文章目录
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前言
本文在LCQMC数据集上，再次对roberta、albert模型进行预训练，详细介绍了预训练的过程并对比了预训练前后的结果。
模型验证集测试集roberta0.885030.86344albert0.856620.84960预训练后roberta
0.89343
0.85328预训练后albert0.84958
0.85224
任务描述
根据选取数据集，转为预训练格式数据，完成roberta、albert的预训练，并对比在该数据集上，预训练前后的具体任务指标。
任务数据集
LCQMC数据集
训练集验证集测试集238766880212500
LCQMC数据集的长度分布如下：
; 实验设置
代码链接：https://github.com/447428054/Pretrain/tree/master/LcqmcExample
预训练环境：tensorflow1.14
预训练代码执行顺序：
- bash create_pretrain_data_lz.sh
- bash pretrain_lz.sh
LCQMC微调代码:
- python task_sentence_similarity_lcqmc_roberta.py
- python task_sentence_similarity_lcqmc_albert.py
TIPS:
记得修改文件路径
预训练数据生成
预训练代码读取生成的record，数据处理代码首先读取不同文件，每个文件格式为： 每一行存放一个句子，不同文档之间以空行分割
我们将LCQMC中相似的句子作为一个文档，不相似的分开
谁有狂三这张高清的
这张高清图，谁有
英雄联盟什么英雄最好
英雄联盟最好英雄是什么
这是什么意思，被蹭网吗
roberta的预训练数据处理
- 每个文件中，一个sentence占一行，不同document之间加一个空行分割
[[‘有’, ‘人’, ‘知’, ‘道’, ‘叫’, ‘什’, ‘么’, ‘名’, ‘字’, ‘吗’, ‘[UNK]’, ‘？’], [‘有’, ‘人’, ‘知’, ‘道’, ‘名’, ‘字’, ‘吗’]] - 从一个文档中连续的获得文本，直到达到最大长度。如果是从下一个文档中获得，那么加上一个分隔符.将长度限制修改了，因为lcqmc句子都偏短
[‘有’, ‘人’, ‘知’, ‘道’, ‘叫’, ‘什’, ‘么’, ‘名’, ‘字’, ‘吗’, ‘[UNK]’, ‘？’, ‘有’, ‘人’, ‘知’, ‘道’, ‘名’, ‘字’, ‘吗’] - 对于获取之后的文本，进行全词分词： 判断每个字符起始长度3以内的，是否在分词里面，在的话添加##标记
[‘有’, ‘##人’, ‘知’, ‘##道’, ‘叫’, ‘什’, ‘##么’, ‘名’, ‘##字’, ‘吗’, ‘[UNK]’, ‘？’, ‘有’, ‘##人’, ‘知’, ‘##道’, ‘名’, ‘##字’, ‘吗’] - 对获得的token序列，进行掩码:返回 掩码结果，掩码的位置，掩码的标签
[‘[CLS]’, ‘有’, ‘人’, ‘知’, ‘道’, ‘叫’, ‘什’, ‘么’, ‘名’, ‘字’, ‘吗’, ‘[UNK]’, ‘[MASK]’, ‘有’, ‘人’, ‘[MASK]’, ‘[MASK]’, ‘名’, ‘字’, ‘吗’, ‘[SEP]’]
[12, 15, 16]
[‘？’, ‘知’, ‘##道’]
run_pretraining.py需要注释TPU的引用
*tpu_cluster_resolver* = tf.contrib.cluster_resolver.TPUClusterResolver( # TODO
tpu=FLAGS.tpu_name, zone=FLAGS.tpu_zone, project=FLAGS.gcp_project)
albert的预训练数据处理
- 每个文件中，一个sentence占一行，不同document之间加一个空行分割
[[‘有’, ‘人’, ‘知’, ‘道’, ‘叫’, ‘什’, ‘么’, ‘名’, ‘字’, ‘吗’, ‘[UNK]’, ‘？’], [‘有’, ‘人’, ‘知’, ‘道’, ‘名’, ‘字’, ‘吗’]] - 从一个文档中获取sentence,sentece进行全词分词，当长度达到最大长度或者遍历完整个文档了，A[SEP]B 随机分割句子，50%概率交换顺序，得到SOP标签
tokenA:[‘有’, ‘##人’, ‘知’, ‘##道’, ‘叫’, ‘什’, ‘##么’, ‘名’, ‘##字’, ‘吗’, ‘[UNK]’, ‘？’]
tokenB:[‘有’, ‘##人’, ‘知’, ‘##道’, ‘名’, ‘##字’, ‘吗’]
只有一句话,构不成SOP任务的就continue
- 对获得的token序列，进行掩码:返回 掩码结果，掩码的位置，掩码的标签
tokens:[‘[CLS]’, ‘有’, ‘人’, ‘知’, ‘道’, ‘叫’, ‘什’, ‘么’, ‘名’, ‘[MASK]’, ‘吗’, ‘[UNK]’, ‘？’, ‘[SEP]’, ‘[MASK]’, ‘人’, ‘知’, ‘[MASK]’, ‘名’, ‘字’, ‘吗’, ‘[SEP]’]
masked_lm_positions:[9, 14, 17]
masked_lm_labels:[‘##字’, ‘有’, ‘##道’]
is_random_next:False
预训练代码
模型结构
Roberta
整个模型结构与BERT相同，整体流程如下：
- 输入的token 经过embedding
- 再加上token type id 与position embedding
- 进入transfomer层，每一个transformer又由多头attention、层归一化、残差结构、前馈神经网络构成
- 获取CLS输出与整个句子的输出
整个代码结构跟流程相同：
with tf.variable_scope(scope, default_name="bert"):
with tf.variable_scope("embeddings"):
