[Test] Align CosyVoice3 hardware mocks across platform layers

Signed-off-by: Sy03 <1370724210@qq.com>

docs/user_guide/examples/offline_inference/text_to_speech.md

@@ -130,7 +130,7 @@ python examples/offline_inference/text_to_speech/cosyvoice3/end2end.py \
 ### Notes
 
 - Stage 0 (`talker`) emits speech tokens; stage 1 (`code2wav`) runs flow matching + HiFiGAN to synthesize waveform.
-- Deploy config auto-loads from `vllm_omni/deploy/cosyvoice3.yaml` based on HF `model_type`. Pass `--deploy-config <path>` to override.
+- H200 automatically selects `cosyvoice3_packed_streaming_optimized_standard.yaml` when the CUDA MPS control tool is available; other devices and environments use `cosyvoice3.yaml`. See the [serving defaults and tradeoffs](../online_serving/text_to_speech.md#cosyvoice3). Pass `--deploy-config cosyvoice3.yaml` for the previous profile or `--deploy-config <path>` to override. The example uses the resolved profile's sampling parameters.
 - `async_chunk: true` is the default; pass `--no-async-chunk` to switch to the legacy synchronous path.
 
 ---

docs/user_guide/examples/online_serving/text_to_speech.md

@@ -155,16 +155,22 @@ vllm serve FunAudioLLM/Fun-CosyVoice3-0.5B-2512 --omni --port 8091 --trust-remot
 ./cosyvoice3/run_server.sh
 ```
 
-Streaming is on by default via `async_chunk: true` in `vllm_omni/deploy/cosyvoice3.yaml`. Pass `--no-async-chunk` (or `NO_ASYNC_CHUNK=1 ./cosyvoice3/run_server.sh`) for the legacy synchronous path.
+Streaming is on by default. On CUDA Hopper GPUs with at least 140 GiB of device memory (H200), serving automatically selects `cosyvoice3_packed_streaming_optimized_standard.yaml` when `nvidia-cuda-mps-control` is available on `PATH`: AR32 / codec32, standard sampling, packed Flow, cached ISTFT, HiFT decode graphs, and local CUDA MPS. No `--hf-overrides` is needed. Other devices and environments without the MPS control tool use `cosyvoice3.yaml` with RAS sampling and AR8 / codec8.
 
-Cross-request Stage-1 flow batching is opt-in. Enable it in the server environment when concurrent requests should share a flow-estimator call:
+The optimized profile is not a universal hardware configuration. Its packed kernels are Hopper-only; the talker's Model Runner V2 is unsupported on NPU/XPU, and CUDA MPS requires a local NVIDIA GPU and its control tool. Larger batches, 25-frame chunks, and graph allocations also require per-device validation on smaller or slower GPUs. Automatic selection retains the generic profile outside the validated device class; an explicit deploy configuration takes precedence.
+
+The H200 default targets streaming throughput at C32/C64. It changes sampling from RAS to standard (temperature 0.7, top-p 0.8, top-k 20, repetition penalty 1.21); output lengths and quality can differ. HiFT captures a recurring exact shape on its third use, which adds latency to that request, and its graph pool needs memory headroom beyond the engine reservations. This profile is not a claim of optimal first-audio latency at low concurrency. Pass `--deploy-config cosyvoice3.yaml` to retain the previous configuration, or `--deploy-config <path>` to select another profile.
+
+Pass `--no-async-chunk` (or `NO_ASYNC_CHUNK=1 ./cosyvoice3/run_server.sh`) for the legacy synchronous path; use `--deploy-config cosyvoice3.yaml` alongside it when selecting that path on H200.
