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[trainer] fix gen_kwarg to eval during training (#5451)

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* Correctly pass gen_kwarg to eval during model runs

* fix

* fix

---------

Co-authored-by: hiyouga <hiyouga@buaa.edu.cn>
Former-commit-id: 11eac71c13cd432322b69ae74a3b8fa17af31bc4
This commit is contained in:
Billy Cao 2025-02-13 02:35:06 +08:00 committed by GitHub
parent 0ad9f7f058
commit 48173b606c
2 changed files with 15 additions and 7 deletions
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9
src/llamafactory/train/sft/trainer.py
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@ -49,7 +49,11 @@ class CustomSeq2SeqTrainer(Seq2SeqTrainer):
""" """
def __init__( def __init__(
self, finetuning_args: "FinetuningArguments", processor: Optional["ProcessorMixin"], **kwargs self,
finetuning_args: "FinetuningArguments",
processor: Optional["ProcessorMixin"],
gen_kwargs: Optional[Dict[str, Any]] = None,
**kwargs,
) -> None: ) -> None:
if is_transformers_version_greater_than("4.46"): if is_transformers_version_greater_than("4.46"):
kwargs["processing_class"] = kwargs.pop("tokenizer") kwargs["processing_class"] = kwargs.pop("tokenizer")
@ -58,6 +62,9 @@ class CustomSeq2SeqTrainer(Seq2SeqTrainer):
super().__init__(**kwargs) super().__init__(**kwargs)
self.finetuning_args = finetuning_args self.finetuning_args = finetuning_args
if gen_kwargs is not None:
# https://github.com/huggingface/transformers/blob/v4.45.0/src/transformers/trainer_seq2seq.py#L287
self._gen_kwargs = gen_kwargs
if processor is not None: if processor is not None:
self.add_callback(SaveProcessorCallback(processor)) self.add_callback(SaveProcessorCallback(processor))

13
src/llamafactory/train/sft/workflow.py
View File

@ -78,6 +78,12 @@ def run_sft(
metric_module["compute_metrics"] = ComputeAccuracy() metric_module["compute_metrics"] = ComputeAccuracy()
metric_module["preprocess_logits_for_metrics"] = eval_logit_processor metric_module["preprocess_logits_for_metrics"] = eval_logit_processor
# Keyword arguments for `model.generate`
gen_kwargs = generating_args.to_dict(obey_generation_config=True)
gen_kwargs["eos_token_id"] = [tokenizer.eos_token_id] + tokenizer.additional_special_tokens_ids
gen_kwargs["pad_token_id"] = tokenizer.pad_token_id
gen_kwargs["logits_processor"] = get_logits_processor()
# Initialize our Trainer # Initialize our Trainer
trainer = CustomSeq2SeqTrainer( trainer = CustomSeq2SeqTrainer(
model=model, model=model,
@ -85,17 +91,12 @@ def run_sft(
finetuning_args=finetuning_args, finetuning_args=finetuning_args,
data_collator=data_collator, data_collator=data_collator,
callbacks=callbacks, callbacks=callbacks,
gen_kwargs=gen_kwargs,
**dataset_module, **dataset_module,
**tokenizer_module, **tokenizer_module,
**metric_module, **metric_module,
) )
# Keyword arguments for `model.generate`
gen_kwargs = generating_args.to_dict(obey_generation_config=True)
gen_kwargs["eos_token_id"] = [tokenizer.eos_token_id] + tokenizer.additional_special_tokens_ids
gen_kwargs["pad_token_id"] = tokenizer.pad_token_id
gen_kwargs["logits_processor"] = get_logits_processor()
# Training # Training
if training_args.do_train: if training_args.do_train:
train_result = trainer.train(resume_from_checkpoint=training_args.resume_from_checkpoint) train_result = trainer.train(resume_from_checkpoint=training_args.resume_from_checkpoint)