mirror of https://github.com/hpcaitech/ColossalAI
180 lines
7.0 KiB
Python
180 lines
7.0 KiB
Python
import os
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from abc import ABC, abstractmethod
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from typing import Any, Callable, Dict, Iterable, List, Optional, Union
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import ray
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import torch
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from coati.experience_buffer.utils import BufferItem
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from coati.experience_maker import Experience
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from torch.utils.data import DataLoader
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from tqdm import tqdm
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from .callbacks import TrainerCallback
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from .detached_replay_buffer import DetachedReplayBuffer
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from .utils import is_rank_0
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class DetachedTrainer(ABC):
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'''
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Base class for detached rlhf trainers.
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'detach' means that the experience maker is detached compared to a normal Trainer.
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Please set name attribute during init:
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>>> trainer = DetachedTrainer.options(..., name = "xxx", ...).remote()
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So an ExperienceMakerHolder can reach the detached_replay_buffer by Actor's name.
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Args:
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detached_strategy (DetachedStrategy): the strategy to use for training
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detached_replay_buffer_ref (ObjectRef[DetachedReplayBuffer]): the replay buffer to use for training
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data_loader_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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experience_maker_holder_name_list: List[str],
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train_batch_size: int = 8,
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buffer_limit: int = 0,
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dataloader_pin_memory: bool = True,
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callbacks: List[TrainerCallback] = [],
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debug: bool = False) -> None:
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super().__init__()
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self.detached_replay_buffer = DetachedReplayBuffer(train_batch_size, limit=buffer_limit)
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self.dataloader_pin_memory = dataloader_pin_memory
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self.callbacks = callbacks
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self.target_holder_name_list = experience_maker_holder_name_list
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self.target_holder_list = []
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self._is_target_holder_initialized = False
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self._debug = debug
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def update_target_holder_list(self):
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# as the length of target_holder_list may be zero, we need to check it by a bool flag
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if not self._is_target_holder_initialized:
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for name in self.target_holder_name_list:
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self.target_holder_list.append(ray.get_actor(name, namespace=os.environ["RAY_NAMESPACE"]))
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self._is_target_holder_initialized = True
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@abstractmethod
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def _update_remote_makers(self, fully_update: bool = False, **kwargs):
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pass
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def sync_models_to_remote_makers(self, **kwargs):
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self._update_remote_makers(fully_update=True, **kwargs)
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@abstractmethod
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def training_step(self, experience: Experience) -> Dict[str, Any]:
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pass
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def _learn(self, update_steps: int, train_epochs: int) -> None:
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data = []
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# warmup
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pbar = tqdm(range(update_steps), desc=f'Train epoch [1/{train_epochs}]', disable=not is_rank_0())
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self._on_epoch_start(0)
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self._learn_epoch(pbar, data)
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self._on_epoch_end(0)
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# item is already a batch
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dataloader = DataLoader(data,
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batch_size=1,
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shuffle=True,
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pin_memory=self.dataloader_pin_memory,
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collate_fn=lambda x: x[0])
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for epoch in range(1, train_epochs):
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pbar = tqdm(dataloader, desc=f'Train epoch [{epoch + 1}/{train_epochs}]', disable=not is_rank_0())
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self._on_epoch_start(epoch)
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self._learn_epoch(pbar, data)
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self._on_epoch_end(epoch)
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def _learn_epoch(self, pbar: tqdm, data: List[Experience]) -> None:
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is_warmup = len(data) == 0
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for x in pbar:
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if self._debug:
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print("[trainer] training step")
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# sample a batch and then train to avoid waiting
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experience = x if not is_warmup else self._buffer_sample()
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experience.to_device(torch.cuda.current_device())
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self._on_batch_start()
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metrics = self.training_step(experience)
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self._on_batch_end(metrics, experience)
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if self._debug:
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print("[trainer] step over")
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experience.to_device("cpu")
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if is_warmup:
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data.append(experience)
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pbar.set_postfix(metrics)
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def fit(self, total_steps: int, update_steps: int, train_epochs: int = 1) -> None:
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self._on_fit_start()
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for i in tqdm(range(total_steps // update_steps), desc='Trainer', disable=not is_rank_0()):
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self._on_episode_start(i)
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self._learn(update_steps, train_epochs)
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self._on_update_start()
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self._update_remote_makers()
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self._on_update_end()
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self._on_episode_end(i)
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self._on_fit_end()
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@ray.method(concurrency_group="buffer_length")
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def buffer_get_length(self):
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# called by ExperienceMakerHolder
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if self._debug:
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print("[trainer] telling length")
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return self.detached_replay_buffer.get_length()
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@ray.method(concurrency_group="buffer_append")
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def buffer_append(self, experience: Experience):
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# called by ExperienceMakerHolder
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if self._debug:
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print(f"[trainer] receiving exp.")
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self.detached_replay_buffer.append(experience)
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@ray.method(concurrency_group="buffer_append")
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def buffer_extend(self, items: List[BufferItem]):
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# called by ExperienceMakerHolder
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if self._debug:
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print(f"[trainer] receiving exp.")
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self.detached_replay_buffer.extend(items)
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@ray.method(concurrency_group="buffer_sample")
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def _buffer_sample(self):
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return self.detached_replay_buffer.sample()
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def _on_fit_start(self) -> None:
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for callback in self.callbacks:
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callback.on_fit_start()
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def _on_fit_end(self) -> None:
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for callback in self.callbacks:
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callback.on_fit_end()
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def _on_episode_start(self, episode: int) -> None:
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for callback in self.callbacks:
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callback.on_episode_start(episode)
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def _on_episode_end(self, episode: int) -> None:
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for callback in self.callbacks:
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callback.on_episode_end(episode)
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def _on_epoch_start(self, epoch: int) -> None:
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for callback in self.callbacks:
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callback.on_epoch_start(epoch)
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def _on_epoch_end(self, epoch: int) -> None:
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for callback in self.callbacks:
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callback.on_epoch_end(epoch)
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def _on_batch_start(self) -> None:
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for callback in self.callbacks:
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callback.on_batch_start()
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def _on_batch_end(self, metrics: dict, experience: Experience) -> None:
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for callback in self.callbacks:
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callback.on_batch_end(metrics, experience)
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def _on_update_start(self) -> None:
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for callback in self.callbacks:
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callback.on_update_start()
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def _on_update_end(self) -> None:
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for callback in self.callbacks:
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callback.on_update_end()
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