本文基于CANN开源社区的pypto和ascend-c仓库进行技术解读

CANN组织地址：https://atomgit.com/cann

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

ascend-c仓库地址：https://atomgit.com/cann/ascend-c

前言

并行Tile操作和C算子开发是NPU编程的两个重要方向。PyPTO（Python Parallel Tile Operation）与Ascend-C（C算子开发）如何协同工作？如何实现高效的并行Tile操作和C算子开发？

本文探讨PyPTO与Ascend-C的协同机制，以及如何通过两者的配合实现高性能的NPU编程。

什么是组合并行Tile操作C算子开发

PyPTO与Ascend-C的组合：

没有协同：
Python Tile操作和C算子开发各自进行 → 性能提升有限

有协同：
Python Tile操作和C算子开发协同进行 → 性能大幅提升

架构：

NPU编程
    ↓
PyPTO（并行Tile操作）
    ↓
Ascend-C（C算子开发）
    ↓
NPU硬件

核心概念

1. 并行Tile操作

并行Tile操作：

#include "pypto/pypto.h"

// Tile配置
typedef struct {
    int tile_size_x;      // Tile大小X
    int tile_size_y;      // Tile大小Y
    int tile_size_z;      // Tile大小Z
    parallel_strategy_t strategy; // 并行策略
    memory_layout_t layout;       // 内存布局
} tile_config_t;

// 创建Tile配置
tile_config_t *create_tile_config(int tile_size_x, int tile_size_y, int tile_size_z);

// 并行策略
typedef enum {
    PARALLEL_STRATEGY_DATA_PARALLEL,     // 数据并行
    PARALLEL_STRATEGY_MODEL_PARALLEL,    // 模型并行
    PARALLEL_STRATEGY_PIPELINE_PARALLEL  // 流水线并行
} parallel_strategy_t;

2. C算子开发

C算子开发：

// 算子开发配置
typedef struct {
    operator_type_t type;     // 算子类型
    input_config_t input;     // 输入配置
    output_config_t output;   // 输出配置
    optimization_level_t level; // 优化级别
} operator_dev_config_t;

// 创建算子开发配置
operator_dev_config_t *create_operator_dev_config(operator_type_t type);

3. 协同开发

协同开发：

// 协同开发配置
typedef struct {
    tile_config_t *tile_config;        // Tile配置
    operator_dev_config_t *op_config;  // 算子配置
    bool enable_auto_tiling;           // 启用自动Tile
    bool enable_auto_optimization;     // 启用自动优化
}协同开发配置_t;

// 创建协同开发配置
协同开发配置_t *create_协同开发配置(tile_config_t *tile_config, operator_dev_config_t *op_config);

协同优化

1. 自动Tile优化

# 自动Tile优化
def auto_tile_optimization(input_tensor, output_tensor):
    # 阶段1：分析数据布局
    print("Phase 1: Analyze Data Layout")
  
    data_layout_analysis = analyze_data_layout(input_tensor)
  
    print(f"  Input shape: {input_tensor.shape}")
    print(f"  Input layout: {data_layout_analysis.layout}")
    print(f"  Memory alignment: {data_layout_analysis.alignment}")
  
    # 阶段2：确定Tile策略
    print("\nPhase 2: Determine Tiling Strategy")
  
    tile_config = create_tile_config(
        tile_size_x=64,
        tile_size_y=64,
        tile_size_z=64
    )
  
    # 选择并行策略
    if input_tensor.shape[0] > 1024:
        tile_config.strategy = PARALLEL_STRATEGY_DATA_PARALLEL
    elif input_tensor.shape[1] > 1024:
        tile_config.strategy = PARALLEL_STRATEGY_MODEL_PARALLEL
    else:
        tile_config.strategy = PARALLEL_STRATEGY_PIPELINE_PARALLEL
  
    print(f"  Tile size: ({tile_config.tile_size_x}, {tile_config.tile_size_y}, {tile_config.tile_size_z})")
    print(f"  Parallel strategy: {tile_config.strategy}")
  
