553 lines
21 KiB
Python
553 lines
21 KiB
Python
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"""Tokenization classes for IQuestCoder."""
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import os
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from shutil import copyfile
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from typing import Any, Dict, List, Optional, Tuple, Union
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import sentencepiece as spm
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from transformers.tokenization_utils import AddedToken, PreTrainedTokenizer
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from transformers.utils import logging
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logger = logging.get_logger(__name__)
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VOCAB_FILES_NAMES = {"vocab_file": "tokenizer.model"}
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PRETRAINED_VOCAB_FILES_MAP = {
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"vocab_file": {},
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"tokenizer_file": {},
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}
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PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES = {}
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class IQuestCoderTokenizer(PreTrainedTokenizer):
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vocab_files_names = VOCAB_FILES_NAMES
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pretrained_vocab_files_map = PRETRAINED_VOCAB_FILES_MAP
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max_model_input_sizes = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
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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="<s>",
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eos_token="</s>",
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pad_token=None,
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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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add_prefix_space=False,
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legacy=None,
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use_default_system_prompt=False,
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chat_template=None,
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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, lstrip=False, rstrip=False) if isinstance(bos_token, str) else bos_token
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eos_token = AddedToken(eos_token, lstrip=False, rstrip=False) if isinstance(eos_token, str) else eos_token
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unk_token = AddedToken(unk_token, lstrip=False, rstrip=False) if isinstance(unk_token, str) else unk_token
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pad_token = AddedToken(pad_token, lstrip=False, rstrip=False) if isinstance(pad_token, str) else pad_token
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# Legacy behavior handling
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if legacy is None:
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logger.warning_once(
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f"You are using the default legacy behaviour of the {self.__class__.__name__}. This is"
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" expected, and simply means that the `legacy` (previous) behavior will be used so nothing changes for you."
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" If you want to use the new behaviour, set `legacy=False`. This should only be set if you understand what it"
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" means, and thoroughly read the reason why this was added as explained in"
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" https://github.com/huggingface/transformers/pull/24565"
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)
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legacy = True
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self.legacy = legacy
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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.add_prefix_space = add_prefix_space
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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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add_prefix_space=add_prefix_space,
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legacy=legacy,
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use_default_system_prompt=use_default_system_prompt,
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chat_template=chat_template,
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**kwargs,
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)
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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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return state
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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.Load(self.vocab_file)
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@property
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def vocab_size(self) -> int:
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"""Returns the vocabulary size."""
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return self.sp_model.get_piece_size()
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def get_vocab(self) -> Dict[str, int]:
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"""Returns the vocabulary as a dictionary of token to index."""
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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: str) -> List[str]:
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"""
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Tokenize a string.
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Args:
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text (`str`): The text to tokenize.
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Returns:
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`List[str]`: The list of tokens.
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"""
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if self.add_prefix_space:
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text = " " + text
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if self.legacy:
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return self.sp_model.encode(text, out_type=str)
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# Non-legacy behavior: handle special tokens properly
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return self.sp_model.encode(text, out_type=str)
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def _convert_token_to_id(self, token: str) -> int:
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"""Converts a token (str) to an id using the vocab."""
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return self.sp_model.piece_to_id(token)
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def _convert_id_to_token(self, index: int) -> str:
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"""Converts an index (integer) to 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 convert_tokens_to_string(self, tokens: List[str]) -> str:
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"""
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Converts a sequence of tokens (strings) to a single string.
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This method handles special tokens separately to ensure they are not
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decoded using the SentencePiece model.
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Args:
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tokens (`List[str]`): The list of tokens to convert.
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Returns:
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`str`: The decoded string.
