rubydragon/metagraphoptimization.jl
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Refactor model into an interface and remove any ABC Model specific code from src/code_gen/. Also generate functions instead of direct code evaluation in execute()

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This commit is contained in:
Anton Reinhard
2023-09-28 17:59:17 +02:00
parent a69dd6018e
commit 7dd9fedf2e
35 changed files with 489 additions and 827 deletions
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2
test/known_graphs.jl
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@ -2,7 +2,7 @@ using Random
function test_known_graph(name::String, n, fusion_test = true)
@testset "Test $name Graph ($n)" begin
graph = parse_abc(joinpath(@__DIR__, "..", "input", "$name.txt"))
graph = parse_dag(joinpath(@__DIR__, "..", "input", "$name.txt"), ABCModel())
props = get_properties(graph)
if (fusion_test)

92
test/unit_tests_execution.jl
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@ -1,93 +1,73 @@
import MetagraphOptimization.A
import MetagraphOptimization.B
import MetagraphOptimization.ParticleType
import MetagraphOptimization.ABCParticle
using QEDbase
include("../examples/profiling_utilities.jl")
@testset "Unit Tests Execution" begin
particles_2_2 = Tuple{Vector{Particle}, Vector{Particle}}((
[
Particle(SFourMomentum(0.823648, 0.0, 0.0, 0.823648), A),
Particle(SFourMomentum(0.823648, 0.0, 0.0, -0.823648), B),
],
[
Particle(
SFourMomentum(0.823648, -0.835061, -0.474802, 0.277915),
A,
),
Particle(SFourMomentum(0.823648, 0.835061, 0.474802, -0.277915), B),
],
))
process_2_2 = ABCProcessDescription(
Dict{Type, Int64}(ParticleA => 1, ParticleB => 1),
Dict{Type, Int64}(ParticleA => 1, ParticleB => 1),
)
expected_result = 5.5320505145845555e-5
particles_2_2 = ABCProcessInput(
process_2_2,
ABCParticle[
ParticleA(SFourMomentum(0.823648, 0.0, 0.0, 0.823648)),
ParticleB(SFourMomentum(0.823648, 0.0, 0.0, -0.823648)),
],
ABCParticle[
ParticleA(SFourMomentum(0.823648, -0.835061, -0.474802, 0.277915)),
ParticleB(SFourMomentum(0.823648, 0.835061, 0.474802, -0.277915)),
],
)
expected_result = 0.00013916495566048735
@testset "AB->AB no optimization" begin
for _ in 1:10 # test in a loop because graph layout should not change the result
graph = parse_abc(joinpath(@__DIR__, "..", "input", "AB->AB.txt"))
@test isapprox(
execute(graph, particles_2_2),
expected_result;
rtol = 0.001,
)
graph = parse_dag(joinpath(@__DIR__, "..", "input", "AB->AB.txt"), ABCModel())
@test isapprox(execute(graph, process_2_2, particles_2_2), expected_result; rtol = 0.001)
#=code = MetagraphOptimization.gen_code(graph)
@test isapprox(
execute(code, particles_2_2),
expected_result;
rtol = 0.001,
)=#
func = get_compute_function(graph, process_2_2)
@test isapprox(func(particles_2_2), expected_result; rtol = 0.001)
end
end
@testset "AB->AB after random walk" begin
for i in 1:100
graph = parse_abc(joinpath(@__DIR__, "..", "input", "AB->AB.txt"))
graph = parse_dag(joinpath(@__DIR__, "..", "input", "AB->AB.txt"), ABCModel())
random_walk!(graph, 50)
@test is_valid(graph)
@test isapprox(
execute(graph, particles_2_2),
expected_result;
rtol = 0.001,
)
@test isapprox(execute(graph, process_2_2, particles_2_2), expected_result; rtol = 0.001)
end
end
particles_2_4 = gen_particles([A, B], [A, B, B, B])
graph = parse_abc(joinpath(@__DIR__, "..", "input", "AB->ABBB.txt"))
expected_result = execute(graph, particles_2_4)
process_2_4 = ABCProcessDescription(
Dict{Type, Int64}(ParticleA => 1, ParticleB => 1),
Dict{Type, Int64}(ParticleA => 1, ParticleB => 3),
)
particles_2_4 = gen_process_input(process_2_4)
graph = parse_dag(joinpath(@__DIR__, "..", "input", "AB->ABBB.txt"), ABCModel())
expected_result = execute(graph, process_2_4, particles_2_4)
@testset "AB->ABBB no optimization" begin
for _ in 1:5 # test in a loop because graph layout should not change the result
graph = parse_abc(joinpath(@__DIR__, "..", "input", "AB->ABBB.txt"))
@test isapprox(
execute(graph, particles_2_4),
expected_result;
rtol = 0.001,
)
graph = parse_dag(joinpath(@__DIR__, "..", "input", "AB->ABBB.txt"), ABCModel())
@test isapprox(execute(graph, process_2_4, particles_2_4), expected_result; rtol = 0.001)
#=code = MetagraphOptimization.gen_code(graph)
@test isapprox(
execute(code, particles_2_4),
expected_result;
rtol = 0.001,
)=#
func = get_compute_function(graph, process_2_4)
@test isapprox(func(particles_2_4), expected_result; rtol = 0.001)
end
end
@testset "AB->ABBB after random walk" begin
for i in 1:20
graph = parse_abc(joinpath(@__DIR__, "..", "input", "AB->ABBB.txt"))
graph = parse_dag(joinpath(@__DIR__, "..", "input", "AB->ABBB.txt"), ABCModel())
random_walk!(graph, 100)
@test is_valid(graph)
@test isapprox(
execute(graph, particles_2_4),
expected_result;
rtol = 0.001,
)
@test isapprox(execute(graph, process_2_4, particles_2_4), expected_result; rtol = 0.001)
end
end

