mirror of https://github.com/hpcaitech/ColossalAI
135 lines
6.9 KiB
Python
135 lines
6.9 KiB
Python
from typing import Any, Callable, Dict, List, Optional
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import torch
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import torch.nn as nn
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from coati.experience_maker import Experience, NaiveExperienceMaker
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from coati.models.base import Actor, Critic
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from coati.models.generation_utils import update_model_kwargs_fn
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from coati.models.loss import PolicyLoss, ValueLoss
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from coati.replay_buffer import NaiveReplayBuffer
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from torch.optim import Optimizer
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from transformers.tokenization_utils_base import PreTrainedTokenizerBase
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from .base import Trainer
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from .callbacks import Callback
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from .strategies import Strategy
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class PPOTrainer(Trainer):
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"""
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Trainer for PPO algorithm.
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Args:
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strategy (Strategy): the strategy to use for training
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actor (Actor): the actor model in ppo algorithm
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critic (Critic): the critic model in ppo algorithm
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reward_model (nn.Module): the reward model in rlhf algorithm to make reward of sentences
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initial_model (Actor): the initial model in rlhf algorithm to generate reference logits to limit the update of actor
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actor_optim (Optimizer): the optimizer to use for actor model
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critic_optim (Optimizer): the optimizer to use for critic model
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kl_coef (float, defaults to 0.1): the coefficient of kl divergence loss
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train_batch_size (int, defaults to 8): the batch size to use for training
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buffer_limit (int, defaults to 0): the max_size limitaiton of replay buffer
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buffer_cpu_offload (bool, defaults to True): whether to offload replay buffer to cpu
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eps_clip (float, defaults to 0.2): the clip coefficient of policy loss
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value_clip (float, defaults to 0.4): the clip coefficient of value loss
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experience_batch_size (int, defaults to 8): the batch size to use for experience generation
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max_epochs (int, defaults to 1): the number of epochs of training process
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tokenier (Callable, optional): the tokenizer to use for tokenizing the input
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sample_replay_buffer (bool, defaults to False): whether to sample from replay buffer
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dataloader_pin_memory (bool, defaults to True): whether to pin memory for data loader
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callbacks (List[Callback], defaults to []): the callbacks to call during training process
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generate_kwargs (dict, optional): the kwargs to use while model generating
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"""
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def __init__(self,
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strategy: Strategy,
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actor: Actor,
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critic: Critic,
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reward_model: nn.Module,
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initial_model: Actor,
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actor_optim: Optimizer,
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critic_optim: Optimizer,
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kl_coef: float = 0.1,
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ptx_coef: float = 0.9,
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train_batch_size: int = 8,
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buffer_limit: int = 0,
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buffer_cpu_offload: bool = True,
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eps_clip: float = 0.2,
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value_clip: float = 0.4,
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experience_batch_size: int = 8,
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max_epochs: int = 1,
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tokenizer: Optional[Callable[[Any], dict]] = None,
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sample_replay_buffer: bool = False,
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dataloader_pin_memory: bool = True,
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callbacks: List[Callback] = [],
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**generate_kwargs) -> None:
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experience_maker = NaiveExperienceMaker(actor, critic, reward_model, initial_model, kl_coef)
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replay_buffer = NaiveReplayBuffer(train_batch_size, buffer_limit, buffer_cpu_offload)
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generate_kwargs = _set_default_generate_kwargs(strategy, generate_kwargs, actor)
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super().__init__(strategy, experience_maker, replay_buffer, experience_batch_size, max_epochs, tokenizer,
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sample_replay_buffer, dataloader_pin_memory, callbacks, **generate_kwargs)
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self.actor = actor
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self.critic = critic
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self.actor_loss_fn = PolicyLoss(eps_clip)
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self.critic_loss_fn = ValueLoss(value_clip)
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self.ptx_loss_fn = nn.CrossEntropyLoss(ignore_index=-100)
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self.ptx_coef = ptx_coef
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self.actor_optim = actor_optim
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self.critic_optim = critic_optim
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def training_step(self, experience: Experience) -> Dict[str, float]:
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self.actor.train()
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self.critic.train()
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# policy loss
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num_actions = experience.action_mask.size(1)
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action_log_probs = self.actor(experience.sequences, num_actions, attention_mask=experience.attention_mask)
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actor_loss = self.actor_loss_fn(action_log_probs,
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experience.action_log_probs,
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experience.advantages,
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action_mask=experience.action_mask)
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# ptx loss
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if self.ptx_coef != 0:
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ptx = next(iter(self.pretrain_dataloader))['input_ids'].to(torch.cuda.current_device())
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label = next(iter(self.pretrain_dataloader))['labels'].to(torch.cuda.current_device())[:, 1:]
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attention_mask = next(iter(self.pretrain_dataloader))['attention_mask'].to(torch.cuda.current_device())
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ptx_log_probs = self.actor.get_base_model()(ptx, attention_mask=attention_mask)['logits'][..., :-1, :]
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ptx_loss = self.ptx_loss_fn(ptx_log_probs.view(-1, ptx_log_probs.size(-1)), label.view(-1))
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actor_loss = ptx_loss * self.ptx_coef + actor_loss * (1 - self.ptx_coef)
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self.strategy.backward(actor_loss, self.actor, self.actor_optim)
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self.strategy.optimizer_step(self.actor_optim)
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self.actor_optim.zero_grad()
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# value loss
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values = self.critic(experience.sequences,
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action_mask=experience.action_mask,
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attention_mask=experience.attention_mask)
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critic_loss = self.critic_loss_fn(values,
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experience.values,
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experience.reward,
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action_mask=experience.action_mask)
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self.strategy.backward(critic_loss, self.critic, self.critic_optim)
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self.strategy.optimizer_step(self.critic_optim)
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self.critic_optim.zero_grad()
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return {'reward': experience.reward.mean().item()}
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def save_model(self, path: str, only_rank0: bool = False, tokenizer: Optional[PreTrainedTokenizerBase] = None) -> None:
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self.strategy.save_model(model=self.actor, path=path, only_rank0=only_rank0, tokenizer=tokenizer)
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def _set_default_generate_kwargs(strategy: Strategy, generate_kwargs: dict, actor: Actor) -> None:
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origin_model = strategy._unwrap_actor(actor)
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new_kwargs = {**generate_kwargs}
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# use huggingface models method directly
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if 'prepare_inputs_fn' not in generate_kwargs and hasattr(origin_model, 'prepare_inputs_for_generation'):
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new_kwargs['prepare_inputs_fn'] = origin_model.prepare_inputs_for_generation
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if 'update_model_kwargs_fn' not in generate_kwargs:
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new_kwargs['update_model_kwargs_fn'] = update_model_kwargs_fn
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return new_kwargs
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