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+ Yi Series Models License Agreement
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+ Version: 2.0
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+ Date of Release: November 4, 2023
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+
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+ 1. Definition
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+ “Agreement” refers to the terms and conditions defined in this Yi Series Models
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+ License Agreement for the use, reproduction and distribution of Yi Series
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+ Models.
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+ status of optimizer).
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+ capabilities named “Yi” provided by the Licensor, including Yi-6B, Yi-34B etc.
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+ “Derivatives” refers to all modifications to Yi Series Models, work based on Yi
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+ for a specific purpose of the Yi Series Models and their output results. You
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+ assume all responsibilities for the risks and consequences arising from the use,
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+ reproduction, distribution of the Yi Series Models, and the creation of
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+ Derivatives.
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+ The Licensor complies with Laws and Regulations at all stages of model training,
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+ maintaining the legality, authenticity, accuracy, objectivity, and diversity of
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+ data and algorithms. The Licensor is not liable for any direct, indirect,
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+ incidental consequences, and other losses or damages related to your use,
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+ reproduction, and distribution of the Yi Series Models, and the creation of
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+ Derivatives under this Agreement. This includes but is not limited to:
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+ 1) The Licensor is not responsible for data security risks resulting from your
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+ use of the Yi Series Models.
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+ 2) The Yi Series Models may contain Personal Information. When you use Yi Series
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+ Models, you acknowledge that you are the data processing entity as defined under
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+ the Laws and Regulations responsible for determining the processing methods and
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+ purposes of Personal Information. You must comply with legal requirements for
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+ processing any Personal Information that may be contained in the Yi Series
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+ Models and assume the associated legal responsibilities, as well as the risks
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+ and consequences of processing Personal Information.
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+ 3) The Licensor is not liable for reputation risks arising from your use of the
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+ Yi Series Models or the output results of the Yi Series Models.
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+ your use of the Yi Series Models’ output results.
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+ If your use, reproduction, distribution of the Yi Series Models, or the creation
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+ of Derivatives result in losses to the Licensor, the Licensor has the right to
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+ seek compensation from you. For any claims made by Third Parties against the
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+ Licensor related to your use, reproduction, and distribution of the Yi Series
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+ from harm.
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+ 5. Dispute Resolution
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+ The stipulation, effectiveness, interpretation, performance, modification, and
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+ termination of the Agreement, the use, copy and Distribute of the Yi Series
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+ Models, and dispute resolution associated with your use, copy and distribution
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+ shall be governed by the laws of the mainland of the People's Republic of China
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+ (for the purposes of this agreement only, excluding Hong Kong, Macau, and
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+ Taiwan), and the application of conflict of laws is excluded.
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+ Any disputes arising from the use, copy or distribution of the Yi Series Models
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+ should first be resolved through amicable negotiations. If negotiations fail,
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+ legal proceedings should be initiated in the People's Court at the location of
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+ the Licensor.
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+
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+
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+ 6. Effectiveness and Termination of the Agreement
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+ Your use of the Yi Series Models signifies that you have read and agreed to be
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+ bound by the terms of the Agreement. The Agreement becomes effective from the
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+ date of your use of the Yi Series Models and will terminate from the date you
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+ cease using the Yi Series Models. If you violate any terms or restrictions in
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+ the Agreement, the Licensor reserves the right to terminate the Agreement.
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+
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+ Upon termination of the Agreement, you must immediately cease using the Yi
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+ Series Models. Section 4, "Disclaimer and Limitation of Liability," and Section
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+ 5, "Dispute Resolution," of this Agreement remain in effect after the
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+ termination of this Agreement.
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+ 7. Updates to the Agreement and Contact Information
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+
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+ The Licensor reserves the right to update the Agreement from time to time. The
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+ latest version of the Agreement will be posted by the Licensor through
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+ https://01.ai.
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+
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+ For any questions related to licensing and copyright, please contact the
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+ Licensor at yi@01.ai.
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+
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+
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+ Yi系列模型许可协议
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+ 版本: 2.0
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+ 发布日期: 2023年11月4日
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+
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+ 1. 定义
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+
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+ “协议”是指本协议中定义Yi系列模型使用、复制和分发的条款和条件。
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+
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+ “模型”是指任何附带的基于机器学习的组件(包括检查点),包括学习的权重、参数(包括优
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+ 化器状态)。
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+
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+ “Yi系列模型”是指许可方开源���以Yi命名的不同规格、不同能力的模型,包括
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+ Yi-6B、Yi-34B等。
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+
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+ “模型衍生品”是指对Yi系列模型的所有修改、基于Yi系列模型的工作,或通过将Yi系列模型
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+ 的权重、参数、激活或输出模式转移到其他模型而创建或初始化的任何其他模型,以使其他
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+ 模型的性能与Yi系列模型类似,包括但不限于需要使用中间数据表示的提取方法或基于Yi系
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+ 列模型生成合成数据来训练其他模型的方法。
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+
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+ “许可方”是指北京零一万物信息技术有限公司。
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+
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+ “您”是指行使本协议授予的权限和/或出于任何目的和在任何使用领域使用Yi系列模型的个
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+ 人或法人实体。
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+
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+ “第三方”是指您之外的任何个人、法人实体或非法人组织。
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+
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+ “分发”是指向第三方传输、复制、发布或以其他方式共享Yi系列模型,包括将Yi系列模型作
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+ 为通过电子或其他远程方式(例如基于 API 或 Web 访问的任何 SaaS 软件或 PaaS 软
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+ 件)。
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+
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+ “商业用途”是指使用Yi系列模型,直接或间接为实体或个人进行运营、推广或产生收入,或
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+ 用于任何其他盈利目的。
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+
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+ “法律法规”是指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)
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+ 的法律及行政法规。
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+
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+ “个人信息”是指以电子或者其他方式记录的与已识别或者可识别的自然人有关的各种信息,
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+ 不包括匿名化处理后的信息。
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+
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+ “标识” 是指任何商标、服务标记、商号、域名、网站名称或其他带有显著品牌特征的标
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+ 记。
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+
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+
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+ 2. 许可及许可限制
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+
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+ 许可方特此授予您非排他性、全球性、不可转让、不可再许可、可撤销、免版税的版权许
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+ 可。您必须满足如下许可限制条件:
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+
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+ 1) 您对Yi系列模型的使用应遵守法律法规以及其他国家/地区适用的法律要求、尊重社会公
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+ 德和伦理道德。包括但不限于您不得将Yi系列模型用作危害国家安全、宣扬恐怖主义、极端
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+ 主义,宣扬民族及种族仇恨、歧视,暴力、色情,以及虚假有害信息等法律法规以及其他国
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+ 家/地区适用的法律要求禁止的目的。
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+
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+ 2) 您不得出于军事或非法目的,或以法律法规以及其他国家/地区适用的法律要求所不允许
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+ 的方式a) 使用、复制、或分发Yi系列模型; 或b) 创建Yi系列模型的全部或部分衍生品。
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+
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+ 3) 您对Yi系列模型的使用(包括使用Yi系列模型的输出)以及模型衍生品的创建不得侵犯
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+ 任何第三方的合法权益,包括但不限于他人肖像权、名誉权、隐私权等人格权,著作权、专
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+ 利权、商业秘密等知识产权,或其他财产权益。
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+
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+ 4) 您必须向Yi系列模型及Yi系列模型衍生品的任何第三方使用者明确Yi系列模型的来源为
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+ 许可方并向其提供本协议的副本。
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+
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+ 5) 若您修改Yi系列模型得到模型衍生品,您必须以显著的方式说明修改的内容,且上述修
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+ 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
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+ 中的许可限制条件。
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+
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+ 如果您计划将 Yi系列模型及模型衍生品用作商业用途,您应当事先通过第7款“协议更新及
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+ 联系方式”中的方式联系许可方进行登记并获得许可方的书面授权。若您取得许可方授权将
