disable fallback for returned and pointwise_logdensities

Author: penelopeysm

HISTORY.md

@@ -1,5 +1,13 @@
 # DynamicPPL Changelog
 
+## 0.38.10
+
+`returned(model, chain)` and `pointwise_logdensities(model, chain)` will now error if a value for a random variable cannot be found in the chain.
+(Previously, they would instead resample such variables, which could lead to silent mistakes.)
+
+If you encounter this error and it is accompanied by a warning about `hasvalue` not being implemented, you should be able to fix this by [using FlexiChains instead of MCMCChains](https://github.com/penelopeysm/FlexiChains.jl).
+(Alternatively, implementations of `hasvalue` for unsupported distributions are more than welcome; these must be provided in the Distributions extension of AbstractPPL.jl.)
+
 ## 0.38.9
 
 Remove warning when using Enzyme as the AD backend.

Project.toml

@@ -1,6 +1,6 @@
 name = "DynamicPPL"
 uuid = "366bfd00-2699-11ea-058f-f148b4cae6d8"
-version = "0.38.9"
+version = "0.38.10"
 
 [deps]
 ADTypes = "47edcb42-4c32-4615-8424-f2b9edc5f35b"

ext/DynamicPPLMCMCChainsExt.jl

@@ -226,7 +226,10 @@ function DynamicPPL.predict(
     )
     predictions = map(params_and_stats) do ps
         _, varinfo = DynamicPPL.init!!(
-            rng, model, varinfo, DynamicPPL.InitFromParams(ps.params)
+            rng,
+            model,
+            varinfo,
+            DynamicPPL.InitFromParams(ps.params, DynamicPPL.InitFromPrior()),
         )
         DynamicPPL.ParamsWithStats(varinfo)
     end
@@ -316,11 +319,7 @@ function DynamicPPL.returned(model::DynamicPPL.Model, chain_full::MCMCChains.Cha
     params_with_stats = AbstractMCMC.to_samples(DynamicPPL.ParamsWithStats, chain)
     return map(params_with_stats) do ps
         first(
-            DynamicPPL.init!!(
-                model,
-                varinfo,
-                DynamicPPL.InitFromParams(ps.params, DynamicPPL.InitFromPrior()),
-            ),
+            DynamicPPL.init!!(model, varinfo, DynamicPPL.InitFromParams(ps.params, nothing))
         )
     end
 end
@@ -426,9 +425,7 @@ function DynamicPPL.pointwise_logdensities(
         values_dict = chain_sample_to_varname_dict(parameter_only_chain, sample_idx, chain_idx)
         # Re-evaluate the model
         _, vi = DynamicPPL.init!!(
-            model,
-            vi,
-            DynamicPPL.InitFromParams(values_dict, DynamicPPL.InitFromPrior()),
+            model, vi, DynamicPPL.InitFromParams(values_dict, nothing)
         )
         DynamicPPL.getacc(vi, Val(accname)).logps
     end