export-model

name	export-model
description	Export a registered PyTorch model to an ExecuTorch .pte artifact for SME2 profiling. Use when Codex needs to convert toy_cnn, EfficientSAM, EdgeTAM image encoder, or another registered model to portable ExecuTorch format with XNNPACK or portable backend, choose dtype or quantization settings, save graph metadata, or prepare artifacts for profiling.

Export Model

Operating Rules

• Run from the repository root with .venv activated.
• Export to model_profiling/out_<model>/artifacts/ for the Learning Path examples unless the user requests another output root.
• Prefer --backend xnnpack for SME2 profiling because the runner comparison is intended to show delegated backend behavior.
• Do not assume .etrecord exists. Check for it and use it when present, but do not fail the export solely because it was not produced.
• If exporting a custom model, verify it is registered in model_profiling/models/__init__.py before running the exporter.

Required Inputs

• Completed setup-workspace.
• Model name, dtype, backend, and output directory.
• For custom models: an implementation of EagerModelBase with stable get_eager_model() and get_example_inputs().

Procedure

1. Activate the environment:

   source .venv/bin/activate
   

2. For the built-in smoke model, export with:

   python model_profiling/export/export_model.py \
     --model toy_cnn \
     --backend xnnpack \
     --dtype fp16 \
     --outdir model_profiling/out_toy_cnn/artifacts
   

3. For a named model, use the same shape:

   MODEL_NAME="<model_name>"
   DTYPE="fp16"
   OUTDIR="model_profiling/out_${MODEL_NAME}/artifacts"
   
   python model_profiling/export/export_model.py \
     --model "${MODEL_NAME}" \
     --backend xnnpack \
     --dtype "${DTYPE}" \
     --outdir "${OUTDIR}" \
     --save-graph
   

4. For quantized exports, add --quantize only after confirming the ExecuTorch checkout supports the model's quantization path:

   python model_profiling/export/export_model.py \
     --model "${MODEL_NAME}" \
     --backend xnnpack \
     --dtype fp32 \
     --quantize \
     --outdir "${OUTDIR}"
   

5. Validate the output:

   find "${OUTDIR}" -maxdepth 1 -name '*.pte' -type f -size +0 -print
   find "${OUTDIR}" -maxdepth 1 -name '*.etrecord' -type f -print
   PTE_FILE="$(find "${OUTDIR}" -maxdepth 1 -name '*.pte' -type f -size +0 | head -1)"
   test -n "${PTE_FILE}"
   

Success Criteria

• A non-empty .pte exists in the selected artifact directory.
• Optional graph.json exists when --save-graph was requested.
• Optional .etrecord is recorded for later analysis if the export flow produced one.

Failure Triage

• Unknown model: register the model under model_profiling/models/ or use an ExecuTorch example model name that exists in the current checkout.
• ImportError from model code: install the model dependency into .venv or adjust the model wrapper.
• Torch export failure: inspect unsupported ops, dynamic control flow, input signatures, dtype casts, and shape constraints.
• XNNPACK partitioning failure: retry with --backend portable to isolate export validity from delegation, then return to XNNPACK after fixing unsupported delegated ops.
• Quantization failure: remove --quantize unless the model appears in the current ExecuTorch quantization options.

Handoff

After export succeeds, use run-profiling with the .pte path and the runner paths for the target platform.