class_docstr: This class serves as a micro library for forward function substitution of OPT models
  under pipeline setting.
class_docstr_tok: ["This","class","serves","as","a","micro","library","for","forward","function","substitution","of","OPT","models","under","pipeline","setting","."]
docstr: This function is modified on the basis of transformers.models.opt.modeling_opt.OPTForSequenceClassification.forward.
  Please refer to original code of transformers for more details.
docstr_tok: ["This","function","is","modified","on","the","basis","of","transformers.models.opt.modeling_opt.OPTForSequenceClassification.forward",".","Please","refer","to","original","code","of","transformers","for","more","details","."]
code: def opt_for_sequence_classification_forward(
      self: OPTForSequenceClassification,
      input_ids: Optional[torch.LongTensor] = None,
      attention_mask: Optional[torch.FloatTensor] = None,
      head_mask: Optional[torch.FloatTensor] = None,
      past_key_values: Optional[Tuple[Tuple[torch.Tensor]]] = None,
      inputs_embeds: Optional[torch.FloatTensor] = None,
      labels: Optional[torch.LongTensor] = None,
      use_cache: Optional[bool] = None,
      output_attentions: Optional[bool] = None,
      output_hidden_states: Optional[bool] = None,
      return_dict: Optional[bool] = None,
      stage_manager: Optional[PipelineStageManager] = None,
      hidden_states: Optional[torch.FloatTensor] = None,
      stage_index: Optional[List[int]] = None,
  ) -> Union[Tuple, SequenceClassifierOutputWithPast]:
      r"""
      This function is modified on the basis of transformers.models.opt.modeling_opt.OPTForSequenceClassification.forward.
      Please refer to original code of transformers for more details.
      """
  
      logger = logging.get_logger(__name__)
  
      return_dict = (
          return_dict
          if return_dict is not None
          else self.config.use_return_dict
      )
  
      transformer_outputs = OPTPipelineForwards.opt_model_forward(
          self.model,
          input_ids,
          past_key_values=past_key_values,
          attention_mask=attention_mask,
          head_mask=head_mask,
          inputs_embeds=inputs_embeds,
          use_cache=use_cache,
          output_attentions=output_attentions,
          output_hidden_states=output_hidden_states,
          return_dict=return_dict,
          stage_manager=stage_manager,
          hidden_states=hidden_states,
          stage_index=stage_index,
      )
  
      if stage_manager.is_last_stage():
          hidden_states = transformer_outputs[0]
          logits = self.score(hidden_states)
  
          batch_size = (
              input_ids.shape[0]
              if input_ids is not None
              else hidden_states.shape[0]
          )
  
          if self.config.pad_token_id is None:
              sequence_lengths = -1
          else:
              if input_ids is not None:
                  sequence_lengths = (
                      torch.ne(input_ids, self.config.pad_token_id).sum(
                          -1
                      )
                      - 1
                  ).to(logits.device)
              else:
                  sequence_lengths = -1
                  logger.warning(
                      f"{self.__class__.__name__} will not detect padding tokens in `inputs_embeds`. Results may be "
                      "unexpected if using padding tokens in conjunction with `inputs_embeds.`"
                  )
  
          pooled_logits = logits[
              torch.arange(batch_size, device=logits.device),
              sequence_lengths,
          ]
  
          loss = None
          if labels is not None:
              if self.config.problem_type is None:
                  if self.num_labels == 1:
                      self.config.problem_type = "regression"
                  elif self.num_labels > 1 and (
                      labels.dtype == torch.long
                      or labels.dtype == torch.int
                  ):
                      self.config.problem_type = (
                          "single_label_classification"
                      )
                  else:
                      self.config.problem_type = (
                          "multi_label_classification"
                      )
  
              if self.config.problem_type == "regression":
                  loss_fct = MSELoss()
                  if self.num_labels == 1:
                      loss = loss_fct(
                          pooled_logits.squeeze(), labels.squeeze()
                      )
                  else:
                      loss = loss_fct(pooled_logits, labels)
              elif (
                  self.config.problem_type
                  == "single_label_classification"
              ):
                  loss_fct = CrossEntropyLoss()
                  loss = loss_fct(
                      pooled_logits.view(-1, self.num_labels),
                      labels.view(-1),
                  )
              elif (
                  self.config.problem_type
                  == "multi_label_classification"
              ):
                  loss_fct = BCEWithLogitsLoss()
                  loss = loss_fct(pooled_logits, labels)
  
          if not return_dict:
              output = (pooled_logits,) + transformer_outputs[1:]
              return ((loss,) + output) if loss is not None else output
  
          return SequenceClassifierOutputWithPast(
              loss=loss,
              logits=pooled_logits,
              past_key_values=transformer_outputs.past_key_values,
              hidden_states=transformer_outputs.hidden_states,
              attentions=transformer_outputs.attentions,
          )
      else:
          hidden_states = transformer_outputs.get("hidden_states")
          return {"hidden_states": hidden_states}
  
file_path: ColossalAI/colossalai/shardformer/modeling/opt.py
imports: import random
  from typing import List, Optional, Tuple, Union
  import torch
  from torch.nn import BCEWithLogitsLoss, CrossEntropyLoss, MSELoss
  from transformers.modeling_outputs import BaseModelOutputWithPast, CausalLMOutputWithPast, QuestionAnsweringModelOutput, SequenceClassifierOutputWithPast
  from transformers.models.opt.modeling_opt import OPTForCausalLM, OPTForQuestionAnswering, OPTForSequenceClassification, OPTModel
  from transformers.utils import logging
  from colossalai.pipeline.stage_manager import PipelineStageManager
label_desc: Use image node_id 5 for calling the OPTPipelineForwards obj's underlying member method code with example usage: obj.opt_for_sequence_classification_forward(input_ids, attention_mask, head_mask, past_key_values, inputs_embeds, labels, use_cache, output_attentions, output_hidden_states, return_dict, stage_manager, hidden_states, stage_index) and returns: SequenceClassifierOutputWithPast, dict, unknown