tilelang/examples/dynamic_shape/example_dynamic.py

import tilelang
import tilelang.language as T


@tilelang.jit
def matmul_dynamic_mnk(
    A,
    B,
    block_M,
    block_N,
    block_K,
    trans_A,
    trans_B,
    in_dtype,
    out_dtype,
    accum_dtype,
    num_stages,
    threads,
):
    M = T.dynamic("m")
    N = T.dynamic("n")
    K = T.dynamic("k")

    A_shape = (K, M) if trans_A else (M, K)
    B_shape = (N, K) if trans_B else (K, N)

    A_shared_shape = (block_K, block_M) if trans_A else (block_M, block_K)
    B_shared_shape = (block_N, block_K) if trans_B else (block_K, block_N)

    A: T.Tensor(A_shape, in_dtype)
    B: T.Tensor(B_shape, in_dtype)
    C = T.empty((M, N), out_dtype)

    with T.Kernel(T.ceildiv(N, block_N), T.ceildiv(M, block_M), threads=threads) as (bx, by):
        A_shared = T.alloc_shared(A_shared_shape, in_dtype)
        B_shared = T.alloc_shared(B_shared_shape, in_dtype)
        C_local = T.alloc_fragment((block_M, block_N), accum_dtype)
        T.clear(C_local)
        for k in T.Pipelined(T.ceildiv(K, block_K), num_stages=num_stages):
            if trans_A:
                T.copy(A[k * block_K, by * block_M], A_shared)
            else:
                T.copy(A[by * block_M, k * block_K], A_shared)
            if trans_B:
                T.copy(B[bx * block_N, k * block_K], B_shared)
            else:
                T.copy(B[k * block_K, bx * block_N], B_shared)
            T.gemm(A_shared, B_shared, C_local, trans_A, trans_B)
        T.copy(C_local, C[by * block_M, bx * block_N])

    return C


def matmul_dynamic(M, N, K, block_M, block_N, block_K, trans_A, trans_B, in_dtype, out_dtype, accum_dtype, num_stages, threads):
    print(
        f"M: {M}, N: {N}, K: {K}, block_M: {block_M}, block_N: {block_N}, block_K: {block_K}, trans_A: {trans_A}, trans_B: {trans_B}, in_dtype: {in_dtype}, out_dtype: {out_dtype}, accum_dtype: {accum_dtype}, num_stages: {num_stages}, threads: {threads}"
    )

    import torch

    if trans_A:
        A = torch.rand(K, M, device="cuda", dtype=getattr(torch, in_dtype))
    else:
        A = torch.rand(M, K, device="cuda", dtype=getattr(torch, in_dtype))
    if trans_B:
        B = torch.rand(N, K, device="cuda", dtype=getattr(torch, in_dtype))
    else:
        B = torch.rand(K, N, device="cuda", dtype=getattr(torch, in_dtype))

    C = matmul_dynamic_mnk(A, B, block_M, block_N, block_K, trans_A, trans_B, in_dtype, out_dtype, accum_dtype, num_stages, threads)

    def ref_program(A, B):
        import torch

        if trans_A:
            A = A.T
        if trans_B:
            B = B.T
        C = torch.matmul(A.to(torch.float), B.to(torch.float))
        C = C.to(torch.__getattribute__(out_dtype))
        return C

    # Get Reference Result
    ref_c = ref_program(A, B)

    torch.testing.assert_close(C, ref_c, rtol=1e-2, atol=1e-2)
    print("Kernel output matches PyTorch reference.")

    from tilelang.profiler import do_bench

    latency = do_bench(
        lambda: matmul_dynamic_mnk(A, B, block_M, block_N, block_K, trans_A, trans_B, in_dtype, out_dtype, accum_dtype, num_stages, threads)
    )
    print(f"Latency: {latency} ms")


def main(M=16384, N=16384, K=16384):
    block_M, block_N, block_K = 128, 128, 32
    trans_A, trans_B = False, False
    in_dtype, out_dtype = "float16", "float16"
    accum_dtype = "float32"
    num_stages = 3
    threads = 128
    matmul_dynamic(M, N, K, block_M, block_N, block_K, trans_A, trans_B, in_dtype, out_dtype, accum_dtype, num_stages, threads)


def run_regression_perf(M=4096, N=4096, K=4096):
    block_M, block_N, block_K = 128, 128, 32
    trans_A, trans_B = False, False
    in_dtype, out_dtype = "float16", "float16"
    accum_dtype = "float32"
    num_stages = 3
    threads = 128

    import torch

    if trans_A:
        A = torch.rand(K, M, device="cuda", dtype=getattr(torch, in_dtype))
    else:
        A = torch.rand(M, K, device="cuda", dtype=getattr(torch, in_dtype))
    if trans_B:
        B = torch.rand(N, K, device="cuda", dtype=getattr(torch, in_dtype))
    else:
        B = torch.rand(K, N, device="cuda", dtype=getattr(torch, in_dtype))

    from tilelang.profiler import do_bench

    return do_bench(
        lambda: matmul_dynamic_mnk(
            A, B, block_M, block_N, block_K, trans_A, trans_B, in_dtype, out_dtype, accum_dtype, num_stages, threads
        ),
        backend="cupti",
    )


if __name__ == "__main__":
    main()