def forward(self,
sequence_tensor: torch.FloatTensor,
span_indices: torch.LongTensor,
sequence_mask: torch.LongTensor = None,
span_indices_mask: torch.LongTensor = None) -> None:
# shape (batch_size, num_spans)
span_starts, span_ends = [index.squeeze(-1) for index in span_indices.split(1, dim=-1)]
if span_indices_mask is not None:
# It's not strictly necessary to multiply the span indices by the mask here,
# but it's possible that the span representation was padded with something other
# than 0 (such as -1, which would be an invalid index), so we do so anyway to
# be safe.
span_starts = span_starts * span_indices_mask
span_ends = span_ends * span_indices_mask
if not self._use_exclusive_start_indices:
start_embeddings = util.batched_index_select(sequence_tensor, span_starts)
end_embeddings = util.batched_index_select(sequence_tensor, span_ends)
else:
# We want `exclusive` span starts, so we remove 1 from the forward span starts
# as the AllenNLP ``SpanField`` is inclusive.
# shape (batch_size, num_spans)
exclusive_span_starts = span_starts - 1
# shape (batch_size, num_spans, 1)
start_sentinel_mask = (exclusive_span_starts == -1).long().unsqueeze(-1)
exclusive_span_starts = exclusive_span_starts * (1 - start_sentinel_mask.squeeze(-1))
# We'll check the indices here at runtime, because it's difficult to debug
# if this goes wrong and it's tricky to get right.
if (exclusive_span_starts < 0).any():
raise ValueError(f"Adjusted span indices must lie inside the the sequence tensor, "
f"but found: exclusive_span_starts: {exclusive_span_starts}.")
start_embeddings = util.batched_index_select(sequence_tensor, exclusive_span_starts)
end_embeddings = util.batched_index_select(sequence_tensor, span_ends)
# We're using sentinels, so we need to replace all the elements which were
# outside the dimensions of the sequence_tensor with the start sentinel.
float_start_sentinel_mask = start_sentinel_mask.float()
start_embeddings = start_embeddings * (1 - float_start_sentinel_mask) \
+ float_start_sentinel_mask * self._start_sentinel
combined_tensors = util.combine_tensors(self._combination, [start_embeddings, end_embeddings])
if self._span_width_embedding is not None:
# Embed the span widths and concatenate to the rest of the representations.
if self._bucket_widths:
span_widths = util.bucket_values(span_ends - span_starts,
num_total_buckets=self._num_width_embeddings)
else:
span_widths = span_ends - span_starts
span_width_embeddings = self._span_width_embedding(span_widths)
return torch.cat([combined_tensors, span_width_embeddings], -1)
if span_indices_mask is not None:
return combined_tensors * span_indices_mask.unsqueeze(-1).float()
return combined_tensors
def _compute_span_pair_embeddings(self,
top_span_embeddings: torch.FloatTensor,
antecedent_embeddings: torch.FloatTensor,
antecedent_offsets: torch.FloatTensor):
"""
Computes an embedding representation of pairs of spans for the pairwise scoring function
to consider. This includes both the original span representations, the element-wise
similarity of the span representations, and an embedding representation of the distance
between the two spans.
Parameters
----------
top_span_embeddings : ``torch.FloatTensor``, required.
Embedding representations of the top spans. Has shape
(batch_size, num_spans_to_keep, embedding_size).
antecedent_embeddings : ``torch.FloatTensor``, required.
Embedding representations of the antecedent spans we are considering
for each top span. Has shape
(batch_size, num_spans_to_keep, max_antecedents, embedding_size).
antecedent_offsets : ``torch.IntTensor``, required.
The offsets between each top span and its antecedent spans in terms
of spans we are considering. Has shape (1, max_antecedents).
Returns
-------
span_pair_embeddings : ``torch.FloatTensor``
Embedding representation of the pair of spans to consider. Has shape
(batch_size, num_spans_to_keep, max_antecedents, embedding_size)
"""
# Shape: (batch_size, num_spans_to_keep, max_antecedents, embedding_size)
target_embeddings = top_span_embeddings.unsqueeze(2).expand_as(
antecedent_embeddings)
# Shape: (1, max_antecedents, embedding_size)
# Shape (coarse-to-fine pruning): (batch_size, num_spans_to_keep, max_antecedents, embedding_size)
antecedent_distance_embeddings = self._distance_embedding(
util.bucket_values(antecedent_offsets,
num_total_buckets=self._num_distance_buckets))
if not self._do_coarse_to_fine_prune:
# Shape: (1, 1, max_antecedents, embedding_size)
antecedent_distance_embeddings = antecedent_distance_embeddings.unsqueeze(
0)
expanded_distance_embeddings_shape = (
antecedent_embeddings.size(0), antecedent_embeddings.size(1),
antecedent_embeddings.size(2),
antecedent_distance_embeddings.size(-1))
# Shape: (batch_size, num_spans_to_keep, max_antecedents, embedding_size)
antecedent_distance_embeddings = antecedent_distance_embeddings.expand(
*expanded_distance_embeddings_shape)
# Shape: (batch_size, num_spans_to_keep, max_antecedents, embedding_size)
span_pair_embeddings = torch.cat([
target_embeddings, antecedent_embeddings, antecedent_embeddings *
target_embeddings, antecedent_distance_embeddings
], -1)
return span_pair_embeddings
def _compute_span_pair_embeddings(self,
top_span_embeddings: torch.FloatTensor,
antecedent_embeddings: torch.FloatTensor,
antecedent_offsets: torch.FloatTensor):
"""
Computes an embedding representation of pairs of spans for the pairwise scoring function
to consider. This includes both the original span representations, the element-wise
similarity of the span representations, and an embedding representation of the distance
between the two spans.
