Define TensorAlgebra.matricize

Project.toml

@@ -1,7 +1,7 @@
 name = "GradedArrays"
 uuid = "bc96ca6e-b7c8-4bb6-888e-c93f838762c2"
 authors = ["ITensor developers <[email protected]> and contributors"]
-version = "0.2.3"
+version = "0.2.4"
 
 [deps]
 BlockArrays = "8e7c35d0-a365-5155-bbbb-fb81a777f24e"
@@ -23,16 +23,16 @@ TensorAlgebra = "68bd88dc-f39d-4e12-b2ca-f046b68fcc6a"
 GradedArraysTensorAlgebraExt = "TensorAlgebra"
 
 [compat]
-BlockArrays = "1.5.0"
-BlockSparseArrays = "0.4.0"
+BlockArrays = "1.6.0"
+BlockSparseArrays = "0.4.2"
 Compat = "4.16.0"
 DerivableInterfaces = "0.4.4"
 FillArrays = "1.13.0"
 HalfIntegers = "1.6.0"
 LinearAlgebra = "1.10.0"
 Random = "1.10.0"
 SplitApplyCombine = "1.2.3"
-TensorAlgebra = "0.2.7"
+TensorAlgebra = "0.3.2"
 TensorProducts = "0.1.3"
 TypeParameterAccessors = "0.3.9"
 julia = "1.10"

ext/GradedArraysTensorAlgebraExt/GradedArraysTensorAlgebraExt.jl

@@ -1,109 +1,76 @@
 module GradedArraysTensorAlgebraExt
 
-using BlockArrays: Block, BlockIndexRange, blockedrange, blocks
-using BlockSparseArrays:
-  BlockSparseArrays,
-  AbstractBlockSparseArray,
-  AbstractBlockSparseArrayInterface,
-  BlockSparseArray,
-  BlockSparseArrayInterface,
-  BlockSparseMatrix,
-  BlockSparseVector,
-  block_merge
-using DerivableInterfaces: @interface
+using BlockArrays: blocks
+using BlockSparseArrays: BlockSparseArray, blockreshape
+using GradedArrays: GradedArray
 using GradedArrays.GradedUnitRanges:
-  GradedUnitRanges,
   AbstractGradedUnitRange,
   blockmergesortperm,
   blocksortperm,
-  dual,
+  flip,
   invblockperm,
-  nondual,
   unmerged_tensor_product
-using LinearAlgebra: Adjoint, Transpose
+using GradedArrays.SymmetrySectors: trivial
 using TensorAlgebra:
-  TensorAlgebra, FusionStyle, BlockReshapeFusion, SectorFusion, fusedims, splitdims
-using TensorProducts: OneToOne
+  TensorAlgebra,
+  AbstractBlockPermutation,
+  BlockedTuple,
+  FusionStyle,
+  trivial_axis,
+  unmatricize
 
-#=
-    reducewhile(f, op, collection, state)
+struct SectorFusion <: FusionStyle end
 
-reducewhile(x -> length(x) < 3, vcat, ["a", "b", "c", "d"], 2; init=String[]) ==
-  (["b", "c"], 4)
-=#
-function reducewhile(f, op, collection, state; init)
-  prev_result = init
-  prev_state = state
-  result = prev_result
-  while f(result)
-    prev_result = result
-    prev_state = state
-    value_and_state = iterate(collection, state)
-    isnothing(value_and_state) && break
-    value, state = value_and_state
-    result = op(result, value)
-  end
-  return prev_result, prev_state
-end
-
-#=
-    groupreducewhile(f, op, collection, ngroups)
-
-groupreducewhile((i, x) -> length(x) ≤ i, vcat, ["a", "b", "c", "d", "e", "f"], 3; init=String[]) ==
-  (["a"], ["b", "c"], ["d", "e", "f"])
-=#
-function groupreducewhile(f, op, collection, ngroups; init)
-  state = firstindex(collection)
-  return ntuple(ngroups) do group_number
-    result, state = reducewhile(x -> f(group_number, x), op, collection, state; init)
-    return result
-  end
-end
+TensorAlgebra.FusionStyle(::Type{<:GradedArray}) = SectorFusion()
 
-TensorAlgebra.FusionStyle(::AbstractGradedUnitRange) = SectorFusion()
+# TBD consider heterogeneous sectors?
+TensorAlgebra.trivial_axis(t::Tuple{Vararg{AbstractGradedUnitRange}}) = trivial(first(t))
 
