More thorough aliasing detection in nonscalar copy, broadcast and assignment

base/abstractarray.jl

@@ -1047,6 +1047,88 @@ function _setindex!(::IndexCartesian, A::AbstractArray, v, I::Vararg{Int,M}) whe
     r
 end
 
+"""
+    parent(A)
+
+Returns the "parent array" of an array view type (e.g., `SubArray`), or the array itself if
+it is not a view.
+
+# Examples
+```jldoctest
+julia> A = [1 2; 3 4]
+2×2 Array{Int64,2}:
+ 1  2
+ 3  4
+
+julia> V = view(A, 1:2, :)
+2×2 view(::Array{Int64,2}, 1:2, :) with eltype Int64:
+ 1  2
+ 3  4
+
+julia> parent(V)
+2×2 Array{Int64,2}:
+ 1  2
+ 3  4
+```
+"""
+parent(a::AbstractArray) = a
+
+## rudimentary aliasing detection ##
+"""
+    unalias(dest, A)
+
+Return either `A` or a copy of `A`, in a rough effort to prevent modifications to `dest` from
+affecting the returned object. No guarantees are provided, and each custom array must
+opt-into aliasing detection by overloading this method by specializing on the second argument.
+
+This function must return an object of exactly the same type as `A` for performance and type stability.
+
+See also [`mightalias`](@ref) and [`dataids`](@ref).
+"""
+unalias(dest, A::AbstractArray) = mightalias(dest, A) ? copypreservingtype(A) : A
+unalias(dest, A::AbstractRange) = A
+unalias(dest, A) = A
+
+copypreservingtype(A::Array) = copy(A)
+copypreservingtype(A::AbstractArray)::typeof(A) = (@_noinline_meta; copy(A))
+copypreservingtype(A) = A
+
+"""
+    mightalias(A::AbstractArray, B::AbstractArray)
+
+Perform a conservative and rudimentary test to check if arrays `A` and `B` might share the same memory.
+
+By default, this simply checks if either of the arrays reference the same memory
+regions, as identified by their [`dataids`](@ref).
+"""
+mightalias(A::AbstractArray, B::AbstractArray) = !_isdisjoint(dataids(A), dataids(B))
+mightalias(x, y) = false
+
+_isdisjoint(as::Tuple{}, bs::Tuple{}) = true
+_isdisjoint(as::Tuple{}, bs::Tuple{Any}) = true
+_isdisjoint(as::Tuple{}, bs::Tuple) = true
+_isdisjoint(as::Tuple{Any}, bs::Tuple{}) = true
+_isdisjoint(as::Tuple{Any}, bs::Tuple{Any}) = as[1] != bs[1]
+_isdisjoint(as::Tuple{Any}, bs::Tuple) = !(as[1] in bs)
+_isdisjoint(as::Tuple, bs::Tuple{}) = true
+_isdisjoint(as::Tuple, bs::Tuple{Any}) = !(bs[1] in as)
+_isdisjoint(as::Tuple, bs::Tuple) = !(as[1] in bs) && _isdisjoint(tail(as), bs)
+
+"""
+    dataids(A::AbstractArray)
+
+Return a tuple of `UInt`s that represent the mutable data segments of an array.
+
+Custom arrays that would like to opt-in to aliasing detection of their component
+parts can specialize this method to return the concatenation of the `dataids` of
+their component parts.  A typical definition for an array that wraps a parent is
+`dataids(C::CustomArray) = dataids(C.parent)`.
+"""
+dataids(A::AbstractArray) = (UInt(objectid(A)),)
+dataids(A::Array) = (UInt(pointer(A)),)
+dataids(::AbstractRange) = ()
+dataids(x) = ()
+
 ## get (getindex with a default value) ##
 
 RangeVecIntList{A<:AbstractVector{Int}} = Union{Tuple{Vararg{Union{AbstractRange, AbstractVector{Int}}}},

base/array.jl

@@ -692,24 +692,20 @@ function setindex! end
 # These are redundant with the abstract fallbacks but needed for bootstrap
 function setindex!(A::Array, x, I::AbstractVector{Int})
     @_propagate_inbounds_meta
-    A === I && (I = copy(I))
-    for i in I
+    I′ = unalias(A, I)
+    for i in I′
         A[i] = x
     end
     return A
 end
 function setindex!(A::Array, X::AbstractArray, I::AbstractVector{Int})
     @_propagate_inbounds_meta
     @boundscheck setindex_shape_check(X, length(I))
+    X′ = unalias(A, X)
+    I′ = unalias(A, I)
     count = 1
-    if X === A
-        X = copy(X)
-        I===A && (I = X::typeof(I))
-    elseif I === A
-        I = copy(I)
-    end
-    for i in I
-        @inbounds x = X[count]
+    for i in I′
+        @inbounds x = X′[count]
         A[i] = x
         count += 1
     end

base/bitarray.jl

@@ -79,6 +79,8 @@ isassigned(B::BitArray, i::Int) = 1 <= i <= length(B)
 
