Qwen-7B / tokenization_qwen.py

update tokenization.py

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	# Copyright (c) Alibaba Cloud.
	#
	# This source code is licensed under the license found in the
	# LICENSE file in the root directory of this source tree.

	"""Tokenization classes for QWen."""

	import base64
	import logging
	import os
	import unicodedata
	from typing import Collection, Dict, List, Set, Tuple, Union

	import tiktoken
	from transformers import PreTrainedTokenizer, AddedToken

	logger = logging.getLogger(__name__)


	VOCAB_FILES_NAMES = {"vocab_file": "qwen.tiktoken"}

	PAT_STR = r"""(?i:'s\|'t\|'re\|'ve\|'m\|'ll\|'d)\|[^\r\n\p{L}\p{N}]?\p{L}+\|\p{N}\| ?[^\s\p{L}\p{N}]+[\r\n]\|\s[\r\n]+\|\s+(?!\S)\|\s+"""
	ENDOFTEXT = "<\|endoftext\|>"
	IMSTART = "<\|im_start\|>"
	IMEND = "<\|im_end\|>"
	# as the default behavior is changed to allow special tokens in
	# regular texts, the surface forms of special tokens need to be
	# as different as possible to minimize the impact
	EXTRAS = tuple((f"<\|extra_{i}\|>" for i in range(205)))
	# changed to use actual index to avoid misconfiguration with vocabulary expansion
	SPECIAL_START_ID = 151643
	SPECIAL_TOKENS = tuple(
	enumerate(
	(
	(
	ENDOFTEXT,
	IMSTART,
	IMEND,
	)
	+ EXTRAS
	),
	start=SPECIAL_START_ID,
	)
	)
	SPECIAL_TOKENS_SET = set(t for i, t in SPECIAL_TOKENS)


	def _load_tiktoken_bpe(tiktoken_bpe_file: str) -> Dict[bytes, int]:
	with open(tiktoken_bpe_file, "rb") as f:
	contents = f.read()
	return {
	base64.b64decode(token): int(rank)
	for token, rank in (line.split() for line in contents.splitlines() if line)
	}


	class QWenTokenizer(PreTrainedTokenizer):
	"""QWen tokenizer."""

	vocab_files_names = VOCAB_FILES_NAMES

	def __init__(
	self,
	vocab_file,
	errors="replace",
	extra_vocab_file=None,
	**kwargs,
	):
	super().__init__(**kwargs)

	# how to handle errors in decoding UTF-8 byte sequences
	# use ignore if you are in streaming inference
	self.errors = errors

	self.mergeable_ranks = _load_tiktoken_bpe(vocab_file) # type: Dict[bytes, int]
	self.special_tokens = {
	token: index
	for index, token in SPECIAL_TOKENS
	}

	# try load extra vocab from file
	if extra_vocab_file is not None:
	used_ids = set(self.mergeable_ranks.values()) \| set(self.special_tokens.values())
	extra_mergeable_ranks = _load_tiktoken_bpe(extra_vocab_file)
	for token, index in extra_mergeable_ranks.items():
	if token in self.mergeable_ranks:
	logger.info(f"extra token {token} exists, skipping")
	continue
	if index in used_ids:
	logger.info(f'the index {index} for extra token {token} exists, skipping')
	continue
	self.mergeable_ranks[token] = index
	# the index may be sparse after this, but don't worry tiktoken.Encoding will handle this

	enc = tiktoken.Encoding(
	"Qwen",
	pat_str=PAT_STR,
	mergeable_ranks=self.mergeable_ranks,
	special_tokens=self.special_tokens,
	)
	assert (
	len(self.mergeable_ranks) + len(self.special_tokens) == enc.n_vocab
	), f"{len(self.mergeable_ranks) + len(self.special_tokens)} != {enc.n_vocab} in encoding"

	self.decoder = {
	v: k for k, v in self.mergeable_ranks.items()
	} # type: dict[int, bytes\|str]
	self.decoder.update({v: k for k, v in self.special_tokens.items()})

	self.tokenizer = enc # type: tiktoken.Encoding

	self.eod_id = self.tokenizer.eot_token
	self.im_start_id = self.special_tokens[IMSTART]
	self.im_end_id = self.special_tokens[IMEND]

	def __getstate__(self):
	# for pickle lovers
	state = self.__dict__.copy()
	del state["tokenizer"]
	return state

	def __setstate__(self, state):
	# tokenizer is not python native; don't pass it; rebuild it
	self.__dict__.update(state)
	enc = tiktoken.Encoding(
	"Qwen",
	pat_str=PAT_STR,
	mergeable_ranks=self.mergeable_ranks,
	special_tokens=self.special_tokens,
	)
	self.tokenizer = enc

