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Model: baichuan-inc/Baichuan2-13B-Base Source: Original Platform
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Baichuan2 模型社区许可协议.pdf
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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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<!-- 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> | <a href="https://github.com/baichuan-inc/Baichuan-7B/blob/main/media/wechat.jpeg?raw=true" target="_blank">💬WeChat</a>
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</div>
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<div align="center">
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百川API支持搜索增强和192K长窗口，新增百川搜索增强知识库、限时免费！<br>
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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/Table of Contents
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- [📖 模型介绍/Introduction](#Introduction)
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- [⚙️ 快速开始/Quick Start](#Start)
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- [📊 Benchmark评估/Benchmark Evaluation](#Benchmark)
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- [📜 声明与协议/Terms and Conditions](#Terms)
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# <span id="Introduction">模型介绍/Introduction</span>
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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，在权威的中文和英文 benchmark
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上均取得同尺寸最好的效果。本次发布包含有 7B、13B 的 Base 和 Chat 版本，并提供了 Chat 版本的 4bits
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量化，所有版本不仅对学术研究完全开放，开发者也仅需[邮件申请]并获得官方商用许可后，即可以免费商用。具体发布版本和下载见下表：
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Baichuan 2 is the new generation of large-scale open-source language models launched by [Baichuan Intelligence inc.](https://www.baichuan-ai.com/).
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It is trained on a high-quality corpus with 2.6 trillion tokens and has achieved the best performance in authoritative Chinese and English benchmarks of the same size.
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This release includes 7B and 13B versions for both Base and Chat models, along with a 4bits quantized version for the Chat model.
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All versions are fully open to academic research, and developers can also use them for free in commercial applications after obtaining an official commercial license through [email request](mailto:opensource@baichuan-inc.com).
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The specific release versions and download links are listed in the table below:
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| | Base Model | Chat Model | 4bits Quantized Chat Model |
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|:---:|:--------------------:|:--------------------:|:--------------------------:|
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| 7B | [Baichuan2-7B-Base](https://huggingface.co/baichuan-inc/Baichuan2-7B-Base) | [Baichuan2-7B-Chat](https://huggingface.co/baichuan-inc/Baichuan2-7B-Chat) | [Baichuan2-7B-Chat-4bits](https://huggingface.co/baichuan-inc/Baichuan2-7B-Base-4bits) |
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| 13B | [Baichuan2-13B-Base](https://huggingface.co/baichuan-inc/Baichuan2-13B-Base) | [Baichuan2-13B-Chat](https://huggingface.co/baichuan-inc/Baichuan2-13B-Chat) | [Baichuan2-13B-Chat-4bits](https://huggingface.co/baichuan-inc/Baichuan2-13B-Chat-4bits) |
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# <span id="Start">快速开始/Quick Start</span>
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在Baichuan2系列模型中，我们为了加快推理速度使用了Pytorch2.0加入的新功能F.scaled_dot_product_attention，因此模型需要在Pytorch2.0环境下运行。
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In the Baichuan 2 series models, we have utilized the new feature `F.scaled_dot_product_attention` introduced in PyTorch 2.0 to accelerate inference speed. Therefore, the model needs to be run in a PyTorch 2.0 environment.
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```python
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import torch
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from transformers import AutoModelForCausalLM, AutoTokenizer
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tokenizer = AutoTokenizer.from_pretrained("baichuan-inc/Baichuan2-13B-Base", use_fast=False, trust_remote_code=True)
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model = AutoModelForCausalLM.from_pretrained("baichuan-inc/Baichuan2-13B-Base", device_map="auto", trust_remote_code=True)
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inputs = tokenizer('登鹳雀楼->王之涣\n夜雨寄北->', return_tensors='pt')
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inputs = inputs.to('cuda:0')
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pred = model.generate(**inputs, max_new_tokens=64, repetition_penalty=1.1)
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print(tokenizer.decode(pred.cpu()[0], skip_special_tokens=True))
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```
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# <span id="Benchmark">Benchmark 结果/Benchmark Evaluation</span>
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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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We have extensively tested the model on authoritative Chinese-English datasets across six domains: [General](https://github.com/baichuan-inc/Baichuan2/blob/main/README_EN.md#general-domain), [Legal](https://github.com/baichuan-inc/Baichuan2/blob/main/README_EN.md#law-and-medicine), [Medical](https://github.com/baichuan-inc/Baichuan2/blob/main/README_EN.md#law-and-medicine), [Mathematics](https://github.com/baichuan-inc/Baichuan2/blob/main/README_EN.md#mathematics-and-code), [Code](https://github.com/baichuan-inc/Baichuan2/blob/main/README_EN.md#mathematics-and-code), and [Multilingual Translation](https://github.com/baichuan-inc/Baichuan2/blob/main/README_EN.md#multilingual-translation). For more detailed evaluation results, please refer to [GitHub](https://github.com/baichuan-inc/Baichuan2/blob/main/README_EN.md).
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### 7B Model Results
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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 Model Results
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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/Training Dynamics
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除了训练了 2.6 万亿 Tokens 的 [Baichuan2-7B-Base](https://huggingface.co/baichuan-inc/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
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（[训练过程checkpoint下载](https://huggingface.co/baichuan-inc/Baichuan2-7B-Intermediate-Checkpoints)）。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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In addition to the [Baichuan2-7B-Base](https://huggingface.co/baichuan-inc/Baichuan2-7B-Base) model trained on 2.6 trillion tokens, we also offer 11 additional intermediate-stage models for community research, corresponding to training on approximately 0.2 to 2.4 trillion tokens each ([Intermediate Checkpoints Download](https://huggingface.co/baichuan-inc/Baichuan2-7B-Intermediate-Checkpoints)). The graph below shows the performance changes of these checkpoints on three benchmarks: C-Eval, MMLU, and CMMLU.
