Quad (128-bit) float dtype for numpy

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NumPy-QuadDType

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A cross-platform Quad (128-bit) float Data-Type for NumPy.

📖 Read the full documentation

Table of Contents

Installation

pip install "numpy>=2.4"
pip install numpy-quaddtype

Or with conda-forge:

conda install numpy_quaddtype

Or with mamba:

mamba install numpy_quaddtype

Or grab the development version with

pip install git+https://github.com/numpy/numpy-quaddtype.git

Usage

import numpy as np
from numpy_quaddtype import QuadPrecDType, QuadPrecision

# using sleef backend (default)
np.array([1,2,3], dtype=QuadPrecDType())
np.array([1,2,3], dtype=QuadPrecDType("sleef"))

# using longdouble backend
np.array([1,2,3], dtype=QuadPrecDType("longdouble"))

Installation from source

Linux/Unix/macOS

Prerequisites: gcc/clang, CMake (≥3.15), Python 3.11+, Git, NumPy ≥ 2.4

# setup the virtual env
python3 -m venv temp
source temp/bin/activate

# Install build and test dependencies
pip install pytest meson meson-python "numpy>=2.4"

# To build without QBLAS (default for MSVC)
# export CFLAGS="-DDISABLE_QUADBLAS"
# export CXXFLAGS="-DDISABLE_QUADBLAS"

python -m pip install ".[test]" -v

# Run the tests
python -m pytest tests

Windows

Prerequisites: Visual Studio 2017+ (with MSVC), CMake (≥3.15), Python 3.11+, Git

  1. Setup Development Environment

    Open a Developer Command Prompt for VS or Developer PowerShell for VS to ensure MSVC is properly configured.

  2. Setup Python Environment

    # Create and activate virtual environment
python -m venv numpy_quad_env
.\numpy_quad_env\Scripts\Activate.ps1

# Install build dependencies
pip install -U pip
pip install numpy pytest ninja meson

  3. Set Environment Variables

    # Note: QBLAS is disabled on Windows due to MSVC compatibility issues
$env:CFLAGS = "/DDISABLE_QUADBLAS"
$env:CXXFLAGS = "/DDISABLE_QUADBLAS"

  4. Build and Install numpy-quaddtype

    # Build and install the package
python -m pip install ".[test]" -v

  5. Test Installation

    # Run tests
pytest -s tests

  6. QBLAS Disabled: QuadBLAS optimization is automatically disabled on Windows builds due to MSVC compatibility issues. This is handled by the -DDISABLE_QUADBLAS compiler flag.

  7. Visual Studio Version: The instructions assume Visual Studio 2022. For other versions, adjust the generator string:

    • VS 2019: "Visual Studio 16 2019"
    • VS 2017: "Visual Studio 15 2017"

  8. Architecture: The instructions are for x64. For x86 builds, change -A x64 to -A Win32.

Build Options

Disabling FMA (Fused Multiply-Add)

On older x86-64 CPUs without FMA support (e.g., Sandy Bridge / x86_64-v2), the SLEEF's PURECFMA scalar code path will cause illegal instruction errors. By default, FMA support is auto-detected at build time, but you can explicitly disable it:

pip install . -Csetup-args=-Ddisable_fma=true

This is a workaround for a SLEEF issue where PURECFMA scalar functions are unconditionally compiled with FMA instructions even on systems that don't support them.

When to use this option:

  • Building on or for x86_64-v2 (Sandy Bridge era) CPUs
  • Cross-compiling for older x86_64 targets
  • Running in emulators/VMs that don't expose FMA capability

Building with ThreadSanitizer (TSan)

This is a development feature to help detect threading issues. To build numpy-quaddtype with TSan enabled, follow these steps:

Use of clang is recommended with machine NOT supporting libquadmath (like ARM64). Set the compiler to clang/clang++ before proceeding.

export CC=clang
export CXX=clang++
  1. Compile free-threaded CPython with TSan support. Follow the Python Free-Threading Guide for detailed instructions.
  2. Create and activate a virtual environment using the TSan-enabled Python build.
  3. Installing dependencies:
python -m pip install meson meson-python wheel ninja
# Need NumPy built with TSan as well
python -m pip install "numpy @ git+https://github.com/numpy/numpy" -C'setup-args=-Db_sanitize=thread'
  1. Building SLEEF with TSan:
# clone the repository
git clone https://github.com/shibatch/sleef.git
cd sleef
git checkout 43a0252ba9331adc7fb10755021f802863678c38

# Build SLEEF with TSan
cmake \
-DCMAKE_C_COMPILER=clang \
-DCMAKE_CXX_COMPILER=clang++ \
-DCMAKE_C_FLAGS="-fsanitize=thread -g -O1" \
-DCMAKE_CXX_FLAGS="-fsanitize=thread -g -O1" \
-DCMAKE_EXE_LINKER_FLAGS="-fsanitize=thread" \
-DCMAKE_SHARED_LINKER_FLAGS="-fsanitize=thread" \
-DSLEEF_BUILD_QUAD=ON \
-DSLEEF_BUILD_TESTS=OFF \
-DCMAKE_INSTALL_PREFIX=/usr/local
-S . -B build

cmake --build build -j --clean-first
cmake --install build
  1. Build and install numpy-quaddtype with TSan:
# SLEEF is already installed with TSan, we need to provide proper flags to numpy-quaddtype's meson file
# So that it does not build SLEEF again and use the installed one.

export CFLAGS="-fsanitize=thread -g -O0"
export CXXFLAGS="-fsanitize=thread -g -O0"
export LDFLAGS="-fsanitize=thread"
python -m pip install . -vv -Csetup-args=-Db_sanitize=thread

Building the documentation

The documentation for the numpy-quaddtype package is built using Sphinx. To build the documentation, follow these steps:

  1. Install the required dependencies:

    pip install ."[docs]"

  2. Navigate to the docs directory and build the documentation:

    cd docs/
make html

  3. The generated HTML documentation can be found in the _build/html directory within the docs folder. Open the index.html file in a web browser to view the documentation, or use a local server to serve the files:

    python3 -m http.server --directory _build/html

Serving the documentation

The documentation is automatically built and served using GitHub Pages. Every time changes are pushed to the main branch, the documentation is rebuilt and deployed to the gh-pages branch of the repository. You can access the documentation at:

https://numpy.org/numpy-quaddtype/

Check the .github/workflows/build_docs.yml file for details.

Development Tips

Cleaning the Build Directory

The subproject folders (subprojects/sleef, subprojects/qblas) are cloned as git repositories. To fully clean them, use double force:

git clean -ffxd

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