InfoQuant: Revolutionizing Low-Bit Activation in Language Models

Low-bit activation quantization is one of the unsung challenges in deploying large language models (LLMs). While these models push the boundaries of language processing, the roadblock of efficiently quantizing activations remains.

Quantization Challenges

The problem isn't just outliers in activation data, but mismatched distributions with low-bit uniform quantizers. Current methods, like post-training quantization (PTQ), try to mitigate these issues by smoothing out peaks or balancing channels. But what they often miss is a deeper understanding of the underlying activation distribution that's easy to discretize.

Many find that despite numerical smoothing, quantization error doesn't significantly drop. Why? Because values often cluster around the mean or the quantization range remains too broad. This is where InfoQuant enters the scene with a fresh perspective.

InfoQuant's Approach

Shifting the focus, InfoQuant treats activation transformation as a distribution design problem aimed at the quantizer. From an information-theoretic stance, the solution lies in creating activations that have both a smaller numerical range and enough dispersion. This isn't just a technical nuance. it's a new lens for engineers and researchers to view quantization.

At the core of InfoQuant is the Peak Suppression Orthogonal Transformation (PSOT). By reshaping activations, PSOT helps forge more quantization-friendly distributions. It doesn't stop there. To enhance robustness, InfoQuant introduces adaptive outlier-token selection, fortifying PSOT during optimization.

Performance and Impact

In testing across multiple LLM families, InfoQuant consistently surpasses existing PTQ and even end-to-end training baselines. Under the W4A4KV4 conditions, it retains an impressive 97% of floating-point accuracy, significantly narrowing LLaMA-2 13B's performance gap by 42% over prior leaders.

Why should we care? Because the AI-AI Venn diagram is getting thicker. Models like InfoQuant aren't mere tweaks. they're a convergence of theory and application, setting the stage for more efficient, powerful AI systems. If agents have wallets, who holds the keys? InfoQuant suggests they might just hold them themselves.

For developers and companies looking to deploy large language models, InfoQuant isn't just an option, it's a necessity. As these models become the backbone of AI applications, the need for efficient deployment methods that maintain precision can't be overstated. The compute layer needs a payment rail, and InfoQuant is laying down new tracks.

With its code available on GitHub, InfoQuant invites further exploration and adaptation. The open-source community can now engage directly with this pioneering project. Are we witnessing the dawn of a new standard in LLM deployment?