# Copyright (c) 2019 PaddlePaddle Authors. All Rights Reserved. # # 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 MRQA""" import six import math import json import random import collections import numpy as np from utils import tokenization from utils.batching import prepare_batch_data def get_input_shape(args): """ define mrqa input shape """ train_input_shape = {"backbone": [([-1, args.max_seq_len, 1], 'int64'), ([-1, args.max_seq_len, 1], 'int64'), ([-1, args.max_seq_len, 1], 'int64'), ([-1, args.max_seq_len, 1], 'float32')], "task": [([-1, 1], 'int64'), ([-1, 1], 'int64')] } test_input_shape = {"backbone": [([-1, args.max_seq_len, 1], 'int64'), ([-1, args.max_seq_len, 1], 'int64'), ([-1, args.max_seq_len, 1], 'int64'), ([-1, args.max_seq_len, 1], 'float32')], "task": [([-1, 1], 'int64')] } return train_input_shape, test_input_shape class DataProcessor(object): def __init__(self, args): self._tokenizer = tokenization.FullTokenizer( vocab_file=args.vocab_path, do_lower_case=args.do_lower_case) self._max_seq_length = args.max_seq_len self._doc_stride = args.doc_stride self._max_query_length = args.max_query_length self._in_tokens = args.in_tokens self._train_file = args.train_file self._predict_file = args.predict_file self._batch_size = args.batch_size self._with_negative = args.with_negative self._epoch = args.epoch self._sample_rate = args.sample_rate self.vocab = self._tokenizer.vocab self.vocab_size = len(self.vocab) self.pad_id = self.vocab["[PAD]"] self.cls_id = self.vocab["[CLS]"] self.sep_id = self.vocab["[SEP]"] self.mask_id = self.vocab["[MASK]"] self.current_train_example = -1 self.num_train_examples = -1 self.current_train_epoch = -1 self.train_examples = None self.predict_examples = None self.predict_features = 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, data_path, is_training, with_negative=False): examples = read_mrqa_examples( input_file=data_path, is_training=is_training, with_negative=with_negative) return examples def get_num_examples(self): """Noted that this API Only support for Training phase.""" return estimate_runtime_examples(self._train_file, self._sample_rate, self._tokenizer, \ self._max_seq_length, self._doc_stride, self._max_query_length, \ remove_impossible_questions=True, filter_invalid_spans=True) def get_features(self, examples, is_training, n_print=0): features = convert_examples_to_features( examples=examples, tokenizer=self._tokenizer, max_seq_length=self._max_seq_length, doc_stride=self._doc_stride, max_query_length=self._max_query_length, is_training=is_training, n_print=n_print) return features def data_generator(self, phase='train', shuffle=False, dev_count=1): if phase == 'train': self.train_examples = self.get_examples( self._train_file, is_training=True, with_negative=self._with_negative) examples = self.train_examples self.num_examples['train'] = len(self.train_examples) elif phase == 'predict': self.predict_examples = self.get_examples( self._predict_file, is_training=False, with_negative=self._with_negative) examples = self.predict_examples self.num_examples['predict'] = len(self.predict_examples) else: raise ValueError( "Unknown phase, which should be in ['train', 'predict'].") def batch_reader(features, batch_size, in_tokens): batch, total_token_num, max_len = [], 0, 0 for (index, feature) in enumerate(features): if phase == 'train': self.current_train_example = index + 1 seq_len = len(feature.input_ids) labels = [feature.unique_id ] if feature.start_position is None else [ feature.start_position, feature.end_position ] example = [ feature.input_ids, feature.segment_ids, range(seq_len) ] + labels max_len = max(max_len, seq_len) if in_tokens: to_append = (len(batch) + 1) * max_len <= batch_size else: to_append = len(batch) < batch_size if to_append: batch.append(example) total_token_num += seq_len else: yield batch, total_token_num batch, total_token_num, max_len = [example ], seq_len, seq_len if len(batch) > 0: yield batch, total_token_num def wrapper(): if phase == "train": epoch = self._epoch else: epoch = 1 for epoch_index in range(epoch): if epoch_index == 0: n_print = 2 else: n_print = 0 if shuffle: random.shuffle(examples) if phase == 'train': self.current_train_epoch = epoch_index features = self.get_features(examples, is_training=True, n_print=n_print) else: features = self.get_features(examples, is_training=False, n_print=n_print) # CAUSIOUS! cannot