class EntityNLM(Model):
"""
Implementation of the Entity Neural Language Model from:
https://arxiv.org/abs/1708.00781
Parameters
----------
vocab : ``Vocabulary``
The model vocabulary.
text_field_embedder : ``TextFieldEmbedder``
Used to embed tokens.
encoder : ``Seq2SeqEncoder``
Used to encode the sequence of token embeddings.
embedding_dim : ``int``
The dimension of entity / length embeddings. Should match the encoder output size.
max_mention_length : ``int``
Maximum entity mention length.
max_embeddings : ``int``
Maximum number of embeddings.
tie_weights : ``bool``
Whether to tie embedding and output weights.
variational_dropout_rate : ``float``, optional
Dropout rate of variational dropout applied to input embeddings. Default: 0.0
dropout_rate : ``float``, optional
Dropout rate applied to hidden states. Default: 0.0
initializer : ``InitializerApplicator``, optional
Used to initialize model parameters.
"""
# pylint: disable=line-too-long
def __init__(
self,
vocab: Vocabulary,
text_field_embedder: TextFieldEmbedder,
encoder: Seq2SeqEncoder,
embedding_dim: int,
max_mention_length: int,
max_embeddings: int,
tie_weights: bool,
variational_dropout_rate: float = 0.0,
dropout_rate: float = 0.0,
initializer: InitializerApplicator = InitializerApplicator()
) -> None:
super(EntityNLM, self).__init__(vocab)
self._text_field_embedder = text_field_embedder
self._encoder = encoder
self._embedding_dim = embedding_dim
self._max_mention_length = max_mention_length
self._max_embeddings = max_embeddings
self._tie_weights = tie_weights
self._variational_dropout_rate = variational_dropout_rate
self._dropout_rate = dropout_rate
self._state: Optional[StateDict] = None
# Input variational dropout
self._variational_dropout = InputVariationalDropout(
variational_dropout_rate)
self._dropout = torch.nn.Dropout(dropout_rate)
# For entity type prediction
self._entity_type_projection = torch.nn.Linear(
in_features=embedding_dim, out_features=2, bias=False)
self._dynamic_embeddings = DynamicEmbedding(
embedding_dim=embedding_dim, max_embeddings=max_embeddings)
# For mention length prediction
self._mention_length_projection = torch.nn.Linear(
in_features=2 * embedding_dim, out_features=max_mention_length)
# For next word prediction
self._dummy_context_embedding = Parameter(
F.normalize(torch.randn(1, embedding_dim))) # TODO: Maybe squeeze
self._entity_output_projection = torch.nn.Linear(
in_features=embedding_dim, out_features=embedding_dim, bias=False)
self._context_output_projection = torch.nn.Linear(
in_features=embedding_dim, out_features=embedding_dim, bias=False)
self._vocab_projection = torch.nn.Linear(
in_features=embedding_dim,
out_features=vocab.get_vocab_size('tokens'))
if tie_weights:
self._vocab_projection.weight = self._text_field_embedder._token_embedders[
'tokens'].weight # pylint: disable=W0212
# self._perplexity = Perplexity()
# self._unknown_penalized_perplexity = UnknownPenalizedPerplexity(self.vocab)
self._entity_type_accuracy = CategoricalAccuracy()
self._entity_id_accuracy = CategoricalAccuracy()
self._mention_length_accuracy = CategoricalAccuracy()
if tie_weights:
self._vocab_projection.weight = self._text_field_embedder._token_embedders[
'tokens'].weight # pylint: disable=W0212
initializer(self)
@overrides
def forward(
self, # pylint: disable=arguments-differ
tokens: Dict[str, torch.Tensor],
entity_types: Optional[torch.Tensor] = None,
entity_ids: Optional[torch.Tensor] = None,
mention_lengths: Optional[torch.Tensor] = None,
reset: bool = False) -> Dict[str, torch.Tensor]:
"""
Computes the loss during training / validation.
