add DI support

Project.toml

@@ -12,6 +12,7 @@ ComputationalResources = "ed09eef8-17a6-5b46-8889-db040fac31e3"
 DataFrames = "a93c6f00-e57d-5684-b7b6-d8193f3e46c0"
 Dates = "ade2ca70-3891-5945-98fb-dc099432e06a"
 DifferentialEquations = "0c46a032-eb83-5123-abaf-570d42b7fbaa"
+DifferentiationInterface = "a0c0ee7d-e4b9-4e03-894e-1c5f64a51d63"
 Distributions = "31c24e10-a181-5473-b8eb-7969acd0382f"
 DistributionsAD = "ced4e74d-a319-5a8a-b0ac-84af2272839c"
 FillArrays = "1a297f60-69ca-5386-bcde-b61e274b549b"
@@ -30,7 +31,6 @@ Random = "9a3f8284-a2c9-5f02-9a11-845980a1fd5c"
 SciMLBase = "0bca4576-84f4-4d90-8ffe-ffa030f20462"
 SciMLSensitivity = "1ed8b502-d754-442c-8d5d-10ac956f44a1"
 ScientificTypes = "321657f4-b219-11e9-178b-2701a2544e81"
-SparseDiffTools = "47a9eef4-7e08-11e9-0b38-333d64bd3804"
 Statistics = "10745b16-79ce-11e8-11f9-7d13ad32a3b2"
 Zygote = "e88e6eb3-aa80-5325-afca-941959d7151f"
 
@@ -50,6 +50,7 @@ ComputationalResources = "0.3"
 DataFrames = "1"
 Dates = "1"
 DifferentialEquations = "7"
+DifferentiationInterface = "0.1"
 Distributions = "0.25"
 DistributionsAD = "0.6"
 FillArrays = "1"
@@ -68,7 +69,6 @@ Random = "1"
 SciMLBase = "2"
 SciMLSensitivity = "7"
 ScientificTypes = "3"
-SparseDiffTools = "2"
 Statistics = "1"
 Zygote = "0.6"
 julia = "1.9"