# Perform embedding lookup on the word ids.
(self.embedding_output, self.embedding_table) = embedding_lookup(
input_ids=input_ids,
vocab_size=config.vocab_size,
embedding_size=config.hidden_size,
initializer_range=config.initializer_range,
word_embedding_name="word_embeddings",
use_one_hot_embeddings=use_one_hot_embeddings)
# Add positional embeddings and token type embeddings, then layer
# normalize and perform dropout.
self.embedding_output = embedding_postprocessor(
input_tensor=self.embedding_output,
use_token_type=True,
token_type_ids=token_type_ids,
token_type_vocab_size=config.type_vocab_size,
token_type_embedding_name="token_type_embeddings",
use_position_embeddings=True,
position_embedding_name="position_embeddings",
initializer_range=config.initializer_range,
max_position_embeddings=config.max_position_embeddings,
dropout_prob=config.hidden_dropout_prob)
with tf.variable_scope("encoder"):
# This converts a 2D mask of shape [batch_size, seq_length] to a 3D
# mask of shape [batch_size, seq_length, seq_length] which is used
# for the attention scores.
attention_mask = create_attention_mask_from_input_mask(
input_ids, input_mask)
# Run the stacked transformer.
# sequence_output shape = [batch_size, seq_length, hidden_size].
self.all_encoder_layers = transformer_model(
input_tensor=self.embedding_output,
attention_mask=attention_mask,
hidden_size=config.hidden_size,
num_hidden_layers=config.num_hidden_layers,
num_attention_heads=config.num_attention_heads,
intermediate_size=config.intermediate_size,
intermediate_act_fn=get_activation(config.hidden_act),
hidden_dropout_prob=config.hidden_dropout_prob,
attention_probs_dropout_prob=config.attention_probs_dropout_prob,
initializer_range=config.initializer_range,
do_return_all_layers=True)
self.sequence_output = self.all_encoder_layers[-1] # [batch_size, seq_length, hidden_size]
# The "pooler" converts the encoded sequence tensor of shape
# [batch_size, seq_length, hidden_size] to a tensor of shape
# [batch_size, hidden_size]. This is necessary for segment-level
# (or segment-pair-level) classification tasks where we need a fixed
# dimensional representation of the segment.
with tf.variable_scope("pooler"):
# We "pool" the model by simply taking the hidden state corresponding
# to the first token. We assume that this has been pre-trained
first_token_tensor = tf.squeeze(self.sequence_output[:, 0:1, :], axis=1)
self.pooled_output = tf.layers.dense(
first_token_tensor,
config.hidden_size,
activation=tf.tanh,
kernel_initializer=create_initializer(config.initializer_range))
embedding_lookup
与常规神经网络中词嵌入类似
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].
"""
# This function assumes that the input is of shape [batch_size, seq_length,
# num_inputs].
#
# If the input is a 2D tensor of shape [batch_size, seq_length], we
# reshape to [batch_size, seq_length, 1].
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)
embedding_postprocessor
加上token type id与可学习的position id 词嵌入，postprocessor指在embedding之后进行层归一化与dropout
def embedding_postprocessor(input_tensor,
use_token_type=False,
token_type_ids=None,
token_type_vocab_size=16,
token_type_embedding_name="token_type_embeddings",
use_position_embeddings=True,
position_embedding_name="position_embeddings",
initializer_range=0.02,
max_position_embeddings=512,
dropout_prob=0.1):
"""Performs various post-processing on a word embedding tensor.
Args:
input_tensor: float Tensor of shape [batch_size, seq_length,
embedding_size].
use_token_type: bool. Whether to add embeddings for token_type_ids.
token_type_ids: (optional) int32 Tensor of shape [batch_size, seq_length].
Must be specified if use_token_type is True.
token_type_vocab_size: int. The vocabulary size of token_type_ids.
token_type_embedding_name: string. The name of the embedding table variable
for token type ids.
use_position_embeddings: bool. Whether to add position embeddings for the
position of each token in the sequence.
position_embedding_name: string. The name of the embedding table variable
for positional embeddings.
initializer_range: float. Range of the weight initialization.
max_position_embeddings: int. Maximum sequence length that might ever be
used with this model. This can be longer than the sequence length of
input_tensor, but cannot be shorter.
dropout_prob: float. Dropout probability applied to the final output tensor.
