refactor: correctly lower quadrature training strategy in NNODE

Project.toml

@@ -44,7 +44,7 @@ AdvancedHMC = "0.6.1"
 Aqua = "0.8"
 ArrayInterface = "7.7"
 CUDA = "5.2"
-ChainRulesCore = "1.18"
+ChainRulesCore = "1.21"
 ComponentArrays = "0.15.8"
 Cubature = "1.5"
 DiffEqBase = "6.144"
@@ -59,7 +59,7 @@ Integrals = "4"
 LineSearches = "7.2"
 LinearAlgebra = "1"
 LogDensityProblems = "2"
-Lux = "0.5.14"
+Lux = "0.5.22"
 LuxCUDA = "0.3.2"
 MCMCChains = "6"
 MethodOfLines = "0.10.7"
@@ -82,7 +82,7 @@ SymbolicUtils = "1.4"
 Symbolics = "5.17"
 Test = "1"
 UnPack = "1"
-Zygote = "0.6.68"
+Zygote = "0.6.69"
 julia = "1.10"
 
 [extras]
@@ -91,12 +91,12 @@ CUDA = "052768ef-5323-5732-b1bb-66c8b64840ba"
 Flux = "587475ba-b771-5e3f-ad9e-33799f191a9c"
 LineSearches = "d3d80556-e9d4-5f37-9878-2ab0fcc64255"
 LuxCUDA = "d0bbae9a-e099-4d5b-a835-1c6931763bda"
+MethodOfLines = "94925ecb-adb7-4558-8ed8-f975c56a0bf4"
 OptimizationOptimJL = "36348300-93cb-4f02-beb5-3c3902f8871e"
 OrdinaryDiffEq = "1dea7af3-3e70-54e6-95c3-0bf5283fa5ed"
 Pkg = "44cfe95a-1eb2-52ea-b672-e2afdf69b78f"
 SafeTestsets = "1bc83da4-3b8d-516f-aca4-4fe02f6d838f"
 Test = "8dfed614-e22c-5e08-85e1-65c5234f0b40"
-MethodOfLines = "94925ecb-adb7-4558-8ed8-f975c56a0bf4"
 
 [targets]
 test = ["Aqua", "Test", "CUDA", "SafeTestsets", "OptimizationOptimJL", "Pkg", "OrdinaryDiffEq", "LineSearches", "LuxCUDA", "Flux", "MethodOfLines"]

docs/src/tutorials/neural_adapter.md

@@ -69,7 +69,7 @@ function loss(cord, θ)
  ch2 .- phi(cord, res.u)
 end
 
-strategy = NeuralPDE.QuadratureTraining()
+strategy = NeuralPDE.QuadratureTraining(; reltol = 1e-6)
 
 prob_ = NeuralPDE.neural_adapter(loss, init_params2, pde_system, strategy)
 res_ = Optimization.solve(prob_, OptimizationOptimisers.Adam(5e-3); maxiters = 10000)
@@ -173,7 +173,7 @@ for i in 1:count_decomp
  bcs_ = create_bcs(domains_[1].domain, phi_bound)
  @named pde_system_ = PDESystem(eq, bcs_, domains_, [x, y], [u(x, y)])
  push!(pde_system_map, pde_system_)
- strategy = NeuralPDE.QuadratureTraining()
+ strategy = NeuralPDE.QuadratureTraining(; reltol = 1e-6)
 
  discretization = NeuralPDE.PhysicsInformedNN(chains[i], strategy;
  init_params = init_params[i])
@@ -243,10 +243,10 @@ callback = function (p, l)
 end
 
