code: def LlamaAttention_fast_forward(
      self,
      hidden_states: torch.Tensor,
      causal_mask: Optional[
          xformers.attn_bias.BlockDiagonalCausalMask
      ] = None,
      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,
      *args,
      **kwargs,
  ) -> Tuple[
      torch.Tensor,
      Optional[torch.Tensor],
      Optional[Tuple[torch.Tensor]],
  ]:
      bsz, q_len, _ = hidden_states.size()
      Q, K, V = self.apply_qkv(self, hidden_states)
  
      # Check for inference
      if use_cache and past_key_value is not None and q_len == 1:
          A, past_key_value = LlamaAttention_fast_forward_inference(
              self,
              hidden_states,
              past_key_value,
              position_ids,
          )
          return A, None, past_key_value
      pass
  
      n_heads = self.num_heads
      n_groups = self.num_key_value_groups
      n_kv_heads = self.num_key_value_heads
      head_dim = self.head_dim
      assert n_kv_heads * n_groups == n_heads
  
      Q = Q.view(bsz, q_len, n_heads, head_dim).transpose(1, 2)
      K = K.view(bsz, q_len, n_kv_heads, head_dim).transpose(1, 2)
      V = V.view(bsz, q_len, n_kv_heads, head_dim).transpose(1, 2)
  
      kv_seq_len = K.shape[-2]
      if past_key_value is not None:
          kv_seq_len += past_key_value[0].shape[-2]
  
      if position_ids is None:
          cos = self.rotary_emb.cos_cached
          sin = self.rotary_emb.sin_cached
          Q, K = fast_rope_embedding(Q, K, cos, sin)
      else:
          cos, sin = self.rotary_emb(V, seq_len=kv_seq_len)
          Q, K = inplace_rope_embedding(Q, K, cos, sin, position_ids)
      pass
  
      if past_key_value is not None:
          K = torch.cat([past_key_value[0], K], dim=2)
          V = torch.cat([past_key_value[1], V], dim=2)
      past_key_value = (K, V) if use_cache else None
  
      # Attention module
      if not HAS_FLASH_ATTENTION:
          # Xformers memory efficient attention
          # Also has Flash Attention v2 dispatching
          Q = Q.transpose(1, 2)
          K = K.transpose(1, 2)
          V = V.transpose(1, 2)
  
          # Group query attention
          if n_groups != 1:
              K = K.view(bsz, q_len, n_kv_heads, 1, head_dim)
              V = V.view(bsz, q_len, n_kv_heads, 1, head_dim)
              K = K.expand(bsz, q_len, n_kv_heads, n_groups, head_dim)
              V = V.expand(bsz, q_len, n_kv_heads, n_groups, head_dim)
              if hidden_states.requires_grad:
                  K = K.reshape(bsz, q_len, n_heads, head_dim)
                  V = V.reshape(bsz, q_len, n_heads, head_dim)
              else:
                  Q = Q.view(bsz, q_len, n_kv_heads, n_groups, head_dim)
          pass
          A = xformers_attention(Q, K, V, attn_bias=causal_mask)
          A = A.view(bsz, q_len, n_heads, head_dim)
  
      elif HAS_FLASH_ATTENTION:
          Q = Q.transpose(1, 2)
          K = K.transpose(1, 2)
          V = V.transpose(1, 2)
          A = flash_attn_func(Q, K, V, causal=True)
      else:
          # Grouped query attention
          if n_groups != 1:
              K = K[:, :, None, :, :].expand(
                  bsz, n_kv_heads, n_groups, q_len, head_dim
              )
              V = V[:, :, None, :, :].expand(
                  bsz, n_kv_heads, n_groups, q_len, head_dim
              )
              K = K.reshape(bsz, n_heads, q_len, head_dim)
              V = V.reshape(bsz, n_heads, q_len, head_dim)
          pass
          # Needs (batch_size, n_heads, seq_len, head_dim)
          # is_casual and attention_mask must not be both set!
          A = scaled_dot_product_attention(
              Q, K, V, attn_mask=attention_mask, is_causal=False
          )
          # Go back to (batch_size, seq_len, n_heads, head_dim)
          A = A.transpose(1, 2)
      pass
      attn_output = A.reshape(bsz, q_len, self.hidden_size)
      attn_output = self.apply_o(self, attn_output)
      attn_weights = None
      return attn_output, attn_weights, past_key_value
  
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 4 for calling a global function with example usage: LlamaAttention_fast_forward(hidden_states, causal_mask, attention_mask, position_ids, past_key_value, output_attentions, use_cache, padding_mask) and returns: attn_output,  attn_weights,  past_key_value, A,  None,  past_key_value