Parameters
----------
top_span_embeddings : ``torch.FloatTensor``, required.
Embedding representations of the top spans. Has shape
(batch_size, num_spans_to_keep, embedding_size).
antecedent_embeddings : ``torch.FloatTensor``, required.
Embedding representations of the antecedent spans we are considering
for each top span. Has shape
(batch_size, num_spans_to_keep, max_antecedents, embedding_size).
antecedent_offsets : ``torch.IntTensor``, required.
The offsets between each top span and its antecedent spans in terms
of spans we are considering. Has shape (1, max_antecedents).
Returns
-------
span_pair_embeddings : ``torch.FloatTensor``
Embedding representation of the pair of spans to consider. Has shape
(batch_size, num_spans_to_keep, max_antecedents, embedding_size)
"""
# Shape: (batch_size, num_spans_to_keep, max_antecedents, embedding_size)
target_embeddings = top_span_embeddings.unsqueeze(2).expand_as(antecedent_embeddings)
# Shape: (1, max_antecedents, embedding_size)
antecedent_distance_embeddings = self._distance_embedding(
util.bucket_values(antecedent_offsets,
num_total_buckets=self._num_distance_buckets))
# Shape: (1, 1, max_antecedents, embedding_size)
antecedent_distance_embeddings = antecedent_distance_embeddings.unsqueeze(0)
expanded_distance_embeddings_shape = (antecedent_embeddings.size(0),
antecedent_embeddings.size(1),
antecedent_embeddings.size(2),
antecedent_distance_embeddings.size(-1))
# Shape: (batch_size, num_spans_to_keep, max_antecedents, embedding_size)
antecedent_distance_embeddings = antecedent_distance_embeddings.expand(*expanded_distance_embeddings_shape)
# Shape: (batch_size, num_spans_to_keep, max_antecedents, embedding_size)
span_pair_embeddings = torch.cat([target_embeddings,
antecedent_embeddings,
antecedent_embeddings * target_embeddings,
antecedent_distance_embeddings], -1)
return span_pair_embeddings
def _compute_span_pair_embeddings(
self,
top_span_embeddings: torch.FloatTensor,
antecedent_embeddings: torch.FloatTensor,
antecedent_offsets: torch.FloatTensor,
):
"""
Computes an embedding representation of pairs of spans for the pairwise scoring function
to consider. This includes both the original span representations, the element-wise
similarity of the span representations, and an embedding representation of the distance
between the two spans.
# Parameters
top_span_embeddings : `torch.FloatTensor`, required.
Embedding representations of the top spans. Has shape
(batch_size, num_spans_to_keep, embedding_size).
antecedent_embeddings : `torch.FloatTensor`, required.
Embedding representations of the antecedent spans we are considering
for each top span. Has shape
(batch_size, num_spans_to_keep, max_antecedents, embedding_size).
antecedent_offsets : `torch.IntTensor`, required.
The offsets between each top span and its antecedent spans in terms
of spans we are considering. Has shape (batch_size, num_spans_to_keep, max_antecedents).
# Returns
span_pair_embeddings : `torch.FloatTensor`
Embedding representation of the pair of spans to consider. Has shape
(batch_size, num_spans_to_keep, max_antecedents, embedding_size)
"""
# Shape: (batch_size, num_spans_to_keep, max_antecedents, embedding_size)
target_embeddings = top_span_embeddings.unsqueeze(2).expand_as(
antecedent_embeddings)
# Shape: (batch_size, num_spans_to_keep, max_antecedents, embedding_size)
antecedent_distance_embeddings = self._distance_embedding(
util.bucket_values(antecedent_offsets,
num_total_buckets=self._num_distance_buckets))
# Shape: (batch_size, num_spans_to_keep, max_antecedents, embedding_size)
span_pair_embeddings = torch.cat(
[
target_embeddings,
antecedent_embeddings,
antecedent_embeddings * target_embeddings,
antecedent_distance_embeddings,
],
-1,
)
return span_pair_embeddings
def forward(
self,
span: torch.Tensor, # SHAPE: (batch_size, num_spans, span_dim)
span_pairs: torch.LongTensor # SHAPE: (batch_size, num_span_pairs)
):
span1 = span2 = span
if self.dim_reduce_layer1 is not None:
span1 = self.dim_reduce_layer1(span)
if self.dim_reduce_layer2 is not None:
span2 = self.dim_reduce_layer2(span)
if not self.pair:
