Title: interleave_datasets seed with multiple workers
State: closed

### Describe the bug

Using interleave_datasets with multiple dataloader workers and a seed set causes the same dataset sampling order across all workers.

Should the seed be modulated with the worker id?

### Steps to reproduce the bug

See above

### Expected behavior

See above

### Environment info

- `datasets` version: 3.5.1
- Platform: macOS-15.4.1-arm64-arm-64bit
- Python version: 3.12.9
- `huggingface_hub` version: 0.30.2
- PyArrow version: 19.0.1
- Pandas version: 2.2.3
- `fsspec` version: 2024.12.0

Comments: Hi ! It's already the case IIRC: the effective seed looks like `seed + worker_id`. Do you have a reproducible example ?here is an example with shuffle

```
import itertools
import datasets
import multiprocessing
import torch.utils.data


def gen(shard):
  worker_info = torch.utils.data.get_worker_info()
  for i in range(10):
    yield {'value': i, 'worker_id': worker_info.id}


def main():
  ds = datasets.IterableDataset.from_generator(gen, gen_kwargs={'shard': list(range(8))})
  ds = ds.shuffle(buffer_size=100, seed=1234)
  dataloader = torch.utils.data.DataLoader(ds, batch_size=None, num_workers=8)
  for i, ex in enumerate(itertools.islice(dataloader, 50)):
    print(i, ex)


if __name__ == '__main__':
  multiprocessing.set_start_method('spawn')
  main()
```

```
python test.py
0 {'value': 8, 'worker_id': 0}
1 {'value': 8, 'worker_id': 1}
2 {'value': 8, 'worker_id': 2}
3 {'value': 8, 'worker_id': 3}
4 {'value': 8, 'worker_id': 4}
5 {'value': 8, 'worker_id': 5}
6 {'value': 8, 'worker_id': 6}
7 {'value': 8, 'worker_id': 7}
8 {'value': 9, 'worker_id': 0}
9 {'value': 9, 'worker_id': 1}
10 {'value': 9, 'worker_id': 2}
11 {'value': 9, 'worker_id': 3}
12 {'value': 9, 'worker_id': 4}
13 {'value': 9, 'worker_id': 5}
14 {'value': 9, 'worker_id': 6}
15 {'value': 9, 'worker_id': 7}
16 {'value': 5, 'worker_id': 0}
17 {'value': 5, 'worker_id': 1}
18 {'value': 5, 'worker_id': 2}
19 {'value': 5, 'worker_id': 3}
```With `interleave_datasets`

```
import itertools
import datasets
import multiprocessing
import torch.utils.data


def gen(shard, value):
  while True:
    yield {'value': value}


def main():
  ds = [
    datasets.IterableDataset.from_generator(gen, gen_kwargs={'shard': list(range(8)), 'value': i})
    for i in range(10)
  ]
  ds = datasets.interleave_datasets(ds, probabilities=[1 / len(ds)] * len(ds), seed=1234)
  dataloader = torch.utils.data.DataLoader(ds, batch_size=None, num_workers=8)
  for i, ex in enumerate(itertools.islice(dataloader, 50)):
    print(i, ex)


if __name__ == '__main__':
  multiprocessing.set_start_method('spawn')
  main()
```

```
python test.py
0 {'value': 9}
1 {'value': 9}
2 {'value': 9}
3 {'value': 9}
4 {'value': 9}
5 {'value': 9}
6 {'value': 9}
7 {'value': 9}
8 {'value': 3}
9 {'value': 3}
10 {'value': 3}
11 {'value': 3}
12 {'value': 3}
13 {'value': 3}
14 {'value': 3}
15 {'value': 3}
16 {'value': 9}
17 {'value': 9}
18 {'value': 9}
19 {'value': 9}
20 {'value': 9}
21 {'value': 9}
22 {'value': 9}
23 {'value': 9}
```Same results after updating to datasets 3.6.0.Ah my bad, `shuffle()` uses a global effective seed which is something like `seed + epoch`, which is used to do the same shards shuffle in each worker so that each worker have a non-overlapping set of shards:

https://github.com/huggingface/datasets/blob/b9efdc64c3bfb8f21f8a4a22b21bddd31ecd5a31/src/datasets/iterable_dataset.py#L2102-L2111

I think we should take into account the `worker_id` in a local seed for the buffer right after this line:

https://github.com/huggingface/datasets/blob/b9efdc64c3bfb8f21f8a4a22b21bddd31ecd5a31/src/datasets/iterable_dataset.py#L2151-L2153

like adding a new step that would propagate in the examples iterables or something like that:

```python
ex_iterable = ex_iterable.shift_rngs(value=worker_id)
```

is this something you'd like to explore ? contributions on this subject are very welcomePotentially, but busy. If anyone wants to take this up please feel free to, otherwise I may or may not revisit when I have free time.

For what it's worth I got around this with

```

class SeedGeneratorWithWorkerIterable(iterable_dataset._BaseExamplesIterable):
  """ExamplesIterable that seeds the rng with worker id."""

  def __init__(
    self,
    ex_iterable: iterable_dataset._BaseExamplesIterable,
    generator: np.random.Generator,
    rank: int = 0,
  ):
    """Constructor."""
    super().__init__()
    self.ex_iterable = ex_iterable
    self.generator = generator
    self.rank = rank

  def _init_state_dict(self) -> dict:
    self._state_dict = self.ex_iterable._init_state_dict()
    return self._state_dict

  def __iter__(self):
    """Data iterator."""
    effective_seed = copy.deepcopy(self.generator).integers(0, 1 << 63) - self.rank
    effective_seed = (1 << 63) + effective_seed if effective_seed < 0 else effective_seed
    generator = np.random.default_rng(effective_seed)
    self.ex_iterable = self.ex_iterable.shuffle_data_sources(generator)
    if self._state_dict:
      self._state_dict = self.ex_iterable._init_state_dict()
    yield from iter(self.ex_iterable)

  def shuffle_data_sources(self, generator):
    """Shuffle data sources."""
    ex_iterable = self.ex_iterable.shuffle_data_sources(generator)
    return SeedGeneratorWithWorkerIterable(ex_iterable, generator=generator, rank=self.rank)

  def shard_data_sources(self, num_shards: int, index: int, contiguous=True):  # noqa: FBT002
    """Shard data sources."""
    ex_iterable = self.ex_iterable.shard_data_sources(num_shards, index, contiguous=contiguous)
    return SeedGeneratorWithWorkerIterable(ex_iterable, generator=self.generator, rank=index)

  @property
  def is_typed(self):
    return self.ex_iterable.is_typed

  @property
  def features(self):
    return self.ex_iterable.features

  @property
  def num_shards(self) -> int:
    """Number of shards."""
    return self.ex_iterable.num_shards
```Thanks for the detailed insights!

After reviewing the issue and the current implementation in `iterable_dataset.py`, I can confirm the cause:

When using `interleave_datasets(..., seed=...)` with `num_workers > 1` (e.g. via `DataLoader`), the same RNG state is shared across workers — which leads to each worker producing identical sample sequences. This is because the seed is not modulated by `worker_id`, unlike the usual approach in `shuffle()` where seed is adjusted using the `epoch`.

As @lhoestq suggested, a proper fix would involve introducing something like:

```python
ex_iterable = ex_iterable.shift_rngs(worker_id)
```

@jonathanasdf Also really appreciate the workaround implementation shared above — that was helpful to validate the behavior and will help shape the general solution.