def create_param(self, name, params, paramt, match, match_id, value0, value1):
"""Append integer-parameter to either params or paramt list"""
if env_isfixed("OPENCL_LIBSMM_SMM_{}".format(name)):
value_fix = getattr(self.args, name.lower())
params.append(IntegerParameter(name, value_fix, value_fix))
else:
value = (
int(match.group(match_id)) if match and match.group(match_id) else None
)
setattr(self, name.lower(), value)
paramt.append(IntegerParameter(name, value0, value1))
def manipulator(self):
m = ConfigurationManipulator()
for i in range(0, 1000):
# bias 3:1 in favor of moving right
m.add_parameter(
EnumParameter('move{}'.format(i), ["R", "L", "RB", "LB", "N", "LR", "LRB", "R2", "RB2", "R3", "RB3"]))
m.add_parameter(IntegerParameter('move_duration{}'.format(i), 1, 60))
# m.add_parameter(BooleanParameter("D"+str(i)))
for i in range(0, 1000):
m.add_parameter(IntegerParameter('jump_frame{}'.format(i), 0, 24000))
m.add_parameter(IntegerParameter('jump_duration{}'.format(i), 1, 32))
return m
def get_tuning_driver(self):
from ctree.opentuner.driver import OpenTunerDriver
from opentuner.search.manipulator import ConfigurationManipulator
from opentuner.search.manipulator import IntegerParameter
from opentuner.search.manipulator import PowerOfTwoParameter
from opentuner.search.objective import MinimizeTime, MinimizeEnergy
manip = ConfigurationManipulator()
manip.add_parameter(PowerOfTwoParameter("rx", 1, 8))
manip.add_parameter(PowerOfTwoParameter("ry", 1, 8))
manip.add_parameter(IntegerParameter("cx", 8, 32))
manip.add_parameter(IntegerParameter("cy", 8, 32))
return OpenTunerDriver(manipulator=manip, objective=MinimizeEnergy())
def build_manipulator(api_config):
"""Build a ConfigurationManipulator object to be used by opentuner.
Parameters
----------
api_config : dict-like of dict-like
Configuration of the optimization variables. See API description.
Returns
-------
manipulator : ConfigurationManipulator
Some over complexified class required by opentuner to run.
"""
manipulator = ConfigurationManipulator()
for pname in api_config:
ptype = api_config[pname]["type"]
pspace = api_config[pname].get("space", None)
pmin, pmax = api_config[pname].get("range", (None, None))
if ptype == "real":
if pspace in ("linear", "logit"):
ot_param = FloatParameter(pname, pmin, pmax)
elif pspace in ("log", "bilog"):
LogFloatParameter_ = ClippedParam(LogFloatParameter)
ot_param = LogFloatParameter_(pname, pmin, pmax)
else:
assert False, "unsupported param space = %s" % pspace
elif ptype == "int":
if pspace in ("linear", "logit"):
ot_param = IntegerParameter(pname, pmin, pmax)
elif pspace in ("log", "bilog"):
ot_param = LogIntegerParameter(pname, pmin, pmax)
else:
assert False, "unsupported param space = %s" % pspace
elif ptype == "bool":
# The actual bool parameter seems not to work in Py3 :(
ot_param = IntegerParameter(pname, 0, 1)
elif ptype in ("cat", "ordinal"):
# Treat ordinal and categorical variables the same for now.
assert "values" in api_config[pname]
pvalues = api_config[pname]["values"]
ot_param = EnumParameter(pname, pvalues)
else:
assert False, "type=%s/space=%s not handled in opentuner yet" % (
ptype, pspace)
manipulator.add_parameter(ot_param)
return manipulator
def main():
parser = argparse.ArgumentParser(parents=opentuner.argparsers())
args = parser.parse_args()
manipulator = ConfigurationManipulator()
manipulator.add_parameter(IntegerParameter('x', -200, 200))
interface = DefaultMeasurementInterface(args=args,
manipulator=manipulator,
project_name='examples',
program_name='api_test',
program_version='0.1')
api = TuningRunManager(interface, args)
for x in xrange(500):
desired_result = api.get_next_desired_result()
if desired_result is None:
