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ArnoStrouwenArnoStrouwen
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format docstrings
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‎.JuliaFormatter.toml

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@@ -1,2 +1,3 @@
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style = "sciml"
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format_markdown = true
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format_markdown = true
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format_docstrings = true

‎src/dense.jl

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@@ -11,7 +11,7 @@ Defined by [ADTypes.jl](https://github.com/SciML/ADTypes.jl).
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# Fields
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- `ruleconfig::RC`: a [`ChainRulesCore.RuleConfig`](https://juliadiff.org/ChainRulesCore.jl/stable/rule_author/superpowers/ruleconfig.html) object.
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- `ruleconfig::RC`: a [`ChainRulesCore.RuleConfig`](https://juliadiff.org/ChainRulesCore.jl/stable/rule_author/superpowers/ruleconfig.html) object.
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"""
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Base.@kwdef struct AutoChainRules{RC} <: AbstractADType
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ruleconfig::RC
@@ -38,18 +38,19 @@ mode(::AutoDiffractor) = ForwardOrReverseMode()
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AutoEnzyme{M}
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Struct used to select the [Enzyme.jl](https://github.com/EnzymeAD/Enzyme.jl) backend for automatic differentiation.
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Defined by [ADTypes.jl](https://github.com/SciML/ADTypes.jl).
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# Constructors
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AutoEnzyme(; mode=nothing)
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# Fields
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- `mode::M`: can be either
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- an object subtyping `EnzymeCore.Mode` (like `EnzymeCore.Forward` or `EnzymeCore.Reverse`) if a specific mode is required
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- `nothing` to choose the best mode automatically
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- `mode::M`: can be either
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+ an object subtyping `EnzymeCore.Mode` (like `EnzymeCore.Forward` or `EnzymeCore.Reverse`) if a specific mode is required
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+ `nothing` to choose the best mode automatically
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"""
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Base.@kwdef struct AutoEnzyme{M} <: AbstractADType
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mode::M = nothing
@@ -85,9 +86,9 @@ Defined by [ADTypes.jl](https://github.com/SciML/ADTypes.jl).
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# Fields
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- `fdtype::T1`: finite difference type
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- `fdjtype::T2`: finite difference type for the Jacobian
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- `fdhtype::T3`: finite difference type for the Hessian
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- `fdtype::T1`: finite difference type
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- `fdjtype::T2`: finite difference type for the Jacobian
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- `fdhtype::T3`: finite difference type for the Hessian
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"""
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Base.@kwdef struct AutoFiniteDiff{T1, T2, T3} <: AbstractADType
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fdtype::T1 = Val(:forward)
@@ -110,7 +111,7 @@ Defined by [ADTypes.jl](https://github.com/SciML/ADTypes.jl).
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# Fields
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- `fdm::T`: a [`FiniteDifferenceMethod`](https://juliadiff.org/FiniteDifferences.jl/stable/pages/api/#FiniteDifferences.FiniteDifferenceMethod)
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- `fdm::T`: a [`FiniteDifferenceMethod`](https://juliadiff.org/FiniteDifferences.jl/stable/pages/api/#FiniteDifferences.FiniteDifferenceMethod)
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"""
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Base.@kwdef struct AutoFiniteDifferences{T} <: AbstractADType
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fdm::T
