mirror of
https://github.com/hiyouga/LLaMA-Factory.git
synced 2025-11-05 18:32:14 +08:00
88 lines
3.2 KiB
Python
88 lines
3.2 KiB
Python
from dataclasses import dataclass
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from typing import Any, Dict, List, Sequence, Tuple
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import torch
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from transformers import DataCollatorForSeq2Seq
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@dataclass
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class PairwiseDataCollatorWithPadding(DataCollatorForSeq2Seq):
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r"""
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Data collator for pairwise data.
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"""
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def _pad_labels(self, batch: torch.Tensor, positions: List[Tuple[int, int]]) -> torch.Tensor:
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r"""
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Masks out the input ids except for the responses.
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"""
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padded_labels = []
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for feature, (prompt_len, answer_len) in zip(batch, positions):
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if self.tokenizer.padding_side == "left":
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start, end = feature.size(0) - answer_len, feature.size(0)
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else:
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start, end = prompt_len, prompt_len + answer_len
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padded_tensor = self.label_pad_token_id * torch.ones_like(feature)
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padded_tensor[start:end] = feature[start:end]
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padded_labels.append(padded_tensor)
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return torch.stack(padded_labels, dim=0).contiguous() # in contiguous memory
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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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concatenated_features = []
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label_positions = []
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for key in ("chosen_ids", "rejected_ids"):
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for feature in features:
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prompt_len, answer_len = len(feature["prompt_ids"]), len(feature[key])
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concatenated_features.append(
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{
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"input_ids": feature["prompt_ids"] + feature[key],
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"attention_mask": [1] * (prompt_len + answer_len),
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}
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)
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label_positions.append((prompt_len, answer_len))
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batch = super().__call__(concatenated_features)
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batch["labels"] = self._pad_labels(batch["input_ids"], label_positions)
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return batch
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@dataclass
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class KTODataCollatorWithPadding(DataCollatorForSeq2Seq):
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r"""
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Data collator for KTO 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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target_features = []
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kl_features = []
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kto_tags = []
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for feature in features:
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target_features.append(
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{
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"input_ids": feature["input_ids"],
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"attention_mask": feature["attention_mask"],
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"labels": feature["labels"],
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}
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)
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kl_features.append(
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{
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"input_ids": feature["kl_input_ids"],
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"attention_mask": feature["kl_attention_mask"],
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"labels": feature["kl_labels"],
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}
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)
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kto_tags.append(feature["kto_tags"])
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batch = super().__call__(target_features)
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kl_batch = super().__call__(kl_features)
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batch["kl_input_ids"] = kl_batch["input_ids"]
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batch["kl_attention_mask"] = kl_batch["attention_mask"]
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batch["kl_labels"] = kl_batch["labels"]
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batch["kto_tags"] = torch.tensor(kto_tags)
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return batch
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