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README.md
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README.md
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# Baichuan2-7B-Chat_a13444794114109440680734
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---
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language:
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- en
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- zh
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license: other
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tasks:
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- text-generation
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---
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百川2-7B-对话模型
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<!-- markdownlint-disable first-line-h1 -->
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<!-- markdownlint-disable html -->
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<div align="center">
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<h1>
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Baichuan 2
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</h1>
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</div>
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<div align="center">
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<a href="https://github.com/baichuan-inc/Baichuan2" target="_blank">🦉GitHub</a> |
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<a href="https://modelscope.cn/models/baichuan-inc/Baichuan-13B-Base/file/view/master/wechat.jpeg" target="_blank">💬WeChat</a>
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</div>
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<div align="center">
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🚀 <a href="https://www.baichuan-ai.com/" target="_blank">百川大模型在线对话平台</a> 已正式向公众开放 🎉
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</div>
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# 目录
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- [📖 模型介绍](#模型介绍)
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- [⚙️ 快速开始](#快速开始)
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- [📊 Benchmark评估](#评估)
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- [📜 声明与协议](#声明与协议)
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# 模型介绍
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- Baichuan 2 是[百川智能]推出的**新一代开源大语言模型**，采用 **2.6 万亿** Tokens 的高质量语料训练。
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- Baichuan 2 在多个权威的中文、英文和多语言的通用、领域 benchmark 上取得同尺寸**最佳**的效果。
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- 本次发布包含有 **7B**、**13B** 的 **Base** 和 **Chat** 版本，并提供了 Chat 版本的 **4bits 量化**。
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- 所有版本对学术研究完全开放。同时，开发者通过邮件申请并获得官方商用许可后，即可**免费商用**，请参考[协议](#协议)章节。
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- 欢迎阅读我们的技术报告 [Baichuan 2: Open Large-scale Language Models] 获取更多信息。
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本次发布版本和下载链接见下表：
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| | 基座模型 | 对齐模型 | 对齐模型 4bits 量化 |
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|:---:|:--------------------:|:--------------------:|:--------------------------:|
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| 7B | [Baichuan2-7B-Base] | [Baichuan2-7B-Chat] | [Baichuan2-7B-Chat-4bits] |
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| 13B | [Baichuan2-13B-Base] | [Baichuan2-13B-Chat] | [Baichuan2-13B-Chat-4bits] |
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# 快速开始
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```python
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import torch
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from modelscope import snapshot_download, AutoModelForCausalLM, AutoTokenizer,GenerationConfig
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model_dir = snapshot_download("baichuan-inc/Baichuan2-7B-Chat", revision='v1.0.4')
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tokenizer = AutoTokenizer.from_pretrained(model_dir, device_map="auto",
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trust_remote_code=True, torch_dtype=torch.float16)
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model = AutoModelForCausalLM.from_pretrained(model_dir, device_map="auto",
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trust_remote_code=True, torch_dtype=torch.float16)
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model.generation_config = GenerationConfig.from_pretrained(model_dir)
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messages = []
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messages.append({"role": "user", "content": "讲解一下“温故而知新”"})
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response = model.chat(tokenizer, messages)
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print(response)
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messages.append({'role': 'assistant', 'content': response})
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messages.append({"role": "user", "content": "背诵一下将进酒"})
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response = model.chat(tokenizer, messages)
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print(response)
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```
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# Benchmark 结果
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我们在[通用]、[法律]、[医疗]、[数学]、[代码]和[多语言翻译]六个领域的中英文权威数据集上对模型进行了广泛测试，更多详细测评结果可查看[GitHub]。
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### 7B 模型结果
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| | **C-Eval** | **MMLU** | **CMMLU** | **Gaokao** | **AGIEval** | **BBH** |
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|:-----------------------:|:----------:|:--------:|:---------:|:----------:|:-----------:|:-------:|
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| | 5-shot | 5-shot | 5-shot | 5-shot | 5-shot | 3-shot |
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| **GPT-4** | 68.40 | 83.93 | 70.33 | 66.15 | 63.27 | 75.12 |
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| **GPT-3.5 Turbo** | 51.10 | 68.54 | 54.06 | 47.07 | 46.13 | 61.59 |
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| **LLaMA-7B** | 27.10 | 35.10 | 26.75 | 27.81 | 28.17 | 32.38 |
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| **LLaMA2-7B** | 28.90 | 45.73 | 31.38 | 25.97 | 26.53 | 39.16 |
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| **MPT-7B** | 27.15 | 27.93 | 26.00 | 26.54 | 24.83 | 35.20 |
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| **Falcon-7B** | 24.23 | 26.03 | 25.66 | 24.24 | 24.10 | 28.77 |
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| **ChatGLM2-6B** | 50.20 | 45.90 | 49.00 | 49.44 | 45.28 | 31.65 |
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| **[Baichuan-7B]** | 42.80 | 42.30 | 44.02 | 36.34 | 34.44 | 32.48 |
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| **[Baichuan2-7B-Base]** | 54.00 | 54.16 | 57.07 | 47.47 | 42.73 | 41.56 |
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### 13B 模型结果
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| | **C-Eval** | **MMLU** | **CMMLU** | **Gaokao** | **AGIEval** | **BBH** |
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|:---------------------------:|:----------:|:--------:|:---------:|:----------:|:-----------:|:-------:|
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| | 5-shot | 5-shot | 5-shot | 5-shot | 5-shot | 3-shot |
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| **GPT-4** | 68.40 | 83.93 | 70.33 | 66.15 | 63.27 | 75.12 |
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| **GPT-3.5 Turbo** | 51.10 | 68.54 | 54.06 | 47.07 | 46.13 | 61.59 |
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| **LLaMA-13B** | 28.50 | 46.30 | 31.15 | 28.23 | 28.22 | 37.89 |
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| **LLaMA2-13B** | 35.80 | 55.09 | 37.99 | 30.83 | 32.29 | 46.98 |
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| **Vicuna-13B** | 32.80 | 52.00 | 36.28 | 30.11 | 31.55 | 43.04 |
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| **Chinese-Alpaca-Plus-13B** | 38.80 | 43.90 | 33.43 | 34.78 | 35.46 | 28.94 |
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| **XVERSE-13B** | 53.70 | 55.21 | 58.44 | 44.69 | 42.54 | 38.06 |
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| **[Baichuan-13B-Base]** | 52.40 | 51.60 | 55.30 | 49.69 | 43.20 | 43.01 |
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| **[Baichuan2-13B-Base]** | 58.10 | 59.17 | 61.97 | 54.33 | 48.17 | 48.78 |
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## 训练过程模型
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除了训练了 2.6 万亿 Tokens 的 [Baichuan2-7B-Base] 模型，我们还提供了在此之前的另外 11 个中间过程的模型（分别对应训练了约 0.2 ~ 2.4 万亿 Tokens）供社区研究使用（[训练过程checkpoint下载]）。下图给出了这些 checkpoints 在 C-Eval、MMLU、CMMLU 三个 benchmark 上的效果变化：
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# 声明与协议
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## 声明
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我们在此声明，我们的开发团队并未基于 Baichuan 2 模型开发任何应用，无论是在 iOS、Android、网页或任何其他平台。我们强烈呼吁所有使用者，不要利用
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Baichuan 2 模型进行任何危害国家社会安全或违法的活动。另外，我们也要求使用者不要将 Baichuan 2
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模型用于未经适当安全审查和备案的互联网服务。我们希望所有的使用者都能遵守这个原则，确保科技的发展能在规范和合法的环境下进行。
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我们已经尽我们所能，来确保模型训练过程中使用的数据的合规性。然而，尽管我们已经做出了巨大的努力，但由于模型和数据的复杂性，仍有可能存在一些无法预见的问题。因此，如果由于使用
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Baichuan 2 开源模型而导致的任何问题，包括但不限于数据安全问题、公共舆论风险，或模型被误导、滥用、传播或不当利用所带来的任何风险和问题，我们将不承担任何责任。
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## 协议
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* Baichuan 2 模型的社区使用需遵循[《Baichuan 2 模型社区许可协议》]。
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* Baichuan 2 支持商用，如果将 Baichuan 2 模型或其衍生品用作商业用途，请您按照如下方式联系许可方，以进行登记并向许可方申请书面授权：联系邮箱 [opensource@baichuan-inc.com]。
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[GitHub]:https://github.com/baichuan-inc/Baichuan2
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[Baichuan2]:https://github.com/baichuan-inc/Baichuan2
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[Baichuan-7B]:https://modelscope.cn/models/baichuan-inc/baichuan-7B/summary
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[Baichuan2-7B-Base]:https://modelscope.cn/models/baichuan-inc/Baichuan2-7B-Base/summary
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[Baichuan2-7B-Chat]:https://modelscope.cn/models/baichuan-inc/Baichuan2-7B-Chat/summary
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[Baichuan2-7B-Chat-4bits]:https://modelscope.cn/models/baichuan-inc/Baichuan2-7B-Chat-4bits/summary
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[Baichuan-13B-Base]:https://modelscope.cn/models/baichuan-inc/Baichuan-13B-Base/summary
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[Baichuan2-13B-Base]:https://modelscope.cn/models/baichuan-inc/Baichuan2-13B-Base/summary
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[Baichuan2-13B-Chat]:https://modelscope.cn/models/baichuan-inc/Baichuan2-13B-Chat/summary
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[Baichuan2-13B-Chat-4bits]:https://modelscope.cn/models/baichuan-inc/Baichuan2-13B-Chat-4bits/summary
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[通用]:https://github.com/baichuan-inc/Baichuan2#%E9%80%9A%E7%94%A8%E9%A2%86%E5%9F%9F
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[法律]:https://github.com/baichuan-inc/Baichuan2#%E6%B3%95%E5%BE%8B%E5%8C%BB%E7%96%97
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[医疗]:https://github.com/baichuan-inc/Baichuan2#%E6%B3%95%E5%BE%8B%E5%8C%BB%E7%96%97
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[数学]:https://github.com/baichuan-inc/Baichuan2#%E6%95%B0%E5%AD%A6%E4%BB%A3%E7%A0%81
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[代码]:https://github.com/baichuan-inc/Baichuan2#%E6%95%B0%E5%AD%A6%E4%BB%A3%E7%A0%81
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[多语言翻译]:https://github.com/baichuan-inc/Baichuan2#%E5%A4%9A%E8%AF%AD%E8%A8%80%E7%BF%BB%E8%AF%91
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[《Baichuan 2 模型社区许可协议》]:https://modelscope.cn/models/baichuan-inc/Baichuan2-7B-Base/file/view/master/Baichuan%202%E6%A8%A1%E5%9E%8B%E7%A4%BE%E5%8C%BA%E8%AE%B8%E5%8F%AF%E5%8D%8F%E8%AE%AE.pdf
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[邮件申请]: mailto:opensource@baichuan-inc.com
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[Email]: mailto:opensource@baichuan-inc.com
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[opensource@baichuan-inc.com]: mailto:opensource@baichuan-inc.com
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[训练过程checkpoint下载]: https://modelscope.cn/models/baichuan-inc/Baichuan2-7B-Intermediate-Checkpoints/summary
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[百川智能]: https://www.baichuan-ai.com
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[Baichuan 2: Open Large-scale Language Models]:https://cdn.baichuan-ai.com/paper/Baichuan2-technical-report.pdf
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{
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"architectures": [
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"BaichuanForCausalLM"
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],
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"auto_map": {
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"AutoConfig": "configuration_baichuan.BaichuanConfig",
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"AutoModelForCausalLM": "modeling_baichuan.BaichuanForCausalLM"
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},
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"tokenizer_class": "BaichuanTokenizer",
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"bos_token_id": 1,
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"eos_token_id": 2,
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"hidden_act": "silu",
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"hidden_size": 4096,
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"initializer_range": 0.02,
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"intermediate_size": 11008,
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"max_position_embeddings": 4096,
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"model_max_length": 4096,
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"model_type": "baichuan",
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"num_attention_heads": 32,
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"num_hidden_layers": 32,
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"pad_token_id": 0,
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"rms_norm_eps": 1e-06,
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"_from_model_config": true,
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"tie_word_embeddings": false,
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"torch_dtype": "bfloat16",
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"transformers_version": "4.29.2",
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"use_cache": true,
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"vocab_size": 125696
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}
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{
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"framework": "pytorch",
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"task": "text-generation",
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"model": {
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"type": "Baichuan2-7B-Chat"
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},
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"pipeline": {
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"type": "Baichuan2-7B-chatbot-pipe"
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},
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"allow_remote": true
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}
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# Copyright 2023 Baichuan Inc. All Rights Reserved.
