def test_viterbi_decode(self):
# Test Viterbi decoding is equal to greedy decoding with no pairwise potentials.
sequence_predictions = torch.nn.functional.softmax(
Variable(torch.rand([5, 9])))
transition_matrix = torch.zeros([9, 9])
indices, _ = viterbi_decode(sequence_predictions.data,
transition_matrix)
_, argmax_indices = torch.max(sequence_predictions, 1)
assert indices == argmax_indices.data.squeeze().tolist()
# Test that pairwise potentials effect the sequence correctly and that
# viterbi_decode can handle -inf values.
sequence_predictions = torch.FloatTensor([[0, 0, 0, 3, 4],
[0, 0, 0, 3, 4],
[0, 0, 0, 3, 4],
[0, 0, 0, 3, 4],
[0, 0, 0, 3, 4],
[0, 0, 0, 3, 4]])
# The same tags shouldn't appear sequentially.
transition_matrix = torch.zeros([5, 5])
for i in range(5):
transition_matrix[i, i] = float("-inf")
indices, _ = viterbi_decode(sequence_predictions, transition_matrix)
assert indices == [4, 3, 4, 3, 4, 3]
# Test that unbalanced pairwise potentials break ties
# between paths with equal unary potentials.
sequence_predictions = torch.FloatTensor([[0, 0, 0, 4, 4],
[0, 0, 0, 4, 4],
[0, 0, 0, 4, 4],
[0, 0, 0, 4, 4],
[0, 0, 0, 4, 4],
[0, 0, 0, 4, 4]])
# The 5th tag has a penalty for appearing sequentially
# or for transitioning to the 4th tag, making the best
# path uniquely to take the 4th tag only.
transition_matrix = torch.zeros([5, 5])
transition_matrix[4, 4] = -10
transition_matrix[4, 3] = -10
indices, _ = viterbi_decode(sequence_predictions, transition_matrix)
assert indices == [3, 3, 3, 3, 3, 3]
sequence_predictions = torch.FloatTensor([[1, 0, 0, 4], [1, 0, 6, 2],
[0, 3, 0, 4]])
# Best path would normally be [3, 2, 3] but we add a
# potential from 2 -> 1, making [3, 2, 1] the best path.
transition_matrix = torch.zeros([4, 4])
transition_matrix[0, 0] = 1
transition_matrix[2, 1] = 5
indices, value = viterbi_decode(sequence_predictions,
transition_matrix)
assert indices == [3, 2, 1]
assert value.numpy() == 18
def decode(
self, output_dict: Dict[str,
torch.Tensor]) -> Dict[str, torch.Tensor]:
"""
Does constrained viterbi decoding on class probabilities output in :func:`forward`. The
constraint simply specifies that the output tags must be a valid BIO sequence. We add a
``"tags"`` key to the dictionary with the result.
"""
all_predictions = output_dict['class_probabilities']
if isinstance(all_predictions, numpy.ndarray):
all_predictions = torch.from_numpy(all_predictions)
if all_predictions.dim() == 3:
predictions_list = [
all_predictions[i] for i in range(all_predictions.shape[0])
]
else:
predictions_list = [all_predictions]
all_tags = []
transition_matrix = self.get_viterbi_pairwise_potentials()
for predictions in predictions_list:
max_likelihood_sequence, _ = viterbi_decode(
predictions, transition_matrix)
tags = [
self.vocab.get_token_from_index(x, namespace="labels")
for x in max_likelihood_sequence
]
all_tags.append(tags)
if len(all_tags) == 1:
all_tags = all_tags[0] # type: ignore
output_dict['tags'] = all_tags
return output_dict
def decode(self, output_dict: Dict[str, torch.Tensor]) -> Dict[str, torch.Tensor]:
"""
ph: Do NOT perform Viterbi decoding - we are interested in learning dynamics, not best performance
"""
all_predictions = output_dict['class_probabilities']
sequence_lengths = get_lengths_from_binary_sequence_mask(output_dict["mask"]).data.tolist()
if all_predictions.dim() == 3:
predictions_list = [all_predictions[i].detach().cpu() for i in range(all_predictions.size(0))]
else:
predictions_list = [all_predictions]
all_tags = []
# ph: transition matrices contain only ones (and no -inf, which would signal illegal transition)
all_labels = self.vocab.get_index_to_token_vocabulary("labels")
num_labels = len(all_labels)
transition_matrix = torch.zeros([num_labels, num_labels])
start_transitions = torch.zeros(num_labels)
for predictions, length in zip(predictions_list, sequence_lengths):
max_likelihood_sequence, _ = viterbi_decode(predictions[:length], transition_matrix,
allowed_start_transitions=start_transitions)
tags = [self.vocab.get_token_from_index(x, namespace="labels")
for x in max_likelihood_sequence]
all_tags.append(tags)
output_dict['tags'] = all_tags
return output_dict
def decode(
self, output_dict: Dict[str,
torch.Tensor]) -> Dict[str, torch.Tensor]:
"""
Does constrained viterbi decoding on class probabilities output in :func:`forward`. The
constraint simply specifies that the output tags must be a valid BIO sequence. We add a
``"tags"`` key to the dictionary with the result.
