Abstract: A method for processing a model operator includes: determining an operator set for model networking, wherein the operator set comprises a plurality of operators; determining a storage amount occupied by an output tensor of each operator in the operator set and a computation time period consumed in a forward computation of each operator in the operator set; and determining a first operator participating in recomputation in a model from the operator set, based on the storage amounts and the computation time periods of the plurality of operators.

Abstract: Provided is a performance optimization method for a model training device, an electronic device, and a storage medium, relating to the fields of deep learning, large model training, and distributed parallel strategies. The method includes: determining communication timing of a current model training device with respect to a target model block at a target sorting position, so as to be able to perform synchronously collective communication with other model training devices of a plurality of model training devices with respect to model blocks at the target sorting position; and performing the collective communication on a backward gradient of the target model block at the communication timing.

Abstract: A method of executing a task for a large language model, a device, and a storage medium are provided, which relate to a field of artificial intelligence technology, and in particular to fields of deep learning, large language model, natural language processing and computer vision technologies. The method includes: determining, by using a determination unit, a target attention task from a plurality of attention tasks to be processed, based on a sparse representation corresponding to a feature to be processed, where the target attention task is a task corresponding to a non-fully masked region of the feature, the sparse representation represents a mask position of the feature, and the mask position represents mask endpoint positions in at least two non-intersecting intervals in a mask matrix corresponding to the feature; and executing the target attention task by using a computing unit, so as to obtain an attention feature.

Abstract: Apparatuses and methods for detecting multilayer multipass welding trajectory are disclosed. The apparatus includes an imaging component, a first and a second directional light source group, a structured light source and a control and processing component. The imaging component is disposed in front of a welding gun and above a weld pass. The first and the second directional light source group are disposed symmetrically above the weld pass and at two sides of the weld pass. The structured light source is disposed above the weld pass. The control and processing component is configured to control the imaging component to capture an image of the workpiece surface separately when the first directional light source group, the second directional light source group or the structured light source emits the light to the workpiece surface and to obtain a multilayer multipass welding trajectory according to the images.

Abstract: Apparatuses and methods for detecting multilayer multipass welding trajectory are disclosed. The apparatus includes an imaging component, a first and a second directional light source group, a structured light source and a control and processing component. The imaging component is disposed in front of a welding gun and above a weld pass. The first and the second directional light source group are disposed symmetrically above the weld pass and at two sides of the weld pass. The structured light source is disposed above the weld pass. The control and processing component is configured to control the imaging component to capture an image of the workpiece surface separately when the first directional light source group, the second directional light source group or the structured light source emits the light to the workpiece surface and to obtain a multilayer multipass welding trajectory according to the images.