return span1, span2
num_spans = span.size(1)
# get span pair embedding
span_pairs_p = span_pairs[:, :, 0]
span_pairs_c = span_pairs[:, :, 1]
# SHAPE: (batch_size * num_span_pairs)
flat_span_pairs_p = util.flatten_and_batch_shift_indices(
span_pairs_p, num_spans)
flat_span_pairs_c = util.flatten_and_batch_shift_indices(
span_pairs_c, num_spans)
# SHAPE: (batch_size, num_span_pairs, span_dim)
span_pair_p_emb = util.batched_index_select(span1, span_pairs_p,
flat_span_pairs_p)
span_pair_c_emb = util.batched_index_select(span2, span_pairs_c,
flat_span_pairs_c)
if self.combine == 'concat':
# SHAPE: (batch_size, num_span_pairs, span_dim * 2)
span_pair_emb = torch.cat([span_pair_p_emb, span_pair_c_emb], -1)
elif self.combine == 'coref':
# use the indices gap as distance, which requires the indices to be consistent
# with the order they appear in the sentences
distance = span_pairs_p - span_pairs_c
# SHAPE: (batch_size, num_span_pairs, dist_emb_dim)
distance_embeddings = self.distance_embedding(
util.bucket_values(
distance, num_total_buckets=self.num_distance_buckets))
# SHAPE: (batch_size, num_span_pairs, span_dim * 3)
span_pair_emb = torch.cat([
span_pair_p_emb, span_pair_c_emb,
span_pair_p_emb * span_pair_c_emb, distance_embeddings
], -1)
if self.repr_layer is not None:
# SHAPE: (batch_size, num_span_pairs, out_dim)
span_pair_emb = self.repr_layer(span_pair_emb)
return span_pair_emb
def _compute_distance_embeddings(self, top_trig_spans, top_arg_spans):
"""
Compute integer distance and positional features of two tensors of span interval indices
of different size. Embeds the bucketed distance values.
:param top_trigger_spans: Size (batch_size, num_trig_spans, 2), 2 for (trigger_start_index, trigger_end_index)
:param top_arg_spans: Size (batch_size, num_arg_spans, 2), 2 for (arg_start_index, arg_end_index)
return res: Tensor of size (batch_size, num_args consists of concat of:
- dist: size (batch_size, num_trig_spans, num_arg_spans) containing the integer distance values.
- trigger_before_feature: size idem size, boolean feature indicating that trigger comes before argument.
- trigger_overlap_feature: size idem, boolean feature indicating that trigger overlaps with argument.
"""
# tile the span matrices
num_trigs = top_trig_spans.size(1)
num_spans = top_arg_spans.size(1)
trig_span_tiled = top_trig_spans.unsqueeze(2).repeat(
1, 1, num_spans, 1)
arg_span_tiled = top_arg_spans.unsqueeze(1).repeat(1, num_trigs, 1, 1)
# get start_idc and end_idc
trig_span_starts = trig_span_tiled[:, :, :, 0]
trig_span_ends = trig_span_tiled[:, :, :, 1]
arg_span_starts = arg_span_tiled[:, :, :, 0]
arg_span_ends = arg_span_tiled[:, :, :, 1]
# compute all distances, abs().min is the correct dist value
dist_start2end = trig_span_starts - arg_span_ends
dist_end2start = trig_span_ends - arg_span_starts
dist = torch.min(dist_start2end.abs(), dist_end2start.abs())
# When the trigger overlaps with the arg span, also set the distance to zero.
# Overlap happens when the trigger is not outside before or after the span.
trigger_before = (trig_span_starts <=
trig_span_ends) & (trig_span_ends < arg_span_starts)
trigger_after = (arg_span_starts <= arg_span_ends) & (arg_span_ends <
trig_span_starts)
trigger_overlap = ~(trigger_before | trigger_after)
dist[trigger_overlap] = 0
# compute bucketed embeddings and add before, overlap boolean feature
dist_buckets = util.bucket_values(dist, self._num_distance_buckets)
dist_emb = self._distance_embedding(dist_buckets)
trigger_before_feature = trigger_before.float().unsqueeze(-1)
trigger_overlap_feature = trigger_overlap.float().unsqueeze(-1)
res = torch.cat(
[dist_emb, trigger_before_feature, trigger_overlap_feature],
dim=-1)
return res
def _compute_distance_embeddings(self, top_trig_indices, top_arg_spans):
top_trig_ixs = top_trig_indices.unsqueeze(2)
arg_span_starts = top_arg_spans[:, :, 0].unsqueeze(1)
arg_span_ends = top_arg_spans[:, :, 1].unsqueeze(1)
dist_from_start = top_trig_ixs - arg_span_starts
dist_from_end = top_trig_ixs - arg_span_ends
# Distance from trigger to arg.
dist = torch.min(dist_from_start.abs(), dist_from_end.abs())
