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@ -0,0 +1,514 @@
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### BERT流程解读
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#### 数据读取模块
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处理MRPC数据的类
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~~~python
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class MrpcProcessor(DataProcessor):
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"""Processor for the MRPC data set (GLUE version)."""
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def get_train_examples(self, data_dir):
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"""See base class."""
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return self._create_examples(
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self._read_tsv(os.path.join(data_dir, "train.tsv")), "train")
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def get_dev_examples(self, data_dir):
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"""See base class."""
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return self._create_examples(
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self._read_tsv(os.path.join(data_dir, "dev.tsv")), "dev")
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def get_test_examples(self, data_dir):
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"""See base class."""
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return self._create_examples(
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self._read_tsv(os.path.join(data_dir, "test.tsv")), "test")
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def get_labels(self):
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"""See base class."""
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return ["0", "1"] # 是否是二分类
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def _create_examples(self, lines, set_type):
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"""Creates examples for the training and dev sets."""
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examples = []
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for (i, line) in enumerate(lines):
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if i == 0:
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continue
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guid = "%s-%s" % (set_type, i)
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text_a = tokenization.convert_to_unicode(line[3]) # 相关的test_a和b怎么切分
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text_b = tokenization.convert_to_unicode(line[4])
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if set_type == "test":
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label = "0"
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else:
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label = tokenization.convert_to_unicode(line[0])
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examples.append(
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InputExample(guid=guid, text_a=text_a, text_b=text_b, label=label))
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return examples
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~~~
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读取训练数据代码：
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~~~python
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if FLAGS.do_train:
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train_examples = processor.get_train_examples(FLAGS.data_dir)
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num_train_steps = int(
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len(train_examples) / FLAGS.train_batch_size * FLAGS.num_train_epochs) # 得到需要迭代的次数，len(train_examples)计算出多少数据量 除以 我们设置的train_batch_size，再乘上epochs次数。
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num_warmup_steps = int(num_train_steps * FLAGS.warmup_proportion) # 在刚开始时，让学习率偏小，经过warmup的百分比后，再还原回原始的学习率
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~~~
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#### 数据预处理模块
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~~~python
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# 衔接上一个
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file_based_convert_examples_to_features(
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train_examples, label_list, FLAGS.max_seq_length, tokenizer, train_file)
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# ctrl点击file_based_xxx函数跳转
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def file_based_convert_examples_to_features(
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examples, label_list, max_seq_length, tokenizer, output_file):
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"""Convert a set of `InputExample`s to a TFRecord file."""
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writer = tf.python_io.TFRecordWriter(output_file) # TFRecord读取数据块，在bert中要求数据是TFRecord的形式。
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for (ex_index, example) in enumerate(examples):
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if ex_index % 10000 == 0:
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tf.logging.info("Writing example %d of %d" % (ex_index, len(examples))) # for循环变量取数据
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feature = convert_single_example(ex_index, example, label_list,
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max_seq_length, tokenizer) # ctrl点击convert_xxx跳转
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def convert_single_example(ex_index, example, label_list, max_seq_length,
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tokenizer):
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"""Converts a single `InputExample` into a single `InputFeatures`."""
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if isinstance(example, PaddingInputExample):
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return InputFeatures(
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input_ids=[0] * max_seq_length,
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input_mask=[0] * max_seq_length,
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segment_ids=[0] * max_seq_length,
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label_id=0,
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is_real_example=False)
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label_map = {} # 构建标签0, 1
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for (i, label) in enumerate(label_list):
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label_map[label] = i
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tokens_a = tokenizer.tokenize(example.text_a) # ctrl点击tokenize，对第一句话分词
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tokens_b = None
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if example.text_b: # 第二句话分词
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tokens_b = tokenizer.tokenize(example.text_b)
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if tokens_b:
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# Modifies `tokens_a` and `tokens_b` in place so that the total
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# length is less than the specified length.
