Remove kludgy VectorStyle and MatrixStyle

These broadcast styles were introduced in response to #23939 (comment) as a way to limit the "greediness" of Sparse's broadcasting implementation -- sparse only wanted to allow known combinations of array types (including Array but not any AbstractArray). The idea was to allow us to gradually improve the sparse broadcast implementation over 1.x in a non-breaking manner.  Unfortunately, these special styles for Array make defining new styles in the heirarchy a bit of a pain (ref. #23939 (comment)), and it was making my life harder in getting the 1.0 breaking changes in.

This commit removes these special broadcast styles in favor of just having Sparse identify the cases itself and re-dispatch back into the default implementation in the cases it doesn't know how to handle.

Author: mbauman

base/broadcast.jl

@@ -160,32 +160,6 @@ BroadcastStyle(a::AbstractArrayStyle{N}, ::DefaultArrayStyle{N}) where N = a
 BroadcastStyle(a::AbstractArrayStyle{M}, ::DefaultArrayStyle{N}) where {M,N} =
     typeof(a)(_max(Val(M),Val(N)))
 
-# FIXME
-# The following definitions are necessary to limit SparseArray broadcasting to "plain Arrays"
-# (see https://github.com/JuliaLang/julia/pull/23939#pullrequestreview-72075382).
-# They should be deleted once the sparse broadcast infrastucture is capable of handling
-# arbitrary AbstractArrays.
-struct VectorStyle <: AbstractArrayStyle{1} end
-struct MatrixStyle <: AbstractArrayStyle{2} end
-const VMStyle = Union{VectorStyle,MatrixStyle}
-# These lose to DefaultArrayStyle
-VectorStyle(::Val{N}) where N = DefaultArrayStyle{N}()
-MatrixStyle(::Val{N}) where N = DefaultArrayStyle{N}()
-
-BroadcastStyle(::Type{<:Vector}) = VectorStyle()
-BroadcastStyle(::Type{<:Matrix}) = MatrixStyle()
-
-BroadcastStyle(::MatrixStyle, ::VectorStyle) = MatrixStyle()
-BroadcastStyle(a::AbstractArrayStyle{Any}, ::VectorStyle) = a
-BroadcastStyle(a::AbstractArrayStyle{Any}, ::MatrixStyle) = a
-BroadcastStyle(a::AbstractArrayStyle{N}, ::VectorStyle) where N = typeof(a)(_max(Val(N), Val(1)))
-BroadcastStyle(a::AbstractArrayStyle{N}, ::MatrixStyle) where N = typeof(a)(_max(Val(N), Val(2)))
-BroadcastStyle(::VectorStyle, ::DefaultArrayStyle{N}) where N = DefaultArrayStyle(_max(Val(N), Val(1)))
-BroadcastStyle(::MatrixStyle, ::DefaultArrayStyle{N}) where N = DefaultArrayStyle(_max(Val(N), Val(2)))
-# to avoid the VectorStyle(::Val) constructor we also need the following
-BroadcastStyle(::VectorStyle, ::MatrixStyle) = MatrixStyle()
-# end FIXME
-
 ## Allocating the output container
 """
     broadcast_similar(f, ::BroadcastStyle, ::Type{ElType}, inds, As...)
@@ -205,17 +179,6 @@ broadcast_similar(f, ::ArrayConflict, ::Type{ElType}, inds::Indices, As...) wher
 broadcast_similar(f, ::ArrayConflict, ::Type{Bool}, inds::Indices, As...) =
     similar(BitArray, inds)
 
-# FIXME: delete when we get rid of VectorStyle and MatrixStyle
-broadcast_similar(f, ::VectorStyle, ::Type{ElType}, inds::Indices{1}, As...) where ElType =
-    similar(Vector{ElType}, inds)
-broadcast_similar(f, ::MatrixStyle, ::Type{ElType}, inds::Indices{2}, As...) where ElType =
-    similar(Matrix{ElType}, inds)
-broadcast_similar(f, ::VectorStyle, ::Type{Bool}, inds::Indices{1}, As...) =
-    similar(BitArray, inds)
-broadcast_similar(f, ::MatrixStyle, ::Type{Bool}, inds::Indices{2}, As...) =
-    similar(BitArray, inds)
-# end FIXME
-
 ## Computing the result's indices. Most types probably won't need to specialize this.
 broadcast_indices() = ()
 broadcast_indices(::Type{T}) where T = ()
@@ -628,7 +591,7 @@ julia> string.(("one","two","three","four"), ": ", 1:4)
     broadcast(f, s, combine_eltypes(f, A, Bs...), combine_indices(A, Bs...),
               A, Bs...)
 
