mirror of https://github.com/hpcaitech/ColossalAI
71 lines
2.5 KiB
Python
Executable File
71 lines
2.5 KiB
Python
Executable File
from typing import List
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import torch
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from coati.experience_buffer.utils import BufferItem, make_experience_batch, split_experience_batch
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from coati.experience_maker.base import Experience
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# from torch.multiprocessing import Queue
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from ray.util.queue import Queue
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class DetachedReplayBuffer:
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"""
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Detached replay buffer. Share Experience across workers on the same node.
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Therefore, a trainer node is expected to have only one instance.
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It is ExperienceMakerHolder's duty to call append(exp) method, remotely.
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Args:
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sample_batch_size: Batch size when sampling. Exp won't enqueue until they formed a batch.
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tp_world_size: Number of workers in the same tp group
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limit: Limit of number of experience sample BATCHs. A number <= 0 means unlimited. Defaults to 0.
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cpu_offload: Whether to offload experience to cpu when sampling. Defaults to True.
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"""
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def __init__(self, sample_batch_size: int, limit: int = 0) -> None:
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self.sample_batch_size = sample_batch_size
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self.limit = limit
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self.items = Queue(self.limit, actor_options={"num_cpus": 1})
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self.batch_collector: List[BufferItem] = []
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@torch.no_grad()
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def append(self, experience: Experience) -> None:
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"""
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Expected to be called remotely.
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"""
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items = split_experience_batch(experience)
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self.extend(items)
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@torch.no_grad()
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def extend(self, items: List[BufferItem]) -> None:
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"""
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Expected to be called remotely.
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"""
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self.batch_collector.extend(items)
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while len(self.batch_collector) >= self.sample_batch_size:
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items = self.batch_collector[: self.sample_batch_size]
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experience = make_experience_batch(items)
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self.items.put(experience, block=True)
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self.batch_collector = self.batch_collector[self.sample_batch_size :]
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def clear(self) -> None:
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# self.items.close()
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self.items.shutdown()
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self.items = Queue(self.limit)
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self.worker_state = [False] * self.tp_world_size
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self.batch_collector = []
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@torch.no_grad()
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def sample(self, worker_rank=0, to_device="cpu") -> Experience:
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ret = self._sample_and_erase()
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ret.to_device(to_device)
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return ret
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@torch.no_grad()
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def _sample_and_erase(self) -> Experience:
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ret = self.items.get(block=True)
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return ret
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def get_length(self) -> int:
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ret = self.items.qsize()
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return ret
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