PyTorch教程-15.9。預訓練 BERT 的數據集

為了預訓練第 15.8 節中實現的 BERT 模型，我們需要以理想的格式生成數據集，以促進兩項預訓練任務：掩碼語言建模和下一句預測。一方面，原始的 BERT 模型是在兩個巨大的語料庫 BookCorpus 和英文維基百科（參見第15.8.5 節）的串聯上進行預訓練的，這使得本書的大多數讀者難以運行。另一方面，現成的預訓練 BERT 模型可能不適合醫學等特定領域的應用。因此，在自定義數據集上預訓練 BERT 變得越來越流行。為了便于演示 BERT 預訓練，我們使用較小的語料庫 WikiText-2 ( Merity et al. , 2016 )。

與 15.3節用于預訓練word2vec的PTB數據集相比，WikiText-2(i)保留了原有的標點符號，適合下一句預測；(ii) 保留原始案例和編號；(iii) 大兩倍以上。

import os
import random
import torch
from d2l import torch as d2l

import os
import random
from mxnet import gluon, np, npx
from d2l import mxnet as d2l

npx.set_np()

在 WikiText-2 數據集中，每一行代表一個段落，其中在任何標點符號及其前面的標記之間插入空格。保留至少兩句話的段落。為了簡單起見，為了拆分句子，我們只使用句點作為分隔符。我們將在本節末尾的練習中討論更復雜的句子拆分技術。

#@save
d2l.DATA_HUB['wikitext-2'] = (
  'https://s3.amazonaws.com/research.metamind.io/wikitext/'
  'wikitext-2-v1.zip', '3c914d17d80b1459be871a5039ac23e752a53cbe')

#@save
def _read_wiki(data_dir):
  file_name = os.path.join(data_dir, 'wiki.train.tokens')
  with open(file_name, 'r') as f:
    lines = f.readlines()
  # Uppercase letters are converted to lowercase ones
  paragraphs = [line.strip().lower().split(' . ')
         for line in lines if len(line.split(' . ')) >= 2]
  random.shuffle(paragraphs)
  return paragraphs

#@save
d2l.DATA_HUB['wikitext-2'] = (
  'https://s3.amazonaws.com/research.metamind.io/wikitext/'
  'wikitext-2-v1.zip', '3c914d17d80b1459be871a5039ac23e752a53cbe')

#@save
def _read_wiki(data_dir):
  file_name = os.path.join(data_dir, 'wiki.train.tokens')
  with open(file_name, 'r') as f:
    lines = f.readlines()
  # Uppercase letters are converted to lowercase ones
  paragraphs = [line.strip().lower().split(' . ')
         for line in lines if len(line.split(' . ')) >= 2]
  random.shuffle(paragraphs)
  return paragraphs

15.9.1。為預訓練任務定義輔助函數

下面，我們首先為兩個 BERT 預訓練任務實現輔助函數：下一句預測和掩碼語言建模。這些輔助函數將在稍后將原始文本語料庫轉換為理想格式的數據集以預訓練 BERT 時調用。

15.9.1.1。生成下一句預測任務

根據15.8.5.2 節的描述，該 _get_next_sentence函數為二元分類任務生成一個訓練樣例。

#@save
def _get_next_sentence(sentence, next_sentence, paragraphs):
  if random.random() < 0.5:
    is_next = True
  else:
    # `paragraphs` is a list of lists of lists
    next_sentence = random.choice(random.choice(paragraphs))
    is_next = False
  return sentence, next_sentence, is_next

#@save
def _get_next_sentence(sentence, next_sentence, paragraphs):
  if random.random() < 0.5:
    is_next = True
  else:
    # `paragraphs` is a list of lists of lists
    next_sentence = random.choice(random.choice(paragraphs))
    is_next = False
  return sentence, next_sentence, is_next

以下函數paragraph通過調用該 _get_next_sentence函數從輸入生成用于下一句預測的訓練示例。這paragraph是一個句子列表，其中每個句子都是一個標記列表。該參數 max_len指定預訓練期間 BERT 輸入序列的最大長度。

#@save
def _get_nsp_data_from_paragraph(paragraph, paragraphs, vocab, max_len):
  nsp_data_from_paragraph = []
  for i in range(len(paragraph) - 1):
    tokens_a, tokens_b, is_next = _get_next_sentence(
      paragraph[i], paragraph[i + 1], paragraphs)
    # Consider 1 '' token and 2 '' tokens
    if len(tokens_a) + len(tokens_b) + 3 > max_len:
      continue
    tokens, segments = d2l.get_tokens_and_segments(tokens_a, tokens_b)
    nsp_data_from_paragraph.append((tokens, segments, is_next))
  return nsp_data_from_paragraph

