Boost Swin Transformer Speed: Profiling, Mixed Precision, and Operator Fusion Techniques

Overview

The session, part of Baidu's "Cloud‑Native AI" series, explains how to combine profiling tools with GPU‑specific optimizations to accelerate Swin Transformer training and inference.

Swin Transformer Basics

Swin Transformer extends Vision Transformer by using window‑based attention, window shift, and relative position bias, forming a hierarchical pyramid similar to ResNet.

Training Optimization

Using Nsight Systems to profile GPU utilization reveals that matrix‑multiply kernels dominate runtime. Optimizations include:

Switching to Tensor Cores with TF32 or FP16 mixed precision, yielding a 1.63× throughput gain.

Operator fusion (e.g., fusing LayerNorm and Adam via Apex) increasing speed to 2.11×.

Custom CUDA kernels for window‑partition/shift operations, raising single‑GPU speedup to 2.85× and 8‑GPU speedup to 2.32×.

Inference Optimization

Inference benefits from similar fusion strategies without backward‑pass constraints. Key techniques:

Pre‑computing position‑bias lookups.

Fusing batch GEMM, softmax, and another GEMM into a single fMHA kernel, achieving ~10× speedup for the block and 1.58× end‑to‑end gain.

Fusing QKV GEMM with bias, adding another 1.1× end‑to‑end improvement.

Kernel‑Level Tricks

Additional low‑level optimizations include matrix‑multiply padding to align dimensions to multiples of 8, using half2 vector types to halve latency, and employing register arrays to cache repeated reads, all reducing memory traffic and kernel launch overhead.

INT8 Quantization

INT8 quantization via PTQ or QAT reduces memory and boosts performance. Using cublasLt with appropriate layouts (column‑major or IMMA‑specific) and fusing quantization/de‑quantization kernels preserves accuracy (≤0.5% loss) while delivering 1.2–1.5× additional speedup on top of FP16 gains.

Results

Combined optimizations deliver:

Training: up to 2.85× single‑GPU and 2.32× 8‑GPU speedup on Swin‑Large.

Inference: 2.82–7.34× FP16 acceleration and an extra 1.2–1.5× with INT8 on GPUs such as T4, A10, and A100.

Conclusion

The presented profiling‑driven workflow—mixed precision, operator fusion, kernel‑level tweaks, and INT8 quantization—provides a reproducible path to substantially accelerate large vision models like Swin Transformer.