Remove LogDensityProblemsAD; wrap adtype in LogDensityFunction

HISTORY.md

@@ -2,7 +2,7 @@
 
 ## 0.35.0
 
-**Breaking**
+**Breaking changes**
 
 ### `.~` right hand side must be a univariate distribution
 
@@ -119,6 +119,52 @@ This release removes the feature of `VarInfo` where it kept track of which varia
 
     This change also affects sampling in Turing.jl.
 
+### `LogDensityFunction` argument order
+
+  - The method `LogDensityFunction(varinfo, model, context)` has been removed.
+    The only accepted order is `LogDensityFunction(model, varinfo, context; adtype)`.
+    (For an explanation of `adtype`, see below.)
+    The varinfo and context arguments are both still optional.
+
+**Other changes**
+
+### `LogDensityProblems` interface
+
+LogDensityProblemsAD is now removed as a dependency.
+Instead of constructing a `LogDensityProblemAD.ADgradient` object, we now directly use `DifferentiationInterface` to calculate the gradient of the log density with respect to model parameters.
+
+Note that if you wish, you can still construct an `ADgradient` out of a `LogDensityFunction` object (there is nothing preventing this).
+
+However, in this version, `LogDensityFunction` now takes an extra AD type argument.
+If this argument is not provided, the behaviour is exactly the same as before, i.e. you can calculate `logdensity` but not its gradient.
+However, if you do pass an AD type, that will allow you to calculate the gradient as well.
+You may thus find that it is easier to instead do this:
+
+```julia
+@model f() = ...
+
+ldf = LogDensityFunction(f(); adtype=AutoForwardDiff())
+```
+
+This will return an object which satisfies the `LogDensityProblems` interface to first-order, i.e. you can now directly call both
+
+```
+LogDensityProblems.logdensity(ldf, params)
+LogDensityProblems.logdensity_and_gradient(ldf, params)
+```
+
+without having to construct a separate `ADgradient` object.
+
+If you prefer, you can also construct a new `LogDensityFunction` with a new AD type afterwards.
+The model, varinfo, and context will be taken from the original `LogDensityFunction`:
+
+```julia
+@model f() = ...
+
+ldf = LogDensityFunction(f())  # by default, no adtype set
+ldf_with_ad = LogDensityFunction(ldf, AutoForwardDiff())
+```
+
 ## 0.34.2
 
   - Fixed bugs in ValuesAsInModelContext as well as DebugContext where underlying PrefixContexts were not being applied.

Project.toml

@@ -12,13 +12,13 @@ Bijectors = "76274a88-744f-5084-9051-94815aaf08c4"
 ChainRulesCore = "d360d2e6-b24c-11e9-a2a3-2a2ae2dbcce4"
 Compat = "34da2185-b29b-5c13-b0c7-acf172513d20"
 ConstructionBase = "187b0558-2788-49d3-abe0-74a17ed4e7c9"
+DifferentiationInterface = "a0c0ee7d-e4b9-4e03-894e-1c5f64a51d63"
 Distributions = "31c24e10-a181-5473-b8eb-7969acd0382f"
 DocStringExtensions = "ffbed154-4ef7-542d-bbb7-c09d3a79fcae"
 InteractiveUtils = "b77e0a4c-d291-57a0-90e8-8db25a27a240"
 KernelAbstractions = "63c18a36-062a-441e-b654-da1e3ab1ce7c"
 LinearAlgebra = "37e2e46d-f89d-539d-b4ee-838fcccc9c8e"
 LogDensityProblems = "6fdf6af0-433a-55f7-b3ed-c6c6e0b8df7c"
-LogDensityProblemsAD = "996a588d-648d-4e1f-a8f0-a84b347e47b1"
 MacroTools = "1914dd2f-81c6-5fcd-8719-6d5c9610ff09"
 OrderedCollections = "bac558e1-5e72-5ebc-8fee-abe8a469f55d"
 Random = "9a3f8284-a2c9-5f02-9a11-845980a1fd5c"
@@ -51,15 +51,14 @@ Bijectors = "0.13.18, 0.14, 0.15"
 ChainRulesCore = "1"
 Compat = "4"
 ConstructionBase = "1.5.4"
+DifferentiationInterface = "0.6.41"
 Distributions = "0.25"
 DocStringExtensions = "0.9"
-KernelAbstractions = "0.9.33"
 EnzymeCore = "0.6 - 0.8"
-ForwardDiff = "0.10"
 JET = "0.9"
+KernelAbstractions = "0.9.33"
 LinearAlgebra = "1.6"
 LogDensityProblems = "2"
-LogDensityProblemsAD = "1.7.0"
 MCMCChains = "6"
 MacroTools = "0.5.6"
 Mooncake = "0.4.95"