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# <span id="Terms">声明与协议/Terms and Conditions</span>
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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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We hereby declare that our team has not developed any applications based on Baichuan 2 models, not on iOS, Android, the web, or any other platform. We strongly call on all users not to use Baichuan 2 models for any activities that harm national / social security or violate the law. Also, we ask users not to use Baichuan 2 models for Internet services that have not undergone appropriate security reviews and filings. We hope that all users can abide by this principle and ensure that the development of technology proceeds in a regulated and legal environment.
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We have done our best to ensure the compliance of the data used in the model training process. However, despite our considerable efforts, there may still be some unforeseeable issues due to the complexity of the model and data. Therefore, if any problems arise due to the use of Baichuan 2 open-source models, including but not limited to data security issues, public opinion risks, or any risks and problems brought about by the model being misled, abused, spread or improperly exploited, we will not assume any responsibility.
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## 协议
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社区使用 Baichuan 2 模型需要遵循 [Apache 2.0](https://github.com/baichuan-inc/Baichuan2/blob/main/LICENSE) 和[《Baichuan 2 模型社区许可协议》](https://huggingface.co/baichuan-inc/Baichuan2-7B-Base/resolve/main/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)。Baichuan 2 模型支持商业用途，如果您计划将 Baichuan 2 模型或其衍生品用于商业目的，请您确认您的主体符合以下情况：
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1. 您或您的关联方的服务或产品的日均用户活跃量（DAU）低于100万。
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2. 您或您的关联方不是软件服务提供商、云服务提供商。
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3. 您或您的关联方不存在将授予您的商用许可，未经百川许可二次授权给其他第三方的可能。
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在符合以上条件的前提下，您需要通过以下联系邮箱 opensource@baichuan-inc.com ，提交《Baichuan 2 模型社区许可协议》要求的申请材料。审核通过后，百川将特此授予您一个非排他性、全球性、不可转让、不可再许可、可撤销的商用版权许可。
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The community usage of Baichuan 2 model requires adherence to [Apache 2.0](https://github.com/baichuan-inc/Baichuan2/blob/main/LICENSE) and [Community License for Baichuan2 Model](https://huggingface.co/baichuan-inc/Baichuan2-7B-Base/resolve/main/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). The Baichuan 2 model supports commercial use. If you plan to use the Baichuan 2 model or its derivatives for commercial purposes, please ensure that your entity meets the following conditions:
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1. The Daily Active Users (DAU) of your or your affiliate's service or product is less than 1 million.
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2. Neither you nor your affiliates are software service providers or cloud service providers.
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3. There is no possibility for you or your affiliates to grant the commercial license given to you, to reauthorize it to other third parties without Baichuan's permission.
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Upon meeting the above conditions, you need to submit the application materials required by the Baichuan 2 Model Community License Agreement via the following contact email: opensource@baichuan-inc.com. Once approved, Baichuan will hereby grant you a non-exclusive, global, non-transferable, non-sublicensable, revocable commercial copyright license.
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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://huggingface.co/baichuan-inc/Baichuan-7B
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[Baichuan2-7B-Base]:https://huggingface.co/baichuan-inc/Baichuan2-7B-Base
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[Baichuan2-7B-Chat]:https://huggingface.co/baichuan-inc/Baichuan2-7B-Chat
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[Baichuan2-7B-Chat-4bits]:https://huggingface.co/baichuan-inc/Baichuan2-7B-Chat-4bits
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[Baichuan-13B-Base]:https://huggingface.co/baichuan-inc/Baichuan-13B-Base
