mirror of https://github.com/hpcaitech/ColossalAI
136 lines
5.9 KiB
Python
136 lines
5.9 KiB
Python
from abc import ABC
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from datetime import datetime
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from typing import Optional
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import pandas as pd
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import torch
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import torch.distributed as dist
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from torch.optim import Optimizer, lr_scheduler
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from torch.utils.data import DataLoader, Dataset, DistributedSampler
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from tqdm import tqdm
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from transformers.tokenization_utils_base import PreTrainedTokenizerBase
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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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loss_fn (callable): the loss function to use for training
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train_dataset (Dataset): the dataset to use for training
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valid_dataset (Dataset): the dataset to use for validation
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eval_dataset (Dataset): 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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"""
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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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loss_fn,
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train_dataset: Dataset,
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valid_dataset: Dataset,
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eval_dataset: Dataset,
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batch_size: int = 1,
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max_epochs: int = 1,
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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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train_sampler = None
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if dist.is_initialized() and dist.get_world_size() > 1:
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train_sampler = DistributedSampler(train_dataset, shuffle=True, seed=42, drop_last=True)
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self.train_dataloader = DataLoader(train_dataset,
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shuffle=(train_sampler is None),
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sampler=train_sampler,
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batch_size=batch_size)
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self.valid_dataloader = DataLoader(valid_dataset, batch_size=batch_size, shuffle=True)
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self.eval_dataloader = DataLoader(eval_dataset, batch_size=batch_size, shuffle=True)
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self.model = strategy.setup_model(model)
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self.loss_fn = loss_fn
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self.optimizer = strategy.setup_optimizer(optim, self.model)
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self.scheduler = lr_scheduler.CosineAnnealingLR(self.optimizer, self.train_dataloader.__len__() // 100)
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def eval_acc(self, dataloader):
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dist = 0
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on = 0
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cnt = 0
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self.model.eval()
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with torch.no_grad():
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for chosen_ids, c_mask, reject_ids, r_mask in dataloader:
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chosen_ids = chosen_ids.squeeze(1).to(torch.cuda.current_device())
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c_mask = c_mask.squeeze(1).to(torch.cuda.current_device())
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reject_ids = reject_ids.squeeze(1).to(torch.cuda.current_device())
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r_mask = r_mask.squeeze(1).to(torch.cuda.current_device())
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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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for i in range(len(chosen_reward)):
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cnt += 1
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if chosen_reward[i] > reject_reward[i]:
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on += 1
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dist += (chosen_reward - reject_reward).mean().item()
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dist_mean = dist / len(dataloader)
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acc = on / cnt
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self.model.train()
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return dist_mean, acc
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def fit(self):
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time = datetime.now()
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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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cnt = 0
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acc = 0
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dist = 0
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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).to(torch.cuda.current_device())
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c_mask = c_mask.squeeze(1).to(torch.cuda.current_device())
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reject_ids = reject_ids.squeeze(1).to(torch.cuda.current_device())
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r_mask = r_mask.squeeze(1).to(torch.cuda.current_device())
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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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cnt += 1
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if cnt == 100:
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self.scheduler.step()
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dist, acc = self.eval_acc(self.valid_dataloader)
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cnt = 0
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if is_rank_0():
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log = pd.DataFrame([[step_bar.n, loss.item(), dist, acc]],
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columns=['step', 'loss', 'dist', 'acc'])
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log.to_csv('log_%s.csv' % time, mode='a', header=False, index=False)
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step_bar.update()
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step_bar.set_postfix({'dist': dist, 'acc': acc})
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# eval
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dist, acc = self.eval_acc(self.eval_dataloader)
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if is_rank_0():
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log = pd.DataFrame([[step_bar.n, loss.item(), dist, acc]], columns=['step', 'loss', 'dist', 'acc'])
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log.to_csv('log.csv', mode='a', header=False, index=False)
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epoch_bar.update()
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step_bar.set_postfix({'dist': dist, 'acc': acc})
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step_bar.close()
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def save_model(self,
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path: str,
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only_rank0: bool = False,
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tokenizer: Optional[PreTrainedTokenizerBase] = None) -> None:
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self.strategy.save_model(model=self.model, path=path, only_rank0=only_rank0, tokenizer=tokenizer)
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