New File forScheduling and defaults for edge sequencing

ITensor · Nov 9, 2023 · 045adc3 · 045adc3
1 parent a196c87
commit 045adc3
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diff --git a/examples/belief_propagation/bpexample.jl b/examples/belief_propagation/bpexample.jl
@@ -35,7 +35,7 @@ function main()
     ψψ; subgraph_vertices=collect(values(group(v -> v[1], vertices(ψψ))))
   )
 
-  mts = belief_propagation(ψψ, mts; contract_kwargs=(; alg="exact"))
+  mts = belief_propagation(ψψ, mts; contract_kwargs=(; alg="exact"), niters=20)
 
   numerator_network = approx_network_region(
     ψψ, mts, [(v, 1)]; verts_tn=ITensorNetwork([apply(op("Sz", s[v]), ψ[v])])
@@ -54,7 +54,7 @@ function main()
   )
   Zpp = partition(ψψ; subgraph_vertices=nested_graph_leaf_vertices(Zp))
   mts = message_tensors(Zpp)
-  mts = belief_propagation(ψψ, mts; contract_kwargs=(; alg="exact"))
+  mts = belief_propagation(ψψ, mts; contract_kwargs=(; alg="exact"), niters=20)
   numerator_network = approx_network_region(
     ψψ, mts, [(v, 1)]; verts_tn=ITensorNetwork([apply(op("Sz", s[v]), ψ[v])])
   )

diff --git a/examples/belief_propagation/bpsequences.jl b/examples/belief_propagation/bpsequences.jl
@@ -12,196 +12,70 @@ using ITensorNetworks:
   approx_network_region,
   contract_inner,
   message_tensors,
-  nested_graph_leaf_vertices
+  nested_graph_leaf_vertices,
+  edge_sequence
 
