  As the size and context length of Large Language Models (LLMs) grow,
weight-activation quantization has emerged as a crucial technique for efficient
deployment of LLMs. Compared to weight-only quantization, weight-activation
quantization presents greater challenges due to the presence of outliers in
activations. Existing methods have made significant progress by exploring
mixed-precision quantization and outlier suppression. However, these methods
primarily focus on optimizing the results of single matrix multiplication,
neglecting the bidirectional propagation of quantization errors in LLMs.
Specifically, errors accumulate vertically within the same token through
layers, and diffuse horizontally across different tokens due to self-attention
mechanisms. To address this issue, we introduce BiSup, a Bidirectional
quantization error Suppression method. By constructing appropriate optimizable
parameter spaces, BiSup utilizes a small amount of data for quantization-aware
parameter-efficient fine-tuning to suppress the error vertical accumulation.
Besides, BiSup employs prompt mixed-precision quantization strategy, which
preserves high precision for the key-value cache of system prompts, to mitigate
the error horizontal diffusion. Extensive experiments on Llama and Qwen
families demonstrate that BiSup can improve performance over two
state-of-the-art methods (the average WikiText2 perplexity decreases from 13.26
to 9.41 for Atom and from 14.33 to 7.85 for QuaRot under the W3A3-g128
configuration), further facilitating the practical applications of low-bit
weight-activation quantization.
