116 lines
5.1 KiB
ReStructuredText
116 lines
5.1 KiB
ReStructuredText
.. _installation_rocm:
|
|
|
|
Installation with ROCm
|
|
======================
|
|
|
|
vLLM supports AMD GPUs with ROCm 6.1.
|
|
|
|
Requirements
|
|
------------
|
|
|
|
* OS: Linux
|
|
* Python: 3.8 -- 3.11
|
|
* GPU: MI200s (gfx90a), MI300 (gfx942), Radeon RX 7900 series (gfx1100)
|
|
* ROCm 6.1
|
|
|
|
Installation options:
|
|
|
|
#. :ref:`Build from source with docker <build_from_source_docker_rocm>`
|
|
#. :ref:`Build from source <build_from_source_rocm>`
|
|
|
|
.. _build_from_source_docker_rocm:
|
|
|
|
Option 1: Build from source with docker (recommended)
|
|
-----------------------------------------------------
|
|
|
|
You can build and install vLLM from source.
|
|
|
|
First, build a docker image from `Dockerfile.rocm <https://github.com/vllm-project/vllm/blob/main/Dockerfile.rocm>`_ and launch a docker container from the image.
|
|
|
|
`Dockerfile.rocm <https://github.com/vllm-project/vllm/blob/main/Dockerfile.rocm>`_ uses ROCm 6.1 by default, but also supports ROCm 5.7 and 6.0 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``, specifically the PyTorch on ROCm base image.
|
|
* `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.
|
|
* `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`
|
|
* `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`
|
|
* `BUILD_TRITON`: specifies whether to build triton flash-attention. The default value is 1.
|
|
|
|
Their values can be passed in when running ``docker build`` with ``--build-arg`` options.
|
|
|
|
|
|
To build vllm on ROCm 6.1 for MI200 and MI300 series, you can use the default:
|
|
|
|
.. code-block:: console
|
|
|
|
$ DOCKER_BUILDKIT=1 docker build -f Dockerfile.rocm -t vllm-rocm .
|
|
|
|
To build vllm on ROCm 6.1 for Radeon RX7900 series (gfx1100), you should specify ``BUILD_FA`` as below:
|
|
|
|
.. code-block:: console
|
|
|
|
$ DOCKER_BUILDKIT=1 docker build --build-arg BUILD_FA="0" -f Dockerfile.rocm -t vllm-rocm .
|
|
|
|
To run the above docker image ``vllm-rocm``, use the below command:
|
|
|
|
.. code-block:: console
|
|
|
|
$ 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 \
|
|
bash
|
|
|
|
Where the `<path/to/model>` is the location where the model is stored, for example, the weights for llama2 or llama3 models.
|
|
|
|
|
|
.. _build_from_source_rocm:
|
|
|
|
Option 2: Build from source
|
|
---------------------------
|
|
|
|
0. Install prerequisites (skip if you are already in an environment/docker with the following installed):
|
|
|
|
- `ROCm <https://rocm.docs.amd.com/en/latest/deploy/linux/index.html>`_
|
|
- `PyTorch <https://pytorch.org/>`_
|
|
- `hipBLAS <https://rocm.docs.amd.com/projects/hipBLAS/en/latest/install.html>`_
|
|
|
|
For installing PyTorch, you can start from a fresh docker image, e.g, `rocm/pytorch:rocm6.1.2_ubuntu20.04_py3.9_pytorch_staging`, `rocm/pytorch-nightly`.
|
|
|
|
Alternatively, you can install PyTorch using PyTorch wheels. You can check PyTorch installation guild in PyTorch `Getting Started <https://pytorch.org/get-started/locally/>`_
|
|
|
|
|
|
1. Install `Triton flash attention for ROCm <https://github.com/ROCm/triton>`_
|
|
|
|
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>`_
|
|
|
|
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>`_
|
|
|
|
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>`_
|
|
Alternatively, wheels intended for vLLM use can be accessed under the releases.
|
|
|
|
.. note::
|
|
- 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`)
|
|
|
|
3. Build vLLM.
|
|
|
|
.. code-block:: console
|
|
|
|
$ cd vllm
|
|
$ pip install -U -r requirements-rocm.txt
|
|
$ python setup.py develop # This may take 5-10 minutes. Currently, `pip install .`` does not work for ROCm installation
|
|
|
|
|
|
.. tip::
|
|
|
|
- Triton flash attention is used by default. For benchmarking purposes, it is recommended to run a warm up step before collecting perf numbers.
|
|
- Triton flash attention does not currently support sliding window attention. If using half precision, please use CK flash-attention for sliding window support.
|
|
- 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.
|
|
- The ROCm version of PyTorch, ideally, should match the ROCm driver version.
|