🚀 Introducing FlashQLA: high-performance linear attention kernels built on TileLang. ⚡ 2–3× forwar...
- FlashQLA实现了2-3倍前向加速和2倍后向加速。
- 采用门控驱动的自动片内CP和硬件友好的代数重构。
- 特别适用于TP设置、小型模型和长上下文工作负载。
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- FlashQLA: 高性能线性注意力内核
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🚀 Introducing FlashQLA: high-performance linear attention kernels built on TileLang.
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下载金句卡 PNG⚡ 2–3× forward speedup. 2× backward speedup.
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下载金句卡 PNG💻 Purpose-built for agentic AI on your personal devices.
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下载金句卡 PNG💡Key insights: 1. Gate-driven automatic intra-card CP. 2. Hardware-friendly algebraic reformulation. 3. TileLang fused warp-specialized kernels.
— 第 2 段
下载金句卡 PNG
⚡ 2–3× forward speedup. 2× backward speedup. 💻 Purpose-built for agentic AI on your personal devices.
💡Key insights: 1. Gate-driven automatic intra-card CP. 2. Hardware-friendly algebraic https://t.co/pA9HCHwFZw" / X
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!Image 2: 🚀 Introducing FlashQLA: high-performance linear attention kernels built on TileLang. !Image 3: ⚡ 2–3× forward speedup. 2× backward speedup. !Image 4: 💻 Purpose-built for agentic AI on your personal devices. !Image 5: 💡Key insights: 1. Gate-driven automatic intra-card CP. 2. Hardware-friendly algebraic reformulation. 3. TileLang fused warp-specialized kernels. FlashQLA boosts SM utilization via automatic intra-device CP. The gains are especially pronounced for TP setups, small models, and long-context workloads. Instead of fusing the entire GDN flow into a single kernel, we split it into two kernels optimized for CP and backward efficiency. At large batch sizes this incurs extra memory I/O overhead vs. a fully fused approach, but it delivers better real-world performance on edge devices and long-context workloads. The backward pass was the hardest part: we built a 16-stage warp-specialized pipeline under extremely tight on-chip memory constraints, ultimately achieving 2×+ kernel-level speedups. We hope this is useful to the community!!Image 6: 🫶!Image 7: 🫶 Learn more: !Image 8: 📖 Blog: qwen.ai/blog?id=flashq!Image 9: 💻 Code: github.com/QwenLM/FlashQLA
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