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1752c7c
1 Parent(s): 6e55444

docs: add comments

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Signed-off-by: jupyterjazz <[email protected]>

Files changed (1) hide show
  1. modeling_lora.py +16 -4
modeling_lora.py CHANGED
@@ -162,6 +162,16 @@ class LoRAParametrization(nn.Module):
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  dropout_p: float,
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  alpha: float,
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  ):
 
 
 
 
 
 
 
 
 
 
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  if isinstance(layer, nn.Linear):
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  parametrize.register_parametrization(
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  layer,
@@ -312,11 +322,11 @@ class XLMRobertaLoRA(XLMRobertaPreTrainedModel):
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  config = XLMRobertaFlashConfig.from_pretrained(
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  pretrained_model_name_or_path, *model_args, **kwargs
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  )
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- if config.load_trained_adapters:
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  return super().from_pretrained(
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  pretrained_model_name_or_path, *model_args, **kwargs
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  )
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- else:
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  roberta = XLMRobertaModel.from_pretrained(
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  pretrained_model_name_or_path, *model_args, **kwargs
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  )
@@ -358,10 +368,12 @@ class XLMRobertaLoRA(XLMRobertaPreTrainedModel):
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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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  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 provide,
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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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  dropout_p: float,
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  alpha: float,
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  ):
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+ """
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+ Registering LoRA adapters to all embedding and linear layers.
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+
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+ Additionally, we implement a custom forward function for LoRA parametrization.
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+ This function modifies the layer's forward pass to optionally use task-specific
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+ parameters. When a `task_id` is provided, it employs a LoRA parametrization
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+ to modify the original weights according to the specific task. This allows
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+ the layer to adapt dynamically to different tasks at runtime. If no `task_id`
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+ is specified, the layer uses its original weights.
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+ """
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  if isinstance(layer, nn.Linear):
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  parametrize.register_parametrization(
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  layer,
 
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  config = XLMRobertaFlashConfig.from_pretrained(
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  pretrained_model_name_or_path, *model_args, **kwargs
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  )
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+ if config.load_trained_adapters: # checkpoint already contains LoRA adapters
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  return super().from_pretrained(
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  pretrained_model_name_or_path, *model_args, **kwargs
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  )
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+ else: # initializing new adapters
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  roberta = XLMRobertaModel.from_pretrained(
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  pretrained_model_name_or_path, *model_args, **kwargs
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  )
 
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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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  """