Problem with RNN and CUDA.

[anbjos]

I want to run a RNN (https://fluxml.ai/Flux.jl/stable/models/recurrence/) on the GPU, using the explicit (https://fluxml.ai/Flux.jl/stable/training/training/#Implicit-or-Explicit?) gradients.

This seems to work fine one CPU, but fails on the GPU with the message pointing to the last statement in the loss function, called from gradient:

ERROR: CUDA error: an illegal memory access was encountered (code 700, ERROR_ILLEGAL_ADDRESS)
Stacktrace:
 [1] throw_api_error(res::CUDA.cudaError_enum)
   @ CUDA C:\Users\XXX\.julia\packages\CUDA\nIZkq\lib\cudadrv\libcuda.jl:27
 [2] isdone
   @ C:\Users\XXX\.julia\packages\CUDA\nIZkq\lib\cudadrv\stream.jl:111 [inlined]
 [3] spinning_synchronization(f::typeof(CUDA.isdone), obj::CuStream)
   @ CUDA C:\Users\XXX\.julia\packages\CUDA\nIZkq\lib\cudadrv\synchronization.jl:79
 [4] device_synchronize(; blocking::Bool, spin::Bool)
   @ CUDA C:\Users\XXX\.julia\packages\CUDA\nIZkq\lib\cudadrv\synchronization.jl:171
 [5] device_synchronize()
   @ CUDA C:\Users\XXX\.julia\packages\CUDA\nIZkq\lib\cudadrv\synchronization.jl:169
 [6] top-level scope
   @ C:\Users\XXX\.julia\packages\CUDA\nIZkq\src\initialization.jl:210

caused by: CUDA error: an illegal memory access was encountered (code 700, ERROR_ILLEGAL_ADDRESS)
Stacktrace:
  [1] throw_api_error(res::CUDA.cudaError_enum)
    @ CUDA C:\Users\XXX\.julia\packages\CUDA\nIZkq\lib\cudadrv\libcuda.jl:27

Since I am dealing with a RNN, the history must be available for the gradient (https://en.wikipedia.org/wiki/Backpropagation_through_time). The recurrence documentation for Flux specifies that the input should be structured as a vector (over time steps) of vectors (over features).

Code below is simplified to expose the problem, the training loop is stripped away.

using Flux
using ChainRulesCore
using CUDA

dev=gpu # cpu is working fine

m = Chain(RNN(2 => 5), Dense(5 => 1)) |> dev

x = [rand(Float32, 2) for i = 1:3] |> dev;
y = [rand(Float32, 1) for i=1:1] |> dev

[m(xi) for xi in x]

using Flux.Losses: mse

function loss(m, x, y)
    @ignore_derivatives Flux.reset!(m)
    m(x[1]) # ignores the output but updates the hidden states
    m(x[2]) # ignore second output
    mse(m(x[3]),y[1])
end
  
loss(m, x, y)

grads = Flux.gradient(m, x, y) do m,x,y
    loss(m, x, y)
end

optim = Flux.setup(Flux.Adam(), m) 
Flux.update!(optim, m, grads[1])

Versions (in clean environment):

Julia 1.9.3
CUDA v5.1.0
ChainRulesCore v1.18.0
Flux v0.14.6

[jeremiedb]

It looks like the issue comes from latest Zygote v0.6.67, which merged this issue: FluxML/Zygote.jl#1328
I could reproduce the issue on Zygote v0.6.67, but it worked fine on Zygote v0.6.66.
@ToucheSir

[ToucheSir]

Changes made on our side should not be directly causing memory access errors on the CUDA one since we operate at a much higher level, so I'm inclined to say the problem lies elsewhere and was exposed by Zygote switching to the ChainRules rule. Can you get a stacktrace which includes Zygote and ChainRules(Core)? Having just CUDA.jl in there is not helpful.

[jeremiedb]

Sorry I'm afraid I missunderstand your comment regarding having CUDA.jl not being useful, as there's no issue raised when the above issue is run on the cpu.

