Erroneous fallback to Base gemv! implementation

[troyrock]

Describe the bug

When trying to train a neural network on the GPU, I'm getting the error message:
[ Info: Training on GPU
[ Info: Start Training
ArgumentError: cannot take the CPU address of a CuArray{Float32, 1}

When I give it a simpler neural network (also included in the MWE below), that works fine.

To reproduce

The Minimal Working Example (MWE) for this bug:

using Flux
using DifferentialEquations
using CUDA

u0 = 0
tspan = (0.0, 2.0)
num_sensors = 100  # number of sensors
num_sensor_sets = 100
num_points = 100
num_train = num_sensor_sets*num_points
lr = 0.001
epochs = 10
width = 10

Base.@kwdef mutable struct Args
    η = lr               # learning rate
    epochs = epochs      # number of epochs
    use_cuda = true      # if true use cuda (if available)
end

using Flux.Data: DataLoader
function get_data(args)
    sensors = range(tspan[1], stop=tspan[2], length=num_sensors)
    bottom_inputs = rand(num_points)*(tspan[2]-tspan[1]) .+ tspan[1]
    inputs = Matrix{Float64}(undef, num_sensors+1, num_train)
    outputs = Vector{Float64}(undef, num_train)
    for loop in 1:num_sensor_sets
        α = rand()*100
        f(u,p,t) = sin(α*t)
        prob = ODEProblem(f, u0, tspan)
        sols = solve(prob, saveat=sensors, reltol=1e-10, abstol=1e-10)
        for loop2 in 1:num_points
            inputs[:, num_points*(loop-1)+loop2] = [sin.(α * sensors); bottom_inputs[loop2]]
            outputs[num_points*(loop-1)+loop2] = sols(bottom_inputs[loop2])
        end
    end
    train_loader = DataLoader((inputs, outputs), shuffle=true)
    return train_loader
end

using Flux.Losses: mse
function loss_checker(data_loader, model, device)
    ls = 0.0f0
    num = 0
    for (x, y) in data_loader
        x, y = device(x), device(y)
        ŷ = model(x)
        ls += mse(ŷ, y, agg=sum)
        num += size(x,2)
    end
    return ls/num
end       

function train(NN; kws...)
    args = Args(; kws...)
    use_cuda = args.use_cuda && CUDA.functional()
    
    if use_cuda
        device = gpu
        @info "Training on GPU"
    else
        device = cpu
        @info "Training on CPU"
    end

    ## DATA
    train_loader = get_data(args)

    ## MODEL AND OPTIMIZER
    model = NN |> device
    
    ps = Flux.params(model)  

    opt = ADAM(args.η) 
    
    ## TRAINING
    @info "Start Training"
    for epoch in 1:args.epochs
        for (x, y) in train_loader
            x, y = device(x), device(y)
            gs = Flux.gradient(ps) do
                ŷ = model(x)
                mse(ŷ, y)
            end

            Flux.Optimise.update!(opt, ps, gs)
        end
    end
end

NN = Chain(Dense(num_sensors+1, width, relu, bias=true),
           Dense(width, width, relu, bias=true),
           Dense(width, 1, bias=true))

train(NN)   # This works!

unstacked_deeponet = Chain(x->Parallel(vcat, 
                        Chain(Dense(num_sensors, 40, relu, bias=true),
                              Dense(40, 40, relu, bias=true)),
                        Chain(Dense(1, 40, relu, bias=true),
                              Dense(40, 40, relu, bias=true)))(x[1:num_sensors], x[num_sensors+1:num_sensors+1]), 
            Dense(80, 1))
train(unstacked_deeponet)  # This doesn't work

Manifest.toml

# This file is machine-generated - editing it directly is not advised

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uuid = "bd369af6-aec1-5ad0-b16a-f7cc5008161c"
version = "1.4.2"

[[Tar]]
deps = ["ArgTools", "SHA"]
uuid = "a4e569a6-e804-4fa4-b0f3-eef7a1d5b13e"

