Revert "Fix some inferability issues in SparseArrays" (JuliaLang#40332)

stdlib/SparseArrays/src/sparsematrix.jl

@@ -2045,7 +2045,7 @@ function _findr(op, A, region, Tv)
             throw(ArgumentError("array slices must be non-empty"))
         else
             ri = Base.reduced_indices0(A, region)
-            return (zeros(Tv, ri), zeros(Ti, ri))
+            return (similar(A, ri), zeros(Ti, ri))
         end
     end
 
@@ -3274,10 +3274,6 @@ 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 ]
@@ -3292,7 +3288,7 @@ function vcat(X::AbstractSparseMatrixCSC...)
     end
 
     Tv = promote_eltype(X...)
-    Ti = promote_idxtype(X...)
+    Ti = promote_eltype(map(x->rowvals(x), X)...)
 
     nnzX = Int[ nnz(x) for x in X ]
     nnz_res = sum(nnzX)
@@ -3344,7 +3340,7 @@ function hcat(X::AbstractSparseMatrixCSC...)
     n = sum(nX)
 
     Tv = promote_eltype(X...)
-    Ti = promote_idxtype(X...)
+    Ti = promote_eltype(map(x->rowvals(x), X)...)
 
     colptr = Vector{Ti}(undef, n+1)
     nnzX = Int[ nnz(x) for x in X ]

stdlib/SparseArrays/src/sparsevector.jl

@@ -1091,16 +1091,17 @@ function vcat(Xin::_SparseConcatGroup...)
     X = map(x -> SparseMatrixCSC(issparse(x) ? x : sparse(x)), Xin)
     vcat(X...)
 end
-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"))
+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]
     end
-    # 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]...)...)
+    vcat(tmp_rows...)
 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 @inferred(hvcat((3, 2), se44, sz42, sz41, sz34, se33)) == se77 # [se44 sz42 sz41; sz34 se33]
+        @test [se44 sz42 sz41; sz34 se33] == se77
         @test length(nonzeros([sp33 0I; 1I 0I])) == 6
     end
 
@@ -1338,10 +1338,10 @@ end
 @testset "argmax, argmin, findmax, findmin" begin
     S = sprand(100,80, 0.5)
     A = Array(S)
-    @test @inferred(argmax(S)) == argmax(A)
-    @test @inferred(argmin(S)) == argmin(A)
-    @test @inferred(findmin(S)) == findmin(A)
-    @test @inferred(findmax(S)) == findmax(A)
+    @test argmax(S) == argmax(A)
+    @test argmin(S) == argmin(A)
+    @test findmin(S) == findmin(A)
+    @test findmax(S) == findmax(A)
     for region in [(1,), (2,), (1,2)], m in [findmax, findmin]
         @test m(S, dims=region) == m(A, dims=region)
     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(@inferred(hvcat((2,), spmat, spmat)))
+    @test issparse(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))