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optim.py
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optim.py
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from collections import defaultdict
import warnings
import math
import torch
from torch.optim import Optimizer
class RAdam(Optimizer):
r"""Implements RAdam optimization algorithm.
It has been proposed in `On the Variance of the Adaptive Learning
Rate and Beyond`__.
Arguments:
params: iterable of parameters to optimize or dicts defining
parameter groups
lr: learning rate (default: 1e-3)
betas: coefficients used for computing
running averages of gradient and its square (default: (0.9, 0.999))
eps: term added to the denominator to improve
numerical stability (default: 1e-8)
weight_decay: weight decay (L2 penalty) (default: 0)
Example:
>>> import torch_optimizer as optim
>>> optimizer = optim.RAdam(model.parameters(), lr=0.1)
>>> optimizer.zero_grad()
>>> loss_fn(model(input), target).backward()
>>> optimizer.step()
__ https://arxiv.org/abs/1908.03265
Note:
Reference code: https://github.com/LiyuanLucasLiu/RAdam
"""
def __init__(
self,
params,
lr: float = 1e-3,
betas=(0.9, 0.999),
eps: float = 1e-8,
weight_decay: float = 0,
) -> None:
if lr <= 0.0:
raise ValueError('Invalid learning rate: {}'.format(lr))
if eps < 0.0:
raise ValueError('Invalid epsilon value: {}'.format(eps))
if not 0.0 <= betas[0] < 1.0:
raise ValueError(
'Invalid beta parameter at index 0: {}'.format(betas[0])
)
if not 0.0 <= betas[1] < 1.0:
raise ValueError(
'Invalid beta parameter at index 1: {}'.format(betas[1])
)
if weight_decay < 0:
raise ValueError(
'Invalid weight_decay value: {}'.format(weight_decay)
)
if (
isinstance(params, (list, tuple))
and len(params) > 0
and isinstance(params[0], dict)
):
for param in params:
if 'betas' in param and (
param['betas'][0] != betas[0]
or param['betas'][1] != betas[1]
):
param['buffer'] = [[None, None, None] for _ in range(10)]
defaults = dict(
lr=lr,
betas=betas,
eps=eps,
weight_decay=weight_decay,
buffer=[[None, None, None] for _ in range(10)],
)
super(RAdam, self).__init__(params, defaults)
def __setstate__(self, state):
super(RAdam, self).__setstate__(state)
def step(self, closure=None):
r"""Performs a single optimization step.
Arguments:
closure: A closure that reevaluates the model and returns the loss.
"""
loss = None
if closure is not None:
loss = closure()
for group in self.param_groups:
lr = group['lr']
weight_decay = group['weight_decay']
beta1, beta2 = group['betas']
eps = group['eps']
for p in group['params']:
if p.grad is None:
continue
grad = p.grad.data.float()
if grad.is_sparse:
msg = (
'RAdam does not support sparse gradients, '
'please consider SparseAdam instead'
)
raise RuntimeError(msg)
p_data_fp32 = p.data.float()
state = self.state[p]
if len(state) == 0:
state['step'] = 0
state['exp_avg'] = torch.zeros_like(
p_data_fp32, memory_format=torch.preserve_format
)
state['exp_avg_sq'] = torch.zeros_like(
p_data_fp32, memory_format=torch.preserve_format
)
else:
state['exp_avg'] = state['exp_avg'].type_as(p_data_fp32)
state['exp_avg_sq'] = state['exp_avg_sq'].type_as(
p_data_fp32
)
exp_avg, exp_avg_sq = state['exp_avg'], state['exp_avg_sq']
exp_avg_sq.mul_(beta2).addcmul_(grad, grad, value=1 - beta2)
exp_avg.mul_(beta1).add_(grad, alpha=1 - beta1)
state['step'] += 1
buffered = group['buffer'][int(state['step'] % 10)]
if state['step'] == buffered[0]:
N_sma, step_size = buffered[1], buffered[2]
else:
buffered[0] = state['step']
beta2_t = beta2 ** state['step']
N_sma_max = 2 / (1 - beta2) - 1
N_sma = N_sma_max - 2 * state['step'] * beta2_t / (
1 - beta2_t
)
buffered[1] = N_sma
# more conservative since it's an approximated value
if N_sma >= 5:
step_size = (
lr
* math.sqrt(
(1 - beta2_t)
* (N_sma - 4)
/ (N_sma_max - 4)
* (N_sma - 2)
/ N_sma
* N_sma_max
/ (N_sma_max - 2)
)
/ (1 - beta1 ** state['step'])
)
else:
step_size = lr / (1 - beta1 ** state['step'])
buffered[2] = step_size
if weight_decay != 0:
p_data_fp32.add_(p_data_fp32, alpha=-weight_decay * lr)
# more conservative since it's an approximated value
if N_sma >= 5:
denom = exp_avg_sq.sqrt().add_(eps)
p_data_fp32.addcdiv_(exp_avg, denom, value=-step_size)
else:
p_data_fp32.add_(exp_avg, alpha=-step_size)
p.data.copy_(p_data_fp32)
return loss
class Lookahead(Optimizer):
r"""Implements Lookahead optimization algorithm.
