fix(v1): decouple async Mamba align D2H counts from InputBatch row shifts (#51571)

[bandham-manikanta]

Target Issue

Closes #51571

Description

When running speculative decoding / MTP in align mode with use_async_scheduling=True, GPUModelRunner._update_states_after_model_execute() was passing input_batch.num_accepted_tokens_cpu_tensor directly as the D2H target tensor for postprocess_mamba_align_gpu().

While the D2H copy is in flight on the CUDA stream, the CPU thread prepares the next step and calls InputBatch.condense(). This compacts finished requests and shifts row indices in input_batch before num_accepted_tokens_event is synchronized in _prepare_inputs(). When _prepare_inputs() later gathers from input_batch.num_accepted_tokens_cpu, it reads shifted/corrupted counts, causing Mamba hidden state copy offsets to misalign for shifted requests.

To fix this race hazard and ensure input_batch is never an asynchronous DMA target:

1. Unconditional D2H target: num_accepted_tokens_cpu_tensor is set to self.num_accepted_tokens.cpu unconditionally across both sync and async modes, isolating input_batch host memory from CUDA stream D2H transfers.
2. Synchronized remapping & write-back: _sync_num_accepted_tokens() handles both modes after num_accepted_tokens_event synchronization:
   • In async mode, it remaps historical counts from self.num_accepted_tokens.np through prev_positions and populates input_batch.num_accepted_tokens_cpu.
   • In sync mode, it writes back self.num_accepted_tokens.np 1:1 into input_batch.num_accepted_tokens_cpu.
3. Empty prev_req_id_to_index handling: When prev_req_id_to_index is empty (on initial steps or all-chunked-prefill steps where all previous requests were discarded), counts default to 1 (accept_token_bias = 0), avoiding reads from host buffers.
4. Unit test suite: Refactored unit tests into TestSyncNumAcceptedTokens in tests/v1/worker/test_gpu_model_runner.py covering async remapping, empty prev-index fallback, sync mode write-back, and D2H target isolation.

Test Plan

• Ran unit tests: pytest tests/v1/worker/test_gpu_model_runner.py -k TestSyncNumAcceptedTokens (4/4 passed).
• Live run: Tested Qwen/Qwen3.5-4B with --async-scheduling, mamba_cache_mode="align", and spec_tokens=3 on an A100 GPU with concurrent prompts (exact match against sync baseline across all requests).
• Checked formatting: ruff check and ruff format --check (passed).

---

AI assistance was used for code inspection, unit test setup, and documentation for this PR.

[claude]

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[github-actions]

✅ @bandham-manikanta, CI is now available for this PR.

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[QwertyJack]

The production fix direction looks correct: under async scheduling, the D2H result should be owned by a runner-side previous-iteration snapshot and remapped only after the existing event synchronization.

One ownership boundary still looks implicit. When use_async_scheduling=True but prev_req_id_to_index is empty, the code falls into the current else branch (commented as "Non-async mode") and reads from InputBatch:

else:
    self.num_accepted_tokens.np[:num_reqs] = (
        self.input_batch.num_accepted_tokens_cpu[:num_reqs]
    )

The usual empty-mapping cases may already contain neutral values, but this relies on an invariant that is not encoded here. Once async D2H ownership moves to self.num_accepted_tokens.cpu, it would be clearer and safer to make the three ownership cases explicit:

if self.use_async_scheduling:
    if prev_req_id_to_index:
        # Remap the previous-iteration runner snapshot through prev_positions.
        ...
    else:
        self.num_accepted_tokens.np[:num_reqs].fill(1)
        self.input_batch.num_accepted_tokens_cpu[:num_reqs].fill(1)
else:
    # InputBatch owns current-request counts and condense/reorder moves them.
    self.num_accepted_tokens.np[:num_reqs] = (
        self.input_batch.num_accepted_tokens_cpu[:num_reqs]
    )

A small _sync_num_accepted_tokens() helper would encode this lifecycle cleanly and give the regression test a production function to exercise. It would also remove the inner if self.use_async_scheduling used to select src_accepted_counts, whose else arm is unreachable inside the outer async-only branch.

This keeps the patch allocation- and synchronization-neutral while making the previous-row snapshot -> current-row state transition explicit.

[bandham-manikanta]

@QwertyJack - thanks for the feedback, updated and pushed.

[bandham-manikanta]

/ci run

[github-actions]

✅ Triggered Buildkite CI #83275 for commit f14a7302baf5.

[robertomeroni]

the regression test doesn't reach that code. It builds a MagicMock() runner and
re-implements the gather in its own body, so _sync_num_accepted_tokens is never called

the test passes even with vLLM not installed

[bandham-manikanta]

Hi @robertomeroni, you are right, thanks for pointing it out! I updated and pushed the testcases to call the production method directly. Please let me know if you have any feedback on it.

[bandham-manikanta]

/ci run

[github-actions]

✅ Triggered Buildkite CI #83586 for commit 152ea62799cb.

[mergify]

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[bandham-manikanta]

/ci run

[github-actions]

✅ Triggered Buildkite CI #83589 for commit 2d89d2dc4d9c.

[mergify]

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

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

Then, commit the changes and push to your branch.

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[bandham-manikanta]

/ci run

[github-actions]

✅ Triggered Buildkite CI #83602 for commit 20db643025f2.

[bandham-manikanta]

Hi @QwertyJack / @njhill
I tried adding the else: fill(1) branch under use_async_scheduling, but it broke standard async Mamba CI tests (amd-language-models-tests-hybrid-2-mi300-1).

