580 lines
21 KiB
Python
580 lines
21 KiB
Python
# coding=utf-8
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# Copyright 2018 Google AI, Google Brain and Carnegie Mellon University Authors and the HuggingFace Inc. team.
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# Copyright (c) 2018, NVIDIA CORPORATION. All rights reserved.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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""" Tokenization classes for Transformer XL model.
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Adapted from https://github.com/kimiyoung/transformer-xl.
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"""
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from __future__ import (absolute_import, division, print_function,
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unicode_literals)
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import glob
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import logging
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import os
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import sys
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from collections import Counter, OrderedDict
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from io import open
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import numpy as np
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from .file_utils import cached_path
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from .tokenization_utils import PreTrainedTokenizer
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try:
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import torch
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except ImportError:
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pass
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# if sys.version_info[0] == 2:
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# import cPickle as pickle
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# else:
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# import pickle
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logger = logging.getLogger(__name__)
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VOCAB_FILES_NAMES = {'pretrained_vocab_file': 'vocab.bin', 'vocab_file': 'vocab.txt'}
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PRETRAINED_VOCAB_FILES_MAP = {
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'pretrained_vocab_file':
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{
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'transfo-xl-wt103': "https://s3.amazonaws.com/models.huggingface.co/bert/transfo-xl-wt103-vocab.bin",
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}
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}
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PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES = {
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'transfo-xl-wt103': None,
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}
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PRETRAINED_CORPUS_ARCHIVE_MAP = {
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'transfo-xl-wt103': "https://s3.amazonaws.com/models.huggingface.co/bert/transfo-xl-wt103-corpus.bin",
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}
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CORPUS_NAME = 'corpus.bin'
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class TransfoXLTokenizer(PreTrainedTokenizer):
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"""
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Transformer-XL tokenizer adapted from Vocab class in https://github.com/kimiyoung/transformer-xl
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"""
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vocab_files_names = VOCAB_FILES_NAMES
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pretrained_vocab_files_map = PRETRAINED_VOCAB_FILES_MAP
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max_model_input_sizes = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
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def __init__(self, special=None, min_freq=0, max_size=None, lower_case=False,
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delimiter=None, vocab_file=None, pretrained_vocab_file=None,
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never_split=None, unk_token="<unk>", eos_token="<eos>",
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additional_special_tokens=["<formula>"], **kwargs):
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super(TransfoXLTokenizer, self).__init__(unk_token=unk_token, eos_token=eos_token,
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additional_special_tokens=additional_special_tokens,
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**kwargs)
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self.max_len_single_sentence = self.max_len # no default special tokens - you can update this value if you add special tokens
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self.max_len_sentences_pair = self.max_len # no default special tokens - you can update this value if you add special tokens
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if never_split is None:
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never_split = self.all_special_tokens
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if special is None:
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special = []
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self.counter = Counter()
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self.special = special
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self.min_freq = min_freq
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self.max_size = max_size
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self.lower_case = lower_case
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self.delimiter = delimiter
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self.vocab_file = vocab_file
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self.never_split = never_split
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if pretrained_vocab_file is not None:
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# Hack because, honestly this tokenizer was not made to be used
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# in a library like ours, at all.
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vocab_dict = torch.load(pretrained_vocab_file)
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for key, value in vocab_dict.items():
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if key not in self.__dict__:
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self.__dict__[key] = value
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if vocab_file is not None:
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self.build_vocab()
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def count_file(self, path, verbose=False, add_eos=False):
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if verbose: logger.info('counting file {} ...'.format(path))
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assert os.path.exists(path)
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sents = []
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with open(path, 'r', encoding='utf-8') as f:
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for idx, line in enumerate(f):
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if verbose and idx > 0 and idx % 500000 == 0:
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logger.info(' line {}'.format(idx))
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symbols = self.tokenize(line, add_eos=add_eos)
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self.counter.update(symbols)
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sents.append(symbols)
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return sents
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def count_sents(self, sents, verbose=False):
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"""
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sents : a list of sentences, each a list of tokenized symbols
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"""
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if verbose: logger.info('counting {} sents ...'.format(len(sents)))
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for idx, symbols in enumerate(sents):
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if verbose and idx > 0 and idx % 500000 == 0:
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logger.info(' line {}'.format(idx))
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self.counter.update(symbols)
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def _build_from_file(self, vocab_file):
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self.idx2sym = []
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self.sym2idx = OrderedDict()
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with open(vocab_file, 'r', encoding='utf-8') as f:
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for line in f:
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symb = line.strip().split()[0]
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self.add_symbol(symb)
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if '<UNK>' in self.sym2idx:
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self.unk_idx = self.sym2idx['<UNK>']
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elif '<unk>' in self.sym2idx:
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self.unk_idx = self.sym2idx['<unk>']
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else:
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raise ValueError('No <unkown> token in vocabulary')
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def save_vocabulary(self, vocab_path):
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"""Save the tokenizer vocabulary to a directory or file."""
