Remove tests that were added back to DPPL

TuringLang · Nov 29, 2024 · f812c69 · f812c69
1 parent 3180c64
commit f812c69
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diff --git a/test/dynamicppl/model.jl b/test/dynamicppl/model.jl
diff --git a/test/dynamicppl/varinfo.jl b/test/dynamicppl/varinfo.jl
@@ -37,58 +37,6 @@ using Turing
         end
     end
 
-    @testset "link!" begin
-        @model gdemo(x, y) = begin
-            s ~ InverseGamma(2, 3)
-            m ~ Uniform(0, 2)
-            x ~ Normal(m, sqrt(s))
-            y ~ Normal(m, sqrt(s))
-        end
-        model = gdemo(1.0, 2.0)
-
-        vi = DynamicPPL.VarInfo()
-        meta = vi.metadata
-        model(vi, DynamicPPL.SampleFromUniform())
-        @test all(x -> !DynamicPPL.istrans(vi, x), meta.vns)
-
-        alg = HMC(0.1, 5)
-        spl = DynamicPPL.Sampler(alg, model)
-        v = copy(meta.vals)
-        DynamicPPL.link!!(vi, spl, model)
-        @test all(x -> DynamicPPL.istrans(vi, x), meta.vns)
-        DynamicPPL.invlink!!(vi, spl, model)
-        @test all(x -> !DynamicPPL.istrans(vi, x), meta.vns)
-        @test meta.vals ≈ v atol = 1e-10
-
-        vi = DynamicPPL.TypedVarInfo(vi)
-        meta = vi.metadata
-        alg = HMC(0.1, 5)
-        spl = DynamicPPL.Sampler(alg, model)
-        @test all(x -> !DynamicPPL.istrans(vi, x), meta.s.vns)
-        @test all(x -> !DynamicPPL.istrans(vi, x), meta.m.vns)
-        v_s = copy(meta.s.vals)
-        v_m = copy(meta.m.vals)
-        DynamicPPL.link!!(vi, spl, model)
-        @test all(x -> DynamicPPL.istrans(vi, x), meta.s.vns)
-        @test all(x -> DynamicPPL.istrans(vi, x), meta.m.vns)
-        DynamicPPL.invlink!!(vi, spl, model)
-        @test all(x -> !DynamicPPL.istrans(vi, x), meta.s.vns)
-        @test all(x -> !DynamicPPL.istrans(vi, x), meta.m.vns)
-        @test meta.s.vals ≈ v_s atol = 1e-10
-        @test meta.m.vals ≈ v_m atol = 1e-10
-
-        # Transforming only a subset of the variables
-        spl = DynamicPPL.Sampler(HMC(0.1, 5, :m), model)
-        DynamicPPL.link!!(vi, spl, model)
-        @test all(x -> !DynamicPPL.istrans(vi, x), meta.s.vns)
-        @test all(x -> DynamicPPL.istrans(vi, x), meta.m.vns)
-        DynamicPPL.invlink!!(vi, spl, model)
-        @test all(x -> !DynamicPPL.istrans(vi, x), meta.s.vns)
-        @test all(x -> !DynamicPPL.istrans(vi, x), meta.m.vns)
-        @test meta.s.vals ≈ v_s atol = 1e-10
-        @test meta.m.vals ≈ v_m atol = 1e-10
-    end
-
     @testset "orders" begin
         csym = gensym() # unique per model
         vn_z1 = @varname z[1]
@@ -176,6 +124,7 @@ using Turing
         @test vi.metadata.b.orders == [2]
         @test DynamicPPL.get_num_produce(vi) == 3
     end
+
     @testset "replay" begin
         # Generate synthesised data
         xs = rand(Normal(0.5, 1), 100)
@@ -196,6 +145,7 @@ using Turing
         # Sampling
         chain = sample(priorsinarray(xs), HMC(0.01, 10), 10)
     end
+
     @testset "varname" begin
         @model function mat_name_test()
             p = Array{Any}(undef, 2, 2)
@@ -288,41 +238,6 @@ using Turing
 
         # Test the update of group IDs
         g_demo_f = igtest()
-
-        # This test section no longer seems as applicable, considering the
-        # user will never end up using an UntypedVarInfo. The `VarInfo`
-        # Varible is also not passed around in the same way as it used to be.
-
-        # TODO: Has to be fixed
-
-        #= g = DynamicPPL.Sampler(Gibbs(PG(10, :x, :y, :z), HMC(0.4, 8, :w, :u)), g_demo_f)
-        vi = VarInfo()
-        g_demo_f(vi, SampleFromPrior())
-        _, state = @inferred AbstractMCMC.step(Random.default_rng(), g_demo_f, g)
-        pg, hmc = state.states
-        @test pg isa TypedVarInfo
-        @test hmc isa Turing.Inference.HMCState
-        vi1 = state.vi
-        @test mapreduce(x -> x.gids, vcat, vi1.metadata) ==
-            [Set([pg.selector]), Set([pg.selector]), Set([pg.selector]), Set{Selector}(), Set{Selector}()]
-
-        @inferred g_demo_f(vi1, hmc)
-        @test mapreduce(x -> x.gids, vcat, vi1.metadata) ==
-            [Set([pg.selector]), Set([pg.selector]), Set([pg.selector]), Set([hmc.selector]), Set([hmc.selector])]
-
-        g = DynamicPPL.Sampler(Gibbs(PG(10, :x, :y, :z), HMC(0.4, 8, :w, :u)), g_demo_f)
-        pg, hmc = g.state.samplers
-        vi = empty!!(TypedVarInfo(vi))
-        @inferred g_demo_f(vi, SampleFromPrior())
-        pg.state.vi = vi
-        step!(Random.default_rng(), g_demo_f, pg, 1)
-        vi = pg.state.vi
-        @inferred g_demo_f(vi, hmc)
-        @test vi.metadata.x.gids[1] == Set([pg.selector])
-        @test vi.metadata.y.gids[1] == Set([pg.selector])
-        @test vi.metadata.z.gids[1] == Set([pg.selector])
-        @test vi.metadata.w.gids[1] == Set([hmc.selector])
-        @test vi.metadata.u.gids[1] == Set([hmc.selector]) =#
     end
 
     @testset "Turing#2151: eltype(vi, spl)" begin
@@ -355,21 +270,6 @@ using Turing
         model = state_space(y, length(t))
         @test size(sample(model, NUTS(; adtype=AutoReverseDiff(; compile=true)), n), 1) == n
     end
-
-    if Threads.nthreads() > 1
-        @testset "DynamicPPL#684: OrderedDict with multiple types when multithreaded" begin
-            @model function f(x)
-                ns ~ filldist(Normal(0, 2.0), 3)
-                m ~ Uniform(0, 1)
-                return x ~ Normal(m, 1)
-            end
-            model = f(1)
-            chain = sample(model, NUTS(), MCMCThreads(), 10, 2)
-            loglikelihood(model, chain)
-            logprior(model, chain)
-            logjoint(model, chain)
-        end
-    end
 end
 
 end