[GradedAxes] Introduce GradedUnitRangeDual (#1531)

NDTensors/src/lib/BlockSparseArrays/ext/BlockSparseArraysGradedAxesExt/test/runtests.jl

@@ -1,13 +1,22 @@
 @eval module $(gensym())
 using Compat: Returns
 using Test: @test, @testset, @test_broken
-using BlockArrays: Block, BlockedOneTo, blockedrange, blocklengths, blocksize
+using BlockArrays:
+  AbstractBlockArray, Block, BlockedOneTo, blockedrange, blocklengths, blocksize
 using NDTensors.BlockSparseArrays: BlockSparseArray, block_nstored
 using NDTensors.GradedAxes:
-  GradedAxes, GradedOneTo, UnitRangeDual, blocklabels, dual, gradedrange
+  GradedAxes,
+  GradedOneTo,
+  GradedUnitRange,
+  GradedUnitRangeDual,
+  blocklabels,
+  dual,
+  gradedrange,
+  isdual
 using NDTensors.LabelledNumbers: label
 using NDTensors.SparseArrayInterface: nstored
 using NDTensors.TensorAlgebra: fusedims, splitdims
+using LinearAlgebra: adjoint
 using Random: randn!
 function blockdiagonal!(f, a::AbstractArray)
   for i in 1:minimum(blocksize(a))
@@ -31,15 +40,15 @@ const elts = (Float32, Float64, Complex{Float32}, Complex{Float64})
     d2 = gradedrange([U1(0) => 2, U1(1) => 2])
     a = BlockSparseArray{elt}(d1, d2, d1, d2)
     blockdiagonal!(randn!, a)
+    @test axes(a, 1) isa GradedOneTo
+    @test axes(view(a, 1:4, 1:4, 1:4, 1:4), 1) isa GradedOneTo
 
     for b in (a + a, 2 * a)
       @test size(b) == (4, 4, 4, 4)
       @test blocksize(b) == (2, 2, 2, 2)
       @test blocklengths.(axes(b)) == ([2, 2], [2, 2], [2, 2], [2, 2])
       @test nstored(b) == 32
       @test block_nstored(b) == 2
-      # TODO: Have to investigate why this fails
-      # on Julia v1.6, or drop support for v1.6.
       for i in 1:ndims(a)
         @test axes(b, i) isa GradedOneTo
       end
@@ -103,16 +112,17 @@ const elts = (Float32, Float64, Complex{Float32}, Complex{Float64})
     @test blocksize(m) == (3, 3)
     @test a == splitdims(m, (d1, d2), (d1, d2))
   end
+
   @testset "dual axes" begin
     r = gradedrange([U1(0) => 2, U1(1) => 2])
     for ax in ((r, r), (dual(r), r), (r, dual(r)), (dual(r), dual(r)))
       a = BlockSparseArray{elt}(ax...)
       @views for b in [Block(1, 1), Block(2, 2)]
         a[b] = randn(elt, size(a[b]))
       end
-      # TODO: Define and use `isdual` here.
       for dim in 1:ndims(a)
         @test typeof(ax[dim]) === typeof(axes(a, dim))
+        @test isdual(ax[dim]) == isdual(axes(a, dim))
       end
       @test @view(a[Block(1, 1)])[1, 1] == a[1, 1]
       @test @view(a[Block(1, 1)])[2, 1] == a[2, 1]
@@ -130,41 +140,149 @@ const elts = (Float32, Float64, Complex{Float32}, Complex{Float64})
         @test a[I] == a_dense[I]
       end
       @test axes(a') == dual.(reverse(axes(a)))
-      # TODO: Define and use `isdual` here.
-      @test typeof(axes(a', 1)) === typeof(dual(axes(a, 2)))
-      @test typeof(axes(a', 2)) === typeof(dual(axes(a, 1)))
+
+      @test isdual(axes(a', 1)) ≠ isdual(axes(a, 2))
+      @test isdual(axes(a', 2)) ≠ isdual(axes(a, 1))
       @test isnothing(show(devnull, MIME("text/plain"), a))
 
       # Check preserving dual in tensor algebra.
       for b in (a + a, 2 * a, 3 * a - a)
         @test Array(b) ≈ 2 * Array(a)
-        # TODO: Define and use `isdual` here.
         for dim in 1:ndims(a)
-          @test typeof(axes(b, dim)) === typeof(axes(b, dim))
+          @test isdual(axes(b, dim)) == isdual(axes(a, dim))
         end
       end
 
       @test isnothing(show(devnull, MIME("text/plain"), @view(a[Block(1, 1)])))
       @test @view(a[Block(1, 1)]) == a[Block(1, 1)]
     end
 
