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README.md ADDED
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+ ---
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+ language:
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+ - zh
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+ - en
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+ tags:
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+ - glm
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+ - chatglm
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+ - thudm
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+ ---
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+ # ChatGLM-6B
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+ ## 介绍
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+ ChatGLM-6B 是一个开源的、支持中英双语问答和对话的预训练语言模型,基于 [GLM](https://github.com/THUDM/GLM) 架构,具有 62 亿参数。ChatGLM-6B 使用了和 ChatGLM(内测中,地址 [https://chatglm.cn](https://chatglm.cn))相同的技术面向中文问答和对话进行优化。
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+
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+ ## 使用方式
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+ 使用前请先安装`transformers>=4.23.1`和`icetk`。
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+
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+ ```shell
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+ pip install "transformers>=4.23.1,icetk"
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+ ```
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+
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+ ### 代码调用
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+
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+ 可以通过如下代码调用 ChatGLM-6B 模型来生成对话。
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+
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+ ```python
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+ from transformers import AutoTokenizer, AutoModel
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+
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+ tokenizer = AutoTokenizer.from_pretrained("THUDM/chatglm-6b", trust_remote_code=True)
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+ model = AutoModel.from_pretrained("THUDM/chatglm-6b", trust_remote_code=True).half().cuda()
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+ model = model.eval()
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+
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+ history = []
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+ query = "你好"
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+ response, history = model.chat(tokenizer, query, history=history)
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+ print(response)
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+
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+ query = "晚上睡不着应该怎么办"
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+ response, history = model.chat(tokenizer, query, history=history)
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+ print(history)
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+ ```
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+
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+ 关于更多的使用说明,以及如何运行命令行和Web版本的demo,请参考我们的[Github repo](https://github.com/THUDM/ChatGLM-6B)。
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+
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+ ## INT8 量化
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+ 默认情况下,模型以 FP16 精度加载,运行上述代码需要大概 13GB 显存。如果你的 GPU 显存有限,可以尝试使用 `transformers` 提供的 8bit 量化功能,即将代码中的
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+
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+ ```python
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+ model = AutoModel.from_pretrained("THUDM/chatglm-6b", trust_remote_code=True).half().cuda()
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+ ```
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+
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+ 替换为
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+
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+ ```python
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+ model = AutoModel.from_pretrained("THUDM/chatglm-6b", device_map="auto", load_in_8bit=True, trust_remote_code=True)
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+ ```
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+
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+ 使用 8-bit 量化之后大约需要 9.5GB 的 GPU 显存。
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+
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+ ## 引用
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+
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+ 如果你觉得我们的工作有帮助的话,请考虑引用下列论文
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+
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+ ```
64
+ @inproceedings{
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+ zeng2023glm-130b,
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+ title={{GLM}-130B: An Open Bilingual Pre-trained Model},
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+ author={Aohan Zeng and Xiao Liu and Zhengxiao Du and Zihan Wang and Hanyu Lai and Ming Ding and Zhuoyi Yang and Yifan Xu and Wendi Zheng and Xiao Xia and Weng Lam Tam and Zixuan Ma and Yufei Xue and Jidong Zhai and Wenguang Chen and Zhiyuan Liu and Peng Zhang and Yuxiao Dong and Jie Tang},
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+ booktitle={The Eleventh International Conference on Learning Representations (ICLR)},
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+ year={2023},
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+ url={https://openreview.net/forum?id=-Aw0rrrPUF}
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+ }
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+ ```
73
+ ```
74
+ @inproceedings{du2022glm,
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+ title={GLM: General Language Model Pretraining with Autoregressive Blank Infilling},
76
+ author={Du, Zhengxiao and Qian, Yujie and Liu, Xiao and Ding, Ming and Qiu, Jiezhong and Yang, Zhilin and Tang, Jie},
77
+ booktitle={Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)},
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+ pages={320--335},
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+ year={2022}
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+ }
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+ ```
config.json ADDED
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+ {
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+ "_name_or_path": "THUDM/chatglm-6b",
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+ "architectures": [
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+ "ChatGLMModel"
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+ ],
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+ "auto_map": {
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+ "AutoConfig": "configuration_chatglm.ChatGLMConfig",
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+ "AutoModel": "modeling_chatglm.ChatGLMForConditionalGeneration",
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+ "AutoModelForSeq2SeqLM": "modeling_chatglm.ChatGLMForConditionalGeneration"
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+ },
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+ "bos_token_id": 150004,
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+ "eos_token_id": 150005,
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+ "hidden_size": 4096,
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+ "inner_hidden_size": 16384,
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+ "layernorm_epsilon": 1e-05,
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+ "max_sequence_length": 2048,
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+ "model_type": "chatglm",
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+ "num_attention_heads": 32,
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+ "num_layers": 28,
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+ "position_encoding_2d": true,
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+ "torch_dtype": "float16",
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+ "transformers_version": "4.23.1",
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+ "use_cache": true,
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+ "vocab_size": 150528
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+ }
configuration_chatglm.py ADDED
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+ """ ChatGLM model configuration """
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+
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+ from transformers.configuration_utils import PretrainedConfig
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+ from transformers.utils import logging
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+
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+ logger = logging.get_logger(__name__)
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+
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+
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+ class ChatGLMConfig(PretrainedConfig):
10
+ r"""
11
+ This is the configuration class to store the configuration of a [`~ChatGLMModel`].
12
+ It is used to instantiate an ChatGLM model according to the specified arguments, defining the model
13
+ architecture. Instantiating a configuration with the defaults will yield a similar configuration to that of
14
+ the ChatGLM-6B [THUDM/ChatGLM-6B](https://huggingface.co/THUDM/chatglm-6b) architecture.
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+
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+ Configuration objects inherit from [`PretrainedConfig`] and can be used
17
+ to control the model outputs. Read the documentation from [`PretrainedConfig`]
18
+ for more information.
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+
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+
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+ Args:
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+ vocab_size (`int`, *optional*, defaults to 150528):
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+ Vocabulary size of the ChatGLM-6B model. Defines the number of different tokens that can be represented by the
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+ `inputs_ids` passed when calling [`~ChatGLMModel`] or
25
+ [`~TFChatGLMModel`].
26
+ hidden_size (`int`, *optional*, defaults to 4096):
27
+ Dimension of the encoder layers and the pooler layer.
28
+ num_hidden_layers (`int`, *optional*, defaults to 28):
29
+ Number of hidden layers in the Transformer encoder.
30
+ num_attention_heads (`int`, *optional*, defaults to 32):
31
+ Number of attention heads for each attention layer in the Transformer encoder.
32
+ inner_hidden_size (`int`, *optional*, defaults to 16384):
33
+ Dimension of the "intermediate" (i.e., feed-forward) layer in the Transformer encoder.
34
+ max_sequence_length (`int`, *optional*, defaults to 512):
35
+ The maximum sequence length that this model might ever be used with.
36
+ Typically set this to something large just in case (e.g., 512 or 1024 or 2048).
37
+ layernorm_epsilon (`float`, *optional*, defaults to 1e-5):
38
+ The epsilon used by the layer normalization layers.
39
+ use_cache (`bool`, *optional*, defaults to `True`):
40
+ Whether the model should return the last key/values attentions (not used by all models).
41
+ Example:
42
+
43
+ ```python
44
+ >>> from configuration_chatglm import ChatGLMConfig
45
+ >>> from modeling_chatglm import ChatGLMModel
46
+
47
+ >>> # Initializing a ChatGLM-6B THUDM/ChatGLM-6B style configuration
48
+ >>> configuration = ChatGLMConfig()
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+
50
+ >>> # Initializing a model from the THUDM/ChatGLM-6B style configuration
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+ >>> model = ChatGLMModel(configuration)
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+
53
+ >>> # Accessing the model configuration
54
+ >>> configuration = model.config
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+ ```
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+ """
57
+ model_type = "chatglm"
58
+
59
+ def __init__(
60
+ self,
61
+ vocab_size=150528,
62
+ hidden_size=4096,
63
+ num_layers=28,
64
+ num_attention_heads=32,
65
+ layernorm_epsilon=1e-5,
66
+ use_cache=False,
67
+ bos_token_id=150004,
68
+ eos_token_id=150005,
69
+ pad_token_id=0,
70
+ max_sequence_length=2048,
71
+ inner_hidden_size=16384,
72
+ position_encoding_2d=True,
73
+ **kwargs
74
+ ):
75
+ self.num_layers = num_layers
76
+ self.vocab_size = vocab_size
77
+ self.hidden_size = hidden_size
78
+ self.num_attention_heads = num_attention_heads
79
+ self.max_sequence_length = max_sequence_length
80
+ self.layernorm_epsilon = layernorm_epsilon
81
+ self.inner_hidden_size = inner_hidden_size
82
+ self.use_cache = use_cache
83
+ self.bos_token_id = bos_token_id
84
+ self.eos_token_id = eos_token_id
85
+ self.pad_token_id = pad_token_id
86
+ self.position_encoding_2d = position_encoding_2d
87
+ super().__init__(
88
+ pad_token_id=pad_token_id,
89
+ bos_token_id=bos_token_id,
90
+ eos_token_id=eos_token_id,
91
+ **kwargs
92
+ )
ice_text.model ADDED
@@ -0,0 +1,3 @@
 
 
 
 
1
+ version https://git-lfs.github.com/spec/v1
2
+ oid sha256:99871e0c85db81ad7af1028854fd091cd5778c8414ae9d94bbbc10d02c831c21
3
+ size 2699926
modeling_chatglm.py ADDED
@@ -0,0 +1,1152 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ """ PyTorch ChatGLM model. """
2
+
3
+ import math
4
+ import copy
5
+ import os
6
+
7
+ import torch
8
+ import torch.utils.checkpoint
9
+ import torch.nn.functional as F
10
+ from torch import nn
11
+ from torch.nn import CrossEntropyLoss, LayerNorm
12
+ from torch.nn.utils import skip_init
13
+ from typing import Optional, Tuple, Union, List
14
+
15
+ from transformers.utils import (
16
+ add_code_sample_docstrings,
17
+ add_start_docstrings,
18
+ add_start_docstrings_to_model_forward,
19
+ )
20
+ from transformers.modeling_outputs import (
21
+ BaseModelOutputWithPast,
22
+ CausalLMOutputWithPast,
23
+ BaseModelOutputWithPastAndCrossAttentions,
24
+ )
25
+ from transformers.modeling_utils import PreTrainedModel
26
+
27
+ from transformers.utils import logging
28
+ from .configuration_chatglm import ChatGLMConfig
29
+
30
+ # flags required to enable jit fusion kernels
31
+ torch._C._jit_set_profiling_mode(False)
32
+ torch._C._jit_set_profiling_executor(False)
33
+ torch._C._jit_override_can_fuse_on_cpu(True)
34
+ torch._C._jit_override_can_fuse_on_gpu(True)
35
+
36
+ logger = logging.get_logger(__name__)
37
+
38
+ _CHECKPOINT_FOR_DOC = "THUDM/ChatGLM-6B"
39
+ _CONFIG_FOR_DOC = "ChatGLM6BConfig"
40
+
41
+ CHATGLM_6B_PRETRAINED_MODEL_ARCHIVE_LIST = [
42
+ "THUDM/chatglm-6b",
43
+ # See all ChatGLM-6B models at https://huggingface.co/models?filter=chatglm
44
+ ]
45
+
46
+
47
+ def load_tf_weights_in_chatglm_6b(model, config, tf_checkpoint_path):
48
+ """Load tf checkpoints in a pytorch model."""
