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[deps] update to transformers 4.52 (#8125)

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hoshi-hiyouga
2025-05-21 05:16:18 +08:00
committed by GitHub
parent 56926d76f9
commit 9ae17cd173
28 changed files with 365 additions and 109 deletions
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4
src/llamafactory/train/dpo/trainer.py
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@@ -121,11 +121,11 @@ class CustomDPOTrainer(DPOTrainer):
return super().create_scheduler(num_training_steps, optimizer)
@override
def _get_train_sampler(self) -> Optional["torch.utils.data.Sampler"]:
def _get_train_sampler(self, *args, **kwargs) -> Optional["torch.utils.data.Sampler"]:
if self.finetuning_args.disable_shuffling:
return torch.utils.data.SequentialSampler(self.train_dataset)
return super()._get_train_sampler()
return super()._get_train_sampler(*args, **kwargs)
@override
def get_batch_samples(self, *args, **kwargs):

5
src/llamafactory/train/kto/trainer.py
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@@ -34,7 +34,6 @@ from ..trainer_utils import create_custom_optimizer, create_custom_scheduler, ge
if TYPE_CHECKING:
import torch.utils.data
from transformers import PreTrainedModel, ProcessorMixin
from ...hparams import FinetuningArguments
@@ -119,12 +118,12 @@ class CustomKTOTrainer(KTOTrainer):
return super().create_scheduler(num_training_steps, optimizer)
@override
def _get_train_sampler(self) -> Optional["torch.utils.data.Sampler"]:
def _get_train_sampler(self, *args, **kwargs) -> Optional["torch.utils.data.Sampler"]:
r"""Replace the sequential sampler of KTO Trainer created by trl with the random sampler."""
if self.finetuning_args.disable_shuffling:
return torch.utils.data.SequentialSampler(self.train_dataset)
return Trainer._get_train_sampler(self)
return Trainer._get_train_sampler(self, *args, **kwargs)
@override
def get_batch_samples(self, *args, **kwargs):

4
src/llamafactory/train/pt/trainer.py
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@@ -70,11 +70,11 @@ class CustomTrainer(Trainer):
return super().create_scheduler(num_training_steps, optimizer)
@override
def _get_train_sampler(self) -> Optional["torch.utils.data.Sampler"]:
def _get_train_sampler(self, *args, **kwargs) -> Optional["torch.utils.data.Sampler"]:
if self.finetuning_args.disable_shuffling:
return torch.utils.data.SequentialSampler(self.train_dataset)
return super()._get_train_sampler()
return super()._get_train_sampler(*args, **kwargs)
@override
def compute_loss(self, model, inputs, *args, **kwargs):

4
src/llamafactory/train/rm/trainer.py
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@@ -78,11 +78,11 @@ class PairwiseTrainer(Trainer):
return super().create_scheduler(num_training_steps, optimizer)
@override
def _get_train_sampler(self) -> Optional["torch.utils.data.Sampler"]:
def _get_train_sampler(self, *args, **kwargs) -> Optional["torch.utils.data.Sampler"]:
if self.finetuning_args.disable_shuffling:
return torch.utils.data.SequentialSampler(self.train_dataset)
return super()._get_train_sampler()
return super()._get_train_sampler(*args, **kwargs)
@override
def compute_loss(

4
src/llamafactory/train/sft/trainer.py
View File

@@ -92,11 +92,11 @@ class CustomSeq2SeqTrainer(Seq2SeqTrainer):
return super().create_scheduler(num_training_steps, optimizer)
@override
def _get_train_sampler(self) -> Optional["torch.utils.data.Sampler"]:
def _get_train_sampler(self, *args, **kwargs) -> Optional["torch.utils.data.Sampler"]:
if self.finetuning_args.disable_shuffling:
return torch.utils.data.SequentialSampler(self.train_dataset)
return super()._get_train_sampler()
return super()._get_train_sampler(*args, **kwargs)
@override
def compute_loss(self, model, inputs, *args, **kwargs):