[Frontend] Enable support for CPU backend in AsyncLLMEngine.

[sighingnow]

FILL IN THE PR DESCRIPTION HERE

FIX #xxxx (link existing issues this PR will resolve)

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[zhouyuan]

should also guard ray enabled case?

[sighingnow]

Fixed. An assertion has been added.

[sighingnow]

Hi @WoosukKwon, could you please take another look on this PR?

Thanks!

[sighingnow]

Rebased to main and conflicits resolved.

[tianyil1]

In the official vLLM-cpu-env docker container, if I used the "python3 -m vllm.entrypoints.openai.api_server --device cpu --model Mistral-7B-v0.1/", it will throw the below error:

If I applied this PR patch into vLLM, it can solved my problem!

[WoosukKwon]

@sighingnow LGTM! Thanks for the PR!

@zhouyuan Thanks for the review! Just curious: Don't we need more optimizations (e.g., process binding) for the API server? If so, do you have a plan to contribute these optimizations?

[zhouyuan]

@sighingnow LGTM! Thanks for the PR!

@zhouyuan Thanks for the review! Just curious: Don't we need more optimizations (e.g., process binding) for the API server? If so, do you have a plan to contribute these optimizations?

@WoosukKwon Hi Woosuk, thanks for checking, yes more optimizations are required for the framework and kernels as vLLM is designed for GPU friendly. In general to make CPU backend happy we are looking on

• separate the threads for tokenizer
• optimize the memory access, inside and outside NUMA node
• optimize the KV cache format to be more CPU friendly

Ideally the components may need to have both GPU and CPU optimized impls, but may introduce too big changes. We are experimenting some local patches. Will try to compile some concrete plans and discuss with vLLM community.

thanks,
-yuan