https://github.com/JuliaLang/julia
Tip revision: 8249ae7c8977282685cc0246977d41bda80faacc authored by Lilith Hafner on 22 April 2023, 15:37:58 UTC
implement pop! for AbstractSet and remove some implementations of pop! for concrete sets
implement pop! for AbstractSet and remove some implementations of pop! for concrete sets
Tip revision: 8249ae7
reinterpretarray.jl
# This file is a part of Julia. License is MIT: https://julialang.org/license
"""
Gives a reinterpreted view (of element type T) of the underlying array (of element type S).
If the size of `T` differs from the size of `S`, the array will be compressed/expanded in
the first dimension. The variant `reinterpret(reshape, T, a)` instead adds or consumes the first dimension
depending on the ratio of element sizes.
"""
struct ReinterpretArray{T,N,S,A<:AbstractArray{S},IsReshaped} <: AbstractArray{T, N}
parent::A
readable::Bool
writable::Bool
function throwbits(S::Type, T::Type, U::Type)
@noinline
throw(ArgumentError("cannot reinterpret `$(S)` as `$(T)`, type `$(U)` is not a bits type"))
end
function throwsize0(S::Type, T::Type, msg)
@noinline
throw(ArgumentError("cannot reinterpret a zero-dimensional `$(S)` array to `$(T)` which is of a $msg size"))
end
function throwsingleton(S::Type, T::Type)
@noinline
throw(ArgumentError("cannot reinterpret a `$(S)` array to `$(T)` which is a singleton type"))
end
global reinterpret
"""
reinterpret(T::DataType, A::AbstractArray)
Construct a view of the array with the same binary data as the given
array, but with `T` as element type.
This function also works on "lazy" array whose elements are not computed until they are explicitly retrieved.
For instance, `reinterpret` on the range `1:6` works similarly as on the dense vector `collect(1:6)`:
```jldoctest
julia> reinterpret(Float32, UInt32[1 2 3 4 5])
1×5 reinterpret(Float32, ::Matrix{UInt32}):
1.0f-45 3.0f-45 4.0f-45 6.0f-45 7.0f-45
julia> reinterpret(Complex{Int}, 1:6)
3-element reinterpret(Complex{$Int}, ::UnitRange{$Int}):
1 + 2im
3 + 4im
5 + 6im
```
"""
function reinterpret(::Type{T}, a::A) where {T,N,S,A<:AbstractArray{S, N}}
function thrownonint(S::Type, T::Type, dim)
@noinline
throw(ArgumentError("""
cannot reinterpret an `$(S)` array to `$(T)` whose first dimension has size `$(dim)`.
The resulting array would have non-integral first dimension.
"""))
end
function throwaxes1(S::Type, T::Type, ax1)
@noinline
throw(ArgumentError("cannot reinterpret a `$(S)` array to `$(T)` when the first axis is $ax1. Try reshaping first."))
end
isbitstype(T) || throwbits(S, T, T)
isbitstype(S) || throwbits(S, T, S)
(N != 0 || sizeof(T) == sizeof(S)) || throwsize0(S, T, "different")
if N != 0 && sizeof(S) != sizeof(T)
ax1 = axes(a)[1]
dim = length(ax1)
if issingletontype(T)
issingletontype(S) || throwsingleton(S, T)
else
rem(dim*sizeof(S),sizeof(T)) == 0 || thrownonint(S, T, dim)
end
first(ax1) == 1 || throwaxes1(S, T, ax1)
end
readable = array_subpadding(T, S)
writable = array_subpadding(S, T)
new{T, N, S, A, false}(a, readable, writable)
end
reinterpret(::Type{T}, a::AbstractArray{T}) where {T} = a
# With reshaping
function reinterpret(::typeof(reshape), ::Type{T}, a::A) where {T,S,A<:AbstractArray{S}}
function throwintmult(S::Type, T::Type)
@noinline
throw(ArgumentError("`reinterpret(reshape, T, a)` requires that one of `sizeof(T)` (got $(sizeof(T))) and `sizeof(eltype(a))` (got $(sizeof(S))) be an integer multiple of the other"))
end
function throwsize1(a::AbstractArray, T::Type)
