Neural Network Runtime对接AI推理框架开发指导

场景介绍

Neural Network Runtime作为AI推理引擎和加速芯片的桥梁，为AI推理引擎提供精简的Native接口，满足推理引擎通过加速芯片执行端到端推理的需求。

本文以图1展示的Add单算子模型为例，介绍Neural Network Runtime的开发流程。Add算子包含两个输入、一个参数和一个输出，其中的activation参数用于指定Add算子中激活函数的类型。

图1 Add单算子网络示意图

环境准备

环境要求

Neural Network Runtime部件的环境要求如下：

• 开发环境：Ubuntu 18.04及以上。
• 接入设备：系统定义的标准设备，系统中内置AI硬件驱动并已接入Neural Network Runtime。

由于Neural Network Runtime通过Native API对外开放，需要通过Native开发套件编译Neural Network Runtime应用。在社区的每日构建中下载对应系统版本的ohos-sdk压缩包，从压缩包中提取对应平台的Native开发套件。以Linux为例，Native开发套件的压缩包命名为native-linux-{版本号}.zip。

环境搭建

1. 打开Ubuntu编译服务器的终端。

2. 把下载好的Native开发套件压缩包拷贝至当前用户根目录下。

3. 执行以下命令解压Native开发套件的压缩包。

   unzip native-linux-{版本号}.zip
   

   解压缩后的内容如下（随版本迭代，目录下的内容可能发生变化，请以最新版本的Native API为准）：

   native/
   ├── build // 交叉编译工具链
   ├── build-tools // 编译构建工具
   ├── docs
   ├── llvm
   ├── nativeapi_syscap_config.json
   ├── ndk_system_capability.json
   ├── NOTICE.txt
   ├── oh-uni-package.json
   └── sysroot // Native API头文件和库
   

接口说明

这里给出Neural Network Runtime开发流程中通用的接口，具体请见下列表格。

结构体

模型构造接口

模型编译接口

张量描述接口

张量接口

执行推理接口

设备管理接口

开发步骤

Neural Network Runtime的开发流程主要包含模型构造、模型编译和推理执行三个阶段。以下开发步骤以Add单算子模型为例，介绍调用Neural Network Runtime接口，开发应用的过程。

1. 创建应用样例文件。

   首先，创建Neural Network Runtime应用样例的源文件。在项目目录下执行以下命令，创建nnrt_example/目录，并在目录下创建 nnrt_example.cpp 源文件。

   mkdir ~/nnrt_example && cd ~/nnrt_example
   touch nnrt_example.cpp
   

2. 导入Neural Network Runtime。

   在 nnrt_example.cpp 文件的开头添加以下代码，引入Neural Network Runtime。

   #include <iostream>
   #include <cstdarg>
   #include "hilog/log.h"
   #include "neural_network_runtime/neural_network_runtime.h"
   

3. 定义日志打印、设置输入数据、数据打印等辅助函数。

   #define LOG_DOMAIN 0xD002101
   #define LOG_TAG "NNRt"
   #define LOGD(...) OH_LOG_DEBUG(LOG_APP, __VA_ARGS__)
   #define LOGI(...) OH_LOG_INFO(LOG_APP, __VA_ARGS__)
   #define LOGW(...) OH_LOG_WARN(LOG_APP, __VA_ARGS__)
   #define LOGE(...) OH_LOG_ERROR(LOG_APP, __VA_ARGS__)
   #define LOGF(...) OH_LOG_FATAL(LOG_APP, __VA_ARGS__)
   
   // 返回值检查宏
   #define CHECKNEQ(realRet, expectRet, retValue, ...) \
       do { \
           if ((realRet) != (expectRet)) { \
               printf(__VA_ARGS__); \
               return (retValue); \
           } \
       } while (0)
   
   #define CHECKEQ(realRet, expectRet, retValue, ...) \
       do { \
           if ((realRet) == (expectRet)) { \
               printf(__VA_ARGS__); \
               return (retValue); \
           } \
       } while (0)
   
