# coding=utf-8 # Copyright 2018 The Google AI 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. # Lint as: python2, python3 """Utility functions for SQuAD v1.1/v2.0 datasets.""" from __future__ import absolute_import from __future__ import division # from __future__ import google_type_annotations from __future__ import print_function import collections import json import math import re import string import sys from albert import fine_tuning_utils from albert import modeling from albert import optimization from albert import tokenization import numpy as np import six from six.moves import map from six.moves import range import tensorflow.compat.v1 as tf from tensorflow.contrib import data as contrib_data from tensorflow.contrib import layers as contrib_layers from tensorflow.contrib import tpu as contrib_tpu _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"]) RawResult = collections.namedtuple("RawResult", ["unique_id", "start_log_prob", "end_log_prob"]) RawResultV2 = collections.namedtuple( "RawResultV2", ["unique_id", "start_top_log_probs", "start_top_index", "end_top_log_probs", "end_top_index", "cls_logits"]) 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, end_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.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 += ", paragraph_text: [%s]" % (" ".join(self.paragraph_text)) 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, tok_start_to_orig_index, tok_end_to_orig_index, token_is_max_context, tokens, input_ids, input_mask, segment_ids, paragraph_len, p_mask=None, 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.tokens = tokens self.input_ids = input_ids self.input_mask = input_mask self.segment_ids = segment_ids self.paragraph_len = paragraph_len self.start_position = start_position self.end_position = end_position self.is_impossible = is_impossible self.p_mask = p_mask def read_squad_examples(input_file, is_training): """Read a SQuAD json file into a list of SquadExample.""" with tf.gfile.Open(input_file, "r") as reader: input_data = json.load(reader)["data"] examples = [] for entry in input_data: for paragraph in entry["paragraphs"]: paragraph_text = paragraph["context"] for qa in paragraph["qas"]: qas_id = qa["id"] question_text = qa["question"] start_position = None orig_answer_text = None is_impossible = False if is_training: is_impossible = qa.get("is_impossible", False) 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"] start_position = answer["answer_start"] else: start_position = -1 orig_answer_text = "" example = SquadExample( qas_id=qas_id, question_text=question_text, paragraph_text=paragraph_text, orig_answer_text=orig_answer_text, start_position=start_position, is_impossible=is_impossible) examples.append(example) return examples def _convert_index(index, pos, m=None, is_start=True): """Converts index.""" 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, tokenizer, max_seq_length, doc_stride, max_query_length, is_training, output_fn, do_lower_case): """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 = tokenization.encode_ids( tokenizer.sp_model, tokenization.preprocess_text( example.question_text, lower=do_lower_case)) if len(query_tokens) > max_query_length: query_tokens = query_tokens[0:max_query_length] paragraph_text = example.paragraph_text para_tokens = tokenization.encode_pieces( tokenizer.sp_model, tokenization.preprocess_text( example.paragraph_text, lower=do_lower_case), return_unicode=False) chartok_to_tok_index = [] tok_start_to_chartok_index = [] tok_end_to_chartok_index = [] char_cnt = 0 para_tokens = [six.ensure_text(token, "utf-8") for token in para_tokens] for i, token in enumerate(para_tokens): new_token = six.ensure_text(token).replace( tokenization.SPIECE_UNDERLINE.decode("utf-8"), " ") chartok_to_tok_index.extend([i] * len(new_token)) tok_start_to_chartok_index.append(char_cnt) char_cnt += len(new_token) tok_end_to_chartok_index.append(char_cnt - 1) tok_cat_text = "".join(para_tokens).replace( tokenization.SPIECE_UNDERLINE.decode("utf-8"), " ") 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) g = {} def _lcs_match(max_dist, n=n, m=m): """Longest-common-substring algorithm.""" 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 (tokenization.preprocess_text( paragraph_text[i], lower=do_lower_case, 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): tf.logging.info("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 = 0 tok_end_position = 0 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, six.text_type): x = six.ensure_binary(x, "utf-8") return tokenizer.