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Documents authored by Rothkopf, Raven

Document
DOI: 10.4230/LIPIcs.ECOOP.2024.15
Rose: Composable Autodiff for the Interactive Web

Authors: Sam Estep, Wode Ni, Raven Rothkopf, and Joshua Sunshine

Published in: LIPIcs, Volume 313, 38th European Conference on Object-Oriented Programming (ECOOP 2024)

Abstract
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.
Cite as

Sam Estep, Wode Ni, Raven Rothkopf, and Joshua Sunshine. Rose: Composable Autodiff for the Interactive Web. In 38th European Conference on Object-Oriented Programming (ECOOP 2024). Leibniz International Proceedings in Informatics (LIPIcs), Volume 313, pp. 15:1-15:27, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2024)

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@InProceedings{estep_et_al:LIPIcs.ECOOP.2024.15,
  author =	{Estep, Sam and Ni, Wode and Rothkopf, Raven and Sunshine, Joshua},
  title =	{{Rose: Composable Autodiff for the Interactive Web}},
  booktitle =	{38th European Conference on Object-Oriented Programming (ECOOP 2024)},
  pages =	{15:1--15:27},
  series =	{Leibniz International Proceedings in Informatics (LIPIcs)},
  ISBN =	{978-3-95977-341-6},
  ISSN =	{1868-8969},
  year =	{2024},
  volume =	{313},
  editor =	{Aldrich, Jonathan and Salvaneschi, Guido},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/entities/document/10.4230/LIPIcs.ECOOP.2024.15},
  URN =		{urn:nbn:de:0030-drops-208642},
  doi =		{10.4230/LIPIcs.ECOOP.2024.15},
  annote =	{Keywords: Automatic differentiation, differentiable programming, compilers, web}
}
Document
Artifact
DOI: 10.4230/DARTS.10.2.7
Rose: Composable Autodiff for the Interactive Web (Artifact)

Authors: Sam Estep, Wode Ni, Raven Rothkopf, and Joshua Sunshine

Published in: DARTS, Volume 10, Issue 2, Special Issue of the 38th European Conference on Object-Oriented Programming (ECOOP 2024)

Abstract
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 document accompanies the research paper introducing 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.
Cite as

Sam Estep, Wode Ni, Raven Rothkopf, and Joshua Sunshine. Rose: Composable Autodiff for the Interactive Web (Artifact). In Special Issue of the 38th European Conference on Object-Oriented Programming (ECOOP 2024). Dagstuhl Artifacts Series (DARTS), Volume 10, Issue 2, pp. 7:1-7:4, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2024)

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@Article{estep_et_al:DARTS.10.2.7,
  author =	{Estep, Sam and Ni, Wode and Rothkopf, Raven and Sunshine, Joshua},
  title =	{{Rose: Composable Autodiff for the Interactive Web (Artifact)}},
  pages =	{7:1--7:4},
  journal =	{Dagstuhl Artifacts Series},
  ISBN =	{978-3-95977-342-3},
  ISSN =	{2509-8195},
  year =	{2024},
  volume =	{10},
  number =	{2},
  editor =	{Estep, Sam and Ni, Wode and Rothkopf, Raven and Sunshine, Joshua},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/entities/document/10.4230/DARTS.10.2.7},
  URN =		{urn:nbn:de:0030-drops-209053},
  doi =		{10.4230/DARTS.10.2.7},
  annote =	{Keywords: Automatic differentiation, differentiable programming, compilers, web}
}
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