pytorch_seq2seq_wrapper

[ allennlp.modules.seq2seq_encoders.pytorch_seq2seq_wrapper ]

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PytorchSeq2SeqWrapper Objects

class PytorchSeq2SeqWrapper(Seq2SeqEncoder):
 | def __init__(
 |     self,
 |     module: torch.nn.Module,
 |     stateful: bool = False
 | ) -> None

Pytorch's RNNs have two outputs: the hidden state for every time step, and the hidden state at the last time step for every layer. We just want the first one as a single output. This wrapper pulls out that output, and adds a get_output_dim method, which is useful if you want to, e.g., define a linear + softmax layer on top of this to get some distribution over a set of labels. The linear layer needs to know its input dimension before it is called, and you can get that from get_output_dim.

In order to be wrapped with this wrapper, a class must have the following members:

- `self.input_size: int`
- `self.hidden_size: int`
- `def forward(inputs: PackedSequence, hidden_state: torch.Tensor) ->
  Tuple[PackedSequence, torch.Tensor]`.
- `self.bidirectional: bool` (optional)

This is what pytorch's RNN's look like - just make sure your class looks like those, and it should work.

Note that we require you to pass a binary mask of shape (batch_size, sequence_length) when you call this module, to avoid subtle bugs around masking. If you already have a PackedSequence you can pass None as the second parameter.

We support stateful RNNs where the final state from each batch is used as the initial state for the subsequent batch by passing stateful=True to the constructor.

get_input_dim

 | @overrides
 | def get_input_dim(self) -> int

get_output_dim

 | @overrides
 | def get_output_dim(self) -> int

is_bidirectional

 | @overrides
 | def is_bidirectional(self) -> bool

forward

 | @overrides
 | def forward(
 |     self,
 |     inputs: torch.Tensor,
 |     mask: torch.BoolTensor,
 |     hidden_state: torch.Tensor = None
 | ) -> torch.Tensor

GruSeq2SeqEncoder Objects

class GruSeq2SeqEncoder(PytorchSeq2SeqWrapper):
 | def __init__(
 |     self,
 |     input_size: int,
 |     hidden_size: int,
 |     num_layers: int = 1,
 |     bias: bool = True,
 |     dropout: float = 0.0,
 |     bidirectional: bool = False,
 |     stateful: bool = False
 | )

Registered as a Seq2SeqEncoder with name "gru".

LstmSeq2SeqEncoder Objects

class LstmSeq2SeqEncoder(PytorchSeq2SeqWrapper):
 | def __init__(
 |     self,
 |     input_size: int,
 |     hidden_size: int,
 |     num_layers: int = 1,
 |     bias: bool = True,
 |     dropout: float = 0.0,
 |     bidirectional: bool = False,
 |     stateful: bool = False
 | )

Registered as a Seq2SeqEncoder with name "lstm".

RnnSeq2SeqEncoder Objects

class RnnSeq2SeqEncoder(PytorchSeq2SeqWrapper):
 | def __init__(
 |     self,
 |     input_size: int,
 |     hidden_size: int,
 |     num_layers: int = 1,
 |     nonlinearity: str = "tanh",
 |     bias: bool = True,
 |     dropout: float = 0.0,
 |     bidirectional: bool = False,
 |     stateful: bool = False
 | )

Registered as a Seq2SeqEncoder with name "rnn".

AugmentedLstmSeq2SeqEncoder Objects

class AugmentedLstmSeq2SeqEncoder(PytorchSeq2SeqWrapper):
 | def __init__(
 |     self,
 |     input_size: int,
 |     hidden_size: int,
 |     go_forward: bool = True,
 |     recurrent_dropout_probability: float = 0.0,
 |     use_highway: bool = True,
 |     use_input_projection_bias: bool = True,
 |     stateful: bool = False
 | ) -> None

Registered as a Seq2SeqEncoder with name "augmented_lstm".

StackedAlternatingLstmSeq2SeqEncoder Objects

class StackedAlternatingLstmSeq2SeqEncoder(PytorchSeq2SeqWrapper):
 | def __init__(
 |     self,
 |     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,
 |     stateful: bool = False
 | ) -> None

Registered as a Seq2SeqEncoder with name "alternating_lstm".

StackedBidirectionalLstmSeq2SeqEncoder Objects

class StackedBidirectionalLstmSeq2SeqEncoder(PytorchSeq2SeqWrapper):
 | def __init__(
 |     self,
 |     input_size: int,
 |     hidden_size: int,
 |     num_layers: int,
 |     recurrent_dropout_probability: float = 0.0,
 |     layer_dropout_probability: float = 0.0,
 |     use_highway: bool = True,
 |     stateful: bool = False
 | ) -> None

Registered as a Seq2SeqEncoder with name "stacked_bidirectional_lstm".