def flattened_index_select(target: torch.Tensor,
indices: torch.LongTensor) -> torch.Tensor:
"""
The given ``indices`` of size ``(set_size, subset_size)`` specifies subsets of the ``target``
that each of the set_size rows should select. The `target` has size
``(batch_size, sequence_length, embedding_size)``, and the resulting selected tensor has size
``(batch_size, set_size, subset_size, embedding_size)``.
Parameters
----------
target : ``torch.Tensor``, required.
A Tensor of shape (batch_size, sequence_length, embedding_size).
indices : ``torch.LongTensor``, required.
A LongTensor of shape (set_size, subset_size). All indices must be < sequence_length
as this tensor is an index into the sequence_length dimension of the target.
Returns
-------
selected : ``torch.Tensor``, required.
A Tensor of shape (batch_size, set_size, subset_size, embedding_size).
"""
if indices.dim() != 2:
raise ConfigurationError("Indices passed to flattened_index_select had shape {} but "
"only 2 dimensional inputs are supported.".format(indices.size()))
# Shape: (batch_size, set_size * subset_size, embedding_size)
flattened_selected = target.index_select(1, indices.view(-1))
# Shape: (batch_size, set_size, subset_size, embedding_size)
selected = flattened_selected.view(target.size(0), indices.size(0), indices.size(1), -1)
return selected
def extract_features(self,
tokens: torch.LongTensor,
return_all_hiddens: bool = False) -> torch.Tensor:
if tokens.dim() == 1:
tokens = tokens.unsqueeze(0)
if tokens.size(-1) > self.model.max_positions():
raise ValueError('tokens exceeds maximum length: {} > {}'.format(
tokens.size(-1), self.model.max_positions()))
features, extra = self.model(
tokens.to(device=self.device),
features_only=True,
return_all_hiddens=return_all_hiddens,
)
if return_all_hiddens:
# convert from T x B x C -> B x T x C
inner_states = extra['inner_states']
return [
inner_state.transpose(0, 1) for inner_state in inner_states
]
else:
return features # just the last layer's features
def decode(
self,
tokens: torch.LongTensor,
skip_special_tokens: bool = True,
remove_bpe: bool = True,
) -> str:
assert tokens.dim() == 1
tokens = tokens.numpy()
if tokens[0] == self.task.source_dictionary.bos(
) and skip_special_tokens:
tokens = tokens[1:] # remove <s>
eos_mask = tokens == self.task.source_dictionary.eos()
doc_mask = eos_mask[1:] & eos_mask[:-1]
sentences = np.split(tokens, doc_mask.nonzero()[0] + 1)
if skip_special_tokens:
sentences = [
np.array(
[c
for c in s
if c != self.task.source_dictionary.eos()])
for s in sentences
]
sentences = [
" ".join([self.task.source_dictionary.symbols[c]
for c in s])
for s in sentences
]
if remove_bpe:
sentences = [
s.replace(" ", "").replace("▁", " ").strip() for s in sentences
]
if len(sentences) == 1:
return sentences[0]
return sentences
def decode(self, tokens: torch.LongTensor, dict):
assert tokens.dim() == 1
tokens = tokens.cpu().numpy()
if tokens[0] == self.src_dict.bos():
tokens = tokens[1:] # remove <s>
eos_mask = (tokens == self.src_dict.eos())
doc_mask = eos_mask[1:] & eos_mask[:-1]
sentences = np.split(tokens, doc_mask.nonzero()[0] + 1)
new_sentences = []
for s in sentences:
_s = dict.string(s,
extra_symbols_to_ignore=[
0, 1, 2, 3, 50262, 50263, 50264, 50265
])
#print(s, _s)
_s = self.bpe.decode(_s)
new_sentences.append(_s)
sentences = new_sentences
#sentences = [self.bpe.decode(self.src_dict.string(s)) for s in sentences]
if len(sentences) == 1:
return sentences[0]
return sentences
def _compute_joint_llh(self, emissions: torch.Tensor,
tags: torch.LongTensor,
mask: torch.ByteTensor) -> torch.Tensor:
# emissions: (seq_length, batch_size, num_tags)
# tags: (seq_length, batch_size)
# mask: (seq_length, batch_size)
assert emissions.dim() == 3 and tags.dim() == 2
assert emissions.size()[:2] == tags.size()
assert emissions.size(2) == self.num_tags
assert mask.size() == tags.size()
assert all(mask[0])
seq_length = emissions.size(0)
mask = mask.float()
# Start transition score
llh = self.start_transitions[tags[0]] # (batch_size,)
for i in range(seq_length - 1):
cur_tag, next_tag = tags[i], tags[i + 1]
# Emission score for current tag
llh += emissions[i].gather(1, cur_tag.view(-1,
1)).squeeze(1) * mask[i]
# Transition score to next tag
transition_score = self.transitions[cur_tag, next_tag]
# Only add transition score if the next tag is not masked (mask == 1)
llh += transition_score * mask[i + 1]
# Find last tag index
last_tag_indices = mask.long().sum(0) - 1 # (batch_size,)
last_tags = tags.gather(0, last_tag_indices.view(1, -1)).squeeze(0)
# End transition score
llh += self.end_transitions[last_tags]
# Emission score for the last tag, if mask is valid (mask == 1)
llh += emissions[-1].gather(1, last_tags.view(-1,
1)).squeeze(1) * mask[-1]
return llh
def _compute_score(self, emissions: torch.Tensor, tags: torch.LongTensor,
mask: torch.ByteTensor) -> torch.Tensor:
# emissions: (seq_length, batch_size, num_tags)
# tags: (seq_length, batch_size)
# mask: (seq_length, batch_size)
assert emissions.dim() == 3 and tags.dim() == 2
assert emissions.shape[:2] == tags.shape
assert emissions.size(2) == self.num_tags
assert mask.shape == tags.shape
assert mask[0].all()
seq_length, batch_size = tags.shape
mask = mask.float()
# Start transition score and first emission
# shape: (batch_size,)
score = self.start_transitions[tags[0]]
score += emissions[0, torch.arange(batch_size), tags[0]]
for i in range(1, seq_length):
# Transition score to next tag, only added if next timestep is valid (mask == 1)
# shape: (batch_size,)
score += self.transitions[tags[i - 1], tags[i]] * mask[i]
# Emission score for next tag, only added if next timestep is valid (mask == 1)
# shape: (batch_size,)
score += emissions[i, torch.arange(batch_size), tags[i]] * mask[i]
# End transition score
# shape: (batch_size,)
seq_ends = mask.long().sum(dim=0) - 1
# shape: (batch_size,)
last_tags = tags[seq_ends, torch.arange(batch_size)]
# shape: (batch_size,)
score += self.end_transitions[last_tags]
return score
def _compute_score(self, emissions: torch.Tensor, tags: torch.LongTensor,
mask: torch.ByteTensor) -> torch.Tensor:
assert emissions.dim() == 3 and tags.dim() == 2
assert emissions.shape[:2] == tags.shape
assert emissions.size(2) == self.num_tags
assert mask.shape == tags.shape
assert mask[0].all()
seq_length, batch_size = tags.shape
mask = mask.float()
score = self.start_transitions[tags[0]]
score += emissions[0, torch.arange(batch_size), tags[0]]
for i in range(1, seq_length):
score += self.transitions[tags[i - 1], tags[i]] * mask[i]
score += emissions[i, torch.arange(batch_size), tags[i]] * mask[i]
seq_ends = mask.long().sum(dim=0) - 1
last_tags = tags[seq_ends, torch.arange(batch_size)]
score += self.end_transitions[last_tags]
return score
def _computer_score(self, emissions: torch.Tensor, tags: torch.LongTensor,
mask: torch.ByteTensor) -> torch.Tensor:
# batch second
assert emissions.dim() == 3 and tags.dim() == 2
assert emissions.shape[:2] == tags.shape
assert emissions.size(2) == self.num_tags
assert mask.shape == tags.shape
assert mask[0].all()
seq_length, batch_size = tags.shape
mask = mask.float()
# self.start_transitions start 到其他tag(不包含end)的得分
score = self.start_transitions[tags[0]]
# emissions.shape (seq_len,batch_size,tag_nums)
score += emissions[0, torch.arange(batch_size), tags[0]]
for i in range(1, seq_length):
# if mask[i].sum() == 0:
# break
score += self.transitions[tags[i - 1], tags[i]] * mask[i]
score += emissions[i, torch.arange(batch_size), tags[i]] * mask[i]
# 这里是为了获取每一个样本最后一个词的tag。
# shape: (batch_size,) 每一个batch 的真实长度
seq_ends = mask.long().sum(dim=0) - 1
# 每个样本最火一个词的tag
last_tags = tags[seq_ends, torch.arange(batch_size)]
# shape: (batch_size,) 每一个样本到最后一个词的得分加上之前的score
score += self.end_transitions[last_tags]
return score
def log_probs(self,
heads: LongTensor,
types: LongTensor,
score_only: bool = False) -> Tensor:
"""Compute the log probability of a labeled dependency tree.
Args:
heads: Tensor of shape (B, N) containing the index/position of the head of
each word.
types: Tensor of shape (B, N) containing the dependency types for the
corresponding head-dependent relation.
score_only: Whether to compute only the score of the tree. Useful for training
with cross-entropy loss.
Returns:
1-D tensor of length B containing the log probabilities.
"""
assert heads.dim() == 2
assert types.shape == heads.shape
assert self.mask is not None
scores = self.scores
bsz, slen, _, n_types = self.scores.shape
# broadcast over types
heads = heads.unsqueeze(2).expand(bsz, slen, n_types) # type: ignore
# shape: (bsz, slen, n_types)
scores = scores.gather(1, heads.unsqueeze(1)).squeeze(1)
# shape: (bsz, slen)
scores = scores.gather(2, types.unsqueeze(2)).squeeze(2)
# mask scores from invalid dependents
scores = scores.masked_fill(~self.mask, 0)
# mask scores of root as dependents
scores = scores.masked_fill(
torch.arange(slen).to(scores.device) == self.ROOT, 0)
return scores.sum(dim=1) - (0 if score_only else self.log_partitions())
def segment_lengths_to_ids(
segment_lengths: torch.LongTensor) -> torch.LongTensor:
"""
Args:
segment_lengths: Non-negative lengths of the tensor segments
Returns:
A tensor containing ids for every element in the tensor to be segmented
Examples:
>>> segments = torch.tensor([2, 4, 3, 1])
>>> segment_lengths_to_slices(segments)
tensor([0, 0, 1, 1, 1, 1, 2, 2, 2, 3])
"""
if segment_lengths.dim() != 1:
raise ValueError(
f'`segment_lengths` should have a single dimension, got shape {segment_lengths.shape}'
)
if (segment_lengths < 0).any():
raise ValueError(
f'All entries in `segment_lengths` should be non-negative')
return segment_lengths.new_tensor(
np.arange(len(segment_lengths)).repeat(segment_lengths.cpu().numpy()))
def count_correct(
heads: LongTensor,
types: LongTensor,
pred_heads: LongTensor,
pred_types: LongTensor,
mask: BoolTensor,
nopunct_mask: BoolTensor,
proj_mask: BoolTensor,
root_idx: int = 0,
type_idx: Optional[int] = None,
) -> Union["Counts", "TypeWiseCounts"]:
# shape: (bsz, slen)
assert heads.dim() == 2
assert types.shape == heads.shape
assert pred_heads.shape == heads.shape
assert pred_types.shape == heads.shape
assert mask.shape == heads.shape
assert nopunct_mask.shape == heads.shape
assert proj_mask.shape == heads.shape
corr_heads = heads == pred_heads
corr_types = types == pred_types
if type_idx is None:
root_mask = heads == root_idx
nonproj_mask = ~torch.all(proj_mask | (~mask), dim=1, keepdim=True)
usents = int(torch.all(corr_heads | (~mask), dim=1).long().sum())
usents_nopunct = int(
torch.all(corr_heads | (~mask) | (~nopunct_mask),
dim=1).long().sum())
lsents = int(
torch.all(corr_heads & corr_types | (~mask), dim=1).long().sum())
lsents_nopunct = int(
torch.all(corr_heads & corr_types | (~mask) | (~nopunct_mask),
dim=1).long().sum())
uarcs = int((corr_heads & mask).long().sum())
uarcs_nopunct = int((corr_heads & mask & nopunct_mask).long().sum())
uarcs_nonproj = int((corr_heads & mask & nonproj_mask).long().sum())
larcs = int((corr_heads & corr_types & mask).long().sum())
larcs_nopunct = int(
(corr_heads & corr_types & mask & nopunct_mask).long().sum())
larcs_nonproj = int(
(corr_heads & corr_types & mask & nonproj_mask).long().sum())
roots = int((corr_heads & mask & root_mask).long().sum())
n_sents = heads.size(0)
n_arcs = int(mask.long().sum())
n_arcs_nopunct = int((mask & nopunct_mask).long().sum())
n_arcs_nonproj = int((mask & nonproj_mask).long().sum())
n_roots = int((mask & root_mask).long().sum())
return Counts(
usents,
usents_nopunct,
lsents,
lsents_nopunct,
uarcs,
uarcs_nopunct,
uarcs_nonproj,
larcs,
larcs_nopunct,
larcs_nonproj,
roots,
n_sents,
n_arcs,
n_arcs_nopunct,
n_arcs_nonproj,
n_roots,
)
assert type_idx is not None
type_mask = types == type_idx
uarcs = int((corr_heads & type_mask & mask).long().sum())
uarcs_nopunct = int(
(corr_heads & type_mask & nopunct_mask & mask).long().sum())
larcs = int((corr_heads & corr_types & type_mask & mask).long().sum())
larcs_nopunct = int((corr_heads & corr_types & type_mask & nopunct_mask
& mask).long().sum())
n_arcs = int((type_mask & mask).long().sum())
n_arcs_nopunct = int((type_mask & nopunct_mask & mask).long().sum())
return TypeWiseCounts(type_idx, uarcs, uarcs_nopunct, larcs, larcs_nopunct,
n_arcs, n_arcs_nopunct)
def forward(
self, # type: ignore
tokens: Dict[str, torch.LongTensor],
verb_indicator: torch.LongTensor,
tags: torch.LongTensor = None) -> Dict[str, torch.Tensor]:
# pylint: disable=arguments-differ
"""
Parameters
----------
tokens : Dict[str, torch.LongTensor], required
The output of ``TextField.as_array()``, which should typically be passed directly to a
``TextFieldEmbedder``. This output is a dictionary mapping keys to ``TokenIndexer``
tensors. At its most basic, using a ``SingleIdTokenIndexer`` this is: ``{"tokens":
Tensor(batch_size, num_tokens)}``. This dictionary will have the same keys as were used
for the ``TokenIndexers`` when you created the ``TextField`` representing your
sequence. The dictionary is designed to be passed directly to a ``TextFieldEmbedder``,
which knows how to combine different word representations into a single vector per
token in your input.
