fix!: update to Lux & Boltz 1.0

.buildkite/pipeline.yml

@@ -18,7 +18,7 @@ steps:
  RETESTITEMS_NWORKERS: 1 # These tests require quite a lot of GPU memory
  GROUP: CUDA
  DATADEPS_ALWAYS_ACCEPT: 'true'
- JULIA_PKG_SERVER: "" # it often struggles with our large artifacts
+ JULIA_PKG_SERVER: ""
 
  - label: "Documentation"
  plugins:
@@ -42,5 +42,6 @@ steps:
  DATADEPS_ALWAYS_ACCEPT: true
  JULIA_DEBUG: "Documenter"
  SECRET_DOCUMENTER_KEY: "AdqcYtp4x3U5j1ELurHIoOwURqXcOan+qmihqVjsjhoGUzS/snTyZNQ5fxaJr8Yawm9CyyGvh+Q5O98St1LJ9S+pi9C5TFSbPWnNp/vXabMmeUEVLHVYHUeR2wgMCciSnM/oLw5sNAEj3hrWFjLslEGKQSptUCTWuU5WRizhQONDxeA3tz9biZUYvKanP8GjsHUkD3te15n1t6o78T1+EJxb1znrBSd9aK1Y4UaVjBEfVtLtTD8Z6VP1L4SVXVipxrDdzwzbzUDaTpvjo3z3e9qx2u6Xn5qa/os6JY81jRa5ZTWFkev73DYhoFmordSI85grOPwNpvrNWqOAs5kTDg==;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"
+ JULIA_PKG_SERVER: ""
  if: build.message !~ /\[skip docs\]/ && !build.pull_request.draft
  timeout_in_minutes: 1000

.github/workflows/CI.yml

@@ -42,8 +42,6 @@ jobs:
  ${{ runner.os }}-
  - uses: julia-actions/julia-buildpkg@v1
  - uses: julia-actions/julia-runtest@v1
- with:
- coverage: false
  env:
  GROUP: ${{ matrix.group }}
  - uses: julia-actions/julia-processcoverage@v1

Project.toml

@@ -1,7 +1,7 @@
 name = "DiffEqFlux"
 uuid = "aae7a2af-3d4f-5e19-a356-7da93b79d9d0"
 authors = ["Chris Rackauckas <[email protected]>"]
-version = "3.6.0"
+version = "4.0.0"
 
 [deps]
 ADTypes = "47edcb42-4c32-4615-8424-f2b9edc5f35b"
@@ -18,6 +18,7 @@ Reexport = "189a3867-3050-52da-a836-e630ba90ab69"
 SciMLBase = "0bca4576-84f4-4d90-8ffe-ffa030f20462"
 SciMLSensitivity = "1ed8b502-d754-442c-8d5d-10ac956f44a1"
 Setfield = "efcf1570-3423-57d1-acb7-fd33fddbac46"
+Static = "aedffcd0-7271-4cad-89d0-dc628f76c6d3"
 
 [weakdeps]
 DataInterpolations = "82cc6244-b520-54b8-b5a6-8a565e85f1d0"
@@ -29,7 +30,7 @@ DiffEqFluxDataInterpolationsExt = "DataInterpolations"
 ADTypes = "1.5"
 Aqua = "0.8.7"
 BenchmarkTools = "1.5.0"
-Boltz = "0.4.2"
+Boltz = "1"
 ChainRulesCore = "1"
 ComponentArrays = "0.15.17"
 ConcreteStructs = "0.2"
@@ -46,11 +47,10 @@ ForwardDiff = "0.10"
 Hwloc = "3"
 InteractiveUtils = "<0.0.1, 1"
 LinearAlgebra = "1.10"
-Lux = "0.5.65"
+Lux = "1"
 LuxCUDA = "0.3.2"
-LuxCore = "0.1"
-LuxLib = "0.3.50"
-MLDatasets = "0.7.14"
+LuxCore = "1"
+LuxLib = "1.2"
 NNlib = "0.9.22"
 OneHotArrays = "0.2.5"
 Optimisers = "0.3"
@@ -65,6 +65,7 @@ Reexport = "0.2, 1"
 SciMLBase = "2"
 SciMLSensitivity = "7"
 Setfield = "1.1.1"
+Static = "1.1.1"
 Statistics = "1.10"
 StochasticDiffEq = "6.68.0"
 Test = "1.10"
@@ -87,7 +88,6 @@ ForwardDiff = "f6369f11-7733-5829-9624-2563aa707210"
 Hwloc = "0e44f5e4-bd66-52a0-8798-143a42290a1d"
 InteractiveUtils = "b77e0a4c-d291-57a0-90e8-8db25a27a240"
 LuxCUDA = "d0bbae9a-e099-4d5b-a835-1c6931763bda"
-MLDatasets = "eb30cadb-4394-5ae3-aed4-317e484a6458"
 NNlib = "872c559c-99b0-510c-b3b7-b6c96a88d5cd"
 OneHotArrays = "0b1bfda6-eb8a-41d2-88d8-f5af5cad476f"
 Optimisers = "3bd65402-5787-11e9-1adc-39752487f4e2"
@@ -105,4 +105,4 @@ Test = "8dfed614-e22c-5e08-85e1-65c5234f0b40"
 Zygote = "e88e6eb3-aa80-5325-afca-941959d7151f"
 
