Unable to sample posterior of the MC example from JuliaCon 2021

[AxLamelas]

Hello, I'm trying to reproduce the example using Markov chains from JuliaCon 2021 but I cannot get the sampling to work. I saw issue #293 I tried the code from there it still fails in the same way. Here is the code I'm using:

using Soss
using Base.Iterators
using SampleChainsDynamicHMC

mc_init = @model begin
	x ~ Normal(0.0, 1.0)
	return (x=x,)
end

mc_step = @model p, s begin
	x ~ Normal(s.x + p.Δμ, p.σ)
	return (x = x,)
end

m = @model begin
	Δμ ~ Normal()
	σ ~ HalfNormal()
	p = (; Δμ, σ)
	mc ~  Chain(mc_init()) do s mc_step(s=s,p=p) end 
	return mc
end

obs = take(rand(m()),400) |> collect
post = m() | (mc = obs,)
chain = sample(post,dynamichmc())

and here is the error

ERROR: MethodError: no method matching logdensity_def(::Soss.ConditionalModel{NamedTuple{()}, GeneralizedGenerated.NGG.TypeLevel{Expr, "Buf{23}()"}, GeneralizedGenerated.NGG.TypeLevel{Module, "Buf{17}()"}, NamedTuple{(), Tuple{}}, NamedTuple{(), Tuple{}}}, ::NamedTuple{(:x,), Tuple{Float64}})
Closest candidates are:
  logdensity_def(::Sorted, ::Any) at C:\Users\AxLamelas\.julia\packages\MeasureTheory\GI8wX\src\transforms\ordered.jl:75
  logdensity_def(::Laplace{()}, ::Any) at C:\Users\AxLamelas\.julia\packages\MeasureTheory\GI8wX\src\parameterized\laplace.jl:24
  logdensity_def(::Laplace{(:μ,)}, ::Any) at C:\Users\AxLamelas\.julia\packages\MeasureBase\Tfp1d\src\proxies.jl:17
  ...
Stacktrace:
  [1] logdensity_def
    @ C:\Users\AxLamelas\.julia\packages\MeasureTheory\GI8wX\src\combinators\chain.jl:28 [inlined]
  [2] unsafe_logdensityof(μ::Chain{GeneralizedGenerated.Closure{function = (p, s;) -> begin
    (Main).mc_step(s = s, p = p)
end, Tuple{NamedTuple{(:Δμ, :σ), Tuple{ForwardDiff.Dual{ForwardDiff.Tag{LogDensityProblems.var"#46#47"{LogDensityProblems.TransformedLogDensity{TransformVariables.TransformTuple{NamedTuple{(:σ, :Δμ), Tuple{TransformVariables.ShiftedExp{true, Int64}, TransformVariables.Identity}}}, Soss.var"#ℓ#154"{Soss.ConditionalModel{NamedTuple{()}, GeneralizedGenerated.NGG.TypeLevel{Expr, "Buf{23}()"}, GeneralizedGenerated.NGG.TypeLevel{Module, "Buf{17}()"}, NamedTuple{(), Tuple{}}, NamedTuple{(:mc,), Tuple{Vector{Any}}}}}}}, Float64}, Float64, 2}, ForwardDiff.Dual{ForwardDiff.Tag{LogDensityProblems.var"#46#47"{LogDensityProblems.TransformedLogDensity{TransformVariables.TransformTuple{NamedTuple{(:σ, :Δμ), Tuple{TransformVariables.ShiftedExp{true, Int64}, TransformVariables.Identity}}}, Soss.var"#ℓ#154"{Soss.ConditionalModel{NamedTuple{()}, GeneralizedGenerated.NGG.TypeLevel{Expr, "Buf{23}()"}, GeneralizedGenerated.NGG.TypeLevel{Module, "Buf{17}()"}, NamedTuple{(), Tuple{}}, NamedTuple{(:mc,), Tuple{Vector{Any}}}}}}}, Float64}, Float64, 2}}}}}, Soss.ConditionalModel{NamedTuple{()}, GeneralizedGenerated.NGG.TypeLevel{Expr, "Buf{23}()"}, GeneralizedGenerated.NGG.TypeLevel{Module, "Buf{17}()"}, NamedTuple{(), Tuple{}}, NamedTuple{(), Tuple{}}}}, x::Vector{Any})
    @ MeasureBase C:\Users\AxLamelas\.julia\packages\MeasureBase\Tfp1d\src\density.jl:139

I get this error both with the windows version of Julia and using WSL2.

