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[BlockSparseArrays] Sub-slices of multiple blocks (#1489)
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* [BlockSparseArrays] Sub-slices of multiple blocks

* [NDTensors] Bump to v0.3.24
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@mtfishman
mtfishman authored Jun 10, 2024
1 parent d70b89e commit 1bc091a
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2 changes: 1 addition & 1 deletion NDTensors/Project.toml
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Original file line number Diff line number Diff line change
@@ -1,7 +1,7 @@
name = "NDTensors"
uuid = "23ae76d9-e61a-49c4-8f12-3f1a16adf9cf"
authors = ["Matthew Fishman <[email protected]>"]
version = "0.3.23"
version = "0.3.24"

[deps]
Accessors = "7d9f7c33-5ae7-4f3b-8dc6-eff91059b697"
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38 changes: 36 additions & 2 deletions NDTensors/src/lib/BlockSparseArrays/src/BlockArraysExtensions/BlockArraysExtensions.jl
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Expand Up @@ -159,8 +159,8 @@ function blockrange(axis::AbstractUnitRange, r::UnitRange)
end

function blockrange(axis::AbstractUnitRange, r::Int)
error("Slicing with integer values isn't supported.")
return findblock(axis, r)
## return findblock(axis, r)
return error("Slicing with integer values isn't supported.")
end

function blockrange(axis::AbstractUnitRange, r::AbstractVector{<:Block{1}})
Expand All @@ -187,6 +187,24 @@ function blockrange(axis::AbstractUnitRange, r::BlockIndexRange)
return Block(r):Block(r)
end

function blockrange(axis::AbstractUnitRange, r::AbstractVector{<:BlockIndexRange{1}})
return error("Slicing not implemented for range of type `$(typeof(r))`.")
end

function blockrange(
axis::AbstractUnitRange,
r::BlockVector{BlockIndex{1},<:AbstractVector{<:BlockIndexRange{1}}},
)
return map(b -> Block(b), blocks(r))
end

# This handles slicing with `:`/`Colon()`.
function blockrange(axis::AbstractUnitRange, r::Base.Slice)
# TODO: Maybe use `BlockRange`, but that doesn't output
# the same thing.
return only(blockaxes(axis))
end

function blockrange(axis::AbstractUnitRange, r)
return error("Slicing not implemented for range of type `$(typeof(r))`.")
end
Expand Down Expand Up @@ -228,6 +246,22 @@ function blockindices(a::AbstractUnitRange, b::Block, r::BlockIndices)
return blockindices(a, b, r.blocks)
end

function blockindices(
a::AbstractUnitRange,
b::Block,
r::BlockVector{BlockIndex{1},<:AbstractVector{<:BlockIndexRange{1}}},
)
# TODO: Change to iterate over `BlockRange(r)`
# once https://github.com/JuliaArrays/BlockArrays.jl/issues/404
# is fixed.
for bl in blocks(r)
if b == Block(bl)
return only(bl.indices)
end
end
return error("Block not found.")
end

function cartesianindices(a::AbstractArray, b::Block)
return cartesianindices(axes(a), b)
end
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27 changes: 24 additions & 3 deletions ...src/lib/BlockSparseArrays/src/abstractblocksparsearray/wrappedabstractblocksparsearray.jl
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Original file line number Diff line number Diff line change
@@ -1,5 +1,6 @@
using Adapt: Adapt, WrappedArray
using BlockArrays: BlockArrays, BlockedUnitRange, BlockRange, blockedrange, unblock
using BlockArrays:
BlockArrays, BlockedUnitRange, BlockIndexRange, BlockRange, blockedrange, mortar, unblock
using SplitApplyCombine: groupcount

const WrappedAbstractBlockSparseArray{T,N} = WrappedArray{
Expand Down Expand Up @@ -46,6 +47,15 @@ function Base.to_indices(
return blocksparse_to_indices(a, I)
end

# Handle case of indexing with `[Block(1)[1:2], Block(2)[1:2]]`
# by converting it to a `BlockVector` with
# `mortar([Block(1)[1:2], Block(2)[1:2]])`.
function Base.to_indices(
a::BlockSparseArrayLike, inds, I::Tuple{AbstractVector{<:BlockIndexRange{1}},Vararg{Any}}
)
return to_indices(a, inds, (mortar(I[1]), Base.tail(I)...))
end

# Fixes ambiguity error with BlockArrays.
function Base.to_indices(a::BlockSparseArrayLike, I::Tuple{BlockRange{1},Vararg{Any}})
return blocksparse_to_indices(a, I)
Expand Down Expand Up @@ -126,14 +136,25 @@ function Base.getindex(
return blocksparse_getindex(a, block...)
end

