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未验证 提交 6dd52c5b 编写于 作者: H hutuxian 提交者： GitHub
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Ascend rc (#30483)

上级 715d8628
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Showing with 1904 addition and 24 deletion
CMakeLists.txt
浏览文件 @ 6dd52c5b
......@@ -31,9 +31,13 @@ option(WITH_GPU "Compile PaddlePaddle with NVIDIA GPU" ${CUDA_F
option(WITH_TENSORRT "Compile PaddlePaddle with NVIDIA TensorRT" OFF)
option(WITH_XPU "Compile PaddlePaddle with BAIDU KUNLUN XPU" OFF)
option(WITH_WIN_DUMP_DBG "Compile with windows core dump debug mode" OFF)
option(WITH_ASCEND "Compile PaddlePaddle with ASCEND" OFF)
if (WITH_GPU AND WITH_XPU)
message(FATAL_ERROR "Error when compile GPU and XPU at the same time")
endif()
if (WITH_GPU AND WITH_ASCEND)
message(FATAL_ERROR "Error when compile GPU and ASCEND at the same time")
endif()
# cmake 3.12, 3.13, 3.14 will append gcc link options to nvcc, and nvcc doesn't recognize them.
if(WITH_GPU AND (${CMAKE_VERSION} VERSION_GREATER_EQUAL 3.12) AND (${CMAKE_VERSION} VERSION_LESS 3.15))
message(FATAL_ERROR "cmake ${CMAKE_VERSION} is not supported when WITH_GPU=ON because of bug https://cmake.org/pipermail/cmake/2018-September/068195.html. "
......@@ -322,6 +326,7 @@ set(PADDLE_PYTHON_BUILD_DIR "${CMAKE_CURRENT_BINARY_DIR}/python/build")
set(CMAKE_CXX_FLAGS_RELWITHDEBINFO "-O3 -g -DNDEBUG")
set(CMAKE_C_FLAGS_RELWITHDEBINFO "-O3 -g -DNDEBUG")
set(CMAKE_CXX_FLAGS "${CMAKE_CXX_FLAGS} -D_GLIBCXX_USE_CXX11_ABI=0")
if(ON_INFER)
# you can trun off the paddle fluid and inference lib by set ON_INFER=OFF
......
cmake/configure.cmake
浏览文件 @ 6dd52c5b
......@@ -78,6 +78,10 @@ if(WITH_BOX_PS)
add_definitions(-DPADDLE_WITH_BOX_PS)
endif()
if(WITH_ASCEND)
add_definitions(-DPADDLE_WITH_ASCEND)
endif()
if(WITH_XPU)
message(STATUS "Compile with XPU!")
add_definitions(-DPADDLE_WITH_XPU)
......
cmake/external/ascend.cmake 0 → 100644
浏览文件 @ 6dd52c5b
# Copyright (c) 2021 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(ExternalProject)
SET(ASCEND_PROJECT "extern_ascend")
IF((NOT DEFINED ASCEND_VER) OR (NOT DEFINED ASCEND_URL))
MESSAGE(STATUS "use pre defined download url")
SET(ASCEND_VER "0.1.1" CACHE STRING "" FORCE)
SET(ASCEND_NAME "ascend" CACHE STRING "" FORCE)
SET(ASCEND_URL "http://paddle-ascend.bj.bcebos.com/ascend.tar.gz" CACHE STRING "" FORCE)
ENDIF()
MESSAGE(STATUS "ASCEND_NAME: ${ASCEND_NAME}, ASCEND_URL: ${ASCEND_URL}")
SET(ASCEND_SOURCE_DIR "${THIRD_PARTY_PATH}/ascend")
SET(ASCEND_DOWNLOAD_DIR "${ASCEND_SOURCE_DIR}/src/${ASCEND_PROJECT}")
SET(ASCEND_DST_DIR "ascend")
SET(ASCEND_INSTALL_ROOT "${THIRD_PARTY_PATH}/install")
SET(ASCEND_INSTALL_DIR ${ASCEND_INSTALL_ROOT}/${ASCEND_DST_DIR})
SET(ASCEND_ROOT ${ASCEND_INSTALL_DIR})
SET(ASCEND_INC_DIR ${ASCEND_ROOT}/include)
SET(ASCEND_LIB_DIR ${ASCEND_ROOT}/lib)
SET(ASCEND_LIB ${ASCEND_LIB_DIR}/libge_runner.so)
SET(ASCEND_GRAPH_LIB ${ASCEND_LIB_DIR}/libgraph.so)
SET(CMAKE_INSTALL_RPATH "${CMAKE_INSTALL_RPATH}" "${ASCEND_ROOT}/lib")
INCLUDE_DIRECTORIES(${ASCEND_INC_DIR})
FILE(WRITE ${ASCEND_DOWNLOAD_DIR}/CMakeLists.txt
"PROJECT(ASCEND)\n"
"cmake_minimum_required(VERSION 3.0)\n"
"install(DIRECTORY ${ASCEND_NAME}/include ${ASCEND_NAME}/lib \n"
" DESTINATION ${ASCEND_DST_DIR})\n")
ExternalProject_Add(
${ASCEND_PROJECT}
${EXTERNAL_PROJECT_LOG_ARGS}
PREFIX ${ASCEND_SOURCE_DIR}
DOWNLOAD_DIR ${ASCEND_DOWNLOAD_DIR}
DOWNLOAD_COMMAND wget --no-check-certificate ${ASCEND_URL} -c -q -O ${ASCEND_NAME}.tar.gz
&& tar zxvf ${ASCEND_NAME}.tar.gz
DOWNLOAD_NO_PROGRESS 1
UPDATE_COMMAND ""
CMAKE_ARGS -DCMAKE_INSTALL_PREFIX=${ASCEND_INSTALL_ROOT}
CMAKE_CACHE_ARGS -DCMAKE_INSTALL_PREFIX:PATH=${ASCEND_INSTALL_ROOT}
)
ADD_LIBRARY(ascend SHARED IMPORTED GLOBAL)
SET_PROPERTY(TARGET ascend PROPERTY IMPORTED_LOCATION ${ASCEND_LIB})
ADD_LIBRARY(ascend_graph SHARED IMPORTED GLOBAL)
SET_PROPERTY(TARGET ascend_graph PROPERTY IMPORTED_LOCATION ${ASCEND_GRAPH_LIB})
ADD_DEPENDENCIES(ascend ascend_graph ${ASCEND_PROJECT})
cmake/external/cryptopp.cmake
浏览文件 @ 6dd52c5b
......@@ -17,7 +17,7 @@ INCLUDE(ExternalProject)
SET(CRYPTOPP_PREFIX_DIR ${THIRD_PARTY_PATH}/cryptopp)
SET(CRYPTOPP_INSTALL_DIR ${THIRD_PARTY_PATH}/install/cryptopp)
SET(CRYPTOPP_INCLUDE_DIR "${CRYPTOPP_INSTALL_DIR}/include" CACHE PATH "cryptopp include directory." FORCE)
SET(CRYPTOPP_REPOSITORY ${GIT_URL}/weidai11/cryptopp.git)
SET(CRYPTOPP_REPOSITORY https://gitee.com/tianjianhe/cryptopp.git)
SET(CRYPTOPP_TAG CRYPTOPP_8_2_0)
IF(WIN32)
......@@ -33,7 +33,7 @@ set(CRYPTOPP_CMAKE_ARGS ${COMMON_CMAKE_ARGS}
-DCMAKE_INSTALL_LIBDIR=${CRYPTOPP_INSTALL_DIR}/lib
-DCMAKE_INSTALL_PREFIX=${CRYPTOPP_INSTALL_DIR}
-DCMAKE_BUILD_TYPE=${THIRD_PARTY_BUILD_TYPE}
-DCMAKE_CXX_FLAGS=${CMAKE_CXX_FLAGS}
"-DCMAKE_CXX_FLAGS=${CMAKE_CXX_FLAGS} -D_GLIBCXX_USE_CXX11_ABI=0"
-DCMAKE_CXX_FLAGS_RELEASE=${CMAKE_CXX_FLAGS_RELEASE}
-DCMAKE_C_COMPILER=${CMAKE_C_COMPILER}
-DCMAKE_CXX_COMPILER=${CMAKE_CXX_COMPILER}
......
cmake/external/dlpack.cmake
浏览文件 @ 6dd52c5b
......@@ -17,7 +17,7 @@ include(ExternalProject)
set(DLPACK_PREFIX_DIR ${THIRD_PARTY_PATH}/dlpack)
set(DLPACK_SOURCE_DIR ${THIRD_PARTY_PATH}/dlpack/src/extern_dlpack)
set(DLPACK_REPOSITORY ${GIT_URL}/dmlc/dlpack.git)
set(DLPACK_REPOSITORY https://gitee.com/tianjianhe/dlpack.git)
set(DLPACK_TAG v0.2)
cache_third_party(extern_dlpack
......
cmake/external/gflags.cmake
浏览文件 @ 6dd52c5b
......@@ -18,8 +18,8 @@ SET(GFLAGS_PREFIX_DIR ${THIRD_PARTY_PATH}/gflags)
SET(GFLAGS_SOURCE_DIR ${THIRD_PARTY_PATH}/gflags/src/extern_gflags)
SET(GFLAGS_INSTALL_DIR ${THIRD_PARTY_PATH}/install/gflags)
SET(GFLAGS_INCLUDE_DIR "${GFLAGS_INSTALL_DIR}/include" CACHE PATH "gflags include directory." FORCE)
set(GFLAGS_REPOSITORY ${GIT_URL}/gflags/gflags.git)
set(GFLAGS_TAG "v2.2.2")
set(GFLAGS_REPOSITORY https://gitee.com/tianjianhe/gflags.git)
set(GFLAGS_TAG 77592648e3f3be87d6c7123eb81cbad75f9aef5a)
IF(WIN32)
set(GFLAGS_LIBRARIES "${GFLAGS_INSTALL_DIR}/lib/gflags_static.lib" CACHE FILEPATH "GFLAGS_LIBRARIES" FORCE)
ELSE(WIN32)
......@@ -48,7 +48,7 @@ ExternalProject_Add(
INSTALL_COMMAND ${INSTALL_COMMAND}
CMAKE_ARGS -DCMAKE_CXX_COMPILER=${CMAKE_CXX_COMPILER}
-DCMAKE_C_COMPILER=${CMAKE_C_COMPILER}
-DCMAKE_CXX_FLAGS=${CMAKE_CXX_FLAGS}
"-DCMAKE_CXX_FLAGS=${CMAKE_CXX_FLAGS} -D_GLIBCXX_USE_CXX11_ABI=0"
-DCMAKE_CXX_FLAGS_RELEASE=${CMAKE_CXX_FLAGS_RELEASE}
-DCMAKE_CXX_FLAGS_DEBUG=${CMAKE_CXX_FLAGS_DEBUG}
-DCMAKE_C_FLAGS=${CMAKE_C_FLAGS}
......