# When the trigger is inside the arg span, also set the distance to zero.
trigger_inside = (top_trig_ixs >= arg_span_starts) & (top_trig_ixs <= arg_span_ends)
dist[trigger_inside] = 0
dist_buckets = util.bucket_values(dist, self._num_distance_buckets)
dist_emb = self._distance_embedding(dist_buckets)
trigger_before_feature = (top_trig_ixs < arg_span_starts).float().unsqueeze(-1)
trigger_inside_feature = trigger_inside.float().unsqueeze(-1)
res = torch.cat([dist_emb, trigger_before_feature, trigger_inside_feature], dim=-1)
return res
def forward(self, # pylint: disable=arguments-differ
sequence_tensor: torch.FloatTensor,
indicies: torch.LongTensor) -> None:
# shape (batch_size, num_spans)
span_starts, span_ends = [index.squeeze(-1) for index in indicies.split(1, dim=-1)]
start_embeddings = batched_index_select(sequence_tensor, span_starts)
end_embeddings = batched_index_select(sequence_tensor, span_ends)
combined_tensors = combine_tensors(self._combination, [start_embeddings, end_embeddings])
if self._span_width_embedding is not None:
# Embed the span widths and concatenate to the rest of the representations.
if self._bucket_widths:
span_widths = bucket_values(span_ends - span_starts,
num_total_buckets=self._num_width_embeddings)
else:
span_widths = span_ends - span_starts
span_width_embeddings = self._span_width_embedding(span_widths)
return torch.cat([combined_tensors, span_width_embeddings], -1)
return combined_tensors
def forward(self,
sequence_tensor: torch.FloatTensor,
span_indices: torch.LongTensor,
sequence_mask: torch.LongTensor = None,
span_indices_mask: torch.LongTensor = None) -> None:
# shape (batch_size, num_spans)
span_starts, span_ends = [
index.squeeze(-1) for index in span_indices.split(1, dim=-1)
]
if span_indices_mask is not None:
# It's not strictly necessary to multiply the span indices by the mask here,
# but it's possible that the span representation was padded with something other
# than 0 (such as -1, which would be an invalid index), so we do so anyway to
# be safe.
span_starts = span_starts * span_indices_mask
span_ends = span_ends * span_indices_mask
start_embeddings = batched_index_select(sequence_tensor, span_starts)
end_embeddings = batched_index_select(sequence_tensor, span_ends)
combined_tensors = combine_tensors(self._combination,
[start_embeddings, end_embeddings])
if self._span_width_embedding is not None:
# Embed the span widths and concatenate to the rest of the representations.
if self._bucket_widths:
span_widths = bucket_values(
span_ends - span_starts,
num_total_buckets=self._num_width_embeddings)
else:
span_widths = span_ends - span_starts
span_width_embeddings = self._span_width_embedding(span_widths)
return torch.cat([combined_tensors, span_width_embeddings], -1)
if span_indices_mask is not None:
return combined_tensors * span_indices_mask.unsqueeze(-1).float()
return combined_tensors
def forward(
self,
sequence_tensor: torch.FloatTensor,
span_indices: torch.LongTensor,
sequence_mask: torch.BoolTensor = None,
span_indices_mask: torch.BoolTensor = None,
):
"""
Given a sequence tensor, extract spans, concatenate width embeddings
when need and return representations of them.
# Parameters
sequence_tensor : `torch.FloatTensor`, required.
A tensor of shape (batch_size, sequence_length, embedding_size)
representing an embedded sequence of words.
span_indices : `torch.LongTensor`, required.
A tensor of shape `(batch_size, num_spans, 2)`, where the last
dimension represents the inclusive start and end indices of the
span to be extracted from the `sequence_tensor`.
sequence_mask : `torch.BoolTensor`, optional (default = `None`).
A tensor of shape (batch_size, sequence_length) representing padded
elements of the sequence.
span_indices_mask : `torch.BoolTensor`, optional (default = `None`).
A tensor of shape (batch_size, num_spans) representing the valid
spans in the `indices` tensor. This mask is optional because
sometimes it's easier to worry about masking after calling this
function, rather than passing a mask directly.
# Returns
A tensor of shape `(batch_size, num_spans, embedded_span_size)`,
where `embedded_span_size` depends on the way spans are represented.
"""
# shape (batch_size, num_spans, embedding_dim)
span_embeddings = self._embed_spans(sequence_tensor, span_indices,
sequence_mask, span_indices_mask)
if self._span_width_embedding is not None:
# width = end_index - start_index + 1 since `SpanField` use inclusive indices.
# But here we do not add 1 beacuse we often initiate the span width
# embedding matrix with `num_width_embeddings = max_span_width`
# shape (batch_size, num_spans)
widths_minus_one = span_indices[..., 1] - span_indices[..., 0]
if self._bucket_widths:
widths_minus_one = util.bucket_values(
widths_minus_one,
num_total_buckets=self.
_num_width_embeddings # type: ignore
)
# Embed the span widths and concatenate to the rest of the representations.
span_width_embeddings = self._span_width_embedding(
widths_minus_one)
span_embeddings = torch.cat(
[span_embeddings, span_width_embeddings], -1)
if span_indices_mask is not None:
# Here we are masking the spans which were originally passed in as padding.
return span_embeddings * span_indices_mask.unsqueeze(-1)
return span_embeddings
def forward(
self,
sequence_tensor: torch.FloatTensor,
span_indices: torch.LongTensor,
sequence_mask: torch.LongTensor = None,
span_indices_mask: torch.LongTensor = None) -> torch.FloatTensor:
# Both of shape (batch_size, sequence_length, embedding_size / 2)
forward_sequence, backward_sequence = sequence_tensor.split(int(
self._input_dim / 2),
dim=-1)
forward_sequence = forward_sequence.contiguous()
backward_sequence = backward_sequence.contiguous()
# shape (batch_size, num_spans)
span_starts, span_ends = [
index.squeeze(-1) for index in span_indices.split(1, dim=-1)
]
if span_indices_mask is not None:
