Add eval_logits_to_cpu
Author: EkhiAzur
Pull request: yes

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Comments:
Hi @EkhiAzur, thanks for opening a PR. Could you provide some more details about the issue this is trying to solve? 

Yes sorry, I missed that part.

With large generative models during evaluation if compute metrics function is provided, OOM errors can occur. This error happens because predictions logits are save in GPU during evaluation. Eval_accumulation_steps is not the best option to avoid this error because it really slows the run. To avoid that, I added a parameter to decide if logits are saved in the GPU or in CPU.

Thanks for you attention

@EkhiAzur Thanks for explaining! Trainer already has very many arguments and so we want to be cautious about each one we add. The one being proposed here is highly specific and I suspect not the only (or best) way to tackle OOM when using trainer and accelerate. I'll let @muellerzr  and @pacman100 chime in on whether this should be added and possible alternatives 

@EkhiAzur I can't see a reason why things would break if we just did this all the time for the logits instead. Since it's after `gather` this should be safe

The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/transformers/pr_27458). All of your documentation changes will be reflected on that endpoint.

Hello, I realized that using `preprocess_logits_for_metrics` to reduce logits shape is better than the PR change. Sorry about that. 

Here is my solution if anyone needs it:


```
def preprocess_logits_for_metrics(logits, labels):
    if type(logits)==tuple:
        logits = logits[0]
    logits = logits.argmax(axis=-1)
    return logits

...

trainer = Trainer(
   model=model,
   args=trainingArgs,
   preprocess_logits_for_metrics = preprocess_logits_for_metrics,
   ...
  )

```

@EkhiAzur can we close this then? :) 

Yes, thanks for your attention and help!