r"""
.. todo::
doc
"""
__all__ = [
"BertEmbedding",
"BertWordPieceEncoder"
]
import collections
import warnings
from itertools import chain
from functools import partial
import numpy as np
import torch
from torch import nn
from .contextual_embedding import ContextualEmbedding
from ..core import logger
from ..core.vocabulary import Vocabulary
from ..io.file_utils import PRETRAINED_BERT_MODEL_DIR
from ..modules.encoder.bert import BertModel
from ..modules.tokenizer import BertTokenizer
[文档]class BertEmbedding(ContextualEmbedding):
r"""
使用BERT对words进行编码的Embedding。建议将输入的words长度限制在430以内，而不要使用512(根据预训练模型参数，可能有变化)。这是由于
预训练的bert模型长度限制为512个token，而因为输入的word是未进行word piece分割的(word piece的分割有BertEmbedding在输入word
时切分)，在分割之后长度可能会超过最大长度限制。
BertEmbedding可以支持自动下载权重，当前支持的模型:
en: base-cased
en-base-uncased:
en-large-cased-wwm:
en-large-cased:
en-large-uncased:
en-large-uncased-wwm
cn: 中文BERT wwm by HIT
cn-base: 中文BERT base-chinese
cn-wwm-ext: 中文BERT wwm by HIT with extra data pretrain.
multi-base-cased: multilingual cased
multi-base-uncased: multilingual uncased
Example::
>>> import torch
>>> from fastNLP import Vocabulary
>>> from fastNLP.embeddings import BertEmbedding
>>> vocab = Vocabulary().add_word_lst("The whether is good .".split())
>>> embed = BertEmbedding(vocab, model_dir_or_name='en-base-uncased', requires_grad=False, layers='4,-2,-1')
>>> words = torch.LongTensor([[vocab.to_index(word) for word in "The whether is good .".split()]])
>>> outputs = embed(words)
>>> outputs.size()
>>> # torch.Size([1, 5, 2304])
"""
[文档] def __init__(self, vocab: Vocabulary, model_dir_or_name: str = 'en-base-uncased', layers: str = '-1',
pool_method: str = 'first', word_dropout=0, dropout=0, include_cls_sep: bool = False,
pooled_cls=True, requires_grad: bool = True, auto_truncate: bool = False, **kwargs):
r"""
:param ~fastNLP.Vocabulary vocab: 词表
:param str model_dir_or_name: 模型所在目录或者模型的名称。当传入模型所在目录时，目录中应该包含一个词表文件(以.txt作为后缀名),
权重文件(以.bin作为文件后缀名), 配置文件(以.json作为后缀名)。
:param str layers: 输出embedding表示来自于哪些层，不同层的结果按照layers中的顺序在最后一维concat起来。以','隔开层数，层的序号是
从0开始，可以以负数去索引倒数几层。 layer=0为embedding层（包括wordpiece embedding,
position embedding和segment embedding）
:param str pool_method: 因为在bert中，每个word会被表示为多个word pieces, 当获取一个word的表示的时候，怎样从它的word pieces
中计算得到它对应的表示。支持 ``last`` , ``first`` , ``avg`` , ``max``。
:param float word_dropout: 以多大的概率将一个词替换为unk。这样既可以训练unk也是一定的regularize。
:param float dropout: 以多大的概率对embedding的表示进行Dropout。0.1即随机将10%的值置为0。
:param bool include_cls_sep: bool，在bert计算句子的表示的时候，需要在前面加上[CLS]和[SEP], 是否在结果中保留这两个内容。 这样
会使得word embedding的结果比输入的结果长两个token。如果该值为True，则在使用 :class::StackEmbedding 可能会与其它类型的
embedding长度不匹配。
:param bool pooled_cls: 返回的[CLS]是否使用预训练中的BertPool映射一下，仅在include_cls_sep时有效。如果下游任务只取[CLS]做预测，
一般该值为True。
:param bool requires_grad: 是否需要gradient以更新Bert的权重。
:param bool auto_truncate: 当句子words拆分为word pieces长度超过bert最大允许长度(一般为512), 自动截掉拆分后的超过510个
word pieces后的内容，并将第512个word piece置为[SEP]。超过长度的部分的encode结果直接全部置零。一般仅有只使用[CLS]
来进行分类的任务将auto_truncate置为True。
:param kwargs:
bool only_use_pretrain_bpe: 仅使用出现在pretrain词表中的bpe，如果该词没法tokenize则使用unk。如果embedding不需要更新
建议设置为True。
int min_freq: 仅在only_use_pretrain_bpe为False有效，大于等于该次数的词会被新加入BERT的BPE词表中
bool truncate_embed: 是否仅保留用到的bpe(这样会减内存占用和加快速度)
"""
super(BertEmbedding, self).__init__(vocab, word_dropout=word_dropout, dropout=dropout)
if word_dropout>0:
assert vocab.unknown != None, "When word_drop>0, Vocabulary must contain the unknown token."
