# Copyright (c) 2020 PaddlePaddle Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import os.path as osp
import numpy as np
import paddle
import paddle.nn as nn
from paddle.utils.download import get_path_from_url
from paddlenlp.data import Vocab, get_idx_from_word
from paddlenlp.utils.env import MODEL_HOME, _get_sub_home
from paddlenlp.utils.log import logger
from .constant import EMBEDDING_NAME_LIST, EMBEDDING_URL_ROOT, PAD_TOKEN, UNK_TOKEN
EMBEDDING_HOME = _get_sub_home("embeddings", parent_home=MODEL_HOME)
__all__ = ["list_embedding_name", "TokenEmbedding"]
[文档]def list_embedding_name():
"""
Lists all names of pretrained embedding models paddlenlp provides.
"""
return list(EMBEDDING_NAME_LIST)
[文档]class TokenEmbedding(nn.Embedding):
"""
A `TokenEmbedding` can load pre-trained embedding model which paddlenlp provides by
specifying embedding name. Furthermore, a `TokenEmbedding` can load extended vocabulary
by specifying extended_vocab_path.
Args:
embedding_name (`str`, optional):
The pre-trained embedding model name. Use `paddlenlp.embeddings.list_embedding_name()` to
list the names of all embedding models that we provide.
Defaults to `w2v.baidu_encyclopedia.target.word-word.dim300`.
unknown_token (`str`, optional):
Specifies unknown token.
Defaults to `[UNK]`.
unknown_token_vector (`list`, optional):
To initialize the vector of unknown token. If it's none, use normal distribution to
initialize the vector of unknown token.
Defaults to `None`.
extended_vocab_path (`str`, optional):
The file path of extended vocabulary.
Defaults to `None`.
trainable (`bool`, optional):
Whether the weight of embedding can be trained.
Defaults to True.
keep_extended_vocab_only (`bool`, optional):
Whether to keep the extended vocabulary only, will be effective only if provides extended_vocab_path.
Defaults to False.
"""
def __init__(
self,
embedding_name=EMBEDDING_NAME_LIST[0],
unknown_token=UNK_TOKEN,
unknown_token_vector=None,
extended_vocab_path=None,
trainable=True,
keep_extended_vocab_only=False,
):
vector_path = osp.join(EMBEDDING_HOME, embedding_name + ".npz")
if not osp.exists(vector_path):
# download
url = EMBEDDING_URL_ROOT + "/" + embedding_name + ".tar.gz"
get_path_from_url(url, EMBEDDING_HOME)
logger.info("Loading token embedding...")
vector_np = np.load(vector_path)
self.embedding_dim = vector_np["embedding"].shape[1]
self.unknown_token = unknown_token
if unknown_token_vector is not None:
unk_vector = np.array(unknown_token_vector).astype(paddle.get_default_dtype())
else:
unk_vector = np.random.normal(scale=0.02, size=self.embedding_dim).astype(paddle.get_default_dtype())
pad_vector = np.array([0] * self.embedding_dim).astype(paddle.get_default_dtype())
if extended_vocab_path is not None:
embedding_table = self._extend_vocab(
extended_vocab_path, vector_np, pad_vector, unk_vector, keep_extended_vocab_only
)
trainable = True
else:
embedding_table = self._init_without_extend_vocab(vector_np, pad_vector, unk_vector)
self.vocab = Vocab.from_dict(self._word_to_idx, unk_token=unknown_token, pad_token=PAD_TOKEN)
self.num_embeddings = embedding_table.shape[0]
# import embedding
super(TokenEmbedding, self).__init__(
self.num_embeddings, self.embedding_dim, padding_idx=self._word_to_idx[PAD_TOKEN]
)
self.weight.set_value(embedding_table)
self.set_trainable(trainable)
logger.info("Finish loading embedding vector.")
s = "Token Embedding info:\
\nUnknown index: {}\
\nUnknown token: {}\
\nPadding index: {}\
\nPadding token: {}\
\nShape :{}".format(
self._word_to_idx[self.unknown_token],
self.unknown_token,
self._word_to_idx[PAD_TOKEN],
PAD_TOKEN,
self.weight.shape,
)
logger.info(s)
def _init_without_extend_vocab(self, vector_np, pad_vector, unk_vector):
"""
Constructs index to word list, word to index dict and embedding weight.
