| | """ 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 |
| |
|
