def adaptive_autorange(
self,
threshold=0.1,
max_run_time=10,
callback: Optional[Callable[[int, float], NoReturn]] = None,
min_run_time=0.01
):
number = self._estimate_block_size(min_run_time=0.05)
def time_hook() -> float:
return self._timer.timeit(number)
def stop_hook(times) -> bool:
if len(times) > 3:
return common.Measurement(
number_per_run=number,
raw_times=times,
task_spec=self._task_spec
).meets_confidence(threshold=threshold)
return False
times = self._threaded_measurement_loop(
number, time_hook, stop_hook, min_run_time, max_run_time, callback=callback)
return common.Measurement(
number_per_run=number,
raw_times=times,
task_spec=self._task_spec
)
def stop_hook(times) -> bool:
if len(times) > 3:
return common.Measurement(
number_per_run=number,
raw_times=times,
task_spec=self._task_spec
).meets_confidence(threshold=threshold)
return False
def timeit(self, number=1000000):
with common.set_torch_threads(self._task_spec.num_threads):
# Warmup
self._timer.timeit(number=max(int(number // 100), 1))
return common.Measurement(
number_per_run=number,
raw_times=[self._timer.timeit(number=number)],
task_spec=self._task_spec
)
def _construct_measurement(self, number_per_run: int, times: List[float]):
return common.Measurement(
number_per_run=number_per_run,
times=times,
num_threads=self._num_threads,
label=self._label,
sub_label=self._sub_label,
description=self._description,
env=self._env,
stmt=self._stmt,
)
def blocked_autorange(self, callback=None, min_run_time=0.2):
number = self._estimate_block_size(min_run_time)
def time_hook() -> float:
return self._timer.timeit(number)
def stop_hook(times) -> bool:
return True
times = self._threaded_measurement_loop(
number, time_hook, stop_hook,
min_run_time=min_run_time,
callback=callback)
return common.Measurement(
number_per_run=number,
raw_times=times,
task_spec=self._task_spec
)