源码解读之Fine-tune

Love The Way You Lie 2022-02-24 11:30 392阅读 0赞

这是我们源码解读的最后一个部分了。fine-tune搞明白之后推断也就没必要再分析了，反正形式都是一样的，重要的是明白根据不同任务调整输入格式和对loss的构建，这两个知识点学会之后，基本上也可以依葫芦画瓢做一些自己的任务了。

bert官方给了两个任务的fine-tune代码:

1.[run\_classifier.py][run_classifier.py]

2.[run\_squad.py][run_squad.py]

其实就是我们在[Bert系列（一）——demo运行][Bert_demo]里运行的demo，下面我就对这两个代码进行展开说明：

# 一、run\_classifier.py #

## 1、参数 ##

## Required parameters
    flags.DEFINE_string(
        "data_dir", None,
        "The input data dir. Should contain the .tsv files (or other data files) "
        "for the task.")
    
    flags.DEFINE_string(
        "bert_config_file", None,
        "The config json file corresponding to the pre-trained BERT model. "
        "This specifies the model architecture.")
    
    flags.DEFINE_string("task_name", None, "The name of the task to train.")
    
    flags.DEFINE_string("vocab_file", None,
                        "The vocabulary file that the BERT model was trained on.")
    
    flags.DEFINE_string(
        "output_dir", None,
        "The output directory where the model checkpoints will be written.")
    
    ## Other parameters
    
    flags.DEFINE_string(
        "init_checkpoint", None,
        "Initial checkpoint (usually from a pre-trained BERT model).")
    
    flags.DEFINE_bool(
        "do_lower_case", True,
        "Whether to lower case the input text. Should be True for uncased "
        "models and False for cased models.")
    
    flags.DEFINE_integer(
        "max_seq_length", 128,
        "The maximum total input sequence length after WordPiece tokenization. "
        "Sequences longer than this will be truncated, and sequences shorter "
        "than this will be padded.")
    
    flags.DEFINE_bool("do_train", False, "Whether to run training.")
    
    flags.DEFINE_bool("do_eval", False, "Whether to run eval on the dev set.")
    
    flags.DEFINE_bool(
        "do_predict", False,
        "Whether to run the model in inference mode on the test set.")
    
    flags.DEFINE_integer("train_batch_size", 32, "Total batch size for training.")
    
    flags.DEFINE_integer("eval_batch_size", 8, "Total batch size for eval.")
    
    flags.DEFINE_integer("predict_batch_size", 8, "Total batch size for predict.")
    
    flags.DEFINE_float("learning_rate", 5e-5, "The initial learning rate for Adam.")
    
    flags.DEFINE_float("num_train_epochs", 3.0,
                       "Total number of training epochs to perform.")
    
    flags.DEFINE_float(
        "warmup_proportion", 0.1,
        "Proportion of training to perform linear learning rate warmup for. "
        "E.g., 0.1 = 10% of training.")
    
    flags.DEFINE_integer("save_checkpoints_steps", 1000,
                         "How often to save the model checkpoint.")
    
    flags.DEFINE_integer("iterations_per_loop", 1000,
                         "How many steps to make in each estimator call.")
    
    flags.DEFINE_bool("use_tpu", False, "Whether to use TPU or GPU/CPU.")
    
    tf.flags.DEFINE_string(
        "tpu_name", None,
        "The Cloud TPU to use for training. This should be either the name "
        "used when creating the Cloud TPU, or a grpc://ip.address.of.tpu:8470 "
        "url.")
    
    tf.flags.DEFINE_string(
        "tpu_zone", None,
        "[Optional] GCE zone where the Cloud TPU is located in. If not "
        "specified, we will attempt to automatically detect the GCE project from "
        "metadata.")
    
    tf.flags.DEFINE_string(
        "gcp_project", None,
        "[Optional] Project name for the Cloud TPU-enabled project. If not "
        "specified, we will attempt to automatically detect the GCE project from "
        "metadata.")
    
    tf.flags.DEFINE_string("master", None, "[Optional] TensorFlow master URL.")
    
    flags.DEFINE_integer(
        "num_tpu_cores", 8,
        "Only used if `use_tpu` is True. Total number of TPU cores to use.")

