Boosting Swin Transformer Speed: Profiling, Mixed Precision, and Kernel Fusion Secrets

Introduction

Swin Transformer extends the Vision Transformer by applying windowed self‑attention and shifted windows to reduce computation while preserving performance, forming a hierarchical pyramid of stages similar to ResNet.

Training Optimization

Using nsight system the authors identified GPU kernel launch and matrix‑multiply (GEMM) as the dominant bottlenecks. Mixed‑precision training with torch.cuda.amp and Tensor‑core execution (TF32 or FP16) raised throughput by 1.63×. Operator fusion via NVIDIA Apex (fused LayerNorm and fused Adam) further increased speed to 2.11×. Custom CUDA kernels were written for window‑partition/shift/reverse operations, which are essentially index‑mapping kernels; fusing these reduced kernel‑launch overhead and global‑memory traffic. A fused multi‑head attention (fMHA) kernel combined the query‑key‑value GEMM, bias addition, and softmax, delivering a 2.19× speedup. Overall, single‑GPU training achieved a 2.85× acceleration and an 8‑GPU (1 × 8) configuration reached 2.32×, with convergence and accuracy unchanged across Swin‑Tiny, Swin‑Base, and Swin‑Large.

Inference Optimization

Inference benefits from operator fusion without backward passes, allowing pre‑processing of invariant data. Common patterns such as GEMM+bias, GEMM+bias+activation, and fused MHA were implemented. Fusing QKV GEMM with bias and eliminating separate transpose steps yielded a 10× speedup for the MHA block and a 1.58× end‑to‑end gain. Half2 vector types were used to pack two FP16 values, halving memory‑access instructions and cutting latency by ~2×. Register‑array caching kept frequently reused tensors in registers, avoiding repeated global loads. INT8 quantization was applied using cublasLt with IMMA‑specific layout; QAT ensured <0.5% accuracy loss, while PTQ required selective de‑quantization to retain precision. The INT8 pipeline added 1.2–1.5× extra speed on top of FP16 gains. Benchmarks on T4, A10, and A100 GPUs showed FP16 inference speedups of 2.82×–7.34× and further improvements with INT8.

Results Summary

Training optimizations maintain model convergence and accuracy across Swin variants. Inference optimizations reduce latency and increase throughput on multiple GPU generations, delivering substantial real‑world performance gains for large‑scale vision models.

Conclusion

By systematically profiling with Nsight, applying mixed‑precision, fusing operators at both library and custom CUDA levels, exploiting half2 and register‑array techniques, and integrating INT8 quantization, Swin Transformer training and inference can be accelerated dramatically on NVIDIA hardware without sacrificing model quality.