test: remove @test_nowarn testing

LuxDL · Jul 19, 2024 · 937efad · 937efad · github-actions · Jul 19, 2024
1 parent 4ace37b
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Showing 11 changed files with 29 additions and 31 deletions.
diff --git a/test/contrib/debug_tests.jl b/test/contrib/debug_tests.jl
@@ -34,7 +34,7 @@
 
  ps, st = Lux.setup(rng, model_fixed) |> device
 
- @test_nowarn model_fixed(x, ps, st)
+ @test model_fixed(x, ps, st) isa Any
 
  model_fixed_debug = Lux.Experimental.@debug_mode model_fixed
 

diff --git a/test/distributed/data_distributedtest.jl b/test/distributed/data_distributedtest.jl
@@ -25,7 +25,7 @@ backend = DistributedUtils.get_distributed_backend(backend_type)
 data = randn(rng, Float32, 10)
 dcontainer = DistributedUtils.DistributedDataContainer(backend, data)
 
-@test_nowarn dcontainer[1]
+@test dcontainer[1] isa Any
 
 rank = DistributedUtils.local_rank(backend)
 tworkers = DistributedUtils.total_workers(backend)

diff --git a/test/distributed/optimizer_distributedtest.jl b/test/distributed/optimizer_distributedtest.jl
@@ -25,7 +25,7 @@ st_dopt = Optimisers.setup(dopt, ps)
 @test st_dopt.a.state == st_opt.a.state
 @test st_dopt.b.state == st_opt.b.state
 
-@test_nowarn DistributedUtils.synchronize!!(backend, st_dopt)
+@test DistributedUtils.synchronize!!(backend, st_dopt) isa Any
 
 gs = (a=ones(4), b=ones(4)) |> dev
 
@@ -35,4 +35,4 @@ _, ps_opt = Optimisers.update(st_opt, ps, gs)
 @test ps_dopt.a≈ps_opt.a atol=1.0e-5 rtol=1.0e-5
 @test ps_dopt.b≈ps_opt.b atol=1.0e-5 rtol=1.0e-5
 
-@test_nowarn Optimisers.adjust(st_dopt, 0.1f0)
+@test Optimisers.adjust(st_dopt, 0.1f0) isa Any
diff --git a/test/distributed/synchronize_distributedtest.jl b/test/distributed/synchronize_distributedtest.jl
@@ -60,7 +60,7 @@ st_opt = DistributedUtils.synchronize!!(backend, st_opt; root)
 opt = Descent(0.001f0)
 st_opt = Optimisers.setup(opt, gs)
 
-@test_nowarn DistributedUtils.synchronize!!(backend, st_opt; root)
+@test DistributedUtils.synchronize!!(backend, st_opt; root) isa Any
 
 ## ComponentArrays
 gs = (

diff --git a/test/helpers/batched_ad_tests.jl b/test/helpers/batched_ad_tests.jl
@@ -42,7 +42,7 @@
  ps, st = Lux.setup(Random.default_rng(), model) |> dev
 
  x = randn(Float32, N, 3) |> dev
- @test_nowarn model(x, ps, st)
+ @test first(model(x, ps, st)) isa AbstractArray{<:Any, 3}
  end
  end
 
@@ -103,7 +103,7 @@ end
  return sum(abs2, J)
  end
 
- @test_nowarn loss_function_batched(model, X, ps, st)
+ @test loss_function_batched(model, X, ps, st) isa Number
  @test loss_function_batched(model, X, ps, st) ≈
  loss_function_simple(model, X, ps, st)
 

diff --git a/test/helpers/compact_tests.jl b/test/helpers/compact_tests.jl
@@ -274,7 +274,7 @@
  end
 
