TuringLang · penelopeysm · Apr 27, 2025 · Apr 27, 2025 · Apr 28, 2025 · Apr 28, 2025
diff --git a/HISTORY.md b/HISTORY.md
@@ -1,3 +1,23 @@
+# Release 0.38.0
+
+DynamicPPL compatibility has been bumped to 0.36.
+This brings with it a number of changes: the ones most likely to affect you are submodel prefixing and conditioning.
+Variables in submodels are now represented correctly with field accessors.
+For example:
+
+```julia
+using Turing
+@model inner() = x ~ Normal()
+@model outer() = a ~ to_submodel(inner())
+```
+
+`keys(VarInfo(outer()))` now returns `[@varname(a.x)]` instead of `[@varname(var"a.x")]`
+
+Furthermore, you can now either condition on the outer model like `outer() | (@varname(a.x) => 1.0)`, or the inner model like `inner() | (@varname(x) => 1.0)`.
+If you use the conditioned inner model as a submodel, the conditioning will still apply correctly.
+
+Please see [the DynamicPPL release notes](https://github.com/TuringLang/DynamicPPL.jl/releases/tag/v0.36.0) for fuller details.
+
 # Release 0.37.1
 
 `maximum_a_posteriori` and `maximum_likelihood` now perform sanity checks on the model before running the optimisation.

diff --git a/Project.toml b/Project.toml
@@ -1,6 +1,6 @@
 name = "Turing"
 uuid = "fce5fe82-541a-59a6-adf8-730c64b5f9a0"
-version = "0.37.1"
+version = "0.38.0"
 
 [deps]
 ADTypes = "47edcb42-4c32-4615-8424-f2b9edc5f35b"
@@ -62,7 +62,7 @@ Distributions = "0.25.77"
 DistributionsAD = "0.6"
 DocStringExtensions = "0.8, 0.9"
 DynamicHMC = "3.4"
-DynamicPPL = "0.35"
+DynamicPPL = "0.36"
 EllipticalSliceSampling = "0.5, 1, 2"
 ForwardDiff = "0.10.3"
 Libtask = "0.8.8"

diff --git a/docs/src/api.md b/docs/src/api.md
@@ -40,7 +40,7 @@ even though [`Prior()`](@ref) is actually defined in the `Turing.Inference` modu
 | `@model`             | [`DynamicPPL.@model`](@extref)             | Define a probabilistic model                                                                 |
 | `@varname`           | [`AbstractPPL.@varname`](@extref)          | Generate a `VarName` from a Julia expression                                                 |
 | `to_submodel`        | [`DynamicPPL.to_submodel`](@extref)        | Define a submodel                                                                            |
-| `prefix`             | [`DynamicPPL.prefix`](@extref)             | Prefix all variable names in a model with a given symbol                                     |
+| `prefix`             | [`DynamicPPL.prefix`](@extref)             | Prefix all variable names in a model with a given VarName                                    |
 | `LogDensityFunction` | [`DynamicPPL.LogDensityFunction`](@extref) | A struct containing all information about how to evaluate a model. Mostly for advanced users |
 
 ### Inference

diff --git a/src/mcmc/Inference.jl b/src/mcmc/Inference.jl
@@ -4,9 +4,8 @@
 using DynamicPPL:
     Metadata,
     VarInfo,
-    TypedVarInfo,
     # TODO(mhauru) all_varnames_grouped_by_symbol isn't exported by DPPL, because it is only
-    # implemented for TypedVarInfo. It is used by mh.jl. Either refactor mh.jl to not use it
+    # implemented for NTVarInfo. It is used by mh.jl. Either refactor mh.jl to not use it
     # or implement it for other VarInfo types and export it from DPPL.
     all_varnames_grouped_by_symbol,
     syms,
@@ -161,7 +160,7 @@
 end
 
 # TODO: make a nicer `set_namedtuple!` and move these functions to DynamicPPL.
-function DynamicPPL.unflatten(vi::TypedVarInfo, θ::NamedTuple)
+function DynamicPPL.unflatten(vi::DynamicPPL.NTVarInfo, θ::NamedTuple)
     set_namedtuple!(deepcopy(vi), θ)
     return vi
 end

diff --git a/src/mcmc/gibbs.jl b/src/mcmc/gibbs.jl
@@ -21,6 +21,11 @@ isgibbscomponent(spl::ExternalSampler) = isgibbscomponent(spl.sampler)
 isgibbscomponent(::AdvancedHMC.HMC) = true
 isgibbscomponent(::AdvancedMH.MetropolisHastings) = true
 
