import tilelang
import tilelang.language as T


@tilelang.jit
def matmul(M,
           N,
           K,
           block_M,
           block_N,
           block_K,
           split_k,
           dtype="float16",
           accum_dtype="float",
           out_dtype="float32"):

    splitK = K // split_k

    @T.prim_func
    def main(
            A: T.Tensor((M, K), dtype),
            B: T.Tensor((N, K), dtype),
            C: T.Tensor((M, N), out_dtype),
    ):
        with T.Kernel(
                T.ceildiv(N, block_N), T.ceildiv(M, block_M), split_k, threads=128) as (bx, by, bz):
            A_shared = T.alloc_shared((block_M, block_K), dtype)
            B_shared = T.alloc_shared((block_K, block_N), dtype)
            C_shared = T.alloc_shared((block_M, block_N), out_dtype)
            C_local = T.alloc_fragment((block_M, block_N), accum_dtype)

            T.clear(C_local)
            for ko in T.Pipelined(T.ceildiv(splitK, block_K), num_stages=0):
                T.copy(A[by * block_M, bz * splitK + ko * block_K], A_shared)
                T.copy(B[bz * splitK + ko * block_K, bx * block_N], B_shared)
                T.gemm(A_shared, B_shared, C_local)

            T.copy(C_local, C_shared)

            # TODO: Automatically add vectorized atomic with enhancement
            # https://github.com/tile-ai/tilelang/issues/523
            # if DataType(dtype).bits == 16:
            #     for i, j in T.Parallel(block_M, block_N // 2):
            #         m, n = by * block_M + i, bx * block_N + j * 2
            #         # vectorized atomic
            #         T.atomic_addx2(C[m, n], C_shared[i, j * 2])

            for i, j in T.Parallel(block_M, block_N):
                T.atomic_add(C[by * block_M + i, bx * block_N + j], C_shared[i, j])

    return main


def main():
    M = 1024
    N = 1024
    K = 1024
    block_M = 128
    block_N = 128
    block_K = 32
    split_k = 4

    kernel = matmul(M, N, K, block_M, block_N, block_K, split_k)

    import torch

    torch.random.manual_seed(42)
    a = torch.randn(M, K).cuda().half()
    b = torch.randn(K, N).cuda().half()
    c = torch.zeros(M, N).cuda().float()
    kernel(a, b, c)

    ref_c = a @ b

    torch.testing.assert_close(c, ref_c.to(c.dtype), rtol=1e-2, atol=1e-2)


if __name__ == "__main__":
    main()