Parameters
----------
tokens : ``Dict[str, torch.Tensor]``
A tensor of shape ``(batch_size, sequence_length)`` containing the sequence of
tokens.
entity_types : ``torch.Tensor``
A tensor of shape ``(batch_size, sequence_length)`` indicating whether or not the
corresponding token belongs to a mention.
entity_ids : ``torch.Tensor``
A tensor of shape ``(batch_size, sequence_length)`` containing the ids of the
entities the corresponding token is mentioning.
mention_lengths : ``torch.Tensor``
A tensor of shape ``(batch_size, sequence_length)`` tracking how many remaining
tokens (including the current one) there are in the mention.
reset : ``bool``
Whether or not to reset the model's state. This should be done at the start of each
new sequence.
Returns
-------
An output dictionary consisting of:
loss : ``torch.Tensor``
The combined loss.
"""
batch_size = tokens['tokens'].shape[0]
if reset:
self.reset_states(batch_size)
else:
self.detach_states()
if entity_types is not None:
output_dict = self._forward_loop(tokens=tokens,
entity_types=entity_types,
entity_ids=entity_ids,
mention_lengths=mention_lengths)
else:
output_dict = {}
if not self.training:
# TODO Some evaluation stuff
pass
return output_dict
def _forward_loop(
self, tokens: Dict[str, torch.Tensor], entity_types: torch.Tensor,
entity_ids: torch.Tensor,
mention_lengths: torch.Tensor) -> Dict[str, torch.Tensor]:
"""
Performs the forward pass to calculate the loss on a chunk of training data.
Parameters
----------
tokens : ``Dict[str, torch.Tensor]``
A tensor of shape ``(batch_size, sequence_length)`` containing the sequence of
tokens.
entity_types : ``torch.Tensor``
A tensor of shape ``(batch_size, sequence_length)`` indicating whether or not the
corresponding token belongs to a mention.
entity_ids : ``torch.Tensor``
A tensor of shape ``(batch_size, sequence_length)`` containing the ids of the
entities the corresponding token is mentioning.
mention_lengths : ``torch.Tensor``
A tensor of shape ``(batch_size, sequence_length)`` tracking how many remaining
tokens (including the current one) there are in the mention.
Returns
-------
An output dictionary consisting of:
entity_type_loss : ``torch.Tensor``
The loss of entity type predictions.
entity_id_loss : ``torch.Tensor``
The loss of entity id predictions.
mention_length_loss : ``torch.Tensor``
The loss of mention length predictions.
vocab_loss : ``torch.Tensor``
The loss of vocab word predictions.
loss : ``torch.Tensor``
The combined loss.
logp : ``torch.Tensor``
Instance level log-probabilities
"""
batch_size, sequence_length = tokens['tokens'].shape
# The model state allows us to recover the last timestep from the previous chunk in the
# split. If it does not exist, then we are processing a new batch.
if self._state is not None:
tokens = {
field: torch.cat(
(self._state['prev_tokens'][field], tokens[field]), dim=1)
for field in tokens
}
entity_types = torch.cat(
(self._state['prev_entity_types'], entity_types), dim=1)
entity_ids = torch.cat(
(self._state['prev_entity_ids'], entity_ids), dim=1)
mention_lengths = torch.cat(
(self._state['prev_mention_lengths'], mention_lengths), dim=1)
contexts = self._state['prev_contexts']
sequence_length += 1
else:
contexts = self._dummy_context_embedding.repeat(batch_size, 1)
# Embed tokens and get RNN hidden state.
mask = get_text_field_mask(tokens)
embeddings = self._text_field_embedder(tokens)
embeddings = self._variational_dropout(embeddings)
hidden = self._encoder(embeddings, mask)
# Initialize losses
entity_type_loss = 0.0
entity_id_loss = 0.0
mention_length_loss = 0.0
vocab_loss = 0.0
logp = hidden.new_zeros(batch_size)
