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Artificial intelligence - Operator interface - Part 1: Basic mathematical classes
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Basic data
| Standard ID | GB/T 46069.1-2025 (GB/T46069.1-2025) |
| Description (Translated English) | Artificial intelligence - Operator interface - Part 1: Basic mathematical classes |
| Sector / Industry | National Standard (Recommended) |
| Classification of Chinese Standard | L70 |
| Classification of International Standard | 35.020 |
| Word Count Estimation | 190,195 |
| Date of Issue | 2025-08-29 |
| Date of Implementation | 2026-03-01 |
| Issuing agency(ies) | State Administration for Market Regulation; Standardization Administration of China |
GB/T 46069.1-2025: Artificial intelligence - Operator interface - Part 1: Basic mathematical classes
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ICS 35.020
CCSL70
National Standards of the People's Republic of China
Artificial intelligence operator interface
Part 1.Basic Mathematics
Published on 2025-08-29
Implemented on 2026-03-01
State Administration for Market Regulation
The State Administration for Standardization issued a statement.
Table of Contents
Preface III
Introduction IV
1.Scope 1
2 Normative References 1
3.Terms and Definitions 1
4.Abbreviations 2
5 General Rules 2
5.1 Starting index 2
5.2 Parameter Information 2
5.3 Programming Languages 3
5.4 Automatic Broadcast 3
5.5 Status Handling 3
5.6 Interface Consistency 3
5.7 Generic Scalar Types 3
6 Data Structures 3
6.1 Summary 3
6.2 Element Types 4
6.3 Shape Information 4
6.4 Layout Information 4
6.5 Equipment Information 4
6.6 Other Extensions 4
7.Basic Mathematical Operator Interfaces 4
7.1 Interface List 4
7.2 Interface Operations and Parameters 6
7.3 Operator Interface Minimal Set 98
Appendix A (Informative) C Language Reference Definition Example 101 for Basic Mathematical Operator Interfaces
A.1 Data Structures 101
A.2 Basic Mathematical Operation Operator Interface 103
References 183
Foreword
This document complies with the provisions of GB/T 1.1-2020 "Standardization Work Guidelines Part 1.Structure and Drafting Rules of Standardization Documents".
Drafting.
This document is Part 1 of GB/T 46069 "Artificial Intelligence Operator Interfaces". GB/T 46069 has already published the following parts.
---Part 1.Basic Mathematics;
---Part 2.Neural Networks
Please note that some content in this document may involve patents. The issuing organization of this document assumes no responsibility for identifying patents.
This document was proposed and is under the jurisdiction of the National Information Technology Standardization Technical Committee (SAC/TC28).
This document was drafted by. Peking University, Peking University Changsha Institute of Computing and Digital Economy, and China Electronics Technology Standardization Institute.
Changsha 1011 Technology Co., Ltd., Pengcheng Laboratory, Institute of Software, Chinese Academy of Sciences, Beijing Baidu Netcom Technology Co., Ltd., Huawei Technologies Co., Ltd.
Cambricon Technologies Corporation Limited, SenseTime Technology Co., Ltd., Shanghai SenseTime Intelligent Technology Co., Ltd., Zhongguancun Audiovisual Industry Technology Innovation Alliance
Alliance, Inspur Electronic Information Industry Co., Ltd., Shanghai Artificial Intelligence Innovation Center, Beijing University of Aeronautics and Astronautics, Harbin Institute of Technology,
Shanghai Suiyuan Technology Co., Ltd., Shanghai Biren Technology Co., Ltd., Henan Kunlun Technology Co., Ltd., and Super Fusion Digital Technology Co., Ltd.
Limited Liability Company, Beijing Jiaotong University.
The main drafters of this document are. Yang Chao, Gou Haipeng, Hu Xiaoguang, Fan Chun, Chen Jun, Bao Wei, Yang Yuze, Ao Yulong, Li Kesen, Ma Yanjun, and Yu Dianhai.