   // 设置输入数据用于推理
   OH_NN_ReturnCode SetInputData(NN_Tensor* inputTensor[], size_t inputSize)
   {
       OH_NN_DataType dataType(OH_NN_FLOAT32);
       OH_NN_ReturnCode ret{OH_NN_FAILED};
       size_t elementCount = 0;
       for (size_t i = 0; i < inputSize; ++i) {
           // 获取张量的数据内存
           auto data = OH_NNTensor_GetDataBuffer(inputTensor[i]);
           CHECKEQ(data, nullptr, OH_NN_FAILED, "Failed to get data buffer.");
           // 获取张量的描述
           auto desc = OH_NNTensor_GetTensorDesc(inputTensor[i]);
           CHECKEQ(desc, nullptr, OH_NN_FAILED, "Failed to get desc.");
           // 获取张量的数据类型
           ret = OH_NNTensorDesc_GetDataType(desc, &dataType);
           CHECKNEQ(ret, OH_NN_SUCCESS, OH_NN_FAILED, "Failed to get data type.");
           // 获取张量的元素个数
           ret = OH_NNTensorDesc_GetElementCount(desc, &elementCount);
           CHECKNEQ(ret, OH_NN_SUCCESS, OH_NN_FAILED, "Failed to get element count.");
           switch(dataType) {
               case OH_NN_FLOAT32: {
                   float* floatValue = reinterpret_cast<float*>(data);
                   for (size_t j = 0; j < elementCount; ++j) {
                       floatValue[j] = static_cast<float>(j);
                   }
                   break;
               }
               case OH_NN_INT32: {
                   int* intValue = reinterpret_cast<int*>(data);
                   for (size_t j = 0; j < elementCount; ++j) {
                       intValue[j] = static_cast<int>(j);
                   }
                   break;
               }
               default:
                   return OH_NN_FAILED;
           }
       }
       return OH_NN_SUCCESS;
   }
   
   OH_NN_ReturnCode Print(NN_Tensor* outputTensor[], size_t outputSize)
   {
       OH_NN_DataType dataType(OH_NN_FLOAT32);
       OH_NN_ReturnCode ret{OH_NN_FAILED};
       size_t elementCount = 0;
       for (size_t i = 0; i < outputSize; ++i) {
           auto data = OH_NNTensor_GetDataBuffer(outputTensor[i]);
           CHECKEQ(data, nullptr, OH_NN_FAILED, "Failed to get data buffer.");
           auto desc = OH_NNTensor_GetTensorDesc(outputTensor[i]);
           CHECKEQ(desc, nullptr, OH_NN_FAILED, "Failed to get desc.");
           ret = OH_NNTensorDesc_GetDataType(desc, &dataType);
           CHECKNEQ(ret, OH_NN_SUCCESS, OH_NN_FAILED, "Failed to get data type.");
           ret = OH_NNTensorDesc_GetElementCount(desc, &elementCount);
           CHECKNEQ(ret, OH_NN_SUCCESS, OH_NN_FAILED, "Failed to get element count.");
           switch(dataType) {
               case OH_NN_FLOAT32: {
                   float* floatValue = reinterpret_cast<float*>(data);
                   for (size_t j = 0; j < elementCount; ++j) {
                       std::cout << "Output index: " << j << ", value is: " << floatValue[j] << "." << std::endl;
                   }
                   break;
               }
               case OH_NN_INT32: {
                   int* intValue = reinterpret_cast<int*>(data);
                   for (size_t j = 0; j < elementCount; ++j) {
                       std::cout << "Output index: " << j << ", value is: " << intValue[j] << "." << std::endl;
                   }
                   break;
               }
               default:
                   return OH_NN_FAILED;
           }
       }
   
       return OH_NN_SUCCESS;
   }
   

4. 构造模型。

   使用Neural Network Runtime的模型构造接口，构造Add单算子样例模型。

   OH_NN_ReturnCode BuildModel(OH_NNModel** pmodel)
   {
       // 创建模型实例model，进行模型构造
       OH_NNModel* model = OH_NNModel_Construct();
       CHECKEQ(model, nullptr, -1, "Create model failed.");
   