span_starts = span_starts * span_indices_mask
span_ends = span_ends * span_indices_mask
# We want `exclusive` span starts, so we remove 1 from the forward span starts
# as the AllenNLP ``SpanField`` is inclusive.
# shape (batch_size, num_spans)
exclusive_span_starts = span_starts - 1
# shape (batch_size, num_spans, 1)
start_sentinel_mask = (
exclusive_span_starts == -1).long().unsqueeze(-1)
# We want `exclusive` span ends for the backward direction
# (so that the `start` of the span in that direction is exlusive), so
# we add 1 to the span ends as the AllenNLP ``SpanField`` is inclusive.
exclusive_span_ends = span_ends + 1
if sequence_mask is not None:
# shape (batch_size)
sequence_lengths = util.get_lengths_from_binary_sequence_mask(
sequence_mask)
else:
# shape (batch_size), filled with the sequence length size of the sequence_tensor.
sequence_lengths = util.ones_like(
sequence_tensor[:, 0, 0]).long() * sequence_tensor.size(1)
# shape (batch_size, num_spans, 1)
end_sentinel_mask = (exclusive_span_ends == sequence_lengths.unsqueeze(
-1)).long().unsqueeze(-1)
# As we added 1 to the span_ends to make them exclusive, which might have caused indices
# equal to the sequence_length to become out of bounds, we multiply by the inverse of the
# end_sentinel mask to erase these indices (as we will replace them anyway in the block below).
# The same argument follows for the exclusive span start indices.
exclusive_span_ends = exclusive_span_ends * (
1 - end_sentinel_mask.squeeze(-1))
exclusive_span_starts = exclusive_span_starts * (
1 - start_sentinel_mask.squeeze(-1))
# We'll check the indices here at runtime, because it's difficult to debug
# if this goes wrong and it's tricky to get right.
if (exclusive_span_starts < 0).any() or (
exclusive_span_ends > sequence_lengths.unsqueeze(-1)).any():
raise ValueError(
f"Adjusted span indices must lie inside the length of the sequence tensor, "
f"but found: exclusive_span_starts: {exclusive_span_starts}, "
f"exclusive_span_ends: {exclusive_span_ends} for a sequence tensor with lengths "
f"{sequence_lengths}.")
# Forward Direction: start indices are exclusive. Shape (batch_size, num_spans, input_size / 2)
forward_start_embeddings = util.batched_index_select(
forward_sequence, exclusive_span_starts)
# Forward Direction: end indices are inclusive, so we can just use span_ends.
# Shape (batch_size, num_spans, input_size / 2)
forward_end_embeddings = util.batched_index_select(
forward_sequence, span_ends)
# Backward Direction: The backward start embeddings use the `forward` end
# indices, because we are going backwards.
# Shape (batch_size, num_spans, input_size / 2)
backward_start_embeddings = util.batched_index_select(
backward_sequence, exclusive_span_ends)
# Backward Direction: The backward end embeddings use the `forward` start
# indices, because we are going backwards.
# Shape (batch_size, num_spans, input_size / 2)
backward_end_embeddings = util.batched_index_select(
backward_sequence, span_starts)
if self._use_sentinels:
# If we're using sentinels, we need to replace all the elements which were
# outside the dimensions of the sequence_tensor with either the start sentinel,
# or the end sentinel.
float_end_sentinel_mask = end_sentinel_mask.float()
float_start_sentinel_mask = start_sentinel_mask.float()
forward_start_embeddings = forward_start_embeddings * (1 - float_start_sentinel_mask) \
+ float_start_sentinel_mask * self._start_sentinel
backward_start_embeddings = backward_start_embeddings * (1 - float_end_sentinel_mask) \
+ float_end_sentinel_mask * self._end_sentinel
# Now we combine the forward and backward spans in the manner specified by the
# respective combinations and concatenate these representations.
# Shape (batch_size, num_spans, forward_combination_dim)
forward_spans = util.combine_tensors(
self._forward_combination,
[forward_start_embeddings, forward_end_embeddings])
# Shape (batch_size, num_spans, backward_combination_dim)
backward_spans = util.combine_tensors(
self._backward_combination,
[backward_start_embeddings, backward_end_embeddings])
# Shape (batch_size, num_spans, forward_combination_dim + backward_combination_dim)
span_embeddings = torch.cat([forward_spans, backward_spans], -1)
if self._span_width_embedding is not None:
