本文基于CANN开源社区的runtime和ops-samples仓库进行技术解读

CANN组织地址：https://atomgit.com/cann

runtime仓库地址：https://atomgit.com/cann/runtime

ops-samples仓库地址：https://atomgit.com/cann/ops-samples

前言

运行时环境和算子样例是开发和学习的重要资源。Runtime（运行时）与Ops-Samples（算子样例）如何协同工作？如何实现高效的运行时环境与算子样例集成？

本文探讨Runtime与Ops-Samples的协同机制，以及如何通过两者的配合为开发者提供便捷的开发和学习体验。

什么是组合运行时样例集成

Runtime与Ops-Samples的组合：

没有协同：
运行时和样例各自独立 → 学习困难 → 开发效率低

有协同：
运行时和样例协同 → 学习容易 → 开发效率高

架构：

开发者
    ↓
Ops-Samples（算子样例）
    ↓
Runtime（运行时）
    ↓
NPU硬件

核心概念

1. 运行时环境

运行时环境：

#include "runtime/runtime.h"
#include "ops_samples/ops_samples.h"

// 运行时配置
typedef struct {
    device_id_t device_id;        // 设备ID
    stream_config_t stream_config; // 流配置
    memory_config_t memory_config; // 内存配置
    performance_config_t perf_config; // 性能配置
} runtime_config_t;

// 创建运行时
runtime_handle_t *create_runtime(runtime_config_t *config);

2. 算子样例

算子样例：

// 算子样例类型
typedef enum {
    SAMPLE_TYPE_BASIC,           // 基础样例
    SAMPLE_TYPE_ADVANCED,        // 高级样例
    SAMPLE_TYPE_PERFORMANCE,     // 性能样例
    SAMPLE_TYPE_OPTIMIZATION     // 优化样例
} sample_type_t;

// 创建算子样例
sample_t *create_sample(sample_type_t type, operator_t *op);

3. 样例框架

样例框架：

// 样例框架配置
typedef struct {
    bool enable_profiling;       // 启用性能分析
    bool enable_logging;         // 启用日志
    bool enable_validation;      // 启用验证
    bool enable_benchmarking;    // 启用基准测试
} sample_framework_config_t;

// 创建样例框架
sample_framework_t *create_sample_framework(sample_framework_config_t *config);

协同机制

1. 基础算子样例

// 基础算子样例
void basic_operator_sample() {
    // 阶段1：创建运行时
    printf("Phase 1: Create Runtime\n");
  
    runtime_config_t runtime_config;
    runtime_config.device_id = 0;
    runtime_config.stream_config.num_streams = 1;
    runtime_config.memory_config.enable_memory_pool = true;
    runtime_config.perf_config.enable_profiling = false;
  
    runtime_handle_t *runtime = create_runtime(&runtime_config);
  
    printf("  Runtime created\n");
  
    // 阶段2：创建算子样例
    printf("\nPhase 2: Create Operator Sample\n");
  
    // 创建卷积算子
    operator_config_t conv_config;
    conv_config.input_channels = 3;
    conv_config.output_channels = 64;
    conv_config.kernel_size = 3;
    conv_config.stride = 1;
    conv_config.padding = 1;
  
    operator_t *conv_op = create_convolution_operator(&conv_config);
  
    // 创建基础样例
    sample_t *sample = create_sample(SAMPLE_TYPE_BASIC, conv_op);
  
    printf("  Operator sample created\n");
  
    // 阶段3：执行样例
    printf("\nPhase 3: Execute Sample\n");
  
    // 准备输入数据
    int batch_size = 1;
    int height = 224;
    int width = 224;
    size_t input_size = batch_size * conv_config.input_channels * height * width * sizeof(float);
  
    float *input = malloc(input_size);
    for (int i = 0; i < input_size / sizeof(float); i++) {
        input[i] = (float)i / input_size;
    }
  
    // 准备输出数据
    size_t output_size = batch_size * conv_config.output_channels * height * width * sizeof(float);
    float *output = malloc(output_size);
  
    // 执行算子
    execute_sample(runtime, sample, input, output);
  
    printf("  Sample executed\n");
  
    // 阶段4：验证结果
    printf("\nPhase 4: Validate Result\n");
  
    bool is_valid = validate_output(output, output_size);
  
    if (is_valid) {
        printf("  Result validation: PASSED\n");
    } else {
        printf("  Result validation: FAILED\n");
    }
  
    // 清理资源
    free(input);
    free(output);
    destroy_sample(sample);
    destroy_operator(conv_op);
    destroy_runtime(runtime);
}

