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+ Bilibili Index 许可协议
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+ 版本 1.0,2024 年 6 月 11 日
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+ 版权所有 (c) 2024 Bilibili Index
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+ 第一部分:前言
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+ 大型生成模型正在被广泛采用和使用,但也存在对其潜在滥用的担忧,无论是由于其技术限制还是伦理考虑。本许可证旨在促进所附模型的开放和负责任的下游使用。
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+ 因此,现在您和 Bilibili Index 同意如下:
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+ 1. 定义
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+ “许可证”是指本文件中定义的使用、复制和分发的条款和条件。
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+ “数据”是指从与模型一起使用的数据集提取的信息和/或内容的集合,包括用于训练、预训练或以其他方式评估模型的数据。数据不受本许可证的许可。
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+ “输出”是指操作模型的结果,以由此产生的信息内容体现。
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+ “模型”是指任何伴随的机器学习基础组件(包括检查点),由学习的权重、参数(包括优化器状态)组成,对应于补充材料中体现的模型架构,这些权重、参数是在数据上使用补充材料进行训练或调整的,全部或部分。
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+ “模型的衍生品”是指对模型的所有修改、基于模型的作品或任何其他通过将模型的权重、参数、激活或输出的模式转移到另一个模型而创建或初始化的模型,以便使另一个模型的性能类似于模型,包括但不限于涉及使用中间数据表示的蒸馏方法或基于模型生成合成数据用于训练另一个模型的方法。
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+ “补充材料”是指用于定义、运行、加载、基准测试或评估模型的伴随源代码和脚本,如果有,还包括用于准备数据进行训练或评估的任何伴随文档、教程、示例等。
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+ “分发”是指将模型或模型的衍生物传输、复制、发布或以其他方式共享给第三方,包括通过电子或其他远程方式提供模型作为托管服务 - 例如基于 API 或 Web 访问。
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+ “Bilibili Index”(或“我们”)是指上海宽娱数码科技有限公司或其任何关联公司。
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+ “您”(或“您的”)是指行使本许可证授予的权限并/或出于任何目的和在任何使用领域使用模型的个人或法律实体,包括在最终使用应用程序(例如聊天机器人、翻译器等)中使用模型。
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+ “第三方”是指与 Bilibili Index 或您没有共同控制的个人或法律实体。
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+ “商业用途”是指使用 Bilibili Index 模型,直接或间接为实体或个人进行运营、推广或产生收入,或用于任何其他盈利目的。
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+
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+ 第二部分、许可及许可限制
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+ 根据本许可协议的条款和条件,许可方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、免版税的版权许可。您可以出于非商业用途使用此许可。许可方对您使用 Bilibili Index 模型的输出或基于 Bilibili Index 模型得到的模型衍生品不主张任何权利,但您必须满足如下许可限制条件:
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+ 1. 您不得出于任何军事或非法目的使用、复制、修改、合并、发布、分发、复制或创建Bilibili Index 模型的全部或部分衍生品。
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+ 2. 如果您计划将 Bilibili Index 模型及模型衍生品用作商业用途,应当按照本协议附则提供的联络方式,事先向许可方登记并获得许可方的书面授权。
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+ 3. 您对 Bilibili Index 模型的使用和修改(包括使用 Bilibili Index 模型的输出或者基于 Bilibili Index 模型得到的模型衍生品)不得违反任何国家的法律法规,尤其是中华人民共和国的法律法规,不得侵犯任何第三方的合法权益,包括但不限于肖像权、名誉权、隐私权等人格权,著作权、专利权、商业秘密等知识产权,或者其他财产权益。
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+ 4. 您必须向 Bilibili Index 模型或其模型衍生品的任何第三方使用者提供 Bilibili Index 模型的来源以及本协议的副本。
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+ 5. 您修改 Bilibili Index 模型得到模型衍生品,必须以显著的方式说明修改的内容,且上述修改不得违反本协议的许可限制条件,也不能允许、协助或以其他方式使得第三方违反本协议中的许可限制条件。
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+
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+ 第三部分:知识产权
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+ 1. Bilibili Index 模型的所有权及其相关知识产权,由许可方单独所有。
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+ 2. 在任何情况下,未经许可方事先书面同意,您不得使用许可方任何商标、服务标记、商号、域名、网站名称或其他显著品牌特征(以下统称为"标识"),包括但不限于明示或暗示您自身为“许可方”。未经许可方事先书面同意,您不得将本条款前述标识以单独或结合的任何方式展示、使用或申请注册商标、进行域名注册等,也不得向他人明示或暗示有权展示、使用、或以其他方式处理这些标识的权利。由于您违反本协议使用许可方上述标识等给许可方或他人造成损失的,由您承担全部法律责任。
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+ 3. 在许可范围内,您可以对 Bilibili Index 模型进行修改以得到模型衍生品,对于模型衍生��中您付出创造性劳动的部分,您可以主张该部分的知识产权。
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+
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+ 第四部分、免责声明及责任限制
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+ 1. 在任何情况下,许可方不对您根据本协议使用 Bilibili Index 模型而产生或与之相关的任何直接、间接、附带的后果、以及其他损失或损害承担责任。若由此导致许可方遭受损失,您应当向许可方承担全部赔偿责任。
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+ 2. 模型中的模型参数仅仅是一种示例,如果您需要满足其他要求,需自行训练,并遵守相应数据集的许可协议。您将对 Bilibili Index 模型的输出及模型衍生品所涉及的知识产权风险或与之相关的任何直接、间接、附带的后果、以及其他损失或损害负责。
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+ 3. 尽管许可方在 Bilibili Index 模型训练的所有阶段,都坚持努力维护数据的合规性和准确性,但受限于 Bilibili Index 模型的规模及其概率固有的随机性因素影响,其输出结果的准确性无法得到保证,模型存在被误导的可能。因此,许可方在此声明,许可方不承担您因使用 Bilibili Index 模型及其源代码而导致的数据安全问题、声誉风险,或任何涉及 Bilibili Index 模型被误导、误用、传播或不正当使用而产生的任何风险和责任。
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+ 4. 本协议所称损失或损害包括但不限于下列任何损失或损害(无论此类损失或损害是不可预见的、可预见的、已知的或其他的):(i)收入损失;(ii)实际或预期利润损失;(ii)货币使用损失;(iv)预期节约的损失;(v)业务损失;(vi)机会损失;(vii)商誉、声誉损失;(viii)软件的使用损失;或(x)任何间接、附带的特殊或间接损害损失。
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+ 5. 除非适用的法律另有要求或经过许可方书面同意,否则许可方将按“现状”授予Bilibili Index 模型的许可。针对本协议中的 Bilibili Index 模型,许可方不提供任何明示、暗示的保证,包括但不限于:关于所有权的任何保证或条件、关于适销性的保证或条件、适用于任何特定目的的保证或条件、过去、现在或未来关于 Bilibili Index 模型不侵权的任何类型的保证、以及因任何交易过程、贸易使用(如建议书、规范或样品)而产生的任何保证。您将对其通过使用、复制或再分发等方式利用 Bilibili Index 模型所产生的风险与后果,独自承担责任。
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+ 6. 您充分知悉并理解同意,Bilibili Index 模型中可能包含个人信息。您承诺将遵守所有适用的法律法规进行个人信息的处理,特别是遵守《中华人民共和国个人信息保护法》的相关规定。请注意,许可方给予您使用 Bilibili Index 模型的授权,并不意味着您已经获得处理相关个人信息的合法性基础。您作为独立的个人信息处理者,需要保证在处理 Bilibili Index 模型中可能包含的个人信息时,完全符合相关法律法规的要求,包括但不限于获得个人信息主体的授权同意等,并愿意独自承担由此可能产生的任何风险和后果。
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+ 7. 您充分理解并同意,许可方有权依合理判断对违反有关法律法规或本协议规定的行为进行处理,对您的违法违规行为采取适当的法律行动,并依据法律法规保存有关信息向有关部门报告等,您应独自承担由此而产生的一切法律责任。
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+
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+ 第五部分、品牌曝光与显著标识
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+ 1. 您同意并理解,如您将您基于 Bilibili Index 模型二次开发的模型衍生品在国内外的开源社区提供开源许可的,您需要在该开源社区以显著方式标注该模型衍生品系基于 Bilibili Index 模型进行的二次开发,标注内容包括但不限于“Bilibili Index ”以及与 Bilibili Index 模型相关的品牌的其他元素。
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+ 2. 您同意并理解,如您将 Bilibili Index 模型二次开发的模型衍生品参加国内外任何组织和个人举行的排名活动,包括但不限于针对模型性能、准确度、算法、算力等任何维度的排名活动,您均需在模型说明中以显著方式标注该模型衍生品系基于 Bilibili Index 模型进行的二次开发,标注内容包括但不限于“Blibili Index Inside”以及与 Bilibili Index 模型相关的品牌的其他元素。
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+
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+ 七、其他
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+ 1. 许可方在法律法规许可的范围内对协议条款享有最终解释权。
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+ 2. 本协议的订立、效力、解释、履行、修改和终止,使用 Bilibili Index 模型以及争议的解决均适用中华人民共和国大陆地区(仅为本协议之目的,不包括香港、澳门和台湾)法律,
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+ 并排除冲突法的适用。
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+ 3. 因使用 Bilibili Index 模型而发生的任何争议,各方应首先通过友好协商的方式加以解决。协商不成时,向许可方所在地人民法院提起诉讼。
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+
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+ 附则
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+ 1. 若您期望基于本协议的许可条件与限制,将 Bilibili Index 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��书面授权:联系邮箱:opensource@bilibili.com
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+
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+ 附件 A 使用限制
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+ 您同意不使用模型或模型的衍生物:
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+ 以任何违反任何适用的国家或国际法律或法规或侵犯任何第三方合法权益的方式;
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+ 用于任何军事目的;
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+ 以任何方式用于剥削、伤害或企图剥削或伤害未成年人;
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+ 生成或传播可验证的虚假信息和/或内容,意图伤害他人;
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+ 生成或传播受适用监管要求限制的不适当内容;
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+ 在未经适当授权或不合理使用的情况下生成或传播个人可识别信息;
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+ 诽谤、贬低或以其他方式骚扰他人;
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+ 用于对个人的法律权利产生不利影响或创建或修改具有约束力的可执行义务的完全自动化决策;
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+ 用于基于在线或离线社会行为或已知或预测的个人或个性特征对个人或群体进行歧视或伤害的任何目的;
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+ 为了对特定群体的个人造成或可能造成身体或心理伤害,利用该群体的年龄、社会、身体或心理特征的任何漏洞,从而严重扭曲属于该群体的个人的行为;
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+ 用于任何旨在或具有基于法律保护的特征或类别对个人或群体进行歧视的目的
README.md ADDED
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+ ---
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+ license: other
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+ license_name: license
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+ license_link: LICENSE
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+ ---
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+ <div align="center">
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+ <h1>
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+ Index-1.9B-Chat
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+ </h1>
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+ </div>
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+
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+ ## 模型介绍
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+
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+ 我们很高兴首次发布Index系列模型中的轻量版本:Index-1.9B系列
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+ 本次开源的Index-1.9B 系列包含以下模型:
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+ - Index-1.9B base : 基座模型,具有 19亿 非词嵌入参数量,在2.8T 中英文为主的语料上预训练,多个评测基准上与同级别模型比处于领先。
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+ - Index-1.9B pure : 基座模型的对照组,与base具有相同的参数和训练策略,不同之处在于我们严格过滤了该版本语料中所有指令相关的数据,以此来验证指令对benchmark的影响。
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+ - **Index-1.9B chat(本仓库模型)** : 基于index-1.9B base通过SFT和DPO对齐后的对话模型,我们发现由于预训练中引入了较多定向清洗的对话类语料,聊天的趣味性明显更强。
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+ - Index-1.9B character : 在SFT和DPO的基础上引入了RAG来实现fewshots角色扮演定制。
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+
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+
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+ 更多细节详见我们的[GitHub](https://github.com/bilibili/Index-1.9B)和[Index-1.9B技术报告](https://github.com/bilibili/Index-1.9B/Index-1.9B技术报告.pdf)
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+
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+ ### Transformers 加载方式
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+
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+ 可通过以下代码加载 Index-1.9B-Chat 模型来进行对话:
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+
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+ ```python
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+ import torch
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+ import argparse
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+ from transformers import AutoTokenizer, AutoModelForCausalLM
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+
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+ # 注意!目录不能含有".",可以替换成"_"
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+ parser = argparse.ArgumentParser()
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+ parser.add_argument('--model_path', default="", type=str, help="")
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+ args = parser.parse_args()
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+
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+ tokenizer = AutoTokenizer.from_pretrained(args.model_path, trust_remote_code=True)
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+ model = AutoModelForCausalLM.from_pretrained(args.model_path, torch_dtype=torch.bfloat16, trust_remote_code=True, device_map='auto')
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+ model = model.eval()
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+ print('model loaded', args.model_path, model.device)
