Google Research

Simple, Distributed, and Accelerated Probabilistic Programming

NeurIPS (2018)


We describe Edward2, a low-level probabilistic programming language. Edward2 distills the core of probabilistic programming down to a single abstraction—the random variable. By blurring the line between model and computation, Edward2 enables numerous applications not shown before: a model-parallel variational auto-encoder (VAE) with tensor processing units (TPUs); a data-parallel autoregressive model (Image Transformer) with TPUs; and multi-GPU No-U-Turn Sampler (NUTS). Edward2 achieves an optimal linear speedup from 4 to 256 TPUs. With VAEs, Edward2 sees up to a 20x speedup on TPUs over Pyro and Edward on GPUs; with Bayesian neural networks, Edward2 sees up to a 51x speedup. With NUTS, Edward2 sees a 20x speedup on GPUs over Stan and 7x over PyMC3.

Research Areas

Learn more about how we do research

We maintain a portfolio of research projects, providing individuals and teams the freedom to emphasize specific types of work