docs: update broken links and fail on linkcheck

LuxDL · Jul 28, 2024 · 8eb392a · 8eb392a · github-actions · Jul 28, 2024
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diff --git a/docs/make.jl b/docs/make.jl
@@ -90,7 +90,6 @@ makedocs(; sitename="Lux.jl Docs",
  repo="github.com/LuxDL/Lux.jl", devbranch="main", devurl="dev",
  deploy_url="https://lux.csail.mit.edu", deploy_decision),
  draft=false,
- warnonly=:linkcheck, # Lately it has been failing quite a lot but those links are actually fine
  pages)
 
 deploydocs(; repo="github.com/LuxDL/Lux.jl.git",

diff --git a/docs/src/manual/autodiff.md b/docs/src/manual/autodiff.md
@@ -14,7 +14,7 @@ Lux. Additionally, we provide some convenience functions for working with AD.
 | [`ForwardDiff.jl`](https://github.com/JuliaDiff/ForwardDiff.jl) | Forward | ✔️ | ✔️ | ✔️ | Tier I |
 | [`ReverseDiff.jl`](https://github.com/JuliaDiff/ReverseDiff.jl) | Reverse | ✔️ | ❌ | ❌ | Tier II |
 | [`Tracker.jl`](https://github.com/FluxML/Tracker.jl) | Reverse | ✔️ | ✔️ | ❌ | Tier II |
-| [`Tapir.jl`](https://github.com/withbayes/Tapir.jl)  | Reverse | ❓[^q] | ❌ | ❌ | Tier III |
+| [`Tapir.jl`](https://github.com/compintell/Tapir.jl) | Reverse | ❓[^q] | ❌ | ❌ | Tier III |
 | [`Diffractor.jl`](https://github.com/JuliaDiff/Diffractor.jl) | Forward | ❓[^q] | ❓[^q] | ❓[^q] | Tier III |
 
 [^e]: Currently Enzyme outperforms other AD packages in terms of CPU performance. However,

diff --git a/docs/src/manual/nested_autodiff.md b/docs/src/manual/nested_autodiff.md
@@ -192,9 +192,9 @@ nothing; # hide
 
 Hutchinson Trace Estimation often shows up in machine learning literature to provide a fast
 estimate of the trace of a Jacobian Matrix. This is based off of
-[Hutchinson 1990](https://www.researchgate.net/publication/243668757_A_Stochastic_Estimator_of_the_Trace_of_the_Influence_Matrix_for_Laplacian_Smoothing_Splines) which
-computes the estimated trace of a matrix ``A \in \mathbb{R}^{D \times D}`` using random
-vectors ``v \in \mathbb{R}^{D}`` s.t. ``\mathbb{E}\left[v v^T\right] = I``.
+[Hutchinson 1990](https://www.nowozin.net/sebastian/blog/thoughts-on-trace-estimation-in-deep-learning.html)
+which computes the estimated trace of a matrix ``A \in \mathbb{R}^{D \times D}`` using
+random vectors ``v \in \mathbb{R}^{D}`` s.t. ``\mathbb{E}\left[v v^T\right] = I``.
 
 ```math
 \text{Tr}(A) = \mathbb{E}\left[v^T A v\right] = \frac{1}{V} \sum_{i = 1}^V v_i^T A v_i

diff --git a/docs/src/manual/performance_pitfalls.md b/docs/src/manual/performance_pitfalls.md
@@ -67,4 +67,4 @@ GPUArraysCore.allowscalar(false)
 `Lux.jl` is integrated with `DispatchDoctor.jl` to catch type instabilities. You can easily
 enable it by setting the `instability_check` preference. This will help you catch type
 instabilities in your code. For more information on how to set preferences, check out
-[`set_dispatch_doctor_preferences`](@ref).
+[`Lux.set_dispatch_doctor_preferences!`](@ref).
diff --git a/docs/src/manual/preferences.md b/docs/src/manual/preferences.md
@@ -50,8 +50,8 @@ By default, both of these preferences are set to `false`.
