype(*scope, input.second[i]);
ss << ":" << dtype;
ss << "[" << GetDims(*scope, input.second[i], true) << "]";
ss << "(" << GetLoD(*scope, input.second[i]) << ")";
}
if (i != input.second.size() - 1) {
ss << ", ";
......@@ -223,14 +238,19 @@ std::string OperatorBase::DebugStringEx(const Scope* scope) const {
auto& output = *it;
ss << output.first << "[";
for (size_t i = 0; i < output.second.size(); ++i) {
ss << output.second[i];
auto var_name = output.second[i];
ss << var_name;
if (scope) {
int row_size = GetRowSize(*scope, output.second[i]);
if (row_size >= 0) {
ss << "[row_size=" << row_size << "]";
if (!VarInited(*scope, var_name)) {
ss << "[uninited]";
} else {
int row_size = GetRowSize(*scope, output.second[i]);
if (row_size >= 0) {
ss << "[row_size=" << row_size << "]";
}
ss << "[" << GetDims(*scope, var_name, true) << "]";
ss << "(" << GetLoD(*scope, var_name) << ")";
}
ss << "[" << GetDims(*scope, output.second[i], true) << "]";
ss << "(" << GetLoD(*scope, output.second[i]) << ")";
}
if (i != output.second.size() - 1) {
ss << ", ";
......
paddle/fluid/framework/rw_lock.h
浏览文件 @ 79d4a99d
......@@ -14,13 +14,16 @@ limitations under the License. */
#pragma once
#if !defined(_WIN32)
#include <pthread.h>
#endif // !_WIN32
#include "paddle/fluid/platform/enforce.h"
namespace paddle {
namespace framework {
#if !defined(_WIN32)
struct RWLock {
RWLock() { pthread_rwlock_init(&lock_, nullptr); }
......@@ -43,6 +46,15 @@ struct RWLock {
private:
pthread_rwlock_t lock_;
};
#else
// https://stackoverflow.com/questions/7125250/making-pthread-rwlock-wrlock-recursive
// In windows, rw_lock seems like a hack. Use empty object and do nothing.
struct RWLock {
void RDLock() {}
void WRLock() {}
void UNLock() {}
};
#endif
} // namespace framework
} // namespace paddle
paddle/fluid/inference/api/demo_ci/CMakeLists.txt
浏览文件 @ 79d4a99d
......@@ -23,9 +23,11 @@ include_directories("${PADDLE_LIB}")
include_directories("${PADDLE_LIB}/third_party/install/protobuf/include")
include_directories("${PADDLE_LIB}/third_party/install/glog/include")
include_directories("${PADDLE_LIB}/third_party/install/gflags/include")
if (NOT WIN32)
include_directories("${PADDLE_LIB}/third_party/install/snappy/include")
include_directories("${PADDLE_LIB}/third_party/install/snappystream/include")
include_directories("${PADDLE_LIB}/third_party/install/zlib/include")
endif(NOT WIN32)
include_directories("${PADDLE_LIB}/third_party/boost")
include_directories("${PADDLE_LIB}/third_party/eigen3")
......
paddle/fluid/memory/detail/system_allocator.cc
浏览文件 @ 79d4a99d
......@@ -11,12 +11,18 @@ 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. */
#define GLOG_NO_ABBREVIATED_SEVERITIES
#include "paddle/fluid/memory/detail/system_allocator.h"
#include <stdlib.h> // for malloc and free
#ifdef _WIN32
#include <malloc.h>
#include <windows.h> // VirtualLock/VirtualUnlock
#else
#include <sys/mman.h> // for mlock and munlock
#include <algorithm> // for std::max
#endif
#include <stdlib.h> // for malloc and free
#include <algorithm> // for std::max
#include "gflags/gflags.h"
#include "paddle/fluid/platform/assert.h"
......@@ -35,31 +41,42 @@ namespace paddle {
namespace memory {
namespace detail {
void* CPUAllocator::Alloc(size_t* index, size_t size) {
// According to http://www.cplusplus.com/reference/cstdlib/malloc/,
// malloc might not return nullptr if size is zero, but the returned
// pointer shall not be dereferenced -- so we make it nullptr.
if (size <= 0) return nullptr;
*index = 0; // unlock memory
void* AlignedMalloc(size_t size) {
void* p = nullptr;
size_t alignment = 32ul;
#ifdef PADDLE_WITH_MKLDNN
// refer to https://github.com/01org/mkl-dnn/blob/master/include/mkldnn.hpp
// memory alignment
PADDLE_ENFORCE_EQ(posix_memalign(&p, 4096ul, size), 0, "Alloc %ld error!",
size);
alignment = 4096ul;
#endif
#ifdef _WIN32
p = _aligned_malloc(size, alignment);
#else
PADDLE_ENFORCE_EQ(posix_memalign(&p, 32ul, size), 0, "Alloc %ld error!",
PADDLE_ENFORCE_EQ(posix_memalign(&p, alignment, size), 0, "Alloc %ld error!",
size);
#endif
PADDLE_ENFORCE(p, "Fail to allocate CPU memory: size = %d .", size);
return p;
}
void* CPUAllocator::Alloc(size_t* index, size_t size) {
// According to http://www.cplusplus.com/reference/cstdlib/malloc/,
// malloc might not return nullptr if size is zero, but the returned
// pointer shall not be dereferenced -- so we make it nullptr.
if (size <= 0) return nullptr;
*index = 0; // unlock memory
void* p = AlignedMalloc(size);
if (p != nullptr) {
if (FLAGS_use_pinned_memory) {
*index = 1;
#ifdef _WIN32
VirtualLock(p, size);
#else
mlock(p, size); // lock memory
#endif
}
}
......@@ -68,7 +85,11 @@ void* CPUAllocator::Alloc(size_t* index, size_t size) {
void CPUAllocator::Free(void* p, size_t size, size_t index) {
if (p != nullptr && index == 1) {
#ifdef _WIN32
VirtualUnlock(p, size);
#else
munlock(p, size);
#endif
}
free(p);
}
......
paddle/fluid/operators/CMakeLists.txt
浏览文件 @ 79d4a99d
......@@ -85,7 +85,7 @@ function(op_library TARGET)
#remove windows unsupported op
if (WIN32)
foreach(windows_unsupport_op "nccl_op" "gen_nccl_id_op")
foreach(windows_unsupport_op "nccl_op" "gen_nccl_id_op" "warpctc_op")
if ("${TARGET}" STREQUAL "${windows_unsupport_op}")
return()
endif()
......@@ -319,8 +319,9 @@ foreach(src ${GENERAL_OPS})
endforeach()
file(APPEND ${pybind_file} "USE_OP(less_than);\nUSE_OP(logical_and);\nUSE_NO_KERNEL_OP(read_from_array);\n")
if (NOT WIN32)
add_subdirectory(reader)
endif(NOT WIN32)
foreach(src ${READER_LIBRARY})
set(OP_LIBRARY ${src} ${OP_LIBRARY})
endforeach()
......
paddle/fluid/operators/attention_lstm_op.cc
浏览文件 @ 79d4a99d
......@@ -232,40 +232,28 @@ use lstm_x_t as input and compute as standard LSTM.
template <typename T>
inline void bias_relu(const int n, const T* x, const T* bias, T* y) {
if (bias) {
for (int i = 0; i < n; ++i) {
y[i] = x[i] + bias[0];
}
math::vec_relu<T>(n, y, y);
math::vec_add_bias<T, platform::jit::avx>(n, *bias, x, y);
math::vec_relu<T, platform::jit::avx>(n, y, y);
} else {
math::vec_relu<T>(n, x, y);
math::vec_relu<T, platform::jit::avx>(n, x, y);
}
}
template <typename DeviceContext, typename T>
inline void vec_softmax(const math::BlasT<DeviceContext, T>& blas, const int n,
const T* x, T* y) {
template <typename T>
inline void vec_softmax(const int n, const T* x, T* y) {
T scalar = x[0];
// max
for (int i = 1; i < n; ++i) {
scalar = scalar < x[i] ? x[i] : scalar;
}
// sub
for (int i = 0; i < n; ++i) {
y[i] = x[i] - scalar;
}
// exp
blas.VEXP(n, y, y);
math::vec_add_bias<T, platform::jit::avx>(n, -scalar, x, y); // sub
math::vec_exp<T>(n, y, y); // exp
// sum
scalar = T(0);
for (int i = 0; i < n; ++i) {
scalar += y[i];
}
// scale
blas.SCAL(n, static_cast<T>(1) / scalar, y);
math::vec_scal<T>(n, static_cast<T>(1) / scalar, y); // scale
}
template <typename T>
......@@ -311,11 +299,21 @@ class AttentionLSTMKernel : public framework::OpKernel<T> {
PADDLE_ENFORCE_EQ(c0->dims()[0], N, "C0 dims should be %d x %d.", N, D);
fc_out->Resize({max_seq_len, 1});
math::VecActivations<T> act_functor;
std::function<void(const int, const T *, T *)> act_gate, act_cell, act_cand;
act_gate = act_functor(ctx.Attr<std::string>("gate_activation"));
act_cell = act_functor(ctx.Attr<std::string>("cell_activation"));
act_cand = act_functor(ctx.Attr<std::string>("candidate_activation"));
auto& act_gate_str = ctx.Attr<std::string>("gate_activation");
auto& act_cell_str = ctx.Attr<std::string>("cell_activation");
auto& act_cand_str = ctx.Attr<std::string>("candidate_activation");
if (platform::jit::MayIUse(platform::jit::avx)) {
math::VecActivations<T, platform::jit::avx> act_functor;
act_gate = act_functor(act_gate_str);
act_cell = act_functor(act_cell_str);
act_cand = act_functor(act_cand_str);
} else {
math::VecActivations<T, platform::jit::isa_any> act_functor;
act_gate = act_functor(act_gate_str);
act_cell = act_functor(act_cell_str);
act_cand = act_functor(act_cand_str);
}
const T* x_data = x->data<T>();
const T* h0_data = h0 ? h0->data<T>() : NULL;
......@@ -363,7 +361,7 @@ class AttentionLSTMKernel : public framework::OpKernel<T> {
fc_out_data);
}
// 1d. softmax
vec_softmax<DeviceContext, T>(blas, seq_len, fc_out_data, fc_out_data);
vec_softmax<T>(seq_len, fc_out_data, fc_out_data);
// mul x(seq_len*M) and sum pool
math::FCCompute<DeviceContext, T>(blas, 1, M, seq_len, fc_out_data,
cur_x_data, lstm_x_data);
......
paddle/fluid/operators/auc_op.h
浏览文件 @ 79d4a99d
......@@ -60,6 +60,20 @@ class AucKernel : public framework::OpKernel<T> {
const T* inference_data = predict->data<T>();
const auto* label_data = label->data<int64_t>();
// check if states are inited.
auto* tp_in = ctx.Input<Tensor>("TP");
auto* fp_in = ctx.Input<Tensor>("FP");
auto* tn_in = ctx.Input<Tensor>("TN");
auto* fn_in = ctx.Input<Tensor>("FN");
PADDLE_ENFORCE(tp_in->IsInitialized(), "true_positive is not inited!");
PADDLE_ENFORCE(fp_in->IsInitialized(), "false_negative is not inited!");
PADDLE_ENFORCE(tn_in->IsInitialized(), "true_negative is not inited!");
PADDLE_ENFORCE(fn_in->IsInitialized(), "false_positive is not inited!");
PADDLE_ENFORCE_EQ(tp_in->numel(), num_thresholds, "");
PADDLE_ENFORCE_EQ(fp_in->numel(), num_thresholds, "");
PADDLE_ENFORCE_EQ(tn_in->numel(), num_thresholds, "");
PADDLE_ENFORCE_EQ(fn_in->numel(), num_thresholds, "");
auto* tp_data = true_positive->mutable_data<int64_t>(ctx.GetPlace());
auto* fn_data = false_negative->mutable_data<int64_t>(ctx.GetPlace());
auto* tn_data = true_negative->mutable_data<int64_t>(ctx.GetPlace());
......