if model_dir_or_name.lower() in PRETRAINED_BERT_MODEL_DIR:
if 'cn' in model_dir_or_name.lower() and pool_method not in ('first', 'last'):
logger.warning("For Chinese bert, pooled_method should choose from 'first', 'last' in order to achieve"
" faster speed.")
warnings.warn("For Chinese bert, pooled_method should choose from 'first', 'last' in order to achieve"
" faster speed.")
self._word_sep_index = -100
if '[SEP]' in vocab:
self._word_sep_index = vocab['[SEP]']
self._word_cls_index = -100
if '[CLS]' in vocab:
self._word_cls_index = vocab['CLS']
only_use_pretrain_bpe = kwargs.get('only_use_pretrain_bpe', False)
truncate_embed = kwargs.get('truncate_embed', True)
min_freq = kwargs.get('min_freq', 2)
self.model = _BertWordModel(model_dir_or_name=model_dir_or_name, vocab=vocab, layers=layers,
pool_method=pool_method, include_cls_sep=include_cls_sep,
pooled_cls=pooled_cls, auto_truncate=auto_truncate, min_freq=min_freq,
only_use_pretrain_bpe=only_use_pretrain_bpe, truncate_embed=truncate_embed)
self.requires_grad = requires_grad
self._embed_size = len(self.model.layers) * self.model.encoder.hidden_size
def _delete_model_weights(self):
del self.model
[文档] def forward(self, words):
r"""
计算words的bert embedding表示。计算之前会在每句话的开始增加[CLS]在结束增加[SEP], 并根据include_cls_sep判断要不要
删除这两个token的表示。
:param torch.LongTensor words: [batch_size, max_len]
:return: torch.FloatTensor. batch_size x max_len x (768*len(self.layers))
"""
words = self.drop_word(words)
outputs = self._get_sent_reprs(words)
if outputs is not None:
return self.dropout(outputs)
outputs = self.model(words)
outputs = torch.cat([*outputs], dim=-1)
return self.dropout(outputs)
[文档] def drop_word(self, words):
r"""
按照设定随机将words设置为unknown_index。
:param torch.LongTensor words: batch_size x max_len
:return:
"""
if self.word_dropout > 0 and self.training:
with torch.no_grad():
mask = torch.full_like(words, fill_value=self.word_dropout, dtype=torch.float, device=words.device)
mask = torch.bernoulli(mask).eq(1) # dropout_word越大，越多位置为1
pad_mask = words.ne(self._word_pad_index)
mask = pad_mask.__and__(mask) # pad的位置不为unk
if self._word_sep_index!=-100:
not_sep_mask = words.ne(self._word_sep_index)
mask = mask.__and__(not_sep_mask)
if self._word_cls_index!=-100:
not_cls_mask = words.ne(self._word_cls_index)
mask = mask.__and__(not_cls_mask)
words = words.masked_fill(mask, self._word_unk_index)
return words
[文档]class BertWordPieceEncoder(nn.Module):
r"""
读取bert模型，读取之后调用index_dataset方法在dataset中生成word_pieces这一列。
BertWordPieceEncoder可以支持自动下载权重，当前支持的模型:
en: base-cased
en-large-cased-wwm:
en-large-cased:
en-large-uncased:
en-large-uncased-wwm
cn: 中文BERT wwm by HIT
cn-base: 中文BERT base-chinese
cn-wwm-ext: 中文BERT wwm by HIT with extra data pretrain.
