bitnet_b1_58-3B / tokenization_bitnet.py

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	# coding=utf-8
	# Copyright 2022 EleutherAI and the HuggingFace Inc. team. All rights reserved.
	#
	# This code is based on EleutherAI's GPT-NeoX library and the GPT-NeoX
	# and OPT implementations in this library. It has been modified from its
	# original forms to accommodate minor architectural differences compared
	# to GPT-NeoX and OPT used by the Meta AI team that trained the model.
	#
	# 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 LLaMA."""
	import os
	from shutil import copyfile
	from typing import TYPE_CHECKING, Any, Dict, List, Optional, Tuple

	import sentencepiece as spm

	from transformers.convert_slow_tokenizer import import_protobuf
	from transformers.tokenization_utils import AddedToken, PreTrainedTokenizer
	from transformers.utils import logging


	if TYPE_CHECKING:
	from transformers.tokenization_utils_base import TextInput

	logger = logging.get_logger(__name__)

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

	PRETRAINED_VOCAB_FILES_MAP = {
	"vocab_file": {
	"hf-internal-testing/llama-tokenizer": "https://huggingface.co/hf-internal-testing/llama-tokenizer/resolve/main/tokenizer.model",
	},
	"tokenizer_file": {
	"hf-internal-testing/llama-tokenizer": "https://huggingface.co/hf-internal-testing/llama-tokenizer/resolve/main/tokenizer_config.json",
	},
	}
	PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES = {
	"hf-internal-testing/llama-tokenizer": 2048,
	}
	SPIECE_UNDERLINE = "▁"

	B_INST, E_INST = "[INST]", "[/INST]"
	B_SYS, E_SYS = "<<SYS>>\n", "\n<</SYS>>\n\n"

	# fmt: off
	DEFAULT_SYSTEM_PROMPT = """You are a helpful, respectful and honest assistant. Always answer as helpfully as possible, while being safe. Your \
	answers should not include any harmful, unethical, racist, sexist, toxic, dangerous, or illegal content. Please ensure\
	that your responses are socially unbiased and positive in nature.

	If a question does not make any sense, or is not factually coherent, explain why instead of answering something not \
	correct. If you don't know the answer to a question, please don't share false information."""
	# fmt: on


	class BitnetTokenizer(PreTrainedTokenizer):
	"""
	Construct a Bitnet 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 `"<s>"`):
	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 `"</s>"`):
	The end of sequence token.
	pad_token (`str` or `tokenizers.AddedToken`, optional):
	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 Bitnet should be used.
	spaces_between_special_tokens (`bool`, optional, defaults to `False`):
	Whether or not to add spaces between special tokens.
	legacy (`bool`, optional):
	Whether or not the `legacy` behavior of the tokenizer should be used. Legacy is before the merge of #24622
	and #25224 which includes fixes to properly handle tokens that appear after special tokens. A simple
	example:

	- `legacy=True`:
	```python
	>>> from transformers import T5Tokenizer

	>>> tokenizer = T5Tokenizer.from_pretrained("google-t5/t5-base", legacy=True)
	>>> tokenizer.encode("Hello <extra_id_0>.")
	[8774, 32099, 3, 5, 1]
	```
	- `legacy=False`:
	```python
	>>> from transformers import T5Tokenizer

	>>> tokenizer = T5Tokenizer.from_pretrained("google-t5/t5-base", legacy=False)
	>>> tokenizer.encode("Hello <extra_id_0>.") # the extra space `[3]` is no longer here
	[8774, 32099, 5, 1]
	```
	Checkout the [pull request](https://github.com/huggingface/transformers/pull/24565) for more details.
	add_prefix_space (`bool`, optional, defaults to `True`):
	Whether or not to add an initial space to the input. This allows to treat the leading word just as any
	other word.

