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提交 92da467c 编写于 作者: P peizhilin
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Merge remote-tracking branch 'upstream/develop' into windows/fixgpuissue

上级 d54133ea fd854183
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Showing with 6794 addition and 1649 deletion
CMakeLists.txt
浏览文件 @ 92da467c
......@@ -55,6 +55,7 @@ option(WITH_DOUBLE "Compile PaddlePaddle with double precision" OFF)
option(WITH_RDMA "Compile PaddlePaddle with RDMA support" OFF)
option(WITH_TIMER "Compile PaddlePaddle with stats timer" OFF)
option(WITH_PROFILER "Compile PaddlePaddle with GPU profiler and gperftools" OFF)
option(WITH_JEMALLOC "Compile PaddlePaddle with jemalloc" OFF)
option(WITH_DOC "Compile PaddlePaddle with documentation" OFF)
option(WITH_COVERAGE "Compile PaddlePaddle with code coverage" OFF)
option(COVERALLS_UPLOAD "Package code coverage data to coveralls" OFF)
......@@ -261,6 +262,12 @@ if (WITH_PROFILER)
add_definitions(-DWITH_GPERFTOOLS)
endif()
if (WITH_JEMALLOC)
find_package(JeMalloc REQUIRED)
include_directories(${JEMALLOC_INCLUDE_DIR})
add_definitions(-DWITH_JEMALLOC)
endif()
include(generic) # simplify cmake module
include(package) # set paddle packages
include(ccache) # set ccache for compilation
......@@ -290,7 +297,7 @@ if(WITH_PSLIB)
list(APPEND EXTERNAL_LIBS pslib_brpc)
list(APPEND EXTERNAL_LIBS libmct)
endif(WITH_PSLIB)
if(WITH_AMD_GPU)
find_package(HIP)
include(hip)
......
cmake/FindJeMalloc.cmake 0 → 100644
浏览文件 @ 92da467c
# - Find JeMalloc library
# Find the native JeMalloc includes and library
#
# JEMALLOC_INCLUDE_DIR - where to find jemalloc.h, etc.
# JEMALLOC_LIBRARIES - List of libraries when using jemalloc.
# JEMALLOC_FOUND - True if jemalloc found.
find_path(JEMALLOC_INCLUDE_DIR
NAMES jemalloc/jemalloc.h
HINTS ${JEMALLOC_ROOT_DIR}/include)
find_library(JEMALLOC_LIBRARIES
NAMES jemalloc
HINTS ${JEMALLOC_ROOT_DIR}/lib)
include(FindPackageHandleStandardArgs)
find_package_handle_standard_args(jemalloc DEFAULT_MSG JEMALLOC_LIBRARIES JEMALLOC_INCLUDE_DIR)
mark_as_advanced(
JEMALLOC_LIBRARIES
JEMALLOC_INCLUDE_DIR)
if (JEMALLOC_FOUND)
add_library(jemalloc::jemalloc UNKNOWN IMPORTED)
set_target_properties(jemalloc::jemalloc PROPERTIES
IMPORTED_LOCATION ${JEMALLOC_LIBRARIES}
INTERFACE_INCLUDE_DIRECTORIES "${JEMALLOC_INCLUDE_DIR}")
endif()
cmake/configure.cmake
浏览文件 @ 92da467c
......@@ -134,6 +134,7 @@ if(WITH_GPU)
message(WARNING "Anakin needs CUDNN >= 7.0 to compile. Force WITH_ANAKIN=OFF")
set(WITH_ANAKIN OFF CACHE STRING "Anakin is valid only when CUDNN >= 7.0." FORCE)
endif()
add_definitions(-DWITH_ANAKIN)
endif()
if(WITH_ANAKIN)
# NOTICE(minqiyang): the end slash is important because $CUDNN_INCLUDE_DIR
......
cmake/cuda.cmake
浏览文件 @ 92da467c
......@@ -5,6 +5,8 @@ endif()
set(paddle_known_gpu_archs "30 35 50 52 60 61 70")
set(paddle_known_gpu_archs7 "30 35 50 52")
set(paddle_known_gpu_archs8 "30 35 50 52 60 61")
set(paddle_known_gpu_archs9 "30 35 50 52 60 61 70")
set(paddle_known_gpu_archs10 "30 35 50 52 60 61 70 75")
######################################################################################
# A function for automatic detection of GPUs installed (if autodetection is enabled)
......@@ -59,7 +61,7 @@ endfunction()
# select_nvcc_arch_flags(out_variable)
function(select_nvcc_arch_flags out_variable)
# List of arch names
set(archs_names "Kepler" "Maxwell" "Pascal" "All" "Manual")
set(archs_names "Kepler" "Maxwell" "Pascal" "Volta" "Turing" "All" "Manual")
set(archs_name_default "All")
if(NOT CMAKE_CROSSCOMPILING)
list(APPEND archs_names "Auto")
......@@ -93,6 +95,8 @@ function(select_nvcc_arch_flags out_variable)
set(cuda_arch_bin "60 61")
elseif(${CUDA_ARCH_NAME} STREQUAL "Volta")
set(cuda_arch_bin "70")
elseif(${CUDA_ARCH_NAME} STREQUAL "Turing")
set(cuda_arch_bin "75")
elseif(${CUDA_ARCH_NAME} STREQUAL "All")
set(cuda_arch_bin ${paddle_known_gpu_archs})
elseif(${CUDA_ARCH_NAME} STREQUAL "Auto")
......@@ -153,6 +157,16 @@ elseif (${CUDA_VERSION} LESS 9.0) # CUDA 8.x
# warning for now.
list(APPEND CUDA_NVCC_FLAGS "-Wno-deprecated-gpu-targets")
add_definitions("-DPADDLE_CUDA_BINVER=\"80\"")
elseif (${CUDA_VERSION} LESS 10.0) # CUDA 9.x
set(paddle_known_gpu_archs ${paddle_known_gpu_archs9})
list(APPEND CUDA_NVCC_FLAGS "-D_MWAITXINTRIN_H_INCLUDED")
list(APPEND CUDA_NVCC_FLAGS "-D__STRICT_ANSI__")
add_definitions("-DPADDLE_CUDA_BINVER=\"90\"")
elseif (${CUDA_VERSION} LESS 11.0) # CUDA 10.x
set(paddle_known_gpu_archs ${paddle_known_gpu_archs10})
list(APPEND CUDA_NVCC_FLAGS "-D_MWAITXINTRIN_H_INCLUDED")
list(APPEND CUDA_NVCC_FLAGS "-D__STRICT_ANSI__")
add_definitions("-DPADDLE_CUDA_BINVER=\"100\"")
endif()
include_directories(${CUDA_INCLUDE_DIRS})
......
cmake/external/boost.cmake
浏览文件 @ 92da467c
......@@ -23,11 +23,8 @@ set(BOOST_PROJECT "extern_boost")
# checked that the devtools package of CentOS 6 installs boost 1.41.0.
# So we use 1.41.0 here.
set(BOOST_VER "1.41.0")
if((NOT DEFINED BOOST_TAR) OR (NOT DEFINED BOOST_URL))
message(STATUS "use pre defined download url")
set(BOOST_TAR "boost_1_41_0" CACHE STRING "" FORCE)
set(BOOST_URL "http://paddlepaddledeps.cdn.bcebos.com/${BOOST_TAR}.tar.gz" CACHE STRING "" FORCE)
endif()
set(BOOST_TAR "boost_1_41_0" CACHE STRING "" FORCE)
set(BOOST_URL "http://paddlepaddledeps.cdn.bcebos.com/${BOOST_TAR}.tar.gz" CACHE STRING "" FORCE)
MESSAGE(STATUS "BOOST_TAR: ${BOOST_TAR}, BOOST_URL: ${BOOST_URL}")
......
cmake/external/mkldnn.cmake
浏览文件 @ 92da467c
......@@ -55,7 +55,7 @@ ExternalProject_Add(
${MKLDNN_PROJECT}
${EXTERNAL_PROJECT_LOG_ARGS}
DEPENDS ${MKLDNN_DEPENDS}
GIT_REPOSITORY "https://github.com/01org/mkl-dnn.git"
GIT_REPOSITORY "https://github.com/intel/mkl-dnn.git"
GIT_TAG "830a10059a018cd2634d94195140cf2d8790a75a"
PREFIX ${MKLDNN_SOURCES_DIR}
UPDATE_COMMAND ""
......
cmake/external/mklml.cmake
浏览文件 @ 92da467c
......@@ -16,6 +16,12 @@ IF(NOT ${WITH_MKLML})
return()
ENDIF(NOT ${WITH_MKLML})
IF(APPLE)
MESSAGE(WARNING "Mac is not supported with MKLML in Paddle yet. Force WITH_MKLML=OFF.")
SET(WITH_MKLML OFF CACHE STRING "Disable MKLML package in MacOS" FORCE)
return()
ENDIF()
INCLUDE(ExternalProject)
SET(MKLML_DST_DIR "mklml")
SET(MKLML_INSTALL_ROOT "${THIRD_PARTY_PATH}/install")
......@@ -23,32 +29,24 @@ SET(MKLML_INSTALL_DIR ${MKLML_INSTALL_ROOT}/${MKLML_DST_DIR})
SET(MKLML_ROOT ${MKLML_INSTALL_DIR})
SET(MKLML_INC_DIR ${MKLML_ROOT}/include)
SET(MKLML_LIB_DIR ${MKLML_ROOT}/lib)
if(WIN32)
SET(CMAKE_INSTALL_RPATH "${CMAKE_INSTALL_RPATH}" "${MKLML_ROOT}/lib")
SET(TIME_VERSION "2019.0.1.20181227")
IF(WIN32)
SET(MKLML_VER "mklml_win_${TIME_VERSION}" CACHE STRING "" FORCE)
SET(MKLML_URL "https://paddlepaddledeps.cdn.bcebos.com/${MKLML_VER}.zip" CACHE STRING "" FORCE)
SET(MKLML_LIB ${MKLML_LIB_DIR}/mklml.lib)
SET(MKLML_IOMP_LIB ${MKLML_LIB_DIR}/libiomp5md.lib)
SET(MKLML_SHARED_LIB ${MKLML_LIB_DIR}/mklml.dll)
SET(MKLML_SHARED_IOMP_LIB ${MKLML_LIB_DIR}/libiomp5md.dll)
else()
ELSE()
SET(MKLML_VER "mklml_lnx_${TIME_VERSION}" CACHE STRING "" FORCE)
SET(MKLML_URL "http://paddlepaddledeps.cdn.bcebos.com/${MKLML_VER}.tgz" CACHE STRING "" FORCE)
SET(MKLML_LIB ${MKLML_LIB_DIR}/libmklml_intel.so)
SET(MKLML_IOMP_LIB ${MKLML_LIB_DIR}/libiomp5.so)
SET(MKLML_SHARED_LIB ${MKLML_LIB_DIR}/libmklml_intel.so)
SET(MKLML_SHARED_IOMP_LIB ${MKLML_LIB_DIR}/libiomp5.so)
endif()
SET(CMAKE_INSTALL_RPATH "${CMAKE_INSTALL_RPATH}" "${MKLML_ROOT}/lib")
IF((NOT DEFINED MKLML_VER) OR (NOT DEFINED MKLML_URL))
MESSAGE(STATUS "use pre defined download url")
if(WIN32)
SET(MKLML_VER "mklml_win_2019.0.1.20180928" CACHE STRING "" FORCE)
SET(MKLML_URL "https://paddlepaddledeps.cdn.bcebos.com/${MKLML_VER}.zip" CACHE STRING "" FORCE)
elseif(APPLE)
SET(MKLML_VER "mklml_mac_2019.0.1.20180928" CACHE STRING "" FORCE)
SET(MKLML_URL "http://paddlepaddledeps.cdn.bcebos.com/${MKLML_VER}.tgz" CACHE STRING "" FORCE)
else()
SET(MKLML_VER "mklml_lnx_2019.0.1.20180928" CACHE STRING "" FORCE)
SET(MKLML_URL "http://paddlepaddledeps.cdn.bcebos.com/${MKLML_VER}.tgz" CACHE STRING "" FORCE)
ENDIF()
endif()
ENDIF()
SET(MKLML_PROJECT "extern_mklml")
MESSAGE(STATUS "MKLML_VER: ${MKLML_VER}, MKLML_URL: ${MKLML_URL}")
......
cmake/external/ngraph.cmake
浏览文件 @ 92da467c
......@@ -37,14 +37,18 @@ INCLUDE(GNUInstallDirs)
INCLUDE(ExternalProject)
SET(NGRAPH_PROJECT "extern_ngraph")
SET(NGRAPH_GIT_TAG "v0.10.1")
SET(NGRAPH_GIT_TAG "20bd8bbc79ae3a81c57313846a2be7313e5d1dab")
SET(NGRAPH_SOURCES_DIR ${THIRD_PARTY_PATH}/ngraph)
SET(NGRAPH_INSTALL_DIR ${THIRD_PARTY_PATH}/install/ngraph)
SET(NGRAPH_INC_DIR ${NGRAPH_INSTALL_DIR}/include)
SET(NGRAPH_LIB_DIR ${NGRAPH_INSTALL_DIR}/${CMAKE_INSTALL_LIBDIR})
SET(NGRAPH_SHARED_LIB_NAME libngraph.so)
SET(NGRAPH_CPU_LIB_NAME libcpu_backend.so)
SET(NGRAPH_TBB_LIB_NAME libtbb.so.2)
if(CMAKE_BUILD_TYPE STREQUAL "Debug")
SET(NGRAPH_TBB_LIB_NAME libtbb_debug.so.2)
else()
SET(NGRAPH_TBB_LIB_NAME libtbb.so.2)
endif()
SET(NGRAPH_GIT_REPO "https://github.com/NervanaSystems/ngraph.git")
SET(NGRAPH_SHARED_LIB ${NGRAPH_LIB_DIR}/${NGRAPH_SHARED_LIB_NAME})
SET(NGRAPH_CPU_LIB ${NGRAPH_LIB_DIR}/${NGRAPH_CPU_LIB_NAME})
......@@ -66,16 +70,7 @@ ExternalProject_Add(
CMAKE_ARGS -DCMAKE_BUILD_TYPE=${CMAKE_BUILD_TYPE}
CMAKE_ARGS -DMKLDNN_INCLUDE_DIR=${MKLDNN_INC_DIR}
CMAKE_ARGS -DMKLDNN_LIB_DIR=${MKLDNN_INSTALL_DIR}/lib
)
# Workaround for nGraph expecting mklml to be in mkldnn install directory.
ExternalProject_Add_Step(
${NGRAPH_PROJECT}
PrepareMKL
COMMAND ${CMAKE_COMMAND} -E create_symlink ${MKLML_LIB} ${MKLDNN_INSTALL_DIR}/lib/libmklml_intel.so
COMMAND ${CMAKE_COMMAND} -E create_symlink ${MKLML_IOMP_LIB} ${MKLDNN_INSTALL_DIR}/lib/libiomp5.so
DEPENDEES download
DEPENDERS configure
CMAKE_ARGS -DMKLML_LIB_DIR=${MKLML_INSTALL_DIR}/lib
)
add_dependencies(ngraph ${NGRAPH_PROJECT})
......
cmake/generic.cmake
浏览文件 @ 92da467c
......@@ -115,6 +115,10 @@ function(common_link TARGET_NAME)
if (WITH_PROFILER)
target_link_libraries(${TARGET_NAME} gperftools::profiler)
endif()
if (WITH_JEMALLOC)
target_link_libraries(${TARGET_NAME} jemalloc::jemalloc)
endif()
endfunction()
......@@ -228,7 +232,7 @@ function(merge_static_libs TARGET_NAME)
# Get the file names of the libraries to be merged
set(libfiles ${libfiles} $<TARGET_FILE:${lib}>)
endforeach()
# msvc will put libarary in directory of "/Release/xxxlib" by default
# msvc will put libarary in directory of "/Release/xxxlib" by default
# COMMAND cmake -E remove "${CMAKE_CURRENT_BINARY_DIR}/${CMAKE_BUILD_TYPE}/${TARGET_NAME}.lib"
add_custom_command(TARGET ${TARGET_NAME} POST_BUILD
COMMAND cmake -E make_directory "${CMAKE_CURRENT_BINARY_DIR}/${CMAKE_BUILD_TYPE}"
......
paddle/fluid/API.spec
浏览文件 @ 92da467c
......@@ -405,28 +405,50 @@ paddle.fluid.nets.glu ArgSpec(args=['input', 'dim'], varargs=None, keywords=None
paddle.fluid.nets.scaled_dot_product_attention ArgSpec(args=['queries', 'keys', 'values', 'num_heads', 'dropout_rate'], varargs=None, keywords=None, defaults=(1, 0.0))
paddle.fluid.nets.img_conv_group ArgSpec(args=['input', 'conv_num_filter', 'pool_size', 'conv_padding', 'conv_filter_size', 'conv_act', 'param_attr', 'conv_with_batchnorm', 'conv_batchnorm_drop_rate', 'pool_stride', 'pool_type', 'use_cudnn'], varargs=None, keywords=None, defaults=(1, 3, None, None, False, 0.0, 1, 'max', True))
paddle.fluid.optimizer.SGDOptimizer.__init__ ArgSpec(args=['self', 'learning_rate', 'regularization', 'name'], varargs=None, keywords=None, defaults=(None, None))
paddle.fluid.optimizer.SGDOptimizer.apply_gradients ArgSpec(args=['self', 'params_grads'], varargs=None, keywords=None, defaults=None)
paddle.fluid.optimizer.SGDOptimizer.backward ArgSpec(args=['self', 'loss', 'startup_program', 'parameter_list', 'no_grad_set', 'callbacks'], varargs=None, keywords=None, defaults=(None, None, None, None))
paddle.fluid.optimizer.SGDOptimizer.minimize ArgSpec(args=['self', 'loss', 'startup_program', 'parameter_list', 'no_grad_set'], varargs=None, keywords=None, defaults=(None, None, None))
paddle.fluid.optimizer.MomentumOptimizer.__init__ ArgSpec(args=['self', 'learning_rate', 'momentum', 'use_nesterov', 'regularization', 'name'], varargs=None, keywords=None, defaults=(False, None, None))
paddle.fluid.optimizer.MomentumOptimizer.apply_gradients ArgSpec(args=['self', 'params_grads'], varargs=None, keywords=None, defaults=None)
paddle.fluid.optimizer.MomentumOptimizer.backward ArgSpec(args=['self', 'loss', 'startup_program', 'parameter_list', 'no_grad_set', 'callbacks'], varargs=None, keywords=None, defaults=(None, None, None, None))
paddle.fluid.optimizer.MomentumOptimizer.minimize ArgSpec(args=['self', 'loss', 'startup_program', 'parameter_list', 'no_grad_set'], varargs=None, keywords=None, defaults=(None, None, None))
paddle.fluid.optimizer.AdagradOptimizer.__init__ ArgSpec(args=['self', 'learning_rate', 'epsilon', 'regularization', 'name'], varargs=None, keywords=None, defaults=(1e-06, None, None))
paddle.fluid.optimizer.AdagradOptimizer.apply_gradients ArgSpec(args=['self', 'params_grads'], varargs=None, keywords=None, defaults=None)
paddle.fluid.optimizer.AdagradOptimizer.backward ArgSpec(args=['self', 'loss', 'startup_program', 'parameter_list', 'no_grad_set', 'callbacks'], varargs=None, keywords=None, defaults=(None, None, None, None))
paddle.fluid.optimizer.AdagradOptimizer.minimize ArgSpec(args=['self', 'loss', 'startup_program', 'parameter_list', 'no_grad_set'], varargs=None, keywords=None, defaults=(None, None, None))
paddle.fluid.optimizer.AdamOptimizer.__init__ ArgSpec(args=['self', 'learning_rate', 'beta1', 'beta2', 'epsilon', 'regularization', 'name', 'lazy_mode'], varargs=None, keywords=None, defaults=(0.001, 0.9, 0.999, 1e-08, None, None, False))
paddle.fluid.optimizer.AdamOptimizer.apply_gradients ArgSpec(args=['self', 'params_grads'], varargs=None, keywords=None, defaults=None)
paddle.fluid.optimizer.AdamOptimizer.backward ArgSpec(args=['self', 'loss', 'startup_program', 'parameter_list', 'no_grad_set', 'callbacks'], varargs=None, keywords=None, defaults=(None, None, None, None))
paddle.fluid.optimizer.AdamOptimizer.minimize ArgSpec(args=['self', 'loss', 'startup_program', 'parameter_list', 'no_grad_set'], varargs=None, keywords=None, defaults=(None, None, None))
paddle.fluid.optimizer.AdamaxOptimizer.__init__ ArgSpec(args=['self', 'learning_rate', 'beta1', 'beta2', 'epsilon', 'regularization', 'name'], varargs=None, keywords=None, defaults=(0.001, 0.9, 0.999, 1e-08, None, None))
paddle.fluid.optimizer.AdamaxOptimizer.apply_gradients ArgSpec(args=['self', 'params_grads'], varargs=None, keywords=None, defaults=None)
paddle.fluid.optimizer.AdamaxOptimizer.backward ArgSpec(args=['self', 'loss', 'startup_program', 'parameter_list', 'no_grad_set', 'callbacks'], varargs=None, keywords=None, defaults=(None, None, None, None))
paddle.fluid.optimizer.AdamaxOptimizer.minimize ArgSpec(args=['self', 'loss', 'startup_program', 'parameter_list', 'no_grad_set'], varargs=None, keywords=None, defaults=(None, None, None))
paddle.fluid.optimizer.DecayedAdagradOptimizer.__init__ ArgSpec(args=['self', 'learning_rate', 'decay', 'epsilon', 'regularization', 'name'], varargs=None, keywords=None, defaults=(0.95, 1e-06, None, None))
paddle.fluid.optimizer.DecayedAdagradOptimizer.apply_gradients ArgSpec(args=['self', 'params_grads'], varargs=None, keywords=None, defaults=None)
paddle.fluid.optimizer.DecayedAdagradOptimizer.backward ArgSpec(args=['self', 'loss', 'startup_program', 'parameter_list', 'no_grad_set', 'callbacks'], varargs=None, keywords=None, defaults=(None, None, None, None))
paddle.fluid.optimizer.DecayedAdagradOptimizer.minimize ArgSpec(args=['self', 'loss', 'startup_program', 'parameter_list', 'no_grad_set'], varargs=None, keywords=None, defaults=(None, None, None))
paddle.fluid.optimizer.FtrlOptimizer.__init__ ArgSpec(args=['self', 'learning_rate', 'l1', 'l2', 'lr_power', 'regularization', 'name'], varargs=None, keywords=None, defaults=(0.0, 0.0, -0.5, None, None))
paddle.fluid.optimizer.FtrlOptimizer.apply_gradients ArgSpec(args=['self', 'params_grads'], varargs=None, keywords=None, defaults=None)
paddle.fluid.optimizer.FtrlOptimizer.backward ArgSpec(args=['self', 'loss', 'startup_program', 'parameter_list', 'no_grad_set', 'callbacks'], varargs=None, keywords=None, defaults=(None, None, None, None))
paddle.fluid.optimizer.FtrlOptimizer.minimize ArgSpec(args=['self', 'loss', 'startup_program', 'parameter_list', 'no_grad_set'], varargs=None, keywords=None, defaults=(None, None, None))
paddle.fluid.optimizer.RMSPropOptimizer.__init__ ArgSpec(args=['self', 'learning_rate', 'rho', 'epsilon', 'momentum', 'centered', 'regularization', 'name'], varargs=None, keywords=None, defaults=(0.95, 1e-06, 0.0, False, None, None))
paddle.fluid.optimizer.RMSPropOptimizer.apply_gradients ArgSpec(args=['self', 'params_grads'], varargs=None, keywords=None, defaults=None)
paddle.fluid.optimizer.RMSPropOptimizer.backward ArgSpec(args=['self', 'loss', 'startup_program', 'parameter_list', 'no_grad_set', 'callbacks'], varargs=None, keywords=None, defaults=(None, None, None, None))
paddle.fluid.optimizer.RMSPropOptimizer.minimize ArgSpec(args=['self', 'loss', 'startup_program', 'parameter_list', 'no_grad_set'], varargs=None, keywords=None, defaults=(None, None, None))
paddle.fluid.optimizer.AdadeltaOptimizer.__init__ ArgSpec(args=['self', 'learning_rate', 'epsilon', 'rho', 'regularization', 'name'], varargs=None, keywords=None, defaults=(1e-06, 0.95, None, None))
paddle.fluid.optimizer.AdadeltaOptimizer.apply_gradients ArgSpec(args=['self', 'params_grads'], varargs=None, keywords=None, defaults=None)
paddle.fluid.optimizer.AdadeltaOptimizer.backward ArgSpec(args=['self', 'loss', 'startup_program', 'parameter_list', 'no_grad_set', 'callbacks'], varargs=None, keywords=None, defaults=(None, None, None, None))
paddle.fluid.optimizer.AdadeltaOptimizer.minimize ArgSpec(args=['self', 'loss', 'startup_program', 'parameter_list', 'no_grad_set'], varargs=None, keywords=None, defaults=(None, None, None))
paddle.fluid.optimizer.ModelAverage.__init__ ArgSpec(args=['self', 'average_window_rate', 'min_average_window', 'max_average_window', 'regularization', 'name'], varargs=None, keywords=None, defaults=(10000, 10000, None, None))
paddle.fluid.optimizer.ModelAverage.apply ArgSpec(args=[], varargs='args', keywords='kwds', defaults=None)
paddle.fluid.optimizer.ModelAverage.apply_gradients ArgSpec(args=['self', 'params_grads'], varargs=None, keywords=None, defaults=None)
paddle.fluid.optimizer.ModelAverage.backward ArgSpec(args=['self', 'loss', 'startup_program', 'parameter_list', 'no_grad_set', 'callbacks'], varargs=None, keywords=None, defaults=(None, None, None, None))
paddle.fluid.optimizer.ModelAverage.minimize ArgSpec(args=['self', 'loss', 'startup_program', 'parameter_list', 'no_grad_set'], varargs=None, keywords=None, defaults=(None, None, None))
paddle.fluid.optimizer.ModelAverage.restore ArgSpec(args=['self', 'executor'], varargs=None, keywords=None, defaults=None)
paddle.fluid.optimizer.LarsMomentumOptimizer.__init__ ArgSpec(args=['self', 'learning_rate', 'momentum', 'lars_coeff', 'lars_weight_decay', 'regularization', 'name'], varargs=None, keywords=None, defaults=(0.001, 0.0005, None, None))
paddle.fluid.optimizer.LarsMomentumOptimizer.apply_gradients ArgSpec(args=['self', 'params_grads'], varargs=None, keywords=None, defaults=None)
paddle.fluid.optimizer.LarsMomentumOptimizer.backward ArgSpec(args=['self', 'loss', 'startup_program', 'parameter_list', 'no_grad_set', 'callbacks'], varargs=None, keywords=None, defaults=(None, None, None, None))
paddle.fluid.optimizer.LarsMomentumOptimizer.minimize ArgSpec(args=['self', 'loss', 'startup_program', 'parameter_list', 'no_grad_set'], varargs=None, keywords=None, defaults=(None, None, None))
paddle.fluid.backward.append_backward ArgSpec(args=['loss', 'parameter_list', 'no_grad_set', 'callbacks'], varargs=None, keywords=None, defaults=(None, None, None))
paddle.fluid.regularizer.L1DecayRegularizer.__init__ ArgSpec(args=['self', 'regularization_coeff'], varargs=None, keywords=None, defaults=(0.0,))
......
paddle/fluid/framework/details/CMakeLists.txt
浏览文件 @ 92da467c
......@@ -94,4 +94,4 @@ cc_library(build_strategy SRCS build_strategy.cc DEPS
graph_viz_pass multi_devices_graph_pass
multi_devices_graph_print_pass multi_devices_graph_check_pass
fuse_elewise_add_act_pass multi_batch_merge_pass
memory_optimize_pass)
memory_optimize_pass lock_free_optimize_pass)
paddle/fluid/framework/details/build_strategy.cc
浏览文件 @ 92da467c
......@@ -18,7 +18,7 @@ limitations under the License. */
#include <memory>
#include "paddle/fluid/framework/details/memory_reuse_types.h"
#include "paddle/fluid/framework/details/multi_devices_graph_check_pass.h"
#include "paddle/fluid/framework/details/multi_devices_graph_pass.h"
#include "paddle/fluid/framework/details/multi_devices_graph_print_pass.h"
#include "paddle/fluid/framework/details/reduce_op_handle.h"
#include "paddle/fluid/framework/details/sequential_execution_pass.h"
......@@ -86,10 +86,8 @@ class ParallelExecutorPassBuilder : public ir::PassBuilder {
if (strategy.memory_optimize_) {
auto analysis_var_pass = AppendPass("analysis_var_pass");
}
// Convert graph to run on multi-devices.
auto multi_devices_pass = AppendPass("multi_devices_pass");
multi_devices_pass->SetNotOwned<const BuildStrategy>("strategy",
&strategy_);
AppendMultiDevPass(strategy);
// Add a graph print pass to record a graph with device info.
if (!strategy_.debug_graphviz_path_.empty()) {
......@@ -115,6 +113,25 @@ class ParallelExecutorPassBuilder : public ir::PassBuilder {
}
}
// Convert graph to run on multi-devices.
void AppendMultiDevPass(const BuildStrategy &strategy) {
ir::Pass *multi_devices_pass;
if (strategy_.is_distribution_) {
multi_devices_pass = AppendPass("dist_multi_devices_pass").get();
} else {
if (strategy.reduce_ == BuildStrategy::ReduceStrategy::kAllReduce) {
multi_devices_pass =
AppendPass("allreduce_mode_multi_devices_pass").get();
} else if (strategy.reduce_ == BuildStrategy::ReduceStrategy::kReduce) {
multi_devices_pass = AppendPass("reduce_mode_multi_devices_pass").get();
} else {
PADDLE_THROW("Unknown reduce strategy.");
}
}
multi_devices_pass->SetNotOwned<const BuildStrategy>("strategy",
&strategy_);
}
private:
BuildStrategy strategy_;
};
......@@ -131,6 +148,10 @@ std::shared_ptr<ir::PassBuilder> BuildStrategy::CreatePassesFromStrategy(
return pass_builder_;
}
bool BuildStrategy::IsMultiDevPass(const std::string &pass_name) const {
return framework::details::MultiDevSSAGraphBuilder().count(pass_name) > 0;
}
std::unique_ptr<ir::Graph> BuildStrategy::Apply(
const ProgramDesc &main_program, const std::vector<platform::Place> &places,
const std::string &loss_var_name, const std::vector<Scope *> &local_scopes,
......@@ -145,22 +166,23 @@ std::unique_ptr<ir::Graph> BuildStrategy::Apply(
std::unique_ptr<ir::Graph> graph(new ir::Graph(main_program));
for (std::shared_ptr<ir::Pass> &pass : pass_builder_->AllPasses()) {
if (pass->Type() == "multi_devices_pass") {
pass->Erase("places");
pass->SetNotOwned<const std::vector<platform::Place>>("places", &places);
pass->Erase("loss_var_name");
pass->SetNotOwned<const std::string>("loss_var_name", &loss_var_name);
pass->Erase("local_scopes");
pass->SetNotOwned<const std::vector<Scope *>>("local_scopes",
if (IsMultiDevPass(pass->Type())) {
pass->Erase(kPlaces);
pass->SetNotOwned<const std::vector<platform::Place>>(kPlaces, &places);
pass->Erase(kLossVarName);
pass->SetNotOwned<const std::string>(kLossVarName, &loss_var_name);
pass->Erase(kLocalScopes);
pass->SetNotOwned<const std::vector<Scope *>>(kLocalScopes,
&local_scopes);
pass->Erase("nranks");
pass->Set<size_t>("nranks", new size_t(nranks));
pass->Erase(kNRanks);
pass->Set<size_t>(kNRanks, new size_t(nranks));
#if defined(PADDLE_WITH_CUDA) && !defined(_WIN32)
platform::NCCLContextMap *nctx = use_cuda ? nccl_ctxs : nullptr;
pass->Erase("nccl_ctxs");
pass->SetNotOwned<platform::NCCLContextMap>("nccl_ctxs", nctx);
#endif
} else if (pass->Type() == "analysis_var_pass") {
const std::vector<OpDesc *> *all_op_descs =
new std::vector<OpDesc *>(main_program.Block(0).AllOps());
......@@ -201,10 +223,13 @@ std::unique_ptr<ir::Graph> BuildStrategy::Apply(
USE_PASS(fuse_elewise_add_act_pass);
USE_PASS(graph_viz_pass);
USE_PASS(multi_batch_merge_pass);
USE_PASS(multi_devices_pass);
USE_PASS(reduce_mode_multi_devices_pass);
USE_PASS(allreduce_mode_multi_devices_pass);
USE_PASS(dist_multi_devices_pass);
USE_PASS(multi_devices_check_pass);
USE_PASS(multi_devices_print_pass);
USE_PASS(analysis_var_pass);
USE_PASS(sequential_execution_pass);
USE_PASS(all_reduce_deps_pass);
USE_PASS(modify_op_lock_and_record_event_pass);
USE_PASS(lock_free_optimize_pass);
paddle/fluid/framework/details/build_strategy.h
浏览文件 @ 92da467c
......@@ -74,8 +74,6 @@ struct BuildStrategy {
bool fuse_elewise_add_act_ops_{false};
bool enable_data_balance_{false};
bool memory_optimize_{false};
bool memory_early_delete_{false};
......@@ -84,6 +82,10 @@ struct BuildStrategy {
bool fuse_broadcast_op_{false};
// FIXME(zcd): is_distribution_ is a temporary field, because in pserver mode,
// num_trainers is 1, so the current fields of build_strategy doesn't tell if
// it's distributed model.
bool is_distribution_{false};
int num_trainers_{1};
int trainer_id_{0};
std::vector<std::string> trainers_endpoints_;
......@@ -104,6 +106,8 @@ struct BuildStrategy {
bool IsFinalized() const { return is_finalized_; }
bool IsMultiDevPass(const std::string &pass_name) const;
// Apply the passes built by the pass_builder_. The passes will be
// applied to the Program and output an ir::Graph.
std::unique_ptr<ir::Graph> Apply(const ProgramDesc &main_program,
......
paddle/fluid/framework/details/multi_devices_graph_check_pass.cc
浏览文件 @ 92da467c
......@@ -12,8 +12,8 @@
// See the License for the specific language governing permissions and
// limitations under the License.
#include "paddle/fluid/framework/details/multi_devices_graph_check_pass.h"
#include <string>
#include "paddle/fluid/framework/details/multi_devices_helper.h"
#include "paddle/fluid/framework/ir/graph.h"
#include "paddle/fluid/framework/ir/graph_helper.h"
......@@ -21,68 +21,78 @@ namespace paddle {
namespace framework {
namespace details {
bool SSAGraghBuilderWithChecker::IsValidGraph(const ir::Graph *graph) const {
std::unordered_map<OpHandleBase *, size_t> pending_ops;
std::unordered_set<VarHandleBase *> pending_vars;
std::unordered_set<VarHandleBase *> ready_vars;
std::unordered_set<OpHandleBase *> ready_ops;
class SSAGraghBuilderWithChecker : public ir::Pass {
protected:
std::unique_ptr<ir::Graph> ApplyImpl(
std::unique_ptr<ir::Graph> graph) const override {
PADDLE_ENFORCE(IsValidGraph(graph.get()));
return graph;
}
auto insert_pending_var = [&](VarHandleBase *var) {
pending_vars.insert(var);
if (var->GeneratedOp() == nullptr) {
ready_vars.emplace(var);
}
};
bool IsValidGraph(const ir::Graph *graph) const {
std::unordered_map<OpHandleBase *, size_t> pending_ops;
std::unordered_set<VarHandleBase *> pending_vars;
std::unordered_set<VarHandleBase *> ready_vars;
std::unordered_set<OpHandleBase *> ready_ops;
for (auto &var_map : graph->Get<GraphVars>(kGraphVars)) {
for (auto &name_pair : var_map) {
for (auto &version_pair : name_pair.second) {
insert_pending_var(version_pair);
auto insert_pending_var = [&](VarHandleBase *var) {
pending_vars.insert(var);
if (var->GeneratedOp() == nullptr) {
ready_vars.emplace(var);
}
}
}
};
for (auto &var : graph->Get<GraphDepVars>(kGraphDepVars)) {
insert_pending_var(var);
}
for (auto &var_map : graph->Get<GraphVars>(kGraphVars)) {
for (auto &name_pair : var_map) {
for (auto &version_pair : name_pair.second) {
insert_pending_var(version_pair);
}
}
}
for (OpHandleBase *op : ir::FilterByNodeWrapper<OpHandleBase>(*graph)) {
if (op->Inputs().empty()) {
ready_ops.insert(op);
} else {
pending_ops.insert({op, op->NoDupInputSize()});
for (auto &var : graph->Get<GraphDepVars>(kGraphDepVars)) {
insert_pending_var(var);
}
}
auto run_all_ops = [&](std::unordered_set<OpHandleBase *> &set) {
for (auto *op : set) {
for (auto out : op->Outputs()) {
ready_vars.emplace(out);
for (OpHandleBase *op : ir::FilterByNodeWrapper<OpHandleBase>(*graph)) {
if (op->Inputs().empty()) {
ready_ops.insert(op);
} else {
pending_ops.insert({op, op->NoDupInputSize()});
}
}
set.clear();
};
while (!pending_vars.empty()) {
run_all_ops(ready_ops);
auto run_all_ops = [&](std::unordered_set<OpHandleBase *> &set) {
for (auto *op : set) {
for (auto out : op->Outputs()) {
ready_vars.emplace(out);
}
}
set.clear();
};
if (ready_vars.empty()) {
return false;
}
while (!pending_vars.empty()) {
run_all_ops(ready_ops);
for (auto ready_var : ready_vars) {
pending_vars.erase(ready_var);
for (auto *op : ready_var->PendingOps()) {
auto &deps = --pending_ops[op];
if (deps == 0) {
ready_ops.insert(op);
if (ready_vars.empty()) {
return false;
}
for (auto ready_var : ready_vars) {
pending_vars.erase(ready_var);
for (auto *op : ready_var->PendingOps()) {
auto &deps = --pending_ops[op];
if (deps == 0) {
ready_ops.insert(op);
}
}
}
ready_vars.clear();
}
ready_vars.clear();
return true;
}
return true;
}
};
} // namespace details
} // namespace framework
} // namespace paddle
......
paddle/fluid/framework/details/multi_devices_graph_pass.cc
浏览文件 @ 92da467c
......@@ -134,15 +134,8 @@ void AddOutputToLeafOps(ir::Graph *graph) {
}
} // namespace
static const char kLossVarName[] = "loss_var_name";
static const char kPlaces[] = "places";
static const char kLocalScopes[] = "local_scopes";
static const char kStrategy[] = "strategy";
static const char kNRanks[] = "nranks";
void MultiDevSSAGraphBuilder::Init() const {
void MultiDevSSAGraphBuilderBase::Init() const {
all_vars_.clear();
balance_vars_.clear();
loss_var_name_ = Get<const std::string>(kLossVarName);
places_ = Get<const std::vector<platform::Place>>(kPlaces);
......@@ -151,31 +144,16 @@ void MultiDevSSAGraphBuilder::Init() const {
#if defined(PADDLE_WITH_CUDA) && !defined(_WIN32)
nccl_ctxs_ = &Get<platform::NCCLContextMap>("nccl_ctxs");
#endif
balance_vars_.resize(places_.size(), 0);
if (strategy_.enable_data_balance_ && places_.size() == 1) {
LOG(WARNING) << "It is no need to enable data balance when there is only "
"one place. enable_data_balance is set to False.";
strategy_.enable_data_balance_ = false;
}
}
std::unique_ptr<ir::Graph> MultiDevSSAGraphBuilder::ApplyImpl(
std::unique_ptr<ir::Graph> MultiDevSSAGraphBuilderBase::ApplyImpl(
std::unique_ptr<ir::Graph> graph) const {
Init();
// Give the topology sort order and rebuild the graph structure.
std::vector<ir::Node *> sorted_ops = ir::TopologySortOperations(*graph);
if (strategy_.reduce_ == BuildStrategy::ReduceStrategy::kReduce) {
sorted_ops = SortForReduceMode(sorted_ops);
}
std::vector<ir::Node *> sorted_ops = SortOperations(*graph);
auto nodes = graph->ReleaseNodes();
ir::Graph &result = *graph;
size_t nranks = Get<size_t>(kNRanks);
for (auto &node : nodes) {
if (node->IsVar() && node->Var()) {
all_vars_.emplace(node->Name(), node->Var());
......@@ -187,217 +165,119 @@ std::unique_ptr<ir::Graph> MultiDevSSAGraphBuilder::ApplyImpl(
result.Set(kGraphDepVars, new GraphDepVars);
result.Set(kGraphOps, new GraphOps);
std::vector<std::unordered_set<std::string>> bcast_var_name_set;
bcast_var_name_set.resize(places_.size());
bool is_forwarding = true;
bool is_dist_train = false;
std::unordered_map<std::string, int> sharded_var_device;
bool insert_collection_ops = NeedCollectiveOps();
for (ir::Node *node : sorted_ops) {
if (OpHaveRole(*node, OpRole::kRPC)) {
int op_dev_id = CreateRPCOp(&result, node, &sharded_var_device);
PADDLE_ENFORCE(op_dev_id != -1,
"Can not schedule the RPC operator to the right place.");
if (node->Op()->Type() == "recv") {
auto recv_vars_attr =
boost::get<std::vector<std::string>>(node->Op()->GetNullableAttr(
OpProtoAndCheckerMaker::OpRoleVarAttrName()));
PADDLE_ENFORCE(recv_vars_attr.size() == 2UL); // [parameter, gradient]
if (recv_vars_attr[0].find(".block") == std::string::npos) {
bcast_var_name_set[op_dev_id].emplace(recv_vars_attr[0]);
}
}
is_dist_train = true;
} else if (OpHaveRole(*node, OpRole::kDist)) {
int op_dev_id = CreateDistTrainOp(&result, node, &sharded_var_device);
if (node->Op()->Type() == "concat") {
auto origin_param_name = node->Op()->OutputArgumentNames()[0];
bcast_var_name_set[op_dev_id].emplace(origin_param_name);
}
} else if (IsScaleLossOp(node)) {
// user can customize loss@grad if not use_default_grad_scale_
if (strategy_.gradient_scale_ !=
BuildStrategy::GradientScaleStrategy::kCustomized) {
// TODO(paddle-dev): Why is there no input for this op_handle?
auto loss_grad_name = node->Op()->OutputArgumentNames()[0];
auto out_dtype = all_vars_.at(loss_grad_name)->GetDataType();
CreateScaleLossGradOp(&result, loss_grad_name, node->outputs[0],
out_dtype);
}
// This assumes the backward generating code will ensure IsScaleLossOp
// is true only for the op that scale the final scalar loss.
// It also assumes backward op will always follow the forward op in
// the block.
is_forwarding = false;
if (DealWithSpecialOp(&result, node)) {
continue;
} else {
int op_dev_id = GetOpDeviceID(node, sharded_var_device);
if (op_dev_id != -1) { // This op only runs on one specific device.
CreateComputationalOp(&result, node, op_dev_id);
for (ir::Node *n : node->outputs) {
sharded_var_device.emplace(n->Name(), op_dev_id);
}
// This op runs on all devices
if (IsScaleLossOp(node)) {
// user can customize loss@grad if not use_default_grad_scale_
InsertScaleLossGradOp(&result, node);
// This assumes the backward generating code will ensure IsScaleLossOp
// is true only for the op that scale the final scalar loss.
// It also assumes backward op will always follow the forward op in
// the block.
is_forwarding = false;
} else {
// This op runs on all devices, and its output may have parameter's
// gradients.
// TODO(paddle-dev): Why is so special about "read" op?
if (node->Op()->Type() == "read" && strategy_.enable_data_balance_) {
node->Op()->SetAttr("throw_eof_exp", false);
CreateComputationalOps(&result, node, places_.size());
const auto &data_var_names = node->Op()->Output("Out");
InsertDataBalanceOp(&result, data_var_names);
} else {
CreateComputationalOps(&result, node, places_.size());
}
CreateComputationalOps(&result, node, places_.size());
}
if (!is_forwarding && nranks > 1UL) {
// Insert collection ops
if (!is_forwarding && insert_collection_ops) {
try {
bool is_bk_op =
static_cast<bool>(boost::get<int>(node->Op()->GetAttr(
OpProtoAndCheckerMaker::OpRoleAttrName())) &
static_cast<int>(OpRole::kBackward));
if (!is_bk_op) continue;
// Currently, we assume that once gradient is generated, it can be
// broadcast, and each gradient is only broadcast once.
try {
auto backward_vars = boost::get<std::vector<std::string>>(
node->Op()->GetNullableAttr(
OpProtoAndCheckerMaker::OpRoleVarAttrName()));
PADDLE_ENFORCE_EQ(backward_vars.size() % 2, 0);
for (size_t i = 0; i < backward_vars.size(); i += 2) {
auto &p_name = backward_vars[i];
auto &g_name = backward_vars[i + 1];
VLOG(10) << "Bcast " << g_name << " for parameter " << p_name;
size_t cur_device_id = -1;
switch (strategy_.reduce_) {
case BuildStrategy::ReduceStrategy::kReduce:
cur_device_id = GetAppropriateDeviceID({g_name});
CreateReduceOp(&result, g_name, cur_device_id);
sharded_var_device.emplace(g_name, cur_device_id);
if (!is_dist_train) {
bcast_var_name_set[cur_device_id].emplace(p_name);
}
break;
case BuildStrategy::ReduceStrategy::kAllReduce:
if (IsSparseGradient(g_name)) {
CreateReduceOp(&result, g_name, 0);
CreateBroadcastOp(&result, g_name, 0);
} else {
InsertAllReduceOp(&result, g_name);
}
break;
default:
LOG(FATAL) << "Unknown reduce strategy ";
break;
}
}
} catch (boost::bad_get e) {
auto backward_vars =
boost::get<std::vector<std::string>>(node->Op()->GetNullableAttr(
OpProtoAndCheckerMaker::OpRoleVarAttrName()));
PADDLE_ENFORCE_EQ(backward_vars.size() % 2, 0);
for (size_t i = 0; i < backward_vars.size(); i += 2) {
auto &p_name = backward_vars[i];
auto &g_name = backward_vars[i + 1];
VLOG(10) << "Bcast " << g_name << " for parameter " << p_name;
InsertCollectiveOp(&result, p_name, g_name);
}
} catch (boost::bad_get e) {
}
}
}
}
bool use_gpu = false;
#if defined(PADDLE_WITH_CUDA) && !defined(_WIN32)
use_gpu = nccl_ctxs_ != nullptr;
#endif
// Insert broadcast operators principle:
// 1. Broadcast optimized parameters in Reduce strategy;
// 2. No need broadcast optimized parameters in AllReduce strategy because of
// the optimization sub-graph would be run on every GPU;
// 3. Allways broadcast received parameters in Distribute Training.
if ((use_gpu &&
strategy_.reduce_ == BuildStrategy::ReduceStrategy::kReduce) ||
is_dist_train) {
if (strategy_.fuse_broadcast_op_) {
CreateFusedBroadcastOp(&result, bcast_var_name_set);
} else {
for (size_t dev_id = 0; dev_id < bcast_var_name_set.size(); ++dev_id) {
auto &to_bcast_set = bcast_var_name_set[dev_id];
for (auto &bcast_name : to_bcast_set) {
CreateBroadcastOp(&result, bcast_name, dev_id);
}
}
}
}
InsertPostprocessOps(&result);
/*
Dependency graph has been constructed. However, there are still data
hazards need to be handled.
*/
*/
PolishGraphToSupportDataHazards(&result);
/*
* Only variables should be the leaves of graph.
*/
AddOutputToLeafOps(&result);
result.Erase<GraphOps>(kGraphOps);
result.Erase(kGraphOps);
return graph;
}
std::vector<ir::Node *> MultiDevSSAGraphBuilder::SortForReduceMode(
const std::vector<ir::Node *> &topo_ops) const {
std::unordered_map<std::string, int> sharded_var_device;
std::vector<ir::Node *> sorted_ops;
std::unordered_map<std::string, std::vector<ir::Node *>> delayed_op;
sorted_ops.reserve(topo_ops.size());
auto insert_delayed_op = [&](const std::string &var_name, int dev_id) {
sharded_var_device.emplace(var_name, dev_id);
if (delayed_op.count(var_name)) {
auto &ops = delayed_op.at(var_name);
sorted_ops.insert(sorted_ops.end(), ops.begin(), ops.end());
delayed_op.at(var_name).clear();
}
};
void MultiDevSSAGraphBuilderBase::InsertScaleLossGradOp(
ir::Graph *result, const ir::Node *node) const {
// user can customize loss@grad if not use_default_grad_scale_
size_t loss_scale = 0;
switch (this->strategy_.gradient_scale_) {
case BuildStrategy::GradientScaleStrategy::kOne:
loss_scale = 1;
break;
case BuildStrategy::GradientScaleStrategy::kCoeffNumDevice:
loss_scale = Get<size_t>(kNRanks);
break;
case BuildStrategy::GradientScaleStrategy::kCustomized:
loss_scale = 0;
break;
default:
LOG(FATAL) << "Unknown gradient scale strategy.";
break;
}
if (loss_scale) {
// TODO(paddle-dev): Why is there no input for this op_handle?
auto loss_grad_name = node->Op()->OutputArgumentNames()[0];
auto out_dtype = this->all_vars_.at(loss_grad_name)->GetDataType();
this->CreateScaleLossGradOp(result, loss_grad_name, node->outputs[0],
loss_scale, out_dtype);
}
}
for (ir::Node *node : topo_ops) {
int op_dev_id = GetOpDeviceID(node, sharded_var_device, &delayed_op);
if (op_dev_id > -1) {
// This op only runs on one specific device.
sorted_ops.emplace_back(node);
for (ir::Node *n : node->outputs) {
insert_delayed_op(n->Name(), op_dev_id);
}
} else if (op_dev_id == -1) {
// This op runs on all devices, and its output may have parameter's
// gradients.
sorted_ops.emplace_back(node);
bool is_bk_op =
static_cast<bool>(boost::get<int>(node->Op()->GetAttr(
OpProtoAndCheckerMaker::OpRoleAttrName())) &
static_cast<int>(OpRole::kBackward));
if (!is_bk_op) continue;
// Currently, we assume that once gradient is generated, it can be
// broadcast, and each gradient is only broadcast once.
std::vector<std::string> backward_vars;
try {
backward_vars =
boost::get<std::vector<std::string>>(node->Op()->GetNullableAttr(
OpProtoAndCheckerMaker::OpRoleVarAttrName()));
} catch (boost::bad_get e) {
}
PADDLE_ENFORCE_EQ(backward_vars.size() % 2, 0);
std::vector<ir::Node *> MultiDevSSAGraphBuilderBase::SortOperations(
const ir::Graph &graph) const {
return ir::TopologySortOperations(graph);
}
for (size_t i = 0; i < backward_vars.size(); i += 2) {
auto &g_name = backward_vars[i + 1];
size_t cur_device_id = GetAppropriateDeviceID({g_name});
insert_delayed_op(g_name, static_cast<int>(cur_device_id));
}
} else if (op_dev_id == -2) {
// The Op on which the Op depends has not yet been generated.