	def __len__(self) -> int:
	return self.tokenizer.n_vocab

	def get_vocab(self) -> Dict[bytes, int]:
	return self.mergeable_ranks

	def convert_tokens_to_ids(
	self, tokens: Union[bytes, str, List[Union[bytes, str]]]
	) -> List[int]:
	ids = []
	if isinstance(tokens, (str, bytes)):
	if tokens in self.special_tokens:
	return self.special_tokens[tokens]
	else:
	return self.mergeable_ranks.get(tokens)
	for token in tokens:
	if token in self.special_tokens:
	ids.append(self.special_tokens[token])
	else:
	ids.append(self.mergeable_ranks.get(token))
	return ids

	def _add_tokens(
	self,
	new_tokens: Union[List[str], List[AddedToken]],
	special_tokens: bool = False,
	) -> int:
	if not special_tokens and new_tokens:
	raise ValueError("Adding regular tokens is not supported")
	for token in new_tokens:
	surface_form = token.content if isinstance(token, AddedToken) else token
	if surface_form not in SPECIAL_TOKENS_SET:
	raise ValueError("Adding unknown special tokens is not supported")
	return 0

	def save_vocabulary(self, save_directory: str, **kwargs) -> Tuple[str]:
	"""
	Save only the vocabulary of the tokenizer (vocabulary).

	Returns:
	`Tuple(str)`: Paths to the files saved.
	"""
	file_path = os.path.join(save_directory, "qwen.tiktoken")
	with open(file_path, "w", encoding="utf8") as w:
	for k, v in self.mergeable_ranks.items():
	line = base64.b64encode(k).decode("utf8") + " " + str(v) + "\n"
	w.write(line)
	return (file_path,)

	def tokenize(
	self,
	text: str,
	allowed_special: Union[Set, str] = "all",
	disallowed_special: Union[Collection, str] = (),
	**kwargs,
	) -> List[Union[bytes, str]]:
	"""
	Converts a string in a sequence of tokens.

	Args:
	text (`str`):
	The sequence to be encoded.
	allowed_special (`Literal["all"]` or `set`):
	The surface forms of the tokens to be encoded as special tokens in regular texts.
	Default to "all".
	disallowed_special (`Literal["all"]` or `Collection`):
	The surface forms of the tokens that should not be in regular texts and trigger errors.
	Default to an empty tuple.

	kwargs (additional keyword arguments, optional):
	Will be passed to the underlying model specific encode method.

	Returns:
	`List[bytes\|str]`: The list of tokens.
	"""
	tokens = []
	text = unicodedata.normalize("NFC", text)

	# this implementation takes a detour: text -> token id -> token surface forms
	for t in self.tokenizer.encode(
	text, allowed_special=allowed_special, disallowed_special=disallowed_special
	):
	tokens.append(self.decoder[t])
	return tokens

	def convert_tokens_to_string(self, tokens: List[Union[bytes, str]]) -> str:
	"""
	Converts a sequence of tokens in a single string.
	"""
	text = ""
	temp = b""
	for t in tokens:
	if isinstance(t, str):
	if temp:
	text += temp.decode("utf-8", errors=self.errors)
	temp = b""
	text += t
	elif isinstance(t, bytes):
	temp += t
	else:
	raise TypeError("token should only be of type types or str")
	if temp:
	text += temp.decode("utf-8", errors=self.errors)
	return text

	@property
	def vocab_size(self):
	return self.tokenizer.n_vocab

	def _convert_id_to_token(self, index: int) -> Union[bytes, str]:
	"""Converts an id to a token, special tokens included"""
	if index in self.decoder:
	return self.decoder[index]
	raise ValueError("unknown ids")

	def _convert_token_to_id(self, token: Union[bytes, str]) -> int:
	"""Converts a token to an id using the vocab, special tokens included"""
	if token in self.special_tokens:
	return self.special_tokens[token]
	if token in self.mergeable_ranks:
	return self.mergeable_ranks[token]
	raise ValueError("unknown token")

	def _tokenize(self, text: str, **kwargs):
	"""
	Converts a string in a sequence of tokens (string), using the tokenizer. Split in words for word-based
	vocabulary or sub-words for sub-word-based vocabularies (BPE/SentencePieces/WordPieces).

	Do NOT take care of added tokens.
	"""
	raise NotImplementedError

	def _decode(
	self,
	token_ids: Union[int, List[int]],
	skip_special_tokens: bool = False,
	errors: str = None,
	**kwargs,
	) -> str:
	if isinstance(token_ids, int):
	token_ids = [token_ids]
	if skip_special_tokens:
	token_ids = [i for i in token_ids if i < self.eod_id]
	return self.tokenizer.decode(token_ids, errors=errors or self.errors)