verb_indicator: torch.LongTensor, required.
A one-hot/all-zeros ``IndexField`` representation of the position of the verb in the sentence.
This should have shape (batch_size, num_tokens) and importantly, can be all zeros, in the case
that the sentence has no verbal predicate.
tags : torch.LongTensor, optional (default = None)
A torch tensor representing the sequence of gold labels. These can either be integer
indexes or one hot arrays of labels, so of shape ``(batch_size, num_tokens)`` or of
shape ``(batch_size, num_tokens, num_tags)``.
Returns
-------
An output dictionary consisting of:
logits : torch.FloatTensor
A tensor of shape ``(batch_size, num_tokens, tag_vocab_size)`` representing
unnormalised log probabilities of the tag classes.
class_probabilities : torch.FloatTensor
A tensor of shape ``(batch_size, num_tokens, tag_vocab_size)`` representing
a distribution of the tag classes per word.
loss : torch.FloatTensor, optional
A scalar loss to be optimised.
"""
embedded_text_input = self.text_field_embedder(tokens)
mask = get_text_field_mask(tokens)
expanded_verb_indicator = verb_indicator.unsqueeze(-1).float()
# Concatenate the verb feature onto the embedded text. This now
# has shape (batch_size, sequence_length, embedding_dim + 1).
embedded_text_with_verb_indicator = torch.cat(
[embedded_text_input, expanded_verb_indicator], -1)
batch_size, sequence_length, embedding_dim_with_binary_feature = embedded_text_with_verb_indicator.size(
)
if self.stacked_encoder.get_input_dim(
) != embedding_dim_with_binary_feature:
raise ConfigurationError(
"The SRL model uses an indicator feature, which makes "
"the embedding dimension one larger than the value "
"specified. Therefore, the 'input_dim' of the stacked_encoder "
"must be equal to total_embedding_dim + 1.")
batch_sequence_lengths = get_lengths_from_binary_sequence_mask(mask)
encoded_text = self.stacked_encoder(embedded_text_with_verb_indicator,
batch_sequence_lengths)
logits = self.tag_projection_layer(encoded_text)
reshaped_log_probs = logits.view(-1, self.num_classes)
class_probabilities = F.softmax(reshaped_log_probs).view(
[batch_size, sequence_length, self.num_classes])
output_dict = {
"logits": logits,
"class_probabilities": class_probabilities
}
if tags is not None:
# Negative log likelihood criterion takes integer labels, not one hot.
if tags.dim() == 3:
_, tags = tags.max(-1)
loss = sequence_cross_entropy_with_logits(logits, tags, mask)
self.span_metric(class_probabilities, tags, mask)
output_dict["loss"] = loss
return output_dict
def eval_probability(
model: transformer.Transformer,
input_seq: torch.LongTensor,
target_seq: torch.LongTensor,
pad_index: int=None
) -> torch.FloatTensor:
"""Computes the probability that the provided model computes a target sequence given an input sequence.
Args:
model (:class:`transformer.Transformer`): The model to use.
input_seq (torch.LongTensor): The input sequence to be provided to the model. This has to be a
(batch-size x input-seq-len)-tensor.
target_seq (torch.LongTensor): The target sequence whose probability is being evaluated. This has to be a
(batch-size x target-seq-len)-tensor.
pad_index (int, optional): The index that indicates a padding token in a sequence. If ``target_seq`` is padded,
then the ``pad_index`` has to be provided in order to allow for computing the probabilities for relevant
parts of the target sequence only.
Returns:
torch.FloatTensor: A 1D-tensor of size (batch-size), which contains one probability for each sample in
``input_seq`` and ``target_seq``, respectively.
"""
if not isinstance(model, transformer.Transformer):
raise TypeError("The <model> has to be a transformer.Transformer!")
if not isinstance(input_seq, torch.LongTensor) and not isinstance(input_seq, torch.cuda.LongTensor):
raise TypeError("The <input_seq> has to be a LongTensor!")
if input_seq.dim() != 2:
raise ValueError("<input_seq> has to be a 2D-tensor!")
if input_seq.is_cuda:
if not isinstance(target_seq, torch.cuda.LongTensor):
raise TypeError("The <target_seq> has to be of the same type as <input_seq>, i.e., cuda.LongTensor!")
elif not isinstance(target_seq, torch.LongTensor):
raise TypeError("The <target_seq> has to be of the same type as <input_seq>, i.e., LongTensor!")
if target_seq.dim() != 2:
raise ValueError("<input_seq> has to be a 2D-tensor!")
if input_seq.size(0) != target_seq.size(0):
raise ValueError("<input_seq> and <target_seq> use different batch sizes!")
if pad_index is not None and not isinstance(pad_index, int):
raise TypeError("The <pad_index>, if provided, has to be an integer!")
batch_size = input_seq.size(0)
max_seq_len = input_seq.size(1)
# put model in evaluation mode
original_mode = model.training # store original mode (train/eval) to be restored eventually
model.eval()
# run the model to compute the needed probabilities
predictions = model(input_seq, target_seq)
# determine the lengths of the target sequences
if pad_index is not None:
mask = util.create_padding_mask(target_seq, pad_index)[:, 0, :]
seq_len = mask.sum(dim=1).cpu().numpy().tolist()
else:
seq_len = (np.ones(batch_size, dtype=np.long) * max_seq_len).tolist()
# compute the probabilities for each of the provided samples
sample_probs = torch.ones(batch_size)
for sample_idx in range(batch_size): # iterate over each sample
for token_idx in range(seq_len[sample_idx]): # iterate over each position in the output sequence
sample_probs[sample_idx] *= predictions[sample_idx, token_idx, target_seq[sample_idx, token_idx]].item()
# restore original mode of the model
model.train(mode=original_mode)
return sample_probs
def forward(
self,
tokens: Dict[str, torch.LongTensor],
spans: torch.LongTensor,
metadata: List[Dict[str, Any]],
pos_tags: Dict[str, torch.LongTensor] = None,
span_labels: torch.LongTensor = None) -> Dict[str, torch.Tensor]:
embedded_text_input = self.text_field_embedder(tokens)
if pos_tags is not None and self.pos_tag_embedding is not None:
embedded_pos_tags = self.pos_tag_embedding(pos_tags)
embedded_text_input = torch.cat(
[embedded_text_input, embedded_pos_tags], -1)
elif self.pos_tag_embedding is not None:
raise ConfigurationError(
"Model uses a POS embedding, but no POS tags were passed.")
mask = get_text_field_mask(tokens)
# Looking at the span start index is enough to know if
# this is padding or not. Shape: (batch_size, num_spans)
span_mask = (spans[:, :, 0] >= 0).squeeze(-1).long()
if span_mask.dim() == 1:
# This happens if you use batch_size 1 and encounter
# a length 1 sentence in PTB, which do exist. -.-
span_mask = span_mask.unsqueeze(-1)
if span_labels is not None and span_labels.dim() == 1:
span_labels = span_labels.unsqueeze(-1)
num_spans = get_lengths_from_binary_sequence_mask(span_mask)
encoded_text = self.encoder(embedded_text_input, mask)
span_representations = self.span_extractor(encoded_text, spans, mask,
span_mask)
if self.feedforward_layer is not None:
span_representations = self.feedforward_layer(span_representations)
logits = self.tag_projection_layer(span_representations)
class_probabilities = masked_softmax(logits, span_mask.unsqueeze(-1))
output_dict = {
"class_probabilities": class_probabilities,
"spans": spans,
"tokens": [meta["tokens"] for meta in metadata],
"pos_tags": [meta.get("pos_tags") for meta in metadata],
"num_spans": num_spans
}
if span_labels is not None:
loss = sequence_cross_entropy_with_logits(logits.float(),
span_labels, span_mask)
self.tag_accuracy(class_probabilities, span_labels, span_mask)
output_dict["loss"] = loss
# The evalb score is expensive to compute, so we only compute
# it for the validation and test sets.
batch_gold_trees = [meta.get("gold_tree") for meta in metadata]
if all(batch_gold_trees
) and self._evalb_score is not None and not self.training:
gold_pos_tags: List[List[str]] = [
list(zip(*tree.pos()))[1] for tree in batch_gold_trees
]
predicted_trees = self.construct_trees(
class_probabilities.cpu().data,
spans.cpu().data, num_spans.data, output_dict["tokens"],
gold_pos_tags)
self._evalb_score(predicted_trees, batch_gold_trees)
return output_dict
def forward(self,
indices: torch.LongTensor,
offsets: Optional[torch.LongTensor] = None,
per_index_weights: Optional[torch.Tensor] = None):
"""
Forward process to the embedding bag layer.
:param indices: Tensor containing bags of indices into the embedding matrix.
:param offsets: Only used when indices is 1D. offsets determines the starting index position of each bag
(sequence)in input.
:param per_index_weights: a tensor of float / double weights, or None to indicate all weights should be taken to
be 1. If specified, per_sample_weights must have exactly the same shape as input and is treated as having the
same offsets, if those are not None.
:return: an #bag x embedding_dim Tensor.