 ## [Dispatch Doctor](@id dispatch-doctor-preference)
 
 1. `instability_check` - Preference controlling the dispatch doctor. See the documentation
- on [`set_dispatch_doctor_preferences!`](@ref) for more details. The preferences need to
- be set for `LuxCore` and `LuxLib` packages. Both of them default to `disable`.
+ on [`Lux.set_dispatch_doctor_preferences!`](@ref) for more details. The preferences need
+ to be set for `LuxCore` and `LuxLib` packages. Both of them default to `disable`.
  - Setting the `LuxCore` preference sets the check at the level of `LuxCore.apply`. This
  essentially activates the dispatch doctor for all Lux layers.
  - Setting the `LuxLib` preference sets the check at the level of functional layer of

diff --git a/examples/Basics/main.jl b/examples/Basics/main.jl
@@ -3,7 +3,7 @@
 # This is a quick intro to [Lux](https://github.com/LuxDL/Lux.jl) loosely based on:
 #
 # 1. [PyTorch's tutorial](https://pytorch.org/tutorials/beginner/deep_learning_60min_blitz.html).
-# 2. [Flux's tutorial](https://fluxml.ai/Flux.jl/stable/tutorials/2020-09-15-deep-learning-flux/).
+# 2. Flux's tutorial (the link for which has now been lost to abyss).
 # 3. [Jax's tutorial](https://jax.readthedocs.io/en/latest/jax-101/index.html).
 #
 # It introduces basic Julia programming, as well `Zygote`, a source-to-source automatic

diff --git a/examples/BayesianNN/main.jl b/examples/BayesianNN/main.jl
@@ -1,11 +1,13 @@
 # # Bayesian Neural Network
 
 # We borrow this tutorial from the
-# [official Turing Docs](https://turinglang.org/stable/tutorials/03-bayesian-neural-network/). We
-# will show how the explicit parameterization of Lux enables first-class composability with
-# packages which expect flattened out parameter vectors.
+# [official Turing Docs](https://turinglang.org/docs/tutorials/03-bayesian-neural-network/index.html).
+# We will show how the explicit parameterization of Lux enables first-class composability
+# with packages which expect flattened out parameter vectors.
 
-# We will use [Turing.jl](https://turinglang.org/stable/) with [Lux.jl](https://lux.csail.mit.edu/)
+# Note: The tutorial in the official Turing docs is now using Lux instead of Flux.
+
+# We will use [Turing.jl](https://turinglang.org/) with [Lux.jl](https://lux.csail.mit.edu/)
 # to implement implementing a classification algorithm. Lets start by importing the relevant
 # libraries.
 

diff --git a/examples/SymbolicOptimalControl/main.jl b/examples/SymbolicOptimalControl/main.jl
@@ -2,8 +2,8 @@
 
 # This tutorial is based on [SciMLSensitivity.jl tutorial](https://docs.sciml.ai/SciMLSensitivity/stable/examples/optimal_control/optimal_control/).
 # Instead of using a classical NN architecture, here we will combine the NN with a symbolic
-# expression from [DynamicExpressions.jl](https://symbolicml.org/DynamicExpressions.jl) (the
-# symbolic engine behind [SymbolicRegression.jl](https://astroautomata.com/SymbolicRegression.jl)
+# expression from [DynamicExpressions.jl](https://symbolicml.org/DynamicExpressions.jl/) (the
+# symbolic engine behind [SymbolicRegression.jl](https://astroautomata.com/SymbolicRegression.jl/)
 # and [PySR](https://github.com/MilesCranmer/PySR/)).
 
 # Here we will solve a classic optimal control problem with a universal differential

diff --git a/src/helpers/losses.jl b/src/helpers/losses.jl
@@ -595,7 +595,7 @@ true
 ## Special Note
 
 This function takes any of the
-[`LossFunctions.jl`](https://juliaml.github.io/LossFunctions.jl/stable) public functions
+[`LossFunctions.jl`](https://juliaml.github.io/LossFunctions.jl/stable/) public functions
 into the Lux Losses API with efficient aggregation.
