class_docstr: Multi-headed attention from 'Attention Is All You Need' paper
class_docstr_tok: ["Multi-headed","attention","from","'Attention","Is","All","You","Need","'","paper"]
code: def 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,
      use_kernel: bool = True,
  ) -> Tuple[
      torch.Tensor,
      Optional[torch.Tensor],
      Optional[Tuple[torch.Tensor]],
  ]:
      bsz, q_len, _ = hidden_states.size()
  
      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)
  
      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
  
      query_states = query_states.transpose(1, 2)
      key_states = key_states.transpose(1, 2)
      max_length = max(query_states.shape[1], key_states.shape[1])
      assert max_length <= self.sin.shape[0]
      sin, cos = self.sin[:max_length], self.cos[:max_length]
      # TODO: for inference, we can add emb kv into cache to avoid computation
      query_states, key_states = apply_rotary_embedding(
          query_states,
          key_states,
          cos,
          sin,
          decode=True if q_len == 1 else False,
          rotary_index=position_ids,
      )
      query_states = query_states.transpose(1, 2)
      key_states = key_states.transpose(1, 2)
  
      # 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 HAS_FLASH_ATTN and use_kernel:
          from flash_attn import flash_attn_func
  
          query_states = query_states.transpose(1, 2)
          key_states = key_states.transpose(1, 2)
          value_states = value_states.transpose(1, 2)
          attn_output = flash_attn_func(
              query_states,
              key_states,
              value_states,
              softmax_scale=1.0,
              causal=True,
          )
          attn_output = attn_output.transpose(1, 2).contiguous()
      else:
          attn_weights = torch.matmul(
              query_states, key_states.transpose(2, 3)
          )
  
          if attn_weights.size() != (
              bsz,
              self.num_heads,
              q_len,
              kv_seq_len,
          ):
              raise ValueError(
                  f"Attention weights should be of size {(bsz, self.num_heads, q_len, kv_seq_len)}, but is"
                  f" {attn_weights.size()}"
              )
  
          if attention_mask is not None:
              if attention_mask.size() != (bsz, 1, q_len, kv_seq_len):
                  raise ValueError(
                      f"Attention mask should be of size {(bsz, 1, q_len, kv_seq_len)}, but is {attention_mask.size()}"
                  )
              if self.training:
                  attention_mask = attention_mask.clone().detach()
              attention_mask[:, :, :, 0] = 0
              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,
          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).contiguous()
      attn_output = attn_output.reshape(
          bsz, q_len, self.num_heads * self.head_dim
      )
  
      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)
  
      if not output_attentions:
          attn_weights = None
  
      return attn_output, attn_weights, past_key_value
  
imports: import math
  from typing import List, Optional, Tuple, Union
  import torch
  import torch.nn.functional
  import torch.utils.checkpoint
  from torch import nn
  from transformers.modeling_outputs import BaseModelOutputWithPast, CausalLMOutputWithPast
  from transformers.modeling_utils import PreTrainedModel
  from transformers.models.llama.modeling_llama import LlamaConfig, LlamaRMSNorm
  from transformers.utils import add_start_docstrings, add_start_docstrings_to_model_forward, logging, replace_return_docstrings
  from colossalai.kernel.cuda_native.mha.flash_attn_2 import HAS_FLASH_ATTN
  from colossalai.kernel.triton.llama_act_combine_kernel import HAS_TRITON
  from colossalai.moe.layers import SparseMLP
  from colossalai.moe.manager import MOE_MANAGER
  from colossalai.moe.utils import get_activation, set_moe_args
label_desc: Use image node_id 3 for calling the OpenMoeAttention obj's underlying member method code with example usage: obj.forward(hidden_states, attention_mask, position_ids, past_key_value, output_attentions, use_cache, use_kernel) and returns: attn_output,  attn_weights,  past_key_value