Switch to DifferentiationInterface

.github/workflows/CI.yml

@@ -17,7 +17,7 @@ jobs:
       fail-fast: false
       matrix:
         version:
-          - '1.6' # Replace this with the minimum Julia version that your package supports. 
+          - '1.10' # Replace this with the minimum Julia version that your package supports. 
           - '1' # Leave this line unchanged. '1' will automatically expand to the latest stable 1.x release of Julia.
         os:
           - ubuntu-latest

Project.toml

@@ -1,43 +1,30 @@
 name = "LogDensityProblemsAD"
 uuid = "996a588d-648d-4e1f-a8f0-a84b347e47b1"
 authors = ["Tamás K. Papp <tkpapp@gmail.com>"]
-version = "1.9.0"
+version = "1.10.0"
 
 [deps]
+ADTypes = "47edcb42-4c32-4615-8424-f2b9edc5f35b"
+DifferentiationInterface = "a0c0ee7d-e4b9-4e03-894e-1c5f64a51d63"
 DocStringExtensions = "ffbed154-4ef7-542d-bbb7-c09d3a79fcae"
 LogDensityProblems = "6fdf6af0-433a-55f7-b3ed-c6c6e0b8df7c"
-Requires = "ae029012-a4dd-5104-9daa-d747884805df"
-SimpleUnPack = "ce78b400-467f-4804-87d8-8f486da07d0a"
 
 [weakdeps]
-ADTypes = "47edcb42-4c32-4615-8424-f2b9edc5f35b"
-BenchmarkTools = "6e4b80f9-dd63-53aa-95a3-0cdb28fa8baf"
 Enzyme = "7da242da-08ed-463a-9acd-ee780be4f1d9"
 FiniteDifferences = "26cc04aa-876d-5657-8c51-4c34ba976000"
 ForwardDiff = "f6369f11-7733-5829-9624-2563aa707210"
-ReverseDiff = "37e2e3b7-166d-5795-8a7a-e32c996b4267"
-Tracker = "9f7883ad-71c0-57eb-9f7f-b5c9e6d3789c"
-Zygote = "e88e6eb3-aa80-5325-afca-941959d7151f"
 
 [extensions]
-LogDensityProblemsADADTypesExt = "ADTypes"
 LogDensityProblemsADEnzymeExt = "Enzyme"
 LogDensityProblemsADFiniteDifferencesExt = "FiniteDifferences"
-LogDensityProblemsADForwardDiffBenchmarkToolsExt = ["BenchmarkTools", "ForwardDiff"]
 LogDensityProblemsADForwardDiffExt = "ForwardDiff"
-LogDensityProblemsADReverseDiffExt = "ReverseDiff"
-LogDensityProblemsADTrackerExt = "Tracker"
-LogDensityProblemsADZygoteExt = "Zygote"
 
 [compat]
-ADTypes = "0.1.7, 0.2, 1"
+ADTypes = "1"
+DifferentiationInterface = "0.3"
 DocStringExtensions = "0.8, 0.9"
-Enzyme = "0.11, 0.12"
-FiniteDifferences = "0.12"
 LogDensityProblems = "1, 2"
-Requires = "0.5, 1"
-SimpleUnPack = "1"
-julia = "1.6"
+julia = "1.10"
 
 [extras]
 ADTypes = "47edcb42-4c32-4615-8424-f2b9edc5f35b"

ext/LogDensityProblemsADEnzymeExt.jl

@@ -1,78 +1,13 @@
-"""
-Gradient AD implementation using Enzyme.
-"""
 module LogDensityProblemsADEnzymeExt
 
-if isdefined(Base, :get_extension)
-    using LogDensityProblemsAD: ADGradientWrapper, logdensity
-    using LogDensityProblemsAD.SimpleUnPack: @unpack
+using ADTypes: AutoEnzyme
+using LogDensityProblemsAD: ADgradient, logdensity
+using Enzyme: Enzyme
 
