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PaddleDetection
提交 90e05a4b 编写于 作者: P peterzhang2029
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Merge branch 'develop' of https://github.com/PaddlePaddle/Paddle into add_bn_eq

上级 5502abb9 67bd4cd6
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内联 并排
Showing with 4705 addition and 10946 deletion
CMakeLists.txt
浏览文件 @ 90e05a4b
......@@ -36,8 +36,7 @@ include(simd)
################################ Configurations #######################################
option(WITH_GPU "Compile PaddlePaddle with NVIDIA GPU" ${CUDA_FOUND})
option(WITH_AVX "Compile PaddlePaddle with AVX intrinsics" ${AVX_FOUND})
option(WITH_MKLDNN "Compile PaddlePaddle with mkl-dnn support." ${AVX_FOUND})
option(WITH_MKLML "Compile PaddlePaddle with mklml package." ${AVX_FOUND})
option(WITH_MKL "Compile PaddlePaddle with MKL support." ${AVX_FOUND})
option(WITH_DSO "Compile PaddlePaddle with dynamic linked CUDA" ON)
option(WITH_TESTING "Compile PaddlePaddle with unit testing" ON)
option(WITH_SWIG_PY "Compile PaddlePaddle with inference api" ON)
......@@ -82,10 +81,8 @@ if(ANDROID OR IOS)
"Disable PYTHON when cross-compiling for Android and iOS" FORCE)
set(WITH_RDMA OFF CACHE STRING
"Disable RDMA when cross-compiling for Android and iOS" FORCE)
set(WITH_MKLDNN OFF CACHE STRING
"Disable MKLDNN when cross-compiling for Android and iOS" FORCE)
set(WITH_MKLML OFF CACHE STRING
"Disable MKLML package when cross-compiling for Android and iOS" FORCE)
set(WITH_MKL OFF CACHE STRING
"Disable MKL when cross-compiling for Android and iOS" FORCE)
# Compile PaddlePaddle mobile inference library
if (NOT WITH_C_API)
......@@ -111,6 +108,14 @@ else()
set(THIRD_PARTY_BUILD_TYPE Release)
endif()
set(WITH_MKLML ${WITH_MKL})
if (WITH_MKL AND AVX2_FOUND)
set(WITH_MKLDNN ON)
else()
message(STATUS "Do not have AVX2 intrinsics and disabled MKL-DNN")
set(WITH_MKLDNN OFF)
endif()
########################################################################################
include(external/mklml) # download mklml package
......@@ -158,14 +163,15 @@ set(EXTERNAL_LIBS
)
if(WITH_GPU)
list(APPEND EXTERNAL_LIBS ${CUDA_LIBRARIES} ${CUDA_rt_LIBRARY})
if(NOT WITH_DSO)
list(APPEND EXTERNAL_LIBS ${CUDNN_LIBRARY} ${CUDA_CUBLAS_LIBRARIES} ${CUDA_curand_LIBRARY} ${NCCL_LIBRARY})
endif(NOT WITH_DSO)
include(cuda)
endif(WITH_GPU)
if(WITH_MKLML)
list(APPEND EXTERNAL_LIBS ${MKLML_IOMP_LIB})
endif()
if(WITH_MKLDNN)
list(APPEND EXTERNAL_LIBS ${MKLDNN_LIB} ${MKLDNN_IOMP_LIB})
list(APPEND EXTERNAL_LIBS ${MKLDNN_LIB})
endif()
if(USE_NNPACK)
......
benchmark/IntelOptimizedPaddle.md
浏览文件 @ 90e05a4b
......@@ -12,11 +12,11 @@ Machine:
System: CentOS release 6.3 (Final), Docker 1.12.1.
PaddlePaddle: paddlepaddle/paddle:latest (TODO: will rerun after 0.11.0)
- MKL-DNN tag v0.10
- MKLML 2018.0.20170720
PaddlePaddle: paddlepaddle/paddle:latest (for MKLML and MKL-DNN), paddlepaddle/paddle:latest-openblas (for OpenBLAS)
- MKL-DNN tag v0.11
- MKLML 2018.0.1.20171007
- OpenBLAS v0.2.20
(TODO: will rerun after 0.11.0)
On each machine, we will test and compare the performance of training on single node using MKL-DNN / MKLML / OpenBLAS respectively.
......@@ -31,15 +31,26 @@ Input image size - 3 * 224 * 224, Time: images/second
| BatchSize | 64 | 128 | 256 |
|--------------|-------| -----| --------|
| OpenBLAS | 7.82 | 8.62 | 10.34 |
| MKLML | 11.02 | 12.86 | 15.33 |
| MKL-DNN | 27.69 | 28.8 | 29.27 |
| OpenBLAS | 7.80 | 9.00 | 10.80 |
| MKLML | 12.12 | 13.70 | 16.18 |
| MKL-DNN | 28.46 | 29.83 | 30.44 |
chart on batch size 128
TBD
- ResNet-50
| BatchSize | 64 | 128 | 256 |
|--------------|-------| ------| -------|
| OpenBLAS | 25.22 | 25.68 | 27.12 |
| MKLML | 32.52 | 31.89 | 33.12 |
| MKL-DNN | 81.69 | 82.35 | 84.08 |
chart on batch size 128
TBD
- ResNet
- GoogLeNet
### Laptop
......
benchmark/paddle/image/googlenet.py
浏览文件 @ 90e05a4b
......@@ -5,6 +5,7 @@ height = 224
width = 224
num_class = 1000
batch_size = get_config_arg('batch_size', int, 128)
use_gpu = get_config_arg('use_gpu', bool, True)
args = {'height': height, 'width': width, 'color': True, 'num_class': num_class}
define_py_data_sources2(
......@@ -16,6 +17,8 @@ settings(
learning_method=MomentumOptimizer(0.9),
regularization=L2Regularization(0.0005 * batch_size))
conv_projection = conv_projection if use_gpu else img_conv_layer
def inception2(name, input, channels, \
filter1,
filter3R, filter3,
......@@ -138,7 +141,7 @@ def inception(name, input, channels, \
cat = concat_layer(
name=name,
input=[cov1, cov3, cov5, covprj],
bias_attr=True,
bias_attr=True if use_gpu else False,
act=ReluActivation())
return cat
......
benchmark/paddle/image/run_mkldnn.sh
浏览文件 @ 90e05a4b
......@@ -40,6 +40,7 @@ fi
for use_mkldnn in True False; do
for batchsize in 64 128 256; do
train vgg 19 $batchsize $use_mkldnn
train resnet 50 $batchsize $use_mkldnn
train resnet 50 $batchsize $use_mkldnn
train googlenet v1 $batchsize $use_mkldnn
done
done
cmake/configure.cmake
浏览文件 @ 90e05a4b
......@@ -76,27 +76,14 @@ else()
include_directories(${CUDA_TOOLKIT_INCLUDE})
endif(NOT WITH_GPU)
if(WITH_MKLDNN)
add_definitions(-DPADDLE_USE_MKLDNN)
if (WITH_MKLML AND MKLDNN_IOMP_DIR)
message(STATUS "Enable Intel OpenMP at ${MKLDNN_IOMP_DIR}")
set(OPENMP_FLAGS "-fopenmp")
set(CMAKE_C_CREATE_SHARED_LIBRARY_FORBIDDEN_FLAGS ${OPENMP_FLAGS})
set(CMAKE_CXX_CREATE_SHARED_LIBRARY_FORBIDDEN_FLAGS ${OPENMP_FLAGS})
set(CMAKE_C_FLAGS "${CMAKE_C_FLAGS} ${OPENMP_FLAGS}")
set(CMAKE_CXX_FLAGS "${CMAKE_CXX_FLAGS} ${OPENMP_FLAGS}")
else()
find_package(OpenMP)
if(OPENMP_FOUND)
set(CMAKE_C_FLAGS "${CMAKE_C_FLAGS} ${OpenMP_C_FLAGS}")
set(CMAKE_CXX_FLAGS "${CMAKE_CXX_FLAGS} ${OpenMP_CXX_FLAGS}")
else()
message(WARNING "Can not find OpenMP."
"Some performance features in MKLDNN may not be available")
endif()
endif()
endif(WITH_MKLDNN)
if (WITH_MKLML AND MKLML_IOMP_LIB)
message(STATUS "Enable Intel OpenMP with ${MKLML_IOMP_LIB}")
set(OPENMP_FLAGS "-fopenmp")
set(CMAKE_C_CREATE_SHARED_LIBRARY_FORBIDDEN_FLAGS ${OPENMP_FLAGS})
set(CMAKE_CXX_CREATE_SHARED_LIBRARY_FORBIDDEN_FLAGS ${OPENMP_FLAGS})
set(CMAKE_C_FLAGS "${CMAKE_C_FLAGS} ${OPENMP_FLAGS}")
set(CMAKE_CXX_FLAGS "${CMAKE_CXX_FLAGS} ${OPENMP_FLAGS}")
endif()
set(CMAKE_C_FLAGS "${CMAKE_C_FLAGS} ${SIMD_FLAG}")
set(CMAKE_CXX_FLAGS "${CMAKE_CXX_FLAGS} ${SIMD_FLAG}")
......
cmake/cuda.cmake 0 → 100644
浏览文件 @ 90e05a4b
if(NOT WITH_GPU)
return()
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")
######################################################################################
# A function for automatic detection of GPUs installed (if autodetection is enabled)
# Usage:
# detect_installed_gpus(out_variable)
function(detect_installed_gpus out_variable)
if(NOT CUDA_gpu_detect_output)
set(cufile ${PROJECT_BINARY_DIR}/detect_cuda_archs.cu)
file(WRITE ${cufile} ""
"#include <cstdio>\n"
"int main() {\n"
" int count = 0;\n"
" if (cudaSuccess != cudaGetDeviceCount(&count)) return -1;\n"
" if (count == 0) return -1;\n"
" for (int device = 0; device < count; ++device) {\n"
" cudaDeviceProp prop;\n"
" if (cudaSuccess == cudaGetDeviceProperties(&prop, device))\n"
" std::printf(\"%d.%d \", prop.major, prop.minor);\n"
" }\n"
" return 0;\n"
"}\n")
execute_process(COMMAND "${CUDA_NVCC_EXECUTABLE}" "-ccbin=${CUDA_HOST_COMPILER}"
"--run" "${cufile}"
WORKING_DIRECTORY "${PROJECT_BINARY_DIR}/CMakeFiles/"
RESULT_VARIABLE nvcc_res OUTPUT_VARIABLE nvcc_out
ERROR_QUIET OUTPUT_STRIP_TRAILING_WHITESPACE)
if(nvcc_res EQUAL 0)
# only keep the last line of nvcc_out
STRING(REGEX REPLACE ";" "\\\\;" nvcc_out "${nvcc_out}")
STRING(REGEX REPLACE "\n" ";" nvcc_out "${nvcc_out}")
list(GET nvcc_out -1 nvcc_out)
string(REPLACE "2.1" "2.1(2.0)" nvcc_out "${nvcc_out}")
set(CUDA_gpu_detect_output ${nvcc_out} CACHE INTERNAL "Returned GPU architetures from detect_installed_gpus tool" FORCE)
endif()
endif()
if(NOT CUDA_gpu_detect_output)
message(STATUS "Automatic GPU detection failed. Building for all known architectures.")
set(${out_variable} ${paddle_known_gpu_archs} PARENT_SCOPE)
else()
set(${out_variable} ${CUDA_gpu_detect_output} PARENT_SCOPE)
endif()
endfunction()
########################################################################
# Function for selecting GPU arch flags for nvcc based on CUDA_ARCH_NAME
# Usage:
# 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_name_default "All")
if(NOT CMAKE_CROSSCOMPILING)
list(APPEND archs_names "Auto")
endif()
# set CUDA_ARCH_NAME strings (so it will be seen as dropbox in CMake-Gui)
set(CUDA_ARCH_NAME ${archs_name_default} CACHE STRING "Select target NVIDIA GPU achitecture.")
set_property( CACHE CUDA_ARCH_NAME PROPERTY STRINGS "" ${archs_names} )
mark_as_advanced(CUDA_ARCH_NAME)
# verify CUDA_ARCH_NAME value
if(NOT ";${archs_names};" MATCHES ";${CUDA_ARCH_NAME};")
string(REPLACE ";" ", " archs_names "${archs_names}")
message(FATAL_ERROR "Only ${archs_names} architeture names are supported.")
endif()
if(${CUDA_ARCH_NAME} STREQUAL "Manual")
set(CUDA_ARCH_BIN ${paddle_known_gpu_archs} CACHE STRING "Specify 'real' GPU architectures to build binaries for, BIN(PTX) format is supported")
set(CUDA_ARCH_PTX "50" CACHE STRING "Specify 'virtual' PTX architectures to build PTX intermediate code for")
mark_as_advanced(CUDA_ARCH_BIN CUDA_ARCH_PTX)
else()
unset(CUDA_ARCH_BIN CACHE)
unset(CUDA_ARCH_PTX CACHE)
endif()
if(${CUDA_ARCH_NAME} STREQUAL "Kepler")
set(cuda_arch_bin "30 35")
elseif(${CUDA_ARCH_NAME} STREQUAL "Maxwell")
set(cuda_arch_bin "50")
elseif(${CUDA_ARCH_NAME} STREQUAL "Pascal")
set(cuda_arch_bin "60 61")
elseif(${CUDA_ARCH_NAME} STREQUAL "Volta")
set(cuda_arch_bin "70")
elseif(${CUDA_ARCH_NAME} STREQUAL "All")
set(cuda_arch_bin ${paddle_known_gpu_archs})
elseif(${CUDA_ARCH_NAME} STREQUAL "Auto")
detect_installed_gpus(cuda_arch_bin)
else() # (${CUDA_ARCH_NAME} STREQUAL "Manual")
set(cuda_arch_bin ${CUDA_ARCH_BIN})
endif()
# remove dots and convert to lists
string(REGEX REPLACE "\\." "" cuda_arch_bin "${cuda_arch_bin}")
string(REGEX REPLACE "\\." "" cuda_arch_ptx "${CUDA_ARCH_PTX}")
string(REGEX MATCHALL "[0-9()]+" cuda_arch_bin "${cuda_arch_bin}")
string(REGEX MATCHALL "[0-9]+" cuda_arch_ptx "${cuda_arch_ptx}")
list(REMOVE_DUPLICATES cuda_arch_bin)
list(REMOVE_DUPLICATES cuda_arch_ptx)
set(nvcc_flags "")
set(nvcc_archs_readable "")
# Tell NVCC to add binaries for the specified GPUs
foreach(arch ${cuda_arch_bin})
if(arch MATCHES "([0-9]+)\\(([0-9]+)\\)")
# User explicitly specified PTX for the concrete BIN
list(APPEND nvcc_flags -gencode arch=compute_${CMAKE_MATCH_2},code=sm_${CMAKE_MATCH_1})
list(APPEND nvcc_archs_readable sm_${CMAKE_MATCH_1})
else()
# User didn't explicitly specify PTX for the concrete BIN, we assume PTX=BIN
list(APPEND nvcc_flags -gencode arch=compute_${arch},code=sm_${arch})
list(APPEND nvcc_archs_readable sm_${arch})
endif()
endforeach()
# Tell NVCC to add PTX intermediate code for the specified architectures
foreach(arch ${cuda_arch_ptx})
list(APPEND nvcc_flags -gencode arch=compute_${arch},code=compute_${arch})
list(APPEND nvcc_archs_readable compute_${arch})
endforeach()
string(REPLACE ";" " " nvcc_archs_readable "${nvcc_archs_readable}")
set(${out_variable} ${nvcc_flags} PARENT_SCOPE)
set(${out_variable}_readable ${nvcc_archs_readable} PARENT_SCOPE)
endfunction()
message(STATUS "CUDA detected: " ${CUDA_VERSION})
if (${CUDA_VERSION} LESS 7.0)
set(paddle_known_gpu_archs ${paddle_known_gpu_archs})
elseif (${CUDA_VERSION} LESS 8.0) # CUDA 7.x
set(paddle_known_gpu_archs ${paddle_known_gpu_archs7})
list(APPEND CUDA_NVCC_FLAGS "-D_MWAITXINTRIN_H_INCLUDED")
list(APPEND CUDA_NVCC_FLAGS "-D__STRICT_ANSI__")
elseif (${CUDA_VERSION} LESS 9.0) # CUDA 8.x
set(paddle_known_gpu_archs ${paddle_known_gpu_archs8})
list(APPEND CUDA_NVCC_FLAGS "-D_MWAITXINTRIN_H_INCLUDED")
list(APPEND CUDA_NVCC_FLAGS "-D__STRICT_ANSI__")
# CUDA 8 may complain that sm_20 is no longer supported. Suppress the
# warning for now.
list(APPEND CUDA_NVCC_FLAGS "-Wno-deprecated-gpu-targets")
endif()
include_directories(${CUDA_INCLUDE_DIRS})
list(APPEND EXTERNAL_LIBS ${CUDA_LIBRARIES} ${CUDA_rt_LIBRARY})
if(NOT WITH_DSO)
list(APPEND EXTERNAL_LIBS ${CUDNN_LIBRARY} ${CUDA_CUBLAS_LIBRARIES} ${CUDA_curand_LIBRARY} ${NCCL_LIBRARY})
endif(NOT WITH_DSO)
# setting nvcc arch flags
select_nvcc_arch_flags(NVCC_FLAGS_EXTRA)
list(APPEND CUDA_NVCC_FLAGS ${NVCC_FLAGS_EXTRA})
message(STATUS "Added CUDA NVCC flags for: ${NVCC_FLAGS_EXTRA_readable}")
# Set C++11 support
set(CUDA_PROPAGATE_HOST_FLAGS OFF)
