---

name: nir-interoperability description: "Export snnTorch models to NIR and import NIR graphs back into snnTorch." metadata: disco-role: operating disable-model-invocation: true license: MIT

nir-interoperability

Use this sub-skill when the task is specifically about NIR export/import.

Use this sub-skill for

• exporting supported snnTorch models to NIR
• importing NIR graphs back into snnTorch
• checking sequential or recurrent NIR round-trips
• validating bundled NIR fixtures and compatibility edge cases

Keep out of scope

• spike encodings and target coding
• training loops and optimization
• plotting and animation
• dataset loading beyond what is needed to build a tiny NIR fixture

Core contract

• sample_data is the trace input used to infer graph shapes.
• ignore_dims trims dimensions from traced shapes; use it for batch-first tensors.
• Sequential graphs round-trip through a plain NIR graph.
• Simple recurrent graphs round-trip as an embedded NIR subgraph.
• Use vector-shaped beta, alpha, threshold, and recurrent V values that match the neuron width.

Bundled helpers

• scripts/nir_roundtrip_smoke.py — self-contained export/import smoke test
• references/fixtures/lif.nir — minimal import fixture
• references/fixtures/conv_pool_limit.nir — current conv/pool compatibility reference

For signatures, node mappings, workflow steps, and known failure modes, see the bundled references.