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# Account for [CLS], [SEP], [SEP] with "- 3" # 保留3个特殊字符
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_truncate_seq_pair(tokens_a, tokens_b, max_seq_length - 3) # 如果太长就截断的操作
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else: # 没有b的时候保留两个字符
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# Account for [CLS] and [SEP] with "- 2"
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if len(tokens_a) > max_seq_length - 2:
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tokens_a = tokens_a[0:(max_seq_length - 2)]
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# The convention in BERT is:
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# (a) For sequence pairs: # 将下面一对话，CLS开始，SEP断点，变成type_ids的0/1形式，0表示前一句，1表示后一句
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# tokens: [CLS] is this jack ##son ##ville ? [SEP] no it is not . [SEP]
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# type_ids: 0 0 0 0 0 0 0 0 1 1 1 1 1 1
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def tokenize(self, text):
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split_tokens = []
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for token in self.basic_tokenizer.tokenize(text): # 词切片，将一个词切片成多个小段，让表达的含义更丰富
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for sub_token in self.wordpiece_tokenizer.tokenize(token):
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split_tokens.append(sub_token)
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return split_tokens
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~~~
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#### tfrecord制作
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~~~~python
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# 延续上面的convert_single_example模块
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# 开始构建，创建两个列表来承接
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tokens = []
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segment_ids = []
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tokens.append("[CLS]") # 第一个词是CLS
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segment_ids.append(0) # 第一个的编码也肯定是0
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for token in tokens_a:
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tokens.append(token)
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segment_ids.append(0) # 遍历获取，a（第一句话）都是0
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tokens.append("[SEP]") # 遍历完增加个SEP连接符/断电
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segment_ids.append(0) # tokens添加完SEP后，ids也添加对应的0
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if tokens_b:
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for token in tokens_b:
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tokens.append(token)
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segment_ids.append(1) # b和a一样，唯一不同的是添加的是1
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tokens.append("[SEP]")
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segment_ids.append(1)
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input_ids = tokenizer.convert_tokens_to_ids(tokens) # 转成ID的映射，就是vocab语料库索引
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# The mask has 1 for real tokens and 0 for padding tokens. Only real
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# tokens are attended to.
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input_mask = [1] * len(input_ids)
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# Zero-pad up to the sequence length. 保证输入的长度是一样的，多退少补
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while len(input_ids) < max_seq_length: # PAD的长度取决于设置的最大长度，小于全补0
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input_ids.append(0)
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input_mask.append(0)
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segment_ids.append(0)
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assert len(input_ids) == max_seq_length
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assert len(input_mask) == max_seq_length
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assert len(segment_ids) == max_seq_length
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label_id = label_map[example.label]
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if ex_index < 5:
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tf.logging.info("*** Example ***") # 打印结果，这时候预处理的部分大致完成
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...
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return feature
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~~~~
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返回原先的convert_single_example
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~~~python
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for (ex_index, example) in enumerate(examples): # 不断遍历处理数据
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if ex_index % 10000 == 0:
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tf.logging.info("Writing example %d of %d" % (ex_index, len(examples)))
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feature = convert_single_example(ex_index, example, label_list,
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max_seq_length, tokenizer) # ctrl点击convert_xxx跳
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def create_int_feature(values):
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f = tf.train.Feature(int64_list=tf.train.Int64List(value=list(values)))
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return f
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features = collections.OrderedDict() # 下面执行格式处理，处理成模型所需的格式
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features["input_ids"] = create_int_feature(feature.input_ids)
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features["input_mask"] = create_int_feature(feature.input_mask)
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features["segment_ids"] = create_int_feature(feature.segment_ids)
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features["label_ids"] = create_int_feature([feature.label_id])
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features["is_real_example"] = create_int_feature(
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[int(feature.is_real_example)])
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tf_example = tf.train.Example(features=tf.train.Features(feature=features)) # 最后转换成tf的数据格式
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writer.write(tf_example.SerializeToString())
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writer.close()
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~~~
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#### Embedding层的作用
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~~~python
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def create_model(bert_config, is_training, input_ids, input_mask, segment_ids,
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labels, num_labels, use_one_hot_embeddings):
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"""Creates a classification model."""
