文章目录
- Bert 论文概述
- Bert 模型结构
-
- 总体结构
- attention 结构
- finetune classifier 结构
- Bert 模型源码解析
-
- preprocess_data
- tokenize
- data result
- BERT
-
- embedding
- encoder
-
- BaseTransformerEncoder
- attention
- positionwise_ffn
- 与 transformer 简单比较
Bert 论文概述 bert 是 Pre-training of Deep Bidirectional Transformers for Language Understanding 的缩写，18年10月份Google出的神作，主要思想是：
- 建立双向 Transformer encoder 模型，前期通过大量语料，进行语言模型的训练，得到 pre-trained 的 word embedding
- 随后采用相同的 Transform 模型，进行下游任务（语句分类、语义分析、问答、命名实体识别）的 fine-tune，此时模型需要训练的参数只有 fine-tune 部分的参数，参数数据很少，从而大大减少了下游任务的训练时间；同时刷新了 11 项 NLP 任务的SOTA。
BERT 原版论文
此文源码解析已整理于 Github – bert source code understand ，包含了全部 bert 代码，可以单步调试进行学习，基本代码跳转不会很乱，不存在函数跳转至其他文件夹下的情况；也可以直接在 repo 中进行源码阅读。
Bert 模型结构 从名字种就可以看出来，BERT 模型的结构事双向 transformer 结构，至于 transformer 就是 Google 的的另一篇论文了：Attention is all you need ，这里不再叙述。
原论文中 bert 结构如下图，：
文章图片
采用了双向的transformer，最下面一行是经过 embedding 之后的模型输入，把语句变为了词向量；随后12层的 transformer 结构，最后输出模型对语言的理解，即 T1, T2… 用来做下游的语言任务。
bert 子结构 transformer 结构体：
文章图片
图片左边是 transformer 的 encoder 结构，右边是 decoder ，transformer 这个模型是用来做机器翻译的，使用的是 S2S 模型，所以又 encoder 和 decoder 结构，即先将要翻译的句子进行编码，得到句子语义的编码，随后更具编码结果再进行解码。 BERT 模型只使用了 encoder 结构，所以 bert 的 transfomer 是只有 multi-head attention、LayerNorm、FeedForward 及残差快链接而成。
使用 netron 工具，进行 mxnet 模型的可视化显示，结果如下：
总体结构 总体结构是12层的 tansformer encoder 结构，因为全局结构图太大，这里这截取一部分，只有两层：
文章图片
attention 结构 截取了一层 encoder 结构，从中标出了 attention 的各个部分：
文章图片
finetune classifier 结构 Bert 模型源码解析 由于代码太多，这里把类中、函数中不重要的代码进行省略，使用三行 … 表示有代码省略。
preprocess_data
train_data, dev_data, num_train_examples = preprocess_data(
bert_tokenizer, task, batch_size, dev_batch_size, args.max_len)
【论文阅读|bert 源码解读（基于gluonnlp finetune-classifier）】data preprocess 是用来生成训练数据集和测试数据集的，处理结果可以查看本节下面的 process result。
def preprocess_data(tokenizer, task, batch_size, dev_batch_size, max_len):
"""Data preparation function."""
# transformation
trans = BERTDatasetTransform(
tokenizer,
max_len,
labels=task.get_labels(),
pad=False,
pair=task.is_pair,
label_dtype='float32' if not task.get_labels() else 'int32')if task.task_name == 'MNLI':
data_train = task('dev_matched').transform(trans, lazy=False)
data_dev = task('dev_mismatched').transform(trans, lazy=False)
else:
data_train = task('train').transform(trans, lazy=False)
data_dev = task('dev').transform(trans, lazy=False)data_train_len = data_train.transform(
lambda input_id, length, segment_id, label_id: length)num_samples_train = len(data_train)
# bucket sampler
batchify_fn = nlp.data.batchify.Tuple(
nlp.data.batchify.Pad(axis=0), nlp.data.batchify.Stack(),
nlp.data.batchify.Pad(axis=0),
nlp.data.batchify.Stack(
'float32' if not task.get_labels() else 'int32'))
batch_sampler = nlp.data.sampler.FixedBucketSampler(
data_train_len,
batch_size=batch_size,
num_buckets=10,
ratio=0,
shuffle=True)
# data loaders
dataloader = gluon.data.DataLoader(
dataset=data_train,
num_workers=1,
batch_sampler=batch_sampler,
batchify_fn=batchify_fn)
dataloader_dev = mx.gluon.data.DataLoader(
data_dev,
batch_size=dev_batch_size,
num_workers=1,
shuffle=False,
batchify_fn=batchify_fn)
return dataloader, dataloader_dev, num_samples_train
基本的逻辑是：tokenize
先通过 tokenize 进行分词并生成 tokens --> 通过 BERTDatasetTransform 生成对应 task 需要的数据 --> 进行不同的 bucket 分装 --> train or inference
class BERTTokenizer(object):
r"""End-to-end tokenization for BERT models.Parameters
----------
vocab : gluonnlp.Vocab or None, default None
Vocabulary for the corpus.