# Embed the span widths and concatenate to the rest of the representations.
if self._bucket_widths:
span_widths = util.bucket_values(
span_ends - span_starts,
num_total_buckets=self._num_width_embeddings)
else:
span_widths = span_ends - span_starts
span_width_embeddings = self._span_width_embedding(span_widths)
return torch.cat([span_embeddings, span_width_embeddings], -1)
if span_indices_mask is not None:
return span_embeddings * span_indices_mask.float().unsqueeze(-1)
return span_embeddings
def test_bucket_values(self):
indices = torch.LongTensor([1, 2, 7, 1, 56, 900])
bucketed_distances = util.bucket_values(indices)
numpy.testing.assert_array_equal(bucketed_distances.numpy(),
numpy.array([1, 2, 5, 1, 8, 9]))
def forward(
self,
sequence_tensor: torch.FloatTensor,
span_indices: torch.LongTensor,
sequence_mask: torch.LongTensor = None,
span_indices_mask: torch.LongTensor = None,
) -> None:
# shape (batch_size, num_spans)
span_starts, span_ends = [
index.squeeze(-1) for index in span_indices.split(1, dim=-1)
]
if span_indices_mask is not None:
# It's not strictly necessary to multiply the span indices by the mask here,
# but it's possible that the span representation was padded with something other
# than 0 (such as -1, which would be an invalid index), so we do so anyway to
# be safe.
span_starts = span_starts * span_indices_mask
span_ends = span_ends * span_indices_mask
if not self._use_exclusive_start_indices:
if sequence_tensor.size(-1) != self._input_dim:
raise ValueError(
f"Dimension mismatch expected ({sequence_tensor.size(-1)}) "
f"received ({self._input_dim}).")
start_embeddings = util.batched_index_select(
sequence_tensor, span_starts)
end_embeddings = util.batched_index_select(sequence_tensor,
span_ends)
else:
# We want `exclusive` span starts, so we remove 1 from the forward span starts
# as the AllenNLP `SpanField` is inclusive.
# shape (batch_size, num_spans)
exclusive_span_starts = span_starts - 1
# shape (batch_size, num_spans, 1)
start_sentinel_mask = (
exclusive_span_starts == -1).long().unsqueeze(-1)
exclusive_span_starts = exclusive_span_starts * (
1 - start_sentinel_mask.squeeze(-1))
# We'll check the indices here at runtime, because it's difficult to debug
# if this goes wrong and it's tricky to get right.
if (exclusive_span_starts < 0).any():
raise ValueError(
f"Adjusted span indices must lie inside the the sequence tensor, "
f"but found: exclusive_span_starts: {exclusive_span_starts}."
)
start_embeddings = util.batched_index_select(
sequence_tensor, exclusive_span_starts)
end_embeddings = util.batched_index_select(sequence_tensor,
span_ends)
# We're using sentinels, so we need to replace all the elements which were
# outside the dimensions of the sequence_tensor with the start sentinel.
float_start_sentinel_mask = start_sentinel_mask.float()
start_embeddings = (
start_embeddings * (1 - float_start_sentinel_mask) +
float_start_sentinel_mask * self._start_sentinel)
combined_tensors = util.combine_tensors(
self._combination, [start_embeddings, end_embeddings])
if self._span_width_embedding is not None:
# Embed the span widths and concatenate to the rest of the representations.
if self._bucket_widths:
span_widths = util.bucket_values(
span_ends - span_starts,
num_total_buckets=self._num_width_embeddings)
else:
span_widths = span_ends - span_starts
span_width_embeddings = self._span_width_embedding(span_widths)
combined_tensors = torch.cat(
[combined_tensors, span_width_embeddings], -1)
if span_indices_mask is not None:
return combined_tensors * span_indices_mask.unsqueeze(-1).float()
return combined_tensors
def forward(self,
sequence_tensor: torch.FloatTensor,
span_indices: torch.LongTensor,
sequence_mask: torch.LongTensor = None,
span_indices_mask: torch.LongTensor = None) -> torch.FloatTensor:
# Both of shape (batch_size, sequence_length, embedding_size / 2)
forward_sequence, backward_sequence = sequence_tensor.split(int(self._input_dim / 2), dim=-1)
forward_sequence = forward_sequence.contiguous()
backward_sequence = backward_sequence.contiguous()
# shape (batch_size, num_spans)
span_starts, span_ends = [index.squeeze(-1) for index in span_indices.split(1, dim=-1)]
if span_indices_mask is not None:
span_starts = span_starts * span_indices_mask
span_ends = span_ends * span_indices_mask
# We want `exclusive` span starts, so we remove 1 from the forward span starts
# as the AllenNLP ``SpanField`` is inclusive.
# shape (batch_size, num_spans)
exclusive_span_starts = span_starts - 1
# shape (batch_size, num_spans, 1)
start_sentinel_mask = (exclusive_span_starts == -1).long().unsqueeze(-1)
# We want `exclusive` span ends for the backward direction
# (so that the `start` of the span in that direction is exlusive), so
# we add 1 to the span ends as the AllenNLP ``SpanField`` is inclusive.
exclusive_span_ends = span_ends + 1
if sequence_mask is not None:
# shape (batch_size)
sequence_lengths = util.get_lengths_from_binary_sequence_mask(sequence_mask)
else:
# shape (batch_size), filled with the sequence length size of the sequence_tensor.
sequence_lengths = util.ones_like(sequence_tensor[:, 0, 0]).long() * sequence_tensor.size(1)
# shape (batch_size, num_spans, 1)
end_sentinel_mask = (exclusive_span_ends == sequence_lengths.unsqueeze(-1)).long().unsqueeze(-1)
