compatable with TreeWidthSolver v0.3 (#47)

* compatable with TreeWidthSolver v0.3

* update docs

* support julia v1.8

* update ci

.github/workflows/ci.yml

@@ -14,6 +14,7 @@ jobs:
       fail-fast: false
       matrix:
         version:
+          - '1.8'
           - '1'
         os:
           - ubuntu-latest

Project.toml

@@ -26,11 +26,12 @@ KaHyPar = "0.3"
 StatsBase = "0.34"
 Suppressor = "0.2"
 LuxorGraphPlot = "0.5.1"
-TreeWidthSolver = "0.2"
-julia = "1.9"
+TreeWidthSolver = "0.3.1"
+julia = "1.8"
 
 [extras]
 Graphs = "86223c79-3864-5bf0-83f7-82e725a168b6"
+KaHyPar = "2a6221f6-aa48-11e9-3542-2d9e0ef01880"
 OMEinsum = "ebe7aa44-baf0-506c-a96f-8464559b3922"
 Random = "9a3f8284-a2c9-5f02-9a11-845980a1fd5c"
 Test = "8dfed614-e22c-5e08-85e1-65c5234f0b40"

src/treewidth.jl

@@ -15,7 +15,7 @@ end
 """
     exact_treewidth_method(incidence_list::IncidenceList{VT,ET}, log2_edge_sizes; α::TA = 0.0, temperature::TT = 0.0, nrepeat=1) where {VT,ET,TA,TT}
 
-This function calculates the exact treewidth of a graph using TreeWidthSolver.jl. It takes an incidence list representation of the graph (`incidence_list`) and a dictionary of logarithm base 2 edge sizes (`log2_edge_sizes`) as input. The function also accepts optional parameters `α`, `temperature`, and `nrepeat` with default values of 0.0, 0.0, and 1 respectively, which are parameter of the GreedyMethod used in the contraction process.
+This function calculates the exact treewidth of a graph using TreeWidthSolver.jl. It takes an incidence list representation of the graph (`incidence_list`) and a dictionary of logarithm base 2 edge sizes (`log2_edge_sizes`) as input. The function also accepts optional parameters `α`, `temperature`, and `nrepeat` with default values of 0.0, 0.0, and 1 respectively, which are parameter of the GreedyMethod used in the contraction process as a sub optimizer.
 
 ## Arguments
 - `incidence_list`: An incidence list representation of the graph.
@@ -67,10 +67,8 @@ function exact_treewidth_method(incidence_list::IncidenceList{VT,ET}, log2_edge_
         lg = induced_subgraph(line_graph, vertice_ids)[1]
         lg_indicies = indicies[vertice_ids]
         lg_weights = weights[vertice_ids]
-        labeled_graph = LabeledSimpleGraph(lg, lg_indicies, lg_weights)
-        tree_decomposition = exact_treewidth(labeled_graph)
-        elimination_order = EliminationOrder(tree_decomposition.tree)
-        contraction_tree = eo2ct(elimination_order, incidence_list, log2_edge_sizes, α, temperature, nrepeat)
+        eo = elimination_order(lg, labels = lg_indicies, weights = lg_weights)
+        contraction_tree = eo2ct(eo, incidence_list, log2_edge_sizes, α, temperature, nrepeat)
         push!(contraction_trees, contraction_tree)
     end
 
@@ -95,8 +93,8 @@ function il2lg(incidence_list::IncidenceList{VT, ET}, indicies::Vector{ET}) wher
 end
 
 # transform elimination order to contraction tree
-function eo2ct(elimination_order::EliminationOrder, incidence_list::IncidenceList{VT, ET}, log2_edge_sizes, α::TA, temperature::TT, nrepeat) where {VT, ET, TA, TT}
-    eo = copy(elimination_order.order)
+function eo2ct(elimination_order::Vector{Vector{TL}}, incidence_list::IncidenceList{VT, ET}, log2_edge_sizes, α::TA, temperature::TT, nrepeat) where {TL, VT, ET, TA, TT}
+    eo = copy(elimination_order)
     incidence_list = copy(incidence_list)
     contraction_tree_nodes = Vector{Union{VT, ContractionTree}}(collect(keys(incidence_list.v2e)))
     tensors_list = Dict{VT, Int}()

test/interfaces.jl

@@ -15,7 +15,17 @@ using OMEinsum
     xs = [[randn(2,2) for i=1:150]..., [randn(2) for i=1:100]...]
 