Returns:
float tensor with same shape as input_tensor.
Raises:
ValueError: One of the tensor shapes or input values is invalid.
"""
input_shape = get_shape_list(input_tensor, expected_rank=3)
batch_size = input_shape[0]
seq_length = input_shape[1]
width = input_shape[2]
output = input_tensor
if use_token_type:
if token_type_ids is None:
raise ValueError("token_type_ids must be specified if"
"use_token_type is True.")
token_type_table = tf.get_variable(
name=token_type_embedding_name,
shape=[token_type_vocab_size, width],
initializer=create_initializer(initializer_range))
# This vocab will be small so we always do one-hot here, since it is always
# faster for a small vocabulary.
flat_token_type_ids = tf.reshape(token_type_ids, [-1])
one_hot_ids = tf.one_hot(flat_token_type_ids, depth=token_type_vocab_size)
token_type_embeddings = tf.matmul(one_hot_ids, token_type_table)
token_type_embeddings = tf.reshape(token_type_embeddings,
[batch_size, seq_length, width])
output += token_type_embeddings
if use_position_embeddings:
assert_op = tf.assert_less_equal(seq_length, max_position_embeddings)
with tf.control_dependencies([assert_op]):
full_position_embeddings = tf.get_variable(
name=position_embedding_name,
shape=[max_position_embeddings, width],
initializer=create_initializer(initializer_range))
# Since the position embedding table is a learned variable, we create it
# using a (long) sequence length max_position_embeddings. The actual
# sequence length might be shorter than this, for faster training of
# tasks that do not have long sequences.
#
# So full_position_embeddings is effectively an embedding table
# for position [0, 1, 2, ..., max_position_embeddings-1], and the current
# sequence has positions [0, 1, 2, ... seq_length-1], so we can just
# perform a slice.
position_embeddings = tf.slice(full_position_embeddings, [0, 0],
[seq_length, -1])
num_dims = len(output.shape.as_list())
# Only the last two dimensions are relevant (seq_length and width), so
# we broadcast among the first dimensions, which is typically just
# the batch size.
position_broadcast_shape = []
for _ in range(num_dims - 2):
position_broadcast_shape.append(1)
position_broadcast_shape.extend([seq_length, width])
position_embeddings = tf.reshape(position_embeddings,
position_broadcast_shape)
output += position_embeddings
output = layer_norm_and_dropout(output, dropout_prob)
return
transformer_model
对于每一个Transformer结构如下：
- 多头attention
- 拼接多头输出，经过隐藏层映射
- 经过dropout+残差+层归一化
- 前馈神经网络
- 经过dropout+残差+层归一化
def transformer_model(input_tensor,
attention_mask=None,
hidden_size=768,
num_hidden_layers=12,
num_attention_heads=12,
intermediate_size=3072,
intermediate_act_fn=gelu,
hidden_dropout_prob=0.1,
attention_probs_dropout_prob=0.1,
initializer_range=0.02,
do_return_all_layers=False):
"""Multi-headed, multi-layer Transformer from "Attention is All You Need".
This is almost an exact implementation of the original Transformer encoder.
See the original paper:
https://arxiv.org/abs/1706.03762
Also see:
https://github.com/tensorflow/tensor2tensor/blob/master/tensor2tensor/models/transformer.py
Args:
input_tensor: float Tensor of shape [batch_size, seq_length, hidden_size].
attention_mask: (optional) int32 Tensor of shape [batch_size, seq_length,
seq_length], with 1 for positions that can be attended to and 0 in
positions that should not be.
hidden_size: int. Hidden size of the Transformer.
num_hidden_layers: int. Number of layers (blocks) in the Transformer.
num_attention_heads: int. Number of attention heads in the Transformer.
intermediate_size: int. The size of the "intermediate" (a.k.a., feed
forward) layer.
intermediate_act_fn: function. The non-linear activation function to apply
to the output of the intermediate/feed-forward layer.
hidden_dropout_prob: float. Dropout probability for the hidden layers.
attention_probs_dropout_prob: float. Dropout probability of the attention
probabilities.
initializer_range: float. Range of the initializer (stddev of truncated
normal).
do_return_all_layers: Whether to also return all layers or just the final
layer.
Returns:
float Tensor of shape [batch_size, seq_length, hidden_size], the final
hidden layer of the Transformer.
Raises:
ValueError: A Tensor shape or parameter is invalid.