 """
 @concrete struct GenericLossFunction <: AbstractLossFunction
Benchmark suite	Current: `8eb392a`	Previous: `f3159bd`	Ratio
`Dense(2 => 2)/cpu/reverse/ReverseDiff (compiled)/(2, 128)`	`3679.375` ns	`4340.625` ns	`0.85`
`Dense(2 => 2)/cpu/reverse/Zygote/(2, 128)`	`7338` ns	`8991.833333333334` ns	`0.82`
`Dense(2 => 2)/cpu/reverse/Tracker/(2, 128)`	`20558` ns	`25788` ns	`0.80`
`Dense(2 => 2)/cpu/reverse/ReverseDiff/(2, 128)`	`9722.2` ns	`12129.333333333334` ns	`0.80`
`Dense(2 => 2)/cpu/reverse/Flux/(2, 128)`	`9037` ns	`10748.1` ns	`0.84`
`Dense(2 => 2)/cpu/reverse/SimpleChains/(2, 128)`	`4463.9375` ns	`4954.25` ns	`0.90`
`Dense(2 => 2)/cpu/reverse/Enzyme/(2, 128)`	`4666.25` ns	`5425.125` ns	`0.86`
`Dense(2 => 2)/cpu/forward/NamedTuple/(2, 128)`	`1114.3974358974358` ns	`1359.1832061068703` ns	`0.82`
`Dense(2 => 2)/cpu/forward/ComponentArray/(2, 128)`	`1184.1407407407407` ns	`1401.2664233576643` ns	`0.85`
`Dense(2 => 2)/cpu/forward/Flux/(2, 128)`	`1810.445652173913` ns	`2176.5833333333335` ns	`0.83`
`Dense(2 => 2)/cpu/forward/SimpleChains/(2, 128)`	`180.16946778711485` ns	`179.46778711484595` ns	`1.00`
`Dense(20 => 20)/cpu/reverse/ReverseDiff (compiled)/(20, 128)`	`17292` ns	`21761` ns	`0.79`
`Dense(20 => 20)/cpu/reverse/Zygote/(20, 128)`	`16852` ns	`20408` ns	`0.83`
`Dense(20 => 20)/cpu/reverse/Tracker/(20, 128)`	`36358` ns	`42780` ns	`0.85`
`Dense(20 => 20)/cpu/reverse/ReverseDiff/(20, 128)`	`28133` ns	`32301` ns	`0.87`
`Dense(20 => 20)/cpu/reverse/Flux/(20, 128)`	`20148` ns	`22652` ns	`0.89`
`Dense(20 => 20)/cpu/reverse/SimpleChains/(20, 128)`	`17052` ns	`18654` ns	`0.91`
`Dense(20 => 20)/cpu/reverse/Enzyme/(20, 128)`	`25548` ns	`28283` ns	`0.90`
`Dense(20 => 20)/cpu/forward/NamedTuple/(20, 128)`	`3795.875` ns	`4363.8125` ns	`0.87`
`Dense(20 => 20)/cpu/forward/ComponentArray/(20, 128)`	`3908.625` ns	`4459.5` ns	`0.88`
`Dense(20 => 20)/cpu/forward/Flux/(20, 128)`	`4793.285714285715` ns	`5581.928571428572` ns	`0.86`
`Dense(20 => 20)/cpu/forward/SimpleChains/(20, 128)`	`1655.1` ns	`1850.5` ns	`0.89`
`Conv((3, 3), 3 => 3)/cpu/reverse/ReverseDiff (compiled)/(64, 64, 3, 128)`	`38607620.5` ns	`45154820.5` ns	`0.86`
`Conv((3, 3), 3 => 3)/cpu/reverse/Zygote/(64, 64, 3, 128)`	`58062464` ns	`62378075.5` ns	`0.93`
`Conv((3, 3), 3 => 3)/cpu/reverse/Tracker/(64, 64, 3, 128)`	`66821661` ns	`82237576.5` ns	`0.81`
