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Add support for specifying RNGs in tensor network constructors, use S…
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…tableRNGs.jl in tests (#184)
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@mtfishman
mtfishman authored May 17, 2024
1 parent e65f9f7 commit de7d4dd
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2 changes: 1 addition & 1 deletion Project.toml
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@@ -1,7 +1,7 @@
name = "ITensorNetworks"
uuid = "2919e153-833c-4bdc-8836-1ea460a35fc7"
authors = ["Matthew Fishman <[email protected]>, Joseph Tindall <[email protected]> and contributors"]
version = "0.11.9"
version = "0.11.10"

[deps]
AbstractTrees = "1520ce14-60c1-5f80-bbc7-55ef81b5835c"
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28 changes: 0 additions & 28 deletions TODO.md
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This file was deleted.

47 changes: 39 additions & 8 deletions src/specialitensornetworks.jl
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Expand Up @@ -2,6 +2,7 @@ using ITensors: delta
using ITensors.NDTensors: dim
using DataGraphs: IsUnderlyingGraph
using Distributions: Distribution
using Random: Random, AbstractRNG

"""
RETURN A TENSOR NETWORK WITH COPY TENSORS ON EACH VERTEX.
Expand All @@ -28,37 +29,67 @@ end
"""
Build an ITensor network on a graph specified by the inds network s. Bond_dim is given by link_space and entries are randomised (normal distribution, mean 0 std 1)
"""
function random_tensornetwork(eltype::Type, s::IndsNetwork; kwargs...)
function random_tensornetwork(rng::AbstractRNG, eltype::Type, s::IndsNetwork; kwargs...)
return ITensorNetwork(s; kwargs...) do v
return inds -> itensor(randn(eltype, dim.(inds)...), inds)
return inds -> itensor(randn(rng, eltype, dim.(inds)...), inds)
end
end

function random_tensornetwork(eltype::Type, s::IndsNetwork; kwargs...)
return random_tensornetwork(Random.default_rng(), eltype, s; kwargs...)
end

function random_tensornetwork(rng::AbstractRNG, s::IndsNetwork; kwargs...)
return random_tensornetwork(rng, Float64, s; kwargs...)
end

function random_tensornetwork(s::IndsNetwork; kwargs...)
return random_tensornetwork(Float64, s; kwargs...)
return random_tensornetwork(Random.default_rng(), s; kwargs...)
end

@traitfn function random_tensornetwork(
rng::AbstractRNG, eltype::Type, g::::IsUnderlyingGraph; kwargs...
)
return random_tensornetwork(rng, eltype, IndsNetwork(g); kwargs...)
end

@traitfn function random_tensornetwork(eltype::Type, g::::IsUnderlyingGraph; kwargs...)
return random_tensornetwork(eltype, IndsNetwork(g); kwargs...)
return random_tensornetwork(Random.default_rng(), eltype, g; kwargs...)
end

@traitfn function random_tensornetwork(rng::AbstractRNG, g::::IsUnderlyingGraph; kwargs...)
return random_tensornetwork(rng, Float64, g; kwargs...)
end

@traitfn function random_tensornetwork(g::::IsUnderlyingGraph; kwargs...)
return random_tensornetwork(Float64, IndsNetwork(g); kwargs...)
return random_tensornetwork(Random.default_rng(), g; kwargs...)
end

"""
Build an ITensor network on a graph specified by the inds network s.
Bond_dim is given by link_space and entries are randomized.
The random distribution is based on the input argument `distribution`.
"""
function random_tensornetwork(distribution::Distribution, s::IndsNetwork; kwargs...)
function random_tensornetwork(
rng::AbstractRNG, distribution::Distribution, s::IndsNetwork; kwargs...
)
return ITensorNetwork(s; kwargs...) do v
return inds -> itensor(rand(distribution, dim.(inds)...), inds)
return inds -> itensor(rand(rng, distribution, dim.(inds)...), inds)
end
end

function random_tensornetwork(distribution::Distribution, s::IndsNetwork; kwargs...)
return random_tensornetwork(Random.default_rng(), distribution, s; kwargs...)
end

@traitfn function random_tensornetwork(
rng::AbstractRNG, distribution::Distribution, g::::IsUnderlyingGraph; kwargs...
)
return random_tensornetwork(rng, distribution, IndsNetwork(g); kwargs...)
end