[[TerminalLoggers]]
deps = ["LeftChildRightSiblingTrees", "Logging", "Markdown", "Printf", "ProgressLogging", "UUIDs"]
git-tree-sha1 = "e185a19bb9172f0cf5bc71233fab92a46f7ae154"
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[[Test]]
deps = ["InteractiveUtils", "Logging", "Random", "Serialization"]
uuid = "8dfed614-e22c-5e08-85e1-65c5234f0b40"

[[ThreadingUtilities]]
deps = ["VectorizationBase"]
git-tree-sha1 = "d9117912ec78dbba3294fcb2962b6826be6107a5"
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[[TimerOutputs]]
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[[Tokenize]]
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[[Tracker]]
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[[TranscodingStreams]]
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[[Trapz]]
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[[TreeViews]]
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[[URIParser]]
deps = ["Unicode"]
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[[URIs]]
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[[UUIDs]]
deps = ["Random", "SHA"]
uuid = "cf7118a7-6976-5b1a-9a39-7adc72f591a4"

[[UnPack]]
git-tree-sha1 = "387c1f73762231e86e0c9c5443ce3b4a0a9a0c2b"
uuid = "3a884ed6-31ef-47d7-9d2a-63182c4928ed"
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[[Unicode]]
uuid = "4ec0a83e-493e-50e2-b9ac-8f72acf5a8f5"

[[Unitful]]
deps = ["ConstructionBase", "Dates", "LinearAlgebra", "Random"]
git-tree-sha1 = "c6bbc170505c5ea36593a0072b61d3be8bf868ae"
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[[VectorizationBase]]
deps = ["ArrayInterface", "Hwloc", "IfElse", "Libdl", "LinearAlgebra", "Static"]
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uuid = "3d5dd08c-fd9d-11e8-17fa-ed2836048c2f"
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[[VertexSafeGraphs]]
deps = ["LightGraphs"]
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[[Wayland_jll]]
deps = ["Artifacts", "Expat_jll", "JLLWrappers", "Libdl", "Libffi_jll", "Pkg", "XML2_jll"]
git-tree-sha1 = "dc643a9b774da1c2781413fd7b6dcd2c56bb8056"
uuid = "a2964d1f-97da-50d4-b82a-358c7fce9d89"
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[[Wayland_protocols_jll]]
deps = ["Artifacts", "JLLWrappers", "Libdl", "Pkg", "Wayland_jll"]
git-tree-sha1 = "2839f1c1296940218e35df0bbb220f2a79686670"
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[[Weave]]
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[[WebIO]]
deps = ["AssetRegistry", "Base64", "Distributed", "FunctionalCollections", "JSON", "Logging", "Observables", "Pkg", "Random", "Requires", "Sockets", "UUIDs", "WebSockets", "Widgets"]
git-tree-sha1 = "adc25e225bc334c7df6eec3b39496edfc451cc38"
uuid = "0f1e0344-ec1d-5b48-a673-e5cf874b6c29"
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[[WebSockets]]
deps = ["Base64", "Dates", "HTTP", "Logging", "Sockets"]
git-tree-sha1 = "f91a602e25fe6b89afc93cf02a4ae18ee9384ce3"
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version = "1.5.9"

[[Widgets]]
deps = ["Colors", "Dates", "Observables", "OrderedCollections"]
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uuid = "cc8bc4a8-27d6-5769-a93b-9d913e69aa62"
version = "0.6.3"

[[WoodburyMatrices]]
deps = ["LinearAlgebra", "SparseArrays"]
git-tree-sha1 = "59e2ad8fd1591ea019a5259bd012d7aee15f995c"
uuid = "efce3f68-66dc-5838-9240-27a6d6f5f9b6"
version = "0.5.3"

[[XML2_jll]]
deps = ["Artifacts", "JLLWrappers", "Libdl", "Libiconv_jll", "Pkg", "Zlib_jll"]
git-tree-sha1 = "1acf5bdf07aa0907e0a37d3718bb88d4b687b74a"
uuid = "02c8fc9c-b97f-50b9-bbe4-9be30ff0a78a"
version = "2.9.12+0"

