MODEL TRAINING METHOD AND RELATED DEVICE

This application relates to the field of artificial intelligence, and provides a model training method. The method includes: obtaining a to-be-trained first neural network model, where the first neural network model includes a first operator, and the first operator is used to perform a product operation on input data and a target weight matrix (301); replacing the first operator in the first neural network model with a second operator, to obtain a second neural network model, where the second operator is used to perform a product operation on input data and a plurality of sub-weight matrices, and the plurality of sub-weight matrices are obtained by performing matrix factorization on the target weight matrix (302); and performing model training on the second neural network model to obtain a target neural network model (303). In this method, the target weight matrix is split into a product of the plurality of sub-weight matrices. Therefore, a training device requires a shorter time period to perform the product operation on the input data and the plurality of sub-weight matrices, thereby reducing a model training time period.