Add chatglm-6b
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You will not use the Software for any act that may undermine China's national security and national unity, harm the public interest of society, or infringe upon the rights and interests of human beings.
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This license shall be governed and construed in accordance with the laws of People’s Republic of China. Any dispute arising from or in connection with this License shall be submitted to Haidian District People's Court in Beijing.
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Note that the license is subject to update to a more comprehensive version. For any questions related to the license and copyright, please contact us at glm-130b@googlegroups.com.
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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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## 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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```shell
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pip install "transformers>=4.23.1,icetk"
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```
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### 代码调用
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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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```python
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from transformers import AutoTokenizer, AutoModel
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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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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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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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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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## 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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替换为
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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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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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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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```
|
||||||
|
@inproceedings{
|
||||||
|
zeng2023glm-130b,
|
||||||
|
title={{GLM}-130B: An Open Bilingual Pre-trained Model},
|
||||||
|
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},
|
||||||
|
booktitle={The Eleventh International Conference on Learning Representations (ICLR)},
|
||||||
|
year={2023},
|
||||||
|
url={https://openreview.net/forum?id=-Aw0rrrPUF}
|
||||||
|
}
|
||||||
|
```
|
||||||
|
```
|
||||||
|
@inproceedings{du2022glm,
|
||||||
|
title={GLM: General Language Model Pretraining with Autoregressive Blank Infilling},
|
||||||
|
author={Du, Zhengxiao and Qian, Yujie and Liu, Xiao and Ding, Ming and Qiu, Jiezhong and Yang, Zhilin and Tang, Jie},
|
||||||
|
booktitle={Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)},
|
||||||
|
pages={320--335},
|
||||||
|
year={2022}
|
||||||
|
}
|
||||||
|
```
|
||||||
|
|
@ -0,0 +1,25 @@
|
||||||
|
{
|
||||||
|
"_name_or_path": "THUDM/chatglm-6b",
|
||||||
|
"architectures": [
|
||||||
|
"ChatGLMModel"
|
||||||
|
],
|
||||||
|
"auto_map": {
|
||||||
|
"AutoConfig": "configuration_chatglm.ChatGLMConfig",
|
||||||
|
"AutoModel": "modeling_chatglm.ChatGLMForConditionalGeneration",
|
||||||
|
"AutoModelForSeq2SeqLM": "modeling_chatglm.ChatGLMForConditionalGeneration"
|
||||||
|
},
|
||||||
|
"bos_token_id": 150004,
|
||||||
|
"eos_token_id": 150005,
|
||||||
|
"hidden_size": 4096,
|
||||||
|
"inner_hidden_size": 16384,
|
||||||
|
"layernorm_epsilon": 1e-05,
|
||||||
|
"max_sequence_length": 2048,
|
||||||
|
"model_type": "chatglm",
|
||||||
|
"num_attention_heads": 32,
|
||||||
|
"num_layers": 28,
|
||||||
|
"position_encoding_2d": true,
|
||||||
|
"torch_dtype": "float16",
|
||||||
|
"transformers_version": "4.23.1",
|
||||||
|
"use_cache": true,
|
||||||
|
"vocab_size": 150528
|
||||||
|
}
|
||||||
|
|
@ -0,0 +1,92 @@
|
||||||
|
""" ChatGLM model configuration """
|
||||||
|
|
||||||
|
from transformers.configuration_utils import PretrainedConfig
|
||||||
|
from transformers.utils import logging
|
||||||
|
|
||||||
|
logger = logging.get_logger(__name__)
|
||||||
|
|
||||||
|
|
||||||
|
class ChatGLMConfig(PretrainedConfig):
|
||||||
|
r"""
|
||||||
|
This is the configuration class to store the configuration of a [`~ChatGLMModel`].
|
||||||
|
It is used to instantiate an ChatGLM model according to the specified arguments, defining the model
|
||||||
|
architecture. Instantiating a configuration with the defaults will yield a similar configuration to that of
|
||||||
|
the ChatGLM-6B [THUDM/ChatGLM-6B](https://huggingface.co/THUDM/chatglm-6b) architecture.
|
||||||
|
|
||||||
|
Configuration objects inherit from [`PretrainedConfig`] and can be used
|
||||||
|
to control the model outputs. Read the documentation from [`PretrainedConfig`]
|
||||||
|
for more information.
|
||||||
|
|
||||||
|
|
||||||
|
Args:
|
||||||
|
vocab_size (`int`, *optional*, defaults to 150528):
|
||||||
|
Vocabulary size of the ChatGLM-6B model. Defines the number of different tokens that can be represented by the
|
||||||
|
`inputs_ids` passed when calling [`~ChatGLMModel`] or
|
||||||
|
[`~TFChatGLMModel`].
|
||||||
|
hidden_size (`int`, *optional*, defaults to 4096):
|
||||||
|
Dimension of the encoder layers and the pooler layer.
|
||||||
|
num_hidden_layers (`int`, *optional*, defaults to 28):
|
||||||
|
Number of hidden layers in the Transformer encoder.
|
||||||
|
num_attention_heads (`int`, *optional*, defaults to 32):
|
||||||
|
Number of attention heads for each attention layer in the Transformer encoder.
|
||||||
|
inner_hidden_size (`int`, *optional*, defaults to 16384):
|
||||||
|
Dimension of the "intermediate" (i.e., feed-forward) layer in the Transformer encoder.
|
||||||
|
max_sequence_length (`int`, *optional*, defaults to 512):
|
||||||
|
The maximum sequence length that this model might ever be used with.
|
||||||
|
Typically set this to something large just in case (e.g., 512 or 1024 or 2048).
|
||||||
|
layernorm_epsilon (`float`, *optional*, defaults to 1e-5):
|
||||||
|
The epsilon used by the layer normalization layers.
|
||||||
|
use_cache (`bool`, *optional*, defaults to `True`):
|
||||||
|
Whether the model should return the last key/values attentions (not used by all models).
