[Spec Decode] Context-length-aware K in DSD (RFC #48627): extend num_speculative_tokens_per_batch_size with a ctx axis

[seongyun1104]

What this PR does

Extends num_speculative_tokens_per_batch_size (Dynamic SD, #32374) with an optional ctx-length axis, so that a schedule can select K based on both batch size and per-request context length instead of batch size alone. The change is a backward-compatible schema extension — a 3-item entry [bs_lo, bs_hi, K] parses and behaves exactly as before, a 5-item entry [bs_lo, bs_hi, ctx_lo, ctx_hi, K] opts in to the ctx axis.

Why

Under memory-bound decode with a shared prefix (RAG serving, agentic sessions, batched summarization), the optimal K depends on ctx as well as batch. A batch-only lookup has to pick one K per BS, either leaving ctx-amortization on the table for long sequences or over-drafting short ones. This PR gives operators a per-(bs, ctx) K table that opts in to this axis when it's useful and stays inert (3-item form) otherwise.

The concept was previously formalized by MagicDec (Sadhukhan et al., 2024) — "Leveraging our observation that there is a sequence length threshold above which decoding becomes memory bound—and that it becomes increasingly memory bound for even longer sequence lengths—we propose choosing the amount of speculation as a function of the sequence length (longer sequence length -> more speculated tokens)." (MagicDec-part1 blog, under Adaptive Sequoia trees; the companion paper is arXiv:2408.11049, which makes the same argument as a critical-sequence-length threshold and an analytic model for the optimal drafting strategy, but does not contain this sentence. An earlier revision of this text cited the paper for the quote and dropped the em-dash clause without marking the elision.) MagicDec's instantiation was self-speculation with StreamingLLM sparse KV in a batch-centric framing; this PR is the general draft-target integration into vLLM's DSD lookup surface. See RFC #48627 for the design discussion.

Evidence (2026-07-27 measurement)

Position-balanced 2-trial, prefix_repetition c=256, H100 NVL 94GB, prithivMLmods/gemma-4-31B-it-qat-FP8 + assistant draft, vLLM c5d967c23. Full methodology + per-cell data in decomposition comment. Per-cell stdev <2%, order-bias max 1.72% (signal 29-36× larger).

Primary contrast — C′ (6-cell 2D schedule) vs A′ (3-item batch-only), same DSD-mode cost:

ctx	A′	C′	C′/A′
400	1875.6	1890.7	1.01× (K=0 tier, tie by design)
900	1453.5	1874.6	1.29×
1900	1416.6	1848.2	1.30×
4000	1232.8	1680.4	1.36×

Zero cost when unused: the original A/B comparison in the PR body (retracted headline, but this specific check reproduces) — 3-item vs 5-item-same-K on spec-bench aggregate: 2627.2 vs 2642.3 (+0.6%, within noise). Users who don't opt in to the ctx axis pay nothing.

Absolute crossover vs no-spec (measured, not projected): above ctx ~2k the 2D schedule pays the full DSD-mode tax and still beats no-speculation in absolute throughput (ctx 1900: 1.02×, ctx 4000: 1.09× vs no_spec). This is the direct mechanism measurement.

Regime guardrail (documented usage)

The ctx axis is intended to raise K for long-ctx buckets, not to lower K for short-ctx buckets in mixed traffic. Using it in the latter direction (as an earlier iteration of my C schedule did) can regress aggregate throughput — the previous −4.0% spec-bench result was an anti-pattern usage plus the DSD-mode baseline tax at short ctx compounding. The docstring for the 5-item schema states this explicitly.

Changes

• vllm/config/speculative.py — 3/5-tuple type union widening, ctx_agg: Literal["median","mean","max"] = "mean" field
• vllm/v1/spec_decode/dynamic/utils.py — DynamicSDLookup(dense, ctx_boundaries) NamedTuple, rectangular grid validation, mixed-arity rejection, 1-indexed ctx range convention
• vllm/v1/core/sched/scheduler.py — 2D dispatch (dense[bs][ctx_bucket]), decode-only pool filter for ctx representative (num_computed_tokens >= num_prompt_tokens with fallback to full pool), aggregator dispatch (median/mean/max)
• tests/v1/spec_decode/test_dynamic_sd.py — 10 new tests for 5-item parsing, 2D routing, rectangular validation, capture-set K-invariance

