Suspension Analysis and Selective Continuation-Passing Style for Universal Probabilistic Programming Languages

Lundén, Daniel; Hummelgren, Lars; Kudlicka, Jan; Eriksson, Oscar; Broman, David

doi:10.1007/978-3-031-57267-8_12

Cited by 2 publications

References 38 publications

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Suspension Analysis and Selective Continuation-Passing Style for Universal Probabilistic Programming Languages

Lundén,

Hummelgren,

Kudlicka

et al. 2024

Lecture Notes in Computer Science

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Universal probabilistic programming languages (PPLs) make it relatively easy to encode and automatically solve statistical inference problems. To solve inference problems, PPL implementations often apply Monte Carlo inference algorithms that rely on execution suspension. State-of-the-art solutions enable execution suspension either through (i) continuation-passing style (CPS) transformations or (ii) efficient, but comparatively complex, low-level solutions that are often not available in high-level languages. CPS transformations introduce overhead due to unnecessary closure allocations—a problem the PPL community has generally overlooked. To reduce overhead, we develop a new efficient selective CPS approach for PPLs. Specifically, we design a novel static suspension analysis technique that determines parts of programs that require suspension, given a particular inference algorithm. The analysis allows selectively CPS transforming the program only where necessary. We formally prove the correctness of the analysis and implement the analysis and transformation in the Miking CorePPL compiler. We evaluate the implementation for a large number of Monte Carlo inference algorithms on real-world models from phylogenetics, epidemiology, and topic modeling. The evaluation results demonstrate significant improvements across all models and inference algorithms.

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Suspension Analysis and Selective Continuation-Passing Style for Universal Probabilistic Programming Languages

Lundén,

Hummelgren,

Kudlicka

et al. 2024

Lecture Notes in Computer Science

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TreePPL: A Universal Probabilistic Programming Language for Phylogenetics

Senderov,

Kudlicka,

Lundén

et al. 2023

Preprint

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We present TreePPL, a language for probabilistic modeling and inference in statistical phylogenetics. Specifically, TreePPL is a domain-specific universal probabilistic programming language (PPL), particularly designed for describing phylogenetic models. The core idea is to express the model as a computer program, which estimates the posterior probability distribution of interest when executed sufficiently many times. The program uses two special probabilistic constructs:assumestatements, which describe latent random variables in the model, andobservestatements, which condition random variables in the model on observed data. Theassumeandobservestatements make it possible for generic inference algorithms, such as sequential Monte Carlo and Markov chain Monte Carlo algorithms, to identify checkpoints that enable them to generate and manipulate simulations from the posterior probability distribution. This means that a user can focus on describing the model, and leave the estimation of the posterior probability distribution to TreePPL’s inference machinery. The TreePPL modeling language is inspired by R, Python, and the functional programming language OCaml. The model script can be conveniently run from a Python environment (an R environment is work in progress), which can be used for pre-processing, feeding the model with the observed data, controlling and running the inference, and receiving and post-processing the output data. The inference machinery is generated by a compiler framework developed specifically for supporting domain-specific modeling and inference, the Miking CorePPL framework. It currently supports a range of inference strategies, including several recent innovations that are important for efficient inference on phylogenetic models. It also supports the implementation of novel inference strategies for models described using TreePPL or other domain-specific modeling languages. We briefly describe the TreePPL modeling language and the Python environment, and give some examples of modeling and inference with TreePPL. The examples illustrate how TreePPL can be used to address a range of common problem types considered in statistical phylogenetics, from diversification and co-speciation analysis to tree inference. Although much progress has been made in recent years, developing efficient algorithms for automatic PPL-based inference is still a very active field. A few major challenges remain to be addressed before the entire phylogenetic model space is adequately covered by efficient automatic inference techniques, but several of them are being addressed in ongoing work on TreePPL. We end the paper by discussing how probabilistic programming can support the use of machine learning in designing and fine-tuning inference strategies and in extending incomplete model descriptions in phylogenetics.

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Suspension Analysis and Selective Continuation-Passing Style for Universal Probabilistic Programming Languages

Cited by 2 publications

References 38 publications

Suspension Analysis and Selective Continuation-Passing Style for Universal Probabilistic Programming Languages

Suspension Analysis and Selective Continuation-Passing Style for Universal Probabilistic Programming Languages

TreePPL: A Universal Probabilistic Programming Language for Phylogenetics

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