Better Steady State Adjoint

.JuliaFormatter.toml

@@ -2,6 +2,6 @@ style = "sciml"
 whitespace_in_kwargs = false
 always_use_return = true
 format_docstrings = true
-join_lines_based_on_source = false
 separate_kwargs_with_semicolon = true
-format_markdown = true
+format_markdown = true
+annotate_untyped_fields_with_any = false

.github/workflows/CI.yml

@@ -27,7 +27,6 @@ jobs:
           - ADJOINT
         version:
           - '1'
-          - '1.6'
     steps:
       - uses: actions/checkout@v4
       - uses: julia-actions/setup-julia@v1

.gitignore

@@ -9,4 +9,5 @@ profs
 logs
 benchmarking
 */tensorflow_datasets/
-checkpoints
+checkpoints
+wip

Project.toml

@@ -1,44 +1,40 @@
 name = "DeepEquilibriumNetworks"
 uuid = "6748aba7-0e9b-415e-a410-ae3cc0ecb334"
 authors = ["Avik Pal <[email protected]>"]
-version = "1.3.0"
+version = "1.4.0"
 
 [deps]
-CUDA = "052768ef-5323-5732-b1bb-66c8b64840ba"
 ChainRulesCore = "d360d2e6-b24c-11e9-a2a3-2a2ae2dbcce4"
+ConcreteStructs = "2569d6c7-a4a2-43d3-a901-331e8e4be471"
 DiffEqBase = "2b5f629d-d688-5b77-993f-72d75c75574e"
 LinearAlgebra = "37e2e46d-f89d-539d-b4ee-838fcccc9c8e"
 LinearSolve = "7ed4a6bd-45f5-4d41-b270-4a48e9bafcae"
 Lux = "b2108857-7c20-44ae-9111-449ecde12c47"
-MLUtils = "f1d291b0-491e-4a28-83b9-f70985020b54"
+NonlinearSolve = "8913a72c-1f9b-4ce2-8d82-65094dcecaec"
 OrdinaryDiffEq = "1dea7af3-3e70-54e6-95c3-0bf5283fa5ed"
 Random = "9a3f8284-a2c9-5f02-9a11-845980a1fd5c"
+Reexport = "189a3867-3050-52da-a836-e630ba90ab69"
 SciMLBase = "0bca4576-84f4-4d90-8ffe-ffa030f20462"
 SciMLSensitivity = "1ed8b502-d754-442c-8d5d-10ac956f44a1"
 Setfield = "efcf1570-3423-57d1-acb7-fd33fddbac46"
-SimpleNonlinearSolve = "727e6d20-b764-4bd8-a329-72de5adea6c7"
-Static = "aedffcd0-7271-4cad-89d0-dc628f76c6d3"
 Statistics = "10745b16-79ce-11e8-11f9-7d13ad32a3b2"
 SteadyStateDiffEq = "9672c7b4-1e72-59bd-8a11-6ac3964bc41f"
 TruncatedStacktraces = "781d530d-4396-4725-bb49-402e4bee1e77"
 Zygote = "e88e6eb3-aa80-5325-afca-941959d7151f"
-ZygoteRules = "700de1a5-db45-46bc-99cf-38207098b444"
 
 [compat]
-CUDA = "3, 4, 5"
 ChainRulesCore = "1"
+ConcreteStructs = "0.2"
 DiffEqBase = "6.119"
 LinearSolve = "1, 2"
-Lux = "0.4, 0.5"
-MLUtils = "0.2, 0.3, 0.4"
+Lux = "0.5.7"
+NonlinearSolve = "2"
 OrdinaryDiffEq = "6"
-SciMLBase = "1.19, 2"
-SciMLSensitivity = "7"
+Reexport = "1"
+SciMLBase = "2"
+SciMLSensitivity = "7.43"
 Setfield = "1"
-SimpleNonlinearSolve = "0.1.14"
-Static = "0.6, 0.7, 0.8"
 SteadyStateDiffEq = "1.16"
 TruncatedStacktraces = "1.1"
 Zygote = "0.6.34"
-ZygoteRules = "0.2"
-julia = "1.6"
+julia = "1.9"

