Evaluate and integrate next-generation attention kernels to boost throughput while safeguarding reproducibility and reliability.
Transformer-style models still spend most of their compute budget inside attention blocks. Novel kernels promise speedups through better tiling, fusion, and memory layout tricks. Yet adopting them blindly can introduce correctness bugs, non-determinism, or hardware-specific quirks. This lesson guides you through dissecting kernel claims, benchmarking responsibly, and integrating improvements without sacrificing trust.