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

TileLang is presented as a Python-based GPU kernel language positioned between Triton’s compiler-managed abstractions and CUTLASS/CuTe’s low-level control, allowing developers to explicitly allocate shared-memory and register tiles, define pipeline stages, and select warp partitioning while relying on layout inference for thread mappings and generated synchronization. A GEMM example illustrates its main primitives, including shared-memory staging, register accumulators, pipelined copies, tensor-core GEMM operations, fused elementwise work, and optional block swizzling. The discussion of DeepSeek MLA decode highlights how TileLang’s warp policies and inferred layouts address register-pressure constraints on Hopper GPUs by splitting output accumulators across warpgroups and coordinating score data through shared memory, enabling a relatively compact implementation of a complex attention kernel. A production RMSNorm-plus-SiLU example from AtlasCloud shows TileLang used as a fallback for unsupported channel dimensions, preserving an existing hand-tuned path while delivering a compatible implementation that reportedly improved over eager PyTorch execution and enabled previously unavailable model configurations. TileLang is characterized as particularly useful for layout-sensitive kernels, attention variants, GEMM-related workloads, and backend-portable implementations where developers need more scheduling and memory control than Triton generally exposes without the implementation complexity of CuTe.

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