# We dynamically add entities and update their representations in sequence. The following
# loop is designed to imitate as closely as possible lines 219-313 in:
# https://github.com/jiyfeng/entitynlm/blob/master/entitynlm.h
# while still being carried out in batch.
for timestep in range(sequence_length - 1):
current_entity_types = entity_types[:, timestep]
current_entity_ids = entity_ids[:, timestep]
current_mention_lengths = mention_lengths[:, timestep]
current_hidden = self._dropout(hidden[:, timestep])
next_entity_types = entity_types[:, timestep + 1]
next_entity_ids = entity_ids[:, timestep + 1]
next_mention_lengths = mention_lengths[:, timestep + 1]
next_mask = mask[:, timestep + 1]
next_tokens = tokens['tokens'][:, timestep + 1]
# We add new entities to any sequence where the current entity id matches the number of
# embeddings that currently exist for that sequence (this means we need a new one since
# there is an additional dummy embedding).
new_entities = current_entity_ids == self._dynamic_embeddings.num_embeddings
self._dynamic_embeddings.add_embeddings(timestep, new_entities)
# We also perform updates of the currently observed entities.
self._dynamic_embeddings.update_embeddings(
hidden=current_hidden,
update_indices=current_entity_ids,
timestep=timestep,
mask=current_entity_types)
# This part is a little counter-intuitive. Because the above code adds a new embedding
# whenever the **current** entity id matches the number of embeddings, we are one
# embedding short if the **next** entity id has not been seen before. To deal with
# this, we use the null embedding (e.g. the first one we created) as a proxy for the
# new entity's embedding (since it is on average what the new entity's embedding will
# be initialized in the next timestep). It might seem more sensible to just create the
# embedding now, but we cannot because of the subsequent update (since this would
# require access to the **next** hidden state, which does not exist during generation).
next_entity_ids = next_entity_ids.clone(
) # This prevents mutating the source data.
next_entity_ids[next_entity_ids ==
self._dynamic_embeddings.num_embeddings] = 0
# We only predict the types / ids / lengths of the next mention if we are not currently
# in the process of generating it (e.g. if the current remaining mention length is 1).
# Indexing / masking with ``predict_all`` makes it possible to do this in batch.
predict_all = (current_mention_lengths == 1) * next_mask.byte()
if predict_all.sum() > 0:
# Equation 3 in the paper.
entity_type_logits = self._entity_type_projection(
current_hidden[predict_all])
_entity_type_loss = F.cross_entropy(
entity_type_logits,
next_entity_types[predict_all].long(),
reduction='none')
entity_type_loss += _entity_type_loss.sum()
entity_type_logp = torch.zeros_like(next_entity_types,
dtype=torch.float32)
entity_type_logp[predict_all] = -_entity_type_loss
logp += entity_type_logp
self._entity_type_accuracy(
predictions=entity_type_logits,
gold_labels=next_entity_types[predict_all].long())
# Only proceed to predict entity and mention length if there is in fact an entity.
predict_em = next_entity_types * predict_all
if predict_em.sum() > 0:
# Equation 4 in the paper.
entity_id_prediction_outputs = self._dynamic_embeddings(
hidden=current_hidden,
target=next_entity_ids,
mask=predict_em)
_entity_id_loss = entity_id_prediction_outputs['loss']
entity_id_loss += _entity_id_loss.sum()
entity_id_logp = torch.zeros_like(next_entity_ids,
dtype=torch.float32)
entity_id_logp[predict_em] = -_entity_id_loss
logp += entity_id_logp
self._entity_id_accuracy(
predictions=entity_id_prediction_outputs['logits'],
gold_labels=next_entity_ids[predict_em])