multi-base-cased: multilingual cased
multi-base-uncased: multilingual uncased
"""
[文档] def __init__(self, model_dir_or_name: str = 'en-base-uncased', layers: str = '-1', pooled_cls: bool = False,
word_dropout=0, dropout=0, requires_grad: bool = True):
r"""
:param str model_dir_or_name: 模型所在目录或者模型的名称。默认值为 ``en-base-uncased``
:param str layers: 最终结果中的表示。以','隔开层数，可以以负数去索引倒数几层。layer=0为embedding层（包括wordpiece embedding,
position embedding和segment embedding）
:param bool pooled_cls: 返回的句子开头的[CLS]是否使用预训练中的BertPool映射一下。如果下游任务取[CLS]做预测，一般该值为True。
:param float word_dropout: 以多大的概率将一个词替换为unk。这样既可以训练unk也是一定的regularize。
:param float dropout: 以多大的概率对embedding的表示进行Dropout。0.1即随机将10%的值置为0。
:param bool requires_grad: 是否需要gradient。
"""
super().__init__()
self.model = _BertWordPieceModel(model_dir_or_name=model_dir_or_name, layers=layers, pooled_cls=pooled_cls)
self._sep_index = self.model._sep_index
self._cls_index = self.model._cls_index
self._wordpiece_pad_index = self.model._wordpiece_pad_index
self._wordpiece_unk_index = self.model._wordpiece_unknown_index
self._embed_size = len(self.model.layers) * self.model.encoder.hidden_size
self.requires_grad = requires_grad
self.word_dropout = word_dropout
self.dropout_layer = nn.Dropout(dropout)
@property
def embed_size(self):
return self._embed_size
@property
def embedding_dim(self):
return self._embed_size
@property
def num_embedding(self):
return self.model.encoder.config.vocab_size
[文档] def index_datasets(self, *datasets, field_name, add_cls_sep=True):
r"""
使用bert的tokenizer新生成word_pieces列加入到datasets中，并将他们设置为input,且将word_pieces这一列的pad value设置为了
bert的pad value。
:param ~fastNLP.DataSet datasets: DataSet对象
:param str field_name: 基于哪一列的内容生成word_pieces列。这一列中每个数据应该是List[str]的形式。
:param bool add_cls_sep: 如果首尾不是[CLS]与[SEP]会在首尾额外加入[CLS]与[SEP]。
:return:
"""
self.model.index_datasets(*datasets, field_name=field_name, add_cls_sep=add_cls_sep)
[文档] def forward(self, word_pieces, token_type_ids=None):
r"""
计算words的bert embedding表示。传入的words中应该自行包含[CLS]与[SEP]的tag。
:param words: batch_size x max_len
:param token_type_ids: batch_size x max_len, 用于区分前一句和后一句话. 如果不传入，则自动生成(大部分情况，都不需要输入),
第一个[SEP]及之前为0, 第二个[SEP]及到第一个[SEP]之间为1; 第三个[SEP]及到第二个[SEP]之间为0，依次往后推。
:return: torch.FloatTensor. batch_size x max_len x (768*len(self.layers))
"""
if token_type_ids is None:
with torch.no_grad():
sep_mask = word_pieces.eq(self._sep_index) # batch_size x max_len
sep_mask_cumsum = sep_mask.long().flip(dims=[-1]).cumsum(dim=-1).flip(dims=[-1])
token_type_ids = sep_mask_cumsum.fmod(2)
if token_type_ids[0, 0].item(): # 如果开头是奇数，则需要flip一下结果，因为需要保证开头为0
token_type_ids = token_type_ids.eq(0).long()
word_pieces = self.drop_word(word_pieces)
outputs = self.model(word_pieces, token_type_ids)
outputs = torch.cat([*outputs], dim=-1)
return self.dropout_layer(outputs)
[文档] def drop_word(self, words):
r"""
按照设定随机将words设置为unknown_index。
:param torch.LongTensor words: batch_size x max_len
:return:
"""
if self.word_dropout > 0 and self.training:
with torch.no_grad():
not_sep_mask = words.ne(self._sep_index)
not_cls_mask = words.ne(self._cls_index)
replaceable_mask = not_sep_mask.__and__(not_cls_mask)
mask = torch.full_like(words, fill_value=self.word_dropout, dtype=torch.float, device=words.device)
mask = torch.bernoulli(mask).eq(1) # dropout_word越大，越多位置为1
pad_mask = words.ne(self._wordpiece_pad_index)
mask = pad_mask.__and__(mask).__and__(replaceable_mask) # pad的位置不为unk
words = words.masked_fill(mask, self._wordpiece_unk_index)
return words
class _BertWordModel(nn.Module):
def __init__(self, model_dir_or_name: str, vocab: Vocabulary, layers: str = '-1', pool_method: str = 'first',
include_cls_sep: bool = False, pooled_cls: bool = False, auto_truncate: bool = False, min_freq=2,
