""" Dpo trainer """ from typing import Any, Optional import torch from coati.models.loss import DpoLoss from coati.models.utils import calc_masked_log_probs from coati.trainer.utils import all_reduce_mean from coati.utils import AccumulativeMeanMeter, save_checkpoint from torch.optim import Optimizer from torch.optim.lr_scheduler import _LRScheduler from torch.utils.data import DataLoader from tqdm import trange from transformers import PreTrainedTokenizerBase from colossalai.booster import Booster from colossalai.cluster import DistCoordinator from colossalai.utils import get_current_device from .base import SLTrainer from .utils import is_rank_0, to_device class DPOTrainer(SLTrainer): """ Trainer for PPO algorithm. Args: actor (Actor): the actor model in ppo algorithm ref_model (Critic): the reference model in ppo algorithm booster (Strategy): the strategy to use for training actor_optim (Optimizer): the optimizer to use for actor model actor_lr_scheduler (_LRScheduler): the lr scheduler to use for actor model tokenizer (PreTrainedTokenizerBase): the tokenizer to use for encoding max_epochs (int, defaults to 1): the max number of epochs to train beta (float, defaults to 0.1): the beta parameter in dpo loss accumulation_steps (int): the number of steps to accumulate gradients start_epoch (int, defaults to 0): the start epoch, non-zero if resumed from a checkpoint save_interval (int): the interval to save model checkpoints, default to 0, which means no checkpoint will be saved during trainning save_dir (str): the directory to save checkpoints coordinator (DistCoordinator): the coordinator to use for distributed logging """ def __init__( self, actor: Any, ref_model: Any, booster: Booster, actor_optim: Optimizer, actor_lr_scheduler: _LRScheduler, tokenizer: PreTrainedTokenizerBase, max_epochs: int = 1, beta: float = 0.1, accumulation_steps: int = 1, start_epoch: int = 0, save_interval: int = 0, save_dir: str = None, coordinator: DistCoordinator = None, ) -> None: super().__init__(booster, max_epochs=max_epochs, model=actor, optimizer=actor_optim, start_epoch=start_epoch) self.ref_model = ref_model self.actor_scheduler = actor_lr_scheduler self.tokenizer = tokenizer self.actor_loss_fn = DpoLoss(beta) self.save_interval = save_interval self.coordinator = coordinator self.save_dir = save_dir self.num_train_step = 0 self.accumulation_steps = accumulation_steps self.device = get_current_device() self.accumulative_meter = AccumulativeMeanMeter() def _before_fit( self, train_preference_dataloader: DataLoader = None, eval_preference_dataloader: DataLoader = None, log_dir: Optional[str] = None, use_wandb: bool = False, ): """ Args: prompt_dataloader (DataLoader): the dataloader to use for prompt data pretrain_dataloader (DataLoader): the dataloader to use for pretrain data """ self.train_dataloader = train_preference_dataloader self.eval_dataloader = eval_preference_dataloader self.writer = None if use_wandb and is_rank_0(): assert log_dir is not None, "log_dir must be provided when use_wandb is True" import wandb self.wandb_run = wandb.init(project="Coati-dpo", sync_tensorboard=True) if log_dir is not None and is_rank_0(): import os import time from torch.utils.tensorboard import SummaryWriter log_dir = os.path.join(log_dir, "dpo") log_dir = os.path.join(log_dir, time.strftime("%Y-%m-%d_%H:%M:%S", time.localtime())) self.writer = SummaryWriter(log_dir=log_dir) def _train(self, epoch: int): """ Args: epoch int: the number of current epoch """ self.model.train() self.accumulative_meter.reset() step_bar = trange( len(self.train_dataloader) // self.accumulation_steps, desc=f"Epoch {epoch + 1}/{self.max_epochs}", disable=not is_rank_0(), ) for i, batch in