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未验证 提交 ec5cb376 编写于 作者: X Xin Pan 提交者： GitHub
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Merge branch 'develop' into ir5

上级 abeb71c8 82671e94
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Showing with 6570 addition and 1142 deletion
CMakeLists.txt
浏览文件 @ ec5cb376
......@@ -24,6 +24,9 @@ message(STATUS "CXX compiler: ${CMAKE_CXX_COMPILER}, version: "
"${CMAKE_CXX_COMPILER_ID} ${CMAKE_CXX_COMPILER_VERSION}")
message(STATUS "C compiler: ${CMAKE_C_COMPILER}, version: "
"${CMAKE_C_COMPILER_ID} ${CMAKE_C_COMPILER_VERSION}")
if(WIN32)
set(CMAKE_STATIC_LIBRARY_PREFIX lib)
endif(WIN32)
if(NOT CMAKE_CROSSCOMPILING)
find_package(CUDA QUIET)
......@@ -138,12 +141,6 @@ else()
set(THIRD_PARTY_BUILD_TYPE Release)
endif()
if(WITH_MKL)
option(MKL_SPLIT_GEMM "PaddlePaddle MKL gemm would split to small ones" OFF)
if (MKL_SPLIT_GEMM)
add_definitions(-DPADDLE_MKL_SPLIT_GEMM)
endif()
endif()
set(WITH_MKLML ${WITH_MKL})
if (NOT DEFINED WITH_MKLDNN)
if (WITH_MKL AND AVX2_FOUND)
......@@ -171,7 +168,6 @@ include(external/python) # download, build, install python
include(external/openblas) # download, build, install openblas
include(external/mkldnn) # download, build, install mkldnn
include(external/swig) # download, build, install swig
include(external/warpctc) # download, build, install warpctc
include(external/boost) # download boost
include(external/any) # download libn::any
include(external/eigen) # download eigen3
......@@ -179,6 +175,14 @@ include(external/pybind11) # download pybind11
include(external/cares)
include(external/cub)
if (NOT WIN32)
# there is no official support of snappystream, warpctc, nccl, cupti in windows
include(external/snappy) # download snappy
include(external/snappystream) # download snappystream
include(external/warpctc) # download, build, install warpctc
include(cupti)
endif (NOT WIN32)
if(WITH_DISTRIBUTE)
if(WITH_GRPC)
include(external/grpc)
......@@ -200,13 +204,10 @@ if(WITH_BRPC_RDMA)
endif()
endif()
include(external/snappy) # download snappy
include(external/snappystream)
include(external/threadpool)
include(external/threadpool)
include(flags) # set paddle compile flags
include(cudnn) # set cudnn libraries, must before configure
include(cupti)
include(configure) # add paddle env configuration
if(WITH_GPU)
......
cmake/configure.cmake
浏览文件 @ ec5cb376
......@@ -61,6 +61,11 @@ if(NOT CMAKE_CROSSCOMPILING)
endif()
endif()
if(WIN32)
# windows stupid compile option for all targets.
add_definitions(-D_XKEYCHECK_H)
endif(WIN32)
if(NOT WITH_GOLANG)
add_definitions(-DPADDLE_WITHOUT_GOLANG)
endif(NOT WITH_GOLANG)
......
cmake/external/boost.cmake
浏览文件 @ ec5cb376
......@@ -28,7 +28,12 @@ if((NOT DEFINED BOOST_TAR) OR (NOT DEFINED BOOST_URL))
set(BOOST_TAR "boost_1_41_0" CACHE STRING "" FORCE)
set(BOOST_URL "http://paddlepaddledeps.cdn.bcebos.com/${BOOST_TAR}.tar.gz" CACHE STRING "" FORCE)
endif()
MESSAGE(STATUS "BOOST_TAR: ${BOOST_TAR}, BOOST_URL: ${BOOST_URL}")
IF (WIN32)
MESSAGE(WARNING, "In windows, boost can not be downloaded automaticlly, please build it manually and put it at " ${THIRD_PARTY_PATH}install/boost)
else()
MESSAGE(STATUS "BOOST_TAR: ${BOOST_TAR}, BOOST_URL: ${BOOST_URL}")
ENDIF(WIN32)
set(BOOST_SOURCES_DIR ${THIRD_PARTY_PATH}/boost)
set(BOOST_DOWNLOAD_DIR "${BOOST_SOURCES_DIR}/src/${BOOST_PROJECT}")
set(BOOST_INCLUDE_DIR "${BOOST_DOWNLOAD_DIR}/${BOOST_TAR}" CACHE PATH "boost include directory." FORCE)
......@@ -36,6 +41,7 @@ set_directory_properties(PROPERTIES CLEAN_NO_CUSTOM 1)
include_directories(${BOOST_INCLUDE_DIR})
if (NOT WIN32)
ExternalProject_Add(
${BOOST_PROJECT}
${EXTERNAL_PROJECT_LOG_ARGS}
......@@ -49,8 +55,9 @@ ExternalProject_Add(
INSTALL_COMMAND ""
UPDATE_COMMAND ""
)
endif(NOT WIN32)
if (${CMAKE_VERSION} VERSION_LESS "3.3.0")
if (${CMAKE_VERSION} VERSION_LESS "3.3.0" OR NOT WIN32)
set(dummyfile ${CMAKE_CURRENT_BINARY_DIR}/boost_dummy.c)
file(WRITE ${dummyfile} "const char *dummy = \"${dummyfile}\";")
add_library(boost STATIC ${dummyfile})
......
cmake/external/gflags.cmake
浏览文件 @ ec5cb376
......@@ -18,7 +18,7 @@ SET(GFLAGS_SOURCES_DIR ${THIRD_PARTY_PATH}/gflags)
SET(GFLAGS_INSTALL_DIR ${THIRD_PARTY_PATH}/install/gflags)
SET(GFLAGS_INCLUDE_DIR "${GFLAGS_INSTALL_DIR}/include" CACHE PATH "gflags include directory." FORCE)
IF(WIN32)
set(GFLAGS_LIBRARIES "${GFLAGS_INSTALL_DIR}/lib/gflags.lib" CACHE FILEPATH "GFLAGS_LIBRARIES" FORCE)
set(GFLAGS_LIBRARIES "${GFLAGS_INSTALL_DIR}/lib/libgflags.lib" CACHE FILEPATH "GFLAGS_LIBRARIES" FORCE)
ELSE(WIN32)
set(GFLAGS_LIBRARIES "${GFLAGS_INSTALL_DIR}/lib/libgflags.a" CACHE FILEPATH "GFLAGS_LIBRARIES" FORCE)
ENDIF(WIN32)
......@@ -45,7 +45,13 @@ ExternalProject_Add(
-DCMAKE_POSITION_INDEPENDENT_CODE:BOOL=ON
-DCMAKE_BUILD_TYPE:STRING=${THIRD_PARTY_BUILD_TYPE}
)
IF(WIN32)
IF(NOT EXISTS "${GFLAGS_INSTALL_DIR}/lib/libgflags.lib")
add_custom_command(TARGET extern_gflags POST_BUILD
COMMAND cmake -E rename ${GFLAGS_INSTALL_DIR}/lib/gflags_static.lib ${GFLAGS_INSTALL_DIR}/lib/libgflags.lib
)
ENDIF()
ENDIF(WIN32)
ADD_LIBRARY(gflags STATIC IMPORTED GLOBAL)
SET_PROPERTY(TARGET gflags PROPERTY IMPORTED_LOCATION ${GFLAGS_LIBRARIES})
ADD_DEPENDENCIES(gflags extern_gflags)
......@@ -60,3 +66,4 @@ IF(WITH_C_API)
INSTALL(FILES ${GFLAGS_LIBRARIES} DESTINATION third_party/gflags/lib)
ENDIF()
ENDIF()
cmake/external/glog.cmake
浏览文件 @ ec5cb376
......@@ -60,6 +60,13 @@ ExternalProject_Add(
-DCMAKE_POSITION_INDEPENDENT_CODE:BOOL=ON
-DCMAKE_BUILD_TYPE:STRING=${THIRD_PARTY_BUILD_TYPE}
)
IF(WIN32)
IF(NOT EXISTS "${GLOG_INSTALL_DIR}/lib/libglog.lib")
add_custom_command(TARGET extern_glog POST_BUILD
COMMAND cmake -E rename ${GLOG_INSTALL_DIR}/lib/glog.lib ${GLOG_INSTALL_DIR}/lib/libglog.lib
)
ENDIF()
ENDIF(WIN32)
ADD_LIBRARY(glog STATIC IMPORTED GLOBAL)
SET_PROPERTY(TARGET glog PROPERTY IMPORTED_LOCATION ${GLOG_LIBRARIES})
......
cmake/external/mkldnn.cmake
浏览文件 @ ec5cb376
......@@ -54,7 +54,7 @@ ExternalProject_Add(
${EXTERNAL_PROJECT_LOG_ARGS}
DEPENDS ${MKLDNN_DEPENDS}
GIT_REPOSITORY "https://github.com/01org/mkl-dnn.git"
GIT_TAG "a29d8487a63afca3d5b8c5bbdbb473cf8ccc6e51"
GIT_TAG "64e03a1939e0d526aa8e9f2e3f7dc0ad8d372944"
PREFIX ${MKLDNN_SOURCES_DIR}
UPDATE_COMMAND ""
CMAKE_ARGS -DCMAKE_CXX_COMPILER=${CMAKE_CXX_COMPILER}
......
cmake/external/openblas.cmake
浏览文件 @ ec5cb376
......@@ -17,20 +17,29 @@ IF(USE_EIGEN_FOR_BLAS)
ENDIF(USE_EIGEN_FOR_BLAS)
INCLUDE(cblas)
# IF(WIN32 AND NOT ${CBLAS_FOUND})
IF(NOT ${CBLAS_FOUND})
INCLUDE(ExternalProject)
SET(CBLAS_SOURCES_DIR ${THIRD_PARTY_PATH}/openblas)
SET(CBLAS_INSTALL_DIR ${THIRD_PARTY_PATH}/install/openblas)
SET(CBLAS_INC_DIR "${CBLAS_INSTALL_DIR}/include" CACHE PATH "openblas include directory." FORCE)
SET(CBLAS_INCLUDE_DIR "${CBLAS_INSTALL_DIR}/include" CACHE PATH "openblas include directory." FORCE)
SET(CBLAS_LIBRARIES
"${CBLAS_INSTALL_DIR}/lib/${CMAKE_STATIC_LIBRARY_PREFIX}openblas${CMAKE_STATIC_LIBRARY_SUFFIX}"
CACHE FILEPATH "openblas library." FORCE)
ADD_DEFINITIONS(-DPADDLE_USE_OPENBLAS)
IF (WIN32)
SET(CBLAS_FOUND true)
MESSAGE(WARNING, "In windows, openblas only support msvc build, please build it manually and put it at " ${CBLAS_INSTALL_DIR})
ENDIF(WIN32)
IF (NOT WIN32)
SET(OPENBLAS_CC "${CMAKE_C_COMPILER} -Wno-unused-but-set-variable -Wno-unused-variable")
SET(OPENBLAS_COMMIT "v0.2.20")
......@@ -69,7 +78,6 @@ IF(NOT ${CBLAS_FOUND})
ENDIF()
SET(COMMON_ARGS CC=${OPENBLAS_CC} NO_SHARED=1 NO_LAPACK=1 libs)
ExternalProject_Add(
extern_openblas
${EXTERNAL_PROJECT_LOG_ARGS}
......@@ -84,9 +92,11 @@ IF(NOT ${CBLAS_FOUND})
UPDATE_COMMAND ""
CONFIGURE_COMMAND ""
)
ELSE()
ENDIF(NOT WIN32)
SET(CBLAS_PROVIDER openblas)
IF(WITH_C_API)
INSTALL(DIRECTORY ${CBLAS_INC_DIR} DESTINATION third_party/openblas)
INSTALL(DIRECTORY ${CBLAS_INCLUDE_DIR} DESTINATION third_party/openblas)
# Because libopenblas.a is a symbolic link of another library, thus need to
# install the whole directory.
IF(ANDROID)
......@@ -107,7 +117,8 @@ IF(NOT ${CBLAS_FOUND})
ENDIF(NOT ${CBLAS_FOUND})
MESSAGE(STATUS "BLAS library: ${CBLAS_LIBRARIES}")
INCLUDE_DIRECTORIES(${CBLAS_INC_DIR})
MESSAGE(STATUS "BLAS Include: ${CBLAS_INCLUDE_DIR}")
INCLUDE_DIRECTORIES(${CBLAS_INCLUDE_DIR})
# FIXME(gangliao): generate cblas target to track all high performance
# linear algebra libraries for cc_library(xxx SRCS xxx.c DEPS cblas)
......
cmake/external/protobuf.cmake
浏览文件 @ ec5cb376
......@@ -14,11 +14,14 @@
INCLUDE(ExternalProject)
# Always invoke `FIND_PACKAGE(Protobuf)` for importing function protobuf_generate_cpp
IF(NOT WIN32)
FIND_PACKAGE(Protobuf QUIET)
ENDIF(NOT WIN32)
macro(UNSET_VAR VAR_NAME)
UNSET(${VAR_NAME} CACHE)
UNSET(${VAR_NAME})
endmacro()
UNSET_VAR(PROTOBUF_INCLUDE_DIR)
UNSET_VAR(PROTOBUF_FOUND)
UNSET_VAR(PROTOBUF_PROTOC_EXECUTABLE)
......@@ -94,12 +97,14 @@ macro(PROMPT_PROTOBUF_LIB)
SET(protobuf_DEPS ${ARGN})
MESSAGE(STATUS "Protobuf protoc executable: ${PROTOBUF_PROTOC_EXECUTABLE}")
MESSAGE(STATUS "Protobuf-lite library: ${PROTOBUF_LITE_LIBRARY}")
MESSAGE(STATUS "Protobuf library: ${PROTOBUF_LIBRARY}")
MESSAGE(STATUS "Protoc library: ${PROTOBUF_PROTOC_LIBRARY}")
MESSAGE(STATUS "Protobuf version: ${PROTOBUF_VERSION}")
INCLUDE_DIRECTORIES(${PROTOBUF_INCLUDE_DIR})
# Assuming that all the protobuf libraries are of the same type.
IF(${PROTOBUF_LIBRARY} MATCHES "${CMAKE_STATIC_LIBRARY_SUFFIX}$")
IF(${PROTOBUF_LIBRARY} MATCHES ${CMAKE_STATIC_LIBRARY_SUFFIX})
SET(protobuf_LIBTYPE STATIC)
ELSEIF(${PROTOBUF_LIBRARY} MATCHES "${CMAKE_SHARED_LIBRARY_SUFFIX}$")
SET(protobuf_LIBTYPE SHARED)
......@@ -137,18 +142,25 @@ macro(SET_PROTOBUF_VERSION)
endmacro()
set(PROTOBUF_ROOT "" CACHE PATH "Folder contains protobuf")
IF (WIN32)
SET(PROTOBUF_ROOT ${THIRD_PARTY_PATH}/install/protobuf)
MESSAGE(WARNING, "In windows, protobuf only support msvc build, please build it manually and put it at " ${PROTOBUF_ROOT})
ENDIF(WIN32)
if (NOT "${PROTOBUF_ROOT}" STREQUAL "")
find_path(PROTOBUF_INCLUDE_DIR google/protobuf/message.h PATHS ${PROTOBUF_ROOT}/include NO_DEFAULT_PATH)
find_library(PROTOBUF_LIBRARY protobuf PATHS ${PROTOBUF_ROOT}/lib NO_DEFAULT_PATH)
find_library(PROTOBUF_LITE_LIBRARY protobuf-lite PATHS ${PROTOBUF_ROOT}/lib NO_DEFAULT_PATH)
find_library(PROTOBUF_PROTOC_LIBRARY protoc PATHS ${PROTOBUF_ROOT}/lib NO_DEFAULT_PATH)
find_library(PROTOBUF_LIBRARY protobuf libprotobuf.lib PATHS ${PROTOBUF_ROOT}/lib NO_DEFAULT_PATH)
find_library(PROTOBUF_LITE_LIBRARY protobuf-lite libprotobuf-lite.lib PATHS ${PROTOBUF_ROOT}/lib NO_DEFAULT_PATH)
find_library(PROTOBUF_PROTOC_LIBRARY protoc libprotoc.lib PATHS ${PROTOBUF_ROOT}/lib NO_DEFAULT_PATH)
find_program(PROTOBUF_PROTOC_EXECUTABLE protoc PATHS ${PROTOBUF_ROOT}/bin NO_DEFAULT_PATH)
if (PROTOBUF_INCLUDE_DIR AND PROTOBUF_LIBRARY AND PROTOBUF_LITE_LIBRARY AND PROTOBUF_PROTOC_LIBRARY AND PROTOBUF_PROTOC_EXECUTABLE)
message(STATUS "Using custom protobuf library in ${PROTOBUF_ROOT}.")
SET(PROTOBUF_FOUND true)
SET_PROTOBUF_VERSION()
PROMPT_PROTOBUF_LIB()
else()
message(WARNING "Cannot find protobuf library in ${PROTOBUF_ROOT}.")
message(WARNING "Cannot find protobuf library in ${PROTOBUF_ROOT}")
endif()
endif()
......@@ -239,6 +251,7 @@ IF(CMAKE_CROSSCOMPILING)
CACHE FILEPATH "protobuf executable." FORCE)
ENDIF()
IF(NOT PROTOBUF_FOUND)
build_protobuf(extern_protobuf FALSE)
......
cmake/generic.cmake
浏览文件 @ ec5cb376
......@@ -148,7 +148,8 @@ function(merge_static_libs TARGET_NAME)
COMMAND rm "${CMAKE_CURRENT_BINARY_DIR}/lib${TARGET_NAME}.a"
COMMAND /usr/bin/libtool -static -o "${CMAKE_CURRENT_BINARY_DIR}/lib${TARGET_NAME}.a" ${libfiles}
)
else() # general UNIX: use "ar" to extract objects and re-add to a common lib
endif(APPLE)
if(LINUX) # general UNIX: use "ar" to extract objects and re-add to a common lib
set(target_DIR ${CMAKE_CURRENT_BINARY_DIR}/${TARGET_NAME}.dir)
foreach(lib ${libs})
......@@ -187,7 +188,36 @@ function(merge_static_libs TARGET_NAME)
COMMAND ${CMAKE_AR} crs ${target_LIBNAME} `find ${target_DIR} -name '*.o'`
COMMAND ${CMAKE_RANLIB} ${target_LIBNAME}
WORKING_DIRECTORY ${target_DIR})
endif()
endif(LINUX)
if(WIN32) # windows do not support gcc/nvcc combined compiling. Use msvc lib.exe to merge libs.
# Make the generated dummy source file depended on all static input
# libs. If input lib changes,the source file is touched
# which causes the desired effect (relink).
add_custom_command(OUTPUT ${target_SRCS}
COMMAND ${CMAKE_COMMAND} -E touch ${target_SRCS}
DEPENDS ${libs})
# Generate dummy staic lib
file(WRITE ${target_SRCS} "const char *dummy_${TARGET_NAME} = \"${target_SRCS}\";")
add_library(${TARGET_NAME} STATIC ${target_SRCS})
target_link_libraries(${TARGET_NAME} ${libs_deps})
foreach(lib ${libs})
# Get the file names of the libraries to be merged
#if(NOT $<TARGET_FILE:${lib}> MATCHES "lib.*\\.lib")
# message("library" ${lib})
# set(libfiles ${libfiles} lib$<TARGET_FILE:${lib}>)
#else()
set(libfiles ${libfiles} $<TARGET_FILE:${lib}>)
#endif()
endforeach()
# windows cmd return error in clean env.
# COMMAND del "${CMAKE_CURRENT_BINARY_DIR}/${CMAKE_BUILD_TYPE}/${TARGET_NAME}.lib"
add_custom_command(TARGET ${TARGET_NAME} POST_BUILD
COMMAND lib /OUT:${CMAKE_CURRENT_BINARY_DIR}/lib${TARGET_NAME}.lib ${libfiles}
)
endif(WIN32)
endfunction(merge_static_libs)
function(cc_library TARGET_NAME)
......@@ -195,6 +225,10 @@ function(cc_library TARGET_NAME)
set(oneValueArgs "")
set(multiValueArgs SRCS DEPS)
cmake_parse_arguments(cc_library "${options}" "${oneValueArgs}" "${multiValueArgs}" ${ARGN})
if(WIN32)
# add libxxx.lib prefix in windows
set(${TARGET_NAME}_LIB_NAME "${CMAKE_STATIC_LIBRARY_PREFIX}${TARGET_NAME}${CMAKE_STATIC_LIBRARY_SUFFIX}" CACHE STRING "output library name for target ${TARGET_NAME}")
endif(WIN32)
if(cc_library_SRCS)
if(cc_library_SHARED OR cc_library_shared) # build *.so
add_library(${TARGET_NAME} SHARED ${cc_library_SRCS})
......
cmake/inference_lib.cmake
浏览文件 @ ec5cb376
......@@ -101,6 +101,7 @@ if(WITH_MKLDNN)
)
endif()
if (NOT WIN32)
if(NOT MOBILE_INFERENCE AND NOT RPI)
set(dst_dir "${FLUID_INSTALL_DIR}/third_party/install/snappy")
copy(snappy_lib
......@@ -120,15 +121,23 @@ if(NOT MOBILE_INFERENCE AND NOT RPI)
DSTS ${dst_dir} ${dst_dir}/lib
DEPS zlib)
endif()
endif(NOT WIN32)
# paddle fluid module
set(src_dir "${PADDLE_SOURCE_DIR}/paddle/fluid")
set(dst_dir "${FLUID_INSTALL_DIR}/paddle/fluid")
set(module "framework")
if (NOT WIN32)
copy(framework_lib DEPS framework_py_proto
SRCS ${src_dir}/${module}/*.h ${src_dir}/${module}/details/*.h ${PADDLE_BINARY_DIR}/paddle/fluid/framework/framework.pb.h
DSTS ${dst_dir}/${module} ${dst_dir}/${module}/details ${dst_dir}/${module}
)
else()
copy(framework_lib
SRCS ${src_dir}/${module}/*.h ${src_dir}/${module}/details/*.h ${PADDLE_BINARY_DIR}/paddle/fluid/framework/framework.pb.h
DSTS ${dst_dir}/${module} ${dst_dir}/${module}/details ${dst_dir}/${module}
)
endif(NOT WIN32)
set(module "memory")
copy(memory_lib
......
doc/fluid/design/dist_train/dist_train_nccl2.md
浏览文件 @ ec5cb376
# Distributed Training with NCCL2
We design a pattern that can enable training with `ParallelExecutor` and
using [NCCL2](https://developer.nvidia.com/nccl) as it's collective
use [NCCL2](https://developer.nvidia.com/nccl) as it's collective
communication library.
In `ParallelExecutor` we can use `AllReduce` or `Reduce` and `Broadcast`
......@@ -9,14 +9,14 @@ to do multi GPU training. And if we initialize NCCL2 communicators as
ranks in a distributed environment, we can simply run the `ParallelExecutor`
as a distributed program! The only thing that may be different than in
the single node version is that we need to broadcast the NCCL unique ID
to all the nodes, and initialize communicators using that ID, so NCCL2
will know each other as ranks.
to all the nodes and initialize communicators using that ID, so NCCL2
can know each other as ranks.
To achieve this feature, we introduce a new operator: `gen_nccl_id` op,
so we are ***not*** "bind to" running NCCL2 with MPI, we can run it in
what ever platform you like.
whatever platform you like.
It have two running modes:
It has two running modes:
1. Generate and broadcast mode, which should be used on trainer 0;
1. Listen and fetch mode, which should be used on trainers other than 0.
......@@ -29,7 +29,7 @@ initialize NCCL communicator objects.
<img src="src/ncc2_design.png">
The above figure indicates the general process when training with NCCL2
distributed. Each trainer have the number of communicators equal to the
distributed. Each trainer has the number of communicators equal to the
number of GPUs, but the ranks should match the global ranks number: here
we have total 8 GPUs, so `nranks==8`, for each trainer, the ranks should
be from 0 ~ 3 on trainer 0 and 4 ~ 7 on trainer 1.
doc/fluid/dev/new_op_cn.md
浏览文件 @ ec5cb376
......@@ -36,19 +36,19 @@
<tbody>
<tr>
<td>OpProtoMake定义 </td>
<td>`.cc`文件，Backward Op不需要定义OpProtoMake </td>
<td>.cc 文件，Backward Op不需要定义OpProtoMake </td>
</tr>
<tr>
<td>Op定义 </td>
<td> `.cc`文件</td>
<td> .cc 文件</td>
</tr>
<tr>
<td>Kernel实现 </td>
<td> CPU、CUDA共享Kernel实现在`.h`文件中，否则，CPU 实现在`.cc`文件中，CUDA 实现在`.cu`文件中。</td>
<td> CPU、CUDA共享Kernel实现在.h 文件中，否则，CPU 实现在.cc 文件中，CUDA 实现在.cu 文件中。</td>
</tr>
<tr>
<td>注册Op </td>
<td> Op注册实现在`.cc`文件；Kernel注册CPU实现在`.cc`文件中，CUDA实现在`.cu`文件中</td>
<td> Op注册实现在.cc 文件；Kernel注册CPU实现在.cc 文件中，CUDA实现在.cu 文件中</td>
</tr>
</tbody>
</table>
......@@ -391,7 +391,7 @@ PADDLE_ENFORCE(ctx->HasInput("X"), "");
```
问题示例2 ：提示信息过于简单
```
PADDLE_ENFORCE(i != nullptr, "I must be set"); // I是什么？
PADDLE_ENFORCE(i != nullptr, "i must be set"); // i是什么？
```
2. 在报错信息中使用开发人员定义的变量缩写，不易理解！
......
doc/fluid/dev/releasing_process_en.md
浏览文件 @ ec5cb376
......@@ -50,6 +50,33 @@ pop-up box, choose the current release branch and click "Run Build" button. You
* pypi does not allow overwrite the already uploaded version of wheel package, even if you delete the
old version. you must change the version number before upload a new one.
### Publish wheel Packages for MacOS
You need to build the binary wheel package for MacOS before publishing, to
make sure that the package can be used by many versions of MacOS
(10.11, 10.12, 10.13) and different python installs (python.org, homebrew, etc.),
you must build the package ***exactly*** following below steps:
Build steps:
1. install python from python.org downloads, and make sure it's currently in use
in your system.
1. `export MACOSX_DEPLOYMENT_TARGET=10.11`, use `10.11` is enough for recent versions.
1. `git clone https://github.com/PaddlePaddle/Paddle.git && cd Paddle && mkdir build && cd build`
1. `cmake -DWITH_GPU=OFF -DWITH_MKL=OFF -DWITH_SYSTEM_BLAS=OFF ..`, make sure the output of `cmake` command is using the correct python interpreter installed from python.org
1. `make -j`
1. `pip install delocate`
1. `mkdir fixed_wheel && delocate-wheel -w fixed_wheel python/dist/*.whl`
Then the whl under `fixed_wheel` is ready to upload.
Install steps:
1. run `pip install paddlepaddle...whl`
1. find the `libpython.dylib` that are currently in use:
- for python.org package installs, do nothing.
- for other python installs, find the path of `libpython*.dylib` and `export LD_LIBRARY_PATH=you path && DYLD_LIBRARY_PATH=your path`
## Publish Docker Images
Our CI tool will push latest images to DockerHub, so we only need to push a version tag like:
......
doc/fluid/howto/cluster/nccl2_rdma_training.md
浏览文件 @ ec5cb376
# Distributed Training with NCCL2 and RDMA
When doing distributed multi-GPU training, network bandwith often becomes the
bottle neck. We introduce a way to use NCCL2 to do such training job to
achieve best performace.
When doing distributed multi-GPU training, network bandwidth often becomes the
bottleneck. We introduce a way to use NCCL2 to do such training job to
achieve best performance.
## Prepare Hardwares with RDMA and Multiple GPUs
## Prepare Hardware with RDMA and Multiple GPUs
I'm using two Linux servers each of them is installed with 8 GPUs and
I'm using two Linux servers each of them installed with 8 GPUs and
one 100Gb RDMA card.
Base environment is:
......@@ -25,7 +25,7 @@ In general, the steps including:
1. Use docker to run tests and make sure GPUs and RDMA can work inside
the container.
I'll ommit section "Install GPU drivers" because we can find it easily
I'll omit the section "Install GPU drivers" because we can find it easily
somewhere else.
### Install RDMA drivers
......@@ -33,7 +33,7 @@ somewhere else.
For my case, I've got two machines with device
"Mellanox Technologies MT27700 Family [ConnectX-4]" installed. The OS was
"CentOS 7.4" and I updated the kernel to version 4.4 so that docker can
work with latest overlay2 filesystem.
work with the latest overlay2 filesystem.
***NOTE: before you start, make sure you have a way to get a console
of the server other than ssh because we may need to re-configure the
......@@ -45,14 +45,14 @@ network device.***
1. Run `./mlnxofedinstall --add-kernel-support` in the software package.
1. Run `/etc/init.d/openibd restart` to make everything work, note that
this operation may cause the network goes down if you are using this
RDMA device as default network device and use ssh to login the server.
RDMA device as default network device and use ssh to log in the server.
1. Re-configure the network interface, for example:
`ifconfig eth2 192.168.16.30/20 up`, then add routes if needed:
`ip route add default via 192.168.16.1 dev eth2`.
1. Do the same thing on the other node.
1. Use `ping` to test if the two nodes have typical ICMP connection.
1. Use either `udaddy` or `ib_write_bw` to test the network connection is
ready and have the desired bandwith.
ready and have the desired bandwidth.
### Prepare Docker Image to Run RDMA Programs
......@@ -60,7 +60,7 @@ network device.***
package in it.
1. Start a docker container and mount GPU driver libs into it (you can
skip this step if you are using nvidia-docker).
1. Mount RDMA dirvers and libs into the docker image (see below section),
1. Mount RDMA drivers and libs into the docker image (see below section),
also `udaddy` and `ib_write_bw` if needed.
1. Mount GPU devices and RDMA devices into the container using `--device`
or just use privileged mode `--privileged`.
......
doc/fluid/new_docs/advanced_usage/deploy/native_infer.rst
浏览文件 @ ec5cb376
......@@ -9,8 +9,6 @@ Paddle 预测 API
- 头文件 ``paddle_inference_api.h`` 定义了所有的接口
- 库文件\ ``libpaddle_fluid.so`` 或 ``libpaddle_fluid.a``
- 库文件 ``libpaddle_inference_api.so`` 或
``libpaddle_inference_api.a``
编译和依赖可以参考 :ref:`install_or_build_cpp_inference_lib` 。
......@@ -97,8 +95,7 @@ engine
CHECK(predictor->Run(slots, &outputs));
// 获取 outputs ...
编译时，联编 ``libpaddle_fluid.a/.so`` 和
``libpaddle_inference_api.a/.so`` 便可。
编译时，联编 ``libpaddle_fluid.a/.so`` 即可。
详细代码参考
------------
......
paddle/fluid/API.spec
浏览文件 @ ec5cb376
......@@ -113,6 +113,7 @@ paddle.fluid.layers.beam_search_decode ArgSpec(args=['ids', 'scores', 'beam_size
paddle.fluid.layers.conv2d_transpose ArgSpec(args=['input', 'num_filters', 'output_size', 'filter_size', 'padding', 'stride', 'dilation', 'groups', 'param_attr', 'bias_attr', 'use_cudnn', 'act', 'name'], varargs=None, keywords=None, defaults=(None, None, 0, 1, 1, None, None, None, True, None, None))
paddle.fluid.layers.conv3d_transpose ArgSpec(args=['input', 'num_filters', 'output_size', 'filter_size', 'padding', 'stride', 'dilation', 'groups', 'param_attr', 'bias_attr', 'use_cudnn', 'act', 'name'], varargs=None, keywords=None, defaults=(None, None, 0, 1, 1, None, None, None, True, None, None))
paddle.fluid.layers.sequence_expand ArgSpec(args=['x', 'y', 'ref_level', 'name'], varargs=None, keywords=None, defaults=(-1, None))
paddle.fluid.layers.sequence_pad ArgSpec(args=['x', 'pad_value', 'maxlen'], varargs=None, keywords=None, defaults=(None,))
paddle.fluid.layers.lstm_unit ArgSpec(args=['x_t', 'hidden_t_prev', 'cell_t_prev', 'forget_bias', 'param_attr', 'bias_attr', 'name'], varargs=None, keywords=None, defaults=(0.0, None, None, None))
paddle.fluid.layers.reduce_sum ArgSpec(args=['input', 'dim', 'keep_dim', 'name'], varargs=None, keywords=None, defaults=(None, False, None))
paddle.fluid.layers.reduce_mean ArgSpec(args=['input', 'dim', 'keep_dim', 'name'], varargs=None, keywords=None, defaults=(None, False, None))
......@@ -146,6 +147,7 @@ paddle.fluid.layers.reshape ArgSpec(args=['x', 'shape', 'actual_shape', 'act', '
paddle.fluid.layers.lod_reset ArgSpec(args=['x', 'y', 'target_lod'], varargs=None, keywords=None, defaults=(None, None))
paddle.fluid.layers.lrn ArgSpec(args=['input', 'n', 'k', 'alpha', 'beta', 'name'], varargs=None, keywords=None, defaults=(5, 1.0, 0.0001, 0.75, None))
paddle.fluid.layers.pad ArgSpec(args=['x', 'paddings', 'pad_value', 'name'], varargs=None, keywords=None, defaults=(0.0, None))
paddle.fluid.layers.pad_constant_like ArgSpec(args=['x', 'y', 'pad_value', 'name'], varargs=None, keywords=None, defaults=(0.0, None))
paddle.fluid.layers.label_smooth ArgSpec(args=['label', 'prior_dist', 'epsilon', 'dtype', 'name'], varargs=None, keywords=None, defaults=(None, 0.1, 'float32', None))
paddle.fluid.layers.roi_pool ArgSpec(args=['input', 'rois', 'pooled_height', 'pooled_width', 'spatial_scale'], varargs=None, keywords=None, defaults=(1, 1, 1.0))
paddle.fluid.layers.dice_loss ArgSpec(args=['input', 'label', 'epsilon'], varargs=None, keywords=None, defaults=(1e-05,))
......@@ -162,6 +164,9 @@ paddle.fluid.layers.crop ArgSpec(args=['x', 'shape', 'offsets', 'name'], varargs
paddle.fluid.layers.rank_loss ArgSpec(args=['label', 'left', 'right', 'name'], varargs=None, keywords=None, defaults=(None,))
paddle.fluid.layers.prelu ArgSpec(args=['x', 'mode', 'param_attr', 'name'], varargs=None, keywords=None, defaults=(None, None))
paddle.fluid.layers.flatten ArgSpec(args=['x', 'axis', 'name'], varargs=None, keywords=None, defaults=(1, None))
paddle.fluid.layers.sequence_mask ArgSpec(args=['x', 'maxlen', 'dtype', 'name'], varargs=None, keywords=None, defaults=(None, 'int64', None))
paddle.fluid.layers.stack ArgSpec(args=['x', 'axis'], varargs=None, keywords=None, defaults=(0,))
paddle.fluid.layers.unstack ArgSpec(args=['x', 'axis', 'num'], varargs=None, keywords=None, defaults=(0, None))
paddle.fluid.layers.data ArgSpec(args=['name', 'shape', 'append_batch_size', 'dtype', 'lod_level', 'type', 'stop_gradient'], varargs=None, keywords=None, defaults=(True, 'float32', 0, VarType.LOD_TENSOR, True))
paddle.fluid.layers.open_recordio_file ArgSpec(args=['filename', 'shapes', 'lod_levels', 'dtypes', 'pass_num', 'for_parallel'], varargs=None, keywords=None, defaults=(1, True))
paddle.fluid.layers.open_files ArgSpec(args=['filenames', 'shapes', 'lod_levels', 'dtypes', 'thread_num', 'buffer_size', 'pass_num', 'is_test'], varargs=None, keywords=None, defaults=(None, None, 1, None))
......@@ -191,7 +196,7 @@ paddle.fluid.layers.argsort ArgSpec(args=['input', 'axis', 'name'], varargs=None
paddle.fluid.layers.ones ArgSpec(args=['shape', 'dtype', 'force_cpu'], varargs=None, keywords=None, defaults=(False,))
paddle.fluid.layers.zeros ArgSpec(args=['shape', 'dtype', 'force_cpu'], varargs=None, keywords=None, defaults=(False,))
paddle.fluid.layers.reverse ArgSpec(args=['x', 'axis'], varargs=None, keywords=None, defaults=None)
paddle.fluid.layers.While.__init__ ArgSpec(args=['self', 'cond', 'name'], varargs=None, keywords=None, defaults=(None,))
paddle.fluid.layers.While.__init__ ArgSpec(args=['self', 'cond', 'is_test', 'name'], varargs=None, keywords=None, defaults=(False, None))
paddle.fluid.layers.While.block ArgSpec(args=['self'], varargs=None, keywords=None, defaults=None)
paddle.fluid.layers.Switch.__init__ ArgSpec(args=['self', 'name'], varargs=None, keywords=None, defaults=(None,))
paddle.fluid.layers.Switch.case ArgSpec(args=['self', 'condition'], varargs=None, keywords=None, defaults=None)
......@@ -376,7 +381,7 @@ paddle.fluid.LoDTensor.__init__ 1. __init__(self: paddle.fluid.core.LoDTensor, a
paddle.fluid.LoDTensor.has_valid_recursive_sequence_lengths has_valid_recursive_sequence_lengths(self: paddle.fluid.core.LoDTensor) -> bool
paddle.fluid.LoDTensor.lod lod(self: paddle.fluid.core.LoDTensor) -> List[List[int]]
paddle.fluid.LoDTensor.recursive_sequence_lengths recursive_sequence_lengths(self: paddle.fluid.core.LoDTensor) -> List[List[int]]
paddle.fluid.LoDTensor.set 1. set(self: paddle.fluid.core.Tensor, arg0: numpy.ndarray[float32], arg1: paddle::platform::CPUPlace) -> None 2. set(self: paddle.fluid.core.Tensor, arg0: numpy.ndarray[int32], arg1: paddle::platform::CPUPlace) -> None 3. set(self: paddle.fluid.core.Tensor, arg0: numpy.ndarray[float64], arg1: paddle::platform::CPUPlace) -> None 4. set(self: paddle.fluid.core.Tensor, arg0: numpy.ndarray[int64], arg1: paddle::platform::CPUPlace) -> None 5. set(self: paddle.fluid.core.Tensor, arg0: numpy.ndarray[bool], arg1: paddle::platform::CPUPlace) -> None 6. set(self: paddle.fluid.core.Tensor, arg0: numpy.ndarray[uint16], arg1: paddle::platform::CPUPlace) -> None 7. set(self: paddle.fluid.core.Tensor, arg0: numpy.ndarray[uint8], arg1: paddle::platform::CPUPlace) -> None 8. set(self: paddle.fluid.core.Tensor, arg0: numpy.ndarray[float32], arg1: paddle::platform::CUDAPlace) -> None 9. set(self: paddle.fluid.core.Tensor, arg0: numpy.ndarray[int32], arg1: paddle::platform::CUDAPlace) -> None 10. set(self: paddle.fluid.core.Tensor, arg0: numpy.ndarray[float64], arg1: paddle::platform::CUDAPlace) -> None 11. set(self: paddle.fluid.core.Tensor, arg0: numpy.ndarray[int64], arg1: paddle::platform::CUDAPlace) -> None 12. set(self: paddle.fluid.core.Tensor, arg0: numpy.ndarray[bool], arg1: paddle::platform::CUDAPlace) -> None 13. set(self: paddle.fluid.core.Tensor, arg0: numpy.ndarray[uint16], arg1: paddle::platform::CUDAPlace) -> None 14. set(self: paddle.fluid.core.Tensor, arg0: numpy.ndarray[uint8], arg1: paddle::platform::CUDAPlace) -> None 15. set(self: paddle.fluid.core.Tensor, arg0: numpy.ndarray[float32], arg1: paddle::platform::CUDAPinnedPlace) -> None 16. set(self: paddle.fluid.core.Tensor, arg0: numpy.ndarray[int32], arg1: paddle::platform::CUDAPinnedPlace) -> None 17. set(self: paddle.fluid.core.Tensor, arg0: numpy.ndarray[float64], arg1: paddle::platform::CUDAPinnedPlace) -> None 18. set(self: paddle.fluid.core.Tensor, arg0: numpy.ndarray[int64], arg1: paddle::platform::CUDAPinnedPlace) -> None 19. set(self: paddle.fluid.core.Tensor, arg0: numpy.ndarray[bool], arg1: paddle::platform::CUDAPinnedPlace) -> None 20. set(self: paddle.fluid.core.Tensor, arg0: numpy.ndarray[uint16], arg1: paddle::platform::CUDAPinnedPlace) -> None 21. set(self: paddle.fluid.core.Tensor, arg0: numpy.ndarray[uint8], arg1: paddle::platform::CUDAPinnedPlace) -> None
paddle.fluid.LoDTensor.set 1. set(self: paddle.fluid.core.Tensor, arg0: numpy.ndarray[float32], arg1: paddle::platform::CPUPlace) -> None 2. set(self: paddle.fluid.core.Tensor, arg0: numpy.ndarray[int32], arg1: paddle::platform::CPUPlace) -> None 3. set(self: paddle.fluid.core.Tensor, arg0: numpy.ndarray[float64], arg1: paddle::platform::CPUPlace) -> None 4. set(self: paddle.fluid.core.Tensor, arg0: numpy.ndarray[int64], arg1: paddle::platform::CPUPlace) -> None 5. set(self: paddle.fluid.core.Tensor, arg0: numpy.ndarray[bool], arg1: paddle::platform::CPUPlace) -> None 6. set(self: paddle.fluid.core.Tensor, arg0: numpy.ndarray[uint16], arg1: paddle::platform::CPUPlace) -> None 7. set(self: paddle.fluid.core.Tensor, arg0: numpy.ndarray[uint8], arg1: paddle::platform::CPUPlace) -> None 8. set(self: paddle.fluid.core.Tensor, arg0: numpy.ndarray[int8], arg1: paddle::platform::CPUPlace) -> None 9. set(self: paddle.fluid.core.Tensor, arg0: numpy.ndarray[float32], arg1: paddle::platform::CUDAPlace) -> None 10. set(self: paddle.fluid.core.Tensor, arg0: numpy.ndarray[int32], arg1: paddle::platform::CUDAPlace) -> None 11. set(self: paddle.fluid.core.Tensor, arg0: numpy.ndarray[float64], arg1: paddle::platform::CUDAPlace) -> None 12. set(self: paddle.fluid.core.Tensor, arg0: numpy.ndarray[int64], arg1: paddle::platform::CUDAPlace) -> None 13. set(self: paddle.fluid.core.Tensor, arg0: numpy.ndarray[bool], arg1: paddle::platform::CUDAPlace) -> None 14. set(self: paddle.fluid.core.Tensor, arg0: numpy.ndarray[uint16], arg1: paddle::platform::CUDAPlace) -> None 15. set(self: paddle.fluid.core.Tensor, arg0: numpy.ndarray[uint8], arg1: paddle::platform::CUDAPlace) -> None 16. set(self: paddle.fluid.core.Tensor, arg0: numpy.ndarray[int8], arg1: paddle::platform::CUDAPlace) -> None 17. set(self: paddle.fluid.core.Tensor, arg0: numpy.ndarray[float32], arg1: paddle::platform::CUDAPinnedPlace) -> None 18. set(self: paddle.fluid.core.Tensor, arg0: numpy.ndarray[int32], arg1: paddle::platform::CUDAPinnedPlace) -> None 19. set(self: paddle.fluid.core.Tensor, arg0: numpy.ndarray[float64], arg1: paddle::platform::CUDAPinnedPlace) -> None 20. set(self: paddle.fluid.core.Tensor, arg0: numpy.ndarray[int64], arg1: paddle::platform::CUDAPinnedPlace) -> None 21. set(self: paddle.fluid.core.Tensor, arg0: numpy.ndarray[bool], arg1: paddle::platform::CUDAPinnedPlace) -> None 22. set(self: paddle.fluid.core.Tensor, arg0: numpy.ndarray[uint16], arg1: paddle::platform::CUDAPinnedPlace) -> None 23. set(self: paddle.fluid.core.Tensor, arg0: numpy.ndarray[uint8], arg1: paddle::platform::CUDAPinnedPlace) -> None 24. set(self: paddle.fluid.core.Tensor, arg0: numpy.ndarray[int8], arg1: paddle::platform::CUDAPinnedPlace) -> None
paddle.fluid.LoDTensor.set_lod set_lod(self: paddle.fluid.core.LoDTensor, arg0: List[List[int]]) -> None
paddle.fluid.LoDTensor.set_recursive_sequence_lengths set_recursive_sequence_lengths(self: paddle.fluid.core.LoDTensor, arg0: List[List[int]]) -> None
paddle.fluid.LoDTensor.shape shape(self: paddle.fluid.core.Tensor) -> List[int]
......
paddle/fluid/CMakeLists.txt
浏览文件 @ ec5cb376
......@@ -2,9 +2,13 @@ add_subdirectory(memory)
add_subdirectory(platform)
add_subdirectory(framework)
add_subdirectory(operators)
add_subdirectory(pybind)
add_subdirectory(string)
if (NOT WIN32)
add_subdirectory(pybind)
add_subdirectory(recordio)
endif(NOT WIN32)
if(WITH_INFERENCE)
# NOTE: please add subdirectory inference at last.
add_subdirectory(inference)
......
paddle/fluid/framework/CMakeLists.txt
浏览文件 @ ec5cb376
add_subdirectory(details)
add_subdirectory(ir)
if (NOT WIN32)
add_subdirectory(details)
endif (NOT WIN32)
# ddim lib
proto_library(framework_proto SRCS framework.proto)
......@@ -28,8 +30,12 @@ if(WITH_GPU)
else()
cc_test(mixed_vector_test SRCS mixed_vector_test.cc DEPS place memory device_context tensor)
endif()
if (NOT WIN32)
cc_library(lod_tensor SRCS lod_tensor.cc DEPS ddim place tensor framework_proto recordio)
else()
cc_library(lod_tensor SRCS lod_tensor.cc DEPS ddim place tensor framework_proto)
endif (NOT WIN32)
cc_test(lod_tensor_test SRCS lod_tensor_test.cc DEPS lod_tensor memory)
nv_test(lod_tensor_gpu_test SRCS lod_tensor_test.cu DEPS lod_tensor)
......@@ -69,14 +75,22 @@ cc_library(op_proto_maker SRCS op_proto_maker.cc DEPS framework_proto attribute
cc_test(op_proto_maker_test SRCS op_proto_maker_test.cc DEPS op_proto_maker)
cc_library(op_info SRCS op_info.cc DEPS attribute framework_proto)
cc_library(shape_inference SRCS shape_inference.cc DEPS ddim attribute device_context)
if (NOT WIN32)
cc_library(operator SRCS operator.cc DEPS op_info device_context tensor scope glog
shape_inference data_transform lod_tensor profiler)
else()
cc_library(operator SRCS operator.cc DEPS op_info device_context tensor scope glog
shape_inference data_transform lod_tensor)
endif(NOT WIN32)
cc_test(operator_test SRCS operator_test.cc DEPS operator op_registry device_context)
cc_library(proto_desc SRCS var_desc.cc op_desc.cc block_desc.cc program_desc.cc DEPS shape_inference op_info operator glog)
cc_library(op_registry SRCS op_registry.cc DEPS op_proto_maker op_info operator glog proto_desc)
nv_test(op_registry_test SRCS op_registry_test.cc DEPS op_registry)
if (NOT WIN32)
py_proto_compile(framework_py_proto SRCS framework.proto)
# Generate an empty __init__.py to make framework_py_proto as a valid python module.
add_custom_target(framework_py_proto_init ALL COMMAND ${CMAKE_COMMAND} -E touch __init__.py)
......@@ -86,17 +100,18 @@ add_custom_command(TARGET framework_py_proto POST_BUILD
COMMAND cp *.py ${PADDLE_BINARY_DIR}/python/paddle/fluid/proto/
COMMENT "Copy generated python proto into directory paddle/fluid/proto."
WORKING_DIRECTORY ${CMAKE_CURRENT_BINARY_DIR})
endif(NOT WIN32)
cc_library(lod_rank_table SRCS lod_rank_table.cc DEPS lod_tensor)
cc_library(feed_fetch_method SRCS feed_fetch_method.cc DEPS lod_tensor scope glog)
if(WITH_DISTRIBUTE)
cc_library(executor SRCS executor.cc DEPS op_registry device_context scope framework_proto glog lod_rank_table feed_fetch_method sendrecvop_grpc cares grpc++_unsecure grpc_unsecure gpr)
cc_library(executor SRCS executor.cc DEPS op_registry device_context scope framework_proto glog lod_rank_table feed_fetch_method sendrecvop_grpc cares grpc++_unsecure grpc_unsecure gpr graph_to_program_pass)
set(DISTRIBUTE_COMPILE_FLAGS "-Wno-non-virtual-dtor -Wno-error=non-virtual-dtor -Wno-error=delete-non-virtual-dtor")
set_source_files_properties(executor.cc PROPERTIES COMPILE_FLAGS ${DISTRIBUTE_COMPILE_FLAGS})
else()
cc_library(executor SRCS executor.cc DEPS op_registry device_context scope framework_proto glog lod_rank_table feed_fetch_method)
cc_library(executor SRCS executor.cc DEPS op_registry device_context scope framework_proto glog lod_rank_table feed_fetch_method graph_to_program_pass)
endif()
if (NOT WIN32)
......@@ -120,7 +135,9 @@ cc_test(cow_ptr_tests SRCS details/cow_ptr_test.cc)
# cc_test(channel_test SRCS channel_test.cc)
cc_test(tuple_test SRCS tuple_test.cc )
if (NOT WIN32)
cc_test(rw_lock_test SRCS rw_lock_test.cc)
endif (NOT WIN32)
# disable test temporarily.
# TODO https://github.com/PaddlePaddle/Paddle/issues/11971
......
paddle/fluid/framework/array.h 0 → 100644
浏览文件 @ ec5cb376
// Copyright (c) 2018 PaddlePaddle Authors. All Rights Reserved.
//
// Licensed under the Apache License, Version 2.0 (the "License");
// you may not use this file except in compliance with the License.
// You may obtain a copy of the License at
//
// http://www.apache.org/licenses/LICENSE-2.0
//
// Unless required by applicable law or agreed to in writing, software
// distributed under the License is distributed on an "AS IS" BASIS,
// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
// See the License for the specific language governing permissions and
// limitations under the License.
