Open
Description
Describe the bug 🐞
The Phi struct abstracts away the state used by Lux models by wrapping the model in a StatefulLuxLayer. Unfortunately, the initial state that's generated is stored on the CPU, and there's nothing that ever moves the state to the GPU when the parameters are there. (Unlike input data, which is moved to the same device as the parameters using safe_get_device.)
The result is that Lux models with a state that needs to be on the same device as the parameters fail during solve.
Expected behavior
The MRE below is from the NeuralPDE tests. The only changes are adding a BatchNorm layer into the model, and cutting off everything after the first call to solve (since that's where the error occurs).
One would expect this to train without erroring, but sadly it instead errors because BatchNorm requires the state and parameters be on the same device.
Minimal Reproducible Example 👇
using Lux, LuxCUDA, Optimization, OptimizationOptimisers, Random, ComponentArrays
import ModelingToolkit: Interval, infimum, supremum
using NeuralPDE
const gpud = gpu_device()
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 = Chain(
Dense(2, inner, σ), Dense(inner, inner, σ),
Dense(inner, inner, σ), Dense(inner, inner, σ),
BatchNorm(inner),
Dense(inner, inner, σ), Dense(inner, inner, σ), Dense(inner, 1)
)
strategy = StochasticTraining(500)
ps = Lux.initialparameters(Random.default_rng(), chain) |> ComponentArray |> gpud |> f64
discretization = PhysicsInformedNN(chain, strategy; init_params = ps)
prob = discretize(pdesys, discretization)
res = solve(prob, Adam(0.01); maxiters = 10)Error & Stacktrace
ERROR: ArgumentError: Objects are on devices with different types: CUDADevice and CPUDevice.
Stacktrace:
[1] combine_devices(T1::Type{CUDADevice}, T2::Type{CPUDevice})
@ MLDataDevices.Internal ~/.julia/packages/MLDataDevices/lMKtX/src/internal.jl:127
[2] macro expansion
@ ~/.julia/packages/MLDataDevices/lMKtX/src/internal.jl:231 [inlined]
[3] unrolled_mapreduce
@ ~/.julia/packages/MLDataDevices/lMKtX/src/internal.jl:218 [inlined]
[4] unrolled_mapreduce(f::typeof(get_device_type), op::typeof(MLDataDevices.Internal.combine_devices), itr::Tuple{…})
@ MLDataDevices.Internal ~/.julia/packages/MLDataDevices/lMKtX/src/internal.jl:209
[5] get_device_type(x::Tuple{CuArray{…}, CuArray{…}, CuArray{…}, Vector{…}, Vector{…}})
@ MLDataDevices.Internal ~/.julia/packages/MLDataDevices/lMKtX/src/internal.jl:186
[6] get_device_type(x::Tuple{CuArray{…}, CuArray{…}, CuArray{…}, Vector{…}, Vector{…}})
@ MLDataDevices ~/.julia/packages/MLDataDevices/lMKtX/src/public.jl:378
[7] internal_operation_mode(xs::Tuple{CuArray{…}, CuArray{…}, CuArray{…}, Vector{…}, Vector{…}})