|
||||||
|
Example:
|
||||||
|
|
||||||
|
```python
|
||||||
|
>>> from configuration_chatglm import ChatGLMConfig
|
||||||
|
>>> from modeling_chatglm import ChatGLMModel
|
||||||
|
|
||||||
|
>>> # Initializing a ChatGLM-6B THUDM/ChatGLM-6B style configuration
|
||||||
|
>>> configuration = ChatGLMConfig()
|
||||||
|
|
||||||
|
>>> # Initializing a model from the THUDM/ChatGLM-6B style configuration
|
||||||
|
>>> model = ChatGLMModel(configuration)
|
||||||
|
|
||||||
|
>>> # Accessing the model configuration
|
||||||
|
>>> configuration = model.config
|
||||||
|
```
|
||||||
|
"""
|
||||||
|
model_type = "chatglm"
|
||||||
|
|
||||||
|
def __init__(
|
||||||
|
self,
|
||||||
|
vocab_size=150528,
|
||||||
|
hidden_size=4096,
|
||||||
|
num_layers=28,
|
||||||
|
num_attention_heads=32,
|
||||||
|
layernorm_epsilon=1e-5,
|
||||||
|
use_cache=False,
|
||||||
|
bos_token_id=150004,
|
||||||
|
eos_token_id=150005,
|
||||||
|
pad_token_id=0,
|
||||||
|
max_sequence_length=2048,
|
||||||
|
inner_hidden_size=16384,
|
||||||
|
position_encoding_2d=True,
|
||||||
|
**kwargs
|
||||||
|
):
|
||||||
|
self.num_layers = num_layers
|
||||||
|
self.vocab_size = vocab_size
|
||||||
|
self.hidden_size = hidden_size
|
||||||
|
self.num_attention_heads = num_attention_heads
|
||||||
|
self.max_sequence_length = max_sequence_length
|
||||||
|
self.layernorm_epsilon = layernorm_epsilon
|
||||||
|
self.inner_hidden_size = inner_hidden_size
|
||||||
|
self.use_cache = use_cache
|
||||||
|
self.bos_token_id = bos_token_id
|
||||||
|
self.eos_token_id = eos_token_id
|
||||||
|
self.pad_token_id = pad_token_id
|
||||||
|
self.position_encoding_2d = position_encoding_2d
|
||||||
|
super().__init__(
|
||||||
|
pad_token_id=pad_token_id,
|
||||||
|
bos_token_id=bos_token_id,
|
||||||
|
eos_token_id=eos_token_id,
|
||||||
|
**kwargs
|
||||||
|
)
|
||||||
|
|
@ -0,0 +1,3 @@
|
||||||
|
version https://git-lfs.github.com/spec/v1
|
||||||
|
oid sha256:99871e0c85db81ad7af1028854fd091cd5778c8414ae9d94bbbc10d02c831c21
|
||||||
|
size 2699926
|
||||||
File diff suppressed because it is too large
Load Diff
|
|
@ -0,0 +1 @@
|
||||||
|
/mnt/vepfs/zxdu/checkpoints/qa-glm-6b-sft-v0.8-v2-original-lr/pytorch_model-00001-of-00008.bin
|
||||||
|
|
@ -0,0 +1 @@
|
||||||
|
/mnt/vepfs/zxdu/checkpoints/qa-glm-6b-sft-v0.8-v2-original-lr/pytorch_model-00002-of-00008.bin
|
||||||
|
|
@ -0,0 +1 @@
|
||||||
|
/mnt/vepfs/zxdu/checkpoints/qa-glm-6b-sft-v0.8-v2-original-lr/pytorch_model-00003-of-00008.bin
|
||||||
|
|
@ -0,0 +1 @@
|
||||||
|
/mnt/vepfs/zxdu/checkpoints/qa-glm-6b-sft-v0.8-v2-original-lr/pytorch_model-00004-of-00008.bin
|
||||||
|
|
@ -0,0 +1 @@
|
||||||
|
/mnt/vepfs/zxdu/checkpoints/qa-glm-6b-sft-v0.8-v2-original-lr/pytorch_model-00005-of-00008.bin
|
||||||
|
|
@ -0,0 +1 @@
|
||||||
|
/mnt/vepfs/zxdu/checkpoints/qa-glm-6b-sft-v0.8-v2-original-lr/pytorch_model-00006-of-00008.bin
|
||||||
|
|
@ -0,0 +1 @@
|
||||||
|
/mnt/vepfs/zxdu/checkpoints/qa-glm-6b-sft-v0.8-v2-original-lr/pytorch_model-00007-of-00008.bin
|
||||||
|
|
@ -0,0 +1 @@
|
||||||
|
/mnt/vepfs/zxdu/checkpoints/qa-glm-6b-sft-v0.8-v2-original-lr/pytorch_model-00008-of-00008.bin
|
||||||
|
|
@ -0,0 +1,375 @@
|
||||||
|
{
|
||||||
|
"metadata": {
|
||||||
|
"total_size": 13744473856
|
||||||
|
},
|
||||||
|
"weight_map": {
|
||||||
|
"lm_head.weight": "pytorch_model-00008-of-00008.bin",
|
||||||
|
"transformer.final_layernorm.bias": "pytorch_model-00007-of-00008.bin",
|
||||||
|
"transformer.final_layernorm.weight": "pytorch_model-00007-of-00008.bin",
|
||||||
|
"transformer.layers.0.attention.dense.bias": "pytorch_model-00001-of-00008.bin",
|
||||||
|
"transformer.layers.0.attention.dense.weight": "pytorch_model-00001-of-00008.bin",
|
||||||
|
"transformer.layers.0.attention.query_key_value.bias": "pytorch_model-00001-of-00008.bin",
|
||||||
|
"transformer.layers.0.attention.query_key_value.weight": "pytorch_model-00001-of-00008.bin",
|
||||||
|
"transformer.layers.0.attention.rotary_emb.inv_freq": "pytorch_model-00001-of-00008.bin",
|
||||||
|
"transformer.layers.0.input_layernorm.bias": "pytorch_model-00001-of-00008.bin",
|
||||||
|
"transformer.layers.0.input_layernorm.weight": "pytorch_model-00001-of-00008.bin",
|
||||||
|
"transformer.layers.0.mlp.dense_4h_to_h.bias": "pytorch_model-00001-of-00008.bin",
|
||||||
|
"transformer.layers.0.mlp.dense_4h_to_h.weight": "pytorch_model-00001-of-00008.bin",
|
||||||
|
"transformer.layers.0.mlp.dense_h_to_4h.bias": "pytorch_model-00001-of-00008.bin",
|
||||||
|
"transformer.layers.0.mlp.dense_h_to_4h.weight": "pytorch_model-00001-of-00008.bin",
|
||||||
|
"transformer.layers.0.post_attention_layernorm.bias": "pytorch_model-00001-of-00008.bin",
|
||||||
|
"transformer.layers.0.post_attention_layernorm.weight": "pytorch_model-00001-of-00008.bin",
|
||||||
|
"transformer.layers.1.attention.dense.bias": "pytorch_model-00001-of-00008.bin",
|
||||||
|
"transformer.layers.1.attention.dense.weight": "pytorch_model-00001-of-00008.bin",
|
||||||
|
"transformer.layers.1.attention.query_key_value.bias": "pytorch_model-00001-of-00008.bin",
|
||||||
|
"transformer.layers.1.attention.query_key_value.weight": "pytorch_model-00001-of-00008.bin",
|
||||||
|
"transformer.layers.1.attention.rotary_emb.inv_freq": "pytorch_model-00001-of-00008.bin",
|
||||||
|
"transformer.layers.1.input_layernorm.bias": "pytorch_model-00001-of-00008.bin",
|
||||||
|
"transformer.layers.1.input_layernorm.weight": "pytorch_model-00001-of-00008.bin",
|
||||||
|
"transformer.layers.1.mlp.dense_4h_to_h.bias": "pytorch_model-00002-of-00008.bin",
|
||||||
|
"transformer.layers.1.mlp.dense_4h_to_h.weight": "pytorch_model-00002-of-00008.bin",
|
||||||
|
"transformer.layers.1.mlp.dense_h_to_4h.bias": "pytorch_model-00001-of-00008.bin",
|
||||||
|