@noinline
throw(ArgumentError("`reinterpret(reshape, $T, a)` where `eltype(a)` is $(eltype(a)) requires that `axes(a, 1)` (got $(axes(a, 1))) be equal to 1:$(sizeof(T) ÷ sizeof(eltype(a))) (from the ratio of element sizes)"))
end
function throwfromsingleton(S, T)
@noinline
throw(ArgumentError("`reinterpret(reshape, $T, a)` where `eltype(a)` is $S requires that $T be a singleton type, since $S is one"))
end
isbitstype(T) || throwbits(S, T, T)
isbitstype(S) || throwbits(S, T, S)
if sizeof(S) == sizeof(T)
N = ndims(a)
elseif sizeof(S) > sizeof(T)
issingletontype(T) && throwsingleton(S, T)
rem(sizeof(S), sizeof(T)) == 0 || throwintmult(S, T)
N = ndims(a) + 1
else
issingletontype(S) && throwfromsingleton(S, T)
rem(sizeof(T), sizeof(S)) == 0 || throwintmult(S, T)
N = ndims(a) - 1
N > -1 || throwsize0(S, T, "larger")
axes(a, 1) == OneTo(sizeof(T) ÷ sizeof(S)) || throwsize1(a, T)
end
readable = array_subpadding(T, S)
writable = array_subpadding(S, T)
new{T, N, S, A, true}(a, readable, writable)
end
reinterpret(::typeof(reshape), ::Type{T}, a::AbstractArray{T}) where {T} = a
end
ReshapedReinterpretArray{T,N,S,A<:AbstractArray{S}} = ReinterpretArray{T,N,S,A,true}
NonReshapedReinterpretArray{T,N,S,A<:AbstractArray{S, N}} = ReinterpretArray{T,N,S,A,false}
"""
reinterpret(reshape, T, A::AbstractArray{S}) -> B
Change the type-interpretation of `A` while consuming or adding a "channel dimension."
If `sizeof(T) = n*sizeof(S)` for `n>1`, `A`'s first dimension must be
of size `n` and `B` lacks `A`'s first dimension. Conversely, if `sizeof(S) = n*sizeof(T)` for `n>1`,
`B` gets a new first dimension of size `n`. The dimensionality is unchanged if `sizeof(T) == sizeof(S)`.
!!! compat "Julia 1.6"
This method requires at least Julia 1.6.
# Examples
```jldoctest
julia> A = [1 2; 3 4]
2×2 Matrix{$Int}:
1 2
3 4
julia> reinterpret(reshape, Complex{Int}, A) # the result is a vector
2-element reinterpret(reshape, Complex{$Int}, ::Matrix{$Int}) with eltype Complex{$Int}:
1 + 3im
2 + 4im
julia> a = [(1,2,3), (4,5,6)]
2-element Vector{Tuple{$Int, $Int, $Int}}:
(1, 2, 3)
(4, 5, 6)
julia> reinterpret(reshape, Int, a) # the result is a matrix
3×2 reinterpret(reshape, $Int, ::Vector{Tuple{$Int, $Int, $Int}}) with eltype $Int:
1 4
2 5
3 6
```
"""
reinterpret(::typeof(reshape), T::Type, a::AbstractArray)
reinterpret(::Type{T}, a::NonReshapedReinterpretArray) where {T} = reinterpret(T, a.parent)
reinterpret(::typeof(reshape), ::Type{T}, a::ReshapedReinterpretArray) where {T} = reinterpret(reshape, T, a.parent)
# Definition of StridedArray
StridedFastContiguousSubArray{T,N,A<:DenseArray} = FastContiguousSubArray{T,N,A}
StridedReinterpretArray{T,N,A<:Union{DenseArray,StridedFastContiguousSubArray},IsReshaped} = ReinterpretArray{T,N,S,A,IsReshaped} where S
StridedReshapedArray{T,N,A<:Union{DenseArray,StridedFastContiguousSubArray,StridedReinterpretArray}} = ReshapedArray{T,N,A}
StridedSubArray{T,N,A<:Union{DenseArray,StridedReshapedArray,StridedReinterpretArray},
I<:Tuple{Vararg{Union{RangeIndex, ReshapedUnitRange, AbstractCartesianIndex}}}} = SubArray{T,N,A,I}
StridedArray{T,N} = Union{DenseArray{T,N}, StridedSubArray{T,N}, StridedReshapedArray{T,N}, StridedReinterpretArray{T,N}}
StridedVector{T} = StridedArray{T,1}
StridedMatrix{T} = StridedArray{T,2}
StridedVecOrMat{T} = Union{StridedVector{T}, StridedMatrix{T}}
strides(a::Union{DenseArray,StridedReshapedArray,StridedReinterpretArray}) = size_to_strides(1, size(a)...)
