def test_stopped(self):
class Fast(Runnable):
def next(self, state):
time.sleep(0.1)
return state.updated(x=state.x + 1)
class Slow(Runnable):
def init(self, state):
self.time_to_stop = threading.Event()
def next(self, state):
self.time_to_stop.wait()
return state.updated(x=state.x + 2)
def stop(self):
self.time_to_stop.set()
# standard case
rb = RacingBranches(Slow(), Fast(), Slow())
res = rb.run(State(x=0)).result()
self.assertEqual([s.x for s in res], [0, 2, 1, 2])
# branches' outputs are of a different type that the inputs
# (i.e. non-endomorphic racing branches)
rb = RacingBranches(Slow(), Fast(), Slow(), endomorphic=False)
res = rb.run(State(x=0)).result()
self.assertEqual([s.x for s in res], [2, 1, 2])
def test_stopped(self):
class Fast(Runnable):
def next(self, state, **runopts):
time.sleep(0.1)
return state.updated(x=state.x + 1)
class Slow(Runnable):
def init(self, state, **runopts):
self.time_to_stop = threading.Event()
def next(self, state, **runopts):
self.time_to_stop.wait()
return state.updated(x=state.x + 2)
def halt(self):
self.time_to_stop.set()
# default case
rb = RacingBranches(Slow(), Fast(), Slow())
res = rb.run(State(x=0)).result()
self.assertEqual([s.x for s in res], [2, 1, 2])
# "endomorphic case"
rb = RacingBranches(BlockingIdentity(), Slow(), Fast(), Slow())
res = rb.run(State(x=0)).result()
self.assertEqual([s.x for s in res], [0, 2, 1, 2])
def test_look_and_feel(self):
br = Runnable(), Runnable()
rb = RacingBranches(*br)
self.assertEqual(rb.name, 'RacingBranches')
self.assertEqual(str(rb), '(Runnable) !! (Runnable)')
self.assertEqual(repr(rb), 'RacingBranches(Runnable(), Runnable())')
self.assertEqual(tuple(rb), br)
def test_iter_walk(self):
flow = Loop(RacingBranches(Runnable(), Runnable()) | ArgMin())
names = [r.name for r in iter_inorder(flow)]
self.assertEqual(names, [
'Loop', 'Branch', 'RacingBranches', 'Runnable', 'Runnable',
'ArgMin'
])
def test_callback_walk(self):
flow = Loop(RacingBranches(Runnable(), Runnable()) | ArgMin())
names = []
walk_inorder(flow, visit=lambda r, _: names.append(r.name))
self.assertEqual(names, [
'Loop', 'Branch', 'RacingBranches', 'Runnable', 'Runnable',
'ArgMin'
])
def test_racing_branches(self):
class A(Runnable, traits.ProblemDecomposer):
def next(self, state):
return state.updated(subproblem=state.problem)
class B(Runnable, traits.SubproblemSampler):
def next(self, state):
return state.updated(subsamples=state.subproblem)
a, b = A(), B()
race = RacingBranches(a, b)
self.assertSetEqual(race.inputs, a.inputs | b.inputs)
self.assertSetEqual(race.outputs, a.outputs & b.outputs)
import dimod
from hybrid.samplers import (QPUSubproblemAutoEmbeddingSampler,
InterruptableTabuSampler)
from hybrid.decomposers import EnergyImpactDecomposer
from hybrid.composers import SplatComposer
from hybrid.core import State
from hybrid.flow import RacingBranches, ArgMin, Loop
from hybrid.utils import min_sample
# load a problem
problem = sys.argv[1]
with open(problem) as fp:
bqm = dimod.BinaryQuadraticModel.from_coo(fp)
# define the solver
iteration = RacingBranches(
InterruptableTabuSampler(),
EnergyImpactDecomposer(max_size=50, rolling=True, rolling_history=0.15)
| QPUSubproblemAutoEmbeddingSampler()
| SplatComposer()) | ArgMin()
main = Loop(iteration, max_iter=10, convergence=3)
# run solver
init_state = State.from_sample(min_sample(bqm), bqm)
solution = main.run(init_state).result()