Working on GPU - Zygote v0.6.67

using Flux
using CUDA
using Flux.Losses: mse
dev = gpu # cpu is working fine

#######################
# no indexing
#######################
m = RNN(2 => 1) |> dev
x = rand(Float32, 2, 3) |> dev;
y = rand(Float32, 1, 3) |> dev

m.state
Flux.reset!(m)
m(x)

loss(m, x, y) = mse(m(x), y)
Flux.reset!(m)
gs = gradient(m) do model
    loss(model, x, y)
end

Failing on GPU - Zygote v0.6.67 (working on Zygote v0.6.66):

m = RNN(2 => 1) |> dev
x = [rand(Float32, 2, 3) for i in 1:4] |> dev;
y = rand(Float32, 1, 3) |> dev

m.state
Flux.reset!(m)
m(x[1])

function loss_1(m, x, y)
    p = [m(xi) for xi in x]
    mse(p[1], y)
end

loss_1(m, x, y)

Flux.reset!(m)
gs = gradient(m) do model
    loss_1(model, x, y)
end

And the above return following error message:

julia> gs = gradient(m) do model
           loss_1(model, x, y)
       end
ERROR: MethodError: no method matching parent(::Type{SubArray{Union{ChainRulesCore.ZeroTangent, CuMatrix{Float32, CUDA.Mem.DeviceBuffer}, DenseCuMatrix{Float32, CUDA.Mem.DeviceBuffer}}, 0, Vector{Union{ChainRulesCore.ZeroTangent, CuMatrix{Float32, CUDA.Mem.DeviceBuffer}, DenseCuMatrix{Float32, CUDA.Mem.DeviceBuffer}}}, Tuple{Int64}, true}})
Closest candidates are:
  parent(::Union{LinearAlgebra.Adjoint{T, S}, LinearAlgebra.Transpose{T, S}} where {T, S}) at C:\Users\jerem\AppData\Local\Programs\Julia-1.8.5\share\julia\stdlib\v1.8\LinearAlgebra\src\adjtrans.jl:218
  parent(::Union{LinearAlgebra.Hermitian{T, S}, LinearAlgebra.Symmetric{T, S}} where {T, S}) at C:\Users\jerem\AppData\Local\Programs\Julia-1.8.5\share\julia\stdlib\v1.8\LinearAlgebra\src\symmetric.jl:275
  parent(::Union{NNlib.BatchedAdjoint{T, S}, NNlib.BatchedTranspose{T, S}} where {T, S}) at C:\Users\jerem\.julia\packages\NNlib\Fg3DQ\src\batched\batchedadjtrans.jl:73
  ...
Stacktrace:
  [1] backend(#unused#::Type{SubArray{Union{ChainRulesCore.ZeroTangent, CuMatrix{Float32, CUDA.Mem.DeviceBuffer}, DenseCuMatrix{Float32, CUDA.Mem.DeviceBuffer}}, 0, Vector{Union{ChainRulesCore.ZeroTangent, CuMatrix{Float32, CUDA.Mem.DeviceBuffer}, DenseCuMatrix{Float32, CUDA.Mem.DeviceBuffer}}}, Tuple{Int64}, true}})
    @ GPUArraysCore C:\Users\jerem\.julia\packages\GPUArraysCore\uOYfN\src\GPUArraysCore.jl:151
  [2] backend(x::SubArray{Union{ChainRulesCore.ZeroTangent, CuMatrix{Float32, CUDA.Mem.DeviceBuffer}, DenseCuMatrix{Float32, CUDA.Mem.DeviceBuffer}}, 0, Vector{Union{ChainRulesCore.ZeroTangent, CuMatrix{Float32, CUDA.Mem.DeviceBuffer}, DenseCuMatrix{Float32, CUDA.Mem.DeviceBuffer}}}, Tuple{Int64}, true})
    @ GPUArraysCore C:\Users\jerem\.julia\packages\GPUArraysCore\uOYfN\src\GPUArraysCore.jl:149
  [3] _copyto!
    @ C:\Users\jerem\.julia\packages\GPUArrays\5XhED\src\host\broadcast.jl:65 [inlined]
  [4] materialize!
    @ C:\Users\jerem\.julia\packages\GPUArrays\5XhED\src\host\broadcast.jl:41 [inlined]
  [5] materialize!