#pragma once
#include <cstdint>
#include "paddle/fluid/platform/hostdevice.h"
namespace paddle {
namespace framework {
template <typename T, size_t N>
class Array {
static_assert(N > 0, "The size of array must be larger than 0");
public:
HOSTDEVICE Array() {}
HOSTDEVICE explicit Array(const T &val) {
for (size_t i = 0; i < N; ++i) data_[i] = val;
}
HOSTDEVICE const T *Get() const { return data_; }
HOSTDEVICE T *GetMutable() { return data_; }
HOSTDEVICE T &operator[](size_t index) { return data_[index]; }
HOSTDEVICE const T &operator[](size_t index) const { return data_[index]; }
HOSTDEVICE constexpr size_t size() const { return N; }
private:
T data_[N];
};
} // namespace framework
} // namespace paddle
paddle/fluid/framework/data_type.cc
浏览文件 @ ec5cb376
......@@ -64,6 +64,7 @@ static DataTypeMap* InitDataTypeMap() {
RegType(size_t, proto::VarType::SIZE_T);
RegType(int16_t, proto::VarType::INT16);
RegType(uint8_t, proto::VarType::UINT8);
RegType(int8_t, proto::VarType::INT8);
#undef RegType
return retv;
......
paddle/fluid/framework/data_type.h
浏览文件 @ ec5cb376
......@@ -26,6 +26,7 @@ namespace framework {
extern proto::VarType::Type ToDataType(std::type_index type);
extern std::type_index ToTypeIndex(proto::VarType::Type type);
#if !defined(_WIN32)
template <typename Visitor>
inline void VisitDataType(proto::VarType::Type type, Visitor visitor) {
switch (type) {
......@@ -53,10 +54,47 @@ inline void VisitDataType(proto::VarType::Type type, Visitor visitor) {
case proto::VarType::INT16:
visitor.template operator()<int16_t>();
break;
case proto::VarType::INT8:
visitor.template operator()<int8_t>();
break;
default:
PADDLE_THROW("Not supported %d", type);
}
}
#else
// the msvc compiler do not implement two-stage name lookup correctly.
template <typename Visitor>
inline void VisitDataType(proto::VarType::Type type, Visitor visitor) {
switch (type) {
case proto::VarType::FP16:
visitor.operator()<platform::float16>();
break;
case proto::VarType::FP32:
visitor.operator()<float>();
break;
case proto::VarType::FP64:
visitor.operator()<double>();
break;
case proto::VarType::INT32:
visitor.operator()<int>();
break;
case proto::VarType::INT64:
visitor.operator()<int64_t>();
break;
case proto::VarType::BOOL:
visitor.operator()<bool>();
break;
case proto::VarType::UINT8:
visitor.operator()<uint8_t>();
break;
case proto::VarType::INT16:
visitor.operator()<int16_t>();
break;
default:
PADDLE_THROW("Not supported %d", type);
}
}
#endif // _WIN32
extern std::string DataTypeToString(const proto::VarType::Type type);
extern size_t SizeOfType(std::type_index type);
......
paddle/fluid/framework/details/multi_devices_graph_pass.cc
浏览文件 @ ec5cb376
......@@ -754,9 +754,20 @@ void MultiDevSSAGraphBuilder::CreateDistTrainOp(ir::Graph *result,
node->Op()->Type());
CreateComputationalOp(result, node, op_dev_id);
if (node->Op()->Type() == "concat") {
ConnectOp(result, result->Get<GraphOps>(kGraphOps).back().get(),
"fetch_barrier");
}
void SetOpInputsAllPlaces(ir::Graph *result, ir::Node *node, int num_places) {
auto *op_handle = result->Get<GraphOps>(kGraphOps).back().get();
for (ir::Node *input : node->inputs) {
VarHandle *var = nullptr;
for (int place_offset = 0; place_offset < num_places; ++place_offset) {
auto &var_holders = result->Get<GraphVars>(kGraphVars)[place_offset];
auto &var_holder = var_holders[input->Name()];
if (!var_holder.empty()) {
var = var_holder.rbegin()->get();
op_handle->AddInput(var);
}
}
}
}
......@@ -771,59 +782,83 @@ void MultiDevSSAGraphBuilder::CreateRPCOp(ir::Graph *result,
"This hack no longer holds, please fix.");
// the variable name which contains .block means it was splited by
// split_byref op
// so that we can balance the variable blocks to all the pserver
// instances.
if (strategy_.reduce_ == BuildStrategy::ReduceStrategy::kAllReduce &&
node->inputs[0]->Name().find(".block") == std::string::npos) {
std::vector<std::string> input_var_names;
for (ir::Node *n : node->inputs) {
input_var_names.push_back(n->Name());
}
op_dev_id = GetAppropriateDeviceID(input_var_names);
auto send_param_grad = boost::get<std::vector<std::string>>(
node->Op()->GetAttr(OpProtoAndCheckerMaker::OpRoleVarAttrName()));
PADDLE_ENFORCE_EQ(send_param_grad.size(), 2U);
op_dev_id = GetAppropriateDeviceID({send_param_grad[1]});
VLOG(10) << "send grad " << input_var_names[0] << " origin "
<< send_param_grad[1] << " place: " << op_dev_id;
for (auto &varname : input_var_names) {
result->Get<ShardedVarDevice>(kShardedVarDevice)
.emplace(varname, op_dev_id);
}
result->Get<ShardedVarDevice>(kShardedVarDevice)
.emplace(send_param_grad[1], op_dev_id);
}
} else if (node->Op()->Type() == "recv") {
std::vector<std::string> output_var_names;
for (ir::Node *n : node->outputs) {
output_var_names.push_back(n->Name());
}
auto recv_param_grad = boost::get<std::vector<std::string>>(
node->Op()->GetAttr(OpProtoAndCheckerMaker::OpRoleVarAttrName()));
if (recv_param_grad.size() == 2U) {
op_dev_id = GetVarDeviceID(*result, recv_param_grad[1]);
VLOG(10) << "recv param " << recv_param_grad[0]
<< " get grad place: " << recv_param_grad[1]
<< " place: " << op_dev_id;
} else {
op_dev_id = GetAppropriateDeviceID(output_var_names);
}
for (auto &varname : output_var_names) {
result->Get<ShardedVarDevice>(kShardedVarDevice)
.emplace(varname, op_dev_id);
}
} else {
// send_barrier and fetch_barrier op can be scheduled on device 0
// send_barrier, fetch_barrier will run on place 0;
op_dev_id = 0;
}
PADDLE_ENFORCE(op_dev_id != -1, "can not find the right place for rpc op: %s",
node->Op()->Type());
result->Get<GraphOps>(kGraphOps).emplace_back(new RPCOpHandle(
result->CreateOpNode(node->Op()), *node->Op(), local_scopes_[op_dev_id],
node->Op()->Type(), places_[op_dev_id]));
// TODO(panyx0718): This might not be needed anymore.
if (node->Op()->Type() == "send_barrier") {
ConnectOp(result, result->Get<GraphOps>(kGraphOps).back().get(), "send");
} else if (node->Op()->Type() == "recv") {
ConnectOp(result, result->Get<GraphOps>(kGraphOps).back().get(),
"send_barrier");
} else if (node->Op()->Type() == "fetch_barrier") {
ConnectOp(result, result->Get<GraphOps>(kGraphOps).back().get(), "recv");
} else if (node->Op()->Type() == "send") {
// do nothing
if (node->Op()->Type() == "send") {
CreateOpHandleIOs(result, node, op_dev_id);
} else {
PADDLE_THROW(
"rpc op should be in ["
"send, send_barrier. recv, fetch_barrier]");
}
// send_barrier, recv, fetch_barrier's inputs are deps var, get them from
// all places
auto p = places_[op_dev_id];
auto *op_handle = result->Get<GraphOps>(kGraphOps).back().get();
op_handle->SetDeviceContext(p,
platform::DeviceContextPool::Instance().Get(p));
CreateOpHandleIOs(result, node, op_dev_id);
SetOpInputsAllPlaces(result, node, places_.size());
for (ir::Node *output : node->outputs) {
int outvar_dev_id = op_dev_id;
if (node->Op()->Type() == "fetch_barrier") {
outvar_dev_id = GetVarDeviceID(*result, output->Name());
PADDLE_ENFORCE_NE(outvar_dev_id, -1);
}
p = places_[outvar_dev_id];
ir::Node *new_node = nullptr;
if (output->Var()) {
new_node = result->CreateVarNode(output->Var());
} else {
new_node =
result->CreateEmptyNode(output->Name(), ir::Node::Type::kVariable);
}
CreateOpOutput(result, op_handle, new_node, p, outvar_dev_id);
}
}
}
bool MultiDevSSAGraphBuilder::IsScaleLossOp(ir::Node *node) const {
......
paddle/fluid/framework/details/multi_devices_graph_print_pass.cc
浏览文件 @ ec5cb376
......@@ -54,7 +54,8 @@ void GraphvizSSAGraphPrinter::Print(const ir::Graph &graph,
sout << "var_" << cur_var_id << " [label=\"" << var_handle_ptr->name_
<< "\\n"
<< var_handle_ptr->place_ << "\\n"
<< var_handle_ptr->version_ << "\"]" << std::endl;
<< "scope: " << var_handle_ptr->scope_idx_ << "\\n"
<< "v" << var_handle_ptr->version_ << "\"]" << std::endl;
} else if (dummy_ptr) {
sout << "var_" << cur_var_id << " [label=\"dummy\"]" << std::endl;
}
......
paddle/fluid/framework/framework.proto
浏览文件 @ ec5cb376
......@@ -107,6 +107,7 @@ message VarType {
// Tensor<size_t> is used in C++.
SIZE_T = 19;
UINT8 = 20;
INT8 = 21;
// Other types that may need additional descriptions
LOD_TENSOR = 7;
......
paddle/fluid/framework/ir/CMakeLists.txt
浏览文件 @ ec5cb376
......@@ -3,6 +3,7 @@ cc_library(graph SRCS graph.cc DEPS node)
cc_library(graph_helper SRCS graph_helper.cc DEPS graph)
cc_library(pass SRCS pass.cc DEPS graph node graph_helper)
cc_library(graph_viz_pass SRCS graph_viz_pass.cc DEPS graph pass graph_helper)
cc_library(graph_to_program_pass SRCS graph_to_program_pass.cc DEPS graph pass graph_helper)
cc_library(graph_traits SRCS graph_traits.cc DEPS graph)
cc_library(graph_pattern_detecter SRCS graph_pattern_detecter.cc DEPS graph graph_helper graph_traits)
cc_library(fc_fuse_pass SRCS fc_fuse_pass.cc DEPS graph graph_pattern_detecter)
......@@ -12,5 +13,6 @@ cc_library(infer_clean_graph_pass SRCS infer_clean_graph_pass.cc DEPS graph pass
cc_test(pass_test SRCS pass_test.cc DEPS graph pass graph_helper)
cc_test(graph_test SRCS graph_test.cc DEPS graph graph_helper op_registry)
cc_test(graph_helper_test SRCS graph_helper_test.cc DEPS graph graph_helper op_registry)
cc_test(graph_to_program_pass_test SRCS graph_to_program_pass_test.cc DEPS graph_to_program_pass)
cc_test(test_graph_pattern_detecter SRCS graph_pattern_detecter_tester.cc DEPS graph_pattern_detecter)
cc_test(test_fc_fuse_pass SRCS fc_fuse_pass_tester.cc DEPS fc_fuse_pass graph_pattern_detecter graph pass graph_traits framework_proto)
paddle/fluid/framework/ir/graph.cc
浏览文件 @ ec5cb376
......@@ -132,63 +132,6 @@ Graph::Graph(const ProgramDesc &program) : program_(program) {
}
}
std::vector<ir::Node *> send_ops;
ir::Node *send_bar = nullptr;
std::vector<ir::Node *> recv_ops;
ir::Node *fetch_bar = nullptr;
for (ir::Node *node : Nodes()) {
if (node->Name() == "send") {
send_ops.push_back(node);
} else if (node->Name() == "send_barrier") {
PADDLE_ENFORCE(!send_bar, "only has one send barrier");
send_bar = node;
} else if (node->Name() == "recv") {
recv_ops.push_back(node);
} else if (node->Name() == "fetch_barrier") {
PADDLE_ENFORCE(!fetch_bar, "only has one fetch barrier");
fetch_bar = node;
}
}
if (send_bar) {
for (ir::Node *send : send_ops) {
ir::Node *dep_var = CreateControlDepVar();
send->outputs.push_back(dep_var);
dep_var->inputs.push_back(send);
send_bar->inputs.push_back(dep_var);
dep_var->outputs.push_back(send_bar);
}
for (ir::Node *recv : recv_ops) {
ir::Node *dep_var = CreateControlDepVar();
recv->inputs.push_back(dep_var);
dep_var->outputs.push_back(recv);
send_bar->outputs.push_back(dep_var);
dep_var->inputs.push_back(send_bar);
}
}
if (fetch_bar) {
for (ir::Node *recv : recv_ops) {
ir::Node *dep_var = CreateControlDepVar();
recv->outputs.push_back(dep_var);
dep_var->inputs.push_back(recv);
fetch_bar->inputs.push_back(dep_var);
dep_var->outputs.push_back(fetch_bar);
}
}
std::vector<std::string> send_vars = FindDistTrainSendVars(send_ops);
std::vector<std::string> recv_vars = FindDistTrainRecvVars(recv_ops);
for (ir::Node *node : Nodes()) {
if (IsDistTrainOp(node, send_vars, recv_vars)) {
if (fetch_bar && node->Name() == "concat") {
ir::Node *dep_var = CreateControlDepVar();
fetch_bar->outputs.push_back(dep_var);
dep_var->inputs.push_back(fetch_bar);
node->inputs.push_back(dep_var);
dep_var->outputs.push_back(node);
}
}
}
/**
* We should handle write after read(WAR) and write after write(WAW) here.
* Because some of the operators of the program can be executed parallelly.
......
paddle/fluid/framework/ir/graph.h
浏览文件 @ ec5cb376
......@@ -142,8 +142,6 @@ class Graph {
nodes_.erase(node);
}
const ProgramDesc &program() const { return program_; }
private:
// This method takes ownership of `node`.
ir::Node *AddNode(ir::Node *node) {
......@@ -154,7 +152,7 @@ class Graph {
}
// NOTE: program_ shouldn't be exposed to user.
const ProgramDesc &program_;
const ProgramDesc program_;
std::map<std::string, boost::any> attrs_;
std::map<std::string, std::function<void(void)>> attr_dels_;
std::map<ir::Node *, std::unique_ptr<ir::Node>> nodes_;
......
paddle/fluid/framework/ir/graph_pattern_detecter_tester.cc
浏览文件 @ ec5cb376
......@@ -163,8 +163,8 @@ TEST(GraphPatternDetecter, MultiSubgraph) {
// 3. Detect op2 -> var2 -> op4
// 4. Detect op2 -> var3 -> op5
// But 2 and 3 and 4 overlapped, so keep 2, so the final choices are 1 and 2
ASSERT_GE(count, 1UL);
ASSERT_LE(count, 2UL);
ASSERT_GE(count, 1);
ASSERT_LE(count, 2);
}
} // namespace ir
......
paddle/fluid/framework/ir/graph_to_program_pass.cc 0 → 100644
浏览文件 @ ec5cb376
/* Copyright (c) 2018 PaddlePaddle Authors. All Rights Reserved.
Licensed under the Apache License, Version 2.0 (the "License");
you may not use this file except in compliance with the License.
You may obtain a copy of the License at
http://www.apache.org/licenses/LICENSE-2.0
Unless required by applicable law or agreed to in writing, software
distributed under the License is distributed on an "AS IS" BASIS,
WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
See the License for the specific language governing permissions and
limitations under the License. */
#include "paddle/fluid/framework/ir/graph_to_program_pass.h"
#include <map>
#include <string>
#include <vector>
#include "paddle/fluid/framework/ir/graph.h"
#include "paddle/fluid/framework/ir/graph_helper.h"
#include "paddle/fluid/framework/program_desc.h"
namespace paddle {
namespace framework {
namespace ir {
std::unique_ptr<Graph> GraphToProgramPass::ApplyImpl(
std::unique_ptr<Graph> graph) const {
ProgramDesc& program = Get<ProgramDesc>("program");
std::unique_ptr<proto::ProgramDesc> program_pb(
new proto::ProgramDesc(*program.Proto()));
auto block = program_pb->mutable_blocks(kRootBlockIndex);
block->clear_vars();
std::unordered_set<std::string> visited_vars;
for (ir::Node* n : graph->Nodes()) {
if (n->NodeType() == ir::Node::Type::kVariable) {
if (n->Var() && visited_vars.count(n->Var()->Name()) == 0) {
visited_vars.insert(n->Var()->Name());
block->add_vars()->MergeFrom(*n->Var()->Proto());
}
}
}
block->clear_ops();
std::vector<ir::Node*> nodes = TopologySortOperations(*graph);
for (ir::Node* n : nodes) {
if (!n->Op()) {
continue;
}
block->add_ops()->MergeFrom(*n->Op()->Proto());
}
program.CopyFrom(*program_pb);
return graph;
}
} // namespace ir
} // namespace framework
} // namespace paddle
REGISTER_PASS(graph_to_program_pass, paddle::framework::ir::GraphToProgramPass);
paddle/fluid/framework/ir/graph_to_program_pass.h 0 → 100644
浏览文件 @ ec5cb376
/* Copyright (c) 2018 PaddlePaddle Authors. All Rights Reserved.
Licensed under the Apache License, Version 2.0 (the "License");
you may not use this file except in compliance with the License.
You may obtain a copy of the License at
http://www.apache.org/licenses/LICENSE-2.0
Unless required by applicable law or agreed to in writing, software
distributed under the License is distributed on an "AS IS" BASIS,
WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
See the License for the specific language governing permissions and
limitations under the License. */
#pragma once
#include "paddle/fluid/framework/ir/pass.h"
namespace paddle {
namespace framework {
namespace ir {
class GraphToProgramPass : public Pass {
protected:
std::unique_ptr<Graph> ApplyImpl(std::unique_ptr<Graph> graph) const override;
};
} // namespace ir
} // namespace framework
} // namespace paddle
paddle/fluid/framework/ir/graph_to_program_pass_test.cc 0 → 100644
浏览文件 @ ec5cb376
/* Copyright (c) 2018 PaddlePaddle Authors. All Rights Reserved.
Licensed under the Apache License, Version 2.0 (the "License");
you may not use this file except in compliance with the License.
You may obtain a copy of the License at
http://www.apache.org/licenses/LICENSE-2.0
Unless required by applicable law or agreed to in writing, software
distributed under the License is distributed on an "AS IS" BASIS,
WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
See the License for the specific language governing permissions and
limitations under the License. */
#include "paddle/fluid/framework/ir/graph_to_program_pass.h"
#include <string>
#include <vector>
#include "gtest/gtest.h"
#include "paddle/fluid/framework/program_desc.h"
namespace paddle {
namespace framework {
namespace ir {
void BuildNoCircleGraph(Graph* g) {
OpDesc op1;
op1.SetType("op1");
OpDesc op2;
op2.SetType("op2");
OpDesc op3;
op3.SetType("op3");
OpDesc op4;
op4.SetType("op4");
OpDesc op5;
op5.SetType("op5");
VarDesc var1("var1");
VarDesc var2("var2");
VarDesc var3("var3");
VarDesc var4("var4");
ir::Node* o1 = g->CreateOpNode(&op1);
ir::Node* o2 = g->CreateOpNode(&op2);
ir::Node* o3 = g->CreateOpNode(&op3);
ir::Node* o4 = g->CreateOpNode(&op4);
ir::Node* o5 = g->CreateOpNode(&op5);
ir::Node* v1 = g->CreateVarNode(&var1);
ir::Node* v2 = g->CreateVarNode(&var2);
ir::Node* v3 = g->CreateVarNode(&var3);
ir::Node* v4 = g->CreateVarNode(&var4);
// o1->v1->o2
o1->outputs.push_back(v1);
o2->inputs.push_back(v1);
v1->inputs.push_back(o1);
v1->outputs.push_back(o2);
// o2->v2->o3
// o2->v2->o4
o2->outputs.push_back(v2);
o3->inputs.push_back(v2);
o4->inputs.push_back(v2);
v2->outputs.push_back(o3);
v2->outputs.push_back(o4);
v2->inputs.push_back(o2);
// o2->v3->o5
o2->outputs.push_back(v3);
o5->inputs.push_back(v3);
v3->inputs.push_back(o2);
v3->outputs.push_back(o5);
// o3-v4->o5
o3->outputs.push_back(v4);
o5->inputs.push_back(v4);
v4->inputs.push_back(o3);
v4->outputs.push_back(o5);
}
TEST(GraphToProgramPass, Basic) {
ProgramDesc prog;
std::unique_ptr<Graph> g(new Graph(prog));
BuildNoCircleGraph(g.get());
auto pass = paddle::framework::ir::PassRegistry::Instance().Get(
"graph_to_program_pass");
ProgramDesc compiled_prog;
pass->SetNotOwned<paddle::framework::ProgramDesc>("program", &compiled_prog);
pass->Apply(std::move(g));
std::vector<OpDesc*> ops = compiled_prog.Block(0).AllOps();
EXPECT_EQ(ops[0]->Type(), "op1");
EXPECT_EQ(ops[1]->Type(), "op2");
if (ops[2]->Type() == "op3") {
EXPECT_EQ(ops[3]->Type(), "op4");
} else if (ops[2]->Type() == "op4") {
EXPECT_EQ(ops[3]->Type(), "op3");
}
EXPECT_EQ(ops[4]->Type(), "op5");
std::unordered_set<std::string> vars;
for (VarDesc* v : compiled_prog.Block(0).AllVars()) {
vars.insert(v->Name());
}
EXPECT_TRUE(vars.find("var1") != vars.end());
EXPECT_TRUE(vars.find("var2") != vars.end());
EXPECT_TRUE(vars.find("var3") != vars.end());
}
} // namespace ir
} // namespace framework
} // namespace paddle
USE_PASS(graph_to_program_pass);
paddle/fluid/framework/ir/node.cc
浏览文件 @ ec5cb376
......@@ -17,7 +17,7 @@ limitations under the License. */
namespace paddle {
namespace framework {
namespace ir {
const char Node::kControlDepVarName[] = "__control_var";
constexpr char Node::kControlDepVarName[];
} // namespace ir
} // namespace framework
} // namespace paddle
paddle/fluid/framework/ir/node.h
浏览文件 @ ec5cb376
......@@ -27,7 +27,7 @@ namespace ir {
class Node {
public:
enum class Type { kOperation, kVariable };
static const char kControlDepVarName[];
static constexpr char kControlDepVarName[] = "__control_var";
explicit Node(const std::string& name, Type type)
: name_(name), var_desc_(nullptr), op_desc_(nullptr), type_(type) {}
......@@ -41,8 +41,7 @@ class Node {
explicit Node(OpDesc* op_desc)
: name_(op_desc->Type()),
var_desc_(nullptr),
op_desc_(new OpDesc(*op_desc)), // TODO(panyx0718) the pointer in the
// original OpDesc might go out.
op_desc_(new OpDesc(*op_desc, op_desc->Block())),
type_(Type::kOperation) {}
Type NodeType() const { return type_; }
......
paddle/fluid/framework/lod_tensor.cc
浏览文件 @ ec5cb376
......@@ -25,8 +25,10 @@ limitations under the License. */
#include "paddle/fluid/memory/memcpy.h"
#include "paddle/fluid/memory/memory.h"
#if !defined(_WIN32)
#include "paddle/fluid/recordio/scanner.h"
#include "paddle/fluid/recordio/writer.h"
#endif // _WIN32
namespace paddle {
namespace framework {
......@@ -300,6 +302,7 @@ void DeserializeFromStream(std::istream &is, LoDTensor *tensor,
TensorFromStream(is, static_cast<Tensor *>(tensor), dev_ctx);
}
#if !defined(_WIN32)
void WriteToRecordIO(recordio::Writer *writer,
const std::vector<LoDTensor> &tensor,
const platform::DeviceContext &dev_ctx) {
......@@ -329,7 +332,19 @@ bool ReadFromRecordIO(recordio::Scanner *scanner,
return true;
}
#else
class Writer {};
class Scanner {};
void WriteToRecordIO(recordio::Writer *writer,
const std::vector<LoDTensor> &tensor,
const platform::DeviceContext &dev_ctx) {}
bool ReadFromRecordIO(recordio::Scanner *scanner,
const platform::DeviceContext &dev_ctx,
std::vector<LoDTensor> *result_ptr) {
PADDLE_ENFORCE("windows didn't supported recordio!.");
return true;
}
#endif // _WIN32
std::vector<LoDTensor> LoDTensor::SplitLoDTensor(
const std::vector<platform::Place> places) const {
check_memory_size();
......
paddle/fluid/framework/lod_tensor_test.cc
浏览文件 @ ec5cb376
......@@ -274,6 +274,7 @@ TEST(LoD, ConvertToOffsetBasedLoD) {
EXPECT_EQ(offset_lod, expected);
}
#if !defined(_WIN32)
template <typename T>
static void TestRecordIO() {
LoDTensor tensor;
......@@ -320,6 +321,7 @@ TEST(LoDTensor, RecordIO) {
TestRecordIO<float>();
TestRecordIO<double>();
}
#endif // !defined(_WIN32)
} // namespace framework
} // namespace paddle
paddle/fluid/framework/operator.cc
浏览文件 @ ec5cb376
......@@ -74,6 +74,12 @@ static DDim GetDims(const Scope& scope, const std::string& name,
}
}
static bool VarInited(const Scope& scope, const std::string& name) {
Variable* var = scope.FindVar(name);
if (var == nullptr) return false;
return var->IsInitialized();
}
static std::string GetDtype(const Scope& scope, const std::string& name) {
Variable* var = scope.FindVar(name);
if (var == nullptr) {
......@@ -87,8 +93,12 @@ static std::string GetDtype(const Scope& scope, const std::string& name) {
}
return DataTypeToString(ToDataType(tensor.type()));
} else if (var->IsType<SelectedRows>()) {
return DataTypeToString(
ToDataType(var->Get<SelectedRows>().value().type()));
auto tensor = var->Get<SelectedRows>().value();
if (UNLIKELY(!tensor.IsInitialized())) {
return "uninited";
} else {
return DataTypeToString(ToDataType(tensor.type()));
}
} else {
return "";
}
......@@ -197,16 +207,21 @@ std::string OperatorBase::DebugStringEx(const Scope* scope) const {
auto& input = *it;
ss << input.first << "[";
for (size_t i = 0; i < input.second.size(); ++i) {
ss << input.second[i];
auto var_name = input.second[i];
ss << var_name;
if (scope) {
int row_size = GetRowSize(*scope, input.second[i]);
if (!VarInited(*scope, var_name)) {
ss << "[uninited]";
} else {
int row_size = GetRowSize(*scope, var_name);
if (row_size >= 0) {
ss << "[row_size=" << row_size << "]";
}
std::string dtype = GetDtype(*scope, input.second[i]);
std::string dtype = GetDtype(*scope, var_name);
ss << ":" << dtype;
ss << "[" << GetDims(*scope, input.second[i], true) << "]";
ss << "(" << GetLoD(*scope, input.second[i]) << ")";
ss << "[" << GetDims(*scope, var_name, true) << "]";
ss << "(" << GetLoD(*scope, var_name) << ")";
}
}
if (i != input.second.size() - 1) {
ss << ", ";
......@@ -223,14 +238,19 @@ std::string OperatorBase::DebugStringEx(const Scope* scope) const {
auto& output = *it;
ss << output.first << "[";
for (size_t i = 0; i < output.second.size(); ++i) {
ss << output.second[i];
auto var_name = output.second[i];
ss << var_name;
if (scope) {
if (!VarInited(*scope, var_name)) {
ss << "[uninited]";
} else {
int row_size = GetRowSize(*scope, output.second[i]);
if (row_size >= 0) {
ss << "[row_size=" << row_size << "]";
}
ss << "[" << GetDims(*scope, output.second[i], true) << "]";
ss << "(" << GetLoD(*scope, output.second[i]) << ")";
ss << "[" << GetDims(*scope, var_name, true) << "]";
ss << "(" << GetLoD(*scope, var_name) << ")";
}
}
if (i != output.second.size() - 1) {
ss << ", ";
......
paddle/fluid/framework/program_desc.cc
浏览文件 @ ec5cb376
......@@ -80,6 +80,12 @@ ProgramDesc::ProgramDesc(const proto::ProgramDesc &desc) {
InitFromProto();
}
void ProgramDesc::CopyFrom(const proto::ProgramDesc &desc) {
blocks_.clear();
desc_ = desc;
InitFromProto();
}
ProgramDesc::ProgramDesc(const std::string &binary_str) {
PADDLE_ENFORCE(desc_.ParseFromString(binary_str),
"Fail to parse program_desc from binary string.");
......@@ -111,10 +117,16 @@ void ProgramDesc::InitFromProto() {
const std::vector<std::string> ProgramDesc::GetFeedTargetNames() {
auto &global_block = Block(0);
// The order of feed_target_names must follow the index specified in `col`.
// since feed operator's order doesn't necessary follow 'col'.
std::vector<std::string> feed_target_names;
for (auto *op : global_block.AllOps()) {
if (op->Type() == kFeedOpType) {
feed_target_names.insert(feed_target_names.begin(), op->Output("Out")[0]);
int col = boost::get<int>(op->GetAttr("col"));
if (col >= feed_target_names.size()) {
feed_target_names.resize(col + 1);
}
feed_target_names[col] = op->Output("Out")[0];
}
}
return feed_target_names;
......@@ -122,10 +134,16 @@ const std::vector<std::string> ProgramDesc::GetFeedTargetNames() {
const std::vector<std::string> ProgramDesc::GetFetchTargetNames() {
auto &global_block = Block(0);
// The order of fetch_target_names must follow the index specified in `col`.
// since fetch operator's order doesn't necessary follow 'col'.
std::vector<std::string> fetch_target_names;
for (auto *op : global_block.AllOps()) {
if (op->Type() == kFetchOpType) {
fetch_target_names.push_back(op->Input("X")[0]);
int col = boost::get<int>(op->GetAttr("col"));
if (col >= fetch_target_names.size()) {
fetch_target_names.resize(col + 1);
}
fetch_target_names[col] = op->Input("X")[0];
}
}
return fetch_target_names;
......
paddle/fluid/framework/program_desc.h
浏览文件 @ ec5cb376
......@@ -53,6 +53,8 @@ class ProgramDesc {
void Flush();
void CopyFrom(const proto::ProgramDesc &desc);
proto::ProgramDesc *Proto();
// The output variable of feed_op is referenced as feed_target.
......
paddle/fluid/framework/rw_lock.h
浏览文件 @ ec5cb376
......@@ -14,13 +14,16 @@ limitations under the License. */
#pragma once
#if !defined(_WIN32)
#include <pthread.h>
#endif // !_WIN32
#include "paddle/fluid/platform/enforce.h"
namespace paddle {
namespace framework {
#if !defined(_WIN32)
struct RWLock {
RWLock() { pthread_rwlock_init(&lock_, nullptr); }
......@@ -43,6 +46,15 @@ struct RWLock {
private:
pthread_rwlock_t lock_;
};
#else
// https://stackoverflow.com/questions/7125250/making-pthread-rwlock-wrlock-recursive
// In windows, rw_lock seems like a hack. Use empty object and do nothing.
struct RWLock {
void RDLock() {}
void WRLock() {}
void UNLock() {}
};
#endif
} // namespace framework
} // namespace paddle
paddle/fluid/framework/selected_rows.cc
浏览文件 @ ec5cb376
......@@ -139,7 +139,7 @@ int64_t SelectedRows::AutoGrownIndex(int64_t key, bool auto_grown) {
}
auto write_iter = id_to_index_.find(key);
if (write_iter == id_to_index_.end()) {
size_t row_num = rows_.size();
int row_num = rows_.size();
if (row_num == value_->dims()[0]) {
rwlock_->UNLock();
PADDLE_THROW("selected rows is full, then length exceed %d", row_num);
......@@ -182,7 +182,7 @@ void SelectedRows::Get(const framework::Tensor& ids, framework::Tensor* value,
PADDLE_ENFORCE_EQ(value_width, value->numel() / value->dims()[0],
"output tensor should have the same shape with table "
"except the dims[0].");
for (size_t i = 0; i < ids.numel(); ++i) {
for (int i = 0; i < ids.numel(); ++i) {
int64_t index = AutoGrownIndex(ids.data<int64_t>()[i], auto_grown);
framework::VisitDataType(
framework::ToDataType(value_->type()),
......
paddle/fluid/framework/tensor.cc
浏览文件 @ ec5cb376
......@@ -31,7 +31,8 @@ size_t Tensor::memory_size() const {
return holder_ == nullptr ? 0UL : holder_->size() - offset_;
}
void* Tensor::mutable_data(platform::Place place, std::type_index type) {
void* Tensor::mutable_data(platform::Place place, std::type_index type,
size_t requested_size) {
if (holder_ != nullptr) {
holder_->set_type(type);
}
......@@ -39,7 +40,11 @@ void* Tensor::mutable_data(platform::Place place, std::type_index type) {
"When calling this method, the Tensor's numel must be "
"equal or larger than zero. "
"Please check Tensor::Resize has been called first.");
int64_t size = numel() * SizeOfType(type);
size_t size = numel() * SizeOfType(type);
if (requested_size) {
PADDLE_ENFORCE_GE(requested_size, size);
size = requested_size;
}
/* some versions of boost::variant don't have operator!= */
if (holder_ == nullptr || !(holder_->place() == place) ||
holder_->size() < size + offset_) {
......@@ -68,10 +73,10 @@ void* Tensor::mutable_data(platform::Place place, std::type_index type) {
offset_);
}
void* Tensor::mutable_data(platform::Place place) {
void* Tensor::mutable_data(platform::Place place, size_t requested_size) {
PADDLE_ENFORCE(this->holder_ != nullptr,
"Cannot invoke mutable data if current hold nothing.");
return mutable_data(place, holder_->type());
return mutable_data(place, holder_->type(), requested_size);
}
Tensor& Tensor::ShareDataWith(const Tensor& src) {
......
paddle/fluid/framework/tensor.h
浏览文件 @ ec5cb376
......@@ -89,22 +89,24 @@ class Tensor {
* @note If not exist, then allocation.
*/
template <typename T>
T* mutable_data(platform::Place place);
T* mutable_data(platform::Place place, size_t requested_size = 0);
void* mutable_data(platform::Place place, std::type_index type);
void* mutable_data(platform::Place place, std::type_index type,
size_t requested_size = 0);
void* mutable_data(platform::Place place);
void* mutable_data(platform::Place place, size_t requested_size = 0);
/**
* @brief Return a pointer to mutable memory block.
*
* @param[in] dims The dimensions of the memory block.
* @param[in] place The place of the memory block.
* @param[in] requested_size The size of the block in bytes.
*
* @note If not exist, then allocation.
*/
template <typename T>
T* mutable_data(DDim dims, platform::Place place);
T* mutable_data(DDim dims, platform::Place place, size_t requested_size = 0);
/*! Return the dimensions of the memory block. */
const DDim& dims() const;
......
paddle/fluid/framework/tensor_impl.h
浏览文件 @ ec5cb376
......@@ -46,16 +46,17 @@ inline T* Tensor::data() {
}
template <typename T>
inline T* Tensor::mutable_data(DDim dims, platform::Place place) {
inline T* Tensor::mutable_data(DDim dims, platform::Place place,
size_t requested_size) {
static_assert(std::is_pod<T>::value, "T must be POD");
Resize(dims);
return mutable_data<T>(place);
return mutable_data<T>(place, requested_size);
}
template <typename T>
inline T* Tensor::mutable_data(platform::Place place) {
inline T* Tensor::mutable_data(platform::Place place, size_t requested_size) {
static_assert(std::is_pod<T>::value, "T must be POD");
return reinterpret_cast<T*>(mutable_data(place, typeid(T)));
return reinterpret_cast<T*>(mutable_data(place, typeid(T), requested_size));
}
inline Tensor ReshapeToMatrix(const Tensor& src, int num_col_dims) {
......
paddle/fluid/framework/var_type.h
浏览文件 @ ec5cb376
......@@ -26,7 +26,7 @@ namespace paddle {
namespace framework {
template <typename T>
bool IsType(const std::type_index& type_index) {
inline bool IsType(const std::type_index& type_index) {
return type_index == std::type_index(typeid(T));
}
......
paddle/fluid/inference/CMakeLists.txt
浏览文件 @ ec5cb376
......@@ -10,7 +10,7 @@ set(FLUID_CORE_MODULES proto_desc memory lod_tensor executor)
# TODO(panyx0718): Should this be called paddle_fluid_inference_api_internal?
cc_library(paddle_fluid_api
SRCS io.cc
DEPS ${FLUID_CORE_MODULES} ${GLOB_OP_LIB})
DEPS ${FLUID_CORE_MODULES} ${GLOB_OP_LIB} graph_to_program_pass)
get_property(fluid_modules GLOBAL PROPERTY FLUID_MODULES)
......
paddle/fluid/inference/analysis/analyzer.cc
浏览文件 @ ec5cb376
......@@ -72,7 +72,7 @@ class DfgPassManagerImpl final : public DfgPassManager {
auto trt_teller = [&](const Node* node) {
std::unordered_set<std::string> teller_set(
{"elementwise_add", "mul", "conv2d", "pool2d", "relu", "softmax",
"depthwise_conv2d", "batch_norm"});
"depthwise_conv2d", "batch_norm", "concat"});
if (!node->IsFunction()) return false;
const auto* func = static_cast<const Function*>(node);
......
paddle/fluid/inference/analysis/analyzer_tester.cc
浏览文件 @ ec5cb376
......@@ -23,6 +23,8 @@
DEFINE_string(infer_ditu_rnn_model, "", "model path for ditu RNN");
DEFINE_string(infer_ditu_rnn_data, "", "data path for ditu RNN");
DEFINE_int32(batch_size, 10, "batch size.");
DEFINE_int32(repeat, 1, "Running the inference program repeat times.");
namespace paddle {
namespace inference {
......@@ -92,7 +94,7 @@ struct DataRecord {
size_t batch_iter{0};
size_t batch_size{1};
DataRecord() = default;
DataRecord(const std::string &path, int batch_size = 1)
explicit DataRecord(const std::string &path, int batch_size = 1)
: batch_size(batch_size) {
Load(path);
}
......@@ -165,7 +167,6 @@ struct DataRecord {
};
void PrepareInputs(std::vector<PaddleTensor> *input_slots, DataRecord *data,
int batch_size) {
// DataRecord data(FLAGS_datapath, batch_size);
PaddleTensor lod_attention_tensor, init_zero_tensor, lod_tensor_tensor,
week_tensor, minute_tensor;
lod_attention_tensor.name = "data_lod_attention";
......@@ -174,28 +175,33 @@ void PrepareInputs(std::vector<PaddleTensor> *input_slots, DataRecord *data,
week_tensor.name = "week";
minute_tensor.name = "minute";
auto one_batch = data->NextBatch();
// clang-format off
std::vector<int> rnn_link_data_shape
({static_cast<int>(one_batch.rnn_link_data.size()), static_cast<int>(one_batch.rnn_link_data.front().size())});
std::vector<int> rnn_link_data_shape(
{static_cast<int>(one_batch.rnn_link_data.size()),
static_cast<int>(one_batch.rnn_link_data.front().size())});
lod_attention_tensor.shape.assign({1, 2});
lod_attention_tensor.lod.assign({one_batch.lod1, one_batch.lod2});
init_zero_tensor.shape.assign({batch_size, 15});
init_zero_tensor.lod.assign({one_batch.lod3});
lod_tensor_tensor.shape = rnn_link_data_shape;
lod_tensor_tensor.lod.assign({one_batch.lod1});
week_tensor.shape.assign({(int) one_batch.rnn_week_datas.size(), (int) one_batch.rnn_week_datas.front().size()});
// clang-format off
week_tensor.shape.assign(
{static_cast<int>(one_batch.rnn_week_datas.size()),
static_cast<int>(one_batch.rnn_week_datas.front().size())});
week_tensor.lod.assign({one_batch.lod3});
minute_tensor.shape.assign({(int) one_batch.rnn_minute_datas.size(),
(int) one_batch.rnn_minute_datas.front().size()});
minute_tensor.shape.assign(
{static_cast<int>(one_batch.rnn_minute_datas.size()),
static_cast<int>(one_batch.rnn_minute_datas.front().size())});
minute_tensor.lod.assign({one_batch.lod3});
// clang-format on
// assign data
TensorAssignData(&lod_attention_tensor, std::vector<std::vector<float>>({{0, 0}}));
TensorAssignData(&lod_attention_tensor,
std::vector<std::vector<float>>({{0, 0}}));
std::vector<float> tmp_zeros(batch_size * 15, 0.);
TensorAssignData(&init_zero_tensor, {tmp_zeros});
TensorAssignData(&lod_tensor_tensor, one_batch.rnn_link_data);
TensorAssignData(&week_tensor, one_batch.rnn_week_datas);
TensorAssignData(&minute_tensor, one_batch.rnn_minute_datas);
// clang-format on
// Set inputs.
auto init_zero_tensor1 = init_zero_tensor;
init_zero_tensor1.name = "hidden_init";
......@@ -231,12 +237,9 @@ std::string DescribeTensor(const PaddleTensor &tensor) {
os << "\n";
os << " - data: ";
// clang-format off
int dim = std::accumulate(tensor.shape.begin(),
tensor.shape.end(),
1,
[](int a, int b) { return a * b; }); // clang-format on
for (size_t i = 0; i < dim; i++) {
int dim = std::accumulate(tensor.shape.begin(), tensor.shape.end(), 1,
[](int a, int b) { return a * b; });
for (int i = 0; i < dim; i++) {
os << static_cast<float *>(tensor.data.data())[i] << " ";
}
os << '\n';
......@@ -300,13 +303,16 @@ void TestDituRNNPrediction(const std::string &model_path,
for (int i = 0; i < num_times; i++) {
predictor->Run(input_slots, &outputs);
}
LOG(INFO) << "time/batch: " << timer.toc() / num_times;
LOG(INFO) << "===========profile result===========";
LOG(INFO) << "batch_size: " << batch_size << ", repeat: " << num_times
<< ", latency: " << timer.toc() / num_times << "ms";
LOG(INFO) << "=====================================";
for (auto &out : outputs) {
size_t size = std::accumulate(out.shape.begin(), out.shape.end(), 1,
[](int a, int b) { return a * b; });
float *data = static_cast<float *>(out.data.data());
for (int i = 0;
for (size_t i = 0;
i < std::min(sizeof(ditu_rnn_target_data) / sizeof(float), size);
i++) {
EXPECT_NEAR(data[i], ditu_rnn_target_data[i], 1e-3);
......@@ -336,7 +342,7 @@ TEST(Analyzer, SupportIRPass) {
// Directly infer with the original model.
TEST(Analyzer, DituRNN_without_analysis) {
TestDituRNNPrediction(FLAGS_infer_ditu_rnn_model, FLAGS_infer_ditu_rnn_data,
10, false, false);
FLAGS_batch_size, false, false, FLAGS_repeat);
}
// Inference with the original model with the analysis turned on, the analysis
......@@ -344,14 +350,14 @@ TEST(Analyzer, DituRNN_without_analysis) {
TEST(Analyzer, DituRNN_with_analysis) {
LOG(INFO) << "ditu rnn with analysis";
TestDituRNNPrediction(FLAGS_infer_ditu_rnn_model, FLAGS_infer_ditu_rnn_data,
10, true, false, 1);
FLAGS_batch_size, true, false, FLAGS_repeat);
}
// Inference with analysis and IR. The IR module will fuse some large kernels.
TEST(Analyzer, DituRNN_with_analysis_with_IR) {
LOG(INFO) << "ditu rnn with analysis and IR fuse";
TestDituRNNPrediction(FLAGS_infer_ditu_rnn_model, FLAGS_infer_ditu_rnn_data,
10, true, true, 1);
FLAGS_batch_size, true, true, FLAGS_repeat);
}
} // namespace analysis
......
paddle/fluid/inference/api/api_tensorrt_subgraph_engine.cc
浏览文件 @ ec5cb376
......@@ -32,6 +32,7 @@ class TensorRTSubgraphPredictor : public NativePaddlePredictor {
: NativePaddlePredictor(config), config_(config) {}
bool Init(const std::shared_ptr<framework::Scope>& parent_scope) {
FLAGS_IA_enable_tensorrt_subgraph_engine = true;
VLOG(3) << "Predictor::init()";
FLAGS_tensorrt_max_batch_size = config_.max_batch_size;
FLAGS_tensorrt_workspace_size = config_.workspace_size;
......@@ -161,3 +162,4 @@ USE_TRT_CONVERTER(fc);
USE_TRT_CONVERTER(pool2d);
USE_TRT_CONVERTER(softmax);
USE_TRT_CONVERTER(batch_norm);
USE_TRT_CONVERTER(concat);
paddle/fluid/inference/api/api_tensorrt_subgraph_engine_tester.cc
浏览文件 @ ec5cb376
......@@ -37,6 +37,7 @@ void CompareTensorRTWithFluid(bool enable_tensorrt) {
config1.use_gpu = true;
config1.fraction_of_gpu_memory = 0.3;
config1.device = 0;
config1.max_batch_size = 10;
auto predictor0 =
CreatePaddlePredictor<NativeConfig, PaddleEngineKind::kNative>(config0);
......
paddle/fluid/inference/api/demo_ci/CMakeLists.txt
浏览文件 @ ec5cb376
......@@ -23,9 +23,11 @@ include_directories("${PADDLE_LIB}")
include_directories("${PADDLE_LIB}/third_party/install/protobuf/include")
include_directories("${PADDLE_LIB}/third_party/install/glog/include")
include_directories("${PADDLE_LIB}/third_party/install/gflags/include")
if (NOT WIN32)
include_directories("${PADDLE_LIB}/third_party/install/snappy/include")
include_directories("${PADDLE_LIB}/third_party/install/snappystream/include")
include_directories("${PADDLE_LIB}/third_party/install/zlib/include")
endif(NOT WIN32)
include_directories("${PADDLE_LIB}/third_party/boost")
include_directories("${PADDLE_LIB}/third_party/eigen3")
......
paddle/fluid/inference/tensorrt/convert/CMakeLists.txt
浏览文件 @ ec5cb376
# Add TRT tests
nv_library(tensorrt_converter
SRCS mul_op.cc conv2d_op.cc fc_op.cc pool2d_op.cc elementwise_op.cc
batch_norm_op.cc activation_op.cc softmax_op.cc
batch_norm_op.cc activation_op.cc softmax_op.cc concat_op.cc
DEPS tensorrt_engine operator scope framework_proto op_registry)
nv_test(test_op_converter SRCS test_op_converter.cc DEPS
......@@ -18,12 +18,12 @@ nv_test(test_trt_conv_op SRCS test_conv2d_op.cc conv2d_op.cc
DEPS ${FLUID_CORE_MODULES} tensorrt_engine conv_op SERIAL)
nv_test(test_trt_pool2d_op SRCS test_pool2d_op.cc pool2d_op.cc
DEPS ${FLUID_CORE_MODULES} tensorrt_engine pool_op SERIAL)
nv_test(test_trt_elementwise_op SRCS test_elementwise_op.cc elementwise_op.cc
DEPS ${FLUID_CORE_MODULES} tensorrt_engine elementwise_add_op SERIAL)
nv_test(test_trt_softmax_op SRCS test_softmax_op.cc softmax_op.cc
DEPS ${FLUID_CORE_MODULES} tensorrt_engine softmax_op SERIAL)
nv_test(test_trt_batch_norm_op SRCS test_batch_norm_op.cc batch_norm_op.cc
DEPS ${FLUID_CORE_MODULES} tensorrt_engine batch_norm_op SERIAL)
nv_test(test_trt_concat_op SRCS test_concat_op.cc concat_op.cc
DEPS ${FLUID_CORE_MODULES} tensorrt_engine concat_op SERIAL)
paddle/fluid/inference/tensorrt/convert/concat_op.cc 0 → 100644
浏览文件 @ ec5cb376
/* Copyright (c) 2018 PaddlePaddle Authors. All Rights Reserved.
Licensed under the Apache License, Version 2.0 (the "License");
you may not use this file except in compliance with the License.
You may obtain a copy of the License at
http://www.apache.org/licenses/LICENSE-2.0
Unless required by applicable law or agreed to in writing, software
distributed under the License is distributed on an "AS IS" BASIS,
WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
See the License for the specific language governing permissions and
limitations under the License. */
#include "paddle/fluid/inference/tensorrt/convert/op_converter.h"
namespace paddle {
namespace inference {
namespace tensorrt {
/*
* MulOp, IMatrixMultiplyLayer in TRT. This Layer doesn't has weights.
*/
class ConcatOpConverter : public OpConverter {
public:
void operator()(const framework::proto::OpDesc& op,
const framework::Scope& scope, bool test_mode) override {
VLOG(4) << "convert a fluid mul op to tensorrt mul layer without bias";
framework::OpDesc op_desc(op, nullptr);
// Declare inputs
std::vector<nvinfer1::ITensor*> itensors;
for (auto& input_name : op_desc.Input("X")) {
itensors.push_back(engine_->GetITensor(input_name));
}
int axis = boost::get<int>(op_desc.GetAttr("axis"));
PADDLE_ENFORCE(axis > 0,
"The axis attr of Concat op should be large than 0 for trt");
auto* layer = TRT_ENGINE_ADD_LAYER(engine_, Concatenation, itensors.data(),
itensors.size());
axis = axis - 1; // Remove batch dim
layer->setAxis(axis);
auto output_name = op_desc.Output("Out")[0];
engine_->SetITensor(output_name, layer->getOutput(0));
if (test_mode) { // the test framework can not determine which is the
// output, so place the declaration inside.
engine_->DeclareOutput(output_name);
}
}
};
} // namespace tensorrt
} // namespace inference
} // namespace paddle
REGISTER_TRT_OP_CONVERTER(concat, ConcatOpConverter);
paddle/fluid/inference/tensorrt/convert/op_converter.h
浏览文件 @ ec5cb376
......@@ -79,6 +79,14 @@ class OpConverter {
it =
Registry<OpConverter>::Lookup("elementwise_" + op_type + "_tensor");
}
PADDLE_ENFORCE_NOT_NULL(it, "no OpConverter for optype [%s]",
op_desc.Type());
}
if (op_desc.Type() == "depthwise_conv2d") {
it = Registry<OpConverter>::Lookup("conv2d");
PADDLE_ENFORCE_NOT_NULL(it, "no OpConverter for optype [%s]",
op_desc.Type());
}
if (!it) {
......
paddle/fluid/inference/tensorrt/convert/test_concat_op.cc 0 → 100644
浏览文件 @ ec5cb376
/* Copyright (c) 2018 PaddlePaddle Authors. All Rights Reserved.
Licensed under the Apache License, Version 2.0 (the "License");
you may not use this file except in compliance with the License.
You may obtain a copy of the License at
http://www.apache.org/licenses/LICENSE-2.0
Unless required by applicable law or agreed to in writing, software
distributed under the License is distributed on an "AS IS" BASIS,
WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
See the License for the specific language governing permissions and
limitations under the License. */
#include <gtest/gtest.h>
#include "paddle/fluid/inference/tensorrt/convert/op_converter.h"
#include "paddle/fluid/inference/tensorrt/convert/ut_helper.h"
namespace paddle {
namespace inference {
namespace tensorrt {
TEST(concat_op, test) {
std::unordered_set<std::string> parameters({""});
framework::Scope scope;
TRTConvertValidation validator(10, parameters, scope, 1000);
validator.DeclInputVar("concat_x1", nvinfer1::DimsCHW(10, 3, 1));
validator.DeclInputVar("concat_x2", nvinfer1::DimsCHW(3, 3, 1));
validator.DeclInputVar("concat_x3", nvinfer1::DimsCHW(7, 3, 1));
validator.DeclOutputVar("concat_out", nvinfer1::DimsCHW(20, 3, 1));
// Prepare Op description
framework::OpDesc desc;
desc.SetType("concat");
desc.SetInput("X", {"concat_x1", "concat_x2", "concat_x3"});
desc.SetOutput("Out", {"concat_out"});
int axis = 1;
desc.SetAttr("axis", axis);
validator.SetOp(*desc.Proto());
validator.Execute(5);
}
} // namespace tensorrt
} // namespace inference
} // namespace paddle
USE_OP(concat);
paddle/fluid/inference/tests/test_helper.h
浏览文件 @ ec5cb376
......@@ -18,6 +18,7 @@ limitations under the License. */
#include <string>
#include <vector>
#include "paddle/fluid/framework/ir/graph_to_program_pass.h"
#include "paddle/fluid/framework/lod_tensor.h"
#include "paddle/fluid/inference/io.h"
#include "paddle/fluid/platform/profiler.h"
......@@ -135,6 +136,15 @@ std::vector<std::vector<int64_t>> GetFeedTargetShapes(
return feed_target_shapes;
}
void Compile(paddle::framework::ProgramDesc* program) {
std::unique_ptr<paddle::framework::ir::Graph> g(
new paddle::framework::ir::Graph(*program));
auto pass = paddle::framework::ir::PassRegistry::Instance().Get(
"graph_to_program_pass");
pass->SetNotOwned<paddle::framework::ProgramDesc>("program", program);
pass->Apply(std::move(g));
}
template <typename Place, bool CreateVars = true, bool PrepareContext = false>
void TestInference(const std::string& dirname,
const std::vector<paddle::framework::LoDTensor*>& cpu_feeds,
......@@ -172,6 +182,8 @@ void TestInference(const std::string& dirname,
paddle::platform::DeviceContextPool::Instance().Get(place));
inference_program = InitProgram(&executor, scope, dirname, is_combined);
}
Compile(inference_program.get());
// Disable the profiler and print the timing information
paddle::platform::DisableProfiler(paddle::platform::EventSortingKey::kDefault,
"load_program_profiler");
......@@ -249,3 +261,5 @@ void TestInference(const std::string& dirname,
delete scope;
}
USE_PASS(graph_to_program_pass);
paddle/fluid/memory/detail/system_allocator.cc
浏览文件 @ ec5cb376
......@@ -11,11 +11,17 @@ distributed under the License is distributed on an "AS IS" BASIS,
WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
See the License for the specific language governing permissions and
limitations under the License. */
#define GLOG_NO_ABBREVIATED_SEVERITIES
#include "paddle/fluid/memory/detail/system_allocator.h"
#include <stdlib.h> // for malloc and free
#ifdef _WIN32
#include <malloc.h>
#include <windows.h> // VirtualLock/VirtualUnlock
#else
#include <sys/mman.h> // for mlock and munlock
#endif
#include <stdlib.h> // for malloc and free
#include <algorithm> // for std::max
#include "gflags/gflags.h"
......@@ -35,31 +41,42 @@ namespace paddle {
namespace memory {
namespace detail {
void* CPUAllocator::Alloc(size_t* index, size_t size) {
// According to http://www.cplusplus.com/reference/cstdlib/malloc/,
// malloc might not return nullptr if size is zero, but the returned
// pointer shall not be dereferenced -- so we make it nullptr.
if (size <= 0) return nullptr;
*index = 0; // unlock memory
void* AlignedMalloc(size_t size) {
void* p = nullptr;
size_t alignment = 32ul;
#ifdef PADDLE_WITH_MKLDNN
// refer to https://github.com/01org/mkl-dnn/blob/master/include/mkldnn.hpp
// memory alignment
PADDLE_ENFORCE_EQ(posix_memalign(&p, 4096ul, size), 0, "Alloc %ld error!",
size);
alignment = 4096ul;
#endif
#ifdef _WIN32
p = _aligned_malloc(size, alignment);
#else
PADDLE_ENFORCE_EQ(posix_memalign(&p, 32ul, size), 0, "Alloc %ld error!",
PADDLE_ENFORCE_EQ(posix_memalign(&p, alignment, size), 0, "Alloc %ld error!",
size);
#endif
PADDLE_ENFORCE(p, "Fail to allocate CPU memory: size = %d .", size);
return p;
}
void* CPUAllocator::Alloc(size_t* index, size_t size) {
// According to http://www.cplusplus.com/reference/cstdlib/malloc/,
// malloc might not return nullptr if size is zero, but the returned
// pointer shall not be dereferenced -- so we make it nullptr.
if (size <= 0) return nullptr;
*index = 0; // unlock memory
void* p = AlignedMalloc(size);
if (p != nullptr) {
if (FLAGS_use_pinned_memory) {
*index = 1;
#ifdef _WIN32
VirtualLock(p, size);
#else
mlock(p, size); // lock memory
#endif
}
}
......@@ -68,7 +85,11 @@ void* CPUAllocator::Alloc(size_t* index, size_t size) {
void CPUAllocator::Free(void* p, size_t size, size_t index) {
if (p != nullptr && index == 1) {
#ifdef _WIN32
VirtualUnlock(p, size);
#else
munlock(p, size);
#endif
}
free(p);
}
......
paddle/fluid/operators/CMakeLists.txt
浏览文件 @ ec5cb376
......@@ -85,7 +85,7 @@ function(op_library TARGET)
#remove windows unsupported op
if (WIN32)
foreach(windows_unsupport_op "nccl_op" "gen_nccl_id_op")
foreach(windows_unsupport_op "nccl_op" "gen_nccl_id_op" "warpctc_op")
if ("${TARGET}" STREQUAL "${windows_unsupport_op}")
return()
endif()
......@@ -291,6 +291,8 @@ op_library(unsqueeze_op DEPS reshape_op)
op_library(squeeze_op DEPS reshape_op)
op_library(extract_rows_op DEPS memory)
op_library(flatten_op DEPS reshape_op)
op_library(sequence_pad_op DEPS sequence_padding)
op_library(unstack_op DEPS stack_op)
if (WITH_GPU)
op_library(conv_op DEPS vol2col depthwise_conv im2col)
......@@ -319,8 +321,9 @@ foreach(src ${GENERAL_OPS})
endforeach()
file(APPEND ${pybind_file} "USE_OP(less_than);\nUSE_OP(logical_and);\nUSE_NO_KERNEL_OP(read_from_array);\n")
if (NOT WIN32)
add_subdirectory(reader)
endif(NOT WIN32)
foreach(src ${READER_LIBRARY})
set(OP_LIBRARY ${src} ${OP_LIBRARY})
endforeach()
......
paddle/fluid/operators/attention_lstm_op.cc 0 → 100644
浏览文件 @ ec5cb376
/* Copyright (c) 2016 PaddlePaddle Authors. All Rights Reserved.
Licensed under the Apache License, Version 2.0 (the "License");
you may not use this file except in compliance with the License.
