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This commit is contained in:
Mike Innes
parent
fc9db7ee74
commit
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@ -46,9 +46,9 @@ version = "0.6.2"
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[[CUDAapi]]
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deps = ["Libdl", "Logging"]
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git-tree-sha1 = "9b2b4b71d6b7f946c9689bb4dea03ff92e3c7091"
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git-tree-sha1 = "e063efb91cfefd7e6afd92c435d01398107a500b"
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uuid = "3895d2a7-ec45-59b8-82bb-cfc6a382f9b3"
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version = "1.1.0"
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version = "1.2.0"
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[[CUDAdrv]]
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deps = ["CUDAapi", "Libdl", "Printf"]
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@ -147,9 +147,9 @@ uuid = "8ba89e20-285c-5b6f-9357-94700520ee1b"
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[[FFTW]]
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deps = ["AbstractFFTs", "BinaryProvider", "Conda", "Libdl", "LinearAlgebra", "Reexport", "Test"]
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git-tree-sha1 = "03f8776fbdae28c20c0d1d2ae4e090cd1dfcd247"
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git-tree-sha1 = "6c5b420da0b8c12098048561b8d58f81adea506f"
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uuid = "7a1cc6ca-52ef-59f5-83cd-3a7055c09341"
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version = "1.0.0"
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version = "1.0.1"
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[[FillArrays]]
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deps = ["LinearAlgebra", "Random", "SparseArrays"]
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@ -170,9 +170,9 @@ version = "0.10.3"
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[[GPUArrays]]
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deps = ["Adapt", "FFTW", "FillArrays", "LinearAlgebra", "Printf", "Random", "Serialization", "StaticArrays", "Test"]
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git-tree-sha1 = "b5009ac44b141ded5e6f04c4db83807970f56e91"
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git-tree-sha1 = "77e27264276fe97a7e7fb928bf8999a145abc018"
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uuid = "0c68f7d7-f131-5f86-a1c3-88cf8149b2d7"
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version = "1.0.2"
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version = "1.0.3"
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[[IRTools]]
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deps = ["InteractiveUtils", "MacroTools", "Test"]
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@ -388,7 +388,7 @@ version = "0.8.3"
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[[Zygote]]
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deps = ["DiffRules", "FFTW", "FillArrays", "ForwardDiff", "IRTools", "InteractiveUtils", "LinearAlgebra", "MacroTools", "NNlib", "NaNMath", "Random", "Requires", "SpecialFunctions", "Statistics", "ZygoteRules"]
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git-tree-sha1 = "ce6d7142d665b1e4c71c678fa7db4da3bbc6743f"
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git-tree-sha1 = "38241b40ebd8748bcacad5e6c7ba3ab3cc7a15c9"
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repo-rev = "master"
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repo-url = "https://github.com/FluxML/Zygote.jl.git"
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uuid = "e88e6eb3-aa80-5325-afca-941959d7151f"
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@ -396,6 +396,8 @@ version = "0.3.4"
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[[ZygoteRules]]
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deps = ["MacroTools"]
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git-tree-sha1 = "def5f96ac2895fd9b48435f6b97020979ee0a4c6"
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git-tree-sha1 = "c4c29b30b8ff3be13d4244e78be7df2a42bc54d0"
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repo-rev = "master"
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repo-url = "https://github.com/FluxML/ZygoteRules.jl.git"
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uuid = "700de1a5-db45-46bc-99cf-38207098b444"
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version = "0.1.0"
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version = "0.2.0"
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@ -24,6 +24,7 @@ StatsBase = "2913bbd2-ae8a-5f71-8c99-4fb6c76f3a91"
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Test = "8dfed614-e22c-5e08-85e1-65c5234f0b40"
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ZipFile = "a5390f91-8eb1-5f08-bee0-b1d1ffed6cea"
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Zygote = "e88e6eb3-aa80-5325-afca-941959d7151f"
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ZygoteRules = "700de1a5-db45-46bc-99cf-38207098b444"
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[compat]
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CUDAapi = "1.1"
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@ -6,7 +6,7 @@ using Base: tail
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using Zygote, MacroTools, Juno, Reexport, Statistics, Random
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using MacroTools: @forward
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@reexport using NNlib
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using Zygote: Params, @adjoint, gradient, forward
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using Zygote: Params, @adjoint, gradient, pullback
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export gradient
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export Chain, Dense, Maxout, RNN, LSTM, GRU, Conv, CrossCor, ConvTranspose, MaxPool, MeanPool,
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@ -1,5 +1,5 @@
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using Flux, CuArrays, Test
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using Flux: forward
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using Flux: pullback
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@testset "CUDNN BatchNorm" begin
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@testset "4D Input" begin
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@ -8,8 +8,8 @@ using Flux: forward
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cx = gpu(x)
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cm = gpu(m)
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y, back = forward((m, x) -> m(x), m, x)
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cy, cback = forward((m, x) -> m(x), cm, cx)
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y, back = pullback((m, x) -> m(x), m, x)
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cy, cback = pullback((m, x) -> m(x), cm, cx)
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@test cpu(cy) ≈ y
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@ -28,8 +28,8 @@ using Flux: forward
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cx = gpu(x)
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cm = gpu(m)
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y, back = forward((m, x) -> m(x), m, x)
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cy, cback = forward((m, x) -> m(x), cm, cx)
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y, back = pullback((m, x) -> m(x), m, x)
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cy, cback = pullback((m, x) -> m(x), cm, cx)
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@test cpu(cy) ≈ y
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@ -1,5 +1,5 @@
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using Flux, CuArrays, Test
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using Flux: forward
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using Flux: pullback
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@testset for R in [RNN, GRU, LSTM]
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m = R(10, 5) |> gpu
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@ -22,8 +22,8 @@ end
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rand(10, batch_size)
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cux = gpu(x)
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y, back = forward((r, x) -> (r(x)), rnn, x)
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cuy, cuback = forward((r, x) -> (r(x)), curnn, cux)
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y, back = pullback((r, x) -> (r(x)), rnn, x)
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cuy, cuback = pullback((r, x) -> (r(x)), curnn, cux)
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@test y ≈ collect(cuy)
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@test haskey(Flux.CUDA.descs, curnn.cell)
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@ -1,7 +1,7 @@
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using Flux, Test, Statistics
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using Zygote: forward
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using Zygote: pullback
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trainmode(f, x...) = forward(f, x...)[1]
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trainmode(f, x...) = pullback(f, x...)[1]
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trainmode(f) = (x...) -> trainmode(f, x...)
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@testset "Dropout" begin
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@ -55,7 +55,7 @@ const ϵ = 1e-7
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y = rand(T, 2)
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ŷ = rand(T, 2)
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for f in (mse, crossentropy, logitcrossentropy)
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fwd, back = Flux.forward(f, ŷ, y)
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fwd, back = Flux.pullback(f, ŷ, y)
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@test fwd isa T
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@test eltype(back(one(T))[1]) == T
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end
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