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[ROCm] [Doc] Update ROCm installation docs (#27327) 


Signed-off-by: default avatarvllmellm <vllm.ellm@embeddedllm.com>
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docs/getting_started/installation/gpu.rocm.inc.md
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# --8<-- [start:installation]
vLLM supports AMD GPUs with ROCm 6.3 or above.
vLLM supports AMD GPUs with ROCm 6.3 or above, and torch 2.8.0 and above.
!!! tip
[Docker](#set-up-using-docker) is the recommended way to use vLLM on ROCm.
......@@ -28,57 +28,63 @@ 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](https://github.com/vllm-project/vllm/blob/main/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](https://rocm.docs.amd.com/en/latest/deploy/linux/index.html)
- [PyTorch](https://pytorch.org/)
For installing PyTorch, you can start from a fresh docker image, e.g, `rocm/pytorch:rocm6.4.3_ubuntu24.04_py3.12_pytorch_release_2.6.0`, `rocm/pytorch-nightly`. If you are using docker image, you can skip to Step 3.
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](https://pytorch.org/get-started/locally/). Example:
```bash
# Install PyTorch
pip uninstall torch -y
pip install --no-cache-dir torch torchvision --index-url https://download.pytorch.org/whl/rocm6.4
pip install --no-cache-dir torch torchvision --index-url https://download.pytorch.org/whl/nightly/rocm7.0
```
1. Install [Triton for ROCm](https://github.com/triton-lang/triton)
1. Install [Triton for ROCm](https://github.com/ROCm/triton.git)
Install ROCm's Triton (the default triton-mlir branch) following the instructions from [ROCm/triton](https://github.com/ROCm/triton/blob/triton-mlir/README.md)
Install ROCm's Triton following the instructions from [ROCm/triton](https://github.com/ROCm/triton.git)
```bash
python3 -m pip install ninja cmake wheel pybind11
pip uninstall -y triton
git clone https://github.com/triton-lang/triton.git
git clone https://github.com/ROCm/triton.git
cd triton
git checkout e5be006
# git checkout $TRITON_BRANCH
git checkout f9e5bf54
if [ ! -f setup.py ]; then cd python; fi
python3 setup.py install
cd ../..
```
!!! note
If you see HTTP issue related to downloading packages during building triton, please try again as the HTTP error is intermittent.
- The validated `$TRITON_BRANCH` can be found in the [docker/Dockerfile.rocm_base](https://github.com/vllm-project/vllm/blob/main/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](https://github.com/Dao-AILab/flash-attention)
2. Optionally, if you choose to use CK flash attention, you can install [flash attention for ROCm](https://github.com/Dao-AILab/flash-attention.git)
Install ROCm's flash attention (v2.7.2) following the instructions from [ROCm/flash-attention](https://github.com/ROCm/flash-attention#amd-rocm-support)
Alternatively, wheels intended for vLLM use can be accessed under the releases.
Install ROCm's flash attention (v2.8.0) following the instructions from [ROCm/flash-attention](https://github.com/Dao-AILab/flash-attention#amd-rocm-support)
For example, for ROCm 6.3, suppose your gfx arch is `gfx90a`. To get your gfx architecture, run `rocminfo |grep gfx`.
For example, for ROCm 7.0, suppose your gfx arch is `gfx942`. To get your gfx architecture, run `rocminfo |grep gfx`.
```bash
git clone https://github.com/Dao-AILab/flash-attention.git
cd flash-attention
git checkout 1a7f4dfa
# git checkout $FA_BRANCH
git checkout 0e60e394
git submodule update --init
GPU_ARCHS="gfx90a" python3 setup.py install
GPU_ARCHS="gfx942" python3 setup.py install
cd ..
```
!!! note
You might need to downgrade the "ninja" version to 1.10 as it is not used when compiling flash-attention-2 (e.g. `pip install ninja==1.10.2.4`)
- The validated `$FA_BRANCH` can be found in the [docker/Dockerfile.rocm_base](https://github.com/vllm-project/vllm/blob/main/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:
......@@ -92,11 +98,13 @@ Currently, there are no pre-built ROCm wheels.
