[Metrics][Spec Decoding] Add per-request acceptance rate Prometheus histogram

[jeffye-dev]

Purpose

This PR adds request-level observability for speculative decoding acceptance.

vLLM already exposes aggregate speculative decoding counters such as draft counts and accepted-token counts. Those counters are useful for overall speculative decoding efficiency, but they are aggregated across scheduler
iterations and do not directly show the distribution of acceptance rates across finished requests.
This PR adds a finished-request-level Prometheus histogram vllm:request_spec_decode_acceptance_rate to inspect request-level acceptance behavior in production.

The motivation is based on the observation: speculative decoding metrics can be useful signals for detecting output-quality anomalies in production inference. In particular:

• Extremely low speculative acceptance can correlate with garbled or rare-character outputs, where draft tokens are almost entirely rejected by the target model.
• Extremely high speculative acceptance can correlate with repetitive outputs, where a corrupted inference state may push generation into a high-confidence loop.

This turns speculative decoding metrics from only a performance signal into a lightweight production monitoring signal for request-level output-quality anomalies.

Test Plan

Unit tests cover:

• Accumulating request-level speculative draft and accepted token counts through
  IterationStats.
• SpecDecodeRequestStats.merge().
• Observing vllm:request_spec_decode_acceptance_rate for a finished request.

Test Result

Local tests are passed

[github-actions]

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[mergify]

Hi @jeffye-dev, the pre-commit checks have failed. Please run:

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[markmc]

Thanks for your interest in this!

Our design philosophy is that we expose raw counters, and let operators compose derived metrics/alerts in their monitoring stack. We don't wish to bake threshold logic and alerting policy into the engine itself.

Instead, you can add e.g. vllm:spec_decode_request_acceptance_rate (Histogram)

observed once per finished request that had spec decode tokens. This lets operators write whatever threshold alerts they want:

  # Alert: >5% of requests have acceptance rate below 0.1
  sum(rate(vllm:spec_decode_request_acceptance_rate_bucket{le="0.1"}[5m]))
  /
  sum(rate(vllm:spec_decode_request_acceptance_rate_count[5m]))
  > 0.05

  # Alert: any requests with suspiciously high acceptance (repetition)
  sum(rate(vllm:spec_decode_request_acceptance_rate_bucket{le="0.96"}[5m]))
  /
  sum(rate(vllm:spec_decode_request_acceptance_rate_count[5m]))
  < 0.95

[jeffye-dev]

Thanks for your interest in this!

Our design philosophy is that we expose raw counters, and let operators compose derived metrics/alerts in their monitoring stack. We don't wish to bake threshold logic and alerting policy into the engine itself.

Instead, you can add e.g. vllm:spec_decode_request_acceptance_rate (Histogram)

observed once per finished request that had spec decode tokens. This lets operators write whatever threshold alerts they want:

  # Alert: >5% of requests have acceptance rate below 0.1
  sum(rate(vllm:spec_decode_request_acceptance_rate_bucket{le="0.1"}[5m]))
  /
  sum(rate(vllm:spec_decode_request_acceptance_rate_count[5m]))
  > 0.05

  # Alert: any requests with suspiciously high acceptance (repetition)
  sum(rate(vllm:spec_decode_request_acceptance_rate_bucket{le="0.96"}[5m]))
  /
  sum(rate(vllm:spec_decode_request_acceptance_rate_count[5m]))
  < 0.95

thanks @markmc , I have updated the PR according to your comment. Now the metric is changed to "vllm:spec_decode_request_acceptance_rate" as histogram metric.

[markmc]

/cc @benchislett @luccafong @MatthewBonanni

Please update the PR description with the latest proposed metrics design ...

I'm still a bit unconvinced by the motivation:

This PR adds request-level observability for speculative decoding acceptance anomalies.

The motivation is based on the observation: speculative decoding metrics can be useful signals for detecting output-quality anomalies in production inference. In particular:

• Extremely low speculative acceptance can correlate with garbled or rare-character outputs, where draft tokens are almost entirely rejected by the target model.
• Extremely high speculative acceptance can correlate with repetitive outputs, where a corrupted inference state may push generation into a high-confidence loop.

vLLM already exposes step-level speculative decoding metrics, but those aggregate counters are not sufficient to identify whether an individual finished request had an abnormal acceptance pattern. This PR adds request-level accounting so operators can monitor such cases directly.

So the operator can find out if there is a certain class of requests that have a lower acceptance rate? In what was is this more actionable in practise than the existing aggregate metrics?

[benchislett]

I'm okay with this as long as we clean it up. Ideally, I would like to have an option to return the per-request metrics to the client in the usage field. But that can be a follow-up or separate project. For monitoring, I'm okay with having a request-level histogram.

[mergify]

Hi @jeffye-dev, the pre-commit checks have failed. Please run:

uv pip install pre-commit>=4.5.1
pre-commit install
pre-commit run --all-files

Then, commit the changes and push to your branch.

For future commits, pre-commit will run automatically on changed files before each commit.

[markmc]

pre-commit issue should be fixed by #45374

[mergify]

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

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

[jeffye-dev]

The buildkite/ci/pr failure seemed not caused by this pr.

[mergify]

Hi @jeffye-dev, the pre-commit checks have failed. Please run:

uv pip install pre-commit>=4.5.1
pre-commit install
pre-commit run --all-files

Then, commit the changes and push to your branch.

For future commits, pre-commit will run automatically on changed files before each commit.

[mergify]

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

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