Data processing method, device and equipment and computer storage medium

Abstract

The invention provides a data processing method, apparatus and device, and a computer storage medium. The method comprises the steps of obtaining an intermediate representation of a deep learning model; wherein the loop expansion factor is related to information of the intermediate representation during execution of the rear-end hardware equipment and / or equipment information of the rear-end hardware equipment; performing loop expansion on the intermediate representation according to the loop expansion factor to obtain an optimized intermediate representation; and compiling the optimized intermediate representation to obtain a target code which can be executed by the back-end hardware equipment, so that the back-end hardware equipment executes the target code to realize the function of the target code. By adopting the embodiment of the invention, the loop expansion factor can be calculated according to the execution information of the rear-end hardware equipment and / or the equipment information of the rear-end hardware equipment to obtain a more accurate loop expansion factor, and the intermediate representation is subjected to loop expansion through the loop expansion factor, so that instruction scheduling is carried out in a larger range; and the transportability of the intermediate representation is improved.

Description

technical field [0001] The present invention relates to the technical field of computer software, in particular to a data processing method, device, equipment and computer storage medium. Background technique [0002] The new wave of artificial intelligence represented by machine learning and deep learning has been heating up for several years. Deep learning is a research hotspot in the field of artificial intelligence in recent years. It has made breakthroughs in many fields. There are many deep learning frameworks today. And a variety of hardware platforms that support multiple deep learning frameworks. Deep learning frameworks and hardware diversity bring great benefits to users and are crucial to maintaining the healthy development of the artificial intelligence ecosystem. However, supporting multiple frameworks and hardware requires a huge workload, which also brings great challenges to artificial intelligence developers. Here comes no small challenge. [0003] With t...

AI Technical Summary

Problems solved by technology

Deep learning frameworks and hardware diversity bring great benefits to users and are crucial to maintaining the healthy development of the artificial intelligence ecosystem. However, supporting multiple frameworks and hardware requires a huge workload, which also brings great challenges to artificial intelligence developers. Here comes the challenge

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