    @ .\broadcast.jl:868 [inlined]
  [6] ∇getindex!(dx::Vector{Union{ChainRulesCore.ZeroTangent, CuMatrix{Float32, CUDA.Mem.DeviceBuffer}, DenseCuMatrix{Float32, CUDA.Mem.DeviceBuffer}}}, dy::CuArray{Float32, 2, CUDA.Mem.DeviceBuffer}, inds::Int64)
    @ ChainRules C:\Users\jerem\.julia\packages\ChainRules\DSuXy\src\rulesets\Base\indexing.jl:147
  [7] ∇getindex(x::Vector{CuArray{Float32, 2, CUDA.Mem.DeviceBuffer}}, dy::CuArray{Float32, 2, CUDA.Mem.DeviceBuffer}, inds::Int64)
    @ ChainRules C:\Users\jerem\.julia\packages\ChainRules\DSuXy\src\rulesets\Base\indexing.jl:89
  [8] (::ChainRules.var"#1583#1585"{Vector{CuArray{Float32, 2, CUDA.Mem.DeviceBuffer}}, CuArray{Float32, 2, CUDA.Mem.DeviceBuffer}, Tuple{Int64}})()
    @ ChainRules C:\Users\jerem\.julia\packages\ChainRules\DSuXy\src\rulesets\Base\indexing.jl:69
  [9] unthunk
    @ C:\Users\jerem\.julia\packages\ChainRulesCore\7MWx2\src\tangent_types\thunks.jl:204 [inlined]
 [10] unthunk(x::ChainRulesCore.InplaceableThunk{ChainRulesCore.Thunk{ChainRules.var"#1583#1585"{Vector{CuArray{Float32, 2, CUDA.Mem.DeviceBuffer}}, CuArray{Float32, 2, CUDA.Mem.DeviceBuffer}, Tuple{Int64}}}, ChainRules.var"#1582#1584"{Vector{CuArray{Float32, 2, CUDA.Mem.DeviceBuffer}}, CuArray{Float32, 2, CUDA.Mem.DeviceBuffer}, Tuple{Int64}}})
    @ ChainRulesCore C:\Users\jerem\.julia\packages\ChainRulesCore\7MWx2\src\tangent_types\thunks.jl:237
 [11] wrap_chainrules_output
    @ C:\Users\jerem\.julia\packages\Zygote\YYT6v\src\compiler\chainrules.jl:110 [inlined]
 [12] map
    @ .\tuple.jl:223 [inlined]
 [13] wrap_chainrules_output
    @ C:\Users\jerem\.julia\packages\Zygote\YYT6v\src\compiler\chainrules.jl:111 [inlined]
 [14] ZBack
    @ C:\Users\jerem\.julia\packages\Zygote\YYT6v\src\compiler\chainrules.jl:211 [inlined]
 [15] Pullback
    @ C:\Users\jerem\.julia\packages\Zygote\YYT6v\src\tools\builtins.jl:12 [inlined]
 [16] (::Zygote.Pullback{Tuple{typeof(Zygote.literal_getindex), Vector{CuArray{Float32, 2, CUDA.Mem.DeviceBuffer}}, Val{1}}, Tuple{Zygote.ZBack{ChainRules.var"#getindex_pullback#1581"{Vector{CuArray{Float32, 2, CUDA.Mem.DeviceBuffer}}, Tuple{Int64}, Tuple{ChainRulesCore.NoTangent}}}}})(Δ::CuArray{Float32, 2, CUDA.Mem.DeviceBuffer})
    @ Zygote C:\Users\jerem\.julia\packages\Zygote\YYT6v\src\compiler\interface2.jl:0
 [17] Pullback
    @ c:\Users\jerem\OneDrive\github\ADTests.jl\FluxRNNTest\rnn-gpu.jl:94 [inlined]
 [18] (::Zygote.Pullback{Tuple{typeof(loss_1), Flux.Recur{Flux.RNNCell{typeof(tanh), CuArray{Float32, 2, CUDA.Mem.DeviceBuffer}, CuArray{Float32, 2, CUDA.Mem.DeviceBuffer}, CuArray{Float32, 1, CUDA.Mem.DeviceBuffer}, CuArray{Float32, 2, CUDA.Mem.DeviceBuffer}}, CuArray{Float32, 2, CUDA.Mem.DeviceBuffer}}, Vector{CuArray{Float32, 2, CUDA.Mem.DeviceBuffer}}, CuArray{Float32, 2, CUDA.Mem.DeviceBuffer}}, Tuple{Zygote.Pullback{Tuple{Type{Base.Generator}, var"#113#114"{Flux.Recur{Flux.RNNCell{typeof(tanh), CuArray{Float32, 