class_docstr: NOTE: If you are using this feature with a container that
  also inherits from `HybridEngineContainer`, ensure that `MetaTensorContainer`
  is inherited before `HybridEngineContainer` in the class definition.
class_docstr_tok: ["NOTE",":","If","you","are","using","this","feature","with","a","container","that","also","inherits","from","`","HybridEngineContainer","`",",","ensure","that","`","MetaTensorContainer","`","is","inherited","before","`","HybridEngineContainer","`","in","the","class","definition","."]
docstr: Load all the transformer parameter from the checkpoint file (sd).
  In addition to the parameter names, we require two
  more parameters to help read the data correctly
  from the checkpoint and split the qkv heads in the
  right order:
      1. `use_load_prefix` (Default: False): this specifies
          whether we need to use the name of first abstraction
          layer of the model for searching the parameter's name
          in a checkpoint file. For more information of how this
          is used please see
          https://github.com/microsoft/DeepSpeed/blob/master/deepspeed/module_inject/load_checkpoint.py
      2. `split_qkv` (Default: True): we use this flag when splitting
          the qkv parameter into heads. If it is False, it means the heads
          of q, k, and v are stored together and needs to split in the
          DeepSpeed-Inference API.
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code: def load_params(
      self, module, sd, weight_quantizer, mp_replace, prefix
  ):
      """
      Load all the transformer parameter from the checkpoint file (sd).
      In addition to the parameter names, we require two
      more parameters to help read the data correctly
      from the checkpoint and split the qkv heads in the
      right order:
          1. `use_load_prefix` (Default: False): this specifies
              whether we need to use the name of first abstraction
              layer of the model for searching the parameter's name
              in a checkpoint file. For more information of how this
              is used please see
              https://github.com/microsoft/DeepSpeed/blob/master/deepspeed/module_inject/load_checkpoint.py
          2. `split_qkv` (Default: True): we use this flag when splitting
              the qkv parameter into heads. If it is False, it means the heads
              of q, k, and v are stored together and needs to split in the
              DeepSpeed-Inference API.
      """
      raise NotImplementedError(
          "A load_params() function must be defined in the model container \
                                    when inheriting the MetaTensorContainer feature"
      )
  
file_path: deepspeed/deepspeed/module_inject/containers/features/meta_tensor.py
imports: from abc import ABC, abstractmethod
  from packaging import version
  import torch
label_desc: Use image node_id 6 for calling the MetaTensorContainer obj's underlying member method code with example usage: obj.load_params(module, sd, weight_quantizer, mp_replace, prefix) without return types