Update amd-installation.md

Dockerfile.rocm

@@ -1,5 +1,5 @@
-# Default ROCm 6.2 base image
-ARG BASE_IMAGE="rocm/pytorch:rocm6.2_ubuntu20.04_py3.9_pytorch_release_2.3.0"
+# Default ROCm 6.3 base image
+ARG BASE_IMAGE="rocm/pytorch:rocm6.3_ubuntu24.04_py3.9_pytorch_release_2.5.0"
 
 # Default ROCm ARCHes to build vLLM for.
 ARG PYTORCH_ROCM_ARCH="gfx908;gfx90a;gfx942;gfx1100"
@@ -48,13 +48,13 @@ RUN apt-get purge -y sccache; python3 -m pip uninstall -y sccache; rm -f "$(whic
 # Install torch == 2.6.0 on ROCm
 RUN --mount=type=cache,target=/root/.cache/pip \
     case "$(ls /opt | grep -Po 'rocm-[0-9]\.[0-9]')" in \
-        *"rocm-6.2"*) \
+        *"rocm-6.3"*) \
             python3 -m pip uninstall -y torch torchvision \
             && python3 -m pip install --pre \
-                torch==2.6.0.dev20241113+rocm6.2 \
+                torch==2.6.0.dev20241224+rocm6.3 \
                 'setuptools-scm>=8' \
-                torchvision==0.20.0.dev20241113+rocm6.2 \
-                --extra-index-url https://download.pytorch.org/whl/nightly/rocm6.2;; \
+                torchvision==0.20.0.dev20241224+rocm6.3 \
+                --extra-index-url https://download.pytorch.org/whl/nightly/rocm6.3;; \
         *) ;; esac
 
 ENV LLVM_SYMBOLIZER_PATH=/opt/rocm/llvm/bin/llvm-symbolizer

docs/source/getting_started/amd-installation.md

@@ -2,14 +2,14 @@
 
 # Installation with ROCm
 
-vLLM supports AMD GPUs with ROCm 6.2.
+vLLM supports AMD GPUs with ROCm 6.3.
 
 ## Requirements
 
 - OS: Linux
 - Python: 3.9 -- 3.12
-- GPU: MI200s (gfx90a), MI300 (gfx942), Radeon RX 7900 series (gfx1100)
-- ROCm 6.2
+- GPU: MI200s (gfx90a), MI300 (gfx942), Radeon Pro, Radeon RX 7900 series (gfx1100)
+- ROCm 6.3
 
 Installation options:
 
@@ -33,7 +33,7 @@ It is important that the user kicks off the docker build using buildkit. Either
 }
 ```
 
-[Dockerfile.rocm](https://github.com/vllm-project/vllm/blob/main/Dockerfile.rocm) uses ROCm 6.2 by default, but also supports ROCm 5.7, 6.0 and 6.1 in older vLLM branches.
+[Dockerfile.rocm](https://github.com/vllm-project/vllm/blob/main/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.
 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.
@@ -44,13 +44,13 @@ 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 6.2 for MI200 and MI300 series, you can use the default:
+To build vllm on ROCm 6.3 for MI200 and MI300 series, you can use the default:
 
 ```console
 $ DOCKER_BUILDKIT=1 docker build -f Dockerfile.rocm -t vllm-rocm .
 ```
 
-To build vllm on ROCm 6.2 for Radeon RX7900 series (gfx1100), you should specify `BUILD_FA` as below:
+To build vllm on ROCm 6.3 for Radeon RX7900 series (gfx1100), you should specify `BUILD_FA` as below:
 
 ```console
 $ DOCKER_BUILDKIT=1 docker build --build-arg BUILD_FA="0" -f Dockerfile.rocm -t vllm-rocm .
@@ -83,7 +83,7 @@ Where the `<path/to/model>` is the location where the model is stored, for examp
 - [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:rocm6.2_ubuntu20.04_py3.9_pytorch_release_2.3.0`, `rocm/pytorch-nightly`.
+For installing PyTorch, you can start from a fresh docker image, e.g, `rocm/pytorch:rocm6.3_ubuntu24.04_py3.12_pytorch_release_2.5.0`, `rocm/pytorch-nightly`.
 
 Alternatively, you can install PyTorch using PyTorch wheels. You can check PyTorch installation guide in PyTorch [Getting Started](https://pytorch.org/get-started/locally/)
 
@@ -94,9 +94,9 @@ Install ROCm's Triton flash attention (the default triton-mlir branch) following
 ```console
 $ python3 -m pip install ninja cmake wheel pybind11
 $ pip uninstall -y triton
-$ git clone https://github.com/OpenAI/triton.git
+$ git clone https://github.com/triton-lang/triton
 $ cd triton
-$ git checkout e192dba
+$ git checkout release/3.2.x
 $ cd python
 $ pip3 install .
 $ cd ../..
@@ -111,12 +111,11 @@ $ cd ../..
 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.
 
-For example, for ROCm 6.2, suppose your gfx arch is `gfx90a`. To get your gfx architecture, run `rocminfo |grep gfx`.
+For example, for ROCm 6.3, suppose your gfx arch is `gfx90a`. To get your gfx architecture, run `rocminfo |grep gfx`.
 
 ```console
 $ git clone https://github.com/ROCm/flash-attention.git
 $ cd flash-attention
-$ git checkout 3cea2fb
 $ git submodule update --init
 $ GPU_ARCHS="gfx90a" python3 setup.py install
 $ cd ..
@@ -126,14 +125,14 @@ $ cd ..
 - 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. For example, vLLM on ROCM 6.2 can be built with the following steps:
+3. Build vLLM. For example, vLLM on ROCM 6.3 can be built with the following steps:
 
 ```bash
 $ pip install --upgrade pip
 
 # Install PyTorch
 $ pip uninstall torch -y
-$ pip install --no-cache-dir --pre torch==2.6.0.dev20241024 --index-url https://download.pytorch.org/whl/nightly/rocm6.2
+$ pip install --no-cache-dir --pre torch==2.6.0.dev20241224 --index-url https://download.pytorch.org/whl/nightly/rocm6.3
 
 # Build & install AMD SMI
 $ pip install /opt/rocm/share/amd_smi