Abstract: A data processing method includes obtaining first data. The data processing method further includes determining a value of r based on a first parameter and/or a second parameter, where r indicates to divide the first data into r pieces of sub-data, the r pieces of sub-data are separately processed by a neural network layer in a machine learning model, a processing process of the first data is divided into tasks of sending the r pieces of sub-data, tasks of processing the r pieces of sub-data, and tasks of sending processing results of the r pieces of sub-data, and time occupied by tasks can overlap. The first parameter indicates a network status of a communication network used to send the sub-data, and the second parameter indicates a load status of a communication node configured to process the sub-data.

Abstract: A data processing method is provided, applied to the field of artificial intelligence. The method includes: determining, based on a target mapping relationship, one or more target network units corresponding to a target word vector and a storage address, where storage space corresponding to the storage address is located in storage space outside a computing unit; obtaining the one or more target network units from the storage space; and performing, through the computing unit based on the target word vector, a training process corresponding to a neural network constructed based on the one or more target network units. Because storage space of the storage location outside the computing unit may be set to be relatively large, through separation of storage and compute, a size of the large-scale model during training can be increased and scalability and flexibility of the large-scale model can be improved.

Abstract: A data processing method includes processing target data through a target neural network to obtain a data processing result, where a target header of the target neural network is used to process, through a first transformation matrix, a first vector corresponding to first subdata, and process, through a second transformation matrix, a second vector corresponding to the first subdata, where the first vector corresponds to position information of the first subdata in the target data, and the second vector corresponds to semantic information of the first subdata.

Abstract: This application relates to the artificial intelligence field, and discloses a neural network search method and a related apparatus. The neural network search method includes: constructing attention heads in transformer layers by sampling a plurality of candidate operators during model search, to construct a plurality of candidate neural networks, and comparing performance of the plurality of candidate neural networks to select a target neural network with higher performance. In this application, a transformer model is constructed with reference to model search, so that a new attention structure with better performance than an original self-attention mechanism can be generated, and effect in a wide range of downstream tasks is significantly improved.

Abstract: 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; 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; and performing model training on the second neural network model to obtain a target neural network model.

Abstract: The invention provides an apparatus for inspecting a light transmissible optical component. The apparatus comprises an image capturing module arranged on a first side of a support configured to hold a light transmissible optical component whilst it is being inspected. The apparatus includes an illumination device configured to shape light from a light source and to illuminate a selected portion of a surface of said light transmissible optical component with said shaped light to enable the image capturing module to capture any of a bright field image, a dark field image, or a combined bright field and dark field image of the light transmissible optical component being held by the support.

Abstract: The invention provides an apparatus for inspecting a light transmissible optical component. The apparatus comprises an image capturing module arranged on a first side of a support configured to hold a light transmissible optical component whilst it is being inspected. The apparatus includes an illumination device configured to shape light from a light source and to illuminate a selected portion of a surface of said light transmissible optical component with said shaped light to enable the image capturing module to capture any of a bright field image, a dark field image, or a combined bright field and dark field image of the light transmissible optical component being held by the support.