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 = [] tokens.append(tokenizer.sp_model.PieceToId("[CLS]")) segment_ids.append(0) p_mask.append(0) for token in query_tokens: tokens.append(token) segment_ids.append(0) p_mask.append(1) tokens.append(tokenizer.sp_model.PieceToId("[SEP]")) segment_ids.append(0) p_mask.append(1) 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(1) p_mask.append(0) tokens.append(tokenizer.sp_model.PieceToId("[SEP]")) segment_ids.append(1) p_mask.append(1) paragraph_len = len(tokens) input_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) 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 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: 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 span_is_impossible: start_position = 0 end_position = 0 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_pieces: %s" % " ".join( [tokenizer.sp_model.IdToPiece(x) for x in tokens])) 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 = [tokenizer.sp_model.IdToPiece(token) for token in tokens[start_position: (end_position + 1)]] answer_text = tokenizer.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" % (tokenization.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, tokens=[tokenizer.sp_model.IdToPiece(x) for x in tokens], input_ids=input_ids, input_mask=input_mask, segment_ids=segment_ids, paragraph_len=paragraph_len, start_position=start_position, end_position=end_position, is_impossible=span_is_impossible, p_mask=p_mask) # 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 _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_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 class FeatureWriter(object): """Writes InputFeature to TF example file.""" def __init__(self, filename, is_training): self.filename = filename self.is_training = is_training self.num_features = 0 self._writer = tf.python_io.TFRecordWriter(filename) def process_feature(self, feature): """Write a InputFeature to the TFRecordWriter as a tf.train.Example.""" self.num_features += 1 def create_int_feature(values): feature = tf.train.Feature( int64_list=tf.train.Int64List(value=list(values))) return feature features = collections.OrderedDict() features["unique_ids"] = create_int_feature([feature.unique_id]) features["input_ids"] = create_int_feature(feature.input_ids) features["input_mask"] = create_int_feature(feature.input_mask) features["segment_ids"] = create_int_feature(feature.segment_ids) features["p_mask"] = create_int_feature(feature.p_mask) if self.is_training: features["start_positions"] = create_int_feature([feature.start_position]) features["end_positions"] = create_int_feature([feature.end_position]) impossible = 0 if feature.is_impossible: impossible = 1 features["is_impossible"] = create_int_feature([impossible]) tf_example = tf.train.Example(features=tf.train.Features(feature=features)) self._writer.write(tf_example.SerializeToString()) def close(self): self._writer.close() def input_fn_builder(input_file, seq_length, is_training, drop_remainder, use_tpu, bsz, is_v2): """Creates an `input_fn` closure to be passed to TPUEstimator.""" name_to_features = { "unique_ids": tf.FixedLenFeature([], tf.int64), "input_ids": tf.FixedLenFeature([seq_length], tf.int64), "input_mask": tf.FixedLenFeature([seq_length], tf.int64), "segment_ids": tf.FixedLenFeature([seq_length], tf.int64), } # p_mask is not required for SQuAD v1.1 if is_v2: name_to_features["p_mask"] = tf.FixedLenFeature([seq_length], tf.int64) if is_training: name_to_features["start_positions"] = tf.FixedLenFeature([], tf.int64) name_to_features["end_positions"] = tf.FixedLenFeature([], tf.int64) name_to_features["is_impossible"] = tf.FixedLenFeature([], tf.int64) def _decode_record(record, name_to_features): """Decodes a record to a TensorFlow example.""" example = tf.parse_single_example(record, name_to_features) # tf.Example only supports tf.int64, but the TPU only supports tf.int32. # So cast all int64 to int32. for name in list(example.keys()): t = example[name] if t.dtype == tf.int64: t = tf.to_int32(t) example[name] = t return example def input_fn(params): """The actual input function.""" if use_tpu: batch_size = params["batch_size"] else: batch_size = bsz # For training, we want a lot of parallel reading and shuffling. # For eval, we want no shuffling and parallel reading doesn't matter. d = tf.data.TFRecordDataset(input_file) if is_training: d = d.repeat() d = d.shuffle(buffer_size=100) d = d.apply( contrib_data.map_and_batch( lambda record: _decode_record(record, name_to_features), batch_size=batch_size, drop_remainder=drop_remainder)) return d return input_fn def create_v1_model(albert_config, is_training, input_ids, input_mask, segment_ids, use_one_hot_embeddings, use_einsum, hub_module): """Creates a classification model.""" (_, final_hidden) = fine_tuning_utils.create_albert( albert_config=albert_config, is_training=is_training, input_ids=input_ids, input_mask=input_mask, segment_ids=segment_ids, use_one_hot_embeddings=use_one_hot_embeddings, use_einsum=use_einsum, hub_module=hub_module) final_hidden_shape = modeling.get_shape_list(final_hidden, expected_rank=3) batch_size = final_hidden_shape[0] seq_length = final_hidden_shape[1] hidden_size = final_hidden_shape[2] output_weights = tf.get_variable( "cls/squad/output_weights", [2, hidden_size], initializer=tf.truncated_normal_initializer(stddev=0.02)) output_bias = tf.get_variable( "cls/squad/output_bias", [2], initializer=tf.zeros_initializer()) final_hidden_matrix = tf.reshape(final_hidden, [batch_size * seq_length, hidden_size]) logits = tf.matmul(final_hidden_matrix, output_weights, transpose_b=True) logits = tf.nn.bias_add(logits, output_bias) logits = tf.reshape(logits, [batch_size, seq_length, 2]) logits = tf.transpose(logits, [2, 0, 1]) unstacked_logits = tf.unstack(logits, axis=0) (start_logits, end_logits) = (unstacked_logits[0], unstacked_logits[1]) return (start_logits, end_logits) def v1_model_fn_builder(albert_config, init_checkpoint, learning_rate, num_train_steps, num_warmup_steps, use_tpu, use_one_hot_embeddings, use_einsum, hub_module): """Returns `model_fn` closure for TPUEstimator.""" def model_fn(features, labels, mode, params): # pylint: disable=unused-argument """The `model_fn` for TPUEstimator.""" tf.logging.info("*** Features ***") for name in sorted(features.keys()): tf.logging.info(" name = %s, shape = %s" % (name, features[name].shape)) if "unique_ids" in features: unique_ids = features["unique_ids"] else: unique_ids = None input_ids = features["input_ids"] input_mask = features["input_mask"] segment_ids = features["segment_ids"] is_training = (mode == tf.estimator.ModeKeys.TRAIN) (start_logits, end_logits) = create_v1_model( albert_config=albert_config, is_training=is_training, input_ids=input_ids, input_mask=input_mask, segment_ids=segment_ids, use_one_hot_embeddings=use_one_hot_embeddings, use_einsum=use_einsum, hub_module=hub_module) # Assign names to the logits so that we can refer to them as output tensors. start_logits = tf.identity(start_logits, name="start_logits") end_logits = tf.identity(end_logits, name="end_logits") tvars = tf.trainable_variables() initialized_variable_names = {} scaffold_fn = None if init_checkpoint: (assignment_map, initialized_variable_names ) = modeling.get_assignment_map_from_checkpoint(tvars, init_checkpoint) if use_tpu: def tpu_scaffold(): tf.train.init_from_checkpoint(init_checkpoint, assignment_map) return tf.train.Scaffold() scaffold_fn = tpu_scaffold else: tf.train.init_from_checkpoint(init_checkpoint, assignment_map) tf.logging.info("**** Trainable Variables ****") for var in tvars: init_string = "" if var.name in initialized_variable_names: init_string = ", *INIT_FROM_CKPT*" tf.logging.info(" name = %s, shape = %s%s", var.name, var.shape, init_string) output_spec = None if mode == tf.estimator.ModeKeys.TRAIN: seq_length = modeling.get_shape_list(input_ids)[1] def compute_loss(logits, positions): one_hot_positions = tf.one_hot( positions, depth=seq_length, dtype=tf.float32) log_probs = tf.nn.log_softmax(logits, axis=-1) loss = -tf.reduce_mean( tf.reduce_sum(one_hot_positions * log_probs, axis=-1)) return loss start_positions = features["start_positions"] end_positions = features["end_positions"] start_loss = compute_loss(start_logits, start_positions) end_loss = compute_loss(end_logits, end_positions) total_loss = (start_loss + end_loss) / 2.0 train_op = optimization.create_optimizer( total_loss, learning_rate, num_train_steps, num_warmup_steps, use_tpu) output_spec = contrib_tpu.TPUEstimatorSpec( mode=mode, loss=total_loss, train_op=train_op, scaffold_fn=scaffold_fn) elif mode == tf.estimator.ModeKeys.PREDICT: predictions = { "start_log_prob": start_logits, "end_log_prob": end_logits, } if unique_ids is not None: predictions["unique_ids"] = unique_ids output_spec = contrib_tpu.TPUEstimatorSpec( mode=mode, predictions=predictions, scaffold_fn=scaffold_fn) else: raise ValueError( "Only TRAIN and PREDICT modes are supported: %s" % (mode)) return output_spec return model_fn def accumulate_predictions_v1(result_dict, all_examples, all_features, all_results, n_best_size, max_answer_length): """accumulate predictions for each positions in a dictionary.""" 