[Model] Accelerate the Qwen2.5-Omni audio encoder: native packed attention + opt-in torch.compile and CUDA graph capture

[pratapyash]

Review copy on the fork — base is pinned at upstream/main tip 08985351f (branch review-base-omni-audio-encoder) so the diff is exactly the feature commits. Not yet submitted upstream; DCO sign-off + a final rebase onto current upstream/main are pending. Delete this note before submitting upstream.

Purpose

Accelerate the Qwen2.5-Omni audio encoder in three layered, independent steps. Layers 2 and 3 are opt-in and default-OFF, so default behavior is byte-identical to today.

1. Native packed audio attention. Replace the audio tower's standalone flash_attn/HF attention with vLLM-native MMEncoderAttention: packed QKVParallelLinear + RowParallelLinear, HF q/k/v → packed qkv remap, biasless k_proj preserved by zeroing the packed K-bias shard. Removes the standalone flash_attn dependency from the thinker path.
2. torch.compile the encoder (compile_mm_encoder). Decorate Qwen2_5OmniAudioEncoderLayer with @support_torch_compile(is_encoder=True, enable_if=should_torch_compile_mm_encoder), matching the Qwen2.5-VL vision pattern. No-op unless compile_mm_encoder is set.
3. CUDA graph capture/replay (cudagraph_mm_encoder) via SupportsEncoderCudaGraph. The thinker implements the model-agnostic protocol; only the fixed-shape encoder layer stack (_run_encoder_layers) is captured. The conv/chunk/pack prologue and the per-clip pool/proj epilogue stay eager (host syncs). Single-path (audio) reuses the qwen2_5_vl idioms: cu_seqlens repeat-padded via padding_logics (a plain zero-pad would break monotonicity); max_seqlen baked at capture as a CPU upper bound (host-side .item(), no D2H sync during capture). No-op unless cudagraph_mm_encoder is set.

Layers 2 and 3 require no changes to the EncoderCudaGraphManager, the protocol, or the compilation_config schema (all already upstream); they are pure model-side opt-ins.

Test Plan

• py_compile the modified model + test files.
• tests/models/multimodal/processing/test_qwen2_5_omni_audio_tower.py — native-attention unit tests.
• tests/compile/fullgraph/test_multimodal_compile.py — compile on/off for the audio encoder.
• tests/models/multimodal/generation/test_audio_cudagraph.py — capture/replay end-to-end on Qwen2.5-Omni-3B (mirrors test_vit_cudagraph.py).
• Offline GPU inference: cudagraph_mm_encoder ON vs OFF byte-identical transcripts on 2 audio assets (L40S).

Test Result (L40S, Qwen2.5-Omni-3B)

Native attention: test_qwen2_5_omni_audio_tower.py → 5 passed.

Compile (compile_mm_encoder=True): num_models_seen 1 → 67; num_graphs_seen 1 → 33 (the +32 = the 32 audio encoder layers actually compiling), with one fresh Inductor compile over the is_encoder (1, 2147483647) range (+31 dedup loads); output identical.
Single-variable control (audio @support_torch_compile decorator removed): num_models_seen=35, num_graphs_seen=1 — confirms the +32 in both counters is the audio encoder, not the (also-decorated, reused) Qwen2.5-VL vision tower.

CUDA graph (cudagraph_mm_encoder=True): EncoderCudaGraphManager built, 9 budget graphs captured; transcripts on 2 audio assets byte-identical to the eager (cudagraph_mm_encoder=False) run, which builds no manager.

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