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Currently we need to call rotary embedding kernel for each LoRA, which makes it hard to serve multiple long context length LoRA. Add batched rotary embedding kernel and pipe it through.

It replaces the rotary embedding layer to the one that is aware of multiple cos-sin-cache per scaling factors.

Follow up of https://github.com/vllm-project/vllm/pull/3095/files

Co-authored-by: Dash Desai <1723932+iamontheinet@users.noreply.github.com>
Co-authored-by: Aurick Qiao <qiao@aurick.net>
Co-authored-by: Aurick Qiao <aurick.qiao@snowflake.com>
Co-authored-by: Aurick Qiao <aurickq@users.noreply.github.com>
Co-authored-by: Cody Yu <hao.yu.cody@gmail.com>

Signed-off-by: Prashant Gupta <prashantgupta@us.ibm.com>
Co-authored-by: Travis Johnson <tjohnson31415@gmail.com>

Co-authored-by: Danny Guinther <dguinther@neuralmagic.com>

Co-authored-by: Roger Wang <136131678+ywang96@users.noreply.github.com>

Signed-off-by: Tao He <sighingnow@gmail.com>

Some simplifications made for clarity.

Also moves detokenization-related functions from tokenizer.py to detokenizer.py.

Co-authored-by: MeloYang <meloyang05@gmail.com>

Co-authored-by: Roy <jasonailu87@gmail.com>
Co-authored-by: Roger Meier <r.meier@siemens.com>

Co-authored-by: Roy <jasonailu87@gmail.com>

This reverts commit 5c976a7e.

Co-authored-by: Chen Shen <scv119@gmail.com>
Co-authored-by: Shreyas Krishnaswamy <shrekris@anyscale.com>
Co-authored-by: Avnish Narayan <avnish@anyscale.com>