Refactor tests and make NNPACK optional

deps/build.jl

@@ -33,10 +33,18 @@ end
 
 # If we have a download, and we are unsatisfied (or the version we're
 # trying to install is not itself installed) then load it up!
-if unsatisfied || !isinstalled(dl_info...; prefix=prefix)
-    # Download and install binaries
+# Download and install binaries
+use_nnpack = get(ENV, "NNLIB_USE_NNPACK", "false") == "true"
+os_support = Sys.islinux() || Sys.isapple()
+if use_nnpack && os_support
+  if unsatisfied || !isinstalled(dl_info...; prefix=prefix)
     install(dl_info...; prefix=prefix, force=true, verbose=verbose)
+  end
+  # Write out a deps.jl file that will contain mappings for our products
+  write_deps_file(joinpath(@__DIR__, "deps.jl"), products, verbose=verbose)
+else
+  open(joinpath(@__DIR__, "deps.jl"), "w") do io
+    write(io, "check_deps() = false")
+  end
 end
 
-# Write out a deps.jl file that will contain mappings for our products
-write_deps_file(joinpath(@__DIR__, "deps.jl"), products, verbose=verbose)

src/NNlib.jl

@@ -5,10 +5,11 @@ using Requires
 include("dim_helpers.jl")
 
 # NNPACK support
-if Sys.islinux() || Sys.isapple()
-    include("nnpack/NNPACK.jl")
+include(joinpath(@__DIR__, "..", "deps", "deps.jl"))
+if check_deps() == nothing
+  include("nnpack/NNPACK.jl")
 else
-    is_nnpack_available() = false
+  is_nnpack_available() = false
 end
 
 include("activation.jl")

src/nnpack/NNPACK.jl

@@ -8,7 +8,6 @@ const depsjl_path = joinpath(dirname(@__FILE__), "..", "..", "deps", "deps.jl")
 if !isfile(depsjl_path)
     error("NNPACK not installed properly, run Pkg.build(\"NNlib\"), restart Julia and try again")
 end
-include(depsjl_path)
 
 const shared_threadpool_dict = Dict{UInt64, Base.RefValue}()
 
@@ -18,7 +17,7 @@ const shared_threadpool_dict = Dict{UInt64, Base.RefValue}()
 Checks if the current hardware is supported by NNPACK.
 """
 function is_nnpack_available()
-    check_deps()
+    check_deps() isa Nothing || return false
     status = nnp_initialize()
     if status == nnp_status_unsupported_hardware
         return false

test/conv.jl

@@ -274,9 +274,13 @@ conv_answer_dict = Dict(
             # A "drop channels and batch dimension" helper
             ddims(x) = dropdims(x, dims=(rank+1, rank+2))
 
-            for conv in (NNlib.conv, NNlib.conv_im2col, NNlib.conv_direct, NNlib.conv_nnpack)
-                if conv == NNlib.conv_nnpack && !NNlib.nnpack_supported_operation(DenseConvDims(x, w))
-                    continue
+            convs = [NNlib.conv, NNlib.conv_im2col, NNlib.conv_direct,]
+            NNlib.is_nnpack_available() && push!(convs, NNlib.conv_nnpack)
+            for conv in convs
+                if NNlib.is_nnpack_available()
+                    if conv == NNlib.conv_nnpack && !NNlib.nnpack_supported_operation(DenseConvDims(x, w))
+                        continue
+                    end
                 end
                 @testset "$(conv)" begin
                     cdims = DenseConvDims(x, w)
@@ -352,12 +356,11 @@ conv_answer_dict = Dict(
             end
         end
     end
+end
 
+if get(ENV,"NNLIB_TEST_FUZZING","false") == "true"
+    # @info("Skipping Convolutional fuzzing tests, set NNLIB_TEST_FUZZING=true to run them")
     @testset "fuzzing" begin
-        if get(ENV,"NNLIB_TEST_FUZZING","false") != "true"
-            @info("Skipping Convolutional fuzzing tests, set NNLIB_TEST_FUZZING=true to run them")
-            return
-        end
         @info("Starting Convolutional fuzzing tests; this can take a few minutes...")
         # Now that we're fairly certain things are working, let's fuzz things a little bit:
         for x_size in (
@@ -441,9 +444,10 @@ conv_answer_dict = Dict(
         end
         println()
     end
+else
+    @info "Skipping Convolutional fuzzing tests, set NNLIB_TEST_FUZZING=true to run them"
 end
 
-
 @testset "Depthwise Convolution" begin
     # Start with some easy-to-debug cases that we have worked through and _know_ work
     for rank in (1,) #2,3)
@@ -552,12 +556,11 @@ end
             end
         end
     end
+end
+
 
+if get(ENV,"NNLIB_TEST_FUZZING","false") == "true"
     @testset "fuzzing" begin
-        if get(ENV,"NNLIB_TEST_FUZZING","false") != "true"
-            @info("Skipping Depthwise Convolutional fuzzing tests, set NNLIB_TEST_FUZZING=true to run them")
-            return
-        end
         @info("Starting Depthwise Convolutional fuzzing tests; this can take a few minutes...")
         # Now that we're fairly certain things are working, let's fuzz things a little bit:
         for x_size in (
@@ -641,8 +644,11 @@ end
         end
         println()
     end
+else
+    @info "Skipping Depthwise Convolutional fuzzing tests, set NNLIB_TEST_FUZZING=true to run them"
 end
 
+
 @testset "conv_wrapper" begin
     x = rand(10, 10, 3, 10)
     w = rand(2, 2, 3, 16)

test/inference.jl

@@ -1,5 +1,4 @@
-using NNlib, Test
-using NNlib: conv_direct, conv_im2col
+import NNlib: conv_direct, conv_im2col
 
 @testset "Conv Inference" begin
     x = rand(10, 10, 3, 2)
@@ -9,6 +8,6 @@ using NNlib: conv_direct, conv_im2col
     NNlib.is_nnpack_available() && push!(impl, NNlib.conv_nnpack)
 
     for T in impl
-        @inferred T(x, w, DenseConvDims(x, w))
+        @test T(x, w, DenseConvDims(x, w)) isa AbstractArray{K,4} where K
     end
 end

test/pooling.jl

@@ -1,4 +1,4 @@
-using NNlib, Test
+#using NNlib, Test
 
 maxpool_answer_dict = Dict(
     1 => Dict(
@@ -298,11 +298,13 @@ for rank in (1, 2, 3)
     end
 end
 
-x = rand(10, 10, 3, 10)
-@test size(maxpool(x, (2, 2))) == (5, 5, 3, 10)
-@test size(maxpool(x, (2, 2); pad = (1, 1), stride = (2, 2))) == (6, 6, 3, 10)
-@test size(meanpool(x, (2, 2))) == (5, 5, 3, 10)
-@test size(meanpool(x, (2, 2); pad = (1, 1), stride = (2, 2))) == (6, 6, 3, 10)
+@testset "Pooling - Check Sizes" begin
+  x = rand(10, 10, 3, 10)
+  @test size(maxpool(x, (2, 2))) == (5, 5, 3, 10)
+  @test size(maxpool(x, (2, 2); pad = (1, 1), stride = (2, 2))) == (6, 6, 3, 10)
+  @test size(meanpool(x, (2, 2))) == (5, 5, 3, 10)
+  @test size(meanpool(x, (2, 2); pad = (1, 1), stride = (2, 2))) == (6, 6, 3, 10)
+end
 
 # Add another test for 2d maxpool that uses an odd-length size:
 @testset "Issue #133" begin