[Bugfix] Align Hub kernel dependency guidance

Signed-off-by: hsliuustc0106 <liuhongsheng4@huawei.com>

.buildkite/cuda/test-merge.yml

@@ -3,6 +3,7 @@
 
 env:
   VLLM_WORKER_MULTIPROC_METHOD: spawn
+  VLLM_USE_FLASHINFER_MOE_FP16: 0
   HF_HUB_DOWNLOAD_TIMEOUT: 300
   HF_HUB_ETAG_TIMEOUT: 60
 

.buildkite/cuda/test-nightly.yml

@@ -4,6 +4,7 @@
 
 env:
   VLLM_WORKER_MULTIPROC_METHOD: spawn
+  VLLM_USE_FLASHINFER_MOE_FP16: 0
   HF_HUB_DOWNLOAD_TIMEOUT: 300
   HF_HUB_ETAG_TIMEOUT: 60
 

.buildkite/cuda/test-ready.yml

@@ -3,6 +3,7 @@
 
 env:
   VLLM_WORKER_MULTIPROC_METHOD: spawn
+  VLLM_USE_FLASHINFER_MOE_FP16: 0
   HF_HUB_DOWNLOAD_TIMEOUT: 300
   HF_HUB_ETAG_TIMEOUT: 60
 

.buildkite/cuda/test-weekly.yml

@@ -3,6 +3,7 @@
 
 env:
   VLLM_WORKER_MULTIPROC_METHOD: spawn
+  VLLM_USE_FLASHINFER_MOE_FP16: 0
   HF_HUB_DOWNLOAD_TIMEOUT: 300
   HF_HUB_ETAG_TIMEOUT: 60
 

docs/design/qwen3_omni_tts_performance_optimization.md

@@ -410,8 +410,8 @@ Notes:
 - `stages[].max_num_seqs` controls stage-level batching.
 - Thinker/Talker commonly use `enforce_eager: false` for CUDA Graph paths.
 - Code2Wav often remains eager (`enforce_eager: true`) depending on runtime behavior.
-- The Qwen3-Omni deploy config pins `moe_backend: triton` for the Thinker and Talker stages. Triton has been more
- stable and faster than the FlashInfer CUTLASS unquantized MoE backend on recent vLLM rebases.
+- Qwen3-Omni defaults `VLLM_USE_FLASHINFER_MOE_FP16=0`. The Triton has been more stable & faster
+ than the FlashInfer CUTLASS unquantized MoE backend on recent vLLM rebases.
 
 #### 2) Enable async chunk
 

docs/user_guide/diffusion/attention_backends/huggingface_hub.md

@@ -12,7 +12,7 @@ implementations to reduce numerical drift and sampling divergence.
 ## Installation
 
 ```bash
-pip install kernels==0.14.1
+pip install kernels==0.15.2
 ```
 
 If `kernels` is unavailable, an explicit `FLASH_ATTN_HUB` or

docs/user_guide/examples/online_serving/qwen3_omni.md

@@ -17,9 +17,9 @@ vllm serve Qwen/Qwen3-Omni-30B-A3B-Instruct --omni --port 8091
 
 The default deployment configuration situated at `vllm_omni/deploy/qwen3_omni_moe.yaml` is resolved and loaded
 automatically via the model registry, obviating the necessity for the `--deploy-config` flag in standard deployment topologies.
-The bundled Qwen3-Omni setup explicitly sets `moe_backend: triton` for the Thinker and Talker stages. This keeps
-both stages on vLLM's Triton unquantized MoE path and avoids the unstable FlashInfer CUTLASS unquantized MoE path on
-some GPUs.
+The bundled Qwen3-Omni setup defaults `VLLM_USE_FLASHINFER_MOE_FP16=0`. This keeps the Thinker & Talker on vLLM's
+Triton unquantized MoE path and avoids the performance regression observed with the FlashInfer CUTLASS unquantized MoE
+backend.
 Asynchronous chunk streaming is **enabled by default** within the bundled configuration.
 
