Remove LogDensityFunctionWrapper and replace VarInfo(mld, ...) with InitFromVector

HISTORY.md

@@ -1,3 +1,10 @@
+# 0.41
+
+Removed `LogDensityFunctionWrapper` and `VarInfo(::MarginalLogDensity, ...)`
+from the MarginalLogDensities extension. Users should now use
+`DynamicPPL.InitFromVector(mld, ...)` to obtain an initialisation strategy
+and pass it to `init!!` to get a consistent `VarInfo`.
+
 # 0.40.20
 
 Added a public function, `DynamicPPL.extract_prefixes(::AbstractContext)`, to more generally handle the removal of `PrefixContext` entries from the context stack.

docs/make.jl

@@ -16,9 +16,6 @@ using AbstractMCMC: AbstractMCMC
 using MarginalLogDensities: MarginalLogDensities
 using Random
 
-# Need this to document a method which uses a type inside the extension...
-DPPLMLDExt = Base.get_extension(DynamicPPL, :DynamicPPLMarginalLogDensitiesExt)
-
 # Doctest setup
 DocMeta.setdocmeta!(
     DynamicPPL, :DocTestSetup, :(using DynamicPPL, MCMCChains); recursive=true

docs/src/api.md

@@ -170,11 +170,7 @@ marginalize
 ```
 
 A `MarginalLogDensity` object acts as a function which maps non-marginalised parameter values to a marginal log-probability.
-To retrieve a VarInfo object from it, you can use:
-
-```@docs
-VarInfo(::MarginalLogDensities.MarginalLogDensity{<:DPPLMLDExt.LogDensityFunctionWrapper}, ::Union{AbstractVector,Nothing})
-```
+To obtain an initialisation strategy reflecting the state of the marginalisation, you can use [`InitFromVector`](@ref).
 
 ## Models within models
 

ext/DynamicPPLMarginalLogDensitiesExt.jl

@@ -3,19 +3,11 @@ module DynamicPPLMarginalLogDensitiesExt
 using DynamicPPL: DynamicPPL, LogDensityProblems, VarName, RangeAndLinked
 using MarginalLogDensities: MarginalLogDensities
 
-# A thin wrapper to adapt a DynamicPPL.LogDensityFunction to the interface expected by
-# MarginalLogDensities. It's helpful to have a struct so that we can dispatch on its type
-# below.
-struct LogDensityFunctionWrapper{
-    L<:DynamicPPL.LogDensityFunction,V<:DynamicPPL.AbstractVarInfo
-}
-    logdensity::L
-    # This field is used only to reconstruct the VarInfo later on; it's not needed for the
-    # actual log-density evaluation.
-    varinfo::V
-end
-function (lw::LogDensityFunctionWrapper)(x, _)
-    return LogDensityProblems.logdensity(lw.logdensity, x)
+# Make LogDensityFunction directly callable with the two-argument interface expected by
+# MarginalLogDensities. The second argument is the gradient and is unused here because
+# MarginalLogDensities handles differentiation separately.
+function (ldf::DynamicPPL.LogDensityFunction)(x, _)
+    return LogDensityProblems.logdensity(ldf, x)
 end
 
 """
@@ -53,7 +45,6 @@ log-density.
   constructor.
 
 ## Example
-
 ```jldoctest
 julia> using DynamicPPL, Distributions, MarginalLogDensities
 