Zhang Chengxing, Fan Ruibo, Jia Mengzhu, Duan Lian, Li Min, Ma Yinping, Fu Zhenxin, Yu Tian, Li Ziyi, Long Tingting, Zhang Yunfei, Guan He, Hu Shuai, Zhao Haiying
Zhang Weimin, Li Jianxin, Liu Xianglong, Yang Muyun, Ma Shanshan, Zhang Jun, Li Hui, Liu Aishan, Zheng Ruolin, Luan Xiaoxu, Tang Yinan, Wang Li, Wu Geng, Jiang Hui
Mei Jingqing, Ding Ruiquan, Qian Chen, Wang Sishan, Xing Feng, Pei Zhilin, Li Xiaoru, Sun Peiyuan, Zhou Hongli, Lu Shun, Wang Hao, Liu Jinnan, Xiao Yisong, Shen Zhiyue
Song Wenlin, Liu Wenfeng, Gao Ge, Nie Jiandi, Chen Deliang, Wang Xinmin, Liu Wei, Yang Zheng, Feng Haijun, Cui Xiaoran, Wang Qunbo.
introduction
In recent years, my country's artificial intelligence industry has shown a prosperous development trend, with related software and hardware developing in a diversified manner, including cloud servers and edge computing.
With the proliferation of different types of processors for devices and terminals, various computing libraries, intermediate representation tools, and programming frameworks have also emerged.
A flourishing landscape. The vast abundance of AI software and hardware has greatly facilitated the efficient deployment of applications, but it has also brought about a diverse range of challenges.
The challenges of increasing complexity and fragmentation. On the one hand, AI software practitioners need to consider the interaction between their software and various mainstream AI processors.
Adaptation involves investing significant effort in improving software portability; on the other hand, the development of each AI hardware device requires fundamentally adapting to commonly used manual processes.
Intelligent software provides support; otherwise, it's difficult to integrate into the existing software ecosystem. This M×N level software-hardware mapping relationship has gradually developed...
This has become a major obstacle hindering the development of artificial intelligence applications.
Artificial intelligence operators are the fundamental computations for building artificial intelligence applications. They encapsulate related hardware operations, and artificial intelligence software calls these operators.
Sub-interfaces are used to utilize hardware resources to complete computations; operator interfaces serve as a bridge between artificial intelligence software and hardware. GB/T 46069, "Artificial Intelligence," is a standard standard for this field.
The "Artificial Intelligence Operator Interface" standardizes and normalizes the core data structures, functions, and interface parameters of artificial intelligence operators, aiming to reduce the cost of artificial intelligence...
This involves fundamental work that addresses the challenges of software and hardware compatibility and promotes ecological development.
GB/T 46069 "Artificial Intelligence Operator Interface" is proposed to consist of five parts.
---Part 1.Fundamental Mathematics. The aim is to establish the general principles and core data structures applicable to artificial intelligence operator interfaces, and...
Standardize the basic functions and parameter requirements of the interface for fundamental mathematical operators.
---Part 2.Neural Networks. The purpose is to standardize the basic functions and parameter requirements of neural network operators.
---Part 3.Machine Learning Classes. The purpose is to standardize the basic functionalities and parameter requirements of machine learning operators.
---Part 4.Large Model Operators. The purpose is to standardize the basic functions and parameter requirements of large model operators.
---Part 5.Automated Testing Framework. The purpose is to provide automated testing methods and reference implementations for operator interfaces, verifying the operators.
Development standards compliance.
Artificial intelligence operator interface
Part 1.Basic Mathematics
1.Scope
This document specifies the basic functions and parameter requirements of the interface for fundamental mathematical operators in the field of artificial intelligence.
This document applies to the design, development, and application of mathematical operator libraries for artificial intelligence, as well as the development of related software, hardware, and systems.
2 Normative references
This document has no normative references.
3.Terms and Definitions
The following terms and definitions apply to this document.
3.1
operator
A functional unit that performs a specific computational task.
Note. These tasks can be encapsulated at different levels, including but not limited to the abstraction and optimization of low-level hardware operations.
3.2
operator interface
A standardized set of rules for describing and implementing interfaces for various mathematical operations, logical operations, or other complex computing units.
Note. The operator interface defines a standardized set of application programming interfaces (APIs) that enable developers to call these operations in a consistent manner.
There's no need to concern yourself with the specific implementation details at the underlying level.
3.3
Encapsulation
The process of binding data and data-related operations together to form an independent unit.
3.4
tensor
A multidimensional array consisting of elements of the same type.
3.5
dense tensor
A tensor in which all or most of its elements are non-zero.
Note. Dense tensors generally use an uncompressed storage method, i.e. dense storage, which stores all elements of the tensor in a certain order.
3.6
sparse tensor
A tensor in which all or most of its elements are zero.
Note. Sparse tensors are generally stored using a compressed storage method, i.e., sparse storage, which stores only the non-zero elements of the tensor.
...