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+
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+ system_message = "你是由哔哩哔哩人工智能平台部自主研发的大语言模型,名为“Index”。你能够根据用户传入的信息,帮助用户完成指定的任务,并生成恰当的、符合要求的回复。"
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+ query = "续写 天不生我金坷垃"
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+ model_input = []
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+ model_input.append({"role": "system", "content": system_message})
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+ model_input.append({"role": "user", "content": query})
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+
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+ inputs = tokenizer.apply_chat_template(model_input, tokenize=False, add_generation_prompt=False)
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+ input_ids = tokenizer.encode(inputs, return_tensors="pt").to(model.device)
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+ history_outputs = model.generate(input_ids, max_new_tokens=300, top_k=5, top_p=0.8, temperature=0.3, repetition_penalty=1.1, do_sample=True)
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+
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+ # 删除</s>
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+ if history_outputs[0][-1] == 2:
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+ history_outputs = history_outputs[:, :-1]
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+
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+ outputs = history_outputs[0][len(input_ids[0]):]
58
+ print('User:', query)
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+ print('\nModel:', tokenizer.decode(outputs))
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+ ```
config.json ADDED
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+ {
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+ "_name_or_path": "Index_1_9B",
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+ "architectures": [
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+ "LlamaForCausalLM"
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+ ],
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+ "auto_map": {
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+ "AutoConfig": "configuration_index.IndexConfig",
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+ "AutoModelForCausalLM": "modeling_index.IndexForCausalLM"
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+ },
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+ "bos_token_id": 1,
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+ "eos_token_id": 2,
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+ "pad_token_id": 0,
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+ "hidden_act": "silu",
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+ "hidden_size": 2048,
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+ "initializer_range": 0.01,
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+ "intermediate_size": 5888,
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+ "max_length": 4096,
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+ "max_position_embeddings": 4096,
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+ "model_type": "index",
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+ "num_attention_heads": 16,
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+ "num_key_value_heads": 16,
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+ "num_hidden_layers": 36,
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+ "rms_norm_eps": 1e-06,
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+ "rope_scaling": null,
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+ "tie_word_embeddings": false,
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+ "norm_head":1,
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+ "torch_dtype": "bfloat16",
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+ "transformers_version": "4.39.2",
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+ "use_cache": true,
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+ "vocab_size": 65029
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+ }
configuration_index.py ADDED
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+ # coding=utf-8
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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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+ """ Index model configuration"""
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+
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+ from transformers.configuration_utils import PretrainedConfig
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+ from transformers.utils import logging
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+
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+
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+ logger = logging.get_logger(__name__)
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+
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+ INDEX_PRETRAINED_CONFIG_ARCHIVE_MAP = {}
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+
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+
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+ class IndexConfig(PretrainedConfig):
32
+ r"""
33
+ Configuration objects inherit from [`PretrainedConfig`] and can be used to control the model outputs. Read the
34
+ documentation from [`PretrainedConfig`] for more information.
35
+
36
+
37
+ Args:
38
+ vocab_size (`int`, *optional*, defaults to 65029):
39
+ Vocabulary size of the Index model. Defines the number of different tokens that can be represented by the
40
+ `inputs_ids` passed when calling [`IndexModel`]
41
+ hidden_size (`int`, *optional*, defaults to 4096):
42
+ Dimension of the hidden representations.
43
+ intermediate_size (`int`, *optional*, defaults to 11008):
44
+ Dimension of the MLP representations.
45
+ num_hidden_layers (`int`, *optional*, defaults to 32):
46
+ Number of hidden layers in the Transformer decoder.
47
+ num_attention_heads (`int`, *optional*, defaults to 32):
48
+ Number of attention heads for each attention layer in the Transformer decoder.
49
+ num_key_value_heads (`int`, *optional*):
50
+ This is the number of key_value heads that should be used to implement Grouped Query Attention. If
51
+ `num_key_value_heads=num_attention_heads`, the model will use Multi Head Attention (MHA), if
52
+ `num_key_value_heads=1 the model will use Multi Query Attention (MQA) otherwise GQA is used. When
53
+ converting a multi-head checkpoint to a GQA checkpoint, each group key and value head should be constructed
54
+ by meanpooling all the original heads within that group. For more details checkout [this
55
+ paper](https://arxiv.org/pdf/2305.13245.pdf). If it is not specified, will default to
56
+ `num_attention_heads`.
57
+ hidden_act (`str` or `function`, *optional*, defaults to `"silu"`):
58
+ The non-linear activation function (function or string) in the decoder.
59
+ max_position_embeddings (`int`, *optional*, defaults to 2048):
60
+ The maximum sequence length that this model might ever be used with. Index 1 supports up to 2048 tokens,
61
+ Index 2 up to 4096, CodeIndex up to 16384.
62
+ initializer_range (`float`, *optional*, defaults to 0.02):
63
+ The standard deviation of the truncated_normal_initializer for initializing all weight matrices.
64
+ rms_norm_eps (`float`, *optional*, defaults to 1e-06):
65
+ The epsilon used by the rms normalization layers.
66
+ use_cache (`bool`, *optional*, defaults to `True`):
67
+ Whether or not the model should return the last key/values attentions (not used by all models). Only
68
+ relevant if `config.is_decoder=True`.
69
+ pad_token_id (`int`, *optional*):
70
+ Padding token id.
71
+ bos_token_id (`int`, *optional*, defaults to 1):
72
+ Beginning of stream token id.
73
+ eos_token_id (`int`, *optional*, defaults to 2):
74
+ End of stream token id.
75
+ pretraining_tp (`int`, *optional*, defaults to 1):
76
+ Experimental feature. Tensor parallelism rank used during pretraining. Please refer to [this
77
+ document](https://huggingface.co/docs/transformers/main/perf_train_gpu_many#tensor-parallelism) to understand more about it. This value is
78
+ necessary to ensure exact reproducibility of the pretraining results. Please refer to [this
79
+ issue](https://github.com/pytorch/pytorch/issues/76232).
80
+ tie_word_embeddings (`bool`, *optional*, defaults to `False`):
81
+ Whether to tie weight embeddings
82
+ rope_theta (`float`, *optional*, defaults to 10000.0):
83
+ The base period of the RoPE embeddings.
84
+ rope_scaling (`Dict`, *optional*):
85
+ Dictionary containing the scaling configuration for the RoPE embeddings. Currently supports two scaling
86
+ strategies: linear and dynamic. Their scaling factor must be a float greater than 1. The expected format is
87
+ `{"type": strategy name, "factor": scaling factor}`. When using this flag, don't update
88
+ `max_position_embeddings` to the expected new maximum. See the following thread for more information on how
89
+ these scaling strategies behave
90
+ attention_bias (`bool`, defaults to `False`, *optional*, defaults to `False`):
91
+ Whether to use a bias in the query, key, value and output projection layers during self-attention.
92
+ attention_dropout (`float`, *optional*, defaults to 0.0):
93
+ The dropout ratio for the attention probabilities.
94
+
95
+ ```python
96
+ >>> from transformers import IndexModel, IndexConfig
97
+
98
+ >>> configuration = IndexConfig()
99
+ >>> model = IndexModel(configuration)
100
+ >>> configuration = model.config
101
+ ```"""
102
+
103
+ model_type = "index"
104
+ keys_to_ignore_at_inference = ["past_key_values"]
105
+
106
+ def __init__(
107
+ self,
108
+ vocab_size=65029,
109
+ hidden_size=4096,
110
+ intermediate_size=11008,
111
+ num_hidden_layers=32,
112
+ num_attention_heads=32,
113
+ num_key_value_heads=None,
114
+ hidden_act="silu",
115
+ max_position_embeddings=2048,
116
+ initializer_range=0.02,
117
+ rms_norm_eps=1e-6,
118
+ use_cache=True,
119
+ pad_token_id=None,
120
+ bos_token_id=1,
121
+ eos_token_id=2,
122
+ pretraining_tp=1,
123
+ tie_word_embeddings=False,
124
+ norm_head=False,
125
+ rope_theta=10000.0,
126
+ rope_scaling=None,
127
+ attention_bias=False,
128
+ attention_dropout=0.0,
129
+ **kwargs,
130
+ ):
131
+ self.vocab_size = vocab_size
132
+ self.max_position_embeddings = max_position_embeddings
133
+ self.hidden_size = hidden_size
134
+ self.intermediate_size = intermediate_size
135
+ self.num_hidden_layers = num_hidden_layers
136
+ self.num_attention_heads = num_attention_heads
137
+
138
+ # for backward compatibility
139
+ if num_key_value_heads is None:
140
+ num_key_value_heads = num_attention_heads
141
+
142
+ self.num_key_value_heads = num_key_value_heads
143
+ self.hidden_act = hidden_act
144
+ self.initializer_range = initializer_range
145
+ self.rms_norm_eps = rms_norm_eps
146
+ self.pretraining_tp = pretraining_tp
147
+ self.use_cache = use_cache
148
+ self.rope_theta = rope_theta
149
+ self.rope_scaling = rope_scaling
150
+ self._rope_scaling_validation()
151
+ self.attention_bias = attention_bias
152
+ self.attention_dropout = attention_dropout
153
+
154
+ self.norm_head = norm_head
155
+
156
+ super().__init__(
157
+ pad_token_id=pad_token_id,
158
+ bos_token_id=bos_token_id,
159
+ eos_token_id=eos_token_id,
160
+ tie_word_embeddings=tie_word_embeddings,
161
+ **kwargs,
162
+ )
163
+
164
+ def _rope_scaling_validation(self):
165
+ """
166
+ Validate the `rope_scaling` configuration.