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# Copyright 2022 EleutherAI and the HuggingFace Inc. team. All rights reserved.
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#
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# This code is based on EleutherAI's GPT-NeoX library and the GPT-NeoX
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# and OPT implementations in this library. It has been modified from its
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# original forms to accommodate minor architectural differences compared
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# to GPT-NeoX and OPT used by the Meta AI team that trained the model.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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from transformers.configuration_utils import PretrainedConfig
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from transformers.utils import logging
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logger = logging.get_logger(__name__)
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class BaichuanConfig(PretrainedConfig):
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model_type = "baichuan"
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keys_to_ignore_at_inference = ["past_key_values"]
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def __init__(
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self,
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vocab_size=125696,
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hidden_size=4096,
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intermediate_size=11008,
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num_hidden_layers=32,
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num_attention_heads=32,
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hidden_act="silu",
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max_position_embeddings=4096,
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initializer_range=0.02,
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rms_norm_eps=1e-6,
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use_cache=True,
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pad_token_id=0,
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bos_token_id=1,
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eos_token_id=2,
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tie_word_embeddings=False,
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z_loss_weight=0,
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**kwargs,
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):
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self.vocab_size = vocab_size
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self.max_position_embeddings = max_position_embeddings
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self.hidden_size = hidden_size
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self.intermediate_size = intermediate_size
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self.num_hidden_layers = num_hidden_layers
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self.num_attention_heads = num_attention_heads
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self.hidden_act = hidden_act
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self.initializer_range = initializer_range
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self.rms_norm_eps = rms_norm_eps
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self.use_cache = use_cache
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self.z_loss_weight = z_loss_weight
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super().__init__(
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pad_token_id=pad_token_id,
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bos_token_id=bos_token_id,
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eos_token_id=eos_token_id,
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tie_word_embeddings=tie_word_embeddings,
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**kwargs,
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)
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{
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"pad_token_id": 0,
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"bos_token_id": 1,
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"eos_token_id": 2,
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"user_token_id": 195,
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"assistant_token_id": 196,
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"max_new_tokens": 2048,
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"temperature": 0.3,
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"top_k": 5,
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"top_p": 0.85,
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"repetition_penalty": 1.05,
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"do_sample": true,
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"transformers_version": "4.29.2"
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}
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from typing import List
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from queue import Queue
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import torch
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def build_chat_input(model, tokenizer, messages: List[dict], max_new_tokens: int=0):
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def _parse_messages(messages, split_role="user"):
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system, rounds = "", []
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round = []
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for i, message in enumerate(messages):
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if message["role"] == "system":
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assert i == 0
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system = message["content"]
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continue
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if message["role"] == split_role and round:
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rounds.append(round)
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round = []
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round.append(message)
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if round:
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rounds.append(round)
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return system, rounds
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max_new_tokens = max_new_tokens or model.generation_config.max_new_tokens
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max_input_tokens = model.config.model_max_length - max_new_tokens
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system, rounds = _parse_messages(messages, split_role="user")
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system_tokens = tokenizer.encode(system)
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max_history_tokens = max_input_tokens - len(system_tokens)
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history_tokens = []
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for round in rounds[::-1]:
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round_tokens = []
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for message in round:
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if message["role"] == "user":
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round_tokens.append(model.generation_config.user_token_id)
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else:
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round_tokens.append(model.generation_config.assistant_token_id)
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round_tokens.extend(tokenizer.encode(message["content"]))
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if len(history_tokens) == 0 or len(history_tokens) + len(round_tokens) <= max_history_tokens:
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history_tokens = round_tokens + history_tokens # concat left
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if len(history_tokens) < max_history_tokens:
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continue
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break
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input_tokens = system_tokens + history_tokens
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if messages[-1]["role"] != "assistant":
|
||||
input_tokens.append(model.generation_config.assistant_token_id)
|
||||
input_tokens = input_tokens[-max_input_tokens:] # truncate left
|
||||
return torch.LongTensor([input_tokens]).to(model.device)
|
||||
|
||||
|
||||
class TextIterStreamer:
|
||||
def __init__(self, tokenizer, skip_prompt=False, skip_special_tokens=False):
|
||||
self.tokenizer = tokenizer
|
||||
self.skip_prompt = skip_prompt
|
||||
self.skip_special_tokens = skip_special_tokens
|
||||
self.tokens = []
|
||||
self.text_queue = Queue()
|
||||
self.next_tokens_are_prompt = True
|
||||
|
||||
def put(self, value):
|
||||
if self.skip_prompt and self.next_tokens_are_prompt:
|
||||
self.next_tokens_are_prompt = False
|
||||
else:
|
||||
if len(value.shape) > 1:
|
||||
value = value[0]
|
||||
self.tokens.extend(value.tolist())
|
||||
self.text_queue.put(
|
||||
self.tokenizer.decode(self.tokens, skip_special_tokens=self.skip_special_tokens))
|
||||
|
||||
def end(self):
|
||||
self.text_queue.put(None)
|
||||
|
||||
def __iter__(self):
|
||||
return self
|
||||
|
||||
def __next__(self):
|
||||
value = self.text_queue.get()
|
||||
if value is None:
|
||||
raise StopIteration()
|
||||
else:
|
||||
return value
|
||||
|
||||
|
|
@ -0,0 +1,785 @@
|
|||
# Copyright 2023 Baichuan Inc. All Rights Reserved.
|
||||
|
||||
# Copyright 2022 EleutherAI and the HuggingFace Inc. team. All rights reserved.
|
||||
#
|
||||
# This code is based on EleutherAI's GPT-NeoX library and the GPT-NeoX
|
||||
# and OPT implementations in this library. It has been modified from its
|
||||
# original forms to accommodate minor architectural differences compared
|
||||
# to GPT-NeoX and OPT used by the Meta AI team that trained the model.
|
||||
#
|
||||
# Licensed under the Apache License, Version 2.0 (the "License");
|
||||
# you may not use this file except in compliance with the License.
|
||||
# You may obtain a copy of the License at
|
||||
#
|
||||
# http://www.apache.org/licenses/LICENSE-2.0
|
||||
#
|
||||
# Unless required by applicable law or agreed to in writing, software
|
||||
# distributed under the License is distributed on an "AS IS" BASIS,
|
||||
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
|
||||
# See the License for the specific language governing permissions and
|
||||
# limitations under the License.
|
||||
|
||||
|
||||
from .configuration_baichuan import BaichuanConfig
|
||||
from .generation_utils import build_chat_input, TextIterStreamer
|
||||
|
||||
import math
|
||||
from typing import List, Optional, Tuple, Union
|
||||
from threading import Thread
|
||||
|
||||
import torch
|
||||
import torch.utils.checkpoint
|
||||
from torch import nn
|
||||
from torch.nn import BCEWithLogitsLoss, CrossEntropyLoss, MSELoss
|
||||
from torch.nn import functional as F
|
||||
from transformers import PreTrainedModel, PretrainedConfig
|
||||
from transformers.activations import ACT2FN
|
||||
from transformers.modeling_outputs import BaseModelOutputWithPast, CausalLMOutputWithPast
|
||||
from transformers.generation.utils import GenerationConfig
|
||||
from transformers.utils import logging, ContextManagers
|
||||
|
||||
import os
|
||||
from contextlib import contextmanager
|
||||
logger = logging.get_logger(__name__)
|
||||
|
||||
try:
|
||||
from xformers import ops as xops
|
||||
except ImportError:
|
||||
xops = None
|
||||
logger.warning(
|
||||
"Xformers is not installed correctly. If you want to use memory_efficient_attention to accelerate training use the following command to install Xformers\npip install xformers."
|
||||
)
|
||||
|
||||
|
||||
# Copied from transformers.models.bart.modeling_bart._make_causal_mask
|
||||
def _make_causal_mask(
|
||||
input_ids_shape: torch.Size, dtype: torch.dtype, device: torch.device, past_key_values_length: int = 0
|
||||
):
|
||||
"""
|
||||
Make causal mask used for bi-directional self-attention.
|
||||
"""
|
||||
bsz, tgt_len = input_ids_shape
|
||||
mask = torch.full((tgt_len, tgt_len), torch.tensor(torch.finfo(dtype).min, device=device), device=device)
|
||||
mask_cond = torch.arange(mask.size(-1), device=device)
|
||||
mask.masked_fill_(mask_cond < (mask_cond + 1).view(mask.size(-1), 1), 0)
|
||||
mask = mask.to(dtype)
|
||||
|
||||
if past_key_values_length > 0:
|
||||
mask = torch.cat([torch.zeros(tgt_len, past_key_values_length, dtype=dtype, device=device), mask], dim=-1)
|
||||
return mask[None, None, :, :].expand(bsz, 1, tgt_len, tgt_len + past_key_values_length)
|
||||
|
||||
def _expand_mask(mask: torch.Tensor, dtype: torch.dtype, tgt_len: Optional[int] = None):
|
||||
"""
|
||||
Expands attention_mask from `[bsz, seq_len]` to `[bsz, 1, tgt_seq_len, src_seq_len]`.
