# coding=utf-8 # Copyright 2018 The Google AI Language Team Authors. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. """Run BERT on SQuAD 1.1 and SQuAD 2.0.""" import re import six import sys import math import json import random import collections import gc import numpy as np sys.path.append('.') import squad_utils from data_utils import SEP_ID, CLS_ID, VOCAB_SIZE import sentencepiece as spm from prepro_utils import preprocess_text, encode_ids, encode_pieces, printable_text SPIECE_UNDERLINE = u'▁' SEG_ID_P = 0 SEG_ID_Q = 1 SEG_ID_CLS = 2 SEG_ID_PAD = 3 class SquadExample(object): """A single training/test example for simple sequence classification. For examples without an answer, the start and end position are -1. """ def __init__(self, qas_id, question_text, paragraph_text, orig_answer_text=None, start_position=None, is_impossible=False): self.qas_id = qas_id self.question_text = question_text self.paragraph_text = paragraph_text self.orig_answer_text = orig_answer_text self.start_position = start_position self.is_impossible = is_impossible def __str__(self): return self.__repr__() def __repr__(self): s = "" s += "qas_id: %s" % (printable_text(self.qas_id)) s += ", question_text: %s" % ( 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 class InputFeatures(object): """A single set of features of data.""" def __init__(self, unique_id, example_index, doc_span_index, tok_start_to_orig_index, tok_end_to_orig_index, token_is_max_context, input_ids, input_mask, p_mask, segment_ids, paragraph_len, cls_index, start_position=None, end_position=None, is_impossible=None): self.unique_id = unique_id self.example_index = example_index self.doc_span_index = doc_span_index self.tok_start_to_orig_index = tok_start_to_orig_index self.tok_end_to_orig_index = tok_end_to_orig_index self.token_is_max_context = token_is_max_context self.input_ids = input_ids self.input_mask = input_mask self.p_mask = p_mask self.segment_ids = segment_ids self.paragraph_len = paragraph_len self.cls_index = cls_index self.start_position = start_position self.end_position = end_position self.is_impossible = is_impossible def read_squad_examples(sample, is_training): """Read a SQuAD json file into a list of SquadExample.""" examples = [] paragraph_text = sample["context"] paragraph_text = re.sub(r'\[TLE\]|\[DOC\]|\[PAR\]', '[SEP]', paragraph_text) for qa in sample["qas"]: qas_id = qa["qid"] question_text = qa["question"] start_position = None orig_answer_text = None is_impossible = False example = SquadExample( qas_id=qas_id, question_text=question_text, paragraph_text=paragraph_text) examples.append(example) return examples def _convert_index(index, pos, M=None, is_start=True): if index[pos] is not None: return index[pos] N = len(index) rear = pos while rear < N - 1 and index[rear] is None: rear += 1 front = pos while front > 0 and index[front] is None: front -= 1 assert index[front] is not None or index[rear] is not None if index[front] is None: if index[rear] >= 1: if is_start: return 0 else: return index[rear] - 1 return index[rear] if index[rear] is None: if M is not None and index[front] < M - 1: if is_start: return index[front] + 1 else: return M - 1 return index[front] if is_start: if index[rear] > index[front] + 1: return index[front] + 1 else: return index[rear] else: if index[rear] > index[front] + 1: return index[rear] - 1 else: return index[front] def convert_examples_to_features(examples, sp_model, max_seq_length, doc_stride, max_query_length, is_training, 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): if example_index % 100 == 0: print('Converting {}/{} pos {} neg {}'.format( example_index, len(examples), cnt_pos, cnt_neg)) query_tokens = encode_ids( sp_model, 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 = encode_pieces( sp_model, 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 = {} 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=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 < 0: print("*** Example ***") print("unique_id: %s" % (unique_id)) print("example_index: %s" % (example_index)) print("doc_span_index: %s" % (doc_span_index)) print("tok_start_to_orig_index: %s" % " ".join( [str(x) for x in cur_tok_start_to_orig_index])) print("tok_end_to_orig_index: %s" % " ".join( [str(x) for x in cur_tok_end_to_orig_index])) print("token_is_max_context: %s" % " ".join([ "%d:%s" % (x, y) for (x, y) in six.iteritems(token_is_max_context) ])) print("input_ids: %s" % " ".join([str(x) for x in input_ids])) print( "input_mask: %s" % " ".join([str(x) for x in input_mask])) print( "segment_ids: %s" % " ".join([str(x) for x in segment_ids])) if is_training and span_is_impossible: print("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) print("start_position: %d" % (start_position)) print("end_position: %d" % (end_position)) print( "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) unique_id += 1 if span_is_impossible: cnt_neg += 1 else: cnt_pos += 1 yield feature print("Total number of instances: {} = pos {} neg {}".format( cnt_pos + cnt_neg, cnt_pos, cnt_neg)) def _check_is_max_context(doc_spans, cur_span_index, position): """Check if this is the 'max context' doc span for the token.""" # Because of the sliding window approach taken to scoring documents, a single # token can appear in multiple documents. E.g. # Doc: the man went to the store and bought a gallon of milk # Span A: the man went to the # Span B: to the store and bought # Span C: and bought a gallon of # ... # # Now the word 'bought' will have two scores from spans B and C. We only # want to consider the score with "maximum context", which we define as # the *minimum* of its left and right context (the *sum* of left and # right context will always be the same, of course). # # In the example the maximum context for 'bought' would be span C since # it has 1 left context and 3 right context, while span B has 4 left context # and 0 right context. best_score = None best_span_index = None for (span_index, doc_span) in enumerate(doc_spans): end = doc_span.start + doc_span.length - 1 if position < doc_span.start: continue if position > end: continue num_left_context = position - doc_span.start num_right_context = end - position score = min(num_left_context, num_right_context) + 0.01 * doc_span.length if best_score is None or score > best_score: best_score = score best_span_index = span_index return cur_span_index == best_span_index class DataProcessor(object): def __init__(self, spiece_model_file, uncased, max_seq_length, doc_stride, max_query_length): self._sp_model = spm.SentencePieceProcessor() self._sp_model.Load(spiece_model_file) self._uncased = uncased self._max_seq_length = max_seq_length self._doc_stride = doc_stride self._max_query_length = max_query_length self.current_train_example = -1 self.num_train_examples = -1 self.current_train_epoch = -1 self.train_examples = None self.predict_examples = None self.num_examples = {'train': -1, 'predict': -1} def get_train_progress(self): """Gets progress for training phase.""" return self.current_train_example, self.current_train_epoch def get_examples(self, sample, is_training): examples = read_squad_examples( sample, is_training=is_training) return examples def get_num_examples(self, phase): if phase not in ['train', 'predict']: raise ValueError( "Unknown phase, which should be in ['train', 'predict'].") return self.num_examples[phase] def get_features(self, examples, is_training): features = convert_examples_to_features( examples=examples, sp_model=self._sp_model, max_seq_length=self._max_seq_length, doc_stride=self._doc_stride, max_query_length=self._max_query_length, is_training=is_training, uncased=self._uncased) return features def data_generator(self, sample, batch_size, phase='predict', shuffle=False, dev_count=1, epoch=1): self.predict_examples = self.get_examples( sample, is_training=False) examples = self.predict_examples self.num_examples['predict'] = len(self.predict_examples) def batch_reader(features, batch_size): batch = [] feats = [] for (index, feature) in enumerate(features): if phase == 