167
+ """
168
+ if self.rope_scaling is None:
169
+ return
170
+
171
+ if not isinstance(self.rope_scaling, dict) or len(self.rope_scaling) != 2:
172
+ raise ValueError(
173
+ "`rope_scaling` must be a dictionary with with two fields, `type` and `factor`, "
174
+ f"got {self.rope_scaling}"
175
+ )
176
+ rope_scaling_type = self.rope_scaling.get("type", None)
177
+ rope_scaling_factor = self.rope_scaling.get("factor", None)
178
+ if rope_scaling_type is None or rope_scaling_type not in ["linear", "dynamic"]:
179
+ raise ValueError(
180
+ f"`rope_scaling`'s type field must be one of ['linear', 'dynamic'], got {rope_scaling_type}"
181
+ )
182
+ if rope_scaling_factor is None or not isinstance(rope_scaling_factor, float) or rope_scaling_factor <= 1.0:
183
+ raise ValueError(f"`rope_scaling`'s factor field must be a float > 1, got {rope_scaling_factor}")
generation_config.json ADDED
@@ -0,0 +1,12 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ {
2
+ "_from_model_config": true,
3
+ "bos_token_id": 1,
4
+ "eos_token_id": 2,
5
+ "pad_token_id": 0,
6
+ "transformers_version": "4.39.2",
7
+ "top_k": 5,
8
+ "top_p": 0.8,
9
+ "temperature": 0.3,
10
+ "repetition_penalty":1.1,
11
+ "do_sample": true
12
+ }
modeling_index.py ADDED
@@ -0,0 +1,1048 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ # coding=utf-8
2
+ # Copyright 2022 EleutherAI and the HuggingFace Inc. team. All rights reserved.
3
+ #
4
+ # This code is based on EleutherAI's GPT-NeoX library and the GPT-NeoX
5
+ # and OPT implementations in this library. It has been modified from its
6
+ # original forms to accommodate minor architectural differences compared
7
+ # to GPT-NeoX and OPT used by the Meta AI team that trained the model.
8
+ #
9
+ # Licensed under the Apache License, Version 2.0 (the "License");
10
+ # you may not use this file except in compliance with the License.
11
+ # You may obtain a copy of the License at
12
+ #
13
+ # http://www.apache.org/licenses/LICENSE-2.0
14
+ #
15
+ # Unless required by applicable law or agreed to in writing, software
16
+ # distributed under the License is distributed on an "AS IS" BASIS,
17
+ # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
18
+ # See the License for the specific language governing permissions and
19
+ # limitations under the License.
20
+ """ PyTorch Index model."""
21
+ import math
22
+ from typing import List, Optional, Tuple, Union
23
+
24
+ import torch
25
+ import torch.nn.functional as F
26
+ import torch.utils.checkpoint
27
+ from torch import nn
28
+ from torch.nn import BCEWithLogitsLoss, CrossEntropyLoss, MSELoss
29
+
30
+ from transformers.activations import ACT2FN
31
+ from transformers.modeling_outputs import BaseModelOutputWithPast, CausalLMOutputWithPast, SequenceClassifierOutputWithPast
32
+ from transformers.modeling_utils import PreTrainedModel
33
+ from transformers.utils import add_start_docstrings, add_start_docstrings_to_model_forward, logging, replace_return_docstrings
34
+ from .configuration_index import IndexConfig
35
+
36
+
37
+ logger = logging.get_logger(__name__)
38
+
39
+ _CONFIG_FOR_DOC = "IndexConfig"
40
+
41
+
42
+ # Copied from transformers.models.bart.modeling_bart._make_causal_mask
43
+ def _make_causal_mask(
44
+ input_ids_shape: torch.Size, dtype: torch.dtype, device: torch.device, past_key_values_length: int = 0
45
+ ):
46
+ """
47
+ Make causal mask used for bi-directional self-attention.
48
+ """
49
+ bsz, tgt_len = input_ids_shape
50
+ mask = torch.full((tgt_len, tgt_len), torch.finfo(dtype).min, device=device)
51
+ mask_cond = torch.arange(mask.size(-1), device=device)
52
+ mask.masked_fill_(mask_cond < (mask_cond + 1).view(mask.size(-1), 1), 0)
53
+ mask = mask.to(dtype)
54
+
55
+ if past_key_values_length > 0:
56
+ mask = torch.cat([torch.zeros(tgt_len, past_key_values_length, dtype=dtype, device=device), mask], dim=-1)
57
+ return mask[None, None, :, :].expand(bsz, 1, tgt_len, tgt_len + past_key_values_length)
58
+
59
+
60
+ # Copied from transformers.models.bart.modeling_bart._expand_mask
61
+ def _expand_mask(mask: torch.Tensor, dtype: torch.dtype, tgt_len: Optional[int] = None):
62
+ """
63
+ Expands attention_mask from `[bsz, seq_len]` to `[bsz, 1, tgt_seq_len, src_seq_len]`.
64
+ """
65
+ bsz, src_len = mask.size()
66
+ tgt_len = tgt_len if tgt_len is not None else src_len
67
+
68
+ expanded_mask = mask[:, None, None, :].expand(bsz, 1, tgt_len, src_len).to(dtype)
69
+
70
+ inverted_mask = 1.0 - expanded_mask
71
+
72
+ return inverted_mask.masked_fill(inverted_mask.to(torch.bool), torch.finfo(dtype).min)
73
+
74
+
75
+ class IndexRMSNorm(nn.Module):
76
+ def __init__(self, hidden_size, eps=1e-6):
77
+ """
78
+ IndexRMSNorm is equivalent to T5LayerNorm
79
+ """
80
+ super().__init__()
81
+ self.weight = nn.Parameter(torch.ones(hidden_size))
82
+ self.variance_epsilon = eps
83
+
84
+ def forward(self, hidden_states):
85
+ input_dtype = hidden_states.dtype
86
+ hidden_states = hidden_states.to(torch.float32)
87
+ variance = hidden_states.pow(2).mean(-1, keepdim=True)
88
+ hidden_states = hidden_states * torch.rsqrt(variance + self.variance_epsilon)
89
+ return self.weight * hidden_states.to(input_dtype)
90
+
91
+
92
+ class IndexRotaryEmbedding(torch.nn.Module):
93
+ def __init__(self, dim, max_position_embeddings=2048, base=10000, device=None):
94
+ super().__init__()
95
+
96
+ self.dim = dim
97
+ self.max_position_embeddings = max_position_embeddings
98
+ self.base = base
99
+ inv_freq = 1.0 / (self.base ** (torch.arange(0, self.dim, 2).float().to(device) / self.dim))
100
+ self.register_buffer("inv_freq", inv_freq, persistent=False)
101
+
102
+ # Build here to make `torch.jit.trace` work.
103
+ self._set_cos_sin_cache(
104
+ seq_len=max_position_embeddings, device=self.inv_freq.device, dtype=torch.get_default_dtype()
105
+ )
106
+
107
+ def _set_cos_sin_cache(self, seq_len, device, dtype):
108
+ self.max_seq_len_cached = seq_len
109
+ t = torch.arange(self.max_seq_len_cached, device=device, dtype=self.inv_freq.dtype)
110
+
111
+ freqs = torch.einsum("i,j->ij", t, self.inv_freq)
112
+ # Different from paper, but it uses a different permutation in order to obtain the same calculation
113
+ emb = torch.cat((freqs, freqs), dim=-1)
114
+ self.register_buffer("cos_cached", emb.cos()[None, None, :, :].to(dtype), persistent=False)
115
+ self.register_buffer("sin_cached", emb.sin()[None, None, :, :].to(dtype), persistent=False)
116
+
117
+ def forward(self, x, seq_len=None):
118
+ # x: [bs, num_attention_heads, seq_len, head_size]
119
+ if seq_len > self.max_seq_len_cached:
120
+ self._set_cos_sin_cache(seq_len=seq_len, device=x.device, dtype=x.dtype)
121
+
122
+ return (
123
+ self.cos_cached[:, :, :seq_len, ...].to(dtype=x.dtype),
124
+ self.sin_cached[:, :, :seq_len, ...].to(dtype=x.dtype),
125
+ )
126
+
127
+
128
+ class IndexLinearScalingRotaryEmbedding(IndexRotaryEmbedding):
129
+ """IndexRotaryEmbedding extended with linear scaling. Credits to the Reddit user /u/kaiokendev"""
130
+
131
+ def __init__(self, dim, max_position_embeddings=2048, base=10000, device=None, scaling_factor=1.0):
132
+ self.scaling_factor = scaling_factor
133
+ super().__init__(dim, max_position_embeddings, base, device)
134
+
135
+ def _set_cos_sin_cache(self, seq_len, device, dtype):
136
+ self.max_seq_len_cached = seq_len
137
+ t = torch.arange(self.max_seq_len_cached, device=device, dtype=self.inv_freq.dtype)
138
+ t = t / self.scaling_factor
139
+
140
+ freqs = torch.einsum("i,j->ij", t, self.inv_freq)
141
+ # Different from paper, but it uses a different permutation in order to obtain the same calculation
142
+ emb = torch.cat((freqs, freqs), dim=-1)
143
+ self.register_buffer("cos_cached", emb.cos()[None, None, :, :].to(dtype), persistent=False)
144
+ self.register_buffer("sin_cached", emb.sin()[None, None, :, :].to(dtype), persistent=False)
145
+
146
+
147
+ class IndexDynamicNTKScalingRotaryEmbedding(IndexRotaryEmbedding):
148
+ """IndexRotaryEmbedding extended with Dynamic NTK scaling. Credits to the Reddit users /u/bloc97 and /u/emozilla"""
149
+
150
+ def __init__(self, dim, max_position_embeddings=2048, base=10000, device=None, scaling_factor=1.0):
151
+ self.scaling_factor = scaling_factor
152
+ super().__init__(dim, max_position_embeddings, base, device)
153
+
154
+ def _set_cos_sin_cache(self, seq_len, device, dtype):
155
+ self.max_seq_len_cached = seq_len
156
+
157
+ if seq_len > self.max_position_embeddings:
158
+ base = self.base * (
159
+ (self.scaling_factor * seq_len / self.max_position_embeddings) - (self.scaling_factor - 1)
160
+ ) ** (self.dim / (self.dim - 2))
161
+ inv_freq = 1.0 / (base ** (torch.arange(0, self.dim, 2).float().to(device) / self.dim))
162
+ self.register_buffer("inv_freq", inv_freq, persistent=False)
163
+
164
+ t = torch.arange(self.max_seq_len_cached, device=device, dtype=self.inv_freq.dtype)
165
+
166
+ freqs = torch.einsum("i,j->ij", t, self.inv_freq)
167
+ # Different from paper, but it uses a different permutation in order to obtain the same calculation
168
+ emb = torch.cat((freqs, freqs), dim=-1)
169
+ self.register_buffer("cos_cached", emb.cos()[None, None, :, :].to(dtype), persistent=False)
170
+ self.register_buffer("sin_cached", emb.sin()[None, None, :, :].to(dtype), persistent=False)
171
+
172
+
173
+ def rotate_half(x):
174
+ """Rotates half the hidden dims of the input."""
175
+ x1 = x[..., : x.shape[-1] // 2]
176
+ x2 = x[..., x.shape[-1] // 2 :]
177
+ return torch.cat((-x2, x1), dim=-1)
178
+
179
+
180
+ def apply_rotary_pos_emb(q, k, cos, sin, position_ids):
181
+ # The first two dimensions of cos and sin are always 1, so we can `squeeze` them.
