ColossalAI/applications/Chat/coati/trainer/sft.py
Wenhao Chen 7b9b86441f
[chat]: update rm, add wandb and fix bugs (#4471)
* feat: modify forward fn of critic and reward model

* feat: modify calc_action_log_probs

* to: add wandb in sft and rm trainer

* feat: update train_sft

* feat: update train_rm

* style: modify type annotation and add warning

* feat: pass tokenizer to ppo trainer

* to: modify trainer base and maker base

* feat: add wandb in ppo trainer

* feat: pass tokenizer to generate

* test: update generate fn tests

* test: update train tests

* fix: remove action_mask

* feat: remove unused code

* fix: fix wrong ignore_index

* fix: fix mock tokenizer

* chore: update requirements

* revert: modify make_experience

* fix: fix inference

* fix: add padding side

* style: modify _on_learn_batch_end

* test: use mock tokenizer

* fix: use bf16 to avoid overflow

* fix: fix workflow

* [chat] fix gemini strategy

* [chat] fix

* sync: update colossalai strategy

* fix: fix args and model dtype

* fix: fix checkpoint test

* fix: fix requirements

* fix: fix missing import and wrong arg

* fix: temporarily skip gemini test in stage 3

* style: apply pre-commit

* fix: temporarily skip gemini test in stage 1&2

---------

Co-authored-by: Mingyan Jiang <1829166702@qq.com>
2023-09-20 15:53:58 +08:00

131 lines
4.8 KiB
Python

from typing import Optional
import torch
import torch.distributed as dist
import tqdm
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
self.num_train_step = 0
self.num_eval_step = 0
def _train(self, epoch: int):
self.model.train()
step_bar = tqdm.trange(
len(self.train_dataloader) // self.accumulation_steps,
desc=f"Epoch {epoch + 1}/{self.max_epochs}",
disable=not is_rank_0(),
)
for i, 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 / self.accumulation_steps
self.total_loss += loss.item()
self.strategy.backward(loss, self.model, self.optimizer)
# gradient accumulation
if (i + 1) % self.accumulation_steps == 0:
self.strategy.optimizer_step(self.optimizer)
self.optimizer.zero_grad()
self.scheduler.step()
if self.writer:
self.writer.add_scalar("train/loss", self.total_loss, self.num_train_step)
self.writer.add_scalar("train/lr", self.scheduler.get_last_lr()[0], self.num_train_step)
self.num_train_step += 1
self.total_loss = 0
step_bar.update()
step_bar.close()
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_sum += outputs.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}")
if self.writer:
self.writer.add_scalar("eval/loss", loss_mean, self.num_eval_step)
self.num_eval_step += 1
def _before_fit(
self,
train_dataloader: DataLoader,
eval_dataloader: Optional[DataLoader] = None,
logger: Optional[DistributedLogger] = None,
log_dir: Optional[str] = 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.writer = None
if use_wandb and is_rank_0():
assert log_dir is not None, "log_dir must be provided when use_wandb is True"
import wandb
wandb.init(project="Coati-sft", sync_tensorboard=True)
if log_dir is not None and is_rank_0():
import os
import time
from torch.utils.tensorboard import SummaryWriter
log_dir = os.path.join(log_dir, "sft")
log_dir = os.path.join(log_dir, time.strftime("%Y-%m-%d_%H:%M:%S", time.localtime()))
self.writer = SummaryWriter(log_dir=log_dir)
self.total_loss = 0