How to set_epoch with interleave_datasets?
State: closed

Let's say I have dataset A which has 100k examples, and dataset B which has 100m examples.

I want to train on an interleaved dataset of A+B, with stopping_strategy='all_exhausted' so dataset B doesn't repeat any examples. But every time A is exhausted I want it to be reshuffled (eg. calling set_epoch)

Of course I want to interleave as IterableDatasets / streaming mode so B doesn't have to get tokenized completely at the start.

How could I achieve this? I was thinking something like, if I wrap dataset A in some new IterableDataset with from_generator() and manually call set_epoch before interleaving it? But I'm not sure how to keep the number of shards in that dataset...

Something like

```
dataset_a = load_dataset(...)
dataset_b = load_dataset(...)

def epoch_shuffled_dataset(ds):
  # How to make this maintain the number of shards in ds??
  for epoch in itertools.count():
    ds.set_epoch(epoch)
    yield from iter(ds)

shuffled_dataset_a = IterableDataset.from_generator(epoch_shuffled_dataset, gen_kwargs={'ds': dataset_a})
interleaved = interleave_datasets([shuffled_dataset_a, dataset_b], probs, stopping_strategy='all_exhausted')
```

Comments:
This is not possible right now afaik :/

Maybe we could have something like this ? wdyt ?

```python
ds = interleave_datasets(
    [shuffled_dataset_a, dataset_b],
    probabilities=probabilities,
    stopping_strategy='all_exhausted',
    reshuffle_each_iteration=True,
)

That would be helpful for this case! 

If there was some way for from_generator to iterate over just a single shard of some dataset that would probably be more ideal. Maybe something like

```
def from_dataset_generator(dataset, generator_fn, gen_kwargs):
  # calls generator_fn(dataset=dataset_shard, **gen_kwargs)
```

Another transform I was trying to implement is an input bucketing transform. Essentially you need to iterate through a dataset and reorder the examples in them, which is not really possible with a `map()` call. But using `from_generator()` causes the final dataset to be a single shard and loses speed gains from multiple dataloader workers

I see, there are some internal functions to get a single shard already but the public `.shard()` method hasn't been implemented yet for `IterableDataset` :/

(see the use of `ex_iterable.shard_data_sources` in `IterableDataset._prepare_ex_iterable_for_iteration` for example)

Would that be something planned on the roadmap for the near future, or do you suggest hacking through with internal APIs for now?

Ok this turned out to be not too difficult. Are there any obvious issues with my implementation?

```
class ShuffleEveryEpochIterable(iterable_dataset._BaseExamplesIterable):
  """ExamplesIterable that reshuffles the dataset every epoch."""

  def __init__(
    self,
    ex_iterable: iterable_dataset._BaseExamplesIterable,
    generator: np.random.Generator,
  ):
    """Constructor."""
    super().__init__()
    self.ex_iterable = ex_iterable
    self.generator = generator

  def _init_state_dict(self) -> dict:
    self._state_dict = {
      'ex_iterable': self.ex_iterable._init_state_dict(),
      'epoch': 0,
    }
    return self._state_dict

  @typing.override
  def __iter__(self):
    epoch = self._state_dict['epoch'] if self._state_dict else 0
    for i in itertools.count(epoch):
      # Create effective seed using i (subtract in order to avoir overflow in long_scalars)
      effective_seed = copy.deepcopy(self.generator).integers(0, 1 << 63) - i
      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['epoch'] = i
        self._state_dict['ex_iterable'] = self.ex_iterable._init_state_dict()
      it = iter(self.ex_iterable)
      yield from it

  @typing.override
  def shuffle_data_sources(self, generator):
    ex_iterable = self.ex_iterable.shuffle_data_sources(generator)
    return ShuffleEveryEpochIterable(ex_iterable, generator=generator)

  @typing.override
  def shard_data_sources(self, worker_id: int, num_workers: int):
    ex_iterable = self.ex_iterable.shard_data_sources(worker_id, num_workers)
    return ShuffleEveryEpochIterable(ex_iterable, generator=self.generator)

  @typing.override
  @property
  def n_shards(self) -> int:
    return self.ex_iterable.n_shards
    
generator = np.random.default_rng(seed)
shuffling = iterable_dataset.ShufflingConfig(generator=generator, _original_seed=seed)
ex_iterable = iterable_dataset.BufferShuffledExamplesIterable(
  dataset._ex_iterable, buffer_size=buffer_size, generator=generator
)
ex_iterable = ShuffleEveryEpochIterable(ex_iterable, generator=generator)
dataset = datasets.IterableDataset(
  ex_iterable=ex_iterable,
  info=dataset._info.copy(),
  split=dataset._split,
  formatting=dataset._formatting,
  shuffling=shuffling,
  distributed=copy.deepcopy(dataset._distributed),
  token_per_repo_id=dataset._token_per_repo_id,
)
```


Nice ! This iterable is infinite though no ? How would `interleave_dataset` know when to stop ?

Maybe the re-shuffling can be implemented directly in `RandomlyCyclingMultiSourcesExamplesIterable` (which is the iterable used by `interleave_dataset`) ?

Infinite is fine for my usecases fortunately.