# The search space for this example is very small, so sometimes
# the techniques have trouble finding a config that hasn't already
# been tested. Change this to a continue to make it try again.
break
cfg = desired_result.configuration.data
result = Result(time=test_func(cfg))
api.report_result(desired_result, result)
best_cfg = api.get_best_configuration()
api.finish()
print 'best x found was', best_cfg['x']
def solve(self, job):
logging.debug('Starting opentuner')
failed_jobs_threshold = self.jobs_limit * _FAILED_JOBS_COEF
manipulator = ConfigurationManipulator()
for var in job.optimization_job.task_variables:
if var.HasField('continuous_variable'):
cont_var = var.continuous_variable
param = FloatParameter(var.name, cont_var.l, cont_var.r)
else:
int_var = var.integer_variable
param = IntegerParameter(var.name, int_var.l, int_var.r)
manipulator.add_parameter(param)
parser = argparse.ArgumentParser(parents=opentuner.argparsers())
args = parser.parse_args([])
args.parallelism = 4
args.no_dups = True
interface = DefaultMeasurementInterface(args=args,
manipulator=manipulator,
project_name=job.job_id)
api = TuningRunManager(interface, args)
jobs = []
current_value = None
failed_jobs = 0
while failed_jobs < failed_jobs_threshold and not self._check_for_termination(
job):
remaining_jobs = []
for job_id, desired_result in jobs:
res = self._get_evaluation_job_result(job_id)
if res is not None:
if current_value is None or current_value > res + _THRESHOLD_EPS:
failed_jobs = 0
else:
failed_jobs += 1
result = Result(time=res)
api.report_result(desired_result, result)
else:
remaining_jobs.append((job_id, desired_result))
jobs = remaining_jobs
while len(jobs) < self.jobs_limit:
desired_result = api.get_next_desired_result()
if desired_result is None:
break
job_id = self._start_evaluation_job(
job, desired_result.configuration.data)
if job_id is None:
api.report_result(desired_result, Result(time=math.inf))
else:
jobs.append((job_id, desired_result))
if not jobs:
break
r = api.get_best_result()
if r is not None:
current_value = r.time
logging.debug('Opentuner current state: %s %s', r.time,
api.get_best_configuration())
time.sleep(5)
res = api.get_best_result().time
vars = api.get_best_configuration()
api.finish()
return res, vars
def test_bruteforce_driver_2d(self):
from ctree.tune import (
BruteForceTuningDriver,
IntegerParameter,
MinimizeTime,
)
params = [
IntegerParameter("foo", 0, 10),
IntegerParameter("bar", 0, 10),
]
driver = BruteForceTuningDriver(params, MinimizeTime())
unsearched = set(range(0, 100))
for config in islice(driver.configs, 100):
entry = config["foo"] * 10 + config["bar"]
unsearched.remove(entry)
driver.report(time=0.4)
self.assertSetEqual(unsearched, set())
def manipulator(self):
'''
Define the search space by creating a ConfigurationManipulator
'''
manipulator = ConfigurationManipulator()
if self.args.window is None:
manipulator.add_parameter(IntegerParameter('window', 1000, 5000))
if self.args.interval is None:
manipulator.add_parameter(IntegerParameter('interval', 2000, 10000))
if self.args.sketch_size is None:
manipulator.add_parameter(IntegerParameter('sketch-size', 1000, 5000))
if self.args.k_hops is None:
manipulator.add_parameter(IntegerParameter('k-hops', 1, 5))
if self.args.chunk_size is None:
manipulator.add_parameter(IntegerParameter('chunk-size', 5, 20))
if self.args.lambda_param is None:
manipulator.add_parameter(FloatParameter('lambda-param', 0.0, 1.0))
# manipulator.add_parameter(EnumParameter('threshold-metric', ['mean', 'max']))
# manipulator.add_parameter(FloatParameter('num-stds', 3.5, 6.0))
return manipulator
