Deep learning model reasoning method and device, equipment and storage medium

A technology of deep learning and reasoning methods, applied in the direction of reasoning methods, neural learning methods, biological neural network models, etc., can solve the problems of large overall errors and achieve the effects of alleviating relatively large errors, improving reasoning efficiency, and ensuring reasoning quality

Pending Publication Date: 2022-07-29
BEIJING QIYI CENTURY SCI & TECH CO LTD
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Problems solved by technology

[0004] This application provides a reasoning method, device, equipment, and storage medium for a deep learning model, which are used to solve the technical problem of large overall errors caused by quantization in the process of model performance optimization in the prior art

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  • Deep learning model reasoning method and device, equipment and storage medium
  • Deep learning model reasoning method and device, equipment and storage medium
  • Deep learning model reasoning method and device, equipment and storage medium

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Embodiment Construction

[0050] In order to make the purposes, technical solutions and advantages of the embodiments of the present application more clear, the technical solutions in the embodiments of the present application will be described clearly and completely below with reference to the accompanying drawings in the embodiments of the present application.

[0051] The term "and / or" in this article is only an association relationship to describe the associated objects, indicating that there can be three kinds of relationships, for example, A and / or B, it can mean that A exists alone, A and B exist at the same time, and A and B exist independently B these three cases. In addition, the character " / " in this document generally indicates that the related objects are an "or" relationship.

[0052] In the training process of deep learning, for the overall target performance, the current mainstream method is to use floating-point float32 data for training, where float represents the floating-point data ...

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Abstract

The invention relates to a deep learning model reasoning method and device, equipment and a storage medium, and the method comprises the steps: obtaining a to-be-reasoned data set; inputting the data set to be reasoned into a reasoning convolution kernel to obtain a floating point type data reasoning result; obtaining an output scaling factor corresponding to the data set to be reasoned; wherein the output scaling factor is determined according to the maximum value of the elements in the data reasoning result; calculating a product of the data reasoning result and the output scaling factor to obtain a quantification result; and continuing reasoning of the deep learning model according to the quantification result. The method is used for solving the technical problem that in the model performance optimization process in the prior art, the overall error is large due to quantification.

Description

technical field [0001] The present application relates to the technical field of deep learning networks, and in particular, to a reasoning method, apparatus, device and storage medium for a deep learning model. Background technique [0002] At present, deep learning has been widely used in all walks of life, and has achieved great results in areas that are difficult to solve by traditional algorithms. However, a problem with the current promotion of deep learning applications is that its operating costs are huge. Even when the computing power of the GPU (graphics processing unit, graphics processing unit) has been greatly improved, the deep learning model with increasing parameters year by year is also Eat up the bonus of GPU performance improvement. Therefore, the performance optimization of the model itself is a key to whether deep learning can be applied in the large-scale production process. [0003] Quantization is one of the methods in the model performance optimizat...

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06N5/04G06N3/08G06N3/04
CPCG06N5/04G06N5/047G06N3/08G06N3/045
Inventor 闻磊
Owner BEIJING QIYI CENTURY SCI & TECH CO LTD
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