[Bugfix][Spec Decode] Preserve constant effective K schedule semantics

[feednetinfra]

Purpose

num_speculative_tokens_per_batch_size is currently treated as runtime-variable whenever the schedule is present. Some schedules are syntactically non-uniform but resolve to one positive effective K after the configured runtime maximum is applied:

num_speculative_tokens = 3
schedule = [(1, 8, 5), (9, 16, 4)]
effective K values = {min(5, 3), min(4, 3)} = {3}

Such a schedule cannot change the target verification width. The current behavior has three effects:

1. V1 downgrades FULL_AND_PIECEWISE to PIECEWISE.
2. Data parallelism disables the schedule and falls back to the raw maximum K, which changes the configured effective K.
3. Prefix-cache-hit requests entering their first decode step are not padded to the effective verification width.

This PR:

• classifies a schedule as constant only when every entry resolves to the same positive K after runtime clamping;
• normalizes the runtime maximum to that effective K so the Scheduler, GPU Runner, metrics, and CUDA Graph descriptor use one width;
• applies the V1 graph downgrade and DP fallback only when the effective verification width can vary; and
• pads prefix-cache-hit requests with the constant effective K.

Malformed schedules, genuinely variable schedules, and K=0 stay on the conservative path. Existing schedule validation remains in place. No CUDA kernel, drafter, verifier, or rejection-sampling rule is changed.

Why this is not duplicate work

Before implementation I checked the issue and searched open PRs by issue number and by the areas constant effective K, speculative schedule, full cudagraph capture sizes, separate decode capture sizes, and related terms.

• [Spec Decode] Context-length-aware K in DSD (RFC #48627): extend num_speculative_tokens_per_batch_size with a ctx axis #48944 adds a context-length axis to K selection; this change only classifies schedules that collapse to one runtime width.
• [Bugfix][Spec Decode] Capture the widest uniform decode batch by default #50488 ensures wide uniform-decode batches are represented in the capture grid; this change addresses an earlier configuration/shape classification boundary.
• [V1][CUDA graph] Dispatch uniform-decode batches to a padded FULL graph instead of falling to eager PIECEWISE #52000 dispatches more uniform-decode batches to padded FULL graphs; it does not preserve a constant schedule's effective K under data parallelism.
• [Bugfix][Spec Decode] Fix autoregressive draft decode capture with dynamic SD #49652 handles fixed-width autoregressive draft-decode capture; it does not classify the target verification schedule.
• [Perf][Spec Decode] Disable EAGLE cache-hit block drop for always-K=0 dynamic DSD #52070 covers always-K=0 behavior. K=0 is deliberately excluded here.

Test plan

Focused behavior tests

HF_HUB_OFFLINE=1 TRANSFORMERS_OFFLINE=1 \
  /vllm-workspace/.venv/bin/python -m pytest -q \
  tests/v1/spec_decode/test_dynamic_sd.py

26 passed, 15 warnings in 7.59s

Coverage includes:

• no schedule, one entry, same-K entries, values clamped to one K, variable K, K=0, and malformed entries;
• preserving effective K=2 under DP when the raw maximum is K=3;
• prefix-cache-hit first-decode padding with the effective K; and
• direct Dispatcher assertions that the normalized runtime shape is uniform and resolves to CUDAGraphMode.FULL.

Repository hooks

uv tool run pre-commit run --files \
  tests/v1/spec_decode/test_dynamic_sd.py \
  vllm/config/speculative.py \
  vllm/config/vllm.py \
  vllm/v1/core/sched/scheduler.py

All applicable hooks passed, including Ruff, mypy, typos, SPDX, forbidden-import, and configuration checks. git diff --check passed.

GPU validation

Environment:

• NVIDIA RTX PRO 6000 Blackwell Server Edition;
• Qwen3-8B BF16, V1 model runner;
• N-gram speculation, raw maximum K=3, effective schedule K=2;
• random input/output length 128/128.

Single-GPU paired matrix

Each concurrency level used five paired repetitions with alternating execution order.