Known limitations

• DSD-mode baseline tax: all DSD-mode arms (including this PR's schedule) pay a substantial throughput tax vs no-spec at short ctx (up to −31% at ctx=400 in the measurements above). The PIECEWISE cudagraph downgrade for DSD is one identified factor; a full decomposition (K=0 fast-path opportunity, drafter forward on K=0, admission cost, tier-boundary ramp) is filed as [Perf] DSD arms pay a large baseline tax vs no-spec under production defaults; PIECEWISE override identified as one factor #49986. This is orthogonal to this PR — it applies to the original 3-item DSD API from [V1][Spec Decode] Add Dynamic SD #32374 too — but is worth noting because the ctx=400 tie in the C′/A′ table is a consequence of it (both arms fire K=0 at that cell, so both eat the same tax).
• Evidence range: measured for ctx ∈ {400, 900, 1900, 4000}. Extension to 8k+ requires re-designing the batch tier to fit within achievable concurrency at longer ctx (per-request KV budget scales with ctx) — a separate measurement.
• Single model/drafter pair: measured on gemma-4-31B-it-qat-FP8 + assistant. Generalization to other pairs (Llama-3.1-8B + EAGLE-3, Qwen, etc.) is a natural next validation.
• APC-high regime: the measurement uses prefix_repetition (num_prefixes=1) which represents high-shared-prefix serving (RAG, agents, batched summarization at ~98% APC hit). Under low-APC-hit workloads the story is different — the gain is bounded by how much of the KV read the amortization can save.
• Aggregator choice: mean is a defensible default (mean × B ≈ Σ ctx_i ≈ verify KV read cost — a cost signal). median is the majority-amortization signal and wins under specific batch-shape skew (documented in the ctx_agg field). Per-sequence K is out of scope for this PR (would require gathered-K verify batching + straggler control) and noted as a longer-term direction in RFC [RFC]: Context-length-aware speculative token scheduling — extending num_speculative_tokens_per_batch_size with a context-length axis #48627 Alternatives.

Roadmap (why this PR is worth landing as the schema base)

Filed as #49986:

1. K=0 fast-path — when the schedule selects K=0 for a given (bs, ctx), route through the no-spec path entirely for that step. Target: recover C′ ctx=400 from 1890.7 → 2711.7 tok/s (+43%), which would make C′ strictly dominant across all four cells (tie-to-win vs no-spec, +26% over static-K3, +29-35% over batch-only). Separate PR, 2-3 weeks.
2. K-keyed FULL cudagraph capture — since the schedule enumerates the K set at startup, (batch bucket × K)-shape graphs are statically capturable, and the DSD-mode PIECEWISE downgrade can be lifted for declarative schedules. Follow-on to the fast-path.
3. 8k+ evidence extension, second model/drafter pair validation, per-bucket AR reporting — natural follow-ups.

Tests

32 unit + integration tests pass on H100 (driver 570, CUDA 12.8): 28 in test_dynamic_sd.py + 4 CUDA-graph regression. E2E verified on H100 (driver 580, CUDA 13.0) via the measurement setup above.

References

• vLLM RFC [RFC]: Context-length-aware speculative token scheduling — extending num_speculative_tokens_per_batch_size with a context-length axis #48627 — Context-length-aware K in DSD (design + Prior Art including MagicDec anchor)
• vLLM PR [V1][Spec Decode] Add Dynamic SD #32374 — original DSD API (ekagra-ranjan)
• MagicDec (Sadhukhan et al., arXiv:2408.11049)
• Follow-up: [Perf] DSD arms pay a large baseline tax vs no-spec under production defaults; PIECEWISE override identified as one factor #49986 — DSD baseline tax decomposition
• Companion: SGLang PR [Spec] Add a ctx axis to the adaptive spec _route (BS × ctx → slot) sgl-project/sglang#31716 (sibling 2D routing implementation)

---

AI assistance

This change was drafted with the help of an AI coding assistant (Anthropic Claude) and reviewed line-by-line before this PR was opened. All code, tests, commit message, and PR text were reviewed by me; the test commands above were run by me on this branch. Filing this disclosure per vLLM's AGENTS.md requirement for AI-assisted contributions.

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[claude]

Claude Code Review

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[benchislett]

Before I get into detailed review, a few high-level notes:

• num_speculative_tokens_per_batch_size is probably not the best name anymore since it's not just by batch size. We might want to rethink how we name this
• It's not clear to me what is the right way to aggregate sequence length across a batch. I feel like mean or max sequence length makes more sense than median, as it feels more in line with the raw kernel performance. We could run some attention microbenchmarks to find out

[seongyun1104]

Thanks for the notes. Running the attention microbenchmark you suggested now — sweeping median/mean/max across mixed-context batch scenarios on H100 NVL (same stack as the A/B/C table above). Will post the numbers alongside a filter-fix commit within 24h.