`Conv((3, 3), 3 => 3)/cpu/reverse/ReverseDiff/(64, 64, 3, 128)`	`79893843` ns	`92405677.5` ns	`0.86`
`Conv((3, 3), 3 => 3)/cpu/reverse/Flux/(64, 64, 3, 128)`	`72220779` ns	`78467695` ns	`0.92`
`Conv((3, 3), 3 => 3)/cpu/reverse/SimpleChains/(64, 64, 3, 128)`	`11898610` ns	`11963195.5` ns	`0.99`
`Conv((3, 3), 3 => 3)/cpu/reverse/Enzyme/(64, 64, 3, 128)`	`79046441.5` ns	`91650799` ns	`0.86`
`Conv((3, 3), 3 => 3)/cpu/forward/NamedTuple/(64, 64, 3, 128)`	`7662156.5` ns	`7710446.5` ns	`0.99`
`Conv((3, 3), 3 => 3)/cpu/forward/ComponentArray/(64, 64, 3, 128)`	`7540729` ns	`7584631` ns	`0.99`
`Conv((3, 3), 3 => 3)/cpu/forward/Flux/(64, 64, 3, 128)`	`10363028` ns	`12087804.5` ns	`0.86`
`Conv((3, 3), 3 => 3)/cpu/forward/SimpleChains/(64, 64, 3, 128)`	`6374425` ns	`6389570.5` ns	`1.00`
`vgg16/cpu/reverse/Zygote/(32, 32, 3, 16)`	`688891366` ns	`691470864.5` ns	`1.00`
`vgg16/cpu/reverse/Zygote/(32, 32, 3, 64)`	`2580089696` ns	`2612057812` ns	`0.99`
`vgg16/cpu/reverse/Zygote/(32, 32, 3, 2)`	`140409324.5` ns	`151710246` ns	`0.93`
`vgg16/cpu/reverse/Tracker/(32, 32, 3, 16)`	`767316784` ns	`847276341` ns	`0.91`
`vgg16/cpu/reverse/Tracker/(32, 32, 3, 64)`	`2911634396` ns	`2998224779` ns	`0.97`
`vgg16/cpu/reverse/Tracker/(32, 32, 3, 2)`	`220564407` ns	`231162547` ns	`0.95`
`vgg16/cpu/reverse/Flux/(32, 32, 3, 16)`	`660516925.5` ns	`712811787.5` ns	`0.93`
`vgg16/cpu/reverse/Flux/(32, 32, 3, 64)`	`2628665253` ns	`2949177680` ns	`0.89`
`vgg16/cpu/reverse/Flux/(32, 32, 3, 2)`	`134431743` ns	`139771741` ns	`0.96`
`vgg16/cpu/forward/NamedTuple/(32, 32, 3, 16)`	`172684244` ns	`175403229` ns	`0.98`
`vgg16/cpu/forward/NamedTuple/(32, 32, 3, 64)`	`660049856.5` ns	`643986392.5` ns	`1.02`
`vgg16/cpu/forward/NamedTuple/(32, 32, 3, 2)`	`34254472` ns	`45299778` ns	`0.76`
`vgg16/cpu/forward/ComponentArray/(32, 32, 3, 16)`	`163069879` ns	`165572824` ns	`0.98`
`vgg16/cpu/forward/ComponentArray/(32, 32, 3, 64)`	`639471793` ns	`648012066` ns	`0.99`
`vgg16/cpu/forward/ComponentArray/(32, 32, 3, 2)`	`29383379` ns	`35881786` ns	`0.82`
`vgg16/cpu/forward/Flux/(32, 32, 3, 16)`	`201738097` ns	`228284686` ns	`0.88`
`vgg16/cpu/forward/Flux/(32, 32, 3, 64)`	`722144211.5` ns	`824337722` ns	`0.88`
`vgg16/cpu/forward/Flux/(32, 32, 3, 2)`	`35370961` ns	`36049946` ns	`0.98`
`Conv((3, 3), 64 => 64)/cpu/reverse/ReverseDiff (compiled)/(64, 64, 64, 128)`	`1247821765` ns	`1305860557.5` ns	`0.96`