Concurrency	Base PIECEWISE	Patched FULL	Paired change	95% CI
1	94.12	92.45	-1.78%	[-1.98%, -1.57%]
2	174.05	169.16	-2.81%	[-2.91%, -2.71%]
4	344.90	367.47	+6.55%	[+6.20%, +6.90%]
8	658.70	638.82	-3.02%	[-3.31%, -2.73%]

A five-run isolation control with the patched build forced to PIECEWISE was approximately neutral at concurrency 1 (94.05 vs 94.12 output tok/s). This indicates that effective-K normalization itself is not the main regression source; FULL-versus-PIECEWISE performance crosses over by workload shape on this setup.

These results do not support a universal performance claim. They show that shape safety and performance admission are separate decisions. A follow-up should select FULL only for empirically winning token/batch buckets, with PIECEWISE as the fail-closed default.

Two-GPU data-parallel stress test

Two DP ranks were tested at temperature 0 for three repetitions at concurrency 7 and 16:

• 2304 total requests completed, 0 failed;
• base logs show the schedule disabled and a fallback to K=3;
• patched logs show both ranks initialized with effective K=2 and retained the schedule.

The throughput numbers are intentionally not presented as a same-K speedup because the base and patched runs execute different K. This test validates configuration semantics, rank consistency, and absence of a DP deadlock.

Current assessment

The DP behavior is a concrete configuration-semantics fix: a schedule whose effective K is always 2 should not silently become K=3 merely because DP is enabled. The effective-width propagation and direct Dispatcher test also fix a gap where graphs could be captured without the runtime descriptor selecting the expected replay path.

The FULL graph policy needs further reviewer discussion or a separate performance-admission change because the measured crossover is non-monotonic. This PR remains a draft while that scope is resolved.

AI assistance disclosure

OpenAI Codex assisted with prior-art checks, implementation and test drafting, GPU benchmark execution, and documentation. The human submitter must review every changed line, rerun the relevant tests, and be able to defend the change end-to-end before requesting merge.

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[claude]

Claude Code Review

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[seongyun1104]

Nice — the constant-effective-width case is a clean win; keeping FULL_AND_PIECEWISE and the DP path when the schedule can't actually vary the width is exactly the right split. Thanks for scoping it against #48944 in the description.

Two coordination notes, since we're both touching the same schedule surface:

1. Field rename in flight. [Spec Decode] Context-length-aware K in DSD (RFC #48627): extend num_speculative_tokens_per_batch_size with a ctx axis #48944 currently proposes (not yet merged — still awaiting maintainer review) renaming num_speculative_tokens_per_batch_size → speculative_token_schedule, keyed on (batch, ctx), with the old name kept as a pydantic validation alias (DeprecationWarning) so existing configs and code paths keep working. Flagging it only so whichever lands first doesn't force a rename churn on the other — your constant_num_speculative_tokens classifier keys off the same schedule object either way.

2. The composition looks clean. Your fast path decides graph/DP treatment for schedules that collapse to one positive K; [Spec Decode] Context-length-aware K in DSD (RFC #48627): extend num_speculative_tokens_per_batch_size with a ctx axis #48944 decides which K per (batch, ctx) for genuinely variable schedules and leaves capture-grid handling to the variable path. They don't overlap — a constant-collapse schedule takes your path, a width-varying one takes the conservative path. If both land, a schedule that's constant after clamping would get your full-graph treatment regardless of which name it's written under.

Happy to rebase #48944's rename around this if yours lands first.

[feednetinfra]

Thanks for the coordination — this is very helpful.

I checked the current #48944 head. If it lands first, I will rebase this PR onto speculative_token_schedule and extend the constant-effective-K classifier to cover both the legacy 3-item form and the new 5-item (batch, ctx, K) form, including constant-after-clamping tests across context buckets.

If this PR lands first, I am happy for #48944 to rebase the field rename around it.

One update from my side: I expanded the GPU validation to a five-run paired matrix and found that FULL-vs-PIECEWISE performance crosses over by workload bucket. I have moved this PR back to draft and removed the universal performance claim. Preserving the configured effective K under DP remains the concrete semantics fix; graph-path performance admission may need to be handled separately.

