mirror of https://github.com/hpcaitech/ColossalAI
353 lines
15 KiB
Python
Executable File
353 lines
15 KiB
Python
Executable File
"""
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KTO trainer
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"""
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import os
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from typing import Any, Optional
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import torch
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import torch.distributed as dist
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from coati.models.loss import KTOLoss
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from coati.models.utils import calc_masked_log_probs
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from coati.trainer.utils import all_reduce_mean
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from coati.utils import AccumulativeMeanMeter, save_checkpoint
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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 tqdm import trange
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from transformers import PreTrainedTokenizerBase
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from colossalai.booster import Booster, Plugin
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from colossalai.cluster import DistCoordinator
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from colossalai.utils import get_current_device
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from .base import SLTrainer
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from .utils import is_rank_0, to_device
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class KTOTrainer(SLTrainer):
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"""
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Trainer for KTO algorithm.
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Args:
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actor (Actor): the actor model in ppo algorithm
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ref_model (Critic): the reference model in ppo algorithm
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booster (Strategy): the strategy to use for training
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actor_optim (Optimizer): the optimizer to use for actor model
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actor_lr_scheduler (_LRScheduler): the lr scheduler to use for actor model
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tokenizer (PreTrainedTokenizerBase): the tokenizer to use for encoding
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max_epochs (int, defaults to 1): the max number of epochs to train
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accumulation_steps (int): the number of steps to accumulate gradients
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start_epoch (int, defaults to 0): the start epoch, non-zero if resumed from a checkpoint
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save_interval (int): the interval to save model checkpoints, default to 0, which means no checkpoint will be saved during trainning
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save_dir (str): the directory to save checkpoints
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coordinator (DistCoordinator): the coordinator to use for distributed logging
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beta (float, defaults to 0.1): the beta parameter in kto loss
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desirable_weight (float, defaults to 1.0): the weight for desirable reward
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undesirable_weight (float, defaults to 1.0): the weight for undesirable reward
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"""
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def __init__(
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self,
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actor: Any,
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ref_model: Any,
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booster: Booster,
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actor_optim: Optimizer,
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plugin: Plugin,
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actor_lr_scheduler: _LRScheduler,
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tokenizer: PreTrainedTokenizerBase,
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max_epochs: int = 1,
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beta: float = 0.1,
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desirable_weight: float = 1.0,
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undesirable_weight: float = 1.0,
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apply_loss_mask: bool = True,
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accumulation_steps: int = 1,
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start_epoch: int = 0,
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save_interval: int = 0,
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save_dir: str = None,
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coordinator: DistCoordinator = None,
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) -> None:
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super().__init__(
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booster, max_epochs=max_epochs, model=actor, optimizer=actor_optim, plugin=plugin, start_epoch=start_epoch
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)
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self.ref_model = ref_model
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self.actor_scheduler = actor_lr_scheduler
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self.tokenizer = tokenizer
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self.kto_loss = KTOLoss(beta=beta, desirable_weight=desirable_weight, undesirable_weight=undesirable_weight)
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self.apply_loss_mask = apply_loss_mask
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self.save_interval = save_interval
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self.coordinator = coordinator
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self.save_dir = save_dir
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self.num_train_step = 0
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self.accumulation_steps = accumulation_steps
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self.device = get_current_device()
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self.accumulative_meter = AccumulativeMeanMeter()
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self.desirable_weight = desirable_weight
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self.undesirable_weight = undesirable_weight
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self.beta = beta
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def _before_fit(
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self,
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train_preference_dataloader: DataLoader = None,
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eval_preference_dataloader: DataLoader = None,
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log_dir: Optional[str] = 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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prompt_dataloader (DataLoader): the dataloader to use for prompt data
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pretrain_dataloader (DataLoader): the dataloader to use for pretrain data
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"""
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self.train_dataloader = train_preference_dataloader
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self.eval_dataloader = eval_preference_dataloader
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self.writer = None
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if use_wandb and is_rank_0():
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assert log_dir is not None, "log_dir must be provided when use_wandb is True"
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import wandb
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self.wandb_run = wandb.init(project="Coati-kto", sync_tensorboard=True)
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if log_dir is not None and is_rank_0():
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import os
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import time
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from torch.utils.tensorboard import SummaryWriter
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log_dir = os.path.join(log_dir, "kto")
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log_dir = os.path.join(log_dir, time.strftime("%Y-%m-%d_%H:%M:%S", time.localtime()))
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self.writer = SummaryWriter(log_dir=log_dir)
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def _train(self, epoch: int):
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"""
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Args:
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epoch int: the number of current epoch
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"""