49
+ try:
50
+ import re
51
+
52
+ import numpy as np
53
+ import tensorflow as tf
54
+ except ImportError:
55
+ logger.error(
56
+ "Loading a TensorFlow model in PyTorch, requires TensorFlow to be installed. Please see "
57
+ "https://www.tensorflow.org/install/ for installation instructions."
58
+ )
59
+ raise
60
+ tf_path = os.path.abspath(tf_checkpoint_path)
61
+ logger.info(f"Converting TensorFlow checkpoint from {tf_path}")
62
+ # Load weights from TF model
63
+ init_vars = tf.train.list_variables(tf_path)
64
+ names = []
65
+ arrays = []
66
+ for name, shape in init_vars:
67
+ logger.info(f"Loading TF weight {name} with shape {shape}")
68
+ array = tf.train.load_variable(tf_path, name)
69
+ names.append(name)
70
+ arrays.append(array)
71
+
72
+ for name, array in zip(names, arrays):
73
+ name = name.split("/")
74
+ # adam_v and adam_m are variables used in AdamWeightDecayOptimizer to calculated m and v
75
+ # which are not required for using pretrained model
76
+ if any(
77
+ n in ["adam_v", "adam_m", "AdamWeightDecayOptimizer", "AdamWeightDecayOptimizer_1", "global_step"]
78
+ for n in name
79
+ ):
80
+ logger.info(f"Skipping {'/'.join(name)}")
81
+ continue
82
+ pointer = model
83
+ for m_name in name:
84
+ if re.fullmatch(r"[A-Za-z]+_\d+", m_name):
85
+ scope_names = re.split(r"_(\d+)", m_name)
86
+ else:
87
+ scope_names = [m_name]
88
+ if scope_names[0] == "kernel" or scope_names[0] == "gamma":
89
+ pointer = getattr(pointer, "weight")
90
+ elif scope_names[0] == "output_bias" or scope_names[0] == "beta":
91
+ pointer = getattr(pointer, "bias")
92
+ elif scope_names[0] == "output_weights":
93
+ pointer = getattr(pointer, "weight")
94
+ elif scope_names[0] == "squad":
95
+ pointer = getattr(pointer, "classifier")
96
+ else:
97
+ try:
98
+ pointer = getattr(pointer, scope_names[0])
99
+ except AttributeError:
100
+ logger.info(f"Skipping {'/'.join(name)}")
101
+ continue
102
+ if len(scope_names) >= 2:
103
+ num = int(scope_names[1])
104
+ pointer = pointer[num]
105
+ if m_name[-11:] == "_embeddings":
106
+ pointer = getattr(pointer, "weight")
107
+ elif m_name == "kernel":
108
+ array = np.transpose(array)
109
+ try:
110
+ assert (
111
+ pointer.shape == array.shape
112
+ ), f"Pointer shape {pointer.shape} and array shape {array.shape} mismatched"
113
+ except AssertionError as e:
114
+ e.args += (pointer.shape, array.shape)
115
+ raise
116
+ logger.info(f"Initialize PyTorch weight {name}")
117
+ pointer.data = torch.from_numpy(array)
118
+ return model
119
+
120
+
121
+ @torch.jit.script
122
+ def gelu_impl(x):
123
+ """OpenAI's gelu implementation."""
124
+ return 0.5 * x * (1.0 + torch.tanh(0.7978845608028654 * x *
125
+ (1.0 + 0.044715 * x * x)))
126
+
127
+
128
+ def gelu(x):
129
+ return gelu_impl(x)
130
+
131
+
132
+ class RotaryEmbedding(torch.nn.Module):
133
+ def __init__(self, dim, base=10000, precision=torch.half, learnable=False):
134
+ super().__init__()
135
+ inv_freq = 1. / (base ** (torch.arange(0, dim, 2).float() / dim))
136
+ inv_freq = inv_freq.half()
137
+ self.learnable = learnable
138
+ if learnable:
139
+ self.inv_freq = torch.nn.Parameter(inv_freq)
140
+ self.max_seq_len_cached = None
141
+ else:
142
+ self.register_buffer('inv_freq', inv_freq)
143
+ self.max_seq_len_cached = None
144
+ self.cos_cached = None
145
+ self.sin_cached = None
146
+ self.precision = precision
147
+
148
+ def _load_from_state_dict(self, state_dict, prefix, local_metadata, strict, missing_keys, unexpected_keys,
149
+ error_msgs):
150
+ pass
151
+
152
+ def forward(self, x, seq_dim=1, seq_len=None):
153
+ if seq_len is None:
154
+ seq_len = x.shape[seq_dim]
155
+ if self.max_seq_len_cached is None or (seq_len > self.max_seq_len_cached):
156
+ self.max_seq_len_cached = None if self.learnable else seq_len
157
+ t = torch.arange(seq_len, device=x.device, dtype=self.inv_freq.dtype)
158
+ freqs = torch.einsum('i,j->ij', t, self.inv_freq)
159
+ # Different from paper, but it uses a different permutation in order to obtain the same calculation
160
+ emb = torch.cat((freqs, freqs), dim=-1).to(x.device)
161
+ if self.precision == torch.bfloat16:
162
+ emb = emb.float()
163
+
164
+ # [sx, 1 (b * np), hn]
165
+ cos_cached = emb.cos()[:, None, :]
166
+ sin_cached = emb.sin()[:, None, :]
167
+ if self.precision == torch.bfloat16:
168
+ cos_cached = cos_cached.bfloat16()
169
+ sin_cached = sin_cached.bfloat16()
170
+ if self.learnable:
171
+ return cos_cached, sin_cached
172
+ self.cos_cached, self.sin_cached = cos_cached, sin_cached
173
+ return self.cos_cached[:seq_len, ...], self.sin_cached[:seq_len, ...]
174
+
175
+
176
+ def rotate_half(x):
177
+ x1, x2 = x[..., :x.shape[-1] // 2], x[..., x.shape[-1] // 2:]
178
+ return torch.cat((-x2, x1), dim=x1.ndim - 1) # dim=-1 triggers a bug in earlier torch versions
179
+
180
+
181
+ @torch.jit.script
182
+ def apply_rotary_pos_emb_index(q, k, cos, sin, position_id):
183
+ # position_id: [sq, b], q, k: [sq, b, np, hn], cos: [sq, 1, hn] -> [sq, b, 1, hn]
184
+ cos, sin = F.embedding(position_id, cos.squeeze(1)).unsqueeze(2), \
185
+ F.embedding(position_id, sin.squeeze(1)).unsqueeze(2)
186
+ q, k = (q * cos) + (rotate_half(q) * sin), (k * cos) + (rotate_half(k) * sin)
187
+ return q, k
188
+
189
+
190
+ def attention_fn(
191
+ self,
192
+ query_layer,
193
+ key_layer,
194
+ value_layer,
195
+ attention_mask,
196
+ hidden_size_per_partition,
197
+ layer_id,
198
+ layer_past=None,
199
+ scaling_attention_score=True,
200
+ use_cache=False,
201
+ ):
202
+ if layer_past is not None:
203
+ past_key, past_value = layer_past
204
+ key_layer = torch.cat((past_key, key_layer), dim=0)
205
+ value_layer = torch.cat((past_value, value_layer), dim=0)
206
+
207
+ # seqlen, batch, num_attention_heads, hidden_size_per_attention_head
208
+ seq_len, b, nh, hidden_size = key_layer.shape
209
+
210
+ if use_cache:
211
+ present = (key_layer, value_layer)
212
+ else:
213
+ present = None
214
+
215
+ query_key_layer_scaling_coeff = float(layer_id + 1)
216
+ if scaling_attention_score:
217
+ query_layer = query_layer / (math.sqrt(hidden_size) * query_key_layer_scaling_coeff)
218
+
219
+ # ===================================
220
+ # Raw attention scores. [b, np, s, s]
221
+ # ===================================
222
+
223
+ # [b, np, sq, sk]
224
+ output_size = (query_layer.size(1), query_layer.size(2), query_layer.size(0), key_layer.size(0))
225
+
226
+ # [sq, b, np, hn] -> [sq, b * np, hn]
227
+ query_layer = query_layer.view(output_size[2], output_size[0] * output_size[1], -1)
228
+ # [sk, b, np, hn] -> [sk, b * np, hn]
229
+ key_layer = key_layer.view(output_size[3], output_size[0] * output_size[1], -1)
230
+
231
+ matmul_result = torch.empty(
232
+ output_size[0] * output_size[1],
233
+ output_size[2],
234
+ output_size[3],
235
+ dtype=query_layer.dtype,
236
+ device=query_layer.device,
237
+ )
238
+
239
+ matmul_result = torch.baddbmm(
240
+ matmul_result,
241
+ query_layer.transpose(0, 1), # [b * np, sq, hn]
242
+ key_layer.transpose(0, 1).transpose(1, 2), # [b * np, hn, sk]
243
+ beta=0.0,
244
+ alpha=1.0,
245
+ )
246
+
247
+ # change view to [b, np, sq, sk]
248
+ attention_scores = matmul_result.view(*output_size)
249
+
250
+ if self.scale_mask_softmax:
251
+ self.scale_mask_softmax.scale = query_key_layer_scaling_coeff
252
+ attention_probs = self.scale_mask_softmax(attention_scores, attention_mask.contiguous())
253
+ else:
254
+ if not (attention_mask == 0).all():
255
+ # if auto-regressive, skip
256
+ attention_scores.masked_fill_(attention_mask, -10000.0)
257
+
258
+ attention_scores = attention_scores.float()
259
+ attention_scores = attention_scores * query_key_layer_scaling_coeff
260
+
261
+ attention_probs = F.softmax(attention_scores, dim=-1)
262
+
263
+ attention_probs = attention_probs.half()
264
+
265
+ # =========================
266
+ # Context layer. [sq, b, hp]
267
+ # =========================
268
+
269
+ # value_layer -> context layer.
