Feature request: add code example of multi-GPU processing

### Feature request

Would be great to add a code example of how to do multi-GPU processing with 🤗 Datasets in the documentation. cc @stevhliu

Currently the docs has a small [section](https://huggingface.co/docs/datasets/v2.3.2/en/process#map) on this saying "your big GPU call goes here", however it didn't work for me out-of-the-box.

Let's say you have a PyTorch model that can do translation, and you have multiple GPUs. In that case, you'd like to duplicate the model on each GPU, each processing (translating) a chunk of the data in parallel.

Here's how I tried to do that:

```
from datasets import load_dataset
from transformers import AutoModelForSeq2SeqLM, AutoTokenizer
from multiprocess import set_start_method
import torch
import os

dataset = load_dataset("mlfoundations/datacomp_small")

tokenizer = AutoTokenizer.from_pretrained("facebook/nllb-200-distilled-600M")
model = AutoModelForSeq2SeqLM.from_pretrained("facebook/nllb-200-distilled-600M")

# put model on each available GPU
# also, should I do it like this or use nn.DataParallel?
model.to("cuda:0")
model.to("cuda:1")

set_start_method("spawn")

def translate_captions(batch, rank):
    os.environ["CUDA_VISIBLE_DEVICES"] = str(rank % torch.cuda.device_count())
    
    texts = batch["text"]
    inputs = tokenizer(texts, padding=True, truncation=True, return_tensors="pt").to(model.device)

    translated_tokens = model.generate(
        **inputs, forced_bos_token_id=tokenizer.lang_code_to_id["eng_Latn"], max_length=30
    )
    translated_texts = tokenizer.batch_decode(translated_tokens, skip_special_tokens=True)

    batch["translated_text"] = translated_texts
    
    return batch

updated_dataset = dataset.map(translate_captions, with_rank=True, num_proc=2, batched=True, batch_size=256)
```

I've personally tried running this script on a machine with 2 A100 GPUs.

## Error 1

Running the code snippet above from the terminal (python script.py) resulted in the following error:

```
Traceback (most recent call last):
  File "<string>", line 1, in <module>
  File "/home/niels/anaconda3/envs/datacomp/lib/python3.10/site-packages/multiprocess/spawn.py", line 116, in spawn_main
    exitcode = _main(fd, parent_sentinel)
  File "/home/niels/anaconda3/envs/datacomp/lib/python3.10/site-packages/multiprocess/spawn.py", line 125, in _main
    prepare(preparation_data)
  File "/home/niels/anaconda3/envs/datacomp/lib/python3.10/site-packages/multiprocess/spawn.py", line 236, in prepare
    _fixup_main_from_path(data['init_main_from_path'])
  File "/home/niels/anaconda3/envs/datacomp/lib/python3.10/site-packages/multiprocess/spawn.py", line 287, in _fixup_main_from_path
    main_content = runpy.run_path(main_path,
  File "/home/niels/anaconda3/envs/datacomp/lib/python3.10/runpy.py", line 289, in run_path
    return _run_module_code(code, init_globals, run_name,
  File "/home/niels/anaconda3/envs/datacomp/lib/python3.10/runpy.py", line 96, in _run_module_code
    _run_code(code, mod_globals, init_globals,
  File "/home/niels/anaconda3/envs/datacomp/lib/python3.10/runpy.py", line 86, in _run_code
    exec(code, run_globals)
  File "/home/niels/python_projects/datacomp/datasets_multi_gpu.py", line 16, in <module>
    set_start_method("spawn")
  File "/home/niels/anaconda3/envs/datacomp/lib/python3.10/site-packages/multiprocess/context.py", line 247, in set_start_method
    raise RuntimeError('context has already been set')
RuntimeError: context has already been set
```

## Error 2
Then, based on [this Stackoverflow answer](https://stackoverflow.com/a/71616344/7762882), I put the `set_start_method("spawn")` section in a try: catch block. This resulted in the following error:
```
File "/home/niels/anaconda3/envs/datacomp/lib/python3.10/site-packages/datasets/dataset_dict.py", line 817, in <dictcomp>
    k: dataset.map(
  File "/home/niels/anaconda3/envs/datacomp/lib/python3.10/site-packages/datasets/arrow_dataset.py", line 2926, in map
    with Pool(nb_of_missing_shards, initargs=initargs, initializer=initializer) as pool:
  File "/home/niels/anaconda3/envs/datacomp/lib/python3.10/site-packages/multiprocess/context.py", line 119, in Pool
    return Pool(processes, initializer, initargs, maxtasksperchild,
  File "/home/niels/anaconda3/envs/datacomp/lib/python3.10/site-packages/multiprocess/pool.py", line 215, in __init__
    self._repopulate_pool()
  File "/home/niels/anaconda3/envs/datacomp/lib/python3.10/site-packages/multiprocess/pool.py", line 306, in _repopulate_pool
    return self._repopulate_pool_static(self._ctx, self.Process,
  File "/home/niels/anaconda3/envs/datacomp/lib/python3.10/site-packages/multiprocess/pool.py", line 329, in _repopulate_pool_static
    w.start()
  File "/home/niels/anaconda3/envs/datacomp/lib/python3.10/site-packages/multiprocess/process.py", line 121, in start
    self._popen = self._Popen(self)
  File "/home/niels/anaconda3/envs/datacomp/lib/python3.10/site-packages/multiprocess/context.py", line 288, in _Popen
    return Popen(process_obj)
  File "/home/niels/anaconda3/envs/datacomp/lib/python3.10/site-packages/multiprocess/popen_spawn_posix.py", line 32, in __init__
    super().__init__(process_obj)
  File "/home/niels/anaconda3/envs/datacomp/lib/python3.10/site-packages/multiprocess/popen_fork.py", line 19, in __init__
    self._launch(process_obj)
  File "/home/niels/anaconda3/envs/datacomp/lib/python3.10/site-packages/multiprocess/popen_spawn_posix.py", line 42, in _launch
    prep_data = spawn.get_preparation_data(process_obj._name)
  File "/home/niels/anaconda3/envs/datacomp/lib/python3.10/site-packages/multiprocess/spawn.py", line 154, in get_preparation_data
    _check_not_importing_main()
  File "/home/niels/anaconda3/envs/datacomp/lib/python3.10/site-packages/multiprocess/spawn.py", line 134, in _check_not_importing_main
    raise RuntimeError('''
RuntimeError: 
        An attempt has been made to start a new process before the
        current process has finished its bootstrapping phase.

        This probably means that you are not using fork to start your
        child processes and you have forgotten to use the proper idiom
        in the main module:

            if __name__ == '__main__':
                freeze_support()
                ...

        The "freeze_support()" line can be omitted if the program
        is not going to be frozen to produce an executable.
```