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model = modeling.BertModel( # ctrl点击BertModel跳转
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config=bert_config, # 配置
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is_training=is_training,
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input_ids=input_ids, # 特征
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input_mask=input_mask, # 特征0/1
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token_type_ids=segment_ids, # 特征维度表示第一句话还是第二句
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use_one_hot_embeddings=use_one_hot_embeddings)
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。。。
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class BertModel(object):
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"""BERT model ("Bidirectional Encoder Representations from Transformers").
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Example usage:
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```python
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# Already been converted into WordPiece token ids
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input_ids = tf.constant([[31, 51, 99], [15, 5, 0]])
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input_mask = tf.constant([[1, 1, 1], [1, 1, 0]])
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token_type_ids = tf.constant([[0, 0, 1], [0, 2, 0]])
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config = modeling.BertConfig(vocab_size=32000, hidden_size=512,
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num_hidden_layers=8, num_attention_heads=6, intermediate_size=1024)
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model = modeling.BertModel(config=config, is_training=True,
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input_ids=input_ids, input_mask=input_mask, token_type_ids=token_type_ids)
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label_embeddings = tf.get_variable(...)
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pooled_output = model.get_pooled_output()
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logits = tf.matmul(pooled_output, label_embeddings)
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...
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```
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"""
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def __init__(self,
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config,
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is_training,
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input_ids,
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input_mask=None,
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token_type_ids=None,
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use_one_hot_embeddings=False,
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scope=None):
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"""Constructor for BertModel.
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Args:
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config: `BertConfig` instance.
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is_training: bool. true for training model, false for eval model. Controls
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whether dropout will be applied.
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input_ids: int32 Tensor of shape [batch_size, seq_length].
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input_mask: (optional) int32 Tensor of shape [batch_size, seq_length].
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token_type_ids: (optional) int32 Tensor of shape [batch_size, seq_length].
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use_one_hot_embeddings: (optional) bool. Whether to use one-hot word
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embeddings or tf.embedding_lookup() for the word embeddings.
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scope: (optional) variable scope. Defaults to "bert".
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Raises:
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ValueError: The config is invalid or one of the input tensor shapes
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is invalid.
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"""
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config = copy.deepcopy(config)
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if not is_training:
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config.hidden_dropout_prob = 0.0
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config.attention_probs_dropout_prob = 0.0
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input_shape = get_shape_list(input_ids, expected_rank=2)
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batch_size = input_shape[0]
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seq_length = input_shape[1]
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if input_mask is None: # 如果没设置mask，默认都是1
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input_mask = tf.ones(shape=[batch_size, seq_length], dtype=tf.int32)
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if token_type_ids is None: # 没设置就默认一句话
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token_type_ids = tf.zeros(shape=[batch_size, seq_length], dtype=tf.int32)
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with tf.variable_scope(scope, default_name="bert"):
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with tf.variable_scope("embeddings"):
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# Perform embedding lookup on the word ids. 词的embeddings
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(self.embedding_output, self.embedding_table) = embedding_lookup( # ctrl点击embedding_lookup跳转
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input_ids=input_ids, # 词
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vocab_size=config.vocab_size, # 语料库
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embedding_size=config.hidden_size, # 编码映射成多少维
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initializer_range=config.initializer_range, # 初始化范围
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word_embedding_name="word_embeddings",
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use_one_hot_embeddings=use_one_hot_embeddings)
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~~~
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~~~python
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def embedding_lookup(input_ids,
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vocab_size,
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embedding_size=128,
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initializer_range=0.02,
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word_embedding_name="word_embeddings",
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use_one_hot_embeddings=False):
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"""Looks up words embeddings for id tensor.
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Args:
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input_ids: int32 Tensor of shape [batch_size, seq_length] containing word
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ids.
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vocab_size: int. Size of the embedding vocabulary.
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embedding_size: int. Width of the word embeddings.
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initializer_range: float. Embedding initialization range.
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word_embedding_name: string. Name of the embedding table.
