Avoid splitting up varnames until absolutely necessary

HISTORY.md

@@ -1,3 +1,9 @@
+# 0.39.8
+
+MCMCChains.jl doesn't understand vector- or matrix-valued variables, and in Turing we split up such values into their individual components.
+This patch carries out some internal refactoring to avoid splitting up VarNames until absolutely necessary.
+There are no user-facing changes in this patch.
+
 # 0.39.7
 
 Update compatibility to AdvancedPS 0.7 and Libtask 0.9.

Project.toml

@@ -1,6 +1,6 @@
 name = "Turing"
 uuid = "fce5fe82-541a-59a6-adf8-730c64b5f9a0"
-version = "0.39.7"
+version = "0.39.8"
 
 [deps]
 ADTypes = "47edcb42-4c32-4615-8424-f2b9edc5f35b"

ext/TuringOptimExt.jl

@@ -186,7 +186,9 @@ function _optimize(
     logdensity_optimum = Optimisation.OptimLogDensity(
         f.ldf.model, vi_optimum, f.ldf.context
     )
-    vns_vals_iter = Turing.Inference.getparams(f.ldf.model, vi_optimum)
+    vals_dict = Turing.Inference.getparams(f.ldf.model, vi_optimum)
+    iters = map(DynamicPPL.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)
     vmat = NamedArrays.NamedArray(vals, varnames)

src/mcmc/Inference.jl

@@ -176,13 +176,7 @@ function getparams(model::DynamicPPL.Model, vi::DynamicPPL.VarInfo)
     # this means that the code below will work both of linked and invlinked `vi`.
     # Ref: https://github.com/TuringLang/Turing.jl/issues/2195
     # NOTE: We need to `deepcopy` here to avoid modifying the original `vi`.
-    vals = DynamicPPL.values_as_in_model(model, true, deepcopy(vi))
-
-    # Obtain an iterator over the flattened parameter names and values.
-    iters = map(DynamicPPL.varname_and_value_leaves, keys(vals), values(vals))
-
-    # Materialize the iterators and concatenate.
-    return mapreduce(collect, vcat, iters)
+    return DynamicPPL.values_as_in_model(model, true, deepcopy(vi))
 end
 function getparams(
     model::DynamicPPL.Model, untyped_vi::DynamicPPL.VarInfo{<:DynamicPPL.Metadata}
@@ -193,14 +187,25 @@ function getparams(
     return getparams(model, DynamicPPL.typed_varinfo(untyped_vi))
 end
 function getparams(::DynamicPPL.Model, ::DynamicPPL.VarInfo{NamedTuple{(),Tuple{}}})
-    return float(Real)[]
+    return Dict{VarName,Any}()
 end
 
 function _params_to_array(model::DynamicPPL.Model, ts::Vector)
     names_set = OrderedSet{VarName}()
     # Extract the parameter names and values from each transition.
     dicts = map(ts) do t
-        nms_and_vs = getparams(model, 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
+        # 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))
+            mapreduce(collect, vcat, iters)
+        end
+
         nms = map(first, nms_and_vs)
         vs = map(last, nms_and_vs)
         for nm in nms
@@ -210,9 +215,7 @@ function _params_to_array(model::DynamicPPL.Model, ts::Vector)
         return OrderedDict(zip(nms, vs))
     end
     names = collect(names_set)
-    vals = [
-        get(dicts[i], key, missing) for i in eachindex(dicts), (j, key) in enumerate(names)
-    ]
+    vals = [get(dicts[i], key, missing) for i in eachindex(dicts), key in names]
 
     return names, vals
 end

src/optimisation/Optimisation.jl

@@ -366,9 +366,10 @@ function Base.get(m::ModeResult, var_symbols::AbstractVector{Symbol})
     # Get all the variable names in the model. This is the same as the list of keys in
     # m.values, but they are more convenient to filter when they are VarNames rather than
     # Symbols.
-    varnames = collect(
-        map(first, Turing.Inference.getparams(log_density.model, log_density.varinfo))
-    )
+    vals_dict = Turing.Inference.getparams(log_density.model, log_density.varinfo)
+    iters = map(DynamicPPL.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
     # variable name has that symbol.
     et = eltype(m.values)
@@ -396,7 +397,9 @@ parameter space in case the optimization was done in a transformed space.
 function ModeResult(log_density::OptimLogDensity, solution::SciMLBase.OptimizationSolution)
     varinfo_new = DynamicPPL.unflatten(log_density.ldf.varinfo, solution.u)
     # `getparams` performs invlinking if needed
-    vns_vals_iter = Turing.Inference.getparams(log_density.ldf.model, varinfo_new)
+    vals = Turing.Inference.getparams(log_density.ldf.model, varinfo_new)
+    iters = map(DynamicPPL.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)
     return ModeResult(

test/mcmc/Inference.jl

@@ -630,16 +630,18 @@ using Turing
             return x ~ Normal()
         end
         fvi = DynamicPPL.VarInfo(f())
-        @test only(Turing.Inference.getparams(f(), fvi)) == (@varname(x), fvi[@varname(x)])
+        fparams = Turing.Inference.getparams(f(), fvi)
+        @test fparams[@varname(x)] == fvi[@varname(x)]
+        @test length(fparams) == 1
 
         @model function g()
             x ~ Normal()
             return y ~ Poisson()
         end
         gvi = DynamicPPL.VarInfo(g())
         gparams = Turing.Inference.getparams(g(), gvi)
-        @test gparams[1] == (@varname(x), gvi[@varname(x)])
-        @test gparams[2] == (@varname(y), gvi[@varname(y)])
+        @test gparams[@varname(x)] == gvi[@varname(x)]
+        @test gparams[@varname(y)] == gvi[@varname(y)]
         @test length(gparams) == 2
     end