test cleanups

src/Flux.jl

@@ -3,10 +3,10 @@ module Flux
 # Zero Flux Given
 
 using Base: tail
-using MacroTools, Juno, Requires, Reexport, Statistics, Random
+using Zygote, MacroTools, Juno, Requires, Reexport, Statistics, Random
 using MacroTools: @forward
 @reexport using NNlib
-using Zygote: Params, @adjoint, gradient
+using Zygote: Params, @adjoint, gradient, forward
 export gradient
 
 export Chain, Dense, Maxout, RNN, LSTM, GRU, Conv, CrossCor, ConvTranspose, MaxPool, MeanPool,

src/cuda/curnn.jl

@@ -265,7 +265,7 @@ function desc(rnn)
   return d
 end
 
-using Zygote: @adjoint
+using ..Flux: @adjoint
 
 function (m::CuRNN{T})(h::CuArray{T}, x::CuArray{T}) where T <: Union{Float32,Float64}
   result = forward(desc(m), x, h)
@@ -295,7 +295,7 @@ for RNN in (CuRNN, CuGRU)
       h_ = hBatch(x, h)
       dx, dh = backwardData(descs[m], y, dy, dho, h_, reserve)
       (dWi, dWh), db = backwardWeights(descs[m], x, h_, y, reserve)
-      nobacksies(:RNN, (dx, unbroadcast(h, dh), transpose(dWi), transpose(dWh), db))
+      (dx, unbroadcast(h, dh), transpose(dWi), transpose(dWh), db)
     end
   end
 end
@@ -309,8 +309,7 @@ end
     c_ = hBatch(x, c)
     dx, dh, dc = backwardData(descs[m], y, dy, dho, dco, h_, c_, reserve)
     (dWi, dWh), db = backwardWeights(descs[m], x, h_, y, reserve)
-    nobacksies(:RNN,
-      (dx, unbroadcast(h, dh), unbroadcast(c, dc),
-       transpose(dWi), transpose(dWh), db))
+    (dx, unbroadcast(h, dh), unbroadcast(c, dc),
+     transpose(dWi), transpose(dWh), db)
   end
 end

test/cuda/cuda.jl

@@ -1,6 +1,5 @@
 using Flux, CuArrays, Test
 using Flux: gpu
-using Zygote
 
 @info "Testing GPU Support"
 

test/cuda/cudnn.jl

@@ -1,5 +1,4 @@
 using Flux, CuArrays, Test
-using Zygote
 trainmode(f, x...) = forward(f, x...)[1]
 
 @testset "CUDNN BatchNorm" begin

test/layers/stateless.jl

@@ -1,7 +1,6 @@
 using Test
 using Flux: onehotbatch, mse, crossentropy, logitcrossentropy,
             σ, binarycrossentropy, logitbinarycrossentropy
-using Zygote
 
 const ϵ = 1e-7
 
@@ -56,7 +55,7 @@ const ϵ = 1e-7
       y = rand(T, 2)
       ŷ = rand(T, 2)
       for f in (mse, crossentropy, logitcrossentropy)
-        fwd, back = Zygote.forward(f, ŷ, y)
+        fwd, back = Flux.forward(f, ŷ, y)
         @test fwd isa T
         @test eltype(back(one(T))[1]) == T
       end

test/optimise.jl

@@ -1,9 +1,11 @@
 using Flux.Optimise
 using Flux.Optimise: runall
-using Zygote
-using Zygote: Params, gradient
+using Flux: Params, gradient
 using Test
-Zygote.@nograd sleep
+
+# TODO move this to Zygote
+Flux.Zygote.@nograd sleep
+
 @testset "Optimise" begin
   w = randn(10, 10)
   @testset for opt in [ADAMW(), ADAGrad(0.1), AdaMax(), ADADelta(0.9), AMSGrad(),