 prob_ = NeuralPDE.neural_adapter(losses, init_params2, pde_system_map,
- NeuralPDE.QuadratureTraining())
+ NeuralPDE.QuadratureTraining(; reltol = 1e-6))
 res_ = Optimization.solve(prob_, OptimizationOptimisers.Adam(5e-3); maxiters = 5000)
 prob_ = NeuralPDE.neural_adapter(losses, res_.u, pde_system_map,
- NeuralPDE.QuadratureTraining())
+ NeuralPDE.QuadratureTraining(; reltol = 1e-6))
 res_ = Optimization.solve(prob_, OptimizationOptimisers.Adam(5e-3); maxiters = 5000)
 
 phi_ = PhysicsInformedNN(chain2, strategy; init_params = res_.u).phi

src/BPINN_ode.jl

@@ -113,7 +113,7 @@ function BNNODE(chain, Kernel = HMC; strategy = nothing, draw_samples = 2000,
  targetacceptancerate = 0.8),
  Integratorkwargs = (Integrator = Leapfrog,),
  autodiff = false, progress = false, verbose = false)
- !(chain isa Lux.AbstractExplicitLayer) && (chain = Lux.transform(chain))
+ !(chain isa Lux.AbstractExplicitLayer) && (chain = adapt(FromFluxAdaptor(false, false), chain))
  BNNODE(chain, Kernel, strategy,
  draw_samples, priorsNNw, param, l2std,
  phystd, dataset, physdt, MCMCkwargs,

src/NeuralPDE.jl

@@ -30,6 +30,7 @@ using DomainSets: Domain, ClosedInterval, AbstractInterval, leftendpoint, righte
 using SciMLBase: @add_kwonly, parameterless_type
 using UnPack: @unpack
 import ChainRulesCore, Lux, ComponentArrays
+using Lux: FromFluxAdaptor
 using ChainRulesCore: @non_differentiable
 
 RuntimeGeneratedFunctions.init(@__MODULE__)

src/advancedHMC_MCMC.jl

@@ -439,7 +439,7 @@ function ahmc_bayesian_pinn_ode(prob::DiffEqBase.ODEProblem, chain;
  MCMCkwargs = (n_leapfrog = 30,),
  progress = false, verbose = false)
 
- !(chain isa Lux.AbstractExplicitLayer) && (chain = Lux.transform(chain))
+ !(chain isa Lux.AbstractExplicitLayer) && (chain = adapt(FromFluxAdaptor(false, false), chain))
  # NN parameter prior mean and variance(PriorsNN must be a tuple)
  if isinplace(prob)
  throw(error("The BPINN ODE solver only supports out-of-place ODE definitions, i.e. du=f(u,p,t)."))

src/dae_solve.jl

@@ -42,7 +42,7 @@ end
 
 function NNDAE(chain, opt, init_params = nothing; strategy = nothing, autodiff = false,
  kwargs...)
- !(chain isa Lux.AbstractExplicitLayer) && (chain = Lux.transform(chain))
+ !(chain isa Lux.AbstractExplicitLayer) && (chain = adapt(FromFluxAdaptor(false, false), chain))
  NNDAE(chain, opt, init_params, autodiff, strategy, kwargs)
 end
 

src/ode_solve.jl

@@ -15,7 +15,7 @@ of the physics-informed neural network which is used as a solver for a standard
 ## Positional Arguments
 
