[DOC] [ROCm] Add ROCm quickstart guide (#26505)

Signed-off-by: vllmellm <vllm.ellm@embeddedllm.com>

docs/getting_started/quickstart.md

@@ -12,32 +12,56 @@ This guide will help you quickly get started with vLLM to perform:
 
 ## Installation
 
-If you are using NVIDIA GPUs, you can install vLLM using [pip](https://pypi.org/project/vllm/) directly.
+=== "NVIDIA CUDA"
 
-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 and install vLLM using the following commands:
+    If you are using NVIDIA GPUs, you can install vLLM using [pip](https://pypi.org/project/vllm/) directly.
 
-```bash
-uv venv --python 3.12 --seed
-source .venv/bin/activate
-uv pip install vllm --torch-backend=auto
-```
+    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 and install vLLM using the following commands:
 
-`uv` can [automatically select the appropriate PyTorch index at runtime](https://docs.astral.sh/uv/guides/integration/pytorch/#automatic-backend-selection) by inspecting the installed CUDA driver version via `--torch-backend=auto` (or `UV_TORCH_BACKEND=auto`). To select a specific backend (e.g., `cu126`), set `--torch-backend=cu126` (or `UV_TORCH_BACKEND=cu126`).
+    ```bash
+    uv venv --python 3.12 --seed
+    source .venv/bin/activate
+    uv pip install vllm --torch-backend=auto
+    ```
 
-Another delightful way is to use `uv run` with `--with [dependency]` option, which allows you to run commands such as `vllm serve` without creating any permanent environment:
+    `uv` can [automatically select the appropriate PyTorch index at runtime](https://docs.astral.sh/uv/guides/integration/pytorch/#automatic-backend-selection) by inspecting the installed CUDA driver version via `--torch-backend=auto` (or `UV_TORCH_BACKEND=auto`). To select a specific backend (e.g., `cu126`), set `--torch-backend=cu126` (or `UV_TORCH_BACKEND=cu126`).
 
-```bash
-uv run --with vllm vllm --help
-```
+    Another delightful way is to use `uv run` with `--with [dependency]` option, which allows you to run commands such as `vllm serve` without creating any permanent environment:
 
-You can also use [conda](https://docs.conda.io/projects/conda/en/latest/user-guide/getting-started.html) to create and manage Python environments. You can install `uv` to the conda environment through `pip` if you want to manage it within the environment.
+    ```bash
+    uv run --with vllm vllm --help
+    ```
 
-```bash
-conda create -n myenv python=3.12 -y
-conda activate myenv
-pip install --upgrade uv
-uv pip install vllm --torch-backend=auto
-```
+    You can also use [conda](https://docs.conda.io/projects/conda/en/latest/user-guide/getting-started.html) to create and manage Python environments. You can install `uv` to the conda environment through `pip` if you want to manage it within the environment.
+
+    ```bash
+    conda create -n myenv python=3.12 -y
+    conda activate myenv
+    pip install --upgrade uv
+    uv pip install vllm --torch-backend=auto
+    ```
+
+=== "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
+        ```
 
 !!! note
     For more detail and non-CUDA platforms, please refer [here](installation/README.md) for specific instructions on how to install vLLM.
@@ -246,7 +270,17 @@ Alternatively, you can use the `openai` Python package:
 
 Currently, vLLM supports multiple backends for efficient Attention computation across different platforms and accelerator architectures. It automatically selects the most performant backend compatible with your system and model specifications.
 
-If desired, you can also manually set the backend of your choice by configuring the environment variable `VLLM_ATTENTION_BACKEND` to one of the following options: `FLASH_ATTN`, `FLASHINFER` or `XFORMERS`.
+If desired, you can also manually set the backend of your choice by configuring the environment variable `VLLM_ATTENTION_BACKEND` to one of the following options:
+
+- On NVIDIA CUDA: `FLASH_ATTN`, `FLASHINFER` or `XFORMERS`.
+- On AMD ROCm: `TRITON_ATTN`, `ROCM_ATTN`, `ROCM_AITER_FA` or `ROCM_AITER_UNIFIED_ATTN`.
+
+For AMD ROCm, you can futher control the specific Attention implementation using the following variables:
+
+- Triton Unified Attention: `VLLM_ROCM_USE_AITER=0 VLLM_V1_USE_PREFILL_DECODE_ATTENTION=0 VLLM_ROCM_USE_AITER_MHA=0`
+- AITER Unified Attention: `VLLM_ROCM_USE_AITER=1 VLLM_USE_AITER_UNIFIED_ATTENTION=1 VLLM_V1_USE_PREFILL_DECODE_ATTENTION=0 VLLM_ROCM_USE_AITER_MHA=0`
+- Triton Prefill-Decode Attention: `VLLM_ROCM_USE_AITER=1 VLLM_V1_USE_PREFILL_DECODE_ATTENTION=1 VLLM_ROCM_USE_AITER_MHA=0`
+- AITER Multi-head Attention: `VLLM_ROCM_USE_AITER=1 VLLM_V1_USE_PREFILL_DECODE_ATTENTION=0 VLLM_ROCM_USE_AITER_MHA=1`
 
 !!! 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.