def encode(self, text): """Encode text for XLNet""" processed_text = prepro_utils.preprocess_text(text, lower=self.lower_case) encoded_ids = prepro_utils.encode_ids(self.sp_processor, processed_text) return encoded_ids
def tokenize_fn(text): text = preprocess_text(text, lower=FLAGS.uncased) return encode_ids(sp, text)
def tokenize_fn(cls, text): text = preprocess_text(text, lower=False) return encode_ids(cls.tokenizer, text)
def _create_data(idx, input_paths): # Load sentence-piece model sp = spm.SentencePieceProcessor() sp.Load(FLAGS.sp_path) input_shards = [] total_line_cnt = 0 for input_path in input_paths: input_data, sent_ids = [], [] sent_id, line_cnt = True, 0 tf.logging.info("Processing %s", input_path) for line in tf.gfile.Open(input_path): if line_cnt % 100000 == 0: tf.logging.info("Loading line %d", line_cnt) line_cnt += 1 if not line.strip(): if FLAGS.use_eod: sent_id = not sent_id cur_sent = [EOD_ID] else: continue else: if FLAGS.from_raw_text: cur_sent = preprocess_text(line.strip(), lower=FLAGS.uncased) cur_sent = encode_ids(sp, cur_sent) else: cur_sent = list(map(int, line.strip().split())) input_data.extend(cur_sent) sent_ids.extend([sent_id] * len(cur_sent)) sent_id = not sent_id tf.logging.info("Finish with line %d", line_cnt) if line_cnt == 0: continue input_data = np.array(input_data, dtype=np.int64) sent_ids = np.array(sent_ids, dtype=np.bool) total_line_cnt += line_cnt input_shards.append((input_data, sent_ids)) tf.logging.info("[Task %d] Total number line: %d", idx, total_line_cnt) tfrecord_dir = os.path.join(FLAGS.save_dir, "tfrecords") filenames, num_batch = [], 0 # Randomly shuffle input shards (with a fixed but distinct random seed) np.random.seed(100 * FLAGS.task + FLAGS.pass_id) perm_indices = np.random.permutation(len(input_shards)) tf.logging.info("Using perm indices %s for pass %d", perm_indices.tolist(), FLAGS.pass_id) input_data_list, sent_ids_list = [], [] prev_sent_id = None for perm_idx in perm_indices: input_data, sent_ids = input_shards[perm_idx] # make sure the `send_ids[0] == not prev_sent_id` if prev_sent_id is not None and sent_ids[0] == prev_sent_id: sent_ids = np.logical_not(sent_ids) # append to temporary list input_data_list.append(input_data) sent_ids_list.append(sent_ids) # update `prev_sent_id` prev_sent_id = sent_ids[-1] input_data = np.concatenate(input_data_list) sent_ids = np.concatenate(sent_ids_list) file_name, cur_num_batch = create_tfrecords( save_dir=tfrecord_dir, basename="{}-{}-{}".format(FLAGS.split, idx, FLAGS.pass_id), data=[input_data, sent_ids], bsz_per_host=FLAGS.bsz_per_host, seq_len=FLAGS.seq_len, bi_data=FLAGS.bi_data, sp=sp, ) filenames.append(file_name) num_batch += cur_num_batch record_info = {"filenames": filenames, "num_batch": num_batch} return record_info
def convert_examples_to_features(examples, sp_model, max_seq_length, doc_stride, max_query_length, is_training, output_fn): """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): if example_index % 100 == 0: tf.logging.info('Converting {}/{} pos {} neg {}'.format( example_index, len(examples), cnt_pos, cnt_neg)) query_tokens = encode_ids( sp_model, preprocess_text(example.question_text, lower=FLAGS.uncased)) if len(query_tokens) > max_query_length: query_tokens = query_tokens[0:max_query_length] paragraph_text = example.paragraph_text para_tokens = encode_pieces( sp_model, preprocess_text(example.paragraph_text, lower=FLAGS.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 = {} def _lcs_match(max_dist): 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_text(paragraph_text[i], lower=FLAGS.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(SEG_ID_P) p_mask.append(0) paragraph_len = len(tokens) tokens.append(SEP_ID) segment_ids.append(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(SEG_ID_Q) p_mask.append(1) tokens.append(SEP_ID) segment_ids.append(SEG_ID_Q) p_mask.append(1) cls_index = len(segment_ids) tokens.append(CLS_ID) segment_ids.append(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(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(zhiliny): 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: tf.logging.info("*** Example ***") tf.logging.info("unique_id: %s" % (unique_id)) tf.logging.info("example_index: %s" % (example_index)) tf.logging.info("doc_span_index: %s" % (doc_span_index)) tf.logging.info( "tok_start_to_orig_index: %s" % " ".join([str(x) for x in cur_tok_start_to_orig_index])) tf.logging.info( "tok_end_to_orig_index: %s" % " ".join([str(x) for x in cur_tok_end_to_orig_index])) tf.logging.info("token_is_max_context: %s" % " ".join([ "%d:%s" % (x, y) for (x, y) in six.iteritems(token_is_max_context) ])) tf.logging.info("input_ids: %s" % " ".join([str(x) for x in input_ids])) tf.logging.info("input_mask: %s" % " ".join([str(x) for x in input_mask])) tf.logging.info("segment_ids: %s" % " ".join([str(x) for x in segment_ids])) if is_training and span_is_impossible: tf.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) tf.logging.info("start_position: %d" % (start_position)) tf.logging.info("end_position: %d" % (end_position)) tf.logging.info("answer: %s" % (printable_text(answer_text))) # note(zhiliny): 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 tf.logging.info("Total number of instances: {} = pos {} neg {}".format( cnt_pos + cnt_neg, cnt_pos, cnt_neg))
def tokenize_fn(text): text = putil.preprocess_text(text, lower=FLAG.uncased) return putil.encode_ids(sp, text)