vaibhavdixit02 / differentiationinterface.jl

An interface to various automatic differentiation backends in Julia.

DifferentiationInterface

Package	Docs
DifferentiationInterface
DifferentiationInterfaceTest

An interface to various automatic differentiation (AD) backends in Julia.

Goal

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

• one-argument functions (allocating): f(x) = y
• two-argument functions (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)
• Thorough validation on standard inputs and outputs (numbers, vectors, matrices)
• Testing and benchmarking utilities accessible to users with DifferentiationInterfaceTest

Compatibility

We support all of the backends defined by ADTypes.jl v1.0:

• ChainRulesCore.jl
• Diffractor.jl
• Enzyme.jl
• FastDifferentiation.jl
• FiniteDiff.jl
• FiniteDifferences.jl
• ForwardDiff.jl
• PolyesterForwardDiff.jl
• ReverseDiff.jl
• SparseDiffTools.jl
• Symbolics.jl
• Tapir.jl
• Tracker.jl
• Zygote.jl

Installation

To install the stable version of the package, run the following code in a Julia REPL:

julia> using Pkg

julia> Pkg.add("DifferentiationInterface")

To install the development version, run this instead:

julia> using Pkg

julia> Pkg.add(
        url="https://github.com/gdalle/DifferentiationInterface.jl",
        subdir="DifferentiationInterface"
    )

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.