  @testset "Keyword Arguments with Anonymous Function" begin
- model = @test_nowarn @compact(x->@return(x+a+b); a=1, b=2)
+ model = @compact(x->@return(x+a+b); a=1, b=2)
  ps, st = Lux.setup(rng, model) |> device
  @test first(model(3, ps, st)) == 1 + 2 + 3
  expected_string = """@compact(

diff --git a/test/helpers/nestedad_tests.jl b/test/helpers/nestedad_tests.jl
@@ -238,7 +238,6 @@ end
  return sum(J * vec(jvp_input))
  end
 
- @test_nowarn loss_function_vjp(model, X, ps, st, vjp_input)
  @test loss_function_vjp(model, X, ps, st, vjp_input) isa Number
  @test loss_function_vjp(model, X, ps, st, vjp_input) ≈
  loss_function_vjp_jacobian(model, X, ps, st, vjp_input)
@@ -250,7 +249,6 @@ end
  @test ∂x≈∂x_vjp rtol=1e-3 atol=1e-3
  @test check_approx(∂ps, ∂ps_vjp; rtol=1e-3, atol=1e-3)
 
- @test_nowarn loss_function_jvp(model, X, ps, st, jvp_input)
  @test loss_function_jvp(model, X, ps, st, jvp_input) isa Number
  @test loss_function_jvp(model, X, ps, st, jvp_input) ≈
  loss_function_jvp_jacobian(model, X, ps, st, jvp_input)
@@ -301,7 +299,7 @@ end
  nt = (functor=functorABC(rand(rng, 3), rand(rng, 3)), tup=(rand(rng, 3), rand(rng, 3)))
  u = (functor=functorABC(rand(rng, 3), rand(rng, 3)), tup=(rand(rng, 3), rand(rng, 3)))
 
- @test_nowarn jacobian_vector_product(ftest, AutoForwardDiff(), nt, u)
+ @test jacobian_vector_product(ftest, AutoForwardDiff(), nt, u) isa Any
 end
 
 @testitem "Nested AD: Issue #743 (eval + gradient)" setup=[SharedTestSetup] tags=[:autodiff] begin

diff --git a/test/helpers/stateful_tests.jl b/test/helpers/stateful_tests.jl
@@ -16,7 +16,7 @@
 
  smodel = StatefulLuxLayer{false}(model, ps, st)
  display(smodel)
- @test_nowarn smodel(1)
+ @test smodel(1) isa Any
 
  smodel = StatefulLuxLayer{true}(model, ps, st)
  display(smodel)

diff --git a/test/layers/conv_tests.jl b/test/layers/conv_tests.jl
@@ -314,19 +314,19 @@ end
 
  @testset "$mode" for (mode, aType, dev, ongpu) in MODES
  @testset "Construction" begin
- @test_nowarn Upsample(:nearest; scale=2)
- @test_nowarn Upsample(:nearest; size=(64, 64))
- @test_nowarn Upsample(:bilinear; scale=2)
- @test_nowarn Upsample(:bilinear; size=(64, 64))
- @test_nowarn Upsample(:trilinear; scale=2)
- @test_nowarn Upsample(:trilinear; size=(64, 64))
+ @test Upsample(:nearest; scale=2) isa Any
+ @test Upsample(:nearest; size=(64, 64)) isa Any
+ @test Upsample(:bilinear; scale=2) isa Any
+ @test Upsample(:bilinear; size=(64, 64)) isa Any
+ @test Upsample(:trilinear; scale=2) isa Any
+ @test Upsample(:trilinear; size=(64, 64)) isa Any
 
  @test_throws ArgumentError Upsample(:linear; scale=2)
  @test_throws ArgumentError Upsample(:nearest; scale=2, size=(64, 64))
  @test_throws ArgumentError Upsample(:nearest)
 
- @test_nowarn Upsample(2)
- @test_nowarn Upsample(2, :nearest)
+ @test Upsample(2) isa Any
+ @test Upsample(2, :nearest) isa Any
  end
 
