def __repr__(self):
s = ""
s += "qas_id: %s" % (preprocess_utils.printable_text(self.qas_id))
s += ", question_text: %s" % (preprocess_utils.printable_text(
self.question_text))
s += ", paragraph_text: [%s]" % (" ".join(self.paragraph_text))
if self.start_position:
s += ", start_position: %d" % (self.start_position)
if self.start_position:
s += ", is_impossible: %r" % (self.is_impossible)
return s
def convert_examples_to_features(examples, sp_model, max_seq_length,
doc_stride, max_query_length, is_training,
output_fn, uncased):
"""Loads a data file into a list of `InputBatch`s."""
cnt_pos, cnt_neg = 0, 0
unique_id = 1000000000
max_N, max_M = 1024, 1024
f = np.zeros((max_N, max_M), dtype=np.float32)
for (example_index, example) in enumerate(examples):
# pylint: disable=logging-format-interpolation
if example_index % 100 == 0:
logging.info("Converting {}/{} pos {} neg {}".format(
example_index, len(examples), cnt_pos, cnt_neg))
query_tokens = preprocess_utils.encode_ids(
sp_model,
preprocess_utils.preprocess_text(example.question_text,
lower=uncased))
if len(query_tokens) > max_query_length:
query_tokens = query_tokens[0:max_query_length]
paragraph_text = example.paragraph_text
para_tokens = preprocess_utils.encode_pieces(
sp_model,
preprocess_utils.preprocess_text(example.paragraph_text,
lower=uncased))
chartok_to_tok_index = []
tok_start_to_chartok_index = []
tok_end_to_chartok_index = []
char_cnt = 0
for i, token in enumerate(para_tokens):
chartok_to_tok_index.extend([i] * len(token))
tok_start_to_chartok_index.append(char_cnt)
char_cnt += len(token)
tok_end_to_chartok_index.append(char_cnt - 1)
tok_cat_text = "".join(para_tokens).replace(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)
gc.collect()
g = {}
# pylint: disable=cell-var-from-loop
def _lcs_match(max_dist):
"""LCS match."""
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):
# note(zhiliny):
# 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 (preprocess_utils.preprocess_text(
paragraph_text[i], lower=uncased,
remove_space=False) == tok_cat_text[j]
and f_prev + 1 > f[i, j]):
g[(i, j)] = 2
f[i, j] = f_prev + 1
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:
print("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 = -1
tok_end_position = -1
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):
if six.PY2 and isinstance(x, unicode):
x = x.encode("utf-8")
return 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 = []
p_mask = []
cur_tok_start_to_orig_index = []
cur_tok_end_to_orig_index = []
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(data_utils.SEG_ID_P)
p_mask.append(0)
paragraph_len = len(tokens)
tokens.append(data_utils.SEP_ID)
segment_ids.append(data_utils.SEG_ID_P)
p_mask.append(1)
# note(zhiliny): we put P before Q
# because during pretraining, B is always shorter than A
for token in query_tokens:
tokens.append(token)
segment_ids.append(data_utils.SEG_ID_Q)
p_mask.append(1)
tokens.append(data_utils.SEP_ID)
segment_ids.append(data_utils.SEG_ID_Q)
p_mask.append(1)
cls_index = len(segment_ids)
tokens.append(data_utils.CLS_ID)
segment_ids.append(data_utils.SEG_ID_CLS)
p_mask.append(0)
input_ids = tokens
# The mask has 0 for real tokens and 1 for padding tokens. Only real
# tokens are attended to.
input_mask = [0] * len(input_ids)
# Zero-pad up to the sequence length.
while len(input_ids) < max_seq_length:
input_ids.append(0)
input_mask.append(1)
segment_ids.append(data_utils.SEG_ID_PAD)
p_mask.append(1)
assert len(input_ids) == max_seq_length
assert len(input_mask) == max_seq_length
assert len(segment_ids) == max_seq_length
assert len(p_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:
# note: we put P before Q, so doc_offset should be zero.
# doc_offset = len(query_tokens) + 2
doc_offset = 0
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 = cls_index
end_position = cls_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 six.iteritems(token_is_max_context)
]))
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]))
if is_training and span_is_impossible:
logging.info("impossible example span")
if is_training and not span_is_impossible:
pieces = [
sp_model.IdToPiece(token)
for token in tokens[start_position:(end_position + 1)]
]
answer_text = sp_model.DecodePieces(pieces)
logging.info("start_position: %d", start_position)
logging.info("end_position: %d", end_position)
logging.info("answer: %s",
preprocess_utils.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,
input_ids=input_ids,
input_mask=input_mask,
p_mask=p_mask,
segment_ids=segment_ids,
paragraph_len=paragraph_len,
cls_index=cls_index,
start_position=start_position,
end_position=end_position,
is_impossible=span_is_impossible)
# Run callback
output_fn(feature)
unique_id += 1
if span_is_impossible:
cnt_neg += 1
else:
cnt_pos += 1
logging.info("Total number of instances: %d = pos %d + neg %d",
cnt_pos + cnt_neg, cnt_pos, cnt_neg)