       // 添加Add算子的第一个输入张量，类型为float32，张量形状为[1, 2, 2, 3]
       NN_TensorDesc* tensorDesc = OH_NNTensorDesc_Create();
       CHECKEQ(tensorDesc, nullptr, -1, "Create TensorDesc failed.");
   
       int32_t inputDims[4] = {1, 2, 2, 3};
       returnCode = OH_NNTensorDesc_SetShape(tensorDesc, inputDims, 4);
       CHECKNEQ(returnCode, OH_NN_SUCCESS, -1, "Set TensorDesc shape failed.");
   
       returnCode = OH_NNTensorDesc_SetDataType(tensorDesc, OH_NN_FLOAT32);
       CHECKNEQ(returnCode, OH_NN_SUCCESS, -1, "Set TensorDesc data type failed.");
   
       returnCode = OH_NNTensorDesc_SetFormat(tensorDesc, OH_NN_FORMAT_NONE);
       CHECKNEQ(returnCode, OH_NN_SUCCESS, -1, "Set TensorDesc format failed.");
   
       returnCode = OH_NNModel_AddTensorToModel(model, tensorDesc);
       CHECKNEQ(returnCode, OH_NN_SUCCESS, -1, "Add first TensorDesc to model failed.");
   
       returnCode = OH_NNModel_SetTensorType(model, 0, OH_NN_TENSOR);
       CHECKNEQ(returnCode, OH_NN_SUCCESS, -1, "Set model tensor type failed.");
   
       // 添加Add算子的第二个输入张量，类型为float32，张量形状为[1, 2, 2, 3]
       tensorDesc = OH_NNTensorDesc_Create();
       CHECKEQ(tensorDesc, nullptr, -1, "Create TensorDesc failed.");
   
       returnCode = OH_NNTensorDesc_SetShape(tensorDesc, inputDims, 4);
       CHECKNEQ(returnCode, OH_NN_SUCCESS, -1, "Set TensorDesc shape failed.");
   
       returnCode = OH_NNTensorDesc_SetDataType(tensorDesc, OH_NN_FLOAT32);
       CHECKNEQ(returnCode, OH_NN_SUCCESS, -1, "Set TensorDesc data type failed.");
   
       returnCode = OH_NNTensorDesc_SetFormat(tensorDesc, OH_NN_FORMAT_NONE);
       CHECKNEQ(returnCode, OH_NN_SUCCESS, -1, "Set TensorDesc format failed.");
   
       returnCode = OH_NNModel_AddTensorToModel(model, tensorDesc);
       CHECKNEQ(returnCode, OH_NN_SUCCESS, -1, "Add second TensorDesc to model failed.");
   
       returnCode = OH_NNModel_SetTensorType(model, 1, OH_NN_TENSOR);
       CHECKNEQ(returnCode, OH_NN_SUCCESS, -1, "Set model tensor type failed.");
   
       // 添加Add算子的参数张量，该参数张量用于指定激活函数的类型，张量的数据类型为int8。
       tensorDesc = OH_NNTensorDesc_Create();
       CHECKEQ(tensorDesc, nullptr, -1, "Create TensorDesc failed.");
   
       int32_t activationDims = 1;
       returnCode = OH_NNTensorDesc_SetShape(tensorDesc, &activationDims, 1);
       CHECKNEQ(returnCode, OH_NN_SUCCESS, -1, "Set TensorDesc shape failed.");
   
       returnCode = OH_NNTensorDesc_SetDataType(tensorDesc, OH_NN_INT8);
       CHECKNEQ(returnCode, OH_NN_SUCCESS, -1, "Set TensorDesc data type failed.");
   
       returnCode = OH_NNTensorDesc_SetFormat(tensorDesc, OH_NN_FORMAT_NONE);
       CHECKNEQ(returnCode, OH_NN_SUCCESS, -1, "Set TensorDesc format failed.");
   
       returnCode = OH_NNModel_AddTensorToModel(model, tensorDesc);
       CHECKNEQ(returnCode, OH_NN_SUCCESS, -1, "Add second TensorDesc to model failed.");
   
       returnCode = OH_NNModel_SetTensorType(model, 2, OH_NN_ADD_ACTIVATIONTYPE);
       CHECKNEQ(returnCode, OH_NN_SUCCESS, -1, "Set model tensor type failed.");
   
       // 将激活函数类型设置为OH_NN_FUSED_NONE，表示该算子不添加激活函数。
       int8_t activationValue = OH_NN_FUSED_NONE;
       returnCode = OH_NNModel_SetTensorData(model, 2, &activationValue, sizeof(int8_t));
       CHECKNEQ(returnCode, OH_NN_SUCCESS, -1, "Set model tensor data failed.");
   
