39 lines
1.4 KiB
Python
39 lines
1.4 KiB
Python
import torch
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from typing import Dict, List, Optional, Tuple, Union
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from transformers.modeling_utils import PreTrainedModel
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from llmtuner.tuner.core.trainer import PeftTrainer
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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)
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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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