Operator Runtime Training Camp

This repository is a training-oriented GPU operator runtime. It is intentionally small, but its workflow mirrors production operator libraries:

1. Define the operator contract in ops/<op>/operator.yaml.
2. Generate build and runtime registries from the manifest.
3. Implement a backend-specific descriptor lifecycle.
4. Expose a Python API with out-of-place, out-variant, and prepared execution.
5. Validate correctness against PyTorch and benchmark steady-state execution.

The project is rooted directly at /workspace; it does not create a nested /workspace/camp project directory.

Operators

Operator	NVIDIA C++	TileLang	MetaX
copy	runnable	runnable when TileLang is installed	stub
vector_add	runnable	runnable when TileLang is installed	stub
reduce_sum	runnable, row-wise fp32	runnable when TileLang is installed	stub
softmax	runnable, row-wise fp32	runnable when TileLang is installed	stub

Build

mkdir -p build
cd build
cmake .. -DCAMP_ENABLE_NVIDIA=ON -DCAMP_ENABLE_METAX=OFF
cmake --build . -j$(nproc)

Validate

python tools/validate_operator_manifest.py --ops-root ops --tests-root tests
CAMP_BUILD_DIR=build pytest tests/ -v --backend nvidia
pytest tests/ -v --backend tilelang
python tests/bench_all.py --backend nvidia --profile tests/perf_profiles/local_gpu.yaml

The TileLang backend requires the tilelang Python package.

Production Mapping

Training concept	Production equivalent
operator.yaml	reviewed operator spec / manifest
descriptor lifecycle	create, workspace, execute, destroy
generated registry	operation table / backend registry
tests/cases/<op>.py	correctness, layout, and API contract coverage
PerformanceResult	profiler report row with latency, bytes, flops, bandwidth