358 lines
13 KiB
Python
358 lines
13 KiB
Python
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# coding=utf-8
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# Copyright 2022 The HuggingFace Team and Microsoft Research AI4Science. 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 BioGPT."""
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import json
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import os
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from typing import List, Optional, Tuple
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from ...tokenization_utils import PreTrainedTokenizer
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from ...utils import logging
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logger = logging.get_logger(__name__)
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VOCAB_FILES_NAMES = {
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"vocab_file": "vocab.json",
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"merges_file": "merges.txt",
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}
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def get_pairs(word):
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"""
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Return set of symbol pairs in a word. word is represented as tuple of symbols (symbols being variable-length
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strings)
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"""
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pairs = set()
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prev_char = word[0]
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for char in word[1:]:
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pairs.add((prev_char, char))
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prev_char = char
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return pairs
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class BioGptTokenizer(PreTrainedTokenizer):
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"""
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Construct an FAIRSEQ Transformer tokenizer. Moses tokenization followed by Byte-Pair Encoding.
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This tokenizer inherits from [`PreTrainedTokenizer`] which contains most of the main methods. Users should refer to
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this superclass for more information regarding those methods.
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Args:
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vocab_file (`str`):
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Path to the vocabulary file.
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merges_file (`str`):
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Merges file.
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unk_token (`str`, *optional*, defaults to `"<unk>"`):
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The unknown token. A token that is not in the vocabulary cannot be converted to an ID and is set to be this
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token instead.
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bos_token (`str`, *optional*, defaults to `"<s>"`):
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The beginning of sequence token that was used during pretraining. Can be used a sequence classifier token.
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<Tip>
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When building a sequence using special tokens, this is not the token that is used for the beginning of
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sequence. The token used is the `cls_token`.
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</Tip>
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eos_token (`str`, *optional*, defaults to `"</s>"`):
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The end of sequence token.
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<Tip>
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When building a sequence using special tokens, this is not the token that is used for the end of sequence.
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The token used is the `sep_token`.
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</Tip>
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sep_token (`str`, *optional*, defaults to `"</s>"`):
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The separator token, which is used when building a sequence from multiple sequences, e.g. two sequences for
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sequence classification or for a text and a question for question answering. It is also used as the last
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token of a sequence built with special tokens.
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pad_token (`str`, *optional*, defaults to `"<pad>"`):
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The token used for padding, for example when batching sequences of different lengths.
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"""
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vocab_files_names = VOCAB_FILES_NAMES
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model_input_names = ["input_ids", "attention_mask"]
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def __init__(
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self,
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vocab_file,
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merges_file,
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unk_token="<unk>",
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bos_token="<s>",
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eos_token="</s>",
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sep_token="</s>",
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pad_token="<pad>",
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**kwargs,
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):
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try:
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import sacremoses
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except ImportError:
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raise ImportError(
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"You need to install sacremoses to use BioGptTokenizer. "
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"See https://pypi.org/project/sacremoses/ for installation."
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)
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self.lang = "en"
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self.sm = sacremoses
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# cache of sm.MosesTokenizer instance
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self.cache_moses_tokenizer = {}
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self.cache_moses_detokenizer = {}
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""" Initialisation"""
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with open(vocab_file, encoding="utf-8") as vocab_handle:
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self.encoder = json.load(vocab_handle)
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self.decoder = {v: k for k, v in self.encoder.items()}
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with open(merges_file, encoding="utf-8") as merges_handle:
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merges = merges_handle.read().split("\n")[:-1]
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merges = [tuple(merge.split()[:2]) for merge in merges]
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self.bpe_ranks = dict(zip(merges, range(len(merges))))
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self.cache = {}
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super().__init__(
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bos_token=bos_token,
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eos_token=eos_token,
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sep_token=sep_token,
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unk_token=unk_token,
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pad_token=pad_token,
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**kwargs,
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)
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@property
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def vocab_size(self):
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"""Returns vocab size"""
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return len(self.encoder)
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def get_vocab(self):
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return dict(self.encoder, **self.added_tokens_encoder)
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def moses_tokenize(self, text, lang):
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if lang not in self.cache_moses_tokenizer:
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moses_tokenizer = self.sm.MosesTokenizer(lang=lang)
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self.cache_moses_tokenizer[lang] = moses_tokenizer
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return self.cache_moses_tokenizer[lang].tokenize(
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text, aggressive_dash_splits=True, return_str=False, escape=True
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)
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def moses_detokenize(self, tokens, lang):
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if lang not in self.cache_moses_detokenizer:
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moses_detokenizer = self.sm.MosesDetokenizer(lang=lang)
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self.cache_moses_detokenizer[lang] = moses_detokenizer
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return self.cache_moses_detokenizer[lang].detokenize(tokens)
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def bpe(self, token):
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word = tuple(token[:-1]) + (token[-1] + "</w>",)
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if token in self.cache:
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return self.cache[token]
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pairs = get_pairs(word)
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if not pairs:
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return token + "</w>"
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while True:
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bigram = min(pairs, key=lambda pair: self.bpe_ranks.get(pair, float("inf")))
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if bigram not in self.bpe_ranks:
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break
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first, second = bigram
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new_word = []
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i = 0
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while i < len(word):
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try:
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j = word.index(first, i)
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except ValueError:
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new_word.extend(word[i:])
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break
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else:
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new_word.extend(word[i:j])
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i = j
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if word[i] == first and i < len(word) - 1 and word[i + 1] == second:
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new_word.append(first + second)
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i += 2
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else:
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new_word.append(word[i])
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i += 1
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new_word = tuple(new_word)
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word = new_word
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if len(word) == 1:
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break
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else:
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pairs = get_pairs(word)
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word = " ".join(word)
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if word == "\n </w>":
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word = "\n</w>"
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self.cache[token] = word
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return word
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def _tokenize(self, text, bypass_tokenizer=False):
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"""Returns a tokenized string."""
