Source code for transformers.models.transfo_xl.tokenization_transfo_xl

# coding=utf-8
# Copyright 2018 Google AI, Google Brain and Carnegie Mellon University Authors and the HuggingFace Inc. team.
# Copyright (c) 2018, NVIDIA CORPORATION.  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
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# See the License for the specific language governing permissions and
# limitations under the License.
 Tokenization classes for Transformer XL model. Adapted from

import glob
import os
import pickle
import re
from collections import Counter, OrderedDict
from typing import List, Optional, Tuple

import numpy as np

import sacremoses as sm

from ...file_utils import cached_path, is_torch_available, torch_only_method
from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging

if is_torch_available():
    import torch

logger = logging.get_logger(__name__)

    "pretrained_vocab_file": "vocab.pkl",
    "pretrained_vocab_file_torch": "vocab.bin",
    "vocab_file": "vocab.txt",

    "pretrained_vocab_file": {
        "transfo-xl-wt103": "",

    "transfo-xl-wt103": None,

    "transfo-xl-wt103": "",
CORPUS_NAME = "corpus.bin"

MATCH_NUMBERS = r"(?<=\d)[,.](?=\d)", r" @\g<0>@ "
DETOKENIZE_NUMBERS = [(r" @\,@ ", r","), (r" @\.@ ", r".")]

def tokenize_numbers(text_array: List[str]) -> List[str]:
    Splits large comma-separated numbers and floating point values. This is done by replacing commas with ' @,@ ' and
    dots with ' @.@ '.

        text_array: An already tokenized text as list.

        A list of strings with tokenized numbers.

        >>> tokenize_numbers(["$", "5,000", "1.73", "m"])
        ["$", "5", "@,@", "000", "1", "@.@", "73", "m"]
    tokenized = []
    for i in range(len(text_array)):
        reg, sub = MATCH_NUMBERS
        replaced = re.sub(reg, sub, text_array[i]).split()

    return tokenized

def detokenize_numbers(text: str) -> str:
    Inverts the operation of `tokenize_numbers`. This is replacing ' @,@ ' and ' @.@' by ',' and '.'.

        text: A string where the number should be detokenized.

        A detokenized string.

        >>> detokenize_numbers("$ 5 @,@ 000 1 @.@ 73 m")
        "$ 5,000 1.73 m"
    for reg, sub in DETOKENIZE_NUMBERS:
        text = re.sub(reg, sub, text)
    return text

