def test_snap_to_nearest_config():
tune_params = OrderedDict()
tune_params['x'] = [0, 1, 2, 3, 4, 5]
tune_params['y'] = [0, 1, 2, 3, 4, 5]
tune_params['z'] = [0, 1, 2, 3, 4, 5]
x = [-5.7, 3.14, 1e6]
expected = [0, 3, 5]
answer = minimize.snap_to_nearest_config(x, tune_params)
assert answer == expected
def test_snap_to_nearest_config():
tune_params = OrderedDict()
tune_params['x'] = [0, 1, 2, 3, 4, 5]
tune_params['y'] = [0, 1, 2, 3, 4, 5]
tune_params['z'] = [0, 1, 2, 3, 4, 5]
x = [-5.7, 3.14, 1e6]
expected = [0, 3, 5]
answer = minimize.snap_to_nearest_config(x, tune_params)
assert answer == expected
def tune(runner, kernel_options, device_options, tuning_options):
""" Find the best performing kernel configuration in the parameter space
:params runner: A runner from kernel_tuner.runners
:type runner: kernel_tuner.runner
:param kernel_options: A dictionary with all options for the kernel.
:type kernel_options: kernel_tuner.interface.Options
:param device_options: A dictionary with all options for the device
on which the kernel should be tuned.
:type device_options: kernel_tuner.interface.Options
:param tuning_options: A dictionary with all options regarding the tuning
process.
:type tuning_options: kernel_tuner.interface.Options
:returns: A list of dictionaries for executed kernel configurations and their
execution times. And a dictionary that contains a information
about the hardware/software environment on which the tuning took place.
:rtype: list(dict()), dict()
"""
results = []
#build a bounds array as needed for the optimizer
bounds = minimize.get_bounds(tuning_options.tune_params)
#call the differential evolution optimizer
opt_result = differential_evolution(
_cost_func,
bounds, [kernel_options, tuning_options, runner, results],
maxiter=1,
polish=False,
disp=tuning_options.verbose)
if tuning_options.verbose:
print(opt_result.message)
print(
'best config:',
minimize.snap_to_nearest_config(opt_result.x,
tuning_options.tune_params))
return results, runner.dev.get_environment()
def _cost_func(x, kernel_options, tuning_options, runner, results):
""" Cost function used by the differential evolution optimizer """
#snap values in x to nearest actual value for each parameter
params = minimize.snap_to_nearest_config(x, tuning_options.tune_params)
#check if this is a legal (non-restricted) parameter instance
if tuning_options.restrictions:
legal = util.check_restrictions(tuning_options.restrictions, params, tuning_options.tune_params.keys(), tuning_options.verbose)
if not legal:
return 1e20
#compile and benchmark this instance
res, _ = runner.run([params], kernel_options, tuning_options)
#append to tuning results
if len(res) > 0:
results.append(res[0])
return res[0]['time']
return 1e20