 To explicitly utilize a custom deployment YAML, specify the configuration path:
@@ -87,9 +87,15 @@ vllm serve Qwen/Qwen3-Omni-30B-A3B-Instruct --omni --port 8091 \
     --stage-overrides '{"1": {"gpu_memory_utilization": 0.5}}'
 ```
 
-To experiment with another supported MoE backend, set `moe_backend` in the
-deployment YAML for both the Thinker and Talker stages. For the stage-based
-CLI, pass `--moe-backend` directly on each stage command.
+To experiment with the FlashInfer FP16 MoE path, set `VLLM_USE_FLASHINFER_MOE_FP16=1` before launching the server:
+```bash
+VLLM_USE_FLASHINFER_MOE_FP16=1 \
+vllm serve Qwen/Qwen3-Omni-30B-A3B-Instruct --omni --port 8091
+```
+
+For the stage-based CLI, you usually do **not** need `--stage-overrides` for
+that kind of change. Since each command launches one stage, just pass the knob
+directly on that stage command:
 
 ```bash
 CUDA_VISIBLE_DEVICES=1 vllm serve Qwen/Qwen3-Omni-30B-A3B-Instruct --omni \

requirements/common.txt

@@ -13,8 +13,9 @@ transformers >= 5.10.1, < 5.15
 av>=14.0.0
 omegaconf>=2.3.0
 diffusers==0.40.0
-# Hub kernels, including LTX-2.5 NATTEN
-kernels==0.14.1
+# Hub kernels, including LTX-2.5 NATTEN. Keep this pin in sync with the
+# supported transformers range above.
+kernels==0.15.2
 safetensors>=0.8.0
 accelerate==1.12.0
 soundfile>=0.13.1

tests/config/test_environment_variables.py

@@ -167,7 +167,7 @@ def test_inventory_matches_reviewed_snapshot_counts():
         EnvironmentVariableCategory.PUBLIC_OMNI: 24,
         EnvironmentVariableCategory.INHERITED_VLLM: 20,
         EnvironmentVariableCategory.PLATFORM_EXTERNAL: 27,
-        EnvironmentVariableCategory.MODEL_SPECIFIC: 57,
+        EnvironmentVariableCategory.MODEL_SPECIFIC: 58,
         EnvironmentVariableCategory.BENCHMARK_TRANSITIONAL: 20,
         EnvironmentVariableCategory.INTERNAL: 2,
     }
@@ -182,7 +182,7 @@ def test_inventory_matches_reviewed_snapshot_counts():
         ModelEnvironmentVariableDisposition.REQUEST_SCOPE: 6,
         ModelEnvironmentVariableDisposition.EXTERNAL: 0,
         ModelEnvironmentVariableDisposition.INTERNALIZE: 11,
-        ModelEnvironmentVariableDisposition.DEPRECATE_REMOVE: 5,
+        ModelEnvironmentVariableDisposition.DEPRECATE_REMOVE: 6,
     }
 
 

tests/diffusion/models/ltx2/test_ltx25_pipeline.py

@@ -386,7 +386,7 @@ def test_ltx25_natten_failure_has_actionable_omni_remedy(monkeypatch):
         MissingNattenProcessor,
     )
 
-    with pytest.raises(RuntimeError, match=r"kernels==0\.14\.1.*supported GPU.*allow Hub access"):
+    with pytest.raises(RuntimeError, match=r"kernels==0\.15\.2.*supported GPU.*allow Hub access"):
         ltx2_components._create_ltx25_natten_processor()
 
 

tests/engine/test_stage_engine_args.py

@@ -104,6 +104,7 @@ _OMNI_ONLY_LLM_STAGE_ENGINE_FIELDS = frozenset(
 def _stable_engine_arg_environment(monkeypatch, tmp_path):
     from vllm_omni import platforms
 
+    monkeypatch.delenv("VLLM_USE_FLASHINFER_MOE_FP16", raising=False)
     xcodec_model = tmp_path / "xcodec-model"
     xcodec_model.mkdir()
     monkeypatch.setenv("XCODEC1_PATH", str(xcodec_model))
@@ -136,25 +137,6 @@ def _stable_engine_arg_environment(monkeypatch, tmp_path):
         monkeypatch.setitem(sys.modules, worker_module.__name__, worker_module)
 
