mirror of
https://github.com/tile-ai/tilelang.git
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Gongen-Ali
4e53b50750
* Fix some bugs on ci and open rocm ci test * fix pre-commit * fix build issue on hip * refactor on hip * pre-commit fix * fix test with pdl * fix pdl test
64 lines
1.9 KiB
Python
64 lines
1.9 KiB
Python
import tilelang
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import tilelang.testing
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import tilelang.language as T
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import torch
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def matmul(M, N, K, block_M, block_N, block_K, dtype=T.float16, accum_dtype=T.float32):
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@T.prim_func
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def main(
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A: T.Tensor((M, K), dtype),
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B: T.Tensor((N, K), dtype),
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C: T.Tensor((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=128) as (
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bx,
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by,
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):
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A_shared = T.alloc_shared((block_M, block_K), dtype)
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B_shared = T.alloc_shared((block_N, block_K), 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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# changing num_stages to 0 gives correct results
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for ko in T.Pipelined(T.ceildiv(K, block_K), num_stages=1):
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T.copy(A[by * block_M, ko * block_K], A_shared)
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for j, k in T.Parallel(block_N, block_K):
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B_shared[j, k] = B[bx * block_N + j, ko * block_K + k]
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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 * block_M, bx * block_N])
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return main
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def run_gemm_pipeline_test(N, block_M=128, block_N=128, block_K=32):
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func = matmul(N, N, N, block_M, block_N, block_K)
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jit_kernel = tilelang.compile(func, out_idx=[2])
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torch.manual_seed(0)
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a = torch.randn(N, N, device="cuda", dtype=torch.float16)
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b = torch.randn(N, N, device="cuda", dtype=torch.float16)
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ref_c = a @ b.T
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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=0.2)
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def test_pipeline_large_matrix():
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"""Test pipeline stages with large matrix multiplication (4096x4096)"""
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run_gemm_pipeline_test(4096)
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def test_pipeline_small_matrix():
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"""Test pipeline stages with smaller matrix multiplication"""
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run_gemm_pipeline_test(1024)
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if __name__ == "__main__":
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tilelang.testing.main()
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