mirror of
https://github.com/tile-ai/tilelang.git
synced 2026-10-24 07:04:00 +08:00
Zhengju Tang
9ddf577042
* [BugFix] Stride check and fix for empty tensor in tvm_ffi * Lint * Lint * Revert * Lint * Fix zero stride check in C host code * Add warning for zero stride * Modify test scripts * Lint * Rename test * Refactor * simplify * Lint * Fix
47 lines
1.5 KiB
Python
47 lines
1.5 KiB
Python
import tilelang.language as T
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import tilelang.testing
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import tilelang
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import torch
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def matmu_jit_kernel(M, N, K, block_M, block_N, block_K):
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@T.prim_func
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def main(
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A: T.Tensor((M, K), T.float16),
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B: T.Tensor((K, N), T.float16),
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C: T.Tensor((M, N), T.float16),
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):
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with T.Kernel(T.ceildiv(N, block_N), T.ceildiv(M, block_M), threads=128) as (bx, by):
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A_shared = T.alloc_shared((block_M, block_K), T.float16)
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B_shared = T.alloc_shared((block_K, block_N), T.float16)
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C_local = T.alloc_fragment((block_M, block_N), T.float32)
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T.clear(C_local)
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for k in T.Pipelined(T.ceildiv(K, block_K)):
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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)
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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_jit_kernel(M, N, K, block_M, block_N, block_K):
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program = matmu_jit_kernel(M, N, K, block_M, block_N, block_K)
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matmul_kernel = tilelang.compile(program, out_idx=-1, execution_backend="tvm_ffi")
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A = torch.randn(M, K, dtype=torch.float16).cuda()
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B = torch.randn(K, N, dtype=torch.float16).cuda()
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C = matmul_kernel(A, B)
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tilelang.testing.torch_assert_close(C, torch.matmul(A, B), atol=1e-2, rtol=1e-2, max_mismatched_ratio=0.05)
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def test_gemm_jit_kernel_zero_dim():
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run_gemm_jit_kernel(512, 1024, 0, 128, 256, 32)
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if __name__ == "__main__":
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tilelang.testing.main()
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