@ LuxLib ~/.julia/packages/LuxLib/Kj0os/src/traits.jl:195
[8] select_fastest_activation(::typeof(identity), ::CuArray{…}, ::CuArray{…}, ::CuArray{…}, ::Vararg{…})
@ LuxLib.Impl ~/.julia/packages/LuxLib/Kj0os/src/impl/activation.jl:144
[9] rrule(::typeof(LuxLib.Impl.select_fastest_activation), ::Function, ::CuArray{…}, ::CuArray{…}, ::CuArray{…}, ::Vector{…}, ::Vector{…})
@ LuxLib.Impl ~/.julia/packages/LuxLib/Kj0os/src/impl/activation.jl:155
[10] rrule(::Zygote.ZygoteRuleConfig{…}, ::Function, ::Function, ::CuArray{…}, ::CuArray{…}, ::CuArray{…}, ::Vector{…}, ::Vector{…})
@ ChainRulesCore ~/.julia/packages/ChainRulesCore/U6wNx/src/rules.jl:138
[11] chain_rrule
@ ~/.julia/packages/Zygote/1GK3J/src/compiler/chainrules.jl:224 [inlined]
[12] macro expansion
@ ~/.julia/packages/Zygote/1GK3J/src/compiler/interface2.jl:0 [inlined]
[13] _pullback(::Zygote.Context{…}, ::typeof(LuxLib.Impl.select_fastest_activation), ::typeof(identity), ::CuArray{…}, ::CuArray{…}, ::CuArray{…}, ::Vector{…}, ::Vector{…})
@ Zygote ~/.julia/packages/Zygote/1GK3J/src/compiler/interface2.jl:91
[14] batchnorm
@ ~/.julia/packages/LuxLib/Kj0os/src/api/batchnorm.jl:42 [inlined]
[15] _pullback(::Zygote.Context{…}, ::typeof(batchnorm), ::CuArray{…}, ::CuArray{…}, ::CuArray{…}, ::Vector{…}, ::Vector{…}, ::Val{…}, ::typeof(identity), ::Float64, ::Float64)
@ Zygote ~/.julia/packages/Zygote/1GK3J/src/compiler/interface2.jl:0
[16] BatchNorm
@ ~/.julia/packages/Lux/L2VO7/src/layers/normalize.jl:146 [inlined]
[17] _pullback(::Zygote.Context{…}, ::BatchNorm{…}, ::CuArray{…}, ::ComponentVector{…}, ::@NamedTuple{…})
@ Zygote ~/.julia/packages/Zygote/1GK3J/src/compiler/interface2.jl:0
[18] apply
@ ~/.julia/packages/LuxCore/Av7WJ/src/LuxCore.jl:155 [inlined]
[19] _pullback(::Zygote.Context{…}, ::typeof(LuxCore.apply), ::BatchNorm{…}, ::CuArray{…}, ::ComponentVector{…}, ::@NamedTuple{…})
@ Zygote ~/.julia/packages/Zygote/1GK3J/src/compiler/interface2.jl:0
[20] applychain
@ ~/.julia/packages/Lux/L2VO7/src/layers/containers.jl:0 [inlined]
[21] _pullback(::Zygote.Context{…}, ::typeof(Lux.applychain), ::@NamedTuple{…}, ::CuArray{…}, ::ComponentVector{…}, ::@NamedTuple{…})
@ Zygote ~/.julia/packages/Zygote/1GK3J/src/compiler/interface2.jl:0
[22] Chain
@ ~/.julia/packages/Lux/L2VO7/src/layers/containers.jl:509 [inlined]
[23] _pullback(::Zygote.Context{…}, ::Chain{…}, ::CuArray{…}, ::ComponentVector{…}, ::@NamedTuple{…})
@ Zygote ~/.julia/packages/Zygote/1GK3J/src/compiler/interface2.jl:0
[24] apply
@ ~/.julia/packages/LuxCore/Av7WJ/src/LuxCore.jl:155 [inlined]
[25] _pullback(::Zygote.Context{…}, ::typeof(LuxCore.apply), ::Chain{…}, ::CuArray{…}, ::ComponentVector{…}, ::@NamedTuple{…})
@ Zygote ~/.julia/packages/Zygote/1GK3J/src/compiler/interface2.jl:0