 [targets]
-test = ["Aqua", "BenchmarkTools", "ComponentArrays", "DataInterpolations", "DelayDiffEq", "DiffEqCallbacks", "Distances", "Distributed", "DistributionsAD", "ExplicitImports", "ForwardDiff", "Flux", "Hwloc", "InteractiveUtils", "LuxCUDA", "MLDatasets", "NNlib", "OneHotArrays", "Optimisers", "Optimization", "OptimizationOptimJL", "OptimizationOptimisers", "OrdinaryDiffEq", "Printf", "Random", "ReTestItems", "Reexport", "Statistics", "StochasticDiffEq", "Test", "Zygote"]
+test = ["Aqua", "BenchmarkTools", "ComponentArrays", "DataInterpolations", "DelayDiffEq", "DiffEqCallbacks", "Distances", "Distributed", "DistributionsAD", "ExplicitImports", "ForwardDiff", "Flux", "Hwloc", "InteractiveUtils", "LuxCUDA", "NNlib", "OneHotArrays", "Optimisers", "Optimization", "OptimizationOptimJL", "OptimizationOptimisers", "OrdinaryDiffEq", "Printf", "Random", "ReTestItems", "Reexport", "Statistics", "StochasticDiffEq", "Test", "Zygote"]

README.md

@@ -63,17 +63,18 @@ explore various ways to integrate the two methodologies:
 
 ## Breaking Changes
 
-### v4 (upcoming)
+### v4
 
  - `TensorLayer` has been removed, use `Boltz.Layers.TensorProductLayer` instead.
  - Basis functions in DiffEqFlux have been removed in favor of `Boltz.Basis` module.
  - `SplineLayer` has been removed, use `Boltz.Layers.SplineLayer` instead.
  - `NeuralHamiltonianDE` has been removed, use `NeuralODE` with `Layers.HamiltonianNN` instead.
  - `HamiltonianNN` has been removed in favor of `Layers.HamiltonianNN`.
+ - `Lux` and `Boltz` are updated to v1.
 
 ### v3
 
- - Flux dependency is dropped. If a non Lux `AbstractExplicitLayer` is passed we try to automatically convert it to a Lux model with `FromFluxAdaptor()(model)`.
+ - Flux dependency is dropped. If a non Lux `AbstractLuxLayer` is passed we try to automatically convert it to a Lux model with `FromFluxAdaptor()(model)`.
  - `Flux` is no longer re-exported from `DiffEqFlux`. Instead we reexport `Lux`.
  - `NeuralDAE` now allows an optional `du0` as input.
  - `TensorLayer` is now a Lux Neural Network.

docs/Project.toml

@@ -42,17 +42,17 @@ CUDA = "5"
 ComponentArrays = "0.15"
 DataDeps = "0.7"
 DataFrames = "1"
-DiffEqFlux = "3"
+DiffEqFlux = "4"
 Distances = "0.10.7"
 Distributions = "0.25.78"
 Documenter = "1"
 Flux = "0.14"
 ForwardDiff = "0.10"
 IterTools = "1"
 LinearAlgebra = "1"
-Lux = "0.5.5"
+Lux = "1"
 LuxCUDA = "0.3"
-MLDatasets = "0.7"
+MLDatasets = "0.7.18"
 MLUtils = "0.4"
 NNlib = "0.9"
 OneHotArrays = "0.2"

docs/src/examples/hamiltonian_nn.md

@@ -33,7 +33,7 @@ dataloader = ncycle(
  for i in 1:(size(data, 2) ÷ B)),
  NEPOCHS)
 
-hnn = HamiltonianNN(Chain(Dense(2 => 64, relu), Dense(64 => 1)); ad = AutoZygote())
+hnn = Layers.HamiltonianNN{true}(Layers.MLP(2, (64, 1)); autodiff = AutoZygote())
 ps, st = Lux.setup(Xoshiro(0), hnn)
 ps_c = ps |> ComponentArray
 
