def _lcs_match(max_dist, n=n, m=m):
"""Longest-common-substring algorithm."""
f.fill(0)
g.clear()
### longest common sub sequence
# f[i, j] = max(f[i - 1, j], f[i, j - 1], f[i - 1, j - 1] + match(i, j))
for i in range(n):
# unlike standard LCS, this is specifically optimized for the setting
# because the mismatch between sentence pieces and original text will
# be small
for j in range(i - max_dist, i + max_dist):
if j >= m or j < 0:
continue
if i > 0:
g[(i, j)] = 0
f[i, j] = f[i - 1, j]
if j > 0 and f[i, j - 1] > f[i, j]:
g[(i, j)] = 1
f[i, j] = f[i, j - 1]
f_prev = f[i - 1, j - 1] if i > 0 and j > 0 else 0
if (tokenization.preprocess_text(paragraph_text[i],
lower=do_lower_case,
remove_space=False)
== tok_cat_text[j] and f_prev + 1 > f[i, j]):
g[(i, j)] = 2
f[i, j] = f_prev + 1
def convert_examples_to_features(examples,
tokenizer,
max_seq_length,
doc_stride,
max_query_length,
is_training,
output_fn,
do_lower_case,
xlnet_format=False,
batch_size=None):
"""Loads a data file into a list of `InputBatch`s."""
cnt_pos, cnt_neg = 0, 0
base_id = 1000000000
unique_id = base_id
max_n, max_m = 1024, 1024
f = np.zeros((max_n, max_m), dtype=np.float32)
for (example_index, example) in enumerate(examples):
if example_index % 100 == 0:
logging.info("Converting %d/%d pos %d neg %d", example_index,
len(examples), cnt_pos, cnt_neg)
query_tokens = tokenization.encode_ids(
tokenizer.sp_model,
tokenization.preprocess_text(example.question_text,
lower=do_lower_case))
if len(query_tokens) > max_query_length:
query_tokens = query_tokens[0:max_query_length]
paragraph_text = example.paragraph_text
para_tokens = tokenization.encode_pieces(
tokenizer.sp_model,
tokenization.preprocess_text(example.paragraph_text,
lower=do_lower_case))
chartok_to_tok_index = []
tok_start_to_chartok_index = []
tok_end_to_chartok_index = []
char_cnt = 0
for i, token in enumerate(para_tokens):
new_token = token.replace(tokenization.SPIECE_UNDERLINE, " ")
chartok_to_tok_index.extend([i] * len(new_token))
tok_start_to_chartok_index.append(char_cnt)
char_cnt += len(new_token)
tok_end_to_chartok_index.append(char_cnt - 1)
tok_cat_text = "".join(para_tokens).replace(
tokenization.SPIECE_UNDERLINE, " ")
n, m = len(paragraph_text), len(tok_cat_text)
if n > max_n or m > max_m:
max_n = max(n, max_n)
max_m = max(m, max_m)
f = np.zeros((max_n, max_m), dtype=np.float32)
g = {}
# pylint: disable=cell-var-from-loop
def _lcs_match(max_dist, n=n, m=m):
"""Longest-common-substring algorithm."""
f.fill(0)
g.clear()
### longest common sub sequence
# f[i, j] = max(f[i - 1, j], f[i, j - 1], f[i - 1, j - 1] + match(i, j))
for i in range(n):
# unlike standard LCS, this is specifically optimized for the setting
# because the mismatch between sentence pieces and original text will
# be small
for j in range(i - max_dist, i + max_dist):
if j >= m or j < 0:
continue
if i > 0:
g[(i, j)] = 0
f[i, j] = f[i - 1, j]
if j > 0 and f[i, j - 1] > f[i, j]:
g[(i, j)] = 1
f[i, j] = f[i, j - 1]
f_prev = f[i - 1, j - 1] if i > 0 and j > 0 else 0
if (tokenization.preprocess_text(paragraph_text[i],
lower=do_lower_case,
remove_space=False)
== tok_cat_text[j] and f_prev + 1 > f[i, j]):
g[(i, j)] = 2
f[i, j] = f_prev + 1
# pylint: enable=cell-var-from-loop
max_dist = abs(n - m) + 5
for _ in range(2):
_lcs_match(max_dist)
if f[n - 1, m - 1] > 0.8 * n:
break
max_dist *= 2
orig_to_chartok_index = [None] * n
chartok_to_orig_index = [None] * m
i, j = n - 1, m - 1
while i >= 0 and j >= 0:
if (i, j) not in g:
break
if g[(i, j)] == 2:
orig_to_chartok_index[i] = j
chartok_to_orig_index[j] = i
i, j = i - 1, j - 1
elif g[(i, j)] == 1:
j = j - 1
else:
i = i - 1
if (all(v is None for v in orig_to_chartok_index)
or f[n - 1, m - 1] < 0.8 * n):
logging.info("MISMATCH DETECTED!")
