import torch import argparse import itertools import tilelang import tilelang.language as T def check_hopper(): if not torch.cuda.is_available(): return None props = torch.cuda.get_device_properties(0) compute_capability = props.major, props.minor return compute_capability == (9, 0) def ref_program(stride, padding, dilation): def main(A, B): A = A.permute(0, 3, 1, 2) # N, H, W, C -> N, C, H, W B = B.permute(3, 2, 0, 1) # H, W, C, F -> F, C, H, W C = torch.conv2d(A, B, stride=stride, padding=padding, dilation=dilation) C = C.permute(0, 2, 3, 1) # N, C, H, W -> N, H, W, C return C return main def get_configs(): block_M = [64, 128, 256] block_N = [64, 128, 256] block_K = [32, 64] num_stages = [0, 1, 2, 3] thread_num = [128, 256] enable_rasterization = [True, False] _configs = list( itertools.product( block_M, block_N, block_K, num_stages, thread_num, enable_rasterization, )) configs = [ { "block_M": c[0], "block_N": c[1], "block_K": c[2], "num_stages": c[3], "thread_num": c[4], "enable_rasteration": c[5], # keep param name for backward-compat } for c in _configs ] return configs def get_heuristic_config() -> dict: # Get CUDA device properties if not torch.cuda.is_available(): raise RuntimeError("CUDA is not available") device = torch.cuda.current_device() sm_major, sm_minor = torch.cuda.get_device_capability(device) sm_version = sm_major * 10 + sm_minor print(f"CUDA device capability: {sm_version}") if sm_version in {80}: return { "block_M": 128, "block_N": 256, "block_K": 32, "num_stages": 2, "thread_num": 128, "enable_rasteration": True } elif sm_version in {90}: return { "block_M": 128, "block_N": 256, "block_K": 64, "num_stages": 3, "thread_num": 256, "enable_rasteration": True } else: return { "block_M": 128, "block_N": 256, "block_K": 32, "num_stages": 0, "thread_num": 128, "enable_rasteration": True } @tilelang.autotune(configs=get_configs()) @tilelang.jit(out_idx=[2]) def convolution(N, C, H, W, F, K, S, D, P, block_M, block_N, block_K, num_stages, thread_num, enable_rasteration, dtype="float16", accum_dtype="float"): KH, KW = K, K OH = (H + 2 * P - D * (K - 1) - 1) // S + 1 OW = (W + 2 * P - D * (K - 1) - 1) // S + 1 dtype = "float16" accum_dtype = "float" is_hopper = check_hopper() @T.prim_func def main( data: T.Tensor((N, H, W, C), dtype), kernel: T.Tensor((KH, KW, C, F), dtype), out: T.Tensor((N, OH, OW, F), dtype), ): with T.Kernel( T.ceildiv(F, block_N), T.ceildiv(N * OH * OW, block_M), threads=thread_num) as (bx, by): data_shared = T.alloc_shared((block_M, block_K), dtype) kernel_shared = T.alloc_shared((block_K, block_N), dtype) out_local = T.alloc_fragment((block_M, block_N), accum_dtype) out_shared = T.alloc_shared((block_M, block_N), dtype) kernel_flat = T.Tensor((KH * KW * C, F), dtype, kernel.data) out_flat = T.Tensor((N * OH * OW, F), dtype, out.data) if is_hopper: T.annotate_layout({ out_shared: tilelang.layout.make_swizzled_layout(out_shared), }) T.clear(out_local) for k_iter in T.Pipelined(T.ceildiv(KH * KW * C, block_K), num_stages=num_stages): if is_hopper: T.c2d_im2col(data, data_shared, by, k_iter, KH, S, D, P) else: for i, j in T.Parallel(block_M, block_K): k = k_iter * block_K + j m = by * block_M + i access_h = m % (OH * OW) // OW * S + k // (KW * C) * D - P access_w = m % OW * S + k // C % KW * D - P in_bound = ((access_h >= 0) and (access_w >= 0) and (access_h < H) and (access_w < W)) data_shared[i, j] = T.if_then_else( in_bound, data[m // (OH * OW), access_h, access_w, k % C], 0) T.copy(kernel_flat[k_iter * block_K, bx * block_N], kernel_shared) T.gemm(data_shared, kernel_shared, out_local) if is_hopper: T.copy(out_local, out_shared) T.copy(out_shared, out_flat[by * block_M, bx * block_N]) else: T.copy(out_local, out_flat[by * block_M, bx * block_N]) return main def main(n: int = 128, c: int = 128, h: int = 64, w: int = 64, f: int = 128, k: int = 3, s: int = 1, d: int = 1, p: int = 1, use_autotune: bool = False, with_roller: bool = True): N, C, H, W, F, K, S, D, P = n, c, h, w, f, k, s, d, p ref_prog = ref_program(S, P, D) if use_autotune: kernel = convolution(N, C, H, W, F, K, S, D, P) else: config = get_heuristic_config() kernel = convolution(N, C, H, W, F, K, S, D, P, **config) profiler = kernel.get_profiler(tensor_supply_type=tilelang.TensorSupplyType.Auto) tilelang_latency = profiler.do_bench() ref_latency = profiler.do_bench(ref_prog) profiler.assert_allclose(ref_prog, atol=1e-2, rtol=1e-2) print(f"TileLang latency: {tilelang_latency}") print(f"Ref latency: {ref_latency}") if __name__ == "__main__": parser = argparse.ArgumentParser(description="Autotuned MatMul Benchmark") parser.add_argument('--n', type=int, default=128, help='n') parser.add_argument('--c', type=int, default=128, help='c') parser.add_argument('--h', type=int, default=64, help='h') parser.add_argument('--w', type=int, default=64, help='w') parser.add_argument('--f', type=int, default=128, help='f') parser.add_argument('--k', type=int, default=3, help='k') parser.add_argument('--s', type=int, default=1, help='s') parser.add_argument('--d', type=int, default=1, help='d') parser.add_argument('--p', type=int, default=1, help='p') parser.add_argument( "--use_autotune", action="store_true", default=False, help="Whether to use autotune for matmul configs") parser.add_argument( "--with_roller", action="store_true", default=True, help="Whether to enable BitBLAS roller for search space") args = parser.parse_args() main(args.n, args.c, args.h, args.w, args.f, args.k, args.s, args.d, args.p, args.use_autotune, args.with_roller)