Issue: Streaming Datasets don't work with Transformers Trainer when dataloader_num_workers>1
State: closed
Labels: name: bug

name: good first issue

## Describe the bug
Streaming Datasets can't be pickled, so any interaction between them and multiprocessing results in a crash.

## Steps to reproduce the bug
```python
import transformers
from transformers import Trainer, AutoModelForCausalLM, TrainingArguments
import datasets

ds = datasets.load_dataset('oscar', "unshuffled_deduplicated_en", split='train', streaming=True).with_format("torch")
model = AutoModelForCausalLM.from_pretrained("distilgpt2")
Trainer(model, train_dataset=ds, args=TrainingArguments("out", max_steps=1000, dataloader_num_workers=4)).train()
```
## Expected results
For this code I'd expect a crash related to not having preprocessed the data, but instead we get a pickling error.

## Actual results
```
  0%|          | 0/1000 [00:00<?, ?it/s]Traceback (most recent call last):
  File "/Users/dlwh/src/mistral/src/stream_fork_crash.py", line 7, in <module>
    Trainer(model, train_dataset=ds, args=TrainingArguments("out", max_steps=1000, dataloader_num_workers=4)).train()
  File "/Users/dlwh/.conda/envs/mistral/lib/python3.8/site-packages/transformers/trainer.py", line 1339, in train
    for step, inputs in enumerate(epoch_iterator):
  File "/Users/dlwh/.conda/envs/mistral/lib/python3.8/site-packages/torch/utils/data/dataloader.py", line 359, in __iter__
    return self._get_iterator()
  File "/Users/dlwh/.conda/envs/mistral/lib/python3.8/site-packages/torch/utils/data/dataloader.py", line 305, in _get_iterator
    return _MultiProcessingDataLoaderIter(self)
  File "/Users/dlwh/.conda/envs/mistral/lib/python3.8/site-packages/torch/utils/data/dataloader.py", line 918, in __init__
    w.start()
  File "/Users/dlwh/.conda/envs/mistral/lib/python3.8/multiprocessing/process.py", line 121, in start
    self._popen = self._Popen(self)
  File "/Users/dlwh/.conda/envs/mistral/lib/python3.8/multiprocessing/context.py", line 224, in _Popen
    return _default_context.get_context().Process._Popen(process_obj)
  File "/Users/dlwh/.conda/envs/mistral/lib/python3.8/multiprocessing/context.py", line 284, in _Popen
    return Popen(process_obj)
  File "/Users/dlwh/.conda/envs/mistral/lib/python3.8/multiprocessing/popen_spawn_posix.py", line 32, in __init__
    super().__init__(process_obj)
  File "/Users/dlwh/.conda/envs/mistral/lib/python3.8/multiprocessing/popen_fork.py", line 19, in __init__
    self._launch(process_obj)
  File "/Users/dlwh/.conda/envs/mistral/lib/python3.8/multiprocessing/popen_spawn_posix.py", line 47, in _launch
    reduction.dump(process_obj, fp)
  File "/Users/dlwh/.conda/envs/mistral/lib/python3.8/multiprocessing/reduction.py", line 60, in dump
    ForkingPickler(file, protocol).dump(obj)
AttributeError: Can't pickle local object 'iterable_dataset.<locals>.TorchIterableDataset'
  0%|          | 0/1000 [00:00<?, ?it/s]
```

This immediate crash can be fixed by not using a local class to make the `TorchIterableDataset` (Note that you have to do with_format("torch") or you get an exception because the dataset has no len) However, any lambdas etc used as maps will also trigger this crash. A more permanent fix would be to move away from multiprocessing and instead use something like pathos or multiprocessing_on_dill (https://stackoverflow.com/questions/19984152/what-can-multiprocessing-and-dill-do-together)

Note that if you bypass this crash you get another crash. (I'll file a separate bug).

## Environment info

- `datasets` version: 2.0.0
- Platform: macOS-12.2-arm64-arm-64bit
- Python version: 3.8.12
- PyArrow version: 7.0.0
- Pandas version: 1.4.1


Hi, thanks for reporting. This could be related to https://github.com/huggingface/datasets/issues/3148 too

We should definitely make `TorchIterableDataset` picklable by moving it in the main code instead of inside a function. If you'd like to contribute, feel free to open a Pull Request :)

I'm also taking a look at your second issue, which is more technical