Adapt to TensorProducts.jl

Project.toml

@@ -1,7 +1,7 @@
 name = "BlockSparseArrays"
 uuid = "2c9a651f-6452-4ace-a6ac-809f4280fbb4"
 authors = ["ITensor developers <[email protected]> and contributors"]
-version = "0.3.8"
+version = "0.3.9"
 
 [deps]
 Adapt = "79e6a3ab-5dfb-504d-930d-738a2a938a0e"
@@ -21,12 +21,12 @@ SplitApplyCombine = "03a91e81-4c3e-53e1-a0a4-9c0c8f19dd66"
 TypeParameterAccessors = "7e5a90cf-f82e-492e-a09b-e3e26432c138"
 
 [weakdeps]
-LabelledNumbers = "f856a3a6-4152-4ec4-b2a7-02c1a55d7993"
 TensorAlgebra = "68bd88dc-f39d-4e12-b2ca-f046b68fcc6a"
+TensorProducts = "decf83d6-1968-43f4-96dc-fdb3fe15fc6d"
 
 [extensions]
 BlockSparseArraysGradedUnitRangesExt = "GradedUnitRanges"
-BlockSparseArraysTensorAlgebraExt = ["LabelledNumbers", "TensorAlgebra"]
+BlockSparseArraysTensorAlgebraExt = ["TensorProducts", "TensorAlgebra"]
 
 [compat]
 Adapt = "4.1.1"
@@ -38,14 +38,14 @@ DiagonalArrays = "0.3"
 Dictionaries = "0.4.3"
 FillArrays = "1.13.0"
 GPUArraysCore = "0.1.0, 0.2"
-GradedUnitRanges = "0.1.0"
-LabelledNumbers = "0.1.0"
+GradedUnitRanges = "0.2.2"
 LinearAlgebra = "1.10"
 MacroTools = "0.5.13"
 MapBroadcast = "0.1.5"
 SparseArraysBase = "0.5"
 SplitApplyCombine = "1.2.3"
-TensorAlgebra = "0.1.0, 0.2"
+TensorAlgebra = "0.2.4"
+TensorProducts = "0.1.2"
 Test = "1.10"
 TypeParameterAccessors = "0.2.0, 0.3"
 julia = "1.10"

ext/BlockSparseArraysTensorAlgebraExt/BlockSparseArraysTensorAlgebraExt.jl

@@ -1,15 +1,10 @@
 module BlockSparseArraysTensorAlgebraExt
 using BlockArrays: AbstractBlockedUnitRange
-using GradedUnitRanges: tensor_product
-using TensorAlgebra: TensorAlgebra, FusionStyle, BlockReshapeFusion
 
-function TensorAlgebra.:⊗(a1::AbstractBlockedUnitRange, a2::AbstractBlockedUnitRange)
-  return tensor_product(a1, a2)
-end
+using TensorAlgebra: TensorAlgebra, FusionStyle, BlockReshapeFusion
+using TensorProducts: OneToOne
 
-using BlockArrays: AbstractBlockedUnitRange
 using BlockSparseArrays: AbstractBlockSparseArray, blockreshape
-using TensorAlgebra: TensorAlgebra, FusionStyle, BlockReshapeFusion
 
 TensorAlgebra.FusionStyle(::AbstractBlockedUnitRange) = BlockReshapeFusion()
 
@@ -46,13 +41,12 @@
 using GradedUnitRanges:
   GradedUnitRanges,
   AbstractGradedUnitRange,
-  OneToOne,
   blockmergesortperm,
   blocksortperm,
   dual,
   invblockperm,
   nondual,
-  tensor_product
+  unmerged_tensor_product
 using LinearAlgebra: Adjoint, Transpose
 using TensorAlgebra:
   TensorAlgebra, FusionStyle, BlockReshapeFusion, SectorFusion, fusedims, splitdims
@@ -77,10 +71,17 @@
 end
 
 function TensorAlgebra.fusedims(
-  ::SectorFusion, a::AbstractArray, axes::AbstractUnitRange...
+  ::SectorFusion, a::AbstractArray, merged_axes::AbstractUnitRange...
 )
   # First perform a fusion using a block reshape.
-  a_reshaped = fusedims(BlockReshapeFusion(), a, axes...)
+  # TODO avoid groupreducewhile. Require refactor of fusedims.
+  unmerged_axes = groupreducewhile(
+    unmerged_tensor_product, axes(a), length(merged_axes); init=OneToOne()
+  ) do i, axis
+    return length(axis) ≤ length(merged_axes[i])
+  end
+
+  a_reshaped = fusedims(BlockReshapeFusion(), a, unmerged_axes...)
   # Sort the blocks by sector and merge the equivalent sectors.
   return block_mergesort(a_reshaped)
 end
@@ -90,10 +91,11 @@
 )
   # First, fuse axes to get `blockmergesortperm`.
   # Then unpermute the blocks.
-  axes_prod =
-    groupreducewhile(tensor_product, split_axes, ndims(a); init=OneToOne()) do i, axis
-      return length(axis) ≤ length(axes(a, i))
-    end
+  axes_prod = groupreducewhile(
+    unmerged_tensor_product, split_axes, ndims(a); init=OneToOne()
+  ) do i, axis
+    return length(axis) ≤ length(axes(a, i))
+  end
   blockperms = blocksortperm.(axes_prod)
   sorted_axes = map((r, I) -> only(axes(r[I])), axes_prod, blockperms)
 
