LLM Weight Rounding via Signed Gradient Descent for Block-Wise Quantization
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Solution Overview
Problem
Conventional quantization approaches for large language models (LLMs) result in accuracy problems when converting high-precision weight data to lower-precision formats, leading to significant errors and reduced performance.
Innovation Solution
A modified rounding operation using signed gradient descent to learn and model weight relationships, applying a tensor to adjust rounding on a per-block basis, optimizing the quantization process to improve accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If weight data is quantized to lower-precision format, then computational demands are reduced, but accuracy deteriorates
Solution Approach 1:
The patent changes the rounding parameters by introducing learnable offset values and block-wise rounding thresholds. Instead of using fixed rounding rules, the system learns optimal rounding parameters through training, allowing weights to be rounded to lower precision while compensating for quantization errors through learned adjustments to the rounding function itself.
Solution Approach 2:
The patent applies preliminary rounding adjustments during the training phase by introducing a rounding operation that learns optimal thresholds and offsets before deployment. This preliminary learning process captures the relationships between weights and determines the best rounding behavior in advance, so that during inference, simple rounded weights can be used without losing accuracy.
2Productivity
If conventional quantization is applied, then computational efficiency is improved, but model performance deteriorates
Solution Approach 1:
The patent implements feedback by using the model's performance on a validation set to guide the rounding optimization process. The rounding parameters are adjusted based on the loss computed from validation predictions, creating a feedback loop that continuously improves rounding accuracy. This feedback mechanism ensures that the rounded model maintains performance comparable to the full-precision model.
Solution Approach 2:
The patent introduces dynamic rounding behavior through block-wise independent rounding thresholds and learnable offsets. Instead of a static rounding function applied uniformly to all weights, the system adapts rounding parameters for different blocks of weights based on their specific characteristics and relationships, allowing each block to be rounded optimally while maintaining overall model performance.
3Device complexity
If weights are rounded independently, then quantization process is simplified, but relationship between weights is lost
Solution Approach 1:
The patent segments the weight matrix into blocks and applies independent rounding optimization to each block. This segmentation allows the system to capture local relationships between weights within each block while maintaining computational tractability. By processing blocks independently rather than all weights together, the method balances the need to preserve relationships with the simplicity of the quantization process.
Solution Approach 2:
The patent introduces learnable offset vectors as intermediaries between the original weights and the rounded weights. These offsets act as mediators that capture the relationships and correlations between weights that would otherwise be lost during rounding. The offsets are learned during training and compensate for the information loss introduced by independent block-wise rounding.
Data Source
AI summary
Systems, apparatuses and methods may provide for technology that determines a tensor based on a signed gradient descent value and modifies a rounding operation with respect to weights in a large language model (LLM) based on the tensor. The rounding operation may be modified on a per block basis.