code: def forward(
      self,
      hidden_states: Tensor,
      key_value_states: Optional[Tensor] = None,
      past_key_value: Optional[Tensor] = None,
      attention_mask: Optional[Tensor] = None,
      layer_head_mask: Optional[Tensor] = None,
      output_attentions: bool = False,
  ) -> Tuple[Tensor, Optional[Tensor], Optional[Tuple[Tensor]]]:
      if not self.training:
          return super().forward(
              hidden_states,
              key_value_states,
              past_key_value,
              attention_mask,
              layer_head_mask,
              output_attentions,
          )
      """Input shape: Batch x Time x Channel"""
      assert (
          layer_head_mask is None
      ), "Xformers attention does not support layer_head_mask"
      assert (
          not output_attentions
      ), "Xformers attention does not support output_attentions"
  
      # if key_value_states are provided this layer is used as a cross-attention layer
      # for the decoder
      is_cross_attention = key_value_states is not None
  
      bsz, tgt_len, _ = hidden_states.size()
  
      # get query proj
      query_states = self.q_proj(hidden_states)
      # get key, value proj
      if is_cross_attention and past_key_value is not None:
          # reuse k,v, cross_attentions
          key_states = past_key_value[0]
          value_states = past_key_value[1]
      elif is_cross_attention:
          # cross_attentions
          key_states = self._shape(
              self.k_proj(key_value_states), -1, bsz
          )
          value_states = self._shape(
              self.v_proj(key_value_states), -1, bsz
          )
      elif past_key_value is not None:
          # reuse k, v, self_attention
          key_states = self._shape(self.k_proj(hidden_states), -1, bsz)
          value_states = self._shape(
              self.v_proj(hidden_states), -1, bsz
          )
          key_states = torch.cat([past_key_value[0], key_states], dim=2)
          value_states = torch.cat(
              [past_key_value[1], value_states], dim=2
          )
      else:
          # self_attention
          key_states = self._shape(self.k_proj(hidden_states), -1, bsz)
          value_states = self._shape(
              self.v_proj(hidden_states), -1, bsz
          )
  
      if self.is_decoder:
          # if cross_attention save Tuple(torch.Tensor, torch.Tensor) of all cross attention key/value_states.
          # Further calls to cross_attention layer can then reuse all cross-attention
          # key/value_states (first "if" case)
          # if uni-directional self-attention (decoder) save Tuple(torch.Tensor, torch.Tensor) of
          # all previous decoder key/value_states. Further calls to uni-directional self-attention
          # can concat previous decoder key/value_states to current projected key/value_states (third "elif" case)
          # if encoder bi-directional self-attention `past_key_value` is always `None`
          past_key_value = (key_states, value_states)
  
      query_states = self._shape(query_states, tgt_len, bsz).transpose(
          1, 2
      )
      key_states = key_states.transpose(1, 2)
      value_states = value_states.transpose(1, 2)
  
      attn_output = xops.memory_efficient_attention(
          query_states,
          key_states,
          value_states,
          attn_bias=xops.LowerTriangularMask(),
          p=self.dropout if self.training else 0.0,
          scale=self.scaling,
      )
  
      # Use the `embed_dim` from the config (stored in the class) rather than `hidden_state` because `attn_output` can be
      # partitioned across GPUs when using tensor-parallelism.
      attn_output = attn_output.reshape(bsz, tgt_len, self.embed_dim)
  
      attn_output = self.out_proj(attn_output)
  
      attn_weights_reshaped = None
  
      return attn_output, attn_weights_reshaped, past_key_value
  
file_path: ColossalAI/applications/Chat/coati/kernels/opt_attn.py
imports: from typing import Optional, Tuple
  import torch
  import xformers.ops
  from torch import Tensor
  from transformers.models.opt.modeling_opt import OPTAttention
label_desc: Use image node_id 1 for calling the XOPTAttention obj's underlying member method code with example usage: obj.forward(hidden_states, key_value_states, past_key_value, attention_mask, layer_head_mask, output_attentions) and returns: attn_output,  attn_weights_reshaped,  past_key_value, super