手把手教你用xFormers做内存优化，打包序列、GQA、ALiBi、SwiGLU全都有，比标准实现省显存还快。
本文通过xFormers实现GPU上内存高效的Transformer模型。对比标准注意力实现，验证了内存高效注意力在不同序列长度下的速度和内存表现。教程涵盖因果掩码、打包变长序列（packed sequences）、分组查询注意力（GQA）以及自定义ALiBi偏置。最后将上述技术结合SwiGLU层和自动混合精度训练，构建了一个可训练的GPT风格模型。
How to Build Memory-Efficient Transformers with xFormers Using Packed Sequences, GQA, ALiBi, SwiGLU, and Causal Attention
We implement xFormers, a practical toolkit for fast, memory-efficient Transformer models on GPUs. We validate memory-efficient attention against a standard implementation, then compare speed and memory across sequence lengths. We work through causal masking, packed variable-length sequences, grouped-query attention, and custom ALiBi biases. Finally, we combine these into a trainable GPT-style model with SwiGLU layers and automatic mixed-precision training. The post How to Build Memory-Efficient Transformers with xFormers Using Packed Sequences, GQA, ALiBi, SwiGLU, and Causal Attention appeared first on MarkTechPost .