+function can_be_wrapped(ctx::DynamicPPL.AbstractContext)
+    return DynamicPPL.NodeTrait(ctx) isa DynamicPPL.IsLeaf
+end
+can_be_wrapped(ctx::DynamicPPL.PrefixContext) = can_be_wrapped(ctx.context)
+
 # Basically like a `DynamicPPL.FixedContext` but
 # 1. Hijacks the tilde pipeline to fix variables.
 # 2. Computes the log-probability of the fixed variables.
@@ -68,14 +73,14 @@ struct GibbsContext{VNs,GVI<:Ref{<:AbstractVarInfo},Ctx<:DynamicPPL.AbstractCont
     context::Ctx
 
     function GibbsContext{VNs}(global_varinfo, context) where {VNs}
-        if !(DynamicPPL.NodeTrait(context) isa DynamicPPL.IsLeaf)
+        if !can_be_wrapped(context)
             error("GibbsContext can only wrap a leaf context, not a $(context).")
         end
         return new{VNs,typeof(global_varinfo),typeof(context)}(global_varinfo, context)
     end
 
     function GibbsContext(target_varnames, global_varinfo, context)
-        if !(DynamicPPL.NodeTrait(context) isa DynamicPPL.IsLeaf)
+        if !can_be_wrapped(context)
             error("GibbsContext can only wrap a leaf context, not a $(context).")
         end
         if any(vn -> DynamicPPL.getoptic(vn) != identity, target_varnames)
@@ -232,9 +237,11 @@ end
 wrap_in_sampler(x::AbstractMCMC.AbstractSampler) = x
 wrap_in_sampler(x::InferenceAlgorithm) = DynamicPPL.Sampler(x)
 
-to_varname_list(x::Union{VarName,Symbol}) = [VarName(x)]
+to_varname(x::VarName) = x
+to_varname(x::Symbol) = VarName{x}()
+to_varname_list(x::Union{VarName,Symbol}) = [to_varname(x)]
 # Any other value is assumed to be an iterable of VarNames and Symbols.
-to_varname_list(t) = collect(map(VarName, t))
+to_varname_list(t) = collect(map(to_varname, t))
 
 """
     Gibbs

diff --git a/test/Project.toml b/test/Project.toml
@@ -40,7 +40,7 @@ TimerOutputs = "a759f4b9-e2f1-59dc-863e-4aeb61b1ea8f"
 
 [compat]
 AbstractMCMC = "5"
-AbstractPPL = "0.9, 0.10"
+AbstractPPL = "0.9, 0.10, 0.11"
 AdvancedMH = "0.6, 0.7, 0.8"
 AdvancedPS = "=0.6.0"
 AdvancedVI = "0.2"
@@ -52,7 +52,7 @@ Combinatorics = "1"
 Distributions = "0.25"
 DistributionsAD = "0.6.3"
 DynamicHMC = "2.1.6, 3.0"
-DynamicPPL = "0.35"
+DynamicPPL = "0.36"
 FiniteDifferences = "0.10.8, 0.11, 0.12"
 ForwardDiff = "0.10.12 - 0.10.32, 0.10"
 HypothesisTests = "0.11"

diff --git a/test/optimisation/Optimisation.jl b/test/optimisation/Optimisation.jl
@@ -71,13 +71,9 @@ using Turing
         end
 
         @testset "With prefixes" begin
-            function prefix_μ(model)
-                return DynamicPPL.contextualize(
-                    model, DynamicPPL.PrefixContext{:inner}(model.context)
-                )
-            end
-            m1 = prefix_μ(model1(x))
-            m2 = prefix_μ(model2() | (var"inner.x"=x,))
+            vn = @varname(inner)
+            m1 = prefix(model1(x), vn)
+            m2 = prefix((model2() | (x=x,)), vn)
             ctx = Turing.Optimisation.OptimizationContext(DynamicPPL.LikelihoodContext())
             @test Turing.Optimisation.OptimLogDensity(m1, ctx)(w) ==
                 Turing.Optimisation.OptimLogDensity(m2, ctx)(w)