'train': self.current_train_example = index + 1 labels = [feature.unique_id] if feature.start_position is None else [ feature.start_position, feature.end_position, feature.is_impossible ] example = [ feature.input_ids, feature.segment_ids, feature.input_mask, feature.cls_index, feature.p_mask ] + labels to_append = len(batch) < batch_size if to_append: batch.append(example) feats.append(feature) else: yield batch, feats batch = [example] feats = [feature] if len(batch) > 0: yield batch, feats def prepare_batch_data(insts): """Generate numpy tensors""" input_ids = np.expand_dims(np.array([inst[0] for inst in insts]).astype('int64'), axis=-1) segment_ids = np.array([inst[1] for inst in insts]).astype('int64') input_mask = np.array([inst[2] for inst in insts]).astype('float32') cls_index = np.expand_dims(np.array([inst[3] for inst in insts]).astype('int64'), axis=-1) p_mask = np.array([inst[4] for inst in insts]).astype('float32') ret_list = [input_ids, segment_ids, input_mask, cls_index, p_mask] if phase == 'train': start_positions = np.expand_dims(np.array([inst[5] for inst in insts]).astype('int64'), axis=-1) end_positions = np.expand_dims(np.array([inst[6] for inst in insts]).astype('int64'), axis=-1) is_impossible = np.expand_dims(np.array([inst[7] for inst in insts]).astype('float32'), axis=-1) ret_list += [start_positions, end_positions, is_impossible] else: unique_ids = np.expand_dims(np.array([inst[5] for inst in insts]).astype('int64'), axis=-1) ret_list += [unique_ids] return ret_list feature_gen = self.get_features(examples, is_training=False) all_dev_batches = [] features = [] for batch_insts, feats in batch_reader(feature_gen, batch_size): batch_data = prepare_batch_data(batch_insts) all_dev_batches.append(batch_data) features.extend(feats) return examples, features, all_dev_batches _PrelimPrediction = collections.namedtuple( # pylint: disable=invalid-name "PrelimPrediction", ["feature_index", "start_index", "end_index", "start_log_prob", "end_log_prob"]) _NbestPrediction = collections.namedtuple( # pylint: disable=invalid-name "NbestPrediction", ["text", "start_log_prob", "end_log_prob"]) def get_answers(all_examples, all_features, all_results, n_best_size, max_answer_length, start_n_top, end_n_top): """Write final predictions to the json file and log-odds of null if needed.""" example_index_to_features = collections.defaultdict(list) for feature in all_features: example_index_to_features[feature.example_index].append(feature) unique_id_to_result = {} for result in all_results: unique_id_to_result[result.unique_id] = result all_predictions = collections.OrderedDict() all_nbest_json = collections.OrderedDict() scores_diff_json = collections.OrderedDict() for (example_index, example) in enumerate(all_examples): features = example_index_to_features[example_index] prelim_predictions = [] # keep track of the minimum score of null start+end of position 0 score_null = 1000000 # large and positive for (feature_index, feature) in enumerate(features): result = unique_id_to_result[feature.unique_id] print("cls_logits", feature.unique_id, result.cls_logits) cur_null_score = result.cls_logits # if we could have irrelevant answers, get the min score of irrelevant score_null = min(score_null, cur_null_score) for i in range(start_n_top): for j in range(end_n_top): start_log_prob = result.start_top_log_probs[i] start_index = result.start_top_index[i] j_index = i * end_n_top + j end_log_prob = result.end_top_log_probs[j_index] end_index = result.end_top_index[j_index] # We could hypothetically create invalid predictions, e.g., predict # that the start of the span is in the question. We throw out all # invalid predictions. if start_index >= feature.paragraph_len - 1: continue if end_index >= feature.paragraph_len - 1: continue if not feature.token_is_max_context.get(start_index, False): continue if end_index < start_index: continue length = end_index - start_index + 1 if length > max_answer_length: continue