improve cat design / performance (#49322)

This used to make a lot of references to design issues with the
SparseArrays package (#2326 /
#20815), which result in a
non-sensical dispatch arrangement, and contribute to a slow loading
experience do to the nonsense Unions that must be checked by subtyping.

base/abstractarray.jl

@@ -1651,7 +1651,7 @@ function _typed_hcat(::Type{T}, A::AbstractVecOrTuple{AbstractVecOrMat}) where T
  for j = 1:nargs
  Aj = A[j]
  if size(Aj, 1) != nrows
- throw(ArgumentError("number of rows of each array must match (got $(map(x->size(x,1), A)))"))
+ throw(DimensionMismatch("number of rows of each array must match (got $(map(x->size(x,1), A)))"))
  end
  dense &= isa(Aj,Array)
  nd = ndims(Aj)
@@ -1686,7 +1686,7 @@ function _typed_vcat(::Type{T}, A::AbstractVecOrTuple{AbstractVecOrMat}) where T
  ncols = size(A[1], 2)
  for j = 2:nargs
  if size(A[j], 2) != ncols
- throw(ArgumentError("number of columns of each array must match (got $(map(x->size(x,2), A)))"))
+ throw(DimensionMismatch("number of columns of each array must match (got $(map(x->size(x,2), A)))"))
  end
  end
  B = similar(A[1], T, nrows, ncols)
@@ -1984,16 +1984,14 @@ julia> cat(1, [2], [3;;]; dims=Val(2))
 
 # The specializations for 1 and 2 inputs are important
 # especially when running with --inline=no, see #11158
-# The specializations for Union{AbstractVecOrMat,Number} are necessary
-# to have more specialized methods here than in LinearAlgebra/uniformscaling.jl
 vcat(A::AbstractArray) = cat(A; dims=Val(1))
 vcat(A::AbstractArray, B::AbstractArray) = cat(A, B; dims=Val(1))
 vcat(A::AbstractArray...) = cat(A...; dims=Val(1))
-vcat(A::Union{AbstractVecOrMat,Number}...) = cat(A...; dims=Val(1))
+vcat(A::Union{AbstractArray,Number}...) = cat(A...; dims=Val(1))
 hcat(A::AbstractArray) = cat(A; dims=Val(2))
 hcat(A::AbstractArray, B::AbstractArray) = cat(A, B; dims=Val(2))
 hcat(A::AbstractArray...) = cat(A...; dims=Val(2))
-hcat(A::Union{AbstractVecOrMat,Number}...) = cat(A...; dims=Val(2))
+hcat(A::Union{AbstractArray,Number}...) = cat(A...; dims=Val(2))
 
 typed_vcat(T::Type, A::AbstractArray) = _cat_t(Val(1), T, A)
 typed_vcat(T::Type, A::AbstractArray, B::AbstractArray) = _cat_t(Val(1), T, A, B)
@@ -2055,8 +2053,8 @@ julia> hvcat((2,2,2), a,b,c,d,e,f) == hvcat(2, a,b,c,d,e,f)
 true
 ```
 """
-hvcat(rows::Tuple{Vararg{Int}}, xs::AbstractVecOrMat...) = typed_hvcat(promote_eltype(xs...), rows, xs...)
-hvcat(rows::Tuple{Vararg{Int}}, xs::AbstractVecOrMat{T}...) where {T} = typed_hvcat(T, rows, xs...)
+hvcat(rows::Tuple{Vararg{Int}}, xs::AbstractArray...) = typed_hvcat(promote_eltype(xs...), rows, xs...)
+hvcat(rows::Tuple{Vararg{Int}}, xs::AbstractArray{T}...) where {T} = typed_hvcat(T, rows, xs...)
 
 function typed_hvcat(::Type{T}, rows::Tuple{Vararg{Int}}, as::AbstractVecOrMat...) where T
  nbr = length(rows) # number of block rows
@@ -2084,16 +2082,16 @@ function typed_hvcat(::Type{T}, rows::Tuple{Vararg{Int}}, as::AbstractVecOrMat..
