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Commit ec8711a
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Zygote CUDA broadcast bug
I'm not sure how to easily isolate this one. It seems deep in the braoa
```julia
using Flux, OptimizationFlux
using DiffEqFlux
using Test, NeuralPDE
using Optimization
using CUDA, QuasiMonteCarlo
import ModelingToolkit: Interval, infimum, supremum
using Random
Random.seed!(100)
@parameters t x
@variables u(..)
Dt = Differential(t)
Dxx = Differential(x)^2
eq = Dt(u(t, x)) ~ Dxx(u(t, x))
bcs = [u(0, x) ~ cos(x),
u(t, 0) ~ exp(-t),
u(t, 1) ~ exp(-t) * cos(1)]
domains = [t ∈ Interval(0.0, 1.0),
x ∈ Interval(0.0, 1.0)]
@nAmed pdesys = PDESystem(eq, bcs, domains, [t, x], [u(t, x)])
inner = 30
chain = FastChain(FastDense(2, inner, Flux.σ),
FastDense(inner, inner, Flux.σ),
FastDense(inner, inner, Flux.σ),
FastDense(inner, inner, Flux.σ),
FastDense(inner, inner, Flux.σ),
FastDense(inner, inner, Flux.σ),
FastDense(inner, 1))#,(u,p)->gpuones .* u)
strategy = NeuralPDE.StochasticTraining(500)
initθ = CuArray(Float64.(DiffEqFlux.initial_params(chain)))
discretization = NeuralPDE.PhysicsInformedNN(chain,
strategy;
init_params = initθ)
prob = NeuralPDE.discretize(pdesys, discretization)
symprob = NeuralPDE.symbolic_discretize(pdesys, discretization)
res = Optimization.solve(prob, ADAM(0.01); maxiters = 1000)
```
```
julia> res = Optimization.solve(prob, ADAM(0.01); maxiters = 1000)
ERROR: type Nothing has no field buffer
Stacktrace:
[1] getproperty(x::Nothing, f::Symbol)
@ Base .\Base.jl:38
[2] unsafe_convert
@ C:\Users\accou\.julia\packages\CUDA\tTK8Y\src\array.jl:321 [inlined]
[3] unsafe_convert
@ C:\Users\accou\.julia\packages\CUDA\tTK8Y\src\pointer.jl:62 [inlined]
[4] macro expansion
@ C:\Users\accou\.julia\packages\CUDA\tTK8Y\lib\cublas\libcublas.jl:1406 [inlined]
[5] macro expansion
@ C:\Users\accou\.julia\packages\CUDA\tTK8Y\src\pool.jl:232 [inlined]
[6] macro expansion
@ C:\Users\accou\.julia\packages\CUDA\tTK8Y\lib\cublas\error.jl:61 [inlined]
[7] cublasGemmEx(handle::Ptr{Nothing}, transa::Char, transb::Char, m::Int64, n::Int64, k::Int64, alpha::Base.RefValue{Float64}, A::CuArray{Float64, 2, CUDA.Mem.DeviceBuffer}, Atype::Type, lda::Int64, B::CuArray{Float64, 2, CUDA.Mem.DeviceBuffer}, Btype::Type, ldb::Int64, beta::Base.RefValue{Float64}, C::CuArray{Float64, 2, CUDA.Mem.DeviceBuffer}, Ctype::Type, ldc::Int64, computeType::CUDA.CUBLAS.cublasComputeType_t, algo::CUDA.CUBLAS.cublasGemmAlgo_t)
@ CUDA.CUBLAS C:\Users\accou\.julia\packages\CUDA\tTK8Y\lib\utils\call.jl:26
[8] gemmEx!(transA::Char, transB::Char, alpha::Number, A::StridedCuVecOrMat, B::StridedCuVecOrMat, beta::Number, C::StridedCuVecOrMat; algo::CUDA.CUBLAS.cublasGemmAlgo_t)
@ CUDA.CUBLAS C:\Users\accou\.julia\packages\CUDA\tTK8Y\lib\cublas\wrappers.jl:920
[9] gemmEx!