only_use_pretrain_bpe=False, truncate_embed=True):
super().__init__()
self.tokenzier = BertTokenizer.from_pretrained(model_dir_or_name)
self.encoder = BertModel.from_pretrained(model_dir_or_name)
self._max_position_embeddings = self.encoder.config.max_position_embeddings
# 检查encoder_layer_number是否合理
encoder_layer_number = len(self.encoder.encoder.layer)
if isinstance(layers, list):
self.layers = [int(l) for l in layers]
elif isinstance(layers, str):
self.layers = list(map(int, layers.split(',')))
else:
raise TypeError("`layers` only supports str or list[int]")
for layer in self.layers:
if layer < 0:
assert -layer <= encoder_layer_number, f"The layer index:{layer} is out of scope for " \
f"a bert model with {encoder_layer_number} layers."
else:
assert layer <= encoder_layer_number, f"The layer index:{layer} is out of scope for " \
f"a bert model with {encoder_layer_number} layers."
assert pool_method in ('avg', 'max', 'first', 'last')
self.pool_method = pool_method
self.include_cls_sep = include_cls_sep
self.pooled_cls = pooled_cls
self.auto_truncate = auto_truncate
# 将所有vocab中word的wordpiece计算出来, 需要额外考虑[CLS]和[SEP]
logger.info("Start to generate word pieces for word.")
self._has_sep_in_vocab = '[SEP]' in vocab # 用来判断传入的数据是否需要生成token_ids
# 第一步统计出需要的word_piece, 然后创建新的embed和word_piece_vocab, 然后填入值
word_piece_dict = {'[CLS]': 1, '[SEP]': 1} # 用到的word_piece以及新增的
new_add_to_bpe_vocab = 0
unsegment_count = 0
if '[sep]' in vocab:
warnings.warn("Lower cased [sep] detected, it cannot be correctly recognized as [SEP] by BertEmbedding.")
if "[CLS]" in vocab:
warnings.warn("[CLS] detected in your vocabulary. BertEmbedding will add [CLS] and [SEP] to the begin "
"and end of the input automatically, make sure you don't add [CLS] and [SEP] at the begin"
" and end.")
for word, index in vocab:
if index == vocab.padding_idx: # pad是个特殊的符号
word = '[PAD]'
elif index == vocab.unknown_idx:
word = '[UNK]'
_words = self.tokenzier.basic_tokenizer._tokenize_chinese_chars(word).split()
word_pieces = []
for w in _words:
word_pieces.extend(self.tokenzier.wordpiece_tokenizer.tokenize(w))
if len(word_pieces) == 1:
if not vocab._is_word_no_create_entry(word): # 如果是train中的值, 但是却没有找到
if index != vocab.unknown_idx and word_pieces[0] == '[UNK]': # 说明这个词不在原始的word里面
if vocab.word_count[word] >= min_freq and not vocab._is_word_no_create_entry(
word) and not only_use_pretrain_bpe: # 出现次数大于这个次数才新增
word_piece_dict[word] = 1 # 新增一个值
new_add_to_bpe_vocab += 1
unsegment_count += 1
continue
for word_piece in word_pieces:
word_piece_dict[word_piece] = 1
original_embed = self.encoder.embeddings.word_embeddings.weight.data
# 特殊词汇要特殊处理
if not truncate_embed:# 如果不删除的话需要将已有的加上
word_piece_dict.update(self.tokenzier.vocab)
embed = nn.Embedding(len(word_piece_dict), original_embed.size(1)) # 新的embed
new_word_piece_vocab = collections.OrderedDict()
for index, token in enumerate(['[PAD]', '[UNK]']):
index = word_piece_dict.pop(token, None)
if index is not None:
new_word_piece_vocab[token] = len(new_word_piece_vocab)
embed.weight.data[new_word_piece_vocab[token]] = original_embed[self.tokenzier.vocab[token]]
for token in word_piece_dict.keys():
if token not in new_word_piece_vocab:
new_word_piece_vocab[token] = len(new_word_piece_vocab)
index = new_word_piece_vocab[token]
if token in self.tokenzier.vocab:
embed.weight.data[index] = original_embed[self.tokenzier.vocab[token]]
else:
embed.weight.data[index] = original_embed[self.tokenzier.vocab['[UNK]']]
self.tokenzier._reinit_on_new_vocab(new_word_piece_vocab)
self.encoder.embeddings.word_embeddings = embed
self.encoder.config.vocab_size = len(new_word_piece_vocab)
if unsegment_count>0:
if only_use_pretrain_bpe or new_add_to_bpe_vocab==0:
logger.info(f"{unsegment_count} words are unsegmented.")
