avoid recording prior components on leaf-prior-context

and avoid recording likelihoods when invoked with leaf-Likelihood context

src/pointwise_logdensities.jl

@@ -73,11 +73,26 @@ function Base.push!(
     return context.logdensities[vn] = logp
 end
 
+
+function _include_prior(context::PointwiseLogdensityContext)
+    return leafcontext(context) isa Union{PriorContext,DefaultContext}
+end
+function _include_likelihood(context::PointwiseLogdensityContext)
+    return leafcontext(context) isa Union{LikelihoodContext,DefaultContext}
+end
+
+
+
 function tilde_observe!!(context::PointwiseLogdensityContext, right, left, vi)
     # Defer literal `observe` to child-context.
     return tilde_observe!!(context.context, right, left, vi)
 end
 function tilde_observe!!(context::PointwiseLogdensityContext, right, left, vn, vi)
+   # Completely defer to child context if we are not tracking likelihoods.
+    if !(_include_likelihood(context))
+        return tilde_observe!!(context.context, right, left, vn, vi)
+    end    
+
     # Need the `logp` value, so we cannot defer `acclogp!` to child-context, i.e.
     # we have to intercept the call to `tilde_observe!`.
     logp, vi = tilde_observe(context.context, right, left, vi)
@@ -93,6 +108,11 @@ function dot_tilde_observe!!(context::PointwiseLogdensityContext, right, left, v
     return dot_tilde_observe!!(context.context, right, left, vi)
 end
 function dot_tilde_observe!!(context::PointwiseLogdensityContext, right, left, vn, vi)
+    # Completely defer to child context if we are not tracking likelihoods.
+    if !(_include_likelihood(context))
+        return dot_tilde_observe!!(context.context, right, left, vn, vi)
+    end
+
     # Need the `logp` value, so we cannot defer `acclogp!` to child-context, i.e.
     # we have to intercept the call to `dot_tilde_observe!`.
 
@@ -132,16 +152,19 @@ end
 function tilde_assume(context::PointwiseLogdensityContext, right::Distribution, vn, vi)
     #@info "PointwiseLogdensityContext tilde_assume called for $vn"
     value, logp, vi = tilde_assume(context.context, right, vn, vi)
-    push!(context, vn, logp)
+    if _include_prior(context)
+        push!(context, vn, logp)
+    end
     return value, logp, vi
 end
 
 function dot_tilde_assume(context::PointwiseLogdensityContext, right, left, vns, vi)
     #@info "PointwiseLogdensityContext dot_tilde_assume called for $vns"
     value, logp, vi_new = dot_tilde_assume(context.context, right, left, vns, vi)
-    # dispatch recording of log-densities based on type of right
-    logps = record_dot_tilde_assume(context, right, left, vns, vi, logp)
-    sum(logps) ≈ logp || error("Expected sum of individual logp equal origina, but differed sum($(join(logps, ","))) != $logp_orig")
+    if _include_prior(context)
+        logps = record_dot_tilde_assume(context, right, left, vns, vi, logp)
+        sum(logps) ≈ logp || error("Expected sum of individual logp equal origina, but differed sum($(join(logps, ","))) != $logp_orig")
+    end
     return value, logp, vi
 end
 
@@ -172,7 +195,7 @@ end
     pointwise_logdensities(model::Model, chain::Chains, keytype = String)
 
 Runs `model` on each sample in `chain` returning a `OrderedDict{String, Matrix{Float64}}`
-with keys corresponding to symbols of the observations, and values being matrices
+with keys corresponding to symbols of the variables, and values being matrices
 of shape `(num_chains, num_samples)`.
 
 `keytype` specifies what the type of the keys used in the returned `OrderedDict` are.

src/test_utils.jl

@@ -1116,10 +1116,8 @@ function TestLogModifyingChildContext(
         mod, context
     )
 end
-# Samplers call leafcontext(model.context) when evaluating log-densities
-# Hence, in order to be used need to say that its a leaf-context
-#DynamicPPL.NodeTrait(::TestLogModifyingChildContext) = DynamicPPL.IsParent()
-DynamicPPL.NodeTrait(::TestLogModifyingChildContext) = DynamicPPL.IsLeaf()
+
+DynamicPPL.NodeTrait(::TestLogModifyingChildContext) = DynamicPPL.IsParent()
 DynamicPPL.childcontext(context::TestLogModifyingChildContext) = context.context
 function DynamicPPL.setchildcontext(context::TestLogModifyingChildContext, child)
     return TestLogModifyingChildContext(context.mod, child)

test/deprecated.jl

@@ -10,15 +10,14 @@
 
         # Compute the pointwise loglikelihoods.
         lls = pointwise_loglikelihoods(m, vi)
-        loglikelihood = sum(sum, values(lls))
 
         #if isempty(lls)
-        if loglikelihood ≈ 0.0 #isempty(lls)
+        if isempty(lls)
             # One of the models with literal observations, so we just skip.
-            # TODO: Think of better way to detect this special case 
             continue
         end
 
+        loglikelihood = sum(sum, values(lls))
         loglikelihood_true = DynamicPPL.TestUtils.loglikelihood_true(m, example_values...)
 