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self.model.train()
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self.accumulative_meter.reset()
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step_bar = trange(
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len(self.train_dataloader) // self.accumulation_steps,
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desc=f"Epoch {epoch + 1}/{self.max_epochs}",
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disable=not is_rank_0(),
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)
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for i, batch in enumerate(self.train_dataloader):
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batch = to_device(batch, self.device)
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(input_ids, attention_mask, loss_mask, label, kl_input_ids, kl_attention_mask, kl_loss_mask) = (
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batch["input_ids"],
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batch["attention_mask"],
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batch["loss_mask"],
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batch["label"],
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batch["kl_input_ids"],
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batch["kl_attention_mask"],
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batch["kl_loss_mask"],
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)
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if not self.apply_loss_mask:
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loss_mask = loss_mask.fill_(1.0)
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kl_loss_mask = kl_loss_mask.fill_(1.0)
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batch_size = input_ids.size()[0]
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# actor logits
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with torch.no_grad():
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# calculate KL term with KT data
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kl_logits = self.model(
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input_ids=kl_input_ids,
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attention_mask=kl_attention_mask,
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)["logits"]
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logits = self.model(
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input_ids=input_ids,
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attention_mask=attention_mask,
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)["logits"]
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logprob = calc_masked_log_probs(logits, input_ids, loss_mask[:, 1:]).sum(-1)
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kl_logprob = calc_masked_log_probs(kl_logits, kl_input_ids, kl_loss_mask[:, 1:]).sum(-1)
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chosen_index = [i for i in range(batch_size) if label[i] == 1]
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rejected_index = [i for i in range(batch_size) if label[i] == 0]
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chosen_logprob = logprob[chosen_index]
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rejected_logprob = logprob[rejected_index]
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with torch.no_grad():
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ref_kl_logits = self.ref_model(
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input_ids=kl_input_ids,
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attention_mask=kl_attention_mask,
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)["logits"]
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ref_logits = self.ref_model(
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input_ids=input_ids,
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attention_mask=attention_mask,
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)["logits"]
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ref_logprob = calc_masked_log_probs(ref_logits, input_ids, loss_mask[:, 1:]).sum(-1)
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ref_kl_logprob = calc_masked_log_probs(ref_kl_logits, kl_input_ids, kl_loss_mask[:, 1:]).sum(-1)
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ref_chosen_logprob = ref_logprob[chosen_index]
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ref_rejected_logprob = ref_logprob[rejected_index]
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loss, chosen_rewards, rejected_rewards, kl = self.kto_loss(
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chosen_logprob, rejected_logprob, kl_logprob, ref_chosen_logprob, ref_rejected_logprob, ref_kl_logprob
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)
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self.booster.backward(loss=loss, optimizer=self.optimizer)
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if self.num_train_step % self.accumulation_steps == self.accumulation_steps - 1:
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self.optimizer.step()
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self.optimizer.zero_grad()
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self.actor_scheduler.step()
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# sync
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loss_mean = all_reduce_mean(tensor=loss)
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chosen_reward_mean = chosen_rewards.mean()
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chosen_rewards_list = [
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torch.tensor(0, dtype=loss.dtype, device=loss.device) for _ in range(dist.get_world_size())
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]
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dist.all_gather(chosen_rewards_list, chosen_reward_mean)
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rejected_reward_mean = rejected_rewards.mean()
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rejected_rewards_list = [
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torch.tensor(0, dtype=loss.dtype, device=loss.device) for _ in range(dist.get_world_size())
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]
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dist.all_gather(rejected_rewards_list, rejected_reward_mean)
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chosen_rewards_list = [i for i in chosen_rewards_list if not i.isnan()]
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rejected_rewards_list = [i for i in rejected_rewards_list if not i.isnan()]
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chosen_rewards_mean = (
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torch.stack(chosen_rewards_list).mean()
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if len(chosen_rewards_list) > 0
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else torch.tensor(torch.nan, dtype=loss.dtype, device=loss.device)
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)
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rejected_rewards_mean = (
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torch.stack(rejected_rewards_list).mean()
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if len(rejected_rewards_list) > 0
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else torch.tensor(torch.nan, dtype=loss.dtype, device=loss.device)
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)
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self.accumulative_meter.add("chosen_rewards", chosen_rewards_mean.to(torch.float16).mean().item())
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self.accumulative_meter.add("rejected_rewards", rejected_rewards_mean.to(torch.float16).mean().item())
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self.accumulative_meter.add("loss", loss_mean.to(torch.float16).detach().item())
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if i % self.accumulation_steps == self.accumulation_steps - 1:
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self.num_train_step += 1
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step_bar.update()
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# logging
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if self.writer and is_rank_0():
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self.writer.add_scalar("train/loss", self.accumulative_meter.get("loss"), self.num_train_step)
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self.writer.add_scalar("train/lr", self.optimizer.param_groups[0]["lr"], self.num_train_step)
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self.writer.add_scalar(
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"train/chosen_rewards", self.accumulative_meter.get("chosen_rewards"), self.num_train_step
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)
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self.writer.add_scalar(
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"train/rejected_rewards",
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self.accumulative_meter.get("rejected_rewards"),
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self.num_train_step,
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)
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self.writer.add_scalar(