be repeated called, 'cause it's a generator! all_dev_batches = [] for batch_data, total_token_num in batch_reader( features, self._batch_size, self._in_tokens): batch_data = prepare_batch_data( batch_data, total_token_num, max_len=self._max_seq_length, voc_size=-1, pad_id=self.pad_id, cls_id=self.cls_id, sep_id=self.sep_id, mask_id=-1, return_input_mask=True, return_max_len=False, return_num_token=False) if len(all_dev_batches) < dev_count: all_dev_batches.append(batch_data) if len(all_dev_batches) == dev_count: for batch in all_dev_batches: yield batch all_dev_batches = [] if phase == 'predict' and len(all_dev_batches) > 0: fake_batch = all_dev_batches[-1] fake_batch = fake_batch[:-1] + [np.array([-1]*len(fake_batch[0]))] all_dev_batches = all_dev_batches + [fake_batch] * (dev_count - len(all_dev_batches)) for batch in all_dev_batches: yield batch return wrapper def write_predictions(self, all_results, n_best_size, max_answer_length, do_lower_case, output_prediction_file, output_nbest_file, output_null_log_odds_file, with_negative, null_score_diff_threshold, verbose): """Write final predictions to the json file and log-odds of null if needed.""" print("Writing predictions to: %s" % (output_prediction_file)) print("Writing nbest to: %s" % (output_nbest_file)) all_examples = self.predict_examples all_features = self.get_features(all_examples, is_training=False, n_print=0) 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 _PrelimPrediction = collections.namedtuple( # pylint: disable=invalid-name "PrelimPrediction", [ "feature_index", "start_index", "end_index", "start_logit", "end_logit" ]) 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 min_null_feature_index = 0 # the paragraph slice with min mull score null_start_logit = 0 # the start logit at the slice with min null score null_end_logit = 0 # the end logit at the slice with min null score for (feature_index, feature) in enumerate(features): result = unique_id_to_result[feature.unique_id] start_indexes = _get_best_indexes(result.start_logits, n_best_size) end_indexes = _get_best_indexes(result.end_logits, n_best_size) # if we could have irrelevant answers, get the min score of irrelevant if with_negative: feature_null_score = result.start_logits[0] + result.end_logits[ 0] if feature_null_score < score_null: score_null = feature_null_score min_null_feature_index = feature_index null_start_logit = result.start_logits[0] null_end_logit = result.end_logits[0] for start_index in start_indexes: for end_index in end_indexes: # 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 >= len(feature.tokens): continue if end_index >= len(feature.tokens): continue if start_index not in feature.token_to_orig_map: continue if end_index not in feature.token_to_orig_map: 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_logit=result.start_logits[start_index], end_logit=result.end_logits[end_index])) if with_negative: prelim_predictions.append( _PrelimPrediction( feature_index=min_null_feature_index, start_index=0, end_index=0, start_logit=null_start_logit, end_logit=null_end_logit)) prelim_predictions = sorted( prelim_predictions, key=lambda x: (x.start_logit + x.end_logit), reverse=True) _NbestPrediction = collections.namedtuple( # pylint: disable=invalid-name "NbestPrediction", ["text", "start_logit", "end_logit"]) seen_predictions = {} nbest = [] for pred in prelim_predictions: if len(nbest) >= n_best_size: break feature = features[pred.feature_index] if pred.start_index > 0: # this is a non-null prediction tok_tokens = feature.tokens[pred.start_index:(pred.end_index + 1 )] orig_doc_start = feature.token_to_orig_map[pred.start_index] orig_doc_end = feature.token_to_orig_map[pred.end_index] orig_tokens = example.doc_tokens[orig_doc_start:(orig_doc_end + 1)] tok_text = " ".join(tok_tokens) # De-tokenize WordPieces that have been split off. tok_text = tok_text.replace(" ##", "") tok_text = tok_text.replace("##", "") # Clean whitespace tok_text = tok_text.strip() tok_text = " ".join(tok_text.split()) orig_text = " ".join(orig_tokens) final_text = get_final_text(tok_text, orig_text, do_lower_case, verbose) if final_text in seen_predictions: continue seen_predictions[final_text] = True else: final_text = "" seen_predictions[final_text] = True nbest.append( _NbestPrediction( text=final_text, start_logit=pred.start_logit, end_logit=pred.end_logit)) # if we didn't