  Aj = as[a+j-1]
  szj = size(Aj,2)
  if size(Aj,1) != szi
- throw(ArgumentError("mismatched height in block row $(i) (expected $szi, got $(size(Aj,1)))"))
+ throw(DimensionMismatch("mismatched height in block row $(i) (expected $szi, got $(size(Aj,1)))"))
  end
  if c-1+szj > nc
- throw(ArgumentError("block row $(i) has mismatched number of columns (expected $nc, got $(c-1+szj))"))
+ throw(DimensionMismatch("block row $(i) has mismatched number of columns (expected $nc, got $(c-1+szj))"))
  end
  out[r:r-1+szi, c:c-1+szj] = Aj
  c += szj
  end
  if c != nc+1
- throw(ArgumentError("block row $(i) has mismatched number of columns (expected $nc, got $(c-1))"))
+ throw(DimensionMismatch("block row $(i) has mismatched number of columns (expected $nc, got $(c-1))"))
  end
  r += szi
  a += rows[i]
@@ -2115,7 +2113,7 @@ function hvcat(rows::Tuple{Vararg{Int}}, xs::T...) where T<:Number
  k = 1
  @inbounds for i=1:nr
  if nc != rows[i]
- throw(ArgumentError("row $(i) has mismatched number of columns (expected $nc, got $(rows[i]))"))
+ throw(DimensionMismatch("row $(i) has mismatched number of columns (expected $nc, got $(rows[i]))"))
  end
  for j=1:nc
  a[i,j] = xs[k]
@@ -2144,14 +2142,14 @@ end
 hvcat(rows::Tuple{Vararg{Int}}, xs::Number...) = typed_hvcat(promote_typeof(xs...), rows, xs...)
 hvcat(rows::Tuple{Vararg{Int}}, xs...) = typed_hvcat(promote_eltypeof(xs...), rows, xs...)
 # the following method is needed to provide a more specific one compared to LinearAlgebra/uniformscaling.jl
-hvcat(rows::Tuple{Vararg{Int}}, xs::Union{AbstractVecOrMat,Number}...) = typed_hvcat(promote_eltypeof(xs...), rows, xs...)
+hvcat(rows::Tuple{Vararg{Int}}, xs::Union{AbstractArray,Number}...) = typed_hvcat(promote_eltypeof(xs...), rows, xs...)
 
 function typed_hvcat(::Type{T}, rows::Tuple{Vararg{Int}}, xs::Number...) where T
  nr = length(rows)
  nc = rows[1]
  for i = 2:nr
  if nc != rows[i]
- throw(ArgumentError("row $(i) has mismatched number of columns (expected $nc, got $(rows[i]))"))
+ throw(DimensionMismatch("row $(i) has mismatched number of columns (expected $nc, got $(rows[i]))"))
  end
  end
  hvcat_fill!(Matrix{T}(undef, nr, nc), xs)
@@ -2319,7 +2317,7 @@ function _typed_hvncat(::Type{T}, ::Val{N}, as::AbstractArray...) where {T, N}
  Ndim += cat_size(as[i], N)
  nd = max(nd, cat_ndims(as[i]))
  for d ∈ 1:N - 1
- cat_size(as[1], d) == cat_size(as[i], d) || throw(ArgumentError("mismatched size along axis $d in element $i"))
+ cat_size(as[1], d) == cat_size(as[i], d) || throw(DimensionMismatch("mismatched size along axis $d in element $i"))
  end
  end
 
@@ -2346,7 +2344,7 @@ function _typed_hvncat(::Type{T}, ::Val{N}, as...) where {T, N}
  nd = max(nd, cat_ndims(as[i]))
  for d ∈ 1:N-1
  cat_size(as[i], d) == 1 ||
- throw(ArgumentError("all dimensions of element $i other than $N must be of length 1"))
+ throw(DimensionMismatch("all dimensions of element $i other than $N must be of length 1"))
  end
  end
 
@@ -2463,7 +2461,7 @@ function _typed_hvncat_dims(::Type{T}, dims::NTuple{N, Int}, row_first::Bool, as
  for dd ∈ 1:N
  dd == d && continue
  if cat_size(as[startelementi], dd) != cat_size(as[i], dd)
- throw(ArgumentError("incompatible shape in element $i"))