enumerate(self.train_dataloader): batch = to_device(batch, self.device) ( chosen_input_ids, chosen_attention_mask, chosen_loss_mask, reject_input_ids, reject_attention_mask, reject_loss_mask, ) = ( batch["chosen_input_ids"], batch["chosen_attention_mask"], batch["chosen_loss_mask"], batch["reject_input_ids"], batch["reject_attention_mask"], batch["reject_loss_mask"], ) reject_loss_mask[:, -1] = False batch_size = chosen_input_ids.size()[0] actor_all_logits = self.model( input_ids=torch.cat([chosen_input_ids, reject_input_ids]), attention_mask=torch.cat([chosen_attention_mask, reject_attention_mask]), )["logits"].to(torch.float32) actor_chosen_logits = actor_all_logits[:batch_size] actor_reject_logits = actor_all_logits[batch_size:] logprob_actor_chosen = calc_masked_log_probs(actor_chosen_logits, chosen_input_ids, chosen_loss_mask[:, 1:]) logprob_actor_reject = calc_masked_log_probs(actor_reject_logits, reject_input_ids, reject_loss_mask[:, 1:]) if self.ref_model is not None: self.ref_model.eval() with torch.no_grad(): ref_all_logits = self.ref_model( input_ids=torch.cat([chosen_input_ids, reject_input_ids]), attention_mask=torch.cat([chosen_attention_mask, reject_attention_mask]), )["logits"].to(torch.float32) ref_chosen_logits = ref_all_logits[:batch_size] ref_reject_logits = ref_all_logits[batch_size:] logprob_ref_chosen = calc_masked_log_probs( ref_chosen_logits, chosen_input_ids, chosen_loss_mask[:, 1:] ) logprob_ref_reject = calc_masked_log_probs( ref_reject_logits, reject_input_ids, reject_loss_mask[:, 1:] ) else: logprob_ref_chosen = None logprob_ref_reject = None losses, chosen_rewards, rejected_rewards = self.actor_loss_fn( logprob_actor_chosen, logprob_actor_reject, logprob_ref_chosen if logprob_ref_chosen is not None else None, logprob_ref_reject if logprob_ref_reject is not None else None, chosen_loss_mask[:, 1:], reject_loss_mask[:, 1:], ) reward_accuracies = (chosen_rewards > rejected_rewards).float().mean() # DPO Loss loss = losses.mean() self.booster.backward(loss=loss, optimizer=self.optimizer) if self.num_train_step % self.accumulation_steps == self.accumulation_steps - 1: self.optimizer.step() self.optimizer.zero_grad() self.actor_scheduler.step() # sync loss_mean = all_reduce_mean(tensor=loss) chosen_rewards_mean = all_reduce_mean(tensor=chosen_rewards) rejected_rewards_mean = all_reduce_mean(tensor=rejected_rewards) reward_accuracies_mean = all_reduce_mean(tensor=reward_accuracies) self.accumulative_meter.add("chosen_rewards", chosen_rewards_mean.to(torch.float16).mean().item()) self.accumulative_meter.add("rejected_rewards", rejected_rewards_mean.to(torch.float16).mean().item()) self.accumulative_meter.add("loss", loss_mean.to(torch.float16).item()) self.accumulative_meter.add("accuracy", reward_accuracies_mean.to(torch.float16).item()) if i % self.accumulation_steps == self.accumulation_steps - 1: self.num_train_step += 1 step_bar.update() # logging if self.writer and is_rank_0(): self.writer.add_scalar("train/loss", self.accumulative_meter.get("loss"), self.num_train_step) self.writer.add_scalar("train/lr", self.optimizer.param_groups[0]["lr"], self.num_train_step) self.writer.add_scalar( "train/chosen_rewards", self.accumulative_meter.get("chosen_rewards"), self.num_train_step ) self.writer.add_scalar( "train/rejected_rewards", self.accumulative_meter.get("rejected_rewards"), self.num_train_step, ) self.writer.add_scalar( "train/margin", self.accumulative_meter.get("chosen_rewards") - self.accumulative_meter.get("rejected_rewards"), self.num_train_step, ) self.writer.add_scalar( "train/accuracy", self.accumulative_meter.get("accuracy"), self.num_train_step, ) self.accumulative_meter.reset() if (self.num_train_step + 