So then I put the last line under a `if __name__ == '__main__':` block. Then the code snippet seemed to work, but it seemed that it's only leveraging a single GPU (based on monitoring `nvidia-smi`):

```
Mon Aug 28 12:19:24 2023       
+-----------------------------------------------------------------------------+
| NVIDIA-SMI 515.65.01    Driver Version: 515.65.01    CUDA Version: 11.7     |
|-------------------------------+----------------------+----------------------+
| GPU  Name        Persistence-M| Bus-Id        Disp.A | Volatile Uncorr. ECC |
| Fan  Temp  Perf  Pwr:Usage/Cap|         Memory-Usage | GPU-Util  Compute M. |
|                               |                      |               MIG M. |
|===============================+======================+======================|
|   0  NVIDIA A100-SXM...  On   | 00000000:01:00.0 Off |                    0 |
| N/A   55C    P0    76W / 275W |   8747MiB / 81920MiB |      0%      Default |
|                               |                      |             Disabled |
+-------------------------------+----------------------+----------------------+
|   1  NVIDIA A100-SXM...  On   | 00000000:47:00.0 Off |                    0 |
| N/A   67C    P0   274W / 275W |  59835MiB / 81920MiB |    100%      Default |
|                               |                      |             Disabled |
```

Both GPUs should have equal GPU usage, but I've always noticed that the last GPU has way more usage than the other ones. This made me think that `os.environ["CUDA_VISIBLE_DEVICES"] = str(rank % torch.cuda.device_count())` might not work inside a Python script, especially if done after importing PyTorch?

### Motivation

Would be great to clarify how to do multi-GPU data processing.

### Your contribution

If my code snippet can be fixed, I can contribute it to the docs :) 

That'd be a great idea! @mariosasko or @lhoestq, would it be possible to fix the code snippet or do you have another suggested way for doing this?

Indeed `if __name__ == "__main__"` is important in this case.

Not sure about the imbalanced GPU usage though, but maybe you can try using the `torch.cuda.device` context manager ?

> also, should I do it like this or use nn.DataParallel?

In this case you wouldn't need a multiprocessed map no ? Since nn.DataParallel would take care of parallelism

Adding this Tweet for reference: https://twitter.com/jxmnop/status/1716834517909119019.

I think the issue is that we set `CUDA_VISIBLE_DEVICES` after pytorch is imported ?

We should use `torch.cuda.set_device(...)` instead

@lhoestq 
> In this case you wouldn't need a multiprocessed map no ?

Yes. But how to load a model to 2 GPU simultaneously without something like accelerate?

> @lhoestq
> 
> > In this case you wouldn't need a multiprocessed map no ?
> 
> Yes. But how to load a model to 2 GPU simultaneously without something like accelerate?

Take a look at this fix #6550 . Basically, you move the model to each GPU inside of the function to be mapped.  



In case someone also runs into this issue, I wrote a [blog post](https://forrestbao.github.io/2024/01/30/datasets_map_with_rank_multiple_GPUs.html) with a complete working example by compiling information from several PRs and issues here. Hope it can help. This issue cost me a few hours. I hope my blog post can save you time before the official document gets fixed. 

Thanks ! I updated the docs in https://github.com/huggingface/datasets/pull/6550

hey @forrestbao , i was too struggling with the same issue for weeks hence i checked out your blog. great work on the blog. 
however i wanted to ask you could we scale up the process by reinitializing the same model on the same GPU multiple times for even more speedups ? 

i mean to say given that on a multi GPU setup where GPU vram is above 40GB each, after intializing the translation model which is barely 1-2GB in VRAM size, the rest of VRAM sits idle, how could i keep creating multiple instances of the same model on the same GPU for all GPUs to maxmize flops ? 

You can use one single instance on your GPU and increase the batch size until you fill the VRAM