"transformer.layers.1.mlp.dense_h_to_4h.weight": "pytorch_model-00001-of-00008.bin",
|
||||||
|
"transformer.layers.1.post_attention_layernorm.bias": "pytorch_model-00001-of-00008.bin",
|
||||||
|
"transformer.layers.1.post_attention_layernorm.weight": "pytorch_model-00001-of-00008.bin",
|
||||||
|
"transformer.layers.10.attention.dense.bias": "pytorch_model-00003-of-00008.bin",
|
||||||
|
"transformer.layers.10.attention.dense.weight": "pytorch_model-00003-of-00008.bin",
|
||||||
|
"transformer.layers.10.attention.query_key_value.bias": "pytorch_model-00003-of-00008.bin",
|
||||||
|
"transformer.layers.10.attention.query_key_value.weight": "pytorch_model-00003-of-00008.bin",
|
||||||
|
"transformer.layers.10.attention.rotary_emb.inv_freq": "pytorch_model-00003-of-00008.bin",
|
||||||
|
"transformer.layers.10.input_layernorm.bias": "pytorch_model-00003-of-00008.bin",
|
||||||
|
"transformer.layers.10.input_layernorm.weight": "pytorch_model-00003-of-00008.bin",
|
||||||
|
"transformer.layers.10.mlp.dense_4h_to_h.bias": "pytorch_model-00003-of-00008.bin",
|
||||||
|
"transformer.layers.10.mlp.dense_4h_to_h.weight": "pytorch_model-00003-of-00008.bin",
|
||||||
|
"transformer.layers.10.mlp.dense_h_to_4h.bias": "pytorch_model-00003-of-00008.bin",
|
||||||
|
"transformer.layers.10.mlp.dense_h_to_4h.weight": "pytorch_model-00003-of-00008.bin",
|
||||||
|
"transformer.layers.10.post_attention_layernorm.bias": "pytorch_model-00003-of-00008.bin",
|
||||||
|
"transformer.layers.10.post_attention_layernorm.weight": "pytorch_model-00003-of-00008.bin",
|
||||||
|
"transformer.layers.11.attention.dense.bias": "pytorch_model-00004-of-00008.bin",
|
||||||
|
"transformer.layers.11.attention.dense.weight": "pytorch_model-00004-of-00008.bin",
|
||||||
|
"transformer.layers.11.attention.query_key_value.bias": "pytorch_model-00003-of-00008.bin",
|
||||||
|
"transformer.layers.11.attention.query_key_value.weight": "pytorch_model-00003-of-00008.bin",
|
||||||
|
"transformer.layers.11.attention.rotary_emb.inv_freq": "pytorch_model-00003-of-00008.bin",
|
||||||
|
"transformer.layers.11.input_layernorm.bias": "pytorch_model-00003-of-00008.bin",
|
||||||
|
"transformer.layers.11.input_layernorm.weight": "pytorch_model-00003-of-00008.bin",
|
||||||
|
"transformer.layers.11.mlp.dense_4h_to_h.bias": "pytorch_model-00004-of-00008.bin",
|
||||||
|
"transformer.layers.11.mlp.dense_4h_to_h.weight": "pytorch_model-00004-of-00008.bin",
|
||||||
|
"transformer.layers.11.mlp.dense_h_to_4h.bias": "pytorch_model-00004-of-00008.bin",
|
||||||
|
"transformer.layers.11.mlp.dense_h_to_4h.weight": "pytorch_model-00004-of-00008.bin",
|
||||||
|
"transformer.layers.11.post_attention_layernorm.bias": "pytorch_model-00004-of-00008.bin",
|
||||||
|
"transformer.layers.11.post_attention_layernorm.weight": "pytorch_model-00004-of-00008.bin",
|
||||||
|
"transformer.layers.12.attention.dense.bias": "pytorch_model-00004-of-00008.bin",
|
||||||
|
"transformer.layers.12.attention.dense.weight": "pytorch_model-00004-of-00008.bin",
|
||||||
|
"transformer.layers.12.attention.query_key_value.bias": "pytorch_model-00004-of-00008.bin",
|
||||||
|
"transformer.layers.12.attention.query_key_value.weight": "pytorch_model-00004-of-00008.bin",
|
||||||
|
"transformer.layers.12.attention.rotary_emb.inv_freq": "pytorch_model-00004-of-00008.bin",
|
||||||
|
"transformer.layers.12.input_layernorm.bias": "pytorch_model-00004-of-00008.bin",
|
||||||
|
"transformer.layers.12.input_layernorm.weight": "pytorch_model-00004-of-00008.bin",
|
||||||
|
"transformer.layers.12.mlp.dense_4h_to_h.bias": "pytorch_model-00004-of-00008.bin",
|
||||||
|
"transformer.layers.12.mlp.dense_4h_to_h.weight": "pytorch_model-00004-of-00008.bin",
|
||||||
|
"transformer.layers.12.mlp.dense_h_to_4h.bias": "pytorch_model-00004-of-00008.bin",
|
||||||
|
"transformer.layers.12.mlp.dense_h_to_4h.weight": "pytorch_model-00004-of-00008.bin",
|
||||||
|
"transformer.layers.12.post_attention_layernorm.bias": "pytorch_model-00004-of-00008.bin",
|
||||||
|
"transformer.layers.12.post_attention_layernorm.weight": "pytorch_model-00004-of-00008.bin",
|
||||||
|
"transformer.layers.13.attention.dense.bias": "pytorch_model-00004-of-00008.bin",
|
||||||
|
"transformer.layers.13.attention.dense.weight": "pytorch_model-00004-of-00008.bin",
|
||||||
|
"transformer.layers.13.attention.query_key_value.bias": "pytorch_model-00004-of-00008.bin",
|
||||||
|
"transformer.layers.13.attention.query_key_value.weight": "pytorch_model-00004-of-00008.bin",
|
||||||
|
"transformer.layers.13.attention.rotary_emb.inv_freq": "pytorch_model-00004-of-00008.bin",
|
||||||
|
"transformer.layers.13.input_layernorm.bias": "pytorch_model-00004-of-00008.bin",
|
||||||
|
"transformer.layers.13.input_layernorm.weight": "pytorch_model-00004-of-00008.bin",
|
||||||
|
"transformer.layers.13.mlp.dense_4h_to_h.bias": "pytorch_model-00004-of-00008.bin",
|
||||||
|
"transformer.layers.13.mlp.dense_4h_to_h.weight": "pytorch_model-00004-of-00008.bin",
|
||||||
|
"transformer.layers.13.mlp.dense_h_to_4h.bias": "pytorch_model-00004-of-00008.bin",
|
||||||
|
"transformer.layers.13.mlp.dense_h_to_4h.weight": "pytorch_model-00004-of-00008.bin",
|
||||||
|
"transformer.layers.13.post_attention_layernorm.bias": "pytorch_model-00004-of-00008.bin",
|
||||||
|
"transformer.layers.13.post_attention_layernorm.weight": "pytorch_model-00004-of-00008.bin",
|
||||||
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||||||
|
"transformer.layers.25.mlp.dense_h_to_4h.weight": "pytorch_model-00007-of-00008.bin",
|
||||||
|
"transformer.layers.25.post_attention_layernorm.bias": "pytorch_model-00006-of-00008.bin",
|
||||||
|
"transformer.layers.25.post_attention_layernorm.weight": "pytorch_model-00006-of-00008.bin",
|
||||||
|
"transformer.layers.26.attention.dense.bias": "pytorch_model-00007-of-00008.bin",
