remove some AD pkgs

impICNF · Aug 1, 2023 · 174813b · 174813b
1 parent 402e5de
commit 174813b
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diff --git a/test/Project.toml b/test/Project.toml
@@ -4,29 +4,23 @@ AbstractDifferentiation = "c29ec348-61ec-40c8-8164-b8c60e9d9f3d"
 Aqua = "4c88cf16-eb10-579e-8560-4a9242c79595"
 BenchmarkTools = "6e4b80f9-dd63-53aa-95a3-0cdb28fa8baf"
 CUDA = "052768ef-5323-5732-b1bb-66c8b64840ba"
-Calculus = "49dc2e85-a5d0-5ad3-a950-438e2897f1b9"
 ComponentArrays = "b0b7db55-cfe3-40fc-9ded-d10e2dbeff66"
 ComputationalResources = "ed09eef8-17a6-5b46-8889-db040fac31e3"
 DataFrames = "a93c6f00-e57d-5684-b7b6-d8193f3e46c0"
 Distances = "b4f34e82-e78d-54a5-968a-f98e89d6e8f7"
 Distributions = "31c24e10-a181-5473-b8eb-7969acd0382f"
-Enzyme = "7da242da-08ed-463a-9acd-ee780be4f1d9"
-FiniteDiff = "6a86dc24-6348-571c-b903-95158fe2bd41"
-FiniteDifferences = "26cc04aa-876d-5657-8c51-4c34ba976000"
 Flux = "587475ba-b771-5e3f-ad9e-33799f191a9c"
 ForwardDiff = "f6369f11-7733-5829-9624-2563aa707210"
 JET = "c3a54625-cd67-489e-a8e7-0a5a0ff4e31b"
 Logging = "56ddb016-857b-54e1-b83d-db4d58db5568"
 Lux = "b2108857-7c20-44ae-9111-449ecde12c47"
 LuxCUDA = "d0bbae9a-e099-4d5b-a835-1c6931763bda"
 MLJBase = "a7f614a8-145f-11e9-1d2a-a57a1082229d"
-ModelingToolkit = "961ee093-0014-501f-94e3-6117800e7a78"
 Optimization = "7f7a1694-90dd-40f0-9382-eb1efda571ba"
 Random = "9a3f8284-a2c9-5f02-9a11-845980a1fd5c"
 ReverseDiff = "37e2e3b7-166d-5795-8a7a-e32c996b4267"
 SciMLBase = "0bca4576-84f4-4d90-8ffe-ffa030f20462"
 Test = "8dfed614-e22c-5e08-85e1-65c5234f0b40"
-Tracker = "9f7883ad-71c0-57eb-9f7f-b5c9e6d3789c"
 TruncatedStacktraces = "781d530d-4396-4725-bb49-402e4bee1e77"
 Zygote = "e88e6eb3-aa80-5325-afca-941959d7151f"
 cuDNN = "02a925ec-e4fe-4b08-9a7e-0d78e3d38ccd"

diff --git a/test/call_tests.jl b/test/call_tests.jl
@@ -32,10 +32,7 @@
         AbstractDifferentiation.ZygoteBackend(),
         AbstractDifferentiation.ReverseDiffBackend(),
         AbstractDifferentiation.ForwardDiffBackend(),
-        AbstractDifferentiation.TrackerBackend(),
-        AbstractDifferentiation.FiniteDifferencesBackend(),
     ]
-    fd_m = FiniteDifferences.central_fdm(5, 1)
     rng = Random.default_rng()
 
     @testset "$at | $tp | $(typeof(adb_u).name.name) | $nvars Vars | $mt" for at in ats,
@@ -45,9 +42,7 @@
         omode in omodes,
         mt in mts
 
-        adb_u isa AbstractDifferentiation.FiniteDifferencesBackend && continue
         adb_u isa AbstractDifferentiation.ReverseDiffBackend && continue
-        adb_u isa AbstractDifferentiation.TrackerBackend && mt <: Planar && continue
 
         data_dist = Distributions.Beta{tp}(convert(Tuple{tp, tp}, (2, 4))...)
         r = convert(at{tp}, rand(data_dist, nvars))
@@ -85,7 +80,6 @@
         diff_loss(x) = loss(icnf, omode, r, x, st)
 
         @testset "Using $(typeof(adb).name.name) For Loss" for adb in adb_list
-            adb isa AbstractDifferentiation.TrackerBackend && continue
             @test_throws MethodError !isnothing(
                 AbstractDifferentiation.derivative(adb, diff_loss, ps),
             )
@@ -126,27 +120,9 @@
         @test_throws DimensionMismatch !isnothing(ForwardDiff.jacobian(diff_loss, ps))
         # @test !isnothing(ForwardDiff.hessian(diff_loss, ps))
 
