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promote: omni-audio native attention delta onto v0.24.0 - #2

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pratapyash merged 7 commits into
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Jul 7, 2026
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promote: omni-audio native attention delta onto v0.24.0#2
pratapyash merged 7 commits into
prod/v0.24.0from
promote/v0.24.0

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Purpose

Promotes PATCHES.md rows #1 (native packed audio attention), #2 (audio-tower hygiene), vllm-project#3 (MRV2 fail-loud guard) onto vLLM v0.24.0. Reason: first prod lane cut on the current upstream release; supersedes the legacy v0.23 full-feature branch (compile/cudagraph deprecated to eager per benchmarks).

Adaptation notes (delta vs the dev lane)

  • Kept the manual qkv loader (W2-3-hardened variant from the validated v0.23 lineage): the AutoWeightsLoader + WeightsMapper(orig_to_new_stacked) idiom requires Remove unnecessary load_weights methods vllm-project/vllm#44589 infra, which landed after v0.24.0 (mapper kwarg would TypeError at class-definition time). K-bias zero-at-construction kept; loader re-zeroes defensively.
  • Everything else: clean cherry-picks from dev (6 commits).

Validation gate (L40S, editable install via VLLM_USE_PRECOMPILED + pinned v0.24.0 release wheel = the exact image path; tip 8342306)

  • py_compile on touched files
  • tower processing tests: 6 passed (includes the loader contract test)
  • K-bias probe on the real checkpoint: {"k_all_zero": true, "q_abs_sum": 117.619, "v_abs_sum": 4.190} — bit-identical to the dev-lane validation
  • generation sanity: coherent transcript; model load 7.59 GiB (vision tower strip active)
  • HF-parity audio entry: 1 passed (149s), no V2-runner override needed
  • runner identity: vllm.v1.worker.gpu_model_runner (V1 default on v0.24.0, as designed)
  • image built from the merge SHA (post-merge)
  • PATCHES.md + BRANCHES.md sync-log row (post-merge)

Review: diff walked through interactively 2026-07-08 (compare v0.24.0...promote/v0.24.0); the config/vllm.py guard questioned and approved ("let it be").

…e branch

Carry the native-attention-relevant fixes from the archived compile/cudagraph
feature branch (archive/omni-audio-cudagraph-full) onto the slim
native-attention-only lineage:
- reject the unsupported FLASHINFER backend for the audio encoder (review W1-1)
- drop dead attrs in Qwen2_5OmniAudioAttention (W3-1) and the tuple return in
  the encoder layer forward (W3-5); use the positional-embedding module forward
  instead of indexing its private buffer (W3-4)
- zero the packed QKV bias at construction (HF k_proj has bias=False) and adopt
  the AutoWeightsLoader + WeightsMapper orig_to_new_stacked idiom from vllm-project#44589,
  mirroring Qwen3OmniMoeAudioEncoder
- adopt the final audio-tower processing test (FLASH_ATTN/TORCH_SDPA
  parametrization) and the qwen2_5_omni_audio HF-parity generation entry
…model runner

The V2 GPU model runner executes multimodal encoders eagerly and never builds
an EncoderCudaGraphManager, so cudagraph_mm_encoder=True was silently ignored
there (no error, no warning, no acceleration). Register it in
_get_v2_model_runner_unsupported_features so the standard V1 fallback warning
fires (or a hard error under a forced VLLM_USE_V2_MODEL_RUNNER=1).
…tion

The loader test asserted the old zero-at-load contract (sentinel-fill the
packed bias, expect load_weights to zero the K slot). With the K-bias zeroing
moved to Qwen2_5OmniAudioAttention.__init__ and load_weights delegating to
AutoWeightsLoader, the contract is: the K slot is zero at construction and is
never written during load (HF provides no k_proj bias). Assert both.
The AutoWeightsLoader + WeightsMapper(orig_to_new_stacked=...) idiom requires
the vllm-project#44589 infrastructure, which landed after the v0.24.0 release — on this
base the mapper kwarg is a TypeError at class-definition time. Restore the
manual stacked-params loader in the W2-3-hardened form proven on the v0.23
lineage (skip non-parameter checkpoint keys via params_dict.get; record only
weights actually loaded). The K-bias zero-at-construction from the hygiene
port is kept; the loader re-zeroes defensively for reload paths.
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@pratapyash
pratapyash merged commit f0a3f65 into prod/v0.24.0 Jul 7, 2026
3 of 4 checks passed
@pratapyash pratapyash self-assigned this Jul 7, 2026
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