182
+ cos = cos.squeeze(1).squeeze(0) # [seq_len, dim]
183
+ sin = sin.squeeze(1).squeeze(0) # [seq_len, dim]
184
+ cos = cos[position_ids].unsqueeze(1) # [bs, 1, seq_len, dim]
185
+ sin = sin[position_ids].unsqueeze(1) # [bs, 1, seq_len, dim]
186
+ q_embed = (q * cos) + (rotate_half(q) * sin)
187
+ k_embed = (k * cos) + (rotate_half(k) * sin)
188
+ return q_embed, k_embed
189
+
190
+
191
+ class IndexMLP(nn.Module):
192
+ def __init__(self, config):
193
+ super().__init__()
194
+ self.config = config
195
+ self.hidden_size = config.hidden_size
196
+ self.intermediate_size = config.intermediate_size
197
+ self.gate_proj = nn.Linear(self.hidden_size, self.intermediate_size, bias=False)
198
+ self.up_proj = nn.Linear(self.hidden_size, self.intermediate_size, bias=False)
199
+ self.down_proj = nn.Linear(self.intermediate_size, self.hidden_size, bias=False)
200
+ self.act_fn = ACT2FN[config.hidden_act]
201
+
202
+ def forward(self, x):
203
+ if self.config.pretraining_tp > 1:
204
+ slice = self.intermediate_size // self.config.pretraining_tp
205
+ gate_proj_slices = self.gate_proj.weight.split(slice, dim=0)
206
+ up_proj_slices = self.up_proj.weight.split(slice, dim=0)
207
+ down_proj_slices = self.down_proj.weight.split(slice, dim=1)
208
+
209
+ gate_proj = torch.cat(
210
+ [F.linear(x, gate_proj_slices[i]) for i in range(self.config.pretraining_tp)], dim=-1
211
+ )
212
+ up_proj = torch.cat([F.linear(x, up_proj_slices[i]) for i in range(self.config.pretraining_tp)], dim=-1)
213
+
214
+ intermediate_states = (self.act_fn(gate_proj) * up_proj).split(slice, dim=2)
215
+ down_proj = [
216
+ F.linear(intermediate_states[i], down_proj_slices[i]) for i in range(self.config.pretraining_tp)
217
+ ]
218
+ down_proj = sum(down_proj)
219
+ else:
220
+ down_proj = self.down_proj(self.act_fn(self.gate_proj(x)) * self.up_proj(x))
221
+
222
+ return down_proj
223
+
224
+
225
+ def repeat_kv(hidden_states: torch.Tensor, n_rep: int) -> torch.Tensor:
226
+ """
227
+ This is the equivalent of torch.repeat_interleave(x, dim=1, repeats=n_rep). The hidden states go from (batch,
228
+ num_key_value_heads, seqlen, head_dim) to (batch, num_attention_heads, seqlen, head_dim)
229
+ """
230
+ batch, num_key_value_heads, slen, head_dim = hidden_states.shape
231
+ if n_rep == 1:
232
+ return hidden_states
233
+ hidden_states = hidden_states[:, :, None, :, :].expand(batch, num_key_value_heads, n_rep, slen, head_dim)
234
+ return hidden_states.reshape(batch, num_key_value_heads * n_rep, slen, head_dim)
235
+
236
+
237
+ class IndexAttention(nn.Module):
238
+ """Multi-headed attention from 'Attention Is All You Need' paper"""
239
+
240
+ def __init__(self, config: IndexConfig):
241
+ super().__init__()
242
+ self.config = config
243
+ self.hidden_size = config.hidden_size
244
+ self.num_heads = config.num_attention_heads
245
+ self.head_dim = self.hidden_size // self.num_heads
246
+ self.num_key_value_heads = config.num_key_value_heads
247
+ self.num_key_value_groups = self.num_heads // self.num_key_value_heads
248
+ self.max_position_embeddings = config.max_position_embeddings
249
+ self.rope_theta = config.rope_theta
250
+
251
+ if (self.head_dim * self.num_heads) != self.hidden_size:
252
+ raise ValueError(
253
+ f"hidden_size must be divisible by num_heads (got `hidden_size`: {self.hidden_size}"
254
+ f" and `num_heads`: {self.num_heads})."
255
+ )
256
+ self.q_proj = nn.Linear(self.hidden_size, self.num_heads * self.head_dim, bias=False)
257
+ self.k_proj = nn.Linear(self.hidden_size, self.num_key_value_heads * self.head_dim, bias=False)
258
+ self.v_proj = nn.Linear(self.hidden_size, self.num_key_value_heads * self.head_dim, bias=False)
259
+ self.o_proj = nn.Linear(self.num_heads * self.head_dim, self.hidden_size, bias=False)
260
+ self._init_rope()
261
+
262
+ def _init_rope(self):
263
+ if self.config.rope_scaling is None:
264
+ self.rotary_emb = IndexRotaryEmbedding(
265
+ self.head_dim,
266
+ max_position_embeddings=self.max_position_embeddings,
267
+ base=self.rope_theta,
268
+ )
269
+ else:
270
+ scaling_type = self.config.rope_scaling["type"]
271
+ scaling_factor = self.config.rope_scaling["factor"]
272
+ if scaling_type == "linear":
273
+ self.rotary_emb = IndexLinearScalingRotaryEmbedding(
274
+ self.head_dim,
275
+ max_position_embeddings=self.max_position_embeddings,
276
+ scaling_factor=scaling_factor,
277
+ base=self.rope_theta,
278
+ )
279
+ elif scaling_type == "dynamic":
280
+ self.rotary_emb = IndexDynamicNTKScalingRotaryEmbedding(
281
+ self.head_dim,
282
+ max_position_embeddings=self.max_position_embeddings,
283
+ scaling_factor=scaling_factor,
284
+ base=self.rope_theta,
285
+ )
286
+ else:
287
+ raise ValueError(f"Unknown RoPE scaling type {scaling_type}")
288
+
289
+ def _shape(self, tensor: torch.Tensor, seq_len: int, bsz: int):
290
+ return tensor.view(bsz, seq_len, self.num_heads, self.head_dim).transpose(1, 2).contiguous()
291
+
292
+ def forward(
293
+ self,
294
+ hidden_states: torch.Tensor,
295
+ attention_mask: Optional[torch.Tensor] = None,
296
+ position_ids: Optional[torch.LongTensor] = None,
297
+ past_key_value: Optional[Tuple[torch.Tensor]] = None,
298
+ output_attentions: bool = False,
299
+ use_cache: bool = False,
300
+ ) -> Tuple[torch.Tensor, Optional[torch.Tensor], Optional[Tuple[torch.Tensor]]]:
301
+ bsz, q_len, _ = hidden_states.size()
302
+
303
+ if self.config.pretraining_tp > 1:
304
+ key_value_slicing = (self.num_key_value_heads * self.head_dim) // self.config.pretraining_tp
305
+ query_slices = self.q_proj.weight.split(
306
+ (self.num_heads * self.head_dim) // self.config.pretraining_tp, dim=0
307
+ )
308
+ key_slices = self.k_proj.weight.split(key_value_slicing, dim=0)
309
+ value_slices = self.v_proj.weight.split(key_value_slicing, dim=0)
310
+
311
+ query_states = [F.linear(hidden_states, query_slices[i]) for i in range(self.config.pretraining_tp)]
312
+ query_states = torch.cat(query_states, dim=-1)
313
+
314
+ key_states = [F.linear(hidden_states, key_slices[i]) for i in range(self.config.pretraining_tp)]
315
+ key_states = torch.cat(key_states, dim=-1)
316
+
317
+ value_states = [F.linear(hidden_states, value_slices[i]) for i in range(self.config.pretraining_tp)]
318
+ value_states = torch.cat(value_states, dim=-1)
319
+
320
+ else:
321
+ query_states = self.q_proj(hidden_states)
322
+ key_states = self.k_proj(hidden_states)
323
+ value_states = self.v_proj(hidden_states)
324
+
325
+ query_states = query_states.view(bsz, q_len, self.num_heads, self.head_dim).transpose(1, 2)
326
+ key_states = key_states.view(bsz, q_len, self.num_key_value_heads, self.head_dim).transpose(1, 2)
327
+ value_states = value_states.view(bsz, q_len, self.num_key_value_heads, self.head_dim).transpose(1, 2)
328
+
329
+ kv_seq_len = key_states.shape[-2]
330
+ if past_key_value is not None:
331
+ kv_seq_len += past_key_value[0].shape[-2]
332
+ cos, sin = self.rotary_emb(value_states, seq_len=kv_seq_len)
333
+ query_states, key_states = apply_rotary_pos_emb(query_states, key_states, cos, sin, position_ids)
334
+
335
+ if past_key_value is not None:
336
+ # reuse k, v, self_attention
337
+ key_states = torch.cat([past_key_value[0], key_states], dim=2)
338
+ value_states = torch.cat([past_key_value[1], value_states], dim=2)
339
+
340
+ past_key_value = (key_states, value_states) if use_cache else None
341
+
342
+ # repeat k/v heads if n_kv_heads < n_heads
343
+ key_states = repeat_kv(key_states, self.num_key_value_groups)
344
+ value_states = repeat_kv(value_states, self.num_key_value_groups)
345
+
346
+ attn_weights = torch.matmul(query_states, key_states.transpose(2, 3)) / math.sqrt(self.head_dim)
347
+
348
+ if attn_weights.size() != (bsz, self.num_heads, q_len, kv_seq_len):
349
+ raise ValueError(
350
+ f"Attention weights should be of size {(bsz, self.num_heads, q_len, kv_seq_len)}, but is"
351
+ f" {attn_weights.size()}"
352
+ )
353
+
354
+ if attention_mask is not None:
355
+ if attention_mask.size() != (bsz, 1, q_len, kv_seq_len):
356
+ raise ValueError(
357
+ f"Attention mask should be of size {(bsz, 1, q_len, kv_seq_len)}, but is {attention_mask.size()}"
358
+ )
359
+ attn_weights = attn_weights + attention_mask
360
+
361
+ # upcast attention to fp32
362
+ attn_weights = nn.functional.softmax(attn_weights, dim=-1, dtype=torch.float32).to(query_states.dtype)
363
+ attn_output = torch.matmul(attn_weights, value_states)
364
+
365
+ if attn_output.size() != (bsz, self.num_heads, q_len, self.head_dim):
366
+ raise ValueError(
367
+ f"`attn_output` should be of size {(bsz, self.num_heads, q_len, self.head_dim)}, but is"
368
+ f" {attn_output.size()}"
369
+ )
370
+
371
+ attn_output = attn_output.transpose(1, 2).contiguous()
372
+ attn_output = attn_output.reshape(bsz, q_len, self.hidden_size)
373
+
374
+ if self.config.pretraining_tp > 1:
375
+ attn_output = attn_output.split(self.hidden_size // self.config.pretraining_tp, dim=2)
376
+ o_proj_slices = self.o_proj.weight.split(self.hidden_size // self.config.pretraining_tp, dim=1)
377
+ attn_output = sum([F.linear(attn_output[i], o_proj_slices[i]) for i in range(self.config.pretraining_tp)])
378
+ else:
379
+ attn_output = self.o_proj(attn_output)
380
+
381
+ if not output_attentions:
382
+ attn_weights = None
383
+
384
+ return attn_output, attn_weights, past_key_value
385
+
386
+
387
+ class IndexDecoderLayer(nn.Module):
388
+ def __init__(self, config: IndexConfig):
389
+ super().__init__()
390
+ self.hidden_size = config.hidden_size
391
+ self.self_attn = IndexAttention(config=config)
392
+ self.mlp = IndexMLP(config)
393
+ self.input_layernorm = IndexRMSNorm(config.hidden_size, eps=config.rms_norm_eps)
394
+ self.post_attention_layernorm = IndexRMSNorm(config.hidden_size, eps=config.rms_norm_eps)
395
+
396
+ def forward(
397
+ self,
398
+ hidden_states: torch.Tensor,
399
+ attention_mask: Optional[torch.Tensor] = None,
400
+ position_ids: Optional[torch.LongTensor] = None,
401
+ past_key_value: Optional[Tuple[torch.Tensor]] = None,
402
+ output_attentions: Optional[bool] = False,
403
+ use_cache: Optional[bool] = False,
404
+ ) -> Tuple[torch.FloatTensor, Optional[Tuple[torch.FloatTensor, torch.FloatTensor]]]:
405
+ """
406
+ Args:
407
+ hidden_states (`torch.FloatTensor`): input to the layer of shape `(batch, seq_len, embed_dim)`
408
+ attention_mask (`torch.FloatTensor`, *optional*): attention mask of size
409
+ `(batch, 1, tgt_len, src_len)` where padding elements are indicated by very large negative values.
