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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