lower : bool, default True
whether the text strips accents and convert to lower case.
If you use the BERT pre-training model,
lower is set to Flase when using the cased model,
otherwise it is set to True.
max_input_chars_per_word : int, default 200Examples
--------
>>> _,vocab = gluonnlp.model.bert_12_768_12(dataset_name='wiki_multilingual',pretrained=False)
>>> tokenizer = gluonnlp.data.BERTTokenizer(vocab=vocab)
>>> tokenizer(u"gluonnlp: 使NLP变得简单。")
['gl', '##uo', '##nn', '##lp', ':', '使', 'nl', '##p', '变', '得', '简', '单', '。']"""def __init__(self, vocab, lower=True, max_input_chars_per_word=200):
self.vocab = vocab
self.max_input_chars_per_word = max_input_chars_per_word
self.basic_tokenizer = BERTBasicTokenizer(lower=lower)def __call__(self, sample):
"""
Parameters
----------
sample: str (unicode for Python 2)
The string to tokenize. Must be unicode.Returns
-------
ret : list of strs
List of tokens
"""return self._tokenizer(sample)def _tokenizer(self, text):
split_tokens = []
for token in self.basic_tokenizer(text):
for sub_token in self._tokenize_wordpiece(token):
split_tokens.append(sub_token)return split_tokensdef _tokenize_wordpiece(self, text):
"""Tokenizes a piece of text into its word pieces.This uses a greedy longest-match-first algorithm to perform tokenization
using the given vocabulary.For example:
input = "unaffable"
output = ["un", "##aff", "##able"]Args:
text: A single token or whitespace separated tokens. This should have
already been passed through `BERTBasicTokenizer.Returns:
A list of wordpiece tokens.
"""...
...
...def convert_tokens_to_ids(self, tokens):
"""Converts a sequence of tokens into ids using the vocab."""
return self.vocab.to_indices(tokens)这里BERT分词的代码，从代码注释中可以看到分词的结果如下：
>>> tokenizer = gluonnlp.data.BERTTokenizer(vocab=vocab)
>>> tokenizer(u"gluonnlp: 使NLP变得简单。")
['gl', '##uo', '##nn', '##lp', ':', '使', 'nl', '##p', '变', '得', '简', '单', '。']
代码中调用的是 self._tokenizer(sample) ，把一句话先进行语句级的分割，得到词汇；随后在进行词汇级的分割，得到 tokens ，此时采用的是贪心算法：首次最大长度匹配（感觉以后可以改进一下，贪心算法在这里肯定不是最优的）。
具体做语句级分割的时候，采用的是 BERTBasicTokenizer 分割器，代码如下：
class BERTBasicTokenizer():
r"""Runs basic tokenizationperforms invalid character removal (e.g. control chars) and whitespace.
tokenize CJK chars.
splits punctuation on a piece of text.
strips accents and convert to lower case.(If lower is true)Parameters
----------
lower : bool, default True
whether the text strips accents and convert to lower case.Examples
--------
>>> tokenizer = gluonnlp.data.BERTBasicTokenizer(lower=True)
>>> tokenizer(u" \tHeLLo!how\n Are yoU?")
['hello', '!', 'how', 'are', 'you', '?']
>>> tokenizer = gluonnlp.data.BERTBasicTokenizer(lower=False)
>>> tokenizer(u" \tHeLLo!how\n Are yoU?")
