Commit ·
db71889
0
Parent(s):
Duplicate from zai-org/codegeex4-all-9b
Browse filesCo-authored-by: Qinkai Zheng <Stanislas@users.noreply.huggingface.co>
- .gitattributes +36 -0
- LICENSE +70 -0
- README.md +102 -0
- README_zh.md +82 -0
- config.json +50 -0
- configuration.json +1 -0
- configuration_chatglm.py +58 -0
- generation_config.json +10 -0
- model-00001-of-00004.safetensors +3 -0
- model-00002-of-00004.safetensors +3 -0
- model-00003-of-00004.safetensors +3 -0
- model-00004-of-00004.safetensors +3 -0
- model.safetensors.index.json +291 -0
- modeling_chatglm.py +1344 -0
- tokenization_chatglm.py +395 -0
- tokenizer.json +3 -0
- tokenizer.model +3 -0
- tokenizer_config.json +165 -0
.gitattributes
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LICENSE
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| 1 |
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The CodeGeeX4 License
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1. 定义
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“许可方”是指分发其软件的 CodeGeeX 团队。
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You should comply with the applicable laws, regulations, policies, ethical standards, and other requirements in the place of use during use.
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THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE
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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
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copyright, please contact us at license@zhipuai.cn.
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README.md
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---
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license: other
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license_name: codegeex4
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license_link: https://huggingface.co/THUDM/codegeex4-all-9b/blob/main/LICENSE
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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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- codegeex
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- thudm
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inference: false
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pipeline_tag: text-generation
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---
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# CodeGeeX4: Open Multilingual Code Generation Model
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<center>
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<img src="https://raw.githubusercontent.com/THUDM/CodeGeeX4/main/resources/logo.jpeg" alt="CodeGeeX4">
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</center>
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[中文](./README_zh.md)
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[GitHub](https://github.com/THUDM/CodeGeeX4)
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We introduce CodeGeeX4-ALL-9B, the open-source version of the latest CodeGeeX4 model series. It is a multilingual code generation model continually trained on the [GLM-4-9B](https://github.com/THUDM/GLM-4), significantly enhancing its code generation capabilities. Using a single CodeGeeX4-ALL-9B model, it can support comprehensive functions such as code completion and generation, code interpreter, web search, function call, repository-level code Q&A, covering various scenarios of software development. CodeGeeX4-ALL-9B has achieved highly competitive performance on public benchmarks, such as [BigCodeBench](https://huggingface.co/spaces/bigcode/bigcodebench-leaderboard) and [NaturalCodeBench](https://github.com/THUDM/NaturalCodeBench). It is currently the most powerful code generation model with less than 10B parameters, even surpassing much larger general-purpose models, achieving the best balance in terms of inference speed and model performance.
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## Get Started
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Use `4.39.0<=transformers<=4.40.2` to quickly launch [codegeex4-all-9b](https://huggingface.co/THUDM/codegeex4-all-9b)：
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```python
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import torch
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from transformers import AutoTokenizer, AutoModelForCausalLM
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device = "cuda" if torch.cuda.is_available() else "cpu"
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tokenizer = AutoTokenizer.from_pretrained("THUDM/codegeex4-all-9b", trust_remote_code=True)
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model = AutoModelForCausalLM.from_pretrained(
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"THUDM/codegeex4-all-9b",
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torch_dtype=torch.bfloat16,
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low_cpu_mem_usage=True,
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trust_remote_code=True
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).to(device).eval()
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inputs = tokenizer.apply_chat_template([{"role": "user", "content": "write a quick sort"}], add_generation_prompt=True, tokenize=True, return_tensors="pt", return_dict=True ).to(device)
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with torch.no_grad():
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outputs = model.generate(**inputs, max_length=256)
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outputs = outputs[:, inputs['input_ids'].shape[1]:]
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print(tokenizer.decode(outputs[0], skip_special_tokens=True))
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```
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If you want to build the **chat** prompt manually, please make sure it follows the following format:
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```
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f"<|system|>\n{system_prompt}\n<|user|>\n{prompt}\n<|assistant|>\n"
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```
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Default system_prompt:
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```
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你是一位智能编程助手，你叫CodeGeeX。你会为用户回答关于编程、代码、计算机方面的任何问题，并提供格式规范、可以执行、准确安全的代码，并在必要时提供详细的解释。
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```
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The English version:
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```
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You are an intelligent programming assistant named CodeGeeX. You will answer any questions users have about programming, coding, and computers, and provide code that is formatted correctly.
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```
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For **infilling** ability, please use (without system prompt):
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```
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f"<|user|>\n<|code_suffix|>{suffix}<|code_prefix|>{prefix}<|code_middle|><|assistant|>\n"
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```
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Additional infos (like file path, programming language, mode) can be added. Example:
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| 69 |
+
```
|
| 70 |
+
<|user|>
|
| 71 |
+
###PATH:src/example.py
|
| 72 |
+
###LANGUAGE:Python
|
| 73 |
+
###MODE:BLOCK
|
| 74 |
+
<|code_suffix|>{suffix}<|code_prefix|>{prefix}<|code_middle|><|assistant|>
|
| 75 |
+
```
|
| 76 |
+
|
| 77 |
+
## Evaluation
|
| 78 |
+
|
| 79 |
+
| **Model** | **Seq Length** | **HumanEval** | **MBPP** | **NCB** | **LCB** | **HumanEvalFIM** | **CRUXEval-O** |
|
| 80 |
+
|-----------------------------|----------------|---------------|----------|---------|---------|------------------|----------------|
|
| 81 |
+
| Llama3-70B-intruct | 8K | 77.4 | 82.3 | 37.0 | 27.4 | - | - |
|
| 82 |
+
| DeepSeek Coder 33B Instruct | 16K | 81.1 | 80.4 | 39.3 | 29.3 | 78.2 | 49.9 |
|
| 83 |
+
| Codestral-22B | 32K | 81.1 | 78.2 | 46.0 | 35.3 | 91.6 | 51.3 |
|
| 84 |
+
| CodeGeeX4-All-9B | 128K | 82.3 | 75.7 | 40.4 | 28.5 | 85.0 | 47.1 |
|
| 85 |
+
|
| 86 |
+
## License
|
| 87 |
+
|
| 88 |
+
The model weights are licensed under the following [License](./LICENSE).
|
| 89 |
+
|
| 90 |
+
## Citation
|
| 91 |
+
|
| 92 |
+
If you find our work helpful, please feel free to cite the following paper:
|
| 93 |
+
|
| 94 |
+
```
|
| 95 |
+
@inproceedings{zheng2023codegeex,
|
| 96 |
+
title={CodeGeeX: A Pre-Trained Model for Code Generation with Multilingual Benchmarking on HumanEval-X},
|
| 97 |
+
author={Qinkai Zheng and Xiao Xia and Xu Zou and Yuxiao Dong and Shan Wang and Yufei Xue and Zihan Wang and Lei Shen and Andi Wang and Yang Li and Teng Su and Zhilin Yang and Jie Tang},
|
| 98 |
+
booktitle={Proceedings of the 29th ACM SIGKDD Conference on Knowledge Discovery and Data Mining},
|
| 99 |
+
pages={5673--5684},
|
| 100 |
+
year={2023}
|
| 101 |
+
}
|
| 102 |
+
```
|
README_zh.md
ADDED
|
@@ -0,0 +1,82 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
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|
|
|
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|
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|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
# CodeGeeX4: 开源多语言代码生成模型
|
| 2 |
+
|
| 3 |
+
<center>
|
| 4 |
+
<img src="https://raw.githubusercontent.com/THUDM/CodeGeeX4/main/resources/logo.jpeg" alt="CodeGeeX4">
|
| 5 |
+
</center>
|
| 6 |
+
|
| 7 |
+
[CodeGeeX4 GitHub](https://github.com/THUDM/CodeGeeX4)
|
| 8 |
+
|
| 9 |
+
我们推出了 CodeGeeX4-ALL-9B，这是最新的 CodeGeeX4 系列模型的开源版本。该模型是在 [GLM-4-9B](https://github.com/THUDM/GLM-4) 基础上持续训练的多语言代码生成模型，显著提升了代码生成能力。使用单个 CodeGeeX4-ALL-9B 模型，可以支持代码补全与生成、代码解释、联网搜索、函数调用、仓库级代码问答等多种功能，覆盖了软件开发的各个场景。CodeGeeX4-ALL-9B 在 [BigCodeBench](https://huggingface.co/spaces/bigcode/bigcodebench-leaderboard) 和 [NaturalCodeBench](https://github.com/THUDM/NaturalCodeBench) 等公开基准测试中取得了极具竞争力的表现。它是目前参数量少于 100 亿的最强代码生成模型，甚至超越了更大的通用模型，在推理速度和模型性能方面达到了最佳平衡。
|
| 10 |
+
|
| 11 |
+
|
| 12 |
+
## 快速开始
|
| 13 |
+
|
| 14 |
+
请使用 `4.39.0<=transformers<=4.40.2` 部署：
|
| 15 |
+
|
| 16 |
+
```python
|
| 17 |
+
from transformers import AutoTokenizer, AutoModelForCausalLM
|
| 18 |
+
|
| 19 |
+
device = "cuda" if torch.cuda.is_available() else "cpu"
|
| 20 |
+
tokenizer = AutoTokenizer.from_pretrained("THUDM/codegeex4-all-9b", trust_remote_code=True)
|
| 21 |
+
model = AutoModelForCausalLM.from_pretrained(
|
| 22 |
+
"THUDM/codegeex4-all-9b",
|
| 23 |
+
torch_dtype=torch.bfloat16,
|
| 24 |
+
low_cpu_mem_usage=True,
|
| 25 |
+
trust_remote_code=True
|
| 26 |
+
).to(device).eval()
|
| 27 |
+
inputs = tokenizer.apply_chat_template([{"role": "user", "content": "write a quick sort"}], add_generation_prompt=True, tokenize=True, return_tensors="pt", return_dict=True).to(device)
|
| 28 |
+
with torch.no_grad():
|
| 29 |
+
outputs = model.generate(**inputs, max_length=256)
|
| 30 |
+
outputs = outputs[:, inputs['input_ids'].shape[1]:]
|
| 31 |
+
print(tokenizer.decode(outputs[0], skip_special_tokens=True))
|
| 32 |
+
```
|
| 33 |
+
|
| 34 |
+
如果希望手动拼接**聊天**prompt，请使用以下格式：
|
| 35 |
+
```
|
| 36 |
+
f"<|system|>\n{system_prompt}\n<|user|>\n{prompt}\n<|assistant|>\n"
|
| 37 |
+
```
|
| 38 |
+
默认系统指令:
|
| 39 |
+
```
|
| 40 |
+
你是一位智能编程助手，你叫CodeGeeX。你会为用户回答关于编程、代码、计算机方面的任何问题，并提供格式规范、可以执行、准确安全的代码，并在必要时提供详细的解释。
|
| 41 |
+
```
|
| 42 |
+
|
| 43 |
+
使用**上下文补全能力**能力，请使用以下格式（不需要系统指令）:
|
| 44 |
+
```
|
| 45 |
+
f"<|user|>\n<|code_suffix|>{suffix}<|code_prefix|>{prefix}<|code_middle|><|assistant|>\n"
|
| 46 |
+
```
|
| 47 |
+
可以添加额外信息（如文件名，编程语言，模式等），示例：
|
| 48 |
+
```
|
| 49 |
+
<|user|>
|
| 50 |
+
###PATH:src/example.py
|
| 51 |
+
###LANGUAGE:Python
|
| 52 |
+
###MODE:BLOCK
|
| 53 |
+
<|code_suffix|>{suffix}<|code_prefix|>{prefix}<|code_middle|><|assistant|>
|
| 54 |
+
```
|
| 55 |
+
|
| 56 |
+
## 评测指标
|
| 57 |
+
|
| 58 |
+
| **模型** | **序列长度** | **HumanEval** | **MBPP** | **NCB** | **LCB** | **HumanEvalFIM** | **CRUXEval-O** |
|
| 59 |
+
|-----------------------------|----------------|---------------|----------|---------|---------|------------------|----------------|
|
| 60 |
+
| Llama3-70B-intruct | 8K | 77.4 | 82.3 | 37.0 | 27.4 | - | - |
|
| 61 |
+
| DeepSeek Coder 33B Instruct | 16K | 81.1 | 80.4 | 39.3 | 29.3 | 78.2 | 49.9 |
|
| 62 |
+
| Codestral-22B | 32K | 81.1 | 78.2 | 46.0 | 35.3 | 91.6 | 51.3 |
|
| 63 |
+
| CodeGeeX4-All-9B | 128K | 82.3 | 75.7 | 40.4 | 28.5 | 85.0 | 47.1 |
|
| 64 |
+
|
| 65 |
+
## License
|
| 66 |
+
|
| 67 |
+
CodeGeeX4-ALL-9B 模型的权重的使用则需要遵循 [License](./LICENSE).