2. 性能样例

// 性能样例
void performance_sample() {
    // 阶段1：创建运行时
    printf("Phase 1: Create Runtime\n");
  
    runtime_config_t runtime_config;
    runtime_config.device_id = 0;
    runtime_config.stream_config.num_streams = 4;
    runtime_config.memory_config.enable_memory_pool = true;
    runtime_config.perf_config.enable_profiling = true;
  
    runtime_handle_t *runtime = create_runtime(&runtime_config);
  
    printf("  Runtime created\n");
  
    // 阶段2：创建性能样例
    printf("\nPhase 2: Create Performance Sample\n");
  
    // 创建矩阵乘法算子
    operator_config_t matmul_config;
    matmul_config.m = 1024;
    matmul_config.n = 1024;
    matmul_config.k = 1024;
  
    operator_t *matmul_op = create_matmul_operator(&matmul_config);
  
    // 创建性能样例
    sample_t *sample = create_sample(SAMPLE_TYPE_PERFORMANCE, matmul_op);
  
    printf("  Performance sample created\n");
  
    // 阶段3：执行性能测试
    printf("\nPhase 3: Execute Performance Test\n");
  
    // 准备输入数据
    size_t a_size = matmul_config.m * matmul_config.k * sizeof(float);
    size_t b_size = matmul_config.k * matmul_config.n * sizeof(float);
    size_t c_size = matmul_config.m * matmul_config.n * sizeof(float);
  
    float *a = malloc(a_size);
    float *b = malloc(b_size);
    float *c = malloc(c_size);
  
    // 初始化数据
    initialize_random(a, a_size / sizeof(float));
    initialize_random(b, b_size / sizeof(float));
  
    // 预热
    for (int i = 0; i < 10; i++) {
        execute_sample(runtime, sample, a, b, c);
    }
  
    // 性能测试
    int num_iterations = 100;
    double start = get_time();
  
    for (int i = 0; i < num_iterations; i++) {
        execute_sample(runtime, sample, a, b, c);
    }
  
    double end = get_time();
  
    // 计算性能指标
    double avg_time = (end - start) / num_iterations;
    double throughput = 2.0 * matmul_config.m * matmul_config.n * matmul_config.k / avg_time;
    double gflops = throughput / 1e9;
  
    printf("  Performance Test Results:\n");
    printf("    Average time: %.2f ms\n", avg_time * 1000);
    printf("    Throughput: %.2f GFLOPS\n", gflops);
  
    // 获取性能分析结果
    performance_profile_t *profile = get_performance_profile(runtime);
  
    printf("  Performance Profile:\n");
    printf("    Compute time: %.2f ms\n", profile->compute_time * 1000);
    printf("    Memory transfer time: %.2f ms\n", profile->memory_transfer_time * 1000);
    printf("    Kernel launch time: %.2f ms\n", profile->kernel_launch_time * 1000);
  
    // 清理资源
    free(a);
    free(b);
    free(c);
    destroy_sample(sample);
    destroy_operator(matmul_op);
    destroy_runtime(runtime);
}

3. 优化样例

// 优化样例
void optimization_sample() {
    // 阶段1：创建运行时
    printf("Phase 1: Create Runtime\n");
  
    runtime_config_t runtime_config;
    runtime_config.device_id = 0;
    runtime_config.stream_config.num_streams = 1;
    runtime_config.memory_config.enable_memory_pool = true;
    runtime_config.perf_config.enable_profiling = true;
  
    runtime_handle_t *runtime = create_runtime(&runtime_config);
  
    printf("  Runtime created\n");
  
    // 阶段2：创建优化样例
    printf("\nPhase 2: Create Optimization Sample\n");
  
    // 创建卷积算子
    operator_config_t conv_config;
    conv_config.input_channels = 3;
    conv_config.output_channels = 64;
    conv_config.kernel_size = 3;
    conv_config.stride = 1;
    conv_config.padding = 1;
  
    operator_t *conv_op = create_convolution_operator(&conv_config);
  
    // 创建优化样例
    sample_t *sample = create_sample(SAMPLE_TYPE_OPTIMIZATION, conv_op);
  
    printf("  Optimization sample created\n");
  
    // 阶段3：测试不同优化策略
    printf("\nPhase 3: Test Optimization Strategies\n");
  
    // 准备输入数据
    int batch_size = 1;
    int height = 224;
    int width = 224;
    size_t input_size = batch_size * conv_config.input_channels * height * width * sizeof(float);
    size_t output_size = batch_size * conv_config.output_channels * height * width * sizeof(float);
  
    float *input = malloc(input_size);
    float *output = malloc(output_size);
  
    initialize_random(input, input_size / sizeof(float));
  