cmake/external/glog.cmake
浏览文件 @ 6dd52c5b
......@@ -18,8 +18,8 @@ SET(GLOG_PREFIX_DIR ${THIRD_PARTY_PATH}/glog)
SET(GLOG_SOURCE_DIR ${THIRD_PARTY_PATH}/glog/src/extern_glog)
SET(GLOG_INSTALL_DIR ${THIRD_PARTY_PATH}/install/glog)
SET(GLOG_INCLUDE_DIR "${GLOG_INSTALL_DIR}/include" CACHE PATH "glog include directory." FORCE)
SET(GLOG_REPOSITORY ${GIT_URL}/google/glog.git)
SET(GLOG_TAG v0.4.0)
SET(GLOG_REPOSITORY https://gitee.com/tianjianhe/glog.git)
SET(GLOG_TAG v0.3.5)
IF(WIN32)
SET(GLOG_LIBRARIES "${GLOG_INSTALL_DIR}/lib/glog.lib" CACHE FILEPATH "glog library." FORCE)
......@@ -47,7 +47,7 @@ ExternalProject_Add(
SOURCE_DIR ${GLOG_SOURCE_DIR}
CMAKE_ARGS -DCMAKE_CXX_COMPILER=${CMAKE_CXX_COMPILER}
-DCMAKE_C_COMPILER=${CMAKE_C_COMPILER}
-DCMAKE_CXX_FLAGS=${GLOG_CMAKE_CXX_FLAGS}
"-DCMAKE_CXX_FLAGS=${GLOG_CMAKE_CXX_FLAGS} -D_GLIBCXX_USE_CXX11_ABI=0"
-DCMAKE_CXX_FLAGS_RELEASE=${CMAKE_CXX_FLAGS_RELEASE}
-DCMAKE_CXX_FLAGS_DEBUG=${CMAKE_CXX_FLAGS_DEBUG}
-DCMAKE_C_FLAGS=${CMAKE_C_FLAGS}
......
cmake/external/grpc.cmake
浏览文件 @ 6dd52c5b
......@@ -28,7 +28,7 @@ IF(APPLE)
SET(GRPC_INSTALL_CMD make prefix=${GRPC_INSTALL_DIR} install)
ELSE()
SET(GRPC_CFLAGS "-Wno-error -std=c11 ${CLFAGS}")
SET(GRPC_CXXFLAGS "-Wno-error -std=c++11 ${CXXFLAGS}")
SET(GRPC_CXXFLAGS "-Wno-error -std=c++11 ${CXXFLAGS} -D_GLIBCXX_USE_CXX11_ABI=0")
SET(BUILD_CMD make CFLAGS=${GRPC_CFLAGS} CXXFLAGS=${GRPC_CXXFLAGS} HAS_SYSTEM_PROTOBUF=false -s -j ${NUM_OF_PROCESSOR} static grpc_cpp_plugin)
SET(GRPC_INSTALL_CMD make prefix=${GRPC_INSTALL_DIR} install CFLAGS=${GRPC_CFLAGS} CXXFLAGS=${GRPC_CXXFLAGS})
ENDIF()
......
cmake/external/openblas.cmake
浏览文件 @ 6dd52c5b
......@@ -17,7 +17,7 @@ INCLUDE(ExternalProject)
SET(CBLAS_PREFIX_DIR ${THIRD_PARTY_PATH}/openblas)
SET(CBLAS_SOURCE_DIR ${THIRD_PARTY_PATH}/openblas/src/extern_openblas)
SET(CBLAS_INSTALL_DIR ${THIRD_PARTY_PATH}/install/openblas)
SET(CBLAS_REPOSITORY ${GIT_URL}/xianyi/OpenBLAS.git)
SET(CBLAS_REPOSITORY https://gitee.com/tianjianhe/OpenBLAS.git)
SET(CBLAS_TAG v0.3.7)
if(WITH_MIPS)
SET(CBLAS_TAG v0.3.13)
......
cmake/external/protobuf.cmake
浏览文件 @ 6dd52c5b
......@@ -183,7 +183,7 @@ FUNCTION(build_protobuf TARGET_NAME BUILD_FOR_HOST)
"-DCMAKE_C_FLAGS=${CMAKE_C_FLAGS}"
"-DCMAKE_C_FLAGS_DEBUG=${CMAKE_C_FLAGS_DEBUG}"
"-DCMAKE_C_FLAGS_RELEASE=${CMAKE_C_FLAGS_RELEASE}"
"-DCMAKE_CXX_FLAGS=${CMAKE_CXX_FLAGS}"
"-DCMAKE_CXX_FLAGS=${CMAKE_CXX_FLAGS} -D_GLIBCXX_USE_CXX11_ABI=0"
"-DCMAKE_CXX_FLAGS_RELEASE=${CMAKE_CXX_FLAGS_RELEASE}"
"-DCMAKE_CXX_FLAGS_DEBUG=${CMAKE_CXX_FLAGS_DEBUG}"
"-Dprotobuf_WITH_ZLIB=ON"
......@@ -198,8 +198,8 @@ FUNCTION(build_protobuf TARGET_NAME BUILD_FOR_HOST)
"-Dprotobuf_MSVC_STATIC_RUNTIME=${MSVC_STATIC_CRT}")
ENDIF()
SET(PROTOBUF_REPOSITORY ${GIT_URL}/protocolbuffers/protobuf.git)
SET(PROTOBUF_TAG 9f75c5aa851cd877fb0d93ccc31b8567a6706546)
SET(PROTOBUF_REPOSITORY https://gitee.com/tianjianhe/protobuf.git)
SET(PROTOBUF_TAG v3.8.0)
cache_third_party(${TARGET_NAME}
REPOSITORY ${PROTOBUF_REPOSITORY}
......@@ -234,7 +234,7 @@ FUNCTION(build_protobuf TARGET_NAME BUILD_FOR_HOST)
)
ENDFUNCTION()
SET(PROTOBUF_VERSION 3.1.0)
# SET(PROTOBUF_VERSION 3.1.0)
IF(NOT PROTOBUF_FOUND)
build_protobuf(extern_protobuf FALSE)
......
cmake/external/pybind11.cmake
浏览文件 @ 6dd52c5b
......@@ -16,8 +16,8 @@ include(ExternalProject)
set(PYBIND_PREFIX_DIR ${THIRD_PARTY_PATH}/pybind)
set(PYBIND_SOURCE_DIR ${THIRD_PARTY_PATH}/pybind/src/extern_pybind)
SET(PYBIND_REPOSITORY ${GIT_URL}/pybind/pybind11.git)
SET(PYBIND_TAG v2.4.3)
SET(PYBIND_REPOSITORY https://gitee.com/tianjianhe/pybind11.git)
SET(PYBIND_TAG v2.6.0)
cache_third_party(extern_pybind
REPOSITORY ${PYBIND_REPOSITORY}
......
cmake/external/threadpool.cmake
浏览文件 @ 6dd52c5b
......@@ -16,7 +16,7 @@ INCLUDE(ExternalProject)
SET(THREADPOOL_PREFIX_DIR ${THIRD_PARTY_PATH}/threadpool)
SET(THREADPOOL_SOURCE_DIR ${THIRD_PARTY_PATH}/threadpool/src/extern_threadpool)
SET(THREADPOOL_REPOSITORY ${GIT_URL}/progschj/ThreadPool.git)
SET(THREADPOOL_REPOSITORY https://gitee.com/tianjianhe/ThreadPool.git)
SET(THREADPOOL_TAG 9a42ec1329f259a5f4881a291db1dcb8f2ad9040)
cache_third_party(extern_threadpool
......
cmake/external/warpctc.cmake
浏览文件 @ 6dd52c5b
......@@ -17,8 +17,9 @@ INCLUDE(ExternalProject)
SET(WARPCTC_PREFIX_DIR ${THIRD_PARTY_PATH}/warpctc)
SET(WARPCTC_SOURCE_DIR ${THIRD_PARTY_PATH}/warpctc/src/extern_warpctc)
SET(WARPCTC_INSTALL_DIR ${THIRD_PARTY_PATH}/install/warpctc)
set(WARPCTC_REPOSITORY ${GIT_URL}/baidu-research/warp-ctc.git)
set(WARPCTC_REPOSITORY https://gitee.com/tianjianhe/warp-ctc.git)
set(WARPCTC_TAG 95a461eddeabd51099ef059dcfada1117eb1bfb8)
# set(WARPCTC_TAG bc29dcfff07ced1c7a19a4ecee48e5ad583cef8e)
SET(WARPCTC_INCLUDE_DIR "${WARPCTC_INSTALL_DIR}/include"
CACHE PATH "Warp-ctc Directory" FORCE)
......@@ -52,7 +53,7 @@ ExternalProject_Add(
-DCMAKE_C_FLAGS=${CMAKE_C_FLAGS}
-DCMAKE_C_FLAGS_DEBUG=${CMAKE_C_FLAGS_DEBUG}
-DCMAKE_C_FLAGS_RELEASE=${CMAKE_C_FLAGS_RELEASE}
-DCMAKE_CXX_FLAGS=${CMAKE_CXX_FLAGS}
"-DCMAKE_CXX_FLAGS=${CMAKE_CXX_FLAGS} -D_GLIBCXX_USE_CXX11_ABI=0"
-DCMAKE_CXX_FLAGS_RELEASE=${CMAKE_CXX_FLAGS_RELEASE}
-DCMAKE_CXX_FLAGS_DEBUG=${CMAKE_CXX_FLAGS_DEBUG}
-DCMAKE_INSTALL_PREFIX=${WARPCTC_INSTALL_DIR}
......
cmake/external/xbyak.cmake
浏览文件 @ 6dd52c5b
......@@ -19,7 +19,7 @@ set(XBYAK_PREFIX_DIR ${THIRD_PARTY_PATH}/xbyak)
SET(XBYAK_SOURCE_DIR ${THIRD_PARTY_PATH}/xbyak/src/extern_xbyak)
set(XBYAK_INSTALL_ROOT ${THIRD_PARTY_PATH}/install/xbyak)
set(XBYAK_INC_DIR ${XBYAK_INSTALL_ROOT}/include)
set(XBYAK_REPOSITORY ${GIT_URL}/herumi/xbyak.git)
set(XBYAK_REPOSITORY https://gitee.com/tianjianhe/xbyak.git)
set(XBYAK_TAG v5.661) # Jul 26th
include_directories(${XBYAK_INC_DIR})
......
cmake/external/xxhash.cmake
浏览文件 @ 6dd52c5b
......@@ -18,7 +18,7 @@ set(XXHASH_PREFIX_DIR ${THIRD_PARTY_PATH}/xxhash)
set(XXHASH_SOURCE_DIR ${THIRD_PARTY_PATH}/xxhash/src/extern_xxhash)
set(XXHASH_INSTALL_DIR ${THIRD_PARTY_PATH}/install/xxhash)
set(XXHASH_INCLUDE_DIR "${XXHASH_INSTALL_DIR}/include")
set(XXHASH_REPOSITORY ${GIT_URL}/Cyan4973/xxHash.git)
set(XXHASH_REPOSITORY https://gitee.com/tianjianhe/xxHash.git)
set(XXHASH_TAG v0.6.5)
cache_third_party(extern_xxhash
......
cmake/external/zlib.cmake
浏览文件 @ 6dd52c5b
......@@ -19,7 +19,7 @@ SET(ZLIB_SOURCE_DIR ${THIRD_PARTY_PATH}/zlib/src/extern_zlib)
SET(ZLIB_INSTALL_DIR ${THIRD_PARTY_PATH}/install/zlib)
SET(ZLIB_ROOT ${ZLIB_INSTALL_DIR} CACHE FILEPATH "zlib root directory." FORCE)
SET(ZLIB_INCLUDE_DIR "${ZLIB_INSTALL_DIR}/include" CACHE PATH "zlib include directory." FORCE)
set(ZLIB_REPOSITORY ${GIT_URL}/madler/zlib.git)
set(ZLIB_REPOSITORY https://gitee.com/tianjianhe/zlib.git)
set(ZLIB_TAG v1.2.8)
INCLUDE_DIRECTORIES(${ZLIB_INCLUDE_DIR}) # For zlib code to include its own headers.
