pytorch_seq2seq_wrapper

allennlp.modules.seq2seq_encoders.pytorch_seq2seq_wrapper

PytorchSeq2SeqWrapper#

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#

class PytorchSeq2SeqWrapper(Seq2SeqEncoder):
 | ...
 | @overrides
 | def get_input_dim(self) -> int

get_output_dim#

class PytorchSeq2SeqWrapper(Seq2SeqEncoder):
 | ...
 | @overrides
 | def get_output_dim(self) -> int

is_bidirectional#

class PytorchSeq2SeqWrapper(Seq2SeqEncoder):
 | ...
 | @overrides
 | def is_bidirectional(self) -> bool

forward#

class PytorchSeq2SeqWrapper(Seq2SeqEncoder):
 | ...
 | @overrides
 | def forward(
 |     self,
 |     inputs: torch.Tensor,
 |     mask: torch.BoolTensor,
 |     hidden_state: torch.Tensor = None
 | ) -> torch.Tensor

GruSeq2SeqEncoder#

@Seq2SeqEncoder.register("gru")
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#

@Seq2SeqEncoder.register("lstm")
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#

@Seq2SeqEncoder.register("rnn")
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#

@Seq2SeqEncoder.register("augmented_lstm")
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#

@Seq2SeqEncoder.register("alternating_lstm")
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#

@Seq2SeqEncoder.register("stacked_bidirectional_lstm")
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".