# Equation 5 in the paper.
next_entity_embeddings = self._dynamic_embeddings.embeddings[
predict_em, next_entity_ids[predict_em]]
next_entity_embeddings = self._dropout(
next_entity_embeddings)
concatenated = torch.cat(
(current_hidden[predict_em], next_entity_embeddings),
dim=-1)
mention_length_logits = self._mention_length_projection(
concatenated)
_mention_length_loss = F.cross_entropy(
mention_length_logits,
next_mention_lengths[predict_em],
reduction='none')
mention_length_loss += _mention_length_loss.sum()
mention_length_logp = torch.zeros_like(
next_mention_lengths, dtype=torch.float32)
mention_length_logp[predict_em] = -_mention_length_loss
logp += mention_length_logp
self._mention_length_accuracy(
predictions=mention_length_logits,
gold_labels=next_mention_lengths[predict_em])
# Always predict the next word. This is done using the hidden state and contextual bias.
entity_embeddings = self._dynamic_embeddings.embeddings[
next_entity_types, next_entity_ids[next_entity_types]]
entity_embeddings = self._entity_output_projection(
entity_embeddings)
context_embeddings = contexts[1 - next_entity_types]
context_embeddings = self._context_output_projection(
context_embeddings)
# The checks in the following block of code are required to prevent adding empty
# tensors to vocab_features (which causes a floating point error).
vocab_features = current_hidden.clone()
if next_entity_types.sum() > 0:
vocab_features[next_entity_types] = vocab_features[
next_entity_types] + entity_embeddings
if (1 - next_entity_types.sum()) > 0:
vocab_features[1 - next_entity_types] = vocab_features[
1 - next_entity_types] + context_embeddings
vocab_logits = self._vocab_projection(vocab_features)
_vocab_loss = F.cross_entropy(vocab_logits,
next_tokens,
reduction='none')
_vocab_loss = _vocab_loss * next_mask.float()
vocab_loss += _vocab_loss.sum()
logp += -_vocab_loss
# self._perplexity(logits=vocab_logits,
# labels=next_tokens,
# mask=next_mask.float())
# self._unknown_penalized_perplexity(logits=vocab_logits,
# labels=next_tokens,
# mask=next_mask.float())
# Lastly update contexts
contexts = current_hidden
# Normalize the losses
entity_type_loss /= mask.sum()
entity_id_loss /= mask.sum()
mention_length_loss /= mask.sum()
vocab_loss /= mask.sum()
total_loss = entity_type_loss + entity_id_loss + mention_length_loss + vocab_loss
output_dict = {
'entity_type_loss': entity_type_loss,
'entity_id_loss': entity_id_loss,
'mention_length_loss': mention_length_loss,
'vocab_loss': vocab_loss,
'loss': total_loss,
'logp': logp
}
# Update the model state
self._state = {
'prev_tokens': {
field: tokens[field][:, -1].unsqueeze(1).detach()
for field in tokens
},
'prev_entity_types': entity_types[:, -1].unsqueeze(1).detach(),
'prev_entity_ids': entity_ids[:, -1].unsqueeze(1).detach(),
'prev_mention_lengths': mention_lengths[:,
-1].unsqueeze(1).detach(),
'prev_contexts': contexts.detach()
}
return output_dict
def reset_states(self, batch_size: int) -> None:
"""Resets the model's internals. Should be called at the start of a new batch."""
self._encoder.reset_states()
self._dynamic_embeddings.reset_states(batch_size)
self._state = None
def detach_states(self):
"""Detaches the model's state to enforce truncated backpropagation."""
self._dynamic_embeddings.detach_states()
@overrides
def get_metrics(self, reset: bool = False) -> Dict[str, float]:
return {
# 'ppl': self._perplexity.get_metric(reset),
# 'upp': self._unknown_penalized_perplexity.get_metric(reset),
'et_acc': self._entity_type_accuracy.get_metric(reset),
'eid_acc': self._entity_id_accuracy.get_metric(reset),
'ml_acc': self._mention_length_accuracy.get_metric(reset)
}
class EntityNLMDiscriminator(Model):
"""
Implementation of the discriminative model from:
https://arxiv.org/abs/1708.00781
used to draw importance samples.
