scripts/tokenizers/add_special_tokens_to_sentencepiece.py

# Copyright (c) 2022, 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
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.

import logging
import sys
from argparse import ArgumentParser

import sentencepiece as spm

try:
    import sentencepiece_model_pb2 as spt
except (ImportError, ModuleNotFoundError):
    raise Exception("Ensure that sentencepiece_model_pb2.py has been generated from the protoc compiler")


"""Utility to add special tokens to existing sentencepiece models.

Generate sentencepiece_model_pb2.py in the directory of this script before running
To generate run `protoc --python_out=<path_to_NeMo>/scripts/tokenizers/ sentencepiece_model.proto`
inside the src folder in sentencepiece repo
Refer: https://github.com/google/sentencepiece/issues/121

Usage:
python edit_spt_model.py \
    --input_file <input_model_dir> \
    --output_file <output_model_dir> \
    --tokens <space separated special tokens> 

Example:
python edit_spt_model.py \
    --input_file test.model \
    --output_file test.model \
    --tokens [CLS] [SEP]
"""


def edit_spt_model():
    parser = ArgumentParser()
    parser.add_argument(
        "--input_file", type=str, required=True, help="Path to sentencepiece model file",
    )
    parser.add_argument(
        "--output_file", type=str, required=True, help="Path to sentencepiece model file",
    )
    parser.add_argument(
        "--tokens", type=str, nargs='+', required=True, help="Special tokens to add to tokenizer",
    )
    parser.add_argument(
        "--is_userdefined", action="store_true", help="When set, the new tokens are set as user_defined tokens",
    )
    args = parser.parse_args()

    token_type = 3
    if args.is_userdefined:
        token_type = 4

    model = spt.ModelProto()
    model.ParseFromString(open(args.input_file, 'rb').read())

    for token in args.tokens:
        piece = model.SentencePiece(piece=token, score=0.0, type=token_type)
        if piece in model.pieces:
            logging.error(f"Special Token '{token}' already exists in the input model!")
            sys.exit(1)
        model.pieces.append(piece)

    sp = spm.SentencePieceProcessor()
    try:
        sp.LoadFromSerializedProto(model.SerializeToString())
        for token in args.tokens:
            id = sp.piece_to_id(token)
            logging.info(f"Created token '{token}' at ID {id}")
        logging.info(f"New tokenizer vocab size: {sp.get_piece_size()}")
    except:
        logging.error("Could not appropriately configure new tokenizer. Verify if the special tokens already exist.")
        sys.exit(1)

    with open(args.output_file, 'wb') as outf:
        outf.write(model.SerializeToString())

    logging.info(f"Created new tokenizer at: {args.output_file}")


if __name__ == "__main__":
    logging.basicConfig(level=logging.INFO, format="%(levelname)s: %(message)s")

    edit_spt_model()