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if os.path.isdir(vocab_path):
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vocab_file = os.path.join(vocab_path, VOCAB_FILES_NAMES['pretrained_vocab_file'])
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torch.save(self.__dict__, vocab_file)
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return (vocab_file,)
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def build_vocab(self):
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if self.vocab_file:
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logger.info('building vocab from {}'.format(self.vocab_file))
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self._build_from_file(self.vocab_file)
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logger.info('final vocab size {}'.format(len(self)))
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else:
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logger.info('building vocab with min_freq={}, max_size={}'.format(
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self.min_freq, self.max_size))
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self.idx2sym = []
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self.sym2idx = OrderedDict()
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for sym in self.special:
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self.add_special(sym)
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for sym, cnt in self.counter.most_common(self.max_size):
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if cnt < self.min_freq: break
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self.add_symbol(sym)
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logger.info('final vocab size {} from {} unique tokens'.format(
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len(self), len(self.counter)))
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def encode_file(self, path, ordered=False, verbose=False, add_eos=True,
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add_double_eos=False):
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if verbose: logger.info('encoding file {} ...'.format(path))
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assert os.path.exists(path)
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encoded = []
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with open(path, 'r', encoding='utf-8') as f:
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for idx, line in enumerate(f):
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if verbose and idx > 0 and idx % 500000 == 0:
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logger.info(' line {}'.format(idx))
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symbols = self.tokenize(line, add_eos=add_eos,
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add_double_eos=add_double_eos)
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encoded.append(self.convert_to_tensor(symbols))
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if ordered:
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encoded = torch.cat(encoded)
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return encoded
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def encode_sents(self, sents, ordered=False, verbose=False):
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if verbose: logger.info('encoding {} sents ...'.format(len(sents)))
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encoded = []
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for idx, symbols in enumerate(sents):
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if verbose and idx > 0 and idx % 500000 == 0:
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logger.info(' line {}'.format(idx))
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encoded.append(self.convert_to_tensor(symbols))
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if ordered:
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encoded = torch.cat(encoded)
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return encoded
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def add_special(self, sym):
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if sym not in self.sym2idx:
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self.idx2sym.append(sym)
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self.sym2idx[sym] = len(self.idx2sym) - 1
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setattr(self, '{}_idx'.format(sym.strip('<>')), self.sym2idx[sym])
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def add_symbol(self, sym):
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if sym not in self.sym2idx:
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self.idx2sym.append(sym)
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self.sym2idx[sym] = len(self.idx2sym) - 1
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def _convert_id_to_token(self, idx):
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"""Converts an id in a token (BPE) using the vocab."""
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assert 0 <= idx < len(self), 'Index {} out of vocabulary range'.format(idx)
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return self.idx2sym[idx]
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def _convert_token_to_id(self, sym):
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""" Converts a token (str/unicode) in an id using the vocab. """
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if sym in self.sym2idx:
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return self.sym2idx[sym]
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else:
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# logger.info('encounter unk {}'.format(sym))
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# assert '<eos>' not in sym
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if hasattr(self, 'unk_idx'):
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return self.sym2idx.get(sym, self.unk_idx)
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# Backward compatibility with pre-trained models
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elif '<unk>' in self.sym2idx:
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return self.sym2idx['<unk>']
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elif '<UNK>' in self.sym2idx:
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return self.sym2idx['<UNK>']
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else:
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raise ValueError('Token not in vocabulary and no <unk> token in vocabulary for replacement')
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def convert_tokens_to_string(self, tokens):
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""" Converts a sequence of tokens (string) in a single string. """
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out_string = ' '.join(tokens).strip()
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return out_string
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def convert_to_tensor(self, symbols):
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return torch.LongTensor(self.convert_tokens_to_ids(symbols))
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@property
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def vocab_size(self):
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return len(self.idx2sym)
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def _tokenize(self, line, add_eos=False, add_double_eos=False):
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line = line.strip()
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# convert to lower case
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if self.lower_case:
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line = line.lower()
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# empty delimiter '' will evaluate False
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if self.delimiter == '':
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symbols = line
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else:
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symbols = line.split(self.delimiter)
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if add_double_eos: # lm1b
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return ['<S>'] + symbols + ['<S>']
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elif add_eos:
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return symbols + ['<eos>']
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else:
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return symbols
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class LMOrderedIterator(object):
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def __init__(self, data, bsz, bptt, device='cpu', ext_len=None):
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"""
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data -- LongTensor -- the LongTensor is strictly ordered
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"""
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self.bsz = bsz
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self.bptt = bptt
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self.ext_len = ext_len if ext_len is not None else 0
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self.device = device
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# Work out how cleanly we can divide the dataset into bsz parts.