+
+Cross-request Stage-1 flow batching is enabled in the H200 profile. For the generic configuration, enable it in the server environment when concurrent requests should share a flow-estimator call:
 
 ```bash
 export COSYVOICE3_BATCH_FLOW=1
 vllm serve FunAudioLLM/Fun-CosyVoice3-0.5B-2512 --omni --port 8091 --trust-remote-code
 ```
 
-Batching preserves output lengths and streaming cache alignment, but the different GEMM shapes can produce small waveform differences compared with processing each request separately. Leave `COSYVOICE3_BATCH_FLOW` unset (or set it to `0`) when request-independent numerical behavior is required. Set `COSYVOICE3_BATCH_FLOW_DEBUG=1` to log the observed group-size distribution and enable detailed Stage-1 profiler scopes; diagnostics are disabled by default to avoid per-step profiling overhead.
+Batching preserves output lengths and streaming cache alignment, but the different GEMM shapes can produce small waveform differences compared with processing each request separately. Use `--deploy-config cosyvoice3.yaml` and leave `COSYVOICE3_BATCH_FLOW` unset (or set it to `0`) when request-independent numerical behavior is required. Set `COSYVOICE3_BATCH_FLOW_DEBUG=1` to log the observed group-size distribution and enable detailed Stage-1 profiler scopes; diagnostics are disabled by default to avoid per-step profiling overhead.
 
 ### CLI client
 
@@ -194,7 +200,7 @@ The client supports `--api-base`, `--model`, `--text`, `--ref-audio`, `--ref-tex
 ### Notes
 
 - Stage 0 (`talker`) emits speech tokens; stage 1 (`code2wav`) runs flow matching + HiFiGAN to synthesize waveform.
-- Deploy config auto-loads from `vllm_omni/deploy/cosyvoice3.yaml` based on HF `model_type`. Pass `--deploy-config <path>` to override.
+- Deploy config auto-loads the device-specific CosyVoice3 default described above. Pass `--deploy-config <path>` to override.
 - For offline inference and the end-to-end script, see the [offline CosyVoice3 section](https://github.com/vllm-project/vllm-omni/tree/main/examples/offline_inference/text_to_speech/README.md#cosyvoice3).
 
 ---

examples/offline_inference/text_to_speech/cosyvoice3/end2end.py

@@ -1,6 +1,7 @@
 # SPDX-License-Identifier: Apache-2.0
-# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
+# SPDX-FileCopyrightText: Copyright contributors to the vLLM-Omni project
 import argparse
+import copy
 import functools
 import json
 import os
@@ -10,13 +11,11 @@ from typing import Any
 
 import numpy as np
 import soundfile as sf
-from vllm import SamplingParams
 from vllm.multimodal.media.audio import load_audio
 
 from vllm_omni.entrypoints.omni import Omni
 from vllm_omni.model_executor.models.cosyvoice3.tokenizer import get_qwen_tokenizer
 from vllm_omni.model_executor.models.cosyvoice3.utils import extract_text_token
-from vllm_omni.transformers_utils.configs.cosyvoice3 import CosyVoice3Config
 
 # Upstream zero-shot reference clip
 ZERO_SHOT_PROMPT_URL = "https://raw.githubusercontent.com/FunAudioLLM/CosyVoice/main/asset/zero_shot_prompt.wav"
@@ -59,8 +58,7 @@ def run_e2e():
         "--deploy-config",
         type=str,
         default=None,
-        help="Override the deploy config path. If unset, auto-loads "
+        help="Override the deploy config path. If unset, selects the bundled CosyVoice3 profile for the device.",
-        "vllm_omni/deploy/cosyvoice3.yaml based on the HF model_type.",
     )
     parser.add_argument("--text", type=str, default="Hello, this is a test of the CosyVoice system capability.")
     parser.add_argument(
@@ -105,8 +103,6 @@ def run_e2e():
         profiler_config=args.profiler_config,
     )
 
-    sampling_cfg = {"top_p": 0.8, "top_k": 25, "eos_token_id": 6561 + 1}
-
     print("Model initialized. Preparing inputs...")