Package verions

Status `C:\Users\AxLamelas\Documents\Personal\Credit simulations\Julia\Manifest.toml`
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[cscherrer]

Hi @AxLamelas , thanks for letting me know about this. I'm checking into it now

[cscherrer]

The logdensity_def problem is easily addressed by adding a primitive for that. I'm still seeing an issue after doing that. This problem can be reduced to

using Soss

mc_init = @model begin
	x ~ Normal(0.0, 1.0)
	return (x=x,)
end

mc_step = @model s begin
	x ~ Normal(s.x + 0.1, 0.2)
	return (x = x,)
end

d = Chain(mc_init()) do s mc_step(s=s) end
x = Iterators.take(rand(d), 10) |> collect
logdensity_def(d,x)

This gives the result

ERROR: MethodError: First argument to `convert` must be a Type, got (x = Float64,)
Stacktrace:
 [1] macro expansion
   @ ~/git/Soss.jl/src/primitives/logdensity.jl:92 [inlined]
 [2] _logdensity_def(M::Type{Soss.GeneralizedGenerated.NGG.TypeLevel{Module, "Buf{17}()"}}, _m::Model{NamedTuple{(:p, :s)}, Soss.GeneralizedGenerated.NGG.TypeLevel{Expr, "Buf{23}()"}, Soss.GeneralizedGenerated.NGG.TypeLevel{Module, "Buf{17}()"}}, _args::NamedTuple{(:s, :p), Tuple{NamedTuple{(:x,), Tuple{Float64}}, NamedTuple{(:Δμ, :σ), Tuple{Float64, Float64}}}}, _data::NamedTuple{(), Tuple{}}, _pars::NamedTuple{(:x,), Tuple{Float64}})
   @ Soss ~/git/Soss.jl/src/primitives/logdensity.jl:92
 [3] logdensity_def(c::Soss.ConditionalModel{NamedTuple{(:p, :s)}, Soss.GeneralizedGenerated.NGG.TypeLevel{Expr, "Buf{23}()"}, Soss.GeneralizedGenerated.NGG.TypeLevel{Module, "Buf{17}()"}, NamedTuple{(:s, :p), Tuple{NamedTuple{(:x,), Tuple{Float64}}, NamedTuple{(:Δμ, :σ), Tuple{Float64, Float64}}}}, NamedTuple{(), Tuple{}}}, x::NamedTuple{(:x,), Tuple{Float64}})
   @ Soss ~/git/Soss.jl/src/primitives/logdensity.jl:51
 [4] logdensity_def(mc::Chain{var"#19#20", Soss.ConditionalModel{NamedTuple{()}, Soss.GeneralizedGenerated.NGG.TypeLevel{Expr, "Buf{23}()"}, Soss.GeneralizedGenerated.NGG.TypeLevel{Module, "Buf{17}()"}, NamedTuple{(), Tuple{}}, NamedTuple{(), Tuple{}}}}, x::Vector{Any})
   @ MeasureTheory ~/.julia/packages/MeasureTheory/MeOXc/src/combinators/chain.jl:28
 [5] top-level scope
   @ REPL[89]:1

[cscherrer]

I had hoped this would just be a matter of adding a logdensity_def method. After adding this in #350, I still get the above error,

ERROR: MethodError: First argument to `convert` must be a Type, got (x = Float64,)

We can also look at the simpler example:

julia> mc_step = @model x begin
               y ~ Normal(x + 0.1, 0.2)
               return y
       end;

julia> d = Chain(Normal()) do s mc_step(s) end;

julia> x = Iterators.take(rand(d), 10) |> collect;

julia> logdensity_def(d,x)
ERROR: MethodError: no method matching keys(::Type{Float64})
Closest candidates are:
  keys(::Union{Tables.AbstractColumns, Tables.AbstractRow}) at ~/.julia/packages/Tables/PxO1m/src/Tables.jl:184
  keys(::Missings.EachReplaceMissing) at ~/.julia/packages/Missings/r1STI/src/Missings.jl:94
  keys(::DataStructures.Trie) at ~/.julia/packages/DataStructures/59MD0/src/trie.jl:82
  ...
Stacktrace:
 [1] loadvals(argstype::Type, datatype::Type, parstype::Type)
   @ Soss ~/git/Soss.jl/src/core/utils.jl:217
 [2] #s112#76
   @ ~/git/Soss.jl/src/primitives/logdensity.jl:92 [inlined]
 [3] var"#s112#76"(::Any, M::Any, _m::Any, _args::Any, _data::Any, _pars::Any)
   @ Soss ./none:0
 [4] (::Core.GeneratedFunctionStub)(::Any, ::Vararg{Any})
   @ Core ./boot.jl:582
 [5] logdensity_def(c::Soss.ConditionalModel{NamedTuple{(:x,)}, Soss.GeneralizedGenerated.NGG.TypeLevel{Expr, "Buf{23}()"}, Soss.GeneralizedGenerated.NGG.TypeLevel{Module, "Buf{17}()"}, NamedTuple{(:x,), Tuple{Float64}}, NamedTuple{(), Tuple{}}}, x::Float64)
   @ Soss ~/git/Soss.jl/src/primitives/logdensity.jl:50
 [6] logdensity_def(mc::Chain{var"#11#12", Normal{(), Tuple{}}}, x::Vector{Any})
   @ MeasureTheory ~/.julia/packages/MeasureTheory/Mrznp/src/combinators/chain.jl:28
 [7] top-level scope
   @ REPL[24]:1

So it looks like there's a place where types are mistakenly being treated as values. That kind of problem usually means it's a metaprogramming issue, maybe a generated function. But then it's also strange that other tests pass, included those for nested models. So it might have to do with the implementation of Chain, or possible its interaction with the generated functions.

The errors I'm getting are kind of tricky to debug. I think a next step might be for me to implement this example in Tilde and see if I hit the same issue.