prelim_predictions.append( _PrelimPrediction( feature_index=feature_index, start_index=start_index, end_index=end_index, start_log_prob=start_log_prob, end_log_prob=end_log_prob)) prelim_predictions = sorted( prelim_predictions, key=lambda x: (x.start_log_prob + x.end_log_prob), reverse=True) seen_predictions = {} nbest = [] for pred in prelim_predictions: if len(nbest) >= n_best_size: break feature = features[pred.feature_index] tok_start_to_orig_index = feature.tok_start_to_orig_index tok_end_to_orig_index = feature.tok_end_to_orig_index start_orig_pos = tok_start_to_orig_index[pred.start_index] end_orig_pos = tok_end_to_orig_index[pred.end_index] paragraph_text = example.paragraph_text final_text = paragraph_text[start_orig_pos: end_orig_pos + 1].strip() if final_text in seen_predictions: continue seen_predictions[final_text] = True nbest.append( _NbestPrediction( text=final_text, start_log_prob=pred.start_log_prob, end_log_prob=pred.end_log_prob)) # In very rare edge cases we could have no valid predictions. So we # just create a nonce prediction in this case to avoid failure. if not nbest: nbest.append( _NbestPrediction(text="", start_log_prob=-1e6, end_log_prob=-1e6)) total_scores = [] best_non_null_entry = None for entry in nbest: total_scores.append(entry.start_log_prob + entry.end_log_prob) if not best_non_null_entry: best_non_null_entry = entry probs = _compute_softmax(total_scores) nbest_json = [] for (i, entry) in enumerate(nbest): output = collections.OrderedDict() output["text"] = entry.text output["probability"] = probs[i] output["start_log_prob"] = entry.start_log_prob output["end_log_prob"] = entry.end_log_prob nbest_json.append(output) assert len(nbest_json) >= 1 assert best_non_null_entry is not None score_diff = score_null scores_diff_json[example.qas_id] = score_diff # note(zhiliny): always predict best_non_null_entry # and the evaluation script will search for the best threshold all_predictions[example.qas_id] = best_non_null_entry.text all_nbest_json[example.qas_id] = nbest_json return all_predictions, all_nbest_json # with open(output_prediction_file, "w") as writer: # writer.write(json.dumps(all_predictions, indent=4) + "\n") # with open(output_nbest_file, "w") as writer: # writer.write(json.dumps(all_nbest_json, indent=4) + "\n") # with open(output_null_log_odds_file, "w") as writer: # writer.write(json.dumps(scores_diff_json, indent=4) + "\n") # qid_to_has_ans = squad_utils.make_qid_to_has_ans(orig_data) # has_ans_qids = [k for k, v in qid_to_has_ans.items() if v] # no_ans_qids = [k for k, v in qid_to_has_ans.items() if not v] # exact_raw, f1_raw = squad_utils.get_raw_scores(orig_data, all_predictions) # out_eval = {} # squad_utils.find_all_best_thresh_v2(out_eval, all_predictions, exact_raw, f1_raw, # scores_diff_json, qid_to_has_ans) # return out_eval def _get_best_indexes(logits, n_best_size): """Get the n-best logits from a list.""" index_and_score = sorted(enumerate(logits), key=lambda x: x[1], reverse=True) best_indexes = [] for i in range(len(index_and_score)): if i >= n_best_size: break best_indexes.append(index_and_score[i][0]) return best_indexes def _compute_softmax(scores): """Compute softmax probability over raw logits.""" if not scores: return [] max_score = None for score in scores: if max_score is None or score > max_score: max_score = score exp_scores = [] total_sum = 0.0 for score in scores: x = math.exp(score - max_score) exp_scores.append(x) total_sum += x probs = [] for score in exp_scores: probs.append(score / total_sum) return probs if __name__ == '__main__': processor = DataProcessor(spiece_model_file="xlnet_cased_L-24_H-1024_A-16/spiece.model", uncased=False, max_seq_length=512, doc_stride=128, max_query_length=64) train_data_generator = processor.data_generator( data_path="squad_v2.0/dev-v2.0.json", batch_size=32, phase='predict', shuffle=True, dev_count=1, epoch=1) for (index, sample) in enumerate(train_data_generator()): if index < 10: print("index:", index) for tensor in sample: print(tensor.shape) else: break #for (index, example) in enumerate(train_examples): # if index < 5: # print(example)