mirror of
https://github.com/hiyouga/LLaMA-Factory.git
synced 2025-11-06 02:42:15 +08:00
72 lines
2.7 KiB
Python
72 lines
2.7 KiB
Python
import torch
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from collections import defaultdict
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from typing import TYPE_CHECKING, Dict, Optional, Tuple, Union
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from transformers import BatchEncoding, Trainer
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from trl import DPOTrainer
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from trl.trainer.utils import disable_dropout_in_model
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from llmtuner.extras.constants import IGNORE_INDEX
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from llmtuner.tuner.core.trainer import PeftModelMixin
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if TYPE_CHECKING:
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from transformers import PreTrainedModel
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from llmtuner.hparams import FinetuningArguments
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class DPOPeftTrainer(PeftModelMixin, DPOTrainer):
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def __init__(
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self,
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finetuning_args: "FinetuningArguments",
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model: Union["PreTrainedModel", torch.nn.Module],
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ref_model: Optional[Union["PreTrainedModel", torch.nn.Module]] = None,
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disable_dropout: Optional[bool] = True,
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**kwargs
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):
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if disable_dropout:
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disable_dropout_in_model(model)
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if ref_model is not None:
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disable_dropout_in_model(ref_model)
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self.finetuning_args = finetuning_args
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self.ref_model = ref_model
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self.use_dpo_data_collator = True # hack to avoid warning
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self.label_pad_token_id = IGNORE_INDEX
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self.padding_value = 0
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self.beta = finetuning_args.dpo_beta
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self._stored_metrics = defaultdict(lambda: defaultdict(list))
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Trainer.__init__(self, model=model, **kwargs)
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if not hasattr(self, "accelerator"):
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raise AttributeError("Please update `transformers`.")
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if ref_model is not None:
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if self.is_deepspeed_enabled:
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self.ref_model = self.accelerator._prepare_deepspeed(self.ref_model)
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self.ref_model.eval()
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else:
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self.ref_model = self.accelerator.prepare_model(self.ref_model, evaluation_mode=True)
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def concatenated_forward(
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self,
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model: Optional[torch.nn.Module] = None,
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batch: Optional[Dict[str, torch.Tensor]] = None
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) -> Tuple[torch.FloatTensor, torch.FloatTensor, torch.FloatTensor, torch.FloatTensor]:
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batch_copied = BatchEncoding({k: v.detach().clone() for k, v in batch.items()}) # avoid error
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all_logits = model(
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input_ids=batch_copied["input_ids"],
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attention_mask=batch_copied["attention_mask"],
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return_dict=True
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).logits.to(torch.float32)
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all_logps = self._get_batch_logps(
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all_logits,
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batch["labels"],
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average_log_prob=False
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)
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batch_size = batch["input_ids"].size(0) // 2
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chosen_logps, rejected_logps = all_logps.split(batch_size, dim=0)
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chosen_logits, rejected_logits = all_logits.split(batch_size, dim=0)
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return chosen_logps, rejected_logps, chosen_logits, rejected_logits
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