[seongyun1104]

Numbers ready. Pushed as 96bb926.

Pool filter (pre-emptive). Before touching the aggregator I noticed the current p50 pool includes fresh prefill entries (num_computed_tokens = 0) alongside decoders, so a fresh prefill joining a decode batch drags the ctx representative toward zero. The commit filters to decode requests (num_computed_tokens >= num_prompt_tokens) with a fallback to the legacy all-request pool when no decoder is scheduled — this preserves 3-item behavior and the existing 32 tests.

Aggregator microbench. Same stack as the A/B/C table above (H100 NVL, driver 580.82.09, CUDA 13.0, gemma-4-31B-it-qat-FP8 + gemma-4-31B-assistant, enforce_eager, greedy seed 0, max_model_len=12288). Batch of 4 requests with varied per-request prompt lengths (which set the decode-time ctx). Schedule [(1,32,1,512,4),(1,32,513,32768,2)] so aggregator choice actually flips the K bucket:

scenario	per-req ctx	median tok/s	mean tok/s	max tok/s
S0 uniform	[2000]×4	144.0	145.3	150.8
S1 mild	[500, 500, 500, 800]	159.6	262.5	149.8
S2 heavy	[100, 100, 100, 8000]	138.3	113.2	99.8
S3 bimodal	[100, 100, 8000, 8000]	88.8	127.7	91.1

S0 is within noise (all three pick the same K under uniform input, correctness sanity). Mean wins the balanced and bimodal scenarios by +64% and +44% over median. Median wins the heavy-skew S2 by +22% (three short + one long — K=4 favoring the majority beats K=2 hedging the outlier). Max never wins outside noise. Defaulting the knob to mean in the commit. Happy to hardcode and drop the ctx_agg field if you'd prefer — the case for keeping it exposed is workloads dominated by S2-style skew where the +22% median edge would matter.

Naming. Deferring to you — happy to rename in this PR (with a deprecated alias for the old name so existing configs keep working) or land the rename as a follow-up. Suggestions if useful: speculative_tokens_schedule, dynamic_sd_schedule, or num_speculative_tokens_schedule.

Caveats: single-seed measurement, so the ±5% spread on S0 is the noise floor; the S1/S2/S3 gaps are well above that but I can push a multi-seed rerun if you want tighter CIs before deciding.

[seongyun1104]

Ran a multi-seed follow-up on the two decision-critical scenarios (S0 uniform + S2 heavy, 3 seeds per aggregator) ahead of your detailed review:

S0 uniform — all aggregators pick the same K, so throughput should tie:

• median avg 176.1, mean 156.4, max 179.7 tok/s. Per-aggregator stdev 4–22 tok/s.
• Noise floor is wider than the ±5% I claimed earlier — closer to ±15% here. Likely FlashInfer autotune cache state differing across subprocess loads.

S2 heavy [100, 100, 100, 8000]:

• median avg 207.5, mean 121.1, max 155.5 tok/s.
• Median's edge over mean is wider than the single-seed pass suggested (+71% vs +22%) and holds even in cold-start seeds (median seed=0 173.9 vs mean seed=0 101.0 — cold, so JIT overhead is symmetric).

Two implications for the decision I flagged in the previous comment:

1. The knob's case is stronger than I suggested. Deployments with S2-shaped batches (one long request mixed with short — realistic under mixed-workload serving) see a real median advantage. Uniform-batch deployments (S0/S1-shape) are within noise.
2. The mean default I picked was on the right side of the S1 evidence but I'd want tighter measurement before hardcoding. Happy to run a longer-warmup pass focused on the deciding scenarios if the choice hinges on that.

No code change here; deferring the default choice to you — can flip to median or drop ctx_agg entirely once you've decided direction.

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[seongyun1104]

@benchislett — I went back over the aggregator microbenchmark I ran for you in July and it does not support what I told you. Correcting it here rather than leaving it in the thread.

What I claimed

The multi-seed microbench earlier in this thread (S0 uniform / S1 mild / S2 heavy-skew / S3 bimodal) showed ... mean wins the balanced/bimodal cases (S1: +64%, S3: +44% over median), median wins the heavy-skew (S2: +71% over mean)

Three problems, in increasing order of seriousness.

1. Only two of the four scenarios were multi-seed

The follow-up run says so plainly — "a multi-seed follow-up on the two decision-critical scenarios (S0 uniform + S2 heavy, 3 seeds per aggregator)". S1 and S3 were single measurements. I described all four as replicated.