-        # @test !isnothing(Tracker.gradient(diff_loss, ps))
-        # @test !isnothing(Tracker.jacobian(diff_loss, ps))
-        # @test !isnothing(Tracker.hessian(diff_loss, ps))
-
-        @test !isnothing(FiniteDifferences.grad(fd_m, diff_loss, ps))
-        @test !isnothing(FiniteDifferences.jacobian(fd_m, diff_loss, ps))
-
-        @test_throws MethodError !isnothing(
-            FiniteDiff.finite_difference_derivative(diff_loss, ps),
-        )
-        @test !isnothing(FiniteDiff.finite_difference_gradient(diff_loss, ps))
-        @test_broken !isnothing(FiniteDiff.finite_difference_jacobian(diff_loss, ps))
-        # @test !isnothing(FiniteDiff.finite_difference_hessian(diff_loss, ps))
-
-        @test_throws MethodError !isnothing(Calculus.gradient(diff_loss, ps))
-        # @test !isnothing(Calculus.hessian(diff_loss, ps))
-
         diff2_loss(x) = loss(icnf, omode, x, ps, st)
 
         @testset "Using $(typeof(adb).name.name) For Loss" for adb in adb_list
-            adb isa AbstractDifferentiation.TrackerBackend && continue
             @test_throws MethodError !isnothing(
                 AbstractDifferentiation.derivative(adb, diff2_loss, r),
             )
@@ -187,23 +163,6 @@
         @test_throws DimensionMismatch !isnothing(ForwardDiff.jacobian(diff2_loss, r))
         # @test !isnothing(ForwardDiff.hessian(diff2_loss, r))
 
-        # @test !isnothing(Tracker.gradient(diff2_loss, r))
-        # @test !isnothing(Tracker.jacobian(diff2_loss, r))
-        # @test !isnothing(Tracker.hessian(diff2_loss, r))
-
-        @test !isnothing(FiniteDifferences.grad(fd_m, diff2_loss, r))
-        @test !isnothing(FiniteDifferences.jacobian(fd_m, diff2_loss, r))
-
-        @test_throws MethodError !isnothing(
-            FiniteDiff.finite_difference_derivative(diff2_loss, r),
-        )
-        @test !isnothing(FiniteDiff.finite_difference_gradient(diff2_loss, r))
-        @test_broken !isnothing(FiniteDiff.finite_difference_jacobian(diff2_loss, r))
-        # @test !isnothing(FiniteDiff.finite_difference_hessian(diff2_loss, r))
-
-        @test_throws MethodError !isnothing(Calculus.gradient(diff2_loss, r))
-        # @test !isnothing(Calculus.hessian(diff2_loss, r))
-
         d = ICNFDist(icnf, omode, ps, st)
 
         @test !isnothing(Distributions.logpdf(d, r))
@@ -256,7 +215,6 @@
         diff_loss(x) = loss(icnf, omode, r_arr, x, st)
 
         @testset "Using $(typeof(adb).name.name) For Loss" for adb in adb_list
-            adb isa AbstractDifferentiation.TrackerBackend && continue
             @test_throws MethodError !isnothing(
                 AbstractDifferentiation.derivative(adb, diff_loss, ps),
             )
@@ -297,27 +255,9 @@
         @test_throws DimensionMismatch !isnothing(ForwardDiff.jacobian(diff_loss, ps))
         # @test !isnothing(ForwardDiff.hessian(diff_loss, ps))
 
-        # @test !isnothing(Tracker.gradient(diff_loss, ps))
-        # @test !isnothing(Tracker.jacobian(diff_loss, ps))
-        # @test !isnothing(Tracker.hessian(diff_loss, ps))
-
-        @test !isnothing(FiniteDifferences.grad(fd_m, diff_loss, ps))
-        @test !isnothing(FiniteDifferences.jacobian(fd_m, diff_loss, ps))
-
-        @test_throws MethodError !isnothing(
-            FiniteDiff.finite_difference_derivative(diff_loss, ps),
-        )
-        @test !isnothing(FiniteDiff.finite_difference_gradient(diff_loss, ps))
-        @test_broken !isnothing(FiniteDiff.finite_difference_jacobian(diff_loss, ps))
-        # @test !isnothing(FiniteDiff.finite_difference_hessian(diff_loss, ps))
-
-        @test_throws MethodError !isnothing(Calculus.gradient(diff_loss, ps))
-        # @test !isnothing(Calculus.hessian(diff_loss, ps))
-
         diff2_loss(x) = loss(icnf, omode, x, ps, st)
 