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if bypass_tokenizer:
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text = text.split()
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else:
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text = self.moses_tokenize(text, self.lang)
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split_tokens = []
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for token in text:
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if token:
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split_tokens.extend(list(self.bpe(token).split(" ")))
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return split_tokens
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def _convert_token_to_id(self, token):
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"""Converts a token (str) in an id using the vocab."""
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return self.encoder.get(token, self.encoder.get(self.unk_token))
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def _convert_id_to_token(self, index):
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"""Converts an index (integer) in a token (str) using the vocab."""
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return self.decoder.get(index, self.unk_token)
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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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# remove BPE
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tokens = [t.replace(" ", "").replace("</w>", " ") for t in tokens]
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tokens = "".join(tokens).split()
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# detokenize
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text = self.moses_detokenize(tokens, self.lang)
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return text
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def build_inputs_with_special_tokens(
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self, token_ids_0: List[int], token_ids_1: Optional[List[int]] = None
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) -> List[int]:
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"""
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Build model inputs from a sequence or a pair of sequence for sequence classification tasks by concatenating and
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adding special tokens. A BioGPT sequence has the following format:
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- single sequence: `</s> X `
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- pair of sequences: `</s> A </s> B `
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Args:
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token_ids_0 (`List[int]`):
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List of IDs to which the special tokens will be added.
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token_ids_1 (`List[int]`, *optional*):
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Optional second list of IDs for sequence pairs.
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Returns:
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`List[int]`: List of [input IDs](../glossary#input-ids) with the appropriate special tokens.
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"""
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if token_ids_1 is None:
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return [self.sep_token_id] + token_ids_0
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sep = [self.sep_token_id]
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return sep + token_ids_0 + sep + token_ids_1
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def get_special_tokens_mask(
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self, token_ids_0: List[int], token_ids_1: Optional[List[int]] = None, already_has_special_tokens: bool = False
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) -> List[int]:
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"""
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Retrieve sequence ids from a token list that has no special tokens added. This method is called when adding
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special tokens using the tokenizer `prepare_for_model` method.
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Args:
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token_ids_0 (`List[int]`):
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List of IDs.
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token_ids_1 (`List[int]`, *optional*):
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Optional second list of IDs for sequence pairs.
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already_has_special_tokens (`bool`, *optional*, defaults to `False`):
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Whether or not the token list is already formatted with special tokens for the model.
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Returns:
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`List[int]`: A list of integers in the range [0, 1]: 1 for a special token, 0 for a sequence token.
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"""
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if already_has_special_tokens:
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return super().get_special_tokens_mask(
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token_ids_0=token_ids_0, token_ids_1=token_ids_1, already_has_special_tokens=True
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)
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# no bos used in fairseq
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if token_ids_1 is not None:
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return [1] + ([0] * len(token_ids_0)) + [1] + ([0] * len(token_ids_1))
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return [1] + ([0] * len(token_ids_0))
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def create_token_type_ids_from_sequences(
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self, token_ids_0: List[int], token_ids_1: Optional[List[int]] = None
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) -> List[int]:
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"""
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Create a mask from the two sequences passed to be used in a sequence-pair classification task. A FAIRSEQ
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Transformer sequence pair mask has the following format:
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```
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0 0 0 0 0 0 0 0 0 0 0 1 1 1 1 1 1 1 1 1
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| first sequence | second sequence |
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```
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If `token_ids_1` is `None`, this method only returns the first portion of the mask (0s).
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Args:
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token_ids_0 (`List[int]`):
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List of IDs.
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token_ids_1 (`List[int]`, *optional*):
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Optional second list of IDs for sequence pairs.
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Returns:
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`List[int]`: List of [token type IDs](../glossary#token-type-ids) according to the given sequence(s).
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"""
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sep = [self.sep_token_id]
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# no bos used in fairseq
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if token_ids_1 is None:
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return len(token_ids_0 + sep) * [0]
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return len(token_ids_0 + sep) * [0] + len(token_ids_1 + sep) * [1]
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def save_vocabulary(self, save_directory: str, filename_prefix: Optional[str] = None) -> Tuple[str]:
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if not os.path.isdir(save_directory):
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logger.error(f"Vocabulary path ({save_directory}) should be a directory")
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return
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vocab_file = os.path.join(
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save_directory, (filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["vocab_file"]
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)
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merge_file = os.path.join(
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save_directory, (filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["merges_file"]
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)
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with open(vocab_file, "w", encoding="utf-8") as f:
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f.write(json.dumps(self.encoder, indent=2, sort_keys=True, ensure_ascii=False) + "\n")
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index = 0
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with open(merge_file, "w", encoding="utf-8") as writer:
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for bpe_tokens, token_index in sorted(self.bpe_ranks.items(), key=lambda kv: kv[1]):
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if index != token_index:
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logger.warning(
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f"Saving vocabulary to {merge_file}: BPE merge indices are not consecutive."
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" Please check that the tokenizer is not corrupted!"
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)
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index = token_index
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writer.write(" ".join(bpe_tokens) + "\n")
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index += 1
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return vocab_file, merge_file
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def __getstate__(self):
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state = self.__dict__.copy()
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state["sm"] = None
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return state
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def __setstate__(self, d):
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self.__dict__ = d
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try:
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import sacremoses
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except ImportError:
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raise ImportError(
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"You need to install sacremoses to use XLMTokenizer. "
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"See https://pypi.org/project/sacremoses/ for installation."
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)
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self.sm = sacremoses
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