327 lines
14 KiB
Python
327 lines
14 KiB
Python
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# coding=utf-8
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# Copyright 2024 The HuggingFace Inc. team. 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 Gemma."""
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import os
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from shutil import copyfile
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from typing import TYPE_CHECKING, Any, Dict, List, Optional, Tuple
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import sentencepiece as spm
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from ...tokenization_utils import AddedToken, PreTrainedTokenizer
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from ...utils import logging
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if TYPE_CHECKING:
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pass
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logger = logging.get_logger(__name__)
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VOCAB_FILES_NAMES = {"vocab_file": "tokenizer.model"}
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SPIECE_UNDERLINE = "▁"
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class GemmaTokenizer(PreTrainedTokenizer):
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"""
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Construct a Gemma tokenizer. Based on byte-level Byte-Pair-Encoding. The default padding token is unset as there is
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no padding token in the original model.
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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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unk_token (`str` or `tokenizers.AddedToken`, *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` or `tokenizers.AddedToken`, *optional*, defaults to `"<bos>"`):
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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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eos_token (`str` or `tokenizers.AddedToken`, *optional*, defaults to `"<eos>"`):
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The end of sequence token.
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pad_token (`str` or `tokenizers.AddedToken`, *optional*, defaults to `"<pad>"`):
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A special token used to make arrays of tokens the same size for batching purpose. Will then be ignored by
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attention mechanisms or loss computation.
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sp_model_kwargs (`Dict[str, Any]`, `Optional`, *optional*):
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Will be passed to the `SentencePieceProcessor.__init__()` method. The [Python wrapper for
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SentencePiece](https://github.com/google/sentencepiece/tree/master/python) can be used, among other things,
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to set:
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- `enable_sampling`: Enable subword regularization.
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- `nbest_size`: Sampling parameters for unigram. Invalid for BPE-Dropout.
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- `nbest_size = {0,1}`: No sampling is performed.
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- `nbest_size > 1`: samples from the nbest_size results.
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- `nbest_size < 0`: assuming that nbest_size is infinite and samples from the all hypothesis (lattice)
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using forward-filtering-and-backward-sampling algorithm.
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- `alpha`: Smoothing parameter for unigram sampling, and dropout probability of merge operations for
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BPE-dropout.
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add_bos_token (`bool`, *optional*, defaults to `True`):
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Whether or not to add an `bos_token` at the start of sequences.
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add_eos_token (`bool`, *optional*, defaults to `False`):
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Whether or not to add an `eos_token` at the end of sequences.
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clean_up_tokenization_spaces (`bool`, *optional*, defaults to `False`):
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Whether or not to cleanup spaces after decoding, cleanup consists in removing potential artifacts like
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extra spaces.
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use_default_system_prompt (`bool`, *optional*, defaults to `False`):
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Whether or not the default system prompt for Gemma should be used.
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spaces_between_special_tokens (`bool`, *optional*, defaults to `False`):
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Whether or not to add spaces between special tokens.
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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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unk_token="<unk>",
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bos_token="<bos>",
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eos_token="<eos>",
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pad_token="<pad>",
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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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clean_up_tokenization_spaces=False,
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use_default_system_prompt=False,
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spaces_between_special_tokens=False,
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**kwargs,
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):
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self.sp_model_kwargs = {} if sp_model_kwargs is None else sp_model_kwargs
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bos_token = AddedToken(bos_token, normalized=False, special=True) if isinstance(bos_token, str) else bos_token
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eos_token = AddedToken(eos_token, normalized=False, special=True) if isinstance(eos_token, str) else eos_token
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unk_token = AddedToken(unk_token, normalized=False, special=True) if isinstance(unk_token, str) else unk_token
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pad_token = AddedToken(pad_token, normalized=False, special=True) if isinstance(pad_token, str) else pad_token
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self.vocab_file = vocab_file
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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.use_default_system_prompt = use_default_system_prompt
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self.sp_model = spm.SentencePieceProcessor(**self.sp_model_kwargs)
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self.sp_model.Load(vocab_file)
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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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unk_token=unk_token,
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pad_token=pad_token,
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add_bos_token=add_bos_token,
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add_eos_token=add_eos_token,
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sp_model_kwargs=self.sp_model_kwargs,
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clean_up_tokenization_spaces=clean_up_tokenization_spaces,
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use_default_system_prompt=use_default_system_prompt,
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spaces_between_special_tokens=spaces_between_special_tokens,
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**kwargs,
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)
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# Copied from transformers.models.llama.tokenization_llama.LlamaTokenizer.__getstate__
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def __getstate__(self):
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state = self.__dict__.copy()
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state["sp_model"] = None
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state["sp_model_proto"] = self.sp_model.serialized_model_proto()
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return state
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# Copied from transformers.models.llama.tokenization_llama.LlamaTokenizer.__setstate__
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def __setstate__(self, d):
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self.__dict__ = d
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self.sp_model = spm.SentencePieceProcessor(**self.sp_model_kwargs)
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self.sp_model.LoadFromSerializedProto(self.sp_model_proto)
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@property
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# Copied from transformers.models.llama.tokenization_llama.LlamaTokenizer.vocab_size
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def vocab_size(self):
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"""Returns vocab size"""
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return self.sp_model.get_piece_size()
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# Copied from transformers.models.llama.tokenization_llama.LlamaTokenizer.get_vocab
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def get_vocab(self):
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"""Returns vocab as a dict"""
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vocab = {self.convert_ids_to_tokens(i): i for i in range(self.vocab_size)}
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vocab.update(self.added_tokens_encoder)
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return vocab
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def _tokenize(self, text, **kwargs):
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"""
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Returns a tokenized string. The Gemma tokenizer never adds a prefix space.