         @testset "Using $(typeof(adb).name.name) For Loss" for adb in adb_list
-            adb isa AbstractDifferentiation.TrackerBackend && continue
             @test_throws MethodError !isnothing(
                 AbstractDifferentiation.derivative(adb, diff2_loss, r),
             )
@@ -358,23 +298,6 @@
         @test_throws DimensionMismatch !isnothing(ForwardDiff.jacobian(diff2_loss, r))
         # @test !isnothing(ForwardDiff.hessian(diff2_loss, r))
 
-        # @test !isnothing(Tracker.gradient(diff2_loss, r))
-        # @test !isnothing(Tracker.jacobian(diff2_loss, r))
-        # @test !isnothing(Tracker.hessian(diff2_loss, r))
-
-        @test !isnothing(FiniteDifferences.grad(fd_m, diff2_loss, r))
-        @test !isnothing(FiniteDifferences.jacobian(fd_m, diff2_loss, r))
-
-        @test_throws MethodError !isnothing(
-            FiniteDiff.finite_difference_derivative(diff2_loss, r),
-        )
-        @test !isnothing(FiniteDiff.finite_difference_gradient(diff2_loss, r))
-        @test_broken !isnothing(FiniteDiff.finite_difference_jacobian(diff2_loss, r))
-        # @test !isnothing(FiniteDiff.finite_difference_hessian(diff2_loss, r))
-
-        @test_throws MethodError !isnothing(Calculus.gradient(diff2_loss, r))
-        # @test !isnothing(Calculus.hessian(diff2_loss, r))
-
         d = ICNFDist(icnf, omode, ps, st)
 
         @test !isnothing(Distributions.logpdf(d, r))
@@ -391,10 +314,7 @@
         omode in omodes,
         mt in cmts
 
-        adb_u isa AbstractDifferentiation.FiniteDifferencesBackend && continue
         adb_u isa AbstractDifferentiation.ReverseDiffBackend && continue
-        adb_u isa AbstractDifferentiation.TrackerBackend && continue
-        adb_u isa AbstractDifferentiation.TrackerBackend && mt <: CondPlanar && continue
 
         data_dist = Distributions.Beta{tp}(convert(Tuple{tp, tp}, (2, 4))...)
         data_dist2 = Distributions.Beta{tp}(convert(Tuple{tp, tp}, (4, 2))...)
@@ -437,7 +357,6 @@
         diff_loss(x) = loss(icnf, omode, r, r2, x, st)
 
         @testset "Using $(typeof(adb).name.name) For Loss" for adb in adb_list
-            adb isa AbstractDifferentiation.TrackerBackend && continue
             @test_throws MethodError !isnothing(
                 AbstractDifferentiation.derivative(adb, diff_loss, ps),
             )
@@ -478,27 +397,9 @@
         @test_throws DimensionMismatch !isnothing(ForwardDiff.jacobian(diff_loss, ps))
         # @test !isnothing(ForwardDiff.hessian(diff_loss, ps))
 
-        # @test !isnothing(Tracker.gradient(diff_loss, ps))
-        # @test !isnothing(Tracker.jacobian(diff_loss, ps))
-        # @test !isnothing(Tracker.hessian(diff_loss, ps))
-
-        @test !isnothing(FiniteDifferences.grad(fd_m, diff_loss, ps))
-        @test !isnothing(FiniteDifferences.jacobian(fd_m, diff_loss, ps))
-
-        @test_throws MethodError !isnothing(
-            FiniteDiff.finite_difference_derivative(diff_loss, ps),
-        )
-        @test !isnothing(FiniteDiff.finite_difference_gradient(diff_loss, ps))
-        @test_broken !isnothing(FiniteDiff.finite_difference_jacobian(diff_loss, ps))
-        # @test !isnothing(FiniteDiff.finite_difference_hessian(diff_loss, ps))
-
-        @test_throws MethodError !isnothing(Calculus.gradient(diff_loss, ps))
-        # @test !isnothing(Calculus.hessian(diff_loss, ps))
-
         diff2_loss(x) = loss(icnf, omode, x, r2, ps, st)
 
