[Renderer] Move Processor out of AsyncLLM

[KKSK-DON]

This pr is point 1 and point 3 of #23869

Purpose

This pr move Processor to API server. So the next step #23873 can be benefit from it

Test Plan

unit test
I think it already be covered

manual test

python -m vllm.entrypoints.openai.api_server \
  --model meta-llama/Llama-3.2-3B-Instruct \
  --host 0.0.0.0 --port 8000

curl -s -X POST http://127.0.0.1:8000/v1/chat/completions \
  -H "Authorization: Bearer dummy" \
  -H "Content-Type: application/json" \
  -d '{
    "model":"meta-llama/Llama-3.2-3B-Instruct",
    "messages":[{"role":"user","content":"Give me two short facts about GPU memory."}],
    "max_tokens":64
  }'

Test Result

---

Essential Elements of an Effective PR Description Checklist

• The purpose of the PR, such as "Fix some issue (link existing issues this PR will resolve)".
• The test plan, such as providing test command.
• The test results, such as pasting the results comparison before and after, or e2e results
• (Optional) The necessary documentation update, such as updating supported_models.md and examples for a new model.
• (Optional) Release notes update. If your change is user facing, please update the release notes draft in the Google Doc.

[ywang96]

Thanks for the contribution! By moving Processor out of AsyncLLM, I meant that this following call should by default happening at the API server layer or under LLM class

vllm/vllm/v1/engine/async_llm.py

Lines 274 to 278 in cb55ad8

	# Convert Input --> Request.
	prompt_str, request = self.processor.process_inputs(
	request_id, prompt, params, arrival_time, lora_request,
	tokenization_kwargs, trace_headers, priority, data_parallel_rank)

This means we will need to change the input type of add_request to support both EngineCoreRequest and PromptType, the change should look something like the following for AsyncLLM.add_request

async def add_request(
    self,
    request_id: str,
    request: Union[EngineCoreRequest, PromptType], # passed from upper layer
    prompt_str: Optional[str],  # passed from upper layer
    params: Union[SamplingParams, PoolingParams],
    arrival_time: Optional[float] = None,
    lora_request: Optional[LoRARequest] = None,
    tokenization_kwargs: Optional[dict[str, Any]] = None,
    trace_headers: Optional[Mapping[str, str]] = None,
    priority: int = 0,
    data_parallel_rank: Optional[int] = None,
) -> RequestOutputCollector:
    ...
    if isinstance(request, PromptType):
        assert prompt_str is None
        logger.warning_once(...) # deprecation warning
        prompt_str, request = self.processor.process_inputs(...)
    ...

Let me know if you have any questions!

[mergify]

This pull request has merge conflicts that must be resolved before it can be
merged. Please rebase the PR, @KKSK-DON.

https://docs.github.com/en/pull-requests/collaborating-with-pull-requests/working-with-forks/syncing-a-fork

[mergify]

This pull request has merge conflicts that must be resolved before it can be
merged. Please rebase the PR, @KKSK-DON.

https://docs.github.com/en/pull-requests/collaborating-with-pull-requests/working-with-forks/syncing-a-fork

[mergify]

This pull request has merge conflicts that must be resolved before it can be
merged. Please rebase the PR, @KKSK-DON.

https://docs.github.com/en/pull-requests/collaborating-with-pull-requests/working-with-forks/syncing-a-fork

[DarkLight1337]

Let's wait for the entrypoints test results, then update the PR according to #26097 (note the updated return type of self.processor.process_inputs)

[DarkLight1337]

I have helped you update it