stride(A::Union{DenseArray,StridedReshapedArray,StridedReinterpretArray}, k::Integer) =
k ≤ ndims(A) ? strides(A)[k] : length(A)
function strides(a::ReinterpretArray{T,<:Any,S,<:AbstractArray{S},IsReshaped}) where {T,S,IsReshaped}
_checkcontiguous(Bool, a) && return size_to_strides(1, size(a)...)
stp = strides(parent(a))
els, elp = sizeof(T), sizeof(S)
els == elp && return stp # 0dim parent is also handled here.
IsReshaped && els < elp && return (1, _checked_strides(stp, els, elp)...)
stp[1] == 1 || throw(ArgumentError("Parent must be contiguous in the 1st dimension!"))
st′ = _checked_strides(tail(stp), els, elp)
return IsReshaped ? st′ : (1, st′...)
end
@inline function _checked_strides(stp::Tuple, els::Integer, elp::Integer)
if elp > els && rem(elp, els) == 0
N = div(elp, els)
return map(i -> N * i, stp)
end
drs = map(i -> divrem(elp * i, els), stp)
all(i->iszero(i[2]), drs) ||
throw(ArgumentError("Parent's strides could not be exactly divided!"))
map(first, drs)
end
_checkcontiguous(::Type{Bool}, A::ReinterpretArray) = _checkcontiguous(Bool, parent(A))
similar(a::ReinterpretArray, T::Type, d::Dims) = similar(a.parent, T, d)
function check_readable(a::ReinterpretArray{T, N, S} where N) where {T,S}
# See comment in check_writable
if !a.readable && !array_subpadding(T, S)
throw(PaddingError(T, S))
end
end
function check_writable(a::ReinterpretArray{T, N, S} where N) where {T,S}
# `array_subpadding` is relatively expensive (compared to a simple arrayref),
# so it is cached in the array. However, it is computable at compile time if,
# inference has the types available. By using this form of the check, we can
# get the best of both worlds for the success case. If the types were not
# available to inference, we simply need to check the field (relatively cheap)
# and if they were we should be able to fold this check away entirely.
if !a.writable && !array_subpadding(S, T)
throw(PaddingError(T, S))
end
end
## IndexStyle specializations
# For `reinterpret(reshape, T, a)` where we're adding a channel dimension and with
# `IndexStyle(a) == IndexLinear()`, it's advantageous to retain pseudo-linear indexing.
struct IndexSCartesian2{K} <: IndexStyle end # K = sizeof(S) ÷ sizeof(T), a static-sized 2d cartesian iterator
IndexStyle(::Type{ReinterpretArray{T,N,S,A,false}}) where {T,N,S,A<:AbstractArray{S,N}} = IndexStyle(A)
function IndexStyle(::Type{ReinterpretArray{T,N,S,A,true}}) where {T,N,S,A<:AbstractArray{S}}
if sizeof(T) < sizeof(S)
IndexStyle(A) === IndexLinear() && return IndexSCartesian2{sizeof(S) ÷ sizeof(T)}()
return IndexCartesian()
end
return IndexStyle(A)
end
IndexStyle(::IndexSCartesian2{K}, ::IndexSCartesian2{K}) where {K} = IndexSCartesian2{K}()
struct SCartesianIndex2{K} # can't make <:AbstractCartesianIndex without N, and 2 would be a bit misleading
i::Int
j::Int
end
to_index(i::SCartesianIndex2) = i
struct SCartesianIndices2{K,R<:AbstractUnitRange{Int}} <: AbstractMatrix{SCartesianIndex2{K}}
indices2::R
end
SCartesianIndices2{K}(indices2::AbstractUnitRange{Int}) where {K} = (@assert K::Int > 1; SCartesianIndices2{K,typeof(indices2)}(indices2))
eachindex(::IndexSCartesian2{K}, A::ReshapedReinterpretArray) where {K} = SCartesianIndices2{K}(eachindex(IndexLinear(), parent(A)))
@inline function eachindex(style::IndexSCartesian2{K}, A::AbstractArray, B::AbstractArray...) where {K}
iter = eachindex(style, A)
_all_match_first(C->eachindex(style, C), iter, B...) || throw_eachindex_mismatch_indices(IndexSCartesian2{K}(), axes(A), axes.(B)...)