else:
logger.info(f"{unsegment_count} words are unsegmented. Among them, {new_add_to_bpe_vocab} added to the BPE vocab.")
word_to_wordpieces = []
word_pieces_lengths = []
for word, index in vocab:
if index == vocab.padding_idx: # pad是个特殊的符号
word = '[PAD]'
elif index == vocab.unknown_idx:
word = '[UNK]'
word_pieces = self.tokenzier.wordpiece_tokenizer.tokenize(word)
word_pieces = self.tokenzier.convert_tokens_to_ids(word_pieces)
word_to_wordpieces.append(word_pieces)
word_pieces_lengths.append(len(word_pieces))
self._cls_index = self.tokenzier.vocab['[CLS]']
self._sep_index = self.tokenzier.vocab['[SEP]']
self._word_pad_index = vocab.padding_idx
self._wordpiece_pad_index = self.tokenzier.vocab['[PAD]'] # 需要用于生成word_piece
self.word_to_wordpieces = np.array(word_to_wordpieces)
self.register_buffer('word_pieces_lengths', torch.LongTensor(word_pieces_lengths))
logger.debug("Successfully generate word pieces.")
def forward(self, words):
r"""
:param words: torch.LongTensor, batch_size x max_len
:return: num_layers x batch_size x max_len x hidden_size或者num_layers x batch_size x (max_len+2) x hidden_size
"""
with torch.no_grad():
batch_size, max_word_len = words.size()
word_mask = words.ne(self._word_pad_index) # 为1的地方有word
seq_len = word_mask.sum(dim=-1)
batch_word_pieces_length = self.word_pieces_lengths[words].masked_fill(word_mask.eq(False),
0) # batch_size x max_len
word_pieces_lengths = batch_word_pieces_length.sum(dim=-1) # batch_size
max_word_piece_length = batch_word_pieces_length.sum(dim=-1).max().item() # 表示word piece的长度(包括padding)
if max_word_piece_length + 2 > self._max_position_embeddings:
if self.auto_truncate:
word_pieces_lengths = word_pieces_lengths.masked_fill(
word_pieces_lengths + 2 > self._max_position_embeddings,
self._max_position_embeddings - 2)
else:
raise RuntimeError(
"After split words into word pieces, the lengths of word pieces are longer than the "
f"maximum allowed sequence length:{self._max_position_embeddings} of bert. You can set "
f"`auto_truncate=True` for BertEmbedding to automatically truncate overlong input.")
