BERT模型核心组件详解及其实现

news2024/11/16 18:09:39

摘要

BERT（Bidirectional Encoder Representations from Transformers）是一种基于Transformer架构的预训练模型，在自然语言处理领域取得了显著的成果。本文详细介绍了BERT模型中的几个关键组件及其实现，包括激活函数、变量初始化、嵌入查找、层归一化等。通过深入理解这些组件，读者可以更好地掌握BERT模型的工作原理，并在实际应用中进行优化和调整。

1. 引言

BERT模型由Google研究人员于2018年提出，通过大规模的无监督预训练和任务特定的微调，显著提升了多个自然语言处理任务的性能。本文将重点介绍BERT模型中的几个核心组件，包括激活函数、变量初始化、嵌入查找、层归一化等，并提供相应的代码实现。

2. 激活函数

2.1 Gaussian Error Linear Unit (GELU)

GELU是一种平滑的ReLU变体，其数学表达式如下：

GELU(x)=x⋅Φ(x)GELU(x)=x⋅Φ(x)

其中，Φ(x)Φ(x)是标准正态分布的累积分布函数（CDF）。在TensorFlow中，GELU可以通过以下方式实现：

def gelu(x):
  """Gaussian Error Linear Unit.

  This is a smoother version of the RELU.
  Original paper: https://arxiv.org/abs/1606.08415
  Args:
    x: float Tensor to perform activation.

  Returns:
    `x` with the GELU activation applied.
  """
  cdf = 0.5 * (1.0 + tf.tanh(
      (np.sqrt(2 / np.pi) * (x + 0.044715 * tf.pow(x, 3)))))
  return x * cdf

2.2 激活函数映射

为了方便使用不同的激活函数，我们定义了一个映射函数get_activation，该函数根据传入的字符串返回相应的激活函数：

def get_activation(activation_string):
  """Maps a string to a Python function, e.g., "relu" => `tf.nn.relu`.

  Args:
    activation_string: String name of the activation function.

  Returns:
    A Python function corresponding to the activation function. If
    `activation_string` is None, empty, or "linear", this will return None.
    If `activation_string` is not a string, it will return `activation_string`.

  Raises:
    ValueError: The `activation_string` does not correspond to a known
      activation.
  """
  if not isinstance(activation_string, six.string_types):
    return activation_string

  if not activation_string:
    return None

  act = activation_string.lower()
  if act == "linear":
    return None
  elif act == "relu":
    return tf.nn.relu
  elif act == "gelu":
    return gelu
  elif act == "tanh":
    return tf.tanh
  else:
    raise ValueError("Unsupported activation: %s" % act)

3. 变量初始化

在深度学习中，合理的变量初始化对于模型的收敛速度和最终性能至关重要。BERT模型中使用了截断正态分布初始化器（truncated_normal_initializer），其标准差为0.02：

def create_initializer(initializer_range=0.02):
  """Creates a `truncated_normal_initializer` with the given range."""
  return tf.truncated_normal_initializer(stddev=initializer_range)

4. 嵌入查找

嵌入查找是将输入的token id转换为向量表示的过程。BERT模型中使用了两种方法：一种是使用tf.gather()，另一种是使用one-hot编码：

def embedding_lookup(input_ids,
                     vocab_size,
                     embedding_size=128,
                     initializer_range=0.02,
                     word_embedding_name="word_embeddings",
                     use_one_hot_embeddings=False):
  """Looks up words embeddings for id tensor.

  Args:
    input_ids: int32 Tensor of shape [batch_size, seq_length] containing word
      ids.
    vocab_size: int. Size of the embedding vocabulary.
    embedding_size: int. Width of the word embeddings.
    initializer_range: float. Embedding initialization range.
    word_embedding_name: string. Name of the embedding table.
    use_one_hot_embeddings: bool. If True, use one-hot method for word
      embeddings. If False, use `tf.gather()`.

  Returns:
    float Tensor of shape [batch_size, seq_length, embedding_size].
  """
  if input_ids.shape.ndims == 2:
    input_ids = tf.expand_dims(input_ids, axis=[-1])

  embedding_table = tf.get_variable(
      name=word_embedding_name,
      shape=[vocab_size, embedding_size],
      initializer=create_initializer(initializer_range))

  flat_input_ids = tf.reshape(input_ids, [-1])
  if use_one_hot_embeddings:
    one_hot_input_ids = tf.one_hot(flat_input_ids, depth=vocab_size)
    output = tf.matmul(one_hot_input_ids, embedding_table)
  else:
    output = tf.gather(embedding_table, flat_input_ids)

  input_shape = get_shape_list(input_ids)
  output = tf.reshape(output,
                      input_shape[0:-1] + [input_shape[-1] * embedding_size])
  return (output, embedding_table)

5. 层归一化

层归一化（Layer Normalization）是一种常用的归一化技术，用于加速训练过程并提高模型的泛化能力。BERT模型中使用了tf.contrib.layers.layer_norm来进行层归一化：

def layer_norm(input_tensor, name=None):
  """Run layer normalization on the last dimension of the tensor."""
  return tf.contrib.layers.layer_norm(
      inputs=input_tensor, begin_norm_axis=-1, begin_params_axis=-1, scope=name)

为了方便使用，我们还定义了一个组合函数layer_norm_and_dropout，该函数先进行层归一化，再进行dropout操作：

def layer_norm_and_dropout(input_tensor, dropout_prob, name=None):
  """Runs layer normalization followed by dropout."""
  output_tensor = layer_norm(input_tensor, name)
  output_tensor = dropout(output_tensor, dropout_prob)
  return output_tensor

6. Dropout

Dropout是一种常用的正则化技术，用于防止模型过拟合。在BERT模型中，dropout的概率可以通过配置参数进行设置：

def dropout(input_tensor, dropout_prob):
  """Perform dropout.

  Args:
    input_tensor: float Tensor.
    dropout_prob: Python float. The probability of dropping out a value (NOT of
      *keeping* a dimension as in `tf.nn.dropout`).

  Returns:
    A version of `input_tensor` with dropout applied.
  """
  if dropout_prob is None or dropout_prob == 0.0:
    return input_tensor

  output = tf.nn.dropout(input_tensor, 1.0 - dropout_prob)
  return output

7. 从检查点加载变量

在微调过程中，通常需要从预训练的模型检查点中加载变量。get_assignment_map_from_checkpoint函数用于计算当前变量与检查点变量的映射关系：

def get_assignment_map_from_checkpoint(tvars, init_checkpoint):
  """Compute the union of the current variables and checkpoint variables."""
  assignment_map = {}
  initialized_variable_names = {}

  name_to_variable = collections.OrderedDict()
  for var in tvars:
    name = var.name
    m = re.match("^(.*):\\d+$", name)
    if m is not None:
      name = m.group(1)
    name_to_variable[name] = var

  init_vars = tf.train.list_variables(init_checkpoint)

  assignment_map = collections.OrderedDict()
  for x in init_vars:
    (name, var) = (x[0], x[1])
    if name not in name_to_variable:
      continue
    assignment_map[name] = name
    initialized_variable_names[name] = 1
    initialized_variable_names[name + ":0"] = 1

  return (assignment_map, initialized_variable_names)