|
| 68 |
+
|
| 69 |
+
|
| 70 |
+
## 引用
|
| 71 |
+
|
| 72 |
+
如果您觉得我们的工作对您有帮助，欢迎引用以下论文：
|
| 73 |
+
|
| 74 |
+
```
|
| 75 |
+
@inproceedings{zheng2023codegeex,
|
| 76 |
+
title={CodeGeeX: A Pre-Trained Model for Code Generation with Multilingual Benchmarking on HumanEval-X},
|
| 77 |
+
author={Qinkai Zheng and Xiao Xia and Xu Zou and Yuxiao Dong and Shan Wang and Yufei Xue and Zihan Wang and Lei Shen and Andi Wang and Yang Li and Teng Su and Zhilin Yang and Jie Tang},
|
| 78 |
+
booktitle={Proceedings of the 29th ACM SIGKDD Conference on Knowledge Discovery and Data Mining},
|
| 79 |
+
pages={5673--5684},
|
| 80 |
+
year={2023}
|
| 81 |
+
}
|
| 82 |
+
```
|
config.json
ADDED
|
@@ -0,0 +1,50 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
{
|
| 2 |
+
"_name_or_path": "THUDM/codegeex4-all-9b",
|
| 3 |
+
"add_bias_linear": false,
|
| 4 |
+
"add_qkv_bias": true,
|
| 5 |
+
"apply_query_key_layer_scaling": true,
|
| 6 |
+
"apply_residual_connection_post_layernorm": false,
|
| 7 |
+
"architectures": [
|
| 8 |
+
"ChatGLMModel"
|
| 9 |
+
],
|
| 10 |
+
"attention_dropout": 0.0,
|
| 11 |
+
"attention_softmax_in_fp32": true,
|
| 12 |
+
"auto_map": {
|
| 13 |
+
"AutoConfig": "configuration_chatglm.ChatGLMConfig",
|
| 14 |
+
"AutoModel": "modeling_chatglm.ChatGLMForConditionalGeneration",
|
| 15 |
+
"AutoModelForCausalLM": "modeling_chatglm.ChatGLMForConditionalGeneration",
|
| 16 |
+
"AutoModelForSeq2SeqLM": "modeling_chatglm.ChatGLMForConditionalGeneration",
|
| 17 |
+
"AutoModelForSequenceClassification": "modeling_chatglm.ChatGLMForSequenceClassification"
|
| 18 |
+
},
|
| 19 |
+
"bias_dropout_fusion": true,
|
| 20 |
+
"classifier_dropout": null,
|
| 21 |
+
"eos_token_id": [
|
| 22 |
+
151329,
|
| 23 |
+
151336,
|
| 24 |
+
151338
|
| 25 |
+
],
|
| 26 |
+
"ffn_hidden_size": 13696,
|
| 27 |
+
"fp32_residual_connection": false,
|
| 28 |
+
"hidden_dropout": 0.0,
|
| 29 |
+
"hidden_size": 4096,
|
| 30 |
+
"kv_channels": 128,
|
| 31 |
+
"layernorm_epsilon": 1e-05,
|
| 32 |
+
"model_type": "chatglm",
|
| 33 |
+
"multi_query_attention": true,
|
| 34 |
+
"multi_query_group_num": 2,
|
| 35 |
+
"num_attention_heads": 32,
|
| 36 |
+
"num_hidden_layers": 40,
|
| 37 |
+
"num_layers": 40,
|
| 38 |
+
"original_rope": true,
|
| 39 |
+
"pad_token_id": 151329,
|
| 40 |
+
"padded_vocab_size": 151552,
|
| 41 |
+
"post_layer_norm": true,
|
| 42 |
+
"rmsnorm": true,
|
| 43 |
+
"rope_ratio": 500,
|
| 44 |
+
"seq_length": 131072,
|
| 45 |
+
"tie_word_embeddings": false,
|
| 46 |
+
"torch_dtype": "bfloat16",
|
| 47 |
+
"transformers_version": "4.40.2",
|
| 48 |
+
"use_cache": true,
|
| 49 |
+
"vocab_size": 151552
|
| 50 |
+
}
|
configuration.json
ADDED
|
@@ -0,0 +1 @@
|
|
|
|
|
|
|
| 1 |
+
{"framework":"Pytorch","task":"text-generation"}
|
configuration_chatglm.py
ADDED
|
@@ -0,0 +1,58 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
from transformers import PretrainedConfig
|
| 2 |
+
|
| 3 |
+
|
| 4 |
+
class ChatGLMConfig(PretrainedConfig):
|
| 5 |
+
model_type = "chatglm"
|
| 6 |
+
|
| 7 |
+
def __init__(
|
| 8 |
+
self,
|
| 9 |
+
num_layers=28,
|
| 10 |
+
padded_vocab_size=65024,
|
| 11 |
+
hidden_size=4096,
|
| 12 |
+
ffn_hidden_size=13696,
|
| 13 |
+
kv_channels=128,
|
| 14 |
+
num_attention_heads=32,
|
| 15 |
+
seq_length=2048,
|
| 16 |
+
hidden_dropout=0.0,
|
| 17 |
+
classifier_dropout=None,
|
| 18 |
+
attention_dropout=0.0,
|
| 19 |
+
layernorm_epsilon=1e-5,
|
| 20 |
+
rmsnorm=True,
|
| 21 |
+
apply_residual_connection_post_layernorm=False,
|
| 22 |
+
post_layer_norm=True,
|
| 23 |
+
add_bias_linear=False,
|
| 24 |
+
add_qkv_bias=False,
|
| 25 |
+
bias_dropout_fusion=True,
|
| 26 |
+
multi_query_attention=False,
|
| 27 |
+
multi_query_group_num=1,
|
| 28 |
+
rope_ratio=1,
|
| 29 |
+
apply_query_key_layer_scaling=True,
|
| 30 |
+
attention_softmax_in_fp32=True,
|
| 31 |
+
fp32_residual_connection=False,
|
| 32 |
+
**kwargs
|
| 33 |
+
):
|
| 34 |
+
self.num_layers = num_layers
|
| 35 |
+
self.vocab_size = padded_vocab_size
|
| 36 |
+
self.padded_vocab_size = padded_vocab_size
|
| 37 |
+
self.hidden_size = hidden_size
|
| 38 |
+
self.ffn_hidden_size = ffn_hidden_size
|
| 39 |
+
self.kv_channels = kv_channels
|
| 40 |
+
self.num_attention_heads = num_attention_heads
|
| 41 |
+
self.seq_length = seq_length
|
| 42 |
+
self.hidden_dropout = hidden_dropout
|
| 43 |
+
self.classifier_dropout = classifier_dropout
|
| 44 |
+
self.attention_dropout = attention_dropout
|
| 45 |
+
self.layernorm_epsilon = layernorm_epsilon
|
| 46 |
+
self.rmsnorm = rmsnorm
|
| 47 |
+
self.apply_residual_connection_post_layernorm = apply_residual_connection_post_layernorm
|
| 48 |
+
self.post_layer_norm = post_layer_norm
|
| 49 |
+
self.add_bias_linear = add_bias_linear
|
| 50 |
+
self.add_qkv_bias = add_qkv_bias
|
| 51 |
+
self.bias_dropout_fusion = bias_dropout_fusion
|
| 52 |
+
self.multi_query_attention = multi_query_attention
|
| 53 |
+
self.multi_query_group_num = multi_query_group_num
|
| 54 |
+
self.rope_ratio = rope_ratio
|
| 55 |
+
self.apply_query_key_layer_scaling = apply_query_key_layer_scaling
|
| 56 |
+
self.attention_softmax_in_fp32 = attention_softmax_in_fp32
|
| 57 |
+
self.fp32_residual_connection = fp32_residual_connection
|
| 58 |
+
super().__init__(**kwargs)
|
generation_config.json
ADDED
|
@@ -0,0 +1,10 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
{
|
| 2 |
+
"_from_model_config": true,
|
| 3 |
+
"eos_token_id": [
|
| 4 |
+
151329,
|
| 5 |
+
151336,
|
| 6 |
+
151338
|
| 7 |
+
],
|
| 8 |
+
"pad_token_id": 151329,
|
| 9 |
+
"transformers_version": "4.40.2"
|
| 10 |
+
}
|
model-00001-of-00004.safetensors
ADDED
|
@@ -0,0 +1,3 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
version https://git-lfs.github.com/spec/v1
|
| 2 |
+
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| 266 |
+
"transformer.encoder.layers.6.self_attention.query_key_value.weight": "model-00001-of-00004.safetensors",
|
| 267 |
+
"transformer.encoder.layers.7.input_layernorm.weight": "model-00001-of-00004.safetensors",
|
| 268 |
+
"transformer.encoder.layers.7.mlp.dense_4h_to_h.weight": "model-00001-of-00004.safetensors",
|
| 269 |
+
"transformer.encoder.layers.7.mlp.dense_h_to_4h.weight": "model-00001-of-00004.safetensors",
|
| 270 |
+
"transformer.encoder.layers.7.post_attention_layernorm.weight": "model-00001-of-00004.safetensors",
|
| 271 |
+
"transformer.encoder.layers.7.self_attention.dense.weight": "model-00001-of-00004.safetensors",
|
| 272 |
+
"transformer.encoder.layers.7.self_attention.query_key_value.bias": "model-00001-of-00004.safetensors",
|
| 273 |
+
"transformer.encoder.layers.7.self_attention.query_key_value.weight": "model-00001-of-00004.safetensors",
|
| 274 |
+
"transformer.encoder.layers.8.input_layernorm.weight": "model-00001-of-00004.safetensors",
|
| 275 |
+
"transformer.encoder.layers.8.mlp.dense_4h_to_h.weight": "model-00001-of-00004.safetensors",
|
| 276 |
+
"transformer.encoder.layers.8.mlp.dense_h_to_4h.weight": "model-00001-of-00004.safetensors",
|
| 277 |
+
"transformer.encoder.layers.8.post_attention_layernorm.weight": "model-00001-of-00004.safetensors",
|
| 278 |
+
"transformer.encoder.layers.8.self_attention.dense.weight": "model-00001-of-00004.safetensors",
|
| 279 |
+
"transformer.encoder.layers.8.self_attention.query_key_value.bias": "model-00001-of-00004.safetensors",
|
| 280 |
+
"transformer.encoder.layers.8.self_attention.query_key_value.weight": "model-00001-of-00004.safetensors",
|
| 281 |
+
"transformer.encoder.layers.9.input_layernorm.weight": "model-00001-of-00004.safetensors",
|
| 282 |
+
"transformer.encoder.layers.9.mlp.dense_4h_to_h.weight": "model-00002-of-00004.safetensors",
|
| 283 |
+
"transformer.encoder.layers.9.mlp.dense_h_to_4h.weight": "model-00002-of-00004.safetensors",
|
| 284 |
+
"transformer.encoder.layers.9.post_attention_layernorm.weight": "model-00001-of-00004.safetensors",
|
| 285 |
+
"transformer.encoder.layers.9.self_attention.dense.weight": "model-00001-of-00004.safetensors",
|
| 286 |
+
"transformer.encoder.layers.9.self_attention.query_key_value.bias": "model-00001-of-00004.safetensors",
|
| 287 |
+
"transformer.encoder.layers.9.self_attention.query_key_value.weight": "model-00001-of-00004.safetensors",
|
| 288 |
+
"transformer.output_layer.weight": "model-00004-of-00004.safetensors",
|
| 289 |
+
"transformer.rotary_pos_emb.inv_freq": "model-00001-of-00004.safetensors"
|
| 290 |
+
}
|
| 291 |
+
}
|
modeling_chatglm.py
ADDED
|
@@ -0,0 +1,1344 @@
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|
| 1 |
+
""" PyTorch ChatGLM model. """
|
| 2 |
+
import json
|
| 3 |
+
import math
|
| 4 |
+
import copy
|
| 5 |
+
import warnings
|
| 6 |
+
import re
|
| 7 |
+
import sys
|
| 8 |
+
|
| 9 |
+
import torch
|
| 10 |
+
import torch.utils.checkpoint
|
| 11 |
+
import torch.nn.functional as F
|
| 12 |
+
from torch import nn
|
| 13 |
+
from torch.nn import CrossEntropyLoss, LayerNorm, MSELoss, BCEWithLogitsLoss
|
| 14 |
+
from torch.nn.utils import skip_init
|
| 15 |
+
from typing import Optional, Tuple, Union, List, Callable, Dict, Any
|
| 16 |
+
from copy import deepcopy
|
| 17 |
+
|
| 18 |
+
from transformers.modeling_outputs import (
|
| 19 |
+
BaseModelOutputWithPast,
|
| 20 |
+
CausalLMOutputWithPast,
|
| 21 |
+
SequenceClassifierOutputWithPast,
|
| 22 |
+
)
|
| 23 |
+
from transformers.modeling_utils import PreTrainedModel
|
| 24 |
+
from transformers.utils import logging, is_torch_npu_available
|
| 25 |
+
from transformers.generation.logits_process import LogitsProcessor
|
| 26 |
+
from transformers.generation.utils import LogitsProcessorList, StoppingCriteriaList, GenerationConfig, ModelOutput
|
| 27 |
+
|
| 28 |
+
from .configuration_chatglm import ChatGLMConfig
|
| 29 |
+
|
| 30 |
+
try:
|
| 31 |
+
from transformers.utils import is_flash_attn_greater_or_equal_2_10, is_flash_attn_2_available
|
| 32 |
+
if is_flash_attn_2_available():
|
| 33 |
+
from flash_attn import flash_attn_func, flash_attn_varlen_func
|
| 34 |
+
from flash_attn.bert_padding import index_first_axis, pad_input, unpad_input # noqa
|
| 35 |
+
except:
|
| 36 |
+
pass
|
| 37 |
+
|
| 38 |
+
|
| 39 |
+
# flags required to enable jit fusion kernels
|
| 40 |
+
|
| 41 |
+
if sys.platform != 'darwin' and not is_torch_npu_available():
|
| 42 |
+
torch._C._jit_set_profiling_mode(False)
|
| 43 |
+
torch._C._jit_set_profiling_executor(False)
|
| 44 |
+
torch._C._jit_override_can_fuse_on_cpu(True)
|
| 45 |
+
torch._C._jit_override_can_fuse_on_gpu(True)
|
| 46 |
+
|
| 47 |
+
logger = logging.get_logger(__name__)
|
| 48 |
+
|
| 49 |
+
_CHECKPOINT_FOR_DOC = "THUDM/ChatGLM"
|
| 50 |
+
_CONFIG_FOR_DOC = "ChatGLMConfig"
|
| 51 |
+
|
| 52 |
+
|
| 53 |
+
def default_init(cls, *args, **kwargs):
|
| 54 |
+
return cls(*args, **kwargs)
|
| 55 |
+
|
| 56 |
+
|
| 57 |
+
class InvalidScoreLogitsProcessor(LogitsProcessor):
|
| 58 |
+
def __call__(self, input_ids: torch.LongTensor, scores: torch.FloatTensor) -> torch.FloatTensor:
|
| 59 |
+
if torch.isnan(scores).any() or torch.isinf(scores).any():
|
| 60 |
+
scores.zero_()
|
| 61 |
+
scores[..., 198] = 5e4
|
| 62 |
+
return scores
|
| 63 |
+
|
| 64 |
+
|
| 65 |
+
def split_tensor_along_last_dim(
|
| 66 |
+
tensor: torch.Tensor,
|
| 67 |
+
num_partitions: int,
|
| 68 |
+
contiguous_split_chunks: bool = False,
|
| 69 |
+
) -> List[torch.Tensor]:
|
| 70 |
+
"""Split a tensor along its last dimension.
|
| 71 |
+
|
| 72 |
+
Arguments:
|
| 73 |
+
tensor: input tensor.
|
| 74 |
+
num_partitions: number of partitions to split the tensor
|
| 75 |
+
contiguous_split_chunks: If True, make each chunk contiguous
|
| 76 |
+
in memory.
|
| 77 |
+
|
| 78 |
+
Returns:
|
| 79 |
+
A list of Tensors
|
| 80 |
+
"""
|
| 81 |
+
# Get the size and dimension.
|
| 82 |
+
last_dim = tensor.dim() - 1
|
| 83 |
+
last_dim_size = tensor.size()[last_dim] // num_partitions
|
| 84 |
+
# Split.
|
| 85 |
+
tensor_list = torch.split(tensor, last_dim_size, dim=last_dim)
|
| 86 |
+
# Note: torch.split does not create contiguous tensors by default.
|
| 87 |
+
if contiguous_split_chunks:
|
| 88 |
+
return tuple(chunk.contiguous() for chunk in tensor_list)
|
| 89 |
+
|
| 90 |
+
return tensor_list
|
| 91 |
+
|
| 92 |
+
|
| 93 |
+
class RotaryEmbedding(nn.Module):
|
| 94 |
+
def __init__(self, dim, rope_ratio=1, original_impl=False, device=None, dtype=None):
|
| 95 |
+
super().__init__()
|
| 96 |
+
inv_freq = 1.0 / (10000 ** (torch.arange(0, dim, 2, device=device).to(dtype=dtype) / dim))
|
| 97 |
+
self.register_buffer("inv_freq", inv_freq)
|
| 98 |
+
self.dim = dim
|
| 99 |
+
self.original_impl = original_impl
|
| 100 |
+
self.rope_ratio = rope_ratio
|
| 101 |
+
|
| 102 |
+
def forward_impl(
|
| 103 |
+
self, seq_len: int, n_elem: int, dtype: torch.dtype, device: torch.device, base: int = 10000
|
| 104 |
+
):
|
| 105 |
+
"""Enhanced Transformer with Rotary Position Embedding.
|
| 106 |
+
|
| 107 |
+
Derived from: https://github.com/labmlai/annotated_deep_learning_paper_implementations/blob/master/labml_nn/
|
| 108 |
+
transformers/rope/__init__.py. MIT License:
|
| 109 |
+
https://github.com/labmlai/annotated_deep_learning_paper_implementations/blob/master/license.
|
| 110 |
+
"""
|
| 111 |
+
# $\Theta = {\theta_i = 10000^{\frac{2(i-1)}{d}}, i \in [1, 2, ..., \frac{d}{2}]}$
|
| 112 |
+
base = base * self.rope_ratio
|
| 113 |
+
theta = 1.0 / (base ** (torch.arange(0, n_elem, 2, dtype=torch.float, device=device) / n_elem))
|
| 114 |
+
|
| 115 |
+
# Create position indexes `[0, 1, ..., seq_len - 1]`
|
| 116 |
+
seq_idx = torch.arange(seq_len, dtype=torch.float, device=device)
|
| 117 |
+
|
| 118 |
+
# Calculate the product of position index and $\theta_i$
|
| 119 |
+
idx_theta = torch.outer(seq_idx, theta).float()
|
| 120 |
+
|
| 121 |
+
cache = torch.stack([torch.cos(idx_theta), torch.sin(idx_theta)], dim=-1)
|
| 122 |
+
|
| 123 |
+
# this is to mimic the behaviour of complex32, else we will get different results
|
| 124 |
+
if dtype in (torch.float16, torch.bfloat16, torch.int8):
|
| 125 |
+
cache = cache.bfloat16() if dtype == torch.bfloat16 else cache.half()
|
| 126 |
+
return cache
|
| 127 |
+
|
| 128 |
+
def forward(self, max_seq_len, offset=0):
|
| 129 |
+
return self.forward_impl(
|
| 130 |
+
max_seq_len, self.dim, dtype=self.inv_freq.dtype, device=self.inv_freq.device
|
| 131 |
+
)
|
| 132 |
+
|
| 133 |
+
|
| 134 |
+
@torch.jit.script
|
| 135 |
+
def apply_rotary_pos_emb(x: torch.Tensor, rope_cache: torch.Tensor) -> torch.Tensor:
|
| 136 |
+
# x: [b, np, sq, hn]
|
| 137 |
+
b, np, sq, hn = x.size(0), x.size(1), x.size(2), x.size(3)
|
| 138 |
+
rot_dim = rope_cache.shape[-2] * 2
|
| 139 |
+
x, x_pass = x[..., :rot_dim], x[..., rot_dim:]
|
| 140 |
+
# truncate to support variable sizes
|
| 141 |
+
rope_cache = rope_cache[:, :sq]
|
| 142 |
+
xshaped = x.reshape(b, np, sq, rot_dim // 2, 2)
|
| 143 |
+
rope_cache = rope_cache.view(-1, 1, sq, xshaped.size(3), 2)
|
| 144 |
+
x_out2 = torch.stack(
|
| 145 |
+
[
|
| 146 |
+
xshaped[..., 0] * rope_cache[..., 0] - xshaped[..., 1] * rope_cache[..., 1],
|
| 147 |
+
xshaped[..., 1] * rope_cache[..., 0] + xshaped[..., 0] * rope_cache[..., 1],
|
| 148 |
+
],
|
| 149 |
+
-1,
|
| 150 |
+
)
|
| 151 |
+
x_out2 = x_out2.flatten(3)
|
| 152 |
+
return torch.cat((x_out2, x_pass), dim=-1)
|
| 153 |
+
|
| 154 |
+
|
| 155 |
+
class RMSNorm(torch.nn.Module):
|
| 156 |
+
def __init__(self, normalized_shape, eps=1e-5, device=None, dtype=None, **kwargs):
|
| 157 |
+
super().__init__()
|
| 158 |
+
self.weight = torch.nn.Parameter(torch.empty(normalized_shape, device=device, dtype=dtype))
|
| 159 |
+
self.eps = eps
|
| 160 |
+
|
| 161 |
+
def forward(self, hidden_states: torch.Tensor):
|
| 162 |
+
input_dtype = hidden_states.dtype
|
| 163 |
+
variance = hidden_states.to(torch.float32).pow(2).mean(-1, keepdim=True)
|
| 164 |
+
hidden_states = hidden_states * torch.rsqrt(variance + self.eps)
|
| 165 |
+
|
| 166 |
+
return (self.weight * hidden_states).to(input_dtype)
|
| 167 |
+
|
| 168 |
+
|
| 169 |
+
class CoreAttention(torch.nn.Module):
|
| 170 |
+
def __init__(self, config: ChatGLMConfig, layer_number):
|
| 171 |
+
super(CoreAttention, self).__init__()
|
| 172 |
+
self.config = config
|
| 173 |
+
self.apply_query_key_layer_scaling = config.apply_query_key_layer_scaling
|
| 174 |
+
self.attention_softmax_in_fp32 = config.attention_softmax_in_fp32
|
| 175 |
+
if self.apply_query_key_layer_scaling:
|
| 176 |
+
self.attention_softmax_in_fp32 = True
|
| 177 |
+
self.layer_number = max(1, layer_number)