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"""
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current_sub_tokens = []
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out_string = ""
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prev_is_special = False
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for i, token in enumerate(tokens):
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# make sure that special tokens are not decoded using sentencepiece model
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if token in self.all_special_tokens:
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if not prev_is_special and i != 0:
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out_string += " "
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out_string += self.sp_model.decode(current_sub_tokens) + token
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prev_is_special = True
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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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prev_is_special = False
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out_string += self.sp_model.decode(current_sub_tokens)
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return out_string
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def save_vocabulary(self, save_directory: str, 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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filename_prefix (`str`, *optional*):
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An optional prefix to add to the named of the saved files.
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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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def build_inputs_with_special_tokens(
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self,
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token_ids_0: List[int],
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token_ids_1: Optional[List[int]] = None
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) -> List[int]:
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"""
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Build model inputs from a sequence or a pair of sequences for sequence classification tasks by concatenating
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and adding special tokens.
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An IQuestCoder sequence has the following format:
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- single sequence: `<s> X </s>` (if add_eos_token is True) or `<s> X` (default)
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- pair of sequences: `<s> A </s> <s> B </s>` (if add_eos_token is True) or `<s> A <s> B` (default)
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Args:
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token_ids_0 (`List[int]`):
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List of IDs to which the special tokens will be added.
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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 input IDs with the appropriate special tokens.
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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 = 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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def get_special_tokens_mask(
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self,
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token_ids_0: List[int],
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token_ids_1: Optional[List[int]] = None,
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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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def create_token_type_ids_from_sequences(
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self,
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token_ids_0: List[int],
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token_ids_1: Optional[List[int]] = None
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) -> List[int]:
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"""
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Create a mask from the two sequences passed to be used in a sequence-pair classification task.
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An IQuestCoder 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`, this method 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 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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@property
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def default_chat_template(self) -> str:
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"""
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Returns the default chat template for IQuestCoder.
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This template formats conversations with system, user, and assistant roles.
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"""
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return DEFAULT_CHAT_TEMPLATE
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def apply_chat_template(
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self,
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conversation: Union[List[Dict[str, str]], "Conversation"],
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chat_template: Optional[str] = None,
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add_generation_prompt: bool = False,
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tokenize: bool = True,
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padding: bool = False,
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truncation: bool = False,
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max_length: Optional[int] = None,
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return_tensors: Optional[str] = None,
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return_dict: bool = False,
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**tokenizer_kwargs,
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):
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"""
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Apply a chat template to format a conversation.
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Args:
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conversation (`List[Dict[str, str]]` or `Conversation`):
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A list of dicts with "role" and "content" keys, representing the conversation history.
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chat_template (`str`, *optional*):
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A Jinja template to use for formatting. If not provided, the tokenizer's default will be used.
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add_generation_prompt (`bool`, *optional*, defaults to `False`):
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Whether to add a generation prompt at the end for the assistant to continue.
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tokenize (`bool`, *optional*, defaults to `True`):
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Whether to tokenize the output. If `False`, returns a string.
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padding (`bool`, *optional*, defaults to `False`):
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Whether to pad sequences.
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truncation (`bool`, *optional*, defaults to `False`):
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Whether to truncate sequences.
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max_length (`int`, *optional*):
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Maximum length of the output.
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return_tensors (`str`, *optional*):
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The type of tensors to return ("pt", "tf", "np", or None).
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return_dict (`bool`, *optional*, defaults to `False`):
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Whether to return a dictionary with additional information.
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**tokenizer_kwargs:
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Additional keyword arguments passed to the tokenizer.
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|
|
|
||
|
|
Returns:
|
||
|
|
`Union[str, List[int], BatchEncoding]`: The formatted (and optionally tokenized) conversation.