"""
# always move indices to cpu, as we need to get its corresponding minhash values from table in memory
indices = indices.cpu()
# Check input validation.
if per_index_weights is not None and indices.size() != per_index_weights.size():
raise ValueError("embedding_bag: If per_index_weights ({}) is not None, "
"then it must have the same shape as the indices ({})"
.format(per_index_weights.shape, indices.shape))
if indices.dim() == 2:
if offsets is not None:
raise ValueError("if input is 2D, then offsets has to be None"
", as input is treated is a mini-batch of"
" fixed length sequences. However, found "
"offsets of type {}".format(type(offsets)))
offsets = torch.arange(0, indices.numel(), indices.size(1), dtype=torch.long, device=indices.device)
indices = indices.reshape(-1)
if per_index_weights is not None:
per_sample_weights = per_index_weights.reshape(-1)
elif indices.dim() == 1:
if offsets is None:
raise ValueError("offsets has to be a 1D Tensor but got None")
if offsets.dim() != 1:
raise ValueError("offsets has to be a 1D Tensor")
else:
ValueError("input has to be 1D or 2D Tensor,"
" but got Tensor of dimension {}".format(input.dim()))
num_bags = offsets.size(0)
# get the min-hash for each category value, note that lsh_weight_index is in cpu memory
lsh_weight_index = self._minhash_table[indices]
# print("In forward: ", lsh_weight_index, indices, self._minhash_table[indices], self.lsh_weight_size)
# move the min-hash values to target device
lsh_weight_index = lsh_weight_index.to(self.hashed_weight.device)
lsh_weight_index %= self.lsh_weight_size
# indices_embedding_vector is a |indices| x |embedding_dim| tensor.
indices_embedding_vectors = self.hashed_weight[lsh_weight_index]
# print('indices_embedding_vectors: ', lsh_weight_index, indices_embedding_vectors)
# multiply embedding vectors by weights
if per_index_weights is not None:
per_index_weights = per_index_weights.to(indices_embedding_vectors.device)
indices_embedding_vectors *= per_index_weights[:, None]
# print("per_index_weights",per_index_weights)
offsets2bag = make_offset2bag(offsets, indices)
# print("offsets2bag: ", offsets2bag)
if self._mode == "sum" or self._mode == "mean":
result = \
torch.zeros(num_bags, self.embedding_dim, dtype=indices_embedding_vectors.dtype,
device=self.hashed_weight.device)
result.index_add_(0, offsets2bag, indices_embedding_vectors)
if self._mode == "sum":
return result
# self._mode == "mean":
bag_size = make_bag_size(offsets, indices).to(result.device)
result /= bag_size[:, None]
return result
def forward(
self, # type: ignore
question_passage: Dict[str, torch.LongTensor],
sentences_mask: torch.LongTensor,
sentences_tokens: torch.LongTensor,
question_sentences_tokens: torch.LongTensor,
number_indices: torch.LongTensor,
mask_indices: torch.LongTensor,
num_spans: torch.LongTensor = None,
answer_as_passage_spans: torch.LongTensor = None,
answer_as_question_spans: torch.LongTensor = None,
answer_as_expressions: torch.LongTensor = None,
answer_as_expressions_extra: torch.LongTensor = None,
answer_as_counts: torch.LongTensor = None,
metadata: List[Dict[str, Any]] = None) -> Dict[str, torch.Tensor]:
# pylint: disable=arguments-differ
# Shape: (batch_size, seqlen) - all questions and passage tokens
question_passage_tokens = question_passage["tokens"]
# Shape: (batch_size, seqlen) - 1 for all question and passage tokens, 0 for padding
pad_mask = question_passage["mask"]
# Shape: (batch_size, seqlen) - 0 for all question tokens, 1 for passage tokens, 0 for padding
seqlen_ids = question_passage["tokens-type-ids"]
max_seqlen = question_passage_tokens.shape[-1]
batch_size = question_passage_tokens.shape[0]
# Shape: (batch_size, 3) - (0, question length - 1, question + passage length - 1)
mask = mask_indices.squeeze(-1)
# Shape: (batch_size, seqlen) - 0 for all [CLS] (question start) and [SEP] (passage start) tokens, 1 for rest
cls_sep_mask = \
torch.ones(pad_mask.shape, device=pad_mask.device).long().scatter(1, mask, torch.zeros(mask.shape, device=mask.device).long())
# Shape: (batch_size, seqlen) - 1 for all passage tokens excluding [CLS], [SEP], 0 for others
passage_mask = seqlen_ids * pad_mask * cls_sep_mask
# Shape: (batch_size, seqlen) -1 for all question tokens excluding [CLS], [SEP], 0 for others
question_mask = (1 - seqlen_ids) * pad_mask * cls_sep_mask
# Contains a single summary vec for each sentence (weighted sum of bert embeddings)
# Shape: (batch_size, sentence_count, bert_dim)
with torch.no_grad():
sentences_embeddings, sentences_embedding_mask = self.extract_sentence_embeddings(
question_sentences_tokens)
sentences_summary_vecs = self.summary_vector(sentences_embeddings,
sentences_embedding_mask,
"sentence")
# Shape: (batch_size, seqlen, bert_dim) ; self.BERT is a BertModel
# seqlen_ids: mask of Sentence A - 0, Sentence B - 1
# pad_mask: 1 for real tokens, 0 for padding
bert_out, _ = self.BERT(question_passage_tokens,
seqlen_ids,
pad_mask,
output_all_encoded_layers=False)
# Shape: (batch_size, qlen, bert_dim)
question_end = max(
mask[:, 1]
) # Contains the index for last question token: Shape (batch_size, )
question_out = bert_out[:, :
question_end] # Contains question token embeddings + some unfiltered embeddings from passage
# Shape: (batch_size, qlen)
question_mask = question_mask[:, :
question_end] # Crop mask to match question_out
# Shape: (batch_size, out)
question_vector = self.summary_vector(question_out, question_mask,
"question")
passage_out = bert_out
del bert_out
# Shape: (batch_size, bert_dim)
passage_vector = self.summary_vector(passage_out, passage_mask)
if "arithmetic" in self.answering_abilities and self.arithmetic == "advanced":
arithmetic_summary = self.summary_vector(passage_out, pad_mask,
"arithmetic")
# arithmetic_summary = self.summary_vector(question_out, question_mask, "arithmetic")
# Shape: (batch_size, # of numbers in the passage)
if number_indices.dim() == 3:
number_indices = number_indices[:, :, 0].long()
number_mask = (number_indices != -1).long()
clamped_number_indices = util.replace_masked_values(
number_indices, number_mask, 0)
encoded_numbers = torch.gather(
passage_out, 1,
clamped_number_indices.unsqueeze(-1).expand(
-1, -1, passage_out.size(-1)))
op_mask = torch.ones((batch_size, self.num_ops + 1),
device=number_mask.device).long()
options_mask = torch.cat([op_mask, number_mask], -1)
ops = self.op_embeddings(
torch.arange(self.num_ops + 1,
device=number_mask.device).expand(batch_size, -1))
options = torch.cat([self.Wo(ops), self.Wc(encoded_numbers)], 1)
if len(self.answering_abilities) > 1:
# Shape: (batch_size, number_of_abilities)
answer_ability_logits = \
self._answer_ability_predictor(torch.cat([passage_vector, question_vector], -1))
answer_ability_log_probs = torch.nn.functional.log_softmax(
answer_ability_logits, -1)
best_answer_ability = torch.argmax(answer_ability_log_probs, 1)
if "counting" in self.answering_abilities:
count_number, best_count_number = self._count_module(
passage_vector)
# count_number, select_probs = self._count_module_per_sentence(passage_vector, sentences_summary_vecs)
# answer_as_counts = answer_as_counts.squeeze(1)
# gold_count_mask = (answer_as_counts != -1).long()
# count_number = util.replace_masked_values(count_number, gold_count_mask, 0)
if "passage_span_extraction" in self.answering_abilities:
passage_span_start_log_probs, passage_span_end_log_probs, best_passage_span = \
self._passage_span_module(passage_out, passage_mask)
if "question_span_extraction" in self.answering_abilities:
question_span_start_log_probs, question_span_end_log_probs, best_question_span = \
self._question_span_module(passage_vector, question_out, question_mask)
if "arithmetic" in self.answering_abilities:
if self.arithmetic == "base":
number_mask = (number_indices[:, :, 0].long() != -1).long()
number_sign_log_probs, best_signs_for_numbers, number_mask = \
self._base_arithmetic_module(passage_vector, passage_out, number_indices, number_mask)
else:
arithmetic_logits, best_expression = \
self._adv_arithmetic_module(arithmetic_summary, self.max_explen, options, options_mask, \
passage_out, pad_mask)
shapes = arithmetic_logits.shape
if (1 - (arithmetic_logits != arithmetic_logits)).sum() != (
shapes[0] * shapes[1] * shapes[2]):
print("bad logits")
arithmetic_logits = torch.rand(
shapes,
device=arithmetic_logits.device,
requires_grad=True)
output_dict = {}
del passage_out, question_out
# If answer is given, compute the loss.
# if answer_as_passage_spans is not None or answer_as_question_spans is not None \
# or answer_as_expressions is not None or answer_as_counts is not None:
#
# log_marginal_likelihood_list = []
# regression_loss = []
#
# for answering_ability in self.answering_abilities:
# if answering_ability == "passage_span_extraction":
# log_marginal_likelihood_for_passage_span = \
# self._passage_span_log_likelihood(answer_as_passage_spans,
# passage_span_start_log_probs,
# passage_span_end_log_probs)
# log_marginal_likelihood_list.append(log_marginal_likelihood_for_passage_span)
#
# elif answering_ability == "question_span_extraction":
# log_marginal_likelihood_for_question_span = \
# self._question_span_log_likelihood(answer_as_question_spans,
# question_span_start_log_probs,
# question_span_end_log_probs)
# log_marginal_likelihood_list.append(log_marginal_likelihood_for_question_span)
#
# elif answering_ability == "arithmetic":
# if self.arithmetic == "base":
# log_marginal_likelihood_for_arithmetic = \
# self._base_arithmetic_log_likelihood(answer_as_expressions,
# number_sign_log_probs,
# number_mask,
# answer_as_expressions_extra)
# else:
# max_explen = answer_as_expressions.shape[-1]
# possible_exps = answer_as_expressions.shape[1]
# limit = min(possible_exps, 1000)
# log_marginal_likelihood_for_arithmetic = \
# self._adv_arithmetic_log_likelihood(arithmetic_logits[:,:max_explen,:],
# answer_as_expressions[:,:limit,:].long())
# log_marginal_likelihood_list.append(log_marginal_likelihood_for_arithmetic)
#
# elif answering_ability == "counting":
# regression_loss_for_count, best_count_number = \
# self._count_regression(answer_as_counts, count_number_regression)
# regression_loss.append(regression_loss_for_count)
# # TODO: This is wrong
# log_marginal_likelihood_list.append(torch.tensor([-1e-7] * batch_size, device=regression_loss_for_count.device))
#
# else:
# raise ValueError(f"Unsupported answering ability: {answering_ability}")
#
# if len(self.answering_abilities) > 1:
# # Add the ability probabilities if there are more than one abilities
# all_log_marginal_likelihoods = torch.stack(log_marginal_likelihood_list, dim=-1)
# all_log_marginal_likelihoods = all_log_marginal_likelihoods + answer_ability_log_probs
# marginal_log_likelihood = util.logsumexp(all_log_marginal_likelihoods)
#
# if len(regression_loss) > 0:
# marginal_log_likelihood -= regression_loss[0]
# else:
# if len(regression_loss) > 0:
# marginal_log_likelihood = -regression_loss[0]
# else:
# marginal_log_likelihood = log_marginal_likelihood_list[0] # TODO: Handle
output_dict["loss"] = -self._count_module(passage_vector)[0].mean()
# output_dict["loss"] = loss_utils.count_loss(answer_as_counts, count_number, select_probs, passage_mask)
with torch.no_grad():
