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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. The core logic of ChainRules is implemented in ChainRulesCore.jl. To add ChainRules support to your package, by defining new rules or frules, you only need to depend on the very light-weight package ChainRulesCore.jl. This repository contains ChainRules.jl, which is what people actually use directly. ChainRules reexports all the ChainRulesCore functionality and has all the rules for the Julia standard library.
Features
- Mixed-mode composability without being coupled to a specific AD implementation
- Extensible rules
- Control-inverted design
- Propagation semantics built-in
- Package authors can add rules (and thus AD support) to the functions in their packages, without needing to make a PR to ChainRules.jl
- Default implementations that allow rule authors to easily opt-in to common optimizations (fusion, increment elision, memoization, etc.)
Project Samples
Categories
Data VisualizationLicense
MIT LicenseFollow ChainRules.jl
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