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 import torch_required from transformers.utils.generic import _is_torch_device import sentencepiece as spm logger = logging.get_logger(__name__) class GLMBatchEncoding(BatchEncoding): # @torch_required 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[:(max_gen_length-1)] + [self.eop_token_id] 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 + [self.pad_token_id] * (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), self.pad_token_id), 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 cls = [self.cls_token_id] eos = [self.eos_token_id] #eop = [self.eop_token_id] #mask = [self.mask_token_id] sep = [self.sep_token_id] #token_ids_0 = cls + token_ids_0 + mask + eos if token_ids_1 is None: return cls + token_ids_0 + eos else: return cls + token_ids_0 + sep + token_ids_1 + 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)