|
import regex as re
|
|
import base64
|
|
import os
|
|
import json
|
|
import tiktoken
|
|
import torch
|
|
from torch import TensorType
|
|
from typing import List, Optional, Union, Dict, Any
|
|
from torchvision import transforms
|
|
from transformers import PreTrainedTokenizer
|
|
from transformers.utils import logging, PaddingStrategy
|
|
from transformers.tokenization_utils_base import EncodedInput, BatchEncoding
|
|
|
|
|
|
class ChatGLM4Tokenizer(PreTrainedTokenizer):
|
|
vocab_files_names = {"vocab_file": "tokenizer.model"}
|
|
model_input_names = ["input_ids", "attention_mask", "position_ids"]
|
|
|
|
def __init__(
|
|
self,
|
|
vocab_file,
|
|
padding_side="left",
|
|
clean_up_tokenization_spaces=False,
|
|
encode_special_tokens=False,
|
|
image_size=None,
|
|
**kwargs
|
|
):
|
|
self.name = "GLM4Tokenizer"
|
|
self.vocab_file = vocab_file
|
|
pat_str = "(?i:'s|'t|'re|'ve|'m|'ll|'d)|[^\\r\\n\\p{L}\\p{N}]?\\p{L}+|\\p{N}{1,3}| ?[^\\s\\p{L}\\p{N}]+[\\r\\n]*|\\s*[\\r\\n]+|\\s+(?!\\S)|\\s+"
|
|
self.pat_str = re.compile(pat_str)
|
|
self.encode_special_tokens = encode_special_tokens
|
|
self.image_size = image_size
|
|
|
|
mergeable_ranks = {}
|
|
with open(vocab_file) as f:
|
|
for line in f:
|
|
token, rank = line.strip().split()
|
|
rank = int(rank)
|
|
token = base64.b64decode(token)
|
|
mergeable_ranks[token] = rank
|
|
|
|
self.mergeable_ranks = mergeable_ranks
|
|
|
|
self.tokenizer = tiktoken.Encoding(
|
|
name="my_tokenizer",
|
|
pat_str=pat_str,
|
|
mergeable_ranks=mergeable_ranks,
|
|
special_tokens={}
|
|
)
|
|
self.decoder = {rank: token for token, rank in mergeable_ranks.items()}
|
|
self.n_words = len(self.decoder)
|
|
|
|
super().__init__(
|
|
padding_side=padding_side,
|
|
clean_up_tokenization_spaces=clean_up_tokenization_spaces,
|
|
**kwargs
|
|
)
|
|
|
|
@property
|
|
def vocab_size(self):
|
|
return self.n_words
|
|
|
|
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 convert_tokens_to_string(self, tokens: List[Union[bytes, str]]) -> str:
|
|
"""
|
|
Converts a sequence of tokens in a single string.
|
|
"""
|
|
text = ""
|
|
temp = b""
|
|
for t in tokens:
|
|
if isinstance(t, str):
|
|
if temp:
|
|
text += temp.decode("utf-8", errors="replace")
|
|
temp = b""
|
|
text += t
|
|
elif isinstance(t, bytes):
|
|
temp += t
|
|
else:
|
|
raise TypeError("token should only be of type types or str")
|
|
if temp:
|
|
text += temp.decode("utf-8", errors="replace")
|
|
return text
|
|
|
|
def _tokenize(self, text, **kwargs):
|
|
tokens = []
|
|
ids = self.tokenizer.encode(text)
|
|
for t in ids:
|
|
tokens.append(self.decoder[t])
|
|
return tokens
|
|
|
|
def _convert_token_to_id(self, token):
|
|
""" Converts a token (str) in an id using the vocab. """
|
|
return self.mergeable_ranks[token]
|
|
|
|
def _convert_id_to_token(self, index):
|
|
"""Converts an index (integer) in a token (str) using the vocab."""
