mirror of https://github.com/hpcaitech/ColossalAI
136 lines
5.5 KiB
Python
136 lines
5.5 KiB
Python
import math
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import time
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from typing import List, Optional
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import torch
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import torch.distributed as dist
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import wandb
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from torch.optim import Optimizer
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from torch.utils.data import DataLoader
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from tqdm import tqdm
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from transformers.tokenization_utils_base import PreTrainedTokenizerBase
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from transformers.trainer import get_scheduler
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from .base import Trainer
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from .callbacks import Callback
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from .strategies import ColossalAIStrategy, Strategy
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from .utils import is_rank_0, to_device
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class SFTTrainer(Trainer):
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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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callbacks (List[Callback], defaults to []): the callbacks to call during training process
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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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max_epochs: int = 2,
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accumulation_steps: int = 8,
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callbacks: List[Callback] = [],
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) -> None:
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if accumulation_steps > 1 and isinstance(strategy, ColossalAIStrategy) and strategy.stage == 3:
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raise ValueError("Accumulation steps are not supported in stage 3 of ColossalAI")
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super().__init__(strategy, max_epochs, callbacks=callbacks)
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self.train_dataloader = train_dataloader
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self.eval_dataloader = eval_dataloader
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self.model = model
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self.optimizer = optim
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self.accumulation_steps = accumulation_steps
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num_update_steps_per_epoch = len(train_dataloader) // self.accumulation_steps
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max_steps = math.ceil(self.max_epochs * num_update_steps_per_epoch)
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self.scheduler = get_scheduler("cosine",
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self.optimizer,
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num_warmup_steps=math.ceil(max_steps * 0.03),
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num_training_steps=max_steps)
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def fit(self, logger, use_wandb: bool = False):
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if use_wandb:
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wandb.init(project="Coati", name=time.strftime("%Y-%m-%d %H:%M:%S", time.localtime()))
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wandb.watch(self.model)
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total_loss = 0
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# epoch_bar = tqdm(range(self.epochs), desc='Epochs', disable=not is_rank_0())
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step_bar = tqdm(range(len(self.train_dataloader) // self.accumulation_steps * self.max_epochs),
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desc=f'steps',
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disable=not is_rank_0())
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for epoch in range(self.max_epochs):
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# process_bar = tqdm(range(len(self.train_dataloader)), desc=f'Train process for{epoch}', disable=not is_rank_0())
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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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batch = to_device(batch, torch.cuda.current_device())
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outputs = self.model(batch["input_ids"], attention_mask=batch["attention_mask"], labels=batch["labels"])
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loss = outputs.loss
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if loss >= 2.5 and is_rank_0():
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logger.warning(f"batch_id:{batch_id}, abnormal loss: {loss}")
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loss = loss / self.accumulation_steps
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self.strategy.backward(loss, self.model, self.optimizer)
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total_loss += loss.item()
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# gradient accumulation
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if (batch_id + 1) % self.accumulation_steps == 0:
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self.strategy.optimizer_step(self.optimizer)
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self.optimizer.zero_grad()
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self.scheduler.step()
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if is_rank_0() and use_wandb:
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wandb.log({
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"loss": total_loss / self.accumulation_steps,
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"lr": self.scheduler.get_last_lr()[0],
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"epoch": epoch,
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"batch_id": batch_id
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})
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total_loss = 0
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step_bar.update()
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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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# wandb.log({"loss": loss.item()})
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# process_bar.update()
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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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batch = to_device(batch, torch.cuda.current_device())
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outputs = self.model(batch["input_ids"],
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attention_mask=batch["attention_mask"],
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labels=batch["labels"])
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loss = outputs.loss
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loss_sum += loss.item()
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num_seen += batch["input_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.max_epochs} loss {loss_mean}')
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# epoch_bar.update()
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