ANL-CEEESA/MIPLearn.jl
1
0
Fork 0
mirror of https://github.com/ANL-CEEESA/MIPLearn.jl.git synced 2025-12-06 08:28:52 -06:00
Code Issues Packages Projects Releases Wiki Activity

Accelerate KnnDualGmiComponent_before_mip; enable precompilation

Browse Source
This commit is contained in:
Alinson S. Xavier
2024-08-23 10:08:07 -05:00
parent 46ed6859f2
commit 006ace00e7
5 changed files with 159 additions and 59 deletions
Download Patch File Download Diff File Expand all files Collapse all files

149
src/Cuts/tableau/gmi_dual.jl
View File

@@ -442,34 +442,69 @@ function _dualgmi_features(h5_filename, extractor)
end
function _dualgmi_generate(train_h5, model)
data = ProblemData(model)
data_s, transforms = convert_to_standard_form(data)
all_cuts = nothing
visited = Set()
for h5_filename in train_h5
h5 = H5File(h5_filename)
cut_basis_vars = h5.get_array("cuts_basis_vars")
cut_basis_sizes = h5.get_array("cuts_basis_sizes")
cut_rows = h5.get_array("cuts_rows")
h5.close()
current_basis = nothing
for (r, row) in enumerate(cut_rows)
if r == 1 || cut_basis_vars[r, :] != cut_basis_vars[r-1, :]
vbb, vnn, cbb, cnn = cut_basis_sizes[r, :]
current_basis = Basis(;
var_basic = cut_basis_vars[r, 1:vbb],
var_nonbasic = cut_basis_vars[r, vbb+1:vbb+vnn],
constr_basic = cut_basis_vars[r, vbb+vnn+1:vbb+vnn+cbb],
constr_nonbasic = cut_basis_vars[r, vbb+vnn+cbb+1:vbb+vnn+cbb+cnn],
)
@timeit "Read problem data" begin
data = ProblemData(model)
end
@timeit "Convert to standard form" begin
data_s, transforms = convert_to_standard_form(data)
end
@timeit "Collect cuts from H5 files" begin
cut_basis_vars = nothing
cut_basis_sizes = nothing
cut_rows = nothing
for h5_filename in train_h5
h5 = H5File(h5_filename)
cut_basis_vars_sample = h5.get_array("cuts_basis_vars")
cut_basis_sizes_sample = h5.get_array("cuts_basis_sizes")
cut_rows_sample = h5.get_array("cuts_rows")
if cut_basis_vars === nothing
cut_basis_vars = cut_basis_vars_sample
cut_basis_sizes = cut_basis_sizes_sample
cut_rows = cut_rows_sample
else
cut_basis_vars = [cut_basis_vars; cut_basis_vars_sample]
cut_basis_sizes = [cut_basis_sizes; cut_basis_sizes_sample]
cut_rows = [cut_rows; cut_rows_sample]
end
h5.close()
end
ncuts, nvars = size(cut_basis_vars)
end
# Detect and skip duplicated cuts
cut_id = (row, cut_basis_vars[r, :])
cut_id ∉ visited || continue
push!(visited, cut_id)
@timeit "Group cuts by tableau basis" begin
vars_to_unique_basis_offset = Dict()
unique_basis_vars = Matrix{Int}(undef, 0, nvars)
unique_basis_sizes = Matrix{Int}(undef, 0, 4)
unique_basis_rows = Dict{Int,Set{Int}}()
for i in 1:ncuts
vars = cut_basis_vars[i, :]
sizes = cut_basis_sizes[i, :]
row = cut_rows[i]
if vars ∉ keys(vars_to_unique_basis_offset)
offset = size(unique_basis_vars)[1] + 1
vars_to_unique_basis_offset[vars] = offset
unique_basis_vars = [unique_basis_vars; vars']
unique_basis_sizes = [unique_basis_sizes; sizes']
unique_basis_rows[offset] = Set()
end
offset = vars_to_unique_basis_offset[vars]
push!(unique_basis_rows[offset], row)
end
end
tableau = compute_tableau(data_s, current_basis, rows = [row])
@timeit "Compute tableaus and cuts" begin
all_cuts = nothing
for (offset, rows) in unique_basis_rows
vbb, vnn, cbb, cnn = unique_basis_sizes[offset, :]
current_basis = Basis(;
var_basic = unique_basis_vars[offset, 1:vbb],
var_nonbasic = unique_basis_vars[offset, vbb+1:vbb+vnn],
constr_basic = unique_basis_vars[offset, vbb+vnn+1:vbb+vnn+cbb],
constr_nonbasic = unique_basis_vars[offset, vbb+vnn+cbb+1:vbb+vnn+cbb+cnn],
)
tableau = compute_tableau(data_s, current_basis; rows=collect(rows))
cuts_s = compute_gmi(data_s, tableau)
cuts = backwards(transforms, cuts_s)
@@ -509,38 +544,51 @@ end
function KnnDualGmiComponent_before_mip(data::_KnnDualGmiData, test_h5, model, _)
x = _dualgmi_features(test_h5, data.extractor)
x = reshape(x, 1, length(x))
neigh_dist, neigh_ind = data.model.kneighbors(x, return_distance = true)
neigh_ind = neigh_ind .+ 1
N = length(neigh_dist)
reset_timer!()
if data.strategy == "near"
selected = collect(1:(data.k))
elseif data.strategy == "far"
selected = collect((N - data.k + 1) : N)
elseif data.strategy == "rand"
selected = shuffle(collect(1:N))[1:(data.k)]
else
error("unknown strategy: $(data.strategy)")
@timeit "Extract features" begin
x = _dualgmi_features(test_h5, data.extractor)
x = reshape(x, 1, length(x))
end
@info "Dual GMI: Selected neighbors ($(data.strategy)):"
neigh_dist = neigh_dist[selected]
neigh_ind = neigh_ind[selected]
for i in 1:data.k
h5_filename = data.train_h5[neigh_ind[i]]
dist = neigh_dist[i]
@info " $(h5_filename) dist=$(dist)"
@timeit "Find neighbors" begin
neigh_dist, neigh_ind = data.model.kneighbors(x, return_distance = true)
neigh_ind = neigh_ind .+ 1
N = length(neigh_dist)
if data.strategy == "near"
selected = collect(1:(data.k))
elseif data.strategy == "far"
selected = collect((N - data.k + 1) : N)
elseif data.strategy == "rand"
selected = shuffle(collect(1:N))[1:(data.k)]
else
error("unknown strategy: $(data.strategy)")
end
@info "Dual GMI: Selected neighbors ($(data.strategy)):"
neigh_dist = neigh_dist[selected]
neigh_ind = neigh_ind[selected]
for i in 1:data.k
h5_filename = data.train_h5[neigh_ind[i]]
dist = neigh_dist[i]
@info " $(h5_filename) dist=$(dist)"
end
end
@info "Dual GMI: Generating cuts..."
time_generate = @elapsed begin
cuts = _dualgmi_generate(data.train_h5[neigh_ind], model)
@timeit "Generate cuts" begin
time_generate = @elapsed begin
cuts = _dualgmi_generate(data.train_h5[neigh_ind], model)
end
@info "Dual GMI: Generated $(length(cuts.lb)) unique cuts in $(time_generate) seconds"
end
@info "Dual GMI: Generated $(length(cuts.lb)) unique cuts in $(time_generate) seconds"
_dualgmi_set_callback(model, cuts)
@timeit "Set callback" begin
_dualgmi_set_callback(model, cuts)
end
print_timer()
stats = Dict()
stats["KnnDualGmi: k"] = data.k
@@ -567,10 +615,11 @@ function __init_gmi_dual__()
KnnDualGmiComponent_fit(self.data, train_h5)
end
function before_mip(self, test_h5, model, stats)
return KnnDualGmiComponent_before_mip(self.data, test_h5, model.inner, stats)
return @time KnnDualGmiComponent_before_mip(self.data, test_h5, model.inner, stats)
end
end
copy!(KnnDualGmiComponent, Class2)
end
export collect_gmi_dual, expert_gmi_dual, ExpertDualGmiComponent, KnnDualGmiComponent