You may obtain a copy of the License at
http://www.apache.org/licenses/LICENSE-2.0
Unless required by applicable law or agreed to in writing, software
distributed under the License is distributed on an "AS IS" BASIS,
WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
See the License for the specific language governing permissions and
limitations under the License. */
#include "paddle/fluid/operators/attention_lstm_op.h"
#include <sys/time.h>
#include <string>
#include "paddle/fluid/operators/math/blas.h"
#include "paddle/fluid/operators/math/cpu_vec.h"
#include "paddle/fluid/operators/math/fc_compute.h"
#include "paddle/fluid/platform/cpu_info.h"
namespace paddle {
namespace operators {
void AttentionLSTMOp::InferShape(framework::InferShapeContext* ctx) const {
PADDLE_ENFORCE(ctx->HasInput("X"),
"Input(X) of AttentionLSTM should not be null.");
PADDLE_ENFORCE(ctx->HasInput("C0"),
"Input(C0) of AttentionLSTM should not be null.");
PADDLE_ENFORCE(ctx->HasInput("LSTMWeight"),
"Input(LSTMWeight) of AttentionLSTM should not be null.");
PADDLE_ENFORCE(ctx->HasInput("LSTMBias"),
"Input(LSTMBias) of AttentionLSTM should not be null.");
PADDLE_ENFORCE(ctx->HasInput("AttentionWeight"),
"Input(AttentionWeight) of AttentionLSTM should not be null.");
PADDLE_ENFORCE(ctx->HasOutput("Hidden"),
"Output(Hidden) of AttentionLSTM should not be null.");
PADDLE_ENFORCE(ctx->HasOutput("Cell"),
"Output(Cell) of AttentionLSTM should not be null.");
PADDLE_ENFORCE(ctx->HasOutput("AttentionedX"),
"Output(AttentionedX) of AttentionLSTM should not be null.");
PADDLE_ENFORCE(ctx->HasOutput("AttentionFCOut"),
"Output(AttentionFCOut) of AttentionLSTM should not be null.");
PADDLE_ENFORCE(ctx->HasOutput("LSTMX"),
"Output(LSTMX) of AttentionLSTM should not be null.");
PADDLE_ENFORCE(ctx->HasOutput("LSTMOUT"),
"Output(LSTMOUT) of AttentionLSTM should not be null.");
auto x_dims = ctx->GetInputDim("X");
const int M = x_dims[1];
PADDLE_ENFORCE_EQ(x_dims.size(), 2, "Input(X)'s rank must be 2.");
auto w_dims = ctx->GetInputDim("LSTMWeight");
const int D = w_dims[1] / 4;
PADDLE_ENFORCE_EQ(w_dims.size(), 2, "Input(LSTMWeight)'s rank must be 2.");
PADDLE_ENFORCE_EQ(w_dims[0], D + M,
"LSTMWeight dims should be (%d + %d) * %d.", D + M, 4 * D);
auto b_dims = ctx->GetInputDim("LSTMBias");
PADDLE_ENFORCE_EQ(b_dims.size(), 2, "Input(LSTMBias)'s rank must be 2.");
PADDLE_ENFORCE_EQ(b_dims[0], 1, "LSTMBias dims should be 1 x %d.", 4 * D);
PADDLE_ENFORCE_EQ(b_dims[1], 4 * D, "LSTMBias dims should be 1 x %d.", 4 * D);
auto c_dims = ctx->GetInputDim("C0");
PADDLE_ENFORCE_EQ(c_dims.size(), 2, "Input(C0)'s rank must be 2.");
PADDLE_ENFORCE_EQ(c_dims[1], D, "C0 dims should be N x %d.", D);
if (ctx->HasInput("H0")) {
auto h_dims = ctx->GetInputDim("H0");
PADDLE_ENFORCE(h_dims == c_dims,
"The dimension of Input(H0) and Input(C0) "
"should be the same.");
}
auto atten_w_dims = ctx->GetInputDim("AttentionWeight");
PADDLE_ENFORCE_EQ(atten_w_dims.size(), 2,
"Input(AttentionWeight)'s rank must be 2.");
PADDLE_ENFORCE_EQ(atten_w_dims[0], M + D,
"AttentionWeight shapes must be (%d + %d) * 1.", M, D);
PADDLE_ENFORCE_EQ(atten_w_dims[1], 1,
"AttentionWeight shapes must be (%d + %d) * 1.", M, D);
if (ctx->HasInput("AttentionBias")) {
auto atten_b_dims = ctx->GetInputDim("AttentionBias");
PADDLE_ENFORCE_EQ(atten_b_dims.size(), 2,
"Input(AttentionBias)'s rank must be 2.");
PADDLE_ENFORCE_EQ(atten_b_dims[0], 1,
"AttentionBias shapes must be 1 * 1.");
PADDLE_ENFORCE_EQ(atten_b_dims[1], 1,
"AttentionBias shapes must be 1 * 1.");
}
if (ctx->HasInput("AttentionScalar")) {
auto dims = ctx->GetInputDim("AttentionScalar");
PADDLE_ENFORCE_EQ(dims.size(), 2,
"Input(AttentionScalar)'s rank must be 2.");
PADDLE_ENFORCE_EQ(dims[0], 1, "AttentionScalar shapes must be 1 * 1.");
PADDLE_ENFORCE_EQ(dims[1], 1, "AttentionScalar shapes must be 1 * 1.");
}
if (ctx->HasInput("AttentionScalarBias")) {
auto dims = ctx->GetInputDim("AttentionScalarBias");
PADDLE_ENFORCE(
ctx->HasInput("AttentionScalar"),
"AttentionScalar should not be null when have AttentionScalarBias.");
PADDLE_ENFORCE_EQ(dims.size(), 2,
"Input(AttentionScalarBias)'s rank must be 2.");
PADDLE_ENFORCE_EQ(dims[0], 1, "AttentionScalarBias shapes must be 1 * 1.");
PADDLE_ENFORCE_EQ(dims[1], 1, "AttentionScalarBias shapes must be 1 * 1.");
}
framework::DDim out_dims({x_dims[0], D});
ctx->SetOutputDim("Hidden", out_dims);
ctx->SetOutputDim("Cell", out_dims);
ctx->SetOutputDim("AttentionedX", {x_dims[0], 1});
ctx->SetOutputDim("LSTMX", {1, M});
ctx->SetOutputDim("LSTMOUT", {1, 4 * D});
// AttentionFCOut should be reshape as (maxseqlen,1) in runtime
ctx->ShareLoD("X", "Hidden");
ctx->ShareLoD("X", "Cell");
}
framework::OpKernelType AttentionLSTMOp::GetExpectedKernelType(
const framework::ExecutionContext& ctx) const {
return framework::OpKernelType(
framework::ToDataType(ctx.Input<framework::LoDTensor>("X")->type()),
ctx.device_context());
}
void AttentionLSTMOpMaker::Make() {
AddInput("X",
"(LoDTensor) the input is a LodTensor, which support "
"variable-time length input sequence. The underlying tensor in "
"this LoDTensor is a matrix with shape (T X M), where T is the "
"total time steps in this mini-batch, M is the dim size of x.");
AddInput("C0",
"(Tensor) LSTM C0"
"This is a tensor with shape (N x D), where N is the batch size, D "
"is the gate size."
"C0 is necessary because of attention.");
AddInput("H0",
"(Tensor, optional) LSTM H0"
"This is a tensor with shape (N x D), where N is the "
"batch size and D is the gate size.")
.AsDispensable();
AddInput("AttentionWeight",
"(Tensor) the weights of attention fc. Always relu the fc result."
"The shape is ((M+D) x 1), where M is the dim size of x, D is the "
"gate size of LSTM.");
AddInput("AttentionBias",
"(Tensor, optional) the bias of attention fc."
"The shape is (1 x 1)")
.AsDispensable();
AddInput("AttentionScalar",
"(Tensor, optional) the scalar on the result of attentioned fc. "
"Always relu the Scalar."
"The shape is (1 x 1)")
.AsDispensable();
AddInput("AttentionScalarBias",
"(Tensor, optional) the scalar bias of attention fc."
"The shape is (1 x 1)")
.AsDispensable();
AddInput("LSTMWeight",
"(Tensor) the combined weight of LSTM"
" - The shape is ((D+M) x 4D), where D is the hidden gate size, M "
"is the dim size of x"
" - Weight = {W_forget, W_input, W_output, W_cell}");
AddInput("LSTMBias",
"(Tensor) the combined bias of LSTM, shape (1x4D)."
"Note: we should add the bias of hidden and context accorindg to "
"the same gate: "
"{B_forget, B_input, B_output, B_cell}");
AddOutput("Hidden",
"(LoDTensor) (same as LSTMOp) the hidden state of LSTM operator. "
"The shape is (T x D), and lod is the same with the `Input`.");
AddOutput("Cell",
"(LoDTensor) (same as LSTMOp) the cell state of LSTM operator. "
"The shape is (T x D), and lod is the same with the `Input`.");
AddOutput("AttentionedX",
"(Tensor) shape is (T x 1), the result after X * AttentionWeight,"
" where T is the total time steps in this mini-batch,"
" D is the hidden size.")
.AsIntermediate();
AddOutput("AttentionFCOut",
"(Tensor) (max_seq_len, 1), compute at each step.")
.AsIntermediate();
AddOutput("LSTMX",
"(Tensor) the input X of LSTM for each step."
"Shape is (1 x M), where M is the x frame size")
.AsIntermediate();
AddOutput(
"LSTMOUT",
"(Tensor) the output of LSTM X(1*(D+M))* weight((D+M)*4D) for each step."
"Shape is (1 x 4D), where M is the x frame size")
.AsIntermediate();
AddAttr<std::string>("gate_activation",
"(string, default: sigmoid)"
"The activation for input gate, forget gate and output "
"gate, `sigmoid` by default.")
.SetDefault("sigmoid")
.InEnum({"sigmoid", "tanh", "relu", "identity"});
AddAttr<std::string>("cell_activation",
"(string, default: tanh)"
"The activation for cell output, `tanh` by defalut.")
.SetDefault("tanh")
.InEnum({"sigmoid", "tanh", "relu", "identity"});
AddAttr<std::string>("candidate_activation",
"(string, default: tanh)"
"The activation for candidate hidden state, "
"`tanh` by default.")
.SetDefault("tanh")
.InEnum({"sigmoid", "tanh", "relu", "identity"});
AddComment(R"DOC(
Attention Long-Short Term Memory (LSTM) Operator.
Attention part:
concat( x(seqlen * M), expand( cell_t-1(1,D) ) ) => tmp(seqlen*(M+D))
tmp(seqlen*(M+D)) * fc((M+D)*1) => fcout(seqlen*1) with bias, relu
fcout(seqlen*1) * scalar => fcout(seqlen*1) with bias, relu
dotmul and sum pool ( fcout(seqlen*1), x(seqlen * M) ) => lstm_x_t(1, M)
LSTM part:
use lstm_x_t as input and compute as standard LSTM.
)DOC");
}
// y[i] = (x[i] + bias[0]) > 0 ? (x[i] + bias[0]) : 0;
template <typename T>
inline void bias_relu(const int n, const T* x, const T* bias, T* y) {
if (bias) {
math::vec_add_bias<T, platform::jit::avx>(n, *bias, x, y);
math::vec_relu<T, platform::jit::avx>(n, y, y);
} else {
math::vec_relu<T, platform::jit::avx>(n, x, y);
}
}
template <typename T>
inline void vec_softmax(const int n, const T* x, T* y) {
T scalar = x[0];
// max
for (int i = 1; i < n; ++i) {
scalar = scalar < x[i] ? x[i] : scalar;
}
math::vec_add_bias<T, platform::jit::avx>(n, -scalar, x, y); // sub
math::vec_exp<T>(n, y, y); // exp
// sum
scalar = T(0);
for (int i = 0; i < n; ++i) {
scalar += y[i];
}
math::vec_scal<T>(n, static_cast<T>(1) / scalar, y); // scale
}
template <typename T>
class AttentionLSTMKernel : public framework::OpKernel<T> {
public:
void Compute(const framework::ExecutionContext& ctx) const override {
using DeviceContext = paddle::platform::CPUDeviceContext;
auto* x = ctx.Input<LoDTensor>("X");
auto* h0 = ctx.Input<Tensor>("H0");
auto* c0 = ctx.Input<Tensor>("C0");
auto* atten_w = ctx.Input<Tensor>("AttentionWeight");
auto* atten_b = ctx.Input<Tensor>("AttentionBias");
auto* atten_scalar = ctx.Input<Tensor>("AttentionScalar");
auto* atten_scalar_bias = ctx.Input<Tensor>("AttentionScalarBias");
auto* lstm_w = ctx.Input<Tensor>("LSTMWeight");
auto* lstm_b = ctx.Input<Tensor>("LSTMBias");
auto* hidden_out = ctx.Output<LoDTensor>("Hidden");
auto* cell_out = ctx.Output<LoDTensor>("Cell");
auto* atted_x = ctx.Output<Tensor>("AttentionedX");
auto* fc_out = ctx.Output<Tensor>("AttentionFCOut");
auto* lstm_x = ctx.Output<Tensor>("LSTMX");
auto* lstm_out = ctx.Output<Tensor>("LSTMOUT");
// some shape should be reshape here since infershape can not get lod info
auto x_lod = x->lod();
const int N = x_lod[0].size() - 1; // batch size
auto x_dims = x->dims(); // T x M
auto w_dims = lstm_w->dims(); // (D+M) x 4D
const int total_T = x_dims[0];
const int M = x_dims[1]; // x frame size
const int D = w_dims[1] / 4; // gate frame size
const int D2 = D * 2;
const int D3 = D * 3;
const int D4 = w_dims[1];
int max_seq_len = x_lod[0][1];
for (int i = 1; i < N; ++i) {
int len = x_lod[0][i + 1] - x_lod[0][i];
max_seq_len = max_seq_len < len ? len : max_seq_len;
}
PADDLE_ENFORCE_EQ(x_lod.size(), 1, "Input(X)'s lod size must be 1.");
PADDLE_ENFORCE_EQ(c0->dims()[0], N, "C0 dims should be %d x %d.", N, D);
fc_out->Resize({max_seq_len, 1});
std::function<void(const int, const T *, T *)> act_gate, act_cell, act_cand;
auto& act_gate_str = ctx.Attr<std::string>("gate_activation");
auto& act_cell_str = ctx.Attr<std::string>("cell_activation");
auto& act_cand_str = ctx.Attr<std::string>("candidate_activation");
if (platform::jit::MayIUse(platform::jit::avx)) {
math::VecActivations<T, platform::jit::avx> act_functor;
act_gate = act_functor(act_gate_str);
act_cell = act_functor(act_cell_str);
act_cand = act_functor(act_cand_str);
} else {
math::VecActivations<T, platform::jit::isa_any> act_functor;
act_gate = act_functor(act_gate_str);
act_cell = act_functor(act_cell_str);
act_cand = act_functor(act_cand_str);
}
const T* x_data = x->data<T>();
const T* h0_data = h0 ? h0->data<T>() : NULL;
const T* c0_data = c0->data<T>();
const T* lstm_w_data = lstm_w->data<T>();
const T* lstm_b_data = lstm_b->data<T>();
const T* atten_w_data = atten_w->data<T>();
const T* atten_b_data = atten_b ? atten_b->data<T>() : NULL;
const T* atten_scalar_data = atten_scalar ? atten_scalar->data<T>() : NULL;
const T* atten_scalar_bias_data =
atten_scalar_bias ? atten_scalar_bias->data<T>() : NULL;
T* hidden_out_data = hidden_out->mutable_data<T>(ctx.GetPlace());
T* cell_out_data = cell_out->mutable_data<T>(ctx.GetPlace());
T* atted_x_data = atted_x->mutable_data<T>(ctx.GetPlace());
T* fc_out_data = fc_out->mutable_data<T>(ctx.GetPlace());
T* lstm_x_data = lstm_x->mutable_data<T>(ctx.GetPlace());
T* lstm_out_data = lstm_out->mutable_data<T>(ctx.GetPlace());
// x(TxM) * fc (Mx1) part of atten_wgt(M+D)x1
auto blas = math::GetBlas<DeviceContext, T>(ctx);
math::FCCompute<DeviceContext, T>(blas, total_T, 1, M, x_data, atten_w_data,
atted_x_data, atten_b_data);
const T* cur_atten_x_data = atted_x_data;
const T* cur_x_data = x_data;
const T* prev_cell_data = NULL;
const T* prev_hidden_data = NULL;
T* cur_cell_out_data = cell_out_data;
T* cur_hidden_out_data = hidden_out_data;
for (int i = 0; i < N; ++i) {
int seq_len = x_lod[0][i + 1] - x_lod[0][i];
prev_cell_data = c0_data + i * D;
prev_hidden_data = h0_data ? h0_data + i * D : NULL;
for (int step = 0; step < seq_len; ++step) {
/// 1. compute attention vector
// 1a. prev_cell(1xD) * fc(D) rest part of atten_wgt
T prev_cell_bias = blas.DOT(D, prev_cell_data, atten_w_data + M);
// 1b. add cell bias and relu
bias_relu<T>(seq_len, cur_atten_x_data, &prev_cell_bias, fc_out_data);
// 1c. fc scalar
if (atten_scalar_data) {
blas.SCAL(seq_len, *atten_scalar_data, fc_out_data);
bias_relu<T>(seq_len, fc_out_data, atten_scalar_bias_data,
fc_out_data);
}
// 1d. softmax
vec_softmax<T>(seq_len, fc_out_data, fc_out_data);
// mul x(seq_len*M) and sum pool
math::FCCompute<DeviceContext, T>(blas, 1, M, seq_len, fc_out_data,
cur_x_data, lstm_x_data);
/// 2. compute LSTM step
// lstm weight : concat[forget , input , output , tilde]
// shape : (D + M) x (4 * D)
// fc inputX(1xM) * weightX(M*(4D)) => 1 x 4D
blas.MatMul(1, D4, M, lstm_x_data, lstm_w_data + D * D4, lstm_out_data);
if (prev_hidden_data) {
blas.GEMM(CblasNoTrans, CblasNoTrans, 1, D4, D, static_cast<T>(1),
prev_hidden_data, D, lstm_w_data, D4, static_cast<T>(1),
lstm_out_data, D4);
}
// since input is 1xM, so can use add bias
blas.VADD(D4, lstm_b_data, lstm_out_data, lstm_out_data);
// gate act: sigmoid
act_gate(D3, lstm_out_data, lstm_out_data);
// candicate act: tanh
act_cand(D, lstm_out_data + D3, lstm_out_data + D3);
// a = forget * prev_cell
blas.VMUL(D, lstm_out_data, prev_cell_data, lstm_out_data);
// b = input * tilde
blas.VMUL(D, lstm_out_data + D, lstm_out_data + D3, lstm_out_data + D);
// cell_out = a + b
blas.VADD(D, lstm_out_data, lstm_out_data + D, cur_cell_out_data);
// state act tanh(cell_out) * output_gate
act_cell(D, cur_cell_out_data, lstm_out_data);
blas.VMUL(D, lstm_out_data, lstm_out_data + D2, cur_hidden_out_data);
prev_hidden_data = cur_hidden_out_data;
prev_cell_data = cur_cell_out_data;
cur_cell_out_data = cur_cell_out_data + D;
cur_hidden_out_data = cur_hidden_out_data + D;
}
cur_x_data = cur_x_data + seq_len * M;
cur_atten_x_data = cur_atten_x_data + seq_len;
}
}
};
} // namespace operators
} // namespace paddle
namespace ops = paddle::operators;
REGISTER_OPERATOR(attention_lstm, ops::AttentionLSTMOp,
ops::AttentionLSTMOpMaker,
paddle::framework::DefaultGradOpDescMaker<true>);
REGISTER_OP_CPU_KERNEL(attention_lstm, ops::AttentionLSTMKernel<float>,
ops::AttentionLSTMKernel<double>);
paddle/fluid/operators/attention_lstm_op.h 0 → 100644
浏览文件 @ ec5cb376
/* Copyright (c) 2016 PaddlePaddle Authors. All Rights Reserved.
Licensed under the Apache License, Version 2.0 (the "License");
you may not use this file except in compliance with the License.
You may obtain a copy of the License at
http://www.apache.org/licenses/LICENSE-2.0
Unless required by applicable law or agreed to in writing, software
distributed under the License is distributed on an "AS IS" BASIS,
WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
See the License for the specific language governing permissions and
limitations under the License. */
#pragma once
#include "paddle/fluid/framework/op_registry.h"
namespace paddle {
namespace operators {
using LoDTensor = framework::LoDTensor;
using Tensor = framework::Tensor;
class AttentionLSTMOp : public framework::OperatorWithKernel {
public:
using framework::OperatorWithKernel::OperatorWithKernel;
void InferShape(framework::InferShapeContext* ctx) const override;
protected:
framework::OpKernelType GetExpectedKernelType(
const framework::ExecutionContext& ctx) const override;
};
class AttentionLSTMOpMaker : public framework::OpProtoAndCheckerMaker {
public:
void Make() override;
};
} // namespace operators
} // namespace paddle
paddle/fluid/operators/batch_norm_mkldnn_op.cc
浏览文件 @ ec5cb376
......@@ -37,6 +37,95 @@ struct bn_type_traits {
using op_prim = typename op_type::primitive_desc;
};
class BatchNormMKLDNNHandler : public platform::MKLDNNHandler {
public:
BatchNormMKLDNNHandler(
std::shared_ptr<batch_norm_fwd::primitive_desc> batch_norm_pd,
const platform::MKLDNNDeviceContext &dev_ctx, mkldnn::engine engine,
const std::string &base_key)
: platform::MKLDNNHandler(dev_ctx, engine, base_key) {
batch_norm_pd_ = batch_norm_pd;
}
std::shared_ptr<memory> AcquireScaleshiftMemoryFromPrimitive(void *ptr) {
return this->AcquireMemoryFromPrimitive(
batch_norm_pd_->weights_primitive_desc(), ptr, "@scaleshift_mem_p");
}
std::shared_ptr<memory> AcquireMeanMemoryFromPrimitive(void *ptr) {
return this->AcquireMemoryFromPrimitive(
batch_norm_pd_->mean_primitive_desc(), ptr, "@mean_mem_p");
}
std::shared_ptr<memory> AcquireVarianceMemoryFromPrimitive(void *ptr) {
return this->AcquireMemoryFromPrimitive(
batch_norm_pd_->variance_primitive_desc(), ptr, "@variance_mem_p");
}
std::shared_ptr<batch_norm_fwd> AcquireTestTrainingBatchNormFwd(
std::shared_ptr<memory> src_memory,
std::shared_ptr<memory> scaleshift_memory,
std::shared_ptr<memory> dst_memory, std::shared_ptr<memory> mean_memory,
std::shared_ptr<memory> variance_memory, bool is_test) {
auto prim_key = key_ + "@batch_norm_p";
auto batch_norm_p =
std::static_pointer_cast<batch_norm_fwd>(dev_ctx_.GetBlob(prim_key));
PADDLE_ENFORCE((batch_norm_p != nullptr) || !is_reusing_,
"Fail to find batch norm primitive in device context");
if (batch_norm_p == nullptr) {
if (is_test) {
batch_norm_p = std::make_shared<batch_norm_fwd>(
*batch_norm_pd_, *src_memory,
(const mkldnn::primitive::at &)*mean_memory,
(const mkldnn::primitive::at &)*variance_memory, *scaleshift_memory,
*dst_memory);
} else {
batch_norm_p = std::make_shared<batch_norm_fwd>(
*batch_norm_pd_, *src_memory, *scaleshift_memory, *dst_memory,
*mean_memory, *variance_memory);
}
dev_ctx_.SetBlob(prim_key, batch_norm_p);
} else {
is_reusing_ = true;
}
return batch_norm_p;
}
static std::string GetHash(const memory::dims &input_dims, float epsilon,
unsigned flag, bool is_test, memory::format format,
const std::string &suffix = "") {
auto dims2str = [](const memory::dims &operand_dims) {
std::string dstr = "";
for (size_t i = 0; i < operand_dims.size(); ++i) {
dstr += std::to_string(operand_dims[i]) + "-";
}
return dstr;
};
return dims2str(input_dims) + std::to_string(epsilon) +
std::to_string(flag) + std::to_string(is_test) +
std::to_string(format) + suffix;
}
private:
std::shared_ptr<batch_norm_fwd::primitive_desc> batch_norm_pd_;
};
std::shared_ptr<memory> UpdateMemoryData(
const platform::MKLDNNDeviceContext &dev_ctx, const std::string &key,
void *new_ptr) {
auto mem = std::static_pointer_cast<memory>(dev_ctx.GetBlob(key));
PADDLE_ENFORCE(
mem != nullptr,
(std::string("Fail to find memory in device context [key: ") + key + "]")
.c_str());
mem->set_data_handle(new_ptr);
return mem;
}
template <typename T, typename Container>
void copy_to_weights(T scale_begin, T scale_end, T shift_begin, T shift_end,
Container *c) {
......@@ -48,15 +137,6 @@ void copy_to_weights(T scale_begin, T scale_end, T shift_begin, T shift_end,
std::inserter(*c, std::next(it, std::distance(scale_begin, scale_end))));
}
template <typename Op, typename... Args>
void run_batch_norm_op(Args &&... args) {
Op batch_norm_op{args...};
std::vector<mkldnn::primitive> pipeline;
pipeline.push_back(batch_norm_op);
mkldnn::stream(mkldnn::stream::kind::eager).submit(pipeline).wait();
}
} // namespace
template <typename T>
......@@ -110,6 +190,14 @@ class BatchNormMKLDNNOpKernel : public paddle::framework::OpKernel<T> {
PADDLE_ENFORCE(scale_tz.size() == 1, "Dims of scale tensor is NOT 1");
const unsigned int ic = scale_tz[0];
// MKLDNN requires a single piece of memory for scale and shift/bias data
const size_t scaleshift_size = 2 * ic;
std::vector<T> scaleshift_data;
scaleshift_data.reserve(scaleshift_size);
copy_to_weights(scale->data<T>(), scale->data<T>() + ic, shift->data<T>(),
shift->data<T>() + ic, &scaleshift_data);
unsigned flags = mkldnn::use_scale_shift;
if (is_test) flags |= mkldnn::use_global_stats;
if (fuse_with_relu) flags |= mkldnn::fuse_bn_relu;
......@@ -118,64 +206,69 @@ class BatchNormMKLDNNOpKernel : public paddle::framework::OpKernel<T> {
mkldnn::memory::format input_format =
platform::MKLDNNFormatForSize(src_tz.size(), x->format());
auto src_memory = memory(
{{{src_tz}, memory::data_type::f32, input_format}, mkldnn_engine},
to_void_cast(x_data));
// keys for backward pass
const std::string key = BatchNormMKLDNNHandler::GetHash(
src_tz, epsilon, flags, is_test, input_format,
ctx.op().Output("SavedMean"));
const std::string key_batch_norm_fwd_pd = key + "@bn_fwd_pd";
auto user_src_md = platform::MKLDNNMemDesc(
{src_tz}, platform::MKLDNNGetDataType<T>(), input_format);
// create primitive descriptor for batch norm forward
using bn_fwd_types = bn_type_traits<mkldnn::batch_normalization_forward>;
auto batch_norm_fwd_desc = bn_fwd_types::op_desc{
propagation, src_memory.get_primitive_desc().desc(), epsilon, flags};
std::shared_ptr<batch_norm_fwd::primitive_desc> batch_norm_fwd_pd =
std::shared_ptr<batch_norm_fwd::primitive_desc>(
new batch_norm_fwd::primitive_desc(batch_norm_fwd_desc,
mkldnn_engine));
// Save the pd to be used in backward pass
const std::string key = ctx.op().Output("SavedMean");
const std::string key_batch_norm_fwd_pd = key + "@bn_fwd_pd";
auto batch_norm_fwd_desc =
bn_fwd_types::op_desc{propagation, user_src_md, epsilon, flags};
auto batch_norm_fwd_pd = std::make_shared<batch_norm_fwd::primitive_desc>(
batch_norm_fwd_desc, mkldnn_engine);
// Save conv_pd/src_memory/weights_memory for backward pass
dev_ctx.SetBlob(key_batch_norm_fwd_pd, batch_norm_fwd_pd);
// MKLDNN requires a single piece of memory for scale and shift/bias data
const size_t scaleshift_size = 2 * ic;
std::vector<T> scaleshift_data;
scaleshift_data.reserve(scaleshift_size);
BatchNormMKLDNNHandler handler(batch_norm_fwd_pd, dev_ctx, mkldnn_engine,
key);
copy_to_weights(scale->data<T>(), scale->data<T>() + ic, shift->data<T>(),
shift->data<T>() + ic, &scaleshift_data);
auto src_memory =
handler.AcquireSrcMemory(user_src_md, to_void_cast(x_data));
// crate mkldnn memory for weights(scale/shift)
auto scaleshift_memory = memory(batch_norm_fwd_pd->weights_primitive_desc(),
scaleshift_data.data());
auto scaleshift_memory =
handler.AcquireScaleshiftMemoryFromPrimitive(scaleshift_data.data());
// create mkldnn memory for output y tensor
auto dst_memory = memory(batch_norm_fwd_pd->dst_primitive_desc(), y_data);
auto dst_memory = handler.AcquireDstMemory(
batch_norm_fwd_pd->dst_primitive_desc().desc(), y_data);
std::shared_ptr<batch_norm_fwd> batch_norm_p;
if (is_test) {
// create mkldnn memory for stats (as input)
auto mean_memory = memory(batch_norm_fwd_pd->mean_primitive_desc(),
to_void_cast(mean_data));
auto variance_memory =
memory(batch_norm_fwd_pd->variance_primitive_desc(),
std::shared_ptr<memory> mean_memory =
handler.AcquireMeanMemoryFromPrimitive(to_void_cast(mean_data));
std::shared_ptr<memory> variance_memory =
handler.AcquireVarianceMemoryFromPrimitive(
to_void_cast(variance_data));
run_batch_norm_op<typename bn_fwd_types::op_type>(
*batch_norm_fwd_pd, src_memory,
(const mkldnn::primitive::at &)mean_memory,
(const mkldnn::primitive::at &)variance_memory, scaleshift_memory,
dst_memory);
batch_norm_p = handler.AcquireTestTrainingBatchNormFwd(
src_memory, scaleshift_memory, dst_memory, mean_memory,
variance_memory, true);
} else {
// create mkldnn memory for stats (as output)
auto mean_memory =
memory(batch_norm_fwd_pd->mean_primitive_desc(), batch_mean_data);
auto variance_memory = memory(
batch_norm_fwd_pd->variance_primitive_desc(), batch_variance_data);
run_batch_norm_op<bn_fwd_types::op_type>(*batch_norm_fwd_pd, src_memory,
scaleshift_memory, dst_memory,
mean_memory, variance_memory);
std::shared_ptr<memory> mean_memory =
handler.AcquireMeanMemoryFromPrimitive(batch_mean_data);
std::shared_ptr<memory> variance_memory =
handler.AcquireVarianceMemoryFromPrimitive(batch_variance_data);
batch_norm_p = handler.AcquireTestTrainingBatchNormFwd(
src_memory, scaleshift_memory, dst_memory, mean_memory,
variance_memory, false);
}
y->set_layout(DataLayout::kMKLDNN);
y->set_format(platform::GetMKLDNNFormat(*dst_memory));
std::vector<mkldnn::primitive> pipeline;
pipeline.push_back(*batch_norm_p);
mkldnn::stream(mkldnn::stream::kind::eager).submit(pipeline).wait();
if (!is_test) {
// mkldnn only compute stats for current batch
// so we need compute momentum stats via Eigen lib
......@@ -192,10 +285,6 @@ class BatchNormMKLDNNOpKernel : public paddle::framework::OpKernel<T> {
running_variance_e =
variance_e * momentum + batch_variance_e * one_minus_momentum;
}
y->set_layout(DataLayout::kMKLDNN);
y->set_format(
(memory::format)dst_memory.get_primitive_desc().desc().data.format);
}
};
......@@ -242,94 +331,169 @@ class BatchNormMKLDNNGradOpKernel : public paddle::framework::OpKernel<T> {
const unsigned int ic = scale_tz[0];
// Retrieve bn_fwd_pd from device context
const std::string key = ctx.op().Input("SavedMean");
const std::string key_batch_norm_fwd_pd = key + "@bn_fwd_pd";
auto batch_norm_fwd_pd =
std::static_pointer_cast<batch_norm_fwd::primitive_desc>(
dev_ctx.GetBlob(key_batch_norm_fwd_pd));
PADDLE_ENFORCE(batch_norm_fwd_pd != nullptr,
"Fail to find batch_norm_fwd_pd in device context");
using bn_bwd_types = bn_type_traits<mkldnn::batch_normalization_backward>;
// create mkldnn memory from input diff_y tensor
mkldnn::memory::format dst_format =
platform::MKLDNNFormatForSize(src_tz.size(), diff_y->format());
mkldnn::memory::format input_format =
platform::MKLDNNFormatForSize(src_tz.size(), x->format());
unsigned flags = mkldnn::use_scale_shift;
// keys from forward pass
const std::string key = BatchNormMKLDNNHandler::GetHash(
src_tz, epsilon, flags, false, input_format,
ctx.op().Input("SavedMean"));
const std::string key_batch_norm_fwd_pd = key + "@bn_fwd_pd";
// keys for primitives reuse
const std::string key_with_hash =
key + BatchNormMKLDNNHandler::GetHash(src_tz, epsilon, flags, false,
input_format);
const std::string key_batch_norm_bwd_p =
key_with_hash + "@batch_norm_bwd_p";
const std::string key_batch_norm_src_mem_p =
key_with_hash + "@batch_norm_bwd_src_mem_p";
const std::string key_batch_norm_mean_mem_p =
key_with_hash + "@batch_norm_bwd_mean_mem_p";
const std::string key_batch_norm_variance_mem_p =
key_with_hash + "@batch_norm_bwd_variance_mem_p";
const std::string key_batch_norm_scaleshift_mem_p =
key_with_hash + "@batch_norm_bwd_scaleshift_mem_p";
const std::string key_batch_norm_diff_scaleshift_mem_p =
key_with_hash + "@batch_norm_bwd_diff_scaleshift_mem_p";
const std::string key_batch_norm_diff_src_mem_p =
key_with_hash + "@batch_norm_bwd_diff_src_mem_p";
const std::string key_batch_norm_diff_dst_mem_p =
key_with_hash + "@batch_norm_bwd_diff_dst_mem_p";
primitive reorder_diff_dst;
bool is_diff_dst_reordered = false;
auto user_diff_dst_memory = memory(
{{{diff_dst_tz}, memory::data_type::f32, dst_format}, mkldnn_engine},
to_void_cast(diff_y_data));
// create mkldnn memory from input x tensor
mkldnn::memory::format input_format =
platform::MKLDNNFormatForSize(src_tz.size(), x->format());
// MKLDNN requires a single piece of memory for scale and shift/bias data
const size_t scaleshift_size = 2 * ic;
auto src_memory = memory(
std::vector<T> scaleshift_data;
scaleshift_data.reserve(scaleshift_size);
copy_to_weights(scale_data, scale_data + ic, shift_data, shift_data + ic,
&scaleshift_data);
std::vector<T> diff_scaleshift_data;
diff_scaleshift_data.reserve(scaleshift_size);
auto batch_norm_fwd_pd =
std::static_pointer_cast<batch_norm_fwd::primitive_desc>(
dev_ctx.GetBlob(key_batch_norm_fwd_pd));
PADDLE_ENFORCE(batch_norm_fwd_pd != nullptr,
"Fail to find batch_norm_fwd_pd in device context");
auto batch_norm_bwd_p = std::static_pointer_cast<batch_norm_bwd>(
dev_ctx.GetBlob(key_batch_norm_bwd_p));
if (batch_norm_bwd_p == nullptr) {
auto src_memory = std::shared_ptr<memory>(new memory(
{{{src_tz}, memory::data_type::f32, input_format}, mkldnn_engine},
to_void_cast(x_data));
to_void_cast(x_data)));
// for diff_dst, try to use same format as dst in forward pass
auto diff_dst_pd = batch_norm_fwd_pd.get()->dst_primitive_desc();
auto diff_dst_md = diff_dst_pd.desc();
// create primitive descriptor for batch norm backward
unsigned flags = mkldnn::use_scale_shift;
auto batch_norm_bwd_desc = bn_bwd_types::op_desc{
mkldnn::prop_kind::backward, diff_dst_md,
src_memory.get_primitive_desc().desc(), epsilon, flags};
src_memory->get_primitive_desc().desc(), epsilon, flags};
auto batch_norm_bwd_pd = bn_bwd_types::op_prim{
batch_norm_bwd_desc, mkldnn_engine, *batch_norm_fwd_pd};
// reorder user_diff_dst if it's not in preferred format
auto diff_dst_memory = user_diff_dst_memory;
primitive reorder_diff_dst;
bool is_diff_dst_reordered = false;
auto diff_dst_memory = std::make_shared<memory>(user_diff_dst_memory);
if (diff_dst_pd != user_diff_dst_memory.get_primitive_desc()) {
diff_dst_memory = memory(diff_dst_pd);
reorder_diff_dst = reorder(user_diff_dst_memory, diff_dst_memory);
diff_dst_memory = std::make_shared<memory>(diff_dst_pd);
reorder_diff_dst = reorder(user_diff_dst_memory, *diff_dst_memory);
is_diff_dst_reordered = true;
}
// create mkldnn memory for input tensors (src/mean/variance)
auto mean_memory = memory(batch_norm_bwd_pd.mean_primitive_desc(),
auto mean_memory =
std::make_shared<memory>(batch_norm_bwd_pd.mean_primitive_desc(),
to_void_cast(batch_mean_data));
auto variance_memory = memory(batch_norm_bwd_pd.variance_primitive_desc(),
auto variance_memory =
std::make_shared<memory>(batch_norm_bwd_pd.variance_primitive_desc(),
to_void_cast(batch_variance_data));
// MKLDNN requires a single piece of memory for scale and shift/bias data
const size_t scaleshift_size = 2 * ic;
std::vector<T> scaleshift_data;
scaleshift_data.reserve(scaleshift_size);
copy_to_weights(scale_data, scale_data + ic, shift_data, shift_data + ic,
&scaleshift_data);
// create mkldnn memory for input tensors (scale/shift)
auto scaleshift_memory = memory(batch_norm_bwd_pd.weights_primitive_desc(),
scaleshift_data.data());
auto scaleshift_memory = std::make_shared<memory>(
batch_norm_bwd_pd.weights_primitive_desc(), scaleshift_data.data());
// create mkldnn memory for output diff weights (combined scale/shift)
std::vector<T> diff_scaleshift_data;
diff_scaleshift_data.reserve(scaleshift_size);
auto diff_scaleshift_memory =
memory(batch_norm_bwd_pd.diff_weights_primitive_desc(),
auto diff_scaleshift_memory = std::make_shared<memory>(
batch_norm_bwd_pd.diff_weights_primitive_desc(),
diff_scaleshift_data.data());
// here assume diff_src is in the same format of src
auto diff_src_memory = memory(src_memory.get_primitive_desc(), diff_x_data);
auto diff_src_memory = std::make_shared<memory>(
src_memory->get_primitive_desc(), diff_x_data);
// finally create batch_norm backward primitive
auto batch_norm_bwd_prim =
batch_norm_bwd(batch_norm_bwd_pd, src_memory, mean_memory,
variance_memory, diff_dst_memory, scaleshift_memory,
diff_src_memory, diff_scaleshift_memory);
batch_norm_bwd_p = std::make_shared<batch_norm_bwd>(
batch_norm_bwd_pd, *src_memory, *mean_memory, *variance_memory,
*diff_dst_memory, *scaleshift_memory, *diff_src_memory,
*diff_scaleshift_memory);
dev_ctx.SetBlob(key_batch_norm_bwd_p, batch_norm_bwd_p);
dev_ctx.SetBlob(key_batch_norm_src_mem_p, src_memory);
dev_ctx.SetBlob(key_batch_norm_mean_mem_p, mean_memory);
dev_ctx.SetBlob(key_batch_norm_variance_mem_p, variance_memory);
dev_ctx.SetBlob(key_batch_norm_scaleshift_mem_p, scaleshift_memory);
dev_ctx.SetBlob(key_batch_norm_diff_scaleshift_mem_p,
diff_scaleshift_memory);
dev_ctx.SetBlob(key_batch_norm_diff_src_mem_p, diff_src_memory);
dev_ctx.SetBlob(key_batch_norm_diff_dst_mem_p, diff_dst_memory);
// set layout/format of output tensors
diff_x->set_layout(DataLayout::kMKLDNN);
diff_x->set_format((memory::format)diff_src_memory->get_primitive_desc()
.desc()
.data.format);
} else {
// primitives already exist
UpdateMemoryData(dev_ctx, key_batch_norm_src_mem_p, to_void_cast(x_data));
UpdateMemoryData(dev_ctx, key_batch_norm_mean_mem_p,
to_void_cast(batch_mean_data));
UpdateMemoryData(dev_ctx, key_batch_norm_variance_mem_p,
to_void_cast(batch_variance_data));
UpdateMemoryData(dev_ctx, key_batch_norm_scaleshift_mem_p,
scaleshift_data.data());
UpdateMemoryData(dev_ctx, key_batch_norm_diff_scaleshift_mem_p,
diff_scaleshift_data.data());
auto diff_src_memory = UpdateMemoryData(
dev_ctx, key_batch_norm_diff_src_mem_p, to_void_cast(diff_x_data));
auto diff_dst_memory = UpdateMemoryData(
dev_ctx, key_batch_norm_diff_dst_mem_p, to_void_cast(diff_y_data));
// reorder user_diff_dst if it's not in preferred format
if (diff_dst_memory->get_primitive_desc() !=
user_diff_dst_memory.get_primitive_desc()) {
reorder_diff_dst = reorder(user_diff_dst_memory, *diff_dst_memory);
is_diff_dst_reordered = true;
}
// set layout/format of output tensors
diff_x->set_layout(DataLayout::kMKLDNN);
diff_x->set_format((memory::format)diff_src_memory->get_primitive_desc()
.desc()
.data.format);
}
// execute optional reorder and batch_norm backward primitive
std::vector<primitive> pipeline;
if (is_diff_dst_reordered) pipeline.push_back(reorder_diff_dst);
pipeline.push_back(batch_norm_bwd_prim);
pipeline.push_back(*batch_norm_bwd_p);
stream(stream::kind::eager).submit(pipeline).wait();
// copy back diff sacle/shift to output tensors (diff scale/shift)
......@@ -338,12 +502,6 @@ class BatchNormMKLDNNGradOpKernel : public paddle::framework::OpKernel<T> {
std::copy(it, std::next(it, ic), diff_scale_data);
std::copy(std::next(it, ic), std::end(diff_scaleshift_data),
diff_shift_data);
// set layout/format of output tensors
diff_x->set_layout(DataLayout::kMKLDNN);
diff_x->set_format((memory::format)diff_src_memory.get_primitive_desc()
.desc()
.data.format);
}
};
} // namespace operators
......
paddle/fluid/operators/concat_op.h
浏览文件 @ ec5cb376
......@@ -62,9 +62,21 @@ class ConcatGradKernel : public framework::OpKernel<T> {
void Compute(const framework::ExecutionContext& ctx) const {
auto* out_grad =
ctx.Input<framework::Tensor>(framework::GradVarName("Out"));
auto ins = ctx.MultiInput<framework::Tensor>("X");
auto ins = ctx.MultiInput<framework::LoDTensor>("X");
auto out_var_names = ctx.Outputs(framework::GradVarName("X"));
auto outs = ctx.MultiOutput<framework::Tensor>(framework::GradVarName("X"));
auto outs =
ctx.MultiOutput<framework::LoDTensor>(framework::GradVarName("X"));
{
auto dx = outs;
auto x = ins;
for (size_t i = 0; i < dx.size(); ++i) {
if (dx[i] != nullptr) {
dx[i]->set_lod(x[i]->lod());
}
}
}
int64_t axis = static_cast<int64_t>(ctx.Attr<int>("axis"));
// get output tensor that the name is not kEmptyVarName
......
paddle/fluid/operators/conv_mkldnn_op.cc
浏览文件 @ ec5cb376
......@@ -53,6 +53,18 @@ class ConvMKLDNNHandler : public platform::MKLDNNHandler {
key_ += "-BWD";
}
size_t GetDstMemorySize() const {
return conv_pd_->dst_primitive_desc().get_size();
}
size_t GetDiffWeightsMemorySize() const {
return conv_bwd_weights_pd_->diff_weights_primitive_desc().get_size();
}
size_t GetDiffSourceMemorySize() const {
return conv_bwd_data_pd_->diff_src_primitive_desc().get_size();
}
std::shared_ptr<mkldnn::memory> AcquireSrcMemoryFromWeightsPrimitive(
const std::shared_ptr<mkldnn::memory> user_memory_p,
std::vector<mkldnn::primitive>& pipeline) { // NOLINT
......@@ -294,7 +306,6 @@ class ConvMKLDNNOpKernel : public paddle::framework::OpKernel<T> {
const T* input_data = input->data<T>();
const T* filter_data = filter->data<T>();
T* output_data = output->mutable_data<T>(ctx.GetPlace());
std::vector<int> src_tz = paddle::framework::vectorize2int(input->dims());
std::vector<int> weights_tz =
......@@ -354,6 +365,8 @@ class ConvMKLDNNOpKernel : public paddle::framework::OpKernel<T> {
auto user_weights_memory_p = handler.AcquireWeightsMemory(
user_weights_md, to_void_cast<T>(filter_data));
T* output_data =
output->mutable_data<T>(ctx.GetPlace(), handler.GetDstMemorySize());
// create reorder primitive if the input format is not the preferred one
auto src_memory_p =
handler.AcquireSrcMemoryFromPrimitive(user_src_memory_p, pipeline);
......@@ -476,13 +489,6 @@ class ConvMKLDNNGradOpKernel : public paddle::framework::OpKernel<T> {
T* input_grad_data = nullptr;
T* filter_grad_data = nullptr;
if (input_grad) {
input_grad_data = input_grad->mutable_data<T>(ctx.GetPlace());
}
if (filter_grad) {
filter_grad_data = filter_grad->mutable_data<T>(ctx.GetPlace());
}
std::vector<int> src_tz = paddle::framework::vectorize2int(input->dims());
std::vector<int> weights_tz =
paddle::framework::vectorize2int(filter->dims());
......@@ -568,6 +574,9 @@ class ConvMKLDNNGradOpKernel : public paddle::framework::OpKernel<T> {
handler.AcquireDiffDstMemoryFromWeightsPrimitive(
user_diff_dst_memory_p, pipeline);
const size_t size = handler.GetDiffWeightsMemorySize();
filter_grad_data = filter_grad->mutable_data<T>(ctx.GetPlace(), size);
auto diff_weights_memory_p =
handler.AcquireDiffWeightsMemoryFromWeightsPrimitive(
reinterpret_cast<void*>(filter_grad_data));
......@@ -590,6 +599,9 @@ class ConvMKLDNNGradOpKernel : public paddle::framework::OpKernel<T> {
handler.AcquireDiffDstMemoryFromDataPrimitive(user_diff_dst_memory_p,
pipeline);
const size_t size = handler.GetDiffSourceMemorySize();
input_grad_data = input_grad->mutable_data<T>(ctx.GetPlace(), size);
auto diff_src_memory_p = handler.AcquireDiffSrcMemoryFromDataPrimitive(
reinterpret_cast<void*>(input_grad_data));
......
paddle/fluid/operators/distributed/variable_response.cc
浏览文件 @ ec5cb376
......@@ -151,6 +151,7 @@ bool VariableResponse::CopySelectRowsData(
::google::protobuf::io::CodedInputStream* input,
const platform::DeviceContext& ctx, int length) {
auto* slr = GetVar()->GetMutable<framework::SelectedRows>();
slr->mutable_rows()->clear();
slr->mutable_rows()->resize(length /
framework::SizeOfType(typeid(int64_t))); // int64
int64_t* rows_data = slr->mutable_rows()->data();
......
paddle/fluid/operators/elementwise_add_mkldnn_op.cc
浏览文件 @ ec5cb376
......@@ -137,9 +137,10 @@ class EltwiseAddMKLDNNKernel : public framework::OpKernel<T> {
};
template <typename T>
class EltwiseAddMKLDNNGradKernel : public framework::OpKernel<T> {
class EltwiseAddMKLDNNGradKernel : public ElemwiseGradKernel<T> {
public:
void Compute(const framework::ExecutionContext& ctx) const override {
ElemwiseGradKernel<T>::Compute(ctx);
using Tensor = framework::Tensor;
auto* dout = ctx.Input<Tensor>(framework::GradVarName("Out"));
......
paddle/fluid/operators/elementwise_add_op.h
浏览文件 @ ec5cb376
......@@ -15,6 +15,7 @@ limitations under the License. */
#pragma once
#include "paddle/fluid/framework/eigen.h"
#include "paddle/fluid/operators/elementwise_op.h"
#include "paddle/fluid/operators/elementwise_op_function.h"
#include "paddle/fluid/operators/math/blas.h"
......@@ -136,9 +137,11 @@ elementwise_add_grad(const framework::ExecutionContext& ctx,
}
template <typename DeviceContext, typename T>
class ElementwiseAddGradKernel : public framework::OpKernel<T> {
class ElementwiseAddGradKernel : public ElemwiseGradKernel<T> {
public:
void Compute(const framework::ExecutionContext& ctx) const override {
ElemwiseGradKernel<T>::Compute(ctx);
using Tensor = framework::Tensor;
auto* dout = ctx.Input<Tensor>(framework::GradVarName("Out"));
......
paddle/fluid/operators/elementwise_div_op.h
浏览文件 @ ec5cb376
......@@ -14,8 +14,8 @@ limitations under the License. */
#pragma once
#include "paddle/fluid/operators/elementwise_op.h"
#include "paddle/fluid/operators/elementwise_op_function.h"
namespace paddle {
namespace operators {
......@@ -53,9 +53,10 @@ struct DivGradDY {
};
template <typename DeviceContext, typename T>
class ElementwiseDivGradKernel : public framework::OpKernel<T> {
class ElementwiseDivGradKernel : public ElemwiseGradKernel<T> {
public:
void Compute(const framework::ExecutionContext& ctx) const override {
ElemwiseGradKernel<T>::Compute(ctx);
using Tensor = framework::Tensor;
auto* x = ctx.Input<Tensor>("X");
......
paddle/fluid/operators/elementwise_max_op.h
浏览文件 @ ec5cb376
......@@ -14,6 +14,7 @@ limitations under the License. */
#pragma once
#include "paddle/fluid/operators/elementwise_op.h"
#include "paddle/fluid/operators/elementwise_op_function.h"
namespace paddle {
......@@ -55,9 +56,10 @@ struct MaxGradDy {
};
template <typename DeviceContext, typename T>
class ElementwiseMaxGradKernel : public framework::OpKernel<T> {
class ElementwiseMaxGradKernel : public ElemwiseGradKernel<T> {
public:
void Compute(const framework::ExecutionContext& ctx) const override {
ElemwiseGradKernel<T>::Compute(ctx);
using Tensor = framework::Tensor;
auto* x = ctx.Input<Tensor>("X");
......
paddle/fluid/operators/elementwise_min_op.h
浏览文件 @ ec5cb376
......@@ -14,8 +14,8 @@ limitations under the License. */
#pragma once
#include "paddle/fluid/operators/elementwise_op.h"
#include "paddle/fluid/operators/elementwise_op_function.h"
namespace paddle {
namespace operators {
......@@ -55,9 +55,10 @@ struct MinGradDy {
};
template <typename DeviceContext, typename T>
class ElementwiseMinGradKernel : public framework::OpKernel<T> {
class ElementwiseMinGradKernel : public ElemwiseGradKernel<T> {
public:
void Compute(const framework::ExecutionContext& ctx) const override {
ElemwiseGradKernel<T>::Compute(ctx);
using Tensor = framework::Tensor;
auto* x = ctx.Input<Tensor>("X");
......
paddle/fluid/operators/elementwise_mul_op.h
浏览文件 @ ec5cb376
......@@ -13,6 +13,7 @@ See the License for the specific language governing permissions and
limitations under the License. */
#pragma once
#include "paddle/fluid/operators/elementwise_op.h"
#include "paddle/fluid/operators/elementwise_op_function.h"
#include "paddle/fluid/operators/math/blas.h"
......@@ -84,9 +85,10 @@ struct MulGradDY {
};
template <typename DeviceContext, typename T>
class ElementwiseMulGradKernel : public framework::OpKernel<T> {
class ElementwiseMulGradKernel : public ElemwiseGradKernel<T> {
public:
void Compute(const framework::ExecutionContext& ctx) const override {
ElemwiseGradKernel<T>::Compute(ctx);
using Tensor = framework::Tensor;
auto* x = ctx.Input<Tensor>("X");
......
paddle/fluid/operators/elementwise_op.h
浏览文件 @ ec5cb376
......@@ -205,6 +205,20 @@ class ElementwiseOpExplicitGrad : public ElementwiseOpGrad {
}
};
template <typename T>
class ElemwiseGradKernel : public framework::OpKernel<T> {
public:
void Compute(const framework::ExecutionContext& context) const override {
auto* dx =
context.Output<framework::LoDTensor>(framework::GradVarName("X"));
if (dx != nullptr) {
auto& dout =
*context.Input<framework::LoDTensor>(framework::GradVarName("Out"));
dx->set_lod(dout.lod());
}
}
};
} // namespace operators
} // namespace paddle
......
paddle/fluid/operators/elementwise_sub_op.h
浏览文件 @ ec5cb376
......@@ -13,6 +13,7 @@ See the License for the specific language governing permissions and
limitations under the License. */
#pragma once
#include "paddle/fluid/operators/elementwise_op.h"
#include "paddle/fluid/operators/elementwise_op_function.h"
namespace paddle {
......@@ -50,9 +51,10 @@ struct SubGradDY {
};
template <typename DeviceContext, typename T>
class ElementwiseSubGradKernel : public framework::OpKernel<T> {
class ElementwiseSubGradKernel : public ElemwiseGradKernel<T> {
public:
void Compute(const framework::ExecutionContext& ctx) const override {
ElemwiseGradKernel<T>::Compute(ctx);
using Tensor = framework::Tensor;
auto* dout = ctx.Input<Tensor>(framework::GradVarName("Out"));
......
paddle/fluid/operators/fake_dequantize_op.cc
浏览文件 @ ec5cb376
......@@ -18,15 +18,32 @@ limitations under the License. */
namespace paddle {
namespace operators {
template <typename T>
struct DequantizeFunctor<platform::CPUDeviceContext, T> {
void operator()(const platform::CPUDeviceContext& dev_ctx,
const framework::Tensor* in, const framework::Tensor* scale,
T max_range, framework::Tensor* out) {
auto in_e = framework::EigenVector<T>::Flatten(*in);
const T* scale_factor = scale->data<T>();
auto out_e = framework::EigenVector<T>::Flatten(*out);
auto& dev = *dev_ctx.eigen_device();
out_e.device(dev) = (scale_factor[0] / max_range) * in_e;
}
};
template struct DequantizeFunctor<platform::CPUDeviceContext, float>;
template struct DequantizeFunctor<platform::CPUDeviceContext, double>;
class FakeDequantizeMaxAbsOp : public framework::OperatorWithKernel {
public:
FakeDequantizeMaxAbsOp(const std::string &type,
const framework::VariableNameMap &inputs,
const framework::VariableNameMap &outputs,
const framework::AttributeMap &attrs)
FakeDequantizeMaxAbsOp(const std::string& type,
const framework::VariableNameMap& inputs,
const framework::VariableNameMap& outputs,
const framework::AttributeMap& attrs)
: OperatorWithKernel(type, inputs, outputs, attrs) {}
void InferShape(framework::InferShapeContext *ctx) const override {
void InferShape(framework::InferShapeContext* ctx) const override {
PADDLE_ENFORCE(ctx->HasInput("X"),
"Input(X) of FakeDequantizeMaxAbsOp should not be null.");
PADDLE_ENFORCE(ctx->HasOutput("Out"),
......@@ -42,21 +59,17 @@ class FakeDequantizeMaxAbsOpMaker : public framework::OpProtoAndCheckerMaker {
AddInput("X",
"(Tensor) The input with float-32/64 type is the "
"low precision tensor.");
AddInput("Scale", "(float) The scale in quantization stage.");
AddOutput("Out",
"(Tensor) The output is the dequantized high "
"precision tensor.");
AddAttr<int>("num_bits",
"(int) `num_bits` is the quantization level bits, "
"such as 2, 5, 8.");
AddAttr<float>("scale",
"(float) The maximum absolute value of low precision tensor."
