Merge pull request #1489 from yuekaizhang/triton

[runtime] Support Cosyvoice2 Nvidia TensorRT-LLM Inference Solution

Author: aluminumbox

runtime/triton_trtllm/Dockerfile.server

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+FROM nvcr.io/nvidia/tritonserver:25.06-trtllm-python-py3
+RUN apt-get update && apt-get install -y cmake
+RUN git clone https://github.com/pytorch/audio.git && cd audio && git checkout c670ad8 && PATH=/usr/local/cuda/bin:$PATH python3 setup.py develop
+COPY ./requirements.txt /workspace/requirements.txt
+RUN pip install -r /workspace/requirements.txt
+WORKDIR /workspace

runtime/triton_trtllm/README.md

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+## Best Practices for Serving CosyVoice with NVIDIA Triton Inference Server
+
+### Quick Start
+Launch the service directly with Docker Compose:
+```sh
+docker compose up
+```
+
+### Build the Docker Image
+Build the image from scratch:
+```sh
+docker build . -f Dockerfile.server -t soar97/triton-cosyvoice:25.06
+```
+
+### Run a Docker Container
+```sh
+your_mount_dir=/mnt:/mnt
+docker run -it --name "cosyvoice-server" --gpus all --net host -v $your_mount_dir --shm-size=2g soar97/triton-cosyvoice:25.06
+```
+
+### Understanding `run.sh`
+The `run.sh` script orchestrates the entire workflow through numbered stages.
+
+Run a subset of stages with:
+```sh
+bash run.sh <start_stage> <stop_stage> [service_type]
+```
+- `<start_stage>` – stage to start from (0-5).
+- `<stop_stage>`  – stage to stop after (0-5).
+
+Stages:
+- **Stage 0** – Download the cosyvoice-2 0.5B model from HuggingFace.
+- **Stage 1** – Convert the HuggingFace checkpoint to TensorRT-LLM format and build TensorRT engines.
+- **Stage 2** – Create the Triton model repository and configure the model files (adjusts depending on whether `Decoupled=True/False` will be used later).
+- **Stage 3** – Launch the Triton Inference Server.
+- **Stage 4** – Run the single-utterance HTTP client.
+- **Stage 5** – Run the gRPC benchmark client.
+
+### Export Models to TensorRT-LLM and Launch the Server
+Inside the Docker container, prepare the models and start the Triton server by running stages 0-3:
+```sh
+# Runs stages 0, 1, 2, and 3
+bash run.sh 0 3
+```
+*Note: Stage 2 prepares the model repository differently depending on whether you intend to run with `Decoupled=False` or `Decoupled=True`. Rerun stage 2 if you switch the service type.*
+
+### Single-Utterance HTTP Client
+Send a single HTTP inference request:
+```sh
+bash run.sh 4 4
+```
+
+### Benchmark with a Dataset
+Benchmark the running Triton server. Pass either `streaming` or `offline` as the third argument.
+```sh
+bash run.sh 5 5
+
+# You can also customise parameters such as num_task and dataset split directly:
+# python3 client_grpc.py --num-tasks 2 --huggingface-dataset yuekai/seed_tts_cosy2 --split-name test_zh --mode [streaming|offline]
+```
+> [!TIP]
+> Only offline CosyVoice TTS is currently supported. Setting the client to `streaming` simply enables NVIDIA Triton’s decoupled mode so that responses are returned as soon as they are ready.
+
+### Benchmark Results
+Decoding on a single L20 GPU with 26 prompt_audio/target_text [pairs](https://huggingface.co/datasets/yuekai/seed_tts) (≈221 s of audio):
+
+| Mode | Note | Concurrency | Avg Latency (ms) | P50 Latency (ms) | RTF |
+|------|------|-------------|------------------|------------------|-----|
+| Decoupled=False | [Commit](https://github.com/yuekaizhang/CosyVoice/commit/b44f12110224cb11c03aee4084b1597e7b9331cb) | 1 | 758.04 | 615.79 | 0.0891 |
+| Decoupled=False | [Commit](https://github.com/yuekaizhang/CosyVoice/commit/b44f12110224cb11c03aee4084b1597e7b9331cb) | 2 | 1025.93 | 901.68 | 0.0657 |
+| Decoupled=False | [Commit](https://github.com/yuekaizhang/CosyVoice/commit/b44f12110224cb11c03aee4084b1597e7b9331cb) | 4 | 1914.13 | 1783.58 | 0.0610 |
+| Decoupled=True  | [Commit](https://github.com/yuekaizhang/CosyVoice/commit/b44f12110224cb11c03aee4084b1597e7b9331cb) | 1 | 659.87 | 655.63 | 0.0891 |
+| Decoupled=True  | [Commit](https://github.com/yuekaizhang/CosyVoice/commit/b44f12110224cb11c03aee4084b1597e7b9331cb) | 2 | 1103.16 | 992.96 | 0.0693 |
+| Decoupled=True  | [Commit](https://github.com/yuekaizhang/CosyVoice/commit/b44f12110224cb11c03aee4084b1597e7b9331cb) | 4 | 1790.91 | 1668.63 | 0.0604 |
+
+### OpenAI-Compatible Server
+To launch an OpenAI-compatible service, run:
+```sh
+git clone https://github.com/yuekaizhang/Triton-OpenAI-Speech.git
+pip install -r requirements.txt
+# After the Triton service is up, start the FastAPI bridge:
+python3 tts_server.py --url http://localhost:8000 --ref_audios_dir ./ref_audios/ --port 10086 --default_sample_rate 24000
+# Test with curl
+bash test/test_cosyvoice.sh
+```
+
+### Acknowledgements
+This section originates from the NVIDIA CISI project. We also provide other multimodal resources—see [mair-hub](https://github.com/nvidia-china-sae/mair-hub) for details.
+