paddlenlp.embeddings.token_embedding 源代码

# Copyright (c) 2020 PaddlePaddle Authors. All Rights Reserved.
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# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
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#     http://www.apache.org/licenses/LICENSE-2.0
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# Unless required by applicable law or agreed to in writing, software
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from enum import Enum
import os
import os.path as osp
import numpy as np
import logging

import paddle
import paddle.nn as nn
from paddle.utils.download import get_path_from_url
from paddlenlp.utils.env import _get_sub_home, MODEL_HOME
from paddlenlp.utils.log import logger
from paddlenlp.data import Vocab, get_idx_from_word
from .constant import EMBEDDING_URL_ROOT, PAD_TOKEN, UNK_TOKEN,\
                      EMBEDDING_NAME_LIST

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).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