|
||||||
|
"transformer.layers.26.attention.dense.weight": "pytorch_model-00007-of-00008.bin",
|
||||||
|
"transformer.layers.26.attention.query_key_value.bias": "pytorch_model-00007-of-00008.bin",
|
||||||
|
"transformer.layers.26.attention.query_key_value.weight": "pytorch_model-00007-of-00008.bin",
|
||||||
|
"transformer.layers.26.attention.rotary_emb.inv_freq": "pytorch_model-00007-of-00008.bin",
|
||||||
|
"transformer.layers.26.input_layernorm.bias": "pytorch_model-00007-of-00008.bin",
|
||||||
|
"transformer.layers.26.input_layernorm.weight": "pytorch_model-00007-of-00008.bin",
|
||||||
|
"transformer.layers.26.mlp.dense_4h_to_h.bias": "pytorch_model-00007-of-00008.bin",
|
||||||
|
"transformer.layers.26.mlp.dense_4h_to_h.weight": "pytorch_model-00007-of-00008.bin",
|
||||||
|
"transformer.layers.26.mlp.dense_h_to_4h.bias": "pytorch_model-00007-of-00008.bin",
|
||||||
|
"transformer.layers.26.mlp.dense_h_to_4h.weight": "pytorch_model-00007-of-00008.bin",
|
||||||
|
"transformer.layers.26.post_attention_layernorm.bias": "pytorch_model-00007-of-00008.bin",
|
||||||
|
"transformer.layers.26.post_attention_layernorm.weight": "pytorch_model-00007-of-00008.bin",
|
||||||
|
"transformer.layers.27.attention.dense.bias": "pytorch_model-00007-of-00008.bin",
|
||||||
|
"transformer.layers.27.attention.dense.weight": "pytorch_model-00007-of-00008.bin",
|
||||||
|
"transformer.layers.27.attention.query_key_value.bias": "pytorch_model-00007-of-00008.bin",
|
||||||
|
"transformer.layers.27.attention.query_key_value.weight": "pytorch_model-00007-of-00008.bin",
|
||||||
|
"transformer.layers.27.attention.rotary_emb.inv_freq": "pytorch_model-00007-of-00008.bin",
|
||||||
|
"transformer.layers.27.input_layernorm.bias": "pytorch_model-00007-of-00008.bin",
|
||||||
|
"transformer.layers.27.input_layernorm.weight": "pytorch_model-00007-of-00008.bin",
|
||||||
|
"transformer.layers.27.mlp.dense_4h_to_h.bias": "pytorch_model-00007-of-00008.bin",
|
||||||
|
"transformer.layers.27.mlp.dense_4h_to_h.weight": "pytorch_model-00007-of-00008.bin",
|
||||||
|
"transformer.layers.27.mlp.dense_h_to_4h.bias": "pytorch_model-00007-of-00008.bin",
|
||||||
|
"transformer.layers.27.mlp.dense_h_to_4h.weight": "pytorch_model-00007-of-00008.bin",
|
||||||
|
"transformer.layers.27.post_attention_layernorm.bias": "pytorch_model-00007-of-00008.bin",
|
||||||
|
"transformer.layers.27.post_attention_layernorm.weight": "pytorch_model-00007-of-00008.bin",
|
||||||
|
"transformer.layers.3.attention.dense.bias": "pytorch_model-00002-of-00008.bin",
|
||||||
|
"transformer.layers.3.attention.dense.weight": "pytorch_model-00002-of-00008.bin",
|
||||||
|
"transformer.layers.3.attention.query_key_value.bias": "pytorch_model-00002-of-00008.bin",
|
||||||
|
"transformer.layers.3.attention.query_key_value.weight": "pytorch_model-00002-of-00008.bin",
|
||||||
|
"transformer.layers.3.attention.rotary_emb.inv_freq": "pytorch_model-00002-of-00008.bin",
|
||||||
|
"transformer.layers.3.input_layernorm.bias": "pytorch_model-00002-of-00008.bin",
|
||||||
|
"transformer.layers.3.input_layernorm.weight": "pytorch_model-00002-of-00008.bin",
|
||||||
|
"transformer.layers.3.mlp.dense_4h_to_h.bias": "pytorch_model-00002-of-00008.bin",
|
||||||
|
"transformer.layers.3.mlp.dense_4h_to_h.weight": "pytorch_model-00002-of-00008.bin",
|
||||||
|
"transformer.layers.3.mlp.dense_h_to_4h.bias": "pytorch_model-00002-of-00008.bin",
|
||||||
|
"transformer.layers.3.mlp.dense_h_to_4h.weight": "pytorch_model-00002-of-00008.bin",
|
||||||
|
"transformer.layers.3.post_attention_layernorm.bias": "pytorch_model-00002-of-00008.bin",
|
||||||
|
"transformer.layers.3.post_attention_layernorm.weight": "pytorch_model-00002-of-00008.bin",
|
||||||
|
"transformer.layers.4.attention.dense.bias": "pytorch_model-00002-of-00008.bin",
|
||||||
|
"transformer.layers.4.attention.dense.weight": "pytorch_model-00002-of-00008.bin",
|
||||||
|
"transformer.layers.4.attention.query_key_value.bias": "pytorch_model-00002-of-00008.bin",
|
||||||
|
"transformer.layers.4.attention.query_key_value.weight": "pytorch_model-00002-of-00008.bin",
|
||||||
|
"transformer.layers.4.attention.rotary_emb.inv_freq": "pytorch_model-00002-of-00008.bin",
|
||||||
|
"transformer.layers.4.input_layernorm.bias": "pytorch_model-00002-of-00008.bin",
|
||||||
|
"transformer.layers.4.input_layernorm.weight": "pytorch_model-00002-of-00008.bin",
|
||||||
|
"transformer.layers.4.mlp.dense_4h_to_h.bias": "pytorch_model-00002-of-00008.bin",
|
||||||
|
"transformer.layers.4.mlp.dense_4h_to_h.weight": "pytorch_model-00002-of-00008.bin",
|
||||||
|
"transformer.layers.4.mlp.dense_h_to_4h.bias": "pytorch_model-00002-of-00008.bin",
|
||||||
|
"transformer.layers.4.mlp.dense_h_to_4h.weight": "pytorch_model-00002-of-00008.bin",
|
||||||
|
"transformer.layers.4.post_attention_layernorm.bias": "pytorch_model-00002-of-00008.bin",
|
||||||
|
"transformer.layers.4.post_attention_layernorm.weight": "pytorch_model-00002-of-00008.bin",
|
||||||
|
"transformer.layers.5.attention.dense.bias": "pytorch_model-00002-of-00008.bin",
|
||||||
|
"transformer.layers.5.attention.dense.weight": "pytorch_model-00002-of-00008.bin",
|
||||||
|
"transformer.layers.5.attention.query_key_value.bias": "pytorch_model-00002-of-00008.bin",
|
||||||
|
"transformer.layers.5.attention.query_key_value.weight": "pytorch_model-00002-of-00008.bin",
|
||||||
|
"transformer.layers.5.attention.rotary_emb.inv_freq": "pytorch_model-00002-of-00008.bin",
|
||||||
|
"transformer.layers.5.input_layernorm.bias": "pytorch_model-00002-of-00008.bin",
|
||||||
|
"transformer.layers.5.input_layernorm.weight": "pytorch_model-00002-of-00008.bin",
|
||||||
|
"transformer.layers.5.mlp.dense_4h_to_h.bias": "pytorch_model-00002-of-00008.bin",
