fix: remove uses of NNPACK

Author: avik-pal

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

src/NNlib.jl

@@ -24,19 +24,6 @@ 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)

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
-########################################################