# As we added 1 to the span_ends to make them exclusive, which might have caused indices
# equal to the sequence_length to become out of bounds, we multiply by the inverse of the
# end_sentinel mask to erase these indices (as we will replace them anyway in the block below).
# The same argument follows for the exclusive span start indices.
exclusive_span_ends = exclusive_span_ends * (1 - end_sentinel_mask.squeeze(-1))
exclusive_span_starts = exclusive_span_starts * (1 - start_sentinel_mask.squeeze(-1))
# We'll check the indices here at runtime, because it's difficult to debug
# if this goes wrong and it's tricky to get right.
if (exclusive_span_starts < 0).any() or (exclusive_span_ends > sequence_lengths.unsqueeze(-1)).any():
raise ValueError(f"Adjusted span indices must lie inside the length of the sequence tensor, "
f"but found: exclusive_span_starts: {exclusive_span_starts}, "
f"exclusive_span_ends: {exclusive_span_ends} for a sequence tensor with lengths "
f"{sequence_lengths}.")
# Forward Direction: start indices are exclusive. Shape (batch_size, num_spans, input_size / 2)
forward_start_embeddings = util.batched_index_select(forward_sequence, exclusive_span_starts)
# Forward Direction: end indices are inclusive, so we can just use span_ends.
# Shape (batch_size, num_spans, input_size / 2)
forward_end_embeddings = util.batched_index_select(forward_sequence, span_ends)
# Backward Direction: The backward start embeddings use the `forward` end
# indices, because we are going backwards.
# Shape (batch_size, num_spans, input_size / 2)
backward_start_embeddings = util.batched_index_select(backward_sequence, exclusive_span_ends)
# Backward Direction: The backward end embeddings use the `forward` start
# indices, because we are going backwards.
# Shape (batch_size, num_spans, input_size / 2)
backward_end_embeddings = util.batched_index_select(backward_sequence, span_starts)
if self._use_sentinels:
# If we're using sentinels, we need to replace all the elements which were
# outside the dimensions of the sequence_tensor with either the start sentinel,
# or the end sentinel.
float_end_sentinel_mask = end_sentinel_mask.float()
float_start_sentinel_mask = start_sentinel_mask.float()
forward_start_embeddings = forward_start_embeddings * (1 - float_start_sentinel_mask) \
+ float_start_sentinel_mask * self._start_sentinel
backward_start_embeddings = backward_start_embeddings * (1 - float_end_sentinel_mask) \
+ float_end_sentinel_mask * self._end_sentinel
# Now we combine the forward and backward spans in the manner specified by the
# respective combinations and concatenate these representations.
# Shape (batch_size, num_spans, forward_combination_dim)
forward_spans = util.combine_tensors(self._forward_combination,
[forward_start_embeddings, forward_end_embeddings])
# Shape (batch_size, num_spans, backward_combination_dim)
backward_spans = util.combine_tensors(self._backward_combination,
[backward_start_embeddings, backward_end_embeddings])
# Shape (batch_size, num_spans, forward_combination_dim + backward_combination_dim)
span_embeddings = torch.cat([forward_spans, backward_spans], -1)
if self._span_width_embedding is not None:
# Embed the span widths and concatenate to the rest of the representations.
if self._bucket_widths:
span_widths = util.bucket_values(span_ends - span_starts,
num_total_buckets=self._num_width_embeddings)
else:
span_widths = span_ends - span_starts
span_width_embeddings = self._span_width_embedding(span_widths)
return torch.cat([span_embeddings, span_width_embeddings], -1)
if span_indices_mask is not None:
return span_embeddings * span_indices_mask.float().unsqueeze(-1)
return span_embeddings
def forward(self,
sequence_tensor: torch.FloatTensor,
span_indices: torch.LongTensor,
sequence_mask: torch.LongTensor = None,
span_indices_mask: torch.LongTensor = None) -> torch.FloatTensor:
# shape (batch_size, num_spans)
# span_starts, span_ends = span_indices.split(1, dim=-1)
# batch_size, max_seq_len, _ = sequence_tensor.shape
# max_span_num = span_indices.shape[1]
# range_vector = util.get_range_vector(max_seq_len, util.get_device_of(sequence_tensor)).repeat(
# (batch_size, max_span_num, 1))
# att_mask = (span_ends >= range_vector) - (span_starts > range_vector)
# att_mask = att_mask * span_mask.unsqueeze(-1)
# res = self._attention(sequence_tensor.repeat((max_span_num,1,1)), att_mask)
# combined_tensors = util.combine_tensors(self._combination, [start_embeddings, end_embeddings])
# both of shape (batch_size, num_spans, 1)
span_starts, span_ends = span_indices.split(1, dim=-1)
# shape (batch_size, num_spans, 1)
# These span widths are off by 1, because the span ends are `inclusive`.
span_widths = span_ends - span_starts
# We need to know the maximum span width so we can
# generate indices to extract the spans from the sequence tensor.
# These indices will then get masked below, such that if the length
# of a given span is smaller than the max, the rest of the values
# are masked.
max_batch_span_width = span_widths.max().item() + 1
# shape (batch_size, sequence_length, 1)
# global_attention_logits = self._global_attention(sequence_tensor)
# Shape: (1, 1, max_batch_span_width)
max_span_range_indices = util.get_range_vector(max_batch_span_width,
util.get_device_of(sequence_tensor)).view(1, 1, -1)
