GitLab

Signed-off-by: Isotr0py <2037008807@qq.com>

Signed-off-by: youkaichao <youkaichao@gmail.com>

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

Signed-off-by: youkaichao <youkaichao@gmail.com>

Signed-off-by: youkaichao <youkaichao@gmail.com>

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

Signed-off-by: Joe Runde <Joseph.Runde@ibm.com>

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

Signed-off-by: Zifei Tong <zifeitong@gmail.com>

Signed-off-by: youkaichao <youkaichao@gmail.com>

Signed-off-by: Muralidhar Andoorveedu <muralidhar.andoorveedu@centml.ai>
Signed-off-by: Murali Andoorveedu <muralidhar.andoorveedu@centml.ai>
Co-authored-by: DarkLight1337 <tlleungac@connect.ust.hk>

Co-authored-by: Robert Shaw <rshaw@neuralmagic.com>
Co-authored-by: ZX <zx@lbx.dev>

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

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

[WIP][Core][Refactor] move vllm/model_executor/parallel_utils into vllm/distributed and vllm/device_communicators (#3950)

Co-authored-by: Antoni Baum <antoni.baum@protonmail.com>

Co-authored-by: Chen Shen <scv119@gmail.com>
Co-authored-by: Antoni Baum <antoni.baum@protonmail.com>