paddle/fluid/operators/fusion_lstm_op.cc
浏览文件 @ 79d4a99d
......@@ -15,10 +15,14 @@ limitations under the License. */
#include "paddle/fluid/operators/fusion_lstm_op.h"
#include <string>
#include "paddle/fluid/operators/math/blas.h"
#include "paddle/fluid/operators/math/cpu_vec.h"
#include "paddle/fluid/operators/math/detail/activation_functions.h"
#include "paddle/fluid/operators/math/fc_compute.h"
#include "paddle/fluid/operators/math/lstm_compute.h"
#include "paddle/fluid/operators/math/sequence2batch.h"
#include "paddle/fluid/platform/cpu_info.h"
DEFINE_bool(seq_mode, true, "Use sequence mode");
namespace paddle {
namespace operators {
......@@ -98,7 +102,12 @@ void FusionLSTMOp::InferShape(framework::InferShapeContext* ctx) const {
ctx->ShareLoD("X", "Hidden");
ctx->ShareLoD("X", "Cell");
int xx_width = x_dims[1] > wx_dims[1] ? wx_dims[1] : x_dims[1];
int xx_width;
if (FLAGS_seq_mode) {
xx_width = wx_dims[1];
} else {
xx_width = x_dims[1] > wx_dims[1] ? wx_dims[1] : x_dims[1];
}
ctx->SetOutputDim("XX", {x_dims[0], xx_width});
ctx->ShareLoD("X", "XX");
}
......@@ -205,10 +214,138 @@ inline void ReorderInitState(const DeviceContext& ctx,
row_shuffle(ctx, src, index_lod, dst, indexed_src);
}
template <typename DeviceContext, typename T>
template <typename T>
class FuisonLSTMKernel : public framework::OpKernel<T> {
public:
void Compute(const framework::ExecutionContext& ctx) const override {
void SeqCompute(const framework::ExecutionContext& ctx) const {
using DeviceContext = paddle::platform::CPUDeviceContext;
auto* x = ctx.Input<LoDTensor>("X");
auto* h0 = ctx.Input<Tensor>("H0");
auto* c0 = ctx.Input<Tensor>("C0");
auto* wx = ctx.Input<Tensor>("WeightX");
auto* wh = ctx.Input<Tensor>("WeightH");
auto* bias = ctx.Input<Tensor>("Bias");
auto* xx = ctx.Output<LoDTensor>("XX");
auto* hidden_out = ctx.Output<LoDTensor>("Hidden");
auto* cell_out = ctx.Output<LoDTensor>("Cell");
bool is_reverse = ctx.Attr<bool>("is_reverse");
std::function<void(const int, const T *, T *)> act_gate, act_cell, act_cand;
auto& act_gate_str = ctx.Attr<std::string>("gate_activation");
auto& act_cell_str = ctx.Attr<std::string>("cell_activation");
auto& act_cand_str = ctx.Attr<std::string>("candidate_activation");
if (platform::jit::MayIUse(platform::jit::avx)) {
math::VecActivations<T, platform::jit::avx> act_functor;
act_gate = act_functor(act_gate_str);
act_cell = act_functor(act_cell_str);
act_cand = act_functor(act_cand_str);
} else {
math::VecActivations<T, platform::jit::isa_any> act_functor;
act_gate = act_functor(act_gate_str);
act_cell = act_functor(act_cell_str);
act_cand = act_functor(act_cand_str);
}
auto x_lod = x->lod();
auto x_dims = x->dims(); // T x M
auto wh_dims = wh->dims(); // D x 4D
const int total_T = x_dims[0];
const int N = x_lod[0].size() - 1; // batch size
const int M = x_dims[1]; // x frame size
const int D = wh_dims[0];
const int D2 = D * 2;
const int D3 = D * 3;
const int D4 = wh_dims[1];
const T* x_data = x->data<T>();
const T* h0_data = h0 ? h0->data<T>() : NULL;
const T* c0_data = c0 ? c0->data<T>() : NULL;
const T* wx_data = wx->data<T>();
const T* wh_data = wh->data<T>();
T* xx_data = xx->mutable_data<T>(ctx.GetPlace());
T* hidden_out_data = hidden_out->mutable_data<T>(ctx.GetPlace());
T* cell_out_data = cell_out->mutable_data<T>(ctx.GetPlace());
auto blas = math::GetBlas<DeviceContext, T>(ctx);
math::FCCompute<DeviceContext, T>(blas, total_T, D4, M, x_data, wx_data,
xx_data, bias->data<T>());
int xx_offset = D4;
int gate_offset = D;
if (is_reverse) {
const int offset = (total_T - 1) * D;
xx_data = xx_data + offset * 4;
hidden_out_data = hidden_out_data + offset;
cell_out_data = cell_out_data + offset;
xx_offset = -D4;
gate_offset = -D;
}
auto move_step = [&]() {
xx_data = xx_data + xx_offset;
hidden_out_data = hidden_out_data + gate_offset;
cell_out_data = cell_out_data + gate_offset;
};
for (int i = 0; i < N; ++i) {
int bid = is_reverse ? N - 1 - i : i;
int seq_len = x_lod[0][bid + 1] - x_lod[0][bid];
const T* prev_cell_data = NULL;
const T* prev_hidden_data = NULL;
int tstart = 0;
if (h0_data) {
prev_hidden_data = h0_data + bid * D;
prev_cell_data = c0_data + bid * D;
} else {
// W_ch, W_ih, W_fh, W_oh
act_gate(D3, xx_data + D, xx_data + D);
act_cand(D, xx_data, xx_data);
// cell out= input*tilde
blas.VMUL(D, xx_data, xx_data + D, cell_out_data);
// hidden out= act_state(cellout) * outgate
act_cell(D, cell_out_data, xx_data + D2);
blas.VMUL(D, xx_data + D2, xx_data + D3, hidden_out_data);
// prev
prev_hidden_data = hidden_out_data;
prev_cell_data = cell_out_data;
tstart = 1;
move_step();
}
for (int step = tstart; step < seq_len; ++step) {
blas.GEMM(CblasNoTrans, CblasNoTrans, 1, D4, D, static_cast<T>(1),
prev_hidden_data, D, wh_data, D4, static_cast<T>(1), xx_data,
D4);
// W_ch, W_ih, W_fh, W_oh
act_gate(D3, xx_data + D, xx_data + D);
act_cand(D, xx_data, xx_data);
// a = forget * prev_cell
blas.VMUL(D, xx_data + D2, prev_cell_data, xx_data + D2);
// b = input * tilde
blas.VMUL(D, xx_data, xx_data + D, xx_data + D);
// cell out= a+b
blas.VADD(D, xx_data + D, xx_data + D2, cell_out_data);
// hidden out= act_state(cellout) * outgate
act_cell(D, cell_out_data, xx_data + D2);
blas.VMUL(D, xx_data + D2, xx_data + D3, hidden_out_data);
// prev
prev_hidden_data = hidden_out_data;
prev_cell_data = cell_out_data;
move_step();
}
}
}
void BatchCompute(const framework::ExecutionContext& ctx) const {
using DeviceContext = platform::CPUDeviceContext;
auto* x = ctx.Input<LoDTensor>("X");
auto* wx = ctx.Input<Tensor>("WeightX");
auto* wh = ctx.Input<Tensor>("WeightH");
......@@ -339,6 +476,13 @@ class FuisonLSTMKernel : public framework::OpKernel<T> {
// restore the output cell state in LoDTensor from the batch cell
to_seq(dev_ctx, batch_cell, cell_out);
}
void Compute(const framework::ExecutionContext& ctx) const override {
if (FLAGS_seq_mode) {
SeqCompute(ctx);
} else {
BatchCompute(ctx);
}
}
};
} // namespace operators
......@@ -348,7 +492,5 @@ namespace ops = paddle::operators;
REGISTER_OPERATOR(fusion_lstm, ops::FusionLSTMOp, ops::FusionLSTMOpMaker,
paddle::framework::DefaultGradOpDescMaker<true>);
REGISTER_OP_CPU_KERNEL(
fusion_lstm,
ops::FuisonLSTMKernel<paddle::platform::CPUDeviceContext, float>,
ops::FuisonLSTMKernel<paddle::platform::CPUDeviceContext, double>);
REGISTER_OP_CPU_KERNEL(fusion_lstm, ops::FuisonLSTMKernel<float>,
ops::FuisonLSTMKernel<double>);
paddle/fluid/operators/math/CMakeLists.txt
浏览文件 @ 79d4a99d
......@@ -65,3 +65,4 @@ if(WITH_GPU)
nv_test(selected_rows_functor_gpu_test SRCS selected_rows_functor_test.cu DEPS selected_rows_functor math_function)
endif()
cc_test(concat_test SRCS concat_test.cc DEPS concat)
cc_test(cpu_vec_test SRCS cpu_vec_test.cc DEPS blas cpu_info)
paddle/fluid/operators/math/cpu_vec.h
浏览文件 @ 79d4a99d
......@@ -13,8 +13,16 @@ See the License for the specific language governing permissions and
limitations under the License. */
#pragma once
#include <cmath>
#include <string>
#include "paddle/fluid/platform/cpu_info.h"
#ifdef __AVX__
#include <immintrin.h>
#endif
#ifdef PADDLE_WITH_MKLML
#include "paddle/fluid/platform/dynload/mklml.h"
#endif
namespace paddle {
namespace operators {
......@@ -22,16 +30,161 @@ namespace math {
#define SIGMOID_THRESHOLD_MIN -40.0
#define SIGMOID_THRESHOLD_MAX 13.0
#define EXP_MAX_INPUT 40.0
#define AVX_FLOAT_BLOCK 8
#define AVX_DOUBLE_BLOCK 4
#define AVX2_FLOAT_BLOCK 8
#define AVX2_DOUBLE_BLOCK 4
#define AVX512_FLOAT_BLOCK 16
#define AVX512_DOUBLE_BLOCK 8
template <typename T>
inline T sigmoid(T x) {
return 1. / (1. + exp(-x));
inline void vec_exp(const int n, const T* x, T* y) {
for (int i = 0; i < n; ++i) {
y[i] = std::exp(x[i]);
}
}
template <typename T>
inline T tanh(T x) {
return 2. * sigmoid(2. * x) - 1.;
inline void vec_scal(const int n, const T a, T* x) {
for (int i = 0; i < n; ++i) {
x[i] = a * x[i];
}
}
#ifdef PADDLE_WITH_MKLML
template <>
inline void vec_exp<float>(const int n, const float* x, float* y) {
platform::dynload::vsExp(n, x, y);
}
template <>
inline void vec_exp<double>(const int n, const double* x, double* y) {
platform::dynload::vdExp(n, x, y);
}
template <>
inline void vec_scal<float>(const int n, const float a, float* x) {
platform::dynload::cblas_sscal(n, a, x, 1);
}
template <>
inline void vec_scal<double>(const int n, const double a, double* x) {
platform::dynload::cblas_dscal(n, a, x, 1);
}
#endif
// MKL scal only support inplace, choose this if src and dst are not equal
template <typename T, platform::jit::cpu_isa_t isa = platform::jit::isa_any>
inline void vec_scal(const int n, const T a, const T* x, T* y) {
for (int i = 0; i < n; ++i) {
y[i] = a * x[i];
}
}
template <>
inline void vec_scal<float, platform::jit::avx>(const int n, const float a,
const float* x, float* y) {
#ifdef __AVX__
constexpr int block = AVX_FLOAT_BLOCK;
if (n < block) {
vec_scal<float, platform::jit::isa_any>(n, a, x, y);
return;
}
const int rest = n % block;
const int end = n - rest;
int i = 0;
__m256 scalar = _mm256_set1_ps(a);
__m256 tmp;
#define MOVE_ONE_STEP \
tmp = _mm256_loadu_ps(x + i); \
tmp = _mm256_mul_ps(tmp, scalar); \
_mm256_storeu_ps(y + i, tmp)
for (i = 0; i < end; i += block) {
MOVE_ONE_STEP;
}
#undef MOVE_ONE_STEP
if (rest == 0) {
return;
}
// can not continue move step if src and dst are inplace
for (i = n - rest; i < n; ++i) {
y[i] = a * x[i];
}
#else
vec_scal<float, platform::jit::isa_any>(n, a, x, y);
#endif
}
template <>
inline void vec_scal<float, platform::jit::avx2>(const int n, const float a,
const float* x, float* y) {
vec_scal<float, platform::jit::avx>(n, a, x, y);
}
template <>
inline void vec_scal<float, platform::jit::avx512_common>(const int n,
const float a,
const float* x,
float* y) {
// TODO(TJ): enable me
vec_scal<float, platform::jit::avx2>(n, a, x, y);
}
template <typename T, platform::jit::cpu_isa_t isa = platform::jit::isa_any>
inline void vec_add_bias(const int n, const T a, const T* x, T* y) {
for (int i = 0; i < n; ++i) {
y[i] = x[i] + a;
}
}
template <>
inline void vec_add_bias<float, platform::jit::avx>(const int n, const float a,
const float* x, float* y) {
#ifdef __AVX__
constexpr int block = AVX_FLOAT_BLOCK;
if (n < block) {
vec_add_bias<float, platform::jit::isa_any>(n, a, x, y);
return;
}
const int rest = n % block;
const int end = n - rest;
int i = 0;
__m256 bias = _mm256_set1_ps(a);
__m256 tmp;
#define MOVE_ONE_STEP \
tmp = _mm256_loadu_ps(x + i); \
tmp = _mm256_add_ps(tmp, bias); \
_mm256_storeu_ps(y + i, tmp)
for (i = 0; i < end; i += block) {
MOVE_ONE_STEP;
}
#undef MOVE_ONE_STEP
if (rest == 0) {
return;
}
// can not continue move step if src and dst are inplace
for (i = n - rest; i < n; ++i) {
y[i] = x[i] + a;
}
#else
vec_add_bias<float, platform::jit::isa_any>(n, a, x, y);
#endif
}
template <>
inline void vec_add_bias<float, platform::jit::avx2>(const int n, const float a,
const float* x, float* y) {
vec_add_bias<float, platform::jit::avx>(n, a, x, y);
}
template <>
inline void vec_add_bias<float, platform::jit::avx512_common>(const int n,
const float a,
const float* x,
float* y) {
// TODO(TJ): enable me
vec_add_bias<float, platform::jit::avx2>(n, a, x, y);
}
template <typename T, platform::jit::cpu_isa_t isa = platform::jit::isa_any>
......@@ -45,18 +198,97 @@ inline void vec_sigmoid(const int n, const T* x, T* y) {
const T min = SIGMOID_THRESHOLD_MIN;
const T max = SIGMOID_THRESHOLD_MAX;
for (int i = 0; i < n; ++i) {
T tmp = (x[i] < min) ? min : ((x[i] > max) ? max : x[i]);
y[i] = 1.0 / (1.0 + std::exp(-tmp));
y[i] = (x[i] < min) ? min : ((x[i] > max) ? max : x[i]);
y[i] = static_cast<T>(0) - y[i];
}
vec_exp<T>(n, y, y);
for (int i = 0; i < n; ++i) {
y[i] = static_cast<T>(1) / (static_cast<T>(1) + y[i]);
}
}
template <>
inline void vec_sigmoid<float, platform::jit::avx>(const int n, const float* x,
float* y) {
#ifdef __AVX__
constexpr int block = AVX_FLOAT_BLOCK;
if (n < block) {
vec_sigmoid<float, platform::jit::isa_any>(n, x, y);
return;
}
const int rest = n % block;
const int end = n - rest;
int i = 0;
__m256 max = _mm256_set1_ps(SIGMOID_THRESHOLD_MAX);
__m256 min = _mm256_set1_ps(SIGMOID_THRESHOLD_MIN);
__m256 zeros = _mm256_setzero_ps();
__m256 tmp;
#define MOVE_ONE_STEP \
tmp = _mm256_loadu_ps(x + i); \
tmp = _mm256_max_ps(tmp, min); \
tmp = _mm256_min_ps(tmp, max); \
tmp = _mm256_sub_ps(zeros, tmp); \
_mm256_storeu_ps(y + i, tmp)
for (i = 0; i < end; i += block) {
MOVE_ONE_STEP;
}
#undef MOVE_ONE_STEP
if (rest != 0) {
// can not continue move step since the src and dst address could be equal
const float xmin = SIGMOID_THRESHOLD_MIN;
const float xmax = SIGMOID_THRESHOLD_MAX;
for (i = n - rest; i < n; ++i) {
y[i] = 0.f - ((x[i] < xmin) ? xmin : ((x[i] > xmax) ? xmax : x[i]));
}
}
vec_exp<float>(n, y, y);
__m256 ones = _mm256_set1_ps(1.0f);
#define MOVE_ONE_STEP \
tmp = _mm256_loadu_ps(y + i); \
tmp = _mm256_add_ps(ones, tmp); \
tmp = _mm256_div_ps(ones, tmp); \
_mm256_storeu_ps(y + i, tmp)
for (i = 0; i < end; i += block) {
MOVE_ONE_STEP;
}
#undef MOVE_ONE_STEP
if (rest == 0) {
return;
}
// can not continue move step
for (i = n - rest; i < n; ++i) {
y[i] = 1.f / (1.f + y[i]);
}
#else
vec_sigmoid<float, platform::jit::isa_any>(n, x, y);
#endif
}
template <>
inline void vec_sigmoid<float, platform::jit::avx2>(const int n, const float* x,
float* y) {
vec_sigmoid<float, platform::jit::avx>(n, x, y);
}
template <>
inline void vec_sigmoid<float, platform::jit::avx512_common>(const int n,
const float* x,
float* y) {
// TODO(TJ): enable me
vec_sigmoid<float, platform::jit::avx2>(n, x, y);
}
template <typename T, platform::jit::cpu_isa_t isa = platform::jit::isa_any>
inline void vec_tanh(const int n, const T* x, T* y) {
for (int i = 0; i < n; ++i) {
y[i] = tanh<T>(x[i]);
}
vec_scal<T, isa>(n, static_cast<T>(2), x, y);
vec_sigmoid<T, isa>(n, y, y);
vec_scal<T>(n, static_cast<T>(2), y);
vec_add_bias<T, isa>(n, static_cast<T>(-1), y, y);
}
// TODO(TJ): make relu clip
template <typename T, platform::jit::cpu_isa_t isa = platform::jit::isa_any>
inline void vec_relu(const int n, const T* x, T* y) {
for (int i = 0; i < n; ++i) {
......@@ -64,24 +296,56 @@ inline void vec_relu(const int n, const T* x, T* y) {
}
}
template <>
inline void vec_relu<float, platform::jit::avx>(const int n, const float* x,
float* y) {
#ifdef __AVX__
constexpr int block = AVX_FLOAT_BLOCK;
if (n < block * 4) {
vec_relu<float, platform::jit::isa_any>(n, x, y);
return;
}
const int rest = n % block;
const int end = n - rest;
int i = 0;
__m256 zeros = _mm256_setzero_ps();
__m256 tmp;
#define MOVE_ONE_STEP \
tmp = _mm256_loadu_ps(x + i); \
tmp = _mm256_max_ps(tmp, zeros); \
_mm256_storeu_ps(y + i, tmp)
for (i = 0; i < end; i += block) {
MOVE_ONE_STEP;
}
if (rest == 0) {
return;
}
i = n - block;
MOVE_ONE_STEP;
#undef MOVE_ONE_STEP
#else
vec_relu<float, platform::jit::isa_any>(n, x, y);
#endif
}
template <>
inline void vec_relu<float, platform::jit::avx2>(const int n, const float* x,
float* y) {
// TODO(TJ): complete me
for (int i = 0; i < n; ++i) {
y[i] = x[i] > 0 ? x[i] : 0;
}
vec_relu<float, platform::jit::avx>(n, x, y);
}
template <>
inline void vec_relu<float, platform::jit::avx>(const int n, const float* x,
float* y) {
// TODO(TJ): complete me
for (int i = 0; i < n; ++i) {
y[i] = x[i] > 0 ? x[i] : 0;
}
inline void vec_relu<float, platform::jit::avx512_common>(const int n,
const float* x,
float* y) {
// TODO(TJ): enable me
vec_relu<float, platform::jit::avx2>(n, x, y);
}
// TODO(TJ): optimize double of sigmoid, tanh and relu if necessary
template <typename T, platform::jit::cpu_isa_t isa = platform::jit::isa_any>
class VecActivations {
public:
......@@ -96,7 +360,7 @@ class VecActivations {
} else if (type == "identity" || type == "") {
return vec_identity<T, isa>;
}
PADDLE_THROW("Not support type %s.", type);
LOG(FATAL) << "Not support type: " << type;
}
};
......
