distributed_sampler_no_evenly_divisible.py

import math
import torch
from torch.utils.data import Sampler
import torch.distributed as dist


class DistributedSamplerNoEvenlyDivisible(Sampler):
    """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 case, each
    process can pass a DistributedSampler instance as a DataLoader sampler,
    and load a subset of the original dataset that is exclusive to it.

    .. note::
        Dataset is assumed to be of constant size.

    Arguments:
        dataset: Dataset used for sampling.
        num_replicas (optional): Number of processes participating in
            distributed training.
        rank (optional): Rank of the current process within num_replicas.
        shuffle (optional): If true (default), sampler will shuffle the indices
    """

    def __init__(self, dataset, num_replicas=None, rank=None, shuffle=True):
        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()
        self.dataset = dataset
        self.num_replicas = num_replicas
        self.rank = rank
        self.epoch = 0
        num_samples = int(math.floor(len(self.dataset) * 1.0 / self.num_replicas))
        rest = len(self.dataset) - num_samples * self.num_replicas
        if self.rank < rest:
            num_samples += 1
        self.num_samples = num_samples
        self.total_size = len(dataset)
        # self.total_size = self.num_samples * self.num_replicas
        self.shuffle = shuffle

    def __iter__(self):
        # deterministically shuffle based on epoch
        g = torch.Generator()
        g.manual_seed(self.epoch)
        if self.shuffle:
            indices = torch.randperm(len(self.dataset), generator=g).tolist()
        else:
            indices = list(range(len(self.dataset)))

        # add extra samples to make it evenly divisible
        # indices += indices[:(self.total_size - len(indices))]
        # assert len(indices) == self.total_size

        # subsample
        indices = indices[self.rank:self.total_size:self.num_replicas]
        self.num_samples = len(indices)
        # assert len(indices) == self.num_samples

        return iter(indices)

    def __len__(self):
        return self.num_samples

    def set_epoch(self, epoch):
        self.epoch = epoch