[[XSLT_jll]]
deps = ["Artifacts", "JLLWrappers", "Libdl", "Libgcrypt_jll", "Pkg", "XML2_jll"]
git-tree-sha1 = "2b3eac39df218762d2d005702d601cd44c997497"
uuid = "aed1982a-8fda-507f-9586-7b0439959a61"
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[[Xorg_libX11_jll]]
deps = ["Artifacts", "JLLWrappers", "Libdl", "Pkg", "Xorg_libxcb_jll", "Xorg_xtrans_jll"]
git-tree-sha1 = "5be649d550f3f4b95308bf0183b82e2582876527"
uuid = "4f6342f7-b3d2-589e-9d20-edeb45f2b2bc"
version = "1.6.9+4"

[[Xorg_libXau_jll]]
deps = ["Artifacts", "JLLWrappers", "Libdl", "Pkg"]
git-tree-sha1 = "4e490d5c960c314f33885790ed410ff3a94ce67e"
uuid = "0c0b7dd1-d40b-584c-a123-a41640f87eec"
version = "1.0.9+4"

[[Xorg_libXcursor_jll]]
deps = ["Artifacts", "JLLWrappers", "Libdl", "Pkg", "Xorg_libXfixes_jll", "Xorg_libXrender_jll"]
git-tree-sha1 = "12e0eb3bc634fa2080c1c37fccf56f7c22989afd"
uuid = "935fb764-8cf2-53bf-bb30-45bb1f8bf724"
version = "1.2.0+4"

[[Xorg_libXdmcp_jll]]
deps = ["Artifacts", "JLLWrappers", "Libdl", "Pkg"]
git-tree-sha1 = "4fe47bd2247248125c428978740e18a681372dd4"
uuid = "a3789734-cfe1-5b06-b2d0-1dd0d9d62d05"
version = "1.1.3+4"

[[Xorg_libXext_jll]]
deps = ["Artifacts", "JLLWrappers", "Libdl", "Pkg", "Xorg_libX11_jll"]
git-tree-sha1 = "b7c0aa8c376b31e4852b360222848637f481f8c3"
uuid = "1082639a-0dae-5f34-9b06-72781eeb8cb3"
version = "1.3.4+4"

[[Xorg_libXfixes_jll]]
deps = ["Artifacts", "JLLWrappers", "Libdl", "Pkg", "Xorg_libX11_jll"]
git-tree-sha1 = "0e0dc7431e7a0587559f9294aeec269471c991a4"
uuid = "d091e8ba-531a-589c-9de9-94069b037ed8"
version = "5.0.3+4"

[[Xorg_libXi_jll]]
deps = ["Artifacts", "JLLWrappers", "Libdl", "Pkg", "Xorg_libXext_jll", "Xorg_libXfixes_jll"]
git-tree-sha1 = "89b52bc2160aadc84d707093930ef0bffa641246"
uuid = "a51aa0fd-4e3c-5386-b890-e753decda492"
version = "1.7.10+4"

[[Xorg_libXinerama_jll]]
deps = ["Artifacts", "JLLWrappers", "Libdl", "Pkg", "Xorg_libXext_jll"]
git-tree-sha1 = "26be8b1c342929259317d8b9f7b53bf2bb73b123"
uuid = "d1454406-59df-5ea1-beac-c340f2130bc3"
version = "1.1.4+4"

[[Xorg_libXrandr_jll]]
deps = ["Artifacts", "JLLWrappers", "Libdl", "Pkg", "Xorg_libXext_jll", "Xorg_libXrender_jll"]
git-tree-sha1 = "34cea83cb726fb58f325887bf0612c6b3fb17631"
uuid = "ec84b674-ba8e-5d96-8ba1-2a689ba10484"
version = "1.5.2+4"

[[Xorg_libXrender_jll]]
deps = ["Artifacts", "JLLWrappers", "Libdl", "Pkg", "Xorg_libX11_jll"]
git-tree-sha1 = "19560f30fd49f4d4efbe7002a1037f8c43d43b96"
uuid = "ea2f1a96-1ddc-540d-b46f-429655e07cfa"
version = "0.9.10+4"

