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Rose: Composable Autodiff for the Interactive Web

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Title: Rose: Composable Autodiff for the Interactive Web
Authors: Estep, Sam, Ni, Wode, Rothkopf, Raven, Sunshine, Joshua
Contributors: Sam Estep and Wode Ni and Raven Rothkopf and Joshua Sunshine
Publisher Information: Schloss Dagstuhl – Leibniz-Zentrum für Informatik
Publication Year: 2024
Collection: DROPS - Dagstuhl Research Online Publication Server (Schloss Dagstuhl - Leibniz Center for Informatics )
Subject Terms: Automatic differentiation, differentiable programming, compilers, web
Description: Reverse-mode automatic differentiation (autodiff) has been popularized by deep learning, but its ability to compute gradients is also valuable for interactive use cases such as bidirectional computer-aided design, embedded physics simulations, visualizing causal inference, and more. Unfortunately, the web is ill-served by existing autodiff frameworks, which use autodiff strategies that perform poorly on dynamic scalar programs, and pull in heavy dependencies that would result in unacceptable webpage sizes. This work introduces Rose, a lightweight autodiff framework for the web using a new hybrid approach to reverse-mode autodiff, blending conventional tracing and transformation techniques in a way that uses the host language for metaprogramming while also allowing the programmer to explicitly define reusable functions that comprise a larger differentiable computation. We demonstrate the value of the Rose design by porting two differentiable physics simulations, and evaluate its performance on an optimization-based diagramming application, showing Rose outperforming the state-of-the-art in web-based autodiff by multiple orders of magnitude.
Document Type: article in journal/newspaper
conference object
File Description: application/pdf
Language: English
Relation: Is Part Of LIPIcs, Volume 313, 38th European Conference on Object-Oriented Programming (ECOOP 2024); https://drops.dagstuhl.de/entities/document/10.4230/LIPIcs.ECOOP.2024.15
DOI: 10.4230/LIPIcs.ECOOP.2024.15
Availability: https://doi.org/10.4230/LIPIcs.ECOOP.2024.15
https://nbn-resolving.org/urn:nbn:de:0030-drops-208642
https://drops.dagstuhl.de/entities/document/10.4230/LIPIcs.ECOOP.2024.15
Rights: https://creativecommons.org/licenses/by/4.0/legalcode
Accession Number: edsbas.4686845B
Database: BASE
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  Data: Rose: Composable Autodiff for the Interactive Web
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  Data: <searchLink fieldCode="AR" term="%22Estep%2C+Sam%22">Estep, Sam</searchLink><br /><searchLink fieldCode="AR" term="%22Ni%2C+Wode%22">Ni, Wode</searchLink><br /><searchLink fieldCode="AR" term="%22Rothkopf%2C+Raven%22">Rothkopf, Raven</searchLink><br /><searchLink fieldCode="AR" term="%22Sunshine%2C+Joshua%22">Sunshine, Joshua</searchLink>
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  Data: Sam Estep and Wode Ni and Raven Rothkopf and Joshua Sunshine
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  Data: Schloss Dagstuhl – Leibniz-Zentrum für Informatik
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  Data: Reverse-mode automatic differentiation (autodiff) has been popularized by deep learning, but its ability to compute gradients is also valuable for interactive use cases such as bidirectional computer-aided design, embedded physics simulations, visualizing causal inference, and more. Unfortunately, the web is ill-served by existing autodiff frameworks, which use autodiff strategies that perform poorly on dynamic scalar programs, and pull in heavy dependencies that would result in unacceptable webpage sizes. This work introduces Rose, a lightweight autodiff framework for the web using a new hybrid approach to reverse-mode autodiff, blending conventional tracing and transformation techniques in a way that uses the host language for metaprogramming while also allowing the programmer to explicitly define reusable functions that comprise a larger differentiable computation. We demonstrate the value of the Rose design by porting two differentiable physics simulations, and evaluate its performance on an optimization-based diagramming application, showing Rose outperforming the state-of-the-art in web-based autodiff by multiple orders of magnitude.
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  Data: https://doi.org/10.4230/LIPIcs.ECOOP.2024.15<br />https://nbn-resolving.org/urn:nbn:de:0030-drops-208642<br />https://drops.dagstuhl.de/entities/document/10.4230/LIPIcs.ECOOP.2024.15
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