class MaskedSelfAttention(Seq2VecEncoder):
def __init__(self, encoder: Seq2SeqEncoder, time_distributed=False):
super().__init__()
self.encoder = TimeDistributed(encoder) if time_distributed else encoder
self.attn_projection = nn.Linear(encoder.get_input_dim(), 1)
def forward(self, x: torch.Tensor, mask: torch.Tensor = None):
encoded = self.encoder(x, mask)
attn_logits = self.attn_projection(encoded).squeeze(-1)
attn_weights = util.masked_softmax(attn_logits, mask)
attn_output = util.weighted_sum(encoded, attn_weights)
return attn_output, attn_weights
@overrides
def get_input_dim(self) -> int:
return self.encoder.get_input_dim()
@overrides
def get_output_dim(self) -> int:
return self.encoder.get_output_dim()
class soft_dictionary_span_classifier_HSCRF(Model):
def __init__(self,
vocab: Vocabulary,
text_field_embedder: TextFieldEmbedder,
feature_size: int,
max_span_width: int,
encoder: Seq2SeqEncoder,
span_label_namespace: str = "span_tags",
token_label_namespace: str = "token_tags",
calculate_span_f1: bool = None,
verbose_metrics: bool = True,
feedforward: Optional[FeedForward] = None,
initializer: InitializerApplicator = InitializerApplicator(),
regularizer: Optional[RegularizerApplicator] = None,
class_weight=None) -> None:
super().__init__(vocab, regularizer)
self.span_label_namespace = span_label_namespace
self.token_label_namespace = token_label_namespace
self.num_span_tags = self.vocab.get_vocab_size(span_label_namespace)
self.num_token_tags = self.vocab.get_vocab_size(token_label_namespace)
self.text_field_embedder = text_field_embedder
self.encoder = TimeDistributed(encoder)
self.length_embedder = torch.nn.Embedding(max_span_width, feature_size)
self.max_span_width = max_span_width
self.feature_size = feature_size
self.soft_maxer = torch.nn.Softmax(dim=3)
self._verbose_metrics = verbose_metrics
self.BILOU_const = 4
if class_weight is not None:
# assert len(class_weight) == self.num_span_tags - 1, "size of class_weight has to be equal to num_class"
self.class_weight = numpy.array(class_weight, dtype=numpy.float32)
else:
self.class_weight = None
if not feedforward:
self.BILOU_tag_projection_layer = torch.nn.Sequential(
TimeDistributed(
Linear(self.encoder.get_output_dim() + feature_size,
self.BILOU_const * (self.num_span_tags - 1))))
else:
self.feedforward = feedforward
self.BILOU_tag_projection_layer = torch.nn.Sequential(
TimeDistributed(self.feedforward),
TimeDistributed(
Linear(self.feedforward.get_output_dim(),
self.BILOU_const * (self.num_span_tags - 1))))
self.metrics = {}
self.calculate_span_f1 = True
# get mask for loss calculation
self.label_to_mask_for_loss = torch.nn.Embedding(
self.num_span_tags, 2 * (self.num_span_tags - 1))
# e.g. PER: [1,0,0,0,| 0,1,1,1], LOC: [0,1,0,0, | ,1,0,1,1], O[0,0,0,0, | ,1,1,1,1]
self.label_to_mask_for_loss.weight.data.copy_(
torch.from_numpy(self._get_label_to_category_mask()))
# get mask for loss calculation
self.HSCRF_scoring_mask = torch.nn.Embedding(
self.max_span_width,
self.max_span_width * self.BILOU_const * (self.num_span_tags - 1))
# e.g. PER: [1,0,0,0,| 0,1,1,1], LOC: [0,1,0,0, | ,1,0,1,1], O[0,0,0,0, | ,1,1,1,1]
self.HSCRF_scoring_mask.weight.data.copy_(
torch.from_numpy(self._get_HSCRF_scoring_mask().reshape(
self.max_span_width, -1)))
self._span_f1_metric = MySpanF1()
initializer(self)
def _get_label_to_category_mask(self):
tag_cnter = 0
label_to_mask = numpy.zeros(
[self.num_span_tags, 2 * (self.num_span_tags - 1)],
dtype='float32')
for i in range(self.num_span_tags):
i_tag = self.vocab.get_token_from_index(i, namespace='span_tags')
if i_tag == 'O':
for j in range(1, 2 * (self.num_span_tags - 1), 2):
label_to_mask[i, j] = 1.0
else:
label_to_mask[i, 2 * tag_cnter] = 1.0
for j in range(1, 2 * (self.num_span_tags - 1), 2):
if j != 2 * tag_cnter + 1:
label_to_mask[i, j] = 1.0
tag_cnter += 1
return label_to_mask
def _get_HSCRF_scoring_mask(self):
HSCRF_mask = numpy.zeros([
self.max_span_width, self.max_span_width, self.BILOU_const *
(self.num_span_tags - 1)
],
dtype='float32')
for i in range(self.max_span_width):
for j in range(i + 1):
for k in range(0, (self.num_span_tags - 1) * self.BILOU_const,
self.BILOU_const):
if i == j == 0:
HSCRF_mask[i, j, k] = 1.0 # U
elif j == 0:
HSCRF_mask[i, j, k + 1] = 1.0 # B
elif j == i:
HSCRF_mask[i, j, k + 3] = 1.0 # L
else:
HSCRF_mask[i, j, k + 2] = 1.0 # I
return HSCRF_mask
@overrides
def forward(
self, # type: ignore
tokens: Dict[str, torch.LongTensor],
spans: torch.LongTensor,
gold_spans: torch.LongTensor,
tags: torch.LongTensor = None,
span_labels: torch.LongTensor = None,
gold_span_labels: torch.LongTensor = None,
metadata: List[Dict[str, Any]] = None,
**kwargs) -> Dict[str, torch.Tensor]:
'''
tags: Shape(batch_size, seq_len)
bilou scheme tags for crf modelling
'''
# Adding mask
token_mask = util.get_text_field_mask(tokens)
batch_size, max_seq_length = token_mask.shape
len_in_token = token_mask.size(1)
#shape (batch_size, num_span, span_width, dim)
token_embedded = self.text_field_embedder(tokens).unsqueeze(1)
token_mask = token_mask.unsqueeze(1)
dim_2_pad = self.max_span_width - token_embedded.size(2)
p2d = (0, 0, 0, dim_2_pad)
# now shape (batch_size, num_span, max_span_width, dim)
token_embedded = F.pad(token_embedded, p2d, "constant", 0.)
