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Asssigning to a Vector of Matrices produces BoundsError #2095

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tiemvanderdeure opened this issue Oct 3, 2023 · 2 comments
Open

Asssigning to a Vector of Matrices produces BoundsError #2095

tiemvanderdeure opened this issue Oct 3, 2023 · 2 comments

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@tiemvanderdeure
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Sampling from LKJ or LKJCholesky and assigning to a vector of matrices errors, regardless of AD backend.

The code compiles but errors with a BoundsError. Sampling from the Prior works does not produce an error.

MWE

@model function vector_of_cor_matrices(n, Type::TV=Matrix{Float64}) where {TV}
    mat = Vector{TV}(undef, n)
    for i = 1:n      
        mat[i] ~ LKJ(3, 3.0)
    end
end

sample(vector_of_cor_matrices(2), NUTS(), 200)

Full stacktrace

ERROR: BoundsError: attempt to access 6-element Vector{ForwardDiff.Dual{ForwardDiff.Tag{Turing.TuringTag, Float64}, Float64, 6}} at index [10:12]
Stacktrace:
  [1] throw_boundserror(A::Vector{ForwardDiff.Dual{ForwardDiff.Tag{Turing.TuringTag, Float64}, Float64, 6}}, I::Tuple{UnitRange{Int64}})
    @ Base .\abstractarray.jl:744
  [2] checkbounds
    @ .\abstractarray.jl:709 [inlined]
  [3] view
    @ .\subarray.jl:177 [inlined]
  [4] getval
    @ C:\Users\tsh371\.julia\packages\DynamicPPL\m0PXI\src\varinfo.jl:318 [inlined]
  [5] getval
    @ C:\Users\tsh371\.julia\packages\DynamicPPL\m0PXI\src\varinfo.jl:317 [inlined]
  [6] getval
    @ C:\Users\tsh371\.julia\packages\DynamicPPL\m0PXI\src\threadsafe.jl:194 [inlined]
  [7] invlink_with_logpdf
    @ C:\Users\tsh371\.julia\packages\DynamicPPL\m0PXI\src\abstract_varinfo.jl:679 [inlined]
  [8] assume
    @ C:\Users\tsh371\.julia\packages\DynamicPPL\m0PXI\src\context_implementations.jl:197 [inlined]
  [9] assume(rng::Random.TaskLocalRNG, spl::DynamicPPL.Sampler{NUTS{Turing.Essential.ForwardDiffAD{0}, (), AdvancedHMC.DiagEuclideanMetric}}, dist::LKJ{Float64, Int64}, vn::AbstractPPL.VarName{:mat, Setfield.IndexLens{Tuple{Int64}}}, vi::DynamicPPL.ThreadSafeVarInfo{DynamicPPL.TypedVarInfo{NamedTuple{(:mat,), Tuple{DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:mat, Setfield.IndexLens{Tuple{Int64}}}, Int64}, Vector{LKJ{Float64, Int64}}, Vector{AbstractPPL.VarName{:mat, Setfield.IndexLens{Tuple{Int64}}}}, Vector{ForwardDiff.Dual{ForwardDiff.Tag{Turing.TuringTag, Float64}, Float64, 6}}, Vector{Set{DynamicPPL.Selector}}}}}, ForwardDiff.Dual{ForwardDiff.Tag{Turing.TuringTag, Float64}, Float64, 6}}, Vector{Base.RefValue{ForwardDiff.Dual{ForwardDiff.Tag{Turing.TuringTag, Float64}, Float64, 6}}}})
    @ Turing.Inference C:\Users\tsh371\.julia\packages\Turing\CP3Ic\src\mcmc\hmc.jl:461
 [10] tilde_assume
    @ C:\Users\tsh371\.julia\packages\DynamicPPL\m0PXI\src\context_implementations.jl:49 [inlined]