[seongyun1104]

Sounds good on both merge orders — and thanks for offering to extend the classifier to the 5-item (batch, ctx, K) form. That's exactly the shape #48944 produces, so a constant-after-clamping bucket test over ctx should carry straight over.

On the FULL-vs-PIECEWISE crossover: that lines up with what we saw isolating the graph-mode component of the DSD K=0-tier cost on a different stack (Gemma-4-31B hybrid + MTP, 1× H100). Toggling only the runner, the K=0-tier tax vs no-spec was +7.29% under V1 (PIECEWISE-downgraded) but +16.64% under V2 (both arms FULL) at ctx 4000 — so removing the PIECEWISE downgrade didn't remove the tax; the K=0 tier was 7–17% slower than no-spec on either runner. Agreed that graph-path performance admission is best kept separate from the DP effective-K semantics fix. There's a running thread on the DSD-vs-no-spec tax with a second, very different stack at #49986 if it's useful context.

[seongyun1104]

Following up on the coordination above with a measurement of what this PR is worth, plus one caveat about what it cannot recover.

We measured the FULL_AND_PIECEWISE → PIECEWISE downgrade in the cleanest arm available: a num_speculative_tokens_per_batch_size schedule pinned to K=0, so the drafter produces zero tokens and the only difference from the no-spec baseline is the graph mode. 1× H100 NVL, vLLM 0.27.1, V1 runner, gemma-4-31B-it-qat-FP8 + drafter.

context	concurrency	tax vs no-spec
400	2	+6.56 %
38 000	2	+6.46 %
400	189	+21.58 %

Zero draft tokens across every measured run, identical workload and step census between arms, 0 preemptions. Baseline captures PIECEWISE=51 and FULL=51; the spec arm captures PIECEWISE=51 only.

Two readings for this PR:

The prize is bigger than a flat few percent, and batch is the axis that sets it. Holding context fixed and moving concurrency from 2 to 189 takes the tax from +6.56 % to +21.58 %; holding batch fixed and moving context by 95× changes it by −0.10 pp. One caveat on attribution before that number is used: this arm carries the graph downgrade and the drafter's KV footprint at the same time, so +21.58 % is the two together and not a measurement of the FULL capture alone. What it does establish for your classifier is which axis decides the size of the prize — batch, not context.

The caveat: this PR recovers the graph half, not the KV half. On the same box, same flags, K=0 throughout, the pool is 142 046 tokens without the drafter and 127 021 with it — 15 025 tokens, 10.6 %, held by a drafter that never drafts. A constant-effective-K schedule still loads the draft model, so that half of the cost survives this fix. Worth stating in the PR description so the win is not read as recovering the whole gap.

Scope honesty on our arm. K=0 is constant, but it is not the "one positive effective K" case your classifier targets — with no draft tokens there is no verification width to preserve. So treat the numbers above as a clean upper bound on the graph-mode term rather than a measurement of your exact case. We have a 2×2 planned that holds the two mechanisms independently (forced cudagraph_mode=PIECEWISE at full pool, versus num_gpu_blocks_override clipped to the drafter-loaded pool at FULL_AND_PIECEWISE), which would split the +21.58 % into the part you recover and the part you do not. If a positive-constant-K arm on this stack would be useful to you before review, say so and we will add it to that run.

Method, raw logs and pre-registration: https://github.com/seongyun1104/depthchart/blob/a346d23/ctx_tax_mechanism/RESULTS.md

---

Edited 2026-08-24, same day as posting: the drafter's KV cost above first read 13 438 tokens / 9.6 %. That pairing crossed a systematic offset in the profiled pool — the first launch of each arm reports ~1 586 tokens less than every launch after it, in both arms, stable to the token thereafter — so it compared one arm's first launch against another arm's second. Pairing like launch to like gives 15 025 tokens (both stable) and 15 024 (both first-launch), two independent pairings one token apart. The corrected figures are in the text above; the correction makes the drafter's cost larger, not smaller. Working: depthchart a346d23 §4.

[mergify]

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

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