270
+ # [sk, b, np, hn] --> [b, np, sq, hn]
271
+
272
+ # context layer shape: [b, np, sq, hn]
273
+ output_size = (value_layer.size(1), value_layer.size(2), query_layer.size(0), value_layer.size(3))
274
+
275
+ # change view [sk, b * np, hn]
276
+ value_layer = value_layer.view(value_layer.size(0), output_size[0] * output_size[1], -1)
277
+
278
+ # change view [b * np, sq, sk]
279
+ attention_probs = attention_probs.view(output_size[0] * output_size[1], output_size[2], -1)
280
+
281
+ # matmul: [b * np, sq, hn]
282
+ context_layer = torch.bmm(attention_probs, value_layer.transpose(0, 1))
283
+
284
+ # change view [b, np, sq, hn]
285
+ context_layer = context_layer.view(*output_size)
286
+
287
+ # [b, np, sq, hn] --> [sq, b, np, hn]
288
+ context_layer = context_layer.permute(2, 0, 1, 3).contiguous()
289
+
290
+ # [sq, b, np, hn] --> [sq, b, hp]
291
+ new_context_layer_shape = context_layer.size()[:-2] + (hidden_size_per_partition,)
292
+ context_layer = context_layer.view(*new_context_layer_shape)
293
+
294
+ outputs = (context_layer, present, attention_probs)
295
+
296
+ return outputs
297
+
298
+
299
+ class SelfAttention(torch.nn.Module):
300
+ def __init__(self, hidden_size, num_attention_heads,
301
+ layer_id, hidden_size_per_attention_head=None, bias=True,
302
+ params_dtype=torch.float, position_encoding_2d=True):
303
+ super(SelfAttention, self).__init__()
304
+
305
+ self.layer_id = layer_id
306
+ self.hidden_size = hidden_size
307
+ self.hidden_size_per_partition = hidden_size
308
+ self.num_attention_heads = num_attention_heads
309
+ self.num_attention_heads_per_partition = num_attention_heads
310
+ self.position_encoding_2d = position_encoding_2d
311
+ self.rotary_emb = RotaryEmbedding(
312
+ self.hidden_size // (self.num_attention_heads * 2)
313
+ if position_encoding_2d
314
+ else self.hidden_size // self.num_attention_heads,
315
+ base=10000,
316
+ precision=torch.half,
317
+ learnable=False,
318
+ )
319
+
320
+ self.scale_mask_softmax = None
321
+
322
+ if hidden_size_per_attention_head is None:
323
+ self.hidden_size_per_attention_head = hidden_size // num_attention_heads
324
+ else:
325
+ self.hidden_size_per_attention_head = hidden_size_per_attention_head
326
+
327
+ self.inner_hidden_size = num_attention_heads * self.hidden_size_per_attention_head
328
+
329
+ # Strided linear layer.
330
+ self.query_key_value = skip_init(
331
+ torch.nn.Linear,
332
+ hidden_size,
333
+ 3 * self.inner_hidden_size,
334
+ bias=bias,
335
+ dtype=params_dtype,
336
+ )
337
+
338
+ self.dense = skip_init(
339
+ torch.nn.Linear,
340
+ self.inner_hidden_size,
341
+ hidden_size,
342
+ bias=bias,
343
+ dtype=params_dtype,
344
+ )
345
+
346
+ @staticmethod
347
+ def attention_mask_func(attention_scores, attention_mask):
348
+ attention_scores.masked_fill_(attention_mask, -10000.0)
349
+ return attention_scores
350
+
351
+ def split_tensor_along_last_dim(self, tensor, num_partitions,
352
+ contiguous_split_chunks=False):
353
+ """Split a tensor along its last dimension.
354
+ Arguments:
355
+ tensor: input tensor.
356
+ num_partitions: number of partitions to split the tensor
357
+ contiguous_split_chunks: If True, make each chunk contiguous
358
+ in memory.
359
+ """
360
+ # Get the size and dimension.
361
+ last_dim = tensor.dim() - 1
362
+ last_dim_size = tensor.size()[last_dim] // num_partitions
363
+ # Split.
364
+ tensor_list = torch.split(tensor, last_dim_size, dim=last_dim)
365
+ # Note: torch.split does not create contiguous tensors by default.
366
+ if contiguous_split_chunks:
367
+ return tuple(chunk.contiguous() for chunk in tensor_list)
368
+
369
+ return tensor_list
370
+
371
+ def forward(
372
+ self,
373
+ hidden_states: torch.Tensor,
374
+ position_ids,
375
+ attention_mask: torch.Tensor,
376
+ layer_id,
377
+ layer_past: Optional[Tuple[torch.Tensor, torch.Tensor]] = None,
378
+ use_cache: bool = False,
379
+ output_attentions: bool = False,
380
+ ):
381
+ """
382
+ hidden_states: [seq_len, batch, hidden_size]
383
+ attention_mask: [(1, 1), seq_len, seq_len]
384
+ """
385
+
386
+ # [seq_len, batch, 3 * hidden_size]
387
+ mixed_raw_layer = self.query_key_value(hidden_states)
388
+
389
+ # [seq_len, batch, 3 * hidden_size] --> [seq_len, batch, num_attention_heads, 3 * hidden_size_per_attention_head]
390
+ new_tensor_shape = mixed_raw_layer.size()[:-1] + (
391
+ self.num_attention_heads_per_partition,
392
+ 3 * self.hidden_size_per_attention_head,
393
+ )
394
+ mixed_raw_layer = mixed_raw_layer.view(*new_tensor_shape)
395
+
396
+ # [seq_len, batch, num_attention_heads, hidden_size_per_attention_head]
397
+ (query_layer, key_layer, value_layer) = self.split_tensor_along_last_dim(mixed_raw_layer, 3)
398
+
399
+ if self.position_encoding_2d:
400
+ q1, q2 = query_layer.chunk(2, dim=(query_layer.ndim - 1))
401
+ k1, k2 = key_layer.chunk(2, dim=(key_layer.ndim - 1))
402
+ cos, sin = self.rotary_emb(q1, seq_len=position_ids.max() + 1)
403
+ position_ids, block_position_ids = position_ids[:, 0, :].transpose(0, 1).contiguous(), \
404
+ position_ids[:, 1, :].transpose(0, 1).contiguous()
405
+ q1, k1 = apply_rotary_pos_emb_index(q1, k1, cos, sin, position_ids)
406
+ q2, k2 = apply_rotary_pos_emb_index(q2, k2, cos, sin, block_position_ids)
407
+ query_layer = torch.concat([q1, q2], dim=(q1.ndim - 1))
408
+ key_layer = torch.concat([k1, k2], dim=(k1.ndim - 1))
409
+ else:
410
+ position_ids = position_ids.transpose(0, 1)
411
+ cos, sin = self.rotary_emb(value_layer, seq_len=position_ids.max() + 1)
412
+ # [seq_len, batch, num_attention_heads, hidden_size_per_attention_head]
413
+ query_layer, key_layer = apply_rotary_pos_emb_index(query_layer, key_layer, cos, sin, position_ids)
414
+
415
+ # [seq_len, batch, hidden_size]
416
+ context_layer, present, attention_probs = attention_fn(
417
+ self=self,
418
+ query_layer=query_layer,
419
+ key_layer=key_layer,
420
+ value_layer=value_layer,
421
+ attention_mask=attention_mask,
422
+ hidden_size_per_partition=self.hidden_size_per_partition,
423
+ layer_id=layer_id,
424
+ layer_past=layer_past,
425
+ use_cache=use_cache
426
+ )
427
+
428
+ output = self.dense(context_layer)
429
+
430
+ outputs = (output, present)
431
+
432
+ if output_attentions:
433
+ outputs += (attention_probs,)
434
+
435
+ return outputs # output, present, attention_probs
436
+
437
+
438
+ class GEGLU(torch.nn.Module):
439
+ def __init__(self):
440
+ super().__init__()
441
+ self.activation_fn = F.gelu
442
+
443
+ def forward(self, x):
444
+ # dim=-1 breaks in jit for pt<1.10
445
+ x1, x2 = x.chunk(2, dim=(x.ndim - 1))
446
+ return x1 * self.activation_fn(x2)
447
+
448
+
449
+ class GLU(torch.nn.Module):
450
+ def __init__(self, hidden_size, inner_hidden_size=None,
451
+ layer_id=None, bias=True, activation_func=gelu, params_dtype=torch.float):
452
+ super(GLU, self).__init__()
453
+ self.layer_id = layer_id
454
+ self.activation_func = activation_func
455
+
456
+ # Project to 4h.
457
+ self.hidden_size = hidden_size
458
+ if inner_hidden_size is None:
459
+ inner_hidden_size = 4 * hidden_size
460
+ self.inner_hidden_size = inner_hidden_size
461
+ self.dense_h_to_4h = skip_init(
462
+ torch.nn.Linear,
463
+ self.hidden_size,
464
+ self.inner_hidden_size,
465
+ bias=bias,
466
+ dtype=params_dtype,
467
+ )
468
+ # Project back to h.
469
+ self.dense_4h_to_h = skip_init(
470
+ torch.nn.Linear,
471
+ self.inner_hidden_size,
472
+ self.hidden_size,
473
+ bias=bias,
474
+ dtype=params_dtype,
475
+ )
476
+
477
+ def forward(self, hidden_states):
478
+ """
479
+ hidden_states: [seq_len, batch, hidden_size]
480
+ """
481
+
482
+ # [seq_len, batch, inner_hidden_size]
483
+ intermediate_parallel = self.dense_h_to_4h(hidden_states)
484
+
485
+ intermediate_parallel = self.activation_func(intermediate_parallel)
486
+
487
+ output = self.dense_4h_to_h(intermediate_parallel)
488
+
489
+ return output
490
+
491
+
492
+ class GLMBlock(torch.nn.Module):
493
+ def __init__(
494
+ self,
495
+ hidden_size,
496
+ num_attention_heads,
497
+ layernorm_epsilon,
498
+ layer_id,
499
+ inner_hidden_size=None,
500
+ hidden_size_per_attention_head=None,
501
+ layernorm=LayerNorm,
502
+ use_bias=True,
503
+ params_dtype=torch.float,
504
+ num_layers=28,
505
+ position_encoding_2d=True
506
+ ):
507
+ super(GLMBlock, self).__init__()
508
+ # Set output layer initialization if not provided.
509
+
510
+ self.layer_id = layer_id
511
+
512
+ # Layernorm on the input data.
513
+ self.input_layernorm = layernorm(hidden_size, eps=layernorm_epsilon)
514
+
515
+ self.position_encoding_2d = position_encoding_2d
516
+
517
+ # Self attention.