# Compute the metrics and add the tokenized input to the output.
if metadata is not None:
output_dict["question_id"] = []
output_dict["answer"] = []
question_tokens = []
passage_tokens = []
for i in range(batch_size):
if len(self.answering_abilities) > 1:
predicted_ability_str = self.answering_abilities[
best_answer_ability[i]]
else:
predicted_ability_str = self.answering_abilities[0]
answer_json: Dict[str, Any] = {}
# We did not consider multi-mention answers here
if predicted_ability_str == "passage_span_extraction":
answer_json["answer_type"] = "passage_span"
answer_json["value"], answer_json["spans"] = \
self._span_prediction(question_passage_tokens[i], best_passage_span[i])
elif predicted_ability_str == "question_span_extraction":
answer_json["answer_type"] = "question_span"
answer_json["value"], answer_json["spans"] = \
self._span_prediction(question_passage_tokens[i], best_question_span[i])
elif predicted_ability_str == "arithmetic": # plus_minus combination answer
answer_json["answer_type"] = "arithmetic"
original_numbers = metadata[i]['original_numbers']
if self.arithmetic == "base":
answer_json["value"], answer_json["numbers"] = \
self._base_arithmetic_prediction(original_numbers, number_indices[i], best_signs_for_numbers[i])
else:
answer_json["value"], answer_json["expression"] = \
self._adv_arithmetic_prediction(original_numbers, best_expression[i])
elif predicted_ability_str == "counting":
answer_json["answer_type"] = "count"
answer_json["value"], answer_json["count"] = \
self._count_prediction(count_number[i])
else:
raise ValueError(
f"Unsupported answer ability: {predicted_ability_str}"
)
output_dict["question_id"].append(
metadata[i]["question_id"])
output_dict["answer"].append(answer_json)
answer_annotations = metadata[i].get(
'answer_annotations', [])
if answer_annotations:
self._drop_metrics(answer_json["value"],
answer_annotations)
return output_dict
def forward(
self, # type: ignore
tokens: Dict[str, torch.LongTensor],
tags: torch.LongTensor = None) -> Dict[str, torch.Tensor]:
# pylint: disable=arguments-differ
"""
Parameters
----------
tokens : Dict[str, torch.LongTensor], required
The output of ``TextField.as_array()``, which should typically be passed directly to a
``TextFieldEmbedder``. This output is a dictionary mapping keys to ``TokenIndexer``
tensors. At its most basic, using a ``SingleIdTokenIndexer`` this is: ``{"tokens":
Tensor(batch_size, num_tokens)}``. This dictionary will have the same keys as were used
for the ``TokenIndexers`` when you created the ``TextField`` representing your
sequence. The dictionary is designed to be passed directly to a ``TextFieldEmbedder``,
which knows how to combine different word representations into a single vector per
token in your input.
tags : torch.LongTensor, optional (default = None)
A torch tensor representing the sequence of gold labels. These can either be integer
indexes or one hot arrays of labels, so of shape ``(batch_size, num_tokens)`` or of
shape ``(batch_size, num_tokens, num_tags)``.
Returns
-------
An output dictionary consisting of:
logits : torch.FloatTensor
A tensor of shape ``(batch_size, num_tokens, tag_vocab_size)`` representing
unnormalised log probabilities of the tag classes.
class_probabilities : torch.FloatTensor
A tensor of shape ``(batch_size, num_tokens, tag_vocab_size)`` representing
a distribution of the tag classes per word.
loss : torch.FloatTensor, optional
A scalar loss to be optimised.
"""
embedded_text_input = self.text_field_embedder(tokens)
batch_size, sequence_length, _ = embedded_text_input.size()
mask = get_text_field_mask(tokens)
batch_sequence_lengths = get_lengths_from_binary_sequence_mask(mask)
encoded_text = self.stacked_encoder(embedded_text_input,
batch_sequence_lengths)
logits = self.tag_projection_layer(encoded_text)
reshaped_log_probs = logits.view(-1, self.num_classes)
class_probabilities = F.softmax(reshaped_log_probs).view(
[batch_size, sequence_length, self.num_classes])
output_dict = {
"logits": logits,
"class_probabilities": class_probabilities
}
if tags is not None:
# Negative log likelihood criterion takes integer labels, not one hot.
if tags.dim() == 3:
_, tags = tags.max(-1)
loss = sequence_cross_entropy_with_logits(logits, tags, mask)
for metric in self.metrics.values():
metric(logits, tags, mask.float())
output_dict["loss"] = loss
return output_dict
def _save_ply(
f,
verts: torch.Tensor,
faces: torch.LongTensor,
verts_normals: torch.Tensor,
ascii: bool,
decimal_places: Optional[int] = None,
) -> None:
"""
Internal implementation for saving 3D data to a .ply file.
Args:
f: File object to which the 3D data should be written.
verts: FloatTensor of shape (V, 3) giving vertex coordinates.
faces: LongTensor of shsape (F, 3) giving faces.
verts_normals: FloatTensor of shape (V, 3) giving vertex normals.
ascii: (bool) whether to use the ascii ply format.
decimal_places: Number of decimal places for saving if ascii=True.
"""
assert not len(verts) or (verts.dim() == 2 and verts.size(1) == 3)
assert not len(faces) or (faces.dim() == 2 and faces.size(1) == 3)
assert not len(verts_normals) or (verts_normals.dim() == 2
and verts_normals.size(1) == 3)
if ascii:
f.write(b"ply\nformat ascii 1.0\n")
elif sys.byteorder == "big":
f.write(b"ply\nformat binary_big_endian 1.0\n")
else:
f.write(b"ply\nformat binary_little_endian 1.0\n")
f.write(f"element vertex {verts.shape[0]}\n".encode("ascii"))
f.write(b"property float x\n")
f.write(b"property float y\n")
f.write(b"property float z\n")
if verts_normals.numel() > 0:
f.write(b"property float nx\n")
f.write(b"property float ny\n")
f.write(b"property float nz\n")
f.write(f"element face {faces.shape[0]}\n".encode("ascii"))
f.write(b"property list uchar int vertex_index\n")
f.write(b"end_header\n")
if not (len(verts) or len(faces)):
warnings.warn("Empty 'verts' and 'faces' arguments provided")
return
vert_data = torch.cat((verts, verts_normals), dim=1).detach().numpy()
if ascii:
if decimal_places is None:
float_str = "%f"
else:
float_str = "%" + ".%df" % decimal_places
np.savetxt(f, vert_data, float_str)
else:
assert vert_data.dtype == np.float32
if isinstance(f, BytesIO):
# tofile only works with real files, but is faster than this.
f.write(vert_data.tobytes())
else:
vert_data.tofile(f)
faces_array = faces.detach().numpy()
_check_faces_indices(faces, max_index=verts.shape[0])
if len(faces_array):
if ascii:
np.savetxt(f, faces_array, "3 %d %d %d")
else:
# rows are 13 bytes: a one-byte 3 followed by three four-byte face indices.
faces_uints = np.full((len(faces_array), 13), 3, dtype=np.uint8)
faces_uints[:, 1:] = faces_array.astype(np.uint32).view(np.uint8)
if isinstance(f, BytesIO):
f.write(faces_uints.tobytes())
else:
faces_uints.tofile(f)
def sample_output(
model: transformer.Transformer,
input_seq: torch.LongTensor,
eos_index: int,
pad_index: int,
max_len: int
) -> torch.LongTensor:
"""Samples an output sequence based on the provided input.
Args:
model (:class:`transformer.Transformer`): The model to use.
input_seq (torch.LongTensor): The input sequence to be provided to the model. This has to be a
(batch-size x input-seq-len)-tensor.
eos_index (int): The index that indicates the end of a sequence.
pad_index (int): The index that indicates a padding token in a sequence.
max_len (int): The maximum length of the generated output.
Returns:
torch.LongTensor: The generated output sequence as (batch-size x output-seq-len)-tensor.
"""
# sanitize args
if not isinstance(model, transformer.Transformer):
raise TypeError("The <model> has to be a transformer.Transformer!")
if not isinstance(input_seq, torch.LongTensor) and not isinstance(input_seq, torch.cuda.LongTensor):
raise TypeError("The <input_seq> has to be a LongTensor!")
if input_seq.dim() != 2:
raise ValueError("<input_seq> has to be a matrix!")
if not isinstance(eos_index, int):
raise TypeError("The <eos_index> has to be an integer!")
if eos_index < 0 or eos_index >= model.output_size:
raise ValueError("The <eos_index> is not a legal index in the vocabulary used by <model>!")
if not isinstance(pad_index, int):
raise TypeError("The <pad_index> has to be an integer!")
if pad_index < 0 or pad_index >= model.output_size:
raise ValueError("The <pad_index> is not a legal index in the vocabulary used by <model>!")
if max_len is not None:
if not isinstance(max_len, int):
raise TypeError("<max_len> has to be an integer!")
if max_len < 1:
raise ValueError("<max_len> has to be > 0!")
original_mode = model.training # the original mode (train/eval) of the provided model
batch_size = input_seq.size(0) # number of samples in the provided input sequence
# put model in evaluation mode
model.eval()
output_seq = [] # used to store the generated outputs for each position
finished = [False] * batch_size
for _ in range(max_len):
# prepare the target to provide to the model
# this is the current output with an additional final entry that is supposed to be predicted next
# (which is why the concrete value does not matter)
current_target = torch.cat(output_seq + [input_seq.new(batch_size, 1).zero_()], dim=1)
# run the model
probs = model(input_seq, current_target)[:, -1, :]
# sample next output form the computed probabilities
output = torch.multinomial(probs, 1)
# determine which samples have been finished, and replace sampled output with padding for those that are already
for sample_idx in range(batch_size):
if finished[sample_idx]:
output[sample_idx, 0] = pad_index
elif output[sample_idx, 0].item() == eos_index:
finished[sample_idx] = True
# store created output
output_seq.append(output)
# check whether generation has been finished
if all(finished):
break
# restore original mode of the model
model.train(mode=original_mode)
return torch.cat(output_seq, dim=1)
def align_bpe_to_words(roberta, bpe_tokens: torch.LongTensor,
other_tokens: List[str]):
"""
Helper to align GPT-2 BPE to other tokenization formats (e.g., spaCy).
Args:
roberta (RobertaHubInterface): RoBERTa instance
bpe_tokens (torch.LongTensor): GPT-2 BPE tokens of shape `(T_bpe)`
other_tokens (List[str]): other tokens of shape `(T_words)`
Returns:
List[str]: mapping from *other_tokens* to corresponding *bpe_tokens*.