[docs]class TransfoXLTokenizer(PreTrainedTokenizer): """ Construct a Transformer-XL tokenizer adapted from Vocab class in `the original code <>`__. The Transformer-XL tokenizer is a word-level tokenizer (no sub-word tokenization). This tokenizer inherits from :class:`~transformers.PreTrainedTokenizer` which contains most of the main methods. Users should refer to this superclass for more information regarding those methods. Args: special (:obj:`List[str]`, `optional`): A list of special tokens (to be treated by the original implementation of this tokenizer). min_freq (:obj:`int`, `optional`, defaults to 0): The minimum number of times a token has to be present in order to be kept in the vocabulary (otherwise it will be mapped to :obj:`unk_token`). max_size (:obj:`int`, `optional`): The maximum size of the vocabulary. If left unset, it will default to the size of the vocabulary found after excluding the tokens according to the :obj:`min_freq` rule. lower_case (:obj:`bool`, `optional`, defaults to :obj:`False`): Whether or not to lowercase the input when tokenizing. delimiter (:obj:`str`, `optional`): The delimiter used between tokens. vocab_file (:obj:`str`, `optional`): File containing the vocabulary (from the original implementation). pretrained_vocab_file (:obj:`str`, `optional`): File containing the vocabulary as saved with the :obj:`save_pretrained()` method. never_split (:obj:`List[str]`, `optional`): List of tokens that should never be split. If no list is specified, will simply use the existing special tokens. unk_token (:obj:`str`, `optional`, defaults to :obj:`"<unk>"`): The unknown token. A token that is not in the vocabulary cannot be converted to an ID and is set to be this token instead. eos_token (:obj:`str`, `optional`, defaults to :obj:`"<eos>"`): The end of sequence token. additional_special_tokens (:obj:`List[str]`, `optional`, defaults to :obj:`["<formula>"]`): A list of additional special tokens (for the HuggingFace functionality). language (:obj:`str`, `optional`, defaults to :obj:`"en"`): The language of this tokenizer (used for mose preprocessing). """ vocab_files_names = VOCAB_FILES_NAMES pretrained_vocab_files_map = PRETRAINED_VOCAB_FILES_MAP max_model_input_sizes = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES model_input_names = ["input_ids"] def __init__( self, special=None, min_freq=0, max_size=None, lower_case=False, delimiter=None, vocab_file=None, pretrained_vocab_file: str = None, never_split=None, unk_token="<unk>", eos_token="<eos>", additional_special_tokens=["<formula>"], language="en", **kwargs ): super().__init__( special=special, min_freq=min_freq, max_size=max_size, lower_case=lower_case, delimiter=delimiter, vocab_file=vocab_file, pretrained_vocab_file=pretrained_vocab_file, never_split=never_split, unk_token=unk_token, eos_token=eos_token, additional_special_tokens=additional_special_tokens, language=language, **kwargs, ) if never_split is None: never_split = self.all_special_tokens if special is None: special = [] self.counter = Counter() self.special = special self.min_freq = min_freq self.max_size = max_size self.lower_case = lower_case self.delimiter = delimiter self.vocab_file = vocab_file self.never_split = never_split self.punctuation_symbols = '!"#$%&()*+,-./\\:;<=>?@[\\]^_`{|}~' self.punction_without_space_before_pattern = re.compile(rf"[^\s][{self.punctuation_symbols}]") self.punctuation_with_space_around_pattern = self._compile_space_around_punctuation_pattern() self.language = language self.moses_punct_normalizer = sm.MosesPunctNormalizer(language) self.moses_tokenizer = sm.MosesTokenizer(language) self.moses_detokenizer = sm.MosesDetokenizer(language) # This try... catch... is not beautiful but honestly this tokenizer was not made to be used # in a library like ours, at all. try: vocab_dict = None if pretrained_vocab_file is not None: # Priority on pickle files (support PyTorch and TF) with open(pretrained_vocab_file, "rb") as f: vocab_dict = pickle.load(f) # Loading a torch-saved transfo-xl vocab dict with pickle results in an integer # Entering this if statement means that we tried to load a torch-saved file with pickle, and we failed. # We therefore load it with torch, if it's available. if type(vocab_dict) == int: if not is_torch_available(): raise ImportError( "Not trying to load dict with PyTorch as you need to install pytorch to load " "from a PyTorch pretrained vocabulary, " "or activate it with environment variables USE_TORCH=1 and USE_TF=0." ) vocab_dict = torch.load(pretrained_vocab_file) if vocab_dict is not None: for key, value in vocab_dict.items(): if key not in self.