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Correctly pass gen_kwarg to eval during model runs #5451

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merged 3 commits into from
Feb 12, 2025
Merged

Correctly pass gen_kwarg to eval during model runs #5451

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ee7e0a7
Correctly pass gen_kwarg to eval during model runs
aliencaocao Sep 16, 2024
6eb7ece
fix
hiyouga Feb 12, 2025
0cc5654
fix
hiyouga Feb 12, 2025
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9 changes: 8 additions & 1 deletion src/llamafactory/train/sft/trainer.py
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Original file line number Diff line number Diff line change
Expand Up @@ -49,7 +49,11 @@ class CustomSeq2SeqTrainer(Seq2SeqTrainer):
"""

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:
if is_transformers_version_greater_than("4.46"):
kwargs["processing_class"] = kwargs.pop("tokenizer")
Expand All @@ -58,6 +62,9 @@ def __init__(

super().__init__(**kwargs)
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:
self.add_callback(SaveProcessorCallback(processor))
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13 changes: 7 additions & 6 deletions src/llamafactory/train/sft/workflow.py
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Original file line number Diff line number Diff line change
Expand Up @@ -78,24 +78,25 @@ def run_sft(
metric_module["compute_metrics"] = ComputeAccuracy()
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
trainer = CustomSeq2SeqTrainer(
model=model,
args=training_args,
finetuning_args=finetuning_args,
data_collator=data_collator,
callbacks=callbacks,
gen_kwargs=gen_kwargs,
**dataset_module,
**tokenizer_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
if training_args.do_train:
train_result = trainer.train(resume_from_checkpoint=training_args.resume_from_checkpoint)
Expand Down