    // 测试不同优化策略
    optimization_strategy_t strategies[] = {
        {"No Optimization", OPTIMIZATION_NONE},
        {"Basic Optimization", OPTIMIZATION_BASIC},
        {"Advanced Optimization", OPTIMIZATION_ADVANCED},
        {"Aggressive Optimization", OPTIMIZATION_AGGRESSIVE}
    };
  
    for (int i = 0; i < 4; i++) {
        printf("\n  Testing: %s\n", strategies[i].name);
      
        // 应用优化策略
        apply_optimization_strategy(sample, strategies[i].strategy);
      
        // 性能测试
        int num_iterations = 100;
        double start = get_time();
      
        for (int j = 0; j < num_iterations; j++) {
            execute_sample(runtime, sample, input, output);
        }
      
        double end = get_time();
      
        double avg_time = (end - start) / num_iterations;
      
        printf("    Average time: %.2f ms\n", avg_time * 1000);
      
        // 验证结果
        bool is_valid = validate_output(output, output_size);
      
        if (is_valid) {
            printf("    Validation: PASSED\n");
        } else {
            printf("    Validation: FAILED\n");
        }
    }
  
    // 清理资源
    free(input);
    free(output);
    destroy_sample(sample);
    destroy_operator(conv_op);
    destroy_runtime(runtime);
}

使用场景

场景一：学习算子开发

// 学习算子开发
void learn_operator_development() {
    // 阶段1：创建学习环境
    printf("Phase 1: Create Learning Environment\n");
  
    runtime_config_t runtime_config;
    runtime_config.device_id = 0;
    runtime_config.stream_config.num_streams = 1;
    runtime_config.memory_config.enable_memory_pool = true;
    runtime_config.perf_config.enable_logging = true;
  
    runtime_handle_t *runtime = create_runtime(&runtime_config);
  
    printf("  Learning environment created\n");
  
    // 阶段2：加载教学样例
    printf("\nPhase 2: Load Tutorial Samples\n");
  
    // 加载基础样例
    sample_t *basic_samples[] = {
        load_sample("conv2d_basic"),
        load_sample("pooling_basic"),
        load_sample("activation_basic")
    };
  
    // 加载高级样例
    sample_t *advanced_samples[] = {
        load_sample("conv2d_advanced"),
        load_sample("attention_advanced"),
        load_sample("normalization_advanced")
    };
  
    printf("  Tutorial samples loaded\n");
  
    // 阶段3：逐步学习
    printf("\nPhase 3: Step-by-Step Learning\n");
  
    // 学习基础算子
    for (int i = 0; i < 3; i++) {
        printf("\n  Learning: %s\n", basic_samples[i]->name);
      
        // 查看样例代码
        print_sample_code(basic_samples[i]);
      
        // 运行样例
        run_sample(runtime, basic_samples[i]);
      
        // 查看结果
        view_sample_result(basic_samples[i]);
    }
  
    // 学习高级算子
    for (int i = 0; i < 3; i++) {
        printf("\n  Learning: %s\n", advanced_samples[i]->name);
      
        // 查看样例代码
        print_sample_code(advanced_samples[i]);
      
        // 运行样例
        run_sample(runtime, advanced_samples[i]);
      
        // 查看结果
        view_sample_result(advanced_samples[i]);
    }
  
    printf("\nLearning completed\n");
  
    // 清理资源
    for (int i = 0; i < 3; i++) {
        destroy_sample(basic_samples[i]);
        destroy_sample(advanced_samples[i]);
    }
    destroy_runtime(runtime);
}

场景二：性能调优

// 性能调优
void performance_tuning() {
    // 阶段1：创建性能分析环境
    printf("Phase 1: Create Performance Analysis Environment\n");
  
    runtime_config_t runtime_config;
    runtime_config.device_id = 0;
    runtime_config.stream_config.num_streams = 4;
    runtime_config.memory_config.enable_memory_pool = true;
    runtime_config.perf_config.enable_profiling = true;
  
    runtime_handle_t *runtime = create_runtime(&runtime_config);
  
    printf("  Performance analysis environment created\n");
  
    // 阶段2：加载性能样例
    printf("\nPhase 2: Load Performance Samples\n");
  
    sample_t *perf_samples[] = {
        load_sample("conv2d_perf"),
        load_sample("matmul_perf"),
        load_sample("attention_perf")
    };
  
    printf("  Performance samples loaded\n");
  