__dict__: self.__dict__[key] = value elif vocab_file is not None: self.build_vocab() except Exception as e: raise ValueError( f"Unable to parse file {pretrained_vocab_file}. Unknown format. " "If you tried to load a model saved through TransfoXLTokenizerFast," "please note they are not compatible." ) from e if vocab_file is not None: self.build_vocab() @property def do_lower_case(self): return self.lower_case def _compile_space_around_punctuation_pattern(self): look_ahead_for_special_token = f"(?=[{self.punctuation_symbols}])" look_ahead_to_match_all_except_space = r"(?=[^\s])" return re.compile(r"" + look_ahead_for_special_token + look_ahead_to_match_all_except_space) def count_file(self, path, verbose=False, add_eos=False): if verbose:"counting file {path} ...") assert os.path.exists(path), f"Input file {path} not found" sents = [] with open(path, "r", encoding="utf-8") as f: for idx, line in enumerate(f): if verbose and idx > 0 and idx % 500000 == 0:" line {idx}") symbols = self.tokenize(line, add_eos=add_eos) self.counter.update(symbols) sents.append(symbols) return sents def count_sents(self, sents, verbose=False): """ sents : a list of sentences, each a list of tokenized symbols """ if verbose:"counting {len(sents)} sents ...") for idx, symbols in enumerate(sents): if verbose and idx > 0 and idx % 500000 == 0:" line {idx}") self.counter.update(symbols) def _build_from_file(self, vocab_file): self.idx2sym = [] self.sym2idx = OrderedDict() with open(vocab_file, "r", encoding="utf-8") as f: for line in f: symb = line.strip().split()[0] self.add_symbol(symb) if "<UNK>" in self.sym2idx: self.unk_idx = self.sym2idx["<UNK>"] elif "<unk>" in self.sym2idx: self.unk_idx = self.sym2idx["<unk>"] else: raise ValueError("No <unknown> token in vocabulary")
[docs] def save_vocabulary(self, save_directory: str, filename_prefix: Optional[str] = None) -> Tuple[str]: if os.path.isdir(save_directory): vocab_file = os.path.join( save_directory, (filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["pretrained_vocab_file"], ) else: vocab_file = (filename_prefix + "-" if filename_prefix else "") + save_directory with open(vocab_file, "wb") as f: pickle.dump(self.__dict__, f) return (vocab_file,)
def build_vocab(self): if self.vocab_file:"building vocab from {self.vocab_file}") self._build_from_file(self.vocab_file)"final vocab size {len(self)}") else:"building vocab with min_freq={self.min_freq}, max_size={self.max_size}") self.idx2sym = [] self.sym2idx = OrderedDict() for sym in self.special: self.add_special(sym) for sym, cnt in self.counter.most_common(self.max_size): if cnt < self.min_freq: break self.add_symbol(sym)"final vocab size {len(self)} from {len(self.counter)} unique tokens") @torch_only_method def encode_file(self, path, ordered=False, verbose=False, add_eos=True, add_double_eos=False): if verbose:"encoding file {path} ...") assert os.path.exists(path), f"Output file {path} not found" encoded = [] with open(path, "r", encoding="utf-8") as f: for idx, line in enumerate(f): if verbose and idx > 0 and idx % 500000 == 0:" line {idx}") symbols = self.tokenize(line, add_eos=add_eos, add_double_eos=add_double_eos) encoded.append(self.convert_to_tensor(symbols)) if ordered: encoded = return encoded @torch_only_method def encode_sents(self, sents, ordered=False, verbose=False): if verbose:"encoding {len(sents)} sents ...") encoded = [] for idx, symbols in enumerate(sents): if verbose and idx > 0 and idx % 500000 == 0:" line {idx}") encoded.append(self.convert_to_tensor(symbols)) if ordered: encoded = return encoded def add_special(self, sym): if sym not in self.sym2idx: self.idx2sym.append(sym) self.sym2idx[sym] = len(self.idx2sym) - 1 setattr(self, f"{sym.strip('<>')}_idx", self.sym2idx[sym]) def add_symbol(self, sym): if sym not in self.sym2idx: self.idx2sym.append(sym) self.sym2idx[sym] = len(self.idx2sym) - 1 def move_added_token(self, token: str, target_idx: int): """ Moves an added token to a specific position in the vocab. This method should be used when resizing an embedding layer other than the last one in the `AdaptiveEmbedding` in order to move the token in the tokenizer from the default position (at the very end) to the desired one. Args: token: The token to move to a specific position in the vocab. target_idx: The position where the token should be moved to. """ assert token in self.added_tokens_encoder, "Token which should be moved has to be an added token" assert token not in self.idx2sym, "Token which should be moved is