}
}
bool MultiDevSSAGraphBuilderBase::UseGPU() const {
bool use_gpu = false;
#if defined(PADDLE_WITH_CUDA) && !defined(_WIN32)
use_gpu = nccl_ctxs_ != nullptr;
#endif
return use_gpu;
}
PADDLE_ENFORCE_EQ(sorted_ops.size(), topo_ops.size());
return sorted_ops;
bool MultiDevSSAGraphBuilderBase::NeedCollectiveOps() const {
return Get<size_t>(kNRanks) > 1;
}
void MultiDevSSAGraphBuilder::CreateOpHandleIOs(ir::Graph *result,
ir::Node *node,
size_t place_id) const {
void MultiDevSSAGraphBuilderBase::CreateOpHandleIOs(ir::Graph *result,
ir::Node *node,
size_t place_id) const {
auto p = places_[place_id];
auto *op_handle = result->Get<GraphOps>(kGraphOps).back();
op_handle->SetDeviceContext(p,
......@@ -420,28 +300,7 @@ void MultiDevSSAGraphBuilder::CreateOpHandleIOs(ir::Graph *result,
}
}
size_t MultiDevSSAGraphBuilder::GetAppropriateDeviceID(
const std::vector<std::string> &var_names) const {
int64_t numel_sum = 0;
for (auto var_name : var_names) {
if (all_vars_.find(var_name) == all_vars_.end()) continue;
auto var_desc = all_vars_.at(var_name);
PADDLE_ENFORCE_NOT_NULL(var_desc);
auto dim = framework::make_ddim(var_desc->GetShape());
int64_t numel = framework::product(dim);
PADDLE_ENFORCE_GT(numel, 0);
numel_sum += numel;
}
auto smallest =
std::min_element(std::begin(balance_vars_), std::end(balance_vars_));
size_t dev_id =
static_cast<size_t>(std::distance(std::begin(balance_vars_), smallest));
balance_vars_[dev_id] += numel_sum;
return dev_id;
}
void MultiDevSSAGraphBuilder::SetCommunicationContext(
void MultiDevSSAGraphBuilderBase::SetCommunicationContext(
OpHandleBase *op_handle, const platform::Place &p) const {
#if defined(PADDLE_WITH_CUDA) && !defined(_WIN32)
if (nccl_ctxs_ == nullptr) {
......@@ -454,9 +313,9 @@ void MultiDevSSAGraphBuilder::SetCommunicationContext(
#endif
}
void MultiDevSSAGraphBuilder::CreateBroadcastOp(ir::Graph *result,
const std::string &p_name,
size_t src_dev_id) const {
void MultiDevSSAGraphBuilderBase::CreateBroadcastOp(ir::Graph *result,
const std::string &p_name,
size_t src_dev_id) const {
#if defined(PADDLE_WITH_CUDA) && !defined(_WIN32)
auto *op_handle = new BroadcastOpHandle(
result->CreateEmptyNode("broadcast", ir::Node::Type::kOperation),
......@@ -484,7 +343,7 @@ void MultiDevSSAGraphBuilder::CreateBroadcastOp(ir::Graph *result,
}
}
void MultiDevSSAGraphBuilder::CreateFusedBroadcastOp(
void MultiDevSSAGraphBuilderBase::CreateFusedBroadcastOp(
ir::Graph *result,
const std::vector<std::unordered_set<std::string>> &bcast_varnames) const {
#if defined(PADDLE_WITH_CUDA) && !defined(_WIN32)
......@@ -522,17 +381,17 @@ void MultiDevSSAGraphBuilder::CreateFusedBroadcastOp(
}
}
void MultiDevSSAGraphBuilder::CreateComputationalOp(ir::Graph *result,
ir::Node *node,
int dev_id) const {
void MultiDevSSAGraphBuilderBase::CreateComputationalOp(ir::Graph *result,
ir::Node *node,
int dev_id) const {
result->Get<GraphOps>(kGraphOps).emplace_back(
new ComputationOpHandle(result->CreateOpNode(node->Op()),
local_scopes_[dev_id], places_[dev_id], dev_id));
CreateOpHandleIOs(result, node, dev_id);
}
void MultiDevSSAGraphBuilder::InsertAllReduceOp(ir::Graph *result,
const std::string &og) const {
void MultiDevSSAGraphBuilderBase::CreateAllReduceOp(
ir::Graph *result, const std::string &og) const {
#if defined(PADDLE_WITH_CUDA) && !defined(_WIN32)
result->Get<GraphOps>(kGraphOps).emplace_back(new AllReduceOpHandle(
result->CreateEmptyNode("allreduce", ir::Node::Type::kOperation),
......@@ -560,102 +419,15 @@ void MultiDevSSAGraphBuilder::InsertAllReduceOp(ir::Graph *result,
}
}
void MultiDevSSAGraphBuilder::InsertDataBalanceOp(
ir::Graph *result, const std::vector<std::string> &datas) const {
#if defined(PADDLE_WITH_CUDA) && !defined(_WIN32)
result->Get<GraphOps>(kGraphOps).emplace_back(new DataBalanceOpHandle(
result->CreateEmptyNode("data_balance", ir::Node::Type::kOperation),
local_scopes_, places_, nccl_ctxs_));
#else
result->Get<GraphOps>(kGraphOps).emplace_back(new DataBalanceOpHandle(
result->CreateEmptyNode("data_balance", ir::Node::Type::kOperation),
local_scopes_, places_));
#endif
auto *op_handle = result->Get<GraphOps>(kGraphOps).back();
for (size_t i = 0; i < places_.size(); ++i) {
auto &p = places_[i];
SetCommunicationContext(op_handle, p);
for (const std::string &d_name : datas) {
auto &vars = result->Get<GraphVars>(kGraphVars)[i][d_name];
PADDLE_ENFORCE(!vars.empty());
op_handle->AddInput(vars.back());
auto var = new VarHandle(
result->CreateEmptyNode(d_name, ir::Node::Type::kVariable),
vars.size(), i, d_name, p);
vars.emplace_back(var);
op_handle->AddOutput(var);
}
}
}
int MultiDevSSAGraphBuilder::GetOpDeviceID(
ir::Node *node,
const std::unordered_map<std::string, int> &sharded_var_device,
std::unordered_map<std::string, std::vector<ir::Node *>> *delay_ops) const {
if (strategy_.reduce_ != BuildStrategy::ReduceStrategy::kReduce) {
return -1;
}
if (!OpHaveRole(*node, framework::OpRole::kOptimize)) {
return -1;
}
auto param_grad = boost::get<std::vector<std::string>>(
node->Op()->GetAttr(OpProtoAndCheckerMaker::OpRoleVarAttrName()));
PADDLE_ENFORCE_EQ(param_grad.size(), 2U);
int dev_id = GetVarDeviceID(param_grad[1], sharded_var_device);
if (dev_id == -1) {
(*delay_ops)[param_grad[1]].push_back(node);
return -2;
}
return dev_id;
}
int MultiDevSSAGraphBuilder::GetOpDeviceID(
ir::Node *node,
const std::unordered_map<std::string, int> &sharded_var_device) const {
if (strategy_.reduce_ != BuildStrategy::ReduceStrategy::kReduce) {
return -1;
}
if (!OpHaveRole(*node, framework::OpRole::kOptimize)) {
return -1;
}
auto param_grad = boost::get<std::vector<std::string>>(
node->Op()->GetAttr(OpProtoAndCheckerMaker::OpRoleVarAttrName()));
PADDLE_ENFORCE_EQ(param_grad.size(), 2U);
int dev_id = GetVarDeviceID(param_grad[1], sharded_var_device);
PADDLE_ENFORCE_NE(dev_id, -1, "dev_id should not be -1.[%s, %s, %s]",
node->Op()->Type(), param_grad[0], param_grad[1]);
return dev_id;
}
int MultiDevSSAGraphBuilder::GetVarDeviceID(
const std::string &varname,
const std::unordered_map<std::string, int> &sharded_var_device) const {
auto got = sharded_var_device.find(varname);
if (got == sharded_var_device.end()) {
auto pos = varname.find(framework::kNewGradSuffix);
if (pos != std::string::npos) {
got = sharded_var_device.find(varname.substr(0, pos));
}
}
return got == sharded_var_device.end() ? -1 : got->second;
}
void MultiDevSSAGraphBuilder::CreateScaleLossGradOp(
void MultiDevSSAGraphBuilderBase::CreateScaleLossGradOp(
ir::Graph *result, const std::string &loss_grad_name,
ir::Node *out_var_node, proto::VarType::Type dtype) const {
size_t nranks = Get<size_t>("nranks");
ir::Node *out_var_node, size_t loss_scale,
proto::VarType::Type dtype) const {
for (size_t i = 0; i < places_.size(); ++i) {
// Insert ScaleCost OpHandle
auto *dev_ctx = platform::DeviceContextPool::Instance().Get(places_[i]);
auto *op_handle = new ScaleLossGradOpHandle(
result->CreateEmptyNode("scale_loss_grad", ir::Node::Type::kOperation),
nranks, local_scopes_[i], places_[i], dev_ctx, dtype);
loss_scale, local_scopes_[i], places_[i], dev_ctx, dtype);
result->Get<GraphOps>(kGraphOps).emplace_back(op_handle);
// FIXME: Currently ScaleLossGradOp only use device_count as scale
......@@ -669,9 +441,8 @@ void MultiDevSSAGraphBuilder::CreateScaleLossGradOp(
}
}
void MultiDevSSAGraphBuilder::CreateComputationalOps(ir::Graph *result,
ir::Node *node,
size_t num_places) const {
void MultiDevSSAGraphBuilderBase::CreateComputationalOps(
ir::Graph *result, ir::Node *node, size_t num_places) const {
for (size_t scope_idx = 0; scope_idx < num_places; ++scope_idx) {
auto p = places_[scope_idx];
auto s = local_scopes_[scope_idx];
......@@ -681,9 +452,9 @@ void MultiDevSSAGraphBuilder::CreateComputationalOps(ir::Graph *result,
}
}
VarHandle *MultiDevSSAGraphBuilder::CreateReduceOp(ir::Graph *result,
const std::string &og,
int dst_dev_id) const {
VarHandle *MultiDevSSAGraphBuilderBase::CreateReduceOp(ir::Graph *result,
const std::string &og,
int dst_dev_id) const {
#if defined(PADDLE_WITH_CUDA) && !defined(_WIN32)
result->Get<GraphOps>(kGraphOps).emplace_back(new ReduceOpHandle(
result->CreateEmptyNode("reduce", ir::Node::Type::kOperation),
......@@ -712,51 +483,273 @@ VarHandle *MultiDevSSAGraphBuilder::CreateReduceOp(ir::Graph *result,
return var;
}
int MultiDevSSAGraphBuilder::CreateDistTrainOp(
ir::Graph *result, ir::Node *node,
std::unordered_map<std::string, int> *sharded_var_device) const {
int op_dev_id = -1;
std::vector<std::string> input_var_names;
std::vector<std::string> output_var_names;
for (ir::Node *input : node->inputs) {
input_var_names.push_back(input->Name());
bool MultiDevSSAGraphBuilderBase::IsScaleLossOp(ir::Node *node) const {
return boost::get<int>(
node->Op()->GetAttr(OpProtoAndCheckerMaker::OpRoleAttrName())) ==
(static_cast<int>(OpRole::kBackward) |
static_cast<int>(OpRole::kLoss)) &&
!loss_var_name_.empty(); // If loss_var is empty. This is test mode
}
bool MultiDevSSAGraphBuilderBase::IsSparseGradient(
const std::string &og) const {
PADDLE_ENFORCE(all_vars_.count(og) != 0);
if (all_vars_.at(og)->GetType() == proto::VarType::SELECTED_ROWS) {
return true;
}
for (ir::Node *output : node->outputs) {
output_var_names.push_back(output->Name());
return false;
}
void AllReduceSSAGraphBuilder::InsertCollectiveOp(
ir::Graph *result, const std::string &p_name,
const std::string &g_name) const {
if (IsSparseGradient(g_name)) {
CreateReduceOp(result, g_name, 0);
CreateBroadcastOp(result, g_name, 0);
} else {
CreateAllReduceOp(result, g_name);
}
}
if (node->Op()->Type() == "split_byref" ||
node->Op()->Type() == "split_selected_rows" ||
node->Op()->Type() == "split_ids") {
// TODO(paddle-dev): getting the first var is not safe.
op_dev_id = GetVarDeviceID(input_var_names[0], *sharded_var_device);
if (strategy_.reduce_ == BuildStrategy::ReduceStrategy::kAllReduce) {
op_dev_id = GetAppropriateDeviceID(input_var_names);
for (auto &varname : input_var_names) {
sharded_var_device->emplace(varname, op_dev_id);
int BalanceVarSSAGraphBuilder::GetVarDeviceID(
const std::string &varname) const {
auto got = sharded_var_device_.find(varname);
if (got == sharded_var_device_.end()) {
auto pos = varname.find(framework::kNewGradSuffix);
if (pos != std::string::npos) {
got = sharded_var_device_.find(varname.substr(0, pos));
}
}
return got == sharded_var_device_.end() ? -1 : got->second;
}
int BalanceVarSSAGraphBuilder::GetOpDeviceID(ir::Node *node) const {
if (strategy_.reduce_ != BuildStrategy::ReduceStrategy::kReduce) {
return -1;
}
if (!OpHaveRole(*node, framework::OpRole::kOptimize)) {
return -1;
}
auto param_grad = boost::get<std::vector<std::string>>(
node->Op()->GetAttr(OpProtoAndCheckerMaker::OpRoleVarAttrName()));
PADDLE_ENFORCE_EQ(param_grad.size(), 2U);
int dev_id = GetVarDeviceID(param_grad[1]);
PADDLE_ENFORCE_NE(dev_id, -1, "dev_id should not be -1.[%s, %s, %s]",
node->Op()->Type(), param_grad[0], param_grad[1]);
return dev_id;
}
size_t BalanceVarSSAGraphBuilder::GetAppropriateDeviceID(
const std::vector<std::string> &var_names) const {
int64_t numel_sum = 0;
for (auto var_name : var_names) {
if (all_vars_.find(var_name) == all_vars_.end()) continue;
auto var_desc = all_vars_.at(var_name);
PADDLE_ENFORCE_NOT_NULL(var_desc);
auto dim = framework::make_ddim(var_desc->GetShape());
int64_t numel = framework::product(dim);
PADDLE_ENFORCE_GT(numel, 0);
numel_sum += numel;
}
auto smallest =
std::min_element(std::begin(balance_vars_), std::end(balance_vars_));
size_t dev_id =
static_cast<size_t>(std::distance(std::begin(balance_vars_), smallest));
balance_vars_[dev_id] += numel_sum;
return dev_id;
}
void BalanceVarSSAGraphBuilder::ResetState() const {
balance_vars_.clear();
sharded_var_device_.clear();
balance_vars_.resize(places_.size(), 0);
}
void ReduceSSAGraphBuilder::Init() const {
MultiDevSSAGraphBuilderBase::Init();
ResetState();
}
void ReduceSSAGraphBuilder::ResetState() const {
BalanceVarSSAGraphBuilder::ResetState();
bcast_var_name_set_.clear();
bcast_var_name_set_.resize(places_.size());
}
void ReduceSSAGraphBuilder::InsertCollectiveOp(
ir::Graph *result, const std::string &p_name,
const std::string &g_name) const {
size_t cur_device_id = GetAppropriateDeviceID({g_name});
CreateReduceOp(result, g_name, cur_device_id);
sharded_var_device_.emplace(g_name, cur_device_id);
bcast_var_name_set_[cur_device_id].emplace(p_name);
}
bool ReduceSSAGraphBuilder::DealWithSpecialOp(ir::Graph *result,
ir::Node *node) const {
int op_dev_id = BalanceVarSSAGraphBuilder::GetOpDeviceID(node);
if (op_dev_id != -1) {
// This op only runs on one specific device.
CreateComputationalOp(result, node, op_dev_id);
for (ir::Node *n : node->outputs) {
sharded_var_device_.emplace(n->Name(), op_dev_id);
}
return true;
}
return false;
}
void ReduceSSAGraphBuilder::InsertPostprocessOps(ir::Graph *result) const {
if (UseGPU()) {
if (strategy_.fuse_broadcast_op_) {
CreateFusedBroadcastOp(result, bcast_var_name_set_);
} else {
for (size_t dev_id = 0; dev_id < bcast_var_name_set_.size(); ++dev_id) {
auto &to_bcast_set = bcast_var_name_set_[dev_id];
for (auto &bcast_name : to_bcast_set) {
CreateBroadcastOp(result, bcast_name, dev_id);
}
}
}
for (auto &varname : output_var_names) {
sharded_var_device->emplace(varname, op_dev_id);
}
}
int ReduceSSAGraphBuilder::GetOpDeviceID(
ir::Node *node,
std::unordered_map<std::string, std::vector<ir::Node *>> *delay_ops) const {
if (!OpHaveRole(*node, framework::OpRole::kOptimize)) {
return -1;
}
auto param_grad = boost::get<std::vector<std::string>>(
node->Op()->GetAttr(OpProtoAndCheckerMaker::OpRoleVarAttrName()));
PADDLE_ENFORCE_EQ(param_grad.size(), 2U);
int dev_id = GetVarDeviceID(param_grad[1]);
if (dev_id == -1) {
(*delay_ops)[param_grad[1]].push_back(node);
return -2;
}
return dev_id;
}
std::vector<ir::Node *> ReduceSSAGraphBuilder::SortOperations(
const ir::Graph &graph) const {
std::vector<ir::Node *> sorted_ops = ir::TopologySortOperations(graph);
return SortForReduceMode(sorted_ops);
}
std::vector<ir::Node *> ReduceSSAGraphBuilder::SortForReduceMode(
const std::vector<ir::Node *> &topo_ops) const {
std::vector<ir::Node *> sorted_ops;
std::unordered_map<std::string, std::vector<ir::Node *>> delayed_op;
sorted_ops.reserve(topo_ops.size());
ResetState();
auto insert_delayed_op = [&](const std::string &var_name, int dev_id) {
sharded_var_device_.emplace(var_name, dev_id);
if (delayed_op.count(var_name)) {
auto &ops = delayed_op.at(var_name);
sorted_ops.insert(sorted_ops.end(), ops.begin(), ops.end());
delayed_op.at(var_name).clear();
}
} else if (node->Op()->Type() == "concat") {
op_dev_id = GetVarDeviceID(input_var_names[0], *sharded_var_device);
for (auto &varname : output_var_names) {
sharded_var_device->emplace(varname, op_dev_id);
};
for (ir::Node *node : topo_ops) {
int op_dev_id = GetOpDeviceID(node, &delayed_op);
if (op_dev_id > -1) {
// This op only runs on one specific device.
sorted_ops.emplace_back(node);
for (ir::Node *n : node->outputs) {
insert_delayed_op(n->Name(), op_dev_id);
}
} else if (op_dev_id == -1) {
// This op runs on all devices, and its output may have parameter's
// gradients.
sorted_ops.emplace_back(node);
bool is_bk_op =
static_cast<bool>(boost::get<int>(node->Op()->GetAttr(
OpProtoAndCheckerMaker::OpRoleAttrName())) &
static_cast<int>(OpRole::kBackward));
if (!is_bk_op) continue;
// Currently, we assume that once gradient is generated, it can be
// broadcast, and each gradient is only broadcast once.
std::vector<std::string> backward_vars;
try {
backward_vars =
boost::get<std::vector<std::string>>(node->Op()->GetNullableAttr(
OpProtoAndCheckerMaker::OpRoleVarAttrName()));
} catch (boost::bad_get e) {
}
PADDLE_ENFORCE_EQ(backward_vars.size() % 2, 0);
for (size_t i = 0; i < backward_vars.size(); i += 2) {
auto &g_name = backward_vars[i + 1];
size_t cur_device_id = GetAppropriateDeviceID({g_name});
insert_delayed_op(g_name, static_cast<int>(cur_device_id));
}
} else if (op_dev_id == -2) {
// The Op on which the Op depends has not yet been generated.
}
} else {
LOG(ERROR) << "got unexpected dist op: " << node->Op()->Type();
PADDLE_THROW(
"the distribute training related op should be in [split_byref, "
"concat].");
}
PADDLE_ENFORCE(op_dev_id != -1,
"can not find right place for distributed op: %s",
node->Op()->Type());
PADDLE_ENFORCE_EQ(sorted_ops.size(), topo_ops.size());
CreateComputationalOp(result, node, op_dev_id);
return op_dev_id;
ResetState();
return sorted_ops;
}
void DistSSAGraphBuilder::Init() const {
MultiDevSSAGraphBuilderBase::Init();
ResetState();
}
void DistSSAGraphBuilder::ResetState() const {
BalanceVarSSAGraphBuilder::ResetState();
bcast_var_name_set_.clear();
bcast_var_name_set_.resize(places_.size());
}
bool DistSSAGraphBuilder::DealWithSpecialOp(ir::Graph *result,
ir::Node *node) const {
bool insert_op = false;
if (OpHaveRole(*node, OpRole::kRPC)) {
int op_dev_id = CreateRPCOp(result, node);
PADDLE_ENFORCE(op_dev_id != -1,
"Can not schedule the RPC operator to the right place.");
if (node->Op()->Type() == "recv") {
auto recv_vars_attr =
boost::get<std::vector<std::string>>(node->Op()->GetNullableAttr(
OpProtoAndCheckerMaker::OpRoleVarAttrName()));
PADDLE_ENFORCE(recv_vars_attr.size() == 2UL); // [parameter, gradient]
if (recv_vars_attr[0].find(".block") == std::string::npos) {
bcast_var_name_set_[op_dev_id].emplace(recv_vars_attr[0]);
}
}
insert_op = true;
need_broadcast_var_ = true;
} else if (OpHaveRole(*node, OpRole::kDist)) {
int op_dev_id = CreateDistTrainOp(result, node);
if (node->Op()->Type() == "concat") {
auto origin_param_name = node->Op()->OutputArgumentNames()[0];
bcast_var_name_set_[op_dev_id].emplace(origin_param_name);
}
insert_op = true;
} else {
int op_dev_id = GetOpDeviceID(node);
if (op_dev_id != -1) { // This op only runs on one specific device.
CreateComputationalOp(result, node, op_dev_id);
for (ir::Node *n : node->outputs) {
sharded_var_device_.emplace(n->Name(), op_dev_id);
}
insert_op = true;
}
}
return insert_op;
}
void SetOpInputsAllPlaces(ir::Graph *result, ir::Node *node, int num_places) {
......@@ -775,13 +768,11 @@ void SetOpInputsAllPlaces(ir::Graph *result, ir::Node *node, int num_places) {
}
// Create RPC related op handles that connects its in ops and out ops.
int MultiDevSSAGraphBuilder::CreateRPCOp(
ir::Graph *result, ir::Node *node,
std::unordered_map<std::string, int> *sharded_var_device) const {
int DistSSAGraphBuilder::CreateRPCOp(ir::Graph *result, ir::Node *node) const {
int op_dev_id = -1;
if (node->Op()->Type() == "send") {
// TODO(paddle-dev): getting the first var is not safe.
op_dev_id = GetVarDeviceID(node->inputs[0]->Name(), *sharded_var_device);
op_dev_id = GetVarDeviceID(node->inputs[0]->Name());
PADDLE_ENFORCE(!ir::IsControlDepVar(*node->inputs[0]),
"This hack no longer holds, please fix.");
// the variable name which contains .block means it was splited by
......@@ -799,9 +790,9 @@ int MultiDevSSAGraphBuilder::CreateRPCOp(
VLOG(10) << "send grad " << input_var_names[0] << " origin "
<< send_param_grad[1] << " place: " << op_dev_id;
for (auto &varname : input_var_names) {
sharded_var_device->emplace(varname, op_dev_id);
sharded_var_device_.emplace(varname, op_dev_id);
}
sharded_var_device->emplace(send_param_grad[1], op_dev_id);
sharded_var_device_.emplace(send_param_grad[1], op_dev_id);
}
} else if (node->Op()->Type() == "recv") {
std::vector<std::string> output_var_names;
......@@ -811,7 +802,7 @@ int MultiDevSSAGraphBuilder::CreateRPCOp(
auto recv_param_grad = boost::get<std::vector<std::string>>(
node->Op()->GetAttr(OpProtoAndCheckerMaker::OpRoleVarAttrName()));
if (recv_param_grad.size() == 2U) {
op_dev_id = GetVarDeviceID(recv_param_grad[1], *sharded_var_device);
op_dev_id = GetVarDeviceID(recv_param_grad[1]);
VLOG(10) << "recv param " << recv_param_grad[0]
<< " get grad place: " << recv_param_grad[1]
<< " place: " << op_dev_id;
......@@ -819,7 +810,7 @@ int MultiDevSSAGraphBuilder::CreateRPCOp(
op_dev_id = GetAppropriateDeviceID(output_var_names);
}
for (auto &varname : output_var_names) {
sharded_var_device->emplace(varname, op_dev_id);
sharded_var_device_.emplace(varname, op_dev_id);
}
} else {
// send_barrier, fetch_barrier will run on place 0;
......@@ -846,7 +837,7 @@ int MultiDevSSAGraphBuilder::CreateRPCOp(
for (ir::Node *output : node->outputs) {
int outvar_dev_id = op_dev_id;
if (node->Op()->Type() == "fetch_barrier") {
outvar_dev_id = GetVarDeviceID(output->Name(), *sharded_var_device);
outvar_dev_id = GetVarDeviceID(output->Name());
PADDLE_ENFORCE_NE(outvar_dev_id, -1, "output name %s", output->Name());
}
p = places_[outvar_dev_id];
......@@ -863,29 +854,124 @@ int MultiDevSSAGraphBuilder::CreateRPCOp(
return op_dev_id;
}
bool MultiDevSSAGraphBuilder::IsSparseGradient(const std::string &og) const {
PADDLE_ENFORCE(all_vars_.count(og) != 0);
if (all_vars_.at(og)->GetType() == proto::VarType::SELECTED_ROWS) {
return true;
int DistSSAGraphBuilder::CreateDistTrainOp(ir::Graph *result,
ir::Node *node) const {
int op_dev_id = -1;
std::vector<std::string> input_var_names;
std::vector<std::string> output_var_names;
for (ir::Node *input : node->inputs) {
input_var_names.push_back(input->Name());
}
return false;
for (ir::Node *output : node->outputs) {
output_var_names.push_back(output->Name());
}
if (node->Op()->Type() == "split_byref" ||
node->Op()->Type() == "split_selected_rows" ||
node->Op()->Type() == "split_ids") {
// TODO(paddle-dev): getting the first var is not safe.
op_dev_id = GetVarDeviceID(input_var_names[0]);
if (strategy_.reduce_ == BuildStrategy::ReduceStrategy::kAllReduce) {
op_dev_id = GetAppropriateDeviceID(input_var_names);
for (auto &varname : input_var_names) {
sharded_var_device_.emplace(varname, op_dev_id);
}
}
for (auto &varname : output_var_names) {
sharded_var_device_.emplace(varname, op_dev_id);
}
} else if (node->Op()->Type() == "concat") {
op_dev_id = GetVarDeviceID(input_var_names[0]);
for (auto &varname : output_var_names) {
sharded_var_device_.emplace(varname, op_dev_id);
}
} else {
LOG(ERROR) << "got unexpected dist op: " << node->Op()->Type();
PADDLE_THROW(
"the distribute training related op should be in [split_byref, "
"concat].");
}
PADDLE_ENFORCE(op_dev_id != -1,
"can not find right place for distributed op: %s",
node->Op()->Type());
CreateComputationalOp(result, node, op_dev_id);
return op_dev_id;
}
bool MultiDevSSAGraphBuilder::IsScaleLossOp(ir::Node *node) const {
return boost::get<int>(
node->Op()->GetAttr(OpProtoAndCheckerMaker::OpRoleAttrName())) ==
(static_cast<int>(OpRole::kBackward) |
static_cast<int>(OpRole::kLoss)) &&
!loss_var_name_.empty(); // If loss_var is empty. This is test mode
void DistSSAGraphBuilder::InsertCollectiveOp(ir::Graph *result,
const std::string &p_name,
const std::string &g_name) const {
size_t cur_device_id = 0;
switch (strategy_.reduce_) {
case BuildStrategy::ReduceStrategy::kReduce:
cur_device_id = GetAppropriateDeviceID({g_name});
CreateReduceOp(result, g_name, cur_device_id);
sharded_var_device_.emplace(g_name, cur_device_id);
break;
case BuildStrategy::ReduceStrategy::kAllReduce:
if (IsSparseGradient(g_name)) {
CreateReduceOp(result, g_name, 0);
CreateBroadcastOp(result, g_name, 0);
} else {
CreateAllReduceOp(result, g_name);
}
break;
default:
LOG(FATAL) << "Unknown reduce strategy.";
break;
}
}
void DistSSAGraphBuilder::InsertPostprocessOps(ir::Graph *result) const {
if (need_broadcast_var_ ||
(UseGPU() &&
strategy_.reduce_ == BuildStrategy::ReduceStrategy::kReduce)) {
if (strategy_.fuse_broadcast_op_) {
CreateFusedBroadcastOp(result, bcast_var_name_set_);
} else {
for (size_t dev_id = 0; dev_id < bcast_var_name_set_.size(); ++dev_id) {
auto &to_bcast_set = bcast_var_name_set_[dev_id];
for (auto &bcast_name : to_bcast_set) {
CreateBroadcastOp(result, bcast_name, dev_id);
}
}
}
}
}
std::unordered_set<std::string> &MultiDevSSAGraphBuilder() {
static std::unordered_set<std::string> regs;
return regs;
}
static int MultiDevSSAGraphBuilderRegister(const std::string &builder_mode) {
MultiDevSSAGraphBuilder().insert(builder_mode);
return 0;
}
} // namespace details
} // namespace framework
} // namespace paddle
REGISTER_PASS(multi_devices_pass,
paddle::framework::details::MultiDevSSAGraphBuilder)
.RequirePassAttr(paddle::framework::details::kLossVarName)
.RequirePassAttr(paddle::framework::details::kPlaces)
.RequirePassAttr(paddle::framework::details::kLocalScopes)
.RequirePassAttr(paddle::framework::details::kStrategy)
.RequirePassAttr(paddle::framework::details::kNRanks);
#define REGISTER_MULTI_DEVICES_PASS(pass_name, pass_class) \
STATIC_ASSERT_GLOBAL_NAMESPACE( \
_reg_ssa_graph_builder_##pass_name, \
"REGISTER_MULTI_DEVICES_PASS must be called in global namespace."); \
int _reg_ssa_graph_builder_entry_##pass_name = \
paddle::framework::details::MultiDevSSAGraphBuilderRegister(#pass_name); \
REGISTER_PASS(pass_name, pass_class) \
.RequirePassAttr(paddle::framework::details::kLossVarName) \
.RequirePassAttr(paddle::framework::details::kPlaces) \
.RequirePassAttr(paddle::framework::details::kLocalScopes) \
.RequirePassAttr(paddle::framework::details::kStrategy) \
.RequirePassAttr(paddle::framework::details::kNRanks)
REGISTER_MULTI_DEVICES_PASS(reduce_mode_multi_devices_pass,
paddle::framework::details::ReduceSSAGraphBuilder);
REGISTER_MULTI_DEVICES_PASS(
allreduce_mode_multi_devices_pass,
paddle::framework::details::AllReduceSSAGraphBuilder);
REGISTER_MULTI_DEVICES_PASS(dist_multi_devices_pass,
paddle::framework::details::DistSSAGraphBuilder);
paddle/fluid/framework/details/multi_devices_graph_pass.h
浏览文件 @ 92da467c
......@@ -13,6 +13,7 @@
// limitations under the License.
#pragma once
#include <string>
#include <utility>
#include <vector>
......@@ -30,78 +31,70 @@ namespace framework {
class Scope;
namespace details {
class MultiDevSSAGraphBuilder : public ir::Pass {
constexpr char kLossVarName[] = "loss_var_name";
constexpr char kPlaces[] = "places";
constexpr char kLocalScopes[] = "local_scopes";
constexpr char kStrategy[] = "strategy";
constexpr char kNRanks[] = "nranks";
class MultiDevSSAGraphBuilderBase : public ir::Pass {
protected:
std::unique_ptr<ir::Graph> ApplyImpl(
std::unique_ptr<ir::Graph> graph) const override;
private:
void CreateOpHandleIOs(ir::Graph *result, ir::Node *node,
size_t device_id) const;
void Init() const;
virtual void Init() const;
#if defined(PADDLE_WITH_CUDA) && !defined(_WIN32)
mutable platform::NCCLContextMap *nccl_ctxs_;
#endif
virtual std::vector<ir::Node *> SortOperations(const ir::Graph &graph) const;
int GetVarDeviceID(
const std::string &varname,
const std::unordered_map<std::string, int> &sharded_var_device) const;
virtual void InsertCollectiveOp(ir::Graph *result, const std::string &p_name,
const std::string &g_name) const = 0;
bool IsScaleLossOp(ir::Node *node) const;
virtual bool DealWithSpecialOp(ir::Graph *result, ir::Node *node) const = 0;
virtual void InsertPostprocessOps(ir::Graph *result) const = 0;
int CreateRPCOp(
ir::Graph *result, ir::Node *node,
std::unordered_map<std::string, int> *sharded_var_device) const;
int CreateDistTrainOp(
ir::Graph *result, ir::Node *node,
std::unordered_map<std::string, int> *sharded_var_device) const;
bool UseGPU() const;
bool NeedCollectiveOps() const;
bool IsScaleLossOp(ir::Node *node) const;
void CreateComputationalOps(ir::Graph *result, ir::Node *node,
size_t num_places) const;
void CreateScaleLossGradOp(ir::Graph *result,
const std::string &loss_grad_name,
ir::Node *out_var_node,
ir::Node *out_var_node, size_t loss_scale,
proto::VarType::Type dtype) const;
VarHandle *CreateReduceOp(ir::Graph *result, const std::string &og,
int dst_dev_id) const;
void CreateComputationalOp(ir::Graph *result, ir::Node *node,
int dev_id) const;
int GetOpDeviceID(
ir::Node *node,
const std::unordered_map<std::string, int> &sharded_var_device) const;
void InsertAllReduceOp(ir::Graph *result, const std::string &og) const;
bool IsSparseGradient(const std::string &og) const;
void InsertDataBalanceOp(ir::Graph *result,
const std::vector<std::string> &datas) const;
void CreateAllReduceOp(ir::Graph *result, const std::string &og) const;
void CreateBroadcastOp(ir::Graph *result, const std::string &p_name,
size_t src_dev_id) const;
void InsertScaleLossGradOp(ir::Graph *result, const ir::Node *node) const;
void CreateFusedBroadcastOp(
ir::Graph *result,
const std::vector<std::unordered_set<std::string>> &bcast_varnames) const;
bool IsSparseGradient(const std::string &og) const;
size_t GetAppropriateDeviceID(
const std::vector<std::string> &var_names) const;
void SetCommunicationContext(OpHandleBase *op_handle,
const platform::Place &p) const;
std::vector<ir::Node *> SortForReduceMode(
const std::vector<ir::Node *> &) const;
void CreateOpHandleIOs(ir::Graph *result, ir::Node *node,
size_t device_id) const;
int GetOpDeviceID(
ir::Node *node,
const std::unordered_map<std::string, int> &shared_var_device,
std::unordered_map<std::string, std::vector<ir::Node *>> *delay_ops)
const;
#if defined(PADDLE_WITH_CUDA) && !defined(_WIN32)
mutable platform::NCCLContextMap *nccl_ctxs_;
#endif
mutable std::string loss_var_name_;
mutable std::vector<platform::Place> places_;
......@@ -109,8 +102,83 @@ class MultiDevSSAGraphBuilder : public ir::Pass {
mutable BuildStrategy strategy_;
mutable std::unordered_map<std::string, VarDesc *> all_vars_;
};
class AllReduceSSAGraphBuilder : public MultiDevSSAGraphBuilderBase {
protected:
virtual void InsertCollectiveOp(ir::Graph *result, const std::string &p_name,
const std::string &g_name) const;
virtual bool DealWithSpecialOp(ir::Graph *result, ir::Node *node) const {
return false;
}
virtual void InsertPostprocessOps(ir::Graph *result) const {}
};
class BalanceVarSSAGraphBuilder : public MultiDevSSAGraphBuilderBase {
protected:
int GetVarDeviceID(const std::string &varname) const;
int GetOpDeviceID(ir::Node *node) const;
size_t GetAppropriateDeviceID(
const std::vector<std::string> &var_names) const;
virtual void ResetState() const;
mutable std::unordered_map<std::string, int> sharded_var_device_;
mutable std::vector<int64_t> balance_vars_;
};
class ReduceSSAGraphBuilder : public BalanceVarSSAGraphBuilder {
protected:
virtual void Init() const;
virtual void InsertCollectiveOp(ir::Graph *result, const std::string &p_name,
const std::string &g_name) const;
virtual bool DealWithSpecialOp(ir::Graph *result, ir::Node *node) const;
virtual void InsertPostprocessOps(ir::Graph *result) const;
virtual std::vector<ir::Node *> SortOperations(const ir::Graph &graph) const;
virtual void ResetState() const;
int GetOpDeviceID(ir::Node *node,
std::unordered_map<std::string, std::vector<ir::Node *>>
*delay_ops) const;
std::vector<ir::Node *> SortForReduceMode(
const std::vector<ir::Node *> &topo_ops) const;
mutable std::vector<std::unordered_set<std::string>> bcast_var_name_set_;
};
class DistSSAGraphBuilder : public BalanceVarSSAGraphBuilder {
protected:
virtual void Init() const;
virtual bool DealWithSpecialOp(ir::Graph *result, ir::Node *node) const;
virtual void InsertPostprocessOps(ir::Graph *result) const;
virtual void InsertCollectiveOp(ir::Graph *result, const std::string &p_name,
const std::string &g_name) const;
virtual void ResetState() const;
int CreateRPCOp(ir::Graph *result, ir::Node *node) const;
int CreateDistTrainOp(ir::Graph *result, ir::Node *node) const;
mutable std::vector<std::unordered_set<std::string>> bcast_var_name_set_;
mutable bool need_broadcast_var_{false};
};
std::unordered_set<std::string> &MultiDevSSAGraphBuilder();
} // namespace details
} // namespace framework
} // namespace paddle
paddle/fluid/framework/ir/CMakeLists.txt
浏览文件 @ 92da467c
......@@ -31,6 +31,7 @@ cc_library(fuse_pass_base SRCS fuse_pass_base.cc DEPS pass)
pass_library(graph_to_program_pass base)
pass_library(graph_viz_pass base)
pass_library(lock_free_optimize_pass base)
pass_library(fc_fuse_pass inference)
pass_library(attention_lstm_fuse_pass inference)
pass_library(infer_clean_graph_pass inference)
......@@ -41,6 +42,7 @@ pass_library(seq_concat_fc_fuse_pass inference)
pass_library(multi_batch_merge_pass base)
pass_library(conv_bn_fuse_pass inference)
pass_library(seqconv_eltadd_relu_fuse_pass inference)
pass_library(seqpool_concat_fuse_pass inference)
pass_library(is_test_pass base)
pass_library(conv_elementwise_add_act_fuse_pass inference)
pass_library(conv_elementwise_add2_act_fuse_pass inference)
......
paddle/fluid/framework/ir/graph.h
浏览文件 @ 92da467c
......@@ -109,7 +109,6 @@ class Graph {
attr_dels_[attr_name] = []() {};
}
template <typename AttrType>
void Erase(const std::string &attr_name) {
PADDLE_ENFORCE(attrs_.count(attr_name) != 0, "%s not set in the graph",
attr_name);
......
paddle/fluid/framework/ir/lock_free_optimize_pass.cc 0 → 100644
浏览文件 @ 92da467c
// Copyright (c) 2018 PaddlePaddle Authors. All Rights Reserved.
//
// Licensed under the Apache License, Version 2.0 (the "License");
// you may not use this file except in compliance with the License.
// You may obtain a copy of the License at
//
// http://www.apache.org/licenses/LICENSE-2.0
//
// Unless required by applicable law or agreed to in writing, software
// distributed under the License is distributed on an "AS IS" BASIS,
// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
// See the License for the specific language governing permissions and
// limitations under the License.
#include "paddle/fluid/framework/ir/lock_free_optimize_pass.h"
#include <string>
#include <unordered_set>
#include <vector>
#include "paddle/fluid/framework/ir/node.h"
#include "paddle/fluid/framework/op_proto_maker.h"
#include "paddle/fluid/framework/operator.h"
#include "paddle/fluid/platform/enforce.h"
namespace paddle {
namespace framework {
namespace ir {
const char kSumGradOpName[] = "sum";
// TODO(minqiyang): only support sgd at current time, please add
// other optimizers later.
const char kOptimizerType[] = "sgd";
std::unique_ptr<ir::Graph> LockFreeOptimizePass::ApplyImpl(
std::unique_ptr<ir::Graph> graph) const {
PADDLE_ENFORCE(graph.get());
// We could collect all weights' name from SGD, where
// W1 <- SGD(W0, Grad0)
std::unordered_set<std::string> weight_var_set;
for (auto* node : graph->Nodes()) {
if (IsOpNamed(node, kOptimizerType)) {
auto& param_out_vars = node->Op()->Output("ParamOut");
PADDLE_ENFORCE(param_out_vars.size() == 1u);
weight_var_set.insert(param_out_vars[0]);
}
}
// find all grad's merge op via weight name, where
// Grad0 <- SUM(Grad1, Grad2, Grad3 ...)
std::unordered_set<ir::Node*> grad_sum_op_set;
for (ir::Node* node : graph->Nodes()) {
if (IsOpNamed(node, kSumGradOpName)) {
for (ir::Node* output : node->outputs) {
// strip the last grad suffix @GRAD
std::string var_name = output->Name();
const std::string suffix(kGradVarSuffix);
if (var_name != suffix && var_name.size() > suffix.size() &&
var_name.substr(var_name.size() - suffix.size()) == suffix) {
// if so then strip them off
var_name = var_name.substr(0, var_name.size() - suffix.size());
if (weight_var_set.find(var_name) != weight_var_set.end()) {
grad_sum_op_set.insert(node);
break;
}
}
}
}
}
// get the forward op and backward op pairs, where
// out <- forward(X, W)
// Grad1 <- backward(out, X')
// Grad0 <- SUM(Grad1, Grad2, Grad3 ...)
// W0 <- SGD(W1, Grad0)
for (ir::Node* node : grad_sum_op_set) {
for (ir::Node* merged_grad_var : node->outputs) {
// find the optimizers connected with sum op
if (IsVarNameEndsWith(merged_grad_var, kGradVarSuffix) &&
merged_grad_var->outputs.size() == 1u) {
ir::Node* opt_node = merged_grad_var->outputs[0];
VLOG(3) << "Found opt node " << opt_node->Name();
// find the backward op connected with sum op
for (ir::Node* unmerged_grad_var : node->inputs) {
if (IsVarNameContains(unmerged_grad_var, kGradVarSuffix) &&
unmerged_grad_var->inputs.size() == 1u) {
ir::Node* backward_op = unmerged_grad_var->inputs[0];
VLOG(3) << "Found backward_op " << backward_op->Name();
// find the forward op related to the backward op
ir::Node* forward_op =
FindForwardOpViaBackwardOp(graph.get(), backward_op);
VLOG(3) << "Found forward_op " << forward_op->Name();
PADDLE_ENFORCE(forward_op);
Node* new_optimizer_node = CreateNewSGDNode(
graph.get(), forward_op, backward_op, node, opt_node);
PADDLE_ENFORCE(new_optimizer_node);
}
}
}
}
}
// Remove the sum_op and its' outputs and connected Optimizers
for (Node* sum_op : grad_sum_op_set) {
for (Node* sum_op_output : sum_op->outputs) {
for (Node* optimize_op : sum_op_output->outputs) {
if (optimize_op->NodeType() == Node::Type::kOperation &&
optimize_op->Name() == kOptimizerType) {
VLOG(3) << "remove optimize_op: " << optimize_op->Name() << "_"
<< optimize_op->id();
graph->RemoveNode(optimize_op);
}
}
VLOG(3) << "remove sum_op_output: " << sum_op_output->Name() << "_"
<< sum_op_output->id();
graph->RemoveNode(sum_op_output);
}
VLOG(3) << "remove sum_op: " << sum_op->Name() << "_" << sum_op->id();
graph->RemoveNode(sum_op);
}
for (auto* node : graph->Nodes()) {
for (Node* output_node : node->outputs) {
if (output_node->Name() == "sgd") {
VLOG(3) << "Node link to SGD: " << node->Name() << "_" << node->id()
<< " --> " << output_node->Name() << "_" << output_node->id();
for (Node* input_node : node->inputs) {
VLOG(3) << "SGD Input link: " << input_node->Name() << "_"
<< input_node->id() << " --> " << node->Name() << "_"
<< node->id();
}
}
}
}
return graph;
}
ir::Node* LockFreeOptimizePass::CreateNewSGDNode(
ir::Graph* graph, ir::Node* forward_node, ir::Node* backward_node,
ir::Node* grad_sum_node, ir::Node* optimize_node) const {
PADDLE_ENFORCE(graph);
PADDLE_ENFORCE(forward_node);
PADDLE_ENFORCE(backward_node);
PADDLE_ENFORCE(grad_sum_node);
PADDLE_ENFORCE(optimize_node);
// find the grad var node between the grad sum node and backward_node
std::vector<ir::Node*> grad_vars =
FindConnectedNode(backward_node, grad_sum_node);
ir::Node* grad_node = nullptr;
for (ir::Node* node : grad_vars) {
if (!ir::IsControlDepVar(*node)) {
grad_node = node;
}
}
PADDLE_ENFORCE(grad_node);
// create a new SGD node
OpDesc* old_desc = optimize_node->Op();
// keep with the same block between new optimizer and the old one
OpDesc new_desc(*old_desc, old_desc->Block());
new_desc.SetInput("Param", old_desc->Input("Param"));
new_desc.SetInput("LearningRate", old_desc->Input("LearningRate"));
new_desc.SetInput("Grad", std::vector<std::string>({grad_node->Name()}));
new_desc.SetOutput("ParamOut", old_desc->Output("ParamOut"));
std::vector<std::string> op_role_vars = boost::get<std::vector<std::string>>(
new_desc.GetAttr(framework::OpProtoAndCheckerMaker::OpRoleVarAttrName()));
// replace the second op role var, because the grad name was
// changed in new optimizer
op_role_vars.pop_back();
op_role_vars.push_back(grad_node->Name());
new_desc.SetAttr(framework::OpProtoAndCheckerMaker::OpRoleVarAttrName(),
op_role_vars);
new_desc.SetType(kOptimizerType);
// set backward op's op role var, this will be used to
// set device_id in multi_device_pass
backward_node->Op()->SetAttr(
framework::OpProtoAndCheckerMaker::OpRoleVarAttrName(), op_role_vars);
// backward_node->Op()->SetAttr(
// framework::OpProtoAndCheckerMaker::OpRoleVarAttrName(), {});
// keep with the same output nodes between new optimizer and the
// old one
Node* sgd_node = graph->CreateOpNode(&new_desc);
// change all outputs of the optimize_node to the new one
ReplaceAllDownstreamNode(optimize_node, sgd_node);
// find connected node between forward node and optimize node
// and replace the optimize node to new sgd node
std::vector<ir::Node*> forward_opt_connected_nodes =
FindConnectedNode(forward_node, optimize_node);
for (ir::Node* node : forward_opt_connected_nodes) {
ReplaceUpstreamNode(node, optimize_node, sgd_node);
}
// find connected node between backward node and optimize node
// and replace the optimize node to new sgd node
std::vector<ir::Node*> backward_opt_connected_nodes =
FindConnectedNode(backward_node, optimize_node);
for (ir::Node* node : backward_opt_connected_nodes) {
ReplaceUpstreamNode(node, optimize_node, sgd_node);
}
// SGD must have only one param and LR in
PADDLE_ENFORCE(old_desc->Input("LearningRate").size() == 1u);
PADDLE_ENFORCE(old_desc->Input("Param").size() == 1u);
// LR and weight nodes should be copied
for (Node* upstream_node : optimize_node->inputs) {
if (upstream_node->Name() == old_desc->Input("LearningRate")[0] ||
upstream_node->Name() == old_desc->Input("Param")[0]) {
ReplaceUpstreamNode(upstream_node, optimize_node, sgd_node);
}
}
VLOG(3) << "Create new opt node" << sgd_node->Name() << "_" << sgd_node->id();
return sgd_node;
}
std::vector<ir::Node*> LockFreeOptimizePass::FindConnectedNode(
ir::Node* upstream_node, ir::Node* downstream_node) const {
std::vector<ir::Node*> result;
for (ir::Node* out_node : upstream_node->outputs) {
for (ir::Node* in_node : downstream_node->inputs) {
if (in_node == out_node) {
result.push_back(in_node);
}
}
}
return result;
}
void LockFreeOptimizePass::ReplaceUpstreamNode(
ir::Node* upstream_node, ir::Node* old_optimizer_node,
ir::Node* new_optimizer_node) const {
PADDLE_ENFORCE(upstream_node);
PADDLE_ENFORCE(old_optimizer_node);
PADDLE_ENFORCE(new_optimizer_node);
// Remove the old_optimizer_node from upstream_node's outputs vector
auto& output_node_vec = upstream_node->outputs;
for (auto output_node_iter = output_node_vec.begin();
output_node_iter != output_node_vec.end();) {
if (*output_node_iter == old_optimizer_node) {
output_node_vec.erase(output_node_iter);
break;
} else {
++output_node_iter;
}
}
// Add the new_optimizer_node to upstream_node's outputs vector
output_node_vec.emplace_back(new_optimizer_node);
new_optimizer_node->inputs.emplace_back(upstream_node);
}
void LockFreeOptimizePass::ReplaceAllDownstreamNode(
ir::Node* old_optimizer_node, ir::Node* new_optimizer_node) const {
PADDLE_ENFORCE(old_optimizer_node);
PADDLE_ENFORCE(new_optimizer_node);
for (ir::Node* downstream_node : old_optimizer_node->outputs) {
// Remove the old_optimizer_node from downstream_node's inputs vector
auto& input_node_vec = downstream_node->inputs;
for (auto input_node_iter = input_node_vec.begin();
input_node_iter != input_node_vec.end();) {
if (*input_node_iter == old_optimizer_node) {
input_node_vec.erase(input_node_iter);
break;
} else {
++input_node_iter;
}
}
// Add the new_optimizer_node to downstream_node's inputs vector
input_node_vec.emplace_back(new_optimizer_node);
new_optimizer_node->outputs.emplace_back(downstream_node);
}
}
ir::Node* LockFreeOptimizePass::FindForwardOpViaBackwardOp(
ir::Graph* graph, ir::Node* backward_node) const {
PADDLE_ENFORCE(graph);
PADDLE_ENFORCE(backward_node);
// strip the suffix _grad of backward_node's name
std::string forward_op_name = backward_node->Name();
const std::string suffix("_grad");
if (forward_op_name != suffix && forward_op_name.size() > suffix.size() &&
forward_op_name.substr(forward_op_name.size() - suffix.size()) ==
suffix) {
// if so then strip them off
forward_op_name =
forward_op_name.substr(0, forward_op_name.size() - suffix.size());
} else {
LOG(WARNING) << "Illegal backward node's name " << backward_node->Name()
<< " id " << backward_node->id();
return nullptr;
}
for (ir::Node* node : graph->Nodes()) {
if (node->Name() == forward_op_name) {
if (node->outputs.size() == 0u) {
// if forward_node has no output, then it has NO grad op
continue;
}
// check whether all inputs of the backward_op that ends_with @GRAD
// comes from the output of forward_op is the input of the backward_op
bool is_related_forward_node = true;
for (ir::Node* backward_input : backward_node->inputs) {
if (IsVarNameEndsWith(backward_input, kGradVarSuffix)) {
bool meets_correct_output = false;
for (ir::Node* forward_output : node->outputs) {
if (forward_output->Name() + kGradVarSuffix ==
backward_input->Name()) {
meets_correct_output = true;
break;
}
}
if (!meets_correct_output) {
is_related_forward_node = false;
break;
}
}
}
if (is_related_forward_node) {
return node;
}
}
}
return nullptr;
}
} // namespace ir
} // namespace framework
} // namespace paddle
REGISTER_PASS(lock_free_optimize_pass,
paddle::framework::ir::LockFreeOptimizePass);
paddle/fluid/framework/ir/lock_free_optimize_pass.h 0 → 100644
浏览文件 @ 92da467c
// 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.
#ifndef PADDLE_FLUID_FRAMEWORK_IR_LOCK_FREE_OPTIMIZE_PASS_H_
#define PADDLE_FLUID_FRAMEWORK_IR_LOCK_FREE_OPTIMIZE_PASS_H_
#include <string>
#include <vector>
#include <boost/algorithm/string/predicate.hpp>
#include "paddle/fluid/framework/ir/graph.h"
#include "paddle/fluid/framework/ir/pass.h"
namespace paddle {
namespace framework {
namespace ir {
class Node;
/*
* Remove the sum op of all gradients of the backward op.
* And remove the dependecies of the optimizer related to the
* same backward op.
*
* Before this pass:
*
* forward_op1 forward_op2
* | |
* grad_op1 grad_op2
* \ /
* \ /
* sum_op
* |
* sgd_op
*
* After this pass:
* forward_op1 forward_op2
* | |
* grad_op1 grad_op2
* | |
* sgd_op1 sgd_op2
*
* sgd_op1 and sgd_op2 will update the same weight which holds the same
* memory, so we could benefits from the acceleration
*/
class LockFreeOptimizePass : public Pass {
public:
virtual ~LockFreeOptimizePass() {}
protected:
std::unique_ptr<ir::Graph> ApplyImpl(std::unique_ptr<ir::Graph> graph) const;
private:
// Create a new sgd node via current optimizer node
ir::Node* CreateNewSGDNode(ir::Graph* graph, ir::Node* forward_node,
ir::Node* backward_node, ir::Node* grad_sum_node,
ir::Node* optimize_node) const;
// Replace the input weight's optimizers
void ReplaceUpstreamNode(ir::Node* upstream_node,
ir::Node* old_optimizer_node,
ir::Node* new_optimizer_node) const;
// Replace the output weight's optimizers
void ReplaceAllDownstreamNode(ir::Node* old_optimizer_node,
ir::Node* new_optimizer_node) const;
// Find all weight variables in graph
bool FindAllWeightVars(ir::Graph* graph) const;
// Find the forward_op node via the backward_op node
ir::Node* FindForwardOpViaBackwardOp(ir::Graph* graph,
ir::Node* backward_node) const;
std::vector<ir::Node*> FindConnectedNode(ir::Node* upstream_node,
ir::Node* downstream_node) const;
inline bool IsOpNamed(ir::Node* node, const std::string& name) const {
PADDLE_ENFORCE(node);
return node->NodeType() == Node::Type::kOperation && node->Name() == name;
}
inline bool IsVarNamed(ir::Node* node, const std::string& name) const {
PADDLE_ENFORCE(node);
return node->NodeType() == Node::Type::kVariable && node->Name() == name;
}
inline bool IsVarNameEndsWith(ir::Node* node, const std::string& name) const {
PADDLE_ENFORCE(node);
return node->NodeType() == Node::Type::kVariable &&
boost::algorithm::ends_with(node->Name(), name);
}
inline bool IsVarNameContains(ir::Node* node, const std::string& name) const {
PADDLE_ENFORCE(node);
return node->NodeType() == Node::Type::kVariable &&
node->Name().find(name) != std::string::npos;
}
inline bool IsControlDepFrom(ir::Node* ctrl_dep_node, ir::Node* node) const {
PADDLE_ENFORCE(ctrl_dep_node);
PADDLE_ENFORCE(node);
return IsControlDepVar(*ctrl_dep_node) &&
ctrl_dep_node->inputs.size() >= 1u &&
ctrl_dep_node->inputs[0] == node;
}
};
} // namespace ir
} // namespace framework
} // namespace paddle
#endif // PADDLE_FLUID_FRAMEWORK_IR_LOCK_FREE_OPTIMIZE_PASS_H_
paddle/fluid/framework/ir/seqpool_concat_fuse_pass.cc 0 → 100644
浏览文件 @ 92da467c
/* Copyright (c) 2018 PaddlePaddle Authors. All Rights Reserved.
*
* Licensed under the Apache License, Version 2.0 (the "License");
* you may not use this file except in compliance with the License.
* You may obtain a copy of the License at
*
* http://www.apache.org/licenses/LICENSE-2.0
*
* Unless required by applicable law or agreed to in writing, software
* distributed under the License is distributed on an "AS IS" BASIS,
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
* See the License for the specific language governing permissions and
* limitations under the License. */
#include "paddle/fluid/framework/ir/seqpool_concat_fuse_pass.h"
#include <string>
#include <vector>
#include "paddle/fluid/framework/lod_tensor.h"
#define MAX_CONCAT_INPUTS 200
namespace paddle {
namespace framework {
namespace ir {
PDNode* BuildSeqPoolConcatPattern(PDPattern* pattern,
const std::string& name_scope,
int num_inputs) {
auto is_concat_op_with_inputs = [](Node* x, int num) -> bool {
return x && x->IsOp() && x->Op()->Type() == "concat" &&
x->Op()->Input("X").size() == static_cast<size_t>(num);
};
auto is_nth_input_var_of_concat = [=](Node* x, int idx) -> bool {
return x && x->IsVar() && VarLinksToOp(x, "concat") &&
x->outputs.size() == 1 && IsNthInput(x, x->outputs[0], "X", idx) &&
is_concat_op_with_inputs(x->outputs[0], num_inputs);
};
auto is_seqpool_op_with_pootype_of_nth_input_of_concat = [=](
Node* x, const std::string& type, int idx) -> bool {
bool ok = x && x->IsOp() && x->Op()->Type() == "sequence_pool" &&
x->Op()->HasAttr("pooltype") &&
boost::get<std::string>(x->Op()->GetAttr("pooltype")) == type &&
x->outputs.size() == 2; // seqpool should only have 2 outputs
if (ok) {
// only one output of seqpool_op is nth_input_var of concat
// the other one should be unused empty var
if (is_nth_input_var_of_concat(x->outputs[0], idx)) {
ok = ok && x->outputs[1]->IsVar() && x->outputs[1]->outputs.size() == 0;
} else {
ok = ok && is_nth_input_var_of_concat(x->outputs[1], idx) &&
x->outputs[0]->IsVar() && x->outputs[0]->outputs.size() == 0;
}
}
return ok;
};
auto* concat_op = pattern->NewNode(
[=](Node* x) { return is_concat_op_with_inputs(x, num_inputs); },
name_scope + "/concat_op");
concat_op->assert_op_attr<int>("axis", 1);
auto* concat_out_var = pattern->NewNode(
[=](Node* x) {
return x && x->IsVar() && VarLinksFromOp(x, "concat") &&
x->inputs.size() == 1 &&
is_concat_op_with_inputs(x->inputs[0], num_inputs);
},
name_scope + "/concat_out_var");
concat_out_var->assert_is_only_output_of_op("concat");
std::vector<PDNode*> seqpool_ops_input_var(num_inputs);
std::vector<PDNode*> seqpool_ops_output_var(num_inputs);
std::vector<PDNode*> seqpool_ops(num_inputs);
for (int i = 0; i < num_inputs; ++i) {
seqpool_ops_output_var[i] = pattern->NewNode(
[=](Node* x) {
return x && x->IsVar() && is_nth_input_var_of_concat(x, i) &&
x->inputs.size() == 1 &&
is_seqpool_op_with_pootype_of_nth_input_of_concat(x->inputs[0],
"SUM", i);
},
name_scope + "/sequence_pool_out_" + std::to_string(i));
seqpool_ops[i] = pattern->NewNode(
[=](Node* x) {
return x && x->IsOp() &&
is_seqpool_op_with_pootype_of_nth_input_of_concat(x, "SUM", i);
},
name_scope + "/sequence_pool_op_" + std::to_string(i));
seqpool_ops_input_var[i] = pattern->NewNode(
[=](Node* x) {
return x && x->IsVar() && x->outputs.size() >= 1 &&
is_seqpool_op_with_pootype_of_nth_input_of_concat(
x->outputs[0], "SUM", i);
},
name_scope + "/sequence_pool_in_" + std::to_string(i));
// Links
seqpool_ops[i]
->LinksFrom({seqpool_ops_input_var[i]})
.LinksTo({seqpool_ops_output_var[i]});
}
concat_op->LinksFrom(seqpool_ops_output_var).LinksTo({concat_out_var});
return concat_out_var;
}
int BuildFusion(Graph* graph, const std::string& name_scope, Scope* scope,
int num_inputs) {
GraphPatternDetector gpd;
auto* pattern = gpd.mutable_pattern();
BuildSeqPoolConcatPattern(pattern, name_scope, num_inputs);
auto retrieve_node = [](const std::string& name,
const GraphPatternDetector::subgraph_t& subgraph,
const PDPattern& pat) -> Node* {
PADDLE_ENFORCE(subgraph.count(pat.RetrieveNode(name)),
"pattern has no Node called %s", name.c_str());
Node* p = subgraph.at(pat.RetrieveNode(name));
PADDLE_ENFORCE_NOT_NULL(p, "subgraph has no node %s", name.c_str());
return p;
};
int fusion_count{0};
auto handler = [&](const GraphPatternDetector::subgraph_t& subgraph,
Graph* g) {
VLOG(4) << "handle SeqPool Concat fuse";
std::vector<std::string> input_names(num_inputs);
std::vector<Node*> input_vars(num_inputs);
auto& fused_pattern = gpd.pattern();
for (int i = 0; i < num_inputs; ++i) {
input_vars[i] =
retrieve_node(name_scope + "/sequence_pool_in_" + std::to_string(i),
subgraph, fused_pattern);
input_names[i] = input_vars[i]->Name();
}
auto* concat_op =
retrieve_node(name_scope + "/concat_op", subgraph, fused_pattern);
auto* concat_out_var =
retrieve_node(name_scope + "/concat_out_var", subgraph, fused_pattern);
auto* seqpool_op0 = retrieve_node(name_scope + "/sequence_pool_op_0",
subgraph, fused_pattern);
// Create New OpDesc
OpDesc op_desc;
op_desc.SetType("fusion_seqpool_concat");
op_desc.SetInput("X", input_names);
op_desc.SetAttr("pooltype", seqpool_op0->Op()->GetAttr("pooltype"));
op_desc.SetAttr("axis", concat_op->Op()->GetAttr("axis"));
op_desc.SetOutput("Out", {concat_out_var->Name()});
auto* op = graph->CreateOpNode(&op_desc);
for (size_t i = 0; i < input_vars.size(); ++i) {
IR_NODE_LINK_TO(input_vars[i], op);
}
IR_NODE_LINK_TO(op, concat_out_var);
std::unordered_set<const Node*> marked_nodes;
for (auto& item : subgraph) {
marked_nodes.insert(item.second);
}
for (size_t i = 0; i < input_vars.size(); ++i) {
marked_nodes.erase(input_vars[i]);
}
marked_nodes.erase(concat_out_var);
GraphSafeRemoveNodes(graph, marked_nodes);
++fusion_count;
};
gpd(graph, handler);
return fusion_count;
}
std::unique_ptr<ir::Graph> SeqPoolConcatFusePass::ApplyImpl(
std::unique_ptr<ir::Graph> graph) const {
FusePassBase::Init(name_scope_, graph.get());
int fusion_count = 0;
for (int i = MAX_CONCAT_INPUTS; i > 0; --i) {
fusion_count += BuildFusion(
graph.get(), name_scope_ + "/" + std::to_string(i), param_scope(), i);
}
AddStatis(fusion_count);
return graph;
}
} // namespace ir
} // namespace framework
} // namespace paddle
REGISTER_PASS(seqpool_concat_fuse_pass,
paddle::framework::ir::SeqPoolConcatFusePass);
paddle/fluid/framework/ir/seqpool_concat_fuse_pass.h 0 → 100644
浏览文件 @ 92da467c
/* Copyright (c) 2018 PaddlePaddle Authors. All Rights Reserved.
*
* Licensed under the Apache License, Version 2.0 (the "License");
* you may not use this file except in compliance with the License.