README.md

@@ -1,33 +1,33 @@
-# ContinuousNormalizingFlows.jl
-
-[![deps](https://juliahub.com/docs/General/ContinuousNormalizingFlows/stable/deps.svg)](https://juliahub.com/ui/Packages/General/ContinuousNormalizingFlows?t=2)
-[![version](https://juliahub.com/docs/General/ContinuousNormalizingFlows/stable/version.svg)](https://juliahub.com/ui/Packages/General/ContinuousNormalizingFlows)
-[![pkgeval](https://juliahub.com/docs/General/ContinuousNormalizingFlows/stable/pkgeval.svg)](https://juliahub.com/ui/Packages/General/ContinuousNormalizingFlows)
-[![Stable](https://img.shields.io/badge/docs-stable-blue.svg)](https://impICNF.github.io/ContinuousNormalizingFlows.jl/stable)
-[![Dev](https://img.shields.io/badge/docs-dev-blue.svg)](https://impICNF.github.io/ContinuousNormalizingFlows.jl/dev)
-[![Build Status](https://github.com/impICNF/ContinuousNormalizingFlows.jl/actions/workflows/CI.yml/badge.svg?branch=main)](https://github.com/impICNF/ContinuousNormalizingFlows.jl/actions/workflows/CI.yml?query=branch%3Amain)
-[![Coverage](https://codecov.io/gh/impICNF/ContinuousNormalizingFlows.jl/branch/main/graph/badge.svg)](https://codecov.io/gh/impICNF/ContinuousNormalizingFlows.jl)
-[![Coverage](https://coveralls.io/repos/github/impICNF/ContinuousNormalizingFlows.jl/badge.svg?branch=main)](https://coveralls.io/github/impICNF/ContinuousNormalizingFlows.jl?branch=main)
-[![Aqua](https://raw.githubusercontent.com/JuliaTesting/Aqua.jl/master/badge.svg)](https://github.com/JuliaTesting/Aqua.jl)
-[![PkgEval](https://JuliaCI.github.io/NanosoldierReports/pkgeval_badges/C/ContinuousNormalizingFlows.svg)](https://JuliaCI.github.io/NanosoldierReports/pkgeval_badges/C/ContinuousNormalizingFlows.html)
-[![ColPrac: Contributor's Guide on Collaborative Practices for Community Packages](https://img.shields.io/badge/ColPrac-Contributor%27s%20Guide-blueviolet)](https://github.com/SciML/ColPrac)
-
-Implementations of Infinitesimal Continuous Normalizing Flows Algorithms in Julia
-
-## Citing
-
-See [`CITATION.bib`](CITATION.bib) for the relevant reference(s).
-
-## Installation
-
-```julia
+# ContinuousNormalizingFlows.jl
+
+[![deps](https://juliahub.com/docs/General/ContinuousNormalizingFlows/stable/deps.svg)](https://juliahub.com/ui/Packages/General/ContinuousNormalizingFlows?t=2)
+[![version](https://juliahub.com/docs/General/ContinuousNormalizingFlows/stable/version.svg)](https://juliahub.com/ui/Packages/General/ContinuousNormalizingFlows)
+[![pkgeval](https://juliahub.com/docs/General/ContinuousNormalizingFlows/stable/pkgeval.svg)](https://juliahub.com/ui/Packages/General/ContinuousNormalizingFlows)
+[![Stable](https://img.shields.io/badge/docs-stable-blue.svg)](https://impICNF.github.io/ContinuousNormalizingFlows.jl/stable)
+[![Dev](https://img.shields.io/badge/docs-dev-blue.svg)](https://impICNF.github.io/ContinuousNormalizingFlows.jl/dev)
+[![Build Status](https://github.com/impICNF/ContinuousNormalizingFlows.jl/actions/workflows/CI.yml/badge.svg?branch=main)](https://github.com/impICNF/ContinuousNormalizingFlows.jl/actions/workflows/CI.yml?query=branch%3Amain)
+[![Coverage](https://codecov.io/gh/impICNF/ContinuousNormalizingFlows.jl/branch/main/graph/badge.svg)](https://codecov.io/gh/impICNF/ContinuousNormalizingFlows.jl)
+[![Coverage](https://coveralls.io/repos/github/impICNF/ContinuousNormalizingFlows.jl/badge.svg?branch=main)](https://coveralls.io/github/impICNF/ContinuousNormalizingFlows.jl?branch=main)
+[![Aqua](https://raw.githubusercontent.com/JuliaTesting/Aqua.jl/master/badge.svg)](https://github.com/JuliaTesting/Aqua.jl)
+[![PkgEval](https://JuliaCI.github.io/NanosoldierReports/pkgeval_badges/C/ContinuousNormalizingFlows.svg)](https://JuliaCI.github.io/NanosoldierReports/pkgeval_badges/C/ContinuousNormalizingFlows.html)
+[![ColPrac: Contributor's Guide on Collaborative Practices for Community Packages](https://img.shields.io/badge/ColPrac-Contributor%27s%20Guide-blueviolet)](https://github.com/SciML/ColPrac)
+
+Implementations of Infinitesimal Continuous Normalizing Flows Algorithms in Julia
+
+## Citing
+
+See [`CITATION.bib`](CITATION.bib) for the relevant reference(s).
+
+## Installation
+
+```julia
 using Pkg
 Pkg.add("ContinuousNormalizingFlows")
-```
-
-## Usage
-
-```julia
+```
+
+## Usage
+
+```julia
 # Enable Logging
 using Logging, TerminalLoggers
 global_logger(TerminalLogger())
@@ -48,7 +48,7 @@ icnf = construct(
     nn,
     nvars, # number of variables
     naugs; # number of augmented dimensions
-    compute_mode = ZygoteMatrixMode, # process data in batches
+    compute_mode = DIVecJacMatrixMode, # process data in batches
     tspan = (0.0f0, 13.0f0), # have bigger time span
     steer_rate = 1.0f-1, # add random noise to end of the time span
     # resource = CUDALibs(), # process data by GPU
@@ -105,4 +105,4 @@ lines!(ax, 0.0f0 .. 1.0f0, x -> pdf(d, vcat(x)); label = "estimated")
 axislegend(ax)
 save("result-fig.svg", f)
 save("result-fig.png", f)
-```
+```

benchmark/Project.toml

@@ -1,14 +1,18 @@
 [deps]
+ADTypes = "47edcb42-4c32-4615-8424-f2b9edc5f35b"
 BenchmarkTools = "6e4b80f9-dd63-53aa-95a3-0cdb28fa8baf"
 ComponentArrays = "b0b7db55-cfe3-40fc-9ded-d10e2dbeff66"
+DifferentiationInterface = "a0c0ee7d-e4b9-4e03-894e-1c5f64a51d63"
 Lux = "b2108857-7c20-44ae-9111-449ecde12c47"
 PkgBenchmark = "32113eaa-f34f-5b0d-bd6c-c81e245fc73d"
 StableRNGs = "860ef19b-820b-49d6-a774-d7a799459cd3"
 Zygote = "e88e6eb3-aa80-5325-afca-941959d7151f"
 
 [compat]
+ADTypes = "0.2"
 BenchmarkTools = "1"
 ComponentArrays = "0.15"
+DifferentiationInterface = "0.1"
 Lux = "0.5"
 PkgBenchmark = "0.2"
 StableRNGs = "1"

benchmark/benchmarks.jl

@@ -1,5 +1,12 @@
 using ContinuousNormalizingFlows,
-    BenchmarkTools, ComponentArrays, Lux, PkgBenchmark, StableRNGs, Zygote
+    ADTypes,
+    BenchmarkTools,
+    ComponentArrays,
+    DifferentiationInterface,
+    Lux,
+    PkgBenchmark,
+    StableRNGs,
+    Zygote
 
