code: def gptneox_attention_forward(
      self,
      hidden_states: torch.FloatTensor,
      attention_mask: torch.FloatTensor,
      position_ids: torch.LongTensor,
      head_mask: Optional[torch.FloatTensor] = None,
      layer_past: Optional[Tuple[torch.Tensor]] = None,
      use_cache: Optional[bool] = False,
      output_attentions: Optional[bool] = False,
  ):
      bsz, q_len, _ = hidden_states.size()
      device = hidden_states.device
      has_layer_past = layer_past is not None
  
      # Compute QKV
      # Attention heads [batch, seq_len, hidden_size]
      #   --> [batch, seq_len, (np * 3 * head_size)]
      qkv = self.query_key_value(hidden_states)
  
      # [batch, seq_len, (num_heads * 3 * head_size)]
      #   --> [batch, seq_len, num_heads, 3 * head_size]
      new_qkv_shape = qkv.size()[:-1] + (
          self.num_attention_heads,
          3 * self.head_size,
      )
      qkv = qkv.view(*new_qkv_shape)
  
      # [batch, seq_len, num_attention_heads, 3 * head_size]
      #   --> 3 [batch, num_attention_heads, seq_len, head_size]
      query = qkv[..., : self.head_size].permute(0, 2, 1, 3)
      key = qkv[..., self.head_size : 2 * self.head_size].permute(
          0, 2, 1, 3
      )
      value = qkv[..., 2 * self.head_size :].permute(0, 2, 1, 3)
  
      # Compute rotary embeddings on rotary_ndims
      query_rot = query[..., : self.rotary_ndims]
      query_pass = query[..., self.rotary_ndims :]
      key_rot = key[..., : self.rotary_ndims]
      key_pass = key[..., self.rotary_ndims :]
  
      # Compute token offset for rotary embeddings (when decoding)
      seq_len = key.shape[-2]
      if has_layer_past:
          seq_len += layer_past[0].shape[-2]
  
      use_fuse_rope = query.device.type == "xpu"
      use_fuse_rope = use_fuse_rope and not (
          self.training and query.requires_grad
      )
  
      if use_fuse_rope:
          query, key = apply_rotary_pos_emb_no_cache_xpu(
              query_rot, key_rot, position_ids, "gpt_neox"
          )
      else:
          cos, sin = self.rotary_emb(value, seq_len=seq_len)
          query, key = apply_rotary_pos_emb(
              query_rot, key_rot, cos, sin, position_ids, "gpt_neox"
          )
  
      query = torch.cat((query, query_pass), dim=-1)
      key = torch.cat((key, key_pass), dim=-1)
  
      # Cache QKV values
      if has_layer_past:
          past_key = layer_past[0]
          past_value = layer_past[1]
          if past_key.stride()[1] <= past_key.size(2) * past_key.size(
              3
          ):
              # allocate new
              new_past_key, new_past_value = extend_kv_cache(
                  bsz,
                  self.num_attention_heads,
                  self.head_size,
                  past_key.size(2),
                  seq_len + KV_CACHE_ALLOC_BLOCK_LENGTH,
                  dtype=past_key.dtype,
                  device=device,
              )
              new_past_key[:] = past_key
              new_past_value[:] = past_value
              past_key = new_past_key
              past_value = new_past_value
  
          key, value = append_kv_cache(past_key, past_value, key, value)
      elif use_cache:
          max_cache_length = seq_len + KV_CACHE_ALLOC_BLOCK_LENGTH
          new_key, new_value = init_kv_cache(
              bsz,
              self.num_attention_heads,
              self.head_size,
              seq_len,
              max_cache_length,
              dtype=key.dtype,
              device=device,
          )
          new_key[:] = key
          new_value[:] = value
          key = new_key
          value = new_value
  
      present = (key, value) if use_cache else None
  
      # Compute attention
      attn_output, attn_weights = self._attn(
          query, key, value, attention_mask, head_mask
      )
  
      # Reshape outputs
      attn_output = self._merge_heads(
          attn_output, self.num_attention_heads, self.head_size
      )
      attn_output = self.dense(attn_output)
  
      outputs = (attn_output, present)
      if output_attentions:
          outputs += (attn_weights,)
  
      return outputs
  
imports: import torch
  from typing import Optional, Tuple
  from bigdl.llm.transformers.models.utils import apply_rotary_pos_emb
  from bigdl.llm.transformers.models.utils import init_kv_cache, extend_kv_cache, append_kv_cache
  from bigdl.llm.transformers.models.utils import apply_rotary_pos_emb_no_cache_xpu
label_desc: Use image node_id 1 for calling a global function with example usage: gptneox_attention_forward(hidden_states, attention_mask, position_ids, head_mask, layer_past, use_cache, output_attentions) and returns: outputs