Parameters
----------
vocab : ``Vocabulary``
The model vocabulary.
text_field_embedder : ``TextFieldEmbedder``
Used to embed tokens.
encoder : ``Seq2SeqEncoder``
Used to encode the sequence of token embeddings.
embedding_dim : ``int``
The dimension of entity / length embeddings. Should match the encoder output size.
max_mention_length : ``int``
Maximum entity mention length.
max_embeddings : ``int``
Maximum number of embeddings.
variational_dropout_rate : ``float``, optional
Dropout rate of variational dropout applied to input embeddings. Default: 0.0
dropout_rate : ``float``, optional
Dropout rate applied to hidden states. Default: 0.0
initializer : ``InitializerApplicator``, optional
Used to initialize model parameters.
"""
# pylint: disable=line-too-long
def __init__(self,
vocab: Vocabulary,
text_field_embedder: TextFieldEmbedder,
encoder: Seq2SeqEncoder,
embedding_dim: int,
max_mention_length: int,
max_embeddings: int,
variational_dropout_rate: float = 0.0,
dropout_rate: float = 0.0,
initializer: InitializerApplicator = InitializerApplicator()) -> None:
super(EntityNLMDiscriminator, self).__init__(vocab)
self._text_field_embedder = text_field_embedder
self._encoder = encoder
self._embedding_dim = embedding_dim
self._max_mention_length = max_mention_length
self._max_embeddings = max_embeddings
self._state: Optional[StateDict] = None
# Input variational dropout
self._variational_dropout = InputVariationalDropout(variational_dropout_rate)
self._dropout = torch.nn.Dropout(dropout_rate)
# For entity type prediction
self._entity_type_projection = torch.nn.Linear(in_features=embedding_dim,
out_features=2,
bias=False)
self._dynamic_embeddings = DynamicEmbedding(embedding_dim=embedding_dim,
max_embeddings=max_embeddings)
# For mention length prediction
self._mention_length_projection = torch.nn.Linear(in_features=2*embedding_dim,
out_features=max_mention_length)
self._entity_type_accuracy = CategoricalAccuracy()
self._entity_id_accuracy = CategoricalAccuracy()
self._mention_length_accuracy = CategoricalAccuracy()
initializer(self)
@overrides
def forward(self, # pylint: disable=arguments-differ
tokens: Dict[str, torch.Tensor],
entity_types: Optional[torch.Tensor] = None,
entity_ids: Optional[torch.Tensor] = None,
mention_lengths: Optional[torch.Tensor] = None,
reset: bool = False)-> Dict[str, torch.Tensor]:
"""
Computes the loss during training / validation.
Parameters
----------
tokens : ``Dict[str, torch.Tensor]``
A tensor of shape ``(batch_size, sequence_length)`` containing the sequence of
tokens.
entity_types : ``torch.Tensor``
A tensor of shape ``(batch_size, sequence_length)`` indicating whether or not the
corresponding token belongs to a mention.
entity_ids : ``torch.Tensor``
A tensor of shape ``(batch_size, sequence_length)`` containing the ids of the
entities the corresponding token is mentioning.
mention_lengths : ``torch.Tensor``
A tensor of shape ``(batch_size, sequence_length)`` tracking how many remaining
tokens (including the current one) there are in the mention.
reset : ``bool``
Whether or not to reset the model's state. This should be done at the start of each
new sequence.
Returns
-------
An output dictionary consisting of:
loss : ``torch.Tensor``
The combined loss.
"""
batch_size = tokens['tokens'].shape[0]
if reset:
self.reset_states(batch_size)
else:
self.detach_states()
if entity_types is not None:
output_dict = self._forward_loop(tokens=tokens,
entity_types=entity_types,
entity_ids=entity_ids,
mention_lengths=mention_lengths)
else:
output_dict = {}
return output_dict
def sample(self,
tokens: Dict[str, torch.Tensor]) -> Dict[str, torch.Tensor]:
"""
Generates a sample from the discriminative model.