paddle/fluid/operators/math/cpu_vec_test.cc 0 → 100644
浏览文件 @ 79d4a99d
/* 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 <sys/time.h>
#include <cmath>
#include <cstring>
#include <random>
#include <vector>
#include "gflags/gflags.h"
#include "glog/logging.h"
#include "gtest/gtest.h"
#include "paddle/fluid/operators/math/cpu_vec.h"
inline double GetCurrentUS() {
struct timeval time;
gettimeofday(&time, NULL);
return 1e+6 * time.tv_sec + time.tv_usec;
}
constexpr int repeat = 1000;
template <typename T>
inline T _sigmoid(T x) {
const T min = SIGMOID_THRESHOLD_MIN;
const T max = SIGMOID_THRESHOLD_MAX;
T tmp = (x < min) ? min : ((x > max) ? max : x);
return static_cast<T>(1) / (static_cast<T>(1) + std::exp(-tmp));
}
template <typename T>
inline T _tanh(T x) {
return static_cast<T>(2) * _sigmoid<T>(static_cast<T>(2) * x) -
static_cast<T>(1);
}
template <typename T>
void ref_sigmoid(const int n, const T* x, T* y) {
for (int i = 0; i < n; ++i) {
y[i] = _sigmoid(x[i]);
}
}
template <typename T>
void ref_tanh(const int n, const T* x, T* y) {
for (int i = 0; i < n; ++i) {
y[i] = _tanh(x[i]);
}
}
template <typename T>
void ref_relu(const int n, const T* x, T* y) {
for (int i = 0; i < n; ++i) {
y[i] = x[i] > 0 ? x[i] : 0;
}
}
template <typename T>
void RandomVec(const int n, T* a) {
static unsigned int seed = 100;
std::mt19937 rng(seed++);
std::uniform_real_distribution<double> uniform_dist(0, 1);
const T lower = static_cast<T>(-20.f);
const T upper = static_cast<T>(20.f);
for (int i = 0; i < n; ++i) {
a[i] = static_cast<T>(uniform_dist(rng) * (upper - lower) + lower);
}
}
template <typename T>
void TestAndBench(const int n, std::function<void(const int, const T*, T*)> tgt,
std::function<void(const int, const T*, T*)> ref) {
std::vector<T> x(n);
std::vector<T> ytgt(n), yref(n);
RandomVec<T>(n, x.data());
const T* x_data = x.data();
T* ytgt_data = ytgt.data();
T* yref_data = yref.data();
auto st = GetCurrentUS();
for (int i = 0; i < repeat; ++i) {
tgt(n, x_data, ytgt_data);
}
auto mt = GetCurrentUS();
for (int i = 0; i < repeat; ++i) {
ref(n, x_data, yref_data);
}
auto et = GetCurrentUS();
VLOG(3) << "Vec size " << n << ": refer takes: " << (et - mt) / repeat
<< " us, tgt takes: " << (mt - st) / repeat;
for (int i = 0; i < n; ++i) {
EXPECT_NEAR(ytgt_data[i], yref_data[i], 1e-3);
}
}
TEST(CpuVecTest, sigmoid) {
namespace jit = paddle::platform::jit;
using namespace paddle::operators::math; // NOLINT
for (auto sz : {1, 2, 15, 16, 30, 32, 128, 200, 512}) {
TestAndBench<float>(sz, vec_sigmoid<float>, ref_sigmoid<float>);
TestAndBench<float>(sz, vec_sigmoid<float, jit::avx>, ref_sigmoid<float>);
TestAndBench<float>(sz, vec_sigmoid<float, jit::avx2>, ref_sigmoid<float>);
TestAndBench<float>(sz, vec_sigmoid<float, jit::avx512_common>,
ref_sigmoid<float>);
}
TestAndBench<double>(30, vec_sigmoid<double>, ref_sigmoid<double>);
}
TEST(CpuVecTest, tanh) {
namespace jit = paddle::platform::jit;
using namespace paddle::operators::math; // NOLINT
for (auto sz : {1, 2, 15, 16, 30, 32, 128, 200, 512}) {
TestAndBench<float>(sz, vec_tanh<float>, ref_tanh<float>);
TestAndBench<float>(sz, vec_tanh<float, jit::avx>, ref_tanh<float>);
TestAndBench<float>(sz, vec_tanh<float, jit::avx2>, ref_tanh<float>);
TestAndBench<float>(sz, vec_tanh<float, jit::avx512_common>,
ref_tanh<float>);
}
TestAndBench<double>(30, vec_tanh<double>, ref_tanh<double>);
}
TEST(CpuVecTest, relu) {
namespace jit = paddle::platform::jit;
using namespace paddle::operators::math; // NOLINT
for (auto sz : {1, 2, 15, 16, 30, 32, 128, 200, 512}) {
TestAndBench<float>(sz, vec_relu<float>, ref_relu<float>);
TestAndBench<float>(sz, vec_relu<float, jit::avx>, ref_relu<float>);
TestAndBench<float>(sz, vec_relu<float, jit::avx2>, ref_relu<float>);
TestAndBench<float>(sz, vec_relu<float, jit::avx512_common>,
ref_relu<float>);
}
TestAndBench<double>(30, vec_relu<double>, ref_relu<double>);
}
template <typename T>
void TestInplace(const int n, std::function<void(const int, const T*, T*)> tgt,
std::function<void(const int, const T*, T*)> ref) {
std::vector<T> x(n);
std::vector<T> ytgt(n), yref(n);
RandomVec<T>(n, x.data());
const T* x_data = x.data();
T* yref_data = yref.data();
T* ytgt_data = ytgt.data();
std::memcpy(yref_data, x_data, sizeof(T) * n);
std::memcpy(ytgt_data, x_data, sizeof(T) * n);
ref(n, yref_data, yref_data);
tgt(n, ytgt_data, ytgt_data);
for (int i = 0; i < n; ++i) {
EXPECT_NEAR(ytgt_data[i], yref_data[i], 1e-3);
}
}
TEST(CpuVecTest, inplace_sigmoid) {
namespace jit = paddle::platform::jit;
using namespace paddle::operators::math; // NOLINT
for (auto sz : {1, 2, 15, 16, 30, 32, 128, 200, 512}) {
TestInplace<float>(sz, vec_sigmoid<float>, ref_sigmoid<float>);
TestInplace<float>(sz, vec_sigmoid<float, jit::avx>, ref_sigmoid<float>);
TestInplace<float>(sz, vec_sigmoid<float, jit::avx2>, ref_sigmoid<float>);
TestInplace<float>(sz, vec_sigmoid<float, jit::avx512_common>,
ref_sigmoid<float>);
}
TestInplace<double>(30, vec_sigmoid<double>, ref_sigmoid<double>);
}
TEST(CpuVecTest, inplace_tanh) {
namespace jit = paddle::platform::jit;
using namespace paddle::operators::math; // NOLINT
for (auto sz : {1, 2, 15, 16, 30, 32, 128, 200, 512}) {
TestInplace<float>(sz, vec_tanh<float>, ref_tanh<float>);
TestInplace<float>(sz, vec_tanh<float, jit::avx>, ref_tanh<float>);
TestInplace<float>(sz, vec_tanh<float, jit::avx2>, ref_tanh<float>);
TestInplace<float>(sz, vec_tanh<float, jit::avx512_common>,
ref_tanh<float>);
}
TestInplace<double>(30, vec_tanh<double>, ref_tanh<double>);
}
TEST(CpuVecTest, inplace_relu) {
namespace jit = paddle::platform::jit;
using namespace paddle::operators::math; // NOLINT
for (auto sz : {1, 2, 15, 16, 30, 32, 128, 200, 512}) {
TestInplace<float>(sz, vec_relu<float>, ref_relu<float>);
TestInplace<float>(sz, vec_relu<float, jit::avx>, ref_relu<float>);
TestInplace<float>(sz, vec_relu<float, jit::avx2>, ref_relu<float>);
TestInplace<float>(sz, vec_relu<float, jit::avx512_common>,
ref_relu<float>);
}
TestInplace<double>(30, vec_relu<double>, ref_relu<double>);
}
paddle/fluid/operators/math/math_function.cc
浏览文件 @ 79d4a99d
......@@ -41,7 +41,8 @@ template struct SetConstant<platform::CPUDeviceContext, uint8_t>;
template struct Transpose<platform::CPUDeviceContext, int64_t, RANK>; \
template struct Transpose<platform::CPUDeviceContext, bool, RANK>; \
template struct Transpose<platform::CPUDeviceContext, int16_t, RANK>; \
template struct Transpose<platform::CPUDeviceContext, uint8_t, RANK>;
template struct Transpose<platform::CPUDeviceContext, uint8_t, RANK>; \
template struct Transpose<platform::CPUDeviceContext, int8_t, RANK>;
DEFINE_CPU_TRANS(1);
DEFINE_CPU_TRANS(2);
......
paddle/fluid/operators/math/math_function.cu
浏览文件 @ 79d4a99d
......@@ -33,10 +33,11 @@ template struct SetConstant<platform::CUDADeviceContext, int>;
template struct SetConstant<platform::CUDADeviceContext, int64_t>;
template struct SetConstant<platform::CUDADeviceContext, bool>;
#define DEFINE_GPU_TRANS(RANK) \
template struct Transpose<platform::CUDADeviceContext, float, RANK>; \
template struct Transpose<platform::CUDADeviceContext, double, RANK>; \
template struct Transpose<platform::CUDADeviceContext, float16, RANK>;
#define DEFINE_GPU_TRANS(RANK) \
template struct Transpose<platform::CUDADeviceContext, float, RANK>; \
template struct Transpose<platform::CUDADeviceContext, double, RANK>; \
template struct Transpose<platform::CUDADeviceContext, float16, RANK>; \
template struct Transpose<platform::CUDADeviceContext, int8_t, RANK>;
DEFINE_GPU_TRANS(1);
DEFINE_GPU_TRANS(2);
......
