Merge branch 'torfjelde/dynamicppl-bump' of github.com:TuringLang/Turing.jl into torfjelde/dynamicppl-bump

Author: torfjelde

src/inference/hmc.jl

@@ -159,7 +159,14 @@ function DynamicPPL.initialstep(
     metricT = getmetricT(spl.alg)
     metric = metricT(length(theta))
     ℓ = LogDensityProblemsAD.ADgradient(
-        Turing.LogDensityFunction(vi, model, spl, DynamicPPL.DefaultContext())
+        Turing.LogDensityFunction(
+            vi,
+            model,
+            # Use the leaf-context from the `model` in case the user has
+            # contextualized the model with something like `PriorContext`
+            # to sample from the prior.
+            DynamicPPL.SamplingContext(rng, spl, DynamicPPL.leafcontext(model.context))
+        )
     )
     logπ = Base.Fix1(LogDensityProblems.logdensity, ℓ)
     ∂logπ∂θ(x) = LogDensityProblems.logdensity_and_gradient(ℓ, x)
@@ -265,7 +272,11 @@ end
 function get_hamiltonian(model, spl, vi, state, n)
     metric = gen_metric(n, spl, state)
     ℓ = LogDensityProblemsAD.ADgradient(
-        Turing.LogDensityFunction(vi, model, spl, DynamicPPL.DefaultContext())
+        Turing.LogDensityFunction(
+            vi,
+            model,
+            DynamicPPL.SamplingContext(spl, DynamicPPL.leafcontext(model.context))
+        )
     )
     ℓπ = Base.Fix1(LogDensityProblems.logdensity, ℓ)
     ∂ℓπ∂θ = Base.Fix1(LogDensityProblems.logdensity_and_gradient, ℓ)
@@ -538,7 +549,12 @@ function HMCState(
 
     # Get the initial log pdf and gradient functions.
     ∂logπ∂θ = gen_∂logπ∂θ(vi, spl, model)
-    logπ = Turing.LogDensityFunction(vi, model, spl, DynamicPPL.DefaultContext())
+    logπ = Turing.LogDensityFunction(
+        vi,
+        model,
+        DynamicPPL.SamplingContext(rng, spl, DynamicPPL.leafcontext(model.context))
+    )
+
 
     # Get the metric type.
     metricT = getmetricT(spl.alg)

src/inference/mh.jl

@@ -375,7 +375,14 @@ function propose!!(
 
     # Make a new transition.
     densitymodel = AMH.DensityModel(
-        Base.Fix1(LogDensityProblems.logdensity, Turing.LogDensityFunction(vi, model, DynamicPPL.SamplingContext(rng, spl)))
+        Base.Fix1(
+            LogDensityProblems.logdensity,
+            Turing.LogDensityFunction(
+                vi,
+                model,
+                DynamicPPL.SamplingContext(rng, spl, DynamicPPL.leafcontext(model.context))
+            )
+        )
     )
     trans, _ = AbstractMCMC.step(rng, densitymodel, mh_sampler, prev_trans)
 
@@ -403,7 +410,14 @@ function propose!!(
 
     # Make a new transition.
     densitymodel = AMH.DensityModel(
-        Base.Fix1(LogDensityProblems.logdensity, Turing.LogDensityFunction(vi, model, DynamicPPL.SamplingContext(rng, spl)))
+        Base.Fix1(
+            LogDensityProblems.logdensity,
+            Turing.LogDensityFunction(
+                vi,
+                model,
+                DynamicPPL.SamplingContext(rng, spl, DynamicPPL.leafcontext(model.context))
+            )
+        )
     )
     trans, _ = AbstractMCMC.step(rng, densitymodel, mh_sampler, prev_trans)
 

test/inference/hmc.jl

@@ -216,4 +216,11 @@
         res3 = sample(StableRNG(123), gdemo_default, alg, 1000)
         @test Array(res1) == Array(res2) == Array(res3)
     end
+
+    @turing_testset "prior" begin
+        alg = NUTS(1000, 0.8)
+        gdemo_default_prior = DynamicPPL.contextualize(gdemo_default, DynamicPPL.PriorContext())
+        chain = sample(gdemo_default_prior, alg, 10_000)
+        check_numerical(chain, [:s, :m], [mean(InverseGamma(2, 3)), 0], atol=0.2)
+    end
 end

test/inference/mh.jl

@@ -216,4 +216,16 @@
         vi = Turing.Inference.maybe_link!!(vi, spl, alg.proposals, gdemo_default)
         @test !DynamicPPL.islinked(vi, spl)
     end
+
+    @turing_testset "prior" begin
+        # HACK: MH can be so bad for this prior model for some reason that it's difficult to
+        # find a non-trivial `atol` where the tests will pass for all seeds. Hence we fix it :/
+        rng = StableRNG(10)
+        alg = MH()
+        gdemo_default_prior = DynamicPPL.contextualize(gdemo_default, DynamicPPL.PriorContext())
+        burnin = 10_000
+        n = 10_000
+        chain = sample(rng, gdemo_default_prior, alg, n; discard_initial = burnin, thinning=10)
+        check_numerical(chain, [:s, :m], [mean(InverseGamma(2, 3)), 0], atol=0.3)
+    end
 end