[26] StatefulLuxLayer
@ ~/.julia/packages/Lux/L2VO7/src/helpers/stateful.jl:123 [inlined]
[27] _pullback(::Zygote.Context{…}, ::StatefulLuxLayer{…}, ::CuArray{…}, ::ComponentVector{…})
@ Zygote ~/.julia/packages/Zygote/1GK3J/src/compiler/interface2.jl:0
[28] Phi
@ ~/.julia/packages/NeuralPDE/nkWKK/src/pinn_types.jl:42 [inlined]
[29] _pullback(::Zygote.Context{…}, ::NeuralPDE.Phi{…}, ::CuArray{…}, ::ComponentVector{…})
@ Zygote ~/.julia/packages/Zygote/1GK3J/src/compiler/interface2.jl:0
[30] #7
@ ~/.julia/packages/NeuralPDE/nkWKK/src/pinn_types.jl:354 [inlined]
[31] _pullback(::Zygote.Context{…}, ::NeuralPDE.var"#7#8", ::CuArray{…}, ::ComponentVector{…}, ::NeuralPDE.Phi{…})
@ Zygote ~/.julia/packages/Zygote/1GK3J/src/compiler/interface2.jl:0
[32] numeric_derivative
@ ~/.julia/packages/NeuralPDE/nkWKK/src/pinn_types.jl:384 [inlined]
[33] _pullback(::Zygote.Context{…}, ::typeof(NeuralPDE.numeric_derivative), ::NeuralPDE.Phi{…}, ::NeuralPDE.var"#7#8", ::CuArray{…}, ::Vector{…}, ::Int64, ::ComponentVector{…})
@ Zygote ~/.julia/packages/Zygote/1GK3J/src/compiler/interface2.jl:0
[34] generated_callfunc
@ ~/.julia/packages/NeuralPDE/nkWKK/src/discretize.jl:130 [inlined]
[35] _pullback(::Zygote.Context{…}, ::typeof(RuntimeGeneratedFunctions.generated_callfunc), ::RuntimeGeneratedFunctions.RuntimeGeneratedFunction{…}, ::CuArray{…}, ::ComponentVector{…}, ::NeuralPDE.Phi{…}, ::typeof(NeuralPDE.numeric_derivative), ::NeuralPDE.var"#284#291"{…}, ::NeuralPDE.var"#7#8", ::Nothing)
@ Zygote ~/.julia/packages/Zygote/1GK3J/src/compiler/interface2.jl:0
[36] _apply(::Function, ::Vararg{Any})
@ Core ./boot.jl:946
[37] adjoint
@ ~/.julia/packages/Zygote/1GK3J/src/lib/lib.jl:202 [inlined]
[38] _pullback
@ ~/.julia/packages/ZygoteRules/CkVIK/src/adjoint.jl:67 [inlined]
[39] RuntimeGeneratedFunction
@ ~/.julia/packages/RuntimeGeneratedFunctions/RrXEW/src/RuntimeGeneratedFunctions.jl:148 [inlined]
[40] _pullback(::Zygote.Context{…}, ::RuntimeGeneratedFunctions.RuntimeGeneratedFunction{…}, ::CuArray{…}, ::ComponentVector{…}, ::NeuralPDE.Phi{…}, ::typeof(NeuralPDE.numeric_derivative), ::NeuralPDE.var"#284#291"{…}, ::NeuralPDE.var"#7#8", ::Nothing)
@ Zygote ~/.julia/packages/Zygote/1GK3J/src/compiler/interface2.jl:0
[41] #242
@ ~/.julia/packages/NeuralPDE/nkWKK/src/discretize.jl:150 [inlined]
[42] _pullback(::Zygote.Context{…}, ::NeuralPDE.var"#242#243"{…}, ::CuArray{…}, ::ComponentVector{…})
@ Zygote ~/.julia/packages/Zygote/1GK3J/src/compiler/interface2.jl:0
[43] #94
@ ~/.julia/packages/NeuralPDE/nkWKK/src/training_strategies.jl:191 [inlined]
[44] _pullback(ctx::Zygote.Context{…}, f::NeuralPDE.var"#94#95"{…}, args::ComponentVector{…})
@ Zygote ~/.julia/packages/Zygote/1GK3J/src/compiler/interface2.jl:0
