Re-add almost all integration tests into DPPL test suite proper

test/ad.jl

@@ -34,4 +34,43 @@
             end
         end
     end
+
+    @testset "Turing#2151: ReverseDiff compilation & eltype(vi, spl)" begin
+        # Failing model
+        t = 1:0.05:8
+        σ = 0.3
+        y = @. rand(sin(t) + Normal(0, σ))
+        @model function state_space(y, TT, ::Type{T}=Float64) where {T}
+            # Priors 
+            α ~ Normal(y[1], 0.001)
+            τ ~ Exponential(1)
+            η ~ filldist(Normal(0, 1), TT - 1)
+            σ ~ Exponential(1)
+            # create latent variable
+            x = Vector{T}(undef, TT)
+            x[1] = α
+            for t in 2:TT
+                x[t] = x[t - 1] + η[t - 1] * τ
+            end
+            # measurement model
+            y ~ MvNormal(x, σ^2 * I)
+            return x
+        end
+        model = state_space(y, length(t))
+
+        # Dummy sampling algorithm for testing. The test case can only be replicated
+        # with a custom sampler, it doesn't work with SampleFromPrior(). We need to
+        # overload assume so that model evaluation doesn't fail due to a lack
+        # of implementation
+        struct MyEmptyAlg end
+        DynamicPPL.getspace(::DynamicPPL.Sampler{MyEmptyAlg}) = ()
+        DynamicPPL.assume(rng, ::DynamicPPL.Sampler{MyEmptyAlg}, dist, vn, vi) =
+            DynamicPPL.assume(dist, vn, vi)
+
+        # Compiling the ReverseDiff tape used to fail here
+        spl = Sampler(MyEmptyAlg())
+        vi = VarInfo(model)
+        ldf = DynamicPPL.LogDensityFunction(vi, model, SamplingContext(spl))
+        @test LogDensityProblemsAD.ADgradient(AutoReverseDiff(; compile=true), ldf) isa Any
+    end
 end

test/model.jl

@@ -29,9 +29,11 @@ is_typed_varinfo(::DynamicPPL.AbstractVarInfo) = false
 is_typed_varinfo(varinfo::DynamicPPL.TypedVarInfo) = true
 is_typed_varinfo(varinfo::DynamicPPL.SimpleVarInfo{<:NamedTuple}) = true
 
+const GDEMO_DEFAULT = DynamicPPL.TestUtils.demo_assume_observe_literal()
+
 @testset "model.jl" begin
     @testset "convenience functions" begin
-        model = gdemo_default # defined in test/test_util.jl
+        model = GDEMO_DEFAULT # defined in test/test_util.jl
 
         # sample from model and extract variables
         vi = VarInfo(model)
@@ -55,53 +57,26 @@ is_typed_varinfo(varinfo::DynamicPPL.SimpleVarInfo{<:NamedTuple}) = true
         @test ljoint ≈ lp
 