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): if example_index not in result_dict: result_dict[example_index] = {} features = example_index_to_features[example_index] prelim_predictions = [] 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): if feature.unique_id not in result_dict[example_index]: result_dict[example_index][feature.unique_id] = {} result = unique_id_to_result[feature.unique_id] start_indexes = _get_best_indexes(result.start_log_prob, n_best_size) end_indexes = _get_best_indexes(result.end_log_prob, n_best_size) for start_index in start_indexes: for end_index in end_indexes: doc_offset = feature.tokens.index("[SEP]") + 1 # 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 - doc_offset >= len(feature.tok_start_to_orig_index): continue if end_index - doc_offset >= len(feature.tok_end_to_orig_index): 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 start_log_prob = result.start_log_prob[start_index] end_log_prob = result.end_log_prob[end_index] start_idx = start_index - doc_offset end_idx = end_index - doc_offset if (start_idx, end_idx) not in result_dict[example_index][feature.unique_id]: result_dict[example_index][feature.unique_id][(start_idx, end_idx)] = [] result_dict[example_index][feature.unique_id][(start_idx, end_idx)].append((start_log_prob, end_log_prob)) def write_predictions_v1(result_dict, all_examples, all_features, all_results, n_best_size, max_answer_length, output_prediction_file, output_nbest_file): """Write final predictions to the json file and log-odds of null if needed.""" tf.logging.info("Writing predictions to: %s" % (output_prediction_file)) tf.logging.info("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 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): for ((start_idx, end_idx), logprobs) in \ result_dict[example_index][feature.unique_id].items(): start_log_prob = 0 end_log_prob = 0 for logprob in logprobs: start_log_prob += logprob[0] end_log_prob += logprob[1] prelim_predictions.append( _PrelimPrediction( feature_index=feature_index, start_index=start_idx, end_index=end_idx, start_log_prob=start_log_prob / len(logprobs), end_log_prob=end_log_prob / len(logprobs))) 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] if pred.start_index >= 0: # this is a non-null prediction 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 else: final_text = "" 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="empty", start_log_prob=0.0, end_log_prob=0.0)) assert len(nbest) >= 1 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: if entry.text: 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 all_predictions[example.qas_id] = nbest_json[0]["text"] all_nbest_json[example.qas_id] = nbest_json with tf.gfile.GFile(output_prediction_file, "w") as writer: writer.write(json.dumps(all_predictions, indent=4) + "\n") with tf.gfile.GFile(output_nbest_file, "w") as writer: writer.write(json.dumps(all_nbest_json, indent=4) + "\n") return all_predictions ####### following are from official SQuAD v1.1 evaluation scripts def normalize_answer_v1(s): """Lower text and remove punctuation, articles and extra whitespace.""" def remove_articles(text): return re.sub(r"\b(a|an|the)\b", " ", text) def white_space_fix(text): return " ".join(text.split()) def remove_punc(text): exclude = set(string.punctuation) return "".join(ch for ch in text if ch not in exclude) def lower(text): return text.lower() return white_space_fix(remove_articles(remove_punc(lower(s)))) def f1_score(prediction, ground_truth): prediction_tokens = normalize_answer_v1(prediction).split() ground_truth_tokens = normalize_answer_v1(ground_truth).split() common = ( collections.Counter(prediction_tokens) & collections.Counter(ground_truth_tokens)) num_same = sum(common.values()) if num_same == 0: return 0 precision = 1.0 * num_same / len(prediction_tokens) recall = 1.0 * num_same / len(ground_truth_tokens) f1 = (2 * precision * recall) / (precision + recall) return f1 def exact_match_score(prediction, ground_truth): return (normalize_answer_v1(prediction) == normalize_answer_v1(ground_truth)) def metric_max_over_ground_truths(metric_fn, prediction, ground_truths): scores_for_ground_truths = [] for ground_truth in ground_truths: score = metric_fn(prediction, ground_truth) scores_for_ground_truths.append(score) return max(scores_for_ground_truths) def evaluate_v1(dataset, predictions): f1 = exact_match = total = 0 for article in dataset: for paragraph in article["paragraphs"]: for qa in paragraph["qas"]: total += 1 if qa["id"] not in predictions: message = ("Unanswered question " + six.ensure_str(qa["id"]) + " will receive score 0.") print(message, file=sys.stderr) continue ground_truths = [x["text"] for x in qa["answers"]] # ground_truths = list(map(lambda x: x["text"], qa["answers"])) prediction = predictions[qa["id"]] exact_match += metric_max_over_ground_truths(exact_match_score, prediction, ground_truths) f1 += metric_max_over_ground_truths(f1_score, prediction, ground_truths) exact_match = 100.0 * exact_match / total f1 = 100.0 * f1 / total return {"exact_match": exact_match, "f1": f1} ####### above are from official SQuAD v1.1 evaluation scripts ####### following are from official SQuAD v2.0 evaluation scripts def make_qid_to_has_ans(dataset): qid_to_has_ans = {} for article in dataset: for p in article['paragraphs']: for qa in p['qas']: qid_to_has_ans[qa['id']] = bool(qa['answers']) return qid_to_has_ans def normalize_answer_v2(s): """Lower text and remove punctuation, articles and extra whitespace.""" def remove_articles(text): regex = re.compile(r'\b(a|an|the)\b', re.UNICODE) return re.sub(regex, ' ', text) def white_space_fix(text): return ' '.join(text.split()) def remove_punc(text): exclude = set(string.punctuation) return ''.join(ch for ch in text if ch not in exclude) def lower(text): return text.lower() return white_space_fix(remove_articles(remove_punc(lower(s)))) def get_tokens(s): if not s: return [] return normalize_answer_v2(s).split() def compute_exact(a_gold, a_pred): return int(normalize_answer_v2(a_gold) == normalize_answer_v2(a_pred)) def compute_f1(a_gold, a_pred): gold_toks = get_tokens(a_gold) pred_toks = get_tokens(a_pred) common = collections.Counter(gold_toks) & collections.Counter(pred_toks) num_same = sum(common.values()) if len(gold_toks) == 0 or len(pred_toks) == 0: # If either is no-answer, then F1 is 1 if they agree, 0 otherwise return int(gold_toks == pred_toks) if num_same == 0: return 0 precision = 1.0 * num_same / len(pred_toks) recall = 1.0 * num_same / len(gold_toks) f1 = (2 * precision * recall) / (precision + recall) return f1 def get_raw_scores(dataset, preds): exact_scores = {} f1_scores = {} for article in dataset: for p in article['paragraphs']: for qa in p['qas']: qid = qa['id'] gold_answers = [a['text'] for a in qa['answers'] if normalize_answer_v2(a['text'])] if not gold_answers: # For unanswerable questions, only correct answer is empty string gold_answers = [''] if qid not in preds: print('Missing prediction for %s' % qid) continue a_pred = preds[qid] # Take max over all gold answers exact_scores[qid] = max(compute_exact(a, a_pred) for a in gold_answers) f1_scores[qid] = max(compute_f1(a, a_pred) for a in gold_answers) return exact_scores, f1_scores def apply_no_ans_threshold(scores, na_probs, qid_to_has_ans, na_prob_thresh): new_scores = {} for qid, s in scores.items(): pred_na = na_probs[qid] > na_prob_thresh if pred_na: new_scores[qid] = float(not qid_to_has_ans[qid]) else: new_scores[qid] = s return new_scores def make_eval_dict(exact_scores, f1_scores, qid_list=None): if not qid_list: total = len(exact_scores) return collections.OrderedDict([ ('exact', 100.0 * sum(exact_scores.values()) / total), ('f1', 100.0 * sum(f1_scores.values()) / total), ('total', total), ]) else: total = len(qid_list) return collections.OrderedDict([ ('exact', 100.0 * sum(exact_scores[k] for k in qid_list) / total), ('f1', 100.0 * sum(f1_scores[k] for k in qid_list) / total), ('total', total), ]) def find_best_thresh(preds, scores, na_probs, qid_to_has_ans): num_no_ans = sum(1 for k in qid_to_has_ans if not qid_to_has_ans[k]) cur_score = num_no_ans best_score = cur_score best_thresh = 0.0 qid_list = sorted(na_probs, key=lambda k: na_probs[k]) for i, qid in enumerate(qid_list): if qid not in scores: continue if qid_to_has_ans[qid]: diff = scores[qid] else: if preds[qid]: diff = -1 else: diff = 0 cur_score += diff if cur_score > best_score: best_score = cur_score best_thresh = na_probs[qid] return 100.0 * best_score / len(scores), best_thresh def find_all_best_thresh(main_eval, preds, exact_raw, f1_raw, na_probs, qid_to_has_ans): best_exact, exact_thresh = find_best_thresh(preds, exact_raw, na_probs, qid_to_has_ans) best_f1, f1_thresh = find_best_thresh(preds, f1_raw, na_probs, qid_to_has_ans) main_eval['best_exact'] = best_exact main_eval['best_exact_thresh'] = exact_thresh main_eval['best_f1'] = best_f1 main_eval['best_f1_thresh'] = f1_thresh def merge_eval(main_eval, new_eval, prefix): for k in new_eval: main_eval['%s_%s' % (prefix, k)] = new_eval[k] ####### above are from official SQuAD v2.0 evaluation scripts def accumulate_predictions_v2(result_dict, cls_dict, all_examples, all_features, all_results, n_best_size, max_answer_length, start_n_top, end_n_top): """accumulate predictions for each positions in a dictionary.""" 