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self.n_step = data.size(0) // bsz
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# Trim off any extra elements that wouldn't cleanly fit (remainders).
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data = data.narrow(0, 0, self.n_step * bsz)
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# Evenly divide the data across the bsz batches.
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self.data = data.view(bsz, -1).t().contiguous().to(device)
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# Number of mini-batches
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self.n_batch = (self.n_step + self.bptt - 1) // self.bptt
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def get_batch(self, i, bptt=None):
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if bptt is None: bptt = self.bptt
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seq_len = min(bptt, self.data.size(0) - 1 - i)
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end_idx = i + seq_len
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beg_idx = max(0, i - self.ext_len)
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data = self.data[beg_idx:end_idx]
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target = self.data[i+1:i+1+seq_len]
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data_out = data.transpose(0, 1).contiguous().to(self.device)
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target_out = target.transpose(0, 1).contiguous().to(self.device)
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return data_out, target_out, seq_len
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def get_fixlen_iter(self, start=0):
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for i in range(start, self.data.size(0) - 1, self.bptt):
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yield self.get_batch(i)
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def get_varlen_iter(self, start=0, std=5, min_len=5, max_deviation=3):
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max_len = self.bptt + max_deviation * std
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i = start
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while True:
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bptt = self.bptt if np.random.random() < 0.95 else self.bptt / 2.
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bptt = min(max_len, max(min_len, int(np.random.normal(bptt, std))))
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data, target, seq_len = self.get_batch(i, bptt)
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i += seq_len
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yield data, target, seq_len
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if i >= self.data.size(0) - 2:
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break
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def __iter__(self):
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return self.get_fixlen_iter()
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class LMShuffledIterator(object):
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def __init__(self, data, bsz, bptt, device='cpu', ext_len=None, shuffle=False):
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"""
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data -- list[LongTensor] -- there is no order among the LongTensors
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"""
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self.data = data
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self.bsz = bsz
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self.bptt = bptt
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self.ext_len = ext_len if ext_len is not None else 0
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self.device = device
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self.shuffle = shuffle
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def get_sent_stream(self):
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# index iterator
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epoch_indices = np.random.permutation(len(self.data)) if self.shuffle \
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else np.array(range(len(self.data)))
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# sentence iterator
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for idx in epoch_indices:
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yield self.data[idx]
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def stream_iterator(self, sent_stream):
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# streams for each data in the batch
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streams = [None] * self.bsz
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data = torch.LongTensor(self.bptt, self.bsz)
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target = torch.LongTensor(self.bptt, self.bsz)
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n_retain = 0
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while True:
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# data : [n_retain+bptt x bsz]
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# target : [bptt x bsz]
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data[n_retain:].fill_(-1)
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target.fill_(-1)
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valid_batch = True
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for i in range(self.bsz):
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n_filled = 0
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try:
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while n_filled < self.bptt:
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if streams[i] is None or len(streams[i]) <= 1:
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streams[i] = next(sent_stream)
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# number of new tokens to fill in
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n_new = min(len(streams[i]) - 1, self.bptt - n_filled)
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# first n_retain tokens are retained from last batch
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data[n_retain+n_filled:n_retain+n_filled+n_new, i] = \
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streams[i][:n_new]
