Merge remote-tracking branch 'origin/main' into codex-api-server-user-guide
Signed-off-by: hsliu_ustc <hsliu_ustc@noreply.gitcode.com> # Conflicts: # docs/user_guide/examples/online_serving/text_to_audio.md # examples/online_serving/stable_audio/README.md
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@@ -1,7 +1,7 @@
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# Installation
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!!! important
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vLLM-Omni is released against the matching upstream vLLM major/minor version. vLLM-Omni 0.26.x requires vLLM 0.26.x; the stable v0.26.0 instructions pin vLLM 0.26.0.
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vLLM-Omni is released against the matching upstream vLLM major/minor version. vLLM-Omni 0.28.x requires vLLM 0.28.x; the stable v0.28.0 instructions pin vLLM 0.28.0.
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vLLM-Omni supports the following hardware platforms:
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@@ -34,7 +34,7 @@ vLLM-Omni is a Python library that supports the following GPU variants. The libr
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### Pre-built wheels
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Note: Pre-built wheels are currently available for vLLM-Omni 0.11.0rc1, 0.12.0rc1, 0.14.0rc1, 0.14.0, 0.16.0, 0.18.0, 0.20.0, 0.21.0, 0.22.0, 0.23.0, 0.24.0, 0.25.0, and 0.26.0. If you need a newer unreleased revision, please [build from source](https://docs.vllm.ai/projects/vllm-omni/en/latest/getting_started/installation/gpu/#build-wheel-from-source).
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Note: Pre-built wheels are currently available for vLLM-Omni 0.11.0rc1, 0.12.0rc1, 0.14.0rc1, 0.14.0, 0.16.0, 0.18.0, 0.20.0, 0.21.0, 0.22.0, 0.23.0, 0.24.0, 0.25.0, and 0.26.0; the 0.28.0 wheels are published together with the v0.28.0 release this branch produces. If you need a newer unreleased revision, please [build from source](https://docs.vllm.ai/projects/vllm-omni/en/latest/getting_started/installation/gpu/#build-wheel-from-source).
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=== "NVIDIA CUDA"
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@@ -20,7 +20,7 @@ Therefore, it is recommended to install vLLM and vLLM-Omni with a **fresh new**
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vLLM-Omni is built based on vLLM. Please install it with command below.
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```bash
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uv pip install vllm==0.26.0 --torch-backend=auto
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uv pip install vllm==0.28.0 --torch-backend=auto
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```
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#### Installation of vLLM-Omni
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@@ -39,13 +39,13 @@ uv pip install 'vllm-omni[demo]'
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# --8<-- [start:build-wheel-from-source]
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#### Installation of vLLM
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If you do not need to modify source code of vLLM, you can directly install the stable 0.26.0 release version of the library
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If you do not need to modify source code of vLLM, you can directly install the stable 0.28.0 release version of the library
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```bash
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uv pip install vllm==0.26.0 --torch-backend=auto
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uv pip install vllm==0.28.0 --torch-backend=auto
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```
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The 0.26.0 release of vLLM ships CUDA 13.0-compatible binaries by default. If you need a different CUDA variant or want to reuse an existing PyTorch installation, build vLLM from source instead.
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The 0.28.0 release of vLLM ships CUDA 13.0-compatible binaries by default. If you need a different CUDA variant or want to reuse an existing PyTorch installation, build vLLM from source instead.
