willtebbutt / differentiationinterface.jl

An interface to various automatic differentiation backends in Julia.

DifferentiationInterface

An interface to various automatic differentiation backends in Julia.

Goal

This package provides a backend-agnostic syntax to differentiate functions of the following types:

• allocating: f(x) = y
• mutating: f!(y, x) = nothing

Features

• First and second order operators
• In-place and out-of-place differentiation
• Preparation mechanism (e.g. to create a config or tape)
• Cross-backend testing and benchmarking utilities
• Thorough validation on standard inputs and outputs (scalars, vectors, matrices)

Compatibility

We support most of the backends defined by ADTypes.jl:

backend	object
ChainRulesCore.jl	AutoChainRules(ruleconfig)
Diffractor.jl	AutoDiffractor()
Enzyme.jl	AutoEnzyme(Enzyme.Forward) or AutoEnzyme(Enzyme.Reverse)
FiniteDiff.jl	AutoFiniteDiff()
FiniteDifferences.jl	AutoFiniteDifferences(fdm)
ForwardDiff.jl	AutoForwardDiff()
PolyesterForwardDiff.jl	AutoPolyesterForwardDiff(; chunksize)
ReverseDiff.jl	AutoReverseDiff()
Tracker.jl	AutoTracker()
Zygote.jl	AutoZygote()

We also provide one additional backend:

backend	object
FastDifferentiation.jl	AutoFastDifferentiation()

Example

julia> import ADTypes, ForwardDiff

julia> using DifferentiationInterface

julia> backend = ADTypes.AutoForwardDiff();

julia> f(x) = sum(abs2, x);

julia> value_and_gradient(f, backend, [1., 2., 3.])
(14.0, [2.0, 4.0, 6.0])

Related packages

• AbstractDifferentiation.jl is the original inspiration for DifferentiationInterface.jl.
• AutoDiffOperators.jl is an attempt to bridge ADTypes.jl with AbstractDifferentiation.jl.