源码解读之Fine-tune_fine-tune代码

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

2.run_squad.py

其实就是我们在Bert系列（一）——demo运行里运行的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。

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系列（二）——模型主体源码解读
Bert系列（三）——源码解读之Pre-train
Bert系列（五）——中文分词实践 F1 97.8%(附代码)

Reference
1.https://github.com/google-research/bert
2.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。