 * `chain`: A neural network architecture, defined as a `Lux.AbstractExplicitLayer` or `Flux.Chain`. 
- `Flux.Chain` will be converted to `Lux` using `Lux.transform`.
+ `Flux.Chain` will be converted to `Lux` using `adapt(FromFluxAdaptor(false, false), chain)`.
 * `opt`: The optimizer to train the neural network.
 * `init_params`: The initial parameter of the neural network. By default, this is `nothing` 
  which thus uses the random initialization provided by the neural network library.
@@ -27,11 +27,10 @@ of the physics-informed neural network which is used as a solver for a standard
  the PDE operators. The reverse mode of the loss function is always
  automatic differentiation (via Zygote), this is only for the derivative
  in the loss function (the derivative with respect to time).
-* `batch`: The batch size to use for the internal quadrature. Defaults to `0`, which
- means the application of the neural network is done at individual time points one
- at a time. `batch>0` means the neural network is applied at a row vector of values
- `t` simultaneously, i.e. it's the batch size for the neural network evaluations.
- This requires a neural network compatible with batched data.
+* `batch`: The batch size for the loss computation. Defaults to `true`, means the neural network is applied at a row vector of values
+ `t` simultaneously, i.e. it's the batch size for the neural network evaluations. This requires a neural network compatible with batched data. 
+ `false` means which means the application of the neural network is done at individual time points one at a time. 
+ This is not applicable to `QuadratureTraining` where `batch` is passed in the `strategy` which is the number of points it can parallelly compute the integrand.
 * `param_estim`: Boolean to indicate whether parameters of the differential equations are learnt along with parameters of the neural network.
 * `strategy`: The training strategy used to choose the points for the evaluations.
  Default of `nothing` means that `QuadratureTraining` with QuadGK is used if no
@@ -88,8 +87,8 @@ struct NNODE{C, O, P, B, PE, K, AL <: Union{Nothing, Function},
 end
 function NNODE(chain, opt, init_params = nothing;
  strategy = nothing,
- autodiff = false, batch = nothing, param_estim = false, additional_loss = nothing, kwargs...)
- !(chain isa Lux.AbstractExplicitLayer) && (chain = Lux.transform(chain))
+ autodiff = false, batch = true, param_estim = false, additional_loss = nothing, kwargs...)
+ !(chain isa Lux.AbstractExplicitLayer) && (chain = adapt(FromFluxAdaptor(false, false), chain))
  NNODE(chain, opt, init_params, autodiff, batch, strategy, param_estim, additional_loss, kwargs)
 end
 
@@ -111,11 +110,7 @@ end
 
 function generate_phi_θ(chain::Lux.AbstractExplicitLayer, t, u0, init_params)
  θ, st = Lux.setup(Random.default_rng(), chain)
- if init_params === nothing
- init_params = ComponentArrays.ComponentArray(θ)
- else
- init_params = ComponentArrays.ComponentArray(init_params)
- end
+ isnothing(init_params) && (init_params = θ)
  ODEPhi(chain, t, u0, st), init_params
 end
 
@@ -182,7 +177,7 @@ function ode_dfdx(phi::ODEPhi, t::AbstractVector, θ, autodiff::Bool)
 end
 
 """
- inner_loss(phi, f, autodiff, t, θ, p)
+ inner_loss(phi, f, autodiff, t, θ, p, param_estim)
 
 Simple L2 inner loss at a time `t` with parameters `θ` of the neural network.
 """
@@ -220,7 +215,7 @@ function inner_loss(phi::ODEPhi{C, T, U}, f, autodiff::Bool, t::AbstractVector,
 end
 
 """
- generate_loss(strategy, phi, f, autodiff, tspan, p, batch)
+ generate_loss(strategy, phi, f, autodiff, tspan, p, batch, param_estim)
 
 Representation of the loss function, parametric on the training strategy `strategy`.
 """
@@ -229,14 +224,13 @@ function generate_loss(strategy::QuadratureTraining, phi, f, autodiff::Bool, tsp
  integrand(t::Number, θ) = abs2(inner_loss(phi, f, autodiff, t, θ, p, param_estim))
 
  integrand(ts, θ) = [abs2(inner_loss(phi, f, autodiff, t, θ, p, param_estim)) for t in ts]
- @assert batch == 0 # not implemented
 
  function loss(θ, _)
- intprob = IntegralProblem(integrand, (tspan[1], tspan[2]), θ)
- sol = solve(intprob, QuadGKJL(); abstol = strategy.abstol, reltol = strategy.reltol)
+ intf = BatchIntegralFunction(integrand, max_batch = strategy.batch)
+ intprob = IntegralProblem(intf, (tspan[1], tspan[2]), θ)
+ sol = solve(intprob, strategy.quadrature_alg; abstol = strategy.abstol, reltol = strategy.reltol, maxiters = strategy.maxiters)
  sol.u
  end
-
  return loss
 end
 