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target[n_filled:n_filled+n_new, i] = \
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streams[i][1:n_new+1]
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streams[i] = streams[i][n_new:]
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n_filled += n_new
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except StopIteration:
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valid_batch = False
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break
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if not valid_batch:
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return
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data_out = data.transpose(0, 1).contiguous().to(self.device)
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target_out = target.transpose(0, 1).contiguous().to(self.device)
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yield data_out, target_out, self.bptt
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n_retain = min(data.size(0), self.ext_len)
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if n_retain > 0:
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data[:n_retain] = data[-n_retain:]
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data.resize_(n_retain + self.bptt, data.size(1))
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def __iter__(self):
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# sent_stream is an iterator
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sent_stream = self.get_sent_stream()
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for batch in self.stream_iterator(sent_stream):
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yield batch
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class LMMultiFileIterator(LMShuffledIterator):
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def __init__(self, paths, vocab, bsz, bptt, device='cpu', ext_len=None,
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shuffle=False):
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self.paths = paths
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self.vocab = vocab
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self.bsz = bsz
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self.bptt = bptt
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self.ext_len = ext_len if ext_len is not None else 0
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self.device = device
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self.shuffle = shuffle
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def get_sent_stream(self, path):
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sents = self.vocab.encode_file(path, add_double_eos=True)
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if self.shuffle:
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np.random.shuffle(sents)
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sent_stream = iter(sents)
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return sent_stream
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def __iter__(self):
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if self.shuffle:
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np.random.shuffle(self.paths)
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for path in self.paths:
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# sent_stream is an iterator
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sent_stream = self.get_sent_stream(path)
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for batch in self.stream_iterator(sent_stream):
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yield batch
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class TransfoXLCorpus(object):
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@classmethod
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def from_pretrained(cls, pretrained_model_name_or_path, cache_dir=None, *inputs, **kwargs):
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"""
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Instantiate a pre-processed corpus.
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"""
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vocab = TransfoXLTokenizer.from_pretrained(pretrained_model_name_or_path, *inputs, **kwargs)
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if pretrained_model_name_or_path in PRETRAINED_CORPUS_ARCHIVE_MAP:
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corpus_file = PRETRAINED_CORPUS_ARCHIVE_MAP[pretrained_model_name_or_path]
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else:
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corpus_file = os.path.join(pretrained_model_name_or_path, CORPUS_NAME)
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# redirect to the cache, if necessary
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try:
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resolved_corpus_file = cached_path(corpus_file, cache_dir=cache_dir)
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except EnvironmentError:
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logger.error(
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"Corpus '{}' was not found in corpus list ({}). "
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"We assumed '{}' was a path or url but couldn't find files {} "
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"at this path or url.".format(
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pretrained_model_name_or_path,
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', '.join(PRETRAINED_CORPUS_ARCHIVE_MAP.keys()),
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pretrained_model_name_or_path,
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corpus_file))
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return None
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if resolved_corpus_file == corpus_file:
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logger.info("loading corpus file {}".format(corpus_file))
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else:
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logger.info("loading corpus file {} from cache at {}".format(
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corpus_file, resolved_corpus_file))
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# Instantiate tokenizer.