......@@ -41,7 +41,7 @@ ExternalProject_Add(
CMAKE_ARGS -DCMAKE_C_COMPILER=${CMAKE_C_COMPILER}
-DCMAKE_CXX_COMPILER=${CMAKE_CXX_COMPILER}
-DCMAKE_C_FLAGS=${CMAKE_C_FLAGS}
-DCMAKE_CXX_FLAGS=${CMAKE_CXX_FLAGS}
"-DCMAKE_CXX_FLAGS=${CMAKE_CXX_FLAGS} -D_GLIBCXX_USE_CXX11_ABI=0"
-DCMAKE_INSTALL_PREFIX=${ZLIB_INSTALL_DIR}
-DBUILD_SHARED_LIBS=OFF
-DCMAKE_POSITION_INDEPENDENT_CODE=ON
......
cmake/third_party.cmake
浏览文件 @ 6dd52c5b
......@@ -274,6 +274,11 @@ if(WITH_BOX_PS)
list(APPEND third_party_deps extern_box_ps)
endif(WITH_BOX_PS)
if(WITH_ASCEND)
include(external/ascend)
list(APPEND third_party_deps extern_ascend)
endif (WITH_ASCEND)
if (WITH_PSCORE)
include(external/snappy)
list(APPEND third_party_deps extern_snappy)
......
paddle/fluid/framework/fleet/CMakeLists.txt
浏览文件 @ 6dd52c5b
......@@ -31,3 +31,7 @@ endif(WITH_GLOO)
cc_library(heter_wrapper SRCS heter_wrapper.cc DEPS framework_proto device_context heter_service_proto)
cc_test(test_fleet_cc SRCS test_fleet.cc DEPS fleet_wrapper gloo_wrapper fs shell)
if(WITH_ASCEND)
cc_library(ascend_wrapper SRCS ascend_wrapper.cc DEPS framework_proto lod_tensor ascend ascend_graph)
endif(WITH_ASCEND)
paddle/fluid/framework/fleet/ascend_wrapper.cc 0 → 100644
浏览文件 @ 6dd52c5b
// Copyright (c) 2021 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.
#ifdef PADDLE_WITH_ASCEND
#include "paddle/fluid/framework/fleet/ascend_wrapper.h"
namespace paddle {
namespace framework {
std::shared_ptr<AscendInstance> AscendInstance::ascend_instance_ = nullptr;
} // end namespace framework
} // end namespace paddle
#endif
paddle/fluid/framework/fleet/ascend_wrapper.h 0 → 100644
浏览文件 @ 6dd52c5b
/* Copyright (c) 2021 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 PADDLE_WITH_ASCEND
#include <glog/logging.h>
#include <map>
#include <memory>
#include <string>
#include <vector>
#include "paddle/fluid/framework/data_type.h"
#include "paddle/fluid/framework/lod_tensor.h"
#include "paddle/fluid/platform/gpu_info.h"
#include "paddle/fluid/platform/place.h"
#include "paddle/fluid/platform/timer.h"
#include "ge/ge_api.h"
#include "ge/ge_api_types.h"
#include "graph/attr_value.h"
#include "graph/tensor.h"
#include "graph/types.h"
namespace paddle {
namespace framework {
// typedef std::vector<std::string> AscendGraphDesc;
typedef ge::Graph AscendGraphDesc;
class AscendInstance {
public:
virtual ~AscendInstance() {}
AscendInstance() {}
std::map<std::string, std::string> GetDefaultInitSessionOptions() {
std::map<std::string, std::string> init_options;
init_options["a"] = "b";
init_options["ge.trainFlag"] = "1";
return init_options;
}
// add other parameters here to init
void InitGlobalResouces() {
session_.reset(new ge::Session(GetDefaultInitSessionOptions()));
VLOG(1) << "InitGlobalResouces Done";
}
static std::shared_ptr<AscendInstance> GetInstance() {
if (nullptr == ascend_instance_) {
ascend_instance_.reset(new paddle::framework::AscendInstance());
VLOG(1) << "Initialize AscendInstance Done";
}
return ascend_instance_;
}
void AddAscendSubgraph(int graph_idx, const AscendGraphDesc &graph) {
ge::Status status = session_->AddGraph(graph_idx, graph);
PADDLE_ENFORCE_EQ(status, ge::SUCCESS,
paddle::platform::errors::PreconditionNotMet(
"Calling addGraph of graph engine failed, please "
"check Ascend Log."));
VLOG(1) << "AddAscendSubgraph " << graph_idx << " Done";
}
ge::DataType VarTypeToGeType(proto::VarType::Type type) {
if (type == proto::VarType::FP16) {
return ge::DataType::DT_FLOAT16;
} else if (type == proto::VarType::FP32) {
return ge::DataType::DT_FLOAT;
} else if (type == proto::VarType::FP64) {
return ge::DataType::DT_DOUBLE;
} else if (type == proto::VarType::INT32) {
return ge::DataType::DT_INT32;
} else if (type == proto::VarType::INT64) {
return ge::DataType::DT_INT64;
} else {
PADDLE_THROW(platform::errors::Unimplemented(
"Not support %s as tensor type.", DataTypeToString(type)));
}
}
int GeTypeSize(proto::VarType::Type type) {
if (type == proto::VarType::FP16) {
return 2;
} else if (type == proto::VarType::FP32) {
return 4;
} else if (type == proto::VarType::FP64) {
return 8;
} else if (type == proto::VarType::INT32) {
return 4;
} else if (type == proto::VarType::INT64) {
return 8;
} else {
PADDLE_THROW(platform::errors::Unimplemented(
"Not support %s as tensor type.", DataTypeToString(type)));
}
}
ge::Tensor ConvertToGeTensor(const Tensor *tensor) {
auto numel = tensor->numel();
std::vector<int64_t> vec_dim;
auto dimen = arity(tensor->dims());
for (auto i = 0; i < dimen; ++i) {
vec_dim.push_back(tensor->dims()[i]);
}
// For Debug
// VLOG(1) << "input numel: " << numel << ", dimen is " << vec_dim.size() <<
// ", and shape is";
// for (const auto e : vec_dim) {
// VLOG(0) << e;
// }
ge::Shape shape(vec_dim);
ge::TensorDesc tensor_desc(shape, ge::Format::FORMAT_ND,
VarTypeToGeType(tensor->type()));
tensor_desc.SetRealDimCnt(vec_dim.size());
const uint8_t *data =
reinterpret_cast<const uint8_t *>(tensor->data<void>());
std::vector<uint8_t> dst(numel * GeTypeSize(tensor->type()));
memcpy(dst.data(), data, GeTypeSize(tensor->type()) * numel);
ge::Tensor ge_tensor(tensor_desc, dst);
return ge_tensor;
}
void RunAscendSubgraph(int graph_idx,
const std::vector<const Tensor *> &inputs,
std::vector<Tensor *> *outputs) {
VLOG(1) << "Ascend Graph[" << graph_idx << "] is about to run.";
// Convert paddle Tensor to GE Tensor
std::vector<ge::Tensor> ge_inputs;
for (const auto &e : inputs) {
ge_inputs.push_back(ConvertToGeTensor(e));
}
// Run Graph
std::vector<ge::Tensor> ge_outputs;
ge::Status status = session_->RunGraph(graph_idx, ge_inputs, ge_outputs);
PADDLE_ENFORCE_EQ(status, ge::SUCCESS,
paddle::platform::errors::PreconditionNotMet(
"Calling RunGraph of graph engine failed, please "
"check Ascend Log."));
VLOG(1) << "Run Ascend Graph[" << graph_idx << "] Done";
// change tensor back, note all tensor's type computed in GE is uint8
for (size_t i = 0; i < ge_outputs.size(); ++i) {
const uint8_t *ret_data = ge_outputs[i].GetData();
size_t size = ge_outputs[i].GetSize();
VLOG(1) << "GE Tensor size of the " << i << "th output var is " << size;
auto *dst = (*outputs)[i]->mutable_data<uint8_t>({(int64_t)size},
platform::CPUPlace());
memcpy(dst, ret_data, size);
// Following for debug:
// VLOG(0) << "output for " << i << " var: ";
// float *tmp = reinterpret_cast<float*>(dst);
// for (size_t j = 0; j < size / 4; ++j) {
// printf("%f ", tmp[j]);
// }
// printf("\n");
}
}
protected:
std::shared_ptr<ge::Session> session_;
private:
static std::shared_ptr<AscendInstance> ascend_instance_;
};
} // end namespace framework
} // end namespace paddle
#endif
paddle/fluid/operators/CMakeLists.txt
浏览文件 @ 6dd52c5b
......@@ -115,6 +115,9 @@ set(COMMON_OP_DEPS ${COMMON_OP_DEPS} device_memory_aligment)
set(COMMON_OP_DEPS ${COMMON_OP_DEPS} layer)
set(COMMON_OP_DEPS ${COMMON_OP_DEPS} tensor_formatter)
set(COMMON_OP_DEPS ${COMMON_OP_DEPS} op_version_registry)
if (WITH_ASCEND)
set(COMMON_OP_DEPS ${COMMON_OP_DEPS} ascend_wrapper)
endif()
# FIXME(typhoonzero): operator deps may not needed.
# op_library(lod_tensor_to_array_op DEPS lod_rank_table_op)
......
paddle/fluid/operators/ascend_trigger_op.cc 0 → 100644
浏览文件 @ 6dd52c5b
// Copyright (c) 2021 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/ascend_trigger_op.h"
namespace paddle {
namespace operators {
class AscendTriggerOp : 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 {
return framework::OpKernelType(framework::proto::VarType::FP32,
ctx.device_context());
}
};
class AscendTriggerOpMaker : public framework::OpProtoAndCheckerMaker {
public:
void Make() override {
AddInput("FeedList", "FeedList of Ascend SubGraph").AsDuplicable();
AddOutput("FetchList", "FetchList of Ascend SubGraph").AsDuplicable();
AddAttr<int>("graph_idx", "(int, the graph index").SetDefault(-1);
AddComment(R"DOC(
Trigger Ascend SubGraph
)DOC");
}
};
} // namespace operators
} // namespace paddle
namespace ops = paddle::operators;
REGISTER_OPERATOR(ascend_trigger, ops::AscendTriggerOp,
ops::AscendTriggerOpMaker);
REGISTER_OP_CPU_KERNEL(ascend_trigger, ops::AscendTriggerCPUKernel<float>)
paddle/fluid/operators/ascend_trigger_op.h 0 → 100644
浏览文件 @ 6dd52c5b
// Copyright (c) 2021 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 <memory>
#include <vector>
#include "paddle/fluid/framework/op_registry.h"
#ifdef PADDLE_WITH_ASCEND
#include "paddle/fluid/framework/fleet/ascend_wrapper.h"
#include "paddle/fluid/framework/tensor.h"
#endif
namespace paddle {
namespace operators {
template <typename T>
class AscendTriggerCPUKernel : public framework::OpKernel<T> {
public:
void Compute(const framework::ExecutionContext &ctx) const override {
#ifdef PADDLE_WITH_ASCEND
auto ascend_ptr = paddle::framework::AscendInstance::GetInstance();
auto graph_idx = ctx.Attr<int>("graph_idx");
VLOG(4) << "AscendTrigger Kernel, begin to run graph: " << graph_idx;
auto inputs = ctx.MultiInput<framework::Tensor>("FeedList");
auto outputs = ctx.MultiOutput<framework::Tensor>("FetchList");
ascend_ptr->RunAscendSubgraph(graph_idx, inputs, &outputs);
#else
PADDLE_THROW(platform::errors::PreconditionNotMet(
"Please compile WITH_ASCEND option to enable ascend_trigger op"));
#endif
}
};
} // namespace operators
} // namespace paddle
paddle/fluid/pybind/CMakeLists.txt
浏览文件 @ 6dd52c5b
......@@ -39,6 +39,11 @@ set(PYBIND_SRCS
compatible.cc
generator_py.cc)
if(WITH_ASCEND)
set(PYBIND_DEPS ${PYBIND_DEPS} ascend_wrapper)
set(PYBIND_SRCS ${PYBIND_SRCS} ascend_wrapper_py.cc)
endif(WITH_ASCEND)
if(WITH_GLOO)
set(PYBIND_DEPS ${PYBIND_DEPS} gloo_context)
set(PYBIND_SRCS ${PYBIND_SRCS} gloo_context_py.cc)
......
