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() __ Note: Reference code: """ 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 = 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( # 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_(, alpha=group['weight_decay']) if group['grad_averaging']: grad.mul_(1 - beta1) exp_avg.mul_(beta1).add_(grad), alpha=-group['lr']) return loss