[[Xorg_libpthread_stubs_jll]]
deps = ["Artifacts", "JLLWrappers", "Libdl", "Pkg"]
git-tree-sha1 = "6783737e45d3c59a4a4c4091f5f88cdcf0908cbb"
uuid = "14d82f49-176c-5ed1-bb49-ad3f5cbd8c74"
version = "0.1.0+3"

[[Xorg_libxcb_jll]]
deps = ["Artifacts", "JLLWrappers", "Libdl", "Pkg", "XSLT_jll", "Xorg_libXau_jll", "Xorg_libXdmcp_jll", "Xorg_libpthread_stubs_jll"]
git-tree-sha1 = "daf17f441228e7a3833846cd048892861cff16d6"
uuid = "c7cfdc94-dc32-55de-ac96-5a1b8d977c5b"
version = "1.13.0+3"

[[Xorg_libxkbfile_jll]]
deps = ["Artifacts", "JLLWrappers", "Libdl", "Pkg", "Xorg_libX11_jll"]
git-tree-sha1 = "926af861744212db0eb001d9e40b5d16292080b2"
uuid = "cc61e674-0454-545c-8b26-ed2c68acab7a"
version = "1.1.0+4"

[[Xorg_xcb_util_image_jll]]
deps = ["Artifacts", "JLLWrappers", "Libdl", "Pkg", "Xorg_xcb_util_jll"]
git-tree-sha1 = "0fab0a40349ba1cba2c1da699243396ff8e94b97"
uuid = "12413925-8142-5f55-bb0e-6d7ca50bb09b"
version = "0.4.0+1"

[[Xorg_xcb_util_jll]]
deps = ["Artifacts", "JLLWrappers", "Libdl", "Pkg", "Xorg_libxcb_jll"]
git-tree-sha1 = "e7fd7b2881fa2eaa72717420894d3938177862d1"
uuid = "2def613f-5ad1-5310-b15b-b15d46f528f5"
version = "0.4.0+1"

[[Xorg_xcb_util_keysyms_jll]]
deps = ["Artifacts", "JLLWrappers", "Libdl", "Pkg", "Xorg_xcb_util_jll"]
git-tree-sha1 = "d1151e2c45a544f32441a567d1690e701ec89b00"
uuid = "975044d2-76e6-5fbe-bf08-97ce7c6574c7"
version = "0.4.0+1"

[[Xorg_xcb_util_renderutil_jll]]
deps = ["Artifacts", "JLLWrappers", "Libdl", "Pkg", "Xorg_xcb_util_jll"]
git-tree-sha1 = "dfd7a8f38d4613b6a575253b3174dd991ca6183e"
uuid = "0d47668e-0667-5a69-a72c-f761630bfb7e"
version = "0.3.9+1"

[[Xorg_xcb_util_wm_jll]]
deps = ["Artifacts", "JLLWrappers", "Libdl", "Pkg", "Xorg_xcb_util_jll"]
git-tree-sha1 = "e78d10aab01a4a154142c5006ed44fd9e8e31b67"
uuid = "c22f9ab0-d5fe-5066-847c-f4bb1cd4e361"
version = "0.4.1+1"

[[Xorg_xkbcomp_jll]]
deps = ["Artifacts", "JLLWrappers", "Libdl", "Pkg", "Xorg_libxkbfile_jll"]
git-tree-sha1 = "4bcbf660f6c2e714f87e960a171b119d06ee163b"
uuid = "35661453-b289-5fab-8a00-3d9160c6a3a4"
version = "1.4.2+4"

[[Xorg_xkeyboard_config_jll]]
deps = ["Artifacts", "JLLWrappers", "Libdl", "Pkg", "Xorg_xkbcomp_jll"]
git-tree-sha1 = "5c8424f8a67c3f2209646d4425f3d415fee5931d"
uuid = "33bec58e-1273-512f-9401-5d533626f822"
version = "2.27.0+4"

