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91 lines
3.7 KiB
91 lines
3.7 KiB
""" Lookahead Optimizer Wrapper.
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Implementation modified from: https://github.com/alphadl/lookahead.pytorch
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Paper: `Lookahead Optimizer: k steps forward, 1 step back` - https://arxiv.org/abs/1907.08610
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"""
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import torch
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from torch.optim.optimizer import Optimizer
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from collections import defaultdict
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class Lookahead(Optimizer):
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def __init__(self, base_optimizer, alpha=0.5, k=6):
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if not 0.0 <= alpha <= 1.0:
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raise ValueError(f'Invalid slow update rate: {alpha}')
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if not 1 <= k:
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raise ValueError(f'Invalid lookahead steps: {k}')
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defaults = dict(lookahead_alpha=alpha, lookahead_k=k, lookahead_step=0)
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self.base_optimizer = base_optimizer
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self.param_groups = self.base_optimizer.param_groups
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self.defaults = base_optimizer.defaults
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self.defaults.update(defaults)
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self.state = defaultdict(dict)
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# manually add our defaults to the param groups
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for name, default in defaults.items():
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for group in self.param_groups:
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group.setdefault(name, default)
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def update_slow(self, group):
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for fast_p in group["params"]:
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if fast_p.grad is None:
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continue
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param_state = self.state[fast_p]
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if 'slow_buffer' not in param_state:
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param_state['slow_buffer'] = torch.empty_like(fast_p.data)
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param_state['slow_buffer'].copy_(fast_p.data)
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slow = param_state['slow_buffer']
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slow.add_(group['lookahead_alpha'], fast_p.data - slow)
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fast_p.data.copy_(slow)
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def sync_lookahead(self):
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for group in self.param_groups:
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self.update_slow(group)
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def step(self, closure=None):
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#assert id(self.param_groups) == id(self.base_optimizer.param_groups)
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loss = self.base_optimizer.step(closure)
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for group in self.param_groups:
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group['lookahead_step'] += 1
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if group['lookahead_step'] % group['lookahead_k'] == 0:
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self.update_slow(group)
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return loss
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def state_dict(self):
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fast_state_dict = self.base_optimizer.state_dict()
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slow_state = {
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(id(k) if isinstance(k, torch.Tensor) else k): v
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for k, v in self.state.items()
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}
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fast_state = fast_state_dict['state']
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param_groups = fast_state_dict['param_groups']
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return {
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'state': fast_state,
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'slow_state': slow_state,
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'param_groups': param_groups,
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}
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def load_state_dict(self, state_dict):
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fast_state_dict = {
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'state': state_dict['state'],
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'param_groups': state_dict['param_groups'],
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}
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self.base_optimizer.load_state_dict(fast_state_dict)
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# We want to restore the slow state, but share param_groups reference
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# with base_optimizer. This is a bit redundant but least code
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slow_state_new = False
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if 'slow_state' not in state_dict:
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print('Loading state_dict from optimizer without Lookahead applied.')
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state_dict['slow_state'] = defaultdict(dict)
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slow_state_new = True
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slow_state_dict = {
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'state': state_dict['slow_state'],
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'param_groups': state_dict['param_groups'], # this is pointless but saves code
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}
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super(Lookahead, self).load_state_dict(slow_state_dict)
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self.param_groups = self.base_optimizer.param_groups # make both ref same container
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if slow_state_new:
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# reapply defaults to catch missing lookahead specific ones
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for name, default in self.defaults.items():
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for group in self.param_groups:
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group.setdefault(name, default)
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