518
+ self.attention = SelfAttention(
519
+ hidden_size,
520
+ num_attention_heads,
521
+ layer_id,
522
+ hidden_size_per_attention_head=hidden_size_per_attention_head,
523
+ bias=use_bias,
524
+ params_dtype=params_dtype,
525
+ position_encoding_2d=self.position_encoding_2d
526
+ )
527
+
528
+ # Layernorm on the input data.
529
+ self.post_attention_layernorm = layernorm(hidden_size, eps=layernorm_epsilon)
530
+
531
+ self.num_layers = num_layers
532
+
533
+ # GLU
534
+ self.mlp = GLU(
535
+ hidden_size,
536
+ inner_hidden_size=inner_hidden_size,
537
+ bias=use_bias,
538
+ layer_id=layer_id,
539
+ params_dtype=params_dtype,
540
+ )
541
+
542
+ def forward(
543
+ self,
544
+ hidden_states: torch.Tensor,
545
+ position_ids,
546
+ attention_mask: torch.Tensor,
547
+ layer_id,
548
+ layer_past: Optional[Tuple[torch.Tensor, torch.Tensor]] = None,
549
+ use_cache: bool = False,
550
+ output_attentions: bool = False,
551
+ ):
552
+ """
553
+ hidden_states: [seq_len, batch, hidden_size]
554
+ attention_mask: [(1, 1), seq_len, seq_len]
555
+ """
556
+
557
+ # Layer norm at the begining of the transformer layer.
558
+ # [seq_len, batch, hidden_size]
559
+ attention_input = self.input_layernorm(hidden_states)
560
+
561
+ # Self attention.
562
+ attention_outputs = self.attention(
563
+ attention_input,
564
+ position_ids,
565
+ attention_mask=attention_mask,
566
+ layer_id=layer_id,
567
+ layer_past=layer_past,
568
+ use_cache=use_cache,
569
+ output_attentions=output_attentions
570
+ )
571
+
572
+ attention_output = attention_outputs[0]
573
+
574
+ outputs = attention_outputs[1:]
575
+
576
+ # Residual connection.
577
+ alpha = (2 * self.num_layers) ** 0.5
578
+ hidden_states = attention_input * alpha + attention_output
579
+
580
+ mlp_input = self.post_attention_layernorm(hidden_states)
581
+
582
+ # MLP.
583
+ mlp_output = self.mlp(mlp_input)
584
+
585
+ # Second residual connection.
586
+ output = mlp_input * alpha + mlp_output
587
+
588
+ if use_cache:
589
+ outputs = (output,) + outputs
590
+ else:
591
+ outputs = (output,) + outputs[1:]
592
+
593
+ return outputs # hidden_states, present, attentions
594
+
595
+
596
+ class ChatGLMPreTrainedModel(PreTrainedModel):
597
+ """
598
+ An abstract class to handle weights initialization and
599
+ a simple interface for downloading and loading pretrained models.
600
+ """
601
+
602
+ is_parallelizable = True
603
+ supports_gradient_checkpointing = False
604
+ config_class = ChatGLMConfig
605
+ base_model_prefix = "transformer"
606
+ _no_split_modules = ["GLM6BBlock"]
607
+
608
+ def __init__(self, *inputs, **kwargs):
609
+ super().__init__(*inputs, **kwargs)
610
+
611
+ def _init_weights(self, module: nn.Module):
612
+ """Initialize the weights."""
613
+ return
614
+
615
+
616
+ CHATGLM_6B_START_DOCSTRING = r"""
617
+ This model is a PyTorch [torch.nn.Module](https://pytorch.org/docs/stable/nn.html#torch.nn.Module) sub-class.
618
+ Use it as a regular PyTorch Module and refer to the PyTorch documentation for all matter related to general
619
+ usage and behavior.
620
+
621
+ Parameters:
622
+ config ([`~ChatGLM6BConfig`]): Model configuration class with all the parameters of the model.
623
+ Initializing with a config file does not load the weights associated with the model, only the configuration.
624
+ Check out the [`~PreTrainedModel.from_pretrained`] method to load the model weights.
625
+ """
626
+
627
+ CHATGLM_6B_INPUTS_DOCSTRING = r"""
628
+ Args:
629
+ input_ids (`torch.LongTensor` of shape `({0})`):
630
+ Indices of input sequence tokens in the vocabulary.
631
+
632
+ Indices can be obtained using [`ChatGLM6BTokenizer`].
633
+ See [`PreTrainedTokenizer.encode`] and
634
+ [`PreTrainedTokenizer.__call__`] for details.
635
+
636
+ [What are input IDs?](../glossary#input-ids)
637
+ attention_mask (`torch.FloatTensor` of shape `({0})`, *optional*):
638
+ Mask to avoid performing attention on padding token indices. Mask values selected in `[0, 1]`:
639
+
640
+ - 1 for tokens that are **not masked**,
641
+ - 0 for tokens that are **masked**.
642
+
643
+ [What are attention masks?](../glossary#attention-mask)
644
+ token_type_ids (`torch.LongTensor` of shape `({0})`, *optional*):
645
+ Segment token indices to indicate first and second portions of the inputs. Indices are selected in `[0, 1]`:
646
+
647
+ - 0 corresponds to a *sentence A* token,
648
+ - 1 corresponds to a *sentence B* token.
649
+
650
+ [What are token type IDs?](../glossary#token-type-ids)
651
+ position_ids (`torch.LongTensor` of shape `({0})`, *optional*):
652
+ Indices of positions of each input sequence tokens in the position embeddings.
653
+ Selected in the range `[0, config.max_position_embeddings - 1]`.
654
+
655
+ [What are position IDs?](../glossary#position-ids)
656
+ head_mask (`torch.FloatTensor` of shape `(num_heads,)` or `(num_layers, num_heads)`, *optional*):
657
+ Mask to nullify selected heads of the self-attention modules. Mask values selected in `[0, 1]`:
658
+
659
+ - 1 indicates the head is **not masked**,
660
+ - 0 indicates the head is **masked**.
661
+
662
+ inputs_embeds (`torch.FloatTensor` of shape `({0}, hidden_size)`, *optional*):
663
+ Optionally, instead of passing `input_ids` you can choose to directly pass an embedded representation.
664
+ This is useful if you want more control over how to convert *input_ids* indices into associated vectors
665
+ than the model's internal embedding lookup matrix.
666
+ output_attentions (`bool`, *optional*):
667
+ Whether or not to return the attentions tensors of all attention layers. See `attentions` under returned
668
+ tensors for more detail.
669
+ output_hidden_states (`bool`, *optional*):
670
+ Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors for
671
+ more detail.
672
+ return_dict (`bool`, *optional*):
673
+ Whether or not to return a [`~utils.ModelOutput`] instead of a plain tuple.
674
+ """
675
+
676
+
677
+ @add_start_docstrings(
678
+ "The bare ChatGLM-6B Model transformer outputting raw hidden-states without any specific head on top.",
679
+ CHATGLM_6B_START_DOCSTRING,
680
+ )
681
+ class ChatGLMModel(ChatGLMPreTrainedModel):
682
+ """
683
+
684
+ The model can behave as an encoder (with only self-attention) as well
685
+ as a decoder, in which case a layer of cross-attention is added between
686
+ the self-attention layers, following the architecture described in [Attention is
687
+ all you need](https://arxiv.org/abs/1706.03762) by Ashish Vaswani,
688
+ Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N. Gomez, Lukasz Kaiser and Illia Polosukhin.
689
+
690
+ To behave as an decoder the model needs to be initialized with the
691
+ `is_decoder` argument of the configuration set to `True`.
692
+ To be used in a Seq2Seq model, the model needs to initialized with both `is_decoder`
693
+ argument and `add_cross_attention` set to `True`; an
694
+ `encoder_hidden_states` is then expected as an input to the forward pass.
695
+ """
696
+
697
+ def __init__(self, config: ChatGLMConfig):
698
+ super().__init__(config)
699
+
700
+ # recording parameters
701
+ self.max_sequence_length = config.max_sequence_length
702
+ self.hidden_size = config.hidden_size
703
+ self.params_dtype = torch.half
704
+ self.num_attention_heads = config.num_attention_heads
705
+ self.vocab_size = config.vocab_size
706
+ self.num_layers = config.num_layers
707
+ self.layernorm_epsilon = config.layernorm_epsilon
708
+ self.inner_hidden_size = config.inner_hidden_size
709
+ self.hidden_size_per_attention_head = self.hidden_size // self.num_attention_heads
710
+ self.position_encoding_2d = config.position_encoding_2d
711
+
712
+ self.word_embeddings = skip_init(
713
+ torch.nn.Embedding,
714
+ num_embeddings=self.vocab_size, embedding_dim=self.hidden_size,
715
+ dtype=self.params_dtype
716
+ )
717
+
718
+ def get_layer(layer_id):
719
+ return GLMBlock(
720
+ self.hidden_size,
721
+ self.num_attention_heads,
722
+ self.layernorm_epsilon,
723
+ layer_id,
724
+ inner_hidden_size=self.inner_hidden_size,
725
+ hidden_size_per_attention_head=self.hidden_size_per_attention_head,
726
+ layernorm=LayerNorm,
727
+ use_bias=True,
728
+ params_dtype=self.params_dtype,
729
+ position_encoding_2d=self.position_encoding_2d,
730
+ )
731
+
732
+ self.layers = torch.nn.ModuleList(
733
+ [get_layer(layer_id) for layer_id in range(self.num_layers)]
734
+ )
735
+
736
+ # Final layer norm before output.
