def neighbor(pos, tune_params):
"""return a random neighbor of pos"""
size = len(pos)
pos_out = []
# random mutation
# expected value is set that values all dimensions attempt to get mutated
for i in range(size):
key = list(tune_params.keys())[i]
values = tune_params[key]
if random.random() < 0.2: #replace with random value
new_value = random_val(i, tune_params)
else: #adjacent value
ind = values.index(pos[i])
if random.random() > 0.5:
ind += 1
else:
ind -= 1
ind = min(max(ind, 0), len(values)-1)
new_value = values[ind]
pos_out.append(new_value)
return pos_out
def neighbor(pos, tune_params):
"""return a random neighbor of pos"""
size = len(pos)
pos_out = []
# random mutation
# expected value is set that values all dimensions attempt to get mutated
for i in range(size):
key = list(tune_params.keys())[i]
values = tune_params[key]
if random.random() < 0.2: #replace with random value
new_value = random_val(i, tune_params)
else: #adjacent value
ind = values.index(pos[i])
if random.random() > 0.5:
ind += 1
else:
ind -= 1
ind = min(max(ind, 0), len(values) - 1)
new_value = values[ind]
pos_out.append(new_value)
return pos_out
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: dict
:param device_options: A dictionary with all options for the device
on which the kernel should be tuned.
:type device_options: dict
:param tuning_options: A dictionary with all options regarding the tuning
process.
:type tuning_options: dict
: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 = []
cache = {}
# SA works with real parameter values and does not need scaling
tuning_options["scaling"] = False
args = (kernel_options, tuning_options, runner, results, cache)
tune_params = tuning_options.tune_params
# optimization parameters
T = 1.0
T_min = 0.001
alpha = 0.9
niter = 20
# generate random starting point and evaluate cost
pos = []
for i, _ in enumerate(tune_params.keys()):
pos.append(random_val(i, tune_params))
old_cost = _cost_func(pos, *args)
if tuning_options.verbose:
c = 0
# main optimization loop
while T > T_min:
if tuning_options.verbose:
print("iteration: ", c, "T", T, "cost: ", old_cost)
c += 1
for i in range(niter):
new_pos = neighbor(pos, tune_params)
new_cost = _cost_func(new_pos, *args)
ap = acceptance_prob(old_cost, new_cost, T)
r = random.random()
if ap > r:
if tuning_options.verbose:
print("new position accepted", new_pos, new_cost, 'old:', pos, old_cost, 'ap', ap, 'r', r, 'T', T)
pos = new_pos
old_cost = new_cost
T = T * alpha
return results, runner.dev.get_environment()
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: dict
:param device_options: A dictionary with all options for the device
on which the kernel should be tuned.
:type device_options: dict
:param tuning_options: A dictionary with all options regarding the tuning
process.
:type tuning_options: dict
: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 = []
cache = {}
# SA works with real parameter values and does not need scaling
tuning_options["scaling"] = False
args = (kernel_options, tuning_options, runner, results, cache)
tune_params = tuning_options.tune_params
# optimization parameters
T = 1.0
T_min = 0.001
alpha = 0.9
niter = 20
# generate random starting point and evaluate cost
pos = []
for i, _ in enumerate(tune_params.keys()):
pos.append(random_val(i, tune_params))
old_cost = _cost_func(pos, *args)
if tuning_options.verbose:
c = 0
# main optimization loop
while T > T_min:
if tuning_options.verbose:
print("iteration: ", c, "T", T, "cost: ", old_cost)
c += 1
for i in range(niter):
new_pos = neighbor(pos, tune_params)
new_cost = _cost_func(new_pos, *args)
ap = acceptance_prob(old_cost, new_cost, T)
r = random.random()
if ap > r:
if tuning_options.verbose:
print("new position accepted", new_pos, new_cost, 'old:',
pos, old_cost, 'ap', ap, 'r', r, 'T', T)
pos = new_pos
old_cost = new_cost
T = T * alpha
return results, runner.dev.get_environment()
def test_random_val():
val0 = ga.random_val(0, tune_params)
assert val0 in tune_params["x"]
val1 = ga.random_val(1, tune_params)
assert val1 in tune_params["y"]