['HeLLo', '!', 'how', 'Are', 'yoU', '?']"""def __init__(self, lower=True):
self.lower = lowerdef __call__(self, sample):
"""Parameters
----------
sample:str (unicode for Python 2)
The string to tokenize. Must be unicode.Returns
-------
ret : list of strs
List of tokens
"""
return self._tokenize(sample)def _tokenize(self, text):
...
...
...
可以看到，语句级分割就是把一句话分割为单词，这里有 lower 的参数，用来做大小写的开关。
data result
: (
array([2,1996,2327,3446,2097,2175,2000,1018,1012,
3429,3867,2005,2035,3901,2007, 23726,3468, 29373,
2682,1002,3156,1010,2199,1012,3,2005,3901,
2007, 29373,2682,1002,3156,1010,2199,1010,1996,
3318,1011,4171,3446,2097,3623,2000,1018,1012,
3429,3867,1012,3], dtype=int32), array(49, dtype=int32), array([0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,
1, 1, 1, 1, 1], dtype=int32), array([1], dtype=int32)
)
这里只贴出来了单句话的处理结果：
- 第一个 array 是句子的词向量表示；
- 第二个 array 记录了句子有效词汇长度；
- 第三个句子是句子标签（0表示第一个句子，1表示第二个句子）；
BERT Bert 结构基本是 transformer encoder 的堆叠，所以懂了 transformer 基本就懂了 bert。 而 transformer 的核心又在 attention，这里代码学习的流程也基本是按这个逻辑来的，下面一一解读。
先从 BERTModel 来进行代码总流程的学习：
class BERTModel(Block):
"""Model for BERT (Bidirectional Encoder Representations from Transformers).
Parameters
----------
encoder : BERTEncoder
Bidirectional encoder that encodes the input sentence.
vocab_size : int or None, default None
The size of the vocabulary.
token_type_vocab_size : int or None, default None
The vocabulary size of token types.
units : int or None, default None
Number of units for the final pooler layer.
embed_size : int or None, default None
Size of the embedding vectors. It is used to generate the word and token type
embeddings if word_embed and token_type_embed are None.
embed_dropout : float, default 0.0
Dropout rate of the embedding weights. It is used to generate the source and target
embeddings if word_embed and token_type_embed are None.
embed_initializer : Initializer, default None
Initializer of the embedding weights. It is used to generate the source and target
embeddings if word_embed and token_type_embed are None.
word_embed : Block or None, default None
The word embedding. If set to None, word_embed will be constructed using embed_size and
embed_dropout.
token_type_embed : Block or None, default None
The token type embedding. If set to None and the token_type_embed will be constructed using
embed_size and embed_dropout.
use_pooler : bool, default True
Whether to include the pooler which converts the encoded sequence tensor of shape
(batch_size, seq_length, units) to a tensor of shape (batch_size, units)
for segment level classification task.
use_decoder : bool, default True
Whether to include the decoder for masked language model prediction.
use_classifier : bool, default True
Whether to include the classifier for next sentence classification.
prefix : str or None
See document of `mx.gluon.Block`.
params : ParameterDict or None
See document of `mx.gluon.Block`.
Inputs:
- **inputs**: input sequence tensor, shape (batch_size, seq_length)
- **token_types**: input token type tensor, shape (batch_size, seq_length).
If the inputs contain two sequences, then the token type of the first
sequence differs from that of the second one.
- **valid_length**: optional tensor of input sequence valid lengths, shape (batch_size,)
- **masked_positions**: optional tensor of position of tokens for masked LM decoding,
shape (batch_size, num_masked_positions).
Outputs:
- **sequence_outputs**: output tensor of sequence encodings.
Shape (batch_size, seq_length, units).
- **pooled_output**: output tensor of pooled representation of the first tokens.
Returned only if use_pooler is True. Shape (batch_size, units)
- **next_sentence_classifier_output**: output tensor of next sentence classification.
Returned only if use_classifier is True. Shape (batch_size, 2)
- **masked_lm_outputs**: output tensor of sequence decoding for masked language model
prediction. Returned only if use_decoder True.