@traitfn function random_tensornetwork(
distribution::Distribution, g::::IsUnderlyingGraph; kwargs...
)
return random_tensornetwork(distribution, IndsNetwork(g); kwargs...)
return random_tensornetwork(Random.default_rng(), distribution, g; kwargs...)
end
1 change: 1 addition & 0 deletions test/Project.toml
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Expand Up @@ -24,6 +24,7 @@ OrdinaryDiffEq = "1dea7af3-3e70-54e6-95c3-0bf5283fa5ed"
Pkg = "44cfe95a-1eb2-52ea-b672-e2afdf69b78f"
Random = "9a3f8284-a2c9-5f02-9a11-845980a1fd5c"
SplitApplyCombine = "03a91e81-4c3e-53e1-a0a4-9c0c8f19dd66"
StableRNGs = "860ef19b-820b-49d6-a774-d7a799459cd3"
Suppressor = "fd094767-a336-5f1f-9728-57cf17d0bbfb"
Test = "8dfed614-e22c-5e08-85e1-65c5234f0b40"
UnicodePlots = "b8865327-cd53-5732-bb35-84acbb429228"
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9 changes: 4 additions & 5 deletions test/test_additensornetworks.jl
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Expand Up @@ -5,11 +5,9 @@ using NamedGraphs.NamedGraphGenerators: named_grid
using ITensorNetworks: ITensorNetwork, inner_network, random_tensornetwork, siteinds
using ITensors: ITensors, apply, op, scalar, inner
using LinearAlgebra: norm_sqr
using Random: Random
using StableRNGs: StableRNG
using Test: @test, @testset

@testset "add_itensornetworks" begin
Random.seed!(5623)
g = named_grid((2, 2))
s = siteinds("S=1/2", g)
ψ1 = ITensorNetwork(v -> "↑", s)
Expand All @@ -32,8 +30,9 @@ using Test: @test, @testset
rem_edge!(s2, NamedEdge((1, 1) => (1, 2)))

v = rand(vertices(g))
ψ1 = random_tensornetwork(s1; link_space=χ)
ψ2 = random_tensornetwork(s2; link_space=χ)
rng = StableRNG(1234)
ψ1 = random_tensornetwork(rng, s1; link_space=χ)
ψ2 = random_tensornetwork(rng, s2; link_space=χ)

ψ12 = ψ1 + ψ2

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15 changes: 3 additions & 12 deletions test/test_apply.jl
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Expand Up @@ -14,39 +14,32 @@ using ITensorNetworks:
using ITensors: ITensors, inner, op
using NamedGraphs.NamedGraphGenerators: named_grid
using NamedGraphs.PartitionedGraphs: PartitionVertex
using Random: Random
using SplitApplyCombine: group
using StableRNGs: StableRNG
using Test: @test, @testset

@testset "apply" begin
Random.seed!(5623)
g_dims = (2, 2)
n = prod(g_dims)
g = named_grid(g_dims)
s = siteinds("S=1/2", g)
χ = 2
ψ = random_tensornetwork(s; link_space=χ)
rng = StableRNG(1234)
ψ = random_tensornetwork(rng, s; link_space=χ)
v1, v2 = (2, 2), (1, 2)
ψψ = norm_sqr_network(ψ)
#Simple Belief Propagation Grouping
bp_cache = BeliefPropagationCache(ψψ, group(v -> v[1], vertices(ψψ)))
bp_cache = update(bp_cache; maxiter=20)
envsSBP = environment(bp_cache, PartitionVertex.([v1, v2]))

ψv = VidalITensorNetwork(ψ)

#This grouping will correspond to calculating the environments exactly (each column of the grid is a partition)
bp_cache = BeliefPropagationCache(ψψ, group(v -> v[1][1], vertices(ψψ)))
bp_cache = update(bp_cache; maxiter=20)
envsGBP = environment(bp_cache, [(v1, "bra"), (v1, "ket"), (v2, "bra"), (v2, "ket")])

inner_alg = "exact"

ngates = 5

for i in 1:ngates
o = op("RandomUnitary", s[v1]..., s[v2]...)