 IndexStyle(::Type{<:BitArray}) = IndexLinear()
 
+copypreservingtype(B::BitArray) = copy(B)
+
 ## aux functions ##
 
 const _msk64 = ~UInt64(0)

base/broadcast.jl

@@ -5,7 +5,7 @@ module Broadcast
 using .Base.Cartesian
 using .Base: Indices, OneTo, linearindices, tail, to_shape,
             _msk_end, unsafe_bitgetindex, bitcache_chunks, bitcache_size, dumpbitcache,
-            isoperator, promote_typejoin
+            isoperator, promote_typejoin, unalias
 import .Base: broadcast, broadcast!
 export BroadcastStyle, broadcast_indices, broadcast_similar,
        broadcast_getindex, broadcast_setindex!, dotview, @__dot__
@@ -472,13 +472,23 @@ end
     return dest
 end
 
+# For broadcasted assignments like `broadcast!(f, A, ..., A, ...)`, where `A`
+# appears on both the LHS and the RHS of the `.=`, then we know we're only
+# going to make one pass through the array, and even though `A` is aliasing
+# against itself, the mutations won't affect the result as the indices on the
+# LHS and RHS will always match. This is not true in general, but with the `.op=`
+# syntax it's fairly common for an argument to be `===` a source.
+broadcast_unalias(dest, src) = dest === src ? src : unalias(dest, src)
+
 # This indirection allows size-dependent implementations.
 @inline function _broadcast!(f, C, A, Bs::Vararg{Any,N}) where N
     shape = broadcast_indices(C)
     @boundscheck check_broadcast_indices(shape, A, Bs...)
-    keeps, Idefaults = map_newindexer(shape, A, Bs)
+    A′ = broadcast_unalias(C, A)
+    Bs′ = map(B->broadcast_unalias(C, B), Bs)
+    keeps, Idefaults = map_newindexer(shape, A′, Bs′)
     iter = CartesianIndices(shape)
-    _broadcast!(f, C, keeps, Idefaults, A, Bs, Val(N), iter)
+    _broadcast!(f, C, keeps, Idefaults, A′, Bs′, Val(N), iter)
     return C
 end
 

base/multidimensional.jl

@@ -675,21 +675,19 @@ _iterable(v) = Iterators.repeated(v)
 @generated function _unsafe_setindex!(::IndexStyle, A::AbstractArray, x, I::Union{Real,AbstractArray}...)
     N = length(I)
     quote
-        X = _iterable(x)
-        @nexprs $N d->(I_d = I[d])
+        x′ = _iterable(unalias(A, x))
+        @nexprs $N d->(I_d = unalias(A, I[d]))
         idxlens = @ncall $N index_lengths I
-        @ncall $N setindex_shape_check X (d->idxlens[d])
-        Xs = start(X)
+        @ncall $N setindex_shape_check x′ (d->idxlens[d])
+        xs = start(x′)
         @inbounds @nloops $N i d->I_d begin
-            v, Xs = next(X, Xs)
+            v, xs = next(x′, xs)
             @ncall $N setindex! A v i
         end
         A
     end
 end
 
-##
-
 # see discussion in #18364 ... we try not to widen type of the resulting array
 # from cumsum or cumprod, but in some cases (+, Bool) we may not have a choice.
 rcum_promote_type(op, ::Type{T}, ::Type{S}) where {T,S<:Number} = promote_op(op, T, S)
@@ -1087,6 +1085,57 @@ end
 
