test fillarray gradients

src/cuda/curnn.jl

@@ -297,11 +297,15 @@ unbroadcast(x::AbstractArray, Δ) =
   length(x) == length(Δ) ? trim(x, Δ) :
     trim(x, sum(Δ, dims = ntuple(i -> size(x, i) == 1 ? i : ndims(Δ)+1, Val(ndims(Δ)))))
 
+coerce_cuda(x::Union{CuArray,Nothing}) = x
+
+coerce_cuda(x) = x .+ CuArrays.fill(0)
+
 for RNN in (CuRNN, CuGRU)
   @eval @adjoint function (m::$RNN{T})(h::CuArray{T}, x::CuArray{T}) where T <: Union{Float32,Float64}
     reserve, (y, ho) = forwardTrain(desc(m), x, h)
     (ho, y), function (Δ)
-      dho, dy = Δ
+      dho, dy = coerce_cuda.(Δ)
       h_ = hBatch(x, h)
       dx, dh = backwardData(descs[m], y, dy, dho, h_, reserve)
       (dWi, dWh), db = backwardWeights(descs[m], x, h_, y, reserve)
@@ -314,7 +318,7 @@ end
 @adjoint function (m::CuLSTM)((h, c)::Tuple{CuArray{T},CuArray{T}}, x::CuArray{T}) where T <: Union{Float32,Float64}
   reserve, (y, ho, co) = forwardTrain(desc(m), x, h, c)
   ((ho, co), y), function (Δ)
-    dhc, dy = Δ
+    dhc, dy = coerce_cuda.(Δ)
     dho, dco = dhc === nothing ? (nothing, nothing) : dhc
     h_ = hBatch(x, h)
     c_ = hBatch(x, c)

test/cuda/curnn.jl

@@ -1,6 +1,12 @@
 using Flux, CuArrays, Test
 using Flux: forward
 
+@testset for R in [RNN, GRU, LSTM]
+  m = R(10, 5) |> gpu
+  x = gpu(rand(10))
+  @test gradient(m -> sum(m(x)), m) isa Tuple
+end
+
 @testset "RNN" begin
   @testset for R in [RNN, GRU, LSTM], batch_size in (1, 5)
     rnn = R(10, 5)