Preparing a Model for {BACKEND_NAME}#

This is a placeholder tutorial.

Step 1: Environment Setup#

This tutorial is intended to be run from a {SUPPORTED_HOST_OS} and uses Conda for Python environment management. For full setup details and system requirements, see Getting Started with ExecuTorch.

Create a Conda environment and install the ExecuTorch Python package.

conda create -y --name executorch python=3.12
conda activate executorch
conda install executorch

{ADDITIONAL_SETUP_STEPS}

Step 2: Model Preparation#

Create a python file named export_{model_filename}.py. This script will be responsible for loading the {EXAMPLE_MODEL} model from {MODEL_SOURCE} and create a {BACKEND_NAME}-targeted .pte file.

# export_{model_filename}.py
from executorch.backends.{backend_name}.partition.{backend_name}_partitioner import {BackendName}Partitioner
from executorch.exir import to_edge_transform_and_lower
import torch
import {MODEL_IMPORT}

Model Instantiation and Example Inputs#

Instantiate the {EXAMPLE_MODEL} model from {MODEL_SOURCE}. The export process also needs an example model input to trace the model. The model takes {MODEL_INPUT_DESCRIPTION}, so we’ll create {INPUT_TUPLE_DESCRIPTION}.

model = {MODEL_INSTANTIATION_CODE}
example_inputs = ({EXAMPLE_INPUTS},)

Lower the Model#

Next, export and lower the model to ExecuTorch. Note that the {BackendName}Partitioner passed to the partitioner parameter tells ExecuTorch to target the {BACKEND_NAME} backend.

exported_program = torch.export.export(model, example_inputs)

executorch_program = to_edge_transform_and_lower(
    exported_program,
    partitioner=[{BackendName}Partitioner()],
).to_executorch()

executorch_program.save("{model_filename}_{backend_name}.pte")

Run the Script#

Save the above script to export_{model_filename}.py and run the script. You should see a file named {model_filename}_{backend_name}.pte in the current directory.

python export_{model_filename}.py

Step 3: Running the Model#

The .pte file created in the previous step can be run on a variety of devices, including {SUPPORTED_PLATFORMS}. ExecuTorch provides runtime APIs and language bindings for a variety of platforms. This tutorial will demonstrate running the model on a desktop using the Python runtime.

Smoke Test#

First, we’ll verify that the model loads and runs correctly by running the model with {TEST_INPUT_DESCRIPTION}. Create a new script, named run_{model_filename}.py, and add the following code.

# run_{model_filename}.py

from executorch.runtime import Runtime
import torch

runtime = Runtime.get()

input_tensor = {TEST_INPUT_TENSOR}
program = runtime.load_program("{model_filename}_{backend_name}.pte")
method = program.load_method("forward")
outputs = method.execute([input_tensor])[0]

print(outputs)

When running the script with python run_{model_filename}.py, you should see {EXPECTED_OUTPUT_DESCRIPTION} printed to the console.

{EXPECTED_OUTPUT_EXAMPLE}

Next Steps#

See Edge Platforms to deploy the .pte file on {SUPPORTED_PLATFORMS}.
See Model Export and Lowering for more information on model preparation.
See {BACKEND_NAME} Overview for more information about the {BACKEND_NAME} backend.