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+ Yi系列模型及模型衍生品用作商业用途时,您应满足许可方上述许可限制条件。
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+
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+
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+ 3. 知识产权
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+
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+ Yi系列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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+
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+ 在任何情况下,未经许可方事先书面同意,您不得以任何方式使用许可方的任何标识。由于
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+ 您违反本协议使用许可方的标识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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+
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+
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+ 4. 免责声明及责任限制
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+
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+ Yi系列模型按“原样”提供。许可方不对Yi系列模型提供任何明示或暗示的保证,包括但不限
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+ 于:模型及输出结果的稳定性、所有权、适销性、非侵权性、或特定用途适用性。您将对适
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+ 用、复制及分发Yi系列模型以及创建模型衍生品所产生的风险与后果承担所有责任。
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+
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+ 许可方在模型训练的所有阶段都遵守法律法规,坚持维护数据和算法的合法、真实、准确、
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+ 客观和多样性。许可方不对您根据本协议使用、复制及分发Yi系列模型,以及创建模型衍生
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+ 品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。包括
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+ 但不限于:
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+
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+ 1) 许可方不承担您因使用Yi系列模型而导致的数据安全风险。
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+
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+ 2) Yi系列模型中可能包含个人信息。在您使用Yi系列模型的过程中,您承认您为法律法规
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+ 定义下决定个人信息处理方式和目的的个人信息处理者。您应遵守法律法规要求处理Yi系列
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+ 模型中可能包含的个人信息,并承担相应的法律责任,以及处理个人信息的风险和后果。
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+
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+ 3) 许可方不承担您使用Yi系列模型或模型输出结果而产生的声誉风险。
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+
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+ 4) 许可方不承担您使用Yi系列模型的输出结果涉及的知识产权风险。
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+
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+ 若由于您对Yi系列模型的使用、复制或分发,或者创建模型衍生品而导致许可方遭受损失,
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+ 许可方有权要求您对许可方的损失进行赔偿。对于任何第三方向许可方提出的因您使用、复
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+ 制或分发Yi系列模型或创建模型衍生品行为的相关索赔,许可方有权要求您为许可方进行辩
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+ 护、赔偿并使许可方免受损害。
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+
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+
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+ 5. 争议解决
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+
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+ 协议的订立、效力、解释、履行、修改和终止,使用、复制和分发Yi系列模型以及争议解决
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+ 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,并
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+ 排除冲突法的适用。
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+
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+ 因使用、复制和分发Yi系列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
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+ 决。协商不成时,应向许可方所在地人民法院提起诉讼。
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+
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+
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+ 6. 协议的生效及终止
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+
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+ 您使用Yi系列模型即表示您已阅读并同意接受协议的约束。协议自您使用Yi系列模型之日起
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+ 生效并将在您停止使用Yi系列模型之日起终止。若您违反协议中的任何条款或限制,许可方
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+ 有权终止协议。
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+
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+ 若协议终止,您需立即停止使用Yi系列模型。本协议第4条“免责声明及责任限制”及第5条
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+ “争议解决”在协议终止后仍有效。
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+
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+
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+ 7. 协议更新及联系方式
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+
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+ 许可方有权对协议进行不时更新。许可方将通过https://01.ai公布协议最新版本。有关许
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+ 可和版权的任何问题,请通过yi@01.ai 与许可方联系。
config.json ADDED
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+ {
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+ "_name_or_path": "/workspace/process/01-ai_yi-34b-200k/source",
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+ "architectures": [
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+ "YiForCausalLM"
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+ ],
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+ "auto_map": {
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+ "AutoConfig": "configuration_yi.YiConfig",
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+ "AutoModel": "modeling_yi.YiModel",
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+ "AutoModelForCausalLM": "modeling_yi.YiForCausalLM"
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+ },
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+ "bos_token_id": 1,
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+ "eos_token_id": 2,
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+ "hidden_act": "silu",
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+ "hidden_size": 7168,
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+ "initializer_range": 0.02,
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+ "intermediate_size": 20480,
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+ "max_position_embeddings": 200000,
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+ "model_type": "Yi",
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+ "num_attention_heads": 56,
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+ "num_hidden_layers": 60,
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+ "num_key_value_heads": 8,
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+ "pad_token_id": 0,
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+ "pretraining_tp": 1,
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+ "quantization_config": {
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+ "bits": 4,
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+ "group_size": 128,
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+ "quant_method": "awq",
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+ "version": "gemm",
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+ "zero_point": true
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+ },
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+ "rms_norm_eps": 1e-05,
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+ "rope_theta": 5000000.0,
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+ "tie_word_embeddings": false,
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+ "torch_dtype": "float16",
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+ "transformers_version": "4.35.0",
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+ "use_cache": true,
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+ "vocab_size": 64000
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+ }
configuration_yi.py ADDED
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+ """ Yi model configuration"""
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+ from transformers.configuration_utils import PretrainedConfig
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+ from transformers.utils import logging
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+
5
+ logger = logging.get_logger(__name__)
6
+
7
+ Yi_PRETRAINED_CONFIG_ARCHIVE_MAP = {}
8
+
9
+
10
+ class YiConfig(PretrainedConfig):
11
+ r"""
12
+ This is the configuration class to store the configuration of a [`YiModel`]. It is used to instantiate an Yi
13
+ model according to the specified arguments, defining the model architecture. Instantiating a configuration with the
14
+ defaults will yield a similar configuration to that of the Yi model.
15
+
16
+ Configuration objects inherit from [`PretrainedConfig`] and can be used to control the model outputs. Read the
17
+ documentation from [`PretrainedConfig`] for more information.
18
+
19
+
20
+ Args:
21
+ vocab_size (`int`, *optional*, defaults to 64000):
22
+ Vocabulary size of the Yi model. Defines the number of different tokens that can be represented by the
23
+ `inputs_ids` passed when calling [`YiModel`]
24
+ hidden_size (`int`, *optional*, defaults to 4096):
25
+ Dimension of the hidden representations.
26
+ intermediate_size (`int`, *optional*, defaults to 11008):
27
+ Dimension of the MLP representations.
28
+ num_hidden_layers (`int`, *optional*, defaults to 32):
29
+ Number of hidden layers in the Transformer encoder.
30
+ num_attention_heads (`int`, *optional*, defaults to 32):
31
+ Number of attention heads for each attention layer in the Transformer encoder.
32
+ num_key_value_heads (`int`, *optional*):
33
+ This is the number of key_value heads that should be used to implement Grouped Query Attention. If
34
+ `num_key_value_heads=num_attention_heads`, the model will use Multi Head Attention (MHA), if
35
+ `num_key_value_heads=1 the model will use Multi Query Attention (MQA) otherwise GQA is used. When
36
+ converting a multi-head checkpoint to a GQA checkpoint, each group key and value head should be constructed
37
+ by meanpooling all the original heads within that group. For more details checkout [this
38
+ paper](https://arxiv.org/pdf/2305.13245.pdf). If it is not specified, will default to
39
+ `num_attention_heads`.
40
+ hidden_act (`str` or `function`, *optional*, defaults to `"silu"`):
41
+ The non-linear activation function (function or string) in the decoder.
42
+ max_position_embeddings (`int`, *optional*, defaults to 4096):
43
+ The maximum sequence length that this model might ever be used with. Typically set this to something large
44
+ just in case (e.g., 512 or 1024 or 2048 or 4096).
45
+ initializer_range (`float`, *optional*, defaults to 0.02):
46
+ The standard deviation of the truncated_normal_initializer for initializing all weight matrices.
47
+ rms_norm_eps (`float`, *optional*, defaults to 1e-5):
48
+ The epsilon used by the rms normalization layers.
49
+ use_cache (`bool`, *optional*, defaults to `True`):
50
+ Whether or not the model should return the last key/values attentions (not used by all models). Only
51
+ relevant if `config.is_decoder=True`.
52
+ tie_word_embeddings(`bool`, *optional*, defaults to `False`):
53
+ Whether to tie weight embeddings
54
+ output_attentions (`bool`, *optional*, defaults to `False`):
55
+ Whether or not to output attentions.
56
+ rope_theta (`float`, *optional*, defaults to 5000000.0):
57
+ The base period of the RoPE embeddings.
58
+ Example:
59
+
60
+ ```python
61
+ >>> from transformers import YiModel, YiConfig
62
+
63
+ >>> # Initializing a Yi style configuration
64
+ >>> configuration = YiConfig()
65
+
66
+ >>> # Initializing a model from the Yi style configuration
67
+ >>> model = YiModel(configuration)
68
+
69
+ >>> # Accessing the model configuration
70
+ >>> configuration = model.config
71
+ ```"""
72
+ model_type = "Yi"
73
+ keys_to_ignore_at_inference = ["past_key_values"]
74
+
75
+ def __init__(
76
+ self,
77
+ vocab_size=64000,
78
+ hidden_size=4096,
79
+ intermediate_size=11008,
80
+ num_hidden_layers=32,
81
+ num_attention_heads=32,
82
+ num_key_value_heads=4,
83
+ hidden_act="silu",
84
+ max_position_embeddings=4096,
85
+ initializer_range=0.02,
86
+ rms_norm_eps=1e-5,
87
+ use_cache=True,
88
+ pad_token_id=0,
89
+ bos_token_id=1,
90
+ eos_token_id=2,
91
+ tie_word_embeddings=False,
92
+ output_attentions=False,
93
+ rope_theta=5000000.0,
94
+ **kwargs,
95
+ ):
96
+ self.vocab_size = vocab_size
97
+ self.max_position_embeddings = max_position_embeddings
98
+ self.hidden_size = hidden_size
99
+ self.intermediate_size = intermediate_size
100
+ self.num_hidden_layers = num_hidden_layers
101
+ self.num_attention_heads = num_attention_heads
102
+
103
+ # for backward compatibility
104
+ if num_key_value_heads is None:
105
+ num_key_value_heads = num_attention_heads
106
+
107
+ self.num_key_value_heads = num_key_value_heads
108
+ self.hidden_act = hidden_act
109
+ self.initializer_range = initializer_range
110
+ self.rms_norm_eps = rms_norm_eps
111
+ self.use_cache = use_cache
112
+ self.output_attentions = output_attentions
113
+ self.rope_theta = rope_theta
114
+
115
+ super().__init__(
116
+ pad_token_id=pad_token_id,
117
+ bos_token_id=bos_token_id,
118
+ eos_token_id=eos_token_id,
119
+ tie_word_embeddings=tie_word_embeddings,
120
+ **kwargs,
121
+ )
generation_config.json ADDED
@@ -0,0 +1,7 @@
 