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use_one_hot_embeddings: bool. If True, use one-hot method for word
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embeddings. If False, use `tf.gather()`.
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Returns:
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float Tensor of shape [batch_size, seq_length, embedding_size].
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"""
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# This function assumes that the input is of shape [batch_size, seq_length,
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# num_inputs].
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#
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# If the input is a 2D tensor of shape [batch_size, seq_length], we
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# reshape to [batch_size, seq_length, 1].
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if input_ids.shape.ndims == 2:
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input_ids = tf.expand_dims(input_ids, axis=[-1])
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embedding_table = tf.get_variable( # 词映射矩阵
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name=word_embedding_name, # 词向量
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shape=[vocab_size, embedding_size], # 获取语料库大表vovab.txt
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initializer=create_initializer(initializer_range))
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flat_input_ids = tf.reshape(input_ids, [-1])
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if use_one_hot_embeddings:
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one_hot_input_ids = tf.one_hot(flat_input_ids, depth=vocab_size) # 查出所有词做one_hot
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output = tf.matmul(one_hot_input_ids, embedding_table) # 运算一个batch里所有的映射结果
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else:
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output = tf.gather(embedding_table, flat_input_ids)
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input_shape = get_shape_list(input_ids)
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output = tf.reshape(output,
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input_shape[0:-1] + [input_shape[-1] * embedding_size]) # 制作返回结果
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return (output, embedding_table) # 返回，词变成了向量
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~~~
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#### 位置编码
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~~~python
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class BertModel(object):
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"""BERT model ("Bidirectional Encoder Representations from Transformers").
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Example usage:
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```python
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# Already been converted into WordPiece token ids
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input_ids = tf.constant([[31, 51, 99], [15, 5, 0]])
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input_mask = tf.constant([[1, 1, 1], [1, 1, 0]])
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token_type_ids = tf.constant([[0, 0, 1], [0, 2, 0]])
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config = modeling.BertConfig(vocab_size=32000, hidden_size=512,
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num_hidden_layers=8, num_attention_heads=6, intermediate_size=1024)
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model = modeling.BertModel(config=config, is_training=True,
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input_ids=input_ids, input_mask=input_mask, token_type_ids=token_type_ids)
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label_embeddings = tf.get_variable(...)
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pooled_output = model.get_pooled_output()
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logits = tf.matmul(pooled_output, label_embeddings)
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...
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```
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"""
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def __init__(self,
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config,
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is_training,
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input_ids,
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input_mask=None,
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token_type_ids=None,
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use_one_hot_embeddings=False,
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scope=None):
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"""Constructor for BertModel.
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Args:
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config: `BertConfig` instance.
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is_training: bool. true for training model, false for eval model. Controls
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whether dropout will be applied.
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input_ids: int32 Tensor of shape [batch_size, seq_length].
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input_mask: (optional) int32 Tensor of shape [batch_size, seq_length].
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token_type_ids: (optional) int32 Tensor of shape [batch_size, seq_length].
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use_one_hot_embeddings: (optional) bool. Whether to use one-hot word
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embeddings or tf.embedding_lookup() for the word embeddings.
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scope: (optional) variable scope. Defaults to "bert".
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Raises:
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ValueError: The config is invalid or one of the input tensor shapes
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is invalid.
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"""
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...
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# Add positional embeddings and token type embeddings, then layer
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# normalize and perform dropout.
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self.embedding_output = embedding_postprocessor( # 制作位置编码，ctrl点击embedding_postprocessor
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input_tensor=self.embedding_output,
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use_token_type=True,
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token_type_ids=token_type_ids,
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token_type_vocab_size=config.type_vocab_size,
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token_type_embedding_name="token_type_embeddings",
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use_position_embeddings=True,
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position_embedding_name="position_embeddings",
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initializer_range=config.initializer_range,
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max_position_embeddings=config.max_position_embeddings,
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dropout_prob=config.hidden_dropout_prob)
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~~~
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~~~python
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def embedding_postprocessor(input_tensor,
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use_token_type=False,
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token_type_ids=None,
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token_type_vocab_size=16,
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token_type_embedding_name="token_type_embeddings",
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use_position_embeddings=True,
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position_embedding_name="position_embeddings",
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initializer_range=0.02,
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max_position_embeddings=512,
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dropout_prob=0.1):
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"""Performs various post-processing on a word embedding tensor.