return iter
end
size(iter::SCartesianIndices2{K}) where K = (K, length(iter.indices2))
axes(iter::SCartesianIndices2{K}) where K = (OneTo(K), iter.indices2)
first(iter::SCartesianIndices2{K}) where {K} = SCartesianIndex2{K}(1, first(iter.indices2))
last(iter::SCartesianIndices2{K}) where {K} = SCartesianIndex2{K}(K, last(iter.indices2))
@inline function getindex(iter::SCartesianIndices2{K}, i::Int, j::Int) where {K}
@boundscheck checkbounds(iter, i, j)
return SCartesianIndex2{K}(i, iter.indices2[j])
end
function iterate(iter::SCartesianIndices2{K}) where {K}
ret = iterate(iter.indices2)
ret === nothing && return nothing
item2, state2 = ret
return SCartesianIndex2{K}(1, item2), (1, item2, state2)
end
function iterate(iter::SCartesianIndices2{K}, (state1, item2, state2)) where {K}
if state1 < K
item1 = state1 + 1
return SCartesianIndex2{K}(item1, item2), (item1, item2, state2)
end
ret = iterate(iter.indices2, state2)
ret === nothing && return nothing
item2, state2 = ret
return SCartesianIndex2{K}(1, item2), (1, item2, state2)
end
SimdLoop.simd_outer_range(iter::SCartesianIndices2) = iter.indices2
SimdLoop.simd_inner_length(::SCartesianIndices2{K}, ::Any) where K = K
@inline function SimdLoop.simd_index(::SCartesianIndices2{K}, Ilast::Int, I1::Int) where {K}
SCartesianIndex2{K}(I1+1, Ilast)
end
_maybe_reshape(::IndexSCartesian2, A::ReshapedReinterpretArray, I...) = A
# fallbacks
function _getindex(::IndexSCartesian2, A::AbstractArray{T,N}, I::Vararg{Int, N}) where {T,N}
@_propagate_inbounds_meta
getindex(A, I...)
end
function _setindex!(::IndexSCartesian2, A::AbstractArray{T,N}, v, I::Vararg{Int, N}) where {T,N}
@_propagate_inbounds_meta
setindex!(A, v, I...)
end
# fallbacks for array types that use "pass-through" indexing (e.g., `IndexStyle(A) = IndexStyle(parent(A))`)
# but which don't handle SCartesianIndex2
function _getindex(::IndexSCartesian2, A::AbstractArray{T,N}, ind::SCartesianIndex2) where {T,N}
@_propagate_inbounds_meta
J = _ind2sub(tail(axes(A)), ind.j)
getindex(A, ind.i, J...)
end
function _setindex!(::IndexSCartesian2, A::AbstractArray{T,N}, v, ind::SCartesianIndex2) where {T,N}
@_propagate_inbounds_meta
J = _ind2sub(tail(axes(A)), ind.j)
setindex!(A, v, ind.i, J...)
end
eachindex(style::IndexSCartesian2, A::AbstractArray) = eachindex(style, parent(A))
## AbstractArray interface
parent(a::ReinterpretArray) = a.parent
dataids(a::ReinterpretArray) = dataids(a.parent)
unaliascopy(a::NonReshapedReinterpretArray{T}) where {T} = reinterpret(T, unaliascopy(a.parent))
unaliascopy(a::ReshapedReinterpretArray{T}) where {T} = reinterpret(reshape, T, unaliascopy(a.parent))
function size(a::NonReshapedReinterpretArray{T,N,S} where {N}) where {T,S}
psize = size(a.parent)
size1 = issingletontype(T) ? psize[1] : div(psize[1]*sizeof(S), sizeof(T))
tuple(size1, tail(psize)...)
end
function size(a::ReshapedReinterpretArray{T,N,S} where {N}) where {T,S}
psize = size(a.parent)
sizeof(S) > sizeof(T) && return (div(sizeof(S), sizeof(T)), psize...)
sizeof(S) < sizeof(T) && return tail(psize)
return psize
end
size(a::NonReshapedReinterpretArray{T,0}) where {T} = ()
function axes(a::NonReshapedReinterpretArray{T,N,S} where {N}) where {T,S}
paxs = axes(a.parent)
f, l = first(paxs[1]), length(paxs[1])
size1 = issingletontype(T) ? l : div(l*sizeof(S), sizeof(T))
tuple(oftype(paxs[1], f:f+size1-1), tail(paxs)...)