@@ -57,7 +57,7 @@ res = Optimization.solve(opt_prob, opt, dataloader; callback)
 
 ps_trained = res.u
 
-model = NeuralHamiltonianDE(
+model = NeuralODE(
  hnn, (0.0f0, 1.0f0), Tsit5(); save_everystep = false, save_start = true, saveat = t)
 
 pred = Array(first(model(data[:, 1], ps_trained, st)))
@@ -97,10 +97,10 @@ dataloader = ncycle(
 
 ### Training the HamiltonianNN
 
-We parameterize the HamiltonianNN with a small MultiLayered Perceptron. HNNs are trained by optimizing the gradients of the Neural Network. Zygote currently doesn't support nesting itself, so we will be using ForwardDiff in the training loop to compute the gradients of the HNN Layer for Optimization.
+We parameterize the with a small MultiLayered Perceptron. HNNs are trained by optimizing the gradients of the Neural Network. Zygote currently doesn't support nesting itself, so we will be using ForwardDiff in the training loop to compute the gradients of the HNN Layer for Optimization.
 
 ```@example hamiltonian
-hnn = HamiltonianNN(Chain(Dense(2 => 64, relu), Dense(64 => 1)); ad = AutoZygote())
+hnn = Layers.HamiltonianNN{true}(Layers.MLP(2, (64, 1)); autodiff = AutoZygote())
 ps, st = Lux.setup(Xoshiro(0), hnn)
 ps_c = ps |> ComponentArray
 
@@ -127,10 +127,11 @@ ps_trained = res.u
 
 ### Solving the ODE using trained HNN
 
-In order to visualize the learned trajectories, we need to solve the ODE. We will use the `NeuralHamiltonianDE` layer, which is essentially a wrapper over `HamiltonianNN` layer, and solves the ODE.
+In order to visualize the learned trajectories, we need to solve the ODE. We will use the
+`NeuralODE` layer with `HamiltonianNN` layer, and solves the ODE.
 
 ```@example hamiltonian
-model = NeuralHamiltonianDE(
+model = NeuralODE(
  hnn, (0.0f0, 1.0f0), Tsit5(); save_everystep = false, save_start = true, saveat = t)
 
 pred = Array(first(model(data[:, 1], ps_trained, st)))

docs/src/examples/neural_gde.md

@@ -14,7 +14,7 @@ using GraphNeuralNetworks, DifferentialEquations
 using DiffEqFlux: NeuralODE
 using GraphNeuralNetworks.GNNGraphs: normalized_adjacency
 using Lux, NNlib, Optimisers, Zygote, Random, ComponentArrays
-using Lux: AbstractExplicitLayer, glorot_normal, zeros32
+using Lux: AbstractLuxLayer, glorot_normal, zeros32
 import Lux: initialparameters, initialstates
 using SciMLSensitivity
 using Statistics: mean
@@ -46,7 +46,7 @@ nout = length(classes)
 epochs = 20
 
 # Define the graph neural network
-struct ExplicitGCNConv{F1, F2, F3, F4} <: AbstractExplicitLayer
+struct ExplicitGCNConv{F1, F2, F3, F4} <: AbstractLuxLayer
  in_chs::Int
  out_chs::Int
  activation::F1
@@ -152,7 +152,7 @@ using GraphNeuralNetworks, DifferentialEquations
 using DiffEqFlux: NeuralODE
 using GraphNeuralNetworks.GNNGraphs: normalized_adjacency
 using Lux, NNlib, Optimisers, Zygote, Random, ComponentArrays
-using Lux: AbstractExplicitLayer, glorot_normal, zeros32
+using Lux: AbstractLuxLayer, glorot_normal, zeros32
 import Lux: initialparameters, initialstates
 using SciMLSensitivity
 using Statistics: mean
@@ -207,10 +207,10 @@ epochs = 20
 
