mirror of https://github.com/hpcaitech/ColossalAI
You can not select more than 25 topics
Topics must start with a letter or number, can include dashes ('-') and can be up to 35 characters long.
243 lines
10 KiB
243 lines
10 KiB
"""
|
|
Reward model trianer
|
|
"""
|
|
|
|
import os
|
|
from typing import Any, Callable, Optional
|
|
|
|
import torch
|
|
import tqdm
|
|
from coati.models import LogSigLoss
|
|
from coati.trainer.utils import all_reduce_mean
|
|
from coati.utils import AccumulativeMeanMeter, save_checkpoint
|
|
from torch.optim import Optimizer
|
|
from torch.optim.lr_scheduler import _LRScheduler
|
|
from torch.utils.data import DataLoader
|
|
from transformers import PreTrainedTokenizerBase
|
|
|
|
from colossalai.booster import Booster
|
|
from colossalai.cluster import DistCoordinator
|
|
from colossalai.utils import get_current_device
|
|
|
|
from .base import SLTrainer
|
|
from .utils import is_rank_0, to_device
|
|
|
|
|
|
class RewardModelTrainer(SLTrainer):
|
|
"""
|
|
Trainer for PPO algorithm.
|
|
|
|
Args:
|
|
actor (Actor): the actor model in ppo algorithm
|
|
ref_model (Critic): the reference model in ppo algorithm
|
|
booster (Strategy): the strategy to use for training
|
|
actor_optim (Optimizer): the optimizer to use for actor model
|
|
actor_lr_scheduler (_LRScheduler): the lr scheduler to use for actor model
|
|
tokenizer (PreTrainedTokenizerBase): the tokenizer to use for encoding
|
|
max_epochs (int, defaults to 1): the max number of epochs to train
|
|
beta (float, defaults to 0.1): the beta parameter in dpo loss
|
|
accumulation_steps (int): the number of steps to accumulate gradients
|
|
start_epoch (int, defaults to 0): the start epoch, non-zero if resumed from a checkpoint
|
|
save_interval (int): the interval to save model checkpoints, default to 0, which means no checkpoint will be saved during trainning
|
|
save_dir (str): the directory to save checkpoints
|
|
coordinator (DistCoordinator): the coordinator to use for distributed logging
|
|
"""
|
|
|
|
def __init__(
|
|
self,
|
|
model: Any,
|
|
booster: Booster,
|
|
optimizer: Optimizer,
|
|
lr_scheduler: _LRScheduler,
|
|
tokenizer: PreTrainedTokenizerBase,
|
|
loss_fn: Optional[Callable] = None,
|
|
max_epochs: int = 1,
|
|
beta: float = 0.1,
|
|
accumulation_steps: int = 1,
|
|
start_epoch: int = 0,
|
|
save_interval: int = 0,
|
|
save_dir: str = None,
|
|
coordinator: DistCoordinator = None,
|
|
) -> None:
|
|
super().__init__(booster, max_epochs=max_epochs, model=model, optimizer=optimizer, start_epoch=start_epoch)
|
|
self.actor_scheduler = lr_scheduler
|
|
self.tokenizer = tokenizer
|
|
self.loss_fn = loss_fn if loss_fn is not None else LogSigLoss(beta=beta)
|
|
self.save_interval = save_interval
|
|
self.coordinator = coordinator
|
|
self.save_dir = save_dir
|
|
self.num_train_step = 0
|
|
self.accumulation_steps = accumulation_steps
|
|
self.device = get_current_device()
|
|
self.accumulative_meter = AccumulativeMeanMeter()
|
|
|
|
def _before_fit(
|
|
self,
|
|
train_preference_dataloader: DataLoader = None,
|
|
eval_preference_dataloader: DataLoader = None,
|
|
log_dir: Optional[str] = None,
|
|
use_wandb: bool = False,
|
|
):
|
|
"""
|
|
Args:
|
|
prompt_dataloader (DataLoader): the dataloader to use for prompt data
|
|
pretrain_dataloader (DataLoader): the dataloader to use for pretrain data
|
|
"""
|
|
self.train_dataloader = train_preference_dataloader
|
|
self.eval_dataloader = eval_preference_dataloader
|
|
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
|
|
|
|
self.wandb_run = wandb.init(project="Coati-rm", 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, "rm")
|
|
log_dir = os.path.join(log_dir, time.strftime("%Y-%m-%d_%H:%M:%S", time.localtime()))
|
|
self.writer = SummaryWriter(log_dir=log_dir)
|
|
|
|
def _train(self, epoch):
|
|
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, self.device)
|
|
|
|
(
|
|
chosen_input_ids,
|
|
chosen_attention_mask,
|
|
reject_input_ids,
|
|
reject_attention_mask,
|
|
) = (
|
|
batch["chosen_input_ids"],
|
|
batch["chosen_attention_mask"],
|
|
batch["reject_input_ids"],
|
|
batch["reject_attention_mask"],
|
|
)
|
|
batch_size = chosen_input_ids.size()[0]
|
|
|
|
# Concatenate for better parrallelism
|
|
reward = self.model(
|
|
torch.cat([chosen_input_ids, reject_input_ids], dim=0),
|
|
attention_mask=torch.cat([chosen_attention_mask, reject_attention_mask], dim=0),
|
|
)
|
|
