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use ceil_div in cutlass block scaling shape check (#17918)

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Lain
2025-05-16 03:02:58 -07:00
committed by GitHub
parent 390ec88905
commit e23564cb70
3 changed files with 62 additions and 25 deletions
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12
benchmarks/cutlass_benchmarks/w8a8_benchmarks.py
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@@ -115,8 +115,16 @@ def bench_fp8(
a_cont = a.contiguous()
scale_a = torch.tensor(1.0, device="cuda", dtype=torch.float32)
scale_b = torch.tensor(1.0, device="cuda", dtype=torch.float32)
block_scale_a = torch.rand((m, k // 128), device="cuda", dtype=torch.float32)
block_scale_b = torch.rand((k // 128, n // 128), device="cuda", dtype=torch.float32)
def ceil_div(x: int, y: int) -> int:
return (x + y - 1) // y
block_scale_a = torch.rand(
(m, ceil_div(k, 128)), device="cuda", dtype=torch.float32
)
block_scale_b = torch.rand(
ceil_div(k, 128), ceil_div(n, 128), device="cuda", dtype=torch.float32
)
block_scale_a_M_major = block_scale_a.t().contiguous().t()
block_scale_b_K_major = block_scale_b.t().contiguous().t()
bias = torch.zeros((n,), device="cuda", dtype=torch.bfloat16)