DimWit – Tensor programming with wit
Session Abstract
We present DimWit, a system for writing readable numerical and machine learning code. DimWit’s core feature is a trait-based implementation of the named tensor notation, which improves readability and type-safety compared to standard tensors. We illustrate how to write machine learning models using DimWit, and discuss our experience training GPT-2.
Session Description
In this session, we motivate why typed tensor computations are needed in machine learning and how named tensor notation helps us to write readable and type-safe code. We discuss the ongoing efforts in the Python community and argue that Scala already today can deliver what many Python library authors would like Python to support eventually. We present DimWit, a Scala library that implements named tensor notation as its core abstraction. We show how DimWit, which is built on top of Python’s JAX, can achieve efficiency on par with state-of-the-art libraries. We will also discuss our efforts to train GPT-2 (a 124M parameters) from scratch in Scala. We hope that this talk will show that Scala can be a better alternative to Python for expressing numerical computation and writing programs that are easier to reason about.
Repository: https://github.com/dimwit-dev/dimwit
Lightning Talk
Benjamin Meyer
ZHAW (Zürcher Hochschule für Angewandte Wissenschaften) at the Center for Artificial Intelligence (CAI)
