code: def LlamaForCausalLM_fast_forward(
      self,
      input_ids: torch.LongTensor = None,
      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,
      labels: Optional[torch.LongTensor] = 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, CausalLMOutputWithPast]:
      if causal_mask is None:
          causal_mask = xformers.attn_bias.LowerTriangularMask()
  
      output_attentions = (
          output_attentions
          if output_attentions is not None
          else self.config.output_attentions
      )
      output_hidden_states = (
          output_hidden_states
          if output_hidden_states is not None
          else self.config.output_hidden_states
      )
      return_dict = (
          return_dict
          if return_dict is not None
          else self.config.use_return_dict
      )
  
      # decoder outputs consists of (dec_features, layer_state, dec_hidden, dec_attn)
      outputs = self.model(
          input_ids=input_ids,
          causal_mask=causal_mask,
          attention_mask=attention_mask,
          position_ids=position_ids,
          past_key_values=past_key_values,
          inputs_embeds=inputs_embeds,
          use_cache=use_cache,
          output_attentions=output_attentions,
          output_hidden_states=output_hidden_states,
          return_dict=return_dict,
      )
  
      hidden_states = outputs[0]
      logits = self.lm_head(hidden_states)
  
      loss = None
      if labels is not None:
          shift_logits = logits
          if not hasattr(self, "extra_ignored_labels"):
              # Fixes https://github.com/unslothai/unsloth/issues/10
              self.extra_ignored_labels = torch.full(
                  (self.max_seq_length, 1), -100, device="cuda"
              )
          pass
  
          shift_labels = torch.hstack(
              (
                  labels[..., 1:],
                  self.extra_ignored_labels[: labels.shape[0]],
              )
          )
          loss = fast_cross_entropy_loss(
              logits=shift_logits,
              labels=shift_labels,
          )
      pass
  
      if not return_dict:
          output = (logits,) + outputs[1:]
          return (loss,) + output if loss is not None else output
  
      return CausalLMOutputWithPast(
          loss=loss,
          logits=logits,
          past_key_values=outputs.past_key_values,
          hidden_states=outputs.hidden_states,
          attentions=outputs.attentions,
      )
  
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 7 for calling a global function with example usage: LlamaForCausalLM_fast_forward(input_ids, causal_mask, attention_mask, position_ids, past_key_values, inputs_embeds, labels, use_cache, output_attentions, output_hidden_states, return_dict) and returns: CausalLMOutputWithPast, unknown