         @testset "Using $(typeof(adb).name.name) For Loss" for adb in adb_list
-            adb isa AbstractDifferentiation.TrackerBackend && continue
             @test_throws MethodError !isnothing(
                 AbstractDifferentiation.derivative(adb, diff2_loss, r),
             )
@@ -539,23 +440,6 @@
         @test_throws DimensionMismatch !isnothing(ForwardDiff.jacobian(diff2_loss, r))
         # @test !isnothing(ForwardDiff.hessian(diff2_loss, r))
 
-        # @test !isnothing(Tracker.gradient(diff2_loss, r))
-        # @test !isnothing(Tracker.jacobian(diff2_loss, r))
-        # @test !isnothing(Tracker.hessian(diff2_loss, r))
-
-        @test !isnothing(FiniteDifferences.grad(fd_m, diff2_loss, r))
-        @test !isnothing(FiniteDifferences.jacobian(fd_m, diff2_loss, r))
-
-        @test_throws MethodError !isnothing(
-            FiniteDiff.finite_difference_derivative(diff2_loss, r),
-        )
-        @test !isnothing(FiniteDiff.finite_difference_gradient(diff2_loss, r))
-        @test_broken !isnothing(FiniteDiff.finite_difference_jacobian(diff2_loss, r))
-        # @test !isnothing(FiniteDiff.finite_difference_hessian(diff2_loss, r))
-
-        @test_throws MethodError !isnothing(Calculus.gradient(diff2_loss, r))
-        # @test !isnothing(Calculus.hessian(diff2_loss, r))
-
         d = CondICNFDist(icnf, omode, r2, ps, st)
 
         @test !isnothing(Distributions.logpdf(d, r))
@@ -613,7 +497,6 @@
         diff_loss(x) = loss(icnf, omode, r_arr, r2_arr, x, st)
 
         @testset "Using $(typeof(adb).name.name) For Loss" for adb in adb_list
-            adb isa AbstractDifferentiation.TrackerBackend && continue
             @test_throws MethodError !isnothing(
                 AbstractDifferentiation.derivative(adb, diff_loss, ps),
             )
@@ -654,27 +537,9 @@
         @test_throws DimensionMismatch !isnothing(ForwardDiff.jacobian(diff_loss, ps))
         # @test !isnothing(ForwardDiff.hessian(diff_loss, ps))
 
-        # @test !isnothing(Tracker.gradient(diff_loss, ps))
-        # @test !isnothing(Tracker.jacobian(diff_loss, ps))
-        # @test !isnothing(Tracker.hessian(diff_loss, ps))
-
-        @test !isnothing(FiniteDifferences.grad(fd_m, diff_loss, ps))
-        @test !isnothing(FiniteDifferences.jacobian(fd_m, diff_loss, ps))
-
-        @test_throws MethodError !isnothing(
-            FiniteDiff.finite_difference_derivative(diff_loss, ps),
-        )
-        @test !isnothing(FiniteDiff.finite_difference_gradient(diff_loss, ps))
-        @test_broken !isnothing(FiniteDiff.finite_difference_jacobian(diff_loss, ps))
-        # @test !isnothing(FiniteDiff.finite_difference_hessian(diff_loss, ps))
-
-        @test_throws MethodError !isnothing(Calculus.gradient(diff_loss, ps))
-        # @test !isnothing(Calculus.hessian(diff_loss, ps))
-
         diff2_loss(x) = loss(icnf, omode, x, r2, ps, st)
 
         @testset "Using $(typeof(adb).name.name) For Loss" for adb in adb_list
-            adb isa AbstractDifferentiation.TrackerBackend && continue
             @test_throws MethodError !isnothing(
                 AbstractDifferentiation.derivative(adb, diff2_loss, r),
             )
@@ -715,23 +580,6 @@
         @test_throws DimensionMismatch !isnothing(ForwardDiff.jacobian(diff2_loss, r))
         # @test !isnothing(ForwardDiff.hessian(diff2_loss, r))
 
-        # @test !isnothing(Tracker.gradient(diff2_loss, r))
-        # @test !isnothing(Tracker.jacobian(diff2_loss, r))
-        # @test !isnothing(Tracker.hessian(diff2_loss, r))
-
-        @test !isnothing(FiniteDifferences.grad(fd_m, diff2_loss, r))
-        @test !isnothing(FiniteDifferences.jacobian(fd_m, diff2_loss, r))
-
-        @test_throws MethodError !isnothing(
-            FiniteDiff.finite_difference_derivative(diff2_loss, r),
-        )
-        @test !isnothing(FiniteDiff.finite_difference_gradient(diff2_loss, r))
-        @test_broken !isnothing(FiniteDiff.finite_difference_jacobian(diff2_loss, r))
-        # @test !isnothing(FiniteDiff.finite_difference_hessian(diff2_loss, r))
-
-        @test_throws MethodError !isnothing(Calculus.gradient(diff2_loss, r))
-        # @test !isnothing(Calculus.hessian(diff2_loss, r))
-
         d = CondICNFDist(icnf, omode, r2_arr, ps, st)
 
