[Bugfix][Model] Fix Qwen3 deepstack buffer device mismatch

[cryptowooser]

Purpose

Fix a Qwen3 deepstack profiling/serving crash caused by returning zero-backed deepstack buffers that were still on their initialization device/dtype.

PR #43617 intentionally made Qwen3-VL and Qwen3-Omni return zero-backed IntermediateTensors when no active deepstack payload exists. That preserves a stable decoder input structure for torch.compile/CUDA graph profiling and avoids skipping the deepstack branch for later real multimodal requests.

However, those buffers are initialized before model weights are moved to the active execution device. If the decoder dummy/profile path requests deepstack tensors before any vision payload has populated the buffer, _get_deepstack_input_embeds() can return stale CPU/float32 tensors while the decoder inputs_embeds are on the active model device/dtype. This can break audio-only or text-only profiling/serving paths for Qwen3-Omni and Qwen3-VL.

This PR keeps the #43617 contract: empty deepstack buffers still return zero-backed IntermediateTensors. The fix is narrower: recreate the buffers when their size, device, or dtype does not match the current inputs_embeds/deepstack payload tensor.

Duplicate check

I searched for existing Qwen3 deepstack PRs/issues before updating this patch. The closest prior work is:

• Fix Qwen3-VL and Qwen3-omni-thinker accuracy degradation from deepstack inputs under torch.compile #43617: merged; intentionally keeps zero-backed deepstack IntermediateTensors for compile stability.
• [Opt] Optimize deepstack buffer handling for multimodal Qwen3 models #40145: older deepstack buffer handling optimization.

This PR is not a duplicate of #43617. It is a follow-up that preserves #43617's stable-input behavior while fixing the stale buffer device/dtype mismatch noted in review of that PR.

Test Plan

• Add regression coverage for Qwen3-Omni empty deepstack buffers matching the current inputs_embeds device/dtype.
• Add matching regression coverage for Qwen3-VL, since the same helper pattern exists there.
• Add payload-preservation coverage for _set_deepstack_input_embeds() so real deepstack payloads are copied into buffers with the payload dtype rather than later being dropped by a getter-side resize.
• Run affected model-executor test files.
• Run compile and ruff checks on changed files.

Test Result

• PYTHONPATH=/home/aomori/vllm XDG_CACHE_HOME=/tmp/$USER/.cache mamba run -n vllm-nightly pytest tests/model_executor/test_qwen3_omni.py tests/model_executor/test_qwen3_vl_mrope.py -q
  • 101 passed, 16 warnings in 26.05s
• PYTHONPATH=/home/aomori/vllm XDG_CACHE_HOME=/tmp/$USER/.cache mamba run -n vllm-nightly python -m compileall vllm/model_executor/models/qwen3_omni_moe_thinker.py vllm/model_executor/models/qwen3_vl.py tests/model_executor/test_qwen3_omni.py tests/model_executor/test_qwen3_vl_mrope.py
  • passed
• PYTHONPATH=/home/aomori/vllm XDG_CACHE_HOME=/tmp/$USER/.cache mamba run -n vllm-nightly python -m ruff check vllm/model_executor/models/qwen3_omni_moe_thinker.py vllm/model_executor/models/qwen3_vl.py tests/model_executor/test_qwen3_omni.py tests/model_executor/test_qwen3_vl_mrope.py
  • All checks passed!
• PYTHONPATH=/home/aomori/vllm XDG_CACHE_HOME=/tmp/$USER/.cache mamba run -n vllm-nightly python -m ruff format --check vllm/model_executor/models/qwen3_omni_moe_thinker.py vllm/model_executor/models/qwen3_vl.py tests/model_executor/test_qwen3_omni.py tests/model_executor/test_qwen3_vl_mrope.py
  • 4 files already formatted

Documentation

No docs update: this is an internal model-executor bugfix and does not change user-facing APIs or documented behavior.

AI Assistance

This PR includes AI-assisted code changes. The commit includes an AI attribution trailer and a Signed-off-by: trailer for DCO compliance.

CI note

The current pre-commit workflow failure is from vLLM's pre-run-check: PRs from authors with fewer than four merged PRs require a maintainer to add the ready or verified label before the actual pre-commit job runs. The workflow listens for pull_request.labeled, so applying either label should rerun and unblock that gate.

[github-actions]

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[cryptowooser]

CI note for reviewers: the current failing pre-commit check is the repository pre-run-check, not a code/lint failure. It blocks PRs from authors with fewer than four merged PRs until a maintainer applies ready or verified; the workflow reruns on pull_request.labeled.

I also expanded the PR body with the concrete Qwen3-Omni audio-only/profile failure mode this fixes.

[cryptowooser]

Update after re-reading the contribution/agent guidelines and related deepstack history:

• Reworked the patch so it no longer returns None for zero-token deepstack buffers. That would conflict with Fix Qwen3-VL and Qwen3-omni-thinker accuracy degradation from deepstack inputs under torch.compile #43617's compiled decoder input-stability fix.
• The PR now preserves Fix Qwen3-VL and Qwen3-omni-thinker accuracy degradation from deepstack inputs under torch.compile #43617's zero-backed IntermediateTensors behavior and instead recreates stale buffers when their size/device/dtype does not match the current inputs_embeds or payload tensor.
• Added Qwen3-VL coverage too, since the same helper pattern exists there.
• Amended the commit with Signed-off-by: and AI attribution trailers, and updated the PR title/body with duplicate-check and AI-assistance notes.

[cryptowooser]

Follow-up after an extra review pass: added regression coverage for _set_deepstack_input_embeds() payload preservation in both Qwen3-Omni and Qwen3-VL. The new tests cover the case where a stale float32 buffer receives a real float16 deepstack payload, ensuring the payload is copied into a correctly typed buffer and is not later dropped by getter-side resizing.

Fresh local validation is now 101 passed for the affected model-executor test files, with compileall/ruff/format checks still passing.

[mergify]

This pull request has merge conflicts that must be resolved before it can be
merged. Please rebase the PR, @cryptowooser.

https://docs.github.com/en/pull-requests/collaborating-with-pull-requests/working-with-forks/syncing-a-fork