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| # 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. | |
| """Utility functions for GLUE classification tasks.""" | |
| from __future__ import absolute_import | |
| from __future__ import division | |
| # from __future__ import google_type_annotations | |
| from __future__ import print_function | |
| import collections | |
| import csv | |
| import os | |
| from albert import fine_tuning_utils | |
| from albert import modeling | |
| from albert import optimization | |
| from albert import tokenization | |
| import tensorflow.compat.v1 as tf | |
| from tensorflow.contrib import data as contrib_data | |
| from tensorflow.contrib import metrics as contrib_metrics | |
| from tensorflow.contrib import tpu as contrib_tpu | |
| class InputExample(object): | |
| """A single training/test example for simple sequence classification.""" | |
| def __init__(self, guid, text_a, text_b=None, label=None): | |
| """Constructs a InputExample. | |
| Args: | |
| guid: Unique id for the example. | |
| text_a: string. The untokenized text of the first sequence. For single | |
| sequence tasks, only this sequence must be specified. | |
| text_b: (Optional) string. The untokenized text of the second sequence. | |
| Only must be specified for sequence pair tasks. | |
| label: (Optional) string. The label of the example. This should be | |
| specified for train and dev examples, but not for test examples. | |
| """ | |
| self.guid = guid | |
| self.text_a = text_a | |
| self.text_b = text_b | |
| self.label = label | |
| class PaddingInputExample(object): | |
| """Fake example so the num input examples is a multiple of the batch size. | |
| When running eval/predict on the TPU, we need to pad the number of examples | |
| to be a multiple of the batch size, because the TPU requires a fixed batch | |
| size. The alternative is to drop the last batch, which is bad because it means | |
| the entire output data won't be generated. | |
| We use this class instead of `None` because treating `None` as padding | |
| battches could cause silent errors. | |
| """ | |
| class InputFeatures(object): | |
| """A single set of features of data.""" | |
| def __init__(self, | |
| input_ids, | |
| input_mask, | |
| segment_ids, | |
| label_id, | |
| guid=None, | |
| example_id=None, | |
| is_real_example=True): | |
| self.input_ids = input_ids | |
| self.input_mask = input_mask | |
| self.segment_ids = segment_ids | |
| self.label_id = label_id | |
| self.example_id = example_id | |
| self.guid = guid | |
| self.is_real_example = is_real_example | |
| class DataProcessor(object): | |
| """Base class for data converters for sequence classification data sets.""" | |
| def __init__(self, use_spm, do_lower_case): | |
| super(DataProcessor, self).__init__() | |
| self.use_spm = use_spm | |
| self.do_lower_case = do_lower_case | |
| def get_train_examples(self, data_dir): | |
| """Gets a collection of `InputExample`s for the train set.""" | |
| raise NotImplementedError() | |
| def get_dev_examples(self, data_dir): | |
| """Gets a collection of `InputExample`s for the dev set.""" | |
| raise NotImplementedError() | |
| def get_test_examples(self, data_dir): | |
| """Gets a collection of `InputExample`s for prediction.""" | |
| raise NotImplementedError() | |
| def get_labels(self): | |
| """Gets the list of labels for this data set.""" | |
| raise NotImplementedError() | |
| def _read_tsv(cls, input_file, quotechar=None): | |
| """Reads a tab separated value file.""" | |
| with tf.gfile.Open(input_file, "r") as f: | |
| reader = csv.reader(f, delimiter="\t", quotechar=quotechar) | |
| lines = [] | |
| for line in reader: | |
| lines.append(line) | |
| return lines | |
| def process_text(self, text): | |
| if self.use_spm: | |
| return tokenization.preprocess_text(text, lower=self.do_lower_case) | |
| else: | |
| return tokenization.convert_to_unicode(text) | |
| class MnliProcessor(DataProcessor): | |
