code: def get_bert_flash_attention_forward():
      try:
          from xformers.ops import (
              memory_efficient_attention as me_attention,
          )
      except:
          raise ImportError(
              "Error: xformers module is not installed. Please install it to use flash attention."
          )
      from transformers.models.bert.modeling_bert import BertAttention
  
      def forward(
          self: BertAttention,
          hidden_states: torch.Tensor,
          attention_mask: Optional[torch.FloatTensor] = None,
          head_mask: Optional[torch.FloatTensor] = None,
          encoder_hidden_states: Optional[torch.FloatTensor] = None,
          encoder_attention_mask: Optional[torch.FloatTensor] = None,
          past_key_value: Optional[
              Tuple[Tuple[torch.FloatTensor]]
          ] = None,
          output_attentions: Optional[bool] = False,
      ) -> Tuple[torch.Tensor]:
          mixed_query_layer = self.query(hidden_states)
  
          # If this is instantiated as a cross-attention module, the keys
          # and values come from an encoder; the attention mask needs to be
          # such that the encoder's padding tokens are not attended to.
          is_cross_attention = encoder_hidden_states is not None
  
          if is_cross_attention and past_key_value is not None:
              # reuse k,v, cross_attentions
              key_layer = past_key_value[0]
              value_layer = past_key_value[1]
              attention_mask = encoder_attention_mask
          elif is_cross_attention:
              key_layer = self.transpose_for_scores(
                  self.key(encoder_hidden_states)
              )
              value_layer = self.transpose_for_scores(
                  self.value(encoder_hidden_states)
              )
              attention_mask = encoder_attention_mask
          elif past_key_value is not None:
              key_layer = self.transpose_for_scores(
                  self.key(hidden_states)
              )
              value_layer = self.transpose_for_scores(
                  self.value(hidden_states)
              )
              key_layer = torch.cat(
                  [past_key_value[0], key_layer], dim=2
              )
              value_layer = torch.cat(
                  [past_key_value[1], value_layer], dim=2
              )
          else:
              key_layer = self.transpose_for_scores(
                  self.key(hidden_states)
              )
              value_layer = self.transpose_for_scores(
                  self.value(hidden_states)
              )
  
          query_layer = self.transpose_for_scores(mixed_query_layer)
  
          use_cache = past_key_value is not None
          if self.is_decoder:
              # if cross_attention save Tuple(torch.Tensor, torch.Tensor) of all cross attention key/value_states.
              # Further calls to cross_attention layer can then reuse all cross-attention
              # key/value_states (first "if" case)
              # if uni-directional self-attention (decoder) save Tuple(torch.Tensor, torch.Tensor) of
              # all previous decoder key/value_states. Further calls to uni-directional self-attention
              # can concat previous decoder key/value_states to current projected key/value_states (third "elif" case)
              # if encoder bi-directional self-attention `past_key_value` is always `None`
              past_key_value = (key_layer, value_layer)
  
          final_attention_mask = None
          if (
              self.position_embedding_type == "relative_key"
              or self.position_embedding_type == "relative_key_query"
          ):
              query_length, key_length = (
                  query_layer.shape[2],
                  key_layer.shape[2],
              )
              if use_cache:
                  position_ids_l = torch.tensor(
                      key_length - 1,
                      dtype=torch.long,
                      device=hidden_states.device,
                  ).view(-1, 1)
              else:
                  position_ids_l = torch.arange(
                      query_length,
                      dtype=torch.long,
                      device=hidden_states.device,
                  ).view(-1, 1)
              position_ids_r = torch.arange(
                  key_length,
                  dtype=torch.long,
                  device=hidden_states.device,
              ).view(1, -1)
              distance = position_ids_l - position_ids_r
  
              positional_embedding = self.distance_embedding(
                  distance + self.max_position_embeddings - 1
              )
              positional_embedding = positional_embedding.to(
                  dtype=query_layer.dtype
              )  # fp16 compatibility
  
              if self.position_embedding_type == "relative_key":
                  relative_position_scores = torch.einsum(
                      "bhld,lrd->bhlr",
                      query_layer,
                      positional_embedding,
                  )
                  final_attention_mask = relative_position_scores
              elif self.position_embedding_type == "relative_key_query":
                  relative_position_scores_query = torch.einsum(
                      "bhld,lrd->bhlr",
                      query_layer,
                      positional_embedding,
                  )
                  relative_position_scores_key = torch.einsum(
                      "bhrd,lrd->bhlr", key_layer, positional_embedding
                  )
                  final_attention_mask = (
                      relative_position_scores_query
                      + relative_position_scores_key
                  )
  
          scale = 1 / math.sqrt(self.attention_head_size)
          if attention_mask is not None:
              if final_attention_mask != None:
                  final_attention_mask = (
                      final_attention_mask * scale + attention_mask
                  )
              else:
                  final_attention_mask = attention_mask
  
          if final_attention_mask is not None:
              batch_size, src_len = (
                  query_layer.size()[0],
                  query_layer.size()[2],
              )
              tgt_len = key_layer.size()[2]
              final_attention_mask = final_attention_mask.expand(
                  batch_size, self.num_attention_heads, src_len, tgt_len
              ).contiguous()
  
          query_layer = query_layer.permute(0, 2, 1, 3).contiguous()
          key_layer = key_layer.permute(0, 2, 1, 3).contiguous()
          value_layer = value_layer.permute(0, 2, 1, 3).contiguous()
  
          context_layer = me_attention(
              query_layer,
              key_layer,
              value_layer,
              attn_bias=final_attention_mask,
              p=self.dropout.p,
              scale=scale,
          )
          new_context_layer_shape = context_layer.size()[:-2] + (
              self.all_head_size,
          )
          context_layer = context_layer.view(new_context_layer_shape)
  
          outputs = (context_layer, None)
  
          if self.is_decoder:
              outputs = outputs + (past_key_value,)
          return outputs
  
      return forward
  
imports: import math
  import warnings
  from typing import List, Optional, Tuple, Union
  import torch
  from torch.nn import BCEWithLogitsLoss, CrossEntropyLoss, MSELoss
  from transformers.modeling_outputs import BaseModelOutputWithPoolingAndCrossAttentions, CausalLMOutputWithCrossAttentions, MaskedLMOutput, MultipleChoiceModelOutput, NextSentencePredictorOutput, QuestionAnsweringModelOutput, SequenceClassifierOutput, TokenClassifierOutput
  from transformers.models.bert.modeling_bert import BertForMaskedLM, BertForMultipleChoice, BertForNextSentencePrediction, BertForPreTraining, BertForPreTrainingOutput, BertForQuestionAnswering, BertForSequenceClassification, BertForTokenClassification, BertLMHeadModel, BertModel
  from transformers.utils import logging
  from colossalai.pipeline.stage_manager import PipelineStageManager
  from colossalai.shardformer import ShardConfig
  from colossalai.shardformer.layer._operation import gather_forward_split_backward, split_forward_gather_backward
label_desc: Use image node_id 1 for calling a global function with example usage: get_bert_flash_attention_forward() and returns: forward, outputs