Shape (batch_size, num_masked_positions, vocab_size)
"""def __init__(self, encoder, vocab_size=None, token_type_vocab_size=None, units=None,
embed_size=None, embed_dropout=0.0, embed_initializer=None,
word_embed=None, token_type_embed=None, use_pooler=True, use_decoder=True,
use_classifier=True, prefix=None, params=None):
super(BERTModel, self).__init__(prefix=prefix, params=params)
self._use_decoder = use_decoder
self._use_classifier = use_classifier
self._use_pooler = use_pooler
self.encoder = encoder
# Construct word embedding
self.word_embed = self._get_embed(word_embed, vocab_size, embed_size,
embed_initializer, embed_dropout, 'word_embed_')
# Construct token type embedding
self.token_type_embed = self._get_embed(token_type_embed, token_type_vocab_size,
embed_size, embed_initializer, embed_dropout,
'token_type_embed_')
if self._use_pooler:
# Construct pooler
self.pooler = self._get_pooler(units, 'pooler_')
if self._use_classifier:
# Construct classifier for next sentence predicition
self.classifier = self._get_classifier('cls_')
else:
assert not use_classifier, 'Cannot use classifier if use_pooler is False'
if self._use_decoder:
# Construct decoder for masked language model
self.decoder = self._get_decoder(units, vocab_size, self.word_embed[0], 'decoder_')def _get_classifier(self, prefix):
""" Construct a decoder for the masked language model task """
with self.name_scope():
classifier = nn.Dense(2, prefix=prefix)
return classifierdef _get_decoder(self, units, vocab_size, embed, prefix):
...
...
...
return decoderdef _get_embed(self, embed, vocab_size, embed_size, initializer, dropout, prefix):
""" Construct an embedding block. """
if embed is None:
assert embed_size is not None, '"embed_size" cannot be None if "word_embed" or ' \
'token_type_embed is not given.'
with self.name_scope():
embed = nn.HybridSequential(prefix=prefix)
with embed.name_scope():
embed.add(nn.Embedding(input_dim=vocab_size, output_dim=embed_size,
weight_initializer=initializer))
if dropout:
embed.add(nn.Dropout(rate=dropout))
assert isinstance(embed, Block)
return embeddef _get_pooler(self, units, prefix):
""" Construct pooler.
The pooler slices and projects the hidden output of first token
in the sequence for segment level classification.
"""
with self.name_scope():
pooler = nn.Dense(units=units, flatten=False, activation='tanh',
prefix=prefix)
return poolerdef forward(self, inputs, token_types, valid_length=None, masked_positions=None):# pylint: disable=arguments-differ
"""Generate the representation given the inputs.
This is used in training or fine-tuning a BERT model.
"""
outputs = []
seq_out, _ = self._encode_sequence(inputs, token_types, valid_length)
outputs.append(seq_out)
if self._use_pooler:
pooled_out = self._apply_pooling(seq_out)
outputs.append(pooled_out)
if self._use_classifier:
next_sentence_classifier_out = self.classifier(pooled_out)
outputs.append(next_sentence_classifier_out)
if self._use_decoder:
assert masked_positions is not None, \
'masked_positions tensor is required for decoding masked language model'
decoder_out = self._decode(seq_out, masked_positions)
outputs.append(decoder_out)
return tuple(outputs) if len(outputs) > 1 else outputs[0]def _encode_sequence(self, inputs, token_types, valid_length=None):
"""Generate the representation given the input sequences.
This is used for pre-training or fine-tuning a BERT model.
"""
# embedding
word_embedding = self.word_embed(inputs)
type_embedding = self.token_type_embed(token_types)
embedding = word_embedding + type_embedding
# encoding
outputs, additional_outputs = self.encoder(embedding, None, valid_length)
return outputs, additional_outputsdef _apply_pooling(self, sequence):
"""Generate the representation given the inputs.
This is used for pre-training or fine-tuning a BERT model.
"""
outputs = sequence[:, 0, :]
return self.pooler(outputs)def _decode(self, sequence, masked_positions):
...
...