"""
assert bpe_tokens.dim() == 1
assert bpe_tokens[0] == 0
def clean(text):
return text.strip()
# remove whitespaces to simplify alignment
bpe_tokens = [
roberta.task.source_dictionary.string([x]) for x in bpe_tokens
]
bpe_tokens = [
clean(roberta.bpe.decode(x) if x not in {'<s>', ''} else x)
for x in bpe_tokens
]
other_tokens = [clean(str(o)) for o in other_tokens]
# strip leading <s>
bpe_tokens = bpe_tokens[1:]
# assert ''.join(bpe_tokens) == ''.join(other_tokens)
# create alignment from every word to a list of BPE tokens
alignment = []
# print(bpe_tokens)
# print(other_tokens, '\n')
bpe_toks = filter(lambda item: item[1] != '', enumerate(bpe_tokens,
start=1))
j, bpe_tok = next(bpe_toks)
for other_tok in other_tokens:
if other_tok == '':
print("empty")
bpe_indices = []
while True:
if bpe_tok == '<unk>':
unk_tok = roberta.bpe.encode(other_tok).split()[0].replace(
'@@', '')
other_tok = other_tok[len(unk_tok):]
try:
j, bpe_tok = next(bpe_toks)
except StopIteration:
j, bpe_tok = None, None
if other_tok.startswith(bpe_tok):
bpe_indices.append(j)
other_tok = other_tok[len(bpe_tok):]
try:
j, bpe_tok = next(bpe_toks)
# break
except StopIteration:
j, bpe_tok = None, None
elif bpe_tok.startswith(other_tok):
# other_tok spans multiple BPE tokens
bpe_indices.append(j)
bpe_tok = bpe_tok[len(other_tok):]
other_tok = ''
else:
raise Exception('Cannot align "{}" and "{}"'.format(
other_tok, bpe_tok))
if other_tok == '':
break
assert len(bpe_indices) > 0
alignment.append(bpe_indices)
assert len(alignment) == len(other_tokens)
return alignment
def forward(
self, # type: ignore
question_passage: Dict[str, torch.LongTensor],
number_indices: torch.LongTensor,
mask_indices: torch.LongTensor,
num_spans: torch.LongTensor = None,
answer_as_passage_spans: torch.LongTensor = None,
answer_as_question_spans: torch.LongTensor = None,
answer_as_expressions: torch.LongTensor = None,
answer_as_expressions_extra: torch.LongTensor = None,
answer_as_counts: torch.LongTensor = None,
metadata: List[Dict[str, Any]] = None) -> Dict[str, torch.Tensor]:
# pylint: disable=arguments-differ
# Shape: (batch_size, seqlen)
question_passage_tokens = question_passage["tokens"]
# Shape: (batch_size, seqlen)
pad_mask = question_passage["mask"]
# Shape: (batch_size, seqlen)
seqlen_ids = question_passage["tokens-type-ids"]
max_seqlen = question_passage_tokens.shape[-1]
batch_size = question_passage_tokens.shape[0]
# Shape: (batch_size, 3)
mask = mask_indices.squeeze(-1)
# Shape: (batch_size, seqlen)
cls_sep_mask = \
torch.ones(pad_mask.shape, device=pad_mask.device).long().scatter(1, mask, torch.zeros(mask.shape, device=mask.device).long())
# Shape: (batch_size, seqlen)
passage_mask = seqlen_ids * pad_mask * cls_sep_mask
# Shape: (batch_size, seqlen)
question_mask = (1 - seqlen_ids) * pad_mask * cls_sep_mask
tags = self.ner_tagger(question_passage_tokens)
# Shape: (batch_size, seqlen, bert_dim)
bert_out, _ = self.BERT(question_passage_tokens,
seqlen_ids,
pad_mask,
output_all_encoded_layers=False)
# Shape: (batch_size, qlen, bert_dim)
question_end = max(mask[:, 1])
question_out = bert_out[:, :question_end]
# Shape: (batch_size, qlen)
question_mask = question_mask[:, :question_end]
# Shape: (batch_size, out)
question_vector = self.summary_vector(question_out, question_mask,
"question")
passage_out = bert_out
del bert_out
# Shape: (batch_size, bert_dim)
passage_vector = self.summary_vector(passage_out, passage_mask)
if "arithmetic" in self.answering_abilities and self.arithmetic == "advanced":
arithmetic_summary = self.summary_vector(passage_out, pad_mask,
"arithmetic")
# arithmetic_summary = self.summary_vector(question_out, question_mask, "arithmetic")
# Shape: (batch_size, # of numbers in the passage)
if number_indices.dim() == 3:
number_indices = number_indices[:, :, 0].long()
number_mask = (number_indices != -1).long()
clamped_number_indices = util.replace_masked_values(
number_indices, number_mask, 0)
encoded_numbers = torch.gather(
passage_out, 1,
clamped_number_indices.unsqueeze(-1).expand(
-1, -1, passage_out.size(-1)))
op_mask = torch.ones((batch_size, self.num_ops + 1),
device=number_mask.device).long()
options_mask = torch.cat([op_mask, number_mask], -1)
ops = self.op_embeddings(
torch.arange(self.num_ops + 1,
device=number_mask.device).expand(batch_size, -1))
options = torch.cat([self.Wo(ops), self.Wc(encoded_numbers)], 1)
if len(self.answering_abilities) > 1:
# Shape: (batch_size, number_of_abilities)
answer_ability_logits = \
self._answer_ability_predictor(torch.cat([passage_vector, question_vector], -1))
answer_ability_log_probs = torch.nn.functional.log_softmax(
answer_ability_logits, -1)
best_answer_ability = torch.argmax(answer_ability_log_probs, 1)
if "counting" in self.answering_abilities:
count_number_log_probs, best_count_number = self._count_module(
passage_vector)
if "passage_span_extraction" in self.answering_abilities:
passage_span_start_log_probs, passage_span_end_log_probs, best_passage_span = \
self._passage_span_module(passage_out, passage_mask)
if "question_span_extraction" in self.answering_abilities:
question_span_start_log_probs, question_span_end_log_probs, best_question_span = \
self._question_span_module(passage_vector, question_out, question_mask)
if "arithmetic" in self.answering_abilities:
if self.arithmetic == "base":
number_mask = (number_indices[:, :, 0].long() != -1).long()
number_sign_log_probs, best_signs_for_numbers, number_mask = \
self._base_arithmetic_module(passage_vector, passage_out, number_indices, number_mask)
else:
arithmetic_logits, best_expression = \
self._adv_arithmetic_module(arithmetic_summary, self.max_explen, options, options_mask, \
passage_out, pad_mask)
shapes = arithmetic_logits.shape
if (1 - (arithmetic_logits != arithmetic_logits)).sum() != (
shapes[0] * shapes[1] * shapes[2]):
print("bad logits")
arithmetic_logits = torch.rand(
shapes,
device=arithmetic_logits.device,
requires_grad=True)
output_dict = {}
del passage_out, question_out
# If answer is given, compute the loss.
if answer_as_passage_spans is not None or answer_as_question_spans is not None \
or answer_as_expressions is not None or answer_as_counts is not None:
log_marginal_likelihood_list = []
for answering_ability in self.answering_abilities:
if answering_ability == "passage_span_extraction":
log_marginal_likelihood_for_passage_span = \
self._passage_span_log_likelihood(answer_as_passage_spans,
passage_span_start_log_probs,
passage_span_end_log_probs)
log_marginal_likelihood_list.append(
log_marginal_likelihood_for_passage_span)
elif answering_ability == "question_span_extraction":
log_marginal_likelihood_for_question_span = \
self._question_span_log_likelihood(answer_as_question_spans,
question_span_start_log_probs,
question_span_end_log_probs)
log_marginal_likelihood_list.append(
log_marginal_likelihood_for_question_span)
elif answering_ability == "arithmetic":
if self.arithmetic == "base":
log_marginal_likelihood_for_arithmetic = \
self._base_arithmetic_log_likelihood(answer_as_expressions,
number_sign_log_probs,
number_mask,
answer_as_expressions_extra)
else:
max_explen = answer_as_expressions.shape[-1]
possible_exps = answer_as_expressions.shape[1]
limit = min(possible_exps, 1000)
log_marginal_likelihood_for_arithmetic = \
self._adv_arithmetic_log_likelihood(arithmetic_logits[:,:max_explen,:],
answer_as_expressions[:,:limit,:].long())
log_marginal_likelihood_list.append(
log_marginal_likelihood_for_arithmetic)
elif answering_ability == "counting":
log_marginal_likelihood_for_count = \
self._count_log_likelihood(answer_as_counts,
count_number_log_probs)
log_marginal_likelihood_list.append(
log_marginal_likelihood_for_count)
else:
raise ValueError(
f"Unsupported answering ability: {answering_ability}")
if len(self.answering_abilities) > 1:
# Add the ability probabilities if there are more than one abilities
all_log_marginal_likelihoods = torch.stack(
log_marginal_likelihood_list, dim=-1)
all_log_marginal_likelihoods = all_log_marginal_likelihoods + answer_ability_log_probs
marginal_log_likelihood = util.logsumexp(
all_log_marginal_likelihoods)
else:
marginal_log_likelihood = log_marginal_likelihood_list[0]
output_dict["loss"] = -marginal_log_likelihood.mean()
with torch.no_grad():
# Compute the metrics and add the tokenized input to the output.
if metadata is not None:
output_dict["question_id"] = []
output_dict["answer"] = []
question_tokens = []
passage_tokens = []
for i in range(batch_size):
if len(self.answering_abilities) > 1:
predicted_ability_str = self.answering_abilities[
best_answer_ability[i]]
else:
predicted_ability_str = self.answering_abilities[0]
answer_json: Dict[str, Any] = {}
# We did not consider multi-mention answers here
if predicted_ability_str == "passage_span_extraction":
answer_json["answer_type"] = "passage_span"
answer_json["value"], answer_json["spans"] = \
self._span_prediction(question_passage_tokens[i], best_passage_span[i])
elif predicted_ability_str == "question_span_extraction":
answer_json["answer_type"] = "question_span"
answer_json["value"], answer_json["spans"] = \
self._span_prediction(question_passage_tokens[i], best_question_span[i])
elif predicted_ability_str == "arithmetic": # plus_minus combination answer
answer_json["answer_type"] = "arithmetic"
original_numbers = metadata[i]['original_numbers']
if self.arithmetic == "base":
answer_json["value"], answer_json["numbers"] = \
self._base_arithmetic_prediction(original_numbers, number_indices[i], best_signs_for_numbers[i])
else:
answer_json["value"], answer_json["expression"] = \
self._adv_arithmetic_prediction(original_numbers, best_expression[i])
elif predicted_ability_str == "counting":
answer_json["answer_type"] = "count"
answer_json["value"], answer_json["count"] = \
self._count_prediction(best_count_number[i])
else:
raise ValueError(
f"Unsupported answer ability: {predicted_ability_str}"
)
output_dict["question_id"].append(
metadata[i]["question_id"])
output_dict["answer"].append(answer_json)
answer_annotations = metadata[i].get(
'answer_annotations', [])
if answer_annotations:
self._drop_metrics(answer_json["value"],
answer_annotations)
return output_dict
def forward(self, batch: torch.LongTensor) -> torch.FloatTensor:
"""Computes the loss function.
Args:
batch (torch.LongTensor): A batch of training data, as (batch-size x max-seq-len)-tensor.
Returns:
torch.FloatTensor: The computed loss.
"""
# sanitize args
insanity.sanitize_type("batch", batch, torch.Tensor)
if batch.dtype != torch.int64:
raise TypeError("<batch> has to be a LongTensor!")
if batch.dim() != 2:
raise ValueError("<batch> has to be a 2d tensor!")
# create the padding mask to use
padding_mask = util.create_padding_mask(batch, self._pad_index)
# create a tensor of indices, which is used to retrieve the according positional embeddings below
index_seq = batch.new(range(batch.size(1))).unsqueeze(0).expand(batch.size(0), -1)
# compute the sequence lengths for all samples in the batch
seq_len = (batch != self._pad_index).sum(dim=1).cpu().numpy().tolist()
# randomly choose the tokens to compute predictions for
pred_mask = padding_mask.new(*batch.size()).zero_().long() # all tokens being predicted
mask_mask = padding_mask.new(*batch.size()).zero_().long() # token replaced with <MASK>
random_mask = padding_mask.new(*batch.size()).zero_().long() # tokens replace with random tokens
for sample_idx, sample_len in enumerate(seq_len): # iterate over all samples in the batch
# determine how many tokens to computed predictions for
num_pred = int(math.ceil(sample_len * self._prediction_rate)) # num of tokens predictions are computed for
num_mask = int(math.floor(num_pred * self._mask_rate)) # num of tokens replaced with <MASK>
num_random = int(math.ceil(num_pred * self._random_rate)) # num of tokens randomly replaced
# randomly select indices to compute predictions for
pred_indices = list(range(sample_len))
random.shuffle(pred_indices)
pred_indices = pred_indices[:num_pred]
# prepare the <MASK>-mask
for token_idx in pred_indices[:num_mask]:
pred_mask[sample_idx, token_idx] = 1
mask_mask[sample_idx, token_idx] = 1
# prepare the random-mask
for token_idx in pred_indices[num_mask:(num_mask + num_random)]:
pred_mask[sample_idx, token_idx] = 1
random_mask[sample_idx, token_idx] = 1
# remaining tokens that predictions are computed for are left untouched
for token_idx in pred_indices[(num_mask + num_random):]:
pred_mask[sample_idx, token_idx] = 1
# replace predicted tokens in the batch appropriately
masked_batch = (
batch * (1 - mask_mask) * (1 - random_mask) +
mask_mask * batch.new(*batch.size()).fill_(self._mask_index) +
random_mask * (batch.new(*batch.size()).double().uniform_() * self._word_emb.num_embeddings).long()
)
# embed the batch
masked_batch = self._word_emb(masked_batch) + self._pos_emb(index_seq)
# encode sequence in the batch using BERT
enc = self._model(masked_batch, padding_mask)
# turn encodings, the target token indices (that we seek to predict), and the prediction mask, into matrices,
# such that each row corresponds with one token
enc = enc.view(enc.size(0) * enc.size(1), enc.size(2))
target = batch.view(-1)
pred_mask = pred_mask.view(-1)
# turn the prediction mask into a tensor of indices (to select below)
pred_mask = pred_mask.new(np.where(pred_mask.detach().cpu().numpy())[0])
# fetch embeddings and target values of those tokens that are being predicted
enc = enc.index_select(0, pred_mask)
target = target.index_select(0, pred_mask)
# compute predictions for each encoded token + the according loss
pred = self._output_layer(enc)
loss = self._loss(pred, target)
return loss
def __call__(self, prediction_labels: torch.Tensor,
gold_labels: torch.Tensor,
mask: torch.LongTensor) -> Dict[str, float]:
"""
计算 metric.
