code: def llama_model_selective_batching_forward_4_31(
      self,
      input_ids: torch.LongTensor = None,
      attention_mask: Optional[torch.Tensor] = None,
      position_ids: Optional[torch.LongTensor] = None,
      past_key_values: Optional[List[torch.FloatTensor]] = None,
      inputs_embeds: Optional[torch.FloatTensor] = None,
      use_cache: Optional[bool] = None,
      output_attentions: Optional[bool] = None,
      output_hidden_states: Optional[bool] = None,
      return_dict: Optional[bool] = None,
  ) -> Union[Tuple, BaseModelOutputWithPast]:
      if output_attentions is not None:
          output_attentions = output_attentions
      else:
          output_attentions = self.config.output_attentions
      output_hidden_states = (
          output_hidden_states
          if output_hidden_states is not None
          else self.config.output_hidden_states
      )
      use_cache = (
          use_cache if use_cache is not None else self.config.use_cache
      )
  
      return_dict = (
          return_dict
          if return_dict is not None
          else self.config.use_return_dict
      )
  
      # retrieve input_ids and inputs_embeds
      if input_ids is not None and inputs_embeds is not None:
          invalidInputError(
              False,
              "You cannot specify both decoder_input_ids"
              " and decoder_inputs_embeds at the same time",
          )
      elif input_ids is not None:
          batch_size, seq_length = input_ids.shape
      elif inputs_embeds is not None:
          batch_size, seq_length, _ = inputs_embeds.shape
      else:
          invalidInputError(
              False,
              "You have to specify either "
              "decoder_input_ids or decoder_inputs_embeds",
          )
  
      # seq_length_with_past = seq_length
      past_key_values_length = 0
  
      # The original position_ids in the format of [1, 1]
      # However, this only applies when kv_len is the same for all the sequences
      # We should set it to format of [batch, position_id]
      # TODO: validate correctness
      device = (
          input_ids.device
          if input_ids is not None
          else inputs_embeds.device
      )
      if position_ids is None:
          invalidInputError(
              False, "vLLM: position_ids should never be None"
          )
      else:
          # print(f"Original position_ids is {position_ids}")
          position_ids = position_ids.view(-1, seq_length)
          # print(f"after position_ids is {position_ids}")
      # if past_key_values is None:
      #     # For prefill
      #     position_ids = torch.arange(0, seq_length, dtype=torch.long, device=device)
      #     position_ids = position_ids.unsqueeze(0).view(-1, seq_length)
      # else:
      #     past_key_values_length = []
      #     for sequence_kv in past_key_values[0]:
      #         key = sequence_kv[0]
      #         past_key_values_length.append(key.shape[-2])
      #     position_ids = torch.tensor(past_key_values_length, dtype=torch.long, device=device)
      #     position_ids = position_ids.unsqueeze(0).view(-1, 1)
  
      if past_key_values is not None:
          # past_key_values in the format of num_layers x num_seqs x 2
          # TODO: this may be incorrect
          past_key_values_length = past_key_values[0][0][0].shape[2]
          # seq_length_with_past = seq_length_with_past + past_key_values_length
  
      # if position_ids is None:
      #     device = input_ids.device if input_ids is not None else inputs_embeds.device
      #     # [start, end)
      #     position_ids = torch.arange(
      #         past_key_values_length, seq_length +
      #         past_key_values_length, dtype=torch.long, device=device
      #     )
      #     position_ids = position_ids.unsqueeze(0).view(-1, seq_length)
      # else:
      #     position_ids = position_ids.view(-1, seq_length).long()
  
      if inputs_embeds is None:
          inputs_embeds = self.embed_tokens(input_ids)
      # embed positions
      if attention_mask is None:
          invalidInputError(
              False, "attention_mask should never be None"
          )
      # print(f"attention_mask before expanding: {attention_mask}")
      if past_key_values is None:
          attention_mask = self._prepare_decoder_attention_mask(
              attention_mask,
              (batch_size, seq_length),
              inputs_embeds,
              past_key_values_length,
          )
      else:
          i = 0
          for attn_mask in attention_mask:
              past_key_value_length = past_key_values[0][i][0].shape[2]
              new_mask = self._prepare_decoder_attention_mask(
                  attn_mask,
                  (1, seq_length),
                  inputs_embeds,
                  past_key_value_length,
              )
              attention_mask[i] = new_mask
              i += 1
  
      hidden_states = inputs_embeds
  
      if self.gradient_checkpointing and self.training:
          invalidInputError(
              False, "gradient_checkpointing is not supported"
          )
  
      # decoder layers
      all_hidden_states = () if output_hidden_states else None
      all_self_attns = () if output_attentions else None
      next_decoder_cache = () if use_cache else None
  
      for idx, decoder_layer in enumerate(self.layers):
          if output_hidden_states:
              all_hidden_states += (hidden_states,)
  
          past_key_value = (
              past_key_values[idx]
              if past_key_values is not None
              else None
          )
  
          if self.gradient_checkpointing and self.training:
  
              def create_custom_forward(module):
                  def custom_forward(*inputs):
                      # None for past_key_value
                      return module(*inputs, output_attentions, None)
  
                  return custom_forward
  
              layer_outputs = torch.utils.checkpoint.checkpoint(
                  create_custom_forward(decoder_layer),
                  hidden_states,
                  attention_mask,
                  position_ids,
                  None,
              )
          else:
              layer_outputs = decoder_layer(
                  hidden_states,
                  attention_mask=attention_mask,
                  position_ids=position_ids,
                  past_key_value=past_key_value,
                  output_attentions=output_attentions,
                  use_cache=use_cache,
              )
  
          hidden_states = layer_outputs[0]
  
          if use_cache:
              next_decoder_cache += (
                  layer_outputs[2 if output_attentions else 1],
              )
  
          if output_attentions:
              all_self_attns += (layer_outputs[1],)
  
      hidden_states = self.norm(hidden_states)
  
      # add hidden states from the last decoder layer
      if output_hidden_states:
          all_hidden_states += (hidden_states,)
  
      next_cache = next_decoder_cache if use_cache else None
      if not return_dict:
          return tuple(
              v
              for v in [
                  hidden_states,
                  next_cache,
                  all_hidden_states,
                  all_self_attns,
              ]
              if v is not None
          )  # noqa
      return BaseModelOutputWithPast(
          last_hidden_state=hidden_states,
          past_key_values=next_cache,
          hidden_states=all_hidden_states,
          attentions=all_self_attns,
      )
  
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 10 for calling a global function with example usage: llama_model_selective_batching_forward_4_31(input_ids, attention_mask, position_ids, past_key_values, inputs_embeds, use_cache, output_attentions, output_hidden_states, return_dict) and returns: BaseModelOutputWithPast, tuple, custom_forward, module