`Conv((3, 3), 64 => 64)/cpu/reverse/Zygote/(64, 64, 64, 128)`	`1872950778` ns	`1872497449` ns	`1.00`
`Conv((3, 3), 64 => 64)/cpu/reverse/Tracker/(64, 64, 64, 128)`	`2218972759.5` ns	`2342556041` ns	`0.95`
`Conv((3, 3), 64 => 64)/cpu/reverse/ReverseDiff/(64, 64, 64, 128)`	`2061757737.5` ns	`2488038610` ns	`0.83`
`Conv((3, 3), 64 => 64)/cpu/reverse/Flux/(64, 64, 64, 128)`	`1829486370` ns	`1951631856` ns	`0.94`
`Conv((3, 3), 64 => 64)/cpu/reverse/Enzyme/(64, 64, 64, 128)`	`2001768856` ns	`2087636119` ns	`0.96`
`Conv((3, 3), 64 => 64)/cpu/forward/NamedTuple/(64, 64, 64, 128)`	`329179369` ns	`338033674` ns	`0.97`
`Conv((3, 3), 64 => 64)/cpu/forward/ComponentArray/(64, 64, 64, 128)`	`328423712` ns	`332696625` ns	`0.99`
`Conv((3, 3), 64 => 64)/cpu/forward/Flux/(64, 64, 64, 128)`	`345125201` ns	`359960422` ns	`0.96`
`Conv((3, 3), 1 => 1)/cpu/reverse/ReverseDiff (compiled)/(64, 64, 1, 128)`	`11790993.5` ns	`11686611` ns	`1.01`
`Conv((3, 3), 1 => 1)/cpu/reverse/Zygote/(64, 64, 1, 128)`	`18100299` ns	`17986098.5` ns	`1.01`
`Conv((3, 3), 1 => 1)/cpu/reverse/Tracker/(64, 64, 1, 128)`	`18996442` ns	`19052140` ns	`1.00`
`Conv((3, 3), 1 => 1)/cpu/reverse/ReverseDiff/(64, 64, 1, 128)`	`23733608` ns	`23778464` ns	`1.00`
`Conv((3, 3), 1 => 1)/cpu/reverse/Flux/(64, 64, 1, 128)`	`17885808.5` ns	`17775034.5` ns	`1.01`
`Conv((3, 3), 1 => 1)/cpu/reverse/SimpleChains/(64, 64, 1, 128)`	`1164034` ns	`1157462` ns	`1.01`
`Conv((3, 3), 1 => 1)/cpu/reverse/Enzyme/(64, 64, 1, 128)`	`22919399` ns	`22924969.5` ns	`1.00`
`Conv((3, 3), 1 => 1)/cpu/forward/NamedTuple/(64, 64, 1, 128)`	`2345181` ns	`2406465` ns	`0.97`
`Conv((3, 3), 1 => 1)/cpu/forward/ComponentArray/(64, 64, 1, 128)`	`2211761` ns	`2242648.5` ns	`0.99`
`Conv((3, 3), 1 => 1)/cpu/forward/Flux/(64, 64, 1, 128)`	`2058975` ns	`2065648` ns	`1.00`
`Conv((3, 3), 1 => 1)/cpu/forward/SimpleChains/(64, 64, 1, 128)`	`195266` ns	`197910` ns	`0.99`
`Dense(200 => 200)/cpu/reverse/ReverseDiff (compiled)/(200, 128)`	`291195` ns	`290874` ns	`1.00`
`Dense(200 => 200)/cpu/reverse/Zygote/(200, 128)`	`263513` ns	`264424` ns	`1.00`
`Dense(200 => 200)/cpu/reverse/Tracker/(200, 128)`	`358852` ns	`364661` ns	`0.98`
`Dense(200 => 200)/cpu/reverse/ReverseDiff/(200, 128)`	`404647` ns	`405888` ns	`1.00`
`Dense(200 => 200)/cpu/reverse/Flux/(200, 128)`	`271508` ns	`272550` ns	`1.00`
`Dense(200 => 200)/cpu/reverse/SimpleChains/(200, 128)`	`405448` ns	`408042` ns	`0.99`
