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[XeTLA] Use stream-k implementation by default for 3072x4096x3072 #2496

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Merged
whitneywhtsang merged 2 commits into from
Oct 17, 2024
Merged

[XeTLA] Use stream-k implementation by default for 3072x4096x3072 #2496

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f7c43d7
[XeTLA] Use stream-k implementation by default for one shape
whitneywhtsang Oct 15, 2024
b2adfdd
address review comment
whitneywhtsang Oct 17, 2024
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4 changes: 4 additions & 0 deletions benchmarks/triton_kernels_benchmark/gemm_benchmark.py
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Expand Up @@ -309,6 +309,10 @@ def benchmark(B, M, N, K, provider):
acc = torch.empty((B, M, N), device='xpu', dtype=torch.float32)
cnt = torch.empty((B, M, N), device='xpu', dtype=torch.int32)
name = f'gemm_shape_{B}_{M}_{K}_{N}'
# FIXME: Use gemm_streamk_benchmark.py when Triton streamk can get
# better performance.
if (B, M, N, K) == (1, 3072, 4096, 3072):
name = 'gemm_streamk_shape_3072_4096_3072'
func = getattr(xetla_kernel, name)
xetla_fn = lambda: func(a, b, c, acc, cnt)
torch_fn = lambda: torch.matmul(a, b).to(torch.float32)
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