这些参数相信运行过demo的同学都已经认识了，不认识读读上面的英文解释也大概能明白什么意思。其中有两个可能需要说明下：

max\_seq\_length：指定WordPiece tokenization 之后的sequence的最大长度，要求小于等于预训练模型的最大sequence长度。当输入的数据长度小于max\_seq\_length时用0补齐，如果长度大于max\_seq\_length则truncate处理；

warmup\_proportion：warm up 步数的比例，比如说总共学习100步，warmup\_proportion=0.1表示前10步用来warm up，warm up时以较低的学习率进行学习(lr = global\_step/num\_warmup\_steps \* init\_lr)，10步之后以正常(或衰减)的学习率来学习。至于这么做的目的不太明白，有知道的同学请务必留言告诉我下，感激不尽。

## 2、数据预处理（以MRPC为例） ##

class InputExample(object):
      """A single training/test example for simple sequence classification."""
      def __init__(self, guid, text_a, text_b=None, label=None):
        self.guid = guid
        self.text_a = text_a
        self.text_b = text_b
        self.label = label

这是输入语料样本的数据结构。

guid是该样本的唯一ID，text\_a和text\_b表示句子对，lable表示句子对关系，如果是test数据集则label统一为0。

class InputFeatures(object):
      """A single set of features of data."""
      def __init__(self, input_ids, input_mask, segment_ids, label_id):
        self.input_ids = input_ids
        self.input_mask = input_mask
        self.segment_ids = segment_ids
        self.label_id = label_id

tokenization过后的样本数据结构，input\_ids其实就是tokens的索引，input\_mask不用解释，segment\_ids对应模型的token\_type\_ids以上三者构成模型输入的X，label\_id是标签，对应Y。

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, "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, "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, "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 = tokenization.convert_to_unicode(line[3])
          text_b = tokenization.convert_to_unicode(line[4])
          if set_type == "test":
            label = "0"
          else:
            label = tokenization.convert_to_unicode(line[0])
          examples.append(
              InputExample(guid=guid, text_a=text_a, text_b=text_b, label=label))
        return examples

MRPC的数据解析器，输入格式：

> label 句子1ID 句子2ID 句子1 句子2

输出的格式为InputExample数据结构。

def file_based_convert_examples_to_features(
        examples, label_list, max_seq_length, tokenizer, output_file):
      """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)
    
        def create_int_feature(values):
          f = tf.train.Feature(int64_list=tf.train.Int64List(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_int_feature([feature.label_id])
    
        tf_example = tf.train.Example(features=tf.train.Features(feature=features))
        writer.write(tf_example.SerializeToString())

将examples转换成features，用到的函数是convert\_single\_example：

def convert_single_example(ex_index, example, label_list, max_seq_length,
                               tokenizer):
      """Converts a single `InputExample` into a single `InputFeatures`."""
      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:
        _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 = []
      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)
    
      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
    
      label_id = label_map[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)
      return feature

把一个InputExample数据转换成InputFeatures数据结构。

（1）构造label\_map ，因为label\_list就\["0", "1"\]所以，label\_map =\{"0":0, "1":1\}；

（2）将text\_a和text\_b转化成token\_a和token\_b，并且将二者截取到长度之和为max\_seq\_length - 3，如果只有token\_a没有token\_b，则将tokens\_a截取到长度为max\_seq\_length - 2；

（3）构造tokens和segment\_ids，如果不满足长度用0补齐，并且构造input\_mask。

## 3、模型构建 ##

def create_model(bert_config, is_training, input_ids, input_mask, segment_ids,
                     labels, num_labels, use_one_hot_embeddings):
      """Creates a classification model."""
      model = modeling.BertModel(
          config=bert_config,
          is_training=is_training,
          input_ids=input_ids,
          input_mask=input_mask,
          token_type_ids=segment_ids,
          use_one_hot_embeddings=use_one_hot_embeddings)
    
      output_layer = model.get_pooled_output()
    
      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)
        probabilities = tf.nn.softmax(logits, axis=-1)
        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)
        loss = tf.reduce_mean(per_example_loss)
    
        return (loss, per_example_loss, logits, probabilities)

X和Y都已经构造好了，将X作为模型的输入，剩下的就是将模型输出和Y进行计算得到loss。

这里的模型输出取的是pooled\_output，之前我们已经说过pooled\_output是模型最后一层的第一个片段。之后再用一个全连接+softmax和labels的one\_hot计算loss。

# 二、run\_squad.py #

run\_squad是基于SQuAD数据进行阅读理解任务的fine-tune，除了X/Y数据的转换、loss构建其他和run\_classifier是一样的，下面我们重点学习下这两块。