 [11] tilde_assume
    @ C:\Users\tsh371\.julia\packages\DynamicPPL\m0PXI\src\context_implementations.jl:46 [inlined]
 [12] tilde_assume
    @ C:\Users\tsh371\.julia\packages\DynamicPPL\m0PXI\src\context_implementations.jl:31 [inlined]
 [13] tilde_assume!!
    @ C:\Users\tsh371\.julia\packages\DynamicPPL\m0PXI\src\context_implementations.jl:117 [inlined]
 [14] macro expansion
    @ C:\Users\tsh371\.julia\packages\DynamicPPL\m0PXI\src\compiler.jl:555 [inlined]
 [15] vector_of_cor_matrices(__model__::DynamicPPL.Model{typeof(vector_of_cor_matrices), (:n, :Type), (), (), Tuple{Int64, Matrix{Float64}}, Tuple{}, DynamicPPL.DefaultContext}, __varinfo__::DynamicPPL.ThreadSafeVarInfo{DynamicPPL.TypedVarInfo{NamedTuple{(:mat,), Tuple{DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:mat, Setfield.IndexLens{Tuple{Int64}}}, Int64}, Vector{LKJ{Float64, Int64}}, Vector{AbstractPPL.VarName{:mat, Setfield.IndexLens{Tuple{Int64}}}}, Vector{ForwardDiff.Dual{ForwardDiff.Tag{Turing.TuringTag, Float64}, Float64, 6}}, Vector{Set{DynamicPPL.Selector}}}}}, ForwardDiff.Dual{ForwardDiff.Tag{Turing.TuringTag, Float64}, Float64, 6}}, Vector{Base.RefValue{ForwardDiff.Dual{ForwardDiff.Tag{Turing.TuringTag, Float64}, Float64, 6}}}}, __context__::DynamicPPL.SamplingContext{DynamicPPL.Sampler{NUTS{Turing.Essential.ForwardDiffAD{0}, (), AdvancedHMC.DiagEuclideanMetric}}, DynamicPPL.DefaultContext, Random.TaskLocalRNG}, n::Int64, Type::Matrix{Float64})
    @ Main 
 [16] _evaluate!!(model::DynamicPPL.Model{typeof(vector_of_cor_matrices), (:n, :Type), (), (), Tuple{Int64, Matrix{Float64}}, Tuple{}, DynamicPPL.DefaultContext}, varinfo::DynamicPPL.ThreadSafeVarInfo{DynamicPPL.TypedVarInfo{NamedTuple{(:mat,), Tuple{DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:mat, Setfield.IndexLens{Tuple{Int64}}}, Int64}, Vector{LKJ{Float64, Int64}}, Vector{AbstractPPL.VarName{:mat, Setfield.IndexLens{Tuple{Int64}}}}, Vector{ForwardDiff.Dual{ForwardDiff.Tag{Turing.TuringTag, Float64}, Float64, 6}}, Vector{Set{DynamicPPL.Selector}}}}}, ForwardDiff.Dual{ForwardDiff.Tag{Turing.TuringTag, Float64}, Float64, 6}}, Vector{Base.RefValue{ForwardDiff.Dual{ForwardDiff.Tag{Turing.TuringTag, Float64}, Float64, 6}}}}, context::DynamicPPL.SamplingContext{DynamicPPL.Sampler{NUTS{Turing.Essential.ForwardDiffAD{0}, (), AdvancedHMC.DiagEuclideanMetric}}, DynamicPPL.DefaultContext, Random.TaskLocalRNG})
    @ DynamicPPL C:\Users\tsh371\.julia\packages\DynamicPPL\m0PXI\src\model.jl:963
 [17] evaluate_threadsafe!!
    @ C:\Users\tsh371\.julia\packages\DynamicPPL\m0PXI\src\model.jl:952 [inlined]
