Skip to content

Fix typo in flops calculation for local attention #1883

New issue

Have a question about this project? Sign up for a free GitHub account to open an issue and contact its maintainers and the community.

Sign up for GitHub

By clicking “Sign up for GitHub”, you agree to our terms of service and privacy statement. We’ll occasionally send you account related emails.

Already on GitHub? Sign in to your account

Merged
tridao merged 1 commit into from
Sep 13, 2025
+2 −2
Merged

Fix typo in flops calculation for local attention #1883

Show file tree
Hide file tree
Changes from all commits
Commits
Show all changes
1 commit
Select commit
File filter

Filter by extension

Filter by extension
Conversations
Failed to load comments.
Loading
Jump to
Jump to file
Failed to load files.
Loading
Diff view
Diff view
2 changes: 1 addition & 1 deletion benchmarks/benchmark_attn.py
View file
Open in desktop
Original file line number Diff line number Diff line change
Expand Up @@ -70,7 +70,7 @@ def flops(batch, nheads, seqlen_q, seqlen_k, headdim, headdim_v, causal=False, w
else:
row_idx = torch.arange(seqlen_q, device='cuda')
col_left = torch.maximum(row_idx + seqlen_k - seqlen_q - window_size[0], torch.tensor(0)) if window_size[0] is not None else torch.zeros_like(row_idx)
col_right = torch.minimum(row_idx + seqlen_k - seqlen_q - window_size[1], torch.tensor(seqlen_k - 1)) if window_size[1] is not None else torch.full_like(row_idx, seqlen_k - 1)
col_right = torch.minimum(row_idx + seqlen_k - seqlen_q + window_size[1], torch.tensor(seqlen_k - 1)) if window_size[1] is not None else torch.full_like(row_idx, seqlen_k - 1)
avg_seqlen = (col_right - col_left + 1).float().mean().item()
return batch * nheads * 2 * seqlen_q * avg_seqlen * (headdim + headdim_v)

Expand Down
2 changes: 1 addition & 1 deletion hopper/benchmark_attn.py
View file
Open in desktop
Original file line number Diff line number Diff line change
Expand Up @@ -68,7 +68,7 @@ def flops(batch, nheads, seqlen_q, seqlen_k, headdim, headdim_v, causal=False, w
else:
row_idx = torch.arange(seqlen_q, device='cuda')
col_left = torch.maximum(row_idx + seqlen_k - seqlen_q - window_size[0], torch.tensor(0))
col_right = torch.minimum(row_idx + seqlen_k - seqlen_q - window_size[1], torch.tensor(seqlen_k - 1))
col_right = torch.minimum(row_idx + seqlen_k - seqlen_q + window_size[1], torch.tensor(seqlen_k - 1))
avg_seqlen = (col_right - col_left + 1).float().mean().item()
return batch * nheads * 2 * seqlen_q * avg_seqlen * (headdim + headdim_v)

Expand Down