Transformer Weight Quantization for Low-Energy LLM Inference
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Solution Overview
Problem
Large Language Models (LLMs) consume significant compute resources and energy, with memory bandwidth and inter-device communication overhead being major bottlenecks, especially when deployed on distributed systems, and existing quantization methods lead to accuracy loss and convergence issues.
Innovation Solution
Implement ultra-low precision (ULP) weight quantization using binary or ternary quantization for LLMs, with parallel matrix arithmetic and group quantization across multiple devices, maintaining high precision for optimizer states and gradients during training.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Use of energy by moving object
If ultra-low precision quantization is applied to weight values, then memory footprint and energy consumption are reduced, but model accuracy may deteriorate
Solution Approach 1:
The patent applies parameter changes by transforming weight values from high-precision floating-point format to ultra-low precision binary or ternary quantized format. This parameter transformation reduces memory footprint and energy consumption while the system maintains model accuracy through specialized quantization algorithms that preserve essential information in the reduced precision representation.
Solution Approach 2:
The patent implements local quality by applying different precision levels to different components of the system. Weight values are quantized to binary or ternary precision, while activation values and gradient computations maintain higher precision. This selective precision allocation optimizes energy consumption for the most resource-intensive operations while preserving accuracy where it matters most.
2Productivity
If distributed computing is used for parallel matrix arithmetic, then processing speed and scalability are improved, but inter-device communication overhead increases
Solution Approach 1:
The patent applies segmentation by dividing the large weight matrix into multiple subgroups that can be processed in parallel across different computing devices. Each device handles a specific subgroup, enabling distributed computation that scales with available hardware while reducing the communication burden through localized processing of partitioned data.
Solution Approach 2:
The patent implements merging by combining results from multiple parallel computations across distributed devices. The system aggregates outputs from different device instances processing weight subgroups, consolidating their contributions to produce the final matrix arithmetic result, thereby achieving scalable parallel processing efficiency.
3Productivity
If weight values are quantized to binary or ternary values, then computational efficiency is improved, but information representation capability is reduced
Solution Approach 1:
The patent transforms the parameter representation of weight values from continuous floating-point to discrete binary or ternary quantized values. This parameter change enables computationally efficient operations using simple arithmetic and logic operations while advanced quantization techniques preserve sufficient information representation capability for maintaining model performance.
Data Source
AI summary
A computer system is provided that includes processing circuitry. The computer system being configured to implement a machine learning (ML) model having a transformer architecture that, during a training operation or inference operation, is configured to receive an activation input matrix of activation input values and obtain a weight matrix of weight values. The ML model is further configured to perform ultra-low precision (ULP) quantization by quantizing each of the weight values in the weight matrix to a corresponding selected value from a predefined set of binary or ternary quantized weight values and compute a matrix arithmetic result based on at least a portion of the weight matrix with the quantized weight values and at least a portion of the activation input matrix.