diff --git a/src/gather.jl b/src/gather.jl
index 1ad69df24..d75f89a2c 100644
--- a/src/gather.jl
+++ b/src/gather.jl
@@ -109,7 +109,7 @@ function gather!(dst::AbstractArray, src::AbstractArray, idx::AbstractArray)
     return dst
 end
 
-function gather!(dst::AbstractGPUArray, src::AbstractGPUArray, idx::AbstractGPUArray)
+function gather!(dst::AnyGPUArray, src::AnyGPUArray, idx::AnyGPUArray)
     n_dims = scatter_dims(src, dst, idx)
     dims = size(src)[1:n_dims]
     max_dims_idx = prod(dims)
diff --git a/src/scatter.jl b/src/scatter.jl
index 6edf6e379..3507b906d 100644
--- a/src/scatter.jl
+++ b/src/scatter.jl
@@ -81,7 +81,7 @@ function scatter!(op::OP, dst::AbstractArray, src::AbstractArray, idx::AbstractA
     dst
 end
 
-for AT in (AbstractArray, AbstractGPUArray)
+for AT in (AbstractArray, AnyGPUArray)
     @eval function scatter!(op::typeof(mean), dst::$AT, src::$AT, idx::$AT)
         Ns = scatter!(+, zero(dst), one.(src), idx)
         dst_ = scatter!(+, zero(dst), src, idx)
@@ -90,7 +90,7 @@ for AT in (AbstractArray, AbstractGPUArray)
     end
 end
 
-function scatter!(op::OP, dst::AbstractGPUArray, src::AbstractGPUArray, idx::AbstractGPUArray) where OP
+function scatter!(op::OP, dst::AnyGPUArray, src::AnyGPUArray, idx::AnyGPUArray) where OP
     n_dims = scatter_dims(dst, src, idx)
     args = if n_dims == 0
         ndrange = length(idx)
@@ -228,7 +228,7 @@ end
 
 function ∇scatter_src(
     op::Union{typeof(*), typeof(/)}, Δ, dst,
-    src::AbstractGPUArray{Tsrc, Nsrc}, idx::AbstractGPUArray{Tidx, Nidx},
+    src::AnyGPUArray{Tsrc, Nsrc}, idx::AnyGPUArray{Tidx, Nidx},
 ) where {Tsrc, Nsrc, Tidx, Nidx}
     n_dims = Nsrc - Nidx
     Δsrc = NNlib.modify_src(op, NNlib.gather(Δ, idx), src)
diff --git a/src/upsample.jl b/src/upsample.jl
index a320ca9e6..babd613fb 100644
--- a/src/upsample.jl
+++ b/src/upsample.jl
@@ -411,7 +411,7 @@ end
 end
 
 # Linear (GPU): parallelization along width dimension.
-# TODO replace AbstractArray -> AbstractGPUArray once device arrays subtype it.
+# TODO replace AbstractArray -> AnyGPUArray once device arrays subtype it.
 
 @kernel function _upsample_linear_kernel!(::B, y::T, x::T, rwidth, align::Val{A}) where {
     B <: GPU, T <: AbstractArray{<:Any, 3}, A,
diff --git a/test/ext_cuda/gather.jl b/test/ext_cuda/gather.jl
index 36d42dbcc..9fa30efa8 100644
--- a/test/ext_cuda/gather.jl
+++ b/test/ext_cuda/gather.jl
@@ -89,4 +89,18 @@
     @test y isa CuArray{Float32,3}
     @test size(y) == (size(src)[1:Nsrc-M]..., size(index)...)
     gputest(src -> NNlib.gather(src, index), src, checkgrad=true)
+
+    @testset "views" begin
+        x = cu(rand(2, 5))
+        v = view(x, axes(x)...)
+        i = cu([1, 2])   
+        outx = NNlib.gather(x, i)
+        outv = NNlib.gather(v, i)
+        @test outx == outv
+
+        # discontinuous view
+        v2 = view(x, :, [1,3,5])
+        outv2 = NNlib.gather(v2, i)
+        @test collect(outv2) == NNlib.gather(collect(v2), collect(i))        
+    end
 end