|
||||
"""
|
||||
if len(mask.size()) == 3:
|
||||
bsz, src_len, _ = mask.size()
|
||||
tgt_len = tgt_len if tgt_len is not None else src_len
|
||||
expanded_mask = mask[:,None,:,:].expand(bsz, 1, tgt_len, src_len).to(dtype)
|
||||
else:
|
||||
bsz, src_len = mask.size()
|
||||
tgt_len = tgt_len if tgt_len is not None else src_len
|
||||
expanded_mask = mask[:, None, None, :].expand(bsz, 1, tgt_len, src_len).to(dtype)
|
||||
|
||||
inverted_mask = 1.0 - expanded_mask
|
||||
|
||||
return inverted_mask.masked_fill(inverted_mask.to(torch.bool), torch.finfo(dtype).min)
|
||||
|
||||
|
||||
class RMSNorm(nn.Module):
|
||||
def __init__(self, hidden_size, eps=1e-6):
|
||||
"""
|
||||
RMSNorm is equivalent to T5LayerNorm
|
||||
"""
|
||||
super().__init__()
|
||||
self.weight = nn.Parameter(torch.ones(hidden_size))
|
||||
self.variance_epsilon = eps
|
||||
|
||||
def forward(self, hidden_states):
|
||||
variance = hidden_states.to(torch.float32).pow(2).mean(-1, keepdim=True)
|
||||
hidden_states = hidden_states * torch.rsqrt(variance + self.variance_epsilon)
|
||||
|
||||
# convert into half-precision if necessary
|
||||
if self.weight.dtype in [torch.float16, torch.bfloat16]:
|
||||
hidden_states = hidden_states.to(self.weight.dtype)
|
||||
|
||||
return self.weight * hidden_states
|
||||
|
||||
|
||||
class RotaryEmbedding(torch.nn.Module):
|
||||
def __init__(self, dim, max_position_embeddings=2048, base=10000, device=None):
|
||||
super().__init__()
|
||||
self.inv_freq = 1.0 / (base ** (torch.arange(0, dim, 2).float().to(device) / dim))
|
||||
self.max_seq_len_cached = max_position_embeddings
|
||||
t = torch.arange(self.max_seq_len_cached, device=self.inv_freq.device, dtype=torch.float32)
|
||||
freqs = torch.outer(t, self.inv_freq)
|
||||
emb = torch.cat((freqs, freqs), dim=-1)
|
||||
self.cos_cached = emb.cos()[None, None, :, :].to(torch.float32)
|
||||
self.sin_cached = emb.sin()[None, None, :, :].to(torch.float32)
|
||||
def forward(self, x, seq_len=None):
|
||||
# x: [bs, num_attention_heads, seq_len, head_size]
|
||||
# This `if` block is unlikely to be run after we build sin/cos in `__init__`. Keep the logic here just in case.
|
||||
if seq_len > self.max_seq_len_cached:
|
||||
self.max_seq_len_cached = seq_len
|
||||
t = torch.arange(self.max_seq_len_cached, device=self.inv_freq.device, dtype=torch.float32)
|
||||
freqs = torch.outer(t, self.inv_freq)
|
||||
emb = torch.cat((freqs, freqs), dim=-1)
|
||||
self.cos_cached = emb.cos()[None, None, :, :].to(torch.float32).to(x.device)
|
||||
self.sin_cached = emb.sin()[None, None, :, :].to(torch.float32).to(x.device)
|
||||
elif self.cos_cached.device != x.device:
|
||||
self.cos_cached = self.cos_cached.to(x.device)
|
||||
self.sin_cached = self.sin_cached.to(x.device)
|
||||
return (
|
||||
self.cos_cached[:, :, :seq_len, ...],
|
||||
self.sin_cached[:, :, :seq_len, ...],
|
||||
)
|
||||
|
||||
|
||||
def rotate_half(x):
|
||||
"""Rotates half the hidden dims of the input."""
|
||||
x1 = x[..., : x.shape[-1] // 2]
|
||||
x2 = x[..., x.shape[-1] // 2:]
|
||||
return torch.cat((-x2, x1), dim=-1)
|
||||
|
||||
|
||||
def apply_rotary_pos_emb(q, k, cos_, sin_, position_ids):
|
||||
cos = cos_.squeeze(1).squeeze(0) # [seq_len, dim]
|
||||
sin = sin_.squeeze(1).squeeze(0) # [seq_len, dim]
|
||||
cos = cos[position_ids].unsqueeze(1) # [bs, 1, seq_len, dim]
|
||||
sin = sin[position_ids].unsqueeze(1) # [bs, 1, seq_len, dim]
|
||||
q_embed = (q.float() * cos) + (rotate_half(q.float()) * sin)
|
||||
k_embed = (k.float() * cos) + (rotate_half(k.float()) * sin)
|
||||
return q_embed.to(q.dtype), k_embed.to(k.dtype)
|
||||
|
||||
|
||||
class MLP(nn.Module):
|
||||
def __init__(
|
||||
self,
|
||||
hidden_size: int,
|
||||
intermediate_size: int,
|
||||
hidden_act: str,
|
||||
):
|
||||
super().__init__()
|
||||
self.gate_proj = nn.Linear(hidden_size, intermediate_size, bias=False)
|
||||
self.down_proj = nn.Linear(intermediate_size, hidden_size, bias=False)
|
||||
self.up_proj = nn.Linear(hidden_size, intermediate_size, bias=False)
|
||||
self.act_fn = ACT2FN[hidden_act]
|
||||
|
||||
def forward(self, x):
|
||||
return self.down_proj(self.act_fn(self.gate_proj(x)) * self.up_proj(x))
|
||||
|
||||
|
||||
class Attention(nn.Module):
|
||||
"""Multi-headed attention from 'Attention Is All You Need' paper"""
|
||||
def __init__(self, config: BaichuanConfig):
|
||||
super().__init__()
|
||||
self.config = config
|
||||
self.hidden_size = config.hidden_size
|
||||
self.num_heads = config.num_attention_heads
|
||||
self.head_dim = self.hidden_size // self.num_heads
|
||||
self.max_position_embeddings = config.max_position_embeddings
|
||||
|
||||
if (self.head_dim * self.num_heads) != self.hidden_size:
|
||||
raise ValueError(
|
||||
f"hidden_size must be divisible by num_heads (got `hidden_size`: {self.hidden_size}"
|
||||
f" and `num_heads`: {self.num_heads})."
|
||||
)
|
||||
self.W_pack = nn.Linear(self.hidden_size, 3 * self.hidden_size, bias=False)
|
||||
self.o_proj = nn.Linear(self.num_heads * self.head_dim, self.hidden_size, bias=False)
|
||||
self.rotary_emb = RotaryEmbedding(self.head_dim, max_position_embeddings=self.max_position_embeddings)
|
||||
|
||||
def _shape(self, tensor: torch.Tensor, seq_len: int, bsz: int):
|
||||
return tensor.view(bsz, seq_len, self.num_heads, self.head_dim).transpose(1, 2).contiguous()
|
||||
|
||||
def forward(
|
||||
self,
|
||||
hidden_states: torch.Tensor,
|
||||
attention_mask: Optional[torch.Tensor] = None,
|
||||
position_ids: Optional[torch.LongTensor] = None,
|
||||
past_key_value: Optional[Tuple[torch.Tensor]] = None,
|
||||
output_attentions: bool = False,
|
||||
use_cache: bool = False,
|
||||
) -> Tuple[torch.Tensor, Optional[torch.Tensor], Optional[Tuple[torch.Tensor]]]:
|
||||
bsz, q_len, _ = hidden_states.size()
|
||||
|
||||
proj = self.W_pack(hidden_states)
|
||||
proj = proj.unflatten(-1, (3, self.hidden_size)).unsqueeze(0).transpose(0, -2).squeeze(-2)
|
||||
query_states = proj[0].view(bsz, q_len, self.num_heads, self.head_dim).transpose(1, 2)
|
||||
key_states = proj[1].view(bsz, q_len, self.num_heads, self.head_dim).transpose(1, 2)
|
||||
value_states = proj[2].view(bsz, q_len, self.num_heads, self.head_dim).transpose(1, 2)
|
||||
|
||||
kv_seq_len = key_states.shape[-2]
|
||||
if past_key_value is not None:
|
||||
kv_seq_len += past_key_value[0].shape[-2]
|
||||
cos, sin = self.rotary_emb(value_states, seq_len=kv_seq_len)
|
||||
query_states, key_states = apply_rotary_pos_emb(query_states, key_states, cos, sin, position_ids)
|
||||
# [bsz, nh, t, hd]
|
||||
|
||||
if past_key_value is not None:
|
||||
# reuse k, v, self_attention
|
||||
key_states = torch.cat([past_key_value[0], key_states], dim=2)
|
||||
value_states = torch.cat([past_key_value[1], value_states], dim=2)
|
||||
|
||||
past_key_value = (key_states, value_states) if use_cache else None
|
||||
if xops is not None and self.training:
|
||||
attn_weights = None
|
||||
query_states = query_states.transpose(1, 2)
|
||||
key_states = key_states.transpose(1, 2)
|
||||
value_states = value_states.transpose(1, 2)
|
||||
attn_output = xops.memory_efficient_attention(
|
||||
query_states, key_states, value_states, attn_bias=xops.LowerTriangularMask()
|
||||
)
|
||||
else:
|
||||
with torch.backends.cuda.sdp_kernel(enable_flash=True, enable_math=True, enable_mem_efficient=True):
|
||||
attn_output = F.scaled_dot_product_attention(query_states, key_states, value_states, attn_mask = attention_mask)
|
||||
attn_output = attn_output.transpose(1, 2)
|
||||
attn_output = attn_output.reshape(bsz, q_len, self.hidden_size)
|
||||
