Low-bit attention, such as SageAttention, has emerged as an effective approach for accelerating model inference, but its applicability to training remains poorly understood. In prior work, we introduced SageBwd, a trainable INT8 attention that quantizes six of seven attention matrix multiplications while preserving fine-tuning performance. However, SageBwd exhibited a persistent performance gap to full-precision attention (FPA) during pre-training. In this work, we investigate why this gap occurs and demonstrate that SageBwd matches full-precision attention during pretraining. Through experiments and theoretical analysis, we reach a few important insights and conclusions: (i) QK-norm is necessary for stable training at large tokens per step, (ii) quantization errors primarily arise from the backward-pass score gradient dS, (iii) reducing tokens per step enables SageBwd to match FPA performance in pre-training, and (iv) K-smoothing remains essential for training stability, while Q-smoothing provides limited benefit during pre-training.

翻译：低比特注意力（如SageAttention）已成为加速模型推理的有效方法，但其在训练中的适用性仍鲜为人知。在先前工作中，我们提出了SageBwd，一种可训练的INT8注意力机制，它在量化七个注意力矩阵乘法中的六个的同时保持了微调性能。然而，SageBwd在预训练阶段始终存在与全精度注意力（FPA）的性能差距。本研究探讨了这一差距产生的原因，并证明SageBwd在预训练中能够匹配全精度注意力的性能。通过实验与理论分析，我们得出了若干重要见解与结论：（i）QK归一化对于大每步令牌数下的稳定训练是必要的；（ii）量化误差主要源于反向传播中的分数梯度dS；（iii）减少每步令牌数可使SageBwd在预训练中匹配FPA性能；（iv）K平滑对训练稳定性仍至关重要，而Q平滑在预训练中带来的收益有限。