code: def split_fused_qkv_in_gpt2_style(
      qkv: torch.Tensor,
      n_fused: int,
      process_group: ProcessGroup,
      is_transposed: bool = False,
  ):
      """
      The fused qkv tensor looks like [Q1, Q2, K1, K2, V1, V2], this function will split them into [Q1, K1, V1] and [Q2, K2, V2].
  
      Args:
          qkv (torch.Tensor): The fused qkv tensor.
          n_fused (int): The number items fused together, defaults to 3 (query, key and value).
          process_group (ProcessGroup): The process group for distributed communication.
          is_transposed (bool): generally the tensor is the shape of (out_features, in_features). Set this to True if the tensor is in the shape (in_features, out_features).
      """
      # get the number of slice for the fused qkv
      rank = dist.get_rank(group=process_group)
      world_size = dist.get_world_size(group=process_group)
      order = torch.arange(world_size * n_fused)
  
      # split the fused qkv
      # from
      # [Q, K, V]
      # to
      # [Q1, Q2, K1, K2, V1, V2]
      if is_transposed:
          weight_chunks = torch.chunk(qkv, world_size * n_fused, dim=-1)
      else:
          weight_chunks = torch.chunk(qkv, world_size * n_fused, dim=0)
  
      # rearrange the slice into the final order
      # from
      # [Q1, Q2, K1, K2, V1, V2]
      # to
      # [Q1, K1, V1], [Q2, K2, V2]
      weight_chunks_of_current_rank = [
          weight_chunks[i] for i in order[rank::world_size]
      ]
  
      if is_transposed:
          weight_of_current_rank = torch.cat(
              weight_chunks_of_current_rank, dim=-1
          )
      else:
          weight_of_current_rank = torch.cat(
              weight_chunks_of_current_rank, dim=0
          )
      return weight_of_current_rank
  
imports: import math
  from typing import Callable, List, Optional, Tuple, Union
  import torch
  import torch.distributed
  import torch.nn
  from torch import Tensor
  from torch.distributed import ProcessGroup
  from torch.nn.parameter import Parameter
  from colossalai.lazy import LazyInitContext
  from colossalai.nn import init
  from colossalai.nn.layer.utils import divide
  from colossalai.tensor.d_tensor.api import customized_distributed_tensor_to_existing_param, distribute_tensor_with_customization, is_customized_distributed_tensor, is_distributed_tensor, shard_rowwise, sharded_tensor_to_existing_param
  from ._operation import gather_forward_split_backward, linear_reducescatter_forward_gather_backward, linear_with_async_comm, matmul_gather_forward_reducescatter_backward, matmul_with_async_comm, reduce_backward, reduce_forward, split_forward_gather_backward
  from .parallel_module import ParallelModule
  from .utils import create_randomizer_with_offset
label_desc: Use image node_id 1 for calling a global function with example usage: split_fused_qkv_in_gpt2_style(qkv, n_fused, process_group, is_transposed) and returns: weight_of_current_rank