#@save
def _get_nsp_data_from_paragraph(paragraph, paragraphs, vocab, max_len):
  nsp_data_from_paragraph = []
  for i in range(len(paragraph) - 1):
    tokens_a, tokens_b, is_next = _get_next_sentence(
      paragraph[i], paragraph[i + 1], paragraphs)
    # Consider 1 '' token and 2 '' tokens
    if len(tokens_a) + len(tokens_b) + 3 > max_len:
      continue
    tokens, segments = d2l.get_tokens_and_segments(tokens_a, tokens_b)
    nsp_data_from_paragraph.append((tokens, segments, is_next))
  return nsp_data_from_paragraph

15.9.1.2。生成掩碼語言建模任務

為了從 BERT 輸入序列為掩碼語言建模任務生成訓練示例，我們定義了以下 _replace_mlm_tokens函數。在它的輸入中，tokens是代表BERT輸入序列的token列表，candidate_pred_positions 是BERT輸入序列的token索引列表，不包括特殊token（masked語言建模任務中不預測特殊token），num_mlm_preds表示預測（召回 15% 的隨機標記來預測）。遵循第 15.8.5.1 節中屏蔽語言建模任務的定義 ，在每個預測位置，輸入可能被特殊的“”標記或隨機標記替換，或者保持不變。最后，該函數返回可能替換后的輸入標記、發生預測的標記索引以及這些預測的標簽。

#@save
def _replace_mlm_tokens(tokens, candidate_pred_positions, num_mlm_preds,
            vocab):
  # For the input of a masked language model, make a new copy of tokens and
  # replace some of them by '' or random tokens
  mlm_input_tokens = [token for token in tokens]
  pred_positions_and_labels = []
  # Shuffle for getting 15% random tokens for prediction in the masked
  # language modeling task
  random.shuffle(candidate_pred_positions)
  for mlm_pred_position in candidate_pred_positions:
    if len(pred_positions_and_labels) >= num_mlm_preds:
      break
    masked_token = None
    # 80% of the time: replace the word with the '' token
    if random.random() < 0.8:
      masked_token = ''
    else:
      # 10% of the time: keep the word unchanged
      if random.random() < 0.5:
        masked_token = tokens[mlm_pred_position]
      # 10% of the time: replace the word with a random word
      else:
        masked_token = random.choice(vocab.idx_to_token)
    mlm_input_tokens[mlm_pred_position] = masked_token
    pred_positions_and_labels.append(
      (mlm_pred_position, tokens[mlm_pred_position]))
  return mlm_input_tokens, pred_positions_and_labels

#@save
def _replace_mlm_tokens(tokens, candidate_pred_positions, num_mlm_preds,
            vocab):
  # For the input of a masked language model, make a new copy of tokens and
  # replace some of them by '' or random tokens
  mlm_input_tokens = [token for token in tokens]
  pred_positions_and_labels = []
  # Shuffle for getting 15% random tokens for prediction in the masked
  # language modeling task
  random.shuffle(candidate_pred_positions)
  for mlm_pred_position in candidate_pred_positions:
    if len(pred_positions_and_labels) >= num_mlm_preds:
      break
    masked_token = None
    # 80% of the time: replace the word with the '' token
    if random.random() < 0.8:
      masked_token = ''
    else:
      # 10% of the time: keep the word unchanged
      if random.random() < 0.5:
        masked_token = tokens[mlm_pred_position]
      # 10% of the time: replace the word with a random word
      else:
        masked_token = random.choice(vocab.idx_to_token)
    mlm_input_tokens[mlm_pred_position] = masked_token
    pred_positions_and_labels.append(
      (mlm_pred_position, tokens[mlm_pred_position]))
  return mlm_input_tokens, pred_positions_and_labels

通過調用上述_replace_mlm_tokens函數，以下函數將 BERT 輸入序列 ( tokens) 作為輸入并返回輸入標記的索引（在可能的標記替換之后，如第15.8.5.1 節所述）、發生預測的標記索引和標簽這些預測的指標。

