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improve cat design / performance
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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.
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vtjnash committed Apr 11, 2023
1 parent 1aa65c3 commit ac6a430
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Showing 10 changed files with 31 additions and 50 deletions.
9 changes: 3 additions & 6 deletions base/abstractarray.jl
Original file line number Diff line number Diff line change
Expand Up @@ -1982,19 +1982,18 @@ julia> cat(1, [2], [3;;]; dims=Val(2))
@inline cat(A...; dims) = _cat(dims, A...)
# `@constprop :aggressive` allows `catdims` to be propagated as constant improving return type inference
@constprop :aggressive _cat(catdims, A::AbstractArray{T}...) where {T} = _cat_t(catdims, T, A...)
const ArrayConcatGroup = Union{Number, AbstractArray}

# 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)
Expand Down Expand Up @@ -2144,8 +2143,6 @@ 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...)

function typed_hvcat(::Type{T}, rows::Tuple{Vararg{Int}}, xs::Number...) where T
nr = length(rows)
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12 changes: 0 additions & 12 deletions base/array.jl
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Expand Up @@ -2001,18 +2001,6 @@ 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 ##
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13 changes: 12 additions & 1 deletion src/staticdata_utils.c
Original file line number Diff line number Diff line change
Expand Up @@ -847,6 +847,9 @@ static jl_array_t *jl_verify_edges(jl_array_t *targets, size_t minworld)
jl_value_t *expected = jl_array_ptr_ref(targets, i * 3 + 2);
size_t min_valid = 0;
size_t max_valid = ~(size_t)0;
uint64_t t0 = uv_hrtime() - jl_gc_total_hrtime();
uint64_t b0; jl_gc_get_total_bytes(&b0);
//if(1);else
if (invokesig) {
assert(callee && "unsupported edge");
jl_methtable_t *mt = jl_method_get_table(((jl_method_instance_t*)callee)->def.method);
Expand Down Expand Up @@ -917,9 +920,16 @@ static jl_array_t *jl_verify_edges(jl_array_t *targets, size_t minworld)
jl_array_ptr_1d_push(_jl_debug_method_invalidation, loctag);
jl_array_ptr_1d_push(_jl_debug_method_invalidation, matches);
}
//uint64_t t1 = uv_hrtime() - jl_gc_total_hrtime();
//uint64_t b1; jl_gc_get_total_bytes(&b1);
//ios_printf(ios_stdout, "\n%u\t%u\t%d\t", ((unsigned)(t1 - t0))/1000u, ((unsigned)(b1 - b0))/1024u, max_valid == ~(size_t)0);
//jl_static_show((JL_STREAM*)ios_stdout, (jl_value_t*)invokesig);
//ios_printf(ios_stdout, "\t");
//jl_static_show((JL_STREAM*)ios_stdout, (jl_value_t*)callee);
////
//jl_static_show((JL_STREAM*)ios_stderr, (jl_value_t*)invokesig);
//jl_static_show((JL_STREAM*)ios_stderr, (jl_value_t*)callee);
//ios_puts(valid ? "valid\n" : "INVALID\n", ios_stderr);
//ios_puts(max_valid == ~(size_t)0 ? "valid\n" : "INVALID\n", ios_stderr);
}
JL_GC_POP();
return maxvalids;
Expand Down Expand Up @@ -1066,6 +1076,7 @@ static void jl_insert_backedges(jl_array_t *edges, jl_array_t *ext_targets, jl_a
// determine which CodeInstance objects are still valid in our image
jl_array_t *valids = jl_verify_edges(ext_targets, minworld);
JL_GC_PUSH1(&valids);

valids = jl_verify_methods(edges, valids); // consumes edges valids, initializes methods valids
jl_verify_graph(edges, valids); // propagates methods valids for each edge
size_t i, l;
Expand Down
26 changes: 6 additions & 20 deletions stdlib/LinearAlgebra/src/special.jl
Original file line number Diff line number Diff line change
Expand Up @@ -323,26 +323,12 @@ end
==(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} = AbstractTriangular{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)
Expand Down
13 changes: 6 additions & 7 deletions stdlib/LinearAlgebra/src/uniformscaling.jl
Original file line number Diff line number Diff line change
Expand Up @@ -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)
Expand All @@ -445,9 +444,9 @@ 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}...) = _hvcat(rows, 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"))
Expand Down
4 changes: 2 additions & 2 deletions stdlib/SparseArrays.version
Original file line number Diff line number Diff line change
@@ -1,4 +1,4 @@
SPARSEARRAYS_BRANCH = main
SPARSEARRAYS_SHA1 = 8affe9e499379616e33fc60a24bb31500e8423d7
SPARSEARRAYS_BRANCH = jn/better-cats
SPARSEARRAYS_SHA1 = 42a02aed4e8499314441ae09f45adc0505ee43b9
SPARSEARRAYS_GIT_URL := https://github.com/JuliaSparse/SparseArrays.jl.git
SPARSEARRAYS_TAR_URL = https://api.github.com/repos/JuliaSparse/SparseArrays.jl/tarball/$1

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