Merge pull request #389 from zsoerenm/complex-convolution-fix

Fix gradient of convolution for complex values

Author: ToucheSir

src/gemm.jl

@@ -35,7 +35,7 @@ for (gemm, elt) in gemm_datatype_mappings
                                beta::$(elt), C::Ptr{$elt})
             # Convert our compile-time transpose marker to a char for BLAS
             convtrans(V::Val{false}) = 'N'
-            convtrans(V::Val{true})  = 'T'
+            convtrans(V::Val{true})  = 'C'
 
             if transA == Val(false)
                 lda = M

src/impl/conv_direct.jl

@@ -169,7 +169,7 @@ Calculate the gradient imposed upon `x` in the convolution `y = x * w`.
 function ∇conv_data_direct!(dx::AbstractArray{xT,5}, dy::AbstractArray{yT,5},
                             w::AbstractArray{wT,5}, cdims::DenseConvDims;
                             alpha::xT=xT(1), beta=false) where {xT, yT, wT}
-    w = transpose_swapbatch(w[end:-1:1, end:-1:1, end:-1:1, :, :])
+    w = conj(transpose_swapbatch(w[end:-1:1, end:-1:1, end:-1:1, :, :]))
     dy = predilate(dy, stride(cdims))
     ctdims = DenseConvDims(dy, w; padding=transpose_pad(cdims),
                                   dilation=dilation(cdims),
@@ -188,7 +188,7 @@ Calculate the gradient imposed upon `w` in the convolution `y = x * w`.
 function ∇conv_filter_direct!(dw::AbstractArray{wT,5}, x::AbstractArray{xT,5},
                               dy::AbstractArray{yT,5}, cdims::DenseConvDims;
                               alpha::wT=wT(1), beta=false) where {xT, yT, wT}
-    x = transpose_swapbatch(x[end:-1:1, end:-1:1, end:-1:1, :, :])
+    x = conj(transpose_swapbatch(x[end:-1:1, end:-1:1, end:-1:1, :, :]))
     dy = transpose_swapbatch(predilate(dy, stride(cdims)))
     ctdims = DenseConvDims(dy, x; padding=transpose_pad(cdims),
                                     stride=dilation(cdims))

test/conv.jl

@@ -359,6 +359,36 @@ ddims(x) = dropdims(x, dims=(ndims(x)-1, ndims(x)))
     end
 end
 
+@testset "Complex Dense Convolution" begin
+    # For now only 1 dimensional 1x1 convolution
+    x = reshape(complex.(Float64[1:4;], Float64[1:4;] .+ 1), 1, 4, 1)
+    w = reshape(complex.(Float64[1:4;] .+ 2, Float64[1:4;] .+ 3), 1, 4, 1)
+    cdims = DenseConvDims(x, w)
+    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(cdims)
+                continue
+            end
+        end
+        @testset "$(conv)" begin
+            @test isapprox(ddims(conv(x, w, cdims)), [transpose(vec(w)) * vec(x)], rtol = 1.0e-7)
+        end
+    end
+    dy = NNlib.conv(x, w, cdims)
+    for (∇conv_filter, ∇conv_data) in (
+        (NNlib.∇conv_filter,        NNlib.∇conv_data),
+        (NNlib.∇conv_filter_im2col, NNlib.∇conv_data_im2col),
+        (NNlib.∇conv_filter_direct, NNlib.∇conv_data_direct),
+    )
+        @testset "$(∇conv_filter)/$(∇conv_data)" begin
+            @test isapprox(∇conv_filter(x, dy, cdims), conj(x) .* dy, rtol = 1.0e-7)
+            @test isapprox(∇conv_data(dy, w, cdims), dy .* conj(w), rtol = 1.0e-7)
+        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