| """Processor for the MultiNLI data set (GLUE version).""" | |
| def get_train_examples(self, data_dir): | |
| """See base class.""" | |
| return self._create_examples( | |
| self._read_tsv(os.path.join(data_dir, "MNLI", "train.tsv")), "train") | |
| def get_dev_examples(self, data_dir): | |
| """See base class.""" | |
| return self._create_examples( | |
| self._read_tsv(os.path.join(data_dir, "MNLI", "dev_matched.tsv")), | |
| "dev_matched") | |
| def get_test_examples(self, data_dir): | |
| """See base class.""" | |
| return self._create_examples( | |
| self._read_tsv(os.path.join(data_dir, "MNLI", "test_matched.tsv")), | |
| "test") | |
| def get_labels(self): | |
| """See base class.""" | |
| return ["contradiction", "entailment", "neutral"] | |
| def _create_examples(self, lines, set_type): | |
| """Creates examples for the training and dev sets.""" | |
| examples = [] | |
| for (i, line) in enumerate(lines): | |
| if i == 0: | |
| continue | |
| # Note(mingdachen): We will rely on this guid for GLUE submission. | |
| guid = self.process_text(line[0]) | |
| text_a = self.process_text(line[8]) | |
| text_b = self.process_text(line[9]) | |
| if set_type == "test": | |
| label = "contradiction" | |
| else: | |
| label = self.process_text(line[-1]) | |
| examples.append( | |
| InputExample(guid=guid, text_a=text_a, text_b=text_b, label=label)) | |
| return examples | |
| class MisMnliProcessor(MnliProcessor): | |
| """Processor for the Mismatched MultiNLI data set (GLUE version).""" | |
| def get_dev_examples(self, data_dir): | |
| """See base class.""" | |
| return self._create_examples( | |
| self._read_tsv(os.path.join(data_dir, "MNLI", "dev_mismatched.tsv")), | |
| "dev") | |
| def get_test_examples(self, data_dir): | |
| """See base class.""" | |
| return self._create_examples( | |
| self._read_tsv(os.path.join(data_dir, "MNLI", "test_mismatched.tsv")), | |
| "test") | |
| class MrpcProcessor(DataProcessor): | |
| """Processor for the MRPC data set (GLUE version).""" | |
| def get_train_examples(self, data_dir): | |
| """See base class.""" | |
| return self._create_examples( | |
| self._read_tsv(os.path.join(data_dir, "MRPC", "train.tsv")), "train") | |
| def get_dev_examples(self, data_dir): | |
| """See base class.""" | |
| return self._create_examples( | |
| self._read_tsv(os.path.join(data_dir, "MRPC", "dev.tsv")), "dev") | |
| def get_test_examples(self, data_dir): | |
| """See base class.""" | |
| return self._create_examples( | |
| self._read_tsv(os.path.join(data_dir, "MRPC", "test.tsv")), "test") | |
| def get_labels(self): | |
| """See base class.""" | |
| return ["0", "1"] | |
| def _create_examples(self, lines, set_type): | |
| """Creates examples for the training and dev sets.""" | |
| examples = [] | |
| for (i, line) in enumerate(lines): | |
| if i == 0: | |
| continue | |
| guid = "%s-%s" % (set_type, i) | |
| text_a = self.process_text(line[3]) | |
| text_b = self.process_text(line[4]) | |
| if set_type == "test": | |
| guid = line[0] | |
| label = "0" | |
| else: | |
| label = self.process_text(line[0]) | |
| examples.append( | |
| InputExample(guid=guid, text_a=text_a, text_b=text_b, label=label)) | |
| return examples | |
| class ColaProcessor(DataProcessor): | |
| """Processor for the CoLA data set (GLUE version).""" | |
| def get_train_examples(self, data_dir): | |
| """See base class.""" | |
| return self._create_examples( | |
| self._read_tsv(os.path.join(data_dir, "CoLA", "train.tsv")), "train") | |
| def get_dev_examples(self, data_dir): | |
| """See base class.""" | |
| return self._create_examples( | |
| self._read_tsv(os.path.join(data_dir, "CoLA", "dev.tsv")), "dev") | |
| def get_test_examples(self, data_dir): | |
| """See base class.""" | |
| return self._create_examples( | |
| self._read_tsv(os.path.join(data_dir, "CoLA", "test.tsv")), "test") | |
| def get_labels(self): | |
| """See base class.""" | |
| return ["0", "1"] | |
| def _create_examples(self, lines, set_type): | |
