remove NNPACK and move ForwardDiff to an extension

Project.toml

@@ -9,16 +9,15 @@ ChainRulesCore = "d360d2e6-b24c-11e9-a2a3-2a2ae2dbcce4"
 GPUArraysCore = "46192b85-c4d5-4398-a991-12ede77f4527"
 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"
 cuDNN = "02a925ec-e4fe-4b08-9a7e-0d78e3d38ccd"
 
 [extensions]
@@ -27,6 +26,7 @@ NNlibCUDACUDNNExt = ["CUDA", "cuDNN"]
 NNlibCUDAExt = "CUDA"
 NNlibEnzymeCoreExt = "EnzymeCore"
 NNlibFFTWExt = "FFTW"
+NNlibForwardDiffExt = "ForwardDiff"
 
 [compat]
 AMDGPU = "0.9.4, 1"
@@ -36,12 +36,11 @@ CUDA = "4, 5"
 ChainRulesCore = "1.13"
 EnzymeCore = "0.5, 0.6, 0.7"
 FFTW = "1.8.0"
+ForwardDiff = "0.10.36"
 GPUArraysCore = "0.1"
 KernelAbstractions = "0.9.2"
 LinearAlgebra = "<0.0.1, 1"
-Pkg = "<0.0.1, 1"
 Random = "<0.0.1, 1"
-Requires = "1.0"
 Statistics = "1"
 cuDNN = "1"
 julia = "1.9"

benchmark/perf_report.jl

@@ -37,10 +37,6 @@ for rank in (2,),
             (NNlib.depthwiseconv_im2col!, NNlib.∇depthwiseconv_data_im2col!, NNlib.∇depthwiseconv_filter_im2col!, DepthwiseConvDims, "im2col"),
     ]
 
-    if NNlib.is_nnpack_available()
-        push!(benchmark_items, (NNlib.conv_nnpack!, NNlib.∇conv_data_nnpack!, NNlib.∇conv_filter_nnpack!, DenseConvDims, "nnpack"))
-    end
-
     for (conv!, ∇conv_data!, ∇conv_filter!, cT, backend) in benchmark_items
 
         x = zeros(Float32, repeat([N], rank)..., C_in, 1)
@@ -105,15 +101,4 @@ for rank in (2,),
         @show(pdims)
         @save "results.jld2" results
     end
-
-    if NNlib.is_nnpack_available()
-        if NNlib.nnpack_supported_operation(pdims)
-            t_fwd  = @benchmark NNlib.maxpool_nnpack!($y, $x, $pdims)
-
-            add_result(t_fwd, "maxpool2d", "nnpack", pdims)
-
-            @show(pdims)
-            @save "results.jld2" results
-        end
-    end
 end

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

src/NNlib.jl

@@ -12,9 +12,7 @@ using KernelAbstractions: @atomic
 using LinearAlgebra
 using LinearAlgebra.BLAS: @blasfunc, BlasInt
 using LinearAlgebra: AdjOrTransAbsMat, Adjoint, BlasFloat, Transpose
-using Pkg
 using Random
-using Requires
 using Statistics
 using Statistics: mean
 
@@ -24,19 +22,6 @@ const Numeric = Union{AbstractArray{<:T}, T} where {T<:Number}
 include("dim_helpers.jl")
 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 +80,6 @@ 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")

src/conv.jl

@@ -76,7 +76,7 @@ end
 # Let's generate auto-allocating versions of all our functions, for all backends.
 # We `@timeit` these methods separately, as we want to know how much time is spent in
 # allocation.  :P
-for backend in (Symbol(), :_direct, :_im2col, :_nnpack)
+for backend in (Symbol(), :_direct, :_im2col)
     # First make auto-allocating versions of the conv()-like calls:
     for name in (:conv, :depthwiseconv)
         @eval begin
@@ -134,7 +134,7 @@ end
 # since we can specialize on sizes.
 for front_name in (:conv, :∇conv_data, :∇conv_filter,
                    :depthwiseconv, :∇depthwiseconv_data, :∇depthwiseconv_filter)
-    for backend in (Symbol(), :_direct, :_im2col) ## NNPACK  is only for 2d conv
+    for backend in (Symbol(), :_direct, :_im2col)
         for N in (3, 4)
             @eval begin
                 function $(Symbol("$(front_name)$(backend)!"))(
@@ -381,26 +381,3 @@ function rrule(::typeof(∇conv_filter), x, dy, cdims; kw...)
     end
     return ∇conv_filter(x, dy, cdims; kw...), ∇conv_filter_pullback
 end
-
-# Use NNPACK if it is available and the operation is supported
-# commented out 'till proper benchmarking and more correctness test are performed
-# if is_nnpack_available()
-#     function conv(x::Array{Float32, 4}, w::Array{Float32, 4},
-#                   cdims::DenseConvDims{2, K, C_in, C_out, (1, 1), P, (1, 1), F};
-#                   kwargs...) where {K, C_in, C_out, P, F}
-#         return conv_nnpack(x, w, cdims; kwargs...)
-#     end
-
-#     function ∇conv_data(dy::Array{Float32, 4}, w::Array{Float32, 4},
-#                 cdims::DenseConvDims{2, K, C_in, C_out, (1, 1), P, (1, 1), F};
-#                 kwargs...) where {K, C_in, C_out, P, F}
-#         return ∇conv_data_nnpack(dy, w, cdims; kwargs...)
-#     end
-
-#     function ∇conv_filter(x::Array{Float32, 4}, dy::Array{Float32, 4},
-#                 cdims::DenseConvDims{2, K, C_in, C_out, (1, 1), P, (1, 1), F};
-#                 kwargs...) where {K, C_in, C_out, P, F}
-#         return ∇conv_filter_nnpack(x, dy, cdims; kwargs...)
-#     end
-# end
-########################################################