Added model which calls C

main.jl

@@ -1,6 +1,7 @@
 using DynamicPPL: DynamicPPL, VarInfo
 using DynamicPPL.TestUtils.AD: run_ad, ADResult, ADIncorrectException
 using ADTypes
+using Random: Xoshiro
 
 import FiniteDifferences: central_fdm
 import ForwardDiff
@@ -11,13 +12,13 @@ import Zygote
 
 # AD backends to test.
 ADTYPES = Dict(
-    "FiniteDifferences" => AutoFiniteDifferences(; fdm=central_fdm(5, 1)),
+    "FiniteDifferences" => AutoFiniteDifferences(; fdm = central_fdm(5, 1)),
     "ForwardDiff" => AutoForwardDiff(),
-    "ReverseDiff" => AutoReverseDiff(; compile=false),
-    "ReverseDiffCompiled" => AutoReverseDiff(; compile=true),
-    "Mooncake" => AutoMooncake(; config=nothing),
-    "EnzymeForward" => AutoEnzyme(; mode=set_runtime_activity(Forward, true)),
-    "EnzymeReverse" => AutoEnzyme(; mode=set_runtime_activity(Reverse, true)),
+    "ReverseDiff" => AutoReverseDiff(; compile = false),
+    "ReverseDiffCompiled" => AutoReverseDiff(; compile = true),
+    "Mooncake" => AutoMooncake(; config = nothing),
+    "EnzymeForward" => AutoEnzyme(; mode = set_runtime_activity(Forward, true)),
+    "EnzymeReverse" => AutoEnzyme(; mode = set_runtime_activity(Reverse, true)),
     "Zygote" => AutoZygote(),
 )
 
@@ -56,14 +57,12 @@ macro include_model(category::AbstractString, model_name::AbstractString)
     if MODELS_TO_LOAD == "__all__" || model_name in split(MODELS_TO_LOAD, ",")
         # Declare a module containing the model. In principle esc() shouldn't
         # be needed, but see https://github.com/JuliaLang/julia/issues/55677
-        Expr(:toplevel, esc(:(
-            module $(gensym())
-            using .Main: @register
-            using Turing
-            include("models/" * $(model_name) * ".jl")
-            @register $(category) model
-            end
-        )))
+        Expr(:toplevel, esc(:(module $(gensym())
+        using .Main: @register
+        using Turing
+        include("models/" * $(model_name) * ".jl")
+        @register $(category) model
+        end)))
     else
         # Empty expression
         :()
@@ -76,6 +75,7 @@ end
 # although it's hardly a big deal.
 @include_model "Base Julia features" "control_flow"
 @include_model "Base Julia features" "multithreaded"
+@include_model "Base Julia features" "call_C"
 @include_model "Core Turing syntax" "broadcast_macro"
 @include_model "Core Turing syntax" "dot_assume"
 @include_model "Core Turing syntax" "dot_observe"
@@ -114,6 +114,7 @@ end
 @include_model "Effect of model size" "n500"
 @include_model "PosteriorDB" "pdb_eight_schools_centered"
 @include_model "PosteriorDB" "pdb_eight_schools_noncentered"
+@include_model "Miscellaneous features" "metabayesian_MH"
 
 # The entry point to this script itself begins here
 if ARGS == ["--list-model-keys"]
@@ -131,9 +132,18 @@ elseif length(ARGS) == 3 && ARGS[1] == "--run"
             # https://github.com/TuringLang/ADTests/issues/4
             vi = DynamicPPL.unflatten(VarInfo(model), [0.5, -0.5])
             params = [-0.5, 0.5]
-            result = run_ad(model, adtype; varinfo=vi, params=params, benchmark=true)
+            result = run_ad(model, adtype; varinfo = vi, params = params, benchmark = true)
         else
-            result = run_ad(model, adtype; benchmark=true)
+            vi = VarInfo(Xoshiro(468), model)
+            linked_vi = DynamicPPL.link!!(vi, model)
+            params = linked_vi[:]
+            result = run_ad(
+                model,
+                adtype;
+                params = params,
+                reference_adtype = ADTYPES["FiniteDifferences"],
+                benchmark = true,
+            )
         end
         # If reached here - nothing went wrong
         println(result.time_vs_primal)

models/broadcast_macro.jl

@@ -1,7 +1,4 @@
-@model function broadcast_macro(
-    x = [1.5, 2.0],
-    ::Type{TV} = Vector{Float64},
-) where {TV}
+@model function broadcast_macro(x = [1.5, 2.0], ::Type{TV} = Vector{Float64}) where {TV}
     a ~ Normal(0, 1)
     b ~ InverseGamma(2, 3)
     @. x ~ Normal(a, $(sqrt(b)))

models/call_C.jl

@@ -0,0 +1,10 @@
+@model function call_C(y = 0.0)
+    x ~ Normal(0, 1)
+
+    # Call C library abs function
+    x_abs = @ccall fabs(x::Cdouble)::Cdouble
+
+    y ~ Normal(0, x_abs)
+end
+
+model = call_C()

models/metabayesian_MH.jl

@@ -0,0 +1,44 @@
+#=
+This is a "meta-Bayesian" model, where the generative model includes an inversion of a different generative model.
+These types of models are common in cognitive modelling, where systems of interest (e.g. human subjects) are thought to use Bayesian inference to navigate their environment.
+Here we use a Metropolis-Hasting sampler implemented with Turing as the inversion of the inner "subjective" model.
+=#
+using Random: Xoshiro
+
+# Inner model function
+@model function inner_model(observation, prior_μ = 0, prior_σ = 1)
+    # The inner model's prior
+    mean ~ Normal(prior_μ, prior_σ)
+    # The inner model's likelihood
+    observation ~ Normal(mean, 1)
+end
+
+# Outer model function
+@model function metabayesian_MH(
+    observation,
+    action,
+    inner_sampler = MH(),
+    inner_n_samples = 20,
+)
+    ### Sample parameters for the inner inference and response ###
+    # The inner model's prior's sufficient statistics
+    subj_prior_μ ~ Normal(0, 1)
+    subj_prior_σ = 1.0
+    # Inverse temperature for actions
+    β ~ Exponential(1)
+
+    ### "Perceptual inference": running the inner model ###
+    # Condition the inner model
+    inner_m = inner_model(observation, subj_prior_μ, subj_prior_σ)
+    # Run the inner Bayesian inference
+    chns = sample(Xoshiro(468), inner_m, inner_sampler, inner_n_samples, progress = false)
+    # Extract subjective point estimate
+    subj_mean_expectationₜ = mean(chns[:mean])
+
+
+    ### "Response model": picking an action ###
+    # The action is a Gaussian-noise report of the subjective point estimate
+    action ~ Normal(subj_mean_expectationₜ, β)
+end
+
+model = metabayesian_MH(0.0, 1.0)