Anton Reinhard
5a30f57e1f
Reviewed-on: Rubydragon/MetagraphOptimization.jl#9 Co-authored-by: Anton Reinhard <anton.reinhard@proton.me> Co-committed-by: Anton Reinhard <anton.reinhard@proton.me>
97 lines
2.6 KiB
Julia
97 lines
2.6 KiB
Julia
using Random
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function test_known_graph(name::String, n, fusion_test = true)
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@testset "Test $name Graph ($n)" begin
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graph = parse_dag(joinpath(@__DIR__, "..", "input", "$name.txt"), ABCModel())
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props = get_properties(graph)
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if (fusion_test)
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test_node_fusion(graph)
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end
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test_random_walk(graph, n)
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end
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end
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function test_node_fusion(g::DAG)
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@testset "Test Node Fusion" begin
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props = get_properties(g)
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options = get_operations(g)
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nodes_number = length(g.nodes)
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data = props.data
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compute_effort = props.computeEffort
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while !isempty(options.nodeFusions)
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fusion = first(options.nodeFusions)
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@test typeof(fusion) <: NodeFusion
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push_operation!(g, fusion)
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props = get_properties(g)
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@test props.data < data
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@test props.computeEffort == compute_effort
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nodes_number = length(g.nodes)
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data = props.data
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compute_effort = props.computeEffort
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options = get_operations(g)
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end
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end
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end
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function test_random_walk(g::DAG, n::Int64)
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@testset "Test Random Walk ($n)" begin
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# the purpose here is to do "random" operations and reverse them again and validate that the graph stays the same and doesn't diverge
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reset_graph!(g)
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@test is_valid(g)
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properties = get_properties(g)
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for i in 1:n
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# choose push or pop
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if rand(Bool)
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# push
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opt = get_operations(g)
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# choose one of fuse/split/reduce
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option = rand(1:3)
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if option == 1 && !isempty(opt.nodeFusions)
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push_operation!(g, rand(collect(opt.nodeFusions)))
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elseif option == 2 && !isempty(opt.nodeReductions)
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push_operation!(g, rand(collect(opt.nodeReductions)))
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elseif option == 3 && !isempty(opt.nodeSplits)
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push_operation!(g, rand(collect(opt.nodeSplits)))
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else
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i = i - 1
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end
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else
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# pop
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if (can_pop(g))
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pop_operation!(g)
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else
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i = i - 1
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end
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end
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end
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reset_graph!(g)
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@test is_valid(g)
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@test properties == get_properties(g)
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end
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end
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Random.seed!(0)
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@testset "Test Known ABC-Graphs" begin
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test_known_graph("AB->AB", 10000)
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test_known_graph("AB->ABBB", 10000)
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test_known_graph("AB->ABBBBB", 1000, false)
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end
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println("Known Graph Testing Complete!")
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