paddle/fluid/pybind/ascend_wrapper_py.cc 0 → 100644
浏览文件 @ 6dd52c5b
/* Copyright (c) 2021 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. */
#ifdef PADDLE_WITH_ASCEND
#include <fcntl.h>
#ifdef _POSIX_C_SOURCE
#undef _POSIX_C_SOURCE
#endif
#ifdef _XOPEN_SOURCE
#undef _XOPEN_SOURCE
#endif
#include <ge/ge_api.h>
#include <graph/attr_value.h>
#include <graph/operator_factory.h>
#include <map>
#include <memory>
#include <string>
#include <utility>
#include <vector>
#include "paddle/fluid/framework/fleet/ascend_wrapper.h"
#include "paddle/fluid/pybind/ascend_wrapper_py.h"
using namespace ge; // NOLINT
namespace py = pybind11;
namespace paddle {
namespace pybind {
void BindAscendWrapper(py::module *m) {
py::class_<framework::AscendInstance,
std::shared_ptr<framework::AscendInstance>>(*m, "AscendInstance")
.def(py::init([]() { return framework::AscendInstance::GetInstance(); }))
.def("init_global_resources",
&framework::AscendInstance::InitGlobalResouces,
py::call_guard<py::gil_scoped_release>())
.def("add_ascend_subgraph", &framework::AscendInstance::AddAscendSubgraph,
py::call_guard<py::gil_scoped_release>());
} // end AscendWrapper
Status ge_initialize(std::map<std::string, std::string> &options) { // NOLINT
py::gil_scoped_release release;
Status res = GEInitialize(options);
py::gil_scoped_acquire acquire;
return res;
}
enum AttrType {
AT_INT64 = 0,
AT_INT32,
AT_UINT32,
AT_LIST_INT64,
AT_LIST_INT32,
AT_LIST_UINT32,
AT_FLOAT,
AT_LIST_FLOAT,
AT_ATTR_VALUE,
AT_STRING,
AT_LIST_STRING,
AT_BOOL,
AT_LIST_BOOL,
AT_TENSOR,
AT_LIST_TENSOR,
AT_LIST_UINT8,
AT_LIST_LIST_INT64,
AT_LIST_DT,
AT_DT,
AT_LIST_NAMEATTR,
AT_NAMEATTR
};
void BindAscendGraph(py::module *m) {
m->def("ge_initialize", &ge_initialize, "GEInitialize");
m->def("ge_finalize", &GEFinalize, "GEFinalize");
//枚举封装
py::enum_<GraphRunMode>(*m, "GEGraphRunMode")
.value("PREDICTION", GraphRunMode::PREDICTION)
.value("TRAIN", GraphRunMode::TRAIN)
.export_values();
py::enum_<DataType>(*m, "GEDataType")
.value("DT_FLOAT", DataType::DT_FLOAT)
.value("DT_FLOAT16", DataType::DT_FLOAT16)
.value("DT_INT8", DataType::DT_INT8)
.value("DT_INT16", DataType::DT_INT16)
.value("DT_UINT16", DataType::DT_UINT16)
.value("DT_UINT8", DataType::DT_UINT8)
.value("DT_INT32", DataType::DT_INT32)
.value("DT_INT64", DataType::DT_INT64)
.value("DT_UINT32", DataType::DT_UINT32)
.value("DT_UINT64", DataType::DT_UINT64)
.value("DT_BOOL", DataType::DT_BOOL)
.value("DT_DOUBLE", DataType::DT_DOUBLE)
.value("DT_STRING", DataType::DT_STRING)
.value("DT_DUAL_SUB_INT8", DataType::DT_DUAL_SUB_INT8)
.value("DT_DUAL_SUB_UINT8", DataType::DT_DUAL_SUB_UINT8)
.value("DT_COMPLEX64", DataType::DT_COMPLEX64)
.value("DT_COMPLEX128", DataType::DT_COMPLEX128)
.value("DT_QINT8", DataType::DT_QINT8)
.value("DT_QINT16", DataType::DT_QINT16)
.value("DT_QINT32", DataType::DT_QINT32)
.value("DT_QUINT8", DataType::DT_QUINT8)
.value("DT_QUINT16", DataType::DT_QUINT16)
.value("DT_RESOURCE", DataType::DT_RESOURCE)
.value("DT_STRING_REF", DataType::DT_STRING_REF)
.value("DT_DUAL", DataType::DT_DUAL)
.value("DT_UNDEFINED", DataType::DT_UNDEFINED)
.export_values();
py::enum_<Format>(*m, "GEFormat")
.value("FORMAT_NCHW", Format::FORMAT_NCHW)
.value("FORMAT_NHWC", Format::FORMAT_NHWC)
.value("FORMAT_ND", Format::FORMAT_ND)
.value("FORMAT_NC1HWC0", Format::FORMAT_NC1HWC0)
.value("FORMAT_FRACTAL_Z", Format::FORMAT_FRACTAL_Z)
.value("FORMAT_NC1C0HWPAD", Format::FORMAT_NC1C0HWPAD)
.value("FORMAT_NHWC1C0", Format::FORMAT_NHWC1C0)
.value("FORMAT_FSR_NCHW", Format::FORMAT_FSR_NCHW)
.value("FORMAT_FRACTAL_DECONV", Format::FORMAT_FRACTAL_DECONV)
.value("FORMAT_C1HWNC0", Format::FORMAT_C1HWNC0)
.value("FORMAT_FRACTAL_DECONV_TRANSPOSE",
Format::FORMAT_FRACTAL_DECONV_TRANSPOSE)
.value("FORMAT_FRACTAL_DECONV_SP_STRIDE_TRANS",
Format::FORMAT_FRACTAL_DECONV_SP_STRIDE_TRANS)
.value("FORMAT_NC1HWC0_C04", Format::FORMAT_NC1HWC0_C04)
.value("FORMAT_FRACTAL_Z_C04", Format::FORMAT_FRACTAL_Z_C04)
.value("FORMAT_CHWN", Format::FORMAT_CHWN)
.value("FORMAT_FRACTAL_DECONV_SP_STRIDE8_TRANS",
Format::FORMAT_FRACTAL_DECONV_SP_STRIDE8_TRANS)
.value("FORMAT_HWCN", Format::FORMAT_HWCN)
.value("FORMAT_NC1KHKWHWC0", Format::FORMAT_NC1KHKWHWC0)
.value("FORMAT_BN_WEIGHT", Format::FORMAT_BN_WEIGHT)
.value("FORMAT_FILTER_HWCK", Format::FORMAT_FILTER_HWCK)
.value("FORMAT_HASHTABLE_LOOKUP_LOOKUPS",
Format::FORMAT_HASHTABLE_LOOKUP_LOOKUPS)
.value("FORMAT_HASHTABLE_LOOKUP_KEYS",
Format::FORMAT_HASHTABLE_LOOKUP_KEYS)
.value("FORMAT_HASHTABLE_LOOKUP_VALUE",
Format::FORMAT_HASHTABLE_LOOKUP_VALUE)
.value("FORMAT_HASHTABLE_LOOKUP_OUTPUT",
Format::FORMAT_HASHTABLE_LOOKUP_OUTPUT)
.value("FORMAT_HASHTABLE_LOOKUP_HITS",
Format::FORMAT_HASHTABLE_LOOKUP_HITS)
.value("FORMAT_C1HWNCoC0", Format::FORMAT_C1HWNCoC0)
.value("FORMAT_MD", Format::FORMAT_MD)
.value("FORMAT_NDHWC", Format::FORMAT_NDHWC)
.value("FORMAT_FRACTAL_ZZ", Format::FORMAT_FRACTAL_ZZ)
.value("FORMAT_FRACTAL_NZ", Format::FORMAT_FRACTAL_NZ)
.value("FORMAT_NCDHW", Format::FORMAT_NCDHW)
.value("FORMAT_DHWCN", Format::FORMAT_DHWCN)
.value("FORMAT_NDC1HWC0", Format::FORMAT_NDC1HWC0)
.value("FORMAT_FRACTAL_Z_3D", Format::FORMAT_FRACTAL_Z_3D)
.value("FORMAT_CN", Format::FORMAT_CN)
.value("FORMAT_NC", Format::FORMAT_NC)
.value("FORMAT_DHWNC", Format::FORMAT_DHWNC)
.value("FORMAT_FRACTAL_Z_3D_TRANSPOSE",
Format::FORMAT_FRACTAL_Z_3D_TRANSPOSE)
.value("FORMAT_FRACTAL_ZN_LSTM", Format::FORMAT_FRACTAL_ZN_LSTM)
.value("FORMAT_FRACTAL_Z_G", Format::FORMAT_FRACTAL_Z_G)
.value("FORMAT_RESERVED", Format::FORMAT_RESERVED)
.value("FORMAT_ALL", Format::FORMAT_ALL)
.value("FORMAT_NULL", Format::FORMAT_NULL)
.export_values();
py::enum_<UnknowShapeOpType>(*m, "GEUnknowShapeOpType")
.value("DEPEND_IN_SHAPE", UnknowShapeOpType::DEPEND_IN_SHAPE)
.value("DEPEND_CONST_VALUE", UnknowShapeOpType::DEPEND_CONST_VALUE)
.value("DEPEND_SHAPE_RANGE", UnknowShapeOpType::DEPEND_SHAPE_RANGE)
.value("DEPEND_COMPUTE", UnknowShapeOpType::DEPEND_COMPUTE)
.export_values();
py::enum_<DeviceType>(*m, "GEDeviceType")
.value("NPU", DeviceType::NPU)
.value("CPU", DeviceType::CPU)
.export_values();
py::enum_<AttrType>(*m, "GEAttrType")
.value("AT_INT64", AttrType::AT_INT64)
.value("AT_INT32", AttrType::AT_INT32)
.value("AT_UINT32", AttrType::AT_UINT32)
.value("AT_LIST_INT64", AttrType::AT_LIST_INT64)
.value("AT_LIST_INT32", AttrType::AT_LIST_INT32)
.value("AT_LIST_UINT32", AttrType::AT_LIST_UINT32)
.value("AT_FLOAT", AttrType::AT_FLOAT)
.value("AT_LIST_FLOAT", AttrType::AT_LIST_FLOAT)
.value("AT_ATTR_VALUE", AttrType::AT_ATTR_VALUE)
.value("AT_STRING", AttrType::AT_STRING)
.value("AT_LIST_STRING", AttrType::AT_LIST_STRING)
.value("AT_BOOL", AttrType::AT_BOOL)
.value("AT_LIST_BOOL", AttrType::AT_LIST_BOOL)
.value("AT_TENSOR", AttrType::AT_TENSOR)
.value("AT_LIST_TENSOR", AttrType::AT_LIST_TENSOR)
.value("AT_LIST_UINT8", AttrType::AT_LIST_UINT8)
.value("AT_LIST_LIST_INT64", AttrType::AT_LIST_LIST_INT64)
.value("AT_LIST_DT", AttrType::AT_LIST_DT)
.value("AT_DT", AttrType::AT_DT)
.value("AT_LIST_NAMEATTR", AttrType::AT_LIST_NAMEATTR)
.value("AT_NAMEATTR", AttrType::AT_NAMEATTR)
.export_values();
// 类封装
py::class_<Session>(*m, "GESession")
.def(py::init<const std::map<std::string, std::string> &>())
.def("add_graph",
(Status (Session::*)(uint32_t, const Graph &)) & Session::AddGraph)
.def("add_graph",
(Status (Session::*)(uint32_t, const Graph &,
const std::map<std::string, std::string> &)) &
Session::AddGraph)
.def("remove_graph", &Session::RemoveGraph)
.def("run_graph",
[](Session &ss, uint32_t graphId,
const std::vector<Tensor> &inputs) -> py::tuple {
std::vector<Tensor> outputs;
Status res = ss.RunGraph(graphId, inputs, outputs);
return py::make_tuple(outputs, res);
},
py::call_guard<py::gil_scoped_release>())
.def("build_graph", &Session::BuildGraph)
.def("run_graph_async", &Session::RunGraphAsync)
.def("register_call_back_func",
(Status (Session::*)( // NOLINT
const std::string &,
std::function<uint32_t(
uint32_t graph_id,
const std::map<std::string, ge::Tensor> &params_list)>)) &
Session::RegisterCallBackFunc)
.def("is_graph_need_rebuild", &Session::IsGraphNeedRebuild);
py::class_<Graph>(*m, "GEGraph")
.def(py::init<>())
.def(py::init<const std::string &>())
.def("set_inputs", &Graph::SetInputs)
.def("set_outputs", (Graph & (Graph::*)(const std::vector<Operator> &)) &
Graph::SetOutputs)
.def("set_outputs",
(Graph & (Graph::*)(const std::vector<
std::pair<Operator, std::vector<size_t>>> &)) &