	"""

	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,
	unk_token="<unk>",
	bos_token="<s>",
	eos_token="</s>",
	pad_token=None,
	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,
	legacy=None,
	add_prefix_space=True,
	**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

	if legacy is None:
	logger.warning_once(
	f"You are using the default legacy behaviour of the {self.__class__}. This is"
	" expected, and simply means that the `legacy` (previous) behavior will be used so nothing changes for you."
	" If you want to use the new behaviour, set `legacy=False`. This should only be set if you understand what it"
	" means, and thoroughly read the reason why this was added as explained in"
	" https://github.com/huggingface/transformers/pull/24565"
	)
	legacy = True

	self.legacy = legacy
	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 = self.get_spm_processor(kwargs.pop("from_slow", False))
	self.add_prefix_space = add_prefix_space

	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,
	legacy=legacy,
	add_prefix_space=add_prefix_space,
	**kwargs,
	)

	@property
	def unk_token_length(self):
	return len(self.sp_model.encode(str(self.unk_token)))

	# Copied from transformers.models.t5.tokenization_t5.T5Tokenizer.get_spm_processor
	def get_spm_processor(self, from_slow=False):
	tokenizer = spm.SentencePieceProcessor(**self.sp_model_kwargs)
	if self.legacy or from_slow: # no dependency on protobuf
	tokenizer.Load(self.vocab_file)
	return tokenizer

	with open(self.vocab_file, "rb") as f:
	sp_model = f.read()
	model_pb2 = import_protobuf(f"The new behaviour of {self.__class__.__name__} (with `self.legacy = False`)")
	model = model_pb2.ModelProto.FromString(sp_model)
	normalizer_spec = model_pb2.NormalizerSpec()
	normalizer_spec.add_dummy_prefix = False
	model.normalizer_spec.MergeFrom(normalizer_spec)
	sp_model = model.SerializeToString()
	tokenizer.LoadFromSerializedProto(sp_model)
	return tokenizer

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

	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
	def vocab_size(self):
	"""Returns vocab size"""
	return self.sp_model.get_piece_size()

	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

	# Copied from transformers.models.t5.tokenization_t5.T5Tokenizer.tokenize
	def tokenize(self, text: "TextInput", **kwargs) -> List[str]:
	"""
	Converts a string to a list of tokens. If `self.legacy` is set to `False`, a prefix token is added unless the
	first token is special.
	"""
	if self.legacy or len(text) == 0:
	return super().tokenize(text, **kwargs)

	text = text.replace(SPIECE_UNDERLINE, " ")
	if self.add_prefix_space:
	text = SPIECE_UNDERLINE + text

	tokens = super().tokenize(text, **kwargs)

	if len(tokens) > 1 and tokens[0] == SPIECE_UNDERLINE and tokens[1] in self.all_special_tokens:
	tokens = tokens[1:]
	return tokens

	# Copied from transformers.models.t5.tokenization_t5.T5Tokenizer._tokenize
	def _tokenize(self, text, **kwargs):
	"""
	Returns a tokenized string.

	We de-activated the `add_dummy_prefix` option, thus the sentencepiece internals will always strip any
	SPIECE_UNDERLINE. For example: `self.sp_model.encode(f"{SPIECE_UNDERLINE}Hey", out_type = str)` will give
	`['H', 'e', 'y']` instead of `['▁He', 'y']`. Thus we always encode `f"{unk_token}text"` and strip the
	`unk_token`. Here is an example with `unk_token = "<unk>"` and `unk_token_length = 4`.
	`self.tokenizer.sp_model.encode("<unk> Hey", out_type = str)[4:]`.
	"""
	tokens = self.sp_model.encode(text, out_type=str)
	if self.legacy or not text.startswith((SPIECE_UNDERLINE, " ")):
	return tokens

	# 1. Encode string + prefix ex: "<unk> Hey"
	tokens = self.sp_model.encode(self.unk_token + text, out_type=str)
	# 2. Remove self.unk_token from ['<','unk','>', '▁Hey']
	return tokens[self.unk_token_length :] if len(tokens) >= self.unk_token_length else tokens

	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)

