NNsight and NDIF: Democratizing Access to Foundation Model Internals

ICLR 2025 Conference Submission11061 Authors

Keywords: interpretability, safety, large language models, distributed inference, scalable infrastructure, deferred execution, computation graphs, resource sharing

TL;DR: We create a framework for separating experimental code and model runtime, enabling researchers to run experiments on very large models without local hosting.

Abstract:

We introduce NNsight and NDIF, technologies that work in tandem to enable scientific study of the representations and computations learned by very large neural networks. NNsight is an open-source system that extends PyTorch to introduce deferred remote execution. NDIF is a scalable inference service that executes NNsight requests, allowing users to share GPU resources and pretrained models. These technologies are enabled by the intervention graph, an architecture developed to decouple experiment design from model runtime. Together, this framework provides transparent and efficient access to the internals of foundation-size deep neural networks without imposing the cost or complexity of hosting customized models individually. We conduct a quantitative survey of the machine learning literature that reveals a growing gap in the study of the internals of large-scale AI. We demonstrate the design and use of our framework to address this gap by enabling a range of research methods on huge models. Finally, we conduct benchmarks to compare performance with previous approaches.

Code and documentation will be made available open-source.

Primary Area: infrastructure, software libraries, hardware, systems, etc.

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Submission Number: 11061