Drop FunctionImplementations, TypeParameterAccessors, and NamedDimsArrays dependencies

Project.toml

@@ -1,6 +1,6 @@
 name = "SparseArraysBase"
 uuid = "0d5efcca-f356-4864-8770-e1ed8d78f208"
-version = "0.9.23"
+version = "0.10.0"
 authors = ["ITensor developers <support@itensor.org> and contributors"]
 
 [workspace]
@@ -12,20 +12,16 @@ Adapt = "79e6a3ab-5dfb-504d-930d-738a2a938a0e"
 ArrayLayouts = "4c555306-a7a7-4459-81d9-ec55ddd5c99a"
 Dictionaries = "85a47980-9c8c-11e8-2b9f-f7ca1fa99fb4"
 FillArrays = "1a297f60-69ca-5386-bcde-b61e274b549b"
-FunctionImplementations = "7c7cc465-9c6a-495f-bdd1-f42428e86d0c"
 GPUArraysCore = "46192b85-c4d5-4398-a991-12ede77f4527"
 LinearAlgebra = "37e2e46d-f89d-539d-b4ee-838fcccc9c8e"
 MapBroadcast = "ebd9b9da-f48d-417c-9660-449667d60261"
 Random = "9a3f8284-a2c9-5f02-9a11-845980a1fd5c"
 SparseArrays = "2f01184e-e22b-5df5-ae63-d93ebab69eaf"
-TypeParameterAccessors = "7e5a90cf-f82e-492e-a09b-e3e26432c138"
 
 [weakdeps]
-NamedDimsArrays = "60cbd0c0-df58-4cb7-918c-6f5607b73fde"
 TensorAlgebra = "68bd88dc-f39d-4e12-b2ca-f046b68fcc6a"
 
 [extensions]
-SparseArraysBaseNamedDimsArraysExt = "NamedDimsArrays"
 SparseArraysBaseTensorAlgebraExt = ["TensorAlgebra", "SparseArrays"]
 
 [compat]
@@ -34,13 +30,10 @@ Adapt = "4.3"
 ArrayLayouts = "1.11"
 Dictionaries = "0.4.3"
 FillArrays = "1.13"
-FunctionImplementations = "0.4"
 GPUArraysCore = "0.2"
 LinearAlgebra = "1.10"
 MapBroadcast = "0.1.5"
-NamedDimsArrays = "0.13, 0.14, 0.15"
 Random = "1.10"
 SparseArrays = "1.10"
-TensorAlgebra = "0.6.2, 0.7, 0.8, 0.9"
-TypeParameterAccessors = "0.4.3"
+TensorAlgebra = "0.10"
 julia = "1.10"

docs/Project.toml

@@ -13,4 +13,4 @@ Dictionaries = "0.4.4"
 Documenter = "1.8.1"
 ITensorFormatter = "0.2.27"
 Literate = "2.20.1"
-SparseArraysBase = "0.9"
+SparseArraysBase = "0.10"

examples/Project.toml

@@ -8,5 +8,5 @@ path = ".."
 
 [compat]
 Dictionaries = "0.4.4"
-SparseArraysBase = "0.9"
+SparseArraysBase = "0.10"
 Test = "<0.0.1, 1"

src/SparseArraysBase.jl

@@ -17,6 +17,13 @@ export SparseArrayDOK,
     storedpairs,
     storedvalues
 
+# `zero!` isn't defined in `Base`, but it is defined in `ArrayLayouts`
+# and is useful for sparse array logic, since it can be used to empty
+# the sparse array storage. SparseArraysBase owns its own `zero!` rather
+# than relying on an external definition.
+function zero! end
+
+include("concatenate.jl")
 include("abstractsparsearraystyle.jl")
 include("sparsearraystyle.jl")
 include("indexing.jl")

src/abstractsparsearray.jl

@@ -23,11 +23,6 @@ const AnyAbstractSparseVecOrMat{T} = Union{
 
 Base.convert(T::Type{<:AbstractSparseArray}, a::AbstractArray) = a isa T ? a : T(a)
 
-using FunctionImplementations: FunctionImplementations
-function FunctionImplementations.ImplementationStyle(::Type{<:AnyAbstractSparseArray})
-    return SparseArrayImplementationStyle()
-end
-
 function Base.copy(a::AnyAbstractSparseArray)
     return copyto!(similar(a), a)
 end
@@ -66,40 +61,66 @@ end
 using ArrayLayouts: ArrayLayouts
 using LinearAlgebra: LinearAlgebra
 
