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Documents authored by Ni, Wode

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}
}
Document
DOI: 10.4230/OASIcs.PLATEAU.2019.4
Designing Declarative Language Tutorials: A Guided and Individualized Approach

Authors: Anael Kuperwajs Cohen, Wode Ni, and Joshua Sunshine

Published in: OASIcs, Volume 76, 10th Workshop on Evaluation and Usability of Programming Languages and Tools (PLATEAU 2019)

Abstract
The ability to declare what a program should include rather than how these features should be implemented makes declarative languages very useful in many visual output programs. The wide-ranging uses of these programs, in domains ranging from architecture to web programming to data visualization, encourages us to find an effective method to teach them. Traditional tutorial systems are usually non-interactive and have a gap between the learning and application. This can leave the user frustrated without a way to move forward in the learning process. A general lack of guidance can lead the student down an incorrect path. To prevent these difficulties, we propose a guided tour followed by novel question types that both direct the student’s learning and creates a focused environment to practice individual skills. Lastly, we propose a study to test the hypothesis that this tutorial is quicker to complete and results in a greater understanding of the declarative language.
Cite as

Anael Kuperwajs Cohen, Wode Ni, and Joshua Sunshine. Designing Declarative Language Tutorials: A Guided and Individualized Approach. In 10th Workshop on Evaluation and Usability of Programming Languages and Tools (PLATEAU 2019). Open Access Series in Informatics (OASIcs), Volume 76, pp. 4:1-4:6, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2020)

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@InProceedings{cohen_et_al:OASIcs.PLATEAU.2019.4,
  author =	{Cohen, Anael Kuperwajs and Ni, Wode and Sunshine, Joshua},
  title =	{{Designing Declarative Language Tutorials: A Guided and Individualized Approach}},
  booktitle =	{10th Workshop on Evaluation and Usability of Programming Languages and Tools (PLATEAU 2019)},
  pages =	{4:1--4:6},
  series =	{Open Access Series in Informatics (OASIcs)},
  ISBN =	{978-3-95977-135-1},
  ISSN =	{2190-6807},
  year =	{2020},
  volume =	{76},
  editor =	{Chasins, Sarah and Glassman, Elena L. and Sunshine, Joshua},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/entities/document/10.4230/OASIcs.PLATEAU.2019.4},
  URN =		{urn:nbn:de:0030-drops-119589},
  doi =		{10.4230/OASIcs.PLATEAU.2019.4},
  annote =	{Keywords: Declarative Programming, Programming Language Tutorial, Visualizations}
}
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