-# Sort the blocks by sector and then merge the common sectors.
-function block_mergesort(a::AbstractArray)
-  I = blockmergesortperm.(axes(a))
-  return a[I...]
+function matricize_axes(
+  blocked_axes::BlockedTuple{2,<:Any,<:Tuple{Vararg{AbstractUnitRange}}}
+)
+  @assert !isempty(blocked_axes)
+  default_axis = trivial_axis(Tuple(blocked_axes))
+  codomain_axes, domain_axes = blocks(blocked_axes)
+  codomain_axis = unmerged_tensor_product(default_axis, codomain_axes...)
+  unflipped_domain_axis = unmerged_tensor_product(default_axis, domain_axes...)
+  return codomain_axis, flip(unflipped_domain_axis)
 end
 
-function TensorAlgebra.fusedims(
-  ::SectorFusion, a::AbstractArray, merged_axes::AbstractUnitRange...
+function TensorAlgebra.matricize(
+  ::SectorFusion, a::AbstractArray, biperm::AbstractBlockPermutation{2}
 )
-  # First perform a fusion using a block reshape.
-  # 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...)
+  a_perm = permutedims(a, Tuple(biperm))
+  codomain_axis, domain_axis = matricize_axes(axes(a)[biperm])
+  a_reshaped = blockreshape(a_perm, (codomain_axis, domain_axis))
   # Sort the blocks by sector and merge the equivalent sectors.
-  return block_mergesort(a_reshaped)
+  return sectormergesort(a_reshaped)
 end
 
-function TensorAlgebra.splitdims(
-  ::SectorFusion, a::AbstractArray, split_axes::AbstractUnitRange...
+function TensorAlgebra.unmatricize(
+  ::SectorFusion,
+  m::AbstractMatrix,
+  blocked_axes::BlockedTuple{2,<:Any,<:Tuple{Vararg{AbstractUnitRange}}},
 )
   # First, fuse axes to get `blockmergesortperm`.
   # Then unpermute the blocks.
-  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)
+  fused_axes = matricize_axes(blocked_axes)
+
+  blockperms = blocksortperm.(fused_axes)
+  sorted_axes = map((r, I) -> only(axes(r[I])), fused_axes, blockperms)
 
   # TODO: This is doing extra copies of the blocks,
   # use `@view a[axes_prod...]` instead.
   # That will require implementing some reindexing logic
   # for this combination of slicing.
-  a_unblocked = a[sorted_axes...]
-  a_blockpermed = a_unblocked[invblockperm.(blockperms)...]
-  return splitdims(BlockReshapeFusion(), a_blockpermed, split_axes...)
+  m_unblocked = m[sorted_axes...]
+  m_blockpermed = m_unblocked[invblockperm.(blockperms)...]
+  return unmatricize(FusionStyle(BlockSparseArray), m_blockpermed, blocked_axes)
 end
 
+# Sort the blocks by sector and then merge the common sectors.
+function sectormergesort(a::AbstractArray)
+  I = blockmergesortperm.(axes(a))
+  return a[I...]
+end
 end

src/gradedarray.jl

@@ -4,11 +4,21 @@ using BlockSparseArrays:
   AbstractBlockSparseMatrix,
   AnyAbstractBlockSparseArray,
   BlockSparseArray,
+  BlockSparseMatrix,
+  BlockSparseVector,
   blocktype
 using ..GradedUnitRanges: AbstractGradedUnitRange, dual
 using LinearAlgebra: Adjoint
 using TypeParameterAccessors: similartype, unwrap_array_type
 
+const GradedArray{T,M,A,Blocks,Axes} = BlockSparseArray{
+  T,M,A,Blocks,Axes
+} where {Axes<:Tuple{AbstractGradedUnitRange,Vararg{AbstractGradedUnitRange}}}
+const GradedMatrix{T,A,Blocks,Axes} =
+  BlockSparseMatrix{T,A,Blocks,Axes} where {Axes<:Tuple{Vararg{AbstractGradedUnitRange}}}
+const GradedVector{T,A,Blocks,Axes} =
+  BlockSparseVector{T,A,Blocks,Axes} where {Axes<:Tuple{Vararg{AbstractGradedUnitRange}}}
+
 # TODO: Handle this through some kind of trait dispatch, maybe
 # a `SymmetryStyle`-like trait to check if the block sparse
 # matrix has graded axes.

test/Project.toml

@@ -15,15 +15,15 @@ TestExtras = "5ed8adda-3752-4e41-b88a-e8b09835ee3a"
 