@@ -395,16 +389,7 @@ function DiffEqBase.__solve(prob::DiffEqBase.AbstractODEProblem,
  alg.strategy
  end
 
- batch = if alg.batch === nothing
- if strategy isa QuadratureTraining
- strategy.batch
- else
- true
- end
- else
- alg.batch
- end
-
+ batch = alg.batch
  inner_f = generate_loss(strategy, phi, f, autodiff, tspan, p, batch, param_estim)
  additional_loss = alg.additional_loss
  (param_estim && isnothing(additional_loss)) && throw(ArgumentError("Please provide `additional_loss` in `NNODE` for parameter estimation (`param_estim` is true)."))

src/pinn_types.jl

@@ -48,7 +48,7 @@ methodology.
 * `chain`: a vector of Lux/Flux chains with a d-dimensional input and a
  1-dimensional output corresponding to each of the dependent variables. Note that this
  specification respects the order of the dependent variables as specified in the PDESystem.
- Flux chains will be converted to Lux internally using `Lux.transform`.
+ Flux chains will be converted to Lux internally using `adapt(FromFluxAdaptor(false, false), chain)`.
 * `strategy`: determines which training strategy will be used. See the Training Strategy
  documentation for more details.
 
@@ -107,7 +107,7 @@ struct PhysicsInformedNN{T, P, PH, DER, PE, AL, ADA, LOG, K} <: AbstractPINN
  if multioutput
  !all(i -> i isa Lux.AbstractExplicitLayer, chain) && (chain = Lux.transform.(chain))
  else
- !(chain isa Lux.AbstractExplicitLayer) && (chain = Lux.transform(chain))
+ !(chain isa Lux.AbstractExplicitLayer) && (chain = adapt(FromFluxAdaptor(false, false), chain))
  end
  if phi === nothing
  if multioutput
@@ -243,7 +243,7 @@ struct BayesianPINN{T, P, PH, DER, PE, AL, ADA, LOG, D, K} <: AbstractPINN
  if multioutput
  !all(i -> i isa Lux.AbstractExplicitLayer, chain) && (chain = Lux.transform.(chain))
  else
- !(chain isa Lux.AbstractExplicitLayer) && (chain = Lux.transform(chain))
+ !(chain isa Lux.AbstractExplicitLayer) && (chain = adapt(FromFluxAdaptor(false, false), chain))
  end
  if phi === nothing
  if multioutput

src/training_strategies.jl

@@ -272,7 +272,7 @@ struct QuadratureTraining{Q <: SciMLBase.AbstractIntegralAlgorithm, T} <:
  batch::Int64
 end
 
-function QuadratureTraining(; quadrature_alg = CubatureJLh(), reltol = 1e-6, abstol = 1e-3,
+function QuadratureTraining(; quadrature_alg = CubatureJLh(), reltol = 1e-3, abstol = 1e-6,
  maxiters = 1_000, batch = 100)
  QuadratureTraining(quadrature_alg, reltol, abstol, maxiters, batch)
 end
@@ -306,11 +306,7 @@ function get_loss_function(loss_function, lb, ub, eltypeθ, strategy::Quadrature
  end
  area = eltypeθ(prod(abs.(ub .- lb)))
  f_ = (lb, ub, loss_, θ) -> begin
- # last_x = 1
  function integrand(x, θ)
- # last_x = x
- # mean(abs2,loss_(x,θ), dims=2)
- # size_x = fill(size(x)[2],(1,1))
  x = adapt(parameterless_type(ComponentArrays.getdata(θ)), x)
  sum(abs2, view(loss_(x, θ), 1, :), dims = 2) #./ size_x
  end