737
+ self.final_layernorm = LayerNorm(self.hidden_size, eps=self.layernorm_epsilon)
738
+
739
+ def get_input_embeddings(self):
740
+ return self.word_embeddings
741
+
742
+ def set_input_embeddings(self, new_embeddings: torch.Tensor):
743
+ self.word_embeddings = new_embeddings
744
+
745
+ @staticmethod
746
+ def get_masks(seq, device):
747
+ context_length = seq.index(150004) + 1
748
+
749
+ attention_mask = torch.ones((1, len(seq), len(seq)), device=device)
750
+ attention_mask.tril_()
751
+ attention_mask[..., :context_length - 1] = 1
752
+ attention_mask.unsqueeze_(1)
753
+ attention_mask = (attention_mask < 0.5).bool()
754
+
755
+ return attention_mask
756
+
757
+ def get_position_ids(self, seq, mask_position, device, gmask=False):
758
+ context_length = seq.index(150004) + 1
759
+ if self.position_encoding_2d:
760
+ seq_length = seq.index(150004)
761
+ position_ids = torch.arange(context_length, dtype=torch.long, device=device)
762
+ if not gmask:
763
+ position_ids[seq_length:] = mask_position
764
+ block_position_ids = torch.cat((
765
+ torch.zeros(seq_length, dtype=torch.long, device=device),
766
+ torch.arange(context_length - seq_length, dtype=torch.long, device=device) + 1
767
+ ))
768
+ position_ids = torch.stack((position_ids, block_position_ids), dim=0)
769
+ else:
770
+ position_ids = torch.arange(context_length, dtype=torch.long, device=device)
771
+ if not gmask:
772
+ position_ids[context_length - 1:] = mask_position
773
+
774
+ position_ids = position_ids.unsqueeze(0)
775
+
776
+ return position_ids
777
+
778
+ @add_start_docstrings_to_model_forward(CHATGLM_6B_INPUTS_DOCSTRING.format("batch_size, sequence_length"))
779
+ @add_code_sample_docstrings(
780
+ checkpoint=_CHECKPOINT_FOR_DOC,
781
+ output_type=BaseModelOutputWithPastAndCrossAttentions,
782
+ config_class=_CONFIG_FOR_DOC,
783
+ )
784
+ def forward(
785
+ self,
786
+ input_ids: Optional[torch.LongTensor] = None,
787
+ position_ids: Optional[torch.LongTensor] = None,
788
+ attention_mask: Optional[torch.Tensor] = None,
789
+ past_key_values: Optional[Tuple[Tuple[torch.Tensor, torch.Tensor], ...]] = None,
790
+ inputs_embeds: Optional[torch.LongTensor] = None,
791
+ use_cache: Optional[bool] = None,
792
+ output_attentions: Optional[bool] = None,
793
+ output_hidden_states: Optional[bool] = None,
794
+ return_dict: Optional[bool] = None,
795
+ ) -> Union[Tuple[torch.Tensor, ...], BaseModelOutputWithPast]:
796
+
797
+ output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions
798
+ output_hidden_states = (
799
+ output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
800
+ )
801
+ use_cache = use_cache if use_cache is not None else self.config.use_cache
802
+ return_dict = return_dict if return_dict is not None else self.config.use_return_dict
803
+
804
+ if input_ids is not None and inputs_embeds is not None:
805
+ raise ValueError("You cannot specify both input_ids and inputs_embeds at the same time")
806
+ elif input_ids is not None:
807
+ batch_size, seq_length = input_ids.shape[:2]
808
+ elif inputs_embeds is not None:
809
+ batch_size, seq_length, _ = inputs_embeds.shape[:2]
810
+ else:
811
+ raise ValueError("You have to specify either input_ids or inputs_embeds")
812
+
813
+ if past_key_values is None:
814
+ past_key_values = tuple([None] * len(self.layers))
815
+
816
+ MASK, gMASK = 150000, 150001
817
+ mask_token = MASK if MASK in input_ids else gMASK
818
+ use_gmask = False if MASK in input_ids else gMASK
819
+ seq = input_ids[0].tolist()
820
+
821
+ mask_position = seq.index(mask_token)
822
+
823
+ if attention_mask is None:
824
+ attention_mask = self.get_masks(
825
+ seq=seq,
826
+ device=input_ids.device
827
+ )
828
+
829
+ if position_ids is None:
830
+ position_ids = self.get_position_ids(
831
+ seq=seq,
832
+ mask_position=mask_position,
833
+ device=input_ids.device,
834
+ gmask=use_gmask
835
+ )
836
+
837
+ if inputs_embeds is None:
838
+ inputs_embeds = self.word_embeddings(input_ids)
839
+
840
+ # [seq_len, batch, hidden_size]
841
+ hidden_states = inputs_embeds.transpose(0, 1)
842
+
843
+ presents = () if use_cache else None
844
+ all_self_attentions = () if output_attentions else None
845
+ all_hidden_states = () if output_hidden_states else None
846
+
847
+ seq_length_with_past = seq_length
848
+ past_key_values_length = 0
849
+ if past_key_values[0] is not None:
850
+ past_key_values_length = past_key_values[0][0].shape[0]
851
+ seq_length_with_past = seq_length_with_past + past_key_values_length
852
+ if attention_mask is None:
853
+ attention_mask = torch.zeros(1, 1, device=input_ids.device).bool()
854
+
855
+ else:
856
+ attention_mask = attention_mask.to(input_ids.device)
857
+
858
+ for i, layer in enumerate(self.layers):
859
+
860
+ if output_hidden_states:
861
+ all_hidden_states = all_hidden_states + (hidden_states,)
862
+
863
+ layer_ret = layer(
864
+ hidden_states,
865
+ position_ids=position_ids,
866
+ attention_mask=attention_mask,
867
+ layer_id=torch.tensor(i),
868
+ layer_past=past_key_values[i],
869
+ use_cache=use_cache,
870
+ output_attentions=output_attentions
871
+ )
872
+
873
+ hidden_states = layer_ret[0]
874
+
875
+ if use_cache:
876
+ presents = presents + (layer_ret[1],)
877
+
878
+ if output_attentions:
879
+ all_self_attentions = all_self_attentions + (layer_ret[2 if use_cache else 1],)
880
+
881
+ # Final layer norm.
882
+ hidden_states = self.final_layernorm(hidden_states)
883
+
884
+ if output_hidden_states:
885
+ all_hidden_states = all_hidden_states + (hidden_states,)
886
+
887
+ if not return_dict:
888
+ return tuple(v for v in [hidden_states, presents, all_hidden_states, all_self_attentions] if v is not None)
889
+
890
+ return BaseModelOutputWithPast(
891
+ last_hidden_state=hidden_states,
892
+ past_key_values=presents,
893
+ hidden_states=all_hidden_states,
894
+ attentions=all_self_attentions,
895
+ )
896
+
897
+
898
+ class ChatGLMForConditionalGeneration(ChatGLMPreTrainedModel):
899
+ def __init__(self, config):
900
+ super().__init__(config)
901
+
902
+ # self.hidden_size = config.hidden_size
903
+ # self.params_dtype = torch.half
904
+ # self.vocab_size = config.vocab_size
905
+ self.max_sequence_length = config.max_sequence_length
906
+
907
+ self.position_encoding_2d = config.position_encoding_2d
908
+
909
+ self.transformer = ChatGLMModel(config)
910
+
911
+ self.lm_head = skip_init(
912
+ nn.Linear,
913
+ config.hidden_size,
914
+ config.vocab_size,
915
+ bias=False,
916
+ dtype=torch.half
917
+ )
918
+
919
+ def get_output_embeddings(self):
920
+ return self.lm_head
921
+
922
+ def set_output_embeddings(self, new_embeddings):
923
+ self.lm_head = new_embeddings
924
+
925
+ def get_masks_and_position_ids(self, seq, mask_position, context_length, device, gmask=False):
926
+ attention_mask = torch.ones((1, context_length, context_length), device=device)
927
+ attention_mask.tril_()
928
+ attention_mask[..., :context_length - 1] = 1
929
+ attention_mask.unsqueeze_(1)
930
+ attention_mask = (attention_mask < 0.5).bool()
931
+
932
+ if self.position_encoding_2d:
933
+ seq_length = seq.index(150004)
934
+ position_ids = torch.arange(context_length, dtype=torch.long, device=device)
935
+ if not gmask:
936
+ position_ids[seq_length:] = mask_position
937
+ block_position_ids = torch.cat((
938
+ torch.zeros(seq_length, dtype=torch.long, device=device),
939
+ torch.arange(context_length - seq_length, dtype=torch.long, device=device) + 1
940
+ ))
941
+ position_ids = torch.stack((position_ids, block_position_ids), dim=0)
942
+ else:
943
+ position_ids = torch.arange(context_length, dtype=torch.long, device=device)
944
+ if not gmask:
945
+ position_ids[context_length - 1:] = mask_position
946
+
947
+ position_ids = position_ids.unsqueeze(0)
948
+
949
+ return attention_mask, position_ids
950
+
951
+ def prepare_inputs_for_generation(
952
+ self,
953
+ input_ids: torch.LongTensor,
954
+ past: Optional[torch.Tensor] = None,
955
+ attention_mask: Optional[torch.Tensor] = None,
956
+ **kwargs
957
+ ) -> dict:
958
+
959
+ MASK, gMASK = 150000, 150001
960
+ mask_token = MASK if MASK in input_ids else gMASK
961
+ use_gmask = False if MASK in input_ids else gMASK
962
+ seq = input_ids[0].tolist()
963
+ mask_position = seq.index(mask_token)
964
+
965
+ if mask_token not in seq:
966
+ raise ValueError("You have to add either [MASK] or [gMASK] in your input")
967
+
968
+ # only last token for input_ids if past is not None
969
+ if past:
970
+ context_length = seq.index(150004)
971
+ last_token = input_ids[:, -1].unsqueeze(-1)
972
+ if self.position_encoding_2d:
973
+ position_ids = torch.tensor([[[mask_position], [len(seq) - context_length]]], dtype=torch.long,
974
+ device=input_ids.device)
975
+ else:
976
+ position_ids = torch.tensor([[mask_position]], dtype=torch.long, device=input_ids.device)
977
+
978
+ return {
979
+ "input_ids": last_token,
980
+ "past_key_values": past,
981
+ "position_ids": position_ids,
982
+ }
983
+ else:
984
+ attention_mask, position_ids = self.get_masks_and_position_ids(
985
+ seq=seq,
986
+ mask_position=mask_position,
987
+ context_length=len(seq),
988
+ device=input_ids.device,
989
+ gmask=use_gmask
990
+ )
991
+
992
+ return {
993
+ "input_ids": input_ids,
994
+ "past_key_values": past,
995
+ "position_ids": position_ids,
996
+ "attention_mask": attention_mask
997
+ }
998
+
999
+ def forward(
1000
+ self,
1001
+ input_ids: Optional[torch.Tensor] = None,
1002
+ position_ids: Optional[torch.Tensor] = None,
1003
+ attention_mask: Optional[torch.Tensor] = None,
1004
+ past_key_values: Optional[Tuple[torch.FloatTensor]] = None,
1005
+ inputs_embeds: Optional[torch.Tensor] = None,
1006
+ labels: Optional[torch.Tensor] = None,
1007
+ use_cache: Optional[bool] = None,
1008
+ output_attentions: Optional[bool] = None,
1009
+ output_hidden_states: Optional[bool] = None,
1010
+ return_dict: Optional[bool] = None,
1011
+ ):
1012
+ use_cache = use_cache if use_cache is not None else self.config.use_cache
1013
+ return_dict = return_dict if return_dict is not None else self.config.use_return_dict
1014
+
1015
+ transformer_outputs = self.transformer(
1016
+ input_ids=input_ids,
1017
+ position_ids=position_ids,
1018
+ attention_mask=attention_mask,
1019
+ past_key_values=past_key_values,
1020
+ inputs_embeds=inputs_embeds,
1021
+ use_cache=use_cache,
1022
+ output_attentions=output_attentions,
1023
+ output_hidden_states=output_hidden_states,
1024
+ return_dict=return_dict,
1025
+ )
1026
+
1027
+ hidden_states = transformer_outputs[0]
1028
+
1029
+ lm_logits = self.lm_head(hidden_states).permute(1, 0, 2).contiguous()
1030
+
1031
+ loss = None
1032
+ if labels is not None:
1033
+ lm_logits = lm_logits.to(torch.float32)
1034
+
1035
+ # Shift so that tokens < n predict n
1036
+ shift_logits = lm_logits[..., :-1, :].contiguous()
1037
+ shift_labels = labels[..., 1:].contiguous()
1038
+ # Flatten the tokens
1039
+ loss_fct = CrossEntropyLoss()
1040
+ loss = loss_fct(shift_logits.view(-1, shift_logits.size(-1)), shift_labels.view(-1))
1041
+
1042
+ lm_logits = lm_logits.to(hidden_states.dtype)
1043
+ loss = loss.to(hidden_states.dtype)
1044
+
1045
+ if not return_dict:
1046
+ output = (lm_logits,) + transformer_outputs[1:]
1047
+ return ((loss,) + output) if loss is not None else output
1048
+
1049
+ return CausalLMOutputWithPast(
1050
+ loss=loss,
1051
+ logits=lm_logits,
1052
+ past_key_values=transformer_outputs.past_key_values,
1053
+ hidden_states=transformer_outputs.hidden_states,
1054
+ attentions=transformer_outputs.attentions,
1055
+ )
1056
+
1057
+ @staticmethod
1058
+ def _reorder_cache(
1059
+ past: Tuple[Tuple[torch.Tensor, torch.Tensor], ...], beam_idx: torch.LongTensor
1060
+ ) -> Tuple[Tuple[torch.Tensor, torch.Tensor], ...]:
1061
+ """
1062
+ This function is used to re-order the `past_key_values` cache if [`~PreTrainedModel.beam_search`] or
1063
+ [`~PreTrainedModel.beam_sample`] is called. This is required to match `past_key_values` with the correct
1064
+ beam_idx at every generation step.
