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])

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