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[Doc] [ROCm] Update ROCm getting started doc (#32580)

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Signed-off-by: tjtanaa <tunjian.tan@embeddedllm.com>
Co-authored-by: Hongxia Yang <hongxia.yang@amd.com>
Co-authored-by: DarkLight1337 <tlleungac@connect.ust.hk>
This commit is contained in:
TJian
2026-01-21 01:20:08 +08:00
committed by GitHub
parent 6c97b9b9b6
commit c025263ddd
2 changed files with 96 additions and 42 deletions
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docs/getting_started/quickstart.md
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@@ -43,25 +43,21 @@ This guide will help you quickly get started with vLLM to perform:
=== "AMD ROCm"
Use a pre-built docker image from Docker Hub. The public stable image is [rocm/vllm:latest](https://hub.docker.com/r/rocm/vllm). There is also a development image at [rocm/vllm-dev](https://hub.docker.com/r/rocm/vllm-dev).
The `-v` flag in the `docker run` command below mounts a local directory into the container. Replace `<path/to/your/models>` with the path on your host machine to the directory containing your models. The models will then be accessible inside the container at `/app/models`.
???+ 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
```
If you are using AMD GPUs, you can install vLLM using `uv`.
It's recommended to use [uv](https://docs.astral.sh/uv/), as it gives the extra index [higher priority than the default index](https://docs.astral.sh/uv/pip/compatibility/#packages-that-exist-on-multiple-indexes). `uv` is also 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 and install vLLM using the following commands:
```bash
uv venv --python 3.12 --seed
source .venv/bin/activate
uv pip install vllm --extra-index-url https://wheels.vllm.ai/rocm/
```
!!! note
It currently supports Python 3.12, ROCm 7.0 and `glibc >= 2.35`.
!!! note
Note that, previously, docker images were published using AMD's docker release pipeline and were located `rocm/vlm-dev`. This is being deprecated by using vLLM's docker release pipeline.
=== "Google TPU"
@@ -294,14 +290,7 @@ python script.py --attention-backend FLASHINFER
Some of the available backend options include:
- On NVIDIA CUDA: `FLASH_ATTN` or `FLASHINFER`.
- On AMD ROCm: `TRITON_ATTN`, `ROCM_ATTN`, `ROCM_AITER_FA` or `ROCM_AITER_UNIFIED_ATTN`.
For AMD ROCm, you can further control the specific Attention implementation using the following options:
- Triton Unified Attention: Set the environment variables `VLLM_ROCM_USE_AITER=0 VLLM_ROCM_USE_AITER_MHA=0` and pass `--attention-config.use_prefill_decode_attention=false` as a CLI argument.
- AITER Unified Attention: Set the environment variables `VLLM_ROCM_USE_AITER=1 VLLM_USE_AITER_UNIFIED_ATTENTION=1 VLLM_ROCM_USE_AITER_MHA=0` and pass `--attention-config.use_prefill_decode_attention=false` as a CLI argument.
- Triton Prefill-Decode Attention: Set the environment variables `VLLM_ROCM_USE_AITER=1 VLLM_ROCM_USE_AITER_MHA=0` and pass `--attention-config.use_prefill_decode_attention=true` as a CLI argument.
- AITER Multi-head Attention: Set the environment variables `VLLM_ROCM_USE_AITER=1 VLLM_ROCM_USE_AITER_MHA=1` and pass `--attention-config.use_prefill_decode_attention=false` as a CLI argument.
- On AMD ROCm: `TRITON_ATTN`, `ROCM_ATTN`, `ROCM_AITER_FA`, `ROCM_AITER_UNIFIED_ATTN`, `TRITON_MLA`, `ROCM_AITER_MLA` or `ROCM_AITER_TRITON_MLA`.
!!! warning
There are no pre-built vllm wheels containing Flash Infer, so you must install it in your environment first. Refer to the [Flash Infer official docs](https://docs.flashinfer.ai/) or see [docker/Dockerfile](../../docker/Dockerfile) for instructions on how to install it.