tokenization_internlm.py

# coding=utf-8
# Copyright 2023 Shanghai Artificial Intelligence Laboratory and the 
# HuggingFace Inc. team. 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.

"""Tokenization classes for IntermLM."""
from transformers.tokenization_utils import LlamaTokenizer


class InternLMTokenizer(LlamaTokenizer):

    @property
    def no_prefix_space_tokens(self):
        if self._no_prefix_space_tokens is None:
            vocab = self.convert_ids_to_tokens(list(range(self.vocab_size)))
            self._no_prefix_space_tokens = {i for i, tok in enumerate(vocab) if not tok.startswith("▁")}
        return self._no_prefix_space_tokens

    def _maybe_add_prefix_space(self, tokens, decoded):
        if tokens and tokens[0] not in self.no_prefix_space_tokens:
            return " " + decoded
        else:
            return decoded

    def convert_tokens_to_string(self, tokens):
        """Converts a sequence of tokens (string) in a single string."""
        current_sub_tokens = []
        out_string = ""
        prev_is_special = False
        for token in tokens:
            # make sure that special tokens are not decoded using sentencepiece model
            if token in self.all_special_tokens:
                if not prev_is_special:
                    out_string += " "
                out_string += self.sp_model.decode(current_sub_tokens) + token
                prev_is_special = True
                current_sub_tokens = []
            else:
                current_sub_tokens.append(token)
                prev_is_special = False
        out_string += self.sp_model.decode(current_sub_tokens)
        out_string = self.clean_up_tokenization(out_string)
        out_string = self._maybe_add_prefix_space(tokens=tokens, decoded=out_string)
        return out_string[1:]