[[Xorg_xtrans_jll]]
deps = ["Artifacts", "JLLWrappers", "Libdl", "Pkg"]
git-tree-sha1 = "79c31e7844f6ecf779705fbc12146eb190b7d845"
uuid = "c5fb5394-a638-5e4d-96e5-b29de1b5cf10"
version = "1.4.0+3"

[[YAML]]
deps = ["Base64", "Dates", "Printf"]
git-tree-sha1 = "78c02bd295bbd0ca330f95e07ccdfcb69f6cbcd4"
uuid = "ddb6d928-2868-570f-bddf-ab3f9cf99eb6"
version = "0.4.6"

[[ZipFile]]
deps = ["Libdl", "Printf", "Zlib_jll"]
git-tree-sha1 = "c3a5637e27e914a7a445b8d0ad063d701931e9f7"
uuid = "a5390f91-8eb1-5f08-bee0-b1d1ffed6cea"
version = "0.9.3"

[[Zlib_jll]]
deps = ["Libdl"]
uuid = "83775a58-1f1d-513f-b197-d71354ab007a"

[[Zstd_jll]]
deps = ["Artifacts", "JLLWrappers", "Libdl", "Pkg"]
git-tree-sha1 = "cc4bf3fdde8b7e3e9fa0351bdeedba1cf3b7f6e6"
uuid = "3161d3a3-bdf6-5164-811a-617609db77b4"
version = "1.5.0+0"

[[Zygote]]
deps = ["AbstractFFTs", "ChainRules", "ChainRulesCore", "DiffRules", "Distributed", "FillArrays", "ForwardDiff", "IRTools", "InteractiveUtils", "LinearAlgebra", "MacroTools", "NaNMath", "Random", "Requires", "SpecialFunctions", "Statistics", "ZygoteRules"]
git-tree-sha1 = "c8b2ac496fd1154fa9020af827f10cced736e8c5"
uuid = "e88e6eb3-aa80-5325-afca-941959d7151f"
version = "0.6.11"

[[ZygoteRules]]
deps = ["MacroTools"]
git-tree-sha1 = "9e7a1e8ca60b742e508a315c17eef5211e7fbfd7"
uuid = "700de1a5-db45-46bc-99cf-38207098b444"
version = "0.2.1"

[[libass_jll]]
deps = ["Artifacts", "Bzip2_jll", "FreeType2_jll", "FriBidi_jll", "JLLWrappers", "Libdl", "Pkg", "Zlib_jll"]
git-tree-sha1 = "acc685bcf777b2202a904cdcb49ad34c2fa1880c"
uuid = "0ac62f75-1d6f-5e53-bd7c-93b484bb37c0"
version = "0.14.0+4"

[[libfdk_aac_jll]]
deps = ["Artifacts", "JLLWrappers", "Libdl", "Pkg"]
git-tree-sha1 = "7a5780a0d9c6864184b3a2eeeb833a0c871f00ab"
uuid = "f638f0a6-7fb0-5443-88ba-1cc74229b280"
version = "0.1.6+4"

[[libpng_jll]]
deps = ["Artifacts", "JLLWrappers", "Libdl", "Pkg", "Zlib_jll"]
git-tree-sha1 = "6abbc424248097d69c0c87ba50fcb0753f93e0ee"
uuid = "b53b4c65-9356-5827-b1ea-8c7a1a84506f"
version = "1.6.37+6"

[[libvorbis_jll]]
deps = ["Artifacts", "JLLWrappers", "Libdl", "Ogg_jll", "Pkg"]
git-tree-sha1 = "fa14ac25af7a4b8a7f61b287a124df7aab601bcd"
uuid = "f27f6e37-5d2b-51aa-960f-b287f2bc3b7a"
version = "1.3.6+6"

[[nghttp2_jll]]
deps = ["Artifacts", "Libdl"]
uuid = "8e850ede-7688-5339-a07c-302acd2aaf8d"

[[p7zip_jll]]
deps = ["Artifacts", "Libdl"]
uuid = "3f19e933-33d8-53b3-aaab-bd5110c3b7a0"