"""
all_predictions = output_dict['class_probabilities']
sequence_lengths = get_lengths_from_binary_sequence_mask(
output_dict["mask"]).data.tolist()
if all_predictions.dim() == 3:
predictions_list = [
all_predictions[i].detach().cpu()
for i in range(all_predictions.size(0))
]
else:
predictions_list = [all_predictions]
all_tags = []
transition_matrix = self.get_viterbi_pairwise_potentials()
for predictions, length in zip(predictions_list, sequence_lengths):
max_likelihood_sequence, _ = viterbi_decode(
predictions[:length], transition_matrix)
tags = [
self.vocab.get_token_from_index(x, namespace="labels")
for x in max_likelihood_sequence
]
all_tags.append(tags)
output_dict['tags'] = all_tags
return output_dict
def make_output_human_readable(
self, output_dict: Dict[str,
torch.Tensor]) -> Dict[str, torch.Tensor]:
"""
Does constrained viterbi decoding on class probabilities output in :func:`forward`. The
constraint simply specifies that the output tags must be a valid BIO sequence. We add a
`"tags"` key to the dictionary with the result.
NOTE: First, we decode a BIO sequence on top of the wordpieces. This is important; viterbi
decoding produces low quality output if you decode on top of word representations directly,
because the model gets confused by the 'missing' positions (which is sensible as it is trained
to perform tagging on wordpieces, not words).
Secondly, it's important that the indices we use to recover words from the wordpieces are the
start_offsets (i.e offsets which correspond to using the first wordpiece of words which are
tokenized into multiple wordpieces) as otherwise, we might get an ill-formed BIO sequence
when we select out the word tags from the wordpiece tags. This happens in the case that a word
is split into multiple word pieces, and then we take the last tag of the word, which might
correspond to, e.g, I-V, which would not be allowed as it is not preceeded by a B tag.
"""
all_predictions = output_dict["class_probabilities"]
sequence_lengths = get_lengths_from_binary_sequence_mask(
output_dict["mask"]).data.tolist()
if all_predictions.dim() == 3:
predictions_list = [
all_predictions[i].detach().cpu()
for i in range(all_predictions.size(0))
]
else:
predictions_list = [all_predictions]
wordpiece_tags = []
word_tags = []
transition_matrix = self.get_viterbi_pairwise_potentials()
start_transitions = self.get_start_transitions()
# **************** Different ********************
# We add in the offsets here so we can compute the un-wordpieced tags.
for predictions, length, offsets in zip(
predictions_list, sequence_lengths,
output_dict["wordpiece_offsets"]):
max_likelihood_sequence, _ = viterbi_decode(
predictions[:length],
transition_matrix,
allowed_start_transitions=start_transitions)
tags = [
self.vocab.get_token_from_index(
x, namespace=self._label_namespace)
for x in max_likelihood_sequence
]
wordpiece_tags.append(tags)
if isinstance(self.bert_model,
PretrainedTransformerMismatchedEmbedder):
word_tags.append(tags)
else:
word_tags.append([tags[i] for i in offsets])
# print(word_tags)
output_dict["wordpiece_tags"] = wordpiece_tags
output_dict["tags"] = word_tags
return output_dict
def viterbi_tags(self, logits: torch.Tensor,
mask: torch.Tensor) -> List[Tuple[List[int], float]]:
"""
Uses viterbi algorithm to find most likely tags for the given inputs.
If constraints are applied, disallows all other transitions.
