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06 — tinygrad Deep Dive (Optional)

Order: Sixth, optional. Hands-on compiler/kernel interface after you've seen graph, kernels, compiler, quantization, and deployment.

Role target: DL Inference Optimization Engineer · MTS Kernels (compiler–kernel interface, custom backends).

Why this is optional and last

tinygrad is a minimal stack that exposes IR, scheduling, and backends in one codebase. Doing it after 01–05 lets you connect everything: graph → scheduler → kernel selection/codegen → runtime. It's optional if your focus is only Triton/CUTLASS in a big framework; it's valuable if you want to add backends or compiler passes.

1. IR and ops

  • Linearized representation — How tinygrad turns a graph into a linear list of ops.
  • Op types — Unary, binary, reduce, movement, load/store; how they map to memory and compute.
  • Memory buffers — How tensors and buffers are assigned and reused.

2. Scheduler

  • How ops are grouped and scheduled — Which ops fuse; how the scheduler makes fusion decisions.
  • BEAM search — Exploring fusion choices; trading kernel count vs kernel size and register pressure.

3. Backends

  • CPU, CUDA, OpenCL — How tinygrad emits code for each. Where to add or tune a backend.
  • Custom backend — What a minimal custom backend needs (codegen, launch, memory).

4. Quantization in tinygrad

  • Passes — How quantization is represented and applied in the graph.
  • Integration with compiler — How quantized ops flow through scheduler and backends.

Resources

Projects

  1. Trace the pipeline — Run a small model in tinygrad. Trace from Python to scheduled kernels; document the pipeline (graph → linearized IR → scheduler → backend code).
  2. Add an optimization — Implement a simple optimization (e.g. constant fold or fuse two ops) in tinygrad. Measure impact on kernel count and runtime.
  3. Backend hook — Add a minimal "identity" or logging hook in a backend (e.g. log each kernel launch). Use it to verify which kernels run for a given model.

You've completed the track

You've gone through: Graph & operators → Kernels → Compiler → Quantization → Runtimes → (optional) tinygrad.

Next steps: deepen the areas that match your role (e.g. more Triton/CUTLASS for MTS Kernels, or more TensorRT/Triton server for inference deployment), and keep building portfolio projects (kernels, benchmarks, portability reports).