自定义算子Codegen：TVM/Triton/CUDA编程技术

# 模拟TensorIR风格的调度策略def schedule_matmul(M, N, K, block_size=32):    # 分块策略    tiles = {        'BM': block_size, 'BN': block_size, 'BK': block_size,    }    # 线程绑定模拟    threads = {'tx': 8, 'ty': 4}    print(f'Matmul调度: M={M}, N={N}, K={K}')    print(f'分块: {tiles}')    print(f'线程: {threads}')    return tiles, threads schedule_matmul(1024, 1024, 1024)

# 模拟Triton风格的Block级算子伪代码def triton_matmul_kernel(A, B, C, M, N, K, BLOCK_SIZE=32):    # Block级索引    pid_m = 'blockIdx.y'    pid_n = 'blockIdx.x'        # 每个Block处理BLOCK_SIZE x BLOCK_SIZE的输出块    offs_m = f'{pid_m} * {BLOCK_SIZE} + arange({BLOCK_SIZE})'    offs_n = f'{pid_n} * {BLOCK_SIZE} + arange({BLOCK_SIZE})'        # 累加器初始化    acc = 'zeros((BLOCK_SIZE, BLOCK_SIZE))'        # 循环遍历K维度分块    for k in range(0, K, BLOCK_SIZE):        a = f'load(A[{offs_m}, {k}:{k+BLOCK_SIZE}])'        b = f'load(B[{k}:{k+BLOCK_SIZE}, {offs_n}])'        acc = f'dot({a}, {b}) + {acc}'        print(f'Triton kernel: Block({pid_m},{pid_n}) -> C[{offs_m}, {offs_n}]')    return acc triton_matmul_kernel(None, None, None, 128, 128, 128)

# 模拟PTX级指令调度分析instructions = [    {'op': 'LDG', 'latency': 4, 'throughput': 1},   # 全局加载    {'op': 'FMA', 'latency': 2, 'throughput': 2},    # 乘加    {'op': 'STG', 'latency': 4, 'throughput': 1},    # 全局存储    {'op': 'BAR', 'latency': 1, 'throughput': 1},    # 同步] print('PTX指令分析:')for inst in instructions:    cycles_per_inst = 1.0 / inst['throughput']    print(f"  {inst['op']}: 延迟={inst['latency']}周期, 吞吐={inst['throughput']}/周期, IPC={cycles_per_inst:.2f}") # 计算理论峰值利用率compute_ops = 512  # 每周期浮点运算memory_bw = 1000   # GB/sprint(f'\n计算峰值: {compute_ops} FLOP/周期')print(f'内存带宽: {memory_bw} GB/s')

import numpy as np # 1. 模拟分块矩阵乘法（TensorIR/Triton核心概念）def blocked_matmul(A, B, C, M, N, K, BLOCK=32):    for i0 in range(0, M, BLOCK):        for j0 in range(0, N, BLOCK):            # 初始化BLOCK x BLOCK累加器            acc = np.zeros((BLOCK, BLOCK))            for k0 in range(0, K, BLOCK):                # 加载A和B的Block                a_block = A[i0:i0+BLOCK, k0:k0+BLOCK]                b_block = B[k0:k0+BLOCK, j0:j0+BLOCK]                # Block级矩阵乘                acc += a_block @ b_block            # 写回C            C[i0:i0+BLOCK, j0:j0+BLOCK] = acc    return C # 2. 模拟性能 Roofline 分析def roofline_analysis(arith_intensity, peak_compute=1000, peak_bw=100):    '''    arith_intensity: 计算强度 (FLOP/Byte)    peak_compute: 峰值算力 (GFLOP/s)    peak_bw: 峰值带宽 (GB/s)    '''    # 带宽限制区域    bw_limited = arith_intensity * peak_bw    # 实际性能取最小值    performance = min(bw_limited, peak_compute)    bound = 'compute' if performance == peak_compute else 'memory'    return performance, bound # 分析不同算子的计算强度ops = [    ('naive_matmul', 8),    ('blocked_matmul', 32),    ('flash_attention', 128),] print('Roofline性能分析:')for name, ai in ops:    perf, bound = roofline_analysis(ai)    print(f'  {name}: AI={ai}, 性能={perf:.1f} GFLOP/s ({bound}-bound)') # 3. 模拟Warp级并行效率WARP_SIZE = 32threads_active = 28  # 28/32线程活跃occupancy = threads_active / WARP_SIZEprint(f'\nWarp效率: {occupancy:.1%} ({threads_active}/{WARP_SIZE})') # 4. 模拟共享内存bank conflict检测def check_bank_conflict(access_pattern, num_banks=32):    banks = set()    for addr in access_pattern:        bank = addr % num_banks        banks.add(bank)    unique_banks = len(banks)    conflict = '无冲突' if unique_banks == len(access_pattern) else f'{len(access_pattern)-unique_banks}路冲突'    return unique_banks, conflict pattern = [0, 32, 64, 96]  # 跨步32，同bankunique, conflict = check_bank_conflict(pattern)print(f'\n共享内存访问: {pattern}')print(f'使用bank数: {unique}/32, {conflict}') pattern2 = [0, 4, 8, 12]  # 连续访问，不同bankunique2, conflict2 = check_bank_conflict(pattern2)print(f'共享内存访问: {pattern2}')print(f'使用bank数: {unique2}/32, {conflict2}')