-
Notifications
You must be signed in to change notification settings - Fork 125
Commit
This commit does not belong to any branch on this repository, and may belong to a fork outside of the repository.
[NDTensors] Introduce
NestedPermutedDimsArrays
submodule (#1589)
- Loading branch information
Showing
5 changed files
with
264 additions
and
0 deletions.
There are no files selected for viewing
This file contains bidirectional Unicode text that may be interpreted or compiled differently than what appears below. To review, open the file in an editor that reveals hidden Unicode characters.
Learn more about bidirectional Unicode characters
237 changes: 237 additions & 0 deletions
237
NDTensors/src/lib/NestedPermutedDimsArrays/src/NestedPermutedDimsArrays.jl
This file contains bidirectional Unicode text that may be interpreted or compiled differently than what appears below. To review, open the file in an editor that reveals hidden Unicode characters.
Learn more about bidirectional Unicode characters
Original file line number | Diff line number | Diff line change |
---|---|---|
@@ -0,0 +1,237 @@ | ||
# Mostly copied from https://github.com/JuliaLang/julia/blob/master/base/permuteddimsarray.jl | ||
# Like `PermutedDimsArrays` but singly nested, similar to `Adjoint` and `Transpose` | ||
# (though those are fully recursive). | ||
module NestedPermutedDimsArrays | ||
|
||
import Base: permutedims, permutedims! | ||
export NestedPermutedDimsArray | ||
|
||
# Some day we will want storage-order-aware iteration, so put perm in the parameters | ||
struct NestedPermutedDimsArray{T,N,perm,iperm,AA<:AbstractArray} <: AbstractArray{T,N} | ||
parent::AA | ||
|
||
function NestedPermutedDimsArray{T,N,perm,iperm,AA}( | ||
data::AA | ||
) where {T,N,perm,iperm,AA<:AbstractArray} | ||
(isa(perm, NTuple{N,Int}) && isa(iperm, NTuple{N,Int})) || | ||
error("perm and iperm must both be NTuple{$N,Int}") | ||
isperm(perm) || | ||
throw(ArgumentError(string(perm, " is not a valid permutation of dimensions 1:", N))) | ||
all(d -> iperm[perm[d]] == d, 1:N) || | ||
throw(ArgumentError(string(perm, " and ", iperm, " must be inverses"))) | ||
return new(data) | ||
end | ||
end | ||
|
||
""" | ||
NestedPermutedDimsArray(A, perm) -> B | ||
Given an AbstractArray `A`, create a view `B` such that the | ||
dimensions appear to be permuted. Similar to `permutedims`, except | ||
that no copying occurs (`B` shares storage with `A`). | ||
See also [`permutedims`](@ref), [`invperm`](@ref). | ||
# Examples | ||
```jldoctest | ||
julia> A = rand(3,5,4); | ||
julia> B = NestedPermutedDimsArray(A, (3,1,2)); | ||
julia> size(B) | ||
(4, 3, 5) | ||
julia> B[3,1,2] == A[1,2,3] | ||
true | ||
``` | ||
""" | ||
Base.@constprop :aggressive function NestedPermutedDimsArray( | ||
data::AbstractArray{T,N}, perm | ||
) where {T,N} | ||
length(perm) == N || | ||
throw(ArgumentError(string(perm, " is not a valid permutation of dimensions 1:", N))) | ||
iperm = invperm(perm) | ||
return NestedPermutedDimsArray{ | ||
PermutedDimsArray{eltype(T),N,(perm...,),(iperm...,),T}, | ||
N, | ||
(perm...,), | ||
(iperm...,), | ||
typeof(data), | ||
}( | ||
data | ||
) | ||
end | ||
|
||
Base.parent(A::NestedPermutedDimsArray) = A.parent | ||
function Base.size(A::NestedPermutedDimsArray{T,N,perm}) where {T,N,perm} | ||
return genperm(size(parent(A)), perm) | ||
end | ||
function Base.axes(A::NestedPermutedDimsArray{T,N,perm}) where {T,N,perm} | ||
return genperm(axes(parent(A)), perm) | ||
end | ||
Base.has_offset_axes(A::NestedPermutedDimsArray) = Base.has_offset_axes(A.parent) | ||
function Base.similar(A::NestedPermutedDimsArray, T::Type, dims::Base.Dims) | ||
return similar(parent(A), T, dims) | ||
end | ||
function Base.cconvert(::Type{Ptr{T}}, A::NestedPermutedDimsArray{T}) where {T} | ||
