Investigating the behavior of PyTorch's DataLoader when using randomness to generate samples.

pytorch_dataloader_randomness.py

# This bug was fixed in PyTorch 1.9. See:
# https://github.com/pytorch/pytorch/commit/aec83ff45ebd2cb3d4890cc97bffb1f367386392.

# See: https://pytorch.org/docs/stable/notes/faq.html#my-data-loader-workers-return-identical-random-numbers
# and: https://pytorch.org/docs/stable/data.html#multi-process-data-loading
# and: https://pytorch.org/docs/stable/data.html#randomness-in-multi-process-data-loading.

import numpy as np
import torch

from torch.utils.data import DataLoader, Dataset


class RandomDataset(Dataset):
    def __len__(self):
        return 20

    def __getitem__(self, idx):
        return torch.LongTensor([np.random.randint(20)])


def worker_init_fn(worker_id):
    # NumPy seed takes a 32-bit unsigned integer.
    np.random.seed(int(torch.utils.data.get_worker_info().seed) % (2 ** 32))


workers = 5
for seed_fn in [None, worker_init_fn]:
    dataset = RandomDataset()
    data_loader = DataLoader(
        dataset=dataset,
        batch_size=None,
        num_workers=workers,
        worker_init_fn=seed_fn,
    )
    epoch2labels = []
    for epoch in range(2):
        data = []
        for tensor in data_loader:
            data.append(tensor[0].item())

        epoch2labels.append(data)

    # When seed_fn is None, every five labels are the same because each of the five
    # workers is generating the same sequence. Further, the sequences for each of the two
    # epochs are identical. When seed_fn is worker_init_fn, the samples for each worker
    # are random and the samples for different epochs are different.
    for labels in epoch2labels:
        print(labels)

    print()

when setting worker_init_fn to None, for torch==2.5.1, it still gives you fully random samples regardless

Indeed, see the comment at the top of the script.