babble: Learning Better Abstractions with E-Graphs and Anti-Unification

TL;DR

BABBLE leverages e-graphs and anti-unification to improve library learning by efficiently discovering reusable functions, achieving significant speedups and better handling of complex, syntactically varied inputs.

Contribution

Introduces LLMT, a novel library learning algorithm using e-graphs and anti-unification with equational theories, enabling scalable and robust program compression.

Findings

BABBLE achieves orders of magnitude faster compression than previous methods.

BABBLE learns reusable functions on more complex and varied inputs.

The approach effectively handles syntactic variations in program inputs.

Abstract

Library learning compresses a given corpus of programs by extracting common structure from the corpus into reusable library functions. Prior work on library learning suffers from two limitations that prevent it from scaling to larger, more complex inputs. First, it explores too many candidate library functions that are not useful for compression. Second, it is not robust to syntactic variation in the input. We propose library learning modulo theory (LLMT), a new library learning algorithm that additionally takes as input an equational theory for a given problem domain. LLMT uses e-graphs and equality saturation to compactly represent the space of programs equivalent modulo the theory, and uses a novel e-graph anti-unification technique to find common patterns in the corpus more directly and efficiently. We implemented LLMT in a tool named BABBLE. Our evaluation shows that BABBLE achieves better compression orders of magnitude faster than the state of the art. We also provide a qualitative evaluation showing that BABBLE learns reusable functions on inputs previously out of reach for library learning.