         #### logprior, logjoint, loglikelihood for MCMC chains ####
-        for model in DynamicPPL.TestUtils.DEMO_MODELS # length(DynamicPPL.TestUtils.DEMO_MODELS)=12
-            var_info = VarInfo(model)
-            vns = DynamicPPL.TestUtils.varnames(model)
-            syms = unique(DynamicPPL.getsym.(vns))
-
-            # generate a chain of sample parameter values.
+        @testset "$(model.f)" for model in DynamicPPL.TestUtils.DEMO_MODELS
             N = 200
-            vals_OrderedDict = mapreduce(hcat, 1:N) do _
-                rand(OrderedDict, model)
-            end
-            vals_mat = mapreduce(hcat, 1:N) do i
-                [vals_OrderedDict[i][vn] for vn in vns]
-            end
-            i = 1
-            for col in eachcol(vals_mat)
-                col_flattened = []
-                [push!(col_flattened, x...) for x in col]
-                if i == 1
-                    chain_mat = Matrix(reshape(col_flattened, 1, length(col_flattened)))
-                else
-                    chain_mat = vcat(
-                        chain_mat, reshape(col_flattened, 1, length(col_flattened))
-                    )
-                end
-                i += 1
-            end
-            chain_mat = convert(Matrix{Float64}, chain_mat)
-
-            # devise parameter names for chain
-            sample_values_vec = collect(values(vals_OrderedDict[1]))
-            symbol_names = []
-            chain_sym_map = Dict()
-            for k in 1:length(keys(var_info))
-                vn_parent = keys(var_info)[k]
+            chain = make_chain_from_prior(model, N)
+            logpriors = logprior(model, chain)
+            loglikelihoods = loglikelihood(model, chain)
+            logjoints = logjoint(model, chain)
+
+            # Construct mapping of varname symbols to varname-parent symbols.
+            # Here, varname_leaves is used to ensure compatibility with the
+            # variables stored in the chain
+            var_info = VarInfo(model)
+            chain_sym_map = Dict{Symbol,Symbol}()
+            for vn_parent in keys(var_info)
                 sym = DynamicPPL.getsym(vn_parent)
-                vn_children = DynamicPPL.varname_leaves(vn_parent, sample_values_vec[k]) # `varname_leaves` defined in src/utils.jl
+                vn_children = DynamicPPL.varname_leaves(vn_parent, var_info[vn_parent])
                 for vn_child in vn_children
                     chain_sym_map[Symbol(vn_child)] = sym
-                    symbol_names = [symbol_names; Symbol(vn_child)]
                 end
             end
-            chain = Chains(chain_mat, symbol_names)
 
-            # calculate the pointwise loglikelihoods for the whole chain using the newly written functions
-            logpriors = logprior(model, chain)
-            loglikelihoods = loglikelihood(model, chain)
-            logjoints = logjoint(model, chain)
             # compare them with true values
             for i in 1:N
                 samples_dict = Dict()
@@ -125,8 +100,21 @@ is_typed_varinfo(varinfo::DynamicPPL.SimpleVarInfo{<:NamedTuple}) = true
         end
     end
 
+    @testset "DynamicPPL#684: threadsafe evaluation with multiple types" begin
+        @model function multiple_types(x)
+            ns ~ filldist(Normal(0, 2.0), 3)
+            m ~ Uniform(0, 1)
+            return x ~ Normal(m, 1)
+        end
+        model = multiple_types(1)
+        chain = make_chain_from_prior(model, 10)
+        loglikelihood(model, chain)
+        logprior(model, chain)
+        logjoint(model, chain)
+    end
+
     @testset "rng" begin
-        model = gdemo_default
+        model = GDEMO_DEFAULT
 
         for sampler in (SampleFromPrior(), SampleFromUniform())
             for i in 1:10
@@ -144,13 +132,15 @@ is_typed_varinfo(varinfo::DynamicPPL.SimpleVarInfo{<:NamedTuple}) = true
     end
 
     @testset "defaults without VarInfo, Sampler, and Context" begin
-        model = gdemo_default
+        model = GDEMO_DEFAULT
 
         Random.seed!(100)
-        s, m = model()
+        retval = model()
 
         Random.seed!(100)
-        @test model(Random.default_rng()) == (s, m)
+        retval2 = model(Random.default_rng())
+        @test retval2.s == retval.s
+        @test retval2.m == retval.m
     end
 
     @testset "nameof" begin
@@ -184,7 +174,7 @@ is_typed_varinfo(varinfo::DynamicPPL.SimpleVarInfo{<:NamedTuple}) = true
     end
 
     @testset "Internal methods" begin
-        model = gdemo_default
+        model = GDEMO_DEFAULT
 
         # sample from model and extract variables
         vi = VarInfo(model)
@@ -224,7 +214,7 @@ is_typed_varinfo(varinfo::DynamicPPL.SimpleVarInfo{<:NamedTuple}) = true
     end
 