[45] #308
@ ./none:0 [inlined]
[46] _pullback(ctx::Zygote.Context{…}, f::NeuralPDE.var"#308#329"{…}, args::NeuralPDE.var"#94#95"{…})
@ Zygote ~/.julia/packages/Zygote/1GK3J/src/compiler/interface2.jl:0
[47] #667
@ ~/.julia/packages/Zygote/1GK3J/src/lib/array.jl:188 [inlined]
[48] iterate
@ ./generator.jl:48 [inlined]
[49] _collect(c::Vector{…}, itr::Base.Generator{…}, ::Base.EltypeUnknown, isz::Base.HasShape{…})
@ Base ./array.jl:811
[50] collect_similar
@ ./array.jl:720 [inlined]
[51] map
@ ./abstractarray.jl:3371 [inlined]
[52] ∇map(cx::Zygote.Context{false}, f::NeuralPDE.var"#308#329"{ComponentVector{…}}, args::Vector{NeuralPDE.var"#94#95"{…}})
@ Zygote ~/.julia/packages/Zygote/1GK3J/src/lib/array.jl:188
[53] _pullback(cx::Zygote.Context{false}, ::typeof(collect), g::Base.Generator{Vector{…}, NeuralPDE.var"#308#329"{…}})
@ Zygote ~/.julia/packages/Zygote/1GK3J/src/lib/array.jl:231
[54] full_loss_function
@ ~/.julia/packages/NeuralPDE/nkWKK/src/discretize.jl:462 [inlined]
[55] _pullback(::Zygote.Context{…}, ::NeuralPDE.var"#full_loss_function#328"{…}, ::ComponentVector{…}, ::SciMLBase.NullParameters)
@ Zygote ~/.julia/packages/Zygote/1GK3J/src/compiler/interface2.jl:0
[56] pullback(::Function, ::Zygote.Context{false}, ::ComponentVector{Float64, CuArray{…}, Tuple{…}}, ::Vararg{Any})
@ Zygote ~/.julia/packages/Zygote/1GK3J/src/compiler/interface.jl:90
[57] pullback(::Function, ::ComponentVector{Float64, CuArray{…}, Tuple{…}}, ::SciMLBase.NullParameters)
@ Zygote ~/.julia/packages/Zygote/1GK3J/src/compiler/interface.jl:88
[58] withgradient(::Function, ::ComponentVector{Float64, CuArray{…}, Tuple{…}}, ::Vararg{Any})
@ Zygote ~/.julia/packages/Zygote/1GK3J/src/compiler/interface.jl:205
[59] value_and_gradient
@ ~/.julia/packages/DifferentiationInterface/Yk2Kt/ext/DifferentiationInterfaceZygoteExt/DifferentiationInterfaceZygoteExt.jl:115 [inlined]
[60] value_and_gradient!(f::Function, grad::ComponentVector{…}, prep::DifferentiationInterface.NoGradientPrep{…}, backend::AutoZygote, x::ComponentVector{…}, contexts::DifferentiationInterface.Constant{…})
@ DifferentiationInterfaceZygoteExt ~/.julia/packages/DifferentiationInterface/Yk2Kt/ext/DifferentiationInterfaceZygoteExt/DifferentiationInterfaceZygoteExt.jl:131
[61] (::OptimizationZygoteExt.var"#fg!#16"{…})(res::ComponentVector{…}, θ::ComponentVector{…})
@ OptimizationZygoteExt ~/.julia/packages/OptimizationBase/UXLhR/ext/OptimizationZygoteExt.jl:53
[62] macro expansion
@ ~/.julia/packages/OptimizationOptimisers/xC7Ic/src/OptimizationOptimisers.jl:101 [inlined]
[63] macro expansion
@ ~/.julia/packages/Optimization/ZeSb8/src/utils.jl:32 [inlined]
[64] __solve(cache::OptimizationCache{…})