## 1、X/Y数据的转换 ##

class SquadExample(object):
      def __init__(self,
                   qas_id,
                   question_text,
                   doc_tokens,
                   orig_answer_text=None,
                   start_position=None,
                   end_position=None,
                   is_impossible=False):
        self.qas_id = qas_id
        self.question_text = question_text 
        self.doc_tokens = doc_tokens 
        self.orig_answer_text = orig_answer_text 
        self.start_position = start_position
        self.end_position = end_position
        self.is_impossible = is_impossible

qas\_id 样本ID，question\_text问题文本，doc\_tokens阅读材料\[word0, word1, ...\]的形式，orig\_answer\_text 原始答案的文本，start\_position答案在文本中开始的位置，end\_position答案在文本中结束的位置，is\_impossible在SQuAD2里才会用到的字段这里可以不用关心。

class InputFeatures(object):
      """A single set of features of data."""
    
      def __init__(self,
                   unique_id,
                   example_index,
                   doc_span_index,
                   tokens,
                   token_to_orig_map,
                   token_is_max_context,
                   input_ids,
                   input_mask,
                   segment_ids,
                   start_position=None,
                   end_position=None,
                   is_impossible=None):
        self.unique_id = unique_id
        self.example_index = example_index
        self.doc_span_index = doc_span_index
        self.tokens = tokens
        self.token_to_orig_map = token_to_orig_map
        self.token_is_max_context = token_is_max_context
        self.input_ids = input_ids
        self.input_mask = input_mask
        self.segment_ids = segment_ids
        self.start_position = start_position
        self.end_position = end_position
        self.is_impossible = is_impossible

unique\_id feature的唯一id，example\_index样本的索引，用于建立feature和example的对应，

doc\_span\_index该feature在doc\_span的索引，如果一个文本很长，那么势必需要对其进行截取，截取成若干片段装进doc\_span，doc\_span里的各个片段会装进各个feature里面，所以一个feature对应的就会有一个doc\_span\_index；

tokens该样本的token序列，token\_to\_orig\_map是tokens里面每一个token在原始doc\_token的索引；

token\_is\_max\_context是一个序列，里面的值表示该位置的token在当前span里面是否是最全上下文的。

例如bought这个词

> Doc: the man went to the store and bought a gallon of milk  
> Span A: the man went to the  
> Span B: to the store and bought  
> Span C: and bought a gallon of

bought在spanB和spanC里都有出现，但很显然span C里bought是语境最全的，既有上文也有下文

input\_ids 是tokens转化为token id作为模型的输入，input\_mask 、segment\_ids、is\_impossible 不用多说了；

start\_position 、 end\_position为答案在当前tokens序列里面的位置（跟上面的不同，不是整个context里面的位置），需要注意的是如果答案不在当前span里的话，start\_position 、 end\_position均为0 。

SquadExample到InputFeatures转换的过程也是类似的，不用细讲，与run\_classifier唯一不同的是classifier的输入是**\[CLS\]句子a\[SEP\]句子b\[SEP\]**, 而squad是**\[CLS\]问题\[SEP\]阅读材料片段\[SEP\]**。

input_ids = features["input_ids"]
        input_mask = features["input_mask"]
        segment_ids = features["segment_ids"]

和这三个元素作为模型的输入X，而start\_position和end\_position作为Y，如果知道了Y就等于知道了答案的位置，然后反向在阅读材料context里面去找出来就可以了，逻辑大概就是这样。

## 2、loss构建 ##

def model_fn_builder(bert_config, init_checkpoint, learning_rate,
                         num_train_steps, num_warmup_steps, use_tpu,
                         use_one_hot_embeddings):
      """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))
    
        unique_ids = features["unique_ids"]
        input_ids = features["input_ids"]
        input_mask = features["input_mask"]
        segment_ids = features["segment_ids"]
    
        is_training = (mode == tf.estimator.ModeKeys.TRAIN)
    
        (start_logits, end_logits) = create_model(
            bert_config=bert_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)
    
        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:
          seq_length = modeling.get_shape_list(input_ids)[1]
    
          def compute_loss(logits, positions):
            one_hot_positions = tf.one_hot(
                positions, depth=seq_length, dtype=tf.float32)
            log_probs = tf.nn.log_softmax(logits, axis=-1)
            loss = -tf.reduce_mean(
                tf.reduce_sum(one_hot_positions * log_probs, axis=-1))
            return loss
    
          start_positions = features["start_positions"]
          end_positions = features["end_positions"]
    
          start_loss = compute_loss(start_logits, start_positions)
          end_loss = compute_loss(end_logits, end_positions)
    
          total_loss = (start_loss + end_loss) / 2.0
    
          train_op = optimization.create_optimizer(
              total_loss, learning_rate, num_train_steps, num_warmup_steps, use_tpu)
    