2. The control was not quiet

S0 is the control: a uniform batch makes every aggregator select the same K, so the three arms are the same engine configuration and the true difference is zero.

pass	S0, three arms that must tie
single-seed	144.0 / 145.3 / 150.8 tok/s — 4.7 %
3-seed, across arms	176.1 / 156.4 / 179.7 tok/s — 14.9 %
3-seed, within the median arm	150 / 191 / 187 tok/s — 27 %

The control's level also moved between the two passes, 146.7 → 170.7 tok/s average, +16 % with nothing changed. I used exactly this spread to withdraw the +21.1 % figure from this PR's body, and did not apply it here.

3. S3 is a second control, and I reported its noise as a result

This is the one that matters. Running the batch shapes through this PR's own aggregation code against the schedule that benchmark used — [(1,32,1,512,4), (1,32,513,32768,2)]:

scenario	median → K	mean → K	max → K	distinct K	reported spread
S0 uniform [2000]×4	2000 → 2	2000 → 2	2000 → 2	{2}	4.7 %
S1 mild [500,500,500,800]	500 → 4	575 → 2	800 → 2	{2,4}	75.2 %
S2 heavy [100,100,100,8000]	100 → 4	2075 → 2	8000 → 2	{2,4}	38.6 %
S3 bimodal [100,100,8000,8000]	8000 → 2	4050 → 2	8000 → 2	{2}	43.8 %

median takes sorted_ctx[len // 2], so on a four-request batch it is the upper median: 8000, not 4050. In S3 all three aggregators select K=2. The three arms are the same configuration, and the "+44 % for mean" I quoted at you is the harness disagreeing with itself — the same failure as S0, at ten times the amplitude. One of the two numbers I used to justify the mean default measured nothing.

And what the two remaining scenarios measure is not an aggregator. With two ctx buckets, aggregator choice is a relabelling of K choice: S1 and S2 are both K=2 against K=4, on two batch shapes, with opposite winners. That is a result about which K suits which batch shape. It says nothing about which statistic should choose it.

S1 is weaker still: its median arm sits at 500 against a bucket boundary of 512, so it crosses into the K=2 bucket after ~13 generated tokens. That arm does not hold a fixed K for the run.

The direction was also wrong for this PR

That benchmark's schedule gives longer context a smaller K (≤512 → K=4, ≥513 → K=2). Every schedule this PR advocates does the opposite — the cell under test in Table 1 is [129,512,1,768,0] and [129,512,769,32768,3], longer context selecting a larger K, which is the MagicDec direction the RFC rests on. Under the PR's own direction the aggregators map to the opposite K's, so even the two scenarios that separate them do not transfer.

Net: that microbenchmark contains no usable evidence about the aggregator, and I should have caught it before answering you with it.

What I think now

Your original instinct was the right one and I over-rode it with a bad measurement. Argued from the cost rather than the bench:

• the verify step reads the KV of every sequence in the batch, so its cost tracks Σ ctx_i, and mean × B is exactly that quantity;
• max is right instead if step latency is set by the longest sequence rather than by total bytes;
• median is defined by ignoring the outlier, which is the one thing you cannot do when the outlier is what sets the step's cost.

mean is already the default here, but it should be presented as an argument from the cost model, not as a benchmark result.

This does not touch Table 1. That campaign ran at ctx_agg="mean" on prefix_repetition with --prefix-repetition-num-prefixes 1, so every request carries the same prompt length and mean, median and max all land in the same bucket and select the same K. It varied the schedule, not the aggregator — so it is unaffected by the above, and equally it is not evidence for mean.

Consequence

The ctx_agg knob existed because S1/S3 and S2 looked like a crossover. Two of those were controls and the others were an inverted-direction K comparison, so there is no measured case for any aggregator other than mean. I would rather delete the knob and hardcode mean — that removes a Literal["median","mean","max"] field, its plumbing and a branch in schedule(), and it makes the shipped behaviour identical to what Table 1 actually ran. Say the word and I will push it.

What would settle it properly

Not end-to-end throughput: the aggregator only acts on steps where it flips a bucket, so most of a serving run measures nothing while the harness drifts. The measurement is at the kernel — verify-step attention time at fixed K and fixed B, across batches with identical Σ ctx but different distributions. If time tracks Σ ctx, mean; if it tracks the longest sequence, max; if neither, median never had a mechanism. That is the microbenchmark you suggested in the first place, done at the level you suggested it at.

I no longer have the H100 those numbers came from. I can write that harness so it is one command for whoever has the hardware, or treat it as a design call and hardcode whichever you prefer.

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merged. Please rebase the PR, @seongyun1104.

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