class_docstr: Implements the LAMB algorithm. Currently GPU-only.
  
  LAMB was proposed in `Large Batch Optimization for Deep Learning: Training BERT in 76 minutes.
  https://arxiv.org/abs/1904.00962
  
  Arguments:
      params (iterable): iterable of parameters to optimize or dicts defining
          parameter groups.
      lr (float, optional): learning rate. (default: 1e-3)
      bias_correction (bool, optional): bias correction (default: True)
      betas (Tuple[float, float], optional): coefficients used for computing
          running averages of gradient and its square. (default: (0.9, 0.999))
      eps (float, optional): term added to the denominator to improve
          numerical stability. (default: 1e-8)
      eps_inside_sqrt (boolean, optional): in the 'update parameters' step,
          adds eps to the bias-corrected second moment estimate before
          evaluating square root instead of adding it to the square root of
          second moment estimate as in the original paper. (default: False)
      weight_decay (float, optional): weight decay (L2 penalty) (default: 0)
      max_grad_norm (float, optional): value used to clip global grad norm
          (default: 0.0)
      max_coeff(float, optional): maximum value of the lamb coefficient (default: 10.0)
      min_coeff(float, optional): minimum value of the lamb coefficient (default: 0.01)
      amsgrad (boolean, optional): NOT SUPPORTED in FusedLamb!
code: def __init__(
      self,
      params,
      lr=1e-3,
      bias_correction=True,
      betas=(0.9, 0.999),
      eps=1e-8,
      eps_inside_sqrt=False,
      weight_decay=0.0,
      max_grad_norm=0.0,
      max_coeff=10.0,
      min_coeff=0.01,
      amsgrad=False,
  ):
      self.fused_lamb_cuda = FusedLambBuilder().load()
  
      if amsgrad:
          raise RuntimeError(
              "FusedLamb does not support the AMSGrad variant."
          )
      defaults = dict(
          lr=lr,
          bias_correction=bias_correction,
          betas=betas,
          eps=eps,
          weight_decay=weight_decay,
          max_grad_norm=max_grad_norm,
          max_coeff=max_coeff,
          min_coeff=min_coeff,
      )
      super(FusedLamb, self).__init__(params, defaults)
      self.eps_mode = 0 if eps_inside_sqrt else 1
      self.lamb_coeffs = []
  
imports: import types
  import torch
  from ..op_builder import FusedLambBuilder
label_desc: Use image node_id 1 to create a new FusedLamb object from inherited base classes: torch.optim with example: obj = FusedLamb(params, lr, bias_correction, betas, eps, eps_inside_sqrt, weight_decay, max_grad_norm, max_coeff, min_coeff, amsgrad)