support only julia >= 1.10

Project.toml

@@ -48,4 +48,4 @@ ScopedValues = "1.3.0"
 SpecialFunctions = "2"
 Statistics = "1"
 cuDNN = "1"
-julia = "1.9"
+julia = "1.10"

src/dim_helpers/ConvDims.jl

@@ -73,7 +73,7 @@ function im2col_dims(c::ConvDims)
         # Size of single dotproduct within convolution
         prod(kernel_size(c))*channels_in(c),
         # One workspace per thread
-        VERSION > v"1.9.0-0" ? Threads.nthreads(:default) : Threads.nthreads(),
+        Threads.nthreads(:default),
     )
 end
 

src/gemm.jl

@@ -95,7 +95,7 @@ for (gemm, elt) in gemm_datatype_mappings
             strC = Base.stride(C, 3)
 
             n_threads = min(
-                VERSION > v"1.9.0-0" ? Threads.nthreads(:default) : Threads.nthreads(),
+                Threads.nthreads(:default),
                 1 + max(length(A), length(B)) ÷ 8000)
             # In some tests, size (20,20,20) is worth splitting between two threads,
             # as is size (32,32,8).

src/utils.jl

@@ -144,21 +144,3 @@ function rrule(cfg::RuleConfig{>:HasReverseMode}, ::typeof(_fast_broadcast!), f:
     rrule_via_ad(cfg, broadcast, f, x, ys...)
 end
 
-# Could get this from Compat.jl instead
-# https://github.com/JuliaLang/julia/pull/39794
-if VERSION < v"1.7.0-DEV.793"
-    struct Returns{V} <: Function
-        value::V
-        Returns{V}(value) where {V} = new{V}(value)
-        Returns(value) = new{Core.Typeof(value)}(value)
-    end
-
-    (obj::Returns)(args...; kw...) = obj.value
-    function Base.show(io::IO, obj::Returns)
-        show(io, typeof(obj))
-        print(io, "(")
-        show(io, obj.value)
-        print(io, ")")
-    end
-end
-

test/conv.jl

@@ -908,7 +908,7 @@ end
   gradtest((y, w) -> sum(∇depthwiseconv_data(y, w, dcdims)), y, w)
 end
 
-@static if Test_Enzyme
+if NNLIB_TEST_ENZYME
 
 @testset "EnzymeRules: conv! spatial_rank=$spatial_rank" for spatial_rank in (1, 2, 3)
   x = rand(rng, repeat([5], spatial_rank)..., 3, 2)

test/dropout.jl

@@ -16,9 +16,6 @@ using Zygote, StableRNGs, ChainRulesCore, Enzyme
     @test size(@inferred dropout(rng, x1, 0.1; dims=2)) == (3, 4)
 
     x2 = Diagonal(randn(Float32, 10))  # Just to check it runs on weird matrices.
-    if VERSION > v"1.8-"  # on 1.6 this makes a sparse array.
-        @test dropout(x2, 0.3) isa Matrix{Float32}  # does not infer, but that's OK?
-    end
 
     # Values
     @test dropout(x1, 0) == x1
@@ -76,7 +73,7 @@ using Zygote, StableRNGs, ChainRulesCore, Enzyme
     @test_throws ArgumentError dropout!(y1, x1, 3)
 end
 
-@static if Test_Enzyme
+if NNLIB_TEST_ENZYME
 
 @testset "EnzymeRules: dropout " begin
     rng = Random.default_rng()

test/pooling.jl

@@ -948,7 +948,7 @@ end
   gradtest(x -> sum(meanpool(x, k)), x)
 end
 
-@static if Test_Enzyme
+if NNLIB_TEST_ENZYME
 
 @testset "EnzymeRules: pooling! $pool spatial_rank=$spatial_rank " for spatial_rank in (1, 2),
                                                                                 (pool, pool!) in ((maxpool, maxpool!), (meanpool, meanpool!))

test/runtests.jl

@@ -18,10 +18,10 @@ import ReverseDiff as RD        # used in `pooling.jl`
 import Pkg
 using SpecialFunctions
 
-const Test_Enzyme = VERSION <= v"1.10-"
 
 DocMeta.setdocmeta!(NNlib, :DocTestSetup, :(using NNlib, UnicodePlots); recursive=true)
 
+const NNLIB_TEST_ENZYME = true
 # ENV["NNLIB_TEST_CUDA"] = "true" # uncomment to run CUDA tests
 # ENV["NNLIB_TEST_AMDGPU"] = "true" # uncomment to run AMDGPU tests
 # ENV["NNLIB_TEST_CPU"] = "false" # uncomment to skip CPU tests

test/testsuite/gather.jl

@@ -154,7 +154,7 @@ function gather_testsuite(Backend)
             gradtest_fn((s, i) -> gather(s, i), src, idx)
     end
 
-    @static if Test_Enzyme
+    if NNLIB_TEST_ENZYME
 
     @testset "EnzymeRules: gather! gradient for scalar index" begin
         src = device(Float64[3, 4, 5, 6, 7])

test/testsuite/scatter.jl

@@ -208,7 +208,7 @@ function scatter_testsuite(Backend)
         end
 
 
-        @static if Test_Enzyme
+        if NNLIB_TEST_ENZYME
 
         @testset "EnzymeRules" begin
             idx = device([2, 2, 3, 4, 4])