#@save
def _get_mlm_data_from_tokens(tokens, vocab):
  candidate_pred_positions = []
  # `tokens` is a list of strings
  for i, token in enumerate(tokens):
    # Special tokens are not predicted in the masked language modeling
    # task
    if token in ['', '']:
      continue
    candidate_pred_positions.append(i)
  # 15% of random tokens are predicted in the masked language modeling task
  num_mlm_preds = max(1, round(len(tokens) * 0.15))
  mlm_input_tokens, pred_positions_and_labels = _replace_mlm_tokens(
    tokens, candidate_pred_positions, num_mlm_preds, vocab)
  pred_positions_and_labels = sorted(pred_positions_and_labels,
                    key=lambda x: x[0])
  pred_positions = [v[0] for v in pred_positions_and_labels]
  mlm_pred_labels = [v[1] for v in pred_positions_and_labels]
  return vocab[mlm_input_tokens], pred_positions, vocab[mlm_pred_labels]

#@save
def _get_mlm_data_from_tokens(tokens, vocab):
  candidate_pred_positions = []
  # `tokens` is a list of strings
  for i, token in enumerate(tokens):
    # Special tokens are not predicted in the masked language modeling
    # task
    if token in ['', '']:
      continue
    candidate_pred_positions.append(i)
  # 15% of random tokens are predicted in the masked language modeling task
  num_mlm_preds = max(1, round(len(tokens) * 0.15))
  mlm_input_tokens, pred_positions_and_labels = _replace_mlm_tokens(
    tokens, candidate_pred_positions, num_mlm_preds, vocab)
  pred_positions_and_labels = sorted(pred_positions_and_labels,
                    key=lambda x: x[0])
  pred_positions = [v[0] for v in pred_positions_and_labels]
  mlm_pred_labels = [v[1] for v in pred_positions_and_labels]
  return vocab[mlm_input_tokens], pred_positions, vocab[mlm_pred_labels]

15.9.2。將文本轉換為預訓練數據集

現在我們幾乎準備好定制一個Dataset用于預訓練 BERT 的類。在此之前，我們仍然需要定義一個輔助函數 _pad_bert_inputs來將特殊的“”標記附加到輸入中。它的參數examples包含輔助函數 _get_nsp_data_from_paragraph和_get_mlm_data_from_tokens兩個預訓練任務的輸出。

#@save
def _pad_bert_inputs(examples, max_len, vocab):
  max_num_mlm_preds = round(max_len * 0.15)
  all_token_ids, all_segments, valid_lens, = [], [], []
  all_pred_positions, all_mlm_weights, all_mlm_labels = [], [], []
  nsp_labels = []
  for (token_ids, pred_positions, mlm_pred_label_ids, segments,
     is_next) in examples:
    all_token_ids.append(torch.tensor(token_ids + [vocab['']] * (
      max_len - len(token_ids)), dtype=torch.long))
    all_segments.append(torch.tensor(segments + [0] * (
      max_len - len(segments)), dtype=torch.long))
    # `valid_lens` excludes count of '' tokens
    valid_lens.append(torch.tensor(len(token_ids), dtype=torch.float32))
    all_pred_positions.append(torch.tensor(pred_positions + [0] * (
      max_num_mlm_preds - len(pred_positions)), dtype=torch.long))
    # Predictions of padded tokens will be filtered out in the loss via
    # multiplication of 0 weights
    all_mlm_weights.append(
      torch.tensor([1.0] * len(mlm_pred_label_ids) + [0.0] * (
        max_num_mlm_preds - len(pred_positions)),
        dtype=torch.float32))
    all_mlm_labels.append(torch.tensor(mlm_pred_label_ids + [0] * (
      max_num_mlm_preds - len(mlm_pred_label_ids)), dtype=torch.long))
    nsp_labels.append(torch.tensor(is_next, dtype=torch.long))
  return (all_token_ids, all_segments, valid_lens, all_pred_positions,
      all_mlm_weights, all_mlm_labels, nsp_labels)