  @testset "Size Correctness" begin
@@ -625,7 +625,7 @@ end
  x = randn(Float32, 28, 28, 42, 3) |> aType
  ps, st = Lux.setup(rng, layer) |> dev
 
- @test_nowarn layer(x, ps, st)
+ @test layer(x, ps, st) isa Any
 
  x = randn(Float32, 28, 28, 46, 3) |> aType
 

diff --git a/test/layers/normalize_tests.jl b/test/layers/normalize_tests.jl
@@ -297,15 +297,15 @@ end
  @test_throws ArgumentError Lux.setup(rng, wn)
 
  wn = WeightNorm(c, (:weight,))
- @test_nowarn Lux.setup(rng, wn)
+ @test Lux.setup(rng, wn) isa Any
 
  c = Conv((3, 3), 3 => 3; init_bias=Lux.ones32)
 
  wn = WeightNorm(c, (:weight, :bias))
- @test_nowarn Lux.setup(rng, wn)
+ @test Lux.setup(rng, wn) isa Any
 
  wn = WeightNorm(c, (:weight,))
- @test_nowarn Lux.setup(rng, wn)
+ @test Lux.setup(rng, wn) isa Any
  end
  end
 end

diff --git a/test/utils_tests.jl b/test/utils_tests.jl
@@ -95,32 +95,32 @@ end
  @test -p_flat == getdata(-ps_c)
  @test zero(p_flat) == getdata(zero(ps_c))
 
- @test_nowarn similar(ps_c, 10)
- @test_nowarn similar(ps_c)
+ @test similar(ps_c, 10) isa Any
+ @test similar(ps_c) isa Any
 
  ps_c_f, ps_c_re = Functors.functor(ps_c)
  @test ps_c_f == ps
  @test ps_c_re(ps_c_f) == ps_c
 
  # Empty ComponentArray test
- @test_nowarn display(ComponentArray(NamedTuple()))
+ @test display(ComponentArray(NamedTuple())) isa Any
  println()
 
  # Optimisers
  opt = Adam(0.001f0)
  ps_c = ps_c |> device
  st_opt = Optimisers.setup(opt, ps_c)
 
- @test_nowarn Optimisers.update(st_opt, ps_c, ps_c)
- @test_nowarn Optimisers.update!(st_opt, ps_c, ps_c)
+ @test Optimisers.update(st_opt, ps_c, ps_c) isa Any
+ @test Optimisers.update!(st_opt, ps_c, ps_c) isa Any
  end
 
  # Ref: https://github.com/LuxDL/Lux.jl/issues/243
  nn = Chain(Dense(4, 3), Dense(3, 2))
  ps, st = Lux.setup(rng, nn)
 
  l2reg(p) = sum(abs2, ComponentArray(p))
- @test_nowarn Zygote.gradient(l2reg, ps)
+ @test length(Zygote.gradient(l2reg, ps)) == 1
 end
 
 @testitem "_init_hidden_state" setup=[SharedTestSetup] tags=[:recurrent_layers] begin
@@ -277,7 +277,7 @@ end
  c = Parallel(+; chain=Chain(; dense_1=Dense(2 => 3), dense_2=Dense(3 => 5)),
  dense_3=Dense(5 => 1))
 