2, CUDA.Mem.DeviceBuffer}, CuArray{Float32, 2, CUDA.Mem.DeviceBuffer}, CuArray{Float32, 1, CUDA.Mem.DeviceBuffer}, CuArray{Float32, 2, CUDA.Mem.DeviceBuffer}}, CuArray{Float32, 2, CUDA.Mem.DeviceBuffer}}}, Vector{CuArray{Float32, 2, CUDA.Mem.DeviceBuffer}}}, Tuple{Zygote.Pullback{Tuple{Type{Base.Generator{Vector{CuArray{Float32, 2, CUDA.Mem.DeviceBuffer}}, var"#113#114"{Flux.Recur{Flux.RNNCell{typeof(tanh), CuArray{Float32, 2, CUDA.Mem.DeviceBuffer}, CuArray{Float32, 2, CUDA.Mem.DeviceBuffer}, CuArray{Float32, 1, CUDA.Mem.DeviceBuffer}, CuArray{Float32, 2, CUDA.Mem.DeviceBuffer}}, CuArray{Float32, 2, CUDA.Mem.DeviceBuffer}}}}}, var"#113#114"{Flux.Recur{Flux.RNNCell{typeof(tanh), CuArray{Float32, 2, CUDA.Mem.DeviceBuffer}, CuArray{Float32, 2, CUDA.Mem.DeviceBuffer}, CuArray{Float32, 1, CUDA.Mem.DeviceBuffer}, CuArray{Float32, 2, CUDA.Mem.DeviceBuffer}}, CuArray{Float32, 2, CUDA.Mem.DeviceBuffer}}}, Vector{CuArray{Float32, 2, CUDA.Mem.DeviceBuffer}}}, Tuple{Zygote.Pullback{Tuple{typeof(convert), Type{var"#113#114"{Flux.Recur{Flux.RNNCell{typeof(tanh), CuArray{Float32, 2, CUDA.Mem.DeviceBuffer}, CuArray{Float32, 2, CUDA.Mem.DeviceBuffer}, CuArray{Float32, 1, CUDA.Mem.DeviceBuffer}, CuArray{Float32, 2, CUDA.Mem.DeviceBuffer}}, CuArray{Float32, 2, CUDA.Mem.DeviceBuffer}}}}, var"#113#114"{Flux.Recur{Flux.RNNCell{typeof(tanh), CuArray{Float32, 2, CUDA.Mem.DeviceBuffer}, CuArray{Float32, 2, CUDA.Mem.DeviceBuffer}, CuArray{Float32, 1, CUDA.Mem.DeviceBuffer}, CuArray{Float32, 2, CUDA.Mem.DeviceBuffer}}, CuArray{Float32, 2, CUDA.Mem.DeviceBuffer}}}}, Tuple{}}, Zygote.var"#2193#back#313"{Zygote.Jnew{Base.Generator{Vector{CuArray{Float32, 2, CUDA.Mem.DeviceBuffer}}, var"#113#114"{Flux.Recur{Flux.RNNCell{typeof(tanh), CuArray{Float32, 2, CUDA.Mem.DeviceBuffer}, CuArray{Float32, 2, CUDA.Mem.DeviceBuffer}, CuArray{Float32, 1, CUDA.Mem.DeviceBuffer}, CuArray{Float32, 2, CUDA.Mem.DeviceBuffer}}, CuArray{Float32, 2, CUDA.Mem.DeviceBuffer}}}}, Nothing, false}}, Zygote.Pullback{Tuple{typeof(convert), Type{Vector{CuArray{Float32, 2, CUDA.Mem.DeviceBuffer}}}, Vector{CuArray{Float32, 2, CUDA.Mem.DeviceBuffer}}}, Any}}}}}, Zygote.var"#2193#back#313"{Zygote.Jnew{var"#113#114"{Flux.Recur{Flux.RNNCell{typeof(tanh), CuArray{Float32, 2, CUDA.Mem.DeviceBuffer}, CuArray{Float32, 2, CUDA.Mem.DeviceBuffer}, CuArray{Float32, 1, CUDA.Mem.DeviceBuffer}, CuArray{Float32, 2, CUDA.Mem.DeviceBuffer}}, CuArray{Float32, 2, CUDA.Mem.DeviceBuffer}}}, Nothing, false}}, Zygote.Pullback{Tuple{typeof(mse), CuArray{Float32, 2, CUDA.Mem.DeviceBuffer}, CuArray{Float32, 2, CUDA.Mem.DeviceBuffer}}, Tuple{Zygote.Pullback{Tuple{Flux.Losses.var"##mse#14", typeof(Statistics.mean), typeof(mse), CuArray{Float32, 2, CUDA.Mem.DeviceBuffer}, CuArray{Float32, 2, CUDA.Mem.DeviceBuffer}}, Tuple{Zygote.ZBack{ChainRules.var"#mean_pullback#1821"{Int64, ChainRules.var"#sum_pullback#1633"{Colon, CuArray{Float32, 