paddle/fluid/operators/math/math_function.h
浏览文件 @ 79d4a99d
......@@ -19,6 +19,10 @@ limitations under the License. */
#ifdef PADDLE_USE_OPENBLAS
#include <cblas.h>
// remove typedef in openblas
#undef FLOAT
#undef INT
#undef SIZE
#endif
#include <cmath>
......
paddle/fluid/operators/math/padding.h 0 → 100644
浏览文件 @ 79d4a99d
/* 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 <utility>
#include <vector>
#include "paddle/fluid/framework/tensor.h"
namespace paddle {
namespace operators {
namespace math {
template <typename T, size_t D, int MajorType = Eigen::RowMajor,
typename IndexType = Eigen::DenseIndex>
using EigenTensor = framework::EigenTensor<T, D, MajorType, IndexType>;
template <typename DeviceContext, typename T, size_t D>
void PadFunction(const framework::ExecutionContext& context,
const std::vector<int>& pads, const framework::Tensor& src,
T pad_value, framework::Tensor* out) {
Eigen::array<std::pair<int, int>, D> paddings;
for (size_t i = 0; i < paddings.size(); ++i) {
paddings[i].first = pads[i * 2];
paddings[i].second = pads[i * 2 + 1];
}
auto src_tensor = EigenTensor<T, D>::From(src);
auto out_tensor = EigenTensor<T, D>::From(*out);
auto& place =
*context.template device_context<DeviceContext>().eigen_device();
out_tensor.device(place) = src_tensor.pad(paddings, pad_value);
}
template <typename DeviceContext, typename T, size_t D>
void PadGradFunction(const framework::ExecutionContext& context,
const std::vector<int>& pads, const framework::Tensor& src,
framework::Tensor* d_out) {
Eigen::array<std::pair<int, int>, D> paddings;
for (size_t i = 0; i < paddings.size(); ++i) {
paddings[i].first = -pads[i * 2];
paddings[i].second = -pads[i * 2 + 1];
}
auto d_out_tensor = EigenTensor<T, D>::From(*d_out);
auto src_tensor = EigenTensor<T, D>::From(src);
auto& place =
*context.template device_context<DeviceContext>().eigen_device();
d_out_tensor.device(place) = src_tensor.pad(paddings, 0);
}
template <typename DeviceContext, typename T>
void PaddingFunctor(int rank, const framework::ExecutionContext& context,
const std::vector<int>& pads, T pad_value,
const framework::Tensor& src, framework::Tensor* out) {
switch (rank) {
case 1:
PadFunction<DeviceContext, T, 1>(context, pads, src, pad_value, out);
break;
case 2:
PadFunction<DeviceContext, T, 2>(context, pads, src, pad_value, out);
break;
case 3:
PadFunction<DeviceContext, T, 3>(context, pads, src, pad_value, out);
break;
case 4:
PadFunction<DeviceContext, T, 4>(context, pads, src, pad_value, out);
break;
case 5:
PadFunction<DeviceContext, T, 5>(context, pads, src, pad_value, out);
break;
case 6:
PadFunction<DeviceContext, T, 6>(context, pads, src, pad_value, out);
break;
default:
PADDLE_THROW(
"PadOp only support tensors with no more than 6 dimensions.");
}
}
template <typename DeviceContext, typename T>
void PaddingGradFunctor(int rank, const framework::ExecutionContext& context,
const std::vector<int>& pads,
const framework::Tensor& src, framework::Tensor* out) {
switch (rank) {
case 1:
PadGradFunction<DeviceContext, T, 1>(context, pads, src, out);
break;
case 2:
PadGradFunction<DeviceContext, T, 2>(context, pads, src, out);
break;
case 3:
PadGradFunction<DeviceContext, T, 3>(context, pads, src, out);
break;
case 4:
PadGradFunction<DeviceContext, T, 4>(context, pads, src, out);
break;
case 5:
PadGradFunction<DeviceContext, T, 5>(context, pads, src, out);
break;
case 6:
PadGradFunction<DeviceContext, T, 6>(context, pads, src, out);
break;
default:
PADDLE_THROW(
"PadOp only support tensors with no more than 6 dimensions.");
}
}
} // namespace math
} // namespace operators
} // namespace paddle
paddle/fluid/operators/pad_constant_like_op.cc 0 → 100644
浏览文件 @ 79d4a99d
/* Copyright (c) 2018 PaddlePaddle Authors. All Rights Reserved.
Licensed under the Apache License, Version 2.0 (the "License");
you may not use this file except in compliance with the License.
You may obtain a copy of the License at
http://www.apache.org/licenses/LICENSE-2.0
Unless required by applicable law or agreed to in writing, software
distributed under the License is distributed on an "AS IS" BASIS,
WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
See the License for the specific language governing permissions and
limitations under the License. */
#include "paddle/fluid/operators/pad_constant_like_op.h"
namespace paddle {
namespace operators {
using framework::Tensor;
class PadConstantLikeOp : public framework::OperatorWithKernel {
public:
using framework::OperatorWithKernel::OperatorWithKernel;
void InferShape(framework::InferShapeContext *ctx) const override {
PADDLE_ENFORCE(ctx->HasInput("X"),
"Input(X) of PadConstantLikeOp should not be null.");
PADDLE_ENFORCE(ctx->HasInput("Y"),
"Input(Y) of PadConstantLikeOp should not be null.");
PADDLE_ENFORCE(ctx->HasOutput("Out"),
"Output(Out) of PadConstantLikeOp should not be null.");
auto x_dim = ctx->GetInputDim("X");
auto y_dim = ctx->GetInputDim("Y");
PADDLE_ENFORCE_EQ(x_dim.size(), y_dim.size(),
"The dimention of X and Y should be the same.");
for (int i = 0; i < x_dim.size(); ++i) {
PADDLE_ENFORCE_GE(x_dim[i], y_dim[i]);
}
ctx->SetOutputDim("Out", x_dim);
ctx->ShareLoD("X", /*->*/ "Out");
}
};
class PadConstantLikeOpMaker : public framework::OpProtoAndCheckerMaker {
public:
void Make() override {
AddInput("X",
"The input of pad_constant_like op. "
"The input should be a k-D tensor(k > 0 and k < 7)");
AddInput("Y",
"The input of pad_constant_like op. "
"The input should be a k-D tensor(k > 0 and k < 7)");
AddOutput("Out",
"The output of pad_constant_like op. "
"A tensor with the same shape as X.");
AddAttr<float>("pad_value",
"(float, default 0.0) "
"The value to fill the padded areas.")
.SetDefault(0.0f);
AddComment(R"DOC(
PadConstantLikeOp Operator.
Pad input(Y) with a pad_value, the number of values padded to the edges of each
axis is specified by the difference of the shape of X and Y.
((0, shape_x_0 - shape_y_0), … (0, shape_x_n - shape_y_n)) unique pad widths for
each axis.
The input should be a k-D tensor(k > 0 and k < 7). As an example:
case1:
Given:
X = [[1, 2],
[3, 4],
[1, 2],
[3, 4]]],
X.shape = (4, 2)
Y = [[5, 6],
[7, 8]],
Y.shape = (2, 2)
And
pad_value = 0,
Return:
Out = [[5, 6],
[7, 8],
[0, 0],
[0, 0]]
Out.shape = (4, 2)
case2:
Given:
X = [[[[ 0, 1, 2],
[ 3, 4, 5]],
[[ 6, 7, 8],
[ 9, 10, 11]],
[[12, 13, 14],
[15, 16, 17]]],
[[[18, 19, 20],
[21, 22, 23]],
[[24, 25, 26],
[27, 28, 29]],
[[30, 31, 32],
[33, 34, 35]]]]
X.shape = (2, 3, 2, 3)
Y = [[[[35, 36, 37]],
[[38, 39, 40]],
[[41, 42, 43]]]]
Y.shape = (1, 3, 1, 3)
And
pad_value = -1,
Return:
Out = [[[[35, 36, 37],
[-1, -1, -1]],
[[38, 39, 40],
[-1, -1, -1]],
[[41, 42, 43],
[-1, -1, -1]]],
[[[-1, -1, -1],
[-1, -1, -1]],
[[-1, -1, -1],
[-1, -1, -1]],
[[-1, -1, -1],
[-1, -1, -1]]]]
Out.shape = (2, 3, 2, 3)
)DOC");
}
};
class PadConstantLikeOpGrad : public framework::OperatorWithKernel {
public:
using framework::OperatorWithKernel::OperatorWithKernel;
void InferShape(framework::InferShapeContext *ctx) const override {
PADDLE_ENFORCE(ctx->HasInput("Y"), "Input(Y) should not be null");
PADDLE_ENFORCE(ctx->HasInput(framework::GradVarName("Out")),
"Input(Out@GRAD) should not be null");
auto y_dim = ctx->GetInputDim("Y");
auto dout_dim = ctx->GetInputDim(framework::GradVarName("Out"));
PADDLE_ENFORCE_EQ(dout_dim.size(), y_dim.size(),
"The dimention of X and Y should be the same.");
auto y_grad_name = framework::GradVarName("Y");
if (ctx->HasOutput(y_grad_name)) {
ctx->SetOutputDim(y_grad_name, y_dim);
ctx->ShareLoD("Y", /*->*/ y_grad_name);
for (int i = 0; i < y_dim.size(); ++i) {
PADDLE_ENFORCE_GE(dout_dim[i], y_dim[i]);
}
}
}
};
class PadConstantLikeOpGradMaker : public framework::SingleGradOpDescMaker {
public:
using framework::SingleGradOpDescMaker::SingleGradOpDescMaker;
protected:
std::unique_ptr<framework::OpDesc> Apply() const override {
auto *bind = new framework::OpDesc();
bind->SetType("pad_constant_like_grad");
bind->SetInput("Y", Input("Y"));
bind->SetInput(framework::GradVarName("Out"), OutputGrad("Out"));
bind->SetOutput(framework::GradVarName("Y"), InputGrad("Y"));
bind->SetAttrMap(Attrs());
return std::unique_ptr<framework::OpDesc>(bind);
}
};
} // namespace operators
} // namespace paddle
namespace ops = paddle::operators;
REGISTER_OPERATOR(pad_constant_like, ops::PadConstantLikeOp,
ops::PadConstantLikeOpMaker, ops::PadConstantLikeOpGradMaker);
REGISTER_OPERATOR(pad_constant_like_grad, ops::PadConstantLikeOpGrad);
REGISTER_OP_CPU_KERNEL(
pad_constant_like,
ops::PadConstantLikeKernel<paddle::platform::CPUDeviceContext, float>,
ops::PadConstantLikeKernel<paddle::platform::CPUDeviceContext, double>);
REGISTER_OP_CPU_KERNEL(
pad_constant_like_grad,
ops::PadConstantLikeGradKernel<paddle::platform::CPUDeviceContext, float>,
ops::PadConstantLikeGradKernel<paddle::platform::CPUDeviceContext, double>);
paddle/fluid/operators/pad_constant_like_op.cu 0 → 100644
浏览文件 @ 79d4a99d
/* 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. */
#define EIGEN_USE_GPU
#include "paddle/fluid/operators/pad_constant_like_op.h"
namespace ops = paddle::operators;
REGISTER_OP_CUDA_KERNEL(
pad_constant_like,
ops::PadConstantLikeKernel<paddle::platform::CUDADeviceContext, float>,
ops::PadConstantLikeKernel<paddle::platform::CUDADeviceContext, double>);
REGISTER_OP_CUDA_KERNEL(
pad_constant_like_grad,
ops::PadConstantLikeGradKernel<paddle::platform::CUDADeviceContext, float>,
ops::PadConstantLikeGradKernel<paddle::platform::CUDADeviceContext,
double>);
paddle/fluid/operators/pad_constant_like_op.h 0 → 100644
浏览文件 @ 79d4a99d
/* 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 <utility>
#include <vector>
#include "paddle/fluid/framework/eigen.h"
#include "paddle/fluid/framework/op_registry.h"
#include "paddle/fluid/framework/tensor.h"
#include "paddle/fluid/framework/tensor_util.h"
#include "paddle/fluid/operators/math/padding.h"
namespace paddle {
namespace operators {
template <typename DeviceContext, typename T>
class PadConstantLikeKernel : public framework::OpKernel<T> {
public:
void Compute(const framework::ExecutionContext& context) const override {
auto in_x = context.Input<framework::Tensor>("X");
auto in_y = context.Input<framework::Tensor>("Y");
auto* out = context.Output<framework::Tensor>("Out");
if (in_x->dims() == in_y->dims()) {
// TensorCopy(in_y, context.GetPlace(), context, out);
out->ShareDataWith(*in_y);
return;
}
T pad_value = context.Attr<T>("pad_value");
out->mutable_data<T>(context.GetPlace());
int rank = context.Input<framework::Tensor>("X")->dims().size();
std::vector<int> pads(rank * 2, 0);
for (int j = 0; j < rank; ++j) {
pads[j * 2] = 0;
pads[j * 2 + 1] = static_cast<int>(in_x->dims()[j] - in_y->dims()[j]);
}
math::PaddingFunctor<DeviceContext, T>(rank, context, pads, pad_value,
*in_y, out);
}
};
template <typename DeviceContext, typename T>
class PadConstantLikeGradKernel : public framework::OpKernel<T> {
public:
void Compute(const framework::ExecutionContext& context) const override {
auto in_y = context.Input<framework::Tensor>("Y");
auto in_dout =
context.Input<framework::Tensor>(framework::GradVarName("Out"));
auto* d_y = context.Output<framework::Tensor>(framework::GradVarName("Y"));
if (d_y == nullptr) {
return;
}
if (in_dout->dims() == in_y->dims()) {
// TensorCopy(in_dout, context.GetPlace(), context, d_y);
d_y->ShareDataWith(*in_dout);
return;
}
d_y->mutable_data<T>(context.GetPlace());
int rank = in_dout->dims().size();
std::vector<int> pads(static_cast<size_t>(rank) * 2, 0);
for (int j = 0; j < rank; ++j) {
pads[j * 2] = 0;
pads[j * 2 + 1] = static_cast<int>(in_dout->dims()[j] - in_y->dims()[j]);
}
math::PaddingGradFunctor<DeviceContext, T>(rank, context, pads, *in_dout,
d_y);
}
};
} // namespace operators
} // namespace paddle
paddle/fluid/operators/pad_op.h
浏览文件 @ 79d4a99d
......@@ -18,117 +18,44 @@ limitations under the License. */
#include <vector>
#include "paddle/fluid/framework/eigen.h"
#include "paddle/fluid/framework/op_registry.h"
#include "paddle/fluid/operators/math/padding.h"
namespace paddle {
namespace operators {
using Tensor = framework::Tensor;
template <typename T, size_t D, int MajorType = Eigen::RowMajor,
typename IndexType = Eigen::DenseIndex>
using EigenTensor = framework::EigenTensor<T, D, MajorType, IndexType>;
template <typename DeviceContext, typename T, size_t D>
void PadFunction(const framework::ExecutionContext& context) {
auto pads = context.Attr<std::vector<int>>("paddings");
Eigen::array<std::pair<int, int>, D> paddings;
for (size_t i = 0; i < paddings.size(); ++i) {