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[Baichuan2-13B-Base]:https://huggingface.co/baichuan-inc/Baichuan2-13B-Base
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[Baichuan2-13B-Chat]:https://huggingface.co/baichuan-inc/Baichuan2-13B-Chat
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[Baichuan2-13B-Chat-4bits]:https://huggingface.co/baichuan-inc/Baichuan2-13B-Chat-4bits
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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
|
||||||
|
[医疗]:https://github.com/baichuan-inc/Baichuan2#%E6%B3%95%E5%BE%8B%E5%8C%BB%E7%96%97
|
||||||
|
[数学]:https://github.com/baichuan-inc/Baichuan2#%E6%95%B0%E5%AD%A6%E4%BB%A3%E7%A0%81
|
||||||
|
[代码]:https://github.com/baichuan-inc/Baichuan2#%E6%95%B0%E5%AD%A6%E4%BB%A3%E7%A0%81
|
||||||
|
[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
|
||||||
|
|
||||||
|
[《Baichuan 2 模型社区许可协议》]:https://huggingface.co/baichuan-inc/Baichuan2-7B-Base/blob/main/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
|
||||||
|
|
||||||
|
[邮件申请]: mailto:opensource@baichuan-inc.com
|
||||||
|
[Email]: mailto:opensource@baichuan-inc.com
|
||||||
|
[opensource@baichuan-inc.com]: mailto:opensource@baichuan-inc.com
|
||||||
|
[训练过程heckpoint下载]: https://huggingface.co/baichuan-inc/Baichuan2-7B-Intermediate-Checkpoints
|
||||||
|
[百川智能]: https://www.baichuan-ai.com
|
||||||
29
config.json
Normal file
29
config.json
Normal file
@@ -0,0 +1,29 @@
|
|||||||
|
{
|
||||||
|
"_from_model_config": true,
|
||||||
|
"architectures": [
|
||||||
|
"BaichuanForCausalLM"
|
||||||
|
],
|
||||||
|
"auto_map": {
|
||||||
|
"AutoConfig": "configuration_baichuan.BaichuanConfig",
|
||||||
|
"AutoModelForCausalLM": "modeling_baichuan.BaichuanForCausalLM"
|
||||||
|
},
|
||||||
|
"tokenizer_class": "BaichuanTokenizer",
|
||||||
|
"bos_token_id": 1,
|
||||||
|
"eos_token_id": 2,
|
||||||
|
"gradient_checkpointing": false,
|
||||||
|
"hidden_act": "silu",
|
||||||
|
"hidden_size": 5120,
|
||||||
|
"initializer_range": 0.02,
|
||||||
|
"intermediate_size": 13696,
|
||||||
|
"model_max_length": 4096,
|
||||||
|
"model_type": "baichuan",
|
||||||
|
"num_attention_heads": 40,
|
||||||
|
"num_hidden_layers": 40,
|
||||||
|
"pad_token_id": 0,
|
||||||
|
"rms_norm_eps": 1e-06,
|
||||||
|
"tie_word_embeddings": false,
|
||||||
|
"torch_dtype": "bfloat16",
|
||||||
|
"transformers_version": "4.29.2",
|
||||||
|
"use_cache": true,
|
||||||
|
"vocab_size": 125696
|
||||||
|
}
|
||||||
11
configuration.json
Normal file
11
configuration.json
Normal file
@@ -0,0 +1,11 @@
|
|||||||
|
{
|
||||||
|
"framework": "pytorch",
|
||||||
|
"task": "text-generation",
|
||||||
|
"model": {
|
||||||
|
"type": "Baichuan2-13B"
|
||||||
|
},
|
||||||
|
"pipeline": {
|
||||||
|
"type": "Baichuan2-13B-text-generation-pipe"
|
||||||
|
},
|
||||||
|
"allow_remote": true
|
||||||
|
}
|
||||||
48
configuration_baichuan.py
Normal file
48
configuration_baichuan.py
Normal file
@@ -0,0 +1,48 @@
|
|||||||
|
# Copyright (c) 2023, Baichuan Intelligent Technology. All rights reserved.
|
||||||
|
|
||||||
|
from transformers.configuration_utils import PretrainedConfig
|
||||||
|
|
||||||
|
|
||||||
|
class BaichuanConfig(PretrainedConfig):
|
||||||
|
model_type = "baichuan"
|
||||||
|
keys_to_ignore_at_inference = ["past_key_values"]
|
||||||
|
|
||||||
|
def __init__(
|
||||||
|
self,
|
||||||
|
vocab_size=64000,
|
||||||
|
hidden_size=5120,
|
||||||
|
intermediate_size=13696,
|
||||||
|
num_hidden_layers=40,
|
||||||
|
num_attention_heads=40,
|
||||||
|
hidden_act="silu",
|
||||||
|
model_max_length=4096,
|
||||||
|
initializer_range=0.02,
|
||||||
|
rms_norm_eps=1e-6,
|
||||||
|
use_cache=True,
|
||||||
|
pad_token_id=0,
|
||||||
|
bos_token_id=1,
|
||||||
|
eos_token_id=2,
|
||||||
|
tie_word_embeddings=False,
|
||||||
|
gradient_checkpointing=False,
|
||||||
|
z_loss_weight=0,
|
||||||
|
**kwargs,
|
||||||
|
):
|
||||||
|
self.vocab_size = vocab_size
|
||||||
|
self.model_max_length = model_max_length
|
||||||
|
self.hidden_size = hidden_size
|
||||||
|
self.intermediate_size = intermediate_size
|
||||||
|
self.num_hidden_layers = num_hidden_layers
|
||||||
|
self.num_attention_heads = num_attention_heads
|
||||||
|
self.hidden_act = hidden_act
|
||||||
|
self.initializer_range = initializer_range
|
||||||
|
self.rms_norm_eps = rms_norm_eps
