code: def sdp_attention_forward(
      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,  # pylint: disable=unused-argument
      **kwargs,  # pylint: disable=unused-argument
  ) -> Tuple[
      torch.Tensor,
      Optional[torch.Tensor],
      Optional[Tuple[torch.Tensor]],
  ]:
      # pylint: disable=duplicate-code
      bsz, q_len, _ = hidden_states.size()
  
      if not hasattr(self, "pretraining_tp"):
          self.pretraining_tp = 1
  
      if self.pretraining_tp > 1:
          key_value_slicing = (
              self.num_key_value_heads * self.head_dim
          ) // self.pretraining_tp
          query_slices = self.q_proj.weight.split(
              (self.num_heads * self.head_dim) // self.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.pretraining_tp)
          ]
          query_states = torch.cat(query_states, dim=-1)
  
          key_states = [
              F.linear(hidden_states, key_slices[i])
              for i in range(self.pretraining_tp)
          ]
          key_states = torch.cat(key_states, dim=-1)
  
          value_states = [
              F.linear(hidden_states, value_slices[i])
              for i in range(self.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)
      # [bsz, q_len, nh, hd]
      # [bsz, nh, q_len, hd]
  
      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
      )
      # [bsz, nh, t, hd]
  
      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)
  
      if output_attentions:
          warnings.warn(
              "Output attentions is not supported for patched `LlamaAttention`, returning `None` instead."
          )
  
      #
      # sdp-attn start
      #
  
      with torch.backends.cuda.sdp_kernel():
          attn_output = (
              torch.nn.functional.scaled_dot_product_attention(
                  query_states,
                  key_states,
                  value_states,
                  attn_mask=attention_mask,
                  is_causal=False,
              )
          )
  
      if attn_output.size() != (
          bsz,
          self.num_heads,
          q_len,
          self.head_dim,
      ):
          raise ValueError(
              f"`attn_output` should be of size {(bsz, self.num_heads, q_len, self.head_dim)}, but is"
              f" {attn_output.size()}"
          )
      attn_output = attn_output.transpose(1, 2)
      attn_output = attn_output.reshape(bsz, q_len, self.hidden_size)
  
      #
      # sdp-attn end
      #
  
      if self.pretraining_tp > 1:
          attn_output = attn_output.split(
              self.hidden_size // self.pretraining_tp, dim=2
          )
          o_proj_slices = self.o_proj.weight.split(
              self.hidden_size // self.pretraining_tp, dim=1
          )
          attn_output = sum(
              F.linear(attn_output[i], o_proj_slices[i])
              for i in range(self.pretraining_tp)
          )
      else:
          attn_output = self.o_proj(attn_output)
  
      return attn_output, None, past_key_value
  
file_path: axolotl/src/axolotl/monkeypatch/llama_attn_hijack_sdp.py
imports: import warnings
  from typing import Optional, Tuple
  import torch
  import torch.nn.functional
  import transformers.models.llama.modeling_llama
  from transformers.models.llama.modeling_llama import apply_rotary_pos_emb, repeat_kv
label_desc: Use image node_id 2 for calling a global function with example usage: sdp_attention_forward(hidden_states, attention_mask, position_ids, past_key_value, output_attentions, use_cache, padding_mask) and returns: attn_output,  None,  past_key_value