[models] - fix: deprecated AdamW - #82
Merged
Merged
Conversation
… cleanup - Unify the `weight_decay_rate` parameter name to `weight_decay` across various modules to align with PyTorch naming conventions - Remove redundant AdamW import from transformers and replace with torch.optim.AdamW for consistency - Optimize the retrieval of learning rate to reduce duplication in optimizer configurations - Clean up lr_scheduler configurations in `configure_optimizers` methods to use consistent key naming
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
1 participant
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Outdated suggestions cannot be applied.This suggestion has been applied or marked resolved.Suggestions cannot be applied from pending reviews.Suggestions cannot be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.
Description
Replaces deprecated
transformers.AdamWwithtorch.optim.AdamWand standardizes optimizer parameter naming across the codebase. Renamesweight_decay_ratetoweight_decayto align with PyTorch conventions, extracts learning rate retrieval logic to reduce duplication, and standardizes lr_scheduler configuration dictionary formatting.Risk
Low - backward-compatible changes that align with PyTorch standards. All optimizer configurations maintain the same behavior with updated parameter names.
Tests
Existing tests cover optimizer configurations.