| # 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. |
| """ |
| Inference for squad/bert using onnx. |
| |
| This is going to do the samem as 'python run_squad.py --do_predict=True ...' using a squad/bert model |
| that was converted to onnx. Lots of code was taken from run_squad.py. |
| You run it with: |
| |
| |
| python onnx_squad.py --model $SQUAD_MODEL/squad.onnx \ |
| --vocab_file $BERT_BASE_DIR/uncased_L-12_H-768_A-12/vocab.txt |
| --predict_file $SQUAD_DATA/dev-v1.1.json \ |
| --bert_config_file $BERT_BASE_DIR/uncased_L-12_H-768_A-12/bert_config.json \ |
| --output /tmp/ |
| """ |
| |
| import collections |
| import json |
| import math |
| |
| import numpy as np |
| import six |
| import tokenization |
| |
| RawResult = collections.namedtuple("RawResult", |
| ["unique_id", "start_logits", "end_logits"]) |
| |
| Feature = collections.namedtuple("Feature", [ |
| "unique_id", "tokens", "example_index", "token_to_orig_map", |
| "token_is_max_context" |
| ]) |
| |
| |
| class SquadExample(object): |
| """A single training/test example for simple sequence classification.""" |
| |
| def __init__(self, |
| qas_id, |
| question_text, |
| doc_tokens, |
| orig_answer_text=None, |
| start_position=None, |
| end_position=None): |
| 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 |
| |
| def __str__(self): |
| return self.__repr__() |
| |
| def __repr__(self): |
| s = [] |
| s.append("qas_id: %s" % (tokenization.printable_text(self.qas_id))) |
| s.append("question_text: %s" % |
| (tokenization.printable_text(self.question_text))) |
| s.append("doc_tokens: [%s]" % (" ".join(self.doc_tokens))) |
| if self.start_position: |
| s.append("start_position: %d" % (self.start_position)) |
| if self.start_position: |
| s.append("end_position: %d" % (self.end_position)) |
| return ", ".join(s) |
| |
| |
| 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 convert_examples_to_features(examples, tokenizer, max_seq_length, |
| doc_stride, max_query_length): |
| """Loads a data file into a list of `InputBatch`s.""" |
| |
| res_input_ids = [] |
| res_input_mask = [] |
| res_segment_ids = [] |
| extra = [] |
| unique_id = 0 |
| |
| 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) |
| |
| # 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("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) |
| |
| # 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(0) |
| res_input_ids.append(np.array(input_ids, dtype=np.int64)) |
| res_input_mask.append(np.array(input_mask, dtype=np.int64)) |
| res_segment_ids.append(np.array(segment_ids, dtype=np.int64)) |
| feature = Feature(unique_id=unique_id, |
| tokens=tokens, |
| example_index=example_index, |
| token_to_orig_map=token_to_orig_map, |
| token_is_max_context=token_is_max_context) |
| extra.append(feature) |
| unique_id += 1 |
| return np.array(res_input_ids), np.array(res_input_mask), np.array( |
| res_segment_ids), extra |
| |
| |
| def read_squad_examples(input_file): |
| """Read a SQuAD json file into a list of SquadExample.""" |
| with open(input_file, "r") as f: |
| input_data = json.load(f)["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 idx, entry in enumerate(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 |
| example = SquadExample(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) |
| examples.append(example) |
| return examples |
| |
| |
| def write_predictions(all_examples, all_features, all_results, n_best_size, |
| max_answer_length, do_lower_case, output_prediction_file, |
| output_nbest_file): |
| """Write final predictions to the json file.""" |
| 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() |
| for (example_index, example) in enumerate(all_examples): |
| features = example_index_to_features[example_index] |
| prelim_predictions = [] |
| for (feature_index, feature) in enumerate(features): |
| if not feature.unique_id in unique_id_to_result: |
| print("feature not in unique_Id", feature.unique_id) |
| continue |
| 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) |
| 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])) |
| |
| 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] |
| |
| 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) |
| if final_text in seen_predictions: |
| continue |
| |
| seen_predictions[final_text] = True |
| nbest.append( |
| _NbestPrediction(text=final_text, |
| start_logit=pred.start_logit, |
| end_logit=pred.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 = [] |
| for entry in nbest: |
| total_scores.append(entry.start_logit + entry.end_logit) |
| |
| 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"] = float(entry.start_logit) |
| output["end_logit"] = float(entry.end_logit) |
| nbest_json.append(output) |
| |
| all_predictions[example.qas_id] = nbest_json[0]["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") |
| |
| |
| def get_final_text(pred_text, orig_text, do_lower_case): |
| """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 SQuAD 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: |
| 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): |
| 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: |
| 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: |
| 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 |