410
+ output_attentions (`bool`, *optional*):
411
+ Whether or not to return the attentions tensors of all attention layers. See `attentions` under
412
+ returned tensors for more detail.
413
+ use_cache (`bool`, *optional*):
414
+ If set to `True`, `past_key_values` key value states are returned and can be used to speed up decoding
415
+ (see `past_key_values`).
416
+ past_key_value (`Tuple(torch.FloatTensor)`, *optional*): cached past key and value projection states
417
+ """
418
+
419
+ residual = hidden_states
420
+
421
+ hidden_states = self.input_layernorm(hidden_states)
422
+
423
+ # Self Attention
424
+ hidden_states, self_attn_weights, present_key_value = self.self_attn(
425
+ hidden_states=hidden_states,
426
+ attention_mask=attention_mask,
427
+ position_ids=position_ids,
428
+ past_key_value=past_key_value,
429
+ output_attentions=output_attentions,
430
+ use_cache=use_cache,
431
+ )
432
+ hidden_states = residual + hidden_states
433
+
434
+ # Fully Connected
435
+ residual = hidden_states
436
+ hidden_states = self.post_attention_layernorm(hidden_states)
437
+ hidden_states = self.mlp(hidden_states)
438
+ hidden_states = residual + hidden_states
439
+
440
+ outputs = (hidden_states,)
441
+
442
+ if output_attentions:
443
+ outputs += (self_attn_weights,)
444
+
445
+ if use_cache:
446
+ outputs += (present_key_value,)
447
+
448
+ return outputs
449
+
450
+
451
+ INDEX_START_DOCSTRING = r"""
452
+ This model inherits from [`PreTrainedModel`]. Check the superclass documentation for the generic methods the
453
+ library implements for all its model (such as downloading or saving, resizing the input embeddings, pruning heads
454
+ etc.)
455
+
456
+ This model is also a PyTorch [torch.nn.Module](https://pytorch.org/docs/stable/nn.html#torch.nn.Module) subclass.
457
+ Use it as a regular PyTorch Module and refer to the PyTorch documentation for all matter related to general usage
458
+ and behavior.
459
+
460
+ Parameters:
461
+ config ([`IndexConfig`]):
462
+ Model configuration class with all the parameters of the model. Initializing with a config file does not
463
+ load the weights associated with the model, only the configuration. Check out the
464
+ [`~PreTrainedModel.from_pretrained`] method to load the model weights.
465
+ """
466
+
467
+
468
+ @add_start_docstrings(
469
+ "The bare Index Model outputting raw hidden-states without any specific head on top.",
470
+ INDEX_START_DOCSTRING,
471
+ )
472
+ class IndexPreTrainedModel(PreTrainedModel):
473
+ config_class = IndexConfig
474
+ base_model_prefix = "model"
475
+ supports_gradient_checkpointing = True
476
+ _no_split_modules = ["IndexDecoderLayer"]
477
+ _skip_keys_device_placement = "past_key_values"
478
+
479
+ def _init_weights(self, module):
480
+ std = self.config.initializer_range
481
+ if isinstance(module, nn.Linear):
482
+ module.weight.data.normal_(mean=0.0, std=std)
483
+ if module.bias is not None:
484
+ module.bias.data.zero_()
485
+ elif isinstance(module, nn.Embedding):
486
+ module.weight.data.normal_(mean=0.0, std=std)
487
+ if module.padding_idx is not None:
488
+ module.weight.data[module.padding_idx].zero_()
489
+
490
+ def _set_gradient_checkpointing(self, module, value=False):
491
+ if isinstance(module, IndexModel):
492
+ module.gradient_checkpointing = value
493
+
494
+
495
+ INDEX_INPUTS_DOCSTRING = r"""
496
+ Args:
497
+ input_ids (`torch.LongTensor` of shape `(batch_size, sequence_length)`):
498
+ Indices of input sequence tokens in the vocabulary. Padding will be ignored by default should you provide
499
+ it.
500
+
501
+ Indices can be obtained using [`AutoTokenizer`]. See [`PreTrainedTokenizer.encode`] and
502
+ [`PreTrainedTokenizer.__call__`] for details.
503
+
504
+ [What are input IDs?](../glossary#input-ids)
505
+ attention_mask (`torch.Tensor` of shape `(batch_size, sequence_length)`, *optional*):
506
+ Mask to avoid performing attention on padding token indices. Mask values selected in `[0, 1]`:
507
+
508
+ - 1 for tokens that are **not masked**,
509
+ - 0 for tokens that are **masked**.
510
+
511
+ [What are attention masks?](../glossary#attention-mask)
512
+
513
+ Indices can be obtained using [`AutoTokenizer`]. See [`PreTrainedTokenizer.encode`] and
514
+ [`PreTrainedTokenizer.__call__`] for details.
515
+
516
+ If `past_key_values` is used, optionally only the last `decoder_input_ids` have to be input (see
517
+ `past_key_values`).
518
+
519
+ If you want to change padding behavior, you should read [`modeling_opt._prepare_decoder_attention_mask`]
520
+ and modify to your needs. See diagram 1 in [the paper](https://arxiv.org/abs/1910.13461) for more
521
+ information on the default strategy.
522
+
523
+ - 1 indicates the head is **not masked**,
524
+ - 0 indicates the head is **masked**.
525
+ position_ids (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*):
526
+ Indices of positions of each input sequence tokens in the position embeddings. Selected in the range `[0,
527
+ config.n_positions - 1]`.
528
+
529
+ [What are position IDs?](../glossary#position-ids)
530
+ past_key_values (`tuple(tuple(torch.FloatTensor))`, *optional*, returned when `use_cache=True` is passed or when `config.use_cache=True`):
531
+ Tuple of `tuple(torch.FloatTensor)` of length `config.n_layers`, with each tuple having 2 tensors of shape
532
+ `(batch_size, num_heads, sequence_length, embed_size_per_head)`) and 2 additional tensors of shape
533
+ `(batch_size, num_heads, encoder_sequence_length, embed_size_per_head)`.
534
+
535
+ Contains pre-computed hidden-states (key and values in the self-attention blocks and in the cross-attention
536
+ blocks) that can be used (see `past_key_values` input) to speed up sequential decoding.
537
+
538
+ If `past_key_values` are used, the user can optionally input only the last `decoder_input_ids` (those that
539
+ don't have their past key value states given to this model) of shape `(batch_size, 1)` instead of all
540
+ `decoder_input_ids` of shape `(batch_size, sequence_length)`.
541
+ inputs_embeds (`torch.FloatTensor` of shape `(batch_size, sequence_length, hidden_size)`, *optional*):
542
+ Optionally, instead of passing `input_ids` you can choose to directly pass an embedded representation. This
543
+ is useful if you want more control over how to convert `input_ids` indices into associated vectors than the
544
+ model's internal embedding lookup matrix.
545
+ use_cache (`bool`, *optional*):
546
+ If set to `True`, `past_key_values` key value states are returned and can be used to speed up decoding (see
547
+ `past_key_values`).
548
+ output_attentions (`bool`, *optional*):
549
+ Whether or not to return the attentions tensors of all attention layers. See `attentions` under returned
550
+ tensors for more detail.
551
+ output_hidden_states (`bool`, *optional*):
552
+ Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors for
553
+ more detail.
554
+ return_dict (`bool`, *optional*):
555
+ Whether or not to return a [`~utils.ModelOutput`] instead of a plain tuple.
556
+ """
557
+
558
+
559
+ @add_start_docstrings(
560
+ "The bare Index Model outputting raw hidden-states without any specific head on top.",
561
+ INDEX_START_DOCSTRING,
562
+ )
563
+ class IndexModel(IndexPreTrainedModel):
564
+ """
565
+ Transformer decoder consisting of *config.num_hidden_layers* layers. Each layer is a [`IndexDecoderLayer`]
566
+
567
+ Args:
568
+ config: IndexConfig
569
+ """
570
+
571
+ def __init__(self, config: IndexConfig):
572
+ super().__init__(config)
573
+ self.padding_idx = config.pad_token_id
574
+ self.vocab_size = config.vocab_size
575
+
576
+ self.embed_tokens = nn.Embedding(config.vocab_size, config.hidden_size, self.padding_idx)
577
+ self.layers = nn.ModuleList([IndexDecoderLayer(config) for _ in range(config.num_hidden_layers)])
578
+ self.norm = IndexRMSNorm(config.hidden_size, eps=config.rms_norm_eps)
579
+
580
+ self.gradient_checkpointing = False
581
+ # Initialize weights and apply final processing
582
+ self.post_init()
583
+
584
+ def get_input_embeddings(self):
585
+ return self.embed_tokens
586
+
587
+ def set_input_embeddings(self, value):
588
+ self.embed_tokens = value
589
+
590
+ # Copied from transformers.models.bart.modeling_bart.BartDecoder._prepare_decoder_attention_mask
591
+ def _prepare_decoder_attention_mask(self, attention_mask, input_shape, inputs_embeds, past_key_values_length):
592
+ # create causal mask
593
+ # [bsz, seq_len] -> [bsz, 1, tgt_seq_len, src_seq_len]
594
+ combined_attention_mask = None
595
+ if input_shape[-1] > 1:
596
+ combined_attention_mask = _make_causal_mask(
597
+ input_shape,
598
+ inputs_embeds.dtype,
599
+ device=inputs_embeds.device,
600
+ past_key_values_length=past_key_values_length,
601
+ )
602
+
603
+ if attention_mask is not None:
604
+ # [bsz, seq_len] -> [bsz, 1, tgt_seq_len, src_seq_len]
605
+ expanded_attn_mask = _expand_mask(attention_mask, inputs_embeds.dtype, tgt_len=input_shape[-1]).to(
606
+ inputs_embeds.device
607
+ )
608
+ combined_attention_mask = (
609
+ expanded_attn_mask if combined_attention_mask is None else expanded_attn_mask + combined_attention_mask
610
+ )
611
+
612
+ return combined_attention_mask
613
+
614
+ @add_start_docstrings_to_model_forward(INDEX_INPUTS_DOCSTRING)
615
+ def forward(
616
+ self,
617
+ input_ids: torch.LongTensor = None,
618
+ attention_mask: Optional[torch.Tensor] = None,
619
+ position_ids: Optional[torch.LongTensor] = None,
620
+ past_key_values: Optional[List[torch.FloatTensor]] = None,
621
+ inputs_embeds: Optional[torch.FloatTensor] = None,
622
+ use_cache: Optional[bool] = None,
623
+ output_attentions: Optional[bool] = None,
624
+ output_hidden_states: Optional[bool] = None,
625
+ return_dict: Optional[bool] = None,
626
+ ) -> Union[Tuple, BaseModelOutputWithPast]:
627
+ output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions
628
+ output_hidden_states = (
629
+ output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
630
+ )
631
+ use_cache = use_cache if use_cache is not None else self.config.use_cache
632
+
633
+ return_dict = return_dict if return_dict is not None else self.config.use_return_dict
634
+
635
+ # retrieve input_ids and inputs_embeds
636
+ if input_ids is not None and inputs_embeds is not None:
637
+ raise ValueError("You cannot specify both decoder_input_ids and decoder_inputs_embeds at the same time")
638
+ elif input_ids is not None:
639
+ batch_size, seq_length = input_ids.shape
640
+ elif inputs_embeds is not None:
641
+ batch_size, seq_length, _ = inputs_embeds.shape
642
+ else:
643
+ raise ValueError("You have to specify either decoder_input_ids or decoder_inputs_embeds")
644
+
645
+ seq_length_with_past = seq_length
646
+ past_key_values_length = 0
647
+
648
+ if past_key_values is not None:
649
+ past_key_values_length = past_key_values[0][0].shape[2]
650
+ seq_length_with_past = seq_length_with_past + past_key_values_length
651
+
652
+ if position_ids is None:
653
+ device = input_ids.device if input_ids is not None else inputs_embeds.device
654
+ position_ids = torch.arange(
655
+ past_key_values_length, seq_length + past_key_values_length, dtype=torch.long, device=device
656
+ )
657
+ position_ids = position_ids.unsqueeze(0).view(-1, seq_length)
658
+ else:
659
+ position_ids = position_ids.view(-1, seq_length).long()
660
+
661
+ if inputs_embeds is None:
662
+ inputs_embeds = self.embed_tokens(input_ids)
663
+ # embed positions
664
+ if attention_mask is None:
665
+ attention_mask = torch.ones(
666
+ (batch_size, seq_length_with_past), dtype=torch.bool, device=inputs_embeds.device
667
+ )
668
+ attention_mask = self._prepare_decoder_attention_mask(
669
+ attention_mask, (batch_size, seq_length), inputs_embeds, past_key_values_length
670
+ )
671
+
672
+ hidden_states = inputs_embeds
673
+
674
+ if self.gradient_checkpointing and self.training:
675
+ if use_cache:
676
+ logger.warning_once(
677
+ "`use_cache=True` is incompatible with gradient checkpointing. Setting `use_cache=False`..."
