Refactor batch handling update
Author: yafshar
Pull request: yes

# What does this PR do?

This PR refactors the batch handling update, which can help to improve performance 

- Added `get_num_items_in_batches` function to calculate the number of items in each batch outside the training loop.
- Updated the `get_batch_samples` function to fetch batch samples only in the loop.

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Comments:
Hi 👋, thank you for opening this pull request! The pull request is converted to draft by default. When it is ready for review, please click the `Ready for review` button (at the bottom of the PR page).

@techkang, @SunMarc, @muellerzr would you please review this. These changes have been added to https://github.com/huggingface/optimum-habana/tree/transformers_4_49 and we get better performance in fine tuning for some cases.

Hello, could you explain on why this refactoring improves performance? Maybe the specific PR in optimum-habana？

> Hello, could you explain on why this refactoring improves performance? Maybe the specific PR in optimum-habana？

I think it's due to calling `.item()` at each training step, thus moving tensors from device to host and syncing the code every time while this could be done once before training.

@yafshar any details on why you did decided to close this PR ? 

> @yafshar any details on why you did decided to close this PR ?

@SunMarc This PR aims to address a performance issue observed with recent changes in the transformers, specifically related to counting the number of items in data. @regisss has submitted another PR (https://github.com/huggingface/transformers/pull/36861) that resolves the issue by directly removing the `item()` in `get_batch_samples` function. As a result, this PR is now obsolete and can be closed.

Thanks for the details !