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