 ### from abstractarray.jl
 
+# In the common case where we have two views into the same parent, aliasing checks
+# are _much_ easier and more important to get right
+function mightalias(A::SubArray{T,<:Any,P}, B::SubArray{T,<:Any,P}) where {T,P}
+    if !_parentsmatch(A.parent, B.parent)
+        # We cannot do any better than the usual dataids check
+        return !_isdisjoint(dataids(A), dataids(B))
+    end
+    # Now we know that A.parent === B.parent. This means that the indices of A
+    # and B are the same length and indexing into the same dimensions. We can
+    # just walk through them and check for overlaps: O(ndims(A)). We must finally
+    # ensure that the indices don't alias with either parent
+    return _indicesmightoverlap(A.indices, B.indices) ||
+        !_isdisjoint(dataids(A.parent), _splatmap(dataids, B.indices)) ||
+        !_isdisjoint(dataids(B.parent), _splatmap(dataids, A.indices))
+end
+_parentsmatch(A::AbstractArray, B::AbstractArray) = A === B
+# Two reshape(::Array)s of the same size aren't `===` because they have different headers
+_parentsmatch(A::Array, B::Array) = pointer(A) == pointer(B) && size(A) == size(B)
+
+_indicesmightoverlap(A::Tuple{}, B::Tuple{}) = true
+_indicesmightoverlap(A::Tuple{}, B::Tuple) = error("malformed subarray")
+_indicesmightoverlap(A::Tuple, B::Tuple{}) = error("malformed subarray")
+# For ranges, it's relatively cheap to construct the intersection
+@inline function _indicesmightoverlap(A::Tuple{AbstractRange, Vararg{Any}}, B::Tuple{AbstractRange, Vararg{Any}})
+    !isempty(intersect(A[1], B[1])) ? _indicesmightoverlap(tail(A), tail(B)) : false
+end
+# But in the common AbstractUnitRange case, there's an even faster shortcut
+@inline function _indicesmightoverlap(A::Tuple{AbstractUnitRange, Vararg{Any}}, B::Tuple{AbstractUnitRange, Vararg{Any}})
+    max(first(A[1]),first(B[1])) <= min(last(A[1]),last(B[1])) ? _indicesmightoverlap(tail(A), tail(B)) : false
+end
+# And we can check scalars against eachother and scalars against arrays quite easily
+@inline _indicesmightoverlap(A::Tuple{Real, Vararg{Any}}, B::Tuple{Real, Vararg{Any}}) =
+    A[1] == B[1] ? _indicesmightoverlap(tail(A), tail(B)) : false
+@inline _indicesmightoverlap(A::Tuple{Real, Vararg{Any}}, B::Tuple{AbstractArray, Vararg{Any}}) =
+    A[1] in B[1] ? _indicesmightoverlap(tail(A), tail(B)) : false
+@inline _indicesmightoverlap(A::Tuple{AbstractArray, Vararg{Any}}, B::Tuple{Real, Vararg{Any}}) =
+    B[1] in A[1] ? _indicesmightoverlap(tail(A), tail(B)) : false
+# And small arrays are quick, too
+@inline function _indicesmightoverlap(A::Tuple{AbstractArray, Vararg{Any}}, B::Tuple{AbstractArray, Vararg{Any}})
+    if length(A[1]) == 1
+        return A[1][1] in B[1] ? _indicesmightoverlap(tail(A), tail(B)) : false
+    elseif length(B[1]) == 1
+        return B[1][1] in A[1] ? _indicesmightoverlap(tail(A), tail(B)) : false
+    else
+        # But checking larger arrays requires O(m*n) and is too much work
+        return true
+    end
+end
+# And in general, checking the intersection is too much work
+_indicesmightoverlap(A::Tuple{Any, Vararg{Any}}, B::Tuple{Any, Vararg{Any}}) = true
+
 """
     fill!(A, x)
 
@@ -1157,33 +1206,35 @@ julia> y
 copyto!(dest, src)
 
 function copyto!(dest::AbstractArray{T,N}, src::AbstractArray{T,N}) where {T,N}
-    @boundscheck checkbounds(dest, axes(src)...)
-    for I in eachindex(IndexStyle(src,dest), src)
-        @inbounds dest[I] = src[I]
+    checkbounds(dest, axes(src)...)
+    src′ = unalias(dest, src)
+    for I in eachindex(IndexStyle(src′,dest), src′)
+        @inbounds dest[I] = src′[I]
     end
     dest
 end
 
 function copyto!(dest::AbstractArray{T1,N}, Rdest::CartesianIndices{N},
-               src::AbstractArray{T2,N}, Rsrc::CartesianIndices{N}) where {T1,T2,N}
+                  src::AbstractArray{T2,N}, Rsrc::CartesianIndices{N}) where {T1,T2,N}
     isempty(Rdest) && return dest
     if size(Rdest) != size(Rsrc)
         throw(ArgumentError("source and destination must have same size (got $(size(Rsrc)) and $(size(Rdest)))"))
     end
-    @boundscheck checkbounds(dest, first(Rdest))
-    @boundscheck checkbounds(dest, last(Rdest))
-    @boundscheck checkbounds(src, first(Rsrc))
-    @boundscheck checkbounds(src, last(Rsrc))
+    checkbounds(dest, first(Rdest))
+    checkbounds(dest, last(Rdest))
+    checkbounds(src, first(Rsrc))
+    checkbounds(src, last(Rsrc))
+    src′ = unalias(dest, src)
     ΔI = first(Rdest) - first(Rsrc)
     if @generated
         quote
             @nloops $N i (n->Rsrc.indices[n]) begin
-                @inbounds @nref($N,dest,n->i_n+ΔI[n]) = @nref($N,src,i)
+                @inbounds @nref($N,dest,n->i_n+ΔI[n]) = @nref($N,src′,i)
             end
         end
     else
         for I in Rsrc
-            @inbounds dest[I + ΔI] = src[I]
+            @inbounds dest[I + ΔI] = src′[I]
         end
     end
     dest