already in vocab" # Insert sym into vocab self.idx2sym.insert(target_idx, token) self.sym2idx[token] = target_idx # Shift following indices in sym2idx for idx in range(target_idx + 1, len(self.idx2sym)): current_sym = self.idx2sym[idx] self.sym2idx[current_sym] = idx # Delete token from added_tokens old_index = self.added_tokens_encoder[token] del self.added_tokens_decoder[old_index] del self.added_tokens_encoder[token] def moses_punct_norm(self, text): return self.moses_punct_normalizer.normalize(text) def moses_tokenize(self, text): return self.moses_tokenizer.tokenize( text, aggressive_dash_splits=True, return_str=False, escape=False, protected_patterns=self.never_split ) def moses_pipeline(self, text: str) -> List[str]: """ Does basic tokenization using :class:`sacremoses.MosesPunctNormalizer` and :class:`sacremoses.MosesTokenizer` with `aggressive_dash_splits=True` (see :func:`sacremoses.tokenize.MosesTokenizer.tokenize`). Additionally, large comma-separated numbers and floating point values are split. E.g. "23,000 people are 1.80m tall" -> "23 @,@ 000 people are 1 @.@ 80m tall" Args: text: Text to be tokenize Returns: A list of tokenized string Example:: >>> tokenizer = TransfoXLTokenizer.from_pretrained("transfo-xl-wt103") >>> tokenizer.moses_pipeline("23,000 people are 1.80 m tall") ['23', '@,@', '000', 'people', 'are', '1', '@.@', '80', 'm', 'tall'] """ text = self.moses_punct_norm(text) text = self.moses_tokenize(text) text = tokenize_numbers(text) return text def _convert_id_to_token(self, idx): """Converts an id in a token (BPE) using the vocab.""" assert 0 <= idx < len(self), f"Index {idx} out of vocabulary range" return self.idx2sym[idx] def _convert_token_to_id(self, sym): """Converts a token (str) in an id using the vocab.""" if sym in self.sym2idx: return self.sym2idx[sym] else: #'encounter unk {sym}') # assert '<eos>' not in sym if hasattr(self, "unk_idx"): return self.sym2idx.get(sym, self.unk_idx) # Backward compatibility with pre-trained models elif "<unk>" in self.sym2idx: return self.sym2idx["<unk>"] elif "<UNK>" in self.sym2idx: return self.sym2idx["<UNK>"] else: raise ValueError("Token not in vocabulary and no <unk> token in vocabulary for replacement") def convert_tokens_to_string(self, tokens): """ Converts a sequence of tokens (string) in a single string. Additionally, the split numbers are converted back into it's original form. """ out_string = self.moses_detokenizer.detokenize(tokens) return detokenize_numbers(out_string).strip() @torch_only_method def convert_to_tensor(self, symbols): return torch.LongTensor(self.convert_tokens_to_ids(symbols)) @property def vocab_size(self): return len(self.idx2sym) def get_vocab(self): return dict(self.sym2idx, **self.added_tokens_encoder) def _tokenize(self, line, add_eos=False, add_double_eos=False): line = line.strip() # convert to lower case if self.lower_case: line = line.lower() # empty delimiter '' will evaluate False if self.delimiter == "": symbols = line else: symbols = self.moses_pipeline(line) if add_double_eos: # lm1b return ["<S>"] + symbols + ["<S>"] elif add_eos: return symbols + ["<eos>"] else: return symbols
class LMOrderedIterator(object): def __init__(self, data, bsz, bptt, device="cpu", ext_len=None): """ data -- LongTensor -- the LongTensor is strictly ordered """ self.bsz = bsz self.bptt = bptt self.ext_len = ext_len if ext_len is not None else 0 self.device = device # Work out how cleanly we can divide the dataset into bsz parts. self.n_step = data.size(0) // bsz # Trim off any extra elements that wouldn't cleanly fit (remainders). data = data.narrow(0, 0, self.n_step * bsz) # Evenly divide the data across the bsz batches. = data.view(bsz, -1).t().contiguous().to(device) # Number of mini-batches self.n_batch = (self.n_step + self.bptt - 1) // self.bptt def get_batch(self, i, bptt=None): if bptt is None: bptt = self.bptt seq_len = min(bptt, - 1 - i) end_idx = i + seq_len beg_idx = max(0, i - self.ext_len) data =[beg_idx:end_idx] target =[i + 1 : i + 1 + seq_len] data_out = data.transpose(0, 1).contiguous().to(self.device) target_out = target.transpose(0, 1).contiguous().to(self.device) return data_out, target_out, seq_len def get_fixlen_iter(self, start=0): for i in range(start, - 1, self.bptt): yield self.get_batch(i) def get_varlen_iter(self, start=0, std=5, min_len=5, max_deviation=3): max_len = self.bptt + max_deviation * std i = start while True: bptt = self.bptt if np.random.random() < 0.95 else self.bptt / 2.0 bptt = min(max_len, max(min_len, int(np.random.normal(bptt, std)))) data, target, seq_len = self.get_batch(i, bptt) i += seq_len yield data, target, seq_len if i >= - 2: break def __iter__(self): return self.get_fixlen_iter() class LMShuffledIterator(object): def __init__(self, data, bsz, bptt, device="cpu", ext_len=None, shuffle=False): """ data -- list[LongTensor] -- there is no order among the LongTensors """ = data self.bsz = bsz self.bptt = bptt self.ext_len = ext_len if ext_len is not None else 0 self.device = device self.shuffle = shuffle def get_sent_stream(self): # index iterator epoch_indices = np.random.permutation(len( if self.shuffle else np.array(range(len( # sentence iterator for idx in epoch_indices: yield[idx] @torch_only_method def stream_iterator(self, sent_stream): # streams for each data in the batch streams = [None] * self.bsz data = torch.LongTensor(self.bptt, self.bsz) target = torch.LongTensor(self.bptt, self.bsz) n_retain = 0 while True: # data : [n_retain+bptt x bsz] # target : [bptt x bsz] data[n_retain:].fill_(-1) target.fill_(-1) valid_batch = True for i in range(self.bsz): n_filled = 0 try: while n_filled < self.bptt: if streams[i] is None or len(streams[i]) <= 1: streams[i] = next(sent_stream) # number of new tokens to fill in n_new = min(len(streams[i]) - 1, self.bptt - n_filled) # first n_retain tokens are retained from last batch data[n_retain + n_filled : n_retain + n_filled + n_new, i] = streams[i][:n_new] target[n_filled : n_filled + n_new, i] = streams[i][1 : n_new + 1] streams[i] = streams[i][n_new:] n_filled += n_new except StopIteration: valid_batch = False break if not valid_batch: return data_out = data.transpose(0, 1).contiguous().to(self.device) target_out = target.transpose(0, 1).contiguous().to(self.device) yield data_out, target_out, self.bptt n_retain = min(data.size(0), self.ext_len) if n_retain > 0: data[:n_retain] = data[-n_retain:] data.resize_(n_retain + self.bptt, data.size(1)) def __iter__(self): # sent_stream is an iterator sent_stream = self.get_sent_stream() for batch in self.stream_iterator(sent_stream): yield batch class LMMultiFileIterator(LMShuffledIterator): def __init__(self, paths, vocab, bsz, bptt, device="cpu", ext_len=None, shuffle=False): self.paths = paths self.vocab = vocab self.bsz = bsz self.bptt = bptt self.ext_len = ext_len if ext_len is not None else 0 self.device = device self.shuffle = shuffle def get_sent_stream(self, path): sents = self.vocab.encode_file(path, add_double_eos=True) if self.shuffle: np.random.shuffle(sents) sent_stream = iter(sents) return sent_stream def __iter__(self): if self.shuffle: np.random.shuffle(self.paths) for path in self.paths: # sent_stream is an iterator sent_stream = self.get_sent_stream(path) for batch in self.stream_iterator(sent_stream): yield batch class TransfoXLCorpus(object): @classmethod @torch_only_method def from_pretrained(cls, pretrained_model_name_or_path, cache_dir=None, *inputs, **kwargs): """ Instantiate a pre-processed corpus. """ vocab = TransfoXLTokenizer.from_pretrained(pretrained_model_name_or_path, *inputs, **kwargs) if pretrained_model_name_or_path in PRETRAINED_CORPUS_ARCHIVE_MAP: corpus_file = PRETRAINED_CORPUS_ARCHIVE_MAP[pretrained_model_name_or_path] else: corpus_file = os.path.join(pretrained_model_name_or_path, CORPUS_NAME) # redirect to the cache, if necessary try: resolved_corpus_file = cached_path(corpus_file, cache_dir=cache_dir) except EnvironmentError: logger.error( f"Corpus '{pretrained_model_name_or_path}' was not found in corpus list " f"({', '.join(PRETRAINED_CORPUS_ARCHIVE_MAP.keys())}. " f"We assumed '{pretrained_model_name_or_path}' was a path or url but couldn't find files {corpus_file} " "at this path or url." ) return None if resolved_corpus_file == corpus_file:"loading corpus file {corpus_file}") else:"loading corpus file {corpus_file} from cache at {resolved_corpus_file}") # Instantiate tokenizer. corpus = cls(*inputs, **kwargs) corpus_dict = torch.load(resolved_corpus_file) for key, value in corpus_dict.items(): corpus.__dict__[key] = value 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) else: data_iter = None raise ValueError(f"Split not recognized: {split}") return data_iter @torch_only_method def get_lm_corpus(datadir, dataset): fn = os.path.join(datadir, "") fn_pickle = os.path.join(datadir, "cache.pkl") if os.path.exists(fn):"Loading cached dataset...") corpus = torch.load(fn_pickle) elif os.path.exists(fn):"Loading cached dataset from pickle...") with open(fn, "rb") as fp: corpus = pickle.load(fp) else:"Producing dataset {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), fn) return corpus