[Perf] Optimize VoxCPM2 first-request latency via startup warmup (#3424)

Signed-off-by: Dan250124 <416947747@qq.com>

tests/entrypoints/openai_api/test_serving_speech_voxcpm.py

@@ -51,6 +51,40 @@ def voxcpm_server(mocker: MockerFixture):
     )
 
 
+@pytest.fixture
+def voxcpm2_server(mocker: MockerFixture):
+    mocker.patch.object(OmniOpenAIServingSpeech, "_load_codec_frame_rate", return_value=None)
+
+    mock_engine_client = mocker.MagicMock()
+    mock_engine_client.errored = False
+    mock_engine_client.model_config = mocker.MagicMock(model="OpenBMB/VoxCPM2")
+    mock_engine_client.default_sampling_params_list = [SimpleNamespace(max_tokens=2048)]
+    mock_engine_client.tts_batch_max_items = 32
+    mock_engine_client.generate = mocker.MagicMock(return_value="generator")
+    mock_engine_client.stage_configs = [
+        SimpleNamespace(
+            engine_args=SimpleNamespace(
+                model_stage="latent_generator",
+                model_arch="VoxCPM2TalkerForConditionalGeneration",
+            ),
+            tts_args={},
+        ),
+        SimpleNamespace(
+            engine_args=SimpleNamespace(model_stage="vae"),
+            tts_args={},
+        ),
+    ]
+
+    mock_models = mocker.MagicMock()
+    mock_models.is_base_model.return_value = True
+
+    return OmniOpenAIServingSpeech(
+        engine_client=mock_engine_client,
+        models=mock_models,
+        request_logger=mocker.MagicMock(),
+    )
+
+
 class TestVoxCPMServing:
     def test_voxcpm_model_type_detection(self, voxcpm_server):
         assert voxcpm_server._tts_model_type == "voxcpm"
@@ -143,3 +177,18 @@ class TestVoxCPMServing:
             "additional_information": tts_params,
             "type": "token",
         }
+
+
+class TestVoxCPM2Serving:
+    """Regression tests for VoxCPM2 serving behavior."""
+
+    def test_voxcpm2_model_type_detection(self, voxcpm2_server):
+        assert voxcpm2_server._tts_model_type == "voxcpm2"
+        assert voxcpm2_server._is_tts is True
+
+    def test_voxcpm2_default_voice_is_supported(self, voxcpm2_server):
+        """VoxCPM2 should accept voice='default' (zero-shot mode, no voice cloning).
+
+        Regression: previously returned 400 Invalid voice 'default'.
+        """
+        assert "default" in voxcpm2_server.supported_speakers

vllm_omni/entrypoints/openai/api_server.py

@@ -916,6 +916,10 @@ async def omni_init_app_state(
         engine_client, state.openai_serving_models, request_logger=request_logger, model_name=model_name
     )
 
+    # Warm up speech pipeline (CUDA Graph capture, torch.compile) so the first
+    # real user request is fast instead of paying a 100s compilation tax.
+    await state.openai_serving_speech.warmup()
+
     state.openai_serving_audio_generate = OmniOpenAIServingAudioGenerate(
         engine_client, state.openai_serving_models, request_logger=request_logger, model_name=model_name
     )

vllm_omni/entrypoints/openai/serving_speech.py

@@ -359,6 +359,46 @@ class OmniOpenAIServingSpeech(OpenAIServing, AudioMixin):
         self._build_fish_speech_prompt_async = make_async(self._build_fish_speech_prompt, executor=self._tts_executor)
         self._estimate_prompt_len_async = make_async(self._estimate_prompt_len, executor=self._tts_executor)
 
+    async def warmup(self) -> None:
+        """Run a synthetic speech request to trigger all first-request warmup.
+
+        Unlike qwen3-tts, whose CUDA Graph warmup targets a standalone tokenizer
+        decoder (no vLLM dependencies) and can complete entirely at model-init
+        time, VoxCPM2 needs to warm up PagedAttention scaffold/residual LLMs.
+        Their CUDA Graph capture requires a vLLM ``ForwardContext``
+        (attn_metadata, slot_mapping, etc.) that only exists during real
+        inference steps.  The same request also pays the one-time torch.compile
+        JIT tax for the LocDiT estimator, feat_encoder, AudioVAE decoder, and
+        projection helpers.
+
+        For VoxCPM2 this shifts ~15s of torch.compile + CUDA Graph capture from
+        the first user request to server startup.
+        """
+        if self._tts_model_type != "voxcpm2":
+            return
+
+        t0 = time.time()
+        logger.info("Running warmup speech request for model_type=%s", self._tts_model_type)
+        # VoxCPM2 has no predefined speaker presets — "default" means zero-shot
+        # mode (no voice cloning).  The voice field is required by the OpenAI
+        # API schema but semantically ignored by the model.
+        warmup_req = OpenAICreateSpeechRequest(
+            input="Warmup.",
+            voice="default",
+            response_format="wav",
+            speed=1.0,
+            stream=False,
+            model=self.model_name,
+        )
+        try:
+            _audio_bytes, _media_type = await self._generate_audio_bytes(warmup_req, request_id="speech-warmup")
+        except Exception as exc:
+            logger.warning("Speech warmup failed (non-fatal): %s", exc)
+            return
+
+        elapsed = time.time() - t0
+        logger.info("Speech warmup complete in %.1fs", elapsed)
+
     def _get_qwen_tts_expected_speaker_embedding_dim(self) -> int | None:
         """Return the loaded Qwen3-TTS speaker embedding dim, if known.
 
@@ -491,6 +531,8 @@ class OmniOpenAIServingSpeech(OpenAIServing, AudioMixin):
         try:
             if self._tts_model_type == "voxcpm":
                 return set()
+            if self._tts_model_type == "voxcpm2":
+                return {"default"}
             if self._tts_model_type == "voxtral_tts":
                 config = self.engine_client.model_config.hf_config.audio_config
             else: