[Doc] Organise installation documentation into categories and tabs (#11935)
Signed-off-by: Harry Mellor <19981378+hmellor@users.noreply.github.com>
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Harry Mellor
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docs/source/getting_started/installation/gpu/rocm.inc.md
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docs/source/getting_started/installation/gpu/rocm.inc.md
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# Installation
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vLLM supports AMD GPUs with ROCm 6.2.
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## Requirements
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- GPU: MI200s (gfx90a), MI300 (gfx942), Radeon RX 7900 series (gfx1100)
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- ROCm 6.2
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## Set up using Python
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### Pre-built wheels
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Currently, there are no pre-built ROCm wheels.
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### Build wheel from source
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0. Install prerequisites (skip if you are already in an environment/docker with the following installed):
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- [ROCm](https://rocm.docs.amd.com/en/latest/deploy/linux/index.html)
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- [PyTorch](https://pytorch.org/)
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For installing PyTorch, you can start from a fresh docker image, e.g, `rocm/pytorch:rocm6.2_ubuntu20.04_py3.9_pytorch_release_2.3.0`, `rocm/pytorch-nightly`.
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Alternatively, you can install PyTorch using PyTorch wheels. You can check PyTorch installation guide in PyTorch [Getting Started](https://pytorch.org/get-started/locally/)
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1. Install [Triton flash attention for ROCm](https://github.com/ROCm/triton)
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Install ROCm's Triton flash attention (the default triton-mlir branch) following the instructions from [ROCm/triton](https://github.com/ROCm/triton/blob/triton-mlir/README.md)
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```console
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python3 -m pip install ninja cmake wheel pybind11
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pip uninstall -y triton
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git clone https://github.com/OpenAI/triton.git
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cd triton
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git checkout e192dba
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cd python
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pip3 install .
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cd ../..
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```
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```{note}
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- If you see HTTP issue related to downloading packages during building triton, please try again as the HTTP error is intermittent.
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```
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2. Optionally, if you choose to use CK flash attention, you can install [flash attention for ROCm](https://github.com/ROCm/flash-attention/tree/ck_tile)
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Install ROCm's flash attention (v2.5.9.post1) following the instructions from [ROCm/flash-attention](https://github.com/ROCm/flash-attention/tree/ck_tile#amd-gpurocm-support)
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Alternatively, wheels intended for vLLM use can be accessed under the releases.
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For example, for ROCm 6.2, suppose your gfx arch is `gfx90a`. To get your gfx architecture, run `rocminfo |grep gfx`.
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```console
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git clone https://github.com/ROCm/flash-attention.git
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cd flash-attention
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git checkout 3cea2fb
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git submodule update --init
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GPU_ARCHS="gfx90a" python3 setup.py install
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cd ..
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```
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```{note}
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- You might need to downgrade the "ninja" version to 1.10 it is not used when compiling flash-attention-2 (e.g. `pip install ninja==1.10.2.4`)
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```
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3. Build vLLM. For example, vLLM on ROCM 6.2 can be built with the following steps:
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```bash
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$ pip install --upgrade pip
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# Install PyTorch
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$ pip uninstall torch -y
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$ pip install --no-cache-dir --pre torch==2.6.0.dev20241024 --index-url https://download.pytorch.org/whl/nightly/rocm6.2
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# Build & install AMD SMI
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$ pip install /opt/rocm/share/amd_smi
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# Install dependencies
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$ pip install --upgrade numba scipy huggingface-hub[cli]
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$ pip install "numpy<2"
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$ pip install -r requirements-rocm.txt
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# Build vLLM for MI210/MI250/MI300.
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$ export PYTORCH_ROCM_ARCH="gfx90a;gfx942"
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$ python3 setup.py develop
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```
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This may take 5-10 minutes. Currently, `pip install .` does not work for ROCm installation.
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```{tip}
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- Triton flash attention is used by default. For benchmarking purposes, it is recommended to run a warm up step before collecting perf numbers.
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- Triton flash attention does not currently support sliding window attention. If using half precision, please use CK flash-attention for sliding window support.
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- To use CK flash-attention or PyTorch naive attention, please use this flag `export VLLM_USE_TRITON_FLASH_ATTN=0` to turn off triton flash attention.
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- The ROCm version of PyTorch, ideally, should match the ROCm driver version.
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```
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```{tip}
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- For MI300x (gfx942) users, to achieve optimal performance, please refer to [MI300x tuning guide](https://rocm.docs.amd.com/en/latest/how-to/tuning-guides/mi300x/index.html) for performance optimization and tuning tips on system and workflow level.
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For vLLM, please refer to [vLLM performance optimization](https://rocm.docs.amd.com/en/latest/how-to/tuning-guides/mi300x/workload.html#vllm-performance-optimization).
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```
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## Set up using Docker
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### Pre-built images
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Currently, there are no pre-built ROCm images.
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### Build image from source
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Building the Docker image from source is the recommended way to use vLLM with ROCm.
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First, build a docker image from <gh-file:Dockerfile.rocm> and launch a docker container from the image.
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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 setup buildkit in the docker daemon configuration /etc/docker/daemon.json as follows and restart the daemon:
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```console
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{
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"features": {
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"buildkit": true
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}
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}
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```
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<gh-file:Dockerfile.rocm> uses ROCm 6.2 by default, but also supports ROCm 5.7, 6.0 and 6.1 in older vLLM branches.
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It provides flexibility to customize the build of docker image using the following arguments:
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- `BASE_IMAGE`: specifies the base image used when running `docker build`, specifically the PyTorch on ROCm base image.
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- `BUILD_FA`: specifies whether to build CK flash-attention. The default is 1. For [Radeon RX 7900 series (gfx1100)](https://rocm.docs.amd.com/projects/radeon/en/latest/index.html), this should be set to 0 before flash-attention supports this target.
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- `FX_GFX_ARCHS`: specifies the GFX architecture that is used to build CK flash-attention, for example, `gfx90a;gfx942` for MI200 and MI300. The default is `gfx90a;gfx942`
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- `FA_BRANCH`: specifies the branch used to build the CK flash-attention in [ROCm's flash-attention repo](https://github.com/ROCmSoftwarePlatform/flash-attention). The default is `ae7928c`
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- `BUILD_TRITON`: specifies whether to build triton flash-attention. The default value is 1.
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Their values can be passed in when running `docker build` with `--build-arg` options.
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To build vllm on ROCm 6.2 for MI200 and MI300 series, you can use the default:
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```console
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DOCKER_BUILDKIT=1 docker build -f Dockerfile.rocm -t vllm-rocm .
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```
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To build vllm on ROCm 6.2 for Radeon RX7900 series (gfx1100), you should specify `BUILD_FA` as below:
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```console
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DOCKER_BUILDKIT=1 docker build --build-arg BUILD_FA="0" -f Dockerfile.rocm -t vllm-rocm .
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```
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To run the above docker image `vllm-rocm`, use the below command:
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```console
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docker run -it \
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--network=host \
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--group-add=video \
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--ipc=host \
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--cap-add=SYS_PTRACE \
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--security-opt seccomp=unconfined \
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--device /dev/kfd \
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--device /dev/dri \
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-v <path/to/model>:/app/model \
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vllm-rocm \
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bash
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```
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Where the `<path/to/model>` is the location where the model is stored, for example, the weights for llama2 or llama3 models.
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## Supported features
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See <project:#feature-x-hardware> compatibility matrix for feature support information.
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