code: def LlamaModel_fast_forward(
      self,
      input_ids: torch.LongTensor,
      causal_mask: Optional[
          xformers.attn_bias.BlockDiagonalCausalMask
      ] = 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,
      *args,
      **kwargs,
  ) -> Union[Tuple, BaseModelOutputWithPast]:
      output_attentions = (
          output_attentions
          if output_attentions is not None
          else self.config.output_attentions
      )
      assert output_attentions is False
      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:
          raise ValueError(
              "Unsloth: 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:
          raise ValueError(
              "Unsloth: You have to specify either decoder_input_ids or decoder_inputs_embeds"
          )
  
      seq_length_with_past = seq_length
  
      # Fix out of bounds tokenization
      if hasattr(self, "max_seq_length"):
          if seq_length > self.max_seq_length:
              logger.warning_once(
                  f"Unsloth: Input IDs of length {seq_length} > the model's max sequence length of {self.max_seq_length}.\n"
                  "We shall truncate it ourselves. It's imperative if you correct this issue first."
              )
          if input_ids is not None:
              input_ids = input_ids[:, : self.max_seq_length]
          elif inputs_embeds is not None:
              inputs_embeds = inputs_embeds[:, : self.max_seq_length, :]
          pass
      pass
  
      past_key_values_length = 0
  
      if past_key_values is not None:
          past_key_values_length = past_key_values[0][0].shape[2]
          seq_length_with_past = (
              seq_length_with_past + past_key_values_length
          )
  
      # We already handle KV cache position_ids ourselves.
      if past_key_values_length != 0:
          position_ids = torch.arange(
              past_key_values_length,
              seq_length + past_key_values_length,
              dtype=torch.int32,
              device="cuda",
          )
          position_ids = position_ids.unsqueeze(0).view(-1, seq_length)
      elif position_ids is not None:
          position_ids = position_ids.view(-1, seq_length).to(
              torch.int32
          )  # .long()
      else:
          position_ids = None
  
      if position_ids is not None:
          if position_ids.shape[0] != batch_size:
              position_ids = position_ids.repeat((batch_size, 1))
  
      # embed positions
      if inputs_embeds is None:
          inputs_embeds = self.embed_tokens(input_ids)
  
      # Ignore attention_mask
      if attention_mask is None:
          padding_mask = None
      else:
          if 0 in attention_mask:
              padding_mask = attention_mask
          else:
              padding_mask = None
  
          attention_mask = _prepare_4d_causal_attention_mask(
              attention_mask,
              (batch_size, seq_length),
              inputs_embeds,
              past_key_values_length,
              sliding_window=None
              if not hasattr(self.config, "sliding_window")
              else self.config.sliding_window,
          )
      pass
  
      hidden_states = inputs_embeds
  
      if self.gradient_checkpointing and self.training:
          if use_cache:
              logger.warning_once(
                  "Unsloth: `use_cache=True` is incompatible with gradient checkpointing. Setting `use_cache=False`"
              )
              use_cache = False
      pass
  
      # 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,
                          past_key_value,
                          output_attentions,
                          padding_mask=padding_mask,
                      )
  
                  return custom_forward
  
              layer_outputs = torch.utils.checkpoint.checkpoint(
                  create_custom_forward(decoder_layer),
                  hidden_states,
                  causal_mask,
                  attention_mask,
                  position_ids,
                  use_reentrant=True,
                  preserve_rng_state=False,
              )
          else:
              layer_outputs = decoder_layer(
                  hidden_states,
                  causal_mask=causal_mask,
                  attention_mask=attention_mask,
                  position_ids=position_ids,
                  past_key_value=past_key_value,
                  output_attentions=output_attentions,
                  use_cache=use_cache,
                  padding_mask=padding_mask,
              )
  
          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],)
      pass
  
      hidden_states = fast_rms_layernorm(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
          )
      return BaseModelOutputWithPast(
          last_hidden_state=hidden_states,
          past_key_values=next_cache,
          hidden_states=all_hidden_states,
          attentions=all_self_attns,
      )
  
imports: import torch
  from typing import Optional, Tuple, List, Union
  from torch.nn.functional import scaled_dot_product_attention
  from transformers.modeling_attn_mask_utils import _prepare_4d_causal_attention_mask
  from transformers.models.llama.modeling_llama import logger, BaseModelOutputWithPast, CausalLMOutputWithPast
  from ..kernels import *
  from ._utils import *
  from ._utils import __version__
  from transformers.models.llama.modeling_llama import LlamaAttention, LlamaDecoderLayer, LlamaModel, LlamaForCausalLM
  from peft import PeftModelForCausalLM
  import gc
  import peft
  import bitsandbytes
  import numpy
  import types
  from transformers import AutoTokenizer, AutoModelForCausalLM, BitsAndBytesConfig, AutoConfig
  from transformers import set_seed
  from peft import LoraConfig, TaskType, get_peft_model
label_desc: Use image node_id 6 for calling a global function with example usage: LlamaModel_fast_forward(input_ids, causal_mask, 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