hunk.code_window: from .lars import Lars
   from .lion import Lion
   from .lookahead import Lookahead
   from .madgrad import MADGRAD
   from .nadam import Nadam
   from .nvnovograd import NvNovoGrad
   from .radam import RAdam
   from .rmsprop_tf import RMSpropTF
  
hunk.labels: keep keep keep keep add keep keep keep
hunk.after_edit: from .nadamw import NAdamW
  
hunk.file_path: timm/optim/optim_factory.py
hunk.type: add
file: """ Nvidia NovoGrad Optimizer.
  Original impl by Nvidia from Jasper example:
      - https://github.com/NVIDIA/DeepLearningExamples/blob/master/PyTorch/SpeechRecognition/Jasper
  Paper: `Stochastic Gradient Methods with Layer-wise Adaptive Moments for Training of Deep Networks`
      - https://arxiv.org/abs/1905.11286
  """
  
  import torch
  from torch.optim.optimizer import Optimizer
  import math
  
  
  class NvNovoGrad(Optimizer):
      """
      Implements Novograd algorithm.
  
      Args:
          params (iterable): iterable of parameters to optimize or dicts defining
              parameter groups
          lr (float, optional): learning rate (default: 1e-3)
          betas (Tuple[float, float], optional): coefficients used for computing
              running averages of gradient and its square (default: (0.95, 0.98))
          eps (float, optional): term added to the denominator to improve
              numerical stability (default: 1e-8)
          weight_decay (float, optional): weight decay (L2 penalty) (default: 0)
          grad_averaging: gradient averaging
          amsgrad (boolean, optional): whether to use the AMSGrad variant of this
              algorithm from the paper `On the Convergence of Adam and Beyond`_
              (default: False)
      """
  
      def __init__(self, params, lr=1e-3, betas=(0.95, 0.98), eps=1e-8,
                   weight_decay=0, grad_averaging=False, amsgrad=False):
          if not 0.0 <= lr:
              raise ValueError("Invalid learning rate: {}".format(lr))
          if not 0.0 <= eps:
              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]))
          defaults = dict(lr=lr, betas=betas, eps=eps,
                          weight_decay=weight_decay,
                          grad_averaging=grad_averaging,
                          amsgrad=amsgrad)
  
          super(NvNovoGrad, self).__init__(params, defaults)
  
      def __setstate__(self, state):
          super(NvNovoGrad, self).__setstate__(state)
          for group in self.param_groups:
              group.setdefault('amsgrad', False)
  
      @torch.no_grad()
      def step(self, closure=None):
          """Performs a single optimization step.
  
          Arguments:
              closure (callable, optional): A closure that reevaluates the model
              and returns the loss.
          """
          loss = None
          if closure is not None:
              with torch.enable_grad():
                  loss = closure()
  
          for group in self.param_groups:
              for p in group['params']:
                  if p.grad is None:
                      continue
                  grad = p.grad
                  if grad.is_sparse:
                      raise RuntimeError('Sparse gradients are not supported.')
                  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)
                      # 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, alpha=group['weight_decay'])
                  if group['grad_averaging']:
                      grad.mul_(1 - beta1)
                  exp_avg.mul_(beta1).add_(grad)
  
                  p.add_(exp_avg, alpha=-group['lr'])
  
          return loss
  