ψOexact = apply(o, ψ; cutoff=1e-16)
ψOSBP = apply(
o,
Expand Down Expand Up @@ -79,9 +72,7 @@ using Test: @test, @testset
fGBP =
inner(ψOGBP, ψOexact; alg=inner_alg) /
sqrt(inner(ψOexact, ψOexact; alg=inner_alg) * inner(ψOGBP, ψOGBP; alg=inner_alg))

@test real(fGBP * conj(fGBP)) >= real(fSBP * conj(fSBP))

@test isapprox(real(fSBP * conj(fSBP)), real(fVidal * conj(fVidal)); atol=1e-3)
end
end
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17 changes: 7 additions & 10 deletions test/test_belief_propagation.jl
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Expand Up @@ -31,31 +31,27 @@ using LinearAlgebra: eigvals, tr
using NamedGraphs: NamedEdge, NamedGraph
using NamedGraphs.NamedGraphGenerators: named_comb_tree, named_grid
using NamedGraphs.PartitionedGraphs: PartitionVertex, partitionedges
using Random: Random
using SplitApplyCombine: group
using StableRNGs: StableRNG
using Test: @test, @testset

@testset "belief_propagation" begin
ITensors.disable_warn_order()

g = named_grid((3, 3))
s = siteinds("S=1/2", g)
χ = 2
Random.seed!(1234)
ψ = random_tensornetwork(s; link_space=χ)
rng = StableRNG(1234)
ψ = random_tensornetwork(rng, s; link_space=χ)
ψψ = ψ ⊗ prime(dag(ψ); sites=[])

bpc = BeliefPropagationCache(ψψ)
bpc = update(bpc; maxiter=50, tol=1e-10)

#Test messages are converged
for pe in partitionedges(partitioned_tensornetwork(bpc))
@test update_message(bpc, pe) ≈ message(bpc, pe) atol = 1e-8
end

#Test updating the underlying tensornetwork in the cache
v = first(vertices(ψψ))
new_tensor = random_itensor(inds(ψψ[v]))
rng = StableRNG(1234)
new_tensor = random_itensor(rng, inds(ψψ[v]))
bpc_updated = update_factor(bpc, v, new_tensor)
ψψ_updated = tensornetwork(bpc_updated)
@test ψψ_updated[v] == new_tensor
Expand All @@ -79,7 +75,8 @@ using Test: @test, @testset
#Test edge case of network which evalutes to 0
χ = 2
g = named_grid((3, 1))
ψ = random_tensornetwork(ComplexF64, g; link_space=χ)
rng = StableRNG(1234)
ψ = random_tensornetwork(rng, ComplexF64, g; link_space=χ)
ψ[(1, 1)] = 0.0 * ψ[(1, 1)]
@test iszero(scalar(ψ; alg="bp"))
end
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4 changes: 3 additions & 1 deletion test/test_binary_tree_partition.jl
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Expand Up @@ -23,6 +23,7 @@ using NamedGraphs.NamedGraphGenerators: named_grid
using NamedGraphs.GraphsExtensions:
is_binary_arborescence, post_order_dfs_vertices, root_vertex
using OMEinsumContractionOrders: OMEinsumContractionOrders
using StableRNGs: StableRNG
using Test: @test, @testset

@testset "test mincut functions on top of MPS" begin
Expand Down Expand Up @@ -60,7 +61,8 @@ end
@testset "test _binary_tree_partition_inds of a 2D network" begin
N = (3, 3, 3)
linkdim = 2
network = random_tensornetwork(IndsNetwork(named_grid(N)); link_space=linkdim)
rng = StableRNG(1234)
network = random_tensornetwork(rng, IndsNetwork(named_grid(N)); link_space=linkdim)
tn = Array{ITensor,length(N)}(undef, N...)
for v in vertices(network)
tn[v...] = network[v...]
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4 changes: 3 additions & 1 deletion test/test_contract_deltas.jl
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Expand Up @@ -17,6 +17,7 @@ using ITensorNetworks:
random_tensornetwork
using NamedGraphs.GraphsExtensions: leaf_vertices, root_vertex
using NamedGraphs.NamedGraphGenerators: named_grid
using StableRNGs: StableRNG
using Test: @test, @testset

@testset "test _contract_deltas with no deltas" begin
Expand All @@ -41,7 +42,8 @@ end
@testset "test _contract_deltas over partition" begin
N = (3, 3, 3)
linkdim = 2
network = random_tensornetwork(IndsNetwork(named_grid(N)); link_space=linkdim)
rng = StableRNG(1234)
network = random_tensornetwork(rng, IndsNetwork(named_grid(N)); link_space=linkdim)
tn = Array{ITensor,length(N)}(undef, N...)
for v in vertices(network)
tn[v...] = network[v...]
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7 changes: 3 additions & 4 deletions test/test_contraction_sequence.jl
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Expand Up @@ -5,17 +5,16 @@ using ITensorNetworks:
using ITensors: ITensors, contract
using NamedGraphs.NamedGraphGenerators: named_grid
using OMEinsumContractionOrders: OMEinsumContractionOrders
using Random: Random
using StableRNGs: StableRNG
using Test: @test, @testset

