`predict` on vector of varinfo is incorrect if the varinfo passed in is linked

[penelopeysm]

julia> @model f() = x ~ Beta(2, 2)
f (generic function with 2 methods)

julia> m = f(); v = link!!(VarInfo(m), m)
VarInfo{@NamedTuple{x::DynamicPPL.Metadata{Dict{VarName{:x, typeof(identity)}, Int64}, Vector{Beta{Float64}}, Vector{VarName{:x, typeof(identity)}}, Vector{Float64}}}, Float64}((x = DynamicPPL.Metadata{Dict{VarName{:x, typeof(identity)}, Int64}, Vector{Beta{Float64}}, Vector{VarName{:x, typeof(identity)}}, Vector{Float64}}(Dict(x => 1), [x], UnitRange{Int64}[1:1], [1.3950616230805561], Beta{Float64}[Beta{Float64}(α=2.0, β=2.0)], [0], Dict{String, BitVector}("del" => [0], "trans" => [1])),), Base.RefValue{Float64}(-1.8839487262608983), Base.RefValue{Int64}(0))

julia> v[@varname(x)] # Should always be in (0, 1), even though it's been linked
0.8013990731398695

julia> pdns = predict(m, [v])
1-element Vector{VarInfo{@NamedTuple{x::DynamicPPL.Metadata{Dict{VarName{:x, typeof(identity)}, Int64}, Vector{Beta{Float64}}, Vector{VarName{:x, typeof(identity)}}, Vector{Float64}}}, Float64}}:
 VarInfo{@NamedTuple{x::DynamicPPL.Metadata{Dict{VarName{:x, typeof(identity)}, Int64}, Vector{Beta{Float64}}, Vector{VarName{:x, typeof(identity)}}, Vector{Float64}}}, Float64}((x = DynamicPPL.Metadata{Dict{VarName{:x, typeof(identity)}, Int64}, Vector{Beta{Float64}}, Vector{VarName{:x, typeof(identity)}}, Vector{Float64}}(Dict(x => 1), [x], UnitRange{Int64}[1:1], [1.3950616230805561], Beta{Float64}[Beta{Float64}(α=2.0, β=2.0)], [0], Dict{String, BitVector}("del" => [0], "trans" => [0])),), Base.RefValue{Float64}(-Inf), Base.RefValue{Int64}(0))

julia> pdns[1][@varname(x)]
1.3950616230805561

julia> getlogp(pdns[1])
-Inf

[penelopeysm]

The removal of setval_and_resample! should fix this, just opening it as a separate issue to track it.

[penelopeysm]

Using #967:

julia> using DynamicPPL, Distributions
Precompiling DynamicPPL...
  2 dependencies successfully precompiled in 6 seconds. 122 already precompiled.

julia> @model f() = x ~ Beta(2, 2)
f (generic function with 2 methods)

julia> m = f(); v = link!!(VarInfo(m), m)
VarInfo{@NamedTuple{x::DynamicPPL.Metadata{Dict{VarName{:x, typeof(identity)}, Int64}, Vector{Beta{Float64}}, Vector{VarName{:x, typeof(identity)}}, Vector{Float64}}}, DynamicPPL.AccumulatorTuple{3, @NamedTuple{LogPrior::LogPriorAccumulator{Float64}, LogLikelihood::LogLikelihoodAccumulator{Float64}, NumProduce::NumProduceAccumulator{Int64}}}}((x = DynamicPPL.Metadata{Dict{VarName{:x, typeof(identity)}, Int64}, Vector{Beta{Float64}}, Vector{VarName{:x, typeof(identity)}}, Vector{Float64}}(Dict(x => 1), [x], UnitRange{Int64}[1:1], [-1.2446528135384165], Beta{Float64}[Beta{Float64}(α=2.0, β=2.0)], [0], Dict{String, BitVector}("del" => [0], "trans" => [1])),), DynamicPPL.AccumulatorTuple{3, @NamedTuple{LogPrior::LogPriorAccumulator{Float64}, LogLikelihood::LogLikelihoodAccumulator{Float64}, NumProduce::NumProduceAccumulator{Int64}}}((LogPrior = LogPriorAccumulator(-1.7100356686475868), LogLikelihood = LogLikelihoodAccumulator(0.0), NumProduce = NumProduceAccumulator(0))))

julia> v[@varname(x)]
0.223627134715461

julia> pdns = predict(m, [v])
1-element Vector{VarInfo{@NamedTuple{x::DynamicPPL.Metadata{Dict{VarName{:x, typeof(identity)}, Int64}, Vector{Beta{Float64}}, Vector{VarName{:x, typeof(identity)}}, Vector{Float64}}}, DynamicPPL.AccumulatorTuple{3, @NamedTuple{LogPrior::LogPriorAccumulator{Float64}, LogLikelihood::LogLikelihoodAccumulator{Float64}, NumProduce::NumProduceAccumulator{Int64}}}}}:
 VarInfo{@NamedTuple{x::DynamicPPL.Metadata{Dict{VarName{:x, typeof(identity)}, Int64}, Vector{Beta{Float64}}, Vector{VarName{:x, typeof(identity)}}, Vector{Float64}}}, DynamicPPL.AccumulatorTuple{3, @NamedTuple{LogPrior::LogPriorAccumulator{Float64}, LogLikelihood::LogLikelihoodAccumulator{Float64}, NumProduce::NumProduceAccumulator{Int64}}}}((x = DynamicPPL.Metadata{Dict{VarName{:x, typeof(identity)}, Int64}, Vector{Beta{Float64}}, Vector{VarName{:x, typeof(identity)}}, Vector{Float64}}(Dict(x => 1), [x], UnitRange{Int64}[1:1], [0.223627134715461], Beta{Float64}[Beta{Float64}(α=2.0, β=2.0)], [0], Dict{String, BitVector}("del" => [0], "trans" => [0])),), DynamicPPL.AccumulatorTuple{3, @NamedTuple{LogPrior::LogPriorAccumulator{Float64}, LogLikelihood::LogLikelihoodAccumulator{Float64}, NumProduce::NumProduceAccumulator{Int64}}}((LogPrior = LogPriorAccumulator(0.0408619002902344), LogLikelihood = LogLikelihoodAccumulator(0.0), NumProduce = NumProduceAccumulator(0))))

julia> pdns[1][@varname(x)]
0.223627134715461

julia> getlogp(pdns[1])
(logprior = 0.0408619002902344, loglikelihood = 0.0)