paddings[i].first = pads[i * 2];
paddings[i].second = pads[i * 2 + 1];
}
T pad_value = context.Attr<T>("pad_value");
auto* x = context.Input<Tensor>("X");
auto* out = context.Output<Tensor>("Out");
out->mutable_data<T>(context.GetPlace());
auto x_tensor = EigenTensor<T, D>::From(*x);
auto out_tensor = EigenTensor<T, D>::From(*out);
auto& place =
*context.template device_context<DeviceContext>().eigen_device();
out_tensor.device(place) = x_tensor.pad(paddings, pad_value);
}
template <typename DeviceContext, typename T>
class PadKernel : public framework::OpKernel<T> {
public:
void Compute(const framework::ExecutionContext& context) const override {
int rank = context.Input<Tensor>("X")->dims().size();
switch (rank) {
case 1:
PadFunction<DeviceContext, T, 1>(context);
break;
case 2:
PadFunction<DeviceContext, T, 2>(context);
break;
case 3:
PadFunction<DeviceContext, T, 3>(context);
break;
case 4:
PadFunction<DeviceContext, T, 4>(context);
break;
case 5:
PadFunction<DeviceContext, T, 5>(context);
break;
case 6:
PadFunction<DeviceContext, T, 6>(context);
break;
default:
PADDLE_THROW(
"PadOp only support tensors with no more than 6 dimensions.");
}
auto pads = context.Attr<std::vector<int>>("paddings");
T pad_value = context.Attr<T>("pad_value");
auto* x = context.Input<Tensor>("X");
auto* out = context.Output<Tensor>("Out");
out->mutable_data<T>(context.GetPlace());
int rank = x->dims().size();
math::PaddingFunctor<DeviceContext, T>(rank, context, pads, pad_value, *x,
out);
}
};
template <typename DeviceContext, typename T, size_t D>
void PadGradFunction(const framework::ExecutionContext& context) {
auto pads = context.Attr<std::vector<int>>("paddings");
Eigen::array<std::pair<int, int>, D> paddings;
for (size_t i = 0; i < paddings.size(); ++i) {
paddings[i].first = -pads[i * 2];
paddings[i].second = -pads[i * 2 + 1];
}
auto* d_out = context.Input<Tensor>(framework::GradVarName("Out"));
auto* d_x = context.Output<Tensor>(framework::GradVarName("X"));
if (d_x != nullptr) {
d_x->mutable_data<T>(context.GetPlace());
auto d_x_tensor = EigenTensor<T, D>::From(*d_x);
auto d_out_tensor = EigenTensor<T, D>::From(*d_out);
auto& place =
*context.template device_context<DeviceContext>().eigen_device();
d_x_tensor.device(place) = d_out_tensor.pad(paddings, 0);
}
}
template <typename DeviceContext, typename T>
class PadGradKernel : public framework::OpKernel<T> {
public:
void Compute(const framework::ExecutionContext& context) const override {
size_t rank =
context.Input<Tensor>(framework::GradVarName("Out"))->dims().size();
switch (rank) {
case 1:
PadGradFunction<DeviceContext, T, 1>(context);
break;
case 2:
PadGradFunction<DeviceContext, T, 2>(context);
break;
case 3:
PadGradFunction<DeviceContext, T, 3>(context);
break;
case 4:
PadGradFunction<DeviceContext, T, 4>(context);
break;
case 5:
PadGradFunction<DeviceContext, T, 5>(context);
break;
case 6:
PadGradFunction<DeviceContext, T, 6>(context);
break;
default:
PADDLE_THROW(
"PadOp only support tensors with no more than 6 dimensions.");
auto pads = context.Attr<std::vector<int>>("paddings");
auto* d_out = context.Input<Tensor>(framework::GradVarName("Out"));
auto* d_x = context.Output<Tensor>(framework::GradVarName("X"));
if (d_x == nullptr) {
return;
}
d_x->mutable_data<T>(context.GetPlace());
int rank = d_out->dims().size();
math::PaddingGradFunctor<DeviceContext, T>(rank, context, pads, *d_out,
d_x);
}
};
......
paddle/fluid/operators/sequence_mask_op.cc 0 → 100644
浏览文件 @ 79d4a99d
// Copyright (c) 2018 PaddlePaddle Authors. All Rights Reserved.
//
// Licensed under the Apache License, Version 2.0 (the "License");
// you may not use this file except in compliance with the License.
// You may obtain a copy of the License at
//
// http://www.apache.org/licenses/LICENSE-2.0
//
// Unless required by applicable law or agreed to in writing, software
// distributed under the License is distributed on an "AS IS" BASIS,
// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
// See the License for the specific language governing permissions and
// limitations under the License.
#include "paddle/fluid/operators/sequence_mask_op.h"
REGISTER_OPERATOR(sequence_mask, paddle::operators::SequenceMaskOp,
paddle::operators::SequenceMaskOpMaker,
paddle::framework::EmptyGradOpMaker);
REGISTER_OP_CPU_KERNEL(
sequence_mask,
paddle::operators::SequenceMaskKernel<paddle::platform::CPUDeviceContext,
int>,
paddle::operators::SequenceMaskKernel<paddle::platform::CPUDeviceContext,
int64_t>);
paddle/fluid/operators/sequence_mask_op.cu 0 → 100644
浏览文件 @ 79d4a99d
// Copyright (c) 2018 PaddlePaddle Authors. All Rights Reserved.
//
// Licensed under the Apache License, Version 2.0 (the "License");
// you may not use this file except in compliance with the License.
// You may obtain a copy of the License at
//
// http://www.apache.org/licenses/LICENSE-2.0
//
// Unless required by applicable law or agreed to in writing, software
// distributed under the License is distributed on an "AS IS" BASIS,
// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
// See the License for the specific language governing permissions and
// limitations under the License.
#include "paddle/fluid/operators/sequence_mask_op.h"
REGISTER_OP_CUDA_KERNEL(
sequence_mask,
paddle::operators::SequenceMaskKernel<paddle::platform::CUDADeviceContext,
int>,
paddle::operators::SequenceMaskKernel<paddle::platform::CUDADeviceContext,
int64_t>);
paddle/fluid/operators/sequence_mask_op.h 0 → 100644
浏览文件 @ 79d4a99d
// 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
#ifdef __NVCC__
#include <thrust/device_ptr.h>
#include <thrust/functional.h>
#include <thrust/reduce.h>
#else
#include <algorithm>
#endif
#include "paddle/fluid/framework/op_registry.h"
#include "paddle/fluid/platform/for_range.h"
namespace paddle {
namespace operators {
class SequenceMaskOp : public framework::OperatorWithKernel {
public:
using framework::OperatorWithKernel::OperatorWithKernel;
void InferShape(framework::InferShapeContext *ctx) const override {
PADDLE_ENFORCE(ctx->HasInput("X"), "Input(X) must exist");
PADDLE_ENFORCE(ctx->HasOutput("Y"), "Output(Y) must exist");
auto maxlen = ctx->Attrs().Get<int>("maxlen");
if (maxlen > 0) { // We can only infershape when maxlen > 0
auto dim = framework::vectorize2int(ctx->GetInputDim("X"));
dim.push_back(maxlen);
ctx->SetOutputDim("Y", framework::make_ddim(dim));
}
}
};
class SequenceMaskOpMaker : public framework::OpProtoAndCheckerMaker {
public:
void Make() override {
AddInput("X", "The input tensor of sequence_mask op.");
AddOutput("Y", "The output mask of sequence_mask op.");
AddAttr<int>("maxlen",
"The maximum length of the sequence. If maxlen < 0, maxlen "
"= max(Input(X)).")
.SetDefault(-1)
.AddCustomChecker([](int &v) {
PADDLE_ENFORCE(v < 0 || v >= 1,
"Attr(maxlen) must be less than 0 or larger than 1");
});
AddAttr<int>("out_dtype", "Output data type");
AddComment(R"DOC(
SequenceMask Operator
This operator outputs a Mask according to Input(X) and Attr(maxlen).
Supposing Input(X) is a Tensor with shape [d_1, d_2, ..., d_n], the
Output(Y) is a mask with shape [d_1, d_2, ..., d_n, maxlen], where:
Y(i_1, i_2, ..., i_n, j) = (j < X(i_1, i_2, ..., i_n))
If maxlen < 0, maxlen = max(X)
)DOC");
}
};
template <typename Tx, typename Ty>
struct SequenceMaskForRangeFunctor {
HOSTDEVICE SequenceMaskForRangeFunctor(const Tx *x, Ty *y, int maxlen)
: x_(x), y_(y), maxlen_(maxlen) {}
HOSTDEVICE void operator()(int y_idx) const {
int x_idx = y_idx / maxlen_;
int j = y_idx % maxlen_;
y_[y_idx] = static_cast<Ty>(j < x_[x_idx] ? 1 : 0);
}
private:
const Tx *x_;
Ty *y_;
int maxlen_;
};
template <typename DeviceContext, typename Tx>
struct SequenceMaskFunctor {
using Tensor = framework::LoDTensor;
SequenceMaskFunctor(const DeviceContext &ctx, const Tx *x, Tensor *y,
int limits, int maxlen)
: ctx_(ctx), x_(x), y_(y), limits_(limits), maxlen_(maxlen) {}
template <typename Ty>
void operator()() const {
auto *y_data = y_->mutable_data<Ty>(ctx_.GetPlace());
platform::ForRange<DeviceContext> for_range(ctx_, limits_);
for_range(SequenceMaskForRangeFunctor<Tx, Ty>(x_, y_data, maxlen_));
}
private:
const DeviceContext &ctx_;
const Tx *x_;
Tensor *y_;
int limits_;
int maxlen_;
};
template <typename DeviceContext, typename Tx>
class SequenceMaskKernel : public framework::OpKernel<Tx> {
using Tensor = framework::LoDTensor;
public:
void Compute(const framework::ExecutionContext &ctx) const override {
auto *x = ctx.Input<Tensor>("X");
auto *y = ctx.Output<Tensor>("Y");
auto maxlen = ctx.Attr<int>("maxlen");
auto *x_data = x->data<Tx>();
auto x_numel = x->numel();
if (maxlen < 0) {
#ifdef __NVCC__
VLOG(10)
<< "SequenceMaskOp on GPU may be slow when maxlen is not provided.";
maxlen = static_cast<int>(
thrust::reduce(thrust::device_pointer_cast(x_data),
thrust::device_pointer_cast(x_data) + x_numel,
static_cast<Tx>(0), thrust::maximum<Tx>()));
#else
maxlen = static_cast<int>(*std::max_element(x_data, x_data + x_numel));
#endif
auto y_dim = framework::vectorize2int(x->dims());
y_dim.push_back(maxlen);
y->Resize(framework::make_ddim(y_dim));
}
auto out_dtype = static_cast<framework::proto::VarType::Type>(
ctx.Attr<int>("out_dtype"));
auto &dev_ctx = ctx.template device_context<DeviceContext>();
framework::VisitDataType(out_dtype,
SequenceMaskFunctor<DeviceContext, Tx>(
dev_ctx, x_data, y, x_numel * maxlen, maxlen));
}
};
} // namespace operators
} // namespace paddle
paddle/fluid/operators/stack_op.h
浏览文件 @ 79d4a99d
......@@ -150,30 +150,17 @@ class StackKernel : public framework::OpKernel<T> {
int total_num = pre * n * post;
auto &dev_ctx = ctx.template device_context<DeviceContext>();
constexpr auto kMaxThreshold = 16;
if (std::is_same<DeviceContext, platform::CPUDeviceContext>::value ||
n > kMaxThreshold) {
#ifdef __NVCC__
VLOG(10) << "Stack more than " << kMaxThreshold
<< " tensors on GPU may be slow.";
thrust::device_vector<const T *> device_x_vec(x_datas);
auto x_data_arr = device_x_vec.data().get();
thrust::device_vector<const T *> device_x_vec(x_datas);
auto x_data_arr = device_x_vec.data().get();
#else
auto x_data_arr = x_datas.data();
auto x_data_arr = x_datas.data();
#endif
StackFunctorForRange(dev_ctx, x_data_arr, y_data, total_num, n, post);
StackFunctorForRange(dev_ctx, x_data_arr, y_data, total_num, n, post);
#ifdef __NVCC__
// Wait() must be called because device_x_vec may be destructed before
// kernel ends
dev_ctx.Wait();
#endif
}
#ifdef __NVCC__
else { // NOLINT
framework::Array<const T *, kMaxThreshold> x_data_arr;
for (int i = 0; i < n; ++i) x_data_arr[i] = x_datas[i];
StackFunctorForRange(dev_ctx, x_data_arr, y_data, total_num, n, post);
}
// Wait() must be called because device_x_vec may be destructed before
// kernel ends
dev_ctx.Wait();
#endif
}
};
......@@ -244,32 +231,17 @@ class StackGradKernel : public framework::OpKernel<T> {
int post = total_num / (n * pre);
auto &dev_ctx = ctx.template device_context<DeviceContext>();
constexpr auto kMaxThreshold = 16;
if (std::is_same<DeviceContext, platform::CPUDeviceContext>::value ||
n > kMaxThreshold) {
#ifdef __NVCC__
VLOG(10) << "Stack more than " << kMaxThreshold
<< " tensors on GPU may be slow.";
thrust::device_vector<T *> device_dx_vec(dx_datas);
auto dx_data_arr = device_dx_vec.data().get();
thrust::device_vector<T *> device_dx_vec(dx_datas);
auto dx_data_arr = device_dx_vec.data().get();
#else
auto dx_data_arr = dx_datas.data();
auto dx_data_arr = dx_datas.data();
#endif
StackGradFunctorForRange(dev_ctx, dx_data_arr, dy_data, total_num, n,
post);
StackGradFunctorForRange(dev_ctx, dx_data_arr, dy_data, total_num, n, post);
#ifdef __NVCC__
// Wait() must be called because device_dx_vec may be destructed before
// kernel ends
dev_ctx.Wait();
#endif
}
#ifdef __NVCC__
else { // NOLINT
framework::Array<T *, kMaxThreshold> dx_data_arr;
for (int i = 0; i < n; ++i) dx_data_arr[i] = dx_datas[i];
StackGradFunctorForRange(dev_ctx, dx_data_arr, dy_data, total_num, n,
post);
}
// Wait() must be called because device_dx_vec may be destructed before
// kernel ends
dev_ctx.Wait();
#endif
}
};
......
paddle/fluid/platform/CMakeLists.txt
浏览文件 @ 79d4a99d
if (NOT WIN32)
proto_library(profiler_proto SRCS profiler.proto DEPS framework_proto)
py_proto_compile(profiler_py_proto SRCS profiler.proto)
......@@ -10,6 +11,7 @@ add_custom_command(TARGET profiler_py_proto POST_BUILD
COMMAND cp *.py ${PADDLE_BINARY_DIR}/python/paddle/fluid/proto/profiler
COMMENT "Copy generated python proto into directory paddle/fluid/proto/profiler."
