调用NN算子接口示例代码
本节介绍了单算子API执行方式下算子调用和编译运行样例。
基本原理
NN(Neural Network)算子主要实现数学基础运算(如加、减、乘、除等)以及CNN相关的操作(如卷积、池化、激活函数)等,详细的算子API介绍参见单算子API执行,接口调用流程参见单算子API执行接口调用流程。
单算子API执行的算子接口一般定义为“两段式接口”:
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aclnnStatus aclnnXxxGetWorkspaceSize(const aclTensor *src, ..., aclTensor *out, ..., uint64_t *workspaceSize, aclOpExecutor **executor); aclnnStatus aclnnXxx(void *workspace, uint64_t workspaceSize, aclOpExecutor *executor, aclrtStream stream); |
其中aclnnXxxGetWorkspaceSize为第一段接口,主要用于计算本次NN算子API调用计算过程中需要多少的workspace内存。获取到本次计算需要的workspace大小后,按照workspaceSize大小申请Device侧内存,然后调用第二段接口aclnnXxx执行计算。
示例代码
这里以Add算子调用过程为例,介绍算子调用的基本逻辑,其他算子的调用过程类似,请根据实际情况自行修改代码。
已知Add算子实现了张量加法运算,计算公式为:y=x1+αxx2。您可以获取如下示例代码,并将文件命名为“test_add.cpp”,代码如下:
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#include <iostream> #include <vector> #include "acl/acl.h" #include "aclnnop/aclnn_add.h" #define CHECK_RET(cond, return_expr) \ do { \ if (!(cond)) { \ return_expr; \ } \ } while (0) #define LOG_PRINT(message, ...) \ do { \ printf(message, ##__VA_ARGS__); \ } while (0) int64_t GetShapeSize(const std::vector<int64_t>& shape) { int64_t shape_size = 1; for (auto i : shape) { shape_size *= i; } return shape_size; } int Init(int32_t deviceId, aclrtStream* stream) { // 固定写法,AscendCL初始化 auto ret = aclInit(nullptr); CHECK_RET(ret == ACL_SUCCESS, LOG_PRINT("aclInit failed. ERROR: %d\n", ret); return ret); ret = aclrtSetDevice(deviceId); CHECK_RET(ret == ACL_SUCCESS, LOG_PRINT("aclrtSetDevice failed. ERROR: %d\n", ret); return ret); ret = aclrtCreateStream(stream); CHECK_RET(ret == ACL_SUCCESS, LOG_PRINT("aclrtCreateStream failed. ERROR: %d\n", ret); return ret); return 0; } template <typename T> int CreateAclTensor(const std::vector<T>& hostData, const std::vector<int64_t>& shape, void** deviceAddr, aclDataType dataType, aclTensor** tensor) { auto size = GetShapeSize(shape) * sizeof(T); // 调用aclrtMalloc申请device侧内存 auto ret = aclrtMalloc(deviceAddr, size, ACL_MEM_MALLOC_HUGE_FIRST); CHECK_RET(ret == ACL_SUCCESS, LOG_PRINT("aclrtMalloc failed. ERROR: %d\n", ret); return ret); // 调用aclrtMemcpy将host侧数据拷贝到device侧内存上 ret = aclrtMemcpy(*deviceAddr, size, hostData.data(), size, ACL_MEMCPY_HOST_TO_DEVICE); CHECK_RET(ret == ACL_SUCCESS, LOG_PRINT("aclrtMemcpy failed. ERROR: %d\n", ret); return ret); // 计算连续tensor的strides std::vector<int64_t> strides(shape.size(), 1); for (int64_t i = shape.size() - 2; i >= 0; i--) { strides[i] = shape[i + 1] * strides[i + 1]; } // 调用aclCreateTensor接口创建aclTensor *tensor = aclCreateTensor(shape.data(), shape.size(), dataType, strides.data(), 0, aclFormat::ACL_FORMAT_ND, shape.data(), shape.size(), *deviceAddr); return 0; } int main() { // 1. (固定写法)device/stream初始化, 参考AscendCL对外接口列表 // 根据自己的实际device填写deviceId int32_t deviceId = 0; aclrtStream stream; auto ret = Init(deviceId, &stream); // check根据自己的需要处理 CHECK_RET(ret == 0, LOG_PRINT("Init acl failed. ERROR: %d\n", ret); return ret); // 2. 