"It is usually calculated by the fake_quantize_max_abs_op.");
AddAttr<float>("max_range", "(float) The max range in quantization stage.");
AddComment(R"DOC(
FakeDequantizeMaxAbsOp operator.
This calculation is an opposite operation of FakeQuantizeMaxAbsOp:
$$Out = \frac{scale*X}{2^{num_bits} - 1}$$
$$Out = \frac{scale*X}{ max_range }$$
)DOC");
}
......
paddle/fluid/operators/fake_dequantize_op.cu
浏览文件 @ ec5cb376
......@@ -14,6 +14,42 @@ limitations under the License. */
#include "paddle/fluid/operators/fake_dequantize_op.h"
namespace paddle {
namespace operators {
template <typename T>
__global__ void KeDequantize(const T* in, const T* scale, T max_range, int num,
T* out) {
const int idx = threadIdx.x + blockIdx.x * blockDim.x;
if (idx < num) {
out[idx] = in[idx] * scale[0] / max_range;
}
}
template <typename T>
struct DequantizeFunctor<platform::CUDADeviceContext, T> {
void operator()(const platform::CUDADeviceContext& dev_ctx,
const framework::Tensor* in, const framework::Tensor* scale,
T max_range, framework::Tensor* out) {
const T* in_data = in->data<T>();
const T* scale_factor = scale->data<T>();
T* out_data = out->mutable_data<T>(dev_ctx.GetPlace());
int num = in->numel();
int block = 512;
int grid = (num + block - 1) / block;
KeDequantize<T><<<grid, block, 0, dev_ctx.stream()>>>(
in_data, scale_factor, max_range, num, out_data);
}
};
template struct DequantizeFunctor<platform::CUDADeviceContext, float>;
template struct DequantizeFunctor<platform::CUDADeviceContext, double>;
} // namespace operators
} // namespace paddle
namespace ops = paddle::operators;
using CUDA = paddle::platform::CUDADeviceContext;
REGISTER_OP_CUDA_KERNEL(fake_dequantize_max_abs,
......
paddle/fluid/operators/fake_dequantize_op.h
浏览文件 @ ec5cb376
......@@ -19,22 +19,29 @@ limitations under the License. */
namespace paddle {
namespace operators {
template <typename DeviceContext, typename T>
struct DequantizeFunctor {
void operator()(const DeviceContext& dev_ctx, const framework::Tensor* in,
const framework::Tensor* scale, T max_range,
framework::Tensor* out);
};
template <typename DeviceContext, typename T>
class FakeDequantizeMaxAbsKernel : public framework::OpKernel<T> {
public:
virtual void Compute(const framework::ExecutionContext& ctx) const {
auto* in = ctx.Input<framework::Tensor>("X");
auto* scale = ctx.Input<framework::Tensor>("Scale");
auto* out = ctx.Output<framework::Tensor>("Out");
out->mutable_data<T>(in->place());
int num_bits = ctx.Attr<int>("num_bits");
T scale = static_cast<T>(ctx.Attr<float>("scale"));
int range = std::pow(2, num_bits) - 1;
float max_range = ctx.Attr<float>("max_range");
auto& dev_ctx = ctx.template device_context<DeviceContext>();
out->mutable_data<T>(dev_ctx.GetPlace());
auto eigen_out = framework::EigenVector<T>::Flatten(*out);
auto eigen_in = framework::EigenVector<T>::Flatten(*in);
auto& dev = *ctx.template device_context<DeviceContext>().eigen_device();
eigen_out.device(dev) = (scale / range) * eigen_in;
DequantizeFunctor<DeviceContext, T>()(dev_ctx, in, scale,
static_cast<T>(max_range), out);
}
};
......
paddle/fluid/operators/fetch_barrier_op.cc
浏览文件 @ ec5cb376
......@@ -52,6 +52,8 @@ class FetchBarrierOp : public framework::OperatorBase {
class FetchBarrierOpMaker : public framework::OpProtoAndCheckerMaker {
public:
void Make() {
AddOutput("Out", "(Any) Dummy outputs, used for control dependency")
.AsDuplicable();
AddComment(R"DOC(
SendBarrier operator
......
paddle/fluid/operators/fill_constant_op.cc
浏览文件 @ ec5cb376
......@@ -15,7 +15,6 @@ limitations under the License. */
#include "paddle/fluid/framework/data_type.h"
#include "paddle/fluid/framework/op_registry.h"
#include "paddle/fluid/operators/math/math_function.h"
#include "paddle/fluid/platform/device_context.h"
namespace paddle {
namespace operators {
......@@ -41,19 +40,33 @@ class FillConstantOp : public framework::OperatorBase {
static_cast<framework::proto::VarType::Type>(Attr<int>("dtype"));
auto value = Attr<float>("value");
auto force_cpu = Attr<bool>("force_cpu");
auto &out =
*scope.FindVar(Output("Out"))->GetMutable<framework::LoDTensor>();
out.Resize(framework::make_ddim(Attr<std::vector<int>>("shape")));
framework::Tensor *tensor = nullptr;
auto &out_var = *scope.FindVar(Output("Out"));
if (out_var.IsType<framework::LoDTensor>()) {
tensor = out_var.GetMutable<framework::LoDTensor>();
tensor->Resize(framework::make_ddim(Attr<std::vector<int>>("shape")));
} else if (out_var.IsType<framework::SelectedRows>()) {
tensor = out_var.GetMutable<framework::SelectedRows>()->mutable_value();
tensor->Resize(framework::make_ddim(Attr<std::vector<int>>("shape")));
} else {
PADDLE_THROW(
"fill constant op's output only"
"supports SelectedRows and LoDTensor");
}
if (force_cpu) {
auto cpu = platform::CPUPlace();
out.mutable_data(cpu, framework::ToTypeIndex(data_type));
tensor->mutable_data(cpu, framework::ToTypeIndex(data_type));
} else {
out.mutable_data(dev_place, framework::ToTypeIndex(data_type));
tensor->mutable_data(dev_place, framework::ToTypeIndex(data_type));
}
platform::DeviceContextPool &pool = platform::DeviceContextPool::Instance();
auto &dev_ctx = *pool.Get(dev_place);
math::set_constant(dev_ctx, &out, value);
math::set_constant(dev_ctx, tensor, value);
}
};
......
paddle/fluid/operators/fusion_lstm_op.cc
浏览文件 @ ec5cb376
......@@ -15,10 +15,14 @@ limitations under the License. */
#include "paddle/fluid/operators/fusion_lstm_op.h"
#include <string>
#include "paddle/fluid/operators/math/blas.h"
#include "paddle/fluid/operators/math/cpu_vec.h"
#include "paddle/fluid/operators/math/detail/activation_functions.h"
#include "paddle/fluid/operators/math/fc_compute.h"
#include "paddle/fluid/operators/math/lstm_compute.h"
#include "paddle/fluid/operators/math/sequence2batch.h"
#include "paddle/fluid/platform/cpu_info.h"
DEFINE_bool(seq_mode, true, "Use sequence mode");
namespace paddle {
namespace operators {
......@@ -98,7 +102,12 @@ void FusionLSTMOp::InferShape(framework::InferShapeContext* ctx) const {
ctx->ShareLoD("X", "Hidden");
ctx->ShareLoD("X", "Cell");
int xx_width = x_dims[1] > wx_dims[1] ? wx_dims[1] : x_dims[1];
int xx_width;
if (FLAGS_seq_mode) {
xx_width = wx_dims[1];
} else {
xx_width = x_dims[1] > wx_dims[1] ? wx_dims[1] : x_dims[1];
}
ctx->SetOutputDim("XX", {x_dims[0], xx_width});
ctx->ShareLoD("X", "XX");
}
......@@ -205,10 +214,138 @@ inline void ReorderInitState(const DeviceContext& ctx,
row_shuffle(ctx, src, index_lod, dst, indexed_src);
}
template <typename DeviceContext, typename T>
template <typename T>
class FuisonLSTMKernel : public framework::OpKernel<T> {
public:
void Compute(const framework::ExecutionContext& ctx) const override {
void SeqCompute(const framework::ExecutionContext& ctx) const {
using DeviceContext = paddle::platform::CPUDeviceContext;
auto* x = ctx.Input<LoDTensor>("X");
auto* h0 = ctx.Input<Tensor>("H0");
auto* c0 = ctx.Input<Tensor>("C0");
auto* wx = ctx.Input<Tensor>("WeightX");
auto* wh = ctx.Input<Tensor>("WeightH");
auto* bias = ctx.Input<Tensor>("Bias");
auto* xx = ctx.Output<LoDTensor>("XX");
auto* hidden_out = ctx.Output<LoDTensor>("Hidden");
auto* cell_out = ctx.Output<LoDTensor>("Cell");
bool is_reverse = ctx.Attr<bool>("is_reverse");
std::function<void(const int, const T *, T *)> act_gate, act_cell, act_cand;
auto& act_gate_str = ctx.Attr<std::string>("gate_activation");
auto& act_cell_str = ctx.Attr<std::string>("cell_activation");
auto& act_cand_str = ctx.Attr<std::string>("candidate_activation");
if (platform::jit::MayIUse(platform::jit::avx)) {
math::VecActivations<T, platform::jit::avx> act_functor;
act_gate = act_functor(act_gate_str);
act_cell = act_functor(act_cell_str);
act_cand = act_functor(act_cand_str);
} else {
math::VecActivations<T, platform::jit::isa_any> act_functor;
act_gate = act_functor(act_gate_str);
act_cell = act_functor(act_cell_str);
act_cand = act_functor(act_cand_str);
}
auto x_lod = x->lod();
auto x_dims = x->dims(); // T x M
auto wh_dims = wh->dims(); // D x 4D
const int total_T = x_dims[0];
const int N = x_lod[0].size() - 1; // batch size
const int M = x_dims[1]; // x frame size
const int D = wh_dims[0];
const int D2 = D * 2;
const int D3 = D * 3;
const int D4 = wh_dims[1];
const T* x_data = x->data<T>();
const T* h0_data = h0 ? h0->data<T>() : NULL;
const T* c0_data = c0 ? c0->data<T>() : NULL;
const T* wx_data = wx->data<T>();
const T* wh_data = wh->data<T>();
T* xx_data = xx->mutable_data<T>(ctx.GetPlace());
T* hidden_out_data = hidden_out->mutable_data<T>(ctx.GetPlace());
T* cell_out_data = cell_out->mutable_data<T>(ctx.GetPlace());
auto blas = math::GetBlas<DeviceContext, T>(ctx);
math::FCCompute<DeviceContext, T>(blas, total_T, D4, M, x_data, wx_data,
xx_data, bias->data<T>());
int xx_offset = D4;
int gate_offset = D;
if (is_reverse) {
const int offset = (total_T - 1) * D;
xx_data = xx_data + offset * 4;
hidden_out_data = hidden_out_data + offset;
cell_out_data = cell_out_data + offset;
xx_offset = -D4;
gate_offset = -D;
}
auto move_step = [&]() {
xx_data = xx_data + xx_offset;
hidden_out_data = hidden_out_data + gate_offset;
cell_out_data = cell_out_data + gate_offset;
};
for (int i = 0; i < N; ++i) {
int bid = is_reverse ? N - 1 - i : i;
int seq_len = x_lod[0][bid + 1] - x_lod[0][bid];
const T* prev_cell_data = NULL;
const T* prev_hidden_data = NULL;
int tstart = 0;
if (h0_data) {
prev_hidden_data = h0_data + bid * D;
prev_cell_data = c0_data + bid * D;
} else {
// W_ch, W_ih, W_fh, W_oh
act_gate(D3, xx_data + D, xx_data + D);
act_cand(D, xx_data, xx_data);
// cell out= input*tilde
blas.VMUL(D, xx_data, xx_data + D, cell_out_data);
// hidden out= act_state(cellout) * outgate
act_cell(D, cell_out_data, xx_data + D2);
blas.VMUL(D, xx_data + D2, xx_data + D3, hidden_out_data);
// prev
prev_hidden_data = hidden_out_data;
prev_cell_data = cell_out_data;
tstart = 1;
move_step();
}
for (int step = tstart; step < seq_len; ++step) {
blas.GEMM(CblasNoTrans, CblasNoTrans, 1, D4, D, static_cast<T>(1),
prev_hidden_data, D, wh_data, D4, static_cast<T>(1), xx_data,
D4);
// W_ch, W_ih, W_fh, W_oh
act_gate(D3, xx_data + D, xx_data + D);
act_cand(D, xx_data, xx_data);
// a = forget * prev_cell
blas.VMUL(D, xx_data + D2, prev_cell_data, xx_data + D2);
// b = input * tilde
blas.VMUL(D, xx_data, xx_data + D, xx_data + D);
// cell out= a+b
blas.VADD(D, xx_data + D, xx_data + D2, cell_out_data);
// hidden out= act_state(cellout) * outgate
act_cell(D, cell_out_data, xx_data + D2);
blas.VMUL(D, xx_data + D2, xx_data + D3, hidden_out_data);
// prev
prev_hidden_data = hidden_out_data;
prev_cell_data = cell_out_data;
move_step();
}
}
}
void BatchCompute(const framework::ExecutionContext& ctx) const {
using DeviceContext = platform::CPUDeviceContext;
auto* x = ctx.Input<LoDTensor>("X");
auto* wx = ctx.Input<Tensor>("WeightX");
auto* wh = ctx.Input<Tensor>("WeightH");
......@@ -339,6 +476,13 @@ class FuisonLSTMKernel : public framework::OpKernel<T> {
// restore the output cell state in LoDTensor from the batch cell
to_seq(dev_ctx, batch_cell, cell_out);
}
void Compute(const framework::ExecutionContext& ctx) const override {
if (FLAGS_seq_mode) {
SeqCompute(ctx);
} else {
BatchCompute(ctx);
}
}
};
} // namespace operators
......@@ -348,7 +492,5 @@ namespace ops = paddle::operators;
REGISTER_OPERATOR(fusion_lstm, ops::FusionLSTMOp, ops::FusionLSTMOpMaker,
paddle::framework::DefaultGradOpDescMaker<true>);
REGISTER_OP_CPU_KERNEL(
fusion_lstm,
ops::FuisonLSTMKernel<paddle::platform::CPUDeviceContext, float>,
ops::FuisonLSTMKernel<paddle::platform::CPUDeviceContext, double>);
REGISTER_OP_CPU_KERNEL(fusion_lstm, ops::FuisonLSTMKernel<float>,
ops::FuisonLSTMKernel<double>);
paddle/fluid/operators/fusion_lstm_op.h
浏览文件 @ ec5cb376
......@@ -13,7 +13,6 @@ See the License for the specific language governing permissions and
limitations under the License. */
#pragma once
// #include <string>
#include "paddle/fluid/framework/op_registry.h"
namespace paddle {
......
paddle/fluid/operators/fusion_seqexpand_concat_fc_op.cc 0 → 100644
浏览文件 @ ec5cb376
/* Copyright (c) 2016 PaddlePaddle Authors. All Rights Reserved.
Licensed under the Apache License, Version 2.0 (the "License");
you may not use this file except in compliance with the License.
You may obtain a copy of the License at
http://www.apache.org/licenses/LICENSE-2.0
Unless required by applicable law or agreed to in writing, software
distributed under the License is distributed on an "AS IS" BASIS,
WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
See the License for the specific language governing permissions and
limitations under the License. */
#include "paddle/fluid/operators/fusion_seqexpand_concat_fc_op.h"
#include <string>
#include "paddle/fluid/operators/math/blas.h"
#include "paddle/fluid/operators/math/cpu_vec.h"
#include "paddle/fluid/operators/math/fc_compute.h"
#include "paddle/fluid/platform/cpu_info.h"
namespace paddle {
namespace operators {
void FusionSeqExpandConcatFCOp::InferShape(
framework::InferShapeContext* ctx) const {
PADDLE_ENFORCE_GT(
ctx->Inputs("X").size(), 1UL,
"Inputs(X) of FusionSeqExpandConcatFCOp should larger than 1.");
PADDLE_ENFORCE(
ctx->HasInput("FCWeight"),
"Input(FCWeight) of FusionSeqExpandConcatFCOp should not be null.");
PADDLE_ENFORCE(
ctx->HasOutput("Out"),
"Output(Out) of FusionSeqExpandConcatFCOp should not be null.");
PADDLE_ENFORCE(
ctx->HasOutput("FCOut"),
"Output(FCOut) of FusionSeqExpandConcatFCOp should not be null.");
auto ins_dims = ctx->GetInputsDim("X");
auto w_dims = ctx->GetInputDim("FCWeight"); // (M0+M1+M2+..) x D
PADDLE_ENFORCE_EQ(w_dims.size(), 2UL, "Input(FCWeight)'s rank must be 2.");
const int D = w_dims[1];
int sum = ins_dims[0][1];
for (size_t i = 1; i < ins_dims.size(); ++i) {
sum += ins_dims[i][1];
}
PADDLE_ENFORCE_EQ(sum, w_dims[0],
"FC height should be sum of all inputs width.");
if (ctx->HasInput("FCBias")) {
auto b_dims = ctx->GetInputDim("FCBias");
PADDLE_ENFORCE_EQ(b_dims.size(), 2, "Input(FCBias)'s rank must be 2.");
PADDLE_ENFORCE_EQ(b_dims[0], 1, "FCBias shapes must be 1 * %d.", D);
PADDLE_ENFORCE_EQ(b_dims[1], D, "FCBias shapes must be 1 * %d.", D);
}
ctx->SetOutputDim("Out", {ins_dims[0][0], D});
// fcout should be reshape when run since can not get lod in infershape
// explicit share the ref lod
ctx->ShareLoD("X", "Out", 0);
}
framework::OpKernelType FusionSeqExpandConcatFCOp::GetExpectedKernelType(
const framework::ExecutionContext& ctx) const {
return framework::OpKernelType(
framework::ToDataType(ctx.MultiInput<LoDTensor>("X")[0]->type()),
ctx.device_context());
}
void FusionSeqExpandConcatFCOpMaker::Make() {
AddInput("X",
"(LoDTensor) input LodDTensors, the first one must be have ref lod "
"for sequence expand, and the rest input should have same lod.")
.AsDuplicable();
AddInput("FCWeight", "(Tensor) the weights of fc.");
AddInput("FCBias", "(Tensor, optional) the bias of fc.").AsDispensable();
AddOutput("Out", "(LoDTensor) Output LodTensor.");
AddOutput(
"FCOut",
"(Tensor) the intermediate tensor to keep the result of fc."
"Shape is (N x D), where N is the batch size, D is the output dim of fc")
.AsIntermediate();
AddAttr<std::string>("fc_activation",
"(string, default: identity)"
"The activation for the result of fc."
"`identity` by default.")
.SetDefault("identity")
.InEnum({"sigmoid", "tanh", "relu", "identity"});
AddComment(R"DOC(
Fusion Sequence expand + concat + fc Operator.
All below conditions should be meet:
The ref_level of seq_expand should be 0.
The ref lod of seq_expand level is the first input of concat.
The other inputs should have same lod and same batch size of ref lod.
The seq len of other inputs should be 1.
The concat axis should be 1.
)DOC");
}
template <typename T>
class FusionSeqExpandConcatFCOpKernel : public framework::OpKernel<T> {
public:
void Compute(const framework::ExecutionContext& ctx) const override {
using DeviceContext = paddle::platform::CPUDeviceContext;
auto ins = ctx.MultiInput<LoDTensor>("X");
auto* w = ctx.Input<Tensor>("FCWeight");
auto* b = ctx.Input<Tensor>("FCBias");
auto* out = ctx.Output<LoDTensor>("Out");
auto* fc_out = ctx.Output<Tensor>("FCOut");
auto* ref_in = ins[0];
auto ref_lod = ref_in->lod();
auto in1_lod = ins[1]->lod();
auto ref_dims = ref_in->dims(); // T x M0
auto in1_dims = ins[1]->dims(); // N x M1
auto w_dims = w->dims();
const int N = ref_lod[0].size() - 1;
const int total_T = ref_dims[0];
const int M0 = ref_dims[1];
const int M1 = in1_dims[1];
const int D = w_dims[1];
// some check and fcout should be reshape here
// since infershape can not get lod info
PADDLE_ENFORCE_EQ(ref_lod.size(), 1UL, "Only support input lod size is 1.");
PADDLE_ENFORCE_EQ(in1_lod.size(), 1UL, "Only support input lod size is 1.");
PADDLE_ENFORCE_EQ(in1_lod[0].size() - 1, N,
"Batch size of all inputs should be equal.");
PADDLE_ENFORCE_EQ(in1_lod[0][N], N,
"Seq_length of other inputs should be 1.");
PADDLE_ENFORCE_EQ(in1_dims[0], N, "input height should be batch size.");
for (size_t i = 2; i < ins.size(); ++i) {
PADDLE_ENFORCE_EQ(ins[i]->dims()[0], N,
"All other inputs height should be equal");
PADDLE_ENFORCE_EQ(ins[i]->lod(), in1_lod,
"All other inputs should have same lod");
}
fc_out->Resize({N, D});
std::function<void(const int, const T*, T*)> fc_act;
auto& fc_act_str = ctx.Attr<std::string>("fc_activation");
if (platform::jit::MayIUse(platform::jit::avx)) {
math::VecActivations<T, platform::jit::avx> act_functor;
fc_act = act_functor(fc_act_str);
} else {
math::VecActivations<T, platform::jit::isa_any> act_functor;
fc_act = act_functor(fc_act_str);
}
const T* ref_in_data = ref_in->data<T>();
const T* in1_data = ins[1]->data<T>();
const T* w_data = w->data<T>();
T* out_data = out->mutable_data<T>(ctx.GetPlace());
T* fc_out_data = fc_out->mutable_data<T>(ctx.GetPlace());
auto blas = math::GetBlas<DeviceContext, T>(ctx);
math::FCCompute<DeviceContext, T>(blas, total_T, D, M0, ref_in_data, w_data,
out_data, b ? b->data<T>() : NULL);
w_data = w_data + M0 * D;
// first write on
blas.MatMul(N, D, M1, in1_data, w_data, fc_out_data);
w_data = w_data + M1 * D;
for (size_t i = 2; i < ins.size(); ++i) {
// add on
const T* in_data = ins[i]->data<T>();
const int K = ins[i]->dims()[1];
blas.GEMM(CblasNoTrans, CblasNoTrans, N, D, K, static_cast<T>(1), in_data,
K, w_data, D, static_cast<T>(1), fc_out_data, D);
w_data = w_data + K * D;
}
T* cur_out_data = out_data;
for (int i = 0; i < N; ++i) {
int seq_len = ref_lod[0][i + 1] - ref_lod[0][i];
T* src = fc_out_data + i * D;
for (int step = 0; step < seq_len; ++step) {
blas.VADD(D, cur_out_data, src, cur_out_data);
cur_out_data = cur_out_data + D;
}
}
fc_act(total_T * D, out_data, out_data);
}
};
} // namespace operators
} // namespace paddle
namespace ops = paddle::operators;
REGISTER_OPERATOR(fusion_seqexpand_concat_fc, ops::FusionSeqExpandConcatFCOp,
ops::FusionSeqExpandConcatFCOpMaker,
paddle::framework::DefaultGradOpDescMaker<true>);
REGISTER_OP_CPU_KERNEL(fusion_seqexpand_concat_fc,
ops::FusionSeqExpandConcatFCOpKernel<float>,
ops::FusionSeqExpandConcatFCOpKernel<double>);
paddle/fluid/operators/fusion_seqexpand_concat_fc_op.h 0 → 100644
浏览文件 @ ec5cb376
/* Copyright (c) 2018 PaddlePaddle Authors. All Rights Reserved.
Licensed under the Apache License, Version 2.0 (the "License");
you may not use this file except in compliance with the License.
You may obtain a copy of the License at
http://www.apache.org/licenses/LICENSE-2.0
Unless required by applicable law or agreed to in writing, software
distributed under the License is distributed on an "AS IS" BASIS,
WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
See the License for the specific language governing permissions and
limitations under the License. */
#pragma once
#include "paddle/fluid/framework/op_registry.h"
namespace paddle {
namespace operators {
using LoDTensor = framework::LoDTensor;
using Tensor = framework::Tensor;
class FusionSeqExpandConcatFCOp : public framework::OperatorWithKernel {
public:
using framework::OperatorWithKernel::OperatorWithKernel;
void InferShape(framework::InferShapeContext* ctx) const override;
protected:
framework::OpKernelType GetExpectedKernelType(
const framework::ExecutionContext& ctx) const override;
};
class FusionSeqExpandConcatFCOpMaker
: public framework::OpProtoAndCheckerMaker {
public:
void Make() override;
};
} // namespace operators
} // namespace paddle
paddle/fluid/operators/listen_and_serv_op.cc
浏览文件 @ ec5cb376
......@@ -165,12 +165,13 @@ void ListenAndServOp::RunSyncLoop(
recv_scope);
VLOG(2) << "run all blocks spent " << GetTimestamp() - ts << "(ms)";
rpc_service_->SetCond(distributed::kRequestGet);
rpc_service_->WaitBarrier(distributed::kRequestGet);
rpc_service_->ResetBarrierCounter();
// reset received sparse vars to avoid reuse it in the next mini-batch
dynamic_cast<distributed::RequestSendHandler *>(request_send_handler_.get())
->ResetSparseVarRecorder();
rpc_service_->SetCond(distributed::kRequestGet);
rpc_service_->WaitBarrier(distributed::kRequestGet);
rpc_service_->ResetBarrierCounter();
} // while(true)
}
......
paddle/fluid/operators/math/CMakeLists.txt
浏览文件 @ ec5cb376
......@@ -65,3 +65,4 @@ if(WITH_GPU)
nv_test(selected_rows_functor_gpu_test SRCS selected_rows_functor_test.cu DEPS selected_rows_functor math_function)
endif()
cc_test(concat_test SRCS concat_test.cc DEPS concat)
cc_test(cpu_vec_test SRCS cpu_vec_test.cc DEPS blas cpu_info)
paddle/fluid/operators/math/blas.h
浏览文件 @ ec5cb376
......@@ -90,6 +90,11 @@ class Blas {
void GEMM(bool transA, bool transB, int M, int N, int K, T alpha, const T* A,
int lda, const T* B, int ldb, T beta, T* C, int ldc) const;
template <typename T>
void GEMM(CBLAS_TRANSPOSE transA, CBLAS_TRANSPOSE transB, int M, int N, int K,
T alpha, const T* A, int lda, const T* B, int ldb, T beta, T* C,
int ldc) const;
#ifdef PADDLE_WITH_MKLML
template <typename T>
T* GEMM_ALLOC(const CBLAS_IDENTIFIER id, const int M, const int N,
......@@ -109,6 +114,10 @@ class Blas {
void GEMM_FREE(T* data) const;
#endif
template <typename T>
void MatMul(const int M, const int N, const int K, const T* A, const T* B,
T* C) const;
template <typename T>
void MatMul(const framework::Tensor& mat_a, bool trans_a,
const framework::Tensor& mat_b, bool trans_b, T alpha,
......@@ -140,10 +149,19 @@ class Blas {
template <typename T>
void VCOPY(int n, const T* x, T* y) const;
template <typename T>
void VEXP(int n, const T* x, T* y) const;
template <typename T>
void GEMV(bool trans_a, int M, int N, T alpha, const T* A, const T* B, T beta,
T* C) const;
template <typename T>
T DOT(int n, const T* x, const T* y) const;
template <typename T>
void SCAL(int n, const T a, T* x) const;
template <typename T>
void BatchedGEMM(CBLAS_TRANSPOSE transA, CBLAS_TRANSPOSE transB, int M, int N,
int K, T alpha, const T* A, const T* B, T beta, T* C,
......@@ -215,11 +233,26 @@ class BlasT : private Blas<DeviceContext> {
Base()->template VCOPY<T>(args...);
}
template <typename... ARGS>
void VEXP(ARGS... args) const {
Base()->template VEXP<T>(args...);
}
template <typename... ARGS>
void GEMV(ARGS... args) const {
Base()->template GEMV<T>(args...);
}
template <typename... ARGS>
T DOT(ARGS... args) const {
return Base()->template DOT<T>(args...);
}
template <typename... ARGS>
void SCAL(ARGS... args) const {
Base()->template SCAL<T>(args...);
}
template <typename... ARGS>
void BatchedGEMM(ARGS... args) const {
Base()->template BatchedGEMM<T>(args...);
......
paddle/fluid/operators/math/blas_impl.h
浏览文件 @ ec5cb376
......@@ -73,6 +73,16 @@ struct CBlas<float> {
platform::dynload::cblas_sgemv(args...);
}
template <typename... ARGS>
static float DOT(ARGS... args) {
return platform::dynload::cblas_sdot(args...);
}
template <typename... ARGS>
static void SCAL(ARGS... args) {
platform::dynload::cblas_sscal(args...);
}
template <typename... ARGS>
static void GEMM_BATCH(ARGS... args) {
platform::dynload::cblas_sgemm_batch(args...);
......@@ -87,6 +97,11 @@ struct CBlas<float> {
static void VMUL(ARGS... args) {
platform::dynload::vsMul(args...);
}
template <typename... ARGS>
static void VEXP(ARGS... args) {
platform::dynload::vsExp(args...);
}
};
template <>
......@@ -138,6 +153,16 @@ struct CBlas<double> {
platform::dynload::cblas_dgemv(args...);
}
template <typename... ARGS>
static double DOT(ARGS... args) {
return platform::dynload::cblas_ddot(args...);
}
template <typename... ARGS>
static void SCAL(ARGS... args) {
platform::dynload::cblas_dscal(args...);
}
template <typename... ARGS>
static void GEMM_BATCH(ARGS... args) {
platform::dynload::cblas_dgemm_batch(args...);
......@@ -152,6 +177,11 @@ struct CBlas<double> {
static void VMUL(ARGS... args) {
platform::dynload::vdMul(args...);
}
template <typename... ARGS>
static void VEXP(ARGS... args) {
platform::dynload::vdExp(args...);
}
};
#else
......@@ -210,6 +240,9 @@ struct CBlas<platform::float16> {
PADDLE_THROW("float16 SMM_GEMM not supported on CPU");
}
static void VMUL(...) { PADDLE_THROW("float16 VMUL not supported on CPU"); }
static void VEXP(...) { PADDLE_THROW("float16 VEXP not supported on CPU"); }
static void DOT(...) { PADDLE_THROW("float16 DOT not supported on CPU"); };
static void SCAL(...) { PADDLE_THROW("float16 SCAL not supported on CPU"); };
#ifdef PADDLE_WITH_MKLML
static void GEMM_BATCH(...) {
PADDLE_THROW("float16 GEMM_BATCH not supported on CPU");
......@@ -217,64 +250,6 @@ struct CBlas<platform::float16> {
#endif
};
template <typename T>
inline bool UseXSMM(const int &m, const int &n, const int &k, bool transa,
bool transb, const T &alpha, const T &beta) {
#ifdef PADDLE_WITH_LIBXSMM
// Refer to https://github.com/hfp/libxsmm/blob/master/README.md
// But the threshold is custom
constexpr int LIBXSMM_THRESHOLD = 20 * 20 * 20;
if (m * n * k > LIBXSMM_THRESHOLD || transa || transb ||
std::abs<T>(alpha - static_cast<T>(1) >
std::numeric_limits<T>::epsilon()) ||
std::abs<T>(beta) > std::numeric_limits<T>::epsilon()) {
return false;
} else {
return true;
}
#endif
return false;
}
template <>
inline bool UseXSMM<platform::float16>(const int &m, const int &n, const int &k,
bool transa, bool transb,
const platform::float16 &alpha,
const platform::float16 &beta) {
return false;
}
template <typename T>
inline void GEMM_WARP(CBLAS_ORDER order, CBLAS_TRANSPOSE transA,
CBLAS_TRANSPOSE transB, int M, int N, int K, T alpha,
const T *A, int lda, const T *B, int ldb, T beta, T *C,
int ldc) {
#ifdef PADDLE_WITH_LIBXSMM
if (UseXSMM<T>(M, N, K, transA != CblasNoTrans, transB != CblasNoTrans, alpha,
beta)) {
// Note: SMM use ColMajor
const char transa = 'N';
const char transb = 'N';
CBlas<T>::SMM_GEMM(&transa, &transb, &N, &M, &K, &alpha, B, &ldb, A, &lda,
&beta, C, &ldc);
return;
}
#endif
#ifdef PADDLE_MKL_SPLIT_GEMM
constexpr int bs = 2;
if (M % bs == 0 && transA == CblasNoTrans && transB == CblasNoTrans) {
for (int off = 0; off < M; off += bs) {
CBlas<T>::GEMM(CblasRowMajor, CblasNoTrans, CblasNoTrans, bs, N, K, alpha,
A + off * lda, lda, B, ldb, beta, C + off * ldb, ldc);
}
return;
}
#endif
CBlas<T>::GEMM(CblasRowMajor, transA, transB, M, N, K, alpha, A, lda, B, ldb,
beta, C, ldc);
}
#ifdef PADDLE_WITH_MKLML
template <>
template <typename T>
......@@ -319,7 +294,7 @@ void Blas<platform::CPUDeviceContext>::GEMM(CBLAS_TRANSPOSE transA,
int lda = (transA == CblasNoTrans) ? K : M;
int ldb = (transB == CblasNoTrans) ? N : K;
int ldc = N;
GEMM_WARP<T>(CblasRowMajor, transA, transB, M, N, K, alpha, A, lda, B, ldb,
CBlas<T>::GEMM(CblasRowMajor, transA, transB, M, N, K, alpha, A, lda, B, ldb,
beta, C, ldc);
}
......@@ -329,11 +304,22 @@ void Blas<platform::CPUDeviceContext>::GEMM(bool transA, bool transB, int M,
int N, int K, T alpha, const T *A,
int lda, const T *B, int ldb,
T beta, T *C, int ldc) const {
GEMM_WARP<T>(CblasRowMajor, transA == false ? CblasNoTrans : CblasTrans,
CBlas<T>::GEMM(CblasRowMajor, transA == false ? CblasNoTrans : CblasTrans,
transB == false ? CblasNoTrans : CblasTrans, M, N, K, alpha, A,
lda, B, ldb, beta, C, ldc);
}
template <>
template <typename T>
void Blas<platform::CPUDeviceContext>::GEMM(CBLAS_TRANSPOSE transA,
CBLAS_TRANSPOSE transB, int M,
int N, int K, T alpha, const T *A,
int lda, const T *B, int ldb,
T beta, T *C, int ldc) const {
CBlas<T>::GEMM(CblasRowMajor, transA, transB, M, N, K, alpha, A, lda, B, ldb,
beta, C, ldc);
}
template <typename DeviceContext>
template <typename T>
void Blas<DeviceContext>::MatMul(const framework::Tensor &mat_a, bool trans_a,
......@@ -399,6 +385,47 @@ void Blas<platform::CPUDeviceContext>::VMUL(int n, const T *x, const T *y,
#endif
}
template <>
template <typename T>
void Blas<platform::CPUDeviceContext>::VEXP(int n, const T *x, T *y) const {
#ifdef PADDLE_WITH_MKLML
CBlas<T>::VEXP(n, x, y);
#else
// try to find if openblas support vexp
for (int i = 0; i < n; ++i) {
y[i] = std::exp(x[i]);
}
#endif
}
template <>
template <typename T>
T Blas<platform::CPUDeviceContext>::DOT(int n, const T *x, const T *y) const {
#ifdef PADDLE_WITH_MKLML
return CBlas<T>::DOT(n, x, 1, y, 1);
#else
// try to find if openblas support cblas_dot
T sum = 0;
for (int i = 0; i < n; ++i) {
sum += x[i] * y[i];
}
return sum;
#endif
}
template <>
template <typename T>
void Blas<platform::CPUDeviceContext>::SCAL(int n, const T a, T *x) const {
#ifdef PADDLE_WITH_MKLML
CBlas<T>::SCAL(n, a, x, 1);
#else
// try to find if openblas support cblas_scal
for (int i = 0; i < n; ++i) {
x[i] = a * x[i];
}
#endif
}
template <>
template <typename T>
void Blas<platform::CPUDeviceContext>::GEMV(bool trans_a, int M, int N, T alpha,
......@@ -440,6 +467,42 @@ void Blas<platform::CPUDeviceContext>::BatchedGEMM(
#endif
}
template <typename DeviceContext>
template <typename T>
void Blas<DeviceContext>::MatMul(const int M, const int N, const int K,
const T *A, const T *B, T *C) const {
this->template GEMM<T>(CblasRowMajor, CblasNoTrans, CblasNoTrans, M, N, K,
static_cast<T>(1), A, K, B, N, static_cast<T>(0), C,
N);
}
template <>
template <typename T>
void Blas<platform::CPUDeviceContext>::MatMul(const int M, const int N,
const int K, const T *A,
const T *B, T *C) const {
#ifdef PADDLE_WITH_LIBXSMM
// Refer to https://github.com/hfp/libxsmm/blob/master/README.md
// But the threshold is custom constexpr int LIBXSMM_THRESHOLD = 20 * 20 * 20;
// Since the matrix is very small,
// so the unit of calculation is already very fast,
// and the if( M*N*K < LIBXSMM_THRESHOLD) would be overhead,
// use xsmm directly.
// Note: SMM use ColMajor
const char transa = 'N';
const char transb = 'N';
const T alpha = static_cast<T>(1);
const T beta = static_cast<T>(0);
CBlas<T>::SMM_GEMM(&transa, &transb, &N, &M, &K, &alpha, B, &N, A, &K, &beta,
C, &N);
return;
#endif
CBlas<T>::GEMM(CblasRowMajor, CblasNoTrans, CblasNoTrans, M, N, K,
static_cast<T>(1), A, K, B, N, static_cast<T>(0), C, N);
}
template <typename DeviceContext>
template <typename T>
void Blas<DeviceContext>::MatMul(const framework::Tensor &mat_a,
......
paddle/fluid/operators/math/concat.cc
浏览文件 @ ec5cb376
......@@ -48,16 +48,16 @@ class ConcatFunctor<platform::CPUDeviceContext, T> {
auto cpu_place = boost::get<platform::CPUPlace>(context.GetPlace());
// computation
for (int k = 0; k < out_rows; ++k) {
T* dst_ptr = output->data<T>() + k * out_cols;
auto output_data = output->data<T>();
int col_idx = 0;
for (int j = 0; j < num; ++j) {
int col_len = input_cols[j];
const T* src_prt = input[j].data<T>() + k * col_len;
memory::Copy(cpu_place, dst_ptr + col_idx, cpu_place, src_prt,
sizeof(T) * col_len);
col_idx += col_len;
auto input_data = input[j].data<T>();
for (int k = 0; k < out_rows; ++k) {
memory::Copy(cpu_place, output_data + k * out_cols + col_idx, cpu_place,
input_data + k * col_len, sizeof(T) * col_len);
}
col_idx += col_len;
}
}
};
......@@ -71,7 +71,7 @@ class ConcatGradFunctor<platform::CPUDeviceContext, T> {
public:
void operator()(const platform::CPUDeviceContext& context,
const framework::Tensor& input,
const std::vector<const framework::Tensor*>& ref_inputs,
const std::vector<const framework::LoDTensor*>& ref_inputs,
const int axis, std::vector<framework::Tensor*>* outputs) {
// TODO(zcd): Add input data validity checking
size_t num = outputs->size();
......
paddle/fluid/operators/math/concat.cu
浏览文件 @ ec5cb376
......@@ -177,6 +177,9 @@ class ConcatFunctor<platform::CUDADeviceContext, T> {
dev_ins_data, dev_ins_col_data, static_cast<int>(inputs_col.size()),
out_row, out_col, output->data<T>());
}
// Wait() must be called because `inputs_data` may be destructed before
// kernel ends
context.Wait();
}
};
......@@ -189,7 +192,7 @@ class ConcatGradFunctor<platform::CUDADeviceContext, T> {
public:
void operator()(const platform::CUDADeviceContext& context,
const framework::Tensor& input,
const std::vector<const framework::Tensor*>& ref_inputs,
const std::vector<const framework::LoDTensor*>& ref_inputs,
const int axis, std::vector<framework::Tensor*>* outputs) {
// TODO(zcd): Add input data validity checking
int o_num = outputs->size();
......@@ -252,6 +255,9 @@ class ConcatGradFunctor<platform::CUDADeviceContext, T> {
input.data<T>(), in_row, in_col, dev_outs_col_data,
static_cast<int>(outputs_cols.size()), dev_out_gpu_data);
}
// Wait() must be called because `outputs_data` may be destructed before
// kernel ends
context.Wait();
}
};
......
paddle/fluid/operators/math/concat.h
浏览文件 @ ec5cb376
......@@ -15,7 +15,7 @@ limitations under the License. */
#pragma once
#include <vector>
#include "paddle/fluid/framework/data_type.h"
#include "paddle/fluid/framework/tensor.h"
#include "paddle/fluid/framework/lod_tensor.h"
namespace paddle {
namespace operators {
......@@ -57,7 +57,7 @@ template <typename DeviceContext, typename T>
class ConcatGradFunctor {
public:
void operator()(const DeviceContext& context, const framework::Tensor& input,
const std::vector<const framework::Tensor*>& ref_inputs,
const std::vector<const framework::LoDTensor*>& ref_inputs,
const int axis, std::vector<framework::Tensor*>* outputs);
};
......
paddle/fluid/operators/math/cpu_vec.h 0 → 100644
浏览文件 @ ec5cb376
/* Copyright (c) 2016 PaddlePaddle Authors. All Rights Reserved.
Licensed under the Apache License, Version 2.0 (the "License");
you may not use this file except in compliance with the License.
You may obtain a copy of the License at
http://www.apache.org/licenses/LICENSE-2.0
Unless required by applicable law or agreed to in writing, software
distributed under the License is distributed on an "AS IS" BASIS,
WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
See the License for the specific language governing permissions and
limitations under the License. */
#pragma once
#include <cmath>
#include <string>
#include "paddle/fluid/platform/cpu_info.h"
#ifdef __AVX__
#include <immintrin.h>
#endif
#ifdef PADDLE_WITH_MKLML
#include "paddle/fluid/platform/dynload/mklml.h"
#endif
namespace paddle {
namespace operators {
namespace math {
#define SIGMOID_THRESHOLD_MIN -40.0
#define SIGMOID_THRESHOLD_MAX 13.0
#define AVX_FLOAT_BLOCK 8
#define AVX_DOUBLE_BLOCK 4
#define AVX2_FLOAT_BLOCK 8
#define AVX2_DOUBLE_BLOCK 4
#define AVX512_FLOAT_BLOCK 16
#define AVX512_DOUBLE_BLOCK 8
template <typename T>
inline void vec_exp(const int n, const T* x, T* y) {
for (int i = 0; i < n; ++i) {
y[i] = std::exp(x[i]);
}
}
template <typename T>
inline void vec_scal(const int n, const T a, T* x) {
for (int i = 0; i < n; ++i) {
x[i] = a * x[i];
}
}
#ifdef PADDLE_WITH_MKLML
template <>
inline void vec_exp<float>(const int n, const float* x, float* y) {
platform::dynload::vsExp(n, x, y);
}
template <>
inline void vec_exp<double>(const int n, const double* x, double* y) {
platform::dynload::vdExp(n, x, y);
}
template <>
inline void vec_scal<float>(const int n, const float a, float* x) {
platform::dynload::cblas_sscal(n, a, x, 1);
}
template <>
inline void vec_scal<double>(const int n, const double a, double* x) {
platform::dynload::cblas_dscal(n, a, x, 1);
}
#endif
// MKL scal only support inplace, choose this if src and dst are not equal
template <typename T, platform::jit::cpu_isa_t isa = platform::jit::isa_any>
inline void vec_scal(const int n, const T a, const T* x, T* y) {
for (int i = 0; i < n; ++i) {
y[i] = a * x[i];
}
}
template <>
inline void vec_scal<float, platform::jit::avx>(const int n, const float a,
const float* x, float* y) {
#ifdef __AVX__
constexpr int block = AVX_FLOAT_BLOCK;
if (n < block) {
vec_scal<float, platform::jit::isa_any>(n, a, x, y);
return;
}
const int rest = n % block;
const int end = n - rest;
int i = 0;
__m256 scalar = _mm256_set1_ps(a);
__m256 tmp;
#define MOVE_ONE_STEP \
tmp = _mm256_loadu_ps(x + i); \
tmp = _mm256_mul_ps(tmp, scalar); \
_mm256_storeu_ps(y + i, tmp)
for (i = 0; i < end; i += block) {
MOVE_ONE_STEP;
}
#undef MOVE_ONE_STEP
if (rest == 0) {
return;
}
// can not continue move step if src and dst are inplace
for (i = n - rest; i < n; ++i) {
y[i] = a * x[i];
}
#else
vec_scal<float, platform::jit::isa_any>(n, a, x, y);
#endif
}
template <>
inline void vec_scal<float, platform::jit::avx2>(const int n, const float a,
const float* x, float* y) {
vec_scal<float, platform::jit::avx>(n, a, x, y);
}
template <>
inline void vec_scal<float, platform::jit::avx512_common>(const int n,
const float a,
const float* x,
float* y) {
// TODO(TJ): enable me
vec_scal<float, platform::jit::avx2>(n, a, x, y);
}
template <typename T, platform::jit::cpu_isa_t isa = platform::jit::isa_any>
inline void vec_add_bias(const int n, const T a, const T* x, T* y) {
for (int i = 0; i < n; ++i) {
y[i] = x[i] + a;
}
}
template <>
inline void vec_add_bias<float, platform::jit::avx>(const int n, const float a,
const float* x, float* y) {
#ifdef __AVX__
constexpr int block = AVX_FLOAT_BLOCK;
if (n < block) {
vec_add_bias<float, platform::jit::isa_any>(n, a, x, y);
return;
}
const int rest = n % block;
const int end = n - rest;
int i = 0;
__m256 bias = _mm256_set1_ps(a);
__m256 tmp;
#define MOVE_ONE_STEP \
tmp = _mm256_loadu_ps(x + i); \
tmp = _mm256_add_ps(tmp, bias); \
_mm256_storeu_ps(y + i, tmp)
for (i = 0; i < end; i += block) {
MOVE_ONE_STEP;
}
#undef MOVE_ONE_STEP
if (rest == 0) {
return;
}
// can not continue move step if src and dst are inplace
for (i = n - rest; i < n; ++i) {
y[i] = x[i] + a;
}
#else
vec_add_bias<float, platform::jit::isa_any>(n, a, x, y);
#endif
}
template <>
inline void vec_add_bias<float, platform::jit::avx2>(const int n, const float a,
const float* x, float* y) {
vec_add_bias<float, platform::jit::avx>(n, a, x, y);
}
template <>
inline void vec_add_bias<float, platform::jit::avx512_common>(const int n,
const float a,
const float* x,
float* y) {
// TODO(TJ): enable me
vec_add_bias<float, platform::jit::avx2>(n, a, x, y);
}
template <typename T, platform::jit::cpu_isa_t isa = platform::jit::isa_any>
inline void vec_identity(const int n, const T* x, T* y) {
// do nothing
return;
}
template <typename T, platform::jit::cpu_isa_t isa = platform::jit::isa_any>
inline void vec_sigmoid(const int n, const T* x, T* y) {
const T min = SIGMOID_THRESHOLD_MIN;
const T max = SIGMOID_THRESHOLD_MAX;
for (int i = 0; i < n; ++i) {
y[i] = (x[i] < min) ? min : ((x[i] > max) ? max : x[i]);
y[i] = static_cast<T>(0) - y[i];
}
vec_exp<T>(n, y, y);
for (int i = 0; i < n; ++i) {
y[i] = static_cast<T>(1) / (static_cast<T>(1) + y[i]);
}
}
template <>
inline void vec_sigmoid<float, platform::jit::avx>(const int n, const float* x,
float* y) {
#ifdef __AVX__
constexpr int block = AVX_FLOAT_BLOCK;
if (n < block) {
vec_sigmoid<float, platform::jit::isa_any>(n, x, y);
return;
}
const int rest = n % block;
const int end = n - rest;
int i = 0;
__m256 max = _mm256_set1_ps(SIGMOID_THRESHOLD_MAX);
__m256 min = _mm256_set1_ps(SIGMOID_THRESHOLD_MIN);
__m256 zeros = _mm256_setzero_ps();
__m256 tmp;
#define MOVE_ONE_STEP \
tmp = _mm256_loadu_ps(x + i); \
tmp = _mm256_max_ps(tmp, min); \
tmp = _mm256_min_ps(tmp, max); \
tmp = _mm256_sub_ps(zeros, tmp); \
_mm256_storeu_ps(y + i, tmp)
for (i = 0; i < end; i += block) {
MOVE_ONE_STEP;
}
#undef MOVE_ONE_STEP
if (rest != 0) {
// can not continue move step since the src and dst address could be equal
const float xmin = SIGMOID_THRESHOLD_MIN;
const float xmax = SIGMOID_THRESHOLD_MAX;
for (i = n - rest; i < n; ++i) {
y[i] = 0.f - ((x[i] < xmin) ? xmin : ((x[i] > xmax) ? xmax : x[i]));
}
}
vec_exp<float>(n, y, y);
__m256 ones = _mm256_set1_ps(1.0f);
#define MOVE_ONE_STEP \
tmp = _mm256_loadu_ps(y + i); \
tmp = _mm256_add_ps(ones, tmp); \
tmp = _mm256_div_ps(ones, tmp); \
_mm256_storeu_ps(y + i, tmp)
for (i = 0; i < end; i += block) {
MOVE_ONE_STEP;
}
#undef MOVE_ONE_STEP
if (rest == 0) {
return;
}
// can not continue move step
for (i = n - rest; i < n; ++i) {
y[i] = 1.f / (1.f + y[i]);
}
#else
vec_sigmoid<float, platform::jit::isa_any>(n, x, y);
#endif
}
template <>
inline void vec_sigmoid<float, platform::jit::avx2>(const int n, const float* x,
float* y) {
vec_sigmoid<float, platform::jit::avx>(n, x, y);
}
template <>
inline void vec_sigmoid<float, platform::jit::avx512_common>(const int n,
const float* x,
float* y) {
// TODO(TJ): enable me
vec_sigmoid<float, platform::jit::avx2>(n, x, y);
}
template <typename T, platform::jit::cpu_isa_t isa = platform::jit::isa_any>
inline void vec_tanh(const int n, const T* x, T* y) {
vec_scal<T, isa>(n, static_cast<T>(2), x, y);
vec_sigmoid<T, isa>(n, y, y);
vec_scal<T>(n, static_cast<T>(2), y);
vec_add_bias<T, isa>(n, static_cast<T>(-1), y, y);
}
// TODO(TJ): make relu clip
template <typename T, platform::jit::cpu_isa_t isa = platform::jit::isa_any>
inline void vec_relu(const int n, const T* x, T* y) {
for (int i = 0; i < n; ++i) {
y[i] = x[i] > 0 ? x[i] : 0;
}
}
template <>
inline void vec_relu<float, platform::jit::avx>(const int n, const float* x,
float* y) {
#ifdef __AVX__
constexpr int block = AVX_FLOAT_BLOCK;
if (n < block * 4) {
vec_relu<float, platform::jit::isa_any>(n, x, y);
return;
}
const int rest = n % block;
const int end = n - rest;
int i = 0;
__m256 zeros = _mm256_setzero_ps();
__m256 tmp;
#define MOVE_ONE_STEP \
tmp = _mm256_loadu_ps(x + i); \
tmp = _mm256_max_ps(tmp, zeros); \
_mm256_storeu_ps(y + i, tmp)
for (i = 0; i < end; i += block) {
MOVE_ONE_STEP;
}
if (rest == 0) {
return;
}
i = n - block;
MOVE_ONE_STEP;
#undef MOVE_ONE_STEP
#else
vec_relu<float, platform::jit::isa_any>(n, x, y);
#endif
}
template <>
inline void vec_relu<float, platform::jit::avx2>(const int n, const float* x,
float* y) {
vec_relu<float, platform::jit::avx>(n, x, y);
}
template <>
inline void vec_relu<float, platform::jit::avx512_common>(const int n,
const float* x,
float* y) {
// TODO(TJ): enable me
vec_relu<float, platform::jit::avx2>(n, x, y);
}
// TODO(TJ): optimize double of sigmoid, tanh and relu if necessary
template <typename T, platform::jit::cpu_isa_t isa = platform::jit::isa_any>
class VecActivations {
public:
std::function<void(const int, const T*, T*)> operator()(
const std::string& type) {
if (type == "sigmoid") {
return vec_sigmoid<T, isa>;
} else if (type == "relu") {
return vec_relu<T, isa>;
} else if (type == "tanh") {
return vec_tanh<T, isa>;
} else if (type == "identity" || type == "") {
return vec_identity<T, isa>;
}
LOG(FATAL) << "Not support type: " << type;
}
};
} // namespace math
} // namespace operators
} // namespace paddle
paddle/fluid/operators/math/cpu_vec_test.cc 0 → 100644
浏览文件 @ ec5cb376
/* Copyright (c) 2018 PaddlePaddle Authors. All Rights Reserved.
Licensed under the Apache License, Version 2.0 (the "License");
you may not use this file except in compliance with the License.
