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DifferentiationInterface

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DifferentiationInterfaceStable Dev
DifferentiationInterfaceTestStable Dev

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 (gradients, Jacobians, Hessians and more)
  • In-place and out-of-place differentiation
  • Preparation mechanism (e.g. to create a config or tape)
  • Built-in sparsity handling
  • 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:

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

using DifferentiationInterface
import ForwardDiff, Enzyme, Zygote  # AD backends you want to use 

f(x) = sum(abs2, x)

x = [1.0, 2.0]

value_and_gradient(f, AutoForwardDiff(), x) # returns (5.0, [2.0, 4.0]) with ForwardDiff.jl
value_and_gradient(f, AutoEnzyme(),      x) # returns (5.0, [2.0, 4.0]) with Enzyme.jl
value_and_gradient(f, AutoZygote(),      x) # returns (5.0, [2.0, 4.0]) with Zygote.jl

For more performance, take a look at the DifferentiationInterface tutorial.