(Kind-of) Type Stability Fixes for No Chunksize Specified

Project.toml

@@ -1,7 +1,7 @@
 name = "SparseDiffTools"
 uuid = "47a9eef4-7e08-11e9-0b38-333d64bd3804"
 authors = ["Pankaj Mishra <[email protected]>", "Chris Rackauckas <[email protected]>"]
-version = "2.9.1"
+version = "2.9.2"
 
 [deps]
 ADTypes = "47edcb42-4c32-4615-8424-f2b9edc5f35b"

ext/SparseDiffToolsSymbolicsExt.jl

@@ -4,7 +4,7 @@ using SparseDiffTools, Symbolics
 import SparseDiffTools: AbstractSparseADType
 
 function (alg::SymbolicsSparsityDetection)(ad::AbstractSparseADType, f, x; fx = nothing,
-    kwargs...)
+        kwargs...)
     fx = fx === nothing ? similar(f(x)) : dx
     f!(y, x) = (y .= f(x))
     J = Symbolics.jacobian_sparsity(f!, fx, x)

ext/SparseDiffToolsZygoteExt.jl

@@ -75,7 +75,7 @@ end
 end
 
 function autoback_hesvec!(dy, f, x, v, cache1 = _default_autoback_hesvec_cache(x, v),
-    cache2 = _default_autoback_hesvec_cache(x, v))
+        cache2 = _default_autoback_hesvec_cache(x, v))
     g = let f = f
         (dx, x) -> dx .= first(Zygote.gradient(f, x))
     end
@@ -140,7 +140,7 @@ end
 
 # prefer non in-place method
 function (L::AutoDiffVJP{<:AutoZygote, IIP, true})(dv, v, p, t;
-    VJP_input = nothing) where {IIP}
+        VJP_input = nothing) where {IIP}
     # ignore VJP_input as pullback was computed in update_coefficients!(...)
 
     _dv = L(v, p, t; VJP_input = VJP_input)

src/coloring/acyclic_coloring.jl

@@ -73,9 +73,9 @@ the induced 2-colored subgraphs/trees where the id of set is an integer
 representing an edge of graph 'g'
 """
 function prevent_cycle!(first_visit_to_tree::AbstractVector{<:Tuple{Integer, Integer}},
-    forbidden_colors::AbstractVector{<:Integer}, v::Integer, w::Integer, x::Integer,
-    g::Graphs.AbstractGraph, two_colored_forest::DisjointSets{<:Integer},
-    color::AbstractVector{<:Integer})
+        forbidden_colors::AbstractVector{<:Integer}, v::Integer, w::Integer, x::Integer,
+        g::Graphs.AbstractGraph, two_colored_forest::DisjointSets{<:Integer},
+        color::AbstractVector{<:Integer})
     e = find(w, x, g, two_colored_forest)
     p, q = first_visit_to_tree[e]
 
@@ -97,8 +97,8 @@ Disjoint set is used to store stars in sets, which are identified through key
 edges present in g.
 """
 function grow_star!(two_colored_forest::DisjointSets{<:Integer},
-    first_neighbor::AbstractVector{<:Tuple{Integer, Integer}}, v::Integer, w::Integer,
-    g::Graphs.AbstractGraph, color::AbstractVector{<:Integer})
+        first_neighbor::AbstractVector{<:Tuple{Integer, Integer}}, v::Integer, w::Integer,
+        g::Graphs.AbstractGraph, color::AbstractVector{<:Integer})
     insert_new_tree!(two_colored_forest, v, w, g)
     p, q = first_neighbor[color[w]]
 
@@ -119,7 +119,7 @@ Subroutine to merge trees present in the disjoint set which have a
 common edge.
 """
 function merge_trees!(two_colored_forest::DisjointSets{<:Integer}, v::Integer, w::Integer,
-    x::Integer, g::Graphs.AbstractGraph)
+        x::Integer, g::Graphs.AbstractGraph)
     e1 = find(v, w, g, two_colored_forest)
     e2 = find(w, x, g, two_colored_forest)
     if e1 != e2
@@ -135,7 +135,7 @@ creates a new singleton set in the disjoint set 'two_colored_forest' consisting
 of the edge connecting v and w in the graph g
 """
 function insert_new_tree!(two_colored_forest::DisjointSets{<:Integer}, v::Integer,
-    w::Integer, g::Graphs.AbstractGraph)
+        w::Integer, g::Graphs.AbstractGraph)
     edge_index = find_edge_index(v, w, g)
     push!(two_colored_forest, edge_index)
 end
@@ -157,7 +157,7 @@ Returns the root of the disjoint set to which the edge connecting vertices w and
 in the graph g belongs to
 """
 function find(w::Integer, x::Integer, g::Graphs.AbstractGraph,
-    two_colored_forest::DisjointSets{<:Integer})
+        two_colored_forest::DisjointSets{<:Integer})
     edge_index = find_edge_index(w, x, g)
     return find_root!(two_colored_forest, edge_index)
 end

