Add YaRN and Dynamic-YaRN RoPE Scaling Methods
Author: mig-mfreitas
Pull request: yes

# What does this PR do?

YaRN (Yet another RoPE extension method) combines the NTK-By-Parts Interpolation and Attention Scaling methods, improving upon existing RoPE interpolation methods for longer context window sizes.

Fine-tuned models maintain their original performance across benchmarks while enabling efficient extrapolation and transfer learning for quicker convergence, especially in compute-limited environments.

We implement YaRN and Dynamic-YaRN for the following list of models:

 - LLaMA
 - Falcon
 - GPT-NeoX
 - Olmo
 - Persimmon
 - Phi
 - StableLM
 - OpenLLaMA

New unit tests are added to assert YaRN's correct behavior on both short and long sequence inputs.

For more details, please refer to https://arxiv.org/abs/2309.00071.

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## Before submitting
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## Who can review?
@gante

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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: @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

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Comments:
cc @ArthurZucker too 

Hey! Thanks a lot for taking the time to implement this! 🤗 
cc @gante with this and phi3's new method, not sure it makes sense to add all of them in the modeling code, WDYT? 

This is unrelated to Llama so we might need some modularity for this! 

🤗

Thank you very much for this in-depth review and suggestions! We'll iterate on it and reach back shortly 🤗

@gante Hi! We believe we've covered the mentioned topics in this iteration. Please tell us what you think! 🤗

Chatting offline: yarn is matching the original repo, dynamic yarn is not. Check [this gist](https://gist.github.com/gante/09fffc31a362ce1f603ba38f70f44fa7).

Chatted with @ArthurZucker offline: let's prepare to merge this PR as is, after removing `dynamic-yarn` (which does not match the original implementation). cc @mig-mfreitas 

In parallel, I will be working on a PR on top of this one to move RoPE scaling code out of the models themselves, into a dedicated place. RoPE scaling would then behave as a plug in to models with RoPE. 

When the two PRs are ready: this PR will get merged and, straight away, mine is applied on top of it. We'll then have YaRN from @mig-mfreitas and the new structure from my PR :)

Done! 🤗🚀

@mig-mfreitas working on the new PR on top of this one today 🤗 

The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/transformers/pr_30910). All of your documentation changes will be reflected on that endpoint. The docs are available until 30 days after the last update.

@mig-mfreitas @miguelm-almeida :D 

![image](https://github.com/user-attachments/assets/f7e4618c-758e-4fff-a985-3412d82dfbff)


😊😊😊

Thank you very much for all the support!

Thank you for all the guidance, this contribution has been a great experience 🤗🚀