remove

ernie-gram/reader/pretraining.py

-#   Copyright (c) 2019 PaddlePaddle Authors. All Rights Reserved.
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-#     http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-
-from __future__ import print_function
-from __future__ import division
-
-import os
-import numpy as np
-import types
-import gzip
-import logging
-import re
-import six
-import collections
-import tokenization
-
-import paddle
-import paddle.fluid as fluid
-
-from batching import prepare_batch_data
-
-
-class ErnieDataReader(object):
-    def __init__(self,
-                 filelist,
-                 vocab_path,
-                 batch_size=4096,
-                 in_tokens=True,
-                 max_seq_len=512,
-                 shuffle_files=True,
-                 random_seed=1,
-                 epoch=100,
-                 voc_size=0,
-                 is_test=False,
-                 generate_neg_sample=False,
-                 hack_old_trainset=False):
-
-        self.vocab = self.load_vocab(vocab_path)
-        self.filelist = filelist
-        self.batch_size = batch_size
-        self.in_tokens = in_tokens
-        self.random_seed = random_seed
-        self.shuffle_files = shuffle_files
-        self.epoch = epoch
-        self.current_epoch = 0
-        self.current_file_index = 0
-        self.total_file = 0
-        self.current_file = None
-        self.voc_size = voc_size
-        self.max_seq_len = max_seq_len
-        self.pad_id = self.vocab["[PAD]"]
-        self.cls_id = self.vocab["[CLS]"]
-        self.sep_id = self.vocab["[SEP]"]
-        self.mask_id = self.vocab["[MASK]"]
-        self.input_slots = 5
-        self.is_test = is_test
-        self.generate_neg_sample = generate_neg_sample
-        
-        self.trainer_id = 0
-        self.trainer_nums = 1
-        self.files = open(filelist).readlines()
-        self.total_file = len(self.files)
-        
-        if self.is_test:
-            self.epoch = 1
-            self.shuffle_files = False
-        
-        self.global_rng = np.random.RandomState(random_seed)
-        if self.shuffle_files:
-            if os.getenv("PADDLE_TRAINER_ID"):
-                self.trainer_id = int(os.getenv("PADDLE_TRAINER_ID"))
-            if os.getenv("PADDLE_NODES_NUM"):
-                self.trainer_nums = int(os.getenv("PADDLE_TRAINERS_NUM"))
-            #renew total_file
-            self.total_file = len(self.files) // self.trainer_nums * self.trainer_nums
-
-            tmp_files = []
-            for each in range(epoch):
-                each_files = [i for i in self.files]
-                self.global_rng.shuffle(each_files)
-                tmp_files += each_files
-            self.files = tmp_files
-            #renew epochs
-            self.epoch = len(self.files) // self.total_file * self.total_file
-        
-        assert self.total_file > 0, \
-            "[Error] data_dir is empty or less than %d" % self.trainer_nums
-
-        if self.in_tokens:
-            assert self.batch_size > 100, "Current batch size means total token's number, \
-                                       it should not be set to too small number."
-
-        if hack_old_trainset:
-            self.input_slots = 4
-
-    def get_progress(self):
-        """return current progress of traning data
-        """
-        return self.current_epoch, self.current_file_index, self.total_file, self.current_file, self.mask_type
-
-    def parse_line(self, line, max_seq_len=512):
-        """ parse one line to token_ids, sentence_ids, pos_ids, label
-        """
-        line = line.strip().split(";")
-        assert len(line) == self.input_slots, \
-                "One sample must have %d fields!" % self.input_slots
-
-        if self.input_slots == 4:
-            (token_ids, sent_ids, pos_ids, label) = line
-            token_ids = [int(token) for token in token_ids.split(" ")]
-            sent_ids = [int(token) for token in sent_ids.split(" ")]
-            pos_ids = [int(token) for token in pos_ids.split(" ")]
-            #fake seg_labels
-            seg_labels = [0, ] * len(sent_ids)
-            id_sent_b = sent_ids[0] + 1
-            len_sent_a = sent_ids.index(id_sent_b)
-            #sent_a, sent_b
-            seg_labels[0] = seg_labels[len_sent_a - 1] = seg_labels[-1] = -1
-
-        if self.input_slots == 5:
-            (token_ids, sent_ids, pos_ids, seg_labels, label) = line
-            token_ids = [int(token) for token in token_ids.split(" ")]
-            sent_ids = [int(token) for token in sent_ids.split(" ")]
-            pos_ids = [int(token) for token in pos_ids.split(" ")]
-            seg_labels = [int(seg_label) for seg_label in seg_labels.split(" ")]
-        
-        assert len(token_ids) == len(sent_ids) == len(pos_ids) == len(
-            seg_labels
-        ), "[Must be true]len(token_ids) == len(sent_ids) == len(pos_ids) == len(seg_labels)"
-        label = int(label)
-        if len(token_ids) > max_seq_len:
-            return None
-        return [token_ids, sent_ids, pos_ids, label, seg_labels]
-
-    def read_file(self, file):
-        assert file.endswith('.gz'), "[ERROR] %s is not a gzip file" % file
-        with gzip.open(file, "rb") as f:
-            for line in f:
-                parsed_line = self.parse_line(
-                    line, max_seq_len=self.max_seq_len)
-                if parsed_line is None:
-                    continue
-                yield parsed_line
-
-    def convert_to_unicode(self, text):
-        """Converts `text` to Unicode (if it's not already), assuming utf-8 input."""
