Fix UnboundLocalError in shard_and_distribute_module for replicated parameters#45675
Merged
3outeille merged 2 commits intohuggingface:mainfrom Apr 28, 2026
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. |
3outeille
approved these changes
Apr 28, 2026
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
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Outdated suggestions cannot be applied.This suggestion has been applied or marked resolved.Suggestions cannot be applied from pending reviews.Suggestions cannot be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.
When a parameter has no entry in the model's TP plan (e.g. the score head in
LlamaForSequenceClassification),shard_and_distribute_modulecorrectly falls through to the replicate branch but then unconditionally callstp_layer.update_module_attributes(...). In that path,tp_layeris unbound, causing anUnboundLocalError. Initializetp_layer = Noneand guard the call.Repro:
LlamaForSequenceClassification.from_pretrained(..., tp_plan="auto")under multi-GPU launch.Found while validating accelerate on 8× AMD MI300X. Not AMD-specific, but discovered while fixing
tests/tp/test_tp.py::TPIntegrationTest::test_working_of_tp.