mirror of https://github.com/hpcaitech/ColossalAI
214 lines
10 KiB
Python
214 lines
10 KiB
Python
from typing import Any, Callable, Dict, List, Optional, Union
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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.loss import GPTLMLoss, PolicyLoss, ValueLoss
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from coati.replay_buffer import NaiveReplayBuffer
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from torch import Tensor
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from torch.optim import Optimizer
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from torch.utils.data import DistributedSampler
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from tqdm import tqdm
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from transformers.tokenization_utils_base import PreTrainedTokenizerBase
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from colossalai.utils import get_current_device
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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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from .utils import is_rank_0, to_device
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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 logics 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 limitation 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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vf_coef (float, defaults to 1.0): the coefficient of value loss
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ptx_coef (float, defaults to 0.9): the coefficient of ptx loss
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value_clip (float, defaults to 0.4): the clip coefficient of value loss
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max_epochs (int, defaults to 1): the number of epochs of training process
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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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offload_inference_models (bool, defaults to True): whether to offload inference models to cpu during training process
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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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vf_coef: float = 1.0,
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value_clip: float = 0.4,
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max_epochs: int = 1,
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sample_replay_buffer: bool = False,
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dataloader_pin_memory: bool = True,
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offload_inference_models: 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, max_epochs, dataloader_pin_memory, callbacks, **generate_kwargs)
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self.experience_maker = experience_maker
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self.replay_buffer = replay_buffer
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self.sample_replay_buffer = sample_replay_buffer
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self.offload_inference_models = offload_inference_models
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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.vf_coef = vf_coef
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self.ptx_loss_fn = GPTLMLoss()
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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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self.device = get_current_device()
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def _make_experience(self, inputs: Union[Tensor, Dict[str, Tensor]]) -> Experience:
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if isinstance(inputs, Tensor):
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return self.experience_maker.make_experience(inputs, **self.generate_kwargs)
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elif isinstance(inputs, dict):
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return self.experience_maker.make_experience(**inputs, **self.generate_kwargs)
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else:
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raise ValueError(f'Unsupported input type "{type(inputs)}"')
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def _learn(self):
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# replay buffer may be empty at first, we should rebuild at each training
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if not self.sample_replay_buffer:
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dataloader = self.strategy.setup_dataloader(self.replay_buffer, self.dataloader_pin_memory)
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if self.sample_replay_buffer:
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pbar = tqdm(range(self.max_epochs), desc='Train epoch', disable=not is_rank_0())
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for _ in pbar:
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experience = self.replay_buffer.sample()
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experience.to_device(self.device)
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metrics = self.training_step(experience)
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pbar.set_postfix(metrics)
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else:
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for epoch in range(self.max_epochs):
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self._on_learn_epoch_start(epoch)
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if isinstance(dataloader.sampler, DistributedSampler):
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dataloader.sampler.set_epoch(epoch)
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pbar = tqdm(dataloader, desc=f'Train epoch [{epoch+1}/{self.max_epochs}]', disable=not is_rank_0())
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for experience in pbar:
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self._on_learn_batch_start()
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experience.to_device(self.device)
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metrics = self.training_step(experience)
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self._on_learn_batch_end(metrics, experience)
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pbar.set_postfix(metrics)
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self._on_learn_epoch_end(epoch)
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def fit(self,
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prompt_dataloader,
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pretrain_dataloader,
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num_episodes: int = 50000,
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max_timesteps: int = 500,
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update_timesteps: int = 5000) -> None:
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time = 0
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self.pretrain_dataloader = pretrain_dataloader
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self.prompt_dataloader = prompt_dataloader
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self._on_fit_start()
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for episode in range(num_episodes):
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self._on_episode_start(episode)
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for timestep in tqdm(range(max_timesteps),
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desc=f'Episode [{episode+1}/{num_episodes}]',
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disable=not is_rank_0()):
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time += 1
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prompts = next(iter(self.prompt_dataloader))
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self._on_make_experience_start()
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if self.offload_inference_models:
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# TODO(ver217): this may be controlled by strategy if they are prepared by strategy
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self.experience_maker.initial_model.to(self.device)
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self.experience_maker.reward_model.to(self.device)
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experience = self._make_experience(prompts)
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self._on_make_experience_end(experience)
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self.replay_buffer.append(experience)
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if time % update_timesteps == 0:
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if self.offload_inference_models:
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self.experience_maker.initial_model.to('cpu')
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self.experience_maker.reward_model.to('cpu')
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self._learn()
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self.replay_buffer.clear()
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self._on_episode_end(episode)
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self._on_fit_end()
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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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batch = next(iter(self.pretrain_dataloader))
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batch = to_device(batch, self.device)
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ptx_log_probs = self.actor.get_base_model()(batch['input_ids'],
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attention_mask=batch['attention_mask'])['logits']
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ptx_loss = self.ptx_loss_fn(ptx_log_probs, batch['labels'])
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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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critic_loss = critic_loss * self.vf_coef
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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 _set_default_generate_kwargs(strategy: Strategy, generate_kwargs: dict, actor: Actor) -> None:
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origin_model = strategy.unwrap_model(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 and hasattr(origin_model, '_update_model_kwargs_for_generation'):
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new_kwargs['update_model_kwargs_fn'] = origin_model._update_model_kwargs_for_generation
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return new_kwargs
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