By sablin39

Inspect a PyTorch baseline with torch.compile graphs/code and torch.profiler before drafting a custom kernel, then compare TileLang and PyTorch timelines, forward/backward regions, and allocations.

sablin39/tilelang-cuda-skills

Write human-facing TileLang design documents for proposed or completed kernel implementations.

sablin39/tilelang-cuda-skills

Draft, debug, and measure CUDA kernels and host launch workflows.

sablin39/tilelang-cuda-skills

Diagnose TileLang compilation, host argument, memory-liveness, runtime, and numerical failures.

sablin39/tilelang-cuda-skills

Describe model or operator HIR with TileFoundry, inspect topology and placement, run static cost analysis, visualize authored dataflow, and check a runtime implementation against its evaluator.

sablin39/tilelang-cuda-skills

Measure TileLang kernel and workflow performance, choose benchmark or timeline tools, and compare implementations fairly.

sablin39/tilelang-cuda-skills

Tune a correct TileLang kernel using measured bottlenecks, tile sizes, layouts, pipelining, fusion, and autotuning.

sablin39/tilelang-cuda-skills

Implement and validate forward and backward TileLang kernels integrated with torch.autograd.Function.

sablin39/tilelang-cuda-skills

Run a bounded TileLang implementation and optimization task from a PyTorch reference or existing kernel through baseline analysis, a written plan, candidate edits, lowering inspection, validation…

sablin39/tilelang-cuda-skills

Draft TileLang operators using JIT, tensor contracts, tiled computation, and reusable kernel patterns.

sablin39/tilelang-cuda-skills