 [18] evaluate!!(model::DynamicPPL.Model{typeof(vector_of_cor_matrices), (:n, :Type), (), (), Tuple{Int64, Matrix{Float64}}, Tuple{}, DynamicPPL.DefaultContext}, varinfo::DynamicPPL.TypedVarInfo{NamedTuple{(:mat,), Tuple{DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:mat, Setfield.IndexLens{Tuple{Int64}}}, Int64}, Vector{LKJ{Float64, Int64}}, Vector{AbstractPPL.VarName{:mat, Setfield.IndexLens{Tuple{Int64}}}}, Vector{ForwardDiff.Dual{ForwardDiff.Tag{Turing.TuringTag, Float64}, Float64, 6}}, Vector{Set{DynamicPPL.Selector}}}}}, ForwardDiff.Dual{ForwardDiff.Tag{Turing.TuringTag, Float64}, Float64, 6}}, context::DynamicPPL.SamplingContext{DynamicPPL.Sampler{NUTS{Turing.Essential.ForwardDiffAD{0}, (), AdvancedHMC.DiagEuclideanMetric}}, DynamicPPL.DefaultContext, Random.TaskLocalRNG})
    @ DynamicPPL C:\Users\tsh371\.julia\packages\DynamicPPL\m0PXI\src\model.jl:887
 [19] logdensity(f::LogDensityFunction{DynamicPPL.TypedVarInfo{NamedTuple{(:mat,), Tuple{DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:mat, Setfield.IndexLens{Tuple{Int64}}}, Int64}, Vector{LKJ{Float64, Int64}}, Vector{AbstractPPL.VarName{:mat, Setfield.IndexLens{Tuple{Int64}}}}, Vector{Float64}, Vector{Set{DynamicPPL.Selector}}}}}, Float64}, DynamicPPL.Model{typeof(vector_of_cor_matrices), (:n, :Type), (), (), Tuple{Int64, Matrix{Float64}}, Tuple{}, DynamicPPL.DefaultContext}, DynamicPPL.SamplingContext{DynamicPPL.Sampler{NUTS{Turing.Essential.ForwardDiffAD{0}, (), AdvancedHMC.DiagEuclideanMetric}}, DynamicPPL.DefaultContext, Random.TaskLocalRNG}}, θ::Vector{ForwardDiff.Dual{ForwardDiff.Tag{Turing.TuringTag, Float64}, Float64, 6}})
    @ DynamicPPL C:\Users\tsh371\.julia\packages\DynamicPPL\m0PXI\src\logdensityfunction.jl:94
 [20] Fix1
    @ .\operators.jl:1108 [inlined]
 [21] vector_mode_dual_eval!
    @ C:\Users\tsh371\.julia\packages\ForwardDiff\PcZ48\src\apiutils.jl:24 [inlined]
 [22] vector_mode_gradient!(result::DiffResults.MutableDiffResult{1, Float64, Tuple{Vector{Float64}}}, f::Base.Fix1{typeof(LogDensityProblems.logdensity), LogDensityFunction{DynamicPPL.TypedVarInfo{NamedTuple{(:mat,), Tuple{DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:mat, Setfield.IndexLens{Tuple{Int64}}}, Int64}, Vector{LKJ{Float64, Int64}}, Vector{AbstractPPL.VarName{:mat, Setfield.IndexLens{Tuple{Int64}}}}, Vector{Float64}, Vector{Set{DynamicPPL.Selector}}}}}, Float64}, DynamicPPL.Model{typeof(vector_of_cor_matrices), (:n, :Type), (), (), Tuple{Int64, Matrix{Float64}}, Tuple{}, DynamicPPL.DefaultContext}, DynamicPPL.SamplingContext{DynamicPPL.Sampler{NUTS{Turing.Essential.ForwardDiffAD{0}, (), AdvancedHMC.DiagEuclideanMetric}}, DynamicPPL.DefaultContext, Random.TaskLocalRNG}}}, x::Vector{Float64}, cfg::ForwardDiff.GradientConfig{ForwardDiff.Tag{Turing.TuringTag, Float64}, Float64, 6, Vector{ForwardDiff.Dual{ForwardDiff.Tag{Turing.TuringTag, Float64}, Float64, 6}}})
    @ ForwardDiff C:\Users\tsh371\.julia\packages\ForwardDiff\PcZ48\src\gradient.jl:96
 [23] gradient!
    @ C:\Users\tsh371\.julia\packages\ForwardDiff\PcZ48\src\gradient.jl:37 [inlined]
 [24] gradient!