attn_output = self.o_proj(attn_output)
|
||||
|
||||
if not output_attentions:
|
||||
attn_weights = None
|
||||
|
||||
return attn_output, attn_weights, past_key_value
|
||||
|
||||
|
||||
class DecoderLayer(nn.Module):
|
||||
def __init__(self, config: BaichuanConfig):
|
||||
super().__init__()
|
||||
self.hidden_size = config.hidden_size
|
||||
self.self_attn = Attention(config=config)
|
||||
self.mlp = MLP(
|
||||
hidden_size=self.hidden_size,
|
||||
intermediate_size=config.intermediate_size,
|
||||
hidden_act=config.hidden_act,
|
||||
)
|
||||
self.input_layernorm = RMSNorm(config.hidden_size, eps=config.rms_norm_eps)
|
||||
self.post_attention_layernorm = RMSNorm(config.hidden_size, eps=config.rms_norm_eps)
|
||||
|
||||
def forward(
|
||||
self,
|
||||
hidden_states: torch.Tensor,
|
||||
attention_mask: Optional[torch.Tensor] = None,
|
||||
position_ids: Optional[torch.LongTensor] = None,
|
||||
past_key_value: Optional[Tuple[torch.Tensor]] = None,
|
||||
output_attentions: Optional[bool] = False,
|
||||
use_cache: Optional[bool] = False,
|
||||
) -> Tuple[torch.FloatTensor, Optional[Tuple[torch.FloatTensor, torch.FloatTensor]]]:
|
||||
|
||||
residual = hidden_states
|
||||
|
||||
hidden_states = self.input_layernorm(hidden_states)
|
||||
|
||||
# Self Attention
|
||||
hidden_states, self_attn_weights, present_key_value = self.self_attn(
|
||||
hidden_states=hidden_states,
|
||||
attention_mask=attention_mask,
|
||||
position_ids=position_ids,
|
||||
past_key_value=past_key_value,
|
||||
output_attentions=output_attentions,
|
||||
use_cache=use_cache,
|
||||
)
|
||||
hidden_states = residual + hidden_states
|
||||
|
||||
# Fully Connected
|
||||
residual = hidden_states
|
||||
hidden_states = self.post_attention_layernorm(hidden_states)
|
||||
hidden_states = self.mlp(hidden_states)
|
||||
hidden_states = residual + hidden_states
|
||||
|
||||
outputs = (hidden_states,)
|
||||
|
||||
if output_attentions:
|
||||
outputs += (self_attn_weights,)
|
||||
|
||||
if use_cache:
|
||||
outputs += (present_key_value,)
|
||||
|
||||
return outputs
|
||||
|
||||
|
||||
class BaichuanPreTrainedModel(PreTrainedModel):
|
||||
config_class = BaichuanConfig
|
||||
base_model_prefix = "model"
|
||||
supports_gradient_checkpointing = True
|
||||
_no_split_modules = ["DecoderLayer"]
|
||||
_keys_to_ignore_on_load_unexpected = [r"decoder\.version"]
|
||||
|
||||
def _init_weights(self, module):
|
||||
std = self.config.initializer_range
|
||||
if isinstance(module, nn.Linear):
|
||||
module.weight.data.normal_(mean=0.0, std=std)
|
||||
if module.bias is not None:
|
||||
module.bias.data.zero_()
|
||||
elif isinstance(module, nn.Embedding):
|
||||
module.weight.data.normal_(mean=0.0, std=std)
|
||||
if module.padding_idx is not None:
|
||||
module.weight.data[module.padding_idx].zero_()
|
||||
|
||||
def _set_gradient_checkpointing(self, module, value=False):
|
||||
if isinstance(module, BaichuanModel):
|
||||
module.gradient_checkpointing = value
|
||||
|
||||
|
||||
class BaichuanModel(BaichuanPreTrainedModel):
|
||||
def __init__(self, config: BaichuanConfig):
|
||||
super().__init__(config)
|
||||
self.padding_idx = config.pad_token_id
|
||||
self.vocab_size = config.vocab_size
|
||||
|
||||
self.embed_tokens = nn.Embedding(config.vocab_size, config.hidden_size, self.padding_idx)
|
||||
self.layers = nn.ModuleList([DecoderLayer(config) for _ in range(config.num_hidden_layers)])
|
||||
self.norm = RMSNorm(config.hidden_size, eps=config.rms_norm_eps)
|
||||
|
||||
self.gradient_checkpointing = False
|
||||
# Initialize weights and apply final processing
|
||||
self.post_init()
|
||||
|
||||
def get_input_embeddings(self):
|
||||
return self.embed_tokens
|
||||
|
||||
def set_input_embeddings(self, value):
|
||||
self.embed_tokens = value
|
||||
|
||||
# Copied from transformers.models.bart.modeling_bart.BartDecoder._prepare_decoder_attention_mask
|
||||
def _prepare_decoder_attention_mask(self, attention_mask, input_shape, inputs_embeds, past_key_values_length):
|
||||
# create causal mask
|
||||
# [bsz, seq_len] -> [bsz, 1, tgt_seq_len, src_seq_len]
|
||||
combined_attention_mask = None
|
||||
if input_shape[-1] > 1:
|
||||
combined_attention_mask = _make_causal_mask(
|
||||
input_shape,
|
||||
inputs_embeds.dtype,
|
||||
device=inputs_embeds.device,
|
||||
past_key_values_length=past_key_values_length,
|
||||
)
|
||||
|
||||
if attention_mask is not None:
|
||||
# [bsz, seq_len] -> [bsz, 1, tgt_seq_len, src_seq_len]
|
||||
expanded_attn_mask = _expand_mask(attention_mask, inputs_embeds.dtype, tgt_len=input_shape[-1]).to(
|
||||
inputs_embeds.device
|
||||
)
|
||||
combined_attention_mask = (
|
||||
expanded_attn_mask if combined_attention_mask is None else expanded_attn_mask + combined_attention_mask
|
||||
)
|
||||
|
||||
return combined_attention_mask
|
||||
|
||||
def forward(
|
||||
self,
|
||||
input_ids: torch.LongTensor = None,
|
||||
attention_mask: Optional[torch.Tensor] = None,
|
||||
position_ids: Optional[torch.LongTensor] = None,
|
||||
past_key_values: Optional[List[torch.FloatTensor]] = None,
|
||||
inputs_embeds: Optional[torch.FloatTensor] = None,
|
||||
use_cache: Optional[bool] = None,
|
||||
output_attentions: Optional[bool] = None,
|
||||
output_hidden_states: Optional[bool] = None,
|
||||
return_dict: Optional[bool] = None,
|
||||
) -> Union[Tuple, BaseModelOutputWithPast]:
|
||||
output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions
|
||||
output_hidden_states = (
|
||||
output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
|
||||
)
|
||||
use_cache = use_cache if use_cache is not None else self.config.use_cache
|
||||
|
||||
return_dict = return_dict if return_dict is not None else self.config.use_return_dict
|
||||
|
||||
# retrieve input_ids and inputs_embeds
|
||||
if input_ids is not None and inputs_embeds is not None:
|
||||
raise ValueError("You cannot specify both decoder_input_ids and decoder_inputs_embeds at the same time")
|
||||
elif input_ids is not None:
|
||||
batch_size, seq_length = input_ids.shape
|
||||
elif inputs_embeds is not None:
|
||||
batch_size, seq_length, _ = inputs_embeds.shape
|
||||
else:
|
||||
raise ValueError("You have to specify either decoder_input_ids or decoder_inputs_embeds")
|
||||
|
||||
seq_length_with_past = seq_length
|
||||
past_key_values_length = 0
|
||||
|
||||
if past_key_values is not None:
|
||||
past_key_values_length = past_key_values[0][0].shape[2]
|
||||
seq_length_with_past = seq_length_with_past + past_key_values_length
|
||||
|
||||
if position_ids is None:
|
||||
device = input_ids.device if input_ids is not None else inputs_embeds.device
|
||||
position_ids = torch.arange(
|
||||
past_key_values_length, seq_length + past_key_values_length, dtype=torch.long, device=device
|
||||
)
|
||||
position_ids = position_ids.unsqueeze(0).view(-1, seq_length)
|
||||
else:
|
||||
position_ids = position_ids.view(-1, seq_length).long()
|
||||
|
||||
if inputs_embeds is None:
|
||||
inputs_embeds = self.embed_tokens(input_ids)
|
||||
# embed positions
|
||||
if attention_mask is None:
|
||||
attention_mask = torch.ones(
|
||||
(batch_size, seq_length_with_past), dtype=torch.bool, device=inputs_embeds.device
|
||||
)
|
||||
attention_mask = self._prepare_decoder_attention_mask(
|
||||
attention_mask, (batch_size, seq_length), inputs_embeds, past_key_values_length
|
||||
)
|
||||
|
||||
hidden_states = inputs_embeds
|
||||
|
||||
if self.gradient_checkpointing and self.training:
|
||||
if use_cache:
|
||||
logger.warning_once(
|
||||
"`use_cache=True` is incompatible with gradient checkpointing. Setting `use_cache=False`..."