inlude the empty option in the n-best, inlcude it if with_negative: if "" not in seen_predictions: nbest.append( _NbestPrediction( text="", start_logit=null_start_logit, end_logit=null_end_logit)) # 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="empty", start_logit=0.0, end_logit=0.0)) assert len(nbest) >= 1 total_scores = [] best_non_null_entry = None for entry in nbest: total_scores.append(entry.start_logit + entry.end_logit) if not best_non_null_entry: if entry.text: best_non_null_entry = entry # debug if best_non_null_entry is None: print("Emmm..., sth wrong") 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_logit"] = entry.start_logit output["end_logit"] = entry.end_logit nbest_json.append(output) assert len(nbest_json) >= 1 if not with_negative: all_predictions[example.qas_id] = nbest_json[0]["text"] else: # predict "" iff the null score - the score of best non-null > threshold score_diff = score_null - best_non_null_entry.start_logit - ( best_non_null_entry.end_logit) scores_diff_json[example.qas_id] = score_diff if score_diff > null_score_diff_threshold: all_predictions[example.qas_id] = "" else: all_predictions[example.qas_id] = best_non_null_entry.text all_nbest_json[example.qas_id] = 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") if with_negative: with open(output_null_log_odds_file, "w") as writer: writer.write(json.dumps(scores_diff_json, indent=4) + "\n") class MRQAExample(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, doc_tokens, orig_answer_text=None, start_position=None, end_position=None, is_impossible=False): self.qas_id = qas_id self.question_text = question_text self.doc_tokens = doc_tokens self.orig_answer_text = orig_answer_text self.start_position = start_position self.end_position = end_position self.is_impossible = is_impossible def __str__(self): return self.__repr__() def __repr__(self): s = "" s += "qas_id: %s" % (tokenization.printable_text(self.qas_id)) s += ", question_text: %s" % ( tokenization.printable_text(self.question_text)) s += ", doc_tokens: [%s]" % (" ".join(self.doc_tokens)) if self.start_position: s += ", start_position: %d" % (self.start_position) if self.start_position: s += ", end_position: %d" % (self.end_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, tokens, token_to_orig_map, token_is_max_context, input_ids, input_mask, segment_ids, 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.tokens = tokens self.token_to_orig_map = token_to_orig_map self.token_is_max_context = token_is_max_context self.input_ids = input_ids self.input_mask = input_mask self.segment_ids = segment_ids self.start_position = start_position self.end_position = end_position self.is_impossible = is_impossible def read_mrqa_examples(input_file, is_training, with_negative=False): """Read a MRQA json file into a list of MRQAExample.""" phase = 'training' if is_training else 'testing' print("loading mrqa {} data...".format(phase)) with open(input_file, "r") as reader: input_data = json.load(reader)["data"] def is_whitespace(c): if c == " " or c == "\t" or c == "\r" or c == "\n" or ord(c) == 0x202F: return True return False examples = [] for entry in input_data: for paragraph in entry["paragraphs"]: paragraph_text = paragraph["context"] doc_tokens = [] char_to_word_offset = [] prev_is_whitespace = True for c in paragraph_text: if is_whitespace(c): prev_is_whitespace = True else: if prev_is_whitespace: doc_tokens.append(c) else: doc_tokens[-1] += c prev_is_whitespace = False char_to_word_offset.append(len(doc_tokens) - 1) for qa in paragraph["qas"]: qas_id = qa["id"] question_text = qa["question"] start_position = None end_position = None orig_answer_text = None is_impossible = False if is_training: if with_negative: is_impossible = qa["is_impossible"] if (len(qa["answers"]) != 1) and (not is_impossible): raise ValueError( "For training, each question should have exactly 1 answer." ) if not is_impossible: answer = qa["answers"][0] orig_answer_text = answer["text"] answer_offset = answer["answer_start"] answer_length = len(orig_answer_text) start_position = char_to_word_offset[answer_offset] end_position = char_to_word_offset[answer_offset + answer_length - 1] # Only add answers where the text can be exactly recovered from the # document. If this CAN'T happen it's likely due to weird Unicode # stuff so we will just skip the example. # # Note that this means for training