    @ C:\Users\tsh371\.julia\packages\ForwardDiff\PcZ48\src\gradient.jl:35 [inlined]
 [25] logdensity_and_gradient
    @ C:\Users\tsh371\.julia\packages\LogDensityProblemsAD\JoNjv\ext\LogDensityProblemsADForwardDiffExt.jl:113 [inlined]
 [26] ∂logπ∂θ
    @ C:\Users\tsh371\.julia\packages\Turing\CP3Ic\src\mcmc\hmc.jl:160 [inlined]
 [27] ∂H∂θ
    @ C:\Users\tsh371\.julia\packages\AdvancedHMC\dgxuI\src\hamiltonian.jl:38 [inlined]
 [28] phasepoint(h::AdvancedHMC.Hamiltonian{AdvancedHMC.DiagEuclideanMetric{Float64, Vector{Float64}}, AdvancedHMC.GaussianKinetic, Base.Fix1{typeof(LogDensityProblems.logdensity), LogDensityProblemsADForwardDiffExt.ForwardDiffLogDensity{LogDensityFunction{DynamicPPL.TypedVarInfo{NamedTuple{(:mat,), Tuple{DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:mat, Setfield.IndexLens{Tuple{Int64}}}, Int64}, Vector{LKJ{Float64, Int64}}, Vector{AbstractPPL.VarName{:mat, Setfield.IndexLens{Tuple{Int64}}}}, Vector{Float64}, Vector{Set{DynamicPPL.Selector}}}}}, Float64}, DynamicPPL.Model{typeof(vector_of_cor_matrices), (:n, :Type), (), (), Tuple{Int64, Matrix{Float64}}, Tuple{}, DynamicPPL.DefaultContext}, DynamicPPL.SamplingContext{DynamicPPL.Sampler{NUTS{Turing.Essential.ForwardDiffAD{0}, (), AdvancedHMC.DiagEuclideanMetric}}, DynamicPPL.DefaultContext, Random.TaskLocalRNG}}, ForwardDiff.Chunk{6}, ForwardDiff.Tag{Turing.TuringTag, Float64}, ForwardDiff.GradientConfig{ForwardDiff.Tag{Turing.TuringTag, Float64}, Float64, 6, Vector{ForwardDiff.Dual{ForwardDiff.Tag{Turing.TuringTag, Float64}, Float64, 6}}}}}, Turing.Inference.var"#∂logπ∂θ#36"{LogDensityProblemsADForwardDiffExt.ForwardDiffLogDensity{LogDensityFunction{DynamicPPL.TypedVarInfo{NamedTuple{(:mat,), Tuple{DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:mat, Setfield.IndexLens{Tuple{Int64}}}, Int64}, Vector{LKJ{Float64, Int64}}, Vector{AbstractPPL.VarName{:mat, Setfield.IndexLens{Tuple{Int64}}}}, Vector{Float64}, Vector{Set{DynamicPPL.Selector}}}}}, Float64}, DynamicPPL.Model{typeof(vector_of_cor_matrices), (:n, :Type), (), (), 
Tuple{Int64, Matrix{Float64}}, Tuple{}, DynamicPPL.DefaultContext}, DynamicPPL.SamplingContext{DynamicPPL.Sampler{NUTS{Turing.Essential.ForwardDiffAD{0}, (), AdvancedHMC.DiagEuclideanMetric}}, DynamicPPL.DefaultContext, Random.TaskLocalRNG}}, ForwardDiff.Chunk{6}, ForwardDiff.Tag{Turing.TuringTag, Float64}, ForwardDiff.GradientConfig{ForwardDiff.Tag{Turing.TuringTag, Float64}, Float64, 6, Vector{ForwardDiff.Dual{ForwardDiff.Tag{Turing.TuringTag, Float64}, Float64, 6}}}}}}, θ::Vector{Float64}, r::Vector{Float64})
    @ AdvancedHMC C:\Users\tsh371\.julia\packages\AdvancedHMC\dgxuI\src\hamiltonian.jl:80
