diff --git a/src/impl/conv_direct.jl b/src/impl/conv_direct.jl
index f2b6ff60e..9f12f1dc9 100644
--- a/src/impl/conv_direct.jl
+++ b/src/impl/conv_direct.jl
@@ -191,9 +191,11 @@ function ∇conv_filter_direct!(dw::AbstractArray{wT,5}, x::AbstractArray{xT,5},
     dy = transpose_swapbatch(predilate(dy, stride(cdims)))
     ctdims = DenseConvDims(dy, x; padding=transpose_pad(cdims),
                                     stride=dilation(cdims))
-    conv_direct!(dw, dy, x, ctdims; alpha=alpha, beta=beta)
-    if flipkernel(cdims)
-        dw .= dw[end:-1:1, end:-1:1, end:-1:1, :, :]
+    dw_ = if flipkernel(cdims)
+        view(dw, reverse(axes(dw, 1)), reverse(axes(dw, 2)), reverse(axes(dw, 3)), :, :)
+    else
+        dw
     end
+    conv_direct!(dw_, dy, x, ctdims; alpha=alpha, beta=beta)
     return dw
 end
diff --git a/test/conv.jl b/test/conv.jl
index 3037930d7..8edc4bf24 100644
--- a/test/conv.jl
+++ b/test/conv.jl
@@ -310,6 +310,49 @@ ddims(x) = dropdims(x, dims=(ndims(x)-1, ndims(x)))
                 end
             end
 
+            # Test all in-place implementations/interfaces 
+            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
+                α, β = 2e0, -1e0
+
+                @testset "$(conv!)" begin
+                    # First, your basic convolution with no parameters
+                    cdims = DenseConvDims(x, w)
+                    y0 = rand(rng, -9e0:9e0, size(y_plain)..., 1, 1)
+                    @test isapprox(ddims(conv!(copy(y0), x, w, cdims; alpha=α, beta=β)), α*y_plain + β*y0, rtol = 1.0e-7)
+
+                    # Next, test convolution on views and alternate datatypes:
+                    @test isapprox(ddims(conv!(copy(y0), view(x, repeat([:], ndims(x))...), w, cdims; alpha=α, beta=β)), α*y_plain + β*y0, rtol = 1.0e-7)
+                    @test isapprox(ddims(conv!(Float32.(copy(y0)), Float32.(x), Float32.(w), cdims; alpha=Float32(α), beta=Float32(β))), Float32.(α*y_plain + β*y0), rtol = 1.0e-7)
+
+                    # Next, introduce stride:
+                    cdims = DenseConvDims(x, w; stride=2)
+                    y0 = rand(rng, -9e0:9e0, size(y_stride)..., 1, 1)
+                    @test isapprox(ddims(conv!(copy(y0), x, w, cdims; alpha=α, beta=β)), α*y_stride + β*y0, rtol = 1.0e-7)
+
+                    # Next, introduce dilation:
+                    cdims = DenseConvDims(x, w; dilation=2)
+                    y0 = rand(rng, -9e0:9e0, size(y_dil)..., 1, 1)
+                    @test isapprox(ddims(conv!(copy(y0), x, w, cdims; alpha=α, beta=β)), α*y_dil + β*y0, rtol = 1.0e-7)
+
+                    # Next, introduce padding:
+                    cdims = DenseConvDims(x, w; padding=1)
+                    y0 = rand(rng, -9e0:9e0, size(y_pad)..., 1, 1)
+                    @test isapprox(ddims(conv!(copy(y0), x, w, cdims; alpha=α, beta=β)), α*y_pad + β*y0, rtol = 1.0e-7)
+
+                    # Next, test crosscor/conv with a flipped kernel
+                    cdims = DenseConvDims(x, w; flipkernel=true)
+                    y0 = rand(rng, -9e0:9e0, size(y_flip)..., 1, 1)
+                    @test isapprox(ddims(conv!(copy(y0), x, w, cdims; alpha=α, beta=β)), α*y_flip + β*y0, rtol = 1.0e-7)
+                end
+            end
+
             # Test all implementations/interfaces
             for (∇conv_filter, ∇conv_data) in (
                     (NNlib.∇conv_filter,        NNlib.∇conv_data),
@@ -355,6 +398,58 @@ ddims(x) = dropdims(x, dims=(ndims(x)-1, ndims(x)))
                     @test isapprox(ddims(∇conv_data(dy, w,  cdims)), dx_flip, rtol = 1.0e-7)
                 end
             end
+
+            # Test all in-place implementations/interfaces 
+            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!),
+                )
+                #α, β = 2*rand(rng) - 1, 2*rand(rng) - 1
+                α, β = 2e0, -1e0
+                flag = ∇conv_data! in (NNlib.∇conv_data!, NNlib.∇conv_data_im2col!)