# Release/Debug flags set by cmake. Such as -O3 -g -DNDEBUG etc.
# So, don't set these flags here.
list(APPEND CUDA_NVCC_FLAGS "-std=c++11")
list(APPEND CUDA_NVCC_FLAGS "--use_fast_math")
list(APPEND CUDA_NVCC_FLAGS "-Xcompiler -fPIC")
# Set :expt-relaxed-constexpr to suppress Eigen warnings
list(APPEND CUDA_NVCC_FLAGS "--expt-relaxed-constexpr")
if(CMAKE_BUILD_TYPE STREQUAL "Debug")
list(APPEND CUDA_NVCC_FLAGS ${CMAKE_CXX_FLAGS_DEBUG})
elseif(CMAKE_BUILD_TYPE STREQUAL "Release")
list(APPEND CUDA_NVCC_FLAGS ${CMAKE_CXX_FLAGS_RELEASE})
elseif(CMAKE_BUILD_TYPE STREQUAL "RelWithDebInfo")
list(APPEND CUDA_NVCC_FLAGS ${CMAKE_CXX_FLAGS_RELWITHDEBINFO})
elseif(CMAKE_BUILD_TYPE STREQUAL "MinSizeRel")
list(APPEND CUDA_NVCC_FLAGS ${CMAKE_CXX_FLAGS_MINSIZEREL})
endif()
mark_as_advanced(CUDA_BUILD_CUBIN CUDA_BUILD_EMULATION CUDA_VERBOSE_BUILD)
mark_as_advanced(CUDA_SDK_ROOT_DIR CUDA_SEPARABLE_COMPILATION)
cmake/external/mkldnn.cmake
浏览文件 @ 90e05a4b
......@@ -40,10 +40,9 @@ INCLUDE_DIRECTORIES(${MKLDNN_INC_DIR})
IF(${CBLAS_PROVIDER} STREQUAL "MKLML")
SET(MKLDNN_DEPENDS ${MKLML_PROJECT})
SET(MKLDNN_MKLROOT ${MKLML_ROOT})
SET(MKLDNN_IOMP_LIB ${MKLML_IOMP_LIB})
SET(MKLDNN_IOMP_DIR ${MKLML_LIB_DIR})
MESSAGE(STATUS "Build MKLDNN with ${MKLDNN_MKLROOT}")
MESSAGE(STATUS "Build MKLDNN with MKLML ${MKLML_ROOT}")
ELSE()
MESSAGE(FATAL_ERROR "Should enable MKLML when build MKLDNN")
ENDIF()
SET(MKLDNN_CFLAG "${CMAKE_C_FLAGS} -Wno-error=strict-overflow")
......@@ -57,15 +56,16 @@ ExternalProject_Add(
PREFIX ${MKLDNN_SOURCES_DIR}
UPDATE_COMMAND ""
CMAKE_ARGS -DCMAKE_INSTALL_PREFIX=${MKLDNN_INSTALL_DIR}
CMAKE_ARGS -DMKLROOT=${MKLDNN_MKLROOT}
CMAKE_ARGS -DMKLROOT=${MKLML_ROOT}
CMAKE_ARGS -DCMAKE_C_FLAGS=${MKLDNN_CFLAG}
CMAKE_ARGS -DCMAKE_CXX_FLAGS=${MKLDNN_CXXFLAG}
CMAKE_CACHE_ARGS -DCMAKE_INSTALL_PREFIX:PATH=${MKLDNN_INSTALL_DIR}
-DMKLROOT:PATH=${MKLDNN_MKLROOT}
-DMKLROOT:PATH=${MKLML_ROOT}
)
ADD_LIBRARY(mkldnn SHARED IMPORTED GLOBAL)
SET_PROPERTY(TARGET mkldnn PROPERTY IMPORTED_LOCATION ${MKLDNN_LIB})
ADD_DEPENDENCIES(mkldnn ${MKLDNN_PROJECT})
MESSAGE(STATUS "Mkldnn library: ${MKLDNN_LIB}")
MESSAGE(STATUS "MKLDNN library: ${MKLDNN_LIB}")
add_definitions(-DPADDLE_USE_MKLDNN)
LIST(APPEND external_project_dependencies mkldnn)
cmake/flags.cmake
浏览文件 @ 90e05a4b
......@@ -149,58 +149,3 @@ endforeach()
foreach(flag ${GPU_COMMON_FLAGS})
safe_set_nvflag(${flag})
endforeach()
set(CUDA_PROPAGATE_HOST_FLAGS OFF)
# Release/Debug flags set by cmake. Such as -O3 -g -DNDEBUG etc.
# So, don't set these flags here.
LIST(APPEND CUDA_NVCC_FLAGS -std=c++11)
LIST(APPEND CUDA_NVCC_FLAGS --use_fast_math)
if(CMAKE_BUILD_TYPE STREQUAL "Debug")
LIST(APPEND CUDA_NVCC_FLAGS ${CMAKE_CXX_FLAGS_DEBUG})
elseif(CMAKE_BUILD_TYPE STREQUAL "Release")
LIST(APPEND CUDA_NVCC_FLAGS ${CMAKE_CXX_FLAGS_RELEASE})
elseif(CMAKE_BUILD_TYPE STREQUAL "RelWithDebInfo")
LIST(APPEND CUDA_NVCC_FLAGS ${CMAKE_CXX_FLAGS_RELWITHDEBINFO})
elseif(CMAKE_BUILD_TYPE STREQUAL "MinSizeRel")
LIST(APPEND CUDA_NVCC_FLAGS ${CMAKE_CXX_FLAGS_MINSIZEREL})
endif()
function(specify_cuda_arch cuda_version cuda_arch)
if(${cuda_version} VERSION_GREATER "8.0")
foreach(capability 61 62)
if(${cuda_arch} STREQUAL ${capability})
list(APPEND __arch_flags " -gencode arch=compute_${cuda_arch},code=sm_${cuda_arch}")
endif()
endforeach()
elseif(${cuda_version} VERSION_GREATER "7.0" and ${cuda_arch} STREQUAL "53")
list(APPEND __arch_flags " -gencode arch=compute_${cuda_arch},code=sm_${cuda_arch}")
endif()
endfunction()
# Common gpu architectures: Kepler, Maxwell
foreach(capability 30 35 50)
list(APPEND __arch_flags " -gencode arch=compute_${capability},code=sm_${capability}")
endforeach()
if (CUDA_VERSION VERSION_GREATER "7.0" OR CUDA_VERSION VERSION_EQUAL "7.0")
list(APPEND __arch_flags " -gencode arch=compute_52,code=sm_52")
endif()
# Modern gpu architectures: Pascal
if (CUDA_VERSION VERSION_GREATER "8.0" OR CUDA_VERSION VERSION_EQUAL "8.0")
list(APPEND __arch_flags " -gencode arch=compute_60,code=sm_60")
list(APPEND CUDA_NVCC_FLAGS --expt-relaxed-constexpr)
endif()
# Custom gpu architecture
set(CUDA_ARCH)
if(CUDA_ARCH)
specify_cuda_arch(${CUDA_VERSION} ${CUDA_ARCH})
endif()
set(CUDA_NVCC_FLAGS ${__arch_flags} ${CUDA_NVCC_FLAGS})
cmake/util.cmake
浏览文件 @ 90e05a4b
......@@ -115,8 +115,8 @@ function(link_paddle_exe TARGET_NAME)
target_link_libraries(${TARGET_NAME} log)
endif(ANDROID)
if(WITH_MKLDNN AND WITH_MKLML AND MKLDNN_IOMP_DIR)
target_link_libraries(${TARGET_NAME} "-L${MKLDNN_IOMP_DIR} -liomp5 -Wl,--as-needed")
if(WITH_MKLML AND MKLML_LIB_DIR AND MKLML_IOMP_LIB)
target_link_libraries(${TARGET_NAME} "-L${MKLML_LIB_DIR} -liomp5 -Wl,--as-needed")
endif()
add_dependencies(${TARGET_NAME} ${external_project_dependencies})
......
doc/api/v2/config/layer.rst
浏览文件 @ 90e05a4b
......@@ -335,6 +335,16 @@ bilinear_interp
.. autoclass:: paddle.v2.layer.bilinear_interp
:noindex:
dot_prod
---------
.. autoclass:: paddle.v2.layer.dot_prod
:noindex:
out_prod
--------
.. autoclass:: paddle.v2.layer.out_prod
:noindex:
power
-----
.. autoclass:: paddle.v2.layer.power
......@@ -372,6 +382,11 @@ cos_sim
.. autoclass:: paddle.v2.layer.cos_sim
:noindex:
l2_distance
-----------
.. autoclass:: paddle.v2.layer.l2_distance
:noindex:
trans
-----
.. autoclass:: paddle.v2.layer.trans
......
doc/design/mkldnn/README.MD
浏览文件 @ 90e05a4b
......@@ -36,13 +36,13 @@ Figure 1. PaddlePaddle on IA.
我们把集成方案大致分为了如下几个方面。
### CMake
我们会在`CMakeLists.txt`中会添加`WITH_MKLDNN`的选项，当设置这个值为`ON`的时候会启用编译MKL-DNN功能。同时会自动开启OpenMP用于提高MKL-DNN的性能。
我们会在`CMakeLists.txt`中会给用户添加一个`WITH_MKL`的开关，他是负责`WITH_MKLML`和`WITH_MKLDNN`的总开关。
同时，我们会引入`WITH_MKLML`选项，用于选择是否使用MKL-DNN自带的MKLML安装包。这个安装包可以独立于MKL-DNN使用，但是建议在开启MKL-DNN的同时也打开MKLML的开关，这样才能发挥最好的性能。
当打开`WITH_MKL`时，会开启MKLML的功能，作为PaddlePaddle的CBLAS和LAPACK库，同时会开启Intel OpenMP用于提高MKLML的性能。 如果系统支持AVX2指令集及以上，同时会开启MKL-DNN功能。
所以，我们会在`cmake/external`目录新建`mkldnn.cmake`和`mklml.cmake`文件，它们会在编译PaddlePaddle的时候下载对应的软件包，并放到PaddlePaddle的third party目录中。
当关闭`WITH_MKL`时，MKLML和MKL-DNN功能会同时关闭。
**备注**：当`WITH_MKLML=ON`的时候，会优先使用这个包作为PaddlePaddle的CBLAS和LAPACK库，所以会稍微改动`cmake/cblas.cmake`中的逻辑。
所以，我们会在`cmake/external`目录新建`mkldnn.cmake`和`mklml.cmake`文件，它们会在编译PaddlePaddle的时候下载对应的软件包，并放到PaddlePaddle的third party目录中。
### Layers
所有MKL-DNN相关的C++ layers，都会按照PaddlePaddle的目录结构存放在
......
doc/howto/dev/write_docs_cn.rst
浏览文件 @ 90e05a4b
......@@ -34,7 +34,7 @@ PaddlePaddle的文档构建有两种方式。
cd TO_YOUR_PADDLE_CLONE_PATH
mkdir -p build
cd build
cmake .. -DCMAKE_BUILD_TYPE=Debug -DWITH_GPU=OFF -DWITH_MKLDNN=OFF -DWITH_MKLML=OFF -DWITH_DOC=ON
cmake .. -DCMAKE_BUILD_TYPE=Debug -DWITH_GPU=OFF -DWITH_MKL=OFF -DWITH_DOC=ON
make gen_proto_py
make paddle_docs paddle_docs_cn
......
paddle/framework/backward.cc
浏览文件 @ 90e05a4b
......@@ -513,19 +513,14 @@ ParamGradInfoMap AppendBackward(
const int root_block_idx = 0;
auto root_block = program_desc.MutableBlock(root_block_idx);
// insert fill one op for target
// TODO(qiao) add some check to the target.
std::string fill_one_op_out = GradVarName(target.Name());
std::vector<int64_t> target_shape_desc = target.Shape();
std::vector<int> target_shape;
std::transform(target_shape_desc.begin(), target_shape_desc.end(),
std::back_inserter(target_shape),
[](int64_t dim) { return static_cast<int>(dim); });
bool is_scalar = target.Shape() == std::vector<int64_t>{1};
PADDLE_ENFORCE(is_scalar, "target should be scalar");
VLOG(3) << "backward from loss=" << target.Name()
<< " data_type=" << target.GetDataType();
std::unique_ptr<OpDescBind> fill_one_op(
new OpDescBind("fill_constant", {}, {{"Out", {fill_one_op_out}}},
{{"shape", target_shape},
{{"shape", std::vector<int>{1}},
{"value", static_cast<float>(1.0)},
{"data_type", target.GetDataType()}}));
// infer var type of fill_one_op
......
paddle/framework/backward_test.cc
浏览文件 @ 90e05a4b
......@@ -508,6 +508,7 @@ TEST(Backward, simple_single_op) {
op->SetOutput("Out", {"out"});
auto target = f::VarDescBind("out");
target.SetShape({1});
auto var_to_grad = AppendBackward(program, target, {});
ASSERT_EQ(block->AllOps().size(), 3UL);
......@@ -544,6 +545,7 @@ TEST(Backward, default_attribute) {
op->CheckAttrs();
auto target = f::VarDescBind("out");
target.SetShape({1});
AppendBackward(program, target, {});
ASSERT_EQ(block->AllOps().size(), 3UL);
......@@ -581,6 +583,7 @@ TEST(Backward, simple_mult_op) {
op3->SetOutput("Out", {"out3"});
auto target = f::VarDescBind("out3");
target.SetShape({1});
size_t forward_len = block->AllOps().size();
auto var_to_grad = AppendBackward(program, target, {});
......@@ -670,6 +673,7 @@ TEST(Backward, intermedia_var_no_grad) {
op4->SetOutput("Out", {"out4"});
auto target = f::VarDescBind("out4");
target.SetShape({1});
size_t forward_len = block->AllOps().size();
auto var_to_grad = AppendBackward(program, target, {"out3"});
......@@ -730,6 +734,7 @@ TEST(Backward, var_no_grad) {
op2->SetOutput("Z", {"z2"});
auto target = f::VarDescBind("z2");
target.SetShape({1});
size_t forward_len = block->AllOps().size();
auto var_to_grad = AppendBackward(program, target, {"z1"});
......@@ -810,6 +815,7 @@ TEST(Backward, shared_var) {
op3->SetOutput("Out", {"out3"});
auto target = f::VarDescBind("out3");
target.SetShape({1});
size_t forward_len = block->AllOps().size();
auto var_to_grad = AppendBackward(program, target, {});
......@@ -888,6 +894,7 @@ TEST(Backward, half_backward) {
op1->SetOutput("Out", {"out"});
auto target = f::VarDescBind("out");
target.SetShape({1});
size_t forward_len = block->AllOps().size();
auto var_to_grad = AppendBackward(program, target, {"b"});
f::OpDescBind *fill_op = block->AllOps()[forward_len];
......
paddle/framework/data_type.h
浏览文件 @ 90e05a4b
......@@ -46,6 +46,8 @@ inline std::type_index ToTypeIndex(DataType type) {
return typeid(int);
case DataType::INT64:
return typeid(int64_t);
case DataType::BOOL:
return typeid(bool);
default:
PADDLE_THROW("Not support type %d", type);
}
......@@ -66,6 +68,9 @@ inline void VisitDataType(DataType type, Visitor visitor) {
case DataType::INT64:
visitor.template operator()<int64_t>();
break;
case DataType::BOOL:
visitor.template operator()<bool>();
break;
default:
PADDLE_THROW("Not supported");
}
......
paddle/gserver/CMakeLists.txt
浏览文件 @ 90e05a4b
......@@ -73,7 +73,6 @@ if(MOBILE_INFERENCE)
list(REMOVE_ITEM GSERVER_SOURCES
dataproviders/DataProvider.cpp
dataproviders/MultiDataProvider.cpp
dataproviders/ProtoDataProvider.cpp
dataproviders/PyDataProvider2.cpp
dataproviders/PyDataProvider.cpp)
......
paddle/gserver/activations/ActivationFunction.cpp
浏览文件 @ 90e05a4b
......@@ -212,6 +212,37 @@ Error __must_check backward(Argument& act) {
}
END_DEFINE_ACTIVATION(sequence_softmax)
/*
* @brief SoftSign Activation.
* \f[
* f(z) = \frac{z}{1 + |z|}
* \f]
*/
BEGIN_DEFINE_ACTIVATION(softsign)
private:
MatrixPtr denominator_;
Error __must_check forward(Argument& act) {
size_t height = act.value->getHeight();
size_t width = act.value->getWidth();
Matrix::resizeOrCreate(
denominator_, height, width, false, useGpu(act.deviceId));
denominator_->assign(*act.value);
denominator_->abs2();
denominator_->add(1.);
act.value->dotDiv(*act.value, *denominator_);
return Error();
}
Error __must_check backward(Argument& act) {
denominator_->square2();
denominator_->scalarDiv(*denominator_, 1.);
act.grad->dotMul(*act.grad, *denominator_);
return Error();
}
END_DEFINE_ACTIVATION(softsign)
/**
* @brief Relu Activation.
* forward. y = max(0, z)
......
paddle/gserver/dataproviders/DataProvider.cpp
浏览文件 @ 90e05a4b
......@@ -16,8 +16,8 @@ limitations under the License. */
#include <unistd.h>
#include <algorithm>
#include "ProtoDataProvider.h"
#include "paddle/utils/Logging.h"
#include "paddle/utils/Stat.h"
#include "paddle/utils/StringUtil.h"
#include "paddle/utils/Util.h"
......@@ -164,8 +164,6 @@ DataProvider* DataProvider::create(const DataConfig& config,
REGISTER_DATA_PROVIDER(simple, SimpleDataProvider);
REGISTER_DATA_PROVIDER(dummy, DummyDataProvider);
REGISTER_DATA_PROVIDER(proto, ProtoDataProvider);
REGISTER_DATA_PROVIDER(proto_sequence, ProtoSequenceDataProvider);
int64_t DataProvider::getNextBatch(int64_t size, DataBatch* batch) {
int64_t batchSize = doubleBuffer_ ? getNextBatchFromBuffer(size, batch)
......
paddle/gserver/dataproviders/ProtoDataProvider.h 已删除 100644 → 0
浏览文件 @ 5502abb9
/* Copyright (c) 2016 PaddlePaddle Authors. All Rights Reserve.
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 <vector>
#include "DataFormat.pb.h"
#include "paddle/utils/Stat.h"
#include "DataProvider.h"
#include "ProtoReader.h"
namespace paddle {
/**
* @brief Provider data from protobuf data file with each sample
* specified by proto message
*
* DataSample defined in DataFormat.proto.
*
* The file format is
*
* header
*
* sample1
*
* sample2
*
* ...
*
* sampleN
*
* @note: In the data file, each message is prefixed with its length.
* The read/write of the protbuf are implemented in ProtoReader.h
*/
class ProtoDataProvider : public DataProvider {
public:
ProtoDataProvider(const DataConfig& config,
bool useGpu,
bool loadDataAll = true);
virtual void reset();
/**
* @note this size includes the sequences which are skipped because they
* are longer than the batch size.
*/
virtual int64_t getSize() {
int64_t size = sampleNums_;
if (usageRatio_ < 1.0f) {
size = static_cast<int64_t>(size * usageRatio_);
}
return size;
}
virtual void shuffle();
void loadData(const std::vector<std::string>& fileList);
virtual int64_t getNextBatchInternal(int64_t size, DataBatch* batch);
protected:
/**
* @brief load protobuf data from a list of file
* @param[in] fileName file name of a file which contains
* a list of file names
*/
void loadData(const std::string& fileName);
/**
* @brief load protobuf data from file
* @param[in] fileName data file name
*/
void loadDataFile(const std::string& fileName);
/** @brief check data header of each data sample
* @param[in] header data header read from protobuf data
*/
void checkDataHeader(const DataHeader& header);
/**
* @brief fill protobuf data into slot_,
* slot_ is a vector of ProtoSlot in memory.
* @param[in] sample data sample read from protobuf data
*/
void fillSlots(const DataSample& sample);
/**
* @brief return true if each sample is one sequence, i.e., independent
* of other samples.
