Differentiable Weight Clustering for LLM Memory Compression
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Solution Overview
Problem
Large language models (LLMs) require significant computational power and memory for training and inference, making them challenging to deploy on resource-constrained devices due to their massive size, typically comprising hundreds of millions to billions of parameters.
Innovation Solution
The implementation of memory-efficient differentiable weight clustering (eDKM) techniques, which include cross-device tensor marshaling and weight uniquification and sharding, to reduce the memory footprint of LLMs during train-time optimization, enabling efficient compression and deployment on mobile devices.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Quantity of substance
If traditional weight clustering methods are applied to compress LLMs, then model size is reduced, but memory consumption during training increases significantly
Solution Approach 1:
The patent segments the weight matrix into multiple smaller matrices by dividing the vocabulary size into multiple buckets. Each weight matrix corresponds to a specific bucket range, allowing the clustering algorithm to process and store only the relevant portion of weights at a time, thereby reducing overall memory consumption while maintaining compression effectiveness.
Solution Approach 2:
The patent introduces a bucket dimension to organize weights based on their magnitude. By sorting weights and assigning them to different buckets based on their value ranges, the system transforms the flat weight matrix into a structured format that enables more efficient memory access patterns and reduces the memory footprint during training.
2Manufacturing precision
If differentiable weight clustering is used to achieve state-of-the-art compression, then model accuracy is maintained, but training time and computational resources increase
Solution Approach 1:
The patent applies partial action by processing only the necessary portions of the weight matrix. Instead of performing clustering on all weights simultaneously, the system processes weights bucket by bucket, focusing computational resources on the most critical weight regions and reducing overall training time while maintaining accuracy.
Solution Approach 2:
The patent performs preliminary sorting of weights by magnitude before applying the clustering algorithm. This pre-processing step organizes the data in advance, allowing the clustering to proceed more efficiently through the buckets in a systematic manner, thereby reducing training time without compromising the quality of weight quantization.
3Adaptability or versatility
If LLMs are deployed on resource-constrained devices, then accessibility is improved, but model performance deteriorates due to limited computational power
Solution Approach 1:
The patent changes the parameter representation from full-precision floating-point numbers to quantized integer representations through weight clustering. By transforming the weight parameters into a compressed format with reduced precision, the system enables deployment on devices with limited computational resources while maintaining sufficient model performance for practical applications.
Data Source
AI summary
The subject technology provides memory-efficient differentiable weight clustering for large language model compression. An apparatus determines a tensor including an attention map between learned weights of a trained machine learning model and corresponding centroids. The apparatus also determines a compressed attention table and a plurality of index lists during compression of the trained machine learning model based on an uniquification of the attention map and sharding of an associated index list. The apparatus determines whether the tensor exists at a destination device during compression of the trained machine learning model using a marshaling layer. The apparatus refrains from copying the tensor to the destination device when the tensor exists at the destination device, or copies the tensor to the destination device when the tensor does not exist at the destination device. The apparatus deploys a compressed machine learning model based on the compression of the trained machine learning model.