[Bugfix][Spec Decode] Reject out-of-range sampled token ids in the async output path

[publee]

Purpose

Fixes the engine crash reported in #424 for a deployment that hits it on decode with MTP4 on a single V100 (SM70), not only above 262k context.

Under async scheduling with speculative decoding, InputBatch.update_async_output_token_ids() counted a sampled id as a real token unless it was exactly -1:

num_sampled_ids = (
    len(sampled_ids) if sampled_ids[-1] != -1 else sampled_ids.index(-1)
)

The SM70 speculative rejection path also emits vocab_size for rejected slots. That sentinel was therefore counted as sampled, written into token_ids_cpu, and from there into input_ids, where the embedding lookup tripped

Indexing.cu:1515: indexSelectSmallIndex: Assertion `srcIndex < srcSelectDimSize` failed

and took the engine down with a device-side assert (reported as EngineDeadError, process exits 0).

The sibling draft path is already guarded — _valid_async_draft_prefix() stops at the first id < 0 or >= vocab_size. The output path was not. This change applies the same rule there.

Root cause evidence

Instrumenting torch.embedding (how VocabParallelEmbedding reaches the ATen index_select) captured the offending call at the moment of the crash:

SM70-TEMB: rows=248320 input=(10,) dtype=torch.int64 min=0 max=248320

rows=248320 is the embedding size and the batch is 10 tokens = 2 requests x (1 accepted + 4 drafts), i.e. the spec-decode row. A probe on the two async write paths localised the source:

SM70-OUTPROBE: OUT-PATH out-of-range sampled ids [248320]
  (vocab_size=248320 num_sampled_ids=1 num_placeholders=5
   first_placeholder=1 old_no_spec_end=23997)

and the Python stack from that probe:

gpu_model_runner.py:6820 _preprocess -> self.model.embed_input_ids(self.input_ids.gpu[:n])

So the value is not the documented -1 placeholder — it is vocab_size, and it is the output path, not the draft path, that admits it.

Test Plan / Test Result

Environment: 1Cat-vLLM 1.5.0, Qwen3.8-27B-INT4 (compressed-tensors) + MTP4, single Tesla V100-PCIE-32GB (SM70), driver 570.211.01 / CUDA 12.8, --attention-backend FLASH_ATTN_V100 --kv-cache-dtype fp8_e5m2 --max-model-len 229376 --max-num-seqs 4 --kv-offloading-size 22 --kv-offloading-backend native, prefix caching on, async scheduling enabled (default).

Reproducer: 2 concurrent requests with long shared-prefix prompts, 32 tool definitions, staggered submission, driven through the OpenAI-compatible endpoint.

• Before (unmodified tree): config 8000w/n=2/max_tokens=600/stagger=10 crashed the engine 7 times out of 7 attempts, always on Indexing.cu:1515 followed by EngineDeadError.
• After (this commit): 0 asserts in 22 attempts over 90 minutes, including that same config.

I am not claiming this replaces a unit test: num_sampled_ids is computed from data the rejection path produces on the GPU, and I could not reach it from a CPU-only test. The numbers above are end-to-end, on the hardware that hits the bug. I am happy to add a test if you can point me at the intended way to fake sampled_token_ids_cpu for this path.

Known limitation, stated honestly: on that same config I also saw 2 of 22 runs where one request stalled client-side (310 s, engine healthy, Accepted: 0 in the spec-decode metrics) and 1 unrelated CUDA out of memory (48 MiB at 38 MiB free).

Follow-up, same day: the exact crash config was then run 7 times in a row after the change — every run completed in 31-50 s with no stall and no assert, so the stall does not reproduce systematically. Note that in the stalling case the engine stayed healthy and kept serving other requests; it is not a deadlock.

Not a duplicate

Checked open PRs and issues on 2026-09-13:

• [Bugfix][Spec Decode] Trim the optimistic spec-decode tokens on every pipeline rank #574 touches gpu_model_runner.py optimistic-token trims for pipeline parallelism (multi-rank NCCL wedge). Different file, different mechanism, does not touch update_async_output_token_ids.
• No other open PR touches vllm/v1/worker/gpu_input_batch.py.

AI assistance

This change was developed with an AI coding agent (Hermes Agent, Nous Research). The diff is 15 lines in one function, it was reviewed line by line, and the end-to-end before/after numbers above were produced on my own hardware. I can defend the change end to end.

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[publee]

Follow-up test, same day, after the numbers above were written: the crash config (8000w/n=2/max_tokens=600/stagger=10) run 8 times in a row with this change - 8/8 completed, 0 stalls, 0 asserts, durations 31.1 / 33.1 / 34.5 / 35.0 / 35.0 / 36.1 / 36.5 / 50.4 s. The 310 s stall mentioned in the description did not reproduce in any of the 8 runs, and the engine was healthy throughout (NRestarts=0).