text: #!/usr/bin/env python
  # -*- encoding: utf-8 -*-
  
  import torch.distributed as dist
  
  from colossalai.registry import DIST_GROUP_INITIALIZER
  from .process_group_initializer import ProcessGroupInitializer
  from ..parallel_mode import ParallelMode
  
  
  @DIST_GROUP_INITIALIZER.register_module
  class Initializer_Tensor(ProcessGroupInitializer):
      """A ProcessGroupInitializer for tensor parallelism.
  
      Args:
          rank (int): The rank of current process.
          world_size (int): Size of whole communication world.
          config (Config): Running configuration.
          data_parallel_size (int): Size of data parallel.
          pipeline_parallel_size (int): Size of pipeline parallel.
          tensor_parallel_size (int): Size of tensor parallel.
      """
  
      def __init__(self, *args, **kwargs):
          super().__init__(*args, **kwargs)
          self.num_tensor_parallel_group = self.world_size // self.tensor_parallel_size
  
      def init_dist_group(self):
          """Initialize tensor parallel groups, and assign local_ranks and groups to each gpu.
  
          Returns:
              Tuple (local_rank, group_world_size, process_group, ranks_in_group, mode):
                  A Tensor parallelism's information tuple.
          """
          local_rank = None
          ranks_in_group = None
          process_group = None
          cpu_group = None
          group_world_size = None
          mode = ParallelMode.TENSOR
  
          for i in range(self.num_tensor_parallel_group):
              ranks = [i * self.tensor_parallel_size + j for j in range(self.tensor_parallel_size)]
              group = dist.new_group(ranks)
              group_cpu = dist.new_group(ranks, backend='gloo') if dist.get_backend() != 'gloo' else group
  
              if self.rank in ranks:
                  local_rank = ranks.index(self.rank)
                  group_world_size = len(ranks)
                  process_group = group
                  cpu_group = group_cpu
                  ranks_in_group = ranks
  
          return local_rank, group_world_size, process_group, cpu_group, ranks_in_group, mode
  