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