It has been proposed in `Lookahead Optimizer: k steps forward, 1
step back`__
Arguments:
optimizer: base inner optimizer optimize, like Yogi, DiffGrad or Adam.
k: number of lookahead steps (default: 5)
alpha: linear interpolation factor. 1.0 recovers the inner optimizer.
(default: 5)
Example:
>>> import torch_optimizer as optim
>>> yogi = optim.Yogi(model.parameters(), lr=0.1)
>>> optimizer = optim.Lookahead(yogi, k=5, alpha=0.5)
>>> optimizer.zero_grad()
>>> loss_fn(model(input), target).backward()
>>> optimizer.step()
__ https://arxiv.org/abs/1907.08610
Note:
Reference code: https://github.com/alphadl/lookahead.pytorch
"""
def __init__(
self, optimizer: Optimizer, k: int = 5, alpha: float = 0.5
) -> None:
if k < 0.0:
raise ValueError('Invalid number of lookahead steps: {}'.format(k))
if alpha < 0:
raise ValueError(
'Invalid linear interpolation factor: {}'.format(alpha)
)
self.optimizer = optimizer
self.k = k
self.alpha = alpha
self.param_groups = self.optimizer.param_groups
self.state = defaultdict(dict)
self.fast_state = self.optimizer.state
for group in self.param_groups:
group['counter'] = 0
def _update(self, group):
for fast in group['params']:
param_state = self.state[fast]
if 'slow_param' not in param_state:
param_state['slow_param'] = torch.clone(fast.data).detach()
slow = param_state['slow_param']
fast.data.mul_(self.alpha).add_(slow, alpha=1.0 - self.alpha)
slow.data.copy_(fast)
def step(self, closure=None):
r"""Performs a single optimization step.
Arguments:
closure: A closure that reevaluates the model and returns the loss.
"""
loss = self.optimizer.step(closure=closure)
for group in self.param_groups:
if group['counter'] == 0:
self._update(group)
group['counter'] = (group['counter'] + 1) % self.k
return loss
def state_dict(self):
r"""Returns the state of the optimizer as a :class:`dict`.
It contains two entries:
* state - a dict holding current optimization state. Its content
differs between optimizer classes.
* param_groups - a dict containing all parameter groups
"""
slow_state_dict = super(Lookahead, self).state_dict()
fast_state_dict = self.optimizer.state_dict()
fast_state = fast_state_dict['state']
param_groups = fast_state_dict['param_groups']
return {
'fast_state': fast_state,
'slow_state': slow_state_dict['state'],
'param_groups': param_groups,
}
def load_state_dict(self, state_dict) -> None:
r"""Loads the optimizer state.
Arguments:
state_dict: optimizer state. Should be an object returned
from a call to :meth:`state_dict`.
"""
slow_state_dict = {
'state': state_dict['slow_state'],
'param_groups': state_dict['param_groups'],
}
fast_state_dict = {
'state': state_dict['fast_state'],
'param_groups': state_dict['param_groups'],
}
super(Lookahead, self).load_state_dict(slow_state_dict)
self.optimizer.load_state_dict(fast_state_dict)
self.fast_state = self.optimizer.state
def zero_grad(self) -> None:
r"""Clears the gradients of all optimized :class:`torch.Tensor` s."""
self.optimizer.zero_grad()
def __repr__(self) -> str:
base_str = self.optimizer.__repr__()
format_string = self.__class__.__name__ + ' ('
format_string += '\n'
format_string += 'k: {}\n'.format(self.k)
format_string += 'alpha: {}\n'.format(self.alpha)
format_string += base_str
format_string += '\n'
format_string += ')'
return format_string