 
 
 
 
 
 
 
1
+ {
2
+ "_from_model_config": true,
3
+ "bos_token_id": 1,
4
+ "eos_token_id": 2,
5
+ "pad_token_id": 0,
6
+ "transformers_version": "4.34.0"
7
+ }
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modeling_yi.py ADDED
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1
+ """ PyTorch Yi model."""
2
+ import math
3
+ from typing import List, Optional, Tuple, Union
4
+
5
+ import torch.utils.checkpoint
6
+ from einops import repeat
7
+ from torch import nn
8
+ from torch.nn import BCEWithLogitsLoss, CrossEntropyLoss, MSELoss
9
+ from transformers.activations import ACT2FN
10
+ from transformers.modeling_outputs import (
11
+ BaseModelOutputWithPast,
12
+ CausalLMOutputWithPast,
13
+ SequenceClassifierOutputWithPast,
14
+ )
15
+ from transformers.modeling_utils import PreTrainedModel
16
+ from transformers.pytorch_utils import ALL_LAYERNORM_LAYERS
17
+ from transformers.utils import (
18
+ add_start_docstrings,
19
+ add_start_docstrings_to_model_forward,
20
+ logging,
21
+ replace_return_docstrings,
22
+ )
23
+
24
+ from .configuration_yi import YiConfig
25
+
26
+ is_flash_attn_available = True
27
+ try:
28
+ from flash_attn import flash_attn_func
29
+ except Exception:
30
+ is_flash_attn_available = False
31
+
32
+ logger = logging.get_logger(__name__)
33
+
34
+ _CONFIG_FOR_DOC = "YiConfig"
35
+
36
+
37
+ # Copied from transformers.models.bart.modeling_bart._make_causal_mask
38
+ def _make_causal_mask(
39
+ input_ids_shape: torch.Size,
40
+ dtype: torch.dtype,
41
+ device: torch.device,
42
+ past_key_values_length: int = 0,
43
+ ):
44
+ """
45
+ Make causal mask used for bi-directional self-attention.
46
+ """
47
+ bsz, tgt_len = input_ids_shape
48
+ mask = torch.full(
49
+ (tgt_len, tgt_len),
50
+ torch.tensor(torch.finfo(dtype).min, device=device),
51
+ device=device,
52
+ )
53
+ mask_cond = torch.arange(mask.size(-1), device=device)
54
+ mask.masked_fill_(mask_cond < (mask_cond + 1).view(mask.size(-1), 1), 0)
55
+ mask = mask.to(dtype)
56
+
57
+ if past_key_values_length > 0:
58
+ mask = torch.cat(
59
+ [
60
+ torch.zeros(
61
+ tgt_len, past_key_values_length, dtype=dtype, device=device
62
+ ),
63
+ mask,
64
+ ],
65
+ dim=-1,
66
+ )
67
+ return mask[None, None, :, :].expand(
68
+ bsz, 1, tgt_len, tgt_len + past_key_values_length
69
+ )
70
+
71
+
72
+ # Copied from transformers.models.bart.modeling_bart._expand_mask
73
+ def _expand_mask(mask: torch.Tensor, dtype: torch.dtype, tgt_len: Optional[int] = None):
74
+ """
75
+ Expands attention_mask from `[bsz, seq_len]` to `[bsz, 1, tgt_seq_len, src_seq_len]`.
76
+ """
77
+ bsz, src_len = mask.size()
78
+ tgt_len = tgt_len if tgt_len is not None else src_len
79
+
80
+ expanded_mask = mask[:, None, None, :].expand(bsz, 1, tgt_len, src_len).to(dtype)
81
+
82
+ inverted_mask = 1.0 - expanded_mask
83
+
84
+ return inverted_mask.masked_fill(
85
+ inverted_mask.to(torch.bool), torch.finfo(dtype).min
86
+ )
87
+
88
+
89
+ class YiRMSNorm(nn.Module):
90
+ def __init__(self, hidden_size, eps=1e-5):
91
+ """
92
+ YiRMSNorm is equivalent to T5LayerNorm
93
+ """
94
+ super().__init__()
95
+ self.weight = nn.Parameter(torch.ones(hidden_size))
96
+ self.variance_epsilon = eps
97
+
98
+ def forward(self, hidden_states):
99
+ input_dtype = hidden_states.dtype
100
+ hidden_states = hidden_states.to(torch.float32)
101
+ variance = hidden_states.pow(2).mean(-1, keepdim=True)
102
+ hidden_states = hidden_states * torch.rsqrt(variance + self.variance_epsilon)
103
+
104
+ return self.weight * hidden_states.to(input_dtype)
105
+
106
+
107
+ ALL_LAYERNORM_LAYERS.append(YiRMSNorm)
108
+
109
+
110
+ class YiRotaryEmbedding(torch.nn.Module):
111
+ def __init__(self, dim, max_position_embeddings=4096, base=5000000, device=None):
112
+ super().__init__()
113
+
114
+ self.dim = dim
115
+ self.max_position_embeddings = max_position_embeddings
116
+ self.base = base
117
+
118
+ # Build here to make `torch.jit.trace` work.
119
+ self._set_cos_sin_cache(seq_len=max_position_embeddings, device=device)
120
+
121
+ def _set_cos_sin_cache(self, seq_len, device):
122
+ self.max_seq_len_cached = seq_len
123
+ inv_freq = 1.0 / (
124
+ self.base ** (torch.arange(0, self.dim, 2).float().to(device) / self.dim)
125
+ )
126
+ t = torch.arange(self.max_seq_len_cached, device=device, dtype=torch.float32)
127
+ freqs = torch.einsum("i,j->ij", t, inv_freq)
128
+ # Different from paper, but it uses a different permutation in order to obtain the same calculation
129
+ emb = torch.cat((freqs, freqs), dim=-1)
130
+ self.register_buffer(
131
+ "cos_cached", emb.cos()[None, None, :, :], persistent=False
132
+ )
133
+ self.register_buffer(
134
+ "sin_cached", emb.sin()[None, None, :, :], persistent=False
135
+ )
136
+
137
+ def forward(self, x, seq_len=None):
138
+ # x: [bs, num_attention_heads, seq_len, head_size]
139
+ if seq_len > self.max_seq_len_cached:
140
+ self._set_cos_sin_cache(seq_len=seq_len, device=x.device)
141
+
142
+ return (
143
+ self.cos_cached[:, :, :seq_len, ...].to(dtype=x.dtype),
144
+ self.sin_cached[:, :, :seq_len, ...].to(dtype=x.dtype),
145
+ )
146
+
147
+
148
+ def rotate_half(x):
149
+ """Rotates half the hidden dims of the input."""
150
+ x1 = x[..., : x.shape[-1] // 2]
151
+ x2 = x[..., x.shape[-1] // 2 :]
152
+ return torch.cat((-x2, x1), dim=-1)
153
+
154
+
155
+ def apply_rotary_pos_emb(q, k, cos, sin, position_ids, flash_attn_available):
156
+ # The first two dimensions of cos and sin are always 1, so we can `squeeze` them.
157
+ cos = cos.squeeze(1).squeeze(0) # [seq_len, dim]
158
+ sin = sin.squeeze(1).squeeze(0) # [seq_len, dim]
159
+ expand_dim = 2 if flash_attn_available else 1
160
+ cos = cos[position_ids].unsqueeze(expand_dim) # [bs, seq_len, dim]
161
+ sin = sin[position_ids].unsqueeze(expand_dim) # [bs, seq_len, dim]
162
+ q_embed = (q * cos) + (rotate_half(q) * sin)
163
+ k_embed = (k * cos) + (rotate_half(k) * sin)
164
+ return q_embed, k_embed
165
+
166
+
167
+ class YiMLP(nn.Module):
168
+ def __init__(self, hidden_size: int, intermediate_size: int, hidden_act: str):
169
+ super().__init__()
170
+ self.gate_proj = nn.Linear(hidden_size, intermediate_size, bias=False)
171
+ self.down_proj = nn.Linear(intermediate_size, hidden_size, bias=False)
172
+ self.up_proj = nn.Linear(hidden_size, intermediate_size, bias=False)
173
+ self.act_fn = ACT2FN[hidden_act]
174
+
175
+ def forward(self, x):
176
+ return self.down_proj(self.act_fn(self.gate_proj(x)) * self.up_proj(x))
177
+
178
+
179
+ class YiAttention(nn.Module):
180
+ """Multi-headed attention from 'Attention Is All You Need' paper"""
181
+
182
+ def __init__(self, config: YiConfig):
183
+ super().__init__()
184
+ self.config = config
185
+ self.hidden_size = config.hidden_size
186
+ self.num_heads = config.num_attention_heads
187
+ self.head_dim = self.hidden_size // self.num_heads
188
+ self.num_key_value_heads = config.num_key_value_heads
189
+ self.num_key_value_groups = self.num_heads // self.num_key_value_heads
190
+ self.max_position_embeddings = config.max_position_embeddings
191
+
192
+ if (self.head_dim * self.num_heads) != self.hidden_size:
193
+ raise ValueError(
194
+ f"hidden_size must be divisible by num_heads (got `hidden_size`: {self.hidden_size}"
195
+ f" and `num_heads`: {self.num_heads})."
196
+ )
197
+ self.q_proj = nn.Linear(
198
+ self.hidden_size, self.num_heads * self.head_dim, bias=False
199
+ )
200
+ self.k_proj = nn.Linear(
201
+ self.hidden_size, self.num_key_value_heads * self.head_dim, bias=False
202
+ )
203
+ self.v_proj = nn.Linear(
204
+ self.hidden_size, self.num_key_value_heads * self.head_dim, bias=False
205
+ )
206
+ self.o_proj = nn.Linear(
207
+ self.num_heads * self.head_dim, self.hidden_size, bias=False
208
+ )
209
+