:param prediction_labels: 预测结果 shape: (B,), 这是模型解码成label的结果，注意是 label，不是 logits
:param gold_labels: 实际结果 shape: (B,)
:param mask: mask, shape: (B,)
:return 每一个label的f1值。这包括 precision, recall, f1。具体结果类似:
{"precision_[label]": [value],
"recall_[label]" : [value],
"f1-measure_[label]": [value],
"precision-overall": [value],
"recall-overall": [value],
"f1-measure-overall": [value]}
说明: "*-overall" 表示的是所有 命中 在初始化参数的 labels 的综合 metric 值。
这是有必要的，作为一个综合的值作为统一衡量。
"""
assert prediction_labels.dim() == 1, "predictions shape 是 (B,)"
assert gold_labels.dim() == 1, "gold_labels shape 是 (B,)"
if mask is not None:
assert mask.dim() == 1, "mask shape 是 (B,)"
# 转换到 cpu 进行计算
prediction_labels, gold_labels = prediction_labels.detach().cpu(
), gold_labels.detach().cpu()
if mask is not None:
bool_mask = mask.detach().cpu().bool()
prediction_labels = prediction_labels.masked_select(bool_mask)
gold_labels = gold_labels.masked_select(bool_mask)
# 当前 batch 下的 true_positives
true_positives = defaultdict(int)
false_positives = defaultdict(int)
false_negatives = defaultdict(int)
for label, label_index in zip(self._labels, self._label_indices):
num_prediction = (prediction_labels == label_index).sum().item()
num_golden = (gold_labels == label_index).sum().item()
# 计算 true positives
label_mask = (prediction_labels == label_index)
label_predictions = prediction_labels.masked_select(label_mask)
label_gold = gold_labels.masked_select(label_mask)
true_positives[label] = (
label_predictions == label_gold).sum().item()
false_positives[label] = num_prediction - true_positives[label]
false_negatives[label] = num_golden - true_positives[label]
for k, v in true_positives.items():
self._true_positives[k] += v
for k, v in false_positives.items():
self._false_positives[k] += v
for k, v in false_negatives.items():
self._false_negatives[k] += v
return self._metric(true_positives=true_positives,
false_positives=false_positives,
false_negatives=false_negatives)
def forward(self, input_seq: torch.LongTensor,
target: torch.LongTensor) -> torch.FloatTensor:
"""Runs the Transformer.
The Transformer expects both an input as well as a target sequence to be provided, and yields a probability
distribution over all possible output tokens for each position in the target sequence.
Args:
input_seq (torch.LongTensor): The input sequence as (batch-size x input-seq-len)-tensor.
target (torch.LongTensor): The target sequence as (batch-size x target-seq-len)-tensor.
Returns:
torch.FloatTensor: The computed probabilities for each position in ``target`` as a
(batch-size x target-seq-len x output-size)-tensor.
"""
# sanitize args
if not isinstance(input_seq, torch.LongTensor) and not isinstance(
input_seq, torch.cuda.LongTensor):
raise TypeError("<input_seq> has to be a LongTensor!")
if input_seq.dim() != 2:
raise ValueError("<input_seq> has to have 2 dimensions!")
if not isinstance(target, torch.LongTensor) and not isinstance(
target, torch.cuda.LongTensor):
raise TypeError("<target> has to be a LongTensor!")
if target.dim() != 2:
raise ValueError("<target> has to have 2 dimensions!")
# create a tensor of indices, which is used to retrieve the according positional embeddings below
index_seq = input_seq.new(range(
input_seq.size(1))).unsqueeze(0).expand(input_seq.size(0), -1)
# create padding mask for input
padding_mask = util.create_padding_mask(input_seq, self._pad_index)
# embed the provided input
input_seq = self._word_emb(input_seq) + self._positional_emb(index_seq)
# project input to the needed size
input_seq = self._input_projection(input_seq)
# run the encoder
input_seq = self._encoder(input_seq, padding_mask=padding_mask)
# create a tensor of indices, which is used to retrieve the positional embeddings for the targets below
index_seq = target.new(range(target.size(1))).unsqueeze(0).expand(
target.size(0), -1)
# embed the provided targets
target = self._word_emb(target) + self._positional_emb(index_seq)
# project target to the needed size
target = self._input_projection(target)
# run the decoder
output = self._decoder(input_seq, target, padding_mask=padding_mask)
# project output to the needed size
output = self._output_projection(output)
# compute softmax
return functional.softmax(output, dim=2)
def forward(self, # type: ignore
tokens: Dict[str, torch.LongTensor],
spans: torch.LongTensor,
metadata: List[Dict[str, Any]],
pos_tags: Dict[str, torch.LongTensor] = None,
span_labels: torch.LongTensor = None) -> Dict[str, torch.Tensor]:
# pylint: disable=arguments-differ
"""
Parameters
----------
tokens : Dict[str, torch.LongTensor], required
The output of ``TextField.as_array()``, which should typically be passed directly to a
``TextFieldEmbedder``. This output is a dictionary mapping keys to ``TokenIndexer``
tensors. At its most basic, using a ``SingleIdTokenIndexer`` this is: ``{"tokens":
Tensor(batch_size, num_tokens)}``. This dictionary will have the same keys as were used
for the ``TokenIndexers`` when you created the ``TextField`` representing your
sequence. The dictionary is designed to be passed directly to a ``TextFieldEmbedder``,
which knows how to combine different word representations into a single vector per
token in your input.
spans : ``torch.LongTensor``, required.
A tensor of shape ``(batch_size, num_spans, 2)`` representing the
inclusive start and end indices of all possible spans in the sentence.
metadata : List[Dict[str, Any]], required.
A dictionary of metadata for each batch element which has keys:
tokens : ``List[str]``, required.
The original string tokens in the sentence.
gold_tree : ``nltk.Tree``, optional (default = None)
Gold NLTK trees for use in evaluation.
pos_tags : ``List[str]``, optional.
The POS tags for the sentence. These can be used in the
model as embedded features, but they are passed here
in addition for use in constructing the tree.
pos_tags : ``torch.LongTensor``, optional (default = None)
The output of a ``SequenceLabelField`` containing POS tags.
span_labels : ``torch.LongTensor``, optional (default = None)
A torch tensor representing the integer gold class labels for all possible
spans, of shape ``(batch_size, num_spans)``.
Returns
-------
An output dictionary consisting of:
class_probabilities : ``torch.FloatTensor``
A tensor of shape ``(batch_size, num_spans, span_label_vocab_size)``
representing a distribution over the label classes per span.
spans : ``torch.LongTensor``
The original spans tensor.
tokens : ``List[List[str]]``, required.
A list of tokens in the sentence for each element in the batch.
pos_tags : ``List[List[str]]``, required.
A list of POS tags in the sentence for each element in the batch.
num_spans : ``torch.LongTensor``, required.
A tensor of shape (batch_size), representing the lengths of non-padded spans
in ``enumerated_spans``.
loss : ``torch.FloatTensor``, optional
A scalar loss to be optimised.
"""
embedded_text_input = self.text_field_embedder(tokens)
if pos_tags is not None and self.pos_tag_embedding is not None:
embedded_pos_tags = self.pos_tag_embedding(pos_tags)
embedded_text_input = torch.cat([embedded_text_input, embedded_pos_tags], -1)
elif self.pos_tag_embedding is not None:
raise ConfigurationError("Model uses a POS embedding, but no POS tags were passed.")
mask = get_text_field_mask(tokens)
# Looking at the span start index is enough to know if
# this is padding or not. Shape: (batch_size, num_spans)
span_mask = (spans[:, :, 0] >= 0).squeeze(-1).long()
if span_mask.dim() == 1:
# This happens if you use batch_size 1 and encounter
# a length 1 sentence in PTB, which do exist. -.-
span_mask = span_mask.unsqueeze(-1)
if span_labels is not None and span_labels.dim() == 1:
span_labels = span_labels.unsqueeze(-1)
num_spans = get_lengths_from_binary_sequence_mask(span_mask)
encoded_text = self.encoder(embedded_text_input, mask)
span_representations = self.span_extractor(encoded_text, spans, mask, span_mask)
if self.feedforward_layer is not None:
span_representations = self.feedforward_layer(span_representations)
logits = self.tag_projection_layer(span_representations)
class_probabilities = last_dim_softmax(logits, span_mask.unsqueeze(-1))
output_dict = {
"class_probabilities": class_probabilities,
"spans": spans,
"tokens": [meta["tokens"] for meta in metadata],
"pos_tags": [meta.get("pos_tags") for meta in metadata],
"num_spans": num_spans
}
if span_labels is not None:
loss = sequence_cross_entropy_with_logits(logits, span_labels, span_mask)
self.tag_accuracy(class_probabilities, span_labels, span_mask)
