# Copyright (c) 2021 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. """data reader for text classification tasks""" import os import csv import numpy as np import copy from collections import namedtuple from model import tokenization from reader.batching import pad_batch_data class ClassifyReader(object): """ClassifyReader""" def __init__(self, tokenizer, args): self.tokenizer = tokenizer self.pad_id = tokenizer.pad_token_id self.cls_id = tokenizer.cls_token_id self.sep_id = tokenizer.sep_token_id self.mask_id = tokenizer.mask_token_id self.max_seq_len = args.max_seq_len self.in_tokens = args.in_tokens self.random_seed = 0 self.global_rng = np.random.RandomState(self.random_seed) self.trainer_id = int(os.getenv("PADDLE_TRAINER_ID", "0")) self.trainer_nums = int(os.getenv("PADDLE_TRAINERS_NUM", "1")) self.current_example = 0 self.current_epoch = 0 self.num_examples = 0 def get_train_progress(self): """Gets progress for training phase.""" return self.current_example, self.current_epoch def _read_tsv(self, input_file, quotechar=None): """Reads a tab separated value file.""" with open(input_file, "r") as f: reader = csv.reader(f, delimiter="\t", quotechar=quotechar) headers = next(reader) text_indices = [ index for index, h in enumerate(headers) if h != "label" ] Example = namedtuple('Example', headers) examples = [] for line in reader: example = Example(*line) examples.append(example) return examples def _pad_batch_records(self, batch_records): batch_token_ids = [record.token_ids for record in batch_records] batch_text_type_ids = [record.text_type_ids for record in batch_records] batch_position_ids = [record.position_ids for record in batch_records] batch_labels = [record.label_id for record in batch_records] batch_labels = np.array(batch_labels).astype('int64').reshape([-1, 1]) if batch_records[0].qid: batch_qids = [record.qid for record in batch_records] batch_qids = np.array(batch_qids).astype('int64').reshape([-1, 1]) else: batch_qids = np.array([]).astype('int64').reshape([-1, 1]) # padding padded_token_ids, input_mask = pad_batch_data( batch_token_ids, pretraining_task='nlu', pad_idx=self.pad_id, return_input_mask=True) padded_text_type_ids = pad_batch_data( batch_text_type_ids, pretraining_task='nlu', pad_idx=self.pad_id) padded_position_ids = pad_batch_data( batch_position_ids, pretraining_task='nlu', pad_idx=self.pad_id) input_mask = np.matmul(input_mask, np.transpose(input_mask, (0, 2, 1))) return_list = [ padded_token_ids, padded_text_type_ids, padded_position_ids, input_mask, batch_labels, batch_qids ] return return_list def _truncate_seq_pair(self, tokens_a, tokens_b, max_length): """Truncates a sequence pair in place to the maximum length.""" 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 _convert_example_to_record(self, example, max_seq_length, tokenizer): """Converts a single `Example` into a single `Record`.""" text_a = tokenization.convert_to_unicode(example.text_a) tokens_a = tokenizer.tokenize(text_a) tokens_b = None if "text_b" in example._fields: text_b = tokenization.convert_to_unicode(example.text_b) tokens_b = tokenizer.tokenize(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" self._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)] tokens = [] text_type_ids = [] tokens.append("[CLS]") text_type_ids.append(0) for token in tokens_a: tokens.append(token) text_type_ids.append(0) tokens.append("[SEP]") text_type_ids.append(0) if tokens_b: for token in tokens_b: tokens.append(token) text_type_ids.append(1) tokens.append("[SEP]") text_type_ids.append(1) token_ids = tokenizer.convert_tokens_to_ids(tokens) position_ids = list(range(2, len(token_ids) + 2)) label_id = example.label Record = namedtuple( 'Record', ['token_ids', 'text_type_ids', 'position_ids', 'label_id', 'qid']) qid = None if "qid" in example._fields: qid = example.qid record = Record( token_ids=token_ids, text_type_ids=text_type_ids, position_ids=position_ids, label_id=label_id, qid=qid) return record def _prepare_batch_data(self, examples, batch_size, phase=None): """generate batch records""" batch_records, max_len = [], 0 for index, example in enumerate(examples): if phase == "train": self.current_example = index record = self._convert_example_to_record(example, self.max_seq_len, self.tokenizer) max_len = max(max_len, len(record.token_ids)) if self.in_tokens: to_append = (len(batch_records) + 1) * max_len <= batch_size else: to_append = len(batch_records) < batch_size if to_append: batch_records.append(record) else: yield self._pad_batch_records(batch_records) batch_records, max_len = [record], len(record.token_ids) if batch_records: yield self._pad_batch_records(batch_records) def get_num_examples(self, input_file): """get_num_examples""" examples = self._read_tsv(input_file) return len(examples) def data_generator(self, input_file, batch_size, epoch, dev_count=1, shuffle=True, phase=None): """data_generator""" examples = self._read_tsv(input_file) def wrapper(): """wrapper""" all_dev_batches = [] trainer_id = 0 for epoch_index in range(epoch): if phase == "train": self.current_example = 0 self.current_epoch = epoch_index self.random_seed = epoch_index self.global_rng = np.random.RandomState(self.random_seed) trainer_id = self.trainer_id else: trainer_id = 0 assert dev_count == 1, "only supports 1 GPU while prediction" current_examples = copy.deepcopy(examples) if shuffle: self.global_rng.shuffle(current_examples) for batch_data in self._prepare_batch_data( current_examples, batch_size, phase=phase): if len(all_dev_batches) < dev_count: all_dev_batches.append(batch_data) if len(all_dev_batches) == dev_count: yield all_dev_batches[trainer_id] all_dev_batches = [] if phase != "train" and self.trainer_id < len(all_dev_batches): yield all_dev_batches[self.trainer_id] return wrapper if __name__ == '__main__': pass