|
| 178 |
+
self.is_causal = True
|
| 179 |
+
|
| 180 |
+
projection_size = config.kv_channels * config.num_attention_heads
|
| 181 |
+
|
| 182 |
+
# Per attention head and per partition values.
|
| 183 |
+
self.hidden_size_per_partition = projection_size
|
| 184 |
+
self.hidden_size_per_attention_head = projection_size // config.num_attention_heads
|
| 185 |
+
self.num_attention_heads_per_partition = config.num_attention_heads
|
| 186 |
+
|
| 187 |
+
coeff = None
|
| 188 |
+
self.norm_factor = math.sqrt(self.hidden_size_per_attention_head)
|
| 189 |
+
if self.apply_query_key_layer_scaling:
|
| 190 |
+
coeff = self.layer_number
|
| 191 |
+
self.norm_factor *= coeff
|
| 192 |
+
self.coeff = coeff
|
| 193 |
+
|
| 194 |
+
self.attention_dropout = torch.nn.Dropout(config.attention_dropout)
|
| 195 |
+
|
| 196 |
+
def forward(self, query_layer, key_layer, value_layer, attention_mask):
|
| 197 |
+
# [b, np, sq, sk]
|
| 198 |
+
output_size = (query_layer.size(0), query_layer.size(1), query_layer.size(2), key_layer.size(2))
|
| 199 |
+
|
| 200 |
+
# [b, np, sq, hn] -> [b * np, sq, hn]
|
| 201 |
+
query_layer = query_layer.view(output_size[0] * output_size[1], output_size[2], -1)
|
| 202 |
+
# [b, np, sk, hn] -> [b * np, sk, hn]
|
| 203 |
+
key_layer = key_layer.view(output_size[0] * output_size[1], output_size[3], -1)
|
| 204 |
+
|
| 205 |
+
# preallocting input tensor: [b * np, sq, sk]
|
| 206 |
+
matmul_input_buffer = torch.empty(
|
| 207 |
+
output_size[0] * output_size[1], output_size[2], output_size[3], dtype=query_layer.dtype,
|
| 208 |
+
device=query_layer.device
|
| 209 |
+
)
|
| 210 |
+
|
| 211 |
+
# Raw attention scores. [b * np, sq, sk]
|
| 212 |
+
matmul_result = torch.baddbmm(
|
| 213 |
+
matmul_input_buffer,
|
| 214 |
+
query_layer, # [b * np, sq, hn]
|
| 215 |
+
key_layer.transpose(1, 2), # [b * np, hn, sk]
|
| 216 |
+
beta=0.0,
|
| 217 |
+
alpha=(1.0 / self.norm_factor),
|
| 218 |
+
)
|
| 219 |
+
|
| 220 |
+
# change view to [b, np, sq, sk]
|
| 221 |
+
attention_scores = matmul_result.view(*output_size)
|
| 222 |
+
|
| 223 |
+
# ===========================
|
| 224 |
+
# Attention probs and dropout
|
| 225 |
+
# ===========================
|
| 226 |
+
|
| 227 |
+
# attention scores and attention mask [b, np, sq, sk]
|
| 228 |
+
if self.attention_softmax_in_fp32:
|
| 229 |
+
attention_scores = attention_scores.float()
|
| 230 |
+
if self.coeff is not None:
|
| 231 |
+
attention_scores = attention_scores * self.coeff
|
| 232 |
+
if attention_mask is None and attention_scores.shape[2] == attention_scores.shape[3]:
|
| 233 |
+
attention_mask = torch.ones(output_size[0], 1, output_size[2], output_size[3],
|
| 234 |
+
device=attention_scores.device, dtype=torch.bool)
|
| 235 |
+
attention_mask.tril_()
|
| 236 |
+
attention_mask = ~attention_mask
|
| 237 |
+
if attention_mask is not None:
|
| 238 |
+
attention_scores = attention_scores.masked_fill(attention_mask, float("-inf"))
|
| 239 |
+
attention_probs = F.softmax(attention_scores, dim=-1)
|
| 240 |
+
attention_probs = attention_probs.type_as(value_layer)
|
| 241 |
+
|
| 242 |
+
# This is actually dropping out entire tokens to attend to, which might
|
| 243 |
+
# seem a bit unusual, but is taken from the original Transformer paper.
|
| 244 |
+
attention_probs = self.attention_dropout(attention_probs)
|
| 245 |
+
|
| 246 |
+
# query layer shape: [b * np, sq, hn]
|
| 247 |
+
# value layer shape: [b, np, sk, hn]
|
| 248 |
+
# attention shape: [b, np, sq, sk]
|
| 249 |
+
# context layer shape: [b, np, sq, hn]
|
| 250 |
+
output_size = (value_layer.size(0), value_layer.size(1), query_layer.size(1), value_layer.size(3))
|
| 251 |
+
# change view [b * np, sk, hn]
|
| 252 |
+
value_layer = value_layer.view(output_size[0] * output_size[1], value_layer.size(2), -1)
|
| 253 |
+
# change view [b * np, sq, sk]
|
| 254 |
+
attention_probs = attention_probs.view(output_size[0] * output_size[1], output_size[2], -1)
|
| 255 |
+
# matmul: [b * np, sq, hn]
|
| 256 |
+
context_layer = torch.bmm(attention_probs, value_layer)
|
| 257 |
+
# change view [b, np, sq, hn]
|
| 258 |
+
context_layer = context_layer.view(*output_size)
|
| 259 |
+
# [b, np, sq, hn] --> [b, sq, np, hn]
|
| 260 |
+
context_layer = context_layer.transpose(1, 2).contiguous()
|
| 261 |
+
# [b, sq, np, hn] --> [b, sq, hp]
|
| 262 |
+
new_context_layer_shape = context_layer.size()[:-2] + (self.hidden_size_per_partition,)
|
| 263 |
+
context_layer = context_layer.reshape(*new_context_layer_shape)
|
| 264 |
+
|
| 265 |
+
return context_layer
|
| 266 |
+
|
| 267 |
+
|
| 268 |
+
class SdpaAttention(CoreAttention):
|
| 269 |
+
def forward(self, query_layer, key_layer, value_layer, attention_mask):
|
| 270 |
+
if attention_mask is None and query_layer.shape[2] == key_layer.shape[2]:
|
| 271 |
+
context_layer = torch.nn.functional.scaled_dot_product_attention(query_layer, key_layer, value_layer,
|
| 272 |
+
is_causal=True,
|
| 273 |
+
dropout_p=self.config.attention_dropout if self.training else 0.0)
|
| 274 |
+
else:
|
| 275 |
+
if attention_mask is not None:
|
| 276 |
+
attention_mask = ~attention_mask
|
| 277 |
+
context_layer = torch.nn.functional.scaled_dot_product_attention(query_layer, key_layer, value_layer,
|
| 278 |
+
attention_mask,
|
| 279 |
+
dropout_p=self.config.attention_dropout if self.training else 0.0)
|
| 280 |
+
context_layer = context_layer.transpose(1, 2).contiguous()
|
| 281 |
+
new_context_layer_shape = context_layer.size()[:-2] + (self.hidden_size_per_partition,)
|
| 282 |
+
context_layer = context_layer.reshape(*new_context_layer_shape)
|
| 283 |
+
return context_layer
|
| 284 |
+
|
| 285 |
+
|
| 286 |
+
def _get_unpad_data(attention_mask):
|
| 287 |
+
seqlens_in_batch = attention_mask.sum(dim=-1, dtype=torch.int32)
|
| 288 |
+
indices = torch.nonzero(attention_mask.flatten(), as_tuple=False).flatten()
|
| 289 |
+
max_seqlen_in_batch = seqlens_in_batch.max().item()
|
| 290 |
+
cu_seqlens = F.pad(torch.cumsum(seqlens_in_batch, dim=0, dtype=torch.int32), (1, 0))
|
| 291 |
+
return (
|
| 292 |
+
indices,
|
| 293 |
+
cu_seqlens,
|
| 294 |
+
max_seqlen_in_batch,
|
| 295 |
+
)
|
| 296 |
+
|
| 297 |
+
|
| 298 |
+
# Copied from transformers.models.llama.modeling_llama.LlamaFlashAttention2
|
| 299 |
+
class FlashAttention2(CoreAttention):
|
| 300 |
+
def __init__(self, *args, **kwargs):
|
| 301 |
+
super().__init__(*args, **kwargs)
|
| 302 |
+
self._flash_attn_uses_top_left_mask = not is_flash_attn_greater_or_equal_2_10()
|
| 303 |
+
|
| 304 |
+
def forward(self, query_states, key_states, value_states, attention_mask):
|
| 305 |
+
query_states = query_states.transpose(1, 2)
|
| 306 |
+
key_states = key_states.transpose(1, 2)
|
| 307 |
+
value_states = value_states.transpose(1, 2)
|
| 308 |
+
batch_size, query_length = query_states.shape[:2]
|
| 309 |
+
if not self._flash_attn_uses_top_left_mask:
|
| 310 |
+
causal = self.is_causal
|
| 311 |
+
else:
|
| 312 |
+
# TODO: Remove the `query_length != 1` check once Flash Attention for RoCm is bumped to 2.1. For details, please see the comment in LlamaFlashAttention2 __init__.
|
| 313 |
+
causal = self.is_causal and query_length != 1
|
| 314 |
+
dropout = self.config.attention_dropout if self.training else 0.0
|
| 315 |
+
# Contains at least one padding token in the sequence
|
| 316 |
+
if attention_mask is not None:
|
| 317 |
+
query_states, key_states, value_states, indices_q, cu_seq_lens, max_seq_lens = self._upad_input(
|
| 318 |
+
query_states, key_states, value_states, attention_mask, query_length
|
| 319 |
+
)
|
| 320 |
+
|
| 321 |
+
cu_seqlens_q, cu_seqlens_k = cu_seq_lens
|
| 322 |
+
max_seqlen_in_batch_q, max_seqlen_in_batch_k = max_seq_lens
|
| 323 |
+
|
| 324 |
+
attn_output_unpad = flash_attn_varlen_func(
|
| 325 |
+
query_states,
|
| 326 |
+
key_states,
|
| 327 |
+
value_states,
|
| 328 |
+
cu_seqlens_q=cu_seqlens_q,
|
| 329 |
+
cu_seqlens_k=cu_seqlens_k,
|
| 330 |
+
max_seqlen_q=max_seqlen_in_batch_q,
|
| 331 |
+
max_seqlen_k=max_seqlen_in_batch_k,
|
| 332 |
+
dropout_p=dropout,
|
| 333 |
+
softmax_scale=None,
|
| 334 |
+
causal=causal,
|
| 335 |
+
)
|
| 336 |
+
|
| 337 |
+
attn_output = pad_input(attn_output_unpad, indices_q, batch_size, query_length)
|
| 338 |
+
else:
|
| 339 |
+
attn_output = flash_attn_func(
|
| 340 |
+
query_states, key_states, value_states, dropout, softmax_scale=None, causal=causal
|
| 341 |
+
)
|
| 342 |
+
attn_output = attn_output.reshape(batch_size, query_length, self.hidden_size_per_partition).contiguous()
|
| 343 |
+
return attn_output
|
| 344 |
+
|
| 345 |
+
def _upad_input(self, query_layer, key_layer, value_layer, attention_mask, query_length):
|
| 346 |
+
indices_k, cu_seqlens_k, max_seqlen_in_batch_k = _get_unpad_data(attention_mask)
|
| 347 |
+
batch_size, kv_seq_len, num_key_value_heads, head_dim = key_layer.shape
|
| 348 |
+
|
| 349 |
+
key_layer = index_first_axis(
|
| 350 |
+
key_layer.reshape(batch_size * kv_seq_len, num_key_value_heads, head_dim), indices_k
|
| 351 |
+
)
|
| 352 |
+
value_layer = index_first_axis(
|
| 353 |
+
value_layer.reshape(batch_size * kv_seq_len, num_key_value_heads, head_dim), indices_k
|
| 354 |
+
)
|
| 355 |
+
if query_length == kv_seq_len:
|
| 356 |
+
query_layer = index_first_axis(
|
| 357 |
+
query_layer.reshape(batch_size * kv_seq_len, self.num_attention_heads_per_partition, head_dim), indices_k
|
| 358 |
+
)
|
| 359 |
+
cu_seqlens_q = cu_seqlens_k
|
| 360 |
+
max_seqlen_in_batch_q = max_seqlen_in_batch_k
|
| 361 |
+
indices_q = indices_k
|
| 362 |
+
elif query_length == 1:
|
| 363 |
+
max_seqlen_in_batch_q = 1
|
| 364 |
+
cu_seqlens_q = torch.arange(
|
| 365 |
+
batch_size + 1, dtype=torch.int32, device=query_layer.device
|
| 366 |
+
) # There is a memcpy here, that is very bad.
|
| 367 |
+
indices_q = cu_seqlens_q[:-1]
|
| 368 |
+
query_layer = query_layer.squeeze(1)
|
| 369 |
+
else:
|
| 370 |
+
# The -q_len: slice assumes left padding.
|
| 371 |
+
attention_mask = attention_mask[:, -query_length:]
|
| 372 |
+
query_layer, indices_q, cu_seqlens_q, max_seqlen_in_batch_q = unpad_input(query_layer, attention_mask)
|
| 373 |
+
|
| 374 |
+
return (
|
| 375 |
+
query_layer,
|
| 376 |
+
key_layer,
|
| 377 |
+
value_layer,
|
| 378 |
+
indices_q,
|
| 379 |
+
(cu_seqlens_q, cu_seqlens_k),
|
| 380 |
+
(max_seqlen_in_batch_q, max_seqlen_in_batch_k),
|
| 381 |
+
)
|
| 382 |
+
|
| 383 |
+
|
| 384 |
+
CORE_ATTENTION_CLASSES = {
|
| 385 |
+
"eager": CoreAttention,
|
| 386 |
+
"sdpa": SdpaAttention,
|
| 387 |
+
"flash_attention_2": FlashAttention2
|
| 388 |
+
}
|
| 389 |
+
|
| 390 |
+
|
| 391 |
+
class SelfAttention(torch.nn.Module):
|
| 392 |
+
"""Parallel self-attention layer abstract class.
|
| 393 |
+
|
| 394 |
+
Self-attention layer takes input with size [s, b, h]
|
| 395 |
+
and returns output of the same size.