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"""
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return self.sp_model.encode(text, out_type=str)
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# Copied from transformers.models.llama.tokenization_llama.LlamaTokenizer._convert_token_to_id
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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.sp_model.piece_to_id(token)
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# Copied from transformers.models.llama.tokenization_llama.LlamaTokenizer._convert_id_to_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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token = self.sp_model.IdToPiece(index)
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return token
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def _decode(
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self,
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token_ids: List[int],
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skip_special_tokens: bool = False,
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spaces_between_special_tokens: bool = False,
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**kwargs,
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) -> str:
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sub_texts = []
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current_sub_text = []
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for ids in token_ids:
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if skip_special_tokens and ids in self.all_special_ids:
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continue
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if ids in self._added_tokens_decoder:
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if current_sub_text:
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sub_texts.append(self.sp_model.decode(current_sub_text))
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sub_texts.append(self._added_tokens_decoder[ids].content)
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current_sub_text = []
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else:
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current_sub_text.append(ids)
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if current_sub_text:
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sub_texts.append(self.sp_model.decode(current_sub_text))
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if spaces_between_special_tokens:
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sub_texts = " ".join(sub_texts)
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else:
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sub_texts = "".join(sub_texts)
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return sub_texts
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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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current_sub_tokens = []
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out_string = ""
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for token in tokens:
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# make sure that special tokens are not decoded using sentencepiece model
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if token in self._added_tokens_encoder:
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out_string += self.sp_model.decode(current_sub_tokens) + token
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current_sub_tokens = []
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else:
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current_sub_tokens.append(token)
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out_string += self.sp_model.decode(current_sub_tokens)
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return out_string
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# Copied from transformers.models.llama.tokenization_llama.LlamaTokenizer.save_vocabulary
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def save_vocabulary(self, save_directory, filename_prefix: Optional[str] = None) -> Tuple[str]:
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"""
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Save the vocabulary and special tokens file to a directory.
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Args:
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save_directory (`str`):
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The directory in which to save the vocabulary.
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Returns:
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`Tuple(str)`: Paths to the files saved.
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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) and os.path.isfile(self.vocab_file):
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copyfile(self.vocab_file, out_vocab_file)
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elif not os.path.isfile(self.vocab_file):
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with open(out_vocab_file, "wb") as fi:
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content_spiece_model = self.sp_model.serialized_model_proto()
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fi.write(content_spiece_model)
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return (out_vocab_file,)
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# Copied from transformers.models.llama.tokenization_llama.LlamaTokenizer.build_inputs_with_special_tokens
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def build_inputs_with_special_tokens(self, token_ids_0, token_ids_1=None):
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bos_token_id = [self.bos_token_id] if self.add_bos_token else []
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eos_token_id = [self.eos_token_id] if self.add_eos_token else []
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output = bos_token_id + token_ids_0 + eos_token_id
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if token_ids_1 is not None:
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output = output + bos_token_id + token_ids_1 + eos_token_id
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return output
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# Copied from transformers.models.llama.tokenization_llama.LlamaTokenizer.get_special_tokens_mask
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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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bos_token_id = [1] if self.add_bos_token else []
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eos_token_id = [1] if self.add_eos_token else []
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if token_ids_1 is None:
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return bos_token_id + ([0] * len(token_ids_0)) + eos_token_id
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return (
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bos_token_id
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+ ([0] * len(token_ids_0))
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+ eos_token_id
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+ bos_token_id
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+ ([0] * len(token_ids_1))
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+ eos_token_id
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)
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# Copied from transformers.models.llama.tokenization_llama.LlamaTokenizer.create_token_type_ids_from_sequences
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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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Creates a mask from the two sequences passed to be used in a sequence-pair classification task. An ALBERT
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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, 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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bos_token_id = [self.bos_token_id] if self.add_bos_token else []
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eos_token_id = [self.eos_token_id] if self.add_eos_token else []
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output = [0] * len(bos_token_id + token_ids_0 + eos_token_id)
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if token_ids_1 is not None:
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output += [1] * len(bos_token_id + token_ids_1 + eos_token_id)
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return output
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