-        if six.PY3:
-            if isinstance(text, str):
-                return text
-            elif isinstance(text, bytes):
-                return text.decode("utf-8", "ignore")
-            else:
-                raise ValueError("Unsupported string type: %s" % (type(text)))
-        elif six.PY2:
-            if isinstance(text, str):
-                return text.decode("utf-8", "ignore")
-            elif isinstance(text, unicode):
-                return text
-            else:
-                raise ValueError("Unsupported string type: %s" % (type(text)))
-        else:
-            raise ValueError("Not running on Python2 or Python 3?")
-
-    def load_vocab(self, vocab_file):
-        """Loads a vocabulary file into a dictionary."""
-        vocab = collections.OrderedDict()
-        fin = open(vocab_file)
-        for num, line in enumerate(fin):
-            items = self.convert_to_unicode(line.strip()).split("\t")
-            if len(items) > 2:
-                break
-            token = items[0]
-            index = items[1] if len(items) == 2 else num
-            token = token.strip()
-            vocab[token] = int(index)
-        return vocab
-
-    def random_pair_neg_samples(self, pos_samples):
-        """ randomly generate negtive samples using pos_samples
-
-            Args:
-                pos_samples: list of positive samples
-            
-            Returns:
-                neg_samples: list of negtive samples
-        """
-        np.random.shuffle(pos_samples)
-        num_sample = len(pos_samples)
-        neg_samples = []
-        miss_num = 0
-
-        def split_sent(sample, max_len, sep_id):
-            token_seq, type_seq, pos_seq, label, seg_labels = sample
-            sep_index = token_seq.index(sep_id)
-            left_len = sep_index - 1
-            if left_len <= max_len:
-                return (token_seq[1:sep_index], seg_labels[1:sep_index])
-            else:
-                return [
-                    token_seq[sep_index + 1:-1], seg_labels[sep_index + 1:-1]
-                ]
-
-        for i in range(num_sample):
-            pair_index = (i + 1) % num_sample
-            left_tokens, left_seg_labels = split_sent(
-                pos_samples[i], (self.max_seq_len - 3) // 2, self.sep_id)
-            right_tokens, right_seg_labels = split_sent(
-                pos_samples[pair_index],
-                self.max_seq_len - 3 - len(left_tokens), self.sep_id)
-
-            token_seq = [self.cls_id] + left_tokens + [self.sep_id] + \
-                    right_tokens + [self.sep_id]
-            if len(token_seq) > self.max_seq_len:
-                miss_num += 1
-                continue
-            type_seq = [0] * (len(left_tokens) + 2) + [1] * (len(right_tokens) +
-                                                             1)
-            pos_seq = range(len(token_seq))
-            seg_label_seq = [-1] + left_seg_labels + [-1] + right_seg_labels + [
-                -1
-            ]
-
-            assert len(token_seq) == len(type_seq) == len(pos_seq) == len(seg_label_seq), \
-                    "[ERROR]len(src_id) == lne(sent_id) == len(pos_id) must be True"
-            neg_samples.append([token_seq, type_seq, pos_seq, 0, seg_label_seq])
-
-        return neg_samples, miss_num
-
-    def mixin_negtive_samples(self, pos_sample_generator, buffer=1000):
-        """ 1. generate negtive samples by randomly group sentence_1 and sentence_2 of positive samples
-            2. combine negtive samples and positive samples
-            
-            Args:
-                pos_sample_generator: a generator producing a parsed positive sample, which is a list: [token_ids, sent_ids, pos_ids, 1]
-
-            Returns:
-                sample: one sample from shuffled positive samples and negtive samples
-        """
-        pos_samples = []
-        num_total_miss = 0
-        pos_sample_num = 0
-        try:
-            while True:
-                while len(pos_samples) < buffer:
-                    pos_sample = next(pos_sample_generator)
-                    label = pos_sample[3]
-                    assert label == 1, "positive sample's label must be 1"
-                    pos_samples.append(pos_sample)
-                    pos_sample_num += 1
-
-                neg_samples, miss_num = self.random_pair_neg_samples(
-                    pos_samples)
-                num_total_miss += miss_num
-                samples = pos_samples + neg_samples
-                pos_samples = []
-                np.random.shuffle(samples)
-                for sample in samples:
-                    yield sample
-        except StopIteration:
-            print("stopiteration: reach end of file")
-            if len(pos_samples) == 1:
-                yield pos_samples[0]
-            elif len(pos_samples) == 0:
-                yield None
-            else:
