Compiling Stan to generative probabilistic languages and extension to deep probabilistic programming
| Autor: | Guillaume Baudart, Javier Burroni, Martin Hirzel, Louis Mandel, Avraham Shinnar |
|---|---|
| Přispěvatelé: | Parallélisme de Kahn Synchrone ( Parkas), Département d'informatique - ENS Paris (DI-ENS), École normale supérieure - Paris (ENS-PSL), Université Paris sciences et lettres (PSL)-Université Paris sciences et lettres (PSL)-Institut National de Recherche en Informatique et en Automatique (Inria)-Centre National de la Recherche Scientifique (CNRS)-École normale supérieure - Paris (ENS-PSL), Université Paris sciences et lettres (PSL)-Université Paris sciences et lettres (PSL)-Institut National de Recherche en Informatique et en Automatique (Inria)-Centre National de la Recherche Scientifique (CNRS)-Centre National de la Recherche Scientifique (CNRS)-Inria de Paris, Institut National de Recherche en Informatique et en Automatique (Inria), University of Massachusetts [Amherst] (UMass Amherst), University of Massachusetts System (UMASS), IBM T. J. Watson Research Centre, Centre National de la Recherche Scientifique (CNRS)-Institut National de Recherche en Informatique et en Automatique (Inria)-École normale supérieure - Paris (ENS Paris), Université Paris sciences et lettres (PSL)-Université Paris sciences et lettres (PSL)-Centre National de la Recherche Scientifique (CNRS)-Institut National de Recherche en Informatique et en Automatique (Inria)-École normale supérieure - Paris (ENS Paris), Université Paris sciences et lettres (PSL)-Université Paris sciences et lettres (PSL)-Centre National de la Recherche Scientifique (CNRS)-Inria de Paris, Département d'informatique de l'École normale supérieure (DI-ENS), École normale supérieure - Paris (ENS Paris), Université Paris sciences et lettres (PSL)-Université Paris sciences et lettres (PSL)-Institut National de Recherche en Informatique et en Automatique (Inria)-Centre National de la Recherche Scientifique (CNRS)-École normale supérieure - Paris (ENS Paris) |
| Jazyk: | angličtina |
| Rok vydání: | 2021 |
| Předmět: |
Scheme (programming language)
FOS: Computer and information sciences Computer Science - Machine Learning Correctness Semantics (computer science) Computer science Computer Science - Artificial Intelligence Theory of computation → Probabilistic computation Probabilistic programming Machine Learning (stat.ML) [INFO.INFO-SE]Computer Science [cs]/Software Engineering [cs.SE] computer.software_genre 01 natural sciences Stan Machine Learning (cs.LG) 010104 statistics & probability CCS Concepts Statistics - Machine Learning 0502 economics and business Pyro 0101 mathematics Probabilistic programming language Software and its engineering → Compilers 050205 econometrics computer.programming_language Computer Science - Programming Languages [INFO.INFO-PL]Computer Science [cs]/Programming Languages [cs.PL] Syntax (programming languages) Programming language 05 social sciences Probabilistic logic Semantics Constructed language Artificial Intelligence (cs.AI) [INFO.INFO-ES]Computer Science [cs]/Embedded Systems Compiler computer Programming Languages (cs.PL) |
| Zdroj: | PLDI '21-42nd ACM SIGPLAN International Conference on Programming Language Design and Implementation PLDI '21-42nd ACM SIGPLAN International Conference on Programming Language Design and Implementation, Jun 2021, Virtual, Canada. pp.497-510, ⟨10.1145/3453483.3454058⟩ PLDI |
| Popis: | International audience; Stan is a probabilistic programming language that is popular in the statistics community, with a high-level syntax for expressing probabilistic models. Stan differs by nature from generative probabilistic programming languages like Church, Anglican, or Pyro. This paper presents a comprehensive compilation scheme to compile any Stan model to a generative language and proves its correctness. We use our compilation scheme to build two new backends for the Stanc3 compiler targeting Pyro and NumPyro. Experimental results show that the NumPyro backend yields a 2.3x speedup compared to Stan in geometric mean over 26 benchmarks. Building on Pyro we extend Stan with support for explicit variational inference guides and deep probabilistic models. That way, users familiar with Stan get access to new features without having to learn a fundamentally new language. |
| Databáze: | OpenAIRE |
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