"""
self._idx_to_word = list(vector_np["vocab"])
self._idx_to_word.append(self.unknown_token)
self._idx_to_word.append(PAD_TOKEN)
self._word_to_idx = self._construct_word_to_idx(self._idx_to_word)
# insert unk, pad embedding
embedding_table = np.append(vector_np["embedding"], [unk_vector, pad_vector], axis=0)
return embedding_table
def _read_vocab_list_from_file(self, extended_vocab_path):
# load new vocab table from file
vocab_list = []
with open(extended_vocab_path, "r", encoding="utf-8") as f:
for line in f.readlines():
vocab = line.rstrip("\n").split("\t")[0]
vocab_list.append(vocab)
return vocab_list
def _extend_vocab(self, extended_vocab_path, vector_np, pad_vector, unk_vector, keep_extended_vocab_only):
"""
Constructs index to word list, word to index dict and embedding weight using
extended vocab.
"""
logger.info("Start extending vocab.")
extend_vocab_list = self._read_vocab_list_from_file(extended_vocab_path)
extend_vocab_set = set(extend_vocab_list)
# update idx_to_word
self._idx_to_word = extend_vocab_list
self._word_to_idx = self._construct_word_to_idx(self._idx_to_word)
# use the Xavier init the embedding
xavier_scale = np.sqrt(6.0 / float(len(self._idx_to_word) + self.embedding_dim))
embedding_table = np.random.uniform(
low=-1.0 * xavier_scale, high=xavier_scale, size=(len(self._idx_to_word), self.embedding_dim)
).astype(paddle.get_default_dtype())
pretrained_idx_to_word = list(vector_np["vocab"])
pretrained_word_to_idx = self._construct_word_to_idx(pretrained_idx_to_word)
pretrained_embedding_table = np.array(vector_np["embedding"])
pretrained_vocab_set = set(pretrained_idx_to_word)
extend_vocab_set = set(self._idx_to_word)
vocab_intersection = pretrained_vocab_set & extend_vocab_set
vocab_subtraction = pretrained_vocab_set - extend_vocab_set
# assignment from pretrained_vocab_embedding to extend_vocab_embedding
pretrained_vocab_intersect_index = [pretrained_word_to_idx[word] for word in vocab_intersection]
pretrained_vocab_subtract_index = [pretrained_word_to_idx[word] for word in vocab_subtraction]
extend_vocab_intersect_index = [self._word_to_idx[word] for word in vocab_intersection]
embedding_table[extend_vocab_intersect_index] = pretrained_embedding_table[pretrained_vocab_intersect_index]
if not keep_extended_vocab_only:
for idx in pretrained_vocab_subtract_index:
word = pretrained_idx_to_word[idx]
self._idx_to_word.append(word)
self._word_to_idx[word] = len(self._idx_to_word) - 1
embedding_table = np.append(
embedding_table, pretrained_embedding_table[pretrained_vocab_subtract_index], axis=0
)
if self.unknown_token not in extend_vocab_set:
self._idx_to_word.append(self.unknown_token)
self._word_to_idx[self.unknown_token] = len(self._idx_to_word) - 1
embedding_table = np.append(embedding_table, [unk_vector], axis=0)
else:
unk_idx = self._word_to_idx[self.unknown_token]
embedding_table[unk_idx] = unk_vector
if PAD_TOKEN not in extend_vocab_set:
self._idx_to_word.append(PAD_TOKEN)
self._word_to_idx[PAD_TOKEN] = len(self._idx_to_word) - 1
embedding_table = np.append(embedding_table, [pad_vector], axis=0)
else:
embedding_table[self._word_to_idx[PAD_TOKEN]] = pad_vector
logger.info("Finish extending vocab.")
return embedding_table
[文档] def set_trainable(self, trainable):
"""
Whether or not to set the weights of token embedding to be trainable.
Args:
trainable (`bool`):
The weights can be trained if trainable is set to True, or the weights are fixed if trainable is False.
"""
self.weight.stop_gradient = not trainable
[文档] def search(self, words):
"""
Gets the vectors of specifying words.