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): if example_index not in result_dict: result_dict[example_index] = {} 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): if feature.unique_id not in result_dict[example_index]: result_dict[example_index][feature.unique_id] = {} result = unique_id_to_result[feature.unique_id] 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) doc_offset = feature.tokens.index("[SEP]") + 1 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 - doc_offset >= len(feature.tok_start_to_orig_index): continue if start_index - doc_offset < 0: continue if end_index - doc_offset >= len(feature.tok_end_to_orig_index): 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 start_idx = start_index - doc_offset end_idx = end_index - doc_offset if (start_idx, end_idx) not in result_dict[example_index][feature.unique_id]: result_dict[example_index][feature.unique_id][(start_idx, end_idx)] = [] result_dict[example_index][feature.unique_id][(start_idx, end_idx)].append((start_log_prob, end_log_prob)) if example_index not in cls_dict: cls_dict[example_index] = [] cls_dict[example_index].append(score_null) def write_predictions_v2(result_dict, cls_dict, all_examples, all_features, all_results, n_best_size, max_answer_length, output_prediction_file, output_nbest_file, output_null_log_odds_file, null_score_diff_threshold): """Write final predictions to the json file and log-odds of null if needed.""" tf.logging.info("Writing predictions to: %s" % (output_prediction_file)) tf.logging.info("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 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): for ((start_idx, end_idx), logprobs) in \ result_dict[example_index][feature.unique_id].items(): start_log_prob = 0 end_log_prob = 0 for logprob in logprobs: start_log_prob += logprob[0] end_log_prob += logprob[1] prelim_predictions.append( _PrelimPrediction( feature_index=feature_index, start_index=start_idx, end_index=end_idx, start_log_prob=start_log_prob / len(logprobs), end_log_prob=end_log_prob / len(logprobs))) 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 = sum(cls_dict[example_index]) / len(cls_dict[example_index]) scores_diff_json[example.qas_id] = score_diff # predict null answers when null threshold is provided if null_score_diff_threshold is None or score_diff < null_score_diff_threshold: all_predictions[example.qas_id] = best_non_null_entry.text else: all_predictions[example.qas_id] = "" all_nbest_json[example.qas_id] = nbest_json assert len(nbest_json) >= 1 with tf.gfile.GFile(output_prediction_file, "w") as writer: writer.write(json.dumps(all_predictions, indent=4) + "\n") with tf.gfile.GFile(output_nbest_file, "w") as writer: writer.write(json.dumps(all_nbest_json, indent=4) + "\n") with tf.gfile.GFile(output_null_log_odds_file, "w") as writer: writer.write(json.dumps(scores_diff_json, indent=4) + "\n") return all_predictions, scores_diff_json def create_v2_model(albert_config, is_training, input_ids, input_mask, segment_ids, use_one_hot_embeddings, features, max_seq_length, start_n_top, end_n_top, dropout_prob, hub_module): """Creates a classification model.""" (_, output) = fine_tuning_utils.create_albert( albert_config=albert_config, is_training=is_training, input_ids=input_ids, input_mask=input_mask, segment_ids=segment_ids, use_one_hot_embeddings=use_one_hot_embeddings, use_einsum=True, hub_module=hub_module) bsz = tf.shape(output)[0] return_dict = {} output = tf.transpose(output, [1, 0, 2]) # invalid position mask such as query and special symbols (PAD, SEP, CLS) p_mask = tf.cast(features["p_mask"], dtype=tf.float32) # logit of the start position with tf.variable_scope("start_logits"): start_logits = tf.layers.dense( output, 1, kernel_initializer=modeling.create_initializer( albert_config.initializer_range)) start_logits = tf.transpose(tf.squeeze(start_logits, -1), [1, 0]) start_logits_masked = start_logits * (1 - p_mask) - 1e30 * p_mask start_log_probs = tf.nn.log_softmax(start_logits_masked, -1) # logit of the end position with tf.variable_scope("end_logits"): if is_training: # during training, compute the end logits