     ref_audio_path = args.ref_audio or _default_ref_audio()
     if not os.path.exists(ref_audio_path):
@@ -138,7 +134,7 @@ def run_e2e():
 
     print(f"Generating for prompt: {args.text}")
 
-    config = CosyVoice3Config()
+    config = omni.engine.stage_vllm_configs[0].model_config.hf_config
     tokenizer = get_qwen_tokenizer(
         token_path=args.tokenizer,
         skip_special_tokens=config.skip_special_tokens,
@@ -149,29 +145,10 @@ def run_e2e():
     min_len = int(base_len * config.min_token_text_ratio)
     max_len = int(base_len * config.max_token_text_ratio)
 
-    # Build SamplingParams for each stage (GPT, S2Mel, Vocoder)
+    # Keep the deploy profile's sampling policy, penalties and required stops.
-    gpt_sampling = SamplingParams(
+    sampling_params_list = copy.deepcopy(omni.default_sampling_params_list)
-        temperature=1.0,
+    sampling_params_list[0].max_tokens = max(1, min(2048, max_len))
-        top_p=sampling_cfg["top_p"],
+    sampling_params_list[0].min_tokens = min(max(1, min_len), sampling_params_list[0].max_tokens)
-        top_k=sampling_cfg["top_k"],
-        repetition_penalty=2.0,
-        min_tokens=min_len,
-        max_tokens=max_len,
-        stop_token_ids=[sampling_cfg["eos_token_id"]],
-        # allowed_token_ids=list(range(6561+3)),
-        detokenize=False,
-    )
-    # Not used
-    s2mel_sampling = SamplingParams(
-        temperature=1.0,
-        top_p=1.0,
-        top_k=-1,
-        repetition_penalty=2.0,
-        max_tokens=256,
-        detokenize=False,
-    )
-
-    sampling_params_list = [gpt_sampling, s2mel_sampling]
 
     profiler_enabled = args.profiler_config is not None
     if profiler_enabled:

examples/online_serving/text_to_speech/cosyvoice3/run_server.sh

@@ -1,11 +1,15 @@
 #!/bin/bash
+# SPDX-License-Identifier: Apache-2.0
+# SPDX-FileCopyrightText: Copyright contributors to the vLLM-Omni project
+
 # Launch vLLM-Omni server for CosyVoice3 TTS
 #
 # Usage:
 #   ./run_server.sh
 #   CUDA_VISIBLE_DEVICES=0 ./run_server.sh
 #
-# Streaming (async-chunk) is on by default via vllm_omni/deploy/cosyvoice3.yaml.
+# Streaming (async-chunk) is on by default. H200 selects the optimized profile;
+# other devices use vllm_omni/deploy/cosyvoice3.yaml.
 # Set NO_ASYNC_CHUNK=1 to use the legacy synchronous path.
 
 set -e

tests/config/test_config_factory.py

@@ -571,6 +571,56 @@ class TestPipelineDiscovery:
         assert "qwen3_omni_moe_thinker_only" in OMNI_PIPELINES
         assert "qwen3_tts" in OMNI_PIPELINES
 
+    @pytest.mark.parametrize(
+        "cuda,major,memory_gib,mps,optimized",
+        [
+            (False, None, 0, True, False),
+            (True, None, 141, True, False),
+            (True, 8, 141, True, False),
+            (True, 9, 80, True, False),
+            (True, 10, 180, True, False),
+            (True, 9, 141, False, False),
+            (True, 9, 141, True, True),
+        ],
+    )
+    def test_cosyvoice3_device_default_and_explicit_overrides(
+        self, monkeypatch, cuda, major, memory_gib, mps, optimized
+    ):
+        from vllm.platforms import current_platform
+        from vllm.platforms.interface import DeviceCapability
+
+        from vllm_omni.platforms import current_omni_platform
+
+        monkeypatch.setattr(current_platform, "is_cuda", lambda: cuda)
+        monkeypatch.setattr(current_omni_platform, "device_name", "cuda" if cuda else "cpu")