Args:
words (`list` or `str` or `int`): The words which need to be searched.
Returns:
`numpy.array`: The vectors of specifying words.
Examples:
.. code-block::
from paddlenlp.embeddings import TokenEmbedding
embed = TokenEmbedding()
vector = embed.search('Welcome to use PaddlePaddle and PaddleNLP!')
"""
idx_list = self.get_idx_list_from_words(words)
idx_tensor = paddle.to_tensor(idx_list)
return self(idx_tensor).cpu().numpy()
[文档] def get_idx_from_word(self, word):
"""
Gets the index of specifying word by searching word_to_idx dict.
Args:
word (`list` or `str` or `int`): The input token word which we want to get the token index converted from.
Returns:
`int`: The index of specifying word.
"""
return get_idx_from_word(word, self.vocab.token_to_idx, self.unknown_token)
[文档] def get_idx_list_from_words(self, words):
"""
Gets the index list of specifying words by searching word_to_idx dict.
Args:
words (`list` or `str` or `int`): The input token words which we want to get the token indices converted from.
Returns:
`list`: The indexes list of specifying words.
Examples:
.. code-block::
from paddlenlp.embeddings import TokenEmbedding
embed = TokenEmbedding()
index = embed.get_idx_from_word('Welcome to use PaddlePaddle and PaddleNLP!')
#635963
"""
if isinstance(words, str):
idx_list = [self.get_idx_from_word(words)]
elif isinstance(words, int):
idx_list = [words]
elif isinstance(words, list) or isinstance(words, tuple):
idx_list = [self.get_idx_from_word(word) if isinstance(word, str) else word for word in words]
else:
raise TypeError
return idx_list
def _dot_np(self, array_a, array_b):
return np.sum(array_a * array_b)
def _calc_word(self, word_a, word_b, calc_kernel):
embeddings = self.search([word_a, word_b])
embedding_a = embeddings[0]
embedding_b = embeddings[1]
return calc_kernel(embedding_a, embedding_b)
[文档] def dot(self, word_a, word_b):
"""
Calculates the dot product of 2 words. Dot product or scalar product is an
algebraic operation that takes two equal-length sequences of numbers (usually
coordinate vectors), and returns a single number.
Args:
word_a (`str`): The first word string.
word_b (`str`): The second word string.
Returns:
float: The dot product of 2 words.
Examples:
.. code-block::
from paddlenlp.embeddings import TokenEmbedding
embed = TokenEmbedding()
dot_product = embed.dot('PaddlePaddle', 'PaddleNLP!')
#0.11827179
"""
dot = self._dot_np
return self._calc_word(word_a, word_b, lambda x, y: dot(x, y))
[文档] def cosine_sim(self, word_a, word_b):
"""
Calculates the cosine similarity of 2 word vectors. Cosine similarity is the
cosine of the angle between two n-dimensional vectors in an n-dimensional space.
Args:
word_a (`str`): The first word string.
word_b (`str`): The second word string.
Returns:
float: The cosine similarity of 2 words.
Examples:
.. code-block::
from paddlenlp.embeddings import TokenEmbedding
embed = TokenEmbedding()
cosine_simi = embed.cosine_sim('PaddlePaddle', 'PaddleNLP!')
#0.99999994
"""
dot = self._dot_np
return self._calc_word(word_a, word_b, lambda x, y: dot(x, y) / (np.sqrt(dot(x, x)) * np.sqrt(dot(y, y))))
def _construct_word_to_idx(self, idx_to_word):
"""
Constructs word to index dict.
Args:
idx_to_word ('list'):
Returns:
`Dict`: The word to index dict constructed by idx_to_word.
"""
word_to_idx = {}
for i, word in enumerate(idx_to_word):
word_to_idx[word] = i
return word_to_idx
def __repr__(self):
"""
Returns:
`Str`: The token embedding infomation.
"""
info = "Object type: {}\
\nUnknown index: {}\
\nUnknown token: {}\
\nPadding index: {}\
\nPadding token: {}\
\n{}".format(
super(TokenEmbedding, self).__repr__(),
self._word_to_idx[self.unknown_token],
self.unknown_token,
self._word_to_idx[PAD_TOKEN],
PAD_TOKEN,
self.weight,
)
return info
