diff --git a/test/conv.jl b/test/conv.jl
index 8edc4bf24..3c4bac0a7 100644
--- a/test/conv.jl
+++ b/test/conv.jl
@@ -407,47 +407,46 @@ ddims(x) = dropdims(x, dims=(ndims(x)-1, ndims(x)))
                 )
                 #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
+                    @test isapprox(ddims(∇conv_data!(copy(x), dy, w,   cdims; alpha=α, beta=β)), α*dx + β*x, rtol = 1.0e-7)
 
                     # 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_data!(copy(x), view(dy, repeat([:], ndims(dy))...), w,   cdims; alpha=α, beta=β)), α*dx + β*x, rtol = 1.0e-7)
 
                     @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
+                    @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)
 
                     # 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
+                    @test isapprox(ddims(∇conv_data!(copy(x), dy, w,   cdims; alpha=α, beta=β)), α*dx_stride + β*x, rtol = 1.0e-7)
 
                     # 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_
+                    @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_
 
                     # 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
+                    @test isapprox(ddims(∇conv_data!(copy(x), dy, w,   cdims; alpha=α, beta=β)), α*dx_pad + β*x, rtol = 1.0e-7)
 
                     # 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
+                    @test isapprox(ddims(∇conv_data!(copy(x), dy, w,   cdims; alpha=α, beta=β)), α*dx_flip + β*x, rtol = 1.0e-7)
                 end
             end
         end