FlatQuant: The Importance of Uniform Distributions for Quantizing Large Language Models

Large language models (LLMs) have achieved tremendous progress in recent years in various fields such as language translation, text generation, and question answering. These models are based on neural networks with billions of parameters, which leads to high demands on computing power and storage space. Quantization has proven to be an effective method for reducing the size and inference speed of LLMs by reducing the precision of model weights and activations.

The Challenge of Outliers in LLMs

A major problem with quantizing LLMs is outliers in the activations, i.e. values that are far from the majority of other values. These outliers can lead to significant quantization errors because they cannot be adequately represented when reducing precision. To address this problem, various pre-quantization transformations have been proposed, such as per-channel scaling and Hadamard transformation. These methods try to suppress outliers and make the distribution of values more uniform.

FlatQuant: A New Approach for Post-Training Quantization

In a recent research paper, scientists from Huawei Noah's Ark Lab and Tsinghua University present a new approach for post-training quantization called FlatQuant (Fast and Learnable Affine Transformation). FlatQuant focuses on improving the uniformity of the distribution of weights and activations to minimize quantization errors. The approach is based on the idea of finding an optimal affine transformation for each linear layer in the neural network that distributes the values more evenly.

How FlatQuant Works

FlatQuant uses a simple but effective strategy to find the optimal affine transformations. Instead of optimizing the transformations for the entire model at once, FlatQuant applies a block-wise training strategy to the calibration data. This allows the transformations to be adapted to the specific characteristics of each layer while reducing computational effort.

To minimize the additional computational overhead introduced by the affine transformations, FlatQuant leverages Kronecker decomposition. This mathematical technique makes it possible to calculate and store the transformations more efficiently, thereby reducing both memory requirements and computation time.

The Benefits of FlatQuant

The researchers evaluated FlatQuant on various tasks and LLM models, comparing the performance to other state-of-the-art quantization methods. The results show that FlatQuant significantly improves the accuracy of the quantized models while increasing inference speed.

Among the key benefits of FlatQuant are:

• Improved accuracy: FlatQuant achieves higher accuracy than other quantization methods by improving the uniformity of the distribution of weights and activations.
• Faster inference: By efficiently implementing affine transformations using Kronecker decomposition, FlatQuant reduces additional computational overhead and increases inference speed.
• Versatility: FlatQuant is compatible with various quantization techniques and can be applied to different quantization settings, such as weight-only quantization or KV cache quantization.

Conclusion

FlatQuant is a promising new approach to post-training quantization of LLMs that improves accuracy and inference speed by optimizing the uniformity of the distribution of weights and activations. The efficient implementation using Kronecker decomposition reduces additional computational overhead and makes FlatQuant an attractive option for use in real-world applications.

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