In this tutorial, we explore TileLang ：https://github.com/tile-ai/tilelang as a high-level Python domain-specific language for designing and compiling performance-oriented GPU kernels through TVM. We begin by validating the CUDA environment and establishing reusable benchmarking and numerical-verification utilities, then progressively implement vector addition, tiled tensor-core matrix multiplication, schedule exploration, fused GEMM epilogues, row-wise softmax, and FlashAttention. Throughout the tutorial, we work directly with TileLang’s shared-memory tiles, register fragments, pipelined loops, parallel iteration primitives, reductions, and tensor-core GEMM operators while allowing the compiler to manage thread mapping, memory layouts, synchronization, vectorization, and low-level CUDA instruction generation. We also compare our kernels against PyTorch and cuBLAS baselines, inspect generated CUDA source, evaluate memory and compute throughput, and use autotuning to identify architecture-dependent kernel configurations.

We configure the Google Colab CUDA environment, install TileLang with a nightly fallback, and import the required PyTorch and TileLang modules. We define reusable benchmarking, validation, and reporting utilities that measure kernel latency and compare numerical outputs using relative error. We then implement a TileLang vector-add kernel, execute it on the GPU, compare its bandwidth with PyTorch, and inspect the CUDA source generated by the compiler.

We implement a tiled tensor-core matrix multiplication kernel that moves input tiles through global memory, shared memory, and register fragments. We control tile dimensions, pipeline stages, thread counts, and L2 swizzling while allowing TileLang to generate tensor-core instructions, synchronization, and memory-transfer logic. We then benchmark several schedule configurations, verify their numerical accuracy, and identify the highest-performing architecture-dependent kernel configuration.

We extend the matrix multiplication kernel by fusing bias addition and the GELU activation directly into the register-resident accumulator. We reduce intermediate global-memory traffic by completing the epilogue before writing the final output tensor and compare the fused implementation with eager PyTorch execution. We also implement a row-wise softmax kernel using fragment-level maximum and sum reductions while keeping the normalization process largely within registers.

We implement a fused FlashAttention forward kernel that processes query, key, and value tiles without materializing the full attention-score matrix in global memory. We apply online softmax updates using running maxima, normalization sums, rescaling factors, and tiled tensor-core matrix multiplications. We validate both causal and non-causal attention against PyTorch scaled dot-product attention and compare their latency and computational throughput.

We define an autotuning search space across matrix tile sizes, K-block dimensions, pipeline depths, and thread counts while filtering configurations that exceed the shared-memory budget. We use TileLang’s autotuning decorator to compile, benchmark, validate, and cache multiple kernel schedules for the same matrix-multiplication workload. We then execute the selected kernel, verify its output against PyTorch, and report the achieved latency and tensor-core throughput.

We introduce TileLang’s debugging and introspection workflow through device-side printing, generated CUDA inspection, and the built-in kernel profiler. We examine compiler-emitted landmarks such as tensor-core operations, asynchronous copies, synchronization barriers, and matrix-load instructions. We finally organize all tutorial sections into a fault-tolerant runner that records execution status, reports timing information, and prints a compact TileLang programming reference.

In conclusion, we built a practical understanding of how TileLang translates tile-level Python programs into optimized GPU kernels without requiring us to manually manage thread indices, warp-level data layouts, tensor-core instructions, or asynchronous memory barriers. We implemented and validated kernels that cover bandwidth-bound elementwise operations, compute-intensive GEMM workloads, fused neural-network epilogues, register-resident reductions, and online-softmax attention. We also examined how block dimensions, shared-memory consumption, pipeline depth, thread count, tile shape, and L2 swizzling influence performance across different GPU architectures. Finally, we used generated-source inspection, device-side debugging, profiling, and automated schedule search to establish a complete workflow for developing, verifying, benchmarking, and refining custom TileLang kernels.

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Sana Hassan, a consulting intern at Marktechpost and dual-degree student at IIT Madras, is passionate about applying technology and AI to address real-world challenges. With a keen interest in solving practical problems, he brings a fresh perspective to the intersection of AI and real-life solutions.