Designing High-Performance GPU Kernels with TileLang: Tensor-Core GEMM, Fused Softmax, FlashAttention, and Autotuning

In this tutorial, we explore 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.