Use inventory links in docs (#260)

Author: gdalle

DifferentiationInterface/docs/Project.toml

@@ -5,6 +5,7 @@ ChainRulesCore = "d360d2e6-b24c-11e9-a2a3-2a2ae2dbcce4"
 DifferentiationInterface = "a0c0ee7d-e4b9-4e03-894e-1c5f64a51d63"
 Diffractor = "9f5e2b26-1114-432f-b630-d3fe2085c51c"
 Documenter = "e30172f5-a6a5-5a46-863b-614d45cd2de4"
+DocumenterInterLinks = "d12716ef-a0f6-4df4-a9f1-a5a34e75c656"
 DocumenterMermaid = "a078cd44-4d9c-4618-b545-3ab9d77f9177"
 Enzyme = "7da242da-08ed-463a-9acd-ee780be4f1d9"
 FastDifferentiation = "eb9bf01b-bf85-4b60-bf87-ee5de06c00be"

DifferentiationInterface/docs/make.jl

@@ -3,6 +3,7 @@ using DifferentiationInterface
 import DifferentiationInterface as DI
 using Documenter
 using DocumenterMermaid
+using DocumenterInterLinks
 
 using ADTypes: ADTypes
 using Diffractor: Diffractor
@@ -18,10 +19,15 @@ using Tapir: Tapir
 using Tracker: Tracker
 using Zygote: Zygote
 
+links = InterLinks(
+    "ADTypes" => "https://sciml.github.io/ADTypes.jl/stable/",
+    "SparseConnectivityTracer" => "https://adrianhill.de/SparseConnectivityTracer.jl/stable/",
+)
+
 cp(joinpath(@__DIR__, "..", "README.md"), joinpath(@__DIR__, "src", "index.md"); force=true)
 
 makedocs(;
-    modules=[DifferentiationInterface, ADTypes],
+    modules=[DifferentiationInterface],
     authors="Guillaume Dalle, Adrian Hill",
     sitename="DifferentiationInterface.jl",
     format=Documenter.HTML(; assets=["assets/favicon.ico"]),
@@ -32,7 +38,7 @@ makedocs(;
         "Advanced" => ["design.md", "overloads.md"],
     ],
     checkdocs=:exports,
-    warnonly=[:missing_docs, :cross_references],
+    plugins=[links],
 )
 
 deploydocs(;

DifferentiationInterface/docs/src/backends.md

@@ -94,28 +94,3 @@ You can check this compatibility using [`check_twoarg`](@ref).
 
 Only some backends are able to compute Hessians.
 You can use [`check_hessian`](@ref) to check this feature (beware that it will try to compute a small Hessian, so it is not instantaneous).
-
-## API reference
-
-!!! warning
-    The following documentation has been borrowed from ADTypes.jl.
-    Refer to the [ADTypes documentation](https://sciml.github.io/ADTypes.jl/stable/) for more information.
-
-```@docs
-ADTypes
-ADTypes.AbstractADType
-AutoChainRules
-AutoDiffractor
-AutoEnzyme
-AutoFastDifferentiation
-AutoFiniteDiff
-AutoFiniteDifferences
-AutoForwardDiff
-AutoPolyesterForwardDiff
-AutoReverseDiff
-AutoSymbolics
-AutoTapir
-AutoTracker
-AutoZygote
-AutoSparse
-```

DifferentiationInterface/docs/src/overview.md

@@ -114,7 +114,7 @@ We offer two ways to perform second-order differentiation (for [`second_derivati
 
 ### Sparsity
 
-[ADTypes.jl](https://github.com/SciML/ADTypes.jl) provides [`AutoSparse`](@ref) to accelerate the computation of sparse Jacobians and Hessians.
+[ADTypes.jl](https://github.com/SciML/ADTypes.jl) provides [`AutoSparse`](@extref ADTypes.AutoSparse) to accelerate the computation of sparse Jacobians and Hessians.
 Just wrap it around any backend, with an appropriate choice of sparsity detector and coloring algorithm, and call `jacobian` or `hessian`: the result will be sparse.
 See the [tutorial section on sparsity](@ref sparsity-tutorial) for details.
 

DifferentiationInterface/docs/src/tutorial.md

@@ -145,12 +145,12 @@ To compute sparse Jacobians or Hessians, you need three ingredients (read [this
 
 1. An underlying (dense) AD backend
 2. A sparsity pattern detector like:
-    - `TracerSparsityDetector`, implemented by [SparseConnectivityTracer.jl](https://github.com/adrhill/SparseConnectivityTracer.jl) (our default recommendation)
-    - [`DifferentiationInterface.SymbolicsSparsityDetector`](@ref), implemented by DifferentiationInterface.jl with [Symbolics.jl](https://github.com/JuliaSymbolics/Symbolics.jl) but not part of the public API (it will soon be [transferred](https://github.com/JuliaSymbolics/Symbolics.jl/pull/1134))
+    - [`TracerSparsityDetector`](@extref SparseConnectivityTracer.TracerSparsityDetector), implemented by [SparseConnectivityTracer.jl](https://github.com/adrhill/SparseConnectivityTracer.jl) (our default recommendation)
+    - [`SymbolicsSparsityDetector`](@ref DifferentiationInterface.SymbolicsSparsityDetector), implemented by DifferentiationInterface.jl with [Symbolics.jl](https://github.com/JuliaSymbolics/Symbolics.jl) but not exported nor part of the public API (it will soon be [transferred](https://github.com/JuliaSymbolics/Symbolics.jl/pull/1134))
 3. A coloring algorithm like:
     - [`GreedyColoringAlgorithm`](@ref), implemented by DifferentiationInterface.jl
 
-ADTypes.jl v1.0 provides the [`AutoSparse`](@ref) wrapper to combine these three ingredients, and DifferentiationInterface.jl re-exports it.
+ADTypes.jl v1.0 provides the [`AutoSparse`](@extref ADTypes.AutoSparse) wrapper to combine these three ingredients, and DifferentiationInterface.jl re-exports it.
 Here's an example:
 
 ```@example tuto
@@ -181,4 +181,4 @@ After preparation, the sparse computation itself will be much faster than the de
 
 !!! info
     The symbolic backends have built-in sparsity handling, so `AutoSparse(AutoSymbolics())` and `AutoSparse(AutoFastDifferentiation())` do not need additional configuration for detection or coloring.
-    However they still benefit from preparation.
+    However they still benefit from preparation.