678
+ )
679
+ use_cache = False
680
+
681
+ # decoder layers
682
+ all_hidden_states = () if output_hidden_states else None
683
+ all_self_attns = () if output_attentions else None
684
+ next_decoder_cache = () if use_cache else None
685
+
686
+ for idx, decoder_layer in enumerate(self.layers):
687
+ if output_hidden_states:
688
+ all_hidden_states += (hidden_states,)
689
+
690
+ past_key_value = past_key_values[idx] if past_key_values is not None else None
691
+
692
+ if self.gradient_checkpointing and self.training:
693
+
694
+ def create_custom_forward(module):
695
+ def custom_forward(*inputs):
696
+ # None for past_key_value
697
+ return module(*inputs, past_key_value, output_attentions)
698
+
699
+ return custom_forward
700
+
701
+ layer_outputs = torch.utils.checkpoint.checkpoint(
702
+ create_custom_forward(decoder_layer),
703
+ hidden_states,
704
+ attention_mask,
705
+ position_ids,
706
+ )
707
+ else:
708
+ layer_outputs = decoder_layer(
709
+ hidden_states,
710
+ attention_mask=attention_mask,
711
+ position_ids=position_ids,
712
+ past_key_value=past_key_value,
713
+ output_attentions=output_attentions,
714
+ use_cache=use_cache,
715
+ )
716
+
717
+ hidden_states = layer_outputs[0]
718
+
719
+ if use_cache:
720
+ next_decoder_cache += (layer_outputs[2 if output_attentions else 1],)
721
+
722
+ if output_attentions:
723
+ all_self_attns += (layer_outputs[1],)
724
+
725
+ hidden_states = self.norm(hidden_states)
726
+
727
+ # add hidden states from the last decoder layer
728
+ if output_hidden_states:
729
+ all_hidden_states += (hidden_states,)
730
+
731
+ next_cache = next_decoder_cache if use_cache else None
732
+ if not return_dict:
733
+ return tuple(v for v in [hidden_states, next_cache, all_hidden_states, all_self_attns] if v is not None)
734
+ return BaseModelOutputWithPast(
735
+ last_hidden_state=hidden_states,
736
+ past_key_values=next_cache,
737
+ hidden_states=all_hidden_states,
738
+ attentions=all_self_attns,
739
+ )
740
+
741
+
742
+ class NormHead(nn.Module):
743
+ def __init__(self, hidden_size, vocab_size, bias=False):
744
+ super().__init__()
745
+ self.weight = nn.Parameter(torch.empty((vocab_size, hidden_size)))
746
+ nn.init.kaiming_uniform_(self.weight, a=math.sqrt(5))
747
+ self.first_flag = True
748
+
749
+ def forward(self, hidden_states):
750
+ if self.training:
751
+ norm_weight = nn.functional.normalize(self.weight)
752
+ self.first_flag = True
753
+ elif self.first_flag:
754
+ self.first_flag = False
755
+ self.weight = nn.Parameter(nn.functional.normalize(self.weight))
756
+ norm_weight = self.weight
757
+ else:
758
+ norm_weight = self.weight
759
+ return nn.functional.linear(hidden_states, norm_weight)
760
+
761
+
762
+ class IndexForCausalLM(IndexPreTrainedModel):
763
+ _tied_weights_keys = ["lm_head.weight"]
764
+
765
+ def __init__(self, config):
766
+ super().__init__(config)
767
+ self.model = IndexModel(config)
768
+ self.vocab_size = config.vocab_size
769
+ if config.norm_head:
770
+ self.lm_head = NormHead(config.hidden_size, config.vocab_size, bias=False)
771
+ else:
772
+ self.lm_head = nn.Linear(config.hidden_size, config.vocab_size, bias=False)
773
+
774
+ # Initialize weights and apply final processing
775
+ self.post_init()
776
+
777
+ def get_input_embeddings(self):
778
+ return self.model.embed_tokens
779
+
780
+ def set_input_embeddings(self, value):
781
+ self.model.embed_tokens = value
782
+
783
+ def get_output_embeddings(self):
784
+ return self.lm_head
785
+
786
+ def set_output_embeddings(self, new_embeddings):
787
+ self.lm_head = new_embeddings
788
+
789
+ def set_decoder(self, decoder):
790
+ self.model = decoder
791
+
792
+ def get_decoder(self):
793
+ return self.model
794
+
795
+ @add_start_docstrings_to_model_forward(INDEX_INPUTS_DOCSTRING)
796
+ @replace_return_docstrings(output_type=CausalLMOutputWithPast, config_class=_CONFIG_FOR_DOC)
797
+ def forward(
798
+ self,
799
+ input_ids: torch.LongTensor = None,
800
+ attention_mask: Optional[torch.Tensor] = None,
801
+ position_ids: Optional[torch.LongTensor] = None,
802
+ past_key_values: Optional[List[torch.FloatTensor]] = None,
803
+ inputs_embeds: Optional[torch.FloatTensor] = None,
804
+ labels: Optional[torch.LongTensor] = None,
805
+ use_cache: Optional[bool] = None,
806
+ output_attentions: Optional[bool] = None,
807
+ output_hidden_states: Optional[bool] = None,
808
+ return_dict: Optional[bool] = None,
809
+ ) -> Union[Tuple, CausalLMOutputWithPast]:
810
+ r"""
811
+ Args:
812
+ labels (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*):
813
+ Labels for computing the masked language modeling loss. Indices should either be in `[0, ...,
814
+ config.vocab_size]` or -100 (see `input_ids` docstring). Tokens with indices set to `-100` are ignored
815
+ (masked), the loss is only computed for the tokens with labels in `[0, ..., config.vocab_size]`.
816
+
817
+ Returns:
818
+
819
+ Example:
820
+
821
+ ```python
822
+ >>> from transformers import AutoTokenizer, IndexForCausalLM
823
+
824
+ >>> model = IndexForCausalLM.from_pretrained(PATH_TO_CONVERTED_WEIGHTS)
825
+ >>> tokenizer = AutoTokenizer.from_pretrained(PATH_TO_CONVERTED_TOKENIZER)
826
+
827
+ >>> prompt = "Hey, are you conscious? Can you talk to me?"
828
+ >>> inputs = tokenizer(prompt, return_tensors="pt")
829
+
830
+ >>> # Generate
831
+ >>> generate_ids = model.generate(inputs.input_ids, max_length=30)
832
+ >>> tokenizer.batch_decode(generate_ids, skip_special_tokens=True, clean_up_tokenization_spaces=False)[0]
833
+ "Hey, are you conscious? Can you talk to me?\nI'm not conscious, but I can talk to you."
834
+ ```"""
835
+
836
+ output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions
837
+ output_hidden_states = (
838
+ output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
839
+ )
840
+ return_dict = return_dict if return_dict is not None else self.config.use_return_dict
841
+
842
+ # decoder outputs consists of (dec_features, layer_state, dec_hidden, dec_attn)
843
+ outputs = self.model(
844
+ input_ids=input_ids,
845
+ attention_mask=attention_mask,
846
+ position_ids=position_ids,
847
+ past_key_values=past_key_values,
848
+ inputs_embeds=inputs_embeds,
849
+ use_cache=use_cache,
850
+ output_attentions=output_attentions,
851
+ output_hidden_states=output_hidden_states,
852
+ return_dict=return_dict,
853
+ )
854
+
855
+ hidden_states = outputs[0]
856
+ if self.config.pretraining_tp > 1:
857
+ lm_head_slices = self.lm_head.weight.split(self.vocab_size // self.config.pretraining_tp, dim=0)
858
+ logits = [F.linear(hidden_states, lm_head_slices[i]) for i in range(self.config.pretraining_tp)]
859
+ logits = torch.cat(logits, dim=-1)
860
+ else:
861
+ logits = self.lm_head(hidden_states)
862
+ logits = logits.float()
863
+
864
+ loss = None
865
+ if labels is not None:
866
+ # Shift so that tokens < n predict n
867
+ shift_logits = logits[..., :-1, :].contiguous()
868
+ shift_labels = labels[..., 1:].contiguous()
869
+ # Flatten the tokens
870
+ loss_fct = CrossEntropyLoss()
871
+ shift_logits = shift_logits.view(-1, self.config.vocab_size)
872
+ shift_labels = shift_labels.view(-1)
873
+ # Enable model parallelism
874
+ shift_labels = shift_labels.to(shift_logits.device)
875
+ loss = loss_fct(shift_logits, shift_labels)
876
+
877
+ if not return_dict:
878
+ output = (logits,) + outputs[1:]
879
+ return (loss,) + output if loss is not None else output
880
+
881
+ return CausalLMOutputWithPast(
882
+ loss=loss,
883
+ logits=logits,
884
+ past_key_values=outputs.past_key_values,
885
+ hidden_states=outputs.hidden_states,
886
+ attentions=outputs.attentions,
887
+ )
888
+
889
+ def prepare_inputs_for_generation(
890
+ self, input_ids, past_key_values=None, attention_mask=None, inputs_embeds=None, **kwargs
891
+ ):
892
+ if past_key_values:
893
+ input_ids = input_ids[:, -1:]
894
+
895
+ position_ids = kwargs.get("position_ids", None)
896
+ if attention_mask is not None and position_ids is None:
897
+ # create position_ids on the fly for batch generation
898
+ position_ids = attention_mask.long().cumsum(-1) - 1
899
+ position_ids.masked_fill_(attention_mask == 0, 1)
900
+ if past_key_values:
901
+ position_ids = position_ids[:, -1].unsqueeze(-1)
902
+
903
+ # if `inputs_embeds` are passed, we only want to use them in the 1st generation step
904
+ if inputs_embeds is not None and past_key_values is None:
905
+ model_inputs = {"inputs_embeds": inputs_embeds}
906
+ else:
907
+ model_inputs = {"input_ids": input_ids}
908
+
909
+ model_inputs.update(
910
+ {
911
+ "position_ids": position_ids,
912
+ "past_key_values": past_key_values,
913
+ "use_cache": kwargs.get("use_cache"),
914
+ "attention_mask": attention_mask,
915
+ }
916
+ )
917
+ return model_inputs
918
+
919
+ @staticmethod
920
+ def _reorder_cache(past_key_values, beam_idx):
921
+ reordered_past = ()
922
+ for layer_past in past_key_values:
923
+ reordered_past += (
924
+ tuple(past_state.index_select(0, beam_idx.to(past_state.device)) for past_state in layer_past),
925
+ )
926
+ return reordered_past
927
+
928
+
929
+ @add_start_docstrings(
930
+ """
931
+ The Index Model transformer with a sequence classification head on top (linear layer).