+    @testset "GradedOneTo" begin
+      r = gradedrange([U1(0) => 2, U1(1) => 2])
+      a = BlockSparseArray{elt}(r, r)
+      @views for i in [Block(1, 1), Block(2, 2)]
+        a[i] = randn(elt, size(a[i]))
+      end
+      b = 2 * a
+      @test block_nstored(b) == 2
+      @test Array(b) == 2 * Array(a)
+      for i in 1:2
+        @test axes(b, i) isa GradedOneTo
+        @test axes(a[:, :], i) isa GradedOneTo
+      end
+
+      I = [Block(1)[1:1]]
+      @test a[I, :] isa AbstractBlockArray
+      @test a[:, I] isa AbstractBlockArray
+      @test size(a[I, I]) == (1, 1)
+      @test !isdual(axes(a[I, I], 1))
+    end
+
+    @testset "GradedUnitRange" begin
+      r = gradedrange([U1(0) => 2, U1(1) => 2])[1:3]
+      a = BlockSparseArray{elt}(r, r)
+      @views for i in [Block(1, 1), Block(2, 2)]
+        a[i] = randn(elt, size(a[i]))
+      end
+      b = 2 * a
+      @test block_nstored(b) == 2
+      @test Array(b) == 2 * Array(a)
+      for i in 1:2
+        @test axes(b, i) isa GradedUnitRange
+        @test axes(a[:, :], i) isa GradedUnitRange
+      end
+
+      I = [Block(1)[1:1]]
+      @test a[I, :] isa AbstractBlockArray
+      @test axes(a[I, :], 1) isa GradedOneTo
+      @test axes(a[I, :], 2) isa GradedUnitRange
+
+      @test a[:, I] isa AbstractBlockArray
+      @test axes(a[:, I], 2) isa GradedOneTo
+      @test axes(a[:, I], 1) isa GradedUnitRange
+      @test size(a[I, I]) == (1, 1)
+      @test !isdual(axes(a[I, I], 1))
+    end
+
     # Test case when all axes are dual.
-    for r in (gradedrange([U1(0) => 2, U1(1) => 2]), blockedrange([2, 2]))
+    @testset "dual GradedOneTo" begin
+      r = gradedrange([U1(-1) => 2, U1(1) => 2])
       a = BlockSparseArray{elt}(dual(r), dual(r))
       @views for i in [Block(1, 1), Block(2, 2)]
         a[i] = randn(elt, size(a[i]))
       end
       b = 2 * a
       @test block_nstored(b) == 2
       @test Array(b) == 2 * Array(a)
-      for ax in axes(b)
-        @test ax isa UnitRangeDual
+      for i in 1:2
+        @test axes(b, i) isa GradedUnitRangeDual
+        @test axes(a[:, :], i) isa GradedUnitRangeDual
       end
+      I = [Block(1)[1:1]]
+      @test a[I, :] isa AbstractBlockArray
+      @test a[:, I] isa AbstractBlockArray
+      @test size(a[I, I]) == (1, 1)
+      @test isdual(axes(a[I, :], 2))
+      @test isdual(axes(a[:, I], 1))
+      @test_broken isdual(axes(a[I, :], 1))
+      @test_broken isdual(axes(a[:, I], 2))
+      @test_broken isdual(axes(a[I, I], 1))
+      @test_broken isdual(axes(a[I, I], 2))
+    end
+
+    @testset "dual GradedUnitRange" begin
+      r = gradedrange([U1(0) => 2, U1(1) => 2])[1:3]
+      a = BlockSparseArray{elt}(dual(r), dual(r))
+      @views for i in [Block(1, 1), Block(2, 2)]
+        a[i] = randn(elt, size(a[i]))
+      end
+      b = 2 * a
+      @test block_nstored(b) == 2
+      @test Array(b) == 2 * Array(a)
+      for i in 1:2
+        @test axes(b, i) isa GradedUnitRangeDual
+        @test axes(a[:, :], i) isa GradedUnitRangeDual
+      end
+
+      I = [Block(1)[1:1]]
+      @test a[I, :] isa AbstractBlockArray
+      @test a[:, I] isa AbstractBlockArray
+      @test size(a[I, I]) == (1, 1)
+      @test isdual(axes(a[I, :], 2))
+      @test isdual(axes(a[:, I], 1))
+      @test_broken isdual(axes(a[I, :], 1))
+      @test_broken isdual(axes(a[:, I], 2))
+      @test_broken isdual(axes(a[I, I], 1))
+      @test_broken isdual(axes(a[I, I], 2))
     end
 