"""
_, max_seq_length, num_tags = logits.size()
# Get the tensors out of the variables
logits, mask = logits.data, mask.data
# Augment transitions matrix with start and end transitions
start_tag = num_tags
end_tag = num_tags + 1
transitions = torch.Tensor(num_tags + 2, num_tags + 2).fill_(-10000.0)
# Apply transition constraints
constrained_transitions = self.transitions * self._constraint_mask[:num_tags, :num_tags] + -10000.0 * (
1 - self._constraint_mask[:num_tags, :num_tags])
transitions[:num_tags, :num_tags] = constrained_transitions.data
if self.include_start_end_transitions:
transitions[start_tag, :num_tags] = self.start_transitions.detach(
) * self._constraint_mask[start_tag, :num_tags].data + -10000.0 * (
1 - self._constraint_mask[start_tag, :num_tags].detach())
transitions[:num_tags, end_tag] = self.end_transitions.detach(
) * self._constraint_mask[:num_tags, end_tag].data + -10000.0 * (
1 - self._constraint_mask[:num_tags, end_tag].detach())
else:
transitions[start_tag, :num_tags] = -10000.0 * (
1 - self._constraint_mask[start_tag, :num_tags].detach())
transitions[:num_tags, end_tag] = -10000.0 * (
1 - self._constraint_mask[:num_tags, end_tag].detach())
best_paths = []
# Pad the max sequence length by 2 to account for start_tag + end_tag.
tag_sequence = torch.Tensor(max_seq_length + 2, num_tags + 2)
for prediction, prediction_mask in zip(logits, mask):
sequence_length = torch.sum(prediction_mask)
# Start with everything totally unlikely
tag_sequence.fill_(-10000.0)
# At timestep 0 we must have the START_TAG
tag_sequence[0, start_tag] = 0.0
# At steps 1, ..., sequence_length we just use the incoming prediction
tag_sequence[1:(sequence_length +
1), :num_tags] = prediction[:sequence_length]
# And at the last timestep we must have the END_TAG
tag_sequence[sequence_length + 1, end_tag] = 0.0
# We pass the tags and the transitions to ``viterbi_decode``.
viterbi_path, viterbi_score = util.viterbi_decode(
tag_sequence[:(sequence_length + 2)], transitions)
# Get rid of START and END sentinels and append.
viterbi_path = viterbi_path[1:-1]
best_paths.append((viterbi_path, viterbi_score.item()))
return best_paths
def _get_gold_answer(self,
gold_answer_representations: Dict[str,
torch.LongTensor],
log_probs: torch.LongTensor,
mask: torch.LongTensor) -> torch.LongTensor:
answer_as_text_to_disjoint_bios = gold_answer_representations[
'answer_as_text_to_disjoint_bios']
answer_as_list_of_bios = gold_answer_representations[
'answer_as_list_of_bios']
span_bio_labels = gold_answer_representations['span_bio_labels']
with torch.no_grad():
answer_as_list_of_bios = answer_as_list_of_bios * mask.unsqueeze(1)
if answer_as_text_to_disjoint_bios.sum() > 0:
# TODO: verify correctness (Elad)
full_bio = span_bio_labels
if self._generation_top_k > 0:
most_likely_predictions, _ = viterbi_decode(
log_probs.cpu(),
self._bio_allowed_transitions,
top_k=self._generation_top_k)
most_likely_predictions = torch.FloatTensor(
most_likely_predictions).to(log_probs.device)
# ^ Should be converted to tensor
most_likely_predictions = most_likely_predictions * mask.unsqueeze(
1)
generated_list_of_bios = self._filter_correct_predictions(
most_likely_predictions,
answer_as_text_to_disjoint_bios, full_bio)
is_pregenerated_answer_format_mask = (
answer_as_list_of_bios.sum(
(1, 2)) > 0).unsqueeze(-1).unsqueeze(-1).long()
bio_seqs = torch.cat(
(answer_as_list_of_bios,
(generated_list_of_bios *
(1 - is_pregenerated_answer_format_mask))),
dim=1)
bio_seqs = self._add_full_bio(bio_seqs, full_bio)
else:
is_pregenerated_answer_format_mask = (
answer_as_list_of_bios.sum((1, 2)) > 0).long()
bio_seqs = torch.cat(
(answer_as_list_of_bios,
(full_bio * (1 - is_pregenerated_answer_format_mask
).unsqueeze(-1)).unsqueeze(1)),
dim=1)
else:
bio_seqs = answer_as_list_of_bios
return bio_seqs
def tag(self, text_field: TextField,
verb_indicator: IndexField) -> Dict[str, Any]:
"""
Perform inference on a ``Instance`` consisting of a single ``TextField`` representing
the sentence and an ``IndexField`` representing an optional index into the sentence
denoting a verbal predicate.
Returned sequence is the maximum likelihood tag sequence under the constraint that
the sequence must be a valid BIO sequence.
Parameters
----------
text_field : ``TextField``, required.
A ``TextField`` containing the text to be tagged.
verb_indicator: ``IndexField``, required.