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#### Installation of vLLM-Omni
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Since vllm-omni is rapidly evolving, it's recommended to install it from source
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@@ -66,12 +66,12 @@ If you want to check, modify or debug with source code of vLLM, install the libr
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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 v0.26.0
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git checkout v0.28.0
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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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#For CUDA 13.0 (the default for v0.26.0; the wheel filename has no `+cu130` suffix)
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export VLLM_PRECOMPILED_WHEEL_LOCATION=https://github.com/vllm-project/vllm/releases/download/v0.26.0/vllm-0.26.0-cp38-abi3-manylinux_2_28_x86_64.whl
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#For CUDA 13.0 (the default for v0.28.0; the wheel filename has no `+cu130` suffix)
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export VLLM_PRECOMPILED_WHEEL_LOCATION=https://github.com/vllm-project/vllm/releases/download/v0.28.0/vllm-0.28.0-cp38-abi3-manylinux_2_28_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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@@ -102,7 +102,7 @@ docker run --runtime nvidia --gpus 2 \
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--env "HF_TOKEN=$HF_TOKEN" \
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-p 8091:8091 \
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--ipc=host \
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vllm/vllm-omni:v0.26.0 \
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vllm/vllm-omni:v0.28.0 \
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vllm serve Qwen/Qwen3-Omni-30B-A3B-Instruct --omni --port 8091
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```
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@@ -124,7 +124,7 @@ If you want to specify the base vLLM version:
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```bash
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DOCKER_BUILDKIT=1 docker build \
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-f docker/Dockerfile.cuda \
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--build-arg BASE_IMAGE=vllm/vllm-openai:v0.26.0 \
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--build-arg BASE_IMAGE=vllm/vllm-openai:v0.28.0 \
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-t vllm-omni-cuda .
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```
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@@ -13,7 +13,7 @@ vLLM-Omni current recommends the steps in under setup through Docker Images.
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vLLM-Omni is built based on vLLM. Please install it with command below.
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```bash
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uv pip install vllm==0.26.0+rocm723 --extra-index-url https://wheels.vllm.ai/rocm/0.26.0/rocm723
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uv pip install vllm==0.28.0+rocm723 --extra-index-url https://wheels.vllm.ai/rocm/0.28.0/rocm723
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```
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#### Installation of vLLM-Omni
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@@ -34,13 +34,13 @@ uv pip install onnxruntime-rocm
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# --8<-- [start:build-wheel-from-source]
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#### Installation of vLLM
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If you do not need to modify source code of vLLM, you can directly install the stable 0.26.0 release version of the library
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If you do not need to modify source code of vLLM, you can directly install the stable 0.28.0 release version of the library
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```bash
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uv pip install vllm==0.26.0+rocm723 --extra-index-url https://wheels.vllm.ai/rocm/0.26.0/rocm723
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uv pip install vllm==0.28.0+rocm723 --extra-index-url https://wheels.vllm.ai/rocm/0.28.0/rocm723
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```
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The pre-built 0.26.0 vLLM wheel targets ROCm 7.2.3. If you need a different ROCm stack or want to reuse an existing PyTorch installation, build vLLM from source instead.
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The pre-built 0.28.0 vLLM wheel targets ROCm 7.2.3. If you need a different ROCm stack or want to reuse an existing PyTorch installation, build vLLM from source instead.
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#### Installation of vLLM-Omni
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Since vllm-omni is rapidly evolving, it's recommended to install it from source
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@@ -58,7 +58,7 @@ If you want to check, modify or debug with source code of vLLM, install the libr
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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 v0.26.0
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git checkout v0.28.0
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python3 -m pip install -r requirements/rocm.txt
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python3 setup.py develop
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```
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@@ -130,7 +130,7 @@ docker run --rm \
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-v ~/.cache/huggingface:/root/.cache/huggingface \
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--env "HF_TOKEN=$HF_TOKEN" \
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-p 8091:8091 \
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vllm/vllm-omni-rocm:v0.26.0 \
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vllm/vllm-omni-rocm:v0.28.0 \
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--model Qwen/Qwen3-Omni-30B-A3B-Instruct --omni --port 8091
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```
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@@ -149,7 +149,7 @@ docker run --rm -it \
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-v ~/.cache/huggingface:/root/.cache/huggingface \
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--env "HF_TOKEN=$HF_TOKEN" \
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--entrypoint bash \
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vllm/vllm-omni-rocm:v0.26.0
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vllm/vllm-omni-rocm:v0.28.0
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```
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# --8<-- [end:pre-built-images]
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@@ -23,6 +23,19 @@ vLLM-Omni currently recommends using the Docker image setup steps below.