-    # Test case when all axes are dual
-    # from taking the adjoint.
-    for r in (gradedrange([U1(0) => 2, U1(1) => 2]), blockedrange([2, 2]))
+    @testset "dual BlockedUnitRange" begin    # self dual
+      r = blockedrange([2, 2])
+      a = BlockSparseArray{elt}(dual(r), dual(r))
+      @views for i in [Block(1, 1), Block(2, 2)]
+        a[i] = randn(elt, size(a[i]))
+      end
+      b = 2 * a
+      @test block_nstored(b) == 2
+      @test Array(b) == 2 * Array(a)
+      @test a[:, :] isa BlockSparseArray
+      for i in 1:2
+        @test axes(b, i) isa BlockedOneTo
+        @test axes(a[:, :], i) isa BlockedOneTo
+      end
+
+      I = [Block(1)[1:1]]
+      @test a[I, :] isa BlockSparseArray
+      @test a[:, I] isa BlockSparseArray
+      @test size(a[I, I]) == (1, 1)
+      @test !isdual(axes(a[I, I], 1))
+    end
+
+    # Test case when all axes are dual from taking the adjoint.
+    for r in (
+      gradedrange([U1(0) => 2, U1(1) => 2]),
+      gradedrange([U1(0) => 2, U1(1) => 2])[begin:end],
+    )
       a = BlockSparseArray{elt}(r, r)
       @views for i in [Block(1, 1), Block(2, 2)]
         a[i] = randn(elt, size(a[i]))
@@ -173,8 +291,13 @@ const elts = (Float32, Float64, Complex{Float32}, Complex{Float64})
       @test block_nstored(b) == 2
       @test Array(b) == 2 * Array(a)'
       for ax in axes(b)
-        @test ax isa UnitRangeDual
+        @test ax isa typeof(dual(r))
       end
+
+      I = [Block(1)[1:1]]
+      @test size(b[I, :]) == (1, 4)
+      @test size(b[:, I]) == (4, 1)
+      @test size(b[I, I]) == (1, 1)
     end
   end
   @testset "Matrix multiplication" begin

NDTensors/src/lib/BlockSparseArrays/src/abstractblocksparsearray/views.jl

@@ -1,5 +1,12 @@
 using BlockArrays:
-  BlockArrays, Block, BlockIndexRange, BlockedVector, blocklength, blocksize, viewblock
+  AbstractBlockedUnitRange,
+  BlockArrays,
+  Block,
+  BlockIndexRange,
+  BlockedVector,
+  blocklength,
+  blocksize,
+  viewblock
 
 # This splits `BlockIndexRange{N}` into
 # `NTuple{N,BlockIndexRange{1}}`.
@@ -191,7 +198,9 @@ function to_blockindexrange(
   # work right now.
   return blocks(a.blocks)[Int(I)]
 end
-function to_blockindexrange(a::Base.Slice{<:BlockedOneTo{<:Integer}}, I::Block{1})
+function to_blockindexrange(
+  a::Base.Slice{<:AbstractBlockedUnitRange{<:Integer}}, I::Block{1}
+)
   @assert I in only(blockaxes(a.indices))
   return I
 end

NDTensors/src/lib/BlockSparseArrays/test/test_basics.jl

@@ -15,9 +15,15 @@ using BlockArrays:
   blocksizes,
   mortar
 using Compat: @compat
-using LinearAlgebra: mul!
+using LinearAlgebra: Adjoint, mul!
 using NDTensors.BlockSparseArrays:
-  @view!, BlockSparseArray, BlockView, block_nstored, block_reshape, view!
+  @view!,
+  BlockSparseArray,
+  BlockView,
+  block_nstored,
+  block_reshape,
+  block_stored_indices,
+  view!
 using NDTensors.SparseArrayInterface: nstored
 using NDTensors.TensorAlgebra: contract
 using Test: @test, @test_broken, @test_throws, @testset
@@ -44,6 +50,17 @@ include("TestBlockSparseArraysUtils.jl")
     a[Block(2, 2)] = randn(elt, 3, 3)
     @test a[2:4, 4] == Array(a)[2:4, 4]
     @test_broken a[4, 2:4]
+
+    @test a[Block(1), :] isa BlockSparseArray{elt}
+    @test adjoint(a) isa Adjoint{elt,<:BlockSparseArray}
+    @test_broken adjoint(a)[Block(1), :] isa Adjoint{elt,<:BlockSparseArray}
+    # could also be directly a BlockSparseArray
+
+    a = BlockSparseArray{elt}([1], [1, 1])
+    a[1, 2] = 1
+    @test [a[Block(Tuple(it))] for it in eachindex(block_stored_indices(a))] isa Vector
+    ah = adjoint(a)
+    @test_broken [ah[Block(Tuple(it))] for it in eachindex(block_stored_indices(ah))] isa Vector
   end
   @testset "Basics" begin
     a = BlockSparseArray{elt}([2, 3], [2, 3])

