Adapt to TensorProducts.jl

Project.toml

@@ -1,14 +1,15 @@
 name = "GradedUnitRanges"
 uuid = "e2de450a-8a67-46c7-b59c-01d5a3d041c5"
 authors = ["ITensor developers <[email protected]> and contributors"]
-version = "0.1.7"
+version = "0.2.0"
 
 [deps]
 BlockArrays = "8e7c35d0-a365-5155-bbbb-fb81a777f24e"
 Compat = "34da2185-b29b-5c13-b0c7-acf172513d20"
 FillArrays = "1a297f60-69ca-5386-bcde-b61e274b549b"
 LabelledNumbers = "f856a3a6-4152-4ec4-b2a7-02c1a55d7993"
 SplitApplyCombine = "03a91e81-4c3e-53e1-a0a4-9c0c8f19dd66"
+TensorProducts = "decf83d6-1968-43f4-96dc-fdb3fe15fc6d"
 
 [weakdeps]
 SymmetrySectors = "f8a8ad64-adbc-4fce-92f7-ffe2bb36a86e"
@@ -23,4 +24,5 @@ FillArrays = "1.13.0"
 LabelledNumbers = "0.1.0"
 SplitApplyCombine = "1.2.3"
 SymmetrySectors = "0.1.4"
+TensorProducts = "0.1.2"
 julia = "1.10"

docs/Project.toml

@@ -6,6 +6,6 @@ Literate = "98b081ad-f1c9-55d3-8b20-4c87d4299306"
 
 [compat]
 Documenter = "1.8.1"
-GradedUnitRanges = "0.1.6"
+GradedUnitRanges = "0.2"
 LabelledNumbers = "0.1.0"
 Literate = "2.20.1"

src/GradedUnitRanges.jl

@@ -7,7 +7,6 @@ include("gradedunitrange.jl")
 include("dual.jl")
 include("labelledunitrangedual.jl")
 include("gradedunitrangedual.jl")
-include("onetoone.jl")
 include("fusion.jl")
 
 end

src/fusion.jl

@@ -1,58 +1,30 @@
 using BlockArrays: AbstractBlockedUnitRange, blocklengths
 using LabelledNumbers: LabelledInteger, label, labelled
 using SplitApplyCombine: groupcount
-
-# 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
-function tensor_product(
-  a1::AbstractUnitRange,
-  a2::AbstractUnitRange,
-  a3::AbstractUnitRange,
-  a_rest::Vararg{AbstractUnitRange},
-)
-  return foldl(tensor_product, (a1, a2, a3, a_rest...))
-end
+using TensorProducts: TensorProducts, OneToOne, tensor_product
 
 flip_dual(r::AbstractUnitRange) = r
 flip_dual(r::GradedUnitRangeDual) = flip(r)
-function tensor_product(a1::AbstractUnitRange, a2::AbstractUnitRange)
-  return tensor_product(flip_dual(a1), flip_dual(a2))
-end
-
-function tensor_product(a1::Base.OneTo, a2::Base.OneTo)
-  return Base.OneTo(length(a1) * length(a2))
-end
-
-function tensor_product(::OneToOne, a2::AbstractUnitRange)
-  return a2
-end
-
-function tensor_product(a1::AbstractUnitRange, ::OneToOne)
-  return a1
-end
-
-function tensor_product(::OneToOne, ::OneToOne)
-  return OneToOne()
-end
 
 function fuse_labels(x, y)
   return error(
     "`fuse_labels` not implemented for object of type `$(typeof(x))` and `$(typeof(y))`."
   )
 end
 
-function fuse_blocklengths(x::Integer, y::Integer)
-  # return blocked unit range to keep non-abelian interface
-  return blockedrange([x * y])
-end
-
 function fuse_blocklengths(x::LabelledInteger, y::LabelledInteger)
   # return blocked unit range to keep non-abelian interface
   return blockedrange([labelled(x * y, fuse_labels(label(x), label(y)))])
 end
 
-function tensor_product(a1::AbstractBlockedUnitRange, a2::AbstractBlockedUnitRange)
+unmerged_tensor_product() = OneToOne()
+unmerged_tensor_product(a) = a
+unmerged_tensor_product(a1, a2) = tensor_product(a1, a2)
+function unmerged_tensor_product(a1, a2, as...)
+  return unmerged_tensor_product(unmerged_tensor_product(a1, a2), as...)
+end
+
+function unmerged_tensor_product(a1::AbstractGradedUnitRange, a2::AbstractGradedUnitRange)
   nested = map(Iterators.flatten((Iterators.product(blocks(a1), blocks(a2)),))) do it
     return mapreduce(length, fuse_blocklengths, it)
   end
@@ -96,15 +68,11 @@
 blockmergesort(g::GradedUnitRangeDual) = flip(blockmergesort(flip(g)))
 blockmergesort(g::AbstractUnitRange) = g
 