|
| 396 |
+
"""
|
| 397 |
+
|
| 398 |
+
def __init__(self, config: ChatGLMConfig, layer_number, device=None):
|
| 399 |
+
super(SelfAttention, self).__init__()
|
| 400 |
+
self.layer_number = max(1, layer_number)
|
| 401 |
+
|
| 402 |
+
self.projection_size = config.kv_channels * config.num_attention_heads
|
| 403 |
+
|
| 404 |
+
# Per attention head and per partition values.
|
| 405 |
+
self.hidden_size_per_attention_head = self.projection_size // config.num_attention_heads
|
| 406 |
+
self.num_attention_heads_per_partition = config.num_attention_heads
|
| 407 |
+
|
| 408 |
+
self.multi_query_attention = config.multi_query_attention
|
| 409 |
+
self.qkv_hidden_size = 3 * self.projection_size
|
| 410 |
+
if self.multi_query_attention:
|
| 411 |
+
self.num_multi_query_groups_per_partition = config.multi_query_group_num
|
| 412 |
+
self.qkv_hidden_size = (
|
| 413 |
+
self.projection_size + 2 * self.hidden_size_per_attention_head * config.multi_query_group_num
|
| 414 |
+
)
|
| 415 |
+
self.query_key_value = nn.Linear(config.hidden_size, self.qkv_hidden_size,
|
| 416 |
+
bias=config.add_bias_linear or config.add_qkv_bias,
|
| 417 |
+
device=device, **_config_to_kwargs(config)
|
| 418 |
+
)
|
| 419 |
+
|
| 420 |
+
self.core_attention = CORE_ATTENTION_CLASSES[config._attn_implementation](config, self.layer_number)
|
| 421 |
+
|
| 422 |
+
# Output.
|
| 423 |
+
self.dense = nn.Linear(self.projection_size, config.hidden_size, bias=config.add_bias_linear,
|
| 424 |
+
device=device, **_config_to_kwargs(config)
|
| 425 |
+
)
|
| 426 |
+
|
| 427 |
+
def _allocate_memory(self, inference_max_sequence_len, batch_size, device=None, dtype=None):
|
| 428 |
+
if self.multi_query_attention:
|
| 429 |
+
num_attention_heads = self.num_multi_query_groups_per_partition
|
| 430 |
+
else:
|
| 431 |
+
num_attention_heads = self.num_attention_heads_per_partition
|
| 432 |
+
return torch.empty(
|
| 433 |
+
inference_max_sequence_len,
|
| 434 |
+
batch_size,
|
| 435 |
+
num_attention_heads,
|
| 436 |
+
self.hidden_size_per_attention_head,
|
| 437 |
+
dtype=dtype,
|
| 438 |
+
device=device,
|
| 439 |
+
)
|
| 440 |
+
|
| 441 |
+
def forward(
|
| 442 |
+
self, hidden_states, attention_mask, rotary_pos_emb, kv_cache=None, use_cache=True
|
| 443 |
+
):
|
| 444 |
+
# hidden_states: [b, sq, h]
|
| 445 |
+
|
| 446 |
+
# =================================================
|
| 447 |
+
# Pre-allocate memory for key-values for inference.
|
| 448 |
+
# =================================================
|
| 449 |
+
# =====================
|
| 450 |
+
# Query, Key, and Value
|
| 451 |
+
# =====================
|
| 452 |
+
|
| 453 |
+
# Attention heads [b, sq, h] --> [b, sq, (np * 3 * hn)]
|
| 454 |
+
mixed_x_layer = self.query_key_value(hidden_states)
|
| 455 |
+
|
| 456 |
+
if self.multi_query_attention:
|
| 457 |
+
(query_layer, key_layer, value_layer) = mixed_x_layer.split(
|
| 458 |
+
[
|
| 459 |
+
self.num_attention_heads_per_partition * self.hidden_size_per_attention_head,
|
| 460 |
+
self.num_multi_query_groups_per_partition * self.hidden_size_per_attention_head,
|
| 461 |
+
self.num_multi_query_groups_per_partition * self.hidden_size_per_attention_head,
|
| 462 |
+
],
|
| 463 |
+
dim=-1,
|
| 464 |
+
)
|
| 465 |
+
query_layer = query_layer.view(
|
| 466 |
+
query_layer.size()[:-1] + (self.num_attention_heads_per_partition, self.hidden_size_per_attention_head)
|
| 467 |
+
)
|
| 468 |
+
key_layer = key_layer.view(
|
| 469 |
+
key_layer.size()[:-1] + (self.num_multi_query_groups_per_partition, self.hidden_size_per_attention_head)
|
| 470 |
+
)
|
| 471 |
+
value_layer = value_layer.view(
|
| 472 |
+
value_layer.size()[:-1]
|
| 473 |
+
+ (self.num_multi_query_groups_per_partition, self.hidden_size_per_attention_head)
|
| 474 |
+
)
|
| 475 |
+
else:
|
| 476 |
+
new_tensor_shape = mixed_x_layer.size()[:-1] + \
|
| 477 |
+
(self.num_attention_heads_per_partition,
|
| 478 |
+
3 * self.hidden_size_per_attention_head)
|
| 479 |
+
mixed_x_layer = mixed_x_layer.view(*new_tensor_shape)
|
| 480 |
+
|
| 481 |
+
# [b, sq, np, 3 * hn] --> 3 [b, sq, np, hn]
|
| 482 |
+
(query_layer, key_layer, value_layer) = split_tensor_along_last_dim(mixed_x_layer, 3)
|
| 483 |
+
|
| 484 |
+
# [b, sq, np, hn] -> [b, np, sq, hn]
|
| 485 |
+
query_layer, key_layer, value_layer = [k.transpose(1, 2) for k in [query_layer, key_layer, value_layer]]
|
| 486 |
+
|
| 487 |
+
# apply relative positional encoding (rotary embedding)
|
| 488 |
+
if rotary_pos_emb is not None:
|
| 489 |
+
query_layer = apply_rotary_pos_emb(query_layer, rotary_pos_emb)
|
| 490 |
+
key_layer = apply_rotary_pos_emb(key_layer, rotary_pos_emb)
|
| 491 |
+
|
| 492 |
+
# adjust key and value for inference
|
| 493 |
+
if kv_cache is not None:
|
| 494 |
+
cache_k, cache_v = kv_cache
|
| 495 |
+
key_layer = torch.cat((cache_k, key_layer), dim=2)
|
| 496 |
+
value_layer = torch.cat((cache_v, value_layer), dim=2)
|
| 497 |
+
if use_cache:
|
| 498 |
+
if kv_cache is None:
|
| 499 |
+
kv_cache = torch.cat((key_layer.unsqueeze(0).unsqueeze(0), value_layer.unsqueeze(0).unsqueeze(0)),
|
| 500 |
+
dim=1)
|
| 501 |
+
else:
|
| 502 |
+
kv_cache = (key_layer, value_layer)
|
| 503 |
+
else:
|
| 504 |
+
kv_cache = None
|
| 505 |
+
|
| 506 |
+
if self.multi_query_attention:
|
| 507 |
+
key_layer = key_layer.unsqueeze(2)
|
| 508 |
+
key_layer = key_layer.expand(
|
| 509 |
+
-1, -1, self.num_attention_heads_per_partition // self.num_multi_query_groups_per_partition, -1, -1
|
| 510 |
+
)
|
| 511 |
+
key_layer = key_layer.contiguous().view(
|
| 512 |
+
key_layer.size()[:1] + (self.num_attention_heads_per_partition,) + key_layer.size()[3:]
|
| 513 |
+
)
|
| 514 |
+
value_layer = value_layer.unsqueeze(2)
|
| 515 |
+
value_layer = value_layer.expand(
|
| 516 |
+
-1, -1, self.num_attention_heads_per_partition // self.num_multi_query_groups_per_partition, -1, -1
|
| 517 |
+
)
|
| 518 |
+
value_layer = value_layer.contiguous().view(
|
| 519 |
+
value_layer.size()[:1] + (self.num_attention_heads_per_partition,) + value_layer.size()[3:]
|
| 520 |
+
)
|
| 521 |
+
|
| 522 |
+
# ==================================
|
| 523 |
+
# core attention computation
|
| 524 |
+
# ==================================
|
| 525 |
+
|
| 526 |
+
context_layer = self.core_attention(query_layer, key_layer, value_layer, attention_mask)
|
| 527 |
+
|
| 528 |
+
# =================
|
| 529 |
+
# Output. [sq, b, h]
|
| 530 |
+
# =================
|
| 531 |
+
|
| 532 |
+
output = self.dense(context_layer)
|
| 533 |
+
|
| 534 |
+
return output, kv_cache
|
| 535 |
+
|
| 536 |
+
|
| 537 |
+
def _config_to_kwargs(args):
|
| 538 |
+
common_kwargs = {
|
| 539 |
+
"dtype": args.torch_dtype,
|
| 540 |
+
}
|
| 541 |
+
return common_kwargs
|
| 542 |
+
|
| 543 |
+
|
| 544 |
+
class MLP(torch.nn.Module):
|
| 545 |
+
"""MLP.
|
| 546 |
+
|
| 547 |
+
MLP will take the input with h hidden state, project it to 4*h
|
| 548 |
+
hidden dimension, perform nonlinear transformation, and project the
|
| 549 |
+
state back into h hidden dimension.
|
| 550 |
+
"""
|
| 551 |
+
|
| 552 |
+
def __init__(self, config: ChatGLMConfig, device=None):
|
| 553 |
+
super(MLP, self).__init__()
|
| 554 |
+
|
| 555 |
+
self.add_bias = config.add_bias_linear
|
| 556 |
+
|
| 557 |
+
# Project to 4h. If using swiglu double the output width, see https://arxiv.org/pdf/2002.05202.pdf
|
| 558 |
+
self.dense_h_to_4h = nn.Linear(
|
| 559 |
+
config.hidden_size,
|
| 560 |
+
config.ffn_hidden_size * 2,
|
| 561 |
+
bias=self.add_bias,
|
| 562 |
+
device=device,
|
| 563 |
+
**_config_to_kwargs(config)
|
| 564 |
+
)
|
| 565 |
+
|
| 566 |
+
def swiglu(x):
|
| 567 |
+
x = torch.chunk(x, 2, dim=-1)
|
| 568 |
+
return F.silu(x[0]) * x[1]
|
| 569 |
+
|
| 570 |
+
self.activation_func = swiglu
|
| 571 |
+
|
| 572 |
+
# Project back to h.
|
| 573 |
+
self.dense_4h_to_h = nn.Linear(
|
| 574 |
+
config.ffn_hidden_size,
|
| 575 |
+
config.hidden_size,
|
| 576 |
+
bias=self.add_bias,
|
| 577 |
+
device=device,
|
| 578 |
+
**_config_to_kwargs(config)
|
| 579 |
+
)
|
| 580 |
+
|
| 581 |
+
def forward(self, hidden_states):
|
| 582 |
+
# [s, b, 4hp]
|
| 583 |
+
intermediate_parallel = self.dense_h_to_4h(hidden_states)
|
| 584 |
+
intermediate_parallel = self.activation_func(intermediate_parallel)
|
| 585 |
+
# [s, b, h]
|
| 586 |
+
output = self.dense_4h_to_h(intermediate_parallel)
|
| 587 |
+
return output
|
| 588 |
+
|
| 589 |
+
|
| 590 |
+
class GLMBlock(torch.nn.Module):
|
| 591 |
+
"""A single transformer layer.
|
| 592 |
+
|
| 593 |
+
Transformer layer takes input with size [s, b, h] and returns an
|
| 594 |
+
output of the same size.
|
| 595 |
+
"""
|
| 596 |
+
|
| 597 |
+
def __init__(self, config: ChatGLMConfig, layer_number, device=None):
|
| 598 |
+
super(GLMBlock, self).__init__()
|
| 599 |
+
self.layer_number = layer_number
|
| 600 |
+
|
| 601 |
+
self.apply_residual_connection_post_layernorm = config.apply_residual_connection_post_layernorm
|
| 602 |
+
|
| 603 |
+
self.fp32_residual_connection = config.fp32_residual_connection
|
| 604 |
+
|
| 605 |
+
LayerNormFunc = RMSNorm if config.rmsnorm else LayerNorm
|
| 606 |
+
# Layernorm on the input data.
|
| 607 |
+
self.input_layernorm = LayerNormFunc(config.hidden_size, eps=config.layernorm_epsilon, device=device,
|
| 608 |
+
dtype=config.torch_dtype)
|
| 609 |
+
|
| 610 |
+
# Self attention.
|
| 611 |
+
self.self_attention = SelfAttention(config, layer_number, device=device)
|
| 612 |
+
self.hidden_dropout = config.hidden_dropout
|
| 613 |
+
|
| 614 |
+
# Layernorm on the attention output
|
| 615 |
+
self.post_attention_layernorm = LayerNormFunc(config.hidden_size, eps=config.layernorm_epsilon, device=device,
|
| 616 |
+
dtype=config.torch_dtype)
|
| 617 |
+
|
| 618 |
+
# MLP
|
| 619 |
+
self.mlp = MLP(config, device=device)
|
| 620 |
+
|
| 621 |
+
def forward(
|
| 622 |
+
self, hidden_states, attention_mask, rotary_pos_emb, kv_cache=None, use_cache=True,
|
| 623 |
+
):
|
| 624 |
+
# hidden_states: [s, b, h]
|
| 625 |
+
|
| 626 |
+
# Layer norm at the beginning of the transformer layer.
|
| 627 |
+
layernorm_output = self.input_layernorm(hidden_states)
|
| 628 |
+
# Self attention.
|
| 629 |
+
attention_output, kv_cache = self.self_attention(
|
| 630 |
+
layernorm_output,
|
| 631 |
+
attention_mask,
|
| 632 |
+
rotary_pos_emb,
|
| 633 |
+
kv_cache=kv_cache,
|
| 634 |
+
use_cache=use_cache
|
| 635 |
+
)
|
| 636 |
+
|
| 637 |
+
# Residual connection.
|
| 638 |
+
if self.apply_residual_connection_post_layernorm:
|
| 639 |
+
residual = layernorm_output
|
| 640 |
+
else:
|
| 641 |
+
residual = hidden_states
|
| 642 |
+
|
| 643 |
+
layernorm_input = torch.nn.functional.dropout(attention_output, p=self.hidden_dropout, training=self.training)
|
| 644 |
+
layernorm_input = residual + layernorm_input
|
| 645 |
+
|
| 646 |
+
# Layer norm post the self attention.
|
| 647 |
+
layernorm_output = self.post_attention_layernorm(layernorm_input)
|
| 648 |
+
|
| 649 |
+
# MLP.
|
| 650 |
+
mlp_output = self.mlp(layernorm_output)
|
| 651 |
+
|
| 652 |
+
# Second residual connection.
|
| 653 |
+
if self.apply_residual_connection_post_layernorm:
|
| 654 |
+
residual = layernorm_output
|
| 655 |
+
else:
|
| 656 |
+
residual = layernorm_input
|
| 657 |
+
|
| 658 |
+
output = torch.nn.functional.dropout(mlp_output, p=self.hidden_dropout, training=self.training)
|
| 659 |
+
output = residual + output
|
| 660 |
+
|
| 661 |
+
return output, kv_cache
|
| 662 |
+
|
| 663 |
+
|
| 664 |
+
class GLMTransformer(torch.nn.Module):
|
| 665 |
+
"""Transformer class."""
|
| 666 |
+
|
| 667 |
+
def __init__(self, config: ChatGLMConfig, device=None):
|
| 668 |
+
super(GLMTransformer, self).__init__()
|
| 669 |
+
|
| 670 |
+
self.fp32_residual_connection = config.fp32_residual_connection
|
| 671 |
+
self.post_layer_norm = config.post_layer_norm
|
| 672 |
+
|
| 673 |
+
# Number of layers.
|
| 674 |
+
self.num_layers = config.num_layers
|
| 675 |
+
|
| 676 |
+
# Transformer layers.
|
| 677 |
+
def build_layer(layer_number):
|
| 678 |
+
return GLMBlock(config, layer_number, device=device)
|
| 679 |
+
|
| 680 |
+
self.layers = torch.nn.ModuleList([build_layer(i + 1) for i in range(self.num_layers)])
|
| 681 |
+
|
| 682 |
+
if self.post_layer_norm:
|
| 683 |
+
LayerNormFunc = RMSNorm if config.rmsnorm else LayerNorm
|
| 684 |
+
# Final layer norm before output.
|
| 685 |
+
self.final_layernorm = LayerNormFunc(config.hidden_size, eps=config.layernorm_epsilon, device=device,
|
| 686 |
+
dtype=config.torch_dtype)
|
| 687 |
+
|
| 688 |
+
self.gradient_checkpointing = False
|
| 689 |
+
|
| 690 |
+
def _get_layer(self, layer_number):
|
| 691 |
+
return self.layers[layer_number]
|
| 692 |
+
|
| 693 |
+
def forward(
|
| 694 |
+
self, hidden_states, attention_mask, rotary_pos_emb, kv_caches=None,
|
| 695 |
+
use_cache: Optional[bool] = True,
|
| 696 |
+
output_hidden_states: Optional[bool] = False,
|
| 697 |
+
):
|
| 698 |
+
if not kv_caches:
|
| 699 |
+
kv_caches = [None for _ in range(self.num_layers)]
|
| 700 |
+
presents = () if use_cache else None
|
| 701 |
+
if self.gradient_checkpointing and self.training:
|
| 702 |
+
if use_cache:
|
| 703 |
+
logger.warning_once(
|
| 704 |
+
"`use_cache=True` is incompatible with gradient checkpointing. Setting `use_cache=False`..."
|
| 705 |
+
)
|
| 706 |
+
use_cache = False
|
| 707 |
+
|
| 708 |
+
all_self_attentions = None
|
| 709 |
+
all_hidden_states = () if output_hidden_states else None
|
| 710 |
+
for index in range(self.num_layers):
|
| 711 |
+
if output_hidden_states:
|
| 712 |
+
all_hidden_states = all_hidden_states + (hidden_states,)
|
| 713 |
+
|
| 714 |
+
layer = self._get_layer(index)
|
| 715 |
+
if self.gradient_checkpointing and self.training:
|
| 716 |
+
layer_ret = torch.utils.checkpoint.checkpoint(
|
| 717 |
+
layer,
|
| 718 |
+
hidden_states,
|
| 719 |
+
attention_mask,
|
| 720 |
+
rotary_pos_emb,
|
| 721 |
+
kv_caches[index],
|
| 722 |
+
use_cache,
|
| 723 |
+
use_reentrant=False
|
| 724 |
+
)
|
| 725 |
+
else:
|
| 726 |
+
layer_ret = layer(
|
| 727 |
+
hidden_states,
|
| 728 |
+
attention_mask,
|
| 729 |
+
rotary_pos_emb,
|
| 730 |
+
kv_cache=kv_caches[index],
|
| 731 |
+
use_cache=use_cache
|
| 732 |
+
)
|
| 733 |
+
hidden_states, kv_cache = layer_ret
|
| 734 |
+
if use_cache:
|
| 735 |
+
# token by token decoding, use tuple format
|
| 736 |
+
if kv_caches[0] is not None:
|
| 737 |
+
presents = presents + (kv_cache,)
|
| 738 |
+
# prefilling in decoding, use tensor format to save cuda memory
|
| 739 |
+
else:
|
| 740 |
+
if len(presents) == 0:
|
| 741 |
+
presents = kv_cache
|
| 742 |
+
else:
|
| 743 |
+
presents = torch.cat((presents, kv_cache.to(presents.device)), dim=0)
|
| 744 |
+
|
| 745 |
+
if output_hidden_states:
|
| 746 |
+
all_hidden_states = all_hidden_states + (hidden_states,)
|
| 747 |
+
|
| 748 |
+
# Final layer norm.
|
| 749 |
+
if self.post_layer_norm:
|
| 750 |
+
hidden_states = self.final_layernorm(hidden_states)
|
| 751 |
+
|
| 752 |
+
return hidden_states, presents, all_hidden_states, all_self_attentions
|
| 753 |
+
|
| 754 |
+
|
| 755 |
+
class ChatGLMPreTrainedModel(PreTrainedModel):
|
| 756 |
+
"""
|
| 757 |
+
An abstract class to handle weights initialization and
|
| 758 |
+
a simple interface for downloading and loading pretrained models.