end
function axes(a::ReshapedReinterpretArray{T,N,S} where {N}) where {T,S}
paxs = axes(a.parent)
sizeof(S) > sizeof(T) && return (OneTo(div(sizeof(S), sizeof(T))), paxs...)
sizeof(S) < sizeof(T) && return tail(paxs)
return paxs
end
axes(a::NonReshapedReinterpretArray{T,0}) where {T} = ()
has_offset_axes(a::ReinterpretArray) = has_offset_axes(a.parent)
elsize(::Type{<:ReinterpretArray{T}}) where {T} = sizeof(T)
unsafe_convert(::Type{Ptr{T}}, a::ReinterpretArray{T,N,S} where N) where {T,S} = Ptr{T}(unsafe_convert(Ptr{S},a.parent))
@inline @propagate_inbounds function getindex(a::NonReshapedReinterpretArray{T,0,S}) where {T,S}
if isprimitivetype(T) && isprimitivetype(S)
reinterpret(T, a.parent[])
else
a[firstindex(a)]
end
end
@inline @propagate_inbounds getindex(a::ReinterpretArray) = a[firstindex(a)]
@inline @propagate_inbounds function getindex(a::ReinterpretArray{T,N,S}, inds::Vararg{Int, N}) where {T,N,S}
check_readable(a)
_getindex_ra(a, inds[1], tail(inds))
end
@inline @propagate_inbounds function getindex(a::ReinterpretArray{T,N,S}, i::Int) where {T,N,S}
check_readable(a)
if isa(IndexStyle(a), IndexLinear)
return _getindex_ra(a, i, ())
end
# Convert to full indices here, to avoid needing multiple conversions in
# the loop in _getindex_ra
inds = _to_subscript_indices(a, i)
isempty(inds) ? _getindex_ra(a, 1, ()) : _getindex_ra(a, inds[1], tail(inds))
end
@inline @propagate_inbounds function getindex(a::ReshapedReinterpretArray{T,N,S}, ind::SCartesianIndex2) where {T,N,S}
check_readable(a)
s = Ref{S}(a.parent[ind.j])
GC.@preserve s begin
tptr = Ptr{T}(unsafe_convert(Ref{S}, s))
return unsafe_load(tptr, ind.i)
end
end
@inline _memcpy!(dst, src, n) = ccall(:memcpy, Cvoid, (Ptr{UInt8}, Ptr{UInt8}, Csize_t), dst, src, n)
@inline @propagate_inbounds function _getindex_ra(a::NonReshapedReinterpretArray{T,N,S}, i1::Int, tailinds::TT) where {T,N,S,TT}
# Make sure to match the scalar reinterpret if that is applicable
if sizeof(T) == sizeof(S) && (fieldcount(T) + fieldcount(S)) == 0
if issingletontype(T) # singleton types
@boundscheck checkbounds(a, i1, tailinds...)
return T.instance
end
return reinterpret(T, a.parent[i1, tailinds...])
else
@boundscheck checkbounds(a, i1, tailinds...)
ind_start, sidx = divrem((i1-1)*sizeof(T), sizeof(S))
# Optimizations that avoid branches
if sizeof(T) % sizeof(S) == 0
# T is bigger than S and contains an integer number of them
n = sizeof(T) ÷ sizeof(S)
t = Ref{T}()
GC.@preserve t begin
sptr = Ptr{S}(unsafe_convert(Ref{T}, t))
for i = 1:n
s = a.parent[ind_start + i, tailinds...]
unsafe_store!(sptr, s, i)
end
end
return t[]
elseif sizeof(S) % sizeof(T) == 0
# S is bigger than T and contains an integer number of them
s = Ref{S}(a.parent[ind_start + 1, tailinds...])