    // 阶段3：性能分析
    printf("\nPhase 3: Performance Analysis\n");
  
    for (int i = 0; i < 3; i++) {
        printf("\n  Analyzing: %s\n", perf_samples[i]->name);
      
        // 运行性能测试
        run_performance_test(runtime, perf_samples[i]);
      
        // 获取性能分析结果
        performance_profile_t *profile = get_performance_profile(runtime);
      
        printf("    Performance Breakdown:\n");
        printf("      Compute: %.2f ms (%.2f%%)\n",
               profile->compute_time * 1000,
               profile->compute_time / profile->total_time * 100);
        printf("      Memory Transfer: %.2f ms (%.2f%%)\n",
               profile->memory_transfer_time * 1000,
               profile->memory_transfer_time / profile->total_time * 100);
        printf("      Kernel Launch: %.2f ms (%.2f%%)\n",
               profile->kernel_launch_time * 1000,
               profile->kernel_launch_time / profile->total_time * 100);
      
        // 提供优化建议
        optimization_suggestion_t *suggestions = get_optimization_suggestions(profile);
      
        printf("    Optimization Suggestions:\n");
        for (int j = 0; j < suggestions->count; j++) {
            printf("      %d. %s\n", j + 1, suggestions->suggestions[j]);
        }
    }
  
    printf("\nPerformance analysis completed\n");
  
    // 清理资源
    for (int i = 0; i < 3; i++) {
        destroy_sample(perf_samples[i]);
    }
    destroy_runtime(runtime);
}

场景三：自定义算子开发

// 自定义算子开发
void custom_operator_development() {
    // 阶段1：创建开发环境
    printf("Phase 1: Create Development Environment\n");
  
    runtime_config_t runtime_config;
    runtime_config.device_id = 0;
    runtime_config.stream_config.num_streams = 1;
    runtime_config.memory_config.enable_memory_pool = true;
    runtime_config.perf_config.enable_profiling = true;
    runtime_config.perf_config.enable_logging = true;
  
    runtime_handle_t *runtime = create_runtime(&runtime_config);
  
    printf("  Development environment created\n");
  
    // 阶段2：参考现有样例
    printf("\nPhase 2: Reference Existing Samples\n");
  
    // 加载参考样例
    sample_t *reference_sample = load_sample("conv2d_reference");
  
    printf("  Reference sample loaded\n");
  
    // 阶段3：开发自定义算子
    printf("\nPhase 3: Develop Custom Operator\n");
  
    // 定义自定义算子
    operator_config_t custom_config;
    custom_config.name = "custom_conv2d";
    custom_config.input_channels = 3;
    custom_config.output_channels = 64;
    custom_config.kernel_size = 3;
    custom_config.stride = 1;
    custom_config.padding = 1;
  
    operator_t *custom_op = create_custom_operator(&custom_config);
  
    printf("  Custom operator created\n");
  
    // 阶段4：测试自定义算子
    printf("\nPhase 4: Test Custom Operator\n");
  
    // 创建测试样例
    sample_t *test_sample = create_sample(SAMPLE_TYPE_BASIC, custom_op);
  
    // 准备测试数据
    int batch_size = 1;
    int height = 224;
    int width = 224;
    size_t input_size = batch_size * custom_config.input_channels * height * width * sizeof(float);
    size_t output_size = batch_size * custom_config.output_channels * height * width * sizeof(float);
  
    float *input = malloc(input_size);
    float *output = malloc(output_size);
  
    initialize_random(input, input_size / sizeof(float));
  
    // 运行测试
    run_sample(runtime, test_sample);
  
    // 验证结果
    bool is_valid = validate_output(output, output_size);
  
    if (is_valid) {
        printf("  Custom operator test: PASSED\n");
    } else {
        printf("  Custom operator test: FAILED\n");
    }
  
    // 性能对比
    printf("\n  Performance Comparison:\n");
  
    double ref_time = measure_performance(runtime, reference_sample);
    double custom_time = measure_performance(runtime, test_sample);
  
    printf("    Reference: %.2f ms\n", ref_time * 1000);
    printf("    Custom: %.2f ms\n", custom_time * 1000);
    printf("    Speedup: %.2fx\n", ref_time / custom_time);
  
    // 清理资源
    free(input);
    free(output);
    destroy_sample(test_sample);
    destroy_sample(reference_sample);
    destroy_operator(custom_op);
    destroy_runtime(runtime);
}

