Enabling Efficient Equivariant Operations in the Fourier Basis via Gaunt Tensor Products

Published: 16 Jan 2024, Last Modified: 14 Mar 2024ICLR 2024 spotlightEveryoneRevisionsBibTeX
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Keywords: Equivariant Operations; Tensor Product; Change of basis; Spherical Harmonics; Fourier Basis; equivariant neural networks
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TL;DR: We develop a method to change the tensor product formulation of equivariant operations in equivariant neural networks, providing improved accuracy and efficiency.
Abstract: Developing equivariant neural networks for the E(3) group plays an important role in modeling 3D data across real-world applications. Enforcing this equivariance primarily involves the tensor products of irreducible representations (irreps). However, the computational complexity of such operations increases significantly as higher-order tensors are used. In this work, we propose a systematic approach to substantially accelerate the computation of the tensor products of irreps. We mathematically connect the commonly used Clebsch-Gordan coefficients to the Gaunt coefficients, which are integrals of products of three spherical harmonics. Through Gaunt coefficients, the tensor product of irreps becomes equivalent to the multiplication between spherical functions represented by spherical harmonics. This perspective further allows us to change the basis for the equivariant operations from spherical harmonics to a 2D Fourier basis. Consequently, the multiplication between spherical functions represented by a 2D Fourier basis can be efficiently computed via the convolution theorem and Fast Fourier Transforms. This transformation reduces the complexity of full tensor products of irreps from $\mathcal{O}(L^6)$ to $\mathcal{O}(L^3)$, where $L$ is the max degree of irreps. Leveraging this approach, we introduce the Gaunt Tensor Product, which serves as a new method to construct efficient equivariant operations across different model architectures. Our experiments on the Open Catalyst Project and 3BPA datasets demonstrate both the increased efficiency and improved performance of our approach. The code and models will be made publicly available at https://github.com/lsj2408/Gaunt-Tensor-Product.
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Primary Area: learning on graphs and other geometries & topologies
Submission Number: 899