You may obtain a copy of the License at
http://www.apache.org/licenses/LICENSE-2.0
Unless required by applicable law or agreed to in writing, software
distributed under the License is distributed on an "AS IS" BASIS,
WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
See the License for the specific language governing permissions and
limitations under the License. */
#include <sys/time.h>
#include <cmath>
#include <cstring>
#include <random>
#include <vector>
#include "gflags/gflags.h"
#include "glog/logging.h"
#include "gtest/gtest.h"
#include "paddle/fluid/operators/math/cpu_vec.h"
inline double GetCurrentUS() {
struct timeval time;
gettimeofday(&time, NULL);
return 1e+6 * time.tv_sec + time.tv_usec;
}
constexpr int repeat = 1000;
template <typename T>
inline T _sigmoid(T x) {
const T min = SIGMOID_THRESHOLD_MIN;
const T max = SIGMOID_THRESHOLD_MAX;
T tmp = (x < min) ? min : ((x > max) ? max : x);
return static_cast<T>(1) / (static_cast<T>(1) + std::exp(-tmp));
}
template <typename T>
inline T _tanh(T x) {
return static_cast<T>(2) * _sigmoid<T>(static_cast<T>(2) * x) -
static_cast<T>(1);
}
template <typename T>
void ref_sigmoid(const int n, const T* x, T* y) {
for (int i = 0; i < n; ++i) {
y[i] = _sigmoid(x[i]);
}
}
template <typename T>
void ref_tanh(const int n, const T* x, T* y) {
for (int i = 0; i < n; ++i) {
y[i] = _tanh(x[i]);
}
}
template <typename T>
void ref_relu(const int n, const T* x, T* y) {
for (int i = 0; i < n; ++i) {
y[i] = x[i] > 0 ? x[i] : 0;
}
}
template <typename T>
void RandomVec(const int n, T* a) {
static unsigned int seed = 100;
std::mt19937 rng(seed++);
std::uniform_real_distribution<double> uniform_dist(0, 1);
const T lower = static_cast<T>(-20.f);
const T upper = static_cast<T>(20.f);
for (int i = 0; i < n; ++i) {
a[i] = static_cast<T>(uniform_dist(rng) * (upper - lower) + lower);
}
}
template <typename T>
void TestAndBench(const int n, std::function<void(const int, const T*, T*)> tgt,
std::function<void(const int, const T*, T*)> ref) {
std::vector<T> x(n);
std::vector<T> ytgt(n), yref(n);
RandomVec<T>(n, x.data());
const T* x_data = x.data();
T* ytgt_data = ytgt.data();
T* yref_data = yref.data();
auto st = GetCurrentUS();
for (int i = 0; i < repeat; ++i) {
tgt(n, x_data, ytgt_data);
}
auto mt = GetCurrentUS();
for (int i = 0; i < repeat; ++i) {
ref(n, x_data, yref_data);
}
auto et = GetCurrentUS();
VLOG(3) << "Vec size " << n << ": refer takes: " << (et - mt) / repeat
<< " us, tgt takes: " << (mt - st) / repeat;
for (int i = 0; i < n; ++i) {
EXPECT_NEAR(ytgt_data[i], yref_data[i], 1e-3);
}
}
TEST(CpuVecTest, sigmoid) {
namespace jit = paddle::platform::jit;
using namespace paddle::operators::math; // NOLINT
for (auto sz : {1, 2, 15, 16, 30, 32, 128, 200, 512}) {
TestAndBench<float>(sz, vec_sigmoid<float>, ref_sigmoid<float>);
TestAndBench<float>(sz, vec_sigmoid<float, jit::avx>, ref_sigmoid<float>);
TestAndBench<float>(sz, vec_sigmoid<float, jit::avx2>, ref_sigmoid<float>);
TestAndBench<float>(sz, vec_sigmoid<float, jit::avx512_common>,
ref_sigmoid<float>);
}
TestAndBench<double>(30, vec_sigmoid<double>, ref_sigmoid<double>);
}
TEST(CpuVecTest, tanh) {
namespace jit = paddle::platform::jit;
using namespace paddle::operators::math; // NOLINT
for (auto sz : {1, 2, 15, 16, 30, 32, 128, 200, 512}) {
TestAndBench<float>(sz, vec_tanh<float>, ref_tanh<float>);
TestAndBench<float>(sz, vec_tanh<float, jit::avx>, ref_tanh<float>);
TestAndBench<float>(sz, vec_tanh<float, jit::avx2>, ref_tanh<float>);
TestAndBench<float>(sz, vec_tanh<float, jit::avx512_common>,
ref_tanh<float>);
}
TestAndBench<double>(30, vec_tanh<double>, ref_tanh<double>);
}
TEST(CpuVecTest, relu) {
namespace jit = paddle::platform::jit;
using namespace paddle::operators::math; // NOLINT
for (auto sz : {1, 2, 15, 16, 30, 32, 128, 200, 512}) {
TestAndBench<float>(sz, vec_relu<float>, ref_relu<float>);
TestAndBench<float>(sz, vec_relu<float, jit::avx>, ref_relu<float>);
TestAndBench<float>(sz, vec_relu<float, jit::avx2>, ref_relu<float>);
TestAndBench<float>(sz, vec_relu<float, jit::avx512_common>,
ref_relu<float>);
}
TestAndBench<double>(30, vec_relu<double>, ref_relu<double>);
}
template <typename T>
void TestInplace(const int n, std::function<void(const int, const T*, T*)> tgt,
std::function<void(const int, const T*, T*)> ref) {
std::vector<T> x(n);
std::vector<T> ytgt(n), yref(n);
RandomVec<T>(n, x.data());
const T* x_data = x.data();
T* yref_data = yref.data();
T* ytgt_data = ytgt.data();
std::memcpy(yref_data, x_data, sizeof(T) * n);
std::memcpy(ytgt_data, x_data, sizeof(T) * n);
ref(n, yref_data, yref_data);
tgt(n, ytgt_data, ytgt_data);
for (int i = 0; i < n; ++i) {
EXPECT_NEAR(ytgt_data[i], yref_data[i], 1e-3);
}
}
TEST(CpuVecTest, inplace_sigmoid) {
namespace jit = paddle::platform::jit;
using namespace paddle::operators::math; // NOLINT
for (auto sz : {1, 2, 15, 16, 30, 32, 128, 200, 512}) {
TestInplace<float>(sz, vec_sigmoid<float>, ref_sigmoid<float>);
TestInplace<float>(sz, vec_sigmoid<float, jit::avx>, ref_sigmoid<float>);
TestInplace<float>(sz, vec_sigmoid<float, jit::avx2>, ref_sigmoid<float>);
TestInplace<float>(sz, vec_sigmoid<float, jit::avx512_common>,
ref_sigmoid<float>);
}
TestInplace<double>(30, vec_sigmoid<double>, ref_sigmoid<double>);
}
TEST(CpuVecTest, inplace_tanh) {
namespace jit = paddle::platform::jit;
using namespace paddle::operators::math; // NOLINT
for (auto sz : {1, 2, 15, 16, 30, 32, 128, 200, 512}) {
TestInplace<float>(sz, vec_tanh<float>, ref_tanh<float>);
TestInplace<float>(sz, vec_tanh<float, jit::avx>, ref_tanh<float>);
TestInplace<float>(sz, vec_tanh<float, jit::avx2>, ref_tanh<float>);
TestInplace<float>(sz, vec_tanh<float, jit::avx512_common>,
ref_tanh<float>);
}
TestInplace<double>(30, vec_tanh<double>, ref_tanh<double>);
}
TEST(CpuVecTest, inplace_relu) {
namespace jit = paddle::platform::jit;
using namespace paddle::operators::math; // NOLINT
for (auto sz : {1, 2, 15, 16, 30, 32, 128, 200, 512}) {
TestInplace<float>(sz, vec_relu<float>, ref_relu<float>);
TestInplace<float>(sz, vec_relu<float, jit::avx>, ref_relu<float>);
TestInplace<float>(sz, vec_relu<float, jit::avx2>, ref_relu<float>);
TestInplace<float>(sz, vec_relu<float, jit::avx512_common>,
ref_relu<float>);
}
TestInplace<double>(30, vec_relu<double>, ref_relu<double>);
}
paddle/fluid/operators/math/fc_compute.h
浏览文件 @ ec5cb376
......@@ -25,17 +25,25 @@ namespace math {
template <typename DeviceContext, typename T>
inline void FCCompute(const BlasT<DeviceContext, T>& blas, const int M,
const int N, const int K, const T* X, const T* W, T* Y,
const T* B = NULL) {
blas.GEMM(CblasNoTrans, CblasNoTrans, M, N, K, static_cast<T>(1), X, W,
static_cast<T>(0), Y);
if (B) {
const T* B = NULL, bool relu = false) {
blas.MatMul(M, N, K, X, W, Y);
if (B == NULL) {
return;
}
#ifdef PADDLE_WITH_MKLML
#pragma omp parallel for if (FLAGS_paddle_num_threads > 1)
#endif
for (int i = 0; i < M; i++) {
blas.AXPY(N, static_cast<T>(1), B, Y + i * N);
}
if (!relu) {
return;
}
// TODO(TJ): fuse relu
LOG(FATAL) << "Not implemented!";
}
} // namespace math
......
paddle/fluid/operators/math/math_function.cc
浏览文件 @ ec5cb376
......@@ -41,7 +41,8 @@ template struct SetConstant<platform::CPUDeviceContext, uint8_t>;
template struct Transpose<platform::CPUDeviceContext, int64_t, RANK>; \
template struct Transpose<platform::CPUDeviceContext, bool, RANK>; \
template struct Transpose<platform::CPUDeviceContext, int16_t, RANK>; \
template struct Transpose<platform::CPUDeviceContext, uint8_t, RANK>;
template struct Transpose<platform::CPUDeviceContext, uint8_t, RANK>; \
template struct Transpose<platform::CPUDeviceContext, int8_t, RANK>;
DEFINE_CPU_TRANS(1);
DEFINE_CPU_TRANS(2);
......
paddle/fluid/operators/math/math_function.cu
浏览文件 @ ec5cb376
......@@ -36,7 +36,8 @@ template struct SetConstant<platform::CUDADeviceContext, bool>;
#define DEFINE_GPU_TRANS(RANK) \
template struct Transpose<platform::CUDADeviceContext, float, RANK>; \
template struct Transpose<platform::CUDADeviceContext, double, RANK>; \
template struct Transpose<platform::CUDADeviceContext, float16, RANK>;
template struct Transpose<platform::CUDADeviceContext, float16, RANK>; \
template struct Transpose<platform::CUDADeviceContext, int8_t, RANK>;
DEFINE_GPU_TRANS(1);
DEFINE_GPU_TRANS(2);
......
paddle/fluid/operators/math/math_function.h
浏览文件 @ ec5cb376
......@@ -19,6 +19,10 @@ limitations under the License. */
#ifdef PADDLE_USE_OPENBLAS
#include <cblas.h>
// remove typedef in openblas
#undef FLOAT
#undef INT
#undef SIZE
#endif
#include <cmath>
......
paddle/fluid/operators/math/padding.h 0 → 100644
浏览文件 @ ec5cb376
/* Copyright (c) 2018 PaddlePaddle Authors. All Rights Reserved.
Licensed under the Apache License, Version 2.0 (the "License");
you may not use this file except in compliance with the License.
You may obtain a copy of the License at
http://www.apache.org/licenses/LICENSE-2.0
Unless required by applicable law or agreed to in writing, software
distributed under the License is distributed on an "AS IS" BASIS,
WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
See the License for the specific language governing permissions and
limitations under the License. */
#pragma once
#include <utility>
#include <vector>
#include "paddle/fluid/framework/tensor.h"
namespace paddle {
namespace operators {
namespace math {
template <typename T, size_t D, int MajorType = Eigen::RowMajor,
typename IndexType = Eigen::DenseIndex>
using EigenTensor = framework::EigenTensor<T, D, MajorType, IndexType>;
template <typename DeviceContext, typename T, size_t D>
void PadFunction(const framework::ExecutionContext& context,
const std::vector<int>& pads, const framework::Tensor& src,
T pad_value, framework::Tensor* out) {
Eigen::array<std::pair<int, int>, D> paddings;
for (size_t i = 0; i < paddings.size(); ++i) {
paddings[i].first = pads[i * 2];
paddings[i].second = pads[i * 2 + 1];
}
auto src_tensor = EigenTensor<T, D>::From(src);
auto out_tensor = EigenTensor<T, D>::From(*out);
auto& place =
*context.template device_context<DeviceContext>().eigen_device();
out_tensor.device(place) = src_tensor.pad(paddings, pad_value);
}
template <typename DeviceContext, typename T, size_t D>
void PadGradFunction(const framework::ExecutionContext& context,
const std::vector<int>& pads, const framework::Tensor& src,
framework::Tensor* d_out) {
Eigen::array<std::pair<int, int>, D> paddings;
for (size_t i = 0; i < paddings.size(); ++i) {
paddings[i].first = -pads[i * 2];
paddings[i].second = -pads[i * 2 + 1];
}
auto d_out_tensor = EigenTensor<T, D>::From(*d_out);
auto src_tensor = EigenTensor<T, D>::From(src);
auto& place =
*context.template device_context<DeviceContext>().eigen_device();
d_out_tensor.device(place) = src_tensor.pad(paddings, 0);
}
template <typename DeviceContext, typename T>
void PaddingFunctor(int rank, const framework::ExecutionContext& context,
const std::vector<int>& pads, T pad_value,
const framework::Tensor& src, framework::Tensor* out) {
switch (rank) {
case 1:
PadFunction<DeviceContext, T, 1>(context, pads, src, pad_value, out);
break;
case 2:
PadFunction<DeviceContext, T, 2>(context, pads, src, pad_value, out);
break;
case 3:
PadFunction<DeviceContext, T, 3>(context, pads, src, pad_value, out);
break;
case 4:
PadFunction<DeviceContext, T, 4>(context, pads, src, pad_value, out);
break;
case 5:
PadFunction<DeviceContext, T, 5>(context, pads, src, pad_value, out);
break;
case 6:
PadFunction<DeviceContext, T, 6>(context, pads, src, pad_value, out);
break;
default:
PADDLE_THROW(
"PadOp only support tensors with no more than 6 dimensions.");
}
}
template <typename DeviceContext, typename T>
void PaddingGradFunctor(int rank, const framework::ExecutionContext& context,
const std::vector<int>& pads,
const framework::Tensor& src, framework::Tensor* out) {
switch (rank) {
case 1:
PadGradFunction<DeviceContext, T, 1>(context, pads, src, out);
break;
case 2:
PadGradFunction<DeviceContext, T, 2>(context, pads, src, out);
break;
case 3:
PadGradFunction<DeviceContext, T, 3>(context, pads, src, out);
break;
case 4:
PadGradFunction<DeviceContext, T, 4>(context, pads, src, out);
break;
case 5:
PadGradFunction<DeviceContext, T, 5>(context, pads, src, out);
break;
case 6:
PadGradFunction<DeviceContext, T, 6>(context, pads, src, out);
break;
default:
PADDLE_THROW(
"PadOp only support tensors with no more than 6 dimensions.");
}
}
} // namespace math
} // namespace operators
} // namespace paddle
paddle/fluid/operators/math/sequence_padding.cc
浏览文件 @ ec5cb376
......@@ -18,65 +18,86 @@ namespace paddle {
namespace operators {
namespace math {
template <typename T>
void CopyValidData(framework::Tensor* dst_tensor,
const framework::Tensor* src_tensor,
const framework::Vector<size_t>& seq_offsets,
int pad_seq_len, int step_width, bool norm_by_len,
CopyType type, PadLayout layout) {
int seq_num = seq_offsets.size() - 1;
const T* src_data = src_tensor->data<T>();
T* dst_data = dst_tensor->data<T>();
int seq_cpy_gap = step_width;
int pad_cpy_gap =
layout == kBatchLengthWidth ? step_width : seq_num * step_width;
for (int seq_idx = 0; seq_idx < seq_num; ++seq_idx) {
int valid_seq_len = seq_offsets[seq_idx + 1] - seq_offsets[seq_idx];
PADDLE_ENFORCE_GE(
pad_seq_len, valid_seq_len,
"The padded sequence length can not be less than its original length.");
int seq_data_offset = seq_offsets[seq_idx] * step_width;
int pad_data_offset = layout == kBatchLengthWidth
? seq_idx * pad_seq_len * step_width
: seq_idx * step_width;
float scale = 1.0f / static_cast<float>(valid_seq_len);
for (int step_idx = 0; step_idx < valid_seq_len; ++step_idx) {
const T* src =
src_data + (type == kSeqToPad ? seq_data_offset : pad_data_offset);
T* dst =
dst_data + (type == kSeqToPad ? pad_data_offset : seq_data_offset);
memcpy(dst, src, step_width * sizeof(T));
if (norm_by_len) {
for (int i = 0; i < step_width; ++i) {
*(dst + i) *= scale;
}
}
seq_data_offset += seq_cpy_gap;
pad_data_offset += pad_cpy_gap;
}
}
}
template <typename T>
class PaddingLoDTensorFunctor<platform::CPUDeviceContext, T> {
public:
void operator()(const platform::CPUDeviceContext& context,
const framework::LoDTensor& seq, framework::Tensor* padding,
bool norm_by_times) {
auto lod = seq.lod();
PADDLE_ENFORCE_GT(lod.size(), 0UL,
"The LoD of LoDTensor seq should not be null.");
const size_t level = 0;
framework::LoD abs_offset_lod = framework::ToAbsOffset(lod);
auto seq_dims = seq.dims();
PADDLE_ENFORCE_EQ(seq_dims[0],
static_cast<int64_t>(abs_offset_lod[level].back()),
"The first dimension of LoDTensor seq should be "
"equal to the sum of all sequences's length.");
auto padding_dims = padding->dims();
PADDLE_ENFORCE_EQ(padding_dims.size(), 3UL,
"The input padding should be a 3-D Tensor of shape "
"[max_sequence_length, num_sequences, sequence_width].");
const int64_t max_sequence_length = MaximumSequenceLength(lod, level);
PADDLE_ENFORCE_EQ(padding_dims[0], max_sequence_length,
"The first dimension of Tensor padding should be the "
"maximum length of all sequences in LoDTensor seq.");
const int64_t num_sequences = abs_offset_lod[level].size() - 1;
PADDLE_ENFORCE_EQ(padding_dims[1], num_sequences,
"The second dimension of Tensor padding should be the "
"number of sequences in LoDTensor seq.");
const int64_t sequence_width = seq.numel() / seq_dims[0];
PADDLE_ENFORCE_EQ(padding_dims[2], sequence_width,
"The third dimension of Tensor padding should be the "
"width of sequence in LoDTensor seq.");
const T* seq_data = seq.data<T>();
T* padding_data = padding->data<T>();
for (int64_t i = 0; i < max_sequence_length; ++i) {
for (int64_t j = 0; j < num_sequences; ++j) {
int64_t start_pos = abs_offset_lod[level][j];
int64_t sequence_length = abs_offset_lod[level][j + 1] - start_pos;
if (i < sequence_length) {
// i > 0 => sequence_length > 0
T scale =
norm_by_times ? (1.0f / static_cast<T>(sequence_length)) : 1.0f;
for (int64_t k = 0; k < sequence_width; ++k) {
padding_data[(i * num_sequences + j) * sequence_width + k] =
seq_data[(start_pos + i) * sequence_width + k] * scale;
const framework::LoDTensor& seq_tensor,
framework::LoDTensor* pad_tensor,
const framework::LoDTensor& pad_value, int pad_seq_len = -1,
int lod_level = 0, bool norm_by_times = false,
const PadLayout layout = kBatchLengthWidth) {
auto seq_lod = seq_tensor.lod();
const auto seq_offsets = framework::ToAbsOffset(seq_lod)[lod_level];
const auto& seq_tensor_dims = seq_tensor.dims();
const auto& pad_tensor_dims = pad_tensor->dims();
if (pad_seq_len == -1) {
pad_seq_len = MaximumSequenceLength(seq_offsets);
}
} else {
memset(padding_data + (i * num_sequences + j) * sequence_width, 0,
sequence_width * sizeof(T));
int step_width = seq_tensor.numel() / seq_tensor_dims[0];
CheckDims(seq_tensor_dims, pad_tensor_dims, seq_offsets, pad_seq_len,
step_width, layout);
PADDLE_ENFORCE(pad_value.numel() == 1 || pad_value.numel() == step_width,
"The numel of 'pad_value' can only be 1 or be equal to the "
"'step_width'.");
// fill padding value
T* pad_data = pad_tensor->data<T>();
const T* pad_value_data = pad_value.data<T>();
if (pad_value.numel() == 1) {
for (int i = 0; i < pad_tensor->numel(); ++i) {
pad_data[i] = *pad_value_data;
}
} else {
for (int i = 0; i < pad_tensor->numel(); i += step_width) {
memcpy(pad_data + i, pad_value_data, step_width * sizeof(T));
}
}
CopyValidData<T>(pad_tensor, &seq_tensor, seq_offsets, pad_seq_len,
step_width, norm_by_times, kSeqToPad, layout);
}
};
......@@ -84,62 +105,35 @@ template <typename T>
class UnpaddingLoDTensorFunctor<platform::CPUDeviceContext, T> {
public:
void operator()(const platform::CPUDeviceContext& context,
framework::LoDTensor* seq, const framework::Tensor& padding,
bool norm_by_times) {
auto lod = seq->lod();
PADDLE_ENFORCE_GT(lod.size(), 0UL,
"The LoD of LoDTensor seq should not be null.");
const size_t level = 0;
framework::LoD abs_offset_lod = framework::ToAbsOffset(lod);
auto seq_dims = seq->dims();
PADDLE_ENFORCE_EQ(seq_dims[0],
static_cast<int64_t>(abs_offset_lod[level].back()),
"The first dimension of LoDTensor seq should be "
"equal to the sum of all sequences's length.");
auto padding_dims = padding.dims();
PADDLE_ENFORCE_EQ(padding_dims.size(), 3UL,
"The input padding should be a 3-D Tensor of shape "
"[max_sequnece_length, num_sequences, sequence_width].");
const int64_t max_sequence_length = MaximumSequenceLength(lod, level);
PADDLE_ENFORCE_EQ(padding_dims[0], max_sequence_length,
"The first dimension of Tensor padding should be "
"the maximum length of all sequences in LoDTensor seq.");
const int64_t num_sequences = abs_offset_lod[level].size() - 1;
PADDLE_ENFORCE_EQ(padding_dims[1], num_sequences,
"The second dimension of Tensor padding should be "
"the number of sequences in LoDTensor seq.");
const int64_t sequence_width = seq->numel() / seq_dims[0];
PADDLE_ENFORCE_EQ(padding_dims[2], sequence_width,
"The third dimension of Tensor padding should be the "
"width of sequence in LoDTensor seq.");
const T* padding_data = padding.data<T>();
T* seq_data = seq->data<T>();
for (int64_t i = 0; i < num_sequences; ++i) {
int64_t start_pos = abs_offset_lod[level][i];
int64_t sequence_length = abs_offset_lod[level][i + 1] - start_pos;
for (int64_t j = 0; j < sequence_length; ++j) {
// sequence_width > j > 0
T scale =
norm_by_times ? (1.0f / static_cast<T>(sequence_length)) : 1.0f;
for (int64_t k = 0; k < sequence_width; ++k) {
seq_data[(start_pos + j) * sequence_width + k] =
padding_data[(j * num_sequences + i) * sequence_width + k] *
scale;
}
}
const framework::LoDTensor& pad_tensor,
framework::LoDTensor* seq_tensor, int pad_seq_len = -1,
int lod_level = 0, bool norm_by_times = false,
const PadLayout layout = kBatchLengthWidth) {
auto seq_offsets = framework::ToAbsOffset(seq_tensor->lod())[lod_level];
const auto& seq_tensor_dims = seq_tensor->dims();
const auto& pad_tensor_dims = pad_tensor.dims();
if (pad_seq_len == -1) {
pad_seq_len = MaximumSequenceLength(seq_offsets);
}
int step_width = seq_tensor->numel() / seq_tensor_dims[0];
CheckDims(seq_tensor_dims, pad_tensor_dims, seq_offsets, pad_seq_len,
step_width, layout);
CopyValidData<T>(seq_tensor, &pad_tensor, seq_offsets, pad_seq_len,
step_width, norm_by_times, kPadToSeq, layout);
}
};
template class PaddingLoDTensorFunctor<platform::CPUDeviceContext, int>;
template class PaddingLoDTensorFunctor<platform::CPUDeviceContext, int64_t>;
template class PaddingLoDTensorFunctor<platform::CPUDeviceContext, float>;
template class PaddingLoDTensorFunctor<platform::CPUDeviceContext, double>;
template class UnpaddingLoDTensorFunctor<platform::CPUDeviceContext, int>;
template class UnpaddingLoDTensorFunctor<platform::CPUDeviceContext, int64_t>;
template class UnpaddingLoDTensorFunctor<platform::CPUDeviceContext, float>;
template class UnpaddingLoDTensorFunctor<platform::CPUDeviceContext, double>;
} // namespace math
} // namespace operators
......
paddle/fluid/operators/math/sequence_padding.cu
浏览文件 @ ec5cb376
......@@ -19,41 +19,32 @@ namespace paddle {
namespace operators {
namespace math {
template <typename T, bool NormByTimes, bool Padding>
__global__ void SequencePaddingKernel(T* padding, T* sequence,
const size_t* sequence_start_positions,
const size_t sequence_width,
const size_t max_sequence_length,
const size_t num_sequences) {
size_t padding_idx = blockIdx.y;
size_t start_pos = sequence_start_positions[padding_idx];
size_t sequence_length =
sequence_start_positions[padding_idx + 1] - start_pos;
size_t sequence_idx = blockIdx.x * blockDim.y + threadIdx.y;
size_t padding_base_idx =
(sequence_idx * num_sequences + padding_idx) * sequence_width;
size_t sequence_base_idx = (start_pos + sequence_idx) * sequence_width;
if (sequence_idx < sequence_length) {
T scale = NormByTimes ? (1.0f / static_cast<T>(sequence_length)) : 1.0f;
if (Padding) {
/* sequence -> padding */
for (size_t i = threadIdx.x; i < sequence_width; i += blockDim.x) {
padding[padding_base_idx + i] = scale * sequence[sequence_base_idx + i];
}
} else {
/* padding -> sequence */
for (size_t i = threadIdx.x; i < sequence_width; i += blockDim.x) {
sequence[sequence_base_idx + i] = scale * padding[padding_base_idx + i];
}
}
} else if (sequence_idx < max_sequence_length) {
if (Padding) {
/* sequence -> padding */
for (size_t i = threadIdx.x; i < sequence_width; i += blockDim.x) {
padding[padding_base_idx + i] = 0;
template <typename T, CopyType Type>
__global__ void SequencePaddingKernel(
T* dst, const T* src, const T* pad_value, bool is_constant_pad,
const size_t* seq_offsets, const size_t seq_num, const size_t pad_seq_len,
const size_t step_width, bool norm_by_len, const PadLayout layout) {
size_t seq_idx = blockIdx.y;
size_t seq_len = seq_offsets[seq_idx + 1] - seq_offsets[seq_idx];
size_t step_idx = blockIdx.x * blockDim.y + threadIdx.y;
size_t seq_data_offset = (seq_offsets[seq_idx] + step_idx) * step_width;
size_t pad_data_offset = layout == kBatchLengthWidth
? (seq_idx * pad_seq_len + step_idx) * step_width
: (step_idx * seq_num + seq_idx) * step_width;
T* dst_data = dst + (Type == kSeqToPad ? pad_data_offset : seq_data_offset);
const T* src_data =
src + (Type == kSeqToPad ? seq_data_offset : pad_data_offset);
if (step_idx < seq_len) {
float scale = norm_by_len ? (1.0f / static_cast<float>(seq_len)) : 1.0f;
for (size_t i = threadIdx.x; i < step_width; i += blockDim.x) {
dst_data[i] = scale * src_data[i];
}
} else if (step_idx < pad_seq_len && Type == kSeqToPad) {
for (size_t i = threadIdx.x; i < step_width; i += blockDim.x) {
dst_data[i] = is_constant_pad ? pad_value[0] : pad_value[i];
}
}
}
......@@ -62,74 +53,59 @@ template <typename T>
class PaddingLoDTensorFunctor<platform::CUDADeviceContext, T> {
public:
void operator()(const platform::CUDADeviceContext& context,
const framework::LoDTensor& seq, framework::Tensor* padding,
bool norm_by_times) {
auto lod = seq.lod();
PADDLE_ENFORCE_GT(lod.size(), 0UL,
"The lod of LoDTensor seq should not be null.");
const size_t level = 0;
framework::LoD abs_offset_lod = framework::ToAbsOffset(lod);
auto seq_dims = seq.dims();
PADDLE_ENFORCE_EQ(seq_dims[0],
static_cast<int64_t>(abs_offset_lod[level].back()),
"The first dimension of LoDTensor seq should be "
"equal to the sum of all sequences's length.");
auto padding_dims = padding->dims();
PADDLE_ENFORCE_EQ(padding_dims.size(), 3UL,
"The input padding should be a 3-D Tensor of shape "
"[max_sequence_length, num_sequences, sequence_width].");
int64_t max_sequence_length = MaximumSequenceLength(lod, level);
PADDLE_ENFORCE_EQ(padding_dims[0], max_sequence_length,
"The first dimension of Tensor padding should be the "
"maximum length of all sequences in LoDTensor seq.");
const int64_t num_sequences = abs_offset_lod[level].size() - 1;
PADDLE_ENFORCE_EQ(padding_dims[1], num_sequences,
"The second dimension of Tensor padding should be the "
"number of sequences in LoDTensor seq.");
const int64_t sequence_width = seq.numel() / seq_dims[0];
PADDLE_ENFORCE_EQ(padding_dims[2], sequence_width,
"The third dimension of Tensor padding should be the "
"width of sequence in LoDTensor seq.");
if (!norm_by_times && num_sequences == 1UL) {
TensorCopy(seq, context.GetPlace(), context, padding);
padding->Resize(padding_dims);
const framework::LoDTensor& seq_tensor,
framework::LoDTensor* pad_tensor,
const framework::LoDTensor& pad_value, int pad_seq_len = -1,
int lod_level = 0, bool norm_by_times = false,
const PadLayout layout = kBatchLengthWidth) {
auto seq_lod = seq_tensor.lod();
const auto seq_offsets = framework::ToAbsOffset(seq_lod)[lod_level];
const auto& seq_tensor_dims = seq_tensor.dims();
const auto& pad_tensor_dims = pad_tensor->dims();
int max_seq_len = MaximumSequenceLength(seq_offsets);
if (pad_seq_len == -1) {
pad_seq_len = max_seq_len;
}
PADDLE_ENFORCE_GE(pad_seq_len, max_seq_len,
"The pad_seq_len must be equal to or greater than the "
"original max sequence length.");
int step_width = seq_tensor.numel() / seq_tensor_dims[0];
int seq_num = seq_offsets.size() - 1;
CheckDims(seq_tensor_dims, pad_tensor_dims, seq_offsets, pad_seq_len,
step_width, layout);
PADDLE_ENFORCE(pad_value.numel() == 1 || pad_value.numel() == step_width,
"The numel of 'pad_value' can only be 1 or be equal to the "
"'step_width'.");
if (!norm_by_times && seq_num == 1UL && pad_seq_len == max_seq_len) {
TensorCopy(seq_tensor, context.GetPlace(), context, pad_tensor);
pad_tensor->Resize(pad_tensor_dims);
return;
}
const int64_t kBlockSize = 512;
const int kBlockSize = 512;
/* At least use 32 threads to copy sequence_width elements,
* and at least 8 elements for each thread.
*/
size_t block_dim_x =
std::min(((((sequence_width + 7) >> 3) + 31) >> 5) << 5, kBlockSize);
std::min(((((step_width + 7) >> 3) + 31) >> 5) << 5, kBlockSize);
size_t block_dim_y = kBlockSize / block_dim_x;
dim3 threads(block_dim_x, block_dim_y);
size_t grid_dim_x = (max_sequence_length + block_dim_y - 1) / block_dim_y;
size_t grid_dim_y = num_sequences;
size_t grid_dim_x = (pad_seq_len + block_dim_y - 1) / block_dim_y;
size_t grid_dim_y = seq_num;
dim3 grid(grid_dim_x, grid_dim_y);
const T* seq_data = seq.data<T>();
T* padding_data = padding->data<T>();
if (norm_by_times) {
SequencePaddingKernel<T, 1, 1><<<grid, threads, 0, context.stream()>>>(
padding_data, const_cast<T*>(seq_data),
abs_offset_lod[level].CUDAData(context.GetPlace()), sequence_width,
max_sequence_length, num_sequences);
} else {
SequencePaddingKernel<T, 0, 1><<<grid, threads, 0, context.stream()>>>(
padding_data, const_cast<T*>(seq_data),
abs_offset_lod[level].CUDAData(context.GetPlace()), sequence_width,
max_sequence_length, num_sequences);
}
const T* seq_data = seq_tensor.data<T>();
T* pad_data = pad_tensor->data<T>();
const T* pad_value_data = pad_value.data<T>();
SequencePaddingKernel<T, kSeqToPad><<<grid, threads, 0, context.stream()>>>(
pad_data, seq_data, pad_value_data, pad_value.numel() == 1,
seq_offsets.CUDAData(context.GetPlace()), seq_num, pad_seq_len,
step_width, norm_by_times, layout);
}
};
......@@ -137,79 +113,62 @@ template <typename T>
class UnpaddingLoDTensorFunctor<platform::CUDADeviceContext, T> {
public:
void operator()(const platform::CUDADeviceContext& context,
framework::LoDTensor* seq, const framework::Tensor& padding,
bool norm_by_times) {
auto lod = seq->lod();
PADDLE_ENFORCE_GT(lod.size(), 0UL,
"The lod of LoDTensor seq should not be null.");
const size_t level = 0;
framework::LoD abs_offset_lod = framework::ToAbsOffset(lod);
auto seq_dims = seq->dims();
PADDLE_ENFORCE_EQ(seq_dims[0],
static_cast<int64_t>(abs_offset_lod[level].back()),
"The first dimension of LoDTensor seq should be "
"equal to the sum of all sequences's length.");
auto padding_dims = padding.dims();
PADDLE_ENFORCE_EQ(padding_dims.size(), 3UL,
"The input padding should be a 3-D Tensor of shape "
"[max_sequnece_length, num_sequences, sequence_width].");
int64_t max_sequence_length = MaximumSequenceLength(lod, level);
PADDLE_ENFORCE_EQ(padding_dims[0], max_sequence_length,
"The first dimension of Tensor padding should be "
"the maximum length of all sequences in LoDTensor seq.");
const int64_t num_sequences = abs_offset_lod[level].size() - 1;
PADDLE_ENFORCE_EQ(padding_dims[1], num_sequences,
"The second dimension of Tensor padding should be "
"the number of sequences in LoDTensor seq.");
const int64_t sequence_width = seq->numel() / seq_dims[0];
PADDLE_ENFORCE_EQ(padding_dims[2], sequence_width,
"The third dimension of Tensor padding should be the "
"width of sequence in LoDTensor seq.");
if (!norm_by_times && num_sequences == 1UL) {
TensorCopy(padding, context.GetPlace(), context, seq);
seq->Resize(seq_dims);
const framework::LoDTensor& pad_tensor,
framework::LoDTensor* seq_tensor, int pad_seq_len = -1,
int lod_level = 0, bool norm_by_times = false,
const PadLayout layout = kBatchLengthWidth) {
auto seq_offsets = framework::ToAbsOffset(seq_tensor->lod())[lod_level];
const auto& seq_tensor_dims = seq_tensor->dims();
const auto& pad_tensor_dims = pad_tensor.dims();
int max_seq_len = MaximumSequenceLength(seq_offsets);
if (pad_seq_len == -1) {
pad_seq_len = max_seq_len;
}
int step_width = seq_tensor->numel() / seq_tensor_dims[0];
int seq_num = seq_offsets.size() - 1;
CheckDims(seq_tensor_dims, pad_tensor_dims, seq_offsets, pad_seq_len,
step_width, layout);
if (!norm_by_times && seq_num == 1UL && pad_seq_len == max_seq_len) {
TensorCopy(pad_tensor, context.GetPlace(), context, seq_tensor);
seq_tensor->Resize(seq_tensor_dims);
return;
}
const int64_t kBlockSize = 512;
const int kBlockSize = 512;
/* At least use 32 threads to copy sequence_width elements,
* and at least 8 elements for each thread.
*/
size_t block_dim_x =
std::min(((((sequence_width + 7) >> 3) + 31) >> 5) << 5, kBlockSize);
std::min(((((step_width + 7) >> 3) + 31) >> 5) << 5, kBlockSize);
size_t block_dim_y = kBlockSize / block_dim_x;
dim3 threads(block_dim_x, block_dim_y);
size_t grid_dim_x = (max_sequence_length + block_dim_y - 1) / block_dim_y;
size_t grid_dim_y = num_sequences;
size_t grid_dim_x = (pad_seq_len + block_dim_y - 1) / block_dim_y;
size_t grid_dim_y = seq_num;
dim3 grid(grid_dim_x, grid_dim_y);
const T* padding_data = padding.data<T>();
T* seq_data = seq->data<T>();
if (norm_by_times) {
SequencePaddingKernel<T, 1, 0><<<grid, threads, 0, context.stream()>>>(
const_cast<T*>(padding_data), seq_data,
abs_offset_lod[level].CUDAData(context.GetPlace()), sequence_width,
max_sequence_length, num_sequences);
} else {
SequencePaddingKernel<T, 0, 0><<<grid, threads, 0, context.stream()>>>(
const_cast<T*>(padding_data), seq_data,
abs_offset_lod[level].CUDAData(context.GetPlace()), sequence_width,
max_sequence_length, num_sequences);
}
const T* pad_data = pad_tensor.data<T>();
T* seq_data = seq_tensor->data<T>();
SequencePaddingKernel<T, kPadToSeq><<<grid, threads, 0, context.stream()>>>(
seq_data, pad_data, nullptr, false,
seq_offsets.CUDAData(context.GetPlace()), seq_num, pad_seq_len,
step_width, norm_by_times, layout);
}
};
template class PaddingLoDTensorFunctor<platform::CUDADeviceContext, int>;
template class PaddingLoDTensorFunctor<platform::CUDADeviceContext, int64_t>;
template class PaddingLoDTensorFunctor<platform::CUDADeviceContext, float>;
template class PaddingLoDTensorFunctor<platform::CUDADeviceContext, double>;
template class UnpaddingLoDTensorFunctor<platform::CUDADeviceContext, int>;
template class UnpaddingLoDTensorFunctor<platform::CUDADeviceContext, int64_t>;
template class UnpaddingLoDTensorFunctor<platform::CUDADeviceContext, float>;
template class UnpaddingLoDTensorFunctor<platform::CUDADeviceContext, double>;
} // namespace math
} // namespace operators
......
paddle/fluid/operators/math/sequence_padding.h
浏览文件 @ ec5cb376
......@@ -15,6 +15,7 @@ limitations under the License. */
#pragma once
#include <algorithm>
#include <vector>
#include "paddle/fluid/framework/lod_tensor.h"
#include "paddle/fluid/platform/device_context.h"
......@@ -22,17 +23,33 @@ namespace paddle {
namespace operators {
namespace math {
inline static size_t MaximumSequenceLength(const framework::LoD& lod,
const size_t level) {
const size_t num_sequences = lod[level].size() - 1;
size_t max_sequence_length = 0;
framework::LoD abs_offset_lod = framework::ToAbsOffset(lod);
for (size_t i = 0; i < num_sequences; ++i) {
max_sequence_length =
std::max(max_sequence_length,
abs_offset_lod[level][i + 1] - abs_offset_lod[level][i]);
enum PadLayout { kBatchLengthWidth = 0, kLengthBatchWidth };
enum CopyType { kSeqToPad, kPadToSeq };
inline static size_t MaximumSequenceLength(
const framework::Vector<size_t>& seq_offset) {
size_t seq_num = seq_offset.size() - 1;
size_t max_seq_len = 0;
for (size_t i = 0; i < seq_num; ++i) {
max_seq_len = std::max(max_seq_len, seq_offset[i + 1] - seq_offset[i]);
}
return max_sequence_length;
return max_seq_len;
}
inline static void CheckDims(const framework::DDim& seq_tensor_dims,
const framework::DDim& pad_tensor_dims,
const framework::Vector<size_t>& seq_offset,
int64_t padded_seq_len, int64_t step_width,
const PadLayout& layout) {
PADDLE_ENFORCE_EQ(static_cast<size_t>(seq_tensor_dims[0]), seq_offset.back(),
"Value of 1st dimension of the sequence tensor should be "
"equal to sum of lengths of all sequences.");
PADDLE_ENFORCE(seq_tensor_dims.size() + 1 == pad_tensor_dims.size() ||
seq_tensor_dims.size() == pad_tensor_dims.size(),
"pad_tensor's rank should be 1 greater than seq_tensor's "
"rank, or be equal with it.");
}
/*
......@@ -64,15 +81,22 @@ inline static size_t MaximumSequenceLength(const framework::LoD& lod,
template <typename DeviceContext, typename T>
class PaddingLoDTensorFunctor {
public:
void operator()(const DeviceContext& context, const framework::LoDTensor& seq,
framework::Tensor* padding, bool norm_by_times);
void operator()(const DeviceContext& context,
const framework::LoDTensor& seq_tensor,
framework::LoDTensor* pad_tensor,
const framework::LoDTensor& pad_value, int pad_seq_len = -1,
int lod_level = 0, bool norm_by_times = false,
const PadLayout layout = kBatchLengthWidth);
};
template <typename DeviceContext, typename T>
class UnpaddingLoDTensorFunctor {
public:
void operator()(const DeviceContext& context, framework::LoDTensor* seq,
const framework::Tensor& padding, bool norm_by_times);
void operator()(const DeviceContext& context,
const framework::LoDTensor& pad_tensor,
framework::LoDTensor* seq_tensor, int pad_seq_len = -1,
int lod_level = 0, bool norm_by_times = false,
const PadLayout layout = kBatchLengthWidth);
};
} // namespace math
......
paddle/fluid/operators/math/sequence_padding_test.cc
浏览文件 @ ec5cb376
......@@ -23,7 +23,9 @@ void TestSequencePadding(const paddle::framework::LoD& lod,
paddle::framework::LoDTensor cpu_seq_back;
paddle::framework::LoDTensor seq;
paddle::framework::LoDTensor seq_back;
paddle::framework::Tensor padding;
paddle::framework::LoDTensor padding;
paddle::framework::LoDTensor cpu_pad_value;
paddle::framework::LoDTensor pad_value;
const size_t level = lod.size() - 1;
auto seq_dims =
......@@ -46,20 +48,33 @@ void TestSequencePadding(const paddle::framework::LoD& lod,
}
const size_t max_sequence_length =
paddle::operators::math::MaximumSequenceLength(lod, level);
paddle::operators::math::MaximumSequenceLength(lod[level]);
const size_t num_sequences = lod[level].size() - 1;
auto padding_dims =
paddle::framework::make_ddim({static_cast<int64_t>(max_sequence_length),
static_cast<int64_t>(num_sequences),
static_cast<int64_t>(sequence_width)});
padding.mutable_data<T>(padding_dims, *place);
T* pad_value_data =
cpu_pad_value.mutable_data<T>({1}, paddle::platform::CPUPlace());
*pad_value_data = static_cast<T>(0);
if (paddle::platform::is_cpu_place(*place)) {
pad_value = cpu_pad_value;
} else {
TensorCopySync(cpu_pad_value, *place, &pad_value);
}
paddle::operators::math::PaddingLoDTensorFunctor<DeviceContext, T>()(
*context, seq, &padding, false);
*context, seq, &padding, pad_value, -1, 0, false,
paddle::operators::math::kLengthBatchWidth);
seq_back.set_lod(lod);
seq_back.mutable_data<T>(seq_dims, *place);
paddle::operators::math::UnpaddingLoDTensorFunctor<DeviceContext, T>()(
*context, &seq_back, padding, false);
*context, padding, &seq_back, -1, 0, false,
paddle::operators::math::kLengthBatchWidth);
if (paddle::platform::is_cpu_place(*place)) {
cpu_seq_back = seq_back;
......
paddle/fluid/operators/mul_op.h
浏览文件 @ ec5cb376
......@@ -62,23 +62,31 @@ class MulGradKernel : public framework::OpKernel<T> {
void Compute(const framework::ExecutionContext& ctx) const override {
int x_num_col_dims = ctx.template Attr<int>("x_num_col_dims");
int y_num_col_dims = ctx.template Attr<int>("y_num_col_dims");
const Tensor* x = ctx.Input<Tensor>("X");
const Tensor* y = ctx.Input<Tensor>("Y");
const Tensor x_matrix = x->dims().size() > 2
auto* x = ctx.Input<framework::LoDTensor>("X");
auto* y = ctx.Input<framework::LoDTensor>("Y");
auto x_matrix = x->dims().size() > 2
? framework::ReshapeToMatrix(*x, x_num_col_dims)
: *x;
const Tensor y_matrix = y->dims().size() > 2
: static_cast<const Tensor&>(*x);
auto y_matrix = y->dims().size() > 2
? framework::ReshapeToMatrix(*y, y_num_col_dims)
: *y;
const Tensor* dout = ctx.Input<Tensor>(framework::GradVarName("Out"));
: static_cast<const Tensor&>(*y);
auto* dout = ctx.Input<framework::LoDTensor>(framework::GradVarName("Out"));
Tensor dout_mat;
dout_mat.ShareDataWith(*dout);
dout_mat.Resize({framework::flatten_to_2d(x->dims(), x_num_col_dims)[0],
framework::flatten_to_2d(y->dims(), y_num_col_dims)[1]});
Tensor* dx = ctx.Output<Tensor>(framework::GradVarName("X"));
Tensor* dy = ctx.Output<Tensor>(framework::GradVarName("Y"));
auto* dx = ctx.Output<framework::LoDTensor>(framework::GradVarName("X"));
auto* dy = ctx.Output<framework::LoDTensor>(framework::GradVarName("Y"));
if (dx != nullptr) {
dx->set_lod(x->lod());
}
if (dy != nullptr) {
dy->set_lod(y->lod());
}
auto& dev_ctx = ctx.template device_context<DeviceContext>();
auto blas = math::GetBlas<DeviceContext, T>(dev_ctx);
if (dx) {
......
paddle/fluid/operators/pad_constant_like_op.cc 0 → 100644
浏览文件 @ ec5cb376
/* Copyright (c) 2018 PaddlePaddle Authors. All Rights Reserved.
Licensed under the Apache License, Version 2.0 (the "License");
you may not use this file except in compliance with the License.
You may obtain a copy of the License at
http://www.apache.org/licenses/LICENSE-2.0
Unless required by applicable law or agreed to in writing, software
distributed under the License is distributed on an "AS IS" BASIS,
WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
See the License for the specific language governing permissions and
limitations under the License. */
#include "paddle/fluid/operators/pad_constant_like_op.h"
namespace paddle {
namespace operators {
using framework::Tensor;
class PadConstantLikeOp : public framework::OperatorWithKernel {
public:
using framework::OperatorWithKernel::OperatorWithKernel;
void InferShape(framework::InferShapeContext *ctx) const override {
PADDLE_ENFORCE(ctx->HasInput("X"),
"Input(X) of PadConstantLikeOp should not be null.");
PADDLE_ENFORCE(ctx->HasInput("Y"),
"Input(Y) of PadConstantLikeOp should not be null.");
PADDLE_ENFORCE(ctx->HasOutput("Out"),
"Output(Out) of PadConstantLikeOp should not be null.");
auto x_dim = ctx->GetInputDim("X");
auto y_dim = ctx->GetInputDim("Y");
PADDLE_ENFORCE_EQ(x_dim.size(), y_dim.size(),
"The dimention of X and Y should be the same.");
for (int i = 0; i < x_dim.size(); ++i) {
PADDLE_ENFORCE_GE(x_dim[i], y_dim[i]);
}
ctx->SetOutputDim("Out", x_dim);
ctx->ShareLoD("X", /*->*/ "Out");
}
protected:
framework::OpKernelType GetExpectedKernelType(
const framework::ExecutionContext &ctx) const override {
return framework::OpKernelType(
framework::ToDataType(ctx.Input<Tensor>("Y")->type()),
ctx.device_context());
}
};
class PadConstantLikeOpMaker : public framework::OpProtoAndCheckerMaker {
public:
void Make() override {
AddInput("X",
"The input of pad_constant_like op. "
"The input should be a k-D tensor(k > 0 and k < 7)");
AddInput("Y",
"The input of pad_constant_like op. "
"The input should be a k-D tensor(k > 0 and k < 7)");
AddOutput("Out",
"The output of pad_constant_like op. "
"A tensor with the same shape as X.");
AddAttr<float>("pad_value",
"(float, default 0.0) "
"The value to fill the padded areas.")
.SetDefault(0.0f);
AddComment(R"DOC(
PadConstantLikeOp Operator.
Pad input(Y) with a pad_value, the number of values padded to the edges of each
axis is specified by the difference of the shape of X and Y.
((0, shape_x_0 - shape_y_0), … (0, shape_x_n - shape_y_n)) unique pad widths for
each axis.
The input should be a k-D tensor(k > 0 and k < 7). As an example:
case1:
Given:
X = [[1, 2],
[3, 4],
[1, 2],
[3, 4]]],
X.shape = (4, 2)
Y = [[5, 6],
[7, 8]],
Y.shape = (2, 2)
And
pad_value = 0,
Return:
Out = [[5, 6],
[7, 8],
[0, 0],
[0, 0]]
Out.shape = (4, 2)
case2:
Given:
X = [[[[ 0, 1, 2],
[ 3, 4, 5]],
[[ 6, 7, 8],
[ 9, 10, 11]],
[[12, 13, 14],
[15, 16, 17]]],
[[[18, 19, 20],
[21, 22, 23]],
[[24, 25, 26],
[27, 28, 29]],
[[30, 31, 32],
[33, 34, 35]]]]
X.shape = (2, 3, 2, 3)
Y = [[[[35, 36, 37]],
[[38, 39, 40]],
[[41, 42, 43]]]]
Y.shape = (1, 3, 1, 3)
And
pad_value = -1,
Return:
Out = [[[[35, 36, 37],
[-1, -1, -1]],
[[38, 39, 40],
[-1, -1, -1]],
[[41, 42, 43],
[-1, -1, -1]]],
[[[-1, -1, -1],
[-1, -1, -1]],
[[-1, -1, -1],
[-1, -1, -1]],
[[-1, -1, -1],
[-1, -1, -1]]]]
Out.shape = (2, 3, 2, 3)
)DOC");
}
};
class PadConstantLikeOpGrad : public framework::OperatorWithKernel {
public:
using framework::OperatorWithKernel::OperatorWithKernel;
void InferShape(framework::InferShapeContext *ctx) const override {
PADDLE_ENFORCE(ctx->HasInput("Y"), "Input(Y) should not be null");
PADDLE_ENFORCE(ctx->HasInput(framework::GradVarName("Out")),
"Input(Out@GRAD) should not be null");
auto y_dim = ctx->GetInputDim("Y");
auto dout_dim = ctx->GetInputDim(framework::GradVarName("Out"));
PADDLE_ENFORCE_EQ(dout_dim.size(), y_dim.size(),
"The dimention of X and Y should be the same.");
auto y_grad_name = framework::GradVarName("Y");
if (ctx->HasOutput(y_grad_name)) {
ctx->SetOutputDim(y_grad_name, y_dim);
ctx->ShareLoD("Y", /*->*/ y_grad_name);
for (int i = 0; i < y_dim.size(); ++i) {
PADDLE_ENFORCE_GE(dout_dim[i], y_dim[i]);
}
}
}
protected:
framework::OpKernelType GetExpectedKernelType(
const framework::ExecutionContext &ctx) const override {
return framework::OpKernelType(
framework::ToDataType(ctx.Input<Tensor>("Y")->type()),
ctx.device_context());
}
};
class PadConstantLikeOpGradMaker : public framework::SingleGradOpDescMaker {
public:
using framework::SingleGradOpDescMaker::SingleGradOpDescMaker;
protected:
std::unique_ptr<framework::OpDesc> Apply() const override {
auto *bind = new framework::OpDesc();
bind->SetType("pad_constant_like_grad");
bind->SetInput("Y", Input("Y"));
bind->SetInput(framework::GradVarName("Out"), OutputGrad("Out"));
bind->SetOutput(framework::GradVarName("Y"), InputGrad("Y"));
bind->SetAttrMap(Attrs());
return std::unique_ptr<framework::OpDesc>(bind);
}
};
} // namespace operators
} // namespace paddle
namespace ops = paddle::operators;
REGISTER_OPERATOR(pad_constant_like, ops::PadConstantLikeOp,
ops::PadConstantLikeOpMaker, ops::PadConstantLikeOpGradMaker);
REGISTER_OPERATOR(pad_constant_like_grad, ops::PadConstantLikeOpGrad);
REGISTER_OP_CPU_KERNEL(
pad_constant_like,
ops::PadConstantLikeKernel<paddle::platform::CPUDeviceContext, float>,
ops::PadConstantLikeKernel<paddle::platform::CPUDeviceContext, double>);
REGISTER_OP_CPU_KERNEL(
pad_constant_like_grad,
ops::PadConstantLikeGradKernel<paddle::platform::CPUDeviceContext, float>,
ops::PadConstantLikeGradKernel<paddle::platform::CPUDeviceContext, double>);
paddle/fluid/operators/pad_constant_like_op.cu 0 → 100644
浏览文件 @ ec5cb376
/* Copyright (c) 2018 PaddlePaddle Authors. All Rights Reserved.
Licensed under the Apache License, Version 2.0 (the "License");
you may not use this file except in compliance with the License.
You may obtain a copy of the License at
http://www.apache.org/licenses/LICENSE-2.0
Unless required by applicable law or agreed to in writing, software
distributed under the License is distributed on an "AS IS" BASIS,
WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
See the License for the specific language governing permissions and
limitations under the License. */
#define EIGEN_USE_GPU
#include "paddle/fluid/operators/pad_constant_like_op.h"
namespace ops = paddle::operators;
REGISTER_OP_CUDA_KERNEL(
pad_constant_like,
ops::PadConstantLikeKernel<paddle::platform::CUDADeviceContext, float>,
ops::PadConstantLikeKernel<paddle::platform::CUDADeviceContext, double>);
REGISTER_OP_CUDA_KERNEL(
pad_constant_like_grad,
ops::PadConstantLikeGradKernel<paddle::platform::CUDADeviceContext, float>,
ops::PadConstantLikeGradKernel<paddle::platform::CUDADeviceContext,
double>);
paddle/fluid/operators/pad_constant_like_op.h 0 → 100644
浏览文件 @ ec5cb376
/* Copyright (c) 2018 PaddlePaddle Authors. All Rights Reserved.
Licensed under the Apache License, Version 2.0 (the "License");
you may not use this file except in compliance with the License.
