Composable Effects for Flexible and Accelerated Probabilistic Programming in NumPyro (1912.11554v1)

Abstract: NumPyro is a lightweight library that provides an alternate NumPy backend to the Pyro probabilistic programming language with the same modeling interface, language primitives and effect handling abstractions. Effect handlers allow Pyro's modeling API to be extended to NumPyro despite its being built atop a fundamentally different JAX-based functional backend. In this work, we demonstrate the power of composing Pyro's effect handlers with the program transformations that enable hardware acceleration, automatic differentiation, and vectorization in JAX. In particular, NumPyro provides an iterative formulation of the No-U-Turn Sampler (NUTS) that can be end-to-end JIT compiled, yielding an implementation that is much faster than existing alternatives in both the small and large dataset regimes.

• The paper presents composable effect handlers integrated with JAX in NumPyro to enhance computational efficiency in probabilistic programming.
• It introduces an iterative No-U-Turn Sampler that leverages JAX’s jit transformation for faster, memory-efficient sampling compared to recursive methods.
• The unified modeling interface ensures smooth interoperability with Pyro, broadening the platform’s applicability in statistical modeling and machine learning.

Composable Effects for Flexible and Accelerated Probabilistic Programming in NumPyro

The paper "Composable Effects for Flexible and Accelerated Probabilistic Programming in NumPyro" presents notable advancements in the field of probabilistic programming languages (PPLs), leveraging the capabilities of the NumPyro library. NumPyro is a lightweight probabilistic programming library that integrates Pyro’s modeling interface with JAX's powerful program transformations for hardware acceleration, automatic differentiation, and vectorization. The paper primarily focuses on the integration of Pyro's effect handlers within NumPyro, enabling efficient and flexible probabilistic programming atop a JAX-based backend.

Key Contributions

1. Effect Handlers Composition: The integration of Pyro’s effect handlers facilitates the extension of Pyro's modeling API to NumPyro. This is achieved through the seamless composition of these handlers with the program transformations inherent in JAX. These include hardware acceleration, automatic differentiation, and vectorization capabilities, which significantly enhance computational efficiency.
2. Iterative No-U-Turn Sampler (NUTS): One of the standout contributions of the paper is the implementation of an iterative version of the No-U-Turn Sampler (NUTS). This is a methodological improvement over traditional NUTS implementations, which are recursive and computationally intensive. By employing JAX’s jit transformation for end-to-end compilation, this iterative NUTS is markedly faster than its existing counterparts. The iterative approach not only facilitates JIT compilation but also retains memory efficiency by requiring only logarithmic memory space relative to the number of integration steps.
3. Unified Modeling Interface: NumPyro retains the same modeling and inference interfaces as Pyro, ensuring a unified and consistent API for users familiar with Pyro. This overlap allows for a smooth transition and interoperability between different backends, thus broadening the applicability and user base.
4. Leverage of JAX Transformations: The paper demonstrates how critical NumPyro subroutines are optimized using JAX transformations such as jit and vmap. For instance, vmap is utilized to vectorize inference utilities like model prediction and log-likelihood computation, allowing for parallel operations without manual batch dimension management.
5. Practical Implications: The authors argue that NumPyro provides substantial speed-ups in various applications, including Hidden Markov Models (HMMs) and logistic regression, particularly in GPU-accelerated settings. The paper’s experiments demonstrate significant performance benefits over existing frameworks like Stan and Pyro, particularly in high-dimensional models or large datasets.

Implications and Future Directions

The implications of this work are multifaceted. On a practical level, the accelerated and flexible inference capabilities of NumPyro make it a compelling choice for researchers and engineers working with large-scale probabilistic models. The integration of effect handlers with JAX’s transformations not only enhances performance but also broadens the scope of applications that can be tackled using PPLs.

Theoretical implications stemming from this research emphasize the functional paradigm and its advantages in probabilistic programming, particularly regarding composability and efficiency. Future developments could explore extending this model to other types of samplers or inference schemes that can benefit from JIT compilation and enhanced parallelization.

In conclusion, this paper contributes to the ongoing evolution of probabilistic programming frameworks by marrying expressive modeling interfaces with cutting-edge computational techniques. NumPyro's architecture and enhancements position it as a robust platform for both research and application in statistical modeling and machine learning. Future research could further refine these computational strategies and explore additional integrations that exploit JAX's flexibility and power.