|
||||||
|
"transformer.layers.5.mlp.dense_4h_to_h.weight": "pytorch_model-00002-of-00008.bin",
|
||||||
|
"transformer.layers.5.mlp.dense_h_to_4h.bias": "pytorch_model-00002-of-00008.bin",
|
||||||
|
"transformer.layers.5.mlp.dense_h_to_4h.weight": "pytorch_model-00002-of-00008.bin",
|
||||||
|
"transformer.layers.5.post_attention_layernorm.bias": "pytorch_model-00002-of-00008.bin",
|
||||||
|
"transformer.layers.5.post_attention_layernorm.weight": "pytorch_model-00002-of-00008.bin",
|
||||||
|
"transformer.layers.6.attention.dense.bias": "pytorch_model-00002-of-00008.bin",
|
||||||
|
"transformer.layers.6.attention.dense.weight": "pytorch_model-00002-of-00008.bin",
|
||||||
|
"transformer.layers.6.attention.query_key_value.bias": "pytorch_model-00002-of-00008.bin",
|
||||||
|
"transformer.layers.6.attention.query_key_value.weight": "pytorch_model-00002-of-00008.bin",
|
||||||
|
"transformer.layers.6.attention.rotary_emb.inv_freq": "pytorch_model-00002-of-00008.bin",
|
||||||
|
"transformer.layers.6.input_layernorm.bias": "pytorch_model-00002-of-00008.bin",
|
||||||
|
"transformer.layers.6.input_layernorm.weight": "pytorch_model-00002-of-00008.bin",
|
||||||
|
"transformer.layers.6.mlp.dense_4h_to_h.bias": "pytorch_model-00003-of-00008.bin",
|
||||||
|
"transformer.layers.6.mlp.dense_4h_to_h.weight": "pytorch_model-00003-of-00008.bin",
|
||||||
|
"transformer.layers.6.mlp.dense_h_to_4h.bias": "pytorch_model-00003-of-00008.bin",
|
||||||
|
"transformer.layers.6.mlp.dense_h_to_4h.weight": "pytorch_model-00003-of-00008.bin",
|
||||||
|
"transformer.layers.6.post_attention_layernorm.bias": "pytorch_model-00002-of-00008.bin",
|
||||||
|
"transformer.layers.6.post_attention_layernorm.weight": "pytorch_model-00002-of-00008.bin",
|
||||||
|
"transformer.layers.7.attention.dense.bias": "pytorch_model-00003-of-00008.bin",
|
||||||
|
"transformer.layers.7.attention.dense.weight": "pytorch_model-00003-of-00008.bin",
|
||||||
|
"transformer.layers.7.attention.query_key_value.bias": "pytorch_model-00003-of-00008.bin",
|
||||||
|
"transformer.layers.7.attention.query_key_value.weight": "pytorch_model-00003-of-00008.bin",
|
||||||
|
"transformer.layers.7.attention.rotary_emb.inv_freq": "pytorch_model-00003-of-00008.bin",
|
||||||
|
"transformer.layers.7.input_layernorm.bias": "pytorch_model-00003-of-00008.bin",
|
||||||
|
"transformer.layers.7.input_layernorm.weight": "pytorch_model-00003-of-00008.bin",
|
||||||
|
"transformer.layers.7.mlp.dense_4h_to_h.bias": "pytorch_model-00003-of-00008.bin",
|
||||||
|
"transformer.layers.7.mlp.dense_4h_to_h.weight": "pytorch_model-00003-of-00008.bin",
|
||||||
|
"transformer.layers.7.mlp.dense_h_to_4h.bias": "pytorch_model-00003-of-00008.bin",
|
||||||
|
"transformer.layers.7.mlp.dense_h_to_4h.weight": "pytorch_model-00003-of-00008.bin",
|
||||||
|
"transformer.layers.7.post_attention_layernorm.bias": "pytorch_model-00003-of-00008.bin",
|
||||||
|
"transformer.layers.7.post_attention_layernorm.weight": "pytorch_model-00003-of-00008.bin",
|
||||||
|
"transformer.layers.8.attention.dense.bias": "pytorch_model-00003-of-00008.bin",
|
||||||
|
"transformer.layers.8.attention.dense.weight": "pytorch_model-00003-of-00008.bin",
|
||||||
|
"transformer.layers.8.attention.query_key_value.bias": "pytorch_model-00003-of-00008.bin",
|
||||||
|
"transformer.layers.8.attention.query_key_value.weight": "pytorch_model-00003-of-00008.bin",
|
||||||
|
"transformer.layers.8.attention.rotary_emb.inv_freq": "pytorch_model-00003-of-00008.bin",
|
||||||
|
"transformer.layers.8.input_layernorm.bias": "pytorch_model-00003-of-00008.bin",
|
||||||
|
"transformer.layers.8.input_layernorm.weight": "pytorch_model-00003-of-00008.bin",
|
||||||
|
"transformer.layers.8.mlp.dense_4h_to_h.bias": "pytorch_model-00003-of-00008.bin",
|
||||||
|
"transformer.layers.8.mlp.dense_4h_to_h.weight": "pytorch_model-00003-of-00008.bin",
|
||||||
|
"transformer.layers.8.mlp.dense_h_to_4h.bias": "pytorch_model-00003-of-00008.bin",
|
||||||
|
"transformer.layers.8.mlp.dense_h_to_4h.weight": "pytorch_model-00003-of-00008.bin",
|
||||||
|
"transformer.layers.8.post_attention_layernorm.bias": "pytorch_model-00003-of-00008.bin",
|
||||||
|
"transformer.layers.8.post_attention_layernorm.weight": "pytorch_model-00003-of-00008.bin",
|
||||||
|
"transformer.layers.9.attention.dense.bias": "pytorch_model-00003-of-00008.bin",
|
||||||
|
"transformer.layers.9.attention.dense.weight": "pytorch_model-00003-of-00008.bin",
|
||||||
|
"transformer.layers.9.attention.query_key_value.bias": "pytorch_model-00003-of-00008.bin",
|
||||||
|
"transformer.layers.9.attention.query_key_value.weight": "pytorch_model-00003-of-00008.bin",
|
||||||
|
"transformer.layers.9.attention.rotary_emb.inv_freq": "pytorch_model-00003-of-00008.bin",
|
||||||
|
"transformer.layers.9.input_layernorm.bias": "pytorch_model-00003-of-00008.bin",
|
||||||
|
"transformer.layers.9.input_layernorm.weight": "pytorch_model-00003-of-00008.bin",
|
||||||
|
"transformer.layers.9.mlp.dense_4h_to_h.bias": "pytorch_model-00003-of-00008.bin",
|
||||||
|
"transformer.layers.9.mlp.dense_4h_to_h.weight": "pytorch_model-00003-of-00008.bin",
|
||||||
|
"transformer.layers.9.mlp.dense_h_to_4h.bias": "pytorch_model-00003-of-00008.bin",
|
||||||
|
"transformer.layers.9.mlp.dense_h_to_4h.weight": "pytorch_model-00003-of-00008.bin",
|
||||||
|
"transformer.layers.9.post_attention_layernorm.bias": "pytorch_model-00003-of-00008.bin",
|
||||||
|
"transformer.layers.9.post_attention_layernorm.weight": "pytorch_model-00003-of-00008.bin",
|
||||||
|
"transformer.word_embeddings.weight": "pytorch_model-00001-of-00008.bin"
|
||||||
|
}
|
||||||
|
}
|
||||||
File diff suppressed because one or more lines are too long
|
|
@ -0,0 +1,347 @@
|
||||||
|
"""Tokenization classes for ChatGLM."""