    # 阶段3：生成Tile代码
    print("\nPhase 3: Generate Tiled Code")
  
    tiled_code = generate_tiled_code(input_tensor, output_tensor, tile_config)
  
    print("  Tiled code generated")
  
    # 阶段4：优化Tile代码
    print("\nPhase 4: Optimize Tiled Code")
  
    optimized_code = optimize_tiled_code(tiled_code, tile_config)
  
    print("  Tiled code optimized")
  
    return optimized_code

2. C算子与Tile集成

// C算子与Tile集成
void integrate_operator_with_tile() {
    // 阶段1：创建Tile配置
    printf("Phase 1: Create Tile Configuration\n");
  
    tile_config_t *tile_config = create_tile_config(64, 64, 64);
    tile_config->strategy = PARALLEL_STRATEGY_DATA_PARALLEL;
    tile_config->layout = MEMORY_LAYOUT_NHWC;
  
    printf("  Tile configuration created\n");
  
    // 阶段2：创建算子开发配置
    printf("\nPhase 2: Create Operator Development Configuration\n");
  
    operator_dev_config_t *op_config = create_operator_dev_config(OPERATOR_TYPE_CONV2D);
  
    op_config->input.shape[0] = 1;
    op_config->input.shape[1] = 3;
    op_config->input.shape[2] = 224;
    op_config->input.shape[3] = 224;
  
    op_config->output.shape[0] = 1;
    op_config->output.shape[1] = 64;
    op_config->output.shape[2] = 224;
    op_config->output.shape[3] = 224;
  
    op_config->level = OPTIMIZATION_LEVEL_ADVANCED;
  
    printf("  Operator development configuration created\n");
  
    // 阶段3：生成Tile代码
    printf("\nPhase 3: Generate Tiled Code\n");
  
    char *tiled_code = generate_tiled_code_c(tile_config, op_config);
  
    printf("  Tiled code generated\n");
  
    // 阶段4：生成算子代码
    printf("\nPhase 4: Generate Operator Code\n");
  
    char *operator_code = generate_operator_code(op_config);
  
    printf("  Operator code generated\n");
  
    // 阶段5：集成代码
    printf("\nPhase 5: Integrate Code\n");
  
    char *integrated_code = integrate_code(tiled_code, operator_code);
  
    printf("  Code integrated\n");
  
    // 阶段6：编译和测试
    printf("\nPhase 6: Compile and Test\n");
  
    // 编译代码
    bool compiled = compile_code(integrated_code);
  
    if (compiled) {
        printf("  Compilation: SUCCESS\n");
      
        // 测试代码
        bool tested = test_operator(integrated_code);
      
        if (tested) {
            printf("  Test: PASSED\n");
        } else {
            printf("  Test: FAILED\n");
        }
    } else {
        printf("  Compilation: FAILED\n");
    }
}

3. 性能优化

# 性能优化
def performance_optimization(input_tensor, output_tensor):
    # 阶段1：基线性能测试
    print("Phase 1: Baseline Performance Test")
  
    baseline_time = measure_baseline_performance(input_tensor, output_tensor)
  
    print(f"  Baseline time: {baseline_time:.2f} ms")
  
    # 阶段2：Tile优化
    print("\nPhase 2: Tile Optimization")
  
    tile_sizes = [(32, 32, 32), (64, 64, 64), (128, 128, 128)]
  
    best_time = baseline_time
    best_config = None
  
    for tile_size in tile_sizes:
        print(f"\n  Testing tile size: {tile_size}")
      
        # 创建Tile配置
        tile_config = create_tile_config(*tile_size)
      
        # 生成Tile代码
        tiled_code = generate_tiled_code(input_tensor, output_tensor, tile_config)
      
        # 编译和测试
        compiled = compile_code(tiled_code)
      
        if compiled:
            time = measure_performance(tiled_code)
          
            print(f"    Performance: {time:.2f} ms")
          
            if time < best_time:
                best_time = time
                best_config = tile_config
                print(f"    New best!")
  