WORKING_DIRECTORY ${CMAKE_CURRENT_BINARY_DIR})
endif(NOT WIN32)
if(WITH_GPU)
nv_library(enforce SRCS enforce.cc)
......@@ -50,7 +52,7 @@ ENDIF()
# memcpy depends on device_context, here add deps individually for
# avoiding cycle dependencies
cc_library(device_context SRCS device_context.cc init.cc DEPS malloc
place eigen3 stringpiece cpu_helper framework_proto ${GPU_CTX_DEPS} ${MKLDNN_CTX_DEPS})
place eigen3 stringpiece cpu_helper cpu_info framework_proto ${GPU_CTX_DEPS} ${MKLDNN_CTX_DEPS})
nv_test(device_context_test SRCS device_context_test.cu DEPS device_context gpu_info)
cc_test(init_test SRCS init_test.cc DEPS device_context)
......@@ -58,9 +60,12 @@ cc_test(init_test SRCS init_test.cc DEPS device_context)
nv_test(cudnn_helper_test SRCS cudnn_helper_test.cc DEPS dynload_cuda)
nv_test(transform_test SRCS transform_test.cu DEPS memory place device_context)
if (NOT WIN32)
cc_library(device_tracer SRCS device_tracer.cc DEPS boost profiler_proto framework_proto ${GPU_CTX_DEPS})
cc_library(profiler SRCS profiler.cc DEPS device_context device_tracer)
cc_test(profiler_test SRCS profiler_test.cc DEPS profiler)
endif(NOT WIN32)
nv_test(float16_gpu_test SRCS float16_test.cu DEPS lod_tensor)
cc_test(float16_test SRCS float16_test.cc DEPS lod_tensor)
......
paddle/fluid/platform/cpu_info.cc
浏览文件 @ 79d4a99d
......@@ -22,9 +22,13 @@ limitations under the License. */
#ifdef __APPLE__
#include <sys/sysctl.h>
#include <sys/types.h>
#elif defined(_WIN32)
#define NOMINMAX // msvc max/min macro conflict with std::min/max
#include <windows.h>
#else
#include <unistd.h>
#endif
#endif // _WIN32
#include <algorithm>
#include "gflags/gflags.h"
......@@ -32,16 +36,20 @@ limitations under the License. */
DEFINE_double(fraction_of_cpu_memory_to_use, 1,
"Default use 100% of CPU memory for PaddlePaddle,"
"reserve the rest for page tables, etc");
#if !defined(_WIN32)
DEFINE_uint64(initial_cpu_memory_in_mb,
#ifdef PADDLE_WITH_MKLDNN
/* Aligned with mozga-intel, MKLDNN need at least 5000 MB
* to obtain the best performance*/
5000,
5000ul,
#else
500,
500ul,
#endif
"Initial CPU memory for PaddlePaddle, in MD unit.");
#else
DEFINE_uint64(initial_cpu_memory_in_mb, 500ul,
"Initial CPU memory for PaddlePaddle, in MD unit.");
#endif // !defined(_WIN32)
DEFINE_double(
fraction_of_cuda_pinned_memory_to_use, 0.5,
......@@ -60,6 +68,11 @@ inline size_t CpuTotalPhysicalMemory() {
size_t len = sizeof(size);
if (sysctl(mib, 2, &size, &len, NULL, 0) == 0) return (size_t)size;
return 0L;
#elif defined(_WIN32)
MEMORYSTATUSEX sMeminfo;
sMeminfo.dwLength = sizeof(sMeminfo);
GlobalMemoryStatusEx(&sMeminfo);
return sMeminfo.ullTotalPhys;
#else
int64_t pages = sysconf(_SC_PHYS_PAGES);
int64_t page_size = sysconf(_SC_PAGE_SIZE);
......
paddle/fluid/platform/cpu_info.h
浏览文件 @ 79d4a99d
......@@ -51,7 +51,7 @@ typedef enum {
} cpu_isa_t; // Instruction set architecture
// May I use some instruction
inline bool MayIUse(const cpu_isa_t cpu_isa);
bool MayIUse(const cpu_isa_t cpu_isa);
} // namespace jit
......
paddle/fluid/platform/device_tracer.h
浏览文件 @ 79d4a99d
......@@ -13,7 +13,12 @@ See the License for the specific language governing permissions and
limitations under the License. */
#pragma once
#if !defined(_WIN32)
#include <sys/time.h>
#else
#include <windows.h>
#endif // !_WIN32
#include <time.h>
#include <chrono> // NOLINT
#include <string>
......@@ -27,12 +32,15 @@ namespace platform {
///////////////////////
// WARN: Under Development. Don't depend on it yet.
//////////////////////
#if !defined(_WIN32)
inline uint64_t PosixInNsec() {
struct timeval tv;
gettimeofday(&tv, nullptr);
return 1000 * (static_cast<uint64_t>(tv.tv_sec) * 1000000 + tv.tv_usec);
}
#else
inline uint64_t PosixInNsec() { return static_cast<uint64_t>(0); }
#endif // !_WIN32
// DeviceTracer performs the following tasks:
// 1. Register cuda callbacks for various events: kernel, memcpy, etc.
......
paddle/fluid/platform/dynload/CMakeLists.txt
浏览文件 @ 79d4a99d
......@@ -16,7 +16,9 @@ if (CUPTI_FOUND)
list(APPEND CUDA_SRCS cupti.cc)
endif(CUPTI_FOUND)
nv_library(dynload_cuda SRCS ${CUDA_SRCS} DEPS dynamic_loader)
if (NOT WIN32)
cc_library(dynload_warpctc SRCS warpctc.cc DEPS dynamic_loader warpctc)
endif(NOT WIN32)
if (WITH_MKLML)
cc_library(dynload_mklml SRCS mklml.cc DEPS dynamic_loader mklml)
endif()
......
paddle/fluid/platform/dynload/dynamic_loader.cc
浏览文件 @ 79d4a99d
......@@ -13,8 +13,6 @@ See the License for the specific language governing permissions and
limitations under the License. */
#include "paddle/fluid/platform/dynload/dynamic_loader.h"
#include <dlfcn.h>
#include <memory>
#include <mutex> // NOLINT
#include <string>
......@@ -23,6 +21,7 @@ limitations under the License. */
#include "glog/logging.h"
#include "paddle/fluid/platform/dynload/cupti_lib_path.h"
#include "paddle/fluid/platform/enforce.h"
#include "paddle/fluid/platform/port.h"
DEFINE_string(cudnn_dir, "",
"Specify path for loading libcudnn.so. For instance, "
......
paddle/fluid/platform/enforce.h
浏览文件 @ 79d4a99d
......@@ -18,6 +18,11 @@ limitations under the License. */
#include <cxxabi.h> // for __cxa_demangle
#endif // __GNUC__
#if defined(_WIN32)
#define NOMINMAX // msvc max/min macro conflict with std::min/max
#define GLOG_NO_ABBREVIATED_SEVERITIES // msvc conflict logging with windows.h
#endif
#ifdef PADDLE_WITH_CUDA
#include <cublas_v2.h>
#include <cudnn.h>
......@@ -117,7 +122,12 @@ struct EOFException : public std::exception {
// always forces branch prediction of true.
// This generates faster binary code. __builtin_expect is since C++11.
// For more details, please check https://stackoverflow.com/a/43870188/724872.
#if !defined(_WIN32)
#define UNLIKELY(condition) __builtin_expect(static_cast<bool>(condition), 0)
#else
// there is no equivalent intrinsics in msvc.
#define UNLIKELY(condition) (condition == 0)
#endif
template <typename... Args>
inline typename std::enable_if<sizeof...(Args) != 0, void>::type throw_on_error(
......@@ -230,6 +240,7 @@ inline void throw_on_error(T e) {
throw_on_error(e, "");
}
#if !defined(_WIN32)
#define PADDLE_THROW(...) \
do { \
throw ::paddle::platform::EnforceNotMet( \
......@@ -248,15 +259,28 @@ inline void throw_on_error(T e) {
__FILE__, __LINE__); \
} \
} while (false)
#else
#define PADDLE_ENFORCE(...) ::paddle::platform::throw_on_error(__VA_ARGS__);
#endif
#define PADDLE_THROW_EOF() \
do { \
throw ::paddle::platform::EOFException("There is no next data.", __FILE__, \
__LINE__); \
} while (false)
#else
#define PADDLE_ENFORCE(...) ::paddle::platform::throw_on_error(__VA_ARGS__)
#endif // REPLACE_ENFORCE_GLOG
#else // !_WIN32
// disable enforce, caused by the varardic macro exception error
#define PADDLE_THROW(x) \
do { \
throw std::make_exception_ptr( \
std::runtime_error("Windows disable the enforce.")); \
} while (false)
#define PADDLE_ENFORCE(x, ...) x
#endif // !_WIN32
/*
* Some enforce helpers here, usage:
* int a = 1;
......
paddle/fluid/platform/float16.h
浏览文件 @ 79d4a99d
......@@ -56,7 +56,11 @@ limitations under the License. */
#include <immintrin.h>
#endif // PADDLE_ARM
#if !defined(_WIN32)
#define PADDLE_ALIGN(x) __attribute__((aligned(x)))
#else
#define PADDLE_ALIGN(x) /*do nothing*/
#endif
namespace paddle {
namespace platform {
......
paddle/fluid/platform/init.cc
浏览文件 @ 79d4a99d
......@@ -18,6 +18,7 @@ limitations under the License. */
#include "paddle/fluid/framework/operator.h"
#include "paddle/fluid/platform/cpu_helper.h"
#include "paddle/fluid/platform/cpu_info.h"
#include "paddle/fluid/platform/device_context.h"
#include "paddle/fluid/platform/init.h"
#include "paddle/fluid/platform/place.h"
......@@ -120,6 +121,22 @@ void InitDevices(bool init_p2p, const std::vector<int> devices) {
#ifndef PADDLE_WITH_MKLDNN
platform::SetNumThreads(FLAGS_paddle_num_threads);
#endif
if (platform::jit::MayIUse(platform::jit::avx512_common)) {
#ifndef __AVX512F__
LOG(WARNING) << "AVX512F is available, Please re-compile on local machine";
#endif
}
if (platform::jit::MayIUse(platform::jit::avx2)) {
#ifndef __AVX2__
LOG(WARNING) << "AVX2 is available, Please re-compile on local machine";
#endif
}
if (platform::jit::MayIUse(platform::jit::avx)) {
#ifndef __AVX__
LOG(WARNING) << "AVX is available, Please re-compile on local machine";
#endif
}
}
void InitGLOG(const std::string &prog_name) {
......
paddle/fluid/platform/profiler.h
浏览文件 @ 79d4a99d
......@@ -69,6 +69,7 @@ void PushEvent(const std::string& name, const DeviceContext* dev_ctx);
void PopEvent(const std::string& name, const DeviceContext* dev_ctx);
#if !defined(_WIN32)
struct RecordEvent {
RecordEvent(const std::string& name, const DeviceContext* dev_ctx);
......@@ -94,6 +95,15 @@ struct RecordBlock {
std::string name_;
uint64_t start_ns_;
};
#else
// windows do not support profiler temporarily.
struct RecordEvent {
RecordEvent(const std::string& name, const DeviceContext* dev_ctx) {}
};
struct RecordBlock {
explicit RecordBlock(int block_id) {}
};
#endif
// Return the event list of all threads. Assumed the returned value calls
// event_lists, event_lists[i][j] represents the j-th Event of i-th thread.
