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94 lines
3.9 KiB
94 lines
3.9 KiB
from abc import ABC
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import loralib as lora
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import torch
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from chatgpt.dataset import RewardDataset
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from chatgpt.models.loss import PairWiseLoss
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from torch.optim import Adam, Optimizer
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from torch.utils.data import DataLoader
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from tqdm import tqdm
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from .strategies import Strategy
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from .utils import is_rank_0
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class RewardModelTrainer(ABC):
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"""
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Trainer to use while training reward model.
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Args:
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model (torch.nn.Module): the model to train
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strategy (Strategy): the strategy to use for training
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optim(Optimizer): the optimizer to use for training
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train_dataset (RewardDataset): the dataset to use for training
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eval_dataset (RewardDataset): the dataset to use for evaluation
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batch_size (int, defaults to 1): the batch size while training
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max_epochs (int, defaults to 2): the number of epochs to train
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optim_kwargs (dict, defaults to {'lr':1e-4}): the kwargs to use while initializing optimizer
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"""
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def __init__(
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self,
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model,
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strategy: Strategy,
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optim: Optimizer,
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train_dataset: RewardDataset,
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eval_dataset: RewardDataset,
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batch_size: int = 1,
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max_epochs: int = 2,
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) -> None:
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super().__init__()
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self.strategy = strategy
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self.epochs = max_epochs
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self.train_dataloader = DataLoader(train_dataset, batch_size=batch_size)
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self.eval_dataloader = DataLoader(eval_dataset, batch_size=batch_size)
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self.model = strategy.setup_model(model)
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if "DDP" in str(self.strategy):
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self.model = self.model.module
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self.loss_fn = PairWiseLoss()
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self.optimizer = strategy.setup_optimizer(optim, self.model)
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def fit(self, use_lora):
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epoch_bar = tqdm(range(self.epochs), desc='Train epoch', disable=not is_rank_0())
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for epoch in range(self.epochs):
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step_bar = tqdm(range(self.train_dataloader.__len__()),
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desc='Train step of epoch %d' % epoch,
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disable=not is_rank_0())
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# train
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self.model.train()
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for chosen_ids, c_mask, reject_ids, r_mask in self.train_dataloader:
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chosen_ids = chosen_ids.squeeze(1).cuda()
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c_mask = c_mask.squeeze(1).cuda()
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reject_ids = reject_ids.squeeze(1).cuda()
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r_mask = r_mask.squeeze(1).cuda()
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chosen_reward = self.model(chosen_ids, attention_mask=c_mask)
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reject_reward = self.model(reject_ids, attention_mask=r_mask)
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loss = self.loss_fn(chosen_reward, reject_reward)
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self.strategy.backward(loss, self.model, self.optimizer)
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self.strategy.optimizer_step(self.optimizer)
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self.optimizer.zero_grad()
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step_bar.update()
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step_bar.set_postfix({'loss': loss.item()})
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# eval
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self.model.eval()
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with torch.no_grad():
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dist = 0
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loss_sum = 0
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for chosen_ids, c_mask, reject_ids, r_mask in self.eval_dataloader:
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chosen_ids = chosen_ids.squeeze(1).cuda()
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c_mask = c_mask.squeeze(1).cuda()
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reject_ids = reject_ids.squeeze(1).cuda()
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r_mask = r_mask.squeeze(1).cuda()
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chosen_reward = self.model(chosen_ids, attention_mask=c_mask)
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reject_reward = self.model(reject_ids, attention_mask=r_mask)
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dist += (chosen_reward - reject_reward).mean().item()
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loss = self.loss_fn(chosen_reward, reject_reward)
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loss_sum += loss.item()
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dist_mean = dist / self.eval_dataloader.__len__()
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loss_mean = loss_sum / self.eval_dataloader.__len__()
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epoch_bar.update()
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step_bar.set_postfix({'loss': loss_mean, 'dist_mean': dist_mean})
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step_bar.close()
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