* You may obtain a copy of the License at
*
* http://www.apache.org/licenses/LICENSE-2.0
*
* Unless required by applicable law or agreed to in writing, software
* distributed under the License is distributed on an "AS IS" BASIS,
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
* See the License for the specific language governing permissions and
* limitations under the License. */
#pragma once
#include <string>
#include "paddle/fluid/framework/ir/fuse_pass_base.h"
#include "paddle/fluid/framework/ir/graph.h"
#include "paddle/fluid/framework/ir/graph_pattern_detector.h"
namespace paddle {
namespace framework {
namespace ir {
class SeqPoolConcatFusePass : public FusePassBase {
public:
virtual ~SeqPoolConcatFusePass() {}
protected:
std::unique_ptr<ir::Graph> ApplyImpl(std::unique_ptr<ir::Graph> graph) const;
const std::string name_scope_{"seqpool_concat_fuse"};
};
} // namespace ir
} // namespace framework
} // namespace paddle
paddle/fluid/framework/naive_executor.cc
浏览文件 @ 92da467c
......@@ -40,14 +40,14 @@ void NaiveExecutor::Prepare(Scope *scope, const ProgramDesc &program_desc,
void NaiveExecutor::Run() {
#ifndef PADDLE_ON_INFERENCE
LOG_FIRST_N(WARNING, 15) << "The NaiveExecutor can not work properly if the "
"cmake flag ON_INFER is not set.";
LOG_FIRST_N(WARNING, 15) << "Unlike the training phase, all the scopes and "
"variables will be reused to save the allocation "
"overhead.";
LOG_FIRST_N(WARNING, 15) << "Please re-compile the inference library by "
"setting the cmake flag ON_INFER=ON if you are "
"running Paddle Inference";
LOG_FIRST_N(WARNING, 5) << "The NaiveExecutor can not work properly if the "
"cmake flag ON_INFER is not set.";
LOG_FIRST_N(WARNING, 5) << "Unlike the training phase, all the scopes and "
"variables will be reused to save the allocation "
"overhead.";
LOG_FIRST_N(WARNING, 5) << "Please re-compile the inference library by "
"setting the cmake flag ON_INFER=ON if you are "
"running Paddle Inference";
#endif // PADDLE_ON_INFERENCE
for (auto &op : ops_) {
VLOG(3) << std::this_thread::get_id() << " run " << op->Type()
......
paddle/fluid/framework/ngraph_bridge.cc
浏览文件 @ 92da467c
......@@ -32,8 +32,11 @@ std::map<std::string,
std::string, std::shared_ptr<ngraph::Node>>>)>>
NgraphBridge::NG_NODE_MAP = {
{"fill_constant", paddle::operators::ngraphs::BuildFillConstantNode},
{"mean", paddle::operators::ngraphs::BuildMeanNode},
{"mean_grad", paddle::operators::ngraphs::BuildMeanGradNode},
{"mul", paddle::operators::ngraphs::BuildMulNode},
{"mul_grad", paddle::operators::ngraphs::BuildMulGradNode},
{"scale", paddle::operators::ngraphs::BuildScaleNode},
{"relu", paddle::operators::ngraphs::BuildUnaryNode<ngraph::op::Relu>},
{"tanh", paddle::operators::ngraphs::BuildUnaryNode<ngraph::op::Tanh>},
{"top_k", paddle::operators::ngraphs::BuildTopKNode}};
......
paddle/fluid/framework/ngraph_operator.cc
浏览文件 @ 92da467c
......@@ -539,7 +539,7 @@ void NgraphEngine::Run(const Scope& scope, const platform::Place& place) const {
}
}
backend_->call(ngraph_function_, t_out, t_in);
backend_->call(backend_->compile(ngraph_function_), t_out, t_in);
} // NgraphEngine::RunImpl
} // namespace framework
} // namespace paddle
paddle/fluid/framework/parallel_executor.cc
浏览文件 @ 92da467c
......@@ -193,15 +193,14 @@ ParallelExecutor::ParallelExecutor(
const std::unordered_set<std::string> &bcast_vars,
const ProgramDesc &main_program, const std::string &loss_var_name,
Scope *scope, const std::vector<Scope *> &local_scopes,
const ExecutionStrategy &exec_strategy, const BuildStrategy &build_strategy,
size_t num_trainers, size_t trainer_id)
const ExecutionStrategy &exec_strategy, const BuildStrategy &build_strategy)
: member_(new ParallelExecutorPrivate(places)) {
member_->global_scope_ = scope;
member_->use_cuda_ = exec_strategy.use_cuda_;
member_->build_strategy_ = build_strategy;
member_->use_all_reduce_ =
build_strategy.reduce_ == BuildStrategy::ReduceStrategy::kAllReduce;
member_->nranks_ = num_trainers * places.size();
member_->nranks_ = build_strategy.num_trainers_ * places.size();
if (!member_->use_all_reduce_) {
PADDLE_ENFORCE(places.size() > 1,
......@@ -253,7 +252,8 @@ ParallelExecutor::ParallelExecutor(
}
member_->nccl_ctxs_.reset(new platform::NCCLContextMap(
member_->places_, nccl_id, num_trainers, trainer_id));
member_->places_, nccl_id, build_strategy.num_trainers_,
build_strategy.trainer_id_));
#else
PADDLE_THROW("Not compiled with CUDA");
#endif
......
paddle/fluid/framework/parallel_executor.h
浏览文件 @ 92da467c
......@@ -50,8 +50,7 @@ class ParallelExecutor {
const std::string &loss_var_name, Scope *scope,
const std::vector<Scope *> &local_scopes,
const ExecutionStrategy &exec_strategy,
const BuildStrategy &build_strategy,
size_t num_trainers = 1, size_t trainer_id = 0);
const BuildStrategy &build_strategy);
~ParallelExecutor();
......
paddle/fluid/framework/scope.cc
浏览文件 @ 92da467c
......@@ -87,11 +87,12 @@ Variable* Scope::Var(const std::string& name) {
}
Variable* Scope::Var(std::string* name) {
auto new_name = string::Sprintf("%p.%d", this, vars_.size());
SCOPE_VARS_WRITER_LOCK
auto new_name = std::to_string(reinterpret_cast<uintptr_t>(this)) + "." +
std::to_string(vars_.size());
if (name != nullptr) {
*name = new_name;
}
SCOPE_VARS_WRITER_LOCK
return VarInternal(new_name);
}
......
paddle/fluid/framework/var_type_traits.cc
浏览文件 @ 92da467c
......@@ -105,13 +105,15 @@ struct VarIdToTypeIndexMapHolder {
} // namespace detail
const std::type_index &ToTypeIndex(int var_id) {
const std::type_index &VarTraitIdToTypeIndex(int var_id) {
return detail::VarIdToTypeIndexMapHolder::ToTypeIndex(var_id);
}
const char *ToTypeName(int var_id) { return ToTypeIndex(var_id).name(); }
const char *ToTypeName(int var_id) {
return VarTraitIdToTypeIndex(var_id).name();
}
int ToTypeId(const std::type_index &type) {
int TypeIndexToVarTraitId(const std::type_index &type) {
return detail::VarIdToTypeIndexMapHolder::ToTypeId(type);
}
......
paddle/fluid/framework/var_type_traits.h
浏览文件 @ 92da467c
......@@ -66,8 +66,8 @@ namespace paddle {
namespace framework {
const char *ToTypeName(int var_id);
const std::type_index &ToTypeIndex(int var_id);
int ToTypeId(const std::type_index &type);
const std::type_index &VarTraitIdToTypeIndex(int var_id);
int TypeIndexToVarTraitId(const std::type_index &type);
namespace detail {
......
paddle/fluid/framework/var_type_traits_test.cc
浏览文件 @ 92da467c
......@@ -45,10 +45,11 @@ struct TypeIndexChecker {
constexpr auto kId = VarTypeTrait<Type>::kId;
std::type_index actual_type(typeid(Type));
EXPECT_EQ(std::string(ToTypeName(kId)), std::string(actual_type.name()));
EXPECT_EQ(ToTypeIndex(kId), actual_type);
EXPECT_EQ(ToTypeId(actual_type), kId);
EXPECT_EQ(ToTypeIndex(ToTypeId(actual_type)), actual_type);
EXPECT_EQ(ToTypeId(ToTypeIndex(kId)), kId);
EXPECT_EQ(VarTraitIdToTypeIndex(kId), actual_type);
EXPECT_EQ(TypeIndexToVarTraitId(actual_type), kId);
EXPECT_EQ(VarTraitIdToTypeIndex(TypeIndexToVarTraitId(actual_type)),
actual_type);
EXPECT_EQ(TypeIndexToVarTraitId(VarTraitIdToTypeIndex(kId)), kId);
EXPECT_TRUE(var_id_set->count(kId) == 0); // NOLINT
EXPECT_TRUE(type_index_set->count(actual_type) == 0); // NOLINT
......
paddle/fluid/imperative/layer.h
浏览文件 @ 92da467c
......@@ -77,6 +77,7 @@ class PreparedOp {
framework::OperatorWithKernel::OpKernelFunc func;
platform::DeviceContext* dev_ctx;
};
class OpBase;
class VarBase {
......
paddle/fluid/inference/analysis/analyzer_tester.cc
浏览文件 @ 92da467c
......@@ -80,8 +80,8 @@ void TestWord2vecPrediction(const std::string& model_path) {
i++) {
LOG(INFO) << "data: " << static_cast<float*>(outputs.front().data.data())[i]
<< " result: " << result[i];
PADDLE_ENFORCE(static_cast<float*>(outputs.front().data.data())[i],
result[i]);
EXPECT_NEAR(static_cast<float*>(outputs.front().data.data())[i], result[i],
1e-3);
}
}
......
paddle/fluid/inference/analysis/argument.h
浏览文件 @ 92da467c
......@@ -123,8 +123,6 @@ struct Argument {
DECL_ARGUMENT_FIELD(use_gpu, UseGPU, bool);
DECL_ARGUMENT_FIELD(gpu_device_id, GPUDeviceId, int);
DECL_ARGUMENT_FIELD(use_tensorrt, UseTensorRT, bool);
DECL_ARGUMENT_FIELD(tensorrt_node_teller, TensorRtNodeTeller,
std::function<bool(const framework::ir::Node*)>);
DECL_ARGUMENT_FIELD(tensorrt_max_batch_size, TensorRtMaxBatchSize, int);
DECL_ARGUMENT_FIELD(tensorrt_workspace_size, TensorRtWorkspaceSize, int);
DECL_ARGUMENT_FIELD(tensorrt_min_subgraph_size, TensorRtMinSubgraphSize, int);
......
paddle/fluid/inference/analysis/ir_pass_manager.cc
浏览文件 @ 92da467c
......@@ -49,13 +49,6 @@ void IRPassManager::CreatePasses(Argument *argument,
for (const std::string &pass_name : passes) {
auto pass = framework::ir::PassRegistry::Instance().Get(pass_name);
// Set some pass attributes.
if (pass_name == "ir_analysis_pass") {
pass->Set("tensorrt_node_teller",
new SubgraphDetector::NodeInsideSubgraphTeller(
argument->tensorrt_node_teller()));
}
if (pass_name == "graph_viz_pass") {
std::string dot_file_path = std::to_string(pass_num) + "_ir_" +
(pre_pass.empty() ? "origin" : pre_pass) +
......@@ -70,9 +63,6 @@ void IRPassManager::CreatePasses(Argument *argument,
}
if (pass_name == "tensorrt_subgraph_pass") {
PADDLE_ENFORCE(argument->tensorrt_node_teller_valid());
pass->SetNotOwned("tensorrt_node_teller",
argument->tensorrt_node_teller_ptr());
pass->Set("workspace_size", new int(argument->tensorrt_workspace_size()));
pass->Set("max_batch_size", new int(argument->tensorrt_max_batch_size()));
pass->Set("min_subgraph_size",
......
paddle/fluid/inference/analysis/ir_passes/CMakeLists.txt
浏览文件 @ 92da467c
cc_library(subgraph_detector SRCS subgraph_detector.cc DEPS proto_desc)
cc_library(tensorrt_subgraph_pass SRCS tensorrt_subgraph_pass.cc DEPS subgraph_detector)
set(analysis_deps ${analysis_deps}
subgraph_detector tensorrt_subgraph_pass
CACHE INTERNAL "")
set(pass_file ${PADDLE_BINARY_DIR}/paddle/fluid/inference/api/paddle_inference_pass.h)
file(APPEND ${pass_file} "USE_PASS(tensorrt_subgraph_pass);\n")
set(INFER_IR_PASSES ${INFER_IR_PASSES} tensorrt_subgraph_pass CACHE INTERNAL "")
if (TENSORRT_FOUND)
cc_library(tensorrt_subgraph_pass SRCS tensorrt_subgraph_pass.cc DEPS subgraph_detector tensorrt_op_teller)
set(analysis_deps ${analysis_deps}
subgraph_detector tensorrt_subgraph_pass
CACHE INTERNAL "")
set(pass_file ${PADDLE_BINARY_DIR}/paddle/fluid/inference/api/paddle_inference_pass.h)
file(APPEND ${pass_file} "USE_PASS(tensorrt_subgraph_pass);\n")
set(INFER_IR_PASSES ${INFER_IR_PASSES} tensorrt_subgraph_pass CACHE INTERNAL "")
endif()
paddle/fluid/inference/analysis/ir_passes/tensorrt_subgraph_pass.cc
浏览文件 @ 92da467c
......@@ -20,6 +20,7 @@
#include "paddle/fluid/inference/analysis/helper.h"
#include "paddle/fluid/inference/analysis/ir_passes/subgraph_detector.h"
#include "paddle/fluid/inference/analysis/ir_passes/tensorrt_subgraph_pass.h"
#include "paddle/fluid/inference/tensorrt/op_teller.h"
namespace paddle {
namespace inference {
......@@ -35,8 +36,10 @@ std::unique_ptr<framework::ir::Graph> analysis::TensorRtSubgraphPass::ApplyImpl(
std::unique_ptr<framework::ir::Graph> graph) const {
framework::ir::FusePassBase::Init("tensorrt_subgraph_pass", graph.get());
auto teller =
Get<SubgraphDetector::NodeInsideSubgraphTeller>("tensorrt_node_teller");
auto teller = [](const framework::ir::Node *node) {
if (!node->IsOp() || !node->Op()) return false;
return tensorrt::OpTeller::Global().Tell(node->Op()->Type(), *node->Op());
};
SubGraphFuser fuser(graph.get(), teller,
Get<int>("min_subgraph_size") /*min subgraph size*/);
......@@ -232,7 +235,6 @@ std::vector<std::string> ExtractParameters(
REGISTER_PASS(tensorrt_subgraph_pass,
paddle::inference::analysis::TensorRtSubgraphPass)
.RequirePassAttr("tensorrt_node_teller")
.RequirePassAttr("max_batch_size")
.RequirePassAttr("workspace_size")
.RequirePassAttr("min_subgraph_size");
paddle/fluid/inference/analysis/passes/CMakeLists.txt
浏览文件 @ 92da467c
......@@ -7,4 +7,5 @@ set(analysis_deps ${analysis_deps}
ir_graph_build_pass
ir_analysis_pass
analysis_passes
subgraph_detector
CACHE INTERNAL "")
paddle/fluid/inference/analysis/passes/ir_analysis_compose_pass.cc
浏览文件 @ 92da467c
......@@ -27,9 +27,6 @@ namespace analysis {
void IrAnalysisComposePass::RunImpl(Argument *argument) {
ARGUMENT_CHECK_FIELD(argument, ir_analysis_passes);
if (argument->use_tensorrt_valid() && argument->use_tensorrt()) {
InitTensorRTAttrs(argument);
}
ApplyIrPasses(argument);
CollectFusionStatis(argument);
}
......@@ -38,26 +35,6 @@ std::string IrAnalysisComposePass::repr() const {
return "ir-analysis-compose-pass";
}
void IrAnalysisComposePass::InitTensorRTAttrs(Argument *argument) {
if (argument->use_tensorrt_valid() && argument->use_tensorrt()) {
LOG(INFO) << "Initing TensorRT pass";
argument->SetTensorRtNodeTeller([](const framework::ir::Node *node) {
std::unordered_set<std::string> teller_set(
{"mul", "conv2d", "pool2d", "relu", "softmax", "sigmoid",
"depthwise_conv2d", "batch_norm", "concat", "tanh", "pad",
"elementwise_add", "elementwise_mul", "dropout", "split", "prelu",
"conv2d_transpose", "leaky_relu"});
if (!node->IsOp()) return false;
if (teller_set.count(node->Op()->Type())) {
return true;
} else {
return false;
}
});
}
}
void IrAnalysisComposePass::ApplyIrPasses(Argument *argument) {
std::vector<std::string> passes({
"ir_graph_build_pass", "ir_analysis_pass",
......
paddle/fluid/inference/analysis/passes/ir_analysis_compose_pass.h
浏览文件 @ 92da467c
......@@ -33,8 +33,6 @@ class IrAnalysisComposePass : public AnalysisPass {
std::string repr() const override;
private:
void InitTensorRTAttrs(Argument* argument);
void ApplyIrPasses(Argument* argument);
void CollectFusionStatis(Argument* argument);
......
paddle/fluid/inference/api/analysis_config.cc
浏览文件 @ 92da467c
......@@ -14,86 +14,101 @@
#include "paddle/fluid/framework/lod_tensor.h"
#include "paddle/fluid/framework/scope.h"
#include "paddle/fluid/inference/api/paddle_analysis_config.h"
#include "paddle/fluid/inference/api/paddle_inference_api.h"
#include "paddle/fluid/inference/api/paddle_pass_builder.h"
#include "paddle/fluid/platform/enforce.h"
#include "paddle_pass_builder.h" // NOLINT
#include "paddle/fluid/platform/gpu_info.h"
namespace paddle {
PassStrategy *contrib::AnalysisConfig::pass_builder() const {
PADDLE_ENFORCE(
pass_builder_.get(),
"Should call constructor first, that will init the pass_builder_.");
if (!pass_builder_.get()) {
if (use_gpu_) {
LOG(INFO) << "Create GPU IR passes";
pass_builder_.reset(new GpuPassStrategy);
} else {
LOG(INFO) << "Create CPU IR passes";
pass_builder_.reset(new CpuPassStrategy);
}
} else if (pass_builder_->use_gpu() ^ use_gpu()) {
LOG(WARNING) << "The use_gpu flag is not compatible between Config and "
"PassBuilder, the flags are "
<< use_gpu() << " " << pass_builder_->use_gpu();
LOG(WARNING) << "Please make them compatible, still use the existing "
"PassBuilder.";
}
return pass_builder_.get();
}
contrib::AnalysisConfig::AnalysisConfig(bool use_gpu) {
this->use_gpu = use_gpu;
if (use_gpu) {
pass_builder_.reset(new GpuPassStrategy);
} else {
pass_builder_.reset(new CpuPassStrategy);
}
contrib::AnalysisConfig::AnalysisConfig(const std::string &model_dir) {
model_dir_ = model_dir;
}
contrib::AnalysisConfig::AnalysisConfig(const std::string &prog_file,
const std::string &params_file) {
prog_file_ = prog_file;
params_file_ = params_file;
}
void contrib::AnalysisConfig::SetModel(const std::string &prog_file_path,
const std::string &params_file_path) {
prog_file_ = prog_file_path;
params_file_ = params_file_path;
}
void contrib::AnalysisConfig::EnableUseGpu(uint64_t memory_pool_init_size_mb,
int device_id) {
#ifdef PADDLE_WITH_CUDA
use_gpu_ = true;
memory_pool_init_size_mb_ = memory_pool_init_size_mb;
device_id_ = device_id;
#else
LOG(ERROR) << "Please compile with gpu to EnableGpu";
use_gpu_ = false;
#endif
}
void contrib::AnalysisConfig::DisableGpu() { use_gpu_ = false; }
contrib::AnalysisConfig::AnalysisConfig(const contrib::AnalysisConfig &other) {
// fields from Config
model_dir = other.model_dir;
// fields from NativeConfig
use_gpu = other.use_gpu;
device = other.device;
fraction_of_gpu_memory = other.fraction_of_gpu_memory;
prog_file = other.prog_file;
param_file = other.param_file;
specify_input_name = other.specify_input_name;
cpu_math_library_num_threads_ = other.cpu_math_library_num_threads_;
// fields from this.
enable_ir_optim = other.enable_ir_optim;
// For mkldnn
use_mkldnn_ = other.use_mkldnn_;
mkldnn_enabled_op_types_ = other.mkldnn_enabled_op_types_;
use_feed_fetch_ops = other.use_feed_fetch_ops;
use_tensorrt_ = other.use_tensorrt_;
tensorrt_max_batchsize_ = other.tensorrt_max_batchsize_;
tensorrt_workspace_size_ = other.tensorrt_workspace_size_;
tensorrt_min_subgraph_size_ = other.tensorrt_min_subgraph_size_;
model_from_memory_ = other.model_from_memory_;
if (use_gpu) {
#define CP_MEMBER(member__) member__ = other.member__;
// Model related.
CP_MEMBER(model_dir_);
CP_MEMBER(prog_file_);
CP_MEMBER(params_file_);
CP_MEMBER(model_from_memory_); // the memory model reuses prog_file_ and
// params_file_ fields.
// Gpu releated.
CP_MEMBER(use_gpu_);
CP_MEMBER(device_id_);
CP_MEMBER(memory_pool_init_size_mb_);
// TensorRT releated.
CP_MEMBER(use_tensorrt_);
CP_MEMBER(tensorrt_workspace_size_);
CP_MEMBER(tensorrt_max_batchsize_);
CP_MEMBER(tensorrt_min_subgraph_size_);
// MKLDNN releated.
CP_MEMBER(use_mkldnn_);
CP_MEMBER(mkldnn_enabled_op_types_);
// Ir related.
CP_MEMBER(enable_ir_optim_);
CP_MEMBER(use_feed_fetch_ops_);
CP_MEMBER(ir_debug_);
CP_MEMBER(specify_input_name_);
CP_MEMBER(cpu_math_library_num_threads_);
CP_MEMBER(serialized_info_cache_);
if (use_gpu_) {
pass_builder_.reset(new GpuPassStrategy(
*static_cast<GpuPassStrategy *>(other.pass_builder())));
} else {
pass_builder_.reset(new CpuPassStrategy(
*static_cast<CpuPassStrategy *>(other.pass_builder())));
}
}
contrib::AnalysisConfig::AnalysisConfig(contrib::AnalysisConfig &&other) {
// fields from Config
model_dir = other.model_dir;
// fields from NativeConfig
use_gpu = other.use_gpu;
device = other.device;
fraction_of_gpu_memory = other.fraction_of_gpu_memory;
prog_file = other.prog_file;
param_file = other.param_file;
specify_input_name = other.specify_input_name;
cpu_math_library_num_threads_ = other.cpu_math_library_num_threads_;
// fields from this.
enable_ir_optim = other.enable_ir_optim;
// For mkldnn
use_mkldnn_ = other.use_mkldnn_;
mkldnn_enabled_op_types_ = other.mkldnn_enabled_op_types_;
use_feed_fetch_ops = other.use_feed_fetch_ops;
use_tensorrt_ = other.use_tensorrt_;
tensorrt_max_batchsize_ = other.tensorrt_max_batchsize_;
tensorrt_workspace_size_ = other.tensorrt_workspace_size_;
tensorrt_min_subgraph_size_ = other.tensorrt_min_subgraph_size_;
model_from_memory_ = other.model_from_memory_;
pass_builder_ = std::move(other.pass_builder_);
#undef CP_MEMBER
}
void contrib::AnalysisConfig::EnableMKLDNN() {
......@@ -112,17 +127,90 @@ void contrib::AnalysisConfig::EnableTensorRtEngine(int workspace_size,
use_tensorrt_ = true;
tensorrt_workspace_size_ = workspace_size;
tensorrt_max_batchsize_ = max_batch_size;
tensorrt_min_subgraph_size_ = min_subgraph_size;
// Append after the conv+affine_channel fuse pass.
pass_builder()->InsertPass(3, "tensorrt_subgraph_pass");
}
void contrib::AnalysisConfig::Update() {
auto info = SerializeInfoCache();
if (info == serialized_info_cache_) return;
if (use_gpu_) {
pass_builder_.reset(new GpuPassStrategy);
} else {
pass_builder_.reset(new CpuPassStrategy);
}
if (use_tensorrt_) {
if (!use_gpu_) {
LOG(ERROR)
<< "TensorRT engine is not available when EnableGpu() not actived.";
} else {
// Append after the infer_clean pass.
pass_builder()->InsertPass(1, "tensorrt_subgraph_pass");
}
}
if (use_mkldnn_) {
if (!enable_ir_optim_) {
LOG(ERROR)
<< "EnableMKLDNN() only works when IR optimization is enabled.";
}
#ifdef PADDLE_WITH_MKLDNN
pass_builder()->EnableMKLDNN();
use_mkldnn_ = true;
#else
LOG(ERROR) << "Please compile with MKLDNN first to use MKLDNN";
use_mkldnn_ = false;
#endif
}
if (ir_debug_) {
pass_builder()->TurnOnDebug();
}
}
std::string contrib::AnalysisConfig::SerializeInfoCache() {
std::stringstream ss;
ss << use_gpu_;
ss << memory_pool_init_size_mb_;
ss << use_tensorrt_;
ss << tensorrt_workspace_size_;
ss << tensorrt_max_batchsize_;
ss << use_mkldnn_;
ss << enable_ir_optim_;
ss << use_feed_fetch_ops_;
ss << ir_debug_;
return ss.str();
}
void contrib::AnalysisConfig::SetCpuMathLibraryNumThreads(
int cpu_math_library_num_threads) {
cpu_math_library_num_threads_ = cpu_math_library_num_threads;
}
float contrib::AnalysisConfig::fraction_of_gpu_memory_for_pool() const {
#ifdef PADDLE_WITH_CUDA
// Get the GPU memory details and calculate the fraction of memory for the
// GPU memory pool.
size_t gpu_used, gpu_available;
platform::GpuMemoryUsage(&gpu_used, &gpu_available);
double total_gpu_memory = (gpu_used + gpu_available) / 1024. / 1024.;
float fraction_of_gpu_memory =
static_cast<double>(memory_pool_init_size_mb()) / total_gpu_memory;
return fraction_of_gpu_memory;
#else
return 0.;
#endif
}
void contrib::AnalysisConfig::SetModelBuffer(const char *prog_buffer,
size_t prog_buffer_size,
const char *param_buffer,
size_t param_buffer_size) {
prog_file = std::string(prog_buffer, prog_buffer + prog_buffer_size);
param_file = std::string(param_buffer, param_buffer + param_buffer_size);
prog_file_ = std::string(prog_buffer, prog_buffer + prog_buffer_size);
params_file_ = std::string(param_buffer, param_buffer + param_buffer_size);
model_from_memory_ = true;
}
......
paddle/fluid/inference/api/analysis_predictor.cc
浏览文件 @ 92da467c
......@@ -33,6 +33,7 @@
#include "paddle/fluid/inference/utils/singleton.h"
#include "paddle/fluid/memory/memcpy.h"
#include "paddle/fluid/platform/cpu_helper.h"
#include "paddle/fluid/platform/gpu_info.h"
#include "paddle/fluid/platform/profiler.h"
DECLARE_bool(profile);
......@@ -59,8 +60,8 @@ bool AnalysisPredictor::Init(
if (FLAGS_profile) {
LOG(WARNING) << "Profiler is actived, might affect the performance";
LOG(INFO) << "You can turn off by set gflags '-profile false'";
auto tracking_device = config_.use_gpu ? platform::ProfilerState::kAll
: platform::ProfilerState::kCPU;
auto tracking_device = config_.use_gpu() ? platform::ProfilerState::kAll
: platform::ProfilerState::kCPU;
platform::EnableProfiler(tracking_device);
}
......@@ -112,7 +113,7 @@ bool AnalysisPredictor::PrepareProgram(
// Optimize the program, and load parameters and modify them in the
// scope_.
// This will change the scope_ address.
if (config_.enable_ir_optim) {
if (config_.ir_optim()) {
status_ir_optim_enabled_ = true;
OptimizeInferenceProgram();
} else {
......@@ -140,9 +141,9 @@ bool AnalysisPredictor::PrepareProgram(
return true;
}
bool AnalysisPredictor::CreateExecutor() {
if (config_.use_gpu) {
if (config_.use_gpu_) {
status_use_gpu_ = true;
place_ = paddle::platform::CUDAPlace(config_.device);
place_ = paddle::platform::CUDAPlace(config_.device_id_);
} else {
place_ = paddle::platform::CPUPlace();
}
......@@ -151,7 +152,7 @@ bool AnalysisPredictor::CreateExecutor() {
}
bool AnalysisPredictor::PrepareExecutor() {
executor_->Prepare(sub_scope_, *inference_program_, 0,
config_.use_feed_fetch_ops);
config_.use_feed_fetch_ops_);
PADDLE_ENFORCE_NOT_NULL(sub_scope_);
......@@ -250,7 +251,7 @@ bool AnalysisPredictor::SetFeed(const std::vector<PaddleTensor> &inputs,
}
input.set_lod(lod);
int idx = -1;
if (config_.specify_input_name) {
if (config_.specify_input_name_) {
auto name = inputs[i].name;
if (feed_names_.find(name) == feed_names_.end()) {
LOG(ERROR) << "feed names from program do not have name: [" << name
......@@ -314,22 +315,22 @@ bool AnalysisPredictor::GetFetch(std::vector<PaddleTensor> *outputs,
void AnalysisPredictor::OptimizeInferenceProgram() {
status_program_optimized_ = true;
argument_.SetUseGPU(config_.use_gpu);
argument_.SetGPUDeviceId(config_.device);
argument_.SetUseGPU(config_.use_gpu());
argument_.SetGPUDeviceId(config_.gpu_device_id());
argument_.SetModelFromMemory(config_.model_from_memory_);
// Analyze inference_program
if (!config_.model_dir.empty()) {
argument_.SetModelDir(config_.model_dir);
if (!config_.model_dir().empty()) {
argument_.SetModelDir(config_.model_dir());
} else {
PADDLE_ENFORCE(
!config_.param_file.empty(),
!config_.params_file().empty(),
"Either model_dir or (param_file, prog_file) should be set.");
PADDLE_ENFORCE(!config_.prog_file.empty());
argument_.SetModelProgramPath(config_.prog_file);
argument_.SetModelParamsPath(config_.param_file);
PADDLE_ENFORCE(!config_.prog_file().empty());
argument_.SetModelProgramPath(config_.prog_file());
argument_.SetModelParamsPath(config_.params_file());
}
if (config_.use_gpu && config_.use_tensorrt_) {
if (config_.use_gpu() && config_.tensorrt_engine_enabled()) {
argument_.SetUseTensorRT(true);
argument_.SetTensorRtWorkspaceSize(config_.tensorrt_workspace_size_);
argument_.SetTensorRtMaxBatchSize(config_.tensorrt_max_batchsize_);
......@@ -341,7 +342,7 @@ void AnalysisPredictor::OptimizeInferenceProgram() {
}
auto passes = config_.pass_builder()->AllPasses();
if (!config_.enable_ir_optim) passes.clear();
if (!config_.ir_optim()) passes.clear();
argument_.SetIrAnalysisPasses(passes);
argument_.SetScopeNotOwned(const_cast<framework::Scope *>(scope_.get()));
Analyzer().Run(&argument_);
......@@ -358,18 +359,26 @@ template <>
std::unique_ptr<PaddlePredictor> CreatePaddlePredictor<
AnalysisConfig, PaddleEngineKind::kAnalysis>(const AnalysisConfig &config) {
VLOG(3) << "create AnalysisConfig";
if (config.use_gpu) {
if (config.use_gpu()) {
// 1. GPU memeroy
PADDLE_ENFORCE_GT(
config.fraction_of_gpu_memory, 0.f,
"fraction_of_gpu_memory in the config should be set to range (0., 1.]");
PADDLE_ENFORCE_GE(config.device, 0, "Invalid device id %d", config.device);
PADDLE_ENFORCE_GT(config.memory_pool_init_size_mb(), 0.f);
PADDLE_ENFORCE_GE(config.gpu_device_id(), 0, "Invalid device id %d",
config.gpu_device_id());
std::vector<std::string> flags;
if (config.fraction_of_gpu_memory >= 0.0f ||
config.fraction_of_gpu_memory <= 0.95f) {
float fraction_of_gpu_memory = config.fraction_of_gpu_memory_for_pool();
if (fraction_of_gpu_memory > 0.95f) {
LOG(ERROR)
<< "Allocate too much memory for the GPU memory pool, assigned "
<< config.memory_pool_init_size_mb() << " MB";
LOG(ERROR)
<< "Try to shink the value by setting AnalysisConfig::EnableGpu(...)";
}
if (fraction_of_gpu_memory >= 0.0f || fraction_of_gpu_memory <= 0.95f) {
flags.push_back("dummpy");
std::string flag = "--fraction_of_gpu_memory_to_use=" +
std::to_string(config.fraction_of_gpu_memory);
std::to_string(fraction_of_gpu_memory);
flags.push_back(flag);
VLOG(3) << "set flag: " << flag;
framework::InitGflags(flags);
......@@ -443,22 +452,22 @@ bool AnalysisPredictor::ZeroCopyRun() {
bool AnalysisPredictor::LoadProgramDesc() {
// Initialize the inference program
std::string filename;
if (!config_.model_dir.empty()) {
filename = config_.model_dir + "/__model__";
} else if (!config_.prog_file.empty() && !config_.param_file.empty()) {
if (!config_.model_dir().empty()) {
filename = config_.model_dir() + "/__model__";
} else if (!config_.prog_file().empty() && !config_.params_file().empty()) {
// All parameters are saved in a single file.
// The file names should be consistent with that used
// in Python API `fluid.io.save_inference_model`.
filename = config_.prog_file;
filename = config_.prog_file();
} else {
if (config_.model_dir.empty() && config_.prog_file.empty()) {
if (config_.model_dir().empty() && config_.prog_file().empty()) {
LOG(ERROR)
<< "Either model_dir or (prog_file, param_file) should be set.";
return false;
}
LOG(ERROR) << string::Sprintf(
"not valid model path '%s' or program path '%s'.", config_.model_dir,
config_.param_file);
"not valid model path '%s' or program path '%s'.", config_.model_dir(),
config_.params_file());
return false;
}
......@@ -478,7 +487,7 @@ bool AnalysisPredictor::LoadProgramDesc() {
proto.ParseFromString(pb_content);
} else {
proto.ParseFromString(config_.prog_file);
proto.ParseFromString(config_.prog_file());
}
inference_program_.reset(new framework::ProgramDesc(proto));
return true;
......@@ -508,27 +517,27 @@ bool AnalysisPredictor::LoadParameters() {
new_var->SetLoDLevel(var->GetLoDLevel());
new_var->SetPersistable(true);
if (!config_.param_file.empty()) {
if (!config_.params_file().empty()) {
params.push_back(new_var->Name());
} else {
// append_op
framework::OpDesc *op = load_block->AppendOp();
op->SetType("load");
op->SetOutput("Out", {new_var->Name()});
op->SetAttr("file_path", {config_.model_dir + "/" + new_var->Name()});
op->SetAttr("file_path", {config_.model_dir() + "/" + new_var->Name()});
op->CheckAttrs();
}
}
}
if (!config_.param_file.empty()) {
if (!config_.params_file().empty()) {
// sort paramlist to have consistent ordering
std::sort(params.begin(), params.end());
// append just the load_combine op
framework::OpDesc *op = load_block->AppendOp();
op->SetType("load_combine");
op->SetOutput("Out", params);
op->SetAttr("file_path", {config_.param_file});
op->SetAttr("file_path", {config_.params_file()});
op->CheckAttrs();
}
......
paddle/fluid/inference/api/analysis_predictor.h
浏览文件 @ 92da467c
......@@ -35,8 +35,11 @@ using framework::proto::ProgramDesc;
using framework::NaiveExecutor;
using contrib::AnalysisConfig;
/* This predictor is based on the original native predictor with IR and Analysis
* support. It will optimize IR and Parameters in the runtime.
/** \brief This predictor is based on the original native predictor with IR and
* Analysis support.
*
* It will optimize IR and Parameters in the runtime.
*
* TODO(Superjomn) Replace the Navive predictor?
*/
class AnalysisPredictor : public PaddlePredictor {
......
paddle/fluid/inference/api/analysis_predictor_tester.cc
浏览文件 @ 92da467c
......@@ -25,9 +25,9 @@ namespace paddle {
using contrib::AnalysisConfig;
TEST(AnalysisPredictor, analysis_off) {
AnalysisConfig config(false);
config.model_dir = FLAGS_dirname;
config.enable_ir_optim = false;
AnalysisConfig config;
config.SetModel(FLAGS_dirname);
config.SwitchIrOptim(false);
auto _predictor = CreatePaddlePredictor<AnalysisConfig>(config);
auto* predictor = static_cast<AnalysisPredictor*>(_predictor.get());
......@@ -55,14 +55,14 @@ TEST(AnalysisPredictor, analysis_off) {
}
TEST(AnalysisPredictor, analysis_on) {
AnalysisConfig config;
config.SetModel(FLAGS_dirname);
config.SwitchIrOptim(true);
#ifdef PADDLE_WITH_CUDA
AnalysisConfig config(true);
config.fraction_of_gpu_memory = 0.15;
config.EnableUseGpu(100, 0);
#else
AnalysisConfig config;
config.DisableGpu();
#endif
config.model_dir = FLAGS_dirname;
config.enable_ir_optim = true;
auto _predictor = CreatePaddlePredictor<AnalysisConfig>(config);
auto* predictor = static_cast<AnalysisPredictor*>(_predictor.get());
......@@ -89,7 +89,8 @@ TEST(AnalysisPredictor, analysis_on) {
}
// compare with NativePredictor
auto naive_predictor = CreatePaddlePredictor<NativeConfig>(config);
auto naive_predictor =
CreatePaddlePredictor<NativeConfig>(config.ToNativeConfig());
std::vector<PaddleTensor> naive_outputs;
ASSERT_TRUE(naive_predictor->Run(inputs, &naive_outputs));
ASSERT_EQ(naive_outputs.size(), 1UL);
......@@ -98,9 +99,8 @@ TEST(AnalysisPredictor, analysis_on) {
TEST(AnalysisPredictor, ZeroCopy) {
AnalysisConfig config;
config.model_dir = FLAGS_dirname;
config.use_feed_fetch_ops = false;
config.SetModel(FLAGS_dirname);
config.SwitchUseFeedFetchOps(false);
auto predictor = CreatePaddlePredictor<AnalysisConfig>(config);
auto w0 = predictor->GetInputTensor("firstw");
......@@ -137,9 +137,9 @@ TEST(AnalysisPredictor, ZeroCopy) {
TEST(AnalysisPredictor, Clone) {
AnalysisConfig config;
config.model_dir = FLAGS_dirname;
config.use_feed_fetch_ops = true;
config.enable_ir_optim = true;
config.SetModel(FLAGS_dirname);
config.SwitchUseFeedFetchOps(true);
config.SwitchIrOptim(true);
std::vector<std::unique_ptr<PaddlePredictor>> predictors;
predictors.emplace_back(CreatePaddlePredictor(config));
......
paddle/fluid/inference/api/api_anakin_engine.h
浏览文件 @ 92da467c
......@@ -19,8 +19,6 @@ limitations under the License. */
#pragma once
#define WITH_ANAKIN
#include <vector>
#include "framework/core/net/net.h"
......
paddle/fluid/inference/api/api_impl.cc
浏览文件 @ 92da467c
......@@ -288,7 +288,7 @@ std::unique_ptr<PaddlePredictor> CreatePaddlePredictor<
VLOG(3) << "create NativePaddlePredictor";
if (config.use_gpu) {
// 1. GPU memeroy
PADDLE_ENFORCE_GT(
PADDLE_ENFORCE_GE(
config.fraction_of_gpu_memory, 0.f,
"fraction_of_gpu_memory in the config should be set to range (0., 1.]");
PADDLE_ENFORCE_GE(config.device, 0, "Invalid device id %d", config.device);
......
paddle/fluid/inference/api/api_impl.h
浏览文件 @ 92da467c
......@@ -19,7 +19,6 @@ limitations under the License. */
#include <memory>
#include <string>
#include <vector>
#include "paddle/fluid/framework/ddim.h"
#include "paddle/fluid/framework/lod_tensor.h"
#include "paddle/fluid/framework/lod_tensor_array.h"
......
paddle/fluid/inference/api/api_impl_tester.cc
浏览文件 @ 92da467c
......@@ -295,7 +295,8 @@ TEST(inference_api_native, image_classification_gpu) {
#endif
TEST(PassBuilder, Delete) {
contrib::AnalysisConfig config(false);
contrib::AnalysisConfig config;
config.DisableGpu();
config.pass_builder()->DeletePass("attention_lstm_fuse_pass");
const auto& passes = config.pass_builder()->AllPasses();
auto it = std::find(passes.begin(), passes.end(), "attention_lstm_fuse_pass");
......
paddle/fluid/inference/api/demo_ci/run.sh
浏览文件 @ 92da467c
......@@ -116,6 +116,10 @@ D
--modeldir=$DATA_DIR/mobilenet/model \
--data=$DATA_DIR/mobilenet/data.txt \
--refer=$DATA_DIR/mobilenet/result.txt
if [ $? -ne 0 ]; then
echo "trt demo trt_mobilenet_demo runs fail."
exit 1
fi
fi
done
set +x
paddle/fluid/inference/api/demo_ci/trt_mobilenet_demo.cc
浏览文件 @ 92da467c
......@@ -36,12 +36,11 @@ namespace demo {
*/
void Main() {
std::unique_ptr<PaddlePredictor> predictor;
paddle::contrib::AnalysisConfig config(true);
config.param_file = FLAGS_modeldir + "/__params__";
config.prog_file = FLAGS_modeldir + "/__model__";
config.device = 0;
paddle::contrib::AnalysisConfig config;
config.EnableUseGpu(100, 0);
config.SetModel(FLAGS_modeldir + "/__model__",
FLAGS_modeldir + "/__params__");
config.EnableTensorRtEngine();
config.fraction_of_gpu_memory = 0.1; // set by yourself
predictor = CreatePaddlePredictor(config);
VLOG(3) << "begin to process data";
......
paddle/fluid/inference/api/demo_ci/vis_demo.cc
浏览文件 @ 92da467c
......@@ -40,15 +40,14 @@ using contrib::AnalysisConfig;
*/
void Main(bool use_gpu) {
std::unique_ptr<PaddlePredictor> predictor, analysis_predictor;
AnalysisConfig config(use_gpu);
config.param_file = FLAGS_modeldir + "/__params__";
config.prog_file = FLAGS_modeldir + "/__model__";
config.device = 0;
if (FLAGS_use_gpu) {
config.fraction_of_gpu_memory = 0.1; // set by yourself
AnalysisConfig config;
if (use_gpu) {
config.EnableUseGpu(100, 0);
}
config.SetModel(FLAGS_modeldir + "/__model__",
FLAGS_modeldir + "/__params__");
predictor = CreatePaddlePredictor<NativeConfig>(config);
predictor = CreatePaddlePredictor<NativeConfig>(config.ToNativeConfig());
analysis_predictor = CreatePaddlePredictor(config);
// Just a single batch of data.
......
paddle/fluid/inference/api/paddle_analysis_config.h
浏览文件 @ 92da467c
......@@ -19,6 +19,8 @@
#include <unordered_set>
#include <vector>
/*! \file */
// Here we include some header files with relative paths, for that in deploy,
// the abstract path of this header file will be changed.
#include "paddle_api.h" // NOLINT
......@@ -34,41 +36,188 @@ class AnalysisPredictor;
namespace contrib {
// NOTE WIP, not stable yet.
struct AnalysisConfig : public NativeConfig {
explicit AnalysisConfig(bool use_gpu = false);
struct AnalysisConfig {
AnalysisConfig() = default;
explicit AnalysisConfig(const AnalysisConfig& other);
explicit AnalysisConfig(AnalysisConfig&& other);
explicit AnalysisConfig(const std::string& model_dir);
explicit AnalysisConfig(const std::string& prog_file,
const std::string& params_file);
// Determine whether to perform graph optimization.
bool enable_ir_optim = true;
/** Set model with a directory.
*/
void SetModel(const std::string& model_dir) { model_dir_ = model_dir; }
/** Set model with two specific pathes for program and parameters.
*/
void SetModel(const std::string& prog_file_path,
const std::string& params_file_path);
/** Set program file path.
*/
void SetProgFile(const std::string& x) { prog_file_ = x; }
/** Set parameter composed file path.
*/
void SetParamsFile(const std::string& x) { params_file_ = x; }
/** Get the model directory path.
*/
const std::string& model_dir() const { return model_dir_; }
/** Get the program file path.
*/
const std::string& prog_file() const { return prog_file_; }
/** Get the composed parameters file.
*/
const std::string& params_file() const { return params_file_; }
// Get a pass builder for customize the passes in IR analysis phase.
PassStrategy* pass_builder() const;
// GPU related.
/**
* \brief Turn on GPU.
* @param memory_pool_init_size_mb initial size of the GPU memory pool in MB.
* @param device_id the GPU card to use (default is 0).
*/
void EnableUseGpu(uint64_t memory_pool_init_size_mb, int device_id = 0);
/** Turn off the GPU.
*/
void DisableGpu();
/** A bool state telling whether the GPU is turned on.
*/
bool use_gpu() const { return use_gpu_; }
/** Get the GPU device id.
*/
int gpu_device_id() const { return device_id_; }
/** Get the initial size in MB of the GPU memory pool.
*/
int memory_pool_init_size_mb() const { return memory_pool_init_size_mb_; }
/** Get the proportion of the initial memory pool size compared to the device.
*/
float fraction_of_gpu_memory_for_pool() const;
/** \brief Control whether to perform IR graph optimization.
*
* If turned off, the AnalysisConfig will act just like a NativeConfig.
*/
void SwitchIrOptim(int x = true) { enable_ir_optim_ = x; }
/** A boolean state tell whether the ir graph optimization is actived.
*/
bool ir_optim() const { return enable_ir_optim_; }
// NOT stable yet.
bool use_feed_fetch_ops{true};
/** \brief INTERNAL Determine whether to use the feed and fetch operators.
* Just for internal development, not stable yet.
* When ZeroCopyTensor is used, this should turned off.
*/
void SwitchUseFeedFetchOps(int x = true) { use_feed_fetch_ops_ = x; }
/** A boolean state telling whether to use the feed and fetch operators.
*/
bool use_feed_fetch_ops_enabled() const { return use_feed_fetch_ops_; }
/** \brief Control whether to specify the inputs' names.
*
* The PaddleTensor type has a `name` member, assign it with the corresponding
* variable name. This is used only when the input PaddleTensors passed to the
* `PaddlePredictor.Run(...)` cannot follow the order in the training phase.
*/
void SwitchSpecifyInputNames(bool x = true) { specify_input_name_ = x; }
/** A boolean state tell whether the input PaddleTensor names specified should
* be used to reorder the inputs in `PaddlePredictor.Run(...)`.
*/
bool specify_input_name() const { return specify_input_name_; }
/**
* \brief Turn on the TensorRT engine.
*
* The TensorRT engine will accelerate some subgraphes in the original Fluid
* computation graph. In some models such as TensorRT50, GoogleNet and so on,
* it gains significant performance acceleration.
*
* @param workspace_size the memory size(in byte) used for TensorRT workspace.
* @param max_batch_size the maximum batch size of this prediction task,
* better set as small as possible, or performance loss.
* @param min_subgrpah_size the minimum TensorRT subgraph size needed, if a
* subgraph is less than this, it will not transfer to TensorRT engine.
*/
void EnableTensorRtEngine(int workspace_size = 1 << 20,
int max_batch_size = 1, int min_subgraph_size = 3);
bool use_tensorrt() const { return use_tensorrt_; }
/** A boolean state telling whether the TensorRT engine is used.
*/
bool tensorrt_engine_enabled() const { return use_tensorrt_; }
/** Control whther to debug IR graph analysis phase.
*/
void SwitchIrDebug(int x = true) { ir_debug_ = x; }
/** Turn on MKLDNN.
*/
void EnableMKLDNN();
bool use_mkldnn() const { return use_mkldnn_; }
/** A boolean state telling whether to use the MKLDNN.
*/
bool mkldnn_enabled() const { return use_mkldnn_; }
/** Set and get the number of cpu math library threads.
*/
void SetCpuMathLibraryNumThreads(int cpu_math_library_num_threads);
/** An int state telling how many threads are used in the CPU math library.
*/
int cpu_math_library_num_threads() const {
return cpu_math_library_num_threads_;
}
/** Transform the AnalysisConfig to NativeConfig.
*/
NativeConfig ToNativeConfig() const {
NativeConfig config;
config.model_dir = model_dir_;
config.prog_file = prog_file_;
config.param_file = params_file_;
config.use_gpu = use_gpu_;
config.device = device_id_;
config.fraction_of_gpu_memory = fraction_of_gpu_memory_for_pool();
config.specify_input_name = specify_input_name_;
return config;
}
/** Specify the operator type list to use MKLDNN acceleration.
* @param op_list the operator type list.
*/
void SetMKLDNNOp(std::unordered_set<std::string> op_list) {
mkldnn_enabled_op_types_ = op_list;
}
// Specify the memory buffer of program and parameter
/** Specify the memory buffer of program and parameter
* @param prog_buffer the memory buffer of program.
* @param prog_buffer_size the size of the data.
* @param params_buffer the memory buffer of the composed parameters file.
* @param params_buffer_size the size of the commposed parameters data.
*/
void SetModelBuffer(const char* prog_buffer, size_t prog_buffer_size,
const char* program_buffer, size_t program_buffer_size);
const char* params_buffer, size_t params_buffer_size);
/** A boolean state telling whether the model is set from the CPU memory.
*/
bool model_from_memory() const { return model_from_memory_; }
friend class ::paddle::AnalysisPredictor;
/** NOTE just for developer, not an official API, easily to be broken.
* Get a pass builder for customize the passes in IR analysis phase.
*/
PassStrategy* pass_builder() const;
protected:
// Update the config.
void Update();
std::string SerializeInfoCache();
protected:
// Model pathes.
std::string model_dir_;
std::string prog_file_;
std::string params_file_;
// GPU releated.
bool use_gpu_{false};
int device_id_{0};
uint64_t memory_pool_init_size_mb_{100}; // initial size is 100MB.
// TensorRT releated.
bool use_tensorrt_{false};
bool use_mkldnn_{false};
std::unordered_set<std::string> mkldnn_enabled_op_types_;
// For workspace_size, refer it from here:
// https://docs.nvidia.com/deeplearning/sdk/tensorrt-developer-guide/index.html#troubleshooting
int tensorrt_workspace_size_;
......@@ -82,17 +231,24 @@ struct AnalysisConfig : public NativeConfig {
// We set this variable to control the minimum number of nodes in the
// subgraph, 3 as default value.
int tensorrt_min_subgraph_size_{3};
std::unique_ptr<PassStrategy> pass_builder_;
bool use_mkldnn_{false};
std::unordered_set<std::string> mkldnn_enabled_op_types_;
bool model_from_memory_{false};
};
// Configurations for Anakin engine.
struct AnakinConfig : public PaddlePredictor::Config {
enum TargetType { NVGPU = 0, X86 };
int device;
std::string model_file;
int max_batch_size{-1};
TargetType target_type;
bool enable_ir_optim_{true};
bool use_feed_fetch_ops_{true};
bool ir_debug_{false};
bool specify_input_name_{false};
int cpu_math_library_num_threads_{1};
// A runtime cache, shouldn't be transferred to others.
std::string serialized_info_cache_;
mutable std::unique_ptr<PassStrategy> pass_builder_;
};
} // namespace contrib
......
paddle/fluid/inference/api/paddle_api.h
浏览文件 @ 92da467c
......@@ -13,61 +13,76 @@
// limitations under the License.
#pragma once
/*! \file paddle_api.h
*/
#include <cassert>
#include <memory>
#include <string>
#include <vector>
/*! \namespace paddle
*/
namespace paddle {
// Data type.
/** paddle data type.
*/
enum PaddleDType {
FLOAT32,
INT64,
// TODO(Superjomn) support more data types if needed.
};
/*
* Memory menage for PaddleTensor.
* The PaddleBuf holds a buffer for data input or output. The memory can be
* allocated by user or by PaddleBuf itself, but in any case, the PaddleBuf
* should be reused for better performance.
/**
*\brief Memory menager for PaddleTensor.
*
* For user allocated memory, the following API can be used:
* - PaddleBuf(void* data, size_t length) to set an external memory by
* specifying
* the memory address and length.
* - Reset(void* data, size_t length) to reset the PaddleBuf with an external
* memory.
* ATTENTION, for user allocated memory, deallocation should be done by users
* externally after the program finished. The PaddleBuf won't do any allocation
* or deallocation.
*The PaddleBuf holds a buffer for data input or output. The memory can be
*allocated by user or by PaddleBuf itself, but in any case, the PaddleBuf
*should be reused for better performance.
*
* To have the PaddleBuf allocate and manage the memory:
* - PaddleBuf(size_t length) will allocate a memory of size `length`.
* - Resize(size_t length) resize the memory to no less than `length`, ATTENTION
* if the allocated memory is larger than `length`, nothing will done.
*For user allocated memory, the following API can be used:
*- PaddleBuf(void* data, size_t length) to set an external memory by
*specifying
* the memory address and length.
*- Reset(void* data, size_t length) to reset the PaddleBuf with an external
*memory.
*ATTENTION, for user allocated memory, deallocation should be done by users
*externally after the program finished. The PaddleBuf won't do any allocation
*or deallocation.
*
*To have the PaddleBuf allocate and manage the memory:
*- PaddleBuf(size_t length) will allocate a memory of size `length`.
*- Resize(size_t length) resize the memory to no less than `length`, ATTENTION
* if the allocated memory is larger than `length`, nothing will done.
*/
class PaddleBuf {
public:
// PaddleBuf allocate memory internally, and manage it.
/** PaddleBuf allocate memory internally, and manage it.
*/
explicit PaddleBuf(size_t length)
: data_(new char[length]), length_(length), memory_owned_(true) {}
// Set external memory, the PaddleBuf won't manage it.
/** Set external memory, the PaddleBuf won't manage it.
*/
PaddleBuf(void* data, size_t length)
: data_(data), length_(length), memory_owned_{false} {}
// Copy only available when memory is managed externally.
/** Copy only available when memory is managed externally.
*/
explicit PaddleBuf(const PaddleBuf&);
// Resize the memory.
/** Resize the memory.
*/
void Resize(size_t length);
// Reset to external memory, with address and length set.
/** Reset to external memory, with address and length set.
*/
void Reset(void* data, size_t length);
// Tell whether the buffer is empty.
/** Tell whether the buffer is empty.
*/
bool empty() const { return length_ == 0; }
// Get the memory address.
/** Get the memory address.
*/
void* data() const { return data_; }
// Get the memory length.
/** Get the memory length.
*/
size_t length() const { return length_; }
~PaddleBuf() { Free(); }
......@@ -83,7 +98,8 @@ class PaddleBuf {
bool memory_owned_{true};
};
// Basic input and output data structure for PaddlePredictor.
/** Basic input and output data structure for PaddlePredictor.
*/
struct PaddleTensor {
PaddleTensor() = default;
std::string name; // variable name.
......@@ -94,19 +110,22 @@ struct PaddleTensor {
};
enum class PaddlePlace { kUNK = -1, kCPU, kGPU };
// Tensor without copy, currently only supports AnalysisPredictor.
/** Tensor without copy, currently only supports AnalysisPredictor.
*/
class ZeroCopyTensor {
public:
void Reshape(const std::vector<int>& shape);
// Get the memory in CPU or GPU with specific data type, should Reshape first
// to tell the data size.
// Once can directly call this data to feed the data.
// This is for write the input tensor.
/** Get the memory in CPU or GPU with specific data type, should Reshape first
* to tell the data size.
* Once can directly call this data to feed the data.
* This is for write the input tensor.
*/
template <typename T>
T* mutable_data(PaddlePlace place);
// Get the memory directly, will return the place and memory size by pointer.
// This is for reading the output tensor.
/** Get the memory directly, will return the place and memory size by pointer.
* This is for reading the output tensor.
*/
template <typename T>
T* data(PaddlePlace* place, int* size) const;
......@@ -128,8 +147,7 @@ class ZeroCopyTensor {
void* scope_{nullptr};
};
/*
* A simple Inference API for Paddle.
/** A simple Inference API for Paddle.
*/
class PaddlePredictor {
public:
......@@ -138,18 +156,20 @@ class PaddlePredictor {
PaddlePredictor(const PaddlePredictor&) = delete;
PaddlePredictor& operator=(const PaddlePredictor&) = delete;
// Predict an record.
// The caller should be responsible for allocating and releasing the memory of
// `inputs`. `inputs` should be available until Run returns. Caller should be
// responsible for the output tensor's buffer, either allocated or passed from
// outside.
/** Predict an record.
* The caller should be responsible for allocating and releasing the memory of
* `inputs`. `inputs` should be available until Run returns. Caller should be
* responsible for the output tensor's buffer, either allocated or passed from
* outside.
*/
virtual bool Run(const std::vector<PaddleTensor>& inputs,
std::vector<PaddleTensor>* output_data,
int batch_size = -1) = 0;
// Zero copy input and output optimization.
// Get the input or output tensors, and operate on their memory directly,
// without copy.
/** Zero copy input and output optimization.
* Get the input or output tensors, and operate on their memory directly,
* without copy.
*/
virtual std::unique_ptr<ZeroCopyTensor> GetInputTensor(
const std::string& name) {
return nullptr;
......@@ -160,16 +180,19 @@ class PaddlePredictor {
}
virtual bool ZeroCopyRun() { return false; }
// Clone a predictor that share the model weights, the Cloned predictor should
// be thread-safe.
/** Clone a predictor that share the model weights, the Cloned predictor
* should be thread-safe.
*/
virtual std::unique_ptr<PaddlePredictor> Clone() = 0;
// Destroy the Predictor.
/** Destroy the Predictor.
*/
virtual ~PaddlePredictor() = default;
// The common configs for all the predictors.
/** The common configs for all the predictors.
*/
struct Config {
std::string model_dir; // path to the model directory.
std::string model_dir; /*!< path to the model directory. */
};
};
......@@ -177,17 +200,21 @@ struct NativeConfig : public PaddlePredictor::Config {
// GPU related fields.
bool use_gpu{false};
int device{0};
float fraction_of_gpu_memory{-1.f}; // Change to a float in (0,1] if needed.
float fraction_of_gpu_memory{
-1.f}; /*!< Change to a float in (0,1] if needed. */
// Specify the exact path of program and parameter files.
std::string prog_file;
std::string param_file;
// Specify the variable's name of each input if input tensors don't follow the
// `feeds` and `fetches` of the phase `save_inference_model`.
/** Specify the variable's name of each input if input tensors don't follow
* the
* `feeds` and `fetches` of the phase `save_inference_model`.
*/
bool specify_input_name{false};
// Set and get the number of cpu math library threads.
/** Set and get the number of cpu math library threads.
*/
void SetCpuMathLibraryNumThreads(int cpu_math_library_num_threads) {
cpu_math_library_num_threads_ = cpu_math_library_num_threads;
}
......@@ -201,28 +228,33 @@ struct NativeConfig : public PaddlePredictor::Config {
int cpu_math_library_num_threads_{1};
};
// A factory to help create different predictors.
//
// Usage:
//
// NativeConfig config;
// ... // change the configs.
// auto native_predictor = CreatePaddlePredictor(config);
//
// FOR EXTENSION DEVELOPER:
// Different predictors are designated by config type. Similar configs can be
// merged, but there shouldn't be a huge config containing different fields for
// more than one kind of predictors.
/*! \fn std::unique_ptr<PaddlePredictor> CreatePaddlePredictor(const ConfigT&
* config);
*
* \brief A factory to help create different predictors.
*
* Usage:
*
* NativeConfig config;
* ... // change the configs.
* auto native_predictor = CreatePaddlePredictor(config);
*
* FOR EXTENSION DEVELOPER:
* Different predictors are designated by config type. Similar configs can be
* merged, but there shouldn't be a huge config containing different fields for
* more than one kind of predictors.
*/
template <typename ConfigT>
std::unique_ptr<PaddlePredictor> CreatePaddlePredictor(const ConfigT& config);
// NOTE The following APIs are too trivial, we will discard it in the following
// versions.
/** NOTE The following APIs are too trivial, we will discard it in the following
* versions.
*/
enum class PaddleEngineKind {
kNative = 0, // Use the native Fluid facility.
kAutoMixedTensorRT, // Automatically mix Fluid with TensorRT.
kAnalysis, // More optimization.
kAnakin // Use Anakin for inference, not mature yet.
kNative = 0, /*!< Use the native Fluid facility. */
kAutoMixedTensorRT, /*!< Automatically mix Fluid with TensorRT. */
kAnalysis, /*!< More optimization. */
kAnakin /*!< Use Anakin for inference, not mature yet. */
};
template <typename ConfigT, PaddleEngineKind engine>
......
paddle/fluid/inference/api/paddle_inference_api.h
浏览文件 @ 92da467c
......@@ -26,9 +26,8 @@ limitations under the License. */
#include <string>
#include <vector>
#include "paddle_api.h" // NOLINT
#ifndef WITH_ANAKIN
#include "paddle_analysis_config.h" // NOLINT
#else
#include "paddle_api.h" // NOLINT
#ifdef WITH_ANAKIN
#include "paddle_anakin_config.h" // NOLINT
#endif
paddle/fluid/inference/api/paddle_pass_builder.h
浏览文件 @ 92da467c
......@@ -18,30 +18,39 @@
#include <string>
#include <vector>
/*! \file */
/*! \namespace paddle */
namespace paddle {
/*
* This is a pass builder based on string. It is part of inference API.