src/coloring/backtracking_coloring.jl

@@ -115,7 +115,7 @@ Returns an uncolored vertex from the partially
 colored graph which has the highest degree
 """
 function uncolored_vertex_of_maximal_degree(A::AbstractVector{<:Integer},
-    F::AbstractVector{<:Integer})
+        F::AbstractVector{<:Integer})
     for v in A
         if F[v] == 0
             return v
@@ -145,8 +145,8 @@ g: Graph to be colored
 opt: Current optimal number of colors to be used in the coloring of graph g
 """
 function free_colors(x::Integer, A::AbstractVector{<:Integer},
-    colors::AbstractVector{<:Integer}, F::Vector{Integer}, g::Graphs.AbstractGraph,
-    opt::Integer)
+        colors::AbstractVector{<:Integer}, F::Vector{Integer}, g::Graphs.AbstractGraph,
+        opt::Integer)
     index = -1
 
     freecolors = zeros(Int, 0)
@@ -187,7 +187,7 @@ Returns least index i such that color of vertex
 A[i] is equal to `opt` (optimal chromatic number)
 """
 function least_index(F::AbstractVector{<:Integer}, A::AbstractVector{<:Integer},
-    opt::Integer)
+        opt::Integer)
     for i in eachindex(A)
         if F[A[i]] == opt
             return i
@@ -202,7 +202,7 @@ Uncolors all vertices A[i] where i is
 greater than or equal to start
 """
 function uncolor_all!(F::AbstractVector{<:Integer}, A::AbstractVector{<:Integer},
-    start::Integer)
+        start::Integer)
     for i in start:length(A)
         F[A[i]] = 0
     end

src/coloring/high_level.jl

@@ -21,8 +21,8 @@ If `ArrayInterface.fast_matrix_colors(A)` is true, then uses
 `ArrayInterface.matrix_colors(A)` to compute the matrix colors.
 """
 function ArrayInterface.matrix_colors(A::AbstractMatrix,
-    alg::SparseDiffToolsColoringAlgorithm = GreedyD1Color();
-    partition_by_rows::Bool = false)
+        alg::SparseDiffToolsColoringAlgorithm = GreedyD1Color();
+        partition_by_rows::Bool = false)
 
     # If fast algorithm for matrix coloring exists use that
     if !partition_by_rows

src/coloring/matrix2graph.jl

@@ -33,7 +33,7 @@ Note that the sparsity pattern is defined by structural nonzeroes, ie includes e
 stored zeros.
 """
 function matrix2graph(sparse_matrix::AbstractSparseMatrix{<:Number},
-    partition_by_rows::Bool = true)
+        partition_by_rows::Bool = true)
     (rows_index, cols_index, _) = findnz(sparse_matrix)
 
     ncols = size(sparse_matrix, 2)

src/differentiation/compute_hessian_ad.jl

@@ -20,9 +20,9 @@ function make_hessian_buffers(colorvec, x)
 end
 
 function ForwardColorHesCache(f, x::AbstractVector{<:Number},
-    colorvec::AbstractVector{<:Integer} = eachindex(x),
-    sparsity::Union{AbstractMatrix, Nothing} = nothing,
-    g! = (G, x, grad_config) -> ForwardDiff.gradient!(G, f, x, grad_config))
+        colorvec::AbstractVector{<:Integer} = eachindex(x),
+        sparsity::Union{AbstractMatrix, Nothing} = nothing,
+        g! = (G, x, grad_config) -> ForwardDiff.gradient!(G, f, x, grad_config))
     ncolors, D, buffer, G, G2 = make_hessian_buffers(colorvec, x)
     grad_config = ForwardDiff.GradientConfig(f, x)
 
@@ -46,7 +46,7 @@ function ForwardColorHesCache(f, x::AbstractVector{<:Number},
 end
 
 function numauto_color_hessian!(H::AbstractMatrix{<:Number}, f, x::AbstractArray{<:Number},
-    hes_cache::ForwardColorHesCache; safe = true)
+        hes_cache::ForwardColorHesCache; safe = true)
     ϵ = cbrt(eps(eltype(x)))
     for j in 1:(hes_cache.ncolors)
         x .+= ϵ .* @view hes_cache.D[:, j]
@@ -67,23 +67,23 @@ function numauto_color_hessian!(H::AbstractMatrix{<:Number}, f, x::AbstractArray
 end
 
