code: def get_vit_flash_self_attention_forward():
      from transformers.models.vit.modeling_vit import ViTSelfAttention
  
      from colossalai.kernel.cuda_native import ColoAttention
  
      def transpose_for_scores(
          x: torch.Tensor, num_attention_heads, attention_head_size
      ) -> torch.Tensor:
          new_x_shape = x.size()[:-1] + (
              num_attention_heads,
              attention_head_size,
          )
          x = x.view(new_x_shape)
          return x
  
      def forward(
          self: ViTSelfAttention,
          hidden_states: torch.Tensor,
          head_mask: Optional[torch.Tensor] = None,
          output_attentions: bool = False,
      ) -> Union[
          Tuple[torch.Tensor, torch.Tensor], Tuple[torch.Tensor]
      ]:
          mixed_query_layer = self.query(hidden_states)
  
          key_layer = transpose_for_scores(
              self.key(hidden_states),
              self.num_attention_heads,
              self.attention_head_size,
          )
          value_layer = transpose_for_scores(
              self.value(hidden_states),
              self.num_attention_heads,
              self.attention_head_size,
          )
          query_layer = transpose_for_scores(
              mixed_query_layer,
              self.num_attention_heads,
              self.attention_head_size,
          )
  
          scale = 1.0 / math.sqrt(self.attention_head_size)
          attention = ColoAttention(
              embed_dim=self.all_head_size,
              num_heads=self.num_attention_heads,
              dropout=self.dropout.p,
              scale=scale,
          )
          context_layer = attention(query_layer, key_layer, value_layer)
  
          outputs = (context_layer,)
  
          return outputs
  
      return forward
  
file_path: ColossalAI/colossalai/shardformer/modeling/vit.py
imports: import math
  from typing import List, Optional, Tuple, Union
  import torch
  from transformers.models.vit.modeling_vit import BaseModelOutput, ViTEncoder
  from transformers.utils import logging
  from colossalai.pipeline.stage_manager import PipelineStageManager
label_desc: Use image node_id 5 for calling a global function with example usage: get_vit_flash_self_attention_forward() and returns: forward, x, outputs