-const NonleafHandlingTypes = Union{DefaultArrayStyle,ArrayConflict,VectorStyle,MatrixStyle}
+const NonleafHandlingTypes = Union{DefaultArrayStyle,ArrayConflict}
 
 @inline function broadcast(f, s::NonleafHandlingTypes, ::Type{ElType}, inds::Indices, As...) where ElType
     if !Base.isconcretetype(ElType)

stdlib/Pkg3/src/precompile.jl

@@ -683,7 +683,6 @@ precompile(Tuple{typeof(Core.Compiler.getindex), Tuple{String, typeof(Base.info)
 precompile(Tuple{typeof(Core.Compiler.getindex), Tuple{UInt128, UInt128}, Int64})
 precompile(Tuple{typeof(Core.Compiler.getindex), Tuple{UInt32}, Int64})
 precompile(Tuple{typeof(Core.Compiler.getindex), Tuple{typeof(Pkg3.BinaryProvider.parse_tar_list)}, Int64})
-precompile(Tuple{typeof(Core.Compiler.getindex), Type{Any}, Core.Compiler.Const, Core.Compiler.Const, Type{Base.Broadcast.VectorStyle}, Core.Compiler.Const, Type{Tuple{Base.OneTo{Int64}}}, Type{Tuple{Array{Base.SubString{String}, 1}}}})
 precompile(Tuple{typeof(Core.Compiler.getindex), Type{Any}, GlobalRef, Bool, typeof(Pkg3.Types.parse_toml), Expr})
 precompile(Tuple{typeof(Core.Compiler.getindex), Type{Any}, GlobalRef, Core.SSAValue, Bool, Expr})
 precompile(Tuple{typeof(Core.Compiler.getindex), Type{Any}, GlobalRef, Core.SlotNumber, QuoteNode, Expr})

stdlib/SparseArrays/src/higherorderfns.jl

@@ -984,22 +984,27 @@ PromoteToSparse(::Val{N}) where N = Broadcast.DefaultArrayStyle{N}()
 Broadcast.BroadcastStyle(::PromoteToSparse, ::SPVM) = PromoteToSparse()
 Broadcast.BroadcastStyle(::PromoteToSparse, ::Broadcast.Style{Tuple}) = Broadcast.DefaultArrayStyle{2}()
 
-# FIXME: switch to DefaultArrayStyle once we can delete VectorStyle and MatrixStyle
-# Broadcast.BroadcastStyle(::SPVM, ::Broadcast.DefaultArrayStyle{0}) = PromoteToSparse()
-# Broadcast.BroadcastStyle(::SPVM, ::Broadcast.DefaultArrayStyle{1}) = PromoteToSparse()
-# Broadcast.BroadcastStyle(::SPVM, ::Broadcast.DefaultArrayStyle{2}) = PromoteToSparse()
-BroadcastStyle(::Type{<:Adjoint{T,<:Vector}}) where T = Broadcast.MatrixStyle() # Adjoint not yet defined when broadcast.jl loaded
-BroadcastStyle(::Type{<:Transpose{T,<:Vector}}) where T = Broadcast.MatrixStyle() # Transpose not yet defined when broadcast.jl loaded
-Broadcast.BroadcastStyle(::SPVM, ::Broadcast.VectorStyle) = PromoteToSparse()
-Broadcast.BroadcastStyle(::SPVM, ::Broadcast.MatrixStyle) = PromoteToSparse()
-Broadcast.BroadcastStyle(::SparseVecStyle, ::Broadcast.DefaultArrayStyle{N}) where N =
-    Broadcast.DefaultArrayStyle(Broadcast._max(Val(N), Val(1)))
-Broadcast.BroadcastStyle(::SparseMatStyle, ::Broadcast.DefaultArrayStyle{N}) where N =
-    Broadcast.DefaultArrayStyle(Broadcast._max(Val(N), Val(2)))
-# end FIXME
+Broadcast.BroadcastStyle(::SPVM, ::Broadcast.DefaultArrayStyle{0}) = PromoteToSparse()
+Broadcast.BroadcastStyle(::SPVM, ::Broadcast.DefaultArrayStyle{1}) = PromoteToSparse()
+Broadcast.BroadcastStyle(::SPVM, ::Broadcast.DefaultArrayStyle{2}) = PromoteToSparse()
 
-broadcast(f, ::PromoteToSparse, ::Nothing, ::Nothing, As::Vararg{Any,N}) where {N} =
-    broadcast(f, map(_sparsifystructured, As)...)
+# FIXME: currently sparse broadcasts are only well-tested on known array types, while any AbstractArray
+# could report itself as a DefaultArrayStyle().
+# See https://github.com/JuliaLang/julia/pull/23939#pullrequestreview-72075382 for more details
+is_supported_sparse_broadcast() = true
+is_supported_sparse_broadcast(::AbstractArray, rest...) = false
+is_supported_sparse_broadcast(::AbstractSparseArray, rest...) = is_supported_sparse_broadcast(rest...)
+is_supported_sparse_broadcast(::StructuredMatrix, rest...) = is_supported_sparse_broadcast(rest...)
+is_supported_sparse_broadcast(::Array, rest...) = is_supported_sparse_broadcast(rest...)
+is_supported_sparse_broadcast(t::Union{Transpose, Adjoint}, rest...) = is_supported_sparse_broadcast(t.parent, rest...)
+is_supported_sparse_broadcast(x, rest...) = BroadcastStyle(typeof(x)) === Broadcast.Scalar() && is_supported_sparse_broadcast(rest...)
+function broadcast(f, s::PromoteToSparse, ::Nothing, ::Nothing, As::Vararg{Any,N}) where {N}
+    if is_supported_sparse_broadcast(As...)
+        return broadcast(f, map(_sparsifystructured, As)...)
+    else
+        return broadcast(f, Broadcast.ArrayConflict(), nothing, nothing, As...)
+    end
+end
 
 # For broadcast! with ::Any inputs, we need a layer of indirection to determine whether
 # the inputs can be promoted to SparseVecOrMat. If it's just SparseVecOrMat and scalars,