You may obtain a copy of the License at
http://www.apache.org/licenses/LICENSE-2.0
Unless required by applicable law or agreed to in writing, software
distributed under the License is distributed on an "AS IS" BASIS,
WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
See the License for the specific language governing permissions and
limitations under the License. */
#pragma once
#include <utility>
#include <vector>
#include "paddle/fluid/framework/eigen.h"
#include "paddle/fluid/framework/op_registry.h"
#include "paddle/fluid/framework/tensor.h"
#include "paddle/fluid/framework/tensor_util.h"
#include "paddle/fluid/operators/math/padding.h"
namespace paddle {
namespace operators {
template <typename DeviceContext, typename T>
class PadConstantLikeKernel : public framework::OpKernel<T> {
public:
void Compute(const framework::ExecutionContext& context) const override {
auto in_x = context.Input<framework::Tensor>("X");
auto in_y = context.Input<framework::Tensor>("Y");
auto* out = context.Output<framework::Tensor>("Out");
if (in_x->dims() == in_y->dims()) {
// TensorCopy(in_y, context.GetPlace(), context, out);
out->ShareDataWith(*in_y);
return;
}
T pad_value = context.Attr<T>("pad_value");
out->mutable_data<T>(context.GetPlace());
int rank = context.Input<framework::Tensor>("X")->dims().size();
std::vector<int> pads(rank * 2, 0);
for (int j = 0; j < rank; ++j) {
pads[j * 2] = 0;
pads[j * 2 + 1] = static_cast<int>(in_x->dims()[j] - in_y->dims()[j]);
}
math::PaddingFunctor<DeviceContext, T>(rank, context, pads, pad_value,
*in_y, out);
}
};
template <typename DeviceContext, typename T>
class PadConstantLikeGradKernel : public framework::OpKernel<T> {
public:
void Compute(const framework::ExecutionContext& context) const override {
auto in_y = context.Input<framework::Tensor>("Y");
auto in_dout =
context.Input<framework::Tensor>(framework::GradVarName("Out"));
auto* d_y = context.Output<framework::Tensor>(framework::GradVarName("Y"));
if (d_y == nullptr) {
return;
}
if (in_dout->dims() == in_y->dims()) {
// TensorCopy(in_dout, context.GetPlace(), context, d_y);
d_y->ShareDataWith(*in_dout);
return;
}
d_y->mutable_data<T>(context.GetPlace());
int rank = in_dout->dims().size();
std::vector<int> pads(static_cast<size_t>(rank) * 2, 0);
for (int j = 0; j < rank; ++j) {
pads[j * 2] = 0;
pads[j * 2 + 1] = static_cast<int>(in_dout->dims()[j] - in_y->dims()[j]);
}
math::PaddingGradFunctor<DeviceContext, T>(rank, context, pads, *in_dout,
d_y);
}
};
} // namespace operators
} // namespace paddle
paddle/fluid/operators/pad_op.h
浏览文件 @ ec5cb376
......@@ -18,117 +18,44 @@ limitations under the License. */
#include <vector>
#include "paddle/fluid/framework/eigen.h"
#include "paddle/fluid/framework/op_registry.h"
#include "paddle/fluid/operators/math/padding.h"
namespace paddle {
namespace operators {
using Tensor = framework::Tensor;
template <typename T, size_t D, int MajorType = Eigen::RowMajor,
typename IndexType = Eigen::DenseIndex>
using EigenTensor = framework::EigenTensor<T, D, MajorType, IndexType>;
template <typename DeviceContext, typename T, size_t D>
void PadFunction(const framework::ExecutionContext& context) {
template <typename DeviceContext, typename T>
class PadKernel : public framework::OpKernel<T> {
public:
void Compute(const framework::ExecutionContext& context) const override {
auto pads = context.Attr<std::vector<int>>("paddings");
Eigen::array<std::pair<int, int>, D> paddings;
for (size_t i = 0; i < paddings.size(); ++i) {
paddings[i].first = pads[i * 2];
paddings[i].second = pads[i * 2 + 1];
}
T pad_value = context.Attr<T>("pad_value");
auto* x = context.Input<Tensor>("X");
auto* out = context.Output<Tensor>("Out");
out->mutable_data<T>(context.GetPlace());
auto x_tensor = EigenTensor<T, D>::From(*x);
auto out_tensor = EigenTensor<T, D>::From(*out);
auto& place =
*context.template device_context<DeviceContext>().eigen_device();
out_tensor.device(place) = x_tensor.pad(paddings, pad_value);
}
int rank = x->dims().size();
math::PaddingFunctor<DeviceContext, T>(rank, context, pads, pad_value, *x,
out);
}
};
template <typename DeviceContext, typename T>
class PadKernel : public framework::OpKernel<T> {
class PadGradKernel : public framework::OpKernel<T> {
public:
void Compute(const framework::ExecutionContext& context) const override {
int rank = context.Input<Tensor>("X")->dims().size();
switch (rank) {
case 1:
PadFunction<DeviceContext, T, 1>(context);
break;
case 2:
PadFunction<DeviceContext, T, 2>(context);
break;
case 3:
PadFunction<DeviceContext, T, 3>(context);
break;
case 4:
PadFunction<DeviceContext, T, 4>(context);
break;
case 5:
PadFunction<DeviceContext, T, 5>(context);
break;
case 6:
PadFunction<DeviceContext, T, 6>(context);
break;
default:
PADDLE_THROW(
"PadOp only support tensors with no more than 6 dimensions.");
}
}
};
template <typename DeviceContext, typename T, size_t D>
void PadGradFunction(const framework::ExecutionContext& context) {
auto pads = context.Attr<std::vector<int>>("paddings");
Eigen::array<std::pair<int, int>, D> paddings;
for (size_t i = 0; i < paddings.size(); ++i) {
paddings[i].first = -pads[i * 2];
paddings[i].second = -pads[i * 2 + 1];
}
auto* d_out = context.Input<Tensor>(framework::GradVarName("Out"));
auto* d_x = context.Output<Tensor>(framework::GradVarName("X"));
if (d_x != nullptr) {
d_x->mutable_data<T>(context.GetPlace());
auto d_x_tensor = EigenTensor<T, D>::From(*d_x);
auto d_out_tensor = EigenTensor<T, D>::From(*d_out);
auto& place =
*context.template device_context<DeviceContext>().eigen_device();
d_x_tensor.device(place) = d_out_tensor.pad(paddings, 0);
if (d_x == nullptr) {
return;
}
}
template <typename DeviceContext, typename T>
class PadGradKernel : public framework::OpKernel<T> {
public:
void Compute(const framework::ExecutionContext& context) const override {
size_t rank =
context.Input<Tensor>(framework::GradVarName("Out"))->dims().size();
switch (rank) {
case 1:
PadGradFunction<DeviceContext, T, 1>(context);
break;
case 2:
PadGradFunction<DeviceContext, T, 2>(context);
break;
case 3:
PadGradFunction<DeviceContext, T, 3>(context);
break;
case 4:
PadGradFunction<DeviceContext, T, 4>(context);
break;
case 5:
PadGradFunction<DeviceContext, T, 5>(context);
break;
case 6:
PadGradFunction<DeviceContext, T, 6>(context);
break;
default:
PADDLE_THROW(
"PadOp only support tensors with no more than 6 dimensions.");
}
d_x->mutable_data<T>(context.GetPlace());
int rank = d_out->dims().size();
math::PaddingGradFunctor<DeviceContext, T>(rank, context, pads, *d_out,
d_x);
}
};
......
paddle/fluid/operators/parallel_do_op.cc
浏览文件 @ ec5cb376
......@@ -355,6 +355,7 @@ class ParallelDoGradOpDescMaker : public framework::SingleGradOpDescMaker {
grad->SetInput(framework::GradVarName(output_param), og_names);
}
}
grad->SetInput("Communicator", {"nccl_com__do_not_change_"});
grad->SetAttrMap(this->Attrs());
grad->SetBlockAttr(kParallelBlock, grad_block_[0]);
......
paddle/fluid/operators/print_op.cc
浏览文件 @ ec5cb376
......@@ -13,14 +13,12 @@
limitations under the License. */
#include <algorithm>
#include <ctime>
#include "paddle/fluid/framework/op_registry.h"
#include "paddle/fluid/framework/var_type.h"
#include "paddle/fluid/framework/variable.h"
namespace paddle {
namespace operators {
using framework::GradVarName;
#define CLOG std::cout
......@@ -35,7 +33,7 @@ struct Formater {
std::type_index dtype{typeid(const char)};
framework::LoD lod;
int summarize;
void* data{nullptr};
void *data{nullptr};
void operator()(size_t size) {
PrintMessage();
......@@ -101,7 +99,7 @@ struct Formater {
template <typename T>
void Display(size_t size) {
auto* d = reinterpret_cast<T*>(data);
auto *d = reinterpret_cast<T *>(data);
CLOG << "\tdata: ";
if (summarize != -1) {
summarize = std::min(size, (size_t)summarize);
......@@ -120,51 +118,36 @@ struct Formater {
// TODO(ChunweiYan) there should be some other printers for TensorArray
class TensorPrintOp : public framework::OperatorBase {
public:
TensorPrintOp(const std::string& type,
const framework::VariableNameMap& inputs,
const framework::VariableNameMap& outputs,
const framework::AttributeMap& attrs)
TensorPrintOp(const std::string &type,
const framework::VariableNameMap &inputs,
const framework::VariableNameMap &outputs,
const framework::AttributeMap &attrs)
: OperatorBase(type, inputs, outputs, attrs) {}
TensorPrintOp(const TensorPrintOp& o)
TensorPrintOp(const TensorPrintOp &o)
: framework::OperatorBase(
static_cast<const framework::OperatorBase&>(o)) {
static_cast<const framework::OperatorBase &>(o)) {
PADDLE_THROW("Not implemented.");
}
private:
void RunImpl(const framework::Scope& scope,
const platform::Place& place) const override {
const framework::Variable* in_var_ptr = nullptr;
std::string phase(kForward);
void RunImpl(const framework::Scope &scope,
const platform::Place &place) const override {
const framework::Variable *in_var_ptr = nullptr;
std::string printed_var_name = "";
auto& inputs = Inputs();
if (inputs.find("In") != inputs.end() && !Inputs("In").empty()) {
in_var_ptr = scope.FindVar(Input("In"));
printed_var_name = Inputs("In").front();
} else if (inputs.find("In@GRAD") != inputs.end() &&
!Inputs("In@GRAD").empty()) {
in_var_ptr = scope.FindVar(Input("In@GRAD"));
printed_var_name = Inputs("In@GRAD").front();
phase = std::string(kBackward);
} else {
PADDLE_THROW("Unknown phase, should be forward or backward.");
}
PADDLE_ENFORCE_NOT_NULL(in_var_ptr);
auto& in_tensor = in_var_ptr->Get<framework::LoDTensor>();
auto* out_var_ptr = scope.FindVar(Output("Out"));
auto& out_tensor = *out_var_ptr->GetMutable<framework::LoDTensor>();
// Just copy data from input tensor to output tensor
// output tensor share same memory with input tensor
out_tensor.ShareDataWith(in_tensor);
out_tensor.set_lod(in_tensor.lod());
auto &in_tensor = in_var_ptr->Get<framework::LoDTensor>();
std::string print_phase = Attr<std::string>("print_phase");
if (print_phase != phase && print_phase != std::string(kBoth)) {
bool is_forward = Attr<bool>("is_forward");
if ((is_forward && print_phase == kBackward) ||
(!is_forward && print_phase == kForward)) {
return;
}
......@@ -192,7 +175,7 @@ class TensorPrintOp : public framework::OperatorBase {
formater.dtype = printed_tensor.type();
}
if (Attr<bool>("print_tensor_shape")) {
auto& dims = printed_tensor.dims();
auto &dims = printed_tensor.dims();
formater.dims.resize(dims.size());
for (int i = 0; i < dims.size(); ++i) formater.dims[i] = dims[i];
}
......@@ -200,7 +183,7 @@ class TensorPrintOp : public framework::OperatorBase {
formater.lod = printed_tensor.lod();
}
formater.summarize = Attr<int>("summarize");
formater.data = reinterpret_cast<void*>(printed_tensor.data<void>());
formater.data = reinterpret_cast<void *>(printed_tensor.data<void>());
formater(printed_tensor.numel());
}
......@@ -219,14 +202,14 @@ class PrintOpProtoAndCheckMaker : public framework::OpProtoAndCheckerMaker {
AddAttr<bool>("print_tensor_type", "Whether to print the tensor's dtype.");
AddAttr<bool>("print_tensor_shape", "Whether to print the tensor's shape.");
AddAttr<bool>("print_tensor_lod", "Whether to print the tensor's lod.");
AddAttr<std::string>(
"print_phase",
"(string, default 'BOTH') Which phase to display including 'FORWARD' "
AddAttr<std::string>("print_phase",
"(string, default 'FORWARD') Which phase to display "
"including 'FORWARD' "
"'BACKWARD' and 'BOTH'.")
.SetDefault(std::string(kBoth))
.InEnum({std::string(kForward), std::string(kBackward),
std::string(kBoth)});
AddOutput("Out", "Output tensor with same data as input tensor.");
AddAttr<bool>("is_forward", "Whether is forward or not").SetDefault(true);
AddComment(R"DOC(
Creates a print op that will print when a tensor is accessed.
......@@ -238,40 +221,21 @@ tensor `t`.)DOC");
class InferShapeForward : public framework::InferShapeBase {
public:
void operator()(framework::InferShapeContext* context) const override {
void operator()(framework::InferShapeContext *context) const override {
PADDLE_ENFORCE(context->HasInput("In"), "Input(In) should not be null.");
context->ShareLoD("In", /*->*/ "Out");
context->SetOutputDim("Out", context->GetInputDim("In"));
}
};
class InferShapeBackward : public framework::InferShapeBase {
public:
void operator()(framework::InferShapeContext* context) const override {
PADDLE_ENFORCE(context->HasInput("In@GRAD"),
"Input(In@GRAD) should not be null.");
context->ShareLoD("In@GRAD", /*->*/ "Out");
context->SetOutputDim("Out", context->GetInputDim("In@GRAD"));
}
};
class InferVarType : public framework::VarTypeInference {
public:
void operator()(const framework::OpDesc& op_desc,
framework::BlockDesc* block) const override {}
};
class PrintOpProtoAndCheckGradOpMaker
: public framework::SingleGradOpDescMaker {
class PrintOpGradientMaker : public framework::SingleGradOpDescMaker {
public:
using framework::SingleGradOpDescMaker::SingleGradOpDescMaker;
std::unique_ptr<framework::OpDesc> Apply() const override {
auto* op_desc_ptr = new framework::OpDesc();
op_desc_ptr->SetType("print_grad");
op_desc_ptr->SetInput("In@GRAD", OutputGrad("Out"));
op_desc_ptr->SetOutput("Out", InputGrad("In"));
auto *op_desc_ptr = new framework::OpDesc();
op_desc_ptr->SetType("print");
op_desc_ptr->SetInput("In", InputGrad("In"));
op_desc_ptr->SetAttrMap(Attrs());
op_desc_ptr->SetAttr("is_forward", false);
return std::unique_ptr<framework::OpDesc>(op_desc_ptr);
}
};
......@@ -282,6 +246,4 @@ class PrintOpProtoAndCheckGradOpMaker
namespace ops = paddle::operators;
REGISTER_OPERATOR(print, ops::TensorPrintOp, ops::PrintOpProtoAndCheckMaker,
ops::PrintOpProtoAndCheckGradOpMaker, ops::InferShapeForward,
ops::InferVarType);
REGISTER_OPERATOR(print_grad, ops::TensorPrintOp, ops::InferShapeBackward);
ops::PrintOpGradientMaker, ops::InferShapeForward);
paddle/fluid/operators/sampling_id_op.h
浏览文件 @ ec5cb376
......@@ -54,7 +54,7 @@ class SamplingIdKernel : public framework::OpKernel<T> {
static_cast<T>(context.Attr<float>("max")));
std::vector<T> ids(batch_size);
for (size_t i = 0; i < batch_size; ++i) {
for (int i = 0; i < batch_size; ++i) {
T r = dist(engine);
int idx = width - 1;
for (int j = 0; j < width; ++j) {
......@@ -63,7 +63,7 @@ class SamplingIdKernel : public framework::OpKernel<T> {
break;
}
}
ids[i] = ins_vector[i * width + idx];
ids[i] = ins_vector[idx];
}
std::vector<int64_t> out_dim;
......
paddle/fluid/operators/scale_op.cc
浏览文件 @ ec5cb376
......@@ -13,8 +13,11 @@ See the License for the specific language governing permissions and
limitations under the License. */
#include "paddle/fluid/operators/scale_op.h"
#include <string>
#include "paddle/fluid/operators/detail/safe_ref.h"
namespace paddle {
namespace operators {
......@@ -52,6 +55,21 @@ $$Out = scale*X$$
}
};
class ScaleOpVarTypeInference : public framework::VarTypeInference {
public:
void operator()(const framework::OpDesc &op_desc,
framework::BlockDesc *block) const override {
auto &in_var_name = op_desc.Input("X").front();
auto &in_var = detail::Ref(block->FindVarRecursive(in_var_name));
auto out_var_name = op_desc.Output("Out").front();
auto *out_var = block->FindVarRecursive(out_var_name);
out_var->SetType(in_var.GetType());
out_var->SetDataType(in_var.GetDataType());
}
};
class ScaleGradMaker : public framework::SingleGradOpDescMaker {
public:
using framework::SingleGradOpDescMaker::SingleGradOpDescMaker;
......@@ -71,7 +89,8 @@ class ScaleGradMaker : public framework::SingleGradOpDescMaker {
namespace ops = paddle::operators;
REGISTER_OPERATOR(scale, ops::ScaleOp, ops::ScaleOpMaker, ops::ScaleGradMaker);
REGISTER_OPERATOR(scale, ops::ScaleOp, ops::ScaleOpMaker, ops::ScaleGradMaker,
ops::ScaleOpVarTypeInference);
REGISTER_OP_CPU_KERNEL(
scale, ops::ScaleKernel<paddle::platform::CPUDeviceContext, float>,
ops::ScaleKernel<paddle::platform::CPUDeviceContext, double>,
......
paddle/fluid/operators/scale_op.h
浏览文件 @ ec5cb376
......@@ -22,17 +22,29 @@ namespace operators {
template <typename DeviceContext, typename T>
class ScaleKernel : public framework::OpKernel<T> {
public:
virtual void Compute(const framework::ExecutionContext& context) const {
auto* tensor = context.Output<framework::Tensor>("Out");
auto* in = context.Input<framework::Tensor>("X");
tensor->mutable_data<T>(in->place());
virtual void Compute(const framework::ExecutionContext& ctx) const {
auto* in_var = ctx.InputVar("X");
auto* in = ctx.Input<framework::Tensor>("X");
auto scale = static_cast<T>(context.Attr<float>("scale"));
auto* out_var = ctx.OutputVar("Out");
auto* out = ctx.Output<framework::Tensor>("Out");
out->mutable_data<T>(in->place());
auto eigen_out = framework::EigenVector<T>::Flatten(*tensor);
PADDLE_ENFORCE_EQ(in->dims(), out->dims(),
"in and out should have the same dim");
auto scale = static_cast<T>(ctx.Attr<float>("scale"));
if (in_var->IsType<framework::SelectedRows>() && in_var != out_var) {
auto& in_slr = in_var->Get<framework::SelectedRows>();
auto* out_slr = out_var->GetMutable<framework::SelectedRows>();
out_slr->set_rows(in_slr.rows());
out_slr->set_height(in_slr.height());
}
auto eigen_out = framework::EigenVector<T>::Flatten(*out);
auto eigen_in = framework::EigenVector<T>::Flatten(*in);
auto& dev =
*context.template device_context<DeviceContext>().eigen_device();
auto& dev = *ctx.template device_context<DeviceContext>().eigen_device();
eigen_out.device(dev) = scale * eigen_in;
}
};
......
paddle/fluid/operators/send_barrier_op.cc
浏览文件 @ ec5cb376
......@@ -56,6 +56,10 @@ class SendBarrierOp : public framework::OperatorBase {
class SendBarrierOpMaker : public framework::OpProtoAndCheckerMaker {
public:
void Make() {
AddInput("X", "(Any) Dummy inputs, used for control dependency")
.AsDuplicable();
AddOutput("Out", "(Any) Dummy outputs, used for control dependency")
.AsDuplicable();
AddComment(R"DOC(
SendBarrier operator
......
paddle/fluid/operators/sequence_expand_op.h
浏览文件 @ ec5cb376
......@@ -53,25 +53,27 @@ struct SequenceExpandFunctor<platform::CPUDeviceContext, T> {
const framework::Vector<size_t>& ref_lod, /*expand referenced lod*/
LoDTensor* out) {
int out_offset = 0;
auto& eigen_place = *context.eigen_device();
int x_item_length = x.numel() / x.dims()[0];
auto out_data = out->data<T>();
auto x_data = x.data<T>();
for (size_t i = 1; i < ref_lod.size(); ++i) {
int repeat_num = ref_lod[i] - ref_lod[i - 1];
int x_start = x_lod[i - 1];
int x_end = x_lod[i];
int x_seq_len = x_end - x_start;
if (repeat_num > 0) {
auto x_sub_tensor = x.Slice(x_start, x_end);
x_sub_tensor.Resize({1, x_sub_tensor.numel()});
int out_start = out_offset;
if (out->lod().size() == 1) {
out_start = out->lod()[0][out_offset];
}
auto out_sub_tensor =
out->Slice(out_start, out_start + x_seq_len * repeat_num);
out_sub_tensor.Resize({repeat_num, x_sub_tensor.dims()[1]});
EigenMatrix<T>::From(out_sub_tensor).device(eigen_place) =
EigenMatrix<T>::From(x_sub_tensor)
.broadcast(Eigen::array<int, 2>({{repeat_num, 1}}));
for (int j = 0; j < repeat_num; j++) {
for (int k = 0; k < x_seq_len; k++) {
for (int l = 0; l < x_item_length; l++) {
out_data[(out_start + j * x_seq_len + k) * x_item_length + l] =
x_data[(x_start + k) * x_item_length + l];
}
}
}
}
out_offset += repeat_num;
}
......
paddle/fluid/operators/sequence_mask_op.cc 0 → 100644
浏览文件 @ ec5cb376
// Copyright (c) 2018 PaddlePaddle Authors. All Rights Reserved.
//
// Licensed under the Apache License, Version 2.0 (the "License");
// you may not use this file except in compliance with the License.
// You may obtain a copy of the License at
//
// http://www.apache.org/licenses/LICENSE-2.0
//
// Unless required by applicable law or agreed to in writing, software
// distributed under the License is distributed on an "AS IS" BASIS,
// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
// See the License for the specific language governing permissions and
// limitations under the License.
#include "paddle/fluid/operators/sequence_mask_op.h"
REGISTER_OPERATOR(sequence_mask, paddle::operators::SequenceMaskOp,
paddle::operators::SequenceMaskOpMaker,
paddle::framework::EmptyGradOpMaker);
REGISTER_OP_CPU_KERNEL(
sequence_mask,
paddle::operators::SequenceMaskKernel<paddle::platform::CPUDeviceContext,
int>,
paddle::operators::SequenceMaskKernel<paddle::platform::CPUDeviceContext,
int64_t>);
paddle/fluid/operators/sequence_mask_op.cu 0 → 100644
浏览文件 @ ec5cb376
// Copyright (c) 2018 PaddlePaddle Authors. All Rights Reserved.
//
// Licensed under the Apache License, Version 2.0 (the "License");
// you may not use this file except in compliance with the License.
// You may obtain a copy of the License at
//
// http://www.apache.org/licenses/LICENSE-2.0
//
// Unless required by applicable law or agreed to in writing, software
// distributed under the License is distributed on an "AS IS" BASIS,
// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
// See the License for the specific language governing permissions and
// limitations under the License.
#include "paddle/fluid/operators/sequence_mask_op.h"
REGISTER_OP_CUDA_KERNEL(
sequence_mask,
paddle::operators::SequenceMaskKernel<paddle::platform::CUDADeviceContext,
int>,
paddle::operators::SequenceMaskKernel<paddle::platform::CUDADeviceContext,
int64_t>);
paddle/fluid/operators/sequence_mask_op.h 0 → 100644
浏览文件 @ ec5cb376
// Copyright (c) 2018 PaddlePaddle Authors. All Rights Reserved.
//
// Licensed under the Apache License, Version 2.0 (the "License");
// you may not use this file except in compliance with the License.
// You may obtain a copy of the License at
//
// http://www.apache.org/licenses/LICENSE-2.0
//
// Unless required by applicable law or agreed to in writing, software
// distributed under the License is distributed on an "AS IS" BASIS,
// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
// See the License for the specific language governing permissions and
// limitations under the License.
#pragma once
#ifdef __NVCC__
#include <thrust/device_ptr.h>
#include <thrust/functional.h>
#include <thrust/reduce.h>
#else
#include <algorithm>
#endif
#include "paddle/fluid/framework/op_registry.h"
#include "paddle/fluid/platform/for_range.h"
namespace paddle {
namespace operators {
class SequenceMaskOp : public framework::OperatorWithKernel {
public:
using framework::OperatorWithKernel::OperatorWithKernel;
void InferShape(framework::InferShapeContext *ctx) const override {
PADDLE_ENFORCE(ctx->HasInput("X"), "Input(X) must exist");
PADDLE_ENFORCE(ctx->HasOutput("Y"), "Output(Y) must exist");
auto maxlen = ctx->Attrs().Get<int>("maxlen");
if (maxlen > 0) { // We can only infershape when maxlen > 0
auto dim = framework::vectorize2int(ctx->GetInputDim("X"));
dim.push_back(maxlen);
ctx->SetOutputDim("Y", framework::make_ddim(dim));
}
}
};
class SequenceMaskOpMaker : public framework::OpProtoAndCheckerMaker {
public:
void Make() override {
AddInput("X", "The input tensor of sequence_mask op.");
AddOutput("Y", "The output mask of sequence_mask op.");
AddAttr<int>("maxlen",
"The maximum length of the sequence. If maxlen < 0, maxlen "
"= max(Input(X)).")
.SetDefault(-1)
.AddCustomChecker([](int &v) {
PADDLE_ENFORCE(v < 0 || v >= 1,
"Attr(maxlen) must be less than 0 or larger than 1");
});
AddAttr<int>("out_dtype", "Output data type");
AddComment(R"DOC(
SequenceMask Operator
This operator outputs a Mask according to Input(X) and Attr(maxlen).
Supposing Input(X) is a Tensor with shape [d_1, d_2, ..., d_n], the
Output(Y) is a mask with shape [d_1, d_2, ..., d_n, maxlen], where:
Y(i_1, i_2, ..., i_n, j) = (j < X(i_1, i_2, ..., i_n))
If maxlen < 0, maxlen = max(X)
)DOC");
}
};
template <typename Tx, typename Ty>
struct SequenceMaskForRangeFunctor {
HOSTDEVICE SequenceMaskForRangeFunctor(const Tx *x, Ty *y, int maxlen)
: x_(x), y_(y), maxlen_(maxlen) {}
HOSTDEVICE void operator()(int y_idx) const {
int x_idx = y_idx / maxlen_;
int j = y_idx % maxlen_;
y_[y_idx] = static_cast<Ty>(j < x_[x_idx] ? 1 : 0);
}
private:
const Tx *x_;
Ty *y_;
int maxlen_;
};
template <typename DeviceContext, typename Tx>
struct SequenceMaskFunctor {
using Tensor = framework::LoDTensor;
SequenceMaskFunctor(const DeviceContext &ctx, const Tx *x, Tensor *y,
int limits, int maxlen)
: ctx_(ctx), x_(x), y_(y), limits_(limits), maxlen_(maxlen) {}
template <typename Ty>
void operator()() const {
auto *y_data = y_->mutable_data<Ty>(ctx_.GetPlace());
platform::ForRange<DeviceContext> for_range(ctx_, limits_);
for_range(SequenceMaskForRangeFunctor<Tx, Ty>(x_, y_data, maxlen_));
}
private:
const DeviceContext &ctx_;
const Tx *x_;
Tensor *y_;
int limits_;
int maxlen_;
};
template <typename DeviceContext, typename Tx>
class SequenceMaskKernel : public framework::OpKernel<Tx> {
using Tensor = framework::LoDTensor;
public:
void Compute(const framework::ExecutionContext &ctx) const override {
auto *x = ctx.Input<Tensor>("X");
auto *y = ctx.Output<Tensor>("Y");
auto maxlen = ctx.Attr<int>("maxlen");
auto *x_data = x->data<Tx>();
auto x_numel = x->numel();
if (maxlen < 0) {
#ifdef __NVCC__
VLOG(10)
<< "SequenceMaskOp on GPU may be slow when maxlen is not provided.";
maxlen = static_cast<int>(
thrust::reduce(thrust::device_pointer_cast(x_data),
thrust::device_pointer_cast(x_data) + x_numel,
static_cast<Tx>(0), thrust::maximum<Tx>()));
#else
maxlen = static_cast<int>(*std::max_element(x_data, x_data + x_numel));
#endif
auto y_dim = framework::vectorize2int(x->dims());
y_dim.push_back(maxlen);
y->Resize(framework::make_ddim(y_dim));
}
auto out_dtype = static_cast<framework::proto::VarType::Type>(
ctx.Attr<int>("out_dtype"));
auto &dev_ctx = ctx.template device_context<DeviceContext>();
framework::VisitDataType(out_dtype,
SequenceMaskFunctor<DeviceContext, Tx>(
dev_ctx, x_data, y, x_numel * maxlen, maxlen));
}
};
} // namespace operators
} // namespace paddle
paddle/fluid/operators/sequence_pad_op.cc 0 → 100644
浏览文件 @ ec5cb376
/* Copyright (c) 2018 PaddlePaddle Authors. All Rights Reserved.
Licensed under the Apache License, Version 2.0 (the "License");
you may not use this file except in compliance with the License.
You may obtain a copy of the License at
http://www.apache.org/licenses/LICENSE-2.0
Unless required by applicable law or agreed to in writing, software
distributed under the License is distributed on an "AS IS" BASIS,
WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
See the License for the specific language governing permissions and
limitations under the License. */
#include "paddle/fluid/operators/sequence_pad_op.h"
namespace paddle {
namespace operators {
class SequencePadOp : public framework::OperatorWithKernel {
public:
using framework::OperatorWithKernel::OperatorWithKernel;
protected:
void InferShape(framework::InferShapeContext* ctx) const override {
PADDLE_ENFORCE(ctx->HasInput("X"),
"Input(X) of SequencePadOp should not be null.");
PADDLE_ENFORCE(ctx->HasInput("PadValue"),
"Input(PadValue) of SequencePadOp should not be null.");
PADDLE_ENFORCE(ctx->HasOutput("Out"),
"Output(Out) of SequencePadOp should not be null.");
auto x_dims = ctx->GetInputDim("X");
PADDLE_ENFORCE_GE(x_dims.size(), 2,
"The rank of Input(x) can't be less than 2.");
auto time_step_dims = framework::slice_ddim(x_dims, 1, x_dims.size());
auto pad_value_dims = ctx->GetInputDim("PadValue");
PADDLE_ENFORCE(pad_value_dims == framework::make_ddim({1}) ||
pad_value_dims == time_step_dims,
"The Input(PadValue) must be a scalar or a tensor whose "
"shape equals to time steps in sequences");
int out_dim_0 = -1;
int out_dim_1 = -1;
if (ctx->IsRuntime()) {
// run time
framework::Variable* x_var =
boost::get<framework::Variable*>(ctx->GetInputVarPtrs("X")[0]);
const auto& x_lod = x_var->Get<LoDTensor>().lod();
PADDLE_ENFORCE(!x_lod.empty(), "The Input(X) must hold lod info.");
const auto& x_lod_0 = x_lod[0];
PADDLE_ENFORCE_GE(x_lod_0.size(), 2,
"The Input(X)'s lod info is corrupted.");
PADDLE_ENFORCE_EQ(
x_dims[0], static_cast<int64_t>(x_lod_0.back()),
"The Input(X)'s lod info mismatches the actual tensor shape.");
int seq_num = x_lod_0.size() - 1;
int max_seq_len = math::MaximumSequenceLength(x_lod_0);
int padded_length = ctx->Attrs().Get<int>("padded_length");
if (padded_length == -1) {
padded_length = max_seq_len;
}
PADDLE_ENFORCE_GE(padded_length, max_seq_len,
"The Attr(padded_length) must be -1 or an int greater "
"than the length of the longest original sequence.");
out_dim_0 = seq_num;
out_dim_1 = padded_length;
} else {
// compile time
framework::VarDesc* x_desc =
boost::get<framework::VarDesc*>(ctx->GetInputVarPtrs("X")[0]);
PADDLE_ENFORCE_GE(x_desc->GetLoDLevel(), 1);
}
std::vector<int> out_dims_vec{out_dim_0, out_dim_1};
auto time_step_dims_vec = framework::vectorize2int(time_step_dims);
out_dims_vec.insert(out_dims_vec.end(), time_step_dims_vec.begin(),
time_step_dims_vec.end());
ctx->SetOutputDim("Out", framework::make_ddim(out_dims_vec));
}
};
class SequencePadOpMaker : public framework::OpProtoAndCheckerMaker {
public:
void Make() override {
AddInput("X",
"(LoDTensor, default LoDTensor<float>) Input variable which "
"should contain lod information.");
AddInput("PadValue",
"(LoDTensor), this Tensor holds values that will be fill into "
"padded steps. It can be a scalar or a tensor whose shape equals "
"to time steps in sequences. If it's a scalar, it will be "
"automatically broadcasted to the shape of time step.");
AddOutput(
"Out",
"(LoDTensor) The output vairable, which contains padded sequences.");
AddAttr<int>(
"padded_length",
"The length of padded sequences. It can be setted to -1 or "
"any positive int. When it is -1, all sequences will be padded up to "
"the length of the longest one among them; when it a certain positive "
"value, it must be greater than the length of the longest original "
"sequence.")
.SetDefault(-1);
AddComment(R"DOC(
Sequence Pad Operator
This operator pads sequences in a same batch to a consistent length.
The length is specified by attribute 'padded_length'. New elements,
whose values are specified by input 'PadValue', will be appended to
the end of each sequence, to make their final lengths consistent.
Following are cases to better explain how this works:
Case 1:
Given a 1-level LoDTensor input(X):
X.lod = [[0, 2, 5]]
X.data = [a, b, c, d, e]
and Input(PadValue):
PadValue.data = [0]
and attribite 'padded_length' = 4,
then we get LoDTensor:
Out.data = [[a, b, 0, 0],
[c, d, e, 0]]
Case 2:
Given a 1-level LoDTensor input(X):
X.lod = [[0, 2, 5]]
X.data = [[a1, a2], [b1, b2], [c1, c2], [d1, d2], [e1, e2]]
and Input(PadValue):
PadValue.data = [0]
and attribite 'padded_length' = -1, which mean using the length
of longest input sequence(3 in this case),
then we get LoDTensor:
Out.data = [[[a1, a2], [b1, b2], [0, 0]],
[[c1, c2], [d1, d2], [e1, e2]]]
Case 3:
Given a 1-level LoDTensor input(X):
X.lod = [[0, 2, 5]]
X.data = [[a1, a2], [b1, b2], [c1, c2], [d1, d2], [e1, e2]]
and Input(PadValue):
PadValue.data = [p1, p2]
and attribite 'padded_length' = -1, which mean using the length
of longest input sequence(3 in this case),
then we get LoDTensor:
Out.data = [[[a1, a2], [b1, b2], [p1, p2]],
[[c1, c2], [d1, d2], [e1, e2]]]
)DOC");
}
};
class SequencePadGradOp : public framework::OperatorWithKernel {
public:
using framework::OperatorWithKernel::OperatorWithKernel;
void InferShape(framework::InferShapeContext* ctx) const override {
PADDLE_ENFORCE(ctx->HasInput("X"),
"Input(X) of SequencePadGradOp should not be null.");
PADDLE_ENFORCE(ctx->HasInput(framework::GradVarName("Out")),
"Input(Out@GRAD) of SequencePadGradOp should not be null.");
if (ctx->HasOutput(framework::GradVarName("X"))) {
ctx->SetOutputDim(framework::GradVarName("X"), ctx->GetInputDim("X"));
ctx->ShareLoD("X", /*->*/ framework::GradVarName("X"));
}
}
};
} // namespace operators
} // namespace paddle
namespace ops = paddle::operators;
REGISTER_OPERATOR(sequence_pad, ops::SequencePadOp, ops::SequencePadOpMaker,
paddle::framework::DefaultGradOpDescMaker<true>);
REGISTER_OPERATOR(sequence_pad_grad, ops::SequencePadGradOp);
REGISTER_OP_CPU_KERNEL(
sequence_pad,
ops::SequencePadOpKernel<paddle::platform::CPUDeviceContext, float>,
ops::SequencePadOpKernel<paddle::platform::CPUDeviceContext, double>,
ops::SequencePadOpKernel<paddle::platform::CPUDeviceContext, int>,
ops::SequencePadOpKernel<paddle::platform::CPUDeviceContext, int64_t>);
REGISTER_OP_CPU_KERNEL(
sequence_pad_grad,
ops::SequencePadGradOpKernel<paddle::platform::CPUDeviceContext, float>,
ops::SequencePadGradOpKernel<paddle::platform::CPUDeviceContext, double>,
ops::SequencePadGradOpKernel<paddle::platform::CPUDeviceContext, int>,
ops::SequencePadGradOpKernel<paddle::platform::CPUDeviceContext, int64_t>);
paddle/fluid/operators/sequence_pad_op.cu 0 → 100644
浏览文件 @ ec5cb376
/* Copyright (c) 2018 PaddlePaddle Authors. All Rights Reserved.
Licensed under the Apache License, Version 2.0 (the "License");
you may not use this file except in compliance with the License.
You may obtain a copy of the License at
http://www.apache.org/licenses/LICENSE-2.0
Unless required by applicable law or agreed to in writing, software
distributed under the License is distributed on an "AS IS" BASIS,
WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
See the License for the specific language governing permissions and
limitations under the License. */
#include "paddle/fluid/operators/sequence_pad_op.h"
namespace ops = paddle::operators;
REGISTER_OP_CUDA_KERNEL(
sequence_pad,
ops::SequencePadOpKernel<paddle::platform::CUDADeviceContext, float>,
ops::SequencePadOpKernel<paddle::platform::CUDADeviceContext, double>,
ops::SequencePadOpKernel<paddle::platform::CUDADeviceContext, int>,
ops::SequencePadOpKernel<paddle::platform::CUDADeviceContext, int64_t>);
REGISTER_OP_CUDA_KERNEL(
sequence_pad_grad,
ops::SequencePadGradOpKernel<paddle::platform::CUDADeviceContext, float>,
ops::SequencePadGradOpKernel<paddle::platform::CUDADeviceContext, double>,
ops::SequencePadGradOpKernel<paddle::platform::CUDADeviceContext, int>,
ops::SequencePadGradOpKernel<paddle::platform::CUDADeviceContext, int64_t>);
paddle/fluid/operators/sequence_pad_op.h 0 → 100644
浏览文件 @ ec5cb376
/* Copyright (c) 2018 PaddlePaddle Authors. All Rights Reserved.
Licensed under the Apache License, Version 2.0 (the "License");
you may not use this file except in compliance with the License.
You may obtain a copy of the License at
http://www.apache.org/licenses/LICENSE-2.0
Unless required by applicable law or agreed to in writing, software
distributed under the License is distributed on an "AS IS" BASIS,
WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
See the License for the specific language governing permissions and
limitations under the License. */
#pragma once
#include <vector>
#include "paddle/fluid/framework/op_registry.h"
#include "paddle/fluid/memory/memcpy.h"
#include "paddle/fluid/operators/math/math_function.h"
#include "paddle/fluid/operators/math/sequence_padding.h"
namespace paddle {
namespace operators {
using LoDTensor = framework::LoDTensor;
using LoD = framework::LoD;
template <typename DeviceContext, typename T>
class SequencePadOpKernel : public framework::OpKernel<T> {
public:
void Compute(const framework::ExecutionContext& ctx) const override {
const auto* x = ctx.Input<LoDTensor>("X");
auto* out = ctx.Output<LoDTensor>("Out");
out->mutable_data<T>(ctx.GetPlace());
const auto* pad_value = ctx.Input<LoDTensor>("PadValue");
int padded_length = ctx.Attr<int>("padded_length");
math::PaddingLoDTensorFunctor<DeviceContext, T>()(
ctx.template device_context<DeviceContext>(), *x, out, *pad_value,
padded_length, 0, false, math::kBatchLengthWidth);
}
};
template <typename DeviceContext, typename T>
class SequencePadGradOpKernel : public framework::OpKernel<T> {
public:
void Compute(const framework::ExecutionContext& ctx) const override {
auto* d_x = ctx.Output<LoDTensor>(framework::GradVarName("X"));
if (d_x) {
const auto* d_out = ctx.Input<LoDTensor>(framework::GradVarName("Out"));
d_x->mutable_data<T>(ctx.GetPlace());
int padded_length = ctx.Attr<int>("padded_length");
math::UnpaddingLoDTensorFunctor<DeviceContext, T>()(
ctx.template device_context<DeviceContext>(), *d_out, d_x,
padded_length, 0, false, math::kBatchLengthWidth);
}
}
};
} // namespace operators
} // namespace paddle
paddle/fluid/operators/sequence_softmax_cudnn_op.cu.cc
浏览文件 @ ec5cb376
......@@ -68,7 +68,9 @@ class SequenceSoftmaxGradCUDNNKernel : public framework::OpKernel<T> {
auto* out_grad = ctx.Input<LoDTensor>(framework::GradVarName("Out"));
auto* x = ctx.Input<LoDTensor>("X");
auto* x_grad = ctx.Output<LoDTensor>(framework::GradVarName("X"));
if (x_grad) {
x_grad->set_lod(x->lod());
}
auto lod = x->lod();
const size_t level = lod.size() - 1;
......
paddle/fluid/operators/sequence_softmax_op.h
浏览文件 @ ec5cb376
......@@ -66,6 +66,9 @@ class SequenceSoftmaxGradKernel : public framework::OpKernel<T> {
auto* out_grad = ctx.Input<LoDTensor>(framework::GradVarName("Out"));
auto* x = ctx.Input<LoDTensor>("X");
auto* x_grad = ctx.Output<LoDTensor>(framework::GradVarName("X"));
if (x_grad) {
x_grad->set_lod(x->lod());
}
auto lod = x->lod();
const size_t level = lod.size() - 1;
......
paddle/fluid/operators/stack_op.cc 0 → 100644
浏览文件 @ ec5cb376
// Copyright (c) 2018 PaddlePaddle Authors. All Rights Reserved.
//
// Licensed under the Apache License, Version 2.0 (the "License");
// you may not use this file except in compliance with the License.
// You may obtain a copy of the License at
//
// http://www.apache.org/licenses/LICENSE-2.0
//
// Unless required by applicable law or agreed to in writing, software
// distributed under the License is distributed on an "AS IS" BASIS,
// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
// See the License for the specific language governing permissions and
// limitations under the License.
#include "paddle/fluid/operators/stack_op.h"
namespace plat = paddle::platform;
namespace ops = paddle::operators;
REGISTER_OPERATOR(stack, ops::StackOp, ops::StackOpMaker,
ops::StackGradOpDescMaker);
REGISTER_OPERATOR(stack_grad, ops::StackOpGrad);
REGISTER_OP_CPU_KERNEL(stack, ops::StackKernel<plat::CPUDeviceContext, float>,
ops::StackKernel<plat::CPUDeviceContext, double>);
REGISTER_OP_CPU_KERNEL(stack_grad,
ops::StackGradKernel<plat::CPUDeviceContext, float>,
ops::StackGradKernel<plat::CPUDeviceContext, double>);
paddle/fluid/operators/stack_op.cu 0 → 100644
浏览文件 @ ec5cb376
// Copyright (c) 2018 PaddlePaddle Authors. All Rights Reserved.
//
// Licensed under the Apache License, Version 2.0 (the "License");
// you may not use this file except in compliance with the License.
// You may obtain a copy of the License at
//
// http://www.apache.org/licenses/LICENSE-2.0
//
// Unless required by applicable law or agreed to in writing, software
// distributed under the License is distributed on an "AS IS" BASIS,
// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
// See the License for the specific language governing permissions and
// limitations under the License.
#include "paddle/fluid/operators/stack_op.h"
namespace plat = paddle::platform;
namespace ops = paddle::operators;
REGISTER_OP_CUDA_KERNEL(stack, ops::StackKernel<plat::CUDADeviceContext, float>,
ops::StackKernel<plat::CUDADeviceContext, double>);
REGISTER_OP_CUDA_KERNEL(stack_grad,
ops::StackGradKernel<plat::CUDADeviceContext, float>,
ops::StackGradKernel<plat::CUDADeviceContext, double>);
paddle/fluid/operators/stack_op.h 0 → 100644
浏览文件 @ ec5cb376
// Copyright (c) 2018 PaddlePaddle Authors. All Rights Reserved.
//
// Licensed under the Apache License, Version 2.0 (the "License");
// you may not use this file except in compliance with the License.
// You may obtain a copy of the License at
//
// http://www.apache.org/licenses/LICENSE-2.0
//
// Unless required by applicable law or agreed to in writing, software
// distributed under the License is distributed on an "AS IS" BASIS,
// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
// See the License for the specific language governing permissions and
// limitations under the License.
#pragma once
#include "paddle/fluid/framework/op_registry.h"
#include "paddle/fluid/platform/for_range.h"
#ifdef __NVCC__
#include <thrust/device_vector.h>
#include "paddle/fluid/framework/array.h"
#endif
namespace paddle {
namespace operators {
class StackOp : public framework::OperatorWithKernel {
public:
using framework::OperatorWithKernel::OperatorWithKernel;
void InferShape(framework::InferShapeContext *ctx) const override {
PADDLE_ENFORCE_GT(ctx->Inputs("X").size(), 0,
"Number of Inputs(X) must be larger than 0");
PADDLE_ENFORCE(ctx->HasOutput("Y"), "Output(Y) must exist.");
auto input_dims = ctx->GetInputsDim("X");
for (size_t i = 1; i < input_dims.size(); ++i) {
PADDLE_ENFORCE_EQ(input_dims[i], input_dims[0],
"Dims of all Inputs(X) must be the same");
}
// Only lod of X[0] would be shared with Y
ctx->ShareLoD("X", /*->*/ "Y");
int axis = ctx->Attrs().Get<int>("axis");
int rank = input_dims[0].size();
PADDLE_ENFORCE(
axis >= -(rank + 1) && axis < rank + 1,
"Attr(axis) must be inside [-(rank+1), rank+1), where rank = %d", rank);
if (axis < 0) axis += (rank + 1);
auto vec = framework::vectorize2int(input_dims[0]);
vec.insert(vec.begin() + axis, input_dims.size());
ctx->SetOutputDim("Y", framework::make_ddim(vec));
}
};
class StackOpMaker : public framework::OpProtoAndCheckerMaker {
public:
void Make() override {
AddInput("X", "The input of stack op.").AsDuplicable();
AddOutput("Y", "The output of stack op.");
AddAttr<int>("axis",
"The axis along which all of the Inputs(X) should be stacked.")
.SetDefault(0);
AddComment(R"DOC(
Stack Operator.
Stack all of the Inputs(X) into one tensor along Attr(axis). The dims of all Inputs(X) must be the same.
)DOC");
}
};
template <typename VecXType, typename T>
struct StackFunctor {
HOSTDEVICE StackFunctor(const VecXType &x, T *y, int n, int post)
: x_(x), y_(y), n_(n), post_(post) {}
HOSTDEVICE void operator()(int idx) {
int i = idx / (n_ * post_);
int which_x = idx / post_ - i * n_;
int x_index = i * post_ + idx % post_;
y_[idx] = x_[which_x][x_index];
}
private:
VecXType x_;
T *y_;
int n_;
int post_;
};
template <typename VecDxType, typename T>
struct StackGradFunctor {
HOSTDEVICE StackGradFunctor(const VecDxType &dx, const T *dy, int n, int post)
: dx_(dx), dy_(dy), n_(n), post_(post) {}
HOSTDEVICE void operator()(int idx) {
int i = idx / (n_ * post_);
int which_x = idx / post_ - i * n_;
int x_index = i * post_ + idx % post_;
dx_[which_x][x_index] = dy_[idx];
}
private:
VecDxType dx_;
const T *dy_;
int n_;
int post_;
};
template <typename DeviceContext, typename VecXType, typename T>
static inline void StackFunctorForRange(const DeviceContext &ctx,
const VecXType &x, T *y, int total_num,
int n, int post) {
platform::ForRange<DeviceContext> for_range(ctx, total_num);
for_range(StackFunctor<VecXType, T>(x, y, n, post));
}
template <typename DeviceContext, typename VecDxType, typename T>
static inline void StackGradFunctorForRange(const DeviceContext &ctx,
const VecDxType &dx, const T *dy,
int total_num, int n, int post) {
platform::ForRange<DeviceContext> for_range(ctx, total_num);
for_range(StackGradFunctor<VecDxType, T>(dx, dy, n, post));
}
template <typename DeviceContext, typename T>
class StackKernel : public framework::OpKernel<T> {
using Tensor = framework::LoDTensor;
public:
void Compute(const framework::ExecutionContext &ctx) const override {
auto x = ctx.MultiInput<Tensor>("X");
auto *y = ctx.Output<Tensor>("Y");
int axis = ctx.Attr<int>("axis");
if (axis < 0) axis += (x[0]->dims().size() + 1);
int n = static_cast<int>(x.size());
auto *y_data = y->mutable_data<T>(ctx.GetPlace());
std::vector<const T *> x_datas(n);
for (int i = 0; i < n; i++) x_datas[i] = x[i]->data<T>();
int pre = 1, post = 1;
auto &dim = x[0]->dims();
for (auto i = 0; i < axis; ++i) pre *= dim[i];
for (auto i = axis; i < dim.size(); ++i) post *= dim[i];
int total_num = pre * n * post;
auto &dev_ctx = ctx.template device_context<DeviceContext>();
#ifdef __NVCC__
thrust::device_vector<const T *> device_x_vec(x_datas);
auto x_data_arr = device_x_vec.data().get();
#else
auto x_data_arr = x_datas.data();
#endif
StackFunctorForRange(dev_ctx, x_data_arr, y_data, total_num, n, post);
#ifdef __NVCC__
// Wait() must be called because device_x_vec may be destructed before
// kernel ends
dev_ctx.Wait();
#endif
}
};
class StackOpGrad : public framework::OperatorWithKernel {
public:
using framework::OperatorWithKernel::OperatorWithKernel;
void InferShape(framework::InferShapeContext *ctx) const override {
PADDLE_ENFORCE(ctx->HasInput(framework::GradVarName("Y")),
"Input(Y@Grad) must exist.");
int axis = ctx->Attrs().Get<int>("axis");
auto dy_dim = ctx->GetInputDim(framework::GradVarName("Y"));
int rank = dy_dim.size();
PADDLE_ENFORCE(axis >= -rank && axis < rank,
"Attr(axis) must be inside [-rank, rank), where rank = %d",
rank);
if (axis < 0) axis += rank;
PADDLE_ENFORCE_EQ(ctx->Outputs(framework::GradVarName("X")).size(),
static_cast<size_t>(dy_dim[axis]),
"Number of Outputs(X@Grad) is wrong");
auto vec = framework::vectorize2int(dy_dim);
vec.erase(vec.begin() + axis);
ctx->SetOutputsDim(
framework::GradVarName("X"),
std::vector<framework::DDim>(dy_dim[axis], framework::make_ddim(vec)));
}
};
class StackGradOpDescMaker : public framework::SingleGradOpDescMaker {
public:
using framework::SingleGradOpDescMaker::SingleGradOpDescMaker;
protected:
std::unique_ptr<framework::OpDesc> Apply() const override {
std::unique_ptr<framework::OpDesc> op(new framework::OpDesc());
op->SetType("stack_grad");
op->SetInput(framework::GradVarName("Y"), OutputGrad("Y"));
op->SetOutput(framework::GradVarName("X"), InputGrad("X", false));
op->SetAttrMap(Attrs());
return op;
}
};
template <typename DeviceContext, typename T>
class StackGradKernel : public framework::OpKernel<T> {
using Tensor = framework::LoDTensor;
public:
void Compute(const framework::ExecutionContext &ctx) const override {
auto *dy = ctx.Input<Tensor>(framework::GradVarName("Y"));
auto dx = ctx.MultiOutput<Tensor>(framework::GradVarName("X"));
int axis = ctx.Attr<int>("axis");
if (axis < 0) axis += dy->dims().size();
int n = dy->dims()[axis];
std::vector<T *> dx_datas(n); // NOLINT
for (int i = 0; i < n; i++) {
dx_datas[i] = dx[i]->mutable_data<T>(ctx.GetPlace());
}
auto dy_data = dy->data<T>();
int pre = 1;
for (int i = 0; i < axis; ++i) pre *= dy->dims()[i];
int total_num = dy->numel();
int post = total_num / (n * pre);
auto &dev_ctx = ctx.template device_context<DeviceContext>();
#ifdef __NVCC__
thrust::device_vector<T *> device_dx_vec(dx_datas);
auto dx_data_arr = device_dx_vec.data().get();
#else
auto dx_data_arr = dx_datas.data();
#endif
StackGradFunctorForRange(dev_ctx, dx_data_arr, dy_data, total_num, n, post);
#ifdef __NVCC__
// Wait() must be called because device_dx_vec may be destructed before
// kernel ends
dev_ctx.Wait();
#endif
}
};
} // namespace operators
} // namespace paddle
paddle/fluid/operators/uniform_random_op.cc
浏览文件 @ ec5cb376
......@@ -37,7 +37,7 @@ class CPUUniformRandomKernel : public framework::OpKernel<T> {
} else {
PADDLE_THROW(
"uniform_random_op's output only"
"supports SelectedRows and Tensor");
"supports SelectedRows and LoDTensor");
}
T* data = tensor->mutable_data<T>(ctx.GetPlace());
unsigned int seed = static_cast<unsigned int>(ctx.Attr<int>("seed"));
......
paddle/fluid/operators/uniform_random_op.cu
浏览文件 @ ec5cb376
......@@ -54,7 +54,7 @@ class GPUUniformRandomKernel : public framework::OpKernel<T> {
} else {
PADDLE_THROW(
"uniform_random_op's output only"
"supports SelectedRows and Tensor");
"supports SelectedRows and LoDTensor");
}
T* data = tensor->mutable_data<T>(context.GetPlace());
unsigned int seed = static_cast<unsigned int>(context.Attr<int>("seed"));
......
paddle/fluid/operators/unstack_op.cc 0 → 100644
浏览文件 @ ec5cb376
// Copyright (c) 2018 PaddlePaddle Authors. All Rights Reserved.
//
// Licensed under the Apache License, Version 2.0 (the "License");
// you may not use this file except in compliance with the License.
// You may obtain a copy of the License at
//
// http://www.apache.org/licenses/LICENSE-2.0
//
// Unless required by applicable law or agreed to in writing, software
// distributed under the License is distributed on an "AS IS" BASIS,
// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
// See the License for the specific language governing permissions and
// limitations under the License.
#include "paddle/fluid/operators/unstack_op.h"
namespace plat = paddle::platform;
namespace ops = paddle::operators;
USE_OP(stack);
REGISTER_OPERATOR(unstack, ops::UnStackOp, ops::UnStackOpMaker,
ops::UnStackOpInferShape, ops::UnStackGradOpDescMaker);
REGISTER_OPERATOR(unstack_grad, ops::UnStackGradOp,
ops::UnStackOpGradInferShape);
paddle/fluid/operators/unstack_op.h 0 → 100644
浏览文件 @ ec5cb376
// Copyright (c) 2018 PaddlePaddle Authors. All Rights Reserved.
