mirror of https://github.com/hpcaitech/ColossalAI
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131 lines
4.5 KiB
131 lines
4.5 KiB
import time
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from typing import Optional
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import torch
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import torch.distributed as dist
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import tqdm
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import wandb
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from torch.optim import Optimizer
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from torch.optim.lr_scheduler import _LRScheduler
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from torch.utils.data import DataLoader
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from colossalai.logging import DistributedLogger
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from .base import SLTrainer
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from .strategies import GeminiStrategy, Strategy
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from .utils import is_rank_0, to_device
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class SFTTrainer(SLTrainer):
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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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lr_scheduler(_LRScheduler): the lr scheduler to use for training
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max_epochs (int, defaults to 2): the number of epochs to train
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accumulation_steps (int, defaults to 8): the number of steps to accumulate gradients
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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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lr_scheduler: _LRScheduler,
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max_epochs: int = 2,
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accumulation_steps: int = 8,
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) -> None:
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if accumulation_steps > 1:
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assert not isinstance(
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strategy, GeminiStrategy
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), "Accumulation steps are not supported in stage 3 of ColossalAI"
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super().__init__(strategy, max_epochs, model, optim)
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self.accumulation_steps = accumulation_steps
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self.scheduler = lr_scheduler
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def _train(self, epoch: int):
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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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if "attention_mask" in batch:
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outputs = self.model(batch["input_ids"], attention_mask=batch["attention_mask"], labels=batch["labels"])
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else:
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outputs = self.model(batch["input_ids"], labels=batch["labels"])
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loss = outputs.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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self.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 self.use_wandb:
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wandb.log(
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{
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"loss": self.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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)
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self.total_loss = 0
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self.step_bar.update()
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def _eval(self, epoch: int):
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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, num_seen = 0, 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(
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batch["input_ids"], attention_mask=batch["attention_mask"], labels=batch["labels"]
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)
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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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self.logger.info(f"Eval Epoch {epoch}/{self.max_epochs} loss {loss_mean}")
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def _before_fit(
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self,
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train_dataloader: DataLoader,
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eval_dataloader: Optional[DataLoader] = None,
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logger: Optional[DistributedLogger] = None,
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use_wandb: bool = False,
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):
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"""
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Args:
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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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"""
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self.train_dataloader = train_dataloader
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self.eval_dataloader = eval_dataloader
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self.logger = logger
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self.use_wandb = use_wandb
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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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self.total_loss = 0
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self.no_epoch_bar = True
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self.step_bar = tqdm.trange(
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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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)
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