-    import LogDensityProblemsAD: ADgradient, logdensity_and_gradient
-    import Enzyme
-else
-    using ..LogDensityProblemsAD: ADGradientWrapper, logdensity
-    using ..LogDensityProblemsAD.SimpleUnPack: @unpack
-
-    import ..LogDensityProblemsAD: ADgradient, logdensity_and_gradient
-    import ..Enzyme
-end
-
-struct EnzymeGradientLogDensity{L,M<:Union{Enzyme.ForwardMode,Enzyme.ReverseMode},S} <: ADGradientWrapper
-    ℓ::L
-    mode::M
-    shadow::S # only used in forward mode
-end
-
-"""
-    ADgradient(:Enzyme, ℓ; kwargs...)
-    ADgradient(Val(:Enzyme), ℓ; kwargs...)
-
-Gradient using algorithmic/automatic differentiation via Enzyme.
-
-# Keyword arguments
-
-- `mode::Enzyme.Mode`: Differentiation mode (default: `Enzyme.Reverse`).
-  Currently only `Enzyme.Reverse` and `Enzyme.Forward` are supported.
-
-- `shadow`: Collection of one-hot vectors for each entry of the inputs `x` to the log density
-  `ℓ`, or `nothing` (default: `nothing`). This keyword argument is only used in forward
-  mode. By default, it will be recomputed in every call of `logdensity_and_gradient(ℓ, x)`.
-  For performance reasons it is recommended to compute it only once when calling `ADgradient`.
-  The one-hot vectors can be constructed, e.g., with `Enzyme.onehot(x)`.
-"""
-function ADgradient(::Val{:Enzyme}, ℓ; mode::Enzyme.Mode = Enzyme.Reverse, shadow = nothing)
-    mode isa Union{Enzyme.ForwardMode,Enzyme.ReverseMode} ||
-        throw(ArgumentError("currently automatic differentiation via Enzyme only supports " *
-                            "`Enzyme.Forward` and `Enzyme.Reverse` modes"))
-    if mode isa Enzyme.ReverseMode && shadow !== nothing
-        @info "keyword argument `shadow` is ignored in reverse mode"
-        shadow = nothing
-    end
-    return EnzymeGradientLogDensity(ℓ, mode, shadow)
-end
-
-function Base.show(io::IO, ∇ℓ::EnzymeGradientLogDensity)
-    print(io, "Enzyme AD wrapper for ", ∇ℓ.ℓ, " with ",
-          ∇ℓ.mode isa Enzyme.ForwardMode ? "forward" : "reverse", " mode")
-end
-
-function logdensity_and_gradient(∇ℓ::EnzymeGradientLogDensity{<:Any,<:Enzyme.ForwardMode},
-                                 x::AbstractVector)
-    @unpack ℓ, mode, shadow = ∇ℓ
-    _shadow = shadow === nothing ? Enzyme.onehot(x) : shadow
-    y, ∂ℓ_∂x = Enzyme.autodiff(mode, logdensity, Enzyme.BatchDuplicated,
-                               Enzyme.Const(ℓ),
-                               Enzyme.BatchDuplicated(x, _shadow))
-    return y, collect(∂ℓ_∂x)
-end
-
-function logdensity_and_gradient(∇ℓ::EnzymeGradientLogDensity{<:Any,<:Enzyme.ReverseMode},
-                                 x::AbstractVector)
-    @unpack ℓ = ∇ℓ
-    ∂ℓ_∂x = zero(x)
-    _, y = Enzyme.autodiff(Enzyme.ReverseWithPrimal, logdensity, Enzyme.Active,
-                           Enzyme.Const(ℓ), Enzyme.Duplicated(x, ∂ℓ_∂x))
-    y, ∂ℓ_∂x
+function ADgradient(::Val{:Enzyme}, ℓ; mode = Enzyme.Reverse, shadow = nothing)
+    @info "keyword argument `shadow` is now ignored"
+    backend = AutoEnzyme(; mode)
+    return ADgradient(backend, ℓ)
 end
 
 end # module

ext/LogDensityProblemsADFiniteDifferencesExt.jl

@@ -1,51 +1,13 @@
-"""
-Gradient implementation using FiniteDifferences.
-"""
 module LogDensityProblemsADFiniteDifferencesExt
 
-if isdefined(Base, :get_extension)
-    using LogDensityProblemsAD: ADGradientWrapper, logdensity
-    using LogDensityProblemsAD.SimpleUnPack: @unpack
+using ADTypes: AutoFiniteDifferences
+using LogDensityProblemsAD: ADgradient
+import FiniteDifferences: central_fdm
 
-    import LogDensityProblemsAD: ADgradient, logdensity_and_gradient
-    import FiniteDifferences
-else
-    using ..LogDensityProblemsAD: ADGradientWrapper, logdensity
-    using ..LogDensityProblemsAD.SimpleUnPack: @unpack
-
-    import ..LogDensityProblemsAD: ADgradient, logdensity_and_gradient
-    import ..FiniteDifferences
-end
-
-struct FiniteDifferencesGradientLogDensity{L,M} <: ADGradientWrapper
-    ℓ::L
-    "finite difference method"
-    fdm::M
-end
-
-"""
-    ADgradient(:FiniteDifferences, ℓ; fdm = central_fdm(5, 1))
-    ADgradient(Val(:FiniteDifferences), ℓ; fdm = central_fdm(5, 1))
-
-Gradient using FiniteDifferences, mainly intended for checking results from other algorithms.
-
-# Keyword arguments
-
-- `fdm`: the finite difference method. Defaults to `central_fdm(5, 1)`.
-"""
-function ADgradient(::Val{:FiniteDifferences}, ℓ; fdm = FiniteDifferences.central_fdm(5, 1))
-    FiniteDifferencesGradientLogDensity(ℓ, fdm)
-end
-
-function Base.show(io::IO, ∇ℓ::FiniteDifferencesGradientLogDensity)
-    print(io, "FiniteDifferences AD wrapper for ", ∇ℓ.ℓ, " with ", ∇ℓ.fdm)
-end
-
-function logdensity_and_gradient(∇ℓ::FiniteDifferencesGradientLogDensity, x::AbstractVector)
-    @unpack ℓ, fdm = ∇ℓ
-    y = logdensity(ℓ, x)
-    ∇y = only(FiniteDifferences.grad(fdm, Base.Fix1(logdensity, ℓ), x))
-    y, ∇y
+function ADgradient(::Val{:FiniteDifferences}, ℓ)
+    fdm = central_fdm(5, 1)
+    backend = AutoFiniteDifferences(; fdm)
+    ADgradient(backend, ℓ)
 end
 
 end # module