def test_bruteforce_driver_2d_parabola(self):
from ctree.tune import (
BruteForceTuningDriver,
IntegerParameter,
MinimizeTime,
)
params = [
IntegerParameter("x", 0, 10),
IntegerParameter("y", 0, 10),
]
driver = BruteForceTuningDriver(params, MinimizeTime())
for config in islice(driver.configs, 100):
# report height on inverted paraboloid with global min at (3,4)
x, y = config["x"], config["y"]
z = (x-3)**2 + (y-4)**2 + 1
driver.report(time=z)
for config in islice(driver.configs, 10):
self.assertEqual((config["x"], config["y"]), (3, 4))
def manipulator(self):
"""
Defines the search space across configuration parameters
"""
manipulator = ConfigurationManipulator()
for flag, param in self.ranged_param_dict.items():
log.info("Adding flag: " + str(flag) + ", " + str(type(param)))
if isinstance(param, SparkIntType):
tuner_param = IntegerParameter(
flag,
param.get_range_start(),
param.get_range_end())
elif isinstance(param, SparkMemoryType):
tuner_param = ScaledIntegerParameter(
flag,
param.get_range_start(),
param.get_range_end(),
param.get_scale())
elif isinstance(param, SparkBooleanType):
tuner_param = BooleanParameter(flag)
else:
raise SparkTunerConfigError(
ValueError, "Invalid type for ConfigurationManipulator")
log.info("Added config: " + str(type(tuner_param)) +
": legal range " + str(tuner_param.legal_range(None)) +
", search space size " +
str(tuner_param.search_space_size()))
manipulator.add_parameter(tuner_param)
return manipulator
def create_test_tuning_run(db):
parser = argparse.ArgumentParser(parents=opentuner.argparsers())
args = parser.parse_args()
args.database = db
manipulator = ConfigurationManipulator()
manipulator.add_parameter(IntegerParameter('x', -200, 200))
interface = DefaultMeasurementInterface(args=args,
manipulator=manipulator,
project_name='examples',
program_name='api_test',
program_version='0.1')
api = TuningRunManager(interface, args)
return api
def manipulator(self):
manipulator = ConfigurationManipulator()
manipulator.add_parameter(IntegerParameter('pixeldiff_similarity_cutoff', 20, 50))
manipulator.add_parameter(IntegerParameter('min_max_colorful_cutoff', 5, 20))
manipulator.add_parameter(IntegerParameter('filter_grayscale_cutoff', 170, 215))
manipulator.add_parameter(IntegerParameter('black_and_white_grayscale_cutoff', 170, 215))
manipulator.add_parameter(IntegerParameter('count_around_delete_cutoff', 2, 4))
manipulator.add_parameter(IntegerParameter('count_around_add_cutoff', 2, 5))
manipulator.add_parameter(IntegerParameter('conway_many_count', 0, 5))
manipulator.add_parameter(BooleanParameter('overlay'))
return manipulator
def build_search_space(self):
"""
Set up search space via ConfigurationManipulator, which is responsible for choosing configurations across runs,
where a configuration is a particular assignment of the parameters.
Tightly coupled with agent/system representations used in src.
Repeating from above:
*initial* search space
- parameter per action (candidate index column in index, where candidate index column is one of the query columns)
per query
- see commit 8f94c4b
*updated* search space
- parameter per action (same, but here candidate index column is not one of query columns but any of columns in query table)
per allowed index / indexing decision
so, at risk of redundancy, an "action" refers to the particular assignment of a parameter, which is
an integer indicating noop or a column for a candidate index column
"""
self.logger.info('Building OpenTuner search space...')
