GitLab

Signed-off-by: maleksan85 <maleksan@amd.com>
Co-authored-by: maleksan85 <maleksan@amd.com>

Signed-off-by: DarkLight1337 <tlleungac@connect.ust.hk>

Signed-off-by: Rafael Vasquez <rafvasq21@gmail.com>

Signed-off-by: DarkLight1337 <tlleungac@connect.ust.hk>

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

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

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

Signed-off-by: Wallas Santos <wallashss@ibm.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: Muralidhar Andoorveedu <muralidhar.andoorveedu@centml.ai>

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