NDTensors/src/lib/GradedAxes/src/GradedAxes.jl

@@ -2,6 +2,7 @@ module GradedAxes
 include("blockedunitrange.jl")
 include("gradedunitrange.jl")
 include("dual.jl")
-include("unitrangedual.jl")
+include("gradedunitrangedual.jl")
+include("onetoone.jl")
 include("fusion.jl")
 end

NDTensors/src/lib/GradedAxes/src/blockedunitrange.jl

@@ -167,7 +167,7 @@ end
 # Slice `a` by `I`, returning a:
 # `BlockVector{<:BlockIndex{1},<:Vector{<:BlockIndexRange{1}}}`
 # with the `BlockIndex{1}` corresponding to each value of `I`.
-function to_blockindices(a::BlockedOneTo{<:Integer}, I::UnitRange{<:Integer})
+function to_blockindices(a::AbstractBlockedUnitRange{<:Integer}, I::UnitRange{<:Integer})
   return mortar(
     map(blocks(blockedunitrange_getindices(a, I))) do r
       bi_first = findblockindex(a, first(r))

NDTensors/src/lib/GradedAxes/src/dual.jl

@@ -1,7 +1,12 @@
-function dual end
+# default behavior: self-dual
+dual(r::AbstractUnitRange) = r
+nondual(r::AbstractUnitRange) = r
+isdual(::AbstractUnitRange) = false
 
 using NDTensors.LabelledNumbers:
   LabelledStyle, IsLabelled, NotLabelled, label, labelled, unlabel
+
+dual(i::LabelledInteger) = labelled(unlabel(i), dual(label(i)))
 label_dual(x) = label_dual(LabelledStyle(x), x)
 label_dual(::NotLabelled, x) = x
 label_dual(::IsLabelled, x) = labelled(unlabel(x), dual(label(x)))

NDTensors/src/lib/GradedAxes/src/fusion.jl

@@ -1,12 +1,5 @@
 using BlockArrays: AbstractBlockedUnitRange, blocklengths
 
-# Represents the range `1:1` or `Base.OneTo(1)`.
-struct OneToOne{T} <: AbstractUnitRange{T} end
-OneToOne() = OneToOne{Bool}()
-Base.first(a::OneToOne) = one(eltype(a))
-Base.last(a::OneToOne) = one(eltype(a))
-BlockArrays.blockaxes(g::OneToOne) = (Block.(g),)  # BlockArrays default crashes for OneToOne{Bool}
-
 # https://github.com/ITensor/ITensors.jl/blob/v0.3.57/NDTensors/src/lib/GradedAxes/src/tensor_product.jl
 # https://en.wikipedia.org/wiki/Tensor_product
 # https://github.com/KeitaNakamura/Tensorial.jl
@@ -20,7 +13,7 @@ function tensor_product(
 end
 
 flip_dual(r::AbstractUnitRange) = r
-flip_dual(r::UnitRangeDual) = flip(r)
+flip_dual(r::GradedUnitRangeDual) = flip(r)
 function tensor_product(a1::AbstractUnitRange, a2::AbstractUnitRange)
   return tensor_product(flip_dual(a1), flip_dual(a2))
 end
@@ -67,7 +60,7 @@ function tensor_product(a1::AbstractBlockedUnitRange, a2::AbstractBlockedUnitRan
   return blockedrange(new_blocklengths)
 end
 
-# convention: sort UnitRangeDual according to nondual blocks
+# convention: sort GradedUnitRangeDual according to nondual blocks
 function blocksortperm(a::AbstractUnitRange)
   return Block.(sortperm(blocklabels(nondual(a))))
 end
@@ -102,7 +95,7 @@ function blockmergesort(g::AbstractGradedUnitRange)
   return gradedrange(new_blocklengths)
 end
 
-blockmergesort(g::UnitRangeDual) = flip(blockmergesort(flip(g)))
+blockmergesort(g::GradedUnitRangeDual) = flip(blockmergesort(flip(g)))
 blockmergesort(g::AbstractUnitRange) = g
 
 # fusion_product produces a sorted, non-dual GradedUnitRange
@@ -111,7 +104,7 @@ function fusion_product(g1, g2)
 end
 
 fusion_product(g::AbstractUnitRange) = blockmergesort(g)
-fusion_product(g::UnitRangeDual) = fusion_product(flip(g))
+fusion_product(g::GradedUnitRangeDual) = fusion_product(flip(g))
 
 # recursive fusion_product. Simpler than reduce + fix type stability issues with reduce
 function fusion_product(g1, g2, g3...)