# -*- coding: UTF-8 -*- # 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. import paddle.fluid as fluid from paddlepalm.head.base_head import Head import collections import numpy as np import os import math import six import paddlepalm.tokenizer.ernie_tokenizer as tokenization import json import io RawResult = collections.namedtuple("RawResult", ["unique_id", "start_logits", "end_logits"]) class MRC(Head): """ Machine Reading Comprehension """ def __init__(self, max_query_len, input_dim, pred_output_path=None, verbose=False, with_negative=False, do_lower_case=False, max_ans_len=None, null_score_diff_threshold=0.0, n_best_size=20, phase='train'): self._is_training = phase == 'train' self._hidden_size = input_dim self._max_sequence_length = max_query_len self._pred_results = [] output_dir = pred_output_path self._max_answer_length = max_ans_len self._null_score_diff_threshold = null_score_diff_threshold self._n_best_size = n_best_size output_dir = pred_output_path self._verbose = verbose self._with_negative = with_negative self._do_lower_case = do_lower_case @property def inputs_attrs(self): if self._is_training: reader = {"start_positions": [[-1], 'int64'], "end_positions": [[-1], 'int64'], } else: reader = {'unique_ids': [[-1], 'int64']} bb = {"encoder_outputs": [[-1, -1, self._hidden_size], 'float32']} return {'reader': reader, 'backbone': bb} @property def epoch_inputs_attrs(self): if not self._is_training: from_reader = {'examples': None, 'features': None} return {'reader': from_reader} @property def outputs_attr(self): if self._is_training: return {'loss': [[1], 'float32']} else: return {'start_logits': [[-1, -1, 1], 'float32'], 'end_logits': [[-1, -1, 1], 'float32'], 'unique_ids': [[-1], 'int64']} def build(self, inputs, scope_name=""): if self._is_training: start_positions = inputs['reader']['start_positions'] end_positions = inputs['reader']['end_positions'] # max_position = inputs["reader"]["seqlen"] - 1 # start_positions = fluid.layers.elementwise_min(start_positions, max_position) # end_positions = fluid.layers.elementwise_min(end_positions, max_position) start_positions.stop_gradient = True end_positions.stop_gradient = True else: unique_id = inputs['reader']['unique_ids'] # It's used to help fetch variable 'unique_ids' that will be removed in the future helper_constant = fluid.layers.fill_constant(shape=[1], value=1, dtype='int64') fluid.layers.elementwise_mul(unique_id, helper_constant) enc_out = inputs['backbone']['encoder_outputs'] logits = fluid.layers.fc( input=enc_out, size=2, num_flatten_dims=2, param_attr=fluid.ParamAttr( name=scope_name+"cls_squad_out_w", initializer=fluid.initializer.TruncatedNormal(scale=0.02)), bias_attr=fluid.ParamAttr( name=scope_name+"cls_squad_out_b", initializer=fluid.initializer.Constant(0.))) logits = fluid.layers.transpose(x=logits, perm=[2, 0, 1]) start_logits, end_logits = fluid.layers.unstack(x=logits, axis=0) def _compute_single_loss(logits, positions): """Compute start/en d loss for mrc model""" inputs = fluid.layers.softmax(logits) loss = fluid.layers.cross_entropy( input=inputs, label=positions) loss = fluid.layers.mean(x=loss) return loss if self._is_training: start_loss = _compute_single_loss(start_logits, start_positions) end_loss = _compute_single_loss(end_logits, end_positions) total_loss = (start_loss + end_loss) / 2.0 return {'loss': total_loss} else: return {'start_logits': start_logits, 'end_logits': end_logits, 'unique_ids': unique_id} def batch_postprocess(self, rt_outputs): """this func will be called after each step(batch) of training/evaluating/predicting process.""" if not self._is_training: unique_ids = rt_outputs['unique_ids'] start_logits = rt_outputs['start_logits'] end_logits = rt_outputs['end_logits'] for idx in range(len(unique_ids)): if unique_ids[idx] < 0: continue if len(self._pred_results) % 1000 == 0: print("Predicting example: {}".format(len(self._pred_results))) uid = int(unique_ids[idx]) s = [float(x) for x in start_logits[idx].flat] e = [float(x) for x in end_logits[idx].flat] self._pred_results.append( RawResult( unique_id=uid, start_logits=s, end_logits=e)) def epoch_postprocess(self, post_inputs, output_dir=None): """(optional interface) this func will be called after evaluation/predicting process and each epoch during training process.""" if not self._is_training: if output_dir is None: raise ValueError('argument output_dir not found in config. Please add it into config dict/file.') examples = post_inputs['reader']['examples'] features = post_inputs['reader']['features'] if not os.path.exists(output_dir): os.makedirs(output_dir) output_prediction_file = os.path.join(output_dir, "predictions.json") output_nbest_file = os.path.join(output_dir, "nbest_predictions.json") output_null_log_odds_file = os.path.join(output_dir, "null_odds.json") _write_predictions(examples, features, self._pred_results, self._n_best_size, self._max_answer_length, self._do_lower_case, output_prediction_file, output_nbest_file, output_null_log_odds_file, self._with_negative, self._null_score_diff_threshold, self._verbose) def _write_predictions(all_examples, all_features, 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)) 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 ull_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.encode('utf-8').decode('utf-8') 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 io.open(output_prediction_file, "w", encoding='utf-8') as writer: writer.write(json.dumps(all_predictions, indent=4, ensure_ascii=False) + "\n") with io.open(output_nbest_file, "w", encoding='utf-8') as writer: writer.write(json.dumps(all_nbest_json, indent=4, ensure_ascii=False) + "\n") if with_negative: with io.open(output_null_log_odds_file, "w", encoding='utf-8') as writer: writer.write(json.dumps(scores_diff_json, indent=4, ensure_ascii=False) + "\n") 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