mode, every example is NOT # guaranteed to be preserved. actual_text = " ".join(doc_tokens[start_position:( end_position + 1)]) cleaned_answer_text = " ".join( tokenization.whitespace_tokenize(orig_answer_text)) if actual_text.find(cleaned_answer_text) == -1: print("Could not find answer: '%s' vs. '%s'", actual_text, cleaned_answer_text) continue else: start_position = -1 end_position = -1 orig_answer_text = "" example = MRQAExample( qas_id=qas_id, question_text=question_text, doc_tokens=doc_tokens, orig_answer_text=orig_answer_text, start_position=start_position, end_position=end_position, is_impossible=is_impossible) examples.append(example) return examples def convert_examples_to_features( examples, tokenizer, max_seq_length, doc_stride, max_query_length, is_training, n_print=0 ): """Loads a data file into a list of `InputBatch`s.""" unique_id = 1000000000 for (example_index, example) in enumerate(examples): query_tokens = tokenizer.tokenize(example.question_text) if len(query_tokens) > max_query_length: query_tokens = query_tokens[0:max_query_length] tok_to_orig_index = [] orig_to_tok_index = [] all_doc_tokens = [] for (i, token) in enumerate(example.doc_tokens): orig_to_tok_index.append(len(all_doc_tokens)) sub_tokens = tokenizer.tokenize(token) for sub_token in sub_tokens: tok_to_orig_index.append(i) all_doc_tokens.append(sub_token) tok_start_position = None 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: tok_start_position = orig_to_tok_index[example.start_position] if example.end_position < len(example.doc_tokens) - 1: tok_end_position = orig_to_tok_index[example.end_position + 1] - 1 else: tok_end_position = len(all_doc_tokens) - 1 (tok_start_position, tok_end_position) = _improve_answer_span( all_doc_tokens, tok_start_position, tok_end_position, tokenizer, example.orig_answer_text) # 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_to_orig_map = {} token_is_max_context = {} segment_ids = [] tokens.append("[CLS]") segment_ids.append(0) for token in query_tokens: tokens.append(token) segment_ids.append(0) tokens.append("[SEP]") segment_ids.append(0) for i in range(doc_span.length): split_token_index = doc_span.start + i token_to_orig_map[len(tokens)] = tok_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(1) tokens.append("[SEP]") segment_ids.append(1) input_ids = tokenizer.convert_tokens_to_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) start_position = None end_position = None if is_training and not example.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: start_position = 0 end_position = 0 continue else: doc_offset = 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 example.is_impossible: start_position = 0 end_position = 0 if n_print > 0: n_print -= 1 print("*** Example ***") print("unique_id: %s" % (unique_id)) print("example_index: %s" % (example_index)) print("doc_span_index: %s" % (doc_span_index)) print("tokens: %s" % " ".join( [tokenization.printable_text(x) for x in tokens])) 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 example.is_impossible: print("impossible example") if is_training and not example.is_impossible: answer_text = " ".join(tokens[start_position:(end_position + 1)]) print("start_position: %d" % (start_position)) print("end_position: %d" % (end_position)) print("answer: %s" % (tokenization.printable_text(answer_text))) feature = InputFeatures( unique_id=unique_id, example_index=example_index, doc_span_index=doc_span_index, tokens=tokens, token_to_orig_map=token_to_orig_map, token_is_max_context=token_is_max_context, input_ids=input_ids, input_mask=input_mask, segment_ids=segment_ids, start_position=start_position, end_position=end_position, is_impossible=example.is_impossible) unique_id += 1 yield feature def estimate_runtime_examples(data_path, sample_rate, tokenizer, \ max_seq_length, doc_stride, max_query_length, \ remove_impossible_questions=True, filter_invalid_spans=True): """Count runtime examples which may differ from number of raw samples due to sliding window operation and etc.. This is useful to get correct warmup steps for training.""" assert sample_rate > 0.0 and sample_rate <= 1.0, "sample_rate must be set between 0.0~1.0" print("loading data with json parser...") with open(data_path, "r") as reader: data = json.load(reader)["data"] num_raw_examples = 0 for entry in data: for paragraph in entry["paragraphs"]: paragraph_text = paragraph["context"] for qa in paragraph["qas"]: num_raw_examples += 1 print("num raw examples:{}".format(num_raw_examples)) def is_whitespace(c): if c == " " or c == "\t" or c == "\r" or c == "\n" or ord(c) == 0x202F: return True return False sampled_examples = [] for entry in data: for paragraph in entry["paragraphs"]: doc_tokens = None for qa in paragraph["qas"]: if sampled_examples and random.random() > sample_rate and sample_rate < 1.0: continue if doc_tokens is None: paragraph_text = paragraph["context"] doc_tokens = [] char_to_word_offset = [] prev_is_whitespace = True for c in paragraph_text: if is_whitespace(c): prev_is_whitespace = True else: if prev_is_whitespace: doc_tokens.append(c) else: doc_tokens[-1] += c prev_is_whitespace = False char_to_word_offset.append(len(doc_tokens) - 1) assert len(qa["answers"]) == 1, "For training, each question should have exactly 1 answer." qas_id = qa["id"] question_text = qa["question"] start_position = None end_position = None orig_answer_text = None is_impossible = False if ('is_impossible' in qa) and (qa["is_impossible"]): if remove_impossible_questions or filter_invalid_spans: continue else: start_position = -1 end_position = -1 orig_answer_text = "" is_impossible = True else: answer = qa["answers"][0] orig_answer_text = answer["text"] answer_offset = answer["answer_start"] answer_length = len(orig_answer_text) start_position = char_to_word_offset[answer_offset] end_position = char_to_word_offset[answer_offset + answer_length - 1] # remove corrupt samples actual_text = " ".join(doc_tokens[start_position:( end_position + 1)]) cleaned_answer_text = " ".join( tokenization.whitespace_tokenize(orig_answer_text)) if actual_text.find(cleaned_answer_text) == -1: print("Could not find answer: '%s' vs. '%s'", actual_text, cleaned_answer_text) continue example = MRQAExample( qas_id=qas_id, question_text=question_text, doc_tokens=doc_tokens, orig_answer_text=orig_answer_text, start_position=start_position, end_position=end_position, is_impossible=is_impossible) sampled_examples.append(example) runtime_sample_rate = len(sampled_examples) / float(num_raw_examples) runtime_samp_cnt = 0 for example in sampled_examples: query_tokens = tokenizer.tokenize(example.question_text) if len(query_tokens) > max_query_length: query_tokens = query_tokens[0:max_query_length] tok_to_orig_index = [] orig_to_tok_index = [] all_doc_tokens = [] for (i, token) in enumerate(example.doc_tokens): orig_to_tok_index.append(len(all_doc_tokens)) sub_tokens = tokenizer.tokenize(token) for sub_token in sub_tokens: tok_to_orig_index.append(i) all_doc_tokens.append(sub_token) tok_start_position = None tok_end_position = None tok_start_position = orig_to_tok_index[example.start_position] if example.end_position < len(example.doc_tokens) - 1: tok_end_position = orig_to_tok_index[example.end_position + 1] - 1 else: tok_end_position = len(all_doc_tokens) - 1 (tok_start_position, tok_end_position) = _improve_answer_span( all_doc_tokens, tok_start_position, tok_end_position, tokenizer, example.orig_answer_text) # The -3 accounts for [CLS], [SEP] and [SEP] max_tokens_for_doc = max_seq_length - len(query_tokens) - 3 _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): doc_start = doc_span.start doc_end = doc_span.start + doc_span.length - 1 if filter_invalid_spans and not (tok_start_position >= doc_start and tok_end_position <= doc_end): continue runtime_samp_cnt += 1 return int(runtime_samp_cnt/runtime_sample_rate) def _improve_answer_span(doc_tokens, input_start, input_end, tokenizer, orig_answer_text): """Returns tokenized answer spans that better match the annotated answer.""" # The MRQA annotations are character based. We first project them to # whitespace-tokenized words. But then after WordPiece tokenization, we can # often find a "better match". For example: # # Question: What year was John Smith born? # Context: The leader was John Smith (1895-1943). # Answer: 1895 # # The original whitespace-tokenized answer will be "(1895-1943).". However # after tokenization, our tokens will be "( 1895 - 1943 ) .". So we can match # the exact answer, 1895. # # However, this is not always possible. Consider the following: # # Question: What country is the top exporter of electornics? # Context: The Japanese electronics industry is the lagest in the world. # Answer: Japan # # In this case, the annotator chose "Japan" as a