astonzhang commented on a change in pull request #8763: Add mxnet.text APIs URL: https://github.com/apache/incubator-mxnet/pull/8763#discussion_r160544045
########## File path: python/mxnet/text/embedding.py ########## @@ -0,0 +1,600 @@ +# Licensed to the Apache Software Foundation (ASF) under one +# or more contributor license agreements. See the NOTICE file +# distributed with this work for additional information +# regarding copyright ownership. The ASF licenses this file +# to you 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. + +# coding: utf-8 +# pylint: disable=consider-iterating-dictionary + +"""Text token embeddings.""" +from __future__ import absolute_import +from __future__ import print_function + +import io +import logging +import os +import tarfile +import warnings +import zipfile + +from . import constants as C +from ..gluon.utils import download +from .indexer import TokenIndexer +from .. import ndarray as nd +from .. import registry + + +class TokenEmbedding(TokenIndexer): + """Token embedding base class. + + + To load token embeddings from an externally hosted pre-trained + token embedding file, such as those of GloVe and FastText, use + `TokenEmbedding.create(embedding_name, pretrained_file_name)`. To get all + the available `embedding_name` and `pretrained_file_name`, use + `TokenEmbedding.get_embedding_and_pretrained_file_names()`. + + Alternatively, to load embedding vectors from a custom pre-trained token + embedding file, use :func:`~mxnet.text.embeddings.CustomEmbedding`. + + For every unknown token, if its representation `self.unknown_token` is + encountered in the pre-trained token embedding file, index 0 of + `self.idx_to_vec` maps to the pre-trained token embedding vector loaded from + the file; otherwise, index 0 of `self.idx_to_vec` maps to the token + embedding vector initialized by `init_unknown_vec`. + + If a token is encountered multiple times in the pre-trained token embedding + file, only the first-encountered token embedding vector will be loaded and + the rest will be skipped. + + For the same token, its index and embedding vector may vary across different + instances of :func:`~mxnet.text.embedding.TokenEmbedding`. + + + Properties + ---------- + vec_len : int + The length of the embedding vector for each token. + idx_to_vec : mxnet.ndarray.NDArray + For all the indexed tokens in this embedding, this NDArray maps each + token's index to an embedding vector. The largest valid index maps + to the initialized embedding vector for every reserved token, such as an + unknown_token token and a padding token. + """ + + def __init__(self, **kwargs): + super(TokenEmbedding, self).__init__(**kwargs) + + @classmethod + def _get_pretrained_file_path_from_url(cls, url, embedding_root, + pretrained_file_name): + """Get the local path to the pre-trained token embedding file from url. + + + The pre-trained embedding file will be downloaded from url if it has not + been downloaded yet or the existing file fails to match its expected + SHA-1 hash. + """ + + embedding_cls = cls.__name__.lower() + embedding_root = os.path.expanduser(embedding_root) + + embedding_dir = os.path.join(embedding_root, embedding_cls) + pretrained_file_path = os.path.join(embedding_dir, pretrained_file_name) + downloaded_file = os.path.basename(url) + downloaded_file_path = os.path.join(embedding_dir, downloaded_file) + + expected_file_hash = cls.pretrained_file_name_sha1[pretrained_file_name] + + if hasattr(cls, 'pretrained_archive_name_sha1'): + expected_downloaded_hash = \ + cls.pretrained_archive_name_sha1[downloaded_file] + else: + expected_downloaded_hash = expected_file_hash + + # If downloaded_file_path exists and matches expected_downloaded_hash, + # there is no need to download. + download(url, downloaded_file_path, sha1_hash=expected_downloaded_hash) + + ext = os.path.splitext(downloaded_file)[1] + if ext == '.zip': + with zipfile.ZipFile(downloaded_file_path, 'r') as zf: + zf.extractall(embedding_dir) + elif ext == '.gz': + with tarfile.open(downloaded_file_path, 'r:gz') as tar: + tar.extractall(path=embedding_dir) + return pretrained_file_path + + def _load_embedding(self, pretrained_file_path, elem_delim, + init_unknown_vec, encoding='utf8'): + """Load embedding vectors from the pre-trained token embedding file. + + + For every