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from typing import Dict, Any |
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import torch |
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class Scheduler: |
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""" Parameter Scheduler Base Class |
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A scheduler base class that can be used to schedule any optimizer parameter groups. |
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Unlike the builtin PyTorch schedulers, this is intended to be consistently called |
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* At the END of each epoch, before incrementing the epoch count, to calculate next epoch's value |
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* At the END of each optimizer update, after incrementing the update count, to calculate next update's value |
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The schedulers built on this should try to remain as stateless as possible (for simplicity). |
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This family of schedulers is attempting to avoid the confusion of the meaning of 'last_epoch' |
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and -1 values for special behaviour. All epoch and update counts must be tracked in the training |
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code and explicitly passed in to the schedulers on the corresponding step or step_update call. |
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Based on ideas from: |
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* https://github.com/pytorch/fairseq/tree/master/fairseq/optim/lr_scheduler |
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* https://github.com/allenai/allennlp/tree/master/allennlp/training/learning_rate_schedulers |
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""" |
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def __init__(self, |
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optimizer: torch.optim.Optimizer, |
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param_group_field: str, |
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noise_range_t=None, |
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noise_type='normal', |
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noise_pct=0.67, |
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noise_std=1.0, |
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noise_seed=None, |
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initialize: bool = True, |
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scheduler_groups=None) -> None: |
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self.optimizer = optimizer |
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print("scheduler_groups:", scheduler_groups) |
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self.scheduler_groups = scheduler_groups |
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if not isinstance(scheduler_groups, list): |
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self.scheduler_groups = [self.scheduler_groups] |
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self.param_group_field = param_group_field |
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self._initial_param_group_field = f"initial_{param_group_field}" |
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if initialize: |
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for i, group in enumerate(self.optimizer.param_groups): |
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if param_group_field not in group: |
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raise KeyError(f"{param_group_field} missing from param_groups[{i}]") |
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group.setdefault(self._initial_param_group_field, group[param_group_field]) |
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else: |
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for i, group in enumerate(self.optimizer.param_groups): |
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if self._initial_param_group_field not in group: |
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raise KeyError(f"{self._initial_param_group_field} missing from param_groups[{i}]") |
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self.base_values = [group[self._initial_param_group_field] for group in self.optimizer.param_groups] |
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self.metric = None |
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self.noise_range_t = noise_range_t |
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self.noise_pct = noise_pct |
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self.noise_type = noise_type |
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self.noise_std = noise_std |
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self.noise_seed = noise_seed if noise_seed is not None else 42 |
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self.update_groups(self.base_values) |
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def state_dict(self) -> Dict[str, Any]: |
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return {key: value for key, value in self.__dict__.items() if key != 'optimizer'} |
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def load_state_dict(self, state_dict: Dict[str, Any]) -> None: |
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self.__dict__.update(state_dict) |
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def get_epoch_values(self, epoch: int): |
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return None |
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def get_update_values(self, num_updates: int): |
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return None |
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def step(self, epoch: int, metric: float = None) -> None: |
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self.metric = metric |
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values = self.get_epoch_values(epoch) |
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if values is not None: |
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values = self._add_noise(values, epoch) |
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self.update_groups(values) |
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def step_update(self, num_updates: int, metric: float = None): |
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self.metric = metric |
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values = self.get_update_values(num_updates) |
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if values is not None: |
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values = self._add_noise(values, num_updates) |
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self.update_groups(values) |
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def update_groups(self, values): |
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if not isinstance(values, (list, tuple)): |
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values = [values] * len(self.optimizer.param_groups) |
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for i, (param_group, value) in enumerate(zip(self.optimizer.param_groups, values)): |
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if i in self.scheduler_groups: |
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param_group[self.param_group_field] = value |
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def _add_noise(self, lrs, t): |
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if self.noise_range_t is not None: |
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if isinstance(self.noise_range_t, (list, tuple)): |
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apply_noise = self.noise_range_t[0] <= t < self.noise_range_t[1] |
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else: |
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apply_noise = t >= self.noise_range_t |
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if apply_noise: |
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g = torch.Generator() |
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g.manual_seed(self.noise_seed + t) |
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if self.noise_type == 'normal': |
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while True: |
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noise = torch.randn(1, generator=g).item() |
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if abs(noise) < self.noise_pct: |
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break |
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else: |
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noise = 2 * (torch.rand(1, generator=g).item() - 0.5) * self.noise_pct |
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lrs = [v + v * noise for v in lrs] |
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return lrs |
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