docs/src/api.md

@@ -54,7 +54,7 @@ logjoint
 
 ### LogDensityProblems.jl interface
 
-The [LogDensityProblems.jl](https://github.com/tpapp/LogDensityProblems.jl) interface is also supported by simply wrapping a [`Model`](@ref) in a `DynamicPPL.LogDensityFunction`:
+The [LogDensityProblems.jl](https://github.com/tpapp/LogDensityProblems.jl) interface is also supported by wrapping a [`Model`](@ref) in a `DynamicPPL.LogDensityFunction`.
 
 ```@docs
 DynamicPPL.LogDensityFunction

ext/DynamicPPLForwardDiffExt.jl

@@ -1,54 +1,40 @@
 module DynamicPPLForwardDiffExt
 
-if isdefined(Base, :get_extension)
-    using DynamicPPL: ADTypes, DynamicPPL, LogDensityProblems, LogDensityProblemsAD
-    using ForwardDiff
-else
-    using ..DynamicPPL: ADTypes, DynamicPPL, LogDensityProblems, LogDensityProblemsAD
-    using ..ForwardDiff
-end
-
-getchunksize(::ADTypes.AutoForwardDiff{chunk}) where {chunk} = chunk
-
-standardtag(::ADTypes.AutoForwardDiff{<:Any,Nothing}) = true
-standardtag(::ADTypes.AutoForwardDiff) = false
-
-function LogDensityProblemsAD.ADgradient(
-    ad::ADTypes.AutoForwardDiff, ℓ::DynamicPPL.LogDensityFunction
-)
-    θ = DynamicPPL.getparams(ℓ)
-    f = Base.Fix1(LogDensityProblems.logdensity, ℓ)
-
-    # Define configuration for ForwardDiff.
-    tag = if standardtag(ad)
-        ForwardDiff.Tag(DynamicPPL.DynamicPPLTag(), eltype(θ))
+using DynamicPPL: ADTypes, DynamicPPL, LogDensityProblems
+using ForwardDiff
+
+# check if the AD type already has a tag
+use_dynamicppl_tag(::ADTypes.AutoForwardDiff{<:Any,Nothing}) = true
+use_dynamicppl_tag(::ADTypes.AutoForwardDiff) = false
+
+function DynamicPPL.tweak_adtype(
+    ad::ADTypes.AutoForwardDiff{chunk_size},
+    ::DynamicPPL.Model,
+    vi::DynamicPPL.AbstractVarInfo,
+    ::DynamicPPL.AbstractContext,
+) where {chunk_size}
+    params = vi[:]
+
+    # Use DynamicPPL tag to improve stack traces
+    # https://www.stochasticlifestyle.com/improved-forwarddiff-jl-stacktraces-with-package-tags/
+    # NOTE: DifferentiationInterface disables tag checking if the
+    # tag inside the AutoForwardDiff type is not nothing. See
+    # https://github.com/JuliaDiff/DifferentiationInterface.jl/blob/1df562180bdcc3e91c885aa5f4162a0be2ced850/DifferentiationInterface/ext/DifferentiationInterfaceForwardDiffExt/onearg.jl#L338-L350.
+    # So we don't currently need to override ForwardDiff.checktag as well.
+    tag = if use_dynamicppl_tag(ad)
+        ForwardDiff.Tag(DynamicPPL.DynamicPPLTag(), eltype(params))
     else
-        ForwardDiff.Tag(f, eltype(θ))
+        ad.tag
     end
-    chunk_size = getchunksize(ad)
+
+    # Optimise chunk size according to size of model
     chunk = if chunk_size == 0 || chunk_size === nothing
-        ForwardDiff.Chunk(θ)
+        ForwardDiff.Chunk(params)
     else
-        ForwardDiff.Chunk(length(θ), chunk_size)
+        ForwardDiff.Chunk(length(params), chunk_size)
     end
 
-    return LogDensityProblemsAD.ADgradient(Val(:ForwardDiff), ℓ; chunk, tag, x=θ)
-end
-
-# Allow Turing tag in gradient etc. calls of the log density function
-function ForwardDiff.checktag(
-    ::Type{ForwardDiff.Tag{DynamicPPL.DynamicPPLTag,V}},
-    ::DynamicPPL.LogDensityFunction,
-    ::AbstractArray{W},
-) where {V,W}
-    return true
-end
-function ForwardDiff.checktag(
-    ::Type{ForwardDiff.Tag{DynamicPPL.DynamicPPLTag,V}},
-    ::Base.Fix1{typeof(LogDensityProblems.logdensity),<:DynamicPPL.LogDensityFunction},
-    ::AbstractArray{W},
-) where {V,W}
-    return true
+    return ADTypes.AutoForwardDiff(; chunksize=ForwardDiff.chunksize(chunk), tag=tag)
 end
 
 end # module

src/DynamicPPL.jl

@@ -14,7 +14,6 @@ using MacroTools: MacroTools
 using ConstructionBase: ConstructionBase
 using Accessors: Accessors
 using LogDensityProblems: LogDensityProblems
-using LogDensityProblemsAD: LogDensityProblemsAD
 
 using LinearAlgebra: LinearAlgebra, Cholesky
 

src/contexts.jl

@@ -184,6 +184,7 @@
 getsampler(context::SamplingContext) = context.sampler
 getsampler(context::AbstractContext) = getsampler(NodeTrait(context), context)
 getsampler(::IsParent, context::AbstractContext) = getsampler(childcontext(context))
+getsampler(::IsLeaf, ::AbstractContext) = error("No sampler found in context")
 
 """
     struct DefaultContext <: AbstractContext end