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Args:
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input_tensor: float Tensor of shape [batch_size, seq_length,
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embedding_size].
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use_token_type: bool. Whether to add embeddings for `token_type_ids`.
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token_type_ids: (optional) int32 Tensor of shape [batch_size, seq_length].
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Must be specified if `use_token_type` is True.
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token_type_vocab_size: int. The vocabulary size of `token_type_ids`.
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token_type_embedding_name: string. The name of the embedding table variable
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for token type ids.
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use_position_embeddings: bool. Whether to add position embeddings for the
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position of each token in the sequence.
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position_embedding_name: string. The name of the embedding table variable
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for positional embeddings.
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initializer_range: float. Range of the weight initialization.
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max_position_embeddings: int. Maximum sequence length that might ever be
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used with this model. This can be longer than the sequence length of
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input_tensor, but cannot be shorter.
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dropout_prob: float. Dropout probability applied to the final output tensor.
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Returns:
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float tensor with same shape as `input_tensor`.
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Raises:
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ValueError: One of the tensor shapes or input values is invalid.
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"""
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input_shape = get_shape_list(input_tensor, expected_rank=3)
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batch_size = input_shape[0]
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seq_length = input_shape[1]
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width = input_shape[2]
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output = input_tensor
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if use_token_type: # 判断是第一句还是第二句，再做相应处理
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if token_type_ids is None:
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raise ValueError("`token_type_ids` must be specified if"
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"`use_token_type` is True.")
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token_type_table = tf.get_variable(
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name=token_type_embedding_name,
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shape=[token_type_vocab_size, width],
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initializer=create_initializer(initializer_range))
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# This vocab will be small so we always do one-hot here, since it is always
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# faster for a small vocabulary.
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flat_token_type_ids = tf.reshape(token_type_ids, [-1])
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one_hot_ids = tf.one_hot(flat_token_type_ids, depth=token_type_vocab_size)
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token_type_embeddings = tf.matmul(one_hot_ids, token_type_table)
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token_type_embeddings = tf.reshape(token_type_embeddings,
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[batch_size, seq_length, width])
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output += token_type_embeddings
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if use_position_embeddings: # 判断是否要做位置编码信息
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assert_op = tf.assert_less_equal(seq_length, max_position_embeddings)
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with tf.control_dependencies([assert_op]):
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full_position_embeddings = tf.get_variable(
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name=position_embedding_name,
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shape=[max_position_embeddings, width],
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initializer=create_initializer(initializer_range))
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# Since the position embedding table is a learned variable, we create it
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# using a (long) sequence length `max_position_embeddings`. The actual
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# sequence length might be shorter than this, for faster training of
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# tasks that do not have long sequences.
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#
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# So `full_position_embeddings` is effectively an embedding table
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# for position [0, 1, 2, ..., max_position_embeddings-1], and the current
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# sequence has positions [0, 1, 2, ... seq_length-1], so we can just
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# perform a slice.
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position_embeddings = tf.slice(full_position_embeddings, [0, 0],
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[seq_length, -1]) # 如果位置编码给的过大，为了加速只需取出部分
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num_dims = len(output.shape.as_list())
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# Only the last two dimensions are relevant (`seq_length` and `width`), so
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# we broadcast among the first dimensions, which is typically just
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# the batch size.
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position_broadcast_shape = []
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for _ in range(num_dims - 2):
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position_broadcast_shape.append(1)
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position_broadcast_shape.extend([seq_length, width])
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position_embeddings = tf.reshape(position_embeddings,
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position_broadcast_shape)
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output += position_embeddings
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output = layer_norm_and_dropout(output, dropout_prob)
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return output
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~~~
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benjas
5 years ago