vllm/docs/getting_started/installation/gpu.rocm.inc.md

--8<-- [start:installation]

vLLM supports AMD GPUs with ROCm 6.3 or above, and torch 2.8.0 and above.

!!! tip Docker is the recommended way to use vLLM on ROCm.

!!! warning There are no pre-built wheels for this device, so you must either use the pre-built Docker image or build vLLM from source.

--8<-- [end:installation]

--8<-- [start:requirements]

• GPU: MI200s (gfx90a), MI300 (gfx942), MI350 (gfx950), Radeon RX 7900 series (gfx1100/1101), Radeon RX 9000 series (gfx1200/1201), Ryzen AI MAX / AI 300 Series (gfx1151/1150)
• ROCm 6.3 or above
  • MI350 requires ROCm 7.0 or above
  • Ryzen AI MAX / AI 300 Series requires ROCm 7.0.2 or above

--8<-- [end:requirements]

--8<-- [start:set-up-using-python]

There is no extra information on creating a new Python environment for this device.

--8<-- [end:set-up-using-python]

--8<-- [start:pre-built-wheels]

Currently, there are no pre-built ROCm wheels.

--8<-- [end:pre-built-wheels]

--8<-- [start:build-wheel-from-source]

!!! tip - If you found that the following installation step does not work for you, please refer to docker/Dockerfile.rocm_base. Dockerfile is a form of installation steps.

0. Install prerequisites (skip if you are already in an environment/docker with the following installed):

   • ROCm
   • PyTorch

   For installing PyTorch, you can start from a fresh docker image, e.g, rocm/pytorch:rocm7.0_ubuntu22.04_py3.10_pytorch_release_2.8.0, rocm/pytorch-nightly. If you are using docker image, you can skip to Step 3.

   Alternatively, you can install PyTorch using PyTorch wheels. You can check PyTorch installation guide in PyTorch Getting Started. Example:

   # Install PyTorch
   pip uninstall torch -y
   pip install --no-cache-dir torch torchvision --index-url https://download.pytorch.org/whl/nightly/rocm7.0
   

1. Install Triton for ROCm

   Install ROCm's Triton following the instructions from ROCm/triton

   python3 -m pip install ninja cmake wheel pybind11
   pip uninstall -y triton
   git clone https://github.com/ROCm/triton.git
   cd triton
   # git checkout $TRITON_BRANCH
   git checkout f9e5bf54
   if [ ! -f setup.py ]; then cd python; fi
   python3 setup.py install
   cd ../..
   

   !!! note - The validated $TRITON_BRANCH can be found in the docker/Dockerfile.rocm_base. - If you see HTTP issue related to downloading packages during building triton, please try again as the HTTP error is intermittent.

2. Optionally, if you choose to use CK flash attention, you can install flash attention for ROCm

   Install ROCm's flash attention (v2.8.0) following the instructions from ROCm/flash-attention

   For example, for ROCm 7.0, suppose your gfx arch is gfx942. To get your gfx architecture, run rocminfo |grep gfx.

   git clone https://github.com/Dao-AILab/flash-attention.git
   cd flash-attention
   # git checkout $FA_BRANCH
   git checkout 0e60e394
   git submodule update --init
   GPU_ARCHS="gfx942" python3 setup.py install
   cd ..
   

   !!! note - The validated $FA_BRANCH can be found in the docker/Dockerfile.rocm_base.

3. If you choose to build AITER yourself to use a certain branch or commit, you can build AITER using the following steps:

   python3 -m pip uninstall -y aiter
   git clone --recursive https://github.com/ROCm/aiter.git
   cd aiter
   git checkout $AITER_BRANCH_OR_COMMIT
   git submodule sync; git submodule update --init --recursive
   python3 setup.py develop
   

   !!! note - You will need to config the $AITER_BRANCH_OR_COMMIT for your purpose. - The validated $AITER_BRANCH_OR_COMMIT can be found in the docker/Dockerfile.rocm_base.

4. Build vLLM. For example, vLLM on ROCM 7.0 can be built with the following steps:

   ???+ console "Commands"

    ```bash
    pip install --upgrade pip
   
    # Build & install AMD SMI
    pip install /opt/rocm/share/amd_smi
   
    # Install dependencies
    pip install --upgrade numba \
        scipy \
        huggingface-hub[cli,hf_transfer] \
        setuptools_scm
    pip install -r requirements/rocm.txt
   
    # To build for a single architecture (e.g., MI300) for faster installation (recommended):
    export PYTORCH_ROCM_ARCH="gfx942"
   
    # To build vLLM for multiple arch MI210/MI250/MI300, use this instead
    # export PYTORCH_ROCM_ARCH="gfx90a;gfx942"
   
    python3 setup.py develop
    ```
   

   This may take 5-10 minutes. Currently, pip install . does not work for ROCm installation.

