Generative Model Quantization for Faster Training Workflows
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Solution Overview
Problem
The training of generative models is time-consuming and inefficient due to the need for manual adjustment of models one by one.
Innovation Solution
A method and apparatus that involves obtaining an initial generative model, determining a target operator, applying a preset quantization policy based on the operator's category and quantity, and performing quantization processing to obtain a trained generative model, which includes operator replacement or insertion of quantized operator combinations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If manual adjustment of generative models is performed one by one, then model training can be conducted, but the training process becomes time-consuming and inefficient
Solution Approach 1:
The patent applies preliminary action by performing quantization processing on the initial generative model before training begins. The system automatically identifies target operators, determines quantization policies, and executes quantization transformations in advance to simplify the model structure. This preliminary model optimization reduces computational complexity during the subsequent training phase, thereby improving training efficiency and reducing training duration without requiring manual adjustment of each model parameter.
2Productivity
If quantization processing is performed on target operators, then model structure is simplified and training efficiency is improved, but the complexity of model processing increases
Solution Approach 1:
The patent implements self-service by enabling the system to automatically identify target operators, determine appropriate quantization policies based on operator category and quantity, and execute quantization processing without manual intervention. The system self-manages the entire quantization process, from selecting operators to applying transformations, thereby simplifying model structure and improving training efficiency while avoiding the complexity increase that would result from manual model adjustment.
Data Source
AI summary
A data processing method includes: obtaining an initial generative model, and determining a target operator to be quantized from the initial generative model; determining, based on a category and a quantity of the target operator, a preset quantization policy for performing quantization processing on the target operator; performing the quantization processing on the target operator by using the preset quantization policy to obtain a generative model to be trained; and training the generative model by using a training input object to obtain a trained generative model.