mirror of
https://github.com/hiyouga/LLaMA-Factory.git
synced 2025-08-23 22:32:54 +08:00
69 lines
2.5 KiB
Python
69 lines
2.5 KiB
Python
import os
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import json
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import torch
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from typing import Dict, List, Optional, Tuple, Union
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from transformers.trainer import PredictionOutput
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from transformers.modeling_utils import PreTrainedModel
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from llmtuner.extras.logging import get_logger
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from llmtuner.tuner.core.trainer import PeftTrainer
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logger = get_logger(__name__)
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class PairwisePeftTrainer(PeftTrainer):
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r"""
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Inherits PeftTrainer to compute pairwise loss.
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"""
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def __init__(self, *args, **kwargs):
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super().__init__(*args, **kwargs)
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self.can_return_loss = True # override property to return eval_loss
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def compute_loss(
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self,
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model: PreTrainedModel,
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inputs: Dict[str, torch.Tensor],
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return_outputs: Optional[bool] = False
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) -> Union[torch.Tensor, Tuple[torch.Tensor, List[torch.Tensor]]]:
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r"""
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Computes pairwise loss. The first n examples are chosen and the last n examples are rejected.
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We use score on the EOS token to represent reward of the whole sentence.
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Subclass and override to inject custom behavior. It should not be directly used by external scripts.
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Note that the first element will be removed from the output tuple.
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See: https://github.com/huggingface/transformers/blob/v4.30.2/src/transformers/trainer.py#L3509
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"""
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batch_size = inputs["input_ids"].size(0) // 2
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_, _, values = model(**inputs, output_hidden_states=True, return_dict=True)
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r_accept, r_reject = values[:, -1].split(batch_size, dim=0)
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loss = -torch.log(torch.sigmoid(r_accept - r_reject)).mean()
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return (loss, [loss, r_accept, r_reject]) if return_outputs else loss
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def save_predictions(
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self,
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predict_results: PredictionOutput
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) -> None:
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r"""
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Saves model predictions to `output_dir`.
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A custom behavior that not contained in Seq2SeqTrainer.
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"""
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if not self.is_world_process_zero():
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return
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output_prediction_file = os.path.join(self.args.output_dir, "generated_predictions.jsonl")
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logger.info(f"Saving prediction results to {output_prediction_file}")
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acc_scores, rej_scores = predict_results.predictions
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with open(output_prediction_file, "w", encoding="utf-8") as writer:
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res: List[str] = []
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for acc_score, rej_score in zip(acc_scores, rej_scores):
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res.append(json.dumps({"accept": round(float(acc_score), 2), "reject": round(float(rej_score), 2)}))
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writer.write("\n".join(res))
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