*/
inline bool iidData() const { return sequenceStartPositions_.empty(); }
/**
* @brief check that sample is consistent with header_
*/
void checkSample(const DataSample& sample);
template <class Op>
int64_t sequenceLoop(Op op, int64_t size);
template <class Op>
int64_t sampleLoop(Op op, int64_t size);
template <class Op>
int64_t subSampleLoop(Op op, int64_t size, int slot);
void showDataStats();
protected:
struct ProtoVarSlot {
std::vector<real> data;
std::vector<int> dims;
};
struct ProtoSlot {
SlotDef::SlotType type;
int dim;
std::vector<int> indexData;
std::vector<real> denseData;
std::vector<sparse_non_value_t> sparseNonValueData;
std::vector<sparse_float_value_t> sparseFloatValueData;
std::vector<int64_t> indices;
std::vector<int64_t> subIndices;
std::vector<ProtoVarSlot> varDenseData;
std::vector<std::vector<int>> varIndices;
std::vector<std::string> strData;
};
DataHeader header_;
int numVecSlots_;
std::vector<ProtoSlot> slots_;
size_t sampleNums_;
/**
* The starting position of each sequence in samples.
* The last element should be num of samples.
* If empty, each sample is one sequence.
*/
std::vector<size_t> sequenceStartPositions_;
int64_t currentSequenceIndex_;
// The size should be the number of sequences.
std::vector<size_t> shuffledSequenceIds_;
ThreadLocalD<DataBatch> cpuBatch_;
ThreadLocalD<DataBatch> gpuBatch_;
RWLock lock_;
std::vector<StatPtr> nnzStats_; // stats for number of none-zeros entries
};
/**
* @brief Special use for Proto data: instances should contain sparse-non-value
* slots
* and label.
*
* @note ProtoSequenceDataProvider treats each SPARSE SLOT as a SEQUENCE
*/
class ProtoSequenceDataProvider : public ProtoDataProvider {
public:
ProtoSequenceDataProvider(const DataConfig& config,
bool useGpu,
bool loadDataAll = true);
~ProtoSequenceDataProvider() {}
virtual int64_t getNextBatchInternal(int64_t size, DataBatch* batch);
};
} // namespace paddle
paddle/gserver/layers/DotProdLayer.cpp 0 → 100644
浏览文件 @ 90e05a4b
/* Copyright (c) 2016 PaddlePaddle Authors. All Rights Reserve.
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 "Layer.h"
#include "paddle/math/Matrix.h"
#include "paddle/utils/Logging.h"
#include "paddle/utils/Stat.h"
namespace paddle {
/**
* @brief A layer for computing the dot product of two vectors.
* Input1: vector (batchSize * dim)
* Input2: vector (batchSize * dim)
* Output: a matrix: (batchSize * 1)
*/
class DotProdLayer : public Layer {
public:
explicit DotProdLayer(const LayerConfig& config) : Layer(config) {}
~DotProdLayer() {}
bool init(const LayerMap& layerMap,
const ParameterMap& parameterMap) override;
void forward(PassType passType) override;
void backward(const UpdateCallback& callback = nullptr) override;
};
REGISTER_LAYER(dot_prod, DotProdLayer);
bool DotProdLayer::init(const LayerMap& layerMap,
const ParameterMap& parameterMap) {
Layer::init(layerMap, parameterMap);
CHECK_EQ(inputLayers_.size(), 2U);
CHECK_EQ(1UL, getSize())
<< "The output dimensionality of this layer should be fixed to 1.";
return true;
}
void DotProdLayer::forward(PassType passType) {
Layer::forward(passType);
MatrixPtr inV0 = getInputValue(0);
MatrixPtr inV1 = getInputValue(1);
size_t batchSize = inV0->getHeight();
CHECK_EQ(inV1->getHeight(), batchSize);
CHECK_EQ(inV0->getWidth(), inV1->getWidth());
{
REGISTER_TIMER_INFO("FwResetTimer", getName().c_str());
reserveOutput(batchSize, 1);
}
MatrixPtr outV = getOutputValue();
{
REGISTER_TIMER_INFO("FwDotProdTimer", getName().c_str());
outV->sumOfProducts(*inV0, *inV1, 1, 0);
}
}
void DotProdLayer::backward(const UpdateCallback& callback) {
MatrixPtr inV0 = getInputValue(0);
MatrixPtr inV1 = getInputValue(1);
MatrixPtr outG = getOutputGrad();
MatrixPtr inG0 = getInputGrad(0);
MatrixPtr inG1 = getInputGrad(1);
{
REGISTER_TIMER_INFO("BwDotProdTimer", getName().c_str());
if (inG0) {
inG0->addRowScale(0, *inV1, *outG);
}
if (inG1) {
inG1->addRowScale(0, *inV0, *outG);
}
}
}
} // namespace paddle
paddle/gserver/layers/L2DistanceLayer.cpp 0 → 100644
浏览文件 @ 90e05a4b
/* Copyright (c) 2016 PaddlePaddle Authors. All Rights Reserve.
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 "L2DistanceLayer.h"
#include "paddle/utils/Logging.h"
#include "paddle/utils/Stat.h"
namespace paddle {
REGISTER_LAYER(l2_distance, L2DistanceLayer);
bool L2DistanceLayer::init(const LayerMap& layerMap,
const ParameterMap& parameterMap) {
/* Initialize the basic parent class */
Layer::init(layerMap, parameterMap);
CHECK_EQ(inputLayers_.size(), 2UL) << "The L2DistanceLayer accepts two and "
<< "only two inputs.";
CHECK_EQ(getSize(), 1UL) << "The output dimensionality of L2DistanceLayer "
<< "is fixed to be 1.";
return true;
}
void L2DistanceLayer::forward(PassType passType) {
Layer::forward(passType);
const auto inV1 = getInputValue(0);
const auto inV2 = getInputValue(1);
CHECK(inV1 && inV2);
CHECK_EQ(inV1->getHeight(), inV2->getHeight())
<< "The height of two inputs of this layer must be the same.";
CHECK_EQ(inV1->getWidth(), inV2->getWidth())
<< "The width of two inputs of this layer must be the same.";
int batchSize = inV1->getHeight();
int output_dim = getSize();
{
REGISTER_TIMER_INFO("L2DistanceBpAtvTimer", getName().c_str());
reserveOutput(batchSize, output_dim);
auto outV = getOutputValue();
CHECK(outV) << "The output matrix should not be null.";
Matrix::resizeOrCreate(
inputSub_, inV1->getHeight(), inV1->getWidth(), false, useGpu_);
inputSub_->assign(*inV1);
inputSub_->sub(*inV2);
outV->sumOfProducts(*inputSub_, *inputSub_, 1, 0);
outV->sqrt2(*outV);
}
}
void L2DistanceLayer::backward(const UpdateCallback& callback) {
const auto outG = getOutputGrad();
const auto outV = getOutputValue();
CHECK(outG && outV);
auto inGrad1 = getInputGrad(0);
auto inGrad2 = getInputGrad(1);
{
REGISTER_TIMER_INFO("L2DistanceBpAtvTimer", getName().c_str());
if (inGrad1 || inGrad2) {
outV->scalarDiv(*outV, 1.);
outV->dotMul(*outG, *outV);
}
if (inGrad1) inGrad1->addRowScale(0, *inputSub_, *outV);
if (inGrad2) {
inputSub_->mulScalar(-1.);
inGrad2->addRowScale(0, *inputSub_, *outV);
}
}
}
} // namespace paddle
paddle/gserver/layers/L2DistanceLayer.h 0 → 100644
浏览文件 @ 90e05a4b
/* Copyright (c) 2016 PaddlePaddle Authors. All Rights Reserve.
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 "Layer.h"
#include "paddle/math/Matrix.h"
namespace paddle {
/**
* @brief The layer calculates the l2 distance between two input vectors.
* \f[
* f(\bf{x}, \bf{y}) = \sqrt{\sum_{i=1}^D(x_i - y_i)}
* \f]
*
* - Input1: A vector (batchSize * dataDim)
* - Input2: A vector (batchSize * dataDim)
* - Output: A vector (batchSize * 1)
*
* The configuration api is: l2_distance_layer.
*/
class L2DistanceLayer : public Layer {
public:
explicit L2DistanceLayer(const LayerConfig& config) : Layer(config) {}
~L2DistanceLayer() {}
bool init(const LayerMap& layerMap,
const ParameterMap& parameterMap) override;
void forward(PassType passType) override;
void backward(const UpdateCallback& callback = nullptr) override;
private:
// Store the result of subtracting Input2 from Input1 in forward computation,
// which will be reused in backward computation.
MatrixPtr inputSub_;
};
} // namespace paddle
paddle/gserver/layers/MKLDNNAddtoLayer.cpp
浏览文件 @ 90e05a4b
......@@ -38,12 +38,13 @@ bool MKLDNNAddtoLayer::init(const LayerMap& layerMap,
}
void MKLDNNAddtoLayer::reshape(
int& bs, int& ic, int& ih, int& iw, int oc, int& oh, int& ow) {
int& bs, int& ic, int& ih, int& iw, int& oc, int& oh, int& ow) {
CHECK_EQ(layerSize_, getSize()) << "this layer size can not be changed";
reshapeInput(bs, ih, iw);
ic = inputLayers_[0]->getSize() / ih / iw;
CHECK_EQ((size_t)ic * ih * iw, inputLayers_[0]->getSize());
CHECK_EQ(inputElemenCnt_, (size_t)bs * ic * ih * iw);
CHECK_EQ(inputLayers_[0]->getOutputValue()->getElementCnt(),
(size_t)bs * ic * ih * iw);
for (size_t i = 0; i < inputLayers_.size(); i++) {
CHECK_EQ(int64_t(bs), inputLayers_[i]->getOutput().getBatchSize());
CHECK_EQ(layerSize_, inputLayers_[i]->getSize());
......@@ -57,47 +58,43 @@ void MKLDNNAddtoLayer::reshape(
}
void MKLDNNAddtoLayer::resetFwd(std::vector<primitive>& pipeline,
MKLDNNMatrixPtr& in,
MKLDNNMatrixPtr& wgt,
MKLDNNMatrixPtr& bias,
std::vector<MKLDNNMatrixPtr>& inputs,
MKLDNNMatrixPtr& out) {
resetFwdBuffers(inVals_, bias, out);
in = inVals_[0];
resetFwdBuffers(inputs, biasVal_, out);
std::shared_ptr<sum::primitive_desc> fwdPD;
std::shared_ptr<sum::primitive_desc> biasPD;
resetFwdPD(fwdPD, biasPD, inVals_, bias, out);
resetFwdPD(fwdPD, biasPD, inputs, biasVal_, out);
resetFwdPipeline(pipeline, fwdPD, biasPD, inVals_, bias, out);
resetFwdPipeline(pipeline, fwdPD, biasPD, inputs, biasVal_, out);
}
void MKLDNNAddtoLayer::resetBwd(std::vector<primitive>& pipeline,
MKLDNNMatrixPtr& in,
MKLDNNMatrixPtr& wgt,
MKLDNNMatrixPtr& bias,
std::vector<MKLDNNMatrixPtr>& inputs,
MKLDNNMatrixPtr& out) {
resetBwdBuffers(inGrads_, bias, out);
in = inGrads_[0];
resetBwdBuffers(inputs, biasGrad_, out);
// backward only need share output grad to input grad
for (size_t i = 0; i < inGrads_.size(); i++) {
if (inGrads_[i] != nullptr) {
inGrads_[i] = out;
inputLayers_[i]->getOutputGrad()->setData(inGrads_[i]->getData());
for (size_t i = 0; i < inputs.size(); i++) {
if (inputs[i] != nullptr) {
inputs[i] = out;
inputLayers_[i]->getOutputGrad()->setData(inputs[i]->getData());
}
}
// backward bias
bwdBias_ = nullptr;
if (bias) {
if (biasGrad_) {
std::vector<float> scales(bs_, 1.0);
std::vector<memory::primitive_desc> srcPDs(bs_, bias->getPrimitiveDesc());
auto biasPD = sum::primitive_desc(bias->getMemoryDesc(), scales, srcPDs);
std::vector<memory::primitive_desc> srcPDs(bs_,
biasGrad_->getPrimitiveDesc());
auto biasPD =
sum::primitive_desc(biasGrad_->getMemoryDesc(), scales, srcPDs);
std::vector<primitive::at> srcs;
for (size_t i = 0; i < grads_.size(); ++i) {
srcs.push_back(*(grads_[i]));
}
bwdBias_.reset(new sum(biasPD, srcs, *bias));
bwdBias_.reset(new sum(biasPD, srcs, *biasGrad_));
pipeline.push_back(*bwdBias_);
}
}
......@@ -208,7 +205,7 @@ void MKLDNNAddtoLayer::resetBwdBuffers(std::vector<MKLDNNMatrixPtr>& inputs,
inputs.resize(inputLayers_.size());
for (size_t i = 0; i < inputs.size(); i++) {
resetInGrad(inputs[i], inVal_->getPrimitiveDesc(), i);
resetInGrad(inputs[i], inVals_[i]->getPrimitiveDesc(), i);
CHECK_PRIMITIVE_DESC_EQ(inputs[i], out->getPrimitiveDesc());
}
......
paddle/gserver/layers/MKLDNNAddtoLayer.h
浏览文件 @ 90e05a4b
......@@ -26,9 +26,6 @@ namespace paddle {
*/
class MKLDNNAddtoLayer : public MKLDNNLayer {
protected:
std::vector<MKLDNNMatrixPtr> inVals_;
std::vector<MKLDNNMatrixPtr> inGrads_;
// layer size == ic * ih * iw == oc * oh *ow, and can not be changed
size_t layerSize_;
......@@ -50,52 +47,19 @@ public:
const ParameterMap& parameterMap) override;
void reshape(
int& bs, int& ic, int& ih, int& iw, int oc, int& oh, int& ow) override;
int& bs, int& ic, int& ih, int& iw, int& oc, int& oh, int& ow) override;
void resetFwd(std::vector<mkldnn::primitive>& pipeline,
MKLDNNMatrixPtr& in,
MKLDNNMatrixPtr& wgt,
MKLDNNMatrixPtr& bias,
std::vector<MKLDNNMatrixPtr>& inputs,
MKLDNNMatrixPtr& out) override;
void resetBwd(std::vector<mkldnn::primitive>& pipeline,
MKLDNNMatrixPtr& in,
MKLDNNMatrixPtr& wgt,
MKLDNNMatrixPtr& bias,
std::vector<MKLDNNMatrixPtr>& inputs,
MKLDNNMatrixPtr& out) override;
void updateWeights(const UpdateCallback& callback) override;
void printValueFormat() override {
for (size_t i = 0; i < inVals_.size(); ++i) {
VLOG(MKLDNN_FMTS) << i << " input: " << inVals_[i]->getFormat() << " >>>";
}
if (outVal_) {
VLOG(MKLDNN_FMTS) << outVal_->getFormat() << " >>> ";
}
if (extOutVal_) {
VLOG(MKLDNN_FMTS) << extOutVal_->getFormat();
}
}
void printGradFormat() override {
if (extOutGrad_) {
VLOG(MKLDNN_FMTS) << extOutGrad_->getFormat();
}
if (outGrad_) {
VLOG(MKLDNN_FMTS) << outGrad_->getFormat() << " <<< ";
}
for (size_t i = 0; i < inGrads_.size(); ++i) {
VLOG(MKLDNN_FMTS) << i << " input: " << inGrads_[i]->getFormat() << "<<<";
}
}
protected:
/**
* Forward functions: reset buffers(inputs, output, bias),
* reset primitive descriptor,
* reset pipeline.
*/
void resetFwdBuffers(std::vector<MKLDNNMatrixPtr>& inputs,
MKLDNNMatrixPtr& bias,
MKLDNNMatrixPtr& out);
......@@ -110,17 +74,10 @@ protected:
std::vector<MKLDNNMatrixPtr>& inputs,
MKLDNNMatrixPtr& bias,
MKLDNNMatrixPtr& out);
/**
* Backward functions: reset buffers(inputs, output, bias)
*/
void resetBwdBuffers(std::vector<MKLDNNMatrixPtr>& inputs,
MKLDNNMatrixPtr& bias,
MKLDNNMatrixPtr& out);
/**
* prepare for bias
*/
void prepareBias(MKLDNNMatrixPtr& bias,
const MatrixPtr& biasMat,
const MKLDNNMatrixPtr& out,
......
paddle/gserver/layers/MKLDNNBatchNormLayer.cpp
浏览文件 @ 90e05a4b
......@@ -116,21 +116,20 @@ void MKLDNNBatchNormLayer::calMovingMeanAndVar() {
}
void MKLDNNBatchNormLayer::reshape(
int& bs, int& ic, int& ih, int& iw, int oc, int& oh, int& ow) {
int& bs, int& ic, int& ih, int& iw, int& oc, int& oh, int& ow) {
reshapeInput(bs, ih, iw);
oh = ih;
ow = iw;
// ic_ and oc can not be changed
CHECK_EQ(inputElemenCnt_ / bs / ih / iw, (size_t)ic)
CHECK_EQ((size_t)ic,
inputLayers_[0]->getOutputValue()->getElementCnt() / bs / ih / iw)
<< "Input channel can not be changed";
reshapeOutput(oh, ow);
resizeOutput(bs, oc * oh * ow);
}
void MKLDNNBatchNormLayer::resetFwd(std::vector<primitive>& pipeline,
MKLDNNMatrixPtr& in,
MKLDNNMatrixPtr& wgt,
MKLDNNMatrixPtr& bias,
std::vector<MKLDNNMatrixPtr>& inputs,
MKLDNNMatrixPtr& out) {
// In training phase, it will always calculate mean and var,
// so useGlobalStats must be false.