 [29] phasepoint(rng::Random.TaskLocalRNG, θ::Vector{Float64}, h::AdvancedHMC.Hamiltonian{AdvancedHMC.DiagEuclideanMetric{Float64, Vector{Float64}}, AdvancedHMC.GaussianKinetic, Base.Fix1{typeof(LogDensityProblems.logdensity), LogDensityProblemsADForwardDiffExt.ForwardDiffLogDensity{LogDensityFunction{DynamicPPL.TypedVarInfo{NamedTuple{(:mat,), Tuple{DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:mat, Setfield.IndexLens{Tuple{Int64}}}, Int64}, Vector{LKJ{Float64, Int64}}, Vector{AbstractPPL.VarName{:mat, Setfield.IndexLens{Tuple{Int64}}}}, Vector{Float64}, Vector{Set{DynamicPPL.Selector}}}}}, Float64}, DynamicPPL.Model{typeof(vector_of_cor_matrices), (:n, :Type), (), (), Tuple{Int64, Matrix{Float64}}, Tuple{}, DynamicPPL.DefaultContext}, DynamicPPL.SamplingContext{DynamicPPL.Sampler{NUTS{Turing.Essential.ForwardDiffAD{0}, (), AdvancedHMC.DiagEuclideanMetric}}, DynamicPPL.DefaultContext, Random.TaskLocalRNG}}, ForwardDiff.Chunk{6}, ForwardDiff.Tag{Turing.TuringTag, Float64}, ForwardDiff.GradientConfig{ForwardDiff.Tag{Turing.TuringTag, Float64}, Float64, 6, Vector{ForwardDiff.Dual{ForwardDiff.Tag{Turing.TuringTag, Float64}, Float64, 6}}}}}, Turing.Inference.var"#∂logπ∂θ#36"{LogDensityProblemsADForwardDiffExt.ForwardDiffLogDensity{LogDensityFunction{DynamicPPL.TypedVarInfo{NamedTuple{(:mat,), Tuple{DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:mat, Setfield.IndexLens{Tuple{Int64}}}, Int64}, Vector{LKJ{Float64, Int64}}, Vector{AbstractPPL.VarName{:mat, Setfield.IndexLens{Tuple{Int64}}}}, Vector{Float64}, Vector{Set{DynamicPPL.Selector}}}}}, Float64}, DynamicPPL.Model{typeof(vector_of_cor_matrices), (:n, :Type), (), (), Tuple{Int64, Matrix{Float64}}, Tuple{}, DynamicPPL.DefaultContext}, DynamicPPL.SamplingContext{DynamicPPL.Sampler{NUTS{Turing.Essential.ForwardDiffAD{0}, (), AdvancedHMC.DiagEuclideanMetric}}, DynamicPPL.DefaultContext, Random.TaskLocalRNG}}, ForwardDiff.Chunk{6}, ForwardDiff.Tag{Turing.TuringTag, Float64}, ForwardDiff.GradientConfig{ForwardDiff.Tag{Turing.TuringTag, Float64}, Float64, 6, Vector{ForwardDiff.Dual{ForwardDiff.Tag{Turing.TuringTag, Float64}, Float64, 6}}}}}})
    @ AdvancedHMC C:\Users\tsh371\.julia\packages\AdvancedHMC\dgxuI\src\hamiltonian.jl:159
 [30] initialstep(rng::Random.TaskLocalRNG, model::DynamicPPL.Model{typeof(vector_of_cor_matrices), (:n, :Type), (), (), Tuple{Int64, Matrix{Float64}}, Tuple{}, DynamicPPL.DefaultContext}, spl::DynamicPPL.Sampler{NUTS{Turing.Essential.ForwardDiffAD{0}, (), AdvancedHMC.DiagEuclideanMetric}}, vi::DynamicPPL.TypedVarInfo{NamedTuple{(:mat,), Tuple{DynamicPPL.Metadata{Dict{AbstractPPL.VarName{:mat, Setfield.IndexLens{Tuple{Int64}}}, Int64}, Vector{LKJ{Float64, Int64}}, Vector{AbstractPPL.VarName{:mat, Setfield.IndexLens{Tuple{Int64}}}}, Vector{Float64}, Vector{Set{DynamicPPL.Selector}}}}}, Float64}; init_params::Nothing, nadapts::Int64, kwargs::Base.Pairs{Symbol, Union{}, Tuple{}, NamedTuple{(), Tuple{}}})
    @ Turing.Inference C:\Users\tsh371\.julia\packages\Turing\CP3Ic\src\mcmc\hmc.jl:164
