quartic-bspline-extension 0.3.1rc1

Quartic B-Spline extension: CPU and CUDA extension for quartic bspline based potential functions.

B-Spline extension

This package implements quartic (midpoint cardinal) b-spline potential functions. By such a potential function we mean a parameter-dependent function $\rho(\cdot, \gamma)$ with

$$ \rho(x, \gamma) = \sum_{\nu = 1}^{N}\gamma_{\nu}M_{4}(\frac{x - t_{\nu}}{s}), $$

where $\lbrace t_\nu\rbrace_{\nu=1}^N$ is an equidistant partition of the Interval $I=[a, b]$, $s>0$ is a scaling parameter and $M_4$ refers to the central quartic midpoint cardinal b-spline (see [1]).

Table of contents

• Features
• Installation
• Usage
• Contributing
• Citation
• References
• Acknowledgements
• License
• References

Features

• CUDA and CPU kernels for forward and backward step
• Custom autograd functions based on these kernels to compute in particular the gradients of the potential w.r.t. to input and parameters.
• Feature-wise implementation:
  • The potential function is designed for input tensors of shape [bs, f, w, h].
  • For a weight tensor of shape [f, N], the potential w.r.t. to the weights [$\nu$, :] is applied to $f$-th channel of the input tensor.

Build

For both building processes a (virtual) Python environment is required.

CUDA extension

Within the Python environment install the packages torch and setuptools. Then, to build/compile the CUDA extension, from the top directory of this repository execute

    python setup.py install

Alternatively call make install.

Python wheel

Activate the Python environment and install the packages setuptools, build and wheel. Then from the top directory of this repository run

    python -m build --wheel --outdir artefacts

Alternatively call make build.

The generate Python wheel is stored in subdirectory artefacts.

Installation

To install the package as a Python wheel simply run

    pip install bspline_cuda_extension-0.2.0-cp311-cp311-linux_x86_64.whl

Usage

from quartic_bspline_extension.functions import QuarticBSplineFunction

box_lower = -3.0
box_upper = 3.0
num_centers = 77
centers = torch.linspace(box_lower, box_upper, num_centers)
weights_1 = torch.log(1 + centers ** 2)
weights_2 = torch.abs(centers)
weights = torch.stack([weights_1, weights_2], dim=0)
scale = (box_upper - box_lower) / (num_centers - 1)

f = 2
t = torch.stack([torch.linspace(box_lower, box_upper, 111)
                    for _ in range(0, f)]).unsqueeze(dim=1).unsqueeze(dim=0)

centers = centers.to(device=device, dtype=dtype)
weights = weights.to(device=device, dtype=dtype)
t = t.to(device=device, dtype=dtype)
t.requires_grad_(True)

y, _ = QuarticBSplineFunction.apply(t, weights, centers, scale)
dy_dt = torch.autograd.grad(inputs=t, outputs=torch.sum(y))[0]

Contributing

1. Fork the repository
2. Create a feature branch
3. Submit a pull request

References

[1] Schoenberg, Isaac J, 1973. Cardinal spline interpolation. SIAM.

License

MIT License