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"""Tokenization classes for ChatGLM."""
from typing import List, Optional, Union
import os
from transformers.tokenization_utils import PreTrainedTokenizer
from icetk.text_tokenizer import TextTokenizer
import icetk.sentencepiece_model_pb2 as sp_model
from transformers.utils import logging, PaddingStrategy
from transformers.tokenization_utils_base import EncodedInput, BatchEncoding
from typing import Dict
import numpy as np
logger = logging.get_logger(__name__)
PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES = {
"THUDM/chatglm-6b": 2048,
}
class SPTokenizer:
def __init__(
self,
vocab_file,
max_blank_length=80,
byte_fallback=True,
):
assert vocab_file is not None
self.vocab_file = vocab_file
self.special_tokens = ["[MASK]", "[gMASK]", "[sMASK]", "<unused_0>", "<sop>", "<eop>", "<ENC>", "<dBLOCK>"]
self.max_blank_length = max_blank_length
self.byte_fallback = byte_fallback
self.text_tokenizer = self._build_text_tokenizer(encode_special_tokens=False)
self.special_text_tokenizer = self._build_text_tokenizer(encode_special_tokens=True)
@staticmethod
def _configure_tokenizer(
text_tokenizer: TextTokenizer,
special_tokens: List[str],
max_blank_length: int,
byte_fallback: bool,
encode_special_tokens=False,
):
# special token
special_token_type = 4 if encode_special_tokens else 3 # 3 - CONTROL, 4 - USER_DEFINE
for token in special_tokens:
text_tokenizer.proto.pieces.append(
sp_model.ModelProto.SentencePiece(piece=token, score=0.0, type=special_token_type)
)
# whitespaces
for token in [SPTokenizer.get_tab_token()] + [
SPTokenizer.get_blank_token(i) for i in range(2, max_blank_length + 1)
]:
text_tokenizer.proto.pieces.append(sp_model.ModelProto.SentencePiece(piece=token, score=0.0, type=4))
# byte fallback
if byte_fallback:
text_tokenizer.proto.trainer_spec.byte_fallback = True
for i in range(256):
text_tokenizer.proto.pieces.append(
sp_model.ModelProto.SentencePiece(piece="<0x{:02X}>".format(i), score=0.0, type=6)
)
text_tokenizer.refresh()
def _build_text_tokenizer(self, encode_special_tokens=False):
tokenizer = TextTokenizer(self.vocab_file)
self._configure_tokenizer(
tokenizer, self.special_tokens, self.max_blank_length, self.byte_fallback, encode_special_tokens
)
return tokenizer
def _get_text_tokenizer(self, encode_special_tokens=False):
if encode_special_tokens:
return self.special_text_tokenizer
else:
return self.text_tokenizer
@staticmethod
def get_blank_token(length: int):
assert length >= 2
return f"<|blank_{length}|>"
@staticmethod
def get_tab_token():
return f"<|tab|>"
@property
def num_image_tokens(self):
return 20000
@property
def num_text_tokens(self):
return self.text_tokenizer.num_tokens
@property
def num_tokens(self):
return self.num_image_tokens + self.num_text_tokens
@staticmethod
def _encode_whitespaces(text: str, max_len: int = 80):
text = text.replace("\t", SPTokenizer.get_tab_token())
for i in range(max_len, 1, -1):
text = text.replace(" " * i, SPTokenizer.get_blank_token(i))
return text
def _preprocess(self, text: str, linebreak=True, whitespaces=True):
if linebreak:
text = text.replace("\n", "<n>")
if whitespaces:
text = self._encode_whitespaces(text, max_len=self.max_blank_length)
return text
def encode(
self, text: str, linebreak=True, whitespaces=True, special_tokens=False, add_dummy_prefix=True
) -> List[int]:
"""
@param text: Text to encode.
@param linebreak: Whether to encode newline (\n) in text.
@param whitespaces: Whether to encode multiple whitespaces or tab in text, useful for source code encoding.
@param special_tokens: Whether to encode special token ([MASK], [gMASK], etc.) in text.
@param add_dummy_prefix: Whether to add dummy blank space in the beginning.