The index of the verb whose arguments we are labeling.
Returns
-------
A Dict containing:
tags : List[str]
A list the length of the text input, containing the predicted (argmax) tag
from the model per token.
class_probabilities : numpy.Array
An array of shape (text_input_length, num_classes), where each row is a
distribution over classes for a given token in the sentence.
"""
instance = Instance({
"tokens": text_field,
"verb_indicator": verb_indicator
})
instance.index_fields(self.vocab)
model_input = arrays_to_variables(instance.as_array_dict(),
add_batch_dimension=True,
for_training=False)
output_dict = self.forward(**model_input)
# Remove batch dimension, as we only had one input.
predictions = output_dict["class_probabilities"].data.squeeze(0)
transition_matrix = self.get_viterbi_pairwise_potentials()
max_likelihood_sequence, _ = viterbi_decode(predictions,
transition_matrix)
tags = [
self.vocab.get_token_from_index(x, namespace="tags")
for x in max_likelihood_sequence
]
return {"tags": tags, "class_probabilities": predictions.numpy()}
def decode_tags(self, output_dict: Dict[str, torch.Tensor]) -> Dict[str, torch.Tensor]:
all_predictions = output_dict['class_probabilities']
sequence_lengths = get_lengths_from_binary_sequence_mask(output_dict["mask"]).data.tolist()
if all_predictions.dim() == 3:
predictions_list = [all_predictions[i].data.cpu() for i in range(all_predictions.size(0))]
else:
predictions_list = [all_predictions]
all_tags = []
transition_matrix = self.get_viterbi_pairwise_potentials()
for predictions, length in zip(predictions_list, sequence_lengths):
max_likelihood_sequence, _ = viterbi_decode(predictions[:length], transition_matrix)
tags = [self.vocab.get_token_from_index(x, namespace="labels")
for x in max_likelihood_sequence]
all_tags.append(tags)
output_dict['tags'] = all_tags
return output_dict
def viterbi_tags(self, logits: Variable, mask: Variable) -> List[List[int]]:
"""
Uses viterbi algorithm to find most likely tags for the given inputs.
If constraints are applied, disallows all other transitions.
"""
_, max_seq_length, num_tags = logits.size()
# Get the tensors out of the variables
logits, mask = logits.data, mask.data
# Augment transitions matrix with start and end transitions
start_tag = num_tags
end_tag = num_tags + 1
transitions = torch.Tensor(num_tags + 2, num_tags + 2).fill_(-10000.)
# Apply transition constraints
constrained_transitions = (self.transitions * self._constraint_mask +
-10000.0 * (1 - self._constraint_mask))
transitions[:num_tags, :num_tags] = constrained_transitions.data
transitions[start_tag, :num_tags] = self.start_transitions.data
transitions[:num_tags, end_tag] = self.end_transitions.data
all_tags = []
# Pad the max sequence length by 2 to account for start_tag + end_tag.
tag_sequence = torch.Tensor(max_seq_length + 2, num_tags + 2)
for prediction, prediction_mask in zip(logits, mask):
sequence_length = torch.sum(prediction_mask)
# Start with everything totally unlikely
tag_sequence.fill_(-10000.)
# At timestep 0 we must have the START_TAG
tag_sequence[0, start_tag] = 0.
# At steps 1, ..., sequence_length we just use the incoming prediction
tag_sequence[1:(sequence_length + 1), :num_tags] = prediction[:sequence_length]
# And at the last timestep we must have the END_TAG
tag_sequence[sequence_length + 1, end_tag] = 0.
# We pass the tags and the transitions to ``viterbi_decode``.
viterbi_path, _ = util.viterbi_decode(tag_sequence[:(sequence_length + 2)], transitions)
# Get rid of START and END sentinels and append.
all_tags.append(viterbi_path[1:-1])
return all_tags
def viterbi_tags(self, logits: Variable,
mask: Variable) -> List[List[int]]:
"""
Uses viterbi algorithm to find most likely tags for the given inputs.
"""
_, max_seq_length, num_tags = logits.size()
# Get the tensors out of the variables
logits, mask = logits.data, mask.data
# Augment transitions matrix with start and end transitions
start_tag = num_tags
end_tag = num_tags + 1
transitions = torch.Tensor(num_tags + 2, num_tags + 2).fill_(-10000.)
transitions[:num_tags, :num_tags] = self.transitions.data
transitions[start_tag, :num_tags] = self.start_transitions.data
transitions[:num_tags, end_tag] = self.end_transitions.data
all_tags = []
# Pad the max sequence length by 2 to account for start_tag + end_tag.
tag_sequence = torch.Tensor(max_seq_length + 2, num_tags + 2)
for prediction, prediction_mask in zip(logits, mask):
sequence_length = torch.sum(prediction_mask)
# Start with everything totally unlikely
tag_sequence.fill_(-10000.)
