| """ Lookahead Optimizer Wrapper. |
| Implementation modified from: https://github.com/alphadl/lookahead.pytorch |
| Paper: `Lookahead Optimizer: k steps forward, 1 step back` - https://arxiv.org/abs/1907.08610 |
| |
| Hacked together by / Copyright 2020 Ross Wightman |
| """ |
| from collections import OrderedDict |
| from typing import Callable, Dict |
|
|
| import torch |
| from torch.optim.optimizer import Optimizer |
| from collections import defaultdict |
|
|
|
|
| class Lookahead(Optimizer): |
| def __init__(self, base_optimizer, alpha=0.5, k=6): |
| |
| self._optimizer_step_pre_hooks: Dict[int, Callable] = OrderedDict() |
| self._optimizer_step_post_hooks: Dict[int, Callable] = OrderedDict() |
| if not 0.0 <= alpha <= 1.0: |
| raise ValueError(f'Invalid slow update rate: {alpha}') |
| if not 1 <= k: |
| raise ValueError(f'Invalid lookahead steps: {k}') |
| defaults = dict(lookahead_alpha=alpha, lookahead_k=k, lookahead_step=0) |
| self._base_optimizer = base_optimizer |
| self.param_groups = base_optimizer.param_groups |
| self.defaults = base_optimizer.defaults |
| self.defaults.update(defaults) |
| self.state = defaultdict(dict) |
| |
| for name, default in defaults.items(): |
| for group in self._base_optimizer.param_groups: |
| group.setdefault(name, default) |
|
|
| @torch.no_grad() |
| def update_slow(self, group): |
| for fast_p in group["params"]: |
| if fast_p.grad is None: |
| continue |
| param_state = self._base_optimizer.state[fast_p] |
| if 'lookahead_slow_buff' not in param_state: |
| param_state['lookahead_slow_buff'] = torch.empty_like(fast_p) |
| param_state['lookahead_slow_buff'].copy_(fast_p) |
| slow = param_state['lookahead_slow_buff'] |
| slow.add_(fast_p - slow, alpha=group['lookahead_alpha']) |
| fast_p.copy_(slow) |
|
|
| def sync_lookahead(self): |
| for group in self._base_optimizer.param_groups: |
| self.update_slow(group) |
|
|
| @torch.no_grad() |
| def step(self, closure=None): |
| loss = self._base_optimizer.step(closure) |
| for group in self._base_optimizer.param_groups: |
| group['lookahead_step'] += 1 |
| if group['lookahead_step'] % group['lookahead_k'] == 0: |
| self.update_slow(group) |
| return loss |
|
|
| def state_dict(self): |
| return self._base_optimizer.state_dict() |
|
|
| def load_state_dict(self, state_dict): |
| self._base_optimizer.load_state_dict(state_dict) |
| self.param_groups = self._base_optimizer.param_groups |
|
|
