The default program from “https://github.com/THUDM/ChatGLM-6B” works out while I was running api.py.
But recently when I try to run it again it suddenly errors:”AttributeError: ‘ChatGLMTokenizer’ object has no attribute ‘sp_tokenizer'”.
I tried to solve it on my own but nothing happens. I reinstalled transformers==4.27.1, updated tokenization_chatglm.py on https://huggingface.co/THUDM/chatglm-6b/blob/main/tokenization_chatglm.py, etc.
I cannot understand how tokenization_chatglm.py or api.py works for they’re too complex for me.
I wonder if there is anything I haven’t done to solve my problem and if there is anyone else having the same problem. Please someone help me out.
the default api.py down here
from fastapi import FastAPI, Request
from transformers import AutoTokenizer, AutoModel
import uvicorn, json, datetime
import torch
DEVICE = "cuda"
DEVICE_ID = "0"
CUDA_DEVICE = f"{DEVICE}:{DEVICE_ID}" if DEVICE_ID else DEVICE
def torch_gc():
if torch.cuda.is_available():
with torch.cuda.device(CUDA_DEVICE):
torch.cuda.empty_cache()
torch.cuda.ipc_collect()
app = FastAPI()
@app.post("/")
async def create_item(request: Request):
global model, tokenizer
json_post_raw = await request.json()
json_post = json.dumps(json_post_raw)
json_post_list = json.loads(json_post)
prompt = json_post_list.get('prompt')
history = json_post_list.get('history')
max_length = json_post_list.get('max_length')
top_p = json_post_list.get('top_p')
temperature = json_post_list.get('temperature')
response, history = model.chat(tokenizer,
prompt,
history=history,
max_length=max_length if max_length else 2048,
top_p=top_p if top_p else 0.7,
temperature=temperature if temperature else 0.95)
now = datetime.datetime.now()
time = now.strftime("%Y-%m-%d %H:%M:%S")
answer = {
"response": response,
"history": history,
"status": 200,
"time": time
}
log = "[" + time + "] " + '", prompt:"' + prompt + '", response:"' + repr(response) + '"'
print(log)
torch_gc()
return answer
if __name__ == '__main__':
model_path = r"E:huggingfacehubmodels_THUDM_chatglm_6b"
tokenizer = AutoTokenizer.from_pretrained(model_path, trust_remote_code=True)
model = AutoModel.from_pretrained(model_path, trust_remote_code=True, resume_download=True).quantize(4).half().cuda()
model.eval()
uvicorn.run(app, host='0.0.0.0', port=8000, workers=1)
the newest tokenization_chatglm.py down here
"""Tokenization classes for ChatGLM."""
from typing import List, Optional, Union
import os
from transformers.tokenization_utils import PreTrainedTokenizer
from transformers.utils import logging, PaddingStrategy
from transformers.tokenization_utils_base import EncodedInput, BatchEncoding
from typing import Dict
import sentencepiece as spm
import numpy as np
logger = logging.get_logger(__name__)
PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES = {
"THUDM/chatglm-6b": 2048,
}
class TextTokenizer:
def __init__(self, model_path):
self.sp = spm.SentencePieceProcessor()
self.sp.Load(model_path)
self.num_tokens = self.sp.vocab_size()
def encode(self, text):
return self.sp.EncodeAsIds(text)
def decode(self, ids: List[int]):
return self.sp.DecodeIds(ids)
def tokenize(self, text):
return self.sp.EncodeAsPieces(text)
def convert_tokens_to_string(self, tokens):
return self.sp.DecodePieces(tokens)
def convert_tokens_to_ids(self, tokens):
return [self.sp.PieceToId(token) for token in tokens]
def convert_token_to_id(self, token):
return self.sp.PieceToId(token)
def convert_id_to_token(self, idx):
return self.sp.IdToPiece(idx)
def __len__(self):
return self.num_tokens
class SPTokenizer:
def __init__(
self,
vocab_file,
num_image_tokens=20000,
max_blank_length=80,
byte_fallback=True,
):
assert vocab_file is not None
self.vocab_file = vocab_file
self.num_image_tokens = num_image_tokens
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 = TextTokenizer(vocab_file)
def _get_text_tokenizer(self):
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_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, 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(text)
tokens = [x + self.num_image_tokens for x in tmp]
return tokens if add_dummy_prefix else tokens[2:]
def postprocess(self, text):
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 decode(self, text_ids: List[int]) -> 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().decode(ids)
text = self.postprocess(text)
return text
def decode_tokens(self, tokens: List[str]) -> str:
text = self._get_text_tokenizer().convert_tokens_to_string(tokens)
text = self.postprocess(text)
return text
def tokenize(
self, text: str, linebreak=True, whitespaces=True, 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().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='<eop>',
end_token='</s>',
mask_token='[MASK]',
gmask_token='[gMASK]',
padding_side="left",
pad_token="<pad>",
unk_token="<unk>",
num_image_tokens=20000,
**kwargs
) -> None:
super().__init__(
do_lower_case=do_lower_case,
remove_space=remove_space,
padding_side=padding_side,
bos_token=bos_token,
eos_token=eos_token,
end_token=end_token,
mask_token=mask_token,
gmask_token=gmask_token,
pad_token=pad_token,
unk_token=unk_token,
num_image_tokens=num_image_tokens,
**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.end_token = end_token
self.mask_token = mask_token
self.gmask_token = gmask_token
self.sp_tokenizer = SPTokenizer(vocab_file, num_image_tokens=num_image_tokens)
""" Initialisation """
@property
def gmask_token_id(self) -> Optional[int]:
if self.gmask_token is None:
return None
return self.convert_tokens_to_ids(self.gmask_token)
@property
def end_token_id(self) -> Optional[int]:
"""
`Optional[int]`: Id of the end of context token in the vocabulary. Returns `None` if the token has not been
set.
"""
if self.end_token is None:
return None
return self.convert_tokens_to_ids(self.end_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 convert_tokens_to_string(self, tokens: List[str]) -> str:
return self.sp_tokenizer.decode_tokens(tokens)
def _decode(
self,
token_ids: Union[int, List[int]],
**kwargs
) -> str:
if isinstance(token_ids, int):
token_ids = [token_ids]
if len(token_ids) == 0:
return ""
if self.pad_token_id in token_ids: # remove pad
token_ids = list(filter((self.pad_token_id).__ne__, token_ids))
return super()._decode(token_ids, **kwargs)
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.
"""
gmask_id = self.sp_tokenizer[self.gmask_token]
eos_id = self.sp_tokenizer[self.eos_token]
token_ids_0 = token_ids_0 + [gmask_id, self.sp_tokenizer[self.bos_token]]
if token_ids_1 is not None:
token_ids_0 = token_ids_0 + token_ids_1 + [eos_id]
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:
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
I reinstalled transformers==4.27.1, updated tokenization_chatglm.py on https://huggingface.co/THUDM/chatglm-6b/blob/main/tokenization_chatglm.py, etc.
Hoping api.py runs successfully so I can use local ChatGLM offline.
New contributor
Tianrun Zhao is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
Check out our Code of Conduct.
Oh, now I got it.
The transformers version I chose was still inappropriate.
Installed transformers-4.33.2 and problem solved.
My bad if I wasted your time, sincerely sorry.
pip uninstall transformers
pip install transformers==4.33.2
New contributor
Tianrun Zhao is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
Check out our Code of Conduct.