mirror of
https://github.com/hiyouga/LLaMA-Factory.git
synced 2025-10-14 23:58:11 +08:00
186 lines
7.9 KiB
Python
186 lines
7.9 KiB
Python
from collections import defaultdict
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from contextlib import nullcontext
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from types import MethodType
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from typing import TYPE_CHECKING, Dict, Literal, Optional, Tuple, Union
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import torch
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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 ...extras.constants import IGNORE_INDEX
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from ..utils import create_custom_optimzer, create_custom_scheduler
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if TYPE_CHECKING:
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from transformers import PreTrainedModel, ProcessorMixin
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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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self,
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model: Union["PreTrainedModel", torch.nn.Module],
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ref_model: Optional[Union["PreTrainedModel", torch.nn.Module]],
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finetuning_args: "FinetuningArguments",
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processor: Optional["ProcessorMixin"],
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disable_dropout: 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.processor = processor
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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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self.label_pad_token_id = IGNORE_INDEX
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self.padding_value = 0
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self.is_encoder_decoder = model.config.is_encoder_decoder
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self.precompute_ref_log_probs = False
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self._precomputed_train_ref_log_probs = False
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self._precomputed_eval_ref_log_probs = False
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self._peft_has_been_casted_to_bf16 = False
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self.ref_model = ref_model
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self.beta = finetuning_args.dpo_beta
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self.label_smoothing = finetuning_args.dpo_label_smoothing
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self.loss_type = finetuning_args.dpo_loss
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self.ftx_gamma = finetuning_args.dpo_ftx
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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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if not (
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getattr(ref_model, "is_loaded_in_8bit", False) or getattr(ref_model, "is_loaded_in_4bit", False)
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): # quantized models are already set on the correct device
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self.ref_model = self._prepare_deepspeed(self.ref_model)
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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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if finetuning_args.use_badam:
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from badam import clip_grad_norm_for_sparse_tensor
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self.accelerator.clip_grad_norm_ = MethodType(clip_grad_norm_for_sparse_tensor, self.accelerator)
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def create_optimizer(self) -> "torch.optim.Optimizer":
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if self.optimizer is None:
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self.optimizer = create_custom_optimzer(self.model, self.args, self.finetuning_args)
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return super().create_optimizer()
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def create_scheduler(
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self, num_training_steps: int, optimizer: Optional["torch.optim.Optimizer"] = None
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) -> "torch.optim.lr_scheduler.LRScheduler":
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create_custom_scheduler(self.args, num_training_steps, optimizer)
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return super().create_scheduler(num_training_steps, optimizer)
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def _save(self, output_dir: Optional[str] = None, state_dict: Optional[Dict[str, "torch.Tensor"]] = None) -> None:
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super()._save(output_dir, state_dict)
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if self.processor is not None:
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output_dir = output_dir if output_dir is not None else self.args.output_dir
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getattr(self.processor, "image_processor").save_pretrained(output_dir)
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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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Returns:
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A tensor of shape (batch_size,) containing the cross-entropy loss of each samples.
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"""
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all_logps = self.get_batch_logps(chosen_logits, chosen_labels, average_log_prob=True)
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return -all_logps
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def concatenated_forward(
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self, model: "PreTrainedModel", batch: Dict[str, "torch.Tensor"]
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) -> Tuple["torch.Tensor", "torch.Tensor", "torch.Tensor", "torch.Tensor"]:
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r"""
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Computes the sum log probabilities of the labels under the given logits if loss_type != IPO.
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Otherwise the average log probabilities.
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"""
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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: "torch.Tensor" = 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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use_cache=False,
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).logits.to(torch.float32)
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all_logps = self.get_batch_logps(
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logits=all_logits,
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labels=batch_copied["labels"],
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average_log_prob=(self.loss_type == "ipo"),
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is_encoder_decoder=self.is_encoder_decoder,
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label_pad_token_id=self.label_pad_token_id,
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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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def get_batch_loss_metrics(
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self,
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model: "PreTrainedModel",
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batch: Dict[str, "torch.Tensor"],
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train_eval: Literal["train", "eval"] = "train",
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) -> Tuple["torch.Tensor", Dict[str, "torch.Tensor"]]:
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r"""
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Computes the DPO loss and other metrics for the given batch of inputs for train or test.
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"""
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metrics = {}
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(
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policy_chosen_logps,
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policy_rejected_logps,
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policy_chosen_logits,
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policy_rejected_logits,
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) = self.concatenated_forward(model, batch)
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with torch.no_grad():
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if self.ref_model is None:
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ref_model = self.model
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ref_context = self.accelerator.unwrap_model(self.model).disable_adapter()
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else:
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ref_model = self.ref_model
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ref_context = nullcontext()
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with ref_context:
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(
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reference_chosen_logps,
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reference_rejected_logps,
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_,
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_,
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) = self.concatenated_forward(ref_model, batch)
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losses, chosen_rewards, rejected_rewards = self.dpo_loss(
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policy_chosen_logps,
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policy_rejected_logps,
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reference_chosen_logps,
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reference_rejected_logps,
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)
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if self.ftx_gamma > 1e-6:
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batch_size = batch["input_ids"].size(0) // 2
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chosen_labels, _ = batch["labels"].split(batch_size, dim=0)
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losses += self.ftx_gamma * self.sft_loss(policy_chosen_logits, chosen_labels)
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reward_accuracies = (chosen_rewards > rejected_rewards).float()
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prefix = "eval_" if train_eval == "eval" else ""
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metrics["{}rewards/chosen".format(prefix)] = chosen_rewards.mean().cpu()
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metrics["{}rewards/rejected".format(prefix)] = rejected_rewards.mean().cpu()
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metrics["{}rewards/accuracies".format(prefix)] = reward_accuracies.mean().cpu()
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metrics["{}rewards/margins".format(prefix)] = (chosen_rewards - rejected_rewards).mean().cpu()
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metrics["{}logps/rejected".format(prefix)] = policy_rejected_logps.detach().mean().cpu()
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metrics["{}logps/chosen".format(prefix)] = policy_chosen_logps.detach().mean().cpu()
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metrics["{}logits/rejected".format(prefix)] = policy_rejected_logits.detach().mean().cpu()
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metrics["{}logits/chosen".format(prefix)] = policy_chosen_logits.detach().mean().cpu()
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return losses.mean(), metrics
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