""" Cosine Scheduler Cosine LR schedule with warmup, cycle/restarts, noise, k-decay. Hacked together by / Copyright 2021 Ross Wightman """ import logging import math from typing import List, Union import torch from .scheduler import Scheduler _logger = logging.getLogger(__name__) class CosineLRScheduler(Scheduler): """ Cosine decay with restarts. This is described in the paper https://arxiv.org/abs/1608.03983. Inspiration from https://github.com/allenai/allennlp/blob/master/allennlp/training/learning_rate_schedulers/cosine.py k-decay option based on `k-decay: A New Method For Learning Rate Schedule` - https://arxiv.org/abs/2004.05909 Args: optimizer (torch.optim.Optimizer): torch optimizer to schedule t_initial (int): Number of epochs it initial (first) cycle. lr_min (float, optional): Minimum learning rate to use during the scheduling. Defaults to 0.. cycle_mul (float, optional): Multiplyer for cycle length. Defaults to 1.. cycle_decay (float, optional): Factor to decay lr at next cycle. Defaults to 1.. cycle_limit (int, optional): Number of cycles. Defaults to 1. warmup_t (int, optional): Number of epochs to warmup. Defaults to 0. warmup_lr_init (float, optional): Initial learning rate during warmup . Defaults to 0. warmup_prefix (bool, optional): If True, after warmup annealing starts from initial LR. Defaults to False. t_in_epochs (bool, optional): If set to False, returned lr are None. Defaults to True. noise_range_t (Union[int, float, List[int, float]], optional): Epoch when noise starts.\ If list or tuple - epoch range, when noise applied. Defaults to None. noise_pct (float, optional): Percentage of noise to add. Defaults to 0.67. noise_std (float, optional): Noise standard deviation. Defaults to 1.0. noise_seed (int, optional): Seed to use to add random noise. Defaults to 42. k_decay (float, optional): Power for k_decay. Defaults to 1.0. initialize (bool, optional): Add initial_{field_name} to optimizer param group. Defaults to True. """ def __init__(self, optimizer: torch.optim.Optimizer, t_initial: int, lr_min: float = 0., cycle_mul: float = 1., cycle_decay: float = 1., cycle_limit: int = 1, warmup_t: int = 0, warmup_lr_init: float = 0, warmup_prefix: bool = False, t_in_epochs: bool = True, noise_range_t: Union[int, float, List[int, float]] = None, noise_pct: float = 0.67, noise_std: float = 1.0, noise_seed: int = 42, k_decay: float = 1.0, initialize: bool = True) -> None: super().__init__( optimizer, param_group_field="lr", noise_range_t=noise_range_t, noise_pct=noise_pct, noise_std=noise_std, noise_seed=noise_seed, initialize=initialize) assert t_initial > 0 assert lr_min >= 0 if t_initial == 1 and cycle_mul == 1 and cycle_decay == 1: _logger.warning("Cosine annealing scheduler will have no effect on the learning " "rate since t_initial = t_mul = eta_mul = 1.") self.t_initial = t_initial self.lr_min = lr_min self.cycle_mul = cycle_mul self.cycle_decay = cycle_decay self.cycle_limit = cycle_limit self.warmup_t = warmup_t self.warmup_lr_init = warmup_lr_init self.warmup_prefix = warmup_prefix self.t_in_epochs = t_in_epochs self.k_decay = k_decay if self.warmup_t: self.warmup_steps = [(v - warmup_lr_init) / self.warmup_t for v in self.base_values] super().update_groups(self.warmup_lr_init) else: self.warmup_steps = [1 for _ in self.base_values] def _get_lr(self, t): if t < self.warmup_t: lrs = [self.warmup_lr_init + t * s for s in self.warmup_steps] else: if self.warmup_prefix: t = t - self.warmup_t if self.cycle_mul != 1: i = math.floor(math.log(1 - t / self.t_initial * (1 - self.cycle_mul), self.cycle_mul)) t_i = self.cycle_mul ** i * self.t_initial t_curr = t - (1 - self.cycle_mul ** i) / (1 - self.cycle_mul) * self.t_initial else: i = t // self.t_initial t_i = self.t_initial t_curr = t - (self.t_initial * i) gamma = self.cycle_decay ** i lr_max_values = [v * gamma for v in self.base_values] k = self.k_decay if i < self.cycle_limit: lrs = [ self.lr_min + 0.5 * (lr_max - self.lr_min) * (1 + math.cos(math.pi * t_curr ** k / t_i ** k)) for lr_max in lr_max_values ] else: lrs = [self.lr_min for _ in self.base_values] return lrs def get_epoch_values(self, epoch: int): if self.t_in_epochs: return self._get_lr(epoch) else: return None def get_update_values(self, num_updates: int): if not self.t_in_epochs: return self._get_lr(num_updates) else: return None def get_cycle_length(self, cycles: int = 0) -> int: """Return total number of epochs. Args: cycles (int, optional): Number of cycles. If 0, takes cycle_limit from sched. Defaults to 0. Returns: int: Total number of epochs """ cycles = max(1, cycles or self.cycle_limit) if self.cycle_mul == 1.0: return self.t_initial * cycles else: return int(math.floor(-self.t_initial * (self.cycle_mul ** cycles - 1) / (1 - self.cycle_mul)))