|
|
return self.decoder.get(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 get_prefix_tokens(self):
|
|
prefix_tokens = [self.convert_tokens_to_ids("[gMASK]"), self.convert_tokens_to_ids("<sop>")]
|
|
return prefix_tokens
|
|
|
|
def build_single_message(self, role, metadata, message, tokenize=True, message_prefix=None):
|
|
assert role in ["system", "user", "assistant", "observation"], role
|
|
if tokenize:
|
|
role_tokens = [self.convert_tokens_to_ids(f"<|{role}|>")] + self.tokenizer.encode(f"{metadata}\n",
|
|
disallowed_special=())
|
|
message_tokens = self.tokenizer.encode(message, disallowed_special=())
|
|
if message_prefix is not None:
|
|
message_tokens = message_prefix + message_tokens
|
|
tokens = role_tokens + message_tokens
|
|
return tokens
|
|
else:
|
|
return str(f"<|{role}|>{metadata}\n{message}")
|
|
|
|
def apply_chat_template(
|
|
self,
|
|
conversation: Union[List[Dict[str, str]], List[List[Dict[str, str]]], "Conversation"],
|
|
add_generation_prompt: bool = False,
|
|
tokenize: bool = True,
|
|
padding: bool = False,
|
|
truncation: bool = False,
|
|
max_length: Optional[int] = None,
|
|
return_tensors: Optional[Union[str, TensorType]] = None,
|
|
return_dict: bool = False,
|
|
tokenizer_kwargs: Optional[Dict[str, Any]] = None,
|
|
add_special_tokens: bool = True,
|
|
**kwargs,
|
|
) -> Union[str, List[int], List[str], List[List[int]], BatchEncoding]:
|
|
|
|
if return_dict and not tokenize:
|
|
raise ValueError(
|
|
"`return_dict=True` is incompatible with `tokenize=False`, because there is no dict "
|
|
"of tokenizer outputs to return."
|
|
)
|
|
|
|
def handle_single_conversation(conversation):
|
|
input_ids = self.get_prefix_tokens() if add_special_tokens else []
|
|
input_message = "[gMASK]<sop>" if add_special_tokens else ""
|
|
input_image = None
|
|
transform = transforms.Compose(
|
|
[
|
|
transforms.Resize(
|
|
(self.image_size, self.image_size), interpolation=transforms.InterpolationMode.BICUBIC
|
|
),
|
|
transforms.ToTensor(),
|
|
transforms.Normalize((0.48145466, 0.4578275, 0.40821073), (0.26862954, 0.26130258, 0.27577711)),
|
|
]
|
|
)
|
|
for item in conversation:
|
|
if item.get("tools"):
|
|
tools = item["tools"]
|
|
content = "你是一个名为 GLM-4 的人工智能助手。你是基于智谱AI训练的语言模型 GLM-4 模型开发的，你的任务是针对用户的问题和要求提供适当的答复和支持。"
|
|
for tool in tools:
|
|
if tool["type"] == "function":
|
|
function = tool["function"]
|
|
content += f"\n\n## {function['name']}\n\n{json.dumps(function, ensure_ascii=False, indent=4)}"
|
|
content += "\n在调用上述函数时，请使用 Json 格式表示调用的参数。"
|
|
elif tool["type"] == "python":
|
|
content += "\n\n## python\n\n当你向 `python` 发送包含 Python 代码的消息时，该代码将会在一个有状态的 Jupyter notebook 环境中执行。\n`python` 返回代码执行的输出，或在执行 60 秒后返回超时。\n`/mnt/data` 将会持久化存储你的文件。在此会话中，`python` 无法访问互联网。不要使用 `python` 进行任何网络请求或者在线 API 调用，这些在线内容的访问将不会成功。"
|
|
elif tool["type"] == "simple_browser":
|
|
content += "\n\n## simple_browser\n\n你可以使用 `simple_browser` 工具。该工具支持以下函数：\n`search(query: str, recency_days: int)`：使用搜索引擎进行查询并显示结果，可以使用 `recency_days` 参数控制搜索内容的时效性。\n`mclick(ids: list[int])`：获取一系列指定 id 的页面内容。每次调用时，须选择3-10个页面。选择多个角度的页面，同时尽可能选择可信任的信息来源。考虑到部分页面是无法加载的，你也可以多打开一些可能有用的页面而不用担心内容过多。\n`open_url(url: str)`：打开指定的 URL。\n\n使用 `【{引用 id}†{引用文本}】` 来引用内容。\n\n操作步骤：1. 使用 `search` 来获得信息列表; 2. 使用 `mclick` 来获取指定 ID 页面的内容; 3. 根据获得的内容进行回复。在回复中应当引用信息来源。\n 如果用户提供了 URL，也可以用 `open_url` 直接打开页面。\n如果初次搜索结果没有找到合适的信息，也可以再次使用 `search` 进行搜索。"
|
|
elif tool["type"] == "cogview":
|
|
content += "\n\n## cogview\n\n如果用户的请求中包含了对图像的描述，你可以使用 `cogview` 来生成图像并展示给用户。你需要向 `cogview` 发送图像描述，规则：\n- 发送给 `cogview` 的消息必须使用英语。用户的中文描述必须完全翻译为英语。\n- 应当尽可能详细地描述图像生成的需求，需求描述约 100 英文单词。\n- 保持用户原始描述的意图。不要虚构内容或者没见过的人物。\n- 如无特殊说明，所在地为中国，持有中国立场并遵循中国社会主义价值观。"