/** This is a pass builder based on string. It is part of inference API.
*/
class PaddlePassBuilder {
public:
explicit PaddlePassBuilder(const std::vector<std::string> &passes)
: passes_(passes) {}
/** Append a pass to the end of the passes. */
void AppendPass(const std::string &pass_type);
/** Insert a pass to a specific position.
* @param idx the position to insert.
* @param pass_type the pass key.
*/
void InsertPass(size_t idx, const std::string &pass_type);
// Delete the `idx`-th pass.
/** Delete the `idx`-th pass. */
void DeletePass(size_t idx);
// Delete all the passes that has type `pass_type`.
/** Delete all the passes that has type `pass_type`. */
void DeletePass(const std::string &pass_type);
// Visualize the computation graph after each pass by generating a DOT
// language file, one can draw them with the Graphviz toolkit.
/** Visualize the computation graph after each pass by generating a DOT
* language file, one can draw them with the Graphviz toolkit.
*/
void TurnOnDebug();
// Human-readible information.
/** Human-readible information. */
std::string DebugString();
const std::vector<std::string> &AllPasses() const { return passes_; }
......@@ -50,23 +59,27 @@ class PaddlePassBuilder {
std::vector<std::string> passes_;
};
/*
* Pass strategy to help control the IR passes.
/**Pass strategy to help control the IR passes.
*/
class PassStrategy : public PaddlePassBuilder {
public:
explicit PassStrategy(const std::vector<std::string> &passes)
: PaddlePassBuilder(passes) {}
// The MKLDNN control exists in both CPU and GPU mode, because there can be
// still some CPU kernels running in CPU mode.
/** The MKLDNN control exists in both CPU and GPU mode, because there can be
* still some CPU kernels running in CPU mode.
*/
virtual void EnableMKLDNN() = 0;
bool use_gpu() const { return use_gpu_; }
virtual ~PassStrategy() = default;
protected:
bool use_gpu_{false};
};
/*
* The CPU passes controller, it is used in AnalysisPredictor with CPU mode.
/** The CPU passes controller, it is used in AnalysisPredictor with CPU mode.
*/
class CpuPassStrategy : public PassStrategy {
public:
......@@ -76,6 +89,7 @@ class CpuPassStrategy : public PassStrategy {
passes_.assign({
"infer_clean_graph_pass", //
"attention_lstm_fuse_pass", //
"seqpool_concat_fuse_pass", //
"seqconv_eltadd_relu_fuse_pass", //
// "embedding_fc_lstm_fuse_pass", //
"fc_lstm_fuse_pass", //
......@@ -88,6 +102,7 @@ class CpuPassStrategy : public PassStrategy {
"conv_eltwiseadd_bn_fuse_pass", //
"is_test_pass", //
});
use_gpu_ = false;
}
virtual ~CpuPassStrategy() = default;
......@@ -111,8 +126,7 @@ class CpuPassStrategy : public PassStrategy {
CpuPassStrategy(const CpuPassStrategy &other) : PassStrategy(other.passes_) {}
};
/*
* The GPU passes strategy, it is used in
/** The GPU passes strategy, it is used in AnalysisPredictor with GPU mode.
*/
class GpuPassStrategy : public PassStrategy {
public:
......@@ -126,10 +140,14 @@ class GpuPassStrategy : public PassStrategy {
"conv_elementwise_add2_act_fuse_pass", //
"conv_elementwise_add_fuse_pass", //
});
use_gpu_ = true;
}
GpuPassStrategy(const GpuPassStrategy &other)
: PassStrategy(other.AllPasses()) {}
: PassStrategy(other.AllPasses()) {
use_gpu_ = true;
}
void EnableMKLDNN() override;
......
paddle/fluid/inference/tensorrt/CMakeLists.txt
浏览文件 @ 92da467c
nv_library(tensorrt_engine SRCS engine.cc DEPS ${GLOB_OPERATOR_DEPS} framework_proto device_context)
nv_library(tensorrt_op_teller SRCS op_teller.cc DEPS framework_proto)
nv_test(test_tensorrt SRCS test_tensorrt.cc DEPS dynload_cuda device_context dynamic_loader)
nv_test(test_tensorrt_engine SRCS test_engine.cc DEPS dynload_cuda tensorrt_engine)
add_subdirectory(plugin)
......
paddle/fluid/inference/tensorrt/op_teller.cc 0 → 100644
浏览文件 @ 92da467c
// Copyright (c) 2019 PaddlePaddle Authors. All Rights Reserved.
//
// Licensed under the Apache License, Version 2.0 (the "License");
// you may not use this file except in compliance with the License.
// You may obtain a copy of the License at
//
// http://www.apache.org/licenses/LICENSE-2.0
//
// Unless required by applicable law or agreed to in writing, software
// distributed under the License is distributed on an "AS IS" BASIS,
// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
// See the License for the specific language governing permissions and
// limitations under the License.
#include "paddle/fluid/inference/tensorrt/op_teller.h"
namespace paddle {
namespace inference {
namespace tensorrt {
// Just tell by the op_types.
struct SimpleOpTypeSetTeller : public Teller {
SimpleOpTypeSetTeller() {}
bool operator()(const std::string& op_type,
const framework::OpDesc& desc) override {
return teller_set.count(op_type);
}
private:
std::unordered_set<std::string> teller_set{
{"mul", "conv2d", "pool2d", "relu", "softmax", "sigmoid",
"depthwise_conv2d", "batch_norm", "concat", "tanh", "pad",
"elementwise_add", "elementwise_mul", "dropout", "split", "prelu",
"conv2d_transpose", "leaky_relu"}};
};
bool OpTeller::Tell(const std::string& op_type, const framework::OpDesc& desc) {
for (auto& teller : tellers_) {
if ((*teller)(op_type, desc)) return true;
}
return false;
}
OpTeller::OpTeller() { tellers_.emplace_back(new SimpleOpTypeSetTeller); }
} // namespace tensorrt
} // namespace inference
} // namespace paddle
paddle/fluid/inference/tensorrt/op_teller.h 0 → 100644
浏览文件 @ 92da467c
// Copyright (c) 2019 PaddlePaddle Authors. All Rights Reserved.
//
// Licensed under the Apache License, Version 2.0 (the "License");
// you may not use this file except in compliance with the License.
// You may obtain a copy of the License at
//
// http://www.apache.org/licenses/LICENSE-2.0
//
// Unless required by applicable law or agreed to in writing, software
// distributed under the License is distributed on an "AS IS" BASIS,
// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
// See the License for the specific language governing permissions and
// limitations under the License.
#pragma once
#include <string>
#include <vector>
#include "paddle/fluid/framework/op_desc.h"
namespace paddle {
namespace inference {
namespace tensorrt {
/*
* Single Op teller definition.
* One can override this and define a more complex tell logic, considerring more
* issues such as op_desc.
*/
struct Teller {
virtual bool operator()(const std::string& op_type,
const framework::OpDesc& desc) = 0;
virtual ~Teller() = default;
};
/*
* A real example:
*
* struct SomeTeller : public Teller {
* bool operator()(const std::string& op_type,
* const framework::OpDesc& desc) override {
* return op_type == "fc" && desc.Inputs().size() == 2;
* }
*};
*/
/*
* class OpTeller helps to tell whether a fluid
* operator can be transformed to a TensorRT layer.
*/
class OpTeller {
public:
static OpTeller& Global() {
static std::unique_ptr<OpTeller> x(new OpTeller);
return *x;
}
bool Tell(const std::string& op_type, const framework::OpDesc& desc);
private:
OpTeller();
private:
std::vector<std::unique_ptr<Teller>> tellers_;
};
} // namespace tensorrt
} // namespace inference
} // namespace paddle
paddle/fluid/inference/tests/api/CMakeLists.txt
浏览文件 @ 92da467c
......@@ -41,7 +41,7 @@ endfunction()
if(NOT APPLE AND WITH_MKLML)
set(RNN1_INSTALL_DIR "${INFERENCE_DEMO_INSTALL_DIR}/rnn1")
download_model_and_data(${RNN1_INSTALL_DIR} "rnn1%2Fmodel.tar.gz" "rnn1%2Fdata.txt.tar.gz")
inference_analysis_api_test(test_analyzer_rnn1 ${RNN1_INSTALL_DIR} analyzer_rnn1_tester.cc)
inference_analysis_api_test(test_analyzer_rnn1 ${RNN1_INSTALL_DIR} analyzer_rnn1_tester.cc SERIAL)
else()
# TODO: fix this test on MACOS and OPENBLAS, the reason is that
# fusion_seqexpand_concat_fc_op is not supported on MACOS and OPENBLAS
......@@ -56,14 +56,14 @@ inference_analysis_api_test(test_analyzer_rnn2 ${RNN2_INSTALL_DIR} analyzer_rnn2
# normal DAM
set(DAM_INSTALL_DIR "${INFERENCE_DEMO_INSTALL_DIR}/dam")
download_model_and_data(${DAM_INSTALL_DIR} "DAM_model.tar.gz" "DAM_data.txt.tar.gz")
inference_analysis_api_test(test_analyzer_dam ${DAM_INSTALL_DIR} analyzer_dam_tester.cc)
inference_analysis_api_test(test_analyzer_dam ${DAM_INSTALL_DIR} analyzer_dam_tester.cc SERIAL)
# small DAM
set(DAM_SMALL_INSTALL_DIR "${INFERENCE_DEMO_INSTALL_DIR}/small_dam")
download_model_and_data(${DAM_SMALL_INSTALL_DIR} "dam_small_model.tar.gz" "dam_small_data.txt.tar.gz")
inference_analysis_test(test_analyzer_small_dam SRCS analyzer_dam_tester.cc
EXTRA_DEPS ${INFERENCE_EXTRA_DEPS}
ARGS --infer_model=${DAM_SMALL_INSTALL_DIR}/model --infer_data=${DAM_SMALL_INSTALL_DIR}/data.txt --max_turn_num=1)
ARGS --infer_model=${DAM_SMALL_INSTALL_DIR}/model --infer_data=${DAM_SMALL_INSTALL_DIR}/data.txt --max_turn_num=1 SERIAL)
# chinese_ner
set(CHINESE_NER_INSTALL_DIR "${INFERENCE_DEMO_INSTALL_DIR}/chinese_ner")
......@@ -111,11 +111,11 @@ inference_analysis_api_test_with_refer_result(test_analyzer_mobilenet_transpose
# resnet50
inference_analysis_api_test_with_fake_data(test_analyzer_resnet50
"${INFERENCE_DEMO_INSTALL_DIR}/resnet50" analyzer_resnet50_tester.cc "resnet50_model.tar.gz")
"${INFERENCE_DEMO_INSTALL_DIR}/resnet50" analyzer_resnet50_tester.cc "resnet50_model.tar.gz" SERIAL)
# mobilenet with depthwise_conv op
inference_analysis_api_test_with_fake_data(test_analyzer_mobilenet_depthwise_conv
"${INFERENCE_DEMO_INSTALL_DIR}/mobilenet_depthwise_conv" analyzer_resnet50_tester.cc "mobilenet_model.tar.gz")
"${INFERENCE_DEMO_INSTALL_DIR}/mobilenet_depthwise_conv" analyzer_resnet50_tester.cc "mobilenet_model.tar.gz" SERIAL)
# anakin
if (WITH_ANAKIN AND WITH_MKL) # only needed in CI
......
paddle/fluid/inference/tests/api/analyzer_dam_tester.cc
浏览文件 @ 92da467c
......@@ -165,12 +165,9 @@ void PrepareInputs(std::vector<PaddleTensor> *input_slots, DataRecord *data,
}
void SetConfig(contrib::AnalysisConfig *cfg) {
cfg->prog_file = FLAGS_infer_model + "/__model__";
cfg->param_file = FLAGS_infer_model + "/param";
cfg->use_gpu = false;
cfg->device = 0;
cfg->specify_input_name = true;
cfg->enable_ir_optim = true;
cfg->SetModel(FLAGS_infer_model + "/__model__", FLAGS_infer_model + "/param");
cfg->SwitchSpecifyInputNames();
cfg->SwitchIrOptim(true);
}
void SetInput(std::vector<std::vector<PaddleTensor>> *inputs) {
......
paddle/fluid/inference/tests/api/analyzer_lac_tester.cc
浏览文件 @ 92da467c
......@@ -105,11 +105,10 @@ void GetOneBatch(std::vector<PaddleTensor> *input_slots, DataRecord *data,
}
void SetConfig(AnalysisConfig *cfg) {
cfg->model_dir = FLAGS_infer_model;
cfg->use_gpu = false;
cfg->device = 0;
cfg->specify_input_name = true;
cfg->enable_ir_optim = true;
cfg->SetModel(FLAGS_infer_model);
cfg->DisableGpu();
cfg->SwitchSpecifyInputNames();
cfg->SwitchIrOptim();
}
void SetInput(std::vector<std::vector<PaddleTensor>> *inputs) {
......
paddle/fluid/inference/tests/api/analyzer_mm_dnn_tester.cc
浏览文件 @ 92da467c
......@@ -76,11 +76,10 @@ void PrepareInputs(std::vector<PaddleTensor> *input_slots, DataRecord *data,
}
void SetConfig(contrib::AnalysisConfig *cfg) {
cfg->model_dir = FLAGS_infer_model;
cfg->use_gpu = false;
cfg->device = 0;
cfg->specify_input_name = true;
cfg->enable_ir_optim = true;
cfg->SetModel(FLAGS_infer_model);
cfg->DisableGpu();
cfg->SwitchSpecifyInputNames();
cfg->SwitchIrOptim();
}
void SetInput(std::vector<std::vector<PaddleTensor>> *inputs) {
......
paddle/fluid/inference/tests/api/analyzer_ner_tester.cc
浏览文件 @ 92da467c
......@@ -84,13 +84,12 @@ void SetConfig(contrib::AnalysisConfig *cfg, bool memory_load = false) {
cfg->SetModelBuffer(&buffer_prog[0], buffer_prog.size(), &buffer_param[0],
buffer_param.size());
} else {
cfg->prog_file = FLAGS_infer_model + "/__model__";
cfg->param_file = FLAGS_infer_model + "/param";
cfg->SetModel(FLAGS_infer_model + "/__model__",
FLAGS_infer_model + "/param");
}
cfg->use_gpu = false;
cfg->device = 0;
cfg->specify_input_name = true;
cfg->enable_ir_optim = true;
cfg->DisableGpu();
cfg->SwitchSpecifyInputNames();
cfg->SwitchIrOptim();
}
void SetInput(std::vector<std::vector<PaddleTensor>> *inputs) {
......
paddle/fluid/inference/tests/api/analyzer_resnet50_tester.cc
浏览文件 @ 92da467c
......@@ -21,12 +21,10 @@ namespace inference {
namespace analysis {
void SetConfig(AnalysisConfig *cfg) {
cfg->param_file = FLAGS_infer_model + "/params";
cfg->prog_file = FLAGS_infer_model + "/model";
cfg->use_gpu = false;
cfg->device = 0;
cfg->enable_ir_optim = true;
cfg->specify_input_name = true;
cfg->SetModel(FLAGS_infer_model + "/model", FLAGS_infer_model + "/params");
cfg->DisableGpu();
cfg->SwitchIrOptim();
cfg->SwitchSpecifyInputNames();
cfg->SetCpuMathLibraryNumThreads(FLAGS_paddle_num_threads);
}
......
paddle/fluid/inference/tests/api/analyzer_rnn1_tester.cc
浏览文件 @ 92da467c
......@@ -204,12 +204,10 @@ void PrepareZeroCopyInputs(ZeroCopyTensor *lod_attention_tensor,
}
void SetConfig(AnalysisConfig *cfg) {
cfg->prog_file = FLAGS_infer_model + "/__model__";
cfg->param_file = FLAGS_infer_model + "/param";
cfg->use_gpu = false;
cfg->device = 0;
cfg->specify_input_name = true;
cfg->enable_ir_optim = true;
cfg->SetModel(FLAGS_infer_model + "/__model__", FLAGS_infer_model + "/param");
cfg->DisableGpu();
cfg->SwitchSpecifyInputNames();
cfg->SwitchIrOptim();
}
void SetInput(std::vector<std::vector<PaddleTensor>> *inputs) {
......@@ -225,10 +223,10 @@ void SetInput(std::vector<std::vector<PaddleTensor>> *inputs) {
// Easy for profiling independently.
TEST(Analyzer_rnn1, profile) {
contrib::AnalysisConfig cfg(false);
contrib::AnalysisConfig cfg;
SetConfig(&cfg);
cfg.fraction_of_gpu_memory = 0.1;
cfg.pass_builder()->TurnOnDebug();
cfg.DisableGpu();
cfg.SwitchIrDebug();
std::vector<PaddleTensor> outputs;
std::vector<std::vector<PaddleTensor>> input_slots_all;
......@@ -285,7 +283,7 @@ TEST(Analyzer_rnn1, multi_thread) {
std::vector<std::vector<PaddleTensor>> input_slots_all;
SetInput(&input_slots_all);
TestPrediction(reinterpret_cast<const PaddlePredictor::Config *>(&cfg),
input_slots_all, &outputs, 4 /* multi_thread */);
input_slots_all, &outputs, 2 /* multi_thread */);
}
// Validate that the AnalysisPredictor + ZeroCopyTensor really works by testing
......@@ -293,16 +291,18 @@ TEST(Analyzer_rnn1, multi_thread) {
TEST(Analyzer_rnn1, ZeroCopy) {
AnalysisConfig config;
SetConfig(&config);
config.use_feed_fetch_ops = false;
config.SwitchUseFeedFetchOps(false);
PaddlePlace place;
auto predictor = CreatePaddlePredictor<AnalysisConfig>(config);
config.use_feed_fetch_ops = true;
auto native_predictor = CreatePaddlePredictor<NativeConfig>(config);
config.SwitchUseFeedFetchOps(true);
auto native_predictor =
CreatePaddlePredictor<NativeConfig>(config.ToNativeConfig());
config.use_feed_fetch_ops = true; // the analysis predictor needs feed/fetch.
config.SwitchUseFeedFetchOps(
true); // the analysis predictor needs feed/fetch.
auto analysis_predictor = CreatePaddlePredictor<AnalysisConfig>(config);
#define NEW_TENSOR(name__) \
......@@ -362,7 +362,7 @@ TEST(Analyzer_rnn1, ZeroCopy) {
TEST(Analyzer_rnn1, ZeroCopyMultiThread) {
AnalysisConfig config;
SetConfig(&config);
config.use_feed_fetch_ops = false;
config.SwitchUseFeedFetchOps(false);
#define NEW_TENSOR(name__) \
auto name__##_tensor = predictor->GetInputTensor(#name__);
......
paddle/fluid/inference/tests/api/analyzer_rnn2_tester.cc
浏览文件 @ 92da467c
......@@ -105,12 +105,10 @@ void PrepareInputs(std::vector<PaddleTensor> *input_slots, DataRecord *data,
}
void SetConfig(AnalysisConfig *cfg) {
cfg->prog_file = FLAGS_infer_model + "/__model__";
cfg->param_file = FLAGS_infer_model + "/param";
cfg->use_gpu = false;
cfg->device = 0;
cfg->specify_input_name = true;
cfg->enable_ir_optim = true;
cfg->SetModel(FLAGS_infer_model + "/__model__", FLAGS_infer_model + "/param");
cfg->DisableGpu();
cfg->SwitchSpecifyInputNames();
cfg->SwitchIrOptim();
}
void SetInput(std::vector<std::vector<PaddleTensor>> *inputs) {
......
paddle/fluid/inference/tests/api/analyzer_seq_conv1_tester.cc
浏览文件 @ 92da467c
......@@ -89,11 +89,10 @@ void PrepareInputs(std::vector<PaddleTensor> *input_slots, DataRecord *data,
}
void SetConfig(AnalysisConfig *cfg) {
cfg->model_dir = FLAGS_infer_model;
cfg->use_gpu = false;
cfg->device = 0;
cfg->specify_input_name = true;
cfg->enable_ir_optim = true;
cfg->SetModel(FLAGS_infer_model);
cfg->DisableGpu();
cfg->SwitchSpecifyInputNames();
cfg->SwitchIrOptim();
}
void SetInput(std::vector<std::vector<PaddleTensor>> *inputs) {
......
paddle/fluid/inference/tests/api/analyzer_seq_pool1_tester.cc
浏览文件 @ 92da467c
......@@ -122,12 +122,9 @@ void PrepareInputs(std::vector<PaddleTensor> *input_slots, DataRecord *data) {
}
void SetConfig(AnalysisConfig *cfg) {
cfg->param_file = FLAGS_infer_model + "/params";
cfg->prog_file = FLAGS_infer_model + "/model";
cfg->use_gpu = false;
cfg->device = 0;
cfg->enable_ir_optim = true;
cfg->specify_input_name = true;
cfg->SetModel(FLAGS_infer_model + "/model", FLAGS_infer_model + "/params");
cfg->DisableGpu();
cfg->SwitchSpecifyInputNames();
cfg->pass_builder()->TurnOnDebug();
cfg->SetCpuMathLibraryNumThreads(FLAGS_paddle_num_threads);
}
......@@ -180,8 +177,12 @@ TEST(Analyzer_seq_pool1, fuse_statis) {
auto predictor = CreatePaddlePredictor<AnalysisConfig>(cfg);
auto fuse_statis = GetFuseStatis(
static_cast<AnalysisPredictor *>(predictor.get()), &num_ops);
ASSERT_TRUE(fuse_statis.count("seqpool_concat_fuse"));
EXPECT_EQ(fuse_statis.at("seqpool_concat_fuse"), 2);
LOG(INFO) << "num_ops: " << num_ops;
EXPECT_EQ(num_ops, 349);
EXPECT_EQ(num_ops, 195);
}
} // namespace analysis
......
paddle/fluid/inference/tests/api/analyzer_text_classification_tester.cc
浏览文件 @ 92da467c
......@@ -47,11 +47,10 @@ struct DataReader {
};
void SetConfig(AnalysisConfig *cfg) {
cfg->model_dir = FLAGS_infer_model;
cfg->use_gpu = false;
cfg->device = 0;
cfg->specify_input_name = true;
cfg->enable_ir_optim = true;
cfg->SetModel(FLAGS_infer_model);
cfg->DisableGpu();
cfg->SwitchSpecifyInputNames();
cfg->SwitchIrOptim();
}
void SetInput(std::vector<std::vector<PaddleTensor>> *inputs) {
......
paddle/fluid/inference/tests/api/analyzer_vis_tester.cc
浏览文件 @ 92da467c
......@@ -51,12 +51,11 @@ Record ProcessALine(const std::string &line) {
}
void SetConfig(AnalysisConfig *cfg) {
cfg->param_file = FLAGS_infer_model + "/__params__";
cfg->prog_file = FLAGS_infer_model + "/__model__";
cfg->use_gpu = false;
cfg->device = 0;
cfg->enable_ir_optim = true;
cfg->specify_input_name = true;
cfg->SetModel(FLAGS_infer_model + "/__model__",
FLAGS_infer_model + "/__params__");
cfg->DisableGpu();
cfg->SwitchIrDebug();
cfg->SwitchSpecifyInputNames();
// TODO(TJ): fix fusion gru
cfg->pass_builder()->DeletePass("fc_gru_fuse_pass");
}
......
paddle/fluid/inference/tests/api/config_printer.h
浏览文件 @ 92da467c
......@@ -62,21 +62,25 @@ std::ostream &operator<<(std::ostream &os,
const contrib::AnalysisConfig &config) {
os << GenSpaces(num_spaces) << "contrib::AnalysisConfig {\n";
num_spaces++;
os << *reinterpret_cast<const NativeConfig *>(&config);
os << config.ToNativeConfig();
if (!config.model_from_memory()) {
os << GenSpaces(num_spaces) << "prog_file: " << config.prog_file << "\n";
os << GenSpaces(num_spaces) << "param_file: " << config.param_file << "\n";
os << GenSpaces(num_spaces) << "prog_file: " << config.prog_file() << "\n";
os << GenSpaces(num_spaces) << "param_file: " << config.params_file()
<< "\n";
} else {
os << GenSpaces(num_spaces)
<< "prog_file and param_file: load from memory \n";
}
os << GenSpaces(num_spaces) << "enable_ir_optim: " << config.enable_ir_optim
os << GenSpaces(num_spaces) << "enable_ir_optim: " << config.ir_optim()
<< "\n";
os << GenSpaces(num_spaces) << "enable_ir_optim: " << config.ir_optim()
<< "\n";
os << GenSpaces(num_spaces)
<< "use_feed_fetch_ops: " << config.use_feed_fetch_ops_enabled() << "\n";
os << GenSpaces(num_spaces)
<< "use_feed_fetch_ops: " << config.use_feed_fetch_ops << "\n";
os << GenSpaces(num_spaces) << "use_tensorrt: " << config.use_tensorrt()
<< "use_tensorrt: " << config.tensorrt_engine_enabled() << "\n";
os << GenSpaces(num_spaces) << "use_mkldnn: " << config.mkldnn_enabled()
<< "\n";
os << GenSpaces(num_spaces) << "use_mkldnn: " << config.use_mkldnn() << "\n";
num_spaces--;
os << GenSpaces(num_spaces) << "}\n";
return os;
......
paddle/fluid/inference/tests/api/tester_helper.h
浏览文件 @ 92da467c
......@@ -54,11 +54,13 @@ namespace paddle {
namespace inference {
void PrintConfig(const PaddlePredictor::Config *config, bool use_analysis) {
const auto *analysis_config =
reinterpret_cast<const contrib::AnalysisConfig *>(config);
if (use_analysis) {
LOG(INFO) << *reinterpret_cast<const contrib::AnalysisConfig *>(config);
LOG(INFO) << *analysis_config;
return;
}
LOG(INFO) << *reinterpret_cast<const NativeConfig *>(config);
LOG(INFO) << analysis_config->ToNativeConfig();
}
void CompareResult(const std::vector<PaddleTensor> &outputs,
......@@ -96,12 +98,13 @@ void CompareResult(const std::vector<PaddleTensor> &outputs,
std::unique_ptr<PaddlePredictor> CreateTestPredictor(
const PaddlePredictor::Config *config, bool use_analysis = true) {
const auto *analysis_config =
reinterpret_cast<const contrib::AnalysisConfig *>(config);
if (use_analysis) {
return CreatePaddlePredictor<contrib::AnalysisConfig>(
*(reinterpret_cast<const contrib::AnalysisConfig *>(config)));
return CreatePaddlePredictor<contrib::AnalysisConfig>(*analysis_config);
}
return CreatePaddlePredictor<NativeConfig>(
*(reinterpret_cast<const NativeConfig *>(config)));
auto native_config = analysis_config->ToNativeConfig();
return CreatePaddlePredictor<NativeConfig>(native_config);
}
size_t GetSize(const PaddleTensor &out) { return VecReduceToInt(out.shape); }
......
paddle/fluid/inference/tests/api/trt_models_tester.cc
浏览文件 @ 92da467c
......@@ -46,22 +46,20 @@ void SetConfig<contrib::AnalysisConfig>(contrib::AnalysisConfig* config,
std::string model_dir, bool use_gpu,
bool use_tensorrt, int batch_size) {
if (!FLAGS_prog_filename.empty() && !FLAGS_param_filename.empty()) {
config->prog_file = model_dir + "/" + FLAGS_prog_filename;
config->param_file = model_dir + "/" + FLAGS_param_filename;
config->SetModel(model_dir + "/" + FLAGS_prog_filename,
model_dir + "/" + FLAGS_param_filename);
} else {
config->model_dir = model_dir;
config->SetModel(model_dir);
}
if (use_gpu) {
config->use_gpu = true;
config->device = 0;
config->fraction_of_gpu_memory = 0.15;
config->EnableUseGpu(100, 0);
if (use_tensorrt) {
config->EnableTensorRtEngine(1 << 10, batch_size);
config->pass_builder()->DeletePass("conv_bn_fuse_pass");
config->pass_builder()->DeletePass("fc_fuse_pass");
config->pass_builder()->TurnOnDebug();
} else {
config->enable_ir_optim = true;
config->SwitchIrOptim();
}
}
}
......@@ -77,7 +75,8 @@ void profile(std::string model_dir, bool use_analysis, bool use_tensorrt) {
std::vector<PaddleTensor> outputs;
if (use_analysis || use_tensorrt) {
contrib::AnalysisConfig config(true);
contrib::AnalysisConfig config;
config.EnableUseGpu(100, 0);
config.pass_builder()->TurnOnDebug();
SetConfig<contrib::AnalysisConfig>(&config, model_dir, true, use_tensorrt,
FLAGS_batch_size);
......@@ -100,23 +99,12 @@ void compare(std::string model_dir, bool use_tensorrt) {
SetFakeImageInput(&inputs_all, model_dir, false, "__model__", "");
}
std::vector<PaddleTensor> native_outputs;
NativeConfig native_config;
SetConfig<NativeConfig>(&native_config, model_dir, true, false,
FLAGS_batch_size);
TestOneThreadPrediction(
reinterpret_cast<PaddlePredictor::Config*>(&native_config), inputs_all,
&native_outputs, false);
std::vector<PaddleTensor> analysis_outputs;
contrib::AnalysisConfig analysis_config(true);
contrib::AnalysisConfig analysis_config;
SetConfig<contrib::AnalysisConfig>(&analysis_config, model_dir, true,
use_tensorrt, FLAGS_batch_size);
TestOneThreadPrediction(
reinterpret_cast<PaddlePredictor::Config*>(&analysis_config), inputs_all,
&analysis_outputs, true);
CompareResult(native_outputs, analysis_outputs);
CompareNativeAndAnalysis(
reinterpret_cast<const PaddlePredictor::Config*>(&analysis_config),
inputs_all);
}
TEST(TensorRT_mobilenet, compare) {
......@@ -154,9 +142,9 @@ TEST(TensorRT_mobilenet, analysis) {
TEST(AnalysisPredictor, use_gpu) {
std::string model_dir = FLAGS_infer_model + "/" + "mobilenet";
AnalysisConfig config(true);
config.model_dir = model_dir;
config.fraction_of_gpu_memory = 0.15;
AnalysisConfig config;
config.EnableUseGpu(100, 0);
config.SetModel(model_dir);
config.pass_builder()->TurnOnDebug();
std::vector<std::vector<PaddleTensor>> inputs_all;
......
paddle/fluid/operators/conv_cudnn_op.cu.cc
浏览文件 @ 92da467c
......@@ -297,6 +297,21 @@ class CUDNNConvGradOpKernel : public framework::OpKernel<T> {
cudnnFilterDescriptor_t cudnn_filter_desc = filter_desc.descriptor<T>(
layout, framework::vectorize2int(filter->dims()), groups);
#if CUDA_VERSION >= 9000 && CUDNN_VERSION_MIN(7, 0, 1)
// Enable Tensor Core for cudnn backward
if (dev_ctx.GetComputeCapability() >= 70 &&
std::type_index(typeid(T)) ==
std::type_index(typeid(platform::float16))) {
CUDNN_ENFORCE(platform::dynload::cudnnSetConvolutionMathType(
cudnn_conv_desc, CUDNN_TENSOR_OP_MATH));
VLOG(5) << "use cudnn_tensor_op_math for backward";
} else {
CUDNN_ENFORCE(platform::dynload::cudnnSetConvolutionMathType(
cudnn_conv_desc, CUDNN_DEFAULT_MATH));
VLOG(5) << "NOT use cudnn_tensor_op_math for backward";
}
#endif
int input_channels = input->dims()[1];
int input_height, input_width, input_depth;
if (input->dims().size() == 5) {
......
paddle/fluid/operators/conv_mkldnn_op.cc
浏览文件 @ 92da467c
......@@ -12,6 +12,7 @@
See the License for the specific language governing permissions and
limitations under the License. */
#include <unordered_map>
#include "paddle/fluid/framework/data_layout_transform.h"
#include "paddle/fluid/memory/malloc.h"
#include "paddle/fluid/operators/conv_op.h"
......@@ -68,13 +69,22 @@ inline mkldnn::memory::format GetWeightsFormat(mkldnn::memory::format format,
}
}
template <typename T>
template <typename T, typename K>
class ConvMKLDNNOpKernel : public paddle::framework::OpKernel<T> {
public:
void Compute(const paddle::framework::ExecutionContext& ctx) const override {
PADDLE_ENFORCE(paddle::platform::is_cpu_place(ctx.GetPlace()),
"It must use CPUPlace.");
bool is_INT8 =
std::is_same<T, int8_t>::value || std::is_same<T, uint8_t>::value;
if (!is_INT8) {
ComputeFP32(ctx);
} else {
ComputeINT8(ctx);
}
}
void ComputeFP32(const paddle::framework::ExecutionContext& ctx) const {
const bool is_test = ctx.Attr<bool>("is_test");
auto& dev_ctx =
......@@ -274,6 +284,271 @@ class ConvMKLDNNOpKernel : public paddle::framework::OpKernel<T> {
output->set_layout(DataLayout::kMKLDNN);
output->set_format(GetMKLDNNFormat(*dst_memory_p));
}
void ComputeINT8(const paddle::framework::ExecutionContext& ctx) const {
const bool is_test = ctx.Attr<bool>("is_test");
auto& dev_ctx =
ctx.template device_context<paddle::platform::MKLDNNDeviceContext>();
const auto& mkldnn_engine = dev_ctx.GetEngine();
auto* input = ctx.Input<Tensor>("Input");
auto* filter = ctx.Input<Tensor>("Filter");
auto* bias = ctx.HasInput("Bias") ? ctx.Input<Tensor>("Bias") : nullptr;
auto* output = ctx.Output<Tensor>("Output");
PADDLE_ENFORCE(input->layout() == DataLayout::kMKLDNN &&
input->format() != memory::format::format_undef,
"Wrong layout/format set for Input tensor");
PADDLE_ENFORCE(filter->layout() == DataLayout::kMKLDNN &&
filter->format() != memory::format::format_undef,
"Wrong layout/format set for Filter tensor");
PADDLE_ENFORCE(input->dims().size() == 4 || input->dims().size() == 5,
"Input must be with 4 or 5 dimensions, i.e. NCHW or NCDHW");
PADDLE_ENFORCE(filter->dims().size() == 4 || filter->dims().size() == 5,
"Filter must be with 4 or 5 dimensions, i.e. OIHW or OIDHW");
if (bias) {
PADDLE_ENFORCE(bias->layout() == DataLayout::kMKLDNN &&
bias->format() != memory::format::format_undef,
"Wrong layout/format set for Bias tensor");
PADDLE_ENFORCE(bias->dims().size() == 1,
"Bias must only have 1 dimension, i.e. X");
}
std::vector<int> strides = ctx.Attr<std::vector<int>>("strides");
std::vector<int> paddings = ctx.Attr<std::vector<int>>("paddings");
std::vector<int> dilations = ctx.Attr<std::vector<int>>("dilations");
int groups = ctx.Attr<int>("groups");
bool fuse_relu = ctx.Attr<bool>("fuse_relu");
bool force_fp32_output = ctx.Attr<bool>("force_fp32_output");
bool is_conv3d = strides.size() == 3U;
// TODO(tpatejko): add support for dilation
PADDLE_ENFORCE(
is_conv3d
? dilations.size() == 3 && dilations[0] == 1 && dilations[1] == 1 &&
dilations[2] == 1
: dilations.size() == 2 && dilations[0] == 1 && dilations[1] == 1,
"dilation in convolution is not implemented yet");
PADDLE_ENFORCE(is_conv3d != true, "int8 does not support conv3d currently");
const T* input_data = input->data<T>();
std::vector<int> src_tz = paddle::framework::vectorize2int(input->dims());
std::vector<int> weights_tz =
paddle::framework::vectorize2int(filter->dims());
int g = std::max(groups, 1);
GetWeightsTz(weights_tz, g, is_conv3d);
std::vector<int> dst_tz = paddle::framework::vectorize2int(output->dims());
mkldnn::memory::data_type src_dt =
paddle::framework::ToMKLDNNDataType(input->type());
auto dst_dt = fuse_relu ? paddle::framework::ToMKLDNNDataType(
framework::DataTypeTrait<uint8_t>::DataType)
: paddle::framework::ToMKLDNNDataType(
framework::DataTypeTrait<int8_t>::DataType);
if (force_fp32_output) {
dst_dt = paddle::framework::ToMKLDNNDataType(
framework::DataTypeTrait<float>::DataType);
}
// Get unique name for storing MKLDNN primitives
std::string key;
key.reserve(MaxKeyLength);
platform::ConvMKLDNNHandler::AppendKey(
&key, src_tz, weights_tz, strides, paddings, dilations, groups, src_dt,
input->format(), dst_dt, ctx.op().Output("Output"));
const std::string key_conv_pd = key + "@conv_pd";
std::shared_ptr<mkldnn::convolution_forward> conv_p = nullptr;
std::shared_ptr<mkldnn::memory> src_memory_p = nullptr;
std::shared_ptr<mkldnn::memory> user_src_memory_p = nullptr;
std::shared_ptr<mkldnn::memory> dst_memory_p = nullptr;
std::vector<primitive> pipeline;
std::shared_ptr<mkldnn::convolution_forward::primitive_desc> conv_pd =
nullptr;
std::shared_ptr<platform::ConvMKLDNNHandler> handler = nullptr;
auto prim_key = key + "@conv_p";
auto dst_key = key + "@dst_mem_p";
auto src_key = key + "@src_mem_p";
auto user_src_key = key + "@user_src_mem_p";
auto src_reorder_key = key + "@src_mem_preorder_p";
conv_p = std::static_pointer_cast<mkldnn::convolution_forward>(
dev_ctx.GetBlob(prim_key));
if (conv_p == nullptr || !is_test) {
const K* filter_data = filter->data<K>();
auto scale_in_data = ctx.Attr<float>("Scale_in");
auto scale_weights_data = ctx.Attr<std::vector<float>>("Scale_weights");
auto scale_out_data =
force_fp32_output ? 1.0f : ctx.Attr<float>("Scale_out");
bool is_multi_channel = scale_weights_data.size() > 1;
int count = is_multi_channel ? (g > 1 ? (weights_tz)[1] * (weights_tz)[0]
: (weights_tz)[0])
: 1;
std::vector<float> output_shift_scale(count);
#pragma omp parallel for if (count > 1)
for (int i = 0; i < count; i++) {
if (scale_weights_data[i] == 0.0)
output_shift_scale[i] =
scale_out_data; // weights data will contain 0
// in some models, then weights
// scale couldn't be calculated
else
output_shift_scale[i] =
scale_out_data / (scale_in_data * scale_weights_data[i]);
}
auto user_src_md =
platform::MKLDNNMemDesc({src_tz}, src_dt, input->format());
auto user_weights_md = platform::MKLDNNMemDesc(
{weights_tz}, platform::MKLDNNGetDataType<K>(),
((g) == 1) ? mkldnn::memory::format::oihw
: mkldnn::memory::format::goihw);
/* create memory descriptor for convolution without specified format
* ('any') which lets a primitive (convolution in this case) choose
* the memory format preferred for best performance
*/
std::string data_format = ctx.Attr<std::string>("data_format");
auto chosen_memory_format =
platform::data_format_to_memory_format(data_format);
std::vector<int> bias_tz;
auto src_md =
platform::MKLDNNMemDesc(src_tz, src_dt, chosen_memory_format);
auto weights_md = platform::MKLDNNMemDesc(
weights_tz, memory::data_type::s8, chosen_memory_format);
auto dst_md =
platform::MKLDNNMemDesc(dst_tz, dst_dt, chosen_memory_format);
// create a conv primitive descriptor and save it for usage in backward
if (bias) {
bias_tz = paddle::framework::vectorize2int(bias->dims());
auto bias_md = platform::MKLDNNMemDesc(bias_tz, memory::data_type::s32,
memory::format::x);
conv_pd = ConvFwdPrimitiveDesc(src_md, weights_md, bias_md, dst_md,
strides, paddings, mkldnn_engine,
fuse_relu, output_shift_scale, is_test);
} else {
conv_pd = ConvFwdPrimitiveDesc(src_md, weights_md, dst_md, strides,
paddings, mkldnn_engine, fuse_relu,
output_shift_scale, is_test);
}
// Save conv_pd/src_memory/weights_memory for backward pass
dev_ctx.SetBlob(key_conv_pd, conv_pd);
handler.reset(new platform::ConvMKLDNNHandler(conv_pd, dev_ctx,
mkldnn_engine, key));
// create mkldnn memory from input tensors (data/weights)
user_src_memory_p =
handler->AcquireSrcMemory(user_src_md, to_void_cast<T>(input_data));
auto user_weights_memory_p = handler->AcquireWeightsMemory(
user_weights_md, to_void_cast<K>(filter_data));
// create reorder primitive if the input format is not the preferred one
src_memory_p =
handler->AcquireSrcMemoryFromPrimitive(user_src_memory_p, pipeline);
std::shared_ptr<mkldnn::memory> weights_memory_p;
int mask_reorder =
is_multi_channel ? ((g != 1) ? (1 << 1) + (1 << 0) : 1 << 0) : 0;
weights_memory_p = handler->AcquireWeightsMemoryFromPrimitive(
user_weights_memory_p, pipeline, is_test, true, scale_weights_data,
mask_reorder);
if (!force_fp32_output) {
if (fuse_relu) {
dst_memory_p = platform::SetDstMemory<uint8_t>(ctx, output, handler);
} else {
dst_memory_p = platform::SetDstMemory<int8_t>(ctx, output, handler);
}
} else {
dst_memory_p = platform::SetDstMemory<float>(ctx, output, handler);
}
// create convolution op primitive
auto scale_bias_key = key + "@scale_bias";
if (bias) {
const float* bias_data = bias->data<float>();
auto user_bias_md = platform::MKLDNNMemDesc(
{bias_tz}, platform::MKLDNNGetDataType<float>(), memory::format::x);
auto user_bias_memory_p = handler->AcquireBiasMemory(
user_bias_md, to_void_cast<float>(bias_data));
std::shared_ptr<mkldnn::memory> bias_memory_p;
int mask_reorder = is_multi_channel ? 1 << 0 : 1;
int count =
is_multi_channel
? (g > 1 ? (weights_tz)[1] * (weights_tz)[0] : (weights_tz)[0])
: 1;
std::vector<float> scale_bias_data(count);
#pragma omp parallel for if (count > 1)
for (int i = 0; i < count; i++) {
scale_bias_data[i] = scale_in_data * scale_weights_data[i];
}
bias_memory_p = handler->AcquireBiasMemoryFromPrimitive(
user_bias_memory_p, pipeline, is_test, true, scale_bias_data,
mask_reorder);
conv_p = handler->AcquireConvolution(src_memory_p, weights_memory_p,
bias_memory_p, dst_memory_p);
} else {
conv_p = handler->AcquireConvolution(src_memory_p, weights_memory_p,
dst_memory_p);
}
// push primitive to stream and wait until it's executed
pipeline.push_back(*conv_p);
} else {
auto src_memory_reorder_p = std::static_pointer_cast<mkldnn::memory>(
dev_ctx.GetBlob(src_reorder_key));
src_memory_p =
std::static_pointer_cast<mkldnn::memory>(dev_ctx.GetBlob(src_key));
if (src_memory_reorder_p) {
user_src_memory_p = std::static_pointer_cast<mkldnn::memory>(
dev_ctx.GetBlob(user_src_key));
user_src_memory_p->set_data_handle(to_void_cast<T>(input_data));
} else if (src_memory_p) {
src_memory_p->set_data_handle(to_void_cast<T>(input_data));
}
dst_memory_p =
std::static_pointer_cast<mkldnn::memory>(dev_ctx.GetBlob(dst_key));
conv_pd =
std::static_pointer_cast<mkldnn::convolution_forward::primitive_desc>(
dev_ctx.GetBlob(key_conv_pd));
if (conv_pd) {
handler.reset(new platform::ConvMKLDNNHandler(conv_pd, dev_ctx,
mkldnn_engine, key));
}
if (!force_fp32_output) {
if (fuse_relu) {
dst_memory_p =
platform::SetDstMemoryHandler<uint8_t>(ctx, output, handler);
} else {
dst_memory_p =
platform::SetDstMemoryHandler<int8_t>(ctx, output, handler);
}
} else {
dst_memory_p =
platform::SetDstMemoryHandler<float>(ctx, output, handler);
}
if (src_memory_reorder_p) {
pipeline.push_back(*src_memory_reorder_p);
}
pipeline.push_back(*conv_p);
}
// push primitive to stream and wait until it's executed
stream(stream::kind::eager).submit(pipeline).wait();
output->set_layout(DataLayout::kMKLDNN);
output->set_format(GetMKLDNNFormat(*dst_memory_p));
}
private:
mkldnn::primitive_attr CreatePostOps(bool fuse_relu,
......@@ -301,6 +576,23 @@ class ConvMKLDNNOpKernel : public paddle::framework::OpKernel<T> {
return conv_attr;
}
mkldnn::primitive_attr CreatePostOps(
bool fuse_relu, const std::vector<float> output_shift_scale) const {
mkldnn::primitive_attr conv_attr;
mkldnn::post_ops post_operations;
int mask = output_shift_scale.size() > 1 ? 1 << 1 : 0;
conv_attr.set_output_scales(mask, output_shift_scale);
if (fuse_relu) {
constexpr float scale = 1.0f;
constexpr float negative_slope = 0.0f;
constexpr float placeholder = 1.0f; // beta
post_operations.append_eltwise(scale, mkldnn::algorithm::eltwise_relu,
negative_slope, placeholder);
}
conv_attr.set_post_ops(post_operations);
return conv_attr;
}
std::unique_ptr<mkldnn::convolution_forward::primitive_desc>
ConvFwdPrimitiveDesc(const memory::desc& src, const memory::desc& weights,
const memory::desc& dst, const std::vector<int>& strides,
......@@ -325,6 +617,33 @@ class ConvMKLDNNOpKernel : public paddle::framework::OpKernel<T> {
p_conv_pd);
}
std::unique_ptr<mkldnn::convolution_forward::primitive_desc>
ConvFwdPrimitiveDesc(const memory::desc& src, const memory::desc& weights,
const memory::desc& dst, const std::vector<int>& strides,
const std::vector<int>& paddings,
const mkldnn::engine& engine, const bool fuse_relu,
const std::vector<float> output_shift_scale,
bool is_test) const {
memory::dims stride_dims = {strides[0], strides[1]};
memory::dims padding_dims = {paddings[0], paddings[1]};
auto propagation = is_test ? mkldnn::prop_kind::forward_scoring
: mkldnn::prop_kind::forward_training;
auto conv_desc = mkldnn::convolution_forward::desc(
propagation, mkldnn::convolution_direct, src, weights, dst, stride_dims,
padding_dims, padding_dims, mkldnn::padding_kind::zero);
mkldnn::primitive_attr conv_attr =
CreatePostOps(fuse_relu, output_shift_scale);
auto p_conv_pd = new mkldnn::convolution_forward::primitive_desc(
conv_desc, conv_attr, engine);
return std::unique_ptr<mkldnn::convolution_forward::primitive_desc>(
p_conv_pd);
}
std::unique_ptr<mkldnn::convolution_forward::primitive_desc>
ConvFwdPrimitiveDesc(const memory::desc& src, const memory::desc& weights,
const memory::desc& bias, const memory::desc& dst,
......@@ -349,6 +668,34 @@ class ConvMKLDNNOpKernel : public paddle::framework::OpKernel<T> {
return std::unique_ptr<mkldnn::convolution_forward::primitive_desc>(
p_conv_pd);
}
std::unique_ptr<mkldnn::convolution_forward::primitive_desc>
ConvFwdPrimitiveDesc(const memory::desc& src, const memory::desc& weights,
const memory::desc& bias, const memory::desc& dst,
const std::vector<int>& strides,
const std::vector<int>& paddings,
const mkldnn::engine& engine, const bool fuse_relu,
const std::vector<float> output_shift_scale,
bool is_test) const {
memory::dims stride_dims = {strides[0], strides[1]};
memory::dims padding_dims = {paddings[0], paddings[1]};
auto propagation = is_test ? mkldnn::prop_kind::forward_scoring
: mkldnn::prop_kind::forward_training;
auto conv_desc = mkldnn::convolution_forward::desc(
propagation, mkldnn::convolution_direct, src, weights, bias, dst,
stride_dims, padding_dims, padding_dims, mkldnn::padding_kind::zero);
mkldnn::primitive_attr conv_attr =
CreatePostOps(fuse_relu, output_shift_scale);
auto p_conv_pd = new mkldnn::convolution_forward::primitive_desc(
conv_desc, conv_attr, engine);
return std::unique_ptr<mkldnn::convolution_forward::primitive_desc>(
p_conv_pd);
}
};
template <typename T>
......@@ -555,7 +902,17 @@ namespace ops = paddle::operators;
REGISTER_OP_KERNEL_WITH_CUSTOM_TYPE(conv2d, MKLDNN,
::paddle::platform::CPUPlace, FP32,
ops::kConvMKLDNNFP32,
ops::ConvMKLDNNOpKernel<float>);
ops::ConvMKLDNNOpKernel<float, float>);
REGISTER_OP_KERNEL_WITH_CUSTOM_TYPE(conv2d, MKLDNN,
::paddle::platform::CPUPlace, U8,
ops::kConvMKLDNNFP32,
ops::ConvMKLDNNOpKernel<uint8_t, float>);
REGISTER_OP_KERNEL_WITH_CUSTOM_TYPE(conv2d, MKLDNN,
::paddle::platform::CPUPlace, S8,
ops::kConvMKLDNNFP32,
ops::ConvMKLDNNOpKernel<int8_t, float>);
REGISTER_OP_KERNEL_WITH_CUSTOM_TYPE(conv2d_grad, MKLDNN,
::paddle::platform::CPUPlace, FP32,
......@@ -565,7 +922,7 @@ REGISTER_OP_KERNEL_WITH_CUSTOM_TYPE(conv2d_grad, MKLDNN,
REGISTER_OP_KERNEL_WITH_CUSTOM_TYPE(conv3d, MKLDNN,
::paddle::platform::CPUPlace, FP32,
ops::kConvMKLDNNFP32,
ops::ConvMKLDNNOpKernel<float>);
ops::ConvMKLDNNOpKernel<float, float>);
REGISTER_OP_KERNEL_WITH_CUSTOM_TYPE(conv3d_grad, MKLDNN,
::paddle::platform::CPUPlace, FP32,
......
paddle/fluid/operators/conv_op.cc
浏览文件 @ 92da467c
......@@ -98,10 +98,12 @@ framework::OpKernelType ConvOp::GetExpectedKernelType(
#endif
auto input_data_type = ctx.Input<Tensor>("Input")->type();
auto filter_data_type = ctx.Input<Tensor>("Filter")->type();
PADDLE_ENFORCE_EQ(input_data_type, filter_data_type,
"input and filter data type should be consistent");
if (input_data_type != framework::proto::VarType::INT8 &&
input_data_type != framework::proto::VarType::UINT8) {
auto filter_data_type = ctx.Input<Tensor>("Filter")->type();
PADDLE_ENFORCE_EQ(input_data_type, filter_data_type,
"input and filter data type should be consistent");
}
if (input_data_type == framework::proto::VarType::FP16) {
PADDLE_ENFORCE_EQ(library, framework::LibraryType::kCUDNN,
"float16 can only be used when CUDNN is used");
......@@ -179,6 +181,26 @@ void Conv2DOpMaker::Make() {
"whenever convolution output is as an input to residual "
"connection.")
.SetDefault(false);
AddAttr<float>("Scale_in",
"Scale_in to be used for int8 input data."
"Only used with MKL-DNN INT8.")
.SetDefault(1.0f);
AddAttr<float>("Scale_out",
"Scale_out to be used for int8 output data."
"Only used with MKL-DNN INT8.")
.SetDefault(1.0f);
AddAttr<float>("Scale_in_eltwise",
"Scale_in_eltwise to be used for int8 eltwise input data."
"Only used with MKL-DNN INT8.")
.SetDefault(1.0f);
AddAttr<std::vector<float>>("Scale_weights",
"Scale_weights to be used for int8 weights data."
"Only used with MKL-DNN INT8.")
.SetDefault({1.0f});
AddAttr<bool>("force_fp32_output",
"(bool, default false) Force INT8 kernel output FP32, only "
"used in MKL-DNN INT8")
.SetDefault(false);
AddAttr<std::string>(
"data_format",
"(string, default NCHW) Only used in "
......@@ -303,6 +325,9 @@ void Conv3DOpMaker::Make() {
"Defaults to \"NHWC\". Specify the data format of the output data, "
"the input will be transformed automatically. ")
.SetDefault("AnyLayout");
AddAttr<bool>("force_fp32_output",
"(bool, default false) Only used in mkldnn INT8 kernel")
.SetDefault(false);
// TODO(dzhwinter): need to registered layout transform function
AddAttr<int>("workspace_size_MB",
"Only used in cudnn kernel. workspace size for cudnn, in MB, "
......
paddle/fluid/operators/conv_op.h
浏览文件 @ 92da467c
......@@ -29,6 +29,7 @@ namespace operators {
using Tensor = framework::Tensor;
constexpr int kConvMKLDNNFP32 = 1;
constexpr int kConvMKLDNNINT8 = 2;
constexpr int MaxKeyLength = 256;
// Base convolution operator definations for other conv
// like operators to reuse the implementation.
......
paddle/fluid/operators/distributed/parameter_prefetch.cc
浏览文件 @ 92da467c
......@@ -32,7 +32,7 @@ namespace paddle {
namespace operators {
namespace distributed {
using Tensor = framework::Tensor;
using LoDTensor = framework::LoDTensor;
using LoDTensor = framework::LoDTensor;
using SelectedRows = framework::SelectedRows;
using DDim = framework::DDim;
......@@ -117,6 +117,12 @@ static void MergeMultipleVarsIntoOneBySection(
auto& id_tensor = scope->FindVar(id_name)->Get<framework::LoDTensor>();
auto* out_tensor =
scope->FindVar(out_name)->GetMutable<framework::LoDTensor>();
PADDLE_ENFORCE_GT(
out_tensor->numel(), 0,
"When calling this method, the LoDTensor's numel must larger than zero. "
"Please check LoDTensor::Resize has been called first.");
auto* out_tensor_data = out_tensor->mutable_data<float>(id_tensor.place());
bool is_on_cpu_place = true;
......@@ -138,7 +144,7 @@ static void MergeMultipleVarsIntoOneBySection(
auto row_numel = dims[1];
for (size_t i = 0; i < dims[0]; ++i) {
for (int64_t i = 0; i < dims[0]; ++i) {
auto id = ids_in_this_section[i];
auto origin_id = id + abs_sections[section_idx];
auto& offsets = id_to_offset[origin_id];
......@@ -172,8 +178,9 @@ void prefetch(const std::string& id_name, const std::string& out_name,
const std::vector<std::string>& table_names,
const std::vector<std::string>& epmap,
const std::vector<int>& height_sections,
const framework::ExecutionContext& context) {
auto& local_scope = context.scope().NewScope();
const framework::ExecutionContext& context,
const framework::Scope& scope) {
auto& local_scope = scope.NewScope();
platform::DeviceContextPool& pool = platform::DeviceContextPool::Instance();
auto& cpu_ctx = *pool.Get(platform::CPUPlace());
......@@ -190,11 +197,11 @@ void prefetch(const std::string& id_name, const std::string& out_name,
out_var_names.push_back(out_name + "@" + epmap[i]);
}
auto& id_tensor = local_scope.FindVar(id_name)->Get<framework::LoDTensor>();
auto& id_tensor = scope.FindVar(id_name)->Get<framework::LoDTensor>();
std::vector<int64_t> ids_vector;
if (platform::is_cpu_place(id_tensor.place())) {
auto* id_data = id_tensor.data<int64_t>();
for (size_t i = 0; i < id_tensor.numel(); ++i) {
for (int64_t i = 0; i < id_tensor.numel(); ++i) {
ids_vector.push_back(id_data[i]);
}
} else {
......@@ -202,7 +209,7 @@ void prefetch(const std::string& id_name, const std::string& out_name,
PADDLE_THROW("paddle is not compiled with CUDA!");
#else
auto cpu_place = platform::CPUPlace();
framework::Tensor cpu_tensor;
framework::LoDTensor cpu_tensor;
auto* cpu_tensor_data =
cpu_tensor.mutable_data<int64_t>(id_tensor.dims(), cpu_place);
auto stream =
......@@ -246,8 +253,7 @@ void prefetch(const std::string& id_name, const std::string& out_name,
MergeMultipleVarsIntoOneBySection(id_name, ids_vector, out_name,
out_var_names, height_sections, splited_ids,
context, &local_scope, &actual_ctx);
context.scope().DeleteScope(&local_scope);
scope.DeleteScope(&local_scope);
}
}; // namespace distributed
......
paddle/fluid/operators/distributed/parameter_prefetch.h
浏览文件 @ 92da467c
......@@ -27,7 +27,56 @@ void prefetch(const std::string& id_name, const std::string& out_name,
const std::vector<std::string>& table_names,
const std::vector<std::string>& epmap,
const std::vector<int>& height_sections,
const framework::ExecutionContext& context);
const framework::ExecutionContext& context,
const framework::Scope& scope);
template <typename T>
void prefetch_with_reconstruct(const std::string& id_name,
const std::string& out_name,
const std::vector<std::string>& table_names,
const std::vector<std::string>& epmap,
const std::vector<int>& height_sections,
const framework::ExecutionContext& context,
const framework::Scope& scope,
framework::LoDTensor* original) {
prefetch(id_name, out_name, table_names, epmap, height_sections, context,
scope);
auto& out = scope.FindVar(out_name)->Get<framework::LoDTensor>();
auto& ids = scope.FindVar(id_name)->Get<framework::LoDTensor>();
auto* original_value = original->data<T>();
auto* out_value = out.data<T>();
size_t original_width = original->numel() / original->dims()[0];
bool is_on_cpu_place = true;
if (!platform::is_cpu_place(ids.place())) {
is_on_cpu_place = false;
}
if (is_on_cpu_place) {
for (int64_t i = 0; i < ids.numel(); i++) {
const T* out_rows = out_value + original_width * i;
T* original_row =
original_value + original_width * ids.data<int64_t>()[i];
std::memcpy(original_row, out_rows, original_width * sizeof(T));
}
} else {
#ifndef PADDLE_WITH_CUDA
PADDLE_THROW("paddle is not compiled with CUDA!");
#else
platform::DeviceContextPool& pool = platform::DeviceContextPool::Instance();
auto& actual_ctx = *pool.Get(context.GetPlace());
for (int64_t i = 0; i < ids.numel(); i++) {
const T* out_rows = out_value + original_width * i;
T* original_row =
original_value + original_width * ids.data<int64_t>()[i];
auto stream =
static_cast<platform::CUDADeviceContext*>(&actual_ctx)->stream();
memory::Copy(boost::get<platform::CUDAPlace>(ids.place()), original_row,
platform::CPUPlace(), out_rows, original_width * sizeof(T),
stream);
}
#endif
}
}
}; // namespace distributed
}; // namespace operators
......
paddle/fluid/operators/elementwise/elementwise_sub_op.cu
浏览文件 @ 92da467c
......@@ -12,18 +12,23 @@ 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/elementwise/elementwise_sub_op.h"
#include "paddle/fluid/platform/float16.h"
namespace ops = paddle::operators;
REGISTER_OP_CUDA_KERNEL(
elementwise_sub,
ops::ElementwiseSubKernel<paddle::platform::CUDADeviceContext, float>,
ops::ElementwiseSubKernel<paddle::platform::CUDADeviceContext,
paddle::platform::float16>,
ops::ElementwiseSubKernel<paddle::platform::CUDADeviceContext, double>,
ops::ElementwiseSubKernel<paddle::platform::CUDADeviceContext, int>,
ops::ElementwiseSubKernel<paddle::platform::CUDADeviceContext, int64_t>);
REGISTER_OP_CUDA_KERNEL(
elementwise_sub_grad,
ops::ElementwiseSubGradKernel<paddle::platform::CUDADeviceContext, float>,
ops::ElementwiseSubGradKernel<paddle::platform::CUDADeviceContext,
paddle::platform::float16>,
ops::ElementwiseSubGradKernel<paddle::platform::CUDADeviceContext, double>,
ops::ElementwiseSubGradKernel<paddle::platform::CUDADeviceContext, int>,
ops::ElementwiseSubGradKernel<paddle::platform::CUDADeviceContext,
......
paddle/fluid/operators/fused/fused_embedding_seq_pool_op.cc 0 → 100644
浏览文件 @ 92da467c
/* Copyright (c) 2016 PaddlePaddle Authors. All Rights Reserved.