......
paddle/fluid/pybind/CMakeLists.txt
浏览文件 @ 79d4a99d
set(PYBIND_DEPS pybind python proto_desc memory executor prune profiler feed_fetch_method
)
set(PYBIND_DEPS pybind python proto_desc memory executor prune feed_fetch_method)
set(PYBIND_SRCS pybind.cc exception.cc protobuf.cc const_value.cc)
if(NOT WIN32)
list(APPEND PYBIND_DEPS parallel_executor)
list(APPEND PYBIND_DEPS parallel_executor profiler)
list(APPEND PYBIND_SRCS recordio.cc)
endif()
if(WITH_PYTHON)
if(WITH_AMD_GPU)
hip_library(paddle_pybind SHARED
SRCS pybind.cc exception.cc protobuf.cc const_value.cc recordio.cc
SRCS ${PYBIND_SRCS}
DEPS ${PYBIND_DEPS}
${GLOB_OP_LIB})
else()
cc_library(paddle_pybind SHARED
SRCS pybind.cc exception.cc protobuf.cc const_value.cc recordio.cc
SRCS ${PYBIND_SRCS}
DEPS ${PYBIND_DEPS}
${GLOB_OP_LIB})
if(NOT APPLE AND NOT ANDROID AND NOT WIN32)
......
paddle/fluid/pybind/protobuf.cc
浏览文件 @ 79d4a99d
......@@ -234,6 +234,7 @@ void BindVarDsec(pybind11::module *m) {
pybind11::enum_<pd::proto::VarType::Type>(var_desc, "VarType", "")
.value("BOOL", pd::proto::VarType::BOOL)
.value("UINT8", pd::proto::VarType::UINT8)
.value("INT8", pd::proto::VarType::INT8)
.value("INT16", pd::proto::VarType::INT16)
.value("INT32", pd::proto::VarType::INT32)
.value("INT64", pd::proto::VarType::INT64)
......
paddle/fluid/pybind/pybind.cc
浏览文件 @ 79d4a99d
......@@ -130,6 +130,7 @@ PYBIND11_PLUGIN(core) {
.def("set", PyCPUTensorSetFromArray<bool>)
.def("set", PyCPUTensorSetFromArray<uint16_t>)
.def("set", PyCPUTensorSetFromArray<uint8_t>)
.def("set", PyCPUTensorSetFromArray<int8_t>)
#ifdef PADDLE_WITH_CUDA
.def("set", PyCUDATensorSetFromArray<float>)
.def("set", PyCUDATensorSetFromArray<int>)
......@@ -138,6 +139,7 @@ PYBIND11_PLUGIN(core) {
.def("set", PyCUDATensorSetFromArray<bool>)
.def("set", PyCUDATensorSetFromArray<uint16_t>)
.def("set", PyCUDATensorSetFromArray<uint8_t>)
.def("set", PyCUDATensorSetFromArray<int8_t>)
.def("set", PyCUDAPinnedTensorSetFromArray<float>)
.def("set", PyCUDAPinnedTensorSetFromArray<int>)
.def("set", PyCUDAPinnedTensorSetFromArray<double>)
......@@ -145,6 +147,7 @@ PYBIND11_PLUGIN(core) {
.def("set", PyCUDAPinnedTensorSetFromArray<bool>)
.def("set", PyCUDAPinnedTensorSetFromArray<uint16_t>)
.def("set", PyCUDAPinnedTensorSetFromArray<uint8_t>)
.def("set", PyCUDAPinnedTensorSetFromArray<int8_t>)
#endif
.def("shape", [](Tensor &self) { return vectorize(self.dims()); })
.def("_set_float_element", TensorSetElement<float>)
......
paddle/fluid/pybind/tensor_py.h
浏览文件 @ 79d4a99d
......@@ -97,7 +97,7 @@ struct CastToPyBufferImpl<true, I, ARGS...> {
inline pybind11::buffer_info CastToPyBuffer(const framework::Tensor &tensor) {
auto buffer_info =
details::CastToPyBufferImpl<true, 0, float, int, double, int64_t, bool,
uint8_t, platform::float16>()(tensor);
uint8_t, int8_t, platform::float16>()(tensor);
return buffer_info;
}
......
paddle/scripts/paddle_build.sh
浏览文件 @ 79d4a99d
......@@ -328,6 +328,11 @@ function assert_api_not_changed() {
source .env/bin/activate
pip install ${PADDLE_ROOT}/build/python/dist/*whl
python ${PADDLE_ROOT}/tools/print_signatures.py paddle.fluid > new.spec
if [ "$1" == "cp35-cp35m" ]; then
# Use sed to make python2 and python3 sepc keeps the same
sed -i 's/arg0: str/arg0: unicode/g' new.spec
sed -i "s/\(.*Transpiler.*\).__init__ ArgSpec(args=\['self'].*/\1.__init__ /g" new.spec
fi
python ${PADDLE_ROOT}/tools/diff_api.py ${PADDLE_ROOT}/paddle/fluid/API.spec new.spec
deactivate
......@@ -621,7 +626,7 @@ function main() {
gen_capi_package
gen_fluid_inference_lib
test_fluid_inference_lib
assert_api_not_changed
assert_api_not_changed ${PYTHON_ABI:-""}
;;
*)
print_usage
......
python/paddle/dataset/flowers.py
浏览文件 @ 79d4a99d
......@@ -42,6 +42,7 @@ from paddle.reader import *
import os
import numpy as np
from multiprocessing import cpu_count
import six
from six.moves import cPickle as pickle
from six.moves import zip
__all__ = ['train', 'test', 'valid']
......
python/paddle/dataset/image.py
浏览文件 @ 79d4a99d
......@@ -36,11 +36,6 @@ import numpy as np
try:
import cv2
except ImportError:
import sys
sys.stderr.write(
'''Warning with paddle image module: opencv-python should be imported,
or paddle image module could NOT work; please install opencv-python first.'''
)
cv2 = None
import os
import tarfile
......@@ -53,6 +48,18 @@ __all__ = [
]
def _check_cv2():
if cv2 is None:
import sys
sys.stderr.write(
'''Warning with paddle image module: opencv-python should be imported,
or paddle image module could NOT work; please install opencv-python first.'''
)
return False
else:
return True
def batch_images_from_tar(data_file,
dataset_name,
img2label,
......@@ -134,7 +141,7 @@ def load_image_bytes(bytes, is_color=True):
load and return a gray image.
:type is_color: bool
"""
assert cv2 is not None
assert _check_cv2() is True
flag = 1 if is_color else 0
file_bytes = np.asarray(bytearray(bytes), dtype=np.uint8)
......@@ -159,7 +166,7 @@ def load_image(file, is_color=True):
load and return a gray image.
:type is_color: bool
"""
assert cv2 is not None
assert _check_cv2() is True
# cv2.IMAGE_COLOR for OpenCV3
# cv2.CV_LOAD_IMAGE_COLOR for older OpenCV Version
......@@ -188,7 +195,7 @@ def resize_short(im, size):
:param size: the shorter edge size of image after resizing.
:type size: int
"""
assert cv2 is not None
assert _check_cv2() is True
h, w = im.shape[:2]
h_new, w_new = size, size
......
python/paddle/fluid/framework.py
浏览文件 @ 79d4a99d
......@@ -95,6 +95,8 @@ def convert_np_dtype_to_dtype_(np_dtype):
return core.VarDesc.VarType.INT16
elif dtype == np.uint8:
return core.VarDesc.VarType.UINT8
elif dtype == np.int8:
return core.VarDesc.VarType.INT8
else:
raise ValueError("Not supported numpy dtype %s" % dtype)
......
python/paddle/fluid/layers/metric_op.py
浏览文件 @ 79d4a99d
......@@ -119,10 +119,14 @@ def auc(input, label, curve='ROC', num_thresholds=200, topk=1):
helper = LayerHelper("auc", **locals())
auc_out = helper.create_tmp_variable(dtype="float64")
# make tp, tn, fp, fn persistable, so that can accumulate all batches.
tp = helper.create_global_variable(persistable=True, dtype='int64')
tn = helper.create_global_variable(persistable=True, dtype='int64')
fp = helper.create_global_variable(persistable=True, dtype='int64')
fn = helper.create_global_variable(persistable=True, dtype='int64')
tp = helper.create_global_variable(
persistable=True, dtype='int64', shape=[num_thresholds])
tn = helper.create_global_variable(
persistable=True, dtype='int64', shape=[num_thresholds])
fp = helper.create_global_variable(
persistable=True, dtype='int64', shape=[num_thresholds])
fn = helper.create_global_variable(
persistable=True, dtype='int64', shape=[num_thresholds])
for var in [tp, tn, fp, fn]:
helper.set_variable_initializer(
var, Constant(
......
python/paddle/fluid/layers/nn.py
浏览文件 @ 79d4a99d
......@@ -103,6 +103,7 @@ __all__ = [
'rank_loss',
'prelu',
'flatten',
'sequence_mask',
'stack',
]
......@@ -5520,7 +5521,75 @@ def flatten(x, axis=1, name=None):
return out
def sequence_mask(x, maxlen=None, dtype='int64', name=None):
"""
**SequenceMask Layer**
This layer outputs a mask according to the input :code:`x` and
:code:`maxlen` with data type of :code:`dtype`.
Supposing :code:`x` is a Tensor with shape [d_1, d_2, ..., d_n], the
:code:`y` is a mask with shape [d_1, d_2, ..., d_n, maxlen], where:
.. math::
y(i_1, i_2,..., i_n, j) = (j < x(i_1, i_2,..., i_n))
Args:
x (Variable): Input tensor of sequence_mask layer,
whose elements are integers less than :code:`maxlen`.
maxlen (int|None): Maximum length of the sequence. If :code:`maxlen`
is None, it would be replace with :math:`max(x)`.
dtype (np.dtype|core.VarDesc.VarType|str): Data type of the output.
name (str|None): A name for this layer(optional). If set None, the
layer will be named automatically.
Returns:
Variable: The output sequence mask.
"""
helper = LayerHelper('sequence_mask', **locals())
if name is None:
out = helper.create_tmp_variable(dtype=dtype)
else:
out = helper.create_tmp_variable(dtype=dtype, name=name)
helper.append_op(
type='sequence_mask',
inputs={'X': [x]},
outputs={'Y': out},
attrs={
'max_len': maxlen if maxlen is not None else -1,
'out_dtype': out.dtype
})
return out
def stack(x, axis=0):
"""
**Stack Layer**
This layer stacks all of the input :code:`x` along axis.
Input :code:`x` can be a single variable, a :code:`list` of variables,
or a :code:`tuple` of variables. If :code:`x` is a :code:`list` or
:code:`tuple`, the shapes of all these variables must be the same.
Supposing the shape of each input is :math:`[d_0, d_1, ..., d_{n-1}]`,
the shape of the output variable would be
:math:`[d_0, d_1, ..., d_{axis}=len(x), ..., d_{n-1}]`.
If :code:`axis` < 0, it would be replaced with :code:`axis+rank(x[0])+1`.
If :code:`axis` is None, it would be replaced with 0.
Args:
x (Variable|list(Variable)|tuple(Variable)): Input variables.
axis (int|None): The axis along which all inputs are stacked.
Returns:
Variable: The stacked variable.
"""
helper = LayerHelper('stack', **locals())
axis = 0 if axis is None else axis
......
python/paddle/fluid/tests/unittests/CMakeLists.txt
浏览文件 @ 79d4a99d
......@@ -64,6 +64,7 @@ if(WITH_DISTRIBUTE)
endif()
py_test_modules(test_parallel_executor_crf MODULES test_parallel_executor_crf SERIAL)
py_test_modules(test_parallel_executor_fetch_feed MODULES test_parallel_executor_fetch_feed SERIAL)
set_tests_properties(test_parallel_executor_fetch_feed PROPERTIES TIMEOUT 150)
py_test_modules(test_dist_transformer MODULES test_dist_transformer SERIAL)
py_test_modules(test_dist_se_resnext MODULES test_dist_se_resnext SERIAL)
py_test_modules(test_parallel_executor_transformer MODULES test_parallel_executor_transformer SERIAL)
......
python/paddle/fluid/tests/unittests/test_attention_lstm_op.py
浏览文件 @ 79d4a99d
......@@ -37,7 +37,7 @@ def attention_lstm(
T = sum(lod[0])
N = len(lod[0])
M = x.shape[1]
D = b.shape[1] / 4
D = b.shape[1] // 4
assert T == x.shape[0]
assert len(fcws) == len(fcbs)
hidden = []
......
python/paddle/fluid/tests/unittests/test_desc_clone.py
浏览文件 @ 79d4a99d
......@@ -120,8 +120,8 @@ def operator_equal(a, b):
raise ValueError("In operator_equal not equal:{0}\n".format(k))
elif isinstance(v, collections.OrderedDict):
v0 = sorted(six.iteritems(v), key=lambda x: x[0])
v1 = sorted(six.iteritems(b.__dict__[k]), key=lambda x: x[0])
v0 = sorted(list(six.iteritems(v)), key=lambda x: x[0])
v1 = sorted(list(six.iteritems(b.__dict__[k])), key=lambda x: x[0])
if v0 != v1:
raise ValueError("In operator_equal not equal:{0}\n".format(k))
......@@ -139,17 +139,15 @@ def block_equal(a, b):
continue
elif k == "ops":
assert (len(a.ops) == len(b.ops))
for i in range(0, len(a.ops)):
if not operator_equal(a.ops[i], b.ops[i]):
raise ValueError("In block_equal not equal:{0}\n".format(k))
assert (len(a.ops) == len(b.ops))
elif isinstance(v, collections.OrderedDict):
v0 = sorted(six.iteritems(v), key=lambda x: x[0])
v1 = sorted(six.iteritems(b.__dict__[k]), key=lambda x: x[0])
if v0 != v1:
raise ValueError("In block_equal not equal:{0}\n".format(k))
for key, value in six.iteritems(v):
if str(value) != str(b.__dict__[k][key]):
raise ValueError("In block_equal not equal:{0}\n".format(k))
elif (v != b.__dict__[k]):
raise ValueError("In block_equal not equal:{0}\n".format(k))
......
python/paddle/fluid/tests/unittests/test_dist_transpiler.py
浏览文件 @ 79d4a99d
......@@ -21,6 +21,7 @@ import paddle.fluid as fluid
from paddle.fluid.transpiler.distribute_transpiler import delete_ops
import traceback
import collections
import six
class TranspilerTest(unittest.TestCase):
......@@ -644,18 +645,18 @@ class TestLoadSliceVar(TranspilerTest):
self.assertTrue(pserver._slice_vars_and_attrs)
self.assertTrue(pserver2._slice_vars_and_attrs)
for idx in xrange(len(pserver._slice_vars_and_attrs)):
for idx in six.moves.xrange(len(pserver._slice_vars_and_attrs)):
self.assertEqual(pserver._slice_vars_and_attrs[idx][0],
pserver2._slice_vars_and_attrs[idx][0])
total_numel = reduce(lambda x, y: x * y,
pserver._slice_vars_and_attrs[idx][0].shape)
total_numel = six.moves.reduce(
lambda x, y: x * y, pserver._slice_vars_and_attrs[idx][0].shape)
self.assertEqual(
total_numel,
reduce(lambda x, y: x * y,
pserver._slice_vars_and_attrs[idx][2].shape) + reduce(
lambda x, y: x * y,
pserver2._slice_vars_and_attrs[idx][2].shape))
six.moves.reduce(lambda x, y: x * y,
pserver._slice_vars_and_attrs[idx][2].shape) +
six.moves.reduce(lambda x, y: x * y,
pserver2._slice_vars_and_attrs[idx][2].shape))
if __name__ == "__main__":
......