return Base.cconvert(Ptr{T}, parent(A)) | ||
end | ||
|
||
# It's OK to return a pointer to the first element, and indeed quite | ||
# useful for wrapping C routines that require a different storage | ||
# order than used by Julia. But for an array with unconventional | ||
# storage order, a linear offset is ambiguous---is it a memory offset | ||
# or a linear index? | ||
function Base.pointer(A::NestedPermutedDimsArray, i::Integer) | ||
throw( | ||
ArgumentError("pointer(A, i) is deliberately unsupported for NestedPermutedDimsArray") | ||
) | ||
end | ||
|
||
function Base.strides(A::NestedPermutedDimsArray{T,N,perm}) where {T,N,perm} | ||
s = strides(parent(A)) | ||
return ntuple(d -> s[perm[d]], Val(N)) | ||
end | ||
function Base.elsize(::Type{<:NestedPermutedDimsArray{<:Any,<:Any,<:Any,<:Any,P}}) where {P} | ||
return Base.elsize(P) | ||
end | ||
|
||
@inline function Base.getindex( | ||
A::NestedPermutedDimsArray{T,N,perm,iperm}, I::Vararg{Int,N} | ||
) where {T,N,perm,iperm} | ||
@boundscheck checkbounds(A, I...) | ||
@inbounds val = PermutedDimsArray(getindex(A.parent, genperm(I, iperm)...), perm) | ||
return val | ||
end | ||
@inline function Base.setindex!( | ||
A::NestedPermutedDimsArray{T,N,perm,iperm}, val, I::Vararg{Int,N} | ||
) where {T,N,perm,iperm} | ||
@boundscheck checkbounds(A, I...) | ||
@inbounds setindex!(A.parent, PermutedDimsArray(val, perm), genperm(I, iperm)...) | ||
return val | ||
end | ||
|
||
function Base.isassigned( | ||
A::NestedPermutedDimsArray{T,N,perm,iperm}, I::Vararg{Int,N} | ||
) where {T,N,perm,iperm} | ||
@boundscheck checkbounds(Bool, A, I...) || return false | ||
@inbounds x = isassigned(A.parent, genperm(I, iperm)...) | ||
return x | ||
end | ||
|
||
@inline genperm(I::NTuple{N,Any}, perm::Dims{N}) where {N} = ntuple(d -> I[perm[d]], Val(N)) | ||
@inline genperm(I, perm::AbstractVector{Int}) = genperm(I, (perm...,)) | ||
|
||
function Base.copyto!( | ||
dest::NestedPermutedDimsArray{T,N}, src::AbstractArray{T,N} | ||
) where {T,N} | ||
checkbounds(dest, axes(src)...) | ||
return _copy!(dest, src) | ||
end | ||
Base.copyto!(dest::NestedPermutedDimsArray, src::AbstractArray) = _copy!(dest, src) | ||
|
||
function _copy!(P::NestedPermutedDimsArray{T,N,perm}, src) where {T,N,perm} | ||
# If dest/src are "close to dense," then it pays to be cache-friendly. | ||
# Determine the first permuted dimension | ||
d = 0 # d+1 will hold the first permuted dimension of src | ||
while d < ndims(src) && perm[d + 1] == d + 1 | ||
d += 1 | ||
end | ||
if d == ndims(src) | ||
copyto!(parent(P), src) # it's not permuted | ||
else | ||
R1 = CartesianIndices(axes(src)[1:d]) | ||
d1 = findfirst(isequal(d + 1), perm)::Int # first permuted dim of dest | ||
R2 = CartesianIndices(axes(src)[(d + 2):(d1 - 1)]) | ||
R3 = CartesianIndices(axes(src)[(d1 + 1):end]) | ||
_permutedims!(P, src, R1, R2, R3, d + 1, d1) | ||
end | ||
return P | ||
end | ||
|
||
@noinline function _permutedims!( | ||
P::NestedPermutedDimsArray, src, R1::CartesianIndices{0}, R2, R3, ds, dp | ||
) | ||
ip, is = axes(src, dp), axes(src, ds) | ||
for jo in first(ip):8:last(ip), io in first(is):8:last(is) | ||
for I3 in R3, I2 in R2 | ||
for j in jo:min(jo + 7, last(ip)) | ||
for i in io:min(io + 7, last(is)) | ||
@inbounds P[i, I2, j, I3] = src[i, I2, j, I3] | ||
end | ||
end | ||
end | ||
end | ||
return P | ||
end | ||
|
||
@noinline function _permutedims!(P::NestedPermutedDimsArray, src, R1, R2, R3, ds, dp) | ||
ip, is = axes(src, dp), axes(src, ds) | ||
for jo in first(ip):8:last(ip), io in first(is):8:last(is) | ||
for I3 in R3, I2 in R2 | ||
for j in jo:min(jo + 7, last(ip)) | ||
for i in io:min(io + 7, last(is)) | ||
for I1 in R1 | ||
@inbounds P[I1, i, I2, j, I3] = src[I1, i, I2, j, I3] | ||
end | ||
end | ||
end | ||
end | ||
