Grainger College of Engineering, All Events

View Full Calendar

CS COMPILER SEMINAR: WhiteFox: White-Box Compiler Fuzzing Empowered by Large Language Models

Event Type
Seminar/Symposium
Sponsor
CS Compilers Area
Location
Room 2406 (Siebel)
Date
Nov 4, 2024   4:30 - 5:30 pm  
Speaker
Chenyuan Yang
Views
8
Originating Calendar
Siebel School Speakers Calendar

CS COMPILER SEMINAR: Please join us Nov 4th, 2024, from 4:30 pm to 5:30pm in the Room 2406 (Siebel) where Chenyuan Yang will give a talk, “WhiteFox: White-Box Compiler Fuzzing Empowered by Large Language Models”.  Please see their abstract below:

Conference: Object-Oriented Programming, Systems, Languages, and Applications 2024 (OOPSLA 2024)

Author(s): Chenyuan Yang, Yinlin Deng, Runyu Lu, Jiayi Yao, Jiawei Liu, Reyhaneh Jabbarvand, Lingming Zhang

Abstract: Compiler correctness is crucial, as miscompilation can falsify program behaviors, leading to serious consequences. Fuzzing has been studied to uncover compiler defects. However, compiler fuzzing remains challenging: Existing arts focus on black- and grey-box fuzzing, which generates tests without sufficient understanding of internal compiler behaviors. Meanwhile, traditional white-box techniques, like symbolic execution, are computationally inapplicable to the giant codebase of compilers. Recent advances demonstrate that Large Language Models (LLMs) excel in code generation/understanding tasks. Nonetheless, guiding LLMs with compiler source-code information remains a missing piece of research in compiler testing. To this end, we propose WhiteFox, the first white-box compiler fuzzer using LLMs with source-code information to test compiler optimization, with a spotlight on detecting deep logic bugs in the deep learning (DL) compilers. WhiteFox adopts a multi-agent framework: an LLM-based analysis agent examines the low-level optimization source code and produces requirements on the high-level test programs that can trigger the optimization; an LLM-based generation agent produces test programs based on the summarized requirements. Additionally, optimization-triggering tests are used as feedback to enhance the generation on the fly. Our evaluation on the three most popular DL compilers (i.e., PyTorch Inductor, TensorFlow-XLA, and TensorFlow Lite) shows WhiteFox can generate high-quality test programs to exercise deep optimizations, practicing up to 8X more than state-of-the-art fuzzers. WhiteFox has found 101 bugs for the DL compilers, with 92 confirmed as previously unknown and 70 fixed. WhiteFox has been acknowledged by the PyTorch team and is being incorporated into its development workflow. Beyond DL compilers, WhiteFox can also be adapted for compilers in different domains.

link for robots only