@@ -106,34 +108,11 @@
   return splitdims(BlockReshapeFusion(), a_blockpermed, split_axes...)
 end
 
-# This is a temporary fix for `eachindex` being broken for BlockSparseArrays
-# with mixed dual and non-dual axes. This shouldn't be needed once
-# GradedUnitRanges is rewritten using BlockArrays v1.
-# TODO: Delete this once GradedUnitRanges is rewritten.
-function Base.eachindex(a::AbstractBlockSparseArray)
-  return CartesianIndices(nondual.(axes(a)))
-end
-
 # TODO: Handle this through some kind of trait dispatch, maybe
 # a `SymmetryStyle`-like trait to check if the block sparse
 # matrix has graded axes.
 function Base.axes(a::Adjoint{<:Any,<:AbstractBlockSparseMatrix})
   return dual.(reverse(axes(a')))
 end
 
-# This definition is only needed since calls like
-# `a[[Block(1), Block(2)]]` where `a isa AbstractGradedUnitRange`
-# returns a `BlockSparseVector` instead of a `BlockVector`
-# due to limitations in the `BlockArray` type not allowing
-# axes with non-Int element types.
-# TODO: Remove this once that issue is fixed,
-# see https://github.com/JuliaArrays/BlockArrays.jl/pull/405.
-using BlockArrays: BlockRange
-using LabelledNumbers: label
-function GradedUnitRanges.blocklabels(a::BlockSparseVector)
-  return map(BlockRange(a)) do block
-    return label(blocks(a)[Int(block)])
-  end
-end
-
 end

test/Project.toml

@@ -29,7 +29,7 @@ BlockArrays = "1"
 BlockSparseArrays = "0.3"
 DiagonalArrays = "0.3"
 GPUArraysCore = "0.2"
-GradedUnitRanges = "0.1"
+GradedUnitRanges = "0.2.2"
 JLArrays = "0.2"
 LabelledNumbers = "0.1"
 LinearAlgebra = "1"
@@ -38,8 +38,8 @@ Random = "1"
 SafeTestsets = "0.1"
 SparseArraysBase = "0.5"
 Suppressor = "0.2"
-SymmetrySectors = "0.1"
-TensorAlgebra = "0.2"
+SymmetrySectors = "0.1.7"
+TensorAlgebra = "0.2.4"
 Test = "1"
 TestExtras = "0.3"
 TypeParameterAccessors = "0.3"

test/test_gradedunitrangesext.jl

@@ -1,4 +1,3 @@
-@eval module $(gensym())
 using Test: @test, @testset
 using BlockArrays:
   AbstractBlockArray, Block, BlockedOneTo, blockedrange, blocklengths, blocksize
@@ -10,6 +9,7 @@ using GradedUnitRanges:
   GradedUnitRange,
   GradedUnitRangeDual,
   blocklabels,
+  dag,
   dual,
   gradedrange,
   isdual
@@ -19,6 +19,7 @@ using SymmetrySectors: U1
 using TensorAlgebra: fusedims, splitdims
 using LinearAlgebra: adjoint
 using Random: randn!
+
 function randn_blockdiagonal(elt::Type, axes::Tuple)
   a = BlockSparseArray{elt}(undef, axes)
   blockdiaglength = minimum(blocksize(a))
@@ -390,4 +391,15 @@ const elts = (Float32, Float64, Complex{Float32}, Complex{Float64})
     @test all(GradedUnitRanges.space_isequal.(axes(b), (r, dual(r))))
   end
 end
+
+@testset "dag" begin
+  elt = ComplexF64
+  r = gradedrange([U1(0) => 2, U1(1) => 3])
+  a = BlockSparseArray{elt}(undef, r, dual(r))
+  a[Block(1, 1)] = randn(elt, 2, 2)
+  a[Block(2, 2)] = randn(elt, 3, 3)
+  @test isdual.(axes(a)) == (false, true)
+  ad = dag(a)
+  @test Array(ad) == conj(Array(a))
+  @test isdual.(axes(ad)) == (true, false)
 end