unknown token, if its representation `self.unknown_token` is + encountered in the pre-trained token embedding file, index 0 of + `self.idx_to_vec` maps to the pre-trained token embedding vector loaded + from the file; otherwise, index 0 of `self.idx_to_vec` maps to the text + embedding vector initialized by `init_unknown_vec`. + + If a token is encountered multiple times in the pre-trained text + embedding file, only the first-encountered token embedding vector will + be loaded and the rest will be skipped. + """ + + pretrained_file_path = os.path.expanduser(pretrained_file_path) + + if not os.path.isfile(pretrained_file_path): + raise ValueError('`pretrained_file_path` must be a valid path to ' + 'the pre-trained token embedding file.') + + with io.open(pretrained_file_path, 'r', encoding=encoding) as f: + lines = f.readlines() + + logging.info('Loading pre-trained token embedding vectors from %s', + pretrained_file_path) + + vec_len = None + all_elems = [] + tokens = set() + loaded_unknown_vec = None + line_num = 0 + for line in lines: + line_num += 1 + elems = line.rstrip().split(elem_delim) + + assert len(elems) > 1, 'At line %d of the pre-trained text ' \ + 'embedding file: the data format of the ' \ + 'pre-trained token embedding file %s is ' \ + 'unexpected.' \ + % (line_num, pretrained_file_path) + + token, elems = elems[0], [float(i) for i in elems[1:]] + + if token == self.unknown_token and loaded_unknown_vec is None: + loaded_unknown_vec = elems + tokens.add(self.unknown_token) + elif token in tokens: + warnings.warn('At line %d of the pre-trained token embedding ' + 'file: the embedding vector for token %s has ' + 'been loaded and a duplicate embedding for the ' + 'same token is seen and skipped.' + % (line_num, token)) + elif len(elems) == 1: + warnings.warn('At line %d of the pre-trained text ' + 'embedding file: token %s with 1-dimensional ' + 'vector %s is likely a header and is ' + 'skipped.' % (line_num, token, elems)) + else: + if vec_len is None: + vec_len = len(elems) + # Reserve a vector slot for the unknown token at the + # very beggining because the unknown index is 0. + all_elems.extend([0] * vec_len) + else: + assert len(elems) == vec_len, \ + 'At line %d of the pre-trained token embedding ' \ + 'file: the dimension of token %s is %d but the ' \ + 'dimension of previous tokens is %d. Dimensions ' \ + 'of all the tokens must be the same.' \ + % (line_num, token, len(elems), vec_len) + all_elems.extend(elems) + self._idx_to_token.append(token) + self._token_to_idx[token] = len(self._idx_to_token) - 1 + tokens.add(token) + + self._vec_len = vec_len + self._idx_to_vec = nd.array(all_elems).reshape((-1, self.vec_len)) + + if loaded_unknown_vec is None: + self._idx_to_vec[C.UNKNOWN_IDX] = init_unknown_vec( + shape=self.vec_len) + else: + self._idx_to_vec[C.UNKNOWN_IDX] = nd.array(loaded_unknown_vec) + + @property + def vec_len(self): + return self._vec_len + + @property + def idx_to_vec(self): + return self._idx_to_vec + + def get_vecs_by_tokens(self, tokens, lower_case_backup=False): + """Look up embedding vectors of tokens. + + + Parameters + ---------- + tokens : str or list of strs + A token or a list of tokens. + lower_case_backup : bool, default False + If False, each token in the original case will be looked up; if + True, each token in the original case will be looked up first, if + not found in the keys of the property `token_to_idx`, the token + in the lower case will be looked up. + + + Returns + ------- + mxnet.ndarray.NDArray: + The embedding vector(s) of the token(s). According to numpy + conventions, if `tokens` is a string, returns a 1-D NDArray of shape + `self.vec_len`; if `tokens` is a list of strings, returns a 2-D + NDArray of shape=(len(tokens), self.vec_len). + """ + + to_reduce = False + if not isinstance(tokens, list): + tokens = [tokens] + to_reduce = True + + if not lower_case_backup: + indices = [self.token_to_idx.get(token, C.UNKNOWN_IDX) + for token in tokens] + else: + indices = [self.token_to_idx[token] if token in self.token_to_idx + else self.token_to_idx.get(token.lower(), C.UNKNOWN_IDX) + for token in tokens] + + vecs = nd.Embedding(nd.array(indices), self.idx_to_vec, + self.idx_to_vec.shape[0], self.idx_to_vec.shape[1]) + + return vecs[0] if to_reduce else vecs + + def