Remove `logits.float()`
Author: ringohoffman
Pull request: yes

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Follow up to:

* https://github.com/huggingface/transformers/pull/31292
* https://github.com/huggingface/transformers/issues/30860

given that 4.45 has been released and 4.46 is next

Llama 3.1 8B FSDP2 peak inference memory usage with `float()` (18.5GiB):

<img width="1728" alt="float()" src="https://github.com/user-attachments/assets/e2aacbd3-f03e-4468-9b0b-33c49572b21e">

Llama 3.1 8B FSDP2 peak inference memory usage without `float()` (10.6GiB):

<img width="1728" alt="no_float()" src="https://github.com/user-attachments/assets/8797e42c-b9b1-40b5-81e5-1e452c3c8917">

In my environment, ~43% reduction in peak memory usage.

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@Cyrilvallez @gante @ArthurZucker 

Comments:
Hey @ringohoffman, I'm curious about the batch size you used to notice such a difference with the `float()` only, given that `num_logits_to_keep=1` by default?

The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/transformers/pr_33902). All of your documentation changes will be reflected on that endpoint. The docs are available until 30 days after the last update.

> batch size you used to notice such

I have observed these severe spikes with batch size 1. At this point I always just comment out that piece of code manually given it does not matter for my workflows at all. 

Humm, are you using `generate`? If not, are you making sure to pass `num_logits_to_keep=1` to the `forward`? By default it is 0 if not using `generate`. Because a batch size of 1 would result in a tensor of shape `(1, 1, 128k)` for Llama 3.1, which is always low independently of the dtype.

We observe these spikes during training and generation, but we are using a different loss than the LM loss, so we don't require full precision during training. 

I see, makes sense then, as long as you don't pass `labels` 🤗