@@ -131,11 +132,11 @@ Defined by [ADTypes.jl](https://github.com/SciML/ADTypes.jl).
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# Type parameters
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- `chunksize`: the preferred [chunk size](https://juliadiff.org/ForwardDiff.jl/stable/user/advanced/#Configuring-Chunk-Size) to evaluate several derivatives at once
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- `chunksize`: the preferred [chunk size](https://juliadiff.org/ForwardDiff.jl/stable/user/advanced/#Configuring-Chunk-Size) to evaluate several derivatives at once
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# Fields
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- `tag::T`: a [custom tag](https://juliadiff.org/ForwardDiff.jl/release-0.10/user/advanced.html#Custom-tags-and-tag-checking-1) to handle nested differentiation calls (usually not necessary)
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- `tag::T`: a [custom tag](https://juliadiff.org/ForwardDiff.jl/release-0.10/user/advanced.html#Custom-tags-and-tag-checking-1) to handle nested differentiation calls (usually not necessary)
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"""
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struct AutoForwardDiff{chunksize, T} <: AbstractADType
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tag::T
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"""
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AutoPolyesterForwardDiff{chunksize,T}
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Struct used to select the [PolyesterForwardDiff.jl](https://github.com/JuliaDiff/PolyesterForwardDiff.jl) backend for automatic differentiation.
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Defined by [ADTypes.jl](https://github.com/SciML/ADTypes.jl).
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# Constructors
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AutoPolyesterForwardDiff(; chunksize=nothing, tag=nothing)
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# Type parameters
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- `chunksize`: the preferred [chunk size](https://juliadiff.org/ForwardDiff.jl/stable/user/advanced/#Configuring-Chunk-Size) to evaluate several derivatives at once
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- `chunksize`: the preferred [chunk size](https://juliadiff.org/ForwardDiff.jl/stable/user/advanced/#Configuring-Chunk-Size) to evaluate several derivatives at once
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# Fields
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- `tag::T`: a [custom tag](https://juliadiff.org/ForwardDiff.jl/release-0.10/user/advanced.html#Custom-tags-and-tag-checking-1) to handle nested differentiation calls (usually not necessary)
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- `tag::T`: a [custom tag](https://juliadiff.org/ForwardDiff.jl/release-0.10/user/advanced.html#Custom-tags-and-tag-checking-1) to handle nested differentiation calls (usually not necessary)
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"""
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struct AutoPolyesterForwardDiff{chunksize, T} <: AbstractADType
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tag::T
@@ -189,7 +190,7 @@ Defined by [ADTypes.jl](https://github.com/SciML/ADTypes.jl).
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# Fields
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- `compile::Bool`: whether to [compile the tape](https://juliadiff.org/ReverseDiff.jl/api/#ReverseDiff.compile) prior to differentiation
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- `compile::Bool`: whether to [compile the tape](https://juliadiff.org/ReverseDiff.jl/api/#ReverseDiff.compile) prior to differentiation
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"""
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Base.@kwdef struct AutoReverseDiff <: AbstractADType
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compile::Bool = false
@@ -225,7 +226,7 @@ Defined by [ADTypes.jl](https://github.com/SciML/ADTypes.jl).
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# Fields
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- `safe_mode::Bool`: whether to run additional checks to catch errors early. On by default. Turn off to maximise performance if your code runs correctly.
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- `safe_mode::Bool`: whether to run additional checks to catch errors early. On by default. Turn off to maximise performance if your code runs correctly.
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"""
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Base.@kwdef struct AutoTapir <: AbstractADType
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safe_mode::Bool = true

