A Language and Compiler View on Differentiable Programming

Fei Wang, Tiark Rompf

Abstract:

Current and emerging deep learning architectures call for an expressive high-level programming style with end-to-end differentiation and for a high-performance implementation at the same time. But the current generation of deep learning
frameworks either limits expressiveness and ease of use for increased performance (e.g., TensorFlow) or vice versa (e.g., PyTorch). In this paper we demonstrate that a “best of both worlds” approach is possible, based on multi-stage programming
and delimited continuations, two orthogonal ideas firmly rooted in programming languages research.

Keywords: multi-stage programming, delimited continuations, computation graph, PyTorch, TensorFlow, compiler