Tests

Marker	File	Needs	What it checks
`sim`	`test_sim.py`	`nki`, `numpy`, `ml_dtypes`	each kernel under `nki.simulate` vs a NumPy fp32 reference (ragged padding, LNC 1/2, query split, padding isolation)
`cpu`	`test_pack_cpu.py`	`torch`, `transformers`	host-side weight packing reproduces the Hugging Face `NewLayer` attention half in fp32 (`GTE_TEST_MODEL` picks the model)
`sim` + `cpu`	`test_patch_sim.py`	all of the above	`patch_model` end to end on a 2-layer model with the kernels run by the simulator
`neuron`	`test_device.py`	trn2 + PyTorch Native	full patched model vs CPU fp32 and vs stock PyTorch Native: score Pearson >= 0.9999, top-1 agreement >= stock, padding isolation bit-exact, fused ops actually used

python -m pytest -m sim                       # local, no device
python -m pytest -m cpu                       # local, downloads the model
NEURON_LOGICAL_NC_CONFIG=1 python -m pytest -m neuron -s tests/test_device.py   # on trn2, once per LNC
NEURON_LOGICAL_NC_CONFIG=2 python -m pytest -m neuron -s tests/test_device.py

A simulator pass is not device evidence. The simulator does not model SBUF/PSUM capacity, and some DMA access patterns behave differently on hardware (for example, one strided DMA spanning several 32-partition blocks is exact in the simulator but copies only the first block on trn2).