fix: apply torchfix to set weights_only=True#3497
Merged
Merged
Conversation
|
The docs for this PR live here. All of your documentation changes will be reflected on that endpoint. The docs are available until 30 days after the last update. |
| else: | ||
| kwargs.pop("weights_only", None) | ||
| loaded_obj = torch.load(f, map_location=map_location, **kwargs) | ||
| loaded_obj = torch.load(f, map_location=map_location, **kwargs, weights_only=True) |
Member
There was a problem hiding this comment.
Some tests are failing with
TypeError: torch.serialization.load() got multiple values for keyword argument 'weights_only'
Probably, weights_only needs to be added to kwargs here.
Contributor
Author
There was a problem hiding this comment.
Oops, sorry. Thanks for the catch!
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
4 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Outdated suggestions cannot be applied.This suggestion has been applied or marked resolved.Suggestions cannot be applied from pending reviews.Suggestions cannot be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.
Since
acceleratedepends on PyTorch >= 2.0 at the minimum, this was introduced in 2.0 which is backward compatible since then. 🎉