Remove conditional loading/exporting of Tapir

Author: penelopeysm

Project.toml

@@ -73,14 +73,13 @@ LogDensityProblems = "2"
 LogDensityProblemsAD = "1.7.0"
 MCMCChains = "5, 6"
 NamedArrays = "0.9, 0.10"
+Optim = "1"
 Optimization = "3"
 OptimizationOptimJL = "0.1, 0.2, 0.3"
 OrderedCollections = "1"
 Printf = "1"
 Random = "1"
-Optim = "1"
 Reexport = "0.2, 1"
-Requires = "0.5, 1.0"
 SciMLBase = "2"
 SpecialFunctions = "0.7.2, 0.8, 0.9, 0.10, 1, 2"
 Statistics = "1.6"

src/Turing.jl

@@ -107,6 +107,7 @@ export @model,                 # modelling
     AutoReverseDiff,
     AutoZygote,
     AutoTracker,
+    AutoTapir,
     setprogress!,           # debugging
     Flat,
     FlatPos,
@@ -136,24 +137,4 @@ export @model,                 # modelling
     MAP,
     MLE
 
-# AutoTapir is only supported by ADTypes v1.0 and above.
-@static if VERSION >= v"1.10" && pkgversion(ADTypes) >= v"1"
-    export AutoTapir
-end
-
-if !isdefined(Base, :get_extension)
-    using Requires
-end
-
-function __init__()
-    @static if !isdefined(Base, :get_extension)
-        @require Optim = "429524aa-4258-5aef-a3af-852621145aeb" include(
-            "../ext/TuringOptimExt.jl"
-        )
-        @require DynamicHMC = "bbc10e6e-7c05-544b-b16e-64fede858acb" include(
-            "../ext/TuringDynamicHMCExt.jl"
-        )
-    end
-end
-
 end

src/essential/Essential.jl

@@ -11,7 +11,7 @@ using Bijectors: PDMatDistribution
 using AdvancedVI
 using StatsFuns: logsumexp, softmax
 @reexport using DynamicPPL
-using ADTypes: ADTypes, AutoForwardDiff, AutoTracker, AutoReverseDiff, AutoZygote
+using ADTypes: ADTypes, AutoForwardDiff, AutoTracker, AutoReverseDiff, AutoZygote, AutoTapir
 
 using AdvancedPS: AdvancedPS
 
@@ -23,13 +23,8 @@ export @model,
     AutoTracker,
     AutoZygote,
     AutoReverseDiff,
+    AutoTapir,
     @logprob_str,
     @prob_str
 
-# AutoTapir is only supported by ADTypes v1.0 and above.
-@static if VERSION >= v"1.10" && pkgversion(ADTypes) >= v"1"
-    using ADTypes: AutoTapir
-    export AutoTapir
-end
-
 end # module

test/Project.toml

@@ -30,6 +30,7 @@ SpecialFunctions = "276daf66-3868-5448-9aa4-cd146d93841b"
 StableRNGs = "860ef19b-820b-49d6-a774-d7a799459cd3"
 StatsBase = "2913bbd2-ae8a-5f71-8c99-4fb6c76f3a91"
 StatsFuns = "4c63d2b9-4356-54db-8cca-17b64c39e42c"
+Tapir = "07d77754-e150-4737-8c94-cd238a1fb45b"
 Test = "8dfed614-e22c-5e08-85e1-65c5234f0b40"
 TimerOutputs = "a759f4b9-e2f1-59dc-863e-4aeb61b1ea8f"
 Tracker = "9f7883ad-71c0-57eb-9f7f-b5c9e6d3789c"
@@ -67,6 +68,7 @@ SpecialFunctions = "0.10.3, 1, 2"
 StableRNGs = "1"
 StatsBase = "0.33, 0.34"
 StatsFuns = "0.9.5, 1"
+Tapir = "0.2.45"
 TimerOutputs = "0.5"
 Tracker = "0.2.11"
 Zygote = "0.5.4, 0.6"

test/mcmc/Inference.jl

@@ -12,11 +12,10 @@ using LinearAlgebra: I
 import MCMCChains
 import Random
 import ReverseDiff
+import Tapir
 using Test: @test, @test_throws, @testset
 using Turing
 
-ADUtils.install_tapir && import Tapir
-
 @testset "Testing inference.jl with $adbackend" for adbackend in ADUtils.adbackends
     # Only test threading if 1.3+.
     if VERSION > v"1.2"

test/mcmc/abstractmcmc.jl

@@ -12,12 +12,11 @@ using LogDensityProblemsAD: LogDensityProblemsAD
 using Random: Random
 using ReverseDiff: ReverseDiff
 using StableRNGs: StableRNG
+import Tapir
 using Test: @test, @test_throws, @testset
 using Turing
 using Turing.Inference: AdvancedHMC
 
-ADUtils.install_tapir && import Tapir
-
 function initialize_nuts(model::Turing.Model)
     # Create a log-density function with an implementation of the
     # gradient so we ensure that we're using the same AD backend as in Turing.

test/mcmc/gibbs.jl

@@ -8,13 +8,12 @@ using Distributions: sample
 using ForwardDiff: ForwardDiff
 using Random: Random
 using ReverseDiff: ReverseDiff
+import Tapir
 using Test: @test, @testset
 using Turing
 using Turing: Inference
 using Turing.RandomMeasures: ChineseRestaurantProcess, DirichletProcess
 
-ADUtils.install_tapir && import Tapir
-
 @testset "Testing gibbs.jl with $adbackend" for adbackend in ADUtils.adbackends
     @testset "gibbs constructor" begin
         N = 500

test/mcmc/gibbs_conditional.jl

@@ -12,11 +12,10 @@ using ReverseDiff: ReverseDiff
 using StableRNGs: StableRNG
 using StatsBase: counts
 using StatsFuns: StatsFuns
+import Tapir
 using Test: @test, @testset
 using Turing
 
-ADUtils.install_tapir && import Tapir
-
 @testset "Testing gibbs conditionals.jl with $adbackend" for adbackend in ADUtils.adbackends
     Random.seed!(1000)
     rng = StableRNG(123)

test/mcmc/hmc.jl

@@ -2,7 +2,6 @@ module HMCTests
 
 using ..Models: gdemo_default
 using ..ADUtils: ADTypeCheckContext
-#using ..Models: gdemo
 using ..NumericalTests: check_gdemo, check_numerical
 import ..ADUtils
 using Distributions: Bernoulli, Beta, Categorical, Dirichlet, Normal, Wishart, sample
@@ -15,11 +14,10 @@ using LinearAlgebra: I, dot, vec
 import Random
 using StableRNGs: StableRNG
 using StatsFuns: logistic
+import Tapir
 using Test: @test, @test_logs, @testset
 using Turing
 
-ADUtils.install_tapir && import Tapir
-
 @testset "Testing hmc.jl with $adbackend" for adbackend in ADUtils.adbackends
     # Set a seed
     rng = StableRNG(123)

test/mcmc/sghmc.jl

@@ -8,11 +8,10 @@ import ForwardDiff
 using LinearAlgebra: dot
 import ReverseDiff
 using StableRNGs: StableRNG
+import Tapir
 using Test: @test, @testset
 using Turing
 
-ADUtils.install_tapir && import Tapir
-
 @testset "Testing sghmc.jl with $adbackend" for adbackend in ADUtils.adbackends
     @testset "sghmc constructor" begin
         alg = SGHMC(; learning_rate=0.01, momentum_decay=0.1, adtype=adbackend)