drop julia v1.9

.github/workflows/ci.yml

@@ -17,7 +17,7 @@ jobs:
       fail-fast: false
       matrix:
         version:
-          - '1.9' # Replace this with the minimum Julia version that your package supports. 
+          - '1.10' # Replace this with the minimum Julia version that your package supports. 
           - '1'
         os: [ubuntu-latest]
         arch: [x64]

Project.toml

@@ -65,4 +65,4 @@ SpecialFunctions = "2.1.2"
 Statistics = "1"
 Zygote = "0.6.67"
 cuDNN = "1"
-julia = "1.9"
+julia = "1.10"

test/layers/normalisation.jl

@@ -446,7 +446,7 @@ end
   @test Zygote.hessian_reverse(sum∘m1, [1.0,2.0,3.0]) == zeros(3, 3)
 
   m2 = Chain(BatchNorm(3), sum)
-  @test_broken Zygote.hessian_reverse(m2, Float32[1 2; 3 4; 5 6]) == zeros(Float32, 6, 6)
+  @test Zygote.hessian_reverse(m2, Float32[1 2; 3 4; 5 6]) == zeros(Float32, 6, 6) broken = VERSION >= v"1.11"
 end
 
 @testset "ForwardDiff" begin

test/runtests.jl

@@ -65,14 +65,6 @@ Random.seed!(0)
     @testset "functors" begin
       include("functors.jl")
     end
-
-    @static if VERSION == v"1.9"
-      using Documenter
-      @testset "Docs" begin
-        DocMeta.setdocmeta!(Flux, :DocTestSetup, :(using Flux); recursive=true)
-        doctest(Flux)
-      end
-    end
   else
       @info "Skipping CPU tests."
   end

test/train.jl

@@ -148,7 +148,7 @@ end
     model.bias .= 0
     pen2(x::AbstractArray) = sum(abs2, x)/2
     opt = Flux.setup(Adam(0.1), model)
-    @test_broken begin
+    @test begin
       trainfn!(model, data, opt) do m, x, y
         err = Flux.mse(m(x), y)
         l2 = sum(pen2, Flux.params(m))
@@ -157,7 +157,7 @@ end
       diff2 = model.weight .- init_weight
       @test diff1 ≈ diff2
       true
-    end
+    end broken = VERSION >= v"1.11"
   end
 
   # Take 3: using WeightDecay instead. Need the /2 above, to match exactly.

test/utils.jl

@@ -273,13 +273,13 @@ end
 @testset "params gradient" begin
   m = (x=[1,2.0], y=[3.0]);
 
-  @test_broken begin
-    # Explicit -- was broken by #2054 / then fixed / now broken again on julia v0.11
+  @test begin
+    # Explicit -- was broken by #2054 / then fixed / now broken again on julia v1.11
     gnew = gradient(m -> (sum(norm, Flux.params(m))), m)[1]
     @test gnew.x ≈ [0.4472135954999579, 0.8944271909999159]
     @test gnew.y ≈ [1.0]
     true
-  end
+  end broken = VERSION >= v"1.11"
 
   # Implicit
   gold = gradient(() -> (sum(norm, Flux.params(m))), Flux.params(m))