    # 阶段3：C算子优化
    print("\nPhase 3: C Operator Optimization")
  
    op_config = create_operator_dev_config(OPERATOR_TYPE_CONV2D)
  
    optimization_levels = [
        OPTIMIZATION_LEVEL_BASIC,
        OPTIMIZATION_LEVEL_INTERMEDIATE,
        OPTIMIZATION_LEVEL_ADVANCED
    ]
  
    for level in optimization_levels:
        print(f"\n  Testing optimization level: {level}")
      
        op_config.level = level
      
        # 生成算子代码
        operator_code = generate_operator_code(op_config)
      
        # 编译和测试
        compiled = compile_code(operator_code)
      
        if compiled:
            time = measure_performance(operator_code)
          
            print(f"    Performance: {time:.2f} ms")
          
            if time < best_time:
                best_time = time
                print(f"    New best!")
  
    # 阶段4：综合优化
    print("\nPhase 4: Comprehensive Optimization")
  
    # 使用最佳Tile配置
    tile_config = best_config if best_config else create_tile_config(64, 64, 64)
  
    # 生成综合优化代码
    optimized_code = generate_optimized_code(tile_config, op_config)
  
    # 编译和测试
    compiled = compile_code(optimized_code)
  
    if compiled:
        time = measure_performance(optimized_code)
      
        speedup = baseline_time / time
      
        print(f"\nFinal Results:")
        print(f"  Baseline: {baseline_time:.2f} ms")
        print(f"  Optimized: {time:.2f} ms")
        print(f"  Speedup: {speedup:.2f}x")

使用场景

场景一：卷积算子开发

# 卷积算子开发
def develop_convolution_operator():
    # 阶段1：定义卷积算子
    print("Phase 1: Define Convolution Operator")
  
    op_config = create_operator_dev_config(OPERATOR_TYPE_CONV2D)
  
    op_config.input.shape = [1, 3, 224, 224]
    op_config.output.shape = [1, 64, 224, 224]
    op_config.kernel_size = 3
    op_config.stride = 1
    op_config.padding = 1
  
    print(f"  Input shape: {op_config.input.shape}")
    print(f"  Output shape: {op_config.output.shape}")
    print(f"  Kernel size: {op_config.kernel_size}")
  
    # 阶段2：Tile优化
    print("\nPhase 2: Tile Optimization")
  
    input_tensor = create_tensor(*op_config.input.shape)
    output_tensor = create_tensor(*op_config.output.shape)
  
    # 自动Tile优化
    tiled_code = auto_tile_optimization(input_tensor, output_tensor)
  
    # 阶段3：生成C算子代码
    print("\nPhase 3: Generate C Operator Code")
  
    operator_code = generate_operator_code(op_config)
  
    # 阶段4：集成和优化
    print("\nPhase 4: Integrate and Optimize")
  
    integrated_code = integrate_code(tiled_code, operator_code)
    optimized_code = optimize_code(integrated_code)
  
    # 阶段5：编译和测试
    print("\nPhase 5: Compile and Test")
  
    compiled = compile_code(optimized_code)
  
    if compiled:
        tested = test_operator(optimized_code)
      
        if tested:
            print("  Convolution operator developed successfully!")
        else:
            print("  Convolution operator test failed!")
    else:
        print("  Convolution operator compilation failed!")

