TensorNetwork type

Project.toml

@@ -1,7 +1,31 @@
 name = "ITensorNetworksNext"
 uuid = "302f2e75-49f0-4526-aef7-d8ba550cb06c"
 authors = ["ITensor developers <[email protected]> and contributors"]
-version = "0.1.0"
+version = "0.1.1"
+
+[deps]
+Adapt = "79e6a3ab-5dfb-504d-930d-738a2a938a0e"
+BackendSelection = "680c2d7c-f67a-4cc9-ae9c-da132b1447a5"
+DataGraphs = "b5a273c3-7e6c-41f6-98bd-8d7f1525a36a"
+Dictionaries = "85a47980-9c8c-11e8-2b9f-f7ca1fa99fb4"
+Graphs = "86223c79-3864-5bf0-83f7-82e725a168b6"
+LinearAlgebra = "37e2e46d-f89d-539d-b4ee-838fcccc9c8e"
+MacroTools = "1914dd2f-81c6-5fcd-8719-6d5c9610ff09"
+NamedDimsArrays = "60cbd0c0-df58-4cb7-918c-6f5607b73fde"
+NamedGraphs = "678767b0-92e7-4007-89e4-4527a8725b19"
+SimpleTraits = "699a6c99-e7fa-54fc-8d76-47d257e15c1d"
+SplitApplyCombine = "03a91e81-4c3e-53e1-a0a4-9c0c8f19dd66"
 
 [compat]
+Adapt = "4.3.0"
+BackendSelection = "0.1.6"
+DataGraphs = "0.2.7"
+Dictionaries = "0.4.5"
+Graphs = "1.13.1"
+LinearAlgebra = "1.10"
+MacroTools = "0.5.16"
+NamedDimsArrays = "0.7.13"
+NamedGraphs = "0.6.9"
+SimpleTraits = "0.9.5"
+SplitApplyCombine = "1.2.3"
 julia = "1.10"

src/ITensorNetworksNext.jl

@@ -1,5 +1,6 @@
 module ITensorNetworksNext
 
-# Write your package code here.
+include("abstracttensornetwork.jl")
+include("tensornetwork.jl")
 