|
||||
)
|
||||
use_cache = False
|
||||
|
||||
# decoder layers
|
||||
all_hidden_states = () if output_hidden_states else None
|
||||
all_self_attns = () if output_attentions else None
|
||||
next_decoder_cache = () if use_cache else None
|
||||
|
||||
for idx, decoder_layer in enumerate(self.layers):
|
||||
if output_hidden_states:
|
||||
all_hidden_states += (hidden_states,)
|
||||
|
||||
past_key_value = past_key_values[idx] if past_key_values is not None else None
|
||||
|
||||
if self.gradient_checkpointing and self.training:
|
||||
|
||||
def create_custom_forward(module):
|
||||
def custom_forward(*inputs):
|
||||
# None for past_key_value
|
||||
return module(*inputs, output_attentions, None)
|
||||
|
||||
return custom_forward
|
||||
|
||||
layer_outputs = torch.utils.checkpoint.checkpoint(
|
||||
create_custom_forward(decoder_layer),
|
||||
hidden_states,
|
||||
attention_mask,
|
||||
position_ids,
|
||||
None,
|
||||
)
|
||||
else:
|
||||
layer_outputs = decoder_layer(
|
||||
hidden_states,
|
||||
attention_mask=attention_mask,
|
||||
position_ids=position_ids,
|
||||
past_key_value=past_key_value,
|
||||
output_attentions=output_attentions,
|
||||
use_cache=use_cache,
|
||||
)
|
||||
|
||||
hidden_states = layer_outputs[0]
|
||||
|
||||
if use_cache:
|
||||
next_decoder_cache += (layer_outputs[2 if output_attentions else 1],)
|
||||
|
||||
if output_attentions:
|
||||
all_self_attns += (layer_outputs[1],)
|
||||
|
||||
hidden_states = self.norm(hidden_states)
|
||||
|
||||
# add hidden states from the last decoder layer
|
||||
if output_hidden_states:
|
||||
all_hidden_states += (hidden_states,)
|
||||
|
||||
next_cache = next_decoder_cache if use_cache else None
|
||||
if not return_dict:
|
||||
return tuple(v for v in [hidden_states, next_cache, all_hidden_states, all_self_attns] if v is not None)
|
||||
return BaseModelOutputWithPast(
|
||||
last_hidden_state=hidden_states,
|
||||
past_key_values=next_cache,
|
||||
hidden_states=all_hidden_states,
|
||||
attentions=all_self_attns,
|
||||
)
|
||||
|
||||
|
||||
class NormHead(nn.Module):
|
||||
def __init__(self, hidden_size, vocab_size, bias=False):
|
||||
super().__init__()
|
||||
self.weight = nn.Parameter(torch.empty((vocab_size, hidden_size)))
|
||||
nn.init.kaiming_uniform_(self.weight, a=math.sqrt(5))
|
||||
self.first_flag = True
|
||||
|
||||
def forward(self, hidden_states):
|
||||
if self.training:
|
||||
norm_weight = nn.functional.normalize(self.weight)
|
||||
self.first_flag = True
|
||||
elif self.first_flag:
|
||||
self.first_flag = False
|
||||
self.weight.data = nn.functional.normalize(self.weight)
|
||||
norm_weight = self.weight
|
||||
else:
|
||||
norm_weight = self.weight
|
||||
return nn.functional.linear(hidden_states, norm_weight)
|
||||
|
||||
_init_weights = True
|
||||
@contextmanager
|
||||
def no_init_weights(_enable=True):
|
||||
global _init_weights
|
||||
old_init_weights = _init_weights
|
||||
if _enable:
|
||||
_init_weights = False
|
||||
try:
|
||||
yield
|
||||
finally:
|
||||
_init_weights = old_init_weights
|
||||
|
||||
class BaichuanForCausalLM(BaichuanPreTrainedModel):
|
||||
def __init__(self, config, *model_args, **model_kwargs):
|
||||
super().__init__(config, *model_args, **model_kwargs)
|
||||
self.model = BaichuanModel(config)
|
||||
|
||||
self.lm_head = NormHead(config.hidden_size, config.vocab_size, bias=False)
|
||||
if hasattr(config, "quantization_config") and isinstance(config.quantization_config, dict) and config.quantization_config.get('load_in_4bit', False):
|
||||
try:
|
||||
from .quantizer import quantize_offline, init_model_weight_int4
|
||||
except ImportError:
|
||||
raise ImportError(f"Needs QLinear to run quantize.")
|
||||
quantize_offline(self, 4)
|
||||
# Initialize weights and apply final processing
|
||||
self.post_init()
|
||||
|
||||
def get_input_embeddings(self):
|
||||
return self.model.embed_tokens
|
||||
|
||||
def set_input_embeddings(self, value):
|
||||
self.model.embed_tokens = value
|
||||
|
||||
def get_output_embeddings(self):
|
||||
return self.lm_head
|
||||
|
||||
def set_output_embeddings(self, new_embeddings):
|
||||
self.lm_head = new_embeddings
|
||||
|
||||
def set_decoder(self, decoder):
|
||||
self.model = decoder
|
||||
|
||||
def get_decoder(self):
|
||||
return self.model
|
||||
|
||||
@classmethod
|
||||
def from_pretrained(
|
||||
cls,
|
||||
pretrained_model_name_or_path: Optional[Union[str, os.PathLike]],
|
||||
*model_args,
|
||||
config: Optional[Union[PretrainedConfig, str, os.PathLike]] = None,
|
||||
cache_dir: Optional[Union[str, os.PathLike]] = None,
|
||||
ignore_mismatched_sizes: bool = False,
|
||||
force_download: bool = False,
|
||||
local_files_only: bool = False,
|
||||
token: Optional[Union[str, bool]] = None,
|
||||
revision: str = "main",
|
||||
use_safetensors: bool = None,
|
||||
**kwargs,
|
||||
):
|
||||
# Load config if we don't provide a configuration
|
||||
if not isinstance(config, PretrainedConfig):
|
||||
config_path = config if config is not None else pretrained_model_name_or_path
|
||||
config, model_kwargs = cls.config_class.from_pretrained(
|
||||
config_path,
|
||||
cache_dir=cache_dir,
|
||||
return_unused_kwargs=True,
|
||||
force_download=force_download,
|
||||
resume_download=False,
|
||||
proxies=None,
|
||||
local_files_only=local_files_only,
|
||||
token=token,
|
||||
revision=revision,
|
||||
subfolder="",
|
||||
_from_auto=False,
|
||||
_from_pipeline=None,
|
||||
**kwargs,
|
||||
)
|
||||
else:
|
||||
model_kwargs = kwargs
|
||||
|
||||
if hasattr(config, "quantization_config") and config.quantization_config['load_in_4bit']:
|
||||
try:
|
||||
from .quantizer import init_model_weight_int4
|
||||
from accelerate import init_empty_weights, dispatch_model, infer_auto_device_map
|
||||
from accelerate.utils import CustomDtype
|
||||
from accelerate.utils import get_balanced_memory
|
||||
except ImportError:
|
||||
raise ImportError(f"Needs import model weight init func to run quantize.")
|
||||
# Instantiate model.
|
||||
init_contexts = [no_init_weights(_enable=True)]
|
||||
init_contexts.append(init_empty_weights())
|
||||
with ContextManagers(init_contexts):
|
||||
model = cls(config)
|
||||
|
||||
model_file = os.path.join(pretrained_model_name_or_path, 'pytorch_model.bin')
|
||||
state_dict = torch.load(model_file, map_location="cpu")
|
||||
model.is_quantized = True
|
||||
|
||||
device_map = kwargs.pop("device_map", None)
|
||||
torch_dtype = kwargs.pop("torch_dtype", None)
|
||||
|
||||
if device_map is not None:
|
||||
kwargs = {"no_split_module_classes": model._no_split_modules}
|
||||
target_dtype = CustomDtype.INT4
|
||||
max_memory = get_balanced_memory(
|
||||
model,
|
||||
dtype=target_dtype,
|
||||
low_zero=(device_map == "balanced_low_0"),
|
||||
max_memory=None,
|
||||
**kwargs,
|
||||
)
|
||||
kwargs["max_memory"] = max_memory
|
||||
device_map = infer_auto_device_map(model, dtype=target_dtype, **kwargs)
|
||||
|
||||
model = init_model_weight_int4(config, model, state_dict)
|
||||
|
||||
# Set model in evaluation mode to deactivate DropOut modules by default
|
||||
model.eval()
|
||||
# If it is a model with generation capabilities, attempt to load the generation config
|
||||
if model.can_generate():
|
||||
try:
|
||||
model.generation_config = GenerationConfig.from_pretrained(
|
||||
pretrained_model_name_or_path,
|
||||
cache_dir=cache_dir,
|
||||
force_download=force_download,
|
||||
resume_download=False,
|
||||
proxies=None,
|
||||
local_files_only=local_files_only,
|
||||
token=token,
|
||||
revision=revision,
|
||||
subfolder="",
|
||||
_from_auto=False,
|
||||
_from_pipeline=None,
|
||||
**kwargs,
|
||||
)
|
||||
except (OSError, TypeError):
|
||||
logger.info(
|
||||
"Generation config file not found, using a generation config created from the model config."
|
||||
)
|
||||
pass
|
||||
|
||||
if device_map is not None:
|
||||
dispatch_model(model, device_map=device_map)
|
||||
|
||||
return model
|
||||
return super(BaichuanForCausalLM, cls).from_pretrained(pretrained_model_name_or_path, *model_args,
|
||||
config=config, cache_dir=cache_dir, ignore_mismatched_sizes=ignore_mismatched_sizes,
|
||||
force_download=force_download, local_files_only=local_files_only, token=token, revision=revision,
|
||||
use_safetensors=use_safetensors, **kwargs)
|
||||
|
||||
def forward(
|
||||
self,
|
||||
input_ids: torch.LongTensor = None,
|
||||
attention_mask: Optional[torch.Tensor] = None,
|
||||
position_ids: Optional[torch.LongTensor] = None,
|
||||
past_key_values: Optional[List[torch.FloatTensor]] = None,
|
||||
inputs_embeds: Optional[torch.FloatTensor] = None,
|
||||
labels: Optional[torch.LongTensor] = None,
|
||||
use_cache: Optional[bool] = None,
|
||||
output_attentions: Optional[bool] = None,
|
||||
output_hidden_states: Optional[bool] = None,
|
||||
return_dict: Optional[bool] = None,
|
||||
) -> Union[Tuple, CausalLMOutputWithPast]:
|
||||
|
||||
output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions
|
||||
output_hidden_states = (
|
||||
output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
|
||||
)
|
||||
return_dict = return_dict if return_dict is not None else self.config.use_return_dict
|
||||
|
||||
# decoder outputs consists of (dec_features, layer_state, dec_hidden, dec_attn)
|
||||
outputs = self.model(
|
||||
input_ids=input_ids,
|
||||
attention_mask=attention_mask,
|
||||
position_ids=position_ids,
|
||||
past_key_values=past_key_values,
|
||||
inputs_embeds=inputs_embeds,
|
||||
use_cache=use_cache,
|
||||
output_attentions=output_attentions,
|
||||
output_hidden_states=output_hidden_states,
|
||||
return_dict=return_dict,
|
||||
)
|
||||
|
||||
hidden_states = outputs[0]
|
||||
logits = self.lm_head(hidden_states)
|
||||
loss = None
|
||||
if labels is not None:
|
||||
# Shift so that tokens < n predict n
|
||||
shift_logits = logits[..., :-1, :].contiguous()
|
||||
shift_labels = labels[..., 1:].contiguous()
|
||||
# Flatten the tokens
|
||||
loss_fct = CrossEntropyLoss()
|
||||
shift_logits = shift_logits.view(-1, self.config.vocab_size)
|
||||
shift_labels = shift_labels.view(-1)
|
||||
softmax_normalizer = shift_logits.max(-1).values ** 2
|
||||
z_loss = self.config.z_loss_weight * softmax_normalizer.mean()
|
||||
# Enable model parallelism
|
||||
shift_labels = shift_labels.to(shift_logits.device)
|
||||
loss = loss_fct(shift_logits, shift_labels) + z_loss
|
||||
|
||||
if not return_dict:
|
||||
output = (logits,) + outputs[1:]
|
||||
return (loss,) + output if loss is not None else output
|
||||
|
||||
return CausalLMOutputWithPast(
|
||||
loss=loss,
|
||||
logits=logits,
|
||||
past_key_values=outputs.past_key_values,
|
||||
hidden_states=outputs.hidden_states,
|
||||
attentions=outputs.attentions,
|
||||
)
|
||||
|
||||
def prepare_inputs_for_generation(
|
||||
self, input_ids, past_key_values=None, attention_mask=None, inputs_embeds=None, **kwargs
|
||||
):
|
||||
if past_key_values:
|
||||
input_ids = input_ids[:, -1:]
|
||||
|
||||
position_ids = kwargs.get("position_ids", None)
|
||||
if attention_mask is not None and position_ids is None:
|
||||
# create position_ids on the fly for batch generation
|
||||
position_ids = attention_mask.long().cumsum(-1) - 1
|
||||
position_ids.masked_fill_(attention_mask == 0, 1)
|
||||
if past_key_values:
|
||||
position_ids = position_ids[:, -1].unsqueeze(-1)
|
||||
|
||||
# if `inputs_embeds` are passed, we only want to use them in the 1st generation step
|
||||
if inputs_embeds is not None and past_key_values is None:
|
||||
model_inputs = {"inputs_embeds": inputs_embeds}
|
||||
else:
|
||||
model_inputs = {"input_ids": input_ids}
|
||||
|
||||
model_inputs.update(
|
||||
{
|
||||
"position_ids": position_ids,
|
||||
"past_key_values": past_key_values,
|
||||
"use_cache": kwargs.get("use_cache"),
|
||||
"attention_mask": attention_mask,
|
||||
}
|
||||
)
|
||||
return model_inputs
|
||||
|
||||
@staticmethod
|
||||
def _reorder_cache(past_key_values, beam_idx):
|
||||
reordered_past = ()
|
||||
for layer_past in past_key_values:
|
||||
reordered_past += (tuple(past_state.index_select(0, beam_idx) for past_state in layer_past),)
|
||||
return reordered_past
|
||||
|
||||
def quantize(self, bits: int):
|
||||
try:
|
||||
from .quantizer import quantize_online
|
||||
except ImportError:
|
||||
raise ImportError(f"Needs QLinear to run quantize.")