 function main()
-  g = named_comb_tree((6, 6))
-  s = siteinds("S=1/2", g)
-  χ = 4
-
-  Random.seed!(5467)
-
-  ψ = randomITensorNetwork(s; link_space=χ)
-  ψψ = ψ ⊗ prime(dag(ψ); sites=[])
-
-  #Initial message tensors for BP
-  mts_init = message_tensors(
-    ψψ; subgraph_vertices=collect(values(group(v -> v[1], vertices(ψψ))))
-  )
-
-  println("\nFirst testing out a comb tree. Random network with bond dim $χ")
-
-  #Now test out various sequences
-  print("Parallel updates (sequence is irrelevant): ")
-  belief_propagation(
-    ψψ,
-    mts_init;
-    contract_kwargs=(; alg="exact"),
-    target_precision=1e-10,
-    niters=100,
-    edges=[[e] for e in edges(mts_init)],
-    verbose=true,
-  )
-  print("Sequential updates (sequence is default edge list of the message tensors): ")
-  belief_propagation(
-    ψψ,
-    mts_init;
-    contract_kwargs=(; alg="exact"),
-    target_precision=1e-10,
-    niters=100,
-    edges=[e for e in edges(mts_init)],
-    verbose=true,
-  )
-  print("Sequential updates (sequence is our custom sequence finder): ")
-  belief_propagation(
-    ψψ,
-    mts_init;
-    contract_kwargs=(; alg="exact"),
-    target_precision=1e-10,
-    niters=100,
-    verbose=true,
-  )
-
-  g = NamedGraph(Graphs.random_regular_graph(100, 3))
-  s = siteinds("S=1/2", g)
-  χ = 4
-
-  Random.seed!(5467)
-
-  ψ = randomITensorNetwork(s; link_space=χ)
-  ψψ = ψ ⊗ prime(dag(ψ); sites=[])
-
-  #Initial message tensors for BP
-  mts_init = message_tensors(
-    ψψ; subgraph_vertices=collect(values(group(v -> v[1], vertices(ψψ))))
-  )
-
-  println("\nNow testing out a z = 3 random regular graph. Random network with bond dim $χ")
-
-  #Now test out various sequences
-  print("Parallel updates (sequence is irrelevant): ")
-  belief_propagation(
-    ψψ,
-    mts_init;
-    contract_kwargs=(; alg="exact"),
-    target_precision=1e-10,
-    niters=100,
-    edges=[[e] for e in edges(mts_init)],
-    verbose=true,
-  )
-  print("Sequential updates (sequence is default edge list of the message tensors): ")
-  belief_propagation(
-    ψψ,
-    mts_init;
-    contract_kwargs=(; alg="exact"),
-    target_precision=1e-10,
-    niters=100,
-    edges=[e for e in edges(mts_init)],
-    verbose=true,
-  )
-  print("Sequential updates (sequence is our custom sequence finder): ")
-  belief_propagation(
-    ψψ,
-    mts_init;
-    contract_kwargs=(; alg="exact"),
-    target_precision=1e-10,
-    niters=100,
-    verbose=true,
-  )
-
-  g = named_grid((6, 6))
-  s = siteinds("S=1/2", g)
-  χ = 2
-
-  Random.seed!(5467)
-
-  ψ = randomITensorNetwork(s; link_space=χ)
-  ψψ = ψ ⊗ prime(dag(ψ); sites=[])
-
-  #Initial message tensors for BP
-  mts_init = message_tensors(
-    ψψ; subgraph_vertices=collect(values(group(v -> v[1], vertices(ψψ))))
-  )
-
-  println("\nNow testing out a 6x6 grid. Random network with bond dim $χ")
-
-  #Now test out various sequences
-  print("Parallel updates (sequence is irrelevant): ")
-  belief_propagation(
-    ψψ,
-    mts_init;
-    contract_kwargs=(; alg="exact"),
-    target_precision=1e-10,
-    niters=100,
-    edges=[[e] for e in edges(mts_init)],
-    verbose=true,
-  )
-  print("Sequential updates (sequence is default edge list of the message tensors): ")
-  belief_propagation(
-    ψψ,
-    mts_init;
-    contract_kwargs=(; alg="exact"),
-    target_precision=1e-10,
-    niters=100,
-    edges=[e for e in edges(mts_init)],
-    verbose=true,
-  )
-  print("Sequential updates (sequence is our custom sequence finder): ")
-  belief_propagation(
-    ψψ,
-    mts_init;
-    contract_kwargs=(; alg="exact"),
-    target_precision=1e-10,
-    niters=100,
-    verbose=true,
-  )
-
-  g = NamedGraphs.hexagonal_lattice_graph(4, 4)
-  s = siteinds("S=1/2", g)
-  χ = 3
-
-  Random.seed!(5467)
-
-  ψ = randomITensorNetwork(s; link_space=χ)
-  ψψ = ψ ⊗ prime(dag(ψ); sites=[])
-
-  #Initial message tensors for BP
-  mts_init = message_tensors(
-    ψψ; subgraph_vertices=collect(values(group(v -> v[1], vertices(ψψ))))
-  )
-
-  println("\nNow testing out a 4 x 4 hexagonal lattice. Random network with bond dim $χ")
-
-  #Now test out various sequences
-  print("Parallel updates (sequence is irrelevant): ")
-  belief_propagation(
-    ψψ,
-    mts_init;
-    contract_kwargs=(; alg="exact"),
-    target_precision=1e-10,
-    niters=100,
-    edges=[[e] for e in edges(mts_init)],
-    verbose=true,
-  )
-  print("Sequential updates (sequence is default edge list of the message tensors): ")
-  belief_propagation(
-    ψψ,
-    mts_init;
-    contract_kwargs=(; alg="exact"),
-    target_precision=1e-10,
-    niters=100,
-    edges=[e for e in edges(mts_init)],
-    verbose=true,
-  )
-  print("Sequential updates (sequence is our custom sequence finder): ")
-  return belief_propagation(
-    ψψ,
-    mts_init;
-    contract_kwargs=(; alg="exact"),
-    target_precision=1e-10,
-    niters=100,
-    verbose=true,
-  )
+  g_labels = [
+    "Comb Tree",
+    "100 Site Random Regular Graph z = 3",
+    "6x6 Square Grid",
+    "4x4 Hexagonal Lattice",
+  ]
+  gs = [
+    named_comb_tree((6, 6)),
+    NamedGraph(Graphs.random_regular_graph(100, 3)),
+    named_grid((6, 6)),
+    NamedGraphs.hexagonal_lattice_graph(4, 4),
+  ]
+  χs = [4, 4, 2, 3]
+
+  for (i, g) in enumerate(gs)
+    Random.seed!(5467)
+    g_label = g_labels[i]
+    χ = χs[i]
+    s = siteinds("S=1/2", g)
+    ψ = randomITensorNetwork(s; link_space=χ)
+    ψψ = ψ ⊗ prime(dag(ψ); sites=[])
+
+    #Initial message tensors for BP
+    mts_init = message_tensors(
+      ψψ; subgraph_vertices=collect(values(group(v -> v[1], vertices(ψψ))))
+    )
+
+    println("\nFirst testing out a $g_label. Random network with bond dim $χ")
+
+    #Now test out various sequences
+    print("Parallel updates (sequence is irrelevant): ")
+    belief_propagation(
+      ψψ,
+      mts_init;
+      contract_kwargs=(; alg="exact"),
+      target_precision=1e-10,
+      niters=100,
+      edges=edge_sequence(mts_init; alg=ITensorNetworks.Parallel()),
+      verbose=true,
+    )
+    print("Sequential updates (sequence is default edge list of the message tensors): ")
+    belief_propagation(
+      ψψ,
+      mts_init;
+      contract_kwargs=(; alg="exact"),
+      target_precision=1e-10,
+      niters=100,
+      edges=[e for e in edges(mts_init)],
+      verbose=true,
+    )
+    print("Sequential updates (sequence is our custom sequence finder): ")
+    belief_propagation(
+      ψψ,
+      mts_init;
+      contract_kwargs=(; alg="exact"),
+      target_precision=1e-10,
+      niters=100,
+      verbose=true,
+    )
+  end
 end
 
 main()
diff --git a/examples/gauging/gauging_itns.jl b/examples/gauging/gauging_itns.jl
@@ -74,7 +74,10 @@ function benchmark_state_gauging(
         )
       else
         times_iters[i] = @elapsed mts, _ = belief_propagation_iteration(
-          ψψ, mts; contract_kwargs=(; alg="exact"), edges=[[e] for e in edges(mts)]
+          ψψ,
+          mts;
+          contract_kwargs=(; alg="exact"),
+          edges=edge_sequence(mts; alg=ITensorNetworks.Parallel()),
         )
       end
 

diff --git a/src/ITensorNetworks.jl b/src/ITensorNetworks.jl
@@ -97,6 +97,7 @@ include("specialitensornetworks.jl")
 include("renameitensornetwork.jl")
 include("boundarymps.jl")
 include(joinpath("beliefpropagation", "beliefpropagation.jl"))
+include(joinpath("beliefpropagation", "beliefpropagation_schedule.jl"))
 include(joinpath("beliefpropagation", "sqrt_beliefpropagation.jl"))
 include("contraction_tree_to_graph.jl")
 include("gauging.jl")