          output_spec = tf.contrib.tpu.TPUEstimatorSpec(
              mode=mode,
              loss=total_loss,
              train_op=train_op,
              scaffold_fn=scaffold_fn)
        elif mode == tf.estimator.ModeKeys.PREDICT:
          predictions = {
              "unique_ids": unique_ids,
              "start_logits": start_logits,
              "end_logits": end_logits,
          }
          output_spec = tf.contrib.tpu.TPUEstimatorSpec(
              mode=mode, predictions=predictions, scaffold_fn=scaffold_fn)
        else:
          raise ValueError(
              "Only TRAIN and PREDICT modes are supported: %s" % (mode))
    
        return output_spec
    
      return model_fn

从上面的代码我们可以发现，loss由两部分组成，答案start\_positions的预测和end\_positions的预测。

def create_model(bert_config, is_training, input_ids, input_mask, segment_ids,
                     use_one_hot_embeddings):
      """Creates a classification model."""
      model = modeling.BertModel(
          config=bert_config,
          is_training=is_training,
          input_ids=input_ids,
          input_mask=input_mask,
          token_type_ids=segment_ids,
          use_one_hot_embeddings=use_one_hot_embeddings)
    
      final_hidden = model.get_sequence_output()
    
      final_hidden_shape = modeling.get_shape_list(final_hidden, expected_rank=3)
      batch_size = final_hidden_shape[0]
      seq_length = final_hidden_shape[1]
      hidden_size = final_hidden_shape[2]
    
      output_weights = tf.get_variable(
          "cls/squad/output_weights", [2, hidden_size],
          initializer=tf.truncated_normal_initializer(stddev=0.02))
    
      output_bias = tf.get_variable(
          "cls/squad/output_bias", [2], initializer=tf.zeros_initializer())
    
      final_hidden_matrix = tf.reshape(final_hidden,
                                       [batch_size * seq_length, hidden_size])
      logits = tf.matmul(final_hidden_matrix, output_weights, transpose_b=True)
      logits = tf.nn.bias_add(logits, output_bias)
    
      logits = tf.reshape(logits, [batch_size, seq_length, 2])
      logits = tf.transpose(logits, [2, 0, 1])
    
      unstacked_logits = tf.unstack(logits, axis=0)
    
      (start_logits, end_logits) = (unstacked_logits[0], unstacked_logits[1])
    
      return (start_logits, end_logits)

模型的输出来自于sequence\_output，即模型最后一层的输出，shape为\[batch\_size, seq\_length, hidden\_size \]，之后再加一个全连接层，unpack成两个部分，分别对应答案的两个位置。

# 总结： #

以上便是这两个demo的全部解读，squad里有很多细节特别是sample到feature的转换过程，比较复杂，但因为时间有限我们不做具体介绍，感兴趣的同学可以自己深入阅读一下。

![Image 1][]

不同任务的输入输出示意图

这两个任务可以和论文里面的示意图结合起来看，句子对分类任务对应的是图(a)，阅读理解任务对应的是图(c)

**本文系列**  
[Bert系列（一）——demo运行][Bert_demo]  
[Bert系列（二）——模型主体源码解读][Bert]  
[Bert系列（三）——源码解读之Pre-train][Bert_Pre-train]  
[Bert系列（五）——中文分词实践 F1 97.8%(附代码)][Bert_ F1 97.8]

**Reference**  
1.[https://github.com/google-research/bert][https_github.com_google-research_bert]  
2.[BERT: Pre-training of Deep Bidirectional Transformers for  
Language Understanding][BERT_ Pre-training of Deep Bidirectional Transformers for Language Understanding]

作者：西溪雷神  
链接：https://www.jianshu.com/p/116bfdb9119a  
来源：简书  
简书著作权归作者所有，任何形式的转载都请联系作者获得授权并注明出处。

[run_classifier.py]: https://links.jianshu.com/go?to=https%3A%2F%2Fgithub.com%2Fgoogle-research%2Fbert%2Fblob%2Fmaster%2Frun_classifier.py
[run_squad.py]: https://links.jianshu.com/go?to=https%3A%2F%2Fgithub.com%2Fgoogle-research%2Fbert%2Fblob%2Fmaster%2Frun_squad.py
[Bert_demo]: https://www.jianshu.com/p/3d0bb34c488a
[Image 1]: 
[Bert]: https://www.jianshu.com/p/d7ce41b58801
[Bert_Pre-train]: https://www.jianshu.com/p/22e462f01d8c
[Bert_ F1 97.8]: https://www.jianshu.com/p/be0a951445f4
[https_github.com_google-research_bert]: https://links.jianshu.com/go?to=https%3A%2F%2Fgithub.com%2Fgoogle-research%2Fbert
[BERT_ Pre-training of Deep Bidirectional Transformers for Language Understanding]: https://links.jianshu.com/go?to=https%3A%2F%2Farxiv.org%2Fabs%2F1810.04805