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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.
デフォルトでは、最初にソースを確認する Prompt が選択されています。直接コマンドに切り替えるか、ローカルコピーをダウンロードすることもできます。
インストールを決める前に、SKILL.md と SkillsMP に表示されている付属ファイルをお読みください。
SOC 職業分類に基づく
Codex または Claude でインストール この Prompt をコピーして Codex、Claude、または他のアシスタントに貼り付けると、Skill ページを確認してインストールできます。
直接コマンドでは確認用 Prompt が省略されます。実行前にソースを確認してください。
npx skills add https://github.com/pytorch/executorch --skill cortex-mコマンドは1行のまま表示されます。コピー前に横へスクロールして全体を確認してください。
ローカルで確認しますか？SkillsMP が現在取得できるファイルをダウンロードできます。
SKILL.md を表示中
Build, test, or develop the QNN (Qualcomm AI Engine Direct) backend. Use when working on backends/qualcomm/, building QNN (use backends/qualcomm/scripts/build.sh), adding new ops or passes, running QNN delegate tests, or exporting models for Qualcomm HTP/GPU targets. Also exposes a Buck-vs-CMake parity workflow — invoke as `/qualcomm buck-fix`, `/qualcomm buck-cmake fix`, `/qualcomm buck-parity`, or any user request to fix `test-qnn-buck-build-linux` CI failures or check buck/cmake drift in backends/qualcomm/. Also covers QNN intermediate-output / per-layer accuracy debugging — trigger on phrases like "QNN accuracy issue", "QNN output doesn't match CPU", "debug per-layer for QNN", "find which QNN layer is wrong".
Build ExecuTorch from source — Python package, C++ runtime, runners, cross-compilation, and backend-specific builds. Use when compiling anything in the ExecuTorch repo, diagnosing build failures, or setting up platform-specific builds.
Build and configure ExecuTorch as a Zephyr RTOS module for embedded boards. Use when setting up a Zephyr workspace with ET, adding board support (overlays, confs, memory layout), building with west, or debugging linker memory overflow.
| 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. |
Not a delegate backend — no partitioner. Custom ops and graph passes replace ATen quantized ops with CMSIS-NN equivalents at the graph level.
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()
| 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/.
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
operators.pybackends/cortex_m/ops/ calling CMSIS-NN APIs.out kernel in operators.yamlcortex_m:: opCortexMTester.test_dialect() (graph correctness) and test_implementation() (numerical accuracy on FVP)