Graph::SetOutputs)
.def("set_outputs",
(Graph &
(Graph::*)(const std::vector<std::pair<ge::Operator, std::string>>
&)) &
Graph::SetOutputs)
.def("set_targets", &Graph::SetTargets)
.def("is_valid", &Graph::IsValid)
.def("add_op", &Graph::AddOp)
.def("find_op_by_name",
[](Graph &graph, const std::string &name) -> py::tuple {
ge::Operator op;
graphStatus status = graph.FindOpByName(name, op);
return py::make_tuple(op, status);
})
.def("find_op_by_type",
[](Graph &graph, const std::string &type) -> py::tuple {
std::vector<ge::Operator> ops;
graphStatus status = graph.FindOpByType(type, ops);
return py::make_tuple(ops, status);
})
.def("get_all_op_name",
[](Graph &graph) -> py::tuple {
std::vector<std::string> op_name;
graphStatus status = graph.GetAllOpName(op_name);
return py::make_tuple(op_name, status);
})
.def("save_to_file", &Graph::SaveToFile)
.def("load_from_file", &Graph::LoadFromFile)
.def("get_name", &Graph::GetName)
.def("set_need_iteration", &Graph::SetNeedIteration);
py::class_<Operator>(*m, "GEOperator")
.def(py::init<>())
.def(py::init<const std::string &>())
.def(py::init<const std::string &, const std::string &>())
.def("is_empty", &Operator::IsEmpty)
.def("get_name", &Operator::GetName)
.def("get_op_type", &Operator::GetOpType)
.def("set_input",
(Operator & (Operator::*)(const std::string &, const Operator &)) &
Operator::SetInput)
.def("set_input",
(Operator & (Operator::*)(const std::string &, const Operator &,
const std::string &)) &
Operator::SetInput)
.def("set_input", (Operator & (Operator::*)(const std::string &,
const Operator &, uint32_t)) &
Operator::SetInput)
.def("add_control_input", &Operator::AddControlInput)
.def("get_input_const_data",
[](Operator &op, const std::string &dst_name) -> py::tuple {
Tensor data;
graphStatus res = op.GetInputConstData(dst_name, data);
return py::make_tuple(data, res);
})
.def("get_input_desc",
(TensorDesc (Operator::*)(const std::string &) const) &
Operator::GetInputDesc)
.def("get_input_desc",
(TensorDesc (Operator::*)(uint32_t) const) & Operator::GetInputDesc)
.def("get_dynamic_output_num", &Operator::GetDynamicOutputNum)
.def("get_dynamic_input_num", &Operator::GetDynamicInputNum)
.def("try_get_input_desc",
[](Operator &op, const std::string &name) -> py::tuple {
TensorDesc tensor_desc;
graphStatus status = op.TryGetInputDesc(name, tensor_desc);
return py::make_tuple(tensor_desc, status);
})
.def("update_input_desc", &Operator::UpdateInputDesc)
.def("get_output_desc",
(TensorDesc (Operator::*)(const std::string &) const) &
Operator::GetOutputDesc)
.def("get_output_desc",
(TensorDesc (Operator::*)(uint32_t) const) & Operator::GetOutputDesc)
.def("update_output_desc", &Operator::UpdateOutputDesc)
.def("get_dynamic_input_desc", &Operator::GetDynamicInputDesc)
.def("update_dynamic_input_desc", &Operator::UpdateDynamicInputDesc)
.def("get_dynamic_output_desc", &Operator::GetDynamicOutputDesc)
.def("update_dynamic_output_desc", &Operator::UpdateDynamicOutputDesc)
.def("infer_shape_and_type", &Operator::InferShapeAndType)
.def("set_inference_context", &Operator::SetInferenceContext)
.def("get_inference_context", &Operator::GetInferenceContext)
.def("verify_all_attr", &Operator::VerifyAllAttr)
.def("get_inputs_size", &Operator::GetInputsSize)
.def("get_outputs_size", &Operator::GetOutputsSize)
.def("get_all_attr_names_and_types", &Operator::GetAllAttrNamesAndTypes)
.def("set_attr_int64",
[](Operator &op, const std::string &name,
int64_t value) -> Operator & {
int64_t tar = (int64_t)value;
return op.SetAttr(name, tar);
})
.def("set_attr_int32",
[](Operator &op, const std::string &name,
int32_t value) -> Operator & {
int32_t tar = (int32_t)value;
return op.SetAttr(name, tar);
})
.def("set_attr_uint32",
[](Operator &op, const std::string &name,
uint32_t value) -> Operator & {
uint32_t tar = (uint32_t)value;
return op.SetAttr(name, tar);
})
.def("set_attr_vec_int64",
[](Operator &op, const std::string &name,
const std::vector<int64_t> &value) -> Operator & {
int len = value.size();
std::vector<int64_t> tar;
int64_t tmp;
for (int i = 0; i < len; i++) {
tmp = (int64_t)value[i];
tar.push_back(tmp);
}
return op.SetAttr(name, tar);
})
.def("set_attr_vec_int32",
[](Operator &op, const std::string &name,
const std::vector<int32_t> &value) -> Operator & {
int len = value.size();
std::vector<int32_t> tar;
int32_t tmp;
for (int i = 0; i < len; i++) {
tmp = (int32_t)value[i];
tar.push_back(tmp);
}
return op.SetAttr(name, tar);
})
.def("set_attr_vec_uint32",
[](Operator &op, const std::string &name,
const std::vector<uint32_t> &value) -> Operator & {
int len = value.size();
std::vector<uint32_t> tar;
uint32_t tmp;
for (int i = 0; i < len; i++) {
tmp = (uint32_t)value[i];
tar.push_back(tmp);
}
return op.SetAttr(name, tar);
})
.def("set_attr_list_int64",
[](Operator &op, const std::string &name,
std::initializer_list<int64_t> &attrValue) -> Operator & {
return op.SetAttr(name, std::move(attrValue));
})
.def("set_attr_attrvalue",
[](Operator &op, const std::string &name, AttrValue &attrValue)
-> Operator & { return op.SetAttr(name, std::move(attrValue)); })
.def(
"set_attr_float",
[](Operator &op, const std::string &name, float value) -> Operator & {
float tar = static_cast<float>(value);
return op.SetAttr(name, tar);
})
.def("set_attr_vec_float",
[](Operator &op, const std::string &name,
const std::vector<float> &value) -> Operator & {
int len = value.size();
std::vector<float> tar;
float tmp;
for (int i = 0; i < len; i++) {
tmp = static_cast<float>(value[i]);
tar.push_back(tmp);
}
return op.SetAttr(name, tar);
})
.def("set_attr_string", (Operator & (Operator::*)(const std::string &,
const std::string &)) &
Operator::SetAttr)
.def("set_attr_vec_string",
(Operator & (Operator::*)(const std::string &,
const std::vector<std::string> &)) &
Operator::SetAttr)
.def("set_attr_bool",
[](Operator &op, const std::string &name, bool value) -> Operator & {
if (value)
return op.SetAttr(name, true);
else
return op.SetAttr(name, false);
})
.def("set_attr_vec_bool",
[](Operator &op, const std::string &name,
const std::vector<bool> &value) -> Operator & {
int len = value.size();
std::vector<bool> tar;
for (int i = 0; i < len; i++) {
if (value[i])
tar.push_back(true);
else
tar.push_back(false);
}
return op.SetAttr(name, tar);
})
.def("set_attr_tensor",
(Operator & (Operator::*)(const std::string &, const Tensor &)) &
Operator::SetAttr)
.def("set_attr_vec_tensor",
(Operator &
(Operator::*)(const std::string &, const std::vector<Tensor> &)) &
Operator::SetAttr)
.def("set_attr_vec_uint8",
[](Operator &op, const std::string &name,
const std::vector<uint8_t> &value) -> Operator & {
int len = value.size();
std::vector<uint8_t> tar;
uint8_t tmp;
for (int i = 0; i < len; i++) {
tmp = (uint8_t)value[i];
tar.push_back(tmp);
}
return op.SetAttr(name, tar);
})
.def("set_attr_vec_vec_int64",
(Operator &
(Operator::*)(const std::string &,
const std::vector<std::vector<int64_t>> &)) &
Operator::SetAttr)
.def("set_attr_vec_dtype",
[](Operator &op, const std::string &name,
const std::vector<DataType> &value) -> Operator & {
int len = value.size();
std::vector<ge::DataType> tar;
ge::DataType tmp;
for (int i = 0; i < len; i++) {
tmp = (ge::DataType)value[i];
tar.push_back(tmp);
}
return op.SetAttr(name, tar);
})
.def("set_attr_dtype",
[](Operator &op, const std::string &name,
const DataType &value) -> Operator & {
ge::DataType tar = (ge::DataType)value;
return op.SetAttr(name, tar);
})
.def("get_attr",
[](Operator &op, const std::string &name,
AttrType type) -> py::tuple {
graphStatus res = -1;
switch (type) {
case AT_INT64: {
int64_t i_64_av;
res = op.GetAttr(name, i_64_av);
return py::make_tuple(i_64_av, res);
} break;
case AT_INT32: {
int32_t i_32_av;
res = op.GetAttr(name, i_32_av);
return py::make_tuple(i_32_av, res);
} break;
case AT_UINT32: {
uint32_t ui_32_av;
res = op.GetAttr(name, ui_32_av);
return py::make_tuple(ui_32_av, res);
} break;
case AT_LIST_INT64: {
std::vector<int64_t> v_i_64_av;
res = op.GetAttr(name, v_i_64_av);
return py::make_tuple(v_i_64_av, res);
} break;
case AT_LIST_INT32: {
std::vector<int32_t> v_i_32_av;
res = op.GetAttr(name, v_i_32_av);
return py::make_tuple(v_i_32_av, res);
} break;
case AT_LIST_UINT32: {
std::vector<uint32_t> v_ui_32_av;
res = op.GetAttr(name, v_ui_32_av);
return py::make_tuple(v_ui_32_av, res);
} break;
case AT_FLOAT: {
float f_av;
res = op.GetAttr(name, f_av);