+        capability = DeviceCapability(major, 0) if major is not None else None
+        monkeypatch.setattr(current_platform, "get_device_capability", lambda: capability)
+        monkeypatch.setattr(current_platform, "get_device_total_memory", lambda: memory_gib * 1024**3)
+        monkeypatch.setattr(
+            "vllm_omni.model_executor.models.cosyvoice3.pipeline.shutil.which",
+            lambda command: "/usr/bin/nvidia-cuda-mps-control" if mps else None,
+        )
+        pipeline = resolve_pipeline_config("cosyvoice3")
+        assert pipeline is not None
+        config = VllmOmniConfig.from_pipeline_config(pipeline)
+        talker, codec = config.stage_configs
+        assert talker.scheduler_config.max_num_seqs == codec.scheduler_config.max_num_seqs == (32 if optimized else 8)
+        assert talker.runtime_config.cuda_mps == codec.runtime_config.cuda_mps == optimized
+        assert talker.model_config.hf_overrides == ({"cosyvoice3_sampling_mode": "standard"} if optimized else None)
+        assert pipeline.stages[0].sampling_constraints["stop_token_ids"] == list(range(6561, 6761))
+        if optimized:
+            assert codec.runtime_config.env["COSYVOICE3_CACHED_ISTFT"] == "1"
+            assert codec.runtime_config.env["COSYVOICE3_HIFT_GRAPH"] == "1"
+            override = VllmOmniConfig.from_pipeline_config(
+                pipeline, cli_overrides={"hf_overrides": {"cosyvoice3_sampling_mode": "ras", "seed": 7}}
+            )
+            assert override.stage_by_id(0).model_config.hf_overrides == {"cosyvoice3_sampling_mode": "ras", "seed": 7}
+        explicit = VllmOmniConfig.from_pipeline_config(
+            pipeline, deploy_config_path=get_deploy_config_path("cosyvoice3.yaml")
+        )
+        assert explicit.stage_by_id(0).scheduler_config.max_num_seqs == 8
+        assert explicit.stage_by_id(0).model_config.hf_overrides is None
+
     def test_registry_resolver_qwen3_omni_all_stages(self):
         """Test that providing the HF config for qwen3 omni with audio enabled uses all stages."""
         pipeline = resolve_pipeline_config(

vllm_omni/config/pipeline_registry.py

@@ -54,7 +54,7 @@ from vllm_omni.model_executor.models.cosmos3.pipeline import (
     COSMOS3_OMNI_DEPLOY_PIPELINE,
     COSMOS3_POLICY_PIPELINE,
 )
-from vllm_omni.model_executor.models.cosyvoice3.pipeline import COSYVOICE3_PIPELINE
+from vllm_omni.model_executor.models.cosyvoice3.pipeline import resolve_cosyvoice3_pipeline
 from vllm_omni.model_executor.models.covo_audio.pipeline import COVO_AUDIO_PIPELINE
 from vllm_omni.model_executor.models.dreamzero.pipeline import DREAMZERO_PIPELINE
 from vllm_omni.model_executor.models.fish_speech.pipeline import FISH_SPEECH_PIPELINE
@@ -150,7 +150,7 @@ OMNI_PIPELINES: dict[str, PipelineConfig | PipelineResolverFunc] = {
     # key (see deploy/cosmos3_policy_droid.yaml and deploy/cosmos3_omni.yaml).
     "cosmos3_omni_deploy": COSMOS3_OMNI_DEPLOY_PIPELINE,
     "cosmos3_policy": COSMOS3_POLICY_PIPELINE,
-    "cosyvoice3": COSYVOICE3_PIPELINE,
+    "cosyvoice3": resolve_cosyvoice3_pipeline,
     "covo_audio": COVO_AUDIO_PIPELINE,
     "dreamzero": DREAMZERO_PIPELINE,
     "fish_qwen3_omni": FISH_SPEECH_PIPELINE,

vllm_omni/deploy/cosyvoice3_packed_streaming_optimized_standard.yaml

@@ -1,5 +1,7 @@
-# Opt-in single-Hopper streaming throughput profile: AR32 / codec32, Flow10.
