[Refactor] Worker/ModelRunner runner correctness fixes (G2/N) (#5452)

Signed-off-by: tzhouam <tzhouam@connect.ust.hk>
Co-authored-by: Claude Fable 5 <noreply@anthropic.com>

tests/model_executor/models/test_gepard_wiring.py

@@ -10,9 +10,10 @@ survives the runner's sparse-audio routing.
 
 These drive the real ``preprocess`` -> ``forward`` -> ``make_omni_output``
 sequence through a stand-in for ``GPUARModelRunner.execute_model``, and use the
-runner's own ``_resolve_sparse_mm_routing`` to decide what actually reaches the
-caller. Only the LM backbone and the frame sampler are stubbed out; every
-method under test is the real one, so a test here goes red when the engine
+shared ``sparse_audio.resolve_sparse_mm_routing`` helper used by the runner to
+decide what actually reaches the caller. Only the LM backbone and the frame
+sampler are stubbed out; every method under test is the real one, so a test
+here goes red when the engine
 path breaks — which a test that calls the flush by hand cannot do.
 """
 
@@ -33,7 +34,7 @@ from vllm_omni.model_executor.models.gepard.gepard_talker import (
 )
 from vllm_omni.model_executor.models.gepard.nanocodec import NanoCodec
 from vllm_omni.model_executor.models.gepard.prompt import build_gepard_prompt_ids
-from vllm_omni.worker.gpu_ar_model_runner import GPUARModelRunner
+from vllm_omni.worker import sparse_audio
 
 pytestmark = [pytest.mark.core_model, pytest.mark.cpu]
 
@@ -219,7 +220,7 @@ class _MiniEngine:
                 self.committed_frames[req_id] = state.frame_count
 
         # --- runner: sparse-audio routing decides what reaches the caller ---
-        downstream, sparse_index, is_sparse = GPUARModelRunner._resolve_sparse_mm_routing(
+        downstream, sparse_index, is_sparse = sparse_audio.resolve_sparse_mm_routing(
             engine_output_type="audio",
             req_ids_output_copy=list(live),
             downstream_req_ids=list(live),

tests/model_executor/models/voxcpm2/test_talker_output_marker.py

@@ -0,0 +1,73 @@
+# SPDX-License-Identifier: Apache-2.0
+# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
+"""VoxCPM2 dense-mode audio outputs must carry the sparse-alignment marker.
+
+Dense mode (`_uses_sparse_audio_outputs()` False) still yields a strict
+SUBSET of the batch whenever some requests emit no audio in a step (e.g.
+prefill phase). The runner routes per-request `model_outputs` lists by batch
+position unless `meta.sparse_audio` declares `meta.req_id` alignment — so a
+subset without the marker misroutes audio across requests (RFC #5450 C3
+review follow-up).
+"""
+
+from __future__ import annotations
+
+import functools
+
+import pytest
+
+pytestmark = [pytest.mark.core_model, pytest.mark.cpu]
+
+torch = pytest.importorskip("torch")
+
+
+@functools.lru_cache(maxsize=1)
+def _talker_cls():
+    """Defer talker import (pulls vLLM model_executor) until first use."""
+    from vllm_omni.model_executor.models.voxcpm2.voxcpm2_talker import (
+        VoxCPM2TalkerForConditionalGeneration,
+    )
+
+    return VoxCPM2TalkerForConditionalGeneration
+
+
+def _make_dense_talker():
+    cls = _talker_cls()
+    talker = cls.__new__(cls)
+    # Dense mode: emit every step, no delayed copy, no batched VAE decode.
+    talker._audio_emit_every = 1
+    talker._vae_decode_every = 1
+    talker._enable_delayed_audio_copy = False
+    talker._coalesce_audio_d2h = False
+    talker._sample_rate = 16000
+    talker._audio_queue = []
+    return talker
+
+
+def test_dense_subset_audio_carries_sparse_alignment_marker():
+    talker = _make_dense_talker()
+    # Batch had [r0, r1, r2]; only r1/r2 produced audio this step.
+    talker._audio_queue = [("r1", torch.zeros(4)), ("r2", torch.ones(4))]
+
+    out = talker.make_omni_output(torch.zeros(1))
+
+    mm = out.multimodal_outputs
+    assert mm["meta"] == {"req_id": ["r1", "r2"], "sparse_audio": ["1"]}
+    assert len(mm["model_outputs"]) == 2
+    assert len(mm["sr"]) == 2
+    # (The coalesce-D2H sibling branch carries the same marker; it needs
+    # device-resident chunks, so its marker contract is pinned here via the
+    # shared dense-subset shape rather than a GPU-only test.)
+
+
+def test_dense_full_batch_audio_still_carries_marker_and_order():
+    # Full coverage is the degenerate subset: the marker stays truthful and
+    # `meta.req_id` order matches the emitted list order.
+    talker = _make_dense_talker()
+    talker._audio_queue = [("r0", torch.zeros(2)), ("r1", torch.ones(2))]
+
+    out = talker.make_omni_output(torch.zeros(1))
+
+    mm = out.multimodal_outputs
+    assert mm["meta"]["req_id"] == ["r0", "r1"]
+    assert mm["meta"]["sparse_audio"] == ["1"]