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公开(公告)号：EP4123514A3
公开(公告)日：2023-07-19
申请号：EP22211848.1
申请日：2022-12-07
发明人： MA, Yanjun , WANG, Haifeng , HU, Xiaoguang , YU, Dianhai , WU, Tian , LI, Qi
摘要： The disclosure provides an access method, an access apparatus, an electronic device and a computer storage medium, and relates to a field of computer technologies, in particular to a field of artificial intelligence technologies such as chip and deep learning. The method includes: determining (S11) a computational graph for calling an access device based on operator representations in a target model; optimizing (S12) the computational graph based on information of the access device; and performing (S13) relevant running operations of the target model on the access device based on the computational graph and an interface for the access device to access to a model framework of the target model, the interface being determined based on kit data of the access device.
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公开(公告)号：EP4191411A1
公开(公告)日：2023-06-07
申请号：EP22211341.7
申请日：2022-12-05
发明人： WANG, Haifeng , WU, Zhihua , YU, Dianhai , MA, Yanjun , WU, Tian
摘要： A distributed training method based on end-to-end adaption, a device and a storage medium. The method includes: obtaining (S101) slicing results by slicing a model to be trained; obtaining (S102) an attribute of computing resources allocated to the model for training by parsing the computing resources, in which the computing resources are determined based on a computing resource requirement of the model, computing resources occupied by another model being trained, and idle computing resources, and the attribute of the computing resources is configured to represent at least one of a topology relation and a task processing capability of the computing resources; determining (S103) a distribution strategy of each of the slicing results in the computing resources based on the attributes of the computing resources; and performing (S104) distributed training on the model using the computing resources based on the distribution strategy.
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3.发明公开
公开(公告)号：EP4350577A1
公开(公告)日：2024-04-10
申请号：EP23200782.3
申请日：2023-09-29
IPC分类号： G06N3/045 , G06N3/08 , G06N3/0475
摘要： The present disclosure provides a data generation method based on a deep learning model, and a training method and apparatus, relates to the field of artificial intelligence technologies, and in particular, to the field of natural language processing and deep learning technologies, and can be used to improve the quality of reply data generated by the deep learning model based on input data of a user. The data generation method includes: determining an initial input of the deep learning model based on input data of a user; obtaining a first output of the model, where in response to the model determining that generating a reply based on the initial input requires calling a first functional component different from the deep learning model, the first output includes a first token for calling the first functional component and a first intermediate inquiry determined based on the initial input and recognizable by the first functional component; obtaining a first intermediate result determined by the first functional component based on the first intermediate inquiry; determining a second input for the model based on the initial input and the first intermediate result; and obtaining a second output of the model for generating a reply to the initial input.
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4.发明公开
公开(公告)号：EP4195108A1
公开(公告)日：2023-06-14
申请号：EP22180958.5
申请日：2022-06-24
发明人： WU, Tian , MA, Yanjun , YU, Dianhai , YANG, Yehua , DU, Yuning
摘要： The present invention provides a method and apparatus for generating and applying a deep learning model based on a deep learning framework, and relates to the field of computers. A specific implementation solution includes that: a basic operating environment is established on a target device, where the basic operating environment is used for providing environment preparation for an overall generation process of a deep learning model; a basic function of the deep learning model is generated in the basic operating environment according to at least one of a service requirement and a hardware requirement, to obtain a first processing result; an extended function of the deep learning model is generated in the basic operating environment based on the first processing result, to obtain a second processing result; and a preset test script is used to perform function test on the second processing result; to output a test result.
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5.发明公开
公开(公告)号：EP3968145A1
公开(公告)日：2022-03-16
申请号：EP20938507.9
申请日：2020-10-28
发明人： XIE, Yongkang , MA, Ruyue , XIN, Zhou , CAO, Hao , SHI, Kuan , ZHOU, Yu , LI, Yashuai , SHI, En , WU, Zhiquan , PAN, Zihao , LI, Shupeng , HU, Mingren , WU, Tian
IPC分类号： G06F8/20
摘要： The present disclosure relates to an apparatus and a method for executing a customized production line using an artificial intelligence development platform, a computing device and a computer readable storage medium, and relates to the technologies of artificial intelligence and cloud platform. The customized production line is an additional development process that is different from a pre-defined development process of the artificial intelligence development platform and defined by a file set. The apparatus includes: a production line executor configured to generate a native form of the artificial intelligence development platform based on the file set, the native form to be sent to a client accessing the artificial intelligence development platform so as to present a native interactive page of the artificial intelligence development platform; and a standardized platform interface configured to provide an interaction channel between the production line executor and the artificial intelligence development platform. The production line executor is further configured to generate an intermediate result by executing processing logic defined in the file set and to process the intermediate result by interacting with the artificial intelligence development platform via the standardized platform interface so as to execute the additional development process.