...
return decoded该类定义初始化都是给各个成员变量进行赋值，这里包括：
self._use_decoder 、self._use_classifier 、self._use_pooler、self.encoder = encoder、word_embed = self._get_embed、self.token_type_embed 等。
mxnet 自定义的模型进行前向传播的时候，其实是在运行 forword函数，所以代码要从 forward 开始看：
这里可以看出，forward 的设计比较简单，依然保留有 decoder 接口，主要工作是在 self._encode_sequence 中进行，接着看这个函数：
- 调用 self._encode_sequence，返回结果 seq_out；
- 判断是否进行 pool 、classifier 和 decoder，对 seq_out 进行相对处理；
- 将处理后的结果进行输出；
这里可以看到 BERTModel 暴露给我们的只有 embedding，主要工作还是在 encoder 核中进行的。
- 进行 word_embedding 和 type_embedding
- 将二者进行相加，随后送入 bert 的 self.encoder 里面进行处理
- 得到输出，返回结果
embedding
下面是 embedding 的代码，也可以看出来， embedding 是通过传参给 nn.embedding 来进行的：
# Construct word embedding
self.word_embed = self._get_embed(word_embed, vocab_size, embed_size, embed_initializer,
embed_dropout, 'word_embed_')
# Construct token type embedding
self.token_type_embed = self._get_embed(token_type_embed, token_type_vocab_size,
embed_size, embed_initializer, embed_dropout, 'token_type_embed_')# embedding
word_embedding = self.word_embed(inputs)
type_embedding = self.token_type_embed(token_types)
embedding = word_embedding + type_embedding
# encoding
outputs, additional_outputs = self.encoder(embedding, None, valid_length)
return outputs, additional_outputsdef _get_embed(self, embed, vocab_size, embed_size, initializer, dropout, prefix):
""" Construct an embedding block. """
if embed is None:
assert embed_size is not None, '"embed_size" cannot be None if "word_embed" or ' \
'token_type_embed is not given.'
with self.name_scope():
embed = nn.HybridSequential(prefix=prefix)
with embed.name_scope():
embed.add(nn.Embedding(input_dim=vocab_size, output_dim=embed_size,
weight_initializer=initializer))
if dropout:
embed.add(nn.Dropout(rate=dropout))
assert isinstance(embed, Block)
return embed
encoder
在上面 BERTModel 中的 调用 self._encode_sequence 可以看到，数据在进行了 embedding 之后，会送入 encoder 中，得到输出，这个是整个模型的核心，基本架构继承自 transformer 。这里以标准 bert_12_768_12 为例，BERTEncoder 参数全部来自与 bert_12_768_12 的配置，如下：
predefined_args = bert_hparams[model_name]
# encoder
encoder = BERTEncoder(attention_cell=predefined_args['attention_cell'],
num_layers=predefined_args['num_layers'],
units=predefined_args['units'],
hidden_size=predefined_args['hidden_size'],
max_length=predefined_args['max_length'],
num_heads=predefined_args['num_heads'],
scaled=predefined_args['scaled'],
dropout=predefined_args['dropout'],
use_residual=predefined_args['use_residual'])bert_12_768_12_hparams = {
'attention_cell': 'multi_head',
'num_layers': 12,
'units': 768,
'hidden_size': 3072,
'max_length': 512,
'num_heads': 12,
'scaled': True,
'dropout': 0.1,
'use_residual': True,
'embed_size': 768,
'embed_dropout': 0.1,
'token_type_vocab_size': 2,
'word_embed': None,
}
class BERTEncoder(BaseTransformerEncoder):
"""Structure of the BERT Encoder.Different from the original encoder for transformer,
`BERTEncoder` uses learnable positional embedding, `BERTPositionwiseFFN`
and `BERTLayerNorm`.Parameters
----------
attention_cell : AttentionCell or str, default 'multi_head'
Arguments of the attention cell.
Can be 'multi_head', 'scaled_luong', 'scaled_dot', 'dot', 'cosine', 'normed_mlp', 'mlp'
num_layers : int
Number of attention layers.
units : int
Number of units for the output.
hidden_size : int
number of units in the hidden layer of position-wise feed-forward networks
max_length : int
Maximum length of the input sequence
num_heads : int
Number of heads in multi-head attention
scaled : bool
Whether to scale the softmax input by the sqrt of the input dimension
in multi-head attention
dropout : float
Dropout probability of the attention probabilities.
use_residual : bool
output_attention: bool
Whether to output the attention weights
weight_initializer : str or Initializer
Initializer for the input weights matrix, used for the linear
transformation of the inputs.
bias_initializer : str or Initializer
Initializer for the bias vector.
prefix : str, default None.
