fix include_all kwarg for predict, improve perf

HISTORY.md

@@ -61,6 +61,10 @@ The `resume_from=chn` keyword argument to `sample` has been removed; please use
 
 **Other changes**
 
+### `predict(model, chain; include_all)`
+
+The `include_all` keyword argument for `predict` now works even when no RNG is specified (previously it would only work when an RNG was explicitly passed).
+
 ### `setleafcontext(model, context)`
 
 This convenience method has been added to quickly modify the leaf context of a model.

ext/DynamicPPLMCMCChainsExt.jl

@@ -116,7 +116,19 @@ function DynamicPPL.predict(
     include_all=false,
 )
     parameter_only_chain = MCMCChains.get_sections(chain, :parameters)
-    varinfo = DynamicPPL.VarInfo(model)
+
+    # Set up a VarInfo with the right accumulators
+    varinfo = DynamicPPL.setaccs!!(
+        DynamicPPL.VarInfo(),
+        (
+            DynamicPPL.LogPriorAccumulator(),
+            DynamicPPL.LogJacobianAccumulator(),
+            DynamicPPL.LogLikelihoodAccumulator(),
+            DynamicPPL.ValuesAsInModelAccumulator(false),
+        ),
+    )
+    _, varinfo = DynamicPPL.init!!(model, varinfo)
+    varinfo = DynamicPPL.typed_varinfo(varinfo)
 
     iters = Iterators.product(1:size(chain, 1), 1:size(chain, 3))
     predictive_samples = map(iters) do (sample_idx, chain_idx)
@@ -129,7 +141,7 @@ function DynamicPPL.predict(
             varinfo,
             DynamicPPL.InitFromParams(values_dict, DynamicPPL.InitFromPrior()),
         )
-        vals = DynamicPPL.values_as_in_model(model, false, varinfo)
+        vals = DynamicPPL.getacc(varinfo, Val(:ValuesAsInModel)).values
         varname_vals = mapreduce(
             collect,
             vcat,
@@ -156,6 +168,13 @@ function DynamicPPL.predict(
     end
     return chain_result[parameter_names]
 end
+function DynamicPPL.predict(
+    model::DynamicPPL.Model, chain::MCMCChains.Chains; include_all=false
+)
+    return DynamicPPL.predict(
+        DynamicPPL.Random.default_rng(), model, chain; include_all=include_all
+    )
+end
 
 function _predictive_samples_to_arrays(predictive_samples)
     variable_names_set = DynamicPPL.OrderedCollections.OrderedSet{DynamicPPL.VarName}()

test/model.jl

@@ -519,6 +519,23 @@ const GDEMO_DEFAULT = DynamicPPL.TestUtils.demo_assume_observe_literal()
                 @test Set(keys(predictions)) == Set([Symbol("y[1]"), Symbol("y[2]")])
             end
 
+            @testset "include_all=true" begin
+                inc_predictions = DynamicPPL.predict(
+                    m_lin_reg_test, β_chain; include_all=true
+                )
+                @test Set(keys(inc_predictions)) ==
+                    Set([:β, Symbol("y[1]"), Symbol("y[2]")])
+                @test inc_predictions[:β] == β_chain[:β]
+                # check rng is respected
+                inc_predictions1 = DynamicPPL.predict(
+                    Xoshiro(468), m_lin_reg_test, β_chain; include_all=true
+                )
+                inc_predictions2 = DynamicPPL.predict(
+                    Xoshiro(468), m_lin_reg_test, β_chain; include_all=true
+                )
+                @test all(Array(inc_predictions1) .== Array(inc_predictions2))
+            end
+
             @testset "accuracy" begin
                 ys_pred = vec(mean(Array(group(predictions, :y)); dims=1))
                 @test ys_pred[1] ≈ ground_truth_β * xs_test[1] atol = 0.01