 end

src/abstracttensornetwork.jl

@@ -0,0 +1,279 @@
+using Adapt: Adapt, adapt, adapt_structure
+using BackendSelection: @Algorithm_str, Algorithm
+using DataGraphs:
+  DataGraphs,
+  AbstractDataGraph,
+  edge_data,
+  underlying_graph,
+  underlying_graph_type,
+  vertex_data
+using Dictionaries: Dictionary
+using Graphs:
+  Graphs,
+  AbstractEdge,
+  AbstractGraph,
+  Graph,
+  add_edge!,
+  add_vertex!,
+  bfs_tree,
+  center,
+  dst,
+  edges,
+  edgetype,
+  ne,
+  neighbors,
+  nv,
+  rem_edge!,
+  src,
+  vertices
+using LinearAlgebra: LinearAlgebra, factorize
+using MacroTools: @capture
+using NamedDimsArrays: dimnames
+using NamedGraphs: NamedGraphs, NamedGraph, not_implemented, steiner_tree
+using NamedGraphs.GraphsExtensions:
+  ⊔, directed_graph, incident_edges, rem_edges!, rename_vertices, vertextype
+using SplitApplyCombine: flatten
+
+abstract type AbstractTensorNetwork{V,VD} <: AbstractDataGraph{V,VD,Nothing} end
+
+function Graphs.rem_edge!(tn::AbstractTensorNetwork, e)
+  rem_edge!(underlying_graph(tn), e)
+  return tn
+end
+
+# TODO: Define a generic fallback for `AbstractDataGraph`?
+DataGraphs.edge_data_eltype(::Type{<:AbstractTensorNetwork}) = error("No edge data")
+
+# Graphs.jl overloads
+function Graphs.weights(graph::AbstractTensorNetwork)
+  V = vertextype(graph)
+  es = Tuple.(edges(graph))
+  ws = Dictionary{Tuple{V,V},Float64}(es, undef)
+  for e in edges(graph)
+    w = log2(dim(commoninds(graph, e)))
+    ws[(src(e), dst(e))] = w
+  end
+  return ws
+end
+
+# Copy
+Base.copy(tn::AbstractTensorNetwork) = error("Not implemented")
+
+# Iteration
+Base.iterate(tn::AbstractTensorNetwork, args...) = iterate(vertex_data(tn), args...)
+
+# TODO: This contrasts with the `DataGraphs.AbstractDataGraph` definition,
+# where it is defined as the `vertextype`. Does that cause problems or should it be changed?
+Base.eltype(tn::AbstractTensorNetwork) = eltype(vertex_data(tn))
+
+# Overload if needed
+Graphs.is_directed(::Type{<:AbstractTensorNetwork}) = false
+
+# Derived interface, may need to be overloaded
+function DataGraphs.underlying_graph_type(G::Type{<:AbstractTensorNetwork})
+  return underlying_graph_type(data_graph_type(G))
+end
+
+# AbstractDataGraphs overloads
+function DataGraphs.vertex_data(graph::AbstractTensorNetwork, args...)
+  return error("Not implemented")
+end
+function DataGraphs.edge_data(graph::AbstractTensorNetwork, args...)
+  return error("Not implemented")
+end
+
+DataGraphs.underlying_graph(tn::AbstractTensorNetwork) = error("Not implemented")
+function NamedGraphs.vertex_positions(tn::AbstractTensorNetwork)
+  return NamedGraphs.vertex_positions(underlying_graph(tn))
+end
+function NamedGraphs.ordered_vertices(tn::AbstractTensorNetwork)
+  return NamedGraphs.ordered_vertices(underlying_graph(tn))
+end
+
+function Adapt.adapt_structure(to, tn::AbstractTensorNetwork)
+  # TODO: Define and use:
+  #
+  # @preserve_graph map_vertex_data(adapt(to), tn)
+  #
+  # or just:
+  #
+  # @preserve_graph map(adapt(to), tn)
+  return map_vertex_data_preserve_graph(adapt(to), tn)
+end
+
+function linkinds(tn::AbstractTensorNetwork, edge::Pair)
+  return linkinds(tn, edgetype(tn)(edge))
+end
+function linkinds(tn::AbstractTensorNetwork, edge::AbstractEdge)
+  return nameddimsindices(tn[src(edge)]) ∩ nameddimsindices(tn[dst(edge)])
+end
+function linkaxes(tn::AbstractTensorNetwork, edge::Pair)
+  return linkaxes(tn, edgetype(tn)(edge))
+end
+function linkaxes(tn::AbstractTensorNetwork, edge::AbstractEdge)
+  return axes(tn[src(edge)]) ∩ axes(tn[dst(edge)])
+end
+function linknames(tn::AbstractTensorNetwork, edge::Pair)
+  return linknames(tn, edgetype(tn)(edge))
+end
+function linknames(tn::AbstractTensorNetwork, edge::AbstractEdge)
+  return dimnames(tn[src(edge)]) ∩ dimnames(tn[dst(edge)])
+end
+
+function siteinds(tn::AbstractTensorNetwork, v)
+  s = nameddimsindices(tn[v])
+  for v′ in neighbors(tn, v)
+    s = setdiff(s, nameddimsindices(tn[v′]))
+  end
+  return s
+end
+function siteaxes(tn::AbstractTensorNetwork, edge::AbstractEdge)
+  s = axes(tn[src(edge)]) ∩ axes(tn[dst(edge)])
+  for v′ in neighbors(tn, v)
+    s = setdiff(s, axes(tn[v′]))
+  end
+  return s
+end
+function sitenames(tn::AbstractTensorNetwork, edge::AbstractEdge)
+  s = dimnames(tn[src(edge)]) ∩ dimnames(tn[dst(edge)])
+  for v′ in neighbors(tn, v)
+    s = setdiff(s, dimnames(tn[v′]))
