[Bugfix][Spec Decode] Size DFlash query buffers for cudagraph-padded batches

[siddhant-bharti]

Purpose

Fix a startup crash (and a latent runtime overflow) in DFlash speculative decoding on the V1 GPU model runner for any odd --max-num-seqs.

DFlashProposer.__init__ allocates its persistent query buffers (positions, _slot_mapping_buffer) to exactly max_query_tokens = max_num_seqs * (1 + num_speculative_tokens) (vllm/v1/spec_decode/dflash.py). But _determine_batch_execution_and_padding → cudagraph_dispatcher.dispatch() pads drafter batches up to the next cudagraph capture size. When max_query_tokens is not itself a capture size, the padded batch exceeds the buffers, and slicing them at the padded size silently returns short tensors. The compiled draft model then receives input_ids at the padded length but positions at the unpadded length, and engine init fails in drafter.dummy_run during cudagraph memory profiling:

AssertionError: expected size 132==136, stride 1==1 at dim=0

(132 = 33 × 4 with max_num_seqs=33, num_speculative_tokens=3; the default capture ladder is multiples of 8 in that range — ..., 128, 136, ... — so 132 pads to 136.) Any odd max_num_seqs hits this; even values happen to land on capture sizes, which is why the bug is easy to miss. The same overflow exists in the runtime propose() path (build_model_inputs_first_pass slices the same buffers) once a full batch is scheduled.

Scope: only the V1 model runner path (vllm/v1/spec_decode/dflash.py) is affected. The V2 model runner has a separate DFlash speculator implementation with its own capture sizing and is unaffected — verified experimentally below. The V1 path remains the fallback for configurations V2 doesn't cover (and is selectable via VLLM_USE_V2_MODEL_RUNNER=0).

Fix: allocate the query buffers to max(max_query_tokens, compilation_config.max_cudagraph_capture_size). Safe by construction: dispatch() never pads a batch beyond max_cudagraph_capture_size (larger batches run unpadded without graphs), and later capture-size adjustment (resolve_cudagraph_mode_and_sizes) only ever shrinks that bound, so the buffer capacity cannot be invalidated. Cost: a few KB of int64 per buffer.

Test Plan

Reproduction and fix verification on a single GPU (the bug is hardware-agnostic), public models only, image vllm/vllm-openai:nightly (0.23.1rc1.dev1533+g49f31d7ce; the affected code is identical on current main):

# bug path: V1 runner, odd max_num_seqs
VLLM_USE_V2_MODEL_RUNNER=0 python3 -m vllm.entrypoints.openai.api_server \
  --model Qwen/Qwen3-8B --max-num-seqs 33 --max-model-len 8192 \
  --speculative-config '{"model": "z-lab/Qwen3-8B-DFlash-b16", "num_speculative_tokens": 3, "method": "dflash"}'

Matrix: {unpatched, patched} × {V2 runner default, V1 runner forced} × {33, 32}, plus a 33-way concurrent full-batch completion burst against each surviving server to exercise the runtime propose() path. Lint: pre-commit run --files ... on both changed files (all hooks incl. mypy pass).

Test Result

Config	Unpatched	Patched
V1 runner, max-num-seqs 33, startup	crash: AssertionError: expected size 132==136, stride 1==1 at dim=0 in drafter.dummy_run	starts clean
V1 runner, max-num-seqs 33, 33-way full-batch burst	n/a (never starts)	33/33 HTTP 200, spec decode active (16374 drafted / 4442 accepted), zero errors
V1 runner, max-num-seqs 32 (control)	starts clean	starts clean
V2 runner (default), max-num-seqs 33	starts clean + 33/33 burst OK (V2 unaffected, as expected)	unchanged

A regression test was validated against a live install in both directions (failed unpatched at 33, passed patched) and then removed per review feedback.

Not a duplicate

Checked open PRs/issues for dflash, drafter padding, and this assertion. Closest is #48725 — same class (spec-decode buffer headroom) but different mechanism (scheduler token budget vs. cudagraph padding), different buffers, different file. No open PR touches DFlash query-buffer sizing.

AI assistance disclosure

This fix was developed with AI assistance (Claude Code): root-cause analysis, patch, reproduction, and test were AI-assisted; the human submitter has reviewed the change and is accountable for it per the contribution policy.

🤖 Generated with Claude Code

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[jcastillo-boop]

looks good

[MatthewBonanni]

Thanks for the fix! Just some nits, this should be a pretty clean and tiny PR

[MatthewBonanni]

LGTM, thanks!