|
|
else:
|
|
raise NotImplementedError(f"Unknown tool type {tool['type']}")
|
|
input = self.build_single_message("system", "", content, tokenize=tokenize)
|
|
if tokenize:
|
|
input_ids.extend(input)
|
|
else:
|
|
input_message += input
|
|
message = ""
|
|
message_prefix = None
|
|
if item.get("image"):
|
|
assert input_image is None, "Multiple images are not supported"
|
|
input_image = transform(item["image"])
|
|
message_prefix = self.convert_tokens_to_ids(
|
|
["<|begin_of_image|>", "<|endoftext|>", "<|end_of_image|>"])
|
|
if item.get("content"):
|
|
message += item["content"]
|
|
if message or message_prefix:
|
|
input = self.build_single_message(
|
|
item["role"],
|
|
item.get("metadata", ""),
|
|
message,
|
|
tokenize=tokenize,
|
|
message_prefix=message_prefix
|
|
)
|
|
if tokenize:
|
|
input_ids.extend(input)
|
|
else:
|
|
input_message += input
|
|
if add_generation_prompt:
|
|
if tokenize:
|
|
input_ids.extend([self.convert_tokens_to_ids("<|assistant|>")])
|
|
else:
|
|
input_message += "<|assistant|>"
|
|
return {"input": input_ids if tokenize else input_message, "image": input_image}
|
|
|
|
|
|
if isinstance(conversation, list) and all(isinstance(i, dict) for i in conversation):
|
|
result = handle_single_conversation(conversation)
|
|
input_ids = result["input"]
|
|
input_images = [result["image"]]
|
|
elif isinstance(conversation, list) and all(isinstance(i, list) for i in conversation):
|
|
results = [handle_single_conversation(c) for c in conversation]
|
|
input_ids = [item["input"] for item in results]
|
|
input_images = [item["image"] for item in results]
|
|
elif hasattr(conversation, "messages"):
|
|
result = handle_single_conversation(conversation.messages)
|
|
input_ids = result["input"]
|
|
input_images = [result["image"]]
|
|
else:
|
|
raise ValueError("Invalid conversation format")
|
|
|
|
if tokenize:
|
|
output = self.batch_encode_plus(
|
|
[input_ids] if isinstance(input_ids[0], int) else input_ids,
|
|
padding=padding,
|
|
truncation=truncation,
|
|
max_length=max_length,
|
|
return_tensors=return_tensors,
|
|
is_split_into_words=True,
|
|
add_special_tokens=False
|
|
)
|
|
if return_dict:
|
|
found_image = False
|
|
for image in input_images:
|
|
if image is not None:
|
|
found_image = True
|
|
break
|
|
if found_image:
|
|
output["images"] = torch.stack(input_images)
|
|
return output
|
|
else:
|
|
return output["input_ids"]
|
|
else:
|
|
return input_ids
|
|
|
|
|
|
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.
|
|
"""
|
|
prefix_tokens = self.get_prefix_tokens()
|
|
token_ids_0 = prefix_tokens + token_ids_0
|
|
if token_ids_1 is not None:
|
|
token_ids_0 = token_ids_0 + token_ids_1 + [self.convert_tokens_to_ids("<eos>")]
|
|
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)
|
|
"""
|
|
|
|
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
|
|
|
|
|
|
if "attention_mask" not in encoded_inputs:
|
|
encoded_inputs["attention_mask"] = [1] * seq_length
|
|
|
|
if "position_ids" not in encoded_inputs:
|
|
encoded_inputs["position_ids"] = list(range(seq_length))
|
|
|
|
if needs_to_be_padded:
|
|
difference = max_length - len(required_input)
|
|
|
|
if "attention_mask" in encoded_inputs:
|
|
encoded_inputs["attention_mask"] = [0] * difference + encoded_inputs["attention_mask"]
|
|
if "position_ids" in encoded_inputs:
|
|
encoded_inputs["position_ids"] = [0] * difference + encoded_inputs["position_ids"]
|
|
encoded_inputs[self.model_input_names[0]] = [self.pad_token_id] * difference + required_input
|
|
|
|
return encoded_inputs
|
|
|
