[FR] Transform Chaining, Lazy Mapping
You can use `with_transform` to get a new dataset object.

Support for lazy `map` has already been discussed [here](https://github.com/huggingface/datasets/issues/3385) a little bit. Personally, I'm not a fan, as this would make `map` even more complex.  > You can use `with_transform` to get a new dataset object.
> 
> Support for lazy `map` has already been discussed [here](https://github.com/huggingface/datasets/issues/3385) a little bit. Personally, I'm not a fan, as this would make `map` even more complex.

I read about IterableDataset, and it seems to have lazy mapping. But I can't figure out how to convert an IterableDataset into a normal one when needed.

`with_transform` still does not chain AFAIU. > I read about IterableDataset, and it seems to have lazy mapping. But I can't figure out how to convert an IterableDataset into a normal one when needed.

You must cache an `IterableDataset` to disk to load it as a `Dataset`. One way to do this is with `Dataset.from_generator`:
```python
from functools import partial
from datasets import Dataset

def gen_from_iterable_dataset(iterable_ds)
    yield from iterable_ds

ds = Dataset.from_generator(partial(gen_from_iterable_dataset, iterable_ds), features=iterable_ds.features})
```

> with_transform still does not chain AFAIU.

Yes, not supported yet - the solution is to combine the transforms into a single one. I wonder if it would be beneficial to have a dedicated method to do that ? Maybe a `.save_to_disk()` so that the user can reload the resulting dataset later ? > ```python
> from functools import partial
> from datasets import Dataset
> 
> def gen_from_iterable_dataset(iterable_ds)
>     yield from iterable_ds
> 
> ds = Dataset.from_generator(partial(gen_from_iterable_dataset, iterable_ds), features=iterable_ds.features})
> ```

@mariosasko With these complex mapping functions, what hash will be used to cache this dataset?
 The params passed to `Dataset.from_generator` will be used to compute the hash (`partial` encapsulates the `iterable_ds` value, so changing it will also change the hash) Hi, I think this feature would be very useful. I want to concatenate large datasets with heterogeneous columns. I dislike `map` since I don't want multiple copy of that datasets locally. I tried to use "set_transform" on each dataset to convert it to a standard features format, but `datasets.concatenate_datasets` ignores the updated format of the datasets.  A work around is to use `torch.utils.data.ConcatDataset`. Is there a neat way to do it using HF datasets?﻿ @mariosasko These features would be handy for large datasets. A typical use case is video datasets: We have millions of videos, each stored in some OSS so they require some custom loading logic.

1) Due to the memory limit, loading the videos a priori into the memory is infeasible. But we can postpone video loading until they are needed with lazy mapping.
2) With chained transforms, we can allow the users to specify their custom video preprocessing logic while keeping the loading logic the same. FYI lazy map is available for `IterableDataset`(map is applied on-the-fly when iterating on the dataset):

```python
ds = load_dataset(...streaming=True)
# or
ds = Dataset.from_list(...).to_iterable_dataset()
# or
ds = IterableDataset.from_generator(...)

# Then you can chain many map/filter/shuffle/etc.
ds = ds.map(...).filter(...).map(...)

# The map functions are applied on-the-fly when iterating on the dataset
for example in ds:
    ...
### Feature request

Currently using a `map` call processes and duplicates the whole dataset, which takes both time and disk space.

The solution is to allow lazy mapping, which is essentially a saved chain of transforms that are applied on the fly whenever a slice of the dataset is requested.

The API should look like `map`, as `set_transform` changes the current dataset while `map` returns another dataset.

### Motivation

Lazy processing allows lower disk usage and faster experimentation.

### Your contribution

_