Import varname_leaves etc from AbstractPPL instead

Author: penelopeysm

ext/TuringOptimExt.jl

@@ -1,7 +1,7 @@
 module TuringOptimExt
 
 using Turing: Turing
-import Turing: DynamicPPL, NamedArrays, Accessors, Optimisation
+import Turing: AbstractPPL, DynamicPPL, NamedArrays, Accessors, Optimisation
 using Optim: Optim
 
 ####################
@@ -186,7 +186,7 @@ function _optimize(
         f.ldf.model, f.ldf.getlogdensity, vi_optimum; adtype=f.ldf.adtype
     )
     vals_dict = Turing.Inference.getparams(f.ldf.model, vi_optimum)
-    iters = map(DynamicPPL.varname_and_value_leaves, keys(vals_dict), values(vals_dict))
+    iters = map(AbstractPPL.varname_and_value_leaves, keys(vals_dict), values(vals_dict))
     vns_vals_iter = mapreduce(collect, vcat, iters)
     varnames = map(Symbol ∘ first, vns_vals_iter)
     vals = map(last, vns_vals_iter)

src/mcmc/Inference.jl

@@ -262,13 +262,13 @@ function _params_to_array(model::DynamicPPL.Model, ts::Vector)
     dicts = map(ts) do t
         # In general getparams returns a dict of VarName => values. We need to also
         # split it up into constituent elements using
-        # `DynamicPPL.varname_and_value_leaves` because otherwise MCMCChains.jl
+        # `AbstractPPL.varname_and_value_leaves` because otherwise MCMCChains.jl
         # won't understand it.
         vals = getparams(model, t)
         nms_and_vs = if isempty(vals)
             Tuple{VarName,Any}[]
         else
-            iters = map(DynamicPPL.varname_and_value_leaves, keys(vals), values(vals))
+            iters = map(AbstractPPL.varname_and_value_leaves, keys(vals), values(vals))
             mapreduce(collect, vcat, iters)
         end
         nms = map(first, nms_and_vs)

src/optimisation/Optimisation.jl

@@ -2,6 +2,7 @@ module Optimisation
 
 using ..Turing
 using NamedArrays: NamedArrays
+using AbstractPPL: AbstractPPL
 using DynamicPPL: DynamicPPL
 using LogDensityProblems: LogDensityProblems
 using Optimization: Optimization
@@ -320,7 +321,7 @@ function Base.get(m::ModeResult, var_symbols::AbstractVector{Symbol})
     # m.values, but they are more convenient to filter when they are VarNames rather than
     # Symbols.
     vals_dict = Turing.Inference.getparams(log_density.model, log_density.varinfo)
-    iters = map(DynamicPPL.varname_and_value_leaves, keys(vals_dict), values(vals_dict))
+    iters = map(AbstractPPL.varname_and_value_leaves, keys(vals_dict), values(vals_dict))
     vns_and_vals = mapreduce(collect, vcat, iters)
     varnames = collect(map(first, vns_and_vals))
     # For each symbol s in var_symbols, pick all the values from m.values for which the
@@ -351,7 +352,7 @@ function ModeResult(log_density::OptimLogDensity, solution::SciMLBase.Optimizati
     varinfo_new = DynamicPPL.unflatten(log_density.ldf.varinfo, solution.u)
     # `getparams` performs invlinking if needed
     vals = Turing.Inference.getparams(log_density.ldf.model, varinfo_new)
-    iters = map(DynamicPPL.varname_and_value_leaves, keys(vals), values(vals))
+    iters = map(AbstractPPL.varname_and_value_leaves, keys(vals), values(vals))
     vns_vals_iter = mapreduce(collect, vcat, iters)
     syms = map(Symbol ∘ first, vns_vals_iter)
     vals = map(last, vns_vals_iter)

test/ext/OptimInterface.jl

@@ -2,6 +2,7 @@ module OptimInterfaceTests
 
 using ..Models: gdemo_default
 using Distributions.FillArrays: Zeros
+using AbstractPPL: AbstractPPL
 using DynamicPPL: DynamicPPL
 using LinearAlgebra: I
 using Optim: Optim
@@ -124,7 +125,7 @@ using Turing
             vals = result.values
 
             for vn in DynamicPPL.TestUtils.varnames(model)
-                for vn_leaf in DynamicPPL.TestUtils.varname_leaves(vn, get(result_true, vn))
+                for vn_leaf in AbstractPPL.varname_leaves(vn, get(result_true, vn))
                     @test get(result_true, vn_leaf) ≈ vals[Symbol(vn_leaf)] atol = 0.05
                 end
             end
@@ -159,7 +160,7 @@ using Turing
             vals = result.values
 
             for vn in DynamicPPL.TestUtils.varnames(model)
-                for vn_leaf in DynamicPPL.TestUtils.varname_leaves(vn, get(result_true, vn))
+                for vn_leaf in AbstractPPL.varname_leaves(vn, get(result_true, vn))
                     if model.f in allowed_incorrect_mle
                         @test isfinite(get(result_true, vn_leaf))
                     else

test/optimisation/Optimisation.jl

@@ -1,6 +1,7 @@
 module OptimisationTests
 
 using ..Models: gdemo, gdemo_default
+using AbstractPPL: AbstractPPL
 using Distributions
 using Distributions.FillArrays: Zeros
 using DynamicPPL: DynamicPPL
@@ -495,7 +496,7 @@ using Turing
             vals = result.values
 
             for vn in DynamicPPL.TestUtils.varnames(model)
-                for vn_leaf in DynamicPPL.TestUtils.varname_leaves(vn, get(result_true, vn))
+                for vn_leaf in AbstractPPL.varname_leaves(vn, get(result_true, vn))
                     @test get(result_true, vn_leaf) ≈ vals[Symbol(vn_leaf)] atol = 0.05
                 end
             end
@@ -534,7 +535,7 @@ using Turing
             vals = result.values
 
             for vn in DynamicPPL.TestUtils.varnames(model)
-                for vn_leaf in DynamicPPL.TestUtils.varname_leaves(vn, get(result_true, vn))
+                for vn_leaf in AbstractPPL.varname_leaves(vn, get(result_true, vn))
                     if model.f in allowed_incorrect_mle
                         @test isfinite(get(result_true, vn_leaf))
                     else