|
| 759 |
+
"""
|
| 760 |
+
|
| 761 |
+
is_parallelizable = False
|
| 762 |
+
supports_gradient_checkpointing = True
|
| 763 |
+
config_class = ChatGLMConfig
|
| 764 |
+
base_model_prefix = "transformer"
|
| 765 |
+
_no_split_modules = ["GLMBlock"]
|
| 766 |
+
_supports_flash_attn_2 = True
|
| 767 |
+
_supports_sdpa = True
|
| 768 |
+
|
| 769 |
+
def _init_weights(self, module: nn.Module):
|
| 770 |
+
"""Initialize the weights."""
|
| 771 |
+
return
|
| 772 |
+
|
| 773 |
+
def get_masks(self, input_ids, past_key_values, padding_mask=None):
|
| 774 |
+
if self.config._attn_implementation == "flash_attention_2":
|
| 775 |
+
if padding_mask is not None and not padding_mask.all():
|
| 776 |
+
return padding_mask
|
| 777 |
+
return None
|
| 778 |
+
batch_size, seq_length = input_ids.shape
|
| 779 |
+
full_attention_mask = torch.ones(batch_size, seq_length, seq_length, device=input_ids.device)
|
| 780 |
+
full_attention_mask.tril_()
|
| 781 |
+
past_length = 0
|
| 782 |
+
if past_key_values:
|
| 783 |
+
past_length = past_key_values[0][0].shape[2]
|
| 784 |
+
if past_length:
|
| 785 |
+
full_attention_mask = torch.cat((torch.ones(batch_size, seq_length, past_length,
|
| 786 |
+
device=input_ids.device), full_attention_mask), dim=-1)
|
| 787 |
+
if padding_mask is not None:
|
| 788 |
+
full_attention_mask = full_attention_mask * padding_mask.unsqueeze(1)
|
| 789 |
+
if not past_length and padding_mask is not None:
|
| 790 |
+
full_attention_mask -= padding_mask.unsqueeze(-1) - 1
|
| 791 |
+
full_attention_mask = (full_attention_mask < 0.5).bool()
|
| 792 |
+
full_attention_mask.unsqueeze_(1)
|
| 793 |
+
return full_attention_mask
|
| 794 |
+
|
| 795 |
+
def get_position_ids(self, input_ids, device):
|
| 796 |
+
batch_size, seq_length = input_ids.shape
|
| 797 |
+
position_ids = torch.arange(seq_length, dtype=torch.long, device=device).unsqueeze(0).repeat(batch_size, 1)
|
| 798 |
+
return position_ids
|
| 799 |
+
|
| 800 |
+
def gradient_checkpointing_enable(self, gradient_checkpointing_kwargs=None):
|
| 801 |
+
if not self.supports_gradient_checkpointing:
|
| 802 |
+
raise ValueError(f"{self.__class__.__name__} does not support gradient checkpointing.")
|
| 803 |
+
|
| 804 |
+
|
| 805 |
+
class Embedding(torch.nn.Module):
|
| 806 |
+
"""Language model embeddings."""
|
| 807 |
+
|
| 808 |
+
def __init__(self, config: ChatGLMConfig, device=None):
|
| 809 |
+
super(Embedding, self).__init__()
|
| 810 |
+
|
| 811 |
+
self.hidden_size = config.hidden_size
|
| 812 |
+
# Word embeddings (parallel).
|
| 813 |
+
self.word_embeddings = nn.Embedding(
|
| 814 |
+
config.padded_vocab_size,
|
| 815 |
+
self.hidden_size,
|
| 816 |
+
dtype=config.torch_dtype,
|
| 817 |
+
device=device
|
| 818 |
+
)
|
| 819 |
+
self.fp32_residual_connection = config.fp32_residual_connection
|
| 820 |
+
|
| 821 |
+
def forward(self, input_ids):
|
| 822 |
+
# Embeddings.
|
| 823 |
+
words_embeddings = self.word_embeddings(input_ids)
|
| 824 |
+
embeddings = words_embeddings
|
| 825 |
+
# If the input flag for fp32 residual connection is set, convert for float.
|
| 826 |
+
if self.fp32_residual_connection:
|
| 827 |
+
embeddings = embeddings.float()
|
| 828 |
+
return embeddings
|
| 829 |
+
|
| 830 |
+
|
| 831 |
+
class ChatGLMModel(ChatGLMPreTrainedModel):
|
| 832 |
+
def __init__(self, config: ChatGLMConfig, device=None, empty_init=True):
|
| 833 |
+
super().__init__(config)
|
| 834 |
+
if empty_init:
|
| 835 |
+
init_method = skip_init
|
| 836 |
+
else:
|
| 837 |
+
init_method = default_init
|
| 838 |
+
init_kwargs = {}
|
| 839 |
+
if device is not None:
|
| 840 |
+
init_kwargs["device"] = device
|
| 841 |
+
self.embedding = init_method(Embedding, config, **init_kwargs)
|
| 842 |
+
self.num_layers = config.num_layers
|
| 843 |
+
self.multi_query_group_num = config.multi_query_group_num
|
| 844 |
+
self.kv_channels = config.kv_channels
|
| 845 |
+
|
| 846 |
+
# Rotary positional embeddings
|
| 847 |
+
self.seq_length = config.seq_length
|
| 848 |
+
rotary_dim = (
|
| 849 |
+
config.hidden_size // config.num_attention_heads if config.kv_channels is None else config.kv_channels
|
| 850 |
+
)
|
| 851 |
+
|
| 852 |
+
self.rotary_pos_emb = RotaryEmbedding(rotary_dim // 2, rope_ratio=config.rope_ratio,
|
| 853 |
+
original_impl=config.original_rope,
|
| 854 |
+
device=device, dtype=config.torch_dtype)
|
| 855 |
+
self.encoder = init_method(GLMTransformer, config, **init_kwargs)
|
| 856 |
+
self.output_layer = init_method(nn.Linear, config.hidden_size, config.padded_vocab_size, bias=False,
|
| 857 |
+
dtype=config.torch_dtype, **init_kwargs)
|
| 858 |
+
|
| 859 |
+
def get_input_embeddings(self):
|
| 860 |
+
return self.embedding.word_embeddings
|
| 861 |
+
|
| 862 |
+
def set_input_embeddings(self, value):
|
| 863 |
+
self.embedding.word_embeddings = value
|
| 864 |
+
|
| 865 |
+
def forward(
|
| 866 |
+
self,
|
| 867 |
+
input_ids,
|
| 868 |
+
position_ids: Optional[torch.Tensor] = None,
|
| 869 |
+
attention_mask: Optional[torch.BoolTensor] = None,
|
| 870 |
+
full_attention_mask: Optional[torch.BoolTensor] = None,
|
| 871 |
+
past_key_values: Optional[Tuple[Tuple[torch.Tensor, torch.Tensor], ...]] = None,
|
| 872 |
+
inputs_embeds: Optional[torch.Tensor] = None,
|
| 873 |
+
use_cache: Optional[bool] = None,
|
| 874 |
+
output_attentions: Optional[bool] = None,
|
| 875 |
+
output_hidden_states: Optional[bool] = None,
|
| 876 |
+
return_dict: Optional[bool] = None,
|
| 877 |
+
):
|
| 878 |
+
output_hidden_states = (
|
| 879 |
+
output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
|
| 880 |
+
)
|
| 881 |
+
use_cache = use_cache if use_cache is not None else self.config.use_cache
|
| 882 |
+
return_dict = return_dict if return_dict is not None else self.config.use_return_dict
|
| 883 |
+
|
| 884 |
+
batch_size, seq_length = input_ids.shape
|
| 885 |
+
|
| 886 |
+
if inputs_embeds is None:
|
| 887 |
+
inputs_embeds = self.embedding(input_ids)
|
| 888 |
+
|
| 889 |
+
if full_attention_mask is None:
|
| 890 |
+
if (attention_mask is not None and not attention_mask.all()) or (past_key_values and seq_length != 1):
|
| 891 |
+
full_attention_mask = self.get_masks(input_ids, past_key_values, padding_mask=attention_mask)
|
| 892 |
+
|
| 893 |
+
# Rotary positional embeddings
|
| 894 |
+
rotary_pos_emb = self.rotary_pos_emb(self.seq_length)
|
| 895 |
+
if position_ids is not None:
|
| 896 |
+
rotary_pos_emb = rotary_pos_emb[position_ids]
|
| 897 |
+
else:
|
| 898 |
+
rotary_pos_emb = rotary_pos_emb[None, :seq_length]
|
| 899 |
+
|
| 900 |
+
# Run encoder.
|
| 901 |
+
hidden_states, presents, all_hidden_states, all_self_attentions = self.encoder(
|
| 902 |
+
inputs_embeds, full_attention_mask, rotary_pos_emb=rotary_pos_emb,
|
| 903 |
+
kv_caches=past_key_values, use_cache=use_cache, output_hidden_states=output_hidden_states
|
| 904 |
+
)
|
| 905 |
+
if presents is not None and type(presents) is torch.Tensor:
|
| 906 |
+
presents = presents.split(1, dim=0)
|
| 907 |
+
presents = list(presents)
|
| 908 |
+
presents = [list(x.squeeze(0).split(1, dim=0)) for x in presents]
|
| 909 |
+
presents = [tuple([x.squeeze(0) for x in y]) for y in presents]
|
| 910 |
+
presents = tuple(presents)
|
| 911 |
+
|
| 912 |
+
if not return_dict:
|
| 913 |
+
return tuple(v for v in [hidden_states, presents, all_hidden_states, all_self_attentions] if v is not None)
|
| 914 |
+
|
| 915 |
+
return BaseModelOutputWithPast(
|
| 916 |
+
last_hidden_state=hidden_states,
|
| 917 |
+
past_key_values=presents,
|
| 918 |
+
hidden_states=all_hidden_states,
|
| 919 |
+
attentions=all_self_attentions,
|
| 920 |
+
)
|
| 921 |
+
|
| 922 |
+
|
| 923 |
+
class ChatGLMForConditionalGeneration(ChatGLMPreTrainedModel):
|
| 924 |
+
def __init__(self, config: ChatGLMConfig, empty_init=True, device=None):
|
| 925 |
+
super().__init__(config)
|
| 926 |
+
|
| 927 |
+
self.max_sequence_length = config.max_length
|
| 928 |
+
self.transformer = ChatGLMModel(config, empty_init=empty_init, device=device)
|
| 929 |
+
self.config = config
|
| 930 |
+
|
| 931 |
+
def _update_model_kwargs_for_generation(
|
| 932 |
+
self,
|
| 933 |
+
outputs: ModelOutput,
|
| 934 |
+
model_kwargs: Dict[str, Any],
|
| 935 |
+
is_encoder_decoder: bool = False,
|
| 936 |
+
standardize_cache_format: bool = False,
|
| 937 |
+
) -> Dict[str, Any]:
|
| 938 |
+
# update past_key_values
|
| 939 |
+
model_kwargs["past_key_values"] = self._extract_past_from_model_output(
|
| 940 |
+
outputs, standardize_cache_format=standardize_cache_format
|
| 941 |
+
)
|
| 942 |
+
|
| 943 |
+
# update attention mask
|
| 944 |
+
if "attention_mask" in model_kwargs:
|
| 945 |
+
attention_mask = model_kwargs["attention_mask"]
|
| 946 |
+
model_kwargs["attention_mask"] = torch.cat(
|
| 947 |
+
[attention_mask, attention_mask.new_ones((attention_mask.shape[0], 1))], dim=-1
|
| 948 |
+
)
|
| 949 |
+
|
| 950 |
+
# update position ids
|
| 951 |
+
if "position_ids" in model_kwargs:
|
| 952 |
+
position_ids = model_kwargs["position_ids"]
|
| 953 |
+
new_position_id = position_ids[..., -1:].clone()
|
| 954 |
+
new_position_id += 1
|
| 955 |
+
model_kwargs["position_ids"] = torch.cat(
|
| 956 |
+
[position_ids, new_position_id], dim=-1
|
| 957 |
+
)
|
| 958 |
+
|
| 959 |
+
model_kwargs["is_first_forward"] = False
|
| 960 |
+
return model_kwargs
|
| 961 |
+
|
| 962 |
+
def prepare_inputs_for_generation(
|
| 963 |
+
self,
|
| 964 |
+
input_ids: torch.LongTensor,
|
| 965 |
+
past_key_values: Optional[torch.Tensor] = None,
|
| 966 |
+
attention_mask: Optional[torch.Tensor] = None,
|
| 967 |
+
position_ids: Optional[torch.Tensor] = None,
|
| 968 |
+
use_cache: Optional[bool] = None,
|
| 969 |
+
is_first_forward: bool = True,
|
| 970 |
+
**kwargs
|
| 971 |
+
) -> dict:
|
| 972 |
+
# only last token for input_ids if past is not None
|
| 973 |
+
if position_ids is None:
|
| 974 |
+
position_ids = self.get_position_ids(input_ids, device=input_ids.device)
|
| 975 |
+
if not is_first_forward:
|
| 976 |
+
if past_key_values is not None:
|
| 977 |
+
position_ids = position_ids[..., -1:]
|
| 978 |
+
input_ids = input_ids[:, -1:]
|
| 979 |
+
return {
|
| 980 |
+
"input_ids": input_ids,
|
| 981 |
+
"past_key_values": past_key_values,
|
| 982 |
+
"position_ids": position_ids,
|
| 983 |
+
"attention_mask": attention_mask,
|
| 984 |
+
"return_last_logit": True,
|
| 985 |
+
"use_cache": use_cache
|
| 986 |
+
}
|
| 987 |
+
|
| 988 |
+
def forward(
|
| 989 |
+
self,
|
| 990 |
+
input_ids: Optional[torch.Tensor] = None,
|
| 991 |
+
position_ids: Optional[torch.Tensor] = None,
|
| 992 |
+
attention_mask: Optional[torch.Tensor] = None,
|
| 993 |
+
past_key_values: Optional[Tuple[torch.FloatTensor]] = None,
|
| 994 |
+
inputs_embeds: Optional[torch.Tensor] = None,
|
| 995 |
+
labels: Optional[torch.Tensor] = None,
|
| 996 |
+
use_cache: Optional[bool] = None,
|
| 997 |
+
output_attentions: Optional[bool] = None,
|
| 998 |
+
output_hidden_states: Optional[bool] = None,
|
| 999 |
+
return_dict: Optional[bool] = None,
|
| 1000 |
+
return_last_logit: Optional[bool] = False,
|
| 1001 |
+
):
|
| 1002 |
+
use_cache = use_cache if use_cache is not None else self.config.use_cache
|
| 1003 |
+
return_dict = return_dict if return_dict is not None else self.config.use_return_dict
|
| 1004 |
+
|
| 1005 |
+
transformer_outputs = self.transformer(
|
| 1006 |
+
input_ids=input_ids,
|
| 1007 |
+
position_ids=position_ids,
|
| 1008 |
+
attention_mask=attention_mask,
|
| 1009 |
+
past_key_values=past_key_values,
|
| 1010 |
+
inputs_embeds=inputs_embeds,
|
| 1011 |
+
use_cache=use_cache,
|
| 1012 |
+
output_hidden_states=output_hidden_states,
|
| 1013 |
+
return_dict=return_dict,
|
| 1014 |
+
)
|
| 1015 |
+
|
| 1016 |
+
hidden_states = transformer_outputs[0]
|
| 1017 |
+
if return_last_logit:
|
| 1018 |
+
hidden_states = hidden_states[:, -1:]
|
| 1019 |
+
lm_logits = self.transformer.output_layer(hidden_states)
|
| 1020 |
+
|
| 1021 |
+
loss = None
|
| 1022 |
+
if labels is not None:
|
| 1023 |
+
lm_logits = lm_logits.to(torch.float32)
|
| 1024 |
+
|
| 1025 |
+
# Shift so that tokens < n predict n
|
| 1026 |
+
shift_logits = lm_logits[..., :-1, :].contiguous()
|
| 1027 |
+
shift_labels = labels[..., 1:].contiguous()
|
| 1028 |
+
# Flatten the tokens
|
| 1029 |
+
loss_fct = CrossEntropyLoss(ignore_index=-100)
|
| 1030 |
+
loss = loss_fct(shift_logits.view(-1, shift_logits.size(-1)), shift_labels.view(-1))
|
| 1031 |
+
|
| 1032 |
+
lm_logits = lm_logits.to(hidden_states.dtype)
|
| 1033 |
+
loss = loss.to(hidden_states.dtype)
|
| 1034 |
+
|
| 1035 |
+
if not return_dict:
|
| 1036 |
+
output = (lm_logits,) + transformer_outputs[1:]
|
| 1037 |
+
return ((loss,) + output) if loss is not None else output
|
| 1038 |
+
|
| 1039 |
+
return CausalLMOutputWithPast(
|
| 1040 |
+
loss=loss,
|
| 1041 |
+
logits=lm_logits,
|
| 1042 |
+
past_key_values=transformer_outputs.past_key_values,
|
| 1043 |
+
hidden_states=transformer_outputs.hidden_states,
|
| 1044 |
+
attentions=transformer_outputs.attentions,
|
| 1045 |
+
)
|
| 1046 |
+
|
| 1047 |
+
@staticmethod
|
| 1048 |
+
def _reorder_cache(
|
| 1049 |
+
past: Tuple[Tuple[torch.Tensor, torch.Tensor], ...], beam_idx: torch.LongTensor
|
| 1050 |
+
) -> Tuple[Tuple[torch.Tensor, torch.Tensor], ...]:
|
| 1051 |
+
"""
|
| 1052 |
+
This function is used to re-order the `past_key_values` cache if [`~PreTrainedModel.beam_search`] or
|
| 1053 |
+
[`~PreTrainedModel.beam_sample`] is called. This is required to match `past_key_values` with the correct
|
| 1054 |
+
beam_idx at every generation step.