49
src/MIPLearn.jl
View File

@@ -6,6 +6,8 @@ module MIPLearn
using PyCall
using SparseArrays
using PrecompileTools: @setup_workload, @compile_workload
include("collectors.jl")
include("components.jl")
@@ -32,4 +34,51 @@ end
include("BB/BB.jl")
include("Cuts/Cuts.jl")
# Precompilation
# =============================================================================
function __precompile_cuts__()
function build_model(mps_filename)
model = read_from_file(mps_filename)
set_optimizer(model, SCIP.Optimizer)
return JumpModel(model)
end
BASEDIR = dirname(@__FILE__)
mps_filename = "$BASEDIR/../test/fixtures/bell5.mps.gz"
h5_filename = "$BASEDIR/../test/fixtures/bell5.h5"
collect_gmi_dual(
mps_filename;
optimizer=HiGHS.Optimizer,
max_rounds = 10,
max_cuts_per_round = 500,
)
knn = KnnDualGmiComponent(
extractor = H5FieldsExtractor(instance_fields = ["static_var_obj_coeffs"]),
k = 2,
)
knn.fit([h5_filename, h5_filename])
solver = LearningSolver(
components = [
ExpertPrimalComponent(action = SetWarmStart()),
knn,
],
skip_lp = true,
)
solver.optimize(mps_filename, build_model)
end
@setup_workload begin
using SCIP
using HiGHS
using MIPLearn.Cuts
using PrecompileTools: @setup_workload, @compile_workload
__init__()
Cuts.__init__()
@compile_workload begin
__precompile_cuts__()
end
end
end # module