The ChainRules package provides a variety of common utilities that can be used by downstream automatic differentiation (AD) tools to define and execute forward-, reverse-, and mixed-mode primitives.

This package is a WIP; the framework is essentially there, but there are a bunch of TODOs, virtually no tests, etc. PRs welcome! Documentation is incoming, which should help if you'd like to contribute.

Here are some of the basic goals for the package:

• First-class support for complex differentiation via Wirtinger derivatives.

• Mixed-mode composability without being coupled to a specific AD implementation.

• Propagation semantics built-in, with default implementations that allow rule authors to easily opt-in to common optimizations (fusion, increment elision, memoization, etc.).

• Control-inverted design: rule authors can fully specify derivatives in a concise manner while naturally allowing the caller to compute only what they need.

The ChainRules source code follows the YASGuide.