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"""Tokenization classes for ChatGLM.""" |
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import sys |
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import unicodedata |
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from typing import List, Optional, Union |
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from functools import lru_cache |
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import os |
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import collections |
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import re |
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from transformers.tokenization_utils import PreTrainedTokenizer |
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from icetk.text_tokenizer import TextTokenizer |
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from icetk.utils import auto_create |
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import icetk.sentencepiece_model_pb2 as sp_model |
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from transformers.utils import logging |
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logger = logging.get_logger(__name__) |
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PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES = { |
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"THUDM/chatglm-6b": 2048, |
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} |
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class SPTokenizer: |
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def __init__( |
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self, |
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vocab_file, |
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max_blank_length=80, |
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byte_fallback=True, |
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): |
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assert vocab_file is not None |
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self.vocab_file = vocab_file |
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self.special_tokens = ["[MASK]", "[gMASK]", "[sMASK]", "<unused_0>", "<sop>", "<eop>", "<ENC>", "<dBLOCK>"] |
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self.max_blank_length = max_blank_length |
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self.byte_fallback = byte_fallback |
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self.text_tokenizer = self._build_text_tokenizer(encode_special_tokens=False) |
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self.special_text_tokenizer = self._build_text_tokenizer(encode_special_tokens=True) |
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@staticmethod |
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def _configure_tokenizer( |
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text_tokenizer: TextTokenizer, |
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special_tokens: List[str], |
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max_blank_length: int, |
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byte_fallback: bool, |
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encode_special_tokens=False, |
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): |
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special_token_type = 4 if encode_special_tokens else 3 |
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for token in special_tokens: |
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text_tokenizer.proto.pieces.append( |
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sp_model.ModelProto.SentencePiece(piece=token, score=0.0, type=special_token_type) |
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) |
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for token in [SPTokenizer.get_tab_token()] + [ |
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SPTokenizer.get_blank_token(i) for i in range(2, max_blank_length + 1) |
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]: |
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text_tokenizer.proto.pieces.append(sp_model.ModelProto.SentencePiece(piece=token, score=0.0, type=4)) |
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if byte_fallback: |
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text_tokenizer.proto.trainer_spec.byte_fallback = True |
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for i in range(256): |
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text_tokenizer.proto.pieces.append( |
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sp_model.ModelProto.SentencePiece(piece="<0x{:02X}>".format(i), score=0.0, type=6) |
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) |
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text_tokenizer.refresh() |
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def _build_text_tokenizer(self, encode_special_tokens=False): |
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tokenizer = TextTokenizer(self.vocab_file) |
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self._configure_tokenizer( |
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tokenizer, self.special_tokens, self.max_blank_length, self.byte_fallback, encode_special_tokens |
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) |
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return tokenizer |
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def _get_text_tokenizer(self, encode_special_tokens=False): |
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if encode_special_tokens: |
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return self.special_text_tokenizer |
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else: |
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return self.text_tokenizer |
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@staticmethod |
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def get_blank_token(length: int): |
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assert length >= 2 |
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return f"<|blank_{length}|>" |
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@staticmethod |
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def get_tab_token(): |
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return f"<|tab|>" |
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@property |
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def num_image_tokens(self): |
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return 20000 |
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@property |
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def num_text_tokens(self): |
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return self.text_tokenizer.num_tokens |
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@property |
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def num_tokens(self): |
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return self.num_image_tokens + self.num_text_tokens |
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@staticmethod |
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def _encode_whitespaces(text: str, max_len: int = 80): |
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text = text.replace("\t", SPTokenizer.get_tab_token()) |
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for i in range(max_len, 1, -1): |
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text = text.replace(" " * i, SPTokenizer.get_blank_token(i)) |
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return text |
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def _preprocess(self, text: str, linebreak=True, whitespaces=True): |
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if linebreak: |
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text = text.replace("\n", "<n>") |
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if whitespaces: |
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text = self._encode_whitespaces(text, max_len=self.max_blank_length) |
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return text |
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def encode( |
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self, text: str, linebreak=True, whitespaces=True, special_tokens=False, add_dummy_prefix=True |
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) -> List[int]: |
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""" |
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@param text: Text to encode. |
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@param linebreak: Whether to encode newline (\n) in text. |
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@param whitespaces: Whether to encode multiple whitespaces or tab in text, useful for source code encoding. |
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@param special_tokens: Whether to encode special token ([MASK], [gMASK], etc.) in text. |
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@param add_dummy_prefix: Whether to add dummy blank space in the beginning. |
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""" |
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text = self._preprocess(text, linebreak, whitespaces) |
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if not add_dummy_prefix: |
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text = "<n>" + text |
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tmp = self._get_text_tokenizer(encode_special_tokens=special_tokens).encode(text) |
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tokens = [x + self.num_image_tokens for x in tmp] |
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return tokens if add_dummy_prefix else tokens[2:] |
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def decode(self, text_ids: List[int], special_tokens=False) -> str: |
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ids = [int(_id) - self.num_image_tokens for _id in text_ids] |
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ids = [_id for _id in ids if _id >= 0] |
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text = self._get_text_tokenizer(encode_special_tokens=special_tokens).decode(ids) |
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text = text.replace("<n>", "\n") |
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text = text.replace(SPTokenizer.get_tab_token(), "\t") |
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for i in range(2, self.max_blank_length + 1): |
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text = text.replace(self.get_blank_token(i), " " * i) |
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return text |
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def tokenize( |