1) % self.save_interval == 0: # save checkpoint self.coordinator.print_on_master("\nStart saving model checkpoint with running states") save_checkpoint( save_dir=self.save_dir, booster=self.booster, model=self.model, optimizer=self.optimizer, lr_scheduler=self.actor_scheduler, epoch=epoch, step=i + 1, batch_size=batch_size, coordinator=self.coordinator, ) self.coordinator.print_on_master( f"Saved checkpoint at epoch {epoch} step {self.save_interval} at folder {self.save_dir}" ) step_bar.close() def _eval(self, epoch: int): """ Args: epoch int: the number of current epoch """ if self.eval_dataloader is None: self.coordinator.print_on_master("No eval dataloader is provided, skip evaluation") return self.model.eval() self.ref_model.eval() self.coordinator.print_on_master("\nStart evaluation...") step_bar = trange( len(self.eval_dataloader), desc=f"Epoch {epoch + 1}/{self.max_epochs}", disable=not is_rank_0(), ) self.accumulative_meter.reset() with torch.no_grad(): for i, batch in enumerate(self.eval_dataloader): batch = to_device(batch, self.device) ( chosen_input_ids, chosen_attention_mask, chosen_loss_mask, reject_input_ids, reject_attention_mask, reject_loss_mask, ) = ( batch["chosen_input_ids"], batch["chosen_attention_mask"], batch["chosen_loss_mask"], batch["reject_input_ids"], batch["reject_attention_mask"], batch["reject_loss_mask"], ) batch_size = chosen_input_ids.size()[0] actor_all_logits = self.model( torch.cat([chosen_input_ids, reject_input_ids]), torch.cat([chosen_attention_mask, reject_attention_mask]), )["logits"].to(torch.float32) actor_chosen_logits = actor_all_logits[:batch_size] actor_reject_logits = actor_all_logits[batch_size:] logprob_actor_chosen = calc_masked_log_probs( actor_chosen_logits, chosen_input_ids, chosen_loss_mask[:, 1:] ) logprob_actor_reject = calc_masked_log_probs( actor_reject_logits, reject_input_ids, reject_loss_mask[:, 1:] ) self.ref_model.eval() ref_all_logits = self.ref_model( torch.cat([chosen_input_ids, reject_input_ids]), torch.cat([chosen_attention_mask, reject_attention_mask]), )["logits"].to(torch.float32) ref_chosen_logits = ref_all_logits[:batch_size] ref_reject_logits = ref_all_logits[batch_size:] logprob_ref_chosen = calc_masked_log_probs(ref_chosen_logits, chosen_input_ids, chosen_loss_mask[:, 1:]) logprob_ref_reject = calc_masked_log_probs(ref_reject_logits, reject_input_ids, reject_loss_mask[:, 1:]) losses, chosen_rewards, rejected_rewards = self.actor_loss_fn( logprob_actor_chosen, logprob_actor_reject, logprob_ref_chosen if logprob_ref_chosen is not None else None, logprob_ref_reject if logprob_ref_reject is not None else None, chosen_loss_mask[:, 1:], reject_loss_mask[:, 1:], ) reward_accuracies = (chosen_rewards > rejected_rewards).float() loss = losses.mean() loss_mean = all_reduce_mean(tensor=loss) chosen_rewards_mean = all_reduce_mean(tensor=chosen_rewards) rejected_rewards_mean = all_reduce_mean(tensor=rejected_rewards) reward_accuracies_mean = all_reduce_mean(tensor=reward_accuracies) self.accumulative_meter.add("chosen_rewards", chosen_rewards_mean.to(torch.float16).mean().item()) self.accumulative_meter.add("rejected_rewards", rejected_rewards_mean.to(torch.float16).mean().item()) self.accumulative_meter.add("loss", loss_mean.to(torch.float16).item()) self.accumulative_meter.add("accuracy", reward_accuracies_mean.to(torch.float16).item()) self.accumulative_meter.add( "margin", (chosen_rewards_mean - rejected_rewards_mean).to(torch.float16).mean().item() ) step_bar.update() msg = "Evaluation Result:\n" for tag in ["loss", "chosen_rewards", "rejected_rewards", "accuracy", "margin"]: msg = msg + f"{tag}: {self.accumulative_meter.get(tag)}\n" self.coordinator.print_on_master(msg) step_bar.close()