@ C:\Users\accou\.julia\packages\CUDA\tTK8Y\lib\cublas\wrappers.jl:895 [inlined]
[10] gemm_dispatch!(C::CuArray{Float64, 2, CUDA.Mem.DeviceBuffer}, A::CuArray{Float64, 2, CUDA.Mem.DeviceBuffer}, B::LinearAlgebra.Adjoint{Float64, CuArray{Float64, 2, CUDA.Mem.DeviceBuffer}}, alpha::Bool, beta::Bool)
@ CUDA.CUBLAS C:\Users\accou\.julia\packages\CUDA\tTK8Y\lib\cublas\linalg.jl:298
[11] mul!
@ C:\Users\accou\.julia\packages\CUDA\tTK8Y\lib\cublas\linalg.jl:321 [inlined]
[12] mul!
@ C:\Users\accou\.julia\juliaup\julia-1.8.0-rc1+0~x64\share\julia\stdlib\v1.8\LinearAlgebra\src\matmul.jl:276 [inlined]
[13] *
@ C:\Users\accou\.julia\juliaup\julia-1.8.0-rc1+0~x64\share\julia\stdlib\v1.8\LinearAlgebra\src\matmul.jl:148 [inlined]
[14] (::DiffEqFlux.var"#FastDense_adjoint#114"{FastDense{typeof(identity), DiffEqFlux.var"#initial_params#107"{Vector{Float32}}, Nothing}, CuArray{Float64, 2, CUDA.Mem.DeviceBuffer}})(ȳ::CuArray{Float64, 2, CUDA.Mem.DeviceBuffer})
@ DiffEqFlux C:\Users\accou\.julia\packages\DiffEqFlux\5e9D2\src\fast_layers.jl:278
[15] #252#back
@ C:\Users\accou\.julia\packages\ZygoteRules\AIbCs\src\adjoint.jl:67 [inlined]
[16] Pullback (repeats 2 times)
@ C:\Users\accou\.julia\packages\DiffEqFlux\5e9D2\src\fast_layers.jl:20 [inlined]
[17] (::typeof(∂(applychain)))(Δ::CuArray{Float64, 2, CUDA.Mem.DeviceBuffer})
@ Zygote C:\Users\accou\.julia\packages\Zygote\DkIUK\src\compiler\interface2.jl:0
--- the last 2 lines are repeated 5 more times ---
[28] Pullback
@ C:\Users\accou\.julia\packages\DiffEqFlux\5e9D2\src\fast_layers.jl:21 [inlined]
[29] Pullback
@ C:\Users\accou\.julia\dev\NeuralPDE\src\pinn_types.jl:228 [inlined]
[30] (::typeof(∂(λ)))(Δ::CuArray{Float64, 2, CUDA.Mem.DeviceBuffer})
@ Zygote C:\Users\accou\.julia\packages\Zygote\DkIUK\src\compiler\interface2.jl:0
[31] Pullback
@ C:\Users\accou\.julia\dev\NeuralPDE\src\pinn_types.jl:232 [inlined]
[32] (::typeof(∂(#12)))(Δ::CuArray{Float64, 2, CUDA.Mem.DeviceBuffer})
@ Zygote C:\Users\accou\.julia\packages\Zygote\DkIUK\src\compiler\interface2.jl:0
[33] Pullback
@ C:\Users\accou\.julia\dev\NeuralPDE\src\pinn_types.jl:261 [inlined]
[34] (::typeof(∂(numeric_derivative)))(Δ::CuArray{Float64, 2, CUDA.Mem.DeviceBuffer})
@ Zygote C:\Users\accou\.julia\packages\Zygote\DkIUK\src\compiler\interface2.jl:0
[35] macro expansion
@ C:\Users\accou\.julia\dev\NeuralPDE\src\discretize.jl:142 [inlined]
[36] macro expansion
@ C:\Users\accou\.julia\packages\RuntimeGeneratedFunctions\KrkGo\src\RuntimeGeneratedFunctions.jl:129 [inlined]
[37] macro expansion
@ .\none:0 [inlined]
[38] Pullback
@ .\none:0 [inlined]
[39] (::Zygote.var"#208#209"{Tuple{Tuple{Nothing}, NTuple{7, Nothing}}, typeof(∂(generated_callfunc))})(Δ::CuArray{Float64, 2, CUDA.Mem.DeviceBuffer})
@ Zygote C:\Users\accou\.julia\packages\Zygote\DkIUK\src\lib\lib.jl:207
[40] #1750#back
@ C:\Users\accou\.julia\packages\ZygoteRules\AIbCs\src\adjoint.jl:67 [inlined]
[41] Pullback