GC.@preserve s begin
tptr = Ptr{T}(unsafe_convert(Ref{S}, s))
return unsafe_load(tptr + sidx)
end
else
i = 1
nbytes_copied = 0
# This is a bit complicated to deal with partial elements
# at both the start and the end. LLVM will fold as appropriate,
# once it knows the data layout
s = Ref{S}()
t = Ref{T}()
GC.@preserve s t begin
sptr = Ptr{S}(unsafe_convert(Ref{S}, s))
tptr = Ptr{T}(unsafe_convert(Ref{T}, t))
while nbytes_copied < sizeof(T)
s[] = a.parent[ind_start + i, tailinds...]
nb = min(sizeof(S) - sidx, sizeof(T)-nbytes_copied)
_memcpy!(tptr + nbytes_copied, sptr + sidx, nb)
nbytes_copied += nb
sidx = 0
i += 1
end
end
return t[]
end
end
end
@inline @propagate_inbounds function _getindex_ra(a::ReshapedReinterpretArray{T,N,S}, i1::Int, tailinds::TT) where {T,N,S,TT}
# Make sure to match the scalar reinterpret if that is applicable
if sizeof(T) == sizeof(S) && (fieldcount(T) + fieldcount(S)) == 0
if issingletontype(T) # singleton types
@boundscheck checkbounds(a, i1, tailinds...)
return T.instance
end
return reinterpret(T, a.parent[i1, tailinds...])
end
@boundscheck checkbounds(a, i1, tailinds...)
if sizeof(T) >= sizeof(S)
t = Ref{T}()
GC.@preserve t begin
sptr = Ptr{S}(unsafe_convert(Ref{T}, t))
if sizeof(T) > sizeof(S)
# Extra dimension in the parent array
n = sizeof(T) ÷ sizeof(S)
if isempty(tailinds) && IndexStyle(a.parent) === IndexLinear()
offset = n * (i1 - firstindex(a))
for i = 1:n
s = a.parent[i + offset]
unsafe_store!(sptr, s, i)
end
else
for i = 1:n
s = a.parent[i, i1, tailinds...]
unsafe_store!(sptr, s, i)
end
end
else
# No extra dimension
s = a.parent[i1, tailinds...]
unsafe_store!(sptr, s)
end
end
return t[]
end
# S is bigger than T and contains an integer number of them
# n = sizeof(S) ÷ sizeof(T)
s = Ref{S}()
GC.@preserve s begin
tptr = Ptr{T}(unsafe_convert(Ref{S}, s))
s[] = a.parent[tailinds...]
return unsafe_load(tptr, i1)
end
end
@inline @propagate_inbounds function setindex!(a::NonReshapedReinterpretArray{T,0,S}, v) where {T,S}
if isprimitivetype(S) && isprimitivetype(T)
a.parent[] = reinterpret(S, v)
return a
end
setindex!(a, v, firstindex(a))
end
@inline @propagate_inbounds setindex!(a::ReinterpretArray, v) = setindex!(a, v, firstindex(a))
@inline @propagate_inbounds function setindex!(a::ReinterpretArray{T,N,S}, v, inds::Vararg{Int, N}) where {T,N,S}
check_writable(a)
_setindex_ra!(a, v, inds[1], tail(inds))
end
@inline @propagate_inbounds function setindex!(a::ReinterpretArray{T,N,S}, v, i::Int) where {T,N,S}
check_writable(a)
if isa(IndexStyle(a), IndexLinear)
return _setindex_ra!(a, v, i, ())
end
inds = _to_subscript_indices(a, i)
_setindex_ra!(a, v, inds[1], tail(inds))
end
@inline @propagate_inbounds function setindex!(a::ReshapedReinterpretArray{T,N,S}, v, ind::SCartesianIndex2) where {T,N,S}
check_writable(a)
v = convert(T, v)::T
s = Ref{S}(a.parent[ind.j])
GC.@preserve s begin
tptr = Ptr{T}(unsafe_convert(Ref{S}, s))
unsafe_store!(tptr, v, ind.i)
end
a.parent[ind.j] = s[]
return a
end
@inline @propagate_inbounds function _setindex_ra!(a::NonReshapedReinterpretArray{T,N,S}, v, i1::Int, tailinds::TT) where {T,N,S,TT}
v = convert(T, v)::T
# Make sure to match the scalar reinterpret if that is applicable
if sizeof(T) == sizeof(S) && (fieldcount(T) + fieldcount(S)) == 0
if issingletontype(T) # singleton types
@boundscheck checkbounds(a, i1, tailinds...)