based on the # ground truth of the start position start_positions = tf.reshape(features["start_positions"], [-1]) start_index = tf.one_hot(start_positions, depth=max_seq_length, axis=-1, dtype=tf.float32) start_features = tf.einsum("lbh,bl->bh", output, start_index) start_features = tf.tile(start_features[None], [max_seq_length, 1, 1]) end_logits = tf.layers.dense( tf.concat([output, start_features], axis=-1), albert_config.hidden_size, kernel_initializer=modeling.create_initializer( albert_config.initializer_range), activation=tf.tanh, name="dense_0") end_logits = contrib_layers.layer_norm(end_logits, begin_norm_axis=-1) end_logits = tf.layers.dense( end_logits, 1, kernel_initializer=modeling.create_initializer( albert_config.initializer_range), name="dense_1") end_logits = tf.transpose(tf.squeeze(end_logits, -1), [1, 0]) end_logits_masked = end_logits * (1 - p_mask) - 1e30 * p_mask end_log_probs = tf.nn.log_softmax(end_logits_masked, -1) else: # during inference, compute the end logits based on beam search start_top_log_probs, start_top_index = tf.nn.top_k( start_log_probs, k=start_n_top) start_index = tf.one_hot(start_top_index, depth=max_seq_length, axis=-1, dtype=tf.float32) start_features = tf.einsum("lbh,bkl->bkh", output, start_index) end_input = tf.tile(output[:, :, None], [1, 1, start_n_top, 1]) start_features = tf.tile(start_features[None], [max_seq_length, 1, 1, 1]) end_input = tf.concat([end_input, start_features], axis=-1) end_logits = tf.layers.dense( end_input, albert_config.hidden_size, kernel_initializer=modeling.create_initializer( albert_config.initializer_range), activation=tf.tanh, name="dense_0") end_logits = contrib_layers.layer_norm(end_logits, begin_norm_axis=-1) end_logits = tf.layers.dense( end_logits, 1, kernel_initializer=modeling.create_initializer( albert_config.initializer_range), name="dense_1") end_logits = tf.reshape(end_logits, [max_seq_length, -1, start_n_top]) end_logits = tf.transpose(end_logits, [1, 2, 0]) end_logits_masked = end_logits * ( 1 - p_mask[:, None]) - 1e30 * p_mask[:, None] end_log_probs = tf.nn.log_softmax(end_logits_masked, -1) end_top_log_probs, end_top_index = tf.nn.top_k( end_log_probs, k=end_n_top) end_top_log_probs = tf.reshape( end_top_log_probs, [-1, start_n_top * end_n_top]) end_top_index = tf.reshape( end_top_index, [-1, start_n_top * end_n_top]) if is_training: return_dict["start_log_probs"] = start_log_probs return_dict["end_log_probs"] = end_log_probs else: return_dict["start_top_log_probs"] = start_top_log_probs return_dict["start_top_index"] = start_top_index return_dict["end_top_log_probs"] = end_top_log_probs return_dict["end_top_index"] = end_top_index # an additional layer to predict answerability with tf.variable_scope("answer_class"): # get the representation of CLS cls_index = tf.one_hot(tf.zeros([bsz], dtype=tf.int32), max_seq_length, axis=-1, dtype=tf.float32) cls_feature = tf.einsum("lbh,bl->bh", output, cls_index) # get the representation of START start_p = tf.nn.softmax(start_logits_masked, axis=-1, name="softmax_start") start_feature = tf.einsum("lbh,bl->bh", output, start_p) # note(zhiliny): no dependency on end_feature so that we can obtain # one single `cls_logits` for each sample ans_feature = tf.concat([start_feature, cls_feature], -1) ans_feature = tf.layers.dense( ans_feature, albert_config.hidden_size, activation=tf.tanh, kernel_initializer=modeling.create_initializer( albert_config.initializer_range), name="dense_0") ans_feature = tf.layers.dropout(ans_feature, dropout_prob, training=is_training) cls_logits = tf.layers.dense( ans_feature, 1, kernel_initializer=modeling.create_initializer( albert_config.initializer_range), name="dense_1", use_bias=False) cls_logits = tf.squeeze(cls_logits, -1) return_dict["cls_logits"] = cls_logits return return_dict def v2_model_fn_builder(albert_config, init_checkpoint, learning_rate, num_train_steps, num_warmup_steps, use_tpu, use_one_hot_embeddings, max_seq_length, start_n_top, end_n_top, dropout_prob, hub_module): """Returns `model_fn` closure for TPUEstimator.""" def model_fn(features, labels, mode, params): # pylint: disable=unused-argument """The `model_fn` for TPUEstimator.""" tf.logging.info("*** Features ***") for name in sorted(features.keys()): tf.logging.info(" name = %s, shape = %s" % (name, features[name].shape)) # unique_ids = features["unique_ids"] input_ids = features["input_ids"] input_mask = features["input_mask"] segment_ids = features["segment_ids"] is_training = (mode == tf.estimator.ModeKeys.TRAIN) outputs = create_v2_model( albert_config=albert_config, is_training=is_training, input_ids=input_ids, input_mask=input_mask, segment_ids=segment_ids, use_one_hot_embeddings=use_one_hot_embeddings, features=features, max_seq_length=max_seq_length, start_n_top=start_n_top, end_n_top=end_n_top, dropout_prob=dropout_prob, hub_module=hub_module) tvars = tf.trainable_variables() initialized_variable_names = {} scaffold_fn = None if init_checkpoint: (assignment_map, initialized_variable_names ) = modeling.get_assignment_map_from_checkpoint(tvars, init_checkpoint) if use_tpu: def tpu_scaffold(): tf.train.init_from_checkpoint(init_checkpoint, assignment_map) return tf.train.Scaffold() scaffold_fn = tpu_scaffold else: tf.train.init_from_checkpoint(init_checkpoint, assignment_map) tf.logging.info("**** Trainable Variables ****") for var in tvars: init_string = "" if var.name in initialized_variable_names: init_string = ", *INIT_FROM_CKPT*" tf.logging.info(" name = %s, shape = %s%s", var.name, var.shape, init_string) output_spec = None if mode == tf.estimator.ModeKeys.TRAIN: seq_length = modeling.get_shape_list(input_ids)[1] def compute_loss(log_probs, positions): one_hot_positions = tf.one_hot( positions, depth=seq_length, dtype=tf.float32) loss = - tf.reduce_sum(one_hot_positions * log_probs, axis=-1) loss = tf.reduce_mean(loss) return loss start_loss = compute_loss( outputs["start_log_probs"], features["start_positions"]) end_loss = compute_loss( outputs["end_log_probs"], features["end_positions"]) total_loss = (start_loss + end_loss) * 0.5 cls_logits = outputs["cls_logits"] is_impossible = tf.reshape(features["is_impossible"], [-1]) regression_loss = tf.nn.sigmoid_cross_entropy_with_logits( labels=tf.cast(is_impossible, dtype=tf.float32), logits=cls_logits) regression_loss = tf.reduce_mean(regression_loss) # note(zhiliny): by default multiply the loss by 0.5 so that the scale is # comparable to start_loss and end_loss total_loss += regression_loss * 0.5 train_op = optimization.create_optimizer( total_loss, learning_rate, num_train_steps, num_warmup_steps, use_tpu) output_spec = contrib_tpu.TPUEstimatorSpec( mode=mode, loss=total_loss, train_op=train_op, scaffold_fn=scaffold_fn) elif mode == tf.estimator.ModeKeys.PREDICT: predictions = { "unique_ids": features["unique_ids"], "start_top_index": outputs["start_top_index"], "start_top_log_probs": outputs["start_top_log_probs"], "end_top_index": outputs["end_top_index"], "end_top_log_probs": outputs["end_top_log_probs"], "cls_logits": outputs["cls_logits"] } output_spec = contrib_tpu.TPUEstimatorSpec( mode=mode, predictions=predictions, scaffold_fn=scaffold_fn) else: raise ValueError( "Only TRAIN and PREDICT modes are supported: %s" % (mode)) return output_spec return model_fn def evaluate_v2(result_dict, cls_dict, prediction_json, eval_examples, eval_features, all_results, n_best_size, max_answer_length, output_prediction_file, output_nbest_file, output_null_log_odds_file): null_score_diff_threshold = None predictions, na_probs = write_predictions_v2( result_dict, cls_dict, eval_examples, eval_features, all_results, n_best_size, max_answer_length, output_prediction_file, output_nbest_file, output_null_log_odds_file, null_score_diff_threshold) na_prob_thresh = 1.0 # default value taken from the eval script qid_to_has_ans = make_qid_to_has_ans(prediction_json) # maps qid to True/False 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 = get_raw_scores(prediction_json, predictions) exact_thresh = apply_no_ans_threshold(exact_raw, na_probs, qid_to_has_ans, na_prob_thresh) f1_thresh = apply_no_ans_threshold(f1_raw, na_probs, qid_to_has_ans, na_prob_thresh) out_eval = make_eval_dict(exact_thresh, f1_thresh) find_all_best_thresh(out_eval, predictions, exact_raw, f1_raw, na_probs, qid_to_has_ans) null_score_diff_threshold = out_eval["best_f1_thresh"] predictions, na_probs = write_predictions_v2( result_dict, cls_dict,eval_examples, eval_features, all_results, n_best_size, max_answer_length, output_prediction_file, output_nbest_file, output_null_log_odds_file, null_score_diff_threshold) qid_to_has_ans = make_qid_to_has_ans(prediction_json) # maps qid to True/False 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 = get_raw_scores(prediction_json, predictions) exact_thresh = apply_no_ans_threshold(exact_raw, na_probs, qid_to_has_ans, na_prob_thresh) f1_thresh = apply_no_ans_threshold(f1_raw, na_probs, qid_to_has_ans, na_prob_thresh) out_eval = make_eval_dict(exact_thresh, f1_thresh) out_eval["null_score_diff_threshold"] = null_score_diff_threshold return out_eval