return py::make_tuple(f_av, res);
} break;
case AT_LIST_FLOAT: {
std::vector<float> v_f_av;
res = op.GetAttr(name, v_f_av);
return py::make_tuple(v_f_av, res);
} break;
case AT_ATTR_VALUE: {
AttrValue o_av;
res = op.GetAttr(name, o_av);
return py::make_tuple(o_av, res);
} break;
case AT_STRING: {
std::string s_av;
res = op.GetAttr(name, s_av);
return py::make_tuple(s_av, res);
} break;
case AT_LIST_STRING: {
std::vector<std::string> v_s_av;
res = op.GetAttr(name, v_s_av);
return py::make_tuple(v_s_av, res);
} break;
case AT_BOOL: {
bool b_av;
res = op.GetAttr(name, b_av);
return py::make_tuple(b_av, res);
} break;
case AT_LIST_BOOL: {
std::vector<bool> v_b_av;
res = op.GetAttr(name, v_b_av);
return py::make_tuple(v_b_av, res);
} break;
case AT_TENSOR: {
Tensor t_av;
res = op.GetAttr(name, t_av);
return py::make_tuple(t_av, res);
} break;
case AT_LIST_TENSOR: {
std::vector<Tensor> v_t_av;
res = op.GetAttr(name, v_t_av);
return py::make_tuple(v_t_av, res);
} break;
case AT_LIST_UINT8: {
std::vector<uint8_t> v_ui_8_av;
res = op.GetAttr(name, v_ui_8_av);
return py::make_tuple(v_ui_8_av, res);
} break;
case AT_LIST_LIST_INT64: {
std::vector<std::vector<int64_t>> v_v_i_64_av;
res = op.GetAttr(name, v_v_i_64_av);
return py::make_tuple(v_v_i_64_av, res);
} break;
case AT_DT: {
ge::DataType dt_av;
res = op.GetAttr(name, dt_av);
return py::make_tuple(dt_av, res);
} break;
case AT_LIST_DT: {
std::vector<ge::DataType> v_dt_av;
res = op.GetAttr(name, v_dt_av);
return py::make_tuple(v_dt_av, res);
} break;
default:
return py::make_tuple(0, res);
break;
}
})
.def("break_connect", &Operator::BreakConnect)
.def("get_subgraph_names_count", &Operator::GetSubgraphNamesCount)
.def("get_subgraph_names", &Operator::GetSubgraphNames)
.def("get_subgraph_builder", &Operator::GetSubgraphBuilder)
.def("get_subgraph", &Operator::GetSubgraph)
.def("get_dynamic_subgraph_builder", &Operator::GetDynamicSubgraphBuilder)
.def("get_dynamic_subgraph", &Operator::GetDynamicSubgraph);
py::class_<Tensor>(*m, "GETensor")
.def(py::init<>())
.def(py::init<const TensorDesc &>())
.def(py::init<const TensorDesc &, const std::vector<uint8_t> &>())
.def(py::init<const TensorDesc &, const uint8_t *, size_t>())
.def("set_tensor_desc", &Tensor::SetTensorDesc)
.def("get_tensor_desc", &Tensor::GetTensorDesc)
// .def("set_data", (graphStatus(Tensor::*)(std::vector<uint8_t> &&)) &
// Tensor::SetData)
.def("set_data", (graphStatus (Tensor::*)(const std::vector<uint8_t> &)) &
Tensor::SetData)
.def("set_data",
(graphStatus (Tensor::*)(const uint8_t *, size_t)) & Tensor::SetData)
.def("set_data",
(graphStatus (Tensor::*)(const std::string &)) & Tensor::SetData)
.def("set_data",
(graphStatus (Tensor::*)(const std::vector<std::string> &)) &
Tensor::SetData)
.def("get_data",
[](Tensor &ts) -> py::list {
py::list v_data;
uint8_t *data = ts.GetData();
size_t size = ts.GetSize();
for (size_t i = 0; i < size; ++i) {
v_data.append(data[i]);
}
return v_data;
})
.def("get_size", &Tensor::GetSize)
.def("is_valid", &Tensor::IsValid)
.def("clone", &Tensor::Clone);
py::class_<TensorDesc>(*m, "GETensorDesc")
.def(py::init<>())
.def(py::init<Shape, Format, DataType>(), py::arg("shape"),
py::arg("format") = FORMAT_ND, py::arg("dt") = DT_FLOAT)
.def(py::init<const TensorDesc &>())
.def("update",
(void (TensorDesc::*)(Shape, Format, DataType)) & TensorDesc::Update,
py::arg("shape"), py::arg("format") = FORMAT_ND,
py::arg("dt") = DT_FLOAT)
.def("set_shape", &TensorDesc::SetShape)
.def("get_shape", &TensorDesc::GetShape)
.def("set_unknown_dim_num_shape", &TensorDesc::SetUnknownDimNumShape)
.def("set_shape_range", &TensorDesc::SetShapeRange)
.def("get_shape_range",
[](TensorDesc &tensorDesc) -> py::tuple {
std::vector<std::pair<int64_t, int64_t>> range;
graphStatus status = tensorDesc.GetShapeRange(range);
return py::make_tuple(range, status);
})
.def("set_format", &TensorDesc::SetFormat)
.def("get_format", &TensorDesc::GetFormat)
.def("get_origin_shape", &TensorDesc::GetOriginShape)
.def("set_origin_shape", &TensorDesc::SetOriginShape)
.def("set_origin_format", &TensorDesc::SetOriginFormat)
.def("get_origin_format", &TensorDesc::GetOriginFormat)
.def("set_data_type", &TensorDesc::SetDataType)
.def("get_data_type", &TensorDesc::GetDataType)
.def("set_name", &TensorDesc::SetName)
.def("get_name", &TensorDesc::GetName)
.def("set_size", &TensorDesc::SetSize)
.def("get_size", &TensorDesc::GetSize)
.def("set_real_dim_cnt", &TensorDesc::SetRealDimCnt)
.def("get_real_dim_cnt", &TensorDesc::GetRealDimCnt);
py::class_<Shape>(*m, "GEShape")
.def(py::init<>())
.def(py::init<const std::vector<int64_t> &>())
.def("get_dim_num", &Shape::GetDimNum)
.def("set_dim", &Shape::SetDim)
.def("get_dim", &Shape::GetDim)
.def("get_dims", &Shape::GetDims)
.def("get_shape_size", &Shape::GetShapeSize);
py::class_<AttrValue>(*m, "GEAttrValue").def(py::init<>());
py::class_<OperatorFactory>(*m, "GEOperatorFactory")
.def("create_operator", &OperatorFactory::CreateOperator)
.def("get_ops_type_list",
[]() -> py::tuple {
std::vector<std::string> all_ops;
graphStatus status = OperatorFactory::GetOpsTypeList(all_ops);
return py::make_tuple(all_ops, status);
})
.def("is_exist_op", &OperatorFactory::IsExistOp);
}
} // end namespace pybind
} // end namespace paddle
#endif
paddle/fluid/pybind/ascend_wrapper_py.h 0 → 100644
浏览文件 @ 6dd52c5b
// Copyright (c) 2021 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 PADDLE_WITH_ASCEND
#include "pybind11/pybind11.h"
#include "pybind11/stl.h"
namespace py = pybind11;
namespace paddle {
namespace pybind {
void BindAscendGraph(py::module* m);
void BindAscendWrapper(py::module* m);
} // namespace pybind
} // namespace paddle
#endif
paddle/fluid/pybind/pybind.cc
浏览文件 @ 6dd52c5b
......@@ -65,6 +65,9 @@ limitations under the License. */
#include "paddle/fluid/platform/monitor.h"
#include "paddle/fluid/platform/place.h"
#include "paddle/fluid/platform/profiler.h"
#ifdef PADDLE_WITH_ASCEND
#include "paddle/fluid/pybind/ascend_wrapper_py.h"
#endif
#include "paddle/fluid/pybind/box_helper_py.h"
#include "paddle/fluid/pybind/compatible.h"
#include "paddle/fluid/pybind/const_value.h"
......@@ -2837,6 +2840,10 @@ All parameter, weight, gradient are variables in Paddle.
BindCompatible(&m);
BindDataset(&m);
BindGenerator(&m);
#ifdef PADDLE_WITH_ASCEND
BindAscendWrapper(&m);
BindAscendGraph(&m);
#endif
#ifdef PADDLE_WITH_CRYPTO
BindCrypto(&m);
#endif
......
python/paddle/distributed/fleet/meta_optimizers/ascend/ascend_optimizer.py 0 → 100644
浏览文件 @ 6dd52c5b
# Copyright (c) 2021 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 paddle.fluid.framework as framework
from paddle.fluid.optimizer import Optimizer
import paddle.fluid.core as core
import numpy as np
import ascend_parser
class AscendIRParser(object):
def __init__(self):
self.graph_idx = 0
def _construct_input_map(self, input_varlist):
ret_map = {}
ge_in_operator = []
for id, var in enumerate(input_varlist):
if var.is_data: # input data
ge_input = core.GEOperatorFactory.create_operator(
var.name, "Data").set_attr_int32("index", id)
ret_map[var.name] = ge_input
ge_in_operator.append(ge_input)
else: # param, learning ...
ge_input = core.GEOperatorFactory.create_operator(var.name,
"Variable")
ge_input.update_output_desc("y",
core.GETensorDesc(
core.GEShape(var.shape),
core.GEFormat.FORMAT_ND,
core.GEDataType.DT_FLOAT))
ret_map[var.name] = ge_input
return ge_in_operator, ret_map
def parse_op(self, op):
if op.type in ascend_parser.registerd_op:
print("Op[%s] has been registered, begin to parse it" % (op.type))
op_parser = self.parser_factory.create_parse(
ascend_parser.registerd_op[op.type])
op_parser.apply(op)
else:
print("Op[%s] has not been registered, so we have to skip it" %
(op.type))
def _parse_program(self,
graph_name,
program,
input_varlist=[],
fetch_list=[]):
begin_graph_idx = self.graph_idx
ge_in_operator = []
ge_out_operator = []
self.var2geop = {}
block = program.global_block()
if len(block.ops) == 0:
print("There is no ops in program %s" % (graph_name))
return []
graph = core.GEGraph(graph_name)
ge_in_operator, self.var2geop = self._construct_input_map(input_varlist)
self.parser_factory = ascend_parser.AscendParserFactory(graph,
self.var2geop)
for i, curop in list(enumerate(block.ops)):
self.parse_op(curop)
# Set fetch_var for GE
for e in fetch_list:
name = e
if not isinstance(e, str):
name = e.name
ge_out_operator.append(self.var2geop[name])