	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 convert_tokens_to_string(self, tokens):
	"""Converts a sequence of tokens (string) in a single string."""
	# since we manually add the prefix space, we have to remove it when decoding
	if tokens[0].startswith(SPIECE_UNDERLINE) and self.add_prefix_space:
	tokens[0] = tokens[0][1:]

	current_sub_tokens = []
	out_string = ""
	prev_is_special = False
	for i, token in enumerate(tokens):
	# make sure that special tokens are not decoded using sentencepiece model
	if token in self.all_special_tokens:
	if not prev_is_special and i != 0 and self.legacy:
	out_string += " "
	out_string += self.sp_model.decode(current_sub_tokens) + token
	prev_is_special = True
	current_sub_tokens = []
	else:
	current_sub_tokens.append(token)
	prev_is_special = False
	out_string += self.sp_model.decode(current_sub_tokens)
	return out_string

	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,)

	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

	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
	)

	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

	@property
	def default_chat_template(self):
	"""
	LLaMA uses [INST] and [/INST] to indicate user messages, and <<SYS>> and <</SYS>> to indicate system messages.
	Assistant messages do not have special tokens, because LLaMA chat models are generally trained with strict
	user/assistant/user/assistant message ordering, and so assistant messages can be identified from the ordering
	rather than needing special tokens. The system message is partly 'embedded' in the first user message, which
	results in an unusual token ordering when it is present. This template should definitely be changed if you wish
	to fine-tune a model with more flexible role ordering!

	The output should look something like:

	<bos>[INST] B_SYS SystemPrompt E_SYS Prompt [/INST] Answer <eos><bos>[INST] Prompt [/INST] Answer <eos>
	<bos>[INST] Prompt [/INST]

	The reference for this chat template is [this code
	snippet](https://github.com/facebookresearch/llama/blob/556949fdfb72da27c2f4a40b7f0e4cf0b8153a28/llama/generation.py#L320-L362)
	in the original repository.
	"""
	logger.warning_once(
	"\nNo chat template is defined for this tokenizer - using the default template "
	f"for the {self.__class__.__name__} class. If the default is not appropriate for "
	"your model, please set `tokenizer.chat_template` to an appropriate template. "
	"See https://huggingface.co/docs/transformers/main/chat_templating for more information.\n"
	)
	template = (
	"{% if messages[0]['role'] == 'system' %}"
	"{% set loop_messages = messages[1:] %}" # Extract system message if it's present
	"{% set system_message = messages[0]['content'] %}"
	"{% elif USE_DEFAULT_PROMPT == true and not '<<SYS>>' in messages[0]['content'] %}"
	"{% set loop_messages = messages %}" # Or use the default system message if the flag is set
	"{% set system_message = 'DEFAULT_SYSTEM_MESSAGE' %}"
	"{% else %}"
	"{% set loop_messages = messages %}"
	"{% set system_message = false %}"
	"{% endif %}"
	"{% for message in loop_messages %}" # Loop over all non-system messages
	"{% if (message['role'] == 'user') != (loop.index0 % 2 == 0) %}"
	"{{ raise_exception('Conversation roles must alternate user/assistant/user/assistant/...') }}"
	"{% endif %}"
	"{% if loop.index0 == 0 and system_message != false %}" # Embed system message in first message
	"{% set content = '<<SYS>>\\n' + system_message + '\\n<</SYS>>\\n\\n' + message['content'] %}"
	"{% else %}"
	"{% set content = message['content'] %}"
	"{% endif %}"
	"{% if message['role'] == 'user' %}" # After all of that, handle messages/roles in a fairly normal way
	"{{ bos_token + '[INST] ' + content.strip() + ' [/INST]' }}"
	"{% elif message['role'] == 'system' %}"
	"{{ '<<SYS>>\\n' + content.strip() + '\\n<</SYS>>\\n\\n' }}"
	"{% elif message['role'] == 'assistant' %}"
	"{{ ' ' + content.strip() + ' ' + eos_token }}"
	"{% endif %}"
	"{% endfor %}"
	)
	template = template.replace("USE_DEFAULT_PROMPT", "true" if self.use_default_system_prompt else "false")
	default_message = DEFAULT_SYSTEM_PROMPT.replace("\n", "\\n").replace("'", "\\'")
	template = template.replace("DEFAULT_SYSTEM_MESSAGE", default_message)

	return template