     results = Float64[]
-    for optimizer in [TreeSA(ntrials=1), TreeSA(ntrials=1, nslices=5), GreedyMethod(), KaHyParBipartite(sc_target=18), SABipartite(sc_target=18, ntrials=1)]
+    for optimizer in [TreeSA(ntrials=1), TreeSA(ntrials=1, nslices=5), GreedyMethod(), SABipartite(sc_target=18, ntrials=1)]
+        for simplifier in (nothing, MergeVectors(), MergeGreedy())
+            @info "optimizer = $(optimizer), simplifier = $(simplifier)"
+            res = optimize_code(code,uniformsize(code, 2), optimizer, simplifier)
+            tc, sc = OMEinsum.timespace_complexity(res, uniformsize(code, 2))
+            @test sc <= 18
+            push!(results, res(xs...)[])
+        end
+    end
+    if isdefined(Base, :get_extension)
+        optimizer = KaHyParBipartite(sc_target=18)
         for simplifier in (nothing, MergeVectors(), MergeGreedy())
             @info "optimizer = $(optimizer), simplifier = $(simplifier)"
             res = optimize_code(code,uniformsize(code, 2), optimizer, simplifier)
@@ -32,7 +42,16 @@ using OMEinsum
     xs = [[randn(2,2) for i=1:15]..., [randn(2) for i=1:10]...]
 
     results = Float64[]
-    for optimizer in [TreeSA(ntrials=1), TreeSA(ntrials=1, nslices=5), GreedyMethod(), KaHyParBipartite(sc_target=18), SABipartite(sc_target=18, ntrials=1), ExactTreewidth()]
+    for optimizer in [TreeSA(ntrials=1), TreeSA(ntrials=1, nslices=5), GreedyMethod(), SABipartite(sc_target=18, ntrials=1), ExactTreewidth()]
+        for simplifier in (nothing, MergeVectors(), MergeGreedy())
+            @info "optimizer = $(optimizer), simplifier = $(simplifier)"
+            res = optimize_code(small_code,uniformsize(small_code, 2), optimizer, simplifier)
+            tc, sc = OMEinsum.timespace_complexity(res, uniformsize(small_code, 2))
+            push!(results, res(xs...)[])
+        end
+    end
+    if isdefined(Base, :get_extension)
+        optimizer = KaHyParBipartite(sc_target=18)
         for simplifier in (nothing, MergeVectors(), MergeGreedy())
             @info "optimizer = $(optimizer), simplifier = $(simplifier)"
             res = optimize_code(small_code,uniformsize(small_code, 2), optimizer, simplifier)
@@ -53,7 +72,7 @@ end
     @test optimize_code(code, sizes, GreedyMethod()) == sne
     @test optimize_code(code, sizes, TreeSA()) == SlicedEinsum(Char[], dne)
     @test optimize_code(code, sizes, TreeSA(nslices=2)) == SlicedEinsum(Char[], dne)
-    @test optimize_code(code, sizes, KaHyParBipartite(sc_target=25)) == dne
+    isdefined(Base, :get_extension) &&  (@test optimize_code(code, sizes, KaHyParBipartite(sc_target=25)) == dne)
     @test optimize_code(code, sizes, SABipartite(sc_target=25)) == dne
 end
 
@@ -79,32 +98,33 @@ end
     @test 10 * 2^sc2 > pm2 > 2^sc2
 end
 
+if isdefined(Base, :get_extension)
+    @testset "kahypar regression test" begin
+        code = ein"i->"
+        optcode = optimize_code(code, Dict('i'=>4), KaHyParBipartite(; sc_target=10, max_group_size=10))
+        @test optcode isa NestedEinsum
+        x = randn(4)
+        @test optcode(x) ≈ code(x)
 