[[x264_jll]]
deps = ["Artifacts", "JLLWrappers", "Libdl", "Pkg"]
git-tree-sha1 = "d713c1ce4deac133e3334ee12f4adff07f81778f"
uuid = "1270edf5-f2f9-52d2-97e9-ab00b5d0237a"
version = "2020.7.14+2"

[[x265_jll]]
deps = ["Artifacts", "JLLWrappers", "Libdl", "Pkg"]
git-tree-sha1 = "487da2f8f2f0c8ee0e83f39d13037d6bbf0a45ab"
uuid = "dfaa095f-4041-5dcd-9319-2fabd8486b76"
version = "3.0.0+3"

[[xkbcommon_jll]]
deps = ["Artifacts", "JLLWrappers", "Libdl", "Pkg", "Wayland_jll", "Wayland_protocols_jll", "Xorg_libxcb_jll", "Xorg_xkeyboard_config_jll"]
git-tree-sha1 = "ece2350174195bb31de1a63bea3a41ae1aa593b6"
uuid = "d8fb68d0-12a3-5cfd-a85a-d49703b185fd"
version = "0.9.1+5"

Expected behavior

I expected that train on NN would be similar to the train on unstacked_deeponet.

Version info

Details on Julia:

Julia Version 1.6.0
Commit f9720dc2eb (2021-03-24 12:55 UTC)
Platform Info:
  OS: Linux (x86_64-pc-linux-gnu)
  CPU: Intel(R) Core(TM) i7-6700 CPU @ 3.40GHz
  WORD_SIZE: 64
  LIBM: libopenlibm
  LLVM: libLLVM-11.0.1 (ORCJIT, skylake)
Environment:
  JULIA_EDITOR = atom  -a
  JULIA_NUM_THREADS = 4

Details on CUDA:

CUDA toolkit 11.1.1, artifact installation
CUDA driver 11.2.0
NVIDIA driver 460.73.1

Libraries: 
- CUBLAS: 11.3.0
- CURAND: 10.2.2
- CUFFT: 10.3.0
- CUSOLVER: 11.0.1
- CUSPARSE: 11.3.0
- CUPTI: 14.0.0
- NVML: 11.0.0+460.73.1
- CUDNN: 8.10.0 (for CUDA 11.2.0)
- CUTENSOR: 1.2.2 (for CUDA 11.1.0)

Toolchain:
- Julia: 1.6.0
- LLVM: 11.0.1
- PTX ISA support: 3.2, 4.0, 4.1, 4.2, 4.3, 5.0, 6.0, 6.1, 6.3, 6.4, 6.5, 7.0
- Device support: sm_35, sm_37, sm_50, sm_52, sm_53, sm_60, sm_61, sm_62, sm_70, sm_72, sm_75, sm_80

1 device:
  0: GeForce GTX 1060 6GB (sm_61, 5.002 GiB / 5.928 GiB available)

Additional context

Add any other context about the problem here.

[maleadt]

Please include the backtrace.

[troyrock]

Here is the whole error:

