class_docstr: Implements stochastic gradient descent (optionally with momentum).
  
  Currently GPU-only.  Requires Apex to be installed via
  ``pip install -v --no-cache-dir --global-option="--cpp_ext" --global-option="--cuda_ext" ./``.
  
  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.optimizers.FusedSGD` may be used as a drop-in replacement for ``torch.optim.SGD``::
  
      opt = apex.optimizers.FusedSGD(model.parameters(), lr = ....)
      ...
      opt.step()
  
  :class:`apex.optimizers.FusedSGD` may be used with or without Amp.  If you wish to use :class:`FusedSGD` with Amp,
  you may choose any ``opt_level``::
  
      opt = apex.optimizers.FusedSGD(model.parameters(), lr = ....)
      model, opt = amp.initialize(model, opt, opt_level="O0" or "O1 or "O2")
      ...
      opt.step()
  
  In general, ``opt_level="O1"`` is recommended.
  
  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:
      >>> optimizer = torch.optim.SGD(model.parameters(), lr=0.1, momentum=0.9)
      >>> optimizer.zero_grad()
      >>> loss_fn(model(input), target).backward()
      >>> optimizer.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 __init__(
      self,
      params,
      lr=required,
      momentum=0,
      dampening=0,
      weight_decay=0,
      nesterov=False,
      wd_after_momentum=False,
      materialize_master_grads=True,
      set_grad_none=False,
  ):
      if lr is not required and lr < 0.0:
          raise ValueError("Invalid learning rate: {}".format(lr))
      if momentum < 0.0:
          raise ValueError(
              "Invalid momentum value: {}".format(momentum)
          )
      if weight_decay < 0.0:
          raise ValueError(
              "Invalid weight_decay value: {}".format(weight_decay)
          )
  
      defaults = dict(
          lr=lr,
          momentum=momentum,
          dampening=dampening,
          weight_decay=weight_decay,
          nesterov=nesterov,
      )
      if nesterov and (momentum <= 0 or dampening != 0):
          raise ValueError(
              "Nesterov momentum requires a momentum and zero dampening"
          )
      super(FusedSGD, self).__init__(params, defaults)
  
      self.wd_after_momentum = wd_after_momentum
      self.materialize_master_grads = materialize_master_grads
      self.most_recent_scale = 1.0
      self.scale_set_by_backward = False
      self.set_grad_none = set_grad_none
  
      if multi_tensor_applier.available:
          import amp_C
  
          # Skip buffer
          self._dummy_overflow_buf = torch.tensor(
              [0],
              dtype=torch.int,
              device=self.param_groups[0]["params"][0].device,
          )
          self.multi_tensor_sgd = amp_C.multi_tensor_sgd
      else:
          raise RuntimeError(
              "apex.optimizers.FusedSGD requires cuda extensions"
          )
  
file_path: apex/apex/optimizers/fused_sgd.py
imports: import torch
  from torch.optim.optimizer import Optimizer, required
  from apex.multi_tensor_apply import multi_tensor_applier
label_desc: Use image node_id 1 to create a new FusedSGD object from inherited base classes: Optimizer with example: obj = FusedSGD(params, lr, momentum, dampening, weight_decay, nesterov, wd_after_momentum, materialize_master_grads, set_grad_none)