[Profiling] Add support for profile_prefix payload in HTTP /start_profile endpoint

[rushabh-46]

Proposal: Upstream API Support for Dynamic Profiling Options

Allow users to pass dynamic settings to the profiler over the API (e.g., toggling native options or experimental_config flags) without requiring engine server restarts. This will help leverage the various options supported by native torch.profiler.profile

Current Gap

Currently, there is a discrepancy in profiling capabilities between online and offline serving:

• ❌ Online Serving (api_server.py): The /start_profile endpoint currently does not let pass any dynamic profiling options to a live production server.
• ✅ Offline Serving (vllm.LLM): Developers can programmatically pass a string argument locally via llm.start_profile(profile_prefix="..."). Downstream hardware-specific profiling backends can rely on this to absorb configuration requirements on-the-fly.

Purpose & Implementation

To safely enable dynamic profiling arguments upstream, we leverage the existing profile_prefix argument in the downstream code paths.

This PR:

1. Patches the REST router (api_router.py): Optionally captures a profile_prefix query parameter string sequentially over HTTP via FastAPI.
2. Updates the Abstract Protocol (EngineClient): Safely permits the signature to accept async def start_profile(self, profile_prefix: str | None = None).

Why this approach? The internal vLLM v1 architecture (including AsyncLLM.start_profile) already natively expects and supports the profile_prefix parameter. We simply bridge the HTTP layer gap.

Test Plan

1. Start an API server:

   vllm serve facebook/opt-125m

2. Trigger the profiler over HTTP, tunnelling some options inside the JSON body:

   curl -X POST "http://localhost:8000/start_profile?profile_prefix=host_tracer_level:3;e2e_enable_fw_throttle:true" # example

3. Issue standard generation requests to capture metrics.
4. Stop the profiler:

   curl -X POST http://localhost:8000/stop_profile

Test Result & Proof of Concept

We captured TPU traces and uploaded and viewed them on XProf and got the results as expected from the passed options in the /start_profile API.

Example trace on XProf

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Signed-off-by: Rushabh Lalwani rlalwani@google.com

[claude]

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[rushabh-46]

@claude review

[simon-mo]

What's the example on gpu path? Is the argument typical for torch profiler? Ideally we don't abuse this arg as a workaround

[rushabh-46]

What's the example on gpu path? Is the argument typical for torch profiler? Ideally we don't abuse this arg as a workaround

In any backend including GPU, we cannot modify the profiling configs dynamically once the server starts.

• Neither online serving (api_router.py).
• Nor offline serving (llm.py). (except the profile_prefix variable)

torch.profiler.profile supports many options (including experimental_config) and we want to support those from the vllm endpoint.

One approach we thought was to overload the profile_prefix. This works for offline serving without any upstream vllm changes, but needs this small change in /start_profile API for online serving. We tried to bridge it using this PR.

The other alternative is adding a new variable like profiler_kwargs: Dict[str, Any] payload through vllm/engine/protocol.py#start_profile down the entire engine stack.

Please let me know your suggestions. cc @kyuyeunk @bdubey

[mgoin]

"Prefix for start profiling" -> I don't understand what this means from the context of vLLM. Can you write a clear docstring for this argument, maybe with reference to the torch consumer?

[rushabh-46]

sure, updated the docstring.

[simon-mo]

The other alternative is adding a new variable like profiler_kwargs: Dict[str, Any] payload through vllm/engine/protocol.py#start_profile down the entire engine stack.

This will be better. Please don't overload and abuse existing args for a hacky workaround.

[mergify]

This pull request has merge conflicts that must be resolved before it can be
merged. Please rebase the PR, @rushabh-46.

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