# show results
print("Solution: sample={s.samples.first}".format(s=solution))
import dimod
from hybrid.samplers import (QPUSubproblemAutoEmbeddingSampler,
TabuProblemSampler, InterruptableTabuSampler)
from hybrid.decomposers import EnergyImpactDecomposer
from hybrid.composers import SplatComposer
from hybrid.core import State, SampleSet
from hybrid.flow import RacingBranches, ArgMin, Loop
from hybrid.utils import min_sample, max_sample, random_sample
problem = sys.argv[1]
with open(problem) as fp:
bqm = dimod.BinaryQuadraticModel.from_coo(fp)
# Run Tabu in parallel with QPU, but post-process QPU samples with very short Tabu
iteration = RacingBranches(
InterruptableTabuSampler(),
EnergyImpactDecomposer(size=50)
| QPUSubproblemAutoEmbeddingSampler(num_reads=100)
| SplatComposer()
| TabuProblemSampler(timeout=1)) | ArgMin()
main = Loop(iteration, max_iter=10, convergence=3)
init_state = State.from_sample(min_sample(bqm), bqm)
solution = main.run(init_state).result()
print("Solution: energy={s.samples.first.energy}".format(s=solution))
from hybrid.samplers import (SimulatedAnnealingSubproblemSampler,
TabuSubproblemSampler, InterruptableTabuSampler)
from hybrid.decomposers import EnergyImpactDecomposer, IdentityDecomposer
from hybrid.composers import SplatComposer
from hybrid.core import State
from hybrid.flow import RacingBranches, ArgMin, Loop
from hybrid.utils import min_sample
problem = sys.argv[1]
with open(problem) as fp:
bqm = dimod.BinaryQuadraticModel.from_coo(fp)
iteration = RacingBranches(
IdentityDecomposer() | SimulatedAnnealingSubproblemSampler()
| SplatComposer(),
EnergyImpactDecomposer(max_size=50)
| RacingBranches(SimulatedAnnealingSubproblemSampler(sweeps=1000),
TabuSubproblemSampler(tenure=20, timeout=10),
endomorphic=False)
| ArgMin(operator.attrgetter('subsamples.first.energy'))
| SplatComposer()) | ArgMin()
main = Loop(iteration, max_iter=10, convergence=3)
init_state = State.from_sample(min_sample(bqm), bqm)
solution = main.run(init_state).result()
print("Solution: energy={s.samples.first.energy}".format(s=solution))
from hybrid.samplers import (QPUSubproblemAutoEmbeddingSampler,
InterruptableTabuSampler)
from hybrid.decomposers import EnergyImpactDecomposer
from hybrid.composers import SplatComposer
from hybrid.core import State
from hybrid.flow import RacingBranches, ArgMin, Loop
from hybrid.utils import min_sample
# Construct a problem
bqm = dimod.BinaryQuadraticModel({}, {
'ab': 1,
'bc': -1,
'ca': 1
}, 0, dimod.SPIN)
# Define the solver
iteration = RacingBranches(
InterruptableTabuSampler(),
EnergyImpactDecomposer(size=2)
| QPUSubproblemAutoEmbeddingSampler()
| SplatComposer()) | ArgMin()
main = Loop(iteration, max_iter=10, convergence=3)
# Solve the problem
init_state = State.from_sample(min_sample(bqm), bqm)
solution = main.run(init_state).result()
# Print results
print("Solution: sample={s.samples.first}".format(s=solution))
# Solution: sample=Sample(sample={'a': 1, 'b': -1, 'c': -1}, energy=-3.0, num_occurrences=1)
def test_racingbranches(self):
rb = RacingBranches(self.RunnableA(), self.RunnableB())
self.assertEqual(self.children(rb), ['RunnableA', 'RunnableB'])
def sample(self,
bqm,
init_sample=None,
max_iter=100,
convergence=10,
num_reads=1,
sa_reads=1,
sa_sweeps=1000,
qpu_reads=100,
qpu_sampler=None,
max_subproblem_size=50):
"""Run Tabu search, Simulated annealing and QPU subproblem sampling (for
high energy impact problem variables) in parallel and return the best
samples.
