Skip to content

slimAttn: Experiments section for Whisper, shorter section 1 - #29

Open
Kayvan-Zahiri wants to merge 1 commit into
OpenMachine-ai:mainfrom
Kayvan-Zahiri:docs/experiments-section
Open

Kayvan-Zahiri wants to merge 1 commit into
OpenMachine-ai:mainfrom
Kayvan-Zahiri:docs/experiments-section

Conversation

@Kayvan-Zahiri

Copy link
Copy Markdown
Contributor

Per your note: my section 1 text is now one paragraph, and the Whisper details are in a new Experiments section.

Corrections to #28:

  • |det W_K| is below 1e-230 in every cross-attention layer of Whisper tiny, small, medium and large-v3, not "nowhere near zero".
  • "Destroys the model at full precision" was never measured for the K-cache in every layer; I only had a logit error (0.54).
  • slimAttn: conditioning, not invertibility, and the V-cache direction #28 credited slimAttn_whisper.py for its numbers, but the logit errors came from a separate script on random encoder output. The new table uses LibriSpeech.
  • slimAttn: conditioning, not invertibility, and the V-cache direction #28 said to choose K or V per layer by condition number, but only compared that with the K-cache in every layer. Against the V-cache in every layer it gives a larger logit error in 7 of 8 cases (four models, two sets of 4 speakers).

Appendix C already had W_VK, so I dropped my duplicate equation. Adds a LibriSpeech reference.

The logit table and determinant row aren't reproducible from the repo yet. Appendix tables renumber to 10-12, doc/slimAttn.pdf isn't regenerated, and in my tectonic build the new tables float after the Conclusion, like Table 7.

Moves the Whisper detail out of section 1 into a new Experiments
section before the Conclusion and corrects OpenMachine-ai#28: |det W_K| is below
1e-230 in every decoder cross-attention layer of Whisper tiny, small,
medium and large-v3 rather than "nowhere near zero", and the merged
logit errors came from random encoder output. On LibriSpeech, choosing
K or V per layer by condition number gives a larger logit error than
the V-cache in every layer in 7 of 8 cases (four models, each on two
sets of 4 speakers).

Drops the duplicated V-cache equation in favor of appendix C, labels
appendices C and F, and adds a LibriSpeech reference.
Sign up for free to join this conversation on GitHub. Already have an account? Sign in to comment

Labels

None yet

Projects

None yet

Development

Successfully merging this pull request may close these issues.

1 participant