Grid Sampler Export Bundle

This bundle contains the code needed to export an ExecuTorch edge program and build a scenario for models that use grid_sample.

Requirements

• Tested with Python 3.10 and 3.12 on Linux and Windows.
• Your own model file. The shipped sample_model.py works out of the box with the included sample_inputs/, and you can adapt it to your own model or copy the relevant snippets into an existing model script.
• A Python environment with the package versions from requirements.txt.
  • Please note that a development version of executorch is needed and several 'monkey-patches' are applied (see arm_backend_monkey_patch.py)
• model-converter and flatc on PATH. In a typical Python environment they are installed under the environment's site-packages/model_converter/binaries/bin and site-packages/bin directories.
• scenario-runner on PATH
  • A recent version (newer than d78c059) may be needed depending on your model and the image formats that are used in the exported scenario.
  • The CMake build flag SCENARIO_RUNNER_EXPERIMENTAL_IMAGE_FORMAT_SUPPORT=ON may be needed depending on your model and the image formats that are used in the exported scenario.
• If the target device does not have native Data Graph Arm extension support, the runtime also needs the appropriate emulation layers enabled. See https://github.com/arm/ai-ml-emulation-layer-for-vulkan.

Typical setup

Bash example:

python3 -m venv .venv
. .venv/bin/activate
python -m pip install -r requirements.txt

SITE_PACKAGES="$(python -c 'import site; print(site.getsitepackages()[0])')"
export PATH="$SITE_PACKAGES/model_converter/binaries/bin:$SITE_PACKAGES/bin:$PATH"

python sample_model.py
scenario-runner --scenario artifacts/scenario/scenario.json --output artifacts/scenario --log-level debug

The script resolves its sample inputs and artifact output relative to its own location, so it can be run from any current working directory. It exports and builds artifacts/scenario/scenario.json, then prints a sample scenario-runner command line you can run manually. The bundled example applies a 3x3 blur, rotates with grid_sample, then applies a 3x3 sharpen filter. It uses INT8 image IO and INT16 symmetric grid coordinates.

Modeling note

When building coordinate tensors for grid_sample, prefer torch.linspace(...) over torch.arange(...). In practice, arange often introduces int64 ops into the exported graph, and those do not lower cleanly through TOSA.

How It Works

• export_executorch.py captures the model, applies the Arm/ExecuTorch export flow, and partitions the graph into regular NN segments and grid_sample segments.
• Regular NN segments are lowered through the Arm VGF path and emitted as section_*.vgf binaries.
• grid_sample segments are lowered by the custom backend into compute-shader work rather than VGF sections.
• export_scenario.py turns those lowered pieces into one scenario.json that references all VGF sections, shader work, tensors, and intermediate resources together.
• sample_model.py supplies the sample inputs, runs export, and leaves everything under artifacts/ so the scenario can be executed later with scenario-runner.