| """Creates examples for the training and dev sets.""" | |
| examples = [] | |
| for (i, line) in enumerate(lines): | |
| # Only the test set has a header | |
| if set_type == "test" and i == 0: | |
| continue | |
| guid = "%s-%s" % (set_type, i) | |
| if set_type == "test": | |
| guid = line[0] | |
| text_a = self.process_text(line[1]) | |
| label = "0" | |
| else: | |
| text_a = self.process_text(line[3]) | |
| label = self.process_text(line[1]) | |
| examples.append( | |
| InputExample(guid=guid, text_a=text_a, text_b=None, label=label)) | |
| return examples | |
| class Sst2Processor(DataProcessor): | |
| """Processor for the SST-2 data set (GLUE version).""" | |
| def get_train_examples(self, data_dir): | |
| """See base class.""" | |
| return self._create_examples( | |
| self._read_tsv(os.path.join(data_dir, "SST-2", "train.tsv")), "train") | |
| def get_dev_examples(self, data_dir): | |
| """See base class.""" | |
| return self._create_examples( | |
| self._read_tsv(os.path.join(data_dir, "SST-2", "dev.tsv")), "dev") | |
| def get_test_examples(self, data_dir): | |
| """See base class.""" | |
| return self._create_examples( | |
| self._read_tsv(os.path.join(data_dir, "SST-2", "test.tsv")), "test") | |
| def get_labels(self): | |
| """See base class.""" | |
| return ["0", "1"] | |
| def _create_examples(self, lines, set_type): | |
| """Creates examples for the training and dev sets.""" | |
| examples = [] | |
| for (i, line) in enumerate(lines): | |
| if i == 0: | |
| continue | |
| if set_type != "test": | |
| guid = "%s-%s" % (set_type, i) | |
| text_a = self.process_text(line[0]) | |
| label = self.process_text(line[1]) | |
| else: | |
| guid = self.process_text(line[0]) | |
| # guid = "%s-%s" % (set_type, line[0]) | |
| text_a = self.process_text(line[1]) | |
| label = "0" | |
| examples.append( | |
| InputExample(guid=guid, text_a=text_a, text_b=None, label=label)) | |
| return examples | |
| class StsbProcessor(DataProcessor): | |
| """Processor for the STS-B data set (GLUE version).""" | |
| def get_train_examples(self, data_dir): | |
| """See base class.""" | |
| return self._create_examples( | |
| self._read_tsv(os.path.join(data_dir, "STS-B", "train.tsv")), "train") | |
| def get_dev_examples(self, data_dir): | |
| """See base class.""" | |
| return self._create_examples( | |
| self._read_tsv(os.path.join(data_dir, "STS-B", "dev.tsv")), "dev") | |
| def get_test_examples(self, data_dir): | |
| """See base class.""" | |
| return self._create_examples( | |
| self._read_tsv(os.path.join(data_dir, "STS-B", "test.tsv")), "test") | |
| def get_labels(self): | |
| """See base class.""" | |
| return [None] | |
| def _create_examples(self, lines, set_type): | |
| """Creates examples for the training and dev sets.""" | |
| examples = [] | |
| for (i, line) in enumerate(lines): | |
| if i == 0: | |
| continue | |
| guid = self.process_text(line[0]) | |
| # guid = "%s-%s" % (set_type, line[0]) | |
| text_a = self.process_text(line[7]) | |
| text_b = self.process_text(line[8]) | |
| if set_type != "test": | |
| label = float(line[-1]) | |
| else: | |
| label = 0 | |
| examples.append( | |
| InputExample(guid=guid, text_a=text_a, text_b=text_b, label=label)) | |
| return examples | |
| class QqpProcessor(DataProcessor): | |
| """Processor for the QQP data set (GLUE version).""" | |
| def get_train_examples(self, data_dir): | |
| """See base class.""" | |
| return self._create_examples( | |
| self._read_tsv(os.path.join(data_dir, "QQP", "train.tsv")), "train") | |
| def get_dev_examples(self, data_dir): | |
| """See base class.""" | |
| return self._create_examples( | |
| self._read_tsv(os.path.join(data_dir, "QQP", "dev.tsv")), "dev") | |
| def get_test_examples(self, data_dir): | |
| """See base class.""" | |
| return self._create_examples( | |
| self._read_tsv(os.path.join(data_dir, "QQP", "test.tsv")), "test") | |
| def get_labels(self): | |
| """See base class.""" | |
| return ["0", "1"] | |