+  end
+  return s
+end
+
+function setindex_preserve_graph!(tn::AbstractTensorNetwork, value, vertex)
+  vertex_data(tn)[vertex] = value
+  return tn
+end
+
+# TODO: Move to `BaseExtensions` module.
+function is_setindex!_expr(expr::Expr)
+  return is_assignment_expr(expr) && is_getindex_expr(first(expr.args))
+end
+is_setindex!_expr(x) = false
+is_getindex_expr(expr::Expr) = (expr.head === :ref)
+is_getindex_expr(x) = false
+is_assignment_expr(expr::Expr) = (expr.head === :(=))
+is_assignment_expr(expr) = false
+
+# TODO: Define this in terms of a function mapping
+# preserve_graph_function(::typeof(setindex!)) = setindex!_preserve_graph
+# preserve_graph_function(::typeof(map_vertex_data)) = map_vertex_data_preserve_graph
+# Also allow annotating codeblocks like `@views`.
+macro preserve_graph(expr)
+  if !is_setindex!_expr(expr)
+    error(
+      "preserve_graph must be used with setindex! syntax (as @preserve_graph a[i,j,...] = value)",
+    )
+  end
+  @capture(expr, array_[indices__] = value_)
+  return :(setindex_preserve_graph!($(esc(array)), $(esc(value)), $(esc.(indices)...)))
+end
+
+# Update the graph of the TensorNetwork `tn` to include
+# edges that should exist based on the tensor connectivity.
+function add_missing_edges!(tn::AbstractTensorNetwork)
+  foreach(v -> add_missing_edges!(tn, v), vertices(tn))
+  return tn
+end
+
+# Update the graph of the TensorNetwork `tn` to include
+# edges that should be incident to the vertex `v`
+# based on the tensor connectivity.
+function add_missing_edges!(tn::AbstractTensorNetwork, v)
+  for v′ in vertices(tn)
+    if v ≠ v′
+      e = v => v′
+      if !isempty(linkinds(tn, e))
+        add_edge!(tn, e)
+      end
+    end
+  end
+  return tn
+end
+
+# Fix the edges of the TensorNetwork `tn` to match
+# the tensor connectivity.
+function fix_edges!(tn::AbstractTensorNetwork)
+  foreach(v -> fix_edges!(tn, v), vertices(tn))
+  return tn
+end
+# Fix the edges of the TensorNetwork `tn` to match
+# the tensor connectivity at vertex `v`.
+function fix_edges!(tn::AbstractTensorNetwork, v)
+  rem_incident_edges!(tn, v)
+  rem_edges!(tn, incident_edges(tn, v))
+  add_missing_edges!(tn, v)
+  return tn
+end
+
+# Customization point.
+using NamedDimsArrays: AbstractNamedUnitRange, namedunitrange, nametype, randname
+function trivial_unitrange(type::Type{<:AbstractUnitRange})
+  return Base.oneto(one(eltype(type)))
+end
+function rand_trivial_namedunitrange(
+  ::Type{<:AbstractNamedUnitRange{<:Any,R,N}}
+) where {R,N}
+  return namedunitrange(trivial_unitrange(R), randname(N))
+end
+
+dag(x) = x
+
+using NamedDimsArrays: nameddimsindices
+function insert_trivial_link!(tn, e)
+  add_edge!(tn, e)
+  l = rand_trivial_namedunitrange(eltype(nameddimsindices(tn[src(e)])))
+  x = similar(tn[src(e)], (l,))
+  x[1] = 1
+  @preserve_graph tn[src(e)] = tn[src(e)] * x
+  @preserve_graph tn[dst(e)] = tn[dst(e)] * dag(x)
+  return tn
+end
+
+function Base.setindex!(tn::AbstractTensorNetwork, value, v)
+  @preserve_graph tn[v] = value
+  fix_edges!(tn, v)
+  return tn
+end
+using NamedGraphs.OrdinalIndexing: OrdinalSuffixedInteger
+# Fix ambiguity error.
+function Base.setindex!(graph::AbstractTensorNetwork, value, vertex::OrdinalSuffixedInteger)
+  graph[vertices(graph)[vertex]] = value
+  return graph
+end
+# Fix ambiguity error.
+function Base.setindex!(tn::AbstractTensorNetwork, value, edge::AbstractEdge)
+  return error("No edge data.")
+end
+# Fix ambiguity error.
+function Base.setindex!(tn::AbstractTensorNetwork, value, edge::Pair)
+  return error("No edge data.")
+end
+using NamedGraphs.OrdinalIndexing: OrdinalSuffixedInteger
+# Fix ambiguity error.
+function Base.setindex!(
+  tn::AbstractTensorNetwork,
+  value,
+  edge::Pair{<:OrdinalSuffixedInteger,<:OrdinalSuffixedInteger},
+)
+  return error("No edge data.")
+end
+
+function Base.show(io::IO, mime::MIME"text/plain", graph::AbstractTensorNetwork)
+  println(io, "$(typeof(graph)) with $(nv(graph)) vertices:")
+  show(io, mime, vertices(graph))
+  println(io, "\n")
+  println(io, "and $(ne(graph)) edge(s):")
+  for e in edges(graph)
+    show(io, mime, e)
+    println(io)
+  end
+  println(io)
+  println(io, "with vertex data:")
+  show(io, mime, axes.(vertex_data(graph)))
+  return nothing
+end
+
+Base.show(io::IO, graph::AbstractTensorNetwork) = show(io, MIME"text/plain"(), graph)

