Incorrect trace

Manifest.toml

@@ -271,9 +271,9 @@ version = "6.144.0"
 
 [[deps.DiffEqCallbacks]]
 deps = ["DataStructures", "DiffEqBase", "ForwardDiff", "Functors", "LinearAlgebra", "Markdown", "NLsolve", "Parameters", "RecipesBase", "RecursiveArrayTools", "SciMLBase", "StaticArraysCore"]
-git-tree-sha1 = "d0b94b3694d55e7eedeee918e7daee9e3b873399"
+git-tree-sha1 = "e48b985459d1cbe8c809de192529f1e25c3382a6"
 uuid = "459566f4-90b8-5000-8ac3-15dfb0a30def"
-version = "2.35.0"
+version = "2.36.0"
 
     [deps.DiffEqCallbacks.weakdeps]
     OrdinaryDiffEq = "1dea7af3-3e70-54e6-95c3-0bf5283fa5ed"

src/utils.jl

@@ -2,7 +2,7 @@
         ::Val{shapes}) where {idxs, shapes}
     dims = [reshape((idxs[i] + 1):idxs[i + 1], shapes[i]...) for i in 1:(length(idxs) - 1)]
     varnames = map(_ -> gensym("x_view"), dims)
-    calls = [:($(varnames[i]) = view(x, $(dims[i]), :)) for i in 1:length(dims)]
+    calls = [:($(varnames[i]) = x[$(dims[i]), :]) for i in 1:length(dims)]
     return quote
         $(calls...)
         return tuple($(varnames...))
@@ -14,7 +14,8 @@ __split_and_reshape(x::AbstractArray, ::Nothing, ::Nothing) = x
 function __split_and_reshape(y::AbstractMatrix, x)
     szs = [prod(size(xᵢ)[1:(end - 1)]) for xᵢ in x]
     counters = vcat(0, cumsum(szs)[1:(end - 1)])
-    return map((sz, c, xᵢ) -> reshape(view(y, (c + 1):(c + sz), :), size(xᵢ)),
+    # Make the data contiguous
+    return map((sz, c, xᵢ) -> copy(reshape(view(y, (c + 1):(c + sz), :), size(xᵢ))),
         szs, counters, x)
 end
 
@@ -95,7 +96,7 @@ CRC.@non_differentiable __gaussian_like(::Any...)
 
 # Jacobian Stabilization
 function __estimate_jacobian_trace(::AutoFiniteDiff, model, ps, z, x, rng)
-    __f = u -> first(model((u, x), ps))
+    __f = u -> model((u, x), ps)
     res = zero(eltype(x))
     ϵ = cbrt(eps(typeof(res)))
     ϵ⁻¹ = inv(ϵ)

test/layers.jl

@@ -23,7 +23,8 @@ end
 
     model_type = (:deq, :skipdeq, :skipregdeq)
     solvers = (VCAB3(), Tsit5(), NewtonRaphson(), SimpleLimitedMemoryBroyden())
-    jacobian_regularizations = (nothing, AutoFiniteDiff(), AutoZygote())
+    jacobian_regularizations = Any[nothing, AutoZygote()]
+    !ongpu && push!(jacobian_regularizations, AutoFiniteDiff())
 
     @testset "Solver: $(__nameof(solver))" for solver in solvers,
         mtype in model_type, jacobian_regularization in jacobian_regularizations
@@ -133,10 +134,10 @@ end
                         jacobian_regularization)
                 end
 
-                ps, st = Lux.setup(rng, model)
+                ps, st = Lux.setup(rng, model) |> dev
                 @test st.solution == DeepEquilibriumSolution()
 
-                x = randn(rng, Float32, x_size...)
+                x = randn(rng, Float32, x_size...) |> dev
                 z, st = model(x, ps, st)
                 z_ = DEQs.__flatten_vcat(z)
 
@@ -157,7 +158,7 @@ end
                 @test __is_finite_gradient(gs_x)
                 @test __is_finite_gradient(gs_ps)
 
-                ps, st = Lux.setup(rng, model)
+                ps, st = Lux.setup(rng, model) |> dev
                 st = Lux.update_state(st, :fixed_depth, Val(10))
                 @test st.solution == DeepEquilibriumSolution()
 

test/test_utils.jl

@@ -2,6 +2,8 @@ using DeepEquilibriumNetworks, Functors, Lux, Random, StableRNGs, Zygote
 import LuxTestUtils: @jet
 using LuxCUDA
 
+CUDA.allowscalar(false)
+
 __nameof(::X) where {X} = nameof(X)
 
 __get_prng(seed::Int) = StableRNG(seed)