210
+ self.rotary_emb = YiRotaryEmbedding(
211
+ self.head_dim,
212
+ max_position_embeddings=self.max_position_embeddings,
213
+ base=self.config.rope_theta,
214
+ )
215
+
216
+ def forward(
217
+ self,
218
+ hidden_states: torch.Tensor,
219
+ attention_mask: Optional[torch.Tensor] = None,
220
+ position_ids: Optional[torch.LongTensor] = None,
221
+ past_key_value: Optional[Tuple[torch.Tensor]] = None,
222
+ output_attentions: bool = False,
223
+ use_cache: bool = False,
224
+ ) -> Tuple[torch.Tensor, Optional[torch.Tensor], Optional[Tuple[torch.Tensor]]]:
225
+ bsz, q_len, _ = hidden_states.size()
226
+
227
+ query_states = self.q_proj(hidden_states).view(
228
+ bsz, q_len, self.num_heads, self.head_dim
229
+ )
230
+
231
+ key_states = self.k_proj(hidden_states).view(
232
+ bsz, q_len, self.num_key_value_heads, self.head_dim
233
+ )
234
+ value_states = self.v_proj(hidden_states).view(
235
+ bsz, q_len, self.num_key_value_heads, self.head_dim
236
+ )
237
+
238
+ if not is_flash_attn_available:
239
+ if self.num_key_value_groups > 1:
240
+ key_states = repeat(
241
+ key_states, f"b n h d -> b n (h {self.num_key_value_groups}) d"
242
+ )
243
+ value_states = repeat(
244
+ value_states, f"b n h d -> b n (h {self.num_key_value_groups}) d"
245
+ )
246
+
247
+ # b n h d -> b h n d
248
+ query_states = query_states.transpose(1, 2)
249
+ key_states = key_states.transpose(1, 2)
250
+ value_states = value_states.transpose(1, 2)
251
+
252
+ seq_dim = 1 if is_flash_attn_available else 2
253
+ kv_seq_len = key_states.shape[seq_dim]
254
+ if past_key_value is not None:
255
+ kv_seq_len += past_key_value[0].shape[seq_dim]
256
+ cos, sin = self.rotary_emb(value_states, seq_len=kv_seq_len)
257
+ query_states, key_states = apply_rotary_pos_emb(
258
+ query_states, key_states, cos, sin, position_ids, is_flash_attn_available
259
+ )
260
+
261
+ if past_key_value is not None:
262
+ # reuse k, v, self_attention
263
+ key_states = torch.cat([past_key_value[0], key_states], dim=seq_dim)
264
+ value_states = torch.cat([past_key_value[1], value_states], dim=seq_dim)
265
+
266
+ past_key_value = (key_states, value_states) if use_cache else None
267
+
268
+ if is_flash_attn_available:
269
+ attn_output = flash_attn_func(
270
+ query_states, key_states, value_states, dropout_p=0.0, causal=True
271
+ )
272
+ else:
273
+ attn_weights = torch.matmul(
274
+ query_states, key_states.transpose(2, 3)
275
+ ) / math.sqrt(self.head_dim)
276
+
277
+ if attn_weights.size() != (bsz, self.num_heads, q_len, kv_seq_len):
278
+ raise ValueError(
279
+ f"Attention weights should be of size {(bsz, self.num_heads, q_len, kv_seq_len)}, but is"
280
+ f" {attn_weights.size()}"
281
+ )
282
+
283
+ if attention_mask is not None:
284
+ if attention_mask.size() != (bsz, 1, q_len, kv_seq_len):
285
+ raise ValueError(
286
+ f"Attention mask should be of size {(bsz, 1, q_len, kv_seq_len)}, but is"
287
+ f"{attention_mask.size()}"
288
+ )
289
+ attn_weights = attn_weights + attention_mask
290
+ dtype_min = torch.tensor(
291
+ torch.finfo(attn_weights.dtype).min,
292
+ device=attn_weights.device,
293
+ dtype=attn_weights.dtype,
294
+ )
295
+ attn_weights = torch.max(attn_weights, dtype_min)
296
+
297
+ # upcast attention to fp32
298
+ attn_weights = nn.functional.softmax(
299
+ attn_weights, dim=-1, dtype=torch.float32
300
+ ).to(query_states.dtype)
301
+ attn_output = torch.matmul(attn_weights, value_states)
302
+
303
+ if attn_output.size() != (bsz, self.num_heads, q_len, self.head_dim):
304
+ raise ValueError(
305
+ f"`attn_output` should be of size {(bsz, self.num_heads, q_len, self.head_dim)}, but is"
306
+ f" {attn_output.size()}"
307
+ )
308
+
309
+ if not is_flash_attn_available:
310
+ attn_output = attn_output.transpose(1, 2)
311
+
312
+ attn_output = attn_output.reshape(bsz, q_len, self.hidden_size)
313
+
314
+ attn_output = self.o_proj(attn_output)
315
+
316
+ if not output_attentions:
317
+ attn_weights = None
318
+
319
+ return attn_output, attn_weights, past_key_value
320
+
321
+
322
+ class YiDecoderLayer(nn.Module):
323
+ def __init__(self, config: YiConfig):
324
+ super().__init__()
325
+
326
+ self.hidden_size = config.hidden_size
327
+ self.self_attn = YiAttention(config=config)
328
+ self.mlp = YiMLP(
329
+ hidden_size=self.hidden_size,
330
+ intermediate_size=config.intermediate_size,
331
+ hidden_act=config.hidden_act,
332
+ )
333
+
334
+ self.ln1 = YiRMSNorm(config.hidden_size, eps=config.rms_norm_eps)
335
+ self.ln2 = YiRMSNorm(config.hidden_size, eps=config.rms_norm_eps)
336
+
337
+ def forward(
338
+ self,
339
+ hidden_states: torch.Tensor,
340
+ attention_mask: Optional[torch.Tensor] = None,
341
+ position_ids: Optional[torch.LongTensor] = None,
342
+ past_key_value: Optional[Tuple[torch.Tensor]] = None,
343
+ output_attentions: Optional[bool] = False,
344
+ use_cache: Optional[bool] = False,
345
+ ) -> Tuple[
346
+ torch.FloatTensor, Optional[Tuple[torch.FloatTensor, torch.FloatTensor]]
347
+ ]:
348
+ """
349
+ Args:
350
+ hidden_states (`torch.FloatTensor`): input to the layer of shape `(batch, seq_len, embed_dim)`
351
+ attention_mask (`torch.FloatTensor`, *optional*): attention mask of size
352
+ `(batch, 1, tgt_len, src_len)` where padding elements are indicated by very large negative values.
353
+ output_attentions (`bool`, *optional*):
354
+ Whether or not to return the attentions tensors of all attention layers. See `attentions` under
355
+ returned tensors for more detail.
356
+ use_cache (`bool`, *optional*):
357
+ If set to `True`, `past_key_values` key value states are returned and can be used to speed up decoding
358
+ (see `past_key_values`).
359
+ past_key_value (`Tuple(torch.FloatTensor)`, *optional*): cached past key and value projection states
360
+ """
361
+
362
+ residual = hidden_states
363
+
364
+ hidden_states = self.ln1(hidden_states)
365
+
366
+ # Self Attention
367
+ hidden_states, self_attn_weights, present_key_value = self.self_attn(
368
+ hidden_states=hidden_states,
369
+ attention_mask=attention_mask,
370
+ position_ids=position_ids,
371
+ past_key_value=past_key_value,
372
+ output_attentions=output_attentions,
373
+ use_cache=use_cache,
374
+ )
375
+ hidden_states = residual + hidden_states
376
+
377
+ # Fully Connected
378
+ residual = hidden_states
379
+ hidden_states = self.ln2(hidden_states)
380
+ hidden_states = self.mlp(hidden_states)
381
+ hidden_states = residual + hidden_states
382
+
383
+ outputs = (hidden_states,)
384
+
385
+ if output_attentions:
386
+ outputs += (self_attn_weights,)
387
+
388
+ if use_cache:
389
+ outputs += (present_key_value,)
390
+
391
+ return outputs
392
+
393
+
394
+ Yi_START_DOCSTRING = r"""
395
+ This model inherits from [`PreTrainedModel`]. Check the superclass documentation for the generic methods the
396
+ library implements for all its model (such as downloading or saving, resizing the input embeddings, pruning heads
397
+ etc.)
398
+
399
+ This model is also a PyTorch [torch.nn.Module](https://pytorch.org/docs/stable/nn.html#torch.nn.Module) subclass.
400
+ Use it as a regular PyTorch Module and refer to the PyTorch documentation for all matter related to general usage
401
+ and behavior.
402
+
403
+ Parameters:
404
+ config ([`YiConfig`]):
405
+ Model configuration class with all the parameters of the model. Initializing with a config file does not
406
+ load the weights associated with the model, only the configuration. Check out the
407
+ [`~PreTrainedModel.from_pretrained`] method to load the model weights.
408
+ """
409
+
410
+
411
+ @add_start_docstrings(
412
+ "The bare Yi Model outputting raw hidden-states without any specific head on top.",
413
+ Yi_START_DOCSTRING,
414
+ )
415
+ class YiPreTrainedModel(PreTrainedModel):
