text: import copy
  
  import torch
  import torch.nn as nn
  from mmcv.runner import OptimizerHook
  from mmcv.utils.parrots_wrapper import _BatchNorm
  
  from ..utils import allreduce_grads
  from .utils import cast_tensor_type
  
  
  class Fp16OptimizerHook(OptimizerHook):
      """FP16 optimizer hook.
  
      The steps of fp16 optimizer is as follows.
      1. Scale the loss value.
      2. BP in the fp16 model.
      2. Copy gradients from fp16 model to fp32 weights.
      3. Update fp32 weights.
      4. Copy updated parameters from fp32 weights to fp16 model.
  
      Refer to https://arxiv.org/abs/1710.03740 for more details.
  
      Args:
          loss_scale (float): Scale factor multiplied with loss.
      """
  
      def __init__(self,
                   grad_clip=None,
                   coalesce=True,
                   bucket_size_mb=-1,
                   loss_scale=512.,
                   distributed=True):
          self.grad_clip = grad_clip
          self.coalesce = coalesce
          self.bucket_size_mb = bucket_size_mb
          self.loss_scale = loss_scale
          self.distributed = distributed
  
      def before_run(self, runner):
          # keep a copy of fp32 weights
          runner.optimizer.param_groups = copy.deepcopy(
              runner.optimizer.param_groups)
          # convert model to fp16
          wrap_fp16_model(runner.model)
  
      def copy_grads_to_fp32(self, fp16_net, fp32_weights):
          """Copy gradients from fp16 model to fp32 weight copy."""
          for fp32_param, fp16_param in zip(fp32_weights, fp16_net.parameters()):
              if fp16_param.grad is not None:
                  if fp32_param.grad is None:
                      fp32_param.grad = fp32_param.data.new(fp32_param.size())
                  fp32_param.grad.copy_(fp16_param.grad)
  
      def copy_params_to_fp16(self, fp16_net, fp32_weights):
          """Copy updated params from fp32 weight copy to fp16 model."""
          for fp16_param, fp32_param in zip(fp16_net.parameters(), fp32_weights):
              fp16_param.data.copy_(fp32_param.data)
  
      def after_train_iter(self, runner):
          # clear grads of last iteration
          runner.model.zero_grad()
          runner.optimizer.zero_grad()
          # scale the loss value
          scaled_loss = runner.outputs['loss'] * self.loss_scale
          scaled_loss.backward()
          # copy fp16 grads in the model to fp32 params in the optimizer
          fp32_weights = []
          for param_group in runner.optimizer.param_groups:
              fp32_weights += param_group['params']
          self.copy_grads_to_fp32(runner.model, fp32_weights)
          # allreduce grads
          if self.distributed:
              allreduce_grads(fp32_weights, self.coalesce, self.bucket_size_mb)
          # scale the gradients back
          for param in fp32_weights:
              if param.grad is not None:
                  param.grad.div_(self.loss_scale)
          if self.grad_clip is not None:
              self.clip_grads(fp32_weights)
          # update fp32 params
          runner.optimizer.step()
          # copy fp32 params to the fp16 model
          self.copy_params_to_fp16(runner.model, fp32_weights)
  
  
  def wrap_fp16_model(model):
      # convert model to fp16
      model.half()
      # patch the normalization layers to make it work in fp32 mode
      patch_norm_fp32(model)
      # set `fp16_enabled` flag
      for m in model.modules():
          if hasattr(m, 'fp16_enabled'):
              m.fp16_enabled = True
  
  
  def patch_norm_fp32(module):
      if isinstance(module, (_BatchNorm, nn.GroupNorm)):
          module.float()
          module.forward = patch_forward_method(module.forward, torch.half,
                                                torch.float)
      for child in module.children():
          patch_norm_fp32(child)
      return module
  
  
  def patch_forward_method(func, src_type, dst_type, convert_output=True):
      """Patch the forward method of a module.
  
      Args:
          func (callable): The original forward method.
          src_type (torch.dtype): Type of input arguments to be converted from.
          dst_type (torch.dtype): Type of input arguments to be converted to.
          convert_output (bool): Whether to convert the output back to src_type.
  
      Returns:
          callable: The patched forward method.
      """
  
      def new_forward(*args, **kwargs):
          output = func(*cast_tensor_type(args, src_type, dst_type),
                        **cast_tensor_type(kwargs, src_type, dst_type))
          if convert_output:
              output = cast_tensor_type(output, dst_type, src_type)
          return output
  
      return new_forward
  