You can not select more than 25 topics
Topics must start with a letter or number, can include dashes ('-') and can be up to 35 characters long.
93 lines
3.7 KiB
93 lines
3.7 KiB
""" 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
|
|
"""
|
|
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):
|
|
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 = self.base_optimizer.param_groups
|
|
self.defaults = base_optimizer.defaults
|
|
self.defaults.update(defaults)
|
|
self.state = defaultdict(dict)
|
|
# manually add our defaults to the param groups
|
|
for name, default in defaults.items():
|
|
for group in self.param_groups:
|
|
group.setdefault(name, default)
|
|
|
|
def update_slow(self, group):
|
|
for fast_p in group["params"]:
|
|
if fast_p.grad is None:
|
|
continue
|
|
param_state = self.state[fast_p]
|
|
if 'slow_buffer' not in param_state:
|
|
param_state['slow_buffer'] = torch.empty_like(fast_p.data)
|
|
param_state['slow_buffer'].copy_(fast_p.data)
|
|
slow = param_state['slow_buffer']
|
|
slow.add_(group['lookahead_alpha'], fast_p.data - slow)
|
|
fast_p.data.copy_(slow)
|
|
|
|
def sync_lookahead(self):
|
|
for group in self.param_groups:
|
|
self.update_slow(group)
|
|
|
|
def step(self, closure=None):
|
|
#assert id(self.param_groups) == id(self.base_optimizer.param_groups)
|
|
loss = self.base_optimizer.step(closure)
|
|
for group in self.param_groups:
|
|
group['lookahead_step'] += 1
|
|
if group['lookahead_step'] % group['lookahead_k'] == 0:
|
|
self.update_slow(group)
|
|
return loss
|
|
|
|
def state_dict(self):
|
|
fast_state_dict = self.base_optimizer.state_dict()
|
|
slow_state = {
|
|
(id(k) if isinstance(k, torch.Tensor) else k): v
|
|
for k, v in self.state.items()
|
|
}
|
|
fast_state = fast_state_dict['state']
|
|
param_groups = fast_state_dict['param_groups']
|
|
return {
|
|
'state': fast_state,
|
|
'slow_state': slow_state,
|
|
'param_groups': param_groups,
|
|
}
|
|
|
|
def load_state_dict(self, state_dict):
|
|
fast_state_dict = {
|
|
'state': state_dict['state'],
|
|
'param_groups': state_dict['param_groups'],
|
|
}
|
|
self.base_optimizer.load_state_dict(fast_state_dict)
|
|
|
|
# We want to restore the slow state, but share param_groups reference
|
|
# with base_optimizer. This is a bit redundant but least code
|
|
slow_state_new = False
|
|
if 'slow_state' not in state_dict:
|
|
print('Loading state_dict from optimizer without Lookahead applied.')
|
|
state_dict['slow_state'] = defaultdict(dict)
|
|
slow_state_new = True
|
|
slow_state_dict = {
|
|
'state': state_dict['slow_state'],
|
|
'param_groups': state_dict['param_groups'], # this is pointless but saves code
|
|
}
|
|
super(Lookahead, self).load_state_dict(slow_state_dict)
|
|
self.param_groups = self.base_optimizer.param_groups # make both ref same container
|
|
if slow_state_new:
|
|
# reapply defaults to catch missing lookahead specific ones
|
|
for name, default in self.defaults.items():
|
|
for group in self.param_groups:
|
|
group.setdefault(name, default)
|