1065
+
1066
+ Output shares the same memory storage as `past`.
1067
+ """
1068
+ return tuple(
1069
+ (
1070
+ layer_past[0].index_select(1, beam_idx.to(layer_past[0].device)),
1071
+ layer_past[1].index_select(1, beam_idx.to(layer_past[1].device)),
1072
+ )
1073
+ for layer_past in past
1074
+ )
1075
+
1076
+ @torch.no_grad()
1077
+ def chat(self, tokenizer, query: str, history: List[Tuple[str, str]] = [], max_length: int = 2048, num_beams=1,
1078
+ do_sample=True, top_p=0.7, temperature=0.95, **kwargs):
1079
+ gen_kwargs = {"max_length": max_length, "num_beams": num_beams, "do_sample": do_sample, "top_p": top_p,
1080
+ "temperature": temperature, **kwargs}
1081
+ if not history:
1082
+ prompt = query
1083
+ else:
1084
+ prompt = ""
1085
+ for i, (old_query, response) in enumerate(history):
1086
+ prompt += "[Round {}]\n问:{}\n答:{}\n".format(i, old_query, response)
1087
+ prompt += "[Round {}]\n问:{}\n答:".format(len(history), query)
1088
+ input_ids = tokenizer([prompt], return_tensors="pt", padding=True)
1089
+ input_ids = input_ids.to(self.device)
1090
+ outputs = self.generate(**input_ids, **gen_kwargs)
1091
+ outputs = outputs.tolist()[0][len(input_ids["input_ids"][0]) - 2:]
1092
+ response = tokenizer.decode(outputs)
1093
+ response = response.strip()
1094
+ response = response.replace("[[训练时间]]", "2023年")
1095
+ history.append((query, response))
1096
+ return response, history
1097
+
1098
+ @torch.no_grad()
1099
+ def generate(
1100
+ self,
1101
+ **kwargs,
1102
+ ):
1103
+ MASK, gMASK = 150000, 150001
1104
+ bos, eos = 150004, 150005
1105
+
1106
+ if "eos_token_id" not in kwargs:
1107
+ kwargs["eos_token_id"] = eos
1108
+
1109
+ stop = False
1110
+
1111
+ return_seqs = []
1112
+
1113
+ while True:
1114
+ output_ids = super().generate(**kwargs)
1115
+
1116
+ return_seqs = []
1117
+ max_length = 0
1118
+
1119
+ for i in range(output_ids.shape[0]):
1120
+ output_seq = output_ids[i].tolist()
1121
+ mask_token = MASK if MASK in output_seq else gMASK
1122
+ mask_position = output_seq.index(mask_token)
1123
+ bos_position = output_seq.index(bos)
1124
+ if eos in output_seq:
1125
+ eos_position = output_seq.index(eos)
1126
+ else:
1127
+ eos_position = len(output_seq)
1128
+
1129
+ return_seq = output_seq[:mask_position] + output_seq[bos_position + 1:eos_position] + output_seq[
1130
+ mask_position + 1:bos_position]
1131
+ max_length = max(max_length, len(return_seq))
1132
+ return_seqs.append(return_seq)
1133
+
1134
+ for i in range(output_ids.shape[0]):
1135
+ return_seqs[i] = [0] * (max_length - len(return_seqs[i])) + return_seqs[i] # padding
1136
+ if mask_token not in return_seqs[i]:
1137
+ stop = True
1138
+
1139
+ if stop:
1140
+ break
1141
+
1142
+ for return_seq in return_seqs:
1143
+ return_seq += [bos]
1144
+
1145
+ kwargs['input_ids'] = torch.tensor(return_seqs, dtype=torch.long, device=kwargs['input_ids'].device)
1146
+
1147
+ return torch.tensor(return_seqs, dtype=torch.long, device=kwargs['input_ids'].device)
1148
+
1149
+ def quantize(self, bits: int):
1150
+ from .quantization import quantize
1151
+ self.transformer = quantize(self.transformer, bits)
1152
+ return self
pytorch_model-00001-of-00008.bin ADDED
@@ -0,0 +1 @@
 
 
1
+ /mnt/vepfs/zxdu/checkpoints/qa-glm-6b-sft-v0.8-v2-original-lr/pytorch_model-00001-of-00008.bin
pytorch_model-00002-of-00008.bin ADDED
@@ -0,0 +1 @@
 
 
1
+ /mnt/vepfs/zxdu/checkpoints/qa-glm-6b-sft-v0.8-v2-original-lr/pytorch_model-00002-of-00008.bin
pytorch_model-00003-of-00008.bin ADDED
@@ -0,0 +1 @@
 
 
1
+ /mnt/vepfs/zxdu/checkpoints/qa-glm-6b-sft-v0.8-v2-original-lr/pytorch_model-00003-of-00008.bin
pytorch_model-00004-of-00008.bin ADDED
@@ -0,0 +1 @@
 
 
1
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pytorch_model-00005-of-00008.bin ADDED
@@ -0,0 +1 @@
 
 
1
+ /mnt/vepfs/zxdu/checkpoints/qa-glm-6b-sft-v0.8-v2-original-lr/pytorch_model-00005-of-00008.bin
pytorch_model-00006-of-00008.bin ADDED
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1
+ /mnt/vepfs/zxdu/checkpoints/qa-glm-6b-sft-v0.8-v2-original-lr/pytorch_model-00006-of-00008.bin
pytorch_model-00007-of-00008.bin ADDED
@@ -0,0 +1 @@
 
 
1
+ /mnt/vepfs/zxdu/checkpoints/qa-glm-6b-sft-v0.8-v2-original-lr/pytorch_model-00007-of-00008.bin
pytorch_model-00008-of-00008.bin ADDED
@@ -0,0 +1 @@
 
 
1
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pytorch_model.bin.index.json ADDED
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+ "transformer.layers.9.post_attention_layernorm.weight": "pytorch_model-00003-of-00008.bin",
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+ "transformer.word_embeddings.weight": "pytorch_model-00001-of-00008.bin"
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+ }
375
+ }
quantization.py ADDED
@@ -0,0 +1,187 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ from torch.nn import Linear
2
+ from torch.nn.parameter import Parameter
3
+
4
+ import bz2
5
+ import torch
6
+ import base64
7
+ import ctypes
8
+
9
+ from typing import List
10
+ from cpm_kernels.kernels.base import LazyKernelCModule, KernelFunction, round_up
11
+
12
+
13
+ class W8A16Linear(torch.autograd.Function):
14
+ @staticmethod
15
+ def forward(ctx, inp: torch.Tensor, quant_w: torch.Tensor, scale_w: torch.Tensor, weight_bit_width):
16
+ ctx.inp_shape = inp.size()
17
+ ctx.weight_shape = quant_w.size()
18
+ ctx.weight_bit_width = weight_bit_width
19
+ out_features = quant_w.size(0)
20
+ inp = inp.contiguous().view(-1, inp.size(-1))
21
+ weight = extract_weight_to_half(quant_w, scale_w, weight_bit_width)
22
+ output = inp.mm(weight.t())
23
+ ctx.save_for_backward(inp, quant_w, scale_w)
24
+ return output.view(*(ctx.inp_shape[:-1] + (out_features,)))
25
+
26
+ @staticmethod
27
+ def backward(ctx, grad_output: torch.Tensor):
28
+ inp, quant_w, scale_w = ctx.saved_tensors
29
+ weight = extract_weight_to_half(quant_w, scale_w, ctx.weight_bit_width)
30
+ grad_output = grad_output.contiguous().view(-1, weight.size(0))
31
+ grad_input = grad_output.mm(weight)
32
+ grad_weight = grad_output.t().mm(inp)
33
+ return grad_input.view(ctx.inp_shape), grad_weight.view(ctx.weight_shape), None
34
+
35
+
36
+ class Kernel:
37
+ def __init__(self, code: bytes, function_names: List[str]):
38
+ self.code = code
39
+ self._function_names = function_names
40
+ self._cmodule = LazyKernelCModule(self.code)
41
+
42
+ for name in self._function_names:
43
+ setattr(self, name, KernelFunction(self._cmodule, name))
44
+
45
+
46
+ quantization_code = "$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"
47
+
48
+ kernels = Kernel(
49
+ bz2.decompress(base64.b64decode(quantization_code)),
50
+ [
51
+ "int4WeightCompression",
52
+ "int4WeightExtractionFloat",
53
+ "int4WeightExtractionHalf",
54
+ "int8WeightExtractionFloat",
55
+ "int8WeightExtractionHalf",
56
+ ],
57
+ )
58
+
59
+
60
+ def compress_int4_weight(weight: torch.Tensor): # (n, m)
61
+ with torch.cuda.device(weight.device):
62
+ n, m = weight.size(0), weight.size(1)
63
+ assert m % 2 == 0
64
+ m = m // 2
65
+ out = torch.empty(n, m, dtype=torch.int8, device="cuda")
66
+ stream = torch.cuda.current_stream()
67
+
68
+ gridDim = (n, 1, 1)
69
+ blockDim = (min(round_up(m, 32), 1024), 1, 1)
70
+
71
+ kernels.int4WeightCompression(
72
+ gridDim,
73
+ blockDim,
74
+ 0,
75
+ stream,
76
+ [ctypes.c_void_p(weight.data_ptr()), ctypes.c_void_p(out.data_ptr()), ctypes.c_int32(n), ctypes.c_int32(m)],
77
+ )
78
+ return out
79
+
80
+
81
+ def extract_weight_to_half(weight: torch.Tensor, scale_list: torch.Tensor, source_bit_width: int):
82
+ if source_bit_width == 8:
83
+ func = kernels.int8WeightExtractionHalf
84
+ elif source_bit_width == 4:
85
+ func = kernels.int4WeightExtractionHalf
86
+ else:
87
+ assert False, "Unsupported bit-width"
88
+
89
+ with torch.cuda.device(weight.device):
90
+ n, m = weight.size(0), weight.size(1)
91
+ out = torch.empty(n, m * (8 // source_bit_width), dtype=torch.half, device="cuda")
