mirror of https://github.com/hpcaitech/ColossalAI
100 lines
3.9 KiB
Python
100 lines
3.9 KiB
Python
from abc import ABC
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from typing import Optional
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import loralib as lora
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import torch
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from chatgpt.models.loss import GPTLMLoss
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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 torch.utils.data.distributed import DistributedSampler
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from tqdm import tqdm
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import torch.distributed as dist
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from .strategies import Strategy
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from .utils import is_rank_0
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from colossalai.logging import get_dist_logger
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class SFTTrainer(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_dataloader: the dataloader to use for training
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eval_dataloader: the dataloader 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_dataloader: DataLoader,
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eval_dataloader: DataLoader = None,
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sampler: Optional[DistributedSampler] = None,
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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.sampler = sampler
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self.train_dataloader = train_dataloader
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self.eval_dataloader = eval_dataloader
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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 = GPTLMLoss()
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self.optimizer = strategy.setup_optimizer(optim, self.model)
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def fit(self, logger, use_lora, log_interval=10):
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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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if isinstance(self.sampler, DistributedSampler):
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self.sampler.set_epoch(epoch)
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# train
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self.model.train()
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for batch_id, batch in enumerate(self.train_dataloader):
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prompt_ids = batch["input_ids"]
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p_mask = batch["attention_mask"]
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prompt_ids = prompt_ids.squeeze(1).cuda()
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p_mask = p_mask.squeeze(1).cuda()
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prompt_logits = self.model(prompt_ids, attention_mask=p_mask)
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loss = self.loss_fn(prompt_logits, prompt_ids)
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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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if batch_id % log_interval == 0:
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logger.info(f'Train Epoch {epoch}/{self.epochs} Batch {batch_id} Rank {dist.get_rank()} loss {loss.item()}')
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# eval
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if self.eval_dataloader is not None:
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self.model.eval()
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with torch.no_grad():
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loss_sum = 0
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num_seen = 0
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for batch in self.eval_dataloader:
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prompt_ids = batch["input_ids"]
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p_mask = batch["attention_mask"]
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prompt_ids = prompt_ids.squeeze(1).cuda()
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p_mask = p_mask.squeeze(1).cuda()
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prompt_logits = self.model(prompt_ids, attention_mask=p_mask)
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loss = self.loss_fn(prompt_logits, prompt_ids)
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loss_sum += loss.item()
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num_seen += prompt_ids.size(0)
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loss_mean = loss_sum / num_seen
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if dist.get_rank() == 0:
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logger.info(f'Eval Epoch {epoch}/{self.epochs} loss {loss_mean}')
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epoch_bar.update()
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