# At timestep 0 we must have the START_TAG
tag_sequence[0, start_tag] = 0.
# At steps 1, ..., sequence_length we just use the incoming prediction
tag_sequence[1:(sequence_length +
1), :num_tags] = prediction[:sequence_length]
# And at the last timestep we must have the END_TAG
tag_sequence[sequence_length + 1, end_tag] = 0.
# We pass the tags and the transitions to ``viterbi_decode``.
viterbi_path, _ = util.viterbi_decode(
tag_sequence[:(sequence_length + 2)], transitions)
# Get rid of START and END sentinels and append.
all_tags.append(viterbi_path[1:-1])
return all_tags
def decode(transition_matrix, vocab,
output_dict: Dict[str, torch.Tensor]) -> Dict[str, torch.Tensor]:
"""
Does constrained viterbi decoding on class probabilities output in :func:`forward`. The
constraint simply specifies that the output tags must be a valid BIO sequence. We add a
``"tags"`` key to the dictionary with the result.
"""
all_predictions = output_dict['class_probabilities']
sequence_lengths = get_lengths_from_binary_sequence_mask(
output_dict["mask"]).data.tolist()
max_seq_length = max(sequence_lengths)
if all_predictions.dim() == 3:
predictions_list = [
all_predictions[i].detach().cpu()
for i in range(all_predictions.size(0))
]
else:
predictions_list = [all_predictions]
all_tags = []
all_prob_seq = []
num_tags = transition_matrix.shape[0] - 2
tag_sequence = torch.Tensor(max_seq_length + 2, num_tags + 2)
for predictions, length in zip(predictions_list, sequence_lengths):
tag_sequence.fill_(-10000.)
tag_sequence[0, num_tags] = 0.
tag_sequence[1:(length + 1), :num_tags] = predictions[:length]
tag_sequence[(length + 1), num_tags + 1] = 0.
max_likelihood_sequence, _ = viterbi_decode(
tag_sequence[:(length + 2)], transition_matrix)
max_likelihood_sequence = max_likelihood_sequence[1:-1]
tags = [
vocab.get_token_from_index(x, namespace="labels")
for x in max_likelihood_sequence
]
all_tags.append(tags)
all_prob_seq.append(max_likelihood_sequence)
output_dict['tags'] = all_tags
output_dict['max_seq'] = all_prob_seq
return output_dict
def decode(self, output_dict: Dict[str, torch.Tensor]) -> Dict[str, torch.Tensor]:
"""
Does constrained viterbi decoding on class probabilities output in :func:`forward`. The
constraint simply specifies that the output tags must be a valid BIO sequence. We add a
``"tags"`` key to the dictionary with the result.
"""
all_predictions = output_dict['class_probabilities']
sequence_lengths = get_lengths_from_binary_sequence_mask(output_dict["mask"]).data.tolist()
if all_predictions.dim() == 3:
predictions_list = [all_predictions[i].detach().cpu() for i in range(all_predictions.size(0))]
else:
predictions_list = [all_predictions]
all_tags = []
transition_matrix = self.get_viterbi_pairwise_potentials()
for predictions, length in zip(predictions_list, sequence_lengths):
max_likelihood_sequence, _ = viterbi_decode(predictions[:length], transition_matrix)
tags = [self.vocab.get_token_from_index(x, namespace="labels")
for x in max_likelihood_sequence]
all_tags.append(tags)
output_dict['tags'] = all_tags
return output_dict
def viterbi_tags(
self,
logits: torch.Tensor,
mask: torch.BoolTensor = None,
top_k: int = None
) -> Union[List[VITERBI_DECODING], List[List[VITERBI_DECODING]]]:
"""
Uses viterbi algorithm to find most likely tags for the given inputs.
If constraints are applied, disallows all other transitions.
Returns a list of results, of the same size as the batch (one result per batch member)
Each result is a List of length top_k, containing the top K viterbi decodings
Each decoding is a tuple (tag_sequence, viterbi_score)
For backwards compatibility, if top_k is None, then instead returns a flat list of
tag sequences (the top tag sequence for each batch item).