@@ -80,12 +71,11 @@ julia> logpdf(Normal(2.0), 1.0)
     marginal log-density can be performed in unconstrained space. However, care must be
     taken if the model contains variables where the link transformation depends on a
     marginalized variable. For example:
-
     ```julia
-    @model function f()
-        x ~ Normal()
-        y ~ truncated(Normal(); lower=x)
-    end
+        @model function f()
+            x ~ Normal()
+            y ~ truncated(Normal(); lower=x)
+        end
     ```
 
     Here, the support of `y`, and hence the link transformation used, depends on the value
@@ -101,7 +91,7 @@ function DynamicPPL.marginalize(
     method::MarginalLogDensities.AbstractMarginalizer=MarginalLogDensities.LaplaceApprox(),
     kwargs...,
 )
-    # Construct the marginal log-density model.
+    # Construct the log-density function directly from the model and varinfo.
     ldf = DynamicPPL.LogDensityFunction(model, getlogprob, varinfo)
     # Determine the indices for the variables to marginalise out.
     varindices = mapreduce(vcat, marginalized_varnames) do vn
@@ -110,121 +100,49 @@ function DynamicPPL.marginalize(
         (ldf._varname_ranges[vn]::RangeAndLinked).range
     end
     mld = MarginalLogDensities.MarginalLogDensity(
-        LogDensityFunctionWrapper(ldf, varinfo),
-        varinfo[:],
-        varindices,
-        (),
-        method;
-        kwargs...,
+        ldf, varinfo[:], varindices, (), method; kwargs...
     )
     return mld
 end
 
 """
-    VarInfo(
-        mld::MarginalLogDensities.MarginalLogDensity{<:LogDensityFunctionWrapper},
+    InitFromVector(
+        mld::MarginalLogDensities.MarginalLogDensity{<:DynamicPPL.LogDensityFunction},
         unmarginalized_params::Union{AbstractVector,Nothing}=nothing
     )
 
-Retrieve the `VarInfo` object used in the marginalisation process.
-
-If a Laplace approximation was used for the marginalisation, the values of the marginalized
-parameters are also set to their mode (note that this only happens if the `mld` object has
-been used to compute the marginal log-density at least once, so that the mode has been
-computed).
+Return an [`InitFromVector`](@ref DynamicPPL.InitFromVector) initialisation strategy whose
+parameter vector reflects the state of `mld`.
 
-If a vector of `unmarginalized_params` is specified, the values for the corresponding
-parameters will also be updated in the returned VarInfo. This vector may be obtained e.g. by
-performing an optimization of the marginal log-density.
+If a Laplace approximation was used for marginalisation, the marginalized parameters are set
+to their modal values (note that this requires `mld` to have been evaluated at least once,
+so that the mode has been found).
 
-All other aspects of the VarInfo, such as link status, are preserved from the original
-VarInfo used in the marginalisation.
-
-!!! note
-
-    The other fields of the VarInfo, e.g. accumulated log-probabilities, will not be
-    updated. If you wish to obtain updated log-probabilities, you should re-evaluate the
-    model with the values inside the returned VarInfo, for example using:
-
-    ```julia
-    init_strategy = DynamicPPL.InitFromParams(varinfo.values, nothing)
-    oavi = DynamicPPL.OnlyAccsVarInfo((
-        DynamicPPL.LogPriorAccumulator(),
-        DynamicPPL.LogLikelihoodAccumulator(),
-        DynamicPPL.RawValueAccumulator(false),
-        # ... whatever else you need
-    ))
-    _, oavi = DynamicPPL.init!!(rng, model, oavi, init_strategy, DynamicPPL.UnlinkAll())
-    ```
-
-    You can then extract all the updated data from `oavi`.
-
-## Example
-
-```jldoctest
-julia> using DynamicPPL, Distributions, MarginalLogDensities
-
-julia> @model function demo()
-           x ~ Normal()
-           y ~ Beta(2, 2)
-       end
-demo (generic function with 2 methods)
+If `unmarginalized_params` is provided, those values are used for the non-marginalized
+parameters. This vector may be obtained e.g. by optimizing the marginal log-density.
 
-julia> # Note that by default `marginalize` uses a linked VarInfo.
-       mld = marginalize(demo(), [@varname(x)]);
-
-julia> using MarginalLogDensities: Optimization, OptimizationOptimJL
-
-julia> # Find the mode of the marginal log-density of `y`, with an initial point of `y0`.
-       y0 = 2.0; opt_problem = Optimization.OptimizationProblem(mld, [y0])
-OptimizationProblem. In-place: true
-u0: 1-element Vector{Float64}:
- 2.0
-
-julia> # This tells us the optimal (linked) value of `y` is around 0.
-       opt_solution = Optimization.solve(opt_problem, OptimizationOptimJL.NelderMead())
-retcode: Success
-u: 1-element Vector{Float64}:
- 4.88281250001733e-5
-
-julia> # Get the VarInfo corresponding to the mode of `y`.
-       vi = VarInfo(mld, opt_solution.u);
-
-julia> # `x` is set to its mode (which for `Normal()` is zero).
-       vi[@varname(x)]
-0.0
-
-julia> # `y` is set to the optimal value we found above.
-       DynamicPPL.getindex_internal(vi, @varname(y))
-1-element Vector{Float64}:
- 4.88281250001733e-5
-
-julia> # To obtain values in the original constrained space, we can either
-       # use `getindex`:
-       vi[@varname(y)]
-0.5000122070312476
-
-julia> # Or invlink the entire VarInfo object using the model:
-       vi_unlinked = DynamicPPL.invlink(vi, demo()); vi_unlinked[:]
-2-element Vector{Float64}:
- 0.0
- 0.5000122070312476
+To obtain a fully consistent `VarInfo` — with updated log-probabilities and correct link
+status — use the returned strategy to re-evaluate the model:
+```julia
+init_strategy = DynamicPPL.InitFromVector(mld, opt_solution.u)
+ldf = mld.logdensity
+_, vi = DynamicPPL.init!!(ldf.model, DynamicPPL.VarInfo(), init_strategy, ldf.transform_strategy)
 ```
 """
-function DynamicPPL.VarInfo(
-    mld::MarginalLogDensities.MarginalLogDensity{<:LogDensityFunctionWrapper},
+function DynamicPPL.InitFromVector(
+    mld::MarginalLogDensities.MarginalLogDensity{<:DynamicPPL.LogDensityFunction},
     unmarginalized_params::Union{AbstractVector,Nothing}=nothing,
 )
-    # Extract the original VarInfo. Its contents will in general be junk.
-    original_vi = mld.logdensity.varinfo
-    # Extract the stored parameters, which includes the modes for any marginalized
-    # parameters
+    # Retrieve the full cached parameter vector (includes modal values for marginalized
+    # parameters if a Laplace approximation has been run).
     full_params = MarginalLogDensities.cached_params(mld)
-    # We can then (if needed) set the values for any non-marginalized parameters
+    # Overwrite the non-marginalized entries if the caller supplied them.
     if unmarginalized_params !== nothing
         full_params[MarginalLogDensities.ijoint(mld)] = unmarginalized_params
     end
-    return DynamicPPL.unflatten!!(original_vi, full_params)
+    # Use the convenience constructor that reads varname_ranges and transform_strategy
+    # directly from the LogDensityFunction stored inside mld.
+    return DynamicPPL.InitFromVector(full_params, mld.logdensity)
 end
 