For non-speculative async Mamba runs, prev_req_id_to_index is None, but input_batch.num_accepted_tokens_cpu holds valid D2H accepted counts copied from GPU. Calling fill(1) overwrites those real counts with 1.

so keeping if self.use_async_scheduling and prev_req_id_to_index: will conver both speculative flows and else block covers non-speculative flows correctly. Let me know if this looks good.

[njhill]

Re-reviewed after the test rewrite. I traced the paths on main rather than taking the issue's diagnosis at face value, and the diagnosis holds: the D2H issued by postprocess_mamba_align_gpu (mamba_utils.py:1361) and InputBatch.condense()'s writes (gpu_input_batch.py:815) target the same pinned, numpy-backed buffer with no sync between them, so whichever lands last is nondeterministic. accept_token_bias in preprocess_mamba (mamba_utils.py:1231) is a real consumer, so it does corrupt state copies. Moving the D2H destination to a runner-owned buffer is the right shape of fix.

The earlier test concern is resolved: TestSyncNumAcceptedTokens now calls _sync_num_accepted_tokens unbound and covers all three ownership branches. I checked the arithmetic - the remap case yields [4, 2, 1], and a regression to reading input_batch would yield [4, 1, 1], so the test does discriminate against the bug.

What's left before merge:

1. Verification (main gap). This is a nondeterministic Mamba-state corruption; unit tests on the remap can't show it's fixed in practice. An accuracy run on a hybrid model with MTP + --async-scheduling + mamba_cache_mode=align is what would make this reviewable - the race shows as sporadic output degradation, not a crash.

2. PR description is stale. It still describes a single test named test_async_mamba_align_accepted_counts_race, and the Test Plan still lists only that -k invocation plus ruff. Per AGENTS.md it also needs an explicit statement that AI assistance was used, which is currently absent.

3. The fix is narrower than the bug. The non-align async path at gpu_model_runner.py:1652 still copies D2H into self.input_batch.num_accepted_tokens_cpu_tensor while condense() mutates it. It's latent today only because needs_cpu_accepted_counts gates the reader off (the comment there names this same race). Targeting self.num_accepted_tokens.cpu unconditionally - adding the input_batch write-back to the sync branch, symmetric with what the async branch already does - would remove the ternary and make "input_batch is never a D2H destination" a global invariant, rather than leaving correctness dependent on a downstream reader gate. If you keep the conditional, worth stating the rationale in the description.

Inline comments below for the smaller items.

[njhill]

This half of the change - the D2H destination - is the part that actually closes the race, and it currently has no test coverage; the helper tests only pin the read side. A small assertion that the tensor handed to postprocess_mamba_align_gpu is the runner buffer rather than input_batch's would stop a future refactor from silently reopening this.

See also the main review comment on making this unconditional.

[njhill]

nit: dict | None -> dict[str, int] | None, matching how the mapping is built in _get_prev_req_id_to_index/prev_req_id_to_index.

[njhill]

This branch is a behavior change beyond the stated race fix, and it isn't mentioned in the description.

prev_req_id_to_index is empty not only on the first step but whenever every previous-step request was discarded (all-chunked-prefill - see the discard_req_indices filter where the mapping is constructed). Previously that case gathered from input_batch; now it forces 1 and additionally clobbers input_batch.num_accepted_tokens_cpu[:num_reqs], which is new in this branch.

I believe the value is correct - a discarded request sampled nothing, so accept_token_bias == 0 - and it's strictly better than gathering from the raced buffer. But it deserves a sentence in the PR description so a reviewer doesn't have to re-derive it. The comment "Default initialization for initial step" is also slightly misleading, since it's not only the initial step.

[njhill]

nit: this comment is now duplicated verbatim inside _sync_num_accepted_tokens. Since the helper covers all three modes, the call site reads better without it.

[njhill]

The decoy array is seeded [4, 2, 1, 0, 0], which is exactly the expected output, so at a glance the assertion looks satisfiable from either source. It does in fact discriminate (reading input_batch through prev_idx would give [4, 1, 1]), but seeding something obviously wrong - e.g. [9, 9, 9, 0, 0] - would make that intent legible without working through the gather.

[njhill]

Sorry for the agent review - MRV1 is low priority right now. I think the most important item is 3. which points out that a larger scoped fix is needed.

[bandham-manikanta]

Sorry for the agent review - MRV1 is low priority right now. I think the most important item is 3. which points out that a larger scoped fix is needed.

Thanks for the feedback @njhill, I am looking into them right now.

[mergify]

Documentation preview: https://vllm--51599.org.readthedocs.build/en/51599/

[bandham-manikanta]

Hi @njhill @QwertyJack,

Quick update on this PR:

1. Cleanly rebased on current main and resolved all conflicts.
2. All 46 worker and model runner tests are passing cleanly (tests/v1/worker/test_gpu_model_runner.py).
3. Independent validation by @amittell confirmed that this patch eliminates the Xid 31 driver crash on multi-GPU hybrid GDN/MTP setups in production.

Whenever you have a moment, would appreciate your final review/approval so we can get this landed!

[maxpla3]

@bandham-manikanta Could you check if following tests pass with your fix?
7fc6a68

Those are from my bugfix #51508

[bandham-manikanta]

@maxpla3 Ran your test suite on this branch — 49 passed, and the 9 new builder/kernel unit tests (test_zero_accepted_tokens_nulls_state_slots, etc.) fail as expected since this PR focuses upstream on the async D2H scheduler race in InputBatch.

Our PRs are complementary: #51599 fixes the upstream async scheduling timing desync, while #51508 hardens the downstream GDN/KDA metadata builder and kernel guards for zero-accept edge cases. Both are needed!