       // 设置Add算子的输出张量，类型为float32，张量形状为[1, 2, 2, 3]
       tensorDesc = OH_NNTensorDesc_Create();
       CHECKEQ(tensorDesc, nullptr, -1, "Create TensorDesc failed.");
   
       returnCode = OH_NNTensorDesc_SetShape(tensorDesc, inputDims, 4);
       CHECKNEQ(returnCode, OH_NN_SUCCESS, -1, "Set TensorDesc shape failed.");
   
       returnCode = OH_NNTensorDesc_SetDataType(tensorDesc, OH_NN_FLOAT32);
       CHECKNEQ(returnCode, OH_NN_SUCCESS, -1, "Set TensorDesc data type failed.");
   
       returnCode = OH_NNTensorDesc_SetFormat(tensorDesc, OH_NN_FORMAT_NONE);
       CHECKNEQ(returnCode, OH_NN_SUCCESS, -1, "Set TensorDesc format failed.");
   
       returnCode = OH_NNModel_AddTensorToModel(model, tensorDesc);
       CHECKNEQ(returnCode, OH_NN_SUCCESS, -1, "Add forth TensorDesc to model failed.");
   
       returnCode = OH_NNModel_SetTensorType(model, 3, OH_NN_TENSOR);
       CHECKNEQ(returnCode, OH_NN_SUCCESS, -1, "Set model tensor type failed.");
   
       // 指定Add算子的输入张量、参数张量和输出张量的索引
       uint32_t inputIndicesValues[2] = {0, 1};
       uint32_t paramIndicesValues = 2;
       uint32_t outputIndicesValues = 3;
       OH_NN_UInt32Array paramIndices = {&paramIndicesValues, 1 * 4};
       OH_NN_UInt32Array inputIndices = {inputIndicesValues, 2 * 4};
       OH_NN_UInt32Array outputIndices = {&outputIndicesValues, 1 * 4};
   
       // 向模型实例添加Add算子
       returnCode = OH_NNModel_AddOperation(model, OH_NN_OPS_ADD, &paramIndices, &inputIndices, &outputIndices);
       CHECKNEQ(returnCode, OH_NN_SUCCESS, -1, "Add operation to model failed.");
   
       // 设置模型实例的输入张量、输出张量的索引
       returnCode = OH_NNModel_SpecifyInputsAndOutputs(model, &inputIndices, &outputIndices);
       CHECKNEQ(returnCode, OH_NN_SUCCESS, -1, "Specify model inputs and outputs failed.");
   
       // 完成模型实例的构建
       returnCode = OH_NNModel_Finish(model);
       CHECKNEQ(returnCode, OH_NN_SUCCESS, -1, "Build model failed.");
   
       // 返回模型实例
       *pmodel = model;
       return OH_NN_SUCCESS;
   }
   

5. 查询Neural Network Runtime已经对接的AI加速芯片。

   Neural Network Runtime支持通过HDI接口，对接多种AI加速芯片。在执行模型编译前，需要查询当前设备下，Neural Network Runtime已经对接的AI加速芯片。每个AI加速芯片对应唯一的ID值，在编译阶段需要通过设备ID，指定模型编译的芯片。

   void GetAvailableDevices(std::vector<size_t>& availableDevice)
   {
       availableDevice.clear();
   
       // 获取可用的硬件ID
       const size_t* devices = nullptr;
       uint32_t deviceCount = 0;
       OH_NN_ReturnCode ret = OH_NNDevice_GetAllDevicesID(&devices, &deviceCount);
       if (ret != OH_NN_SUCCESS) {
           std::cout << "GetAllDevicesID failed, get no available device." << std::endl;
           return;
       }
   
       for (uint32_t i = 0; i < deviceCount; i++) {
           availableDevice.emplace_back(devices[i]);
       }
   }
   

6. 在指定的设备上编译模型。

   Neural Network Runtime使用抽象的模型表达描述AI模型的拓扑结构。在AI加速芯片上执行前，需要通过Neural Network Runtime提供的编译模块来创建编译实例，并由编译实例将抽象的模型表达下发至芯片驱动层，转换成可以直接推理计算的格式，即模型编译。