"""
if hidden_size % num_attention_heads != 0:
raise ValueError(
"The hidden size (%d) is not a multiple of the number of attention "
"heads (%d)" % (hidden_size, num_attention_heads))
attention_head_size = int(hidden_size / num_attention_heads)
input_shape = get_shape_list(input_tensor, expected_rank=3)
batch_size = input_shape[0]
seq_length = input_shape[1]
input_width = input_shape[2]
# The Transformer performs sum residuals on all layers so the input needs
# to be the same as the hidden size.
if input_width != hidden_size:
raise ValueError("The width of the input tensor (%d) != hidden size (%d)" %
(input_width, hidden_size))
# We keep the representation as a 2D tensor to avoid re-shaping it back and
# forth from a 3D tensor to a 2D tensor. Re-shapes are normally free on
# the GPU/CPU but may not be free on the TPU, so we want to minimize them to
# help the optimizer.
prev_output = reshape_to_matrix(input_tensor)
all_layer_outputs = []
for layer_idx in range(num_hidden_layers):
with tf.variable_scope("layer_%d" % layer_idx):
layer_input = prev_output
with tf.variable_scope("attention"):
attention_heads = []
with tf.variable_scope("self"):
attention_head = attention_layer(
from_tensor=layer_input,
to_tensor=layer_input,
attention_mask=attention_mask,
num_attention_heads=num_attention_heads,
size_per_head=attention_head_size,
attention_probs_dropout_prob=attention_probs_dropout_prob,
initializer_range=initializer_range,
do_return_2d_tensor=True,
batch_size=batch_size,
from_seq_length=seq_length,
to_seq_length=seq_length)
attention_heads.append(attention_head)
attention_output = None
if len(attention_heads) == 1:
attention_output = attention_heads[0]
else:
# In the case where we have other sequences, we just concatenate
# them to the self-attention head before the projection.
attention_output = tf.concat(attention_heads, axis=-1)
# Run a linear projection of hidden_size then add a residual
# with layer_input.
with tf.variable_scope("output"):
attention_output = tf.layers.dense(
attention_output,
hidden_size,
kernel_initializer=create_initializer(initializer_range))
attention_output = dropout(attention_output, hidden_dropout_prob)
attention_output = layer_norm(attention_output + layer_input)
# The activation is only applied to the "intermediate" hidden layer.
with tf.variable_scope("intermediate"):
intermediate_output = tf.layers.dense(
attention_output,
intermediate_size,
activation=intermediate_act_fn,
kernel_initializer=create_initializer(initializer_range))
# Down-project back to hidden_size then add the residual.
with tf.variable_scope("output"):
layer_output = tf.layers.dense(
intermediate_output,
hidden_size,
kernel_initializer=create_initializer(initializer_range))
layer_output = dropout(layer_output, hidden_dropout_prob)
layer_output = layer_norm(layer_output + attention_output)
prev_output = layer_output
all_layer_outputs.append(layer_output)
if do_return_all_layers:
final_outputs = []
for layer_output in all_layer_outputs:
final_output = reshape_from_matrix(layer_output, input_shape)
final_outputs.append(final_output)
return final_outputs
else:
final_output = reshape_from_matrix(prev_output, input_shape)
return final_output
其中，多头attention结构如下：
- 针对输入向量与输出向量，生成Q、K、V向量，隐藏层维度为num heads * head size self-attention中 输入输出来源相同。Q来源于输入向量，V来源于输出向量。”目的在于计算输入向量 对于 不同输出的 权重” 若需要对注意力进行掩码，对得分减去很大的值，最终softmax之后得到的影响就非常小
- 针对Q，K 进行缩放点积计算，再经过softmax
- 将第二步结果与V相乘得到上下文向量
def attention_layer(from_tensor,
to_tensor,
attention_mask=None,
num_attention_heads=1,
size_per_head=512,
query_act=None,
key_act=None,
value_act=None,
attention_probs_dropout_prob=0.0,
initializer_range=0.02,
do_return_2d_tensor=False,
batch_size=None,
from_seq_length=None,
to_seq_length=None):
"""Performs multi-headed attention from from_tensor to to_tensor.
This is an implementation of multi-headed attention based on "Attention
is all you Need". If from_tensor and to_tensor are the same, then
this is self-attention. Each timestep in from_tensor attends to the
corresponding sequence in to_tensor, and returns a fixed-with vector.
This function first projects from_tensor into a "query" tensor and
to_tensor into "key" and "value" tensors. These are (effectively) a list
of tensors of length num_attention_heads, where each tensor is of shape
[batch_size, seq_length, size_per_head].
Then, the query and key tensors are dot-producted and scaled. These are
softmaxed to obtain attention probabilities. The value tensors are then
interpolated by these probabilities, then concatenated back to a single
tensor and returned.
In practice, the multi-headed attention are done with transposes and
reshapes rather than actual separate tensors.