WARNING: Unlike during training, this function expects the full (unsplit) sequence of
tokens.
Parameters
----------
tokens : ``Dict[str, torch.Tensor]``
A tensor of shape ``(batch_size, sequence_length)`` containing the sequence of
tokens.
Returns
-------
An output dictionary consisting of:
logp : ``torch.Tensor``
A tensor containing the log-probability of the sample (averaged over time)
entity_types : ``torch.Tensor``
A tensor of shape ``(batch_size, sequence_length)`` indicating whether or not the
corresponding token belongs to a mention.
entity_ids : ``torch.Tensor``
A tensor of shape ``(batch_size, sequence_length)`` containing the ids of the
entities the corresponding token is mentioning.
mention_lengths : ``torch.Tensor``
A tensor of shape ``(batch_size, sequence_length)`` tracking how many remaining
tokens (including the current one) there are in the mention.
"""
batch_size, sequence_length = tokens['tokens'].shape
# We will use a standard iterator during evaluation instead of a split iterator. Otherwise
# it will be a pain to handle generating multiple samples for a sequence since there's no
# way to get back to the first split.
self.reset_states(batch_size)
# Embed tokens and get RNN hidden state.
mask = get_text_field_mask(tokens)
embeddings = self._text_field_embedder(tokens)
hidden = self._encoder(embeddings, mask)
prev_mention_lengths = tokens['tokens'].new_ones(batch_size)
# Initialize outputs
logp = hidden.new_zeros(batch_size) # Track total logp for **each** generated sample
entity_types = torch.zeros_like(tokens['tokens'], dtype=torch.uint8)
entity_ids = torch.zeros_like(tokens['tokens'])
mention_lengths = torch.ones_like(tokens['tokens'])
# Generate outputs
for timestep in range(sequence_length):
current_hidden = hidden[:, timestep]
# We only predict types / ids / lengths if the previous mention is terminated.
predict_mask = prev_mention_lengths == 1
predict_mask = predict_mask * mask[:, timestep].byte()
if predict_mask.sum() > 0:
# Predict entity types
entity_type_logits = self._entity_type_projection(current_hidden[predict_mask])
entity_type_logp = F.log_softmax(entity_type_logits, dim=-1)
entity_type_prediction_logp, entity_type_predictions = sample_from_logp(entity_type_logp)
entity_type_predictions = entity_type_predictions.byte()
entity_types[predict_mask, timestep] = entity_type_predictions
logp[predict_mask] += entity_type_prediction_logp
# Only predict entity and mention lengths if we predicted that there was a mention
predict_em = entity_types[:, timestep] * predict_mask
if predict_em.sum() > 0:
# Predict entity ids
entity_id_prediction_outputs = self._dynamic_embeddings(hidden=current_hidden,
mask=predict_em)
entity_id_logits = entity_id_prediction_outputs['logits']
entity_id_logp = F.log_softmax(entity_id_logits, dim=-1)
entity_id_prediction_logp, entity_id_predictions = sample_from_logp(entity_id_logp)
