code: def forward_noflashattn(
      self,
      hidden_states: torch.Tensor,
      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,
  ) -> Tuple[
      torch.Tensor,
      Optional[torch.Tensor],
      Optional[Tuple[torch.Tensor]],
  ]:
      bsz, q_len, _ = hidden_states.size()
  
      group_size = int(q_len * group_size_ratio)
  
      if q_len % group_size > 0:
          raise ValueError(
              "q_len %d should be divisible by group size %d."
              % (q_len, group_size)
          )
      num_group = q_len // group_size
  
      if self.config.pretraining_tp > 1:
          key_value_slicing = (
              self.num_key_value_heads * self.head_dim
          ) // self.config.pretraining_tp
          query_slices = self.q_proj.weight.split(
              (self.num_heads * self.head_dim)
              // self.config.pretraining_tp,
              dim=0,
          )
          key_slices = self.k_proj.weight.split(
              key_value_slicing, dim=0
          )
          value_slices = self.v_proj.weight.split(
              key_value_slicing, dim=0
          )
  
          query_states = [
              F.linear(hidden_states, query_slices[i])
              for i in range(self.config.pretraining_tp)
          ]
          query_states = torch.cat(query_states, dim=-1)
  
          key_states = [
              F.linear(hidden_states, key_slices[i])
              for i in range(self.config.pretraining_tp)
          ]
          key_states = torch.cat(key_states, dim=-1)
  
          value_states = [
              F.linear(hidden_states, value_slices[i])
              for i in range(self.config.pretraining_tp)
          ]
          value_states = torch.cat(value_states, dim=-1)
  
      else:
          query_states = self.q_proj(hidden_states)
          key_states = self.k_proj(hidden_states)
          value_states = self.v_proj(hidden_states)
  
      query_states = query_states.view(
          bsz, q_len, self.num_heads, self.head_dim
      ).transpose(1, 2)
      key_states = key_states.view(
          bsz, q_len, self.num_key_value_heads, self.head_dim
      ).transpose(1, 2)
      value_states = value_states.view(
          bsz, q_len, self.num_key_value_heads, self.head_dim
      ).transpose(1, 2)
  
      kv_seq_len = key_states.shape[-2]
      if past_key_value is not None:
          kv_seq_len += past_key_value[0].shape[-2]
      cos, sin = self.rotary_emb(value_states, seq_len=kv_seq_len)
      query_states, key_states = apply_rotary_pos_emb(
          query_states, key_states, cos, sin, position_ids
      )
  
      if past_key_value is not None:
          # reuse k, v, self_attention
          key_states = torch.cat([past_key_value[0], key_states], dim=2)
          value_states = torch.cat(
              [past_key_value[1], value_states], dim=2
          )
  
      past_key_value = (key_states, value_states) if use_cache else None
  
      # repeat k/v heads if n_kv_heads < n_heads
      key_states = repeat_kv(key_states, self.num_key_value_groups)
      value_states = repeat_kv(value_states, self.num_key_value_groups)
  
      # shift
      def shift(qkv, bsz, q_len, group_size, num_heads, head_dim):
          qkv[:, num_heads // 2 :] = qkv[:, num_heads // 2 :].roll(
              -group_size // 2, dims=2
          )
          qkv = (
              qkv.transpose(1, 2)
              .reshape(
                  bsz * (q_len // group_size),
                  group_size,
                  num_heads,
                  head_dim,
              )
              .transpose(1, 2)
          )
          return qkv
  
      query_states = shift(
          query_states,
          bsz,
          q_len,
          group_size,
          self.num_heads,
          self.head_dim,
      )
      key_states = shift(
          key_states,
          bsz,
          q_len,
          group_size,
          self.num_heads,
          self.head_dim,
      )
      value_states = shift(
          value_states,
          bsz,
          q_len,
          group_size,
          self.num_heads,
          self.head_dim,
      )
  
      attn_weights = torch.matmul(
          query_states, key_states.transpose(2, 3)
      ) / math.sqrt(self.head_dim)
  
      if attn_weights.size() != (
          bsz * num_group,
          self.num_heads,
          group_size,
          group_size,
      ):
          raise ValueError(
              f"Attention weights should be of size {(bsz * num_group, self.num_heads, group_size, group_size)}, but is"
              f" {attn_weights.size()}"
          )
  
      attention_mask = attention_mask[
          :, :, :group_size, :group_size
      ].repeat(num_group, 1, 1, 1)
      if attention_mask is not None:
          if attention_mask.size() != (
              bsz * num_group,
              1,
              group_size,
              group_size,
          ):
              raise ValueError(
                  f"Attention mask should be of size {(bsz * num_group, 1, group_size, group_size)}, but is {attention_mask.size()}"
              )
          attn_weights = attn_weights + attention_mask
  
      # upcast attention to fp32
      attn_weights = nn.functional.softmax(
          attn_weights, dim=-1, dtype=torch.float32
      ).to(query_states.dtype)
      attn_output = torch.matmul(attn_weights, value_states)
  
      if attn_output.size() != (
          bsz * num_group,
          self.num_heads,
          group_size,
          self.head_dim,
      ):
          raise ValueError(
              f"`attn_output` should be of size {(bsz * num_group, self.num_heads, group_size, self.head_dim)}, but is"
              f" {attn_output.size()}"
          )
      attn_output = attn_output.transpose(1, 2).contiguous()
  
      attn_output = attn_output.reshape(
          bsz, q_len, self.num_heads, self.head_dim
      )
  
      # shift back
      attn_output[:, :, self.num_heads // 2 :] = attn_output[
          :, :, self.num_heads // 2 :
      ].roll(group_size // 2, dims=1)
  
      attn_output = attn_output.reshape(bsz, q_len, self.hidden_size)
  
      if self.config.pretraining_tp > 1:
          attn_output = attn_output.split(
              self.hidden_size // self.config.pretraining_tp, dim=2
          )
          o_proj_slices = self.o_proj.weight.split(
              self.hidden_size // self.config.pretraining_tp, dim=1
          )
          attn_output = sum(
              [
                  F.linear(attn_output[i], o_proj_slices[i])
                  for i in range(self.config.pretraining_tp)
              ]
          )
      else:
          attn_output = self.o_proj(attn_output)
  
      if not output_attentions:
          attn_weights = None
  
      return attn_output, attn_weights, past_key_value
  
file_path: long-lora/llama_attn_replace_sft.py
imports: import warnings
  from typing import Optional, Tuple
  import torch
  from torch import nn
  import transformers
  from einops import rearrange
  from flash_attn import __version__
  from flash_attn.bert_padding import pad_input, unpad_input
  from flash_attn.flash_attn_interface import flash_attn_func, flash_attn_varlen_kvpacked_func, flash_attn_varlen_qkvpacked_func
  from transformers.models.llama.modeling_llama import apply_rotary_pos_emb, repeat_kv, rotate_half
  from flash_attn.bert_padding import unpad_input, pad_input
  import math
label_desc: Use image node_id 3 for calling a global function with example usage: forward_noflashattn(hidden_states, attention_mask, position_ids, past_key_value, output_attentions, use_cache, padding_mask) and returns: attn_output,  attn_weights,  past_key_value, qkv