932
+
933
+ [`IndexForSequenceClassification`] uses the last token in order to do the classification, as other causal models
934
+ (e.g. GPT-2) do.
935
+
936
+ Since it does classification on the last token, it requires to know the position of the last token. If a
937
+ `pad_token_id` is defined in the configuration, it finds the last token that is not a padding token in each row. If
938
+ no `pad_token_id` is defined, it simply takes the last value in each row of the batch. Since it cannot guess the
939
+ padding tokens when `inputs_embeds` are passed instead of `input_ids`, it does the same (take the last value in
940
+ each row of the batch).
941
+ """,
942
+ INDEX_START_DOCSTRING,
943
+ )
944
+ class IndexForSequenceClassification(IndexPreTrainedModel):
945
+ def __init__(self, config):
946
+ super().__init__(config)
947
+ self.num_labels = config.num_labels
948
+ self.model = IndexModel(config)
949
+ self.score = nn.Linear(config.hidden_size, self.num_labels, bias=False)
950
+
951
+ # Initialize weights and apply final processing
952
+ self.post_init()
953
+
954
+ def get_input_embeddings(self):
955
+ return self.model.embed_tokens
956
+
957
+ def set_input_embeddings(self, value):
958
+ self.model.embed_tokens = value
959
+
960
+ @add_start_docstrings_to_model_forward(INDEX_INPUTS_DOCSTRING)
961
+ def forward(
962
+ self,
963
+ input_ids: torch.LongTensor = None,
964
+ attention_mask: Optional[torch.Tensor] = None,
965
+ position_ids: Optional[torch.LongTensor] = None,
966
+ past_key_values: Optional[List[torch.FloatTensor]] = None,
967
+ inputs_embeds: Optional[torch.FloatTensor] = None,
968
+ labels: Optional[torch.LongTensor] = None,
969
+ use_cache: Optional[bool] = None,
970
+ output_attentions: Optional[bool] = None,
971
+ output_hidden_states: Optional[bool] = None,
972
+ return_dict: Optional[bool] = None,
973
+ ) -> Union[Tuple, SequenceClassifierOutputWithPast]:
974
+ r"""
975
+ labels (`torch.LongTensor` of shape `(batch_size,)`, *optional*):
976
+ Labels for computing the sequence classification/regression loss. Indices should be in `[0, ...,
977
+ config.num_labels - 1]`. If `config.num_labels == 1` a regression loss is computed (Mean-Square loss), If
978
+ `config.num_labels > 1` a classification loss is computed (Cross-Entropy).
979
+ """
980
+ return_dict = return_dict if return_dict is not None else self.config.use_return_dict
981
+
982
+ transformer_outputs = self.model(
983
+ input_ids,
984
+ attention_mask=attention_mask,
985
+ position_ids=position_ids,
986
+ past_key_values=past_key_values,
987
+ inputs_embeds=inputs_embeds,
988
+ use_cache=use_cache,
989
+ output_attentions=output_attentions,
990
+ output_hidden_states=output_hidden_states,
991
+ return_dict=return_dict,
992
+ )
993
+ hidden_states = transformer_outputs[0]
994
+ logits = self.score(hidden_states)
995
+
996
+ if input_ids is not None:
997
+ batch_size = input_ids.shape[0]
998
+ else:
999
+ batch_size = inputs_embeds.shape[0]
1000
+
1001
+ if self.config.pad_token_id is None and batch_size != 1:
1002
+ raise ValueError("Cannot handle batch sizes > 1 if no padding token is defined.")
1003
+ if self.config.pad_token_id is None:
1004
+ sequence_lengths = -1
1005
+ else:
1006
+ if input_ids is not None:
1007
+ sequence_lengths = (torch.eq(input_ids, self.config.pad_token_id).long().argmax(-1) - 1).to(
1008
+ logits.device
1009
+ )
1010
+ else:
1011
+ sequence_lengths = -1
1012
+
1013
+ pooled_logits = logits[torch.arange(batch_size, device=logits.device), sequence_lengths]
1014
+
1015
+ loss = None
1016
+ if labels is not None:
1017
+ labels = labels.to(logits.device)
1018
+ if self.config.problem_type is None:
1019
+ if self.num_labels == 1:
1020
+ self.config.problem_type = "regression"
1021
+ elif self.num_labels > 1 and (labels.dtype == torch.long or labels.dtype == torch.int):
1022
+ self.config.problem_type = "single_label_classification"
1023
+ else:
1024
+ self.config.problem_type = "multi_label_classification"
1025
+
1026
+ if self.config.problem_type == "regression":
1027
+ loss_fct = MSELoss()
1028
+ if self.num_labels == 1:
1029
+ loss = loss_fct(pooled_logits.squeeze(), labels.squeeze())
1030
+ else:
1031
+ loss = loss_fct(pooled_logits, labels)
1032
+ elif self.config.problem_type == "single_label_classification":
1033
+ loss_fct = CrossEntropyLoss()
1034
+ loss = loss_fct(pooled_logits.view(-1, self.num_labels), labels.view(-1))
1035
+ elif self.config.problem_type == "multi_label_classification":
1036
+ loss_fct = BCEWithLogitsLoss()
1037
+ loss = loss_fct(pooled_logits, labels)
1038
+ if not return_dict:
1039
+ output = (pooled_logits,) + transformer_outputs[1:]
1040
+ return ((loss,) + output) if loss is not None else output
1041
+
1042
+ return SequenceClassifierOutputWithPast(
1043
+ loss=loss,
1044
+ logits=pooled_logits,
1045
+ past_key_values=transformer_outputs.past_key_values,
1046
+ hidden_states=transformer_outputs.hidden_states,
1047
+ attentions=transformer_outputs.attentions,
1048
+ )
pytorch_model.bin ADDED
@@ -0,0 +1,3 @@
 
 
 
 
1
+ version https://git-lfs.github.com/spec/v1
2
+ oid sha256:2d5cf8e663977e64946246ae5baadf930951006a024ac025a8b7fe33667e8728
3
+ size 4345787766
special_tokens_map.json ADDED
@@ -0,0 +1,23 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ {
2
+ "bos_token": {
3
+ "content": "<s>",
4
+ "lstrip": false,
5
+ "normalized": false,
6
+ "rstrip": false,
7
+ "single_word": false
8
+ },
9
+ "eos_token": {
10
+ "content": "</s>",
11
+ "lstrip": false,
12
+ "normalized": false,
13
+ "rstrip": false,
14
+ "single_word": false
15
+ },
16
+ "unk_token": {
17
+ "content": "<unk>",
18
+ "lstrip": false,
19
+ "normalized": false,
20
+ "rstrip": false,
21
+ "single_word": false
22
+ }
23
+ }
tokenization_index.py ADDED
@@ -0,0 +1,266 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ # coding=utf-8
2
+ # Copyright 2022 EleutherAI and the HuggingFace Inc. team. All rights reserved.
3
+ #
4
+ # This code is based on EleutherAI's GPT-NeoX library and the GPT-NeoX
5
+ # and OPT implementations in this library. It has been modified from its
6
+ # original forms to accommodate minor architectural differences compared
7
+ # to GPT-NeoX and OPT used by the Meta AI team that trained the model.
8
+ #
9
+ # Licensed under the Apache License, Version 2.0 (the "License");
10
+ # you may not use this file except in compliance with the License.
11
+ # You may obtain a copy of the License at
12
+ #
13
+ # http://www.apache.org/licenses/LICENSE-2.0
14
+ #
15
+ # Unless required by applicable law or agreed to in writing, software
16
+ # distributed under the License is distributed on an "AS IS" BASIS,
17
+ # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
18
+ # See the License for the specific language governing permissions and
19
+ # limitations under the License.
20
+
21
+ """Tokenization classes for Index, Modify from llama tokenzier."""
22
+ import os
23
+ from shutil import copyfile
24
+ from typing import Any, Dict, List, Optional, Tuple
25
+
26
+ import sentencepiece as spm
27
+
28
+ from transformers.tokenization_utils import AddedToken, PreTrainedTokenizer
29
+ from transformers.utils import logging
30
+
31
+
32
+ logger = logging.get_logger(__name__)
33
+
34
+ VOCAB_FILES_NAMES = {"vocab_file": "tokenizer.model"}
35
+
36
+ PRETRAINED_VOCAB_FILES_MAP = {}
37
+
38
+ SPIECE_UNDERLINE = "▁"
39
+
40
+
41
+ class IndexTokenizer(PreTrainedTokenizer):
42
+ """
43
+ Construct a Index tokenizer. Based on Byte-Pair-Encoding.
44
+
45
+ Args:
46
+ vocab_file (`str`):
47
+ Path to the vocabulary file.