   OH_NN_ReturnCode CreateCompilation(OH_NNModel* model, const std::vector<size_t>& availableDevice,
                                      OH_NNCompilation** pCompilation)
   {
       // 创建编译实例compilation，将构图的模型实例或MSLite传下来的模型实例传入
       OH_NNCompilation* compilation = OH_NNCompilation_Construct(model);
       CHECKEQ(compilation, nullptr, -1, "OH_NNCore_ConstructCompilationWithNNModel failed.");
   
       // 设置编译的硬件、缓存路径、性能模式、计算优先级、是否开启float16低精度计算等选项
       // 选择在第一个设备上编译模型
       returnCode = OH_NNCompilation_SetDevice(compilation, availableDevice[0]);
       CHECKNEQ(returnCode, OH_NN_SUCCESS, -1, "OH_NNCompilation_SetDevice failed.");
   
       // 将模型编译结果缓存在/data/local/tmp目录下，版本号指定为1
       returnCode = OH_NNCompilation_SetCache(compilation, "/data/local/tmp", 1);
       CHECKNEQ(returnCode, OH_NN_SUCCESS, -1, "OH_NNCompilation_SetCache failed.");
   
       // 设置硬件性能模式
       returnCode = OH_NNCompilation_SetPerformanceMode(compilation, OH_NN_PERFORMANCE_EXTREME);
       CHECKNEQ(returnCode, OH_NN_SUCCESS, -1, "OH_NNCompilation_SetPerformanceMode failed.");
   
       // 设置推理执行优先级
       returnCode = OH_NNCompilation_SetPriority(compilation, OH_NN_PRIORITY_HIGH);
       CHECKNEQ(returnCode, OH_NN_SUCCESS, -1, "OH_NNCompilation_SetPriority failed.");
   
       // 是否开启FP16计算模式
       returnCode = OH_NNCompilation_EnableFloat16(compilation, false);
       CHECKNEQ(returnCode, OH_NN_SUCCESS, -1, "OH_NNCompilation_EnableFloat16 failed.");
   
       // 执行模型编译
       returnCode = OH_NNCompilation_Build(compilation);
       CHECKNEQ(returnCode, OH_NN_SUCCESS, -1, "OH_NNCompilation_Build failed.");
   
       *pCompilation = compilation;
       return OH_NN_SUCCESS;
   }
   

7. 创建执行器。

   完成模型编译后，需要调用Neural Network Runtime的执行模块，通过编译实例创建执行器。模型推理阶段中的设置模型输入、触发推理计算以及获取模型输出等操作均需要围绕执行器完成。

   OH_NNExecutor* CreateExecutor(OH_NNCompilation* compilation)
   {
       // 通过编译实例compilation创建执行器executor
       OH_NNExecutor *executor = OH_NNExecutor_Construct(compilation);
       CHECKEQ(executor, nullptr, -1, "OH_NNExecutor_Construct failed.");
       return executor;
   }
   

8. 执行推理计算，并打印推理结果。

   通过执行模块提供的接口，将推理计算所需要的输入数据传递给执行器，触发执行器完成一次推理计算，获取模型的推理结果并打印。

   OH_NN_ReturnCode Run(OH_NNExecutor* executor, const std::vector<size_t>& availableDevice)
   {
       // 从executor获取输入输出信息
       // 获取输入张量的个数
       size_t inputCount = 0;
       returnCode = OH_NNExecutor_GetInputCount(executor, &inputCount);
       CHECKNEQ(returnCode, OH_NN_SUCCESS, -1, "OH_NNExecutor_GetInputCount failed.");
       std::vector<NN_TensorDesc*> inputTensorDescs;
       NN_TensorDesc* tensorDescTmp = nullptr;
       for (size_t i = 0; i < inputCount; ++i) {
           // 创建输入张量的描述
           tensorDescTmp = OH_NNExecutor_CreateInputTensorDesc(executor, i);
           CHECKEQ(tensorDescTmp, nullptr, -1, "OH_NNExecutor_CreateInputTensorDesc failed.");
           inputTensorDescs.emplace_back(tensorDescTmp);
       }
       // 获取输出张量的个数
       size_t outputCount = 0;
       returnCode = OH_NNExecutor_GetOutputCount(executor, &outputCount);
       CHECKNEQ(returnCode, OH_NN_SUCCESS, -1, "OH_NNExecutor_GetOutputCount failed.");
       std::vector<NN_TensorDesc*> outputTensorDescs;
       for (size_t i = 0; i < outputCount; ++i) {
           // 创建输出张量的描述
           tensorDescTmp = OH_NNExecutor_CreateOutputTensorDesc(executor, i);
           CHECKEQ(tensorDescTmp, nullptr, -1, "OH_NNExecutor_CreateOutputTensorDesc failed.");
           outputTensorDescs.emplace_back(tensorDescTmp);
       }
   