| def _create_examples(self, lines, set_type): | |
| """Creates examples for the training and dev sets.""" | |
| examples = [] | |
| for (i, line) in enumerate(lines): | |
| if i == 0: | |
| continue | |
| guid = line[0] | |
| # guid = "%s-%s" % (set_type, line[0]) | |
| if set_type != "test": | |
| try: | |
| text_a = self.process_text(line[3]) | |
| text_b = self.process_text(line[4]) | |
| label = self.process_text(line[5]) | |
| except IndexError: | |
| continue | |
| else: | |
| text_a = self.process_text(line[1]) | |
| text_b = self.process_text(line[2]) | |
| label = "0" | |
| examples.append( | |
| InputExample(guid=guid, text_a=text_a, text_b=text_b, label=label)) | |
| return examples | |
| class QnliProcessor(DataProcessor): | |
| """Processor for the QNLI data set (GLUE version).""" | |
| def get_train_examples(self, data_dir): | |
| """See base class.""" | |
| return self._create_examples( | |
| self._read_tsv(os.path.join(data_dir, "QNLI", "train.tsv")), "train") | |
| def get_dev_examples(self, data_dir): | |
| """See base class.""" | |
| return self._create_examples( | |
| self._read_tsv(os.path.join(data_dir, "QNLI", "dev.tsv")), | |
| "dev_matched") | |
| def get_test_examples(self, data_dir): | |
| """See base class.""" | |
| return self._create_examples( | |
| self._read_tsv(os.path.join(data_dir, "QNLI", "test.tsv")), | |
| "test_matched") | |
| def get_labels(self): | |
| """See base class.""" | |
| return ["entailment", "not_entailment"] | |
| def _create_examples(self, lines, set_type): | |
| """Creates examples for the training and dev sets.""" | |
| examples = [] | |
| for (i, line) in enumerate(lines): | |
| if i == 0: | |
| continue | |
| guid = self.process_text(line[0]) | |
| # guid = "%s-%s" % (set_type, line[0]) | |
| text_a = self.process_text(line[1]) | |
| text_b = self.process_text(line[2]) | |
| if set_type == "test_matched": | |
| label = "entailment" | |
| else: | |
| label = self.process_text(line[-1]) | |
| examples.append( | |
| InputExample(guid=guid, text_a=text_a, text_b=text_b, label=label)) | |
| return examples | |
| class RteProcessor(DataProcessor): | |
| """Processor for the RTE data set (GLUE version).""" | |
| def get_train_examples(self, data_dir): | |
| """See base class.""" | |
| return self._create_examples( | |
| self._read_tsv(os.path.join(data_dir, "RTE", "train.tsv")), "train") | |
| def get_dev_examples(self, data_dir): | |
| """See base class.""" | |
| return self._create_examples( | |
| self._read_tsv(os.path.join(data_dir, "RTE", "dev.tsv")), "dev") | |
| def get_test_examples(self, data_dir): | |
| """See base class.""" | |
| return self._create_examples( | |
| self._read_tsv(os.path.join(data_dir, "RTE", "test.tsv")), "test") | |
| def get_labels(self): | |
| """See base class.""" | |
| return ["entailment", "not_entailment"] | |
| def _create_examples(self, lines, set_type): | |
| """Creates examples for the training and dev sets.""" | |
| examples = [] | |
| for (i, line) in enumerate(lines): | |
| if i == 0: | |
| continue | |
| guid = self.process_text(line[0]) | |
| # guid = "%s-%s" % (set_type, line[0]) | |
| text_a = self.process_text(line[1]) | |
| text_b = self.process_text(line[2]) | |
| if set_type == "test": | |
| label = "entailment" | |
| else: | |
| label = self.process_text(line[-1]) | |
| examples.append( | |
| InputExample(guid=guid, text_a=text_a, text_b=text_b, label=label)) | |
| return examples | |
| class WnliProcessor(DataProcessor): | |
| """Processor for the WNLI data set (GLUE version).""" | |
| def get_train_examples(self, data_dir): | |
| """See base class.""" | |
| return self._create_examples( | |
| self._read_tsv(os.path.join(data_dir, "WNLI", "train.tsv")), "train") | |
| def get_dev_examples(self, data_dir): | |
| """See base class.""" | |
| return self._create_examples( | |
| self._read_tsv(os.path.join(data_dir, "WNLI", "dev.tsv")), "dev") | |
| def get_test_examples(self, data_dir): | |