         #priors = 

test/pointwise_logdensities.jl

@@ -3,9 +3,12 @@
     prior_context = PriorContext()
     mod_ctx = DynamicPPL.TestUtils.TestLogModifyingChildContext(1.2)
     mod_ctx2 = DynamicPPL.TestUtils.TestLogModifyingChildContext(1.4, mod_ctx)
-    #m = DynamicPPL.TestUtils.DEMO_MODELS[12]
+    #m = DynamicPPL.TestUtils.DEMO_MODELS[1]
     #m = model = DynamicPPL.TestUtils.demo_dot_assume_matrix_dot_observe_matrix2()
-    @testset "$(m.f)" for (i, m) in enumerate(DynamicPPL.TestUtils.DEMO_MODELS)
+    demo_models = (
+        DynamicPPL.TestUtils.DEMO_MODELS..., 
+        DynamicPPL.TestUtils.demo_dot_assume_matrix_dot_observe_matrix2())
+    @testset "$(m.f)" for (i, m) in enumerate(demo_models)
         #@show i
         example_values = DynamicPPL.TestUtils.rand_prior_true(m)
 
@@ -26,23 +29,19 @@
         # Compute the pointwise loglikelihoods.
         lls = pointwise_logdensities(m, vi, likelihood_context)
         #lls2 = pointwise_loglikelihoods(m, vi)
-        loglikelihood_sum = sum(sum, values(lls))
-        if loglikelihood_sum ≈ 0.0 #isempty(lls)
+        if isempty(lls)
             # One of the models with literal observations, so we just skip.
-            # TODO: Think of better way to detect this special case 
             loglikelihood_true = 0.0
+        else
+            loglikelihood_sum = sum(sum, values(lls))
+            @test loglikelihood_sum ≈ loglikelihood_true
         end
-        @test loglikelihood_sum ≈ loglikelihood_true
 
         # Compute the pointwise logdensities of the priors.
         lps_prior = pointwise_logdensities(m, vi, prior_context)
         logp = sum(sum, values(lps_prior))
-        if false # isempty(lps_prior)
-            # One of the models with only observations so we just skip.
-        else
-            logp1 = getlogp(vi)
-            @test !isfinite(logp_true) || logp ≈ logp_true
-        end
+        logp1 = getlogp(vi)
+        @test !isfinite(logp_true) || logp ≈ logp_true
 
         # Compute both likelihood and logdensity of prior
         # using the default DefaultContex        
@@ -57,7 +56,6 @@
     end
 end
 
-
 @testset "pointwise_logdensities chain" begin
     @model function demo(x, ::Type{TV}=Vector{Float64}) where {TV}
         s ~ InverseGamma(2, 3)
@@ -73,9 +71,9 @@ end
         # generate the sample used below
         chain = sample(model, MH(), MCMCThreads(), 10, 2)
         arr0 = stack(Array(chain, append_chains=false))
-        @show(arr0);
+        @show(arr0[1:2,:,:]);
     end
-    arr0 = [5.590726417006858 -3.3407908212996493 -3.5126580698975687 -0.02830755634462317; 5.590726417006858 -3.3407908212996493 -3.5126580698975687 -0.02830755634462317; 0.9199555480151707 -0.1304320097505629 1.0669120062696917 -0.05253734412139093; 1.0982766276744311 0.9593277181079177 0.005558174156359029 -0.45842032209694183; 1.0982766276744311 0.9593277181079177 0.005558174156359029 -0.45842032209694183; 1.0982766276744311 0.9593277181079177 0.005558174156359029 -0.45842032209694183; 1.0982766276744311 0.9593277181079177 0.005558174156359029 -0.45842032209694183; 1.0982766276744311 0.9593277181079177 0.005558174156359029 -0.45842032209694183; 1.0982766276744311 0.9593277181079177 0.005558174156359029 -0.45842032209694183; 1.0982766276744311 0.9593277181079177 0.005558174156359029 -0.45842032209694183;;; 3.5612802961176797 -5.167692608117693 1.3768066487740864 -0.9154694769223497; 3.5612802961176797 -5.167692608117693 1.3768066487740864 -0.9154694769223497; 2.5409470583244933 1.7838744695696407 0.7013562890105632 -3.0843947804314658; 0.8296370582311665 1.5360702767879642 -1.5964695255693102 0.16928084806166913; 2.6246697053824954 0.8096845024785173 -1.2621822861663752 1.1414885535466166; 1.1304261861894538 0.7325784741344005 -1.1866016911837542 -0.1639319562090826; 2.5669872989791473 -0.43642462460747317 0.07057300935786101 0.5168578624259272; 2.5669872989791473 -0.43642462460747317 0.07057300935786101 0.5168578624259272; 2.5669872989791473 -0.43642462460747317 0.07057300935786101 0.5168578624259272; 0.9838526141898173 -0.20198797220982412 2.0569535882007006 -1.1560724118010939]
+    arr0[1:2, :, :] = [5.590726417006858 -3.3407908212996493 -3.5126580698975687 -0.02830755634462317; 5.590726417006858 -3.3407908212996493 -3.5126580698975687 -0.02830755634462317;;; 3.5612802961176797 -5.167692608117693 1.3768066487740864 -0.9154694769223497; 3.5612802961176797 -5.167692608117693 1.3768066487740864 -0.9154694769223497]
     chain = Chains(arr0, [:s, Symbol("m[1]"), Symbol("m[2]"), Symbol("m[3]")]);
     tmp1 = pointwise_logdensities(model, chain)
     vi = VarInfo(model)