Self-Satisfied: An end-to-end framework for SAT generation and prediction

TL;DR

This paper introduces SaT, a transformer-based framework for SAT problem generation and prediction, leveraging hardware acceleration and geometric encoding to handle large-scale problems effectively.

Contribution

It presents a novel end-to-end approach combining hardware-accelerated SAT generation, geometric encoding, and head slicing for transformers, enabling scalable SAT prediction on large datasets.

Findings

Achieved high prediction accuracy on large SAT problems from SATComp 2022.

Demonstrated the effectiveness of geometric encoding and head slicing in scaling transformer models.

Validated the approach's ability to handle problems with thousands of variables and tens of thousands of clauses.

Abstract

The boolean satisfiability (SAT) problem asks whether there exists an assignment of boolean values to the variables of an arbitrary boolean formula making the formula evaluate to True. It is well-known that all NP-problems can be coded as SAT problems and therefore SAT is important both practically and theoretically. From both of these perspectives, better understanding the patterns and structure implicit in SAT data is of significant value. In this paper, we describe several advances that we believe will help open the door to such understanding: we introduce hardware accelerated algorithms for fast SAT problem generation, a geometric SAT encoding that enables the use of transformer architectures typically applied to vision tasks, and a simple yet effective technique we term head slicing for reducing sequence length representation inside transformer architectures. These advances allow us to scale our approach to SAT problems with thousands of variables and tens of thousands of clauses. We validate our architecture, termed Satisfiability Transformer (SaT), on the SAT prediction task with data from the SAT Competition (SATComp) 2022 problem sets. Prior related work either leveraged a pure machine learning approach, but could not handle SATComp-sized problems, or was hybrid in the sense of integrating a machine learning component in a standard SAT solving tool. Our pure machine learning approach achieves prediction accuracies comparable to recent work, but on problems that are an order of magnitude larger than previously demonstrated. A fundamental aspect of our work concerns the very nature of SAT data and its suitability for training machine learning models. We both describe experimental results that probe the landscape of where SAT data can be successfully used for learning and position these results within the broader context of complexity and learning.