---

name: executorch description: "Use ExecuTorch to export PyTorch models to edge runtime artifacts, build host/device runtimes, choose delegates, profile/debug execution, and operate specialized Qualcomm, Cortex-M, LLM, and binary-size workflows." disable-model-invocation: true metadata: disco-role: operating license: NOASSERTION

ExecuTorch

Use this repo skill when the task involves ExecuTorch, the executorch Python package, .pte or .ptd artifacts, EXIR/export lowering, on-device runtime integration, ExecuTorch backends/delegates, or ExecuTorch build/profiling/debugging workflows.

Start Here

1. Classify the user's goal before reading deep references:
   • Install, build, configure CMake, cross-compile, or pick tests: sub-skills/setup-build/SKILL.md.
   • Export a PyTorch model, create .pte/.ptd, or validate runtime loading: sub-skills/export-runtime/SKILL.md.
   • Choose or configure XNNPACK, Core ML, MPS/Metal, Vulkan, CUDA/AOTI, OpenVINO, MediaTek, NXP, Samsung, Cadence, or Arm VGF backends: sub-skills/backend-selection/SKILL.md.
   • Use the Qualcomm QNN backend, QNN SDK, Android device tests, Buck/CMake parity, or QNN intermediate-output debugging: sub-skills/qualcomm/SKILL.md.
   • Use Cortex-M/CMSIS-NN, Arm embedded quantization, FVP, or bare-metal tests: sub-skills/cortex-m/SKILL.md.
   • Generate or inspect ETRecord/ETDump, use Inspector, visualize graphs, or triage accuracy/performance: sub-skills/profiling-debugging/SKILL.md.
   • Export/run LLMs, plan export_llm or Optimum ExecuTorch commands, or build LLM runners: sub-skills/llm-workflows/SKILL.md.
   • Measure/reduce binary size or compare stripped binaries: sub-skills/binary-size/SKILL.md.
2. For mixed workflows, route through the narrowest owner first, then cross-link. Example: a dynamic-shape CV model with XNNPACK and ETRecord starts in export-runtime, reads backend-selection for XNNPACK, then reads profiling-debugging for ETRecord/Inspector.
3. Treat hardware/vendor SDK/device workflows as conditional. A CPU import or export smoke proves Python/export plumbing, not QNN/Core ML/MPS/Vulkan/CUDA/Arm implementation behavior.

Package and Runtime Baseline

• Public package name: executorch; import namespace: executorch.
• Python support from package metadata: Python 3.10 through 3.14.
• The direct export path is torch.export.export(...) followed by executorch.exir.to_edge_transform_and_lower(...) and .to_executorch().
• The higher-level experimental export surface is executorch.export.export(...) with ExportRecipe, LoweringRecipe, and QuantizationRecipe.
• Python runtime loading with executorch.runtime.Runtime and executorch.extension.pybindings.portable_lib requires a build or wheel that includes the native pybindings. If imports fail with missing _portable_lib, route to setup-build before debugging model logic.

Minimal Checks

Use these checks only in an environment where ExecuTorch is installed or a source checkout is intentionally on PYTHONPATH:

python - <<'PY'
from executorch.exir import to_edge_transform_and_lower
print("EXIR import OK", callable(to_edge_transform_and_lower))
PY

For a more complete read-only diagnostic, run the bundled helper:

python scripts/check_import_surface.py

The helper reports import surfaces and optional backend modules without installing packages, downloading models, or building targets.

Shared References

• Read references/package-overview.md for source/package layout, public artifacts, and terminology.
• Read references/troubleshooting.md for cross-cutting installation, import, backend, runtime, and source-build failures.
• Read references/repo-provenance.md before deciding whether this skill is current for another ExecuTorch checkout.