@ C:\Users\accou\.julia\packages\RuntimeGeneratedFunctions\KrkGo\src\RuntimeGeneratedFunctions.jl:117 [inlined]
[42] Pullback
@ C:\Users\accou\.julia\dev\NeuralPDE\src\discretize.jl:157 [inlined]
[43] (::typeof(∂(λ)))(Δ::CuArray{Float64, 2, CUDA.Mem.DeviceBuffer})
@ Zygote C:\Users\accou\.julia\packages\Zygote\DkIUK\src\compiler\interface2.jl:0
[44] Pullback
@ C:\Users\accou\.julia\dev\NeuralPDE\src\training_strategies.jl:89 [inlined]
[45] (::typeof(∂(λ)))(Δ::Float64)
@ Zygote C:\Users\accou\.julia\packages\Zygote\DkIUK\src\compiler\interface2.jl:0
[46] Pullback
@ .\none:0 [inlined]
[47] (::typeof(∂(λ)))(Δ::Float64)
@ Zygote C:\Users\accou\.julia\packages\Zygote\DkIUK\src\compiler\interface2.jl:0
[48] #546
@ C:\Users\accou\.julia\packages\Zygote\DkIUK\src\lib\array.jl:198 [inlined]
[49] #4
@ .\generator.jl:36 [inlined]
[50] iterate
@ .\generator.jl:47 [inlined]
[51] collect(itr::Base.Generator{Base.Iterators.Zip{Tuple{Vector{Tuple{Float64, typeof(∂(λ))}}, Vector{Float64}}}, Base.var"#4#5"{Zygote.var"#546#551"}})
@ Base .\array.jl:787
[52] map
@ .\abstractarray.jl:3053 [inlined]
[53] (::Zygote.var"#map_back#548"{NeuralPDE.var"#318#329"{CuArray{Float64, 1, CUDA.Mem.DeviceBuffer}}, 1, Tuple{Vector{NeuralPDE.var"#87#88"{NeuralPDE.var"#231#232"{RuntimeGeneratedFunctions.RuntimeGeneratedFunction{(:cord, Symbol("##θ#313"), :phi, :derivative, :integral, :u, :p), NeuralPDE.var"#_RGF_ModTag", NeuralPDE.var"#_RGF_ModTag", (0x26bb95dc, 0xdf5e9632, 0x817f3943, 0xb81f03ec, 0x97e64e44)}, NeuralPDE.var"#12#13", NeuralPDE.var"#297#304"{NeuralPDE.var"#297#298#305"{typeof(NeuralPDE.numeric_derivative)}, Dict{Symbol, Int64}, Dict{Symbol, Int64}, StochasticTraining}, typeof(NeuralPDE.numeric_derivative), NeuralPDE.Phi{FastChain{Tuple{FastDense{typeof(sigmoid_fast), DiffEqFlux.var"#initial_params#107"{Vector{Float32}}, Nothing}, FastDense{typeof(sigmoid_fast), DiffEqFlux.var"#initial_params#107"{Vector{Float32}}, Nothing}, FastDense{typeof(sigmoid_fast), DiffEqFlux.var"#initial_params#107"{Vector{Float32}}, Nothing}, FastDense{typeof(sigmoid_fast), DiffEqFlux.var"#initial_params#107"{Vector{Float32}}, Nothing}, FastDense{typeof(sigmoid_fast), DiffEqFlux.var"#initial_params#107"{Vector{Float32}}, Nothing}, FastDense{typeof(sigmoid_fast), DiffEqFlux.var"#initial_params#107"{Vector{Float32}}, Nothing}, FastDense{typeof(identity), DiffEqFlux.var"#initial_params#107"{Vector{Float32}}, Nothing}}}}, Nothing}, Vector{Any}, DataType, Int64}}}, Tuple{Tuple{Base.OneTo{Int64}}}, Vector{Tuple{Float64, typeof(∂(λ))}}})(Δ::Vector{Float64})
@ Zygote C:\Users\accou\.julia\packages\Zygote\DkIUK\src\lib\array.jl:198