416
+ config_class = YiConfig
417
+ base_model_prefix = "model"
418
+ supports_gradient_checkpointing = True
419
+ _no_split_modules = ["YiDecoderLayer"]
420
+ _skip_keys_device_placement = "past_key_values"
421
+
422
+ def _init_weights(self, module):
423
+ std = self.config.initializer_range
424
+ if isinstance(module, nn.Linear):
425
+ module.weight.data.normal_(mean=0.0, std=std)
426
+ if module.bias is not None:
427
+ module.bias.data.zero_()
428
+ elif isinstance(module, nn.Embedding):
429
+ module.weight.data.normal_(mean=0.0, std=std)
430
+ if module.padding_idx is not None:
431
+ module.weight.data[module.padding_idx].zero_()
432
+
433
+ def _set_gradient_checkpointing(self, module, value=False):
434
+ if isinstance(module, YiModel):
435
+ module.gradient_checkpointing = value
436
+
437
+
438
+ Yi_INPUTS_DOCSTRING = r"""
439
+ Args:
440
+ input_ids (`torch.LongTensor` of shape `(batch_size, sequence_length)`):
441
+ Indices of input sequence tokens in the vocabulary. Padding will be ignored by default should you provide
442
+ it.
443
+
444
+ Indices can be obtained using [`AutoTokenizer`]. See [`PreTrainedTokenizer.encode`] and
445
+ [`PreTrainedTokenizer.__call__`] for details.
446
+
447
+ [What are input IDs?](../glossary#input-ids)
448
+ attention_mask (`torch.Tensor` of shape `(batch_size, sequence_length)`, *optional*):
449
+ Mask to avoid performing attention on padding token indices. Mask values selected in `[0, 1]`:
450
+
451
+ - 1 for tokens that are **not masked**,
452
+ - 0 for tokens that are **masked**.
453
+
454
+ [What are attention masks?](../glossary#attention-mask)
455
+
456
+ Indices can be obtained using [`AutoTokenizer`]. See [`PreTrainedTokenizer.encode`] and
457
+ [`PreTrainedTokenizer.__call__`] for details.
458
+
459
+ If `past_key_values` is used, optionally only the last `decoder_input_ids` have to be input (see
460
+ `past_key_values`).
461
+
462
+ If you want to change padding behavior, you should read [`modeling_opt._prepare_decoder_attention_mask`]
463
+ and modify to your needs. See diagram 1 in [the paper](https://arxiv.org/abs/1910.13461) for more
464
+ information on the default strategy.
465
+
466
+ - 1 indicates the head is **not masked**,
467
+ - 0 indicates the head is **masked**.
468
+ position_ids (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*):
469
+ Indices of positions of each input sequence tokens in the position embeddings. Selected in the range `[0,
470
+ config.n_positions - 1]`.
471
+
472
+ [What are position IDs?](../glossary#position-ids)
473
+ past_key_values (`tuple(tuple(torch.FloatTensor))`, *optional*, returned when `use_cache=True` is passed or when `config.use_cache=True`):
474
+ Tuple of `tuple(torch.FloatTensor)` of length `config.n_layers`, with each tuple having 2 tensors of shape
475
+ `(batch_size, num_heads, sequence_length, embed_size_per_head)`) and 2 additional tensors of shape
476
+ `(batch_size, num_heads, encoder_sequence_length, embed_size_per_head)`.
477
+
478
+ Contains pre-computed hidden-states (key and values in the self-attention blocks and in the cross-attention
479
+ blocks) that can be used (see `past_key_values` input) to speed up sequential decoding.
480
+
481
+ If `past_key_values` are used, the user can optionally input only the last `decoder_input_ids` (those that
482
+ don't have their past key value states given to this model) of shape `(batch_size, 1)` instead of all
483
+ `decoder_input_ids` of shape `(batch_size, sequence_length)`.
484
+ inputs_embeds (`torch.FloatTensor` of shape `(batch_size, sequence_length, hidden_size)`, *optional*):
485
+ Optionally, instead of passing `input_ids` you can choose to directly pass an embedded representation. This
486
+ is useful if you want more control over how to convert `input_ids` indices into associated vectors than the
487
+ model's internal embedding lookup matrix.
488
+ use_cache (`bool`, *optional*):
489
+ If set to `True`, `past_key_values` key value states are returned and can be used to speed up decoding (see
490
+ `past_key_values`).
491
+ output_attentions (`bool`, *optional*):
492
+ Whether or not to return the attentions tensors of all attention layers. See `attentions` under returned
493
+ tensors for more detail.
494
+ output_hidden_states (`bool`, *optional*):
495
+ Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors for
496
+ more detail.
497
+ return_dict (`bool`, *optional*):
498
+ Whether or not to return a [`~utils.ModelOutput`] instead of a plain tuple.
499
+ """
500
+
501
+
502
+ @add_start_docstrings(
503
+ "The bare Yi Model outputting raw hidden-states without any specific head on top.",
504
+ Yi_START_DOCSTRING,
505
+ )
506
+ class YiModel(YiPreTrainedModel):
507
+ """
508
+ Transformer decoder consisting of *config.num_hidden_layers* layers. Each layer is a [`YiDecoderLayer`]
509
+
510
+ Args:
511
+ config: YiConfig
512
+ """
513
+
514
+ def __init__(self, config: YiConfig):
515
+ super().__init__(config)
516
+ self.padding_idx = config.pad_token_id
517
+ self.vocab_size = config.vocab_size
518
+
519
+ self.embed_tokens = nn.Embedding(
520
+ config.vocab_size, config.hidden_size, self.padding_idx
521
+ )
522
+ self.layers = nn.ModuleList(
523
+ [YiDecoderLayer(config) for _ in range(config.num_hidden_layers)]
524
+ )
525
+
526
+ self.norm = YiRMSNorm(config.hidden_size, eps=config.rms_norm_eps)
527
+
528
+ self.gradient_checkpointing = False
529
+ # Initialize weights and apply final processing
530
+ self.post_init()
531
+
532
+ def get_input_embeddings(self):
533
+ return self.embed_tokens
534
+
535
+ def set_input_embeddings(self, value):
536
+ self.embed_tokens = value
537
+
538
+ # Copied from transformers.models.bart.modeling_bart.BartDecoder._prepare_decoder_attention_mask
539
+ def _prepare_decoder_attention_mask(
540
+ self, attention_mask, input_ids, inputs_embeds, past_key_values_length
541
+ ):
542
+ input_shape = input_ids.shape if input_ids is not None else inputs_embeds.shape[:-1]
543
+ # create causal mask
544
+ # [bsz, seq_len] -> [bsz, 1, tgt_seq_len, src_seq_len]
545
+ combined_attention_mask = None
546
+ if input_shape[-1] > 1:
547
+ combined_attention_mask = _make_causal_mask(
548
+ input_shape,
549
+ inputs_embeds.dtype,
550
+ device=inputs_embeds.device,
551
+ past_key_values_length=past_key_values_length,
552
+ )
553
+
554
+ if attention_mask is not None:
555
+ # [bsz, seq_len] -> [bsz, 1, tgt_seq_len, src_seq_len]
556
+ expanded_attn_mask = _expand_mask(
557
+ attention_mask, inputs_embeds.dtype, tgt_len=input_shape[-1]
558
+ ).to(inputs_embeds.device)
559
+ combined_attention_mask = (
560
+ expanded_attn_mask
561
+ if combined_attention_mask is None
562
+ else expanded_attn_mask + combined_attention_mask
563
+ )
564
+
565
+ return combined_attention_mask
566
+
567
+ @add_start_docstrings_to_model_forward(Yi_INPUTS_DOCSTRING)
568
+ def forward(
569
+ self,
570
+ input_ids: torch.LongTensor = None,
571
+ attention_mask: Optional[torch.Tensor] = None,
572
+ position_ids: Optional[torch.LongTensor] = None,
573
+ past_key_values: Optional[List[torch.FloatTensor]] = None,
574
+ inputs_embeds: Optional[torch.FloatTensor] = None,
575
+ use_cache: Optional[bool] = None,
576
+ output_attentions: Optional[bool] = None,
577
+ output_hidden_states: Optional[bool] = None,
578
+ return_dict: Optional[bool] = None,
579
+ ) -> Union[Tuple, BaseModelOutputWithPast]:
580
+ output_attentions = (
581
+ output_attentions
582
+ if output_attentions is not None
583
+ else self.config.output_attentions
584
+ )
585
+ output_hidden_states = (
586
+ output_hidden_states
587
+ if output_hidden_states is not None
588
+ else self.config.output_hidden_states
589
+ )
590