- @test_nowarn fmap(println, c)
+ @test fmap(println, c) isa Any
 
  l = Dense(2 => 2)
  new_model = fmap(x -> l, c)
Benchmark suite	Current: `937efad`	Previous: `4ace37b`	Ratio
`Dense(2 => 2)/cpu/reverse/ReverseDiff (compiled)/(2, 128)`	`3669.375` ns	`3889.75` ns	`0.94`
`Dense(2 => 2)/cpu/reverse/Zygote/(2, 128)`	`7200` ns	`7213.428571428572` ns	`1.00`
`Dense(2 => 2)/cpu/reverse/Tracker/(2, 128)`	`21054.5` ns	`20608` ns	`1.02`
`Dense(2 => 2)/cpu/reverse/ReverseDiff/(2, 128)`	`9781.3` ns	`9816.3` ns	`1.00`
`Dense(2 => 2)/cpu/reverse/Flux/(2, 128)`	`8908.6` ns	`9185.2` ns	`0.97`
`Dense(2 => 2)/cpu/reverse/SimpleChains/(2, 128)`	`4449.5` ns	`4510.875` ns	`0.99`
`Dense(2 => 2)/cpu/reverse/Enzyme/(2, 128)`	`1157.9930555555557` ns	`1166.159420289855` ns	`0.99`
`Dense(2 => 2)/cpu/forward/NamedTuple/(2, 128)`	`1171.8985507246377` ns	`1111.1946308724832` ns	`1.05`
`Dense(2 => 2)/cpu/forward/ComponentArray/(2, 128)`	`1185.8125` ns	`1187.955223880597` ns	`1.00`
`Dense(2 => 2)/cpu/forward/Flux/(2, 128)`	`1791.6440677966102` ns	`1795.1666666666667` ns	`1.00`
`Dense(2 => 2)/cpu/forward/SimpleChains/(2, 128)`	`180.02920723226703` ns	`179.85854341736695` ns	`1.00`
`Dense(20 => 20)/cpu/reverse/ReverseDiff (compiled)/(20, 128)`	`17283` ns	`17242` ns	`1.00`
`Dense(20 => 20)/cpu/reverse/Zygote/(20, 128)`	`16841` ns	`16641` ns	`1.01`
`Dense(20 => 20)/cpu/reverse/Tracker/(20, 128)`	`37390` ns	`36418` ns	`1.03`
`Dense(20 => 20)/cpu/reverse/ReverseDiff/(20, 128)`	`28994` ns	`28994` ns	`1`
`Dense(20 => 20)/cpu/reverse/Flux/(20, 128)`	`20027` ns	`21290` ns	`0.94`
`Dense(20 => 20)/cpu/reverse/SimpleChains/(20, 128)`	`17182` ns	`17383` ns	`0.99`
`Dense(20 => 20)/cpu/reverse/Enzyme/(20, 128)`	`4328` ns	`4282.285714285715` ns	`1.01`
`Dense(20 => 20)/cpu/forward/NamedTuple/(20, 128)`	`3870.875` ns	`3815.875` ns	`1.01`
`Dense(20 => 20)/cpu/forward/ComponentArray/(20, 128)`	`3954.875` ns	`3921` ns	`1.01`
`Dense(20 => 20)/cpu/forward/Flux/(20, 128)`	`4847.642857142857` ns	`4816.142857142857` ns	`1.01`
`Dense(20 => 20)/cpu/forward/SimpleChains/(20, 128)`	`1658.1` ns	`1651.1` ns	`1.00`
`Conv((3, 3), 3 => 3)/cpu/reverse/ReverseDiff (compiled)/(64, 64, 3, 128)`	`40009085` ns	`38826337.5` ns	`1.03`
`Conv((3, 3), 3 => 3)/cpu/reverse/Zygote/(64, 64, 3, 128)`	`58044866` ns	`57888052` ns	`1.00`
`Conv((3, 3), 3 => 3)/cpu/reverse/Tracker/(64, 64, 3, 128)`	`81652371` ns	`75523456` ns	`1.08`