性能优化

1. 内存优化

// 内存优化
void optimize_memory_usage(runtime_handle_t *runtime, sample_t *sample) {
    // 启用内存池
    enable_memory_pool(runtime);
  
    // 优化内存分配
    optimize_memory_allocation(runtime);
  
    // 使用零拷贝
    enable_zero_copy(runtime);
}

2. 流优化

// 流优化
void optimize_streams(runtime_handle_t *runtime, sample_t *sample) {
    // 启用多流
    enable_multiple_streams(runtime, 4);
  
    // 流水线执行
    enable_pipeline_execution(runtime);
  
    // 异步执行
    enable_async_execution(runtime);
}

3. 算子优化

// 算子优化
void optimize_operator(sample_t *sample) {
    // 算子融合
    enable_operator_fusion(sample);
  
    // 算子替换
    enable_operator_replacement(sample);
  
    // 算子调优
    enable_operator_tuning(sample);
}

与其他组件的关系

组件	关系
runtime	运行时环境
ops-samples	算子样例
ops-nn	神经网络算子
ops-cv	计算机视觉算子

关系：

开发者
    ↓
Ops-Samples（算子样例）
    ↓
Runtime（运行时）
    ↓
NPU硬件

调试技巧

1. 样例调试

// 样例调试
void debug_sample(runtime_handle_t *runtime, sample_t *sample) {
    // 启用调试模式
    enable_debug_mode(runtime);
  
    // 设置断点
    set_breakpoint(runtime, sample, 100);
  
    // 单步执行
    step_through_sample(runtime, sample);
  
    // 查看变量
    inspect_variables(runtime, sample);
}

2. 性能分析

// 性能分析
void analyze_performance(runtime_handle_t *runtime, sample_t *sample) {
    // 启用性能分析
    enable_profiling(runtime);
  
    // 运行样例
    run_sample(runtime, sample);
  
    // 获取性能分析结果
    performance_profile_t *profile = get_performance_profile(runtime);
  
    printf("Performance Profile:\n");
    printf("  Total time: %.2f ms\n", profile->total_time * 1000);
    printf("  Compute time: %.2f ms\n", profile->compute_time * 1000);
    printf("  Memory transfer time: %.2f ms\n", profile->memory_transfer_time * 1000);
}

3. 错误诊断

// 错误诊断
void diagnose_errors(runtime_handle_t *runtime, sample_t *sample) {
    // 运行样例
    error_code_t error = run_sample(runtime, sample);
  
    if (error != ERROR_SUCCESS) {
        // 获取错误信息
        error_info_t *info = get_error_info(error);
      
        printf("Error: %s\n", info->message);
        printf("  Code: %d\n", info->code);
        printf("  File: %s\n", info->file);
        printf("  Line: %d\n", info->line);
      
        // 提供修复建议
        fix_suggestion_t *suggestions = get_fix_suggestions(error);
      
        printf("Fix Suggestions:\n");
        for (int i = 0; i < suggestions->count; i++) {
            printf("  %d. %s\n", i + 1, suggestions->suggestions[i]);
        }
    }
}

常见问题

问题1：样例运行失败

// 错误：运行时未初始化
run_sample(runtime, sample);  // 运行时未初始化！

// 正确：先初始化运行时
runtime = create_runtime(&config);
run_sample(runtime, sample);  // 成功

问题2：性能不佳

// 错误：未使用优化
run_sample(runtime, sample);  // 未优化！

// 正确：使用优化
apply_optimization_strategy(sample, OPTIMIZATION_ADVANCED);
run_sample(runtime, sample);  // 优化后，快！

问题3：内存不足

// 错误：数据太大
prepare_large_data();  // 太大，内存不足！

// 正确：使用合理的数据大小
prepare_reasonable_data();  // 合理

应用场景总结

场景一：学习算子开发

用于学习算子开发。

场景二：性能调优

用于性能调优。

场景三：自定义算子开发

用于自定义算子开发。

场景四：算法验证

用于算法验证。

总结

Runtime与Ops-Samples的组合：

• 运行时环境
• 算子样例
• 学习资源
• 性能分析
• 开发支持

通过运行时环境和算子样例的协同，为开发者提供了便捷的开发和学习体验，是CANN生态的重要组成部分。

相关链接

runtime仓库地址：https://atomgit.com/cann/runtime

ops-samples仓库地址：https://atomgit.com/cann/ops-samples

CANN组织地址：https://atomgit.com/cann

ops-nn仓库地址：https://atomgit.com/cann/ops-nn

ops-cv仓库地址：https://atomgit.com/cann/ops-cv