base/reinterpretarray.jl

@@ -36,6 +36,7 @@ struct ReinterpretArray{T,N,S,A<:AbstractArray{S, N}} <: AbstractArray{T, N}
 end
 
 parent(a::ReinterpretArray) = a.parent
+dataids(a::ReinterpretArray) = dataids(a.parent)
 
 function size(a::ReinterpretArray{T,N,S} where {N}) where {T,S}
     psize = size(a.parent)

base/reshapedarray.jl

@@ -180,6 +180,9 @@ parent(A::ReshapedArray) = A.parent
 parentindices(A::ReshapedArray) = map(s->1:s, size(parent(A)))
 reinterpret(::Type{T}, A::ReshapedArray, dims::Dims) where {T} = reinterpret(T, parent(A), dims)
 
+copypreservingtype(A::ReshapedArray) = typeof(A)(copypreservingtype(A.parent), A.dims, A.mi)
+dataids(A::ReshapedArray) = dataids(A.parent)
+
 @inline ind2sub_rs(::Tuple{}, i::Int) = i
 @inline ind2sub_rs(strds, i) = _ind2sub_rs(strds, i - 1)
 @inline _ind2sub_rs(::Tuple{}, ind) = (ind + 1,)

base/subarray.jl

@@ -59,41 +59,34 @@ similar(V::SubArray, T::Type, dims::Dims) = similar(V.parent, T, dims)
 
 sizeof(V::SubArray) = length(V) * sizeof(eltype(V))
 
-"""
-    parent(A)
-
-Returns the "parent array" of an array view type (e.g., `SubArray`), or the array itself if
-it is not a view.
-
-# Examples
-```jldoctest
-julia> A = [1 2; 3 4]
-2×2 Array{Int64,2}:
- 1  2
- 3  4
-
-julia> V = view(A, 1:2, :)
-2×2 view(::Array{Int64,2}, 1:2, :) with eltype Int64:
- 1  2
- 3  4
-
-julia> parent(V)
-2×2 Array{Int64,2}:
- 1  2
- 3  4
-```
-"""
 parent(V::SubArray) = V.parent
 parentindices(V::SubArray) = V.indices
 
-parent(a::AbstractArray) = a
 """
     parentindices(A)
 
 From an array view `A`, returns the corresponding indices in the parent.
 """
 parentindices(a::AbstractArray) = ntuple(i->OneTo(size(a,i)), ndims(a))
 
+## Aliasing detection
+dataids(A::SubArray) = (dataids(A.parent)..., _splatmap(dataids, A.indices)...)
+_splatmap(f, ::Tuple{}) = ()
+_splatmap(f, t::Tuple) = (f(t[1])..., _splatmap(f, tail(t))...)
+copypreservingtype(A::SubArray) = typeof(A)(copypreservingtype(A.parent), map(copypreservingtype, A.indices), A.offset1, A.stride1)
+
+# 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 copypreservingtype(V::SubArray{T,N,A,I,LD}) where {T,N,A<:Array,I<:Tuple{Vararg{Union{Real,AbstractRange,Array}}},LD}
+    dest = Array{T}(uninitialized, index_lengths(V.indices...))
+    copyto!(dest, V)
+    SubArray{T,N,A,I,LD}(dest, map(_trimmedindex, V.indices), 0, Int(LD))
+end
+# Transform indices to be "dense"
+_trimmedindex(i::Real) = oftype(i, 1)
+_trimmedindex(i::AbstractUnitRange) = i
+_trimmedindex(i::AbstractArray) = oftype(i, reshape(linearindices(i), axes(i)))
+
 ## SubArray creation
 # We always assume that the dimensionality of the parent matches the number of
 # indices that end up getting passed to it, so we store the parent as a
@@ -135,7 +128,7 @@ julia> A # Note A has changed even though we modified b
 """
 function view(A::AbstractArray, I::Vararg{Any,N}) where {N}
     @_inline_meta
-    J = to_indices(A, I)
+    J = map(i->unalias(A,i), to_indices(A, I))
     @boundscheck checkbounds(A, J...)
     unsafe_view(_maybe_reshape_parent(A, index_ndims(J...)), J...)
 end

stdlib/LinearAlgebra/src/adjtrans.jl

@@ -47,6 +47,9 @@ end
 Adjoint(A) = Adjoint{Base.promote_op(adjoint,eltype(A)),typeof(A)}(A)
 Transpose(A) = Transpose{Base.promote_op(transpose,eltype(A)),typeof(A)}(A)
 
+Base.dataids(A::Union{Adjoint, Transpose}) = Base.dataids(A.parent)
+Base.copypreservingtype(A::Union{Adjoint,Transpose}) = typeof(A)(Base.copypreservingtype(A.parent))
+
 # wrapping lowercase quasi-constructors
 """
     adjoint(A)