| """See base class.""" | |
| return self._create_examples( | |
| self._read_tsv(os.path.join(data_dir, "WNLI", "test.tsv")), "test") | |
| def get_labels(self): | |
| """See base class.""" | |
| return ["0", "1"] | |
| def _create_examples(self, lines, set_type): | |
| """Creates examples for the training and dev sets.""" | |
| examples = [] | |
| for (i, line) in enumerate(lines): | |
| if i == 0: | |
| continue | |
| guid = self.process_text(line[0]) | |
| # guid = "%s-%s" % (set_type, line[0]) | |
| text_a = self.process_text(line[1]) | |
| text_b = self.process_text(line[2]) | |
| if set_type != "test": | |
| label = self.process_text(line[-1]) | |
| else: | |
| label = "0" | |
| examples.append( | |
| InputExample(guid=guid, text_a=text_a, text_b=text_b, label=label)) | |
| return examples | |
| class AXProcessor(DataProcessor): | |
| """Processor for the AX data set (GLUE version).""" | |
| def get_test_examples(self, data_dir): | |
| """See base class.""" | |
| return self._create_examples( | |
| self._read_tsv(os.path.join(data_dir, "diagnostic", "diagnostic.tsv")), | |
| "test") | |
| def get_labels(self): | |
| """See base class.""" | |
| return ["contradiction", "entailment", "neutral"] | |
| def _create_examples(self, lines, set_type): | |
| """Creates examples for the training and dev sets.""" | |
| examples = [] | |
| for (i, line) in enumerate(lines): | |
| if i == 0: | |
| continue | |
| # Note(mingdachen): We will rely on this guid for GLUE submission. | |
| guid = self.process_text(line[0]) | |
| text_a = self.process_text(line[1]) | |
| text_b = self.process_text(line[2]) | |
| if set_type == "test": | |
| label = "contradiction" | |
| else: | |
| label = self.process_text(line[-1]) | |
| examples.append( | |
| InputExample(guid=guid, text_a=text_a, text_b=text_b, label=label)) | |
| return examples | |
| def convert_single_example(ex_index, example, label_list, max_seq_length, | |
| tokenizer, task_name): | |
| """Converts a single `InputExample` into a single `InputFeatures`.""" | |
| if isinstance(example, PaddingInputExample): | |
| return InputFeatures( | |
| input_ids=[0] * max_seq_length, | |
| input_mask=[0] * max_seq_length, | |
| segment_ids=[0] * max_seq_length, | |
| label_id=0, | |
| is_real_example=False) | |
| if task_name != "sts-b": | |
| label_map = {} | |
| for (i, label) in enumerate(label_list): | |
| label_map[label] = i | |
| tokens_a = tokenizer.tokenize(example.text_a) | |
| tokens_b = None | |
| if example.text_b: | |
| tokens_b = tokenizer.tokenize(example.text_b) | |
| if tokens_b: | |
| # Modifies `tokens_a` and `tokens_b` in place so that the total | |
| # length is less than the specified length. | |
| # Account for [CLS], [SEP], [SEP] with "- 3" | |
| _truncate_seq_pair(tokens_a, tokens_b, max_seq_length - 3) | |
| else: | |
| # Account for [CLS] and [SEP] with "- 2" | |
| if len(tokens_a) > max_seq_length - 2: | |
| tokens_a = tokens_a[0:(max_seq_length - 2)] | |
| # The convention in ALBERT is: | |
| # (a) For sequence pairs: | |
| # tokens: [CLS] is this jack ##son ##ville ? [SEP] no it is not . [SEP] | |
| # type_ids: 0 0 0 0 0 0 0 0 1 1 1 1 1 1 | |
| # (b) For single sequences: | |
| # tokens: [CLS] the dog is hairy . [SEP] | |
| # type_ids: 0 0 0 0 0 0 0 | |
| # | |
| # Where "type_ids" are used to indicate whether this is the first | |
| # sequence or the second sequence. The embedding vectors for `type=0` and | |
| # `type=1` were learned during pre-training and are added to the | |
| # embedding vector (and position vector). This is not *strictly* necessary | |
| # since the [SEP] token unambiguously separates the sequences, but it makes | |
| # it easier for the model to learn the concept of sequences. | |
| # | |
| # For classification tasks, the first vector (corresponding to [CLS]) is | |
| # used as the "sentence vector". Note that this only makes sense because | |
| # the entire model is fine-tuned. | |
| tokens = [] | |
| segment_ids = [] | |