|
||||||
|
import sys
|
||||||
|
import unicodedata
|
||||||
|
from typing import List, Optional, Union
|
||||||
|
from functools import lru_cache
|
||||||
|
import os
|
||||||
|
import collections
|
||||||
|
import re
|
||||||
|
|
||||||
|
from transformers.tokenization_utils import PreTrainedTokenizer
|
||||||
|
from icetk.text_tokenizer import TextTokenizer
|
||||||
|
from icetk.utils import auto_create
|
||||||
|
import icetk.sentencepiece_model_pb2 as sp_model
|
||||||
|
from transformers.utils import logging
|
||||||
|
|
||||||
|
logger = logging.get_logger(__name__)
|
||||||
|
|
||||||
|
VOCAB_FILES_NAMES = {"vocab_file": "ice_text.model"}
|
||||||
|
|
||||||
|
PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES = {
|
||||||
|
"THUDM/chatglm-6b": 2048,
|
||||||
|
}
|
||||||
|
|
||||||
|
|
||||||
|
class SPTokenizer:
|
||||||
|
def __init__(
|
||||||
|
self,
|
||||||
|
vocab_file,
|
||||||
|
max_blank_length=80,
|
||||||
|
byte_fallback=True,
|
||||||
|
):
|
||||||
|
assert vocab_file is not None
|
||||||
|
self.vocab_file = vocab_file
|
||||||
|
self.special_tokens = ["[MASK]", "[gMASK]", "[sMASK]", "<unused_0>", "<sop>", "<eop>", "<ENC>", "<dBLOCK>"]
|
||||||
|
self.max_blank_length = max_blank_length
|
||||||
|
self.byte_fallback = byte_fallback
|
||||||
|
self.text_tokenizer = self._build_text_tokenizer(encode_special_tokens=False)
|
||||||
|
self.special_text_tokenizer = self._build_text_tokenizer(encode_special_tokens=True)
|
||||||
|
|
||||||
|
@staticmethod
|
||||||
|
def _configure_tokenizer(
|
||||||
|
text_tokenizer: TextTokenizer,
|
||||||
|
special_tokens: List[str],
|
||||||
|
max_blank_length: int,
|
||||||
|
byte_fallback: bool,
|
||||||
|
encode_special_tokens=False,
|
||||||
|
):
|
||||||
|
# special token
|
||||||
|
special_token_type = 4 if encode_special_tokens else 3 # 3 - CONTROL, 4 - USER_DEFINE
|
||||||
|
for token in special_tokens:
|
||||||
|
text_tokenizer.proto.pieces.append(
|
||||||
|
sp_model.ModelProto.SentencePiece(piece=token, score=0.0, type=special_token_type)
|
||||||
|
)
|
||||||
|
# whitespaces
|
||||||
|
for token in [SPTokenizer.get_tab_token()] + [
|
||||||
|
SPTokenizer.get_blank_token(i) for i in range(2, max_blank_length + 1)
|
||||||
|
]:
|
||||||
|
text_tokenizer.proto.pieces.append(sp_model.ModelProto.SentencePiece(piece=token, score=0.0, type=4))
|
||||||
|
# byte fallback
|
||||||
|
if byte_fallback:
|
||||||
|
text_tokenizer.proto.trainer_spec.byte_fallback = True
|
||||||
|
for i in range(256):
|
||||||
|
text_tokenizer.proto.pieces.append(
|
||||||
|
sp_model.ModelProto.SentencePiece(piece="<0x{:02X}>".format(i), score=0.0, type=6)
|
||||||
|
)
|
||||||
|
text_tokenizer.refresh()
|
||||||
|
|
||||||
|
def _build_text_tokenizer(self, encode_special_tokens=False):
|
||||||
|
tokenizer = TextTokenizer(self.vocab_file)
|
||||||
|
self._configure_tokenizer(
|
||||||
|
tokenizer, self.special_tokens, self.max_blank_length, self.byte_fallback, encode_special_tokens
|
||||||
|
)
|
||||||
|
return tokenizer
|
||||||
|
|
||||||
|
def _get_text_tokenizer(self, encode_special_tokens=False):
|
||||||
|
if encode_special_tokens:
|
||||||
|
return self.special_text_tokenizer
|
||||||
|
else:
|
||||||
|
return self.text_tokenizer
|
||||||
|
|
||||||
|
@staticmethod
|
||||||
|
def get_blank_token(length: int):
|
||||||
|
assert length >= 2
|
||||||
|
return f"<|blank_{length}|>"
|
||||||
|
|
||||||
|
@staticmethod
|
||||||
|
def get_tab_token():
|
||||||
|
return f"<|tab|>"
|
||||||
|
|
||||||
|
@property
|
||||||
|
def num_image_tokens(self):
|
||||||
|
return 20000
|
||||||
|
|
||||||
|
@property
|
||||||
|
def num_text_tokens(self):
|
||||||
|
return self.text_tokenizer.num_tokens
|
||||||
|
|
||||||
|
@property
|
||||||
|
def num_tokens(self):
|
||||||
|
return self.num_image_tokens + self.num_text_tokens
|
||||||
|
|
||||||
|
@staticmethod
|
||||||
|
def _encode_whitespaces(text: str, max_len: int = 80):
|
||||||
|
text = text.replace("\t", SPTokenizer.get_tab_token())
|
||||||
|
for i in range(max_len, 1, -1):
|
||||||
|
text = text.replace(" " * i, SPTokenizer.get_blank_token(i))
|
||||||
|
return text
|
||||||
|
|
||||||
|
def _preprocess(self, text: str, linebreak=True, whitespaces=True):
|
||||||
|
if linebreak:
|
||||||
|
text = text.replace("\n", "<n>")
|
||||||
|
if whitespaces:
|
||||||
|
text = self._encode_whitespaces(text, max_len=self.max_blank_length)
|
||||||
|
return text
|
||||||
|
|
||||||
|
def encode(
|
||||||
|
self, text: str, linebreak=True, whitespaces=True, special_tokens=False, add_dummy_prefix=True
|
||||||
|
) -> List[int]:
|
||||||
|
"""
|
||||||
|
@param text: Text to encode.
