# Copyright 2024 HuggingFace Inc. and the LlamaFactory team. # # This code is inspired by the HuggingFace's TRL library. # https://github.com/huggingface/trl/blob/v0.8.0/trl/trainer/dpo_trainer.py # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. import warnings from collections import defaultdict from contextlib import nullcontext from types import MethodType from typing import TYPE_CHECKING, Literal, Optional, Union import torch import torch.nn.functional as F from transformers import Trainer from trl import DPOTrainer from trl.trainer import disable_dropout_in_model from typing_extensions import override from ...extras.constants import IGNORE_INDEX from ...extras.packages import is_transformers_version_greater_than from ..callbacks import SaveProcessorCallback from ..trainer_utils import create_custom_optimizer, create_custom_scheduler, get_batch_logps, nested_detach if TYPE_CHECKING: from transformers import PreTrainedModel, ProcessorMixin from ...hparams import FinetuningArguments class CustomDPOTrainer(DPOTrainer): def __init__( self, model: Union["PreTrainedModel", torch.nn.Module], ref_model: Optional[Union["PreTrainedModel", torch.nn.Module]], finetuning_args: "FinetuningArguments", processor: Optional["ProcessorMixin"], disable_dropout: bool = True, **kwargs, ): if is_transformers_version_greater_than("4.46"): kwargs["processing_class"] = kwargs.pop("tokenizer") 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.f_divergence_type = "reverse_kl" 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)) # dpo hyperparams self.beta = finetuning_args.pref_beta self.loss_type = finetuning_args.pref_loss self.ftx_gamma = finetuning_args.pref_ftx self.label_smoothing = finetuning_args.dpo_label_smoothing self.simpo_gamma = finetuning_args.simpo_gamma Trainer.__init__(self, model=model, **kwargs) self.model_accepts_loss_kwargs = False # overwrite trainer's default behavior if not hasattr(self, "accelerator"): raise AttributeError("Please update `transformers`.") warnings.simplefilter("ignore") # remove gc warnings on ref model 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) self.ref_model.eval() if processor is not None: self.add_callback(SaveProcessorCallback(processor)) if finetuning_args.use_badam: from badam import BAdamCallback, clip_grad_norm_old_version # type: ignore self.accelerator.clip_grad_norm_ = MethodType(clip_grad_norm_old_version, self.accelerator) self.add_callback(BAdamCallback) @override def create_optimizer(self) -> "torch.optim.Optimizer": if self.optimizer is None: self.optimizer = create_custom_optimizer(self.model, self.args, self.finetuning_args) return super().create_optimizer() @override 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) @override def _get_train_sampler(self) -> Optional["torch.utils.data.Sampler"]: if self.finetuning_args.disable_shuffling: return torch.utils.data.SequentialSampler(self.train_dataset) return super()._get_train_sampler() @override def get_batch_samples(self, epoch_iterator, num_batches): r"""Replace the method of DPO Trainer with the one of the standard Trainer.""" return Trainer.get_batch_samples(self, epoch_iterator, num_batches) def odds_ratio_loss(self, chosen_logps: "torch.Tensor", rejected_logps: "torch.Tensor") -> "torch.Tensor": r"""Compute ORPO's odds ratio (OR) loss for batched log probabilities of the policy model.""" log_odds = (chosen_logps - rejected_logps) - ( torch.log1p(-torch.exp(chosen_logps)) - torch.log1p(-torch.exp(rejected_logps)) ) sft_loss = -chosen_logps odds_ratio_loss = -F.logsigmoid(log_odds) orpo_loss = sft_loss + self.beta * odds_ratio_loss return orpo_loss def simpo_loss(self, chosen_logps: "torch.Tensor", rejected_logps: "torch.Tensor") -> "torch.Tensor": r"""Compute SimPO loss for batched log probabilities of the policy model.""" pi_logratios = chosen_logps - rejected_logps gamma_logratios = self.simpo_gamma / self.beta logits = pi_logratios - gamma_logratios simpo_loss = -F.logsigmoid(self.beta * logits) return simpo_loss def compute_preference_loss( self, policy_chosen_logps: "torch.Tensor", policy_rejected_logps: "torch.Tensor", reference_chosen_logps: Optional["torch.Tensor"], reference_rejected_logps: Optional["torch.Tensor"], ) -> tuple["torch.Tensor", "torch.Tensor", "torch.Tensor"]: r"""Compute loss for preference learning.""" if not self.finetuning_args.use_ref_model: if self.loss_type == "orpo": losses = self.odds_ratio_loss(policy_chosen_logps, policy_rejected_logps) elif self.loss_type == "simpo": losses = self.simpo_loss(policy_chosen_logps, policy_rejected_logps) else: raise NotImplementedError(f"Unknown loss type: {self.loss_type}.") chosen_rewards = self.beta * policy_chosen_logps.to(self.accelerator.device).detach() rejected_rewards = self.beta * policy_rejected_logps.to(self.accelerator.device).detach() else: losses, chosen_rewards, rejected_rewards = self.dpo_loss( policy_chosen_logps, policy_rejected_logps, reference_chosen_logps, reference_rejected_logps ) return losses, chosen_rewards, rejected_rewards @override def concatenated_forward( self, model: "PreTrainedModel", batch: dict[str, "torch.Tensor"] ) -> tuple["torch.Tensor", "torch.Tensor", "torch.Tensor", "torch.Tensor", "torch.Tensor"]: r"""Compute the sum log probabilities of the labels under given logits if loss_type is not IPO, ORPO or SimPO. Otherwise the average log probabilities. """ if self.finetuning_args.use_ref_model: batch = nested_detach(batch, clone=True) # avoid error all_logits: torch.Tensor = model(**batch, return_dict=True, use_cache=False).logits.to(torch.float32) all_logps, valid_length = get_batch_logps(logits=all_logits, labels=batch["labels"]) if self.loss_type in ["ipo", "orpo", "simpo"]: all_logps = all_logps / valid_length batch_size = batch["input_ids"].size(0) // 2 chosen_logps, rejected_logps = all_logps.split(batch_size, dim=0) chosen_logits, rejected_logits = all_logits.split(batch_size, dim=0) chosen_length, _ = valid_length.split(batch_size, dim=0) if self.loss_type in ["ipo", "orpo", "simpo"]: return chosen_logps, rejected_logps, chosen_logits, rejected_logits, chosen_logps else: return chosen_logps, rejected_logps, chosen_logits, rejected_logits, chosen_logps / chosen_length @override def compute_reference_log_probs( self, model: "PreTrainedModel", batch: dict[str, "torch.Tensor"] ) -> tuple[Optional["torch.Tensor"], Optional["torch.Tensor"]]: r"""Compute log probabilities of the reference model.""" if not self.finetuning_args.use_ref_model: return None, None if self.ref_model is None: ref_model = model ref_context = self.accelerator.unwrap_model(model).disable_adapter() else: ref_model = self.ref_model ref_context = nullcontext() with torch.no_grad(), ref_context: reference_chosen_logps, reference_rejected_logps, *_ = self.concatenated_forward(ref_model, batch) return reference_chosen_logps, reference_rejected_logps @override def get_batch_loss_metrics( self, model: "PreTrainedModel", batch: dict[str, "torch.Tensor"], train_eval: Literal["train", "eval"] = "train", ) -> tuple["torch.Tensor", dict[str, "torch.Tensor"]]: r"""Compute the DPO loss and other metrics for the given batch of inputs for train or test.""" metrics = {} ( policy_chosen_logps, policy_rejected_logps, policy_chosen_logits, policy_rejected_logits, policy_chosen_logps_avg, ) = self.concatenated_forward(model, batch) reference_chosen_logps, reference_rejected_logps = self.compute_reference_log_probs(model, batch) losses, chosen_rewards, rejected_rewards = self.compute_preference_loss( policy_chosen_logps, policy_rejected_logps, reference_chosen_logps, reference_rejected_logps, ) sft_loss = -policy_chosen_logps_avg if self.ftx_gamma > 1e-6: losses += self.ftx_gamma * sft_loss prefix = "eval_" if train_eval == "eval" else "" metrics[f"{prefix}rewards/chosen"] = chosen_rewards.mean().item() metrics[f"{prefix}rewards/rejected"] = rejected_rewards.mean().item() metrics[f"{prefix}rewards/accuracies"] = (chosen_rewards > rejected_rewards).float().mean().item() metrics[f"{prefix}rewards/margins"] = (chosen_rewards - rejected_rewards).mean().item() metrics[f"{prefix}logps/chosen"] = policy_chosen_logps.mean().item() metrics[f"{prefix}logps/rejected"] = policy_rejected_logps.mean().item() metrics[f"{prefix}logits/chosen"] = policy_chosen_logits.mean().item() metrics[f"{prefix}logits/rejected"] = policy_rejected_logits.mean().item() if self.loss_type == "orpo": metrics[f"{prefix}sft_loss"] = sft_loss.mean().item() metrics[f"{prefix}odds_ratio_loss"] = ((losses - sft_loss) / self.beta).mean().item() return losses.mean(), metrics @override def compute_loss( self, model: "PreTrainedModel", inputs: dict[str, "torch.Tensor"], return_outputs: bool = False, **kwargs ) -> Union["torch.Tensor", tuple["torch.Tensor", list["torch.Tensor"]]]: r"""Subclass and override to accept extra kwargs.""" return super().compute_loss(model, inputs, return_outputs) @override def log(self, logs: dict[str, float], *args, **kwargs) -> None: r"""Log `logs` on the various objects watching training, including stored metrics.""" # logs either has "loss" or "eval_loss" train_eval = "train" if "loss" in logs else "eval" # Add averaged stored metrics to logs key_list, metric_list = [], [] for key, metrics in self._stored_metrics[train_eval].items(): key_list.append(key) metric_list.append(torch.tensor(metrics, dtype=torch.float).to(self.accelerator.device).mean().item()) del self._stored_metrics[train_eval] if len(metric_list) < 10: # pad to for all reduce for i in range(10 - len(metric_list)): key_list.append(f"dummy_{i}") metric_list.append(0.0) metric_list = torch.tensor(metric_list, dtype=torch.float).to(self.accelerator.device) metric_list = self.accelerator.reduce(metric_list, "mean").tolist() for key, metric in zip(key_list, metric_list): # add remaining items if not key.startswith("dummy_"): logs[key] = metric return Trainer.log(self, logs, *args, **kwargs)