| tokens.append("[CLS]") | |
| segment_ids.append(0) | |
| for token in tokens_a: | |
| tokens.append(token) | |
| segment_ids.append(0) | |
| tokens.append("[SEP]") | |
| segment_ids.append(0) | |
| if tokens_b: | |
| for token in tokens_b: | |
| tokens.append(token) | |
| 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) | |
| assert len(input_ids) == max_seq_length | |
| assert len(input_mask) == max_seq_length | |
| assert len(segment_ids) == max_seq_length | |
| if task_name != "sts-b": | |
| label_id = label_map[example.label] | |
| else: | |
| label_id = example.label | |
| if ex_index < 5: | |
| tf.logging.info("*** Example ***") | |
| tf.logging.info("guid: %s" % (example.guid)) | |
| tf.logging.info("tokens: %s" % " ".join( | |
| [tokenization.printable_text(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])) | |
| tf.logging.info("label: %s (id = %d)" % (example.label, label_id)) | |
| feature = InputFeatures( | |
| input_ids=input_ids, | |
| input_mask=input_mask, | |
| segment_ids=segment_ids, | |
| label_id=label_id, | |
| is_real_example=True) | |
| return feature | |
| def file_based_convert_examples_to_features( | |
| examples, label_list, max_seq_length, tokenizer, output_file, task_name): | |
| """Convert a set of `InputExample`s to a TFRecord file.""" | |
| writer = tf.python_io.TFRecordWriter(output_file) | |
| for (ex_index, example) in enumerate(examples): | |
| if ex_index % 10000 == 0: | |
| tf.logging.info("Writing example %d of %d" % (ex_index, len(examples))) | |
| feature = convert_single_example(ex_index, example, label_list, | |
| max_seq_length, tokenizer, task_name) | |
| def create_int_feature(values): | |
| f = tf.train.Feature(int64_list=tf.train.Int64List(value=list(values))) | |
| return f | |
| def create_float_feature(values): | |
| f = tf.train.Feature(float_list=tf.train.FloatList(value=list(values))) | |
| return f | |
| features = collections.OrderedDict() | |
| 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["label_ids"] = create_float_feature([feature.label_id])\ | |
| if task_name == "sts-b" else create_int_feature([feature.label_id]) | |
| features["is_real_example"] = create_int_feature( | |
| [int(feature.is_real_example)]) | |
| tf_example = tf.train.Example(features=tf.train.Features(feature=features)) | |
| writer.write(tf_example.SerializeToString()) | |
| writer.close() | |
| def file_based_input_fn_builder(input_file, seq_length, is_training, | |
| drop_remainder, task_name, use_tpu, bsz, | |
| multiple=1): | |
| """Creates an `input_fn` closure to be passed to TPUEstimator.""" | |
| labeltype = tf.float32 if task_name == "sts-b" else tf.int64 | |
| name_to_features = { | |
| "input_ids": tf.FixedLenFeature([seq_length * multiple], tf.int64), | |
| "input_mask": tf.FixedLenFeature([seq_length * multiple], tf.int64), | |
| "segment_ids": tf.FixedLenFeature([seq_length * multiple], tf.int64), | |
| "label_ids": tf.FixedLenFeature([], labeltype), | |
| "is_real_example": 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 _truncate_seq_pair(tokens_a, tokens_b, max_length): | |
| """Truncates a sequence pair in place to the maximum length.""" | |
| # This is a simple heuristic which will always truncate the longer sequence | |
| # one token at a time. This makes more sense than truncating an equal percent | |
| # of tokens from each, since if one sequence is very short then each token | |
| # that's truncated likely contains more information than a longer sequence. | |
| while True: | |
| total_length = len(tokens_a) + len(tokens_b) | |
| if total_length <= max_length: | |
| break | |
| if len(tokens_a) > len(tokens_b): | |
| tokens_a.pop() | |
| else: | |
| tokens_b.pop() | |
| def create_model(albert_config, is_training, input_ids, input_mask, segment_ids, | |
| labels, num_labels, use_one_hot_embeddings, task_name, | |
| hub_module): | |
| """Creates a classification model.""" | |
| (output_layer, _) = 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) | |