+ throw(DimensionMismatch("incompatible shape in element $i"))
  end
  end
  end
@@ -2500,18 +2498,18 @@ function _typed_hvncat_dims(::Type{T}, dims::NTuple{N, Int}, row_first::Bool, as
  elseif currentdims[d] < outdims[d] # dimension in progress
  break
  else # exceeded dimension
- throw(ArgumentError("argument $i has too many elements along axis $d"))
+ throw(DimensionMismatch("argument $i has too many elements along axis $d"))
  end
  end
  end
  elseif currentdims[d1] > outdims[d1] # exceeded dimension
- throw(ArgumentError("argument $i has too many elements along axis $d1"))
+ throw(DimensionMismatch("argument $i has too many elements along axis $d1"))
  end
  end
 
  outlen = prod(outdims)
  elementcount == outlen ||
- throw(ArgumentError("mismatched number of elements; expected $(outlen), got $(elementcount)"))
+ throw(DimensionMismatch("mismatched number of elements; expected $(outlen), got $(elementcount)"))
 
  # copy into final array
  A = cat_similar(as[1], T, outdims)
@@ -2572,8 +2570,8 @@ function _typed_hvncat_shape(::Type{T}, shape::NTuple{N, Tuple}, row_first, as::
  if d == 1 || i == 1 || wasstartblock
  currentdims[d] += dsize
  elseif dsize != cat_size(as[i - 1], ad)
- throw(ArgumentError("argument $i has a mismatched number of elements along axis $ad; \
- expected $(cat_size(as[i - 1], ad)), got $dsize"))
+ throw(DimensionMismatch("argument $i has a mismatched number of elements along axis $ad; \
+  expected $(cat_size(as[i - 1], ad)), got $dsize"))
  end
 
  wasstartblock = blockcounts[d] == 1 # remember for next dimension
@@ -2583,15 +2581,15 @@ function _typed_hvncat_shape(::Type{T}, shape::NTuple{N, Tuple}, row_first, as::
  if outdims[d] == -1
  outdims[d] = currentdims[d]
  elseif outdims[d] != currentdims[d]
- throw(ArgumentError("argument $i has a mismatched number of elements along axis $ad; \
- expected $(abs(outdims[d] - (currentdims[d] - dsize))), got $dsize"))
+ throw(DimensionMismatch("argument $i has a mismatched number of elements along axis $ad; \
+  expected $(abs(outdims[d] - (currentdims[d] - dsize))), got $dsize"))
  end
  currentdims[d] = 0
  blockcounts[d] = 0
  shapepos[d] += 1
  d > 1 && (blockcounts[d - 1] == 0 ||
- throw(ArgumentError("shape in level $d is inconsistent; level counts must nest \
- evenly into each other")))
+ throw(DimensionMismatch("shape in level $d is inconsistent; level counts must nest \
+  evenly into each other")))
  end
  end
  end

base/array.jl

@@ -2041,18 +2041,6 @@ function vcat(arrays::Vector{T}...) where T
 end
 vcat(A::Vector...) = cat(A...; dims=Val(1)) # more special than SparseArrays's vcat
 
-# disambiguation with LinAlg/special.jl
-# Union{Number,Vector,Matrix} is for LinearAlgebra._DenseConcatGroup
-# VecOrMat{T} is for LinearAlgebra._TypedDenseConcatGroup
-hcat(A::Union{Number,Vector,Matrix}...) = cat(A...; dims=Val(2))
-hcat(A::VecOrMat{T}...) where {T} = typed_hcat(T, A...)
-vcat(A::Union{Number,Vector,Matrix}...) = cat(A...; dims=Val(1))
-vcat(A::VecOrMat{T}...) where {T} = typed_vcat(T, A...)
-hvcat(rows::Tuple{Vararg{Int}}, xs::Union{Number,Vector,Matrix}...) =
- typed_hvcat(promote_eltypeof(xs...), rows, xs...)
-hvcat(rows::Tuple{Vararg{Int}}, xs::VecOrMat{T}...) where {T} =
- typed_hvcat(T, rows, xs...)