......@@ -140,25 +139,23 @@ void MKLDNNBatchNormLayer::resetFwd(std::vector<primitive>& pipeline,
useGlobalStats_ = false;
}
resetFwdBuffers(in, wgt, out);
resetFwdBuffers(inputs[0], wgtVal_, out);
resetFwdPD(fwdPD_, in, wgt, out);
resetFwdPD(fwdPD_, inputs[0], wgtVal_, out);
resetFwdPipeline(pipeline, fwdPD_, in, wgt, out);
resetFwdPipeline(pipeline, fwdPD_, inputs[0], wgtVal_, out);
}
void MKLDNNBatchNormLayer::resetBwd(std::vector<primitive>& pipeline,
MKLDNNMatrixPtr& in,
MKLDNNMatrixPtr& wgt,
MKLDNNMatrixPtr& bias,
std::vector<MKLDNNMatrixPtr>& inputs,
MKLDNNMatrixPtr& out) {
std::shared_ptr<bn_bwd::primitive_desc> pd;
resetBwdBuffers(in, wgt, out);
resetBwdBuffers(inputs[0], wgtGrad_, out);
resetBwdPD(pd, in, wgt, out);
resetBwdPD(pd, inputs[0], wgtGrad_, out);
resetBwdPipeline(pipeline, pd, in, wgt, out);
resetBwdPipeline(pipeline, pd, inputs[0], wgtGrad_, out);
}
void MKLDNNBatchNormLayer::forward(PassType passType) {
......@@ -260,9 +257,9 @@ void MKLDNNBatchNormLayer::resetFwdPipeline(
void MKLDNNBatchNormLayer::resetBwdBuffers(MKLDNNMatrixPtr& in,
MKLDNNMatrixPtr& wgt,
MKLDNNMatrixPtr& out) {
CHECK(inVal_ && outVal_);
CHECK(inVals_[0] && outVal_);
resetOutGrad(out, outVal_->getPrimitiveDesc());
resetInGrad(in, inVal_->getPrimitiveDesc());
resetInGrad(in, inVals_[0]->getPrimitiveDesc());
if (gradScaleShift_) {
CHECK(wgtVal_);
resetWithMatrix(wgt, gradScaleShift_, wgtVal_->getPrimitiveDesc());
......@@ -297,11 +294,12 @@ void MKLDNNBatchNormLayer::resetBwdPipeline(
if (pd == nullptr) {
return;
}
CHECK(inVal_);
CHECK(inVals_[0]);
bwdData_.reset(
wgt && wgtVal_
? new bn_bwd(*pd, *inVal_, *mean_, *var_, *out, *wgtVal_, *in, *wgt)
: new bn_bwd(*pd, *inVal_, *mean_, *var_, *out, *in));
? new bn_bwd(
*pd, *inVals_[0], *mean_, *var_, *out, *wgtVal_, *in, *wgt)
: new bn_bwd(*pd, *inVals_[0], *mean_, *var_, *out, *in));
pipeline.push_back(*bwdData_);
}
......
paddle/gserver/layers/MKLDNNBatchNormLayer.h
浏览文件 @ 90e05a4b
......@@ -74,18 +74,14 @@ public:
void forward(PassType passType) override;
void reshape(
int& bs, int& ic, int& ih, int& iw, int oc, int& oh, int& ow) override;
int& bs, int& ic, int& ih, int& iw, int& oc, int& oh, int& ow) override;
void resetFwd(std::vector<mkldnn::primitive>& pipeline,
MKLDNNMatrixPtr& in,
MKLDNNMatrixPtr& wgt,
MKLDNNMatrixPtr& bias,
std::vector<MKLDNNMatrixPtr>& inputs,
MKLDNNMatrixPtr& out) override;
void resetBwd(std::vector<mkldnn::primitive>& pipeline,
MKLDNNMatrixPtr& in,
MKLDNNMatrixPtr& wgt,
MKLDNNMatrixPtr& bias,
std::vector<MKLDNNMatrixPtr>& inputs,
MKLDNNMatrixPtr& out) override;
void updateWeights(const UpdateCallback& callback) override;
......@@ -99,11 +95,7 @@ protected:
* moving = moving * AvgFraction + local * (1 - AvgFraction)
*/
void calMovingMeanAndVar();
/**
* Forward functions: reset buffers(input, weight, output),
* reset primitive descriptor,
* reset pipeline.
*/
void resetFwdBuffers(MKLDNNMatrixPtr& in,
MKLDNNMatrixPtr& wgt,
MKLDNNMatrixPtr& out);
......@@ -116,12 +108,6 @@ protected:
MKLDNNMatrixPtr& in,
MKLDNNMatrixPtr& wgt,
MKLDNNMatrixPtr& out);
/**
* Backward functions: reset buffers(input, weight, output),
* reset primitive descriptor,
* reset pipeline.
*/
void resetBwdBuffers(MKLDNNMatrixPtr& in,
MKLDNNMatrixPtr& wgt,
MKLDNNMatrixPtr& out);
......
paddle/gserver/layers/MKLDNNConcatLayer.cpp 0 → 100644
浏览文件 @ 90e05a4b
/* Copyright (c) 2017 PaddlePaddle Authors. All Rights Reserve.
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 "MKLDNNConcatLayer.h"
using namespace mkldnn; // NOLINT
typedef memory::format format;
namespace paddle {
REGISTER_LAYER(mkldnn_concat, MKLDNNConcatLayer);
bool MKLDNNConcatLayer::init(const LayerMap& layerMap,
const ParameterMap& parameterMap) {
if (!MKLDNNLayer::init(layerMap, parameterMap)) {
return false;
}
CHECK_GT(inputLayers_.size(), 1UL);
CHECK(!biasParameter_);
return true;
}
void MKLDNNConcatLayer::reshape(
int& bs, int& ic, int& ih, int& iw, int& oc, int& oh, int& ow) {
reshapeInput(bs, ih, iw);
ic = inputLayers_[0]->getSize() / ih / iw;
CHECK_EQ((size_t)ic * ih * iw, inputLayers_[0]->getSize());
CHECK_EQ(inputLayers_[0]->getOutputValue()->getElementCnt(),
(size_t)bs * ic * ih * iw);
CHECK_GT(inputLayers_.size(), 1UL);
channels_.resize(inputLayers_.size());
channels_[0] = ic;
oc = ic;
for (size_t i = 1; i < inputLayers_.size(); i++) {
int batchsize, height, witdh;
reshapeInput(batchsize, height, witdh, i);
CHECK_EQ(bs, batchsize);
CHECK_EQ(ih, height);
CHECK_EQ(iw, witdh);
channels_[i] = inputLayers_[i]->getSize() / height / witdh;
CHECK_EQ((size_t)channels_[i] * height * witdh, inputLayers_[i]->getSize());
oc += channels_[i];
}
oh = ih;
ow = iw;
reshapeOutput(oh, ow);
resizeOutput(bs, oc * oh * ow);
}
void MKLDNNConcatLayer::resetFwd(std::vector<primitive>& pipeline,
std::vector<MKLDNNMatrixPtr>& inputs,
MKLDNNMatrixPtr& out) {
resetFwdBuffers(inputs, out);
std::shared_ptr<concat::primitive_desc> fwdPD;
resetFwdPD(fwdPD, inputs, out);
resetFwdPipeline(pipeline, fwdPD, inputs, out);
}
void MKLDNNConcatLayer::resetBwd(std::vector<primitive>& pipeline,
std::vector<MKLDNNMatrixPtr>& inputs,
MKLDNNMatrixPtr& out) {
resetBwdBuffers(inputs, out);
resetBwdPipeline(pipeline, bwds_, inputs, out);
}
void MKLDNNConcatLayer::resetFwdBuffers(std::vector<MKLDNNMatrixPtr>& inputs,
MKLDNNMatrixPtr& out) {
inputs.resize(inputLayers_.size());
bool has8c = false, has16c = false, hasnc = false;
for (size_t i = 0; i < inputs.size(); i++) {
resetInValue(inputs[i], nullptr, i, channels_[i]);
CHECK(inputs[i]);
auto dm = inputs[i]->getDims();
// inputs format can be different, but ndims must equal
CHECK(i == 0 || dm.size() == inputs[0]->getDims().size());
CHECK_EQ(bs_, dm[0]);
CHECK_EQ(channels_[i], dm[1]);
if (dm.size() > 2) {
CHECK_EQ(ih_, dm[2]);
CHECK_EQ(iw_, dm[3]);
}
if (inputs[i]->getFormat() == format::nc) {
hasnc = true;
}
if (inputs[i]->getFormat() == format::nChw8c) {
has8c = true;
}
if (inputs[i]->getFormat() == format::nChw16c) {
has16c = true;
}
}
format outFmt;
if (has16c && oc_ % 16 == 0) {
outFmt = format::nChw16c;
} else if (has8c && oc_ % 8 == 0) {
outFmt = format::nChw8c;
} else if (hasnc) {
CHECK(oh_ == 1 && ow_ == 1);
outFmt = format::nc;
} else {
outFmt = format::nchw;
}
memory::dims outDims =
hasnc ? memory::dims{bs_, oc_} : memory::dims{bs_, oc_, oh_, ow_};
auto outPD = MKLDNNMatrix::createPrimitiveDesc(outDims, outFmt, engine_);
resetOutValue(out, outPD);
}
void MKLDNNConcatLayer::resetFwdPD(std::shared_ptr<concat::primitive_desc>& pd,
std::vector<MKLDNNMatrixPtr>& inputs,
MKLDNNMatrixPtr out) {
std::vector<memory::primitive_desc> srcPDs;
for (size_t i = 0; i < inputs.size(); i++) {
srcPDs.push_back(inputs[i]->getPrimitiveDesc());
}
CHECK(out);
pd.reset(new concat::primitive_desc(out->getMemoryDesc(), axis_, srcPDs));
CHECK_PRIMITIVE_DESC_EQ(out, pd->dst_primitive_desc());
}
void MKLDNNConcatLayer::resetFwdPipeline(
std::vector<primitive>& pipeline,
std::shared_ptr<concat::primitive_desc>& pd,
std::vector<MKLDNNMatrixPtr>& inputs,
MKLDNNMatrixPtr& out) {
std::vector<primitive::at> srcs;
for (size_t i = 0; i < inputs.size(); i++) {
srcs.push_back(*(inputs[i]));
}
fwd_.reset(new concat(*pd, srcs, *out));
pipeline.push_back(*fwd_);
}
void MKLDNNConcatLayer::resetBwdBuffers(std::vector<MKLDNNMatrixPtr>& inputs,
MKLDNNMatrixPtr& out) {
CHECK(outVal_);
resetOutGrad(out, outVal_->getPrimitiveDesc());
CHECK(out);
inputs.resize(inputLayers_.size());
for (size_t i = 0; i < inputs.size(); i++) {
CHECK(inVals_[i]);
resetInGrad(inputs[i], inVals_[i]->getPrimitiveDesc(), i);
CHECK_PRIMITIVE_DESC_EQ(inputs[i], inVals_[i]->getPrimitiveDesc());
}
}
void MKLDNNConcatLayer::resetBwdPipeline(
std::vector<mkldnn::primitive>& pipeline,
std::vector<std::shared_ptr<mkldnn::primitive>>& prims,
std::vector<MKLDNNMatrixPtr>& inputs,
MKLDNNMatrixPtr& out) {
// reset the backward primitives
memory::dims offsets = {0, 0, 0, 0};
prims.resize(inputs.size());
CHECK_EQ(inputs.size(), channels_.size());
for (size_t i = 0; i < inputs.size(); i++) {
auto viewPD = view::primitive_desc(
out->getPrimitiveDesc(), inputs[i]->getDims(), offsets);
auto bwdPD = reorder::primitive_desc(viewPD.dst_primitive_desc(),
inputs[i]->getPrimitiveDesc());
prims[i].reset(new reorder(bwdPD, *out, *(inputs[i])));
offsets[axis_] += channels_[i];
// push to pipeline
pipeline.push_back(*prims[i]);
}
}
} // namespace paddle
paddle/gserver/layers/MKLDNNConcatLayer.h 0 → 100644
浏览文件 @ 90e05a4b
/* Copyright (c) 2017 PaddlePaddle Authors. All Rights Reserve.
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 "MKLDNNLayer.h"
#include "mkldnn.hpp"
namespace paddle {
/**
* @brief A subclass of MKLDNNLayer Concatenate layer.
*
* The config file api is mkldnn_concat
*/
class MKLDNNConcatLayer : public MKLDNNLayer {
protected:
std::vector<std::shared_ptr<mkldnn::primitive>> bwds_;
// input channel numbers
std::vector<int> channels_;
// concat_dimension in MKLDNN
// if axis_ == 0, concat batchsize
// if axis_ == 1, concat channel (default)
int axis_;
public:
explicit MKLDNNConcatLayer(const LayerConfig& config)
: MKLDNNLayer(config), axis_(1) {}
~MKLDNNConcatLayer() {}
bool init(const LayerMap& layerMap,
const ParameterMap& parameterMap) override;
void reshape(
int& bs, int& ic, int& ih, int& iw, int& oc, int& oh, int& ow) override;
void resetFwd(std::vector<mkldnn::primitive>& pipeline,
std::vector<MKLDNNMatrixPtr>& inputs,
MKLDNNMatrixPtr& out) override;
void resetBwd(std::vector<mkldnn::primitive>& pipeline,
std::vector<MKLDNNMatrixPtr>& inputs,
MKLDNNMatrixPtr& out) override;
void printSizeInfo() override {
CHECK_EQ(channels_.size(), inputLayers_.size());
for (size_t i = 0; i < channels_.size(); ++i) {
VLOG(MKLDNN_SIZES) << "Input " << i << ", " << inputLayers_[i]->getName()
<< ": " << bs_ << ", " << channels_[i] << ", " << ih_
<< ", " << iw_;
}
VLOG(MKLDNN_SIZES) << "Output: " << bs_ << ", " << oc_ << ", " << oh_
<< ", " << ow_;
}
size_t keepCondition() {
// reset when the total element size of all inputs changed
size_t totalSize = inputLayers_[0]->getOutputValue()->getElementCnt();
for (size_t i = 1; i < inputLayers_.size(); ++i) {
totalSize += inputLayers_[i]->getOutputValue()->getElementCnt();
}
return totalSize;
}
protected:
void resetFwdBuffers(std::vector<MKLDNNMatrixPtr>& inputs,
MKLDNNMatrixPtr& out);
void resetFwdPD(std::shared_ptr<mkldnn::concat::primitive_desc>& pd,
std::vector<MKLDNNMatrixPtr>& inputs,
MKLDNNMatrixPtr out);
void resetFwdPipeline(std::vector<mkldnn::primitive>& pipeline,
std::shared_ptr<mkldnn::concat::primitive_desc>& pd,
std::vector<MKLDNNMatrixPtr>& inputs,
MKLDNNMatrixPtr& out);
void resetBwdBuffers(std::vector<MKLDNNMatrixPtr>& inputs,
MKLDNNMatrixPtr& out);
void resetBwdPipeline(std::vector<mkldnn::primitive>& pipeline,
std::vector<std::shared_ptr<mkldnn::primitive>>& prims,
std::vector<MKLDNNMatrixPtr>& inputs,
MKLDNNMatrixPtr& out);
};
} // namespace paddle
paddle/gserver/layers/MKLDNNConvLayer.cpp
浏览文件 @ 90e05a4b
......@@ -90,7 +90,7 @@ void MKLDNNConvLayer::convertWeightsToPaddle() {
}
void MKLDNNConvLayer::reshape(
int& bs, int& ic, int& ih, int& iw, int oc, int& oh, int& ow) {
int& bs, int& ic, int& ih, int& iw, int& oc, int& oh, int& ow) {
reshapeInput(bs, ih, iw);
// cal output sizes
......@@ -105,21 +105,17 @@ void MKLDNNConvLayer::reshape(
}
void MKLDNNConvLayer::resetFwd(std::vector<primitive>& pipeline,
MKLDNNMatrixPtr& in,
MKLDNNMatrixPtr& wgt,
MKLDNNMatrixPtr& bias,
std::vector<MKLDNNMatrixPtr>& inputs,
MKLDNNMatrixPtr& out) {
resetFwdPD(fwdPD_);
resetFwdBuffers(fwdPD_, in, wgt, bias, out);
resetFwdBuffers(fwdPD_, inputs[0], wgtVal_, biasVal_, out);
resetFwdPipeline(pipeline, fwdPD_, in, wgt, bias, out);
resetFwdPipeline(pipeline, fwdPD_, inputs[0], wgtVal_, biasVal_, out);
}
void MKLDNNConvLayer::resetBwd(std::vector<primitive>& pipeline,
MKLDNNMatrixPtr& in,
MKLDNNMatrixPtr& wgt,
MKLDNNMatrixPtr& bias,
std::vector<MKLDNNMatrixPtr>& inputs,
MKLDNNMatrixPtr& out) {
std::shared_ptr<conv_bwdWgt::primitive_desc> bwdWgtPD;
std::shared_ptr<conv_bwdData::primitive_desc> bwdDataPD;
......@@ -128,9 +124,10 @@ void MKLDNNConvLayer::resetBwd(std::vector<primitive>& pipeline,
resetBwdDataPD(bwdDataPD);
resetBwdBuffers(bwdWgtPD, bwdDataPD, in, wgt, bias, out);
resetBwdBuffers(bwdWgtPD, bwdDataPD, inputs[0], wgtGrad_, biasGrad_, out);
resetBwdPipeline(pipeline, bwdWgtPD, bwdDataPD, in, wgt, bias, out);
resetBwdPipeline(
pipeline, bwdWgtPD, bwdDataPD, inputs[0], wgtGrad_, biasGrad_, out);
}
void MKLDNNConvLayer::updateWeights(const UpdateCallback& callback) {
......@@ -236,14 +233,14 @@ void MKLDNNConvLayer::resetBwdWgtPD(
loadConvSettings(wgtDims, biasDims, strides, dilations, padL, padR);
// create backward weight using input, output and weight value memory desc
CHECK(inVal_) << "Should have internal input value";
CHECK(inVals_[0]) << "Should have internal input value";
CHECK(outVal_) << "Should have internal output value";
CHECK(wgtVal_) << "Should have weight value";
algorithm algo = algorithm::convolution_direct;
padding_kind padKind = padding_kind::zero;
auto bwdWgtDesc = biasVal_ != nullptr
? conv_bwdWgt::desc(algo,
inVal_->getMemoryDesc(),
inVals_[0]->getMemoryDesc(),
wgtVal_->getMemoryDesc(),
biasVal_->getMemoryDesc(),
outVal_->getMemoryDesc(),
......@@ -252,7 +249,7 @@ void MKLDNNConvLayer::resetBwdWgtPD(
padR,
padKind)
: conv_bwdWgt::desc(algo,
inVal_->getMemoryDesc(),
inVals_[0]->getMemoryDesc(),
wgtVal_->getMemoryDesc(),
outVal_->getMemoryDesc(),
strides,
......@@ -260,7 +257,7 @@ void MKLDNNConvLayer::resetBwdWgtPD(
padR,
padKind);
pd.reset(new conv_bwdWgt::primitive_desc(bwdWgtDesc, engine_, *fwdPD_));
CHECK_PRIMITIVE_DESC_EQ(inVal_, pd->src_primitive_desc());
CHECK_PRIMITIVE_DESC_EQ(inVals_[0], pd->src_primitive_desc());
CHECK_PRIMITIVE_DESC_EQ(
outVal_,
pd->diff_dst_primitive_desc(),
......@@ -280,12 +277,12 @@ void MKLDNNConvLayer::resetBwdDataPD(
memory::dims wgtDims, biasDims, strides, dilations, padL, padR;
loadConvSettings(wgtDims, biasDims, strides, dilations, padL, padR);
CHECK(inVal_) << "Should have internal input value";
CHECK(inVals_[0]) << "Should have internal input value";
CHECK(outVal_) << "Should have internal output value";
// create backward data using input and output value memory desc
// but using weight memory desc with any format
auto bwdDataDesc = conv_bwdData::desc(algorithm::convolution_direct,
inVal_->getMemoryDesc(),
inVals_[0]->getMemoryDesc(),
MKLDNNMatrix::createMemoryDesc(wgtDims),
outVal_->getMemoryDesc(),
strides,
......@@ -294,7 +291,7 @@ void MKLDNNConvLayer::resetBwdDataPD(
padding_kind::zero);
pd.reset(new conv_bwdData::primitive_desc(bwdDataDesc, engine_, *fwdPD_));
CHECK_PRIMITIVE_DESC_EQ(
inVal_,
inVals_[0],
pd->diff_src_primitive_desc(),
"primitive desc of in value and grad should be equal");
CHECK_PRIMITIVE_DESC_EQ(
......@@ -346,12 +343,12 @@ void MKLDNNConvLayer::resetBwdPipeline(
MKLDNNMatrixPtr& wgt,
MKLDNNMatrixPtr& bias,
MKLDNNMatrixPtr& out) {
CHECK(inVal_);
CHECK(inVals_[0]);
// add bwdWgt handle
if (bias) {
bwdWgt_.reset(new conv_bwdWgt(*wgtPD, *inVal_, *out, *wgt, *bias));
bwdWgt_.reset(new conv_bwdWgt(*wgtPD, *inVals_[0], *out, *wgt, *bias));
} else {
bwdWgt_.reset(new conv_bwdWgt(*wgtPD, *inVal_, *out, *wgt));
bwdWgt_.reset(new conv_bwdWgt(*wgtPD, *inVals_[0], *out, *wgt));
}
pipeline.push_back(*bwdWgt_);
......
paddle/gserver/layers/MKLDNNConvLayer.h
浏览文件 @ 90e05a4b
......@@ -69,18 +69,14 @@ public:
const ParameterMap& parameterMap) override;
void reshape(
int& bs, int& ic, int& ih, int& iw, int oc, int& oh, int& ow) override;
int& bs, int& ic, int& ih, int& iw, int& oc, int& oh, int& ow) override;
void resetFwd(std::vector<mkldnn::primitive>& pipeline,
MKLDNNMatrixPtr& in,
MKLDNNMatrixPtr& wgt,
MKLDNNMatrixPtr& bias,
std::vector<MKLDNNMatrixPtr>& inputs,
MKLDNNMatrixPtr& out) override;
void resetBwd(std::vector<mkldnn::primitive>& pipeline,
MKLDNNMatrixPtr& in,
MKLDNNMatrixPtr& wgt,
MKLDNNMatrixPtr& bias,
std::vector<MKLDNNMatrixPtr>& inputs,
MKLDNNMatrixPtr& out) override;
void updateWeights(const UpdateCallback& callback) override;
......@@ -107,48 +103,26 @@ protected:
mkldnn::memory::dims& padL,
mkldnn::memory::dims& padR);
/**
* reset the forward primitive descriptor.