"""
if mask is None:
mask = torch.ones(*logits.shape[:2],
dtype=torch.bool,
device=logits.device)
if top_k is None:
top_k = 1
flatten_output = True
else:
flatten_output = False
_, max_seq_length, num_tags = logits.size()
# Get the tensors out of the variables
logits, mask = logits.data, mask.data
# Augment transitions matrix with start and end transitions
start_tag = num_tags
end_tag = num_tags + 1
transitions = torch.Tensor(num_tags + 2, num_tags + 2).fill_(-10000.0)
# Apply transition constraints
constrained_transitions = self.transitions * self._constraint_mask[:num_tags, :num_tags] + -10000.0 * (
1 - self._constraint_mask[:num_tags, :num_tags])
transitions[:num_tags, :num_tags] = constrained_transitions.data
if self.include_start_end_transitions:
transitions[start_tag, :num_tags] = self.start_transitions.detach(
) * self._constraint_mask[start_tag, :num_tags].data + -10000.0 * (
1 - self._constraint_mask[start_tag, :num_tags].detach())
transitions[:num_tags, end_tag] = self.end_transitions.detach(
) * self._constraint_mask[:num_tags, end_tag].data + -10000.0 * (
1 - self._constraint_mask[:num_tags, end_tag].detach())
else:
transitions[start_tag, :num_tags] = -10000.0 * (
1 - self._constraint_mask[start_tag, :num_tags].detach())
transitions[:num_tags, end_tag] = -10000.0 * (
1 - self._constraint_mask[:num_tags, end_tag].detach())
best_paths = []
# Pad the max sequence length by 2 to account for start_tag + end_tag.
tag_sequence = torch.Tensor(max_seq_length + 2, num_tags + 2)
for prediction, prediction_mask in zip(logits, mask):
mask_indices = prediction_mask.nonzero().squeeze()
masked_prediction = torch.index_select(prediction, 0, mask_indices)
sequence_length = masked_prediction.shape[0]
# Start with everything totally unlikely
tag_sequence.fill_(-10000.0)
# At timestep 0 we must have the START_TAG
tag_sequence[0, start_tag] = 0.0
# At steps 1, ..., sequence_length we just use the incoming prediction
tag_sequence[1:(sequence_length +
1), :num_tags] = masked_prediction
# And at the last timestep we must have the END_TAG
tag_sequence[sequence_length + 1, end_tag] = 0.0
# We pass the tags and the transitions to `viterbi_decode`.
viterbi_paths, viterbi_scores = util.viterbi_decode(
tag_sequence=tag_sequence[:(sequence_length + 2)],
transition_matrix=transitions,
top_k=top_k,
)
top_k_paths = []
for viterbi_path, viterbi_score in zip(viterbi_paths,
viterbi_scores):
# Get rid of START and END sentinels and append.
viterbi_path = viterbi_path[1:-1]
top_k_paths.append((viterbi_path, viterbi_score.item()))
best_paths.append(top_k_paths)
if flatten_output:
return [top_k_paths[0] for top_k_paths in best_paths]
return best_paths
def test_viterbi_decode(self):
# Test Viterbi decoding is equal to greedy decoding with no pairwise potentials.
sequence_logits = torch.nn.functional.softmax(torch.rand([5, 9]), dim=-1)
transition_matrix = torch.zeros([9, 9])
indices, _ = util.viterbi_decode(sequence_logits.data, transition_matrix)
_, argmax_indices = torch.max(sequence_logits, 1)
assert indices == argmax_indices.data.squeeze().tolist()
# Test that pairwise potentials effect the sequence correctly and that
# viterbi_decode can handle -inf values.
sequence_logits = torch.FloatTensor([[0, 0, 0, 3, 4],
[0, 0, 0, 3, 4],
[0, 0, 0, 3, 4],
[0, 0, 0, 3, 4],
[0, 0, 0, 3, 4],
[0, 0, 0, 3, 4]])
# The same tags shouldn't appear sequentially.
transition_matrix = torch.zeros([5, 5])
for i in range(5):
transition_matrix[i, i] = float("-inf")
indices, _ = util.viterbi_decode(sequence_logits, transition_matrix)
assert indices == [4, 3, 4, 3, 4, 3]
# Test that unbalanced pairwise potentials break ties
# between paths with equal unary potentials.
sequence_logits = torch.FloatTensor([[0, 0, 0, 4, 4],
[0, 0, 0, 4, 4],
[0, 0, 0, 4, 4],
[0, 0, 0, 4, 4],
[0, 0, 0, 4, 4],
[0, 0, 0, 4, 4]])
