class_docstr: This class serves as a micro library for falcon pipeline forwards.
class_docstr_tok: ["This","class","serves","as","a","micro","library","for","falcon","pipeline","forwards","."]
docstr: labels (`torch.LongTensor` of shape `(batch_size,)`, *optional*):
      Labels for computing the sequence classification/regression loss. Indices should be in `[0, ...,
      config.num_labels - 1]`. If `config.num_labels == 1` a regression loss is computed (Mean-Square loss), If
      `config.num_labels > 1` a classification loss is computed (Cross-Entropy).
docstr_tok: ["labels","(","`","torch.LongTensor","`","of","shape","`","(","batch_size",",",")","`",",","*","optional","*",")",":","Labels","for","computing","the","sequence","classification\/regression","loss",".","Indices","should","be","in","`","[","0",",","...",",","config.num_labels","-","1","]","`",".","If","`","config.num_labels","==","1","`","a","regression","loss","is","computed","(","Mean-Square","loss",")",",","If","`","config.num_labels",">","1","`","a","classification","loss","is","computed","(","Cross-Entropy",")","."]
code: def falcon_for_sequence_classification_forward(
      self: FalconForSequenceClassification,
      input_ids: Optional[torch.LongTensor] = None,
      past_key_values: Optional[
          Tuple[Tuple[torch.Tensor, torch.Tensor], ...]
      ] = None,
      attention_mask: Optional[torch.Tensor] = None,
      head_mask: Optional[torch.Tensor] = None,
      inputs_embeds: Optional[torch.Tensor] = None,
      labels: Optional[torch.Tensor] = 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,
      shard_config: ShardConfig = None,
  ) -> Union[Tuple[torch.Tensor], SequenceClassifierOutputWithPast]:
      r"""
      labels (`torch.LongTensor` of shape `(batch_size,)`, *optional*):
          Labels for computing the sequence classification/regression loss. Indices should be in `[0, ...,
          config.num_labels - 1]`. If `config.num_labels == 1` a regression loss is computed (Mean-Square loss), If
          `config.num_labels > 1` a classification loss is computed (Cross-Entropy).
      """
      logger = logging.get_logger(__name__)
  
      return_dict = (
          return_dict
          if return_dict is not None
          else self.config.use_return_dict
      )
  
      if output_attentions:
          logger.warning_once(
              "output_attentions=True is not supported for pipeline models at the moment."
          )
          output_attentions = False
      if output_hidden_states:
          logger.warning_once(
              "output_hidden_states=True is not supported for pipeline models at the moment."
          )
          output_hidden_states = False
  
      transformer_outputs = FalconPipelineForwards.falcon_model_forward(
          self.transformer,
          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,
          shard_config=shard_config,
      )
  
      past_key_values = None
      if stage_manager.is_last_stage():
          batch_size = hidden_states.shape[0]
          hidden_states = transformer_outputs[0]
          logits = self.score(hidden_states)
  
          if self.config.pad_token_id is None and batch_size != 1:
              raise ValueError(
                  "Cannot handle batch sizes > 1 if no padding token is defined."
              )
          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(
                          dim=-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, labels)
              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/falcon.py
imports: from typing import List, Optional, Tuple, Union
  import torch
  import torch.distributed
  from torch.distributed import ProcessGroup
  from torch.nn import BCEWithLogitsLoss, CrossEntropyLoss, MSELoss
  from transformers.modeling_outputs import BaseModelOutputWithPastAndCrossAttentions, CausalLMOutputWithCrossAttentions, QuestionAnsweringModelOutput, SequenceClassifierOutputWithPast, TokenClassifierOutput
  from transformers.models.falcon.modeling_falcon import FalconForCausalLM, FalconForQuestionAnswering, FalconForSequenceClassification, FalconForTokenClassification, FalconModel, build_alibi_tensor
  from transformers.utils import logging
  from colossalai.pipeline.stage_manager import PipelineStageManager
  from colossalai.shardformer.shard import ShardConfig
label_desc: Use image node_id 3 for calling the FalconPipelineForwards obj's underlying member method code with example usage: obj.falcon_for_sequence_classification_forward(input_ids, past_key_values, attention_mask, head_mask, inputs_embeds, labels, use_cache, output_attentions, output_hidden_states, return_dict, stage_manager, hidden_states, stage_index, shard_config) and returns: SequenceClassifierOutputWithPast, dict, unknown