```
!!! note
You will need to config the `$AITER_BRANCH_OR_COMMIT` for your purpose.
- You will need to config the `$AITER_BRANCH_OR_COMMIT` for your purpose.
- The validated `$AITER_BRANCH_OR_COMMIT` can be found in the [docker/Dockerfile.rocm_base](https://github.com/vllm-project/vllm/blob/main/docker/Dockerfile.rocm_base).
4. Build vLLM. For example, vLLM on ROCM 6.3 can be built with the following steps:
4. Build vLLM. For example, vLLM on ROCM 7.0 can be built with the following steps:
??? console "Commands"
???+ console "Commands"
```bash
pip install --upgrade pip
......@@ -109,31 +117,48 @@ Currently, there are no pre-built ROCm wheels.
scipy \
huggingface-hub[cli,hf_transfer] \
setuptools_scm
pip install "numpy<2"
pip install -r requirements/rocm.txt
# Build vLLM for MI210/MI250/MI300.
export PYTORCH_ROCM_ARCH="gfx90a;gfx942"
# 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
- 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.
!!! tip
- 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.
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).
For vLLM, please refer to [vLLM performance optimization](https://rocm.docs.amd.com/en/latest/how-to/rocm-for-ai/inference-optimization/vllm-optimization.html).
# --8<-- [end:build-wheel-from-source]
# --8<-- [start:pre-built-images]
The [AMD Infinity hub for vLLM](https://hub.docker.com/r/rocm/vllm/tags) 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](https://hub.docker.com/r/rocm/vllm-dev), 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](https://rocm.docs.amd.com/en/latest/how-to/performance-validation/mi300x/vllm-benchmark.html)
......@@ -144,29 +169,29 @@ docker image designed for validating inference performance on the AMD Instinct
Building the Docker image from source is the recommended way to use vLLM with ROCm.
#### (Optional) Build an image with ROCm software stack
??? 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.
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:
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
{
```json
{
"features": {
"buildkit": true
}
}
```
}
```
To build vllm on ROCm 6.3 for MI200 and MI300 series, you can use the default:
To build vllm on ROCm 7.0 for MI200 and MI300 series, you can use the default:
```bash
DOCKER_BUILDKIT=1 docker build \
```bash
DOCKER_BUILDKIT=1 docker build \
-f docker/Dockerfile.rocm_base \
-t rocm/vllm-dev:base .
```
```
#### Build an image with vLLM
......@@ -181,7 +206,7 @@ It is important that the user kicks off the docker build using buildkit. Either
}
```
[docker/Dockerfile.rocm](https://github.com/vllm-project/vllm/blob/main/docker/Dockerfile.rocm) uses ROCm 6.3 by default, but also supports ROCm 5.7, 6.0, 6.1, and 6.2, in older vLLM branches.
[docker/Dockerfile.rocm](https://github.com/vllm-project/vllm/blob/main/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](https://github.com/vllm-project/vllm/blob/main/docker/Dockerfile.rocm_base)
......@@ -189,16 +214,16 @@ It provides flexibility to customize the build of docker image using the followi
Their values can be passed in when running `docker build` with `--build-arg` options.
To build vllm on ROCm 6.3 for MI200 and MI300 series, you can use the default:
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 -t vllm-rocm .
```
???+ 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 "Command"
???+ console "Commands"
```bash
docker run -it \
--network=host \
......
docs/getting_started/installation/python_env_setup.inc.md
View file @ 5b3c35a6
It's recommended to use [uv](https://docs.astral.sh/uv/), a very fast Python environment manager, to create and manage Python environments. Please follow the [documentation](https://docs.astral.sh/uv/#getting-started) to install `uv`. After installing `uv`, you can create a new Python environment using the following commands:
On NVIDIA CUDA only, it's recommended to use [uv](https://docs.astral.sh/uv/), a very fast Python environment manager, to create and manage Python environments. Please follow the [documentation](https://docs.astral.sh/uv/#getting-started) to install `uv`. After installing `uv`, you can create a new Python environment using the following commands:
```bash
uv venv --python 3.12 --seed
......
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