"""
text = self._preprocess(text, linebreak, whitespaces)
if not add_dummy_prefix:
text = "<n>" + text
tmp = self._get_text_tokenizer(encode_special_tokens=special_tokens).encode(text)
tokens = [x + self.num_image_tokens for x in tmp]
return tokens if add_dummy_prefix else tokens[2:]
def decode(self, text_ids: List[int], special_tokens=False) -> str:
ids = [int(_id) - self.num_image_tokens for _id in text_ids]
ids = [_id for _id in ids if _id >= 0]
text = self._get_text_tokenizer(encode_special_tokens=special_tokens).decode(ids)
text = text.replace("<n>", "\n")
text = text.replace(SPTokenizer.get_tab_token(), "\t")
for i in range(2, self.max_blank_length + 1):
text = text.replace(self.get_blank_token(i), " " * i)
return text
def tokenize(
self, text: str, linebreak=True, whitespaces=True, special_tokens=False, add_dummy_prefix=True
) -> List[str]:
"""
@param text: Text to encode.
@param linebreak: Whether to encode newline (\n) in text.
@param whitespaces: Whether to encode multiple whitespaces or tab in text, useful for source code encoding.
@param special_tokens: Whether to encode special token ([MASK], [gMASK], etc.) in text.
@param add_dummy_prefix: Whether to add dummy blank space in the beginning.
"""
text = self._preprocess(text, linebreak, whitespaces)
if not add_dummy_prefix:
text = "<n>" + text
tokens = self._get_text_tokenizer(encode_special_tokens=special_tokens).tokenize(text)
return tokens if add_dummy_prefix else tokens[2:]
def __getitem__(self, x: Union[int, str]):
if isinstance(x, int):
if x < self.num_image_tokens:
return "<image_{}>".format(x)
else:
return self.text_tokenizer.convert_id_to_token(x - self.num_image_tokens)
elif isinstance(x, str):
if x.startswith("<image_") and x.endswith(">") and x[7:-1].isdigit():
return int(x[7:-1])
else:
return self.text_tokenizer.convert_token_to_id(x) + self.num_image_tokens
else:
raise ValueError("The key should be str or int.")
class ChatGLMTokenizer(PreTrainedTokenizer):
"""
Construct a ChatGLM tokenizer. Based on byte-level Byte-Pair-Encoding.
Args:
vocab_file (`str`):
Path to the vocabulary file.
"""
vocab_files_names = {"vocab_file": "ice_text.model"}
max_model_input_sizes = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
model_input_names = ["input_ids", "attention_mask", "position_ids"]
def __init__(
self,
vocab_file,
do_lower_case=False,
remove_space=False,
bos_token='sop',
eos_token='eos',
eop_token='eop',
mask_token='[MASK]',
gmask_token='[gMASK]',
padding_side="left",
**kwargs
) -> None:
super().__init__(
do_lower_case=do_lower_case,
remove_space=remove_space,
padding_side=padding_side,
**kwargs
)
self.do_lower_case = do_lower_case
self.remove_space = remove_space
self.vocab_file = vocab_file
self.bos_token = bos_token
self.eos_token = eos_token
self.eop_token = eop_token
self.mask_token = mask_token
self.gmask_token = gmask_token
self.sp_tokenizer = SPTokenizer(vocab_file)
""" Initialisation """
@property
def eop_token_id(self) -> Optional[int]:
"""
`Optional[int]`: Id of the end of sentence token in the vocabulary. Returns `None` if the token has not been
set.
"""
if self.eop_token is None:
return None
return self.convert_tokens_to_ids(self.eop_token)
@property
def vocab_size(self):
""" Returns vocab size """
return self.sp_tokenizer.num_tokens
def get_vocab(self):
""" Returns vocab as a dict """
vocab = {self._convert_id_to_token(i): i for i in range(self.vocab_size)}
vocab.update(self.added_tokens_encoder)
return vocab
def preprocess_text(self, inputs):
if self.remove_space:
outputs = " ".join(inputs.strip().split())
else:
outputs = inputs
if self.do_lower_case:
outputs = outputs.lower()
return outputs
def _tokenize(self, text, **kwargs):
""" Returns a tokenized string. """
text = self.preprocess_text(text)
seq = self.sp_tokenizer.tokenize(text)
return seq
def decode(
self,
token_ids: Union[List[int], List[List[int]]],
skip_special_tokens: bool = False,
clean_up_tokenization_spaces: bool = True,
spaces_between_special_tokens: bool = True,
**kwargs
) -> str:
if not isinstance(token_ids, list):
token_ids = [token_ids]
if len(token_ids) == 0:
return ""
if isinstance(token_ids[0], list):
tokens = []
for single_token_ids in token_ids:
if self.pad_token_id in single_token_ids: # remove pad
single_token_ids = list(filter((self.pad_token_id).__ne__, single_token_ids))
tokens.append(self.sp_tokenizer.decode(single_token_ids))
return (tokens)
else:
if self.pad_token_id in token_ids: # remove pad
token_ids = list(filter((self.pad_token_id).__ne__, token_ids))
return self.sp_tokenizer.decode(token_ids)
def _convert_token_to_id(self, token):
""" Converts a token (str) in an id using the vocab. """
return self.sp_tokenizer[token]
def _convert_id_to_token(self, index):
"""Converts an index (integer) in a token (str) using the vocab."""
