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https://github.com/tile-ai/tilelang.git
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zqh-wzandLei Wang
c5bbc60855
* add test for issue 101 * use ss_smem_selector from cutlass * fix mismatch between smem layout and mma * only fix for sm90 * Add CUDA requirements to GEMM thread tests * lint fix --------- Co-authored-by: Lei Wang <34334180+LeiWang1999@users.noreply.github.com>
59 lines
1.8 KiB
Python
59 lines
1.8 KiB
Python
import torch
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import tilelang
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import tilelang.testing
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import tilelang.language as T
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def matmul(M, N, K, block_M, block_N, block_K, threads, dtype="float16", accum_dtype="float"):
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@T.prim_func
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def main(
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A: T.Buffer((M, K), dtype),
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B: T.Buffer((K, N), dtype),
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C: T.Buffer((M, N), dtype),
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):
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with T.Kernel(T.ceildiv(N, block_N), T.ceildiv(M, block_M), threads=threads) as (bx, by):
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A_shared = T.alloc_shared((block_M, block_K), dtype)
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B_shared = T.alloc_shared((block_K, block_N), dtype)
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C_local = T.alloc_fragment((block_M, block_N), accum_dtype)
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T.clear(C_local)
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for k in T.Pipelined(T.ceildiv(K, block_K), num_stages=3):
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T.copy(A[by * block_M, k * block_K], A_shared)
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T.copy(B[k * block_K, bx * block_N], B_shared)
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T.gemm(A_shared, B_shared, C_local, policy=T.GemmWarpPolicy.FullCol)
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T.copy(C_local, C[by * block_M, bx * block_N])
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return main
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def run_gemm_threads_test(threads, M=1024, N=192, K=1024, block_M=64, block_N=192, block_K=32):
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func = matmul(M, N, K, block_M, block_N, block_K, threads)
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jit_kernel = tilelang.compile(func, out_idx=-1, target="cuda")
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torch.manual_seed(0)
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a = torch.randn(M, K, device="cuda", dtype=torch.float16)
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b = torch.randn(K, N, device="cuda", dtype=torch.float16)
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ref_c = a @ b
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c = jit_kernel(a, b)
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tilelang.testing.torch_assert_close(c, ref_c, rtol=1e-2, atol=1e-2)
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@tilelang.testing.requires_cuda
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@tilelang.testing.requires_cuda_compute_version(9, 0)
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def test_gemm_threads_2wgs():
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run_gemm_threads_test(128 * 2)
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@tilelang.testing.requires_cuda
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@tilelang.testing.requires_cuda_compute_version(9, 0)
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def test_gemm_threads_4wgs():
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run_gemm_threads_test(128 * 4)
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if __name__ == "__main__":
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tilelang.testing.main()
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