rubydragon/metagraphoptimization.jl
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Remove occurrences of Fusion/Fuse
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This commit is contained in:
Anton Reinhard
2024-08-13 17:57:16 +02:00
parent 8a5e49429b
commit 97ccb3f3fb
31 changed files with 33 additions and 917 deletions
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47
test/known_graphs.jl
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@@ -3,48 +3,15 @@ using Random
RNG = Random.MersenneTwister(321)
function test_known_graph(name::String, n, fusion_test = true)
function test_known_graph(name::String, n)
@testset "Test $name Graph ($n)" begin
graph = parse_dag(joinpath(@__DIR__, "..", "input", "$name.txt"), ABCModel())
props = get_properties(graph)
if (fusion_test)
test_node_fusion(graph)
end
test_random_walk(RNG, graph, n)
end
end
function test_node_fusion(g::DAG)
@testset "Test Node Fusion" begin
props = get_properties(g)
options = get_operations(g)
nodes_number = length(g.nodes)
data = props.data
compute_effort = props.computeEffort
while !isempty(options.nodeFusions)
fusion = first(options.nodeFusions)
@test typeof(fusion) <: NodeFusion
push_operation!(g, fusion)
props = get_properties(g)
@test props.data < data
@test props.computeEffort == compute_effort
nodes_number = length(g.nodes)
data = props.data
compute_effort = props.computeEffort
options = get_operations(g)
end
end
end
function test_random_walk(RNG, g::DAG, n::Int64)
@testset "Test Random Walk ($n)" begin
# the purpose here is to do "random" operations and reverse them again and validate that the graph stays the same and doesn't diverge
@@ -60,13 +27,11 @@ function test_random_walk(RNG, g::DAG, n::Int64)
# push
opt = get_operations(g)
# choose one of fuse/split/reduce
option = rand(RNG, 1:3)
if option == 1 && !isempty(opt.nodeFusions)
push_operation!(g, rand(RNG, collect(opt.nodeFusions)))
elseif option == 2 && !isempty(opt.nodeReductions)
# choose one of split/reduce
option = rand(RNG, 1:2)
if option == 1 && !isempty(opt.nodeReductions)
push_operation!(g, rand(RNG, collect(opt.nodeReductions)))
elseif option == 3 && !isempty(opt.nodeSplits)
elseif option == 2 && !isempty(opt.nodeSplits)
push_operation!(g, rand(RNG, collect(opt.nodeSplits)))
else
i = i - 1
@@ -91,4 +56,4 @@ end
test_known_graph("AB->AB", 10000)
test_known_graph("AB->ABBB", 10000)
test_known_graph("AB->ABBBBB", 1000, false)
test_known_graph("AB->ABBBBB", 1000)

16
test/node_reduction.jl
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@@ -61,17 +61,14 @@ insert_edge!(graph, CD, C1C, track = false)
opt = get_operations(graph)
@test length(opt) == (nodeFusions = 6, nodeReductions = 1, nodeSplits = 1)
#println("Initial State:\n", opt)
@test length(opt) == (nodeReductions = 1, nodeSplits = 1)
nr = first(opt.nodeReductions)
@test Set(nr.input) == Set([B1C_1, B1C_2])
push_operation!(graph, nr)
opt = get_operations(graph)
@test length(opt) == (nodeFusions = 4, nodeReductions = 1, nodeSplits = 1)
#println("After 1 Node Reduction:\n", opt)
@test length(opt) == (nodeReductions = 1, nodeSplits = 1)
nr = first(opt.nodeReductions)
@test Set(nr.input) == Set([B1D_1, B1D_2])
@@ -80,19 +77,16 @@ opt = get_operations(graph)
@test is_valid(graph)
@test length(opt) == (nodeFusions = 4, nodeReductions = 0, nodeSplits = 1)
#println("After 2 Node Reductions:\n", opt)
@test length(opt) == (nodeReductions = 0, nodeSplits = 1)
pop_operation!(graph)
opt = get_operations(graph)
@test length(opt) == (nodeFusions = 4, nodeReductions = 1, nodeSplits = 1)
#println("After reverting the second Node Reduction:\n", opt)
@test length(opt) == (nodeReductions = 1, nodeSplits = 1)
reset_graph!(graph)
opt = get_operations(graph)
@test length(opt) == (nodeFusions = 6, nodeReductions = 1, nodeSplits = 1)
#println("After reverting to the initial state:\n", opt)
@test length(opt) == (nodeReductions = 1, nodeSplits = 1)
@test is_valid(graph)