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self, text: str, linebreak=True, whitespaces=True, special_tokens=False, add_dummy_prefix=True |
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) -> List[str]: |
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""" |
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@param text: Text to encode. |
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@param linebreak: Whether to encode newline (\n) in text. |
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@param whitespaces: Whether to encode multiple whitespaces or tab in text, useful for source code encoding. |
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@param special_tokens: Whether to encode special token ([MASK], [gMASK], etc.) in text. |
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@param add_dummy_prefix: Whether to add dummy blank space in the beginning. |
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""" |
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text = self._preprocess(text, linebreak, whitespaces) |
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if not add_dummy_prefix: |
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text = "<n>" + text |
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tokens = self._get_text_tokenizer(encode_special_tokens=special_tokens).tokenize(text) |
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return tokens if add_dummy_prefix else tokens[2:] |
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def __getitem__(self, x: Union[int, str]): |
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if isinstance(x, int): |
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if x < self.num_image_tokens: |
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return "<image_{}>".format(x) |
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else: |
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return self.text_tokenizer.convert_id_to_token(x - self.num_image_tokens) |
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elif isinstance(x, str): |
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if x.startswith("<image_") and x.endswith(">") and x[7:-1].isdigit(): |
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return int(x[7:-1]) |
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else: |
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return self.text_tokenizer.convert_token_to_id(x) + self.num_image_tokens |
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else: |
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raise ValueError("The key should be str or int.") |
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class ChatGLMTokenizer(PreTrainedTokenizer): |
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""" |
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Construct a ChatGLM tokenizer. Based on byte-level Byte-Pair-Encoding. |
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Args: |
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vocab_file (`str`): |
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Path to the vocabulary file. |
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""" |
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vocab_files_names = {"vocab_file": "ice_text.model"} |
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max_model_input_sizes = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES |
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model_input_names = ["input_ids"] |
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def __init__( |
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self, |
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vocab_file, |
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do_lower_case=False, |
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remove_space=False, |
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bos_token='sop', |
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eos_token='eos', |
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eop_token='eop', |
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mask_token='[MASK]', |
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gmask_token='[gMASK]', |
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padding_side="left", |
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**kwargs |
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) -> None: |
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super().__init__( |
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do_lower_case=do_lower_case, |
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remove_space=remove_space, |
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padding_side=padding_side, |
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**kwargs |
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) |
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self.do_lower_case = do_lower_case |
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self.remove_space = remove_space |
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self.vocab_file = vocab_file |
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self.bos_token = bos_token |
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self.eos_token = eos_token |
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self.eop_token = eop_token |
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self.mask_token = mask_token |
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self.gMASK_token = gmask_token |
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self.sp_tokenizer = SPTokenizer(vocab_file) |
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""" Initialisation """ |
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@property |
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def eop_token_id(self) -> Optional[int]: |
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""" |
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`Optional[int]`: Id of the end of sentence token in the vocabulary. Returns `None` if the token has not been |
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set. |
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""" |
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if self.eop_token is None: |
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return None |
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return self.convert_tokens_to_ids(self.eop_token) |
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@property |
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def vocab_size(self): |
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""" Returns vocab size """ |
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return self.sp_tokenizer.num_tokens |
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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 preprocess_text(self, inputs): |
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if self.remove_space: |
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outputs = " ".join(inputs.strip().split()) |
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else: |
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outputs = inputs |
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if self.do_lower_case: |
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outputs = outputs.lower() |
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return outputs |
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def _tokenize(self, text, **kwargs): |
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""" Returns a tokenized string. """ |
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text = self.preprocess_text(text) |
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seq = self.sp_tokenizer.tokenize(text) |
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return seq |
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def decode( |
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self, |
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token_ids: Union[List[int], List[List[int]]], |
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skip_special_tokens: bool = False, |
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clean_up_tokenization_spaces: bool = True, |
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spaces_between_special_tokens: bool = True, |
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**kwargs |
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) -> str: |
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if isinstance(token_ids[0], list): |
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tokens = [] |
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for single_token_ids in token_ids: |
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if self.pad_token_id in single_token_ids: |
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single_token_ids = list(filter((self.pad_token_id).__ne__, single_token_ids)) |
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tokens.append(self.sp_tokenizer.decode(single_token_ids)) |
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return (tokens) |
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else: |
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if self.pad_token_id in token_ids: |
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token_ids = list(filter((self.pad_token_id).__ne__, token_ids)) |
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return self.sp_tokenizer.decode(token_ids) |
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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.sp_tokenizer[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.sp_tokenizer[index] |
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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, 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 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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if token_ids_1 is not None: |
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token_ids_0 += token_ids_1 |
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mask_ids = self.sp_tokenizer[self.mask_token] |
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gmask_ids = self.sp_tokenizer[self.gMASK_token] |
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if mask_ids not in token_ids_0 and gmask_ids not in token_ids_0: |
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token_ids_0 += [gmask_ids] |
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if token_ids_0[-1] != mask_ids and token_ids_0[-1] != gmask_ids: |
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token_ids_0 += [self.sp_tokenizer[self.eos_token]] |
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token_ids_0 += [self.sp_tokenizer[self.bos_token]] |
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return token_ids_0 |
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