+# Single-Hopper streaming throughput profile: AR32 / codec32, Flow10.
-# Use --hf-overrides '{"cosyvoice3_sampling_mode": "standard"}' and stream=true.
+# Automatically selected on Hopper devices with at least 140 GiB (H200)
+# when nvidia-cuda-mps-control is available on PATH.
+# Standard sampling is configured below; request stream=true for first audio.
 # F0 and HiFT stay FP32; only deterministic HiFT decode is graphed.
 # HiFT graphs capture on the third use of each exact shape, adding latency then.
 # The codec graph pool is not capped by gpu_memory_utilization; leave memory headroom.
@@ -7,6 +9,8 @@ base_config: cosyvoice3_packed_streaming.yaml
 stages:
   - stage_id: 0
     max_num_seqs: 32
+    hf_overrides:
+      cosyvoice3_sampling_mode: standard
     default_sampling_params:
       max_tokens: 2048
       repetition_penalty: 1.21

vllm_omni/model_executor/models/cosyvoice3/pipeline.py

@@ -1,5 +1,5 @@
 # SPDX-License-Identifier: Apache-2.0
-# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
+# SPDX-FileCopyrightText: Copyright contributors to the vLLM-Omni project
 """CosyVoice3 pipeline topology (frozen).
 
 Stage 0: Talker   — text prompt → speech tokens (LLM autoregressive).
@@ -12,11 +12,17 @@ Stage 1: Code2Wav — flow-matching decoder → acoustic features → waveform.
     through the shared-memory connector.
 """
 
+import shutil
+from dataclasses import replace
+
+from transformers import PretrainedConfig
+
 from vllm_omni.config.stage_config import (
     PipelineConfig,
     StageExecutionType,
     StagePipelineConfig,
 )
+from vllm_omni.transformers_utils.configs.cosyvoice3 import CosyVoice3Config
 
 _PROC = "vllm_omni.model_executor.stage_input_processors.cosyvoice3"
 
@@ -35,8 +41,9 @@ COSYVOICE3_PIPELINE = PipelineConfig(
             async_chunk_process_next_stage_input_func=(f"{_PROC}.talker2code2wav_async_chunk"),
             custom_process_next_stage_input_func=f"{_PROC}.text2flow_full_payload",
             sampling_constraints={
-                # merged speech stop token (logsumexp of all 200 stop logits)
+                # Standard sampling can emit any of the 200 control tokens.
-                "stop_token_ids": [6562],
+                # RAS merges their logits into 6562, which is also in this range.
+                "stop_token_ids": list(range(6561, 6761)),
             },
         ),
         StagePipelineConfig(
@@ -52,3 +59,28 @@ COSYVOICE3_PIPELINE = PipelineConfig(
         ),
     ),
 )
+
+
+def resolve_cosyvoice3_pipeline(hf_config: PretrainedConfig | None = None) -> PipelineConfig | None:
+    """Select a streaming default compatible with the device and runtime.
+
+    Query the platform through NVML without initializing CUDA in the parent.
+    Explicit deploy configs still take precedence over this pipeline default.
+    """
+    from vllm.platforms import current_platform
+
+    if hf_config is not None and not isinstance(hf_config, CosyVoice3Config):
+        return None
+    if current_platform.is_cuda():
+        capability = current_platform.get_device_capability()
+        if (
+            capability is not None
+            and capability.major == 9
+            and current_platform.get_device_total_memory() >= 140 * 1024**3
+            and shutil.which("nvidia-cuda-mps-control") is not None
+        ):
+            return replace(
+                COSYVOICE3_PIPELINE,
+                default_deploy_config_name="cosyvoice3_packed_streaming_optimized_standard.yaml",
+            )
+    return COSYVOICE3_PIPELINE