         @test !isnothing(Distributions.logpdf(d, r))

diff --git a/test/fit_tests.jl b/test/fit_tests.jl
@@ -31,17 +31,11 @@
         AbstractDifferentiation.ZygoteBackend(),
         AbstractDifferentiation.ReverseDiffBackend(),
         AbstractDifferentiation.ForwardDiffBackend(),
-        AbstractDifferentiation.TrackerBackend(),
-        AbstractDifferentiation.FiniteDifferencesBackend(),
     ]
     go_ads = ADTypes.AbstractADType[
-        ADTypes.AutoEnzyme(),
         ADTypes.AutoZygote(),
         ADTypes.AutoReverseDiff(),
         ADTypes.AutoForwardDiff(),
-        ADTypes.AutoTracker(),
-        ADTypes.AutoFiniteDiff(),
-        ADTypes.AutoModelingToolkit(),
     ]
 
     @testset "$at | $tp | $(typeof(adb_u).name.name) for internal | $(typeof(go_ad).name.name) for fitting | $nvars Vars | $mt" for at in
@@ -52,12 +46,7 @@
         nvars in nvars_,
         mt in mts
 
-        adb_u isa AbstractDifferentiation.FiniteDifferencesBackend && continue
         adb_u isa AbstractDifferentiation.ReverseDiffBackend && continue
-        adb_u isa AbstractDifferentiation.TrackerBackend && mt <: Planar && continue
-        go_ad isa ADTypes.AutoTracker && continue
-        go_ad isa ADTypes.AutoEnzyme && continue
-        go_ad isa ADTypes.AutoModelingToolkit && continue
 
         data_dist = Distributions.Beta{tp}(convert(Tuple{tp, tp}, (2, 4))...)
         r = convert(at{tp}, rand(data_dist, nvars, 2))
@@ -99,9 +88,6 @@
         mt in mts
 
         cmode <: SDJacVecMatrixMode && continue
-        go_ad isa ADTypes.AutoTracker && continue
-        go_ad isa ADTypes.AutoEnzyme && continue
-        go_ad isa ADTypes.AutoModelingToolkit && continue
 
         data_dist = Distributions.Beta{tp}(convert(Tuple{tp, tp}, (2, 4))...)
         r = convert(at{tp}, rand(data_dist, nvars, 2))
@@ -136,13 +122,7 @@
         nvars in nvars_,
         mt in cmts
 
-        adb_u isa AbstractDifferentiation.FiniteDifferencesBackend && continue
         adb_u isa AbstractDifferentiation.ReverseDiffBackend && continue
-        adb_u isa AbstractDifferentiation.TrackerBackend && continue
-        adb_u isa AbstractDifferentiation.TrackerBackend && mt <: CondPlanar && continue
-        go_ad isa ADTypes.AutoTracker && continue
-        go_ad isa ADTypes.AutoEnzyme && continue
-        go_ad isa ADTypes.AutoModelingToolkit && continue
 
         data_dist = Distributions.Beta{tp}(convert(Tuple{tp, tp}, (2, 4))...)
         data_dist2 = Distributions.Beta{tp}(convert(Tuple{tp, tp}, (4, 2))...)
@@ -187,9 +167,6 @@
         mt in cmts
 
         cmode <: SDJacVecMatrixMode && continue
-        go_ad isa ADTypes.AutoTracker && continue
-        go_ad isa ADTypes.AutoEnzyme && continue
-        go_ad isa ADTypes.AutoModelingToolkit && continue
 
         data_dist = Distributions.Beta{tp}(convert(Tuple{tp, tp}, (2, 4))...)
         data_dist2 = Distributions.Beta{tp}(convert(Tuple{tp, tp}, (4, 2))...)

diff --git a/test/instability _tests.jl → test/instability_tests.jl b/test/instability _tests.jl → test/instability_tests.jl
@@ -1,5 +1,5 @@
-@testset "Instability" begin
-    JET.report_package("ContinuousNormalizingFlows")
-
-    @test true
-end
+@testset "Instability" begin
+    JET.report_package("ContinuousNormalizingFlows")
+
+    @test true
+end