       // 创建输入和输出张量
       NN_Tensor* inputTensors[inputCount];
       NN_Tensor* tensor = nullptr;
       for (size_t i = 0; i < inputCount; ++i) {
           tensor = nullptr;
           tensor = OH_NNTensor_Create(availableDevice[0], inputTensorDescs[i]);
           CHECKEQ(tensor, nullptr, -1, "OH_NNTensor_Create failed.");
           inputTensors[i] = tensor;
       }
       NN_Tensor* outputTensors[outputCount];
       for (size_t i = 0; i < outputCount; ++i) {
           tensor = nullptr;
           tensor = OH_NNTensor_Create(availableDevice[0], outputTensorDescs[i]);
           CHECKEQ(tensor, nullptr, -1, "OH_NNTensor_Create failed.");
           outputTensors[i] = tensor;
       }
   
       // 设置输入张量的数据
       returnCode = SetInputData(inputTensors, inputCount);
       CHECKNEQ(returnCode, OH_NN_SUCCESS, -1, "SetInputData failed.");
   
       // 执行推理
       returnCode = OH_NNExecutor_RunSync(executor, inputTensors, inputCount, outputTensors, outputCount);
       CHECKNEQ(returnCode, OH_NN_SUCCESS, -1, "OH_NNExecutor_RunSync failed.");
   
       // 打印输出张量的数据
       Print(outputTensors, outputCount);
   
       // 清理输入和输出张量以及张量描述
       for (size_t i = 0; i < inputCount; ++i) {
           returnCode = OH_NNTensor_Destroy(&inputTensors[i]);
           CHECKNEQ(returnCode, OH_NN_SUCCESS, -1, "OH_NNTensor_Destroy failed.");
           returnCode = OH_NNTensorDesc_Destroy(&inputTensorDescs[i]);
           CHECKNEQ(returnCode, OH_NN_SUCCESS, -1, "OH_NNTensorDesc_Destroy failed.");
       }
       for (size_t i = 0; i < outputCount; ++i) {
           returnCode = OH_NNTensor_Destroy(&outputTensors[i]);
           CHECKNEQ(returnCode, OH_NN_SUCCESS, -1, "OH_NNTensor_Destroy failed.");
           returnCode = OH_NNTensorDesc_Destroy(&outputTensorDescs[i]);
           CHECKNEQ(returnCode, OH_NN_SUCCESS, -1, "OH_NNTensorDesc_Destroy failed.");
       }
   
       return OH_NN_SUCCESS;
   }
   

9. 构建端到端模型构造-编译-执行流程。

   步骤4-步骤8实现了模型的模型构造、编译和执行流程，并封装成多个函数，便于模块化开发。以下示例代码将串联这些函数， 形成一个完整的Neural Network Runtime使用流程。

   int main()
   {
       OH_NNModel* model = nullptr;
       OH_NNCompilation* compilation = nullptr;
       OH_NNExecutor* executor = nullptr;
       std::vector<size_t> availableDevices;
   
       // 模型构造
       OH_NNModel* model = nullptr;
       OH_NN_ReturnCode ret = BuildModel(&model);
       if (ret != OH_NN_SUCCESS) {
           std::cout << "BuildModel failed." << std::endl;
           OH_NNModel_Destroy(&model);
           return -1;
       }
   
       // 获取可执行的设备
       GetAvailableDevices(availableDevices);
       if (availableDevices.empty()) {
           std::cout << "No available device." << std::endl;
           OH_NNModel_Destroy(&model);
           return -1;
       }
   