-# fusion_product produces a sorted, non-dual GradedUnitRange
-function fusion_product(g1, g2)
-  return blockmergesort(tensor_product(g1, g2))
-end
+# tensor_product produces a sorted, non-dual GradedUnitRange
+TensorProducts.tensor_product(g::AbstractGradedUnitRange) = blockmergesort(flip_dual(g))
 
-fusion_product(g::AbstractUnitRange) = blockmergesort(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...)
-  return fusion_product(fusion_product(g1, g2), g3...)
+function TensorProducts.tensor_product(
+  g1::AbstractGradedUnitRange, g2::AbstractGradedUnitRange
+)
+  return blockmergesort(unmerged_tensor_product(g1, g2))
 end

test/Project.toml

@@ -1,19 +1,19 @@
 [deps]
 Aqua = "4c88cf16-eb10-579e-8560-4a9242c79595"
 BlockArrays = "8e7c35d0-a365-5155-bbbb-fb81a777f24e"
-BlockSparseArrays = "2c9a651f-6452-4ace-a6ac-809f4280fbb4"
 GradedUnitRanges = "e2de450a-8a67-46c7-b59c-01d5a3d041c5"
 LabelledNumbers = "f856a3a6-4152-4ec4-b2a7-02c1a55d7993"
 SafeTestsets = "1bc83da4-3b8d-516f-aca4-4fe02f6d838f"
 Suppressor = "fd094767-a336-5f1f-9728-57cf17d0bbfb"
+TensorProducts = "decf83d6-1968-43f4-96dc-fdb3fe15fc6d"
 Test = "8dfed614-e22c-5e08-85e1-65c5234f0b40"
 
 [compat]
 Aqua = "0.8.9"
 BlockArrays = "1.4.0"
-BlockSparseArrays = "0.2.28, 0.3"
-GradedUnitRanges = "0.1.6"
+GradedUnitRanges = "0.2"
 LabelledNumbers = "0.1.0"
 SafeTestsets = "0.1"
 Suppressor = "0.2"
+TensorProducts = "0.1.0"
 Test = "1.10"

test/test_basics.jl

@@ -1,4 +1,3 @@
-@eval module $(gensym())
 using BlockArrays:
   Block,
   BlockRange,
@@ -13,34 +12,13 @@ using BlockArrays:
   combine_blockaxes,
   mortar
 using GradedUnitRanges:
-  GradedOneTo,
-  GradedUnitRange,
-  OneToOne,
-  blocklabels,
-  gradedrange,
-  sector_type,
-  space_isequal
+  GradedOneTo, GradedUnitRange, blocklabels, gradedrange, sector_type, space_isequal
 using LabelledNumbers:
   LabelledUnitRange, islabelled, label, labelled, labelled_isequal, unlabel
 using Test: @test, @test_broken, @testset
 
-@testset "OneToOne" begin
-  a0 = OneToOne()
-  @test a0 isa OneToOne{Bool}
-  @test eltype(a0) == Bool
-  @test length(a0) == 1
-  @test labelled_isequal(a0, a0)
-  @test a0[1] == true
-  @test a0[[1]] == [true]
-
-  @test labelled_isequal(a0, 1:1)
-  @test labelled_isequal(1:1, a0)
-  @test !labelled_isequal(a0, 1:2)
-  @test !labelled_isequal(1:2, a0)
-end
-
 @testset "GradedUnitRanges basics" begin
-  a0 = OneToOne()
+  a0 = Base.OneTo(1)
   for a in (
     blockedrange([labelled(2, "x"), labelled(3, "y")]),
     gradedrange([labelled(2, "x"), labelled(3, "y")]),
@@ -260,4 +238,3 @@ end
   @test length(a) == 1
   @test label(first(a)) == "x"
 end
-end

test/test_dual.jl

@@ -1,4 +1,3 @@
-@eval module $(gensym())
 using BlockArrays:
   Block,
   BlockedOneTo,
@@ -13,13 +12,11 @@ using BlockArrays:
   findblock,
   mortar,
   combine_blockaxes
-using BlockSparseArrays: BlockSparseArray
 using GradedUnitRanges:
   AbstractGradedUnitRange,
   GradedUnitRanges,
   GradedUnitRangeDual,
   LabelledUnitRangeDual,
-  OneToOne,
   blocklabels,
   blockmergesortperm,
   blocksortperm,
@@ -36,6 +33,8 @@ using GradedUnitRanges:
 using LabelledNumbers:
   LabelledInteger, LabelledUnitRange, label, label_type, labelled, labelled_isequal, unlabel
 using Test: @test, @test_broken, @testset
+using TensorProducts: OneToOne, tensor_product
+
 struct U1
   n::Int
 end
@@ -306,16 +305,3 @@ end
     @test !isdual(dual(flip(a)))
   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
-end