Args:
bqm (:obj:`~dimod.BinaryQuadraticModel`):
Binary quadratic model to be sampled from.
init_sample (:class:`~dimod.SampleSet`, callable, ``None``):
Initial sample set (or sample generator) used for each "read".
Use a random sample for each read by default.
max_iter (int):
Number of iterations in the hybrid algorithm.
convergence (int):
Number of iterations with no improvement that terminates sampling.
num_reads (int):
Number of reads. Each sample is the result of a single run of the
hybrid algorithm.
sa_reads (int):
Number of reads in the simulated annealing branch.
sa_sweeps (int):
Number of sweeps in the simulated annealing branch.
qpu_reads (int):
Number of reads in the QPU branch.
qpu_sampler (:class:`dimod.Sampler`, optional, default=DWaveSampler()):
Quantum sampler such as a D-Wave system.
max_subproblem_size (int):
Maximum size of the subproblem selected in the QPU branch.
Returns:
:obj:`~dimod.SampleSet`: A `dimod` :obj:`.~dimod.SampleSet` object.
"""
if callable(init_sample):
init_state_gen = lambda: State.from_sample(init_sample(), bqm)
elif init_sample is None:
init_state_gen = lambda: State.from_sample(random_sample(bqm), bqm)
elif isinstance(init_sample, dimod.SampleSet):
init_state_gen = lambda: State.from_sample(init_sample, bqm)
else:
raise TypeError(
"'init_sample' should be a SampleSet or a SampleSet generator")
subproblem_size = min(len(bqm), max_subproblem_size)
iteration = RacingBranches(
InterruptableTabuSampler(),
InterruptableSimulatedAnnealingProblemSampler(num_reads=sa_reads,
sweeps=sa_sweeps),
EnergyImpactDecomposer(size=subproblem_size,
rolling=True,
rolling_history=0.3,
traversal='bfs')
| QPUSubproblemAutoEmbeddingSampler(num_reads=qpu_reads,
qpu_sampler=qpu_sampler)
| SplatComposer(),
) | ArgMin()
self.runnable = Loop(iteration,
max_iter=max_iter,
convergence=convergence)
samples = []
energies = []
for _ in range(num_reads):
init_state = init_state_gen()
final_state = self.runnable.run(init_state)
# the best sample from each run is one "read"
ss = final_state.result().samples
ss.change_vartype(bqm.vartype, inplace=True)
samples.append(ss.first.sample)
energies.append(ss.first.energy)
return dimod.SampleSet.from_samples(samples,
vartype=bqm.vartype,
energy=energies)
problems = chain(
sorted(glob('problems/qbsolv/bqp100_*'))[:5],
sorted(glob('problems/qbsolv/bqp2500_*'))[:5],
sorted(glob('problems/random-chimera/2048*'))[:5],
sorted(glob('problems/random-chimera/8192*'))[:5],
sorted(glob('problems/ac3/*'))[:5],
)
solver_factories = [
("10 second Tabu", lambda **kw: TabuProblemSampler(timeout=10000)),
("10k sweeps Simulated Annealing", lambda **kw: IdentityDecomposer() |
SimulatedAnnealingSubproblemSampler(sweeps=10000) | SplatComposer()),
("qbsolv-like solver", lambda qpu, **kw: Loop(RacingBranches(
InterruptableTabuSampler(quantum_timeout=200),
EnergyImpactDecomposer(max_size=50, rolling=True, rolling_history=0.15)
| QPUSubproblemAutoEmbeddingSampler(qpu_sampler=qpu)
| SplatComposer()) | ArgMin(),
max_iter=100,
convergence=10)),
("tiling chimera solver", lambda qpu, **kw: Loop(RacingBranches(
InterruptableTabuSampler(quantum_timeout=200),
TilingChimeraDecomposer(size=(16, 16, 4))
| QPUSubproblemExternalEmbeddingSampler(qpu_sampler=qpu)
| SplatComposer(),
) | ArgMin(),
max_iter=100,
convergence=10)),
("qbsolv-classic", lambda **kw: QBSolvProblemSampler()),
("qbsolv-qpu", lambda qpu, **kw: QBSolvProblemSampler(qpu_sampler=qpu)),
]
