FlexModel: A Framework for Interpretability of Distributed Large Language Models

TL;DR

FlexModel is a software framework that simplifies interaction with distributed large language models, making interpretability and research more accessible for users without extensive distributed computing expertise.

Contribution

It introduces a user-friendly interface that integrates with existing distributed model tools, enabling easier interpretability and interaction with large-scale models.

Findings

Enhances accessibility for model interpretability in distributed settings

Bridges the gap between distributed and single-device model paradigms

Facilitates inclusive research in large-scale neural networks

Abstract

With the growth of large language models, now incorporating billions of parameters, the hardware prerequisites for their training and deployment have seen a corresponding increase. Although existing tools facilitate model parallelization and distributed training, deeper model interactions, crucial for interpretability and responsible AI techniques, still demand thorough knowledge of distributed computing. This often hinders contributions from researchers with machine learning expertise but limited distributed computing background. Addressing this challenge, we present FlexModel, a software package providing a streamlined interface for engaging with models distributed across multi-GPU and multi-node configurations. The library is compatible with existing model distribution libraries and encapsulates PyTorch models. It exposes user-registerable HookFunctions to facilitate straightforward interaction with distributed model internals, bridging the gap between distributed and single-device model paradigms. Primarily, FlexModel enhances accessibility by democratizing model interactions and promotes more inclusive research in the domain of large-scale neural networks. The package is found at https://github.com/VectorInstitute/flex_model.