-@testset "kahypar regression test" begin
-    code = ein"i->"
-    optcode = optimize_code(code, Dict('i'=>4), KaHyParBipartite(; sc_target=10, max_group_size=10))
-    @test optcode isa NestedEinsum
-    x = randn(4)
-    @test optcode(x) ≈ code(x)
-
-    code = ein"i,j->"
-    optcode = optimize_code(code, Dict('i'=>4, 'j'=>4), KaHyParBipartite(; sc_target=10, max_group_size=10))
-    @test optcode isa NestedEinsum
-    x = randn(4)
-    y = randn(4)
-    @test optcode(x, y) ≈ code(x, y)
+        code = ein"i,j->"
+        optcode = optimize_code(code, Dict('i'=>4, 'j'=>4), KaHyParBipartite(; sc_target=10, max_group_size=10))
+        @test optcode isa NestedEinsum
+        x = randn(4)
+        y = randn(4)
+        @test optcode(x, y) ≈ code(x, y)
 
-    code = ein"ij,jk,kl->ijl"
-    println(code)
-    optcode = optimize_code(code, Dict('i'=>4, 'j'=>4, 'k'=>4, 'l'=>4), KaHyParBipartite(; sc_target=4, max_group_size=2))
-    println(optcode)
-    @test optcode isa NestedEinsum
-    a, b, c = [rand(4,4) for i=1:4]
-    @test optcode(a, b, c) ≈ code(a, b, c)
+        code = ein"ij,jk,kl->ijl"
+        println(code)
+        optcode = optimize_code(code, Dict('i'=>4, 'j'=>4, 'k'=>4, 'l'=>4), KaHyParBipartite(; sc_target=4, max_group_size=2))
+        println(optcode)
+        @test optcode isa NestedEinsum
+        a, b, c = [rand(4,4) for i=1:4]
+        @test optcode(a, b, c) ≈ code(a, b, c)
 
-    code = ein"ij,jk,kl->ijl"
-    optcode = optimize_code(code, Dict('i'=>3, 'j'=>3, 'k'=>3, 'l'=>3), KaHyParBipartite(; sc_target=4, max_group_size=2))
-    @test optcode isa NestedEinsum
-    a, b, c = [rand(3,3) for i=1:4]
-    @test optcode(a, b, c) ≈ code(a, b, c)
+        code = ein"ij,jk,kl->ijl"
+        optcode = optimize_code(code, Dict('i'=>3, 'j'=>3, 'k'=>3, 'l'=>3), KaHyParBipartite(; sc_target=4, max_group_size=2))
+        @test optcode isa NestedEinsum
+        a, b, c = [rand(3,3) for i=1:4]
+        @test optcode(a, b, c) ≈ code(a, b, c)
+    end
 end

test/runtests.jl

@@ -13,8 +13,10 @@ end
     include("sa.jl")
 end
 
-@testset "kahypar" begin
-    include("kahypar.jl")
+if isdefined(Base, :get_extension)
+    @testset "kahypar" begin
+        include("kahypar.jl")
+    end
 end
 
 @testset "treesa" begin
@@ -38,6 +40,8 @@ end
 end
 
 # testing the extension `LuxorTensorPlot` for visualization
-@testset "visualization" begin
-    include("visualization.jl")
+if isdefined(Base, :get_extension)
+    @testset "visualization" begin
+        include("visualization.jl")
+    end
 end

test/treewidth.jl

@@ -15,7 +15,7 @@ using Test, Random
     cc = contraction_complexity(optcode, size_dict)
     # test flop
     @test cc.tc ≈ log2(flop(optcode, size_dict))
-    @test 16 <= cc.tc <= log2(exp2(10)+exp2(16)+exp2(15)+exp2(9))
+    @test (16 <= cc.tc <= log2(exp2(10)+exp2(16)+exp2(15)+exp2(9))) | (cc.tc ≈ log2(exp2(10)+exp2(16)+exp2(15)+exp2(9)))
     @test cc.sc == 11
     @test decorate(eincode)(tensors...) ≈ decorate(optcode)(tensors...)