Merge branch 'master' into compathelper/new_version/2024-09-20-16-09-35-712-03414958508

Author: ChrisRackauckas

Project.toml

@@ -4,8 +4,8 @@ authors = ["Christopher Rackauckas <[email protected]>", "Yingbo Ma <
 version = "7.67.0"
 
 [deps]
-Accessors = "7d9f7c33-5ae7-4f3b-8dc6-eff91059b697"
 ADTypes = "47edcb42-4c32-4615-8424-f2b9edc5f35b"
+Accessors = "7d9f7c33-5ae7-4f3b-8dc6-eff91059b697"
 Adapt = "79e6a3ab-5dfb-504d-930d-738a2a938a0e"
 ArrayInterface = "4fba245c-0d91-5ea0-9b3e-6abc04ee57a9"
 ChainRulesCore = "d360d2e6-b24c-11e9-a2a3-2a2ae2dbcce4"
@@ -45,8 +45,8 @@ TruncatedStacktraces = "781d530d-4396-4725-bb49-402e4bee1e77"
 Zygote = "e88e6eb3-aa80-5325-afca-941959d7151f"
 
 [compat]
-Accessors = "0.1.36"
 ADTypes = "0.1, 0.2, 1"
+Accessors = "0.1.36"
 Adapt = "1.0, 2.0, 3.0, 4"
 AlgebraicMultigrid = "0.6.0"
 Aqua = "0.8.4"
@@ -61,7 +61,7 @@ DiffEqNoiseProcess = "5.19"
 Distributed = "1"
 Distributions = "0.25"
 EllipsisNotation = "1"
-Enzyme = "0.12"
+Enzyme = "0.12, 0.13"
 FiniteDiff = "2"
 ForwardDiff = "0.10"
 FunctionProperties = "0.1"

docs/Project.toml

@@ -41,15 +41,15 @@ DiffEqCallbacks = "2.24, 3"
 DiffEqNoiseProcess = "5.14"
 DifferentialEquations = "7"
 Documenter = "1"
-Enzyme = "0.12"
+Enzyme = "0.12, 0.13"
 Flux = "0.14"
 ForwardDiff = "0.10"
 IterTools = "1"
 Lux = "0.5.7, 1"
 LuxCUDA = "0.3"
 Optimization = "3.9, 4"
-OptimizationOptimJL = "0.2, 0.3"
-OptimizationOptimisers = "0.2"
+OptimizationOptimJL = "0.2, 0.3, 0.4"
+OptimizationOptimisers = "0.2, 0.3"
 OptimizationPolyalgorithms = "0.2"
 OrdinaryDiffEq = "6.31"
 Plots = "1.36"

test/gpu/Project.toml

@@ -7,5 +7,5 @@ LuxCUDA = "d0bbae9a-e099-4d5b-a835-1c6931763bda"
 [compat]
 CUDA = "3.12, 4, 5"
 DiffEqCallbacks = "2.24, 3"
-DiffEqFlux = "3"
+DiffEqFlux = "3, 4"
 LuxCUDA = "0.3.1"

test/gpu/mixed_gpu_cpu_adjoint.jl

@@ -1,6 +1,8 @@
 using SciMLSensitivity, OrdinaryDiffEq
 using Lux, LuxCUDA, Test, Zygote, Random, LinearAlgebra, ComponentArrays
 
+const gdev = gpu_device()
+
 CUDA.allowscalar(false)
 
 H = CuArray(rand(Float32, 2, 2))
@@ -42,10 +44,10 @@ grad = Zygote.gradient(cost, p)[1]
 rng = MersenneTwister(1234)
 m = 32
 n = 16
-Z = randn(rng, Float32, (n, m)) |> gpu
+Z = randn(rng, Float32, (n, m)) |> gdev
 𝒯 = 2.0f0
 Δτ = 1.0f-1
-ca_init = [zeros(1); ones(m)] |> gpu
+ca_init = [zeros(1); ones(m)] |> gdev
 
 function f(ca, Z, t)
     a = ca[2:end]
@@ -54,7 +56,7 @@ function f(ca, Z, t)
     Ka_unit = Z' * w_unit
     z_unit = dot(abs.(Ka_unit), a_unit)
     aKa_over_z = a .* Ka_unit / z_unit
-    [sum(aKa_over_z) / m; -abs.(aKa_over_z)] |> gpu
+    [sum(aKa_over_z) / m; -abs.(aKa_over_z)] |> gdev
 end
 
 function c(Z)