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6.发明公开
公开(公告)号：EP3996009A1
公开(公告)日：2022-05-11
申请号：EP21197920.8
申请日：2021-09-21
发明人： ZHANG, Liujie , LI, Yamei , ZHENG, Huihuang , LIU, Hongyu , LAN, Xiang , YU, Dianhai , MA, Yanjun , WU, Tian , WANG, Haifeng
IPC分类号： G06N20/00
摘要： According to exemplary embodiments of the present disclosure, there is provided a method and apparatus of converting a schema in a deep learning framework, and a computer storage medium, and a computer program product, which may be used for a construction of the deep learning framework. The method of converting the schema in the deep learning framework includes: updating a first schema, based on first syntax elements in the first schema and a context relationship between the first syntax elements in the first schema, so as to obtain an updated first schema; generating second syntax elements corresponding to updated first syntax elements in the updated first schema, based on a mapping relationship between the updated first syntax elements in the updated first schema and second syntax elements in a second schema system; and combining the second syntax elements according to a context relationship between the updated first syntax elements, so as to generate a second schema. According to a solution of the present disclosure, the schema conversion may be performed efficiently.
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7.发明公开
公开(公告)号：EP3926555A2
公开(公告)日：2021-12-22
申请号：EP21198055.2
申请日：2021-09-21
发明人： DANG, Qingqing , DENG, Kaipeng , JIANG, Lielin , GUO, Sheng , HU, Xiaoguang , ZHANG, Chunyu , MA, Yanjun , WU, Tian , WANG, Haifeng
IPC分类号： G06N20/00
摘要： Embodiments of the present disclosure provide a method and apparatus of training a model, an electronic device, a storage medium and a development system, which relate to a field of deep learning. The method may include calling a training preparation component to set at least a loss function and an optimization function for training the model, in response to determining that a training preparation instruction is received. The method further includes calling a training component to set a first data reading component, in response to determining that a training instruction is received. The first data reading component is configured to load a training data set for training the model. In addition, the method may further include training the model based on the training data set from the first data reading component, by using the loss function and the optimization function through the training component. Technical solutions of the present disclosure may reduce an input of codes, so that research and development resources and time costs are significantly saved.
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公开(公告)号：EP4195092A1
公开(公告)日：2023-06-14
申请号：EP22188472.9
申请日：2022-08-03
发明人： CHEN, Zeyu , WANG, Haifeng , WU, Tian , YU, Dianhai , MA, Yanjun , HU, Xiaoguang
IPC分类号： G06F40/279 , G06F40/284 , G06F40/53 , G06N3/0455 , G06N3/08
摘要： Provided are a text processing method and apparatus, a system, a device and a storage medium, which relate to a field of computer technology, and especially to a field of artificial intelligence, such as natural language processing and deep learning. The specific implementation scheme includes: performing (S101) text processing on first text, by using a text processing acceleration operator; and processing (S 102), in parallel and faster, content after the text processing, by using the text processing acceleration operator. In the embodiments of the present disclosure, text processing and parallel acceleration are carried out by the text processing acceleration operator, which can improve the speed of text processing.
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公开(公告)号：EP4123514A2
公开(公告)日：2023-01-25
申请号：EP22211848.1
申请日：2022-12-07
发明人： MA, Yanjun , WANG, Haifeng , HU, Xiaoguang , YU, Dianhai , WU, Tian , LI, Qi
摘要： The disclosure provides an access method, an access apparatus, an electronic device and a computer storage medium, and relates to a field of computer technologies, in particular to a field of artificial intelligence technologies such as chip and deep learning. The method includes: determining (S11) a computational graph for calling an access device based on operator representations in a target model; optimizing (S12) the computational graph based on information of the access device; and performing (S13) relevant running operations of the target model on the access device based on the computational graph and an interface for the access device to access to a model framework of the target model, the interface being determined based on kit data of the access device.
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10.发明公开
公开(公告)号：EP3926555A3
公开(公告)日：2022-04-27
申请号：EP21198055.2
申请日：2021-09-21
发明人： DANG, Qingqing , DENG, Kaipeng , JIANG, Lielin , GUO, Sheng , HU, Xiaoguang , ZHANG, Chunyu , MA, Yanjun , WU, Tian , WANG, Haifeng
IPC分类号： G06N20/00
摘要： Embodiments of the present disclosure provide a method and apparatus of training a model, an electronic device, a storage medium and a development system, which relate to a field of deep learning. The method may include calling a training preparation component to set at least a loss function and an optimization function for training the model, in response to determining that a training preparation instruction is received. The method further includes calling a training component to set a first data reading component, in response to determining that a training instruction is received. The first data reading component is configured to load a training data set for training the model. In addition, the method may further include training the model based on the training data set from the first data reading component, by using the loss function and the optimization function through the training component. Technical solutions of the present disclosure may reduce an input of codes, so that research and development resources and time costs are significantly saved.
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