code: def forward(self, hidden_states, cache, buffer, lora):
      bsz, q_len, _ = hidden_states.size()
      past_len = cache.current_seq_len
  
      # Project q, k, v, apply position embeddings to k and v
  
      query_states = self.q_proj.forward(hidden_states, lora)
      key_states = self.k_proj.forward(hidden_states, lora)
  
      cuda_ext.exllama_ext.rope_(
          query_states,
          self.sin,
          self.cos,
          past_len,
          self.config.num_attention_heads,
          self.config.head_dim,
      )
      cuda_ext.exllama_ext.rope_(
          key_states,
          self.sin,
          self.cos,
          past_len,
          self.config.num_key_value_heads,
          self.config.head_dim,
      )
  
      query_states = query_states.view(
          bsz,
          q_len,
          self.config.num_attention_heads,
          self.config.head_dim,
      ).transpose(1, 2)
      key_states = key_states.view(
          bsz,
          q_len,
          self.config.num_key_value_heads,
          self.config.head_dim,
      ).transpose(1, 2)
      value_states = (
          self.v_proj.forward(hidden_states, lora)
          .view(
              bsz,
              q_len,
              self.config.num_key_value_heads,
              self.config.head_dim,
          )
          .transpose(1, 2)
      )
  
      # Add keys and values to cache
  
      new_keys = (
          cache.key_states[self.index]
          .narrow(2, past_len, q_len)
          .narrow(0, 0, bsz)
      )
      new_values = (
          cache.value_states[self.index]
          .narrow(2, past_len, q_len)
          .narrow(0, 0, bsz)
      )
      new_keys.copy_(key_states)
      new_values.copy_(value_states)
  
      # Key/value tensors with past
  
      key_states = (
          cache.key_states[self.index]
          .narrow(2, 0, past_len + q_len)
          .narrow(0, 0, bsz)
      )
      value_states = (
          cache.value_states[self.index]
          .narrow(2, 0, past_len + q_len)
          .narrow(0, 0, bsz)
      )
  
      # Attention
  
      # -- Flash Attention 2.0
  
      if self.config.use_flash_attn_2 and (past_len == 0 or q_len == 1):
          key_states = key_states.transpose(1, 2)
          value_states = value_states.transpose(1, 2)
          query_states = query_states.transpose(1, 2)
          attn_output = flash_attn_func(
              query_states,
              key_states,
              value_states,
              causal=(past_len == 0),
          )
  
      # -- HF Transformers regular attention, faster on shorter sequences, same VRAM usage
  
      elif self.config.sdp_thd == 0 or q_len < self.config.sdp_thd:
          key_states = self.repeat_kv(
              key_states, self.config.num_key_value_groups
          )
          value_states = self.repeat_kv(
              value_states, self.config.num_key_value_groups
          )
  
          attn_weights = torch.matmul(
              query_states, key_states.transpose(2, 3)
          )
          attn_weights /= math.sqrt(self.config.head_dim)
          if buffer.attn_mask is not None:
              attn_weights = attn_weights + buffer.attn_mask
          attn_weights = nn.functional.softmax(
              attn_weights, dim=-1, dtype=torch.float16
          )
          attn_output = torch.matmul(attn_weights, value_states)
          attn_output = attn_output.transpose(1, 2)
  
      # -- Scaled dot-product attention from PyTorch 2, should be comparable to xformers (?)
  
      else:
          # Torch's SDP attention has a built-in causal mask feature which we can use only when there is no past, i.e.
          # it can only apply a square attention mask. It saves quite a bit of VRAM but in practice Torch seems to use
          # the same amount of memory at peak anyway.
          #
          # TODO: Apparently flash attention is disabled when supplying an attention mask tensor. Figure out if this
          # is true and maybe drop SDP altogether. If causal masking in flash-attn is updated eventually there should
          # be no need for this anyway.
  
          key_states = self.repeat_kv(
              key_states, self.config.num_key_value_groups
          )
          value_states = self.repeat_kv(
              value_states, self.config.num_key_value_groups
          )
  
          if past_len > 0 or (bsz > 1 and buffer.attn_mask is not None):
              attn_output = F.scaled_dot_product_attention(
                  query_states,
                  key_states,
                  value_states,
                  attn_mask=buffer.attn_mask,
                  is_causal=False,
              )
          else:
              attn_output = F.scaled_dot_product_attention(
                  query_states,
                  key_states,
                  value_states,
                  attn_mask=None,
                  is_causal=True,
              )
  
          attn_output = attn_output.transpose(1, 2)
  
      # Output projection
  
      attn_output = attn_output.reshape(
          bsz, q_len, self.config.hidden_size
      )
      attn_output = self.o_proj.forward(attn_output, lora)
  
      return attn_output
  
imports: import sys
  import torch
  from torch import nn
  import torch.nn.functional
  from safetensors import safe_open
  import cuda_ext
  import json
  import math
  import gc
  from enum import Enum
label_desc: Use image node_id 4 for calling the ExLlamaAttention obj's underlying member method code with example usage: obj.forward(hidden_states, cache, buffer, lora) and returns: attn_output