code: def get_gptj_flash_attention_forward():
      from transformers.models.gptj.modeling_gptj import GPTJAttention
  
      from colossalai.kernel.cuda_native import (
          AttnMaskType,
          ColoAttention,
      )
  
      def split_heads(
          tensor, num_attention_heads, attn_head_size, rotary
      ):
          """
          Splits hidden dim into attn_head_size and num_attention_heads
          """
          new_shape = tensor.size()[:-1] + (
              num_attention_heads,
              attn_head_size,
          )
          tensor = tensor.view(new_shape)
          if rotary or len(tensor.shape) in [4, 5]:
              return tensor
          else:
              raise ValueError(
                  f"Input tensor rank should be one of [4, 5], but is: {len(tensor.shape)}"
              )
  
      def forward(
          self: GPTJAttention,
          hidden_states: torch.FloatTensor,
          layer_past: Optional[Tuple[torch.Tensor]] = None,
          attention_mask: Optional[torch.FloatTensor] = None,
          position_ids: Optional[torch.LongTensor] = None,
          head_mask: Optional[torch.FloatTensor] = None,
          use_cache: Optional[bool] = False,
          output_attentions: Optional[bool] = False,
      ) -> Union[
          Tuple[torch.Tensor, Tuple[torch.Tensor]],
          Optional[
              Tuple[
                  torch.Tensor,
                  Tuple[torch.Tensor],
                  Tuple[torch.Tensor, ...],
              ]
          ],
      ]:
          query = self.q_proj(hidden_states)
          key = self.k_proj(hidden_states)
          value = self.v_proj(hidden_states)
  
          query = split_heads(
              query, self.num_attention_heads, self.head_dim, True
          )
          key = split_heads(
              key, self.num_attention_heads, self.head_dim, True
          )
          value = split_heads(
              value, self.num_attention_heads, self.head_dim, False
          )
  
          if is_torch_fx_proxy(position_ids) or torch.jit.is_tracing():
              # The logic to conditionally copy to GPU could not be traced, so we do this
              # every time in the torch.fx case
              embed_positions = get_embed_positions(
                  self.embed_positions, position_ids
              )
          else:
              embed_positions = self._get_embed_positions(position_ids)
  
          repeated_position_ids = position_ids.unsqueeze(-1).repeat(
              1, 1, embed_positions.shape[-1]
          )
          sincos = torch.gather(
              embed_positions, 1, repeated_position_ids
          )
          sin, cos = torch.split(sincos, sincos.shape[-1] // 2, dim=-1)
  
          if self.rotary_dim is not None:
              k_rot = key[:, :, :, : self.rotary_dim]
              k_pass = key[:, :, :, self.rotary_dim :]
  
              q_rot = query[:, :, :, : self.rotary_dim]
              q_pass = query[:, :, :, self.rotary_dim :]
  
              k_rot = apply_rotary_pos_emb(k_rot, sin, cos)
              q_rot = apply_rotary_pos_emb(q_rot, sin, cos)
  
              key = torch.cat([k_rot, k_pass], dim=-1)
              query = torch.cat([q_rot, q_pass], dim=-1)
          else:
              key = apply_rotary_pos_emb(key, sin, cos)
              query = apply_rotary_pos_emb(query, sin, cos)
  
          # key = key.permute(0, 2, 1, 3)
          # query = query.permute(0, 2, 1, 3)
          key = key.to(dtype=value.dtype)  # fp16 compatability
          query = query.to(dtype=value.dtype)
  
          if layer_past is not None:
              past_key = layer_past[0]
              past_value = layer_past[1]
              key = torch.cat((past_key, key), dim=1)
              value = torch.cat((past_value, value), dim=1)
  
          if use_cache is True:
              present = (key, value)
          else:
              present = None
  
          # use AttnMaskType and ColoAttention
          attn_mask_type = AttnMaskType.causal
          flash_attention_mask = None
          if attention_mask != None:
              if attn_mask_type == AttnMaskType.causal:
                  attn_mask_type == AttnMaskType.paddedcausal
              else:
                  attn_mask_type = AttnMaskType.padding
              flash_attention_mask = ~(
                  attention_mask[:, :, -1].squeeze(1).to(torch.bool)
              ).contiguous()
  
          # use coloattention
          scale = value.size(-1) ** -0.5
  
          attention = ColoAttention(
              embed_dim=self.embed_dim,
              num_heads=self.num_attention_heads,
              dropout=self.attn_dropout.p,
              scale=scale,
          )
  
          attn_output = attention(
              query,
              key,
              value,
              attn_mask=flash_attention_mask,
              attn_mask_type=attn_mask_type,
          )
  
          attn_output = self.out_proj(attn_output)
          attn_output = self.resid_dropout(attn_output)
          outputs = (attn_output, present, None)
  
          return outputs  # a, present, (attentions)
  
      return forward
  
file_path: ColossalAI/colossalai/shardformer/modeling/gptj.py
imports: from typing import Dict, List, Optional, Tuple, Union
  import torch
  from torch.nn import BCEWithLogitsLoss, CrossEntropyLoss, MSELoss
  from transformers.modeling_outputs import BaseModelOutputWithPast, CausalLMOutputWithPast, QuestionAnsweringModelOutput, SequenceClassifierOutputWithPast
  from transformers.models.gptj.modeling_gptj import GPTJForCausalLM, GPTJForQuestionAnswering, GPTJForSequenceClassification, GPTJModel, apply_rotary_pos_emb, get_embed_positions
  from transformers.utils import is_torch_fx_proxy, 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 1 for calling a global function with example usage: get_gptj_flash_attention_forward() and returns: forward, outputs, tensor