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158 lines
6.3 KiB
158 lines
6.3 KiB
import math
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from collections.abc import Iterator
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from typing import TypeVar
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import torch
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import torch.distributed as dist
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from torch.utils.data.dataset import Dataset
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from torch.utils.data.sampler import Sampler
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__all__ = ["DistributedSampler"]
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_T_co = TypeVar("_T_co", covariant=True)
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class DistributedSampler(Sampler[_T_co]):
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r"""Sampler that restricts data loading to a subset of the dataset.
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It is especially useful in conjunction with
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:class:`torch.nn.parallel.DistributedDataParallel`. In such a case, each
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process can pass a :class:`~torch.utils.data.DistributedSampler` instance as a
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:class:`~torch.utils.data.DataLoader` sampler, and load a subset of the
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original dataset that is exclusive to it.
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.. note::
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Dataset is assumed to be of constant size and that any instance of it always
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returns the same elements in the same order.
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Args:
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dataset: Dataset used for sampling.
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num_replicas (int, optional): Number of processes participating in
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distributed training. By default, :attr:`world_size` is retrieved from the
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current distributed group.
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rank (int, optional): Rank of the current process within :attr:`num_replicas`.
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By default, :attr:`rank` is retrieved from the current distributed
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group.
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shuffle (bool, optional): If ``True`` (default), sampler will shuffle the
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indices.
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seed (int, optional): random seed used to shuffle the sampler if
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:attr:`shuffle=True`. This number should be identical across all
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processes in the distributed group. Default: ``0``.
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drop_last (bool, optional): if ``True``, then the sampler will drop the
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tail of the data to make it evenly divisible across the number of
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replicas. If ``False``, the sampler will add extra indices to make
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the data evenly divisible across the replicas. Default: ``False``.
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.. warning::
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In distributed mode, calling the :meth:`set_epoch` method at
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the beginning of each epoch **before** creating the :class:`DataLoader` iterator
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is necessary to make shuffling work properly across multiple epochs. Otherwise,
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the same ordering will be always used.
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Example::
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>>> # xdoctest: +SKIP
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>>> sampler = DistributedSampler(dataset) if is_distributed else None
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>>> loader = DataLoader(dataset, shuffle=(sampler is None),
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... sampler=sampler)
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>>> for epoch in range(start_epoch, n_epochs):
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... if is_distributed:
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... sampler.set_epoch(epoch)
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... train(loader)
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"""
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def __init__(
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self,
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dataset: Dataset,
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num_replicas: int | None = None,
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rank: int | None = None,
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shuffle: bool = True,
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seed: int = 0,
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drop_last: bool = False,
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) -> None:
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if num_replicas is None:
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if not dist.is_available():
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raise RuntimeError("Requires distributed package to be available")
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num_replicas = dist.get_world_size()
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if rank is None:
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if not dist.is_available():
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raise RuntimeError("Requires distributed package to be available")
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rank = dist.get_rank()
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if rank >= num_replicas or rank < 0:
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raise ValueError(
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f"Invalid rank {rank}, rank should be in the interval [0, {num_replicas - 1}]"
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)
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self.dataset = dataset
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self.num_replicas = num_replicas
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self.rank = rank
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self.epoch = 0
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self.drop_last = drop_last
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# If the dataset length is evenly divisible by # of replicas, then there
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# is no need to drop any data, since the dataset will be split equally.
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if self.drop_last and len(self.dataset) % self.num_replicas != 0: # type: ignore[arg-type]
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# Split to nearest available length that is evenly divisible.
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# This is to ensure each rank receives the same amount of data when
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# using this Sampler.
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self.num_samples = math.ceil(
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(len(self.dataset) - self.num_replicas) / self.num_replicas # type: ignore[arg-type]
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)
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else:
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self.num_samples = math.ceil(len(self.dataset) / self.num_replicas) # type: ignore[arg-type]
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self.total_size = self.num_samples * self.num_replicas
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self.shuffle = shuffle
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self.seed = seed
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def __iter__(self) -> Iterator[_T_co]:
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if self.shuffle:
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# deterministically shuffle based on epoch and seed
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g = torch.Generator()
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g.manual_seed(self.seed + self.epoch)
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indices = torch.randperm(len(self.dataset), generator=g).tolist() # type: ignore[arg-type]
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else:
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indices = list(range(len(self.dataset))) # type: ignore[arg-type]
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if not self.drop_last:
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# add extra samples to make it evenly divisible
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padding_size = self.total_size - len(indices)
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if padding_size <= len(indices):
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indices += indices[:padding_size]
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else:
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indices += (indices * math.ceil(padding_size / len(indices)))[
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:padding_size
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]
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else:
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# remove tail of data to make it evenly divisible.
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indices = indices[: self.total_size]
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if len(indices) != self.total_size:
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raise AssertionError(
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f"Number of indices ({len(indices)}) does not match total_size ({self.total_size})"
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)
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# subsample
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indices = indices[self.rank : self.total_size : self.num_replicas]
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if len(indices) != self.num_samples:
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raise AssertionError(
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f"Number of subsampled indices ({len(indices)}) does not match num_samples ({self.num_samples})"
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)
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# pyrefly: ignore [bad-return]
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return iter(indices)
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def __len__(self) -> int:
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return self.num_samples
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def set_epoch(self, epoch: int) -> None:
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r"""
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Set the epoch for this sampler.
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When :attr:`shuffle=True`, this ensures all replicas
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use a different random ordering for each epoch. Otherwise, the next iteration of this
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sampler will yield the same ordering.
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Args:
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epoch (int): Epoch number.
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"""
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self.epoch = epoch
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