GitLab

Signed-off-by: NickLucche <nlucches@redhat.com>

Signed-off-by: Aaron Pham <contact@aarnphm.xyz>

Co-authored-by: Zhuohan Li <zhuohan123@gmail.com>

Signed-off-by: Russell Bryant <rbryant@redhat.com>

Signed-off-by: Wallas Santos <wallashss@ibm.com>

Co-authored-by: Brendan Wong <bjwpokemon@gmail.com>

Co-authored-by: Varun Sundar Rabindranath <varun@neuralmagic.com>

Co-authored-by: Varun Sundar Rabindranath <varun@neuralmagic.com>

Signed-off-by: Travis Johnson <tsjohnso@us.ibm.com>

Co-authored-by: Alexander Matveev <alexm@neuralmagic.com>

Co-authored-by: Woosuk Kwon <woosuk.kwon@berkeley.edu>
Co-authored-by: Fei <dfdfcai4@gmail.com>

Signed-off-by: Travis Johnson <tsjohnso@us.ibm.com>

Co-authored-by: Zifei Tong <zifeitong@gmail.com>

Signed-off-by: Muralidhar Andoorveedu <muralidhar.andoorveedu@centml.ai>

Co-authored-by: Roger Wang <ywang@roblox.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: Yun Ding <yunding@nvidia.com>
Co-authored-by: Roger Wang <ywang@roblox.com>