Merge remote-tracking branch 'origin/main' into breaking

Author: penelopeysm

.github/workflows/Benchmarking.yml

@@ -10,7 +10,7 @@ jobs:
       results: ${{ steps.benchmark.outputs.results }}
       sha: ${{ steps.benchmark.outputs.sha }}
     steps:
-      - uses: actions/checkout@v5
+      - uses: actions/checkout@v6
         with:
           ref: ${{ github.base_ref }}
       - uses: julia-actions/setup-julia@v2
@@ -35,7 +35,7 @@ jobs:
       results: ${{ steps.benchmark.outputs.results }}
       sha: ${{ steps.benchmark.outputs.sha }}
     steps:
-      - uses: actions/checkout@v5
+      - uses: actions/checkout@v6
         with:
           ref: ${{ github.event.pull_request.head.sha }}
       - uses: julia-actions/setup-julia@v2
@@ -58,7 +58,7 @@ jobs:
     runs-on: ubuntu-latest
     needs: [benchmark-base, benchmark-head]
     steps:
-      - uses: actions/checkout@v5
+      - uses: actions/checkout@v6
         with:
           ref: ${{ github.event.pull_request.head.sha }}
       - uses: julia-actions/setup-julia@v2

.github/workflows/CI.yml

@@ -56,7 +56,7 @@ jobs:
           - Group2
 
     steps:
-      - uses: actions/checkout@v5
+      - uses: actions/checkout@v6
 
       - uses: julia-actions/setup-julia@v2
         with:

.github/workflows/DocTest.yml

@@ -26,7 +26,7 @@ jobs:
     runs-on: ubuntu-latest
 
     steps:
-      - uses: actions/checkout@v5
+      - uses: actions/checkout@v6
 
       - uses: julia-actions/setup-julia@v2
         with:

.github/workflows/DocsNav.yml

@@ -17,7 +17,7 @@ jobs:
 
     steps:
       - name: Checkout gh-pages branch
-        uses: actions/checkout@v5
+        uses: actions/checkout@v6
         with:
           ref: gh-pages
 

.github/workflows/DocsPreviewCleanup.yml

@@ -9,7 +9,7 @@ jobs:
     runs-on: ubuntu-latest
     steps:
       - name: Checkout gh-pages branch
-        uses: actions/checkout@v5
+        uses: actions/checkout@v6
         with:
           ref: gh-pages
       - name: Delete preview and history + push changes

.github/workflows/Enzyme.yml

@@ -20,7 +20,7 @@ jobs:
   enzyme:
     runs-on: ubuntu-latest
     steps:
-      - uses: actions/checkout@v5
+      - uses: actions/checkout@v6
 
       - uses: julia-actions/setup-julia@v2
         with:

.github/workflows/IntegrationTest.yml

@@ -21,7 +21,7 @@ jobs:
           - {user: TuringLang, repo: Turing.jl}
 
     steps:
-      - uses: actions/checkout@v5
+      - uses: actions/checkout@v6
 
       - uses: julia-actions/setup-julia@v2
         with:
@@ -30,7 +30,7 @@ jobs:
       - uses: julia-actions/julia-buildpkg@v1
 
       - name: Clone Downstream
-        uses: actions/checkout@v5
+        uses: actions/checkout@v6
         with:
           repository: ${{ matrix.package.user }}/${{ matrix.package.repo }}
           path: downstream

.github/workflows/JuliaPre.yml

@@ -24,7 +24,7 @@ jobs:
           - Group2
 
     steps:
-      - uses: actions/checkout@v5
+      - uses: actions/checkout@v6
       - uses: julia-actions/setup-julia@v2
         with:
           version: 'pre' # pre-release

HISTORY.md

@@ -77,6 +77,14 @@ This is a generalisation of the previous behaviour, where `init` would always re
 The family of functions `returned(model, chain)`, along with the same signatures of `pointwise_logdensities`, `logjoint`, `loglikelihood`, and `logprior`, have been changed such that if the chain does not contain all variables in the model, an error is thrown.
 Previously the behaviour would have been to sample missing variables.
 
+## 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.

test/ext/DynamicPPLMCMCChainsExt.jl

@@ -3,19 +3,6 @@ module DynamicPPLMCMCChainsExtTests
 using DynamicPPL, Distributions, MCMCChains, Test, AbstractMCMC
 
 @testset "DynamicPPLMCMCChainsExt" begin
-    @model demo() = x ~ Normal()
-    model = demo()
-
-    chain = MCMCChains.Chains(
-        randn(1000, 2, 1),
-        [:x, :y],
-        Dict(:internals => [:y]);
-        info=(; varname_to_symbol=Dict(@varname(x) => :x)),
-    )
-    chain_generated = @test_nowarn returned(model, chain)
-    @test size(chain_generated) == (1000, 1)
-    @test mean(chain_generated) ≈ 0 atol = 0.1
-
     @testset "from_samples" begin
         @model function f(z)
             x ~ Normal()
@@ -61,6 +48,42 @@ using DynamicPPL, Distributions, MCMCChains, Test, AbstractMCMC
             @test new_p.stats == p.stats
         end
     end
+
+    @testset "returned (basic)" begin
+        @model demo() = x ~ Normal()
+        model = demo()
+
+        chain = MCMCChains.Chains(
+            randn(1000, 2, 1),
+            [:x, :y],
+            Dict(:internals => [:y]);
+            info=(; varname_to_symbol=Dict(@varname(x) => :x)),
+        )
+        chain_generated = @test_nowarn returned(model, chain)
+        @test size(chain_generated) == (1000, 1)
+        @test mean(chain_generated) ≈ 0 atol = 0.1
+    end
+
+    @testset "returned: errors on missing variable" begin
+        # Create a chain that only has `m`.
+        @model function m_only()
+            return m ~ Normal()
+        end
+        model_m_only = m_only()
+        chain_m_only = AbstractMCMC.from_samples(
+            MCMCChains.Chains,
+            hcat([ParamsWithStats(VarInfo(model_m_only), model_m_only) for _ in 1:50]),
+        )
+
+        # Define a model that needs both `m` and `s`.
+        @model function f()
+            m ~ Normal()
+            s ~ Exponential()
+            return y ~ Normal(m, s)
+        end
+        model = f() | (; y=1.0)
+        @test_throws "No value was provided" returned(model, chain_m_only)
+    end
 end
 
 # test for `predict` is in `test/model.jl`