output_dict["loss"] = loss
# The evalb score is expensive to compute, so we only compute
# it for the validation and test sets.
batch_gold_trees = [meta.get("gold_tree") for meta in metadata]
if all(batch_gold_trees) and self._evalb_score is not None and not self.training:
gold_pos_tags: List[List[str]] = [list(zip(*tree.pos()))[1]
for tree in batch_gold_trees]
predicted_trees = self.construct_trees(class_probabilities.cpu().data,
spans.cpu().data,
num_spans.data,
output_dict["tokens"],
gold_pos_tags)
self._evalb_score(predicted_trees, batch_gold_trees)
return output_dict
def _compute_score(self, emissions: torch.Tensor, tags1: torch.LongTensor,
tags2: torch.LongTensor,
sims: torch.LongTensor) -> torch.Tensor:
# emissions: (seq_length, batch_size, num_tags)
# tags: (seq_length, batch_size)
assert emissions.dim() == 3 and tags1.dim() == 2 and tags2.dim(
) == 2 and sims.dim() == 2
assert emissions.shape[:2] == tags1.shape == tags2.shape == sims.shape
assert emissions.size(2) == self.num_tags
seq_length, batch_size = tags1.shape
# Start transition score and first emission
# shape: (batch_size,)
if sims[0]: #当前标签一样
score = self.start_transitions[tags1[0]]
score = score + emissions[0, torch.arange(batch_size), tags1[0]]
elif sims[1]: #当前标签不一样，下一个词标签一样
score1 = self.start_transitions[tags1[0]]
score2 = self.start_transitions[tags2[0]]
score1 = score1 + emissions[0, torch.arange(batch_size), tags1[0]]
score2 = score2 + emissions[0, torch.arange(batch_size), tags2[0]]
score = torch.logsumexp(torch.stack((score1, score2), 1), dim=1)
else: #当前标签不一样，下一个词标签也不一样
score1 = self.start_transitions[tags1[0]]
score2 = self.start_transitions[tags2[0]]
score1 = score1 + emissions[0, torch.arange(batch_size), tags1[0]]
score2 = score2 + emissions[0, torch.arange(batch_size), tags2[0]]
for i in range(1, seq_length - 1):
# Transition score to next tag, only added if next timestep is valid (mask == 1)
# shape: (batch_size,)
# Emission score for next tag, only added if next timestep is valid (mask == 1)
# shape: (batch_size,)
if sims[i]: #当前标签一样
score = score + self.transitions[tags1[i - 1], tags1[i]]
score = score + emissions[i,
torch.arange(batch_size), tags1[i]]
elif sims[i - 1] and sims[i + 1]: #上一个词标签一样,当前标签不一样，下一个词标签一样
score1 = score + self.transitions[tags1[i - 1], tags1[i]]
score1 = score1 + emissions[i,
torch.arange(batch_size), tags1[i]]
score2 = score + self.transitions[tags2[i - 1], tags2[i]]
score2 = score2 + emissions[i,
torch.arange(batch_size), tags2[i]]
score = torch.logsumexp(torch.stack((score1, score2), 1),
dim=1)
elif sims[i - 1]: #上一个词标签一样,当前标签不一样，下一个词标签不一样
score1 = score + self.transitions[tags1[i - 1], tags1[i]]
score1 = score1 + emissions[i,
torch.arange(batch_size), tags1[i]]
score2 = score + self.transitions[tags2[i - 1], tags2[i]]
score2 = score2 + emissions[i,
torch.arange(batch_size), tags2[i]]
elif sims[i + 1]: #上一个词标签不一样,当前标签不一样，下一个词标签一样
score1 = score1 + self.transitions[
tags1[i - 1], tags1[i]] + self.transitions[tags2[i - 1],
tags1[i]]
score1 = score1 + emissions[i,
torch.arange(batch_size), tags1[i]]
score2 = score2 + self.transitions[
tags2[i - 1], tags2[i]] + self.transitions[tags1[i - 1],
tags2[i]]
score2 = score2 + emissions[i,
torch.arange(batch_size), tags2[i]]
score = torch.logsumexp(torch.stack((score1, score2), 1),
dim=1)
else: #上一个词标签不一样,当前标签不一样，下一个词标签也不一样
score1 = score1 + self.transitions[
tags1[i - 1], tags1[i]] + self.transitions[tags2[i - 1],
tags1[i]]
score1 = score1 + emissions[i,
torch.arange(batch_size), tags1[i]]
score2 = score2 + self.transitions[
tags2[i - 1], tags2[i]] + self.transitions[tags1[i - 1],
tags2[i]]
score2 = score2 + emissions[i,
torch.arange(batch_size), tags2[i]]
# End transition score
# shape: (batch_size,)
seq_ends = seq_length - 1
if sims[seq_ends]: #当前标签一样
score = score + self.transitions[tags1[seq_ends - 1],
tags1[seq_ends]]
score = score + emissions[seq_ends,
torch.arange(batch_size),
tags1[seq_ends]]
# shape: (batch_size,)
last_tags = tags1[seq_ends, torch.arange(batch_size)]
# shape: (batch_size,)
score = score + self.end_transitions[last_tags]
elif sims[seq_ends - 1]: #上一个词标签一样,当前标签不一样
score1 = score + self.transitions[tags1[seq_ends - 1],
tags1[seq_ends]]
score1 = score1 + emissions[seq_ends,
torch.arange(batch_size),
tags1[seq_ends]]
last_tags1 = tags1[seq_ends, torch.arange(batch_size)]
score1 = score1 + self.end_transitions[last_tags1]
score2 = score + self.transitions[tags2[seq_ends - 1],
tags2[seq_ends]]
score2 = score2 + emissions[seq_ends,
torch.arange(batch_size),
tags2[seq_ends]]
last_tags2 = tags2[seq_ends, torch.arange(batch_size)]
score2 = score2 + self.end_transitions[last_tags2]
score = torch.logsumexp(torch.stack((score1, score2), 1), dim=1)
else: #上一个词标签不一样,当前标签不一样
score1 = score1 + self.transitions[
tags1[seq_ends - 1],
tags1[seq_ends]] + self.transitions[tags2[seq_ends - 1],
tags1[seq_ends]]
score1 = score1 + emissions[seq_ends,
torch.arange(batch_size),
tags1[seq_ends]]
last_tags1 = tags1[seq_ends, torch.arange(batch_size)]
score1 = score1 + self.end_transitions[last_tags1]
score2 = score2 + self.transitions[
tags2[seq_ends - 1],
tags2[seq_ends]] + self.transitions[tags1[seq_ends - 1],
tags2[seq_ends]]
score2 = score2 + emissions[seq_ends,
torch.arange(batch_size),
tags2[seq_ends]]
last_tags2 = tags2[seq_ends, torch.arange(batch_size)]
score2 = score2 + self.end_transitions[last_tags2]
score = torch.logsumexp(torch.stack((score1, score2), 1), dim=1)
return score
def compute_partial_decoded_loss(
self,
batch: Batch,
latent: torch.Tensor,
encoder_states: Tuple[torch.Tensor, ...],
cand_vecs: torch.LongTensor,
label_inds: torch.LongTensor,
) -> torch.Tensor:
"""
Compute partial loss from decoding outputs.
Here, we consider each partially decoded sequence as a separate
item from which to compute multiobjective scores.
:param batch:
batch being considered
:param latent:
decoder output representations
:param encoder_states:
encoder output representations
:param cand_vecs:
character candidate vectors
:param label_inds:
list of indices indicating which character is correct in the character candidates
:return partial_loss:
return loss for each batch item as a sum of the partial losses.
"""
assert self.opt['multiobjective_latent_representation'] == 'decoder_final_layer'
assert latent.dim() == 3 and latent.size(0) == cand_vecs.size(0)
bsz, seq_len, dim = latent.size()
seq_lens = []
partial_char_losses = []
seq_scores = []
stride_length = 2
for stride in range(0, bsz, stride_length): # arbitrary stride for now
# Compute new batches of items; latent reps, candidate vectors, etc.
end_idx = min(stride + stride_length, bsz)
new_bsz = batch.label_vec[stride:end_idx].ne(self.NULL_IDX).sum().item()
new_latent = latent.new(new_bsz, seq_len, dim).fill_(0)
new_cand_vecs = cand_vecs.new(new_bsz, *cand_vecs.shape[1:]).fill_(
self.NULL_IDX
)
if new_cand_vecs.dim() == 2:
new_cand_vecs = new_cand_vecs.unsqueeze(1).repeat(
1, cand_vecs.size(0), 1
)
new_label_inds = label_inds[stride:end_idx].new(new_bsz).fill_(0)
# For each batch item in the stride, we compute seq_length examples
# where each example represents a partial output of the decoder.
offset = 0
for i in range(stride, end_idx):
cand_vecs_i = cand_vecs if cand_vecs.dim() == 2 else cand_vecs[i]
seq_len_i = batch.label_vec[i].ne(self.NULL_IDX).sum().item()
seq_lens.append(seq_len_i)
for j in range(seq_len_i):
new_latent[offset + j, 0 : j + 1, :] = latent[
i : i + 1, 0 : j + 1, :
]
new_cand_vecs[offset : offset + seq_len_i] = cand_vecs_i
new_label_inds[offset : offset + seq_len_i] = label_inds[
i : i + 1
].repeat(seq_len_i)
offset += seq_len_i
assert isinstance(new_cand_vecs, torch.LongTensor)
seq_score = self.get_multiobjective_output(
new_latent, encoder_states, new_cand_vecs, 'partial'
)
partial_char_losses.append(
self.multiobj_criterion(seq_score, new_label_inds)
)
seq_scores.append(seq_score)
partial_char_loss = torch.cat(partial_char_losses, dim=0)
seq_scores = torch.cat(seq_scores, dim=0)
partial_char_loss_metric = partial_char_loss.new(bsz).fill_(0)
offset = 0
partial_char_scores = torch.zeros(
batch.batchsize,
batch.batchsize if cand_vecs.dim() == 2 else cand_vecs.size(1),
).to(latent)
for i in range(bsz):
partial_char_loss_metric[i] = partial_char_loss[
offset : offset + seq_lens[i]
].mean()
partial_char_scores[i] = seq_scores[
partial_char_loss[offset : offset + seq_lens[i]].argmin()
]
self.compute_multiobj_metrics(
partial_char_loss_metric, partial_char_scores, label_inds, prefix='partial'
)
return partial_char_loss
def forward(self,
sentence: LongTensor,
entity_tag: LongTensor,
event_type: LongTensor,
metadata: Dict = None) -> EventModelOutputs:
"""
模型运行
:param sentence: shape: (B, SeqLen), 句子的 index tensor
:param entity_tag: shape: (B, SeqLen), 句子的 实体 index tensor
:param event_type: shape: (B,), event type 的 tensor
:param metadata: metadata 数据，不参与模型运算
"""
assert sentence.dim(
) == 2, f"Sentence 的维度 {sentence.dim()} !=2, 应该是(B, seq_len)"
assert entity_tag.dim(
) == 2, f"entity_tag 维度 {entity_tag.dim()} != 2, 应该是 (B, seq_len)"
assert event_type.dim(
) == 1, f"event_type 维度 {event_type.dim()} != 1, 应该是 (B,)"
batch_size = sentence.size(0)
seq_len = sentence.size(1)
# sentence, entity_tag 使用的是同一个 mask
mask = nn_util.sequence_mask(
sentence, self._sentence_vocab.index(self._sentence_vocab.padding))
assert mask.shape == (batch_size, seq_len), f"mask 维度是: (B, seq_len)"
# shape: B * SeqLen * sentence_embedding_dim
sentence_embedding = self._sentence_embedder(sentence)
assert sentence_embedding.shape == (batch_size, seq_len,
self._sentence_embedding_dim)
# shape: B * SeqLen * entity_tag_embedding_dim
entity_tag_embedding = self._entity_tag_embedder(entity_tag)
assert entity_tag_embedding.shape == (batch_size, seq_len,
self._entity_tag_embedding_dim)
# shape: B * SeqLen * InputSize, InputSize = sentence_embedding_dim + entity_tag_embedding_dim
sentence_embedding = torch.cat(
(sentence_embedding, entity_tag_embedding), dim=-1)
assert sentence_embedding.shape, (batch_size, seq_len,
self._sentence_embedding_dim +
self._entity_tag_embedding_dim)
# 使用 mask 计算 sentence 实际长度, shape: (B,)
sentence_length = mask.long().sum(dim=-1)
assert sentence_length.shape == (batch_size, )
# 使用 lstm sequence encoder 进行 encoder
packed_sentence_embedding = pack_padded_sequence(
input=sentence_embedding,
lengths=sentence_length,
batch_first=True,
enforce_sorted=False)
packed_sequence, (h_n, c_n) = self._lstm(packed_sentence_embedding)
# Tuple, sentence: shape: B * SeqLen * InputSize 和 sentence length
(sentence_encoding, _) = pad_packed_sequence(packed_sequence,
batch_first=True)
assert sentence_encoding.shape == (batch_size, seq_len,
self._lstm_hidden_size)
# shape: B * InputSize
event_type_embedding_1: Tensor = self._event_type_embedder_1(
event_type)
assert event_type_embedding_1.shape == (batch_size,
self._event_type_embedding_dim)
# attention
# shape: B * InputSize * 1
event_type_embedding_1_tmp = event_type_embedding_1.unsqueeze(-1)
assert event_type_embedding_1_tmp.shape == (
batch_size, self._event_type_embedding_dim, 1)
# shape: (B * SeqLen * InputSize) bmm (B * InputSize * 1) = B * SeqLen * 1
attention_logits = sentence_encoding.bmm(event_type_embedding_1_tmp)
# shape: B * SeqLen
attention_logits = attention_logits.squeeze(-1)
assert attention_logits.shape == (batch_size, seq_len)
# Shape: B * SeqLen
tmp_attention_logits = torch.exp(attention_logits) * mask.float()
# Shape: B * Seqlen
tmp_attenttion_logits_sum = torch.sum(tmp_attention_logits,
dim=-1,
keepdim=True)
# Shape: B * SeqLen
attention = tmp_attention_logits / tmp_attenttion_logits_sum
assert attention.shape == (batch_size, seq_len)
# Score1 计算, Shape: B * 1
score1 = torch.sum(attention_logits * attention, dim=-1, keepdim=True)
assert score1.shape == (batch_size, 1)
score1 = score1.squeeze(dim=-1)
# global score
# 获取最后一个hidden, shape: B * INPUT_SIZE
hidden_last = h_n.squeeze(dim=0)
assert hidden_last.shape == (batch_size, self._lstm_hidden_size)
# event type 2, shape: B * INPUT_SIZE
event_type_embedding_2: Tensor = self._event_type_embedder_2(
event_type)
assert event_type_embedding_2.shape == (batch_size,
self._event_type_embedding_dim)
# shape: B * INPUT_SIZE
tmp = hidden_last * event_type_embedding_2
# shape: B * 1
score2 = torch.sum(tmp, dim=-1, keepdim=True)
assert score2.shape == (batch_size, 1)
score2 = score2.squeeze(dim=-1)
# 最终的score, B
score = score1 * self._alpha + score2 * (1 - self._alpha)
assert score.shape == (batch_size, )
if self._activate_score: # 使用 sigmoid函数激活
score = torch.sigmoid(score)
return EventModelOutputs(logits=score, event_type=event_type)
def forward(
self, # type: ignore
tokens: TextFieldTensors,
spans: torch.LongTensor,
metadata: List[Dict[str, Any]],
pos_tags: TextFieldTensors = None,
span_labels: torch.LongTensor = None,
) -> Dict[str, torch.Tensor]:
"""
# Parameters
tokens : `TextFieldTensors`, required
The output of `TextField.as_array()`, which should typically be passed directly to a
`TextFieldEmbedder`. This output is a dictionary mapping keys to `TokenIndexer`
tensors. At its most basic, using a `SingleIdTokenIndexer` this is : `{"tokens":
Tensor(batch_size, num_tokens)}`. This dictionary will have the same keys as were used
for the `TokenIndexers` when you created the `TextField` representing your
sequence. The dictionary is designed to be passed directly to a `TextFieldEmbedder`,
which knows how to combine different word representations into a single vector per
token in your input.
spans : `torch.LongTensor`, required.
