midm-bitext-S-7B-inst-v1 / midm_bitext_tokenization.py

taehyeong_kim

Fix - potential bug in checkpoint files #5

ffe82b7 8 months ago

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12.8 kB

	# coding=utf-8
	# 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 class for model Midm_bitext_tonkenizer."""
	import os
	import re
	import warnings
	from shutil import copyfile
	from typing import Any, Dict, List, Optional, Tuple

	import sentencepiece as spm

	from transformers.tokenization_utils import PreTrainedTokenizer
	from transformers.utils import logging


	logger = logging.get_logger(__name__)

	VOCAB_FILES_NAMES = {"vocab_file": "midm_bitext_tokenizer.model"}

	PRETRAINED_VOCAB_FILES_MAP = {}

	PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES = {}


	class Midm_bitext_Tokenizer(PreTrainedTokenizer):
	"""
	Construct a Midm bitext tonkenizer. Based on [SentencePiece](https://github.com/google/sentencepiece).

	This tokenizer inherits from [`PreTrainedTokenizer`] which contains most of the main methods. Users should refer to
	this superclass for more information regarding those methods.

	Args:
	vocab_file (`str`):
	[SentencePiece](https://github.com/google/sentencepiece) file (generally has a .spm extension) that
	contains the vocabulary necessary to instantiate a tokenizer.
	eos_token (`str`, optional, defaults to `"</s>"`):
	The end of sequence token.

	<Tip>

	When building a sequence using special tokens, this is not the token that is used for the end of sequence.
	The token used is the `sep_token`.

	</Tip>

	unk_token (`str`, 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.
	pad_token (`str`, optional, defaults to `"<pad>"`):
	The token used for padding, for example when batching sequences of different lengths.
	extra_ids (`int`, optional, defaults to 100):
	Add a number of extra ids added to the end of the vocabulary for use as sentinels. These tokens are
	accessible as "<extra_id_{%d}>" where "{%d}" is a number between 0 and extra_ids-1. Extra tokens are
	indexed from the end of the vocabulary up to beginning.
	additional_special_tokens (`List[str]`, optional):
	Additional special tokens used by the tokenizer.
	sp_model_kwargs (`dict`, 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.

	Attributes:
	sp_model (`SentencePieceProcessor`):
	The SentencePiece processor that is used for every conversion (string, tokens and IDs).
	"""

	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"]

	def __init__(
	self,
	vocab_file,
	eos_token="</s>",
	unk_token="<unk>",
	pad_token="<pad>",
	extra_ids=100,
	additional_special_tokens=None,
	sp_model_kwargs: Optional[Dict[str, Any]] = None,
	**kwargs
	) -> None:
	# Add extra_ids to the special token list
	if extra_ids > 0 and additional_special_tokens is None:
	additional_special_tokens = [f"<extra_id_{i}>" for i in range(extra_ids)]
	elif extra_ids > 0 and additional_special_tokens is not None:
	# Check that we have the right number of extra_id special tokens
	extra_tokens = len(set(filter(lambda x: bool("extra_id" in str(x)), additional_special_tokens)))
	if extra_tokens != extra_ids:
	raise ValueError(
	f"Both extra_ids ({extra_ids}) and additional_special_tokens ({additional_special_tokens}) are provided to Midm_bitext_Tonkenizer. "
	"In this case the additional_special_tokens must include the extra_ids tokens"
	)

	self.sp_model_kwargs = {} if sp_model_kwargs is None else sp_model_kwargs

	# custom special tokens
	# convert \n, \t in input text -> <[!newline]>, <[!tab]>
	self.newline_token = "<[!newline]>"
	self.tab_token = "<[!tab]>"

	self.vocab_file = vocab_file
	self._extra_ids = extra_ids

	self.sp_model = spm.SentencePieceProcessor(**self.sp_model_kwargs)
	self.sp_model.Load(vocab_file)
	super().__init__(
	eos_token=eos_token,
	unk_token=unk_token,
	pad_token=pad_token,
	extra_ids=extra_ids,
	additional_special_tokens=additional_special_tokens,
	sp_model_kwargs=self.sp_model_kwargs,
	**kwargs,
	)