|
||||||
|
self.use_cache = use_cache
|
||||||
|
self.z_loss_weight = z_loss_weight
|
||||||
|
self.gradient_checkpointing = (gradient_checkpointing,)
|
||||||
|
super().__init__(
|
||||||
|
pad_token_id=pad_token_id,
|
||||||
|
bos_token_id=bos_token_id,
|
||||||
|
eos_token_id=eos_token_id,
|
||||||
|
tie_word_embeddings=tie_word_embeddings,
|
||||||
|
**kwargs,
|
||||||
|
)
|
||||||
83
generation_utils.py
Normal file
83
generation_utils.py
Normal file
@@ -0,0 +1,83 @@
|
|||||||
|
from typing import List
|
||||||
|
from queue import Queue
|
||||||
|
|
||||||
|
import torch
|
||||||
|
|
||||||
|
|
||||||
|
def build_chat_input(model, tokenizer, messages: List[dict], max_new_tokens: int=0):
|
||||||
|
def _parse_messages(messages, split_role="user"):
|
||||||
|
system, rounds = "", []
|
||||||
|
round = []
|
||||||
|
for i, message in enumerate(messages):
|
||||||
|
if message["role"] == "system":
|
||||||
|
assert i == 0
|
||||||
|
system = message["content"]
|
||||||
|
continue
|
||||||
|
if message["role"] == split_role and round:
|
||||||
|
rounds.append(round)
|
||||||
|
round = []
|
||||||
|
round.append(message)
|
||||||
|
if round:
|
||||||
|
rounds.append(round)
|
||||||
|
return system, rounds
|
||||||
|
|
||||||
|
max_new_tokens = max_new_tokens or model.generation_config.max_new_tokens
|
||||||
|
max_input_tokens = model.config.model_max_length - max_new_tokens
|
||||||
|
system, rounds = _parse_messages(messages, split_role="user")
|
||||||
|
system_tokens = tokenizer.encode(system)
|
||||||
|
max_history_tokens = max_input_tokens - len(system_tokens)
|
||||||
|
|
||||||
|
history_tokens = []
|
||||||
|
for round in rounds[::-1]:
|
||||||
|
round_tokens = []
|
||||||
|
for message in round:
|
||||||
|
if message["role"] == "user":
|
||||||
|
round_tokens.append(model.generation_config.user_token_id)
|
||||||
|
else:
|
||||||
|
round_tokens.append(model.generation_config.assistant_token_id)
|
||||||
|
round_tokens.extend(tokenizer.encode(message["content"]))
|
||||||
|
if len(history_tokens) == 0 or len(history_tokens) + len(round_tokens) <= max_history_tokens:
|
||||||
|
history_tokens = round_tokens + history_tokens # concat left
|
||||||
|
if len(history_tokens) < max_history_tokens:
|
||||||
|
continue
|
||||||
|
break
|
||||||
|
|
||||||
|
input_tokens = system_tokens + history_tokens
|
||||||
|
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
|
||||||
|
|
||||||
829
modeling_baichuan.py
Normal file
829
modeling_baichuan.py
Normal file
@@ -0,0 +1,829 @@
|
|||||||
|
# Copyright (c) 2023, Baichuan Intelligent Technology. All rights reserved.
|
||||||
|
|
||||||
|
from .configuration_baichuan import BaichuanConfig
|
||||||
|
from .generation_utils import build_chat_input, TextIterStreamer
|
||||||
|
|
||||||
|
import math
|
||||||
|
from threading import Thread
|
||||||
|
from typing import List, Optional, Tuple, Union
|
||||||
|
|
||||||
|
import torch
|
||||||
|
from torch import nn
|
||||||
|
from torch.nn import CrossEntropyLoss
|
||||||
|
from torch.nn import functional as F
|
||||||
|
from transformers import PreTrainedModel, PretrainedConfig
|
||||||
|
from transformers.activations import ACT2FN
|
||||||
|
from transformers.generation.utils import GenerationConfig
|
||||||
|
from transformers.modeling_outputs import BaseModelOutputWithPast, CausalLMOutputWithPast
|
||||||
|
from transformers.utils import logging, ContextManagers
|
||||||
|
|
||||||
|
import os
|
||||||
|
from contextlib import contextmanager
|
||||||
|
from accelerate import init_empty_weights
|
||||||
|
|
||||||
|
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."
|
||||||
|
)
|
||||||
|
|
||||||
|
|
||||||
|
def _get_interleave(n):
|
||||||
|
def _get_interleave_power_of_2(n):
|
||||||
|
start = 2 ** (-(2 ** -(math.log2(n) - 3)))
|
||||||
|
ratio = start
|
||||||
|
return [start * ratio**i for i in range(n)]
|
||||||
|
|
||||||
|
if math.log2(n).is_integer():
|
||||||
|
return _get_interleave_power_of_2(n)
|
||||||
|
else:
|
||||||
|
closest_power_of_2 = 2 ** math.floor(math.log2(n))
|
||||||
|
return (
|
||||||
|
_get_interleave_power_of_2(closest_power_of_2)
|
||||||
|
+ _get_interleave(2 * closest_power_of_2)[0::2][: n - closest_power_of_2]
|
||||||
|
)
|
||||||
|
|
||||||
|
|
||||||
|
def _fill_with_neg_inf(t):
|
||||||
|
"""FP16-compatible function that fills a tensor with -inf."""