|
||
|
|
|
||
|
|
Example:
|
||
|
|
```python
|
||
|
|
>>> tokenizer = IQuestCoderTokenizer.from_pretrained("path/to/model")
|
||
|
|
>>> conversation = [
|
||
|
|
... {"role": "system", "content": "You are a helpful assistant."},
|
||
|
|
... {"role": "user", "content": "Hello!"},
|
||
|
|
... {"role": "assistant", "content": "Hi there! How can I help you today?"},
|
||
|
|
... {"role": "user", "content": "What's the weather like?"},
|
||
|
|
... ]
|
||
|
|
>>> tokenizer.apply_chat_template(conversation, add_generation_prompt=True, tokenize=False)
|
||
|
|
'<|system|>\\nYou are a helpful assistant.\\n</|system|><|user|>\\nHello!\\n</|user|>...'
|
||
|
|
```
|
||
|
|
"""
|
||
|
|
# Use parent class implementation with our template
|
||
|
|
return super().apply_chat_template(
|
||
|
|
conversation,
|
||
|
|
chat_template=chat_template,
|
||
|
|
add_generation_prompt=add_generation_prompt,
|
||
|
|
tokenize=tokenize,
|
||
|
|
padding=padding,
|
||
|
|
truncation=truncation,
|
||
|
|
max_length=max_length,
|
||
|
|
return_tensors=return_tensors,
|
||
|
|
return_dict=return_dict,
|
||
|
|
**tokenizer_kwargs,
|
||
|
|
)
|
||
|
|
|
||
|
|
|
||
|
|
# Try to import and create Fast tokenizer version
|
||
|
|
try:
|
||
|
|
from transformers import PreTrainedTokenizerFast
|
||
|
|
from tokenizers import Tokenizer, decoders, models, normalizers, pre_tokenizers, processors
|
||
|
|
|
||
|
|
class IQuestCoderTokenizerFast(PreTrainedTokenizerFast):
|
||
|
|
"""
|
||
|
|
Construct a "fast" IQuestCoder tokenizer (backed by HuggingFace's *tokenizers* library).
|
||
|
|
|
||
|
|
This is a fast implementation of [`IQuestCoderTokenizer`] using the 🤗 Tokenizers library.
|
||
|
|
|
||
|
|
Args:
|
||
|
|
vocab_file (`str`, *optional*):
|
||
|
|
Path to the vocabulary file (SentencePiece model).
|
||
|
|
tokenizer_file (`str`, *optional*):
|
||
|
|
Path to a tokenizer JSON file.
|
||
|
|
unk_token (`str`, *optional*, defaults to `"<unk>"`):
|
||
|
|
The unknown token.
|
||
|
|
bos_token (`str`, *optional*, defaults to `"<s>"`):
|
||
|
|
The beginning of sequence token.
|
||
|
|
eos_token (`str`, *optional*, defaults to `"</s>"`):
|
||
|
|
The end of sequence token.
|
||
|
|
pad_token (`str`, *optional*):
|
||
|
|
The token used for padding.
|
||
|
|
add_bos_token (`bool`, *optional*, defaults to `True`):
|
||
|
|
Whether to add a BOS token at the start of sequences.
|
||
|
|
add_eos_token (`bool`, *optional*, defaults to `False`):
|
||
|
|
Whether to add an EOS token at the end of sequences.
|
||
|
|
add_prefix_space (`bool`, *optional*, defaults to `False`):
|
||
|
|
Whether to add an initial space to the input.
|
||
|
|
use_default_system_prompt (`bool`, *optional*, defaults to `False`):
|
||
|
|
Whether to use the default system prompt.
|
||
|
|
chat_template (`str`, *optional*):
|
||
|
|
A Jinja template for formatting conversations.
|
||
|
|
|
||
|
|
Example:
|
||
|
|
```python
|
||
|
|
>>> from tokenization_iquestcoder import IQuestCoderTokenizerFast
|
||
|
|
|
||
|
|
>>> tokenizer = IQuestCoderTokenizerFast.from_pretrained("path/to/model")
|
||
|
|
>>> tokenizer.encode("Hello, world!")