[54] (::Zygote.var"#back#578"{Zygote.var"#map_back#548"{NeuralPDE.var"#318#329"{CuArray{Float64, 1, CUDA.Mem.DeviceBuffer}}, 1, Tuple{Vector{NeuralPDE.var"#87#88"{NeuralPDE.var"#231#232"{RuntimeGeneratedFunctions.RuntimeGeneratedFunction{(:cord, Symbol("##θ#313"), :phi, :derivative, :integral, :u, :p), NeuralPDE.var"#_RGF_ModTag", NeuralPDE.var"#_RGF_ModTag", (0x26bb95dc, 0xdf5e9632, 0x817f3943, 0xb81f03ec, 0x97e64e44)}, NeuralPDE.var"#12#13", NeuralPDE.var"#297#304"{NeuralPDE.var"#297#298#305"{typeof(NeuralPDE.numeric_derivative)}, Dict{Symbol, Int64}, Dict{Symbol, Int64}, StochasticTraining}, typeof(NeuralPDE.numeric_derivative), NeuralPDE.Phi{FastChain{Tuple{FastDense{typeof(sigmoid_fast), DiffEqFlux.var"#initial_params#107"{Vector{Float32}}, Nothing}, FastDense{typeof(sigmoid_fast), DiffEqFlux.var"#initial_params#107"{Vector{Float32}}, Nothing}, FastDense{typeof(sigmoid_fast), DiffEqFlux.var"#initial_params#107"{Vector{Float32}}, Nothing}, FastDense{typeof(sigmoid_fast), DiffEqFlux.var"#initial_params#107"{Vector{Float32}}, Nothing}, FastDense{typeof(sigmoid_fast), DiffEqFlux.var"#initial_params#107"{Vector{Float32}}, Nothing}, FastDense{typeof(sigmoid_fast), DiffEqFlux.var"#initial_params#107"{Vector{Float32}}, Nothing}, FastDense{typeof(identity), DiffEqFlux.var"#initial_params#107"{Vector{Float32}}, Nothing}}}}, Nothing}, Vector{Any}, DataType, Int64}}}, Tuple{Tuple{Base.OneTo{Int64}}}, Vector{Tuple{Float64, typeof(∂(λ))}}}})(ȳ::Vector{Float64})
@ Zygote C:\Users\accou\.julia\packages\Zygote\DkIUK\src\lib\array.jl:234
[55] Pullback
@ C:\Users\accou\.julia\dev\NeuralPDE\src\discretize.jl:445 [inlined]
[56] (::typeof(∂(λ)))(Δ::Float64)
@ Zygote C:\Users\accou\.julia\packages\Zygote\DkIUK\src\compiler\interface2.jl:0
[57] #208
@ C:\Users\accou\.julia\packages\Zygote\DkIUK\src\lib\lib.jl:207 [inlined]
[58] #1750#back
@ C:\Users\accou\.julia\packages\ZygoteRules\AIbCs\src\adjoint.jl:67 [inlined]
[59] Pullback
@ C:\Users\accou\.julia\dev\SciMLBase\src\scimlfunctions.jl:2887 [inlined]
[60] (::typeof(∂(λ)))(Δ::Float64)
@ Zygote C:\Users\accou\.julia\packages\Zygote\DkIUK\src\compiler\interface2.jl:0
[61] #208
@ C:\Users\accou\.julia\packages\Zygote\DkIUK\src\lib\lib.jl:207 [inlined]
[62] #1750#back
@ C:\Users\accou\.julia\packages\ZygoteRules\AIbCs\src\adjoint.jl:67 [inlined]
[63] Pullback
@ C:\Users\accou\.julia\dev\Optimization\src\function\zygote.jl:30 [inlined]
[64] (::typeof(∂(λ)))(Δ::Float64)
@ Zygote C:\Users\accou\.julia\packages\Zygote\DkIUK\src\compiler\interface2.jl:0
[65] #208
@ C:\Users\accou\.julia\packages\Zygote\DkIUK\src\lib\lib.jl:207 [inlined]
[66] #1750#back
@ C:\Users\accou\.julia\packages\ZygoteRules\AIbCs\src\adjoint.jl:67 [inlined]
[67] Pullback
@ C:\Users\accou\.julia\dev\Optimization\src\function\zygote.jl:32 [inlined]
[68] (::typeof(∂(λ)))(Δ::Float64)
@ Zygote C:\Users\accou\.julia\packages\Zygote\DkIUK\src\compiler\interface2.jl:0
[69] (::Zygote.var"#52#53"{typeof(∂(λ))})(Δ::Float64)
@ Zygote C:\Users\accou\.julia\packages\Zygote\DkIUK\src\compiler\interface.jl:41
[70] gradient(f::Function, args::CuArray{Float64, 1, CUDA.Mem.DeviceBuffer})
@ Zygote C:\Users\accou\.julia\packages\Zygote\DkIUK\src\compiler\interface.jl:76