|
||||||
|
return t.float().fill_(float("-inf")).type_as(t)
|
||||||
|
|
||||||
|
|
||||||
|
def _buffered_future_mask(tensor, maxpos, alibi, attn_heads):
|
||||||
|
_future_mask = torch.triu(_fill_with_neg_inf(torch.zeros([maxpos, maxpos])), 1)
|
||||||
|
_future_mask = _future_mask.unsqueeze(0) + alibi
|
||||||
|
new_future_mask = _future_mask.to(tensor)
|
||||||
|
return new_future_mask[: tensor.shape[0] * attn_heads, :maxpos, :maxpos]
|
||||||
|
|
||||||
|
|
||||||
|
def _gen_alibi_mask(tensor, n_head, max_pos):
|
||||||
|
slopes = torch.Tensor(_get_interleave(n_head))
|
||||||
|
position_point = torch.arange(max_pos) - max_pos + 1
|
||||||
|
position_point = position_point.unsqueeze(0).unsqueeze(0).expand(n_head, -1, -1)
|
||||||
|
diag = torch.diag(position_point[0])
|
||||||
|
position_point = position_point - diag.unsqueeze(0).unsqueeze(0).transpose(-1, -2)
|
||||||
|
alibi = slopes.unsqueeze(1).unsqueeze(1) * position_point
|
||||||
|
alibi = alibi.view(n_head, 1, max_pos)
|
||||||
|
alibi_mask = torch.triu(_fill_with_neg_inf(torch.zeros([max_pos, max_pos])), 1)
|
||||||
|
alibi_mask = alibi_mask.unsqueeze(0) + alibi
|
||||||
|
return alibi_mask
|
||||||
|
|
||||||
|
|
||||||
|
class RMSNorm(torch.nn.Module):
|
||||||
|
def __init__(self, hidden_size, epsilon=1e-6):
|
||||||
|
super().__init__()
|
||||||
|
self.weight = torch.nn.Parameter(torch.empty(hidden_size))
|
||||||
|
self.epsilon = epsilon
|
||||||
|
|
||||||
|
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.epsilon)
|
||||||
|
|
||||||
|
# convert into half-precision
|
||||||
|
if self.weight.dtype in [torch.float16, torch.bfloat16]:
|
||||||
|
hidden_states = hidden_states.to(self.weight.dtype)
|
||||||
|
|
||||||
|
return self.weight * hidden_states
|
||||||
|
|
||||||
|
|
||||||
|
class MLP(torch.nn.Module):
|
||||||
|
def __init__(
|
||||||
|
self,
|
||||||
|
hidden_size: int,
|
||||||
|
intermediate_size: int,
|
||||||
|
hidden_act: str,
|
||||||
|
):
|
||||||
|
super().__init__()
|
||||||
|
self.gate_proj = torch.nn.Linear(hidden_size, intermediate_size, bias=False)
|
||||||
|
self.down_proj = torch.nn.Linear(intermediate_size, hidden_size, bias=False)
|
||||||
|
self.up_proj = torch.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 BaichuanAttention(torch.nn.Module):
|
||||||
|
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.model_max_length
|
||||||
|
|
||||||
|
if (self.head_dim * self.num_heads) != self.hidden_size:
|
||||||
|
raise ValueError(
|
||||||
|
f"hidden_size {self.hidden_size} is not divisible by num_heads {self.num_heads}"
|
||||||
|
)
|
||||||
|
self.W_pack = torch.nn.Linear(
|
||||||
|
self.hidden_size, 3 * self.hidden_size, bias=False
|
||||||
|
)
|
||||||
|
self.o_proj = torch.nn.Linear(
|
||||||
|
self.num_heads * self.head_dim, self.hidden_size, bias=False
|
||||||
|
)
|
||||||
|
|
||||||
|
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,
|
||||||
|
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]
|
||||||
|
|
||||||
|
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=attention_mask
|
||||||
|
# )
|
||||||
|
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)
|
||||||
|
else:
|
||||||
|
attn_weights = torch.matmul(
|
||||||
|
query_states, key_states.transpose(2, 3)
|
||||||
|
) / math.sqrt(self.head_dim)
|
||||||
|
|
||||||
|
if attention_mask is not None:
|
||||||
|
if q_len == 1: # inference with cache
|
||||||
|
if len(attention_mask.size()) == 4:
|
||||||
|
attention_mask = attention_mask[:, :, -1:, :]
|
||||||
|
else:
|
||||||
|
attention_mask = attention_mask[:, -1:, :]
|
||||||
|
attn_weights = attn_weights + attention_mask
|
||||||
|
attn_weights = torch.max(
|
||||||
|
attn_weights, torch.tensor(torch.finfo(attn_weights.dtype).min)
|
||||||
|
)
|
||||||
|
|
||||||
|
attn_weights = torch.nn.functional.softmax(attn_weights, dim=-1)
|
||||||
|
attn_output = torch.matmul(attn_weights, value_states)
|
||||||
|
|
||||||
|
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 BaichuanLayer(torch.nn.Module):
|
||||||
|
def __init__(self, config: BaichuanConfig):
|
||||||
|
super().__init__()
|
||||||
|
self.hidden_size = config.hidden_size
|
||||||
|
self.self_attn = BaichuanAttention(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, epsilon=config.rms_norm_eps)
|
||||||
|
self.post_attention_layernorm = RMSNorm(
|
||||||
|
config.hidden_size, epsilon=config.rms_norm_eps
|
||||||
|
)
|
||||||
|
|
||||||
|