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corpus = cls(*inputs, **kwargs)
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corpus_dict = torch.load(resolved_corpus_file)
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for key, value in corpus_dict.items():
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corpus.__dict__[key] = value
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corpus.vocab = vocab
|
|
if corpus.train is not None:
|
|
corpus.train = torch.tensor(corpus.train, dtype=torch.long)
|
|
if corpus.valid is not None:
|
|
corpus.valid = torch.tensor(corpus.valid, dtype=torch.long)
|
|
if corpus.test is not None:
|
|
corpus.test = torch.tensor(corpus.test, dtype=torch.long)
|
|
return corpus
|
|
|
|
def __init__(self, *args, **kwargs):
|
|
self.vocab = TransfoXLTokenizer(*args, **kwargs)
|
|
self.dataset = None
|
|
self.train = None
|
|
self.valid = None
|
|
self.test = None
|
|
|
|
def build_corpus(self, path, dataset):
|
|
self.dataset = dataset
|
|
|
|
if self.dataset in ['ptb', 'wt2', 'enwik8', 'text8']:
|
|
self.vocab.count_file(os.path.join(path, 'train.txt'))
|
|
self.vocab.count_file(os.path.join(path, 'valid.txt'))
|
|
self.vocab.count_file(os.path.join(path, 'test.txt'))
|
|
elif self.dataset == 'wt103':
|
|
self.vocab.count_file(os.path.join(path, 'train.txt'))
|
|
elif self.dataset == 'lm1b':
|
|
train_path_pattern = os.path.join(
|
|
path, '1-billion-word-language-modeling-benchmark-r13output',
|
|
'training-monolingual.tokenized.shuffled', 'news.en-*')
|
|
train_paths = glob.glob(train_path_pattern)
|
|
# the vocab will load from file when build_vocab() is called
|
|
|
|
self.vocab.build_vocab()
|
|
|
|
if self.dataset in ['ptb', 'wt2', 'wt103']:
|
|
self.train = self.vocab.encode_file(
|
|
os.path.join(path, 'train.txt'), ordered=True)
|
|
self.valid = self.vocab.encode_file(
|
|
os.path.join(path, 'valid.txt'), ordered=True)
|
|
self.test = self.vocab.encode_file(
|
|
os.path.join(path, 'test.txt'), ordered=True)
|
|
elif self.dataset in ['enwik8', 'text8']:
|
|
self.train = self.vocab.encode_file(
|
|
os.path.join(path, 'train.txt'), ordered=True, add_eos=False)
|
|
self.valid = self.vocab.encode_file(
|
|
os.path.join(path, 'valid.txt'), ordered=True, add_eos=False)
|
|
self.test = self.vocab.encode_file(
|
|
os.path.join(path, 'test.txt'), ordered=True, add_eos=False)
|
|
elif self.dataset == 'lm1b':
|
|
self.train = train_paths
|
|
self.valid = self.vocab.encode_file(
|
|
os.path.join(path, 'valid.txt'), ordered=False, add_double_eos=True)
|
|
self.test = self.vocab.encode_file(
|
|
os.path.join(path, 'test.txt'), ordered=False, add_double_eos=True)
|
|
|
|
def get_iterator(self, split, *args, **kwargs):
|
|
if split == 'train':
|
|
if self.dataset in ['ptb', 'wt2', 'wt103', 'enwik8', 'text8']:
|
|
data_iter = LMOrderedIterator(self.train, *args, **kwargs)
|
|
elif self.dataset == 'lm1b':
|
|
kwargs['shuffle'] = True
|
|
data_iter = LMMultiFileIterator(self.train, self.vocab, *args, **kwargs)
|
|
elif split in ['valid', 'test']:
|
|
data = self.valid if split == 'valid' else self.test
|
|
if self.dataset in ['ptb', 'wt2', 'wt103', 'enwik8', 'text8']:
|
|
data_iter = LMOrderedIterator(data, *args, **kwargs)
|
|
elif self.dataset == 'lm1b':
|
|
data_iter = LMShuffledIterator(data, *args, **kwargs)
|
|
|
|
return data_iter
|
|
|
|
|
|
def get_lm_corpus(datadir, dataset):
|
|
fn = os.path.join(datadir, 'cache.pt')
|
|
fn_pickle = os.path.join(datadir, 'cache.pkl')
|
|
if os.path.exists(fn):
|
|
logger.info('Loading cached dataset...')
|
|
corpus = torch.load(fn_pickle)
|
|
elif os.path.exists(fn):
|
|
logger.info('Loading cached dataset from pickle...')
|
|
with open(fn, "rb") as fp:
|
|
corpus = pickle.load(fp)
|
|
else:
|
|
logger.info('Producing dataset {}...'.format(dataset))
|
|
kwargs = {}
|
|
if dataset in ['wt103', 'wt2']:
|
|
kwargs['special'] = ['<eos>']
|
|
kwargs['lower_case'] = False
|
|
elif dataset == 'ptb':
|
|
kwargs['special'] = ['<eos>']
|
|
kwargs['lower_case'] = True
|
|
elif dataset == 'lm1b':
|
|
kwargs['special'] = []
|
|
kwargs['lower_case'] = False
|
|
kwargs['vocab_file'] = os.path.join(datadir, '1b_word_vocab.txt')
|
|
elif dataset in ['enwik8', 'text8']:
|
|
pass
|
|
|
|
corpus = TransfoXLCorpus(datadir, dataset, **kwargs)
|
|
torch.save(corpus, fn)
|
|
|
|
return corpus
|