update spec decode metrics to use throughput

[qandrew]

Purpose

In encountering vllm speculative decoding code, we noticed that throughput was a more useful metric to read than the total number of tokens. Worked with @Jialin to devise better metrics

Test Plan

send a curl request to a vllm server running spec decoding

server

vllm serve facebook/opt-125m \
  --swap-space 16 \
  --disable-log-requests \
  --host :: \
  --dtype float16 \
  --speculative_config \
    "{\"method\":\"ngram\",\"num_speculative_tokens\":5,\"prompt_lookup_min\":5,\"prompt_lookup_max\":10}" \
  2>&1 | tee /data/users/$USER/logs/vllm_serving.$(date +%Y%m%d_%H%M%S).log

Test Result

metrics

[axia@devvm30969.cln0 ~/uv_env/gpt_oss_edit/bin]$ curl http://localhost:8000/v1/completions   -H "Content-Type: application/json"   -d '{
    "model": "facebook/opt-125m",
    "prompt": "Write a short story about plants.",
    "max_tokens": 1000,
    "temperature": 0.7
  }'


(APIServer pid=2236552) INFO 09-09 11:51:35 [metrics.py:96] SpecDecoding metrics: Draft acceptance rate: 88.8%, Mean acceptance length: 5.44, Accepted throughput: 4.37 tokens/s, Drafted throughput: 4.92 tokens/s, Accepted: 262 tokens, Drafted: 295 tokens, Per-position acceptance rate: 0.983, 0.949, 0.915, 0.881, 0.712

---

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.

[github-actions]

👋 Hi! Thank you for contributing to the vLLM project.

💬 Join our developer Slack at https://slack.vllm.ai to discuss your PR in #pr-reviews, coordinate on features in #feat- channels, or join special interest groups in #sig- channels.

Just a reminder: PRs would not trigger full CI run by default. Instead, it would only run fastcheck CI which starts running only a small and essential subset of CI tests to quickly catch errors.

You ask your reviewers to trigger select CI tests on top of fastcheck CI.

Once the PR is approved and ready to go, your PR reviewer(s) can run CI to test the changes comprehensively before merging.

To run CI, PR reviewers can either: Add ready label to the PR or enable auto-merge.

If you have any questions, please reach out to us on Slack at https://slack.vllm.ai.

🚀

[gemini-code-assist]

Code Review

This pull request updates the speculative decoding metrics to include throughput for drafted and accepted tokens. The changes correctly use time.monotonic() to measure the elapsed time for accurate throughput calculation. The implementation is sound, adding last_log_time to SpecDecodingLogging, calculating throughput in the log method, and updating the log message accordingly. The code handles potential division-by-zero errors. Overall, the changes are a good addition for better performance monitoring, and I have no high or critical severity feedback.

[Jialin]

Looks good to me. Thanks for patching the change to OSS. Please add some screen shot in the test plan.

[Jialin]

CC @yeqcharlotte @houseroad

[luccafong]

lgtm!

[benchislett]

LGTM