|
||||
return quantize_online(self, bits)
|
||||
|
||||
def chat(self, tokenizer, messages: List[dict], stream=False,
|
||||
generation_config: Optional[GenerationConfig]=None):
|
||||
generation_config = generation_config or self.generation_config
|
||||
input_ids = build_chat_input(self, tokenizer, messages, generation_config.max_new_tokens)
|
||||
if stream:
|
||||
streamer = TextIterStreamer(tokenizer, skip_prompt=True, skip_special_tokens=True)
|
||||
Thread(target=self.generate, kwargs=dict(
|
||||
inputs=input_ids, streamer=streamer,
|
||||
generation_config=generation_config,
|
||||
)).start()
|
||||
return streamer
|
||||
else:
|
||||
outputs = self.generate(input_ids, generation_config=generation_config)
|
||||
response = tokenizer.decode(outputs[0][len(input_ids[0]):], skip_special_tokens=True)
|
||||
return response
|
||||
|
|
@ -0,0 +1,74 @@
|
|||
import os
|
||||
import torch
|
||||
from typing import Union, Dict, Any
|
||||
from modelscope.pipelines.builder import PIPELINES
|
||||
from modelscope.models.builder import MODELS
|
||||
from modelscope.utils.constant import Tasks
|
||||
from modelscope.pipelines.base import Pipeline
|
||||
from modelscope.outputs import OutputKeys
|
||||
from modelscope.pipelines.nlp.text_generation_pipeline import TextGenerationPipeline
|
||||
from modelscope.models.base import Model, TorchModel
|
||||
from modelscope.utils.logger import get_logger
|
||||
from transformers import AutoModelForCausalLM, AutoTokenizer, GenerationConfig
|
||||
from transformers.generation.utils import GenerationConfig
|
||||
|
||||
@PIPELINES.register_module(Tasks.text_generation, module_name='Baichuan2-7B-chatbot-pipe')
|
||||
class Baichuan7BChatTextGenerationPipeline(TextGenerationPipeline):
|
||||
def __init__(
|
||||
self,
|
||||
model: Union[Model, str],
|
||||
*args,
|
||||
**kwargs):
|
||||
self.model = Baichuan7BChatTextGeneration(model) if isinstance(model, str) else model
|
||||
super().__init__(model=model, **kwargs)
|
||||
|
||||
def preprocess(self, inputs, **preprocess_params) -> Dict[str, Any]:
|
||||
return inputs
|
||||
|
||||
def _sanitize_parameters(self, **pipeline_parameters):
|
||||
return {},pipeline_parameters,{}
|
||||
|
||||
# define the forward pass
|
||||
def forward(self, inputs: Dict, **forward_params) -> Dict[str, Any]:
|
||||
output = {}
|
||||
device = self.model.model.device
|
||||
input_ids = self.model.tokenizer(inputs, return_tensors="pt").input_ids.to(device)
|
||||
pred = self.model.model.generate(input_ids,**forward_params)
|
||||
out = self.model.tokenizer.decode(pred.cpu()[0], skip_special_tokens=True)
|
||||
output['text'] = out
|
||||
return output
|
||||
|
||||
# format the outputs from pipeline
|
||||
def postprocess(self, input, **kwargs) -> Dict[str, Any]:
|
||||
return input
|
||||
|
||||
|
||||
@MODELS.register_module(Tasks.text_generation, module_name='Baichuan2-7B-Chat')
|
||||
class Baichuan7BChatTextGeneration(TorchModel):
|
||||
def __init__(self, model_dir=None, *args, **kwargs):
|
||||
super().__init__(model_dir, *args, **kwargs)
|
||||
self.logger = get_logger()
|
||||
# loading tokenizer
|
||||
self.tokenizer = AutoTokenizer.from_pretrained(model_dir, trust_remote_code=True)
|
||||
self.model = AutoModelForCausalLM.from_pretrained(model_dir, device_map="auto", torch_dtype=torch.float16, trust_remote_code=True)
|
||||
# self.model = AutoModelForCausalLM.from_pretrained(model_dir, device_map="auto",trust_remote_code=True)
|
||||
self.model.generation_config = GenerationConfig.from_pretrained(model_dir)
|
||||
self.model = self.model.eval()
|
||||
|
||||
def forward(self,input: Dict, *args, **kwargs) -> Dict[str, Any]:
|
||||
output = {}
|
||||
response = self.model.chat(self.tokenizer, input, *args, **kwargs)
|
||||
history = input.copy()
|
||||
history.append({'role': 'assistant', 'content': response})
|
||||
return {OutputKeys.RESPONSE:response, OutputKeys.HISTORY: history}
|
||||
|
||||
def quantize(self, bits: int):
|
||||
self.model = self.model.quantize(bits)
|
||||
return self
|
||||
|
||||
def infer(self, input, **kwargs):
|
||||
device = self.model.device
|
||||
input_ids = self.tokenizer(input, return_tensors="pt").input_ids.to(device)
|
||||
pred = self.model.generate(input_ids,**kwargs)
|
||||
out = self.tokenizer.decode(pred.cpu()[0], skip_special_tokens=True)
|
||||
return out
|
||||
Binary file not shown.
|
|
@ -0,0 +1,210 @@
|
|||
import bitsandbytes as bnb
|
||||
from bitsandbytes.nn.modules import Params4bit, Int8Params
|
||||
import torch
|
||||
|
||||
def Params4bitCuda(self, device):
|
||||
self.data = self.data.cuda(device)
|
||||
self.quant_state[0] = self.quant_state[0].cuda(device)
|
||||
self.quant_state[4][0] = self.quant_state[4][0].cuda(device)
|
||||
self.quant_state[4][1][0] = self.quant_state[4][1][0].cuda(device)
|
||||
self.quant_state[4][1][1] = self.quant_state[4][1][1].cuda(device)
|
||||
|
||||
self.quant_state[6] = self.quant_state[6].cuda(device)
|
||||
return self
|
||||
|
||||
class Linear4bitOnline(torch.nn.Module):
|
||||
def __init__(self, weight, bias, quant_type):
|
||||
super().__init__()
|
||||
self.weight = Params4bit(
|
||||
weight.data, requires_grad=False, compress_statistics=True, quant_type=quant_type
|
||||
)
|
||||
self.compute_dtype = None
|
||||
#self.weight.cuda(weight.device)
|
||||
self.bias = bias
|
||||
|
||||
def forward(self, x: torch.Tensor):
|
||||
# weights are cast automatically as Int8Params, but the bias has to be cast manually
|
||||
if self.bias is not None and self.bias.dtype != x.dtype:
|
||||
self.bias.data = self.bias.data.to(x.dtype)
|
||||
|
||||
if getattr(self.weight, "quant_state", None) is None:
|
||||
print(
|
||||
"FP4 quantization state not initialized. Please call .cuda() or .to(device) on the LinearFP4 layer first."