@ OptimizationOptimisers ~/.julia/packages/OptimizationOptimisers/xC7Ic/src/OptimizationOptimisers.jl:83
[65] solve!(cache::OptimizationCache{…})
@ SciMLBase ~/.julia/packages/SciMLBase/7BHQj/src/solve.jl:187
[66] solve(::OptimizationProblem{…}, ::Adam{…}; kwargs::@Kwargs{…})
@ SciMLBase ~/.julia/packages/SciMLBase/7BHQj/src/solve.jl:95Environment (please complete the following information):
- Output of
using Pkg; Pkg.status()
Status `~/julia_projects/NeuralPDEtests/Project.toml`
[b0b7db55] ComponentArrays v0.15.26
[b2108857] Lux v1.12.4
[d0bbae9a] LuxCUDA v0.3.3
[961ee093] ModelingToolkit v9.76.0
[315f7962] NeuralPDE v5.18.1
[7f7a1694] Optimization v4.2.0
[42dfb2eb] OptimizationOptimisers v0.3.7
[9a3f8284] Random v1.11.0- Output of
using Pkg; Pkg.status(; mode = PKGMODE_MANIFEST)
Status `~/julia_projects/NeuralPDEtests/Manifest.toml`
[47edcb42] ADTypes v1.14.0
[621f4979] AbstractFFTs v1.5.0
[80f14c24] AbstractMCMC v5.6.0
[1520ce14] AbstractTrees v0.4.5
[7d9f7c33] Accessors v0.1.42
[79e6a3ab] Adapt v4.3.0
[0bf59076] AdvancedHMC v0.7.1
[66dad0bd] AliasTables v1.1.3
[dce04be8] ArgCheck v2.5.0
[ec485272] ArnoldiMethod v0.4.0
[4fba245c] ArrayInterface v7.18.0
[4c555306] ArrayLayouts v1.11.1
[a9b6321e] Atomix v1.1.1
[13072b0f] AxisAlgorithms v1.1.0
[39de3d68] AxisArrays v0.4.7
[ab4f0b2a] BFloat16s v0.5.1
[198e06fe] BangBang v0.4.4
[9718e550] Baselet v0.1.1
[e2ed5e7c] Bijections v0.1.9
[62783981] BitTwiddlingConvenienceFunctions v0.1.6
[8e7c35d0] BlockArrays v1.6.3
[70df07ce] BracketingNonlinearSolve v1.2.0
[fa961155] CEnum v0.5.0
[2a0fbf3d] CPUSummary v0.2.6
[052768ef] CUDA v5.7.3
[1af6417a] CUDA_Runtime_Discovery v0.3.5
[082447d4] ChainRules v1.72.3
[d360d2e6] ChainRulesCore v1.25.1
[fb6a15b2] CloseOpenIntervals v0.1.13
[3da002f7] ColorTypes v0.12.1
[5ae59095] Colors v0.13.0
[861a8166] Combinatorics v1.0.2
[a80b9123] CommonMark v0.9.1
[38540f10] CommonSolve v0.2.4
[bbf7d656] CommonSubexpressions v0.3.1
[f70d9fcc] CommonWorldInvalidations v1.0.0
[34da2185] Compat v4.16.0
[b0b7db55] ComponentArrays v0.15.26
[b152e2b5] CompositeTypes v0.1.4
[a33af91c] CompositionsBase v0.1.2
[2569d6c7] ConcreteStructs v0.2.3
[88cd18e8] ConsoleProgressMonitor v0.1.2
[187b0558] ConstructionBase v1.5.8
[adafc99b] CpuId v0.3.1
[a8cc5b0e] Crayons v4.1.1
[667455a9] Cubature v1.5.1
[9a962f9c] DataAPI v1.16.0
[a93c6f00] DataFrames v1.7.0
[864edb3b] DataStructures v0.18.22
[e2d170a0] DataValueInterfaces v1.0.0
[244e2a9f] DefineSingletons v0.1.2
[8bb1440f] DelimitedFiles v1.9.1
[2b5f629d] DiffEqBase v6.170.1
[459566f4] DiffEqCallbacks v4.4.1
[77a26b50] DiffEqNoiseProcess v5.24.1
[163ba53b] DiffResults v1.1.0
[b552c78f] DiffRules v1.15.1
[a0c0ee7d] DifferentiationInterface v0.6.52
[8d63f2c5] DispatchDoctor v0.4.19