Args:
from_tensor: float Tensor of shape [batch_size, from_seq_length,
from_width].
to_tensor: float Tensor of shape [batch_size, to_seq_length, to_width].
attention_mask: (optional) int32 Tensor of shape [batch_size,
from_seq_length, to_seq_length]. The values should be 1 or 0. The
attention scores will effectively be set to -infinity for any positions in
the mask that are 0, and will be unchanged for positions that are 1.
num_attention_heads: int. Number of attention heads.
size_per_head: int. Size of each attention head.
query_act: (optional) Activation function for the query transform.
key_act: (optional) Activation function for the key transform.
value_act: (optional) Activation function for the value transform.
attention_probs_dropout_prob: (optional) float. Dropout probability of the
attention probabilities.
initializer_range: float. Range of the weight initializer.
do_return_2d_tensor: bool. If True, the output will be of shape [batch_size
* from_seq_length, num_attention_heads * size_per_head]. If False, the
output will be of shape [batch_size, from_seq_length, num_attention_heads
* size_per_head].
batch_size: (Optional) int. If the input is 2D, this might be the batch size
of the 3D version of the from_tensor and to_tensor.
from_seq_length: (Optional) If the input is 2D, this might be the seq length
of the 3D version of the from_tensor.
to_seq_length: (Optional) If the input is 2D, this might be the seq length
of the 3D version of the to_tensor.
Returns:
float Tensor of shape [batch_size, from_seq_length,
num_attention_heads * size_per_head]. (If do_return_2d_tensor is
true, this will be of shape [batch_size * from_seq_length,
num_attention_heads * size_per_head]).
Raises:
ValueError: Any of the arguments or tensor shapes are invalid.
"""
def transpose_for_scores(input_tensor, batch_size, num_attention_heads,
seq_length, width):
output_tensor = tf.reshape(
input_tensor, [batch_size, seq_length, num_attention_heads, width])
output_tensor = tf.transpose(output_tensor, [0, 2, 1, 3])
return output_tensor
from_shape = get_shape_list(from_tensor, expected_rank=[2, 3])
to_shape = get_shape_list(to_tensor, expected_rank=[2, 3])
if len(from_shape) != len(to_shape):
raise ValueError(
"The rank of from_tensor must match the rank of to_tensor.")
if len(from_shape) == 3:
batch_size = from_shape[0]
from_seq_length = from_shape[1]
to_seq_length = to_shape[1]
elif len(from_shape) == 2:
if (batch_size is None or from_seq_length is None or to_seq_length is None):
raise ValueError(
"When passing in rank 2 tensors to attention_layer, the values "
"for batch_size, from_seq_length, and to_seq_length "
"must all be specified.")
# Scalar dimensions referenced here:
# B = batch size (number of sequences)
# F = from_tensor sequence length
# T = to_tensor sequence length
# N = num_attention_heads
# H = size_per_head
from_tensor_2d = reshape_to_matrix(from_tensor)
to_tensor_2d = reshape_to_matrix(to_tensor)
# query_layer = [B*F, N*H]
query_layer = tf.layers.dense(
from_tensor_2d,
num_attention_heads * size_per_head,
activation=query_act,
name="query",
kernel_initializer=create_initializer(initializer_range))
# key_layer = [B*T, N*H]
key_layer = tf.layers.dense(
to_tensor_2d,
num_attention_heads * size_per_head,
activation=key_act,
name="key",
kernel_initializer=create_initializer(initializer_range))
# value_layer = [B*T, N*H]
value_layer = tf.layers.dense(
to_tensor_2d,
num_attention_heads * size_per_head,
activation=value_act,
name="value",
kernel_initializer=create_initializer(initializer_range))
# query_layer = [B, N, F, H]
query_layer = transpose_for_scores(query_layer, batch_size,
num_attention_heads, from_seq_length,
size_per_head)
# key_layer = [B, N, T, H]
key_layer = transpose_for_scores(key_layer, batch_size, num_attention_heads,
to_seq_length, size_per_head)
# Take the dot product between "query" and "key" to get the raw
# attention scores.
# attention_scores = [B, N, F, T]
attention_scores = tf.matmul(query_layer, key_layer, transpose_b=True)
attention_scores = tf.multiply(attention_scores,
1.0 / math.sqrt(float(size_per_head)))
if attention_mask is not None:
# attention_mask = [B, 1, F, T]
attention_mask = tf.expand_dims(attention_mask, axis=[1])
