mirror of https://github.com/hpcaitech/ColossalAI
446 lines
17 KiB
Python
446 lines
17 KiB
Python
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"""
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This code is copied from https://huggingface.co/THUDM/chatglm-6b/blob/main/tokenization_chatglm.py
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"""
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"""Tokenization classes for ChatGLM."""
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from typing import List, Optional, Union
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import os
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from transformers.tokenization_utils 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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from typing import Dict
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import sentencepiece as spm
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import numpy as np
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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 TextTokenizer:
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def __init__(self, model_path):
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self.sp = spm.SentencePieceProcessor()
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self.sp.Load(model_path)
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self.num_tokens = self.sp.vocab_size()
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def encode(self, text):
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return self.sp.EncodeAsIds(text)
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def decode(self, ids: List[int]):
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return self.sp.DecodeIds(ids)
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def tokenize(self, text):
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return self.sp.EncodeAsPieces(text)
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def convert_tokens_to_string(self, tokens):
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return self.sp.DecodePieces(tokens)
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def convert_tokens_to_ids(self, tokens):
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return [self.sp.PieceToId(token) for token in tokens]
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def convert_token_to_id(self, token):
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return self.sp.PieceToId(token)
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def convert_id_to_token(self, idx):
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return self.sp.IdToPiece(idx)
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def __len__(self):
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return self.num_tokens
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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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num_image_tokens=20000,
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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.num_image_tokens = num_image_tokens
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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 = TextTokenizer(vocab_file)
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def _get_text_tokenizer(self):
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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_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, 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(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 postprocess(self, text):
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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 decode(self, text_ids: List[int]) -> 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().decode(ids)
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text = self.postprocess(text)
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return text
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def decode_tokens(self, tokens: List[str]) -> str:
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text = self._get_text_tokenizer().convert_tokens_to_string(tokens)
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text = self.postprocess(text)
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return text
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def tokenize(
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self, text: str, linebreak=True, whitespaces=True, 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().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", "attention_mask", "position_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='<eop>',
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end_token='</s>',
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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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pad_token="<pad>",
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unk_token="<unk>",
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num_image_tokens=20000,
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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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bos_token=bos_token,
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eos_token=eos_token,
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end_token=end_token,
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mask_token=mask_token,
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gmask_token=gmask_token,
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pad_token=pad_token,
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unk_token=unk_token,
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num_image_tokens=num_image_tokens,
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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.end_token = end_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, num_image_tokens=num_image_tokens)
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""" Initialisation """
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@property
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def gmask_token_id(self) -> Optional[int]:
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if self.gmask_token is None:
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return None
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return self.convert_tokens_to_ids(self.gmask_token)
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@property
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def end_token_id(self) -> Optional[int]:
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"""
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`Optional[int]`: Id of the end of context 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.end_token is None:
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return None
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return self.convert_tokens_to_ids(self.end_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 convert_tokens_to_string(self, tokens: List[str]) -> str:
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return self.sp_tokenizer.decode_tokens(tokens)
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def _decode(
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self,
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token_ids: Union[int, List[int]],
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**kwargs
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) -> str:
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if isinstance(token_ids, int):
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token_ids = [token_ids]
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if len(token_ids) == 0:
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return ""
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if self.pad_token_id in token_ids: # remove pad
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token_ids = list(filter((self.pad_token_id).__ne__, token_ids))
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return super()._decode(token_ids, **kwargs)
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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, 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 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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gmask_id = self.sp_tokenizer[self.gmask_token]
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eos_id = self.sp_tokenizer[self.eos_token]
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token_ids_0 = token_ids_0 + [gmask_id, self.sp_tokenizer[self.bos_token]]
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if token_ids_1 is not None:
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token_ids_0 = token_ids_0 + token_ids_1
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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).
|
||
|
|
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
|
||
|
|
bos_token_id = self.sp_tokenizer[self.bos_token]
|
||
|
|
mask_token_id = self.sp_tokenizer[self.mask_token]
|
||
|
|
gmask_token_id = self.sp_tokenizer[self.gmask_token]
|
||
|
|
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 max_length is not None:
|
||
|
|
if "attention_mask" not in encoded_inputs:
|
||
|
|
if bos_token_id in required_input:
|
||
|
|
context_length = required_input.index(bos_token_id)
|
||
|
|
else:
|
||
|
|
context_length = seq_length
|
||
|
|
attention_mask = np.ones((1, seq_length, seq_length))
|
||
|
|
attention_mask = np.tril(attention_mask)
|
||
|
|
attention_mask[:, :, :context_length] = 1
|
||
|
|
attention_mask = np.bool_(attention_mask < 0.5)
|
||
|
|
encoded_inputs["attention_mask"] = attention_mask
|
||
|
|
|
||
|
|
if "position_ids" not in encoded_inputs:
|
||
|
|
if bos_token_id in required_input:
|
||
|
|
context_length = required_input.index(bos_token_id)
|
||
|
|
else:
|
||
|
|
context_length = seq_length
|
||
|
|
position_ids = np.arange(seq_length, dtype=np.int64)
|
||
|
|
mask_token = mask_token_id if mask_token_id in required_input else gmask_token_id
|
||
|
|
if mask_token in required_input:
|
||
|
|
mask_position = required_input.index(mask_token)
|
||
|
|
position_ids[context_length:] = mask_position
|
||
|
|
block_position_ids = np.concatenate(
|
||
|
|
[np.zeros(context_length, dtype=np.int64),
|
||
|
|
np.arange(1, seq_length - context_length + 1, dtype=np.int64)])
|
||
|
|
encoded_inputs["position_ids"] = np.stack([position_ids, block_position_ids], axis=0)
|
||
|
|
|
||
|
|
if needs_to_be_padded:
|
||
|
|
difference = max_length - len(required_input)
|
||
|
|
|
||
|
|
if "attention_mask" in encoded_inputs:
|
||
|
|
encoded_inputs["attention_mask"] = np.pad(encoded_inputs["attention_mask"],
|
||
|
|
pad_width=[(0, 0), (difference, 0), (difference, 0)],
|
||
|
|
mode='constant', constant_values=True)
|
||
|
|
if "token_type_ids" in encoded_inputs:
|
||
|
|
encoded_inputs["token_type_ids"] = [self.pad_token_type_id] * difference + encoded_inputs[
|
||
|
|
"token_type_ids"
|
||
|
|
]
|
||
|
|
if "special_tokens_mask" in encoded_inputs:
|
||
|
|
encoded_inputs["special_tokens_mask"] = [1] * difference + encoded_inputs["special_tokens_mask"]
|
||
|
|
if "position_ids" in encoded_inputs:
|
||
|
|
encoded_inputs["position_ids"] = np.pad(encoded_inputs["position_ids"],
|
||
|
|
pad_width=[(0, 0), (difference, 0)])
|
||
|
|
encoded_inputs[self.model_input_names[0]] = [self.pad_token_id] * difference + required_input
|
||
|
|
|
||
|
|
return encoded_inputs
|