pyro-ppl/pyro

Implement Nonparametric Hamiltonian Monte Carlo

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#2,903 opened on Jul 15, 2021

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Description

This issue proposes adding an implementation of nonparametric Hamiltonian Monte Carlo, a modified version of HMC that provably correctly handles models with stochastic control flow, unlike vanilla HMC.

@fzaiser (one of the paper's authors) has open-sourced a PyTorch reference implementation, and turning this into a Pyro inference algorithm would be a great project for contributors with some preexisting interest in and knowledge of the line of research (on extensions of gradient-based approximate inference algorithms to discontinuous models) embodied in the paper.

Some questions for discussion:

  1. What extra information about the model is necessary to support the algorithms used in the paper's experiments?
  2. Can this information be extracted automatically, or would it require additional user-supplied annotations?
  3. Would it make more sense to implement this functionality in a new MCMC kernel or to modify our existing HMC kernels?
  4. Would NumPyro be a better substrate for this algorithm since Jax does not compile away control flow?

Contributor guide

Tech stack
pythonpytorch
Domain
machine learningai
Issue type
feature
DifficultyEstimated implementation difficulty for a new contributor, from 1 for very small changes to 5 for expert-level work.
5
Estimated timeA rough time range for an experienced contributor to investigate, implement, test, and prepare a pull request.
over 1 week
Activity statusHow available the issue appears right now: fresh, active, stale, blocked, or waiting on maintainer input.
stale
ClarityHow clearly the issue explains the expected change, acceptance criteria, and next step.
unclear
Prerequisites
Knowledge of Hamiltonian Monte CarloFamiliarity with PyTorchUnderstanding of probabilistic programming
Newbie friendlinessA 1-100 score estimating how approachable this issue is for first-time contributors.
10
Research direction
Research the paper referenced (arxiv.org/abs/2106.10238) and the PyTorch reference implementation at github.com/fzaiser/nonparametric hmc. Investigate how to adapt the algorithm to Pyro's existing MCMC kernel structure. The issue raises several open questions: determining necessary model information, automation vs. user annotations, whether to implement as new kernel or modify existing HMC kernels, and whether NumPyro (with JAX) would be a better substrate due to JAX's handling of control flow. Consider exploring these questions through prototyping and consulting Pyro's source code for examples of existing inference algorithms.