#@save
def _pad_bert_inputs(examples, max_len, vocab):
  max_num_mlm_preds = round(max_len * 0.15)
  all_token_ids, all_segments, valid_lens, = [], [], []
  all_pred_positions, all_mlm_weights, all_mlm_labels = [], [], []
  nsp_labels = []
  for (token_ids, pred_positions, mlm_pred_label_ids, segments,
     is_next) in examples:
    all_token_ids.append(np.array(token_ids + [vocab['']] * (
      max_len - len(token_ids)), dtype='int32'))
    all_segments.append(np.array(segments + [0] * (
      max_len - len(segments)), dtype='int32'))
    # `valid_lens` excludes count of '' tokens
    valid_lens.append(np.array(len(token_ids), dtype='float32'))
    all_pred_positions.append(np.array(pred_positions + [0] * (
      max_num_mlm_preds - len(pred_positions)), dtype='int32'))
    # Predictions of padded tokens will be filtered out in the loss via
    # multiplication of 0 weights
    all_mlm_weights.append(
      np.array([1.0] * len(mlm_pred_label_ids) + [0.0] * (
        max_num_mlm_preds - len(pred_positions)), dtype='float32'))
    all_mlm_labels.append(np.array(mlm_pred_label_ids + [0] * (
      max_num_mlm_preds - len(mlm_pred_label_ids)), dtype='int32'))
    nsp_labels.append(np.array(is_next))
  return (all_token_ids, all_segments, valid_lens, all_pred_positions,
      all_mlm_weights, all_mlm_labels, nsp_labels)

將兩個預訓練任務生成訓練樣例的輔助函數和填充輸入的輔助函數放在一起，我們自定義如下類_WikiTextDataset作為預訓練 BERT 的 WikiText-2 數據集。通過實現該 __getitem__功能，我們可以任意訪問從 WikiText-2 語料庫中的一對句子生成的預訓練（掩碼語言建模和下一句預測）示例。

原始 BERT 模型使用詞匯量為 30000 的 WordPiece 嵌入( Wu et al. , 2016 )。WordPiece 的標記化方法是對15.6.2 節中原始字節對編碼算法的輕微修改。為簡單起見，我們使用該d2l.tokenize函數進行標記化。過濾掉出現次數少于五次的不常見標記。

#@save
class _WikiTextDataset(torch.utils.data.Dataset):
  def __init__(self, paragraphs, max_len):
    # Input `paragraphs[i]` is a list of sentence strings representing a
    # paragraph; while output `paragraphs[i]` is a list of sentences
    # representing a paragraph, where each sentence is a list of tokens
    paragraphs = [d2l.tokenize(
      paragraph, token='word') for paragraph in paragraphs]
    sentences = [sentence for paragraph in paragraphs
           for sentence in paragraph]
    self.vocab = d2l.Vocab(sentences, min_freq=5, reserved_tokens=[
      '', '', '', ''])
    # Get data for the next sentence prediction task
    examples = []
    for paragraph in paragraphs:
      examples.extend(_get_nsp_data_from_paragraph(
        paragraph, paragraphs, self.vocab, max_len))
    # Get data for the masked language model task
    examples = [(_get_mlm_data_from_tokens(tokens, self.vocab)
           + (segments, is_next))
           for tokens, segments, is_next in examples]
    # Pad inputs
    (self.all_token_ids, self.all_segments, self.valid_lens,
     self.all_pred_positions, self.all_mlm_weights,
     self.all_mlm_labels, self.nsp_labels) = _pad_bert_inputs(
      examples, max_len, self.vocab)

  def __getitem__(self, idx):
    return (self.all_token_ids[idx], self.all_segments[idx],
        self.valid_lens[idx], self.all_pred_positions[idx],
        self.all_mlm_weights[idx], self.all_mlm_labels[idx],
        self.nsp_labels[idx])

  def __len__(self):
    return len(self.all_token_ids)

#@save
class _WikiTextDataset(gluon.data.Dataset):
  def __init__(self, paragraphs, max_len):
    # Input `paragraphs[i]` is a list of sentence strings representing a
    # paragraph; while output `paragraphs[i]` is a list of sentences
    # representing a paragraph, where each sentence is a list of tokens
    paragraphs = [d2l.tokenize(
      paragraph, token='word') for paragraph in paragraphs]
    sentences = [sentence for paragraph in paragraphs
           for sentence in paragraph]
    self.vocab = d2l.Vocab(sentences, min_freq=5, reserved_tokens=[
      '', '', '', ''])
    # Get data for the next sentence prediction task
    examples = []
    for paragraph in paragraphs:
      examples.extend(_get_nsp_data_from_paragraph(
        paragraph, paragraphs, self.vocab, max_len))
    # Get data for the masked language model task
    examples = [(_get_mlm_data_from_tokens(tokens, self.vocab)
           + (segments, is_next))
           for tokens, segments, is_next in examples]
    # Pad inputs
    (self.all_token_ids, self.all_segments, self.valid_lens,
     self.all_pred_positions, self.all_mlm_weights,
     self.all_mlm_labels, self.nsp_labels) = _pad_bert_inputs(
      examples, max_len, self.vocab)

  def __getitem__(self, idx):
    return (self.all_token_ids[idx], self.all_segments[idx],
        self.valid_lens[idx], self.all_pred_positions[idx],
        self.all_mlm_weights[idx], self.all_mlm_labels[idx],
        self.nsp_labels[idx])

  def __len__(self):
    return len(self.all_token_ids)

