Distribution Encoding for LLM Weight Compression
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Solution Overview
Problem
Large language models (LLMs) face challenges in processing and storage efficiencies, requiring multiple GPUs for inference and consuming significant resources and costs.
Innovation Solution
The distribution encoding method optimizes the rounding process in LLMs by exploiting the non-uniform distribution of model weights, improving numerical accuracy and compression, and reducing the number of GPUs needed for inferencing.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Quantity of substance
If quantization is applied to reduce model storage requirements, then storage efficiency is improved, but numerical accuracy deteriorates
Solution Approach 1:
The patent applies different precision levels to different weight values based on their distribution characteristics. Frequently occurring weight values are represented with lower precision (fewer bits), while rare weight values maintain higher precision. This local differentiation resolves the contradiction by optimizing storage for common cases without sacrificing accuracy for rare but important cases.
Solution Approach 2:
The patent changes the parameter of bit depth dynamically based on the weight value's position in the distribution. Instead of using a fixed bit depth for all weights, the system adjusts the number of bits allocated to represent each weight based on its frequency and magnitude, thereby improving overall storage efficiency while maintaining numerical accuracy where needed.
2Productivity
If multiple GPUs are used for inference, then processing capacity is improved, but resource consumption and cost increase
Solution Approach 1:
The patent segments the model weights into different precision groups based on their distribution characteristics. This segmentation allows the model to be stored and processed more efficiently, reducing the memory bandwidth requirements and enabling the same processing capacity to be achieved with fewer GPUs or lower resource consumption per GPU.
3Quantity of substance
If compression is applied to model weights, then storage efficiency is improved, but numerical accuracy deteriorates
Solution Approach 1:
The patent implements local quality by applying different compression ratios to different portions of the weight distribution. High-frequency weight values that are less sensitive to precision changes are compressed more aggressively, while low-frequency values maintain higher precision. This resolves the contradiction between compression and accuracy by making compression adaptive to the local characteristics of the weight distribution.
Data Source
AI summary
Methods are described for improving processing and storage efficiencies in large language models (LLMs) while also improving numerical accuracy. The methods are referred as distribution encoding. The disclosed distribution encoding techniques exploit the non-uniform distribution of model weights to provide improved numerical accuracy and compression, and consequently can reduce the number of GPU's needed for inferencing. This in turn enables the reduction of resources and cost necessary to implement such models.