+ use_cache = use_cache if use_cache is not None else self.config.use_cache
591
+
592
+ return_dict = (
593
+ return_dict if return_dict is not None else self.config.use_return_dict
594
+ )
595
+
596
+ # retrieve input_ids and inputs_embeds
597
+ if input_ids is not None and inputs_embeds is not None:
598
+ raise ValueError(
599
+ "You cannot specify both decoder_input_ids and decoder_inputs_embeds at the same time"
600
+ )
601
+ elif input_ids is not None:
602
+ batch_size, seq_length = input_ids.shape
603
+ elif inputs_embeds is not None:
604
+ batch_size, seq_length, _ = inputs_embeds.shape
605
+ else:
606
+ raise ValueError(
607
+ "You have to specify either decoder_input_ids or decoder_inputs_embeds"
608
+ )
609
+
610
+ seq_length_with_past = seq_length
611
+ past_key_values_length = 0
612
+
613
+ if past_key_values is not None:
614
+ past_key_values_length = past_key_values[0][0].shape[2]
615
+ seq_length_with_past = seq_length_with_past + past_key_values_length
616
+
617
+ if position_ids is None:
618
+ device = input_ids.device if input_ids is not None else inputs_embeds.device
619
+ position_ids = torch.arange(
620
+ past_key_values_length,
621
+ seq_length + past_key_values_length,
622
+ dtype=torch.long,
623
+ device=device,
624
+ )
625
+ position_ids = position_ids.unsqueeze(0).view(-1, seq_length)
626
+ else:
627
+ position_ids = position_ids.view(-1, seq_length).long()
628
+
629
+ if inputs_embeds is None:
630
+ inputs_embeds = self.embed_tokens(input_ids)
631
+
632
+ if not is_flash_attn_available:
633
+ # embed positions
634
+ if attention_mask is None:
635
+ attention_mask = torch.ones(
636
+ (batch_size, seq_length_with_past),
637
+ dtype=torch.bool,
638
+ device=inputs_embeds.device,
639
+ )
640
+ attention_mask = self._prepare_decoder_attention_mask(
641
+ attention_mask,
642
+ input_ids,
643
+ inputs_embeds,
644
+ past_key_values_length,
645
+ )
646
+ else:
647
+ attention_mask = None
648
+
649
+ hidden_states = inputs_embeds
650
+ if self.gradient_checkpointing and self.training:
651
+ if use_cache:
652
+ logger.warning_once(
653
+ "`use_cache=True` is incompatible with gradient checkpointing. Setting `use_cache=False`..."
654
+ )
655
+ use_cache = False
656
+
657
+ # decoder layers
658
+ all_hidden_states = () if output_hidden_states else None
659
+ all_self_attns = () if output_attentions else None
660
+ next_decoder_cache = () if use_cache else None
661
+
662
+ for idx, decoder_layer in enumerate(self.layers):
663
+ if output_hidden_states:
664
+ all_hidden_states += (hidden_states,)
665
+
666
+ past_key_value = (
667
+ past_key_values[idx] if past_key_values is not None else None
668
+ )
669
+
670
+ if self.gradient_checkpointing and self.training:
671
+
672
+ def create_custom_forward(module):
673
+ def custom_forward(*inputs):
674
+ # None for past_key_value
675
+ return module(*inputs, past_key_value, output_attentions)
676
+
677
+ return custom_forward
678
+
679
+ layer_outputs = torch.utils.checkpoint.checkpoint(
680
+ create_custom_forward(decoder_layer),
681
+ hidden_states,
682
+ attention_mask,
683
+ position_ids,
684
+ )
685
+ else:
686
+ layer_outputs = decoder_layer(
687
+ hidden_states,
688
+ attention_mask=attention_mask,
689
+ position_ids=position_ids,
690
+ past_key_value=past_key_value,
691
+ output_attentions=output_attentions,
692
+ use_cache=use_cache,
693
+ )
694
+
695
+ hidden_states = layer_outputs[0]
696
+
697
+ if use_cache:
698
+ next_decoder_cache += (layer_outputs[2 if output_attentions else 1],)
699
+
700
+ if output_attentions:
701
+ all_self_attns += (layer_outputs[1],)
702
+
703
+ hidden_states = self.norm(hidden_states)
704
+ # add hidden states from the last decoder layer
705
+ if output_hidden_states:
706
+ all_hidden_states += (hidden_states,)
707
+
708
+ next_cache = next_decoder_cache if use_cache else None
709
+ if not return_dict:
710
+ return tuple(
711
+ v
712
+ for v in [hidden_states, next_cache, all_hidden_states, all_self_attns]
713
+ if v is not None
714
+ )
715
+ return BaseModelOutputWithPast(
716
+ last_hidden_state=hidden_states,
717
+ past_key_values=next_cache,
718
+ hidden_states=all_hidden_states,
719
+ attentions=all_self_attns,
720
+ )
721
+
722
+
723
+ class YiForCausalLM(YiPreTrainedModel):
724
+ _tied_weights_keys = ["lm_head.weight"]
725
+
726
+ def __init__(self, config):
727
+ super().__init__(config)
728
+ self.model = YiModel(config)
729
+
730
+ self.lm_head = nn.Linear(config.hidden_size, config.vocab_size, bias=False)
731
+
732
+ # Initialize weights and apply final processing
733
+ self.post_init()
734
+
735
+ def get_input_embeddings(self):
736
+ return self.model.embed_tokens
737
+
738
+ def set_input_embeddings(self, value):
739
+ self.model.embed_tokens = value
740
+
741
+ def get_output_embeddings(self):
742
+ return self.lm_head
743
+
744
+ def set_output_embeddings(self, new_embeddings):
745
+ self.lm_head = new_embeddings
746
+
747
+ def set_decoder(self, decoder):
748
+ self.model = decoder
749
+
750
+ def get_decoder(self):
751
+ return self.model
752
+
753
+ @add_start_docstrings_to_model_forward(Yi_INPUTS_DOCSTRING)
754
+ @replace_return_docstrings(
755
+ output_type=CausalLMOutputWithPast, config_class=_CONFIG_FOR_DOC
756
+ )
757
+ def forward(
758
+ self,
759
+ input_ids: torch.LongTensor = None,
760
+ attention_mask: Optional[torch.Tensor] = None,
761
+ position_ids: Optional[torch.LongTensor] = None,
762
+ past_key_values: Optional[List[torch.FloatTensor]] = None,
763
+ inputs_embeds: Optional[torch.FloatTensor] = None,
764
+ labels: Optional[torch.LongTensor] = None,
765
+ use_cache: Optional[bool] = None,
766
+ output_attentions: Optional[bool] = None,
767
+ output_hidden_states: Optional[bool] = None,
768
+ return_dict: Optional[bool] = None,
769
+ ) -> Union[Tuple, CausalLMOutputWithPast]:
770
+ r"""
771
+ Args:
772
+ labels (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*):
773
+ Labels for computing the masked language modeling loss. Indices should either be in `[0, ...,
774
+ config.vocab_size]` or -100 (see `input_ids` docstring). Tokens with indices set to `-100` are ignored
775
+ (masked), the loss is only computed for the tokens with labels in `[0, ..., config.vocab_size]`.
776
+
777
+ Returns:
778
+
779
+ Example:
780
+
781
+ ```python
782
+ >>> from transformers import AutoTokenizer, YiForCausalLM
783
+
784
+ >>> model = YiForCausalLM.from_pretrained(PATH_TO_CONVERTED_WEIGHTS)
785
+ >>> tokenizer = AutoTokenizer.from_pretrained(PATH_TO_CONVERTED_TOKENIZER)
786
+
787
+ >>> prompt = "Hey, are you conscious? Can you talk to me?"
788
+ >>> inputs = tokenizer(prompt, return_tensors="pt")
789
+
790
+ >>> # Generate
791
+ >>> generate_ids = model.generate(inputs.input_ids, max_length=30)
792
+ >>> tokenizer.batch_decode(generate_ids, skip_special_tokens=True, clean_up_tokenization_spaces=False)[0]
793
+ "Hey, are you conscious? Can you talk to me?\nI'm not conscious, but I can talk to you."
794
+ ```"""
795
+
796
+ output_attentions = (
797
+ output_attentions
798
+ if output_attentions is not None
799
+ else self.config.output_attentions
800
+ )
801
+ output_hidden_states = (
802
+ output_hidden_states
803
+ if output_hidden_states is not None
804
+ else self.config.output_hidden_states
805
+ )
806
+ return_dict = (
807
+ return_dict if return_dict is not None else self.config.use_return_dict
808
+ )
809
+
810
+ # decoder outputs consists of (dec_features, layer_state, dec_hidden, dec_attn)
811
+ outputs = self.model(
812
+ input_ids=input_ids,
813
+ attention_mask=attention_mask,
814
+ position_ids=position_ids,
815
+ past_key_values=past_key_values,
816
+ inputs_embeds=inputs_embeds,
817
+ use_cache=use_cache,
818
+ output_attentions=output_attentions,
819
