Optimize Qwen2VL vision model by precomputing cos/sin embeds before ViT blocks
Author: li-plus
Pull request: yes
Labels: name: optimization

name: Multimodal

# 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 -->

The current implementation of Qwen2VL recomputes cos/sin embeddings and repeats them on **each** layer of the ViT model. This is computational inefficient. This PR attempts to precompute the rotary embeddings at the beginning of the ViT forward function to save computation and memory.

## Before submitting
- [ ] This PR fixes a typo or improves the docs (you can dismiss the other checks if that's the case).
- [x] Did you read the [contributor guideline](https://github.com/huggingface/transformers/blob/main/CONTRIBUTING.md#create-a-pull-request),
      Pull Request section?
- [ ] 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.
- [ ] 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).
- [ ] Did you write any new necessary tests?


## Who can review?

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.

@ArthurZucker @amyeroberts  @qubvel 

<!-- 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
- vision models: @amyeroberts, @qubvel
- speech models: @ylacombe, @eustlb
- graph models: @clefourrier

Library:

- flax: @sanchit-gandhi
- generate: @zucchini-nlp (visual-language models) or @gante (all others)
- pipelines: @Rocketknight1
- tensorflow: @gante and @Rocketknight1
- tokenizers: @ArthurZucker
- trainer: @muellerzr and @SunMarc
- chat templates: @Rocketknight1

Integrations:

- deepspeed: HF Trainer/Accelerate: @muellerzr
- ray/raytune: @richardliaw, @amogkam
- Big Model Inference: @SunMarc
- quantization (bitsandbytes, autogpt): @SunMarc @MekkCyber

Documentation: @stevhliu

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:
cc @zucchini-nlp for vlms

I just rebased and the CI is failing on this check. Is it related to this PR?

```
...
No differences found for 
src/transformers/models/moonshine/modeling_moonshine.py.
Traceback (most recent call last):
  File "/root/transformers/utils/check_modular_conversion.py", line 80, in <module>
    raise ValueError("Some diff and their modeling code did not match.")
ValueError: Some diff and their modeling code did not match.

Exited with code exit status 1
```

@li-plus Right, you need to add the changes in a "modular_model.py" file and then running `make fix-copies` will copy everything to the actual "modeling_model.py". So you make CI green by adding the same changes to the `modular_qwen2vl.py` and then with `make fix-copies`

I see. It seems that this change also affects the implementation of Qwen2.5VL since it reuses the modules in Qwen2VL. I need to get a Qwen2.5VL model to ensure it's bug-free.

@li-plus running `make- fix-copies` should fix it and copy content from Qwen2VL to 2.5VL

Running `make fix-copies` ports changes to Qwen2.5VL incompatible with other modules in it. Fortunately just now Qwen2.5VL has been released so I have tested and fixed it. Now this PR is compatible for both Qwen2VL & Qwen2.5VL.

@ArthurZucker Hi, would you take a look at this PR? It's been approved and the CI turns green.

The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/transformers/pr_35837). All of your documentation changes will be reflected on that endpoint. The docs are available until 30 days after the last update.

Could you update to fix the conflicts? 🤗 

Conflicts are just solved.