 [compat]
 Aqua = "0.8.11"
-BlockArrays = "1.5.0"
-BlockSparseArrays = "0.4.0"
+BlockArrays = "1.6.0"
+BlockSparseArrays = "0.4.2"
 GradedArrays = "0.2.0"
 LinearAlgebra = "1.10.0"
 Random = "1.10.0"
 SafeTestsets = "0.1.0"
 SparseArraysBase = "0.5.4"
 Suppressor = "0.2.8"
-TensorAlgebra = "0.2.7"
+TensorAlgebra = "0.3.2"
 TensorProducts = "0.1.3"
 Test = "1.10.0"
 TestExtras = "0.3.1"

test/test_gradedarray.jl

@@ -2,12 +2,12 @@ using BlockArrays:
   AbstractBlockArray, Block, BlockedOneTo, blockedrange, blocklengths, blocksize
 using BlockSparseArrays:
   BlockSparseArray, BlockSparseMatrix, BlockSparseVector, blockstoredlength
-using GradedArrays:
+using GradedArrays: GradedArray, GradedMatrix, GradedVector
+using GradedArrays.GradedUnitRanges:
   GradedUnitRanges,
   GradedOneTo,
   GradedUnitRange,
   GradedUnitRangeDual,
-  blocklabels,
   dag,
   dual,
   gradedrange,
@@ -31,6 +31,19 @@ end
 
 const elts = (Float32, Float64, Complex{Float32}, Complex{Float64})
 @testset "GradedArray (eltype=$elt)" for elt in elts
+  @testset "definitions" begin
+    r = gradedrange([U1(0) => 2, U1(1) => 2])
+    a = BlockSparseArray{elt}(undef)
+    @test !(a isa GradedArray)  # no type piracy
+    v = BlockSparseArray{elt}(undef, r)
+    @test v isa GradedArray
+    @test v isa GradedVector
+    m = BlockSparseArray{elt}(undef, r, r)
+    @test m isa GradedArray
+    @test m isa GradedMatrix
+    a = BlockSparseArray{elt}(undef, r, r, r)
+    @test a isa GradedArray
+  end
   @testset "map" begin
     d1 = gradedrange([U1(0) => 2, U1(1) => 2])
     d2 = gradedrange([U1(0) => 2, U1(1) => 2])
@@ -60,6 +73,7 @@ const elts = (Float32, Float64, Complex{Float32}, Complex{Float64})
     r = gradedrange([U1(0) => 2, U1(1) => 2])
     a = zeros(r, r, r, r)
     @test a isa BlockSparseArray{Float64}
+    @test a isa GradedArray
     @test eltype(a) === Float64
     @test size(a) == (4, 4, 4, 4)
     @test iszero(a)

test/test_tensoralgebraext.jl

@@ -1,9 +1,9 @@
 using BlockArrays: Block, blocksize
-using BlockSparseArrays: BlockSparseArray
-using GradedArrays: GradedOneTo, blocklabels, dual, gradedrange
+using BlockSparseArrays: BlockSparseArray, BlockSparseMatrix
+using GradedArrays: GradedOneTo, blocklabels, dual, flip, gradedrange, space_isequal
 using GradedArrays.SymmetrySectors: U1
 using Random: randn!
-using TensorAlgebra: contract, fusedims, splitdims
+using TensorAlgebra: contract, matricize, unmatricize
 using Test: @test, @test_broken, @testset
 
