[CI][ROCm] Align AMD image with vLLM 0.29 (#7395)
Signed-off-by: andyluo7 <andy.luo@amd.com> Co-authored-by: Hongsheng Liu <liuhongsheng4@huawei.com>
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@@ -1,11 +1,23 @@
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<!-- markdownlint-disable MD001 MD025 -->
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# --8<-- [start:requirements]
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- GPU: Validated on gfx942 (It should be supported on the AMD GPUs that are supported by vLLM.)
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# --8<-- [end:requirements]
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# --8<-- [start:set-up-using-python]
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vLLM-Omni current recommends the steps in under setup through Docker Images.
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For ROCm, vLLM-Omni currently recommends the setup steps through Docker Images.
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vLLM-Omni depends on the matching major/minor release of vLLM. The 0.29
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development line uses vLLM 0.29.x. Published 0.28.0 wheels and images use vLLM
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0.28.x.
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The Dockerfile's `BASE_IMAGE` pin applies only to Docker builds. The
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`vllm-omni` package does not install vLLM as a dependency, so non-Docker source
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installs must install the matching ROCm vLLM release explicitly before
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installing vLLM-Omni, as shown below.
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# --8<-- [start:pre-built-wheels]
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@@ -14,6 +26,7 @@ vLLM-Omni current recommends the steps in under setup through Docker Images.
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These pre-built wheel instructions install the published vLLM-Omni 0.28.0 release. For the 0.29 development line, use the source-install instructions below.
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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==0.28.0+rocm723 --extra-index-url https://wheels.vllm.ai/rocm/0.28.0/rocm723
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```
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@@ -36,6 +49,7 @@ 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 0.29.0 release version of the library
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```bash
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@@ -45,7 +59,9 @@ uv pip install vllm==0.29.0+rocm723 --extra-index-url https://wheels.vllm.ai/roc
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The pre-built 0.29.0 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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```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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@@ -71,15 +87,31 @@ python3 setup.py develop
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#### Build docker image
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The source build defaults to the published upstream base image
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`vllm/vllm-openai-rocm:v0.29.0`, aligned with the vLLM release used by CI.
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Older or custom bases must provide the vLLM APIs checked by the Dockerfile's
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image-build canary. This upstream base is distinct from the prebuilt
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`vllm/vllm-omni-rocm` images discussed below; their published-tag availability
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is tracked in [#7405](https://github.com/vllm-project/vllm-omni/issues/7405).
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```bash
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DOCKER_BUILDKIT=1 docker build -f docker/Dockerfile.rocm -t vllm-omni-rocm .
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```
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To select the upstream ROCm base explicitly, pass `BASE_IMAGE`:
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```bash
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DOCKER_BUILDKIT=1 docker build \
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-f docker/Dockerfile.rocm \
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--build-arg BASE_IMAGE=vllm/vllm-openai-rocm:v0.29.0 \
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-t vllm-omni-rocm .
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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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```
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```bash
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docker run --rm \
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--group-add=video \
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--ipc=host \
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@@ -97,7 +129,7 @@ vllm-omni-rocm \
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##### Launch with interactive session for development
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```
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```bash
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docker run --rm -it \
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--network=host \
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--group-add=video \
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@@ -119,7 +151,9 @@ vllm-omni-rocm
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vLLM-Omni offers an official docker image for deployment. These images are built on top of vLLM docker images and available on Docker Hub as [vllm/vllm-omni-rocm](https://hub.docker.com/r/vllm/vllm-omni-rocm/tags). The version of vLLM-Omni indicates which release of vLLM it is based on.
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#### Launch vLLM-Omni Server
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Here's an example deployment command that has been verified on 2 x MI300's:
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```bash
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docker run --rm \
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--group-add=video \
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@@ -136,8 +170,10 @@ docker run --rm \
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--model Qwen/Qwen3-Omni-30B-A3B-Instruct --omni --port 8091
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```
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#### Launch an interactive terminal with prebuilt docker image.
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#### Launch an interactive terminal with prebuilt docker image
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If you want to run in dev environment you can launch the docker image as follows:
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```bash
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docker run --rm -it \
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--network=host \
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