import json import os import re from typing import List, Optional, Union, Dict from sentencepiece import SentencePieceProcessor from transformers import PreTrainedTokenizer from transformers.utils import logging, PaddingStrategy from transformers.tokenization_utils_base import EncodedInput, BatchEncoding class SPTokenizer: def __init__(self, model_path: str): # reload tokenizer assert os.path.isfile(model_path), model_path self.sp_model = SentencePieceProcessor(model_file=model_path) # BOS / EOS token IDs self.n_words: int = self.sp_model.vocab_size() self.bos_id: int = self.sp_model.bos_id() self.eos_id: int = self.sp_model.eos_id() self.pad_id: int = self.sp_model.unk_id() assert self.sp_model.vocab_size() == self.sp_model.get_piece_size() special_tokens = ["[MASK]", "[gMASK]", "[sMASK]", "sop", "eop"] self.special_tokens = {} self.index_special_tokens = {} for token in special_tokens: self.special_tokens[token] = self.n_words self.index_special_tokens[self.n_words] = token self.n_words += 1 def tokenize(self, s: str): return self.sp_model.EncodeAsPieces(s) def encode(self, s: str, bos: bool = False, eos: bool = False) -> List[int]: assert type(s) is str t = self.sp_model.encode(s) if bos: t = [self.bos_id] + t if eos: t = t + [self.eos_id] return t def decode(self, t: List[int]) -> str: text, buffer = "", [] for token in t: if token in self.index_special_tokens: if buffer: text += self.sp_model.decode(buffer) buffer = [] text += self.index_special_tokens[token] else: buffer.append(token) if buffer: text += self.sp_model.decode(buffer) return text def decode_tokens(self, tokens: List[str]) -> str: text = self.sp_model.DecodePieces(tokens) return text def convert_token_to_id(self, token): """ Converts a token (str) in an id using the vocab. """ if token in self.special_tokens: return self.special_tokens[token] return self.sp_model.PieceToId(token) def convert_id_to_token(self, index): """Converts an index (integer) in a token (str) using the vocab.""" if index in self.index_special_tokens: return self.index_special_tokens[index] if index in [self.eos_id, self.bos_id, self.pad_id] or index < 0 or index > self.sp_model.vocab_size(): return "" return self.sp_model.IdToPiece(index) class ChatGLMTokenizer(PreTrainedTokenizer): vocab_files_names = {"vocab_file": "tokenizer.model"} model_input_names = ["input_ids", "attention_mask", "position_ids"] def __init__(self, vocab_file, padding_side="left", clean_up_tokenization_spaces=False, encode_special_tokens=False, **kwargs): self.name = "GLMTokenizer" self.vocab_file = vocab_file self.tokenizer = SPTokenizer(vocab_file) self.special_tokens = { "": self.tokenizer.bos_id, "": self.tokenizer.eos_id, "": self.tokenizer.pad_id } self.encode_special_tokens = encode_special_tokens super().__init__(padding_side=padding_side, clean_up_tokenization_spaces=clean_up_tokenization_spaces, encode_special_tokens=encode_special_tokens, **kwargs) def get_command(self, token): if token in self.special_tokens: return self.special_tokens[token] assert token in self.tokenizer.special_tokens, f"{token} is not a special token for {self.name}" return self.tokenizer.special_tokens[token] @property def unk_token(self) -> str: return "" @property def pad_token(self) -> str: return "" @property def pad_token_id(self): return self.get_command("") @property def eos_token(self) -> str: return "" @property def eos_token_id(self): return self.get_command("") @property def vocab_size(self): return self.tokenizer.n_words def get_vocab(self): """ Returns vocab as a dict """ vocab = {self._convert_id_to_token(i): i for i in range(self.vocab_size)} vocab.update(self.added_tokens_encoder) return vocab def _tokenize(self, text, **kwargs): return self.tokenizer.tokenize(text) def _convert_token_to_id(self, token): """ Converts a token (str) in an id using the vocab. """ return self.tokenizer.convert_token_to_id(token) def _convert_id_to_token(self, index): """Converts an index (integer) in a token (str) using the vocab.""" return self.tokenizer.convert_id_to_token(index) def