2, CUDA.Mem.DeviceBuffer}, ChainRulesCore.ProjectTo{AbstractArray, NamedTuple{(:element, :axes), Tuple{ChainRulesCore.ProjectTo{Float32, NamedTuple{(), Tuple{}}}, Tuple{Base.OneTo{Int64}, Base.OneTo{Int64}}}}}}}}, Zygote.var"#3960#back#1287"{Zygote.var"#1283#1286"{CuArray{Float32, 2, CUDA.Mem.DeviceBuffer}}}, Zygote.var"#3752#back#1189"{Zygote.var"#1185#1188"{CuArray{Float32, 2, CUDA.Mem.DeviceBuffer}, CuArray{Float32, 2, CUDA.Mem.DeviceBuffer}}}, Zygote.ZBack{Flux.Losses.var"#_check_sizes_pullback#12"}, Zygote.Pullback{Tuple{typeof(Base.Broadcast.materialize), CuArray{Float32, 2, CUDA.Mem.DeviceBuffer}}, Tuple{}}}}}}, Zygote.var"#collect_pullback#704"{Zygote.var"#map_back#666"{var"#113#114"{Flux.Recur{Flux.RNNCell{typeof(tanh), CuArray{Float32, 2, CUDA.Mem.DeviceBuffer}, CuArray{Float32, 2, CUDA.Mem.DeviceBuffer}, CuArray{Float32, 1, CUDA.Mem.DeviceBuffer}, CuArray{Float32, 2, CUDA.Mem.DeviceBuffer}}, CuArray{Float32, 2, CUDA.Mem.DeviceBuffer}}}, 1, Tuple{Vector{CuArray{Float32, 2, CUDA.Mem.DeviceBuffer}}}, Tuple{Tuple{Base.OneTo{Int64}}}, Vector{Tuple{CuArray{Float32, 2, CUDA.Mem.DeviceBuffer}, Zygote.Pullback{Tuple{var"#113#114"{Flux.Recur{Flux.RNNCell{typeof(tanh), CuArray{Float32, 2, CUDA.Mem.DeviceBuffer}, CuArray{Float32, 2, CUDA.Mem.DeviceBuffer}, CuArray{Float32, 1, CUDA.Mem.DeviceBuffer}, CuArray{Float32, 2, CUDA.Mem.DeviceBuffer}}, CuArray{Float32, 2, CUDA.Mem.DeviceBuffer}}}, CuArray{Float32, 2, CUDA.Mem.DeviceBuffer}}, Tuple{Zygote.var"#2166#back#303"{Zygote.var"#back#302"{:m, Zygote.Context{false}, var"#113#114"{Flux.Recur{Flux.RNNCell{typeof(tanh), CuArray{Float32, 2, CUDA.Mem.DeviceBuffer}, CuArray{Float32, 2, CUDA.Mem.DeviceBuffer}, CuArray{Float32, 1, CUDA.Mem.DeviceBuffer}, CuArray{Float32, 2, CUDA.Mem.DeviceBuffer}}, CuArray{Float32, 2, CUDA.Mem.DeviceBuffer}}}, Flux.Recur{Flux.RNNCell{typeof(tanh), CuArray{Float32, 2, CUDA.Mem.DeviceBuffer}, CuArray{Float32, 2, CUDA.Mem.DeviceBuffer}, CuArray{Float32, 1, CUDA.Mem.DeviceBuffer}, CuArray{Float32, 2, CUDA.Mem.DeviceBuffer}}, CuArray{Float32, 2, CUDA.Mem.DeviceBuffer}}}}, Zygote.Pullback{Tuple{Flux.Recur{Flux.RNNCell{typeof(tanh), CuArray{Float32, 2, CUDA.Mem.DeviceBuffer}, CuArray{Float32, 2, CUDA.Mem.DeviceBuffer}, CuArray{Float32, 1, CUDA.Mem.DeviceBuffer}, CuArray{Float32, 2, CUDA.Mem.DeviceBuffer}}, CuArray{Float32, 2, CUDA.Mem.DeviceBuffer}}, CuArray{Float32, 2, CUDA.Mem.DeviceBuffer}}, Tuple{Zygote.var"#2166#back#303"{Zygote.var"#back#302"{:cell, Zygote.Context{false}, Flux.Recur{Flux.RNNCell{typeof(tanh), CuArray{Float32, 2, CUDA.Mem.DeviceBuffer}, CuArray{Float32, 2, CUDA.Mem.DeviceBuffer}, CuArray{Float32, 1, CUDA.Mem.DeviceBuffer}, CuArray{Float32, 2, CUDA.Mem.DeviceBuffer}}, CuArray{Float32, 2, CUDA.Mem.DeviceBuffer}}, Flux.RNNCell{typeof(tanh), CuArray{Float32, 