[Spec Decode] Add graph-aware adaptive K for DFlash

[mo-ke-ke]

Purpose

Addresses the throughput degradation reported in #49730 when a fixed DFlash
draft length remains enabled after target verification becomes more expensive
than ordinary decoding at larger batches.

This change extends enable_adaptive_verification to DFlash and selects a
batch-level K from graph-compatible candidates (0, 1, 3, 7, 15 for K15).
The policy combines startup-measured drafter/target costs, CUDA graph coverage,
an EMA of accepted-prefix survival, and calibrated shared runtime overhead.
K=0 skips the drafter and trims the current target verification batch to
ordinary decode immediately.

The current PR intentionally does not optimize sampling, the non-causal
drafter, or fuse Qwen3.5 hybrid-GDN/FP8 target verification. H200 profiling
showed target verification is the dominant cost; that kernel-fusion work is a
separate follow-up.

This is not a duplicate of #52522. That PR makes stochastic speculative
decoding batch-invariant and recovers proposals after preemption; it does not
choose K from runtime costs or disable speculation when it is predicted to be
slower. Searches for 49730 in:body, DFlash adaptive K, and DFlash performance found no open PR implementing this policy.

AI assistance was used for implementation, review, testing, and documentation.
The human submitter reviewed the changes and remains responsible for them.

Test Plan

.venv/bin/python -m pytest -q --noconftest \
  tests/test_config.py \
  tests/test_sampling_params.py \
  tests/v1/spec_decode/test_adaptive_verification.py \
  tests/v1/spec_decode/test_dynamic_sd_cug.py \
  tests/v1/worker/test_gpu_batch_ordering.py \
  -k 'adaptive or dflash or dspark'

SKIP=shellcheck .venv/bin/pre-commit run

H200 serving A/B:

• Target: Qwen/Qwen3.5-4B, FP8 target path
• Drafter: z-lab/Qwen3.5-4B-DFlash, K15
• CUDA graph capture sizes: 1 through 512
• Dataset: GSM8K, 256 prompts, 32 warmups, temperature 0
• Concurrency: 1, 8, 32
• Arms: ordinary decode, fixed K15, automatic K
• Gate: 256/256 complete and zero failures. Fixed K15 must exactly match
  ordinary decode; automatic K must retain at least 99% exact full-text matches
  under the FP8 target path, with the mismatch count reported.

Test Result

• Focused pytest: 37 passed

• Staged pre-commit: passed

• H200 A/B (output tokens/s):

  Concurrency	Ordinary	Fixed K15	Automatic K
  1	340.84	583.96	557.78
  8	2331.09	2387.12	2392.29
  32	6863.32	4505.21	4528.87

• At concurrency 32 the policy moved from K15 to K7. Automatic K was 0.53%
  faster than fixed K15, instead of the previous false K0 fallback, while all
  256 outputs exactly matched ordinary decoding.

• All nine cells completed 256/256 requests with zero failures. Automatic K
  exact full-text match rate was 100% at concurrency 1, 8, and 32.

• Ordinary decoding remains faster at concurrency 32. This isolates the next
  optimization to the hybrid-GDN/FP8 target verification path rather than
  sampling or the non-causal drafter.

• Durable H200 result:
  /threed-code/mokashliu/h200/vllm-dflash-adaptive-k/dflash-adaptive-k-h200-20260817-r30

[claude]

Claude Code Review

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[mergify]

Documentation preview: https://vllm--52559.org.readthedocs.build/en/52559/

[mo-ke-ke]

Maintainers: please add the ready label to authorize CI. The focused suite passes (37 tests), staged pre-commit passes locally, and the PR body includes the completed H200 A/B with exact-output gates. The current pre-run-check failure is only the new-contributor authorization gate.

[mergify]

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

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

[seanyourhighness]

I isolated a complementary hybrid-GDN runtime-K cost and opened a focused
current-main PR: #53542

With max K7 and runtime K3, GDNAttentionMetadataBuilder selected four active
state columns but returned the full eight-column FULL-graph staging view. The
GDN kernel derives its active query width from that tensor's last dimension.
The PR preserves the active width through block-table selection and cudagraph
staging; it does not duplicate this PR's adaptive-K policy.

Current-main focused tests: 10/10 GDN metadata-builder tests passed, including
the new max-K7/runtime-K3 (8, 4) regression; staged pre-commit passed.

Production backport A/B on RTX 5090, same max-K7/runtime-K3 c8 workload:
212.17 -> 298.91 aggregate decode tok/s (+40.9%). This removes one accidental
max-K specialization but does not close the remaining gap to true per-K
compiled graphs.

Disclosure: AI-assisted implementation and write-up; I reviewed the submitted
diff and verified the reported runs.