Make sparse hvcat inferable

This also requires making sparse `vcat` and `hcat` inferable in the
vararg case which in turn requires a different way to determine the
resulting index type, now implemented similar to `promote_eltype`.

stdlib/SparseArrays/src/sparsematrix.jl

@@ -3274,6 +3274,10 @@ dropstored!(A::AbstractSparseMatrixCSC, ::Colon) = dropstored!(A, :, :)
 
 # Sparse concatenation
 
+promote_idxtype(::AbstractSparseMatrixCSC{<:Any, Ti}) where {Ti} = Ti
+promote_idxtype(::AbstractSparseMatrixCSC{<:Any, Ti}, X::AbstractSparseMatrixCSC...) where {Ti} =
+ promote_type(Ti, promote_idxtype(X...))
+
 function vcat(X::AbstractSparseMatrixCSC...)
  num = length(X)
  mX = Int[ size(x, 1) for x in X ]
@@ -3288,7 +3292,7 @@ function vcat(X::AbstractSparseMatrixCSC...)
  end
 
  Tv = promote_eltype(X...)
- Ti = promote_eltype(map(x->rowvals(x), X)...)
+ Ti = promote_idxtype(X...)
 
  nnzX = Int[ nnz(x) for x in X ]
  nnz_res = sum(nnzX)
@@ -3340,7 +3344,7 @@ function hcat(X::AbstractSparseMatrixCSC...)
  n = sum(nX)
 
  Tv = promote_eltype(X...)
- Ti = promote_eltype(map(x->rowvals(x), X)...)
+ Ti = promote_idxtype(X...)
 
  colptr = Vector{Ti}(undef, n+1)
  nnzX = Int[ nnz(x) for x in X ]

stdlib/SparseArrays/src/sparsevector.jl

@@ -1091,17 +1091,16 @@ function vcat(Xin::_SparseConcatGroup...)
  X = map(x -> SparseMatrixCSC(issparse(x) ? x : sparse(x)), Xin)
  vcat(X...)
 end
-function hvcat(rows::Tuple{Vararg{Int}}, X::_SparseConcatGroup...)
- nbr = length(rows) # number of block rows
-
- tmp_rows = Vector{SparseMatrixCSC}(undef, nbr)
- k = 0
- @inbounds for i = 1 : nbr
- tmp_rows[i] = hcat(X[(1 : rows[i]) .+ k]...)
- k += rows[i]
+hvcat(rows::Tuple{Vararg{Int}}, X::_SparseConcatGroup...) =
+ vcat(_hvcat_rows(rows, X...)...)
+function _hvcat_rows((row1, rows...)::Tuple{Vararg{Int}}, X::_SparseConcatGroup...)
+ if row1 ≤ 0
+ throw(ArgumentError("length of block row must be positive, got $row1"))
  end
- vcat(tmp_rows...)
+ # provide X[1] separately to convince inference that we don't call hcat() without arguments
+ return (hcat(X[1], X[2 : row1]...), _hvcat_rows(rows, X[row1+1:end]...)...)
 end
+_hvcat_rows(::Tuple{}, X::_SparseConcatGroup...) = ()
 
 # make sure UniformScaling objects are converted to sparse matrices for concatenation
 promote_to_array_type(A::Tuple{Vararg{Union{_SparseConcatGroup,UniformScaling}}}) = SparseMatrixCSC

stdlib/SparseArrays/test/sparse.jl

@@ -165,7 +165,7 @@ end
  sz34 = spzeros(3, 4)
  se77 = sparse(1.0I, 7, 7)
  @testset "h+v concatenation" begin
- @test [se44 sz42 sz41; sz34 se33] == se77
+ @test @inferred(hvcat((3, 2), se44, sz42, sz41, sz34, se33)) == se77 # [se44 sz42 sz41; sz34 se33]
  @test length(nonzeros([sp33 0I; 1I 0I])) == 6
  end
 
@@ -2201,7 +2201,7 @@ end
  # Test that concatenations of pairs of sparse matrices yield sparse arrays
  @test issparse(vcat(spmat, spmat))
  @test issparse(hcat(spmat, spmat))
- @test issparse(hvcat((2,), spmat, spmat))
+ @test issparse(@inferred(hvcat((2,), spmat, spmat)))
  @test issparse(cat(spmat, spmat; dims=(1,2)))
  # Test that concatenations of a sparse matrice with a dense matrix/vector yield sparse arrays
  @test issparse(vcat(spmat, densemat))