2, CUDA.Mem.DeviceBuffer}, CuArray{Float32, 2, CUDA.Mem.DeviceBuffer}, CuArray{Float32, 1, CUDA.Mem.DeviceBuffer}, CuArray{Float32, 2, CUDA.Mem.DeviceBuffer}}}}, Zygote.var"#2015#back#213"{Zygote.var"#back#211"{2, 2, Zygote.Context{false}, Int64}}, Zygote.Pullback{Tuple{typeof(setproperty!), Flux.Recur{Flux.RNNCell{typeof(tanh), CuArray{Float32, 2, CUDA.Mem.DeviceBuffer}, CuArray{Float32, 2, CUDA.Mem.DeviceBuffer}, CuArray{Float32, 1, CUDA.Mem.DeviceBuffer}, CuArray{Float32, 2, CUDA.Mem.DeviceBuffer}}, CuArray{Float32, 2, CUDA.Mem.DeviceBuffer}}, Symbol, CuArray{Float32, 2, CUDA.Mem.DeviceBuffer}}, Tuple{Zygote.ZBack{ChainRules.var"#typeof_pullback#45"}, Zygote.Pullback{Tuple{typeof(convert), Type{CuArray{Float32, 2, CUDA.Mem.DeviceBuffer}}, CuArray{Float32, 2, CUDA.Mem.DeviceBuffer}}, Tuple{}}, Zygote.ZBack{ChainRules.var"#fieldtype_pullback#421"}, Zygote.var"#2182#back#311"{Zygote.var"#309#310"{Symbol, Base.RefValue{Any}}}}}, Zygote.var"#back#246"{Zygote.var"#2015#back#213"{Zygote.var"#back#211"{2, 2, Zygote.Context{false}, CuArray{Float32, 2, CUDA.Mem.DeviceBuffer}}}}, Zygote.var"#back#245"{Zygote.var"#2015#back#213"{Zygote.var"#back#211"{2, 1, Zygote.Context{false}, CuArray{Float32, 2, CUDA.Mem.DeviceBuffer}}}}, Zygote.Pullback{Tuple{Flux.RNNCell{typeof(tanh), CuArray{Float32, 2, CUDA.Mem.DeviceBuffer}, CuArray{Float32, 2, CUDA.Mem.DeviceBuffer}, CuArray{Float32, 1, CUDA.Mem.DeviceBuffer}, CuArray{Float32, 2, CUDA.Mem.DeviceBuffer}}, CuArray{Float32, 2, CUDA.Mem.DeviceBuffer}, CuArray{Float32, 2, CUDA.Mem.DeviceBuffer}}, Tuple{Zygote.ZBack{Flux.var"#174#175"}, Zygote.var"#3736#back#1181"{Zygote.var"#1175#1179"{Tuple{CuArray{Float32, 2, CUDA.Mem.DeviceBuffer}, CuArray{Float32, 1, 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    @ Zygote C:\Users\jerem\.julia\packages\Zygote\YYT6v\src\compiler\interface2.jl:0
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Zygote.Pullback{Tuple{typeof(Zygote.literal_getindex), Vector{CuArray{Float32, 2, CUDA.Mem.DeviceBuffer}}, Val{1}}, Tuple{Zygote.ZBack{ChainRules.var"#getindex_pullback#1581"{Vector{CuArray{Float32, 2, CUDA.Mem.DeviceBuffer}}, Tuple{Int64}, Tuple{ChainRulesCore.NoTangent}}}}}}}, Zygote.var"#1972#back#194"{Zygote.var"#190#193"{Zygote.Context{false}, GlobalRef, CuArray{Float32, 2, CUDA.Mem.DeviceBuffer}}}}}})(Δ::Float32)
    @ Zygote C:\Users\jerem\.julia\packages\Zygote\YYT6v\src\compiler\interface.jl:45
 [22] gradient(f::Function, args::Flux.Recur{Flux.RNNCell{typeof(tanh), CuArray{Float32, 2, CUDA.Mem.DeviceBuffer}, CuArray{Float32, 2, CUDA.Mem.DeviceBuffer}, CuArray{Float32, 1, CUDA.Mem.DeviceBuffer}, CuArray{Float32, 2, CUDA.Mem.DeviceBuffer}}, CuArray{Float32, 2, CUDA.Mem.DeviceBuffer}})
    @ Zygote C:\Users\jerem\.julia\packages\Zygote\YYT6v\src\compiler\interface.jl:97
 [23] top-level scope
    @ c:\Users\jerem\OneDrive\github\ADTests.jl\FluxRNNTest\rnn-gpu.jl:100