15
test/unit_tests_estimator.jl
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@@ -1,16 +1,5 @@
using MetagraphOptimization
function test_op_specific(estimator, graph, nf::NodeFusion)
estimate = operation_effect(estimator, graph, nf)
data_reduce = data(nf.input[2].task)
@test isapprox(estimate.data, -data_reduce)
@test isapprox(estimate.computeEffort, 0; atol = eps(Float64))
@test isapprox(estimate.computeIntensity, 0; atol = eps(Float64))
return nothing
end
function test_op_specific(estimator, graph, nr::NodeReduction)
estimate = operation_effect(estimator, graph, nr)
@@ -74,13 +63,9 @@ end
@testset "Operation Cost" begin
ops = get_operations(graph)
nfs = copy(ops.nodeFusions)
nrs = copy(ops.nodeReductions)
nss = copy(ops.nodeSplits)
for nf in nfs
test_op(estimator, graph, nf)
end
for nr in nrs
test_op(estimator, graph, nr)
end

99
test/unit_tests_execution.jl
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@@ -136,105 +136,6 @@ TODO: fix precision(?) issues
end
=#
@testset "AB->AB large sum fusion" begin
for _ in 1:20
graph = parse_dag(joinpath(@__DIR__, "..", "input", "AB->AB.txt"), ABCModel())
# push a fusion with the sum node
ops = get_operations(graph)
for fusion in ops.nodeFusions
if isa(fusion.input[3].task, ComputeTaskABC_Sum)
push_operation!(graph, fusion)
break
end
end
# push two more fusions with the fused node
for _ in 1:15
ops = get_operations(graph)
for fusion in ops.nodeFusions
if isa(fusion.input[3].task, FusedComputeTask)
push_operation!(graph, fusion)
break
end
end
end
# try execute
@test is_valid(graph)
expected_result = ground_truth_graph_result(particles_2_2)
@test isapprox(execute(graph, process_2_2, machine, particles_2_2), expected_result; rtol = RTOL)
end
end
@testset "AB->AB large sum fusion" begin
for _ in 1:20
graph = parse_dag(joinpath(@__DIR__, "..", "input", "AB->AB.txt"), ABCModel())
# push a fusion with the sum node
ops = get_operations(graph)
for fusion in ops.nodeFusions
if isa(fusion.input[3].task, ComputeTaskABC_Sum)
push_operation!(graph, fusion)
break
end
end
# push two more fusions with the fused node
for _ in 1:15
ops = get_operations(graph)
for fusion in ops.nodeFusions
if isa(fusion.input[3].task, FusedComputeTask)
push_operation!(graph, fusion)
break
end
end
end
# try execute
@test is_valid(graph)
expected_result = ground_truth_graph_result(particles_2_2)
@test isapprox(execute(graph, process_2_2, machine, particles_2_2), expected_result; rtol = RTOL)
end
end
@testset "AB->AB fusion edge case" begin
for _ in 1:20
graph = parse_dag(joinpath(@__DIR__, "..", "input", "AB->AB.txt"), ABCModel())
# push two fusions with ComputeTaskABC_V
for _ in 1:2
ops = get_operations(graph)
for fusion in ops.nodeFusions
if isa(fusion.input[1].task, ComputeTaskABC_V)
push_operation!(graph, fusion)
break
end
end
end
# push fusions until the end
cont = true
while cont
cont = false
ops = get_operations(graph)
for fusion in ops.nodeFusions
if isa(fusion.input[1].task, FusedComputeTask)
push_operation!(graph, fusion)
cont = true
break
end
end
end
# try execute
@test is_valid(graph)
expected_result = ground_truth_graph_result(particles_2_2)
@test isapprox(execute(graph, process_2_2, machine, particles_2_2), expected_result; rtol = RTOL)
end
end
@testset "$(process) after random walk" for process in ["ke->ke", "ke->kke", "ke->kkke"]
process = parse_process("ke->kkke", QEDModel())
inputs = [gen_process_input(process) for _ in 1:100]