convert_tokens_to_string(self, tokens: List[str]) -> str: return self.tokenizer.decode_tokens(tokens) def save_vocabulary(self, save_directory, filename_prefix=None): """ Save the vocabulary and special tokens file to a directory. Args: save_directory (`str`): The directory in which to save the vocabulary. filename_prefix (`str`, *optional*): An optional prefix to add to the named of the saved files. Returns: `Tuple(str)`: Paths to the files saved. """ if os.path.isdir(save_directory): vocab_file = os.path.join( save_directory, self.vocab_files_names["vocab_file"] ) else: vocab_file = save_directory with open(self.vocab_file, 'rb') as fin: proto_str = fin.read() with open(vocab_file, "wb") as writer: writer.write(proto_str) return (vocab_file,) def get_prefix_tokens(self): prefix_tokens = [self.get_command("[gMASK]"), self.get_command("sop")] return prefix_tokens def build_prompt(self, query, history=None): if history is None: history = [] prompt = "" for i, (old_query, response) in enumerate(history): prompt += "[Round {}]\n\n问：{}\n\n答：{}\n\n".format(i + 1, old_query, response) prompt += "[Round {}]\n\n问：{}\n\n答：".format(len(history) + 1, query) return prompt 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 (`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. """ prefix_tokens = self.get_prefix_tokens() token_ids_0 = prefix_tokens + token_ids_0 if token_ids_1 is not None: token_ids_0 = token_ids_0 + token_ids_1 + [self.get_command("")] return token_ids_0 def _pad( self, encoded_inputs: Union[Dict[str, EncodedInput], BatchEncoding], max_length: Optional[int] = None, padding_strategy: PaddingStrategy = PaddingStrategy.DO_NOT_PAD, pad_to_multiple_of: Optional[int] = None, return_attention_mask: Optional[bool] = None, ) -> dict: """ Pad encoded inputs (on left/right and up to predefined length or max length in the batch) Args: encoded_inputs: Dictionary of tokenized inputs (`List[int]`) or batch of tokenized inputs (`List[List[int]]`). max_length: maximum length of the returned list and optionally padding length (see below). Will truncate by taking into account the special tokens. padding_strategy: PaddingStrategy to use for padding. - PaddingStrategy.LONGEST Pad to the longest sequence in the batch - PaddingStrategy.MAX_LENGTH: Pad to the max length (default) - PaddingStrategy.DO_NOT_PAD: Do not pad The tokenizer padding sides are defined in self.padding_side: - 'left': pads on the left of the sequences - 'right': pads on the right of the sequences pad_to_multiple_of: (optional) Integer if set will pad the sequence to a multiple of the provided value. This is especially useful to enable the use of Tensor Core on NVIDIA hardware with compute capability `>= 7.5` (Volta). return_attention_mask: (optional) Set to False to avoid returning attention mask (default: set to model specifics) """ # Load from model defaults assert self.padding_side == "left" required_input = encoded_inputs[self.model_input_names[0]] seq_length = len(required_input) if padding_strategy == PaddingStrategy.LONGEST: max_length = len(required_input) if max_length is not None and pad_to_multiple_of is not None and (max_length % pad_to_multiple_of != 0): max_length = ((max_length // pad_to_multiple_of) + 1) * pad_to_multiple_of needs_to_be_padded = padding_strategy != PaddingStrategy.DO_NOT_PAD and len(required_input) != max_length # Initialize attention mask if not present. if "attention_mask" not in encoded_inputs: encoded_inputs["attention_mask"] = [1] * seq_length if "position_ids" not in encoded_inputs: encoded_inputs["position_ids"] = list(range(seq_length)) if needs_to_be_padded: difference = max_length - len(required_input) if "attention_mask" in encoded_inputs: encoded_inputs["attention_mask"] = [0] * difference + encoded_inputs["attention_mask"] if "position_ids" in encoded_inputs: encoded_inputs["position_ids"] = [0] * difference + encoded_inputs["position_ids"] encoded_inputs[self.model_input_names[0]] = [self.pad_token_id] * difference + required_input return encoded_inputs