Source code for ding.worker.replay_buffer.utils
from typing import Any
import time
from queue import Queue
from typing import Union, Tuple
from threading import Thread
from functools import partial
from ding.utils.autolog import LoggedValue, LoggedModel
from ding.utils import LockContext, LockContextType, remove_file
def generate_id(name, data_id: int) -> str:
"""
Overview:
Use ``self.name`` and input ``id`` to generate a unique id for next data to be inserted.
Arguments:
- data_id (:obj:`int`): Current unique id.
Returns:
- id (:obj:`str`): Id in format "BufferName_DataId".
"""
return "{}_{}".format(name, str(data_id))
[docs]class UsedDataRemover:
"""
Overview:
UsedDataRemover is a tool to remove file datas that will no longer be used anymore.
Interface:
start, close, add_used_data
"""
def __init__(self) -> None:
self._used_data = Queue()
self._delete_used_data_thread = Thread(target=self._delete_used_data, name='delete_used_data')
self._delete_used_data_thread.daemon = True
self._end_flag = True
[docs] def start(self) -> None:
"""
Overview:
Start the `delete_used_data` thread.
"""
self._end_flag = False
self._delete_used_data_thread.start()
[docs] def close(self) -> None:
"""
Overview:
Delete all datas in `self._used_data`. Then join the `delete_used_data` thread.
"""
while not self._used_data.empty():
data_id = self._used_data.get()
remove_file(data_id)
self._end_flag = True
[docs] def add_used_data(self, data: Any) -> None:
"""
Overview:
Delete all datas in `self._used_data`. Then join the `delete_used_data` thread.
Arguments:
- data (:obj:`Any`): Add a used data item into `self._used_data` for further remove.
"""
assert data is not None and isinstance(data, dict) and 'data_id' in data
self._used_data.put(data['data_id'])
def _delete_used_data(self) -> None:
while not self._end_flag:
if not self._used_data.empty():
data_id = self._used_data.get()
remove_file(data_id)
else:
time.sleep(0.001)
[docs]class SampledDataAttrMonitor(LoggedModel):
"""
Overview:
SampledDataAttrMonitor is to monitor read-out indicators for ``expire`` times recent read-outs.
Indicators include: read out time; average and max of read out data items' use; average, max and min of
read out data items' priorityl; average and max of staleness.
Interface:
__init__, fixed_time, current_time, freeze, unfreeze, register_attribute_value, __getattr__
Property:
time, expire
"""
use_max = LoggedValue(int)
use_avg = LoggedValue(float)
priority_max = LoggedValue(float)
priority_avg = LoggedValue(float)
priority_min = LoggedValue(float)
staleness_max = LoggedValue(int)
staleness_avg = LoggedValue(float)
def __init__(self, time_: 'BaseTime', expire: Union[int, float]): # noqa
LoggedModel.__init__(self, time_, expire)
self.__register()
def __register(self):
def __avg_func(prop_name: str) -> float:
records = self.range_values[prop_name]()
_list = [_value for (_begin_time, _end_time), _value in records]
return sum(_list) / len(_list) if len(_list) != 0 else 0
def __max_func(prop_name: str) -> Union[float, int]:
records = self.range_values[prop_name]()
_list = [_value for (_begin_time, _end_time), _value in records]
return max(_list) if len(_list) != 0 else 0
def __min_func(prop_name: str) -> Union[float, int]:
records = self.range_values[prop_name]()
_list = [_value for (_begin_time, _end_time), _value in records]
return min(_list) if len(_list) != 0 else 0
self.register_attribute_value('avg', 'use', partial(__avg_func, prop_name='use_avg'))
self.register_attribute_value('max', 'use', partial(__max_func, prop_name='use_max'))
self.register_attribute_value('avg', 'priority', partial(__avg_func, prop_name='priority_avg'))
self.register_attribute_value('max', 'priority', partial(__max_func, prop_name='priority_max'))
self.register_attribute_value('min', 'priority', partial(__min_func, prop_name='priority_min'))
self.register_attribute_value('avg', 'staleness', partial(__avg_func, prop_name='staleness_avg'))
self.register_attribute_value('max', 'staleness', partial(__max_func, prop_name='staleness_max'))
[docs]class PeriodicThruputMonitor:
"""
Overview:
PeriodicThruputMonitor is a tool to record and print logs(text & tensorboard) how many datas are
pushed/sampled/removed/valid in a period of time. For tensorboard, you can view it in 'buffer_{$NAME}_sec'.
