[Benchmarks] Add speculative K schedule tuner

[RichApple123]

Purpose

Add vllm bench sweep tune_speculative_k, an offline tuner that converts
repeated serving sweep results into a deployable
num_speculative_tokens_per_batch_size schedule.

Dynamic speculative decoding currently requires users to choose the mapping in
advance. The command makes that choice from measured serving behavior while
leaving runtime scheduling unchanged.

The selection policy:

• groups repeated results by batch-size anchor and effective K;
• uses the median objective with a configurable MAD uncertainty penalty;
• rejects noisy measurements above a relative-MAD threshold;
• treats candidates within 1% of the best conservative result as tied and
  chooses the smaller K;
• preserves non-monotonic local optima instead of assuming K must decrease;
• requires the same candidate set at every anchor, including batch 1 and the
  measured deployment maximum;
• rejects serving records with failed or incomplete requests when the standard
  benchmark health fields are present;
• rejects candidate K above the configured global maximum and sweeps that
  change the global maximum between candidates; and
• optionally reports or strictly enforces acceptance-metric stability, while
  correctly allowing undefined K=0 acceptance.

Output TPS is maximized by default. Users can minimize TPOT or maximize
latency-SLO-aware request_goodput. The JSON output includes the merged
schedule, all candidate statistics, selection parameters, and input-summary
provenance. It also records both the configured global speculative-token
maximum and the largest measured candidate, so deployment configuration does
not silently drift when a sweep covers only a subset of valid K values.

No open PR or issue found by the required duplicate-work searches implements a
sweep-result-to-dynamic-K tuner. #48692 consumes a user-provided batch-size
schedule but does not choose one.

Test Plan

pytest -q tests/benchmarks/sweep

mypy \
  vllm/benchmarks/sweep/tune_speculative_k.py \
  tests/benchmarks/sweep/test_tune_speculative_k.py

vllm bench sweep tune_speculative_k results/spec-k \
  --max-batch-size 256 \
  --acceptance-var spec_decode_acceptance_length

Relevant Ruff, Ruff format, typos, and Markdownlint hooks were run on all
changed files.

Test Result

• Tuner plus existing sweep tests: 48 passed.
• Mypy: no issues.
• Relevant pre-commit hooks: passed.
• CLI help, parser registration, JSON generation, and default output path:
  passed.

Official serving sweep

A single-H100 vllm bench sweep serve run covers K=0/1/3/7 and concurrency
1/64/128/256, with three repetitions per combination (48 runs total), async
scheduling, compiled CUDA graphs, fixed 128-token input/output lengths, and
zero failed requests.

Random-token continuation gives very low draft acceptance (positive-K
acceptance length 1.09-1.15). TPS, goodput under tpot:50, and TPOT objectives
all correctly select:

[[1, 256, 0]]

For example, robust output TPS at batch 256 is 5016.94 for K=0 versus 2118.30,
2005.52, and 1776.67 for K=1/3/7. This is a negative control showing that the
tuner disables harmful speculation rather than assuming positive K wins.

Standard Sonnet serving validation

A second single-H100 serving sweep uses vLLM's standard Sonnet dataset with
550-token inputs, 128-token outputs, and the same 48-run K/load matrix. All
runs complete with zero failed requests. TPS, tpot:50 goodput, and mean TPOT
independently select:

[[1, 63, 7], [64, 256, 0]]

At concurrency 1, K=7 robust TPS is 317.27 versus 204.33 for K=0 (+55.3%). At
concurrency 64, K=0 is 3511.24 versus 3042.50 for the best positive candidate,
K=3. Acceptance length remains approximately load-invariant for each fixed K,
so the transition comes from measured end-to-end serving efficiency rather
than an acceptance-only rule.

Natural-text validation

An independent mixed natural-prompt matrix contains 84 records: batch sizes
1/8/32, K=0/1/3/7, and seven repeats per point. The final CLI selects:

[[1, 32, 7]]

K=7 robust TPS improves over K=0 by 207.28%, 162.38%, and 95.76% at the three
anchors. A separate five-repeat high-load A/B finds local throughput optima
K=7 at batch 64, K=3 at batch 128, and K=0 at batch 256 even though acceptance
length remains approximately batch-invariant for each fixed K. This supports
measuring TPS instead of deriving K from acceptance alone.

Limitations

The generated schedule is specific to the measured hardware, model/drafter,
workload, compilation mode, and latency constraints. max_concurrency is a
load ceiling, not a scheduler trace, so the documentation requires saturated,
fixed-length traffic and production validation. The tuner optimizes one scalar
metric and does not replace workload selection or SLO design.

AI assistance

This patch was developed with OpenAI Codex assistance and the commit includes
the required attribution trailer. This is a Draft PR; the human submitter must
review every changed line and confirm the test/benchmark evidence before
marking it ready for review.

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[mergify]

Documentation preview: https://vllm--49163.org.readthedocs.build/en/49163/

[RichApple123]

Closing this draft while re-scoping the contribution around vLLM's upstream Dynamic SD architecture and existing adaptive-K work. The offline tuner is not the original online-adaptation objective, and I do not want to advance it independently without a clearer non-overlapping role. The branch and benchmark artifacts remain available for reference.