Prior should use PriorContext (#2170)

* `Prior` now uses `PriorContext`

* `Prior` sampler now implements `step` instead of relying on `SampleFromPrior`

* remove unnecessary `make_prior_model`

* bump patch version

Project.toml

@@ -1,6 +1,6 @@
 name = "Turing"
 uuid = "fce5fe82-541a-59a6-adf8-730c64b5f9a0"
-version = "0.30.3"
+version = "0.30.4"
 
 [deps]
 ADTypes = "47edcb42-4c32-4615-8424-f2b9edc5f35b"

src/mcmc/Inference.jl

@@ -115,6 +115,23 @@ DynamicPPL.unflatten(vi::SimpleVarInfo, θ::NamedTuple) = SimpleVarInfo(θ, vi.l
 # Algorithm for sampling from the prior
 struct Prior <: InferenceAlgorithm end
 
+function AbstractMCMC.step(
+    rng::Random.AbstractRNG,
+    model::DynamicPPL.Model,
+    sampler::DynamicPPL.Sampler{<:Prior},
+    state=nothing;
+    kwargs...,
+)
+    vi = last(DynamicPPL.evaluate!!(
+        model,
+        VarInfo(),
+        SamplingContext(
+            rng, DynamicPPL.SampleFromPrior(), DynamicPPL.PriorContext()
+        )
+    ))
+    return vi, nothing
+end
+
 """
     mh_accept(logp_current::Real, logp_proposal::Real, log_proposal_ratio::Real)
 
@@ -230,36 +247,6 @@ function AbstractMCMC.sample(
                                    chain_type=chain_type, progress=progress, kwargs...)
 end
 
-function AbstractMCMC.sample(
-    rng::AbstractRNG,
-    model::AbstractModel,
-    alg::Prior,
-    ensemble::AbstractMCMC.AbstractMCMCEnsemble,
-    N::Integer,
-    n_chains::Integer;
-    chain_type=DynamicPPL.default_chain_type(alg),
-    progress=PROGRESS[],
-    kwargs...
-)
-    return AbstractMCMC.sample(rng, model, SampleFromPrior(), ensemble, N, n_chains;
-                                    chain_type, progress, kwargs...)
-end
-
-function AbstractMCMC.sample(
-    rng::AbstractRNG,
-    model::AbstractModel,
-    alg::Prior,
-    N::Integer;
-    chain_type=DynamicPPL.default_chain_type(alg),
-    resume_from=nothing,
-    initial_state=DynamicPPL.loadstate(resume_from),
-    progress=PROGRESS[],
-    kwargs...
-)
-    return AbstractMCMC.mcmcsample(rng, model, SampleFromPrior(), N;
-                                    chain_type, initial_state, progress, kwargs...)
-end
-
 ##########################
 # Chain making utilities #
 ##########################

test/mcmc/Inference.jl

@@ -140,6 +140,21 @@
         @test all(haskey(x, :lp) for x in chains)
         @test mean(x[:s][1] for x in chains) ≈ 3 atol=0.1
         @test mean(x[:m][1] for x in chains) ≈ 0 atol=0.1
+
+        @testset "#2169" begin
+            # Not exactly the same as the issue, but similar.
+            @model function issue2169_model()
+                if DynamicPPL.leafcontext(__context__) isa DynamicPPL.PriorContext
+                    x ~ Normal(0, 1)
+                else
+                    x ~ Normal(1000, 1)
+                end
+            end
+
+            model = issue2169_model()
+            chain = sample(model, Prior(), 10)
+            @test all(mean(chain[:x]) .< 5)
+        end
     end
 
     @testset "chain ordering" begin