Fix batch size in evaluation loop #16763
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
|
The documentation is not available anymore as the PR was closed or merged. |
LysandreJik
approved these changes
Apr 13, 2022
src/transformers/trainer.py
Outdated
| # Number of losses has been rounded to a multiple of batch_size and in a distributed training, the number of | ||
| # samplers has been rounded to a multiple of batch_size, so we truncate. | ||
| if all_losses is not None: | ||
| print(all_losses) |
There was a problem hiding this comment.
Oops! Thanks for catching.
elusenji
pushed a commit
to elusenji/transformers
that referenced
this pull request
Jun 12, 2022
* Fix batch size in evaluation loop * remove debug statement
Sign up for free
to join this conversation on GitHub.
Already have an account?
Sign in to comment
3 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Outdated suggestions cannot be applied.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?
The batch size used in the evaluation loop is wrong: it's using the per device batch size, which is different from the actual batch size when using DataParallel with more than one GPU. As a result, the
test_evaluatetest is failing for 2 GPUs (see #16716).This PR fixes that.