LLM Weight Rounding Optimization Balancing Accuracy and Efficiency
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing quantization methods for Large Language Models (LLMs) either compromise accuracy or computational efficiency, with weight-only quantization techniques like SignRound focusing only on rounding tasks and introducing performance overhead, while other methods struggle with substantial compression errors and limited applicability.
Innovation Solution
Implementing block-wise optimization with three trainable parameters (α, β, V) for weight rounding in LLM-based quantization, enhancing the rounding quantization operation to improve accuracy and efficiency without performance overhead, applicable to various deep learning domains.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If weight-only quantization is used to reduce computational costs, then computational efficiency is improved, but accuracy deteriorates due to substantial compression errors
Solution Approach 1:
The patent changes the quantization parameter from simple rounding to a learned rounding scheme where the quantization function is optimized during training. This allows the system to adapt the quantization parameters to minimize accuracy loss while maintaining computational efficiency benefits
Solution Approach 2:
The patent introduces an intermediary optimization process that bridges the gap between high-precision original weights and low-precision quantized weights. By learning an optimal rounding function as an intermediary step, the system reduces compression errors without sacrificing computational efficiency
2Measurement precision
If SignRound method is used for weight rounding, then rounding accuracy is improved, but computational overhead increases due to focusing only on rounding tasks
Solution Approach 1:
The patent merges the rounding operation with the existing quantization pipeline by learning the rounding function within the same training framework. This integration eliminates separate rounding computation steps and reduces overall computational overhead while maintaining rounding accuracy
3Productivity
If broader quantization grid is used to emphasize up and down rounding, then weight-only quantization efficiency is improved, but compression errors increase
Solution Approach 1:
The patent makes the quantization grid dynamic by learning optimal rounding parameters during training. Instead of using a fixed broad quantization grid, the system adapts the grid characteristics to balance efficiency and accuracy requirements for different weight distributions
Data Source
AI summary
Example apparatus disclosed includes at least one memory, machine readable instructions, and programmable circuitry to at least one of instantiate or execute the machine readable instructions to determine a plurality of weights of a large language model, initialize a first parameter and a second parameter associated with the large language model, perform rounding quantization of the large language model weights using at least the first parameter or the second parameter, generate a quantized large language model using the large language model weights after the rounding quantization, determine model loss between the large language model and corresponding quantized large language model, and update the first parameter and the second parameter based on the model loss using backpropagation.