code: def llama_attention_selective_batching_forward_4_31(
      self,
      hidden_states: torch.Tensor,
      attention_mask: Optional[torch.Tensor] = None,
      position_ids: Optional[torch.LongTensor] = None,
      past_key_value: Optional[Tuple[torch.Tensor]] = None,
      output_attentions: bool = False,
      use_cache: bool = False,
      padding_mask: Optional[torch.LongTensor] = None,
      **kwargs,
  ) -> Tuple[
      torch.Tensor,
      Optional[torch.Tensor],
      Optional[Tuple[torch.Tensor]],
  ]:
      # Minimize this value to reduce memory allocation.
      VLLM_KV_CACHE_ALLOC_BLOCK_LENGTH = int(
          os.environ.get("VLLM_KV_CACHE_ALLOC_BLOCK", 64)
      )
      bsz, q_len, _ = hidden_states.size()
      device = hidden_states.device
      # for flash attention
      original_dtype = hidden_states.dtype
      # TODO: consider this later - flash attention
      # if not self.training and not hidden_states.requires_grad:
      #     fsdp_flag = use_flash_attention(hidden_states)
      # else:
      #     fsdp_flag = False
      # if fsdp_flag and q_len > 1:
      #     attention_dtype = torch.float16  # use fp16 for flash attention
      # else:
      #     attention_dtype = original_dtype
  
      attention_dtype = original_dtype
  
      # TODO: decoding fast path
      use_fuse_rope = should_use_fuse_rope(
          self, hidden_states, position_ids
      )
      enough_kv_room = (
          past_key_value is not None
          and is_enough_kv_cache_room_4_31(past_key_value[0])
      )
      is_q4_0 = self.q_proj.qtype == SYM_INT4
      no_tp = not self.config.pretraining_tp > 1
      decoding_fast_path = (
          no_tp and is_q4_0 and use_fuse_rope and bsz * q_len == 1
      )
  
      updated_past_key_values = []
      # single batch decoding fast path
      # forward_qkv takes will perform QKV projection, rotary position embedding
      # and save the key/value states to cache, then return query states and the
      # extended key/value cache
      if decoding_fast_path:
          past_k = past_key_value[0][0]
          past_v = past_key_value[0][1]
          kv_seq_len = past_k.shape[-2]
          if not enough_kv_room:
              new_cache_k, new_cache_v = extend_kv_cache(
                  1,
                  self.num_key_value_heads,  # Support GQA
                  self.head_dim,
                  kv_seq_len,
                  kv_seq_len + VLLM_KV_CACHE_ALLOC_BLOCK_LENGTH,
                  dtype=past_k.dtype,
                  device=device,
              )
              new_cache_k[:] = past_k
              new_cache_v[:] = past_v
              past_k = new_cache_k
              past_v = new_cache_v
          hidden_states = hidden_states.view(1, -1)
          import linear_q4_0
  
          (
              query_states,
              key_states,
              value_states,
          ) = linear_q4_0.forward_qkv(
              hidden_states,
              self.q_proj.weight,
              self.k_proj.weight,
              self.v_proj.weight,
              position_ids,
              past_k,
              past_v,
              self.q_proj.weight.qtype,
              kv_seq_len,
              self.head_dim,
          )
          kv_seq_len += 1
      else:
          if self.config.pretraining_tp > 1:
              invalidInputError(
                  False,
                  f"vLLM: config.pretraining_tp > 1 not supported yet",
              )
          else:
              query_states = self.q_proj(hidden_states)
              key_states = self.k_proj(hidden_states)
              value_states = self.v_proj(hidden_states)
  
          query_states = query_states.view(
              bsz, q_len, self.num_heads, self.head_dim
          ).transpose(1, 2)
          key_states = key_states.view(
              bsz, q_len, self.num_key_value_heads, self.head_dim
          ).transpose(1, 2)
          value_states = value_states.view(
              bsz, q_len, self.num_key_value_heads, self.head_dim
          ).transpose(1, 2)
  
          kv_seq_len = key_states.shape[-2]
          if past_key_value is not None:
              kv_seq_len += max(
                  kv_pair[0].shape[-2] for kv_pair in past_key_value
              )
  
          if use_fuse_rope:
              (
                  query_states,
                  key_states,
              ) = apply_rotary_pos_emb_no_cache_xpu(
                  query_states, key_states, position_ids, "llama"
              )
          else:
              cos, sin = self.rotary_emb(
                  value_states, seq_len=kv_seq_len
              )
              query_states, key_states = apply_rotary_pos_emb(
                  query_states,
                  key_states,
                  cos,
                  sin,
                  position_ids,
                  "llama",
              )
  
          if past_key_value is not None:
              batched_attention_output = []
              # print(f"type of attention_mask is {type(attention_mask)}")
              for batch in range(bsz):
                  enough_kv_room = is_enough_kv_cache_room_4_31(
                      past_key_value[batch]
                  )
                  past_k, past_v = past_key_value[batch]
                  current_kv_len = past_k.shape[-2] + 1
                  if not enough_kv_room:
                      # allocate new
                      new_cache_k, new_cache_v = extend_kv_cache(
                          1,
                          self.num_key_value_heads,
                          self.head_dim,
                          past_k.size(2),
                          current_kv_len
                          + VLLM_KV_CACHE_ALLOC_BLOCK_LENGTH,
                          dtype=past_k.dtype,
                          device=device,
                      )
                      new_cache_k[:] = past_k
                      new_cache_v[:] = past_v
                      past_k = new_cache_k
                      past_v = new_cache_v
  
