Neural network compiler configuration method and device, computer equipment and storage medium

Abstract

The invention relates to a neural network compiler configuration method and device, computer equipment and a storage medium. The method comprises the following steps: acquiring a target neural network model and an initial calculation graph corresponding to the target neural network model, wherein the initial calculation graph comprises a plurality of operators; dividing the plurality of operators to obtain a plurality of operator sets; determining a plurality of operator type combinations respectively corresponding to each operator set, and obtaining operator operation time corresponding to each operator type combination to obtain a plurality of operator operation times respectively corresponding to each operator set; taking the operator type combination with the shortest operator operation time in the plurality of operator type combinations as a target operator type combination corresponding to each operator set; and generating a target calculation graph of the target neural network model according to the target operator type combination corresponding to each operator set, and generating compiler configuration information for the target neural network model according to the target calculation graph. By adopting the method, the configuration optimization effect of the neural network model compiler can be improved.

Description

technical field [0001] The present application relates to the field of computer technology, in particular to a neural network compiler configuration method, device, computer equipment and storage medium. Background technique [0002] With the development of computer technology, neural network models have been used more and more widely, but neural network models still face huge challenges in computing performance. At present, in order to realize the efficient calculation of the neural network model, the calculation performance of the neural network model is usually improved by optimizing the configuration of the compiler of the neural network model. [0003] In traditional technology, the optimization configuration of the compiler of the neural network model is usually based on the handwritten rules of experts. By splitting the calculation graph of the neural network model into multiple sub-graphs according to the splitting rules experienced by experts, and then separately M...

AI Technical Summary

Problems solved by technology

However, the current neural network compiler configuration method needs to rely on experts' handwritten rules to optimize the calculation graph, so the compiler configuration optimization effect for neural network models with emerging operators or model structures is poor

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