 [31] step(rng::Random.TaskLocalRNG, model::DynamicPPL.Model{typeof(vector_of_cor_matrices), (:n, :Type), (), (), Tuple{Int64, Matrix{Float64}}, Tuple{}, DynamicPPL.DefaultContext}, spl::DynamicPPL.Sampler{NUTS{Turing.Essential.ForwardDiffAD{0}, (), AdvancedHMC.DiagEuclideanMetric}}; resume_from::Nothing, init_params::Nothing, kwargs::Base.Pairs{Symbol, Int64, Tuple{Symbol}, NamedTuple{(:nadapts,), Tuple{Int64}}})
    @ DynamicPPL C:\Users\tsh371\.julia\packages\DynamicPPL\m0PXI\src\sampler.jl:111
 [32] step
    @ C:\Users\tsh371\.julia\packages\DynamicPPL\m0PXI\src\sampler.jl:84 [inlined]
 [33] macro expansion
    @ C:\Users\tsh371\.julia\packages\AbstractMCMC\fWWW0\src\sample.jl:125 [inlined]
 [34] macro expansion
    @ C:\Users\tsh371\.julia\packages\ProgressLogging\6KXlp\src\ProgressLogging.jl:328 [inlined]
 [35] macro expansion
    @ C:\Users\tsh371\.julia\packages\AbstractMCMC\fWWW0\src\logging.jl:9 [inlined]
 [36] mcmcsample(rng::Random.TaskLocalRNG, model::DynamicPPL.Model{typeof(vector_of_cor_matrices), (:n, :Type), (), (), Tuple{Int64, Matrix{Float64}}, Tuple{}, DynamicPPL.DefaultContext}, sampler::DynamicPPL.Sampler{NUTS{Turing.Essential.ForwardDiffAD{0}, (), AdvancedHMC.DiagEuclideanMetric}}, N::Int64; progress::Bool, progressname::String, callback::Nothing, discard_initial::Int64, thinning::Int64, chain_type::Type, kwargs::Base.Pairs{Symbol, Int64, Tuple{Symbol}, NamedTuple{(:nadapts,), Tuple{Int64}}})
    @ AbstractMCMC C:\Users\tsh371\.julia\packages\AbstractMCMC\fWWW0\src\sample.jl:116
 [37] sample(rng::Random.TaskLocalRNG, model::DynamicPPL.Model{typeof(vector_of_cor_matrices), (:n, :Type), (), (), Tuple{Int64, Matrix{Float64}}, Tuple{}, DynamicPPL.DefaultContext}, sampler::DynamicPPL.Sampler{NUTS{Turing.Essential.ForwardDiffAD{0}, (), AdvancedHMC.DiagEuclideanMetric}}, N::Int64; chain_type::Type, resume_from::Nothing, progress::Bool, nadapts::Int64, discard_adapt::Bool, discard_initial::Int64, kwargs::Base.Pairs{Symbol, Union{}, Tuple{}, NamedTuple{(), Tuple{}}})
    @ Turing.Inference C:\Users\tsh371\.julia\packages\Turing\CP3Ic\src\mcmc\hmc.jl:121
 [38] sample
    @ C:\Users\tsh371\.julia\packages\Turing\CP3Ic\src\mcmc\hmc.jl:91 [inlined]
 [39] #sample#2
    @ C:\Users\tsh371\.julia\packages\Turing\CP3Ic\src\mcmc\Inference.jl:194 [inlined]
 [40] sample
    @ C:\Users\tsh371\.julia\packages\Turing\CP3Ic\src\mcmc\Inference.jl:187 [inlined]
 [41] #sample#1
    @ C:\Users\tsh371\.julia\packages\Turing\CP3Ic\src\mcmc\Inference.jl:184 [inlined]
 [42] sample(model::DynamicPPL.Model{typeof(vector_of_cor_matrices), (:n, :Type), (), (), Tuple{Int64, Matrix{Float64}}, Tuple{}, DynamicPPL.DefaultContext}, alg::NUTS{Turing.Essential.ForwardDiffAD{0}, (), AdvancedHMC.DiagEuclideanMetric}, N::Int64)
    @ Turing.Inference C:\Users\tsh371\.julia\packages\Turing\CP3Ic\src\mcmc\Inference.jl:178
 [43] top-level scope
@torfjelde
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torfjelde commented Oct 3, 2023 •
edited

