from collections import defaultdict from contextlib import nullcontext from types import MethodType from typing import TYPE_CHECKING, Dict, List, Optional, Tuple, Union import torch from transformers import Trainer from trl import KTOTrainer from trl.trainer.utils import disable_dropout_in_model from ...extras.constants import IGNORE_INDEX from ..utils import create_custom_optimzer, create_custom_scheduler if TYPE_CHECKING: from transformers import PreTrainedModel from ...hparams import FinetuningArguments class CustomKTOTrainer(KTOTrainer): def __init__( self, model: Union["PreTrainedModel", torch.nn.Module], ref_model: Optional[Union["PreTrainedModel", torch.nn.Module]], finetuning_args: "FinetuningArguments", disable_dropout: bool = True, **kwargs, ): if disable_dropout: disable_dropout_in_model(model) if ref_model is not None: disable_dropout_in_model(ref_model) self.finetuning_args = finetuning_args self.reference_free = False self.use_dpo_data_collator = True # hack to avoid warning self.generate_during_eval = False # disable at evaluation self.label_pad_token_id = IGNORE_INDEX self.padding_value = 0 self.is_encoder_decoder = model.config.is_encoder_decoder self.precompute_ref_log_probs = False self._precomputed_train_ref_log_probs = False self._precomputed_eval_ref_log_probs = False self._peft_has_been_casted_to_bf16 = False self.ref_model = ref_model self._stored_metrics = defaultdict(lambda: defaultdict(list)) # KTO parameter self.beta = finetuning_args.kto_beta self.ftx_gamma = finetuning_args.kto_ftx self.desirable_weight = finetuning_args.kto_desirable_weight self.undesirable_weight = finetuning_args.kto_undesirable_weight Trainer.__init__(self, model=model, **kwargs) if not hasattr(self, "accelerator"): raise AttributeError("Please update `transformers`.") if ref_model is not None: if self.is_deepspeed_enabled: if not ( getattr(ref_model, "is_loaded_in_8bit", False) or getattr(ref_model, "is_loaded_in_4bit", False) ): # quantized models are already set on the correct device self.ref_model = self._prepare_deepspeed(self.ref_model) else: self.ref_model = self.accelerator.prepare_model(self.ref_model, evaluation_mode=True) if finetuning_args.use_badam: from badam import clip_grad_norm_for_sparse_tensor self.accelerator.clip_grad_norm_ = MethodType(clip_grad_norm_for_sparse_tensor, self.accelerator) def create_optimizer(self) -> "torch.optim.Optimizer": if self.optimizer is None: self.optimizer = create_custom_optimzer(self.model, self.args, self.finetuning_args) return super().create_optimizer() def create_scheduler( self, num_training_steps: int, optimizer: Optional["torch.optim.Optimizer"] = None ) -> "torch.optim.lr_scheduler.LRScheduler": create_custom_scheduler(self.args, num_training_steps, optimizer) return super().create_scheduler(num_training_steps, optimizer) def sft_loss(self, chosen_logits: "torch.FloatTensor", chosen_labels: "torch.LongTensor") -> "torch.Tensor": r""" Computes supervised cross-entropy loss of given labels under the given logits. Returns: A tensor of shape (batch_size,) containing the cross-entropy loss of each samples. """ all_logps = self.get_batch_logps(chosen_logits, chosen_labels, average_log_prob=True) return -all_logps.nanmean() def forward( self, model: "PreTrainedModel", batch: Dict[str, "torch.Tensor"] ) -> Tuple["torch.Tensor", "torch.Tensor", "torch.Tensor", "torch.Tensor", "torch.Tensor"]: with torch.no_grad(): KL_logits = model( batch["KL_completion_input_ids"], attention_mask=batch["KL_completion_attention_mask"], ).logits completion_logits = model( batch["input_ids"], attention_mask=batch["attention_mask"], ).logits completion_logps = self.get_batch_logps( completion_logits, batch["labels"], average_log_prob=False, is_encoder_decoder=self.is_encoder_decoder, label_pad_token_id=self.label_pad_token_id, ) KL_logps = self.get_batch_logps( KL_logits, batch["kl_labels"], average_log_prob=False, is_encoder_decoder=self.is_encoder_decoder, label_pad_token_id=self.label_pad_token_id, ) if completion_logps.shape[0] != len(batch["tag"]): raise ValueError( "There is a mismatch between the number of examples in this batch and the number of " "examples for which an output sequence was predicted." ) chosen_idx = [i for i in range(completion_logps.shape[0]) if batch["tag"][i]] rejected_idx = [i for i in range(completion_logps.shape[0]) if not batch["tag"][i]] chosen_logps = completion_logps[chosen_idx, ...] rejected_logps = completion_logps[rejected_idx, ...] chosen_logits = completion_logits[chosen_idx, ...] rejected_logits = completion_logits[rejected_idx, ...] return (chosen_logps, rejected_logps, chosen_logits, rejected_logits, KL_logps) def get_batch_loss_metrics( self, model, batch: Dict[str, Union[List, torch.LongTensor]], ): """Compute the KTO loss and other metrics for the given batch of inputs for train or test.""" metrics = {} batch = {k: (v.to(self.accelerator.device) if isinstance(v, torch.Tensor) else v) for k, v in batch.items()} ( policy_chosen_logps, policy_rejected_logps, policy_chosen_logits, policy_rejected_logits, policy_KL_logps, ) = self.forward(model, batch) with torch.no_grad(): if self.ref_model is None: ref_model = self.model ref_context = self.accelerator.unwrap_model(self.model).disable_adapter() else: ref_model = self.ref_model ref_context = nullcontext() with ref_context: ( reference_chosen_logps, reference_rejected_logps, _, _, reference_KL_logps, ) = self.forward(ref_model, batch) losses, chosen_rewards, rejected_rewards, kl = self.kto_loss( policy_chosen_logps, policy_rejected_logps, policy_KL_logps, reference_chosen_logps, reference_rejected_logps, reference_KL_logps, ) losses = losses.nanmean() if self.ftx_gamma > 1e-6 and len(batch["labels"][batch['tag']])>0: losses += self.ftx_gamma * self.sft_loss(policy_chosen_logits, batch["labels"][batch['tag']]) num_chosen = torch.Tensor([len(chosen_rewards)]).to(self.accelerator.device) num_rejected = torch.Tensor([len(rejected_rewards)]).to(self.accelerator.device) all_num_chosen = self.accelerator.gather(num_chosen).sum().item() all_num_rejected = self.accelerator.gather(num_rejected).sum().item() if all_num_chosen > 0: metrics["rewards/chosen_sum"] = self.accelerator.gather(chosen_rewards.nansum()).nansum().item() metrics["logps/chosen_sum"] = self.accelerator.gather(policy_chosen_logps.nansum()).nansum().item() metrics["count/chosen"] = all_num_chosen if all_num_rejected > 0: metrics["rewards/rejected_sum"] = self.accelerator.gather(rejected_rewards.nansum()).nansum().item() metrics["logps/rejected_sum"] = self.accelerator.gather(policy_rejected_logps.nansum()).nansum().item() metrics["count/rejected"] = all_num_rejected metrics["kl"] = kl.item() return losses, metrics