Add Cross-Attention to Bloom Model for VisionEncoderDecoder Compatibility
Author: tommy-21
Pull request: yes

# What does this PR do?

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This Pull Request introduces the cross-attention mechanisms into the Bloom model. The main goal is to make model from this family usable as a decoder within the VisionEncoderDecoderModel framework. 

The cross-attention is optional and actually added to the model's architecture only when the `add_cross_attention` parameter is set to `True` in the model's config, as it has been done for GPT2 and BERT models.



@ArthurZucker, @younesbelkada



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Models:

- text models: @ArthurZucker and @younesbelkada
- vision models: @amyeroberts
- speech models: @sanchit-gandhi
- graph models: @clefourrier

Library:

- flax: @sanchit-gandhi
- generate: @zucchini-nlp (visual-language models) or @gante (all others)
- pipelines: @Narsil
- tensorflow: @gante and @Rocketknight1
- tokenizers: @ArthurZucker
- trainer: @muellerzr and @SunMarc

Integrations:

- deepspeed: HF Trainer/Accelerate: @muellerzr
- 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

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Comments:
Hi! 
Bloom models are relatively popular among users and have this interesting advantage of being multilingual from the pretraining over the currently supported models in the VisionEncoderDecoder framework (BERT, GPT2, etc). The motivation of this add is to extend the possibilities of Bloom models by allowing users to easily use them in EncoderDecoder or VisionEncoderDecoder models. 

I for example, needed for my professional work to fine-tune a VisionEncoderDecoder model using the Bloom model "cmarkea/bloomz-560m-sft-chat" as the decoder because it has good performances for his size and demonstrates good efficiency in French and English languages.

Regarding the checkpoints, unfortunately, I am not authorized to publish them 😅

This issue has been automatically marked as stale because it has not had recent activity. If you think this still needs to be addressed please comment on this thread.

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