Final few tests

SciML · Dec 18, 2023 · 1e76987 · 1e76987
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diff --git a/docs/Project.toml b/docs/Project.toml
@@ -2,6 +2,12 @@
 DeepEquilibriumNetworks = "6748aba7-0e9b-415e-a410-ae3cc0ecb334"
 Documenter = "e30172f5-a6a5-5a46-863b-614d45cd2de4"
 DocumenterCitations = "daee34ce-89f3-4625-b898-19384cb65244"
+Lux = "b2108857-7c20-44ae-9111-449ecde12c47"
+NonlinearSolve = "8913a72c-1f9b-4ce2-8d82-65094dcecaec"
+OrdinaryDiffEq = "1dea7af3-3e70-54e6-95c3-0bf5283fa5ed"
+Random = "9a3f8284-a2c9-5f02-9a11-845980a1fd5c"
+SciMLSensitivity = "1ed8b502-d754-442c-8d5d-10ac956f44a1"
+Zygote = "e88e6eb3-aa80-5325-afca-941959d7151f"
 
 [compat]
 DeepEquilibriumNetworks = "2"

diff --git a/docs/src/index.md b/docs/src/index.md
@@ -16,8 +16,8 @@ Pkg.add("DeepEquilibriumNetworks")
 
 ## Quick-start
 
-```julia
-using DeepEquilibriumNetworks, Lux, Random, NonlinearSolve, Zygote
+```@example
+using DeepEquilibriumNetworks, Lux, Random, NonlinearSolve, Zygote, SciMLSensitivity
 # using LuxCUDA, LuxAMDGPU ## Install and Load for GPU Support
 
 seed = 0
@@ -46,14 +46,11 @@ If you are using this project for research or other academic purposes, consider
 paper:
 
 ```bibtex
-@misc{pal2022mixing,
-  title={Mixing Implicit and Explicit Deep Learning with Skip DEQs and Infinite Time Neural
-         ODEs (Continuous DEQs)}, 
-  author={Avik Pal and Alan Edelman and Christopher Rackauckas},
-  year={2022},
-  eprint={2201.12240},
-  archivePrefix={arXiv},
-  primaryClass={cs.LG}
+@article{pal2022continuous,
+  title={Continuous Deep Equilibrium Models: Training Neural ODEs Faster by Integrating Them to Infinity},
+  author={Pal, Avik and Edelman, Alan and Rackauckas, Christopher},
+  booktitle={2023 IEEE High Performance Extreme Computing Conference (HPEC)}, 
+  year={2023}
 }
 ```
 

diff --git a/src/layers.jl b/src/layers.jl
@@ -27,13 +27,8 @@ function CRC.rrule(::Type{<:DeepEquilibriumSolution}, z_star, u0, residual, jaco
     sol = DeepEquilibriumSolution(z_star, u0, residual, jacobian_loss, nfe, original)
     ∇DeepEquilibriumSolution(::CRC.NoTangent) = ntuple(_ -> CRC.NoTangent(), 7)
     function ∇DeepEquilibriumSolution(∂sol)
-        ∂z_star = ∂sol.z_star
-        ∂u0 = ∂sol.u0
-        ∂residual = ∂sol.residual
-        ∂jacobian_loss = ∂sol.jacobian_loss
-        ∂nfe = ∂sol.nfe
-        ∂original = CRC.NoTangent()
-        return (CRC.NoTangent(), ∂z_star, ∂u0, ∂residual, ∂jacobian_loss, ∂nfe, ∂original)
+        return (CRC.NoTangent(), ∂sol.z_star, ∂sol.u0, ∂sol.residual, ∂sol.jacobian_loss,
+            ∂sol.nfe, CRC.NoTangent())
     end
     return sol, ∇DeepEquilibriumSolution
 end
@@ -149,11 +144,11 @@ Deep Equilibrium Network as proposed in [baideep2019](@cite) and [pal2022mixing]
 
 ## Example
 
-```julia
+```@example
 using DeepEquilibriumNetworks, Lux, Random, OrdinaryDiffEq
 
 model = DeepEquilibriumNetwork(Parallel(+, Dense(2, 2; use_bias=false),
-        Dense(2, 2; use_bias=false)), VCABM3(); save_everystep=true)
+        Dense(2, 2; use_bias=false)), VCABM3())
 
 rng = Random.default_rng()
 ps, st = Lux.setup(rng, model)
@@ -218,7 +213,7 @@ For keyword arguments, see [`DeepEquilibriumNetwork`](@ref).
 
 ## Example
 
-```julia
+```@example
 using DeepEquilibriumNetworks, Lux, Random, OrdinaryDiffEq
 
 main_layers = (Parallel(+, Dense(4 => 4, tanh; use_bias=false),

diff --git a/test/layers.jl b/test/layers.jl
@@ -1,5 +1,5 @@
 using ADTypes, DeepEquilibriumNetworks, DiffEqBase, NonlinearSolve, OrdinaryDiffEq,
-    SciMLBase, Test, NLsolve
+    SciMLSensitivity, SciMLBase, Test, NLsolve
 
 include("test_utils.jl")
 
@@ -152,6 +152,11 @@ end
                     @test maximum(abs, st.solution.residual) ≤ 1e-3
                 end
 
+                _, gs_x, gs_ps, _ = Zygote.gradient(loss_function, model, x, ps, st)
+
+                @test __is_finite_gradient(gs_x)
+                @test __is_finite_gradient(gs_ps)
+
                 ps, st = Lux.setup(rng, model)
                 st = Lux.update_state(st, :fixed_depth, Val(10))
                 @test st.solution == DeepEquilibriumSolution()
@@ -165,6 +170,11 @@ end
                 @test size(z_) == (sum(prod, scale), size(x, ndims(x)))
                 @test st.solution isa DeepEquilibriumSolution
                 @test st.solution.nfe == 10
+
+                _, gs_x, gs_ps, _ = Zygote.gradient(loss_function, model, x, ps, st)
+
+                @test __is_finite_gradient(gs_x)
+                @test __is_finite_gradient(gs_ps)
             end
         end
     end