Pyro: Deep Universal Probabilistic Programming (1810.09538v1)

Abstract: Pyro is a probabilistic programming language built on Python as a platform for developing advanced probabilistic models in AI research. To scale to large datasets and high-dimensional models, Pyro uses stochastic variational inference algorithms and probability distributions built on top of PyTorch, a modern GPU-accelerated deep learning framework. To accommodate complex or model-specific algorithmic behavior, Pyro leverages Poutine, a library of composable building blocks for modifying the behavior of probabilistic programs.

• The paper introduces Pyro as a deep probabilistic programming language that leverages Python and PyTorch to model complex, data-dependent scenarios.
• It demonstrates the use of stochastic variational inference with GPU acceleration, ensuring scalability for large datasets and high-dimensional models.
• It emphasizes a minimalistic design by separating model implementation from inference using effect handlers, enabling customizable and efficient research applications.

Pyro: Deep Universal Probabilistic Programming

The paper "Pyro: Deep Universal Probabilistic Programming" by Bingham et al. discusses the development and implementation of the probabilistic programming language (PPL) Pyro. Constructed atop Python and leveraging the PyTorch framework, Pyro aims for flexibility, scalability, expressivity, and minimalistic design needed for advanced probabilistic modeling.

Key Design Principles

The paper identifies four critical design principles for a PPL intended for state-of-the-art AI research models: expressive, scalable, flexible, and minimal. These principles are integrated into Pyro to address the demands of constructing complex models.

1. Expressivity: Pyro can manage models with data-dependent control flows and latent variable dependencies. It supports arbitrary Python code and unnormalized joint distributions, as exemplified by the use of the obs keyword in pyro.sample.
2. Scalability: To ensure scalability, Pyro leverages stochastic variational inference (SVI) and supports GPU acceleration via PyTorch. This allows it to handle large datasets and high-dimensional models efficiently.
3. Flexibility: Pyro's flexibility is shown through its separation of model and inference implementations. It allows users to customize inference algorithms, which is crucial for advanced research applications.
4. Minimality: Pyro maintains a low cognitive overhead by adhering closely to Python syntax and semantics and integrates seamlessly with existing tools and libraries, such as PyTorch for deep learning and visualization.

Implementation and Tools

Pyro introduces the constructs pyro.sample and pyro.param for incorporating randomness and learnable parameters in the models. Moreover, it integrates seamlessly with Python’s control flow structures, enhancing its expressivity. Pyro's primary inference algorithm, SVI, optimizes the ELBO objective over mini-batches of data, ensuring scalability.

To manage enhanced modularity and control, Pyro employs Poutine, a library of effect handlers, for separating inference execution from the underlying model implementation. This provides a clean mechanism for creating and modifying inference algorithms without altering the high-level model code.

Comparative Analysis

The paper contrasts Pyro with other existing PPLs such as Stan, Church, Venture, webPPL, Edward, ProbTorch, and Turing (Table \ref{ppl_comparison}). Pyro excels in balancing expressive power, scalability, flexibility, and minimalistic integration with host languages and libraries. While other systems demonstrate strengths in specific areas, Pyro is designed to achieve a more balanced approach, supporting dynamic control flow and high-dimensional models effectively.

Experimental Validation

The paper validates Pyro by implementing state-of-the-art models such as the Variational Autoencoder (VAE) and the Deep Markov Model (DMM). Performance comparison with a pure PyTorch VAE implementation shows moderate overhead, which diminishes as computational demand increases. For the DMM, Pyro successfully replicates the original model results and enhances them using autoregressive flows (IAFs), demonstrating Pyro’s flexibility and efficiency.

Implications and Future Directions

Pyro's design principles position it well for complex AI research and practical applications requiring sophisticated probabilistic modeling and approximate inference. Its open-source nature and integration with PyTorch encourage widespread adoption and further enhancements, potentially accelerating advancements in probabilistic programming and AI.

Future developments may focus on optimizing performance overhead, further expanding the library of inference algorithms, and enhancing the integration with emerging AI frameworks. Pyro’s modular and extensible design lays a strong foundation for ongoing innovation in probabilistic programming.

Conclusion

The paper by Bingham et al. articulates the design, implementation, and capabilities of Pyro as a deep universal probabilistic programming language. By adhering to core design principles, leveraging the computational power of PyTorch, and facilitating complex model specifications and custom inference algorithms, Pyro represents a versatile tool for advancing AI research and applications.