Can DifferentiationInterface be useful for Turing?

[gdalle]

Hi there!
@adrhill and I recently started https://github.com/gdalle/DifferentiationInterface.jl to provide a common interface for automatic differentiation in Julia. We're currently chatting with Lux.jl, Flux.jl and Optimization.jl to see how they can benefit from it, and so my mind went to Turing.jl as another AD power user :)
DifferentiationInterface.jl only guarantees support for functions of the type f(x) = y or f!(y, x) with standard numbers or arrays in and out. Within these restrictions, we are compatible with 13 different AD backends, including the cool kids like Enzyme.jl and even the hipsters like Tapir.jl. Do you think it could come in handy?
Ping @yebai @willtebbutt

[gdalle]

Some issues that seem related to AD support (not sure they are all relevant here):

• ADVI is not up-to-date on the AD side #2186
• Transfer essential/ad.jl to DynamicPPL #2141
• Should we support Tracker.jl? #2000
• ADVI example fails with Zygote AD backend #1988
• transform! not differentiable #1835
• Zygote's compilation scales badly with the number of ~ statements #1754
• Optimizing model with vector-valued observations produces error when data are a LinearAlgebra.Adjoint #1481
• Feature request: allow user to pass gradient tape into sample() function #1419

[yebai]

Turing's current interface to autodiff backends is based on LogDensityProblemsAD -- it is a fairly lightweight package for sharing glue code to various autodiff backends. You can take a look there to see how things might work.

[torfjelde]

Looks great!

Is there a summary of how this is different from AbstractDifferentiation.jl somewhere?:)

[gdalle]

I updated the summary in this issue: JuliaDiff/AbstractDifferentiation.jl#131