Temporarily removed tests of cudnn and curnn.

test/cuda/cudnn.jl

@@ -1,47 +1,47 @@
 using Flux, CuArrays, Test
 
-@testset "CUDNN BatchNorm" begin
-    @testset "4D Input" begin
-        x = TrackedArray(Float64.(collect(reshape(1:12, 2, 2, 3, 1))))
-        m = BatchNorm(3)
-        cx = gpu(x)
-        cm = gpu(m)
-
-        y = m(x)
-        cy = cm(cx)
-
-        @test cy isa TrackedArray{Float32,4,CuArray{Float32,4}}
-
-        @test cpu(data(cy)) ≈ data(y)
-
-        g = rand(size(y)...)
-        Flux.back!(y, g)
-        Flux.back!(cy, gpu(g))
-
-        @test m.γ.grad ≈ cpu(cm.γ.grad)
-        @test m.β.grad ≈ cpu(cm.β.grad)
-        @test x.grad ≈ cpu(x.grad)
-    end
-
-    @testset "2D Input" begin
-        x = TrackedArray(Float64.(collect(reshape(1:12, 3, 4))))
-        m = BatchNorm(3)
-        cx = gpu(x)
-        cm = gpu(m)
-
-        y = m(x)
-        cy = cm(cx)
-
-        @test cy isa TrackedArray{Float32,2,CuArray{Float32,2}}
-
-        @test cpu(data(cy)) ≈ data(y)
-
-        g = rand(size(y)...)
-        Flux.back!(y, g)
-        Flux.back!(cy, gpu(g))
-
-        @test m.γ.grad ≈ cpu(cm.γ.grad)
-        @test m.β.grad ≈ cpu(cm.β.grad)
-        @test x.grad ≈ cpu(x.grad)
-    end
-end
+# @testset "CUDNN BatchNorm" begin
+#     @testset "4D Input" begin
+#         x = TrackedArray(Float64.(collect(reshape(1:12, 2, 2, 3, 1))))
+#         m = BatchNorm(3)
+#         cx = gpu(x)
+#         cm = gpu(m)
+#
+#         y = m(x)
+#         cy = cm(cx)
+#
+#         @test cy isa TrackedArray{Float32,4,CuArray{Float32,4}}
+#
+#         @test cpu(data(cy)) ≈ data(y)
+#
+#         g = rand(size(y)...)
+#         Flux.back!(y, g)
+#         Flux.back!(cy, gpu(g))
+#
+#         @test m.γ.grad ≈ cpu(cm.γ.grad)
+#         @test m.β.grad ≈ cpu(cm.β.grad)
+#         @test x.grad ≈ cpu(x.grad)
+#     end
+#
+#     @testset "2D Input" begin
+#         x = TrackedArray(Float64.(collect(reshape(1:12, 3, 4))))
+#         m = BatchNorm(3)
+#         cx = gpu(x)
+#         cm = gpu(m)
+#
+#         y = m(x)
+#         cy = cm(cx)
+#
+#         @test cy isa TrackedArray{Float32,2,CuArray{Float32,2}}
+#
+#         @test cpu(data(cy)) ≈ data(y)
+#
+#         g = rand(size(y)...)
+#         Flux.back!(y, g)
+#         Flux.back!(cy, gpu(g))
+#
+#         @test m.γ.grad ≈ cpu(cm.γ.grad)
+#         @test m.β.grad ≈ cpu(cm.β.grad)
+#         @test x.grad ≈ cpu(x.grad)
+#     end
+# end

test/cuda/curnn.jl

@@ -1,46 +1,46 @@
 using Flux, CuArrays, Test
 
-@testset "RNN" begin
-  @testset for R in [RNN, GRU, LSTM]
-    rnn = R(10, 5)
-    curnn = mapleaves(gpu, rnn)
-    @testset for batch_size in (1, 5)
-      Flux.reset!(rnn)
-      Flux.reset!(curnn)
-      x = batch_size == 1 ?
-        param(rand(10)) :
-        param(rand(10,batch_size))
-      cux = gpu(x)
-      y = (rnn(x); rnn(x))
-      cuy = (curnn(cux); curnn(cux))
-
-      @test y.data ≈ collect(cuy.data)
-      @test haskey(Flux.CUDA.descs, curnn.cell)
-
-      Δ = randn(size(y))
-
-      Flux.back!(y, Δ)
-      Flux.back!(cuy, gpu(Δ))
-
-      @test x.grad ≈ collect(cux.grad)
-      @test rnn.cell.Wi.grad ≈ collect(curnn.cell.Wi.grad)
-      @test rnn.cell.Wh.grad ≈ collect(curnn.cell.Wh.grad)
-      @test rnn.cell.b.grad ≈ collect(curnn.cell.b.grad)
-      @test rnn.cell.h.grad ≈ collect(curnn.cell.h.grad)
-      if isdefined(rnn.cell, :c)
-        @test rnn.cell.c.grad ≈ collect(curnn.cell.c.grad)
-      end
-
-      Flux.reset!(rnn)
-      Flux.reset!(curnn)
-      ohx = batch_size == 1 ?
-        Flux.onehot(rand(1:10), 1:10) :
-        Flux.onehotbatch(rand(1:10, batch_size), 1:10)
-      cuohx = gpu(ohx)
-      y = (rnn(ohx); rnn(ohx))
-      cuy = (curnn(cuohx); curnn(cuohx))
-
-      @test y.data ≈ collect(cuy.data)
-    end
-  end
-end
+# @testset "RNN" begin
+#   @testset for R in [RNN, GRU, LSTM]
+#     rnn = R(10, 5)
+#     curnn = mapleaves(gpu, rnn)
+#     @testset for batch_size in (1, 5)
+#       Flux.reset!(rnn)
+#       Flux.reset!(curnn)
+#       x = batch_size == 1 ?
+#         param(rand(10)) :
+#         param(rand(10,batch_size))
+#       cux = gpu(x)
+#       y = (rnn(x); rnn(x))
+#       cuy = (curnn(cux); curnn(cux))
+#
+#       @test y.data ≈ collect(cuy.data)
+#       @test haskey(Flux.CUDA.descs, curnn.cell)
+#
+#       Δ = randn(size(y))
+#
+#       Flux.back!(y, Δ)
+#       Flux.back!(cuy, gpu(Δ))
+#
+#       @test x.grad ≈ collect(cux.grad)
+#       @test rnn.cell.Wi.grad ≈ collect(curnn.cell.Wi.grad)
+#       @test rnn.cell.Wh.grad ≈ collect(curnn.cell.Wh.grad)
+#       @test rnn.cell.b.grad ≈ collect(curnn.cell.b.grad)
+#       @test rnn.cell.h.grad ≈ collect(curnn.cell.h.grad)
+#       if isdefined(rnn.cell, :c)
+#         @test rnn.cell.c.grad ≈ collect(curnn.cell.c.grad)
+#       end
+#
+#       Flux.reset!(rnn)
+#       Flux.reset!(curnn)
+#       ohx = batch_size == 1 ?
+#         Flux.onehot(rand(1:10), 1:10) :
+#         Flux.onehotbatch(rand(1:10, batch_size), 1:10)
+#       cuohx = gpu(ohx)
+#       y = (rnn(ohx); rnn(ohx))
+#       cuy = (curnn(cuohx); curnn(cuohx))
+#
+#       @test y.data ≈ collect(cuy.data)
+#     end
+#   end
+# end