code: def gptj_attention_forward(
      self,
      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 = self._split_heads(
          query, self.num_attention_heads, self.head_dim, True
      )
      key = self._split_heads(
          key, self.num_attention_heads, self.head_dim, True
      )
      value = self._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 :]
  
          q_rot, k_rot = apply_rotary_pos_emb(
              q_rot, k_rot, cos, sin, position_ids, "gptj"
          )
  
          key = torch.cat([k_rot, k_pass], dim=-1)
          query = torch.cat([q_rot, q_pass], dim=-1)
      else:
          query, key = apply_rotary_pos_emb(
              query, k_rot, cos, sin, position_ids, "gptj"
          )
  
      batch_size, q_len, _ = hidden_states.size()
  
      key = key.permute(0, 2, 1, 3).contiguous()
      query = query.permute(0, 2, 1, 3).contiguous()
  
      kv_seq_len = key.size(-2)
      device = hidden_states.device
  
      if layer_past is not None:
          kv_seq_len += layer_past[0].size(1)
  
      if layer_past is not None:
          cache_k = layer_past[0]
          cache_v = layer_past[1]
          cache_k = cache_k.permute(0, 2, 1, 3)
          cache_v = cache_v.permute(0, 2, 1, 3)
          past_length = cache_k.size(2)
  
          if cache_k.stride()[1] <= cache_k.size(2) * cache_k.size(3):
              new_cache_k, new_cache_v = extend_kv_cache(
                  batch_size,
                  self.num_attention_heads,
                  self.head_dim,
                  past_length,
                  kv_seq_len + KV_CACHE_ALLOC_BLOCK_LENGTH,
                  dtype=cache_k.dtype,
                  device=device,
              )
              new_cache_k[:] = cache_k
              new_cache_v[:] = cache_v
              cache_k = new_cache_k
              cache_v = new_cache_v
          key, value = append_kv_cache(cache_k, cache_v, key, value)
  
      elif use_cache:
          key_cache, value_cache = init_kv_cache(
              batch_size,
              self.num_attention_heads,
              self.head_dim,
              kv_seq_len,
              kv_seq_len + KV_CACHE_ALLOC_BLOCK_LENGTH,
              dtype=key.dtype,
              device=device,
          )
          key_cache[:] = key
          value_cache[:] = value
          key = key_cache
          value = value_cache
  
      if use_cache is True:
          present = (key.permute(0, 2, 1, 3), value.permute(0, 2, 1, 3))
      else:
          present = None
  
      # compute self-attention: V x Softmax(QK^T)
      attn_output, attn_weights = self._attn(
          query, key, value, attention_mask, head_mask
      )
  
      attn_output = self._merge_heads(
          attn_output, self.num_attention_heads, self.head_dim
      )
      attn_output = self.out_proj(attn_output)
      attn_output = self.resid_dropout(attn_output)
  
      outputs = (attn_output, present)
      if output_attentions:
          outputs += (attn_weights,)
  
      return outputs
  
imports: import torch
  from typing import Optional, Tuple, Union
  from bigdl.llm.transformers.models.utils import init_kv_cache, extend_kv_cache, apply_rotary_pos_emb, append_kv_cache
  from transformers.utils.import_utils import is_torch_fx_proxy
label_desc: Use image node_id 3 for calling a global function with example usage: gptj_attention_forward(hidden_states, layer_past, attention_mask, position_ids, head_mask, use_cache, output_attentions) and returns: outputs