AutoSharding IterableDataset's when num_workers > 1

### Feature request

Minimal Example

```
import torch
from datasets import IterableDataset

d = IterableDataset.from_file()
dl = torch.utils.data.dataloader.DataLoader(d,num_workers=3)

for sample in dl:
print(sample)

```

Warning:
Too many dataloader workers: 2 (max is dataset.n_shards=1). Stopping 1 dataloader workers.
To parallelize data loading, we give each process some shards (or data sources) to process. Therefore it's unnecessary to have a number of workers greater than dataset.n_shards=1. To enable more parallelism, please split the dataset in more files than 1.

Expected Behavior:
Dataset is sharded each cpu uses subset (contiguously - so you can do checkpoint loading/saving)

### Motivation

I have a lot of unused cpu's and would like to be able to shard iterable datasets with pytorch's dataloader when num_workers > 1. This is for a very large single file. I am aware that we can use the `split_dataset_by_node` to ensure that each node (for distributed) gets different shards, but we should extend it so that this also continues for multiple workers.

### Your contribution

If someone points me to what needs to change, I can create a PR.