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Co-authored-by: LiuXiaoxuanPKU &lt;llilyliupku@gmail.com&gt;, bong-furiosa <bongwon.jang@furiosa.ai>

Co-authored-by: ywang96 <ywang@roblox.com>

Co-authored-by: Abhinav Goyal <abhinav.goyal@flipkart.com>

Signed-off-by: Stephanie Wang <swang@cs.berkeley.edu>
Signed-off-by: Stephanie <swang@anyscale.com>
Co-authored-by: Stephanie <swang@anyscale.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>

Co-authored-by: Jiang Li <jiang1.li@intel.com>
Co-authored-by: Abhilash Majumder <abhilash.majumder@intel.com>
Co-authored-by: Abhilash Majumder <30946547+abhilash1910@users.noreply.github.com>

[Core][Distributed] add coordinator to reduce code duplication in tp and pp (#5293)

Signed-off-by: Travis Johnson <tsjohnso@us.ibm.com>
Co-authored-by: Sanger Steel <sangersteel@gmail.com>
Co-authored-by: Roger Wang <ywang@roblox.com>

[Core][CUDA Graph] add output buffer for cudagraph to reduce memory footprint (#5074)

Co-authored-by: Ruth Evans <ruthevans@Ruths-MacBook-Pro.local>

The 2nd PR for #4532.

This PR supports loading FP8 kv-cache scaling factors from a FP8 checkpoint (with .kv_scale parameter).

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