79 lines
2.4 KiB
Python
79 lines
2.4 KiB
Python
import torch
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import torch.distributed as dist
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from torch.utils.data import Dataset, Sampler
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from typing import Optional
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class ResumableDistributedSampler(Sampler[int]):
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def __init__(
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self,
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data_source: Dataset,
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start_epoch: int = 0,
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start_iter: int = 0,
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seed: int = 42,
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drop_last: bool = False,
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shuffle: bool = True,
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process_group: Optional[dist.ProcessGroup] = None,
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):
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self.epoch = start_epoch
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self.iter = start_iter
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self.seed = seed
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self.num_samples = len(data_source)
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if process_group is not None:
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# input process group
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self.rank = dist.get_rank(process_group)
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self.num_replicas = dist.get_world_size(process_group)
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elif dist.is_available() and dist.is_initialized():
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# use default process group
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process_group = dist.group.WORLD
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self.rank = dist.get_rank()
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self.num_replicas = dist.get_world_size()
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else:
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# single process
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self.rank = 0
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self.num_replicas = 1
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self.drop_last = drop_last
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self.shuffle = shuffle
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offset = 0 if drop_last else self.num_replicas - 1
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self.num_samples_per_replica = (self.num_samples + offset) // self.num_replicas
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self.total_size = self.num_samples_per_replica * self.num_replicas
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self._indices = None
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def _get_indices(self):
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if self.shuffle:
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generator = torch.Generator()
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generator.manual_seed(self.seed + self.epoch)
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indices = torch.randperm(self.num_samples, generator=generator).tolist()
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else:
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indices = torch.arange(self.num_samples).tolist()
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if not self.drop_last and self.num_samples < self.total_size:
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padding_size = self.total_size - len(indices)
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indices += indices[:padding_size]
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local_indices = indices[self.rank : self.total_size : self.num_replicas]
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self.iter = self.iter % self.num_samples_per_replica
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self._indices = local_indices[self.iter :]
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def __iter__(self):
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if self._indices is None:
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self._get_indices()
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for i in self._indices:
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self.iter += 1
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yield i
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self.epoch += 1
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self._indices = None
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def __len__(self):
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return self.num_samples_per_replica
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