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* Add YaRN and Dynamic-YaRN RoPE Scaling Methods

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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Co-authored-by: Miguel Almeida <miguel.pessanha.almeida@tecnico.ulisboa.pt>

* Refactor YaRN implementation for LLaMA

Iterate on YaRN implementation for LLaMA and remove diff from remaining
models for increased PR modularity.

This commit includes the following changes:
- Merge 'yarn_rope_scaling' and 'rope_scaling' dictionaries
- Remove unnecessary attributes ('extrapolation_factor' and 'finetuned')
  from YaRN classes
- Inherit 'forward' method in YaRN classes from superclass
- Rename 'yarn' method to 'compute_yarn_scaling'
- Extend YaRN tests with further assertions
- Fix style inconsistencies
Co-authored-by: Miguel Monte e Freitas <miguelmontefreitas@tecnico.ulisboa.pt>

* Refactor Tensor Building Logic for YaRN

- Comply with the the tensor building logic introduced in #30743
- Add referencing to the optimized Attention Factor equation
- Remove Dynamic YaRN for a more agile deployment
Co-authored-by: mig-mfreitas <mig-mfreitas@users.noreply.github.com>

* remove unwanted file

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Co-authored-by: Miguel Almeida <miguel.pessanha.almeida@tecnico.ulisboa.pt>
Co-authored-by: mig-mfreitas <mig-mfreitas@users.noreply.github.com>
Co-authored-by: Joao Gante <joao@huggingface.co>