Extend trimming optimization to all range type.

Author: N5N3

base/subarray.jl

@@ -110,27 +110,35 @@ unaliascopy(A::SubArray) = typeof(A)(unaliascopy(A.parent), map(unaliascopy, A.i
 
 # When the parent is an Array we can trim the size down a bit. In the future this
 # could possibly be extended to any mutable array.
-function unaliascopy(V::SubArray{T,N,A,I,LD}) where {T,N,A<:Array,I<:Tuple{Vararg{Union{Real,AbstractRange,Array}}},LD}
-    dest = Array{T}(undef, _trimmed_shape(V.indices...))
-    copyto!(dest, view(V, _trimmed_vind(V.indices...)...))
+function unaliascopy(V::SubArray{T,N,A,I,LD}) where {T,N,A<:Array,I<:Tuple{Vararg{Union{ScalarIndex,AbstractRange{<:ScalarIndex},Array{<:Union{ScalarIndex,AbstractCartesianIndex}}}}},LD}
+    dest = Array{T}(undef, _trimmedshape(V.indices...))
+    trimmedpind = _trimmedpind(V.indices...)
+    vdest = trimmedpind isa Tuple{Vararg{Union{Slice,Colon}}} ? dest : view(dest, trimmedpind...)
+    copyto!(vdest, view(V, _trimmedvind(V.indices...)...))
     SubArray{T,N,A,I,LD}(dest, map(_trimmedindex, V.indices), 0, Int(LD))
 end
-# We can avoid the repeation from `AbstractArray{CartesianIndex{0}}`
-@inline _trimmed_vind(A, rest...) = (_trimmed_vind1(A)..., _trimmed_vind(rest...)...)
-_trimmed_vind() = ()
-_trimmed_vind1(i::AbstractArray{<:AbstractCartesianIndex{0}}) = map(firstindex, axes(i))
-_trimmed_vind1(i) = axes(i)
 # Get the proper trimmed shape
-@inline _trimmed_shape(A, rest...) = (_trimmed_shape1(A)..., _trimmed_shape(rest...)...)
-_trimmed_shape() = ()
-_trimmed_shape1(::Real) = (1,)
-_trimmed_shape1(i::AbstractArray{<:Integer}) = (length(i),)
-_trimmed_shape1(i::AbstractArray{<:AbstractCartesianIndex{0}}) = ()
-_trimmed_shape1(i::AbstractArray{<:AbstractCartesianIndex{N}}) where {N} = (length(i), ntuple(Returns(1), Val(N - 1))...)
+_trimmedshape(::ScalarIndex, rest...) = (1, _trimmedshape(rest...)...)
+_trimmedshape(i::AbstractRange, rest...) = (maximum(i), _trimmedshape(rest...)...)
+_trimmedshape(i::Union{UnitRange,StepRange,OneTo}, rest...) = (length(i), _trimmedshape(rest...)...)
+_trimmedshape(i::AbstractArray{<:ScalarIndex}, rest...) = (length(i), _trimmedshape(rest...)...)
+_trimmedshape(i::AbstractArray{<:AbstractCartesianIndex{0}}, rest...) = _trimmedshape(rest...)
+_trimmedshape(i::AbstractArray{<:AbstractCartesianIndex{N}}, rest...) where {N} = (length(i), ntuple(Returns(1), Val(N - 1))..., _trimmedshape(rest...)...)
+_trimmedshape() = ()
+# We can avoid the repeation from `AbstractArray{CartesianIndex{0}}`
+_trimmedpind(i, rest...) = (map(Returns(:), axes(i))..., _trimmedpind(rest...)...)
+_trimmedpind(i::AbstractRange, rest...) = (i, _trimmedpind(rest...)...)
+_trimmedpind(i::Union{UnitRange,StepRange,OneTo}, rest...) = ((:), _trimmedpind(rest...)...)
+_trimmedpind(i::AbstractArray{<:AbstractCartesianIndex{0}}, rest...) = _trimmedpind(rest...)
+_trimmedpind() = ()
+_trimmedvind(i, rest...) = (map(Returns(:), axes(i))..., _trimmedvind(rest...)...)
+_trimmedvind(i::AbstractArray{<:AbstractCartesianIndex{0}}, rest...) = (map(first, axes(i))..., _trimmedvind(rest...)...)
+_trimmedvind() = ()
 # Transform indices to be "dense"
-_trimmedindex(i::Real) = oftype(i, 1)
-_trimmedindex(i::AbstractUnitRange{<:Integer}) = oftype(i, oneto(length(i)))
-_trimmedindex(i::AbstractArray{<:Integer}) = oftype(i, reshape(eachindex(IndexLinear(), i), axes(i)))
+_trimmedindex(i::ScalarIndex) = oftype(i, 1)
+_trimmedindex(i::AbstractRange) = i
+_trimmedindex(i::Union{UnitRange,StepRange,OneTo}) = oftype(i, oneto(length(i)))
+_trimmedindex(i::AbstractArray{<:ScalarIndex}) = oftype(i, reshape(eachindex(IndexLinear(), i), axes(i)))
 _trimmedindex(i::AbstractArray{<:AbstractCartesianIndex{0}}) = oftype(i, copy(i))
 function _trimmedindex(i::AbstractArray{<:AbstractCartesianIndex{N}}) where {N}
     padding = ntuple(Returns(1), Val(N - 1))

test/subarray.jl

@@ -660,8 +660,14 @@ end
 @testset "unaliascopy trimming; Issue #26263" begin
     A = rand(5,5,5,5)
     V = view(A, 2:5, :, 2:5, 1:2:5)
-    @test @inferred(Base.unaliascopy(V))::typeof(V) == V == A[2:5, :, 2:5, 1:2:5]
-    @test @inferred(sum(Base.unaliascopy(V))) ≈ sum(V) ≈ sum(A[2:5, :, 2:5, 1:2:5])
+    V′ = @inferred(Base.unaliascopy(V))
+    @test size(V′.parent) == size(V)
+    @test V′::typeof(V) == V == A[2:5, :, 2:5, 1:2:5]
+    @test @inferred(sum(V′)) ≈ sum(V) ≈ sum(A[2:5, :, 2:5, 1:2:5])
+    V = view(A, Base.IdentityUnitRange(2:4), :, Base.StepRangeLen(1,1,3), 1:2:5)
+    V′ = @inferred(Base.unaliascopy(V))
+    @test size(V.parent) != size(V′.parent)
+    @test V′ == V && V′ isa typeof(V)
     i1 = collect(CartesianIndices((2:5)))
     i2 = [CartesianIndex(), CartesianIndex()]
     i3 = collect(CartesianIndices((2:5, 1:2:5)))