continue
tok_start_to_orig_index = []
tok_end_to_orig_index = []
for i in range(len(para_tokens)):
start_chartok_pos = tok_start_to_chartok_index[i]
end_chartok_pos = tok_end_to_chartok_index[i]
start_orig_pos = _convert_index(chartok_to_orig_index,
start_chartok_pos,
n,
is_start=True)
end_orig_pos = _convert_index(chartok_to_orig_index,
end_chartok_pos,
n,
is_start=False)
tok_start_to_orig_index.append(start_orig_pos)
tok_end_to_orig_index.append(end_orig_pos)
if not is_training:
tok_start_position = tok_end_position = None
if is_training and example.is_impossible:
tok_start_position = 0
tok_end_position = 0
if is_training and not example.is_impossible:
start_position = example.start_position
end_position = start_position + len(example.orig_answer_text) - 1
start_chartok_pos = _convert_index(orig_to_chartok_index,
start_position,
is_start=True)
tok_start_position = chartok_to_tok_index[start_chartok_pos]
end_chartok_pos = _convert_index(orig_to_chartok_index,
end_position,
is_start=False)
tok_end_position = chartok_to_tok_index[end_chartok_pos]
assert tok_start_position <= tok_end_position
def _piece_to_id(x):
return tokenizer.sp_model.PieceToId(x)
all_doc_tokens = list(map(_piece_to_id, para_tokens))
# The -3 accounts for [CLS], [SEP] and [SEP]
max_tokens_for_doc = max_seq_length - len(query_tokens) - 3
# We can have documents that are longer than the maximum sequence length.
# To deal with this we do a sliding window approach, where we take chunks
# of the up to our max length with a stride of `doc_stride`.
_DocSpan = collections.namedtuple( # pylint: disable=invalid-name
"DocSpan", ["start", "length"])
doc_spans = []
start_offset = 0
while start_offset < len(all_doc_tokens):
length = len(all_doc_tokens) - start_offset
if length > max_tokens_for_doc:
length = max_tokens_for_doc
doc_spans.append(_DocSpan(start=start_offset, length=length))
if start_offset + length == len(all_doc_tokens):
break
start_offset += min(length, doc_stride)
for (doc_span_index, doc_span) in enumerate(doc_spans):
tokens = []
token_is_max_context = {}
segment_ids = []
# Paragraph mask used in XLNet.
# 1 represents paragraph and class tokens.
# 0 represents query and other special tokens.
paragraph_mask = []
cur_tok_start_to_orig_index = []
cur_tok_end_to_orig_index = []
# pylint: disable=cell-var-from-loop
def process_query(seg_q):
for token in query_tokens:
tokens.append(token)
segment_ids.append(seg_q)
paragraph_mask.append(0)
tokens.append(tokenizer.sp_model.PieceToId("[SEP]"))
segment_ids.append(seg_q)
paragraph_mask.append(0)
def process_paragraph(seg_p):
for i in range(doc_span.length):
split_token_index = doc_span.start + i
cur_tok_start_to_orig_index.append(
tok_start_to_orig_index[split_token_index])
cur_tok_end_to_orig_index.append(
tok_end_to_orig_index[split_token_index])
is_max_context = _check_is_max_context(
doc_spans, doc_span_index, split_token_index)
token_is_max_context[len(tokens)] = is_max_context
tokens.append(all_doc_tokens[split_token_index])
segment_ids.append(seg_p)
paragraph_mask.append(1)
tokens.append(tokenizer.sp_model.PieceToId("[SEP]"))
segment_ids.append(seg_p)
paragraph_mask.append(0)
return len(tokens)
def process_class(seg_class):
class_index = len(segment_ids)
tokens.append(tokenizer.sp_model.PieceToId("[CLS]"))
segment_ids.append(seg_class)
paragraph_mask.append(1)
return class_index
if xlnet_format:
seg_p, seg_q, seg_class, seg_pad = 0, 1, 2, 3
paragraph_len = process_paragraph(seg_p)
process_query(seg_q)
class_index = process_class(seg_class)
else:
seg_p, seg_q, seg_class, seg_pad = 1, 0, 0, 0
class_index = process_class(seg_class)
process_query(seg_q)
paragraph_len = process_paragraph(seg_p)