A tensor of shape `(batch_size, num_spans, 2)` representing the
inclusive start and end indices of all possible spans in the sentence.
metadata : `List[Dict[str, Any]]`, required.
A dictionary of metadata for each batch element which has keys:
tokens : `List[str]`, required.
The original string tokens in the sentence.
gold_tree : `nltk.Tree`, optional (default = `None`)
Gold NLTK trees for use in evaluation.
pos_tags : `List[str]`, optional.
The POS tags for the sentence. These can be used in the
model as embedded features, but they are passed here
in addition for use in constructing the tree.
pos_tags : `torch.LongTensor`, optional (default = `None`)
The output of a `SequenceLabelField` containing POS tags.
span_labels : `torch.LongTensor`, optional (default = `None`)
A torch tensor representing the integer gold class labels for all possible
spans, of shape `(batch_size, num_spans)`.
# Returns
An output dictionary consisting of:
class_probabilities : `torch.FloatTensor`
A tensor of shape `(batch_size, num_spans, span_label_vocab_size)`
representing a distribution over the label classes per span.
spans : `torch.LongTensor`
The original spans tensor.
tokens : `List[List[str]]`, required.
A list of tokens in the sentence for each element in the batch.
pos_tags : `List[List[str]]`, required.
A list of POS tags in the sentence for each element in the batch.
num_spans : `torch.LongTensor`, required.
A tensor of shape (batch_size), representing the lengths of non-padded spans
in `enumerated_spans`.
loss : `torch.FloatTensor`, optional
A scalar loss to be optimised.
"""
embedded_text_input = self.text_field_embedder(tokens)
if pos_tags is not None and self.pos_tag_embedding is not None:
embedded_pos_tags = self.pos_tag_embedding(pos_tags)
embedded_text_input = torch.cat([embedded_text_input, embedded_pos_tags], -1)
elif self.pos_tag_embedding is not None:
raise ConfigurationError("Model uses a POS embedding, but no POS tags were passed.")
mask = get_text_field_mask(tokens)
# Looking at the span start index is enough to know if
# this is padding or not. Shape: (batch_size, num_spans)
span_mask = (spans[:, :, 0] >= 0).squeeze(-1)
if span_mask.dim() == 1:
# This happens if you use batch_size 1 and encounter
# a length 1 sentence in PTB, which do exist. -.-
span_mask = span_mask.unsqueeze(-1)
if span_labels is not None and span_labels.dim() == 1:
span_labels = span_labels.unsqueeze(-1)
num_spans = get_lengths_from_binary_sequence_mask(span_mask)
encoded_text = self.encoder(embedded_text_input, mask)
span_representations = self.span_extractor(encoded_text, spans, mask, span_mask)
if self.feedforward_layer is not None:
span_representations = self.feedforward_layer(span_representations)
logits = self.tag_projection_layer(span_representations)
class_probabilities = masked_softmax(logits, span_mask.unsqueeze(-1))
output_dict = {
"class_probabilities": class_probabilities,
"spans": spans,
"tokens": [meta["tokens"] for meta in metadata],
"pos_tags": [meta.get("pos_tags") for meta in metadata],
"num_spans": num_spans,
}
if span_labels is not None:
loss = sequence_cross_entropy_with_logits(logits, span_labels, span_mask)
self.tag_accuracy(class_probabilities, span_labels, span_mask)
output_dict["loss"] = loss
# The evalb score is expensive to compute, so we only compute
# it for the validation and test sets.
batch_gold_trees = [meta.get("gold_tree") for meta in metadata]
if all(batch_gold_trees) and self._evalb_score is not None and not self.training:
gold_pos_tags: List[List[str]] = [
list(zip(*tree.pos()))[1] for tree in batch_gold_trees
]
predicted_trees = self.construct_trees(
class_probabilities.cpu().data,
spans.cpu().data,
num_spans.data,
output_dict["tokens"],
gold_pos_tags,
)
self._evalb_score(predicted_trees, batch_gold_trees)
return output_dict
def forward(self, sentence: LongTensor, category: LongTensor) -> ACSAModelOutputs:
"""
模型运行
:param sentence: 句子的 index tensor
:param category: category index tensor
:return:
"""
assert sentence.dim() == 2, f"sentence dim: {sentence.dim()} 与 (batch_size, seq_len) 不匹配"
assert category.dim() == 1, f"category dim: {category.dim()} 与 （batch_size,) 不匹配"
bool_mask: BoolTensor = sequence_mask(sequence=sentence,
padding_index=self._token_vocabulary.padding_index)
long_mask = bool_mask.long()
sentence_length = long_mask.sum(dim=-1)
assert sentence_length.dim() == 1, f"sentence_length dim: {sentence_length.dim()} 与 (batch_size,) 不匹配"
# sentence embedding, shape: (batch_size, seq_len, embedding_dim)
sentence_embedding = self.token_embedding(sentence)
assert sentence_embedding.dim() == 3, \
f"sentence_embedding dim: {sentence_embedding.dim()} 与 (batch_size, seq_len, embedding_dim) 不匹配"
# 对 category expand，(batch_size,) -> (batch_size, seq_len)
# category.unsequeeze, (batch_size,) -> (batch_size, 1)
category = category.unsqueeze(dim=1)
# category.expand_as, (batch_size, 1) -> (batch_size, seq_len)
category = category.expand_as(sentence)
# category embedding, shape: (batch_size, seq_len, category_embedding_dim)
category_embedding = self.category_embedding(category)
assert category_embedding.dim() == 3, \
f"category_embedding dim: {category_embedding.dim()} 与 (batch_size, seq_len, category_embedding_dim) 不匹配"
# 将word embedding 与 category embedding 合并在一起
input_embedding = torch.cat((category_embedding, sentence_embedding), dim=-1)
# 使用 lstm sequence encoder 进行 encoder
packed_sentence_embedding = pack_padded_sequence(input=input_embedding,
lengths=sentence_length,
batch_first=True,
enforce_sorted=False)
packed_sequence, (h_n, c_n) = self.lstm(packed_sentence_embedding)
# Tuple, sentence: shape: B * SeqLen * InputSize 和 sentence length
(sentence_encoding, _) = pad_packed_sequence(packed_sequence, batch_first=True)
# h_n shape (num_layers * num_directions, batch_size, hidden_size)
h_n = torch.transpose(h_n, 0, 1)
last_index = -2 if self.lstm.bidirectional else -1
hidden_size = self.lstm.hidden_size * 2 if self.lstm.bidirectional else self.lstm.hidden_size
# hn_last shape: (batch_size, hidden_size * (1 or 2))
hn_last = h_n[:, last_index:, :].contiguous().view(-1, hidden_size)
# 将 lstm 输出与 aspect embedding 合并在一起，准备做 attention
# attention_inputs shape: (batch_size, seq_len, attention_dim = (lstm_hidden_size + category_dim))
attention_inputs = torch.cat((sentence_encoding, category_embedding), dim=-1)
# attention_seq_vec shape: (B, lstm_hidden_size + category_dim)
attention_seq_vec = self.attention_seq2vec(sequence=attention_inputs, mask=long_mask)
# sentiment_vec shape: (B, lstm_hidden_size + category_dim + lstm_hidden_size)
sentiment_vec = torch.cat((attention_seq_vec, hn_last), dim=-1)
logits = self.fc(sentiment_vec)
model_outputs = ACSAModelOutputs(logits=logits)
return model_outputs
def forward(
self, # type: ignore
tokens: Dict[str, torch.LongTensor],
spans: torch.LongTensor,
metadata: List[Dict[str, Any]],
pos_tags: Dict[str, torch.LongTensor] = None,
span_labels: torch.LongTensor = None) -> Dict[str, torch.Tensor]:
# pylint: disable=arguments-differ
"""
Parameters
----------
tokens : Dict[str, torch.LongTensor], required
The output of ``TextField.as_array()``, which should typically be passed directly to a
``TextFieldEmbedder``. This output is a dictionary mapping keys to ``TokenIndexer``
tensors. At its most basic, using a ``SingleIdTokenIndexer`` this is: ``{"tokens":
Tensor(batch_size, num_tokens)}``. This dictionary will have the same keys as were used
for the ``TokenIndexers`` when you created the ``TextField`` representing your
sequence. The dictionary is designed to be passed directly to a ``TextFieldEmbedder``,
which knows how to combine different word representations into a single vector per
token in your input.
spans : ``torch.LongTensor``, required.
A tensor of shape ``(batch_size, num_spans, 2)`` representing the
inclusive start and end indices of all possible spans in the sentence.
metadata : List[Dict[str, Any]], required.
A dictionary of metadata for each batch element which has keys:
tokens : ``List[str]``, required.
The original string tokens in the sentence.
gold_tree : ``nltk.Tree``, optional (default = None)
Gold NLTK trees for use in evaluation.
pos_tags : ``List[str]``, optional.
The POS tags for the sentence. These can be used in the
model as embedded features, but they are passed here
in addition for use in constructing the tree.
pos_tags : ``torch.LongTensor``, optional (default = None)
The output of a ``SequenceLabelField`` containing POS tags.
span_labels : ``torch.LongTensor``, optional (default = None)
A torch tensor representing the integer gold class labels for all possible
spans, of shape ``(batch_size, num_spans)``.
Returns
-------
An output dictionary consisting of:
class_probabilities : ``torch.FloatTensor``
A tensor of shape ``(batch_size, num_spans, span_label_vocab_size)``
representing a distribution over the label classes per span.
spans : ``torch.LongTensor``
The original spans tensor.
tokens : ``List[List[str]]``, required.
A list of tokens in the sentence for each element in the batch.
pos_tags : ``List[List[str]]``, required.
A list of POS tags in the sentence for each element in the batch.
num_spans : ``torch.LongTensor``, required.
A tensor of shape (batch_size), representing the lengths of non-padded spans
in ``enumerated_spans``.
loss : ``torch.FloatTensor``, optional
A scalar loss to be optimised.
"""
embedded_text_input = self.text_field_embedder(tokens)
if pos_tags is not None and self.pos_tag_embedding is not None:
embedded_pos_tags = self.pos_tag_embedding(pos_tags)
embedded_text_input = torch.cat(
[embedded_text_input, embedded_pos_tags], -1)
elif self.pos_tag_embedding is not None:
raise ConfigurationError(
"Model uses a POS embedding, but no POS tags were passed.")
mask = get_text_field_mask(tokens)
# Looking at the span start index is enough to know if
# this is padding or not. Shape: (batch_size, num_spans)
span_mask = (spans[:, :, 0] >= 0).squeeze(-1).long()
if span_mask.dim() == 1:
# This happens if you use batch_size 1 and encounter
# a length 1 sentence in PTB, which do exist. -.-
span_mask = span_mask.unsqueeze(-1)
if span_labels is not None and span_labels.dim() == 1:
span_labels = span_labels.unsqueeze(-1)
num_spans = get_lengths_from_binary_sequence_mask(span_mask)
encoded_text = self.encoder(embedded_text_input, mask)
encoder_final_state = get_final_encoder_states(encoded_text, mask)
output_dict = {
"encoder_final_state": encoder_final_state,
"encoded_text": encoded_text,
}
return output_dict
def decode(self, tokens: torch.LongTensor):
assert tokens.dim() == 1
tokens = list(tokens.cpu().numpy())
sentences = self.tokenizer.decode(tokens)
return sentences
