Former-commit-id: 54d5f62d29456a8d9d0c0dd3d0bbfffe48935803
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@@ -8,11 +8,14 @@ from trl import DPOTrainer
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from trl.trainer.utils import disable_dropout_in_model
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from ...extras.constants import IGNORE_INDEX
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from ..utils import create_custom_optimzer
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if TYPE_CHECKING:
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from transformers import PreTrainedModel
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from ...hparams import FinetuningArguments
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class CustomDPOTrainer(DPOTrainer):
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def __init__(
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@@ -21,6 +24,7 @@ class CustomDPOTrainer(DPOTrainer):
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loss_type: Literal["sigmoid", "hinge", "ipo", "kto_pair"],
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ftx_gamma: float,
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model: Union["PreTrainedModel", torch.nn.Module],
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finetuning_args: "FinetuningArguments",
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ref_model: Optional[Union["PreTrainedModel", torch.nn.Module]] = None,
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disable_dropout: bool = True,
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**kwargs,
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@@ -30,6 +34,7 @@ class CustomDPOTrainer(DPOTrainer):
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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.reference_free = False
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self.use_dpo_data_collator = True # hack to avoid warning
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self.generate_during_eval = False # disable at evaluation
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@@ -61,6 +66,13 @@ class CustomDPOTrainer(DPOTrainer):
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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 create_optimizer_and_scheduler(self, num_training_steps: int) -> None:
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self.optimizer = create_custom_optimzer(self.model, self.args, self.finetuning_args, num_training_steps)
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if self.optimizer is None:
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self.create_optimizer()
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self.create_scheduler(num_training_steps=num_training_steps, optimizer=self.optimizer)
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def sft_loss(self, chosen_logits: torch.FloatTensor, chosen_labels: torch.LongTensor) -> torch.Tensor:
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r"""
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Computes supervised cross-entropy loss of given labels under the given logits.
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@@ -7,7 +7,7 @@ from ...extras.constants import IGNORE_INDEX
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from ...extras.ploting import plot_loss
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from ...hparams import ModelArguments
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from ...model import load_model, load_tokenizer
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from ..utils import create_custom_optimzer, create_modelcard_and_push, create_ref_model
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from ..utils import create_modelcard_and_push, create_ref_model
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from .collator import DPODataCollatorWithPadding
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from .trainer import CustomDPOTrainer
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@@ -44,18 +44,17 @@ def run_dpo(
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training_args.remove_unused_columns = False # important for pairwise dataset
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# Initialize our Trainer
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optimizer = create_custom_optimzer(model, dataset, training_args, finetuning_args)
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trainer = CustomDPOTrainer(
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beta=finetuning_args.dpo_beta,
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loss_type=finetuning_args.dpo_loss,
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ftx_gamma=finetuning_args.dpo_ftx,
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finetuning_args=finetuning_args,
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model=model,
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ref_model=ref_model,
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args=training_args,
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tokenizer=tokenizer,
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data_collator=data_collator,
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callbacks=callbacks,
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optimizers=(optimizer, None),
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**split_dataset(dataset, data_args, training_args),
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)
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