Cortex M Agent Skill
Build, test, or develop the Cortex-M (CMSIS-NN) backend. Use when working on backends/cortex_m/, running Cortex-M tests, or exporting models for Cortex-M targets.
Install
one command, takes just this skill from the repositorynpx skills add https://github.com/pytorch/executorch --skill cortex-mThe instruction itself
6 sections, as written by the authorCortex-M (CMSIS-NN) Backend
Architecture
Not a delegate backend — no partitioner. Custom ops and graph passes replace ATen quantized ops with CMSIS-NN equivalents at the graph level.
Pipeline
Uses standard PT2E quantization (prepare_pt2e / convert_pt2e), then CortexMPassManager rewrites quantized ops to cortex_m:: equivalents.
from executorch.backends.cortex_m.quantizer.quantizer import CortexMQuantizer
from executorch.backends.cortex_m.passes.cortex_m_pass_manager import CortexMPassManager
from torch.export import export
from torchao.quantization.pt2e.quantize_pt2e import convert_pt2e, prepare_pt2e
from executorch.exir import to_edge_transform_and_lower, EdgeCompileConfig
quantizer = CortexMQuantizer()
captured = export(model, example_inputs).module()
prepared = prepare_pt2e(captured, quantizer)
prepared(*example_inputs) # calibration
quantized = convert_pt2e(prepared)
exported = export(quantized, example_inputs)
edge = to_edge_transform_and_lower(
exported,
compile_config=EdgeCompileConfig(_check_ir_validity=False),
)
edge._edge_programs["forward"] = CortexMPassManager(
edge.exported_program(), CortexMPassManager.pass_list
).transform()
et_program = edge.to_executorch()
In tests, CortexMTester wraps this pipeline:
from executorch.backends.cortex_m.test.tester import CortexMTester
tester = CortexMTester(model, example_inputs)
tester.quantize().export().to_edge().run_passes().to_executorch()
Key Files
| File | Purpose |
|------|---------|
| backends/cortex_m/quantizer/quantizer.py | CortexMQuantizer — quantizes model for CMSIS-NN |
| backends/cortex_m/passes/cortex_m_pass_manager.py | CortexMPassManager — rewrites ATen ops → cortex_m:: ops |
| backends/cortex_m/test/tester.py | CortexMTester — test harness with test_dialect() and test_implementation() |
| backends/cortex_m/ops/operators.py | Python op definitions and reference implementations (cortex_m:: namespace) |
| backends/cortex_m/ops/operators.yaml | C++ kernel registration schemas (used by build system) |
C++ kernels calling CMSIS-NN APIs live under backends/cortex_m/ops/.
Testing
Toolchain setup (required for test_implementation tests):
./examples/arm/setup.sh --i-agree-to-the-contained-eula
source ./examples/arm/arm-scratch/setup_path.sh
Run all tests:
source ./examples/arm/arm-scratch/setup_path.sh
pytest backends/cortex_m/test/
test_dialect_* tests verify graph correctness (pure Python, no toolchain needed).
test_implementation_* tests verify numerical accuracy on the Corstone-300 FVP (requires toolchain on PATH).
Baremetal build:
backends/cortex_m/test/build_test_runner.sh
Adding a New Op
- Define the op schema, meta function, and reference implementation in
operators.py - Write the C++ kernel in
backends/cortex_m/ops/calling CMSIS-NN APIs - Register the
.outkernel inoperators.yaml - Add a pass to rewrite the ATen op →
cortex_m::op - Test with
CortexMTester.test_dialect()(graph correctness) andtest_implementation()(numerical accuracy on FVP)
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How to use it
Take pytorch/cortex-m from the repository into ~/.claude/skills for personal
use, or into .claude/skills inside a project.
The agent identifies a skill by the name field in its header. Two skills with the
same name cannot sit side by side — one of them will be ignored.