# Since attention_mask is 1.0 for positions we want to attend and 0.0 for
# masked positions, this operation will create a tensor which is 0.0 for
# positions we want to attend and -10000.0 for masked positions.
adder = (1.0 - tf.cast(attention_mask, tf.float32)) * -10000.0
# Since we are adding it to the raw scores before the softmax, this is
# effectively the same as removing these entirely.
attention_scores += adder
# Normalize the attention scores to probabilities.
# attention_probs = [B, N, F, T]
attention_probs = tf.nn.softmax(attention_scores)
# This is actually dropping out entire tokens to attend to, which might
# seem a bit unusual, but is taken from the original Transformer paper.
attention_probs = dropout(attention_probs, attention_probs_dropout_prob)
# value_layer = [B, T, N, H]
value_layer = tf.reshape(
value_layer,
[batch_size, to_seq_length, num_attention_heads, size_per_head])
# value_layer = [B, N, T, H]
value_layer = tf.transpose(value_layer, [0, 2, 1, 3])
# context_layer = [B, N, F, H]
context_layer = tf.matmul(attention_probs, value_layer)
# context_layer = [B, F, N, H]
context_layer = tf.transpose(context_layer, [0, 2, 1, 3])
if do_return_2d_tensor:
# context_layer = [B*F, N*H]
context_layer = tf.reshape(
context_layer,
[batch_size * from_seq_length, num_attention_heads * size_per_head])
else:
# context_layer = [B, F, N*H]
context_layer = tf.reshape(
context_layer,
[batch_size, from_seq_length, num_attention_heads * size_per_head])
return context_layer
Albert
Albert整体结构与BERT相似，改动有三点：
- 词嵌入层由Vocab * Hidden 分解为 Vocab * Embedding + Embedding * Hidden
- 跨层参数共享，主要是全连接层与注意力层的共享 Tensorflow 中 通过get variable 与 变量域Variable Scope完成参数共享
- 段落连续的SOP任务替换原先NSP任务，SOP任务中文档连续语句为正例，调换顺序后为负例
代码中同时更新了层归一化的顺序：pre-Layer Normalization can converge fast and better. check paper: ON LAYER NORMALIZATION IN THE TRANSFORMER ARCHITECTURE
模型结构改动主要涉及前两点，接下来我们从代码层面来看这些改动：
embedding_lookup_factorized
主要拆分为两次矩阵运算，embedding size在中间过渡
def embedding_lookup_factorized(input_ids, # Factorized embedding parameterization provide by albert
vocab_size,
hidden_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, but in a factorized style followed by albert. it is used to reduce much percentage of parameters previous exists.
Check "Factorized embedding parameterization" session in the paper.
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].
"""
# This function assumes that the input is of shape [batch_size, seq_length,
# num_inputs].
#
# If the input is a 2D tensor of shape [batch_size, seq_length], we
# reshape to [batch_size, seq_length, 1].
# 1.first project one-hot vectors into a lower dimensional embedding space of size E
print("embedding_lookup_factorized. factorized embedding parameterization is used.")
if input_ids.shape.ndims == 2:
input_ids = tf.expand_dims(input_ids, axis=[-1]) # shape of input_ids is:[ batch_size, seq_length, 1]
embedding_table = tf.get_variable( # [vocab_size, embedding_size]
name=word_embedding_name,
shape=[vocab_size, embedding_size],
initializer=create_initializer(initializer_range))
flat_input_ids = tf.reshape(input_ids, [-1]) # one rank. shape as (batch_size * sequence_length,)
if use_one_hot_embeddings:
one_hot_input_ids = tf.one_hot(flat_input_ids,depth=vocab_size) # one_hot_input_ids=[batch_size * sequence_length,vocab_size]
output_middle = tf.matmul(one_hot_input_ids, embedding_table) # output=[batch_size * sequence_length,embedding_size]
else:
output_middle = tf.gather(embedding_table,flat_input_ids) # [vocab_size, embedding_size]*[batch_size * sequence_length,]--->[batch_size * sequence_length,embedding_size]
# 2. project vector(output_middle) to the hidden space
project_variable = tf.get_variable( # [embedding_size, hidden_size]
name=word_embedding_name+"_2",
shape=[embedding_size, hidden_size],
initializer=create_initializer(initializer_range))
output = tf.matmul(output_middle, project_variable) # ([batch_size * sequence_length, embedding_size] * [embedding_size, hidden_size])--->[batch_size * sequence_length, hidden_size]
# reshape back to 3 rank
input_shape = get_shape_list(input_ids) # input_shape=[ batch_size, seq_length, 1]
batch_size, sequene_length, _=input_shape
output = tf.reshape(output, (batch_size,sequene_length,hidden_size)) # output=[batch_size, sequence_length, hidden_size]
return (output, embedding_table, project_variable)
prelln_transformer_model
将Layer Norm放在Attention前面，使训练过程收敛的更快更好。使用Tensorflow的变量域，完成参数共享。
def prelln_transformer_model(input_tensor,
attention_mask=None,
hidden_size=768,
num_hidden_layers=12,
num_attention_heads=12,
intermediate_size=3072,
intermediate_act_fn=gelu,
hidden_dropout_prob=0.1,
attention_probs_dropout_prob=0.1,
initializer_range=0.02,
do_return_all_layers=False,
shared_type='all', # None,
adapter_fn=None):
"""Multi-headed, multi-layer Transformer from "Attention is All You Need".
This is almost an exact implementation of the original Transformer encoder.
