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