[71] (::Optimization.var"#85#95"{Optimization.var"#84#94"{OptimizationFunction{true, Optimization.AutoZygote, NeuralPDE.var"#full_loss_function#328"{NeuralPDE.var"#null_nonadaptive_loss#127", Vector{NeuralPDE.var"#87#88"{loss_function, Vector{Any}, DataType, Int64} where loss_function}, Vector{NeuralPDE.var"#87#88"{NeuralPDE.var"#231#232"{RuntimeGeneratedFunctions.RuntimeGeneratedFunction{(:cord, Symbol("##θ#313"), :phi, :derivative, :integral, :u, :p), NeuralPDE.var"#_RGF_ModTag", NeuralPDE.var"#_RGF_ModTag", (0x26bb95dc, 0xdf5e9632, 0x817f3943, 0xb81f03ec, 0x97e64e44)}, NeuralPDE.var"#12#13", NeuralPDE.var"#297#304"{NeuralPDE.var"#297#298#305"{typeof(NeuralPDE.numeric_derivative)}, Dict{Symbol, Int64}, Dict{Symbol, Int64}, StochasticTraining}, typeof(NeuralPDE.numeric_derivative), NeuralPDE.Phi{FastChain{Tuple{FastDense{typeof(sigmoid_fast), DiffEqFlux.var"#initial_params#107"{Vector{Float32}}, Nothing}, FastDense{typeof(sigmoid_fast), DiffEqFlux.var"#initial_params#107"{Vector{Float32}}, Nothing}, FastDense{typeof(sigmoid_fast), DiffEqFlux.var"#initial_params#107"{Vector{Float32}}, Nothing}, FastDense{typeof(sigmoid_fast), DiffEqFlux.var"#initial_params#107"{Vector{Float32}}, Nothing}, FastDense{typeof(sigmoid_fast), DiffEqFlux.var"#initial_params#107"{Vector{Float32}}, Nothing}, FastDense{typeof(sigmoid_fast), DiffEqFlux.var"#initial_params#107"{Vector{Float32}}, Nothing}, FastDense{typeof(identity), DiffEqFlux.var"#initial_params#107"{Vector{Float32}}, Nothing}}}}, Nothing}, Vector{Any}, DataType, Int64}}, NeuralPDE.PINNRepresentation, Bool, Vector{Int64}, Int64, NeuralPDE.Phi{FastChain{Tuple{FastDense{typeof(sigmoid_fast), DiffEqFlux.var"#initial_params#107"{Vector{Float32}}, Nothing}, FastDense{typeof(sigmoid_fast), DiffEqFlux.var"#initial_params#107"{Vector{Float32}}, Nothing}, FastDense{typeof(sigmoid_fast), DiffEqFlux.var"#initial_params#107"{Vector{Float32}}, Nothing}, FastDense{typeof(sigmoid_fast), DiffEqFlux.var"#initial_params#107"{Vector{Float32}}, Nothing}, FastDense{typeof(sigmoid_fast), DiffEqFlux.var"#initial_params#107"{Vector{Float32}}, Nothing}, FastDense{typeof(sigmoid_fast), DiffEqFlux.var"#initial_params#107"{Vector{Float32}}, Nothing}, FastDense{typeof(identity), DiffEqFlux.var"#initial_params#107"{Vector{Float32}}, Nothing}}}}, NonAdaptiveLoss{Float64}, Nothing, Bool, Nothing}, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing}, SciMLBase.NullParameters}})(::CuArray{Float64, 1, CUDA.Mem.DeviceBuffer}, ::CuArray{Float64, 1, CUDA.Mem.DeviceBuffer})
@ Optimization C:\Users\accou\.julia\dev\Optimization\src\function\zygote.jl:32
[72] macro expansion
@ C:\Users\accou\.julia\packages\OptimizationFlux\cpWyO\src\OptimizationFlux.jl:32 [inlined]
[73] macro expansion
@ C:\Users\accou\.julia\dev\Optimization\src\utils.jl:35 [inlined]
