Writing High-Performance Kernels in TileLang, from GEMM to MLA

TileLang is a framework that offers an intermediate path between Triton and CUTLASS/CuTe for writing GPU kernels, allowing developers to explicitly manage shared memory, pipeline staging, and warp work distribution using Python. It balances control and simplicity by enabling users to specify where data lives in memory and how operations are staged, while a layout inference pass handles thread mapping and memory layout decisions. TileLang is illustrated through examples like the GEMM kernel, which demonstrates its explicit buffer allocation and pipelining capabilities, and the MLA decode kernel, which showcases its ability to manage complex memory layouts and register pressures effectively. Furthermore, TileLang's strength lies in its ability to adapt to configurations not supported by hand-tuned kernels, as demonstrated by its use in a production kernel at AtlasCloud, where it provided a drop-in solution that enhanced performance and flexibility. The tool allows developers to make high-level decisions about work distribution and memory placement, with the framework handling intricate details like layout optimization and warp specialization, offering the benefits of both high control and ease of use.

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