+ output_hidden_states=output_hidden_states,
820
+ return_dict=return_dict,
821
+ )
822
+
823
+ hidden_states = outputs[0]
824
+ logits = self.lm_head(hidden_states)
825
+
826
+ loss = None
827
+ if labels is not None:
828
+ # Shift so that tokens < n predict n
829
+ shift_logits = logits[..., :-1, :].contiguous()
830
+ shift_labels = labels[..., 1:].contiguous()
831
+ # Flatten the tokens
832
+ loss_fct = CrossEntropyLoss()
833
+ shift_logits = shift_logits.view(-1, self.config.vocab_size)
834
+ shift_labels = shift_labels.view(-1)
835
+ # Enable model parallelism
836
+ shift_labels = shift_labels.to(shift_logits.device)
837
+ loss = loss_fct(shift_logits, shift_labels)
838
+
839
+ if not return_dict:
840
+ output = (logits,) + outputs[1:]
841
+ return (loss,) + output if loss is not None else output
842
+
843
+ return CausalLMOutputWithPast(
844
+ loss=loss,
845
+ logits=logits,
846
+ past_key_values=outputs.past_key_values,
847
+ hidden_states=outputs.hidden_states,
848
+ attentions=outputs.attentions,
849
+ )
850
+
851
+ def prepare_inputs_for_generation(
852
+ self,
853
+ input_ids,
854
+ past_key_values=None,
855
+ attention_mask=None,
856
+ inputs_embeds=None,
857
+ **kwargs,
858
+ ):
859
+ if past_key_values:
860
+ input_ids = input_ids[:, -1:]
861
+
862
+ position_ids = kwargs.get("position_ids", None)
863
+ if attention_mask is not None and position_ids is None:
864
+ # create position_ids on the fly for batch generation
865
+ position_ids = attention_mask.long().cumsum(-1) - 1
866
+ position_ids.masked_fill_(attention_mask == 0, 1)
867
+ if past_key_values:
868
+ position_ids = position_ids[:, -1].unsqueeze(-1)
869
+
870
+ # if `inputs_embeds` are passed, we only want to use them in the 1st generation step
871
+ if inputs_embeds is not None and past_key_values is None:
872
+ model_inputs = {"inputs_embeds": inputs_embeds}
873
+ else:
874
+ model_inputs = {"input_ids": input_ids}
875
+
876
+ model_inputs.update(
877
+ {
878
+ "position_ids": position_ids,
879
+ "past_key_values": past_key_values,
880
+ "use_cache": kwargs.get("use_cache"),
881
+ "attention_mask": attention_mask,
882
+ }
883
+ )
884
+ return model_inputs
885
+
886
+ @staticmethod
887
+ def _reorder_cache(past_key_values, beam_idx):
888
+ reordered_past = ()
889
+ for layer_past in past_key_values:
890
+ reordered_past += (
891
+ tuple(
892
+ past_state.index_select(0, beam_idx.to(past_state.device))
893
+ for past_state in layer_past
894
+ ),
895
+ )
896
+ return reordered_past
897
+
898
+
899
+ @add_start_docstrings(
900
+ """
901
+ The Yi Model transformer with a sequence classification head on top (linear layer).
902
+
903
+ [`YiForSequenceClassification`] uses the last token in order to do the classification, as other causal models
904
+ (e.g. GPT-2) do.
905
+
906
+ Since it does classification on the last token, it requires to know the position of the last token. If a
907
+ `pad_token_id` is defined in the configuration, it finds the last token that is not a padding token in each row. If
908
+ no `pad_token_id` is defined, it simply takes the last value in each row of the batch. Since it cannot guess the
909
+ padding tokens when `inputs_embeds` are passed instead of `input_ids`, it does the same (take the last value in
910
+ each row of the batch).
911
+ """,
912
+ Yi_START_DOCSTRING,
913
+ )
914
+ class YiForSequenceClassification(YiPreTrainedModel):
915
+ def __init__(self, config):
916
+ super().__init__(config)
917
+ self.num_labels = config.num_labels
918
+ self.model = YiModel(config)
919
+ self.score = nn.Linear(config.hidden_size, self.num_labels, bias=False)
920
+
921
+ # Initialize weights and apply final processing
922
+ self.post_init()
923
+
924
+ def get_input_embeddings(self):
925
+ return self.model.embed_tokens
926
+
927
+ def set_input_embeddings(self, value):
928
+ self.model.embed_tokens = value
929
+
930
+ @add_start_docstrings_to_model_forward(Yi_INPUTS_DOCSTRING)
931
+ def forward(
932
+ self,
933
+ input_ids: torch.LongTensor = None,
934
+ attention_mask: Optional[torch.Tensor] = None,
935
+ position_ids: Optional[torch.LongTensor] = None,
936
+ past_key_values: Optional[List[torch.FloatTensor]] = None,
937
+ inputs_embeds: Optional[torch.FloatTensor] = None,
938
+ labels: Optional[torch.LongTensor] = None,
939
+ use_cache: Optional[bool] = None,
940
+ output_attentions: Optional[bool] = None,
941
+ output_hidden_states: Optional[bool] = None,
942
+ return_dict: Optional[bool] = None,
943
+ ) -> Union[Tuple, SequenceClassifierOutputWithPast]:
944
+ r"""
945
+ labels (`torch.LongTensor` of shape `(batch_size,)`, *optional*):
946
+ Labels for computing the sequence classification/regression loss. Indices should be in `[0, ...,
947
+ config.num_labels - 1]`. If `config.num_labels == 1` a regression loss is computed (Mean-Square loss), If
948
+ `config.num_labels > 1` a classification loss is computed (Cross-Entropy).
949
+ """
950
+ return_dict = (
951
+ return_dict if return_dict is not None else self.config.use_return_dict
952
+ )
953
+
954
+ transformer_outputs = self.model(
955
+ input_ids,
956
+ attention_mask=attention_mask,
957
+ position_ids=position_ids,
958
+ past_key_values=past_key_values,
959
+ inputs_embeds=inputs_embeds,
960
+ use_cache=use_cache,
961
+ output_attentions=output_attentions,
962
+ output_hidden_states=output_hidden_states,
963
+ return_dict=return_dict,
964
+ )
965
+ hidden_states = transformer_outputs[0]
966
+ logits = self.score(hidden_states)
967
+
968
+ if input_ids is not None:
969
+ batch_size = input_ids.shape[0]
970
+ else:
971
+ batch_size = inputs_embeds.shape[0]
972
+
973
+ if self.config.pad_token_id is None and batch_size != 1:
974
+ raise ValueError(
975
+ "Cannot handle batch sizes > 1 if no padding token is defined."
976
+ )
977
+ if self.config.pad_token_id is None:
978
+ sequence_lengths = -1
979
+ else:
980
+ if input_ids is not None:
981
+ sequence_lengths = (
982
+ torch.eq(input_ids, self.config.pad_token_id).long().argmax(-1) - 1
983
+ ).to(logits.device)
984
+ else:
985
+ sequence_lengths = -1
986
+
987
+ pooled_logits = logits[
988
+ torch.arange(batch_size, device=logits.device), sequence_lengths
989
+ ]
990
+
991
+ loss = None
992
+ if labels is not None:
993
+ labels = labels.to(logits.device)
994
+ if self.config.problem_type is None:
995
+ if self.num_labels == 1:
996
+ self.config.problem_type = "regression"
997
+ elif self.num_labels > 1 and (
998
+ labels.dtype == torch.long or labels.dtype == torch.int
999
+ ):
1000
+ self.config.problem_type = "single_label_classification"
1001
+ else:
1002
+ self.config.problem_type = "multi_label_classification"
1003
+
1004
+ if self.config.problem_type == "regression":
1005
+ loss_fct = MSELoss()
1006
+ if self.num_labels == 1:
1007
+ loss = loss_fct(pooled_logits.squeeze(), labels.squeeze())
1008
+ else:
1009
+ loss = loss_fct(pooled_logits, labels)
1010
+ elif self.config.problem_type == "single_label_classification":
1011
+ loss_fct = CrossEntropyLoss()
1012
+ loss = loss_fct(
1013
+ pooled_logits.view(-1, self.num_labels), labels.view(-1)
1014
+ )
1015
+ elif self.config.problem_type == "multi_label_classification":
1016
+ loss_fct = BCEWithLogitsLoss()
1017
+ loss = loss_fct(pooled_logits, labels)
1018
+ if not return_dict:
1019
+ output = (pooled_logits,) + transformer_outputs[1:]
1020
+ return ((loss,) + output) if loss is not None else output
1021
+
1022
+ return SequenceClassifierOutputWithPast(
1023
+ loss=loss,
1024
+ logits=pooled_logits,
1025
+ past_key_values=transformer_outputs.past_key_values,
1026
+ hidden_states=transformer_outputs.hidden_states,
1027
+ attentions=transformer_outputs.attentions,
1028
+ )
quant_config.json ADDED
@@ -0,0 +1,6 @@
 
