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arXiv · 1811.02091

Simple, Distributed, and Accelerated Probabilistic Programming

Dustin Tran·Matthew Hoffman·Dave Moore·Christopher Suter·Srinivas Vasudevan·Alexey Radul·Matthew Johnson·Rif A. Saurous
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Abstract

We describe a simple, low-level approach for embedding probabilistic programming in a deep learning ecosystem. In particular, we distill probabilistic programming down to a single abstraction---the random variable. Our lightweight implementation in TensorFlow enables numerous applications: a model-parallel variational auto-encoder (VAE) with 2nd-generation tensor processing units (TPUv2s); a data-parallel autoregressive model (Image Transformer) with TPUv2s; and multi-GPU No-U-Turn Sampler (NUTS). For both a state-of-the-art VAE on 64x64 ImageNet and Image Transformer on 256x256 CelebA-HQ, our approach achieves an optimal linear speedup from 1 to 256 TPUv2 chips. With NUTS, we see a 100x speedup on GPUs over Stan and 37x over PyMC3.

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Dustin Tran, Matthew Hoffman, Dave Moore, Christopher Suter, Srinivas Vasudevan, Alexey Radul, Matthew Johnson, Rif A. Saurous. 2018-11-05. Simple, Distributed, and Accelerated Probabilistic Programming. https://arxiv.org/abs/1811.02091

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