def forward(
|
||||||
|
self,
|
||||||
|
hidden_states: torch.Tensor,
|
||||||
|
attention_mask: Optional[torch.Tensor] = 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,
|
||||||
|
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 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 = ["BaichuanLayer"]
|
||||||
|
_keys_to_ignore_on_load_unexpected = [r"decoder\.version"]
|
||||||
|
|
||||||
|
def _init_weights(self, module):
|
||||||
|
std = self.config.initializer_range
|
||||||
|
if isinstance(module, torch.nn.Linear):
|
||||||
|
module.weight.data.normal_(mean=0.0, std=std)
|
||||||
|
if module.bias is not None:
|
||||||
|
module.bias.data.zero_()
|
||||||
|
elif isinstance(module, torch.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.n_head = config.num_attention_heads
|
||||||
|
self.embed_tokens = torch.nn.Embedding(
|
||||||
|
config.vocab_size, config.hidden_size, self.padding_idx
|
||||||
|
)
|
||||||
|
self.layers = torch.nn.ModuleList(
|
||||||
|
[BaichuanLayer(config) for _ in range(config.num_hidden_layers)]
|
||||||
|
)
|
||||||
|
self.norm = RMSNorm(config.hidden_size, epsilon=config.rms_norm_eps)
|
||||||
|
|
||||||
|
self.gradient_checkpointing = config.gradient_checkpointing
|
||||||
|
self.post_init()
|
||||||
|
self.max_cache_pos = config.model_max_length
|
||||||
|
self.first_run = True
|
||||||
|
self.alibi_mask = None
|
||||||
|
|
||||||
|
def get_input_embeddings(self):
|
||||||
|
return self.embed_tokens
|
||||||
|
|
||||||
|
def set_input_embeddings(self, value):
|
||||||
|
self.embed_tokens = value
|
||||||
|
|
||||||
|
def get_alibi_mask(self, tensor, seq_length_with_past):
|
||||||
|
if self.training:
|
||||||
|
slopes = torch.Tensor(_get_interleave(self.n_head))
|
||||||
|
position_point = (
|
||||||
|
torch.arange(seq_length_with_past) - seq_length_with_past + 1
|
||||||
|
)
|
||||||
|
position_point = (
|
||||||
|
position_point.unsqueeze(0)
|
||||||
|
.unsqueeze(0)
|
||||||
|
.expand(self.n_head, seq_length_with_past, -1)
|
||||||
|
)
|
||||||
|
diag = torch.diag(position_point[0])
|
||||||
|
position_point = position_point - diag.unsqueeze(0).unsqueeze(0).transpose(
|
||||||
|
-1, -2
|
||||||
|
)
|
||||||
|
alibi = slopes.unsqueeze(1).unsqueeze(1) * position_point
|
||||||
|
mask = _buffered_future_mask(
|
||||||
|
tensor, seq_length_with_past, alibi, self.n_head
|
||||||
|
)
|
||||||
|
else:
|
||||||
|
if self.first_run:
|
||||||
|
self.first_run = False
|
||||||
|
self.register_buffer(
|
||||||
|
"future_mask",
|
||||||
|
_gen_alibi_mask(tensor, self.n_head, self.max_cache_pos).to(
|
||||||
|
tensor
|
||||||
|
),
|
||||||
|
persistent=False,
|
||||||
|
)
|
||||||
|
if seq_length_with_past > self.max_cache_pos:
|
||||||
|
self.max_cache_pos = seq_length_with_past
|
||||||
|
self.register_buffer(
|
||||||
|
"future_mask",
|
||||||
|
_gen_alibi_mask(tensor, self.n_head, self.max_cache_pos).to(
|
||||||
|
tensor
|
||||||
|
),
|
||||||
|
persistent=False,
|
||||||
|
)
|
||||||
|
mask = self.future_mask[
|
||||||
|
: self.n_head, :seq_length_with_past, :seq_length_with_past
|
||||||
|
]
|
||||||
|
return mask
|
||||||
|
|
||||||
|
def forward(
|
||||||
|
self,
|
||||||
|
input_ids: torch.LongTensor = None,
|
||||||
|
attention_mask: Optional[torch.Tensor] = None,
|
||||||
|
past_key_values: Optional[List[torch.FloatTensor]] = None,
|
||||||
|
inputs_embeds: Optional[torch.FloatTensor] = None,
|
||||||
|
use_cache: Optional[bool] = False,
|
||||||
|
output_attentions: Optional[bool] = False,
|
||||||
|
output_hidden_states: Optional[bool] = False,
|
||||||
|
return_dict: Optional[bool] = None,
|
||||||
|
) -> Union[Tuple, BaseModelOutputWithPast]:
|
||||||
|
if input_ids is not None and inputs_embeds is not None:
|
||||||
|
raise ValueError(
|
||||||
|
"You cannot provide both input_ids and inputs_embeds simultaneously"
|
||||||
|
)
|
||||||
|
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 need to provide input_ids or inputs_embeds")
|
||||||
|
|
||||||
|
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
|
||||||
|
)
|
||||||
|
|
||||||
|
seq_length_with_past = seq_length
|
||||||
|
|
||||||
|
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 inputs_embeds is None:
|
||||||
|
inputs_embeds = self.embed_tokens(input_ids)
|
||||||
|
|
||||||
|
if self.training:
|
||||||
|
if (
|
||||||
|
self.alibi_mask is None
|
||||||
|
or self.alibi_mask.shape[-1] != seq_length_with_past
|
||||||
|
):