# The 5th tag has a penalty for appearing sequentially
# or for transitioning to the 4th tag, making the best
# path uniquely to take the 4th tag only.
transition_matrix = torch.zeros([5, 5])
transition_matrix[4, 4] = -10
transition_matrix[4, 3] = -10
indices, _ = util.viterbi_decode(sequence_logits, transition_matrix)
assert indices == [3, 3, 3, 3, 3, 3]
sequence_logits = torch.FloatTensor([[1, 0, 0, 4],
[1, 0, 6, 2],
[0, 3, 0, 4]])
# Best path would normally be [3, 2, 3] but we add a
# potential from 2 -> 1, making [3, 2, 1] the best path.
transition_matrix = torch.zeros([4, 4])
transition_matrix[0, 0] = 1
transition_matrix[2, 1] = 5
indices, value = util.viterbi_decode(sequence_logits, transition_matrix)
assert indices == [3, 2, 1]
assert value.numpy() == 18
# Test that providing evidence results in paths containing specified tags.
sequence_logits = torch.FloatTensor([[0, 0, 0, 7, 7],
[0, 0, 0, 7, 7],
[0, 0, 0, 7, 7],
[0, 0, 0, 7, 7],
[0, 0, 0, 7, 7],
[0, 0, 0, 7, 7]])
# The 5th tag has a penalty for appearing sequentially
# or for transitioning to the 4th tag, making the best
# path to take the 4th tag for every label.
transition_matrix = torch.zeros([5, 5])
transition_matrix[4, 4] = -10
transition_matrix[4, 3] = -2
# The 1st, 4th and 5th sequence elements are observed - they should be
# equal to 2, 0 and 4. The last tag should be equal to 3, because although
# the penalty for transitioning to the 4th tag is -2, the unary potential
# is 7, which is greater than the combination for any of the other labels.
observations = [2, -1, -1, 0, 4, -1]
indices, _ = util.viterbi_decode(sequence_logits,
transition_matrix,
observations)
assert indices == [2, 3, 3, 0, 4, 3]
def test_viterbi_decode(self):
# Test Viterbi decoding is equal to greedy decoding with no pairwise potentials.
sequence_logits = torch.nn.functional.softmax(torch.rand([5, 9]), dim=-1)
transition_matrix = torch.zeros([9, 9])
indices, _ = util.viterbi_decode(sequence_logits.data, transition_matrix)
_, argmax_indices = torch.max(sequence_logits, 1)
assert indices == argmax_indices.data.squeeze().tolist()
# Test that pairwise potentials effect the sequence correctly and that
# viterbi_decode can handle -inf values.
sequence_logits = torch.FloatTensor([[0, 0, 0, 3, 4],
[0, 0, 0, 3, 4],
[0, 0, 0, 3, 4],
[0, 0, 0, 3, 4],
[0, 0, 0, 3, 4],
[0, 0, 0, 3, 4]])
# The same tags shouldn't appear sequentially.
transition_matrix = torch.zeros([5, 5])
for i in range(5):
transition_matrix[i, i] = float("-inf")
indices, _ = util.viterbi_decode(sequence_logits, transition_matrix)
assert indices == [4, 3, 4, 3, 4, 3]
# Test that unbalanced pairwise potentials break ties
# between paths with equal unary potentials.
sequence_logits = torch.FloatTensor([[0, 0, 0, 4, 4],
[0, 0, 0, 4, 4],
[0, 0, 0, 4, 4],
[0, 0, 0, 4, 4],
[0, 0, 0, 4, 4],
[0, 0, 0, 4, 4]])
# The 5th tag has a penalty for appearing sequentially
# or for transitioning to the 4th tag, making the best
# path uniquely to take the 4th tag only.
transition_matrix = torch.zeros([5, 5])
transition_matrix[4, 4] = -10
transition_matrix[4, 3] = -10
indices, _ = util.viterbi_decode(sequence_logits, transition_matrix)
assert indices == [3, 3, 3, 3, 3, 3]
sequence_logits = torch.FloatTensor([[1, 0, 0, 4],
[1, 0, 6, 2],
[0, 3, 0, 4]])
# Best path would normally be [3, 2, 3] but we add a
# potential from 2 -> 1, making [3, 2, 1] the best path.
transition_matrix = torch.zeros([4, 4])
transition_matrix[0, 0] = 1
transition_matrix[2, 1] = 5
indices, value = util.viterbi_decode(sequence_logits, transition_matrix)
assert indices == [3, 2, 1]