chosen_reward = reward[:batch_size]
|
|
reject_reward = reward[batch_size:]
|
|
loss = self.loss_fn(chosen_reward, reject_reward).mean()
|
|
|
|
self.booster.backward(loss=loss, optimizer=self.optimizer)
|
|
|
|
accuracy = (chosen_reward > reject_reward).float()
|
|
|
|
# Sync
|
|
loss_mean = all_reduce_mean(tensor=loss)
|
|
chosen_rewards_mean = all_reduce_mean(tensor=chosen_reward)
|
|
rejected_rewards_mean = all_reduce_mean(tensor=reject_reward)
|
|
accuracy_mean = all_reduce_mean(tensor=accuracy)
|
|
self.accumulative_meter.add("chosen_rewards", chosen_rewards_mean.to(torch.float16).mean().item())
|
|
self.accumulative_meter.add("rejected_rewards", rejected_rewards_mean.to(torch.float16).mean().item())
|
|
self.accumulative_meter.add("loss", loss_mean.to(torch.float16).item())
|
|
self.accumulative_meter.add("accuracy", accuracy_mean.mean().to(torch.float16).item())
|
|
|
|
if (i + 1) % self.accumulation_steps == 0:
|
|
self.optimizer.step()
|
|
self.optimizer.zero_grad()
|
|
self.actor_scheduler.step()
|
|
step_bar.update()
|
|
self.num_train_step += 1
|
|
|
|
# Logging
|
|
if self.writer and is_rank_0():
|
|
self.writer.add_scalar("train/loss", self.accumulative_meter.get("loss"), self.num_train_step)
|
|
self.writer.add_scalar("train/lr", self.optimizer.param_groups[0]["lr"], self.num_train_step)
|
|
self.writer.add_scalar(
|
|
"train/dist",
|
|
self.accumulative_meter.get("chosen_rewards") - self.accumulative_meter.get("rejected_rewards"),
|
|
self.num_train_step,
|
|
)
|
|
self.writer.add_scalar(
|
|
"train/reward_chosen", self.accumulative_meter.get("chosen_rewards"), self.num_train_step
|
|
)
|
|
self.writer.add_scalar(
|
|
"train/reward_reject", self.accumulative_meter.get("rejected_rewards"), self.num_train_step
|
|
)
|
|
self.writer.add_scalar("train/acc", self.accumulative_meter.get("accuracy"), self.num_train_step)
|
|
|
|
self.accumulative_meter.reset()
|
|
|
|
# Save checkpoint
|
|
if self.save_interval > 0 and (self.num_train_step + 1) % self.save_interval == 0:
|
|
self.coordinator.print_on_master("\nStart saving model checkpoint with running states")
|
|
save_checkpoint(
|
|
save_dir=self.save_dir,
|
|
booster=self.booster,
|
|
model=self.model,
|
|
optimizer=self.optimizer,
|
|
lr_scheduler=self.actor_scheduler,
|
|
epoch=epoch,
|
|
step=i + 1,
|
|
batch_size=batch_size,
|
|
coordinator=self.coordinator,
|
|
)
|
|
self.coordinator.print_on_master(
|
|
f"Saved checkpoint at epoch {epoch} step {(i + 1)/self.accumulation_steps} at folder {self.save_dir}"
|
|
)
|
|
step_bar.close()
|
|
|
|
def _eval(self, epoch):
|
|
if self.eval_dataloader is None:
|
|
self.coordinator.print_on_master("No eval dataloader is provided, skip evaluation")
|
|
return
|
|
self.model.eval()
|
|
step_bar = tqdm.trange(
|
|
len(self.eval_dataloader), desc=f"Epoch {epoch + 1}/{self.max_epochs}", disable=not is_rank_0()
|
|
)
|
|
with torch.no_grad():
|
|
for i, batch in enumerate(self.eval_dataloader):
|
|
batch = to_device(batch, self.device)
|
|
(
|
|
chosen_input_ids,
|
|
chosen_attention_mask,
|
|
reject_input_ids,
|
|
reject_attention_mask,
|
|
) = (
|
|
batch["chosen_input_ids"],
|
|
batch["chosen_attention_mask"],
|
|
batch["reject_input_ids"],
|
|
batch["reject_attention_mask"],
|
|
)
|
|
|
|
chosen_reward = self.model(chosen_input_ids, attention_mask=chosen_attention_mask)
|
|
reject_reward = self.model(reject_input_ids, attention_mask=reject_attention_mask)
|
|
loss = self.loss_fn(chosen_reward, reject_reward).mean()
|
|
|
|
# Sync
|
|
loss_mean = all_reduce_mean(tensor=loss)
|
|
chosen_rewards_mean = all_reduce_mean(tensor=chosen_reward)
|
|
rejected_rewards_mean = all_reduce_mean(tensor=reject_reward)
|
|
self.accumulative_meter.add("chosen_rewards", chosen_rewards_mean.to(torch.float16).mean().item())
|
|
self.accumulative_meter.add("rejected_rewards", rejected_rewards_mean.to(torch.float16).mean().item())
|
|
self.accumulative_meter.add("loss", loss_mean.to(torch.float16).item())
|
|
|
|
step_bar.update()
|
|
|
|
msg = "Evaluation Result:\n"
|
|
for tag in ["loss", "chosen_rewards", "rejected_rewards"]:
|
|
msg = msg + f"{tag}: {self.accumulative_meter.get(tag)}\n"
|
|
msg = (
|
|
msg
|
|
+ f"distance: {self.accumulative_meter.get('chosen_rewards')-self.accumulative_meter.get('rejected_rewards')}\n"
|
|
)
|
|
self.coordinator.print_on_master(msg)
|
|
with open(os.path.join(self.save_dir, f"eval_result_epoch{epoch}.txt"), "w") as f:
|
|
f.write(msg)
|
|
step_bar.close()
|