       // 模型编译
       ret = CreateCompilation(model, availableDevices, &compilation);
       if (ret != OH_NN_SUCCESS) {
           std::cout << "CreateCompilation failed." << std::endl;
           OH_NNModel_Destroy(&model);
           OH_NNCompilation_Destroy(&compilation);
           return -1;
       }
   
       // 销毁模型实例
       OH_NNModel_Destroy(&model);
   
       // 创建模型的推理执行器
       executor = CreateExecutor(compilation);
       if (executor == nullptr) {
           std::cout << "CreateExecutor failed, no executor is created." << std::endl;
           OH_NNCompilation_Destroy(&compilation);
           return -1;
       }
   
       // 销毁编译实例
       OH_NNCompilation_Destroy(&compilation);
   
       // 使用上一步创建的执行器，执行推理计算
       ret = Run(executor, availableDevices);
       if (ret != OH_NN_SUCCESS) {
           std::cout << "Run failed." << std::endl;
           OH_NNExecutor_Destroy(&executor);
           return -1;
       }
   
       // 销毁执行器实例
       OH_NNExecutor_Destroy(&executor);
   
       return 0;
   }
   

调测验证

1. 准备应用样例的编译配置文件。

   新建一个 CMakeLists.txt 文件，为开发步骤中的应用样例文件 nnrt_example.cpp 添加编译配置。以下提供简单的 CMakeLists.txt 示例：

   cmake_minimum_required(VERSION 3.16)
   project(nnrt_example C CXX)
   
   add_executable(nnrt_example
       ./nnrt_example.cpp
   )
   
   target_link_libraries(nnrt_example
       neural_network_runtime
       neural_network_core
   )
   

2. 编译应用样例。

   执行以下命令，在当前目录下新建build/目录，在build/目录下编译 nnrt_example.cpp，得到二进制文件 nnrt_example。

   mkdir build && cd build
   cmake -DCMAKE_TOOLCHAIN_FILE={交叉编译工具链的路径}/build/cmake/ohos.toolchain.cmake -DOHOS_ARCH=arm64-v8a -DOHOS_PLATFORM=OHOS -DOHOS_STL=c++_static ..
   make
   

3. 执行以下代码，将样例推送到设备上执行。

   # 将编译得到的 `nnrt_example` 推送到设备上，执行样例。
   hdc_std file send ./nnrt_example /data/local/tmp/.
   
   # 给测试用例可执行文件加上权限。
   hdc_std shell "chmod +x /data/local/tmp/nnrt_example"
   
   # 执行测试用例
   hdc_std shell "/data/local/tmp/nnrt_example"
   

   如果样例执行正常，应该得到以下输出。

   Output index: 0, value is: 0.000000.
   Output index: 1, value is: 2.000000.
   Output index: 2, value is: 4.000000.
   Output index: 3, value is: 6.000000.
   Output index: 4, value is: 8.000000.
   Output index: 5, value is: 10.000000.
   Output index: 6, value is: 12.000000.
   Output index: 7, value is: 14.000000.
   Output index: 8, value is: 16.000000.
   Output index: 9, value is: 18.000000.
   Output index: 10, value is: 20.000000.
   Output index: 11, value is: 22.000000.
   

4. 检查模型缓存（可选）。

   如果在调测环境下，Neural Network Runtime对接的HDI服务支持模型缓存功能，执行完 nnrt_example, 可以在 /data/local/tmp 目录下找到生成的缓存文件。

   说明：

   模型的IR需要传递到硬件驱动层，由HDI服务将统一的IR图，编译成硬件专用的计算图，编译的过程非常耗时。Neural Network Runtime支持计算图缓存的特性，可以将HDI服务编译生成的计算图，缓存到设备存储中。当下一次在同一个加速芯片上编译同一个模型时，通过指定缓存的路径，Neural Network Runtime可以直接加载缓存文件中的计算图，减少编译消耗的时间。

   检查缓存目录下的缓存文件：

   ls /data/local/tmp
   

   以下为打印结果：

   # 0.nncache 1.nncache 2.nncache cache_info.nncache
   

   如果缓存不再使用，需要手动删除缓存，可以参考以下命令，删除缓存文件。

   rm /data/local/tmp/*nncache