 function numauto_color_hessian!(H::AbstractMatrix{<:Number}, f, x::AbstractArray{<:Number},
-    colorvec::AbstractVector{<:Integer} = eachindex(x),
-    sparsity::Union{AbstractMatrix, Nothing} = nothing)
+        colorvec::AbstractVector{<:Integer} = eachindex(x),
+        sparsity::Union{AbstractMatrix, Nothing} = nothing)
     hes_cache = ForwardColorHesCache(f, x, colorvec, sparsity)
     numauto_color_hessian!(H, f, x, hes_cache)
     return H
 end
 
 function numauto_color_hessian(f, x::AbstractArray{<:Number},
-    hes_cache::ForwardColorHesCache)
+        hes_cache::ForwardColorHesCache)
     H = convert.(eltype(x), hes_cache.sparsity)
     numauto_color_hessian!(H, f, x, hes_cache)
     return H
 end
 
 function numauto_color_hessian(f, x::AbstractArray{<:Number},
-    colorvec::AbstractVector{<:Integer} = eachindex(x),
-    sparsity::Union{AbstractMatrix, Nothing} = nothing)
+        colorvec::AbstractVector{<:Integer} = eachindex(x),
+        sparsity::Union{AbstractMatrix, Nothing} = nothing)
     hes_cache = ForwardColorHesCache(f, x, colorvec, sparsity)
     H = convert.(eltype(x), hes_cache.sparsity)
     numauto_color_hessian!(H, f, x, hes_cache)
@@ -102,9 +102,9 @@ end
 struct AutoAutoTag end
 
 function ForwardAutoColorHesCache(f, x::AbstractVector{V},
-    colorvec::AbstractVector{<:Integer} = eachindex(x),
-    sparsity::Union{AbstractMatrix, Nothing} = nothing,
-    tag::ForwardDiff.Tag = ForwardDiff.Tag(AutoAutoTag(), V)) where {V}
+        colorvec::AbstractVector{<:Integer} = eachindex(x),
+        sparsity::Union{AbstractMatrix, Nothing} = nothing,
+        tag::ForwardDiff.Tag = ForwardDiff.Tag(AutoAutoTag(), V)) where {V}
     if sparsity === nothing
         sparsity = sparse(ones(length(x), length(x)))
     end
@@ -124,28 +124,28 @@ function ForwardAutoColorHesCache(f, x::AbstractVector{V},
 end
 
 function autoauto_color_hessian!(H::AbstractMatrix{<:Number}, f, x::AbstractArray{<:Number},
-    hes_cache::ForwardAutoColorHesCache)
+        hes_cache::ForwardAutoColorHesCache)
     forwarddiff_color_jacobian!(H, hes_cache.grad!, x, hes_cache.jac_cache)
 end
 
 function autoauto_color_hessian!(H::AbstractMatrix{<:Number}, f, x::AbstractArray{<:Number},
-    colorvec::AbstractVector{<:Integer} = eachindex(x),
-    sparsity::Union{AbstractMatrix, Nothing} = nothing)
+        colorvec::AbstractVector{<:Integer} = eachindex(x),
+        sparsity::Union{AbstractMatrix, Nothing} = nothing)
     hes_cache = ForwardAutoColorHesCache(f, x, colorvec, sparsity)
     autoauto_color_hessian!(H, f, x, hes_cache)
     return H
 end
 
 function autoauto_color_hessian(f, x::AbstractArray{<:Number},
-    hes_cache::ForwardAutoColorHesCache)
+        hes_cache::ForwardAutoColorHesCache)
     H = convert.(eltype(x), hes_cache.sparsity)
     autoauto_color_hessian!(H, f, x, hes_cache)
     return H
 end
 
 function autoauto_color_hessian(f, x::AbstractArray{<:Number},
-    colorvec::AbstractVector{<:Integer} = eachindex(x),
-    sparsity::Union{AbstractMatrix, Nothing} = nothing)
+        colorvec::AbstractVector{<:Integer} = eachindex(x),
+        sparsity::Union{AbstractMatrix, Nothing} = nothing)
     hes_cache = ForwardAutoColorHesCache(f, x, colorvec, sparsity)
     H = convert.(eltype(x), hes_cache.sparsity)
     autoauto_color_hessian!(H, f, x, hes_cache)

src/differentiation/compute_jacobian_ad.jl

@@ -20,7 +20,7 @@ const default_chunk_size = ForwardDiff.pickchunksize
 const SMALLTAG = typeof(ForwardDiff.Tag(missing, Float64))
 
 function ForwardColorJacCache(f::F, x, _chunksize = nothing; dx = nothing, tag = nothing,
-    colorvec = 1:length(x), sparsity::Union{AbstractArray, Nothing} = nothing) where {F}
+        colorvec = 1:length(x), sparsity::Union{AbstractArray, Nothing} = nothing) where {F}
     if _chunksize isa Nothing
         chunksize = ForwardDiff.pickchunksize(maximum(colorvec))
     else
@@ -105,13 +105,13 @@ end
 end
 
