[Core] Add torch profiler CPU traces for AsyncLLM.

[huachenheli]

Summary:
vLLM currently lacks ability to generate CPU profiles, and the existing torch profile does not cover frontend/core processes. @WoosukKwon previously suggested profiling CPU by setting VLLM_ENABLE_V1_MULTIPROCESSING=0 but we still lack the ability to profile when processes are split.

For AsyncLLM class, which runs multimodal processor, the CPU work can be slow and worth profiling. Here we added support to also profile CPU.

For simplicity, I'm reusing the env var VLLM_TORCH_PROFILER_DIR for CPU traces as well, so both CPU & GPU profiles are consolidated into the same directory.

Test Plan:

export VLLM_ENABLE_V1_MULTIPROCESSING=0 ; VLLM_TORCH_PROFILER_DIR=/home/huachenheli/vllm_profile python -m vllm.entrypoints.openai.api_server --model Qwen/Qwen2.5-VL-7B-Instruct --tensor-parallel-size 1 --limit-mm-per-prompt '{"image": 1}'

VLLM_TORCH_PROFILER_DIR=/home/huachenheli/vllm_profile python -m vllm.entrypoints.openai.api_server --model Qwen/Qwen2.5-VL-7B-Instruct --tensor-parallel-size 1 --limit-mm-per-prompt '{"image": 1}'

Both CPU (AsyncLLM) & GPU (workers) profiles are produced:

Example profile with image inputs:
AsyncLLM:

Reviewers:

@youkaichao @WoosukKwon

Subscribers:

Tasks:

Tags:

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[youkaichao]

LGTM, thanks for the contribution!