Source code for torch_optimizer.novograd
import torch
from torch.optim.optimizer import Optimizer
from .types import Betas2, OptFloat, OptLossClosure, Params
__all__ = ('NovoGrad',)
[docs]class NovoGrad(Optimizer):
r"""Implements Novograd optimization algorithm.
It has been proposed in `Stochastic Gradient Methods with Layer-wise
Adaptive Moments for Training of Deep Networks`__.
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: (0.95, 0))
eps: term added to the denominator to improve
numerical stability (default: 1e-8)
weight_decay: weight decay (L2 penalty) (default: 0)
grad_averaging: gradient averaging (default: False)
amsgrad: whether to use the AMSGrad variant of this
algorithm from the paper `On the Convergence of Adam and Beyond`
(default: False)
Example:
>>> import torch_optimizer as optim
>>> optimizer = optim.Yogi(model.parameters(), lr=0.1)
>>> optimizer.zero_grad()
>>> loss_fn(model(input), target).backward()
>>> scheduler = StepLR(optimizer, step_size=1, gamma=0.7)
>>> optimizer.step()
>>> scheduler.step()
__ https://arxiv.org/abs/1905.11286
Note:
Reference code: https://github.com/NVIDIA/DeepLearningExamples
"""
def __init__(
self,
params: Params,
lr: float = 1e-3,
betas: Betas2 = (0.95, 0),
eps: float = 1e-8,
weight_decay: float = 0,
grad_averaging: bool = False,
amsgrad: bool = False,
):
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)
)
defaults = dict(
lr=lr,
betas=betas,
eps=eps,
weight_decay=weight_decay,
grad_averaging=grad_averaging,
amsgrad=amsgrad,
)
super(NovoGrad, self).__init__(params, defaults)
def __setstate__(self, state: dict) -> None:
super(NovoGrad, self).__setstate__(state)
for group in self.param_groups:
group.setdefault('amsgrad', False)
[docs] def step(self, closure: OptLossClosure = None) -> OptFloat:
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:
for p in group['params']:
if p.grad is None:
continue
grad = p.grad.data
if grad.is_sparse:
msg = (
'NovoGrad does not support sparse gradients, '
'please consider SparseAdam instead'
)
raise RuntimeError(msg)
amsgrad = group['amsgrad']
state = self.state[p]
# State initialization
if len(state) == 0:
state['step'] = 0
# Exponential moving average of gradient values
state['exp_avg'] = torch.zeros_like(p.data)
# Exponential moving average of squared gradient values
state['exp_avg_sq'] = torch.zeros([]).to(
state['exp_avg'].device
)
if amsgrad:
# Maintains max of all exp. moving avg. of sq.
# grad. values
state['max_exp_avg_sq'] = torch.zeros([]).to(
state['exp_avg'].device
)
exp_avg, exp_avg_sq = state['exp_avg'], state['exp_avg_sq']
if amsgrad:
max_exp_avg_sq = state['max_exp_avg_sq']
beta1, beta2 = group['betas']
state['step'] += 1
norm = torch.sum(torch.pow(grad, 2))
if exp_avg_sq == 0:
exp_avg_sq.copy_(norm)
else:
exp_avg_sq.mul_(beta2).add_(norm, alpha=1 - beta2)
if amsgrad:
# Maintains the maximum of all 2nd moment running avg.
# till now
torch.max(max_exp_avg_sq, exp_avg_sq, out=max_exp_avg_sq)
# Use the max. for normalizing running avg. of gradient
denom = max_exp_avg_sq.sqrt().add_(group['eps'])
else:
denom = exp_avg_sq.sqrt().add_(group['eps'])
grad.div_(denom)
if group['weight_decay'] != 0:
grad.add_(p.data, alpha=group['weight_decay'])
if group['grad_averaging']:
grad.mul_(1 - beta1)
exp_avg.mul_(beta1).add_(grad)
p.data.add_(exp_avg, alpha=-group['lr'])
return loss