 
-def test_qwen3_omni_defaults_to_triton_moe_backend():
-    engine_args = {"model_arch": "Qwen3OmniMoeForConditionalGeneration"}
-
-    stage_init_utils._maybe_set_qwen3_omni_moe_backend(engine_args)
-
-    assert engine_args["moe_backend"] == "triton"
-
-
-def test_qwen3_omni_preserves_explicit_moe_backend():
-    engine_args = {
-        "model_arch": "Qwen3OmniMoeForConditionalGeneration",
-        "moe_backend": "flashinfer",
-    }
-
-    stage_init_utils._maybe_set_qwen3_omni_moe_backend(engine_args)
-
-    assert engine_args["moe_backend"] == "flashinfer"
-
-
 def _engine_arg_inputs(tmp_path: Path) -> tuple[PipelineConfig, DeployConfig, str]:
     parent_model = tmp_path / "parent-model"
     stage_model = tmp_path / "stage-model"
@@ -535,8 +517,6 @@ def test_typed_engine_args_preserve_explicit_backend_default_overrides(tmp_path)
 
     assert {name: legacy_args[name] for name in expected} == expected
     assert {name: typed_args[name] for name in expected} == expected
-    assert legacy_args["moe_backend"] == "triton"
-    assert typed_args["moe_backend"] == "triton"
 
 
 @pytest.mark.parametrize("stage_scoped", [False, True], ids=["global", "stage-scoped"])

vllm_omni/config/environment_variable_inventory.py

@@ -203,6 +203,7 @@ _MODEL_DEPRECATE_REMOVE = (
     "HIGGS_AUDIO_V2_TOKENIZER_PATH",
     "VLLM_OMNI_LINGBOT_ACTION_ROOT",
     "VLLM_OMNI_VOXCPM_CODE_PATH",
+    "VLLM_USE_FLASHINFER_MOE_FP16",
     "XCODEC1_PATH",
     "model_stage",
 )

vllm_omni/deploy/qwen3_omni_moe.yaml

@@ -27,7 +27,6 @@ stages:
     max_num_batched_tokens: 32768
     max_num_seqs: 64
     gpu_memory_utilization: 0.9
-    moe_backend: triton
     trust_remote_code: true
     enable_prefix_caching: false
     devices: "0"
@@ -39,7 +38,6 @@ stages:
     max_num_batched_tokens: 32768
     max_num_seqs: 64
     gpu_memory_utilization: 0.6
-    moe_backend: triton
     trust_remote_code: true
     enable_prefix_caching: false
     devices: "1"

vllm_omni/diffusion/models/ltx2/ltx2_components.py

@@ -298,7 +298,7 @@ def _create_ltx25_natten_processor() -> LTX2VideoVaeNeighborhoodNattenProcessor:
     except (ImportError, OSError, RuntimeError, ValueError) as exc:
         raise RuntimeError(
             "LTX-2.5 DiffVAE requires the shi-labs/natten Hub kernel. "
-            "Install kernels==0.14.1, use a supported GPU, leave "
+            "Install kernels==0.15.2, use a supported GPU, leave "
             "DIFFUSERS_DISABLE_REMOTE_CODE unset, and allow Hub access during kernel initialization."
         ) from exc
 

vllm_omni/engine/stage_init_utils.py

@@ -763,14 +763,13 @@ def prepare_engine_environment() -> None:
         pass
 
 
-def _maybe_set_qwen3_omni_moe_backend(engine_args_dict: dict[str, Any]) -> None:
-    """Choose the stable MoE backend when Qwen3-Omni has no explicit choice."""
+def _maybe_set_qwen3_omni_moe_env(engine_args_dict: dict[str, Any]) -> None:
     if (
         engine_args_dict.get("model_arch") == "Qwen3OmniMoeForConditionalGeneration"
-        and engine_args_dict.get("moe_backend", "auto") == "auto"
+        and "VLLM_USE_FLASHINFER_MOE_FP16" not in os.environ
     ):
-        engine_args_dict["moe_backend"] = "triton"
-        logger.info("[stage_init] Set moe_backend=triton for Qwen3-Omni stage")
+        os.environ["VLLM_USE_FLASHINFER_MOE_FP16"] = "0"
+        logger.info("[stage_init] Set VLLM_USE_FLASHINFER_MOE_FP16=0 for Qwen3-Omni stage")
 
 
 def split_devices_for_replicas(
@@ -1281,8 +1280,9 @@ def _finalize_engine_args_dict(
     engine_args_dict["has_sampling_extra_args"] = has_sampling_extra_args
     engine_args_dict["sampling_extra_args_keys"] = sampling_extra_args_keys
 
-    # Select the typed backend option during stage argument finalization.
-    _maybe_set_qwen3_omni_moe_backend(engine_args_dict)
+    # TODO: Remove this after the performance regression is fixed
+    # Set VLLM_USE_FLASHINFER_MOE_FP16=0 for Qwen3-Omni to avoid performance regression
+    _maybe_set_qwen3_omni_moe_env(engine_args_dict)
     return engine_args_dict