Top-Down Synthesis for Library Learning
Name
bowers-mlbowers-sm-eecs-2023-thesis.pdf
Description
Thesis PDF
Size
4.38 MB
Format
Adobe PDF
Checksum (MD5)
0ede9885b6f095491efaf33f8d461e2f
Author(s)
Bowers, Matthew L.
Advisor(s)
Solar-Lezama, Armando
Date Issued
June 2023
Publisher
Massachusetts Institute of Technology
Abstract
This thesis introduces corpus-guided top-down synthesis as a mechanism for synthesizing library functions that capture common functionality from a corpus of programs in a domain specific language (DSL). The algorithm builds abstractions directly from initial DSL primitives, using syntactic pattern matching of intermediate abstractions to intelligently prune the search space and guide the algorithm towards abstractions that maximally capture shared structures in the corpus. We present an implementation of the approach in a tool called Stitch and evaluate it against the state-of-the-art deductive library learning algorithm from DreamCoder. Our evaluation shows that Stitch is 3-4 orders of magnitude faster and uses 2 orders of magnitude less memory while maintaining comparable or better library quality (as measured by compressivity). We also demonstrate Stitch’s scalability on corpora containing hundreds of complex programs that are intractable with prior deductive approaches and show empirically that it is robust to terminating the search procedure early—further allowing it to scale to challenging datasets by means of early stopping. We publish the code, the documentation, a tutorial, and a Python library for interfacing with our for our Rust implementation of Stitch.
Tutorial & Documentation (Python Library): https://stitch-bindings.read thedocs.io/en/stable/intro/tutorial.html
Rust Implementation: https://github.com/mlb2251/stitch
Artifact (Awarded: Reusable): https://github.com/mlb2251/stitch-artifact
MIT Department
Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science
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