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SHF: Small: Practical Analyses and Safe Transformations for Imperative Deep Learning Programs

SHF：小型：命令式深度学习程序的实用分析和安全转换

基本信息

批准号：
2200343
负责人：
Raffi Khatchadourian
金额：
$ 60万
依托单位：
CUNY Hunter College
依托单位国家：
美国
项目类别：
Standard Grant
财政年份：
2022
资助国家：
美国
起止时间：
2022-06-01 至 2025-05-31
项目状态：
未结题

来源：
https://www.nsf.gov/awardsearch/showAward?AWD_ID=2200343&HistoricalAwards=false
关键词：
SHF Small Practical Analyses Safe

项目摘要

Learning often occurs by pattern recognition. Software systems learn by using algorithms to recognize patterns and draw inferences from existing data and apply the inferences to previously unseen data. Deep Learning (DL) is a kind of machine-learning algorithm inspired by neural networks of human brains. A DL model learns decision logic from a large set of examples. A classic application is image processing, where a model may learn to recognize particular images through training with many sample images. While software systems that incorporate DL models involve large amounts of data, they still have to be efficient and responsive. This project is expected to increase DL system robustness, reliability, and scalability, positively impacting computer vision, autonomous driving, medicine, and extremism identification. Tools developed as a result of this project are also expected to democratize the Artificial Intelligence workforce, as they will assist data scientists and software engineers of varying proficiencies in writing quality DL code. Such tools can potentially contribute to a diverse, globally competitive STEM workforce and increase US economic competitiveness. This project will also promote software engineering concepts in machine learning by augmenting and creating several undergraduate and graduate courses. Dissemination will occur through publicly distributing datasets, papers, open-source software, and Open Educational Resources.DL frameworks increasingly make various tradeoffs to balance the often competing requirements of reliability, usability, and generality. Popular DL frameworks have historically embraced graph-based, deferred execution-style (low-level) Application Programming Interfaces (APIs). While efficient, (legacy) systems using such interfaces are cumbersome, error-prone, and difficult to debug, maintain, and port. Contrarily, (modern) eager execution-style DL APIs facilitate higher-level, imperative, and Object-Oriented (Python) programs that are easier to debug, less error-prone, and more extensible have consequently emerged at the expense of run-time performance. Though hybrid approaches aim to bridge the two paradigms, they necessitate a non-trivial amount of technical metadata and exhibit several limitations and known issues on the use of native program constructs. This project is expected to contribute practical analyses and safe transformations for modern imperative and Object-Oriented DL programs that markedly improve their reliability and scalability. First, various software engineering artifacts will be mined for bug fixes, (manual) refactorings (semantics-preserving source-to-source program transformations), and missed opportunities in efficiently executing imperative DL code. Then, novel analyses and refactorings for automatically (i) migrating legacy, deferred execution-style DL code to more robust imperative DL code and (ii) specifying how their otherwise eagerly-executed imperative DL code should be reliably and efficiently executed as graphs at run-time will be formulated. Finally, novel analyses for detecting performance bottlenecks and semantic errors associated with graph-based execution of imperative, otherwise eagerly-executed DL code will be designed. This contribution is significant because it fills the void of techniques, methodologies, and tools for effectively developing---and evolving long-lived---trustworthy and efficient DL systems that pervasively use imperative and Object-Oriented DL programming.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

学习通常是通过模式识别来进行的。软件系统通过使用算法来识别模式并从现有数据中得出推论并将推论应用于以前未见过的数据来进行学习。深度学习（DL）是一种受人脑神经网络启发的机器学习算法。深度学习模型从大量示例中学习决策逻辑。一个经典的应用是图像处理，其中模型可以通过使用许多样本图像进行训练来学习识别特定图像。虽然包含深度学习模型的软件系统涉及大量数据，但它们仍然必须高效且响应迅速。该项目预计将提高深度学习系统的稳健性、可靠性和可扩展性，对计算机视觉、自动驾驶、医学和极端主义识别产生积极影响。该项目开发的工具也有望使人工智能劳动力民主化，因为它们将协助不同熟练程度的数据科学家和软件工程师编写高质量的深度学习代码。这些工具有可能有助于培养多元化、具有全球竞争力的 STEM 劳动力，并提高美国的经济竞争力。该项目还将通过增强和创建多个本科生和研究生课程来推广机器学习中的软件工程概念。传播将通过公开分发数据集、论文、开源软件和开放教育资源来进行。DL 框架越来越多地进行各种权衡，以平衡可靠性、可用性和通用性方面经常相互竞争的要求。流行的深度学习框架历来都采用基于图的延迟执行式（低级）应用程序编程接口（API）。虽然使用此类接口的（传统）系统高效，但它们很麻烦、容易出错，并且难以调试、维护和移植。相反，（现代）急切执行式的 DL API 促进了更高级别、命令式和面向对象 (Python) 程序，这些程序更易于调试、不易出错且更具可扩展性，但以牺牲运行时性能为代价。尽管混合方法旨在弥合这两种范式，但它们需要大量的技术元数据，并且在使用本机程序构造时表现出一些限制和已知问题。该项目预计将为现代命令式和面向对象的 DL 程序提供实用的分析和安全转换，从而显着提高其可靠性和可扩展性。首先，将挖掘各种软件工程工件来修复错误、（手动）重构（保留语义的源到源程序转换），并错过有效执行命令式深度学习代码的机会。然后，将制定新颖的分析和重构，以自动 (i) 将遗留的延迟执行式 DL 代码迁移到更健壮的命令式 DL 代码，以及 (ii) 指定如何在运行时将急切执行的命令式 DL 代码可靠且高效地执行为图形。最后，将设计新颖的分析来检测与基于图形的命令式执行（否则急切执行的深度学习代码）相关的性能瓶颈和语义错误。这一贡献意义重大，因为它填补了技术、方法论和工具的空白，用于有效开发和发展持久的、值得信赖和高效的深度学习系统，这些系统普遍使用命令式和面向对象的深度学习编程。该奖项反映了 NSF 的法定使命，并通过使用基金会的智力价值和更广泛的影响审查标准进行评估，被认为值得支持。

项目成果

期刊论文数量（5）

专著数量（0）

科研奖励数量（0）

会议论文数量（0）

专利数量（0）

Challenges in Migrating Imperative Deep Learning Programs to Graph Execution: An Empirical Study

DOI：
10.1145/3524842.3528455
发表时间：
2022-01
期刊：
2022 IEEE/ACM 19th International Conference on Mining Software Repositories (MSR)
影响因子：
0
作者：
Tatiana Castro V'elez;Raffi Khatchadourian;M. Bagherzadeh;A. Raja
通讯作者：
Tatiana Castro V'elez;Raffi Khatchadourian;M. Bagherzadeh;A. Raja

How many mutex bugs can a simple analysis find in Go programs?

简单分析一下Go程序中可以发现多少互斥量bug？

DOI：
发表时间：
2022
期刊：
Annual Conference of the Japanese Society for Software Science and Technology
影响因子：
0
作者：
Fumi Takeuchi. Hidehiko Masuhara. Raffi Khatchadourian, Youyou Cong
通讯作者：
Fumi Takeuchi. Hidehiko Masuhara. Raffi Khatchadourian, Youyou Cong

A Tool for Rejuvenating Feature Logging Levels via Git Histories and Degree of Interest

通过 Git 历史记录和兴趣程度恢复功能日志级别的工具