|
| 1055 |
+
|
| 1056 |
+
Output shares the same memory storage as `past`.
|
| 1057 |
+
"""
|
| 1058 |
+
return tuple(
|
| 1059 |
+
(
|
| 1060 |
+
layer_past[0].index_select(0, beam_idx.to(layer_past[0].device)),
|
| 1061 |
+
layer_past[1].index_select(0, beam_idx.to(layer_past[1].device)),
|
| 1062 |
+
)
|
| 1063 |
+
for layer_past in past
|
| 1064 |
+
)
|
| 1065 |
+
|
| 1066 |
+
def process_response(self, output, history):
|
| 1067 |
+
content = ""
|
| 1068 |
+
history = deepcopy(history)
|
| 1069 |
+
for response in output.split("<|assistant|>"):
|
| 1070 |
+
if "\n" in response:
|
| 1071 |
+
metadata, content = response.split("\n", maxsplit=1)
|
| 1072 |
+
else:
|
| 1073 |
+
metadata, content = "", response
|
| 1074 |
+
if not metadata.strip():
|
| 1075 |
+
content = content.strip()
|
| 1076 |
+
history.append({"role": "assistant", "metadata": metadata, "content": content})
|
| 1077 |
+
content = content.replace("[[训练时间]]", "2023年")
|
| 1078 |
+
else:
|
| 1079 |
+
history.append({"role": "assistant", "metadata": metadata, "content": content})
|
| 1080 |
+
if history[0]["role"] == "system" and "tools" in history[0]:
|
| 1081 |
+
parameters = json.loads(content)
|
| 1082 |
+
content = {"name": metadata.strip(), "parameters": parameters}
|
| 1083 |
+
else:
|
| 1084 |
+
content = {"name": metadata.strip(), "content": content}
|
| 1085 |
+
return content, history
|
| 1086 |
+
|
| 1087 |
+
@torch.inference_mode()
|
| 1088 |
+
def chat(self, tokenizer, query: str, history: List[Dict] = None, role: str = "user",
|
| 1089 |
+
max_length: int = 8192, num_beams=1, do_sample=True, top_p=0.8, temperature=0.8, logits_processor=None,
|
| 1090 |
+
**kwargs):
|
| 1091 |
+
if history is None:
|
| 1092 |
+
history = []
|
| 1093 |
+
if logits_processor is None:
|
| 1094 |
+
logits_processor = LogitsProcessorList()
|
| 1095 |
+
logits_processor.append(InvalidScoreLogitsProcessor())
|
| 1096 |
+
gen_kwargs = {"max_length": max_length, "num_beams": num_beams, "do_sample": do_sample, "top_p": top_p,
|
| 1097 |
+
"temperature": temperature, "logits_processor": logits_processor, **kwargs}
|
| 1098 |
+
history.append({"role": role, "content": query})
|
| 1099 |
+
inputs = tokenizer.apply_chat_template(history, add_generation_prompt=True, tokenize=True,
|
| 1100 |
+
return_tensors="pt", return_dict=True)
|
| 1101 |
+
inputs = inputs.to(self.device)
|
| 1102 |
+
eos_token_id = [tokenizer.eos_token_id, tokenizer.convert_tokens_to_ids("<|user|>"),
|
| 1103 |
+
tokenizer.convert_tokens_to_ids("<|observation|>")]
|
| 1104 |
+
outputs = self.generate(**inputs, **gen_kwargs, eos_token_id=eos_token_id)
|
| 1105 |
+
outputs = outputs.tolist()[0][len(inputs["input_ids"][0]):-1]
|
| 1106 |
+
response = tokenizer.decode(outputs)
|
| 1107 |
+
response, history = self.process_response(response, history)
|
| 1108 |
+
return response, history
|
| 1109 |
+
|
| 1110 |
+
@torch.inference_mode()
|
| 1111 |
+
def stream_chat(self, tokenizer, query: str, history: List[Dict] = None, role: str = "user",
|
| 1112 |
+
past_key_values=None, max_length: int = 8192, do_sample=True, top_p=0.8, temperature=0.8,
|
| 1113 |
+
logits_processor=None, return_past_key_values=False, **kwargs):
|
| 1114 |
+
if history is None:
|
| 1115 |
+
history = []
|
| 1116 |
+
if logits_processor is None:
|
| 1117 |
+
logits_processor = LogitsProcessorList()
|
| 1118 |
+
logits_processor.append(InvalidScoreLogitsProcessor())
|
| 1119 |
+
eos_token_id = [tokenizer.eos_token_id, tokenizer.convert_tokens_to_ids("<|user|>"),
|
| 1120 |
+
tokenizer.convert_tokens_to_ids("<|observation|>")]
|
| 1121 |
+
gen_kwargs = {"max_length": max_length, "do_sample": do_sample, "top_p": top_p,
|
| 1122 |
+
"temperature": temperature, "logits_processor": logits_processor, **kwargs}
|
| 1123 |
+
if past_key_values is None:
|
| 1124 |
+
inputs = tokenizer.apply_chat_template(history + [{"role": role, "content": query}],
|
| 1125 |
+
add_generation_prompt=True, tokenize=True, return_tensors="pt",
|
| 1126 |
+
return_dict=True)
|
| 1127 |
+
else:
|
| 1128 |
+
inputs = tokenizer.apply_chat_template([{"role": role, "content": query}], add_special_tokens=False,
|
| 1129 |
+
add_generation_prompt=True, tokenize=True, return_tensors="pt",
|
| 1130 |
+
return_dict=True)
|
| 1131 |
+
inputs = inputs.to(self.device)
|
| 1132 |
+
if past_key_values is not None:
|
| 1133 |
+
past_length = past_key_values[0][0].shape[2]
|
| 1134 |
+
inputs.position_ids += past_length
|
| 1135 |
+
attention_mask = inputs.attention_mask
|
| 1136 |
+
attention_mask = torch.cat((attention_mask.new_ones(1, past_length), attention_mask), dim=1)
|
| 1137 |
+
inputs['attention_mask'] = attention_mask
|
| 1138 |
+
history.append({"role": role, "content": query})
|
| 1139 |
+
for outputs in self.stream_generate(**inputs, past_key_values=past_key_values,
|
| 1140 |
+
eos_token_id=eos_token_id, return_past_key_values=return_past_key_values,
|
| 1141 |
+
**gen_kwargs):
|
| 1142 |
+
if return_past_key_values:
|
| 1143 |
+
outputs, past_key_values = outputs
|
| 1144 |
+
outputs = outputs.tolist()[0][len(inputs["input_ids"][0]):-1]
|
| 1145 |
+
response = tokenizer.decode(outputs)
|
| 1146 |
+
if response and response[-1] != "�":
|
| 1147 |
+
response, new_history = self.process_response(response, history)
|
| 1148 |
+
if return_past_key_values:
|
| 1149 |
+
yield response, new_history, past_key_values
|
| 1150 |
+
else:
|
| 1151 |
+
yield response, new_history
|
| 1152 |
+
|
| 1153 |
+
@torch.inference_mode()
|
| 1154 |
+
def stream_generate(
|
| 1155 |
+
self,
|
| 1156 |
+
input_ids,
|
| 1157 |
+
generation_config: Optional[GenerationConfig] = None,
|
| 1158 |
+
logits_processor: Optional[LogitsProcessorList] = None,
|
| 1159 |
+
stopping_criteria: Optional[StoppingCriteriaList] = None,
|
| 1160 |
+
prefix_allowed_tokens_fn: Optional[Callable[[int, torch.Tensor], List[int]]] = None,
|
| 1161 |
+
return_past_key_values=False,
|
| 1162 |
+
**kwargs,
|
| 1163 |
+
):
|
| 1164 |
+
batch_size, input_ids_seq_length = input_ids.shape[0], input_ids.shape[-1]
|
| 1165 |
+
|
| 1166 |
+
if generation_config is None:
|
| 1167 |
+
generation_config = self.generation_config
|
| 1168 |
+
generation_config = copy.deepcopy(generation_config)
|
| 1169 |
+
model_kwargs = generation_config.update(**kwargs)
|
| 1170 |
+
model_kwargs["use_cache"] = generation_config.use_cache
|
| 1171 |
+
bos_token_id, eos_token_id = generation_config.bos_token_id, generation_config.eos_token_id
|
| 1172 |
+
|
| 1173 |
+
if isinstance(eos_token_id, int):
|
| 1174 |
+
eos_token_id = [eos_token_id]
|
| 1175 |
+
eos_token_id_tensor = torch.tensor(eos_token_id).to(input_ids.device) if eos_token_id is not None else None
|
| 1176 |
+
|
| 1177 |
+
has_default_max_length = kwargs.get("max_length") is None and generation_config.max_length is not None
|
| 1178 |
+
if has_default_max_length and generation_config.max_new_tokens is None:
|
| 1179 |
+
warnings.warn(
|
| 1180 |
+
f"Using `max_length`'s default ({generation_config.max_length}) to control the generation length. "
|
| 1181 |
+
"This behaviour is deprecated and will be removed from the config in v5 of Transformers -- we"
|
| 1182 |
+
" recommend using `max_new_tokens` to control the maximum length of the generation.",
|
| 1183 |
+
UserWarning,
|
| 1184 |
+
)
|
| 1185 |
+
elif generation_config.max_new_tokens is not None:
|
| 1186 |
+
generation_config.max_length = generation_config.max_new_tokens + input_ids_seq_length
|
| 1187 |
+
if not has_default_max_length:
|
| 1188 |
+
logger.warn(
|
| 1189 |
+
f"Both `max_new_tokens` (={generation_config.max_new_tokens}) and `max_length`(="
|
| 1190 |
+
f"{generation_config.max_length}) seem to have been set. `max_new_tokens` will take precedence. "
|
| 1191 |
+
"Please refer to the documentation for more information. "
|
| 1192 |
+
"(https://huggingface.co/docs/transformers/main/en/main_classes/text_generation)",
|
| 1193 |
+
UserWarning,
|
| 1194 |
+
)
|
| 1195 |
+
|
| 1196 |
+
if input_ids_seq_length >= generation_config.max_length:
|
| 1197 |
+
input_ids_string = "decoder_input_ids" if self.config.is_encoder_decoder else "input_ids"
|
| 1198 |
+
logger.warning(
|
| 1199 |
+
f"Input length of {input_ids_string} is {input_ids_seq_length}, but `max_length` is set to"
|
| 1200 |
+
f" {generation_config.max_length}. This can lead to unexpected behavior. You should consider"
|
| 1201 |
+
" increasing `max_new_tokens`."
|
| 1202 |
+
)
|
| 1203 |
+
|
| 1204 |
+
# 2. Set generation parameters if not already defined
|
| 1205 |
+
logits_processor = logits_processor if logits_processor is not None else LogitsProcessorList()
|
| 1206 |
+
stopping_criteria = stopping_criteria if stopping_criteria is not None else StoppingCriteriaList()
|
| 1207 |
+
|
| 1208 |
+
logits_processor = self._get_logits_processor(
|
| 1209 |
+
generation_config=generation_config,
|
| 1210 |
+
input_ids_seq_length=input_ids_seq_length,
|
| 1211 |
+
encoder_input_ids=input_ids,
|
| 1212 |
+
prefix_allowed_tokens_fn=prefix_allowed_tokens_fn,
|
| 1213 |
+
logits_processor=logits_processor,
|
| 1214 |
+
)
|
| 1215 |
+
|
| 1216 |
+
stopping_criteria = self._get_stopping_criteria(
|
| 1217 |
+
generation_config=generation_config, stopping_criteria=stopping_criteria
|
| 1218 |
+
)
|
| 1219 |
+
logits_warper = self._get_logits_warper(generation_config)
|
| 1220 |
+
|
| 1221 |
+
unfinished_sequences = input_ids.new(input_ids.shape[0]).fill_(1)
|
| 1222 |
+
scores = None
|
| 1223 |
+
while True:
|
| 1224 |
+
model_inputs = self.prepare_inputs_for_generation(input_ids, **model_kwargs)
|
| 1225 |
+
# forward pass to get next token
|
| 1226 |
+
outputs = self(
|
| 1227 |
+
**model_inputs,
|
| 1228 |
+
return_dict=True,
|
| 1229 |
+
output_attentions=False,
|
| 1230 |
+
output_hidden_states=False,
|
| 1231 |
+
)
|
| 1232 |
+
|
| 1233 |
+
next_token_logits = outputs.logits[:, -1, :]
|
| 1234 |
+
|
| 1235 |
+
# pre-process distribution
|
| 1236 |
+
next_token_scores = logits_processor(input_ids, next_token_logits)
|
| 1237 |
+
next_token_scores = logits_warper(input_ids, next_token_scores)
|
| 1238 |
+
|
| 1239 |
+
# sample
|
| 1240 |
+
probs = nn.functional.softmax(next_token_scores, dim=-1)
|
| 1241 |
+
if generation_config.do_sample:
|
| 1242 |
+
next_tokens = torch.multinomial(probs, num_samples=1).squeeze(1)
|
| 1243 |
+
else:
|
| 1244 |
+
next_tokens = torch.argmax(probs, dim=-1)
|
| 1245 |
+
# update generated ids, model inputs, and length for next step
|
| 1246 |
+
input_ids = torch.cat([input_ids, next_tokens[:, None]], dim=-1)
|
| 1247 |
+
model_kwargs = self._update_model_kwargs_for_generation(
|
| 1248 |
+
outputs, model_kwargs, is_encoder_decoder=self.config.is_encoder_decoder
|
| 1249 |
+
)
|
| 1250 |
+
unfinished_sequences = unfinished_sequences.mul(
|
| 1251 |
+
next_tokens.tile(eos_token_id_tensor.shape[0], 1).ne(eos_token_id_tensor.unsqueeze(1)).prod(dim=0)
|
| 1252 |
+
)
|
| 1253 |
+
if return_past_key_values:
|
| 1254 |
+
yield input_ids, outputs.past_key_values
|
| 1255 |
+
else:
|
| 1256 |
+
yield input_ids
|
| 1257 |
+
# stop when each sentence is finished, or if we exceed the maximum length
|
| 1258 |
+
if unfinished_sequences.max() == 0 or stopping_criteria(input_ids, scores):
|
| 1259 |
+
break
|
| 1260 |
+
|
| 1261 |
+
|
| 1262 |
+
class ChatGLMForSequenceClassification(ChatGLMPreTrainedModel):
|
| 1263 |
+
def __init__(self, config: ChatGLMConfig, empty_init=True, device=None):
|
| 1264 |
+
super().__init__(config)
|
| 1265 |
+
|
| 1266 |
+
self.num_labels = config.num_labels
|
| 1267 |
+
self.transformer = ChatGLMModel(config, empty_init=empty_init, device=device)
|
| 1268 |
+
|
| 1269 |
+
self.classifier_head = nn.Linear(config.hidden_size, config.num_labels, bias=True, dtype=config.torch_dtype)
|
| 1270 |
+
if config.classifier_dropout is not None:
|
| 1271 |
+
self.dropout = nn.Dropout(config.classifier_dropout)
|
| 1272 |
+
else:
|
| 1273 |
+
self.dropout = None
|
| 1274 |
+
self.config = config
|
| 1275 |
+
|
| 1276 |
+
def forward(
|
| 1277 |
+
self,
|
| 1278 |
+
input_ids: Optional[torch.LongTensor] = None,
|
| 1279 |
+
position_ids: Optional[torch.LongTensor] = None,
|
| 1280 |
+
attention_mask: Optional[torch.Tensor] = None,
|
| 1281 |
+
full_attention_mask: Optional[torch.Tensor] = None,
|
| 1282 |
+
past_key_values: Optional[Tuple[Tuple[torch.Tensor, torch.Tensor], ...]] = None,
|
| 1283 |
+
inputs_embeds: Optional[torch.LongTensor] = None,
|
| 1284 |
+
labels: Optional[torch.LongTensor] = None,
|
| 1285 |
+
use_cache: Optional[bool] = None,
|
| 1286 |
+
output_attentions: Optional[bool] = None,
|
| 1287 |
+
output_hidden_states: Optional[bool] = None,
|
| 1288 |
+
return_dict: Optional[bool] = None,
|
| 1289 |
+
) -> Union[Tuple[torch.Tensor, ...], SequenceClassifierOutputWithPast]:
|
| 1290 |
+
return_dict = return_dict if return_dict is not None else self.config.use_return_dict
|
| 1291 |
+
|
| 1292 |
+
transformer_outputs = self.transformer(