92
+ stream = torch.cuda.current_stream()
93
+
94
+ gridDim = (n, 1, 1)
95
+ blockDim = (min(round_up(m, 32), 1024), 1, 1)
96
+
97
+ func(
98
+ gridDim,
99
+ blockDim,
100
+ 0,
101
+ stream,
102
+ [
103
+ ctypes.c_void_p(weight.data_ptr()),
104
+ ctypes.c_void_p(scale_list.data_ptr()),
105
+ ctypes.c_void_p(out.data_ptr()),
106
+ ctypes.c_int32(n),
107
+ ctypes.c_int32(m),
108
+ ],
109
+ )
110
+ return out
111
+
112
+
113
+ class QuantizedLinear(Linear):
114
+ def __init__(self, weight_bit_width: int, weight_tensor=None, bias_tensor=None, *args, **kwargs):
115
+ super(QuantizedLinear, self).__init__(*args, **kwargs)
116
+ self.weight_bit_width = weight_bit_width
117
+
118
+ shape = self.weight.shape
119
+ del self.weight
120
+
121
+ if weight_tensor is None:
122
+ self.weight = torch.empty(
123
+ shape[0], shape[1] * weight_bit_width // 8, dtype=torch.int8, device=kwargs["device"]
124
+ )
125
+ self.weight_scale = torch.empty(shape[0], dtype=kwargs["params_dtype"], device=kwargs["device"])
126
+ else:
127
+ self.weight_scale = (weight_tensor.abs().max(dim=-1).values / ((2 ** (weight_bit_width - 1)) - 1)).half()
128
+ self.weight = torch.round(weight_tensor / self.weight_scale[:, None]).to(torch.int8)
129
+ if weight_bit_width == 4:
130
+ self.weight = compress_int4_weight(self.weight)
131
+
132
+ self.weight = Parameter(self.weight.to(kwargs["device"]), requires_grad=False)
133
+ self.weight_scale = Parameter(self.weight_scale.to(kwargs["device"]), requires_grad=False)
134
+ self.bias = Parameter(bias_tensor.to(kwargs["device"]), requires_grad=False)
135
+
136
+ def forward(self, input):
137
+ output = W8A16Linear.apply(input, self.weight, self.weight_scale, self.weight_bit_width)
138
+ if self.bias is not None:
139
+ output = output + self.bias
140
+ return output
141
+
142
+
143
+ def quantize(model, weight_bit_width):
144
+ """Replace fp16 linear with quantized linear"""
145
+
146
+ for layer in model.layers:
147
+ layer.attention.query_key_value = QuantizedLinear(
148
+ weight_bit_width=weight_bit_width,
149
+ weight_tensor=layer.attention.query_key_value.weight.to(torch.cuda.current_device()),
150
+ bias_tensor=layer.attention.query_key_value.bias,
151
+ in_features=layer.attention.query_key_value.in_features,
152
+ out_features=layer.attention.query_key_value.out_features,
153
+ bias=True,
154
+ dtype=torch.half,
155
+ device=layer.attention.query_key_value.weight.device,
156
+ )
157
+ layer.attention.dense = QuantizedLinear(
158
+ weight_bit_width=weight_bit_width,
159
+ weight_tensor=layer.attention.dense.weight.to(torch.cuda.current_device()),
160
+ bias_tensor=layer.attention.dense.bias,
161
+ in_features=layer.attention.dense.in_features,
162
+ out_features=layer.attention.dense.out_features,
163
+ bias=True,
164
+ dtype=torch.half,
165
+ device=layer.attention.dense.weight.device,
166
+ )
167
+ layer.mlp.dense_h_to_4h = QuantizedLinear(
168
+ weight_bit_width=weight_bit_width,
169
+ weight_tensor=layer.mlp.dense_h_to_4h.weight.to(torch.cuda.current_device()),
170
+ bias_tensor=layer.mlp.dense_h_to_4h.bias,
171
+ in_features=layer.mlp.dense_h_to_4h.in_features,
172
+ out_features=layer.mlp.dense_h_to_4h.out_features,
173
+ bias=True,
174
+ dtype=torch.half,
175
+ device=layer.mlp.dense_h_to_4h.weight.device,
176
+ )
177
+ layer.mlp.dense_4h_to_h = QuantizedLinear(
178
+ weight_bit_width=weight_bit_width,
179
+ weight_tensor=layer.mlp.dense_4h_to_h.weight.to(torch.cuda.current_device()),
180
+ bias_tensor=layer.mlp.dense_4h_to_h.bias,
181
+ in_features=layer.mlp.dense_4h_to_h.in_features,
182
+ out_features=layer.mlp.dense_4h_to_h.out_features,
183
+ bias=True,
184
+ dtype=torch.half,
185
+ device=layer.mlp.dense_4h_to_h.weight.device,
186
+ )
187
+ return model
tokenization_chatglm.py ADDED
@@ -0,0 +1,347 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ """Tokenization classes for ChatGLM."""
2
+ import sys
3
+ import unicodedata
4
+ from typing import List, Optional, Union
5
+ from functools import lru_cache
6
+ import os
7
+ import collections
8
+ import re
9
+
10
+ from transformers.tokenization_utils import PreTrainedTokenizer
11
+ from icetk.text_tokenizer import TextTokenizer
12
+ from icetk.utils import auto_create
13
+ import icetk.sentencepiece_model_pb2 as sp_model
14
+ from transformers.utils import logging
15
+
16
+ logger = logging.get_logger(__name__)
17
+
18
+ VOCAB_FILES_NAMES = {"vocab_file": "ice_text.model"}
19
+
20
+ PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES = {
21
+ "THUDM/chatglm-6b": 2048,
22
+ }
23
+
24
+
25
+ class SPTokenizer:
26
+ def __init__(
27
+ self,
28
+ vocab_file,
29
+ max_blank_length=80,
30
+ byte_fallback=True,
31
+ ):
32
+ assert vocab_file is not None
33
+ self.vocab_file = vocab_file
34
+ self.special_tokens = ["[MASK]", "[gMASK]", "[sMASK]", "<unused_0>", "<sop>", "<eop>", "<ENC>", "<dBLOCK>"]
35
+ self.max_blank_length = max_blank_length
36
+ self.byte_fallback = byte_fallback
37
+ self.text_tokenizer = self._build_text_tokenizer(encode_special_tokens=False)
38
+ self.special_text_tokenizer = self._build_text_tokenizer(encode_special_tokens=True)
39
+
40
+ @staticmethod
41
+ def _configure_tokenizer(
42
+ text_tokenizer: TextTokenizer,
43
+ special_tokens: List[str],
44
+ max_blank_length: int,
45
+ byte_fallback: bool,
46
+ encode_special_tokens=False,
47
+ ):
48
+ # special token
49
+ special_token_type = 4 if encode_special_tokens else 3 # 3 - CONTROL, 4 - USER_DEFINE
50
+ for token in special_tokens:
51
+ text_tokenizer.proto.pieces.append(
52
+ sp_model.ModelProto.SentencePiece(piece=token, score=0.0, type=special_token_type)
53
+ )
54
+ # whitespaces
55
+ for token in [SPTokenizer.get_tab_token()] + [
56
+ SPTokenizer.get_blank_token(i) for i in range(2, max_blank_length + 1)
57
+ ]:
58
+ text_tokenizer.proto.pieces.append(sp_model.ModelProto.SentencePiece(piece=token, score=0.0, type=4))
59
+ # byte fallback
60
+ if byte_fallback:
61
+ text_tokenizer.proto.trainer_spec.byte_fallback = True
62
+ for i in range(256):
63
+ text_tokenizer.proto.pieces.append(
64
+ sp_model.ModelProto.SentencePiece(piece="<0x{:02X}>".format(i), score=0.0, type=6)
65
+ )
66
+ text_tokenizer.refresh()
67
+
68
+ def _build_text_tokenizer(self, encode_special_tokens=False):
69
+ tokenizer = TextTokenizer(self.vocab_file)
70
+ self._configure_tokenizer(
71
+ tokenizer, self.special_tokens, self.max_blank_length, self.byte_fallback, encode_special_tokens
72
+ )
73
+ return tokenizer
74
+
75
+ def _get_text_tokenizer(self, encode_special_tokens=False):
76
+ if encode_special_tokens:
77
+ return self.special_text_tokenizer
78
+ else:
79
+ return self.text_tokenizer
80
+
81
+ @staticmethod
82
+ def get_blank_token(length: int):
83
+ assert length >= 2
84
+ return f"<|blank_{length}|>"
85
+
86
+ @staticmethod
87
+ def get_tab_token():
88
+ return f"<|tab|>"
89
+
90
+ @property
91
+ def num_image_tokens(self):
92
+ return 20000
93
+
94
+ @property
95
+ def num_text_tokens(self):
96
+ return self.text_tokenizer.num_tokens
97
+
98
+ @property
99
+ def num_tokens(self):
100
+ return self.num_image_tokens + self.num_text_tokens
101
+
102
+ @staticmethod
103
+ def _encode_whitespaces(text: str, max_len: int = 80):
104
+ text = text.replace("\t", SPTokenizer.get_tab_token())
105
+ for i in range(max_len, 1, -1):
106
+ text = text.replace(" " * i, SPTokenizer.get_blank_token(i))
107
+ return text
108
+
109
+ def _preprocess(self, text: str, linebreak=True, whitespaces=True):
110
+ if linebreak:
111
+ text = text.replace("\n", "<n>")
112
+ if whitespaces:
113
+ text = self._encode_whitespaces(text, max_len=self.max_blank_length)
114
+ return text
115
+
116
+ def encode(
117
+ self, text: str, linebreak=True, whitespaces=True, special_tokens=False, add_dummy_prefix=True
118
+ ) -> List[int]:
119
+ """
120
+ @param text: Text to encode.
