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 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 run_session(self):
params_set = dict(self.planner_config[self.planner].items())
manipulator = self._load_search_space(params_set)
interface = DefaultMeasurementInterface(args=self.args,
manipulator=manipulator)
# Using OpenTuner API
# https://github.com/jansel/opentuner/blob/master/examples/py_api/api_example.py
api = TuningRunManager(interface, self.args)
self.n_trial = 0 # Reset to n_trials to zero for each planner
start_time = timer()
params = {'planner': self.planner, 'start_time': start_time}
if (self.MAX_RUNTIME != 'None'):
params['end_time'] = timer() + self.MAX_RUNTIME
print('\n')
rospy.loginfo('Executing %s on %s for %d secs', self.MODE,
params['planner'], self.MAX_RUNTIME)
else:
print('\n')
rospy.loginfo('Executing %s on %s for %d trials', self.MODE,
params['planner'], self.MAX_TRIALS)
for _ in range(self.MAX_TRIALS):
desired_result = api.get_next_desired_result()
params_set = desired_result.configuration.data
params['params_set'] = params_set
run_result = self._opentuner_obj(params)
result = Result(time=run_result['loss'])
api.report_result(desired_result, result)
def create_benchmark_api(benchmark):
args = argparser.parse_args()
args.benchmark = benchmark
args.database = "{}_{}{}".format(
os.path.splitext(args.database)[0], benchmark,
os.path.splitext(args.database)[1])
interface = ReproBLASTuner(args)
api = TuningRunManager(interface, args)
return api
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 __init__(self, *ot_args, **ot_kwargs):
"""
Creates communication queues and spawn a thread
to run the tuning logic.
"""
super(OpenTunerDriver, self).__init__()
self._best_config = None
interface = CtreeMeasurementInterface(self, *ot_args, **ot_kwargs)
arg_parser = argparse.ArgumentParser(parents=opentuner.argparsers())
config_args = CONFIG.get("opentuner", "args").split()
tuner_args = arg_parser.parse_args(config_args)
self.manager = TuningRunManager(interface, tuner_args)
self._converged = False
def __init__(self, train_queries, test_queries, experiment_config,
result_dir, system_environment):
"""
Args:
train_queries (list of queries (SQLQuery queries))
test_queries (list of queries (SQLQuery queries))
result_dir (str)
system (PostgresSystemEnvironment): encapsulates environment
"""
self.logger = logging.getLogger(__name__)
self.logger.info('Setting up OpenTuner...')
self.train_queries = train_queries
self.test_queries = test_queries
self.experiment_config = experiment_config
self.max_size = experiment_config['max_size']
self.size_weight = experiment_config['size_weight']
self.max_runtime = experiment_config['max_runtime']
self.runtime_weight = experiment_config['runtime_weight']
self.reward_penalty = experiment_config['reward_penalty']
self.system_environment = system_environment
self.result_dir = result_dir
# 2nd search space
self.n_idxs = experiment_config['n_queries_per_episode']
self.n_cols_per_idx = 3 # TODO hardcode
self.tbls = experiment_config['tables']
self.n_idxs_per_tbl = int(self.n_idxs / len(self.tbls))
# maps tbls to mapping of tbl's column indices to tbl's columns, per search space representation
self.tbl_2_col_idx_2_col = {}
for tbl in self.tbls:
self.tbl_2_col_idx_2_col[tbl] = {}
for col_idx, col in enumerate(tpch_table_columns[tbl].keys()):
self.tbl_2_col_idx_2_col[tbl][col_idx +
1] = col # + 1 b/c 0 is noop
# api
sys.argv = [sys.argv[0]] # opentuner expects own args
parser = argparse.ArgumentParser(parents=opentuner.argparsers())
args = parser.parse_args()
manipulator = self.build_search_space()
interface = DefaultMeasurementInterface(args=args,
manipulator=manipulator)
self.api = TuningRunManager(interface, args)
def __init__(self,
api_config,
techniques=DEFAULT_TECHNIQUES,
n_suggestions=1):
"""Build wrapper class to use opentuner optimizer in benchmark.