 SUITE = BenchmarkGroup()
 
@@ -26,7 +33,7 @@ icnf = construct(
     nn,
     nvars,
     naugs;
-    compute_mode = ZygoteMatrixMode,
+    compute_mode = DIVecJacMatrixMode,
     tspan = (0.0f0, 13.0f0),
     steer_rate = 1.0f-1,
     λ₃ = 1.0f-2,
@@ -41,24 +48,24 @@ diff_loss_tt(x) = loss(icnf, TestMode(), r, x, st)
 
 diff_loss_tn(ps)
 diff_loss_tt(ps)
-Zygote.gradient(diff_loss_tn, ps)
-Zygote.gradient(diff_loss_tt, ps)
+DifferentiationInterface.gradient(diff_loss_tn, AutoZygote(), ps)
+DifferentiationInterface.gradient(diff_loss_tt, AutoZygote(), ps)
 GC.gc()
 
 SUITE["main"]["no_inplace"]["direct"]["train"] = @benchmarkable diff_loss_tn(ps)
 SUITE["main"]["no_inplace"]["direct"]["test"] = @benchmarkable diff_loss_tt(ps)
 SUITE["main"]["no_inplace"]["AD-1-order"]["train"] =
-    @benchmarkable Zygote.gradient(diff_loss_tn, ps)
+    @benchmarkable DifferentiationInterface.gradient(diff_loss_tn, AutoZygote(), ps)
 SUITE["main"]["no_inplace"]["AD-1-order"]["test"] =
-    @benchmarkable Zygote.gradient(diff_loss_tt, ps)
+    @benchmarkable DifferentiationInterface.gradient(diff_loss_tt, AutoZygote(), ps)
 
 icnf2 = construct(
     RNODE,
     nn,
     nvars,
     naugs;
     inplace = true,
-    compute_mode = ZygoteMatrixMode,
+    compute_mode = DIVecJacMatrixMode,
     tspan = (0.0f0, 13.0f0),
     steer_rate = 1.0f-1,
     λ₃ = 1.0f-2,
@@ -70,13 +77,13 @@ diff_loss_tt2(x) = loss(icnf2, TestMode(), r, x, st)
 
 diff_loss_tn2(ps)
 diff_loss_tt2(ps)
-Zygote.gradient(diff_loss_tn2, ps)
-Zygote.gradient(diff_loss_tt2, ps)
+DifferentiationInterface.gradient(diff_loss_tn2, AutoZygote(), ps)
+DifferentiationInterface.gradient(diff_loss_tt2, AutoZygote(), ps)
 GC.gc()
 
 SUITE["main"]["inplace"]["direct"]["train"] = @benchmarkable diff_loss_tn2(ps)
 SUITE["main"]["inplace"]["direct"]["test"] = @benchmarkable diff_loss_tt2(ps)
 SUITE["main"]["inplace"]["AD-1-order"]["train"] =
-    @benchmarkable Zygote.gradient(diff_loss_tn2, ps)
+    @benchmarkable DifferentiationInterface.gradient(diff_loss_tn2, AutoZygote(), ps)
 SUITE["main"]["inplace"]["AD-1-order"]["test"] =
-    @benchmarkable Zygote.gradient(diff_loss_tt2, ps)
+    @benchmarkable DifferentiationInterface.gradient(diff_loss_tt2, AutoZygote(), ps)

src/ContinuousNormalizingFlows.jl

@@ -9,6 +9,7 @@ using AbstractDifferentiation,
     DataFrames,
     Dates,
     DifferentialEquations,
+    DifferentiationInterface,
     Distributions,
     DistributionsAD,
     FillArrays,
@@ -27,7 +28,6 @@ using AbstractDifferentiation,
     ScientificTypes,
     SciMLBase,
     SciMLSensitivity,
-    SparseDiffTools,
     Statistics,
     Zygote
 

src/base_icnf.jl

@@ -21,11 +21,7 @@ function construct(
         Eye{data_type}(nvars + naugmented),
     ),
     differentiation_backend::AbstractDifferentiation.AbstractBackend = AbstractDifferentiation.ZygoteBackend(),
-    autodiff_backend::ADTypes.AbstractADType = ifelse(
-        compute_mode <: SDJacVecMatrixMode,
-        AutoForwardDiff(),
-        AutoZygote(),
-    ),
+    autodiff_backend::ADTypes.AbstractADType = AutoZygote(),
     sol_kwargs::NamedTuple = (
         save_everystep = false,
         alg = Tsit5(; thread = OrdinaryDiffEq.True()),