Fix depreciation for AutoReverseDiff. (#638)

* Update DynamicPPLReverseDiffExt.jl * Update Project.toml * Update ext/DynamicPPLReverseDiffExt.jl Co-authored-by: github-actions[bot] <41898282+github-actions[bot]@users.noreply.github.com> * Update DynamicPPLReverseDiffExt.jl * Update Project.toml --------- Co-authored-by: github-actions[bot] <41898282+github-actions[bot]@users.noreply.github.com>
TuringLang · Aug 10, 2024 · 3b8dcea · 3b8dcea
1 parent 949d153
commit 3b8dcea
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diff --git a/Project.toml b/Project.toml
@@ -41,7 +41,7 @@ DynamicPPLReverseDiffExt = ["ReverseDiff"]
 DynamicPPLZygoteRulesExt = ["ZygoteRules"]
 
 [compat]
-ADTypes = "0.2, 1"
+ADTypes = "1"
 AbstractMCMC = "5"
 AbstractPPL = "0.8.4"
 Accessors = "0.1"

diff --git a/ext/DynamicPPLReverseDiffExt.jl b/ext/DynamicPPLReverseDiffExt.jl
@@ -9,12 +9,12 @@ else
 end
 
 function LogDensityProblemsAD.ADgradient(
-    ad::ADTypes.AutoReverseDiff, ℓ::DynamicPPL.LogDensityFunction
-)
+    ad::ADTypes.AutoReverseDiff{Tcompile}, ℓ::DynamicPPL.LogDensityFunction
+) where {Tcompile}
     return LogDensityProblemsAD.ADgradient(
         Val(:ReverseDiff),
         ℓ;
-        compile=Val(ad.compile),
+        compile=Val(Tcompile),
         # `getparams` can return `Vector{Real}`, in which case, `ReverseDiff` will initialize the gradients to Integer 0
         # because at https://github.com/JuliaDiff/ReverseDiff.jl/blob/c982cde5494fc166965a9d04691f390d9e3073fd/src/tracked.jl#L473
         # `zero(D)` will return 0 when D is Real.