return self.sp_tokenizer[index]
def save_vocabulary(self, save_directory, filename_prefix=None):
"""
Save the vocabulary and special tokens file to a directory.
Args:
save_directory (`str`):
The directory in which to save the vocabulary.
filename_prefix (`str`, *optional*):
An optional prefix to add to the named of the saved files.
Returns:
`Tuple(str)`: Paths to the files saved.
"""
if os.path.isdir(save_directory):
vocab_file = os.path.join(
save_directory, self.vocab_files_names["vocab_file"]
)
else:
vocab_file = save_directory
with open(self.vocab_file, 'rb') as fin:
proto_str = fin.read()
with open(vocab_file, "wb") as writer:
writer.write(proto_str)
return (vocab_file,)
def build_inputs_with_special_tokens(
self, token_ids_0: List[int], token_ids_1: Optional[List[int]] = None
) -> List[int]:
"""
Build model inputs from a sequence or a pair of sequence for sequence classification tasks by concatenating and
adding special tokens. A BERT sequence has the following format:
- single sequence: `[CLS] X [SEP]`
- pair of sequences: `[CLS] A [SEP] B [SEP]`
Args:
token_ids_0 (`List[int]`):
List of IDs to which the special tokens will be added.
token_ids_1 (`List[int]`, *optional*):
Optional second list of IDs for sequence pairs.
Returns:
`List[int]`: List of [input IDs](../glossary#input-ids) with the appropriate special tokens.
"""
mask_ids = self.sp_tokenizer[self.mask_token]
gmask_ids = self.sp_tokenizer[self.gmask_token]
eop_id = self.sp_tokenizer[self.eop_token]
if mask_ids not in token_ids_0 and gmask_ids not in token_ids_0:
token_ids_0 += [gmask_ids]
if token_ids_0[-1] != mask_ids and token_ids_0[-1] != gmask_ids:
token_ids_0 += [self.sp_tokenizer[self.eos_token]]
token_ids_0 += [self.sp_tokenizer[self.bos_token]]
if token_ids_1 is not None:
if not token_ids_1 or token_ids_1[-1] != eop_id:
token_ids_1 += [eop_id]
token_ids_0 += token_ids_1
return token_ids_0
def _pad(
self,
encoded_inputs: Union[Dict[str, EncodedInput], BatchEncoding],
max_length: Optional[int] = None,
padding_strategy: PaddingStrategy = PaddingStrategy.DO_NOT_PAD,
pad_to_multiple_of: Optional[int] = None,
return_attention_mask: Optional[bool] = None,
) -> dict:
"""
Pad encoded inputs (on left/right and up to predefined length or max length in the batch)
Args:
encoded_inputs:
Dictionary of tokenized inputs (`List[int]`) or batch of tokenized inputs (`List[List[int]]`).
max_length: maximum length of the returned list and optionally padding length (see below).
Will truncate by taking into account the special tokens.
padding_strategy: PaddingStrategy to use for padding.
- PaddingStrategy.LONGEST Pad to the longest sequence in the batch
- PaddingStrategy.MAX_LENGTH: Pad to the max length (default)
- PaddingStrategy.DO_NOT_PAD: Do not pad
The tokenizer padding sides are defined in self.padding_side:
- 'left': pads on the left of the sequences
- 'right': pads on the right of the sequences
pad_to_multiple_of: (optional) Integer if set will pad the sequence to a multiple of the provided value.
This is especially useful to enable the use of Tensor Core on NVIDIA hardware with compute capability
`>= 7.5` (Volta).
return_attention_mask:
(optional) Set to False to avoid returning attention mask (default: set to model specifics)
"""
# Load from model defaults
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:
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
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