src/tensornetwork.jl

@@ -0,0 +1,75 @@
+using DataGraphs: DataGraphs, AbstractDataGraph, DataGraph
+using Dictionaries: AbstractDictionary, Indices, dictionary
+using Graphs: AbstractSimpleGraph
+using NamedDimsArrays: AbstractNamedDimsArray, dimnames, nameddimsarray
+using NamedGraphs: NamedGraphs, NamedEdge, NamedGraph, vertextype
+using NamedGraphs.GraphsExtensions: arranged_edges, vertextype
+
+function _TensorNetwork end
+
+struct TensorNetwork{V,VD,UG<:AbstractGraph{V},Tensors<:AbstractDictionary{V,VD}} <:
+       AbstractTensorNetwork{V,VD}
+  underlying_graph::UG
+  tensors::Tensors
+  global @inline function _TensorNetwork(
+    underlying_graph::UG, tensors::Tensors
+  ) where {V,VD,UG<:AbstractGraph{V},Tensors<:AbstractDictionary{V,VD}}
+    # This assumes the tensor connectivity matches the graph structure.
+    return new{V,VD,UG,Tensors}(underlying_graph, tensors)
+  end
+end
+
+DataGraphs.underlying_graph(tn::TensorNetwork) = getfield(tn, :underlying_graph)
+DataGraphs.vertex_data(tn::TensorNetwork) = getfield(tn, :tensors)
+function DataGraphs.underlying_graph_type(type::Type{<:TensorNetwork})
+  return fieldtype(type, :underlying_graph)
+end
+
+# Determine the graph structure from the tensors.
+function TensorNetwork(t::AbstractDictionary)
+  g = NamedGraph(eachindex(t))
+  for v1 in vertices(g)
+    for v2 in vertices(g)
+      if v1 ≠ v2
+        if !isdisjoint(dimnames(t[v1]), dimnames(t[v2]))
+          add_edge!(g, v1 => v2)
+        end
+      end
+    end
+  end
+  return _TensorNetwork(g, t)
+end
+function TensorNetwork(tensors::AbstractDict)
+  return TensorNetwork(Dictionary(tensors))
+end
+
+function TensorNetwork(graph::AbstractGraph, tensors::AbstractDictionary)
+  tn = TensorNetwork(tensors)
+  arranged_edges(tn) ⊆ arranged_edges(graph) ||
+    error("The edges in the tensors do not match the graph structure.")
+  for e in setdiff(arranged_edges(graph), arranged_edges(tn))
+    insert_trivial_link!(tn, e)
+  end
+  return tn
+end
+function TensorNetwork(graph::AbstractGraph, tensors::AbstractDict)
+  return TensorNetwork(graph, Dictionary(tensors))
+end
+function TensorNetwork(f, graph::AbstractGraph)
+  return TensorNetwork(graph, Dict(v => f(v) for v in vertices(graph)))
+end
+
+function Base.copy(tn::TensorNetwork)
+  TensorNetwork(copy(underlying_graph(tn)), copy(vertex_data(tn)))
+end
+TensorNetwork(tn::TensorNetwork) = copy(tn)
+TensorNetwork{V}(tn::TensorNetwork{V}) where {V} = copy(tn)
+function TensorNetwork{V}(tn::TensorNetwork) where {V}
+  g′ = convert_vertextype(V, underlying_graph(tn))
+  d = vertex_data(tn)
+  d′ = dictionary(V(k) => d[k] for k in eachindex(d))
+  return TensorNetwork(g′, d′)
+end
+
+NamedGraphs.convert_vertextype(::Type{V}, tn::TensorNetwork{V}) where {V} = tn
+NamedGraphs.convert_vertextype(V::Type, tn::TensorNetwork) = TensorNetwork{V}(tn)