def _select_span_with_token(
text: str, tensorizer: Tensorizer, token_str: str = "[START_ENT]"
) -> T:
id = tensorizer.get_token_id(token_str)
query_tensor = tensorizer.text_to_tensor(text)
if id not in query_tensor:
query_tensor_full = tensorizer.text_to_tensor(text, apply_max_len=False)
token_indexes = (query_tensor_full == id).nonzero()
if token_indexes.size(0) > 0:
start_pos = token_indexes[0, 0].item()
# add some randomization to avoid overfitting to a specific token position
left_shit = int(tensorizer.max_length / 2)
rnd_shift = int((rnd.random() - 0.5) * left_shit / 2)
left_shit += rnd_shift
query_tensor = query_tensor_full[start_pos - left_shit :]
cls_id = tensorizer.tokenizer.cls_token_id
if query_tensor[0] != cls_id:
query_tensor = torch.cat([torch.tensor([cls_id]), query_tensor], dim=0)
from dpr.models.reader import _pad_to_len
query_tensor = _pad_to_len(
query_tensor, tensorizer.get_pad_id(), tensorizer.max_length
)
query_tensor[-1] = tensorizer.tokenizer.sep_token_id
# logger.info('aligned query_tensor %s', query_tensor)
assert id in query_tensor, "query_tensor={}".format(query_tensor)
return query_tensor
else:
raise RuntimeError(
"[START_ENT] toke not found for Entity Linking sample query={}".format(
text
)
)
else:
return query_tensor
def _create_question_passages_tensors(
wiki_data: TokenizedWikipediaPassages,
question_token_ids: np.ndarray,
tensorizer: Tensorizer,
positives: List[ReaderPassage],
negatives: List[ReaderPassage],
total_size: int,
empty_ids: T,
max_n_answers: int,
is_train: bool,
is_random: bool = True
):
max_len = empty_ids.size(0)
pad_token_id = tensorizer.get_pad_id()
if is_train:
# select just one positive
positive_idx = _get_positive_idx(positives, max_len, is_random)
if positive_idx is None:
return None
positive = positives[positive_idx]
if getattr(positive, "sequence_ids", None) is None:
# Load in passage tokens and title tokens
positive.load_tokens(
question_token_ids=question_token_ids,
**wiki_data.get_tokenized_data(int(positive.id))
)
sequence_ids, passage_offset = tensorizer.concatenate_inputs({
"question": positive.question_token_ids,
"passage_title": positive.title_token_ids,
"passage": positive.passage_token_ids,
}, get_passage_offset=True)
positive.sequence_ids = sequence_ids
positive.passage_offset = passage_offset
positive.answers_spans = [
(start + passage_offset, end + passage_offset)
for start, end in positive.answers_spans
]
positive_a_spans = _get_answer_spans(positive_idx, positives, max_len)[0: max_n_answers]
answer_starts = [span[0] for span in positive_a_spans]
answer_ends = [span[1] for span in positive_a_spans]
assert all(s < max_len for s in answer_starts)
assert all(e < max_len for e in answer_ends)
positive_input_ids = tensorizer.to_max_length(positive.sequence_ids.numpy(), apply_max_len=True)
positive_input_ids = torch.from_numpy(positive_input_ids)
answer_starts_tensor = torch.zeros((total_size, max_n_answers)).long()
answer_starts_tensor[0, 0:len(answer_starts)] = torch.tensor(answer_starts) # only first passage contains the answer
answer_ends_tensor = torch.zeros((total_size, max_n_answers)).long()
answer_ends_tensor[0, 0:len(answer_ends)] = torch.tensor(answer_ends) # only first passage contains the answer
answer_mask = torch.zeros((total_size, max_n_answers), dtype=torch.long)
answer_mask[0, 0:len(answer_starts)] = torch.tensor([1 for _ in range(len(answer_starts))])
positives_IDs: List[int] = [positive.id]
positives_selected = [positive_input_ids]
else:
positives_IDs: List[int] = []
positives_selected = []
answer_starts_tensor = None
answer_ends_tensor = None
answer_mask = None
positives_num = len(positives_selected)
negative_idxs = np.random.permutation(range(len(negatives))) if is_random else range(
len(negatives) - positives_num)
