215 lines
7.5 KiB
Python
215 lines
7.5 KiB
Python
# coding=utf-8
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# Copyright (c) The InternLM team and The HuggingFace Inc. team. All rights reserved.
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#
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# This code is based on transformers/src/transformers/models/llama/tokenization_llama_fast.py
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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 Fast class for InternLM."""
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import os
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from shutil import copyfile
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from typing import Any, Dict, Optional, Tuple
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from tokenizers import processors, decoders, Tokenizer, normalizers
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from tokenizers.models import BPE
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from transformers.tokenization_utils_fast import PreTrainedTokenizerFast
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from transformers.utils import logging
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from transformers.convert_slow_tokenizer import (
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SLOW_TO_FAST_CONVERTERS,
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SpmConverter,
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SentencePieceExtractor,
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)
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from .tokenization_internlm2 import InternLM2Tokenizer
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logger = logging.get_logger(__name__)
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VOCAB_FILES_NAMES = {"vocab_file": "./tokenizer.model"}
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# Modified from transformers.convert_slow_tokenizer.LlamaConverter
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class InternLM2Converter(SpmConverter):
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handle_byte_fallback = True
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def vocab(self, proto):
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vocab = [
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("<unk>", 0.0),
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("<s>", 0.0),
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("</s>", 0.0),
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]
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vocab += [(piece.piece, piece.score) for piece in proto.pieces[3:]]
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return vocab
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def unk_id(self, proto):
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unk_id = 0
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return unk_id
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def decoder(self, replacement, add_prefix_space):
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return decoders.Sequence(
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[
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decoders.Replace("▁", " "),
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decoders.ByteFallback(),
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decoders.Fuse(),
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decoders.Strip(content=" ", left=1),
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]
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)
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def tokenizer(self, proto):
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model_type = proto.trainer_spec.model_type
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vocab_scores = self.vocab(proto)
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# special tokens
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added_tokens = self.original_tokenizer.added_tokens_decoder
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for i in range(len(vocab_scores)):
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piece, score = vocab_scores[i]
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if i in added_tokens:
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vocab_scores[i] = (added_tokens[i].content, score)
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if model_type == 1:
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raise RuntimeError("InternLM2 is supposed to be a BPE model!")
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elif model_type == 2:
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_, merges = SentencePieceExtractor(self.original_tokenizer.vocab_file).extract(vocab_scores)
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bpe_vocab = {word: i for i, (word, _score) in enumerate(vocab_scores)}
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tokenizer = Tokenizer(
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BPE(bpe_vocab, merges, unk_token=proto.trainer_spec.unk_piece, fuse_unk=True, byte_fallback=True)
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)
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tokenizer.add_special_tokens(
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[ added_token for index, added_token in added_tokens.items()]
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)
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else:
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raise Exception(
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"You're trying to run a `Unigram` model but you're file was trained with a different algorithm"
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)
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return tokenizer
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def normalizer(self, proto):
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normalizers_list = []
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if proto.normalizer_spec.add_dummy_prefix:
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normalizers_list.append(normalizers.Prepend(prepend="▁"))
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normalizers_list.append(normalizers.Replace(pattern=" ", content="▁"))
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return normalizers.Sequence(normalizers_list)
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def pre_tokenizer(self, replacement, add_prefix_space):
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return None
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SLOW_TO_FAST_CONVERTERS["InternLM2Tokenizer"] = InternLM2Converter
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# Modified from transformers.model.llama.tokenization_llama_fast.LlamaTokenizerFast -> InternLM2TokenizerFast
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class InternLM2TokenizerFast(PreTrainedTokenizerFast):
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vocab_files_names = VOCAB_FILES_NAMES
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slow_tokenizer_class = InternLM2Tokenizer
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padding_side = "left"
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model_input_names = ["input_ids", "attention_mask"]
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_auto_class = "AutoTokenizer"
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def __init__(
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self,
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vocab_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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pad_token="</s>",
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sp_model_kwargs: Optional[Dict[str, Any]] = None,
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add_bos_token=True,
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add_eos_token=False,
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decode_with_prefix_space=False,
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clean_up_tokenization_spaces=False,
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**kwargs,
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):
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super().__init__(
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vocab_file=vocab_file,
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unk_token=unk_token,
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bos_token=bos_token,
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eos_token=eos_token,
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pad_token=pad_token,
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sp_model_kwargs=sp_model_kwargs,
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add_bos_token=add_bos_token,
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add_eos_token=add_eos_token,
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decode_with_prefix_space=decode_with_prefix_space,
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clean_up_tokenization_spaces=clean_up_tokenization_spaces,
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**kwargs,
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)
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self._add_bos_token = add_bos_token
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self._add_eos_token = add_eos_token
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self.update_post_processor()
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self.vocab_file = vocab_file
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@property
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def can_save_slow_tokenizer(self) -> bool:
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return os.path.isfile(self.vocab_file) if self.vocab_file else False
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def update_post_processor(self):
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"""
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Updates the underlying post processor with the current `bos_token` and `eos_token`.
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"""
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bos = self.bos_token
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bos_token_id = self.bos_token_id
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if bos is None and self.add_bos_token:
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raise ValueError("add_bos_token = True but bos_token = None")
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eos = self.eos_token
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eos_token_id = self.eos_token_id
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if eos is None and self.add_eos_token:
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raise ValueError("add_eos_token = True but eos_token = None")
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single = f"{(bos+':0 ') if self.add_bos_token else ''}$A:0{(' '+eos+':0') if self.add_eos_token else ''}"
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pair = f"{single}{(' '+bos+':1') if self.add_bos_token else ''} $B:1{(' '+eos+':1') if self.add_eos_token else ''}"
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special_tokens = []
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if self.add_bos_token:
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special_tokens.append((bos, bos_token_id))
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if self.add_eos_token:
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special_tokens.append((eos, eos_token_id))
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self._tokenizer.post_processor = processors.TemplateProcessing(
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single=single, pair=pair, special_tokens=special_tokens
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)
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@property
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def add_eos_token(self):
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return self._add_eos_token
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@property
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def add_bos_token(self):
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return self._add_bos_token
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@add_eos_token.setter
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def add_eos_token(self, value):
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self._add_eos_token = value
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self.update_post_processor()
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@add_bos_token.setter
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def add_bos_token(self, value):
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self._add_bos_token = value
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self.update_post_processor()
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def save_vocabulary(self, save_directory: str, filename_prefix: Optional[str] = None) -> Tuple[str]:
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if not self.can_save_slow_tokenizer:
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raise ValueError(
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"Your fast tokenizer does not have the necessary information to save the vocabulary for a slow "
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"tokenizer."
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)
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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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out_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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if os.path.abspath(self.vocab_file) != os.path.abspath(out_vocab_file):
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copyfile(self.vocab_file, out_vocab_file)
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return (out_vocab_file,)
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