self.idx_2_idx_cols = {
} # store candidate index columns (OpenTuner operates on this) with candidate index (system operates on this)
self.idx_2_tbl = {}
manipulator = ConfigurationManipulator()
for tbl in self.tbls:
n_cols_per_tbl = len(tpch_table_columns[tbl])
for candidate_idx in range(self.n_idxs_per_tbl):
idx_id = "tbl_{}_idx_{}".format(tbl, candidate_idx)
idx_col_ids = []
for candidate_idx_col in range(self.n_cols_per_idx):
idx_col_id = idx_id + "_idx_col_{}".format(
candidate_idx_col)
idx_col_ids.append(idx_col_id)
manipulator.add_parameter(
IntegerParameter(idx_col_id, 0, n_cols_per_tbl))
self.idx_2_idx_cols[idx_id] = idx_col_ids
self.idx_2_tbl[idx_id] = tbl
self.logger.info("... actions are: {}".format(self.idx_2_idx_cols))
return manipulator
def test_bruteforce_driver_1d(self):
from ctree.tune import (
BruteForceTuningDriver,
IntegerParameter,
MinimizeTime,
)
objective = MinimizeTime()
params = [IntegerParameter("foo", 0, 100)]
driver = BruteForceTuningDriver(params, objective)
unsearched = set(range(0, 100))
for config in islice(driver.configs, 100):
unsearched.remove(config["foo"])
driver.report(time=0.4)
self.assertSetEqual(unsearched, set())
def test_1d_config(self):
from ctree.opentuner.driver import OpenTunerDriver
from opentuner.search.objective import MinimizeTime
from opentuner.search.manipulator import (
ConfigurationManipulator,
IntegerParameter,
)
cfg = ConfigurationManipulator()
cfg.add_parameter( IntegerParameter("foo", 0, 199) )
driver = OpenTunerDriver(manipulator=cfg, objective=MinimizeTime())
unsearched = set(range(200))
for cfg in islice(driver.configs, 20):
val = cfg["foo"]
driver.report(time=val)
unsearched.remove(val)
self.assertEqual(len(unsearched), 180)
def test_bruteforce_driver_other_params(self):
from ctree.tune import (
BruteForceTuningDriver,
IntegerParameter,
BooleanParameter,
EnumParameter,
MinimizeTime,
)
params = [
IntegerParameter("foo", 0, 10),
BooleanParameter("bar"),
EnumParameter("baz", ['monty', 'python', 'rocks']),
]
driver = BruteForceTuningDriver(params, MinimizeTime())
nConfigs = 10*2*3
configs = list(islice(driver.configs, nConfigs))
self.assertEqual(len(configs), nConfigs)
def manipulator(self):
"""create the configuration manipulator, from example config"""
upper_limit = self.program_settings['n'] + 1
cfg = open(self.args.program_cfg_default).read()
manipulator = ConfigurationManipulator()
self.choice_sites = dict()
for m in re.finditer(
r" *([a-zA-Z0-9_-]+)[ =]+([0-9e.+-]+) *"
r"[#] *([a-z]+).* ([0-9]+) to ([0-9]+)", cfg):
k, v, valtype, minval, maxval = m.group(1, 2, 3, 4, 5)
minval = float(minval)
maxval = float(maxval)
if upper_limit:
maxval = min(maxval, upper_limit)
assert valtype == 'int'
# log.debug("param %s %f %f", k, minval, maxval)
m1 = re.match(r'(.*)_lvl[0-9]+_rule', k)
m2 = re.match(r'(.*)_lvl[0-9]+_cutoff', k)
if m1:
self.choice_sites[m1.group(1)] = int(maxval)
elif m2:
pass
elif k == 'worker_threads':
manipulator.add_parameter(IntegerParameter(k, 1, 16))
elif k == 'distributedcutoff':
pass
elif minval == 0 and maxval < 64:
manipulator.add_parameter(SwitchParameter(k, maxval))
else:
manipulator.add_parameter(
LogIntegerParameter(k, minval, maxval))
for name, choices in list(self.choice_sites.items()):
manipulator.add_parameter(
SelectorParameter('.' + name, list(range(choices + 1)),
old_div(upper_limit, choices)))
self.manipulator = manipulator
return manipulator
#!/usr/bin/env python
#
from opentuner import ConfigurationManipulator
from opentuner.search.manipulator import PowerOfTwoParameter, IntegerParameter
mdriver_manipulator = ConfigurationManipulator()
"""
See opentuner/search/manipulator.py for more parameter types,
like IntegerParameter, EnumParameter, etc.