|
||||||
|
@param linebreak: Whether to encode newline (\n) in text.
|
||||||
|
@param whitespaces: Whether to encode multiple whitespaces or tab in text, useful for source code encoding.
|
||||||
|
@param special_tokens: Whether to encode special token ([MASK], [gMASK], etc.) in text.
|
||||||
|
@param add_dummy_prefix: Whether to add dummy blank space in the beginning.
|
||||||
|
"""
|
||||||
|
text = self._preprocess(text, linebreak, whitespaces)
|
||||||
|
if not add_dummy_prefix:
|
||||||
|
text = "<n>" + text
|
||||||
|
tmp = self._get_text_tokenizer(encode_special_tokens=special_tokens).encode(text)
|
||||||
|
tokens = [x + self.num_image_tokens for x in tmp]
|
||||||
|
return tokens if add_dummy_prefix else tokens[2:]
|
||||||
|
|
||||||
|
def decode(self, text_ids: List[int], special_tokens=False) -> str:
|
||||||
|
ids = [int(_id) - self.num_image_tokens for _id in text_ids]
|
||||||
|
text = self._get_text_tokenizer(encode_special_tokens=special_tokens).decode(ids)
|
||||||
|
text = text.replace("<n>", "\n")
|
||||||
|
text = text.replace(SPTokenizer.get_tab_token(), "\t")
|
||||||
|
for i in range(2, self.max_blank_length + 1):
|
||||||
|
text = text.replace(self.get_blank_token(i), " " * i)
|
||||||
|
return text
|
||||||
|
|
||||||
|
def tokenize(
|
||||||
|
self, text: str, linebreak=True, whitespaces=True, special_tokens=False, add_dummy_prefix=True
|
||||||
|
) -> List[str]:
|
||||||
|
"""
|
||||||
|
@param text: Text to encode.
|
||||||
|
@param linebreak: Whether to encode newline (\n) in text.
|
||||||
|
@param whitespaces: Whether to encode multiple whitespaces or tab in text, useful for source code encoding.
|
||||||
|
@param special_tokens: Whether to encode special token ([MASK], [gMASK], etc.) in text.
|
||||||
|
@param add_dummy_prefix: Whether to add dummy blank space in the beginning.
|
||||||
|
"""
|
||||||
|
text = self._preprocess(text, linebreak, whitespaces)
|
||||||
|
if not add_dummy_prefix:
|
||||||
|
text = "<n>" + text
|
||||||
|
tokens = self._get_text_tokenizer(encode_special_tokens=special_tokens).tokenize(text)
|
||||||
|
return tokens if add_dummy_prefix else tokens[2:]
|
||||||
|
|
||||||
|
def __getitem__(self, x: Union[int, str]):
|
||||||
|
if isinstance(x, int):
|
||||||
|
if x < self.num_image_tokens:
|
||||||
|
return "<image_{}>".format(x)
|
||||||
|
else:
|
||||||
|
return self.text_tokenizer.convert_id_to_token(x - self.num_image_tokens)
|
||||||
|
elif isinstance(x, str):
|
||||||
|
if x.startswith("<image_") and x.endswith(">") and x[7:-1].isdigit():
|
||||||
|
return int(x[7:-1])
|
||||||
|
else:
|
||||||
|
return self.text_tokenizer.convert_token_to_id(x) + self.num_image_tokens
|
||||||
|
else:
|
||||||
|
raise ValueError("The key should be str or int.")
|
||||||
|
|
||||||
|
|
||||||
|
class ChatGLMTokenizer(PreTrainedTokenizer):
|
||||||
|
"""
|
||||||
|
Construct a ChatGLM tokenizer. Based on byte-level Byte-Pair-Encoding.
|
||||||
|
|
||||||
|
Args:
|
||||||
|
vocab_file (`str`):
|
||||||
|
Path to the vocabulary file.
|
||||||
|
"""
|
||||||
|
|
||||||
|
vocab_files_names = VOCAB_FILES_NAMES
|
||||||
|
max_model_input_sizes = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
|
||||||
|
model_input_names = ["input_ids"]
|
||||||
|
|
||||||
|
def __init__(
|
||||||
|
self,
|
||||||
|
vocab_file,
|
||||||
|
do_lower_case=False,
|
||||||
|
remove_space=False,
|
||||||
|
bos_token='sop',
|
||||||
|
eos_token='eos',
|
||||||
|
eop_token='eop',
|
||||||
|
mask_token='[MASK]',
|
||||||
|
gmask_token='[gMASK]',
|
||||||
|
padding_side="left",
|
||||||
|
**kwargs
|
||||||
|
) -> None:
|
||||||
|
super().__init__(
|
||||||
|
do_lower_case=do_lower_case,
|
||||||
|
remove_space=remove_space,
|
||||||
|
padding_side=padding_side,
|
||||||
|
**kwargs
|
||||||
|
)
|
||||||
|
|
||||||
|
self.do_lower_case = do_lower_case
|
||||||
|
self.remove_space = remove_space
|
||||||
|
self.vocab_file = vocab_file
|
||||||
|
|
||||||
|
self.bos_token = bos_token
|
||||||
|
self.eos_token = eos_token
|
||||||
|
self.eop_token = eop_token
|
||||||
|
self.mask_token = mask_token
|
||||||
|
self.gMASK_token = gmask_token
|
||||||
|
|
||||||
|
self.sp_tokenizer = SPTokenizer(vocab_file)
|
||||||
|
|
||||||
|
""" Initialisation """
|
||||||
|
|
||||||
|
@property
|
||||||
|
def eop_token_id(self) -> Optional[int]:
|
||||||
|
"""
|
||||||
|
`Optional[int]`: Id of the end of sentence token in the vocabulary. Returns `None` if the token has not been
|
||||||
|
set.
