code: def get_bloom_flash_attention_forward(enabel_jit_fused=False):
      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.bloom.modeling_bloom import (
          BloomAttention,
      )
  
      def forward(
          self: BloomAttention,
          hidden_states: torch.Tensor,
          residual: torch.Tensor,
          alibi: torch.Tensor,
          attention_mask: torch.Tensor,
          layer_past: Optional[
              Tuple[torch.Tensor, torch.Tensor]
          ] = None,
          head_mask: Optional[torch.Tensor] = None,
          use_cache: bool = False,
          output_attentions: bool = False,
      ):
          fused_qkv = self.query_key_value(hidden_states)
          (query_layer, key_layer, value_layer) = self._split_heads(
              fused_qkv
          )
          batch_size, tgt_len, _, _ = query_layer.size()
  
          _, kv_length, _, _ = key_layer.size()
  
          proj_shape = (
              batch_size,
              tgt_len,
              self.num_heads,
              self.head_dim,
          )
          query_layer = query_layer.contiguous().view(*proj_shape)
          key_layer = key_layer.contiguous().view(*proj_shape)
          value_layer = value_layer.contiguous().view(*proj_shape)
  
          if layer_past is not None:
              past_key, past_value = layer_past
              # concatenate along seq_length dimension:
              #  - key: [batch_size * self.num_heads, head_dim, kv_length]
              #  - value: [batch_size * self.num_heads, kv_length, head_dim]
              key_layer = torch.cat((past_key, key_layer), dim=1)
              value_layer = torch.cat((past_value, value_layer), dim=1)
  
          if use_cache is True:
              present = (key_layer, value_layer)
          else:
              present = None
  
          tgt_len = key_layer.size()[1]
  
          attention_numerical_mask = torch.zeros(
              (batch_size, self.num_heads, tgt_len, kv_length),
              dtype=torch.float32,
              device=query_layer.device,
              requires_grad=True,
          )
          attention_numerical_mask = (
              attention_numerical_mask
              + alibi.view(batch_size, self.num_heads, 1, kv_length)
              * self.beta
          )
          attention_numerical_mask = torch.masked_fill(
              attention_numerical_mask,
              attention_mask,
              torch.finfo(torch.float32).min,
          )
          attention_numerical_mask = attention_numerical_mask.to(
              query_layer.dtype
          )
  
          context_layer = me_attention(
              query_layer,
              key_layer,
              value_layer,
              attn_bias=attention_numerical_mask,
              scale=self.inv_norm_factor,
              p=self.attention_dropout.p,
          )
          context_layer = context_layer.reshape(
              -1, kv_length, self.hidden_size
          )
          if self.pretraining_tp > 1 and self.slow_but_exact:
              slices = self.hidden_size / self.pretraining_tp
              output_tensor = torch.zeros_like(context_layer)
              for i in range(self.pretraining_tp):
                  output_tensor = output_tensor + F.linear(
                      context_layer[
                          :, :, int(i * slices) : int((i + 1) * slices)
                      ],
                      self.dense.weight[
                          :, int(i * slices) : int((i + 1) * slices)
                      ],
                  )
          else:
              output_tensor = self.dense(context_layer)
  
          # TODO to replace with the bias_dropout_add function in jit
          output_tensor = self.dropout_add(
              output_tensor,
              residual,
              self.hidden_dropout,
              self.training,
          )
          outputs = (output_tensor, present, None)
  
          return outputs
  
      return forward
  
imports: import warnings
  from typing import List, Optional, Tuple, Union
  import torch
  import torch.distributed
  from torch.distributed import ProcessGroup
  from torch.nn import BCEWithLogitsLoss, CrossEntropyLoss, MSELoss
  from torch.nn import functional
  from transformers.modeling_outputs import BaseModelOutputWithPastAndCrossAttentions, CausalLMOutputWithCrossAttentions, QuestionAnsweringModelOutput, SequenceClassifierOutputWithPast, TokenClassifierOutput
  from transformers.models.bloom.modeling_bloom import BloomForCausalLM, BloomForQuestionAnswering, BloomForSequenceClassification, BloomForTokenClassification, BloomModel
  from transformers.utils import logging
  from colossalai.pipeline.stage_manager import PipelineStageManager
  from colossalai.shardformer.layer._operation import gather_forward_split_backward, split_forward_gather_backward
  from colossalai.shardformer.shard import ShardConfig
label_desc: Use image node_id 2 for calling a global function with example usage: get_bloom_flash_attention_forward(enabel_jit_fused) and returns: forward, outputs