Licensed under the Apache License, Version 2.0 (the "License");
you may not use this file except in compliance with the License.
You may obtain a copy of the License at
http://www.apache.org/licenses/LICENSE-2.0
Unless required by applicable law or agreed to in writing, software
distributed under the License is distributed on an "AS IS" BASIS,
WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
See the License for the specific language governing permissions and
limitations under the License. */
#include "paddle/fluid/operators/fused/fused_embedding_seq_pool_op.h"
#include "paddle/fluid/framework/var_type_inference.h"
namespace paddle {
namespace operators {
class FusedEmbeddingSeqPoolOp : public framework::OperatorWithKernel {
public:
using framework::OperatorWithKernel::OperatorWithKernel;
void InferShape(framework::InferShapeContext* ctx) const override {
PADDLE_ENFORCE(ctx->HasInput("W"),
"Input W of FusedEmbeddingSeqPoolOp should not be null.");
PADDLE_ENFORCE(ctx->HasInput("Ids"),
"Input Ids of FusedEmbeddingSeqPoolOp should not be null.");
PADDLE_ENFORCE(ctx->HasOutput("Out"),
"Output of FusedEmbeddingSeqPoolOp should not be null.");
auto table_dims = ctx->GetInputDim("W");
auto ids_dims = ctx->GetInputDim("Ids");
const std::string& combiner = ctx->Attrs().Get<std::string>("combiner");
PADDLE_ENFORCE_EQ(table_dims.size(), 2);
PADDLE_ENFORCE_GE(ids_dims.size(), 1,
"The dim size of the 'Ids' tensor must greater than 1.");
PADDLE_ENFORCE_EQ(ids_dims[ids_dims.size() - 1], 1,
"The last dimension of the 'Ids' tensor must be 1.");
// we only support sum now
PADDLE_ENFORCE_EQ(combiner, "sum");
int64_t last_dim = table_dims[1];
for (int i = 1; i != ids_dims.size(); ++i) {
last_dim *= ids_dims[i];
}
if (ctx->IsRuntime()) {
framework::Variable* ids_var =
boost::get<framework::Variable*>(ctx->GetInputVarPtrs("Ids")[0]);
const auto& ids_lod = ids_var->Get<LoDTensor>().lod();
// in run time, the LoD of ids must be 1
PADDLE_ENFORCE(ids_lod.size(), 1u,
"The LoD level of Input(Ids) must be 1");
PADDLE_ENFORCE_GE(ids_lod[0].size(), 1u, "The LoD could NOT be empty");
int64_t batch_size = ids_lod[0].size() - 1;
// in run time, the shape from Ids -> output
// should be [seq_length, 1] -> [batch_size, embedding_size]
ctx->SetOutputDim("Out", framework::make_ddim({batch_size, last_dim}));
} else {
// in compile time, the lod level of ids must be 1
framework::VarDesc* ids_desc =
boost::get<framework::VarDesc*>(ctx->GetInputVarPtrs("Ids")[0]);
PADDLE_ENFORCE_EQ(ids_desc->GetLoDLevel(), 1);
// in compile time, the shape from Ids -> output
// should be [-1, 1] -> [-1, embedding_size]
ctx->SetOutputDim("Out", framework::make_ddim({-1, last_dim}));
}
}
protected:
framework::OpKernelType GetExpectedKernelType(
const framework::ExecutionContext& ctx) const override {
auto data_type = framework::GetDataTypeOfVar(ctx.InputVar("W"));
return framework::OpKernelType(data_type, ctx.device_context());
}
};
class FusedEmbeddingSeqPoolOpMaker : public framework::OpProtoAndCheckerMaker {
public:
void Make() override {
AddInput("W",
"(Tensor) The input represents embedding tensors, "
"which is a learnable parameter.");
AddInput("Ids",
"An input with type int32 or int64 "
"contains the ids to be looked up in W. "
"The last dimension size must be 1.");
AddOutput("Out", "The lookup results, which have the same type as W.");
AddAttr<std::string>("combiner",
"(string, default sum) "
"A string specifying the reduction op. Currently sum "
"are supported, sum computes the weighted sum of the "
"embedding results for each row.")
.SetDefault("sum");
// NOTE(minqiyang): grad_inplace is an temporal attribute,
// please do NOT set this attribute in python layer.
AddAttr<bool>("grad_inplace",
"(boolean, default false) "
"If the grad op reuse the input's variable.")
.SetDefault(false);
AddAttr<bool>("is_sparse",
"(boolean, default false) "
"Sparse update.")
.SetDefault(false);
AddComment(R"DOC(
FusedEmbeddingSeqPool Operator.
Computes embeddings for the given ids and weights.
This operator is used to perform lookups on the parameter W,
then computes the weighted sum of the lookups results for each row
and concatenated into a dense tensor.
The input Ids should carry the LoD (Level of Details) information.
And the output will change the LoD information with input Ids.
)DOC");
}
};
class FusedEmbeddingSeqPoolOpGradDescMaker
: public framework::DefaultGradOpDescMaker<true> {
using ::paddle::framework::DefaultGradOpDescMaker<
true>::DefaultGradOpDescMaker;
protected:
virtual std::string GradOpType() const {
return "fused_embedding_seq_pool_grad";
}
};
class FusedEmbeddingSeqPoolOpGrad : public framework::OperatorWithKernel {
public:
using framework::OperatorWithKernel::OperatorWithKernel;
void InferShape(framework::InferShapeContext* ctx) const override {
auto table_dims = ctx->GetInputDim("W");
ctx->SetOutputDim(framework::GradVarName("W"), table_dims);
}
protected:
framework::OpKernelType GetExpectedKernelType(
const framework::ExecutionContext& ctx) const override {
auto data_type = framework::GetDataTypeOfVar(ctx.InputVar("W"));
return framework::OpKernelType(data_type, ctx.device_context());
}
};
class FusedEmbeddingSeqPoolOpGradVarTypeInference
: public framework::VarTypeInference {
public:
void operator()(const framework::OpDesc& op_desc,
framework::BlockDesc* block) const override {
auto out_var_name = op_desc.Output(framework::GradVarName("W")).front();
auto attr = op_desc.GetAttr("is_sparse");
bool is_sparse = boost::get<bool>(attr);
if (is_sparse) {
VLOG(3) << "fused_embedding_seq_pool_grad op "
<< framework::GradVarName("W") << " is set to SelectedRows";
block->Var(out_var_name)
->SetType(framework::proto::VarType::SELECTED_ROWS);
} else {
VLOG(3) << "fused_embedding_seq_pool_grad op "
<< framework::GradVarName("W") << " is set to LoDTensor";
block->Var(out_var_name)->SetType(framework::proto::VarType::LOD_TENSOR);
}
block->Var(out_var_name)->SetDataType(block->Var("W")->GetDataType());
}
};
} // namespace operators
} // namespace paddle
namespace ops = paddle::operators;
REGISTER_OPERATOR(fused_embedding_seq_pool, ops::FusedEmbeddingSeqPoolOp,
ops::FusedEmbeddingSeqPoolOpGradDescMaker,
ops::FusedEmbeddingSeqPoolOpMaker);
REGISTER_OPERATOR(fused_embedding_seq_pool_grad,
ops::FusedEmbeddingSeqPoolOpGrad,
ops::FusedEmbeddingSeqPoolOpGradVarTypeInference);
REGISTER_OP_CPU_KERNEL(fused_embedding_seq_pool,
ops::FusedEmbeddingSeqPoolKernel<float>,
ops::FusedEmbeddingSeqPoolKernel<double>);
REGISTER_OP_CPU_KERNEL(fused_embedding_seq_pool_grad,
ops::FusedEmbeddingSeqPoolGradKernel<float>,
ops::FusedEmbeddingSeqPoolGradKernel<double>);
paddle/fluid/operators/fused/fused_embedding_seq_pool_op.h 0 → 100644
浏览文件 @ 92da467c
/* Copyright (c) 2016 PaddlePaddle Authors. All Rights Reserved.
Licensed under the Apache License, Version 2.0 (the "License");
you may not use this file except in compliance with the License.
You may obtain a copy of the License at
http://www.apache.org/licenses/LICENSE-2.0
Unless required by applicable law or agreed to in writing, software
distributed under the License is distributed on an "AS IS" BASIS,
WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
See the License for the specific language governing permissions and
limitations under the License. */
#pragma once
#include <string>
#include <vector>
#include "paddle/fluid/framework/eigen.h"
#include "paddle/fluid/framework/lod_tensor.h"
#include "paddle/fluid/framework/op_registry.h"
#include "paddle/fluid/framework/selected_rows.h"
#include "paddle/fluid/operators/math/blas.h"
namespace paddle {
namespace operators {
using Tensor = framework::Tensor;
using LoDTensor = framework::LoDTensor;
using SelectedRows = framework::SelectedRows;
using DDim = framework::DDim;
template <typename T>
struct EmbeddingVSumFunctor {
void operator()(const framework::ExecutionContext &context,
const LoDTensor *table_t, const LoDTensor *ids_t,
LoDTensor *output_t) {
auto *table = table_t->data<T>();
int64_t row_number = table_t->dims()[0];
int64_t row_width = table_t->dims()[1];
int64_t last_dim = output_t->dims()[1];
const int64_t *ids = ids_t->data<int64_t>();
auto ids_lod = ids_t->lod()[0];
int64_t ids_count = ids_t->numel() / ids_lod.back();
auto *output = output_t->mutable_data<T>(context.GetPlace());
auto blas = math::GetBlas<platform::CPUDeviceContext, T>(context);
for (int64_t i = 0; i != ids_lod.size() - 1; ++i) {
size_t begin = ids_lod[i] * ids_count;
for (int64_t j = 0; j != ids_count; ++j) {
PADDLE_ENFORCE_LT(ids[begin], row_number);
PADDLE_ENFORCE_GE(ids[begin], 0, "ids %d", i);
blas.VCOPY(row_width, table + ids[begin + j] * row_width,
output + i * last_dim + j * row_width);
}
for (int64_t r = (ids_lod[i] + 1) * ids_count;
r < ids_lod[i + 1] * ids_count; ++r) {
PADDLE_ENFORCE_LT(ids[r], row_number);
PADDLE_ENFORCE_GE(ids[r], 0, "ids %d", i);
blas.AXPY(row_width, 1., table + ids[r] * row_width,
output + i * last_dim + (r % ids_count) * row_width);
}
}
}
};
template <typename T>
class FusedEmbeddingSeqPoolKernel : public framework::OpKernel<T> {
public:
void Compute(const framework::ExecutionContext &context) const override {
const LoDTensor *ids_t = context.Input<LoDTensor>("Ids"); // int tensor
LoDTensor *output_t = context.Output<LoDTensor>("Out"); // float tensor
const LoDTensor *table_var = context.Input<LoDTensor>("W");
const std::string &combiner_type = context.Attr<std::string>("combiner");
if (combiner_type == "sum") {
EmbeddingVSumFunctor<T> functor;
functor(context, table_var, ids_t, output_t);
}
}
};
template <typename T>
class FusedEmbeddingSeqPoolGradKernel : public framework::OpKernel<T> {
public:
void Compute(const framework::ExecutionContext &context) const override {
auto *table_var = context.InputVar("W");
DDim table_dim;
if (table_var->IsType<LoDTensor>()) {
table_dim = context.Input<LoDTensor>("W")->dims();
} else if (table_var->IsType<SelectedRows>()) {
auto *table_t = context.Input<SelectedRows>("W");
table_dim = table_t->value().dims();
} else {
PADDLE_THROW(
"The parameter W of a LookupTable "
"must be either LoDTensor or SelectedRows");
}
bool is_sparse = context.Attr<bool>("is_sparse");
// Since paddings are not trainable and fixed in forward, the gradient of
// paddings makes no sense and we don't deal with it in backward.
if (is_sparse) {
auto *ids = context.Input<LoDTensor>("Ids");
auto *d_output = context.Input<LoDTensor>(framework::GradVarName("Out"));
auto *d_table = context.Output<SelectedRows>(framework::GradVarName("W"));
auto *ids_data = ids->data<int64_t>();
int64_t ids_num = ids->numel();
auto lod = ids->lod()[0];
int64_t row_width = d_output->dims()[1];
framework::Vector<int64_t> *new_rows = d_table->mutable_rows();
new_rows->resize(ids_num);
std::memcpy(&(*new_rows)[0], ids_data, ids_num * sizeof(int64_t));
auto *d_table_value = d_table->mutable_value();
d_table_value->Resize({ids_num, table_dim[1]});
T *d_table_data = d_table_value->mutable_data<T>(context.GetPlace());
const T *d_output_data = d_output->data<T>();
auto blas = math::GetBlas<platform::CPUDeviceContext, T>(context);
for (int i = 0; i < static_cast<int>(lod.size()) - 1; ++i) {
int64_t h = static_cast<int64_t>(lod[i + 1] - lod[i]);
int64_t in_offset = lod[i] * row_width;
const T *out_pos = d_output_data + i * row_width;
T *in_pos = d_table_data + in_offset;
for (int r = 0; r != h; ++r) {
blas.VCOPY(row_width, out_pos, in_pos + r * row_width);
}
}
} else {
LOG(ERROR) << "Dense is not supported in fused_embedding_seq_pool_op now";
}
}
};
} // namespace operators
} // namespace paddle
paddle/fluid/operators/fused/fusion_seqpool_concat_op.cc 0 → 100644
浏览文件 @ 92da467c
/* 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/fused/fusion_seqpool_concat_op.h"
#include <string>
#include <vector>
#include "paddle/fluid/operators/jit/kernels.h"
namespace paddle {
namespace operators {
void FusionSeqPoolConcatOp::InferShape(
framework::InferShapeContext* ctx) const {
PADDLE_ENFORCE_GE(ctx->Inputs("X").size(), 1UL,
"Inputs(X) of FusionSeqPoolConcatOp should be empty.");
PADDLE_ENFORCE(ctx->HasOutput("Out"),
"Output(Out) of FusionSeqPoolConcatOp should not be null.");
int axis = ctx->Attrs().Get<int>("axis");
PADDLE_ENFORCE_EQ(axis, 1,
"FusionSeqPoolConcatOp only supports concat axis=1 yet.");
auto ins_dims = ctx->GetInputsDim("X");
const size_t n = ins_dims.size();
PADDLE_ENFORCE_GT(n, 0UL, "Input tensors count should > 0.");
if (n == 1) {
LOG(WARNING) << "Only have one input, may waste memory";
}
// The output height should be confirmed in Compute,
// since input lod is not accessible here.
PADDLE_ENFORCE_EQ(ins_dims[0].size(), 2UL,
"The dims size of first input should be 2.");
ctx->SetOutputDim("Out", {-1, ins_dims[0][axis] * static_cast<int>(n)});
}
framework::OpKernelType FusionSeqPoolConcatOp::GetExpectedKernelType(
const framework::ExecutionContext& ctx) const {
return framework::OpKernelType(
framework::GetDataTypeOfVar(ctx.MultiInputVar("X")[0]), ctx.GetPlace());
}
void FusionSeqPoolConcatOpMaker::Make() {
AddInput("X", "(LoDTensor) Input tensors of this operator.").AsDuplicable();
AddOutput("Out", "(LoDTensor) Output tensor of concat operator.");
AddAttr<std::string>("pooltype",
"(string, default 'AVERAGE') some of the pooling "
"pooltype of SequencePoolOp.")
.SetDefault("SUM")
.InEnum({"AVERAGE", "SUM", "SQRT"});
AddAttr<int>("axis",
"The axis along which the input tensors will be concatenated.")
.SetDefault(1);
AddComment(R"DOC(
Fusion Sequence Pool of pooltype(sum, average and sqrt) and Concat Operator.
)DOC");
}
template <typename T>
class FusionSeqPoolConcatKernel : public framework::OpKernel<T> {
public:
void Compute(const framework::ExecutionContext& ctx) const override {
auto ins = ctx.MultiInput<LoDTensor>("X");
auto* out = ctx.Output<LoDTensor>("Out");
std::string pooltype = ctx.Attr<std::string>("pooltype");
auto x0_lod = ins[0]->lod();
auto x0_dims = ins[0]->dims();
auto y_dims = out->dims();
size_t bs = x0_lod[0].size() - 1;
out->Resize({static_cast<int64_t>(bs), y_dims[1]});
framework::LoD y_lod(1);
y_lod[0].resize(bs + 1);
for (size_t i = 0; i <= bs; ++i) {
y_lod[0][i] = i;
}
out->set_lod(y_lod);
auto place = ctx.GetPlace();
T* y_data = out->mutable_data<T>(place);
int w = ins[0]->numel() / x0_dims[0];
PADDLE_ENFORCE_EQ(y_dims[1] % w, 0,
"The output of dims[1] should be dividable of w");
jit::seq_pool_attr_t attr(w, jit::SeqPoolType::kSum);
if (pooltype == "AVERAGE") {
attr.type = jit::SeqPoolType::kAvg;
} else if (pooltype == "SQRT") {
attr.type = jit::SeqPoolType::kSqrt;
}
auto seqpool =
jit::Get<jit::kSeqPool, jit::SeqPoolTuples<T>, platform::CPUPlace>(
attr);
size_t n = ins.size();
for (size_t i = 0; i < n; ++i) {
auto x_dims = ins[i]->dims();
auto x_lod = ins[i]->lod()[0];
const T* src = ins[i]->data<T>();
T* dst = y_data + i * w;
PADDLE_ENFORCE_EQ(static_cast<int>(ins[i]->numel() / x_dims[0]), w,
"Width of all inputs should be equal.");
PADDLE_ENFORCE_EQ(x_lod.size(), bs + 1,
"Batchsize of all inputs should be equal.");
for (size_t j = 0; j < bs; ++j) {
attr.h = static_cast<int>(x_lod[j + 1] - x_lod[j]);
seqpool(src, dst, &attr);
dst += n * w;
src += attr.h * attr.w;
}
}
}
};
} // namespace operators
} // namespace paddle
namespace ops = paddle::operators;
REGISTER_OPERATOR(fusion_seqpool_concat, ops::FusionSeqPoolConcatOp,
ops::FusionSeqPoolConcatOpMaker,
paddle::framework::DefaultGradOpDescMaker<true>);
REGISTER_OP_CPU_KERNEL(fusion_seqpool_concat,
ops::FusionSeqPoolConcatKernel<float>,
ops::FusionSeqPoolConcatKernel<double>);
paddle/fluid/operators/fused/fusion_seqpool_concat_op.h 0 → 100644
浏览文件 @ 92da467c
/* 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 "paddle/fluid/framework/op_registry.h"
namespace paddle {
namespace operators {
using LoDTensor = framework::LoDTensor;
using Tensor = framework::Tensor;
class FusionSeqPoolConcatOp : public framework::OperatorWithKernel {
public:
using framework::OperatorWithKernel::OperatorWithKernel;
void InferShape(framework::InferShapeContext* ctx) const override;
protected:
framework::OpKernelType GetExpectedKernelType(
const framework::ExecutionContext& ctx) const override;
};
class FusionSeqPoolConcatOpMaker : public framework::OpProtoAndCheckerMaker {
public:
void Make() override;
};
} // namespace operators
} // namespace paddle
paddle/fluid/operators/hierarchical_sigmoid_op.cc
浏览文件 @ 92da467c
......@@ -67,6 +67,11 @@ class HierarchicalSigmoidOp : public framework::OperatorWithKernel {
PADDLE_ENFORCE(ctx->HasOutput("Out"), "Output(Out) should not be null.");
PADDLE_ENFORCE(ctx->HasOutput("PreOut"),
"Output(PreOut) should not be null.");
auto with_prefetch = ctx->Attrs().Get<bool>("remote_prefetch");
if (with_prefetch) {
PADDLE_ENFORCE(ctx->HasOutput("W_Out"),
"Output(W_Out) should not be null.");
}
const int64_t batch_size = ctx->GetInputDim("X")[0];
std::vector<int64_t> output_shape({batch_size, 1});
ctx->SetOutputDim("Out", framework::make_ddim(output_shape));
......@@ -95,7 +100,7 @@ class HierarchicalSigmoidOpMaker : public framework::OpProtoAndCheckerMaker {
AddInput("Label",
"(LoDTensor, required), The labels of training data. It's a"
"tensor with shape [N, 1].");
AddInput("PTable",
AddInput("PathTable",
"(LoDTensor, optional), The Path Table from root to current word"
"it should have shape like [N, L], L is the length of the Path")
.AsDispensable();
......@@ -119,8 +124,30 @@ class HierarchicalSigmoidOpMaker : public framework::OpProtoAndCheckerMaker {
"[batch_size, code_length], where code_length represents the "
"maximum path length from root to leaf nodes.")
.AsIntermediate();
AddOutput(
"W_Out",
"(LoDTensor, optinal) using input 'W' as Output to make it mutable"
"When we are using prefetch")
.AsIntermediate();
AddAttr<AttrType>("num_classes", "(int, optional), The number of classes")
.SetDefault(2);
// for parameter prefetch
AddAttr<bool>("remote_prefetch", "").SetDefault(false);
AddAttr<int>("trainer_id", "trainer id from 0 ~ worker_num.").SetDefault(0);
AddAttr<std::vector<int>>("height_sections",
"Height for each output SelectedRows.")
.SetDefault(std::vector<int>({}));
AddAttr<std::vector<std::string>>(
"epmap",
"(string vector, default 127.0.0.1:6164)"
"Server endpoints in the order of input variables for mapping")
.SetDefault({});
AddAttr<std::vector<std::string>>(
"table_names",
"(string vector, the splited table names that will be fetched from "
"parameter server)"
"in the order of input variables for mapping")
.SetDefault({});
AddComment(R"DOC(
The hierarchical sigmoid operator organize the classes into a binary tree.
At each node, a sigmoid function is used to calculate the probability of
......@@ -189,23 +216,17 @@ class HierarchicalSigmoidGradOpGradVarTypeInference
<< " is set to SelectedRows";
block->Var(w_grad_var_name)
->SetType(framework::proto::VarType::SELECTED_ROWS);
if (hasBias) {
VLOG(30) << "hierarchical_sigmoid_grad op "
<< framework::GradVarName("Bias") << " is set to SelectedRows";
block->Var(bias_grad_var_name)
->SetType(framework::proto::VarType::SELECTED_ROWS);
}
} else {
VLOG(30) << "hierarchical_sigmoid_grad op " << framework::GradVarName("W")
<< " is set to LoDTensor";
block->Var(w_grad_var_name)
->SetType(framework::proto::VarType::LOD_TENSOR);
if (hasBias) {
VLOG(30) << "hierarchical_sigmoid_grad op "
<< framework::GradVarName("Bias") << " is set to LoDTensor";
block->Var(bias_grad_var_name)
->SetType(framework::proto::VarType::LOD_TENSOR);
}
}
if (hasBias) {
VLOG(30) << "hierarchical_sigmoid_grad op "
<< framework::GradVarName("Bias") << " is set to LoDTensor";
block->Var(bias_grad_var_name)
->SetType(framework::proto::VarType::LOD_TENSOR);
}
block->Var(w_grad_var_name)->SetDataType(block->Var("W")->GetDataType());
}
......
paddle/fluid/operators/hierarchical_sigmoid_op.h
浏览文件 @ 92da467c
......@@ -14,7 +14,9 @@ limitations under the License. */
#pragma once
#include <iostream>
#include <iterator>
#include <set>
#include <string>
#include <vector>
#include "paddle/fluid/framework/mixed_vector.h"
#include "paddle/fluid/framework/op_registry.h"
......@@ -24,6 +26,10 @@ limitations under the License. */
#include "paddle/fluid/operators/math/matrix_bit_code.h"
#include "paddle/fluid/platform/transform.h"
#ifdef PADDLE_WITH_DISTRIBUTE
#include "paddle/fluid/operators/distributed/parameter_prefetch.h"
#endif
namespace paddle {
namespace operators {
......@@ -34,8 +40,9 @@ using platform::Transform;
static std::vector<int64_t> PathToRows(const framework::LoDTensor& path) {
std::set<int64_t> rows;
const int64_t* paths = path.data<int64_t>();
for (int64_t i = 0; i < path.numel(); ++i) {
int64_t row = path.data<int64_t>()[i];
int64_t row = paths[i];
if (row < 0) {
continue;
}
......@@ -49,13 +56,54 @@ class HierarchicalSigmoidOpKernel : public framework::OpKernel<T> {
void Compute(const framework::ExecutionContext& ctx) const override {
auto& in = detail::Ref(ctx.Input<framework::LoDTensor>("X"));
auto& w = detail::Ref(ctx.Input<framework::LoDTensor>("W"));
auto* path = ctx.Input<framework::LoDTensor>("PTable");
auto* path = ctx.Input<framework::LoDTensor>("PathTable");
auto* code = ctx.Input<framework::LoDTensor>("PathCode");
auto& label = detail::Ref(ctx.Input<framework::LoDTensor>("Label"));
auto* bias = ctx.Input<framework::LoDTensor>("Bias");
auto* out = ctx.Output<framework::LoDTensor>("Out");
auto* pre_out = ctx.Output<framework::LoDTensor>("PreOut");
size_t num_classes = static_cast<size_t>(ctx.Attr<int>("num_classes"));
// for remote prefetch
auto epmap = ctx.Attr<std::vector<std::string>>("epmap");
if (!epmap.empty()) {
// if epmap is not empty, then the parameter will be fetched from remote
// parameter
// server
auto height_sections = ctx.Attr<std::vector<int>>("height_sections");
auto table_names = ctx.Attr<std::vector<std::string>>("table_names");
std::vector<int64_t> real_rows = PathToRows(*path);
framework::Scope& local_scope = ctx.scope().NewScope();
auto* ids = local_scope.Var("Ids@Prefetch");
auto* x_tensor = ids->GetMutable<framework::LoDTensor>();
x_tensor->mutable_data<int64_t>(
framework::make_ddim({static_cast<int64_t>(real_rows.size()), 1}),
ctx.GetPlace());
// copy.
std::memcpy(x_tensor->data<int64_t>(), real_rows.data(),
real_rows.size() * sizeof(int64_t));
framework::DDim w_dims = ctx.Input<Tensor>("W")->dims();
w_dims[0] = x_tensor->dims()[0];
auto* w_tensor =
local_scope.Var("W@Prefetch")->GetMutable<framework::LoDTensor>();
w_tensor->Resize(w_dims);
#ifdef PADDLE_WITH_DISTRIBUTE
// w_Out is set to used by prefetch, never change it in other cases
auto* w_out = ctx.Output<framework::LoDTensor>("W_Out");
operators::distributed::prefetch_with_reconstruct<T>(
"Ids@Prefetch", "W@Prefetch", table_names, epmap, height_sections,
ctx, local_scope, w_out);
#else
PADDLE_THROW(
"paddle is not compiled with distribute support, can not do "
"parameter prefetch!");
#endif
}
bool is_custom = false;
if (path) {
is_custom = true;
......@@ -116,9 +164,8 @@ class HierarchicalSigmoidGradOpKernel : public framework::OpKernel<T> {
void Compute(const framework::ExecutionContext& ctx) const override {
auto& in = detail::Ref(ctx.Input<framework::LoDTensor>("X"));
auto& w = detail::Ref(ctx.Input<framework::LoDTensor>("W"));
auto* path = ctx.Input<framework::LoDTensor>("PTable");
auto* path = ctx.Input<framework::LoDTensor>("PathTable");
auto* code = ctx.Input<framework::LoDTensor>("PathCode");
auto* bias = ctx.Input<framework::LoDTensor>("Bias");
auto* in_grad =
ctx.Output<framework::LoDTensor>(framework::GradVarName("X"));
bool is_sparse = ctx.Attr<bool>("is_sparse");
......@@ -173,15 +220,14 @@ class HierarchicalSigmoidGradOpKernel : public framework::OpKernel<T> {
}
// TODO(guosheng): multiply pre_out_grad with subgradient of clipping to
// be consistent with the clipping in forward.
auto* bias_grad =
ctx.Output<framework::LoDTensor>(framework::GradVarName("Bias"));
if (bias_grad) {
bias_grad->mutable_data<T>(ctx.GetPlace());
zero(dev_ctx, bias_grad, static_cast<T>(0.0));
bit_code->AddGrad(pre_out_grad, bias_grad);
}
if (!is_sparse) {
auto* bias_grad =
ctx.Output<framework::LoDTensor>(framework::GradVarName("Bias"));
if (bias_grad) {
bias_grad->mutable_data<T>(ctx.GetPlace());
zero(dev_ctx, bias_grad, static_cast<T>(0.0));
bit_code->AddGrad(pre_out_grad, bias_grad);
}
auto* w_grad =
ctx.Output<framework::LoDTensor>(framework::GradVarName("W"));
w_grad->mutable_data<T>(ctx.GetPlace());
......@@ -200,21 +246,6 @@ class HierarchicalSigmoidGradOpKernel : public framework::OpKernel<T> {
w_grad_value->mutable_data<T>(temp_dim, ctx.GetPlace());
zero(dev_ctx, w_grad_value, static_cast<T>(0.0));
auto* bias_grad =
ctx.Output<framework::SelectedRows>(framework::GradVarName("Bias"));
if (bias_grad) {
bias_grad->set_rows(real_rows);
// build ids -> rows index map
bias_grad->SyncIndex();
bias_grad->set_height(bias->dims()[0]);
auto* bias_grad_value = bias_grad->mutable_value();
std::vector<int64_t> dims = {static_cast<int64_t>(real_rows.size()),
bias->dims()[1]};
bias_grad_value->mutable_data<T>(framework::make_ddim(dims),
ctx.GetPlace());
zero(dev_ctx, bias_grad_value, static_cast<T>(0.0));
bit_code->AddGrad(pre_out_grad, bias_grad);
}
bit_code->MulGradWeight(pre_out_grad, w_grad, in);
}
bit_code->MulGradError(pre_out_grad, w, in_grad);
......
paddle/fluid/operators/jit/benchmark.cc
浏览文件 @ 92da467c
......@@ -190,6 +190,26 @@ void BenchGRUKernel() {
}
}
template <paddle::operators::jit::KernelType KT, typename T, typename PlaceType>
void BenchSeqPoolKernel() {
std::vector<jit::SeqPoolType> pool_types = {
jit::SeqPoolType::kSum, jit::SeqPoolType::kAvg, jit::SeqPoolType::kSqrt};
for (auto type : pool_types) {
for (int w : TestSizes()) {
jit::seq_pool_attr_t attr(w, type);
for (int h : TestSizes()) {
attr.h = h;
std::vector<T> x(h * w), y(w);
RandomVec<T>(h * w, x.data(), -2.f, 2.f);
const T* x_data = x.data();
T* y_data = y.data();
BenchAllImpls<KT, jit::SeqPoolTuples<T>, PlaceType>(attr, x_data,
y_data, &attr);
}
}
}
}
// Benchmark all jit kernels including jitcode, mkl and refer.
// To use this tool, run command: ./benchmark [options...]
// Options:
......@@ -228,4 +248,7 @@ int main(int argc, char* argv[]) {
BenchGRUKernel<jit::kGRUH1, T, PlaceType>();
BenchGRUKernel<jit::kGRUHtPart1, T, PlaceType>();
BenchGRUKernel<jit::kGRUHtPart2, T, PlaceType>();
// seq pool function
BenchSeqPoolKernel<jit::kSeqPool, T, PlaceType>();
}
paddle/fluid/operators/jit/gen/CMakeLists.txt
浏览文件 @ 92da467c
......@@ -26,3 +26,4 @@ USE_JITKERNEL_GEN(kGRUH1)
USE_JITKERNEL_GEN(kGRUHtPart1)
USE_JITKERNEL_GEN(kGRUHtPart2)
USE_JITKERNEL_GEN(kNCHW16CMulNC)
USE_JITKERNEL_GEN(kSeqPool)
paddle/fluid/operators/jit/gen/seqpool.cc 0 → 100644
浏览文件 @ 92da467c
/* 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/jit/gen/seqpool.h"
#include "paddle/fluid/operators/jit/gen/act.h" // for exp_float_consts ones
#include "paddle/fluid/operators/jit/registry.h"
#include "paddle/fluid/platform/cpu_info.h"
namespace paddle {
namespace operators {
namespace jit {
namespace gen {
void SeqPoolJitCode::genCode() {
constexpr int block = YMM_FLOAT_BLOCK;
constexpr int max_num_regs = 8;
const int num_block = w_ / block;
const int num_groups = num_block / max_num_regs;
int rest_num_regs = num_block % max_num_regs;
mov(reg32_int_h, dword[param_attr]);
if (type_ == SeqPoolType::kAvg || type_ == SeqPoolType::kSqrt) {
mov(reg_tmp, reinterpret_cast<size_t>(exp_float_consts));
vmovups(xmm_t(1), ptr[reg_tmp + OFFSET_EXP_ONE]);
mov(reg_tmp, reinterpret_cast<size_t>(fp_h_));
fild(dword[param_attr]);
fstp(dword[reg_tmp]);
vmovss(xmm_t(0), ptr[reg_tmp]);
if (type_ == SeqPoolType::kSqrt) {
vsqrtps(xmm_t(0), xmm_t(0));
}
vdivps(xmm_t(1), xmm_t(1), xmm_t(0));
vmovss(ptr[reg_tmp], xmm_t(1));
}
const int group_len = max_num_regs * block * sizeof(float);
for (int g = 0; g < num_groups; ++g) {
pool_height<ymm_t>(g * group_len, block, max_num_regs);
}
if (rest_num_regs > 0) {
pool_height<ymm_t>(num_groups * group_len, block, rest_num_regs);
}
// part of rest_w * height
const int rest = w_ % block;
pool_height_of_rest_width(rest, (w_ - rest) * sizeof(float), max_num_regs);
ret();
}
class SeqPoolCreator : public JitCodeCreator<seq_pool_attr_t> {
public:
bool UseMe(const seq_pool_attr_t& attr) const override {
return platform::MayIUse(platform::avx);
}
size_t CodeSize(const seq_pool_attr_t& attr) const override {
return 96 +
((attr.w / YMM_FLOAT_BLOCK + 4 /* for rest */) *
4 /* load, mul and save */ +
256) *
8;
}
std::unique_ptr<GenBase> CreateJitCode(
const seq_pool_attr_t& attr) const override {
PADDLE_ENFORCE_GT(attr.w, 0);
PADDLE_ENFORCE_GT(attr.h, 0);
return make_unique<SeqPoolJitCode>(attr, CodeSize(attr));
}
};
} // namespace gen
} // namespace jit
} // namespace operators
} // namespace paddle
namespace gen = paddle::operators::jit::gen;
REGISTER_JITKERNEL_GEN(kSeqPool, gen::SeqPoolCreator);
paddle/fluid/operators/jit/gen/seqpool.h 0 → 100644
浏览文件 @ 92da467c
/* Copyright (c) 2018 PaddlePaddle Authors. All Rights Reserved.
*
* Licensed under the Apache License, Version 2.0 (the "License");
* you may not use this file except in compliance with the License.
* You may obtain a copy of the License at
*
* http://www.apache.org/licenses/LICENSE-2.0
*
* Unless required by applicable law or agreed to in writing, software
* distributed under the License is distributed on an "AS IS" BASIS,
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
* See the License for the specific language governing permissions and
* limitations under the License. */
#pragma once
#include <string>
#include "glog/logging.h"
#include "paddle/fluid/operators/jit/gen/jitcode.h"
#include "paddle/fluid/platform/enforce.h"
namespace paddle {
namespace operators {
namespace jit {
namespace gen {
class SeqPoolJitCode : public JitCode {
public:
explicit SeqPoolJitCode(const seq_pool_attr_t& attr,
size_t code_size = 256 * 1024,
void* code_ptr = nullptr)
: JitCode(code_size, code_ptr), w_(attr.w), type_(attr.type) {
if (!(type_ == SeqPoolType::kSum || type_ == SeqPoolType::kAvg ||
type_ == SeqPoolType::kSqrt)) {
LOG(FATAL) << "Only support sum pool yet ";
}
fp_h_[0] = 1.f;
this->genCode();
}
virtual const char* name() const {
std::string base = "SeqPoolJitCode";
if (type_ == SeqPoolType::kSum) {
base += "_Sum";
} else if (type_ == SeqPoolType::kAvg) {
base += "_Avg";
} else if (type_ == SeqPoolType::kSqrt) {
base += "_Sqrt";
}
base += ("_W" + std::to_string(w_));
return base.c_str();
}
void genCode() override;
protected:
template <typename JMM>
void pool_height(int w_offset, int block, int max_num_regs) {
int offset = w_offset;
for (int i = 0; i < max_num_regs; ++i) {
vmovups(JMM(i), ptr[param_src + offset]);
offset += sizeof(float) * block;
}
cmp(reg32_int_h, 1);
Label l_next_h, l_h_done;
jle(l_h_done, T_NEAR);
mov(reg_h_i, 1);
mov(reg_tmp, param_src);
add(reg_tmp, w_ * sizeof(float) + w_offset);
L(l_next_h);
{
mov(reg_ptr_src_i, reg_tmp);
for (int i = 0; i < max_num_regs; ++i) {
vmovups(JMM(i + max_num_regs), ptr[reg_ptr_src_i]);
// sum anyway
vaddps(JMM(i), JMM(i), JMM(i + max_num_regs));
add(reg_ptr_src_i, sizeof(float) * block);
}
inc(reg_h_i);
add(reg_tmp, w_ * sizeof(float));
cmp(reg_h_i, reg32_int_h);
jl(l_next_h, T_NEAR);
}
L(l_h_done);
// save right now
if (type_ == SeqPoolType::kAvg || type_ == SeqPoolType::kSqrt) {
mov(reg_tmp, reinterpret_cast<size_t>(fp_h_));
vbroadcastss(JMM(max_num_regs), ptr[reg_tmp]);
}
offset = w_offset;
for (int i = 0; i < max_num_regs; ++i) {
if (type_ == SeqPoolType::kAvg || type_ == SeqPoolType::kSqrt) {
vmulps(JMM(i), JMM(i), JMM(max_num_regs));
}
vmovups(ptr[param_dst + offset], JMM(i));
offset += sizeof(float) * block;
}
}
void pool_height_of_rest_width(int rest, int w_offset, int max_num_regs) {
const int rest_used_num_regs = load_rest(rest, w_offset, 0);
const bool has_block4 = rest / 4 > 0;
const bool has_block2 = (rest % 4) / 2 > 0;
const bool has_block1 = (rest % 2) == 1;
cmp(reg32_int_h, 1);
Label l_next_h, l_h_done;
jle(l_h_done, T_NEAR);
mov(reg_h_i, 1);
mov(reg_tmp, param_src);
add(reg_tmp, w_ * sizeof(float) + w_offset);
L(l_next_h);
{
int reg_idx = 0;
mov(reg_ptr_src_i, reg_tmp);
if (has_block4) {
vmovups(xmm_t(reg_idx + max_num_regs), ptr[reg_ptr_src_i]);
add(reg_ptr_src_i, sizeof(float) * 4);
reg_idx++;
}
if (has_block2) {
vmovups(xmm_t(reg_idx + max_num_regs), ptr[reg_ptr_src_i]);
add(reg_ptr_src_i, sizeof(float) * 2);
reg_idx++;
}
if (has_block1) {
vmovss(xmm_t(reg_idx + max_num_regs), ptr[reg_ptr_src_i]);
reg_idx++;
}
PADDLE_ENFORCE_EQ(reg_idx, rest_used_num_regs,
"All heights should use same regs");
for (int i = 0; i < reg_idx; ++i) {
vaddps(xmm_t(i), xmm_t(i), xmm_t(i + max_num_regs));
}
inc(reg_h_i);
add(reg_tmp, w_ * sizeof(float));
cmp(reg_h_i, reg32_int_h);
jl(l_next_h, T_NEAR);
}
L(l_h_done);
// save right now
if (type_ == SeqPoolType::kAvg || type_ == SeqPoolType::kSqrt) {
mov(reg_tmp, reinterpret_cast<size_t>(fp_h_));
vbroadcastss(xmm_t(max_num_regs), ptr[reg_tmp]);
for (int i = 0; i < rest_used_num_regs; ++i) {
vmulps(xmm_t(i), xmm_t(i), xmm_t(max_num_regs));
}
}
save_rest(rest, w_offset);
}
// return the number of used regs, use start from reg 0
int load_rest(int rest, int w_offset, const int num_shift_regs,
const int reg_start = 0) {
const bool has_block4 = rest / 4 > 0;
const bool has_block2 = (rest % 4) / 2 > 0;
const bool has_block1 = (rest % 2) == 1;
int reg_idx = reg_start;
if (has_block4) {
vmovups(xmm_t(reg_idx + num_shift_regs), ptr[param_src + w_offset]);
w_offset += sizeof(float) * 4;
reg_idx++;
}
if (has_block2) {
vmovq(xmm_t(reg_idx + num_shift_regs), ptr[param_src + w_offset]);
w_offset += sizeof(float) * 2;
reg_idx++;
}
if (has_block1) {
vmovss(xmm_t(reg_idx + num_shift_regs), ptr[param_src + w_offset]);
reg_idx++;
}
return reg_idx;
}
// use reg start from 0
void save_rest(int rest, int w_offset, int reg_start = 0) {
const bool has_block4 = rest / 4 > 0;
const bool has_block2 = (rest % 4) / 2 > 0;
const bool has_block1 = (rest % 2) == 1;
int reg_idx = reg_start;
if (has_block4) {
vmovups(ptr[param_dst + w_offset], xmm_t(reg_idx));
w_offset += sizeof(float) * 4;
reg_idx++;
}
if (has_block2) {
vmovq(ptr[param_dst + w_offset], xmm_t(reg_idx));
w_offset += sizeof(float) * 2;
reg_idx++;
}
if (has_block1) {
vmovss(ptr[param_dst + w_offset], xmm_t(reg_idx));
}
}
private:
float ALIGN32_BEG fp_h_[1] ALIGN32_END;
int w_;
SeqPoolType type_;
reg64_t param_src{abi_param1};
reg64_t param_dst{abi_param2};
reg64_t param_attr{abi_param3};
reg64_t reg_tmp{rax};
reg32_t reg32_int_h{r8d};
reg32_t reg32_fp_h{r9d};
reg64_t reg_h_i{r10};
reg64_t reg_ptr_src_i{r11};
};
} // namespace gen
} // namespace jit
} // namespace operators
} // namespace paddle
paddle/fluid/operators/jit/helper.cc
浏览文件 @ 92da467c
......@@ -26,6 +26,7 @@ namespace jit {
const char* to_string(KernelType kt) {
switch (kt) {
ONE_CASE(kNone);
ONE_CASE(kVMul);
ONE_CASE(kVAdd);
ONE_CASE(kVAddRelu);
......@@ -45,12 +46,26 @@ const char* to_string(KernelType kt) {
ONE_CASE(kCRFDecoding);
ONE_CASE(kLayerNorm);
ONE_CASE(kNCHW16CMulNC);
ONE_CASE(kSeqPool);
default:
PADDLE_THROW("Not support type: %d, or forget to add it.", kt);
return "NOT JITKernel";
}
return nullptr;
}
const char* to_string(SeqPoolType tp) {
switch (tp) {
ONE_CASE(kNonePoolType);
ONE_CASE(kSum);
ONE_CASE(kAvg);
ONE_CASE(kSqrt);
default:
PADDLE_THROW("Not support type: %d, or forget to add it.", tp);
return "NOT PoolType";
}
return nullptr;
}
#undef ONE_CASE
KernelType to_kerneltype(const std::string& act) {
......
paddle/fluid/operators/jit/helper.h
浏览文件 @ 92da467c
......@@ -119,6 +119,7 @@ typename KernelTuples::func_type Get(
}
const char* to_string(KernelType kt);
const char* to_string(SeqPoolType kt);
KernelType to_kerneltype(const std::string& act);
......@@ -134,6 +135,11 @@ inline std::ostream& operator<<(std::ostream& os, const gru_attr_t& attr) {
<< "],act_cand[" << to_string(attr.act_cand) << "]";
return os;
}
inline std::ostream& operator<<(std::ostream& os, const seq_pool_attr_t& attr) {
os << "height_size[" << attr.h << "],width_size[" << attr.w << "],pool_type["
<< to_string(attr.type) << "]";
return os;
}
} // namespace jit
} // namespace operators
......
paddle/fluid/operators/jit/kernel_base.h
浏览文件 @ 92da467c
......@@ -41,8 +41,16 @@ typedef enum {
kCRFDecoding,
kLayerNorm,
kNCHW16CMulNC,
kSeqPool,
} KernelType;
typedef enum {
kNonePoolType = 0,
kSum = 1,
kAvg,
kSqrt,
} SeqPoolType;
template <typename T>
struct XYZNTuples {
typedef T data_type;
......@@ -112,6 +120,21 @@ struct GRUTuples {
typedef void (*func_type)(gru_t*, const gru_attr_t*);
};
typedef struct seq_pool_attr_s {
int h, w; // h should always be the first one
SeqPoolType type;
seq_pool_attr_s() = default;
explicit seq_pool_attr_s(int width, SeqPoolType pool_type, int height = 1)
: h(height), w(width), type(pool_type) {}
} seq_pool_attr_t;
template <typename T>
struct SeqPoolTuples {
typedef T data_type;
typedef seq_pool_attr_t attr_type;
typedef void (*func_type)(const T*, T*, const seq_pool_attr_t*);
};
template <typename T>
struct CRFDecodingTuples {
typedef T data_type;
......
paddle/fluid/operators/jit/kernel_key.cc
浏览文件 @ 92da467c
......@@ -42,6 +42,13 @@ size_t JitCodeKey<gru_attr_t>(const gru_attr_t& attr) {
(static_cast<int>(attr.act_cand) << act_type_shift);
}
template <>
size_t JitCodeKey<seq_pool_attr_t>(const seq_pool_attr_t& attr) {
size_t key = attr.w;
constexpr int pool_type_shift = 3;
return (key << pool_type_shift) + static_cast<int>(attr.type);
}
} // namespace jit
} // namespace operators
} // namespace paddle
paddle/fluid/operators/jit/more/mkl/CMakeLists.txt
浏览文件 @ 92da467c
......@@ -9,3 +9,4 @@ USE_JITKERNEL_MORE(kVScal, mkl)
USE_JITKERNEL_MORE(kVExp, mkl)
USE_JITKERNEL_MORE(kVSigmoid, mkl)
USE_JITKERNEL_MORE(kVTanh, mkl)
USE_JITKERNEL_MORE(kSeqPool, mkl)
paddle/fluid/operators/jit/more/mkl/mkl.cc
浏览文件 @ 92da467c
......@@ -72,6 +72,26 @@ void VExp<double>(const double* x, double* y, int n) {
platform::dynload::vdExp(n, x, y);
}
template <>
void VCopy<float>(const float* x, float* y, int n) {
platform::dynload::cblas_scopy(n, x, 1, y, 1);
}
template <>
void VCopy<double>(const double* x, double* y, int n) {
platform::dynload::cblas_dcopy(n, x, 1, y, 1);
}
template <>
void VAXPY<float>(float a, const float* x, float* y, int n) {
platform::dynload::cblas_saxpy(n, a, x, 1, y, 1);
}
template <>
void VAXPY<double>(double a, const double* x, double* y, int n) {
platform::dynload::cblas_daxpy(n, a, x, 1, y, 1);
}
// TODO(TJ): tuning me carefully on AVX, AVX2 and AVX512
template <>
bool VMulKernel<float>::UseMe(const int& d) const {
......@@ -103,6 +123,16 @@ bool VTanhKernel<float>::UseMe(const int& d) const {
return d > 7;
}
template <>
bool SeqPoolKernel<float>::UseMe(const seq_pool_attr_t& attr) const {
return true;
}
template <>
bool SeqPoolKernel<double>::UseMe(const seq_pool_attr_t& attr) const {
return true;
}
#define AWALYS_USE_ME_WITH_DOUBLE(func) \
template <> \
bool func##Kernel<double>::UseMe(const int& d) const { \
......@@ -135,5 +165,6 @@ REGISTER_MKL_KERNEL(kVScal, VScal);
REGISTER_MKL_KERNEL(kVExp, VExp);
REGISTER_MKL_KERNEL(kVSigmoid, VSigmoid);
REGISTER_MKL_KERNEL(kVTanh, VTanh);
REGISTER_MKL_KERNEL(kSeqPool, SeqPool);
#undef REGISTER_MKL_KERNEL
paddle/fluid/operators/jit/more/mkl/mkl.h
浏览文件 @ 92da467c
......@@ -14,6 +14,7 @@
#pragma once
#include <cmath>
#include <type_traits>
#include "paddle/fluid/operators/jit/kernel_base.h"
......@@ -35,6 +36,12 @@ void VScal(const T* a, const T* x, T* y, int n);
template <typename T>
void VExp(const T* x, T* y, int n);
template <typename T>
void VCopy(const T* x, T* y, int n);
template <typename T>
void VAXPY(T a, const T* x, T* y, int n);
template <typename T>
void VSigmoid(const T* x, T* y, int n) {
const T min = SIGMOID_THRESHOLD_MIN;
......@@ -60,6 +67,23 @@ void VTanh(const T* x, T* y, int n) {
}
}
template <typename T>
void SeqPool(const T* x, T* y, const seq_pool_attr_t* attr) {
VCopy<T>(x, y, attr->w);
for (int h = 1; h != attr->h; ++h) {
VAXPY<T>(static_cast<T>(1), x + h * attr->w, y, attr->w);
}
if (attr->type == SeqPoolType::kAvg || attr->type == SeqPoolType::kSqrt) {
T scalar = static_cast<T>(1);
if (attr->type == SeqPoolType::kAvg) {
scalar = scalar / static_cast<T>(attr->h);
} else {
scalar = scalar / std::sqrt(static_cast<T>(attr->h));
}
VScal<T>(&scalar, y, y, attr->w);
}
}
#define DECLARE_MKL_KERNEL(name, tuples) \
template <typename T> \
class name##Kernel : public KernelMore<tuples<T>> { \
......@@ -81,6 +105,8 @@ DECLARE_MKL_KERNEL(VExp, XYNTuples);
DECLARE_MKL_KERNEL(VSigmoid, XYNTuples);
DECLARE_MKL_KERNEL(VTanh, XYNTuples);
DECLARE_MKL_KERNEL(SeqPool, SeqPoolTuples);
#undef DECLARE_MKL_KERNEL
} // namespace mkl
......
paddle/fluid/operators/jit/refer/CMakeLists.txt
浏览文件 @ 92da467c
......@@ -26,3 +26,4 @@ USE_JITKERNEL_REFER(kGRUHtPart2)
USE_JITKERNEL_REFER(kCRFDecoding)
USE_JITKERNEL_REFER(kLayerNorm)
USE_JITKERNEL_REFER(kNCHW16CMulNC)
USE_JITKERNEL_REFER(kSeqPool)
paddle/fluid/operators/jit/refer/refer.cc
浏览文件 @ 92da467c
......@@ -47,4 +47,6 @@ REGISTER_REFER_KERNEL(kLayerNorm, LayerNorm);
REGISTER_REFER_KERNEL(kNCHW16CMulNC, NCHW16CMulNC);
REGISTER_REFER_KERNEL(kSeqPool, SeqPool);
#undef REGISTER_REFER_KERNEL
paddle/fluid/operators/jit/refer/refer.h
浏览文件 @ 92da467c
......@@ -332,6 +332,28 @@ void NCHW16CMulNC(const T* x, const T* y, T* z, int height, int width) {
}
}
template <typename T>
void SeqPool(const T* x, T* y, const seq_pool_attr_t* attr) {
for (int w = 0; w < attr->w; ++w) {
const T* src = x + w;
T* dst = y + w;
*dst = static_cast<T>(0);
for (int h = 0; h < attr->h; ++h) {
*dst = *dst + *src;
src += attr->w;
}
}
if (attr->type == SeqPoolType::kAvg || attr->type == SeqPoolType::kSqrt) {
T scalar = static_cast<T>(1);
if (attr->type == SeqPoolType::kAvg) {
scalar = scalar / static_cast<T>(attr->h);
} else {
scalar = scalar / std::sqrt(static_cast<T>(attr->h));
}
VScal<T>(&scalar, y, y, attr->w);
}
}
#define DECLARE_REFER_KERNEL(name, tuples) \
template <typename T> \
class name##Kernel : public ReferKernel<tuples<T>> { \
......@@ -370,6 +392,8 @@ DECLARE_REFER_KERNEL(LayerNorm, LayerNormTuples);
DECLARE_REFER_KERNEL(NCHW16CMulNC, NCHW16CMulNCTuples);
DECLARE_REFER_KERNEL(SeqPool, SeqPoolTuples);
#undef DECLARE_REFER_KERNEL
} // namespace refer
......
paddle/fluid/operators/jit/test.cc
浏览文件 @ 92da467c
......@@ -211,6 +211,24 @@ struct TestFuncWithRefer<jit::GRUTuples<T>, std::vector<T>, std::vector<T>,
}
};
template <typename T>
struct TestFuncWithRefer<jit::SeqPoolTuples<T>, std::vector<T>,
std::vector<T>> {
void operator()(const typename jit::SeqPoolTuples<T>::func_type tgt,
const std::vector<T>& x, const std::vector<T>& yref,
const typename jit::SeqPoolTuples<T>::attr_type& attr) {
EXPECT_TRUE(tgt != nullptr);
EXPECT_EQ(x.size() % yref.size(), 0);
int w = yref.size();
std::vector<T> y(w);
const T* x_data = x.data();
const T* yref_data = yref.data();
T* y_data = y.data();
tgt(x_data, y_data, &attr);
ExpectEQ<T>(y_data, yref_data, w);
}
};
template <paddle::operators::jit::KernelType KT, typename KernelTuples,
typename PlaceType, typename... Args>
void TestAllImpls(const typename KernelTuples::attr_type& attr, Args... args) {
......@@ -415,6 +433,31 @@ void TestGRUKernel() {
}
}
template <paddle::operators::jit::KernelType KT, typename T, typename PlaceType>
void TestSeqPoolKernel() {
VLOG(10) << "===== Test JITKernel " << jit::to_string(KT);
std::vector<jit::SeqPoolType> pool_types = {
jit::SeqPoolType::kSum, jit::SeqPoolType::kAvg, jit::SeqPoolType::kSqrt};
for (auto type : pool_types) {
for (int w : TestSizes()) {
jit::seq_pool_attr_t attr(w, type);
for (int h : TestSizes()) {
attr.h = h;
auto ref = jit::GetRefer<KT, jit::SeqPoolTuples<T>>();
EXPECT_TRUE(ref != nullptr);
std::vector<T> x(h * w), yref(w);
RandomVec<T>(h * w, x.data(), -2.f, 2.f);
const T* x_data = x.data();
T* yref_data = yref.data();
ref(x_data, yref_data, &attr);
VLOG(10) << attr;
TestAllImpls<KT, jit::SeqPoolTuples<T>, PlaceType, std::vector<T>,
std::vector<T>>(attr, x, yref, attr);
}
}
}
}
template <paddle::operators::jit::KernelType KT, typename T, typename PlaceType>
void TestNCHW16CMulNCKernel() {
VLOG(10) << "===== Test JITKernel " << jit::to_string(KT);
......@@ -569,6 +612,12 @@ TEST(JITKernel, kGRUHtPart2) {
TestGRUKernel<jit::kGRUHtPart2, double, paddle::platform::CPUPlace>();
}
TEST(JITKernel, kSeqPool) {
namespace jit = paddle::operators::jit;
TestSeqPoolKernel<jit::kSeqPool, float, paddle::platform::CPUPlace>();
TestSeqPoolKernel<jit::kSeqPool, double, paddle::platform::CPUPlace>();
}
TEST(JITKernel, kNCHW16CMulNC) {
namespace jit = paddle::operators::jit;
TestNCHW16CMulNCKernel<jit::kNCHW16CMulNC, float,
......
paddle/fluid/operators/linear_chain_crf_op.cc
浏览文件 @ 92da467c
......@@ -230,10 +230,12 @@ class LinearChainCRFGradOp : public framework::OperatorWithKernel {
if (ctx->HasOutput(framework::GradVarName("Emission"))) {
ctx->SetOutputDim(framework::GradVarName("Emission"), emission_exps_dims);
ctx->ShareLoD("Emission", framework::GradVarName("Emission"));
}
if (ctx->HasOutput(framework::GradVarName("Transition"))) {
ctx->SetOutputDim(framework::GradVarName("Transition"),
transition_exps_dims);
ctx->ShareLoD("Transition", framework::GradVarName("Transition"));
}
}
......
paddle/fluid/operators/lookup_table_op.cu
浏览文件 @ 92da467c
......@@ -92,7 +92,8 @@ class LookupTableCUDAKernel : public framework::OpKernel<T> {
// server
#ifdef PADDLE_WITH_DISTRIBUTE
operators::distributed::prefetch(id_name, out_name, table_names, epmap,
height_sections, context);
height_sections, context,
context.scope());
#else
PADDLE_THROW(
"paddle is not compiled with distribute support, can not do "
......
paddle/fluid/operators/lookup_table_op.h
浏览文件 @ 92da467c
......@@ -59,7 +59,8 @@ class LookupTableKernel : public framework::OpKernel<T> {
// server
#ifdef PADDLE_WITH_DISTRIBUTE
operators::distributed::prefetch(id_name, out_name, table_names, epmap,
height_sections, context);
height_sections, context,
context.scope());
#else
PADDLE_THROW(
"paddle is not compiled with distribute support, can not do "
......
paddle/fluid/operators/math/CMakeLists.txt
浏览文件 @ 92da467c
......@@ -51,7 +51,7 @@ math_library(pooling)
math_library(selected_rows_functor DEPS selected_rows math_function blas)
math_library(sequence2batch)
math_library(sequence_padding)
math_library(sequence_pooling DEPS math_function)
math_library(sequence_pooling DEPS math_function jit_kernel_helper)
math_library(sequence_scale)
math_library(softmax DEPS math_function)
......
paddle/fluid/operators/math/blas_impl.cu.h
浏览文件 @ 92da467c
......@@ -62,27 +62,19 @@ struct CUBlas<float> {
cudaDataType_t Atype, int lda, const void *B,
cudaDataType_t Btype, int ldb, const float *beta, void *C,
cudaDataType_t Ctype, int ldc) {
// Because the gcc 4.8 doesn't expand template parameter pack that
// appears in a lambda-expression, I can not use template parameter pack
// here.
auto cublas_call = [&]() {
// Because the gcc 4.8 doesn't expand template parameter pack that
// appears in a lambda-expression, I can not use template parameter pack
// here.
