class_docstr: Transformer decoder consisting of *config.num_hidden_layers* layers. Each layer is a [`LlamaDecoderLayer`]
  
  Args:
      config: LlamaConfig
class_docstr_tok: ["Transformer","decoder","consisting","of","*","config.num_hidden_layers","*","layers",".","Each","layer","is","a","[","`","LlamaDecoderLayer","`","]","Args",":","config",":","LlamaConfig"]
code: def forward(
      self,
      input_ids: torch.LongTensor = None,
      attention_mask: Optional[torch.Tensor] = None,
      position_ids: Optional[torch.LongTensor] = None,
      past_key_values: Optional[List[torch.FloatTensor]] = None,
      inputs_embeds: Optional[torch.FloatTensor] = None,
      use_cache: Optional[bool] = None,
      output_attentions: Optional[bool] = None,
      output_hidden_states: Optional[bool] = None,
      return_dict: Optional[bool] = None,
  ) -> Union[Tuple, BaseModelOutputWithPast]:
      output_attentions = (
          output_attentions
          if output_attentions is not None
          else self.config.output_attentions
      )
      output_hidden_states = (
          output_hidden_states
          if output_hidden_states is not None
          else self.config.output_hidden_states
      )
      use_cache = (
          use_cache if use_cache is not None else self.config.use_cache
      )
  
      return_dict = (
          return_dict
          if return_dict is not None
          else self.config.use_return_dict
      )
  
      # retrieve input_ids and inputs_embeds
      if input_ids is not None and inputs_embeds is not None:
          raise ValueError(
              "You cannot specify both decoder_input_ids and decoder_inputs_embeds at the same time"
          )
      elif input_ids is not None:
          batch_size, seq_length = input_ids.shape
      elif inputs_embeds is not None:
          batch_size, seq_length, _ = inputs_embeds.shape
      else:
          raise ValueError(
              "You have to specify either decoder_input_ids or decoder_inputs_embeds"
          )
  
      seq_length_with_past = seq_length
      past_key_values_length = 0
  
      if past_key_values is not None:
          past_key_values_length = past_key_values[0][0].shape[2]
          seq_length_with_past = (
              seq_length_with_past + past_key_values_length
          )
  
      if position_ids is None:
          device = (
              input_ids.device
              if input_ids is not None
              else inputs_embeds.device
          )
          position_ids = torch.arange(
              past_key_values_length,
              seq_length + past_key_values_length,
              dtype=torch.long,
              device=device,
          )
          position_ids = position_ids.unsqueeze(0).view(-1, seq_length)
      else:
          position_ids = position_ids.view(-1, seq_length).long()
  
      if inputs_embeds is None:
          inputs_embeds = self.embed_tokens(input_ids)
      # embed positions
      if attention_mask is None:
          attention_mask = torch.ones(
              (batch_size, seq_length_with_past),
              dtype=torch.bool,
              device=inputs_embeds.device,
          )
      attention_mask = self._prepare_decoder_attention_mask(
          attention_mask,
          (batch_size, seq_length),
          inputs_embeds,
          past_key_values_length,
      )
  
      hidden_states = inputs_embeds
  
      if self.gradient_checkpointing and self.training:
          if use_cache:
              logger.warning_once(
                  "`use_cache=True` is incompatible with gradient checkpointing. Setting `use_cache=False`..."
              )
              use_cache = False
  
      # decoder layers
      all_hidden_states = () if output_hidden_states else None
      all_self_attns = () if output_attentions else None
      next_decoder_cache = () if use_cache else None
  
      for idx, decoder_layer in enumerate(self.layers):
          if output_hidden_states:
              all_hidden_states += (hidden_states,)
  
          past_key_value = (
              past_key_values[idx]
              if past_key_values is not None
              else None
          )
  
          if self.gradient_checkpointing and self.training:
  
              def create_custom_forward(module):
                  def custom_forward(*inputs):
                      # None for past_key_value
                      return module(*inputs, output_attentions, None)
  
                  return custom_forward
  
              layer_outputs = torch.utils.checkpoint.checkpoint(
                  create_custom_forward(decoder_layer),
                  hidden_states,
                  attention_mask,
                  position_ids,
                  None,
              )
          else:
              layer_outputs = decoder_layer(
                  hidden_states,
                  attention_mask=attention_mask,
                  position_ids=position_ids,
                  past_key_value=past_key_value,
                  output_attentions=output_attentions,
                  use_cache=use_cache,
              )
  
          hidden_states = layer_outputs[0]
  
          if use_cache:
              next_decoder_cache += (
                  layer_outputs[2 if output_attentions else 1],
              )
  
          if output_attentions:
              all_self_attns += (layer_outputs[1],)
  
      hidden_states = self.norm(hidden_states)
  
      # add hidden states from the last decoder layer
      if output_hidden_states:
          all_hidden_states += (hidden_states,)
  
      next_cache = next_decoder_cache if use_cache else None
      if not return_dict:
          return tuple(
              v
              for v in [
                  hidden_states,
                  next_cache,
                  all_hidden_states,
                  all_self_attns,
              ]
              if v is not None
          )
      return BaseModelOutputWithPast(
          last_hidden_state=hidden_states,
          past_key_values=next_cache,
          hidden_states=all_hidden_states,
          attentions=all_self_attns,
      )
  
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 5 for calling the OpenMoeModel obj's underlying member method code with example usage: obj.forward(input_ids, attention_mask, position_ids, past_key_values, inputs_embeds, use_cache, output_attentions, output_hidden_states, return_dict) and returns: BaseModelOutputWithPast, tuple, custom_forward, module