# TODO: Define `issasigned(a, ::Block{N})`.
# TODO: Define `blocksparse_isassigned`.
function Base.isassigned(
a::BlockSparseArrayLike{<:Any,N}, index::Vararg{Block{1},N}
) where {N}
# TODO: Define `blocksparse_isassigned`.
return isassigned(blocks(a), Int.(index)...)
end

function Base.isassigned(a::BlockSparseArrayLike{<:Any,N}, index::Block{N}) where {N}
return isassigned(a, Tuple(index)...)
end

# TODO: Define `blocksparse_isassigned`.
function Base.isassigned(
a::BlockSparseArrayLike{<:Any,N}, index::Vararg{BlockIndex{1},N}
) where {N}
b = block.(index)
return isassigned(a, b...) && isassigned(@view(a[b...]), blockindex.(index)...)
end

function Base.setindex!(a::BlockSparseArrayLike{<:Any,N}, value, I::BlockIndex{N}) where {N}
blocksparse_setindex!(a, value, I)
return a
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72 changes: 68 additions & 4 deletions NDTensors/src/lib/BlockSparseArrays/test/test_basics.jl
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@@ -1,6 +1,14 @@
@eval module $(gensym())
using BlockArrays:
Block, BlockRange, BlockedUnitRange, BlockVector, blockedrange, blocklength, blocksize
Block,
BlockRange,
BlockedUnitRange,
BlockVector,
blockedrange,
blocklength,
blocklengths,
blocksize,
mortar
using LinearAlgebra: mul!
using NDTensors.BlockSparseArrays: BlockSparseArray, block_nstored, block_reshape
using NDTensors.SparseArrayInterface: nstored
Expand Down Expand Up @@ -331,13 +339,69 @@ include("TestBlockSparseArraysUtils.jl")
@test b[4, 4] == 44
end

# This is outputting only zero blocks.
a = BlockSparseArray{elt}(undef, ([2, 3], [3, 4]))
a[Block(1, 2)] = randn(elt, size(@view(a[Block(1, 2)])))
a[Block(2, 1)] = randn(elt, size(@view(a[Block(2, 1)])))
@views for b in [Block(1, 2), Block(2, 1)]
a[b] = randn(elt, size(a[b]))
end
b = a[Block(2):Block(2), Block(1):Block(2)]
@test block_nstored(b) == 1
@test b == Array(a)[3:5, 1:end]

a = BlockSparseArray{elt}(undef, ([2, 3, 4], [2, 3, 4]))
# TODO: Define `block_diagindices`.
@views for b in [Block(1, 1), Block(2, 2), Block(3, 3)]
a[b] = randn(elt, size(a[b]))
end
for (I1, I2) in (
(mortar([Block(2)[2:3], Block(3)[1:3]]), mortar([Block(2)[2:3], Block(3)[2:3]])),
([Block(2)[2:3], Block(3)[1:3]], [Block(2)[2:3], Block(3)[2:3]]),
)
for b in (a[I1, I2], @view(a[I1, I2]))
# TODO: Rename `block_stored_length`.
@test block_nstored(b) == 2
@test b[Block(1, 1)] == a[Block(2, 2)[2:3, 2:3]]
@test b[Block(2, 2)] == a[Block(3, 3)[1:3, 2:3]]
end
end

a = BlockSparseArray{elt}(undef, ([3, 3], [3, 3]))
# TODO: Define `block_diagindices`.
@views for b in [Block(1, 1), Block(2, 2)]
a[b] = randn(elt, size(a[b]))
end
I = mortar([Block(1)[1:2], Block(2)[1:2]])
b = a[:, I]
@test b[Block(1, 1)] == a[Block(1, 1)][:, 1:2]
@test b[Block(2, 1)] == a[Block(2, 1)][:, 1:2]
@test b[Block(1, 2)] == a[Block(1, 2)][:, 1:2]
@test b[Block(2, 2)] == a[Block(2, 2)][:, 1:2]
@test blocklengths.(axes(b)) == ([3, 3], [2, 2])
# TODO: Rename `block_stored_length`.
@test blocksize(b) == (2, 2)
@test block_nstored(b) == 2

