[Bugfix] Avoid racy accepted counts in async spec decode

[sunnweiwei]

Purpose

Fix an async speculative decoding race for hybrid non-align Mamba/GDN models (observed with Qwen3.5 MTP) where _prepare_inputs consumes a stale CPU copy of num_accepted_tokens.

In the failing path, _update_states_after_model_execute writes accepted-token counts from GPU to input_batch.num_accepted_tokens_cpu_tensor with a non-blocking D2H copy. Under async scheduling, the next _prepare_inputs may also remap request rows after input_batch.swap_states() / condense(). At a request's prefill-to-first-spec-decode transition, this can make GDN see another row's accepted-token count, e.g. 4 instead of 1. GDN then restores the recurrent state from the wrong speculative state slot; for Qwen3.5 this loses prompt memory and the request often ends quickly with garbled text plus EOS.

This PR keeps the async non-align path device-authoritative:

• initialize num_accepted_tokens to 1 in _prepare_inputs
• keep the existing GPU correction kernel to overwrite draft-participating rows from valid_sampled_token_count
• leave mamba_cache_mode == "align" on the old synchronized CPU path, because align-mode preprocessing consumes CPU-side accepted counts
• keep GDN FULL cudagraph per-request metadata padded by m.num_reqs, not token-padded m.num_actual_tokens

This is not a verl-specific issue. The repro and validation below use a native vLLM AsyncLLM harness without verl training code.

AI assistance disclosure: this contribution was human-reviewed and validated; GPT-5.5 and Claude Fable 5 were used as debugging / code-review / PR-drafting assistants.

Test Plan

Unit / focused checks:

python -m py_compile \
  vllm/v1/worker/gpu_model_runner.py \
  vllm/v1/attention/backends/gdn_attn.py \
  tests/v1/attention/test_gdn_metadata_builder.py

pytest -q tests/v1/attention/test_gdn_metadata_builder.py

Native runtime A/B harness:

python /mnt/verl-data/scripts/repeat_vllm_native_async_from_rollout_dump.py \
  --model /mnt/verl-data/models/Qwen3.5-27B \
  --input /mnt/verl-data/debug/qwen35_mtp3_hybrid_sleepwake_nosync_rolloutonly_20260609_061929/rollout_tokens.jsonl \
  --mode mtp3 \
  --rounds 40 \
  --num-prompts 64 \
  --rollout-n 8 \
  --max-tokens 512 \
  --tensor-parallel-size 4 \
  --num-servers 2 \
  --visible-gpu-groups "0,1,2,3;4,5,6,7" \
  --gpu-memory-utilization 0.75 \
  --max-model-len 9216 \
  --max-num-batched-tokens 9216 \
  --max-num-seqs 1024 \
  --cudagraph-mode FULL_AND_PIECEWISE

The runtime A/B was run on official main commit 6deb05e0e4f6c298a60975f188cd044773ae6dec. This PR is rebased onto 2c9c07c85e56c799afffd5a671a8a0bace377a39; the two newer upstream commits only touched benchmark/docs/Rust frontend files, not the files changed here.

Test Result

Focused unit test:

9 passed, 17 warnings in 8.36s

Primary A/B: Qwen3.5-27B, MTP=3, async scheduling enabled, FULL_AND_PIECEWISE, native AsyncLLM, 2 servers with TP=4 each, 64 prompts x 8 samples x 40 rounds = 20,480 generations.

Code	Count	Min len	<32	<64	<128	<256	Stop counts	Tok/s incl. init	Notes
official main, unpatched	20,480	5	6	7	7	16	{"length": 20417, "stop": 63}	14,565.6	reproducible garbled early EOS
patched	20,480	213	0	0	0	1	{"length": 20450, "stop": 30}	14,739.0	no hard-short failures; only short was a complete answer ending #### 42

Examples from the unpatched FULL run:

Thinking ProcessCO 

ts-matic<|im_end|>

The user
bra<|im_end|>

Here's a a stepke isks
USER<|im_end|>

Additional controls with the same model / input dump / two TP=4 workers / gpu_memory_utilization=0.75:

Code	Config	Count	Min len	<32	<64	<128	<256	Notes
patched	no MTP, async=true, FULL_AND_PIECEWISE	10,240	413	0	0	0	0	non-speculative path clean
patched	MTP=3, async=false, FULL_AND_PIECEWISE	10,240	227	0	0	0	1	only short was a complete answer ending #### 5
unpatched	MTP=3, async=true, PIECEWISE	10,240	6	10	13	15	16	many garbled early-EOS outputs
patched	MTP=3, async=true, PIECEWISE	10,240	68	0	0	2	2	no <64; no obvious garbled text in inspected short outputs
patched	MTP=3, async=true, NONE	10,240	294	0	0	0	0	eager path clean

Examples from the unpatched PIECEWISE run:

Thinking

I


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Here user
 organisms Is is Your is <|im_end|>

The "]user'"** John inhalishment
seeing sconfitta toughhtorion just force comp * apple** notion
cing**imes with this**<|im_end|>

The GDN metadata unit test added in this PR covers the second fix directly: for FULL cudagraph spec-decode batches, request-indexed metadata tensors (spec_state_indices_tensor, spec_sequence_masks, spec_query_start_loc, num_accepted_tokens) are sized by request count, not by token-padded count.

---

Essential Elements of an Effective PR Description Checklist

• The purpose of the PR is explained above.
• The test plan includes concrete commands.
• The test results include before/after runtime comparison and focused unit results.
• Documentation update is not needed for this bug fix.

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[sunnweiwei]

The current pre-run-check failure is the repository gate: this account has fewer than 4 merged vLLM PRs and cannot add labels. Could a maintainer add the ready or verified label to trigger the pre-commit workflow? Local focused tests and the full native AsyncLLM A/B validation are included in the PR description.

[ZJY0516]

Thanks for catching this. Will take a deeper look further. Let's run CI first

[mergify]

Hi @sunnweiwei, the pre-commit checks have failed. Please run:

uv pip install pre-commit>=4.5.1
pre-commit install
pre-commit run --all-files

Then, commit the changes and push to your branch.

For future commits, pre-commit will run automatically on changed files before each commit.

[ZJY0516]

Thanks for fixing it!

[ZJY0516]

@sunnweiwei Do you know how to reproduce garbage output using main branch?

[tdoublep]

Is this change also needed for all mode or specifically for self.cache_config.mamba_cache_mode == None?

[sunnweiwei]

Both. Only "align" is excluded, as its preprocess_mamba genuinely consumes the CPU-side counts (reads num_accepted_tokens_cpu[i], resets it to 1, re-syncs to GPU). "none" and "all" have no CPU consumer; the GDN kernels read only the GPU buffer, so both take the device-authoritative path.

[tdoublep]

Is there perhaps a way to rework the logic so that we can re-use this code instead of having it in two branches?

[sunnweiwei]

Good point! done in 066f691. Inverted the condition so it reuses the existing fallback instead of duplicating it.

[sunnweiwei]

@sunnweiwei Do you know how to reproduce garbage output using main branch?

created a repo script: https://gist.github.com/sunnweiwei/259017be1925f5e9c5a447feda64d346

bad case example:

'Thinkinging a<|im_end|>'

[tdoublep]

Thanks!