negative_idxs = negative_idxs[:total_size - positives_num]
negatives_IDs: List[int] = []
negatives_selected = []
for negative_idx in negative_idxs:
negative = negatives[negative_idx]
if getattr(negative, "sequence_ids", None) is None:
# Load in passage tokens and title tokens
negative.load_tokens(
question_token_ids=question_token_ids,
**wiki_data.get_tokenized_data(int(negative.id))
)
# Concatenate input tokens
sequence_ids, passage_offset = tensorizer.concatenate_inputs({
"question": negative.question_token_ids,
"passage_title": negative.title_token_ids,
"passage": negative.passage_token_ids,
}, get_passage_offset=True)
negative.sequence_ids = sequence_ids
negative.passage_offset = passage_offset
negatives_IDs.append(negative.id)
negative_input_ids = tensorizer.to_max_length(negative.sequence_ids.numpy(), apply_max_len=True)
negatives_selected.append(torch.from_numpy(negative_input_ids))
while len(negatives_selected) < total_size - positives_num:
negatives_IDs.append(-1)
negatives_selected.append(empty_ids.clone())
context_IDs = torch.tensor(positives_IDs + negatives_IDs, dtype=torch.int64)
input_ids = torch.stack([t for t in positives_selected + negatives_selected], dim=0)
assert len(context_IDs) == len(input_ids)
return context_IDs, input_ids, answer_starts_tensor, answer_ends_tensor, answer_mask
def create_reader_input(
wiki_data: TokenizedWikipediaPassages,
tensorizer: Tensorizer,
samples: List[ReaderSample],
passages_per_question: int,
max_length: int,
max_n_answers: int,
is_train: bool,
shuffle: bool,
) -> ReaderBatch:
"""
Creates a reader batch instance out of a list of ReaderSample-s. This is compatible with `GeneralDataset`.
:param wiki_data: all tokenized wikipedia passages
:param tensorizer: initialized tensorizer (which contains the tokenizer)
:param samples: list of samples to create the batch for
:param passages_per_question: amount of passages for every question in a batch
:param max_length: max model input sequence length
:param max_n_answers: max num of answers per single question
:param is_train: if the samples are for a train set
:param shuffle: should passages selection be randomized
:return: ReaderBatch instance
"""
context_IDs = []
input_ids = []
start_positions = []
end_positions = []
answers_masks = []
empty_sequence = torch.Tensor().new_full((max_length,), tensorizer.get_pad_id(), dtype=torch.long)
for sample in samples:
if is_train:
positive_ctxs = sample.positive_passages
negative_ctxs = sample.negative_passages
else:
positive_ctxs = []
negative_ctxs = sample.positive_passages + sample.negative_passages
# Need to re-sort samples based on their scores
negative_ctxs = sorted(negative_ctxs, key=lambda x: x.score, reverse=True)
question_token_ids = sample.question_token_ids
sample_tensors = _create_question_passages_tensors(
wiki_data,
question_token_ids,
tensorizer,
positive_ctxs,
negative_ctxs,
passages_per_question,
empty_sequence,
max_n_answers,
is_train,
is_random=shuffle
)
if not sample_tensors:
logger.warning('No valid passages combination for question=%s ', sample.question)
continue
context_ID, sample_input_ids, starts_tensor, ends_tensor, answer_mask = sample_tensors
context_IDs.append(context_ID)
input_ids.append(sample_input_ids)
if is_train:
start_positions.append(starts_tensor)
end_positions.append(ends_tensor)
answers_masks.append(answer_mask)
context_IDs = torch.cat([IDs.unsqueeze(0) for IDs in context_IDs], dim=0) # (N, M)
input_ids = torch.cat([ids.unsqueeze(0) for ids in input_ids], dim=0) # (N, M)
if is_train:
start_positions = torch.stack(start_positions, dim=0)
end_positions = torch.stack(end_positions, dim=0)
answers_masks = torch.stack(answers_masks, dim=0)
return ReaderBatch(context_IDs, input_ids, start_positions, end_positions, answers_masks)