|
| 1293 |
+
input_ids=input_ids,
|
| 1294 |
+
position_ids=position_ids,
|
| 1295 |
+
attention_mask=attention_mask,
|
| 1296 |
+
full_attention_mask=full_attention_mask,
|
| 1297 |
+
past_key_values=past_key_values,
|
| 1298 |
+
inputs_embeds=inputs_embeds,
|
| 1299 |
+
use_cache=use_cache,
|
| 1300 |
+
output_attentions=output_attentions,
|
| 1301 |
+
output_hidden_states=output_hidden_states,
|
| 1302 |
+
return_dict=return_dict,
|
| 1303 |
+
)
|
| 1304 |
+
|
| 1305 |
+
hidden_states = transformer_outputs[0]
|
| 1306 |
+
pooled_hidden_states = hidden_states[:, -1]
|
| 1307 |
+
if self.dropout is not None:
|
| 1308 |
+
pooled_hidden_states = self.dropout(pooled_hidden_states)
|
| 1309 |
+
logits = self.classifier_head(pooled_hidden_states)
|
| 1310 |
+
|
| 1311 |
+
loss = None
|
| 1312 |
+
if labels is not None:
|
| 1313 |
+
if self.config.problem_type is None:
|
| 1314 |
+
if self.num_labels == 1:
|
| 1315 |
+
self.config.problem_type = "regression"
|
| 1316 |
+
elif self.num_labels > 1 and (labels.dtype == torch.long or labels.dtype == torch.int):
|
| 1317 |
+
self.config.problem_type = "single_label_classification"
|
| 1318 |
+
else:
|
| 1319 |
+
self.config.problem_type = "multi_label_classification"
|
| 1320 |
+
|
| 1321 |
+
if self.config.problem_type == "regression":
|
| 1322 |
+
loss_fct = MSELoss()
|
| 1323 |
+
if self.num_labels == 1:
|
| 1324 |
+
loss = loss_fct(logits.squeeze().float(), labels.squeeze())
|
| 1325 |
+
else:
|
| 1326 |
+
loss = loss_fct(logits.float(), labels)
|
| 1327 |
+
elif self.config.problem_type == "single_label_classification":
|
| 1328 |
+
loss_fct = CrossEntropyLoss()
|
| 1329 |
+
loss = loss_fct(logits.view(-1, self.num_labels).float(), labels.view(-1))
|
| 1330 |
+
elif self.config.problem_type == "multi_label_classification":
|
| 1331 |
+
loss_fct = BCEWithLogitsLoss()
|
| 1332 |
+
loss = loss_fct(logits.float(), labels.view(-1, self.num_labels))
|
| 1333 |
+
|
| 1334 |
+
if not return_dict:
|
| 1335 |
+
output = (logits,) + transformer_outputs[1:]
|
| 1336 |
+
return ((loss,) + output) if loss is not None else output
|
| 1337 |
+
|
| 1338 |
+
return SequenceClassifierOutputWithPast(
|
| 1339 |
+
loss=loss,
|
| 1340 |
+
logits=logits,
|
| 1341 |
+
past_key_values=transformer_outputs.past_key_values,
|
| 1342 |
+
hidden_states=transformer_outputs.hidden_states,
|
| 1343 |
+
attentions=transformer_outputs.attentions,
|
| 1344 |
+
)
|
tokenization_chatglm.py
ADDED
|
@@ -0,0 +1,395 @@
|
|
|
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|
| 1 |
+
import base64
|
| 2 |
+
import json
|
| 3 |
+
import os
|
| 4 |
+
from typing import List, Optional, Union, Dict, Any
|
| 5 |
+
|
| 6 |
+
import regex as re
|
| 7 |
+
import tiktoken
|
| 8 |
+
from torch import TensorType
|
| 9 |
+
from transformers import PreTrainedTokenizer
|
| 10 |
+
from transformers.tokenization_utils_base import EncodedInput, BatchEncoding
|
| 11 |
+
from transformers.utils import PaddingStrategy
|
| 12 |
+
|
| 13 |
+
|
| 14 |
+
class ChatGLM4Tokenizer(PreTrainedTokenizer):
|
| 15 |
+
vocab_files_names = {"vocab_file": "tokenizer.model"}
|
| 16 |
+
model_input_names = ["input_ids", "attention_mask", "position_ids"]
|
| 17 |
+
|
| 18 |
+
def __init__(
|
| 19 |
+
self,
|
| 20 |
+
vocab_file,
|
| 21 |
+
padding_side="left",
|
| 22 |
+
clean_up_tokenization_spaces=False,
|
| 23 |
+
encode_special_tokens=False,
|
| 24 |
+
**kwargs
|
| 25 |
+
):
|
| 26 |
+
self.name = "GLM4Tokenizer"
|
| 27 |
+
self.vocab_file = vocab_file
|
| 28 |
+
pat_str = "(?i:'s|'t|'re|'ve|'m|'ll|'d)|[^\\r\\n\\p{L}\\p{N}]?\\p{L}+|\\p{N}{1,3}| ?[^\\s\\p{L}\\p{N}]+[\\r\\n]*|\\s*[\\r\\n]+|\\s+(?!\\S)|\\s+"
|
| 29 |
+
self.pat_str = re.compile(pat_str)
|
| 30 |
+
self.encode_special_tokens = encode_special_tokens
|
| 31 |
+
|
| 32 |
+
mergeable_ranks = {}
|
| 33 |
+
with open(vocab_file) as f:
|
| 34 |
+
for line in f:
|
| 35 |
+
token, rank = line.strip().split()
|
| 36 |
+
rank = int(rank)
|
| 37 |
+
token = base64.b64decode(token)
|
| 38 |
+
mergeable_ranks[token] = rank
|
| 39 |
+
|
| 40 |
+
self.mergeable_ranks = mergeable_ranks
|
| 41 |
+
|
| 42 |
+
self.tokenizer = tiktoken.Encoding(
|
| 43 |
+
name="my_tokenizer",
|
| 44 |
+
pat_str=pat_str,
|
| 45 |
+
mergeable_ranks=mergeable_ranks,
|
| 46 |
+
special_tokens={v.content: int(k) for k, v in kwargs['added_tokens_decoder'].items()}
|
| 47 |
+
# special_tokens={}
|
| 48 |
+
)
|
| 49 |
+
self.decoder = {rank: token for token, rank in mergeable_ranks.items()}
|
| 50 |
+
self.n_words = len(self.decoder)
|
| 51 |
+
|
| 52 |
+
super().__init__(
|
| 53 |
+
padding_side=padding_side,
|
| 54 |
+
clean_up_tokenization_spaces=clean_up_tokenization_spaces,
|
| 55 |
+
**kwargs
|
| 56 |
+
)
|
| 57 |
+
|
| 58 |
+
@property
|
| 59 |
+
def vocab_size(self):
|
| 60 |
+
return self.n_words
|
| 61 |
+
|
| 62 |
+
def get_vocab(self):
|
| 63 |
+
""" Returns vocab as a dict """
|
| 64 |
+
vocab = {self._convert_id_to_token(i): i for i in range(self.vocab_size)}
|
| 65 |
+
vocab.update(self.added_tokens_encoder)
|
| 66 |
+
return vocab
|
| 67 |
+
|
| 68 |
+
@staticmethod
|
| 69 |
+
def convert_tokens_to_string(tokens: List[Union[bytes, str]]) -> str:
|
| 70 |
+
"""
|
| 71 |
+
Converts a sequence of tokens in a single string.
|
| 72 |
+
"""
|
| 73 |
+
text = ""
|
| 74 |
+
temp = b""
|
| 75 |
+
for t in tokens:
|
| 76 |
+
if isinstance(t, str):
|
| 77 |
+
if temp:
|
| 78 |
+
text += temp.decode("utf-8", errors="replace")
|
| 79 |
+
temp = b""
|
| 80 |
+
text += t
|
| 81 |
+
elif isinstance(t, bytes):
|
| 82 |
+
temp += t
|
| 83 |
+
else:
|
| 84 |
+
raise TypeError("token should only be of type types or str")
|
| 85 |
+
if temp:
|
| 86 |
+
text += temp.decode("utf-8", errors="replace")
|
| 87 |
+
return text
|
| 88 |
+
|
| 89 |
+
def _tokenize(self, text, **kwargs):
|
| 90 |
+
tokens = []
|
| 91 |
+
ids = self.tokenizer.encode(text)
|
| 92 |
+
for t in ids:
|
| 93 |
+
tokens.append(self.decoder[t])
|
| 94 |
+
return tokens
|
| 95 |
+
|
| 96 |
+
def _convert_token_to_id(self, token):
|
| 97 |
+
""" Converts a token (str) in an id using the vocab. """
|
| 98 |
+
return self.mergeable_ranks[token]
|
| 99 |
+
|
| 100 |
+
def _convert_id_to_token(self, index):
|
| 101 |
+
"""Converts an index (integer) in a token (str) using the vocab."""
|
| 102 |
+
return self.decoder.get(index, "")
|
| 103 |
+
|
| 104 |
+
def save_vocabulary(self, save_directory, filename_prefix=None):
|
| 105 |
+
"""
|
| 106 |
+
Save the vocabulary and special tokens file to a directory.
|
| 107 |
+
|
| 108 |
+
Args:
|
| 109 |
+
save_directory (`str`):
|
| 110 |
+
The directory in which to save the vocabulary.
|
| 111 |
+
filename_prefix (`str`, *optional*):
|
| 112 |
+
An optional prefix to add to the named of the saved files.
|
| 113 |
+
|
| 114 |
+
Returns:
|
| 115 |
+
`Tuple(str)`: Paths to the files saved.
|
| 116 |
+
"""
|
| 117 |
+
if os.path.isdir(save_directory):
|
| 118 |
+
vocab_file = os.path.join(
|
| 119 |
+
save_directory, self.vocab_files_names["vocab_file"]
|
| 120 |
+
)
|
| 121 |
+
else:
|
| 122 |
+
vocab_file = save_directory
|
| 123 |
+
|
| 124 |
+
with open(self.vocab_file, 'rb') as fin:
|
| 125 |
+
proto_str = fin.read()
|
| 126 |
+
|
| 127 |
+
with open(vocab_file, "wb") as writer:
|
| 128 |
+
writer.write(proto_str)
|
| 129 |
+
|
| 130 |
+
return (vocab_file,)
|
| 131 |
+
|
| 132 |
+
def get_prefix_tokens(self):
|
| 133 |
+
prefix_tokens = [self.convert_tokens_to_ids("[gMASK]"), self.convert_tokens_to_ids("<sop>")]
|
| 134 |
+
return prefix_tokens
|
| 135 |
+
|
| 136 |
+
def apply_chat_template(
|
| 137 |
+
self,
|
| 138 |
+
conversation: Union[List[Dict[str, str]], List[List[Dict[str, str]]]],
|
| 139 |
+
add_generation_prompt: bool = False,
|
| 140 |
+
tokenize: bool = True,
|
| 141 |
+
padding: bool = False,
|
| 142 |
+
truncation: bool = False,
|
| 143 |
+
max_length: Optional[int] = None,
|
| 144 |
+
return_tensors: Optional[Union[str, TensorType]] = None,
|
| 145 |
+
return_dict: bool = False,
|
| 146 |
+
tokenizer_kwargs: Optional[Dict[str, Any]] = None,
|
| 147 |
+
add_special_tokens: bool = True,
|
| 148 |
+
**kwargs,
|
| 149 |
+
) -> Union[str, List[int], List[str], List[List[int]], BatchEncoding]:
|
| 150 |
+
|
| 151 |
+
if return_dict and not tokenize:
|
| 152 |
+
raise ValueError(
|
| 153 |
+
"`return_dict=True` is incompatible with `tokenize=False`, because there is no dict "
|
| 154 |
+
"of tokenizer outputs to return."
|
| 155 |
+
)
|
| 156 |
+
|
| 157 |
+
def handle_single_conversation(messages):
|
| 158 |
+
content = "你是一位智能编程助手，你叫CodeGeeX。你会为用户回答关于编程、代码、计算机方面的任何问题，并提供格式规范、可以执行、准确安全的代码，并在必要时提供详细的解释。"
|
| 159 |
+
input_message = self.build_single_message("system", "", content)
|
| 160 |
+
for item in messages:
|
| 161 |
+
role = item.get("role", "")
|
| 162 |
+
if not role:
|
| 163 |
+
raise ValueError("Invalid conversation format, 'role' must be given")
|
| 164 |
+
# function call
|
| 165 |
+
elif role == "tool":
|
| 166 |
+
content = self.build_function_sys_prompt(item["content"])
|
| 167 |
+
input_message = self.build_single_message("system", "", content)
|
| 168 |
+
# chat
|
| 169 |
+
elif role == "system":
|
| 170 |
+
input_message = self.build_single_message("system", item.get("metadata", ""), item["content"])
|
| 171 |
+
else:
|
| 172 |
+
input_message += self.build_single_message(item["role"], item.get("metadata", ""), item["content"])
|
| 173 |
+
|
| 174 |
+
if add_generation_prompt:
|
| 175 |
+
input_message += "<|assistant|>\n"
|
| 176 |
+
if tokenize:
|
| 177 |
+
input_ids = self.get_prefix_tokens() if add_special_tokens else []
|
| 178 |
+
input_ids += self.tokenizer.encode(input_message, allowed_special='all', disallowed_special=set())
|
| 179 |
+
return input_ids
|
| 180 |
+
else:
|
| 181 |
+
return input_message
|
| 182 |
+
|
| 183 |
+
# Main logic to handle different conversation formats
|
| 184 |
+
if isinstance(conversation, list) and all(isinstance(i, dict) for i in conversation):
|
| 185 |
+
result = handle_single_conversation(conversation)
|
| 186 |
+
elif isinstance(conversation, list) and all(isinstance(i, list) for i in conversation):
|
| 187 |
+
result = [handle_single_conversation(c) for c in conversation]
|
| 188 |
+
elif hasattr(conversation, "messages"):
|
| 189 |
+
result = handle_single_conversation(conversation.messages)
|
| 190 |
+
else:
|
| 191 |
+
raise ValueError("Invalid conversation format")
|
| 192 |
+
|
| 193 |
+
if tokenize:
|
| 194 |
+
output = self.batch_encode_plus(
|
| 195 |
+
[result] if isinstance(result[0], int) else result,
|
| 196 |
+
padding=padding,
|
| 197 |
+
truncation=truncation,
|
| 198 |
+
max_length=max_length,
|
| 199 |
+
return_tensors=return_tensors,
|
| 200 |
+
is_split_into_words=True,
|
| 201 |
+
add_special_tokens=False
|
| 202 |
+
)
|
| 203 |
+
if return_dict:
|
| 204 |
+
return output
|
| 205 |
+
else:
|
| 206 |
+
return output["input_ids"]
|
| 207 |
+
else:
|
| 208 |
+
return result
|
| 209 |
+
|
| 210 |
+
def build_inputs_with_special_tokens(
|
| 211 |
+
self, token_ids_0: List[int], token_ids_1: Optional[List[int]] = None
|
| 212 |
+
) -> List[int]:
|
| 213 |
+
"""
|
| 214 |
+
Build model inputs from a sequence or a pair of sequence for sequence classification tasks by concatenating and
|
| 215 |
+
adding special tokens. A BERT sequence has the following format:
|
| 216 |
+
|
| 217 |
+
- single sequence: `[CLS] X [SEP]`
|
| 218 |
+
- pair of sequences: `[CLS] A [SEP] B [SEP]`
|
| 219 |
+
|
| 220 |
+
Args:
|
| 221 |
+
token_ids_0 (`List[int]`):
|
| 222 |
+
List of IDs to which the special tokens will be added.
|
| 223 |
+
token_ids_1 (`List[int]`, *optional*):
|
| 224 |
+
Optional second list of IDs for sequence pairs.
|
| 225 |
+
|
| 226 |
+
Returns:
|
| 227 |
+
`List[int]`: List of [input IDs](../glossary#input-ids) with the appropriate special tokens.
|
| 228 |
+
"""
|
| 229 |
+
prefix_tokens = self.get_prefix_tokens()
|
| 230 |
+
token_ids_0 = prefix_tokens + token_ids_0
|
| 231 |
+
if token_ids_1 is not None:
|
| 232 |
+
token_ids_0 = token_ids_0 + token_ids_1 + [self.convert_tokens_to_ids("<eos>")]
|
| 233 |
+
return token_ids_0
|
| 234 |
+
|
| 235 |
+
def _pad(
|
| 236 |
+
self,
|
| 237 |
+
encoded_inputs: Union[Dict[str, EncodedInput], BatchEncoding],
|
| 238 |
+
max_length: Optional[int] = None,
|
| 239 |
+
padding_strategy: PaddingStrategy = PaddingStrategy.DO_NOT_PAD,
|
| 240 |
+
pad_to_multiple_of: Optional[int] = None,
|
| 241 |
+
return_attention_mask: Optional[bool] = None,
|
| 242 |
+
) -> dict:
|
| 243 |
+
"""
|
| 244 |
+
Pad encoded inputs (on left/right and up to predefined length or max length in the batch)
|
| 245 |
+
|
| 246 |
+
Args:
|
| 247 |
+
encoded_inputs:
|
| 248 |
+
Dictionary of tokenized inputs (`List[int]`) or batch of tokenized inputs (`List[List[int]]`).
|
| 249 |
+
max_length: maximum length of the returned list and optionally padding length (see below).
|
| 250 |
+
Will truncate by taking into account the special tokens.