-                neg_samples, miss_num = self.random_pair_neg_samples(
-                    pos_samples)
-                num_total_miss += miss_num
-                samples = pos_samples + neg_samples
-                pos_samples = []
-                np.random.shuffle(samples)
-                for sample in samples:
-                    yield sample
-            print("miss_num:%d\tideal_total_sample_num:%d\tmiss_rate:%f" %
-                  (num_total_miss, pos_sample_num * 2,
-                   num_total_miss / (pos_sample_num * 2)))
-    
-    def shuffle_samples(self, sample_generator, buffer=1000):
-        samples = []
-        try:
-            while True:
-                while len(samples) < buffer:
-                    sample = next(sample_generator)
-                    samples.append(sample)
-                np.random.shuffle(samples)
-                for sample in samples:
-                    yield sample
-                samples = []
-        except StopIteration:
-            print("stopiteration: reach end of file")
-            if len(samples) == 0:
-                yield None
-            else:
-                np.random.shuffle(samples)
-                for sample in samples:
-                    yield sample
-
-    def data_generator(self):
-        """
-        data_generator
-        """
-        def wrapper():
-            def reader():
-                for epoch in range(self.epoch):
-                    self.current_epoch = epoch + 1
-                    files = self.files
-                    #during training, data are sliced by trainers
-                    if self.shuffle_files:
-                        start = epoch * self.total_file
-                        end = start + self.total_file
-                        files = [file_ for index, file_ in enumerate(self.files[start:end]) \
-                            if index % self.trainer_nums == self.trainer_id]
-                    
-                    for index, file_ in enumerate(files):
-                        file_, mask_word_prob = file_.strip().split("\t")
-                        mask_word = (np.random.random() < float(mask_word_prob))
-                        self.current_file_index = (index + 1) * self.trainer_nums
-                        self.current_file = file_
-                        if mask_word:
-                            self.mask_type = "mask_word"
-                        else:
-                            self.mask_type = "mask_char"
-
-                        sample_generator = self.read_file(file_)
-                        if not self.is_test:
-                            if self.generate_neg_sample:
-                                sample_generator = self.mixin_negtive_samples(
-                                    sample_generator)
-                            else:
-                                #shuffle buffered sample
-                                sample_generator = self.shuffle_samples(
-                                    sample_generator)
-
-                        for sample in sample_generator:
-                            if sample is None:
-                                continue
-                            sample.append(mask_word)
-                            yield sample
-
-            def batch_reader(reader, batch_size):
-                batch, total_token_num, max_len = [], 0, 0
-                for parsed_line in reader():
-                    token_ids, sent_ids, pos_ids, label, seg_labels, mask_word = parsed_line
-                    max_len = max(max_len, len(token_ids))
-                    if self.in_tokens:
-                        to_append = (len(batch) + 1) * max_len <= batch_size
-                    else:
-                        to_append = len(batch) < batch_size
-                    if to_append:
-                        batch.append(parsed_line)
-                        total_token_num += len(token_ids)
-                    else:
-                        yield batch, total_token_num
-                        batch, total_token_num, max_len = [parsed_line], len(
-                            token_ids), len(token_ids)
-
-                if len(batch) > 0:
-                    yield batch, total_token_num
-
-            for batch_data, total_token_num in batch_reader(reader,
-                                                            self.batch_size):
-                yield prepare_batch_data(
-                    batch_data,
-                    total_token_num,
-                    voc_size=self.voc_size,
-                    pad_id=self.pad_id,
-                    cls_id=self.cls_id,
-                    sep_id=self.sep_id,
-                    mask_id=self.mask_id,
-                    return_input_mask=True,
-                    return_max_len=False,
-                    return_num_token=False)
-
-        return wrapper
-
-
-if __name__ == "__main__":
-    pass