Examples

This dir contains scripts and other helper utilities to illustrate an end-to-end workflow to run a torch.nn.module on the Executorch runtime. It also includes a list of modules, from a simple Add to a full model like MobileNetv2 and MobileNetv3, with more to come.

Directory structure

examples
├── custom_ops      # Contains examples to register custom operators into PyTorch as well as register its kernels into Executorch runtime
├── executor_runner # This is an example C++ wrapper around the ET runtime
├── export          # Python helper scripts to illustrate export workflow
├── ios_demo_apps   # Contains iOS demo apps
├── models          # Contains a set of simple to PyTorch models
├── quantization    # Contains examples of quantization workflow
└── README.md       # This file

Using the examples

We will walk through an example model to generate a binary file from a python torch.nn.module from the models dir using scripts from the export dir. Then we will run on these binary model files on the Executorch (ET) runtime. For that we will use executor_runner. It is a simple wrapper for the Executorch runtime to serve as an example. Although simple, it is capable of loading and executing previously exported binary file(s).

1. Following the setup guide in Setting up ExecuTorch from GitHub you should be able to get the basic development environment for Executorch working.

2. Using the script export/export_example.py generate a model binary file by selecting a model name from the list of available models in the models dir.

cd executorch # To the top level dir

bash examples/install_requirements.sh

# To get a list of example models
python3 -m examples.export.export_example -h

# To generate a specific pte model
python3 -m examples.export.export_example --model_name="mv2" # for MobileNetv2

# This should generate ./mv2.pte file, if successful.

Use -h (or --help) to see all the supported models.

3. Once we have the model binary (pte) file, then let's run it with Executorch runtime using the executor_runner.

buck2 run examples/executor_runner:executor_runner -- --model_path mv2.pte

Quantization

Here is the Quantization Flow Docs.

You can run quantization test with the following command:

python3 -m examples.quantization.example --model_name "mv2" # for MobileNetv2

It will print both the original model after capture and quantized model.

The flow produces a quantized model that could be lowered through partitioner or the runtime directly.

you can also find the valid quantized example models by running:

buck2 run executorch/examples/quantization:example -- --help

Dependencies

Various models listed in this directory have dependencies on some other packages, e.g. torchvision, torchaudio. In order to make sure model's listed in examples are importable, e.g. via

from executorch.examples.models.mobilenet_v3d import MV3Model
m = MV3Model.get_model()

we need to have appropriate packages installed. You should install these deps via install_requirements.sh.