Achieving binary weight and activation for LLMs using Post-Training Quantization

• URL: http://arxiv.org/abs/2504.05352v1
• Date: Mon, 07 Apr 2025 04:50:04 GMT
• Title: Achieving binary weight and activation for LLMs using Post-Training Quantization
• Authors: Siqing Song, Chuang Wang, Ruiqi Wang, Yi Yang, Xuyao Zhang,
• Abstract summary: Quantizing large language models (LLMs) to 1-bit precision significantly reduces computational costs.<n>Existing quantization techniques suffer from noticeable performance degradation when using weight and activation precisions below 4 bits (W4A4)<n>We propose a post-training quantization framework with W (1+1)A (1*4) configuration, where weights are quantized to 1 bit with an additional 1 bit for fine-grain grouping and activations are quantized to 1 bit with a 4-fold increase in the number of channels.
• Score: 32.247768389896144
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Quantizing large language models (LLMs) to 1-bit precision significantly reduces computational costs, but existing quantization techniques suffer from noticeable performance degradation when using weight and activation precisions below 4 bits (W4A4). In this paper, we propose a post-training quantization framework with W(1+1)A(1*4) configuration, where weights are quantized to 1 bit with an additional 1 bit for fine-grain grouping and activations are quantized to 1 bit with a 4-fold increase in the number of channels. For weight quantization, we propose utilizing Hessian-aware fine-grained grouping along with an EM-based quantization scheme. For activation quantization, we decompose INT4-quantized activations into a 4 * INT1 format equivalently and simultaneously smooth the scaling factors based on quantization errors, which further reduces the quantization errors in activations. Our method surpasses state-of-the-art (SOTA) LLM quantization baselines on W2A4 across multiple tasks, pushing the boundaries of existing LLM quantization methods toward fully binarized models.

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