//
// Licensed under the Apache License, Version 2.0 (the "License");
// you may not use this file except in compliance with the License.
// You may obtain a copy of the License at
//
// http://www.apache.org/licenses/LICENSE-2.0
//
// Unless required by applicable law or agreed to in writing, software
// distributed under the License is distributed on an "AS IS" BASIS,
// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
// See the License for the specific language governing permissions and
// limitations under the License.
#pragma once
#include "paddle/fluid/framework/op_registry.h"
namespace paddle {
namespace operators {
class UnStackOpInferShape : public framework::InferShapeBase {
public:
void operator()(framework::InferShapeContext *ctx) const override {
PADDLE_ENFORCE(ctx->HasInput("X"), "Input(X) must exist.");
int axis = ctx->Attrs().Get<int>("axis");
int num = ctx->Attrs().Get<int>("num");
auto x_dim = ctx->GetInputDim("X");
int rank = x_dim.size();
PADDLE_ENFORCE(axis >= -rank && axis < rank,
"Attr(axis) must be inside [-rank, rank), where rank = %d",
rank);
if (axis < 0) axis += rank;
PADDLE_ENFORCE_EQ(ctx->Outputs("Y").size(), static_cast<size_t>(num),
"Number of Outputs(Y) is wrong");
if (x_dim[axis] > 0) {
PADDLE_ENFORCE_EQ(num, x_dim[axis], "Number of Outputs(Y) is wrong");
}
auto vec = framework::vectorize2int(x_dim);
vec.erase(vec.begin() + axis);
ctx->SetOutputsDim("Y", std::vector<framework::DDim>( // NOLINT
x_dim[axis], framework::make_ddim(vec)));
}
};
class UnStackOpMaker : public framework::OpProtoAndCheckerMaker {
public:
void Make() override {
AddInput("X", "The input of unstack op.");
AddOutput("Y", "The output of unstack op.").AsDuplicable();
AddAttr<int>("axis", "The axis along which Input(X) should be unstacked.")
.SetDefault(0);
AddAttr<int>("num", "The number of outputs(Y).").GreaterThan(0);
AddComment(R"DOC(
UnStack Operator.
UnStack Input(X) into several tensors along Attr(axis).
)DOC");
}
};
class UnStackOp : public framework::OperatorBase {
public:
using OperatorBase::OperatorBase;
private:
void RunImpl(const framework::Scope &scope,
const platform::Place &place) const override {
auto stack_grad_op = framework::OpRegistry::CreateOp(
"stack_grad", {{framework::GradVarName("Y"), {Input("X")}}},
{{framework::GradVarName("X"), Outputs("Y")}}, Attrs());
stack_grad_op->Run(scope, place);
}
};
class UnStackOpGradInferShape : public framework::InferShapeBase {
public:
void operator()(framework::InferShapeContext *ctx) const override {
PADDLE_ENFORCE_GT(ctx->Inputs(framework::GradVarName("Y")).size(), 0,
"Number of Inputs(Y@Grad) must be larger than 0");
PADDLE_ENFORCE(ctx->HasOutput(framework::GradVarName("X")),
"Output(X@Grad) must exist.");
auto input_dims = ctx->GetInputsDim(framework::GradVarName("Y"));
for (size_t i = 1; i < input_dims.size(); ++i) {
PADDLE_ENFORCE_EQ(input_dims[i], input_dims[0],
"Dims of all Inputs(Y@Grad) must be the same");
}
int axis = ctx->Attrs().Get<int>("axis");
int rank = input_dims[0].size();
PADDLE_ENFORCE(
axis >= -(rank + 1) && axis < rank + 1,
"Attr(axis) must be inside [-(rank+1), rank+1), where rank = %d", rank);
if (axis < 0) axis += (rank + 1);
auto vec = framework::vectorize2int(input_dims[0]);
vec.insert(vec.begin() + axis, input_dims.size());
ctx->SetOutputDim(framework::GradVarName("X"), framework::make_ddim(vec));
}
};
class UnStackGradOpDescMaker : public framework::SingleGradOpDescMaker {
public:
using framework::SingleGradOpDescMaker::SingleGradOpDescMaker;
protected:
std::unique_ptr<framework::OpDesc> Apply() const override {
std::unique_ptr<framework::OpDesc> op(new framework::OpDesc());
op->SetType("unstack_grad");
op->SetInput(framework::GradVarName("Y"), OutputGrad("Y"));
op->SetOutput(framework::GradVarName("X"), InputGrad("X"));
op->SetAttrMap(Attrs());
return op;
}
};
class UnStackGradOp : public framework::OperatorBase {
public:
using OperatorBase::OperatorBase;
private:
void RunImpl(const framework::Scope &scope,
const platform::Place &place) const override {
auto stack_op = framework::OpRegistry::CreateOp(
"stack", {{"X", Inputs(framework::GradVarName("Y"))}},
{{"Y", {Output(framework::GradVarName("X"))}}}, Attrs());
stack_op->Run(scope, place);
}
};
} // namespace operators
} // namespace paddle
paddle/fluid/operators/warpctc_op.h
浏览文件 @ ec5cb376
......@@ -153,17 +153,29 @@ class WarpCTCKernel : public framework::OpKernel<T> {
framework::make_ddim({static_cast<int64_t>(num_sequences), 1});
// warpctc needs sequences data stored in transposed padding format
Tensor warpctc_logits;
LoDTensor warpctc_logits;
const size_t max_sequence_length =
math::MaximumSequenceLength(logits_lod, level);
math::MaximumSequenceLength(logits_lod[level]);
auto warpctc_logits_dims =
framework::make_ddim({static_cast<int64_t>(max_sequence_length),
static_cast<int64_t>(num_sequences),
static_cast<int64_t>(sequence_width)});
warpctc_logits.mutable_data<T>(warpctc_logits_dims, ctx.GetPlace());
LoDTensor cpu_pad_value;
T* pad_value_data =
cpu_pad_value.mutable_data<T>({1}, platform::CPUPlace());
*pad_value_data = static_cast<T>(0);
LoDTensor pad_value;
if (platform::is_cpu_place(ctx.GetPlace())) {
pad_value = cpu_pad_value;
} else {
TensorCopySync(cpu_pad_value, ctx.GetPlace(), &pad_value);
}
math::PaddingLoDTensorFunctor<DeviceContext, T>()(
ctx.template device_context<DeviceContext>(), *logits, &warpctc_logits,
false);
pad_value, -1, 0, false /* norm_by_times */, math::kLengthBatchWidth);
const T* warpctc_logits_data = warpctc_logits.data<T>();
std::vector<int> warpctc_label_lengths(num_sequences);
......@@ -209,15 +221,15 @@ template <typename DeviceContext, typename T>
class WarpCTCGradKernel : public framework::OpKernel<T> {
public:
void Compute(const framework::ExecutionContext& ctx) const override {
auto* warpctc_grad = ctx.Input<Tensor>("WarpCTCGrad");
auto* warpctc_grad = ctx.Input<LoDTensor>("WarpCTCGrad");
auto* logits_grad = ctx.Output<LoDTensor>(framework::GradVarName("Logits"));
const Tensor* loss_grad = ctx.Input<Tensor>(framework::GradVarName("Loss"));
logits_grad->mutable_data<T>(ctx.GetPlace());
bool norm_by_times = ctx.Attr<bool>("norm_by_times");
math::UnpaddingLoDTensorFunctor<DeviceContext, T>()(
ctx.template device_context<DeviceContext>(), logits_grad,
*warpctc_grad, norm_by_times);
ctx.template device_context<DeviceContext>(), *warpctc_grad,
logits_grad, -1, 0, norm_by_times, math::kLengthBatchWidth);
const T* loss_grad_data = loss_grad->data<T>();
math::ScaleLoDTensorFunctor<DeviceContext, T>()(
......
paddle/fluid/operators/while_op.cc
浏览文件 @ ec5cb376
......@@ -58,11 +58,15 @@ class WhileOp : public framework::OperatorBase {
PADDLE_ENFORCE(platform::is_cpu_place(cond.place()),
"Condition of while op must in CPU memory.");
bool is_test = Attr<bool>("is_test");
auto ctx = executor.Prepare(*program, block->ID());
while (cond.data<bool>()[0]) {
auto &current_scope = scope.NewScope();
step_scopes->push_back(&current_scope);
executor.RunPreparedContext(ctx.get(), &current_scope, false);
if (is_test) {
scope.DeleteScope(&current_scope);
}
}
}
};
......@@ -88,6 +92,7 @@ class WhileOpMaker : public framework::OpProtoAndCheckerMaker {
"variables generated in the i'th step.");
AddAttr<framework::BlockDesc *>(kStepBlock,
"The step block inside WhileOp");
AddAttr<bool>("is_test", "True if in test phase.").SetDefault(false);
AddComment(R"DOC(
)DOC");
}
......@@ -103,6 +108,8 @@ class WhileGradOp : public framework::OperatorBase {
private:
void RunImpl(const framework::Scope &scope,
const platform::Place &dev_place) const override {
PADDLE_ENFORCE(!Attr<bool>("is_test"),
"GradOp is only callable when is_test is false");
// get device context from pool
platform::DeviceContextPool &pool = platform::DeviceContextPool::Instance();
auto &dev_ctx = *pool.Get(dev_place);
......
paddle/fluid/platform/CMakeLists.txt
浏览文件 @ ec5cb376
if (NOT WIN32)
proto_library(profiler_proto SRCS profiler.proto DEPS framework_proto)
py_proto_compile(profiler_py_proto SRCS profiler.proto)
......@@ -10,6 +11,7 @@ add_custom_command(TARGET profiler_py_proto POST_BUILD
COMMAND cp *.py ${PADDLE_BINARY_DIR}/python/paddle/fluid/proto/profiler
COMMENT "Copy generated python proto into directory paddle/fluid/proto/profiler."
WORKING_DIRECTORY ${CMAKE_CURRENT_BINARY_DIR})
endif(NOT WIN32)
if(WITH_GPU)
nv_library(enforce SRCS enforce.cc)
......@@ -50,7 +52,7 @@ ENDIF()
# memcpy depends on device_context, here add deps individually for
# avoiding cycle dependencies
cc_library(device_context SRCS device_context.cc init.cc DEPS malloc
place eigen3 stringpiece cpu_helper framework_proto ${GPU_CTX_DEPS} ${MKLDNN_CTX_DEPS})
place eigen3 stringpiece cpu_helper cpu_info framework_proto ${GPU_CTX_DEPS} ${MKLDNN_CTX_DEPS})
nv_test(device_context_test SRCS device_context_test.cu DEPS device_context gpu_info)
cc_test(init_test SRCS init_test.cc DEPS device_context)
......@@ -58,9 +60,12 @@ cc_test(init_test SRCS init_test.cc DEPS device_context)
nv_test(cudnn_helper_test SRCS cudnn_helper_test.cc DEPS dynload_cuda)
nv_test(transform_test SRCS transform_test.cu DEPS memory place device_context)
if (NOT WIN32)
cc_library(device_tracer SRCS device_tracer.cc DEPS boost profiler_proto framework_proto ${GPU_CTX_DEPS})
cc_library(profiler SRCS profiler.cc DEPS device_context device_tracer)
cc_test(profiler_test SRCS profiler_test.cc DEPS profiler)
endif(NOT WIN32)
nv_test(float16_gpu_test SRCS float16_test.cu DEPS lod_tensor)
cc_test(float16_test SRCS float16_test.cc DEPS lod_tensor)
......
paddle/fluid/platform/cpu_info.cc
浏览文件 @ ec5cb376
......@@ -22,9 +22,13 @@ limitations under the License. */
#ifdef __APPLE__
#include <sys/sysctl.h>
#include <sys/types.h>
#elif defined(_WIN32)
#define NOMINMAX // msvc max/min macro conflict with std::min/max
#include <windows.h>
#else
#include <unistd.h>
#endif
#endif // _WIN32
#include <algorithm>
#include "gflags/gflags.h"
......@@ -32,16 +36,20 @@ limitations under the License. */
DEFINE_double(fraction_of_cpu_memory_to_use, 1,
"Default use 100% of CPU memory for PaddlePaddle,"
"reserve the rest for page tables, etc");
#if !defined(_WIN32)
DEFINE_uint64(initial_cpu_memory_in_mb,
#ifdef PADDLE_WITH_MKLDNN
/* Aligned with mozga-intel, MKLDNN need at least 5000 MB
* to obtain the best performance*/
5000,
5000ul,
#else
500,
500ul,
#endif
"Initial CPU memory for PaddlePaddle, in MD unit.");
#else
DEFINE_uint64(initial_cpu_memory_in_mb, 500ul,
"Initial CPU memory for PaddlePaddle, in MD unit.");
#endif // !defined(_WIN32)
DEFINE_double(
fraction_of_cuda_pinned_memory_to_use, 0.5,
......@@ -60,6 +68,11 @@ inline size_t CpuTotalPhysicalMemory() {
size_t len = sizeof(size);
if (sysctl(mib, 2, &size, &len, NULL, 0) == 0) return (size_t)size;
return 0L;
#elif defined(_WIN32)
MEMORYSTATUSEX sMeminfo;
sMeminfo.dwLength = sizeof(sMeminfo);
GlobalMemoryStatusEx(&sMeminfo);
return sMeminfo.ullTotalPhys;
#else
int64_t pages = sysconf(_SC_PHYS_PAGES);
int64_t page_size = sysconf(_SC_PAGE_SIZE);
......@@ -103,15 +116,16 @@ size_t CUDAPinnedMaxChunkSize() {
return CUDAPinnedMaxAllocSize() / 256;
}
#ifdef PADDLE_WITH_XBYAK
namespace jit {
#ifdef PADDLE_WITH_XBYAK
static Xbyak::util::Cpu cpu;
bool MayIUse(const cpu_isa_t cpu_isa) {
using namespace Xbyak::util; // NOLINT
switch (cpu_isa) {
case sse42:
return cpu.has(Cpu::tSSE42);
case avx:
return cpu.has(Cpu::tAVX);
case avx2:
return cpu.has(Cpu::tAVX2);
case avx512_common:
......@@ -134,8 +148,16 @@ bool MayIUse(const cpu_isa_t cpu_isa) {
}
return false;
}
#else
bool MayIUse(const cpu_isa_t cpu_isa) {
if (cpu_isa == isa_any) {
return true;
} else {
return false;
}
}
#endif
} // namespace jit
#endif
} // namespace platform
} // namespace paddle
paddle/fluid/platform/cpu_info.h
浏览文件 @ ec5cb376
......@@ -37,12 +37,11 @@ size_t CUDAPinnedMinChunkSize();
//! Get the maximum chunk size for buddy allocator.
size_t CUDAPinnedMaxChunkSize();
#ifdef PADDLE_WITH_XBYAK
namespace jit {
typedef enum {
isa_any,
sse42,
avx,
avx2,
avx512_common,
avx512_core,
......@@ -52,10 +51,9 @@ typedef enum {
} cpu_isa_t; // Instruction set architecture
// May I use some instruction
inline bool MayIUse(const cpu_isa_t cpu_isa);
bool MayIUse(const cpu_isa_t cpu_isa);
} // namespace jit
#endif
} // namespace platform
} // namespace paddle
paddle/fluid/platform/device_tracer.h
浏览文件 @ ec5cb376
......@@ -13,7 +13,12 @@ See the License for the specific language governing permissions and
limitations under the License. */
#pragma once
#if !defined(_WIN32)
#include <sys/time.h>
#else
#include <windows.h>
#endif // !_WIN32
#include <time.h>
#include <chrono> // NOLINT
#include <string>
......@@ -27,12 +32,15 @@ namespace platform {
///////////////////////
// WARN: Under Development. Don't depend on it yet.
//////////////////////
#if !defined(_WIN32)
inline uint64_t PosixInNsec() {
struct timeval tv;
gettimeofday(&tv, nullptr);
return 1000 * (static_cast<uint64_t>(tv.tv_sec) * 1000000 + tv.tv_usec);
}
#else
inline uint64_t PosixInNsec() { return static_cast<uint64_t>(0); }
#endif // !_WIN32
// DeviceTracer performs the following tasks:
// 1. Register cuda callbacks for various events: kernel, memcpy, etc.
......
paddle/fluid/platform/dynload/CMakeLists.txt
浏览文件 @ ec5cb376
......@@ -16,7 +16,9 @@ if (CUPTI_FOUND)
list(APPEND CUDA_SRCS cupti.cc)
endif(CUPTI_FOUND)
nv_library(dynload_cuda SRCS ${CUDA_SRCS} DEPS dynamic_loader)
if (NOT WIN32)
cc_library(dynload_warpctc SRCS warpctc.cc DEPS dynamic_loader warpctc)
endif(NOT WIN32)
if (WITH_MKLML)
cc_library(dynload_mklml SRCS mklml.cc DEPS dynamic_loader mklml)
endif()
......
paddle/fluid/platform/dynload/dynamic_loader.cc
浏览文件 @ ec5cb376
......@@ -13,8 +13,6 @@ See the License for the specific language governing permissions and
limitations under the License. */
#include "paddle/fluid/platform/dynload/dynamic_loader.h"
#include <dlfcn.h>
#include <memory>
#include <mutex> // NOLINT
#include <string>
......@@ -23,6 +21,7 @@ limitations under the License. */
#include "glog/logging.h"
#include "paddle/fluid/platform/dynload/cupti_lib_path.h"
#include "paddle/fluid/platform/enforce.h"
#include "paddle/fluid/platform/port.h"
DEFINE_string(cudnn_dir, "",
"Specify path for loading libcudnn.so. For instance, "
......
paddle/fluid/platform/dynload/mklml.h
浏览文件 @ ec5cb376
......@@ -66,10 +66,16 @@ extern void* mklml_dso_handle;
__macro(cblas_dgemm_free); \
__macro(cblas_sgemm_batch); \
__macro(cblas_dgemm_batch); \
__macro(cblas_sdot); \
__macro(cblas_ddot); \
__macro(cblas_sscal); \
__macro(cblas_dscal); \
__macro(vsAdd); \
__macro(vdAdd); \
__macro(vsMul); \
__macro(vdMul); \
__macro(vsExp); \
__macro(vdExp); \
__macro(MKL_Set_Num_Threads)
MKLML_ROUTINE_EACH(DECLARE_DYNAMIC_LOAD_MKLML_WRAP);
......
paddle/fluid/platform/enforce.h
浏览文件 @ ec5cb376
......@@ -18,6 +18,11 @@ limitations under the License. */
#include <cxxabi.h> // for __cxa_demangle
#endif // __GNUC__
#if defined(_WIN32)
#define NOMINMAX // msvc max/min macro conflict with std::min/max
#define GLOG_NO_ABBREVIATED_SEVERITIES // msvc conflict logging with windows.h
#endif
#ifdef PADDLE_WITH_CUDA
#include <cublas_v2.h>
#include <cudnn.h>
......@@ -117,7 +122,12 @@ struct EOFException : public std::exception {
// always forces branch prediction of true.
// This generates faster binary code. __builtin_expect is since C++11.
// For more details, please check https://stackoverflow.com/a/43870188/724872.
#if !defined(_WIN32)
#define UNLIKELY(condition) __builtin_expect(static_cast<bool>(condition), 0)
#else
// there is no equivalent intrinsics in msvc.
#define UNLIKELY(condition) (condition == 0)
#endif
template <typename... Args>
inline typename std::enable_if<sizeof...(Args) != 0, void>::type throw_on_error(
......@@ -230,6 +240,7 @@ inline void throw_on_error(T e) {
throw_on_error(e, "");
}
#if !defined(_WIN32)
#define PADDLE_THROW(...) \
do { \
throw ::paddle::platform::EnforceNotMet( \
......@@ -248,15 +259,28 @@ inline void throw_on_error(T e) {
__FILE__, __LINE__); \
} \
} while (false)
#else
#define PADDLE_ENFORCE(...) ::paddle::platform::throw_on_error(__VA_ARGS__);
#endif
#define PADDLE_THROW_EOF() \
do { \
throw ::paddle::platform::EOFException("There is no next data.", __FILE__, \
__LINE__); \
} while (false)
#else
#define PADDLE_ENFORCE(...) ::paddle::platform::throw_on_error(__VA_ARGS__)
#endif // REPLACE_ENFORCE_GLOG
#else // !_WIN32
// disable enforce, caused by the varardic macro exception error
#define PADDLE_THROW(x) \
do { \
throw std::make_exception_ptr( \
std::runtime_error("Windows disable the enforce.")); \
} while (false)
#define PADDLE_ENFORCE(x, ...) x
#endif // !_WIN32
/*
* Some enforce helpers here, usage:
* int a = 1;
......
paddle/fluid/platform/float16.h
浏览文件 @ ec5cb376
......@@ -56,7 +56,11 @@ limitations under the License. */
#include <immintrin.h>
#endif // PADDLE_ARM
#if !defined(_WIN32)
#define PADDLE_ALIGN(x) __attribute__((aligned(x)))
#else
#define PADDLE_ALIGN(x) /*do nothing*/
#endif
namespace paddle {
namespace platform {
......
paddle/fluid/platform/init.cc
浏览文件 @ ec5cb376
......@@ -18,6 +18,7 @@ limitations under the License. */
#include "paddle/fluid/framework/operator.h"
#include "paddle/fluid/platform/cpu_helper.h"
#include "paddle/fluid/platform/cpu_info.h"
#include "paddle/fluid/platform/device_context.h"
#include "paddle/fluid/platform/init.h"
#include "paddle/fluid/platform/place.h"
......@@ -120,6 +121,22 @@ void InitDevices(bool init_p2p, const std::vector<int> devices) {
#ifndef PADDLE_WITH_MKLDNN
platform::SetNumThreads(FLAGS_paddle_num_threads);
#endif
if (platform::jit::MayIUse(platform::jit::avx512_common)) {
#ifndef __AVX512F__
LOG(WARNING) << "AVX512F is available, Please re-compile on local machine";
#endif
}
if (platform::jit::MayIUse(platform::jit::avx2)) {
#ifndef __AVX2__
LOG(WARNING) << "AVX2 is available, Please re-compile on local machine";
#endif
}
if (platform::jit::MayIUse(platform::jit::avx)) {
#ifndef __AVX__
LOG(WARNING) << "AVX is available, Please re-compile on local machine";
#endif
}
}
void InitGLOG(const std::string &prog_name) {
......
paddle/fluid/platform/profiler.h
浏览文件 @ ec5cb376
......@@ -69,6 +69,7 @@ void PushEvent(const std::string& name, const DeviceContext* dev_ctx);
void PopEvent(const std::string& name, const DeviceContext* dev_ctx);
#if !defined(_WIN32)
struct RecordEvent {
RecordEvent(const std::string& name, const DeviceContext* dev_ctx);
......@@ -94,6 +95,15 @@ struct RecordBlock {
std::string name_;
uint64_t start_ns_;
};
#else
// windows do not support profiler temporarily.
struct RecordEvent {
RecordEvent(const std::string& name, const DeviceContext* dev_ctx) {}
};
struct RecordBlock {
explicit RecordBlock(int block_id) {}
};
#endif
// Return the event list of all threads. Assumed the returned value calls
// event_lists, event_lists[i][j] represents the j-th Event of i-th thread.
......
paddle/fluid/pybind/CMakeLists.txt
浏览文件 @ ec5cb376
set(PYBIND_DEPS pybind python proto_desc memory executor prune profiler feed_fetch_method
)
set(PYBIND_DEPS pybind python proto_desc memory executor prune feed_fetch_method)
set(PYBIND_SRCS pybind.cc exception.cc protobuf.cc const_value.cc)
if(NOT WIN32)
list(APPEND PYBIND_DEPS parallel_executor)
list(APPEND PYBIND_DEPS parallel_executor profiler)
list(APPEND PYBIND_SRCS recordio.cc)
endif()
if(WITH_PYTHON)
if(WITH_AMD_GPU)
hip_library(paddle_pybind SHARED
SRCS pybind.cc exception.cc protobuf.cc const_value.cc recordio.cc
SRCS ${PYBIND_SRCS}
DEPS ${PYBIND_DEPS}
${GLOB_OP_LIB})
else()
cc_library(paddle_pybind SHARED
SRCS pybind.cc exception.cc protobuf.cc const_value.cc recordio.cc
SRCS ${PYBIND_SRCS}
DEPS ${PYBIND_DEPS}
${GLOB_OP_LIB})
if(NOT APPLE AND NOT ANDROID AND NOT WIN32)
......
paddle/fluid/pybind/const_value.cc
浏览文件 @ ec5cb376
......@@ -13,7 +13,8 @@ See the License for the specific language governing permissions and
limitations under the License. */
#include "paddle/fluid/pybind/const_value.h"
#include <paddle/fluid/framework/op_proto_maker.h>
#include "paddle/fluid/framework/ir/node.h"
#include "paddle/fluid/framework/op_proto_maker.h"
#include "paddle/fluid/framework/operator.h"
namespace paddle {
......@@ -24,6 +25,8 @@ void BindConstValue(pybind11::module* m) {
m->def("kTempVarName", [] { return framework::kTempVarName; });
m->def("kGradVarSuffix", [] { return framework::kGradVarSuffix; });
m->def("kZeroVarSuffix", [] { return framework::kZeroVarSuffix; });
m->def("kControlDepVarName",
[] { return framework::ir::Node::kControlDepVarName; });
auto op_proto_and_checker_maker =
m->def_submodule("op_proto_and_checker_maker");
......
paddle/fluid/pybind/protobuf.cc
浏览文件 @ ec5cb376
......@@ -234,6 +234,7 @@ void BindVarDsec(pybind11::module *m) {
pybind11::enum_<pd::proto::VarType::Type>(var_desc, "VarType", "")
.value("BOOL", pd::proto::VarType::BOOL)
.value("UINT8", pd::proto::VarType::UINT8)
.value("INT8", pd::proto::VarType::INT8)
.value("INT16", pd::proto::VarType::INT16)
.value("INT32", pd::proto::VarType::INT32)
.value("INT64", pd::proto::VarType::INT64)
......
paddle/fluid/pybind/pybind.cc
浏览文件 @ ec5cb376
......@@ -130,6 +130,7 @@ PYBIND11_PLUGIN(core) {
.def("set", PyCPUTensorSetFromArray<bool>)
.def("set", PyCPUTensorSetFromArray<uint16_t>)
.def("set", PyCPUTensorSetFromArray<uint8_t>)
.def("set", PyCPUTensorSetFromArray<int8_t>)
#ifdef PADDLE_WITH_CUDA
.def("set", PyCUDATensorSetFromArray<float>)
.def("set", PyCUDATensorSetFromArray<int>)
......@@ -138,6 +139,7 @@ PYBIND11_PLUGIN(core) {
.def("set", PyCUDATensorSetFromArray<bool>)
.def("set", PyCUDATensorSetFromArray<uint16_t>)
.def("set", PyCUDATensorSetFromArray<uint8_t>)
.def("set", PyCUDATensorSetFromArray<int8_t>)
.def("set", PyCUDAPinnedTensorSetFromArray<float>)
.def("set", PyCUDAPinnedTensorSetFromArray<int>)
.def("set", PyCUDAPinnedTensorSetFromArray<double>)
......@@ -145,6 +147,7 @@ PYBIND11_PLUGIN(core) {
.def("set", PyCUDAPinnedTensorSetFromArray<bool>)
.def("set", PyCUDAPinnedTensorSetFromArray<uint16_t>)
.def("set", PyCUDAPinnedTensorSetFromArray<uint8_t>)
.def("set", PyCUDAPinnedTensorSetFromArray<int8_t>)
#endif
.def("shape", [](Tensor &self) { return vectorize(self.dims()); })
.def("_set_float_element", TensorSetElement<float>)
......
paddle/fluid/pybind/tensor_py.h
浏览文件 @ ec5cb376
......@@ -97,7 +97,7 @@ struct CastToPyBufferImpl<true, I, ARGS...> {
inline pybind11::buffer_info CastToPyBuffer(const framework::Tensor &tensor) {
auto buffer_info =
details::CastToPyBufferImpl<true, 0, float, int, double, int64_t, bool,
uint8_t, platform::float16>()(tensor);
uint8_t, int8_t, platform::float16>()(tensor);
return buffer_info;
}
......
paddle/scripts/paddle_build.sh
浏览文件 @ ec5cb376
......@@ -116,7 +116,6 @@ function cmake_gen() {
-DCMAKE_EXPORT_COMPILE_COMMANDS=ON
-DWITH_CONTRIB=${WITH_CONTRIB:-ON}
-DWITH_ANAKIN=${WITH_ANAKIN:-OFF}
-DWITH_INFERENCE_DEMO=${WITH_INFERENCE_DEMO:-ON}
-DPY_VERSION=${PY_VERSION:-2.7}
========================================
EOF
......@@ -146,7 +145,6 @@ EOF
-DCMAKE_EXPORT_COMPILE_COMMANDS=ON \
-DWITH_CONTRIB=${WITH_CONTRIB:-ON} \
-DWITH_ANAKIN=${WITH_ANAKIN:-OFF} \
-DWITH_INFERENCE_DEMO=${WITH_INFERENCE_DEMO:-ON} \
-DPY_VERSION=${PY_VERSION:-2.7}
}
......@@ -330,14 +328,25 @@ function assert_api_not_changed() {
source .env/bin/activate
pip install ${PADDLE_ROOT}/build/python/dist/*whl
python ${PADDLE_ROOT}/tools/print_signatures.py paddle.fluid > new.spec
if [ "$1" == "cp35-cp35m" ]; then
# Use sed to make python2 and python3 sepc keeps the same
sed -i 's/arg0: str/arg0: unicode/g' new.spec
sed -i "s/\(.*Transpiler.*\).__init__ ArgSpec(args=\['self'].*/\1.__init__ /g" new.spec
fi
python ${PADDLE_ROOT}/tools/diff_api.py ${PADDLE_ROOT}/paddle/fluid/API.spec new.spec
deactivate
}
function assert_api_spec_approvals() {
if [ -z ${BRANCH} ]; then
BRANCH="develop"
fi
API_CHANGE=`git diff --name-only upstream/develop | grep "paddle/fluid/API.spec" || true`
API_CHANGE=`git diff --name-only upstream/$BRANCH | grep "paddle/fluid/API.spec" || true`
echo "checking API.spec change, PR: ${GIT_PR_ID}, changes: ${API_CHANGE}"
if [ ${API_CHANGE} ] && [ "${GIT_PR_ID}" != "" ]; then
# TODO: curl -H 'Authorization: token ${TOKEN}'
APPROVALS=`curl -H "Authorization: token ${GITHUB_API_TOKEN}" https://api.github.com/repos/PaddlePaddle/Paddle/pulls/${GIT_PR_ID}/reviews | \
# NOTE: per_page=10000 should be ok for all cases, a PR review > 10000 is not human readable.
APPROVALS=`curl -H "Authorization: token ${GITHUB_API_TOKEN}" https://api.github.com/repos/PaddlePaddle/Paddle/pulls/${GIT_PR_ID}/reviews?per_page=10000 | \
python ${PADDLE_ROOT}/tools/check_pr_approval.py 2 7845005 2887803 728699 13348433`
echo "current pr ${GIT_PR_ID} got approvals: ${APPROVALS}"
if [ "${APPROVALS}" == "FALSE" ]; then
......@@ -619,11 +628,12 @@ function main() {
cicheck)
cmake_gen ${PYTHON_ABI:-""}
build
assert_api_not_changed ${PYTHON_ABI:-""}
run_test
gen_capi_package
gen_fluid_inference_lib
test_fluid_inference_lib
assert_api_not_changed
assert_api_spec_approvals
;;
*)
print_usage
......
python/paddle/dataset/common.py
浏览文件 @ ec5cb376
......@@ -19,6 +19,7 @@ import hashlib
import os
import errno
import shutil
import six
import sys
import importlib
import paddle.dataset
......@@ -94,6 +95,8 @@ def download(url, module_name, md5sum, save_name=None):
dl = 0
total_length = int(total_length)
for data in r.iter_content(chunk_size=4096):
if six.PY2:
data = six.b(data)
dl += len(data)
f.write(data)
done = int(50 * dl / total_length)
......
python/paddle/dataset/flowers.py
浏览文件 @ ec5cb376
......@@ -35,20 +35,22 @@ import itertools
import functools
from .common import download
import tarfile
import six
import scipy.io as scio
from paddle.dataset.image import *
from paddle.reader import *
import os
import numpy as np
from multiprocessing import cpu_count
import six
from six.moves import cPickle as pickle
from six.moves import zip
__all__ = ['train', 'test', 'valid']
DATA_URL = 'http://www.robots.ox.ac.uk/~vgg/data/flowers/102/102flowers.tgz'
LABEL_URL = 'http://www.robots.ox.ac.uk/~vgg/data/flowers/102/imagelabels.mat'
SETID_URL = 'http://www.robots.ox.ac.uk/~vgg/data/flowers/102/setid.mat'
DATA_MD5 = '33bfc11892f1e405ca193ae9a9f2a118'
DATA_URL = 'http://paddlemodels.cdn.bcebos.com/flowers/102flowers.tgz'
LABEL_URL = 'http://paddlemodels.cdn.bcebos.com/flowers/imagelabels.mat'
SETID_URL = 'http://paddlemodels.cdn.bcebos.com/flowers/setid.mat'
DATA_MD5 = '52808999861908f626f3c1f4e79d11fa'
LABEL_MD5 = 'e0620be6f572b9609742df49c70aed4d'
SETID_MD5 = 'a5357ecc9cb78c4bef273ce3793fc85c'
# In official 'readme', tstid is the flag of test data
......@@ -120,7 +122,10 @@ def reader_creator(data_file,
file = file.strip()
batch = None
with open(file, 'rb') as f:
if six.PY2:
batch = pickle.load(f)
else:
batch = pickle.load(f, encoding='bytes')
data = batch['data']
labels = batch['label']
for sample, label in zip(data, batch['label']):
......
python/paddle/dataset/image.py
浏览文件 @ ec5cb376
......@@ -36,11 +36,6 @@ import numpy as np
try:
import cv2
except ImportError:
import sys
sys.stderr.write(
'''Warning with paddle image module: opencv-python should be imported,
or paddle image module could NOT work; please install opencv-python first.'''
)
cv2 = None
import os
import tarfile
......@@ -53,6 +48,18 @@ __all__ = [
]
def _check_cv2():
if cv2 is None:
import sys
sys.stderr.write(
'''Warning with paddle image module: opencv-python should be imported,
or paddle image module could NOT work; please install opencv-python first.'''
)
return False
else:
return True
def batch_images_from_tar(data_file,
dataset_name,
img2label,
......@@ -134,7 +141,7 @@ def load_image_bytes(bytes, is_color=True):
load and return a gray image.
:type is_color: bool
"""
assert cv2 is not None
assert _check_cv2() is True
flag = 1 if is_color else 0
file_bytes = np.asarray(bytearray(bytes), dtype=np.uint8)
......@@ -159,7 +166,7 @@ def load_image(file, is_color=True):
load and return a gray image.
:type is_color: bool
"""
assert cv2 is not None
assert _check_cv2() is True
# cv2.IMAGE_COLOR for OpenCV3
# cv2.CV_LOAD_IMAGE_COLOR for older OpenCV Version
......@@ -188,7 +195,7 @@ def resize_short(im, size):
:param size: the shorter edge size of image after resizing.
:type size: int
"""
assert cv2 is not None
assert _check_cv2() is True
h, w = im.shape[:2]
h_new, w_new = size, size
......@@ -196,7 +203,7 @@ def resize_short(im, size):
h_new = size * h // w
else:
w_new = size * w // h
im = cv2.resize(im, (h_new, w_new), interpolation=cv2.INTER_CUBIC)
im = cv2.resize(im, (w_new, h_new), interpolation=cv2.INTER_CUBIC)
return im
......@@ -338,7 +345,6 @@ def simple_transform(im,
if np.random.randint(2) == 0:
im = left_right_flip(im, is_color)
else:
im = center_crop(im, crop_size, is_color)
im = center_crop(im, crop_size, is_color=is_color)
if len(im.shape) == 3:
im = to_chw(im)
......
python/paddle/dataset/movielens.py
浏览文件 @ ec5cb376
......@@ -24,6 +24,7 @@ set and test set into paddle reader creators.
from __future__ import print_function
import numpy as np
import zipfile
import paddle.dataset.common
import re
......@@ -150,12 +151,12 @@ def __initialize_meta_info__():
def __reader__(rand_seed=0, test_ratio=0.1, is_test=False):
fn = __initialize_meta_info__()
rand = random.Random(x=rand_seed)
np.random.seed(rand_seed)
with zipfile.ZipFile(file=fn) as package:
with package.open('ml-1m/ratings.dat') as rating:
for line in rating:
line = cpt.to_text(line, encoding='latin')
if (rand.random() < test_ratio) == is_test:
if (np.random.random() < test_ratio) == is_test:
uid, mov_id, rating, _ = line.strip().split("::")
uid = int(uid)
mov_id = int(mov_id)
......
python/paddle/fluid/framework.py
浏览文件 @ ec5cb376
......@@ -49,6 +49,12 @@ EMPTY_VAR_NAME = core.kEmptyVarName()
TEMP_VAR_NAME = core.kTempVarName()
GRAD_VAR_SUFFIX = core.kGradVarSuffix()
ZERO_VAR_SUFFIX = core.kZeroVarSuffix()
CONTROL_DEP_VAR_PREFIX = core.kControlDepVarName()
def generate_control_dev_var_name():
import random
return CONTROL_DEP_VAR_PREFIX + "@" + str(random.random())
def grad_var_name(var_name):
......@@ -89,6 +95,8 @@ def convert_np_dtype_to_dtype_(np_dtype):
return core.VarDesc.VarType.INT16
elif dtype == np.uint8:
return core.VarDesc.VarType.UINT8
elif dtype == np.int8:
return core.VarDesc.VarType.INT8
else:
raise ValueError("Not supported numpy dtype %s" % dtype)
......
python/paddle/fluid/io.py
浏览文件 @ ec5cb376
......@@ -406,6 +406,9 @@ def load_vars(executor,
attrs={'file_path': os.path.join(dirname, filename)})
executor.run(load_prog)
if main_program is None:
main_program = default_main_program()
# load slice vars on pserver, if have it.
_load_slice_up_vars(executor, dirname,
main_program._slice_vars_and_attrs)
......
python/paddle/fluid/layers/control_flow.py
浏览文件 @ ec5cb376
......@@ -189,7 +189,6 @@ def Print(input,
message="The content of some_layer: ")
'''
helper = LayerHelper('print', **locals())
out = helper.create_tmp_variable(dtype=helper.input_dtype())
helper.append_op(
type='print',
inputs={'In': input},
......@@ -202,9 +201,7 @@ def Print(input,
'print_tensor_shape': print_tensor_shape,
'print_tensor_lod': print_tensor_lod,
'print_phase': print_phase.upper()
},
outputs={'Out': out})
return out
})
class BlockGuard(object):
......@@ -661,6 +658,7 @@ class While(object):
Args:
cond (Variable): condition used to compare.
is_test(bool): A flag indicating whether execution is in test phase.
name (str): The name of this layer.
Examples:
......@@ -683,7 +681,7 @@ class While(object):
IN_WHILE_BLOCK = 1
AFTER_WHILE_BLOCK = 2
def __init__(self, cond, name=None):
def __init__(self, cond, is_test=False, name=None):
self.helper = LayerHelper("while", name=name)
self.status = While.BEFORE_WHILE_BLOCK
if not isinstance(cond, Variable):
......@@ -694,6 +692,7 @@ class While(object):
if reduce(lambda a, b: a * b, cond.shape, 1) != 1:
raise TypeError("condition should be a bool scalar")
self.cond_var = cond
self.is_test = is_test
def block(self):
return WhileGuard(self)
......@@ -735,7 +734,8 @@ class While(object):
},
outputs={'Out': out_vars,
'StepScopes': [step_scope]},
attrs={'sub_block': while_block})
attrs={'sub_block': while_block,
"is_test": self.is_test})
def lod_rank_table(x, level=0):
......
python/paddle/fluid/layers/io.py
浏览文件 @ ec5cb376
......@@ -246,7 +246,11 @@ def Send(endpoints, send_vars, dummy_output=None, sync=True):
rpc_op_role_name: core.op_proto_and_checker_maker.OpRole.RPC
})
if sync:
helper.append_op(type="send_barrier", attrs={"endpoints": endpoints})
helper.append_op(
type="send_barrier",
inputs={"X": dummy_output},
outputs={"Out": []},
attrs={"endpoints": endpoints})
def Recv(endpoints, get_vars, dummy_input=None, sync=True):
......@@ -282,7 +286,10 @@ def Recv(endpoints, get_vars, dummy_input=None, sync=True):
attrs={"endpoints": endpoints,
"epmap": epmap})
if sync:
helper.append_op(type="fetch_barrier", attrs={"endpoints": endpoints})
helper.append_op(
type="fetch_barrier",
outputs={"Out": get_vars},
attrs={"endpoints": endpoints})
return get_vars
......
python/paddle/fluid/layers/metric_op.py
浏览文件 @ ec5cb376
......@@ -119,10 +119,14 @@ def auc(input, label, curve='ROC', num_thresholds=200, topk=1):
helper = LayerHelper("auc", **locals())
auc_out = helper.create_tmp_variable(dtype="float64")
# make tp, tn, fp, fn persistable, so that can accumulate all batches.
tp = helper.create_global_variable(persistable=True, dtype='int64')
tn = helper.create_global_variable(persistable=True, dtype='int64')
fp = helper.create_global_variable(persistable=True, dtype='int64')
fn = helper.create_global_variable(persistable=True, dtype='int64')
tp = helper.create_global_variable(
persistable=True, dtype='int64', shape=[num_thresholds])
tn = helper.create_global_variable(
persistable=True, dtype='int64', shape=[num_thresholds])
fp = helper.create_global_variable(
persistable=True, dtype='int64', shape=[num_thresholds])
fn = helper.create_global_variable(
persistable=True, dtype='int64', shape=[num_thresholds])
for var in [tp, tn, fp, fn]:
helper.set_variable_initializer(
var, Constant(
......
python/paddle/fluid/layers/nn.py
浏览文件 @ ec5cb376
......@@ -17,6 +17,7 @@ All layers just related to the neural network.
from __future__ import print_function
import numpy as np
from ..layer_helper import LayerHelper
from ..initializer import Normal, Constant
from ..framework import Variable
......@@ -24,7 +25,6 @@ from ..param_attr import ParamAttr
from .layer_function_generator import autodoc, templatedoc
from .tensor import concat
from . import utils
import random
from .. import unique_name
from functools import reduce
......@@ -54,6 +54,7 @@ __all__ = [
'conv2d_transpose',
'conv3d_transpose',
'sequence_expand',
'sequence_pad',
'lstm_unit',
'reduce_sum',
'reduce_mean',
......@@ -87,6 +88,7 @@ __all__ = [
'lod_reset',
'lrn',
'pad',
'pad_constant_like',
'label_smooth',
'roi_pool',
'dice_loss',
......@@ -103,6 +105,9 @@ __all__ = [
'rank_loss',
'prelu',
'flatten',
'sequence_mask',
'stack',
'unstack',
]
......@@ -2653,6 +2658,51 @@ def sequence_expand(x, y, ref_level=-1, name=None):
return tmp
@templatedoc()
def sequence_pad(x, pad_value, maxlen=None):
"""
${comment}
Args:
x(Variable): Input variable which should contain lod information.
pad_value(Variable): The Variable that holds values that will be fill
into padded steps. It can be a scalar or a tensor whose shape
equals to time steps in sequences. If it's a scalar, it will be
automatically broadcasted to the shape of time step.
maxlen(int, default None): The length of padded sequences. It can be
None or any positive int. When it is None, all sequences will be
padded up to the length of the longest one among them; when it a
certain positive value, it must be greater than the length of the
longest original sequence."
Returns:
Variable: The padded sequence batch. All sequences has the same length.
Examples:
.. code-block:: python
import numpy
x = fluid.layers.data(name='y', shape=[10, 5],
dtype='float32', lod_level=1)
pad_value = fluid.layers.assign(input=numpy.array([0]))
out = fluid.layers.sequence_pad(x=x, pad_value=pad_value)
"""
helper = LayerHelper('sequence_pad', input=x, **locals())
dtype = helper.input_dtype()
out = helper.create_tmp_variable(dtype)
if maxlen is None:
maxlen = -1
helper.append_op(
type='sequence_pad',
inputs={'X': x,
'PadValue': pad_value},
outputs={'Out': out},
attrs={'padded_length': maxlen})
return out
def beam_search(pre_ids,
pre_scores,
ids,
......@@ -4706,6 +4756,86 @@ def pad(x, paddings, pad_value=0., name=None):
return out
def pad_constant_like(x, y, pad_value=0., name=None):
"""
Pad input(Y) with :attr:`pad_value`, the number of values padded to
the edges of each axis is specified by the difference of the shape
of X and Y. ((0, shape_x_0 - shape_y_0), ... (0, shape_x_n - shape_y_n))
unique pad widths for each axis. The input should be a k-D
tensor(k > 0 and k < 7).
See below for an example.
.. code-block:: text
Given:
X = [[[[ 0, 1, 2],
[ 3, 4, 5]],
[[ 6, 7, 8],
[ 9, 10, 11]],
[[12, 13, 14],
[15, 16, 17]]],
[[[18, 19, 20],
[21, 22, 23]],
[[24, 25, 26],
[27, 28, 29]],
[[30, 31, 32],
[33, 34, 35]]]]
X.shape = (2, 3, 2, 3)
Y = [[[[35, 36, 37]],
[[38, 39, 40]],
[[41, 42, 43]]]]
Y.shape = (1, 3, 1, 3)
And
pad_value = -1,
Return:
Out = [[[[35, 36, 37],
[-1, -1, -1]],
[[38, 39, 40],
[-1, -1, -1]],
[[41, 42, 43],
[-1, -1, -1]]],
[[[-1, -1, -1],
[-1, -1, -1]],
[[-1, -1, -1],
[-1, -1, -1]],
[[-1, -1, -1],
[-1, -1, -1]]]]
Out.shape = (2, 3, 2, 3)
Args:
x (Variable): The input tensor variable.
y (Variable): The input tensor variable.
pad_value (float): The constant value used to pad.
name(str|None): A name for this layer(optional). If set None, the layer
will be named automatically.
Returns:
Variable: The padded tensor variable.
Examples:
.. code-block:: python
# x is a rank 4 tensor variable, x.shape = (2, 3, 2, 3)
# y is a rank 4 tensor variable, y.shape = (1, 3, 1, 3)
out = fluid.layers.pad_constant_like(x=x, y=y, pad_value=0.)
# out is a rank 4 tensor variable, and out.shape = [2, 3 ,2 , 3]
"""
helper = LayerHelper('pad_constant_like', input=x, **locals())
dtype = helper.input_dtype()
out = helper.create_tmp_variable(dtype)
helper.append_op(
type='pad_constant_like',
inputs={'X': x,
'Y': y},
outputs={'Out': out},
attrs={'pad_value': float(pad_value)})
return out
def label_smooth(label,
prior_dist=None,
epsilon=0.1,
......@@ -5100,7 +5230,7 @@ def random_crop(x, shape, seed=None):
dtype = x.dtype
out = helper.create_tmp_variable(dtype)
if seed is None:
seed = random.randint(-65536, 65535)
seed = np.random.randint(-65536, 65536)
op_attrs = {"shape": shape}
if isinstance(seed, int):
op_attrs["startup_seed"] = seed
......@@ -5517,3 +5647,126 @@ def flatten(x, axis=1, name=None):
outputs={'Out': out},
attrs={"axis": axis})
return out
def sequence_mask(x, maxlen=None, dtype='int64', name=None):
"""
**SequenceMask Layer**
This layer outputs a mask according to the input :code:`x` and
:code:`maxlen` with data type of :code:`dtype`.
Supposing :code:`x` is a Tensor with shape [d_1, d_2, ..., d_n], the
:code:`y` is a mask with shape [d_1, d_2, ..., d_n, maxlen], where:
.. math::
y(i_1, i_2,..., i_n, j) = (j < x(i_1, i_2,..., i_n))
Args:
x (Variable): Input tensor of sequence_mask layer,
whose elements are integers less than :code:`maxlen`.
maxlen (int|None): Maximum length of the sequence. If :code:`maxlen`
is None, it would be replace with :math:`max(x)`.
dtype (np.dtype|core.VarDesc.VarType|str): Data type of the output.
name (str|None): A name for this layer(optional). If set None, the
layer will be named automatically.
Returns:
Variable: The output sequence mask.
"""
helper = LayerHelper('sequence_mask', **locals())
if name is None:
out = helper.create_tmp_variable(dtype=dtype)
else:
out = helper.create_tmp_variable(dtype=dtype, name=name)
helper.append_op(
type='sequence_mask',
inputs={'X': [x]},
outputs={'Y': out},
attrs={
'max_len': maxlen if maxlen is not None else -1,
'out_dtype': out.dtype
})
return out
def stack(x, axis=0):
"""
**Stack Layer**
This layer stacks all of the input :code:`x` along axis.
Input :code:`x` can be a single variable, a :code:`list` of variables,
or a :code:`tuple` of variables. If :code:`x` is a :code:`list` or
:code:`tuple`, the shapes of all these variables must be the same.
Supposing the shape of each input is :math:`[d_0, d_1, ..., d_{n-1}]`,
the shape of the output variable would be
:math:`[d_0, d_1, ..., d_{axis}=len(x), ..., d_{n-1}]`.
If :code:`axis` < 0, it would be replaced with :code:`axis+rank(x[0])+1`.
If :code:`axis` is None, it would be replaced with 0.
Args:
x (Variable|list(Variable)|tuple(Variable)): Input variables.
axis (int|None): The axis along which all inputs are stacked.
Returns:
Variable: The stacked variable.
"""
helper = LayerHelper('stack', **locals())
axis = 0 if axis is None else axis
if not isinstance(x, list) and not isinstance(x, tuple):
x = [x]
out = helper.create_tmp_variable(x[0].dtype)
helper.append_op(
type='stack', inputs={'X': x}, outputs={'Y': out},
attrs={'axis': axis})
return out
def unstack(x, axis=0, num=None):
"""
**UnStack Layer**
This layer unstacks input :code:`x` into several tensors along axis.
If :code:`axis` < 0, it would be replaced with :code:`axis+rank(x)`.
If :code:`num` is None, it would be inferred from :code:`x.shape[axis]`,
and if :code:`x.shape[axis]` <= 0 or is unknown, :code:`ValueError` is
raised.
Args:
x (Variable): Input variable.
axis (int): The axis along which the input is unstacked.
num (int|None): The number of output variables.
Returns:
list(Variable): The unstacked variables.
"""
helper = LayerHelper('unstack', **locals())
if num is None:
if axis is None or x.shape[axis] <= 0:
raise ValueError('unknown unstack number')
else:
num = x.shape[axis]
outs = []
for _ in num:
outs.append(helper.create_tmp_variable(x.dtype))
helper.append_op(
type='unstack',
inputs={'X': [x]},
outputs={'Y': outs},
attrs={'axis': axis,
'num': num})
return outs
python/paddle/fluid/optimizer.py
浏览文件 @ ec5cb376
......@@ -46,10 +46,12 @@ class Optimizer(object):
def __init__(self,
learning_rate,
regularization=None,
LARS_weight_decay=0.0):
LARS_weight_decay=0.0,
name=None):
if not isinstance(learning_rate, float) and \
not isinstance(learning_rate, framework.Variable):
raise TypeError("learning rate should be float or Variable")
self._name = name
self.regularization = regularization
self._learning_rate = learning_rate
# the learning rate type should be inferenced from loss
......@@ -153,6 +155,8 @@ class Optimizer(object):
dtype: data type of the accumulator variable
fill_value: value to initialize the accumulator variable
"""
if self._name is not None:
name = self._name + "_" + name
if (name in self._accumulators and
param.name in self._accumulators[name]):
raise Exception("Accumulator {} already exists for parameter {}".
......@@ -181,6 +185,8 @@ class Optimizer(object):
Returns:
accumulator variable for the parameter
"""
if self._name is not None:
name = self._name + "_" + name
if (name not in self._accumulators or
param.name not in self._accumulators[name]):
raise Exception("Accumulator {} does not exist for parameter {}".
