code: def chatglm2_32k_attention_forward(
      self,
      hidden_states,
      attention_mask,
      rotary_pos_emb,
      kv_cache=None,
      use_cache=True,
  ):
      # hidden_states: [sq, b, h]
  
      # =================================================
      # Pre-allocate memory for key-values for inference.
      # =================================================
      # =====================
      # Query, Key, and Value
      # =====================
  
      # Attention heads [sq, b, h] --> [sq, b, (np * 3 * hn)]
      device = hidden_states.device
      mixed_x_layer = self.query_key_value(hidden_states)
  
      if self.multi_query_attention:
          (query_layer, key_layer, value_layer) = mixed_x_layer.split(
              [
                  self.num_attention_heads_per_partition
                  * self.hidden_size_per_attention_head,
                  self.num_multi_query_groups_per_partition
                  * self.hidden_size_per_attention_head,
                  self.num_multi_query_groups_per_partition
                  * self.hidden_size_per_attention_head,
              ],
              dim=-1,
          )
          query_layer = query_layer.view(
              query_layer.size()[:-1]
              + (
                  self.num_attention_heads_per_partition,
                  self.hidden_size_per_attention_head,
              )
          )
          key_layer = key_layer.view(
              key_layer.size()[:-1]
              + (
                  self.num_multi_query_groups_per_partition,
                  self.hidden_size_per_attention_head,
              )
          )
          value_layer = value_layer.view(
              value_layer.size()[:-1]
              + (
                  self.num_multi_query_groups_per_partition,
                  self.hidden_size_per_attention_head,
              )
          )
      else:
          new_tensor_shape = mixed_x_layer.size()[:-1] + (
              self.num_attention_heads_per_partition,
              3 * self.hidden_size_per_attention_head,
          )
          mixed_x_layer = mixed_x_layer.view(*new_tensor_shape)
  
          # [sq, b, np, 3 * hn] --> 3 [sq, b, np, hn]
          (
              query_layer,
              key_layer,
              value_layer,
          ) = split_tensor_along_last_dim(mixed_x_layer, 3)
  
      # apply relative positional encoding (rotary embedding)
      if rotary_pos_emb is not None:
          query_layer = apply_rotary_pos_emb(
              query_layer, rotary_pos_emb
          )
          key_layer = apply_rotary_pos_emb(key_layer, rotary_pos_emb)
  
      cur_length, batch_size = (
          query_layer.shape[0],
          query_layer.shape[1],
      )
  
      if self.multi_query_attention:
          key_length = key_layer.size(0)
          query_group_size = (
              self.num_attention_heads_per_partition
              // self.num_multi_query_groups_per_partition
          )
          key_layer = key_layer.permute(1, 2, 0, 3).unsqueeze(
              -3
          )  # [bs, nh/k, sl, hn]
          key_layer = key_layer.expand(-1, -1, query_group_size, -1, -1)
          key_layer = key_layer.contiguous().view(
              (
                  batch_size,
                  self.num_attention_heads_per_partition,
                  key_length,
                  self.hidden_size_per_attention_head,
              )
          )
          value_layer = value_layer.permute(1, 2, 0, 3).unsqueeze(-3)
          value_layer = value_layer.expand(
              -1, -1, query_group_size, -1, -1
          )
          value_layer = value_layer.contiguous().view(
              (
                  batch_size,
                  self.num_attention_heads_per_partition,
                  key_length,
                  self.hidden_size_per_attention_head,
              )
          )
  
      # adjust key and value for inference
      if kv_cache is not None:
          cache_k, cache_v = kv_cache
          cache_k = cache_k.permute(1, 2, 0, 3)
          cache_v = cache_v.permute(1, 2, 0, 3)
          past_length = cache_k.size(2)
  
          if cache_k.stride()[1] <= cache_k.size(2) * cache_k.size(3):
              max_cache_length = (
                  past_length + cur_length + KV_CACHE_ALLOC_BLOCK_LENGTH
              )
              new_cache_k, new_cache_v = extend_kv_cache(
                  batch_size,
                  self.num_attention_heads_per_partition,
                  self.hidden_size_per_attention_head,
                  past_length,
                  max_cache_length,
                  dtype=query_layer.dtype,
                  device=device,
              )
              new_cache_k[:] = cache_k
              new_cache_v[:] = cache_v
              cache_k = new_cache_k
              cache_v = new_cache_v
  
          key_layer, value_layer = append_kv_cache(
              cache_k, cache_v, key_layer, value_layer
          )
      elif use_cache:
          max_cache_length = (
              max(KV_CACHE_ALLOC_MIN_LENGTH, cur_length)
              + KV_CACHE_ALLOC_BLOCK_LENGTH
          )
          key_cache, value_cache = init_kv_cache(
              batch_size,
              self.num_attention_heads_per_partition,
              self.hidden_size_per_attention_head,
              cur_length,
              max_cache_length,
              dtype=query_layer.dtype,
              device=device,
          )
          key_cache[:] = key_layer
          value_cache[:] = value_layer
          key_layer = key_cache
          value_layer = value_cache
  
      key_layer = key_layer.permute(2, 0, 1, 3)
      value_layer = value_layer.permute(2, 0, 1, 3)
  
      if use_cache:
          if kv_cache is None:
              kv_cache = torch.cat(
                  (
                      key_layer.unsqueeze(0).unsqueeze(0),
                      value_layer.unsqueeze(0).unsqueeze(0),
                  ),
                  dim=1,
              )
          else:
              kv_cache = (key_layer, value_layer)
      else:
          kv_cache = None
  
      # ==================================
      # core attention computation
      # ==================================
  
      context_layer = self.core_attention(
          query_layer, key_layer, value_layer, attention_mask
      )
  
      # =================
      # Output. [sq, b, h]
      # =================
  
      output = self.dense(context_layer)
  
      return output, kv_cache
  
imports: import torch
  from typing import Optional, Tuple, Union, List, Callable, Dict, Any
  import torch.nn.functional
  from bigdl.llm.transformers.models.utils import init_kv_cache, extend_kv_cache, append_kv_cache
label_desc: Use image node_id 3 for calling a global function with example usage: chatglm2_32k_attention_forward(hidden_states, attention_mask, rotary_pos_emb, kv_cache, use_cache) and returns: output,  kv_cache