allennlp.modules.seq2seq_decoders.auto_regressive_seq_decoder#

AutoRegressiveSeqDecoder#

AutoRegressiveSeqDecoder(self, vocab:allennlp.data.vocabulary.Vocabulary, decoder_net:allennlp.modules.seq2seq_decoders.decoder_net.DecoderNet, max_decoding_steps:int, target_embedder:allennlp.modules.token_embedders.embedding.Embedding, target_namespace:str='tokens', tie_output_embedding:bool=False, scheduled_sampling_ratio:float=0, label_smoothing_ratio:Union[float, NoneType]=None, beam_size:int=4, tensor_based_metric:allennlp.training.metrics.metric.Metric=None, token_based_metric:allennlp.training.metrics.metric.Metric=None) -> None

An autoregressive decoder that can be used for most seq2seq 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.
  • decoder_net : DecoderNet, required Module that contains implementation of neural network for decoding output elements
  • max_decoding_steps : int, required Maximum length of decoded sequences.
  • target_embedder : Embedding Embedder for target tokens.
  • target_namespace : str, optional (default = '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.
  • beam_size : int, optional (default = 4) Width of the beam for beam search.
  • tensor_based_metric : Metric, optional (default = None) A metric to track on validation data that takes raw tensors when its called.
  • This metric must accept two arguments when called: a batched tensor of predicted token indices, and a batched tensor of gold token indices.
  • token_based_metric : Metric, optional (default = None) A metric to track on validation data that takes lists of lists of tokens as input. This metric must accept two arguments when called, both of type List[List[str]]. The first is a predicted sequence for each item in the batch and the second is a gold sequence for each item in the batch.
  • scheduled_sampling_ratio : float optional (default = 0) Defines ratio between teacher forced training and real output usage. If its zero (teacher forcing only) and decoder_netsupports parallel decoding, we get the output predictions in a single forward pass of the decoder_net.

take_step#

AutoRegressiveSeqDecoder.take_step(self, last_predictions:torch.Tensor, state:Dict[str, torch.Tensor]) -> Tuple[torch.Tensor, Dict[str, torch.Tensor]]

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.

get_metrics#

AutoRegressiveSeqDecoder.get_metrics(self, reset:bool=False) -> Dict[str, float]

The decoder is responsible for computing metrics using the target tokens.

forward#

AutoRegressiveSeqDecoder.forward(self, encoder_out:Dict[str, torch.LongTensor], target_tokens:Dict[str, torch.LongTensor]=None) -> Dict[str, torch.Tensor]

Decoding from encoded states to sequence of outputs also computes loss if target_tokens are given.

Parameters

  • encoder_out : Dict[str, torch.LongTensor], required Dictionary with encoded state, ideally containing the encoded vectors and the source mask.
  • target_tokens : Dict[str, torch.LongTensor], optional The output of TextField.as_array() applied on the target TextField.

post_process#

AutoRegressiveSeqDecoder.post_process(self, output_dict:Dict[str, torch.Tensor]) -> Dict[str, torch.Tensor]

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.