[avik-pal]

I don't know why exactly this is happening, but I had to push a temporary patch for this. See https://github.com/LuxDL/Lux.jl/pull/442/files

[ToucheSir]

@jeremiedb I meant that the stacktrace @kvantitative posted only had stack frames from CUDA.jl and not Zygote or ChainRules. It wasn't at all clear how higher-level libraries would've been responsible for a low-level illegal memory access error because of that.

The issue you found is FluxML/Zygote.jl#1470 (comment). If you have a better understanding of what's going on with GPU broadcast styles there or know someone who does, additional guidance would be much appreciated.

[anbjos]

I have attached the complete stack trace as a file.
stacktrace.txt

[nomadbl]

I commented on the issue @ToucheSir raised here FluxML/Zygote.jl#1470 (comment) but it does not bear directly on the RNN.
@jeremiedb 's second example works for me using JLArrays if I modify it slightly to

...
dev = m-> fmap(jl, m; exclude=Flux.Optimisers.maywrite) # move weights/inputs to "gpu"
x = tuple((rand(Float32, 2, 3) for i in 1:4)...) |> dev; # Tuple{JLArray}
...

So I think the RNN is fine, the issue is the problematic use of Vectors

I used a temp env with

ChainRulesCore v1.18.0
Flux v0.14.6
JLArrays v0.1.

[CarloLucibello]

the example in the OP works now