Interface:
close
Property:
push_data_count, sample_data_count, remove_data_count, valid_count
.. note::
`thruput_log` thread is initialized and started in `__init__` method, so PeriodicThruputMonitor only provide
one signle interface `close`
"""
def __init__(self, name, cfg, logger, tb_logger) -> None:
self.name = name
self._end_flag = False
self._logger = logger
self._tb_logger = tb_logger
self._thruput_print_seconds = cfg.seconds
self._thruput_print_times = 0
self._thruput_start_time = time.time()
self._history_push_count = 0
self._history_sample_count = 0
self._remove_data_count = 0
self._valid_count = 0
self._thruput_log_thread = Thread(target=self._thrput_print_periodically, args=(), name='periodic_thruput_log')
self._thruput_log_thread.daemon = True
self._thruput_log_thread.start()
def _thrput_print_periodically(self) -> None:
while not self._end_flag:
time_passed = time.time() - self._thruput_start_time
if time_passed >= self._thruput_print_seconds:
self._logger.info('In the past {:.1f} seconds, buffer statistics is as follows:'.format(time_passed))
count_dict = {
'pushed_in': self._history_push_count,
'sampled_out': self._history_sample_count,
'removed': self._remove_data_count,
'current_have': self._valid_count,
}
self._logger.info(self._logger.get_tabulate_vars_hor(count_dict))
for k, v in count_dict.items():
self._tb_logger.add_scalar('{}_sec/'.format(self.name) + k, v, self._thruput_print_times)
self._history_push_count = 0
self._history_sample_count = 0
self._remove_data_count = 0
self._thruput_start_time = time.time()
self._thruput_print_times += 1
else:
time.sleep(min(1, self._thruput_print_seconds * 0.2))
def close(self) -> None:
"""
Overview:
Join the `thruput_log` thread by setting `self._end_flag` to `True`.
"""
self._end_flag = True
def __del__(self) -> None:
self.close()
@property
def push_data_count(self) -> int:
return self._history_push_count
@push_data_count.setter
def push_data_count(self, count) -> None:
self._history_push_count = count
@property
def sample_data_count(self) -> int:
return self._history_sample_count
@sample_data_count.setter
def sample_data_count(self, count) -> None:
self._history_sample_count = count
@property
def remove_data_count(self) -> int:
return self._remove_data_count
@remove_data_count.setter
def remove_data_count(self, count) -> None:
self._remove_data_count = count
@property
def valid_count(self) -> int:
return self._valid_count
@valid_count.setter
def valid_count(self, count) -> None:
self._valid_count = count
class ThruputController:
def __init__(self, cfg) -> None:
self._push_sample_rate_limit = cfg.push_sample_rate_limit
assert 'min' in self._push_sample_rate_limit and self._push_sample_rate_limit['min'] >= 0
assert 'max' in self._push_sample_rate_limit and self._push_sample_rate_limit['max'] <= float("inf")
window_seconds = cfg.window_seconds
self._decay_factor = 0.01 ** (1 / window_seconds)
self._push_lock = LockContext(lock_type=LockContextType.THREAD_LOCK)
self._sample_lock = LockContext(lock_type=LockContextType.THREAD_LOCK)
self._history_push_count = 0
self._history_sample_count = 0
self._end_flag = False
self._count_decay_thread = Thread(target=self._count_decay, name='count_decay')
self._count_decay_thread.daemon = True
self._count_decay_thread.start()
def _count_decay(self) -> None:
while not self._end_flag:
time.sleep(1)
with self._push_lock:
self._history_push_count *= self._decay_factor
with self._sample_lock:
self._history_sample_count *= self._decay_factor
def can_push(self, push_size: int) -> Tuple[bool, str]:
if abs(self._history_sample_count) < 1e-5:
return True, "Can push because `self._history_sample_count` < 1e-5"
rate = (self._history_push_count + push_size) / self._history_sample_count
if rate > self._push_sample_rate_limit['max']:
return False, "push({}+{}) / sample({}) > limit_max({})".format(
self._history_push_count, push_size, self._history_sample_count, self._push_sample_rate_limit['max']
)
return True, "Can push."
def can_sample(self, sample_size: int) -> Tuple[bool, str]:
rate = self._history_push_count / (self._history_sample_count + sample_size)
if rate < self._push_sample_rate_limit['min']:
return False, "push({}) / sample({}+{}) < limit_min({})".format(
self._history_push_count, self._history_sample_count, sample_size, self._push_sample_rate_limit['min']
)
return True, "Can sample."
def close(self) -> None:
self._end_flag = True
@property
def history_push_count(self) -> int:
return self._history_push_count
@history_push_count.setter
def history_push_count(self, count) -> None:
with self._push_lock:
self._history_push_count = count
@property
def history_sample_count(self) -> int:
return self._history_sample_count
@history_sample_count.setter
def history_sample_count(self, count) -> None:
with self._sample_lock:
self._history_sample_count = count