`Conv((3, 3), 3 => 3)/cpu/reverse/ReverseDiff/(64, 64, 3, 128)`	`84691568` ns	`88301980` ns	`0.96`
`Conv((3, 3), 3 => 3)/cpu/reverse/Flux/(64, 64, 3, 128)`	`75567907` ns	`72229490` ns	`1.05`
`Conv((3, 3), 3 => 3)/cpu/reverse/SimpleChains/(64, 64, 3, 128)`	`11702113` ns	`11634194` ns	`1.01`
`Conv((3, 3), 3 => 3)/cpu/reverse/Enzyme/(64, 64, 3, 128)`	`6962047.5` ns	`6911425` ns	`1.01`
`Conv((3, 3), 3 => 3)/cpu/forward/NamedTuple/(64, 64, 3, 128)`	`7131554` ns	`7082578.5` ns	`1.01`
`Conv((3, 3), 3 => 3)/cpu/forward/ComponentArray/(64, 64, 3, 128)`	`7068401` ns	`6994755` ns	`1.01`
`Conv((3, 3), 3 => 3)/cpu/forward/Flux/(64, 64, 3, 128)`	`12275161.5` ns	`9789325` ns	`1.25`
`Conv((3, 3), 3 => 3)/cpu/forward/SimpleChains/(64, 64, 3, 128)`	`6382810.5` ns	`6381274` ns	`1.00`
`vgg16/cpu/reverse/Zygote/(32, 32, 3, 16)`	`714086754` ns	`706120562` ns	`1.01`
`vgg16/cpu/reverse/Zygote/(32, 32, 3, 64)`	`2579434858` ns	`2529871556` ns	`1.02`
`vgg16/cpu/reverse/Zygote/(32, 32, 3, 2)`	`148383143` ns	`131797096` ns	`1.13`
`vgg16/cpu/reverse/Tracker/(32, 32, 3, 16)`	`891741497` ns	`757974733` ns	`1.18`
`vgg16/cpu/reverse/Tracker/(32, 32, 3, 64)`	`3088439700` ns	`2932092144` ns	`1.05`
`vgg16/cpu/reverse/Tracker/(32, 32, 3, 2)`	`212058426` ns	`189094496` ns	`1.12`
`vgg16/cpu/reverse/Flux/(32, 32, 3, 16)`	`653749242` ns	`663524511.5` ns	`0.99`
`vgg16/cpu/reverse/Flux/(32, 32, 3, 64)`	`2642538349` ns	`2577224378.5` ns	`1.03`
`vgg16/cpu/reverse/Flux/(32, 32, 3, 2)`	`128699402` ns	`125424056` ns	`1.03`
`vgg16/cpu/forward/NamedTuple/(32, 32, 3, 16)`	`175013829.5` ns	`174066006` ns	`1.01`
`vgg16/cpu/forward/NamedTuple/(32, 32, 3, 64)`	`654368735` ns	`653654001` ns	`1.00`
`vgg16/cpu/forward/NamedTuple/(32, 32, 3, 2)`	`45671588` ns	`34513878` ns	`1.32`
`vgg16/cpu/forward/ComponentArray/(32, 32, 3, 16)`	`165691299` ns	`164808835` ns	`1.01`
`vgg16/cpu/forward/ComponentArray/(32, 32, 3, 64)`	`644989507` ns	`634945112.5` ns	`1.02`
`vgg16/cpu/forward/ComponentArray/(32, 32, 3, 2)`	`45474454` ns	`29766865` ns	`1.53`
`vgg16/cpu/forward/Flux/(32, 32, 3, 16)`	`227248905` ns	`187595950` ns	`1.21`
`vgg16/cpu/forward/Flux/(32, 32, 3, 64)`	`775116259` ns	`722751963` ns	`1.07`
`vgg16/cpu/forward/Flux/(32, 32, 3, 2)`	`40575003.5` ns	`37544548` ns	`1.08`
`Conv((3, 3), 64 => 64)/cpu/reverse/ReverseDiff (compiled)/(64, 64, 64, 128)`	`1266387049.5` ns	`1226815927.5` ns	`1.03`
