mirror of
https://github.com/tile-ai/tilelang.git
synced 2026-10-25 07:14:16 +08:00
Tong WU
727d108315
* support tcgen5mma lower for int8/uint8->int32 * add test * add template for u8 and fix a bug in codegen * lint * fix ai suggestions * add testing to maint
84 lines
2.9 KiB
Python
84 lines
2.9 KiB
Python
import torch
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import tilelang.testing
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import tilelang.language as T
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def calc_diff(x, y):
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x, y = x.double(), y.double()
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denominator = (x * x + y * y).sum()
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sim = 2 * (x * y).sum() / denominator
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return 1 - sim
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def matmul_nt(M, N, K, bM, bN, bK, in_dtype, out_dtype, accum_dtype):
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@T.prim_func
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def main(
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A: T.Tensor((M, K), in_dtype),
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B: T.Tensor((N, K), in_dtype),
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C: T.Tensor((M, N), out_dtype),
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):
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with T.Kernel(T.ceildiv(N, bN), T.ceildiv(M, bM), threads=256) as (bx, by):
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A_shared = T.alloc_shared((bM, bK), in_dtype)
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B_shared = T.alloc_shared((bN, bK), in_dtype)
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C_tmem = T.alloc_tmem((bM, bN), accum_dtype)
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mbar = T.alloc_barrier(1)
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C_local = T.alloc_fragment((bM, bN), accum_dtype)
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C_shared = T.alloc_shared((bM, bN), out_dtype)
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for k in T.Pipelined(T.ceildiv(K, bK), num_stages=2):
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T.copy(A[by * bM, k * bK], A_shared)
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T.copy(B[bx * bN, k * bK], B_shared)
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T.gemm(A_shared, B_shared, C_tmem, transpose_B=True, mbar=mbar, wg_wait=-1, clear_accum=k == 0)
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T.mbarrier_wait_parity(mbar, k % 2)
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T.copy(C_tmem, C_local)
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T.copy(C_local, C_shared)
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T.copy(C_shared, C[by * bM, bx * bN])
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return main
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def assert_matmul_correctness(M, N, K, block_M, block_N, block_K, in_dtype, out_dtype, accum_dtype):
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func = matmul_nt(M, N, K, block_M, block_N, block_K, in_dtype, out_dtype, accum_dtype)
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kernel = tilelang.compile(
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func,
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out_idx=-1,
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target="cuda",
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pass_configs={
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tilelang.PassConfigKey.TL_DISABLE_TMA_LOWER: True,
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tilelang.PassConfigKey.TL_DISABLE_WARP_SPECIALIZED: True,
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},
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)
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assert out_dtype in [T.int32], "Currently only int32 is supported"
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assert accum_dtype in [T.int32], "Currently only int32 is supported"
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if in_dtype is T.int8:
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A = torch.randint(-128, 128, (M, K), device="cuda", dtype=torch.int8)
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B = torch.randint(-128, 128, (N, K), device="cuda", dtype=torch.int8)
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elif in_dtype is T.uint8:
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A = torch.randint(0, 256, (M, K), device="cuda", dtype=torch.uint8)
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B = torch.randint(0, 256, (N, K), device="cuda", dtype=torch.uint8)
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else:
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raise ValueError(f"Unsupported input dtype: {in_dtype}")
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C = kernel(A, B)
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ref_c = (A.float() @ B.T.float()).to(torch.int32)
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print(C)
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print(ref_c)
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diff = calc_diff(C, ref_c)
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print(f"diff: {diff}")
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assert diff < 1e-3
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@tilelang.testing.requires_cuda
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@tilelang.testing.requires_cuda_compute_version(10)
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def test_assert_matmul():
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assert_matmul_correctness(1024, 1024, 1024, 128, 128, 128, T.int8, T.int32, T.int32)
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assert_matmul_correctness(1024, 1024, 1024, 128, 128, 128, T.uint8, T.int32, T.int32)
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if __name__ == "__main__":
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test_assert_matmul()
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