-
 _cat(n::Integer, x::Integer...) = reshape([x...], (ntuple(Returns(1), n-1)..., length(x)))
 
 ## find ##

deps/checksums/SparseArrays-b4b0e721ada6e8cf5f6391aff4db307be69b0401.tar.gz/md5

@@ -0,0 +1 @@
+e59c1c57b97e17a73eba758d65022bd7

deps/checksums/SparseArrays-b4b0e721ada6e8cf5f6391aff4db307be69b0401.tar.gz/sha512

@@ -0,0 +1 @@
+ad88ebe77aaf1580e6d7ee7649ac5b812a23b9d9bf947f26babe9dd79902f6da11aa69bf63f22f67f6eae92a4c6e665cc3b950bb7c648c623e9cb4b9cb4daac4

stdlib/LinearAlgebra/src/special.jl

@@ -330,27 +330,11 @@ end
 ==(A::Bidiagonal, B::SymTridiagonal) = iszero(_evview(B)) && iszero(A.ev) && A.dv == B.dv
 ==(B::SymTridiagonal, A::Bidiagonal) = A == B
 
-# concatenation
-const _SpecialArrays = Union{Diagonal, Bidiagonal, Tridiagonal, SymTridiagonal}
-const _Symmetric_DenseArrays{T,A<:Matrix} = Symmetric{T,A}
-const _Hermitian_DenseArrays{T,A<:Matrix} = Hermitian{T,A}
-const _Triangular_DenseArrays{T,A<:Matrix} = UpperOrLowerTriangular{T,A}
-const _Annotated_DenseArrays = Union{_SpecialArrays, _Triangular_DenseArrays, _Symmetric_DenseArrays, _Hermitian_DenseArrays}
-const _Annotated_Typed_DenseArrays{T} = Union{_Triangular_DenseArrays{T}, _Symmetric_DenseArrays{T}, _Hermitian_DenseArrays{T}}
-const _DenseConcatGroup = Union{Number, Vector, Adjoint{<:Any,<:Vector}, Transpose{<:Any,<:Vector}, Matrix, _Annotated_DenseArrays}
-const _TypedDenseConcatGroup{T} = Union{Vector{T}, Adjoint{T,Vector{T}}, Transpose{T,Vector{T}}, Matrix{T}, _Annotated_Typed_DenseArrays{T}}
-
-promote_to_array_type(::Tuple{Vararg{Union{_DenseConcatGroup,UniformScaling}}}) = Matrix
-
-Base._cat(dims, xs::_DenseConcatGroup...) = Base._cat_t(dims, promote_eltype(xs...), xs...)
-vcat(A::_DenseConcatGroup...) = Base.typed_vcat(promote_eltype(A...), A...)
-hcat(A::_DenseConcatGroup...) = Base.typed_hcat(promote_eltype(A...), A...)
-hvcat(rows::Tuple{Vararg{Int}}, xs::_DenseConcatGroup...) = Base.typed_hvcat(promote_eltype(xs...), rows, xs...)
-# For performance, specially handle the case where the matrices/vectors have homogeneous eltype
-Base._cat(dims, xs::_TypedDenseConcatGroup{T}...) where {T} = Base._cat_t(dims, T, xs...)
-vcat(A::_TypedDenseConcatGroup{T}...) where {T} = Base.typed_vcat(T, A...)
-hcat(A::_TypedDenseConcatGroup{T}...) where {T} = Base.typed_hcat(T, A...)
-hvcat(rows::Tuple{Vararg{Int}}, xs::_TypedDenseConcatGroup{T}...) where {T} = Base.typed_hvcat(T, rows, xs...)
+# TODO: remove these deprecations (used by SparseArrays in the past)
+const _DenseConcatGroup = Union{}
+const _SpecialArrays = Union{}
+
+promote_to_array_type(::Tuple) = Matrix
 
 # factorizations
 function cholesky(S::RealHermSymComplexHerm{<:Real,<:SymTridiagonal}, ::NoPivot = NoPivot(); check::Bool = true)

stdlib/LinearAlgebra/src/uniformscaling.jl