场景二：注意力算子开发

# 注意力算子开发
def develop_attention_operator():
    # 阶段1：定义注意力算子
    print("Phase 1: Define Attention Operator")
  
    op_config = create_operator_dev_config(OPERATOR_TYPE_ATTENTION)
  
    op_config.input.shape = [1, 512, 768]
    op_config.output.shape = [1, 512, 768]
    op_config.num_heads = 12
    op_config.head_dim = 64
  
    print(f"  Input shape: {op_config.input.shape}")
    print(f"  Output shape: {op_config.output.shape}")
    print(f"  Num heads: {op_config.num_heads}")
    print(f"  Head dim: {op_config.head_dim}")
  
    # 阶段2：Tile优化
    print("\nPhase 2: Tile Optimization")
  
    input_tensor = create_tensor(*op_config.input.shape)
    output_tensor = create_tensor(*op_config.output.shape)
  
    # 使用流水线并行策略
    tile_config = create_tile_config(64, 64, 64)
    tile_config.strategy = PARALLEL_STRATEGY_PIPELINE_PARALLEL
  
    tiled_code = generate_tiled_code(input_tensor, output_tensor, tile_config)
  
    # 阶段3：生成C算子代码
    print("\nPhase 3: Generate C Operator Code")
  
    operator_code = generate_operator_code(op_config)
  
    # 阶段4：集成和优化
    print("\nPhase 4: Integrate and Optimize")
  
    integrated_code = integrate_code(tiled_code, operator_code)
    optimized_code = optimize_code(integrated_code)
  
    # 阶段5：编译和测试
    print("\nPhase 5: Compile and Test")
  
    compiled = compile_code(optimized_code)
  
    if compiled:
        tested = test_operator(optimized_code)
      
        if tested:
            print("  Attention operator developed successfully!")
        else:
            print("  Attention operator test failed!")
    else:
        print("  Attention operator compilation failed!")

场景三：矩阵乘法算子开发

// 矩阵乘法算子开发
void develop_matmul_operator() {
    // 阶段1：定义矩阵乘法算子
    printf("Phase 1: Define Matrix Multiplication Operator\n");
  
    operator_dev_config_t *op_config = create_operator_dev_config(OPERATOR_TYPE_MATMUL);
  
    op_config->input.shape[0] = 1024;
    op_config->input.shape[1] = 1024;
    op_config->output.shape[0] = 1024;
    op_config->output.shape[1] = 1024;
  
    printf("  Input shape: (%d, %d)\n", op_config->input.shape[0], op_config->input.shape[1]);
    printf("  Output shape: (%d, %d)\n", op_config->output.shape[0], op_config->output.shape[1]);
  
    // 阶段2：Tile优化
    printf("\nPhase 2: Tile Optimization\n");
  
    tile_config_t *tile_config = create_tile_config(128, 128, 128);
    tile_config->strategy = PARALLEL_STRATEGY_DATA_PARALLEL;
    tile_config->layout = MEMORY_LAYOUT_ROW_MAJOR;
  
    char *tiled_code = generate_tiled_code_c(tile_config, op_config);
  
    printf("  Tiled code generated\n");
  
    // 阶段3：生成C算子代码
    printf("\nPhase 3: Generate C Operator Code\n");
  
    char *operator_code = generate_operator_code(op_config);
  
    printf("  Operator code generated\n");
  
    // 阶段4：集成和优化
    printf("\nPhase 4: Integrate and Optimize\n");
  
    char *integrated_code = integrate_code(tiled_code, operator_code);
    char *optimized_code = optimize_code(integrated_code);
  
    printf("  Code integrated and optimized\n");
  
    // 阶段5：编译和测试
    printf("\nPhase 5: Compile and Test\n");
  
    bool compiled = compile_code(optimized_code);
  
    if (compiled) {
        printf("  Compilation: SUCCESS\n");
      
        bool tested = test_operator(optimized_code);
      
        if (tested) {
            printf("  Test: PASSED\n");
            printf("  Matrix multiplication operator developed successfully!\n");
        } else {
            printf("  Test: FAILED\n");
            printf("  Matrix multiplication operator test failed!\n");
        }
    } else {
        printf("  Compilation: FAILED\n");
        printf("  Matrix multiplication operator compilation failed!\n");
    }
}

