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https://github.com/tile-ai/tilelang.git
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2cccf1f5dd
* upgrade cutlass to upstream v3.8.0 * Implement fp8 gemm and add example script * Fix dtype retrieval with map_torch_type for fp8 inputs * Disable vectorization of fp8 values * Make MMA declaration compatible with cutlass 3.4.0+ * Add test for fp8 T.gemm * fix indent * fix indent * Add copyright and license header * Add copyright and license header * lint fix * Refactor matmul_nt and assert_matmul_correctness functions for improved readability by consolidating parameter definitions and adjusting formatting. * clang format lint --------- Co-authored-by: Lei Wang <34334180+LeiWang1999@users.noreply.github.com> Co-authored-by: LeiWang1999 <leiwang1999@outlook.com>
65 lines
2.1 KiB
Python
65 lines
2.1 KiB
Python
import torch
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import tilelang.testing
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import tilelang.language as T
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from tilelang.utils.tensor import map_torch_type
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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.Buffer((M, K), in_dtype),
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B: T.Buffer((N, K), in_dtype),
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C: T.Buffer((M, N), out_dtype),
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):
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with T.Kernel(T.ceildiv(N, bN), T.ceildiv(M, bM), threads=128) 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_local = T.alloc_fragment((bM, bN), accum_dtype)
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T.clear(C_local)
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for k in T.Pipelined(T.ceildiv(K, bK), num_stages=3):
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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_local, transpose_B=True)
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T.copy(C_local, 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(func, out_idx=-1)
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A = torch.randn(M, K).to(map_torch_type(in_dtype)).cuda()
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B = torch.randn(N, K).to(map_torch_type(in_dtype)).cuda()
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C = kernel(A, B)
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ref_c = torch.matmul(A.to(map_torch_type(accum_dtype)),
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B.T.to(map_torch_type(accum_dtype))).to(map_torch_type(out_dtype))
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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(9)
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def test_assert_matmul():
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assert_matmul_correctness(1024, 1024, 1024, 128, 128, 64, "e4m3_float8", "float32", "float32")
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assert_matmul_correctness(1024, 1024, 1024, 128, 128, 64, "e5m2_float8", "float32", "float32")
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if __name__ == "__main__":
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tilelang.testing.main()
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