update_token_vectors(self, tokens, new_vectors): + """Updates embedding vectors for tokens. + + + Parameters + ---------- + tokens : str or a list of strs. + A token or a list of tokens whose embedding vector are to be + updated. + new_vectors : mxnet.ndarray.NDArray + An NDArray to be assigned to the embedding vectors of `tokens`. + Its length must be equal to the number of `tokens` and its width + must be equal to the dimension of embeddings of the glossary. If + `tokens` is a singleton, it must be 1-D or 2-D. If `tokens` is a + list of multiple strings, it must be 2-D. + """ + + assert self.idx_to_vec is not None, \ + 'The property `idx_to_vec` has not been properly set.' + + if not isinstance(tokens, list) or len(tokens) == 1: + assert isinstance(new_vectors, nd.NDArray) and \ + len(new_vectors.shape) in [1, 2], \ + '`new_vectors` must be a 1-D or 2-D NDArray if `tokens` is a ' \ + 'singleton.' + if not isinstance(tokens, list): + tokens = [tokens] + if len(new_vectors.shape) == 1: + new_vectors = new_vectors.expand_dims(0) + + else: + assert isinstance(new_vectors, nd.NDArray) and \ + len(new_vectors.shape) == 2, \ + '`new_vectors` must be a 2-D NDArray if `tokens` is a list ' \ + 'of multiple strings.' + assert new_vectors.shape == (len(tokens), self.vec_len), \ + 'The length of new_vectors must be equal to the number of tokens ' \ + 'and the width of new_vectors must be equal to the dimension of ' \ + 'embeddings of the glossary.' + + indices = [] + for token in tokens: + if token in self.token_to_idx: + indices.append(self.token_to_idx[token]) + else: + raise ValueError('Token %s is unknown. To update the embedding ' + 'vector for an unknown token, please specify ' + 'it explicitly as the `unknown_token` %s in ' + '`tokens`. This is to avoid unintended ' + 'updates.' % + (token, self.idx_to_token[C.UNKNOWN_IDX])) + + self._idx_to_vec[nd.array(indices)] = new_vectors + + @staticmethod + def register(embedding_cls): + """Registers a new token embedding. + + Once an embedding is registered, we can create an instance of this + embedding with :func:`~mxnet.text.embedding.TokenEmbedding.create`. + + + Examples + -------- + >>> @mxnet.text.embedding.TokenEmbedding.register + ... class MyTextEmbed(mxnet.text.embedding.TokenEmbedding): + ... def __init__(self, pretrained_file_name='my_pretrain_file'): + ... pass + >>> embed = mxnet.text.embedding.TokenEmbedding.create('MyTokenEmbed') + >>> print(type(embed)) + <class '__main__.MyTokenEmbed'> + """ + + register_text_embedding = registry.get_register_func( + TokenEmbedding, 'token embedding') + return register_text_embedding(embedding_cls) + + @staticmethod + def create(embedding_name, **kwargs): + """Creates an instance of :func:`~mxnet.text.embedding.TokenEmbedding`. + + Creates a token embedding instance by loading embedding vectors from an + externally hosted pre-trained token embedding file, such as those + of GloVe and FastText. To get all the valid `embedding_name` and + `pretrained_file_name`, use `mxnet.text.embedding.TokenEmbedding. + get_embedding_and_pretrained_file_names()`. + + + Parameters + ---------- + embedding_name : str + The token embedding name (case-insensitive). + + + Returns + ------- + mxnet.text.glossary.TokenEmbedding: + A token embedding instance that loads embedding vectors from an + externally hosted pre-trained token embedding file. + """ + + create_text_embedding = registry.get_create_func( + TokenEmbedding, 'token embedding') + return create_text_embedding(embedding_name, **kwargs) + + @classmethod + def _check_pretrained_file_names(cls, pretrained_file_name): + """Checks if a pre-trained token embedding file name is valid. + + + Parameters + ---------- + pretrained_file_name : str + The pre-trained token embedding file. + """ + + embedding_name = cls.