#if CUDA_VERSION >= 8000
VLOG(5) << "use_tensor_op_math: "
<< (platform::TensorCoreAvailable() ? "True" : "False");
VLOG(5) << "use_tensor_op_math: "
<< (dev_ctx->tensor_core_available() ? "True" : "False");
dev_ctx->TensorCoreCublasCallIfAvailable([&](cublasHandle_t handle) {
PADDLE_ENFORCE(platform::dynload::cublasSgemmEx(
dev_ctx->cublas_handle(), transa, transb, m, n, k, alpha, A, Atype,
lda, B, Btype, ldb, beta, C, Ctype, ldc));
handle, transa, transb, m, n, k, alpha, A, Atype, lda, B, Btype, ldb,
beta, C, Ctype, ldc));
});
#else
PADDLE_THROW("cublasSgemmEx is supported on cuda >= 8.0");
#endif
};
#if CUDA_VERSION >= 9000
// NOTES: To use Tensor Core, we should change the cublas config,
// but the cublas may be hold by multi-thread.
dev_ctx->CublasCall(cublas_call, CUBLAS_TENSOR_OP_MATH);
#else
cublas_call();
PADDLE_THROW("cublasSgemmEx is supported on cuda >= 8.0");
#endif
}
};
......@@ -170,32 +162,24 @@ struct CUBlas<platform::float16> {
cudaDataType_t Btype, int ldb, const void *beta, void *C,
cudaDataType_t Ctype, int ldc,
cudaDataType_t computeType) {
auto cublas_call = [&]() {
#if CUDA_VERSION >= 8000
cublasGemmAlgo_t algo = CUBLAS_GEMM_DFALT;
cublasGemmAlgo_t algo = CUBLAS_GEMM_DFALT;
#if CUDA_VERSION >= 9000
bool use_tensor_op_math = platform::TensorCoreAvailable();
if (use_tensor_op_math) {
algo = CUBLAS_GEMM_DFALT_TENSOR_OP;
}
VLOG(5) << "use_tensor_op_math: "
<< (use_tensor_op_math ? "True" : "False");
bool use_tensor_op_math = dev_ctx->tensor_core_available();
if (use_tensor_op_math) {
algo = CUBLAS_GEMM_DFALT_TENSOR_OP;
}
VLOG(5) << "use_tensor_op_math: "
<< (use_tensor_op_math ? "True" : "False");
#endif // CUDA_VERSION >= 9000
dev_ctx->TensorCoreCublasCallIfAvailable([&](cublasHandle_t handle) {
PADDLE_ENFORCE(platform::dynload::cublasGemmEx(
dev_ctx->cublas_handle(), transa, transb, m, n, k, alpha, A, Atype,
lda, B, Btype, ldb, beta, C, Ctype, ldc, computeType, algo));
handle, transa, transb, m, n, k, alpha, A, Atype, lda, B, Btype, ldb,
beta, C, Ctype, ldc, computeType, algo));
});
#else
PADDLE_THROW("cublasGemmEx is supported on cuda >= 8.0");
#endif
};
#if CUDA_VERSION >= 9000
// NOTES: To use Tensor Core, we should change the cublas config,
// but the cublas may be hold by multi-thread.
dev_ctx->CublasCall(cublas_call, CUBLAS_TENSOR_OP_MATH);
#else
cublas_call();
PADDLE_THROW("cublasGemmEx is supported on cuda >= 8.0");
#endif
}
};
......@@ -223,9 +207,10 @@ void Blas<platform::CUDADeviceContext>::GEMM(CBLAS_TRANSPOSE transA,
CUDA_R_32F, N);
} else {
#endif // CUDA_VERSION >= 8000
CUBlas<T>::GEMM(context_.cublas_handle(), cuTransB, cuTransA, N, M, K,
&alpha, B, ldb, A, lda, &beta, C, N);
context_.CublasCall([&](cublasHandle_t handle) {
CUBlas<T>::GEMM(handle, cuTransB, cuTransA, N, M, K, &alpha, B, ldb, A,
lda, &beta, C, N);
});
#if CUDA_VERSION >= 8000
}
......@@ -266,9 +251,12 @@ inline void Blas<platform::CUDADeviceContext>::GEMM(
CUDA_R_16F, lda, &h_beta, C, CUDA_R_16F, N, CUDA_R_32F);
#else
// CUDA 7.5 does not support cublasGemmEx, hence we fall back to use hgemm
CUBlas<platform::float16>::GEMM(context_.cublas_handle(), cuTransB, cuTransA,
N, M, K, &h_alpha, h_B, ldb, h_A, lda,
&h_beta, h_C, N);
context_.CublasCall([&](cublasHandle_t handle) {
CUBlas<platform::float16>::GEMM(handle, cuTransB, cuTransA, N, M, K,
&h_alpha, h_B, ldb, h_A, lda, &h_beta, h_C,
N);
});
#endif // CUDA_VERSION >= 8000
}
......@@ -292,8 +280,10 @@ void Blas<platform::CUDADeviceContext>::GEMM(bool transA, bool transB, int M,
} else {
#endif // CUDA_VERSION >= 8000
CUBlas<T>::GEMM(context_.cublas_handle(), cuTransB, cuTransA, N, M, K,
&alpha, B, ldb, A, lda, &beta, C, ldc);
context_.CublasCall([&](cublasHandle_t handle) {
CUBlas<T>::GEMM(handle, cuTransB, cuTransA, N, M, K, &alpha, B, ldb, A,
lda, &beta, C, ldc);
});
#if CUDA_VERSION >= 8000
}
......@@ -311,16 +301,19 @@ inline void Blas<platform::CUDADeviceContext>::GEMM(
cublasOperation_t cuTransA = transA ? CUBLAS_OP_T : CUBLAS_OP_N;
cublasOperation_t cuTransB = transB ? CUBLAS_OP_T : CUBLAS_OP_N;
CUBlas<platform::float16>::GEMM(context_.cublas_handle(), cuTransB, cuTransA,
N, M, K, &alpha, B, ldb, A, lda, &beta, C,
ldc);
context_.CublasCall([&](cublasHandle_t handle) {
CUBlas<platform::float16>::GEMM(handle, cuTransB, cuTransA, N, M, K, &alpha,
B, ldb, A, lda, &beta, C, ldc);
});
}
template <>
template <typename T>
void Blas<platform::CUDADeviceContext>::AXPY(int n, T alpha, const T *x,
T *y) const {
CUBlas<T>::AXPY(context_.cublas_handle(), n, &alpha, x, 1, y, 1);
context_.CublasCall([&](cublasHandle_t handle) {
CUBlas<T>::AXPY(handle, n, &alpha, x, 1, y, 1);
});
}
template <>
......@@ -330,8 +323,9 @@ void Blas<platform::CUDADeviceContext>::GEMV(bool trans_a, int M, int N,
T beta, T *C) const {
cublasOperation_t cuTransA = !trans_a ? CUBLAS_OP_T : CUBLAS_OP_N;
CUBlas<T>::GEMV(context_.cublas_handle(), cuTransA, N, M, &alpha, A, N, B, 1,
&beta, C, 1);
context_.CublasCall([&](cublasHandle_t handle) {
CUBlas<T>::GEMV(handle, cuTransA, N, M, &alpha, A, N, B, 1, &beta, C, 1);
});
}
template <>
......@@ -353,28 +347,28 @@ void Blas<platform::CUDADeviceContext>::BatchedGEMM(
#if CUDA_VERSION >= 9010
if (FLAGS_enable_cublas_tensor_op_math && std::is_same<T, float>::value) {
auto cublas_call = [&]() {
cublasGemmAlgo_t algo = CUBLAS_GEMM_DFALT;
bool use_tensor_op_math = platform::TensorCoreAvailable();
if (use_tensor_op_math) {
algo = CUBLAS_GEMM_DFALT_TENSOR_OP;
}
VLOG(5) << "use_tensor_op_math: "
<< (use_tensor_op_math ? "True" : "False");
cublasGemmAlgo_t algo = CUBLAS_GEMM_DFALT;
bool use_tensor_op_math = context_.tensor_core_available();
if (use_tensor_op_math) {
algo = CUBLAS_GEMM_DFALT_TENSOR_OP;
}
VLOG(5) << "use_tensor_op_math: "
<< (use_tensor_op_math ? "True" : "False");
context_.TensorCoreCublasCallIfAvailable([&](cublasHandle_t handle) {
PADDLE_ENFORCE(platform::dynload::cublasGemmStridedBatchedEx(
context_.cublas_handle(), cuTransB, cuTransA, N, M, K, &alpha, B,
CUDA_R_32F, ldb, strideB, A, CUDA_R_32F, lda, strideA, &beta, C,
CUDA_R_32F, ldc, strideC, batchCount, CUDA_R_32F, algo));
};
auto &dev_ctx = const_cast<platform::CUDADeviceContext &>(context_);
dev_ctx.CublasCall(cublas_call, CUBLAS_TENSOR_OP_MATH);
handle, cuTransB, cuTransA, N, M, K, &alpha, B, CUDA_R_32F, ldb,
strideB, A, CUDA_R_32F, lda, strideA, &beta, C, CUDA_R_32F, ldc,
strideC, batchCount, CUDA_R_32F, algo));
});
} else {
#endif // CUDA_VERSION >= 9010
CUBlas<T>::GEMM_STRIDED_BATCH(context_.cublas_handle(), cuTransB, cuTransA,
N, M, K, &alpha, B, ldb, strideB, A, lda,
strideA, &beta, C, ldc, strideC, batchCount);
context_.CublasCall([&](cublasHandle_t handle) {
CUBlas<T>::GEMM_STRIDED_BATCH(handle, cuTransB, cuTransA, N, M, K, &alpha,
B, ldb, strideB, A, lda, strideA, &beta, C,
ldc, strideC, batchCount);
});
#if CUDA_VERSION >= 9010
}
......
paddle/fluid/operators/math/matrix_bit_code.cc
浏览文件 @ 92da467c
......@@ -84,41 +84,6 @@ void MatrixBitCodeFunctor<T>::AddGrad(const framework::Tensor &tmat,
code_table_.apply_visitor(func);
}
template <typename T>
struct MatrixBitCodeFunctorSelectedRowsAddGrad
: public boost::static_visitor<void> {
const framework::Tensor &tmat_;
framework::SelectedRows *vec_;
MatrixBitCodeFunctorSelectedRowsAddGrad(const framework::Tensor &tmat,
framework::SelectedRows *vec)
: tmat_(tmat), vec_(vec) {}
template <typename CodeTable>
void operator()(const CodeTable &code_table) {
size_t batch_size = tmat_.dims()[0];
size_t width = tmat_.dims()[1];
auto *vec_data = vec_->mutable_value()->template data<T>();
auto *tmat_data = tmat_.data<T>();
for (size_t i = 0; i < batch_size; ++i) {
auto code = code_table.get_code(i);
int code_length = code.get_length();
for (int j = 0; j < code_length; ++j) {
size_t index = code.calc_index(j);
int64_t row_index = vec_->GetIndexFromId(static_cast<int64_t>(index));
vec_data[row_index] += tmat_data[i * width + j];
}
}
}
};
template <typename T>
void MatrixBitCodeFunctor<T>::AddGrad(const framework::Tensor &tmat,
framework::SelectedRows *vec) {
MatrixBitCodeFunctorSelectedRowsAddGrad<T> func(tmat, vec);
code_table_.apply_visitor(func);
}
template <typename T>
struct MatrixBitCodeFunctorSum : public boost::static_visitor<void> {
const framework::Tensor &tmat_;
......
paddle/fluid/operators/math/matrix_bit_code.h
浏览文件 @ 92da467c
......@@ -124,11 +124,12 @@ class SimpleCode {
template <typename T>
class CustomCode {
public:
CustomCode(const framework::Tensor& ptable, const framework::Tensor& pcode,
const int64_t* ids, int index) {
seq_len_ = ptable.dims()[1];
ptable_data_ = ptable.data<T>() + seq_len_ * index;
pcode_data_ = pcode.data<T>() + seq_len_ * index;
CustomCode(const framework::Tensor& path_table,
const framework::Tensor& path_code, const int64_t* ids,
int index) {
seq_len_ = path_table.dims()[1];
path_table_data_ = path_table.data<T>() + seq_len_ * index;
path_code_data_ = path_code.data<T>() + seq_len_ * index;
}
/**
* Here the id of root should be 1 rather than 0, thus the encoding of class c
......@@ -139,25 +140,25 @@ class CustomCode {
* Binary classification path is the suffixes of encoding, thus leave out the
* left most bit in calc_bit.
*/
size_t calc_index(int bit) const { return ptable_data_[bit]; }
bool calc_bit(int bit) const { return pcode_data_[bit]; }
size_t calc_index(int bit) const { return path_table_data_[bit]; }
bool calc_bit(int bit) const { return path_code_data_[bit]; }
// NOTE: this function is not thread-safe.
int get_length() const {
if (length_ < 0) {
auto len = seq_len_;
length_ =
static_cast<int>(std::find_if(ptable_data_, ptable_data_ + len,
[](const T& val) { return val < 0; }) -
ptable_data_);
length_ = static_cast<int>(
std::find_if(path_table_data_, path_table_data_ + len,
[](const T& val) { return val < 0; }) -
path_table_data_);
}
return length_;
}
private:
int64_t seq_len_;
const T* ptable_data_;
const T* pcode_data_;
const T* path_table_data_;
const T* path_code_data_;
mutable int length_{-1};
};
......@@ -181,9 +182,9 @@ class SimpleCodeTable {
template <typename T>
class CustomCodeTable {
public:
CustomCodeTable(const framework::Tensor& ptable,
const framework::Tensor& pcode, const int64_t* ids)
: ptable_(ptable), pcode_(pcode), ids_(ids) {}
CustomCodeTable(const framework::Tensor& path_table,
const framework::Tensor& path_code, const int64_t* ids)
: ptable_(path_table), pcode_(path_code), ids_(ids) {}
CustomCode<T> get_code(int64_t code) const {
return CustomCode<T>(ptable_, pcode_, ids_, code);
......@@ -210,11 +211,11 @@ class MatrixBitCodeFunctor {
ids_(ids),
code_table_(SimpleCodeTable(num_classes, ids)) {}
MatrixBitCodeFunctor(const framework::Tensor& ptable,
const framework::Tensor& pcode, const int64_t* ids)
: num_classes_(static_cast<size_t>(ptable.dims()[1])),
MatrixBitCodeFunctor(const framework::Tensor& path_table,
const framework::Tensor& path_code, const int64_t* ids)
: num_classes_(static_cast<size_t>(path_table.dims()[1])),
ids_(ids),
code_table_(CustomCodeTable<int64_t>(ptable, pcode, ids)) {}
code_table_(CustomCodeTable<int64_t>(path_table, path_code, ids)) {}
/* For j < code_length
tmat(i, j) += vec(0, index(i, j))
*/
......@@ -225,11 +226,6 @@ class MatrixBitCodeFunctor {
*/
void AddGrad(const framework::Tensor& tmat, framework::Tensor* vec);
/* For selected rows For j < code_length
vec(0, index(i, j)) += tmat(i, j)
*/
void AddGrad(const framework::Tensor& tmat, framework::SelectedRows* vec);
/* For j < code_length
sum(i, 0) = \sum_j bit(i, j) * tmat(i, j)
*/
......
paddle/fluid/operators/math/selected_rows_functor.cc
浏览文件 @ 92da467c
......@@ -195,6 +195,10 @@ struct SelectedRowsAddToTensor<platform::CPUDeviceContext, T> {
void operator()(const platform::CPUDeviceContext& context,
const framework::SelectedRows& input1,
framework::Tensor* input2) {
if (UNLIKELY(input1.rows().size() == 0)) {
LOG(WARNING) << "input selected rows is empty!";
return;
}
auto in1_height = input1.height();
auto in2_dims = input2->dims();
PADDLE_ENFORCE_EQ(in1_height, in2_dims[0]);
......
paddle/fluid/operators/math/sequence_pooling.cc
浏览文件 @ 92da467c
......@@ -14,6 +14,7 @@ limitations under the License. */
#include <string>
#include "paddle/fluid/operators/jit/kernels.h"
#include "paddle/fluid/operators/math/blas.h"
#include "paddle/fluid/operators/math/math_function.h"
#include "paddle/fluid/operators/math/sequence_pooling.h"
......@@ -239,15 +240,33 @@ class SequencePoolFunctor<platform::CPUDeviceContext, T> {
last_pool(context, input, output);
return;
}
if (pooltype == "FIRST") {
math::FirstSeqPoolFunctor<T> first_pool;
first_pool(context, input, output);
return;
}
auto lod = input.lod()[0];
if (pooltype == "SUM") {
auto place = context.GetPlace();
PADDLE_ENFORCE(platform::is_cpu_place(place));
const T* src = input.data<T>();
T* dst = output->mutable_data<T>(place);
jit::seq_pool_attr_t attr(
static_cast<int>(input.numel() / input.dims()[0]),
jit::SeqPoolType::kSum);
auto seqpool =
jit::Get<jit::kSeqPool, jit::SeqPoolTuples<T>, platform::CPUPlace>(
attr);
for (int i = 0; i < static_cast<int>(lod.size()) - 1; ++i) {
attr.h = static_cast<int>(lod[i + 1] - lod[i]);
seqpool(src, dst, &attr);
dst += attr.w;
src += attr.h * attr.w;
}
return;
}
auto& place = *context.eigen_device();
auto blas = math::GetBlas<platform::CPUDeviceContext, T>(context);
for (int i = 0; i < static_cast<int>(lod.size()) - 1; ++i) {
Tensor in_t =
input.Slice(static_cast<int>(lod[i]), static_cast<int>(lod[i + 1]));
......@@ -258,15 +277,6 @@ class SequencePoolFunctor<platform::CPUDeviceContext, T> {
auto out_e = EigenVector<T>::Flatten(out_t);
if (pooltype == "AVERAGE") {
out_e.device(place) = in_e.mean(Eigen::array<int, 1>({{0}}));
} else if (pooltype == "SUM") {
if (h > 0) {
const T* in_data = in_t.data<T>();
T* out_data = out_t.mutable_data<T>(context.GetPlace());
blas.VCOPY(w, in_data, out_data);
for (int64_t r = 1; r != h; ++r) {
blas.AXPY(w, 1., in_data + r * w, out_data);
}
}
} else if (pooltype == "SQRT") {
out_e.device(place) = in_e.sum(Eigen::array<int, 1>({{0}})) /
std::sqrt(static_cast<T>(h));
......
paddle/fluid/operators/math/softmax.h
浏览文件 @ 92da467c
......@@ -49,6 +49,7 @@ class SoftmaxGradCUDNNFunctor {
const framework::Tensor* Y, const framework::Tensor* y_grad,
framework::Tensor* x_grad);
};
#endif
} // namespace math
......
paddle/fluid/operators/nce_op.cc
浏览文件 @ 92da467c
......@@ -153,6 +153,24 @@ class NCEOpMaker : public framework::OpProtoAndCheckerMaker {
AddAttr<bool>("is_sparse", "(boolean, default false) Sparse update.")
.SetDefault(false);
// for parameter prefetch
AddAttr<bool>("remote_prefetch", "").SetDefault(false);
AddAttr<int>("trainer_id", "trainer id from 0 ~ worker_num.").SetDefault(0);
AddAttr<std::vector<int>>("height_sections",
"Height for each output SelectedRows.")
.SetDefault(std::vector<int>({}));
AddAttr<std::vector<std::string>>(
"epmap",
"(string vector, default 127.0.0.1:6164)"
"Server endpoints in the order of input variables for mapping")
.SetDefault({});
AddAttr<std::vector<std::string>>(
"table_names",
"(string vector, the splited table names that will be fetched from "
"parameter server)"
"in the order of input variables for mapping")
.SetDefault({});
AddAttr<std::vector<int>>("custom_neg_classes",
"This attribute only be used in unitest. Classes "
"in this list wiil be used as negative classes "
......@@ -222,24 +240,20 @@ class NCEOpGradVarTypeInference : public framework::VarTypeInference {
void operator()(const framework::OpDesc &op_desc,
framework::BlockDesc *block) const override {
auto weight_grad = op_desc.Output(framework::GradVarName("Weight")).front();
auto bias_grad = op_desc.Output(framework::GradVarName("Bias")).front();
auto attr = op_desc.GetAttr("is_sparse");
bool is_sparse = boost::get<bool>(attr);
if (is_sparse) {
VLOG(3) << "nce_op_grad op " << weight_grad << " and " << bias_grad
VLOG(3) << "nce_op_grad op " << weight_grad << " and "
<< " is set to SelectedRows";
block->Var(weight_grad)
->SetType(framework::proto::VarType::SELECTED_ROWS);
block->Var(bias_grad)->SetType(framework::proto::VarType::SELECTED_ROWS);
} else {
VLOG(3) << "nce_op_grad op " << weight_grad << " and " << bias_grad
VLOG(3) << "nce_op_grad op " << weight_grad << " and "
<< " is set to LoDTensor";
block->Var(weight_grad)->SetType(framework::proto::VarType::LOD_TENSOR);
block->Var(bias_grad)->SetType(framework::proto::VarType::LOD_TENSOR);
}
block->Var(weight_grad)->SetDataType(block->Var("Input")->GetDataType());
block->Var(bias_grad)->SetDataType(block->Var("Input")->GetDataType());
}
};
......
paddle/fluid/operators/nce_op.h
浏览文件 @ 92da467c
......@@ -15,8 +15,10 @@ limitations under the License. */
#pragma once
#include <math.h>
#include <iterator>
#include <random>
#include <set>
#include <string>
#include <vector>
#include "paddle/fluid/framework/eigen.h"
#include "paddle/fluid/framework/op_registry.h"
......@@ -24,6 +26,10 @@ limitations under the License. */
#include "paddle/fluid/operators/math/sampler.h"
#include "unsupported/Eigen/CXX11/Tensor"
#ifdef PADDLE_WITH_DISTRIBUTE
#include "paddle/fluid/operators/distributed/parameter_prefetch.h"
#endif
namespace paddle {
namespace operators {
......@@ -43,7 +49,6 @@ void PrepareSamples(const framework::ExecutionContext &context,
auto label = context.Input<Tensor>("Label");
const int64_t *label_data = label->data<int64_t>();
auto label_dims = label->dims();
// int num_total_classes = context.Attr<int>("num_total_classes");
// for unitest
std::vector<int> custom_neg_classes =
context.Attr<std::vector<int>>("custom_neg_classes");
......@@ -144,15 +149,82 @@ class NCEKernel : public framework::OpKernel<T> {
}
// forward mul
auto input_mat = EigenMatrix<T>::From(*(context.Input<Tensor>("Input")));
auto weight_mat = EigenMatrix<T>::From(*(context.Input<Tensor>("Weight")));
for (int64_t i = 0; i < sample_labels->numel(); ++i) {
Eigen::Tensor<T, 0, Eigen::RowMajor, Eigen::DenseIndex> result =
(input_mat.chip(static_cast<int>(i / sample_labels->dims()[1]), 0) *
weight_mat.chip(sample_labels_data[i], 0))
.sum();
sample_out_data[i] += result(0);
sample_out_data[i] = (1. / (1. + exp(-sample_out_data[i])));
// for remote prefetch
auto epmap = context.Attr<std::vector<std::string>>("epmap");
if (!epmap.empty()) {
// if epmap is not empty, then the parameter will be fetched from remote
// parameter
// server
std::vector<int64_t> labels;
for (int64_t i = 0; i < sample_labels->numel(); ++i) {
labels.push_back(sample_labels_data[i]);
}
std::set<T> st(labels.begin(), labels.end());
labels.assign(st.begin(), st.end());
framework::Scope &local_scope = context.scope().NewScope();
auto height_sections = context.Attr<std::vector<int>>("height_sections");
auto table_names = context.Attr<std::vector<std::string>>("table_names");
auto *ids = local_scope.Var("Ids@Prefetch");
auto *x_tensor = ids->GetMutable<framework::LoDTensor>();
x_tensor->mutable_data<int64_t>(
framework::make_ddim({static_cast<int64_t>(labels.size()), 1}),
context.GetPlace());
// copy.
std::memcpy(x_tensor->data<int64_t>(), labels.data(),
labels.size() * sizeof(int64_t));
std::vector<int> w_dims = paddle::framework::vectorize2int(
context.Input<Tensor>("Weight")->dims());
w_dims[0] = static_cast<int>(labels.size());
auto *w_tensor = local_scope.Var("Weight@Prefetch")
->GetMutable<framework::LoDTensor>();
w_tensor->Resize(framework::make_ddim(w_dims));
#ifdef PADDLE_WITH_DISTRIBUTE
operators::distributed::prefetch("Ids@Prefetch", "Weight@Prefetch",
table_names, epmap, height_sections,
context, local_scope);
#else
PADDLE_THROW(
"paddle is not compiled with distribute support, can not do "
"parameter prefetch!");
#endif
auto weight_mat = EigenMatrix<T>::From(
(local_scope.Var("Weight@Prefetch")->Get<framework::LoDTensor>()));
for (int64_t i = 0; i < sample_labels->numel(); ++i) {
std::vector<int64_t>::iterator it =
std::find(labels.begin(), labels.end(), sample_labels_data[i]);
int idx = std::distance(labels.begin(), it);
Eigen::Tensor<T, 0, Eigen::RowMajor, Eigen::DenseIndex> result =
(input_mat.chip(static_cast<int>(i / sample_labels->dims()[1]), 0) *
weight_mat.chip(idx, 0))
.sum();
sample_out_data[i] += result(0);
sample_out_data[i] = (1. / (1. + exp(-sample_out_data[i])));
}
context.scope().DeleteScope(&local_scope);
} else {
auto weight_mat =
EigenMatrix<T>::From(*(context.Input<Tensor>("Weight")));
for (int64_t i = 0; i < sample_labels->numel(); ++i) {
Eigen::Tensor<T, 0, Eigen::RowMajor, Eigen::DenseIndex> result =
(input_mat.chip(static_cast<int>(i / sample_labels->dims()[1]), 0) *
weight_mat.chip(sample_labels_data[i], 0))
.sum();
sample_out_data[i] += result(0);
sample_out_data[i] = (1. / (1. + exp(-sample_out_data[i])));
}
}
// forward cost
for (int64_t i = 0; i < sample_labels->dims()[0]; ++i) {
out_data[i] = 0;
......@@ -240,18 +312,19 @@ class NCEGradKernel : public framework::OpKernel<T> {
sample_grad_data[i] *= d_out_data[sample_idx];
}
// get d_bias
auto d_bias = context.Output<Tensor>(framework::GradVarName("Bias"));
if (d_bias != nullptr) {
T *d_bias_data = d_bias->mutable_data<T>(context.GetPlace());
std::fill(d_bias_data, d_bias_data + d_bias->numel(), 0.0);
for (int64_t i = 0; i < sample_labels->numel(); ++i) {
d_bias_data[sample_labels_data[i]] += sample_grad_data[i];
}
}
bool is_sparse = context.Attr<bool>("is_sparse");
if (!is_sparse) {
// get d_bias
auto d_bias = context.Output<Tensor>(framework::GradVarName("Bias"));
if (d_bias != nullptr) {
T *d_bias_data = d_bias->mutable_data<T>(context.GetPlace());
std::fill(d_bias_data, d_bias_data + d_bias->numel(), 0.0);
for (int64_t i = 0; i < sample_labels->numel(); ++i) {
d_bias_data[sample_labels_data[i]] += sample_grad_data[i];
}
}
// get d_w
auto d_w = context.Output<Tensor>(framework::GradVarName("Weight"));
if (d_w != nullptr) {
......@@ -273,34 +346,6 @@ class NCEGradKernel : public framework::OpKernel<T> {
std::set<T> st(labels.begin(), labels.end());
labels.assign(st.begin(), st.end());
auto *bias_var = context.InputVar("Bias");
DDim bias_dim;
if (bias_var->IsType<LoDTensor>()) {
bias_dim = context.Input<LoDTensor>("Bias")->dims();
} else if (bias_var->IsType<SelectedRows>()) {
auto *table_t = context.Input<SelectedRows>("Bias");
bias_dim = table_t->value().dims();
} else {
PADDLE_THROW(
"The parameter Bias of a NCE_OP "
"must be either LoDTensor or SelectedRows");
}
auto d_bias =
context.Output<SelectedRows>(framework::GradVarName("Bias"));
d_bias->set_rows(labels);
d_bias->set_height(bias_dim[0]);
d_bias->mutable_value()->Resize(
{static_cast<int64_t>(labels.size()), bias_dim[1]});
T *d_bias_data =
d_bias->mutable_value()->mutable_data<T>(context.GetPlace());
std::fill(d_bias_data, d_bias_data + labels.size(), 0.0);
for (int64_t i = 0; i < sample_labels->numel(); ++i) {
d_bias_data[d_bias->Index(sample_labels_data[i])] +=
sample_grad_data[i];
}
auto *table_var = context.InputVar("Weight");
DDim table_dim;
if (table_var->IsType<LoDTensor>()) {
......
paddle/fluid/operators/ngraph/ngraph_ops.h
浏览文件 @ 92da467c
......@@ -23,5 +23,7 @@ limitations under the License. */
#include "ops/binary_unnary_op.h"
#include "ops/fill_constant_op.h"
#include "ops/mean_op.h"
#include "ops/mul_op.h"
#include "ops/scale_op.h"
#include "ops/top_k_op.h"
paddle/fluid/operators/ngraph/ops/binary_unnary_op.h
浏览文件 @ 92da467c
......@@ -12,7 +12,6 @@ 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. */
#ifdef PADDLE_WITH_NGRAPH
#pragma once
#include <string>
......@@ -48,4 +47,3 @@ static void BuildUnaryNode(
} // namespace ngraphs
} // namespace operators
} // namespace paddle
#endif
paddle/fluid/operators/ngraph/ops/elementwise_scalar_op.h 0 → 100644
浏览文件 @ 92da467c
/*Copyright (c) 2018 PaddlePaddle Authors. All Rights Reserved.
Licensed under the Apache License, Version 2.0 (the "License");
you may not use this file except in compliance with the License.
You may obtain a copy of the License at
http://www.apache.org/licenses/LICENSE-2.0
Unless required by applicable law or agreed to in writing, software
distributed under the License is distributed on an "AS IS" BASIS,
WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
See the License for the specific language governing permissions and
limitations under the License. */
#pragma once
#include <string>
#include "ngraph/ngraph.hpp"
#include "paddle/fluid/platform/ngraph_helper.h"
namespace paddle {
namespace operators {
namespace ngraphs {
template <typename T>
std::shared_ptr<ngraph::Node> ElementwiseScalar(
float scale, std::shared_ptr<ngraph::Node> node) {
auto node_shape = node->get_shape();
auto scale_const = ngraph::op::Constant::create(node->get_element_type(),
node_shape, {scale});
return std::make_shared<T>(scale_const, node);
}
template <typename T>
std::shared_ptr<ngraph::Node> ElementwiseScalar(
std::shared_ptr<ngraph::Node> scale_1d,
std::shared_ptr<ngraph::Node> node) {
auto scale_shape = scale_1d->get_shape();
PADDLE_ENFORCE_EQ(scale_shape.size(), 1, "Supporting 1d scale node");
PADDLE_ENFORCE_EQ(scale_shape.at(0), 1, "scale 1d in in shape {1}");
auto node_shape = node->get_shape();
ngraph::AxisSet axis_set;
for (size_t i = 0; i < node_shape.size(); ++i) {
axis_set.insert(i);
}
node_shape.push_back(1);
auto scale_bcast =
std::make_shared<ngraph::op::Broadcast>(scale_1d, node_shape, axis_set);
auto scale_reshape =
paddle::platform::NgReshaper(scale_bcast, node->get_shape());
return std::make_shared<T>(scale_reshape, node);
}
} // namespace ngraphs
} // namespace operators
} // namespace paddle
paddle/fluid/operators/ngraph/ops/fill_constant_op.h
浏览文件 @ 92da467c
......@@ -12,7 +12,6 @@ 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. */
#ifdef PADDLE_WITH_NGRAPH
#pragma once
#include <string>
......@@ -58,4 +57,3 @@ void BuildFillConstantNode(
} // namespace ngraphs
} // namespace operators
} // namespace paddle
#endif
paddle/fluid/operators/ngraph/ops/mean_op.h 0 → 100644
浏览文件 @ 92da467c
/*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 <functional>
#include <string>
#include "ngraph/ngraph.hpp"
#include "paddle/fluid/operators/ngraph/ops/elementwise_scalar_op.h"
#include "paddle/fluid/platform/ngraph_helper.h"
namespace paddle {
namespace operators {
namespace ngraphs {
void BuildMeanNode(
const std::shared_ptr<paddle::framework::OperatorBase>& op,
std::shared_ptr<
std::unordered_map<std::string, std::shared_ptr<ngraph::Node>>>
ngb_node_map) {
auto input = paddle::platform::GetInputNode(op, "X", ngb_node_map);
ngraph::AxisSet axes;
for (size_t i = 0; i < input->get_shape().size(); ++i) {
axes.insert(i);
}
auto mean = ngraph::builder::mean(input, axes);
auto mean_1d = std::make_shared<ngraph::op::Reshape>(
mean, ngraph::AxisVector{}, ngraph::Shape{1});
paddle::platform::SetOutputNode(op, "Out", mean_1d, ngb_node_map);
}
void BuildMeanGradNode(
const std::shared_ptr<paddle::framework::OperatorBase>& op,
std::shared_ptr<
std::unordered_map<std::string, std::shared_ptr<ngraph::Node>>>
ngb_node_map) {
auto x = paddle::platform::GetInputNode(op, "X", ngb_node_map);
auto og = paddle::platform::GetInputNode(op, "Out@GRAD", ngb_node_map);
auto x_shape = x->get_shape();
float x_size = std::accumulate(std::begin(x_shape), std::end(x_shape), 1,
std::multiplies<float>());
auto node_const = ngraph::op::Constant::create(og->get_element_type(),
ngraph::Shape{1}, {x_size});
auto node_div = std::make_shared<ngraph::op::Divide>(og, node_const);
auto result = ElementwiseScalar<ngraph::op::Add>(
og / node_const,
ngraph::op::Constant::create(og->get_element_type(), x_shape, {0}));
paddle::platform::SetOutputNode(op, "X@GRAD", result, ngb_node_map);
}
} // namespace ngraphs
} // namespace operators
} // namespace paddle
paddle/fluid/operators/ngraph/ops/mul_op.h
浏览文件 @ 92da467c
......@@ -12,7 +12,6 @@ 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. */
#ifdef PADDLE_WITH_NGRAPH
#pragma once
#include <string>
......@@ -131,4 +130,3 @@ static void BuildMulGradNode(
} // namespace ngraphs
} // namespace operators
} // namespace paddle
#endif
paddle/fluid/operators/ngraph/ops/scale_op.h 0 → 100644
浏览文件 @ 92da467c
/*Copyright (c) 2018 PaddlePaddle Authors. All Rights Reserved.
Licensed under the Apache License, Version 2.0 (the "License");
you may not use this file except in compliance with the License.
You may obtain a copy of the License at
http://www.apache.org/licenses/LICENSE-2.0
Unless required by applicable law or agreed to in writing, software
distributed under the License is distributed on an "AS IS" BASIS,
WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
See the License for the specific language governing permissions and
limitations under the License. */
#pragma once
#include <string>
#include "ngraph/ngraph.hpp"
#include "paddle/fluid/operators/ngraph/ops/elementwise_scalar_op.h"
#include "paddle/fluid/platform/ngraph_helper.h"
namespace paddle {
namespace operators {
namespace ngraphs {
void BuildScaleNode(
const std::shared_ptr<paddle::framework::OperatorBase>& op,
std::shared_ptr<
std::unordered_map<std::string, std::shared_ptr<ngraph::Node>>>
ngb_node_map) {
auto op_attrs = paddle::framework::AttrReader(op->Attrs());
float scale = op_attrs.Get<float>("scale");
auto x = paddle::platform::GetInputNode(op, "X", ngb_node_map);
auto out = ElementwiseScalar<ngraph::op::Multiply>(scale, x);
paddle::platform::SetOutputNode(op, "Out", out, ngb_node_map);
}
} // namespace ngraphs
} // namespace operators
} // namespace paddle
paddle/fluid/operators/ngraph/ops/top_k_op.h
浏览文件 @ 92da467c
......@@ -12,7 +12,6 @@ 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. */
#ifdef PADDLE_WITH_NGRAPH
#pragma once
#include <string>
......@@ -48,4 +47,3 @@ void BuildTopKNode(
} // namespace ngraphs
} // namespace operators
} // namespace paddle
#endif
paddle/fluid/operators/optimizers/adam_op.h
浏览文件 @ 92da467c
......@@ -424,16 +424,23 @@ class AdamOpKernel : public framework::OpKernel<T> {
}
}
framework::SelectedRows cpu_grad_merge;
const framework::SelectedRows* grad_merge_ptr;
if (is_strict_sorted) {
grad_merge_ptr = &grad;
} else {
// merge duplicated rows if any.
// The rows of grad_merge have been sorted inside MergeAdd functor
framework::SelectedRows* grad_merge_var;
scatter::MergeAdd<DeviceContext, T> merge_func;
auto* grad_merge_var = const_cast<framework::Scope&>(ctx.scope())
.Var()
->GetMutable<framework::SelectedRows>();
if (platform::is_cpu_place(ctx.GetPlace())) {
grad_merge_var = &cpu_grad_merge;
} else {
// FIXME(qiao): GPU also need to fix this
grad_merge_var = const_cast<framework::Scope&>(ctx.scope())
.Var()
->GetMutable<framework::SelectedRows>();
}
merge_func(ctx.template device_context<DeviceContext>(), grad,
grad_merge_var, true);
grad_merge_ptr = grad_merge_var;
......
paddle/fluid/operators/softmax_with_cross_entropy_op.cu
浏览文件 @ 92da467c
/* 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.
......@@ -58,12 +55,24 @@ __global__ void SoftCrossEntropyGradientKernel(T* logit_grad,
} // namespace
static __device__ __forceinline__ float real_exp(float x) { return expf(x); }
static __device__ __forceinline__ double real_exp(double x) { return exp(x); }
static __device__ __forceinline__ float real_log(float x) {
static __device__ __forceinline__ platform::float16 exp_on_device(
platform::float16 x) {
return ::Eigen::numext::exp(x);
}
static __device__ __forceinline__ float exp_on_device(float x) {
return expf(x);
}
static __device__ __forceinline__ double exp_on_device(double x) {
return exp(x);
}
static __device__ __forceinline__ platform::float16 log_on_device(
platform::float16 x) {
return math::TolerableValue<platform::float16>()(::Eigen::numext::log(x));
}
static __device__ __forceinline__ float log_on_device(float x) {
return math::TolerableValue<float>()(logf(x));
}
static __device__ __forceinline__ double real_log(double x) {
static __device__ __forceinline__ double log_on_device(double x) {
return math::TolerableValue<double>()(log(x));
}
......@@ -72,25 +81,20 @@ static __device__ __forceinline__ double real_log(double x) {
/*
Supposing the x is `logits` and y is `labels`, the equations are as
followings:
cross\_entropy_i = \sum_{j}[- y_i_j * log({e^{x_i_j}/\sum_{j}e^{x_i_j}})]
= \sum_{j}[- y_i_j * log({e^{x_i_j - max_i}/\sum_{j}e^{x_i_j-max_i}})]
= \sum_{j}[-y_i_j * (x_i_j - max_i - log\sum_{j}e^{x_i_j - max_i})]
= \sum_{j}[-y_i_j * (x_i_j - max_i - logDiffMaxSum_i)]
= \sum_{j}(-y_i_j * tmp_i_j)
softmax_i_j = e^{tmp_i_j}
where:
max_i = \max_{j}{x_i_j}
logDiffMaxSum_i = log\sum_{j}e^{x_i_j - max_i}
tmp_i_j = x_i_j - max_i - logDiffMaxSum_i
Therefore, the calculation can be separated into 3 steps:
Step 1: row-wise operation to calculate max_i
Step 2: row-wise operation to calculate logDiffMaxSum_i
Step 3: caculate tmp_i_j, and finally get softmax_i_j and cross\_entropy_i
To save memory, we can share memory among max_i, logDiffMaxSum_i and
cross\_entropy_i.
In this way, the 3 steps should be changed to:
......@@ -134,7 +138,8 @@ static __global__ void RowReductionForMax(const T* logits_data, T* max_data,
cur_max = BlockReduce<T, BlockDim>(temp_storage).Reduce(cur_max, cub::Max());
if (threadIdx.x == 0) {
max_data[blockIdx.x] = cur_max < -64 ? -64 : cur_max;
max_data[blockIdx.x] =
cur_max < static_cast<T>(-64) ? static_cast<T>(-64) : cur_max;
}
}
......@@ -151,17 +156,17 @@ static __global__ void RowReductionForDiffMaxSum(const T* logits_data,
auto block_max = max_data[blockIdx.x];
softmax[beg_idx] = logits_data[beg_idx] - block_max;
T diff_max_sum = real_exp(softmax[beg_idx]);
T diff_max_sum = exp_on_device(softmax[beg_idx]);
auto idx = beg_idx + BlockDim;
while (idx < end_idx) {
softmax[idx] = logits_data[idx] - block_max;
diff_max_sum += real_exp(softmax[idx]);
diff_max_sum += exp_on_device(softmax[idx]);
idx += BlockDim;
}
diff_max_sum =
BlockReduce<T, BlockDim>(temp_storage).Reduce(diff_max_sum, cub::Sum());
if (threadIdx.x == 0) max_data[blockIdx.x] = real_log(diff_max_sum);
if (threadIdx.x == 0) max_data[blockIdx.x] = log_on_device(diff_max_sum);
if (!CalculateLogSoftmax) return;
__syncthreads();
......@@ -188,12 +193,12 @@ static __global__ void RowReductionForSoftmaxAndCrossEntropy(
// log_diff_max_sum shares memory with loss
auto block_log_diff_max_sum = loss_data[blockIdx.x];
auto tmp = softmax[beg_idx] - block_log_diff_max_sum;
softmax[beg_idx] = real_exp(tmp);
softmax[beg_idx] = exp_on_device(tmp);
auto loss = -labels_data[beg_idx] * tmp;
beg_idx += BlockDim;
while (beg_idx < end_idx) {
tmp = softmax[beg_idx] - block_log_diff_max_sum;
softmax[beg_idx] = real_exp(tmp);
softmax[beg_idx] = exp_on_device(tmp);
loss -= (labels_data[beg_idx] * tmp);
beg_idx += BlockDim;
}
......@@ -218,10 +223,10 @@ struct HardLabelSoftmaxWithCrossEntropyFunctor {
auto row_idx = idx / feature_size_;
auto col_idx = idx % feature_size_;
if (col_idx != labels_[row_idx]) {
log_softmax_[idx] = real_exp(log_softmax_[idx]);
log_softmax_[idx] = exp_on_device(log_softmax_[idx]);
} else {
auto softmax = log_softmax_[idx];
log_softmax_[idx] = real_exp(softmax);
log_softmax_[idx] = exp_on_device(softmax);
loss_[row_idx] = -softmax;
}
}
......@@ -253,10 +258,10 @@ struct HardLabelSoftmaxWithCrossEntropyFunctorWithIgnoreIdx {
auto row_idx = idx / feature_size_;
auto col_idx = idx % feature_size_;
if (col_idx != labels_[row_idx] || col_idx == ignore_idx_) {
log_softmax_[idx] = real_exp(log_softmax_[idx]);
log_softmax_[idx] = exp_on_device(log_softmax_[idx]);
} else {
auto softmax = log_softmax_[idx];
log_softmax_[idx] = real_exp(softmax);
log_softmax_[idx] = exp_on_device(softmax);
loss_[row_idx] = -softmax;
}
}
......@@ -464,9 +469,12 @@ class SoftmaxWithCrossEntropyGradCUDAKernel : public framework::OpKernel<T> {
} // namespace paddle
namespace ops = paddle::operators;
REGISTER_OP_CUDA_KERNEL(softmax_with_cross_entropy,
ops::SoftmaxWithCrossEntropyCUDAKernel<float>,
ops::SoftmaxWithCrossEntropyCUDAKernel<double>);
REGISTER_OP_CUDA_KERNEL(softmax_with_cross_entropy_grad,
ops::SoftmaxWithCrossEntropyGradCUDAKernel<float>,
ops::SoftmaxWithCrossEntropyGradCUDAKernel<double>);
REGISTER_OP_CUDA_KERNEL(
softmax_with_cross_entropy, ops::SoftmaxWithCrossEntropyCUDAKernel<float>,
ops::SoftmaxWithCrossEntropyCUDAKernel<paddle::platform::float16>,
ops::SoftmaxWithCrossEntropyCUDAKernel<double>);
REGISTER_OP_CUDA_KERNEL(
softmax_with_cross_entropy_grad,
ops::SoftmaxWithCrossEntropyGradCUDAKernel<float>,
ops::SoftmaxWithCrossEntropyGradCUDAKernel<paddle::platform::float16>,
ops::SoftmaxWithCrossEntropyGradCUDAKernel<double>);
paddle/fluid/operators/sum_op.cc
浏览文件 @ 92da467c
......@@ -41,7 +41,9 @@ class SumOp : public framework::OperatorWithKernel {
return; // skip runtime infershape when is tensor array;
}
auto x_var_types = ctx->GetInputsVarType("X");
auto x_dims = ctx->GetInputsDim("X");
size_t N = x_dims.size();
PADDLE_ENFORCE_GT(N, 0, "Input tensors count should > 0.");
if (N == 1) {
......@@ -49,7 +51,13 @@ class SumOp : public framework::OperatorWithKernel {
}
framework::DDim in_dim({0});
for (auto& x_dim : x_dims) {
for (size_t i = 0; i < x_dims.size(); ++i) {
auto& x_dim = x_dims[i];
// x_dim.size() == 1 means the real dim of selected rows is [0]
if (x_var_types[i] == framework::proto::VarType::SELECTED_ROWS &&
x_dim.size() == 1) {
continue;
}
if (framework::product(x_dim) == 0) {
continue;
}
......
paddle/fluid/platform/cuda_helper.h 0 → 100644
浏览文件 @ 92da467c
// Copyright (c) 2019 PaddlePaddle Authors. All Rights Reserved.
//
// Licensed under the Apache License, Version 2.0 (the "License");
// you may not use this file except in compliance with the License.
// 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 <mutex> // NOLINT
#include "paddle/fluid/platform/dynload/cublas.h"
#include "paddle/fluid/platform/macros.h"
#if CUDA_VERSION < 9000
enum cublasMath_t { CUBLAS_DEFAULT_MATH = 0 };
#endif
namespace paddle {
namespace platform {
class CublasHandleHolder {
public:
CublasHandleHolder(cudaStream_t stream, cublasMath_t math_type) {
PADDLE_ENFORCE(dynload::cublasCreate(&handle_));
PADDLE_ENFORCE(dynload::cublasSetStream(handle_, stream));
#if CUDA_VERSION >= 9000
if (math_type == CUBLAS_TENSOR_OP_MATH) {
PADDLE_ENFORCE(
dynload::cublasSetMathMode(handle_, CUBLAS_TENSOR_OP_MATH));
}
#endif
}
~CublasHandleHolder() { PADDLE_ENFORCE(dynload::cublasDestroy(handle_)); }
template <typename Callback>
inline void Call(Callback &&callback) const {
std::lock_guard<std::mutex> guard(mtx_);
callback(handle_);
}
private:
DISABLE_COPY_AND_ASSIGN(CublasHandleHolder);
cublasHandle_t handle_;
mutable std::mutex mtx_;
};
} // namespace platform
} // namespace paddle
paddle/fluid/platform/device_context.cc
浏览文件 @ 92da467c
......@@ -245,8 +245,15 @@ CUDADeviceContext::CUDADeviceContext(CUDAPlace place)
eigen_stream_.reset(new EigenCudaStreamDevice());
eigen_stream_->Reinitialize(&stream_, place);
eigen_device_.reset(new Eigen::GpuDevice(eigen_stream_.get()));
PADDLE_ENFORCE(dynload::cublasCreate(&cublas_handle_));
PADDLE_ENFORCE(dynload::cublasSetStream(cublas_handle_, stream_));
cublas_handle_.reset(new CublasHandleHolder(stream_, CUBLAS_DEFAULT_MATH));
if (TensorCoreAvailable()) {
#if CUDA_VERSION >= 9000
cublas_tensor_core_handle_.reset(
new CublasHandleHolder(stream_, CUBLAS_TENSOR_OP_MATH));
#endif
}
if (dynload::HasCUDNN()) {
cudnn_holder_.reset(new CudnnHolder(&stream_, place));
}
......@@ -285,7 +292,7 @@ CUDADeviceContext::CUDADeviceContext(CUDAPlace place)
if (dynload::HasCUDNN()) {
auto local_cudnn_version = cudnn_dso_ver / 100;
auto compile_cudnn_version = CUDNN_VERSION / 100;
if (local_cuda_version < compile_cuda_version) {
if (local_cudnn_version < compile_cudnn_version) {
LOG_FIRST_N(WARNING, 1)
<< "WARNING: device: " << place_.device
<< ". The installed Paddle is compiled with CUDNN "
......@@ -306,7 +313,8 @@ CUDADeviceContext::~CUDADeviceContext() {
SetDeviceId(place_.device);
Wait();
WaitStreamCallback();
PADDLE_ENFORCE(dynload::cublasDestroy(cublas_handle_));
cublas_handle_.reset();
cublas_tensor_core_handle_.reset();
eigen_stream_.reset();
eigen_device_.reset();
PADDLE_ENFORCE(cudaStreamDestroy(stream_));
......@@ -335,8 +343,8 @@ Eigen::GpuDevice* CUDADeviceContext::eigen_device() const {
return eigen_device_.get();
}
cublasHandle_t CUDADeviceContext::cublas_handle() const {
return cublas_handle_;
bool CUDADeviceContext::tensor_core_available() const {
return cublas_tensor_core_handle_ != nullptr;
}
cudnnHandle_t CUDADeviceContext::cudnn_handle() const {
......
paddle/fluid/platform/device_context.h
浏览文件 @ 92da467c
......@@ -20,6 +20,7 @@ limitations under the License. */
#include "paddle/fluid/memory/malloc.h"
#include "paddle/fluid/platform/temporary_allocator.h"
#ifdef PADDLE_WITH_CUDA
#include "paddle/fluid/platform/cuda_helper.h"
#include "paddle/fluid/platform/dynload/cublas.h"
#include "paddle/fluid/platform/dynload/cudnn.h"
#include "paddle/fluid/platform/gpu_info.h"
......@@ -209,39 +210,6 @@ class CudnnWorkspaceHandle {
std::unique_ptr<std::lock_guard<std::mutex>> guard_;
};
#if CUDA_VERSION >= 9000
class ScopedCublasMathMode {
public:
ScopedCublasMathMode(cublasHandle_t handle, cublasMath_t new_math_mode)
: handle_(handle) {
need_reset = false;
PADDLE_ENFORCE(
platform::dynload::cublasGetMathMode(handle_, &old_math_mode_),
"Failed to get old cublas math mode");
if (old_math_mode_ != new_math_mode) {
PADDLE_ENFORCE(
platform::dynload::cublasSetMathMode(handle_, new_math_mode),
"Failed to set old cublas math mode");
need_reset = true;
}
}
~ScopedCublasMathMode() {
if (need_reset) {
PADDLE_ENFORCE(
platform::dynload::cublasSetMathMode(handle_, old_math_mode_),
"Failed to set old cublas math mode");
}
}
private:
cublasHandle_t handle_;
cublasMath_t old_math_mode_;
bool need_reset;
};
#endif
class CUDADeviceContext : public DeviceContext {
public:
explicit CUDADeviceContext(CUDAPlace place);
......@@ -262,8 +230,25 @@ class CUDADeviceContext : public DeviceContext {
/*! \brief Return eigen device in the device context. */
Eigen::GpuDevice* eigen_device() const;
/*! \brief Return cublas handle in the device context. */
cublasHandle_t cublas_handle() const;
/*! \brief Call cublas function safely. */
template <typename Callback>
inline void CublasCall(Callback&& callback) const {
cublas_handle_->Call(std::forward<Callback>(callback));
}
/*! \brief Check whether tensor core is supported */
bool tensor_core_available() const;
/*! \brief Call cublas function with Tensor Core safely. If
Tensor Core is not available, use DEFAULT_MATH instead. */
template <typename Callback>
inline void TensorCoreCublasCallIfAvailable(Callback&& callback) const {
if (cublas_tensor_core_handle_) {
cublas_tensor_core_handle_->Call(std::forward<Callback>(callback));
} else {
cublas_handle_->Call(std::forward<Callback>(callback));
}
}
/*! \brief Return cudnn handle in the device context. */
cudnnHandle_t cudnn_handle() const;
......@@ -282,7 +267,6 @@ class CUDADeviceContext : public DeviceContext {
template <typename Callback>
void RecordEvent(cudaEvent_t ev, Callback callback) {
std::lock_guard<std::mutex> guard(mtx_);
callback();
PADDLE_ENFORCE(cudaEventRecord(ev, stream_));
}
......@@ -294,18 +278,6 @@ class CUDADeviceContext : public DeviceContext {
void WaitStreamCallback() const { callback_manager_->Wait(); }
#if CUDA_VERSION >= 9000
/*! \brief CublasCall may need to change cublas's config,
* but the cublas may be hold by multi-thread, so we should
* add lock here. */
template <typename Callback>
void CublasCall(Callback callback, cublasMath_t new_math) {
std::lock_guard<std::mutex> guard(cublas_mtx_);
ScopedCublasMathMode scoped_cublas_math(cublas_handle_, new_math);
callback();
}
#endif
private:
CUDAPlace place_;
......@@ -313,7 +285,9 @@ class CUDADeviceContext : public DeviceContext {
std::unique_ptr<EigenCudaStreamDevice> eigen_stream_;
std::unique_ptr<CudnnHolder> cudnn_holder_;
cudaStream_t stream_;
cublasHandle_t cublas_handle_;
std::unique_ptr<CublasHandleHolder> cublas_handle_;
std::unique_ptr<CublasHandleHolder> cublas_tensor_core_handle_;
int compute_capability_;
int runtime_version_;
......@@ -321,12 +295,10 @@ class CUDADeviceContext : public DeviceContext {
int multi_process_;
int max_threads_per_mp_;
mutable std::mutex mtx_;
// StreamCallbackManager is thread-safe
std::unique_ptr<StreamCallbackManager> callback_manager_;
mutable std::mutex cublas_mtx_;
DISABLE_COPY_AND_ASSIGN(CUDADeviceContext);
};
template <>
......
paddle/fluid/platform/device_context_test.cu
浏览文件 @ 92da467c
......@@ -43,9 +43,6 @@ TEST(Device, CUDADeviceContext) {
ASSERT_NE(nullptr, gpu_device);
cudnnHandle_t cudnn_handle = device_context->cudnn_handle();
ASSERT_NE(nullptr, cudnn_handle);
cublasHandle_t cublas_handle = device_context->cublas_handle();
ASSERT_NE(nullptr, cublas_handle);
ASSERT_NE(nullptr, device_context->stream());
delete device_context;
}
}
......
paddle/fluid/platform/mkldnn_reuse.h
浏览文件 @ 92da467c
......@@ -145,7 +145,8 @@ class MKLDNNHandler {
const std::shared_ptr<mkldnn::memory> user_memory_p,
const std::string& suffix,
std::vector<mkldnn::primitive>& pipeline, // NOLINT
bool is_persistent = false) {
bool is_persistent = false, bool is_INT8 = false,
std::vector<float> scale_data = {1.0f}, int mask = 0) {
// create reorder primitive if the input format is not the preferred one
auto local_key = key_ + suffix;
auto key_reorder_p = key_ + suffix + "reorder_p";
......@@ -159,8 +160,20 @@ class MKLDNNHandler {
std::shared_ptr<mkldnn::primitive> reorder_p;
if (mpd != user_mpd) {
target_memory_p = std::make_shared<mkldnn::memory>(mpd);
auto reorder_p =
std::make_shared<mkldnn::reorder>(*user_memory_p, *target_memory_p);
std::shared_ptr<mkldnn::reorder> reorder_p;
if (is_INT8) {
mkldnn::primitive_attr
attri; // attribute for int8 weights and bias data reorder.
attri.set_output_scales(mask, scale_data);
auto reorder_pd = std::shared_ptr<mkldnn::reorder::primitive_desc>(
new mkldnn::reorder::primitive_desc(user_mpd, mpd, attri));
reorder_p = std::shared_ptr<mkldnn::reorder>(new mkldnn::reorder(
*reorder_pd, *user_memory_p, *target_memory_p));
} else {
reorder_p = std::make_shared<mkldnn::reorder>(*user_memory_p,
*target_memory_p);
}
dev_ctx_.SetBlob(key_reorder_p, reorder_p);
pipeline.push_back(*reorder_p);
}
......@@ -182,22 +195,58 @@ class MKLDNNHandler {
return dims2str(operand_dims) + suffix;
}
template <typename M>
template <typename T>
static void SetDstMemory(
const framework::ExecutionContext& ctx, framework::Tensor* output,
std::vector<int> dst_tz, const mkldnn::engine& engine,
std::shared_ptr<mkldnn::memory::primitive_desc>& dst_pd, // NOLINT
std::shared_ptr<mkldnn::memory>& dst_memory) { // NOLINT
M* output_data = output->mutable_data<M>(ctx.GetPlace());
T* output_data = output->mutable_data<T>(ctx.GetPlace());
auto dst_md = platform::MKLDNNMemDesc(
{dst_tz}, paddle::framework::ToMKLDNNDataType(
framework::DataTypeTrait<M>::DataType),
framework::DataTypeTrait<T>::DataType),
mkldnn::memory::format::nhwc);
dst_pd.reset(new mkldnn::memory::primitive_desc(dst_md, engine));
dst_memory.reset(new mkldnn::memory(*dst_pd, to_void_cast<M>(output_data)));
dst_memory.reset(new mkldnn::memory(*dst_pd, to_void_cast<T>(output_data)));
}
static void AppendKey(
std::string* key, const mkldnn::memory::dims& input_dims,
const mkldnn::memory::dims& weights_dims, const std::vector<int>& strides,
const std::vector<int>& paddings, const std::vector<int>& dilations,
const int& groups, const mkldnn::memory::data_type& srcdt,
const mkldnn::memory::format& format,
const mkldnn::memory::data_type& dstdt, const std::string& suffix) {
AppendKeyDims(key, input_dims);
AppendKeyDims(key, weights_dims);
AppendKeyVec(key, strides);
AppendKeyVec(key, paddings);
AppendKeyVec(key, dilations);
AppendKey(key, std::to_string(groups));
AppendKey(key, std::to_string(srcdt));
AppendKey(key, std::to_string(format));
AppendKey(key, std::to_string(dstdt));
AppendKey(key, suffix);
}
protected:
static void AppendKeyDims(std::string* key,
const mkldnn::memory::dims& dims) {
for (unsigned int i = 0; i < dims.size(); i++) {
AppendKey(key, std::to_string(dims[i]));
}
}
static void AppendKeyVec(std::string* key, const std::vector<int>& dims) {
for (unsigned int i = 0; i < dims.size(); i++) {
AppendKey(key, std::to_string(dims[i]));
}
}
static void AppendKey(std::string* key, const std::string& s) {
key->append(s);
}
static std::string dims2str(const mkldnn::memory::dims& operand_dims) {
std::string dstr = "";
for (size_t i = 0; i < operand_dims.size(); ++i) {
......@@ -215,7 +264,8 @@ class MKLDNNHandler {
class TransposeMKLDNNHandler : public MKLDNNHandler {
public:
TransposeMKLDNNHandler(std::vector<int>& dims, std::vector<int>& axis,
TransposeMKLDNNHandler(std::vector<int>& dims, // NOLINT
std::vector<int>& axis, // NOLINT
const platform::MKLDNNDeviceContext& dev_ctx,
mkldnn::engine engine, const std::string& base_key)
: platform::MKLDNNHandler(dev_ctx, engine, base_key),
......@@ -303,8 +353,9 @@ class TransposeMKLDNNHandler : public MKLDNNHandler {
}
protected:
mkldnn_memory_desc_t Axis2MemoryDesc(std::vector<int>& nchw_tz,
std::vector<int>& axis) {
mkldnn_memory_desc_t Axis2MemoryDesc(std::vector<int>& nchw_tz, // NOLINT
std::vector<int>& axis // NOLINT
) {
mkldnn_memory_desc_t mem_fmt;
mem_fmt.primitive_kind = mkldnn_memory;
......@@ -462,21 +513,26 @@ class ConvMKLDNNTemplateHandler : public MKLDNNHandler {
std::shared_ptr<mkldnn::memory> AcquireWeightsMemoryFromPrimitive(
const std::shared_ptr<mkldnn::memory> user_weights_memory_p,
std::vector<mkldnn::primitive>& pipeline, // NOLINT
bool is_persistent = false) {
bool is_persistent = false, bool is_INT8 = false,
std::vector<float> scale_data = {1.0f}, int mask = 0) {
auto user_weights_pd = user_weights_memory_p->get_primitive_desc();
auto weights_pd = conv_pd_->weights_primitive_desc();
return this->AcquireMemory(weights_pd, user_weights_pd,
user_weights_memory_p, "@weights_mem_p",
pipeline, is_persistent);
return this->AcquireMemory(
weights_pd, user_weights_pd, user_weights_memory_p, "@weights_mem_p",
pipeline, is_persistent, is_INT8, scale_data, mask);
}
std::shared_ptr<mkldnn::memory> AcquireBiasMemoryFromPrimitive(
const std::shared_ptr<mkldnn::memory> user_bias_memory_p,
std::vector<mkldnn::primitive>& pipeline) { // NOLINT
std::vector<mkldnn::primitive>& pipeline, // NOLINT
bool is_persistent = false, bool is_INT8 = false,
std::vector<float> scale_data = {1.0f},
int mask = 0) { // NOLINT
auto user_bias_pd = user_bias_memory_p->get_primitive_desc();
auto bias_pd = conv_pd_->bias_primitive_desc();
return this->AcquireMemory(bias_pd, user_bias_pd, user_bias_memory_p,
"@bias_mem_p", pipeline);
"@bias_mem_p", pipeline, is_persistent, is_INT8,
scale_data, mask);
}
std::shared_ptr<forward_t> AcquireConvolution(
......@@ -594,5 +650,29 @@ using ConvTransposeMKLDNNHandler =
ConvMKLDNNTemplateHandler<mkldnn::deconvolution_forward,
mkldnn::deconvolution_backward_data,
mkldnn::deconvolution_backward_weights>;
template <typename T>
static std::shared_ptr<mkldnn::memory> SetDstMemory(
const framework::ExecutionContext& ctx, framework::Tensor* output,
const std::shared_ptr<ConvMKLDNNHandler>& handler) {
T* output_data = output->mutable_data<T>(
ctx.GetPlace(), ::paddle::memory::Allocator::kDefault,
handler->GetDstMemorySize());
std::shared_ptr<mkldnn::memory> dst_memory_p =
handler->AcquireDstMemoryFromPrimitive(to_void_cast<T>(output_data));
return dst_memory_p;
}
template <typename T>
static std::shared_ptr<mkldnn::memory> SetDstMemoryHandler(
const framework::ExecutionContext& ctx, framework::Tensor* output,
const std::shared_ptr<ConvMKLDNNHandler>& handler) {
T* output_data = output->mutable_data<T>(
ctx.GetPlace(), ::paddle::memory::Allocator::kDefault,
handler->GetDstMemorySize());
std::shared_ptr<mkldnn::memory> dst_memory_p;
dst_memory_p->set_data_handle(to_void_cast<T>(output_data));
return dst_memory_p;
}
} // namespace platform
} // namespace paddle
paddle/fluid/pybind/CMakeLists.txt
浏览文件 @ 92da467c
......@@ -3,7 +3,7 @@ set(PYBIND_DEPS pybind python proto_desc memory executor async_executor prune fe
if(WITH_PYTHON)
list(APPEND PYBIND_DEPS py_func_op)
endif()
set(PYBIND_SRCS pybind.cc exception.cc protobuf.cc const_value.cc recordio.cc async_executor_py.cc imperative.cc)
set(PYBIND_SRCS pybind.cc exception.cc protobuf.cc const_value.cc recordio.cc async_executor_py.cc imperative.cc ir.cc)
if(WITH_PYTHON)
if(WITH_AMD_GPU)
......
paddle/fluid/pybind/ir.cc 0 → 100644
浏览文件 @ 92da467c
// 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/pybind/ir.h"
#include <string>
#include <unordered_map>
#include "paddle/fluid/framework/ir/graph.h"
#include "paddle/fluid/framework/ir/node.h"
#include "paddle/fluid/framework/op_desc.h"
#include "paddle/fluid/framework/var_desc.h"
#include "pybind11/stl.h"
namespace py = pybind11;
using paddle::framework::ir::Graph;
using paddle::framework::ir::Node;
using paddle::framework::OpDesc;
using paddle::framework::ProgramDesc;
using paddle::framework::VarDesc;
using pybind11::return_value_policy;
namespace paddle {
namespace pybind {
void BindGraph(py::module *m) {
py::class_<Graph, std::shared_ptr<Graph>>(
*m, "Graph",
"The graph is a Directed Acyclic Single Static Assignment Graph, see "
"`paddle::ir::Graph` for details.")
