EasyQuant: An Efficient Data-free Quantization Algorithm for LLMs
Hanlin Tang Yifu Sun Decheng Wu Kai Liu Jianchen Zhu Zhanhui Kang
Large language models (LLMs) have proven to be very superior to conventional
methods in various tasks. However, their expensive computations and high memory
requirements are prohibitive for deployment. Model quantization is an effective
method for reducing this overhead. The problem is that in most previous works,
the quantized model was calibrated using few samples from the training data,
which might affect the generalization of the quantized LLMs to unknown cases
and tasks. Hence in this work, we explore an important question: Can we design
a data-independent quantization method for LLMs to guarantee its generalization
performance? In this work, we propose EasyQuant, a training-free and
data-independent weight-only quantization algorithm for LLMs. Our observation
indicates that two factors: outliers in the weight and quantization ranges, are
essential for reducing the quantization error. Therefore, in EasyQuant, we
leave the outliers (less than 1%) unchanged and optimize the quantization range
to reduce the reconstruction error. With these methods, we surprisingly find
that EasyQuant achieves comparable performance to the original model. Since
EasyQuant does not depend on any training data, the generalization performance
of quantized LLMs is safely guaranteed. Moreover, EasyQuant can be implemented
in parallel so that the quantized model could be attained in a few minutes even
for LLMs over 100B. To our best knowledge, we are the first work that achieves
almost lossless quantization performance for LLMs under a data-independent
setting and our algorithm runs over 10 times faster than the data-dependent
methods.