# Predict mention lengths - we do this before writing the
# entity id predictions since we'll need to reindex the new
# entities, but need the null embeddings here.
predicted_entity_embeddings = self._dynamic_embeddings.embeddings[predict_em, entity_id_predictions]
concatenated = torch.cat((current_hidden[predict_em], predicted_entity_embeddings), dim=-1)
mention_length_logits = self._mention_length_projection(concatenated)
mention_length_logp = F.log_softmax(mention_length_logits, dim=-1)
mention_length_prediction_logp, mention_length_predictions = sample_from_logp(mention_length_logp)
# Write predictions
new_entity_mask = entity_id_predictions == 0
new_entity_labels = self._dynamic_embeddings.num_embeddings[predict_em]
entity_id_predictions[new_entity_mask] = new_entity_labels[new_entity_mask]
entity_ids[predict_em, timestep] = entity_id_predictions
logp[predict_em] += entity_id_prediction_logp
mention_lengths[predict_em, timestep] = mention_length_predictions
logp[predict_em] += mention_length_prediction_logp
# Add / update entity embeddings
new_entities = entity_ids[:, timestep] == self._dynamic_embeddings.num_embeddings
self._dynamic_embeddings.add_embeddings(timestep, new_entities)
self._dynamic_embeddings.update_embeddings(hidden=current_hidden,
update_indices=entity_ids[:, timestep],
timestep=timestep,
mask=predict_em)
# If the previous mentions are ongoing, we assign the output deterministically. Mention
# lengths decrease by 1, all other outputs are copied from the previous timestep. Do
# not need to add anything to logp since these 'predictions' have probability 1 under
# the model.
deterministic_mask = prev_mention_lengths > 1
deterministic_mask = deterministic_mask * mask[:, timestep].byte()
if deterministic_mask.sum() > 1:
entity_types[deterministic_mask, timestep] = entity_types[deterministic_mask, timestep - 1]
entity_ids[deterministic_mask, timestep] = entity_ids[deterministic_mask, timestep - 1]
mention_lengths[deterministic_mask, timestep] = mention_lengths[deterministic_mask, timestep - 1] - 1
# Update mention lengths for next timestep
prev_mention_lengths = mention_lengths[:, timestep]
return {
'logp': logp,
'sample': {
'entity_types': entity_types,
'entity_ids': entity_ids,
'mention_lengths': mention_lengths
}
}
def _forward_loop(self,
tokens: Dict[str, torch.Tensor],
entity_types: torch.Tensor,
entity_ids: torch.Tensor,
mention_lengths: torch.Tensor) -> Dict[str, torch.Tensor]:
"""
Performs the forward pass to calculate the loss on a chunk of training data.
Parameters
----------
tokens : ``Dict[str, torch.Tensor]``
A tensor of shape ``(batch_size, sequence_length)`` containing the sequence of
tokens.
entity_types : ``torch.Tensor``
A tensor of shape ``(batch_size, sequence_length)`` indicating whether or not the
corresponding token belongs to a mention.
entity_ids : ``torch.Tensor``
A tensor of shape ``(batch_size, sequence_length)`` containing the ids of the
entities the corresponding token is mentioning.
mention_lengths : ``torch.Tensor``
A tensor of shape ``(batch_size, sequence_length)`` tracking how many remaining
tokens (including the current one) there are in the mention.
Returns
-------
An output dictionary consisting of:
entity_type_loss : ``torch.Tensor``
The loss of entity type predictions.
entity_id_loss : ``torch.Tensor``
The loss of entity id predictions.
mention_length_loss : ``torch.Tensor``
The loss of mention length predictions.
loss : ``torch.Tensor``
The combined loss.
"""
batch_size, sequence_length = tokens['tokens'].shape
# Need to track previous mention lengths in order to know when to measure loss.
if self._state is None:
prev_mention_lengths = mention_lengths.new_ones(batch_size)
else:
prev_mention_lengths = self._state['prev_mention_lengths']
# Embed tokens and get RNN hidden state.
mask = get_text_field_mask(tokens)
embeddings = self._text_field_embedder(tokens)
embeddings = self._variational_dropout(embeddings)
hidden = self._encoder(embeddings, mask)
# Initialize losses
entity_type_loss = torch.tensor(0.0, requires_grad=True, device=hidden.device)
entity_id_loss = torch.tensor(0.0, requires_grad=True, device=hidden.device)
mention_length_loss = torch.tensor(0.0, requires_grad=True, device=hidden.device)
for timestep in range(sequence_length):
current_entity_types = entity_types[:, timestep]
current_entity_ids = entity_ids[:, timestep]
current_mention_lengths = mention_lengths[:, timestep]
current_hidden = hidden[:, timestep]
current_hidden = self._dropout(hidden[:, timestep])