-Base.getindex(a::AnyAbstractSparseArray, I::Any...) = style(a)(getindex)(a, I...)
-Base.getindex(a::AnyAbstractSparseArray, I::Int...) = style(a)(getindex)(a, I...)
-Base.setindex!(a::AnyAbstractSparseArray, x, I::Any...) = style(a)(setindex!)(a, x, I...)
-Base.setindex!(a::AnyAbstractSparseArray, x, I::Int...) = style(a)(setindex!)(a, x, I...)
-Base.copy!(dst::AbstractArray, src::AnyAbstractSparseArray) = style(src)(copy!)(dst, src)
+# `copy!` and `real` are routed to sparse implementations that are not (yet)
+# defined, matching the prior behavior where these threw a `MethodError`.
+function copy!_sparse end
+function real_sparse end
+
+Base.getindex(a::AnyAbstractSparseArray, I::Any...) = getindex_sparse(a, I...)
+Base.getindex(a::AnyAbstractSparseArray, I::Int...) = getindex_sparse(a, I...)
+Base.setindex!(a::AnyAbstractSparseArray, x, I::Any...) = setindex!_sparse(a, x, I...)
+Base.setindex!(a::AnyAbstractSparseArray, x, I::Int...) = setindex!_sparse(a, x, I...)
+Base.copy!(dst::AbstractArray, src::AnyAbstractSparseArray) = copy!_sparse(dst, src)
 function Base.copyto!(dst::AbstractArray, src::AnyAbstractSparseArray)
-    return style(src)(copyto!)(dst, src)
+    return copyto!_sparse(dst, src)
 end
-Base.map(f, as::AnyAbstractSparseArray...) = style(as...)(map)(f, as...)
+Base.map(f, as::AnyAbstractSparseArray...) = map_sparse(f, as...)
 function Base.map!(f, dst::AbstractArray, as::AnyAbstractSparseArray...)
-    return style(as...)(map!)(f, dst, as...)
+    return map!_sparse(f, dst, as...)
 end
 function Base.mapreduce(f, op, as::AnyAbstractSparseArray...; kwargs...)
-    return style(as...)(mapreduce)(f, op, as...; kwargs...)
+    return mapreduce_sparse(f, op, as...; kwargs...)
 end
 function Base.reduce(f, as::AnyAbstractSparseArray...; kwargs...)
-    return style(as...)(reduce)(f, as...; kwargs...)
-end
-Base.all(f::Function, a::AnyAbstractSparseArray) = style(a)(all)(f, a)
-Base.all(a::AnyAbstractSparseArray) = style(a)(all)(a)
-Base.iszero(a::AnyAbstractSparseArray) = style(a)(iszero)(a)
-Base.isreal(a::AnyAbstractSparseArray) = style(a)(isreal)(a)
-Base.real(a::AnyAbstractSparseArray) = style(a)(real)(a)
-Base.fill!(a::AnyAbstractSparseArray, x) = style(a)(fill!)(a, x)
-FunctionImplementations.zero!(a::AnyAbstractSparseArray) = style(a)(zero!)(a)
-Base.zero(a::AnyAbstractSparseArray) = style(a)(zero)(a)
+    return reduce_sparse(f, as...; kwargs...)
+end
+Base.all(f::Function, a::AnyAbstractSparseArray) = all_sparse(f, a)
+Base.all(a::AnyAbstractSparseArray) = all_sparse(a)
+Base.iszero(a::AnyAbstractSparseArray) = iszero_sparse(a)
+Base.isreal(a::AnyAbstractSparseArray) = isreal_sparse(a)
+Base.real(a::AnyAbstractSparseArray) = real_sparse(a)
+Base.fill!(a::AnyAbstractSparseArray, x) = fill!_sparse(a, x)
+zero!(a::AnyAbstractSparseArray) = zero!_sparse(a)
+Base.zero(a::AnyAbstractSparseArray) = zero_sparse(a)
 function Base.permutedims!(dst, a::AnyAbstractSparseArray, perm)
-    return style(a)(permutedims!)(dst, a, perm)
+    return permutedims!_sparse(dst, a, perm)
 end
 function LinearAlgebra.mul!(
         dst::AbstractMatrix, a1::AnyAbstractSparseArray, a2::AnyAbstractSparseArray,
         α::Number, β::Number
     )
-    return style(a1, a2)(mul!)(dst, a1, a2, α, β)
+    return mul!_sparse(dst, a1, a2, α, β)
+end
+
+# Wire the sparse stored-entry implementations (defined in `indexing.jl`) to the
+# generic interface functions for sparse array types. Concrete sparse types and
+# wrappers may override the canonical methods directly.
+@inline getstoredindex(a::AnyAbstractSparseArray, I::Int...) =
+    getstoredindex_sparse(a, I...)
+@inline function getunstoredindex(a::AnyAbstractSparseArray, I::Int...)
+    return getunstoredindex_sparse(a, I...)
+end
+@inline isstored(a::AbstractSparseArray, i::Int, I::Int...) = isstored_sparse(a, i, I...)
+@inline function setstoredindex!(a::AnyAbstractSparseArray, v, I::Int...)
+    return setstoredindex!_sparse(a, v, I...)
+end
+@inline function setunstoredindex!(a::AnyAbstractSparseArray, v, I::Int...)
+    return setunstoredindex!_sparse(a, v, I...)
+end
+storedvalues(a::AnyAbstractSparseArray) = storedvalues_sparse(a)
+storedpairs(a::AnyAbstractSparseArray) = storedpairs_sparse(a)
+function eachstoredindex(style::IndexStyle, a::AnyAbstractSparseArray, bs::AbstractArray...)
+    return eachstoredindex_sparse(style, a, bs...)
 end
 