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DOCKER_BUILDKIT=1 docker build -f docker/Dockerfile.xpu -t vllm-omni-xpu --shm-size=4g .
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```
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This layers vLLM-Omni on top of the published `vllm/vllm-openai-xpu:<VLLM_VERSION>`
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base image, which Docker pulls automatically. To target a different vLLM release,
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pass `--build-arg VLLM_VERSION=<tag>`. If that tag has not been published yet, build
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the base from upstream's own Dockerfile first and point the build at it:
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```bash
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git clone --depth 1 --branch "$VLLM_VERSION" https://github.com/vllm-project/vllm /tmp/vllm
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DOCKER_BUILDKIT=1 docker build -t vllm-openai-xpu:local --target vllm-openai \
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-f /tmp/vllm/docker/Dockerfile.xpu /tmp/vllm
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DOCKER_BUILDKIT=1 docker build -f docker/Dockerfile.xpu -t vllm-omni-xpu --shm-size=4g \
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--build-arg VLLM_BASE=vllm-openai-xpu:local .
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```
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#### Launch the docker image
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##### Launch with OpenAI API Server
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@@ -19,10 +19,10 @@ uv venv --python 3.12 --seed
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source .venv/bin/activate
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# On CUDA
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uv pip install vllm==0.26.0 --torch-backend=auto
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uv pip install vllm==0.28.0 --torch-backend=auto
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# On ROCm
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uv pip install vllm==0.26.0+rocm723 --extra-index-url https://wheels.vllm.ai/rocm/0.26.0/rocm723
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uv pip install vllm==0.28.0+rocm723 --extra-index-url https://wheels.vllm.ai/rocm/0.28.0/rocm723
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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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@@ -34,7 +34,7 @@ For additional installation methods — please see the [installation guide](inst
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!!! note
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It is important to install the same major & minor version of vLLM and vLLM Omni, otherwise things may not work as expected. If the versions are misaligned, you will see a warning when you import vLLM Omni.
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If you are seeing strange behavior with the `vllm` command not handling the `--omni` flag correctly, you most likely have a version mismatch with vLLM < `0.26.0` and vLLM Omni `0.26.0`, as vLLM Omni no longer hijacks the vLLM entrypoint. Updating vLLM should resolve this issue.
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If you are seeing strange behavior with the `vllm` command not handling the `--omni` flag correctly, you most likely have a version mismatch with vLLM < `0.28.0` and vLLM Omni `0.28.0`, as vLLM Omni no longer hijacks the vLLM entrypoint. Updating vLLM should resolve this issue.
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## Offline Inference
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@@ -47,7 +47,7 @@ if __name__ == "__main__":
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omni = Omni(model="Tongyi-MAI/Z-Image-Turbo")
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prompt = "a cup of coffee on the table"
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outputs = omni.generate(prompt)
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images = outputs[0].request_output.images
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images = outputs[0].images
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images[0].save("coffee.png")
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```
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@@ -66,7 +66,7 @@ from vllm_omni.entrypoints.omni import Omni
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if __name__ == "__main__":
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omni = Omni(
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model="Tongyi-MAI/Z-Image-Turbo",
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# stage_configs_path="./stage-config.yaml", # See below
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# deploy_config="./deploy-config.yaml", # Optional deploy override
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)
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prompts = [
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"a cup of coffee on a table",
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@@ -75,8 +75,7 @@ if __name__ == "__main__":
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]
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omni_outputs = omni.generate(prompts)
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for i_prompt, prompt_output in enumerate(omni_outputs):
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this_request_output = prompt_output.request_output
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this_images = this_request_output.images
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this_images = prompt_output.images
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for i_image, image in enumerate(this_images):
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image.save(f"p{i_prompt}-img{i_image}.jpg")
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print("saved to", f"p{i_prompt}-img{i_image}.jpg")
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