   !!! tip - The ROCm version of PyTorch, ideally, should match the ROCm driver version.

!!! tip - For MI300x (gfx942) users, to achieve optimal performance, please refer to MI300x tuning guide for performance optimization and tuning tips on system and workflow level. For vLLM, please refer to vLLM performance optimization.

--8<-- [end:build-wheel-from-source]

--8<-- [start:pre-built-images]

The AMD Infinity hub for vLLM offers a prebuilt, optimized docker image designed for validating inference performance on the AMD Instinct™ MI300X accelerator. AMD also offers nightly prebuilt docker image from Docker Hub, which has vLLM and all its dependencies installed.

???+ console "Commands" bash docker pull rocm/vllm-dev:nightly # to get the latest image docker run -it --rm \ --network=host \ --group-add=video \ --ipc=host \ --cap-add=SYS_PTRACE \ --security-opt seccomp=unconfined \ --device /dev/kfd \ --device /dev/dri \ -v <path/to/your/models>:/app/models \ -e HF_HOME="/app/models" \ rocm/vllm-dev:nightly

!!! tip Please check LLM inference performance validation on AMD Instinct MI300X for instructions on how to use this prebuilt docker image.

--8<-- [end:pre-built-images]

--8<-- [start:build-image-from-source]

Building the Docker image from source is the recommended way to use vLLM with ROCm.

??? info "(Optional) Build an image with ROCm software stack"

Build a docker image from [docker/Dockerfile.rocm_base](https://github.com/vllm-project/vllm/blob/main/docker/Dockerfile.rocm_base) which setup ROCm software stack needed by the vLLM.
**This step is optional as this rocm_base image is usually prebuilt and store at [Docker Hub](https://hub.docker.com/r/rocm/vllm-dev) under tag `rocm/vllm-dev:base` to speed up user experience.**
If you choose to build this rocm_base image yourself, the steps are as follows.

It is important that the user kicks off the docker build using buildkit. Either the user put DOCKER_BUILDKIT=1 as environment variable when calling docker build command, or the user needs to set up buildkit in the docker daemon configuration /etc/docker/daemon.json as follows and restart the daemon:

```json
{
    "features": {
        "buildkit": true
    }
}
```

To build vllm on ROCm 7.0 for MI200 and MI300 series, you can use the default:

```bash
DOCKER_BUILDKIT=1 docker build \
    -f docker/Dockerfile.rocm_base \
    -t rocm/vllm-dev:base .
```

Build an image with vLLM

First, build a docker image from docker/Dockerfile.rocm and launch a docker container from the image. It is important that the user kicks off the docker build using buildkit. Either the user put DOCKER_BUILDKIT=1 as environment variable when calling docker build command, or the user needs to set up buildkit in the docker daemon configuration /etc/docker/daemon.json as follows and restart the daemon:

{
    "features": {
        "buildkit": true
    }
}

docker/Dockerfile.rocm uses ROCm 7.0 by default, but also supports ROCm 5.7, 6.0, 6.1, 6.2, 6.3, and 6.4, in older vLLM branches. It provides flexibility to customize the build of docker image using the following arguments:

• BASE_IMAGE: specifies the base image used when running docker build. The default value rocm/vllm-dev:base is an image published and maintained by AMD. It is being built using docker/Dockerfile.rocm_base
• ARG_PYTORCH_ROCM_ARCH: Allows to override the gfx architecture values from the base docker image

Their values can be passed in when running docker build with --build-arg options.

To build vllm on ROCm 7.0 for MI200 and MI300 series, you can use the default:

???+ console "Commands" bash DOCKER_BUILDKIT=1 docker build -f docker/Dockerfile.rocm -t vllm-rocm .

To run the above docker image vllm-rocm, use the below command:

???+ console "Commands" bash docker run -it \ --network=host \ --group-add=video \ --ipc=host \ --cap-add=SYS_PTRACE \ --security-opt seccomp=unconfined \ --device /dev/kfd \ --device /dev/dri \ -v <path/to/model>:/app/model \ vllm-rocm

Where the <path/to/model> is the location where the model is stored, for example, the weights for llama2 or llama3 models.

--8<-- [end:build-image-from-source]

--8<-- [start:supported-features]

See Feature x Hardware compatibility matrix for feature support information.

--8<-- [end:supported-features]