Building Blocks of TileLang
TileLang is a high-level Python domain-specific language designed for compiling performance-oriented GPU kernels through TVM. The tutorial begins by validating the CUDA environment and establishing reusable benchmarking utilities. It then progressively implements foundational operations, starting with a simple vector addition kernel and comparing its bandwidth directly against PyTorch baselines.
Fusing Epilogues and Optimizing Memory
The guide advances to tiled tensor-core matrix multiplication, demonstrating how to move input tiles through global memory, shared memory, and register fragments. By controlling tile dimensions, pipeline stages, and thread counts, developers can fuse bias addition and GELU activation directly into the register-resident accumulator. This reduces intermediate global-memory traffic significantly before writing the final output tensor.
Autotuning for Peak Throughput
The tutorial concludes by implementing a fused FlashAttention forward kernel and utilizing TileLang's autotuning decorator. By defining a search space across matrix tile sizes, K-block dimensions, and pipeline depths, the compiler can automatically identify the highest-performing, architecture-dependent kernel configuration. This workflow allows developers to achieve peak throughput without manually managing low-level CUDA instructions or asynchronous memory barriers.