A stacked LSTM with LSTM layers which alternate between going forwards over the sequence and going backwards.


    input_size: int,
    hidden_size: int,
    num_layers: int,
    recurrent_dropout_probability: float = 0.0,
    use_highway: bool = True,
    use_input_projection_bias: bool = True,
) -> None

A stacked LSTM with LSTM layers which alternate between going forwards over the sequence and going backwards. This implementation is based on the description in [Deep Semantic Role Labelling - What works and what's next] (


  • input_size : int, required The dimension of the inputs to the LSTM.
  • hidden_size : int, required The dimension of the outputs of the LSTM.
  • num_layers : int, required The number of stacked LSTMs to use.
  • recurrent_dropout_probability : float, optional (default = 0.0) The dropout probability to be used in a dropout scheme as stated in [A Theoretically Grounded Application of Dropout in Recurrent Neural Networks]
  • (
  • use_input_projection_bias : bool, optional (default = True) Whether or not to use a bias on the input projection layer. This is mainly here for backwards compatibility reasons and will be removed (and set to False) in future releases.


output_accumulator: PackedSequence The outputs of the interleaved LSTMs per timestep. A tensor of shape (batch_size, max_timesteps, hidden_size) where for a given batch element, all outputs past the sequence length for that batch are zero tensors.


    inputs: torch.nn.utils.rnn.PackedSequence,
    initial_state: Optional[Tuple[torch.Tensor, torch.Tensor]] = None,
) -> Tuple[Union[torch.Tensor, torch.nn.utils.rnn.PackedSequence], Tuple[torch.Tensor, torch.Tensor]]


  • inputs : PackedSequence, required. A batch first PackedSequence to run the stacked LSTM over.
  • initial_state : Tuple[torch.Tensor, torch.Tensor], optional, (default = None) A tuple (state, memory) representing the initial hidden state and memory of the LSTM. Each tensor has shape (1, batch_size, output_dimension).


output_sequence: PackedSequence The encoded sequence of shape (batch_size, sequence_length, hidden_size) final_states: Tuple[torch.Tensor, torch.Tensor] The per-layer final (state, memory) states of the LSTM, each with shape (num_layers, batch_size, hidden_size).