# +2是由于需要加入[CLS]与[SEP]
word_pieces = words.new_full((batch_size, min(max_word_piece_length + 2, self._max_position_embeddings)),
fill_value=self._wordpiece_pad_index)
attn_masks = torch.zeros_like(word_pieces)
# 1. 获取words的word_pieces的id，以及对应的span范围
word_indexes = words.cpu().numpy()
for i in range(batch_size):
word_pieces_i = list(chain(*self.word_to_wordpieces[word_indexes[i, :seq_len[i]]]))
if self.auto_truncate and len(word_pieces_i) > self._max_position_embeddings - 2:
word_pieces_i = word_pieces_i[:self._max_position_embeddings - 2]
word_pieces[i, 1:word_pieces_lengths[i] + 1] = torch.LongTensor(word_pieces_i)
attn_masks[i, :word_pieces_lengths[i] + 2].fill_(1)
# 添加[cls]和[sep]
word_pieces[:, 0].fill_(self._cls_index)
batch_indexes = torch.arange(batch_size).to(words)
word_pieces[batch_indexes, word_pieces_lengths + 1] = self._sep_index
if self._has_sep_in_vocab: # 但[SEP]在vocab中出现应该才会需要token_ids
sep_mask = word_pieces.eq(self._sep_index).long() # batch_size x max_len
sep_mask_cumsum = sep_mask.flip(dims=[-1]).cumsum(dim=-1).flip(dims=[-1])
token_type_ids = sep_mask_cumsum.fmod(2)
if token_type_ids[0, 0].item(): # 如果开头是奇数，则需要flip一下结果，因为需要保证开头为0
token_type_ids = token_type_ids.eq(0).long()
else:
token_type_ids = torch.zeros_like(word_pieces)
# 2. 获取hidden的结果，根据word_pieces进行对应的pool计算
# all_outputs: [batch_size x max_len x hidden_size, batch_size x max_len x hidden_size, ...]
bert_outputs, pooled_cls = self.encoder(word_pieces, token_type_ids=token_type_ids, attention_mask=attn_masks,
output_all_encoded_layers=True)
# output_layers = [self.layers] # len(self.layers) x batch_size x real_word_piece_length x hidden_size
if self.include_cls_sep:
s_shift = 1
outputs = bert_outputs[-1].new_zeros(len(self.layers), batch_size, max_word_len + 2,
bert_outputs[-1].size(-1))
else:
s_shift = 0
outputs = bert_outputs[-1].new_zeros(len(self.layers), batch_size, max_word_len,
bert_outputs[-1].size(-1))
batch_word_pieces_cum_length = batch_word_pieces_length.new_zeros(batch_size, max_word_len + 1)
batch_word_pieces_cum_length[:, 1:] = batch_word_pieces_length.cumsum(dim=-1) # batch_size x max_len
if self.pool_method == 'first':
batch_word_pieces_cum_length = batch_word_pieces_cum_length[:, :seq_len.max()]
batch_word_pieces_cum_length.masked_fill_(batch_word_pieces_cum_length.ge(max_word_piece_length), 0)
_batch_indexes = batch_indexes[:, None].expand((batch_size, batch_word_pieces_cum_length.size(1)))
elif self.pool_method == 'last':
batch_word_pieces_cum_length = batch_word_pieces_cum_length[:, 1:seq_len.max()+1] - 1
batch_word_pieces_cum_length.masked_fill_(batch_word_pieces_cum_length.ge(max_word_piece_length), 0)
_batch_indexes = batch_indexes[:, None].expand((batch_size, batch_word_pieces_cum_length.size(1)))
for l_index, l in enumerate(self.layers):
output_layer = bert_outputs[l]
real_word_piece_length = output_layer.size(1) - 2
if max_word_piece_length > real_word_piece_length: # 如果实际上是截取出来的
paddings = output_layer.new_zeros(batch_size,
max_word_piece_length - real_word_piece_length,
output_layer.size(2))
output_layer = torch.cat((output_layer, paddings), dim=1).contiguous()
# 从word_piece collapse到word的表示
truncate_output_layer = output_layer[:, 1:-1] # 删除[CLS]与[SEP] batch_size x len x hidden_size
if self.pool_method == 'first':
tmp = truncate_output_layer[_batch_indexes, batch_word_pieces_cum_length]
tmp = tmp.masked_fill(word_mask[:, :batch_word_pieces_cum_length.size(1), None].eq(False), 0)
outputs[l_index, :, s_shift:batch_word_pieces_cum_length.size(1)+s_shift] = tmp
elif self.pool_method == 'last':
tmp = truncate_output_layer[_batch_indexes, batch_word_pieces_cum_length]
tmp = tmp.masked_fill(word_mask[:, :batch_word_pieces_cum_length.size(1), None].eq(False), 0)
outputs[l_index, :, s_shift:batch_word_pieces_cum_length.size(1)+s_shift] = tmp
elif self.pool_method == 'max':
for i in range(batch_size):
for j in range(seq_len[i]):
start, end = batch_word_pieces_cum_length[i, j], batch_word_pieces_cum_length[i, j + 1]
outputs[l_index, i, j + s_shift], _ = torch.max(truncate_output_layer[i, start:end], dim=-2)
else:
for i in range(batch_size):
for j in range(seq_len[i]):
start, end = batch_word_pieces_cum_length[i, j], batch_word_pieces_cum_length[i, j + 1]
outputs[l_index, i, j + s_shift] = torch.mean(truncate_output_layer[i, start:end], dim=-2)
if self.include_cls_sep:
if l in (len(bert_outputs) - 1, -1) and self.pooled_cls:
outputs[l_index, :, 0] = pooled_cls
else:
outputs[l_index, :, 0] = output_layer[:, 0]
outputs[l_index, batch_indexes, seq_len + s_shift] = output_layer[batch_indexes, word_pieces_lengths + s_shift]
# 3. 最终的embedding结果
return outputs
class _BertWordPieceModel(nn.Module):
r"""
这个模块用于直接计算word_piece的结果.