+
+                @testset "$(∇conv_filter!)/$(∇conv_data!)" begin
+                    # First, your basic convolution with no parameters
+                    cdims = DenseConvDims(x, w)
+                    dy = NNlib.conv(x, w, cdims)
+                    @test isapprox(ddims(∇conv_filter!(copy(w), x, dy, cdims; alpha=α, beta=β)), α*dw + β*w, rtol = 1.0e-7) 
+                    @test isapprox(ddims(∇conv_data!(copy(x), dy, w,   cdims; alpha=α, beta=β)), α*dx + β*x, rtol = 1.0e-7) broken=flag
+
+                    # Next, test convolution on views and alternate datatypes:
+                    @test isapprox(ddims(∇conv_filter!(copy(w), x, view(dy, repeat([:], ndims(dy))...), cdims; alpha=α, beta=β)), α*dw + β*w, rtol = 1.0e-7) 
+                    @test isapprox(ddims(∇conv_data!(copy(x), view(dy, repeat([:], ndims(dy))...), w,   cdims; alpha=α, beta=β)), α*dx + β*x, rtol = 1.0e-7) broken=flag
+
+                    @test isapprox(ddims(∇conv_filter!(Float32.(copy(w)), Float32.(x), Float32.(dy), cdims; alpha=Float32(α), beta=Float32(β))), α*dw + β*w, rtol = 1.0e-7) 
+                    @test isapprox(ddims(∇conv_data!(Float32.(copy(x)), Float32.(dy),  Float32.(w),  cdims; alpha=Float32(α), beta=Float32(β))), α*dx + β*x, rtol = 1.0e-7) broken=flag
+
+                    # Next, introduce stride:
+                    cdims = DenseConvDims(x, w; stride=2)
+                    dy = NNlib.conv(x, w, cdims)
+                    flag_ = ∇conv_filter! == NNlib.∇conv_filter_direct! && rank in (1,3)
+                    @test isapprox(ddims(∇conv_filter!(copy(w), x, dy, cdims; alpha=α, beta=β)), α*dw_stride + β*w, rtol = 1.0e-7) broken=flag_
+                    @test isapprox(ddims(∇conv_data!(copy(x), dy, w,   cdims; alpha=α, beta=β)), α*dx_stride + β*x, rtol = 1.0e-7) broken=flag
+
+                    # Next, introduce dilation:
+                    cdims = DenseConvDims(x, w; dilation=2)
+                    dy = NNlib.conv(x, w, cdims)
+                    flag_ = ∇conv_data! == NNlib.∇conv_data_direct! && rank == 3
+                    @test isapprox(ddims(∇conv_filter!(copy(w), x, dy, cdims; alpha=α, beta=β)), α*dw_dil + β*w, rtol = 1.0e-7) 
+                    @test isapprox(ddims(∇conv_data!(copy(x), dy, w,   cdims; alpha=α, beta=β)), α*dx_dil + β*x, rtol = 1.0e-7) broken=flag || flag_
+
+                    # Next, introduce padding:
+                    cdims = DenseConvDims(x, w; padding=1)
+                    dy = NNlib.conv(x, w, cdims)
+                    @test isapprox(ddims(∇conv_filter!(copy(w), x, dy, cdims; alpha=α, beta=β)), α*dw_pad + β*w, rtol = 1.0e-7) 
+                    @test isapprox(ddims(∇conv_data!(copy(x), dy, w,   cdims; alpha=α, beta=β)), α*dx_pad + β*x, rtol = 1.0e-7) broken=flag
+
+                    # Next, test crosscor/conv with a flipped kernel
+                    cdims = DenseConvDims(x, w; flipkernel=true)
+                    dy = NNlib.conv(x, w, cdims)
+                    @test isapprox(ddims(∇conv_filter!(copy(w), x, dy, cdims; alpha=α, beta=β)), α*dw_flip + β*w, rtol = 1.0e-7) 
+                    @test isapprox(ddims(∇conv_data!(copy(x), dy, w,   cdims; alpha=α, beta=β)), α*dx_flip + β*x, rtol = 1.0e-7) broken=flag
+                end
+            end
         end
     end
 end