Prefix for name of `Block`s. (and name of weight if params is `None`).
params : Parameter or None
Container for weight sharing between cells. Created if `None`.Inputs:
- **inputs** : input sequence of shape (batch_size, length, C_in)
- **states** : list of tensors for initial states and masks.
- **valid_length** : valid lengths of each sequence. Usually used when part of sequence
has been padded. Shape is (batch_size, )Outputs:
- **outputs** : the output of the encoder. Shape is (batch_size, length, C_out)
- **additional_outputs** : list of tensors.
Either be an empty list or contains the attention weights in this step.
The attention weights will have shape (batch_size, num_heads, length, mem_length)
"""def __init__(self, attention_cell='multi_head', num_layers=2,
units=512, hidden_size=2048, max_length=50,
num_heads=4, scaled=True, dropout=0.0,
use_residual=True, output_attention=False,
weight_initializer=None, bias_initializer='zeros',
prefix=None, params=None):
super(BERTEncoder, self).__init__(attention_cell=attention_cell,
num_layers=num_layers, units=units,
hidden_size=hidden_size, max_length=max_length,
num_heads=num_heads, scaled=scaled, dropout=dropout,
use_residual=use_residual,
output_attention=output_attention,
weight_initializer=weight_initializer,
bias_initializer=bias_initializer,
prefix=prefix, params=params,
# extra configurations for BERT
positional_weight='learned',
use_bert_encoder=True,
use_layer_norm_before_dropout=False,
scale_embed=False)
BaseTransformerEncoder
class BaseTransformerEncoder(HybridBlock, Seq2SeqEncoder):
"""Base Structure of the Transformer Encoder.Parameters
----------
attention_cell : AttentionCell or str, default 'multi_head'
Arguments of the attention cell.
Can be 'multi_head', 'scaled_luong', 'scaled_dot', 'dot', 'cosine', 'normed_mlp', 'mlp'
num_layers : int
Number of attention layers.
units : int
Number of units for the output.
hidden_size : int
number of units in the hidden layer of position-wise feed-forward networks
max_length : int
Maximum length of the input sequence
num_heads : int
Number of heads in multi-head attention
scaled : bool
Whether to scale the softmax input by the sqrt of the input dimension
in multi-head attention
dropout : float
Dropout probability of the attention probabilities.
use_residual : bool
output_attention: bool
Whether to output the attention weights
weight_initializer : str or Initializer
Initializer for the input weights matrix, used for the linear
transformation of the inputs.
bias_initializer : str or Initializer
Initializer for the bias vector.
positional_weight: str, default 'sinusoidal'
Type of positional embedding. Can be 'sinusoidal', 'learned'.
If set to 'sinusoidal', the embedding is initialized as sinusoidal values and keep constant.
use_bert_encoder : bool, default False
Whether to use BERTEncoderCell and BERTLayerNorm. Set to True for pre-trained BERT model
use_layer_norm_before_dropout: bool, default False
Before passing embeddings to attention cells, whether to perform `layernorm -> dropout` or
`dropout -> layernorm`. Set to True for pre-trained BERT models.
scale_embed : bool, default True
Scale the input embeddings by sqrt(embed_size). Set to False for pre-trained BERT models.
prefix : str, default 'rnn_'
Prefix for name of `Block`s
(and name of weight if params is `None`).
params : Parameter or None
Container for weight sharing between cells.
Created if `None`.