.def(py::init<const ProgramDesc &>())
.def("has", &Graph::Has)
.def("get_int", &Graph::Get<int>)
.def("get_float", &Graph::Get<float>)
.def("get_double", &Graph::Get<double>)
.def("get_string", &Graph::Get<std::string>)
.def("set", [](Graph &self, const std::string &attr_name,
int attr) { return self.Set(attr_name, new int(attr)); })
.def("set",
[](Graph &self, const std::string &attr_name,
const std::string &attr) {
return self.Set(attr_name, new std::string(attr));
})
.def("set",
[](Graph &self, const std::string &attr_name, float attr) {
return self.Set(attr_name, new float(attr));
})
.def("set",
[](Graph &self, const std::string &attr_name, double attr) {
return self.Set(attr_name, new double(attr));
})
.def("erase", &Graph::Erase)
.def("nodes", &Graph::Nodes, return_value_policy::reference)
.def("create_var_node",
[](Graph &self, VarDesc &var_desc) {
return self.CreateVarNode(&var_desc);
},
return_value_policy::reference)
.def("create_op_node",
[](Graph &self, OpDesc &op_desc) {
return self.CreateOpNode(&op_desc);
},
return_value_policy::reference)
.def("create_control_dep_var", &Graph::CreateControlDepVar,
return_value_policy::reference)
.def("create_empty_node", &Graph::CreateEmptyNode,
return_value_policy::reference)
.def("release_nodes", &Graph::ReleaseNodes)
.def("remove_node",
[](Graph &self, Node &node) { return self.RemoveNode(&node); })
.def("retrieve_node", &Graph::RetrieveNode,
return_value_policy::reference)
.def("resolve_hazard", &Graph::ResolveHazard);
}
void BindNode(py::module *m) {
py::class_<Node> node(*m, "Node");
node.def("name", &Node::Name)
.def("node_type", &Node::NodeType)
.def("var", &Node::Var)
.def("op", &Node::Op)
.def("id", &Node::id)
.def("is_op", &Node::IsOp)
.def("is_var", &Node::IsVar)
.def("is_ctrl_var", &Node::IsCtrlVar)
.def_readwrite("inputs", &Node::inputs)
.def_readwrite("outputs", &Node::outputs);
py::enum_<Node::Type>(node, "Type")
.value("Operation", Node::Type::kOperation)
.value("Variable", Node::Type::kVariable)
.export_values();
}
} // namespace pybind
} // namespace paddle
paddle/fluid/framework/details/multi_devices_graph_check_pass.h → paddle/fluid/pybind/ir.h
浏览文件 @ 92da467c
......@@ -14,25 +14,12 @@
#pragma once
#include "paddle/fluid/framework/details/multi_devices_helper.h"
#include <string>
#include <pybind11/pybind11.h>
#include "paddle/fluid/framework/ir/graph.h"
namespace paddle {
namespace framework {
namespace details {
class SSAGraghBuilderWithChecker : public ir::Pass {
protected:
std::unique_ptr<ir::Graph> ApplyImpl(
std::unique_ptr<ir::Graph> graph) const override {
PADDLE_ENFORCE(IsValidGraph(graph.get()));
return graph;
}
bool IsValidGraph(const ir::Graph* graph) const;
};
} // namespace details
} // namespace framework
namespace pybind {
void BindGraph(pybind11::module *m);
void BindNode(pybind11::module *m);
} // namespace pybind
} // namespace paddle
paddle/fluid/pybind/pybind.cc
浏览文件 @ 92da467c
......@@ -49,6 +49,7 @@ limitations under the License. */
#include "paddle/fluid/pybind/const_value.h"
#include "paddle/fluid/pybind/exception.h"
#include "paddle/fluid/pybind/imperative.h"
#include "paddle/fluid/pybind/ir.h"
#include "paddle/fluid/pybind/protobuf.h"
#include "paddle/fluid/pybind/pybind.h" // NOLINT
#include "paddle/fluid/pybind/recordio.h"
......@@ -775,7 +776,12 @@ All parameter, weight, gradient are variables in Paddle.
})
.def("set_int", [](ir::Pass &self, const std::string &name,
int val) { self.Set<const int>(name, new int(val)); })
.def("type", &ir::Pass::Type);
.def("type", &ir::Pass::Type)
.def("apply", [](ir::Pass &self, std::shared_ptr<ir::Graph> graph) {
std::unique_ptr<ir::Graph> origin_graph(graph.get());
auto optim_graph = self.Apply(std::move(origin_graph));
graph.reset(optim_graph.release());
});
py::class_<ir::PassBuilder, std::shared_ptr<ir::PassBuilder>> pb(
m, "PassBuilder");
......@@ -946,13 +952,6 @@ All parameter, weight, gradient are variables in Paddle.
R"DOC(The type is STR, debug_graphviz_path indicate the path that
writing the SSA Graph to file in the form of graphviz, you.
It is useful for debugging. Default "")DOC")
.def_property(
"enable_data_balance",
[](const BuildStrategy &self) { return self.enable_data_balance_; },
[](BuildStrategy &self, bool b) {
PADDLE_ENFORCE(!self.IsFinalized(), "BuildStrategy is finlaized.");
self.enable_data_balance_ = b;
}) // FIXME(chengudo): enable_data_balance seems not important
.def_property(
"enable_sequential_execution",
[](const BuildStrategy &self) {
......@@ -1007,6 +1006,10 @@ All parameter, weight, gradient are variables in Paddle.
"memory_optimize",
[](const BuildStrategy &self) { return self.memory_optimize_; },
[](BuildStrategy &self, bool b) { self.memory_optimize_ = b; })
.def_property(
"is_distribution",
[](const BuildStrategy &self) { return self.is_distribution_; },
[](BuildStrategy &self, bool b) { self.is_distribution_ = b; })
.def_property(
"memory_early_delete",
[](const BuildStrategy &self) { return self.memory_early_delete_; },
......@@ -1022,8 +1025,7 @@ All parameter, weight, gradient are variables in Paddle.
pe.def(py::init<const std::vector<platform::Place> &,
const std::unordered_set<std::string> &, const ProgramDesc &,
const std::string &, Scope *, std::vector<Scope *> &,
const ExecutionStrategy &, const BuildStrategy &, size_t,
size_t>())
const ExecutionStrategy &, const BuildStrategy &>())
// NOTE: even we return a vec<Scope*>* to Python use reference policy.
// We still cannot get local_scope from this vector, since the element
// of vec<Scope*> will be freed by Python GC. We can only return Scope*
......@@ -1046,6 +1048,9 @@ All parameter, weight, gradient are variables in Paddle.
BindRecordIOWriter(&m);
BindAsyncExecutor(&m);
BindGraph(&m);
BindNode(&m);
}
} // namespace pybind
} // namespace paddle
paddle/scripts/paddle_build.sh
浏览文件 @ 92da467c
......@@ -199,6 +199,7 @@ function cmake_gen() {
-DANAKIN_BUILD_CROSS_PLANTFORM=${ANAKIN_BUILD_CROSS_PLANTFORM:ON}
-DPY_VERSION=${PY_VERSION:-2.7}
-DCMAKE_INSTALL_PREFIX=${INSTALL_PREFIX:-/paddle/build}
-DWITH_JEMALLOC=${WITH_JEMALLOC:-OFF}
========================================
EOF
# Disable UNITTEST_USE_VIRTUALENV in docker because
......@@ -232,7 +233,8 @@ EOF
-DANAKIN_BUILD_FAT_BIN=${ANAKIN_BUILD_FAT_BIN:OFF}\
-DANAKIN_BUILD_CROSS_PLANTFORM=${ANAKIN_BUILD_CROSS_PLANTFORM:ON}\
-DPY_VERSION=${PY_VERSION:-2.7} \
-DCMAKE_INSTALL_PREFIX=${INSTALL_PREFIX:-/paddle/build}
-DCMAKE_INSTALL_PREFIX=${INSTALL_PREFIX:-/paddle/build} \
-DWITH_JEMALLOC=${WITH_JEMALLOC:-OFF}
}
......@@ -447,7 +449,7 @@ EOF
elif [ "$1" == "cp37-cp37m" ]; then
pip3.7 install --user ${INSTALL_PREFIX:-/paddle/build}/opt/paddle/share/wheels/*.whl
fi
if [[ ${WITH_FLUID_ONLY:-OFF} == "OFF" ]] ; then
paddle version
fi
......@@ -918,11 +920,11 @@ function main() {
cmake_gen ${PYTHON_ABI:-""}
build
assert_api_not_changed ${PYTHON_ABI:-""}
assert_api_spec_approvals
run_test
gen_capi_package
gen_fluid_lib
test_fluid_lib
assert_api_spec_approvals
;;
assert_api)
assert_api_not_changed ${PYTHON_ABI:-""}
......
python/paddle/fluid/__init__.py
浏览文件 @ 92da467c
......@@ -102,13 +102,6 @@ def __bootstrap__():
import sys
import os
import platform
if os.name == 'nt':
third_lib_path = os.path.abspath(os.path.dirname(
__file__)) + os.sep + '..' + os.sep + 'libs'
os.environ['path'] += ';' + third_lib_path
sys.path.append(third_lib_path)
from . import core
in_test = 'unittest' in sys.modules
......@@ -162,7 +155,7 @@ def __bootstrap__():
'fraction_of_gpu_memory_to_use', 'cudnn_deterministic',
'enable_cublas_tensor_op_math', 'conv_workspace_size_limit',
'cudnn_exhaustive_search', 'memory_optimize_debug', 'selected_gpus',
'cudnn_exhaustive_search_times', 'sync_nccl_allreduce'
'sync_nccl_allreduce'
]
core.init_gflags([sys.argv[0]] +
......
python/paddle/fluid/compiler.py 0 → 100644
浏览文件 @ 92da467c
# Copyright (c) 2018 PaddlePaddle Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import multiprocessing
import os
import six
import sys
from .. import compat as cpt
from . import core
ExecutionStrategy = core.ParallelExecutor.ExecutionStrategy
BuildStrategy = core.ParallelExecutor.BuildStrategy
def _place_obj(place):
p = core.Place()
p.set_place(place)
return p
class CompiledProgram(object):
"""
Compiles a Program for execution.
1. Users first create the program with layers.
2. Optionally, users use CompiledProgram to optimize the program before run.
3. The original program or CompiledProgram is run by executor.
The CompiledProgram is used to transform a program for various
optimizations, for example.
* Pre-compute some logic once so that each run is faster.
* Transform the program so that it can run in multiple devices.
* TODO: transform the program for optimized inference or distributed
training.
Example:
.. code-block:: python
place = fluid.CUDAPlace(0) if use_cuda else fluid.CPUPlace()
exe = fluid.Executor(place)
exe.run(startup)
compiled_prog = compiler.CompiledProgram(main).with_data_parallel(
loss_name=loss.name)
for i in range(5):
test_loss, = exe.run(compiled_prog,
feed=feed_dict,
fetch_list=[loss.name])
Args:
program: Program instance that contains the model logic.
"""
def __init__(self, program):
self._program = program
self._scope = None
self._place = None
self._executor = None
self._compiled = False
self._is_data_parallel = False
def with_data_parallel(self,
loss_name=None,
build_strategy=None,
exec_strategy=None,
share_vars_from=None):
"""Configs the program to run in data parallel way.
Args:
loss_name (str): The loss name must set in training. Default None.
build_strategy(BuildStrategy): build_strategy is used to
build the graph so it can run on multiple devices/cores with
optimized topology.
For more information, please refer to fluid.BuildStrategy.
Default None.
exec_strategy(ExecutionStrategy): exec_strategy is used to
to select the a way to execute the graph, for example how many
threads are used, how many iterations to clean up the temp
variables. For more information, please refer
to fluid.ExecutionStrategy. Default None.
share_vars_from(CompiledProgram): If provide, this CompiledProgram
will share variables from `share_vars_from`. `share_vars_from`
must be run by the executor before this CompiledProgram so that
vars are ready.
Returns:
self
"""
assert not self._is_data_parallel, "Already compiled with parallel."
self._is_data_parallel = True
self._build_strategy = build_strategy
self._exec_strategy = exec_strategy
self._loss_name = loss_name
self._share_vars_from = share_vars_from
if self._exec_strategy is None:
self._exec_strategy = ExecutionStrategy()
if self._build_strategy is None:
self._build_strategy = BuildStrategy()
return self
def _with_distributed(self):
raise NotImplementedError()
def _with_inference_optimize(self):
raise NotImplementedError()
def _compile_data_parallel(self):
if self._share_vars_from:
if self._scope:
sys.stderr.write("share_vars_from is set, scope is ignored.\n")
if not self._share_vars_from._is_data_parallel:
raise ValueError("share_vars_from is not data parallel. Cannot "
"share vars from it.")
if self._share_vars_from._executor is None:
raise ValueError(
"share_vars_from is not compiled and run, so there is no "
"var to share.")
self._local_scopes = self._share_vars_from._executor.local_scopes()
else:
self._local_scopes = []
self._exec_strategy.use_cuda = isinstance(self._place, core.CUDAPlace)
if self._exec_strategy.use_cuda:
gpus_env = os.getenv("FLAGS_selected_gpus")
if gpus_env:
gpus = [int(s) for s in gpus_env.split(",")]
else:
gpus = [
i for i in six.moves.range(core.get_cuda_device_count())
]
self._places = [core.CUDAPlace(i) for i in gpus]
else:
cpu_num = int(
os.environ.get('CPU_NUM', multiprocessing.cpu_count()))
self._places = [core.CPUPlace() for _ in six.moves.range(cpu_num)]
assert self._places, "no place for execution"
if self._exec_strategy.num_threads == 0:
if self._exec_strategy.use_cuda:
# Experiments on se-resnext shows that too many threads hurt
# performance. Worth tunning for other models in the future.
self._exec_strategy.num_threads = len(self._places) * 4
else:
cpu_num = int(
os.environ.get('CPU_NUM', multiprocessing.cpu_count()))
self._exec_strategy.num_threads = cpu_num * 2
trainers_endpoints = self._program._trainers_endpoints
if self._build_strategy.num_trainers > 1 and trainers_endpoints:
assert self._build_strategy.num_trainers == len(
trainers_endpoints), "num_trainers == len(end_points)"
self._build_strategy.trainers_endpoints = trainers_endpoints
self._persistable_vars = set([
cpt.to_text(v.name)
for v in [
var for var in self._program.list_vars()
if var.persistable and var.type != core.VarDesc.VarType.RAW
]
])
places = list(map(_place_obj, self._places))
return core.ParallelExecutor(
places, self._persistable_vars, self._program.desc,
cpt.to_text(self._loss_name)
if self._loss_name else six.u(''), self._scope, self._local_scopes,
self._exec_strategy, self._build_strategy)
def _compile(self, scope, place):
"""Compile the program based on the configs.
Args:
scope: The variables (resources) that are associated with
this compiled program.
place: The location that the compiled program will be run on.
Returns:
self
"""
if self._compiled:
if scope and self._scope != scope:
raise ValueError("Cannot compile with different scope")
if place and self._place != place:
raise ValueError("Cannot compile with different place")
return self
self._compiled = True
self._scope = scope
self._place = place
if self._is_data_parallel:
self._executor = self._compile_data_parallel()
else:
p = _place_obj(self._place)
self._executor = core.Executor(p)
return self
python/paddle/fluid/data_feeder.py
浏览文件 @ 92da467c
......@@ -71,10 +71,25 @@ class DataToLoDTensorConverter(object):
for each_data in data:
self._feed_impl_(each_data, lod[1:], lod_level - 1)
def _check_shape(self, shape):
for s1, s2 in zip(self.shape, shape):
if s1 != s2 and s1 >= 0 and s2 >= 0:
raise ValueError(
"Shape not match. What is defined in data layer is {}, but receive {}".
format(self.shape, shape))
def done(self):
arr = numpy.array(self.data, dtype=self.dtype)
if self.shape and len(arr.shape) != len(self.shape):
arr = arr.reshape(self.shape)
if self.shape:
if len(arr.shape) != len(self.shape):
try:
arr = arr.reshape(self.shape)
except ValueError:
raise ValueError(
"Reshape error. What is defined in data layer is {}, but receive {}"
.format(self.shape, arr.shape))
else:
self._check_shape(arr.shape)
t = core.LoDTensor()
t.set(arr, self.place)
if self.lod_level > 0:
......@@ -152,17 +167,8 @@ class DataFeeder(object):
raise TypeError("Feed list should contain a list of variable")
self.feed_dtypes.append(each_var.dtype)
self.feed_names.append(each_var.name)
shape = each_var.shape
batch_size_dim = -1
for i, s in enumerate(shape):
if s < 0:
batch_size_dim = i
break
if batch_size_dim == -1:
raise ValueError("Variable {0} must has a batch size dimension",
each_var.name)
self.feed_lod_level.append(each_var.lod_level)
self.feed_shapes.append(shape)
self.feed_shapes.append(each_var.shape)
self.place = place
......
python/paddle/fluid/executor.py
浏览文件 @ 92da467c
......@@ -14,11 +14,15 @@
from __future__ import print_function
import os
import multiprocessing
import numpy as np
import contextlib
import six
from .framework import Program, default_main_program, Variable
from . import core
from . import compiler
from .. import compat as cpt
__all__ = ['Executor', 'global_scope', 'scope_guard']
......@@ -204,20 +208,20 @@ def _fetch_var(name, scope=None, return_numpy=True):
return tensor
def _get_program_cache_key(feed, fetch_list):
feed_var_names = list(feed.keys())
def _to_name_str(var):
if isinstance(var, Variable):
return var.desc.name()
elif isinstance(var, str):
return var
elif isinstance(var, six.string_types):
return str(var)
else:
raise TypeError(str(var) + " should be Variable or str")
def to_name_str(var):
if isinstance(var, Variable):
return var.desc.name()
elif isinstance(var, str):
return var
elif isinstance(var, six.string_types):
return str(var)
else:
raise TypeError(str(var) + " should be Variable or str")
fetch_var_names = list(map(to_name_str, fetch_list))
def _get_program_cache_key(feed, fetch_list):
feed_var_names = list(feed.keys())
fetch_var_names = list(map(_to_name_str, fetch_list))
return str(feed_var_names + fetch_var_names)
......@@ -266,6 +270,29 @@ class Executor(object):
But the global scope variables will be persistent through different runs.
All of ops in program will be running in sequence.
Example:
.. code-block:: python
# First create the Executor.
place = fluid.CUDAPlace(0) if use_cuda else fluid.CPUPlace()
exe = fluid.Executor(place)
# Run the startup program once and only once.
# Not need to optimize/compile the startup program.
exe.run(fluid.default_startup_program())
# Run the main program directly without compile.
loss, = exe.run(fluid.default_main_program(),
feed=feed_dict,
fetch_list=[loss.name])
# Or, compiled the program and run. See `CompiledProgram` for more detail.
compiled_prog = compiler.CompiledProgram(
fluid.default_main_program()).with_data_parallel(
loss_name=loss.name)
loss, = exe.run(compiled_prog,
feed=feed_dict,
fetch_list=[loss.name])
Args:
place(core.CPUPlace|core.CUDAPlace(n)): indicate the executor run on which device
......@@ -275,11 +302,8 @@ class Executor(object):
def __init__(self, place):
self.place = place
p = core.Place()
p.set_place(place)
self.executor = core.Executor(p)
self.program_caches = dict()
self.executor = None
self._closed = False
def _get_program_cache(self, program_cache_key):
......@@ -361,6 +385,7 @@ class Executor(object):
You can no long use this executor after calling this method.
For the distributed training, this method would free the resource on PServers related to
the current Trainer.
TODO(panyx0718): Why ParallelExecutor doesn't have close?
Example:
>>> cpu = core.CPUPlace()
......@@ -368,10 +393,55 @@ class Executor(object):
>>> ...
>>> exe.close()
"""
if not self._closed:
if not self._closed and self.executor:
self.executor.close()
self._closed = True
def _run_parallel(self, scope, feed, fetch_list, fetch_var_name,
return_numpy):
if isinstance(feed, dict):
feed_tensor_dict = dict()
for feed_name in feed:
feed_tensor = feed[feed_name]
if not isinstance(feed_tensor, core.LoDTensor):
feed_tensor = core.LoDTensor()
# always set to CPU place, since the tensor need to be splitted
# it is fast in CPU
feed_tensor.set(feed[feed_name], core.CPUPlace())
feed_tensor_dict[feed_name] = feed_tensor
self.executor.feed_and_split_tensor_into_local_scopes(
feed_tensor_dict)
elif isinstance(feed, list) or isinstance(feed, tuple):
if len(feed) != len(self._places):
raise ValueError(
"Feed a list of tensor, the list should be the same size as places"
)
res = list()
for i, each in enumerate(feed):
if not isinstance(each, dict):
raise TypeError(
"Each element of feed list should be a dict")
res_dict = dict()
for feed_name in each:
tensor = each[feed_name]
if not isinstance(tensor, core.LoDTensor):
tmp = core.LoDTensor()
tmp.set(tensor, self._places[i])
tensor = tmp
res_dict[feed_name] = tensor
res.append(res_dict)
self.executor.feed_tensors_into_local_scopes(res)
fetch_var_names = list(map(_to_name_str, fetch_list))
self.executor.run(fetch_var_names, fetch_var_name)
arr = scope.find_var(fetch_var_name).get_lod_tensor_array()
if return_numpy:
return as_numpy(arr)
return [arr[i] for i in range(len(arr))]
def run(self,
program=None,
feed=None,
......@@ -391,8 +461,9 @@ class Executor(object):
operators in the program but not only the operators dependent by the fetch_list
Args:
program(Program): the program that need to run, if not provied, then default_main_program will be used.
feed(dict): feed variable map, e.g. {"image": ImageData, "label": LableData}
program(Program|CompiledProgram): the program that need to run,
if not provided, then default_main_program will be used.
feed(dict): feed variable map, e.g. {"image": ImageData, "label": LabelData}
fetch_list(list): a list of variable or variable names that user want to get, run will return them according to this list.
feed_var_name(str): the name for the input variable of feed Operator.
fetch_var_name(str): the name for the output variable of fetch Operator.
......@@ -428,14 +499,59 @@ class Executor(object):
if self._closed:
raise RuntimeError("Attempted to use a closed Executor")
if scope is None:
scope = global_scope()
if fetch_list is None:
fetch_list = []
compiled = isinstance(program, compiler.CompiledProgram)
# For backward compatibility, run directly.
if not compiled:
if not self.executor:
p = core.Place()
p.set_place(self.place)
self.executor = core.Executor(p)
return self._run(
program,
feed=feed,
fetch_list=fetch_list,
feed_var_name=feed_var_name,
fetch_var_name=fetch_var_name,
scope=scope,
return_numpy=return_numpy,
use_program_cache=use_program_cache)
program._compile(scope, self.place)
self.executor = program._executor
if program._is_data_parallel:
return self._run_parallel(
scope=scope,
feed=feed,
fetch_list=fetch_list,
fetch_var_name=fetch_var_name,
return_numpy=return_numpy)
else:
# TODO(panyx0718): Can compile program to optimize executor
# performance.
return self._run(
program._program,
feed=feed,
fetch_list=fetch_list,
feed_var_name=feed_var_name,
fetch_var_name=fetch_var_name,
scope=scope,
return_numpy=return_numpy,
use_program_cache=use_program_cache)
def _run(self, program, feed, fetch_list, feed_var_name, fetch_var_name,
scope, return_numpy, use_program_cache):
if feed is None:
feed = {}
if not isinstance(feed, dict):
raise TypeError(
"feed requires dict as its Parameter. But you passed in %s" %
(type(feed)))
if fetch_list is None:
fetch_list = []
if program is None:
program = default_main_program()
......@@ -444,9 +560,6 @@ class Executor(object):
"Executor requires Program as its Parameter. But you passed in %s"
% (type(program)))
if scope is None:
scope = global_scope()
cache_key = _get_program_cache_key(feed, fetch_list)
if use_program_cache:
cached_program = self._get_program_cache(cache_key)
......
python/paddle/fluid/framework.py
浏览文件 @ 92da467c
......@@ -26,6 +26,13 @@ import numpy as np
from .. import compat as cpt
from .proto import framework_pb2
try:
if os.name == 'nt':
import sys
third_lib_path = os.path.abspath(os.path.dirname(
__file__)) + os.sep + '..' + os.sep + 'libs'
os.environ['path'] += ';' + third_lib_path
sys.path.append(third_lib_path)
from . import core
except ImportError as e:
if os.name == 'nt':
......
python/paddle/fluid/layers/nn.py
浏览文件 @ 92da467c
......@@ -26,7 +26,7 @@ from ..initializer import Normal, Constant
from ..framework import Variable, OpProtoHolder
from ..param_attr import ParamAttr
from .layer_function_generator import autodoc, templatedoc, _generate_doc_string_
from .tensor import concat
from .tensor import concat, assign
from . import utils
from .. import unique_name
from functools import reduce
......@@ -340,9 +340,7 @@ def embedding(input,
"""
helper = LayerHelper('embedding', **locals())
remote_prefetch = False
if os.environ.get('PADDLE_ENABLE_REMOTE_PREFETCH'):
remote_prefetch = True
remote_prefetch = is_sparse and (not is_distributed)
if remote_prefetch:
assert is_sparse is True and is_distributed is False
w = helper.create_parameter(
......@@ -5032,12 +5030,18 @@ def nce(input,
else:
num_neg_samples = int(num_neg_samples)
remote_prefetch = is_sparse
print(
"With sparse mode, if your models has only small parameter prefetch may cause speed down"
)
attrs = {
'num_total_classes': int(num_total_classes),
'num_neg_samples': num_neg_samples,
'seed': seed,
'sampler': sampler,
'is_sparse': is_sparse
'is_sparse': is_sparse,
'remote_prefetch': remote_prefetch
}
helper.append_op(
......@@ -5147,7 +5151,10 @@ def hsigmoid(input,
pass
weights = None
remote_prefetch = is_sparse
print(
"With sparse mode, if your models has only small parameter prefetch may cause speed down"
)
if not is_custom:
weights = helper.create_parameter(
attr=helper.param_attr,
......@@ -5163,7 +5170,7 @@ def hsigmoid(input,
inputs = {
"X": input,
"W": weights,
"PTable": path_table,
"PathTable": path_table,
"PathCode": path_code,
"Label": label
}
......@@ -5186,9 +5193,13 @@ def hsigmoid(input,
type="hierarchical_sigmoid",
inputs=inputs,
outputs={"Out": out,
"PreOut": pre_out},
attrs={"num_classes": num_classes,
"is_sparse": is_sparse})
"PreOut": pre_out,
"W_Out": weights},
attrs={
"num_classes": num_classes,
"is_sparse": is_sparse,
"remote_prefetch": remote_prefetch
})
return out
......@@ -7684,7 +7695,7 @@ def brelu(x, t_min=0.0, t_max=24.0, name=None):
Examples:
.. code-block:: python
.. code-block:: python
x = fluid.layers.data(name="x", shape=[2,3,16,16], dtype="float32")
y = fluid.layers.brelu(x, t_min=1.0, t_max=20.0)
......
python/paddle/fluid/optimizer.py
浏览文件 @ 92da467c
# Copyright (c) 2018 PaddlePaddle Authors. All Rights Reserved.
# Copyright (c) 2018 PaddlePaddle Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
......@@ -195,22 +195,18 @@ class Optimizer(object):
format(name, param.name))
return self._accumulators[name][param.name]
def _create_optimization_pass(self,
parameters_and_grads,
loss,
startup_program=None):
def _create_optimization_pass(self, parameters_and_grads):
"""Add optimization operators to update gradients to variables.
Args:
loss(Variable): the target that this optimization is for.
parameters_and_grads(list(tuple(Variable, Variable))):
a list of (variable, gradient) pair to update.
a list of (variable, gradient) pair to update.
Returns:
return_op_list: a list of operators that will complete one step of
optimization. This will include parameter update ops, global step
update ops and any other custom ops required by subclasses to manage
their internal state.
optimization. This will include parameter update ops, global step
update ops and any other custom ops required by subclasses to manage
their internal state.
"""
# This is a default implementation of create_optimization_pass that
# can be shared by most optimizers. This implementation assumes that
......@@ -219,37 +215,33 @@ class Optimizer(object):
# _create_accumulators method if it needs to create accumulators
# for parameters and extend _finish_update method to add custom ops.
# Create any accumulators
program = loss.block.program
self._dtype = loss.dtype
with program_guard(program, startup_program):
global_block = framework.default_main_program().global_block()
start = len(global_block.ops)
self.helper = LayerHelper(self.__class__.__name__)
self._create_accumulators(loss.block,
[p[0] for p in parameters_and_grads])
self._create_global_learning_rate()
optimize_ops = []
for param_and_grad in parameters_and_grads:
if param_and_grad[1] is None:
continue
with param_and_grad[0].block.program._optimized_guard(
param_and_grad), name_scope("optimizer"):
if param_and_grad[0].trainable is True:
optimize_op = self._append_optimize_op(loss.block,
param_and_grad)
optimize_ops.append(optimize_op)
# Get custom finish ops for subclasses
# FIXME: Need to fix this once we figure out how to handle dependencies
self._finish_update(loss.block, parameters_and_grads)
end = len(global_block.ops)
return global_block._slice_ops(start, end)
def _process_distribute_lookuptable(self, param_grads, loss,
startup_program):
# Allways called under program_guard use global block as loss block
global_block = framework.default_main_program().global_block()
start = len(global_block.ops)
self.helper = LayerHelper(self.__class__.__name__)
self._create_accumulators(global_block,
[p[0] for p in parameters_and_grads])
self._create_global_learning_rate()
optimize_ops = []
for param_and_grad in parameters_and_grads:
if param_and_grad[1] is None:
continue
with param_and_grad[0].block.program._optimized_guard(
param_and_grad), name_scope("optimizer"):
if param_and_grad[0].trainable is True:
optimize_op = self._append_optimize_op(global_block,
param_and_grad)
optimize_ops.append(optimize_op)
# Get custom finish ops for subclasses
# FIXME: Need to fix this once we figure out how to handle dependencies
self._finish_update(global_block, parameters_and_grads)
end = len(global_block.ops)
return global_block._slice_ops(start, end)
def _process_distribute_lookuptable(self, param_grads):
"""
Because distribute lookup table only support SGD optimizer for now, not support
other optimizer and regularization, so we should find the table parameter out,
......@@ -259,7 +251,8 @@ class Optimizer(object):
:param loss: the loss variable.
:param startup_program: the startup program
"""
program = loss.block.program
program = framework.default_main_program()
global_block = framework.default_main_program().global_block()
table_name = find_distributed_lookup_table(program)
table_param = None
table_grad = None
......@@ -275,38 +268,121 @@ class Optimizer(object):
new_param_grads.append((p, g))
sgd_op = None
if table_param is not None:
with program_guard(program, startup_program):
param_and_grad = [table_param, table_grad]
with table_param.block.program._optimized_guard(param_and_grad), \
framework.name_scope("optimizer"):
self._create_global_learning_rate()
# create the optimize op
sgd_op = loss.block.append_op(
type='sgd',
inputs={
"Param": table_param,
"Grad": table_grad,
"LearningRate":
self._create_param_lr(param_and_grad)
},
outputs={"ParamOut": param_and_grad[0]})
param_and_grad = [table_param, table_grad]
with table_param.block.program._optimized_guard(param_and_grad), \
framework.name_scope("optimizer"):
self._create_global_learning_rate()
# create the optimize op
sgd_op = global_block.append_op(
type='sgd',
inputs={
"Param": table_param,
"Grad": table_grad,
"LearningRate": self._create_param_lr(param_and_grad)
},
outputs={"ParamOut": param_and_grad[0]})
return new_param_grads, (table_param, table_grad), sgd_op
def backward(self,
loss,
startup_program=None,
parameter_list=None,
no_grad_set=None,
callbacks=None):
"""
First part of `minimize`, do auto-diff to append backward ops for
the current program.
Args:
loss (Variable): loss variable to run optimizations.
startup_program (Program): startup_program for initializing parameters
in `parameter_list`.
parameter_list (list): list of Variables to update.
no_grad_set (set|None): set of Variables should be ignored.
callbacks (list|None): list of callables to run when appending backward
operator for one parameter.
Return:
list: list of (param, grad) pair, grad is the output of backward.
Examples:
See examples in `apply_gradients`.
"""
if callbacks is None:
callbacks = [error_clip_callback]
else:
assert (isinstance(callbacks, list))
callbacks.append(error_clip_callback)
return append_backward(loss, parameter_list, no_grad_set, callbacks)
def apply_gradients(self, params_grads):
"""
Second part of `minimize`, appending optimization operators for
given `params_grads` pairs.
Args:
params_grads (list): list of (param, grad) pair to do optimization.
Returns:
list: A list of operators appended to the current program.
Examples:
.. code-block:: python
loss = network()
optimizer = fluid.optimizer.SGD(learning_rate=0.1)
params_grads = optimizer.backward(loss)
# you may append operations for params_grads here
# ...
optimizer.apply_gradients(params_grads)
"""
params_grads = sorted(params_grads, key=lambda x: x[0].name)
params_grads, table_param_and_grad, table_optimize_op = \
self._process_distribute_lookuptable(params_grads)
params_grads = append_gradient_clip_ops(params_grads)
# Add regularization if any
params_grads = append_regularization_ops(params_grads,
self.regularization)
optimize_ops = self._create_optimization_pass(params_grads)
if table_optimize_op is not None:
optimize_ops.append(table_optimize_op)
params_grads.append(table_param_and_grad)
return optimize_ops
def minimize(self,
loss,
startup_program=None,
parameter_list=None,
no_grad_set=None):
"""Add operations to minimize `loss` by updating `parameter_list`.
"""
Add operations to minimize `loss` by updating `parameter_list`.
This method combines interface `append_backward()` and
`create_optimization_pass()` into one.
This method combines interface `backward()` and
`apply_gradients()` into one.
Args:
loss (Variable): loss variable to run optimizations.
startup_program (Program): startup_program for initializing parameters
in `parameter_list`.
parameter_list (list): list of Variables to update.
no_grad_set (set|None): set of Variables should be ignored.
Returns:
tuple: (optimize_ops, params_grads) which are, list of operators appended;
and list of (param, grad) Variables pair for optimization.
"""
self._dtype = loss.dtype
program = loss.block.program
optimize_ops = []
if imperative_base.enabled():
if parameter_list is not None:
params_grads = parameter_list
else:
program = loss.block.program
parameters = program.global_block().all_parameters()
params_grads = []
for param in parameters:
......@@ -317,29 +393,13 @@ class Optimizer(object):
stop_gradient=True)
grad_var._value = param._ivar.grad_value
params_grads.append((param, grad_var))
optimize_ops = self._create_optimization_pass(params_grads, loss,
startup_program)
with program_guard(program, startup_program):
optimize_ops = self._create_optimization_pass(params_grads)
else:
params_grads = append_backward(loss, parameter_list, no_grad_set,
[error_clip_callback])
params_grads = sorted(params_grads, key=lambda x: x[0].name)
params_grads, table_param_and_grad, table_optimize_op = \
self._process_distribute_lookuptable(params_grads, loss, startup_program)
params_grads = append_gradient_clip_ops(params_grads)
# Add regularization if any
params_grads = append_regularization_ops(params_grads,
self.regularization)
optimize_ops = self._create_optimization_pass(params_grads, loss,
startup_program)
if table_optimize_op is not None:
optimize_ops.append(table_optimize_op)
params_grads.append(table_param_and_grad)
with program_guard(program, startup_program):
params_grads = self.backward(loss, startup_program,
parameter_list, no_grad_set)
optimize_ops = self.apply_gradients(params_grads)
return optimize_ops, params_grads
......
python/paddle/fluid/parallel_executor.py
浏览文件 @ 92da467c
......@@ -29,6 +29,15 @@ ExecutionStrategy = core.ParallelExecutor.ExecutionStrategy
BuildStrategy = core.ParallelExecutor.BuildStrategy
def _is_pserver_mode(main_program):
main = main_program if main_program \
else framework.default_main_program()
for op in main.global_block().ops:
if op.type in ["send", "recv"]:
return True
return False
class ParallelExecutor(object):
"""
ParallelExecutor is designed for data parallelism, which focuses on distributing
......@@ -128,6 +137,11 @@ class ParallelExecutor(object):
build_strategy = BuildStrategy()
build_strategy.num_trainers = num_trainers
build_strategy.trainer_id = trainer_id
# FIXME(zcd): is_distribution_ is a temporary field, because in pserver mode,
# num_trainers is 1, so the current fields of build_strategy doesn't tell if
# it's distributed model.
build_strategy.is_distribution = _is_pserver_mode(
main_program) or num_trainers > 1
# step4: get main_program, scope, local_scopes
main = main_program if main_program \
......@@ -167,9 +181,8 @@ class ParallelExecutor(object):
# step7: init ParallelExecutor
self.executor = core.ParallelExecutor(
places, persistable_vars, main.desc,
cpt.to_text(loss_name)
if loss_name else six.u(''), scope, local_scopes, exec_strategy,
build_strategy, num_trainers, trainer_id)
cpt.to_text(loss_name) if loss_name else six.u(''), scope,
local_scopes, exec_strategy, build_strategy)
self.scope = scope
......@@ -280,7 +293,7 @@ class ParallelExecutor(object):
res.append(res_dict)
self.executor.feed_tensors_into_local_scopes(res)
fetch_var_name = '@FETCHED_VAR_NAME@'
fetch_var_name = 'fetch'
self.executor.run(fetch_list, fetch_var_name)
arr = self.scope.find_var(fetch_var_name).get_lod_tensor_array()
......
python/paddle/fluid/tests/test_data_feeder.py
浏览文件 @ 92da467c
......@@ -30,6 +30,12 @@ class TestDataFeeder(unittest.TestCase):
self.assertEqual(result['image'].recursive_sequence_lengths(), [])
self.assertEqual(result['label'].recursive_sequence_lengths(), [])
try:
result = feeder.feed([([0] * 783, [9]), ([1] * 783, [1])])
self.assertTrue(False)
except ValueError:
self.assertTrue(True)
def test_lod_level_1_converter(self):
# lod_level = 1
# each sentence has a different number of words
......
python/paddle/fluid/tests/unittests/CMakeLists.txt
浏览文件 @ 92da467c
......@@ -21,6 +21,8 @@ if(NOT WITH_DISTRIBUTE)
LIST(REMOVE_ITEM TEST_OPS test_dist_simnet_bow)
LIST(REMOVE_ITEM TEST_OPS test_dist_mnist_batch_merge)
LIST(REMOVE_ITEM TEST_OPS test_dist_text_classification)
LIST(REMOVE_ITEM TEST_OPS test_nce_remote_table_op)
LIST(REMOVE_ITEM TEST_OPS test_hsigmoid_remote_table_op)
endif(NOT WITH_DISTRIBUTE)
if (NOT ${WITH_GPU})
......@@ -32,7 +34,6 @@ endif()
list(REMOVE_ITEM TEST_OPS test_seq_concat_op) # FIXME(helin): https://github.com/PaddlePaddle/Paddle/issues/8290
list(REMOVE_ITEM TEST_OPS test_modified_huber_loss_op) # FIXME(qijun) https://github.com/PaddlePaddle/Paddle/issues/5184
list(REMOVE_ITEM TEST_OPS test_lstm_unit_op) # # FIXME(qijun) https://github.com/PaddlePaddle/Paddle/issues/5185
list(REMOVE_ITEM TEST_OPS test_nce) # FIXME(qijun) https://github.com/PaddlePaddle/Paddle/issues/7778
list(REMOVE_ITEM TEST_OPS test_recurrent_op) # FIXME(qijun) https://github.com/PaddlePaddle/Paddle/issues/6152
list(REMOVE_ITEM TEST_OPS test_cond_op) # FIXME(qijun): https://github.com/PaddlePaddle/Paddle/issues/5101#issuecomment-339814957
......
python/paddle/fluid/tests/unittests/dist_ctr.py
浏览文件 @ 92da467c
......@@ -31,6 +31,7 @@ fluid.default_main_program().random_seed = 1
class TestDistCTR2x2(TestDistRunnerBase):
def get_model(self, batch_size=2):
dnn_input_dim, lr_input_dim = dist_ctr_reader.load_data_meta()
""" network definition """
dnn_data = fluid.layers.data(
......@@ -97,7 +98,14 @@ class TestDistCTR2x2(TestDistRunnerBase):
inference_program = paddle.fluid.default_main_program().clone()
sgd_optimizer = fluid.optimizer.SGD(learning_rate=0.0001)
regularization = None
use_l2_decay = bool(os.getenv('USE_L2_DECAY', 0))
if use_l2_decay:
regularization = fluid.regularizer.L2DecayRegularizer(
regularization_coeff=1e-1)
sgd_optimizer = fluid.optimizer.SGD(learning_rate=0.0001,
regularization=regularization)
sgd_optimizer.minimize(avg_cost)
dataset = dist_ctr_reader.Dataset()
......
python/paddle/fluid/tests/unittests/dist_se_resnext.py
浏览文件 @ 92da467c
......@@ -235,7 +235,6 @@ class DistSeResneXt2x2(TestDistRunnerBase):
bd = [step * e for e in epochs]
base_lr = 0.1
lr = []
lr = [base_lr * (0.1**i) for i in range(len(bd) + 1)]
optimizer = fluid.optimizer.Momentum(
......
python/paddle/fluid/tests/unittests/ngraph/test_mean_ngraph_op.py 0 → 100644
浏览文件 @ 92da467c
# 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
from paddle.fluid.tests.unittests.test_mean_op import TestMeanOp, TestFP16MeanOp
class TestNGRAPHMeanOp(TestMeanOp):
def setUp(self):
super(TestNGRAPHMeanOp, self).setUp()
class TestNGRAPHFP16MeanOp(TestFP16MeanOp):
def setUp(self):
super(TestNGRAPHFP16MeanOp, self).setUp()
if __name__ == "__main__":
unittest.main()
python/paddle/fluid/tests/unittests/ngraph/test_scale_ngraph_op.py 0 → 100644
浏览文件 @ 92da467c
# 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
from paddle.fluid.tests.unittests.test_scale_op import TestScaleOp, TestScaleOpSelectedRows, TestScaleFp16Op, TestScaleFp16OpSelectedRows
class TestNGRAPHScaleOp(TestScaleOp):
def init_dtype_type(self):
pass
class TestNGRAPHScaleOpSelectedRows(TestScaleOpSelectedRows):
def init_dtype_type(self):
pass
class TestNGRAPHScaleFp16Op(TestScaleFp16Op):
def init_dtype_type(self):
pass
class TestNGRAPHScaleFp16OpSelectedRows(TestScaleFp16OpSelectedRows):
def init_dtype_type(self):
pass
if __name__ == "__main__":
unittest.main()
python/paddle/fluid/tests/unittests/parallel_executor_test_base.py
浏览文件 @ 92da467c
......@@ -19,6 +19,7 @@ import os
import unittest
import paddle.fluid as fluid
import paddle.fluid.core as core
from paddle.fluid import compiler
import time
import numpy as np
import math
......@@ -44,15 +45,8 @@ class TestParallelExecutorBase(unittest.TestCase):
optimizer=fluid.optimizer.Adam,
use_fast_executor=False,
enable_sequential_execution=False):
def run_executor(exe, feed, fetch_list, program=None):
if isinstance(exe, fluid.ParallelExecutor):
res = exe.run(fetch_list=fetch_list, feed=feed)
elif isinstance(exe, fluid.Executor):
if program is None:
program = fluid.default_main_program()
res = exe.run(program=program, feed=feed, fetch_list=fetch_list)
else:
raise ValueError('Unkown type exe')
def run_executor(exe, binary, feed, fetch_list):
res = exe.run(binary, feed=feed, fetch_list=fetch_list)
return res
main = fluid.Program()
......@@ -72,8 +66,8 @@ class TestParallelExecutorBase(unittest.TestCase):
fluid.memory_optimize(main)
place = fluid.CUDAPlace(0) if use_cuda else fluid.CPUPlace()
startup_exe = fluid.Executor(place)
startup_exe.run(startup)
exe = fluid.Executor(place)
exe.run(startup)
exec_strategy = fluid.ExecutionStrategy()
exec_strategy.allow_op_delay = allow_op_delay
if use_fast_executor:
......@@ -86,15 +80,13 @@ class TestParallelExecutorBase(unittest.TestCase):
build_strategy.enable_sequential_execution = enable_sequential_execution
if use_cuda and core.is_compiled_with_cuda():
build_strategy.remove_unnecessary_lock = True
if use_parallel_executor:
exe = fluid.ParallelExecutor(
use_cuda,
binary = compiler.CompiledProgram(main).with_data_parallel(
loss_name=loss.name,
exec_strategy=exec_strategy,
build_strategy=build_strategy)
build_strategy=build_strategy,
exec_strategy=exec_strategy)
else:
exe = fluid.Executor(place=place)
binary = compiler.CompiledProgram(main)
if batch_size is not None:
batch_size *= fluid.core.get_cuda_device_count(
......@@ -102,13 +94,14 @@ class TestParallelExecutorBase(unittest.TestCase):
os.environ.get('CPU_NUM', multiprocessing.cpu_count()))
begin = time.time()
first_loss, = run_executor(
exe=exe, feed=feed_dict, fetch_list=[loss.name])
exe=exe, binary=binary, feed=feed_dict, fetch_list=[loss.name])
for i in range(iter):
run_executor(exe=exe, feed=feed_dict, fetch_list=[])
run_executor(
exe=exe, binary=binary, feed=feed_dict, fetch_list=[])
last_loss, = run_executor(
exe=exe, feed=feed_dict, fetch_list=[loss.name])
exe=exe, binary=binary, feed=feed_dict, fetch_list=[loss.name])
end = time.time()
if batch_size is not None:
......
python/paddle/fluid/tests/unittests/test_conv2d_fusion_op.py
浏览文件 @ 92da467c
......@@ -51,8 +51,9 @@ class TestConv2dFusionOp(OpTest):
input = np.random.random(self.input_size).astype(self.dtype)
filter = np.random.random(self.filter_size).astype(self.dtype)
self.output = conv2d_forward_naive(input, filter, self.groups,
conv2d_param).astype(self.dtype)
self.output, _, _, _, _ = conv2d_forward_naive(
input, filter, self.groups, conv2d_param)
self.output = self.output.astype(self.dtype)
self.inputs = {
'Input': OpTest.np_dtype_to_fluid_dtype(input),
......
python/paddle/fluid/tests/unittests/test_conv2d_int8_mkldnn_op.py 0 → 100644
浏览文件 @ 92da467c
# 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
import paddle.fluid.core as core
from op_test import OpTest
from test_conv2d_op import conv2d_forward_naive, TestConv2dOp
def conv2d_forward_refer(input, filter, group, conv_param):
out, in_n, out_h, out_w, out_c = conv2d_forward_naive(input, filter, group,
conv_param)
out_tmp = np.zeros((in_n, out_h, out_w, out_c))
for n in range(in_n):
for i in range(out_h):
for j in range(out_w):
for m in range(out_c):
out_tmp[n, i, j, m] = out[n, m, i, j]
return out_tmp.reshape(in_n, out_c, out_h, out_w)
class TestConv2dInt8Op(TestConv2dOp):
def setUp(self):
self.op_type = "conv2d"
self.use_cudnn = False
self.exhaustive_search = False
self.use_cuda = False
self.use_mkldnn = False
self.data_format = "AnyLayout"
self.weighttype = np.float32
self.use_mkldnn = True
self.init_group()
self.init_dilation()
self.init_test_case()
self.init_fuse_relu()
self.init_data_type()
conv2d_param = {
'stride': self.stride,
'pad': self.pad,
'dilation': self.dilations
}
filter = np.random.random(self.filter_size).astype(self.weighttype)
if self.srctype == np.uint8:
input = np.random.randint(0, 10,
self.input_size).astype(self.srctype)
else:
input = np.random.randint(-5, 5,
self.input_size).astype(self.srctype)
input_shift = (np.ones(self.input_size) * 128).astype(np.uint8)
if self.srctype == np.int8:
filter_int = np.round(filter * self.scale_weights[0] *
0.5).astype(np.int32)
scale_output_shift = self.scale_out / (self.scale_in *
self.scale_weights[0] * 0.5)
output1 = conv2d_forward_refer(
np.round((input.astype(np.int32) + input_shift) *
self.scale_in).astype(np.int32), filter_int,
self.groups,
conv2d_param).astype(np.float32) * scale_output_shift
output2 = conv2d_forward_refer(
np.round((input_shift) * self.scale_in).astype(np.int32),
filter_int, self.groups,
conv2d_param).astype(np.float32) * scale_output_shift
if self.fuse_relu:
output = np.maximum(np.round(output1 - output2),
0).astype(self.dsttype)
else:
output = np.round(output1 - output2).astype(self.dsttype)
else:
filter_int = np.round(filter *
self.scale_weights[0]).astype(np.int32)
scale_output_shift = self.scale_out / (self.scale_in *
self.scale_weights[0])
output1 = conv2d_forward_refer(
input.astype(np.int32), filter_int, self.groups,
conv2d_param).astype(np.float32)
if self.fuse_relu:
output = np.maximum(
np.round(output1 * (self.scale_out / (
self.scale_in * self.scale_weights[0]))),
0).astype(self.dsttype)
else:
output = np.round(output1 * (self.scale_out / (
self.scale_in *
self.scale_weights[0]))).astype(self.dsttype)
self.inputs = {
'Input':
OpTest.np_dtype_to_fluid_dtype(input.astype(self.srctype)),
'Filter': OpTest.np_dtype_to_fluid_dtype(filter)
}
self.attrs = {
'strides': self.stride,
'paddings': self.pad,
'groups': self.groups,
'dilations': self.dilations,
'use_cudnn': self.use_cudnn,
'use_mkldnn': self.use_mkldnn,
'data_format': self.data_format,
'exhaustive_search': self.exhaustive_search,
'Scale_in': self.scale_in,
'Scale_out': self.scale_out,
'Scale_weights': self.scale_weights,
'fuse_relu': self.fuse_relu
}
self.outputs = {'Output': output}
def test_check_output(self):
self.check_output_with_place(core.CPUPlace(), atol=0)
def test_check_grad(self):
pass
def test_check_grad_no_filter(self):
pass
def test_check_grad_no_input(self):
pass
def init_test_case(self):
TestConv2dOp.init_test_case(self)
f_c = self.input_size[1] // self.groups
self.filter_size = [1, f_c, 3, 3]
self.scale_in = 1.0
self.scale_out = 0.5
self.scale_weights = [10.0]
def init_data_type(self):
self.srctype = np.uint8
self.dsttype = np.int8
def init_fuse_relu(self):
self.fuse_relu = True
#--------------------test conv2d u8 in and u8 out--------------------
class TestConv2d(TestConv2dInt8Op):
def init_test_case(self):
self.pad = [0, 0]
self.stride = [1, 1]
self.input_size = [2, 3, 5, 5] # NCHW
assert np.mod(self.input_size[1], self.groups) == 0
f_c = self.input_size[1] // self.groups
self.filter_size = [6, f_c, 3, 3]
self.scale_in = 1.0
self.scale_out = 0.5
self.scale_weights = [10.0]
class TestWithPad(TestConv2d):
def init_test_case(self):
TestConv2d.init_test_case(self)
self.pad = [1, 1]
class TestWithGroup(TestConv2d):
def init_group(self):
self.groups = 3
class TestWithStride(TestConv2dInt8Op):
def init_test_case(self):
self.pad = [1, 1]
self.stride = [2, 2]
self.input_size = [2, 3, 6, 6]
assert np.mod(self.input_size[1], self.groups) == 0
f_c = self.input_size[1] // self.groups
self.filter_size = [6, f_c, 3, 3]
self.scale_in = 1.0
self.scale_out = 0.8
self.scale_weights = [10.0]
class TestWith1x1(TestConv2dInt8Op):
def init_test_case(self):
self.pad = [0, 0]
self.stride = [1, 1]
self.input_size = [1, 3, 5, 5]
assert np.mod(self.input_size[1], self.groups) == 0
f_c = self.input_size[1] // self.groups
self.filter_size = [6, f_c, 1, 1]
self.scale_in = 1.0
self.scale_out = 0.5
self.scale_weights = [12.0]
class TestWithInput1x1Filter1x1(TestConv2dInt8Op):
def init_test_case(self):
self.pad = [0, 0]
self.stride = [1, 1]
self.input_size = [2, 3, 1, 1]
assert np.mod(self.input_size[1], self.groups) == 0
f_c = self.input_size[1] // self.groups
self.filter_size = [6, f_c, 1, 1]
self.scale_in = 1.0
self.scale_out = 0.5
self.scale_weights = [10.0]
def init_group(self):
self.groups = 3
def init_data_type_with_fusion(self, input_dt, fuse_relu):
self.srctype = input_dt
self.dsttype = np.uint8 if fuse_relu else np.int8
def init_fuse_relu(self):
self.fuse_relu = fuse_relu
def create_test_int8_class(parent):
#--------------------test conv2d s8 in and u8 out--------------------
class TestS8U8Case(parent):
def init_data_type(self):
init_data_type_with_fusion(self, np.int8, True)
#--------------------test conv2d s8 in and s8 out--------------------
class TestS8S8Case(parent):
def init_data_type(self):
init_data_type_with_fusion(self, np.int8, False)
#--------------------test conv2d u8 in and s8 out--------------------
class TestU8S8Case(parent):
def init_data_type(self):
init_data_type_with_fusion(self, np.uint8, False)
cls_name_s8u8 = "{0}_relu_{1}".format(parent.__name__, "1")
cls_name_s8s8 = "{0}_relu_{1}".format(parent.__name__, "0")
cls_name_u8s8 = "{0}_relu_{1}".format(parent.__name__, "0")
TestS8U8Case.__name__ = cls_name_s8u8
TestS8S8Case.__name__ = cls_name_s8s8
TestU8S8Case.__name__ = cls_name_u8s8
globals()[cls_name_s8u8] = TestS8U8Case
globals()[cls_name_s8s8] = TestS8S8Case
globals()[cls_name_u8s8] = TestU8S8Case
create_test_int8_class(TestConv2dInt8Op)
create_test_int8_class(TestWithPad)
create_test_int8_class(TestWithStride)
create_test_int8_class(TestWithGroup)
create_test_int8_class(TestWith1x1)
create_test_int8_class(TestWithInput1x1Filter1x1)
if __name__ == '__main__':
unittest.main()
python/paddle/fluid/tests/unittests/test_conv2d_op.py
浏览文件 @ 92da467c
......@@ -60,7 +60,7 @@ def conv2d_forward_naive(input, filter, group, conv_param):
np.sum(input_pad_masked * f_sub[k, :, :, :],
axis=(1, 2, 3))
return out
return out, in_n, out_h, out_w, out_c
class TestConv2dOp(OpTest):
......@@ -85,8 +85,9 @@ class TestConv2dOp(OpTest):
input = np.random.random(self.input_size).astype(self.dtype)
filter = np.random.random(self.filter_size).astype(self.dtype)
output = conv2d_forward_naive(input, filter, self.groups,
conv2d_param).astype(self.dtype)
output, _, _, _, _ = conv2d_forward_naive(input, filter, self.groups,
conv2d_param)
output = output.astype(self.dtype)
self.inputs = {
'Input': OpTest.np_dtype_to_fluid_dtype(input),
......