token_mask = F.pad(token_mask, (0, dim_2_pad), "constant", 0.)
length_vec = torch.autograd.Variable(
torch.LongTensor(range(self.max_span_width))).cuda(
util.get_device_of(spans))
length_vec = self.length_embedder(length_vec).unsqueeze(0).unsqueeze(
0).expand(batch_size, token_embedded.size(1), -1, -1)
token_encoded = self.encoder(token_embedded, token_mask)
token_encoded = torch.cat((token_encoded, length_vec), 3).contiguous()
# Shape (batch_size, 1)
lengths = token_mask.sum(-1).long() - 1
HSCRF_scoring_mask = self.HSCRF_scoring_mask(lengths).detach()
# shape (batch_size, 1 (only 1 span), max_span_wid, 4* span_tags)
span_logits = self.BILOU_tag_projection_layer(token_encoded)
span_logits = span_logits.view(
batch_size, 1, self.max_span_width, (self.num_span_tags - 1),
self.BILOU_const) * HSCRF_scoring_mask.view(
batch_size, 1, self.max_span_width,
(self.num_span_tags - 1), self.BILOU_const)
final_logits = span_logits.sum(-1).sum(2)
# add dummy zero for O
final_logits = torch.stack(
[final_logits,
final_logits.new_zeros(*final_logits.size())], -1)
spans = gold_spans
span_labels = gold_span_labels
# spans Shape: (batch_size, num_spans, 2)
spans = F.relu(spans.float()).long().view(batch_size, -1, 2)
num_spans = spans.size(1)
# shape (batch_size, 1 (only 1 span), span_tags, 2) is PER / not PER , is LOC / not LOC ...
span_probs = self.soft_maxer(final_logits).view(
batch_size, 1, 2 * (self.num_span_tags - 1))
# shape (batch_size, 1 (only 1 span), 2*(self.num_span_tags-1))
span_probs_mask = self.label_to_mask_for_loss(
span_labels).detach().float()
# TODO:
# Predict results
pred_results = []
mx_prob, mx_ind = span_probs.view(batch_size, 1, -1, 2)[:, :, :,
0].max(2)
larger_than_half = mx_prob > 0.5
# Shape: mx_prob:(batch_size, 1), mx_ind:(batch_size,1)
for i in range(batch_size):
pred_span = {}
phrase_len = len(metadata[i]["words"])
if mx_prob[i, 0] > 0.5:
pred_span = {
(0, phrase_len - 1):
self.vocab.get_index_to_token_vocabulary(
self.span_label_namespace)[int(mx_ind[i, 0])]
}
pass
else:
pass
pred_results.append(pred_span)
output = {}
output['span_logits'] = span_logits
ce_loss = span_probs_mask * (1e-6 + span_probs).log(
) # may cause NaN error..., possibly use (eps + span_probs).log ?
output['span_probs_mask'] = span_probs_mask
output['ce_loss'] = ce_loss.view(batch_size, 1, -1, 2)
if self.class_weight is not None:
# re-weight classes during training
pass #ce_loss = ce_loss.view(batch_size,1,-1,2) * torch.cuda.FloatTensor(self.class_weight).view(-1,1)
ce_loss = -ce_loss.sum() / (batch_size * num_spans)
output['loss'] = ce_loss
output['span_probs'] = span_probs.view(batch_size, 1, -1, 2)
if metadata is not None:
output["words"] = [x["words"] for x in metadata]
return output
@overrides
def get_metrics(self, reset: bool = False) -> Dict[str, float]:
metrics_to_return = {}
if self.calculate_span_f1:
span_f1_dict = self._span_f1_metric.get_metric(reset=reset)
span_kys = list(span_f1_dict.keys())
if self._verbose_metrics:
metrics_to_return.update(span_f1_dict)
else:
metrics_to_return.update(
{x: y
for x, y in span_f1_dict.items() if "overall" in x})
return metrics_to_return