性能优化

1. 内存优化

# 内存优化
def memory_optimization(tile_config, op_config):
    # 启用内存池
    enable_memory_pool()
  
    # 优化内存布局
    optimize_memory_layout(tile_config)
  
    # 使用零拷贝
    enable_zero_copy()
  
    # 优化内存访问模式
    optimize_memory_access_pattern(tile_config)

2. 计算优化

// 计算优化
void computation_optimization(tile_config_t *tile_config, operator_dev_config_t *op_config) {
    // 启用SIMD指令
    enable_simd_instructions();
  
    // 优化循环展开
    optimize_loop_unrolling(tile_config);
  
    // 使用向量化
    enable_vectorization(op_config);
  
    // 优化指令流水线
    optimize_instruction_pipeline();
}

3. 并行优化

# 并行优化
def parallel_optimization(tile_config):
    # 启用多线程
    enable_multithreading()
  
    # 优化任务调度
    optimize_task_scheduling(tile_config)
  
    # 使用异步执行
    enable_async_execution()
  
    # 优化负载均衡
    optimize_load_balancing(tile_config)

与其他组件的关系

组件	关系
pypto	并行Tile操作
ascend-c	C算子开发
runtime	运行时支持
ops-nn	神经网络算子

关系：

NPU编程
    ↓
PyPTO（并行Tile操作）
    ↓
Ascend-C（C算子开发）
    ↓
Runtime（运行时）
    ↓
NPU硬件

调试技巧

1. Tile调试

# Tile调试
def debug_tiling(input_tensor, output_tensor, tile_config):
    # 启用调试模式
    enable_debug_mode()
  
    # 设置断点
    set_breakpoint("tile_compute", 100)
  
    # 单步执行
    step_through_tiling()
  
    # 查看Tile数据
    inspect_tile_data()
  
    # 验证Tile结果
    validate_tile_results()

2. 算子调试

// 算子调试
void debug_operator(operator_dev_config_t *op_config) {
    // 启用调试模式
    enable_debug_mode();
  
    // 设置断点
    set_breakpoint("operator_compute", 100);
  
    // 单步执行
    step_through_operator();
  
    // 查看变量
    inspect_variables();
  
    // 验证结果
    validate_results();
}

3. 性能分析

# 性能分析
def analyze_performance(tile_config, op_config):
    # 启用性能分析
    enable_profiling()
  
    # 运行算子
    run_operator(tile_config, op_config)
  
    # 获取性能分析结果
    profile = get_performance_profile()
  
    print("Performance Profile:")
    print(f"  Total time: {profile.total_time:.2f} ms")
    print(f"  Compute time: {profile.compute_time:.2f} ms")
    print(f"  Memory transfer time: {profile.memory_transfer_time:.2f} ms")
    print(f"  Tile overhead: {profile.tile_overhead:.2f} ms")

常见问题

问题1：Tile效果不佳

# 错误：Tile大小不当
tile_config = create_tile_config(1024, 1024, 1024)  # 太大！

# 正确：使用合理的Tile大小
tile_config = create_tile_config(64, 64, 64)  # 合理

问题2：编译失败

// 错误：代码生成错误
char *code = generate_operator_code(op_config);  // 可能错误！

// 正确：检查代码生成
if (validate_operator_config(op_config)) {
    char *code = generate_operator_code(op_config);  // 安全
}

问题3：性能不佳

# 错误：未使用优化
run_operator(tile_config, op_config)  # 未优化！

# 正确：使用优化
optimize_code(code)
run_operator(tile_config, op_config)  # 优化后，快！

应用场景总结

场景一：卷积算子开发

用于卷积算子开发。

场景二：注意力算子开发

用于注意力算子开发。

场景三：矩阵乘法算子开发

用于矩阵乘法算子开发。

场景四：自定义算子开发

用于自定义算子开发。

总结

PyPTO与Ascend-C的组合：

• 并行Tile操作
• C算子开发
• 自动优化
• 性能提升
• 开发效率

通过并行Tile操作和C算子开发的协同，实现了高效的NPU编程，是算子开发的重要工具。

相关链接

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

ascend-c仓库地址：https://atomgit.com/cann/ascend-c

CANN组织地址：https://atomgit.com/cann

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