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https://github.com/hiyouga/LLaMA-Factory.git
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52 lines
1.8 KiB
Python
52 lines
1.8 KiB
Python
import torch
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from typing import Dict, Sequence, Union
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from .data_collator import DynamicDataCollatorWithPadding
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from .peft_trainer import PeftTrainer
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from .other import get_main_logger
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logger = get_main_logger(__name__)
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class PairwiseDataCollatorWithPadding(DynamicDataCollatorWithPadding):
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r"""
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Data collator for pairwise data.
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"""
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def __call__(self, features: Sequence[Dict[str, Union[torch.Tensor, Sequence[int]]]]) -> Dict[str, torch.Tensor]:
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r"""
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Pads batched data to the longest sequence in the batch.
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We generate 2 * n examples where the first n examples represent chosen examples and
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the last n examples represent rejected examples.
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"""
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features = [{"input_ids": feature[key]} for key in ("accept_ids", "reject_ids") for feature in features]
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return super().__call__(features)
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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(self, model, inputs, return_outputs=False):
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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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"""
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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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outputs = {"r_accept": r_accept, "r_reject": r_reject}
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return (loss, outputs) if return_outputs else loss
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