allennlp.models.encoder_decoders

class allennlp.models.encoder_decoders.simple_seq2seq.SimpleSeq2Seq(vocab: allennlp.data.vocabulary.Vocabulary, source_embedder: allennlp.modules.text_field_embedders.text_field_embedder.TextFieldEmbedder, encoder: allennlp.modules.seq2seq_encoders.seq2seq_encoder.Seq2SeqEncoder, max_decoding_steps: int, attention: allennlp.modules.attention.attention.Attention = None, attention_function: allennlp.modules.similarity_functions.similarity_function.SimilarityFunction = None, beam_size: int = None, target_namespace: str = 'tokens', target_embedding_dim: int = None, scheduled_sampling_ratio: float = 0.0, use_bleu: bool = True) → None[source]

Bases: allennlp.models.model.Model

This SimpleSeq2Seq class is a Model which takes a sequence, encodes it, and then uses the encoded representations to decode another sequence. You can use this as the basis for a neural machine translation system, an abstractive summarization system, or any other common seq2seq problem. The model here is simple, but should be a decent starting place for implementing recent models for these tasks.

Parameters:
vocab : Vocabulary, required

Vocabulary containing source and target vocabularies. They may be under the same namespace (tokens) or the target tokens can have a different namespace, in which case it needs to be specified as target_namespace.

source_embedder : TextFieldEmbedder, required

Embedder for source side sequences

encoder : Seq2SeqEncoder, required

The encoder of the “encoder/decoder” model

max_decoding_steps : int

Maximum length of decoded sequences.

target_namespace : str, optional (default = ‘target_tokens’)

If the target side vocabulary is different from the source side’s, you need to specify the target’s namespace here. If not, we’ll assume it is “tokens”, which is also the default choice for the source side, and this might cause them to share vocabularies.

target_embedding_dim : int, optional (default = source_embedding_dim)

You can specify an embedding dimensionality for the target side. If not, we’ll use the same value as the source embedder’s.

attention : Attention, optional (default = None)

If you want to use attention to get a dynamic summary of the encoder outputs at each step of decoding, this is the function used to compute similarity between the decoder hidden state and encoder outputs.

attention_function: ``SimilarityFunction``, optional (default = None)

This is if you want to use the legacy implementation of attention. This will be deprecated since it consumes more memory than the specialized attention modules.

beam_size : int, optional (default = None)

Width of the beam for beam search. If not specified, greedy decoding is used.

scheduled_sampling_ratio : float, optional (default = 0.)

At each timestep during training, we sample a random number between 0 and 1, and if it is not less than this value, we use the ground truth labels for the whole batch. Else, we use the predictions from the previous time step for the whole batch. If this value is 0.0 (default), this corresponds to teacher forcing, and if it is 1.0, it corresponds to not using target side ground truth labels. See the following paper for more information: Scheduled Sampling for Sequence Prediction with Recurrent Neural Networks. Bengio et al., 2015.

use_bleu : bool, optional (default = True)

If True, the BLEU metric will be calculated during validation.

decode(output_dict: typing.Dict[str, torch.Tensor]) → typing.Dict[str, torch.Tensor][source]

Finalize predictions.

This method overrides Model.decode, which gets called after Model.forward, at test time, to finalize predictions. The logic for the decoder part of the encoder-decoder lives within the forward method.

This method trims the output predictions to the first end symbol, replaces indices with corresponding tokens, and adds a field called predicted_tokens to the output_dict.

forward(source_tokens: typing.Dict[str, torch.LongTensor], target_tokens: typing.Dict[str, torch.LongTensor] = None) → typing.Dict[str, torch.Tensor][source]

Make foward pass with decoder logic for producing the entire target sequence.

Parameters:
source_tokens : Dict[str, torch.LongTensor]

The output of TextField.as_array() applied on the source TextField. This will be passed through a TextFieldEmbedder and then through an encoder.

target_tokens : Dict[str, torch.LongTensor], optional (default = None)

Output of Textfield.as_array() applied on target TextField. We assume that the target tokens are also represented as a TextField.

Returns:
Dict[str, torch.Tensor]
get_metrics(reset: bool = False) → typing.Dict[str, float][source]

Returns a dictionary of metrics. This method will be called by allennlp.training.Trainer in order to compute and use model metrics for early stopping and model serialization. We return an empty dictionary here rather than raising as it is not required to implement metrics for a new model. A boolean reset parameter is passed, as frequently a metric accumulator will have some state which should be reset between epochs. This is also compatible with Metrics should be populated during the call to ``forward`, with the Metric handling the accumulation of the metric until this method is called.

take_step(last_predictions: torch.Tensor, state: typing.Dict[str, torch.Tensor]) → typing.Tuple[torch.Tensor, typing.Dict[str, torch.Tensor]][source]

Take a decoding step. This is called by the beam search class.

Parameters:
last_predictions : torch.Tensor

A tensor of shape (group_size,), which gives the indices of the predictions during the last time step.

state : Dict[str, torch.Tensor]

A dictionary of tensors that contain the current state information needed to predict the next step, which includes the encoder outputs, the source mask, and the decoder hidden state and context. Each of these tensors has shape (group_size, *), where * can be any other number of dimensions.

Returns:
Tuple[torch.Tensor, Dict[str, torch.Tensor]]

A tuple of (log_probabilities, updated_state), where log_probabilities is a tensor of shape (group_size, num_classes) containing the predicted log probability of each class for the next step, for each item in the group, while updated_state is a dictionary of tensors containing the encoder outputs, source mask, and updated decoder hidden state and context.

Notes

We treat the inputs as a batch, even though group_size is not necessarily equal to batch_size, since the group may contain multiple states for each source sentence in the batch.