See the original paper:
https://arxiv.org/abs/1706.03762
Also see:
https://github.com/tensorflow/tensor2tensor/blob/master/tensor2tensor/models/transformer.py
Args:
input_tensor: float Tensor of shape [batch_size, seq_length, hidden_size].
attention_mask: (optional) int32 Tensor of shape [batch_size, seq_length,
seq_length], with 1 for positions that can be attended to and 0 in
positions that should not be.
hidden_size: int. Hidden size of the Transformer.
num_hidden_layers: int. Number of layers (blocks) in the Transformer.
num_attention_heads: int. Number of attention heads in the Transformer.
intermediate_size: int. The size of the "intermediate" (a.k.a., feed
forward) layer.
intermediate_act_fn: function. The non-linear activation function to apply
to the output of the intermediate/feed-forward layer.
hidden_dropout_prob: float. Dropout probability for the hidden layers.
attention_probs_dropout_prob: float. Dropout probability of the attention
probabilities.
initializer_range: float. Range of the initializer (stddev of truncated
normal).
do_return_all_layers: Whether to also return all layers or just the final
layer.
Returns:
float Tensor of shape [batch_size, seq_length, hidden_size], the final
hidden layer of the Transformer.
Raises:
ValueError: A Tensor shape or parameter is invalid.
"""
if hidden_size % num_attention_heads != 0:
raise ValueError(
"The hidden size (%d) is not a multiple of the number of attention "
"heads (%d)" % (hidden_size, num_attention_heads))
attention_head_size = int(hidden_size / num_attention_heads)
input_shape = bert_utils.get_shape_list(input_tensor, expected_rank=3)
batch_size = input_shape[0]
seq_length = input_shape[1]
input_width = input_shape[2]
# The Transformer performs sum residuals on all layers so the input needs
# to be the same as the hidden size.
if input_width != hidden_size:
raise ValueError("The width of the input tensor (%d) != hidden size (%d)" %
(input_width, hidden_size))
# We keep the representation as a 2D tensor to avoid re-shaping it back and
# forth from a 3D tensor to a 2D tensor. Re-shapes are normally free on
# the GPU/CPU but may not be free on the TPU, so we want to minimize them to
# help the optimizer.
prev_output = bert_utils.reshape_to_matrix(input_tensor)
all_layer_outputs = []
def layer_scope(idx, shared_type):
if shared_type == 'all':
tmp = {
"layer":"layer_shared",
'attention':'attention',
'intermediate':'intermediate',
'output':'output'
}
elif shared_type == 'attention':
tmp = {
"layer":"layer_shared",
'attention':'attention',
'intermediate':'intermediate_{}'.format(idx),
'output':'output_{}'.format(idx)
}
elif shared_type == 'ffn':
tmp = {
"layer":"layer_shared",
'attention':'attention_{}'.format(idx),
'intermediate':'intermediate',
'output':'output'
}
else:
tmp = {
"layer":"layer_{}".format(idx),
'attention':'attention',
'intermediate':'intermediate',
'output':'output'
}
return tmp
all_layer_outputs = []
for layer_idx in range(num_hidden_layers):
idx_scope = layer_scope(layer_idx, shared_type)
with tf.variable_scope(idx_scope['layer'], reuse=tf.AUTO_REUSE):
layer_input = prev_output
with tf.variable_scope(idx_scope['attention'], reuse=tf.AUTO_REUSE):
attention_heads = []
with tf.variable_scope("output", reuse=tf.AUTO_REUSE):
layer_input_pre = layer_norm(layer_input)
with tf.variable_scope("self"):
attention_head = attention_layer(
from_tensor=layer_input_pre,
to_tensor=layer_input_pre,
attention_mask=attention_mask,
num_attention_heads=num_attention_heads,
size_per_head=attention_head_size,
attention_probs_dropout_prob=attention_probs_dropout_prob,
initializer_range=initializer_range,
do_return_2d_tensor=True,
batch_size=batch_size,
from_seq_length=seq_length,
to_seq_length=seq_length)
attention_heads.append(attention_head)
attention_output = None
if len(attention_heads) == 1:
attention_output = attention_heads[0]
else:
# In the case where we have other sequences, we just concatenate
# them to the self-attention head before the projection.
attention_output = tf.concat(attention_heads, axis=-1)
# Run a linear projection of hidden_size then add a residual
# with layer_input.
with tf.variable_scope("output", reuse=tf.AUTO_REUSE):
attention_output = tf.layers.dense(
attention_output,
hidden_size,
kernel_initializer=create_initializer(initializer_range))
attention_output = dropout(attention_output, hidden_dropout_prob)
# attention_output = layer_norm(attention_output + layer_input)
attention_output = attention_output + layer_input
with tf.variable_scope(idx_scope['output'], reuse=tf.AUTO_REUSE):
attention_output_pre = layer_norm(attention_output)
