A Visual Guide to LLM Quantization
Quantization reduces the memory footprint of large language models by converting high-precision parameters to lower-precision formats, maintaining accuracy while minimizing storage. Various methods include symmetric and asymmetric quantization.
Read original articleLarge Language Models (LLMs) often require significant computational resources due to their size, typically containing billions of parameters. To address the challenges of running these models on consumer hardware, quantization has emerged as a key technique for reducing their memory footprint. This process involves converting high-precision parameters, such as 32-bit floating-point numbers, into lower-precision formats like 8-bit integers. While quantization can lead to some loss of precision, it aims to maintain model accuracy while minimizing storage requirements.
The article outlines various quantization methods, including symmetric and asymmetric quantization. Symmetric quantization maps the range of floating-point values to a symmetric range around zero, while asymmetric quantization shifts the mapping to accommodate different minimum and maximum values. Calibration techniques are essential for determining the optimal range of values to minimize quantization error, particularly for weights and biases, which are static, compared to activations that vary with input data.
Post-Training Quantization (PTQ) is a common approach where model parameters are quantized after training, using either dynamic or static methods to handle activations. Dynamic quantization collects activation distributions during inference to calculate necessary parameters for quantization. Overall, quantization is a critical area of research aimed at making LLMs more accessible and efficient for practical applications, balancing the trade-off between model size and performance.
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A beginner's guide to LLM quantization and testing
Quantization in machine learning involves reducing model parameters to lower precision for efficiency. Methods like GGUF are explored, impacting model size and performance. Extreme quantization to 1-bit values is discussed, along with practical steps using tools like Llama.cpp for optimizing deployment on various hardware.
Q-Sparse: All Large Language Models Can Be Fully Sparsely-Activated
The paper introduces Q-Sparse, a method for training sparsely-activated large language models, achieving full sparsity in activations for efficiency gains during inference. Q-Sparse is effective across various LLM settings, including full-precision and 1-bit models like BitNet b1.58, promising enhanced efficiency and reduced costs.
It uses asymmetric quantization and does so layer by layer such that each layer is processed independently before continuing to the next
GPTQ also supports symmetric quantization and almost everyone uses it. The problem with GPTQ asymmetric quantization is that all popular implementations have a bug [1] where all zero/bias values of 0 are reset to 1 during packing (out of 16 possible biases in 4-bit quantization), leading to quite a large loss in quality. Interestingly, it seems that people initially observed that symmetric quantization worked better than asymmetric quantization (which is very counter-intuitive, but made GPTQ symmetric quantization far more popular) and only discovered later that it is due to a bug.
[1] https://notes.danieldk.eu/ML/Formats/GPTQ#Packing+integers
Intuitively, I like the idea of asymmetric scales as well. Treating all values as equal seems like it's probably wasteful in terms of memory. It would be interesting to see where typical values fall statistically in an LLM. I bet it's nowhere near a random distribution of values.
Floats are not distributed evenly across the number line. The number of floats between 0 and 1 is the same as the number of floats between 1 and 3, then between 3 and 7 and so on. Quantising well to integers means that you take this sensitivity into account since the spacing between integers is always the same.
Related
A beginner's guide to LLM quantization and testing
Quantization in machine learning involves reducing model parameters to lower precision for efficiency. Methods like GGUF are explored, impacting model size and performance. Extreme quantization to 1-bit values is discussed, along with practical steps using tools like Llama.cpp for optimizing deployment on various hardware.
Q-Sparse: All Large Language Models Can Be Fully Sparsely-Activated
The paper introduces Q-Sparse, a method for training sparsely-activated large language models, achieving full sparsity in activations for efficiency gains during inference. Q-Sparse is effective across various LLM settings, including full-precision and 1-bit models like BitNet b1.58, promising enhanced efficiency and reduced costs.