ArgumentError: cannot take the CPU address of a CuArray{Float32, 1}
unsafe_convert(#unused#::Type{Ptr{Float32}}, x::CuArray{Float32, 1}) at array.jl:211
gemv!(trans::Char, alpha::Float32, A::Matrix{Float32}, X::CuArray{Float32, 1}, beta::Float32, Y::CuArray{Float32, 1}) at blas.jl:704
gemv!(y::CuArray{Float32, 1}, tA::Char, A::Matrix{Float32}, x::CuArray{Float32, 1}, α::Bool, β::Bool) at matmul.jl:544
mul! at matmul.jl:66 [inlined]
mul! at matmul.jl:275 [inlined]
at matmul.jl:47 [inlined]
rrule at arraymath.jl:40 [inlined]
chain_rrule at chainrules.jl:89 [inlined]
macro expansion at interface2.jl:0 [inlined]
_pullback(::Zygote.Context, ::typeof(*), ::Matrix{Float32}, ::CuArray{Float32, 1}) at interface2.jl:9
_pullback at basic.jl:147 [inlined]
_pullback(ctx::Zygote.Context, f::Dense{typeof(relu), Matrix{Float32}, Vector{Float32}}, args::CuArray{Float32, 1}) at interface2.jl:0
_pullback at basic.jl:36 [inlined]
_pullback(::Zygote.Context, ::typeof(Flux.applychain), ::Tuple{Dense{typeof(relu), Matrix{Float32}, Vector{Float32}}, Dense{typeof(relu), Matrix{Float32}, Vector{Float32}}}, ::CuArray{Float32, 1}) at interface2.jl:0
_pullback at basic.jl:38 [inlined]
_pullback(ctx::Zygote.Context, f::Chain{Tuple{Dense{typeof(relu), Matrix{Float32}, Vector{Float32}}, Dense{typeof(relu), Matrix{Float32}, Vector{Float32}}}}, args::CuArray{Float32, 1}) at interface2.jl:0
_pullback at basic.jl:414 [inlined]
_pullback(::Zygote.Context, ::Flux.var"#148#149", ::Chain{Tuple{Dense{typeof(relu), Matrix{Float32}, Vector{Float32}}, Dense{typeof(relu), Matrix{Float32}, Vector{Float32}}}}, ::CuArray{Float32, 1}) at interface2.jl:0
_apply at boot.jl:804 [inlined]
adjoint at lib.jl:191 [inlined]
_pullback at adjoint.jl:57 [inlined]
_pullback at generator.jl:36 [inlined]
_pullback(ctx::Zygote.Context, f::Base.var"#4#5"{Flux.var"#148#149"}, args::Tuple{Chain{Tuple{Dense{typeof(relu), Matrix{Float32}, Vector{Float32}}, Dense{typeof(relu), Matrix{Float32}, Vector{Float32}}}}, CuArray{Float32, 1}}) at interface2.jl:0
_pullback at reduce.jl:93 [inlined]
_pullback at reduce.jl:58 [inlined]
_pullback(::Zygote.Context, ::typeof(Base._foldl_impl), ::Base.MappingRF{Base.var"#4#5"{Flux.var"#148#149"}, Base.BottomRF{typeof(vcat)}}, ::Base._InitialValue, ::Base.Iterators.Zip{Tuple{Tuple{Chain{Tuple{Dense{typeof(relu), Matrix{Float32}, Vector{Float32}}, Dense{typeof(relu), Matrix{Float32}, Vector{Float32}}}}, Chain{Tuple{Dense{typeof(relu), Matrix{Float32}, Vector{Float32}}, Dense{typeof(relu), Matrix{Float32}, Vector{Float32}}}}}, Tuple{CuArray{Float32, 1}, CuArray{Float32, 1}}}}) at interface2.jl:0
_pullback at reduce.jl:48 [inlined]
_pullback(::Zygote.Context, ::typeof(Base.foldl_impl), ::Base.MappingRF{Base.var"#4#5"{Flux.var"#148#149"}, Base.BottomRF{typeof(vcat)}}, ::Base._InitialValue, ::Base.Iterators.Zip{Tuple{Tuple{Chain{Tuple{Dense{typeof(relu), Matrix{Float32}, Vector{Float32}}, Dense{typeof(relu), Matrix{Float32}, Vector{Float32}}}}, Chain{Tuple{Dense{typeof(relu), Matrix{Float32}, Vector{Float32}}, Dense{typeof(relu), Matrix{Float32}, Vector{Float32}}}}}, Tuple{CuArray{Float32, 1}, CuArray{Float32, 1}}}}) at interface2.jl:0
_pullback at reduce.jl:44 [inlined]
_pullback(::Zygote.Context, ::typeof(Base.mapfoldl_impl), ::typeof(identity), ::typeof(vcat), ::Base._InitialValue, ::Base.Generator{Base.Iterators.Zip{Tuple{Tuple{Chain{Tuple{Dense{typeof(relu), Matrix{Float32}, Vector{Float32}}, Dense{typeof(relu), Matrix{Float32}, Vector{Float32}}}}, Chain{Tuple{Dense{typeof(relu), Matrix{Float32}, Vector{Float32}}, Dense{typeof(relu), Matrix{Float32}, Vector{Float32}}}}}, Tuple{CuArray{Float32, 1}, CuArray{Float32, 1}}}}, Base.var"#4#5"{Flux.var"#148#149"}}) at interface2.jl:0
_pullback at reduce.jl:160 [inlined]
_pullback(::Zygote.Context, ::Base.var"##mapfoldl#214", ::Base._InitialValue, ::typeof(mapfoldl), ::typeof(identity), ::typeof(vcat), ::Base.Generator{Base.Iterators.Zip{Tuple{Tuple{Chain{Tuple{Dense{typeof(relu), Matrix{Float32}, Vector{Float32}}, Dense{typeof(relu), Matrix{Float32}, Vector{Float32}}}}, Chain{Tuple{Dense{typeof(relu), Matrix{Float32}, Vector{Float32}}, Dense{typeof(relu), Matrix{Float32}, Vector{Float32}}}}}, Tuple{CuArray{Float32, 1}, CuArray{Float32, 1}}}}, Base.var"#4#5"{Flux.var"#148#149"}}) at interface2.jl:0
_pullback at reduce.jl:160 [inlined]
_pullback(::Zygote.Context, ::typeof(mapfoldl), ::typeof(identity), ::typeof(vcat), ::Base.Generator{Base.Iterators.Zip{Tuple{Tuple{Chain{Tuple{Dense{typeof(relu), Matrix{Float32}, Vector{Float32}}, Dense{typeof(relu), Matrix{Float32}, Vector{Float32}}}}, Chain{Tuple{Dense{typeof(relu), Matrix{Float32}, Vector{Float32}}, Dense{typeof(relu), Matrix{Float32}, Vector{Float32}}}}}, Tuple{CuArray{Float32, 1}, CuArray{Float32, 1}}}}, Base.var"#4#5"{Flux.var"#148#149"}}) at interface2.jl:0
_pullback at reduce.jl:287 [inlined]
_pullback(::Zygote.Context, ::Base.var"##mapreduce#218", ::Base.Iterators.Pairs{Union{}, Union{}, Tuple{}, NamedTuple{(), Tuple{}}}, ::typeof(mapredu...