|
||
|
|
[1, 15043, 29892, 3186, 29991]
|
||
|
|
```
|
||
|
|
"""
|
||
|
|
|
||
|
|
vocab_files_names = VOCAB_FILES_NAMES
|
||
|
|
pretrained_vocab_files_map = PRETRAINED_VOCAB_FILES_MAP
|
||
|
|
max_model_input_sizes = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
|
||
|
|
model_input_names = ["input_ids", "attention_mask"]
|
||
|
|
slow_tokenizer_class = IQuestCoderTokenizer
|
||
|
|
|
||
|
|
def __init__(
|
||
|
|
self,
|
||
|
|
vocab_file=None,
|
||
|
|
tokenizer_file=None,
|
||
|
|
unk_token="<unk>",
|
||
|
|
bos_token="<s>",
|
||
|
|
eos_token="</s>",
|
||
|
|
pad_token=None,
|
||
|
|
add_bos_token=True,
|
||
|
|
add_eos_token=False,
|
||
|
|
add_prefix_space=False,
|
||
|
|
use_default_system_prompt=False,
|
||
|
|
chat_template=None,
|
||
|
|
**kwargs,
|
||
|
|
):
|
||
|
|
self.add_bos_token = add_bos_token
|
||
|
|
self.add_eos_token = add_eos_token
|
||
|
|
self.add_prefix_space = add_prefix_space
|
||
|
|
self.use_default_system_prompt = use_default_system_prompt
|
||
|
|
|
||
|
|
if chat_template is None:
|
||
|
|
chat_template = DEFAULT_CHAT_TEMPLATE
|
||
|
|
|
||
|
|
super().__init__(
|
||
|
|
vocab_file=vocab_file,
|
||
|
|
tokenizer_file=tokenizer_file,
|
||
|
|
unk_token=unk_token,
|
||
|
|
bos_token=bos_token,
|
||
|
|
eos_token=eos_token,
|
||
|
|
pad_token=pad_token,
|
||
|
|
add_bos_token=add_bos_token,
|
||
|
|
add_eos_token=add_eos_token,
|
||
|
|
add_prefix_space=add_prefix_space,
|
||
|
|
use_default_system_prompt=use_default_system_prompt,
|
||
|
|
chat_template=chat_template,
|
||
|
|
**kwargs,
|
||
|
|
)
|
||
|
|
|
||
|
|
@property
|
||
|
|
def can_save_slow_tokenizer(self) -> bool:
|
||
|
|
return os.path.isfile(self.vocab_file) if self.vocab_file else False
|
||
|
|
|
||
|
|
@property
|
||
|
|
def default_chat_template(self) -> str:
|
||
|
|
"""Returns the default chat template."""
|
||
|
|
return DEFAULT_CHAT_TEMPLATE
|
||
|
|
|
||
|
|
def build_inputs_with_special_tokens(
|
||
|
|
self,
|
||
|
|
token_ids_0: List[int],
|
||
|
|
token_ids_1: Optional[List[int]] = None
|
||
|
|
) -> List[int]:
|
||
|
|
"""Build model inputs with special tokens."""
|
||
|
|
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
|
||
|
|
|
||
|
|
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 special tokens mask."""
|
||
|
|
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
|
||
|
|
)
|
||
|
|
|
||
|
|
def create_token_type_ids_from_sequences(
|
||
|
|
self,
|
||
|
|
token_ids_0: List[int],
|
||
|
|
token_ids_1: Optional[List[int]] = None
|
||
|
|
) -> List[int]:
|
||
|
|
"""Create token type IDs from sequences."""
|
||
|
|
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
|
||
|
|
|
||
|
|
except ImportError:
|
||
|
|
# tokenizers library not available, Fast tokenizer not supported
|
||
|
|
IQuestCoderTokenizerFast = None
|
||
|
|
logger.info(
|
||
|
|
"The `tokenizers` library is not installed. "
|
||
|
|
"IQuestCoderTokenizerFast will not be available. "
|
||
|
|
"Install it with `pip install tokenizers`."
|
||
|
|
)
|
||
|
|
|