[RoBERTa] model conversion, inference, tests 🔥
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Julien Chaumond
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pytorch_transformers/tokenization_roberta.py
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218
pytorch_transformers/tokenization_roberta.py
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# coding=utf-8
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# Copyright 2018 The Open AI Team Authors and The HuggingFace Inc. team.
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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 RoBERTa."""
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from __future__ import (absolute_import, division, print_function,
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unicode_literals)
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import json
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import logging
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import re
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from .tokenization_utils import PreTrainedTokenizer
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from .tokenization_gpt2 import GPT2Tokenizer
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logger = logging.getLogger(__name__)
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VOCAB_FILES_NAMES = {
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'dict_file': 'dict.txt',
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}
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PRETRAINED_VOCAB_FILES_MAP = {
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'dict_file':
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{
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'roberta-base': "https://s3.amazonaws.com/models.huggingface.co/bert/roberta-base-dict.txt",
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'roberta-large': "https://s3.amazonaws.com/models.huggingface.co/bert/roberta-base-dict.txt",
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'roberta-large-mnli': "https://s3.amazonaws.com/models.huggingface.co/bert/roberta-base-dict.txt",
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},
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}
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PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES = {
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'roberta-base': 512,
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'roberta-large': 512,
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'roberta-large-mnli': 512,
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}
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SPACE_NORMALIZER = re.compile(r"\s+")
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def tokenize_line(line):
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line = SPACE_NORMALIZER.sub(" ", line)
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line = line.strip()
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return line.split()
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class Dictionary(object):
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"""
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A mapping from symbols to consecutive integers
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From Facebook's fairseq.
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"""
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def __init__(
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self,
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pad='<pad>',
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eos='</s>',
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unk='<unk>',
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bos='<s>',
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extra_special_symbols=None,
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):
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self.unk_word, self.pad_word, self.eos_word = unk, pad, eos
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self.symbols = []
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self.count = []
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self.indices = {}
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self.bos_index = self.add_symbol(bos)
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self.pad_index = self.add_symbol(pad)
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self.eos_index = self.add_symbol(eos)
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self.unk_index = self.add_symbol(unk)
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if extra_special_symbols:
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for s in extra_special_symbols:
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self.add_symbol(s)
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self.nspecial = len(self.symbols)
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def __getitem__(self, idx):
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if idx < len(self.symbols):
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return self.symbols[idx]
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return self.unk_word
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def index(self, sym):
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"""Returns the index of the specified symbol"""
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assert isinstance(sym, str)
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if sym in self.indices:
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return self.indices[sym]
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return self.unk_index
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def add_symbol(self, word, n=1):
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"""Adds a word to the dictionary"""
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if word in self.indices:
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idx = self.indices[word]
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self.count[idx] = self.count[idx] + n
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return idx
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else:
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idx = len(self.symbols)
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self.indices[word] = idx
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self.symbols.append(word)
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self.count.append(n)
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return idx
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@classmethod
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def load(cls, f, ignore_utf_errors=False):
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"""Loads the dictionary from a text file with the format:
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```
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<symbol0> <count0>
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<symbol1> <count1>
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...
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```
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"""
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d = cls()
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d.add_from_file(f, ignore_utf_errors)
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return d
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def add_from_file(self, f, ignore_utf_errors=False):
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"""
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Loads a pre-existing dictionary from a text file and adds its symbols
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to this instance.
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"""
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if isinstance(f, str):
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try:
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if not ignore_utf_errors:
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with open(f, 'r', encoding='utf-8') as fd:
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self.add_from_file(fd)
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else:
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with open(f, 'r', encoding='utf-8', errors='ignore') as fd:
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self.add_from_file(fd)
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except FileNotFoundError as fnfe:
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raise fnfe
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except UnicodeError:
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raise Exception("Incorrect encoding detected in {}, please "
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"rebuild the dataset".format(f))
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return
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lines = f.readlines()
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for line in lines:
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idx = line.rfind(' ')
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if idx == -1:
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raise ValueError("Incorrect dictionary format, expected '<token> <cnt>'")
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word = line[:idx]
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count = int(line[idx + 1:])
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self.indices[word] = len(self.symbols)
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self.symbols.append(word)
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self.count.append(count)
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def encode_line(self, line, line_tokenizer=tokenize_line, add_if_not_exist=True,
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consumer=None, append_eos=True, reverse_order=False):
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words = line_tokenizer(line)
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if reverse_order:
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words = list(reversed(words))
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nwords = len(words)
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ids = [0] * (nwords + 1 if append_eos else nwords)
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for i, word in enumerate(words):
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if add_if_not_exist:
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idx = self.add_symbol(word)
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else:
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idx = self.index(word)
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if consumer is not None:
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consumer(word, idx)
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ids[i] = idx
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if append_eos:
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ids[nwords] = self.eos_index
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return ids
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class RobertaTokenizer(PreTrainedTokenizer):
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"""
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RoBERTa tokenizer. Peculiarities:
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- GPT-2 tokenizer with a different integer mapping on top.
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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, dict_file,
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bos_token="<s>", eos_token="</s>", **kwargs):
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super(RobertaTokenizer, self).__init__(bos_token=bos_token, eos_token=eos_token, **kwargs)
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self.gpt2_tokenizer = GPT2Tokenizer.from_pretrained('gpt2')
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self.dictionary = Dictionary.load(dict_file)
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def _tokenize(self, text):
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""" Use GPT-2 Tokenizer """
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return self.gpt2_tokenizer._tokenize(text)
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def encode(self, text):
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""" Converts a string in a sequence of ids (integer), using the tokenizer and vocabulary.
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"""
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gpt2_tokens_joined = " ".join(
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str(x) for x in self.gpt2_tokenizer.convert_tokens_to_ids(self.tokenize(text))
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)
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bpe_sentence = '<s> ' + gpt2_tokens_joined + ' </s>'
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return self.dictionary.encode_line(bpe_sentence, append_eos=False)
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def _convert_token_to_id(self, token):
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return self.dictionary.index(token)
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def _convert_id_to_token(self, index):
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symbol = self.dictionary[index]
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try:
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idx = int(symbol)
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return self.gpt2_tokenizer._convert_id_to_token(idx)
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except:
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return symbol
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def convert_tokens_to_string(self, tokens):
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return self.gpt2_tokenizer.convert_tokens_to_string(tokens)
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