| hidden_size = output_layer.shape[-1].value | |
| output_weights = tf.get_variable( | |
| "output_weights", [num_labels, hidden_size], | |
| initializer=tf.truncated_normal_initializer(stddev=0.02)) | |
| output_bias = tf.get_variable( | |
| "output_bias", [num_labels], initializer=tf.zeros_initializer()) | |
| with tf.variable_scope("loss"): | |
| if is_training: | |
| # I.e., 0.1 dropout | |
| output_layer = tf.nn.dropout(output_layer, keep_prob=0.9) | |
| logits = tf.matmul(output_layer, output_weights, transpose_b=True) | |
| logits = tf.nn.bias_add(logits, output_bias) | |
| if task_name != "sts-b": | |
| probabilities = tf.nn.softmax(logits, axis=-1) | |
| predictions = tf.argmax(probabilities, axis=-1, output_type=tf.int32) | |
| log_probs = tf.nn.log_softmax(logits, axis=-1) | |
| one_hot_labels = tf.one_hot(labels, depth=num_labels, dtype=tf.float32) | |
| per_example_loss = -tf.reduce_sum(one_hot_labels * log_probs, axis=-1) | |
| else: | |
| probabilities = logits | |
| logits = tf.squeeze(logits, [-1]) | |
| predictions = logits | |
| per_example_loss = tf.square(logits - labels) | |
| loss = tf.reduce_mean(per_example_loss) | |
| return (loss, per_example_loss, probabilities, logits, predictions) | |
| def model_fn_builder(albert_config, num_labels, init_checkpoint, learning_rate, | |
| num_train_steps, num_warmup_steps, use_tpu, | |
| use_one_hot_embeddings, task_name, hub_module=None, | |
| optimizer="adamw"): | |
| """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)) | |
| input_ids = features["input_ids"] | |
| input_mask = features["input_mask"] | |
| segment_ids = features["segment_ids"] | |
| label_ids = features["label_ids"] | |
| is_real_example = None | |
| if "is_real_example" in features: | |
| is_real_example = tf.cast(features["is_real_example"], dtype=tf.float32) | |
| else: | |
| is_real_example = tf.ones(tf.shape(label_ids), dtype=tf.float32) | |
| is_training = (mode == tf.estimator.ModeKeys.TRAIN) | |
| (total_loss, per_example_loss, probabilities, logits, predictions) = \ | |
| create_model(albert_config, is_training, input_ids, input_mask, | |
| segment_ids, label_ids, num_labels, use_one_hot_embeddings, | |
| task_name, 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: | |
| train_op = optimization.create_optimizer( | |
| total_loss, learning_rate, num_train_steps, num_warmup_steps, | |
| use_tpu, optimizer) | |
| output_spec = contrib_tpu.TPUEstimatorSpec( | |
| mode=mode, | |
| loss=total_loss, | |
| train_op=train_op, | |
| scaffold_fn=scaffold_fn) | |
| elif mode == tf.estimator.ModeKeys.EVAL: | |
| if task_name not in ["sts-b", "cola"]: | |
| def metric_fn(per_example_loss, label_ids, logits, is_real_example): | |
| predictions = tf.argmax(logits, axis=-1, output_type=tf.int32) | |
| accuracy = tf.metrics.accuracy( | |
| labels=label_ids, predictions=predictions, | |
| weights=is_real_example) | |
| loss = tf.metrics.mean( | |
| values=per_example_loss, weights=is_real_example) | |
| return { | |
| "eval_accuracy": accuracy, | |
| "eval_loss": loss, | |
| } | |
| elif task_name == "sts-b": | |
| def metric_fn(per_example_loss, label_ids, logits, is_real_example): | |
| """Compute Pearson correlations for STS-B.""" | |
| # Display labels and predictions | |
| concat1 = contrib_metrics.streaming_concat(logits) | |
| concat2 = contrib_metrics.streaming_concat(label_ids) | |
| # Compute Pearson correlation | |
| pearson = contrib_metrics.streaming_pearson_correlation( | |
| logits, label_ids, weights=is_real_example) | |
| # Compute MSE | |
| # mse = tf.metrics.mean(per_example_loss) | |
| mse = tf.metrics.mean_squared_error( | |
| label_ids, logits, weights=is_real_example) | |
| loss = tf.metrics.mean( | |
| values=per_example_loss, | |
| weights=is_real_example) | |
| return {"pred": concat1, "label_ids": concat2, "pearson": pearson, | |
| "MSE": mse, "eval_loss": loss,} | |
| elif task_name == "cola": | |
