aurora

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import torch
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from dataclasses import dataclass
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from typing import Any, Dict, Sequence
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from transformers import DataCollatorWithPadding
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@dataclass
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class PairwiseDataCollatorWithPadding(DataCollatorWithPadding):
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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, Any]]) -> 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 = [
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            {
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                "input_ids": feature["prompt_ids"] + feature[key],
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                "attention_mask": [1] * (len(feature["prompt_ids"]) + len(feature[key]))
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            }
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            for key in ("chosen_ids", "rejected_ids") for feature in features
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        ]
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        return super().__call__(features)
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