mirror of https://github.com/hpcaitech/ColossalAI
You can not select more than 25 topics
Topics must start with a letter or number, can include dashes ('-') and can be up to 35 characters long.
58 lines
2.0 KiB
58 lines
2.0 KiB
import random
|
|
from typing import List
|
|
|
|
import torch
|
|
from chatgpt.experience_maker.base import Experience
|
|
|
|
from .base import ReplayBuffer
|
|
from .utils import BufferItem, make_experience_batch, split_experience_batch
|
|
|
|
|
|
class NaiveReplayBuffer(ReplayBuffer):
|
|
"""Naive replay buffer class. It stores experience.
|
|
|
|
Args:
|
|
sample_batch_size (int): Batch size when sampling.
|
|
limit (int, optional): Limit of number of experience samples. A number <= 0 means unlimited. Defaults to 0.
|
|
cpu_offload (bool, optional): Whether to offload experience to cpu when sampling. Defaults to True.
|
|
"""
|
|
|
|
def __init__(self, sample_batch_size: int, limit: int = 0, cpu_offload: bool = True) -> None:
|
|
super().__init__(sample_batch_size, limit)
|
|
self.cpu_offload = cpu_offload
|
|
self.target_device = torch.device(f'cuda:{torch.cuda.current_device()}')
|
|
# TODO(ver217): add prefetch
|
|
self.items: List[BufferItem] = []
|
|
|
|
@torch.no_grad()
|
|
def append(self, experience: Experience) -> None:
|
|
if self.cpu_offload:
|
|
experience.to_device(torch.device('cpu'))
|
|
items = split_experience_batch(experience)
|
|
self.items.extend(items)
|
|
if self.limit > 0:
|
|
samples_to_remove = len(self.items) - self.limit
|
|
if samples_to_remove > 0:
|
|
self.items = self.items[samples_to_remove:]
|
|
|
|
def clear(self) -> None:
|
|
self.items.clear()
|
|
|
|
@torch.no_grad()
|
|
def sample(self) -> Experience:
|
|
items = random.sample(self.items, self.sample_batch_size)
|
|
experience = make_experience_batch(items)
|
|
if self.cpu_offload:
|
|
experience.to_device(self.target_device)
|
|
return experience
|
|
|
|
def __len__(self) -> int:
|
|
return len(self.items)
|
|
|
|
def __getitem__(self, idx: int) -> BufferItem:
|
|
return self.items[idx]
|
|
|
|
def collate_fn(self, batch) -> Experience:
|
|
experience = make_experience_batch(batch)
|
|
return experience
|