`Conv((3, 3), 64 => 64)/cpu/reverse/Zygote/(64, 64, 64, 128)`	`1873245890` ns	`1859890354` ns	`1.01`
`Conv((3, 3), 64 => 64)/cpu/reverse/Tracker/(64, 64, 64, 128)`	`2337050962` ns	`2339835190` ns	`1.00`
`Conv((3, 3), 64 => 64)/cpu/reverse/ReverseDiff/(64, 64, 64, 128)`	`2598176961` ns	`2525615729` ns	`1.03`
`Conv((3, 3), 64 => 64)/cpu/reverse/Flux/(64, 64, 64, 128)`	`1906864622.5` ns	`1865477869.5` ns	`1.02`
`Conv((3, 3), 64 => 64)/cpu/reverse/Enzyme/(64, 64, 64, 128)`	`323660356.5` ns	`322877161` ns	`1.00`
`Conv((3, 3), 64 => 64)/cpu/forward/NamedTuple/(64, 64, 64, 128)`	`323114250` ns	`320721263` ns	`1.01`
`Conv((3, 3), 64 => 64)/cpu/forward/ComponentArray/(64, 64, 64, 128)`	`319854782` ns	`317208348` ns	`1.01`
`Conv((3, 3), 64 => 64)/cpu/forward/Flux/(64, 64, 64, 128)`	`431236413` ns	`418779532` ns	`1.03`
`Conv((3, 3), 1 => 1)/cpu/reverse/ReverseDiff (compiled)/(64, 64, 1, 128)`	`11856906` ns	`11864932` ns	`1.00`
`Conv((3, 3), 1 => 1)/cpu/reverse/Zygote/(64, 64, 1, 128)`	`17995791` ns	`17886839.5` ns	`1.01`
`Conv((3, 3), 1 => 1)/cpu/reverse/Tracker/(64, 64, 1, 128)`	`19308830` ns	`19045196` ns	`1.01`
`Conv((3, 3), 1 => 1)/cpu/reverse/ReverseDiff/(64, 64, 1, 128)`	`24002610` ns	`23830079.5` ns	`1.01`
`Conv((3, 3), 1 => 1)/cpu/reverse/Flux/(64, 64, 1, 128)`	`18021078` ns	`17899726` ns	`1.01`
`Conv((3, 3), 1 => 1)/cpu/reverse/SimpleChains/(64, 64, 1, 128)`	`1168729` ns	`1148335` ns	`1.02`
`Conv((3, 3), 1 => 1)/cpu/reverse/Enzyme/(64, 64, 1, 128)`	`2067715` ns	`2052685` ns	`1.01`
`Conv((3, 3), 1 => 1)/cpu/forward/NamedTuple/(64, 64, 1, 128)`	`2082044` ns	`2063356` ns	`1.01`
`Conv((3, 3), 1 => 1)/cpu/forward/ComponentArray/(64, 64, 1, 128)`	`2083795` ns	`2066360` ns	`1.01`
`Conv((3, 3), 1 => 1)/cpu/forward/Flux/(64, 64, 1, 128)`	`2078152` ns	`2051643` ns	`1.01`
`Conv((3, 3), 1 => 1)/cpu/forward/SimpleChains/(64, 64, 1, 128)`	`198179` ns	`196415` ns	`1.01`
`Dense(200 => 200)/cpu/reverse/ReverseDiff (compiled)/(200, 128)`	`294960` ns	`289318` ns	`1.02`
`Dense(200 => 200)/cpu/reverse/Zygote/(200, 128)`	`268370` ns	`263239` ns	`1.02`
`Dense(200 => 200)/cpu/reverse/Tracker/(200, 128)`	`372856` ns	`359839` ns	`1.04`
`Dense(200 => 200)/cpu/reverse/ReverseDiff/(200, 128)`	`414518.5` ns	`405253` ns	`1.02`
`Dense(200 => 200)/cpu/reverse/Flux/(200, 128)`	`278368` ns	`272277` ns	`1.02`
`Dense(200 => 200)/cpu/reverse/SimpleChains/(200, 128)`	`410746` ns	`405564` ns	`1.01`