|
||||||
|
"""
|
||||||
|
if self.eop_token is None:
|
||||||
|
return None
|
||||||
|
return self.convert_tokens_to_ids(self.eop_token)
|
||||||
|
|
||||||
|
@property
|
||||||
|
def vocab_size(self):
|
||||||
|
""" Returns vocab size """
|
||||||
|
return self.sp_tokenizer.num_tokens
|
||||||
|
|
||||||
|
def get_vocab(self):
|
||||||
|
""" Returns vocab as a dict """
|
||||||
|
vocab = {self._convert_id_to_token(i): i for i in range(self.vocab_size)}
|
||||||
|
vocab.update(self.added_tokens_encoder)
|
||||||
|
return vocab
|
||||||
|
|
||||||
|
def preprocess_text(self, inputs):
|
||||||
|
if self.remove_space:
|
||||||
|
outputs = " ".join(inputs.strip().split())
|
||||||
|
else:
|
||||||
|
outputs = inputs
|
||||||
|
|
||||||
|
if self.do_lower_case:
|
||||||
|
outputs = outputs.lower()
|
||||||
|
|
||||||
|
return outputs
|
||||||
|
|
||||||
|
def _tokenize(self, text, **kwargs):
|
||||||
|
""" Returns a tokenized string. """
|
||||||
|
text = self.preprocess_text(text)
|
||||||
|
|
||||||
|
seq = self.sp_tokenizer.tokenize(text)
|
||||||
|
|
||||||
|
return seq
|
||||||
|
|
||||||
|
def decode(
|
||||||
|
self,
|
||||||
|
token_ids: Union[List[int], List[List[int]]],
|
||||||
|
skip_special_tokens: bool = False,
|
||||||
|
clean_up_tokenization_spaces: bool = True,
|
||||||
|
spaces_between_special_tokens: bool = True,
|
||||||
|
**kwargs
|
||||||
|
) -> str:
|
||||||
|
if isinstance(token_ids[0], list):
|
||||||
|
tokens = []
|
||||||
|
for single_token_ids in token_ids:
|
||||||
|
if self.pad_token_id in single_token_ids: # remove pad
|
||||||
|
single_token_ids = list(filter((self.pad_token_id).__ne__, single_token_ids))
|
||||||
|
tokens.append(self.sp_tokenizer.decode(single_token_ids))
|
||||||
|
return (tokens)
|
||||||
|
else:
|
||||||
|
if self.pad_token_id in token_ids: # remove pad
|
||||||
|
token_ids = list(filter((self.pad_token_id).__ne__, token_ids))
|
||||||
|
return self.sp_tokenizer.decode(token_ids)
|
||||||
|
|
||||||
|
def _convert_token_to_id(self, token):
|
||||||
|
""" Converts a token (str) in an id using the vocab. """
|
||||||
|
return self.sp_tokenizer[token]
|
||||||
|
|
||||||
|
def _convert_id_to_token(self, index):
|
||||||
|
"""Converts an index (integer) in a token (str) using the vocab."""
|
||||||
|
return self.sp_tokenizer[index]
|
||||||
|
|
||||||
|
def save_vocabulary(self, save_directory, filename_prefix=None):
|
||||||
|
"""
|
||||||
|
Save the vocabulary and special tokens file to a directory.
|
||||||
|
|
||||||
|
Args:
|
||||||
|
save_directory (`str`):
|
||||||
|
The directory in which to save the vocabulary.
|
||||||
|
filename_prefix (`str`, *optional*):
|
||||||
|
An optional prefix to add to the named of the saved files.
|
||||||
|
|
||||||
|
Returns:
|
||||||
|
`Tuple(str)`: Paths to the files saved.
|
||||||
|
"""
|
||||||
|
if os.path.isdir(save_directory):
|
||||||
|
vocab_file = os.path.join(
|
||||||
|
save_directory, VOCAB_FILES_NAMES["vocab_file"]
|
||||||
|
)
|
||||||
|
else:
|
||||||
|
vocab_file = save_directory
|
||||||
|
|
||||||
|
with open(self.vocab_file, 'rb') as fin:
|
||||||
|
proto_str = fin.read()
|
||||||
|
|
||||||
|
with open(vocab_file, "wb") as writer:
|
||||||
|
writer.write(proto_str)
|
||||||
|
|
||||||
|
return (vocab_file,)
|
||||||
|
|
||||||
|
def build_inputs_with_special_tokens(
|
||||||
|
self, token_ids_0: List[int], token_ids_1: Optional[List[int]] = None
|
||||||
|
) -> List[int]:
|
||||||
|
"""
|
||||||
|
Build model inputs from a sequence or a pair of sequence for sequence classification tasks by concatenating and
|
||||||
|
adding special tokens. A BERT sequence has the following format:
|
||||||
|
|
||||||
|
- single sequence: `[CLS] X [SEP]`
|
||||||
|
- pair of sequences: `[CLS] A [SEP] B [SEP]`
|
||||||
|
|
||||||
|
Args:
|
||||||
|
token_ids_0 (`List[int]`):
|
||||||
|
List of IDs to which the special tokens will be added.
|
||||||
|
token_ids_1 (`List[int]`, *optional*):
|
||||||
|
Optional second list of IDs for sequence pairs.
|
||||||
|
|
||||||
|
Returns:
|
||||||
|
`List[int]`: List of [input IDs](../glossary#input-ids) with the appropriate special tokens.
|
||||||
|
"""
|
||||||
|
if token_ids_1 is not None:
|
||||||
|
token_ids_0 += token_ids_1
|
||||||
|
mask_ids = self.sp_tokenizer[self.mask_token]
|
||||||
|
gmask_ids = self.sp_tokenizer[self.gMASK_token]
|
||||||
|
if mask_ids not in token_ids_0 and gmask_ids not in token_ids_0:
|
||||||
|
token_ids_0 += [gmask_ids]
|
||||||
|
|
||||||
|
if token_ids_0[-1] != mask_ids and token_ids_0[-1] != gmask_ids:
|
||||||
|
token_ids_0 += [self.sp_tokenizer[self.eos_token]]
|
||||||
|
|
||||||
|
token_ids_0 += [self.sp_tokenizer[self.bos_token]]
|
||||||
|
|
||||||
|
return token_ids_0
|
||||||
|
|
@ -0,0 +1,19 @@
|
||||||
|
{
|
||||||
|
"name_or_path": "THUDM/chatglm-6b",
|
||||||
|
"bos_token": "<sop>",
|
||||||
|
"eop_token": "<eop>",
|
||||||
|
"eos_token": "</s>",
|
||||||
|
"gmask_token": "[gMASK]",
|
||||||
|
"mask_token": "[MASK]",
|
||||||
|
"pad_token": "<pad>",
|
||||||
|
"unk_token": "<unk>",
|
||||||
|
"remove_space": false,
|
||||||
|
"do_lower_case": false,
|
||||||
|
"tokenizer_class": "ChatGLMTokenizer",
|
||||||
|
"auto_map": {
|
||||||
|
"AutoTokenizer": [
|
||||||
|
"tokenization_chatglm.ChatGLMTokenizer",
|
||||||
|
null
|
||||||
|
]
|
||||||
|
}
|
||||||
|
}
|
||||||
Loading…
Reference in New Issue