	@property
	def vocab_size(self):
	return self.sp_model.get_piece_size() + self._extra_ids

	def get_vocab(self):
	vocab = {self.convert_ids_to_tokens(i): i for i in range(self.vocab_size)}
	vocab.update(self.added_tokens_encoder)
	return vocab

	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
	)

	# normal case: some special tokens
	if token_ids_1 is None:
	return ([0] * len(token_ids_0)) + [1]
	return ([0] * len(token_ids_0)) + [1] + ([0] * len(token_ids_1)) + [1]

	def _add_eos_if_not_present(self, token_ids: List[int]) -> List[int]:
	"""Do not add eos again if user already added it."""
	if len(token_ids) > 0 and token_ids[-1] == self.eos_token_id:
	warnings.warn(
	f"This sequence already has {self.eos_token}. In future versions this behavior may lead to duplicated eos tokens being added."
	)
	return token_ids
	else:
	return token_ids + [self.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 a mask from the two sequences passed to be used in a sequence-pair classification task. Midm does not make
	use of token type ids, therefore a list of zeros is returned.

	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 zeros.
	"""
	eos = [self.eos_token_id]

	if token_ids_1 is None:
	return len(token_ids_0 + eos) * [0]
	return len(token_ids_0 + eos + token_ids_1 + eos) * [0]

	def build_inputs_with_special_tokens(
	self, token_ids_0: List[int], token_ids_1: Optional[List[int]] = None
	) -> List[int]:
	"""
	Build model inputs from a sequence or a pair of sequence for sequence classification tasks by concatenating and
	adding special tokens. A sequence has the following format:

	- single sequence: `X </s>`
	- pair of sequences: `A </s> B </s>`

	Args:
	token_ids_0 (`List[int]`):
	List of IDs to which the special tokens will be added.
	token_ids_1 (`List[int]`, optional):
	Optional second list of IDs for sequence pairs.

	Returns:
	`List[int]`: List of [input IDs](../glossary#input-ids) with the appropriate special tokens.
	"""
	token_ids_0 = self._add_eos_if_not_present(token_ids_0)
	if token_ids_1 is None:
	return token_ids_0
	else:
	token_ids_1 = self._add_eos_if_not_present(token_ids_1)
	return token_ids_0 + token_ids_1

	def __getstate__(self):
	state = self.__dict__.copy()
	state["sp_model"] = None
	return state

	def __setstate__(self, d):
	self.__dict__ = d

	# for backward compatibility
	if not hasattr(self, "sp_model_kwargs"):
	self.sp_model_kwargs = {}

	self.sp_model = spm.SentencePieceProcessor(**self.sp_model_kwargs)
	self.sp_model.Load(self.vocab_file)

	def _tokenize(self, text: str) -> List[str]:
	"""Take as input a string and return a list of strings (tokens) for words/sub-words"""
	text = text.replace("\n", self.newline_token)
	text = text.replace("\t", self.tab_token)

	return self.sp_model.encode(text, out_type=str)

	def _convert_token_to_id(self, token):
	"""Converts a token (str) in an id using the vocab."""
	if token.startswith("<extra_id_"):
	match = re.match(r"<extra_id_(\d+)>", token)
	num = int(match.group(1))
	return self.vocab_size - num - 1
	return self.sp_model.piece_to_id(token)

	def _convert_id_to_token(self, index):
	"""Converts an index (integer) in a token (str) using the vocab."""
	if index < self.sp_model.get_piece_size():
	token = self.sp_model.IdToPiece(index)
	else:
	token = f"<extra_id_{self.vocab_size - 1 - index}>"
	return token

	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.all_special_tokens:
	out_string += self.sp_model.decode_pieces(current_sub_tokens) + token + " "
	current_sub_tokens = []
	else:
	current_sub_tokens.append(token)
	out_string += self.sp_model.decode_pieces(current_sub_tokens)

	out_string.replace(self.newline_token, "\n")
	out_string.replace(self.tab_token, "\t")

	return out_string.strip()

	def save_vocabulary(self, save_directory: str, filename_prefix: Optional[str] = None) -> Tuple[str]:
	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,)