`Dense(200 => 200)/cpu/reverse/Enzyme/(200, 128)`	`83776` ns	`83124` ns	`1.01`
`Dense(200 => 200)/cpu/forward/NamedTuple/(200, 128)`	`81953` ns	`80740` ns	`1.02`
`Dense(200 => 200)/cpu/forward/ComponentArray/(200, 128)`	`83195` ns	`82383` ns	`1.01`
`Dense(200 => 200)/cpu/forward/Flux/(200, 128)`	`87764` ns	`86060` ns	`1.02`
`Dense(200 => 200)/cpu/forward/SimpleChains/(200, 128)`	`104685` ns	`104325` ns	`1.00`
`Conv((3, 3), 16 => 16)/cpu/reverse/ReverseDiff (compiled)/(64, 64, 16, 128)`	`198244459` ns	`197459994` ns	`1.00`
`Conv((3, 3), 16 => 16)/cpu/reverse/Zygote/(64, 64, 16, 128)`	`329497937.5` ns	`327332523.5` ns	`1.01`
`Conv((3, 3), 16 => 16)/cpu/reverse/Tracker/(64, 64, 16, 128)`	`407311865.5` ns	`367989344` ns	`1.11`
`Conv((3, 3), 16 => 16)/cpu/reverse/ReverseDiff/(64, 64, 16, 128)`	`452728713` ns	`439797244.5` ns	`1.03`
`Conv((3, 3), 16 => 16)/cpu/reverse/Flux/(64, 64, 16, 128)`	`388579645` ns	`374931694` ns	`1.04`
`Conv((3, 3), 16 => 16)/cpu/reverse/SimpleChains/(64, 64, 16, 128)`	`319868232.5` ns	`326044951` ns	`0.98`
`Conv((3, 3), 16 => 16)/cpu/reverse/Enzyme/(64, 64, 16, 128)`	`44289039` ns	`44053216` ns	`1.01`
`Conv((3, 3), 16 => 16)/cpu/forward/NamedTuple/(64, 64, 16, 128)`	`44316868` ns	`44122534.5` ns	`1.00`
`Conv((3, 3), 16 => 16)/cpu/forward/ComponentArray/(64, 64, 16, 128)`	`43960351` ns	`43811761` ns	`1.00`
`Conv((3, 3), 16 => 16)/cpu/forward/Flux/(64, 64, 16, 128)`	`51507810` ns	`53017284.5` ns	`0.97`
`Conv((3, 3), 16 => 16)/cpu/forward/SimpleChains/(64, 64, 16, 128)`	`28435005` ns	`27849167` ns	`1.02`
`Dense(2000 => 2000)/cpu/reverse/ReverseDiff (compiled)/(2000, 128)`	`18968878` ns	`18739042.5` ns	`1.01`
`Dense(2000 => 2000)/cpu/reverse/Zygote/(2000, 128)`	`19614768` ns	`19499913` ns	`1.01`
`Dense(2000 => 2000)/cpu/reverse/Tracker/(2000, 128)`	`23514329` ns	`23187751` ns	`1.01`
`Dense(2000 => 2000)/cpu/reverse/ReverseDiff/(2000, 128)`	`24219437.5` ns	`24095441` ns	`1.01`
`Dense(2000 => 2000)/cpu/reverse/Flux/(2000, 128)`	`19657817` ns	`19512675` ns	`1.01`
`Dense(2000 => 2000)/cpu/reverse/Enzyme/(2000, 128)`	`6533333` ns	`6502673` ns	`1.00`
`Dense(2000 => 2000)/cpu/forward/NamedTuple/(2000, 128)`	`6543909` ns	`6533912` ns	`1.00`
`Dense(2000 => 2000)/cpu/forward/ComponentArray/(2000, 128)`	`6522365` ns	`6481314` ns	`1.01`
`Dense(2000 => 2000)/cpu/forward/Flux/(2000, 128)`	`6506154.5` ns	`6489178` ns	`1.00`