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Co-authored-by: simon-mo <xmo@berkeley.edu>
Co-authored-by: Chang Su <chang.s.su@oracle.com>
Co-authored-by: Simon Mo <simon.mo@hey.com>
Co-authored-by: Roger Wang <136131678+ywang96@users.noreply.github.com>
Co-authored-by: Roger Wang <ywang@roblox.com>

Co-authored-by: Michael Goin <michael@neuralmagic.com>

Co-authored-by: Nick Hill <nickhill@us.ibm.com>

Co-authored-by: Roger Wang <ywang@roblox.com>

Signed-off-by: Thomas Parnell <tpa@zurich.ibm.com>
Co-authored-by: Joshua Rosenkranz <jmrosenk@us.ibm.com>

Signed-off-by: Thomas Parnell <tpa@zurich.ibm.com>
Co-authored-by: Thomas Parnell <tpa@zurich.ibm.com>
Co-authored-by: Nick Hill <nickhill@us.ibm.com>
Co-authored-by: Davis Wertheimer <Davis.Wertheimer@ibm.com>

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>

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

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