251 lines
9.8 KiB
Python
251 lines
9.8 KiB
Python
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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# reload tokenizer
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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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# BOS / EOS token IDs
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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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# Load from model defaults
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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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# Initialize attention mask if not present.
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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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