end | ||
return P | ||
end | ||
|
||
const CommutativeOps = Union{ | ||
typeof(+), | ||
typeof(Base.add_sum), | ||
typeof(min), | ||
typeof(max), | ||
typeof(Base._extrema_rf), | ||
typeof(|), | ||
typeof(&), | ||
} | ||
|
||
function Base._mapreduce_dim( | ||
f, op::CommutativeOps, init::Base._InitialValue, A::NestedPermutedDimsArray, dims::Colon | ||
) | ||
return Base._mapreduce_dim(f, op, init, parent(A), dims) | ||
end | ||
function Base._mapreduce_dim( | ||
f::typeof(identity), | ||
op::Union{typeof(Base.mul_prod),typeof(*)}, | ||
init::Base._InitialValue, | ||
A::NestedPermutedDimsArray{<:Union{Real,Complex}}, | ||
dims::Colon, | ||
) | ||
return Base._mapreduce_dim(f, op, init, parent(A), dims) | ||
end | ||
|
||
function Base.mapreducedim!( | ||
f, op::CommutativeOps, B::AbstractArray{T,N}, A::NestedPermutedDimsArray{S,N,perm,iperm} | ||
) where {T,S,N,perm,iperm} | ||
C = NestedPermutedDimsArray{T,N,iperm,perm,typeof(B)}(B) # make the inverse permutation for the output | ||
Base.mapreducedim!(f, op, C, parent(A)) | ||
return B | ||
end | ||
function Base.mapreducedim!( | ||
f::typeof(identity), | ||
op::Union{typeof(Base.mul_prod),typeof(*)}, | ||
B::AbstractArray{T,N}, | ||
A::NestedPermutedDimsArray{<:Union{Real,Complex},N,perm,iperm}, | ||
) where {T,N,perm,iperm} | ||
C = NestedPermutedDimsArray{T,N,iperm,perm,typeof(B)}(B) # make the inverse permutation for the output | ||
Base.mapreducedim!(f, op, C, parent(A)) | ||
return B | ||
end | ||
|
||
function Base.showarg( | ||
io::IO, A::NestedPermutedDimsArray{T,N,perm}, toplevel | ||
) where {T,N,perm} | ||
print(io, "NestedPermutedDimsArray(") | ||
Base.showarg(io, parent(A), false) | ||
print(io, ", ", perm, ')') | ||
toplevel && print(io, " with eltype ", eltype(A)) | ||
return nothing | ||
end | ||
|
||
end |
This file contains bidirectional Unicode text that may be interpreted or compiled differently than what appears below. To review, open the file in an editor that reveals hidden Unicode characters.
Learn more about bidirectional Unicode characters
Original file line number | Diff line number | Diff line change |
---|---|---|
@@ -0,0 +1,2 @@ | ||
[deps] | ||
NDTensors = "23ae76d9-e61a-49c4-8f12-3f1a16adf9cf" |
23 changes: 23 additions & 0 deletions
23
NDTensors/src/lib/NestedPermutedDimsArrays/test/runtests.jl
This file contains bidirectional Unicode text that may be interpreted or compiled differently than what appears below. To review, open the file in an editor that reveals hidden Unicode characters.
Learn more about bidirectional Unicode characters
Original file line number | Diff line number | Diff line change |
---|---|---|
@@ -0,0 +1,23 @@ | ||
@eval module $(gensym()) | ||
using NDTensors.NestedPermutedDimsArrays: NestedPermutedDimsArray | ||
using Test: @test, @testset | ||
@testset "NestedPermutedDimsArrays" for elt in ( | ||
Float32, Float64, Complex{Float32}, Complex{Float64} | ||
) | ||
a = map(_ -> randn(elt, 2, 3, 4), CartesianIndices((2, 3, 4))) | ||
perm = (3, 2, 1) | ||
p = NestedPermutedDimsArray(a, perm) | ||
T = PermutedDimsArray{elt,3,perm,invperm(perm),eltype(a)} | ||
@test typeof(p) === NestedPermutedDimsArray{T,3,perm,invperm(perm),typeof(a)} | ||
@test size(p) == (4, 3, 2) | ||
@test eltype(p) === T | ||
for I in eachindex(p) | ||
@test size(p[I]) == (4, 3, 2) | ||
@test p[I] == permutedims(a[CartesianIndex(reverse(Tuple(I)))], perm) | ||
end | ||
x = randn(elt, 4, 3, 2) | ||
p[3, 2, 1] = x | ||
@test p[3, 2, 1] == x | ||
@test a[1, 2, 3] == permutedims(x, perm) | ||
end | ||
end |
This file contains bidirectional Unicode text that may be interpreted or compiled differently than what appears below. To review, open the file in an editor that reveals hidden Unicode characters.
Learn more about bidirectional Unicode characters