44
test/unit_tests_graph.jl
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@@ -13,7 +13,7 @@ graph = MetagraphOptimization.DAG()
@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(get_operations(graph)) == (nodeReductions = 0, nodeSplits = 0)
# s to output (exit node)
d_exit = insert_node!(graph, make_node(DataTask(10)), track = false)
@@ -133,13 +133,12 @@ insert_edge!(graph, s0, d_exit, track = false)
@test length(siblings(s0)) == 1
operations = get_operations(graph)
@test length(operations) == (nodeFusions = 10, nodeReductions = 0, nodeSplits = 0)
@test length(operations) == (nodeReductions = 0, nodeSplits = 0)
@test length(graph.dirtyNodes) == 0
@test sum(length(operations)) == 10
@test operations == get_operations(graph)
nf = first(operations.nodeFusions)
properties = get_properties(graph)
@test properties.computeEffort == 28
@@ -148,46 +147,11 @@ properties = get_properties(graph)
@test properties.noNodes == 26
@test properties.noEdges == 25
push_operation!(graph, nf)
# **does not immediately apply the operation**
@test length(graph.nodes) == 26
@test length(graph.appliedOperations) == 0
@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)
# this applies pending operations
properties = get_properties(graph)
@test length(graph.nodes) == 24
@test length(graph.appliedOperations) == 1
@test length(graph.operationsToApply) == 0
@test length(graph.dirtyNodes) != 0
@test properties.noNodes == 24
@test properties.noEdges == 23
@test properties.computeEffort == 28
@test properties.data < 62
@test properties.computeIntensity > 28 / 62
operations = get_operations(graph)
@test length(graph.dirtyNodes) == 0
@test length(operations) == (nodeFusions = 9, nodeReductions = 0, nodeSplits = 0)
@test !isempty(operations)
possibleNF = 9
while !isempty(operations.nodeFusions)
push_operation!(graph, first(operations.nodeFusions))
global operations = get_operations(graph)
global possibleNF = possibleNF - 1
@test length(operations) == (nodeFusions = possibleNF, nodeReductions = 0, nodeSplits = 0)
end
@test length(operations) == (nodeReductions = 0, nodeSplits = 0)
@test isempty(operations)
@test length(operations) == (nodeFusions = 0, nodeReductions = 0, nodeSplits = 0)
@test length(graph.dirtyNodes) == 0
@test length(graph.nodes) == 6
@test length(graph.appliedOperations) == 10
@@ -208,6 +172,6 @@ properties = get_properties(graph)
@test properties.computeIntensity ≈ 28 / 62
operations = get_operations(graph)
@test length(operations) == (nodeFusions = 10, nodeReductions = 0, nodeSplits = 0)
@test length(operations) == (nodeReductions = 0, nodeSplits = 0)
@test is_valid(graph)

3
test/unit_tests_optimization.jl
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@@ -6,8 +6,7 @@ RNG = Random.MersenneTwister(0)
graph = parse_dag(joinpath(@__DIR__, "..", "input", "AB->ABBB.txt"), ABCModel())
# create the optimizers
FIXPOINT_OPTIMIZERS =
[GreedyOptimizer(GlobalMetricEstimator()), ReductionOptimizer(), SplitOptimizer(), FusionOptimizer()]
FIXPOINT_OPTIMIZERS = [GreedyOptimizer(GlobalMetricEstimator()), ReductionOptimizer(), SplitOptimizer()]
NO_FIXPOINT_OPTIMIZERS = [RandomWalkOptimizer(RNG)]
@testset "Optimizer $optimizer" for optimizer in vcat(NO_FIXPOINT_OPTIMIZERS, FIXPOINT_OPTIMIZERS)