# setindex! is a noop except for the index check
else
setindex!(a.parent, reinterpret(S, v), i1, tailinds...)
end
else
@boundscheck checkbounds(a, i1, tailinds...)
ind_start, sidx = divrem((i1-1)*sizeof(T), sizeof(S))
# Optimizations that avoid branches
if sizeof(T) % sizeof(S) == 0
# T is bigger than S and contains an integer number of them
t = Ref{T}(v)
GC.@preserve t begin
sptr = Ptr{S}(unsafe_convert(Ref{T}, t))
n = sizeof(T) ÷ sizeof(S)
for i = 1:n
s = unsafe_load(sptr, i)
a.parent[ind_start + i, tailinds...] = s
end
end
elseif sizeof(S) % sizeof(T) == 0
# S is bigger than T and contains an integer number of them
s = Ref{S}(a.parent[ind_start + 1, tailinds...])
GC.@preserve s begin
tptr = Ptr{T}(unsafe_convert(Ref{S}, s))
unsafe_store!(tptr + sidx, v)
a.parent[ind_start + 1, tailinds...] = s[]
end
else
t = Ref{T}(v)
s = Ref{S}()
GC.@preserve t s begin
tptr = Ptr{UInt8}(unsafe_convert(Ref{T}, t))
sptr = Ptr{UInt8}(unsafe_convert(Ref{S}, s))
nbytes_copied = 0
i = 1
# Deal with any partial elements at the start. We'll have to copy in the
# element from the original array and overwrite the relevant parts
if sidx != 0
s[] = a.parent[ind_start + i, tailinds...]
nb = min((sizeof(S) - sidx) % UInt, sizeof(T) % UInt)
_memcpy!(sptr + sidx, tptr, nb)
nbytes_copied += nb
a.parent[ind_start + i, tailinds...] = s[]
i += 1
sidx = 0
end
# Deal with the main body of elements
while nbytes_copied < sizeof(T) && (sizeof(T) - nbytes_copied) > sizeof(S)
nb = min(sizeof(S), sizeof(T) - nbytes_copied)
_memcpy!(sptr, tptr + nbytes_copied, nb)
nbytes_copied += nb
a.parent[ind_start + i, tailinds...] = s[]
i += 1
end
# Deal with trailing partial elements
if nbytes_copied < sizeof(T)
s[] = a.parent[ind_start + i, tailinds...]
nb = min(sizeof(S), sizeof(T) - nbytes_copied)
_memcpy!(sptr, tptr + nbytes_copied, nb)
a.parent[ind_start + i, tailinds...] = s[]
end
end
end
end
return a
end
@inline @propagate_inbounds function _setindex_ra!(a::ReshapedReinterpretArray{T,N,S}, v, i1::Int, tailinds::TT) where {T,N,S,TT}
v = convert(T, v)::T
# Make sure to match the scalar reinterpret if that is applicable
if sizeof(T) == sizeof(S) && (fieldcount(T) + fieldcount(S)) == 0
if issingletontype(T) # singleton types
@boundscheck checkbounds(a, i1, tailinds...)
# setindex! is a noop except for the index check
else
setindex!(a.parent, reinterpret(S, v), i1, tailinds...)
end
end
@boundscheck checkbounds(a, i1, tailinds...)
if sizeof(T) >= sizeof(S)
t = Ref{T}(v)
GC.@preserve t begin
sptr = Ptr{S}(unsafe_convert(Ref{T}, t))
if sizeof(T) > sizeof(S)
# Extra dimension in the parent array
n = sizeof(T) ÷ sizeof(S)
if isempty(tailinds) && IndexStyle(a.parent) === IndexLinear()
offset = n * (i1 - firstindex(a))
for i = 1:n
s = unsafe_load(sptr, i)
a.parent[i + offset] = s
end
else
for i = 1:n
s = unsafe_load(sptr, i)
a.parent[i, i1, tailinds...] = s
end
end
else # sizeof(T) == sizeof(S)
# No extra dimension
s = unsafe_load(sptr)
a.parent[i1, tailinds...] = s
end
end
else
# S is bigger than T and contains an integer number of them
s = Ref{S}()
GC.@preserve s begin
tptr = Ptr{T}(unsafe_convert(Ref{S}, s))
s[] = a.parent[tailinds...]
unsafe_store!(tptr, v, i1)
a.parent[tailinds...] = s[]
end
end
return a
end
# Padding
struct Padding
offset::Int
size::Int
end
function intersect(p1::Padding, p2::Padding)
start = max(p1.offset, p2.offset)
stop = min(p1.offset + p1.size, p2.offset + p2.size)
Padding(start, max(0, stop-start))
end
struct PaddingError <: Exception
S::Type
T::Type
end
function showerror(io::IO, p::PaddingError)
print(io, "Padding of type $(p.S) is not compatible with type $(p.T).")
end
"""
CyclePadding(padding, total_size)
Cylces an iterator of `Padding` structs, restarting the padding at `total_size`.