python/paddle/fluid/tests/unittests/test_fusion_lstm_op.py
浏览文件 @ 79d4a99d
......@@ -43,13 +43,13 @@ def fusion_lstm(
act_cell, act_cand)
class TestLstmOp(OpTest):
def set_argument(self):
self.lod = [[2, 3, 2]]
class TestFusionLSTMOp(OpTest):
def set_conf(self):
pass
def setUp(self):
self.op_type = 'fusion_lstm'
self.lod = [[2, 3, 2]]
self.lod = [[2, 3, 5, 4]]
self.M = 8
self.D = 16
self.has_initial_state = False
......@@ -58,33 +58,33 @@ class TestLstmOp(OpTest):
self.act_cell = 'tanh'
self.act_cand = 'tanh'
self.use_peepholes = False
self.set_argument()
self.set_conf()
T = sum(self.lod[0])
bs = len(self.lod[0])
x = np.random.normal(size=(T, self.M)).astype('float64')
x = np.random.normal(size=(T, self.M)).astype('float32')
if self.has_initial_state:
h0 = np.random.normal(size=(bs, self.D)).astype('float64')
c0 = np.random.normal(size=(bs, self.D)).astype('float64')
h0 = np.random.normal(size=(bs, self.D)).astype('float32')
c0 = np.random.normal(size=(bs, self.D)).astype('float32')
else:
h0 = np.zeros((bs, self.D)).astype('float64')
c0 = np.zeros((bs, self.D)).astype('float64')
h0 = np.zeros((bs, self.D)).astype('float32')
c0 = np.zeros((bs, self.D)).astype('float32')
wh = np.random.normal(size=(self.D, 4 * self.D)).astype('float64')
wh = np.random.normal(size=(self.D, 4 * self.D)).astype('float32')
if self.use_peepholes:
b = np.random.normal(size=(1, 7 * self.D)).astype('float64')
b = np.random.normal(size=(1, 7 * self.D)).astype('float32')
else:
b = np.random.normal(size=(1, 4 * self.D)).astype('float64')
b = np.random.normal(size=(1, 4 * self.D)).astype('float32')
w_b = np.copy(b[:, 0:4 * self.D])
w_c = b[:, 4 * self.D:] if self.use_peepholes else None
# this is the weight of fc
wx = np.random.normal(size=(self.M, 4 * self.D)).astype('float64')
wx = np.random.normal(size=(self.M, 4 * self.D)).astype('float32')
# this is the bias of fc
# and it should be manually added into the bias of this fusion LSTM
bx = np.random.normal(size=(1, 4 * self.D)).astype('float64')
bx = np.random.normal(size=(1, 4 * self.D)).astype('float32')
b[0, 0:4 * self.D] += bx[0, :]
h, c = fusion_lstm(x, self.lod, wx, bx, h0, c0, wh, w_b, w_c,
self.is_reverse, ACTIVATION[self.act_gate],
......@@ -114,35 +114,45 @@ class TestLstmOp(OpTest):
}
def test_check_output(self):
self.check_output(atol=1e-8)
self.check_output()
class TestLstmOpInitReverse(TestLstmOp):
def set_argument(self):
class TestFusionLSTMOpInit(TestFusionLSTMOp):
def set_conf(self):
self.has_initial_state = True
class TestFusionLSTMOpReverse(TestFusionLSTMOp):
def set_conf(self):
self.is_reverse = True
class TestFusionLSTMOpInitReverse(TestFusionLSTMOp):
def set_conf(self):
self.has_initial_state = True
self.is_reverse = True
class TestLstmOpMD1(TestLstmOp):
def set_argument(self):
class TestFusionLSTMOpMD1(TestFusionLSTMOp):
def set_conf(self):
self.M = 36
self.D = 8
class TestLstmOpMD2(TestLstmOp):
def set_argument(self):
class TestFusionLSTMOpMD2(TestFusionLSTMOp):
def set_conf(self):
self.M = 8
self.D = 8
class TestLstmOpMD3(TestLstmOp):
def set_argument(self):
class TestFusionLSTMOpMD3(TestFusionLSTMOp):
def set_conf(self):
self.M = 15
self.D = 3
class TestLstmOpBS1(TestLstmOp):
def set_argument(self):
class TestFusionLSTMOpBS1(TestFusionLSTMOp):
def set_conf(self):
self.lod = [[3]]
self.D = 16
......
python/paddle/fluid/tests/unittests/test_pad_constant_like.py 0 → 100644
浏览文件 @ 79d4a99d
# 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.
from __future__ import print_function
import unittest
import numpy as np
from op_test import OpTest
class TestPadOp(OpTest):
def setUp(self):
self.initTestCase()
self.op_type = "pad_constant_like"
self.inputs = {
'X': np.random.random(self.x_shape).astype("float32"),
'Y': np.random.random(self.y_shape).astype("float32")
}
self.attrs = {}
self.attrs['pad_value'] = self.pad_value
self.outputs = {
'Out': np.pad(self.inputs['Y'],
self.paddings,
mode='constant',
constant_values=self.pad_value)
}
def test_check_output(self):
self.check_output()
def test_check_grad_normal(self):
self.check_grad(['Y'], 'Out', max_relative_error=0.006)
def initTestCase(self):
self.x_shape = (16, 16)
self.y_shape = (3, 16)
self.pad_value = 0.1
self.paddings = [(0, 13), (0, 0)]
class TestCase1(TestPadOp):
def initTestCase(self):
self.x_shape = (4, 3, 4, 4)
self.y_shape = (2, 3, 4, 4)
self.paddings = [(0, 2), (0, 0), (0, 0), (0, 0)]
self.pad_value = 0.5
class TestCase2(TestPadOp):
def initTestCase(self):
self.x_shape = (4, 3, 4, 4)
self.y_shape = (2, 3, 2, 4)
self.paddings = [(0, 2), (0, 0), (0, 2), (0, 0)]
self.pad_value = 0.5
if __name__ == '__main__':
unittest.main()
python/paddle/fluid/tests/unittests/test_prelu_op.py
浏览文件 @ 79d4a99d
......@@ -51,30 +51,28 @@ class PReluTest(OpTest):
def test_check_output(self):
self.check_output()
def test_check_grad(self):
self.check_grad(['X', 'Alpha'], 'Out')
def test_check_grad_ignore_x(self):
def test_check_grad_1_ignore_x(self):
self.check_grad(['Alpha'], 'Out', no_grad_set=set('X'))
def test_check_grad_ignore_alpha(self):
self.check_grad(['X'], 'Out', no_grad_set=set('Alpha'))
class TestCase1(PReluTest):
def initTestCase(self):
self.attrs = {'mode': "all"}
def test_check_grad_2(self):
self.check_grad(['X', 'Alpha'], 'Out')
def test_check_grad_3_ignore_alpha(self):
self.check_grad(['X'], 'Out', no_grad_set=set('Alpha'))
class TestCase2(PReluTest):
def initTestCase(self):
self.attrs = {'mode': "channel"}
# TODO(minqiyang): Resume these test cases after fixing Python3 CI job issues
# class TestCase1(PReluTest):
# def initTestCase(self):
# self.attrs = {'mode': "all"}
class TestCase3(PReluTest):
def initTestCase(self):
self.attrs = {'mode': "element"}
# class TestCase2(PReluTest):
# def initTestCase(self):
# self.attrs = {'mode': "channel"}
# class TestCase3(PReluTest):
# def initTestCase(self):
# self.attrs = {'mode': "element"}
if __name__ == "__main__":
unittest.main()
python/paddle/fluid/tests/unittests/test_sequence_mask.py 0 → 100644
浏览文件 @ 79d4a99d
# 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.
from op_test import OpTest
import paddle.fluid as fluid
from paddle.fluid.framework import convert_np_dtype_to_dtype_
import paddle.fluid.core as core
import numpy as np
import copy
import unittest
class SequenceMaskTestBase(OpTest):
def initDefaultParameters(self):
self.op_type = 'sequence_mask'
self.maxlen = 10
self.mask_dtype = 'int64'
self.x = [[0, 3, 4], [5, 7, 9]]
def initParameters(self):
pass
def setUp(self):
self.initDefaultParameters()
self.initParameters()
if not isinstance(self.x, np.ndarray):
self.x = np.array(self.x)
self.inputs = {'X': self.x}
self.outputs = {'Y': self.calc_ground_truth_mask()}
self.attrs = {
'maxlen': self.maxlen,
'out_dtype': convert_np_dtype_to_dtype_(self.mask_dtype)
}
def calc_ground_truth_mask(self):
maxlen = np.max(self.x) if self.maxlen < 0 else self.maxlen
shape = self.x.shape + (maxlen, )
index_broadcast = np.broadcast_to(
np.reshape(
range(maxlen), newshape=[1] * self.x.ndim + [-1]),
shape=shape)
x_broadcast = np.broadcast_to(
np.reshape(
self.x, newshape=self.x.shape + (-1, )), shape=shape)
return (index_broadcast < x_broadcast).astype(self.mask_dtype)
def test_check_output(self):
self.check_output()
class SequenceMaskTest1(SequenceMaskTestBase):
def initParameters(self):
self.mask_dtype = 'bool'
class SequenceMaskTest2(SequenceMaskTestBase):
def initParameters(self):
self.mask_dtype = 'uint8'
class SequenceMaskTest3(SequenceMaskTestBase):
def initParameters(self):
self.mask_dtype = 'int32'
class SequenceMaskTest4(SequenceMaskTestBase):
def initParameters(self):
self.mask_dtype = 'float32'
class SequenceMaskTest5(SequenceMaskTestBase):
def initParameters(self):
self.mask_dtype = 'float64'
class SequenceMaskTest6(SequenceMaskTestBase):
def initParameters(self):
self.maxlen = -1
if __name__ == '__main__':
unittest.main()
python/paddle/fluid/tests/unittests/test_tensor.py
浏览文件 @ 79d4a99d
......@@ -59,6 +59,27 @@ class TestTensor(unittest.TestCase):
self.assertAlmostEqual(1.0, tensor_array_2[3, 9])
self.assertAlmostEqual(2.0, tensor_array_2[19, 11])
def test_int8_tensor(self):
scope = core.Scope()
var = scope.var("int8_tensor")
cpu_tensor = var.get_tensor()
tensor_array = numpy.random.randint(
-127, high=128, size=[100, 200], dtype=numpy.int8)
place = core.CPUPlace()
cpu_tensor.set(tensor_array, place)
cpu_tensor_array_2 = numpy.array(cpu_tensor)
self.assertAlmostEqual(cpu_tensor_array_2.all(), tensor_array.all())
if core.is_compiled_with_cuda():
cuda_tensor = var.get_tensor()
tensor_array = numpy.random.randint(
-127, high=128, size=[100, 200], dtype=numpy.int8)
place = core.CUDAPlace(0)
cuda_tensor.set(tensor_array, place)
cuda_tensor_array_2 = numpy.array(cuda_tensor)
self.assertAlmostEqual(cuda_tensor_array_2.all(),
tensor_array.all())
def test_int_lod_tensor(self):
place = core.CPUPlace()
scope = core.Scope()
......
python/paddle/fluid/tests/unittests/test_variable.py
浏览文件 @ 79d4a99d
......@@ -31,7 +31,8 @@ class TestVariable(unittest.TestCase):
self.assertEqual(DT.INT16, convert("int16"))
self.assertEqual(DT.INT64, convert("int64"))
self.assertEqual(DT.BOOL, convert("bool"))
self.assertRaises(ValueError, lambda: convert("int8"))
self.assertEqual(DT.INT8, convert("int8"))
self.assertEqual(DT.UINT8, convert("uint8"))
def test_var(self):
b = default_main_program().current_block()
......
python/paddle/fluid/transpiler/details/program_utils.py
浏览文件 @ 79d4a99d
......@@ -159,7 +159,7 @@ def program_to_code(prog):
get_indent_space(indent), '{', block_idx))
indent += 1
# sort all vars
all_vars = sorted(block.vars.iteritems(), key=lambda x: x[0])
all_vars = sorted(six.iteritems(block.vars), key=lambda x: x[0])
for var in all_vars:
print("{}{}".format(
get_indent_space(indent), variable_to_code(var[1])))
......
tools/check_ctest_hung.py
浏览文件 @ 79d4a99d
......@@ -12,6 +12,8 @@
# See the License for the specific language governing permissions and
# limitations under the License.
from __future__ import print_function
import sys
import re
......@@ -46,7 +48,7 @@ Diff: set(['test_parallel_executor_crf'])
start_parts = escape(l).split(" ")
m = re.search("Start\s+[0-9]+\:\s([a-z0-9_]+)", escape(l))
started.add(m.group(1))
print "Diff: ", started - passed
print("Diff: ", started - passed)
if __name__ == "__main__":
......
tools/print_signatures.py
浏览文件 @ 79d4a99d
......@@ -17,6 +17,8 @@ Print all signature of a python module in alphabet order.
Usage:
./print_signature "paddle.fluid" > signature.txt
"""
from __future__ import print_function
import importlib
import inspect
import collections
......@@ -64,4 +66,4 @@ def visit_all_module(mod):
visit_all_module(importlib.import_module(sys.argv[1]))
for name in member_dict:
print name, member_dict[name]
print(name, member_dict[name])
tools/timeline.py
浏览文件 @ 79d4a99d
......@@ -14,6 +14,7 @@
import argparse
import json
import six
import sys
import unittest
......@@ -124,7 +125,7 @@ class Timeline(object):
return cur_pid
def _allocate_pids(self):
for k, profile_pb in self._profile_dict.iteritems():
for k, profile_pb in six.iteritems(self._profile_dict):
for event in profile_pb.events:
if event.type == profiler_pb2.Event.CPU:
if (k, event.device_id, "CPU") not in self._devices:
......@@ -140,7 +141,7 @@ class Timeline(object):
(k, event.device_id), pid)
def _allocate_events(self):
for k, profile_pb in self._profile_dict.iteritems():
for k, profile_pb in six.iteritems(self._profile_dict):
for event in profile_pb.events:
if event.type == profiler_pb2.Event.CPU:
type = "CPU"
......
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