|
| 251 |
+
padding_strategy: PaddingStrategy to use for padding.
|
| 252 |
+
|
| 253 |
+
- PaddingStrategy.LONGEST Pad to the longest sequence in the batch
|
| 254 |
+
- PaddingStrategy.MAX_LENGTH: Pad to the max length (default)
|
| 255 |
+
- PaddingStrategy.DO_NOT_PAD: Do not pad
|
| 256 |
+
The tokenizer padding sides are defined in self.padding_side:
|
| 257 |
+
|
| 258 |
+
- 'left': pads on the left of the sequences
|
| 259 |
+
- 'right': pads on the right of the sequences
|
| 260 |
+
pad_to_multiple_of: (optional) Integer if set will pad the sequence to a multiple of the provided value.
|
| 261 |
+
This is especially useful to enable the use of Tensor Core on NVIDIA hardware with compute capability
|
| 262 |
+
`>= 7.5` (Volta).
|
| 263 |
+
return_attention_mask:
|
| 264 |
+
(optional) Set to 'False' to avoid returning attention mask (default: set to model specifics)
|
| 265 |
+
"""
|
| 266 |
+
# Load from model defaults
|
| 267 |
+
assert self.padding_side == "left"
|
| 268 |
+
|
| 269 |
+
required_input = encoded_inputs[self.model_input_names[0]]
|
| 270 |
+
seq_length = len(required_input)
|
| 271 |
+
|
| 272 |
+
if padding_strategy == PaddingStrategy.LONGEST:
|
| 273 |
+
max_length = len(required_input)
|
| 274 |
+
|
| 275 |
+
if max_length is not None and pad_to_multiple_of is not None and (max_length % pad_to_multiple_of != 0):
|
| 276 |
+
max_length = ((max_length // pad_to_multiple_of) + 1) * pad_to_multiple_of
|
| 277 |
+
|
| 278 |
+
needs_to_be_padded = padding_strategy != PaddingStrategy.DO_NOT_PAD and len(required_input) != max_length
|
| 279 |
+
|
| 280 |
+
# Initialize attention mask if not present.
|
| 281 |
+
if "attention_mask" not in encoded_inputs:
|
| 282 |
+
encoded_inputs["attention_mask"] = [1] * seq_length
|
| 283 |
+
|
| 284 |
+
if "position_ids" not in encoded_inputs:
|
| 285 |
+
encoded_inputs["position_ids"] = list(range(seq_length))
|
| 286 |
+
|
| 287 |
+
if needs_to_be_padded:
|
| 288 |
+
difference = max_length - len(required_input)
|
| 289 |
+
|
| 290 |
+
if "attention_mask" in encoded_inputs:
|
| 291 |
+
encoded_inputs["attention_mask"] = [0] * difference + encoded_inputs["attention_mask"]
|
| 292 |
+
if "position_ids" in encoded_inputs:
|
| 293 |
+
encoded_inputs["position_ids"] = [0] * difference + encoded_inputs["position_ids"]
|
| 294 |
+
encoded_inputs[self.model_input_names[0]] = [self.pad_token_id] * difference + required_input
|
| 295 |
+
|
| 296 |
+
return encoded_inputs
|
| 297 |
+
|
| 298 |
+
@staticmethod
|
| 299 |
+
def build_single_message(role, metadata, message):
|
| 300 |
+
assert role in ["system", "user", "assistant", "observation"], role
|
| 301 |
+
return f"<|{role}|>{metadata}\n{message}"
|
| 302 |
+
|
| 303 |
+
@staticmethod
|
| 304 |
+
def build_function_sys_prompt(item: dict) -> str:
|
| 305 |
+
prompt = """
|
| 306 |
+
你将接收到一个用户提出的问题，并请撰写清晰、简洁且准确的答案。
|
| 307 |
+
|
| 308 |
+
# Note
|
| 309 |
+
- 我将给你提供一些函数工具的接口信息，包括函数的定义、用途、名字、参数名和参数类型。
|
| 310 |
+
- 请根据这些信息，为用户的指令，从中选择最合适的函数，并给出调用时需要使用的参数。
|
| 311 |
+
- **返回类型为一个json格式的字符串，包含函数名和参数字典。**
|
| 312 |
+
- name: 函数名
|
| 313 |
+
- arguments: 参数字典，其中key为参数名，value为参数类型。
|
| 314 |
+
- **只需要生成答案即可，无需在你的回答之前或之后做出解释，也不要直接回答用户的问题。**
|
| 315 |
+
- 只用当提供的函数工具不足以完成任务时，请你用正常的语气告知用户并解释原因。
|
| 316 |
+
|
| 317 |
+
# Functions
|
| 318 |
+
以下是可使用的函数工具的接口信息。
|
| 319 |
+
""".lstrip()
|
| 320 |
+
|
| 321 |
+
if isinstance(item['function'], dict):
|
| 322 |
+
func = item['function']
|
| 323 |
+
prompt += f"\n## Function 1\n"
|
| 324 |
+
prompt += f"\n### Name\n{func['name']}\n"
|
| 325 |
+
prompt += f"\n### Description\n{func['description']}\n"
|
| 326 |
+
prompt += f"\n### Parameters\n```json\n{json.dumps(func['parameters'], ensure_ascii=False)}\n```\n"
|
| 327 |
+
return prompt
|
| 328 |
+
elif isinstance(item['function'], list):
|
| 329 |
+
for idx, func in enumerate(item['function']):
|
| 330 |
+
prompt += f"\n## Function {idx + 1}\n"
|
| 331 |
+
prompt += f"\n### Name\n{func['name']}\n"
|
| 332 |
+
prompt += f"\n### Description\n{func['description']}\n"
|
| 333 |
+
prompt += f"\n### Parameters\n```json\n{json.dumps(func['parameters'], ensure_ascii=False)}\n```\n"
|
| 334 |
+
return prompt
|
| 335 |
+
|
| 336 |
+
def apply_infilling_template(
|
| 337 |
+
self,
|
| 338 |
+
message: dict,
|
| 339 |
+
add_generation_prompt: bool = False,
|
| 340 |
+
tokenize: bool = True,
|
| 341 |
+
padding: bool = False,
|
| 342 |
+
truncation: bool = False,
|
| 343 |
+
max_length: Optional[int] = None,
|
| 344 |
+
return_tensors: Optional[Union[str, TensorType]] = None,
|
| 345 |
+
return_dict: bool = False,
|
| 346 |
+
add_special_tokens: bool = True,
|
| 347 |
+
) -> Union[str, List[int], List[str], List[List[int]], BatchEncoding]:
|
| 348 |
+
if return_dict and not tokenize:
|
| 349 |
+
raise ValueError(
|
| 350 |
+
"`return_dict=True` is incompatible with `tokenize=False`, because there is no dict "
|
| 351 |
+
"of tokenizer outputs to return."
|
| 352 |
+
)
|
| 353 |
+
|
| 354 |
+
if not isinstance(message, dict):
|
| 355 |
+
raise ValueError("Invalid conversation format")
|
| 356 |
+
content = self.build_infilling_prompt(message)
|
| 357 |
+
input_message = self.build_single_message("user", "", content)
|
| 358 |
+
if add_generation_prompt:
|
| 359 |
+
input_message += "<|assistant|>\n"
|
| 360 |
+
if not tokenize:
|
| 361 |
+
return input_message
|
| 362 |
+
|
| 363 |
+
input_ids = self.get_prefix_tokens() if add_special_tokens else []
|
| 364 |
+
input_ids += self.tokenizer.encode(input_message, allowed_special='all', disallowed_special=set())
|
| 365 |
+
output = self.batch_encode_plus(
|
| 366 |
+
[input_ids] if isinstance(input_ids[0], int) else input_ids,
|
| 367 |
+
padding=padding,
|
| 368 |
+
truncation=truncation,
|
| 369 |
+
max_length=max_length,
|
| 370 |
+
return_tensors=return_tensors,
|
| 371 |
+
is_split_into_words=True,
|
| 372 |
+
add_special_tokens=False
|
| 373 |
+
)
|
| 374 |
+
if return_dict:
|
| 375 |
+
return output
|
| 376 |
+
else:
|
| 377 |
+
return output["input_ids"]
|
| 378 |
+
|
| 379 |
+
@staticmethod
|
| 380 |
+
def build_infilling_prompt(item: dict) -> str:
|
| 381 |
+
prompt = ""
|
| 382 |
+
if "path" in item:
|
| 383 |
+
prompt += f"###PATH:{item['path']}\n"
|
| 384 |
+
if "language" in item:
|
| 385 |
+
prompt += f"###LANGUAGE:{item['language']}\n"
|
| 386 |
+
elif "lang" in item:
|
| 387 |
+
prompt += f"###LANGUAGE:{item['lang']}\n"
|
| 388 |
+
if "mode" in item and item['mode'].lower() == "line":
|
| 389 |
+
prompt += "###MODE:LINE\n"
|
| 390 |
+
else:
|
| 391 |
+
prompt += "###MODE:BLOCK\n"
|
| 392 |
+
prompt += f"<|code_suffix|>{item['suffix']}"
|
| 393 |
+
prompt += f"<|code_prefix|>{item['prefix']}"
|
| 394 |
+
prompt += "<|code_middle|>"
|
| 395 |
+
return prompt
|
tokenizer.json
ADDED
|
@@ -0,0 +1,3 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
version https://git-lfs.github.com/spec/v1
|
| 2 |
+
oid sha256:b3bbfa7d61e43c6ce23a59c2fd2c8a4365684a48d69d33dafa67e75e9a884c93
|
| 3 |
+
size 10743038
|
tokenizer.model
ADDED
|
@@ -0,0 +1,3 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
version https://git-lfs.github.com/spec/v1
|
| 2 |
+
oid sha256:5a493598071550244b2ee7f26118f3edec2150b9dfa967929a99052ac83fe716
|
| 3 |
+
size 2623634
|
tokenizer_config.json
ADDED
|
@@ -0,0 +1,165 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
{
|
| 2 |
+
"auto_map": {
|
| 3 |
+
"AutoTokenizer": [
|
| 4 |
+
"tokenization_chatglm.ChatGLM4Tokenizer",
|
| 5 |
+
null
|
| 6 |
+
]
|
| 7 |
+
},
|
| 8 |
+
"added_tokens_decoder": {
|
| 9 |
+
"151329": {
|
| 10 |
+
"content": "<|endoftext|>",
|
| 11 |
+
"lstrip": false,
|
| 12 |
+
"normalized": false,
|
| 13 |
+
"rstrip": false,
|
| 14 |
+
"single_word": false,
|
| 15 |
+
"special": true
|
| 16 |
+
},
|
| 17 |
+
"151330": {
|
| 18 |
+
"content": "[MASK]",
|
| 19 |
+
"lstrip": false,
|
| 20 |
+
"normalized": false,
|
| 21 |
+
"rstrip": false,
|
| 22 |
+
"single_word": false,
|
| 23 |
+
"special": true
|
| 24 |
+
},
|
| 25 |
+
"151331": {
|
| 26 |
+
"content": "[gMASK]",
|
| 27 |
+
"lstrip": false,
|
| 28 |
+
"normalized": false,
|
| 29 |
+
"rstrip": false,
|
| 30 |
+
"single_word": false,
|
| 31 |
+
"special": true
|
| 32 |
+
},
|
| 33 |
+
"151332": {
|
| 34 |
+
"content": "[sMASK]",
|
| 35 |
+
"lstrip": false,
|
| 36 |
+
"normalized": false,
|
| 37 |
+
"rstrip": false,
|
| 38 |
+
"single_word": false,
|
| 39 |
+
"special": true
|
| 40 |
+
},
|
| 41 |
+
"151333": {
|
| 42 |
+
"content": "<sop>",
|
| 43 |
+
"lstrip": false,
|
| 44 |
+
"normalized": false,
|
| 45 |
+
"rstrip": false,
|
| 46 |
+
"single_word": false,
|
| 47 |
+
"special": true
|
| 48 |
+
},
|
| 49 |
+
"151334": {
|
| 50 |
+
"content": "<eop>",
|
| 51 |
+
"lstrip": false,
|
| 52 |
+
"normalized": false,
|
| 53 |
+
"rstrip": false,
|
| 54 |
+
"single_word": false,
|
| 55 |
+
"special": true
|
| 56 |
+
},
|
| 57 |
+
"151335": {
|
| 58 |
+
"content": "<|system|>",
|
| 59 |
+
"lstrip": false,
|
| 60 |
+
"normalized": false,
|
| 61 |
+
"rstrip": false,
|
| 62 |
+
"single_word": false,
|
| 63 |
+
"special": true
|
| 64 |
+
},
|
| 65 |
+
"151336": {
|
| 66 |
+
"content": "<|user|>",
|
| 67 |
+
"lstrip": false,
|
| 68 |
+
"normalized": false,
|
| 69 |
+
"rstrip": false,
|
| 70 |
+
"single_word": false,
|
| 71 |
+
"special": true
|
| 72 |
+
},
|
| 73 |
+
"151337": {
|
| 74 |
+
"content": "<|assistant|>",
|
| 75 |
+
"lstrip": false,
|
| 76 |
+
"normalized": false,
|
| 77 |
+
"rstrip": false,
|
| 78 |
+
"single_word": false,
|
| 79 |
+
"special": true
|
| 80 |
+
},
|
| 81 |
+
"151338": {
|
| 82 |
+
"content": "<|observation|>",
|
| 83 |
+
"lstrip": false,
|
| 84 |
+
"normalized": false,
|
| 85 |
+
"rstrip": false,
|
| 86 |
+
"single_word": false,
|
| 87 |
+
"special": true
|
| 88 |
+
},
|
| 89 |
+
"151339": {
|
| 90 |
+
"content": "<|begin_of_image|>",
|
| 91 |
+
"lstrip": false,
|
| 92 |
+
"normalized": false,
|
| 93 |
+
"rstrip": false,
|
| 94 |
+
"single_word": false,
|
| 95 |
+
"special": true
|
| 96 |
+
},
|
| 97 |
+
"151340": {
|
| 98 |
+
"content": "<|end_of_image|>",
|
| 99 |
+
"lstrip": false,
|
| 100 |
+
"normalized": false,
|
| 101 |
+
"rstrip": false,
|
| 102 |
+
"single_word": false,
|
| 103 |
+
"special": true
|
| 104 |
+
},
|
| 105 |
+
"151341": {
|
| 106 |
+
"content": "<|begin_of_video|>",
|
| 107 |
+
"lstrip": false,
|
| 108 |
+
"normalized": false,
|
| 109 |
+
"rstrip": false,
|
| 110 |
+
"single_word": false,
|
| 111 |
+
"special": true
|
| 112 |
+
},
|
| 113 |
+
"151342": {
|
| 114 |
+
"content": "<|end_of_video|>",
|
| 115 |
+
"lstrip": false,
|
| 116 |
+
"normalized": false,
|
| 117 |
+
"rstrip": false,
|
| 118 |
+
"single_word": false,
|
| 119 |
+
"special": true
|
| 120 |
+
},
|
| 121 |
+
"151343": {
|
| 122 |
+
"content": "<|code_prefix|>",
|
| 123 |
+
"lstrip": false,
|
| 124 |
+
"normalized": false,
|
| 125 |
+
"rstrip": false,
|
| 126 |
+
"single_word": false,
|
| 127 |
+
"special": false
|
| 128 |
+
},
|
| 129 |
+
"151344": {
|
| 130 |
+
"content": "<|code_middle|>",
|
| 131 |
+
"lstrip": false,
|
| 132 |
+
"normalized": false,
|
| 133 |
+
"rstrip": false,
|
| 134 |
+
"single_word": false,
|
| 135 |
+
"special": false
|
| 136 |
+
},
|
| 137 |
+
"151345": {
|
| 138 |
+
"content": "<|code_suffix|>",
|
| 139 |
+
"lstrip": false,
|
| 140 |
+
"normalized": false,
|
| 141 |
+
"rstrip": false,
|
| 142 |
+
"single_word": false,
|
| 143 |
+
"special": false
|
| 144 |
+
},
|
| 145 |
+
"151346": {
|
| 146 |
+
"content": "<|cursor|>",
|
| 147 |
+
"lstrip": false,
|
| 148 |
+
"normalized": false,
|
| 149 |
+
"rstrip": false,
|
| 150 |
+
"single_word": false,
|
| 151 |
+
"special": false
|
| 152 |
+
}
|
| 153 |
+
},
|
| 154 |
+
"additional_special_tokens": ["<|endoftext|>", "[MASK]", "[gMASK]", "[sMASK]", "<sop>", "<eop>", "<|system|>",
|
| 155 |
+
"<|user|>", "<|assistant|>", "<|observation|>", "<|begin_of_image|>", "<|end_of_image|>",
|
| 156 |
+
"<|begin_of_video|>", "<|end_of_video|>", "<|code_prefix|>", "<|code_middle|>", "<|code_suffix|>", "<|cursor|>"],
|
| 157 |
+
"clean_up_tokenization_spaces": false,
|
| 158 |
+
"do_lower_case": false,
|
| 159 |
+
"eos_token": "<|endoftext|>",
|
| 160 |
+
"pad_token": "<|endoftext|>",
|
| 161 |
+
"model_max_length": 1000000000000000019884624838656,
|
| 162 |
+
"padding_side": "left",
|
| 163 |
+
"remove_space": false,
|
| 164 |
+
"tokenizer_class": "ChatGLM4Tokenizer"
|
| 165 |
+
}
|