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Export a PyTorch model to .pte format for ExecuTorch. Use when converting models, lowering to edge, or generating .pte files.
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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".
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Search the ExecuTorch tribal knowledge base covering QNN, XNNPACK, Vulkan, CoreML, Arm, and Cadence backends, quantization recipes, export pitfalls, runtime errors, and SoC compatibility. Use when debugging ExecuTorch errors, choosing quantization configs, checking backend op support, or answering questions about Qualcomm HTP / Snapdragon / Apple Neural Engine behavior.
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| name | export |
| description | Export a PyTorch model to .pte format for ExecuTorch. Use when converting models, lowering to edge, or generating .pte files. |
from executorch.exir import to_edge_transform_and_lower
from torch.export import export
exported = export(model.eval(), example_inputs)
edge = to_edge_transform_and_lower(exported)
with open("model.pte", "wb") as f:
f.write(edge.to_executorch().buffer)
| Model | Script |
|---|---|
| Llama | examples/models/llama/export_llama.py |
| Whisper | examples/models/whisper/ |
| Parakeet | examples/models/parakeet/export_parakeet_tdt.py |
export(model, inputs, strict=False)TORCH_LOGS="+dynamo,+export" python script.py 2>&1 | tlparsetorch.export.export(), dynamic shapes, strict mode, debugging