Parameters
----------
api_config : dict-like of dict-like
Configuration of the optimization variables. See API description.
techniques : iterable of strings
A list or tuple of techniques to use in opentuner. If the list
has only one technique, then that technique will be used. If the
list has multiple techniques a bandit over those techniques
will be used.
n_suggestions : int
Default number of suggestions to be made in parallel.
"""
AbstractOptimizer.__init__(self, api_config)
# Opentuner requires DesiredResult to reference suggestion when making
# its observation. x_to_dr maps the dict suggestion to DesiredResult.
self.x_to_dr = {}
# Keep last suggested x and repeat it whenever opentuner gives up.
self.dummy_suggest = None
"""Setting up the arguments for opentuner. You can see all possible
arguments using:
```
>>> import opentuner
>>> opentuner.default_argparser().parse_args(['-h'])
```
We only change a few arguments (other arguments are set to defaults):
* database = MEMORY_ONLY_DB: to use an in-memory sqlite database
* parallelism = n_suggestions: num of suggestions to give in parallel
* technique = techniques: a list of techniques to be used by opentuner
* print_params = False: to avoid opentuner from exiting after printing
param spaces
"""
args = Namespace(
bail_threshold=500,
database=MEMORY_ONLY_DB,
display_frequency=10,
generate_bandit_technique=False,
label=None,
list_techniques=False,
machine_class=None,
no_dups=False,
parallel_compile=False,
parallelism=n_suggestions,
pipelining=0,
print_params=False,
print_search_space_size=False,
quiet=False,
results_log=None,
results_log_details=None,
seed_configuration=[],
stop_after=None,
technique=techniques,
test_limit=5000,
)
# Setup some dummy classes required by opentuner to actually run.
manipulator = OpentunerOptimizer.build_manipulator(api_config)
interface = DMI(args=args, manipulator=manipulator)
self.api = TuningRunManager(interface, args)
def tuning_loop():
report_delay = 5
last_time = time.time()
start_time = last_time
parser = argparse.ArgumentParser(parents=opentuner.argparsers())
parser.add_argument("--processes",
type=int,
help="Number of Python threads available.")
parser.add_argument(
"--no-wait",
action="store_true",
help="Do not wait for requested results to generate more requests.")
args = parser.parse_args()
pool = ThreadPool(args.processes)
manipulator = ConfigurationManipulator()
for name in legup_parameters.parameters:
parameter_type = legup_parameters.parameter_type(name)
values = legup_parameters.parameter_values(name)
if parameter_type == int:
manipulator.add_parameter(
IntegerParameter(name, values[0], values[1]))
elif parameter_type == bool:
manipulator.add_parameter(BooleanParameter(name))
elif parameter_type == Enum:
manipulator.add_parameter(EnumParameter(name, values))
else:
print("ERROR: No such parameter type \"{0}\"".format(name))
interface = DefaultMeasurementInterface(args=args,
manipulator=manipulator,
project_name='HLS-FPGAs',
program_name='legup-tuner',
program_version='0.0.1')
manager = TuningRunManager(interface, args)
current_time = time.time()
computing_results = []
computed_results = []
desired_results = manager.get_desired_results()
while current_time - start_time < args.stop_after:
if args.no_wait:
if len(desired_results) != 0 or len(computing_results) != 0:
for desired_result in desired_results:
computing_results.append([
desired_result,
pool.apply_async(get_wallclock_time,
(desired_result.configuration.data, ))
])
for result in computing_results:
if result[1].ready() and result[0] not in computed_results:
cost = result[1].get()
manager.report_result(result[0], Result(time=cost))
computed_results.append(result)
for result in computed_results:
if result in computing_results:
computing_results.remove(result)
computed_results = []
else:
if len(desired_results) != 0:
cfgs = [dr.configuration.data for dr in desired_results]
results = pool.map_async(get_wallclock_time,
cfgs).get(timeout=None)
for dr, result in zip(desired_results, results):
manager.report_result(dr, Result(time=result))
desired_results = manager.get_desired_results()
current_time = time.time()
if (current_time - last_time) >= report_delay:
log_intermediate(current_time - start_time, manager)
last_time = current_time
current_time = time.time()
log_intermediate(current_time - start_time, manager)
save_final_configuration(manager.get_best_configuration())
manager.finish()
import argparse
def get_next_desired_result():
global desired_result
desired_result = api.get_next_desired_result()
while desired_result is None:
desired_result = api.get_next_desired_result()
return desired_result.configuration.data
def report_result(runtime):
api.report_result(desired_result, Result(time=runtime))
def finish():
api.finish()
parser = argparse.ArgumentParser(parents=opentuner.argparsers())
args = parser.parse_args()
manipulator = ConfigurationManipulator()
:::parameters:::
interface = DefaultMeasurementInterface(args=args,
manipulator=manipulator,
project_name='atf_library',
program_name='atf_library',
program_version='0.1')
api = TuningRunManager(interface, args)
)"
def tuning_loop():
report_delay = 30
last_time = time.time()
start_time = last_time
iterations = 5
parser = argparse.ArgumentParser(parents=opentuner.argparsers())
parser.add_argument("--processes",
type=int,
help="Number of Python threads available.")