Random.seed!(1234)
@testset "contraction_sequence" begin
ITensors.@disable_warn_order begin
dims = (2, 3)
g = named_grid(dims)
s = siteinds("S=1/2", g)
χ = 10
ψ = random_tensornetwork(s; link_space=χ)
rng = StableRNG(1234)
ψ = random_tensornetwork(rng, s; link_space=χ)
tn = norm_sqr_network(ψ)
seq_optimal = contraction_sequence(tn; alg="optimal")
res_optimal = contract(tn; sequence=seq_optimal)[]
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10 changes: 4 additions & 6 deletions test/test_contraction_sequence_to_graph.jl
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Expand Up @@ -6,20 +6,18 @@ using ITensorNetworks:
contraction_sequence_to_graph,
contraction_tree_leaf_bipartition,
random_tensornetwork
using Test: @test, @testset
using NamedGraphs.GraphsExtensions:
is_leaf_vertex, leaf_vertices, non_leaf_edges, root_vertex
using NamedGraphs.NamedGraphGenerators: named_grid

using StableRNGs: StableRNG
using Test: @test, @testset
@testset "contraction_sequence_to_graph" begin
n = 3
dims = (n, n)
g = named_grid(dims)

tn = random_tensornetwork(g; link_space=2)

rng = StableRNG(1234)
tn = random_tensornetwork(rng, g; link_space=2)
seq = contraction_sequence(tn)

g_directed_seq = contraction_sequence_to_digraph(seq)
g_seq_leaves = leaf_vertices(g_directed_seq)
@test length(g_seq_leaves) == n * n
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12 changes: 5 additions & 7 deletions test/test_expect.jl
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@@ -1,5 +1,4 @@
@eval module $(gensym())

using Graphs: SimpleGraph, uniform_tree
using NamedGraphs: NamedGraph, vertices
using NamedGraphs.NamedGraphGenerators: named_grid
Expand All @@ -10,18 +9,16 @@ using ITensorNetworks:
expect,
random_tensornetwork,
original_state_vertex
using Random: Random
using SplitApplyCombine: group
using StableRNGs: StableRNG
using Test: @test, @testset

@testset "Test Expect" begin
Random.seed!(1234)

#Test on a tree
L, χ = 4, 2
g = NamedGraph(SimpleGraph(uniform_tree(L)))
s = siteinds("S=1/2", g)
ψ = random_tensornetwork(s; link_space=χ)
rng = StableRNG(1234)
ψ = random_tensornetwork(rng, s; link_space=χ)
sz_bp = expect(ψ, "Sz"; alg="bp")
sz_exact = expect(ψ, "Sz"; alg="exact")
@test sz_bp ≈ sz_exact
Expand All @@ -30,7 +27,8 @@ using Test: @test, @testset
L, χ = 2, 2
g = named_grid((L, L))
s = siteinds("S=1/2", g)
ψ = random_tensornetwork(s; link_space=χ)
rng = StableRNG(1234)
ψ = random_tensornetwork(rng, s; link_space=χ)
cache_construction_function =
f -> BeliefPropagationCache(
f; partitioned_vertices=group(v -> (original_state_vertex(f, v)[1]), vertices(f))
Expand Down
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Registration pull request created: JuliaRegistries/General/107075

Tip: Release Notes

Did you know you can add release notes too? Just add markdown formatted text underneath the comment after the text
"Release notes:" and it will be added to the registry PR, and if TagBot is installed it will also be added to the
release that TagBot creates. i.e.

@JuliaRegistrator register

Release notes:

## Breaking changes

- blah

To add them here just re-invoke and the PR will be updated.

Tagging

After the above pull request is merged, it is recommended that a tag is created on this repository for the registered package version.

This will be done automatically if the Julia TagBot GitHub Action is installed, or can be done manually through the github interface, or via:

git tag -a v0.11.10 -m "<description of version>" de7d4dd9e981d39d9cc0866301aaaa55f4b8eb53
git push origin v0.11.10

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