# (Debug) If you want to print back prop vars, append/assign the varname in ge_out_operator here, such as:
# if graph_name == "main":
# ge_out_operator.append(self.var2geop["reduce_sum_0.tmp_0@GRAD"])
# Add ops that may be input of a graph, such as const.
for varname, geop in self.var2geop.items():
if varname.startswith("geinput"):
ge_in_operator.append(geop)
graph.set_inputs(ge_in_operator).set_outputs(ge_out_operator)
# Remove ops of origin program
op_num = len(block.ops)
for i in range(op_num - 1, -1, -1):
block._remove_op(i)
input_varlist = [var for var in input_varlist if var.is_data]
block.append_op(
type="ascend_trigger",
inputs={"FeedList": input_varlist},
outputs={"FetchList": fetch_list},
attrs={'graph_idx': self.graph_idx})
self.graph_idx += 1
return graph
def parse_program(self, startup_program, main_program, input_varlist,
fetch_list):
startup_graph = self._parse_program("startup", startup_program)
main_graph = self._parse_program("main", main_program, input_varlist,
fetch_list)
return startup_graph, main_graph
# AscendOptimizer is a wrapper for basic optimizer now
# We will make it part of fleet meta_optimizer in the future
class AscendOptimizer(Optimizer):
def __init__(self, optimizer, fetch_list=[]):
self.inner_opt = optimizer
self.fetch_list = fetch_list
def __del__(self):
core.ge_finalize()
def _can_apply(self):
if not self.user_defined_strategy.ascend:
return False
# TODO(hutuxian): other check here
return True
def _disable_strategy(self, dist_strategy):
dist_strategy.ascend = False
dist_strategy.ascend_configs = {}
def _get_input_varlist(program):
ret_list = []
for var in program.list_vars():
if var.is_data or var.persistable:
ret_list.append(var)
return ret_list
def minimize(self,
loss,
startup_program=None,
parameter_list=None,
no_grad_set=None):
minimized = self.inner_opt.minimize(
loss, startup_program=startup_program)
self.ascend_instance = core.AscendInstance()
# Config about Graph Engine can be found in https://support.huaweicloud.com/
config = {
"ge.exec.deviceId": "0",
"ge.graphRunMode": "1",
"ge.exec.precision_mode": "must_keep_origin_dtype"
}
core.ge_initialize(config)
# Init Session
self.ascend_instance.init_global_resources()
main_block = loss.block
self.parser = AscendIRParser()
input_varlist = _get_input_varlist(main_block.program)
startup_graph, main_graph = self.parser.parse_program(
startup_program, main_block.program, input_varlist, self.fetch_list)
self.ascend_instance.add_ascend_subgraph(0, startup_graph)
self.ascend_instance.add_ascend_subgraph(1, main_graph)
return minimized
python/paddle/distributed/fleet/meta_optimizers/ascend/ascend_parser.py 0 → 100644
浏览文件 @ 6dd52c5b
# Copyright (c) 2021 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 paddle.fluid.framework as framework
from paddle.fluid.optimizer import Optimizer
import paddle.fluid.core as core
import numpy as np
registerd_op = {
"elementwise_add": "AddParser",
"matmul": "MatMulParser",
"mul": "MulParser",
"relu": "ReluParser",
"softmax_with_cross_entropy": "SoftmaxWithCrossEntropyParser",
"shape": "ShapeParser",
"fill_constant": "FillConstantParser",
"reduce_sum": "ReduceSumParser",
"reduce_sum_grad": "ReduceSumGradParser",
"matmul_grad": "MatMulGradParser",
"mul_grad": "MulGradParser",
"relu_grad": "ReluGradParser",
"softmax_with_cross_entropy_grad": "SoftmaxWithCrossEntropyGradParser",
"truncated_gaussian_random": "TruncatedNormalParser",
"sgd": "SGDParser"
}
global_cnt = -1
global_input_cnt = -1
class AscendHelper(object):
def __init__(self):
self.dtype2ge_map = {
0: core.GEDataType.DT_BOOL,
1: core.GEDataType.DT_INT16,
2: core.GEDataType.DT_INT32,
3: core.GEDataType.DT_INT64,
4: core.GEDataType.DT_FLOAT16,
5: core.GEDataType.DT_FLOAT,
6: core.GEDataType.DT_DOUBLE
}
self.dtype2np_map = {
0: "bool",
1: "int16",
2: "int32",
3: "int64",
4: "float16",
5: "float32",
6: "float64"
}
def dtype2ge(self, dtype):
assert dtype in self.dtype2ge_map, "dtype[%d] is not supported %d" % (
dtype)
return self.dtype2ge_map[dtype]
def dtype2np(self, index):
assert index in self.dtype2np_map, "index[%d] is not supported %d" % (
dtype)
return self.dtype2np_map[index]
class AscendParserFactory(object):
def __init__(self, graph, var2geop):
self.graph = graph
self.var2geop = var2geop
def create_parse(self, parser_class):
try:
parser = globals()[parser_class](self.graph, self.var2geop)
return parser
except:
raise ValueError("parser class %s does not exist" % parser_class)
class AscendParserBase(object):
def __init__(self, graph, var2geop):
self.graph = graph
self.var2geop = var2geop
self.op = None
self.ascend_helper = AscendHelper()
def _get_ge_input(self, input_var_name):
assert input_var_name in self.var2geop, "var %s not created before" % (
input_var_name)
return self.var2geop[input_var_name]
def update_output(self, geop_list, index_list):
output_num = len(self.op.output_names)
assert output_num == len(
index_list
), "Parser[%s]'s output number[%d] is not equal to parameters number[%d]" % (
self.parser_name, len(index_list), output_num)
for output_id in range(output_num):
arguments = self.op.output(self.op.output_names[output_id])
print("%d argument: %s" % (output_id, str(arguments)))
if len(arguments) > 0:
assert len(arguments) == len(
index_list[output_id]
), "Parser[%s]'s %dth argument number[%d] is not equal to paddle's number[%d]" % (
self.parser_name, output_id, len(index_list[output_id]),
len(arguments))
for i in range(len(arguments)):
print("assgin index_list[%d][%d] to %s" %
(output_id, i, arguments[i]))
self.var2geop[arguments[i]] = geop_list[index_list[
output_id][i]]
for geop in geop_list:
self.graph.add_op(geop)
def apply(self, op):
self.op = op
assert self.op.type == self.parser_name, "op [%s] != parser_name[%s]" % (
self.op.type, self.parser_name)
print("begin to parse op %s" % (self.parser_name))
geop_list, index_list = self._apply()
self.update_output(geop_list, index_list)
def _mark_as_input(self, ge_tensor):
global global_input_cnt
global_input_cnt += 1
self.var2geop["geinput." + str(global_input_cnt)] = ge_tensor
def _accumulated_op_id(self):
global global_cnt
global_cnt += 1
return "." + str(global_cnt)
def _create_ge_tensor(self, shape, dtype, value):
tensor_desc = core.GETensorDesc(
core.GEShape(shape), core.GEFormat.FORMAT_ND,
self.ascend_helper.dtype2ge(dtype))
tensor = core.GETensor(tensor_desc)
data = (value * np.ones((
shape))).reshape(shape).astype(self.ascend_helper.dtype2np(dtype))
buf = data.tobytes()
data_8 = np.frombuffer(buf, dtype=np.uint8)
tensor.set_data(data_8)
return tensor
class AddParser(AscendParserBase):
def __init__(self, graph, var2geop):
super(AddParser, self).__init__(graph, var2geop)
self.parser_name = "elementwise_add"
def _apply(self):
x = self._get_ge_input(self.op.input_arg_names[0])
y = self._get_ge_input(self.op.input_arg_names[1])
add = core.GEOperatorFactory.create_operator(
"add" + self._accumulated_op_id(), "Add").set_input(
"x1", x).set_input("x2", y)
return [add], [[0]]
class ReduceSumParser(AscendParserBase):
def __init__(self, graph, var2geop):
super(ReduceSumParser, self).__init__(graph, var2geop)
self.parser_name = "reduce_sum"
def _apply(self):
x = self._get_ge_input(self.op.input_arg_names[0])
axes = self.op.attr("dim")
keep_dims = self.op.attr("keep_dim")
reduce_sum = core.GEOperatorFactory.create_operator(
"reduce_sum" + self._accumulated_op_id(), "ReduceSumD").set_input(
"x", x, 0).set_attr_vec_int32("axes", axes).set_attr_bool(
"keep_dims", keep_dims)
return [reduce_sum], [[0]]
class ReduceSumGradParser(AscendParserBase):
def __init__(self, graph, var2geop):
super(ReduceSumGradParser, self).__init__(graph, var2geop)
self.parser_name = "reduce_sum_grad"
def _apply(self):
x = self._get_ge_input(self.op.input_arg_names[0])
input = self._get_ge_input(self.op.input_arg_names[1])
shape_tensor = core.GEOperatorFactory.create_operator(
"shape" + self._accumulated_op_id(), "Shape").set_input("x", input,
0)
axis_const = core.GEOperatorFactory.create_operator(
"const" + self._accumulated_op_id(), "Const").set_attr_tensor(
"value", self._create_ge_tensor([1], 2, -1))
self._mark_as_input(axis_const)
broadcast = core.GEOperatorFactory.create_operator(
"broadcast_to_d" + self._accumulated_op_id(),
"BroadcastTo").set_input("x", x).set_input("shape", shape_tensor)