通過使用_read_wiki函數和_WikiTextDataset類，我們定義了以下內容load_data_wiki來下載 WikiText-2 數據集并從中生成預訓練示例。

#@save
def load_data_wiki(batch_size, max_len):
  """Load the WikiText-2 dataset."""
  num_workers = d2l.get_dataloader_workers()
  data_dir = d2l.download_extract('wikitext-2', 'wikitext-2')
  paragraphs = _read_wiki(data_dir)
  train_set = _WikiTextDataset(paragraphs, max_len)
  train_iter = torch.utils.data.DataLoader(train_set, batch_size,
                    shuffle=True, num_workers=num_workers)
  return train_iter, train_set.vocab

#@save
def load_data_wiki(batch_size, max_len):
  """Load the WikiText-2 dataset."""
  num_workers = d2l.get_dataloader_workers()
  data_dir = d2l.download_extract('wikitext-2', 'wikitext-2')
  paragraphs = _read_wiki(data_dir)
  train_set = _WikiTextDataset(paragraphs, max_len)
  train_iter = gluon.data.DataLoader(train_set, batch_size, shuffle=True,
                    num_workers=num_workers)
  return train_iter, train_set.vocab

將批量大小設置為 512，將 BERT 輸入序列的最大長度設置為 64，我們打印出 BERT 預訓練示例的小批量形狀。請注意，在每個 BERT 輸入序列中，10 (64×0.15) 位置是為掩碼語言建模任務預測的。

batch_size, max_len = 512, 64
train_iter, vocab = load_data_wiki(batch_size, max_len)

for (tokens_X, segments_X, valid_lens_x, pred_positions_X, mlm_weights_X,
   mlm_Y, nsp_y) in train_iter:
  print(tokens_X.shape, segments_X.shape, valid_lens_x.shape,
     pred_positions_X.shape, mlm_weights_X.shape, mlm_Y.shape,
     nsp_y.shape)
  break

Downloading ../data/wikitext-2-v1.zip from https://s3.amazonaws.com/research.metamind.io/wikitext/wikitext-2-v1.zip...
torch.Size([512, 64]) torch.Size([512, 64]) torch.Size([512]) torch.Size([512, 10]) torch.Size([512, 10]) torch.Size([512, 10]) torch.Size([512])

batch_size, max_len = 512, 64
train_iter, vocab = load_data_wiki(batch_size, max_len)

for (tokens_X, segments_X, valid_lens_x, pred_positions_X, mlm_weights_X,
   mlm_Y, nsp_y) in train_iter:
  print(tokens_X.shape, segments_X.shape, valid_lens_x.shape,
     pred_positions_X.shape, mlm_weights_X.shape, mlm_Y.shape,
     nsp_y.shape)
  break

(512, 64) (512, 64) (512,) (512, 10) (512, 10) (512, 10) (512,)

最后，讓我們看一下詞匯量。即使在過濾掉不常見的標記后，它仍然比 PTB 數據集大兩倍以上。

len(vocab)

20256

len(vocab)

20256

15.9.3。概括

與 PTB 數據集相比，WikiText-2 數據集保留了原始標點符號、大小寫和數字，并且大了一倍多。

我們可以任意訪問從 WikiText-2 語料庫中的一對句子生成的預訓練（掩碼語言建模和下一句預測）示例。

15.9.4。練習

為簡單起見，句點用作拆分句子的唯一分隔符。嘗試其他句子拆分技術，例如 spaCy 和 NLTK。以 NLTK 為例。您需要先安裝 NLTK：. 在代碼中，首先. 然后，下載 Punkt 句子分詞器： 。要拆分諸如 之類的句子 ，調用 將返回兩個句子字符串的列表：。pip install nltkimport nltknltk.download('punkt')sentences = 'This is great ! Why not ?'nltk.tokenize.sent_tokenize(sentences)['This is great !', 'Why not ?']

如果我們不過濾掉任何不常見的標記，詞匯表的大小是多少？