 function forwarddiff_color_jacobian(f::F,
-    x::AbstractArray{<:Number};
-    colorvec = 1:length(x),
-    sparsity = nothing,
-    jac_prototype = nothing,
-    chunksize = nothing,
-    dx = sparsity === nothing && jac_prototype === nothing ?
-         nothing : copy(x)) where {F} #if dx is nothing, we will estimate dx at the cost of a function call
+        x::AbstractArray{<:Number};
+        colorvec = 1:length(x),
+        sparsity = nothing,
+        jac_prototype = nothing,
+        chunksize = nothing,
+        dx = sparsity === nothing && jac_prototype === nothing ?
+             nothing : copy(x)) where {F} #if dx is nothing, we will estimate dx at the cost of a function call
     if sparsity === nothing && jac_prototype === nothing
         cfg = if chunksize === nothing
             if typeof(x) <: StaticArrays.StaticArray
@@ -136,12 +136,12 @@ function forwarddiff_color_jacobian(f::F,
 end
 
 function forwarddiff_color_jacobian(J::AbstractArray{<:Number}, f::F,
-    x::AbstractArray{<:Number};
-    colorvec = 1:length(x),
-    sparsity = nothing,
-    jac_prototype = nothing,
-    chunksize = nothing,
-    dx = similar(x, size(J, 1))) where {F} #dx kwarg can be used to avoid re-allocating dx every time
+        x::AbstractArray{<:Number};
+        colorvec = 1:length(x),
+        sparsity = nothing,
+        jac_prototype = nothing,
+        chunksize = nothing,
+        dx = similar(x, size(J, 1))) where {F} #dx kwarg can be used to avoid re-allocating dx every time
     if sparsity === nothing && jac_prototype === nothing
         cfg = chunksize === nothing ? ForwardDiff.JacobianConfig(f, x) :
               ForwardDiff.JacobianConfig(f, x, ForwardDiff.Chunk(getsize(chunksize)))
@@ -154,8 +154,8 @@ function forwarddiff_color_jacobian(J::AbstractArray{<:Number}, f::F,
 end
 
 function forwarddiff_color_jacobian(f::F, x::AbstractArray{<:Number},
-    jac_cache::ForwardColorJacCache,
-    jac_prototype = nothing) where {F}
+        jac_cache::ForwardColorJacCache,
+        jac_prototype = nothing) where {F}
     if jac_prototype isa Nothing ? ArrayInterface.ismutable(x) :
        ArrayInterface.ismutable(jac_prototype)
         # Whenever J is mutable, we mutate it to avoid allocations
@@ -174,8 +174,8 @@ end
 
 # When J is mutable, this version of forwarddiff_color_jacobian will mutate J to avoid allocations
 function forwarddiff_color_jacobian(J::AbstractMatrix{<:Number}, f::F,
-    x::AbstractArray{<:Number},
-    jac_cache::ForwardColorJacCache) where {F}
+        x::AbstractArray{<:Number},
+        jac_cache::ForwardColorJacCache) where {F}
     t = jac_cache.t
     dx = jac_cache.dx
     p = jac_cache.p
@@ -248,8 +248,8 @@ end
 
 # When J is immutable, this version of forwarddiff_color_jacobian will avoid mutating J
 function forwarddiff_color_jacobian_immutable(f, x::AbstractArray{<:Number},
-    jac_cache::ForwardColorJacCache,
-    jac_prototype = nothing)
+        jac_cache::ForwardColorJacCache,
+        jac_prototype = nothing)
     t = jac_cache.t
     dx = jac_cache.dx
     p = jac_cache.p
@@ -313,15 +313,15 @@ function forwarddiff_color_jacobian_immutable(f, x::AbstractArray{<:Number},
 end
 
 function forwarddiff_color_jacobian!(J::AbstractMatrix{<:Number}, f,
-    x::AbstractArray{<:Number}; dx = similar(x, size(J, 1)), colorvec = 1:length(x),
-    sparsity = ArrayInterface.has_sparsestruct(J) ? J : nothing)
+        x::AbstractArray{<:Number}; dx = similar(x, size(J, 1)), colorvec = 1:length(x),
+        sparsity = ArrayInterface.has_sparsestruct(J) ? J : nothing)
     forwarddiff_color_jacobian!(J, f, x, ForwardColorJacCache(f, x; dx, colorvec, sparsity))
 end
 
 function forwarddiff_color_jacobian!(J::AbstractMatrix{<:Number},
-    f,
-    x::AbstractArray{<:Number},
-    jac_cache::ForwardColorJacCache)
+        f,
+        x::AbstractArray{<:Number},
+        jac_cache::ForwardColorJacCache)
     t = jac_cache.t
     fx = jac_cache.fx
     dx = jac_cache.dx