class_docstr: Implements LAMB algorithm.
  
  `FusedLAMB` requires CUDA extensions which can be built during installation or runtime.
  
  This version of fused LAMB implements 2 fusions.
  
    * Fusion of the LAMB 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:`colossalai.nn.optimizer.FusedLAMB`'s usage is identical to any ordinary Pytorch optimizer
  
  :class:`colossalai.nn.optimizer.FusedLAMB` may be used with or without Amp.
  
  LAMB was proposed in `Large Batch Optimization for Deep Learning: Training BERT in 76 minutes`_.
  
  Arguments:
      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 norm. (default: (0.9, 0.999))
      eps (float, optional): term added to the denominator to improve
          numerical stability. (default: 1e-6)
      weight_decay (float, optional): weight decay (L2 penalty) (default: 0.01)
      amsgrad (boolean, optional): whether to use the AMSGrad variant of this
          algorithm from the paper `On the Convergence of Adam and Beyond`_
          NOT SUPPORTED now! (default: False)
      adam_w_mode (boolean, optional): Apply L2 regularization or weight decay
          True for decoupled weight decay(also known as AdamW) (default: True)
      grad_averaging (bool, optional): whether apply (1-beta2) to grad when
          calculating running averages of gradient. (default: True)
      set_grad_none (bool, optional): whether set grad to None when zero_grad()
          method is called. (default: True)
      max_grad_norm (float, optional): value used to clip global grad norm
          (default: 1.0)
      use_nvlamb (boolean, optional): Apply adaptive learning rate to 0.0
          weight decay parameter (default: False)
  
  .. _Large Batch Optimization for Deep Learning: Training BERT in 76 minutes:
      https://arxiv.org/abs/1904.00962
  .. _On the Convergence of Adam and Beyond:
      https://openreview.net/forum?id=ryQu7f-RZ
code: def __init__(
      self,
      params,
      lr=1e-3,
      bias_correction=True,
      betas=(0.9, 0.999),
      eps=1e-6,
      weight_decay=0.01,
      amsgrad=False,
      adam_w_mode=True,
      grad_averaging=True,
      set_grad_none=True,
      max_grad_norm=1.0,
      use_nvlamb=False,
  ):
      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,
          grad_averaging=grad_averaging,
          max_grad_norm=max_grad_norm,
      )
      super(FusedLAMB, self).__init__(params, defaults)
      if multi_tensor_applier.available:
          from colossalai.kernel.op_builder import FusedOptimBuilder
  
          fused_optim = FusedOptimBuilder().load()
  
          self.multi_tensor_l2norm = fused_optim.multi_tensor_l2norm
          # Skip buffer
          self._dummy_overflow_buf = torch.tensor(
              [0],
              dtype=torch.int,
              device=self.param_groups[0]["params"][0].device,
          )
          self.multi_tensor_lamb = fused_optim.multi_tensor_lamb
      else:
          raise RuntimeError("FusedLAMB requires cuda extensions")
  
      self.adam_w_mode = 1 if adam_w_mode else 0
      self.set_grad_none = set_grad_none
      self.use_nvlamb = use_nvlamb
  
imports: import torch
  from colossalai.utils import multi_tensor_applier
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, weight_decay, amsgrad, adam_w_mode, grad_averaging, set_grad_none, max_grad_norm, use_nvlamb)