Preserve message type on normalization

Author: JoeyT1994

Project.toml

@@ -38,7 +38,6 @@ EinExprs = "b1794770-133b-4de1-afb4-526377e9f4c5"
 GraphsFlows = "06909019-6f44-4949-96fc-b9d9aaa02889"
 OMEinsumContractionOrders = "6f22d1fd-8eed-4bb7-9776-e7d684900715"
 Observers = "338f10d5-c7f1-4033-a7d1-f9dec39bcaa0"
-TensorOperations = "6aa20fa7-93e2-5fca-9bc0-fbd0db3c71a2"
 
 [extensions]
 ITensorNetworksEinExprsExt = "EinExprs"

src/caches/abstractbeliefpropagationcache.jl

@@ -382,8 +382,8 @@ function rescale_partitions(
     isempty(pv_vs) && continue
 
     vn = region_scalar(bpc, pv)
-    s = isreal(vn) ? sign(vn) : 1.0
-    vn = s * inv(vn^(1 / length(pv_vs)))
+    s = isreal(vn) ? sign(vn) : 1
+    vn = s * inv(vn^(typeof(vn)((1 / length(pv_vs)))))
     set!(vertices_weights, first(pv_vs), s*vn)
     for v in pv_vs[2:length(pv_vs)]
       set!(vertices_weights, v, vn)

src/caches/beliefpropagationcache.jl

@@ -141,7 +141,7 @@ function rescale_messages(bp_cache::BeliefPropagationCache, pes::Vector{<:Partit
       n *= sign(n)
     end
 
-    sf = (1 / sqrt(n)) ^ (1 / length(me))
+    sf = (1 / sqrt(n)) ^ (typeof(n)((1 / length(me))))
     set!(mts, pe, sf .* me)
     set!(mts, reverse(pe), sf .* mer)
   end

test/test_normalize.jl

@@ -4,7 +4,9 @@ using ITensorNetworks:
   QuadraticFormNetwork,
   edge_scalars,
   norm_sqr_network,
+  messages,
   random_tensornetwork,
+  scalartype,
   siteinds,
   vertex_scalars,
   rescale
@@ -39,7 +41,7 @@ using Test: @test, @testset
 
   g = named_grid((Lx, Ly))
   s = siteinds("S=1/2", g)
-  x = random_tensornetwork(rng, s; link_space=χ)
+  x = random_tensornetwork(rng, ComplexF32, s; link_space=χ)
 
   ψ = normalize(x; alg="exact")
   @test scalar(norm_sqr_network(ψ); alg="exact") ≈ 1.0
@@ -49,6 +51,7 @@ using Test: @test, @testset
     x; alg="bp", (cache!)=ψIψ_bpc, update_cache=true, cache_update_kwargs=(; maxiter=20)
   )
   ψIψ_bpc = ψIψ_bpc[]
+  @test all(m -> scalartype(only(m)) == ComplexF32, messages(ψIψ_bpc))
   @test all(x -> x ≈ 1.0, edge_scalars(ψIψ_bpc))
   @test all(x -> x ≈ 1.0, vertex_scalars(ψIψ_bpc))
   @test scalar(QuadraticFormNetwork(ψ); alg="bp", cache_update_kwargs=(; maxiter=20)) ≈ 1.0