[troyrock]

Another user on Slack gave the following solution which does fix the problem. I will close the error but thought it might be useful to also include the solution in case there IS something amiss.

Troy Rockwood the line x -> Parallel(vcat, ...) isn’t doing what I think want. You created an anonymous function that for any input x will construct a new Parallel layer then apply it to x split up. Contrast that with:
layer = Parallel(vcat, ...)
x -> layer(x)
This closes over layer. For each time you call the anonymous function, it reuses layer for every x instead of constructing a new one. I recommend writing your model as:

udo = Chain(x-> (x[1:numsensors], x[numsensors+1:numsensors+1]),
                      Parallel(vcat,
                        Chain(Dense(num_sensors, 40, relu, bias=true),
                              Dense(40, 40, relu, bias=true)),
                        Chain(Dense(1, 40, relu, bias=true),
                              Dense(40, 40, relu, bias=true))),
            Dense(80, 1))

Note the difference where only one Parallel is constructed. Instead we add an anonymous function before the Parallel that splits the array into a tuple of each part. Not sure if this will fix your GPU errors. There may be a separate issue. The GPU error is specifically related to a mixing of CPU arrays and GPU arrays in a call to gemm. Right now, I can’t tell where the CPU array is appearing from. But maybe you can try the fix above, then post again.

[maleadt]

The LinAlg seems sensible, and like something we should implement:

gemv!(y::CuArray{Float32, 1}, tA::Char, A::Matrix{Float32}, x::CuArray{Float32, 1}, α::Bool, β::Bool) at matmul.jl:544
mul! at matmul.jl:66 [inlined]
mul! at matmul.jl:275 [inlined]

Too bad we can't see the mul! invocation though.