speculative: Ensure draft and target model vocab matches #3812
Add this suggestion to a batch that can be applied as a single commit.
This suggestion is invalid because no changes were made to the code.
Suggestions cannot be applied while the pull request is closed.
Suggestions cannot be applied while viewing a subset of changes.
Only one suggestion per line can be applied in a batch.
Add this suggestion to a batch that can be applied as a single commit.
Applying suggestions on deleted lines is not supported.
You must change the existing code in this line in order to create a valid suggestion.
Outdated suggestions cannot be applied.
This suggestion has been applied or marked resolved.
Suggestions cannot be applied from pending reviews.
Suggestions cannot be applied on multi-line comments.
Suggestions cannot be applied while the pull request is queued to merge.
Suggestion cannot be applied right now. Please check back later.
It's currently possible to shoot yourself in the foot by trying to speculate using a draft model with vocab that doesn't match the target, and weird stuff will happen in that case. Naturally the draft model will fail 100% of the time, but looking at the logs it'll appear that the draft is just generating random unrelated stuff (even draft candidates with NaN as the probability).
When there's a mismatch you'll now get an error like:
or
This approach may be too strict, since one might possibly want to use a draft model with a few special tokens that differ. One way to deal with that might be to just say there can be X mismatches at most.
strcmpon every entry might also be overkill. On my system, checking 32,000 entries is instant but doing something like looping with a step of 10 would probably be fine also.