"""
def __init__(self, model_dir_or_name: str, layers: str = '-1', pooled_cls: bool=False):
super().__init__()
self.tokenzier = BertTokenizer.from_pretrained(model_dir_or_name)
self.encoder = BertModel.from_pretrained(model_dir_or_name)
# 检查encoder_layer_number是否合理
encoder_layer_number = len(self.encoder.encoder.layer)
if isinstance(layers, list):
self.layers = [int(l) for l in layers]
elif isinstance(layers, str):
self.layers = list(map(int, layers.split(',')))
else:
raise TypeError("`layers` only supports str or list[int]")
for layer in self.layers:
if layer < 0:
assert -layer <= encoder_layer_number, f"The layer index:{layer} is out of scope for " \
f"a bert model with {encoder_layer_number} layers."
else:
assert layer <= encoder_layer_number, f"The layer index:{layer} is out of scope for " \
f"a bert model with {encoder_layer_number} layers."
self._cls_index = self.tokenzier.cls_index
self._sep_index = self.tokenzier.sep_index
self._wordpiece_unknown_index = self.tokenzier.unk_index
self._wordpiece_pad_index = self.tokenzier.pad_index # 需要用于生成word_piece
self.pooled_cls = pooled_cls
def index_datasets(self, *datasets, field_name, add_cls_sep=True):
r"""
使用bert的tokenizer新生成word_pieces列加入到datasets中，并将他们设置为input。如果首尾不是
[CLS]与[SEP]会在首尾额外加入[CLS]与[SEP], 且将word_pieces这一列的pad value设置为了bert的pad value。
:param datasets: DataSet对象
:param field_name: 基于哪一列index
:return:
"""
encode_func = partial(self.tokenzier.encode, add_special_tokens=add_cls_sep)
for index, dataset in enumerate(datasets):
try:
dataset.apply_field(encode_func, field_name=field_name, new_field_name='word_pieces',
is_input=True)
dataset.set_pad_val('word_pieces', self._wordpiece_pad_index)
except Exception as e:
logger.error(f"Exception happens when processing the {index} dataset.")
raise e
def forward(self, word_pieces, token_type_ids=None):
r"""
:param word_pieces: torch.LongTensor, batch_size x max_len
:param token_type_ids: torch.LongTensor, batch_size x max_len
:return: num_layers x batch_size x max_len x hidden_size或者num_layers x batch_size x (max_len+2) x hidden_size
"""
batch_size, max_len = word_pieces.size()
attn_masks = word_pieces.ne(self._wordpiece_pad_index)
bert_outputs, pooled_cls = self.encoder(word_pieces, token_type_ids=token_type_ids, attention_mask=attn_masks,
output_all_encoded_layers=True)
# output_layers = [self.layers] # len(self.layers) x batch_size x max_word_piece_length x hidden_size
outputs = bert_outputs[0].new_zeros((len(self.layers), batch_size, max_len, bert_outputs[0].size(-1)))
for l_index, l in enumerate(self.layers):
bert_output = bert_outputs[l]
if l in (len(bert_outputs)-1, -1) and self.pooled_cls:
bert_output[:, 0] = pooled_cls
outputs[l_index] = bert_output
return outputs