parser.add_argument(
"--no-wait",
action="store_true",
help="Do not wait for requested results to generate more requests.")
parser.add_argument("--application", type=str, help="Application name.")
parser.add_argument("--verilog-file",
type=str,
help="Verilog file for the application.")
args = parser.parse_args()
pool = ThreadPool(args.processes)
manipulator = ConfigurationManipulator()
global application
global verilog_file
global application_path
global container_path
global host_path
global image_name
global script_name
global tuning_init
application = args.application
verilog_file = args.verilog_file
application_path = "/root/legup_src/legup-4.0/examples/chstone/{0}".format(
application)
container_path = "/root/legup_src/legup-4.0/examples/chstone/{0}/tuner".format(
application)
host_path = "/home/bruelp/legup-tuner/post_place_and_route/py"
image_name = "legup_quartus"
script_name = "measure.sh"
print(application, container_path, application_path)
for name in legup_parameters.parameters:
parameter_type = legup_parameters.parameter_type(name)
values = legup_parameters.parameter_values(name)
if parameter_type == int:
manipulator.add_parameter(
IntegerParameter(name, values[0], values[1]))
elif parameter_type == bool:
manipulator.add_parameter(BooleanParameter(name))
elif parameter_type == Enum:
manipulator.add_parameter(EnumParameter(name, values))
else:
print("ERROR: No such parameter type \"{0}\"".format(name))
interface = DefaultMeasurementInterface(args=args,
manipulator=manipulator,
project_name='HLS-FPGAs',
program_name='legup-tuner',
program_version='0.0.1')
manager = TuningRunManager(interface, args)
current_time = time.time()
computing_results = []
computed_results = []
desired_results = manager.get_desired_results()
while current_time - start_time < args.stop_after:
if args.no_wait:
if len(desired_results) != 0 or len(computing_results) != 0:
for desired_result in desired_results:
computing_results.append([
desired_result,
pool.apply_async(get_wallclock_time,
(desired_result.configuration.data, ))
])
for result in computing_results:
if result[1].ready() and result[0] not in computed_results:
cost = result[1].get()
manager.report_result(result[0], Result(time=cost))
computed_results.append(result)
for result in computed_results:
if result in computing_results:
computing_results.remove(result)
computed_results = []
else:
if len(desired_results) != 0:
cfgs = [dr.configuration.data for dr in desired_results]
results = pool.map_async(get_wallclock_time,
cfgs).get(timeout=None)
for dr, result in zip(desired_results, results):
manager.report_result(
dr,
Result(time=result['value'],
cycles=result['cycles'],
fmax=result['fmax'],
LU=result['lu'],
pins=result['pins'],
regs=result['regs'],
block=result['block'],
ram=result['ram'],
dsp=result['dsp']))
desired_results = manager.get_desired_results()
current_time = time.time()
if (current_time - last_time) >= report_delay:
log_intermediate(current_time - start_time, manager)
last_time = current_time
current_time = time.time()
log_intermediate(current_time - start_time, manager)
save_final_configuration(manager.get_best_configuration())
manager.finish()