Here's an MWE:

using Random, Turing, DynamicPPL, LogDensityProblems

@model function vector_of_cor_matrices(n, Type::TV=Matrix{Float64}) where {TV}
    mat = Vector{TV}(undef, n)
    for i = 1:n      
        mat[i] ~ LKJ(3, 3.0)
    end
end
model = vector_of_cor_matrices(2)
varinfo = DynamicPPL.VarInfo(model)
varinfo_linked = DynamicPPL.link!!(varinfo, model)
f_linked = DynamicPPL.LogDensityFunction(model, varinfo_linked)
LogDensityProblems.logdensity(f_linked, varinfo_linked[:])

which results in

julia> LogDensityProblems.logdensity(f_linked, varinfo_linked[:])
ERROR: BoundsError: attempt to access 6-element Vector{Float64} at index [10:12]
Stacktrace:
  [1] throw_boundserror(A::Vector{Float64}, I::Tuple{UnitRange{Int64}})
    @ Base ./abstractarray.jl:744
  [2] checkbounds
    @ ./abstractarray.jl:709 [inlined]
  [3] view
    @ ./subarray.jl:177 [inlined]
  [4] getval
    @ ~/.julia/packages/DynamicPPL/m0PXI/src/varinfo.jl:318 [inlined]
  [5] getval
    @ ~/.julia/packages/DynamicPPL/m0PXI/src/varinfo.jl:317 [inlined]
  [6] invlink_with_logpdf
    @ ~/.julia/packages/DynamicPPL/m0PXI/src/abstract_varinfo.jl:679 [inlined]
  [7] assume
    @ ~/.julia/packages/DynamicPPL/m0PXI/src/context_implementations.jl:197 [inlined]
  [8] tilde_assume
    @ ~/.julia/packages/DynamicPPL/m0PXI/src/context_implementations.jl:39 [inlined]
  [9] tilde_assume
    @ ~/.julia/packages/DynamicPPL/m0PXI/src/context_implementations.jl:36 [inlined]
 [10] tilde_assume!!
    @ ~/.julia/packages/DynamicPPL/m0PXI/src/context_implementations.jl:117 [inlined]
 [11] macro expansion
    @ ~/.julia/packages/DynamicPPL/m0PXI/src/compiler.jl:555 [inlined]
 [12] vector_of_cor_matrices(__model__::Model{typeof(vector_of_cor_matrices), (:n, :Type), (), (), Tuple{Int64, DataType}, Tuple{}, DefaultContext}, __varinfo__::TypedVarInfo{NamedTuple{(:mat,), Tuple{DynamicPPL.Metadata{Dict{VarName{:mat, Setfield.IndexLens{Tuple{Int64}}}, Int64}, Vector{LKJ{Float64, Int64}}, Vector{VarName{:mat, Setfield.IndexLens{Tuple{Int64}}}}, Vector{Float64}, Vector{Set{DynamicPPL.Selector}}}}}, Float64}, __context__::DefaultContext, n::Int64, Type::Type{Matrix{Float64}})
    @ Main ./REPL[20]:3
 [13] _evaluate!!(model::Model{typeof(vector_of_cor_matrices), (:n, :Type), (), (), Tuple{Int64, DataType}, Tuple{}, DefaultContext}, varinfo::TypedVarInfo{NamedTuple{(:mat,), Tuple{DynamicPPL.Metadata{Dict{VarName{:mat, Setfield.IndexLens{Tuple{Int64}}}, Int64}, Vector{LKJ{Float64, Int64}}, Vector{VarName{:mat, Setfield.IndexLens{Tuple{Int64}}}}, Vector{Float64}, Vector{Set{DynamicPPL.Selector}}}}}, Float64}, context::DefaultContext)
    @ DynamicPPL ~/.julia/packages/DynamicPPL/m0PXI/src/model.jl:963
 [14] evaluate_threadunsafe!!
    @ ~/.julia/packages/DynamicPPL/m0PXI/src/model.jl:936 [inlined]
 [15] evaluate!!
    @ ~/.julia/packages/DynamicPPL/m0PXI/src/model.jl:889 [inlined]
 [16] logdensity(f::LogDensityFunction{TypedVarInfo{NamedTuple{(:mat,), Tuple{DynamicPPL.Metadata{Dict{VarName{:mat, Setfield.IndexLens{Tuple{Int64}}}, Int64}, Vector{LKJ{Float64, Int64}}, Vector{VarName{:mat, Setfield.IndexLens{Tuple{Int64}}}}, Vector{Float64}, Vector{Set{DynamicPPL.Selector}}}}}, Float64}, Model{typeof(vector_of_cor_matrices), (:n, :Type), (), (), Tuple{Int64, DataType}, Tuple{}, DefaultContext}, DefaultContext}, θ::Vector{Float64})
    @ DynamicPPL ~/.julia/packages/DynamicPPL/m0PXI/src/logdensityfunction.jl:94
 [17] top-level scope
    @ REPL[46]:1
Manifest 
(jl_bPalDn) pkg> st --manifest
Status `/tmp/jl_bPalDn/Manifest.toml`
  [47edcb42] ADTypes v0.2.4
  [621f4979] AbstractFFTs v1.5.0
  [80f14c24] AbstractMCMC v4.4.2
  [7a57a42e] AbstractPPL v0.6.2
  [1520ce14] AbstractTrees v0.4.4
  [79e6a3ab] Adapt v3.6.2
  [0bf59076] AdvancedHMC v0.5.5
  [5b7e9947] AdvancedMH v0.7.5
  [576499cb] AdvancedPS v0.4.3
  [b5ca4192] AdvancedVI v0.2.4
  [dce04be8] ArgCheck v2.3.0
  [4fba245c] ArrayInterface v7.4.11
  [a9b6321e] Atomix v0.1.0
  [13072b0f] AxisAlgorithms v1.0.1
  [39de3d68] AxisArrays v0.4.7
  [198e06fe] BangBang v0.3.39
  [9718e550] Baselet v0.1.1
  [76274a88] Bijectors v0.13.7
  [fa961155] CEnum v0.4.2
  [49dc2e85] Calculus v0.5.1
  [082447d4] ChainRules v1.54.0
  [d360d2e6] ChainRulesCore v1.16.0
  [9e997f8a] ChangesOfVariables v0.1.8
  [861a8166] Combinatorics v1.0.2
  [38540f10] CommonSolve v0.2.4
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Info Packages marked with ⌃ and ⌅ have new versions available, but those with ⌅ are restricted by compatibility constraints from upgrading. To see why use `status --outdated -m`

@torfjelde torfjelde mentioned this issue Oct 3, 2023
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So I have a fix coming for the BoundsError, but then you immediately run into the numerical issues because we're using LKJ 😕

@devmotion devmotion mentioned this issue Oct 5, 2023
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yebai added a commit that referenced this issue Mar 4, 2024
@torfjelde @yebai
Partial fix for #2095 (#2096)
0b56415 
* use immutable link in the initialstep for HMC

* bump patch version

* added test

* Update hmc.jl

---------

Co-authored-by: Hong Ge <3279477+yebai@users.noreply.github.com>
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