*/
void resetFwdPD(std::shared_ptr<conv_fwd::primitive_desc>& pd);
/**
* reset the MKLDNNMatrix buffers used in forward.
*/
void resetFwdBuffers(std::shared_ptr<conv_fwd::primitive_desc>& pd,
MKLDNNMatrixPtr& in,
MKLDNNMatrixPtr& wgt,
MKLDNNMatrixPtr& bias,
MKLDNNMatrixPtr& out);
/**
* reset the forward pipeline.
*/
void resetFwdPipeline(std::vector<mkldnn::primitive>& pipeline,
std::shared_ptr<conv_fwd::primitive_desc>& pd,
MKLDNNMatrixPtr& in,
MKLDNNMatrixPtr& wgt,
MKLDNNMatrixPtr& bias,
MKLDNNMatrixPtr& out);
/**
* reset the backward weight primitive descriptor.
*/
void resetBwdWgtPD(std::shared_ptr<conv_bwdWgt::primitive_desc>& pd);
/**
* reset the backward data primitive descriptor.
*/
void resetBwdDataPD(std::shared_ptr<conv_bwdData::primitive_desc>& pd);
/**
* reset the MKLDNNMatrix buffers used in backward.
*/
void resetBwdBuffers(std::shared_ptr<conv_bwdWgt::primitive_desc>& wgtPD,
std::shared_ptr<conv_bwdData::primitive_desc>& dataPD,
MKLDNNMatrixPtr& in,
MKLDNNMatrixPtr& wgt,
MKLDNNMatrixPtr& bias,
MKLDNNMatrixPtr& out);
/**
* reset the backward pipeline.
*/
void resetBwdPipeline(std::vector<mkldnn::primitive>& pipeline,
std::shared_ptr<conv_bwdWgt::primitive_desc>& wgtPD,
std::shared_ptr<conv_bwdData::primitive_desc>& dataPD,
......
paddle/gserver/layers/MKLDNNFcLayer.cpp
浏览文件 @ 90e05a4b
......@@ -74,7 +74,7 @@ void MKLDNNFcLayer::convertWeightsToPaddle() {
}
void MKLDNNFcLayer::reshape(
int& bs, int& ic, int& ih, int& iw, int oc, int& oh, int& ow) {
int& bs, int& ic, int& ih, int& iw, int& oc, int& oh, int& ow) {
reshapeInput(bs, ih, iw);
CHECK_EQ(iLayerSize_, inputLayers_[0]->getSize());
......@@ -87,32 +87,29 @@ void MKLDNNFcLayer::reshape(
}
void MKLDNNFcLayer::resetFwd(std::vector<primitive>& pipeline,
MKLDNNMatrixPtr& in,
MKLDNNMatrixPtr& wgt,
MKLDNNMatrixPtr& bias,
std::vector<MKLDNNMatrixPtr>& inputs,
MKLDNNMatrixPtr& out) {
resetFwdBuffers(in, wgt, bias, out);
resetFwdBuffers(inputs[0], wgtVal_, biasVal_, out);
resetFwdPD(fwdPD_, in, wgt, bias, out);
resetFwdPD(fwdPD_, inputs[0], wgtVal_, biasVal_, out);
resetFwdPipeline(pipeline, fwdPD_, in, wgt, bias, out);
resetFwdPipeline(pipeline, fwdPD_, inputs[0], wgtVal_, biasVal_, out);
}
void MKLDNNFcLayer::resetBwd(std::vector<primitive>& pipeline,
MKLDNNMatrixPtr& in,
MKLDNNMatrixPtr& wgt,
MKLDNNMatrixPtr& bias,
std::vector<MKLDNNMatrixPtr>& inputs,
MKLDNNMatrixPtr& out) {
std::shared_ptr<fc_bwdWgt::primitive_desc> bwdWgtPD;
std::shared_ptr<fc_bwdData::primitive_desc> bwdDataPD;
resetBwdBuffers(in, wgt, bias, out);
resetBwdBuffers(inputs[0], wgtGrad_, biasGrad_, out);
resetBwdWgtPD(bwdWgtPD, wgt, bias, out);
resetBwdWgtPD(bwdWgtPD, wgtGrad_, biasGrad_, out);
resetBwdDataPD(bwdDataPD, in, out);
resetBwdDataPD(bwdDataPD, inputs[0], out);
resetBwdPipeline(pipeline, bwdWgtPD, bwdDataPD, in, wgt, bias, out);
resetBwdPipeline(
pipeline, bwdWgtPD, bwdDataPD, inputs[0], wgtGrad_, biasGrad_, out);
}
void MKLDNNFcLayer::updateWeights(const UpdateCallback& callback) {
......@@ -193,9 +190,9 @@ void MKLDNNFcLayer::resetBwdBuffers(MKLDNNMatrixPtr& in,
MKLDNNMatrixPtr& wgt,
MKLDNNMatrixPtr& bias,
MKLDNNMatrixPtr& out) {
CHECK(inVal_ && outVal_);
CHECK(inVals_[0] && outVal_);
resetOutGrad(out, outVal_->getPrimitiveDesc());
resetInGrad(in, inVal_->getPrimitiveDesc());
resetInGrad(in, inVals_[0]->getPrimitiveDesc());
CHECK(wgtVal_);
resetWithMatrix(wgt, weight_->getWGrad(), wgtVal_->getPrimitiveDesc());
......@@ -212,14 +209,15 @@ void MKLDNNFcLayer::resetBwdWgtPD(
MKLDNNMatrixPtr& wgt,
MKLDNNMatrixPtr& bias,
MKLDNNMatrixPtr& out) {
CHECK(inVal_);
fc_bwdWgt::desc bwdWgtDesc = bias ? fc_bwdWgt::desc(inVal_->getMemoryDesc(),
wgt->getMemoryDesc(),
bias->getMemoryDesc(),
out->getMemoryDesc())
: fc_bwdWgt::desc(inVal_->getMemoryDesc(),
wgt->getMemoryDesc(),
out->getMemoryDesc());
CHECK(inVals_[0]);
fc_bwdWgt::desc bwdWgtDesc =
bias ? fc_bwdWgt::desc(inVals_[0]->getMemoryDesc(),
wgt->getMemoryDesc(),
bias->getMemoryDesc(),
out->getMemoryDesc())
: fc_bwdWgt::desc(inVals_[0]->getMemoryDesc(),
wgt->getMemoryDesc(),
out->getMemoryDesc());
pd.reset(new fc_bwdWgt::primitive_desc(bwdWgtDesc, engine_, *fwdPD_));
}
......@@ -245,11 +243,11 @@ void MKLDNNFcLayer::resetBwdPipeline(
MKLDNNMatrixPtr& wgt,
MKLDNNMatrixPtr& bias,
MKLDNNMatrixPtr& out) {
CHECK(inVal_);
CHECK(inVals_[0]);
if (bias) {
bwdWgt_.reset(new fc_bwdWgt(*bwdWgtPD, *inVal_, *out, *wgt, *bias));
bwdWgt_.reset(new fc_bwdWgt(*bwdWgtPD, *inVals_[0], *out, *wgt, *bias));
} else {
bwdWgt_.reset(new fc_bwdWgt(*bwdWgtPD, *inVal_, *out, *wgt));
bwdWgt_.reset(new fc_bwdWgt(*bwdWgtPD, *inVals_[0], *out, *wgt));
}
pipeline.push_back(*bwdWgt_);
......
paddle/gserver/layers/MKLDNNFcLayer.h
浏览文件 @ 90e05a4b
......@@ -52,18 +52,14 @@ public:
const ParameterMap& parameterMap) override;
void reshape(
int& bs, int& ic, int& ih, int& iw, int oc, int& oh, int& ow) override;
int& bs, int& ic, int& ih, int& iw, int& oc, int& oh, int& ow) override;
void resetFwd(std::vector<mkldnn::primitive>& pipeline,
MKLDNNMatrixPtr& in,
MKLDNNMatrixPtr& wgt,
MKLDNNMatrixPtr& bias,
std::vector<MKLDNNMatrixPtr>& inputs,
MKLDNNMatrixPtr& out) override;
void resetBwd(std::vector<mkldnn::primitive>& pipeline,
MKLDNNMatrixPtr& in,
MKLDNNMatrixPtr& wgt,
MKLDNNMatrixPtr& bias,
std::vector<MKLDNNMatrixPtr>& inputs,
MKLDNNMatrixPtr& out) override;
void updateWeights(const UpdateCallback& callback) override;
......@@ -73,11 +69,6 @@ public:
void convertWeightsToPaddle() override;
protected:
/**
* Forward functions: reset buffers(input, output, weight and bias),
* reset primitive descriptor,
* reset pipeline.
*/
void resetFwdBuffers(MKLDNNMatrixPtr& in,
MKLDNNMatrixPtr& wgt,
MKLDNNMatrixPtr& bias,
......@@ -93,13 +84,6 @@ protected:
MKLDNNMatrixPtr& wgt,
MKLDNNMatrixPtr& bias,
MKLDNNMatrixPtr& out);
/**
* Backward functions: reset buffers(input, output, weight and bias),
* reset primitive descriptor for backward weight,
* reset primitive descriptor for backward data,
* reset pipeline.
*/
void resetBwdBuffers(MKLDNNMatrixPtr& in,
MKLDNNMatrixPtr& wgt,
MKLDNNMatrixPtr& bias,
......
paddle/gserver/layers/MKLDNNLayer.cpp
浏览文件 @ 90e05a4b
......@@ -21,8 +21,8 @@ namespace paddle {
bool MKLDNNLayer::init(const LayerMap& layerMap,
const ParameterMap& parameterMap) {
CHECK(FLAGS_use_mkldnn) << "MkldnnLayers only support use_mkldnn."
<< "Please set WITH_MKLDNN=ON "
CHECK(FLAGS_use_mkldnn) << "MKLDNNLayers only support use_mkldnn."
<< "Please set WITH_MKL=ON "
<< "and set use_mkldnn=True";
CHECK(!useGpu_) << "Do not support GPU yet";
......@@ -48,31 +48,20 @@ void MKLDNNLayer::forward(PassType passType) {
REGISTER_TIMER_INFO("mkldnn_FwdTimer", getName().c_str());
CHECK(!inputLayers_.empty());
copySeqInfoToOutputs();
size_t elemenCnt = inputLayers_[0]->getOutputValue()->getElementCnt();
if (inputElemenCnt_ != elemenCnt) {
if (condition_ != keepCondition()) {
VLOG(MKLDNN_BASE) << getName() << " reset mkldnn forward";
// reset when input total sizes changed, not only the batchsize
inputElemenCnt_ = elemenCnt;
pipelineFwd_.clear();
condition_ = keepCondition();
reshape(bs_, ic_, ih_, iw_, oc_, oh_, ow_);
// all cpu device output grad or value share output's
printSizeInfo();
// the output_.value and output_.grad are shared with CPU device
shareCPUDevice();
resetFwd(pipelineFwd_, inVal_, wgtVal_, biasVal_, outVal_);
// MKLDNNLayer output value should be MKLDNNMatrix
// so external output value is necessary.
// Then external input value is not necessary,
// since input may be mkldnn internal buffer.
CHECK(extOutVal_) << "external output value is necessary";
output_.value = std::dynamic_pointer_cast<Matrix>(extOutVal_);
CHECK(inVal_ && outVal_) << "internal memories are necessary";
if (cvtInVal_) {
pipelineFwd_.insert(pipelineFwd_.begin(), *cvtInVal_);
}
if (cvtOutVal_) {
pipelineFwd_.push_back(*cvtOutVal_);
}
pipelineFwd_.clear();
inVals_.resize(inputLayers_.size(), nullptr);
extInVals_.resize(inputLayers_.size(), nullptr);
cvtInVals_.resize(inputLayers_.size(), nullptr);
resetFwd(pipelineFwd_, inVals_, outVal_);
prepareValueConversions(pipelineFwd_);
convertWeightsFromPaddle();
printSizeInfo();
printValueFormat();
needResetBwd_ = true;
}
......@@ -80,8 +69,8 @@ void MKLDNNLayer::forward(PassType passType) {
if (inputLayers_[0]->getType() == "data" && inputLayers_.size() == 1) {
// Update input value data when input layer is "data" type,
// since the input value data address might be changed.
CHECK(extInVal_);
extInVal_->setData(getInputValue(0, CPU_DEVICE)->getData());
CHECK(extInVals_[0]);
extInVals_[0]->setData(getInputValue(0, CPU_DEVICE)->getData());
}
if (!outputOnlyMKLDNN_) {
......@@ -99,22 +88,13 @@ void MKLDNNLayer::backward(const UpdateCallback& callback) {
if (needResetBwd_) {
VLOG(MKLDNN_BASE) << getName() << " reset mkldnn backward";
pipelineBwd_.clear();
inGrads_.resize(inputLayers_.size(), nullptr);
extInGrads_.resize(inputLayers_.size(), nullptr);
cvtInGrads_.resize(inputLayers_.size(), nullptr);
pipelineMergeGrad_.clear();
mergeGrad_ = nullptr;
resetBwd(pipelineBwd_, inGrad_, wgtGrad_, biasGrad_, outGrad_);
// external output grad is not necessary
// since output may be mkldnn internal buffer or merge them directly.
CHECK(outGrad_) << "internal output grad is necessary";
if (extOutGrad_) {
CHECK_EQ(extOutGrad_->getData(), output_.grad->getData())
<< "the external buffer should share the same data with output_.grad";
}
if (cvtOutGrad_) {
pipelineBwd_.insert(pipelineBwd_.begin(), *cvtOutGrad_);
}
if (cvtInGrad_) {
pipelineBwd_.push_back(*cvtInGrad_);
}
resetBwd(pipelineBwd_, inGrads_, outGrad_);
prepareGradConversions(pipelineBwd_);
printGradFormat();
needResetBwd_ = false;
}
......@@ -138,8 +118,11 @@ void MKLDNNLayer::backward(const UpdateCallback& callback) {
}
}
void MKLDNNLayer::reshapeInput(int& batchsize, int& height, int& width) {
const Argument& input = inputLayers_[0]->getOutput();
void MKLDNNLayer::reshapeInput(int& batchsize,
int& height,
int& width,
size_t idx) {
const Argument& input = inputLayers_[idx]->getOutput();
batchsize = input.getBatchSize();
int h = input.getFrameHeight();
int w = input.getFrameWidth();
......@@ -173,27 +156,30 @@ void MKLDNNLayer::resetWithMatrix(MKLDNNMatrixPtr& dnn,
void MKLDNNLayer::resetInValue(
MKLDNNMatrixPtr& in,
const std::shared_ptr<memory::primitive_desc>& intPD,
size_t inputIdx) {
cvtInVal_ = nullptr;
extInVal_ = nullptr;
size_t idx,
int inputChannel) {
cvtInVals_[idx] = nullptr;
extInVals_[idx] = nullptr;
in = nullptr;
CHECK_GT(bs_ * ic_ * ih_ * iw_, 0);
inputChannel = inputChannel == 0 ? ic_ : inputChannel;
CHECK_GT(bs_ * inputChannel * ih_ * iw_, 0);
auto extPD = MKLDNNMatrix::createPrimitiveDesc(
{bs_, ic_, ih_, iw_}, format::nchw, engine_);
const MatrixPtr& inMat = inputLayers_[inputIdx]->getOutputValue();
extInVal_ = std::dynamic_pointer_cast<MKLDNNMatrix>(inMat);
CHECK_EQ(inputIsOnlyMKLDNN(), extInVal_ != nullptr);
if (extInVal_ == nullptr || extInVal_->getFormat() == format::nc) {
extInVal_ = MKLDNNMatrix::create(extPD, inMat);
{bs_, inputChannel, ih_, iw_}, format::nchw, engine_);
const MatrixPtr& inMat = inputLayers_[idx]->getOutputValue();
extInVals_[idx] = std::dynamic_pointer_cast<MKLDNNMatrix>(inMat);
CHECK_EQ(inputIsOnlyMKLDNN(), extInVals_[idx] != nullptr);
if (extInVals_[idx] == nullptr ||
extInVals_[idx]->getFormat() == format::nc) {
extInVals_[idx] = MKLDNNMatrix::create(extPD, inMat);
}
in = extInVal_;
in = extInVals_[idx];
if (nullptr == intPD || in->getPrimitiveDesc() == *intPD) {
return;
}
// need create reorder
in = MKLDNNMatrix::create(*intPD);
cvtInVal_ = MKLDNNMatrix::createReorder(extInVal_, in);
CHECK(cvtInVal_) << "should not be emptry";
cvtInVals_[idx] = MKLDNNMatrix::createReorder(extInVals_[idx], in);
CHECK(cvtInVals_[idx]) << "should not be emptry";
}
void MKLDNNLayer::resetOutValue(MKLDNNMatrixPtr& out,
......@@ -215,11 +201,11 @@ void MKLDNNLayer::resetOutValue(MKLDNNMatrixPtr& out,
void MKLDNNLayer::resetInGrad(MKLDNNMatrixPtr& in,
memory::primitive_desc intPD,
size_t inputIdx) {
cvtInGrad_ = nullptr;
extInGrad_ = nullptr;
size_t idx) {
cvtInGrads_[idx] = nullptr;
extInGrads_[idx] = nullptr;
in = nullptr;
LayerPtr& input = inputLayers_[inputIdx];
LayerPtr& input = inputLayers_[idx];
if (input->getOutputGrad() == nullptr) {
// no need input grad
return;
......@@ -234,23 +220,25 @@ void MKLDNNLayer::resetInGrad(MKLDNNMatrixPtr& in,
in = MKLDNNMatrix::create(intPD, inMat);
Argument& arg = input->getOutput(this->getName());
arg.grad = std::dynamic_pointer_cast<Matrix>(in);
CHECK_PRIMITIVE_DESC_EQ(inVal_, intPD);
CHECK_PRIMITIVE_DESC_EQ(inVals_[idx], intPD);
if (inputIsOnlyMKLDNN()) {
return;
}
extInGrad_ = in;
if (isPaddleFormat(extInGrad_->getFormat())) {
extInGrads_[idx] = in;
if (isPaddleFormat(extInGrads_[idx]->getFormat())) {
return;
}
// need create reorder
CHECK(extInVal_ != nullptr && isPaddleFormat(extInVal_->getFormat()))
CHECK(extInVals_[idx] != nullptr &&
isPaddleFormat(extInVals_[idx]->getFormat()))
<< "should have external input value and the format must be nchw(nc)";
extInGrad_ = MKLDNNMatrix::create(extInVal_->getPrimitiveDesc(), inMat);
CHECK_PRIMITIVE_DESC_EQ(inVal_, intPD);
extInGrads_[idx] =
MKLDNNMatrix::create(extInVals_[idx]->getPrimitiveDesc(), inMat);
CHECK_PRIMITIVE_DESC_EQ(inVals_[idx], intPD);
in = MKLDNNMatrix::create(intPD);
cvtInGrad_ = MKLDNNMatrix::createReorder(in, extInGrad_);
CHECK(cvtInGrad_);
cvtInGrads_[idx] = MKLDNNMatrix::createReorder(in, extInGrads_[idx]);
CHECK(cvtInGrads_[idx]);
}
void MKLDNNLayer::resetOutGrad(MKLDNNMatrixPtr& out,
......