121
+ @param linebreak: Whether to encode newline (\n) in text.
122
+ @param whitespaces: Whether to encode multiple whitespaces or tab in text, useful for source code encoding.
123
+ @param special_tokens: Whether to encode special token ([MASK], [gMASK], etc.) in text.
124
+ @param add_dummy_prefix: Whether to add dummy blank space in the beginning.
125
+ """
126
+ text = self._preprocess(text, linebreak, whitespaces)
127
+ if not add_dummy_prefix:
128
+ text = "<n>" + text
129
+ tmp = self._get_text_tokenizer(encode_special_tokens=special_tokens).encode(text)
130
+ tokens = [x + self.num_image_tokens for x in tmp]
131
+ return tokens if add_dummy_prefix else tokens[2:]
132
+
133
+ def decode(self, text_ids: List[int], special_tokens=False) -> str:
134
+ ids = [int(_id) - self.num_image_tokens for _id in text_ids]
135
+ text = self._get_text_tokenizer(encode_special_tokens=special_tokens).decode(ids)
136
+ text = text.replace("<n>", "\n")
137
+ text = text.replace(SPTokenizer.get_tab_token(), "\t")
138
+ for i in range(2, self.max_blank_length + 1):
139
+ text = text.replace(self.get_blank_token(i), " " * i)
140
+ return text
141
+
142
+ def tokenize(
143
+ self, text: str, linebreak=True, whitespaces=True, special_tokens=False, add_dummy_prefix=True
144
+ ) -> List[str]:
145
+ """
146
+ @param text: Text to encode.
147
+ @param linebreak: Whether to encode newline (\n) in text.
148
+ @param whitespaces: Whether to encode multiple whitespaces or tab in text, useful for source code encoding.
149
+ @param special_tokens: Whether to encode special token ([MASK], [gMASK], etc.) in text.
150
+ @param add_dummy_prefix: Whether to add dummy blank space in the beginning.
151
+ """
152
+ text = self._preprocess(text, linebreak, whitespaces)
153
+ if not add_dummy_prefix:
154
+ text = "<n>" + text
155
+ tokens = self._get_text_tokenizer(encode_special_tokens=special_tokens).tokenize(text)
156
+ return tokens if add_dummy_prefix else tokens[2:]
157
+
158
+ def __getitem__(self, x: Union[int, str]):
159
+ if isinstance(x, int):
160
+ if x < self.num_image_tokens:
161
+ return "<image_{}>".format(x)
162
+ else:
163
+ return self.text_tokenizer.convert_id_to_token(x - self.num_image_tokens)
164
+ elif isinstance(x, str):
165
+ if x.startswith("<image_") and x.endswith(">") and x[7:-1].isdigit():
166
+ return int(x[7:-1])
167
+ else:
168
+ return self.text_tokenizer.convert_token_to_id(x) + self.num_image_tokens
169
+ else:
170
+ raise ValueError("The key should be str or int.")
171
+
172
+
173
+ class ChatGLMTokenizer(PreTrainedTokenizer):
174
+ """
175
+ Construct a ChatGLM tokenizer. Based on byte-level Byte-Pair-Encoding.
176
+
177
+ Args:
178
+ vocab_file (`str`):
179
+ Path to the vocabulary file.
180
+ """
181
+
182
+ vocab_files_names = VOCAB_FILES_NAMES
183
+ max_model_input_sizes = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
184
+ model_input_names = ["input_ids"]
185
+
186
+ def __init__(
187
+ self,
188
+ vocab_file,
189
+ do_lower_case=False,
190
+ remove_space=False,
191
+ bos_token='sop',
192
+ eos_token='eos',
193
+ eop_token='eop',
194
+ mask_token='[MASK]',
195
+ gmask_token='[gMASK]',
196
+ padding_side="left",
197
+ **kwargs
198
+ ) -> None:
199
+ super().__init__(
200
+ do_lower_case=do_lower_case,
201
+ remove_space=remove_space,
202
+ padding_side=padding_side,
203
+ **kwargs
204
+ )
205
+
206
+ self.do_lower_case = do_lower_case
207
+ self.remove_space = remove_space
208
+ self.vocab_file = vocab_file
209
+
210
+ self.bos_token = bos_token
211
+ self.eos_token = eos_token
212
+ self.eop_token = eop_token
213
+ self.mask_token = mask_token
214
+ self.gMASK_token = gmask_token
215
+
216
+ self.sp_tokenizer = SPTokenizer(vocab_file)
217
+
218
+ """ Initialisation """
219
+
220
+ @property
221
+ def eop_token_id(self) -> Optional[int]:
222
+ """
223
+ `Optional[int]`: Id of the end of sentence token in the vocabulary. Returns `None` if the token has not been
224
+ set.
225
+ """
226
+ if self.eop_token is None:
227
+ return None
228
+ return self.convert_tokens_to_ids(self.eop_token)
229
+
230
+ @property
231
+ def vocab_size(self):
232
+ """ Returns vocab size """
233
+ return self.sp_tokenizer.num_tokens
234
+
235
+ def get_vocab(self):
236
+ """ Returns vocab as a dict """
237
+ vocab = {self._convert_id_to_token(i): i for i in range(self.vocab_size)}
238
+ vocab.update(self.added_tokens_encoder)
239
+ return vocab
240
+
241
+ def preprocess_text(self, inputs):
242
+ if self.remove_space:
243
+ outputs = " ".join(inputs.strip().split())
244
+ else:
245
+ outputs = inputs
246
+
247
+ if self.do_lower_case:
248
+ outputs = outputs.lower()
249
+
250
+ return outputs
251
+
252
+ def _tokenize(self, text, **kwargs):
253
+ """ Returns a tokenized string. """
254
+ text = self.preprocess_text(text)
255
+
256
+ seq = self.sp_tokenizer.tokenize(text)
257
+
258
+ return seq
259
+
260
+ def decode(
261
+ self,
262
+ token_ids: Union[List[int], List[List[int]]],
263
+ skip_special_tokens: bool = False,
264
+ clean_up_tokenization_spaces: bool = True,
265
+ spaces_between_special_tokens: bool = True,
266
+ **kwargs
267
+ ) -> str:
268
+ if isinstance(token_ids[0], list):
269
+ tokens = []
270
+ for single_token_ids in token_ids:
271
+ if self.pad_token_id in single_token_ids: # remove pad
272
+ single_token_ids = list(filter((self.pad_token_id).__ne__, single_token_ids))
273
+ tokens.append(self.sp_tokenizer.decode(single_token_ids))
274
+ return (tokens)
275
+ else:
276
+ if self.pad_token_id in token_ids: # remove pad
277
+ token_ids = list(filter((self.pad_token_id).__ne__, token_ids))
278
+ return self.sp_tokenizer.decode(token_ids)
279
+
280
+ def _convert_token_to_id(self, token):
281
+ """ Converts a token (str) in an id using the vocab. """
282
+ return self.sp_tokenizer[token]
283
+
284
+ def _convert_id_to_token(self, index):
285
+ """Converts an index (integer) in a token (str) using the vocab."""
286
+ return self.sp_tokenizer[index]
287
+
288
+ def save_vocabulary(self, save_directory, filename_prefix=None):
289
+ """
290
+ Save the vocabulary and special tokens file to a directory.
291
+
292
+ Args:
293
+ save_directory (`str`):
294
+ The directory in which to save the vocabulary.
295
+ filename_prefix (`str`, *optional*):
296
+ An optional prefix to add to the named of the saved files.
297
+
298
+ Returns:
299
+ `Tuple(str)`: Paths to the files saved.
300
+ """
301
+ if os.path.isdir(save_directory):
302
+ vocab_file = os.path.join(
303
+ save_directory, VOCAB_FILES_NAMES["vocab_file"]
304
+ )
305
+ else:
306
+ vocab_file = save_directory
307
+
308
+ with open(self.vocab_file, 'rb') as fin:
309
+ proto_str = fin.read()
310
+
311
+ with open(vocab_file, "wb") as writer:
312
+ writer.write(proto_str)
313
+
314
+ return (vocab_file,)
315
+
316
+ def build_inputs_with_special_tokens(
317
+ self, token_ids_0: List[int], token_ids_1: Optional[List[int]] = None
318
+ ) -> List[int]:
319
+ """
320
+ Build model inputs from a sequence or a pair of sequence for sequence classification tasks by concatenating and
321
+ adding special tokens. A BERT sequence has the following format:
322
+
323
+ - single sequence: `[CLS] X [SEP]`
324
+ - pair of sequences: `[CLS] A [SEP] B [SEP]`
325
+
326
+ Args:
327
+ token_ids_0 (`List[int]`):
328
+ List of IDs to which the special tokens will be added.
329
+ token_ids_1 (`List[int]`, *optional*):
330
+ Optional second list of IDs for sequence pairs.
331
+
332
+ Returns:
333
+ `List[int]`: List of [input IDs](../glossary#input-ids) with the appropriate special tokens.
334
+ """
335
+ if token_ids_1 is not None:
336
+ token_ids_0 += token_ids_1
337
+ mask_ids = self.sp_tokenizer[self.mask_token]
338
+ gmask_ids = self.sp_tokenizer[self.gMASK_token]
339
+ if mask_ids not in token_ids_0 and gmask_ids not in token_ids_0:
340
+ token_ids_0 += [gmask_ids]
341
+
342
+ if token_ids_0[-1] != mask_ids and token_ids_0[-1] != gmask_ids:
343
+ token_ids_0 += [self.sp_tokenizer[self.eos_token]]
344
+
345
+ token_ids_0 += [self.sp_tokenizer[self.bos_token]]
346
+
347
+ return token_ids_0
tokenizer_config.json ADDED
@@ -0,0 +1,19 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ {
2
+ "name_or_path": "THUDM/chatglm-6b",
3
+ "bos_token": "<sop>",
4
+ "eop_token": "<eop>",
5
+ "eos_token": "</s>",
6
+ "gmask_token": "[gMASK]",
7
+ "mask_token": "[MASK]",
8
+ "pad_token": "<pad>",
9
+ "unk_token": "<unk>",
10
+ "remove_space": false,
11
+ "do_lower_case": false,
12
+ "tokenizer_class": "ChatGLMTokenizer",
13
+ "auto_map": {
14
+ "AutoTokenizer": [
15
+ "tokenization_chatglm.ChatGLMTokenizer",
16
+ null
17
+ ]
18
+ }
19
+ }