                  current_key_states = key_states[
                      batch : batch + 1, :, :, :
                  ]
                  current_value_states = value_states[
                      batch : batch + 1, :, :, :
                  ]
                  (
                      current_key_states,
                      current_value_states,
                  ) = append_kv_cache(
                      past_k,
                      past_v,
                      current_key_states,
                      current_value_states,
                  )
                  updated_past_key_values.append(
                      (current_key_states, current_value_states)
                  )
  
                  current_key_states = repeat_kv(
                      current_key_states, self.num_key_value_groups
                  )
                  current_value_states = repeat_kv(
                      current_value_states, self.num_key_value_groups
                  )
  
                  current_query_states = query_states[
                      batch : batch + 1, :, :, :
                  ]
                  attn_output, attn_weights = native_sdp(
                      current_query_states,
                      current_key_states,
                      current_value_states,
                      attention_mask[batch],
                      1,
                      1,
                      current_kv_len,
                      self.head_dim,
                      self.num_heads,
                  )
                  if attn_output.size() != (
                      1,
                      self.num_heads,
                      1,
                      self.head_dim,
                  ):
                      invalidInputError(
                          False,
                          f"`attn_output` should be of size "
                          f"{(1, self.num_heads, 1, self.head_dim)}, but is"
                          f" {attn_output.size()}",
                      )
                  batched_attention_output.append(attn_output)
              # For loop ends
              # TODO: handle attention_weights later
              attn_output = torch.concat(
                  batched_attention_output, dim=0
              )
              batched_attention_output.clear()
              if attn_output.size() != (
                  bsz,
                  self.num_heads,
                  q_len,
                  self.head_dim,
              ):
                  invalidInputError(
                      False,
                      f"`attn_output` should be of size "
                      f"{(bsz, self.num_heads, q_len, self.head_dim)}, but is"
                      f" {attn_output.size()}",
                  )
              attn_output = attn_output.transpose(1, 2).contiguous()
              attn_output = attn_output.reshape(
                  bsz, q_len, self.hidden_size
              )
              attn_output = self.o_proj(attn_output)
              return attn_output, None, updated_past_key_values
  
      # Assume always use_cache
      # prefill or decoding fast path
      for batch in range(bsz):
          updated_past_key_values.append(
              (
                  key_states[batch : batch + 1, :, :, :],
                  value_states[batch : batch + 1, :, :, :],
              )
          )
  
      # repeat k/v heads if n_kv_heads < n_heads
      key_states = repeat_kv(key_states, self.num_key_value_groups).to(
          device, dtype=attention_dtype
      )
      value_states = repeat_kv(
          value_states, self.num_key_value_groups
      ).to(device, dtype=attention_dtype)
      # Can also happens for decoding fast path
      if isinstance(attention_mask, list):
          # For decoding fast path
          attention_mask = attention_mask[0]
      attn_output, attn_weights = native_sdp(
          query_states,
          key_states,
          value_states,
          attention_mask,
          bsz,
          q_len,
          kv_seq_len,
          self.head_dim,
          self.num_heads,
      )
  
      if attn_output.size() != (
          bsz,
          self.num_heads,
          q_len,
          self.head_dim,
      ):
          invalidInputError(
              False,
              f"`attn_output` should be of size "
              f"{(bsz, self.num_heads, q_len, self.head_dim)}, but is"
              f" {attn_output.size()}",
          )
      attn_output = attn_output.transpose(1, 2).contiguous()
      attn_output = attn_output.reshape(bsz, q_len, self.hidden_size)
  
      attn_output = self.o_proj(attn_output)
      return (
          attn_output.to(original_dtype),
          attn_weights,
          updated_past_key_values,
      )
  
imports: import torch
  import warnings
  import importlib
  import torch.nn
  from typing import Optional, Tuple, Union, List
  import math
  import os
  import torch.nn.functional
  from bigdl.llm.transformers.models.utils import init_kv_cache, extend_kv_cache, append_kv_cache
  from bigdl.llm.transformers.models.utils import is_enough_kv_cache_room_4_31, apply_rotary_pos_emb, is_enough_kv_cache_room_4_36
  from bigdl.llm.transformers.models.utils import apply_rotary_pos_emb_no_cache_xpu
  from bigdl.llm.transformers.models.utils import use_flash_attention, use_esimd_sdp
  from bigdl.llm.transformers.models.utils import mlp_fusion_check
  from transformers.modeling_outputs import BaseModelOutputWithPast
  from bigdl.llm.transformers.low_bit_linear import SYM_INT4, FP8E5
  from bigdl.llm.ggml.quantize import ggml_tensor_qtype
  from bigdl.llm.utils.common import invalidInputError
label_desc: Use image node_id 7 for calling a global function with example usage: llama_attention_selective_batching_forward_4_31(hidden_states, attention_mask, position_ids, past_key_value, output_attentions, use_cache, padding_mask) and returns: attn_output,  attn_weights,  updated_past_key_values, attn_output,  None,  updated_past_key_values