Quadrants on ROCm installation

Quadrants on ROCm installation#

2026-10-30

3 min read time

Applies to Linux

System requirements#

To use Quadrants 1.3.3, you need the following prerequisites:

  • ROCm version: 10.1.0

  • Operating system: Ubuntu 24.04

  • GPU platform: AMD Instinct™ MI300X, MI350X, and MI355X

  • Python: 3.12

  • PyTorch: 2.13.0

Install Quadrants#

This branch builds Quadrants for AMD GPUs on ROCm 10.1.0. The image installs PyTorch 2.13.0+rocm10.1.0 and the Quadrants wheel compiled with AMDGPU on, and CUDA and Vulkan off. It keeps the C++ test binary and this source tree so the suites can run. It does not install Genesis. The base image is rocm/dev-ubuntu-24.04:10.1.0-full. The host kernel driver must match ROCm 10.1.

Build the Docker image and run the container#

Clone this branch and build from the repository root.

  1. Clone the Quadrants repository:

    git clone -b rocm10.1.0_r26.10 https://github.com/AMD-Ecosystem/quadrants.git
cd quadrants

  2. Build the Docker image:

    docker build -t quadrants-release-rocm10.1 .

  3. Run the container on the AMD GPU. On a machine that also has a display GPU, set all three device variables to the CDNA device. That is GPU 0 on the machine used for this release. The shell starts in /src/quadrants.

    docker run --rm -it \
  --device=/dev/kfd --device=/dev/dri \
  --group-add "$(stat -c '%g' /dev/kfd)" \
  -e HIP_VISIBLE_DEVICES=0 \
  -e ROCR_VISIBLE_DEVICES=0 \
  -e CUDA_VISIBLE_DEVICES=0 \
  quadrants-release-rocm10.1 \
  bash

    Note

    Do not set QD_AMDGPU_V520.

Test Quadrants#

Inside that shell, set the C++ runtime path once, then run the four suites in this order. Importing Quadrants prints a banner, so QD_LIB_DIR takes the last line of the command output (tail -n 1).

export QD_LIB_DIR=$(python -c 'import os, quadrants as qd; print(os.path.join(qd.__path__[0], "_lib", "runtime"))' | tail -n 1)

/opt/quadrants/quadrants_cpp_tests --gtest_filter='AMDGPU.*'
/opt/quadrants/quadrants_cpp_tests --gtest_filter='-AMDGPU.*'
python tests/run_tests.py -t 4 -r 1 -v --arch amdgpu -m "not needs_torch"
python tests/run_tests.py -t 4 -r 1 -v --arch amdgpu -m "needs_torch"

The first command is the C++ suite on the AMD GPU. The second is the C++ suite that does not use the AMD GPU. The third is the Python suite without torch. The fourth is the Python suite that needs torch.

Example Quadrants kernel#

Paste this into the shell of the container. It compiles one kernel on the AMD GPU, adds 1 to each element of an array, and checks the result.

python << 'EOF'
import numpy as np
import quadrants as qd
qd.init(arch=qd.amdgpu)
n = 8
@qd.kernel
def add_one(x: qd.types.ndarray(qd.i32, ndim=1)):
    for i in range(x.shape[0]):
        x[i] = x[i] + 1
values = qd.ndarray(qd.i32, shape=(n,))
values.from_numpy(np.zeros(n, dtype=np.int32))
add_one(values)
result = values.to_numpy()
print(result)
assert np.array_equal(result, np.ones(n, dtype=np.int32))
print("Quadrants kernel passed")
EOF

A passing run prints eight 1s and then Quadrants kernel passed.

Contents