Year: 2024
Type: conference-paper
Abstract: This study presents an in-depth manual analysis of memory-related bugs within the PyTorch deep learning framework, leveraging a filtered dataset of 1,678 closed issues from the official PyTorch GitHub repository. The selected issues span a three-year period from January 1, 2020, to March 23, 2023, allowing for a comprehensive examination of trends, patterns, and solutions. This study aims to understand the correlations between the characteristics of PyTorch bugs and also the composition of the root causes behind memory bugs. The findings reveal that Correctness and Runtime Error bugs occur most frequently, with a lack of a correlation between Affected Components and Bug Symptoms. Our results highlight the need for more integrated inter-component debugging tools. Furthermore, the findings show that indexing errors occur most frequently among memory bugs. We determine that, to address the severe impact of such memory bugs, there exists a need for more comprehensive and redundant test cases. Through this analysis, this work aims to provide actionable insights for developers to improve the robustness of PyTorch, improving its reliability in machine learning applications.
Authors: Brian Yu, Rubayet Rahman Rongon, Chen Cao, Xuechen Zhang
Language: English
Subfield: Software
Field: Computer Science
Domain: Physical Sciences
Open Access status: green
Funder: National Science Foundation
Awards: Collaborative Research: REU Site: Advancing Data-Driven Deep Coupling of Computational Simulations and Experiments, Research Infrastructure: MRI: Acquisition of a GPU Cluster to Advance the Land Grant Mission at Washington State University Using AI-Driven Research, CRII: CSR: Understanding and Enhancing Unsafe Rust Code in Resource-constrained Systems
OpenAlex ID: W4406460689