27
test/unit_tests_graph.jl
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@ -11,10 +11,8 @@ import MetagraphOptimization.partners
@test length(graph.appliedOperations) == 0
@test length(graph.operationsToApply) == 0
@test length(graph.dirtyNodes) == 0
@test length(graph.diff) ==
(addedNodes = 0, removedNodes = 0, addedEdges = 0, removedEdges = 0)
@test length(get_operations(graph)) ==
(nodeFusions = 0, nodeReductions = 0, nodeSplits = 0)
@test length(graph.diff) == (addedNodes = 0, removedNodes = 0, addedEdges = 0, removedEdges = 0)
@test length(get_operations(graph)) == (nodeFusions = 0, nodeReductions = 0, nodeSplits = 0)
# s to output (exit node)
d_exit = insert_node!(graph, make_node(DataTask(10)), track = false)
@ -107,8 +105,7 @@ import MetagraphOptimization.partners
@test length(graph.appliedOperations) == 0
@test length(graph.operationsToApply) == 0
@test length(graph.dirtyNodes) == 26
@test length(graph.diff) ==
(addedNodes = 0, removedNodes = 0, addedEdges = 0, removedEdges = 0)
@test length(graph.diff) == (addedNodes = 0, removedNodes = 0, addedEdges = 0, removedEdges = 0)
@test is_valid(graph)
@ -135,8 +132,7 @@ import MetagraphOptimization.partners
@test length(siblings(s0)) == 1
operations = get_operations(graph)
@test length(operations) ==
(nodeFusions = 10, nodeReductions = 0, nodeSplits = 0)
@test length(operations) == (nodeFusions = 10, nodeReductions = 0, nodeSplits = 0)
@test length(graph.dirtyNodes) == 0
@test operations == get_operations(graph)
@ -157,8 +153,7 @@ import MetagraphOptimization.partners
@test length(graph.operationsToApply) == 1
@test first(graph.operationsToApply) == nf
@test length(graph.dirtyNodes) == 0
@test length(graph.diff) ==
(addedNodes = 0, removedNodes = 0, addedEdges = 0, removedEdges = 0)
@test length(graph.diff) == (addedNodes = 0, removedNodes = 0, addedEdges = 0, removedEdges = 0)
# this applies pending operations
properties = get_properties(graph)
@ -176,8 +171,7 @@ import MetagraphOptimization.partners
operations = get_operations(graph)
@test length(graph.dirtyNodes) == 0
@test length(operations) ==
(nodeFusions = 9, nodeReductions = 0, nodeSplits = 0)
@test length(operations) == (nodeFusions = 9, nodeReductions = 0, nodeSplits = 0)
@test !isempty(operations)
possibleNF = 9
@ -185,14 +179,12 @@ import MetagraphOptimization.partners
push_operation!(graph, first(operations.nodeFusions))
operations = get_operations(graph)
possibleNF = possibleNF - 1
@test length(operations) ==
(nodeFusions = possibleNF, nodeReductions = 0, nodeSplits = 0)
@test length(operations) == (nodeFusions = possibleNF, nodeReductions = 0, nodeSplits = 0)
end
@test isempty(operations)
@test length(operations) ==
(nodeFusions = 0, nodeReductions = 0, nodeSplits = 0)
@test length(operations) == (nodeFusions = 0, nodeReductions = 0, nodeSplits = 0)
@test length(graph.dirtyNodes) == 0
@test length(graph.nodes) == 6
@test length(graph.appliedOperations) == 10
@ -213,8 +205,7 @@ import MetagraphOptimization.partners
@test properties.computeIntensity ≈ 28 / 62
operations = get_operations(graph)
@test length(operations) ==
(nodeFusions = 10, nodeReductions = 0, nodeSplits = 0)
@test length(operations) == (nodeFusions = 10, nodeReductions = 0, nodeSplits = 0)
@test is_valid(graph)
end

3
test/unit_tests_nodes.jl
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@ -3,8 +3,7 @@
nC1 = MetagraphOptimization.make_node(MetagraphOptimization.ComputeTaskU())
nC2 = MetagraphOptimization.make_node(MetagraphOptimization.ComputeTaskV())
nC3 = MetagraphOptimization.make_node(MetagraphOptimization.ComputeTaskP())
nC4 =
MetagraphOptimization.make_node(MetagraphOptimization.ComputeTaskSum())
nC4 = MetagraphOptimization.make_node(MetagraphOptimization.ComputeTaskSum())
nD1 = MetagraphOptimization.make_node(MetagraphOptimization.DataTask(10))
nD2 = MetagraphOptimization.make_node(MetagraphOptimization.DataTask(20))

6
test/unit_tests_utility.jl
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@ -5,9 +5,7 @@
@test MetagraphOptimization.bytes_to_human_readable(1025) == "1.001 KiB"
@test MetagraphOptimization.bytes_to_human_readable(684235) == "668.2 KiB"
@test MetagraphOptimization.bytes_to_human_readable(86214576) == "82.22 MiB"
@test MetagraphOptimization.bytes_to_human_readable(9241457698) ==
"8.607 GiB"
@test MetagraphOptimization.bytes_to_human_readable(3218598654367) ==
"2.927 TiB"
@test MetagraphOptimization.bytes_to_human_readable(9241457698) == "8.607 GiB"
@test MetagraphOptimization.bytes_to_human_readable(3218598654367) == "2.927 TiB"
end
println("Utility Unit Tests Complete!")