构造输入与输出,需要根据API的接口自定义构造 std::vector<int64_t> selfShape = {4, 2}; std::vector<int64_t> otherShape = {4, 2}; std::vector<int64_t> outShape = {4, 2}; void* selfDeviceAddr = nullptr; void* otherDeviceAddr = nullptr; void* outDeviceAddr = nullptr; aclTensor* self = nullptr; aclTensor* other = nullptr; aclScalar* alpha = nullptr; aclTensor* out = nullptr; std::vector<float> selfHostData = {0, 1, 2, 3, 4, 5, 6, 7}; std::vector<float> otherHostData = {1, 1, 1, 2, 2, 2, 3, 3}; std::vector<float> outHostData = {0, 0, 0, 0, 0, 0, 0, 0}; float alphaValue = 1.2f; // 创建self aclTensor ret = CreateAclTensor(selfHostData, selfShape, &selfDeviceAddr, aclDataType::ACL_FLOAT, &self); CHECK_RET(ret == ACL_SUCCESS, return ret); // 创建other aclTensor ret = CreateAclTensor(otherHostData, otherShape, &otherDeviceAddr, aclDataType::ACL_FLOAT, &other); CHECK_RET(ret == ACL_SUCCESS, return ret); // 创建alpha aclScalar alpha = aclCreateScalar(&alphaValue, aclDataType::ACL_FLOAT); CHECK_RET(alpha != nullptr, return ret); // 创建out aclTensor ret = CreateAclTensor(outHostData, outShape, &outDeviceAddr, aclDataType::ACL_FLOAT, &out); CHECK_RET(ret == ACL_SUCCESS, return ret); // 3. 调用CANN算子库API,需要修改为具体的算子接口 uint64_t workspaceSize = 0; aclOpExecutor* executor; // 调用aclnnAdd第一段接口 ret = aclnnAddGetWorkspaceSize(self, other, alpha, out, &workspaceSize, &executor); CHECK_RET(ret == ACL_SUCCESS, LOG_PRINT("aclnnAddGetWorkspaceSize failed. ERROR: %d\n", ret); return ret); // 根据第一段接口计算出的workspaceSize申请device内存 void* workspaceAddr = nullptr; if (workspaceSize > 0) { ret = aclrtMalloc(&workspaceAddr, workspaceSize, ACL_MEM_MALLOC_HUGE_FIRST); CHECK_RET(ret == ACL_SUCCESS, LOG_PRINT("allocate workspace failed. ERROR: %d\n", ret); return ret;); } // 调用aclnnAdd第二段接口 ret = aclnnAdd(workspaceAddr, workspaceSize, executor, stream); CHECK_RET(ret == ACL_SUCCESS, LOG_PRINT("aclnnAdd failed. ERROR: %d\n", ret); return ret); // 4.( 固定写法)同步等待任务执行结束 ret = aclrtSynchronizeStream(stream); CHECK_RET(ret == ACL_SUCCESS, LOG_PRINT("aclrtSynchronizeStream failed. ERROR: %d\n", ret); return ret); // 5. 获取输出的值,将device侧内存上的结果拷贝至host侧,需要根据具体API的接口定义修改 auto size = GetShapeSize(outShape); std::vector<float> resultData(size, 0); ret = aclrtMemcpy(resultData.data(), resultData.size() * sizeof(resultData[0]), outDeviceAddr, size * sizeof(float), ACL_MEMCPY_DEVICE_TO_HOST); CHECK_RET(ret == ACL_SUCCESS, LOG_PRINT("copy result from device to host failed. ERROR: %d\n", ret); return ret); for (int64_t i = 0; i < size; i++) { LOG_PRINT("result[%ld] is: %f\n", i, resultData[i]); } // 6. 释放aclTensor和aclScalar,需要根据具体API的接口定义修改 aclDestroyTensor(self); aclDestroyTensor(other); aclDestroyScalar(alpha); aclDestroyTensor(out); // 7. 释放device资源,需要根据具体API的接口定义修改 aclrtFree(selfDeviceAddr); aclrtFree(otherDeviceAddr); aclrtFree(outDeviceAddr); if (workspaceSize > 0) { aclrtFree(workspaceAddr); } aclrtDestroyStream(stream); aclrtResetDevice(deviceId); aclFinalize(); return 0; } |
CMakeLists文件(动态库)
这里以Add算子动态编译为例,其他算子的CMakeLists动态编译脚本类似,请根据实际情况自行修改脚本。
