00e17a377c
https://github.com/huggingface/trl/pull/1491 Former-commit-id: 3b15d495264b00a4f8716bafea334778874963d7
151 lines
6.6 KiB
Python
151 lines
6.6 KiB
Python
from collections import defaultdict
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from typing import TYPE_CHECKING, Dict, Literal, Optional, Tuple, Union
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import torch
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import torch.nn.functional as F
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from transformers import 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
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from ...hparams import FinetuningArguments
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class CustomORPOTrainer(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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finetuning_args: "FinetuningArguments",
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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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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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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.beta = finetuning_args.orpo_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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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 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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# Borrowed from:
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# https://github.com/huggingface/trl/blob/0ee349dcd43b0f4b3169449f16751c38ac4a609f/trl/trainer/orpo_trainer.py#L592
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def odds_ratio_loss(
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self, chosen_logps: "torch.Tensor", rejected_logps: "torch.Tensor"
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) -> Tuple["torch.Tensor", "torch.Tensor", "torch.Tensor", "torch.Tensor", "torch.Tensor"]:
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r"""
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Computes ORPO's odds ratio (OR) loss.
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Args:
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policy_chosen_logps: Log probabilities of the policy model for the chosen responses. Shape: (batch_size,)
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policy_rejected_logps: Log probabilities of the policy model for the rejected responses. Shape: (batch_size,)
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Returns:
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A tuple of five tensors: (losses, chosen_rewards, rejected_rewards, log_odds_ratio, log_odds_chosen).
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"""
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# Derived from Eqs. (4) and (7) from https://arxiv.org/abs/2403.07691 by using log identities and exp(log(P(y|x)) = P(y|x)
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log_odds = (chosen_logps - rejected_logps) - (
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torch.log1p(-torch.exp(chosen_logps)) - torch.log1p(-torch.exp(rejected_logps))
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)
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ratio = F.logsigmoid(log_odds)
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losses = self.beta * ratio
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chosen_rewards = self.beta * chosen_logps.detach()
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rejected_rewards = self.beta * rejected_logps.detach()
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return losses, chosen_rewards, rejected_rewards, ratio, log_odds
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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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all_logits = model(
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input_ids=batch["input_ids"], attention_mask=batch["attention_mask"], 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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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 ORPO 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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chosen_logps, rejected_logps, chosen_logits, rejected_logits = self.concatenated_forward(model, batch)
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losses, chosen_rewards, rejected_rewards, log_odds_ratio, log_odds_chosen = self.odds_ratio_loss(
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chosen_logps, rejected_logps
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)
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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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sft_loss = self.sft_loss(chosen_logits, chosen_labels)
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batch_loss = (sft_loss - losses).mean()
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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.cpu().mean()
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metrics["{}rewards/rejected".format(prefix)] = rejected_rewards.cpu().mean()
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metrics["{}rewards/accuracies".format(prefix)] = reward_accuracies.cpu().mean()
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metrics["{}rewards/margins".format(prefix)] = (chosen_rewards - rejected_rewards).cpu().mean()
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metrics["{}logps/rejected".format(prefix)] = rejected_logps.detach().cpu().mean()
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metrics["{}logps/chosen".format(prefix)] = chosen_logps.detach().cpu().mean()
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metrics["{}logits/rejected".format(prefix)] = rejected_logits.detach().cpu().mean()
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metrics["{}logits/chosen".format(prefix)] = chosen_logits.detach().cpu().mean()
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metrics["{}sft_loss".format(prefix)] = sft_loss.detach().cpu().mean()
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metrics["{}log_odds_ratio".format(prefix)] = log_odds_ratio.detach().cpu().mean()
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metrics["{}log_odds_chosen".format(prefix)] = log_odds_chosen.detach().cpu().mean()
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return batch_loss, metrics
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