| Package | Docs |
|---|---|
| DifferentiationInterface |   |
| DifferentiationInterfaceTest | |
An interface to various automatic differentiation (AD) backends in Julia.
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
- 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
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
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"
)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])
- AbstractDifferentiation.jl is the original inspiration for DifferentiationInterface.jl.
- AutoDiffOperators.jl is an attempt to bridge ADTypes.jl with AbstractDifferentiation.jl.