__name__.lower() + if pretrained_file_name not in cls.pretrained_file_name_sha1: + raise KeyError('Cannot find pretrained file %s for token embedding ' + '%s. Valid pretrained files for embedding %s: %s' % + (pretrained_file_name, embedding_name, + embedding_name, + ', '.join(cls.pretrained_file_name_sha1.keys()))) + + @staticmethod + def get_embedding_and_pretrained_file_names(embedding_name=None): + """Get valid token embedding names and their pre-trained file names. + + To load token embedding vectors from an externally hosted pre-trained + token embedding file, such as those of GloVe and FastText, one should use + `mxnet.text.embedding.TokenEmbedding.create(embedding_name, + pretrained_file_name)`. This method returns all the valid names of + `pretrained_file_name` for the specified `embedding_name`. If + `embedding_name` is set to None, this method returns all the valid names + of `embedding_name` with associated `pretrained_file_name`. + + + Parameters + ---------- + embedding_name : str or None, default None + The pre-trained token embedding name. + + + Returns + ------- + dict or list: + A list of all the valid pre-trained token embedding file names + (`pretrained_file_name`) for the specified token embedding name + (`embedding_name`). If the text embeding name is set to None, + returns a dict mapping each valid token embedding name to a list + of valid pre-trained files (`pretrained_file_name`). They can be + plugged into `mxnet.text.embedding.TokenEmbedding.create( + embedding_name, pretrained_file_name)`. + """ + + text_embedding_reg = registry.get_registry(TokenEmbedding) + + if embedding_name is not None: + if embedding_name not in text_embedding_reg: + raise KeyError('Cannot find `embedding_name` %s. Use ' + '`get_embedding_and_pretrained_file_names(' + 'embedding_name=None).keys()` to get all the ' + 'valid embedding names.' % embedding_name) + return list(text_embedding_reg[ + embedding_name].pretrained_file_name_sha1.keys()) + else: + return {embedding_name: list( + embedding_cls.pretrained_file_name_sha1.keys()) + for embedding_name, embedding_cls in + registry.get_registry(TokenEmbedding).items()} + + [email protected] +class GloVe(TokenEmbedding): + """The GloVe token embedding. + + GloVe is an unsupervised learning algorithm for obtaining vector + representations for words. Training is performed on aggregated global + word-word co-occurrence statistics from a corpus, and the resulting + representations showcase interesting linear substructures of the word vector + space. (Source from https://nlp.stanford.edu/projects/glove/) + + Reference: + GloVe: Global Vectors for Word Representation + Jeffrey Pennington, Richard Socher, and Christopher D. Manning + https://nlp.stanford.edu/pubs/glove.pdf + + Website: + https://nlp.stanford.edu/projects/glove/ + + To get the updated URLs to the externally hosted pre-trained token embedding + files, visit https://nlp.stanford.edu/projects/glove/ + + + Parameters + ---------- + pretrain_file : str, default 'glove.840B.300d.txt' + The name of the pre-trained token embedding file. + embed_root : str, default '~/.mxnet/embeddings/' + The root directory for storing embedding-related files. + unknown_vec : callback + The callback used to initialize the embedding vector for the unknown + token. + """ + + # Map a pre-trained token embedding archive file and its SHA-1 hash. + pretrained_archive_name_sha1 = \ + {'glove.42B.300d.zip': 'f8e722b39578f776927465b71b231bae2ae8776a', + 'glove.6B.zip': 'b64e54f1877d2f735bdd000c1d7d771e25c7dfdc', + 'glove.840B.300d.zip': '8084fbacc2dee3b1fd1ca4cc534cbfff3519ed0d', + 'glove.twitter.27B.zip': 'dce69c404025a8312c323197347695e81fd529fc'} + + # Map a pre-trained token embedding file and its SHA-1 hash. + pretrained_file_name_sha1 = \ + {'glove.42B.300d.txt': '876767977d6bd4d947c0f84d44510677bc94612a', + 'glove.6B.50d.txt': '21bf566a9d27f84d253e0cd4d4be9dcc07976a6d', + 'glove.6B.100d.txt': '16b1dbfaf35476790bd9df40c83e2dfbd05312f1', + 'glove.6B.200d.txt': '17d0355ddaa253e298ede39877d1be70f99d9148', + 'glove.6B.300d.txt': '646443dd885090927f8215ecf7a677e9f703858d', + 'glove.840B.300d.txt': '294b9f37fa64cce31f9ebb409c266fc379527708', + 'glove.twitter.27B.25d.txt': + '767d80889d8c8a22ae7cd25e09d0650a6ff0a502', + 'glove.twitter.27B.50d.txt': + '9585f4be97e286339bf0112d0d3aa7c15a3e864d', + 'glove.twitter.27B.100d.txt': + '1bbeab8323c72332bd46ada0fc3c99f2faaa8ca8', + 'glove.twitter.27B.200d.txt': + '7921c77a53aa5977b1d9ce3a7c4430cbd9d1207a'} + + url_prefix = 'http://nlp.stanford.edu/data/' + + def __init__(self, pretrained_file_name='glove.840B.300d.txt', + embedding_root='~/.mxnet/embeddings/', Review comment: Resolved. ---------------------------------------------------------------- This is an automated message from the Apache Git Service. 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