Fix skipped gradient update of the last step when using gradient accumulation.
Author: richardSHkim
Pull request: yes

# What does this PR do?

This PR fixes skipped gradient update of the last step when using gradient accumulation in trainer.py.

I'm not sure what is the meaning of ['step is always smaller than gradient_accumulation_steps'](https://github.com/huggingface/transformers/blob/main/src/transformers/trainer.py#L1995), but [steps_in_epoch <= args.gradient_accumulation_steps](https://github.com/huggingface/transformers/blob/main/src/transformers/trainer.py#L1989) looks like always `False`.
Thus, the update of the last iteration is skipped when gradient_accumulation_step > 1.

This also leads to different total iterations (-1 iteration per epoch).

It might be negligible, and the skipped gradients are used in next iteration, but I believe the gradient update behavior should be the same whether or not using gradient accumulation if the total batch size is the same.

I have tested with [example script](https://github.com/huggingface/transformers/blob/main/examples/pytorch/image-classification/run_image_classification.py) and the comparison results are [here](https://wandb.ai/kshtrue95/fix_grad_accumulation/workspace?nw=nwuserkshtrue95).

accelerator.accumulate() context manager now can handle the last step of data loader, so I'm trying to use it.

## Before submitting
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## Who can review?

@muellerzr @pacman100

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Comments:
I found this implementation was intended in [PR#24415](https://github.com/huggingface/transformers/pull/24415), so close this PR.