jupyterjazz commited on
Commit
ffd672d
1 Parent(s): b20a611

refactor-task-type-to-task (#43)

Browse files

- rename task type (3afddee7275504c48afc63049db9124f9e2871ce)

Files changed (2) hide show
  1. modeling_lora.py +10 -10
  2. modeling_xlm_roberta.py +1 -1
modeling_lora.py CHANGED
@@ -367,35 +367,35 @@ class XLMRobertaLoRA(XLMRobertaPreTrainedModel):
367
  self,
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  sentences: Union[str, List[str]],
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  *args,
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- task_type: Optional[str] = None,
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  **kwargs,
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  ) -> Union[List[torch.Tensor], np.ndarray, torch.Tensor]:
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  """
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  Computes sentence embeddings.
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  sentences(`str` or `List[str]`):
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  Sentence or sentences to be encoded
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- task_type(`str`, *optional*, defaults to `None`):
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- Specifies the task for which the encoding is intended. If `task_type` is not provided,
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  all LoRA adapters are disabled, and the model reverts to its original,
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  general-purpose weights.
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  """
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- if task_type and task_type not in self._lora_adaptations:
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  raise ValueError(
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- f"Unsupported task '{task_type}'. "
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  f"Supported tasks are: {', '.join(self.config.lora_adaptations)}."
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- f"Alternatively, don't pass the `task_type` argument to disable LoRA."
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  )
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  adapter_mask = None
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- if task_type:
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- task_id = self._adaptation_map[task_type]
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  num_examples = 1 if isinstance(sentences, str) else len(sentences)
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  adapter_mask = torch.full(
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  (num_examples,), task_id, dtype=torch.int32, device=self.device
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  )
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  if isinstance(sentences, str):
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- sentences = self._task_instructions[task_type] + sentences
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  else:
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- sentences = [self._task_instructions[task_type] + sentence for sentence in sentences]
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  return self.roberta.encode(
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  sentences, *args, adapter_mask=adapter_mask, **kwargs
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  )
 
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  self,
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  sentences: Union[str, List[str]],
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  *args,
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+ task: Optional[str] = None,
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  **kwargs,
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  ) -> Union[List[torch.Tensor], np.ndarray, torch.Tensor]:
373
  """
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  Computes sentence embeddings.
375
  sentences(`str` or `List[str]`):
376
  Sentence or sentences to be encoded
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+ task(`str`, *optional*, defaults to `None`):
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+ Specifies the task for which the encoding is intended. If `task` is not provided,
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  all LoRA adapters are disabled, and the model reverts to its original,
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  general-purpose weights.
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  """
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+ if task and task not in self._lora_adaptations:
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  raise ValueError(
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+ f"Unsupported task '{task}'. "
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  f"Supported tasks are: {', '.join(self.config.lora_adaptations)}."
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+ f"Alternatively, don't pass the `task` argument to disable LoRA."
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  )
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  adapter_mask = None
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+ if task:
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+ task_id = self._adaptation_map[task]
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  num_examples = 1 if isinstance(sentences, str) else len(sentences)
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  adapter_mask = torch.full(
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  (num_examples,), task_id, dtype=torch.int32, device=self.device
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  )
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  if isinstance(sentences, str):
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+ sentences = self._task_instructions[task] + sentences
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  else:
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+ sentences = [self._task_instructions[task] + sentence for sentence in sentences]
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  return self.roberta.encode(
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  sentences, *args, adapter_mask=adapter_mask, **kwargs
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  )
modeling_xlm_roberta.py CHANGED
@@ -473,7 +473,7 @@ class XLMRobertaModel(XLMRobertaPreTrainedModel):
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  normalize_embeddings: bool = True,
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  truncate_dim: Optional[int] = None,
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  adapter_mask: Optional[torch.Tensor] = None,
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- task_type: Optional[str] = None,
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  **tokenizer_kwargs,
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  ) -> Union[List[torch.Tensor], np.ndarray, torch.Tensor]:
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  """
 
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  normalize_embeddings: bool = True,
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  truncate_dim: Optional[int] = None,
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  adapter_mask: Optional[torch.Tensor] = None,
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+ task: Optional[str] = None,
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  **tokenizer_kwargs,
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  ) -> Union[List[torch.Tensor], np.ndarray, torch.Tensor]:
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  """