fix

Author: penelopeysm

src/debug_utils.jl

@@ -451,9 +451,8 @@ function check_model_and_trace(
     issuccess = check_model_pre_evaluation(debug_context, model)
 
     # Force single-threaded execution.
-    retval, varinfo_result = DynamicPPL.evaluate_threadunsafe!!(
-        model, varinfo, debug_context
-    )
+    debug_model = DynamicPPL.contextualize(model, debug_context)
+    DynamicPPL.evaluate_threadunsafe!!(debug_model, varinfo)
 
     # Perform checks after evaluating the model.
     issuccess &= check_model_post_evaluation(debug_context, model)

src/model.jl

@@ -929,7 +929,8 @@ end
 Evaluate the `model` with the given `varinfo`. If an additional `context` is provided,
 the model's context is combined with that context.
 
-This function does not wrap the varinfo in a `ThreadSafeVarInfo`.
+This function does not wrap the varinfo in a `ThreadSafeVarInfo`. It also does not
+reset the log probability of the `varinfo` before running.
 """
 function _evaluate!!(model::Model, varinfo::AbstractVarInfo)
     args, kwargs = make_evaluate_args_and_kwargs(model, varinfo)

src/sampler.jl

@@ -58,7 +58,7 @@ function AbstractMCMC.step(
     kwargs...,
 )
     vi = VarInfo()
-    model(rng, vi, sampler)
+    DynamicPPL.sample!!(rng, model, vi, sampler)
     return vi, nothing
 end
 

test/ad.jl

@@ -110,7 +110,7 @@ using DynamicPPL: LogDensityFunction
         # Compiling the ReverseDiff tape used to fail here
         spl = Sampler(MyEmptyAlg())
         vi = VarInfo(model)
-        sampling_model = contextualize(model, SamplingConext(model.context))
+        sampling_model = contextualize(model, SamplingContext(model.context))
         ldf = LogDensityFunction(sampling_model, vi; adtype=AutoReverseDiff(; compile=true))
         @test LogDensityProblems.logdensity_and_gradient(ldf, vi[:]) isa Any
     end

test/context_implementations.jl

@@ -5,12 +5,12 @@
             μ ~ MvNormal(zeros(2), 4 * I)
             z = Vector{Int}(undef, length(x))
             z ~ product_distribution(Categorical.(fill([0.5, 0.5], length(x))))
-            for i in 1:length(x)
+            for i in eachindex(x)
                 x[i] ~ Normal(μ[z[i]], 0.1)
             end
         end
 
-        test([1, 1, -1])(VarInfo(), SampleFromPrior(), DefaultContext())
+        test([1, 1, -1])(VarInfo())
     end
 
     @testset "dot tilde with varying sizes" begin

test/model.jl

@@ -332,7 +332,7 @@ const GDEMO_DEFAULT = DynamicPPL.TestUtils.demo_assume_observe_literal()
             @test logjoint(model, x) !=
                 DynamicPPL.TestUtils.logjoint_true_with_logabsdet_jacobian(model, x...)
             # Ensure `varnames` is implemented.
-            vi = last(DynamicPPL.sample!!(sampling_model, SimpleVarInfo(OrderedDict())))
+            vi = last(DynamicPPL.sample!!(model, SimpleVarInfo(OrderedDict())))
             @test all(collect(keys(vi)) .== DynamicPPL.TestUtils.varnames(model))
             # Ensure `posterior_mean` is implemented.
             @test DynamicPPL.TestUtils.posterior_mean(model) isa typeof(x)