|
||||
)
|
||||
inp_dtype = x.dtype
|
||||
if self.compute_dtype is not None:
|
||||
x = x.to(self.compute_dtype)
|
||||
|
||||
bias = None if self.bias is None else self.bias.to(self.compute_dtype)
|
||||
out = bnb.matmul_4bit(
|
||||
x, self.weight.t(), bias=bias, quant_state=self.weight.quant_state
|
||||
)
|
||||
|
||||
out = out.to(inp_dtype)
|
||||
|
||||
return out
|
||||
|
||||
class Linear8bitLtOnline(torch.nn.Module):
|
||||
def __init__(
|
||||
self,
|
||||
weight,
|
||||
bias,
|
||||
has_fp16_weights=True,
|
||||
memory_efficient_backward=False,
|
||||
threshold=0.0,
|
||||
index=None,
|
||||
):
|
||||
super().__init__()
|
||||
assert (
|
||||
not memory_efficient_backward
|
||||
), "memory_efficient_backward is no longer required and the argument is deprecated in 0.37.0 and will be removed in 0.39.0"
|
||||
self.state = bnb.MatmulLtState()
|
||||
self.index = index
|
||||
|
||||
# Necessary for stacked layers
|
||||
self.state.threshold = threshold
|
||||
self.state.has_fp16_weights = has_fp16_weights
|
||||
self.state.memory_efficient_backward = memory_efficient_backward
|
||||
if threshold > 0.0 and not has_fp16_weights:
|
||||
self.state.use_pool = True
|
||||
|
||||
self.weight = Int8Params(
|
||||
weight.data,
|
||||
has_fp16_weights=has_fp16_weights,
|
||||
requires_grad=has_fp16_weights,
|
||||
)
|
||||
self.bias = bias
|
||||
|
||||
def init_8bit_state(self):
|
||||
self.state.CB = self.weight.CB
|
||||
self.state.SCB = self.weight.SCB
|
||||
self.weight.CB = None
|
||||
self.weight.SCB = None
|
||||
|
||||
def forward(self, x: torch.Tensor):
|
||||
self.state.is_training = self.training
|
||||
if self.weight.CB is not None:
|
||||
self.init_8bit_state()
|
||||
|
||||
# weights are cast automatically as Int8Params, but the bias has to be cast manually
|
||||
if self.bias is not None and self.bias.dtype != x.dtype:
|
||||
self.bias.data = self.bias.data.to(x.dtype)
|
||||
|
||||
out = bnb.matmul(x, self.weight, bias=self.bias, state=self.state)
|
||||
|
||||
if not self.state.has_fp16_weights:
|
||||
if self.state.CB is not None and self.state.CxB is not None:
|
||||
# we converted 8-bit row major to turing/ampere format in the first inference pass
|
||||
# we no longer need the row-major weight
|
||||
del self.state.CB
|
||||
self.weight.data = self.state.CxB
|
||||
return out
|
||||
|
||||
def quantize_offline(model, bits: int):
|
||||
assert (bits == 4), f'bits: {bits} is not supported'
|
||||
|
||||
for i, layer in enumerate(model.model.layers):
|
||||
layer.self_attn.W_pack = bnb.nn.Linear4bit(
|
||||
layer.self_attn.W_pack.weight.shape[1],
|
||||
layer.self_attn.W_pack.weight.shape[0],
|
||||
False,
|
||||
torch.float16,
|
||||
compress_statistics=True,
|
||||
quant_type="nf4",
|
||||
)
|
||||
layer.self_attn.o_proj = bnb.nn.Linear4bit(
|
||||
layer.self_attn.o_proj.weight.shape[1],
|
||||
layer.self_attn.o_proj.weight.shape[0],
|
||||
False,
|
||||
torch.float16,
|
||||
compress_statistics=True,
|
||||
quant_type="nf4",
|
||||
)
|
||||
|
||||
layer.mlp.gate_proj = bnb.nn.Linear4bit(
|
||||
layer.mlp.gate_proj.weight.shape[1],
|
||||
layer.mlp.gate_proj.weight.shape[0],
|
||||
False,
|
||||
torch.float16,
|
||||
compress_statistics=True,
|
||||
quant_type="nf4",
|
||||
)
|
||||
layer.mlp.down_proj = bnb.nn.Linear4bit(
|
||||
layer.mlp.down_proj.weight.shape[1],
|
||||
layer.mlp.down_proj.weight.shape[0],
|
||||
False,
|
||||
torch.float16,
|
||||
compress_statistics=True,
|
||||
quant_type="nf4",
|
||||
)
|
||||
layer.mlp.up_proj = bnb.nn.Linear4bit(
|
||||
layer.mlp.up_proj.weight.shape[1],
|
||||
layer.mlp.up_proj.weight.shape[0],
|
||||
False,
|
||||
torch.float16,
|
||||
compress_statistics=True,
|
||||
quant_type="nf4",
|
||||
)
|
||||
return model
|
||||
|
||||
def quantize_online(model, bits: int):
|
||||
def quant(weight, bias=None):
|
||||
if bits == 8:
|
||||
linear = Linear8bitLtOnline(
|
||||
weight,
|
||||
bias,
|
||||
has_fp16_weights=False,
|
||||
threshold=6.0,
|
||||
)
|
||||
if bias is not None:
|
||||
linear.bias = torch.nn.Parameter(bias)
|
||||
elif bits == 4:
|
||||
linear = Linear4bitOnline(
|
||||
weight,
|
||||
bias,
|
||||
quant_type="nf4", #fp4/nf4
|
||||
)
|
||||
else:
|
||||
raise ValueError("quantize only support 4/8 bit")
|
||||
return linear
|
||||
|
||||
for i, layer in enumerate(model.model.layers):
|
||||
layer.self_attn.W_pack = quant(layer.self_attn.W_pack.weight)
|
||||
layer.self_attn.o_proj = quant(layer.self_attn.o_proj.weight)
|
||||
layer.mlp.gate_proj = quant(layer.mlp.gate_proj.weight)
|
||||
layer.mlp.down_proj = quant(layer.mlp.down_proj.weight)
|
||||
layer.mlp.up_proj = quant(layer.mlp.up_proj.weight)
|
||||
return model
|
||||
|
||||
def init_model_weight_int4(config, model, state_dict):
|
||||
#replace Params4bit.cuda with Params4bitCuda
|
||||
Params4bit.cuda = Params4bitCuda
|
||||
|
||||
for i in range(config.num_hidden_layers):
|
||||
weight_data = state_dict[f'model.layers.{i}.self_attn.W_pack.weight.data']
|
||||
weight_quant_state = state_dict[f'model.layers.{i}.self_attn.W_pack.weight.quant_state']
|
||||
model.model.layers[i].self_attn.W_pack.weight = Params4bit(weight_data, requires_grad=False, quant_state=weight_quant_state)
|
||||
|
||||
weight_data = state_dict[f'model.layers.{i}.self_attn.o_proj.weight.data']
|
||||
weight_quant_state = state_dict[f'model.layers.{i}.self_attn.o_proj.weight.quant_state']
|
||||
model.model.layers[i].self_attn.o_proj.weight = Params4bit(weight_data, requires_grad=False, quant_state=weight_quant_state)
|
||||
|
||||
weight_data = state_dict[f'model.layers.{i}.mlp.gate_proj.weight.data']
|
||||
weight_quant_state = state_dict[f'model.layers.{i}.mlp.gate_proj.weight.quant_state']
|
||||
model.model.layers[i].mlp.gate_proj.weight = Params4bit(weight_data, requires_grad=False, quant_state=weight_quant_state)
|
||||
|
||||
weight_data = state_dict[f'model.layers.{i}.mlp.up_proj.weight.data']
|
||||
weight_quant_state = state_dict[f'model.layers.{i}.mlp.up_proj.weight.quant_state']
|
||||
model.model.layers[i].mlp.up_proj.weight = Params4bit(weight_data, requires_grad=False, quant_state=weight_quant_state)
|
||||
|
||||
weight_data = state_dict[f'model.layers.{i}.mlp.down_proj.weight.data']
|
||||
weight_quant_state = state_dict[f'model.layers.{i}.mlp.down_proj.weight.quant_state']
|
||||
model.model.layers[i].mlp.down_proj.weight = Params4bit(weight_data, requires_grad=False, quant_state=weight_quant_state)
|
||||
|
||||
model.model.layers[i].input_layernorm.weight = state_dict[f'model.layers.{i}.input_layernorm.weight']
|
||||
model.model.layers[i].post_attention_layernorm.weight = state_dict[f'model.layers.{i}.post_attention_layernorm.weight']
|
||||
|
||||
model.model.embed_tokens.weight = state_dict['model.embed_tokens.weight']
|
||||
model.model.norm.weight = state_dict['model.norm.weight']
|
||||
model.lm_head.weight = state_dict['lm_head.weight']
|
||||
return model
|
||||
|
|
@ -0,0 +1,30 @@
|
|||
{
|
||||
"bos_token": {
|
||||
"content": "<s>",
|
||||
"lstrip": false,
|
||||
"normalized": true,
|
||||
"rstrip": false,
|
||||
"single_word": false
|
||||
},
|
||||
"eos_token": {
|
||||
"content": "</s>",
|
||||
"lstrip": false,
|
||||
"normalized": true,
|
||||
"rstrip": false,
|
||||
"single_word": false
|
||||
},
|
||||
"pad_token": {
|
||||
"content": "<unk>",
|
||||
"lstrip": false,
|
||||
"normalized": true,
|
||||
"rstrip": false,
|
||||
"single_word": false
|
||||
},
|
||||
"unk_token": {
|
||||
"content": "<unk>",
|
||||
"lstrip": false,
|
||||
"normalized": true,
|
||||
"rstrip": false,
|
||||
"single_word": false
|
||||
}
|
||||
}
|
||||
|
|
@ -0,0 +1,253 @@
|
|||
# Copyright 2023 Baichuan Inc. All Rights Reserved.
|
||||
|
||||
# Copyright 2022 EleutherAI and the HuggingFace Inc. team. All rights reserved.
|
||||
#
|
||||
# This code is based on EleutherAI's GPT-NeoX library and the GPT-NeoX
|
||||
# and OPT implementations in this library. It has been modified from its
|
||||
# original forms to accommodate minor architectural differences compared
|
||||
# to GPT-NeoX and OPT used by the Meta AI team that trained the model.
|
||||
#
|
||||
# Licensed under the Apache License, Version 2.0 (the "License");
|
||||
# you may not use this file except in compliance with the License.
|
||||
# You may obtain a copy of the License at
|
||||
#
|
||||
# http://www.apache.org/licenses/LICENSE-2.0
|
||||
#
|
||||
# Unless required by applicable law or agreed to in writing, software
|
||||
# distributed under the License is distributed on an "AS IS" BASIS,
|
||||
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
|
||||
# See the License for the specific language governing permissions and
|
||||
# limitations under the License.
|
||||
|
||||
import os
|
||||
from shutil import copyfile
|
||||
from typing import Any, Dict, List, Optional, Tuple
|
||||
|
||||
import sentencepiece as spm
|
||||
|
||||
from transformers.tokenization_utils import AddedToken, PreTrainedTokenizer
|
||||
from transformers.utils import logging
|
||||
|
||||
|
||||
logger = logging.get_logger(__name__)
|
||||
|
||||
VOCAB_FILES_NAMES = {"vocab_file": "tokenizer.model"}
|
||||
|
||||
PRETRAINED_VOCAB_FILES_MAP = {
|
||||
"vocab_file": {},
|
||||
"tokenizer_file": {},
|
||||
}
|
||||
PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES = {}
|
||||
|
||||
|
||||
class BaichuanTokenizer(PreTrainedTokenizer):
|
||||
"""
|
||||
Construct a Baichuan tokenizer. Based on byte-level Byte-Pair-Encoding.
|
||||
|
||||
Args:
|
||||
vocab_file (`str`):
|
||||
Path to the vocabulary file.
