Spaces:
Running
Running
File size: 6,518 Bytes
08ccc8e |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 |
# https://github.com/huggingface/transformers/blob/main/examples/flax/language-modeling/t5_tokenizer_model.py
import argparse
import json
import os
import sys
from typing import Iterator, List, Union
import datasets
from datasets import load_dataset
from tokenizers import (
AddedToken,
Regex,
Tokenizer,
decoders,
normalizers,
pre_tokenizers,
trainers,
)
from tokenizers.implementations.base_tokenizer import BaseTokenizer
from tokenizers.models import Unigram
from tokenizers.processors import TemplateProcessing
from transformers import AutoTokenizer, T5Config
sys.path.append(os.path.abspath(os.path.join(os.path.dirname(__file__), "..")))
from utils import seed_everything
seed_everything(seed=42)
script_dir = os.path.abspath(os.path.dirname(__file__))
project_root = os.path.abspath(os.path.join(script_dir, ".."))
data_dir = os.path.join(project_root, "data")
class SentencePieceUnigramTokenizer(BaseTokenizer):
"""
This class is a copy of `DeDLOC's tokenizer implementation <https://github.com/yandex-research/DeDLOC/blob/main/sahajbert/tokenizer/tokenizer_model.py>`__ .
Custom SentencePiece Unigram Tokenizer with NMT, NKFC, spaces and lower-casing characters normalization
Represents the Unigram algorithm, with the pretokenization used by SentencePiece
"""
def __init__(
self,
replacement: str = "▁",
add_prefix_space: bool = True,
unk_token: Union[str, AddedToken] = "<unk>",
eos_token: Union[str, AddedToken] = "</s>",
pad_token: Union[str, AddedToken] = "<pad>",
):
self.special_tokens = {
"pad": {"id": 0, "token": pad_token},
"eos": {"id": 1, "token": eos_token},
"unk": {"id": 2, "token": unk_token},
}
self.special_tokens_list = [None] * len(self.special_tokens)
for token_dict in self.special_tokens.values():
self.special_tokens_list[token_dict["id"]] = token_dict["token"]
tokenizer = Tokenizer(Unigram())
tokenizer.normalizer = normalizers.Sequence(
[
normalizers.Nmt(),
normalizers.NFKC(),
normalizers.Replace(Regex(" {2,}"), " "),
# normalizers.Lowercase(),
]
)
tokenizer.pre_tokenizer = pre_tokenizers.Sequence(
[
pre_tokenizers.Metaspace(
replacement=replacement, add_prefix_space=add_prefix_space
),
pre_tokenizers.Digits(individual_digits=True),
pre_tokenizers.Punctuation(),
]
)
tokenizer.decoder = decoders.Metaspace(
replacement=replacement, add_prefix_space=add_prefix_space
)
tokenizer.post_processor = TemplateProcessing(
single=f"$A {self.special_tokens['eos']['token']}",
special_tokens=[
(self.special_tokens["eos"]["token"], self.special_tokens["eos"]["id"])
],
)
parameters = {
"model": "SentencePieceUnigram",
"replacement": replacement,
"add_prefix_space": add_prefix_space,
}
super().__init__(tokenizer, parameters)
def train(
self,
files: Union[str, List[str]],
vocab_size: int = 8000,
show_progress: bool = True,
):
"""Train the model using the given files"""
trainer = trainers.UnigramTrainer(
vocab_size=vocab_size,
special_tokens=self.special_tokens_list,
show_progress=show_progress,
)
if isinstance(files, str):
files = [files]
self._tokenizer.train(files, trainer=trainer)
self.add_unk_id()
def train_from_iterator(
self,
iterator: Union[Iterator[str], Iterator[Iterator[str]]],
vocab_size: int = 8000,
show_progress: bool = True,
):
"""Train the model using the given iterator"""
trainer = trainers.UnigramTrainer(
vocab_size=vocab_size,
special_tokens=self.special_tokens_list,
show_progress=show_progress,
)
self._tokenizer.train_from_iterator(iterator, trainer=trainer)
self.add_unk_id()
def add_unk_id(self):
tokenizer_json = json.loads(self._tokenizer.to_str())
tokenizer_json["model"]["unk_id"] = self.special_tokens["unk"]["id"]
self._tokenizer = Tokenizer.from_str(json.dumps(tokenizer_json))
def create_normal_tokenizer(dataset, model_name):
if isinstance(dataset, datasets.dataset_dict.DatasetDict):
training_corpus = (
dataset["train"][i : i + 1000]["smiles"]
for i in range(0, len(dataset), 1000)
)
else:
training_corpus = (
dataset[i : i + 1000]["smiles"] for i in range(0, len(dataset), 1000)
)
if "deberta" in model_name:
# Train tokenizer
old_tokenizer = AutoTokenizer.from_pretrained(model_name)
tokenizer = old_tokenizer.train_new_from_iterator(training_corpus, 1000)
elif "t5" in model_name:
tokenizer = SentencePieceUnigramTokenizer(
unk_token="<unk>", eos_token="</s>", pad_token="<pad>"
)
tokenizer.train_from_iterator(training_corpus, 1000)
return tokenizer
def create_character_level_tokenizer(dataset, model_name):
df = dataset["train"].to_pandas()
df["smiles"] = [" ".join(list(i)) for i in df["smiles"]]
dataset = datasets.Dataset.from_pandas(df)
tokenizer = create_normal_tokenizer(dataset, model_name)
return tokenizer
def parse_args():
parser = argparse.ArgumentParser()
parser.add_argument(
"--use_character_level_tokenizer",
action="store_true",
default=False,
required=False,
)
return parser.parse_args()
CFG = parse_args()
# Initialize a dataset
dataset = load_dataset(
"csv", data_files=os.path.join(data_dir, "ZINC-canonicalized.csv")
)
if CFG.use_character_level_tokenizer:
tokenizer = create_character_level_tokenizer(dataset, "t5")
else:
tokenizer = create_normal_tokenizer(dataset, "t5")
# Save files to disk
tokenizer.save(os.path.join(script_dir, "CompoundT5/CompoundT5-config/tokenizer.json"))
config = T5Config.from_pretrained(
"google/t5-v1_1-base", vocab_size=tokenizer.get_vocab_size()
)
config.save_pretrained(os.path.join(script_dir, "CompoundT5/CompoundT5-config/"))
|