class_docstr: Float16 optimizer for fp16 and bf16 data types.
  
  Arguments:
      optimizer: base optimizer such as Adam or SGD
      clip_grad: clip gradeints with this global L2 norm. Note
          that clipping is ignored if clip_grad == 0
      log_num_zeros_in_grad: return number of zeros in the gradients.
      check_for_nan_in_grad: check if gradients have a NaN.
      params_have_main_grad: flag indicating if parameters have
          a `main_grad` field. If this is set, we are assuming
          that the model parameters are store in the `main_grad`
          field instead of the typical `grad` field. This happens
          for the DDP cases where there is a continuous buffer
          holding the gradients. For example for bfloat16, we want
          to do gradient accumulation and all-reduces in float32
          and as a result we store those gradients in the main_grad.
          Note that main grad is not necessarily in float32.
      fp16: if true, the model is running in fp16.
      bf16: if true, the model is running in bfloat16.
      grad_scaler: used for scaling gradients. Note that this can be
          None. This case happens when `bf16 = True` and we don't
          use any loss scale. Note that for `bf16 = True`, we can have
          a constnat gradient scaler. Also for `bf16 = False`, we
          always require a grad scaler.
      models: list of models (i.e., the virtual pipelining models). This
          is used by the distributed optimizer for mapping parameters.
code: def _copy_model_grads_to_main_grads(self):
      # This only needs to be done for the float16 group.
      for model_group, main_group in zip(
          self.float16_groups, self.fp32_from_float16_groups
      ):
          for model_param, main_param in zip(model_group, main_group):
              if self.params_have_main_grad and hasattr(
                  model_param, "main_grad"
              ):
                  main_param.grad = model_param.main_grad.float()
              else:
                  if model_param.grad is not None:
                      main_param.grad = model_param.grad.float()
  
              # Safe to deallocate model's grad/main_grad after copying.
              # (If using contiguous buffers, main_grad's memory should
              # persist and therefore should not be deallocated.)
              model_param.grad = None
  
      # For fp32 grads, we need to reset the grads to main grad.
      if self.params_have_main_grad:
          for model_group in self.fp32_from_fp32_groups:
              for model_param in model_group:
                  model_param.grad = model_param.main_grad
  
imports: from abc import ABC
  from abc import abstractmethod
  from apex.multi_tensor_apply import multi_tensor_applier
  import amp_C
  import torch
  from megatron import get_timers
  from megatron import print_rank_0
  from megatron.core import mpu, tensor_parallel
  from megatron.model import Float16Module
  from megatron.model.module import param_is_not_shared
  from .clip_grads import clip_grad_norm_fp32, count_zeros_fp32
label_desc: Use image node_id 5 for calling the Float16OptimizerWithFloat16Params obj's underlying member method code with example usage: obj._copy_model_grads_to_main_grads() without return types