input_ids = tokens
# The mask has 1 for real tokens and 0 for padding tokens. Only real
# tokens are attended to.
input_mask = [1] * len(input_ids)
# Zero-pad up to the sequence length.
while len(input_ids) < max_seq_length:
input_ids.append(0)
input_mask.append(0)
segment_ids.append(seg_pad)
paragraph_mask.append(0)
assert len(input_ids) == max_seq_length
assert len(input_mask) == max_seq_length
assert len(segment_ids) == max_seq_length
assert len(paragraph_mask) == max_seq_length
span_is_impossible = example.is_impossible
start_position = None
end_position = None
if is_training and not span_is_impossible:
# For training, if our document chunk does not contain an annotation
# we throw it out, since there is nothing to predict.
doc_start = doc_span.start
doc_end = doc_span.start + doc_span.length - 1
out_of_span = False
if not (tok_start_position >= doc_start
and tok_end_position <= doc_end):
out_of_span = True
if out_of_span:
# continue
start_position = 0
end_position = 0
span_is_impossible = True
else:
doc_offset = 0 if xlnet_format else len(query_tokens) + 2
start_position = tok_start_position - doc_start + doc_offset
end_position = tok_end_position - doc_start + doc_offset
if is_training and span_is_impossible:
start_position = class_index
end_position = class_index
if example_index < 20:
logging.info("*** Example ***")
logging.info("unique_id: %s", (unique_id))
logging.info("example_index: %s", (example_index))
logging.info("doc_span_index: %s", (doc_span_index))
logging.info(
"tok_start_to_orig_index: %s",
" ".join([str(x) for x in cur_tok_start_to_orig_index]))
logging.info(
"tok_end_to_orig_index: %s",
" ".join([str(x) for x in cur_tok_end_to_orig_index]))
logging.info(
"token_is_max_context: %s", " ".join([
"%d:%s" % (x, y)
for (x, y) in token_is_max_context.items()
]))
logging.info(
"input_pieces: %s",
" ".join([tokenizer.sp_model.IdToPiece(x)
for x in tokens]))
logging.info("input_ids: %s",
" ".join([str(x) for x in input_ids]))
logging.info("input_mask: %s",
" ".join([str(x) for x in input_mask]))
logging.info("segment_ids: %s",
" ".join([str(x) for x in segment_ids]))
logging.info("paragraph_mask: %s",
" ".join([str(x) for x in paragraph_mask]))
logging.info("class_index: %d", class_index)
if is_training and span_is_impossible:
logging.info("impossible example span")
if is_training and not span_is_impossible:
pieces = [
tokenizer.sp_model.IdToPiece(token)
for token in tokens[start_position:(end_position + 1)]
]
answer_text = tokenizer.sp_model.DecodePieces(pieces)
logging.info("start_position: %d", (start_position))
logging.info("end_position: %d", (end_position))
logging.info("answer: %s",
(tokenization.printable_text(answer_text)))
# With multi processing, the example_index is actually the index
# within the current process therefore we use example_index=None
# to avoid being used in the future.
# The current code does not use example_index of training data.
if is_training:
feat_example_index = None
else:
feat_example_index = example_index
feature = InputFeatures(
unique_id=unique_id,
example_index=feat_example_index,
doc_span_index=doc_span_index,
tok_start_to_orig_index=cur_tok_start_to_orig_index,
tok_end_to_orig_index=cur_tok_end_to_orig_index,
token_is_max_context=token_is_max_context,
tokens=[tokenizer.sp_model.IdToPiece(x) for x in tokens],
input_ids=input_ids,
input_mask=input_mask,
paragraph_mask=paragraph_mask,
segment_ids=segment_ids,
paragraph_len=paragraph_len,
class_index=class_index,
start_position=start_position,
end_position=end_position,
is_impossible=span_is_impossible)
# Run callback
if is_training:
output_fn(feature)
else:
output_fn(feature, is_padding=False)
unique_id += 1
if span_is_impossible:
cnt_neg += 1
else:
cnt_pos += 1
if not is_training and feature:
assert batch_size
num_padding = 0
num_examples = unique_id - base_id
if unique_id % batch_size != 0:
num_padding = batch_size - (num_examples % batch_size)
dummy_feature = copy.deepcopy(feature)
for _ in range(num_padding):
dummy_feature.unique_id = unique_id
# Run callback
output_fn(feature, is_padding=True)
unique_id += 1
logging.info("Total number of instances: %d = pos %d neg %d",
cnt_pos + cnt_neg, cnt_pos, cnt_neg)
return unique_id - base_id
