logitorch.pl_models.fld
=======================

.. py:module:: logitorch.pl_models.fld


Classes
-------

.. autoapisummary::

   logitorch.pl_models.fld.PLFLDAllAtOnceProver


Module Contents
---------------

.. py:class:: PLFLDAllAtOnceProver(pretrained_model: str = 't5-base', learning_rate: float = None, weight_decay=0.1, warmup_steps: Optional[int] = 1000)

   Bases: :py:obj:`lightning.pytorch.LightningModule`


   PyTorch Lightning module for Fine-tuned Language Decoder (FLD) all-at-once prover.

   Args:
       pretrained_model (str): Pretrained model name or path (default: "t5-base").
       learning_rate (float): Learning rate for optimizer.
       weight_decay (float): Weight decay for optimizer (default: 0.1).
       warmup_steps (int, optional): Number of warmup steps for learning rate scheduler (default: 1000).

   Attributes:
       model (FLDAllAtOnceProver): FLD model.
       pretrained_model (str): Pretrained model name or path.
       learning_rate (float): Learning rate for optimizer.
       weight_decay (float): Weight decay for optimizer.
       warmup_steps (int): Number of warmup steps for learning rate scheduler.
       optimizer (AdamW): Optimizer for training.


   .. py:method:: configure_optimizers()

      Configure the optimizer and learning rate scheduler.

      Returns:
          Tuple[List[Optimizer], List[Dict[str, Any]]]: Optimizers and schedulers.



   .. py:method:: forward(x, y) -> transformers.modeling_outputs.SequenceClassifierOutput

      Forward pass of the model.

      Args:
          x: Input data.
          y: Target data.

      Returns:
          SequenceClassifierOutput: Model output.



   .. py:method:: predict(prompt: str, num_beams: int = 5, max_length: int = 1000, device: str = 'cpu')

      Generate predictions using the model.

      Args:
          prompt (str): Input prompt.
          num_beams (int): Number of beams for beam search (default: 5).
          max_length (int): Maximum length of generated sequence (default: 1000).
          device (str): Device to use for prediction (default: "cpu").

      Returns:
          Model predictions.



   .. py:method:: training_step(train_batch: Tuple[Dict[str, torch.Tensor], torch.Tensor], batch_idx: int) -> torch.Tensor

      Training step.

      Args:
          train_batch: Batch of training data.
          batch_idx: Index of the batch.

      Returns:
          torch.Tensor: Loss value.



   .. py:method:: validation_step(val_batch: Tuple[Dict[str, torch.Tensor], torch.Tensor], batch_idx: int) -> None

      Validation step.

      Args:
          val_batch: Batch of validation data.
          batch_idx: Index of the batch.



   .. py:attribute:: learning_rate
      :value: None



   .. py:attribute:: model


   .. py:attribute:: optimizer
      :value: None



   .. py:attribute:: pretrained_model
      :value: 't5-base'



   .. py:attribute:: warmup_steps
      :value: 1000



   .. py:attribute:: weight_decay
      :value: 0.1