# Shape: (batch_size, num_spans, max_batch_span_width)
# This is a broadcasted comparison - for each span we are considering,
# we are creating a range vector of size max_span_width, but masking values
# which are greater than the actual length of the span.
#
# We're using <= here (and for the mask below) because the span ends are
# inclusive, so we want to include indices which are equal to span_widths rather
# than using it as a non-inclusive upper bound.
span_mask = (max_span_range_indices <= span_widths).float()
raw_span_indices = span_ends - max_span_range_indices
# We also don't want to include span indices which are less than zero,
# which happens because some spans near the beginning of the sequence
# have an end index < max_batch_span_width, so we add this to the mask here.
span_mask = span_mask * (raw_span_indices >= 0).float()
span_indices = torch.nn.functional.relu(raw_span_indices.float()).long()
# Shape: (batch_size * num_spans * max_batch_span_width)
flat_span_indices = util.flatten_and_batch_shift_indices(span_indices, sequence_tensor.size(1))
# Shape: (batch_size, num_spans, max_batch_span_width, embedding_dim)
span_embeddings = util.batched_index_select(sequence_tensor, span_indices, flat_span_indices)
span_embeddings = span_embeddings * span_mask.unsqueeze(-1)
span_embeddings = span_embeddings.max(2)[0]
if self._span_width_embedding is not None:
# Embed the span widths and concatenate to the rest of the representations.
if self._bucket_widths:
span_widths = util.bucket_values(span_ends - span_starts,
num_total_buckets=self._num_width_embeddings)
else:
span_widths = span_ends - span_starts
span_widths = span_widths.squeeze(-1)
span_width_embeddings = self._span_width_embedding(span_widths)
combined_tensors = torch.cat([span_embeddings, span_width_embeddings], -1)
else:
combined_tensors = span_embeddings
if span_indices_mask is not None:
return combined_tensors * span_indices_mask.unsqueeze(-1).float()
return combined_tensors
def forward(
self,
sequence_tensor: torch.FloatTensor,
span_indices: torch.LongTensor,
sequence_mask: torch.BoolTensor = None,
span_indices_mask: torch.BoolTensor = None,
) -> torch.FloatTensor:
forward_sequence, backward_sequence = sequence_tensor.split(
int(self._input_dim / 2), dim=-1
)
span_starts, span_ends = [index.squeeze(-1) for index in span_indices.split(1, dim=-1)]
if span_indices_mask is not None:
span_starts = span_starts * span_indices_mask
span_ends = span_ends * span_indices_mask
exclusive_span_starts = span_starts - 1
start_sentinel_mask = (exclusive_span_starts == -1).unsqueeze(-1)
exclusive_span_ends = span_ends + 1
if sequence_mask is not None:
else:
sequence_lengths = torch.ones_like(
sequence_tensor[:, 0, 0], dtype=torch.long
end_sentinel_mask = (exclusive_span_ends >= sequence_lengths.unsqueeze(-1)).unsqueeze(-1)
exclusive_span_ends = exclusive_span_ends * ~end_sentinel_mask.squeeze(-1)
exclusive_span_starts = exclusive_span_starts * ~start_sentinel_mask.squeeze(-1)
if (exclusive_span_starts < 0).any() or (
exclusive_span_ends > sequence_lengths.unsqueeze(-1)
).any():
raise ValueError(
f"Adjusted span indices must lie inside the length of the sequence tensor, "
f"but found: exclusive_span_starts: {exclusive_span_starts}, "
f"exclusive_span_ends: {exclusive_span_ends} for a sequence tensor with lengths "
f"{sequence_lengths}."
)
forward_start_embeddings = util.batched_index_select(
forward_sequence, exclusive_span_starts
)
backward_start_embeddings = util.batched_index_select(
backward_sequence, exclusive_span_ends
)
backward_end_embeddings = util.batched_index_select(backward_sequence, span_starts)
if self._use_sentinels:
forward_start_embeddings = (
forward_start_embeddings * ~start_sentinel_mask
+ start_sentinel_mask * self._start_sentinel
)
backward_start_embeddings = (
backward_start_embeddings * ~end_sentinel_mask
+ end_sentinel_mask * self._end_sentinel
)
forward_spans = util.combine_tensors(
self._forward_combination, [forward_start_embeddings, forward_end_embeddings]
)
backward_spans = util.combine_tensors(
self._backward_combination, [backward_start_embeddings, backward_end_embeddings]
)
if self._span_width_embedding is not None:
if self._bucket_widths:
span_widths = util.bucket_values(
span_ends - span_starts, num_total_buckets=self._num_width_embeddings
)
else:
span_widths = span_ends - span_starts
span_width_embeddings = self._span_width_embedding(span_widths)
return torch.cat([span_embeddings, span_width_embeddings], -1)
if span_indices_mask is not None:)
reveal_type(span_indices_mask)
def test_bucket_values(self):
indices = torch.LongTensor([1, 2, 7, 1, 56, 900])
bucketed_distances = util.bucket_values(indices)
numpy.testing.assert_array_equal(bucketed_distances.numpy(),
numpy.array([1, 2, 5, 1, 8, 9]))