Flashlight: Enabling Innovation in Tools for Machine Learning

Jacob Kahn, Vineel Pratap, Tatiana Likhomanenko, Qiantong Xu, Awni Hannun, Jeff Cai, Paden Tomasello, Ann Lee, Edouard Grave, Gilad Avidov, Benoit Steiner, Vitaliy Liptchinsky, Gabriel Synnaeve, Ronan Collobert

Keywords: machine learning, deep learning, systems, frameworks, autograd library, tensor library

Abstract: As the computational requirements for machine learning systems and the size and complexity of machine learning frameworks increases, essential framework innovation has become challenging. While computational needs have driven recent compiler, networking, and hardware advancements, utilization of those advancements by machine learning tools is occurring at a slower pace. This is in part due to the difficulties involved in prototyping new computational paradigms with existing frameworks. Large frameworks prioritize machine learning researchers and practitioners as end users and pay comparatively little attention to systems researchers who can push frameworks forward --- we argue that both are equally-important stakeholders. We introduce Flashlight, an open source library built to spur innovation in machine learning tools and systems by prioritizing open, modular, customizable internals and state-of-the-art, research-ready models and training setups across a variety of domains. Flashlight enables systems researchers to rapidly prototype and experiment with novel ideas in machine learning computation and has low overhead, competing with and often outperforming other popular machine learning frameworks. We see Flashlight as a tool enabling research that can benefit widely-used libraries downstream and bring machine learning and systems researchers closer together.

One-sentence Summary: We introduce Flashlight, a lightweight machine learning library built for internal customizability and framework research.

Community Implementations: [ 6 code implementations](https://www.catalyzex.com/paper/arxiv:2201.12465/code)