Hongsheng LiuandRoger Wang
1fb46ddd62
Signed-off-by: hsliu <liuhongsheng4@huawei.com> Signed-off-by: Hongsheng Liu <liuhongsheng4@huawei.com> Co-authored-by: Roger Wang <hey@rogerw.io>
138 lines
4.5 KiB
Markdown
138 lines
4.5 KiB
Markdown
<p align="center">
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<picture>
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<source media="(prefers-color-scheme: dark)" src="./docs/source/logos/vllm-omni-logo-text-dark.png">
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<img alt="vllm-omni" src="./docs/source/logos/vllm-omni-logo-text-dark.png" width=55%>
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</picture>
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</p>
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<h3 align="center">
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Easy, fast, and cheap omni-modality model serving for everyone
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</h3>
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<p align="center">
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| <a href="https://vllm-omni.readthedocs.io/en/latest/"><b>Documentation</b></a> | <a href="https://tinyurl.com/vllm-omni-meeting"><b>Weekly Meeting</b></a> |
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</p>
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---
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*Latest News* 🔥
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- [2025/11] vLLM community officially released [vllm-project/vllm-omni](https://github.com/vllm-project/vllm-omni) in order to support omni-modality models serving.
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---
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## About
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[vLLM](https://github.com/vllm-project/vllm) was originally designed to support large language models for text-based autoregressive generation tasks. vLLM-Omni extends its support for omni-modality model inference and serving:
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- **Omni-modality**: Text, image, video, and audio data processing
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- **Non-autoregressive Architectures**: extend the AR support of vLLM to Diffusion Transformers (DiT) and other parallel generation models
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- **Heterogeneous outputs**: from traditional text generation to multimodal outputs
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<p align="center">
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<picture>
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<img alt="vllm-omni" src="./docs/source/architecture/omni-modality model architecture.png" width=55%>
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</picture>
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</p>
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vLLM-omni is fast with:
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- Seamless AR support for efficient KV Cache management with vLLM
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- Pipelined stage execution overlapping
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- Fully disaggregation based on omniConnector and dynamic resource allocation across stages
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vLLM-omni is flexible and easy to use with:
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- Seamless integration with popular Hugging Face models
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- High-throughput serving with various decoding algorithms, including *parallel sampling*, *beam search*, and more
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- Tensor, pipeline, data and expert parallelism support for distributed inference
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- Streaming outputs
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- OpenAI-compatible API server
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vLLM seamlessly supports most popular open-source models on HuggingFace, including:
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- Multi-modality generation models (e.g. Qwen-image)
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- Omni-modality models (e.g. Qwen-omni)
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## Getting Started
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Install vLLM with `pip` or [from source](https://docs.vllm.ai/en/latest/getting_started/installation/gpu/index.html#build-wheel-from-source):
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```bash
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pip install vllm
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```
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## 🛠️ Installation
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### Environment setup
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Use Docker to keep consistent basic environment (Optional, Recommended)
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```bash
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docker run --gpus all --ipc=host --network=host -v $source_dir:$container_dir --name $container_name -it nvcr.io/nvidia/pytorch:25.01-py3 bash
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```
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Set up basic uv environment
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```bash
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pip install uv
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uv venv --python 3.12 --seed
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source .venv/bin/activate
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```
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### Installation of vLLM (for users)
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Now we build it based on vLLM v0.11.0. Please install it with command below.
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```bash
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uv pip install vllm==0.11.0 --torch-backend=auto
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```
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### Installation of vLLM (for developers)
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If you want to check or debug with source code of vLLM, install stable release version of vllm with 0.11.0 from source with pre-built wheel file.
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```bash
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git clone https://github.com/vllm-project/vllm.git
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cd vllm
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git checkout v0.11.0
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```
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Set up environment variables to get pre-built wheels. If there are internet problems, just download the whl file manually. And set VLLM_PRECOMPILED_WHEEL_LOCATION as your local absolute path of whl file.
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```bash
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export VLLM_PRECOMPILED_WHEEL_LOCATION=https://github.com/vllm-project/vllm/releases/download/v0.11.0/vllm-0.11.0-cp38-abi3-manylinux1_x86_64.whl
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```
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Install vllm with command below (If you have no existing PyTorch).
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```bash
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uv pip install --editable .
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```
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Install vllm with command below (If you already have PyTorch).
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```bash
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python use_existing_torch.py
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uv pip install -r requirements/build.txt
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uv pip install --no-build-isolation --editable .
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```
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### Installation of vLLM-omni
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Install additional requirements for vllm-omni
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```bash
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git clone https://github.com/vllm-project/vllm-omni.git
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cd vllm_omni
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uv pip install -e .
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```
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## Run examples (Qwen2.5-omni)
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Please check the folder of [examples](examples)
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## Contributing
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We welcome and value any contributions and collaborations.
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Please check out [Contributing to vLLM-omni](https://docs.vllm.ai/en/latest/contributing/index.html) for how to get involved.
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## Weekly Meeting
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- vLLM Omni Weekly Meeting: https://tinyurl.com/vllm-omni-meeting
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- Wednesday, 11:30 - 12:30 (UTC+8, [Convert to your timezone](https://dateful.com/convert/gmt8?t=15))
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## License
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Apache License 2.0, as found in the [LICENSE](./LICENSE) file.
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