paddle/gserver/layers/MKLDNNLayer.h
浏览文件 @ 90e05a4b
......@@ -34,15 +34,16 @@ typedef std::shared_ptr<MKLDNNLayer> MKLDNNLayerPtr;
*/
class MKLDNNLayer : public Layer {
protected:
// input value element count
size_t inputElemenCnt_;
// batch size
int bs_;
// their sizes are always from the first input layer
// input image channel, height and width
int ic_, ih_, iw_;
// output image channel, height and width
int oc_, oh_, ow_;
// the condition that forward need be reset
size_t condition_;
// backward also need reset after reset forward handle
bool needResetBwd_;
......@@ -67,18 +68,18 @@ protected:
* When all layers are mkldnn layers, they could save internal data.
*/
// below MKLDNNMatrix buffers are all internal buffers
MKLDNNMatrixPtr inVal_;
MKLDNNMatrixPtr inGrad_;
std::vector<MKLDNNMatrixPtr> inVals_;
std::vector<MKLDNNMatrixPtr> inGrads_;
MKLDNNMatrixPtr outVal_;
MKLDNNMatrixPtr outGrad_;
// below are external value and grad
MKLDNNMatrixPtr extInVal_;
MKLDNNMatrixPtr extInGrad_;
std::vector<MKLDNNMatrixPtr> extInVals_;
std::vector<MKLDNNMatrixPtr> extInGrads_;
MKLDNNMatrixPtr extOutVal_;
MKLDNNMatrixPtr extOutGrad_;
// convert handle between external and internal buffers
std::shared_ptr<mkldnn::reorder> cvtInVal_;
std::shared_ptr<mkldnn::reorder> cvtInGrad_;
std::vector<std::shared_ptr<mkldnn::reorder>> cvtInVals_;
std::vector<std::shared_ptr<mkldnn::reorder>> cvtInGrads_;
std::shared_ptr<mkldnn::reorder> cvtOutVal_;
std::shared_ptr<mkldnn::reorder> cvtOutGrad_;
......@@ -102,14 +103,7 @@ protected:
public:
explicit MKLDNNLayer(const LayerConfig& config)
: Layer(config),
inputElemenCnt_(0),
bs_(0),
ic_(0),
ih_(0),
iw_(0),
oc_(0),
oh_(0),
ow_(0),
condition_(0),
needResetBwd_(true),
outputOnlyMKLDNN_(false),
engine_(mkldnn::engine::cpu, 0),
......@@ -125,31 +119,28 @@ public:
virtual void backward(const UpdateCallback& callback);
/**
* reshape the input image sizes
* and reset output image and buffer size
* output channel can not be changed
* reshape the input and output channels and image sizes
* and reset output buffer size
*/
virtual void reshape(
int& bs, int& ic, int& ih, int& iw, int oc, int& oh, int& ow) = 0;
int& bs, int& ic, int& ih, int& iw, int& oc, int& oh, int& ow) = 0;
/**
* reset the mkldnn forward primitve and memories
* only would be called when input size changes
* weight and bias buffers should be coverd by child class itself
*/
virtual void resetFwd(std::vector<mkldnn::primitive>& pipeline,
MKLDNNMatrixPtr& in,
MKLDNNMatrixPtr& wgt,
MKLDNNMatrixPtr& bias,
std::vector<MKLDNNMatrixPtr>& inputs,
MKLDNNMatrixPtr& out) = 0;
/**
* reset the mkldnn backward primitve and memories
* only would be called when needed
* weight and bias buffers should be coverd by child class itself
*/
virtual void resetBwd(std::vector<mkldnn::primitive>& pipeline,
MKLDNNMatrixPtr& in,
MKLDNNMatrixPtr& wgt,
MKLDNNMatrixPtr& bias,
std::vector<MKLDNNMatrixPtr>& inputs,
MKLDNNMatrixPtr& out) = 0;
/**
......@@ -175,10 +166,19 @@ public:
void addOutputArgument(int deviceId) { Layer::addOutputArgument(deviceId); }
protected:
/**
* Some layers may have different condition to reset the forward.
* The function returns the condition that do not need reset forward.
*/
inline virtual size_t keepCondition() {
// reset when the first input element size changed, not only the batchsize
return inputLayers_[0]->getOutputValue()->getElementCnt();
}
/**
* reshape the input image sizes and input batchsize
*/
void reshapeInput(int& batchsize, int& height, int& width);
void reshapeInput(int& batchsize, int& height, int& width, size_t idx = 0);
/**
* reshape output image sizes
......@@ -196,11 +196,13 @@ protected:
/**
* reset input value from input MKLDNNMatrix and internal primitive desc.
* reset both internal and external buffer and create reorder if necessary.
* input channel may be different in concat.
*/
void resetInValue(
MKLDNNMatrixPtr& in,
const std::shared_ptr<mkldnn::memory::primitive_desc>& intPD = nullptr,
size_t inputIdx = 0);
size_t idx = 0,
int inputChannel = 0);
/**
* reset output value from internal primitive desc.
......@@ -215,7 +217,7 @@ protected:
*/
void resetInGrad(MKLDNNMatrixPtr& in,
mkldnn::memory::primitive_desc intPD,
size_t inputIdx = 0);
size_t idx = 0);
/**
* reset output grad from internal primitive desc.
......@@ -293,17 +295,19 @@ protected:
* print the mkldnn memory format of value
*/
virtual void printValueFormat() {
if (extInVal_) {
VLOG(MKLDNN_FMTS) << extInVal_->getFormat() << " >>> ";
}
if (inVal_) {
VLOG(MKLDNN_FMTS) << inVal_->getFormat() << " >>>";
for (size_t i = 0; i < inVals_.size(); ++i) {
if (!inVals_[i]) {
continue;
}
VLOG(MKLDNN_FMTS) << "Input " << i << ", " << inputLayers_[i]->getName()
<< ": " << (extInVals_[i] ? extInVals_[i]->getFormat()
: inVals_[i]->getFormat())
<< " >>> " << inVals_[i]->getFormat() << " >>>";
}
if (outVal_) {
VLOG(MKLDNN_FMTS) << outVal_->getFormat() << " >>> ";
}
if (extOutVal_) {
VLOG(MKLDNN_FMTS) << extOutVal_->getFormat();
VLOG(MKLDNN_FMTS) << outVal_->getFormat() << " >>> "
<< (extOutVal_ ? extOutVal_->getFormat()
: outVal_->getFormat());
}
if (wgtVal_) {
VLOG(MKLDNN_FMTS) << "Weight value format: " << wgtVal_->getFormat();
......@@ -317,17 +321,19 @@ protected:
* print the mkldnn memory format of grad
*/
virtual void printGradFormat() {
if (extOutGrad_) {
VLOG(MKLDNN_FMTS) << extOutGrad_->getFormat();
}
if (outGrad_) {
VLOG(MKLDNN_FMTS) << outGrad_->getFormat() << " <<< ";
VLOG(MKLDNN_FMTS) << outGrad_->getFormat() << " <<< "
<< (extOutGrad_ ? extOutGrad_->getFormat()
: outGrad_->getFormat());
}
if (inGrad_) {
VLOG(MKLDNN_FMTS) << inGrad_->getFormat() << " <<<";
}
if (extInGrad_) {
VLOG(MKLDNN_FMTS) << extInGrad_->getFormat() << " <<< ";
for (size_t i = 0; i < inGrads_.size(); ++i) {
if (!inGrads_[i]) {
continue;
}
VLOG(MKLDNN_FMTS) << "Input " << i << ", " << inputLayers_[i]->getName()
<< ": " << (extInGrads_[i] ? extInGrads_[i]->getFormat()
: inGrads_[i]->getFormat())
<< " <<< " << inGrads_[i]->getFormat() << " <<<";
}
if (wgtGrad_) {
VLOG(MKLDNN_FMTS) << "Weight grad format: " << wgtGrad_->getFormat();
......@@ -434,6 +440,41 @@ private:
outputOtherDevice_[i].cpuSequenceDims = output_.cpuSequenceDims;
}
}
void prepareValueConversions(std::vector<mkldnn::primitive>& pipeline) {
// MKLDNNLayer output value should be MKLDNNMatrix
// so external output value is necessary.
// Then external input value is not necessary,
// since input may be mkldnn internal buffer.
CHECK(extOutVal_) << "external output value is necessary";
output_.value = std::dynamic_pointer_cast<Matrix>(extOutVal_);
CHECK(inVals_[0] && outVal_) << "internal memories are necessary";
for (size_t i = 0; i < cvtInVals_.size(); ++i) {
if (cvtInVals_[i]) {
pipeline.insert(pipeline.begin(), *cvtInVals_[i]);
}
}
if (cvtOutVal_) {
pipeline.push_back(*cvtOutVal_);
}
}
void prepareGradConversions(std::vector<mkldnn::primitive>& pipeline) {
// external output grad is not necessary
// since output may be mkldnn internal buffer or merge them directly.
CHECK(outGrad_) << "internal output grad is necessary";
if (extOutGrad_) {
CHECK_EQ(extOutGrad_->getData(), output_.grad->getData())
<< "the external buffer should share the same data with output_.grad";
}
if (cvtOutGrad_) {
pipeline.insert(pipeline.begin(), *cvtOutGrad_);
}
for (size_t i = 0; i < cvtInGrads_.size(); ++i) {
if (cvtInGrads_[i]) {
pipeline.push_back(*cvtInGrads_[i]);
}
}
}
};
} // namespace paddle
paddle/gserver/layers/MKLDNNPoolLayer.cpp
浏览文件 @ 90e05a4b
......@@ -58,10 +58,11 @@ bool MKLDNNPoolLayer::init(const LayerMap& layerMap,
}
void MKLDNNPoolLayer::reshape(
int& bs, int& ic, int& ih, int& iw, int oc, int& oh, int& ow) {
int& bs, int& ic, int& ih, int& iw, int& oc, int& oh, int& ow) {
reshapeInput(bs, ih, iw);
// ic_ and oc can not be changed
CHECK_EQ(inputElemenCnt_ / bs / ih / iw, (size_t)ic)
CHECK_EQ((size_t)ic,
inputLayers_[0]->getOutputValue()->getElementCnt() / bs / ih / iw)
<< "Input channel can not be changed";
// cal output sizes
......@@ -74,29 +75,25 @@ void MKLDNNPoolLayer::reshape(
}
void MKLDNNPoolLayer::resetFwd(std::vector<primitive>& pipeline,
MKLDNNMatrixPtr& in,
MKLDNNMatrixPtr& wgt,
MKLDNNMatrixPtr& bias,
std::vector<MKLDNNMatrixPtr>& inputs,
MKLDNNMatrixPtr& out) {
resetFwdBuffers(in, out);
resetFwdBuffers(inputs[0], out);
resetFwdPD(fwdPD_, in, out);
resetFwdPD(fwdPD_, inputs[0], out);
resetFwdPipeline(pipeline, fwdPD_, in, out);
resetFwdPipeline(pipeline, fwdPD_, inputs[0], out);
}
void MKLDNNPoolLayer::resetBwd(std::vector<primitive>& pipeline,
MKLDNNMatrixPtr& in,
MKLDNNMatrixPtr& wgt,
MKLDNNMatrixPtr& bias,
std::vector<MKLDNNMatrixPtr>& inputs,
MKLDNNMatrixPtr& out) {
std::shared_ptr<pool_bwd::primitive_desc> pd;
resetBwdBuffers(in, out);
resetBwdBuffers(inputs[0], out);
resetBwdPD(pd, in, out);
resetBwdPD(pd, inputs[0], out);
resetBwdPipeline(pipeline, pd, in, out);
resetBwdPipeline(pipeline, pd, inputs[0], out);
}
void MKLDNNPoolLayer::resetFwdBuffers(MKLDNNMatrixPtr& in,
......@@ -151,9 +148,9 @@ void MKLDNNPoolLayer::resetFwdPipeline(
void MKLDNNPoolLayer::resetBwdBuffers(MKLDNNMatrixPtr& in,
MKLDNNMatrixPtr& out) {
CHECK(inVal_ && outVal_);
CHECK(inVals_[0] && outVal_);
resetOutGrad(out, outVal_->getPrimitiveDesc());
resetInGrad(in, inVal_->getPrimitiveDesc());
resetInGrad(in, inVals_[0]->getPrimitiveDesc());
}
void MKLDNNPoolLayer::resetBwdPD(std::shared_ptr<pool_bwd::primitive_desc>& pd,
......
paddle/gserver/layers/MKLDNNPoolLayer.h
浏览文件 @ 90e05a4b
......@@ -53,18 +53,14 @@ public:
const ParameterMap& parameterMap) override;
void reshape(
int& bs, int& ic, int& ih, int& iw, int oc, int& oh, int& ow) override;
int& bs, int& ic, int& ih, int& iw, int& oc, int& oh, int& ow) override;
void resetFwd(std::vector<mkldnn::primitive>& pipeline,
MKLDNNMatrixPtr& in,
MKLDNNMatrixPtr& wgt,
MKLDNNMatrixPtr& bias,
std::vector<MKLDNNMatrixPtr>& inputs,
MKLDNNMatrixPtr& out) override;
void resetBwd(std::vector<mkldnn::primitive>& pipeline,
MKLDNNMatrixPtr& in,
MKLDNNMatrixPtr& wgt,
MKLDNNMatrixPtr& bias,
std::vector<MKLDNNMatrixPtr>& inputs,
MKLDNNMatrixPtr& out) override;
void printSizeInfo() override {
......@@ -75,11 +71,6 @@ public:
}
protected:
/**
* Forward functions: reset buffers(input, output),
* reset primitive descriptor,
* reset pipeline.
*/
void resetFwdBuffers(MKLDNNMatrixPtr& in, MKLDNNMatrixPtr& out);
void resetFwdPD(std::shared_ptr<pool_fwd::primitive_desc>& pd,
MKLDNNMatrixPtr in,
......@@ -88,12 +79,6 @@ protected:
std::shared_ptr<pool_fwd::primitive_desc>& pd,
MKLDNNMatrixPtr& in,
MKLDNNMatrixPtr& out);
/**
* Backward functions: reset buffers(input, output),
* reset primitive descriptor,
* reset pipeline.
