The quantization constant in the context of QLORA (or any neural network quantization process) plays a crucial role in effectively reducing the model size while maintaining its performance.
Scaling Factor: The quantization constant acts as a scaling factor. During quantization, the continuous or high-precision values (like weights in a neural network) are converted into a more compact format. The quantization constant determines how these values are scaled down to fit within the limited range of the quantized format (e.g., 4-bit integers).
Maximising Use of Range: By scaling the maximum value in a vector to align with the quantized range, the quantization constant ensures that the available range is used optimally. This helps in maintaining the relative differences in the values, which is crucial for preserving the behavior of the neural network.
Accuracy and Nuance: Different blocks of weights in a neural network may have different distributions. Using a single quantization constant for the entire network might not be optimal for all weight blocks. By computing separate quantization constants for each block, QLORA can tailor the quantization process to the specific distribution of each block, leading to more accurate quantization.
Minimising Information Loss: This tailored approach helps in minimizing the loss of information that typically occurs during quantization. Since each block is quantized according to its own characteristics, crucial details are less likely to be lost in the compression process.
Recovering Original Data: In the dequantization process, the quantized values are scaled back to their original range. The quantization constant is essential here to accurately reconstruct the original values.
Approximating Original Data: While exact recovery of the original data is often not possible due to the lossy nature of quantization, the use of the quantization constant allows for a close approximation, which is vital for maintaining the performance of the neural network.
In essence, the quantization constant in QLORA's approach is pivotal for efficiently compressing the neural network without significantly compromising its effectiveness.
By customizing this constant for each weight block, QLORA enhances the precision of the quantization process, thereby achieving a balance between model compactness and performance retention.
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