Enable dynamic resolution input for Beit
Author: OmarManzoor
Pull request: yes

# What does this PR do?

<!--
Congratulations! You've made it this far! You're not quite done yet though.

Once merged, your PR is going to appear in the release notes with the title you set, so make sure it's a great title that fully reflects the extent of your awesome contribution.

Then, please replace this with a description of the change and which issue is fixed (if applicable). Please also include relevant motivation and context. List any dependencies (if any) that are required for this change.

Once you're done, someone will review your PR shortly (see the section "Who can review?" below to tag some potential reviewers). They may suggest changes to make the code even better. If no one reviewed your PR after a week has passed, don't hesitate to post a new comment @-mentioning the same persons---sometimes notifications get lost.
-->

<!-- Remove if not applicable -->

Towards #30579


## Before submitting
- [x] Did you read the [contributor guideline](https://github.com/huggingface/transformers/blob/main/CONTRIBUTING.md#create-a-pull-request),
      Pull Request section?
- [x] Was this discussed/approved via a Github issue or the [forum](https://discuss.huggingface.co/)? Please add a link
      to it if that's the case.
- [X] Did you make sure to update the documentation with your changes? Here are the
      [documentation guidelines](https://github.com/huggingface/transformers/tree/main/docs), and
      [here are tips on formatting docstrings](https://github.com/huggingface/transformers/tree/main/docs#writing-source-documentation).
- [x] Did you write any new necessary tests?


## Who can review?
CC: @amyeroberts 
Anyone in the community is free to review the PR once the tests have passed. Feel free to tag
members/contributors who may be interested in your PR.

<!-- Your PR will be replied to more quickly if you can figure out the right person to tag with @

 If you know how to use git blame, that is the easiest way, otherwise, here is a rough guide of **who to tag**.
 Please tag fewer than 3 people.

Models:

- text models: @ArthurZucker and @younesbelkada
- vision models: @amyeroberts
- speech models: @sanchit-gandhi
- graph models: @clefourrier

Library:

- flax: @sanchit-gandhi
- generate: @gante
- pipelines: @Narsil
- tensorflow: @gante and @Rocketknight1
- tokenizers: @ArthurZucker
- trainer: @muellerzr and @pacman100

Integrations:

- deepspeed: HF Trainer/Accelerate: @pacman100
- ray/raytune: @richardliaw, @amogkam
- Big Model Inference: @SunMarc
- quantization (bitsandbytes, autogpt): @SunMarc and @younesbelkada

Documentation: @stevhliu and @MKhalusova

HF projects:

- accelerate: [different repo](https://github.com/huggingface/accelerate)
- datasets: [different repo](https://github.com/huggingface/datasets)
- diffusers: [different repo](https://github.com/huggingface/diffusers)
- rust tokenizers: [different repo](https://github.com/huggingface/tokenizers)

Maintained examples (not research project or legacy):

- Flax: @sanchit-gandhi
- PyTorch: See Models above and tag the person corresponding to the modality of the example.
- TensorFlow: @Rocketknight1

 -->


Comments:
Hi @amyeroberts 

This PR is incomplete right now because I am unsure how to proceed. It seems that the BeitEncoder takes the original patch embeddings configuration as an input and hence the original window size
https://github.com/huggingface/transformers/pull/31053/files#diff-3f84bebd6be8d9c0f5c5068199f5c49eac8489d5fa466fb6fa08b0365e78dba4R679-R685
This is used to initialize the `BeitRelativePositionBias` and the dimensions mismatch when we interpolate the embeddings during the forward calls. Could you kindly guide regarding what I am missing?


@OmarManzoor Regarding the relative position bias, looking at the modeling code, I think whenever the output of this model is used, then it will also need to be interpolated if `interpolate_pos_encoding=True` 

> @OmarManzoor Regarding the relative position bias, looking at the modeling code, I think whenever the output of this model is used, then it will also need to be interpolated if `interpolate_pos_encoding=True`

Could you kindly clarify a bit where exactly this should be added? Do we need to add a new interpolation function that works for BeitSelfAttention?

@OmarManzoor You need to make sure that the relative position biases are interpolated wherever that is needed. This might be as an argument to the relative position class, or within the modules that use its output 

@amyeroberts How do I calculate the interpolations for the relative position biases similar to how we calculated them for the embeddings?

Thanks again for adding this!