README.md

@@ -24,30 +24,31 @@ Pkg.add("DeepEquilibriumNetworks")
 ## Quickstart
 
 ```julia
-import DeepEquilibriumNetworks as DEQs
-import Lux
-import Random
-import Zygote
+using DeepEquilibriumNetworks, Lux, Random, Zygote
+# using LuxCUDA, LuxAMDGPU ## Install and Load for GPU Support
 
 seed = 0
 rng = Random.default_rng()
 Random.seed!(rng, seed)
 
-model = Lux.Chain(Lux.Dense(2, 2),
-    DEQs.DeepEquilibriumNetwork(Lux.Parallel(+,
-            Lux.Dense(2, 2; use_bias=false),
-            Lux.Dense(2, 2; use_bias=false)),
-        DEQs.ContinuousDEQSolver(;
-            abstol=0.1f0,
-            reltol=0.1f0,
-            abstol_termination=0.1f0,
-            reltol_termination=0.1f0)))
-
-ps, st = gpu.(Lux.setup(rng, model))
-x = gpu(rand(rng, Float32, 2, 1))
-y = gpu(rand(rng, Float32, 2, 1))
-
-gs = Zygote.gradient(p -> sum(abs2, model(x, p, st)[1][1] .- y), ps)[1]
+model = Chain(Dense(2 => 2),
+    DeepEquilibriumNetwork(Parallel(+,
+            Dense(2 => 2; use_bias=false),
+            Dense(2 => 2; use_bias=false)),
+        ContinuousDEQSolver(; abstol=0.1f0, reltol=0.1f0, abstol_termination=0.1f0,
+            reltol_termination=0.1f0);
+        save_everystep=true))
+
+gdev = gpu_device()
+cdev = cpu_device()
+
+ps, st = Lux.setup(rng, model) |> gdev
+x = rand(rng, Float32, 2, 1) |> gdev
+y = rand(rng, Float32, 2, 1) |> gdev
+
+model(x, ps, st)
+
+gs = only(Zygote.gradient(p -> sum(abs2, first(first(model(x, p, st))) .- y), ps))
 ```
 
 ## Citation

docs/src/index.md

@@ -2,7 +2,7 @@
 
 DeepEquilibriumNetworks.jl is a framework built on top of
 [DifferentialEquations.jl](https://docs.sciml.ai/DiffEqDocs/stable/) and
-[Lux.jl](https://docs.sciml.ai/Lux/stable/), enabling the efficient training and inference for
+[Lux.jl](https://lux.csail.mit.edu/), enabling the efficient training and inference for
 Deep Equilibrium Networks (Infinitely Deep Neural Networks).
 
 ## Installation
@@ -17,30 +17,30 @@ Pkg.add("DeepEquilibriumNetworks")
 ## Quick-start
 
 ```julia
-import DeepEquilibriumNetworks as DEQs
-import Lux
-import Random
-import Zygote
+using DeepEquilibriumNetworks, Lux, Random, Zygote
+# using LuxCUDA, LuxAMDGPU ## Install and Load for GPU Support
 
 seed = 0
 rng = Random.default_rng()
 Random.seed!(rng, seed)
+model = Chain(Dense(2 => 2),
+    DeepEquilibriumNetwork(Parallel(+,
+            Dense(2 => 2; use_bias=false),
+            Dense(2 => 2; use_bias=false)),
+        ContinuousDEQSolver(; abstol=0.1f0, reltol=0.1f0, abstol_termination=0.1f0,
+            reltol_termination=0.1f0);
+        save_everystep=true))
 
-model = Lux.Chain(Lux.Dense(2, 2),
-    DEQs.DeepEquilibriumNetwork(Lux.Parallel(+,
-            Lux.Dense(2, 2; use_bias=false),
-            Lux.Dense(2, 2; use_bias=false)),
-        DEQs.ContinuousDEQSolver(;
-            abstol=0.1f0,
-            reltol=0.1f0,
-            abstol_termination=0.1f0,
-            reltol_termination=0.1f0)))
-
-ps, st = gpu.(Lux.setup(rng, model))
-x = gpu(rand(rng, Float32, 2, 1))
-y = gpu(rand(rng, Float32, 2, 1))
-
-gs = Zygote.gradient(p -> sum(abs2, model(x, p, st)[1][1] .- y), ps)[1]
+gdev = gpu_device()
+cdev = cpu_device()
+
+ps, st = Lux.setup(rng, model) |> gdev
+x = rand(rng, Float32, 2, 1) |> gdev
+y = rand(rng, Float32, 2, 1) |> gdev
+
+model(x, ps, st)
+
+gs = only(Zygote.gradient(p -> sum(abs2, first(first(model(x, p, st))) .- y), ps))
 ```
 
 ## Citation

docs/src/manual/misc.md

@@ -4,6 +4,5 @@
 DeepEquilibriumSolution
 EquilibriumSolution
 DeepEquilibriumNetworks.split_and_reshape
-DeepEquilibriumNetworks.init_identity_matrix
 DeepEquilibriumNetworks.estimate_jacobian_trace
 ```