mirror of
https://github.com/tile-ai/tilelang.git
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72 lines
1.8 KiB
Python
72 lines
1.8 KiB
Python
import tilelang
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import tilelang.language as T
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@tilelang.jit
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def matmul(M,
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N,
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K,
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block_M,
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block_N,
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block_K,
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split_k,
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dtype="float16",
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accum_dtype="float",
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out_dtype="float32"):
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splitK = K // split_k
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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), out_dtype),
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):
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with T.Kernel(
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T.ceildiv(N, block_N), T.ceildiv(M, block_M), split_k, threads=128) as (bx, by, bz):
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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_shared = T.alloc_shared((block_M, block_N), out_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 ko in T.Pipelined(T.ceildiv(splitK, block_K), num_stages=0):
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T.copy(A[by * block_M, bz * splitK + ko * block_K], A_shared)
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T.copy(B[bz * splitK + ko * block_K, bx * block_N], B_shared)
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T.gemm(A_shared, B_shared, C_local)
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T.copy(C_local, C_shared)
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for i, j in T.Parallel(block_M, block_N):
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T.atomic_add(C[by * block_M + i, bx * block_N + j], C_shared[i, j])
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return main
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def main():
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M = 1024
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N = 1024
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K = 1024
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block_M = 128
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block_N = 128
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block_K = 32
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split_k = 4
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kernel = matmul(M, N, K, block_M, block_N, block_K, split_k)
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import torch
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torch.random.manual_seed(42)
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a = torch.randn(M, K).cuda().half()
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b = torch.randn(K, N).cuda().half()
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c = torch.zeros(M, N).cuda().float()
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kernel(a, b, c)
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ref_c = a @ b
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torch.testing.assert_close(c, ref_c.to(c.dtype), rtol=1e-2, atol=1e-2)
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if __name__ == "__main__":
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main()
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