"""def __init__(self, attention_cell='multi_head', num_layers=2,
units=512, hidden_size=2048, max_length=50,
num_heads=4, scaled=True, dropout=0.0,
use_residual=True, output_attention=False,
weight_initializer=None, bias_initializer='zeros',
positional_weight='sinusoidal', use_bert_encoder=False,
use_layer_norm_before_dropout=False, scale_embed=True,
prefix=None, params=None):
super(BaseTransformerEncoder, self).__init__(prefix=prefix, params=params)
assert units % num_heads == 0, \
'In TransformerEncoder, The units should be divided exactly ' \
'by the number of heads. Received units={}, num_heads={}' \
.format(units, num_heads)
self._num_layers = num_layers
self._max_length = max_length
self._num_heads = num_heads
self._units = units
self._hidden_size = hidden_size
self._output_attention = output_attention
self._dropout = dropout
self._use_residual = use_residual
self._scaled = scaled
self._use_layer_norm_before_dropout = use_layer_norm_before_dropout
self._scale_embed = scale_embed
with self.name_scope():
self.dropout_layer = nn.Dropout(dropout)
self.layer_norm = _get_layer_norm(use_bert_encoder, units)
self.position_weight = self._get_positional(positional_weight, max_length, units,
weight_initializer)
self.transformer_cells = nn.HybridSequential()
for i in range(num_layers):
cell = self._get_encoder_cell(use_bert_encoder, units, hidden_size, num_heads,
attention_cell, weight_initializer, bias_initializer,
dropout, use_residual, scaled, output_attention, i)
self.transformer_cells.add(cell)def _get_positional(self, weight_type, max_length, units, initializer):
if weight_type == 'sinusoidal':
encoding = _position_encoding_init(max_length, units)
position_weight = self.params.get_constant('const', encoding)
elif weight_type == 'learned':
position_weight = self.params.get('position_weight', shape=(max_length, units),
init=initializer)
else:
raise ValueError('Unexpected value for argument position_weight: %s' % (position_weight))
return position_weightdef _get_encoder_cell(self, use_bert, units, hidden_size, num_heads, attention_cell,
weight_initializer, bias_initializer, dropout, use_residual,
scaled, output_attention, i):
from bert_func import BERTEncoderCell
cell = BERTEncoderCell if use_bert else TransformerEncoderCell
return cell(units=units, hidden_size=hidden_size,
num_heads=num_heads, attention_cell=attention_cell,
weight_initializer=weight_initializer,
bias_initializer=bias_initializer,
dropout=dropout, use_residual=use_residual,
scaled=scaled, output_attention=output_attention,
prefix='transformer%d_' % i)def __call__(self, inputs, states=None, valid_length=None):# pylint: disable=arguments-differ
"""Encoder the inputs given the states and valid sequence length.Parameters
----------
inputs : NDArray
Input sequence. Shape (batch_size, length, C_in)
states : list of NDArrays or None
Initial states. The list of initial states and masks
valid_length : NDArray or None
Valid lengths of each sequence. This is usually used when part of sequence has
been padded. Shape (batch_size,)Returns
-------
encoder_outputs: list
Outputs of the encoder. Contains:- outputs of the transformer encoder. Shape (batch_size, length, C_out)
- additional_outputs of all the transformer encoder
"""
return super(BaseTransformerEncoder, self).__call__(inputs, states, valid_length)def forward(self, inputs, states=None, valid_length=None, steps=None):# pylint: disable=arguments-differ
"""Parameters
----------
inputs : NDArray, Shape(batch_size, length, C_in)
states : list of NDArray
valid_length : NDArray
steps : NDArray
Stores value [0, 1, ..., length].
It is used for lookup in positional encoding matrixReturns
-------
outputs : NDArray
The output of the encoder. Shape is (batch_size, length, C_out)
additional_outputs : list
Either be an empty list or contains the attention weights in this step.
The attention weights will have shape (batch_size, length, length) or
(batch_size, num_heads, length, length)"""
length = inputs.shape[1]
if valid_length is not None:
mask = mx.nd.broadcast_lesser(
mx.nd.arange(length, ctx=valid_length.context).reshape((1, -1)),
valid_length.reshape((-1, 1)))
mask = mx.nd.broadcast_axes(mx.nd.expand_dims(mask, axis=1), axis=1, size=length)
if states is None:
states = [mask]
else:
states.append(mask)
if self._scale_embed:
inputs = inputs * math.sqrt(inputs.shape[-1])
steps = mx.nd.arange(length, ctx=inputs.context)
if states is None:
states = [steps]
else:
states.append(steps)
if valid_length is not None:
step_output, additional_outputs = \
super(BaseTransformerEncoder, self).forward(inputs, states, valid_length)
else:
step_output, additional_outputs = \
super(BaseTransformerEncoder, self).forward(inputs, states)
return step_output, additional_outputsdef hybrid_forward(self, F, inputs, states=None, valid_length=None, position_weight=None):
# pylint: disable=arguments-differ
"""Parameters
----------
inputs : NDArray or Symbol, Shape(batch_size, length, C_in)
states : list of NDArray or Symbol
valid_length : NDArray or Symbol
position_weight : NDArray or SymbolReturns
-------
outputs : NDArray or Symbol
The output of the encoder. Shape is (batch_size, length, C_out)
additional_outputs : list
Either be an empty list or contains the attention weights in this step.
