refactor: remove Requires and move everything to extensions

Project.toml

@@ -11,14 +11,15 @@ KernelAbstractions = "63c18a36-062a-441e-b654-da1e3ab1ce7c"
 LinearAlgebra = "37e2e46d-f89d-539d-b4ee-838fcccc9c8e"
 Pkg = "44cfe95a-1eb2-52ea-b672-e2afdf69b78f"
 Random = "9a3f8284-a2c9-5f02-9a11-845980a1fd5c"
-Requires = "ae029012-a4dd-5104-9daa-d747884805df"
 Statistics = "10745b16-79ce-11e8-11f9-7d13ad32a3b2"
 
 [weakdeps]
 AMDGPU = "21141c5a-9bdb-4563-92ae-f87d6854732e"
 CUDA = "052768ef-5323-5732-b1bb-66c8b64840ba"
 EnzymeCore = "f151be2c-9106-41f4-ab19-57ee4f262869"
 FFTW = "7a1cc6ca-52ef-59f5-83cd-3a7055c09341"
+ForwardDiff = "f6369f11-7733-5829-9624-2563aa707210"
+NNPACK_jll = "a6bfbf70-4841-5cb9-aa18-3a8ad3c413ee"
 cuDNN = "02a925ec-e4fe-4b08-9a7e-0d78e3d38ccd"
 
 [extensions]
@@ -27,6 +28,8 @@ NNlibCUDACUDNNExt = ["CUDA", "cuDNN"]
 NNlibCUDAExt = "CUDA"
 NNlibEnzymeCoreExt = "EnzymeCore"
 NNlibFFTWExt = "FFTW"
+NNlibForwardDiffExt = "ForwardDiff"
+NNlibNNPACK_jllExt = "NNPACK_jll"
 
 [compat]
 AMDGPU = "0.9.4"
@@ -41,7 +44,6 @@ KernelAbstractions = "0.9.2"
 LinearAlgebra = "1.10"
 Pkg = "1.10"
 Random = "1.10"
-Requires = "1.0"
 Statistics = "1"
 cuDNN = "1"
 julia = "1.10"

ext/NNlibForwardDiffExt.jl

@@ -0,0 +1,9 @@
+module NNlibForwardDiffExt
+
+using ForwardDiff: ForwardDiff
+using NNlib: NNlib
+
+NNlib.within_gradient(x::ForwardDiff.Dual) = true
+NNlib.within_gradient(x::AbstractArray{<:ForwardDiff.Dual}) = true
+
+end

ext/NNlibNNPACK_jllExt/NNlibNNPACK_jllExt.jl

@@ -0,0 +1,15 @@
+module NNlibNNPACK_jllExt
+
+using NNlib: NNlib
+using NNPACK_jll, Pkg
+
+if isdefined(NNPACK_jll, :libnnpack)
+    include("NNPACK.jl")
+else
+    @warn "NNPACK not available for your platform: " *
+          "$( Pkg.BinaryPlatforms.platform_name(Pkg.BinaryPlatforms.platform_key_abi()))" *
+          "($( Pkg.BinaryPlatforms.triplet(Pkg.BinaryPlatforms.platform_key_abi())))
+          You will be able to use only the default Julia NNlib backend"
+end
+
+end

src/nnpack/interface.jl → ext/NNlibNNPACK_jllExt/interface.jl

@@ -7,7 +7,7 @@ for (front_name, backend) in (
         :∇conv_filter  => :_nnpack,
     )
     @eval begin
-        function $(Symbol("$(front_name)$(backend)!"))(
+        function NNlib.$(Symbol("$(front_name)$(backend)!"))(
                         out::Array{T1,4}, in1::Array{T2,4}, in2::Array{T3,4},
                         cdims::ConvDims; kwargs...) where {T1, T2, T3}
             @warn "Automatically converting input tensor to Float32. This will have performance implications" maxlog=1

src/NNlib.jl

@@ -14,7 +14,6 @@ using LinearAlgebra.BLAS: @blasfunc, BlasInt
 using LinearAlgebra: AdjOrTransAbsMat, Adjoint, BlasFloat, Transpose
 using Pkg
 using Random
-using Requires
 using Statistics
 using Statistics: mean
 
@@ -26,17 +25,6 @@ export ConvDims, DenseConvDims, PoolDims, DepthwiseConvDims
 
 is_nnpack_available() = false
 
-@init @require NNPACK_jll="a6bfbf70-4841-5cb9-aa18-3a8ad3c413ee"  begin
-  if isdefined(NNPACK_jll, :libnnpack)
-    include("nnpack/NNPACK.jl")
-  else
-    @warn "NNPACK not available for your platform: " *
-          "$( Pkg.BinaryPlatforms.platform_name(Pkg.BinaryPlatforms.platform_key_abi()))" *
-          "($( Pkg.BinaryPlatforms.triplet(Pkg.BinaryPlatforms.platform_key_abi())))
-          You will be able to use only the default Julia NNlib backend"
-  end
-end
-
 include("activations.jl")
 for f in ACTIVATIONS
     @eval export $(f)
@@ -95,11 +83,6 @@ export upsample_nearest, ∇upsample_nearest,
 include("gather.jl")
 include("scatter.jl")
 include("utils.jl")
-@init @require ForwardDiff = "f6369f11-7733-5829-9624-2563aa707210" begin
-    using .ForwardDiff
-    within_gradient(x::ForwardDiff.Dual) = true
-    within_gradient(x::AbstractArray{<:ForwardDiff.Dual}) = true
-end
 
 include("sampling.jl")
 include("functions.jl")