*/
void resetBwdBuffers(MKLDNNMatrixPtr& in, MKLDNNMatrixPtr& out);
void resetBwdPD(std::shared_ptr<pool_bwd::primitive_desc>& pd,
MKLDNNMatrixPtr& in,
......
paddle/gserver/tests/CMakeLists.txt
浏览文件 @ 90e05a4b
......@@ -29,7 +29,7 @@ gserver_test(test_KmaxSeqScore)
gserver_test(test_Expand)
gserver_test(test_MaxPoolingWithMaskOutput)
########## test_Mkldnn layers and activations ##########
########## test_MKLDNN layers and activations ##########
if(WITH_MKLDNN)
add_unittest_without_exec(test_MKLDNN
test_MKLDNN.cpp
......@@ -62,17 +62,6 @@ if(NOT WITH_DOUBLE AND NOT MOBILE_INFERENCE)
endif()
if(NOT MOBILE_INFERENCE)
################### test_ProtoDataProvider ############
add_unittest_without_exec(test_ProtoDataProvider
test_ProtoDataProvider.cpp)
# test_ProtoDataProvider will mkdir as same name,
# so if WORKING_DIRECTORY is default directory, then
# mkdir will get error.
add_test(NAME test_ProtoDataProvider
COMMAND ${CMAKE_CURRENT_BINARY_DIR}/test_ProtoDataProvider
WORKING_DIRECTORY ${PADDLE_SOURCE_DIR}/paddle)
################## test_Evaluator #######################
add_unittest(test_Evaluator
test_Evaluator.cpp)
......@@ -110,3 +99,24 @@ add_test(NAME test_PyDataProvider2
COMMAND .set_python_path.sh -d ${PADDLE_SOURCE_DIR}/paddle/gserver/tests:${PADDLE_SOURCE_DIR}/python ${CMAKE_CURRENT_BINARY_DIR}/test_PyDataProvider2
WORKING_DIRECTORY ${PADDLE_SOURCE_DIR}/paddle
)
################# test_CompareSparse ##################
add_unittest_without_exec(test_CompareSparse
test_CompareSparse.cpp)
if(NOT ON_TRAVIS)
add_test(NAME test_CompareSparse
COMMAND ${PADDLE_SOURCE_DIR}/paddle/.set_python_path.sh -d
${PADDLE_SOURCE_DIR}/python:${PADDLE_SOURCE_DIR}/paddle/gserver/tests
./.set_port.sh -p port -n 6
${CMAKE_CURRENT_BINARY_DIR}/test_CompareSparse
WORKING_DIRECTORY ${PADDLE_SOURCE_DIR}/paddle/)
endif()
################ test_CompareTwoNets ######################
add_unittest_without_exec(test_CompareTwoNets
test_CompareTwoNets.cpp)
add_test(NAME test_CompareTwoNets
COMMAND ${PADDLE_SOURCE_DIR}/paddle/.set_python_path.sh -d
${PADDLE_SOURCE_DIR}/python:${PADDLE_SOURCE_DIR}/paddle/gserver/tests
${CMAKE_CURRENT_BINARY_DIR}/test_CompareTwoNets
WORKING_DIRECTORY ${PADDLE_SOURCE_DIR}/paddle/)
paddle/gserver/tests/MKLDNNTester.h
浏览文件 @ 90e05a4b
......@@ -23,7 +23,7 @@ limitations under the License. */
namespace paddle {
/**
* @brief test the functionality of Mkldnnlayers
* @brief test the functionality of MKLDNNlayers and MKLDNNActivations
* refer to paddle original function
*/
class MKLDNNTester {
......
paddle/gserver/tests/proto_files.txt 已删除 100644 → 0
浏览文件 @ 5502abb9
./test_ProtoDataProvider/data1.bin
./test_ProtoDataProvider/data2.bin
paddle/gserver/tests/proto_files_compressed.txt 已删除 100644 → 0
浏览文件 @ 5502abb9
./test_ProtoDataProvider/data1.bin.gz
./test_ProtoDataProvider/data2.bin.gz
paddle/trainer/tests/sample_trainer_config_opt_a.conf → paddle/gserver/tests/sequence_lstm.conf
浏览文件 @ 90e05a4b
#!/usr/bin/env python
# Copyright (c) 2016 PaddlePaddle Authors. All Rights Reserved
#
# Licensed under the Apache License, Version 2.0 (the "License");
......@@ -14,27 +15,50 @@
from paddle.trainer_config_helpers import *
################################### Data Configuration ###################################
TrainData(ProtoData(files = "trainer/tests/mnist.list"))
################################### Algorithm Configuration ###################################
settings(batch_size = 1000,
learning_method = MomentumOptimizer(momentum=0.5, sparse=False))
################################### Network Configuration ###################################
data = data_layer(name ="input", size=784)
######################## data source ################################
dict_path = 'gserver/tests/Sequence/tour_dict_phrase.dict'
dict_file = dict()
for line_count, line in enumerate(open(dict_path, "r")):
dict_file[line.strip()] = line_count
fc1 = fc_layer(input=data, size=800,
bias_attr=True,
act=SigmoidActivation())
define_py_data_sources2(
train_list='gserver/tests/Sequence/train.list',
test_list=None,
module='sequenceGen',
obj='process',
args={"dict_file": dict_file})
fc2 = fc_layer(input=fc1, size=800,
bias_attr=True,
act=SigmoidActivation())
settings(batch_size=5)
######################## network configure ################################
dict_dim = len(open(dict_path, 'r').readlines())
word_dim = 128
hidden_dim = 256
label_dim = 3
sparse_update = get_config_arg("sparse_update", bool, False)
output = fc_layer(input=[fc1, fc2], size=10,
bias_attr=True,
act=SoftmaxActivation())
data = data_layer(name="word", size=dict_dim)
lbl = data_layer(name ="label", size=1)
emb = embedding_layer(
input=data,
size=word_dim,
param_attr=ParamAttr(sparse_update=sparse_update))
cost = classification_cost(input=output, label=lbl)
outputs(cost)
with mixed_layer(size=hidden_dim * 4) as lstm_input:
lstm_input += full_matrix_projection(input=emb)
lstm = lstmemory(
input=lstm_input,
act=TanhActivation(),
gate_act=SigmoidActivation(),
state_act=TanhActivation())
lstm_last = last_seq(input=lstm)
with mixed_layer(
size=label_dim, act=SoftmaxActivation(), bias_attr=True) as output:
output += full_matrix_projection(input=lstm_last)
outputs(
classification_cost(
input=output, label=data_layer(
name="label", size=1)))
paddle/trainer/tests/sample_trainer_config_opt_b.conf → paddle/gserver/tests/sequence_recurrent.py
浏览文件 @ 90e05a4b
#!/usr/bin/env python
# Copyright (c) 2016 PaddlePaddle Authors. All Rights Reserved
#
# Licensed under the Apache License, Version 2.0 (the "License");
......@@ -14,27 +15,42 @@
from paddle.trainer_config_helpers import *
################################### Data Configuration ###################################
TrainData(ProtoData(files = "trainer/tests/mnist.list"))
################################### Algorithm Configuration ###################################
settings(batch_size = 1000,
learning_method = MomentumOptimizer(momentum=0.5, sparse=False))
################################### Network Configuration ###################################
data = data_layer(name ="input", size=784)
######################## data source ################################
dict_path = 'gserver/tests/Sequence/tour_dict_phrase.dict'
dict_file = dict()
for line_count, line in enumerate(open(dict_path, "r")):
dict_file[line.strip()] = line_count
fc1 = fc_layer(input=data, size=800,
bias_attr=True,
act=SigmoidActivation())
define_py_data_sources2(
train_list='gserver/tests/Sequence/train.list',
test_list=None,
module='sequenceGen',
obj='process',
args={"dict_file": dict_file})
fc2 = fc_layer(input=fc1, size=800,
bias_attr=True,
act=SigmoidActivation())
settings(batch_size=5)
######################## network configure ################################
dict_dim = len(open(dict_path, 'r').readlines())
word_dim = 128
hidden_dim = 128
label_dim = 3
output = fc_layer(input=[fc1, fc2], size=10,
bias_attr=True,
act=SoftmaxActivation())
# This config is designed to be equivalent with sequence_recurrent_group.py
lbl = data_layer(name ="label", size=1)
data = data_layer(name="word", size=dict_dim)
cost = classification_cost(input=output, label=lbl)
outputs(cost)
emb = embedding_layer(
input=data, size=word_dim, param_attr=ParamAttr(name="emb"))
recurrent = recurrent_layer(input=emb, bias_attr=False, act=SoftmaxActivation())
recurrent_last = last_seq(input=recurrent)
with mixed_layer(
size=label_dim, act=SoftmaxActivation(), bias_attr=True) as output:
output += full_matrix_projection(input=recurrent_last)
outputs(
classification_cost(
input=output, label=data_layer(
name="label", size=1)))
paddle/gserver/tests/sequence_recurrent_group.py 0 → 100644
浏览文件 @ 90e05a4b
#!/usr/bin/env python
# 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.
from paddle.trainer_config_helpers import *
######################## data source ################################
dict_path = 'gserver/tests/Sequence/tour_dict_phrase.dict'
dict_file = dict()
for line_count, line in enumerate(open(dict_path, "r")):
dict_file[line.strip()] = line_count
define_py_data_sources2(
train_list='gserver/tests/Sequence/train.list',
test_list=None,
module='sequenceGen',
obj='process',
args={"dict_file": dict_file})
settings(batch_size=5)
######################## network configure ################################
dict_dim = len(open(dict_path, 'r').readlines())
word_dim = 128
hidden_dim = 128
label_dim = 3
# This config is designed to be equivalent with sequence_recurrent.py
data = data_layer(name="word", size=dict_dim)
emb = embedding_layer(
input=data, size=word_dim, param_attr=ParamAttr(name="emb"))
def step(y):
mem = memory(name="rnn_state", size=hidden_dim)
with mixed_layer(
name="rnn_state",
size=hidden_dim,
bias_attr=False,
act=SoftmaxActivation()) as out:
out += identity_projection(input=y)
out += full_matrix_projection(
input=mem, param_attr=ParamAttr(name="___recurrent_layer_0__"))
return out
recurrent = recurrent_group(name="rnn", step=step, input=emb)
recurrent_last = last_seq(input=recurrent)
with mixed_layer(
size=label_dim, act=SoftmaxActivation(), bias_attr=True) as output:
output += full_matrix_projection(input=recurrent_last)
outputs(
classification_cost(
input=output, label=data_layer(
name="label", size=1)))
paddle/trainer/tests/test_CompareSparse.cpp → paddle/gserver/tests/test_CompareSparse.cpp
浏览文件 @ 90e05a4b
......@@ -22,8 +22,7 @@ limitations under the License. */
using namespace paddle; // NOLINT
using namespace std; // NOLINT
static const string& configFile1 =
"trainer/tests/sample_trainer_config_compare_sparse.conf";
static const string& configFile1 = "gserver/tests/sequence_lstm.conf";
DECLARE_bool(use_gpu);
DECLARE_string(config);
......
paddle/trainer/tests/test_CompareTwoNets.cpp → paddle/gserver/tests/test_CompareTwoNets.cpp
浏览文件 @ 90e05a4b
......@@ -30,8 +30,6 @@ DECLARE_bool(use_gpu);
DECLARE_string(config);
DECLARE_string(nics);
DEFINE_string(config_file_a, "", "config of one network to compare");
DEFINE_string(config_file_b, "", "config of another network to compare");
DEFINE_bool(need_high_accuracy,
false,
"whether need to run in double accuracy");
......@@ -42,6 +40,10 @@ DEFINE_double(
DECLARE_bool(thread_local_rand_use_global_seed);
DECLARE_int32(seed);
static const string& config_file_a = "gserver/tests/sequence_recurrent.py";
static const string& config_file_b =
"gserver/tests/sequence_recurrent_group.py";
struct ComData {
vector<Argument> outArgs;
vector<ParameterPtr> parameters;
......@@ -66,6 +68,7 @@ void calcGradient(ComData& data, const string configFile) {
DataBatch dataBatch;
int32_t batchSize = trainer.getConfig().opt_config().batch_size();
trainer.getDataProvider()->reset();
trainer.getDataProvider()->setSkipShuffle();
trainer.getDataProvider()->getNextBatch(batchSize, &dataBatch);
......@@ -167,11 +170,11 @@ void compareGradient(ComData& comDataA, ComData& comDataB) {
TEST(Trainer, create) {
ComData dataA;
calcGradient(dataA, FLAGS_config_file_a);
calcGradient(dataA, config_file_a);
LOG(INFO) << "\n\nforwardBackward of Network A is finished\n\n";
ComData dataB;
calcGradient(dataB, FLAGS_config_file_b);
calcGradient(dataB, config_file_b);
LOG(INFO) << "\n\nforwardBackward of the Network B is finished\n\n";
compareGradient(dataA, dataB);
......
paddle/gserver/tests/test_LayerGrad.cpp
浏览文件 @ 90e05a4b
......@@ -583,6 +583,7 @@ TEST(Layer, maxoutLayer) {
testLayerGrad(config, "maxout", 10, false, useGpu);
}
}
void testFcLayer(string format, size_t nnz) {
TestConfig config;
config.biasSize = 1024;
......@@ -1081,6 +1082,21 @@ TEST(Layer, InterpolationLayer) {
}
}
TEST(Layer, DotProdLayer) {
TestConfig config;
config.layerConfig.set_type("dot_prod");
config.layerConfig.set_size(1);
config.inputDefs.push_back({INPUT_DATA, "layer_0", 10, 0});
config.layerConfig.add_inputs();
config.inputDefs.push_back({INPUT_DATA, "layer_1", 10, 0});
config.layerConfig.add_inputs();
for (auto useGpu : {false, true}) {
testLayerGrad(config, "dot_prod", 10, false, useGpu);
}
}
TEST(Layer, OuterProdLayer) {
TestConfig config;
config.layerConfig.set_type("out_prod");
......@@ -2429,6 +2445,25 @@ TEST(Layer, ScaleSubRegionLayer) {
}
}
TEST(Layer, L2DistanceLayer) {
TestConfig config;
config.layerConfig.set_type("l2_distance");
config.layerConfig.set_size(1);
config.biasSize = 0;
const size_t input_dim = 27;
const size_t batch_size = 11;
config.inputDefs.push_back({INPUT_DATA, "layer_0", input_dim, 0});
config.inputDefs.push_back({INPUT_DATA, "layer_1", input_dim, 0});
config.layerConfig.add_inputs();
config.layerConfig.add_inputs();
for (auto useGpu : {false, true}) {
testLayerGrad(config, "l2_distance", batch_size, false, useGpu);
}
}
int main(int argc, char** argv) {
testing::InitGoogleTest(&argc, argv);
initMain(argc, argv);
......
paddle/gserver/tests/test_MKLDNN.cpp
浏览文件 @ 90e05a4b
......@@ -313,6 +313,47 @@ TEST(MKLDNNLayer, AddtoLayer) {
testAddtoLayer({4, 12, 1, 1}, 3);
}
static void getMKLDNNConcatConfig(TestConfig& cfg,
const std::vector<testImageDesc>& inputs) {
CHECK_GE(inputs.size(), 2UL) << "at least two inputs";
int oc = inputs[0].ic;
for (size_t i = 1; i < inputs.size(); ++i) {
CHECK_EQ(inputs[i].bs, inputs[0].bs);
CHECK_EQ(inputs[i].ih, inputs[0].ih);
CHECK_EQ(inputs[i].iw, inputs[0].iw);
oc += inputs[i].ic;
}
cfg.biasSize = 0;
cfg.layerConfig.set_type("mkldnn_concat");
cfg.layerConfig.set_size(oc * inputs[0].ih * inputs[0].iw);
cfg.layerConfig.set_active_type("relu");
for (size_t i = 0; i < inputs.size(); ++i) {
std::stringstream ss;
ss << "layer_" << i;
cfg.inputDefs.push_back(
{INPUT_DATA,
ss.str(),
(size_t)(inputs[i].ic) * inputs[i].ih * inputs[i].iw,
0});
LayerInputConfig* input = cfg.layerConfig.add_inputs();
ImageConfig* img_conf = input->mutable_image_conf();
img_conf->set_channels(inputs[i].ic);
img_conf->set_img_size_y(inputs[i].ih);
img_conf->set_img_size(inputs[i].iw);
}
}
void testConcatLayer(const std::vector<testImageDesc>& inputs) {
TestConfig dnnConfig;
getMKLDNNConcatConfig(dnnConfig, inputs);
RUN_MKLDNN_TEST_LAYER(dnnConfig, "concat", inputs[0])
}
TEST(MKLDNNLayer, ConcatLayer) {
testConcatLayer({{64, 128, 1, 1}, {64, 32, 1, 1}, {64, 64, 1, 1}});
testConcatLayer({{32, 100, 8, 8}, {32, 10, 8, 8}});
}
void testActivation(std::string actType, const testImageDesc& pm) {
// TODO(TJ): remove me when paddle support elu activation
if (actType == "mkldnn_elu") {
......
paddle/operators/CMakeLists.txt
浏览文件 @ 90e05a4b
......@@ -61,6 +61,18 @@ function(op_library TARGET)
set(pybind_flag 1)
endif()
if ("${TARGET}" STREQUAL "compare_op")
set(pybind_flag 1)
file(APPEND ${pybind_file} "USE_OP(less_than);\nUSE_OP(equal);\n")
endif()
# conv_op contains several operators
if ("${TARGET}" STREQUAL "conv_op")
set(pybind_flag 1)
# It's enough to just adding one operator to pybind
file(APPEND ${pybind_file} "USE_OP(conv2d);\n")
endif()
# pool_op contains several operators
if ("${TARGET}" STREQUAL "pool_op")
set(pybind_flag 1)
......@@ -68,23 +80,23 @@ function(op_library TARGET)
file(APPEND ${pybind_file} "USE_OP(pool2d);\n")
endif()
if ("${TARGET}" STREQUAL "compare_op")
# pool_cudnn_op contains several operators
if ("${TARGET}" STREQUAL "pool_cudnn_op")
set(pybind_flag 1)
file(APPEND ${pybind_file} "USE_OP(less_than);\nUSE_OP(equal);\n")
# It's enough to just adding one operator to pybind
file(APPEND ${pybind_file} "USE_OP(pool2d_cudnn);\n")
endif()
# pool_with_index_op contains several operators
if ("${TARGET}" STREQUAL "pool_with_index_op")
if ("${TARGET}" STREQUAL "logical_op")
set(pybind_flag 1)
# It's enough to just adding one operator to pybind
file(APPEND ${pybind_file} "USE_OP(max_pool2d_with_index);\n")
file(APPEND ${pybind_file} "USE_OP(logical_and);\n")
endif()
# conv_op contains several operators
if ("${TARGET}" STREQUAL "conv_op")
# pool_with_index_op contains several operators
if ("${TARGET}" STREQUAL "pool_with_index_op")
set(pybind_flag 1)
# It's enough to just adding one operator to pybind
file(APPEND ${pybind_file} "USE_OP(conv2d);\n")
file(APPEND ${pybind_file} "USE_OP(max_pool2d_with_index);\n")
endif()
# conv_transpose_op contains several operators
......@@ -93,12 +105,12 @@ function(op_library TARGET)
# It's enough to just adding one operator to pybind
file(APPEND ${pybind_file} "USE_OP(conv2d_transpose);\n")
endif()
# pool_cudnn_op contains several operators
if ("${TARGET}" STREQUAL "pool_cudnn_op")
# conv_transpose_cudnn_op contains two operators
if ("${TARGET}" STREQUAL "conv_transpose_cudnn_op")
set(pybind_flag 1)
# It's enough to just adding one operator to pybind
file(APPEND ${pybind_file} "USE_OP(pool2d_cudnn);\n")
file(APPEND ${pybind_file} "USE_OP(conv2d_transpose_cudnn);\n")
endif()
# save_restore_op contains several operators
......@@ -172,6 +184,7 @@ set(DEPS_OPS
sequence_softmax_op
sum_op
pool_op
maxout_op
pool_with_index_op
conv_op
conv_transpose_op
......@@ -198,6 +211,7 @@ op_library(sgd_op DEPS selected_rows_functor)
op_library(adagrad_op DEPS selected_rows_functor)
op_library(conv_op DEPS vol2col)
op_library(pool_op DEPS pooling)
op_library(maxout_op DEPS maxouting)
op_library(pool_with_index_op DEPS pooling)
op_library(lod_rank_table_op SRCS lod_rank_table_op.cc DEPS lod_rank_table)
op_library(lod_tensor_to_array_op SRCS lod_tensor_to_array_op.cc DEPS lod_rank_table_op)
......
paddle/operators/activation_op.cc
浏览文件 @ 90e05a4b
......@@ -98,7 +98,6 @@ $y = \max(x, 0)$
}
};
template <typename AttrType>
class LeakyReluOpMaker : public framework::OpProtoAndCheckerMaker {
public:
LeakyReluOpMaker(framework::OpProto *proto,
......@@ -106,8 +105,7 @@ class LeakyReluOpMaker : public framework::OpProtoAndCheckerMaker {
: OpProtoAndCheckerMaker(proto, op_checker) {
AddInput("X", "Input of LeakyRelu operator");
AddOutput("Y", "Output of LeakyRelu operator");
AddAttr<AttrType>("alpha", "The small negative slope")
.SetDefault(static_cast<AttrType>(0.02f));
AddAttr<float>("alpha", "The small negative slope").SetDefault(0.02f);
AddComment(R"DOC(
LeakyRelu Activation Operator.
