TileLang explained: write AI kernels at tile granularity
TileLang next steps: a controlled operator pilot
Move from a passing README example to evidence for one real model bottleneck
What you will learn
- Define the pilot
- Test breadth before speed
- Choose a release boundary
Before you start
- One target operator and device
- A correct framework reference
- A compatible compiler and runtime
Move from a passing README example to evidence for one real model bottleneck
Key takeaways
- A model bottleneck should motivate the kernel.
- Cold, warm and model-level metrics are distinct.
- Fallback and device tests make the result operable.
Define the pilot
Choose one matrix or attention operator, one GPU architecture and a fixed shape distribution. Preserve a framework reference, tolerance, latency objective and peak-memory limit.
Write down Python, PyTorch, TileLang, toolkit and driver versions. Start with a disposable workload so that a compiler or numeric surprise cannot silently affect users.
Test breadth before speed
Run correctness across representative and edge shapes, dtypes and masks. Compare cold compile and warm execution, then put the kernel into the actual model path and measure end-to-end effect.
When a case fails, keep the smallest reproducer and compiler diagnostics. Autotune only after the original implementation is correct and measurable.
Choose a release boundary
Ship a pinned kernel with a fallback and a per-device test matrix only if the accepted objective improves. Otherwise keep the framework operator and record what the experiment taught.
Possible future backend coverage and compiler tooling in upstream news are project claims, not a promise in this guide. This series has not executed the pilot.
Decision guide
| Criterion | Option A | Option B |
|---|---|---|
| Best when | You need predictable behavior and easy auditing | You need adaptive optimization and have reliable telemetry |
| Main risk | May leave performance on the table | Can become difficult to explain or debug |
Implementation steps
- 1
Choose one operator, device and accepted objective.
- 2
Prove correctness across shapes before tuning.
- 3
Release pinned code with fallback or stop the pilot.
Copy-ready example
operator: chosen-bottleneck
backend: one-tested-device
shapes: representative-and-edge
reference: framework-op
metrics: [correctness, compile, warm, model, memory]
fallback: requiredFrequently asked questions
What would count as a completed pilot?
Correctness, cold and warm measurements, model-level impact and a tested fallback on the target device.
Does this series validate production performance?
No. It provides a fixed-source reading and an experiment plan, not GPU measurements.
Sources
- TileLang / README.mdSource checked 2026-10-04
- TileLang / docs/get_started/Installation.mdSource checked 2026-10-04
- TileLang / examples/gemm/README.mdSource checked 2026-10-04
- TileLang / examples/flash_attention/README.mdSource checked 2026-10-04
- TileLang / docs/tutorials/auto_tuning.mdSource checked 2026-10-04
- TileLang / docs/tutorials/debug_tools_for_tilelang.mdSource checked 2026-10-04