‎src/mode.jl

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@@ -5,10 +5,10 @@ Abstract supertype for the traits identifying differentiation modes.
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# Subtypes
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- [`ForwardMode`](@ref)
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- [`ReverseMode`](@ref)
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- [`ForwardOrReverseMode`](@ref)
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- [`SymbolicMode`](@ref)
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- [`ForwardMode`](@ref)
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- [`ReverseMode`](@ref)
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- [`ForwardOrReverseMode`](@ref)
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- [`SymbolicMode`](@ref)
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"""
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abstract type AbstractMode end
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Trait for AD choices that can work either in [`ForwardMode`](@ref) or [`ReverseMode`](@ref), depending on their configuration.
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!!! warning
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This trait should rarely be used, because more precise dispatches to [`ForwardMode`](@ref) or [`ReverseMode`](@ref) should be defined.
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This trait should rarely be used, because more precise dispatches to [`ForwardMode`](@ref) or [`ReverseMode`](@ref) should be defined.
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"""
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struct ForwardOrReverseMode <: AbstractMode end
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‎src/sparse.jl

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@@ -7,8 +7,8 @@ Abstract supertype for sparsity pattern detectors.
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# Required methods
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- [`jacobian_sparsity`](@ref)
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- [`hessian_sparsity`](@ref)
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- [`jacobian_sparsity`](@ref)
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- [`hessian_sparsity`](@ref)
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"""
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abstract type AbstractSparsityDetector end
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# See also
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- [`AbstractSparsityDetector`](@ref)
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- [`AbstractSparsityDetector`](@ref)
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"""
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struct NoSparsityDetector <: AbstractSparsityDetector end
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# Required methods
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- [`column_coloring`](@ref)
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- [`row_coloring`](@ref)
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- [`symmetric_coloring`](@ref)
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- [`column_coloring`](@ref)
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- [`row_coloring`](@ref)
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- [`symmetric_coloring`](@ref)
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# Note
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The terminology and definitions are taken from the following paper:
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> "What Color Is Your Jacobian? Graph Coloring for Computing Derivatives"
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>
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>
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> Assefaw Hadish Gebremedhin, Fredrik Manne, and Alex Pothen (2005)
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>
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>
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> https://epubs.siam.org/doi/10.1137/S0036144504444711
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"""
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abstract type AbstractColoringAlgorithm end
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The result is a coloring vector `c` of length `size(M, 1) == size(M, 2)` such that for every non-zero coefficient `M[i, j]`, at least one of the following conditions holds:
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- column `j` is the only column of its color `c[j]` with a non-zero coefficient in row `i`;
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- column `i` is the only column of its color `c[i]` with a non-zero coefficient in row `j`.
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- column `j` is the only column of its color `c[j]` with a non-zero coefficient in row `i`;
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- column `i` is the only column of its color `c[i]` with a non-zero coefficient in row `j`.
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"""
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function symmetric_coloring end
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# See also
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- [`AbstractColoringAlgorithm`](@ref)
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- [`AbstractColoringAlgorithm`](@ref)
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"""
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struct NoColoringAlgorithm <: AbstractColoringAlgorithm end
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# Fields
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- `dense_ad::D`: the underlying AD package, subtyping [`AbstractADType`](@ref)
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- `sparsity_detector::S`: the sparsity pattern detector, subtyping [`AbstractSparsityDetector`](@ref)
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- `coloring_algorithm::C`: the coloring algorithm, subtyping [`AbstractColoringAlgorithm`](@ref)
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- `dense_ad::D`: the underlying AD package, subtyping [`AbstractADType`](@ref)
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- `sparsity_detector::S`: the sparsity pattern detector, subtyping [`AbstractSparsityDetector`](@ref)
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- `coloring_algorithm::C`: the coloring algorithm, subtyping [`AbstractColoringAlgorithm`](@ref)
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# Constructors
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dense_ad(ad::AutoSparse)::AbstractADType
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Return the underlying AD package for a sparse AD choice.
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# See also
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- [`AutoSparse`](@ref)
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- [`AutoSparse`](@ref)
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"""
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dense_ad(ad::AutoSparse) = ad.dense_ad
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# See also
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- [`AutoSparse`](@ref)
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- [`AbstractSparsityDetector`](@ref)
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- [`AutoSparse`](@ref)
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- [`AbstractSparsityDetector`](@ref)
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"""
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sparsity_detector(ad::AutoSparse) = ad.sparsity_detector
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# See also
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- [`AutoSparse`](@ref)
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- [`AbstractColoringAlgorithm`](@ref)
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- [`AutoSparse`](@ref)
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- [`AbstractColoringAlgorithm`](@ref)
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"""
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coloring_algorithm(ad::AutoSparse) = ad.coloring_algorithm

‎test/dense.jl

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@@ -139,8 +139,8 @@ end
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@test ad isa AutoTapir
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@test mode(ad) isa ReverseMode
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@test ad.safe_mode
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ad = AutoTapir(; safe_mode=false)
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ad = AutoTapir(; safe_mode = false)
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@test !ad.safe_mode
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end
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