[Bugfix] Fix vllm bench serve to count multimodal tokens in "total input tokens"

[mgehre-amd]

Purpose

When benchmarking multimodal models, vllm bench serve reports total_input_tokens based on the client-side text-only prompt length, excluding image/encoder tokens. The server already reports the correct count (text + image tokens) via usage.prompt_tokens in the streaming response, but it was not captured.

Capture prompt_tokens from the streaming usage chunk and use it for input token metrics so total_input_tokens reflects the actual prefill size.

Test Plan

vllm bench serve --model Qwen/Qwen2.5-VL-7B-Instruct --port 8000 --random-input-len 512 --output-len 128 --num-prompts 1  --backend openai-chat --endpoint /v1/chat/completions --dataset-name
   random-mm --random-mm-base-items-per-request 1 --random-mm-limit-mm-per-prompt '{"image": 1, "video": 0}' --random-mm-bucket-config '{(1024, 800, 1): 1.0}

Before:

  Total input tokens:                      512

After:

  Total input tokens:                      1606

[gemini-code-assist]

Code Review

This pull request updates the OpenAI benchmark request functions to capture prompt_tokens from the server's usage response and utilizes this value for metric calculations in serve.py. Feedback indicates that the current implementation uses an elif block for usage data, which may lead to missed metrics if a provider includes both choices and usage in a single streaming chunk; it is recommended to check for usage independently to ensure robustness across different backends.

[gemini-code-assist]

Similar to the completions endpoint, the elif usage := data.get("usage"): block might be skipped if a provider sends both choices and usage in the same streaming chunk. To ensure that prompt_len and output_tokens are always captured when provided by the server, it is safer to use a separate if statement for the usage data rather than an elif tied to the presence of choices.

[mgehre-amd]

This is a pre-existing issue, not related to this PR

[shen-shanshan]

LGTM. This 1606 = text inputs + chat template + ViT inputs.