mirror of
https://github.com/tile-ai/tilelang.git
synced 2026-11-02 08:33:19 +08:00
Liu YunuoandLeiWang1999
b9a5907a20
* [Example] Migrate eligible examples to eager style * fix wrong shape annotation in gemm_split_k examples * fix incorrect dtype in gemm_streamk example * fix * revert some changes * Refactor attention functions to use new parameter structures and improve readability. Updated `example_amd_flash_attn_bwd.py` and `example_amd_flash_attn_fwd.py` to define input parameters more clearly. Enhanced shared memory usage in `flashattn_fwd` and `flashattn_sink` functions across various examples, ensuring compatibility with new configurations. Adjusted kernel definitions and added necessary checks for GPU capabilities. * lint fix * FIX --------- Co-authored-by: LeiWang1999 <leiwang1999@outlook.com>
59 lines
1.6 KiB
Python
59 lines
1.6 KiB
Python
import tilelang
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import tilelang.language as T
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# use pass_configs to enable layout visualization
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@tilelang.jit(
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pass_configs={
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tilelang.PassConfigKey.TL_LAYOUT_VISUALIZATION_ENABLE: True,
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tilelang.PassConfigKey.TL_LAYOUT_VISUALIZATION_FORMATS: "svg",
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}
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)
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def matmul(A, B, block_M, block_N, block_K, dtype=T.float16, accum_dtype=T.float32):
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M, N, K = T.const("M, N, K")
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A: T.Tensor((M, K), dtype)
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B: T.Tensor((K, N), dtype)
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C = T.empty((M, N), dtype)
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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), 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)
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T.copy(C_local, C[by * block_M, bx * block_N])
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return C
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def main():
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import torch
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a = torch.randn(128, 128).cuda().half()
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b = torch.randn(128, 128).cuda().half()
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c = matmul(a, b, 32, 32, 32)
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ref_c = a @ b
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torch.testing.assert_close(c, ref_c, rtol=1e-2, atol=1e-2)
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print("All check passed.")
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# print the layout visualization result and save figures to ./tmp.
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"""
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C_local inferenced layout:
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Shape: [32, 32] -> [8]
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Thread: _j // 16 * 64 + _i // 16 * 32 + _i % 8 * 4 + _j % 8 // 2
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Index: [_j % 16 // 8 * 4 + _i % 16 // 8 * 2 + _j % 2]
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"""
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if __name__ == "__main__":
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main()
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