a = BlockSparseArray{elt}(undef, ([2, 3], [3, 4]))
@views for b in [Block(1, 2), Block(2, 1)]
a[b] = randn(elt, size(a[b]))
end
@test isassigned(a, 1, 1)
@test isassigned(a, 5, 7)
@test !isassigned(a, 0, 1)
@test !isassigned(a, 5, 8)
@test isassigned(a, Block(1), Block(1))
@test isassigned(a, Block(2), Block(2))
@test !isassigned(a, Block(1), Block(0))
@test !isassigned(a, Block(3), Block(2))
@test isassigned(a, Block(1, 1))
@test isassigned(a, Block(2, 2))
@test !isassigned(a, Block(1, 0))
@test !isassigned(a, Block(3, 2))
@test isassigned(a, Block(1)[1], Block(1)[1])
@test isassigned(a, Block(2)[3], Block(2)[4])
@test !isassigned(a, Block(1)[0], Block(1)[1])
@test !isassigned(a, Block(2)[3], Block(2)[5])
@test !isassigned(a, Block(1)[1], Block(0)[1])
@test !isassigned(a, Block(3)[3], Block(2)[4])
end
@testset "LinearAlgebra" begin
a1 = BlockSparseArray{elt}([2, 3], [2, 3])
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13 changes: 4 additions & 9 deletions NDTensors/src/lib/SparseArrayInterface/src/sparsearrayinterface/indexing.jl
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Expand Up @@ -160,16 +160,11 @@ function sparse_setindex!(a::AbstractArray, value, I::NotStoredIndex)
end

# isassigned
function sparse_isassigned(a::AbstractArray, I::Integer...)
return sparse_isassigned(a, CartesianIndex(I))
function sparse_isassigned(a::AbstractArray{<:Any,N}, I::CartesianIndex{N}) where {N}
return sparse_isassigned(a, Tuple(I)...)
end
sparse_isassigned(a::AbstractArray, I::NotStoredIndex) = true
sparse_isassigned(a::AbstractArray, I::StoredIndex) = sparse_isassigned(a, StorageIndex(I))
function sparse_isassigned(a::AbstractArray, I::StorageIndex)
return isassigned(sparse_storage(a), index(I))
end
function sparse_isassigned(a::AbstractArray, I::CartesianIndex)
return sparse_isassigned(a, storage_index(a, I))
function sparse_isassigned(a::AbstractArray{<:Any,N}, I::Vararg{Integer,N}) where {N}
return all(dim -> I[dim] ∈ axes(a, dim), 1:ndims(a))
end

# A set of indices into the storage of the sparse array.
Expand Down
16 changes: 16 additions & 0 deletions NDTensors/src/lib/SparseArrayInterface/test/test_abstractsparsearray.jl
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Expand Up @@ -21,6 +21,14 @@ using Test: @test, @testset
for I in eachindex(a)
@test iszero(a)
end
for I in CartesianIndices(a)
@test isassigned(a, Tuple(I)...)
@test isassigned(a, I)
end
@test !isassigned(a, 0, 1)
@test !isassigned(a, CartesianIndex(0, 1))
@test !isassigned(a, 1, 4)
@test !isassigned(a, CartesianIndex(1, 4))

a = SparseArray{elt}(2, 3)
fill!(a, 0)
Expand Down Expand Up @@ -60,6 +68,14 @@ using Test: @test, @testset
@test iszero(a[I])
end
end
for I in CartesianIndices(a)
@test isassigned(a, Tuple(I)...)
@test isassigned(a, I)
end
@test !isassigned(a, 0, 1)
@test !isassigned(a, CartesianIndex(0, 1))
@test !isassigned(a, 1, 4)
@test !isassigned(a, CartesianIndex(1, 4))

a = SparseArray{elt}(2, 3)
a[1, 2] = 12
Expand Down

2 comments on commit 1bc091a

@mtfishman
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@JuliaRegistrator register subdir=NDTensors

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Registration pull request created: JuliaRegistries/General/108609

Tip: Release Notes

Did you know you can add release notes too? Just add markdown formatted text underneath the comment after the text
"Release notes:" and it will be added to the registry PR, and if TagBot is installed it will also be added to the
release that TagBot creates. i.e.

@JuliaRegistrator register

Release notes:

## Breaking changes

- blah

To add them here just re-invoke and the PR will be updated.

Tagging

After the above pull request is merged, it is recommended that a tag is created on this repository for the registered package version.

This will be done automatically if the Julia TagBot GitHub Action is installed, or can be done manually through the github interface, or via:

git tag -a NDTensors-v0.3.24 -m "<description of version>" 1bc091a03ca2b9fd5b205c785d01f4626c66d267
git push origin NDTensors-v0.3.24

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