......
python/paddle/fluid/tests/book_memory_optimization/test_memopt_image_classification_train.py
浏览文件 @ ec5cb376
......@@ -125,8 +125,8 @@ opts = optimizer.minimize(avg_cost)
batch_size = fluid.layers.create_tensor(dtype='int64')
batch_acc = fluid.layers.accuracy(input=predict, label=label, total=batch_size)
# fluid.memory_optimize(fluid.default_main_program(), level=0)
fluid.release_memory(fluid.default_main_program())
fluid.memory_optimize(fluid.default_main_program(), level=0)
# fluid.release_memory(fluid.default_main_program())
BATCH_SIZE = 16
PASS_NUM = 1
......
python/paddle/fluid/tests/book_memory_optimization/test_memopt_machine_translation.py
浏览文件 @ ec5cb376
......@@ -92,8 +92,8 @@ def main():
optimizer = fluid.optimizer.Adagrad(learning_rate=1e-4)
optimizer.minimize(avg_cost)
# fluid.memory_optimize(fluid.default_main_program())
fluid.release_memory(fluid.default_main_program())
fluid.memory_optimize(fluid.default_main_program())
# fluid.release_memory(fluid.default_main_program())
# fix the order of training data
train_data = paddle.batch(
......
python/paddle/fluid/tests/unittests/CMakeLists.txt
浏览文件 @ ec5cb376
......@@ -64,6 +64,7 @@ if(WITH_DISTRIBUTE)
endif()
py_test_modules(test_parallel_executor_crf MODULES test_parallel_executor_crf SERIAL)
py_test_modules(test_parallel_executor_fetch_feed MODULES test_parallel_executor_fetch_feed SERIAL)
set_tests_properties(test_parallel_executor_fetch_feed PROPERTIES TIMEOUT 150)
py_test_modules(test_dist_transformer MODULES test_dist_transformer SERIAL)
py_test_modules(test_dist_se_resnext MODULES test_dist_se_resnext SERIAL)
py_test_modules(test_parallel_executor_transformer MODULES test_parallel_executor_transformer SERIAL)
......
python/paddle/fluid/tests/unittests/dist_se_resnext.py
浏览文件 @ ec5cb376
......@@ -134,7 +134,7 @@ class SE_ResNeXt():
size=class_dim,
act='softmax',
param_attr=fluid.ParamAttr(
initializer=fluid.initializer.Constant(value=0.2)))
initializer=fluid.initializer.Constant(value=0.05)))
return out
def shortcut(self, input, ch_out, stride):
......@@ -184,7 +184,7 @@ class SE_ResNeXt():
act=None,
# avoid pserver CPU init differs from GPU
param_attr=fluid.ParamAttr(
initializer=fluid.initializer.Constant(value=0.2)),
initializer=fluid.initializer.Constant(value=0.05)),
bias_attr=False)
return fluid.layers.batch_norm(input=conv, act=act)
......@@ -192,12 +192,18 @@ class SE_ResNeXt():
pool = fluid.layers.pool2d(
input=input, pool_size=0, pool_type='avg', global_pooling=True)
stdv = 1.0 / math.sqrt(pool.shape[1] * 1.0)
squeeze = fluid.layers.fc(input=pool,
squeeze = fluid.layers.fc(
input=pool,
size=num_channels // reduction_ratio,
param_attr=fluid.ParamAttr(
initializer=fluid.initializer.Constant(value=0.05)),
act='relu')
stdv = 1.0 / math.sqrt(squeeze.shape[1] * 1.0)
excitation = fluid.layers.fc(input=squeeze,
excitation = fluid.layers.fc(
input=squeeze,
size=num_channels,
param_attr=fluid.ParamAttr(
initializer=fluid.initializer.Constant(value=0.05)),
act='sigmoid')
scale = fluid.layers.elementwise_mul(x=input, y=excitation, axis=0)
return scale
......
python/paddle/fluid/tests/unittests/dist_word2vec.py
浏览文件 @ ec5cb376
......@@ -49,28 +49,32 @@ class TestDistWord2vec2x2(TestDistRunnerBase):
dtype='float32',
is_sparse=IS_SPARSE,
param_attr=fluid.ParamAttr(
name='shared_w', initializer=fluid.initializer.Constant()))
name='shared_w',
initializer=fluid.initializer.Constant(value=0.1)))
embed_second = fluid.layers.embedding(
input=words[1],
size=[dict_size, EMBED_SIZE],
dtype='float32',
is_sparse=IS_SPARSE,
param_attr=fluid.ParamAttr(
name='shared_w', initializer=fluid.initializer.Constant()))
name='shared_w',
initializer=fluid.initializer.Constant(value=0.1)))
embed_third = fluid.layers.embedding(
input=words[2],
size=[dict_size, EMBED_SIZE],
dtype='float32',
is_sparse=IS_SPARSE,
param_attr=fluid.ParamAttr(
name='shared_w', initializer=fluid.initializer.Constant()))
name='shared_w',
initializer=fluid.initializer.Constant(value=0.1)))
embed_forth = fluid.layers.embedding(
input=words[3],
size=[dict_size, EMBED_SIZE],
dtype='float32',
is_sparse=IS_SPARSE,
param_attr=fluid.ParamAttr(
name='shared_w', initializer=fluid.initializer.Constant()))
name='shared_w',
initializer=fluid.initializer.Constant(value=0.1)))
concat_embed = fluid.layers.concat(
input=[embed_first, embed_second, embed_third, embed_forth],
......@@ -80,13 +84,13 @@ class TestDistWord2vec2x2(TestDistRunnerBase):
size=HIDDEN_SIZE,
act='sigmoid',
param_attr=fluid.ParamAttr(
initializer=fluid.initializer.Constant()))
initializer=fluid.initializer.Constant(value=0.1)))
predict_word = fluid.layers.fc(
input=hidden1,
size=dict_size,
act='softmax',
param_attr=fluid.ParamAttr(
initializer=fluid.initializer.Constant()))
initializer=fluid.initializer.Constant(value=0.1)))
cost = fluid.layers.cross_entropy(
input=predict_word, label=words[4])
avg_cost = fluid.layers.mean(cost)
......
python/paddle/fluid/tests/unittests/op_test.py
浏览文件 @ ec5cb376
......@@ -56,8 +56,8 @@ def get_numeric_gradient(place,
def get_output():
sum = []
for output_name in output_names:
op.run(scope, place)
for output_name in output_names:
sum.append(
np.array(scope.find_var(output_name).get_tensor()).mean())
return np.array(sum).mean()
......
python/paddle/fluid/tests/unittests/test_attention_lstm_op.py 0 → 100644
浏览文件 @ ec5cb376
# Copyright (c) 2018 PaddlePaddle Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from __future__ import print_function
import unittest
import numpy as np
from op_test import OpTest
from test_fusion_lstm_op import fc, ACTIVATION
from test_softmax_op import stable_softmax
def attention_lstm(
x, # T x M
lod, # 1 x N
h0, # N x D
c0, # N x D
fcws, # (M+D) x 1, 1x1
fcbs, # 1 x 1, 1x1
w, # (M+D) x 4D
b, # 1 x 4D
act_gate,
act_cell,
act_cand):
T = sum(lod[0])
N = len(lod[0])
M = x.shape[1]
D = b.shape[1] // 4
assert T == x.shape[0]
assert len(fcws) == len(fcbs)
hidden = []
cell = []
start_offset = 0
for bid in range(N):
seq_len = lod[0][bid]
xi = np.copy(x[start_offset:start_offset + seq_len, :]).reshape(seq_len,
M)
prev_cell = np.copy(c0[bid]).reshape([1, D])
prev_hidden = np.copy(h0[bid]).reshape([1, D])
for step in range(seq_len):
expanded_cell = np.repeat(prev_cell, seq_len, axis=0)
tmp = np.concatenate((xi, expanded_cell), axis=1)
assert tmp.shape[0] == seq_len
assert tmp.shape[1] == M + D
for fcid in range(len(fcbs)):
tmp = fc(tmp, fcws[fcid], fcbs[fcid])
tmp = ACTIVATION['relu'](tmp)
tmp = np.reshape(tmp, (1, seq_len))
tmp = stable_softmax(tmp).reshape(seq_len, 1)
lstmx = xi * tmp # seq * M
lstmx = np.sum(lstmx.reshape(seq_len, M), axis=0).reshape([1, M])
lstmin = np.concatenate((prev_hidden, lstmx), axis=1)
lstmout = fc(lstmin, w, b).reshape([1, 4 * D])
g_f, g_i, g_o, cand = np.split(lstmout, 4, axis=1)
g_f = act_gate(g_f).reshape([1, D])
g_i = act_gate(g_i).reshape([1, D])
g_o = act_gate(g_o).reshape([1, D])
cand = act_cand(cand).reshape([1, D])
cell_t = (prev_cell * g_f) + (g_i * cand)
hidden_t = g_o * act_cell(cell_t)
hidden.append(hidden_t.flatten())
cell.append(cell_t.flatten())
prev_cell = cell_t.reshape([1, D])
prev_hidden = hidden_t.reshape([1, D])
start_offset += seq_len
hidden = np.array(hidden).astype('float32').reshape([T, D])
cell = np.array(cell).astype('float32').reshape([T, D])
return hidden, cell
class TestAttentionLSTMOp(OpTest):
def set_conf(self):
pass
def setUp(self):
self.op_type = 'attention_lstm'
self.lod = [[3]]
self.M = 30
self.D = 15
self.has_initial_hidden = True
self.act_gate = 'sigmoid'
self.act_cell = 'tanh'
self.act_cand = 'tanh'
self.set_conf()
T = sum(self.lod[0])
bs = len(self.lod[0])
x = np.random.normal(size=(T, self.M)).astype('float32')
c0 = np.random.normal(size=(bs, self.D)).astype('float32')
if self.has_initial_hidden:
h0 = np.random.normal(size=(bs, self.D)).astype('float32')
else:
h0 = np.zeros((bs, self.D)).astype('float32')
fcw1 = np.random.normal(size=(self.M + self.D, 1)).astype('float32')
fcb1 = np.random.normal(size=(1, 1)).astype('float32')
fcw2 = np.random.normal(size=(1, 1)).astype('float32')
fcb2 = np.random.normal(size=(1, 1)).astype('float32')
# lstm weight and bias
w = np.random.normal(size=(self.M + self.D,
self.D * 4)).astype('float32')
b = np.random.normal(size=(1, self.D * 4)).astype('float32')
h, c = attention_lstm(x, self.lod, h0, c0, [fcw1, fcw2], [fcb1, fcb2],
w, b, ACTIVATION[self.act_gate],
ACTIVATION[self.act_cell],
ACTIVATION[self.act_cand])
self.inputs = {
'X': (x, self.lod),
'C0': c0,
'AttentionWeight': fcw1,
'AttentionBias': fcb1,
'AttentionScalar': fcw2,
'AttentionScalarBias': fcb2,
'LSTMWeight': w,
'LSTMBias': b
}
if self.has_initial_hidden:
self.inputs['H0'] = h0
self.outputs = {
'Hidden': (h, self.lod),
'Cell': (c, self.lod),
}
self.attrs = {
'gate_activation': self.act_gate,
'cell_activation': self.act_cell,
'candidate_activation': self.act_cand
}
def test_check_output(self):
self.check_output()
class TestAttentionOpNonInit(TestAttentionLSTMOp):
def set_conf(self):
self.has_initial_hidden = False
class TestAttentionOpAct(TestAttentionLSTMOp):
def set_conf(self):
self.M = 3
self.D = 2
self.act_gate = 'relu'
self.act_cell = 'tanh'
self.act_cand = 'sigmoid'
class TestAttentionOpMD1(TestAttentionLSTMOp):
def set_conf(self):
self.M = 36
self.D = 8
class TestAttentionOpMD2(TestAttentionLSTMOp):
def set_conf(self):
self.M = 8
self.D = 8
class TestAttentionOpMD3(TestAttentionLSTMOp):
def set_conf(self):
self.M = 15
self.D = 30
class TestAttentionOpBS1(TestAttentionLSTMOp):
def set_conf(self):
self.lod = [[5]]
self.M = 16
self.D = 32
class TestAttentionOpBS2(TestAttentionLSTMOp):
def set_conf(self):
self.lod = [[3, 6]]
class TestAttentionOpBS5(TestAttentionLSTMOp):
def set_conf(self):
self.lod = [[3, 2, 4, 7, 5]]
if __name__ == '__main__':
unittest.main()
python/paddle/fluid/tests/unittests/test_desc_clone.py
浏览文件 @ ec5cb376
......@@ -120,8 +120,8 @@ def operator_equal(a, b):
raise ValueError("In operator_equal not equal:{0}\n".format(k))
elif isinstance(v, collections.OrderedDict):
v0 = sorted(six.iteritems(v), key=lambda x: x[0])
v1 = sorted(six.iteritems(b.__dict__[k]), key=lambda x: x[0])
v0 = sorted(list(six.iteritems(v)), key=lambda x: x[0])
v1 = sorted(list(six.iteritems(b.__dict__[k])), key=lambda x: x[0])
if v0 != v1:
raise ValueError("In operator_equal not equal:{0}\n".format(k))
......@@ -139,16 +139,14 @@ def block_equal(a, b):
continue
elif k == "ops":
assert (len(a.ops) == len(b.ops))
for i in range(0, len(a.ops)):
if not operator_equal(a.ops[i], b.ops[i]):
raise ValueError("In block_equal not equal:{0}\n".format(k))
assert (len(a.ops) == len(b.ops))
elif isinstance(v, collections.OrderedDict):
v0 = sorted(six.iteritems(v), key=lambda x: x[0])
v1 = sorted(six.iteritems(b.__dict__[k]), key=lambda x: x[0])
if v0 != v1:
for key, value in six.iteritems(v):
if str(value) != str(b.__dict__[k][key]):
raise ValueError("In block_equal not equal:{0}\n".format(k))
elif (v != b.__dict__[k]):
......
python/paddle/fluid/tests/unittests/test_dist_train.py
浏览文件 @ ec5cb376
......@@ -100,7 +100,7 @@ class TestSendOp(unittest.TestCase):
main.global_block().append_op(
type="fetch_barrier",
inputs={},
outputs={},
outputs={"Out": []},
attrs={
"endpoints": ["127.0.0.1:{0}".format(port)],
RPC_OP_ROLE_ATTR_NAME: RPC_OP_ROLE_ATTR_VALUE
......
python/paddle/fluid/tests/unittests/test_dist_transpiler.py
浏览文件 @ ec5cb376
......@@ -21,6 +21,7 @@ import paddle.fluid as fluid
from paddle.fluid.transpiler.distribute_transpiler import delete_ops
import traceback
import collections
import six
class TranspilerTest(unittest.TestCase):
......@@ -644,17 +645,17 @@ class TestLoadSliceVar(TranspilerTest):
self.assertTrue(pserver._slice_vars_and_attrs)
self.assertTrue(pserver2._slice_vars_and_attrs)
for idx in xrange(len(pserver._slice_vars_and_attrs)):
for idx in six.moves.xrange(len(pserver._slice_vars_and_attrs)):
self.assertEqual(pserver._slice_vars_and_attrs[idx][0],
pserver2._slice_vars_and_attrs[idx][0])
total_numel = reduce(lambda x, y: x * y,
pserver._slice_vars_and_attrs[idx][0].shape)
total_numel = six.moves.reduce(
lambda x, y: x * y, pserver._slice_vars_and_attrs[idx][0].shape)
self.assertEqual(
total_numel,
reduce(lambda x, y: x * y,
pserver._slice_vars_and_attrs[idx][2].shape) + reduce(
lambda x, y: x * y,
six.moves.reduce(lambda x, y: x * y,
pserver._slice_vars_and_attrs[idx][2].shape) +
six.moves.reduce(lambda x, y: x * y,
pserver2._slice_vars_and_attrs[idx][2].shape))
......
python/paddle/fluid/tests/unittests/test_dist_word2vec.py
浏览文件 @ ec5cb376
......@@ -22,7 +22,7 @@ class TestDistSeResneXt2x2(TestDistBase):
self._sync_mode = True
def test_se_resnext(self):
self.check_with_place("dist_word2vec.py", delta=1e-7)
self.check_with_place("dist_word2vec.py", delta=1e-4)
class TestDistSeResneXt2x2Async(TestDistBase):
......
python/paddle/fluid/tests/unittests/test_fake_dequantize_op.py
浏览文件 @ ec5cb376
......@@ -20,41 +20,50 @@ import math
from op_test import OpTest
def quantize_max_abs(x, num_bits):
range = math.pow(2, num_bits) - 1
def quantize_max_abs(x, max_range):
scale = np.max(np.abs(x).flatten())
y = np.round(x / scale * range)
y = np.round(x / scale * max_range)
return y, scale
def dequantize_max_abs(x, num_bits, scale):
range = math.pow(2, num_bits) - 1
y = (scale / range) * x
def dequantize_max_abs(x, scale, max_range):
y = (scale / max_range) * x
return y
class TestFakeDequantizeMaxAbsOp(OpTest):
def set_args(self):
self.num_bits = 8
self.max_range = math.pow(2, self.num_bits - 1) - 1
self.data_type = "float32"
def setUp(self):
self.set_args()
self.op_type = "fake_dequantize_max_abs"
x = np.random.randn(31, 65).astype("float32")
yq, scale = quantize_max_abs(x, self.num_bits)
ydq = dequantize_max_abs(yq, self.num_bits, scale)
x = np.random.randn(31, 65).astype(self.data_type)
yq, scale = quantize_max_abs(x, self.max_range)
ydq = dequantize_max_abs(yq, scale, self.max_range)
self.inputs = {'X': yq}
self.attrs = {'num_bits': self.num_bits, 'scale': float(scale)}
self.inputs = {'X': yq, 'Scale': np.array(scale).astype(self.data_type)}
self.attrs = {'max_range': self.max_range}
self.outputs = {'Out': ydq}
def test_check_output(self):
self.check_output()
class TestFakeDequantizeMaxAbsOp5Bits(OpTest):
class TestFakeDequantizeMaxAbsOpDouble(TestFakeDequantizeMaxAbsOp):
def set_args(self):
self.num_bits = 8
self.max_range = math.pow(2, self.num_bits - 1) - 1
self.data_type = "float64"
class TestFakeDequantizeMaxAbsOp5Bits(TestFakeDequantizeMaxAbsOp):
def set_args(self):
self.num_bits = 5
self.max_range = math.pow(2, self.num_bits - 1) - 1
self.data_type = "float32"
if __name__ == "__main__":
......
python/paddle/fluid/tests/unittests/test_fill_constant_op.py
浏览文件 @ ec5cb376
......@@ -18,6 +18,9 @@ import unittest
import numpy as np
from op_test import OpTest
import paddle.fluid.core as core
from paddle.fluid.op import Operator
class TestFillConstantOp1(OpTest):
def setUp(self):
......@@ -47,5 +50,31 @@ class TestFillConstantOp2(OpTest):
self.check_output()
class TestFillConstantOpWithSelectedRows(OpTest):
def check_with_place(self, place):
scope = core.Scope()
# create Out Variable
out = scope.var('Out').get_selected_rows()
# create and run fill_constant_op operator
fill_constant_op = Operator(
"fill_constant", shape=[123, 92], value=3.8, Out='Out')
fill_constant_op.run(scope, place)
# get result from Out
result_array = np.array(out.get_tensor())
full_array = np.full((123, 92), 3.8, 'float32')
self.assertTrue(np.array_equal(result_array, full_array))
def test_fill_constant_with_selected_rows(self):
places = [core.CPUPlace()]
if core.is_compiled_with_cuda():
places.append(core.CUDAPlace(0))
for place in places:
self.check_with_place(place)
if __name__ == "__main__":
unittest.main()
python/paddle/fluid/tests/unittests/test_fusion_lstm_op.py
浏览文件 @ ec5cb376
......@@ -43,13 +43,13 @@ def fusion_lstm(
act_cell, act_cand)
class TestLstmOp(OpTest):
def set_argument(self):
self.lod = [[2, 3, 2]]
class TestFusionLSTMOp(OpTest):
def set_conf(self):
pass
def setUp(self):
self.op_type = 'fusion_lstm'
self.lod = [[2, 3, 2]]
self.lod = [[2, 3, 5, 4]]
self.M = 8
self.D = 16
self.has_initial_state = False
......@@ -58,33 +58,33 @@ class TestLstmOp(OpTest):
self.act_cell = 'tanh'
self.act_cand = 'tanh'
self.use_peepholes = False
self.set_argument()
self.set_conf()
T = sum(self.lod[0])
bs = len(self.lod[0])
x = np.random.normal(size=(T, self.M)).astype('float64')
x = np.random.normal(size=(T, self.M)).astype('float32')
if self.has_initial_state:
h0 = np.random.normal(size=(bs, self.D)).astype('float64')
c0 = np.random.normal(size=(bs, self.D)).astype('float64')
h0 = np.random.normal(size=(bs, self.D)).astype('float32')
c0 = np.random.normal(size=(bs, self.D)).astype('float32')
else:
h0 = np.zeros((bs, self.D)).astype('float64')
c0 = np.zeros((bs, self.D)).astype('float64')
h0 = np.zeros((bs, self.D)).astype('float32')
c0 = np.zeros((bs, self.D)).astype('float32')
wh = np.random.normal(size=(self.D, 4 * self.D)).astype('float64')
wh = np.random.normal(size=(self.D, 4 * self.D)).astype('float32')
if self.use_peepholes:
b = np.random.normal(size=(1, 7 * self.D)).astype('float64')
b = np.random.normal(size=(1, 7 * self.D)).astype('float32')
else:
b = np.random.normal(size=(1, 4 * self.D)).astype('float64')
b = np.random.normal(size=(1, 4 * self.D)).astype('float32')
w_b = np.copy(b[:, 0:4 * self.D])
w_c = b[:, 4 * self.D:] if self.use_peepholes else None
# this is the weight of fc
wx = np.random.normal(size=(self.M, 4 * self.D)).astype('float64')
wx = np.random.normal(size=(self.M, 4 * self.D)).astype('float32')
# this is the bias of fc
# and it should be manually added into the bias of this fusion LSTM
bx = np.random.normal(size=(1, 4 * self.D)).astype('float64')
bx = np.random.normal(size=(1, 4 * self.D)).astype('float32')
b[0, 0:4 * self.D] += bx[0, :]
h, c = fusion_lstm(x, self.lod, wx, bx, h0, c0, wh, w_b, w_c,
self.is_reverse, ACTIVATION[self.act_gate],
......@@ -114,35 +114,45 @@ class TestLstmOp(OpTest):
}
def test_check_output(self):
self.check_output(atol=1e-8)
self.check_output()
class TestLstmOpInitReverse(TestLstmOp):
def set_argument(self):
class TestFusionLSTMOpInit(TestFusionLSTMOp):
def set_conf(self):
self.has_initial_state = True
class TestFusionLSTMOpReverse(TestFusionLSTMOp):
def set_conf(self):
self.is_reverse = True
class TestFusionLSTMOpInitReverse(TestFusionLSTMOp):
def set_conf(self):
self.has_initial_state = True
self.is_reverse = True
class TestLstmOpMD1(TestLstmOp):
def set_argument(self):
class TestFusionLSTMOpMD1(TestFusionLSTMOp):
def set_conf(self):
self.M = 36
self.D = 8
class TestLstmOpMD2(TestLstmOp):
def set_argument(self):
class TestFusionLSTMOpMD2(TestFusionLSTMOp):
def set_conf(self):
self.M = 8
self.D = 8
class TestLstmOpMD3(TestLstmOp):
def set_argument(self):
class TestFusionLSTMOpMD3(TestFusionLSTMOp):
def set_conf(self):
self.M = 15
self.D = 3
class TestLstmOpBS1(TestLstmOp):
def set_argument(self):
class TestFusionLSTMOpBS1(TestFusionLSTMOp):
def set_conf(self):
self.lod = [[3]]
self.D = 16
......
python/paddle/fluid/tests/unittests/test_fusion_seqexpand_concat_fc_op.py 0 → 100644
浏览文件 @ ec5cb376
# Copyright (c) 2018 PaddlePaddle Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from __future__ import print_function
import unittest
import numpy as np
from op_test import OpTest
from test_fusion_lstm_op import fc, ACTIVATION
def fusion_seqexpand_concat_fc(xs, lod, w, b, fc_act):
T = sum(lod[0])
N = len(lod[0])
num_inputs = len(xs)
D = w.shape[1]
expanded_inputs = [xs[0]]
for i in range(num_inputs - 1):
x = xs[i + 1]
assert x.shape[0] == N
expanded = np.repeat(x, lod[0], axis=0)
assert expanded.shape[0] == T
assert expanded.shape[1] == x.shape[1]
expanded_inputs.append(expanded)
fc_input = np.concatenate(expanded_inputs, axis=1)
assert fc_input.shape[0] == T
assert fc_input.shape[1] == w.shape[0]
fc_out = fc(fc_input, w, b)
fc_out = fc_act(fc_out)
assert fc_out.shape[0] == T
assert fc_out.shape[1] == D
return fc_out
class TestFusionSeqExpandConcatFCOp(OpTest):
def set_conf(self):
pass
def setUp(self):
self.op_type = 'fusion_seqexpand_concat_fc'
self.lod = [[3, 5, 8, 2]]
self.inputs_M = [15, 10, 10]
self.D = 20
self.with_bias = True
self.fc_act = 'relu'
self.set_conf()
T = sum(self.lod[0])
bs = len(self.lod[0])
num_inputs = len(self.inputs_M)
x0 = np.random.normal(size=(T, self.inputs_M[0])).astype('float32')
xs = [x0]
for i in range(num_inputs - 1):
xi = np.random.normal(size=(bs,
self.inputs_M[i + 1])).astype('float32')
xs.append(xi)
# fc weight and bias
w = np.random.normal(size=(sum(self.inputs_M),
self.D)).astype('float32')
b = np.random.normal(size=(
1, self.D)).astype('float32') if self.with_bias else np.zeros(
(1, self.D)).astype('float32')
out = fusion_seqexpand_concat_fc(xs, self.lod, w, b,
ACTIVATION[self.fc_act])
self.inputs = {'X': [('x0', (x0, self.lod))], 'FCWeight': w}
normal_lod = [[1] * bs]
for i in range(num_inputs - 1):
self.inputs['X'].append(('x%d' % (i + 1), (xs[i + 1], normal_lod)))
if self.with_bias:
self.inputs['FCBias'] = b
self.outputs = {'Out': (out, self.lod)}
self.attrs = {'fc_activation': self.fc_act}
def test_check_output(self):
self.check_output()
class TestFusionSECFCOpNonBias(TestFusionSeqExpandConcatFCOp):
def set_conf(self):
self.with_bias = False
class TestFusionSECFCOpNonAct(TestFusionSeqExpandConcatFCOp):
def set_conf(self):
self.fc_act = 'identity'
class TestFusionSECFCOpMD1(TestFusionSeqExpandConcatFCOp):
def set_conf(self):
self.inputs_M = [3, 4, 2, 1, 5]
self.D = 8
class TestFusionSECFCOpMD2(TestFusionSeqExpandConcatFCOp):
def set_conf(self):
self.lod = [[5, 6]]
self.inputs_M = [1, 1]
class TestFusionSECFCOpBS1_1(TestFusionSeqExpandConcatFCOp):
def set_conf(self):
self.lod = [[1]]
self.inputs_M = [3, 4, 2]
class TestFusionSECFCOpBS1_2(TestFusionSeqExpandConcatFCOp):
def set_conf(self):
self.lod = [[1]]
self.inputs_M = [3, 4]
class TestFusionSECFCOpBS1_3(TestFusionSeqExpandConcatFCOp):
def set_conf(self):
self.lod = [[5]]
self.inputs_M = [6, 3]
if __name__ == '__main__':
unittest.main()
python/paddle/fluid/tests/unittests/test_pad_constant_like.py 0 → 100644
浏览文件 @ ec5cb376
# Copyright (c) 2018 PaddlePaddle Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from __future__ import print_function
import unittest
import numpy as np
from op_test import OpTest
class TestPadOp(OpTest):
def setUp(self):
self.initTestCase()
self.op_type = "pad_constant_like"
self.inputs = {
'X': np.random.random(self.x_shape).astype("float32"),
'Y': np.random.random(self.y_shape).astype("float32")
}
self.attrs = {}
self.attrs['pad_value'] = self.pad_value
self.outputs = {
'Out': np.pad(self.inputs['Y'],
self.paddings,
mode='constant',
constant_values=self.pad_value)
}
def test_check_output(self):
self.check_output()
def test_check_grad_normal(self):
self.check_grad(['Y'], 'Out', max_relative_error=0.006)
def initTestCase(self):
self.x_shape = (16, 16)
self.y_shape = (3, 16)
self.pad_value = 0.1
self.paddings = [(0, 13), (0, 0)]
class TestCase1(TestPadOp):
def initTestCase(self):
self.x_shape = (4, 3, 4, 4)
self.y_shape = (2, 3, 4, 4)
self.paddings = [(0, 2), (0, 0), (0, 0), (0, 0)]
self.pad_value = 0.5
class TestCase2(TestPadOp):
def initTestCase(self):
self.x_shape = (4, 3, 4, 4)
self.y_shape = (2, 3, 2, 4)
self.paddings = [(0, 2), (0, 0), (0, 2), (0, 0)]
self.pad_value = 0.5
if __name__ == '__main__':
unittest.main()
python/paddle/fluid/tests/unittests/test_prelu_op.py
浏览文件 @ ec5cb376
......@@ -51,30 +51,28 @@ class PReluTest(OpTest):
def test_check_output(self):
self.check_output()
def test_check_grad(self):
self.check_grad(['X', 'Alpha'], 'Out')
def test_check_grad_ignore_x(self):
def test_check_grad_1_ignore_x(self):
self.check_grad(['Alpha'], 'Out', no_grad_set=set('X'))
def test_check_grad_ignore_alpha(self):
self.check_grad(['X'], 'Out', no_grad_set=set('Alpha'))
class TestCase1(PReluTest):
def initTestCase(self):
self.attrs = {'mode': "all"}
def test_check_grad_2(self):
self.check_grad(['X', 'Alpha'], 'Out')
def test_check_grad_3_ignore_alpha(self):
self.check_grad(['X'], 'Out', no_grad_set=set('Alpha'))
class TestCase2(PReluTest):
def initTestCase(self):
self.attrs = {'mode': "channel"}
# TODO(minqiyang): Resume these test cases after fixing Python3 CI job issues
# class TestCase1(PReluTest):
# def initTestCase(self):
# self.attrs = {'mode': "all"}
class TestCase3(PReluTest):
def initTestCase(self):
self.attrs = {'mode': "element"}
# class TestCase2(PReluTest):
# def initTestCase(self):
# self.attrs = {'mode': "channel"}
# class TestCase3(PReluTest):
# def initTestCase(self):
# self.attrs = {'mode': "element"}
if __name__ == "__main__":
unittest.main()
python/paddle/fluid/tests/unittests/test_print_op.py
浏览文件 @ ec5cb376
......@@ -35,9 +35,8 @@ class TestPrintOpCPU(unittest.TestCase):
def build_network(self, only_forward, **kargs):
x = layers.data('x', shape=[3], dtype='float32', lod_level=1)
x.stop_gradient = False
printed = layers.Print(input=x, **kargs)
if only_forward: return printed
loss = layers.mean(printed)
layers.Print(input=x, **kargs)
loss = layers.mean(x)
append_backward(loss=loss)
return loss
......
python/paddle/fluid/tests/unittests/test_program_code.py 0 → 100644
浏览文件 @ ec5cb376
# Copyright (c) 2018 PaddlePaddle Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import os
import time
import unittest
from multiprocessing import Process
import signal
import numpy
import paddle.fluid as fluid
import paddle.fluid.layers as layers
from paddle.fluid.layers.io import ListenAndServ
from paddle.fluid.layers.io import Recv
from paddle.fluid.layers.io import Send
from paddle.fluid.transpiler.details import program_to_code
class TestProgram2Code(unittest.TestCase):
def test_print(self):
place = fluid.CPUPlace()
self.init_serv(place)
self.init_client(place, 9123)
def init_serv(self, place):
main = fluid.Program()
with fluid.program_guard(main):
serv = ListenAndServ("127.0.0.1:0", ["X"], optimizer_mode=False)
with serv.do():
out_var = main.global_block().create_var(
name="scale_0.tmp_0",
psersistable=True,
dtype="float32",
shape=[32, 32])
x = layers.data(
shape=[32, 32],
dtype='float32',
name="X",
append_batch_size=False)
fluid.initializer.Constant(value=1.0)(x, main.global_block())
layers.scale(x=x, scale=10.0, out=out_var)
program_to_code(main)
def init_client(self, place, port):
main = fluid.Program()
with fluid.program_guard(main):
x = layers.data(
shape=[32, 32],
dtype='float32',
name='X',
append_batch_size=False)
fluid.initializer.Constant(value=2.3)(x, main.global_block())
get_var = main.global_block().create_var(
name="scale_0.tmp_0", # server side var
dtype="float32",
persistable=False,
shape=[32, 32])
fluid.initializer.Constant(value=2.3)(get_var, main.global_block())
Send("127.0.0.1:%d" % port, [x])
o = Recv("127.0.0.1:%d" % port, [get_var])
program_to_code(main)
if __name__ == "__main__":
unittest.main()
python/paddle/fluid/tests/unittests/test_scale_op.py
浏览文件 @ ec5cb376
......@@ -17,6 +17,8 @@ from __future__ import print_function
import unittest
import numpy as np
from op_test import OpTest
import paddle.fluid.core as core
from paddle.fluid.op import Operator
class TestScaleOp(OpTest):
......@@ -33,5 +35,57 @@ class TestScaleOp(OpTest):
self.check_grad(['X'], 'Out')
class TestScaleOpSelectedRows(unittest.TestCase):
def check_with_place(self, place, in_name, out_name):
scope = core.Scope()
# create and initialize Grad Variable
in_height = 10
in_rows = [0, 4, 7]
in_row_numel = 12
scale = 2.0
in_selected_rows = scope.var(in_name).get_selected_rows()
in_selected_rows.set_height(in_height)
in_selected_rows.set_rows(in_rows)
in_array = np.random.random(
(len(in_rows), in_row_numel)).astype("float32")
in_tensor = in_selected_rows.get_tensor()
in_tensor.set(in_array, place)
# create and initialize Param Variable
out_selected_rows = scope.var(out_name).get_selected_rows()
out_tensor = out_selected_rows.get_tensor()
out_tensor._set_dims(in_tensor._get_dims())
# create and run sgd operator
scale_op = Operator("scale", X=in_name, Out=out_name, scale=scale)
scale_op.run(scope, place)
# get and compare result
out_height = out_selected_rows.height()
out_rows = out_selected_rows.rows()
result_array = np.array(out_tensor)
assert (in_array * scale == result_array).all()
assert in_height == out_height
assert in_rows == out_rows
def test_scale_selected_rows(self):
places = [core.CPUPlace()]
if core.is_compiled_with_cuda():
places.append(core.CUDAPlace(0))
for place in places:
self.check_with_place(place, 'in', 'out')
def test_scale_selected_rows_inplace(self):
places = [core.CPUPlace()]
if core.is_compiled_with_cuda():
places.append(core.CUDAPlace(0))
for place in places:
self.check_with_place(place, 'in', 'in')
if __name__ == "__main__":
unittest.main()
python/paddle/fluid/tests/unittests/test_sequence_mask.py 0 → 100644
浏览文件 @ ec5cb376
# Copyright (c) 2018 PaddlePaddle Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from op_test import OpTest
import paddle.fluid as fluid
from paddle.fluid.framework import convert_np_dtype_to_dtype_
import paddle.fluid.core as core
import numpy as np
import copy
import unittest
class SequenceMaskTestBase(OpTest):
def initDefaultParameters(self):
self.op_type = 'sequence_mask'
self.maxlen = 10
self.mask_dtype = 'int64'
self.x = [[0, 3, 4], [5, 7, 9]]
def initParameters(self):
pass
def setUp(self):
self.initDefaultParameters()
self.initParameters()
if not isinstance(self.x, np.ndarray):
self.x = np.array(self.x)
self.inputs = {'X': self.x}
self.outputs = {'Y': self.calc_ground_truth_mask()}
self.attrs = {
'maxlen': self.maxlen,
'out_dtype': convert_np_dtype_to_dtype_(self.mask_dtype)
}
def calc_ground_truth_mask(self):
maxlen = np.max(self.x) if self.maxlen < 0 else self.maxlen
shape = self.x.shape + (maxlen, )
index_broadcast = np.broadcast_to(
np.reshape(
range(maxlen), newshape=[1] * self.x.ndim + [-1]),
shape=shape)
x_broadcast = np.broadcast_to(
np.reshape(
self.x, newshape=self.x.shape + (-1, )), shape=shape)
return (index_broadcast < x_broadcast).astype(self.mask_dtype)
def test_check_output(self):
self.check_output()
class SequenceMaskTest1(SequenceMaskTestBase):
def initParameters(self):
self.mask_dtype = 'bool'
class SequenceMaskTest2(SequenceMaskTestBase):
def initParameters(self):
self.mask_dtype = 'uint8'
class SequenceMaskTest3(SequenceMaskTestBase):
def initParameters(self):
self.mask_dtype = 'int32'
class SequenceMaskTest4(SequenceMaskTestBase):
def initParameters(self):
self.mask_dtype = 'float32'
class SequenceMaskTest5(SequenceMaskTestBase):
def initParameters(self):
self.mask_dtype = 'float64'
class SequenceMaskTest6(SequenceMaskTestBase):
def initParameters(self):
self.maxlen = -1
if __name__ == '__main__':
unittest.main()
python/paddle/fluid/tests/unittests/test_sequence_pad_op.py 0 → 100644
浏览文件 @ ec5cb376
# Copyright (c) 2018 PaddlePaddle Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import unittest
import numpy as np
from op_test import OpTest
class TestSequencePadOp(OpTest):
def set_attr(self):
self.x_shape = [12, 4]
self.x_len_lod = [[2, 3, 4, 3]]
self.pad_value = [1.0]
self.padded_length = -1
self.dtype = 'float32'
def set_data(self):
x_data = np.random.uniform(0.1, 0.5, self.x_shape).astype(self.dtype)
pad_value_data = np.array(self.pad_value).astype(self.dtype)
self.inputs = {
'X': (x_data, self.x_len_lod),
'PadValue': pad_value_data
}
self.attrs = {'padded_length': self.padded_length}
def compute(self):
# get padded length
padded_length = self.padded_length
x_len_lod_0 = self.x_len_lod[0]
if padded_length == -1:
max_seq_len = 0
for l in x_len_lod_0:
max_seq_len = max(max_seq_len, l)
padded_length = max_seq_len
# do padding
x_data = self.inputs['X'][0]
pad_value_data = self.inputs['PadValue']
if pad_value_data.shape == (1, ):
pad_value_data = np.broadcast_to(
pad_value_data, shape=x_data.shape[1:])
padded_sequences = []
start_idx = 0
for l in x_len_lod_0:
end_idx = start_idx + l
seq = x_data[start_idx:end_idx]
to_pad_len = padded_length - l
for _ in range(to_pad_len):
seq = np.append(seq, pad_value_data[np.newaxis, :], axis=0)
padded_sequences.append(seq)
start_idx = end_idx
out_data = np.array(padded_sequences)
self.outputs = {'Out': out_data}
def setUp(self):
self.op_type = 'sequence_pad'
self.set_attr()
self.set_data()
self.compute()
def test_check_output(self):
self.check_output()
def test_check_grad(self):
self.check_grad(["X"], "Out")
class TestSequencePadOp2(TestSequencePadOp):
def set_attr(self):
self.x_shape = [12, 4]
self.x_len_lod = [[2, 3, 4, 3]]
self.pad_value = [1.0, 2.0, 3.0, 4.0]
self.padded_length = -1
self.dtype = 'float32'
class TestSequencePadOp3(TestSequencePadOp):
def set_attr(self):
self.x_shape = [12, 4]
self.x_len_lod = [[2, 3, 4, 3]]
self.pad_value = [1.0]
self.padded_length = 7
self.dtype = 'float32'
class TestSequencePadOp4(TestSequencePadOp):
def set_attr(self):
self.x_shape = [12, 4]
self.x_len_lod = [[2, 3, 4, 3]]
self.pad_value = [1.0, 2.0, 3.0, 4.0]
self.padded_length = 7
self.dtype = 'float32'
class TestSequencePadOp5(TestSequencePadOp):
def set_attr(self):
self.x_shape = [12, 2, 2]
self.x_len_lod = [[2, 3, 4, 3]]
self.pad_value = [1.0]
self.padded_length = -1
self.dtype = 'float32'
class TestSequencePadOp6(TestSequencePadOp):
def set_attr(self):
self.x_shape = [12, 2, 2]
self.x_len_lod = [[2, 3, 4, 3]]
self.pad_value = [[1.0, 2.0], [3.0, 4.0]]
self.padded_length = -1
self.dtype = 'float32'
class TestSequencePadOp7(TestSequencePadOp):
def set_attr(self):
self.x_shape = [12, 2, 2]
self.x_len_lod = [[2, 3, 4, 3]]
self.pad_value = [1.0]
self.padded_length = 7
self.dtype = 'float32'
python/paddle/fluid/tests/unittests/test_stack_op.py 0 → 100644
浏览文件 @ ec5cb376
# Copyright (c) 2018 PaddlePaddle Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from op_test import OpTest
import numpy as np
import unittest
class TestStackOpBase(OpTest):
def initDefaultParameters(self):
self.num_inputs = 4
self.input_dim = (5, 6, 7)
self.axis = 0
self.dtype = 'float32'
def initParameters(self):
pass
def get_x_names(self):
x_names = []
for i in range(self.num_inputs):
x_names.append('x{}'.format(i))
return x_names
def setUp(self):
self.initDefaultParameters()
self.initParameters()
self.op_type = 'stack'
self.x = []
for i in range(self.num_inputs):
self.x.append(
np.random.random(size=self.input_dim).astype(self.dtype))
tmp = []
x_names = self.get_x_names()
for i in range(self.num_inputs):
tmp.append((x_names[i], self.x[i]))
self.inputs = {'X': tmp}
self.outputs = {'Y': np.stack(self.x, axis=self.axis)}
self.attrs = {'axis': self.axis}
def test_check_output(self):
self.check_output()
def test_check_grad(self):
self.check_grad(self.get_x_names(), 'Y')
class TestStackOp1(TestStackOpBase):
def initParameters(self):
self.num_inputs = 16
class TestStackOp2(TestStackOpBase):
def initParameters(self):
self.num_inputs = 20
class TestStackOp3(TestStackOpBase):
def initParameters(self):
self.axis = -1
class TestStackOp4(TestStackOpBase):
def initParameters(self):
self.axis = -4
class TestStackOp5(TestStackOpBase):
def initParameters(self):
self.axis = 1
class TestStackOp6(TestStackOpBase):
def initParameters(self):
self.axis = 3
if __name__ == '__main__':
unittest.main()
python/paddle/fluid/tests/unittests/test_tensor.py
浏览文件 @ ec5cb376
......@@ -59,6 +59,27 @@ class TestTensor(unittest.TestCase):
self.assertAlmostEqual(1.0, tensor_array_2[3, 9])
self.assertAlmostEqual(2.0, tensor_array_2[19, 11])
def test_int8_tensor(self):
scope = core.Scope()
var = scope.var("int8_tensor")
cpu_tensor = var.get_tensor()
tensor_array = numpy.random.randint(
-127, high=128, size=[100, 200], dtype=numpy.int8)
place = core.CPUPlace()
cpu_tensor.set(tensor_array, place)
cpu_tensor_array_2 = numpy.array(cpu_tensor)
self.assertAlmostEqual(cpu_tensor_array_2.all(), tensor_array.all())
if core.is_compiled_with_cuda():
cuda_tensor = var.get_tensor()
tensor_array = numpy.random.randint(
-127, high=128, size=[100, 200], dtype=numpy.int8)
place = core.CUDAPlace(0)
cuda_tensor.set(tensor_array, place)
cuda_tensor_array_2 = numpy.array(cuda_tensor)
self.assertAlmostEqual(cuda_tensor_array_2.all(),
tensor_array.all())
def test_int_lod_tensor(self):
place = core.CPUPlace()
scope = core.Scope()
......
python/paddle/fluid/tests/unittests/test_unstack_op.py 0 → 100644
浏览文件 @ ec5cb376
# Copyright (c) 2018 PaddlePaddle Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from op_test import OpTest
import numpy as np
import unittest
class TestUnStackOpBase(OpTest):
def initDefaultParameters(self):
self.input_dim = (5, 6, 7)
self.axis = 0
self.dtype = 'float32'
def initParameters(self):
pass
def get_y_names(self):
y_names = []
for i in range(self.input_dim[self.axis]):
y_names.append('y{}'.format(i))
return y_names
def setUp(self):
self.initDefaultParameters()
self.initParameters()
self.op_type = 'unstack'
self.x = np.random.random(size=self.input_dim).astype(self.dtype)
outs = np.split(self.x, self.input_dim[self.axis], self.axis)
new_shape = list(self.input_dim)
del new_shape[self.axis]
y_names = self.get_y_names()
tmp = []
for i in range(self.input_dim[self.axis]):
tmp.append((y_names[i], np.reshape(outs[i], new_shape)))
self.inputs = {'X': self.x}
self.outputs = {'Y': tmp}
self.attrs = {'axis': self.axis, 'num': self.input_dim[self.axis]}
def test_check_output(self):
self.check_output()
def test_check_grad(self):
self.check_grad('X', self.get_y_names())
class TestStackOp3(TestUnStackOpBase):
def initParameters(self):
self.axis = -1
class TestStackOp4(TestUnStackOpBase):
def initParameters(self):
self.axis = -3
class TestStackOp5(TestUnStackOpBase):
def initParameters(self):
self.axis = 1
class TestStackOp6(TestUnStackOpBase):
def initParameters(self):
self.axis = 2
if __name__ == '__main__':
unittest.main()
python/paddle/fluid/tests/unittests/test_variable.py
浏览文件 @ ec5cb376
......@@ -31,7 +31,8 @@ class TestVariable(unittest.TestCase):
self.assertEqual(DT.INT16, convert("int16"))
self.assertEqual(DT.INT64, convert("int64"))
self.assertEqual(DT.BOOL, convert("bool"))
self.assertRaises(ValueError, lambda: convert("int8"))
self.assertEqual(DT.INT8, convert("int8"))
self.assertEqual(DT.UINT8, convert("uint8"))
def test_var(self):
b = default_main_program().current_block()
......
python/paddle/fluid/tests/unittests/testsuite.py
浏览文件 @ ec5cb376
......@@ -153,9 +153,6 @@ def append_input_output(block, op_proto, np_list, is_input, dtype):
def append_loss_ops(block, output_names):
mean_inputs = list(map(block.var, output_names))
# for item in mean_inputs:
# print(item)
# print("Item", item.dtype)
if len(mean_inputs) == 1:
loss = block.create_var(dtype=mean_inputs[0].dtype, shape=[1])
......
python/paddle/fluid/transpiler/details/program_utils.py
浏览文件 @ ec5cb376
......@@ -16,6 +16,9 @@ from __future__ import print_function
import six
from paddle.fluid import core
import paddle
def delete_ops(block, ops):
try:
......@@ -39,3 +42,133 @@ def find_op_by_output_arg(block, arg_name):
if arg_name in op.output_arg_names:
return index
return -1
def get_indent_space(indent, space_num=4):
ret = ""
for i in range(0, indent * space_num):
ret += " "
return ret
def variable_to_code(var):
"""
Get readable codes of fluid variable.
Args:
var: A fluid operator.
Returns:
string: The formatted string.
"""
var_str = "{name} : fluid.{type}.shape{shape}.astype({dtype})".\
format(i="{", e="}", name=var.name, type=var.type, shape=var.shape, dtype=var.dtype)
if type(var) == paddle.fluid.framework.Parameter:
if var.trainable:
var_str = "trainable parameter " + var_str
else:
var_str = "parameter " + var_str
else:
var_str = "var " + var_str
if var.persistable:
var_str = "persist " + var_str
return var_str
def op_to_code(op):
"""
Get readable codes of fluid operator.
Args:
op: A fluid operator.
Returns:
string: The foramtted string.
"""
outputs_str = "{"
for i in range(0, len(op.output_names)):
outputs_str += "{name}=".format(name=op.output_names[i])
o = op.output(op.output_names[i])
outputs_str += "{value}".format(value=o)
if i != len(op.output_names) - 1:
outputs_str += ", "
outputs_str += "}"
inputs_str = "{"
for i in range(0, len(op.input_names)):
inputs_str += "{name}=".format(name=op.input_names[i])
o = op.input(op.input_names[i])
inputs_str += "{value}".format(value=o)
if i != len(op.input_names) - 1:
inputs_str += ", "
inputs_str += "}"
attrs_str = ""
for i in range(0, len(op.attr_names)):
name = op.attr_names[i]
attr_type = op.desc.attr_type(name)
if attr_type == core.AttrType.BLOCK:
a = "{name} = block[{value}]".format(
name=name, type=attr_type, value=op.block_attr_id(name))
attrs_str += a
continue
if attr_type == core.AttrType.BLOCKS:
a = "{name} = blocks{value}".format(
name=name, type=attr_type, value=op.blocks_attr_ids(name))
attrs_str += a
continue
a = "{name} = {value}".format(
name=name, type=attr_type, value=op.desc.attr(name))
attrs_str += a
if i != len(op.attr_names) - 1:
attrs_str += ", "
if outputs_str != "{}":
op_str = "{outputs} = {op_type}(inputs={inputs}, {attrs})".\
format(outputs = outputs_str, op_type=op.type, inputs=inputs_str, attrs=attrs_str)
else:
op_str = "{op_type}(inputs={inputs}, {attrs})".\
format(op_type=op.type, inputs=inputs_str, attrs=attrs_str)
return op_str
def program_to_code(prog):
"""
Print readable codes of fluid program.
Args:
prog : A fluid program.
An example result like bellow:
https://github.com/PaddlePaddle/Paddle/pull/12673
"""
indent = 0
block_idx = 0
for block in prog.blocks:
print("{0}{1} // block {2}".format(
get_indent_space(indent), '{', block_idx))
indent += 1
# sort all vars
all_vars = sorted(six.iteritems(block.vars), key=lambda x: x[0])
for var in all_vars:
print("{}{}".format(
get_indent_space(indent), variable_to_code(var[1])))
if len(all_vars) > 0:
print("")
for op in block.ops:
print("{}{}".format(get_indent_space(indent), op_to_code(op)))
indent -= 1
print("{0}{1}".format(get_indent_space(indent), '}'))
block_idx += 1
python/paddle/fluid/transpiler/distribute_transpiler.py
浏览文件 @ ec5cb376
......@@ -31,7 +31,6 @@ Steps to transpile pserver:
"""
import math
import random
import numpy as np
import collections
import six
......@@ -212,8 +211,10 @@ class DistributeTranspiler(object):
ps_dispatcher = self.config.split_method(self.pserver_endpoints)
self.has_distributed_lookup_table = self._has_distributed_lookup_table()
self.param_name_to_grad_name = dict()
self.grad_name_to_param_name = dict()
for param_var, grad_var in self.params_grads:
self.param_name_to_grad_name[param_var.name] = grad_var.name
self.grad_name_to_param_name[grad_var.name] = param_var.name
# add distributed attrs to program
self.origin_program._is_distributed = True
......@@ -237,8 +238,8 @@ class DistributeTranspiler(object):
grad_var_mapping_items = list(six.iteritems(self.grad_var_mapping))
if not self.config.slice_var_up:
random.seed(self.origin_program.random_seed)
random.shuffle(grad_var_mapping_items)
np.random.seed(self.origin_program.random_seed)
np.random.shuffle(grad_var_mapping_items)
grad_name_to_send_dummy_out = dict()
for grad_varname, splited_vars in grad_var_mapping_items:
......@@ -262,8 +263,10 @@ class DistributeTranspiler(object):
AssertionError("Can not insert the send op by original "
"variable name :", splited_grad_varname)
dummy_output = program.global_block().create_var()
dummy_output = program.global_block().create_var(
name=framework.generate_control_dev_var_name())
grad_name_to_send_dummy_out[grad_varname] = dummy_output
program.global_block()._insert_op(
index=index + 1,
type="send",
......@@ -272,16 +275,21 @@ class DistributeTranspiler(object):
attrs={
"epmap": eplist,
RPC_OP_ROLE_ATTR_NAME: RPC_OP_ROLE_ATTR_VALUE,
OP_ROLE_VAR_ATTR_NAME:
[self.grad_name_to_param_name[grad_varname], grad_varname],
"sync_mode": not self.sync_mode,
})
for _, var in enumerate(splited_vars):
send_vars.append(var)
if self.sync_mode:
send_barrier_out = program.global_block().create_var(
name=framework.generate_control_dev_var_name())
input_deps = grad_name_to_send_dummy_out.values()
program.global_block().append_op(
type="send_barrier",
inputs={},
outputs={},
inputs={"X": input_deps},
outputs={"Out": send_barrier_out},
attrs={
"endpoints": pserver_endpoints,
RPC_OP_ROLE_ATTR_NAME: RPC_OP_ROLE_ATTR_VALUE
......@@ -299,28 +307,39 @@ class DistributeTranspiler(object):
self.param_grad_ep_mapping[ep]["grads"].append(send_vars[i])
# step4: Concat the parameters splits together after recv.
all_recv_outputs = []
for param_varname, splited_var in six.iteritems(self.param_var_mapping):
eps = []
for var in splited_var:
index = [v.name for v in recv_vars].index(var.name)
eps.append(eplist[index])
grad_send_dummy_out = grad_name_to_send_dummy_out[
if self.sync_mode:
recv_dep_in = send_barrier_out
else:
# connect deps to send op in async mode
recv_dep_in = grad_name_to_send_dummy_out[
self.param_name_to_grad_name[param_varname]]
all_recv_outputs.extend(splited_var)
program.global_block().append_op(
type="recv",
inputs={"X": [grad_send_dummy_out]},
inputs={"X": [recv_dep_in]},
outputs={"Out": splited_var},
attrs={
"epmap": eps,
RPC_OP_ROLE_ATTR_NAME: RPC_OP_ROLE_ATTR_VALUE,
OP_ROLE_VAR_ATTR_NAME: [
param_varname,
self.param_name_to_grad_name[param_varname]
],
"sync_mode": not self.sync_mode
})
if self.sync_mode:
# form a WAW dependency
program.global_block().append_op(
type="fetch_barrier",
inputs={},
outputs={},
outputs={"Out": all_recv_outputs},
attrs={
"endpoints": pserver_endpoints,
RPC_OP_ROLE_ATTR_NAME: RPC_OP_ROLE_ATTR_VALUE
......@@ -404,10 +423,12 @@ class DistributeTranspiler(object):
RPC_OP_ROLE_ATTR_NAME: RPC_OP_ROLE_ATTR_VALUE
})
fetch_barrier_out = startup_program.global_block().create_var(
name=framework.generate_control_dev_var_name())
startup_program.global_block().append_op(
type="fetch_barrier",
inputs={},
outputs={},
outputs={"Out": fetch_barrier_out},
attrs={
"endpoints": self.pserver_endpoints,
RPC_OP_ROLE_ATTR_NAME: RPC_OP_ROLE_ATTR_VALUE
......@@ -971,7 +992,11 @@ class DistributeTranspiler(object):
attrs={
"sync_mode": True,
"epmap": pserver_endpoints,
RPC_OP_ROLE_ATTR_NAME: RPC_OP_ROLE_ATTR_VALUE
RPC_OP_ROLE_ATTR_NAME: RPC_OP_ROLE_ATTR_VALUE,
OP_ROLE_VAR_ATTR_NAME: [
self.grad_name_to_param_name[table_grad_name],
table_grad_name
]
})
break
......
tools/check_ctest_hung.py
浏览文件 @ ec5cb376
......@@ -12,6 +12,8 @@
# See the License for the specific language governing permissions and
# limitations under the License.
from __future__ import print_function
import sys
import re
......@@ -46,7 +48,7 @@ Diff: set(['test_parallel_executor_crf'])
start_parts = escape(l).split(" ")
m = re.search("Start\s+[0-9]+\:\s([a-z0-9_]+)", escape(l))
started.add(m.group(1))
print "Diff: ", started - passed
print("Diff: ", started - passed)
if __name__ == "__main__":
......
tools/print_signatures.py
浏览文件 @ ec5cb376
......@@ -17,6 +17,8 @@ Print all signature of a python module in alphabet order.
Usage:
./print_signature "paddle.fluid" > signature.txt
"""
from __future__ import print_function
import importlib
import inspect
import collections
......@@ -64,4 +66,4 @@ def visit_all_module(mod):
visit_all_module(importlib.import_module(sys.argv[1]))
for name in member_dict:
print name, member_dict[name]
print(name, member_dict[name])
tools/timeline.py
浏览文件 @ ec5cb376
......@@ -14,6 +14,7 @@
import argparse
import json
import six
import sys
import unittest
......@@ -124,7 +125,7 @@ class Timeline(object):
return cur_pid
def _allocate_pids(self):
for k, profile_pb in self._profile_dict.iteritems():
for k, profile_pb in six.iteritems(self._profile_dict):
for event in profile_pb.events:
if event.type == profiler_pb2.Event.CPU:
if (k, event.device_id, "CPU") not in self._devices:
......@@ -140,7 +141,7 @@ class Timeline(object):
(k, event.device_id), pid)
def _allocate_events(self):
for k, profile_pb in self._profile_dict.iteritems():
for k, profile_pb in six.iteritems(self._profile_dict):
for event in profile_pb.events:
if event.type == profiler_pb2.Event.CPU:
type = "CPU"
......
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