Add new model RoFormer (use rotary position embedding )
Author: JunnYu
Pull request: yes

# What does this PR do?

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## Add new model RoFormer
[RoFormer: Enhanced Transformer with Rotary Position Embedding](https://arxiv.org/pdf/2104.09864v1.pdf) by Jianlin Su and Yu Lu and Shengfeng Pan and Bo Wen and Yunfeng Liu.
The original code can be found [here](https://github.com/ZhuiyiTechnology/roformer).


## The abstract from the paper is the following:
*Position encoding in transformer architecture provides supervision for dependency modeling between elements at
different positions in the sequence. We investigate various methods to encode positional information in
transformer-based language models and propose a novel implementation named Rotary Position Embedding(RoPE). The
proposed RoPE encodes absolute positional information with rotation matrix and naturally incorporates explicit relative
position dependency in self-attention formulation. Notably, RoPE comes with valuable properties such as flexibility of
being expand to any sequence lengths, decaying inter-token dependency with increasing relative distances, and
capability of equipping the linear self-attention with relative position encoding. As a result, the enhanced
transformer with rotary position embedding, or RoFormer, achieves superior performance in tasks with long texts. We
release the theoretical analysis along with some preliminary experiment results on Chinese data. The undergoing
experiment for English benchmark will soon be updated.*

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Models:

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Comments:
@patil-suraj I have updated some codes, please review again. Thanks~

@patil-suraj  
- I fixed the docstrings format and the build_doc tests pass
- I have resolved the merge conflicts
- I have run make style and make quality

Thank you for reviewing on this PR. ∩▂∩


@patrickvonplaten i have done it，thanks;)

Tests are fine I think (PyTorch times out :-/).
Good to merge for me

Thanks a lot @JunnYu, fantastic addition!