|
||||
"""
|
||||
|
||||
vocab_files_names = VOCAB_FILES_NAMES
|
||||
pretrained_vocab_files_map = PRETRAINED_VOCAB_FILES_MAP
|
||||
max_model_input_sizes = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
|
||||
model_input_names = ["input_ids", "attention_mask"]
|
||||
|
||||
def __init__(
|
||||
self,
|
||||
vocab_file,
|
||||
unk_token="<unk>",
|
||||
bos_token="<s>",
|
||||
eos_token="</s>",
|
||||
pad_token=None,
|
||||
sp_model_kwargs: Optional[Dict[str, Any]] = None,
|
||||
add_bos_token=True,
|
||||
add_eos_token=False,
|
||||
clean_up_tokenization_spaces=False,
|
||||
**kwargs,
|
||||
):
|
||||
self.sp_model_kwargs = {} if sp_model_kwargs is None else sp_model_kwargs
|
||||
bos_token = AddedToken(bos_token, lstrip=False, rstrip=False) if isinstance(bos_token, str) else bos_token
|
||||
eos_token = AddedToken(eos_token, lstrip=False, rstrip=False) if isinstance(eos_token, str) else eos_token
|
||||
unk_token = AddedToken(unk_token, lstrip=False, rstrip=False) if isinstance(unk_token, str) else unk_token
|
||||
pad_token = AddedToken(pad_token, lstrip=False, rstrip=False) if isinstance(pad_token, str) else pad_token
|
||||
|
||||
self.vocab_file = vocab_file
|
||||
self.add_bos_token = add_bos_token
|
||||
self.add_eos_token = add_eos_token
|
||||
self.sp_model = spm.SentencePieceProcessor(**self.sp_model_kwargs)
|
||||
self.sp_model.Load(vocab_file)
|
||||
|
||||
super().__init__(
|
||||
bos_token=bos_token,
|
||||
eos_token=eos_token,
|
||||
unk_token=unk_token,
|
||||
pad_token=pad_token,
|
||||
add_bos_token=add_bos_token,
|
||||
add_eos_token=add_eos_token,
|
||||
sp_model_kwargs=self.sp_model_kwargs,
|
||||
clean_up_tokenization_spaces=clean_up_tokenization_spaces,
|
||||
**kwargs,
|
||||
)
|
||||
|
||||
def __getstate__(self):
|
||||
state = self.__dict__.copy()
|
||||
state["sp_model"] = None
|
||||
return state
|
||||
|
||||
def __setstate__(self, d):
|
||||
self.__dict__ = d
|
||||
self.sp_model = spm.SentencePieceProcessor(**self.sp_model_kwargs)
|
||||
self.sp_model.Load(self.vocab_file)
|
||||
|
||||
@property
|
||||
def vocab_size(self):
|
||||
"""Returns vocab size"""
|
||||
return self.sp_model.get_piece_size()
|
||||
|
||||
def get_vocab(self):
|
||||
"""Returns vocab as a dict"""
|
||||
vocab = {self.convert_ids_to_tokens(i): i for i in range(self.vocab_size)}
|
||||
vocab.update(self.added_tokens_encoder)
|
||||
return vocab
|
||||
|
||||
def _tokenize(self, text):
|
||||
"""Returns a tokenized string."""
|
||||
return self.sp_model.encode(text, out_type=str)
|
||||
|
||||
def _convert_token_to_id(self, token):
|
||||
"""Converts a token (str) in an id using the vocab."""
|
||||
return self.sp_model.piece_to_id(token)
|
||||
|
||||
def _convert_id_to_token(self, index):
|
||||
"""Converts an index (integer) in a token (str) using the vocab."""
|
||||
token = self.sp_model.IdToPiece(index)
|
||||
return token
|
||||
|
||||
def convert_tokens_to_string(self, tokens):
|
||||
"""Converts a sequence of tokens (string) in a single string."""
|
||||
current_sub_tokens = []
|
||||
out_string = ""
|
||||
prev_is_special = False
|
||||
for i, token in enumerate(tokens):
|
||||
# make sure that special tokens are not decoded using sentencepiece model
|
||||
if token in self.all_special_tokens:
|
||||
if not prev_is_special and i != 0:
|
||||
out_string += " "
|
||||
out_string += self.sp_model.decode(current_sub_tokens) + token
|
||||
prev_is_special = True
|
||||
current_sub_tokens = []
|
||||
else:
|
||||
current_sub_tokens.append(token)
|
||||
prev_is_special = False
|
||||
out_string += self.sp_model.decode(current_sub_tokens)
|
||||
return out_string
|
||||
|
||||
def save_vocabulary(self, save_directory, filename_prefix: Optional[str] = None) -> Tuple[str]:
|
||||
"""
|
||||
Save the vocabulary and special tokens file to a directory.
|
||||
|
||||
Args:
|
||||
save_directory (`str`):
|
||||
The directory in which to save the vocabulary.
|
||||
|
||||
Returns:
|
||||
`Tuple(str)`: Paths to the files saved.
|
||||
"""
|
||||
if not os.path.isdir(save_directory):
|
||||
logger.error(f"Vocabulary path ({save_directory}) should be a directory")
|
||||
return
|
||||
out_vocab_file = os.path.join(
|
||||
save_directory, (filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["vocab_file"]
|
||||
)
|
||||
|
||||
if os.path.abspath(self.vocab_file) != os.path.abspath(out_vocab_file) and os.path.isfile(self.vocab_file):
|
||||
copyfile(self.vocab_file, out_vocab_file)
|
||||
elif not os.path.isfile(self.vocab_file):
|
||||
with open(out_vocab_file, "wb") as fi:
|
||||
content_spiece_model = self.sp_model.serialized_model_proto()
|
||||
fi.write(content_spiece_model)
|
||||
|
||||
return (out_vocab_file,)
|
||||
|
||||
def build_inputs_with_special_tokens(self, token_ids_0, token_ids_1=None):
|
||||
bos_token_id = [self.bos_token_id] if self.add_bos_token else []
|
||||
eos_token_id = [self.eos_token_id] if self.add_eos_token else []
|
||||
|
||||
output = bos_token_id + token_ids_0 + eos_token_id
|
||||
|
||||
if token_ids_1 is not None:
|
||||
output = output + bos_token_id + token_ids_1 + eos_token_id
|
||||
|
||||
return output
|
||||
|
||||
def get_special_tokens_mask(
|
||||
self, token_ids_0: List[int], token_ids_1: Optional[List[int]] = None, already_has_special_tokens: bool = False
|
||||
) -> List[int]:
|
||||
"""
|
||||
Retrieve sequence ids from a token list that has no special tokens added. This method is called when adding
|
||||
special tokens using the tokenizer `prepare_for_model` method.
|
||||
|
||||
Args:
|
||||
token_ids_0 (`List[int]`):
|
||||
List of IDs.
|
||||
token_ids_1 (`List[int]`, *optional*):
|
||||
Optional second list of IDs for sequence pairs.
|
||||
already_has_special_tokens (`bool`, *optional*, defaults to `False`):
|
||||
Whether or not the token list is already formatted with special tokens for the model.
|
||||
|
||||
Returns:
|
||||
`List[int]`: A list of integers in the range [0, 1]: 1 for a special token, 0 for a sequence token.
|
||||
"""
|
||||
if already_has_special_tokens:
|
||||
return super().get_special_tokens_mask(
|
||||
token_ids_0=token_ids_0, token_ids_1=token_ids_1, already_has_special_tokens=True
|
||||
)
|
||||
|
||||
bos_token_id = [1] if self.add_bos_token else []
|
||||
eos_token_id = [1] if self.add_eos_token else []
|
||||
|
||||
if token_ids_1 is None:
|
||||
return bos_token_id + ([0] * len(token_ids_0)) + eos_token_id
|
||||
return (
|
||||
bos_token_id
|
||||
+ ([0] * len(token_ids_0))
|
||||
+ eos_token_id
|
||||
+ bos_token_id
|
||||
+ ([0] * len(token_ids_1))
|
||||
+ eos_token_id
|
||||
)
|
||||
|
||||
def create_token_type_ids_from_sequences(
|
||||
self, token_ids_0: List[int], token_ids_1: Optional[List[int]] = None
|
||||
) -> List[int]:
|
||||
"""
|
||||
Creates a mask from the two sequences passed to be used in a sequence-pair classification task. An ALBERT
|
||||
sequence pair mask has the following format:
|
||||
|
||||
```
|
||||
0 0 0 0 0 0 0 0 0 0 0 1 1 1 1 1 1 1 1 1
|
||||
| first sequence | second sequence |
|
||||
```
|
||||
|
||||
if token_ids_1 is None, only returns the first portion of the mask (0s).
|
||||
|
||||
Args:
|
||||
token_ids_0 (`List[int]`):
|
||||
List of ids.
|
||||
token_ids_1 (`List[int]`, *optional*):
|
||||
Optional second list of IDs for sequence pairs.
|
||||
|
||||
Returns:
|
||||
`List[int]`: List of [token type IDs](../glossary#token-type-ids) according to the given sequence(s).
|
||||
"""
|
||||
bos_token_id = [self.bos_token_id] if self.add_bos_token else []
|
||||
eos_token_id = [self.eos_token_id] if self.add_eos_token else []
|
||||
|
||||
output = [0] * len(bos_token_id + token_ids_0 + eos_token_id)
|
||||
|
||||
if token_ids_1 is not None:
|
||||
output += [1] * len(bos_token_id + token_ids_1 + eos_token_id)
|
||||
|
||||
return output
|
||||
Binary file not shown.
|
|
@ -0,0 +1,36 @@
|
|||
{
|
||||
"auto_map": {
|
||||
"AutoTokenizer": ["tokenization_baichuan.BaichuanTokenizer", null]
|
||||
},
|
||||
"add_bos_token": false,
|
||||
"add_eos_token": false,
|
||||
"use_fast": false,
|
||||
"clean_up_tokenization_spaces": false,
|
||||
"eos_token": {
|
||||
"__type": "AddedToken",
|
||||
"content": "</s>",
|
||||
"lstrip": false,
|
||||
"normalized": true,
|
||||
"rstrip": false,
|
||||
"single_word": true
|
||||
},
|
||||
"model_max_length": 4096,
|
||||
"sp_model_kwargs": {},
|
||||
"tokenizer_class": "BaichuanTokenizer",
|
||||
"pad_token": {
|
||||
"__type": "AddedToken",
|
||||
"content": "<unk>",
|
||||
"lstrip": false,
|
||||
"normalized": true,
|
||||
"rstrip": false,
|
||||
"single_word": true
|
||||
},
|
||||
"unk_token": {
|
||||
"__type": "AddedToken",
|
||||
"content": "<unk>",
|
||||
"lstrip": false,
|
||||
"normalized": true,
|
||||
"rstrip": false,
|
||||
"single_word": true
|
||||
}
|
||||
}
|
||||
Loading…
Reference in New Issue