 ## Define the Graph Neural Network
 
-Here, we define a type of graph neural networks called `GCNConv`. We use the name `ExplicitGCNConv` to avoid naming conflicts with `GraphNeuralNetworks`. For more information on defining a layer with `Lux`, please consult to the [doc](http://lux.csail.mit.edu/dev/introduction/overview/#AbstractExplicitLayer-API).
+Here, we define a type of graph neural networks called `GCNConv`. We use the name `ExplicitGCNConv` to avoid naming conflicts with `GraphNeuralNetworks`. For more information on defining a layer with `Lux`, please consult to the [doc](http://lux.csail.mit.edu/dev/introduction/overview/#AbstractLuxLayer-API).
 
 ```julia
-struct ExplicitGCNConv{F1, F2, F3} <: AbstractExplicitLayer
+struct ExplicitGCNConv{F1, F2, F3} <: AbstractLuxLayer
  Ã::AbstractMatrix # nomalized_adjacency matrix
  in_chs::Int
  out_chs::Int

docs/src/examples/tensor_layer.md

@@ -33,8 +33,8 @@ Now, we create a TensorLayer that will be able to perform 10th order expansions
 a Legendre Basis:
 
 ```@example tensor
-A = [LegendreBasis(10), LegendreBasis(10)]
-nn = TensorLayer(A, 1)
+A = [Basis.Legendre(10), Basis.Legendre(10)]
+nn = Layers.TensorProductLayer(A, 1)
 ps, st = Lux.setup(Xoshiro(0), nn)
 ps = ComponentArray(ps)
 nn = StatefulLuxLayer{true}(nn, nothing, st)

src/DiffEqFlux.jl

@@ -6,7 +6,7 @@ using ConcreteStructs: @concrete
 using Distributions: Distributions, ContinuousMultivariateDistribution, Distribution, logpdf
 using LinearAlgebra: LinearAlgebra, Diagonal, det, tr, mul!
 using Lux: Lux, Chain, Dense, StatefulLuxLayer, FromFluxAdaptor
-using LuxCore: LuxCore, AbstractExplicitLayer, AbstractExplicitContainerLayer
+using LuxCore: LuxCore, AbstractLuxLayer, AbstractLuxContainerLayer, AbstractLuxWrapperLayer
 using LuxLib: batched_matmul
 using Random: Random, AbstractRNG, randn!
 using Reexport: @reexport
@@ -20,22 +20,22 @@ using SciMLSensitivity: SciMLSensitivity, AdjointLSS, BacksolveAdjoint, EnzymeVJ
  SteadyStateAdjoint, TrackerAdjoint, TrackerVJP, ZygoteAdjoint,
  ZygoteVJP
 using Setfield: @set!
+using Static: True, False
 
 const CRC = ChainRulesCore
 
 @reexport using ADTypes, Lux, Boltz
 
 fixed_state_type(_) = true
-fixed_state_type(::Layers.HamiltonianNN{FST}) where {FST} = FST
+fixed_state_type(::Layers.HamiltonianNN{True}) = true
+fixed_state_type(::Layers.HamiltonianNN{False}) = false
 
 include("ffjord.jl")
 include("neural_de.jl")
 
 include("collocation.jl")
 include("multiple_shooting.jl")
 
-include("deprecated.jl")
-
 export NeuralODE, NeuralDSDE, NeuralSDE, NeuralCDDE, NeuralDAE, AugmentedNDELayer,
  NeuralODEMM
 export FFJORD, FFJORDDistribution

src/ffjord.jl

@@ -1,4 +1,4 @@
-abstract type CNFLayer <: LuxCore.AbstractExplicitContainerLayer{(:model,)} end
+abstract type CNFLayer <: AbstractLuxWrapperLayer{:model} end
 
 """
  FFJORD(model, tspan, input_dims, args...; ad = nothing, basedist = nothing, kwargs...)
@@ -21,7 +21,7 @@ for new values of x.
 
 Arguments:
 
- - `model`: A `Flux.Chain` or `Lux.AbstractExplicitLayer` neural network that defines the
+ - `model`: A `Flux.Chain` or `Lux.AbstractLuxLayer` neural network that defines the
  dynamics of the model.
  - `basedist`: Distribution of the base variable. Set to the unit normal by default.
  - `input_dims`: Input Dimensions of the model.
@@ -49,7 +49,7 @@ Information Processing Systems, pp. 6572-6583. 2018.
 preprint arXiv:1810.01367 (2018).
 """
 @concrete struct FFJORD <: CNFLayer
- model <: AbstractExplicitLayer
+ model <: AbstractLuxLayer
  basedist <: Union{Nothing, Distribution}
  ad
  input_dims
@@ -65,7 +65,7 @@ end
 
 function FFJORD(
  model, tspan, input_dims, args...; ad = nothing, basedist = nothing, kwargs...)
- !(model isa AbstractExplicitLayer) && (model = FromFluxAdaptor()(model))
+ !(model isa AbstractLuxLayer) && (model = FromFluxAdaptor()(model))
  return FFJORD(model, basedist, ad, input_dims, tspan, args, kwargs)
 end