[31c24e10] Distributions v0.25.119
[ffbed154] DocStringExtensions v0.9.4
[5b8099bc] DomainSets v0.7.15
[7c1d4256] DynamicPolynomials v0.6.1
[06fc5a27] DynamicQuantities v1.8.0
[4e289a0a] EnumX v1.0.5
[f151be2c] EnzymeCore v0.8.8
[e2ba6199] ExprTools v0.1.10
[55351af7] ExproniconLite v0.10.14
[7a1cc6ca] FFTW v1.8.1
[7034ab61] FastBroadcast v0.3.5
[9aa1b823] FastClosures v0.3.2
[a4df4552] FastPower v1.1.2
[1a297f60] FillArrays v1.13.0
[64ca27bc] FindFirstFunctions v1.4.1
[6a86dc24] FiniteDiff v2.27.0
[53c48c17] FixedPointNumbers v0.8.5
[1fa38f19] Format v1.3.7
⌅ [f6369f11] ForwardDiff v0.10.38
[069b7b12] FunctionWrappers v1.1.3
[77dc65aa] FunctionWrappersWrappers v0.1.3
[d9f16b24] Functors v0.5.2
[0c68f7d7] GPUArrays v11.2.2
[46192b85] GPUArraysCore v0.2.0
[61eb1bfa] GPUCompiler v1.4.0
[096a3bc2] GPUToolbox v0.2.0
[c145ed77] GenericSchur v0.5.5
[c27321d9] Glob v1.3.1
[86223c79] Graphs v1.12.1
[19dc6840] HCubature v1.7.0
[076d061b] HashArrayMappedTries v0.2.0
[3e5b6fbb] HostCPUFeatures v0.1.17
[34004b35] HypergeometricFunctions v0.3.28
[7869d1d1] IRTools v0.4.14
[615f187c] IfElse v0.1.1
[d25df0c9] Inflate v0.1.5
[22cec73e] InitialValues v0.3.1
[842dd82b] InlineStrings v1.4.3
[18e54dd8] IntegerMathUtils v0.1.2
[de52edbc] Integrals v4.5.0
[a98d9a8b] Interpolations v0.15.1
[8197267c] IntervalSets v0.7.11
[3587e190] InverseFunctions v0.1.17
[41ab1584] InvertedIndices v1.3.1
[92d709cd] IrrationalConstants v0.2.4
[c8e1da08] IterTools v1.10.0
[82899510] IteratorInterfaceExtensions v1.0.0
[692b3bcd] JLLWrappers v1.7.0
[ae98c720] Jieko v0.2.1
[98e50ef6] JuliaFormatter v2.1.2
⌅ [70703baa] JuliaSyntax v0.4.10
[ccbc3e58] JumpProcesses v9.14.3
[63c18a36] KernelAbstractions v0.9.34
[5ab0869b] KernelDensity v0.6.9
[ba0b0d4f] Krylov v0.10.0
[5be7bae1] LBFGSB v0.4.1
[929cbde3] LLVM v9.3.1
[8b046642] LLVMLoopInfo v1.0.0
[b964fa9f] LaTeXStrings v1.4.0
[23fbe1c1] Latexify v0.16.7
[73f95e8e] LatticeRules v0.0.1
[10f19ff3] LayoutPointers v0.1.17
[5078a376] LazyArrays v2.6.1
[1d6d02ad] LeftChildRightSiblingTrees v0.2.0
[87fe0de2] LineSearch v0.1.4
[d3d80556] LineSearches v7.3.0
[7ed4a6bd] LinearSolve v3.9.0
[6fdf6af0] LogDensityProblems v2.1.2
[996a588d] LogDensityProblemsAD v1.13.0
[2ab3a3ac] LogExpFunctions v0.3.29
[e6f89c97] LoggingExtras v1.1.0
[b2108857] Lux v1.12.4
[d0bbae9a] LuxCUDA v0.3.3
[bb33d45b] LuxCore v1.2.4
[82251201] LuxLib v1.7.3
[c7f686f2] MCMCChains v6.0.7
[be115224] MCMCDiagnosticTools v0.3.14
[7e8f7934] MLDataDevices v1.9.1
[e80e1ace] MLJModelInterface v1.11.1
[d8e11817] MLStyle v0.4.17
[1914dd2f] MacroTools v0.5.16
[d125e4d3] ManualMemory v0.1.8
[bb5d69b7] MaybeInplace v0.1.4
[128add7d] MicroCollections v0.2.0
[e1d29d7a] Missings v1.2.0
[961ee093] ModelingToolkit v9.76.0