# The activation is only applied to the "intermediate" hidden layer.
with tf.variable_scope(idx_scope['intermediate'], reuse=tf.AUTO_REUSE):
intermediate_output = tf.layers.dense(
attention_output_pre,
intermediate_size,
activation=intermediate_act_fn,
kernel_initializer=create_initializer(initializer_range))
# Down-project back to hidden_size then add the residual.
with tf.variable_scope(idx_scope['output'], reuse=tf.AUTO_REUSE):
layer_output = tf.layers.dense(
intermediate_output,
hidden_size,
kernel_initializer=create_initializer(initializer_range))
layer_output = dropout(layer_output, hidden_dropout_prob)
# layer_output = layer_norm(layer_output + attention_output)
layer_output = layer_output + attention_output
prev_output = layer_output
all_layer_outputs.append(layer_output)
if do_return_all_layers:
final_outputs = []
for layer_output in all_layer_outputs:
final_output = bert_utils.reshape_from_matrix(layer_output, input_shape)
final_outputs.append(final_output)
return final_outputs
else:
final_output = bert_utils.reshape_from_matrix(prev_output, input_shape)
return final_output
损失函数
MLM
主要流程为：
- 提取模型输出中mask position位置的向量
- 经过变换 每一个输出vocab size大小
- 与标签计算交叉熵损失
def get_masked_lm_output(albert_config, input_tensor, output_weights, positions,
label_ids, label_weights):
"""Get loss and log probs for the masked LM."""
input_tensor = gather_indexes(input_tensor, positions)
with tf.variable_scope("cls/predictions"):
# We apply one more non-linear transformation before the output layer.
# This matrix is not used after pre-training.
with tf.variable_scope("transform"):
input_tensor = tf.layers.dense(
input_tensor,
units=albert_config.embedding_size,
activation=modeling.get_activation(albert_config.hidden_act),
kernel_initializer=modeling.create_initializer(
albert_config.initializer_range))
input_tensor = modeling.layer_norm(input_tensor)
# The output weights are the same as the input embeddings, but there is
# an output-only bias for each token.
output_bias = tf.get_variable(
"output_bias",
shape=[albert_config.vocab_size],
initializer=tf.zeros_initializer())
logits = tf.matmul(input_tensor, output_weights, transpose_b=True)
logits = tf.nn.bias_add(logits, output_bias)
log_probs = tf.nn.log_softmax(logits, axis=-1)
label_ids = tf.reshape(label_ids, [-1])
label_weights = tf.reshape(label_weights, [-1])
one_hot_labels = tf.one_hot(
label_ids, depth=albert_config.vocab_size, dtype=tf.float32)
# The positions tensor might be zero-padded (if the sequence is too
# short to have the maximum number of predictions). The label_weights
# tensor has a value of 1.0 for every real prediction and 0.0 for the
# padding predictions.
per_example_loss = -tf.reduce_sum(log_probs * one_hot_labels, axis=[-1])
numerator = tf.reduce_sum(label_weights * per_example_loss)
denominator = tf.reduce_sum(label_weights) + 1e-5
loss = numerator / denominator
return (loss, per_example_loss, log_probs)
SOP
主要流程为：
- 模型输出向量 转换为 输出为2维的向量
- 与标签计算交叉熵损失
def get_sentence_order_output(albert_config, input_tensor, labels):
"""Get loss and log probs for the next sentence prediction."""
# Simple binary classification. Note that 0 is "next sentence" and 1 is
# "random sentence". This weight matrix is not used after pre-training.
with tf.variable_scope("cls/seq_relationship"):
output_weights = tf.get_variable(
"output_weights",
shape=[2, albert_config.hidden_size],
initializer=modeling.create_initializer(
albert_config.initializer_range))
output_bias = tf.get_variable(
"output_bias", shape=[2], initializer=tf.zeros_initializer())
logits = tf.matmul(input_tensor, output_weights, transpose_b=True)
logits = tf.nn.bias_add(logits, output_bias)
log_probs = tf.nn.log_softmax(logits, axis=-1)
labels = tf.reshape(labels, [-1])
one_hot_labels = tf.one_hot(labels, depth=2, 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, log_probs)
实验结果
模型验证集测试集roberta0.885030.86344albert0.856620.84960预训练后roberta
0.89343
0.85328预训练后albert0.84958
0.85224
总结
模型根据验证集结果保存最优模型，因此测试集上表现不一定是最优的，我们主要看在验证集上的表现。以上模型在相同参数下只跑了一次，因此结果会略有浮动。
- roberta在预训练后效果取得提升，经过再次预训练，模型领域与微调领域更加接近，效果更好
- Albert预训练后效果下降，可能与我们构建数据的方式有关，构建的数据与SOP任务并不符合，可以尝试更符合要求的数据进行测试。
TO DO
- MACBert-20220319暂未开源预训练代码
- 根据SpanBert改为n-gram 掩码与SBO任务
- Pytorch与keras的预训练
Original: https://blog.csdn.net/qq_40676033/article/details/123619606
Author: JMXGODLZ
Title: 不要停止预训练实战-Roberta与Albert
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