class_docstr: Implements stochastic gradient descent (optionally with momentum).
  
  This version of fused SGD implements 2 fusions.
    * Fusion of the SGD update's elementwise operations
    * A multi-tensor apply launch that batches the elementwise updates applied to all the model's parameters into one or a few kernel launches.
  
  :class:`apex.contrib.optimizers.FusedSGD` should be used without AMP.
  
  :class:`apex.contrib.optimizers.FusedSGD` only works in the case where all parameters require grad. 
  
  Nesterov momentum is based on the formula from
  `On the importance of initialization and momentum in deep learning`__.
  
  Args:
      params (iterable): iterable of parameters to optimize or dicts defining
          parameter groups
      lr (float): learning rate
      momentum (float, optional): momentum factor (default: 0)
      weight_decay (float, optional): weight decay (L2 penalty) (default: 0)
      dampening (float, optional): dampening for momentum (default: 0)
      nesterov (bool, optional): enables Nesterov momentum (default: False)
  
  Example:
      model = ...
      model.half()
      optimizer = apex.contrib.optimizers.FusedSGD(model.parameters())
      # wrap with FP16_Optimizer
      optimizer = FP16_Optimizer(optimizer, dynamic_loss_scale=True)
      optimizer.zero_grad()
      ...
      optimizer.backward(loss)
      optmizer.step()
  
  __ http://www.cs.toronto.edu/%7Ehinton/absps/momentum.pdf
  
  .. note::
      The implementation of SGD with Momentum/Nesterov subtly differs from
      Sutskever et. al. and implementations in some other frameworks.
  
      Considering the specific case of Momentum, the update can be written as
  
      .. math::
                v = \rho * v + g \\
                p = p - lr * v
  
      where p, g, v and :math:`\rho` denote the parameters, gradient,
      velocity, and momentum respectively.
  
      This is in contrast to Sutskever et. al. and
      other frameworks which employ an update of the form
  
      .. math::
           v = \rho * v + lr * g \\
           p = p - v
  
      The Nesterov version is analogously modified.
code: def __setstate__(self, state):
      super(FusedSGD, self).__setstate__(state)
      for group in self.param_groups:
          group.setdefault("nesterov", False)
  
file_path: apex/apex/contrib/optimizers/fused_sgd.py
imports: import types
  import torch
  from torch.optim.optimizer import Optimizer, required
  from apex.multi_tensor_apply import multi_tensor_applier
label_desc: Use image node_id 2 for calling the FusedSGD obj's underlying member method code with example usage: obj.__setstate__(state) without return types