mirror of https://github.com/hpcaitech/ColossalAI
201 lines
9.0 KiB
Python
201 lines
9.0 KiB
Python
from typing import Any, Callable, Dict, List, Optional, Tuple
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import ray
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import torch
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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 PolicyLoss, ValueLoss
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from coati.trainer.callbacks import Callback
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from coati.trainer.strategies import DDPStrategy, GeminiStrategy, LowLevelZeroStrategy, Strategy
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from torch.optim import Adam
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from colossalai.nn.optimizer import HybridAdam
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from .callbacks import TrainerCallback, TrainerPerformanceEvaluator
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from .detached_trainer_base import DetachedTrainer
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from .lora_constructor import LoRAConstructor
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from .utils import (
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get_actor_from_args,
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get_critic_from_args,
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get_model_numel,
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get_rank,
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get_strategy_from_args,
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is_rank_0,
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set_dist_env,
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state_dict_to,
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)
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@ray.remote(concurrency_groups={
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"buffer_length": 1,
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"buffer_append": 1,
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"buffer_sample": 1,
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"model_io": 1,
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"compute": 1
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})
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class DetachedPPOTrainer(DetachedTrainer):
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'''
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Detached 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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model (str) : for actor / critic init
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pretrained (str) : for actor / critic init
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lora_rank (int) : for actor / critic init
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train_batch_size (int, defaults to 8): the batch size to use for training
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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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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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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__(
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self,
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experience_maker_holder_name_list: List[str],
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strategy_fn: Callable[[], Strategy],
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model_fn: Callable[[], Tuple[Actor, Critic]],
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env_info: Dict[str, str] = None,
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train_batch_size: int = 8,
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buffer_limit: int = 0,
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eps_clip: float = 0.2,
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value_clip: float = 0.4,
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dataloader_pin_memory: bool = True,
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callbacks: List[TrainerCallback] = [],
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eval_performance: bool = False,
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debug: bool = False,
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update_lora_weights: bool = False,
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) -> None:
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# set environment variables
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if env_info:
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set_dist_env(env_info=env_info)
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# configure strategy
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self.strategy = strategy_fn()
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# configure models, loss and optimizers
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with self.strategy.model_init_context():
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self.actor, self.critic = model_fn()
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if eval_performance:
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actor_numel = get_model_numel(self.actor)
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critic_numel = get_model_numel(self.critic)
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evaluator = TrainerPerformanceEvaluator(actor_numel, critic_numel)
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callbacks = callbacks + [evaluator]
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if isinstance(self.strategy, (LowLevelZeroStrategy, GeminiStrategy)):
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self.actor_optim = HybridAdam(self.actor.parameters(), lr=1e-7)
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self.critic_optim = HybridAdam(self.critic.parameters(), lr=1e-7)
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else:
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self.actor_optim = Adam(self.actor.parameters(), lr=1e-7)
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self.critic_optim = Adam(self.critic.parameters(), lr=1e-7)
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(self.actor, self.actor_optim), (self.critic, self.critic_optim) = \
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self.strategy.prepare((self.actor, self.actor_optim), (self.critic, self.critic_optim))
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# configure trainer
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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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super().__init__(experience_maker_holder_name_list,
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train_batch_size=train_batch_size,
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buffer_limit=buffer_limit,
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dataloader_pin_memory=dataloader_pin_memory,
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callbacks=callbacks,
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debug=debug)
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if self._debug:
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print(f'[trainer{get_rank()}] will send state dict to {experience_maker_holder_name_list}')
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self._update_lora_weights = update_lora_weights
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@ray.method(concurrency_group="model_io")
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@torch.no_grad()
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def _update_remote_makers(self, fully_update: bool = False, **config):
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# TODO: balance duties
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if not fully_update:
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config['requires_grad_only'] = True
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self.update_target_holder_list()
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# mark start, ensure order
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tasks = []
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for target_holder in self.target_holder_list:
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tasks.append(target_holder.update_experience_maker.remote(chunk_start=True, fully_update=fully_update))
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ray.get(tasks)
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# sending loop
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tasks = []
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for state_dict_shard in self._get_model_state_dict_shard(self.actor, fully_update=fully_update, **config):
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for target_holder in self.target_holder_list:
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tasks.append(
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target_holder.update_experience_maker.remote(
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new_actor_state_dict=state_dict_shard,
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new_actor_lora_config_dict=self._get_model_lora_config_dict(self.actor),
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fully_update=fully_update))
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# sending loop
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for state_dict_shard in self._get_model_state_dict_shard(self.critic, fully_update=fully_update, **config):
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for target_holder in self.target_holder_list:
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tasks.append(
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target_holder.update_experience_maker.remote(
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new_critic_state_dict=state_dict_shard,
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new_critic_lora_config_dict=self._get_model_lora_config_dict(self.critic),
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fully_update=fully_update))
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ray.get(tasks)
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# mark end
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for target_holder in self.target_holder_list:
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target_holder.update_experience_maker.remote(chunk_end=True, fully_update=fully_update)
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@ray.method(concurrency_group="compute")
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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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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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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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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 {'actor_loss': actor_loss.item(), 'critic_loss': critic_loss.item()}
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def strategy_save_actor(self, path: str, only_rank0: bool = False) -> None:
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self.strategy.save_model(self.actor, path, only_rank0)
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def strategy_save_critic(self, path: str, only_rank0: bool = False) -> None:
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self.strategy.save_model(self.critic, path, only_rank0)
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def strategy_save_actor_optim(self, path: str, only_rank0: bool = False) -> None:
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self.strategy.save_optimizer(self.actor_optim, path, only_rank0)
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def strategy_save_critic_optim(self, path: str, only_rank0: bool = False) -> None:
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self.strategy.save_optimizer(self.critic_optim, path, only_rank0)
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def _get_model_state_dict_shard(self, model: torch.nn.Module, fully_update=False, **config):
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for state_dict in self.strategy.get_model_state_dict_shard(model, **config):
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if not self._update_lora_weights or fully_update:
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yield state_dict_to(state_dict)
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else:
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state_dict_lora, _ = LoRAConstructor.filter_state_dict_lora(state_dict)
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yield state_dict_to(state_dict_lora)
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def _get_model_lora_config_dict(self, model: torch.nn.Module):
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if not self._update_lora_weights:
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return None
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unwrapped_model = self.strategy.unwrap_model(model)
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return LoRAConstructor.extract_lora_config(unwrapped_model)
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