[4886b29c] MonteCarloIntegration v0.2.0
[0987c9cc] MonteCarloMeasurements v1.4.5
[2e0e35c7] Moshi v0.3.5
[46d2c3a1] MuladdMacro v0.2.4
[102ac46a] MultivariatePolynomials v0.5.7
[d8a4904e] MutableArithmetics v1.6.4
[d41bc354] NLSolversBase v7.9.1
[872c559c] NNlib v0.9.30
[5da4648a] NVTX v1.0.0
[77ba4419] NaNMath v1.1.3
[c020b1a1] NaturalSort v1.0.0
[ea5c82af] NeuralOperators v0.5.3
[315f7962] NeuralPDE v5.18.1
[8913a72c] NonlinearSolve v4.8.0
[be0214bd] NonlinearSolveBase v1.6.0
[5959db7a] NonlinearSolveFirstOrder v1.4.0
[9a2c21bd] NonlinearSolveQuasiNewton v1.3.0
[26075421] NonlinearSolveSpectralMethods v1.2.0
[6fe1bfb0] OffsetArrays v1.17.0
[429524aa] Optim v1.12.0
[3bd65402] Optimisers v0.4.6
[7f7a1694] Optimization v4.2.0
[bca83a33] OptimizationBase v2.5.0
[42dfb2eb] OptimizationOptimisers v0.3.7
[bac558e1] OrderedCollections v1.8.0
[90014a1f] PDMats v0.11.34
[d96e819e] Parameters v0.12.3
[e409e4f3] PoissonRandom v0.4.4
[f517fe37] Polyester v0.7.16
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⌅ [aea7be01] PrecompileTools v1.2.1
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[731186ca] RecursiveArrayTools v3.33.0
[189a3867] Reexport v1.2.2
[ae029012] Requires v1.3.1
[ae5879a3] ResettableStacks v1.1.1
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[efcf1570] Setfield v1.1.2
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[699a6c99] SimpleTraits v0.9.4
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Info Packages marked with ⌃ and ⌅ have new versions available. Those with ⌃ may be upgradable, but those with ⌅ are restricted by compatibility constraints from upgrading. To see why use `status --outdated -m`- Output of
versioninfo()
Julia Version 1.11.3
Commit d63adeda50d (2025-01-21 19:42 UTC)
Build Info:
Official https://julialang.org/ release
Platform Info:
OS: Linux (x86_64-linux-gnu)
CPU: 20 × 12th Gen Intel(R) Core(TM) i9-12900H
WORD_SIZE: 64
LLVM: libLLVM-16.0.6 (ORCJIT, alderlake)
Threads: 20 default, 0 interactive, 10 GC (on 20 virtual cores)
Environment:
JULIA_NUM_THREADS = auto
JULIA_EDITOR = codeAdditional context
This probably hasn't shown up as an issue yet because the most common Lux layers (e.g., Dense), don't use the state (it's an empty NamedTuple). I'm hoping to use a Boltz.Layers.PeriodicEmbedding to handle periodic boundary conditions, and that stores a list of which inputs are periodic in the state and a list of the periods. Those lists are stored in the state specifically so that they can be on the same device as the parameters. This issue means that PeriodicEmbedding can't be used with NeuralPDE for periodic boundary conditions (when you want to use a GPU), even though that's what it was made for.