TorchPfaffian: PyTorch-Based Pfaffian Computation

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Description

TorchPfaffian is a Python package for efficiently computing the Pfaffian of skew-symmetric matrices using PyTorch. Designed as a PyTorch-based alternative to pfapack, it enables GPU acceleration and supports automatic differentiation, making it particularly useful in physics, quantum computing, and machine learning applications.

Features:

  • Efficient Pfaffian computation for skew-symmetric matrices
  • GPU acceleration via PyTorch
  • Support for automatic differentiation
  • Seamless integration with PyTorch tensors

Installation

With python and pip installed, run the following commands to install TorchPfaffian:

pip install torchpfaffian

For development, this project uses uv. Clone the repository and set up the environment with:

uv sync --dev --extra cpu

Use the cu128 or cu130 extra instead of cpu to install a CUDA-enabled build of PyTorch. See .github/CONTRIBUTING.md for the full contribution workflow.

Native acceleration (optional)

A Rust-accelerated signed-Pfaffian strategy (RustPfaffianParlettReid) is available when the package is built with its native extension. Building from source requires a Rust toolchain (https://rustup.rs); the project builds with maturin:

uv run maturin develop --release -m rust/Cargo.toml

Use --release for an optimized build: maturin develop compiles in debug mode by default, which makes the Rust kernel much slower. Installing a prebuilt wheel (or maturin build) is already optimized, so this only matters for local development builds.

If the native extension is not present, the package still works using the pure-Python strategies.

Usage

import torch

from torch_pfaffian import pfaffian

# Any skew-symmetric matrix of shape (..., 2n, 2n).
matrix = torch.tensor([[0.0, -3.0], [3.0, 0.0]])

pf = pfaffian(matrix)                 # signed Pfaffian (default)
magnitude = pfaffian(matrix, sign=False)  # |pf|, using the faster det-based path

pfaffian() selects a strategy from the input: sign=True (the default) returns the signed Pfaffian, using the native RustPfaffianParlettReid when the extension is built and falling back to the pure-Python PfaffianParlettReid otherwise; sign=False returns the magnitude using a determinant-based strategy (PfaffianFDBPf when gradients are needed, otherwise PfaffianDet). For explicit strategy selection, use get_pfaffian_function(name).

Important Links

Found a bug or have a feature request?

License

Apache License 2.0

Acknowledgements

Citation

Repository:

@misc{torchpfaffian_Gince2025,
  title={Torch Pfaffian},
  author={Jérémie Gince},
  year={2025},
  publisher={Université de Sherbrooke},
  url={https://github.com/MatchCake/TorchPfaffian},
}

Project links

Data verified by PyPI on Jul 13, 2026
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Avatar for Gince from gravatar.com Gince

Credits

Author: Jérémie Gince

GitHub Statistics

Data verified by PyPI on Jul 13, 2026
The GitHub source repository was provided by the project maintainers and verified by PyPI at the time of upload. Stars, forks, and open issues/PRs are derived from that repository and have not been independently verified.

License

Apache Software License

Requires

Python <3.15, >=3.10

Provides Extra

cpu cu128 cu130

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