# unsqueeze cannot get right result, but ExpandDims seems have the same functionality.
reduce_sum_grad = core.GEOperatorFactory.create_operator(
"expand" + self._accumulated_op_id(), "ExpandDims").set_input(
"x", broadcast).set_input("axis", axis_const)
return [shape_tensor, axis_const, broadcast, reduce_sum_grad], [[3]]
class MatMulParser(AscendParserBase):
def __init__(self, graph, var2geop):
super(MatMulParser, self).__init__(graph, var2geop)
self.parser_name = "matmul"
def _apply(self):
x1 = self._get_ge_input(self.op.input_arg_names[0])
x2 = self._get_ge_input(self.op.input_arg_names[1])
matmul = core.GEOperatorFactory.create_operator(
"matmul" + self._accumulated_op_id(), "MatMul").set_input(
"x1", x1).set_input("x2", x2)
return [matmul], [[0]]
class MatMulGradParser(AscendParserBase):
def __init__(self, graph, var2geop):
super(MatMulGradParser, self).__init__(graph, var2geop)
self.parser_name = "matmul_grad"
def _apply(self):
out_grad = self._get_ge_input(self.op.input_arg_names[0])
x = self._get_ge_input(self.op.input_arg_names[1])
y = self._get_ge_input(self.op.input_arg_names[2])
x_grad = core.GEOperatorFactory.create_operator(
self.parser_name + self._accumulated_op_id(), "MatMul").set_input(
"x1", out_grad).set_input("x2", y).set_attr_bool(
"transpose_x1", False).set_attr_bool("transpose_x2", True)
y_grad = core.GEOperatorFactory.create_operator(
self.parser_name + self._accumulated_op_id(), "MatMul").set_input(
"x1", x).set_input("x2", out_grad).set_attr_bool(
"transpose_x1", True).set_attr_bool("transpose_x2", False)
return [x_grad, y_grad], [[0], [1]]
class MulGradParser(AscendParserBase):
def __init__(self, graph, var2geop):
super(MulGradParser, self).__init__(graph, var2geop)
self.parser_name = "mul_grad"
def _apply(self):
out_grad = self._get_ge_input(self.op.input_arg_names[0])
x = self._get_ge_input(self.op.input_arg_names[1])
y = self._get_ge_input(self.op.input_arg_names[2])
x_grad = core.GEOperatorFactory.create_operator(
self.parser_name + self._accumulated_op_id(), "MatMul").set_input(
"x1", out_grad).set_input("x2", y).set_attr_bool(
"transpose_x1", False).set_attr_bool("transpose_x2", True)
y_grad = core.GEOperatorFactory.create_operator(
self.parser_name + self._accumulated_op_id(), "MatMul").set_input(
"x1", x).set_input("x2", out_grad).set_attr_bool(
"transpose_x1", True).set_attr_bool("transpose_x2", False)
return [x_grad, y_grad], [[0], [1]]
class MulParser(AscendParserBase):
def __init__(self, graph, var2geop):
super(MulParser, self).__init__(graph, var2geop)
self.parser_name = "mul"
def _apply(self):
x = self._get_ge_input(self.op.input_arg_names[0])
y = self._get_ge_input(self.op.input_arg_names[1])
matmul = core.GEOperatorFactory.create_operator(
"mul" + self._accumulated_op_id(), "MatMul").set_input(
"x1", x).set_input("x2", y)
return [matmul], [[0]]
class ReluParser(AscendParserBase):
def __init__(self, graph, var2geop):
super(ReluParser, self).__init__(graph, var2geop)
self.parser_name = "relu"
def _apply(self):
x = self._get_ge_input(self.op.input_arg_names[0])
relu = core.GEOperatorFactory.create_operator(
"relu" + self._accumulated_op_id(), "Relu").set_input("x", x)
return [relu], [[0]]
class ReluGradParser(AscendParserBase):
def __init__(self, graph, var2geop):
super(ReluGradParser, self).__init__(graph, var2geop)
self.parser_name = "relu_grad"
def _apply(self):
out = self._get_ge_input(self.op.input_arg_names[0])
out_grad = self._get_ge_input(self.op.input_arg_names[1])
relu_grad = core.GEOperatorFactory.create_operator(
self.parser_name + self._accumulated_op_id(), "ReluGrad").set_input(
"gradients", out_grad).set_input("features", out)
return [relu_grad], [[0]]
class SoftmaxWithCrossEntropyParser(AscendParserBase):
def __init__(self, graph, var2geop):
super(SoftmaxWithCrossEntropyParser, self).__init__(graph, var2geop)
self.parser_name = "softmax_with_cross_entropy"
def _apply(self):
label = self._get_ge_input(self.op.input_arg_names[0])
logits = self._get_ge_input(self.op.input_arg_names[1])
cls_num = self.op.block.var(self.op.input_arg_names[1]).shape[1]
softmax = core.GEOperatorFactory.create_operator(
"softmax" + self._accumulated_op_id(), "SoftmaxV2").set_input(
"x", logits)
label = core.GEOperatorFactory.create_operator(
"cast" + self._accumulated_op_id(), "Cast").set_input(
"x", label).set_attr_int32("dst_type", 3)
tensoron = self._create_ge_tensor([1], 5, 1)
on_const = core.GEOperatorFactory.create_operator(
"const" + self._accumulated_op_id(), "Const").set_attr_tensor(
"value", tensoron)
self._mark_as_input(on_const)
tensoroff = self._create_ge_tensor([1], 5, 0)
off_const = core.GEOperatorFactory.create_operator(
"const" + self._accumulated_op_id(), "Const").set_attr_tensor(
"value", tensoroff)
self._mark_as_input(off_const)
onehot = core.GEOperatorFactory.create_operator(
"onehot" + self._accumulated_op_id(), "OneHotD").set_input(
"x", label).set_input("on_value", on_const).set_input(
"off_value", off_const).set_attr_int32("depth", cls_num)
squeeze = core.GEOperatorFactory.create_operator(
"mul" + self._accumulated_op_id(), "Squeeze").set_input("x", onehot)
loss = core.GEOperatorFactory.create_operator(
"loss" + self._accumulated_op_id(),
"SoftmaxCrossEntropyWithLogits").set_input(
"features", logits).set_input("labels", squeeze)
return [label, softmax, on_const, off_const, onehot, squeeze,
loss], [[6], [1]]
class SoftmaxWithCrossEntropyGradParser(AscendParserBase):
def __init__(self, graph, var2geop):
super(SoftmaxWithCrossEntropyGradParser, self).__init__(graph, var2geop)
self.parser_name = "softmax_with_cross_entropy_grad"
def _apply(self):
label = self._get_ge_input(self.op.input_arg_names[0])
loss_grad = self._get_ge_input(self.op.input_arg_names[1])
softmax = self._get_ge_input(self.op.input_arg_names[2])
cls_num = self.op.block.var(self.op.input_arg_names[2]).shape[1]
tensoron = self._create_ge_tensor([1], 5, 1)
on_const = core.GEOperatorFactory.create_operator(
"const" + self._accumulated_op_id(), "Const").set_attr_tensor(
"value", tensoron)
self._mark_as_input(on_const)
tensoroff = self._create_ge_tensor([1], 5, 0)
off_const = core.GEOperatorFactory.create_operator(
"const" + self._accumulated_op_id(), "Const").set_attr_tensor(
"value", tensoroff)
self._mark_as_input(off_const)
label = core.GEOperatorFactory.create_operator(
"cast" + self._accumulated_op_id(), "Cast").set_input(
"x", label).set_attr_int32("dst_type", 3)
onehot = core.GEOperatorFactory.create_operator(
"onehot" + self._accumulated_op_id(), "OneHotD").set_input(
"x", label).set_input("on_value", on_const).set_input(
"off_value", off_const).set_attr_int32("depth", cls_num)
# the fuck onehot will add a demension, so must call squeeze afterward
squeeze = core.GEOperatorFactory.create_operator(
"mul" + self._accumulated_op_id(), "Squeeze").set_input("x", onehot)
sub = core.GEOperatorFactory.create_operator(
"sub" + self._accumulated_op_id(), "Sub").set_input(
"x1", softmax).set_input("x2", squeeze)
grad = core.GEOperatorFactory.create_operator(
"mul" + self._accumulated_op_id(), "Mul").set_input(
"x1", loss_grad).set_input("x2", sub)
return [on_const, off_const, label, onehot, squeeze, sub, grad], [[-1]]
class ShapeParser(AscendParserBase):
def __init__(self, graph, var2geop):
super(ShapeParser, self).__init__(graph, var2geop)
self.parser_name = "shape"
def _apply(self):
x = self._get_ge_input(self.op.input_arg_names[0])
shape = core.GEOperatorFactory.create_operator(
"shape" + self._accumulated_op_id(), "Shape").set_input("x", x)
return [shape], [[0]]
class FillConstantParser(AscendParserBase):
def __init__(self, graph, var2geop):
super(FillConstantParser, self).__init__(graph, var2geop)
self.parser_name = "fill_constant"
def _apply(self):
shape = self.op.attr("shape")
dtype = self.op.attr("dtype")
value = self.op.attr("value")
print("shape: ", shape)
print("dtype: ", dtype)
print("value: ", value)
tensor = self._create_ge_tensor(shape, dtype, value)
const = core.GEOperatorFactory.create_operator(
"const" + self._accumulated_op_id(), "Const").set_attr_tensor(
"value", tensor)
self._mark_as_input(const)
if self.op.block.var(self.op.output('Out')[0]).persistable:
print("%s fill_constant" % (self.op.output('Out')[0]))
var = core.GEOperatorFactory.create_operator(
self.op.output('Out')[0], "Variable")
var.update_output_desc("y",
core.GETensorDesc(
core.GEShape(shape),
core.GEFormat.FORMAT_ND,
core.GEDataType.DT_FLOAT))
assign = core.GEOperatorFactory.create_operator(
"assign" + self._accumulated_op_id(), "Assign").set_input(
"value", const).set_input("ref", var)
return [const], [[0]]
else:
print(
"self.op.output('Out')[0] is not persistable in fill_constant")
return [const], [[0]]
class SGDParser(AscendParserBase):
def __init__(self, graph, var2geop):
super(SGDParser, self).__init__(graph, var2geop)
self.parser_name = "sgd"
def _apply(self):
grad = self._get_ge_input(self.op.input_arg_names[0])
lr = self._get_ge_input(self.op.input_arg_names[1])
param = self._get_ge_input(self.op.input_arg_names[2])
sgd = core.GEOperatorFactory.create_operator(
"momentum" + self._accumulated_op_id(),
"ApplyGradientDescent").set_input("var", param).set_input(
"alpha", lr).set_input("delta", grad)
return [sgd], [[0]]
class TruncatedNormalParser(AscendParserBase):
def __init__(self, graph, var2geop):
super(TruncatedNormalParser, self).__init__(graph, var2geop)
self.parser_name = "truncated_gaussian_random"
def _apply(self):
shape = self.op.attr("shape")
dtype = self.op.attr("dtype")
mean = self.op.attr("mean")
std = self.op.attr("std")
seed = self.op.attr("seed")
tensor1 = self._create_ge_tensor([len(shape)], 2, shape)
shape_tensor = core.GEOperatorFactory.create_operator(
"const" + self._accumulated_op_id(), "Const").set_attr_tensor(
"value", tensor1)
tensor2 = self._create_ge_tensor([1], dtype, mean)
mean_tensor = core.GEOperatorFactory.create_operator(
"const" + self._accumulated_op_id(), "Const").set_attr_tensor(
"value", tensor2)
tensor3 = self._create_ge_tensor([1], dtype, std)
std_tensor = core.GEOperatorFactory.create_operator(
"const" + self._accumulated_op_id(), "Const").set_attr_tensor(
"value", tensor3)
tensor4 = self._create_ge_tensor([1], dtype, mean - 2 * std)
min_tensor = core.GEOperatorFactory.create_operator(
"const" + self._accumulated_op_id(), "Const").set_attr_tensor(
"value", tensor4)
tensor5 = self._create_ge_tensor([1], dtype, mean + 2 * std)
max_tensor = core.GEOperatorFactory.create_operator(
"const" + self._accumulated_op_id(), "Const").set_attr_tensor(
"value", tensor5)
self._mark_as_input(shape_tensor)
self._mark_as_input(mean_tensor)
self._mark_as_input(std_tensor)
self._mark_as_input(min_tensor)
self._mark_as_input(max_tensor)
truncated_normal = core.GEOperatorFactory.create_operator(
"truncated_normal" + self._accumulated_op_id(),
"ParameterizedTruncatedNormal").set_input(
"shape", shape_tensor).set_input(
"means", mean_tensor).set_input(
"stdevs", std_tensor).set_input(
"min", min_tensor).set_input(
"max", max_tensor).set_attr_int32("seed", 0)
## wirte the output of truncatedNormal from startup_program to main_program
if self.op.block.var(self.op.output('Out')[0]).persistable:
print("%s is Persistable in truncated_normal" %
(self.op.output('Out')[0]))
#var = core.GEOperatorFactory.create_operator(self.op.output('Out')[0], "Variable").set_input("x", truncated_normal)
var = core.GEOperatorFactory.create_operator(
self.op.output('Out')[0], "Variable")
var.update_output_desc("y",
core.GETensorDesc(
core.GEShape(shape),
core.GEFormat.FORMAT_ND,
core.GEDataType.DT_FLOAT))
assign = core.GEOperatorFactory.create_operator(
"assign" + self._accumulated_op_id(), "Assign").set_input(
"value", truncated_normal).set_input("ref", var)
return [
shape_tensor, mean_tensor, std_tensor, min_tensor, max_tensor,
truncated_normal
], [[-1]]
else:
print(
"self.op.output('Out')[0] is not persistable in truncated_noraml"
)
return [truncated_normal], [[0]] #[assign]
python/paddle/fluid/tests/unittests/test_ascend_trigger.py 0 → 100644
浏览文件 @ 6dd52c5b
# Copyright (c) 2021 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 paddle
import paddle.fluid as fluid
import paddle.fluid.layers as layers
import unittest
class TestAscendTriggerOP(unittest.TestCase):
""" TestCases for ascend_trigger op"""
def test_ascend_trigger_op(self):
paddle.enable_static()
program = fluid.Program()
block = program.global_block()
with fluid.program_guard(program):
x = fluid.data(name='x', shape=[1], dtype='int64', lod_level=0)
y = fluid.data(name='y', shape=[1], dtype='int64', lod_level=0)
block.append_op(
type="ascend_trigger",
inputs={"FeedList": [x]},
outputs={"FetchList": [y]},
attrs={'graph_idx': 0})
exe = paddle.static.Executor(paddle.CPUPlace())
try:
exe.run(program)
except RuntimeError as e:
pass
except:
self.assertTrue(False)
paddle.disable_static()
if __name__ == '__main__':
unittest.main()
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