[74] __solve(prob::OptimizationProblem{true, OptimizationFunction{true, Optimization.AutoZygote, NeuralPDE.var"#full_loss_function#328"{NeuralPDE.var"#null_nonadaptive_loss#127", Vector{NeuralPDE.var"#87#88"{loss_function, Vector{Any}, DataType, Int64} where loss_function}, Vector{NeuralPDE.var"#87#88"{NeuralPDE.var"#231#232"{RuntimeGeneratedFunctions.RuntimeGeneratedFunction{(:cord, Symbol("##θ#313"), :phi, :derivative, :integral, :u, :p), NeuralPDE.var"#_RGF_ModTag", NeuralPDE.var"#_RGF_ModTag", (0x26bb95dc, 0xdf5e9632, 0x817f3943, 0xb81f03ec, 0x97e64e44)}, NeuralPDE.var"#12#13", NeuralPDE.var"#297#304"{NeuralPDE.var"#297#298#305"{typeof(NeuralPDE.numeric_derivative)}, Dict{Symbol, Int64}, Dict{Symbol, Int64}, StochasticTraining}, typeof(NeuralPDE.numeric_derivative), NeuralPDE.Phi{FastChain{Tuple{FastDense{typeof(sigmoid_fast), DiffEqFlux.var"#initial_params#107"{Vector{Float32}}, Nothing}, FastDense{typeof(sigmoid_fast), DiffEqFlux.var"#initial_params#107"{Vector{Float32}}, Nothing}, FastDense{typeof(sigmoid_fast), DiffEqFlux.var"#initial_params#107"{Vector{Float32}}, Nothing}, FastDense{typeof(sigmoid_fast), DiffEqFlux.var"#initial_params#107"{Vector{Float32}}, Nothing}, FastDense{typeof(sigmoid_fast), DiffEqFlux.var"#initial_params#107"{Vector{Float32}}, Nothing}, FastDense{typeof(sigmoid_fast), DiffEqFlux.var"#initial_params#107"{Vector{Float32}}, Nothing}, FastDense{typeof(identity), DiffEqFlux.var"#initial_params#107"{Vector{Float32}}, Nothing}}}}, Nothing}, Vector{Any}, DataType, Int64}}, NeuralPDE.PINNRepresentation, Bool, Vector{Int64}, Int64, NeuralPDE.Phi{FastChain{Tuple{FastDense{typeof(sigmoid_fast), DiffEqFlux.var"#initial_params#107"{Vector{Float32}}, Nothing}, FastDense{typeof(sigmoid_fast), DiffEqFlux.var"#initial_params#107"{Vector{Float32}}, Nothing}, FastDense{typeof(sigmoid_fast), DiffEqFlux.var"#initial_params#107"{Vector{Float32}}, Nothing}, FastDense{typeof(sigmoid_fast), DiffEqFlux.var"#initial_params#107"{Vector{Float32}}, Nothing}, FastDense{typeof(sigmoid_fast), DiffEqFlux.var"#initial_params#107"{Vector{Float32}}, Nothing}, FastDense{typeof(sigmoid_fast), DiffEqFlux.var"#initial_params#107"{Vector{Float32}}, Nothing}, FastDense{typeof(identity), DiffEqFlux.var"#initial_params#107"{Vector{Float32}}, Nothing}}}}, NonAdaptiveLoss{Float64}, Nothing, Bool, Nothing}, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing}, CuArray{Float64, 1, CUDA.Mem.DeviceBuffer}, SciMLBase.NullParameters, Nothing, Nothing, Nothing, Nothing, Base.Pairs{Symbol, Union{}, Tuple{}, NamedTuple{(), Tuple{}}}}, opt::ADAM, data::Base.Iterators.Cycle{Tuple{Optimization.NullData}}; maxiters::Int64, callback::Function, progress::Bool, save_best::Bool, kwargs::Base.Pairs{Symbol, Union{}, Tuple{}, NamedTuple{(), Tuple{}}})
@ OptimizationFlux C:\Users\accou\.julia\packages\OptimizationFlux\cpWyO\src\OptimizationFlux.jl:30
[75] #solve#494
@ C:\Users\accou\.julia\dev\SciMLBase\src\solve.jl:71 [inlined]
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