|
||||||
|
self.alibi_mask = self.get_alibi_mask(
|
||||||
|
inputs_embeds, seq_length_with_past
|
||||||
|
)
|
||||||
|
alibi_mask = self.alibi_mask
|
||||||
|
else:
|
||||||
|
alibi_mask = self.get_alibi_mask(inputs_embeds, seq_length_with_past)
|
||||||
|
|
||||||
|
if attention_mask is not None:
|
||||||
|
if len(attention_mask.shape) == 2:
|
||||||
|
expanded_mask = attention_mask.to(alibi_mask.dtype)
|
||||||
|
expanded_mask = torch.tril(
|
||||||
|
torch.gt(expanded_mask[:, :, None] * expanded_mask[:, None, :], 0)
|
||||||
|
) * torch.eq(expanded_mask[:, :, None] - expanded_mask[:, None, :], 0)
|
||||||
|
else:
|
||||||
|
expanded_mask = attention_mask
|
||||||
|
bsz = inputs_embeds.size(0)
|
||||||
|
src_len, tgt_len = alibi_mask.size()[-2:]
|
||||||
|
expanded_mask = (
|
||||||
|
expanded_mask.unsqueeze(1)
|
||||||
|
.expand(bsz, 1, src_len, tgt_len)
|
||||||
|
.to(alibi_mask.dtype)
|
||||||
|
)
|
||||||
|
inverted_mask = 1.0 - expanded_mask
|
||||||
|
inverted_mask = inverted_mask.masked_fill(
|
||||||
|
inverted_mask.to(torch.bool), torch.finfo(alibi_mask.dtype).min
|
||||||
|
)
|
||||||
|
attention_mask = inverted_mask + alibi_mask.unsqueeze(0)
|
||||||
|
else:
|
||||||
|
attention_mask = alibi_mask
|
||||||
|
|
||||||
|
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,
|
||||||
|
None,
|
||||||
|
)
|
||||||
|
else:
|
||||||
|
layer_outputs = decoder_layer(
|
||||||
|
hidden_states,
|
||||||
|
attention_mask=attention_mask,
|
||||||
|
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 = nn.Parameter(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 config.quantization_config['load_in_4bit']:
|
||||||
|
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 quantize_offline 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,
|
||||||
|
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] = False,
|
||||||
|
output_hidden_states: Optional[bool] = False,
|
||||||
|
return_dict: Optional[bool] = None,
|
||||||
|
**kwargs,
|
||||||
|
) -> Union[Tuple, CausalLMOutputWithPast]:
|
||||||
|
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,
|
||||||
|
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 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 prepare_inputs_for_generation(
|
||||||
|
self,
|
||||||
|
input_ids: torch.LongTensor,
|
||||||
|
past_key_values: Optional[torch.Tensor] = None,
|
||||||
|
attention_mask: Optional[torch.Tensor] = None,
|
||||||
|
inputs_embeds: Optional[torch.Tensor] = None,
|
||||||
|
**kwargs,
|
||||||
|
):
|
||||||
|
if past_key_values:
|
||||||
|
input_ids = input_ids[:, -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(
|
||||||
|
{
|
||||||
|
"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):
|
||||||
|
return tuple(
|
||||||
|
tuple(past_state.index_select(0, beam_idx) for past_state in layer_past)
|
||||||
|
for layer_past in past_key_values
|
||||||
|
)
|
||||||
|
|
||||||
|
def _build_chat_input(
|
||||||
|
self, tokenizer, messages: List[dict], max_new_tokens: int = 0
|
||||||
|
):
|
||||||
|
max_new_tokens = max_new_tokens or self.generation_config.max_new_tokens
|
||||||
|
max_input_tokens = self.config.model_max_length - max_new_tokens
|
||||||
|
max_input_tokens = max(self.config.model_max_length // 2, max_input_tokens)
|
||||||
|
total_input, round_input = [], []
|
||||||
|
for i, message in enumerate(messages[::-1]):
|
||||||
|
content_tokens = tokenizer.encode(message["content"])
|
||||||
|
if message["role"] == "user":
|
||||||
|
round_input = (
|
||||||
|
[self.generation_config.user_token_id]
|
||||||
|
+ content_tokens
|
||||||
|
+ round_input
|
||||||
|
)
|
||||||
|
if (
|
||||||
|
total_input
|
||||||
|
and len(total_input) + len(round_input) > max_input_tokens
|
||||||
|
):
|
||||||
|
break
|
||||||
|
else:
|
||||||
|
total_input = round_input + total_input
|
||||||
|
if len(total_input) >= max_input_tokens:
|
||||||
|
break
|
||||||
|
else:
|
||||||
|
round_input = []
|
||||||
|
elif message["role"] == "assistant":
|
||||||
|
round_input = (
|
||||||
|
[self.generation_config.assistant_token_id]
|
||||||
|
+ content_tokens
|
||||||
|
+ [self.generation_config.eos_token_id]
|
||||||
|
+ round_input
|
||||||
|
)
|
||||||
|
else:
|
||||||
|
raise ValueError(f"message role not supported yet: {message['role']}")
|
||||||
|
total_input = total_input[-max_input_tokens:] # truncate left
|
||||||
|
total_input.append(self.generation_config.assistant_token_id)