 function randn_blockdiagonal(elt::Type, axes::Tuple)
@@ -18,7 +18,51 @@ end
 
 const elts = (Float32, Float64, Complex{Float32}, Complex{Float64})
 @testset "`contract` `GradedArray` (eltype=$elt)" for elt in elts
-  @testset "GradedOneTo with U(1)" begin
+  @testset "matricize" begin
+    d1 = gradedrange([U1(0) => 1, U1(1) => 1])
+    d2 = gradedrange([U1(0) => 1, U1(1) => 1])
+    a = randn_blockdiagonal(elt, (d1, d2, dual(d1), dual(d2)))
+    m = matricize(a, (1, 2), (3, 4))
+    @test m isa BlockSparseMatrix
+    @test space_isequal(axes(m, 1), gradedrange([U1(0) => 1, U1(1) => 2, U1(2) => 1]))
+    @test space_isequal(
+      axes(m, 2), flip(gradedrange([U1(0) => 1, U1(-1) => 2, U1(-2) => 1]))
+    )
+
+    for I in CartesianIndices(m)
+      if I ∈ CartesianIndex.([(1, 1), (4, 4)])
+        @test !iszero(m[I])
+      else
+        @test iszero(m[I])
+      end
+    end
+    @test a[1, 1, 1, 1] == m[1, 1]
+    @test a[2, 2, 2, 2] == m[4, 4]
+    @test blocksize(m) == (3, 3)
+    @test a == unmatricize(m, (d1, d2), (dual(d1), dual(d2)))
+
+    # check block fusing and splitting
+    d = gradedrange([U1(0) => 2, U1(1) => 1])
+    b = randn_blockdiagonal(elt, (d, d, dual(d), dual(d)))
+    @test unmatricize(
+      matricize(b, (1, 2), (3, 4)), (axes(b, 1), axes(b, 2)), (axes(b, 3), axes(b, 4))
+    ) == b
+
+    d1234 = gradedrange([U1(-2) => 1, U1(-1) => 4, U1(0) => 6, U1(1) => 4, U1(2) => 1])
+    m = matricize(a, (1, 2, 3, 4), ())
+    @test m isa BlockSparseMatrix
+    @test space_isequal(axes(m, 1), d1234)
+    @test space_isequal(axes(m, 2), flip(gradedrange([U1(0) => 1])))
+    @test a == unmatricize(m, (d1, d2, dual(d1), dual(d2)), ())
+
+    m = matricize(a, (), (1, 2, 3, 4))
+    @test m isa BlockSparseMatrix
+    @test space_isequal(axes(m, 1), gradedrange([U1(0) => 1]))
+    @test space_isequal(axes(m, 2), dual(d1234))
+    @test a == unmatricize(m, (), (d1, d2, dual(d1), dual(d2)))
+  end
+
+  @testset "contract with U(1)" begin
     d = gradedrange([U1(0) => 2, U1(1) => 3])
     a1 = randn_blockdiagonal(elt, (d, d, dual(d), dual(d)))
     a2 = randn_blockdiagonal(elt, (d, d, dual(d), dual(d)))
@@ -38,14 +82,12 @@ const elts = (Float32, Float64, Complex{Float32}, Complex{Float64})
     @test a_dest ≈ a_dest_dense
 
     # matrix vector
-    @test_broken a_dest, dimnames_dest = contract(a1, (2, -1, -2, 1), a3, (1, 2))
-    #=
+    a_dest, dimnames_dest = contract(a1, (2, -1, -2, 1), a3, (1, 2))
     a_dest_dense, dimnames_dest_dense = contract(a1_dense, (2, -1, -2, 1), a3_dense, (1, 2))
     @test dimnames_dest == dimnames_dest_dense
     @test size(a_dest) == size(a_dest_dense)
     @test a_dest isa BlockSparseArray
     @test a_dest ≈ a_dest_dense
-    =#
 
     #  vector matrix
     a_dest, dimnames_dest = contract(a3, (1, 2), a1, (2, -1, -2, 1))
@@ -71,30 +113,4 @@ const elts = (Float32, Float64, Complex{Float32}, Complex{Float64})
     @test a_dest isa BlockSparseArray
     @test a_dest ≈ a_dest_dense
   end
-  @testset "fusedims" begin
-    d1 = gradedrange([U1(0) => 1, U1(1) => 1])
-    d2 = gradedrange([U1(0) => 1, U1(1) => 1])
-    a = randn_blockdiagonal(elt, (d1, d2, d1, d2))
-    m = fusedims(a, (1, 2), (3, 4))
-    for ax in axes(m)
-      @test ax isa GradedOneTo
-      @test blocklabels(ax) == [U1(0), U1(1), U1(2)]
-    end
-    for I in CartesianIndices(m)
-      if I ∈ CartesianIndex.([(1, 1), (4, 4)])
-        @test !iszero(m[I])
-      else
-        @test iszero(m[I])
-      end
-    end
-    @test a[1, 1, 1, 1] == m[1, 1]
-    @test a[2, 2, 2, 2] == m[4, 4]
-    @test blocksize(m) == (3, 3)
-    @test a == splitdims(m, (d1, d2), (d1, d2))
-
-    # check block fusing and splitting
-    d = gradedrange([U1(0) => 2, U1(1) => 1])
-    a = randn_blockdiagonal(elt, (d, d, dual(d), dual(d)))
-    @test splitdims(fusedims(a, (1, 2), (3, 4)), axes(a)...) == a
-  end
 end