tilelang/examples/convolution/example_convolution.py

import torch
import tilelang
from tilelang.autotuner import *
import tilelang.language as T
import itertools
import argparse
from functools import partial


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 get_configs():
    block_M = [64, 128, 256]
    block_N = [64, 128, 256]
    block_K = [32, 64]
    num_stages = [1, 2, 3, 4]
    threads = [128, 256]
    _configs = list(itertools.product(block_M, block_N, block_K, num_stages, threads))

    configs = [{
        'block_M': c[0],
        'block_N': c[1],
        'block_K': c[2],
        'num_stages': c[3],
        'threads': c[4]
    } for c in _configs]
    return configs


def convolution(N, C, H, W, F, K, S, D, P, tune=False):
    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()

    def kernel_func(block_M, block_N, block_K, num_stages, threads):

        @T.prim_func
        def main(
                data: T.Buffer((N, H, W, C), dtype),
                kernel: T.Buffer((KH, KW, C, F), dtype),
                out: T.Buffer((N, OH, OW, F), dtype),
        ):
            with T.Kernel(
                    T.ceildiv(F, block_N), T.ceildiv(N * OH * OW, block_M),
                    threads=threads) 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.Buffer((KH * KW * C, F), dtype, kernel.data)
                out_flat = T.Buffer((N * OH * OW, F), dtype, out.data)

                T.annotate_layout({
                    out_shared: tilelang.layout.make_swizzled_layout(out_shared),
                    data_shared: tilelang.layout.make_swizzled_layout(data_shared),
                    kernel_shared: tilelang.layout.make_swizzled_layout(kernel_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)

                T.copy(out_local, out_shared)
                T.copy(out_shared, out_flat[by * block_M, bx * block_N])

        return main

    if tune:

        @autotune(
            configs=get_configs(),
            keys=["block_M", "block_N", "block_K", "num_stages", "threads"],
            warmup=10,
            rep=10)
        @jit(
            out_idx=[2],
            supply_type=tilelang.TensorSupplyType.Integer,
            ref_prog=None,
            profiler="auto")
        def kernel(block_M=None, block_N=None, block_K=None, num_stages=None, threads=None):
            return kernel_func(block_M, block_N, block_K, num_stages, threads)

        return kernel()
    else:

        def kernel(block_M, block_N, block_K, num_stages, threads):
            return kernel_func(block_M, block_N, block_K, num_stages, threads)

        return kernel


def ref_program(A, B, stride, padding, dilation):
    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


if __name__ == "__main__":
    parser = argparse.ArgumentParser()
    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('--tune', action='store_true', help='tune configs')
    args = parser.parse_args()
    N, C, H, W, F, K, S, D, P = args.n, args.c, args.h, args.w, args.f, args.k, args.s, args.d, args.p
    OH = (H + 2 * P - D * (K - 1) - 1) // S + 1
    OW = (W + 2 * P - D * (K - 1) - 1) // S + 1
    total_flops = 2 * N * C * OH * OW * F * K * K

    if (not args.tune):
        program = convolution(
            N, C, H, W, F, K, S, D, P, tune=args.tune)(
                block_M=256, block_N=128, block_K=64, num_stages=4, threads=256)
        ref_program = partial(ref_program, stride=S, padding=P, dilation=D)
        kernel = tilelang.compile(program, out_idx=[2])
        profiler = kernel.get_profiler(tilelang.TensorSupplyType.Normal)
        profiler.assert_allclose(ref_program, rtol=0.01, atol=0.01)
        print("All checks pass.")
        latency = profiler.do_bench(ref_program, warmup=500)
        print("Ref: {:.2f} ms".format(latency))
        print("Ref: {:.2f} TFlops".format(total_flops / latency * 1e-9))
        latency = profiler.do_bench(warmup=500)
        print("Tile-lang: {:.2f} ms".format(latency))
        print("Tile-lang: {:.2f} TFlops".format(total_flops / latency * 1e-9))
    else:
        best_latency, best_config, ref_latency = convolution(
            N, C, H, W, F, K, S, D, P, tune=args.tune)
        print(f"Best latency: {best_latency}")
        print(f"Best TFlops: {total_flops / best_latency * 1e-9}")
        print(f"Best config: {best_config}")