Load a cached dataset as iterable

The idea would be to allow something like 

```python 
ds = load_dataset("c4", "en", as_iterable=True)
```

To be used to train models. It would load an IterableDataset from the cached Arrow files.

Cc @stas00 

Edit : from the discussions we may load from cache when streaming=True

Can I work on this issue? I am pretty new to this.

Hi ! Sure :) you can comment `#self-assign` to assign yourself to this issue.

I can give you some pointers to get started:

`load_dataset` works roughly this way:
1. it instantiate a dataset builder using `load_dataset_builder()`
2. the builder download and prepare the dataset as Arrow files in the cache using `download_and_prepare()`
3. the builder returns a Dataset object with `as_dataset()`

One way to approach this would be to implement `as_iterable_dataset()` in `builder.py`.

And similarly to `as_dataset()`, you can use the `ArrowReader`. It has a `get_file_instructions()` method that can be helpful. It gives you the files to read as list of dictionaries with those keys: `filename`, `skip` and `take`.

The `skip` and `take` arguments are used in case the user wants to load a subset of the dataset, e.g.
```python
load_dataset(..., split="train[:10]")
```

Let me know if you have questions or if I can help :)

This use-case is a bit specific, and `load_dataset` already has enough parameters (plus, `streaming=True` also returns an iterable dataset, so we would have to explain the difference), so I think it would be better to add `IterableDataset.from_file` to the API (more flexible and aligned with the goal from https://github.com/huggingface/datasets/issues/3444) instead.

> This use-case is a bit specific

This allows to use `datasets` for large scale training where map-style datasets are too slow and use too much memory in PyTorch. So I would still consider adding it.

Alternatively we could add this feature one level bellow:
```python
builder = load_dataset_builder(...)
builder.download_and_prepare()
ids = builder.as_iterable_dataset()
```

Yes, I see how this can be useful. Still, I think `Dataset.to_iterable` + `IterableDataset.from_file` would be much cleaner in terms of the API design (and more flexible since `load_dataset` can only access the "initial" (unprocessed) version of a dataset).

And since it can be tricky to manually find the "initial" version of a dataset in the cache, maybe `load_dataset` could return an iterable dataset streamed from the cache if `streaming=True` and the cache is up-to-date. 

> This allows to use datasets for large scale training where map-style datasets are too slow and use too much memory in PyTorch.

I second that. e.g. In my last experiment Oscar-en uses 16GB RSS RAM per process and when using multiple processes the host quickly runs out cpu memory. 

>And since it can be tricky to manually find the "initial" version of a dataset in the cache, maybe load_dataset could return an iterable dataset streamed from the cache if streaming=True and the cache is up-to-date.

This is exactly the need on JeanZay (HPC) - I have the dataset cache ready, but the compute node is offline, so making streaming work off a local cache would address that need.

If you will have a working POC I can be the tester. 

> Yes, I see how this can be useful. Still, I think Dataset.to_iterable + IterableDataset.from_file would be much cleaner in terms of the API design (and more flexible since load_dataset can only access the "initial" (unprocessed) version of a dataset).

I like `IterableDataset.from_file` as well. On the other hand `Dataset.to_iterable` first requires to load a Dataset object, which can take time depending on your hardware and your dataset size (sometimes 1h+).

> And since it can be tricky to manually find the "initial" version of a dataset in the cache, maybe load_dataset could return an iterable dataset streamed from the cache if streaming=True and the cache is up-to-date.

That would definitely do the job. I was suggesting a different parameter just to make explicit the difference between
- streaming from the raw data
- streaming from the local cache

But I'd be fine with streaming from cache is the cache is up-to-date since it's always faster. We could log a message as usual to make it explicit that the cache is used

> I was suggesting a different parameter just to make explicit the difference between

MosaicML's `streaming` library does the same (tries to stream from the local cache if possible), so logging a message should be explicit enough :).

Ok ! Sounds good then :)

Hi Both! It has been a while since my first issue so I am gonna go for this one ! #self-assign

#self-assign

I like idea of `IterableDataset.from_file`. 

https://github.com/huggingface/datasets/pull/5821 should be helpful to implement `IterableDataset.from_file`, since it defines a new ArrowExamplesIterable that takes an Arrow tables generator function (e.g. from a file) and can be used in an IterableDataset

@lhoestq I have just started working on this issue. 

@lhoestq Thank you for taking over.

So what's recommanded usage of `IterableDataset.from_file` and `load_dataset`? How about I have multiple arrow files and `load_dataset` is often convenient to handle that.

If you have multiple Arrow files you can load them using

```python
from datasets import load_dataset

data_files = {"train": ["path/to/0.arrow", "path/to/1.arrow", ..., "path/to/n.arrow"]}

ds = load_dataset("arrow", data_files=data_files, streaming=True)
```

This is equivalent to calling `IterableDataset.from_file` and `concatenate_datasets`.

Hi! 👋 I’d love to help with this feature and was wondering if @mariusz-jachimowicz-83 is still working on it. If not, I’d be happy to pick it up and continue. Let me know! 🙌

I don't think anyone is working on this at the moment, imo the simplest would be to do it one level below

```python
builder = load_dataset_builder(...)
builder.download_and_prepare()
ids = builder.as_iterable_dataset()
```

Thanks for the clarification @lhoestq 🙌 Will share a draft soon

Follow-up test added in #7629. for as_iterable_dataset() method.
I’ve added the unit test in a separate PR to keep this one focused on the feature implementation, as the test is optional and can be reviewed independently.