3 results about "Transformation operator" patented technology

The application provides a method for inserting a conversion operator, which comprises: determining an initial data format of a first operator in a neural network; determining a current data format of the first operator; when the initial data format of the first operator is different from the current data format, inserting at least one first conversion operator and at least one second conversion operator around the first operator, the first conversion operator being used for converting input data of the first operator from the initial data format to the current data format, and the second conversion operator being used for converting output data of the first operator from the current data format to the initial data format. In the method, the initial data format is a data format beneficial to hardware performance, and the current data format is a data format beneficial to improving processing efficiency of the first operator; therefore, the conversion operator is inserted according to the initial data format and the current data format of the first operator, so that the requirements of the operator and the requirements of hardware can be met simultaneously.

The invention provides a large language model operator conversion system and method and an AI accelerator, the system comprises an operator identification module, a conversion module and a model reconstruction module, the operator identification module analyzes a computational graph structure of an input model to identify convertible operators meeting preset conversion conditions in the computational graph structure; the conversion module converts the convertible operator into an equivalent operator sequence based on a preset conversion strategy; and the model reconstruction module replaces nodes corresponding to convertible operators in the computational graph of the input model with an equivalent operator sequence, updates the connection relation and the weight initializer, and generates an updated model. According to the system, pipeline processing of model optimization is realized through an identification rule and a conversion strategy, and adaptation for different hardware platforms can be realized by updating a preset conversion condition and a conversion strategy, so that the problems of low deployment efficiency and low expandability on a CNN (Convolutional Neural Network) accelerator are solved.

The application discloses a model design method and device, equipment and a storage medium, and relates to the technical field of computers, which comprises the following steps: obtaining an original model, and obtaining an operator satisfying a preset condition from the original model to obtain a target operator; inserting a first conversion operator and a second conversion operator in front of the target operator; distributing the target operator to a target compiler, and distributing the first conversion operator and the second conversion operator to a central processing unit to obtain a compiled model. The application increases the conversion operator in front of the operator satisfying the preset condition, and distributes the conversion operator to the central processing unit, so that the central processing unit converts the data that cannot be used by the target operator into usable data, and then sends the data to the target compiler for processing, thereby realizing the conversion of the data mode, improving the equipment utilization rate of the target compiler and the processing speed of the model.