Refactor gptj output tracing to use standardized decorators#44722
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chandan11248 wants to merge 3 commits intohuggingface:mainfrom
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Refactor gptj output tracing to use standardized decorators#44722chandan11248 wants to merge 3 commits intohuggingface:mainfrom
chandan11248 wants to merge 3 commits intohuggingface:mainfrom
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…tuple Migrate the GPT-J model to use the new standardized output collection decorators, replacing manual accumulation of hidden states and attention weights with hook-based capturing. Changes: - Add `_can_record_outputs` to `GPTJPreTrainedModel` mapping hidden_states to GPTJBlock and attentions to GPTJAttention - Add `@capture_outputs` and `@merge_with_config_defaults` to `GPTJModel.forward()` - Add `@can_return_tuple` to all task head models (ForCausalLM, ForSequenceClassification, ForQuestionAnswering) - Remove `output_attentions`, `output_hidden_states`, and `return_dict` parameters from all forward signatures - Remove manual accumulator loops and return_dict branching - Simplify GPTJBlock to return plain `torch.Tensor` instead of tuple - Update attention forward signatures to always return `(attn_output, attn_weights)` without conditional logic Resolves huggingface#43979
The CodeGenBlock is a documented copy of GPTJBlock. This syncs it to match the updated signature after removing output_attentions parameter and simplifying the return type to plain torch.Tensor. Generated via `python utils/check_copies.py --fix_and_overwrite`.
The previous commit auto-synced CodeGenBlock.forward() with the refactored GPTJBlock, but CodeGenModel still passes output_attentions to CodeGenBlock and expects a tuple return. Since the CodeGen model has not been refactored to use the new decorators yet, restore CodeGenBlock's original forward() signature and remove the '# Copied from' directive to decouple it from GPTJBlock until CodeGen gets its own output tracing refactor.
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[For maintainers] Suggested jobs to run (before merge) run-slow: codegen, gptj |
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What does this PR do?
Migrates the GPT-J model to use the new
@capture_outputsand@can_return_tupledecorators for standardized output collection, as described in #43979.Changes
_can_record_outputstoGPTJPreTrainedModel, mapping"hidden_states"→GPTJBlockand"attentions"→GPTJAttention@capture_outputsand@merge_with_config_defaultsdecorators toGPTJModel.forward()@can_return_tupledecorator toGPTJForCausalLM,GPTJForSequenceClassification, andGPTJForQuestionAnsweringoutput_attentions,output_hidden_states, andreturn_dictparameters from allforward()signaturesall_hidden_states,all_self_attentions) andreturn_dictbranching fromGPTJModel.forward()GPTJBlock.forward()to return a plaintorch.Tensorinstead of a tuple(attn_output, attn_weights)with a simplified type annotationNet result: 38 insertions, 108 deletions — cleaner architecture with no manual output collection boilerplate.
Before submitting
Fixes #43979 (gptj model)