# We only predict types / ids / lengths if we are not currently in the process of
# generating a mention (e.g. if the previous remaining mention length is 1). Indexing /
# masking with ``predict_all`` makes it possible to do this in batch.
predict_all = prev_mention_lengths == 1
predict_all = predict_all * mask[:, timestep].byte()
if predict_all.sum() > 0:
# Equation 3 in the paper.
entity_type_logits = self._entity_type_projection(current_hidden[predict_all])
entity_type_loss = entity_type_loss + F.cross_entropy(
entity_type_logits,
current_entity_types[predict_all].long(),
reduction='sum')
self._entity_type_accuracy(predictions=entity_type_logits,
gold_labels=current_entity_types[predict_all].long())
# Only proceed to predict entity and mention length if there is in fact an entity.
predict_em = current_entity_types * predict_all
if predict_em.sum() > 0:
# Equation 4 in the paper. We want new entities to correspond to a prediction of
# zero, their embedding should be added after they've been predicted for the first
# time.
modified_entity_ids = current_entity_ids.clone()
modified_entity_ids[modified_entity_ids == self._dynamic_embeddings.num_embeddings] = 0
entity_id_prediction_outputs = self._dynamic_embeddings(hidden=current_hidden,
target=modified_entity_ids,
mask=predict_em)
entity_id_loss = entity_id_loss + entity_id_prediction_outputs['loss'].sum()
self._entity_id_accuracy(predictions=entity_id_prediction_outputs['logits'],
gold_labels=modified_entity_ids[predict_em])
# Equation 5 in the paper.
predicted_entity_embeddings = self._dynamic_embeddings.embeddings[predict_em, modified_entity_ids[predict_em]]
predicted_entity_embeddings = self._dropout(predicted_entity_embeddings)
concatenated = torch.cat((current_hidden[predict_em], predicted_entity_embeddings), dim=-1)
mention_length_logits = self._mention_length_projection(concatenated)
mention_length_loss = mention_length_loss + F.cross_entropy(
mention_length_logits,
current_mention_lengths[predict_em])
self._mention_length_accuracy(predictions=mention_length_logits,
gold_labels=current_mention_lengths[predict_em])
# We add new entities to any sequence where the current entity id matches the number of
# embeddings that currently exist for that sequence (this means we need a new one since
# there is an additional dummy embedding).
new_entities = current_entity_ids == self._dynamic_embeddings.num_embeddings
self._dynamic_embeddings.add_embeddings(timestep, new_entities)
# We also perform updates of the currently observed entities.
self._dynamic_embeddings.update_embeddings(hidden=current_hidden,
update_indices=current_entity_ids,
timestep=timestep,
mask=current_entity_types)
prev_mention_lengths = current_mention_lengths
# Normalize the losses
entity_type_loss = entity_type_loss / mask.sum()
entity_id_loss = entity_id_loss / mask.sum()
mention_length_loss = mention_length_loss / mask.sum()
total_loss = entity_type_loss + entity_id_loss + mention_length_loss
output_dict = {
'entity_type_loss': entity_type_loss,
'entity_id_loss': entity_id_loss,
'mention_length_loss': mention_length_loss,
'loss': total_loss
}
# Update state
self._state = {
'prev_mention_lengths': prev_mention_lengths.detach()
}
return output_dict
def reset_states(self, batch_size: int) -> None:
"""Resets the model's internals. Should be called at the start of a new batch."""
self._encoder.reset_states()
self._dynamic_embeddings.reset_states(batch_size)
self._state = None
def detach_states(self):
"""Detaches the model's state to enforce truncated backpropagation."""
self._dynamic_embeddings.detach_states()
@overrides
def get_metrics(self, reset: bool = False) -> Dict[str, float]:
return {
'et_acc': self._entity_type_accuracy.get_metric(reset),
'eid_acc': self._entity_id_accuracy.get_metric(reset),
'ml_acc': self._mention_length_accuracy.get_metric(reset)
}