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arXiv · 2305.04684

ASDL: A Unified Interface for Gradient Preconditioning in PyTorch

Kazuki Osawa·Satoki Ishikawa·Rio Yokota·Shigang Li·Torsten Hoefler
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Abstract

Gradient preconditioning is a key technique to integrate the second-order information into gradients for improving and extending gradient-based learning algorithms. In deep learning, stochasticity, nonconvexity, and high dimensionality lead to a wide variety of gradient preconditioning methods, with implementation complexity and inconsistent performance and feasibility. We propose the Automatic Second-order Differentiation Library (ASDL), an extension library for PyTorch, which offers various implementations and a plug-and-play unified interface for gradient preconditioning. ASDL enables the study and structured comparison of a range of gradient preconditioning methods.

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Kazuki Osawa, Satoki Ishikawa, Rio Yokota, Shigang Li, Torsten Hoefler. 2023-05-08. ASDL: A Unified Interface for Gradient Preconditioning in PyTorch. https://arxiv.org/abs/2305.04684

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