CompatHelper: bump compat for Bijectors to 0.13, (keep existing compat) (#2018)

* CompatHelper: bump compat for Bijectors to 0.13, (keep existing compat)

* Update Project.toml

* Replacement for #2039 (#2040)

* Fix testset for external samplers

* Update abstractmcmc.jl

* Update test/contrib/inference/abstractmcmc.jl

Co-authored-by: Tor Erlend Fjelde <[email protected]>

* Update test/contrib/inference/abstractmcmc.jl

Co-authored-by: Tor Erlend Fjelde <[email protected]>

* Transfer some test utility function into DynamicPPL (#2049)

* Update OptimInterface.jl

* Only run optimisation tests in numerical stage.

* fix function lookup after moving functions

---------

Co-authored-by: Xianda Sun <[email protected]>

* Remove tracker tests.

* Update Project.toml

* Update Project.toml

* Update Project.toml

---------

Co-authored-by: CompatHelper Julia <[email protected]>
Co-authored-by: Hong Ge <[email protected]>
Co-authored-by: haris organtzidis <[email protected]>
Co-authored-by: Tor Erlend Fjelde <[email protected]>
Co-authored-by: Xianda Sun <[email protected]>
Co-authored-by: Cameron Pfiffer <[email protected]>

Author: 7 people

Project.toml

@@ -51,7 +51,7 @@ AdvancedMH = "0.6.8, 0.7"
 AdvancedPS = "0.4"
 AdvancedVI = "0.2"
 BangBang = "0.3"
-Bijectors = "0.12"
+Bijectors = "0.13.6"
 DataStructures = "0.18"
 Distributions = "0.23.3, 0.24, 0.25"
 DistributionsAD = "0.6"

test/contrib/inference/abstractmcmc.jl

@@ -41,7 +41,7 @@ function initialize_mh(model)
 end
 
 @testset "External samplers" begin
-    @testset "AdvancedHMC.jl" begin
+    @turing_testset "AdvancedHMC.jl" begin
         for model in DynamicPPL.TestUtils.DEMO_MODELS
             # Need some functionality to initialize the sampler.
             # TODO: Remove this once the constructors in the respective packages become "lazy".
@@ -52,12 +52,13 @@ end
                 5_000;
                 n_adapts=1_000,
                 discard_initial=1_000,
-                rtol=0.2
+                rtol=0.2,
+                sampler_name="AdvancedHMC"
             )
         end
     end
 
-    @testset "AdvancedMH.jl" begin
+    @turing_testset "AdvancedMH.jl" begin
         for model in DynamicPPL.TestUtils.DEMO_MODELS
             # Need some functionality to initialize the sampler.
             # TODO: Remove this once the constructors in the respective packages become "lazy".
@@ -68,7 +69,8 @@ end
                 10_000;
                 discard_initial=1_000,
                 thinning=10,
-                rtol=0.2
+                rtol=0.2,
+                sampler_name="AdvancedMH"
             )
         end
     end

test/essential/ad.jl

@@ -84,8 +84,6 @@
         @model function dir()
             theta ~ Dirichlet(1 ./ fill(4, 4))
         end
-        Turing.setadbackend(:tracker)
-        sample(dir(), HMC(0.01, 1), 1000);
         Turing.setadbackend(:zygote)
         sample(dir(), HMC(0.01, 1), 1000)
         Turing.setadbackend(:reversediff)
@@ -99,8 +97,6 @@
         @model function wishart()
             theta ~ Wishart(4, Matrix{Float64}(I, 4, 4))
         end
-        Turing.setadbackend(:tracker)
-        sample(wishart(), HMC(0.01, 1), 1000);
         Turing.setadbackend(:reversediff)
         sample(wishart(), HMC(0.01, 1), 1000);
         Turing.setadbackend(:zygote)
@@ -109,8 +105,6 @@
         @model function invwishart()
             theta ~ InverseWishart(4, Matrix{Float64}(I, 4, 4))
         end
-        Turing.setadbackend(:tracker)
-        sample(invwishart(), HMC(0.01, 1), 1000);
         Turing.setadbackend(:reversediff)
         sample(invwishart(), HMC(0.01, 1), 1000);
         Turing.setadbackend(:zygote)

test/modes/OptimInterface.jl

@@ -1,38 +1,3 @@
-# TODO: Remove these once the equivalent is present in `DynamicPPL.TestUtils.
-function likelihood_optima(::DynamicPPL.TestUtils.UnivariateAssumeDemoModels)
-    return (s=1/16, m=7/4)
-end
-function posterior_optima(::DynamicPPL.TestUtils.UnivariateAssumeDemoModels)
-    # TODO: Figure out exact for `s`.
-    return (s=0.907407, m=7/6)
-end
-
-function likelihood_optima(model::DynamicPPL.TestUtils.MultivariateAssumeDemoModels)
-    # Get some containers to fill.
-    vals = Random.rand(model)
-
-    # NOTE: These are "as close to zero as we can get".
-    vals.s[1] = 1e-32
-    vals.s[2] = 1e-32
-
-    vals.m[1] = 1.5
-    vals.m[2] = 2.0
-
-    return vals
-end
-function posterior_optima(model::DynamicPPL.TestUtils.MultivariateAssumeDemoModels)
-    # Get some containers to fill.
-    vals = Random.rand(model)
-
-    # TODO: Figure out exact for `s[1]`.
-    vals.s[1] = 0.890625
-    vals.s[2] = 1
-    vals.m[1] = 3/4
-    vals.m[2] = 1
-
-    return vals
-end
-
 # Used for testing how well it works with nested contexts.
 struct OverrideContext{C,T1,T2} <: DynamicPPL.AbstractContext
     context::C
@@ -57,7 +22,7 @@ function DynamicPPL.tilde_observe(context::OverrideContext, right, left, vi)
     return context.loglikelihood_weight, vi
 end
 
-@testset "OptimInterface.jl" begin
+@numerical_testset "OptimInterface.jl" begin
     @testset "MLE" begin
         Random.seed!(222)
         true_value = [0.0625, 1.75]
@@ -157,7 +122,7 @@ end
     # FIXME: Some models doesn't work for Tracker and ReverseDiff.
     if Turing.Essential.ADBACKEND[] === :forwarddiff
         @testset "MAP for $(model.f)" for model in DynamicPPL.TestUtils.DEMO_MODELS
-            result_true = posterior_optima(model)
+            result_true = DynamicPPL.TestUtils.posterior_optima(model)
 
             @testset "$(nameof(typeof(optimizer)))" for optimizer in [LBFGS(), NelderMead()]
                 result = optimize(model, MAP(), optimizer)
@@ -188,7 +153,7 @@ end
             DynamicPPL.TestUtils.demo_dot_assume_matrix_dot_observe_matrix,
         ]
         @testset "MLE for $(model.f)" for model in DynamicPPL.TestUtils.DEMO_MODELS
-            result_true = likelihood_optima(model)
+            result_true = DynamicPPL.TestUtils.likelihood_optima(model)
 
             # `NelderMead` seems to struggle with convergence here, so we exclude it.
             @testset "$(nameof(typeof(optimizer)))" for optimizer in [LBFGS(),]