| def metric_fn(per_example_loss, label_ids, logits, is_real_example): | |
| """Compute Matthew's correlations for STS-B.""" | |
| predictions = tf.argmax(logits, axis=-1, output_type=tf.int32) | |
| # https://en.wikipedia.org/wiki/Matthews_correlation_coefficient | |
| tp, tp_op = tf.metrics.true_positives( | |
| predictions, label_ids, weights=is_real_example) | |
| tn, tn_op = tf.metrics.true_negatives( | |
| predictions, label_ids, weights=is_real_example) | |
| fp, fp_op = tf.metrics.false_positives( | |
| predictions, label_ids, weights=is_real_example) | |
| fn, fn_op = tf.metrics.false_negatives( | |
| predictions, label_ids, weights=is_real_example) | |
| # Compute Matthew's correlation | |
| mcc = tf.div_no_nan( | |
| tp * tn - fp * fn, | |
| tf.pow((tp + fp) * (tp + fn) * (tn + fp) * (tn + fn), 0.5)) | |
| # Compute accuracy | |
| accuracy = tf.metrics.accuracy( | |
| labels=label_ids, predictions=predictions, | |
| weights=is_real_example) | |
| loss = tf.metrics.mean( | |
| values=per_example_loss, | |
| weights=is_real_example) | |
| return {"matthew_corr": (mcc, tf.group(tp_op, tn_op, fp_op, fn_op)), | |
| "eval_accuracy": accuracy, "eval_loss": loss,} | |
| eval_metrics = (metric_fn, | |
| [per_example_loss, label_ids, logits, is_real_example]) | |
| output_spec = contrib_tpu.TPUEstimatorSpec( | |
| mode=mode, | |
| loss=total_loss, | |
| eval_metrics=eval_metrics, | |
| scaffold_fn=scaffold_fn) | |
| else: | |
| output_spec = contrib_tpu.TPUEstimatorSpec( | |
| mode=mode, | |
| predictions={ | |
| "probabilities": probabilities, | |
| "predictions": predictions | |
| }, | |
| scaffold_fn=scaffold_fn) | |
| return output_spec | |
| return model_fn | |
| # This function is not used by this file but is still used by the Colab and | |
| # people who depend on it. | |
| def input_fn_builder(features, seq_length, is_training, drop_remainder): | |
| """Creates an `input_fn` closure to be passed to TPUEstimator.""" | |
| all_input_ids = [] | |
| all_input_mask = [] | |
| all_segment_ids = [] | |
| all_label_ids = [] | |
| for feature in features: | |
| all_input_ids.append(feature.input_ids) | |
| all_input_mask.append(feature.input_mask) | |
| all_segment_ids.append(feature.segment_ids) | |
| all_label_ids.append(feature.label_id) | |
| def input_fn(params): | |
| """The actual input function.""" | |
| batch_size = params["batch_size"] | |
| num_examples = len(features) | |
| # This is for demo purposes and does NOT scale to large data sets. We do | |
| # not use Dataset.from_generator() because that uses tf.py_func which is | |
| # not TPU compatible. The right way to load data is with TFRecordReader. | |
| d = tf.data.Dataset.from_tensor_slices({ | |
| "input_ids": | |
| tf.constant( | |
| all_input_ids, shape=[num_examples, seq_length], | |
| dtype=tf.int32), | |
| "input_mask": | |
| tf.constant( | |
| all_input_mask, | |
| shape=[num_examples, seq_length], | |
| dtype=tf.int32), | |
| "segment_ids": | |
| tf.constant( | |
| all_segment_ids, | |
| shape=[num_examples, seq_length], | |
| dtype=tf.int32), | |
| "label_ids": | |
| tf.constant(all_label_ids, shape=[num_examples], dtype=tf.int32), | |
| }) | |
| if is_training: | |
| d = d.repeat() | |
| d = d.shuffle(buffer_size=100) | |
| d = d.batch(batch_size=batch_size, drop_remainder=drop_remainder) | |
| return d | |
| return input_fn | |
| # This function is not used by this file but is still used by the Colab and | |
| # people who depend on it. | |
| def convert_examples_to_features(examples, label_list, max_seq_length, | |
| tokenizer, task_name): | |
| """Convert a set of `InputExample`s to a list of `InputFeatures`.""" | |
| features = [] | |
| for (ex_index, example) in enumerate(examples): | |
| if ex_index % 10000 == 0: | |
| tf.logging.info("Writing example %d of %d" % (ex_index, len(examples))) | |
| feature = convert_single_example(ex_index, example, label_list, | |
| max_seq_length, tokenizer, task_name) | |
| features.append(feature) | |
| return features | |