......@@ -117,7 +115,6 @@ $y = \max(x, \alpha * x)$
}
};
template <typename AttrType>
class SoftShrinkOpMaker : public framework::OpProtoAndCheckerMaker {
public:
SoftShrinkOpMaker(framework::OpProto *proto,
......@@ -125,8 +122,7 @@ class SoftShrinkOpMaker : public framework::OpProtoAndCheckerMaker {
: OpProtoAndCheckerMaker(proto, op_checker) {
AddInput("X", "Input of Softshrink operator");
AddOutput("Y", "Output of Softshrink operator");
AddAttr<AttrType>("lambda", "non-negative offset")
.SetDefault(static_cast<AttrType>(0.5f));
AddAttr<float>("lambda", "non-negative offset").SetDefault(0.5f);
AddComment(R"DOC(
Softshrink Activation Operator.
......@@ -173,7 +169,6 @@ $$y = x - \frac{e^{x} - e^{-x}}{e^{x} + e^{-x}}$$
}
};
template <typename AttrType>
class HardShrinkOpMaker : public framework::OpProtoAndCheckerMaker {
public:
HardShrinkOpMaker(framework::OpProto *proto,
......@@ -181,8 +176,8 @@ class HardShrinkOpMaker : public framework::OpProtoAndCheckerMaker {
: OpProtoAndCheckerMaker(proto, op_checker) {
AddInput("X", "Input of HardShrink operator");
AddOutput("Y", "Output of HardShrink operator");
AddAttr<AttrType>("threshold", "The value of threshold for HardShrink")
.SetDefault(static_cast<AttrType>(0.5));
AddAttr<float>("threshold", "The value of threshold for HardShrink")
.SetDefault(0.5f);
AddComment(R"DOC(
HardShrink Activation Operator.
......@@ -308,17 +303,16 @@ $$y = \frac{x}{1 + |x|}$$
}
};
template <typename AttrType>
class BReluOpMaker : public framework::OpProtoAndCheckerMaker {
public:
BReluOpMaker(framework::OpProto *proto, framework::OpAttrChecker *op_checker)
: OpProtoAndCheckerMaker(proto, op_checker) {
AddInput("X", "Input of BRelu operator");
AddOutput("Y", "Output of BRelu operator");
AddAttr<AttrType>("t_min", "The min marginal value of BRelu")
.SetDefault(static_cast<AttrType>(0));
AddAttr<AttrType>("t_max", "The max marginal value of BRelu")
.SetDefault(static_cast<AttrType>(24));
AddAttr<float>("t_min", "The min marginal value of BRelu")
.SetDefault(static_cast<float>(0));
AddAttr<float>("t_max", "The max marginal value of BRelu")
.SetDefault(static_cast<float>(24));
AddComment(R"DOC(
BRelu Activation Operator.
......@@ -328,7 +322,6 @@ $y = \max(\min(x, t_{min}), t_{max})$
}
};
template <typename AttrType>
class SoftReluOpMaker : public framework::OpProtoAndCheckerMaker {
public:
SoftReluOpMaker(framework::OpProto *proto,
......@@ -336,8 +329,8 @@ class SoftReluOpMaker : public framework::OpProtoAndCheckerMaker {
: OpProtoAndCheckerMaker(proto, op_checker) {
AddInput("X", "Input of SoftRelu operator");
AddOutput("Y", "Output of SoftRelu operator");
AddAttr<AttrType>("threshold", "The threshold value of SoftRelu")
.SetDefault(static_cast<AttrType>(40));
AddAttr<float>("threshold", "The threshold value of SoftRelu")
.SetDefault(40.0f);
AddComment(R"DOC(
SoftRelu Activation Operator.
......@@ -347,15 +340,13 @@ $y = \ln(1 + \exp(\max(\min(x, threshold), threshold))$
}
};
template <typename AttrType>
class ELUOpMaker : public framework::OpProtoAndCheckerMaker {
public:
ELUOpMaker(framework::OpProto *proto, framework::OpAttrChecker *op_checker)
: OpProtoAndCheckerMaker(proto, op_checker) {
AddInput("X", "Input of ELU operator");
AddOutput("Y", "Output of ELU operator");
AddAttr<AttrType>("alpha", "The alpha value of ELU")
.SetDefault(static_cast<AttrType>(1.0f));
AddAttr<float>("alpha", "The alpha value of ELU").SetDefault(1.0f);
AddComment(R"DOC(
ELU Activation Operator.
......@@ -368,15 +359,14 @@ $y = \max(0, x) + \min(0, \alpha * (e^x - 1))$
}
};
template <typename AttrType>
class Relu6OpMaker : public framework::OpProtoAndCheckerMaker {
public:
Relu6OpMaker(framework::OpProto *proto, framework::OpAttrChecker *op_checker)
: OpProtoAndCheckerMaker(proto, op_checker) {
AddInput("X", "Input of Relu6 operator");
AddOutput("Y", "Output of Relu6 operator");
AddAttr<AttrType>("threshold", "The threshold value of Relu6")
.SetDefault(static_cast<AttrType>(6));
AddAttr<float>("threshold", "The threshold value of Relu6")
.SetDefault(6.0f);
AddComment(R"DOC(
Relu6 Activation Operator.
......@@ -386,15 +376,13 @@ $y = \min(\max(0, x), 6)$
}
};
template <typename AttrType>
class PowOpMaker : public framework::OpProtoAndCheckerMaker {
public:
PowOpMaker(framework::OpProto *proto, framework::OpAttrChecker *op_checker)
: OpProtoAndCheckerMaker(proto, op_checker) {
AddInput("X", "Input of Pow operator");
AddOutput("Y", "Output of Pow operator");
AddAttr<AttrType>("factor", "The exponential factor of Pow")
.SetDefault(static_cast<AttrType>(1));
AddAttr<float>("factor", "The exponential factor of Pow").SetDefault(1.0f);
AddComment(R"DOC(
Pow Activation Operator.
......@@ -404,17 +392,16 @@ $y = x^{factor}$
}
};
template <typename AttrType>
class STanhOpMaker : public framework::OpProtoAndCheckerMaker {
public:
STanhOpMaker(framework::OpProto *proto, framework::OpAttrChecker *op_checker)
: OpProtoAndCheckerMaker(proto, op_checker) {
AddInput("X", "Input of STanh operator");
AddOutput("Y", "Output of STanh operator");
AddAttr<AttrType>("scale_a", "The scale parameter of a for the input")
.SetDefault(static_cast<AttrType>(2 / 3));
AddAttr<AttrType>("scale_b", "The scale parameter of b for the input")
.SetDefault(static_cast<AttrType>(1.7159));
AddAttr<float>("scale_a", "The scale parameter of a for the input")
.SetDefault(2.0f / 3.0f);
AddAttr<float>("scale_b", "The scale parameter of b for the input")
.SetDefault(1.7159f);
AddComment(R"DOC(
STanh Activation Operator.
......@@ -424,7 +411,6 @@ $$y = b * \frac{e^{a * x} - e^{-a * x}}{e^{a * x} + e^{-a * x}}$$
}
};
template <typename AttrType>
class ThresholdedReluOpMaker : public framework::OpProtoAndCheckerMaker {
public:
ThresholdedReluOpMaker(framework::OpProto *proto,
......@@ -432,8 +418,8 @@ class ThresholdedReluOpMaker : public framework::OpProtoAndCheckerMaker {
: OpProtoAndCheckerMaker(proto, op_checker) {
AddInput("X", "Input of ThresholdedRelu operator");
AddOutput("Y", "Output of ThresholdedRelu operator");
AddAttr<AttrType>("threshold", "The threshold location of activation")
.SetDefault(static_cast<AttrType>(1.0));
AddAttr<float>("threshold", "The threshold location of activation")
.SetDefault(1.0f);
AddComment(R"DOC(
ThresholdedRelu Activation Operator.
......@@ -448,7 +434,6 @@ $$
}
};
template <typename AttrType>
class HardSigmoidOpMaker : public framework::OpProtoAndCheckerMaker {
public:
HardSigmoidOpMaker(framework::OpProto *proto,
......@@ -456,10 +441,10 @@ class HardSigmoidOpMaker : public framework::OpProtoAndCheckerMaker {
: OpProtoAndCheckerMaker(proto, op_checker) {
AddInput("X", "Input of HardSigmoid operator");
AddOutput("Y", "Output of HardSigmoid operator");
AddAttr<AttrType>("slope", "Slope for linear approximation of sigmoid")
.SetDefault(static_cast<AttrType>(0.2));
AddAttr<AttrType>("offset", "Offset for linear approximation of sigmoid")
.SetDefault(static_cast<AttrType>(0.5));
AddAttr<float>("slope", "Slope for linear approximation of sigmoid")
.SetDefault(0.2f);
AddAttr<float>("offset", "Offset for linear approximation of sigmoid")
.SetDefault(0.5f);
AddComment(R"DOC(
HardSigmoid Activation Operator.
......@@ -499,7 +484,7 @@ REGISTER_OP(tanh, ops::ActivationOp, ops::TanhOpMaker, tanh_grad,
REGISTER_OP(tanh_shrink, ops::ActivationOp, ops::TanhShrinkOpMaker,
tanh_shrink_grad, ops::ActivationOpGrad);
REGISTER_OP(softshrink, ops::ActivationOp, ops::SoftShrinkOpMaker<float>,
REGISTER_OP(softshrink, ops::ActivationOp, ops::SoftShrinkOpMaker,
softshrink_grad, ops::ActivationOpGrad);
REGISTER_OP(sqrt, ops::ActivationOp, ops::SqrtOpMaker, sqrt_grad,
......@@ -523,35 +508,34 @@ REGISTER_OP(softplus, ops::ActivationOp, ops::SoftplusOpMaker, softplus_grad,
REGISTER_OP(softsign, ops::ActivationOp, ops::SoftsignOpMaker, softsign_grad,
ops::ActivationOpGrad);
REGISTER_OP(brelu, ops::ActivationOp, ops::BReluOpMaker<float>, brelu_grad,
REGISTER_OP(brelu, ops::ActivationOp, ops::BReluOpMaker, brelu_grad,
ops::ActivationOpGrad);
REGISTER_OP(leaky_relu, ops::ActivationOp, ops::LeakyReluOpMaker<float>,
REGISTER_OP(leaky_relu, ops::ActivationOp, ops::LeakyReluOpMaker,
leaky_relu_grad, ops::ActivationOpGrad);
REGISTER_OP(soft_relu, ops::ActivationOp, ops::SoftReluOpMaker<float>,
soft_relu_grad, ops::ActivationOpGrad);
REGISTER_OP(soft_relu, ops::ActivationOp, ops::SoftReluOpMaker, soft_relu_grad,
ops::ActivationOpGrad);
REGISTER_OP(elu, ops::ActivationOp, ops::ELUOpMaker<float>, elu_grad,
REGISTER_OP(elu, ops::ActivationOp, ops::ELUOpMaker, elu_grad,
ops::ActivationOpGrad);
REGISTER_OP(relu6, ops::ActivationOp, ops::Relu6OpMaker<float>, relu6_grad,
REGISTER_OP(relu6, ops::ActivationOp, ops::Relu6OpMaker, relu6_grad,
ops::ActivationOpGrad);
REGISTER_OP(pow, ops::ActivationOp, ops::PowOpMaker<float>, pow_grad,
REGISTER_OP(pow, ops::ActivationOp, ops::PowOpMaker, pow_grad,
ops::ActivationOpGrad);
REGISTER_OP(stanh, ops::ActivationOp, ops::STanhOpMaker<float>, stanh_grad,
REGISTER_OP(stanh, ops::ActivationOp, ops::STanhOpMaker, stanh_grad,
ops::ActivationOpGrad);
REGISTER_OP(hard_shrink, ops::ActivationOp, ops::HardShrinkOpMaker<float>,
REGISTER_OP(hard_shrink, ops::ActivationOp, ops::HardShrinkOpMaker,
hard_shrink_grad, ops::ActivationOpGrad);
REGISTER_OP(thresholded_relu, ops::ActivationOp,
ops::ThresholdedReluOpMaker<float>, thresholded_relu_grad,
ops::ActivationOpGrad);
REGISTER_OP(thresholded_relu, ops::ActivationOp, ops::ThresholdedReluOpMaker,
thresholded_relu_grad, ops::ActivationOpGrad);
REGISTER_OP(hard_sigmoid, ops::ActivationOp, ops::HardSigmoidOpMaker<float>,
REGISTER_OP(hard_sigmoid, ops::ActivationOp, ops::HardSigmoidOpMaker,
hard_sigmoid_grad, ops::ActivationOpGrad);
#define REGISTER_ACTIVATION_CPU_KERNEL(act_type, functor, grad_functor) \
......
paddle/operators/adadelta_op.cc
浏览文件 @ 90e05a4b
......@@ -109,4 +109,5 @@ paramOut = param + paramUpdate$$
namespace ops = paddle::operators;
REGISTER_OP_WITHOUT_GRADIENT(adadelta, ops::AdadeltaOp, ops::AdadeltaOpMaker);
REGISTER_OP_CPU_KERNEL(
adadelta, ops::AdadeltaOpKernel<paddle::platform::CPUPlace, float>);
adadelta, ops::AdadeltaOpKernel<paddle::platform::CPUPlace, float>,
ops::AdadeltaOpKernel<paddle::platform::CPUPlace, double>);
paddle/operators/adadelta_op.cu
浏览文件 @ 90e05a4b
......@@ -17,4 +17,5 @@
namespace ops = paddle::operators;
REGISTER_OP_GPU_KERNEL(
adadelta, ops::AdadeltaOpKernel<paddle::platform::GPUPlace, float>);
adadelta, ops::AdadeltaOpKernel<paddle::platform::GPUPlace, float>,
ops::AdadeltaOpKernel<paddle::platform::GPUPlace, double>);
paddle/operators/adadelta_op.h
浏览文件 @ 90e05a4b
......@@ -33,8 +33,8 @@ class AdadeltaOpKernel : public framework::OpKernel<T> {
avg_squared_grad_out_tensor->mutable_data<T>(ctx.GetPlace());
avg_squared_update_out_tensor->mutable_data<T>(ctx.GetPlace());
float rho = ctx.Attr<float>("rho");
float epsilon = ctx.Attr<float>("epsilon");
T rho = static_cast<T>(ctx.Attr<float>("rho"));
T epsilon = static_cast<T>(ctx.Attr<float>("epsilon"));
auto param = framework::EigenVector<T>::Flatten(
*ctx.Input<framework::Tensor>("Param"));
......
paddle/operators/adagrad_op.cu
浏览文件 @ 90e05a4b
......@@ -14,8 +14,8 @@
#define EIGEN_USE_GPU
#include "paddle/operators/adagrad_op.h"
#include "paddle/operators/math/selected_rows_functor.h"
#include "paddle/operators/math/math_function.h"
#include "paddle/operators/math/selected_rows_functor.h"
#include "paddle/platform/cuda_helper.h"
namespace paddle {
......@@ -134,8 +134,8 @@ struct SparseAdagradFunctor<platform::GPUPlace, T> {
T, 256><<<grid2, threads, 0,
reinterpret_cast<const platform::CUDADeviceContext&>(context)
.stream()>>>(grad_merge_data, grad_merge->rows().data(),
lr, param_data,
moment_data, grad_width, epsilon);
lr, param_data, moment_data, grad_width,
epsilon);
}
};
......
paddle/operators/adam_op.cc
浏览文件 @ 90e05a4b
......@@ -127,4 +127,5 @@ paramOut = param - learningRate * moment_1/ ($\sqrt{(moment_2)} + \epsilon)$$
namespace ops = paddle::operators;
REGISTER_OP_WITHOUT_GRADIENT(adam, ops::AdamOp, ops::AdamOpMaker);
REGISTER_OP_CPU_KERNEL(adam,
ops::AdamOpKernel<paddle::platform::CPUPlace, float>);
ops::AdamOpKernel<paddle::platform::CPUPlace, float>,
ops::AdamOpKernel<paddle::platform::CPUPlace, double>);
paddle/operators/adam_op.cu
浏览文件 @ 90e05a4b
......@@ -17,4 +17,5 @@
namespace ops = paddle::operators;
REGISTER_OP_GPU_KERNEL(adam,
ops::AdamOpKernel<paddle::platform::GPUPlace, float>);
ops::AdamOpKernel<paddle::platform::GPUPlace, float>,
ops::AdamOpKernel<paddle::platform::GPUPlace, double>);
paddle/operators/adam_op.h
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......@@ -31,9 +31,9 @@ class AdamOpKernel : public framework::OpKernel<T> {
moment1_out_tensor->mutable_data<T>(ctx.GetPlace());
moment2_out_tensor->mutable_data<T>(ctx.GetPlace());
float beta1 = ctx.Attr<float>("beta1");
float beta2 = ctx.Attr<float>("beta2");
float epsilon = ctx.Attr<float>("epsilon");
T beta1 = static_cast<T>(ctx.Attr<float>("beta1"));
T beta2 = static_cast<T>(ctx.Attr<float>("beta2"));
T epsilon = static_cast<T>(ctx.Attr<float>("epsilon"));
auto param = framework::EigenVector<T>::Flatten(
*ctx.Input<framework::Tensor>("Param"));
......
paddle/operators/adamax_op.cc
浏览文件 @ 90e05a4b
......@@ -126,4 +126,5 @@ division by 0 error.
namespace ops = paddle::operators;
REGISTER_OP_WITHOUT_GRADIENT(adamax, ops::AdamaxOp, ops::AdamaxOpMaker);
REGISTER_OP_CPU_KERNEL(adamax,
ops::AdamaxOpKernel<paddle::platform::CPUPlace, float>);
ops::AdamaxOpKernel<paddle::platform::CPUPlace, float>,
ops::AdamaxOpKernel<paddle::platform::CPUPlace, double>);
paddle/operators/adamax_op.cu
浏览文件 @ 90e05a4b
......@@ -17,4 +17,5 @@
namespace ops = paddle::operators;
REGISTER_OP_GPU_KERNEL(adamax,
ops::AdamaxOpKernel<paddle::platform::GPUPlace, float>);
ops::AdamaxOpKernel<paddle::platform::GPUPlace, float>,
ops::AdamaxOpKernel<paddle::platform::GPUPlace, double>);
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