python/paddle/fluid/tests/unittests/test_dist_base.py
浏览文件 @ 92da467c
......@@ -26,6 +26,7 @@ import pickle
import numpy as np
import paddle.fluid as fluid
from paddle.fluid import compiler
RUN_STEP = 10
DEFAULT_BATCH_SIZE = 2
......@@ -104,8 +105,8 @@ class TestDistRunnerBase(object):
else:
place = fluid.CPUPlace()
startup_exe = fluid.Executor(place)
startup_exe.run(fluid.default_startup_program())
exe = fluid.Executor(place)
exe.run(fluid.default_startup_program())
strategy = fluid.ExecutionStrategy()
strategy.num_threads = 1
......@@ -125,19 +126,16 @@ class TestDistRunnerBase(object):
mypass.set_int("num_repeats", args.batch_merge_repeat)
if args.update_method == "nccl2":
num_trainers = len(args.endpoints.split(","))
trainer_id = args.trainer_id
build_stra.num_trainers = len(args.endpoints.split(","))
build_stra.trainer_id = args.trainer_id
else:
num_trainers = 1
trainer_id = 0
build_stra.num_trainers = 1
build_stra.trainer_id = 0
exe = fluid.ParallelExecutor(
args.use_cuda,
binary = compiler.CompiledProgram(trainer_prog).with_data_parallel(
loss_name=avg_cost.name,
exec_strategy=strategy,
build_strategy=build_stra,
num_trainers=num_trainers,
trainer_id=trainer_id)
exec_strategy=strategy)
feed_var_list = [
var for var in trainer_prog.global_block().vars.values()
......@@ -160,7 +158,8 @@ class TestDistRunnerBase(object):
out_losses = []
for _ in six.moves.xrange(RUN_STEP):
loss, = exe.run(fetch_list=[avg_cost.name],
loss, = exe.run(binary,
fetch_list=[avg_cost.name],
feed=feeder.feed(get_data()))
out_losses.append(loss[0])
if six.PY2:
......
python/paddle/fluid/tests/unittests/test_dist_ctr.py
浏览文件 @ 92da467c
......@@ -18,7 +18,6 @@ import unittest
from test_dist_base import TestDistBase
# FIXME(tangwei): sum op can not handle when inputs is empty.
class TestDistCTR2x2(TestDistBase):
def _setup_config(self):
self._sync_mode = True
......@@ -28,5 +27,19 @@ class TestDistCTR2x2(TestDistBase):
self.check_with_place("dist_ctr.py", delta=1e-7, check_error_log=False)
class TestDistCTRWithL2Decay2x2(TestDistBase):
def _setup_config(self):
self._sync_mode = True
self._enforce_place = "CPU"
def test_dist_ctr(self):
need_envs = {"USE_L2_DECAY": "1"}
self.check_with_place(
"dist_ctr.py",
delta=1e-7,
check_error_log=False,
need_envs=need_envs)
if __name__ == "__main__":
unittest.main()
python/paddle/fluid/tests/unittests/test_dist_transpiler.py
浏览文件 @ 92da467c
......@@ -14,14 +14,15 @@
from __future__ import print_function
import traceback
import math
import collections
import six
import unittest
import numpy as np
import paddle.fluid as fluid
from paddle.fluid.transpiler.distribute_transpiler import delete_ops
import traceback
import collections
import six
class TranspilerTest(unittest.TestCase):
......@@ -520,7 +521,7 @@ class TestLocalLookupTable(TestDistLookupTableBase):
'split_selected_rows', 'send', 'sequence_pool_grad',
'lookup_table_grad', 'sequence_pool_grad', 'lookup_table_grad',
'sum', 'split_selected_rows', 'send', 'send_barrier', 'recv',
'recv', 'recv', 'recv', 'fetch_barrier', 'concat', 'concat'
'recv', 'fetch_barrier'
]
self.assertEqual([op.type for op in trainer.blocks[0].ops], ops)
......@@ -560,7 +561,7 @@ class TestDistLookupTable(TestDistLookupTableBase):
'lookup_table_grad', 'split_selected_rows', 'send',
'sequence_pool_grad', 'lookup_table_grad', 'sequence_pool_grad',
'lookup_table_grad', 'sum', 'split_ids', 'send', 'send_barrier',
'recv', 'recv', 'recv', 'fetch_barrier', 'concat'
'recv', 'recv', 'fetch_barrier'
]
self.assertEqual([op.type for op in trainer.blocks[0].ops], ops)
startup_ops = [
......@@ -607,8 +608,7 @@ class TestAsyncLocalLookupTable(TestDistLookupTableBase):
'send', 'concat_grad', 'sequence_pool_grad', 'lookup_table_grad',
'split_selected_rows', 'send', 'sequence_pool_grad',
'lookup_table_grad', 'sequence_pool_grad', 'lookup_table_grad',
'sum', 'split_selected_rows', 'send', 'recv', 'recv', 'recv',
'recv', 'concat', 'concat'
'sum', 'split_selected_rows', 'send', 'recv', 'recv'
]
self.assertEqual([op.type for op in trainer.blocks[0].ops], ops)
......@@ -648,8 +648,7 @@ class TestAsyncDistLookupTable(TestDistLookupTableBase):
'mul_grad', 'send', 'concat_grad', 'sequence_pool_grad',
'lookup_table_grad', 'split_selected_rows', 'send',
'sequence_pool_grad', 'lookup_table_grad', 'sequence_pool_grad',
'lookup_table_grad', 'sum', 'split_ids', 'send', 'recv', 'recv',
'recv', 'concat'
'lookup_table_grad', 'sum', 'split_ids', 'send', 'recv', 'recv'
]
self.assertEqual([op.type for op in trainer.blocks[0].ops], ops)
startup_ops = [
......@@ -824,5 +823,142 @@ class TestRemoteLookupTable(TestDistLookupTableBase):
self.assertEqual([op.type for op in trainer.blocks[0].ops], ops)
# test for remote prefetch
class TestRemoteNce(TestDistLookupTableBase):
def network_with_table(self, is_sparse, is_distributed):
num_total_classes = 20
sampler = "uniform"
nid_freq_arr = np.random.dirichlet(np.ones(20) * 1000).astype('float32')
input = fluid.layers.data(name="input", shape=[10], dtype="float32")
label = fluid.layers.data(name="label", shape=[1], dtype="int64")
w_param = fluid.default_main_program().global_block().create_parameter(
shape=[num_total_classes, 10],
dtype='float32',
name='nce_w',
initializer=fluid.initializer.ConstantInitializer())
b_param = fluid.default_main_program().global_block().create_parameter(
shape=[num_total_classes, 1],
dtype='float32',
name='nce_b',
initializer=fluid.initializer.ConstantInitializer())
cost = fluid.layers.nce(input=input,
label=label,
num_total_classes=num_total_classes,
sampler=sampler,
custom_dist=nid_freq_arr.tolist(),
sample_weight=None,
param_attr='nce_w',
bias_attr='nce_b',
seed=1,
num_neg_samples=5,
is_sparse=is_sparse)
avg_cost = fluid.layers.mean(cost)
# optimizer
optimizer = fluid.optimizer.Adam(learning_rate=0.003)
optimizer.minimize(avg_cost)
def net_conf(self):
import os
os.environ['PADDLE_ENABLE_REMOTE_PREFETCH'] = "1"
self.network_with_table(is_sparse=True, is_distributed=False)
def transpiler_test_impl(self):
trainer, _ = self.get_trainer()
out_vars = ["nce_w"]
in_vars = ["nce_b"]
recv_var_names = []
for op in trainer.blocks[0].ops:
if op.type == "recv":
for var in op.output("Out"):
recv_var_names.append(var)
for out_var in out_vars:
self.assertFalse(out_var in recv_var_names)
for in_var in in_vars:
self.assertTrue(in_var in recv_var_names)
# test for remote prefetch
class TestRemoteHsigmoid(TestDistLookupTableBase):
def network_with_table(self, is_sparse, is_distributed):
num_total_classes = 3
input = fluid.layers.data(name="input", shape=[1], dtype="float32")
label = fluid.layers.data(name="label", shape=[1], dtype="int64")
path_table = fluid.layers.data(
name='path_table', shape=[3], dtype='int64')
path_code = fluid.layers.data(
name='path_code', shape=[3], dtype='int64')
w_param = fluid.default_main_program().global_block().create_parameter(
shape=[num_total_classes, 10],
dtype='float32',
name='hs_w',
initializer=fluid.initializer.ConstantInitializer())
b_param = fluid.default_main_program().global_block().create_parameter(
shape=[3, 1],
dtype='float32',
name='hs_b',
initializer=fluid.initializer.ConstantInitializer())
emb = fluid.layers.embedding(
input=input,
is_sparse=is_sparse,
size=[3, 3],
param_attr=fluid.ParamAttr(initializer=fluid.initializer.Normal(
scale=1 / math.sqrt(num_total_classes))))
cost = fluid.layers.hsigmoid(
input=emb,
label=label,
num_classes=num_total_classes,
path_table=path_table,
path_code=path_code,
is_custom=True,
is_sparse=is_sparse)
avg_cost = fluid.layers.mean(cost)
# optimizer
optimizer = fluid.optimizer.SGD(learning_rate=0.003)
optimizer.minimize(avg_cost)
def net_conf(self):
import os
os.environ['PADDLE_ENABLE_REMOTE_PREFETCH'] = "1"
self.network_with_table(is_sparse=True, is_distributed=False)
def transpiler_test_impl(self):
trainer, _ = self.get_trainer()
params_to_check = list()
for op in trainer.blocks[0].ops:
if op.type == "hierarchical_sigmoid":
params_to_check = [op.input("W")[0], op.input("Bias")[0]]
for name in ["epmap", "table_names", "epmap"]:
assert op.has_attr(name)
if name == "epmap":
assert op.attr(name)[0] == u'127.0.0.1:6174'
elif name == "table_names":
assert op.attr(name)[0] == u'hierarchical_sigmoid_0.w_0'
else:
assert op.attr(name) == 3
elif op.type == "lookup_table":
params_to_check.append(op.input("W")[0])
else:
pass
op_count = 0
for op in trainer.blocks[0].ops:
if op.type == "recv":
assert len(op.output("Out")) == 1
assert op.output("Out")[0] == u'hierarchical_sigmoid_0.b_0'
op_count += 1
assert op_count == 1
if __name__ == "__main__":
unittest.main()
python/paddle/fluid/tests/unittests/test_eager_deletion_dynamic_rnn_base.py
浏览文件 @ 92da467c
......@@ -29,6 +29,12 @@ def train(network, use_cuda, use_parallel_executor, batch_size=32, pass_num=2):
print('Skip use_cuda=True because Paddle is not compiled with cuda')
return
if use_parallel_executor and os.name == 'nt':
print(
'Skip use_parallel_executor=True because Paddle comes without parallel support on windows'
)
return
word_dict = paddle.dataset.imdb.word_dict()
train_reader = paddle.batch(
paddle.dataset.imdb.train(word_dict), batch_size=batch_size)
......
python/paddle/fluid/tests/unittests/test_fused_emb_seq_pool_op.py 0 → 100644
浏览文件 @ 92da467c
# 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
import paddle.fluid.core as core
import paddle.fluid as fluid
from paddle.fluid.op import Operator
import paddle.compat as cpt
class TestFusedEmbeddingSeqPoolOp(OpTest):
def setUp(self):
self.op_type = "fused_embedding_seq_pool"
self.emb_size = 2
table = np.random.random((17, self.emb_size)).astype("float32")
ids = np.array([[[4], [3]], [[4], [3]], [[2], [1]],
[[16], [1]]]).astype("int64")
merged_ids = np.array([4, 2, 16]).astype("int64")
ids_expand = np.expand_dims(ids, axis=1)
self.lod = [[3, 1]]
self.attrs = {'is_sparse': True}
self.inputs = {'W': table, 'Ids': (ids_expand, self.lod)}
self.outputs = {
'Out': np.reshape(
np.array([
table[[4, 3]] + table[[4, 3]] + table[[2, 1]],
table[[16, 1]]
]), [len(self.lod[0]), 2 * self.emb_size])
}
def test_check_output(self):
self.check_output()
if __name__ == "__main__":
unittest.main()
python/paddle/fluid/tests/unittests/test_fusion_seqpool_concat_op.py 0 → 100644
浏览文件 @ 92da467c
# 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
from test_reorder_lod_tensor import convert_to_offset
from test_seq_pool import compute_seqpool_sum, compute_seqpool_avg, compute_seqpool_sqrt
class TestFusionSeqPoolConcatOp(OpTest):
def setUp(self):
self.w = 11
self.lods = [[[2, 3, 5]], [[1, 5, 2]]]
self.set_conf()
self.set_pooltype()
self.op_type = 'fusion_seqpool_concat'
self.axis = 1
bs = len(self.lods[0][0])
inputs = []
outs = []
i = 0
for lod in self.lods:
assert bs == len(lod[0]), 'All lod size should be equal'
x = np.random.uniform(0.1, 1,
[sum(lod[0]), self.w]).astype('float32')
offset = convert_to_offset(lod)
out = np.zeros((bs, self.w)).astype('float32')
if self.pooltype == "SUM":
compute_seqpool_sum(x, offset, out)
elif self.pooltype == "AVERAGE":
compute_seqpool_avg(x, offset, out)
elif self.pooltype == "SQRT":
compute_seqpool_sqrt(x, offset, out)
else:
raise Exception("Unsupported pool type!")
inputs.append(('x_{0}'.format(i), (x, lod)))
outs.append(out)
i = i + 1
self.inputs = {'X': inputs}
self.outputs = {'Out': np.concatenate(outs, axis=self.axis)}
self.attrs = {
'pooltype': self.pooltype,
'axis': self.axis,
}
def set_pooltype(self):
self.pooltype = "SUM"
def set_conf(self):
pass
def test_check_output(self):
self.check_output()
class TestFusionSeqPoolConcatOpCase1(TestFusionSeqPoolConcatOp):
def set_conf(self):
self.lods = [[[1]]]
class TestFusionSeqPoolConcatOpCase2(TestFusionSeqPoolConcatOp):
def set_conf(self):
self.lods = [[[1]], [[1]], [[1]]]
class TestFusionSeqPoolConcatOpCase3(TestFusionSeqPoolConcatOp):
def set_conf(self):
self.lods = [[[1, 3, 4, 6]]]
self.w = 10
class TestFusionSeqPoolConcatOpCase4(TestFusionSeqPoolConcatOp):
def set_conf(self):
self.lods = [[[2, 13, 4]], [[1, 1, 1]], [[5, 3, 1]], [[9, 10, 3]]]
self.w = 3
## test avg pool and sqrt
def create_test_avg_sqrt_class(parent):
class TestSeqPoolAvgCase(parent):
def set_pooltype(self):
self.pooltype = "AVERAGE"
class TestSeqPoolSqrtCase(parent):
def set_pooltype(self):
self.pooltype = "SQRT"
cls_name_avg = "{0}_{1}".format(parent.__name__, "avg")
cls_name_sqrt = "{0}_{1}".format(parent.__name__, "sqrt")
TestSeqPoolAvgCase.__name__ = cls_name_avg
TestSeqPoolSqrtCase.__name__ = cls_name_sqrt
globals()[cls_name_avg] = TestSeqPoolAvgCase
globals()[cls_name_sqrt] = TestSeqPoolSqrtCase
create_test_avg_sqrt_class(TestFusionSeqPoolConcatOp)
create_test_avg_sqrt_class(TestFusionSeqPoolConcatOpCase1)
create_test_avg_sqrt_class(TestFusionSeqPoolConcatOpCase2)
create_test_avg_sqrt_class(TestFusionSeqPoolConcatOpCase3)
create_test_avg_sqrt_class(TestFusionSeqPoolConcatOpCase4)
if __name__ == '__main__':
unittest.main()
python/paddle/fluid/tests/unittests/test_hsigmoid_op.py
浏览文件 @ 92da467c
......@@ -185,7 +185,7 @@ class TestHSigmoidOpSparse(OpTest):
self.inputs = {
'X': x,
'W': w,
'PTable': path_table,
'PathTable': path_table,
'PathCode': path_code,
'Label': label,
'Bias': bias
......@@ -287,7 +287,7 @@ class TestHSigmoidOpWithCostumTree(OpTest):
self.inputs = {
'X': x,
'W': w,
'PTable': path_table,
'PathTable': path_table,
'PathCode': path_code,
'Label': label,
'Bias': bias
......@@ -324,7 +324,7 @@ class TestHSigmoidOpWithCostumTreeWithoutBias(OpTest):
self.inputs = {
'X': x,
'W': w,
'PTable': path_table,
'PathTable': path_table,
'PathCode': path_code,
'Label': label,
}
......
python/paddle/fluid/tests/unittests/test_hsigmoid_remote_table_op.py 0 → 100644
浏览文件 @ 92da467c
# 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 os
import signal
import time
import unittest
from multiprocessing import Process
import numpy as np
import paddle.fluid as fluid
import paddle.fluid.core as core
from paddle.fluid.op import Operator
from paddle.fluid.framework import Program, program_guard
def run_pserver(pserver_id, use_cuda, sync_mode):
scope = fluid.core.Scope()
program = Program()
with fluid.scope_guard(scope):
with program_guard(program, startup_program=Program()):
# create table parameter in scope
place = fluid.CUDAPlace(0) if use_cuda else fluid.CPUPlace()
# create and initialize Param Variable
param = scope.var('table').get_tensor()
param_array = np.ones((5, 8)).astype("float32")
for i in range(len(param_array)):
param_array[i] *= param_array[i] * i + pserver_id * 10 + 1
param.set(param_array, place)
optimize_block = program._create_block(program.global_block().idx)
program.global_block().append_op(
type="listen_and_serv",
inputs={'X': []},
outputs={},
attrs={
"optimize_blocks": [optimize_block],
"endpoint": '127.0.0.1:0',
"Fanin": 1,
"sync_mode": True,
"grad_to_block_id": []
})
exe = fluid.Executor(place)
exe.run(program)
class TestListenAndServOp(unittest.TestCase):
def setUp(self):
self.ps_timeout = 5
def _start_pserver(self, pserver_id, use_cuda, sync_mode, pserver_func):
p = Process(target=pserver_func, args=(pserver_id, use_cuda, sync_mode))
p.daemon = True
p.start()
return p
def _wait_ps_ready(self, pid):
start_left_time = self.ps_timeout
sleep_time = 0.5
while True:
assert start_left_time >= 0, "wait ps ready failed"
time.sleep(sleep_time)
try:
# the listen_and_serv_op would touch a file which contains the listen port
# on the /tmp directory until it was ready to process all the RPC call.
os.stat("/tmp/paddle.%d.port" % pid)
return
except os.error:
start_left_time -= sleep_time
def _get_pserver_port(self, pid):
with open("/tmp/paddle.%d.port" % pid, 'r') as f:
port = int(f.read().strip())
return port
def _run_hsigmoid_op_one_pserver(self, place, port):
scope = fluid.core.Scope()
program = Program()
with fluid.scope_guard(scope):
with program_guard(program, startup_program=Program()):
x = scope.var('X').get_tensor()
x_array = np.random.random((4, 8)).astype("float32") * 2
x.set(x_array, place)
# create and initialize Param Variable
param = scope.var('W').get_tensor()
param_array = np.zeros((5, 8)).astype("float32") * 2
param.set(param_array, place)
path_table = scope.var('PathTable').get_tensor()
path_table_array = np.array(
[(0, 2, -1, -1, -1), (0, 1, 2, -1, -1), (0, 1, 4, -1, -1),
(0, 2, -1, -1, -1)]).astype(
"int64"
) #np.array to store 1,2,5,6s' non-leaf path(root -> leaf)
path_table.set(path_table_array, place)
path_code = scope.var('PathCode').get_tensor()
path_code_array = np.array(
[(0, 0, -1, -1, -1), (1, 1, 1, -1, -1), (1, 0, 0, -1, -1),
(0, 1, -1, -1, -1)]).astype("int64") #np.array to store
path_code.set(path_code_array, place)
label = scope.var('Label').get_tensor()
label_array = np.array([0, 1, 4, 5])
label.set(label_array, place)
bias = scope.var('Bias').get_tensor()
bias_array = np.random.random((5, 1)).astype("float32")
bias.set(bias_array, place)
out = scope.var('Out').get_tensor()
pre_out = scope.var('PreOut').get_tensor
w_out = scope.var('W_Out').get_tensor()
w_out.set(param_array, place)
emaps = ['127.0.0.1:' + str(port)]
table_names = ['table']
height_sections = [2]
# create and run sgd operator
hsigmoid_op = Operator(
"hierarchical_sigmoid",
X='X',
W='W',
PathTable='PathTable',
PathCode='PathCode',
Label='Label',
Bias='Bias',
Out='Out',
PreOut='PreOut',
W_Out='W_Out',
remote_prefetch=True,
epmap=emaps,
table_names=table_names,
height_sections=height_sections)
hsigmoid_op.run(scope, place)
# get and compare result
result_array = np.array(w_out)
self.assertEqual(list(result_array.shape), [5, 8])
correct = None
for i in range(5):
if i != 3:
correct = np.full((1, 8), i + 1).astype("float32")
self.assertTrue((result_array[i] == correct).all())
else:
correct = np.full((1, 8), 0).astype("float32")
self.assertTrue((result_array[i] == correct).all())
def _run_hsigmoid_op_two_pserver(self, place, port0, port1):
scope = fluid.core.Scope()
program = Program()
with fluid.scope_guard(scope):
with program_guard(program, startup_program=Program()):
x = scope.var('X').get_tensor()
x_array = np.random.random((4, 8)).astype("float32") * 2
x.set(x_array, place)
# create and initialize Param Variable
param = scope.var('W').get_tensor()
param_array = np.zeros((5, 8)).astype("float32") * 2
param.set(param_array, place)
path_table = scope.var('PathTable').get_tensor()
path_table_array = np.array(
[(0, 2, -1, -1, -1), (0, 1, 3, -1, -1), (0, 1, 4, -1, -1),
(0, 2, -1, -1, -1)]).astype(
"int64"
) #np.array to store 1,2,5,6s' non-leaf path(root -> leaf)
path_table.set(path_table_array, place)
path_code = scope.var('PathCode').get_tensor()
path_code_array = np.array(
[(0, 0, -1, -1, -1), (1, 1, 1, -1, -1), (1, 0, 0, -1, -1),
(0, 1, -1, -1, -1)]).astype("int64") #np.array to store
path_code.set(path_code_array, place)
label = scope.var('Label').get_tensor()
label_array = np.array([0, 1, 4, 5])
label.set(label_array, place)
bias = scope.var('Bias').get_tensor()
bias_array = np.random.random((5, 1)).astype("float32")
bias.set(bias_array, place)
out = scope.var('Out').get_tensor()
pre_out = scope.var('PreOut').get_tensor
w_out = scope.var('W_Out').get_tensor()
w_out.set(param_array, place)
emaps = ['127.0.0.1:' + str(port0), '127.0.0.1:' + str(port1)]
table_names = ['table', 'table']
height_sections = [2, 3]
# create and run sgd operator
hsigmoid_op = Operator(
"hierarchical_sigmoid",
X='X',
W='W',
PathTable='PathTable',
PathCode='PathCode',
Label='Label',
Bias='Bias',
Out='Out',
PreOut='PreOut',
W_Out='W_Out',
remote_prefetch=True,
epmap=emaps,
table_names=table_names,
height_sections=height_sections)
hsigmoid_op.run(scope, place)
# get and compare result
result_array = np.array(w_out)
self.assertEqual(list(result_array.shape), [5, 8])
correct = None
for i in range(5):
if i < 2:
correct = np.full((1, 8), i + 1).astype("float32")
self.assertTrue((result_array[i] == correct).all())
else:
correct = np.full((1, 8), i + 9).astype("float32")
self.assertTrue((result_array[i] == correct).all())
def test_hsigmoid_op_remote(self):
os.environ['PADDLE_ENABLE_REMOTE_PREFETCH'] = "1"
# run pserver on CPU in sync mode
p0 = self._start_pserver(0, False, True, run_pserver)
self._wait_ps_ready(p0.pid)
port0 = self._get_pserver_port(p0.pid)
p1 = self._start_pserver(1, False, True, run_pserver)
self._wait_ps_ready(p1.pid)
port1 = self._get_pserver_port(p1.pid)
places = [core.CPUPlace()]
for place in places:
self._run_hsigmoid_op_one_pserver(place, port0)
self._run_hsigmoid_op_two_pserver(place, port0, port1)
# raise SIGTERM to pserver
os.kill(p0.pid, signal.SIGINT)
p0.join()
os.kill(p1.pid, signal.SIGINT)
p1.join()
if __name__ == '__main__':
unittest.main()
python/paddle/fluid/tests/unittests/test_ir_graph.py 0 → 100644
浏览文件 @ 92da467c
# Copyright (c) 2018 PaddlePaddle Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import os
import unittest
import six
from paddle import fluid
class TestIRGraph(unittest.TestCase):
"""
TODO(fc500110): `resolve_hazard` api will be tested when it can be used.
"""
def test_nodes(self):
graph = build_graph()
self.assertTrue(
{node.name()
for node in graph.nodes()} == {"x1", "x2", "out", "sum"})
def test_has_set_get(self):
graph = build_graph()
for attr_name in ["int", "float", "string"]:
self.assertFalse(graph.has(attr_name))
graph.set("int", 1)
graph.set("float", 0.5)
graph.set("string", "string")
for attr_name in ["int", "float", "string"]:
self.assertTrue(graph.has(attr_name))
self.assertTrue(graph.get_int("int") == 1)
self.assertTrue(graph.get_float("float") == 0.5)
self.assertTrue(graph.get_string("string") == "string")
def test_erase(self):
graph = build_graph()
graph.set("test", 0)
self.assertTrue(graph.has("test"))
graph.erase("test")
self.assertFalse(graph.has("test"))
def test_create_var_node(self):
prog = fluid.core.ProgramDesc()
block = prog.block(0)
shape = [10, 20]
x1 = block.var(six.b("x1"))
x1.set_type(fluid.core.VarDesc.VarType.LOD_TENSOR)
x1.set_shape(shape)
graph = fluid.core.Graph(prog)
node = graph.create_var_node(x1)
self.assertTrue(node.node_type() == fluid.core.Node.Type.Variable)
def test_create_op_node(self):
prog = fluid.core.ProgramDesc()
block = prog.block(0)
sum_op_desc = block.append_op()
graph = fluid.core.Graph(prog)
node = graph.create_op_node(sum_op_desc)
self.assertTrue(node.node_type() == fluid.core.Node.Type.Operation)
def test_create_control_dep_var(self):
graph = build_graph()
name = "__control_var@{}".format(len(graph.nodes()))
node = graph.create_control_dep_var()
self.assertTrue(node.name() == name)
def test_create_empty_node(self):
prog = fluid.core.ProgramDesc()
graph = fluid.core.Graph(prog)
n1 = graph.create_empty_node('x', fluid.core.Node.Type.Operation)
self.assertTrue(n1.name() == 'x')
n2 = graph.create_empty_node('y', fluid.core.Node.Type.Variable)
self.assertTrue(n2.name() == 'y')
def test_release_nodes(self):
graph = build_graph()
nodes = graph.release_nodes()
self.assertTrue(len(graph.nodes()) == 0)
self.assertTrue({node.name()
for node in nodes} == {"x1", "x2", "out", "sum"})
def test_remove_node(self):
graph = build_graph()
nodes = graph.nodes()
for node in nodes:
if node.name() == "sum":
break
self.assertTrue({node.name()
for node in nodes} == {"x1", "x2", "out", "sum"})
nodes.remove(node)
self.assertTrue({node.name() for node in nodes} == {"x1", "x2", "out"})
def test_retrieve_node(self):
graph = build_graph()
nodes = []
for i in range(len(graph.nodes())):
nodes.append(graph.retrieve_node(i))
for node in nodes:
self.assertTrue(node in graph.nodes())
def resolve_hazard(self):
pass
def build_graph():
prog = fluid.core.ProgramDesc()
block = prog.block(0)
shape = [10, 20]
# prepare input/output
x1 = block.var(six.b("x1"))
x1.set_type(fluid.core.VarDesc.VarType.LOD_TENSOR)
x1.set_shape(shape)
x2 = block.var(six.b("x2"))
x2.set_type(fluid.core.VarDesc.VarType.LOD_TENSOR)
x2.set_shape(shape)
out = block.var(six.b("out"))
out.set_type(fluid.core.VarDesc.VarType.LOD_TENSOR)
sum_op_desc = block.append_op()
sum_op_desc.set_type("sum")
sum_op_desc.set_input("X", ["x1", "x2"])
sum_op_desc.set_output("Out", ["out"])
sum_op_desc.check_attrs()
sum_op_desc.infer_shape(block)
graph = fluid.core.Graph(prog)
return graph
if __name__ == "__main__":
unittest.main()
python/paddle/fluid/tests/unittests/test_nce_remote_table_op.py 0 → 100644
浏览文件 @ 92da467c
# 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 os
import signal
import time
import unittest
from multiprocessing import Process
import numpy as np
import paddle.fluid as fluid
import paddle.fluid.core as core
from paddle.fluid.op import Operator
from paddle.fluid.framework import Program, program_guard
def nce(input, weight, bias, sample_weight, labels, num_classes,
num_sample_class):
samples = []
sample_labels = []
batch_size = input.shape[0]
num_true_class = labels.shape[1]
for i in range(batch_size):
w = 1 if sample_weight is None else sample_weight[i]
for label in labels[i]:
samples.append((i, label, True, w))
sample_labels.append(label)
for num in range(num_sample_class):
samples.append((i, num, False, w))
sample_labels.append(num)
# forward bias
sample_out = np.zeros(len(samples)).astype(np.float32)
if bias is not None:
for i in range(len(samples)):
sample_out[i] = bias[samples[i][1]]
# forward weight
for i in range(len(samples)):
sample_out[i] += np.dot(input[samples[i][0]], weight[samples[i][1]])
# forward activation
sample_out = 1.0 / (1.0 + np.exp(-sample_out))
# forward cost
out = np.zeros(batch_size).astype(np.float32)
b = 1.0 / num_classes * num_sample_class
for i in range(len(samples)):
o = sample_out[i]
cost = -np.log(o / (o + b)) if samples[i][2] else -np.log(b / (o + b))
out[samples[i][0]] += cost * samples[i][3]
return (out[:, np.newaxis], np.array(sample_out).reshape(
batch_size, num_sample_class + num_true_class),
np.array(sample_labels).reshape(batch_size,
num_sample_class + num_true_class))
def run_pserver(pserver_id, use_cuda, sync_mode):
scope = fluid.core.Scope()
program = Program()
with fluid.scope_guard(scope):
with program_guard(program, startup_program=Program()):
# create table parameter in scope
place = fluid.CUDAPlace(0) if use_cuda else fluid.CPUPlace()
# create and initialize Param Variable
param = scope.var('table').get_tensor()
param_array = np.ones((5, 8)).astype("float32")
for i in range(len(param_array)):
param_array[i] *= param_array[i] * i + pserver_id * 10 + 1
param.set(param_array, place)
optimize_block = program._create_block(program.global_block().idx)
program.global_block().append_op(
type="listen_and_serv",
inputs={'X': []},
outputs={},
attrs={
"optimize_blocks": [optimize_block],
"endpoint": '127.0.0.1:0',
"Fanin": 1,
"sync_mode": True,
"grad_to_block_id": []
})
exe = fluid.Executor(place)
exe.run(program)
class TestListenAndServOp(unittest.TestCase):
def setUp(self):
self.ps_timeout = 5
def _start_pserver(self, pserver_id, use_cuda, sync_mode, pserver_func):
p = Process(target=pserver_func, args=(pserver_id, use_cuda, sync_mode))
p.daemon = True
p.start()
return p
def _wait_ps_ready(self, pid):
start_left_time = self.ps_timeout
sleep_time = 0.5
while True:
assert start_left_time >= 0, "wait ps ready failed"
time.sleep(sleep_time)
try:
# the listen_and_serv_op would touch a file which contains the listen port
# on the /tmp directory until it was ready to process all the RPC call.
os.stat("/tmp/paddle.%d.port" % pid)
return
except os.error:
start_left_time -= sleep_time
def _get_pserver_port(self, pid):
with open("/tmp/paddle.%d.port" % pid, 'r') as f:
port = int(f.read().strip())
return port
def _run_nce_op_two_pserver(self, place, port0, port1):
scope = fluid.core.Scope()
program = Program()
with fluid.scope_guard(scope):
with program_guard(program, startup_program=Program()):
x = scope.var('Input').get_tensor()
x_array = np.random.random((4, 8)).astype("float32")
x.set(x_array, place)
# create and initialize Param Variable
param = scope.var('Weight').get_tensor()
param_array = np.zeros((5, 8)).astype("float32")
param.set(param_array, place)
bias = scope.var('Bias').get_tensor()
bias_array = np.random.random((5, 1)).astype("float32")
bias.set(bias_array, place)
sample_w = scope.var('SampleWeight').get_tensor()
sample_weight = np.random.random((4, 1)).astype("float32")
sample_w.set(sample_weight, place)
label = scope.var('Label').get_tensor()
label_array = np.array([[0], [1], [4], [3]])
label.set(label_array, place)
cost = scope.var('Cost').get_tensor()
cost_w = np.zeros((4, 1)).astype("float32")
cost.set(cost_w, place)
sample_l = scope.var('SampleLogits').get_tensor()
sample_l_w = np.zeros((4, 3)).astype("float32")
sample_l.set(sample_l_w, place)
sample_la = scope.var('SampleLabels').get_tensor()
sample_la_w = np.zeros((4, 3)).astype("int")
sample_la.set(sample_la_w, place)
emaps = ['127.0.0.1:' + str(port0), '127.0.0.1:' + str(port1)]
table_names = ['table', 'table']
height_sections = [2, 3]
# create and run nce operator
nce_op = Operator(
"nce",
Input='Input',
Weight='Weight',
Label='Label',
Bias='Bias',
Cost='Cost',
SampleLogits='SampleLogits',
SampleLabels='SampleLabels',
SampleWeight='SampleWeight',
num_total_classes=5,
num_neg_samples=2,
custom_neg_classes=list(range(2)),
sampler=0,
seed=0,
is_sparse=True,
remote_prefetch=True,
epmap=emaps,
table_names=table_names,
height_sections=height_sections)
nce_op.run(scope, place)
# get and compare result
o_cost = np.array(scope.var('Cost').get_tensor())
o_logits = np.array(scope.var('SampleLogits').get_tensor())
o_labels = np.array(scope.var('SampleLabels').get_tensor())
param_array = np.ones((5, 8)).astype("float32")
for i in range(2):
param_array[i] *= param_array[i] * i + 0 * 10 + 1
for i in range(2, 5):
param_array[i] *= param_array[i] * i + 1 * 10 + 1
out = nce(x_array, param_array, bias_array, sample_weight,
label_array, 5, 2)
self.assertAlmostEqual(o_cost.all(), out[0].all(), delta=1e-6)
self.assertAlmostEqual(o_logits.all(), out[1].all(), delta=1e-6)
self.assertAlmostEqual(o_labels.all(), out[2].all(), delta=1e-6)
def test_nce_op_remote(self):
os.environ['PADDLE_ENABLE_REMOTE_PREFETCH'] = "1"
# run pserver on CPU in sync mode
p0 = self._start_pserver(0, False, True, run_pserver)
self._wait_ps_ready(p0.pid)
port0 = self._get_pserver_port(p0.pid)
p1 = self._start_pserver(1, False, True, run_pserver)
self._wait_ps_ready(p1.pid)
port1 = self._get_pserver_port(p1.pid)
places = [core.CPUPlace()]
for place in places:
self._run_nce_op_two_pserver(place, port0, port1)
# raise SIGTERM to pserver
os.kill(p0.pid, signal.SIGINT)
p0.join()
os.kill(p1.pid, signal.SIGINT)
p1.join()
if __name__ == '__main__':
unittest.main()
python/paddle/fluid/tests/unittests/test_optimizer.py
浏览文件 @ 92da467c
......@@ -61,6 +61,48 @@ class TestOptimizer(unittest.TestCase):
self.assertEqual([op.type for op in opts], ["sgd"])
class TestOptimizerBackwardApplygrad(unittest.TestCase):
def test_sgd_optimizer(self):
def check_sgd_optimizer(optimizer_attr):
init_program = framework.Program()
program = framework.Program()
block = program.global_block()
mul_x = block.create_parameter(
dtype="float32",
shape=[5, 10],
lod_level=0,
name="mul.x",
optimize_attr=optimizer_attr)
mul_y = block.create_var(
dtype="float32", shape=[10, 8], lod_level=0, name="mul.y")
mul_out = block.create_var(
dtype="float32", shape=[5, 8], lod_level=0, name="mul.out")
mean_out = block.create_var(
dtype="float32", shape=[1], lod_level=0, name="mean.out")
block.append_op(
type="mul",
inputs={"X": mul_x,
"Y": mul_y},
outputs={"Out": mul_out},
attrs={"x_num_col_dims": 1})
block.append_op(
type="mean", inputs={"X": mul_out}, outputs={"Out": mean_out})
sgd_optimizer = optimizer.SGDOptimizer(learning_rate=0.01)
with framework.program_guard(program, init_program):
p_g = sgd_optimizer.backward(mean_out)
opts = sgd_optimizer.apply_gradients(p_g)
return opts
opts = check_sgd_optimizer({'learning_rate': 1.1})
self.assertEqual(len(opts), 3)
self.assertEqual([op.type for op in opts],
["fill_constant", "elementwise_mul", "sgd"])
opts = check_sgd_optimizer({'learning_rate': 1.0})
self.assertEqual(len(opts), 1)
self.assertEqual([op.type for op in opts], ["sgd"])
class TestMomentumOptimizer(unittest.TestCase):
class MockMomentum(optimizer.MomentumOptimizer):
def get_accumulators(self):
......@@ -99,8 +141,8 @@ class TestMomentumOptimizer(unittest.TestCase):
params_grads = append_backward(mean_out)
self.assertEqual(len(params_grads), 1)
self.assertEqual(len(momentum_optimizer.get_accumulators()), 0)
opts = momentum_optimizer._create_optimization_pass(
params_grads, mul_out, init_program)
with framework.program_guard(program, init_program):
opts = momentum_optimizer.apply_gradients(params_grads)
self.assertEqual(len(opts), 3)
sgd_op = opts[-1]
self.assertEqual([op.type for op in opts],
......@@ -153,8 +195,8 @@ class TestMomentumOptimizer(unittest.TestCase):
params_grads = append_backward(mean_out)
self.assertEqual(len(params_grads), 1)
self.assertEqual(len(momentum_optimizer.get_accumulators()), 0)
opts = momentum_optimizer._create_optimization_pass(
params_grads, mul_out, init_program)
with framework.program_guard(program, init_program):
opts = momentum_optimizer.apply_gradients(params_grads)
self.assertEqual(len(opts), 3)
sgd_op = opts[-1]
self.assertEqual([op.type for op in opts],
......@@ -216,8 +258,8 @@ class TestAdagradOptimizer(unittest.TestCase):
params_grads = append_backward(mean_out)
self.assertEqual(len(params_grads), 1)
self.assertEqual(len(adagrad_optimizer.get_accumulators()), 0)
opts = adagrad_optimizer._create_optimization_pass(
params_grads, mul_out, init_program)
with framework.program_guard(program, init_program):
opts = adagrad_optimizer.apply_gradients(params_grads)
self.assertEqual(len(opts), 3)
self.assertEqual([op.type for op in opts],
["fill_constant", "elementwise_mul", "adagrad"])
......@@ -280,8 +322,8 @@ class TestAdamOptimizer(unittest.TestCase):
params_grads = append_backward(mean_out)
self.assertEqual(len(params_grads), 1)
self.assertEqual(len(adam_optimizer.get_accumulators()), 0)
opts = adam_optimizer._create_optimization_pass(params_grads, mul_out,
init_program)
with framework.program_guard(program, init_program):
opts = adam_optimizer.apply_gradients(params_grads)
self.assertEqual(len(opts), 5)
self.assertEqual(
[op.type for op in opts],
......@@ -347,8 +389,8 @@ class TestAdamaxOptimizer(unittest.TestCase):
params_grads = append_backward(mean_out)
self.assertEqual(len(params_grads), 1)
self.assertEqual(len(adamax_optimizer.get_accumulators()), 0)
opts = adamax_optimizer._create_optimization_pass(params_grads, mul_out,
init_program)
with framework.program_guard(program, init_program):
opts = adamax_optimizer.apply_gradients(params_grads)
self.assertEqual(len(opts), 4)
self.assertEqual(
[op.type for op in opts],
......@@ -411,8 +453,8 @@ class TestDecayedAdagradOptimizer(unittest.TestCase):
params_grads = append_backward(mean_out)
self.assertEqual(len(params_grads), 1)
self.assertEqual(len(decayed_adagrad_optimizer.get_accumulators()), 0)
opts = decayed_adagrad_optimizer._create_optimization_pass(
params_grads, mul_out, init_program)
with framework.program_guard(program, init_program):
opts = decayed_adagrad_optimizer.apply_gradients(params_grads)
self.assertEqual(len(opts), 3)
self.assertEqual(
[op.type for op in opts],
......@@ -477,8 +519,8 @@ class TestFtrlOptimizer(unittest.TestCase):
params_grads = append_backward(mean_out)
self.assertEqual(len(params_grads), 1)
self.assertEqual(len(ftrl_optimizer.get_accumulators()), 0)
opts = ftrl_optimizer._create_optimization_pass(params_grads, mul_out,
init_program)
with framework.program_guard(program, init_program):
opts = ftrl_optimizer.apply_gradients(params_grads)
self.assertEqual(len(opts), 3)
self.assertEqual([op.type for op in opts],
["fill_constant", "elementwise_mul", "ftrl"])
......
python/paddle/fluid/tests/unittests/test_parallel_executor_test_while_train.py
浏览文件 @ 92da467c
......@@ -15,6 +15,7 @@
from __future__ import print_function
import paddle.fluid as fluid
from paddle.fluid import compiler
import paddle.fluid.core as core
import numpy as np
import unittest
......@@ -61,22 +62,21 @@ class ParallelExecutorTestingDuringTraining(unittest.TestCase):
exe.run(startup)
feed_dict = {'image': image, 'label': label}
train_exe = fluid.ParallelExecutor(
use_cuda=use_cuda,
train_cp = compiler.CompiledProgram(main).with_data_parallel(
loss_name=loss.name, build_strategy=build_strategy)
test_cp = compiler.CompiledProgram(test_program).with_data_parallel(
loss_name=loss.name,
main_program=main,
build_strategy=build_strategy)
test_exe = fluid.ParallelExecutor(
use_cuda=use_cuda,
main_program=test_program,
share_vars_from=train_exe,
build_strategy=build_strategy)
build_strategy=build_strategy,
share_vars_from=train_cp)
for i in range(5):
test_loss, = test_exe.run([loss.name], feed=feed_dict)
train_loss, = train_exe.run([loss.name], feed=feed_dict)
exe.run(train_cp, feed=feed_dict, fetch_list=[loss.name])
test_loss, = exe.run(test_cp,
feed=feed_dict,
fetch_list=[loss.name])
train_loss, = exe.run(train_cp,
feed=feed_dict,
fetch_list=[loss.name])
avg_test_loss_val = np.array(test_loss).mean()
if math.isnan(float(avg_test_loss_val)):
......
python/paddle/fluid/tests/unittests/test_reader_reset.py
浏览文件 @ 92da467c
......@@ -75,8 +75,6 @@ class TestReaderReset(unittest.TestCase):
exe.run(startup_prog)
build_strategy = fluid.BuildStrategy()
if with_double_buffer:
build_strategy.enable_data_balance = True
exec_strategy = fluid.ExecutionStrategy()
parallel_exe = fluid.ParallelExecutor(
use_cuda=self.use_cuda,
......
python/paddle/fluid/tests/unittests/test_reorder_lod_tensor.py
浏览文件 @ 92da467c
......@@ -22,6 +22,14 @@ import numpy
import functools
def convert_to_offset(lod):
offset = [[0] for i in lod]
for i, level in enumerate(lod):
for seq_len in level:
offset[i].append(offset[i][-1] + seq_len)
return offset
class TestReorderLoDTensor(unittest.TestCase):
num_seq = 5
# [name, shape, lod_level] pair indicating data info of source and target
......@@ -91,13 +99,6 @@ class TestReorderLoDTensor(unittest.TestCase):
self.inputs[desc[0]] = tensor
def reorder(self):
def convert_to_offset(lod):
offset_lod = [[0] for i in lod]
for i, level in enumerate(lod):
for seq_len in level:
offset_lod[i].append(offset_lod[i][-1] + seq_len)
return offset_lod
level = 0
# compute the rank_table according to ref_lod
ref_lod = self.data[self.data_desc[1][0]][1][level]
......
python/paddle/fluid/tests/unittests/test_seq_pool.py
浏览文件 @ 92da467c
......@@ -17,33 +17,43 @@ from __future__ import print_function
import unittest
import numpy as np
from op_test import OpTest
from test_reorder_lod_tensor import convert_to_offset
class TestSeqAvgPool(OpTest):
def convert_to_offset(self, lod):
offset = [[0] for i in lod]
for i, level in enumerate(lod):
for seq_len in level:
offset[i].append(offset[i][-1] + seq_len)
return offset
def compute_seqpool_sum(x, offset, out):
for i in range(len(offset[0]) - 1):
sub_x = x[offset[0][i]:offset[0][i + 1], :]
out[i] = sub_x.sum(axis=0)
def compute_seqpool_avg(x, offset, out):
for i in range(len(offset[0]) - 1):
sub_x = x[offset[0][i]:offset[0][i + 1], :]
out[i] = sub_x.mean(axis=0)
def compute_seqpool_sqrt(x, offset, out):
for i in range(len(offset[0]) - 1):
sub_x = x[offset[0][i]:offset[0][i + 1], :]
seq_len = offset[0][i + 1] - offset[0][i]
out[i] = sub_x.sum(axis=0) / np.sqrt(seq_len)
class TestSeqAvgPool(OpTest):
def set_data(self):
self.op_type = 'sequence_pool'
# one level, batch size is 4
x = np.random.uniform(0.1, 1, [11, 23]).astype('float32')
lod = [[11]]
self.inputs = {'X': (x, lod)}
offset = self.convert_to_offset(lod)
offset = convert_to_offset(lod)
out = np.zeros((len(lod[0]), 23)).astype('float32')
self.outputs = {'Out': out}
return x, offset, out
def compute(self, x, offset, out):
self.attrs = {'pooltype': "AVERAGE"}
for i in range(len(offset[0]) - 1):
sub_x = x[offset[0][i]:offset[0][i + 1], :]
out[i] = sub_x.mean(axis=0)
compute_seqpool_avg(x, offset, out)
def setUp(self):
x, offset, out = self.set_data()
......@@ -62,9 +72,7 @@ class TestSeqAvgPool(OpTest):
class TestSeqSumPool(TestSeqAvgPool):
def compute(self, x, offset, out):
self.attrs = {'pooltype': "SUM"}
for i in range(len(offset[0]) - 1):
sub_x = x[offset[0][i]:offset[0][i + 1], :]
out[i] = sub_x.sum(axis=0)
compute_seqpool_sum(x, offset, out)
class TestSeqMaxPool(TestSeqAvgPool):
......@@ -72,7 +80,7 @@ class TestSeqMaxPool(TestSeqAvgPool):
self.op_type = 'sequence_pool'
x = np.random.uniform(0.1, 1, [13, 23]).astype('float32')
lod = [[13]]
offset = self.convert_to_offset(lod)
offset = convert_to_offset(lod)
for i in range(len(offset[0]) - 1):
l = offset[0][i + 1] - offset[0][i]
x[offset[0][i] + np.random.randint(l), :] += 2.0
......@@ -93,10 +101,7 @@ class TestSeqMaxPool(TestSeqAvgPool):
class TestSeqSqrtPool(TestSeqAvgPool):
def compute(self, x, offset, out):
self.attrs = {'pooltype': "SQRT"}
for i in range(len(offset[0]) - 1):
sub_x = x[offset[0][i]:offset[0][i + 1], :]
seq_len = offset[0][i + 1] - offset[0][i]
out[i] = sub_x.sum(axis=0) / np.sqrt(seq_len)
compute_seqpool_sqrt(x, offset, out)
class TestSeqLastPool(TestSeqAvgPool):
......@@ -122,7 +127,7 @@ class TestSeqAvgPool2D(TestSeqAvgPool):
x = np.random.uniform(0.1, 1, [13, 3, 17]).astype('float32')
lod = [[4, 1, 3, 5]]
self.inputs = {'X': (x, lod)}
offset = self.convert_to_offset(lod)
offset = convert_to_offset(lod)
out = np.zeros((4, 3, 17)).astype('float32')
self.outputs = {'Out': out}
......@@ -167,7 +172,7 @@ class TestSeqMaxPool2D(TestSeqAvgPool2D):
x = np.random.uniform(0.1, 1, [13, 3, 11]).astype('float32')
lod = [[4, 1, 3, 5]]
self.inputs = {'X': (x, lod)}
offset = self.convert_to_offset(lod)
offset = convert_to_offset(lod)
for i in range(len(offset[0]) - 1):
l = offset[0][i + 1] - offset[0][i]
x[offset[0][i] + np.random.randint(l), :] += 1.0
......
python/paddle/fluid/tests/unittests/test_softmax_with_cross_entropy_op.py
浏览文件 @ 92da467c
......@@ -28,6 +28,7 @@ class TestSoftmaxWithCrossEntropyOp(OpTest):
def initParams(self):
self.numeric_stable_mode = False
self.dtype = np.float64
def setUp(self):
self.initParams()
......@@ -36,19 +37,19 @@ class TestSoftmaxWithCrossEntropyOp(OpTest):
class_num = 37
logits = np.random.uniform(0.1, 1.0,
[batch_size, class_num]).astype("float64")
[batch_size, class_num]).astype(self.dtype)
softmax = np.apply_along_axis(stable_softmax, 1, logits)
labels = np.random.randint(0, class_num, [batch_size, 1], dtype="int64")
cross_entropy = np.asmatrix(
[[-np.log(softmax[i][labels[i][0]])]
for i in range(softmax.shape[0])],
dtype="float64")
dtype=self.dtype)
self.inputs = {"Logits": logits, "Label": labels}
self.outputs = {
"Softmax": softmax.astype("float64"),
"Loss": cross_entropy.astype("float64")
"Softmax": softmax.astype(self.dtype),
"Loss": cross_entropy.astype(self.dtype)
}
self.attrs = {"numeric_stable_mode": self.numeric_stable_mode}
......@@ -56,7 +57,7 @@ class TestSoftmaxWithCrossEntropyOp(OpTest):
self.check_output()
def test_check_grad(self):
self.check_grad(["Logits"], "Loss")
self.check_grad(["Logits"], "Loss", max_relative_error=0.05)
class TestSoftmaxWithCrossEntropyOpNoCudnn(TestSoftmaxWithCrossEntropyOp):
......@@ -64,6 +65,55 @@ class TestSoftmaxWithCrossEntropyOpNoCudnn(TestSoftmaxWithCrossEntropyOp):
self.numeric_stable_mode = True
class TestSoftmaxWithCrossEntropyOpFp16(TestSoftmaxWithCrossEntropyOp):
def initParams(self):
self.numeric_stable_mode = False
self.dtype = np.float16
def setUp(self):
self.initParams()
self.op_type = "softmax_with_cross_entropy"
batch_size = 41
class_num = 37
# NOTE: numpy float16 have very low accuracy, use float32 for numpy check.
logits = np.random.uniform(0.1, 1.0,
[batch_size, class_num]).astype(np.float32)
softmax = np.apply_along_axis(stable_softmax, 1, logits)
labels = np.random.randint(0, class_num, [batch_size, 1], dtype="int64")
cross_entropy = np.asmatrix(
[[-np.log(softmax[i][labels[i][0]])]
for i in range(softmax.shape[0])],
dtype=np.float32)
self.inputs = {
"Logits": logits.astype(self.dtype).view(np.uint16),
"Label": labels
}
self.outputs = {
"Softmax": softmax.astype(self.dtype),
"Loss": cross_entropy.astype(self.dtype)
}
self.attrs = {"numeric_stable_mode": self.numeric_stable_mode}
def test_check_output(self):
self.check_output(atol=1e-2)
def test_check_grad(self):
self.check_grad(["Logits"], "Loss", max_relative_error=0.1)
class TestSoftmaxWithCrossEntropyOpNoCudnnFp16(
TestSoftmaxWithCrossEntropyOpFp16):
def initParams(self):
self.numeric_stable_mode = True
self.dtype = np.float16
def test_check_grad(self):
self.check_grad(["Logits"], "Loss", max_relative_error=0.1)
class TestSoftmaxWithCrossEntropyOp2(OpTest):
"""
Test softmax with cross entropy operator with soft labels.
......
python/paddle/fluid/transpiler/distribute_transpiler.py
浏览文件 @ 92da467c
......@@ -251,11 +251,10 @@ class DistributeTranspiler(object):
def _get_all_remote_sparse_update_op(self, main_program):
sparse_update_ops = []
sparse_update_op_types = ["lookup_table"]
sparse_update_op_types = ["lookup_table", "nce", "hierarchical_sigmoid"]
for op in main_program.global_block().ops:
if op.type in sparse_update_op_types and op.attr(
'remote_prefetch') is True and not op.attr(
'is_distributed'):
'remote_prefetch') is True:
sparse_update_ops.append(op)
return sparse_update_ops
......@@ -753,12 +752,6 @@ class DistributeTranspiler(object):
elif op not in lr_ops:
self._append_pserver_non_opt_ops(block, op)
def __op_have_grad_input__(op):
for varname in op.input_arg_names:
if varname.find("@GRAD") >= 0:
return varname
return ""
def __clone_lr_op_sub_block__(op, program, lr_block):
if not op.has_attr('sub_block'):
return
......@@ -809,7 +802,7 @@ class DistributeTranspiler(object):
merged_var = None
for _, op in enumerate(self.optimize_ops):
# find the origin grad var before clipping/L2Decay,
# merged_var should be the input var name of L2Decaybuil
# merged_var should be the input var name of L2Decay
grad_varname_for_block = op.attr(OP_ROLE_VAR_ATTR_NAME)[1]
if op.attr(OP_ROLE_VAR_ATTR_NAME)[
0] == optimize_target_param_name:
......@@ -1685,7 +1678,16 @@ class DistributeTranspiler(object):
if self.config.enable_dc_asgd:
new_inputs[key] = dc
else:
new_inputs[key] = merged_var
# Note!! This is for l2decay on sparse gradient, because it will create a new tensor for
# decayed gradient but not inplace modify the origin one
origin_grad_name = opt_op.input(key)[0]
if core.kNewGradSuffix(
) in origin_grad_name and pserver_block.has_var(
origin_grad_name):
new_grad = pserver_block.var(origin_grad_name)
new_inputs[key] = new_grad
else:
new_inputs[key] = merged_var
elif key == "Param":
param_block = _get_param_block(opt_op)
if not param_block:
......
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