 
 
 
 
 
 
1
+ {
2
+ "zero_point": true,
3
+ "q_group_size": 128,
4
+ "w_bit": 4,
5
+ "version": "GEMM"
6
+ }
special_tokens_map.json ADDED
@@ -0,0 +1,30 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ {
2
+ "bos_token": {
3
+ "content": "<|startoftext|>",
4
+ "lstrip": false,
5
+ "normalized": true,
6
+ "rstrip": false,
7
+ "single_word": false
8
+ },
9
+ "eos_token": {
10
+ "content": "<|endoftext|>",
11
+ "lstrip": false,
12
+ "normalized": true,
13
+ "rstrip": false,
14
+ "single_word": false
15
+ },
16
+ "pad_token": {
17
+ "content": "<unk>",
18
+ "lstrip": false,
19
+ "normalized": true,
20
+ "rstrip": false,
21
+ "single_word": false
22
+ },
23
+ "unk_token": {
24
+ "content": "<unk>",
25
+ "lstrip": false,
26
+ "normalized": true,
27
+ "rstrip": false,
28
+ "single_word": false
29
+ }
30
+ }
tokenization_yi.py ADDED
@@ -0,0 +1,255 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ import os
2
+ from shutil import copyfile
3
+ from typing import Any, Dict, List, Optional, Tuple
4
+
5
+ import sentencepiece as spm
6
+ from transformers.tokenization_utils import AddedToken, PreTrainedTokenizer
7
+ from transformers.utils import logging
8
+
9
+ logger = logging.get_logger(__name__)
10
+
11
+ VOCAB_FILES_NAMES = {"vocab_file": "tokenizer.model"}
12
+
13
+ PRETRAINED_VOCAB_FILES_MAP = {
14
+ "vocab_file": {},
15
+ "tokenizer_file": {},
16
+ }
17
+ PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES = {}
18
+
19
+
20
+ class YiTokenizer(PreTrainedTokenizer):
21
+ """
22
+ Construct a Yi tokenizer. Based on byte-level Byte-Pair-Encoding.
23
+
24
+ Args:
25
+ vocab_file (`str`):
26
+ Path to the vocabulary file.
27
+ """
28
+
29
+ vocab_files_names = VOCAB_FILES_NAMES
30
+ pretrained_vocab_files_map = PRETRAINED_VOCAB_FILES_MAP
31
+ max_model_input_sizes = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
32
+ model_input_names = ["input_ids", "attention_mask"]
33
+
34
+ def __init__(
35
+ self,
36
+ vocab_file,
37
+ unk_token="<unk>",
38
+ bos_token="<|startoftext|>",
39
+ eos_token="<|endoftext|>",
40
+ pad_token="<unk>",
41
+ sp_model_kwargs: Optional[Dict[str, Any]] = None,
42
+ add_bos_token=True,
43
+ add_eos_token=False,
44
+ clean_up_tokenization_spaces=False,
45
+ **kwargs,
46
+ ):
47
+ self.sp_model_kwargs = {} if sp_model_kwargs is None else sp_model_kwargs
48
+ bos_token = (
49
+ AddedToken(bos_token, lstrip=False, rstrip=False)
50
+ if isinstance(bos_token, str)
51
+ else bos_token
52
+ )
53
+ eos_token = (
54
+ AddedToken(eos_token, lstrip=False, rstrip=False)
55
+ if isinstance(eos_token, str)
56
+ else eos_token
57
+ )
58
+ unk_token = (
59
+ AddedToken(unk_token, lstrip=False, rstrip=False)
60
+ if isinstance(unk_token, str)
61
+ else unk_token
62
+ )
63
+ pad_token = (
64
+ AddedToken(pad_token, lstrip=False, rstrip=False)
65
+ if isinstance(pad_token, str)
66
+ else pad_token
67
+ )
68
+ self.vocab_file = vocab_file
69
+ self.add_bos_token = add_bos_token
70
+ self.add_eos_token = add_eos_token
71
+ self.sp_model = spm.SentencePieceProcessor(**self.sp_model_kwargs)
72
+ self.sp_model.Load(vocab_file)
73
+ super().__init__(
74
+ bos_token=bos_token,
75
+ eos_token=eos_token,
76
+ unk_token=unk_token,
77
+ pad_token=pad_token,
78
+ add_bos_token=add_bos_token,
79
+ add_eos_token=add_eos_token,
80
+ sp_model_kwargs=self.sp_model_kwargs,
81
+ clean_up_tokenization_spaces=clean_up_tokenization_spaces,
82
+ **kwargs,
83
+ )
84
+
85
+ def __getstate__(self):
86
+ state = self.__dict__.copy()
87
+ state["sp_model"] = None
88
+ return state
89
+
90
+ def __setstate__(self, d):
91
+ self.__dict__ = d
92
+ self.sp_model = spm.SentencePieceProcessor(**self.sp_model_kwargs)
93
+ self.sp_model.Load(self.vocab_file)
94
+
95
+ @property
96
+ def vocab_size(self):
97
+ """Returns vocab size"""
98
+ return self.sp_model.get_piece_size()
99
+
100
+ def get_vocab(self):
101
+ """Returns vocab as a dict"""
102
+ vocab = {self.convert_ids_to_tokens(i): i for i in range(self.vocab_size)}
103
+ vocab.update(self.added_tokens_encoder)
104
+ return vocab
105
+
106
+ def _tokenize(self, text):
107
+ """Returns a tokenized string."""
108
+ return self.sp_model.encode(text, out_type=str)
109
+
110
+ def _convert_token_to_id(self, token):
111
+ """Converts a token (str) in an id using the vocab."""
112
+ return self.sp_model.piece_to_id(token)
113
+
114
+ def _convert_id_to_token(self, index):
115
+ """Converts an index (integer) in a token (str) using the vocab."""
116
+ token = self.sp_model.IdToPiece(index)
117
+ return token
118
+
119
+ def convert_tokens_to_string(self, tokens):
120
+ """Converts a sequence of tokens (string) in a single string."""
121
+ current_sub_tokens = []
122
+ out_string = ""
123
+ prev_is_special = False
124
+ for i, token in enumerate(tokens):
125
+ # make sure that special tokens are not decoded using sentencepiece model
126
+ if token in self.all_special_tokens:
127
+ if not prev_is_special and i != 0:
128
+ out_string += " "
129
+ out_string += self.sp_model.decode(current_sub_tokens) + token
130
+ prev_is_special = True
131
+ current_sub_tokens = []
132
+ else:
133
+ current_sub_tokens.append(token)
134
+ prev_is_special = False
135
+ out_string += self.sp_model.decode(current_sub_tokens)
136
+ return out_string
137
+
138
+ def save_vocabulary(
139
+ self, save_directory, filename_prefix: Optional[str] = None
140
+ ) -> Tuple[str]:
141
+ """
142
+ Save the vocabulary and special tokens file to a directory.
143
+
144
+ Args:
145
+ save_directory (`str`):
146
+ The directory in which to save the vocabulary.
147
+
148
+ Returns:
149
+ `Tuple(str)`: Paths to the files saved.
150
+ """
151
+ if not os.path.isdir(save_directory):
152
+ logger.error(f"Vocabulary path ({save_directory}) should be a directory")
153
+ return
154
+ out_vocab_file = os.path.join(
155
+ save_directory,
156
+ (filename_prefix + "-" if filename_prefix else "")
157
+ + VOCAB_FILES_NAMES["vocab_file"],
158
+ )
159
+
160
+ if os.path.abspath(self.vocab_file) != os.path.abspath(
161
+ out_vocab_file
162
+ ) and os.path.isfile(self.vocab_file):
163
+ copyfile(self.vocab_file, out_vocab_file)
164
+ elif not os.path.isfile(self.vocab_file):
165
+ with open(out_vocab_file, "wb") as fi:
166
+ content_spiece_model = self.sp_model.serialized_model_proto()
167
+ fi.write(content_spiece_model)
168
+
169
+ return (out_vocab_file,)
170
+
171
+ def build_inputs_with_special_tokens(self, token_ids_0, token_ids_1=None):
172
+ bos_token_id = [self.bos_token_id] if self.add_bos_token else []
173
+ eos_token_id = [self.eos_token_id] if self.add_eos_token else []
174
+
175
+ output = bos_token_id + token_ids_0 + eos_token_id
176
+
177
+ if token_ids_1 is not None:
178
+ output = output + bos_token_id + token_ids_1 + eos_token_id
179
+
180
+ return output
181
+
182
+ def get_special_tokens_mask(
183
+ self,
184
+ token_ids_0: List[int],
185
+ token_ids_1: Optional[List[int]] = None,
186
+ already_has_special_tokens: bool = False,
187
+ ) -> List[int]:
188
+ """
189
+ Retrieve sequence ids from a token list that has no special tokens added. This method is called when adding
190
+ special tokens using the tokenizer `prepare_for_model` method.
191
+
192
+ Args:
193
+ token_ids_0 (`List[int]`):
194
+ List of IDs.
195
+ token_ids_1 (`List[int]`, *optional*):
196
+ Optional second list of IDs for sequence pairs.
197
+ already_has_special_tokens (`bool`, *optional*, defaults to `False`):
198
+ Whether or not the token list is already formatted with special tokens for the model.
199
+
200
+ Returns:
201
+ `List[int]`: A list of integers in the range [0, 1]: 1 for a special token, 0 for a sequence token.
202
+ """
203
+ if already_has_special_tokens:
204
+ return super().get_special_tokens_mask(
205
+ token_ids_0=token_ids_0,
206
+ token_ids_1=token_ids_1,
207
+ already_has_special_tokens=True,
208
+ )
209
+
210
+ bos_token_id = [1] if self.add_bos_token else []
211
+ eos_token_id = [1] if self.add_eos_token else []
212
+
213
+ if token_ids_1 is None:
214
+ return bos_token_id + ([0] * len(token_ids_0)) + eos_token_id
215
+ return (
216
+ bos_token_id
217
+ + ([0] * len(token_ids_0))
218
+ + eos_token_id
219
+ + bos_token_id
220
+ + ([0] * len(token_ids_1))
221
+ + eos_token_id
222
+ )
223
+
224
+ def create_token_type_ids_from_sequences(
225
+ self, token_ids_0: List[int], token_ids_1: Optional[List[int]] = None
226
+ ) -> List[int]:
227
+ """
228
+ Creates a mask from the two sequences passed to be used in a sequence-pair classification task. An ALBERT
229
+ sequence pair mask has the following format:
230
+
231
+ ```
232
+ 0 0 0 0 0 0 0 0 0 0 0 1 1 1 1 1 1 1 1 1
233
+ | first sequence | second sequence |
234
+ ```
235
+
236
+ if token_ids_1 is None, only returns the first portion of the mask (0s).
237
+
238
+ Args:
239
+ token_ids_0 (`List[int]`):
240
+ List of ids.
241
+ token_ids_1 (`List[int]`, *optional*):
242
+ Optional second list of IDs for sequence pairs.
243
+
244
+ Returns:
245
+ `List[int]`: List of [token type IDs](../glossary#token-type-ids) according to the given sequence(s).
246
+ """
247
+ bos_token_id = [self.bos_token_id] if self.add_bos_token else []
248
+ eos_token_id = [self.eos_token_id] if self.add_eos_token else []
249
+
250
+ output = [0] * len(bos_token_id + token_ids_0 + eos_token_id)
251
+
252
+ if token_ids_1 is not None:
253
+ output += [1] * len(bos_token_id + token_ids_1 + eos_token_id)
254
+
255
+ return output
tokenizer.json ADDED
The diff for this file is too large to render. See raw diff
 
tokenizer.model ADDED
@@ -0,0 +1,3 @@
 
 
 
 
1
+ version https://git-lfs.github.com/spec/v1
2
+ oid sha256:386c49cf943d71aa110361135338c50e38beeff0a66593480421f37b319e1a39
3
+ size 1033105
tokenizer_config.json ADDED
@@ -0,0 +1,9 @@
 
 
 
 
 
 
 
 
 
 
1
+ {
2
+ "auto_map": {
3
+ "AutoTokenizer": ["tokenization_yi.YiTokenizer", null]
4
+ },
5
+ "add_bos_token": false,
6
+ "add_eos_token": false,
7
+ "model_max_length": 200000,
8
+ "tokenizer_class": "YiTokenizer"
9
+ }