Ignore past_key_values during GPT-Neo inference
#13521
Merged
Conversation
This file contains hidden or bidirectional Unicode text that may be interpreted or compiled differently than what appears below. To review, open the file in an editor that reveals hidden Unicode characters.
Learn more about bidirectional Unicode characters
Sign up for free
to join this conversation on GitHub.
Already have an account?
Sign in to comment
2 participants
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Outdated suggestions cannot be applied.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.
What does this PR do?
Applies #8633 to GPT-Neo.
I was getting errors like
RuntimeError: Sizes of tensors must match except in dimension 2. Got 20 and 19 (The offending index is 0)during the evaluation step ofTrainer.train()with GPT-Neo.This was fixed in an internal implementation of
GPTNeoForSequenceClassificationby @manuelciosici before an official version of the class was released fortransformers. I converted our code to use thetransformersversion of the class and realized the solution should be upstreamed. I'm just mentioning this here because I don't get all the credit for this patch.This might be needed for other models as well, but I don't know of a good way to figure that out without testing them all.
Who can review?
trainer: @sgugger