# CMake lowest version requirement cmake_minimum_required(VERSION 3.14) # 设置工程名 project(ACLNN_EXAMPLE) # Compile options add_compile_options(-std=c++11) # 设置编译选项 set(CMAKE_RUNTIME_OUTPUT_DIRECTORY "./bin") set(CMAKE_CXX_FLAGS_DEBUG "-fPIC -O0 -g -Wall") set(CMAKE_CXX_FLAGS_RELEASE "-fPIC -O2 -Wall") # 设置可执行文件名(如opapi_test),并指定待运行算子文件*.cpp所在目录 add_executable(opapi_test test_add.cpp) # 设置ASCEND_PATH(CANN软件包目录,请根据实际路径修改)和INCLUDE_BASE_DIR(头文件目录) if(NOT "$ENV{ASCEND_CUSTOM_PATH}" STREQUAL "") set(ASCEND_PATH $ENV{ASCEND_CUSTOM_PATH}) else() set(ASCEND_PATH "/usr/local/Ascend/ascend-toolkit/latest") endif() set(INCLUDE_BASE_DIR "${ASCEND_PATH}/include") include_directories( ${INCLUDE_BASE_DIR} ${INCLUDE_BASE_DIR}/aclnn ) # 设置链接的动态库文件路径 # arch表示操作系统架构,os表示操作系统 target_link_libraries(opapi_test PRIVATE ${ASCEND_PATH}/${arch-os}/devlib/libascendcl.so ${ASCEND_PATH}/${arch-os}/devlib/libnnopbase.so ${ASCEND_PATH}/lib64/libopapi.so) # 可执行文件在CMakeLists文件所在目录的bin目录下 install(TARGETS opapi_test DESTINATION ${CMAKE_RUNTIME_OUTPUT_DIRECTORY})
CMakeLists文件(静态库)
这里以Add算子动态编译为例,其他算子的CMakeLists静态编译脚本类似,请根据实际情况自行修改脚本。
# Copyright (c) Huawei Technologies Co., Ltd. 2019. All rights reserved. # CMake lowest version requirement cmake_minimum_required(VERSION 3.14) # 设置工程名 project(ACLNN_EXAMPLE) # Compile options add_compile_options(-std=c++11) # 设置编译选项 set(CMAKE_RUNTIME_OUTPUT_DIRECTORY "./bin") set(CMAKE_CXX_FLAGS_DEBUG "-fPIC -O0 -g -Wall") set(CMAKE_CXX_FLAGS_RELEASE "-fPIC -O2 -Wall") # 设置可执行文件名(如opapi_test),并指定待运行算子文件*.cpp所在目录 add_executable(opapi_test test_add.cpp) # 设置ASCEND_PATH(CANN软件包目录,请根据实际路径修改)和INCLUDE_BASE_DIR(头文件目录) if(NOT "$ENV{ASCEND_CUSTOM_PATH}" STREQUAL "") set(ASCEND_PATH $ENV{ASCEND_CUSTOM_PATH}) else() set(ASCEND_PATH "/usr/local/Ascend/ascend-toolkit/latest") endif() set(INCLUDE_BASE_DIR "${ASCEND_PATH}/include") include_directories( ${INCLUDE_BASE_DIR} ${INCLUDE_BASE_DIR}/aclnn ) # 设置链接的静态库文件路径 # 注意1:opmaster_static.a和aclnn_math_static.a必选,其余.a文件按需设置,支持设置1个或多个。 # 注意2:so文件是静态库.a文件依赖的动态库文件,必须设置。 target_link_directories(opapi_test PRIVATE ${ASCEND_PATH}/lib64/) target_link_libraries(opapi_test PRIVATE aclnn_rand_static aclnn_math_static aclnn_ops_infer_static aclnn_ops_train_static opmaster_static c_sec platform error_manager ascendalog profapi ascendcl ge_common_base graph_base exe_graph graph register ascend_dump nnopbase) # 可执行文件在CMakeLists文件所在目录的bin目录下 install(TARGETS opapi_test DESTINATION ${CMAKE_RUNTIME_OUTPUT_DIRECTORY})
编译与运行
- 根据前文提供的示例代码、CMakeLists文件(动态库)或CMakeLists文件(静态库),提前准备好算子的调用代码(*.cpp)和编译脚本(CMakeLists.txt)。
- 配置环境变量。
安装CANN软件后,使用CANN运行用户(如HwHiAiUser)登录环境,执行如下命令设置环境变量。其中${install_path}为CANN软件安装后文件存储路径,请根据实际情况替换该路径。
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source ${install_path}/set_env.sh
- 编译并运行。
- 进入CMakeLists.txt所在目录,执行如下命令,新建build目录存放生成的编译文件。
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mkdir -p build
- 进入build所在目录,执行cmake命令编译,再执行make命令生成可执行文件。
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cmake ./ -DCMAKE_CXX_COMPILER=g++ -DCMAKE_SKIP_RPATH=TRUE make
编译成功后,会在当前目录的bin目录下生成opapi_test可执行文件。
- 进入bin目录,运行可执行文件opapi_test。
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./opapi_test
以Add算子的运行结果为例,运行后的结果如下:
- 进入CMakeLists.txt所在目录,执行如下命令,新建build目录存放生成的编译文件。
父主题: 单算子API执行