Papers
Topics
Authors
Recent
Gemini 2.5 Flash
Gemini 2.5 Flash
134 tokens/sec
GPT-4o
10 tokens/sec
Gemini 2.5 Pro Pro
47 tokens/sec
o3 Pro
4 tokens/sec
GPT-4.1 Pro
38 tokens/sec
DeepSeek R1 via Azure Pro
28 tokens/sec
2000 character limit reached

Axon: A Language for Dynamic Shapes in Deep Learning Graphs (2210.02374v1)

Published 5 Oct 2022 in cs.PL

Abstract: Axon is a language that enables shape and rank inference for tensors in a Deep Learning graphs. It aims to make shapes implicit and inferred, in a similar manner to how types are implicit and inferred in many functional programming languages. Tensor dimensions are represented by expressions consisting of symbolic variables, constants, and arithmetic operators. Tensor shapes can be expressed as either a sequence of these dimension expressions, as a symbolic variable, or as an appending of other shapes. This allows complex constraints on shapes to be expressed. Axon is functional in style, with a type system similar in to Standard ML, extended to include shape information. It provides a suite of built in operators over tensors, including pointwise arithmetic operators, maps, reduction, loops and user defined functions. We describe a shape inference algorithm based on constraint solving which infers information about shapes, from both shape information provided by the programmer and the structure of the program. This allows fully automatic inference of the shapes of tensors for complex Deep Learning graphs. This approach reduces programmer effort when specifying graphs, as tensor shapes are not explicit, allows composition of Deep Learning graphs while maintaining input and output tensor shape compatibility, and aids in automated error detection by identifying shape mismatches at runtime.

Citations (2)

Summary

We haven't generated a summary for this paper yet.