docstr: Partitioning Parameters that were not partitioned usually if parameters
  of modules whose input parameters do not require grad computation do not
  trigger post call and will therefore will remain unpartitioned
docstr_tok: ["Partitioning","Parameters","that","were","not","partitioned","usually","if","parameters","of","modules","whose","input","parameters","do","not","require","grad","computation","do","not","trigger","post","call","and","will","therefore","will","remain","unpartitioned"]
code: def partition_all_parameters(self):
      """Partitioning Parameters that were not partitioned usually if parameters
      of modules whose input parameters do not require grad computation do not
      trigger post call and will therefore will remain unpartitioned"""
      self.get_param_coordinator(
          training=self.module.training
      ).release_and_reset_all(self.module)
      for param in iter_params(self.module, recurse=True):
          if param.ds_status != ZeroParamStatus.NOT_AVAILABLE:
              raise RuntimeError(
                  f"{param.ds_summary()} expected to be released"
              )
  
imports: import sys
  import torch
  from collections import OrderedDict
  from deepspeed.runtime.utils import see_memory_usage
  from deepspeed.runtime.zero.offload_config import OffloadDeviceEnum
  from deepspeed.runtime.zero.partition_parameters import _init_external_params
  from deepspeed.runtime.zero.partition_parameters import *
  from deepspeed.runtime.zero.partitioned_param_coordinator import PartitionedParameterCoordinator, InflightParamRegistry, iter_params
  from deepspeed import comm
  from deepspeed.accelerator import get_accelerator
label_desc: Use image node_id 2 for calling the DeepSpeedZeRoOffload obj's underlying member method code with example usage: obj.partition_all_parameters() without return types