assert value.numpy() == 18
# Test that providing evidence results in paths containing specified tags.
sequence_logits = torch.FloatTensor([[0, 0, 0, 7, 7],
[0, 0, 0, 7, 7],
[0, 0, 0, 7, 7],
[0, 0, 0, 7, 7],
[0, 0, 0, 7, 7],
[0, 0, 0, 7, 7]])
# The 5th tag has a penalty for appearing sequentially
# or for transitioning to the 4th tag, making the best
# path to take the 4th tag for every label.
transition_matrix = torch.zeros([5, 5])
transition_matrix[4, 4] = -10
transition_matrix[4, 3] = -2
# The 1st, 4th and 5th sequence elements are observed - they should be
# equal to 2, 0 and 4. The last tag should be equal to 3, because although
# the penalty for transitioning to the 4th tag is -2, the unary potential
# is 7, which is greater than the combination for any of the other labels.
observations = [2, -1, -1, 0, 4, -1]
indices, _ = util.viterbi_decode(sequence_logits,
transition_matrix,
observations)
assert indices == [2, 3, 3, 0, 4, 3]
def viterbi_tags(
self,
logits: torch.Tensor,
mask: torch.Tensor,
constraint_mask: torch.Tensor = None
) -> List[Tuple[List[int], float]]:
"""
Uses viterbi algorithm to find most likely tags for the given inputs.
If constraints are applied, disallows all other transitions.
Parameters
----------
logits: torch.Tensor
Shape: (batch_size, max_seq_length, num_tags) Tensor of logits.
mask: torch.Tensor
Shape: (batch_size, max_seq_length, num_tags) Tensor of logits.
constraint_mask: torch.Tensor, optional (default=None)
Shape: (batch_size, num_tags+2, num_tags+2) Tensor of the allowed
transitions for each example in the batch.
"""
# pylint: disable=arguments-differ
if constraint_mask is None:
# Defer to superclass function if there is no custom constraint mask.
return super().viterbi_tags(logits=logits, mask=mask)
# We have a custom constraint mask for each example, so we need to re-mask
# when we make each prediction.
batch_size, max_seq_length, num_tags = logits.size()
assert list(constraint_mask.size()) == [
batch_size, num_tags + 2, num_tags + 2
]
# Get the tensors out of the variables
logits, mask = logits.data, mask.data
start_tag = num_tags
end_tag = num_tags + 1
best_paths = []
# Pad the max sequence length by 2 to account for start_tag + end_tag.
tag_sequence = torch.Tensor(max_seq_length + 2, num_tags + 2)
for prediction, prediction_mask, prediction_constraint_mask in zip(
logits, mask, constraint_mask):
prediction_constraint_mask = torch.nn.Parameter(
prediction_constraint_mask, requires_grad=False)
# Augment transitions matrix with start and end transitions
transitions = torch.Tensor(num_tags + 2,
num_tags + 2).fill_(-10000.)
# Apply transition constraints
constrained_transitions = (
self.transitions *
prediction_constraint_mask[:num_tags, :num_tags] + -10000.0 *
(1 - prediction_constraint_mask[:num_tags, :num_tags]))
transitions[:num_tags, :num_tags] = constrained_transitions.data
if self.include_start_end_transitions:
transitions[start_tag, :num_tags] = (
self.start_transitions.detach() *
prediction_constraint_mask[start_tag, :num_tags].data +
-10000.0 *
(1 -
prediction_constraint_mask[start_tag, :num_tags].detach())
)
transitions[:num_tags, end_tag] = (
self.end_transitions.detach() *
prediction_constraint_mask[:num_tags, end_tag].data +
-10000.0 *
(1 -
prediction_constraint_mask[:num_tags, end_tag].detach()))
else:
transitions[start_tag, :num_tags] = (-10000.0 * (
1 -
prediction_constraint_mask[start_tag, :num_tags].detach()))
transitions[:num_tags, end_tag] = (
-10000.0 *
(1 -
prediction_constraint_mask[:num_tags, end_tag].detach()))
sequence_length = torch.sum(prediction_mask)
# Start with everything totally unlikely
tag_sequence.fill_(-10000.)
# At timestep 0 we must have the START_TAG
tag_sequence[0, start_tag] = 0.
# At steps 1, ..., sequence_length we just use the incoming prediction
tag_sequence[1:(sequence_length +
1), :num_tags] = prediction[:sequence_length]
# And at the last timestep we must have the END_TAG
tag_sequence[sequence_length + 1, end_tag] = 0.
# We pass the tags and the transitions to ``viterbi_decode``.
viterbi_path, viterbi_score = util.viterbi_decode(
tag_sequence[:(sequence_length + 2)], transitions)
# Get rid of START and END sentinels and append.
viterbi_path = viterbi_path[1:-1]
best_paths.append((viterbi_path, viterbi_score.item()))
return best_paths