 function Base.Broadcast.BroadcastStyle(type::Type{<:AnyAbstractSparseArray})
@@ -109,7 +130,7 @@ end
 using ArrayLayouts: ArrayLayouts
 ArrayLayouts.MemoryLayout(type::Type{<:AnyAbstractSparseArray}) = SparseLayout()
 
-using FunctionImplementations.Concatenate: concatenate
+using .Concatenate: concatenate
 # We overload `Base._cat` instead of `Base.cat` since it
 # is friendlier for invalidations/compile times, see:
 # https://github.com/ITensor/SparseArraysBase.jl/issues/25

src/abstractsparsearraystyle.jl

@@ -1,6 +1,5 @@
 using Base: @_propagate_inbounds_meta
 using FillArrays: Zeros
-using FunctionImplementations: FunctionImplementations
 
 function unstored end
 function eachstoredindex end
@@ -38,7 +37,28 @@ unstoredsimilar(a::AbstractArray) = a
 # Generic functionality for converting to a
 # dense array, trying to preserve information
 # about the array (such as which device it is on).
-using TypeParameterAccessors: unspecify_type_parameters, unwrap_array, unwrap_array_type
+# Local equivalents of the `TypeParameterAccessors` helpers; usage is
+# limited to `densetype`/`dense`.
+unspecify_type_parameters(::Type{T}) where {T} = Base.typename(T).wrapper
+function unwrap_array(a::AbstractArray)
+    p = parent(a)
+    p isa typeof(a) && return a
+    return unwrap_array(p)
+end
+using LinearAlgebra: Adjoint, Transpose
+unwrap_array_type(arraytype::Type{<:AbstractArray}) = arraytype
+unwrap_array_type(a::AbstractArray) = unwrap_array_type(typeof(a))
+unwrap_array_type(::Type{<:Adjoint{<:Any, P}}) where {P} = unwrap_array_type(P)
+unwrap_array_type(::Type{<:Transpose{<:Any, P}}) where {P} = unwrap_array_type(P)
+function unwrap_array_type(
+        ::Type{<:PermutedDimsArray{<:Any, <:Any, <:Any, <:Any, P}}
+    ) where {P}
+    return unwrap_array_type(P)
+end
+function unwrap_array_type(::Type{<:Base.ReshapedArray{<:Any, <:Any, P}}) where {P}
+    return unwrap_array_type(P)
+end
+unwrap_array_type(::Type{<:SubArray{<:Any, <:Any, P}}) where {P} = unwrap_array_type(P)
 function densetype(arraytype::Type{<:AbstractArray})
     return unspecify_type_parameters(unwrap_array_type(arraytype))
 end
@@ -51,34 +71,6 @@ function dense(a::AbstractArray)
     return @allowscalar convert(densetype(a), a)
 end
 
-# Minimal interface for `SparseArrayImplementationStyle`.
-# Fallbacks for dense/non-sparse arrays.
-
-using FunctionImplementations: AbstractArrayImplementationStyle
-abstract type AbstractSparseArrayImplementationStyle <: AbstractArrayImplementationStyle end
-
-function FunctionImplementations.ImplementationStyle(
-        style1::AbstractSparseArrayImplementationStyle,
-        style2::AbstractSparseArrayImplementationStyle
-    )
-    return SparseArrayImplementationStyle()
-end
-function FunctionImplementations.ImplementationStyle(
-        style1::AbstractSparseArrayImplementationStyle,
-        style2::AbstractArrayImplementationStyle
-    )
-    return style1
-end
-# Fix ambiguity error with
-# `ImplementationStyle(::AbstractSparseArrayImplementationStyle, ::AbstractArrayImplementationStyle)`.
-using FunctionImplementations: DefaultArrayImplementationStyle
-function FunctionImplementations.ImplementationStyle(
-        style1::AbstractSparseArrayImplementationStyle,
-        style2::DefaultArrayImplementationStyle
-    )
-    return style1
-end
-
 to_vec(x) = vec(collect(x))
 to_vec(x::AbstractArray) = vec(x)