The attention weights will have shape (batch_size, length, length) or
(batch_size, num_heads, length, length)"""
if states is not None:
steps = states[-1]
# Positional Encoding
positional_embed = F.Embedding(steps, position_weight, self._max_length, self._units)
inputs = F.broadcast_add(inputs, F.expand_dims(positional_embed, axis=0))
if self._use_layer_norm_before_dropout:
inputs = self.layer_norm(inputs)
inputs = self.dropout_layer(inputs)
else:
inputs = self.dropout_layer(inputs)
inputs = self.layer_norm(inputs)
outputs = inputs
if valid_length is not None:
mask = states[-2]
else:
mask = None
additional_outputs = []
for cell in self.transformer_cells:
outputs, attention_weights = cell(inputs, mask)
inputs = outputs
if self._output_attention:
additional_outputs.append(attention_weights)
if valid_length is not None:
outputs = F.SequenceMask(outputs, sequence_length=valid_length,
use_sequence_length=True, axis=1)
return outputs, additional_outputsdef _get_layer_norm(use_bert, units):
from bert_func import BERTLayerNorm
layer_norm = BERTLayerNorm if use_bert else nn.LayerNorm
return layer_norm(in_channels=units)def _position_encoding_init(max_length, dim):
"""Init the sinusoid position encoding table """
position_enc = np.arange(max_length).reshape((-1, 1)) \
/ (np.power(10000, (2. / dim) * np.arange(dim).reshape((1, -1))))
# Apply the cosine to even columns and sin to odds.
position_enc[:, 0::2] = np.sin(position_enc[:, 0::2])# dim 2i
position_enc[:, 1::2] = np.cos(position_enc[:, 1::2])# dim 2i+1
return position_encdef _get_attention_cell(attention_cell, units=None,
scaled=True, num_heads=None,
use_bias=False, dropout=0.0):
"""Parameters
----------
attention_cell : AttentionCell or str
units : int or NoneReturns
-------
attention_cell : AttentionCell
"""
if isinstance(attention_cell, str):
if attention_cell == 'scaled_luong':
return DotProductAttentionCell(units=units, scaled=True, normalized=False,
use_bias=use_bias, dropout=dropout, luong_style=True)
elif attention_cell == 'scaled_dot':
return DotProductAttentionCell(units=units, scaled=True, normalized=False,
use_bias=use_bias, dropout=dropout, luong_style=False)
elif attention_cell == 'dot':
return DotProductAttentionCell(units=units, scaled=False, normalized=False,
use_bias=use_bias, dropout=dropout, luong_style=False)
elif attention_cell == 'cosine':
return DotProductAttentionCell(units=units, scaled=False, use_bias=use_bias,
dropout=dropout, normalized=True)
elif attention_cell == 'mlp':
return MLPAttentionCell(units=units, normalized=False)
elif attention_cell == 'normed_mlp':
return MLPAttentionCell(units=units, normalized=True)
elif attention_cell == 'multi_head':
base_cell = DotProductAttentionCell(scaled=scaled, dropout=dropout)
return MultiHeadAttentionCell(base_cell=base_cell, query_units=units, use_bias=use_bias,
key_units=units, value_units=units, num_heads=num_heads)
else:
raise NotImplementedError
else:
assert isinstance(attention_cell, AttentionCell),\
'attention_cell must be either string or AttentionCell. Received attention_cell={}'\
.format(attention_cell)
return attention_cell
attention
WIP
positionwise_ffn
WIP
与 transformer 简单比较 WIP
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