repo_description: MindSpore +  🤗Huggingface: Run any Transformers/Diffusers model on MindSpore with seamless compatibility and acceleration.
repo_topics: deep-learning diffusion-models huggingface large-language-models llm mindspore natural-language-processing nlp nlp-library python vlm
code: _overwrite_module_params_on_conversion: bool = False
  _swap_module_params_on_conversion: bool = False
  
  
  def set_overwrite_module_params_on_conversion(value: bool) -> None:
      """
      Sets whether to assign new tensors to the parameters instead of changing the
      existing parameters in-place when converting an ``nn.Module``.
  
      When enabled, the following methods will assign new parameters to the module:
  
      #. ``module.{device}()`` (e.g. :meth:`nn.Module.cuda()`) for moving a module between devices
      #. ``module.{dtype}()`` (e.g. :meth:`nn.Module.float()`) for converting a module to a different dtype
      #. :meth:`nn.Module.to`
      #. :meth:`nn.Module.to_empty`
  
      Args:
          value (bool): Whether to assign new tensors or not.
  
      """
      global _overwrite_module_params_on_conversion
      _overwrite_module_params_on_conversion = value
  
  
  def get_overwrite_module_params_on_conversion() -> bool:
      """
      Returns whether to assign new tensors to the parameters instead of changing the
      existing parameters in-place when converting an :class:`torch.nn.Module`. Defaults to ``False``.
  
      See :func:`~torch.__future__.set_overwrite_module_params_on_conversion` for more information.
      """
      return _overwrite_module_params_on_conversion
  
  
  def set_swap_module_params_on_conversion(value: bool) -> None:
      """
      Sets whether to use :func:`~torch.utils.swap_tensors` instead of setting ``.data`` to
      change the existing parameters in-place when converting an ``nn.Module`` and instead
      of ``param.copy_(state_dict[key])`` when loading a state dict into an ``nn.Module``.
  
      .. note::
          This function takes precedence over :func:`~torch.__future__.get_overwrite_module_params_on_conversion`
  
      When enabled, the following methods will swap the existing parameters in-place:
  
      #. ``module.{device}()`` (e.g. :meth:`nn.Module.cuda()`) for moving a module between devices
      #. ``module.{dtype}()`` (e.g. :meth:`nn.Module.float()`) for converting a module to a different dtype
      #. :meth:`nn.Module.to`
      #. :meth:`nn.Module.to_empty`
      #. :meth:`nn.Module.load_state_dict`
  
      The semantics for :meth:`~nn.Module.load_state_dict` when this is set are as follows:
  
      #. For each parameter/buffer, its corresponding ``state_dict['key']`` is transformed via
         :meth:`~torch.Tensor.module_load` (i.e. ``res = param.module_load(state_dict['key'])``)
      #. If necessary, ``res`` will be wrapped in an :class:`~nn.Parameter`
      #. The parameter/buffer in the module will be swapped via :func:`~torch.utils.swap_tensors`
         with ``res``
  
      Args:
          value (bool): Whether to use :func:`~torch.utils.swap_tensors` or not.
  
      """
      global _swap_module_params_on_conversion
      _swap_module_params_on_conversion = value
  
  
  def get_swap_module_params_on_conversion() -> bool:
      """
      Returns whether to use :func:`~torch.utils.swap_tensors` instead of setting .data to
      change the existing parameters in-place when converting an ``nn.Module``. Defaults to ``False``.
  
      See :func:`~torch.__future__.set_swap_module_params_on_conversion` for more information.
      """
      return _swap_module_params_on_conversion