`Dense(200 => 200)/cpu/reverse/Enzyme/(200, 128)`	`394609` ns	`394657` ns	`1.00`
`Dense(200 => 200)/cpu/forward/NamedTuple/(200, 128)`	`80831` ns	`80941` ns	`1.00`
`Dense(200 => 200)/cpu/forward/ComponentArray/(200, 128)`	`81954` ns	`81403` ns	`1.01`
`Dense(200 => 200)/cpu/forward/Flux/(200, 128)`	`86211` ns	`86461` ns	`1.00`
`Dense(200 => 200)/cpu/forward/SimpleChains/(200, 128)`	`104485` ns	`104525` ns	`1.00`
`Conv((3, 3), 16 => 16)/cpu/reverse/ReverseDiff (compiled)/(64, 64, 16, 128)`	`187479900` ns	`206243721` ns	`0.91`
`Conv((3, 3), 16 => 16)/cpu/reverse/Zygote/(64, 64, 16, 128)`	`328956491` ns	`327524051.5` ns	`1.00`
`Conv((3, 3), 16 => 16)/cpu/reverse/Tracker/(64, 64, 16, 128)`	`419428117` ns	`410738774` ns	`1.02`
`Conv((3, 3), 16 => 16)/cpu/reverse/ReverseDiff/(64, 64, 16, 128)`	`479472814.5` ns	`439152524.5` ns	`1.09`
`Conv((3, 3), 16 => 16)/cpu/reverse/Flux/(64, 64, 16, 128)`	`380882756` ns	`382717413` ns	`1.00`
`Conv((3, 3), 16 => 16)/cpu/reverse/SimpleChains/(64, 64, 16, 128)`	`306226546` ns	`325870578.5` ns	`0.94`
`Conv((3, 3), 16 => 16)/cpu/reverse/Enzyme/(64, 64, 16, 128)`	`468205037` ns	`455578258` ns	`1.03`
`Conv((3, 3), 16 => 16)/cpu/forward/NamedTuple/(64, 64, 16, 128)`	`47148555` ns	`47303828.5` ns	`1.00`
`Conv((3, 3), 16 => 16)/cpu/forward/ComponentArray/(64, 64, 16, 128)`	`46601077.5` ns	`46793042.5` ns	`1.00`
`Conv((3, 3), 16 => 16)/cpu/forward/Flux/(64, 64, 16, 128)`	`59226820` ns	`57476949` ns	`1.03`
`Conv((3, 3), 16 => 16)/cpu/forward/SimpleChains/(64, 64, 16, 128)`	`28155899` ns	`27955898` ns	`1.01`
`Dense(2000 => 2000)/cpu/reverse/ReverseDiff (compiled)/(2000, 128)`	`18845148.5` ns	`18906265` ns	`1.00`
`Dense(2000 => 2000)/cpu/reverse/Zygote/(2000, 128)`	`19457185` ns	`19556819` ns	`0.99`
`Dense(2000 => 2000)/cpu/reverse/Tracker/(2000, 128)`	`23071936.5` ns	`23268022` ns	`0.99`
`Dense(2000 => 2000)/cpu/reverse/ReverseDiff/(2000, 128)`	`23991051.5` ns	`24118160` ns	`0.99`
`Dense(2000 => 2000)/cpu/reverse/Flux/(2000, 128)`	`19527991.5` ns	`19557901` ns	`1.00`
`Dense(2000 => 2000)/cpu/reverse/Enzyme/(2000, 128)`	`20815286.5` ns	`20840171` ns	`1.00`
`Dense(2000 => 2000)/cpu/forward/NamedTuple/(2000, 128)`	`6510728` ns	`6539910.5` ns	`1.00`
`Dense(2000 => 2000)/cpu/forward/ComponentArray/(2000, 128)`	`6483627` ns	`6488719` ns	`1.00`
`Dense(2000 => 2000)/cpu/forward/Flux/(2000, 128)`	`6472777` ns	`6511897` ns	`0.99`