E.g. if `padding` is all the padding in a struct and `total_size` is the total
aligned size of that array, `CyclePadding` will correspond to the padding in an
infinite vector of such structs.
"""
struct CyclePadding{P}
padding::P
total_size::Int
end
eltype(::Type{<:CyclePadding}) = Padding
IteratorSize(::Type{<:CyclePadding}) = IsInfinite()
isempty(cp::CyclePadding) = isempty(cp.padding)
function iterate(cp::CyclePadding)
y = iterate(cp.padding)
y === nothing && return nothing
y[1], (0, y[2])
end
function iterate(cp::CyclePadding, state::Tuple)
y = iterate(cp.padding, tail(state)...)
y === nothing && return iterate(cp, (state[1]+cp.total_size,))
Padding(y[1].offset+state[1], y[1].size), (state[1], tail(y)...)
end
"""
Compute the location of padding in a type.
"""
function padding(T)
padding = Padding[]
last_end::Int = 0
for i = 1:fieldcount(T)
offset = fieldoffset(T, i)
fT = fieldtype(T, i)
if offset != last_end
push!(padding, Padding(offset, offset-last_end))
end
last_end = offset + sizeof(fT)
end
padding
end
function CyclePadding(T::DataType)
a, s = datatype_alignment(T), sizeof(T)
as = s + (a - (s % a)) % a
pad = padding(T)
s != as && push!(pad, Padding(s, as - s))
CyclePadding(pad, as)
end
@assume_effects :total function array_subpadding(S, T)
lcm_size = lcm(sizeof(S), sizeof(T))
s, t = CyclePadding(S), CyclePadding(T)
checked_size = 0
# use of Stateful harms inference and makes this vulnerable to invalidation
(pad, tstate) = let
it = iterate(t)
it === nothing && return true
it
end
(ps, sstate) = let
it = iterate(s)
it === nothing && return false
it
end
while checked_size < lcm_size
while true
# See if there's corresponding padding in S
ps.offset > pad.offset && return false
intersect(ps, pad) == pad && break
ps, sstate = iterate(s, sstate)
end
checked_size = pad.offset + pad.size
pad, tstate = iterate(t, tstate)
end
return true
end
# Reductions with IndexSCartesian2
function _mapreduce(f::F, op::OP, style::IndexSCartesian2{K}, A::AbstractArrayOrBroadcasted) where {F,OP,K}
inds = eachindex(style, A)
n = size(inds)[2]
if n == 0
return mapreduce_empty_iter(f, op, A, IteratorEltype(A))
else
return mapreduce_impl(f, op, A, first(inds), last(inds))
end
end
@noinline function mapreduce_impl(f::F, op::OP, A::AbstractArrayOrBroadcasted,
ifirst::SCI, ilast::SCI, blksize::Int) where {F,OP,SCI<:SCartesianIndex2{K}} where K
if ilast.j - ifirst.j < blksize
# sequential portion
@inbounds a1 = A[ifirst]
@inbounds a2 = A[SCI(2,ifirst.j)]
v = op(f(a1), f(a2))
@simd for i = ifirst.i + 2 : K
@inbounds ai = A[SCI(i,ifirst.j)]
v = op(v, f(ai))
end
# Remaining columns
for j = ifirst.j+1 : ilast.j
@simd for i = 1:K
@inbounds ai = A[SCI(i,j)]
v = op(v, f(ai))
end
end
return v
else
# pairwise portion
jmid = ifirst.j + (ilast.j - ifirst.j) >> 1
v1 = mapreduce_impl(f, op, A, ifirst, SCI(K,jmid), blksize)
v2 = mapreduce_impl(f, op, A, SCI(1,jmid+1), ilast, blksize)
return op(v1, v2)
end
end
mapreduce_impl(f::F, op::OP, A::AbstractArrayOrBroadcasted, ifirst::SCartesianIndex2, ilast::SCartesianIndex2) where {F,OP} =
mapreduce_impl(f, op, A, ifirst, ilast, pairwise_blocksize(f, op))