 end

test/ext/DynamicPPLMarginalLogDensitiesExt.jl

@@ -79,20 +79,34 @@ using ADTypes: AutoForwardDiff
 
         @testset "unlinked VarInfo" begin
             mx = marginalize(model, [@varname(x)]; varinfo=vi_unlinked)
-            mx([0.5]) # evaluate at some point to force calculation of Laplace approx
-            vi = VarInfo(mx)
+            mx([0.5]) # evaluate to force the Laplace approximation to run and cache modal values
+            strategy = DynamicPPL.InitFromVector(mx) # build init strategy from cached modal values
+            ldf = mx.logdensity
+            _, vi = DynamicPPL.init!!(
+                ldf.model, DynamicPPL.VarInfo(), strategy, ldf.transform_strategy
+            )
             @test vi[@varname(x)] ≈ mode(Normal())
-            vi = VarInfo(mx, [0.5]) # this 0.5 is unlinked
+            strategy = DynamicPPL.InitFromVector(mx, [0.5]) # same, but override the unmarginalized parameter with 0.5
+            _, vi = DynamicPPL.init!!(
+                ldf.model, DynamicPPL.VarInfo(), strategy, ldf.transform_strategy
+            )
             @test vi[@varname(x)] ≈ mode(Normal())
             @test vi[@varname(y)] ≈ 0.5
         end
 
         @testset "linked VarInfo" begin
             mx = marginalize(model, [@varname(x)]; varinfo=vi_linked)
             mx([0.5]) # evaluate at some point to force calculation of Laplace approx
-            vi = VarInfo(mx)
+            strategy = DynamicPPL.InitFromVector(mx) # build init strategy from cached modal values
+            ldf = mx.logdensity
+            _, vi = DynamicPPL.init!!(
+                ldf.model, DynamicPPL.VarInfo(), strategy, ldf.transform_strategy
+            )
             @test vi[@varname(x)] ≈ mode(Normal())
-            vi = VarInfo(mx, [0.5]) # this 0.5 is linked
+            strategy = DynamicPPL.InitFromVector(mx, [0.5]) # this 0.5 is a linked value for the unmarginalized parameter y
+            _, vi = DynamicPPL.init!!(
+                ldf.model, DynamicPPL.VarInfo(), strategy, ldf.transform_strategy
+            )
             binv = Bijectors.inverse(Bijectors.bijector(Beta(2, 2)))
             @test vi[@varname(x)] ≈ mode(Normal())
             # when using getindex it always returns unlinked values