T5gemma2
Author: bzhangGo
Pull request: yes
Labels: name: New model

# 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 -->

Add support for T5Gemma2 with multi-modal and long-context capability.


## Before submitting
- [ ] This PR fixes a typo or improves the docs (you can dismiss the other checks if that's the case).
- [ ] 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.

<!-- 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 @Cyrilvallez
- vision models: @yonigozlan @molbap
- audio models: @eustlb @ebezzam @vasqu
- multimodal models: @zucchini-nlp
- graph models: @clefourrier

Library:

- generate: @zucchini-nlp (visual-language models) or @gante (all others)
- continuous batching: @remi-or @ArthurZucker @McPatate
- pipelines: @Rocketknight1
- tokenizers: @ArthurZucker and @itazap
- trainer: @SunMarc
- attention: @vasqu @ArthurZucker @CyrilVallez
- model loading (from pretrained, etc): @CyrilVallez
- distributed: @3outeille @ArthurZucker
- CIs: @ydshieh

Integrations:

- ray/raytune: @richardliaw, @amogkam
- Big Model Inference: @SunMarc
- quantization: @SunMarc @MekkCyber
- kernels: @MekkCyber @drbh
- peft: @BenjaminBossan @githubnemo

Devices/Backends:

- AMD ROCm: @ivarflakstad
- Intel XPU: @IlyasMoutawwakil
- Ascend NPU: @ivarflakstad 

Documentation: @stevhliu

Research projects are not maintained and should be taken as is.

 -->


Comments:
Hey! Is this a pre-release model? I don't see the checkpoints like `google/t5gemma-2-270m` anywhere

The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/transformers/pr_41834). All of your documentation changes will be reflected on that endpoint. The docs are available until 30 days after the last update.

> Hey! Is this a pre-release model? I don't see the checkpoints like `google/t5gemma-2-270m` anywhere
yeah, coming soon


cc @arthurzucker in that case!

@vasqu Thanks for the comments. Re the major issues,

> We definitely should be able to have a SWA for bidirectional masks directly in the utils.

I agree with this move but I think this is better done by transformers side since it's a big behavior change. wdyt?

> The process fn to process pixel values should not be passed around imo, it should be handled a level above

This relates to how the transformers handle generation for encoder-decoder models. Two abnormal behaviors:
1. the cache was created by generationmix, not the model: https://github.com/huggingface/transformers/blob/main/src/transformers/generation/utils.py#L1924
2. the encoder outputs was created from encoder directly, not the model: https://github.com/huggingface/transformers/blob/main/src/transformers/generation/utils.py#L902.

This is the main reason that we have some wired designs in T5Gemma2, including the dynamic adjustment for sliding window size, and the special handle of the vision preprocessor. Please let me know if you have any suggestions!

> We should have one level for the encoder, decoder models to be wrapped under language model

I'm not sure if it's a good idea to have another level of wrapper for encoder-decoders, as it's common to put encoders and decoders into the model jointly, like T5/Bart/T5Gemma.



cc @ArthurZucker @Cyrilvallez for core maintainer

This is an encoder-decoder model with multimodal capabilities. To properly interact with our generation pipeline, the vision backbone is within the encoder. 

**[For maintainers]** Suggested jobs to run (before merge)

run-slow: auto, t5gemma, t5gemma2

Thx a lot for sticking through this and iterating @bzhangGo :hugs: