Simple VarInfo illegal instruction

[willtebbutt]

As discussed with @torfjelde on slack:

using Distributions, Turing

# LDA example -- copied over from
# https://github.com/TuringLang/Turing.jl/issues/668#issuecomment-1153124051
function _make_data(D, K, V, N, α, η)
    β = Matrix{Float64}(undef, V, K)
    for k in 1:K
        β[:,k] .= rand(Dirichlet(η))
    end

    θ = Matrix{Float64}(undef, K, D)
    z = Vector{Int}(undef, D * N)
    w = Vector{Int}(undef, D * N)
    doc = Vector{Int}(undef, D * N)
    i = 0
    for d in 1:D
        θ[:,d] .= rand(Dirichlet(α))
        for n in 1:N
            i += 1
            z[i] = rand(Categorical(θ[:, d]))
            w[i] = rand(Categorical(β[:, z[i]]))
            doc[i] = d
        end
    end
    return (D=D, K=K, V=V, N=N, α=α, η=η, z=z, w=w, doc=doc, θ=θ, β=β)
end

data = let D = 2, K = 2, V = 160, N = 290
    _make_data(D, K, V, N, ones(K), ones(V))
end

# LDA with vectorization and manual log-density accumulation
@model function LatentDirichletAllocationVectorizedCollapsedMannual(
    D, K, V, α, η, w, doc
)
    β ~ filldist(Dirichlet(η), K)
    θ ~ filldist(Dirichlet(α), D)
    
    log_product = log.(β * θ)
    Turing.@addlogprob! sum(log_product[CartesianIndex.(w, doc)])
    # Above is equivalent to below
    #product = β * θ
    #dist = [Categorical(product[:,i]) for i in 1:D]    
    #w ~ arraydist([dist[doc[i]] for i in 1:length(doc)])
end

model = LatentDirichletAllocationVectorizedCollapsedMannual(
    data.D, data.K, data.V, data.α, data.η, data.w, data.doc,
)

ctx = Turing.DefaultContext()
vi = Turing.SimpleVarInfo(model)
vi_linked = Turing.link(vi, model)

the final line yields

[1]    38239 illegal hardware instruction  julia

Run on Julia v1.10.0, with

  [31c24e10] Distributions v0.25.107
  [fce5fe82] Turing v0.30.3