@@ -408,7 +408,7 @@ end
 # so that we can re-use this code for sparse-matrix hcat etcetera.
 promote_to_arrays_(n::Int, ::Type, a::Number) = a
 promote_to_arrays_(n::Int, ::Type{Matrix}, J::UniformScaling{T}) where {T} = Matrix(J, n, n)
-promote_to_arrays_(n::Int, ::Type, A::AbstractVecOrMat) = A
+promote_to_arrays_(n::Int, ::Type, A::AbstractArray) = A
 promote_to_arrays(n,k, ::Type) = ()
 promote_to_arrays(n,k, ::Type{T}, A) where {T} = (promote_to_arrays_(n[k], T, A),)
 promote_to_arrays(n,k, ::Type{T}, A, B) where {T} =
@@ -417,17 +417,16 @@ promote_to_arrays(n,k, ::Type{T}, A, B, C) where {T} =
  (promote_to_arrays_(n[k], T, A), promote_to_arrays_(n[k+1], T, B), promote_to_arrays_(n[k+2], T, C))
 promote_to_arrays(n,k, ::Type{T}, A, B, Cs...) where {T} =
  (promote_to_arrays_(n[k], T, A), promote_to_arrays_(n[k+1], T, B), promote_to_arrays(n,k+2, T, Cs...)...)
-promote_to_array_type(A::Tuple{Vararg{Union{AbstractVecOrMat,UniformScaling,Number}}}) = Matrix
 
 _us2number(A) = A
 _us2number(J::UniformScaling) = J.λ
 
 for (f, _f, dim, name) in ((:hcat, :_hcat, 1, "rows"), (:vcat, :_vcat, 2, "cols"))
  @eval begin
- @inline $f(A::Union{AbstractVecOrMat,UniformScaling}...) = $_f(A...)
+ @inline $f(A::Union{AbstractArray,UniformScaling}...) = $_f(A...)
  # if there's a Number present, J::UniformScaling must be 1x1-dimensional
- @inline $f(A::Union{AbstractVecOrMat,UniformScaling,Number}...) = $f(map(_us2number, A)...)
- function $_f(A::Union{AbstractVecOrMat,UniformScaling,Number}...; array_type = promote_to_array_type(A))
+ @inline $f(A::Union{AbstractArray,UniformScaling,Number}...) = $f(map(_us2number, A)...)
+ function $_f(A::Union{AbstractArray,UniformScaling,Number}...; array_type = promote_to_array_type(A))
  n = -1
  for a in A
  if !isa(a, UniformScaling)
@@ -445,9 +444,8 @@ for (f, _f, dim, name) in ((:hcat, :_hcat, 1, "rows"), (:vcat, :_vcat, 2, "cols"
  end
 end
 
-hvcat(rows::Tuple{Vararg{Int}}, A::Union{AbstractVecOrMat,UniformScaling}...) = _hvcat(rows, A...)
-hvcat(rows::Tuple{Vararg{Int}}, A::Union{AbstractVecOrMat,UniformScaling,Number}...) = _hvcat(rows, A...)
-function _hvcat(rows::Tuple{Vararg{Int}}, A::Union{AbstractVecOrMat,UniformScaling,Number}...; array_type = promote_to_array_type(A))
+hvcat(rows::Tuple{Vararg{Int}}, A::Union{AbstractArray,UniformScaling,Number}...) = _hvcat(rows, A...)
+function _hvcat(rows::Tuple{Vararg{Int}}, A::Union{AbstractArray,UniformScaling,Number}...; array_type = promote_to_array_type(A))
  require_one_based_indexing(A...)
  nr = length(rows)
  sum(rows) == length(A) || throw(ArgumentError("mismatch between row sizes and number of arguments"))

stdlib/SparseArrays.version

@@ -1,4 +1,4 @@
 SPARSEARRAYS_BRANCH = main
-SPARSEARRAYS_SHA1 = 2c7f4d6d839e9a97027454a037bfa004c1eb34b0
+SPARSEARRAYS_SHA1 = b4b0e721ada6e8cf5f6391aff4db307be69b0401
 SPARSEARRAYS_GIT_URL := https://github.com/JuliaSparse/SparseArrays.jl.git
 SPARSEARRAYS_TAR_URL = https://api.github.com/repos/JuliaSparse/SparseArrays.jl/tarball/$1