48
+ """
49
+
50
+ vocab_files_names = VOCAB_FILES_NAMES
51
+ pretrained_vocab_files_map = PRETRAINED_VOCAB_FILES_MAP
52
+ model_input_names = ["input_ids", "attention_mask"]
53
+
54
+ def __init__(
55
+ self,
56
+ vocab_file,
57
+ unk_token="<unk>",
58
+ bos_token="<s>",
59
+ eos_token="</s>",
60
+ pad_token=None,
61
+ sp_model_kwargs: Optional[Dict[str, Any]] = None,
62
+ add_bos_token=False,
63
+ add_eos_token=False,
64
+ decode_with_prefix_space=False,
65
+ clean_up_tokenization_spaces=False,
66
+ legacy=False,
67
+ **kwargs,
68
+ ):
69
+ self.sp_model_kwargs = {} if sp_model_kwargs is None else sp_model_kwargs
70
+ bos_token = AddedToken(bos_token, lstrip=False, rstrip=False) if isinstance(bos_token, str) else bos_token
71
+ eos_token = AddedToken(eos_token, lstrip=False, rstrip=False) if isinstance(eos_token, str) else eos_token
72
+ unk_token = AddedToken(unk_token, lstrip=False, rstrip=False) if isinstance(unk_token, str) else unk_token
73
+ pad_token = AddedToken(pad_token, lstrip=False, rstrip=False) if isinstance(pad_token, str) else pad_token
74
+ self.legacy = legacy
75
+
76
+ self.vocab_file = vocab_file
77
+ self.add_bos_token = add_bos_token
78
+ self.add_eos_token = add_eos_token
79
+ self.decode_with_prefix_space = decode_with_prefix_space
80
+ self.sp_model = spm.SentencePieceProcessor(**self.sp_model_kwargs)
81
+ self.sp_model.Load(vocab_file)
82
+ self._no_prefix_space_tokens = None
83
+
84
+ super().__init__(
85
+ bos_token=bos_token,
86
+ eos_token=eos_token,
87
+ unk_token=unk_token,
88
+ pad_token=pad_token,
89
+ add_bos_token=add_bos_token,
90
+ add_eos_token=add_eos_token,
91
+ sp_model_kwargs=self.sp_model_kwargs,
92
+ decode_with_prefix_space=decode_with_prefix_space,
93
+ clean_up_tokenization_spaces=clean_up_tokenization_spaces,
94
+ legacy=legacy,
95
+ **kwargs,
96
+ )
97
+
98
+ @property
99
+ def no_prefix_space_tokens(self):
100
+ if self._no_prefix_space_tokens is None:
101
+ vocab = self.convert_ids_to_tokens(list(range(self.vocab_size)))
102
+ self._no_prefix_space_tokens = {i for i, tok in enumerate(vocab) if not tok.startswith("▁")}
103
+ return self._no_prefix_space_tokens
104
+
105
+ @property
106
+ def vocab_size(self):
107
+ """Returns vocab size"""
108
+ return self.sp_model.get_piece_size()
109
+
110
+ @property
111
+ def bos_token_id(self) -> Optional[int]:
112
+ return self.sp_model.bos_id()
113
+
114
+ @property
115
+ def eos_token_id(self) -> Optional[int]:
116
+ return self.sp_model.eos_id()
117
+
118
+ def get_vocab(self):
119
+ """Returns vocab as a dict"""
120
+ vocab = {self.convert_ids_to_tokens(i): i for i in range(self.vocab_size)}
121
+ vocab.update(self.added_tokens_encoder)
122
+ return vocab
123
+
124
+ def _tokenize(self, text):
125
+ """Returns a tokenized string."""
126
+ return self.sp_model.encode(text, out_type=str)
127
+
128
+ def _convert_token_to_id(self, token):
129
+ """Converts a token (str) in an id using the vocab."""
130
+ return self.sp_model.piece_to_id(token)
131
+
132
+ def _convert_id_to_token(self, index):
133
+ """Converts an index (integer) in a token (str) using the vocab."""
134
+ token = self.sp_model.IdToPiece(index)
135
+ return token
136
+
137
+ def convert_tokens_to_string(self, tokens):
138
+ """Converts a sequence of tokens (string) in a single string."""
139
+ # since we manually add the prefix space, we have to remove it when decoding
140
+ if tokens[0].startswith(SPIECE_UNDERLINE):
141
+ tokens[0] = tokens[0][1:]
142
+
143
+ current_sub_tokens = []
144
+ out_string = ""
145
+ prev_is_special = False
146
+ for i, token in enumerate(tokens):
147
+ # make sure that special tokens are not decoded using sentencepiece model
148
+ if token in self.all_special_tokens:
149
+ if not prev_is_special and i != 0 and self.legacy:
150
+ out_string += " "
151
+ out_string += self.sp_model.decode(current_sub_tokens) + token
152
+ prev_is_special = True
153
+ current_sub_tokens = []
154
+ else:
155
+ current_sub_tokens.append(token)
156
+ prev_is_special = False
157
+ out_string += self.sp_model.decode(current_sub_tokens)
158
+ return out_string
159
+
160
+ def save_vocabulary(self, save_directory, filename_prefix: Optional[str] = None) -> Tuple[str]:
161
+ """
162
+ Save the vocabulary and special tokens file to a directory.
163
+
164
+ Args:
165
+ save_directory (`str`):
166
+ The directory in which to save the vocabulary.
167
+
168
+ Returns:
169
+ `Tuple(str)`: Paths to the files saved.
170
+ """
171
+ if not os.path.isdir(save_directory):
172
+ logger.error(f"Vocabulary path ({save_directory}) should be a directory")
173
+ return
174
+ out_vocab_file = os.path.join(
175
+ save_directory, (filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["vocab_file"]
176
+ )
177
+
178
+ if os.path.abspath(self.vocab_file) != os.path.abspath(out_vocab_file) and os.path.isfile(self.vocab_file):
179
+ copyfile(self.vocab_file, out_vocab_file)
180
+ elif not os.path.isfile(self.vocab_file):
181
+ with open(out_vocab_file, "wb") as fi:
182
+ content_spiece_model = self.sp_model.serialized_model_proto()
183
+ fi.write(content_spiece_model)
184
+
185
+ return (out_vocab_file,)
186
+
187
+ def build_inputs_with_special_tokens(self, token_ids_0, token_ids_1=None):
188
+ bos_token_id = [self.bos_token_id] if self.add_bos_token else []
189
+ eos_token_id = [self.eos_token_id] if self.add_eos_token else []
190
+
191
+ output = bos_token_id + token_ids_0 + eos_token_id
192
+
193
+ if token_ids_1 is not None:
194
+ output = output + bos_token_id + token_ids_1 + eos_token_id
195
+
196
+ return output
197
+
198
+ def get_special_tokens_mask(
199
+ self, token_ids_0: List[int], token_ids_1: Optional[List[int]] = None, already_has_special_tokens: bool = False
200
+ ) -> List[int]:
201
+ """
202
+ Retrieve sequence ids from a token list that has no special tokens added. This method is called when adding
203
+ special tokens using the tokenizer `prepare_for_model` method.
204
+
205
+ Args:
206
+ token_ids_0 (`List[int]`):
207
+ List of IDs.
208
+ token_ids_1 (`List[int]`, *optional*):
209
+ Optional second list of IDs for sequence pairs.
210
+ already_has_special_tokens (`bool`, *optional*, defaults to `False`):
211
+ Whether or not the token list is already formatted with special tokens for the model.
212
+
213
+ Returns:
214
+ `List[int]`: A list of integers in the range [0, 1]: 1 for a special token, 0 for a sequence token.
215
+ """
216
+ if already_has_special_tokens:
217
+ return super().get_special_tokens_mask(
218
+ token_ids_0=token_ids_0, token_ids_1=token_ids_1, already_has_special_tokens=True
219
+ )
220
+
221
+ bos_token_id = [1] if self.add_bos_token else []
222
+ eos_token_id = [1] if self.add_eos_token else []
223
+
224
+ if token_ids_1 is None:
225
+ return bos_token_id + ([0] * len(token_ids_0)) + eos_token_id
226
+ return (
227
+ bos_token_id
228
+ + ([0] * len(token_ids_0))
229
+ + eos_token_id
230
+ + bos_token_id
231
+ + ([0] * len(token_ids_1))
232
+ + eos_token_id
233
+ )
234
+
235
+ def create_token_type_ids_from_sequences(
236
+ self, token_ids_0: List[int], token_ids_1: Optional[List[int]] = None
237
+ ) -> List[int]:
238
+ """
239
+ Creates a mask from the two sequences passed to be used in a sequence-pair classification task. An ALBERT
240
+ sequence pair mask has the following format:
241
+
242
+ ```
243
+ 0 0 0 0 0 0 0 0 0 0 0 1 1 1 1 1 1 1 1 1
244
+ | first sequence | second sequence |
245
+ ```
246
+
247
+ if token_ids_1 is None, only returns the first portion of the mask (0s).
248
+
249
+ Args:
250
+ token_ids_0 (`List[int]`):
251
+ List of ids.
252
+ token_ids_1 (`List[int]`, *optional*):
253
+ Optional second list of IDs for sequence pairs.
254
+
255
+ Returns:
256
+ `List[int]`: List of [token type IDs](../glossary#token-type-ids) according to the given sequence(s).
257
+ """
258
+ bos_token_id = [self.bos_token_id] if self.add_bos_token else []
259
+ eos_token_id = [self.eos_token_id] if self.add_eos_token else []
260
+
261
+ output = [0] * len(bos_token_id + token_ids_0 + eos_token_id)
262
+
263
+ if token_ids_1 is not None:
264
+ output += [1] * len(bos_token_id + token_ids_1 + eos_token_id)
265
+
266
+ return output
tokenizer.model ADDED
@@ -0,0 +1,3 @@
 
 
 
 
1
+ version https://git-lfs.github.com/spec/v1
2
+ oid sha256:3a11626b3c80cbfdd5f4b5585fce8938d178f3a0df739c3dd1c708ce944878af
3
+ size 1010047
tokenizer_config.json ADDED
@@ -0,0 +1,69 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ {
2
+ "add_bos_token": false,
3
+ "add_eos_token": false,
4
+ "bos_token": {
5
+ "__type": "AddedToken",
6
+ "content": "<s>",
7
+ "lstrip": false,
8
+ "normalized": false,
9
+ "rstrip": false,
10
+ "single_word": false
11
+ },
12
+ "eos_token": {
13
+ "__type": "AddedToken",
14
+ "content": "</s>",
15
+ "lstrip": false,
16
+ "normalized": false,
17
+ "rstrip": false,
18
+ "single_word": false
19
+ },
20
+ "unk_token": {
21
+ "__type": "AddedToken",
22
+ "content": "<unk>",
23
+ "lstrip": false,
24
+ "normalized": false,
25
+ "rstrip": false,
26
+ "single_word": false
27
+ },
28
+ "pad_token": {
29
+ "__type": "AddedToken",
30
+ "content": "<unk>",
31
+ "lstrip": false,
32
+ "normalized": false,
33
+ "rstrip": false,
34
+ "single_word": true
35
+ },
36
+ "added_tokens_decoder": {
37
+ "3": {
38
+ "content": "reserved_0",
39
+ "lstrip": false,
40
+ "normalized": false,
41
+ "rstrip": false,
42
+ "single_word": false,
43
+ "special": true
44
+ },
45
+ "4": {
46
+ "content": "reserved_1",
47
+ "lstrip": false,
48
+ "normalized": false,
49
+ "rstrip": false,
50
+ "single_word": false,
51
+ "special": true
52
+ }
53
+ },
54
+ "clean_up_tokenization_spaces": false,
55
+ "spaces_between_special_tokens": false,
56
+ "add_prefix_space": false,
57
+ "legacy": false,
58
+ "model_max_length": 1e+30,
59
+ "sp_model_kwargs": {},
60
+ "tokenizer_class": "IndexTokenizer",
61
+ "auto_map": {
62
+ "AutoTokenizer": [
63
+ "tokenization_index.IndexTokenizer",
64
+ null
65
+ ]
66
+ },
67
+ "use_default_system_prompt": false,
68
+ "chat_template": "{% if messages[0]['role'] == 'system' %}{% set system_message = messages[0]['content'] %}{% endif %}{% if system_message is defined %}{{ unk_token + system_message }}{% endif %}{% for message in messages %}{% set content = message['content'] %}{% if message['role'] == 'user' %}{{ 'reserved_0' + content.strip() + 'reserved_1' }}{% elif message['role'] == 'assistant' %}{{ content.strip() }}{% endif %}{% endfor %}"
69
+ }