     @testset "rand" begin
-        model = gdemo_default
+        model = GDEMO_DEFAULT
 
         Random.seed!(1776)
         s, m = model()
@@ -309,7 +299,7 @@ is_typed_varinfo(varinfo::DynamicPPL.SimpleVarInfo{<:NamedTuple}) = true
         end
     end
 
-    @testset "generated_quantities on `LKJCholesky`" begin
+    @testset "returned() on `LKJCholesky`" begin
         n = 10
         d = 2
         model = DynamicPPL.TestUtils.demo_lkjchol(d)
@@ -333,7 +323,7 @@ is_typed_varinfo(varinfo::DynamicPPL.SimpleVarInfo{<:NamedTuple}) = true
         )
 
         # Test!
-        results = generated_quantities(model, chain)
+        results = returned(model, chain)
         for (x_true, result) in zip(xs, results)
             @test x_true.UL == result.x.UL
         end
@@ -352,7 +342,7 @@ is_typed_varinfo(varinfo::DynamicPPL.SimpleVarInfo{<:NamedTuple}) = true
             info=(varname_to_symbol=vns_to_syms_with_extra,),
         )
         # Test!
-        results = generated_quantities(model, chain_with_extra)
+        results = returned(model, chain_with_extra)
         for (x_true, result) in zip(xs, results)
             @test x_true.UL == result.x.UL
         end

test/model_utils.jl

@@ -0,0 +1,16 @@
+@testset "model_utils.jl" begin
+    @testset "value_iterator_from_chain" begin
+        @testset "$model" for model in DynamicPPL.TestUtils.DEMO_MODELS
+            chain = make_chain_from_prior(model, 10)
+            for (i, d) in enumerate(value_iterator_from_chain(model, chain))
+                for vn in keys(d)
+                    val = DynamicPPL.getvalue(d, vn)
+                    for vn_leaf in DynamicPPL.varname_leaves(vn, val)
+                        val_leaf = DynamicPPL.getvalue(d, vn_leaf)
+                        @test val_leaf == chain[i, Symbol(vn_leaf), 1]
+                    end
+                end
+            end
+        end
+    end
+end

test/runtests.jl

@@ -29,6 +29,7 @@ using Distributions
 using LinearAlgebra # Diagonal
 
 using Combinatorics: combinations
+using OrderedCollections: OrderedSet
 
 using DynamicPPL: getargs_dottilde, getargs_tilde, Selector
 

test/test_util.jl

@@ -43,40 +43,6 @@ function test_model_ad(model, logp_manual)
     @test back(1)[1] ≈ grad
 end
 
-"""
-    test_setval!(model, chain; sample_idx = 1, chain_idx = 1)
-
-Test `setval!` on `model` and `chain`.
-
-Worth noting that this only supports models containing symbols of the forms
-`m`, `m[1]`, `m[1, 2]`, not `m[1][1]`, etc.
-"""
-function test_setval!(model, chain; sample_idx=1, chain_idx=1)
-    var_info = VarInfo(model)
-    spl = SampleFromPrior()
-    θ_old = var_info[spl]
-    DynamicPPL.setval!(var_info, chain, sample_idx, chain_idx)
-    θ_new = var_info[spl]
-    @test θ_old != θ_new
-    vals = DynamicPPL.values_as(var_info, OrderedDict)
-    iters = map(DynamicPPL.varname_and_value_leaves, keys(vals), values(vals))
-    for (n, v) in mapreduce(collect, vcat, iters)
-        n = string(n)
-        if Symbol(n) ∉ keys(chain)
-            # Assume it's a group
-            chain_val = vec(
-                MCMCChains.group(chain, Symbol(n)).value[sample_idx, :, chain_idx]
-            )
-            v_true = vec(v)
-        else
-            chain_val = chain[sample_idx, n, chain_idx]
-            v_true = v
-        end
-
-        @test v_true == chain_val
-    end
-end
-
 """
     short_varinfo_name(vi::AbstractVarInfo)