code: def MistralForCausalLM_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:
          bsz, q_len = input_ids.shape
          sliding_window = getattr(self.config, "sliding_window")
          if sliding_window is None or sliding_window <= 0:
              causal_mask = xformers.attn_bias.LowerTriangularMask()
          elif q_len <= sliding_window:
              causal_mask = xformers.attn_bias.LowerTriangularMask()
          else:
              # Fix from https://github.com/Rypo
              causal_mask = xformers.attn_bias.BlockDiagonalCausalMask.from_seqlens(
                  [q_len] * bsz
              ).make_local_attention(
                  window_size=sliding_window
              )
      pass
  
      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,
      )
  
file_path: unsloth/unsloth/models/mistral.py
imports: from .llama import *
  from ._utils import __version__
  from transformers.models.mistral.modeling_mistral import MistralAttention, MistralDecoderLayer, MistralModel, MistralForCausalLM
label_desc: Use image node_id 2 for calling a global function with example usage: MistralForCausalLM_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