character sub-span of # the word "Japanese". Since our WordPiece tokenizer does not split # "Japanese", we just use "Japanese" as the annotation. This is fairly rare # in MRQA, but does happen. tok_answer_text = " ".join(tokenizer.tokenize(orig_answer_text)) for new_start in range(input_start, input_end + 1): for new_end in range(input_end, new_start - 1, -1): text_span = " ".join(doc_tokens[new_start:(new_end + 1)]) if text_span == tok_answer_text: return (new_start, new_end) return (input_start, input_end) 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 def get_final_text(pred_text, orig_text, do_lower_case, verbose): """Project the tokenized prediction back to the original text.""" # When we created the data, we kept track of the alignment between original # (whitespace tokenized) tokens and our WordPiece tokenized tokens. So # now `orig_text` contains the span of our original text corresponding to the # span that we predicted. # # However, `orig_text` may contain extra characters that we don't want in # our prediction. # # For example, let's say: # pred_text = steve smith # orig_text = Steve Smith's # # We don't want to return `orig_text` because it contains the extra "'s". # # We don't want to return `pred_text` because it's already been normalized # (the MRQA eval script also does punctuation stripping/lower casing but # our tokenizer does additional normalization like stripping accent # characters). # # What we really want to return is "Steve Smith". # # Therefore, we have to apply a semi-complicated alignment heruistic between # `pred_text` and `orig_text` to get a character-to-charcter alignment. This # can fail in certain cases in which case we just return `orig_text`. def _strip_spaces(text): ns_chars = [] ns_to_s_map = collections.OrderedDict() for (i, c) in enumerate(text): if c == " ": continue ns_to_s_map[len(ns_chars)] = i ns_chars.append(c) ns_text = "".join(ns_chars) return (ns_text, ns_to_s_map) # We first tokenize `orig_text`, strip whitespace from the result # and `pred_text`, and check if they are the same length. If they are # NOT the same length, the heuristic has failed. If they are the same # length, we assume the characters are one-to-one aligned. tokenizer = tokenization.BasicTokenizer(do_lower_case=do_lower_case) tok_text = " ".join(tokenizer.tokenize(orig_text)) start_position = tok_text.find(pred_text) if start_position == -1: if verbose: print("Unable to find text: '%s' in '%s'" % (pred_text, orig_text)) return orig_text end_position = start_position + len(pred_text) - 1 (orig_ns_text, orig_ns_to_s_map) = _strip_spaces(orig_text) (tok_ns_text, tok_ns_to_s_map) = _strip_spaces(tok_text) if len(orig_ns_text) != len(tok_ns_text): if verbose: print("Length not equal after stripping spaces: '%s' vs '%s'", orig_ns_text, tok_ns_text) return orig_text # We then project the characters in `pred_text` back to `orig_text` using # the character-to-character alignment. tok_s_to_ns_map = {} for (i, tok_index) in six.iteritems(tok_ns_to_s_map): tok_s_to_ns_map[tok_index] = i orig_start_position = None if start_position in tok_s_to_ns_map: ns_start_position = tok_s_to_ns_map[start_position] if ns_start_position in orig_ns_to_s_map: orig_start_position = orig_ns_to_s_map[ns_start_position] if orig_start_position is None: if verbose: print("Couldn't map start position") return orig_text orig_end_position = None if end_position in tok_s_to_ns_map: ns_end_position = tok_s_to_ns_map[end_position] if ns_end_position in orig_ns_to_s_map: orig_end_position = orig_ns_to_s_map[ns_end_position] if orig_end_position is None: if verbose: print("Couldn't map end position") return orig_text output_text = orig_text[orig_start_position:(orig_end_position + 1)] return output_text 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__': train_file = 'data/mrqa-combined.all_dev.raw.json' vocab_file = 'uncased_L-12_H-768_A-12/vocab.txt' do_lower_case = True tokenizer = tokenization.FullTokenizer( vocab_file=vocab_file, do_lower_case=do_lower_case) train_examples = read_mrqa_examples( input_file=train_file, is_training=True) print("begin converting") for (index, feature) in enumerate( convert_examples_to_features( examples=train_examples, tokenizer=tokenizer, max_seq_length=384, doc_stride=128, max_query_length=64, is_training=True, )): if index < 10: print(index, feature.input_ids, feature.input_mask, feature.segment_ids)