mirror of https://github.com/hpcaitech/ColossalAI
126 lines
4.4 KiB
Python
126 lines
4.4 KiB
Python
import time
|
|
from typing import Optional
|
|
|
|
import torch
|
|
import torch.distributed as dist
|
|
import tqdm
|
|
import wandb
|
|
from torch.optim import Optimizer
|
|
from torch.optim.lr_scheduler import _LRScheduler
|
|
from torch.utils.data import DataLoader
|
|
|
|
from colossalai.logging import DistributedLogger
|
|
|
|
from .base import SLTrainer
|
|
from .strategies import GeminiStrategy, Strategy
|
|
from .utils import is_rank_0, to_device
|
|
|
|
|
|
class SFTTrainer(SLTrainer):
|
|
"""
|
|
Trainer to use while training reward model.
|
|
|
|
Args:
|
|
model (torch.nn.Module): the model to train
|
|
strategy (Strategy): the strategy to use for training
|
|
optim(Optimizer): the optimizer to use for training
|
|
lr_scheduler(_LRScheduler): the lr scheduler to use for training
|
|
max_epochs (int, defaults to 2): the number of epochs to train
|
|
accumulation_steps (int, defaults to 8): the number of steps to accumulate gradients
|
|
"""
|
|
|
|
def __init__(
|
|
self,
|
|
model,
|
|
strategy: Strategy,
|
|
optim: Optimizer,
|
|
lr_scheduler: _LRScheduler,
|
|
max_epochs: int = 2,
|
|
accumulation_steps: int = 8,
|
|
) -> None:
|
|
if accumulation_steps > 1:
|
|
assert not isinstance(strategy, GeminiStrategy), \
|
|
"Accumulation steps are not supported in stage 3 of ColossalAI"
|
|
|
|
super().__init__(strategy, max_epochs, model, optim)
|
|
|
|
self.accumulation_steps = accumulation_steps
|
|
self.scheduler = lr_scheduler
|
|
|
|
def _train(self, epoch: int):
|
|
self.model.train()
|
|
for batch_id, batch in enumerate(self.train_dataloader):
|
|
|
|
batch = to_device(batch, torch.cuda.current_device())
|
|
outputs = self.model(batch["input_ids"],
|
|
attention_mask=batch["attention_mask"],
|
|
labels=batch["labels"])
|
|
|
|
loss = outputs.loss
|
|
loss = loss / self.accumulation_steps
|
|
|
|
self.strategy.backward(loss, self.model, self.optimizer)
|
|
|
|
self.total_loss += loss.item()
|
|
|
|
# gradient accumulation
|
|
if (batch_id + 1) % self.accumulation_steps == 0:
|
|
self.strategy.optimizer_step(self.optimizer)
|
|
self.optimizer.zero_grad()
|
|
self.scheduler.step()
|
|
if is_rank_0() and self.use_wandb:
|
|
wandb.log({
|
|
"loss": self.total_loss / self.accumulation_steps,
|
|
"lr": self.scheduler.get_last_lr()[0],
|
|
"epoch": epoch,
|
|
"batch_id": batch_id
|
|
})
|
|
self.total_loss = 0
|
|
self.step_bar.update()
|
|
|
|
def _eval(self, epoch: int):
|
|
if self.eval_dataloader is not None:
|
|
self.model.eval()
|
|
with torch.no_grad():
|
|
loss_sum, num_seen = 0, 0
|
|
for batch in self.eval_dataloader:
|
|
batch = to_device(batch, torch.cuda.current_device())
|
|
outputs = self.model(batch["input_ids"],
|
|
attention_mask=batch["attention_mask"],
|
|
labels=batch["labels"])
|
|
loss = outputs.loss
|
|
|
|
loss_sum += loss.item()
|
|
num_seen += batch["input_ids"].size(0)
|
|
|
|
loss_mean = loss_sum / num_seen
|
|
if dist.get_rank() == 0:
|
|
self.logger.info(f'Eval Epoch {epoch}/{self.max_epochs} loss {loss_mean}')
|
|
|
|
def _before_fit(self,
|
|
train_dataloader: DataLoader,
|
|
eval_dataloader: Optional[DataLoader] = None,
|
|
logger: Optional[DistributedLogger] = None,
|
|
use_wandb: bool = False):
|
|
"""
|
|
Args:
|
|
train_dataloader: the dataloader to use for training
|
|
eval_dataloader: the dataloader to use for evaluation
|
|
"""
|
|
self.train_dataloader = train_dataloader
|
|
self.eval_dataloader = eval_dataloader
|
|
|
|
self.logger = logger
|
|
self.use_wandb = use_wandb
|
|
if use_wandb:
|
|
wandb.init(project="Coati", name=time.strftime("%Y-%m-%d %H:%M:%S", time.localtime()))
|
|
wandb.watch(self.model)
|
|
|
|
self.total_loss = 0
|
|
self.no_epoch_bar = True
|
|
self.step_bar = tqdm.trange(
|
|
len(self.train_dataloader) // self.accumulation_steps * self.max_epochs,
|
|
desc=f'steps',
|
|
disable=not is_rank_0()
|
|
)
|