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"train/margin",
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self.accumulative_meter.get("chosen_rewards") - self.accumulative_meter.get("rejected_rewards"),
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self.num_train_step,
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)
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self.accumulative_meter.reset()
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if self.save_dir is not None and (self.num_train_step + 1) % self.save_interval == 0:
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# save checkpoint
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self.coordinator.print_on_master("\nStart saving model checkpoint with running states")
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save_checkpoint(
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save_dir=self.save_dir,
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booster=self.booster,
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model=self.model,
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optimizer=self.optimizer,
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lr_scheduler=self.actor_scheduler,
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epoch=epoch,
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step=i + 1,
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batch_size=batch_size,
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coordinator=self.coordinator,
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)
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self.coordinator.print_on_master(
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f"Saved checkpoint at epoch {epoch} step {self.save_interval} at folder {self.save_dir}"
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)
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step_bar.close()
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def _eval(self, epoch: int):
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"""
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Args:
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epoch int: the number of current epoch
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"""
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if self.eval_dataloader is None:
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self.coordinator.print_on_master("No eval dataloader is provided, skip evaluation")
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return
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self.model.eval()
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self.accumulative_meter.reset()
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step_bar = trange(
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len(self.train_dataloader) // self.accumulation_steps,
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desc=f"Epoch {epoch + 1}/{self.max_epochs}",
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disable=not is_rank_0(),
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)
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for i, batch in enumerate(self.train_dataloader):
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batch = to_device(batch, self.device)
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(input_ids, attention_mask, loss_mask, label, kl_input_ids, kl_attention_mask, kl_loss_mask) = (
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batch["input_ids"],
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batch["attention_mask"],
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batch["loss_mask"],
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batch["label"],
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batch["kl_input_ids"],
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batch["kl_attention_mask"],
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batch["kl_loss_mask"],
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)
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if not self.apply_loss_mask:
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loss_mask = loss_mask.fill_(1.0)
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kl_loss_mask = kl_loss_mask.fill_(1.0)
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batch_size = input_ids.size()[0]
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# actor logits
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with torch.no_grad():
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# calculate KL term with KT data
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kl_logits = self.model(
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input_ids=kl_input_ids,
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attention_mask=kl_attention_mask,
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)["logits"]
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logits = self.model(
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input_ids=input_ids,
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attention_mask=attention_mask,
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)["logits"]
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logprob = calc_masked_log_probs(logits, input_ids, loss_mask[:, 1:]).sum(-1)
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kl_logprob = calc_masked_log_probs(kl_logits, kl_input_ids, kl_loss_mask[:, 1:]).sum(-1)
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chosen_index = [i for i in range(batch_size) if label[i] == 1]
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rejected_index = [i for i in range(batch_size) if label[i] == 0]
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chosen_logprob = logprob[chosen_index]
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rejected_logprob = logprob[rejected_index]
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with torch.no_grad():
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ref_kl_logits = self.ref_model(
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input_ids=kl_input_ids,
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attention_mask=kl_attention_mask,
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)["logits"]
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ref_logits = self.ref_model(
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input_ids=input_ids,
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attention_mask=attention_mask,
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)["logits"]
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ref_logprob = calc_masked_log_probs(ref_logits, input_ids, loss_mask[:, 1:]).sum(-1)
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ref_kl_logprob = calc_masked_log_probs(ref_kl_logits, kl_input_ids, kl_loss_mask[:, 1:]).sum(-1)
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ref_chosen_logprob = ref_logprob[chosen_index]
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ref_rejected_logprob = ref_logprob[rejected_index]
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loss, chosen_rewards, rejected_rewards, kl = self.kto_loss(
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chosen_logprob, rejected_logprob, kl_logprob, ref_chosen_logprob, ref_rejected_logprob, ref_kl_logprob
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)
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# sync
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loss_mean = all_reduce_mean(tensor=loss)
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chosen_rewards_mean = all_reduce_mean(tensor=chosen_rewards.mean())
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rejected_rewards_mean = all_reduce_mean(tensor=rejected_rewards.mean())
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self.accumulative_meter.add("chosen_rewards", chosen_rewards_mean.to(torch.float16).mean().item())
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self.accumulative_meter.add("rejected_rewards", rejected_rewards_mean.to(torch.float16).mean().item())
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self.accumulative_meter.add("loss", loss_mean.to(torch.float16).detach().item())
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self.accumulative_meter.add(
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"margin", (chosen_rewards_mean - rejected_rewards_mean).to(torch.float16).mean().item()
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)
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step_bar.update()
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msg = "Evaluation Result:\n"
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for tag in ["loss", "chosen_rewards", "rejected_rewards", "margin"]:
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msg = msg + f"{tag}: {self.accumulative_meter.get(tag)}\n"
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self.coordinator.print_on_master(msg)
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os.makedirs(self.save_dir, exist_ok=True)
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with open(os.path.join(self.save_dir, f"eval_result_epoch{epoch}.txt"), "w") as f:
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f.write(msg)
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step_bar.close()
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