|
||||||
|
total_input = torch.LongTensor([total_input]).to(self.device)
|
||||||
|
return total_input
|
||||||
|
|
||||||
|
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
|
||||||
3
pytorch_model-00001-of-00003.bin
Normal file
3
pytorch_model-00001-of-00003.bin
Normal file
@@ -0,0 +1,3 @@
|
|||||||
|
version https://git-lfs.github.com/spec/v1
|
||||||
|
oid sha256:015ac5047b08f017dccd0c509c7425d17932d33af28affe170ada3c96c0c5041
|
||||||
|
size 9973568983
|
||||||
3
pytorch_model-00002-of-00003.bin
Normal file
3
pytorch_model-00002-of-00003.bin
Normal file
@@ -0,0 +1,3 @@
|
|||||||
|
version https://git-lfs.github.com/spec/v1
|
||||||
|
oid sha256:37f8f5e13e4a4e4d90a94f527c0ff89b10c78bd0f78af6f7c0bea77f4203714b
|
||||||
|
size 9947421360
|
||||||
3
pytorch_model-00003-of-00003.bin
Normal file
3
pytorch_model-00003-of-00003.bin
Normal file
@@ -0,0 +1,3 @@
|
|||||||
|
version https://git-lfs.github.com/spec/v1
|
||||||
|
oid sha256:6fcde694f470709e24b7fa4c8a1939d798c60bdb9ad6f3ab096c4729a16074b4
|
||||||
|
size 7872446643
|
||||||
3
pytorch_model.bin.index.json
Normal file
3
pytorch_model.bin.index.json
Normal file
@@ -0,0 +1,3 @@
|
|||||||
|
version https://git-lfs.github.com/spec/v1
|
||||||
|
oid sha256:3106a4c80764eef23c6bfec7fa8593b996c614130c55a1a3ccac9966132842b6
|
||||||
|
size 23274
|
||||||
211
quantizer.py
Normal file
211
quantizer.py
Normal file
@@ -0,0 +1,211 @@
|
|||||||
|
import bitsandbytes as bnb
|
||||||
|
from accelerate import init_empty_weights
|
||||||
|
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
|
||||||
30
special_tokens_map.json
Normal file
30
special_tokens_map.json
Normal file
@@ -0,0 +1,30 @@
|
|||||||
|
{
|
||||||
|
"bos_token": {
|
||||||
|
"content": "<s>",
|
||||||
|
"lstrip": false,
|
||||||
|
"normalized": true,
|
||||||
|
"rstrip": false,
|
||||||
|
"single_word": true
|
||||||
|
},
|
||||||
|
"eos_token": {
|
||||||
|
"content": "</s>",
|
||||||
|
"lstrip": false,
|
||||||
|
"normalized": true,
|
||||||
|
"rstrip": false,
|
||||||
|
"single_word": true
|
||||||
|
},
|
||||||
|
"unk_token": {
|
||||||
|
"content": "<unk>",
|
||||||
|
"lstrip": false,
|
||||||
|
"normalized": true,
|
||||||
|
"rstrip": false,
|
||||||
|
"single_word": true
|
||||||
|
},
|
||||||
|
"pad_token": {
|
||||||
|
"content": "<unk>",
|
||||||
|
"lstrip": false,
|
||||||
|
"normalized": true,
|
||||||
|
"rstrip": false,
|
||||||
|
"single_word": true
|
||||||
|
}
|
||||||
|
}
|
||||||
260
tokenization_baichuan.py
Normal file
260
tokenization_baichuan.py
Normal file
@@ -0,0 +1,260 @@
|
|||||||
|
# Copyright (c) 2023, Baichuan Intelligent Technology. All rights reserved.
|
||||||
|
|
||||||
|
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
|
||||||
3
tokenizer.model
Normal file
3
tokenizer.model
Normal file
@@ -0,0 +1,3 @@
|
|||||||
|
version https://git-lfs.github.com/spec/v1
|
||||||
|
oid sha256:79452955be6b419a65984273a9f08af86042e1c2a75ee3ba989cbf620a133cc2
|
||||||
|
size 2001107
|
||||||
46
tokenizer_config.json
Normal file
46
tokenizer_config.json
Normal file
@@ -0,0 +1,46 @@
|
|||||||
|
{
|
||||||
|
"add_bos_token": false,
|
||||||
|
"add_eos_token": false,
|
||||||
|
"auto_map": {
|
||||||
|
"AutoTokenizer": [
|
||||||
|
"tokenization_baichuan.BaichuanTokenizer",
|
||||||
|
null
|
||||||
|
]
|
||||||
|
},
|
||||||
|
"bos_token": {
|
||||||
|
"__type": "AddedToken",
|
||||||
|
"content": "<s>",
|
||||||
|
"lstrip": false,
|
||||||
|
"normalized": true,
|
||||||
|
"rstrip": false,
|
||||||
|
"single_word": true
|
||||||
|
},
|
||||||
|
"clean_up_tokenization_spaces": false,
|
||||||
|
"eos_token": {
|
||||||
|
"__type": "AddedToken",
|
||||||
|
"content": "</s>",
|
||||||
|
"lstrip": false,
|
||||||
|
"normalized": true,
|
||||||
|
"rstrip": false,
|
||||||
|
"single_word": true
|
||||||
|
},
|
||||||
|
"model_max_length": 4096,
|
||||||
|
"pad_token": {
|
||||||
|
"__type": "AddedToken",
|
||||||
|
"content": "<unk>",
|
||||||
|
"lstrip": false,
|
||||||
|
"normalized": true,
|
||||||
|
"rstrip": false,
|
||||||
|
"single_word": true
|
||||||
|
},
|
||||||
|
"sp_model_kwargs": {},
|
||||||
|
"tokenizer_class": "BaichuanTokenizer",
|
||||||
|
"unk_token": {
|
||||||
|
"__type": "AddedToken",
|
||||||
|
"content": "<unk>",
|
||||||
|
"lstrip": false,
|
||||||
|
"normalized": true,
|
||||||
|
"rstrip": false,
|
||||||
|
"single_word": true
|
||||||
|
}
|
||||||
|
}
|
||||||
Reference in New Issue
Block a user