TODO(project3): Implement the parameters of your allocator. Once
you have at least one other parameters, feel free to remove ALIGNMENT.
"""
# mdriver_manipulator.add_parameter(PowerOfTwoParameter('ALIGNMENT', 8, 8))
mdriver_manipulator.add_parameter(IntegerParameter('NUMBUCKETS', 2, 128))
mdriver_manipulator.add_parameter(IntegerParameter('BASESIZE', 2, 1000))
def manipulator(self):
# FIXME: should some of these be expressed as booleans or switch parameters?
# FIXME: how to express P and Q, given PxQ=nprocs, with nprocs being fixed?
# FIXME: how to express logscaled parameter with a particular base?
manipulator = ConfigurationManipulator()
manipulator.add_parameter(IntegerParameter("blocksize", 1, 64))
manipulator.add_parameter(IntegerParameter("row_or_colmajor_pmapping", 0, 1))
manipulator.add_parameter(IntegerParameter("pfact", 0, 2))
manipulator.add_parameter(IntegerParameter("nbmin", 1, 4))
manipulator.add_parameter(IntegerParameter("ndiv", 2, 2))
manipulator.add_parameter(IntegerParameter("rfact", 0, 4))
manipulator.add_parameter(IntegerParameter("bcast", 0, 5))
manipulator.add_parameter(IntegerParameter("depth", 0, 4))
manipulator.add_parameter(IntegerParameter("swap", 0, 2))
manipulator.add_parameter(IntegerParameter("swapping_threshold", 64, 128))
manipulator.add_parameter(IntegerParameter("L1_transposed", 0, 1))
manipulator.add_parameter(IntegerParameter("U_transposed", 0, 1))
manipulator.add_parameter(IntegerParameter("mem_alignment", 4, 16))
return manipulator
#!/usr/bin/env python
#
from opentuner import ConfigurationManipulator
from opentuner.search.manipulator import PowerOfTwoParameter
from opentuner.search.manipulator import IntegerParameter
mdriver_manipulator = ConfigurationManipulator()
"""
See opentuner/search/manipulator.py for more parameter types,
like IntegerParameter, EnumParameter, etc.
TODO(project3): Implement the parameters of your allocator. Once
you have at least one other parameters, feel free to remove ALIGNMENT.
"""
#mdriver_manipulator.add_parameter(IntegerParameter('SMALL_BIN_SEARCH_MAX', 0, 32))
mdriver_manipulator.add_parameter(
IntegerParameter('LARGE_BIN_SEARCH_MAX', 0, 32))
mdriver_manipulator.add_parameter(IntegerParameter('EXTENSION_SIZE', 8, 2000))
#mdriver_manipulator.add_parameter(IntegerParameter('INITIAL_CHUNK_SIZE', 0, 40000))
if p is not victim:
p.set_value(config,
p.get_value(config) + v / (len(nonzeroes) - 1))
def zeroes(self, config):
return [p for p in self.sub_parameters() if p.get_value(config) == 0]
def nonzeroes(self, config):
return [p for p in self.sub_parameters() if p.get_value(config) > 0]
def update_value_for_json(self, config):
for (i, p) in zip(xrange(self.count), self.sub_parameters()):
self.values[i] = p.get_value(config)
def json_replacement(self):
return {
"name": self.name,
"values": self.values,
"class": self.javaClass
}
if __name__ == '__main__':
ip = IntegerParameter("suman", 2, 7)
sjip = sjIntegerParameter("ss", 3, 56, 45)
sjsw = sjSwitchParameter(
'sjswtch', 'java.lang.Integer', ['AAA', 'BBB', 'CCC', 'DDD'], 2,
'edu.mit.streamjit.impl.common.Configuration$SwitchParameter')
print sjsw.getName()
print sjsw.getUniverse()
print sjsw.getValue()
