Skip to content

metal : use F32 attention accumulators in FA kernels #13975

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.

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
merged 1 commit into from
Jun 2, 2025

Conversation

ggerganov
Copy link
Member

fix #12433 (comment)

It seems that the attention output lo overflows F16 at large context (more than 32k). This fixes Gemma 3 27B at large contexts with Metal.

@github-actions github-actions bot added ggml changes relating to the ggml tensor library for machine learning Apple Metal https://en.wikipedia.org/wiki/Metal_(API) labels Jun 2, 2025
@ggerganov ggerganov merged commit ea394d7 into master Jun 2, 2025
53 checks passed
@ggerganov ggerganov deleted the gg/metal-fa-acc-f32 branch June 2, 2025 18:33
Sign up for free to join this conversation on GitHub. Already have an account? Sign in to comment
Labels
Apple Metal https://en.wikipedia.org/wiki/Metal_(API) ggml changes relating to the ggml tensor library for machine learning
Projects
None yet
Development

Successfully merging this pull request may close these issues.

Eval bug: Gemma3 <unused32> spam
1 participant