cortex-m
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.
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تعليمات المصدر · معاينة للقراءة فقط- name
- cortex-m
- description
- 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.
# Cortex-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.
```python
from executorch.backends.cortex_m.quantizer.quantizer import CortexMQuantizer
from executorch.backends.cortex_m.passes.cortex_m_pass_manager import CortexMPassManager
from executorch.backends.cortex_m.edge_compile_config import cortex_m_edge_compile_config
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
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)
# The backend's own config: linear and the activations must survive to_edge or the
# lowering either fails or silently falls back to portable float kernels.
edge = to_edge_transform_and_lower(
exported,
compile_config=cortex_m_edge_compile_config(),
transform_passes=CortexMPassManager(),
)
et_program = edge.to_executorch()
```
In tests, `CortexMTester` wraps this pipeline:
```python
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):**
```bash
./examples/arm/setup.sh --i-agree-to-the-contained-eula
source ./examples/arm/arm-scratch/setup_path.sh
```
**Run all tests:**
```bash
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:**
```bash
backends/cortex_m/test/build_test_runner.sh
```
## Adding a New Op
1. Define the op schema, meta function, and reference implementation in `operators.py`
2. Write the C++ kernel in `backends/cortex_m/ops/` calling CMSIS-NN APIs
3. Register the `.out` kernel in `operators.yaml`
4. Add a pass to rewrite the ATen op → `cortex_m::` op
5. Test with `CortexMTester.test_dialect()` (graph correctness) and `test_implementation()` (numerical accuracy on FVP)
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