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.. _sphx_glr_tutorials_sdk-integration-tutorial.py:


SDK Integration Tutorial
========================

**Author:** `Jack Khuu <https://github.com/Jack-Khuu>`__

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The `ExecuTorch SDK <../sdk-overview.html>`__ is a set of tools designed to
provide users with the ability to profile, debug, and visualize ExecuTorch
models.

This tutorial will show a full end-to-end flow of how to utilize the SDK.
Specifically, it will:

1. Generate the artifacts consumed by the SDK (`ETRecord <../sdk-etrecord>`__, `ETDump <../sdk-etdump.html>`__).
2. Create an Inspector class consuming these artifacts.
3. Utilize the Inspector class to analyze the model.

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Prerequisites
-------------

To run this tutorial, you’ll need to install ExecuTorch.

Set up a conda environment. To set up a conda environment in Google Colab::

  !pip install -q condacolab
  import condacolab
  condacolab.install()

  !conda create --name executorch python=3.10
  !conda install -c conda-forge flatbuffers

Install ExecuTorch from source. If cloning is failing on Google Colab, make
sure Colab -> Setting -> Github -> Access Private Repo is checked::

  !git clone https://{github_username}:{token}@github.com/pytorch/executorch.git
  !cd executorch && bash ./install_requirements.sh

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Generate ETRecord (Optional)
----------------------------

The first step is to generate an ``ETRecord``. ``ETRecord`` contains model
graphs and metadata for linking runtime results (such as profiling) to
the eager model. This is generated via ``executorch.sdk.generate_etrecord``.

``executorch.sdk.generate_etrecord`` takes in an output file path (str), the
edge dialect model (ExirExportedProgram), the ExecuTorch dialect model
(ExecutorchProgram), and an optional dictionary of additional models

In this tutorial, the mobilenet v2 example model is used to demonstrate::

  # Imports
  import copy

  import torch

  from executorch import exir
  from executorch.examples.models.mobilenet_v2 import MV2Model
  from executorch.exir import ExecutorchProgram, ExirExportedProgram, ExportedProgram
  from executorch.sdk import generate_etrecord

  # Generate MV2 Model
  model: torch.nn.Module = MV2Model()
  aten_model: ExportedProgram = exir.capture(
      model.get_eager_model().eval(),
      model.get_example_inputs(),
      exir.CaptureConfig(),
  )

  edge_model: ExirExportedProgram = aten_model.to_edge(
      exir.EdgeCompileConfig(_check_ir_validity=True)
  )
  edge_copy: ExirExportedProgram = copy.deepcopy(edge_model)

  et_model: ExecutorchProgram = edge_model.to_executorch()

  # Generate ETRecord
  etrecord_path = "etrecord.bin"
  generate_etrecord(etrecord_path, edge_copy, et_model)


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Generate ETDump
---------------

Next step is to generate an ``ETDump``. ``ETDump`` contains runtime results
from executing the model. To generate, simply pass the ExecuTorch model
to the ``executor_runner``::

  buck2 run executorch/examples/portable/scripts:export -- -m mv2
  buck2 run @mode/opt -c executorch.event_tracer_enabled=true executorch/sdk/runners:executor_runner -- --model_path mv2.pte

TODO: Add Instructions for CMake, when landed

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Creating an Inspector
---------------------

Final step is to create the ``Inspector`` by passing in the artifact paths.
Inspector takes the runtime results from ``ETDump`` and correlates them to
the operators of the Edge Dialect Graph.

Note: An ``ETRecord`` is not required. If an ``ETRecord`` is not provided,
the Inspector will show runtime results without operator correlation.

To visualize all runtime events, call ``print_data_tabular``::

  from executorch.sdk import Inspector

  etdump_path = "etdump.etdp"
  inspector = Inspector(etdump_path=etdump_path, etrecord_path=etrecord_path)
  inspector.print_data_tabular()


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Conclusion
----------

In this tutorial, we learned about the steps required to consume an ExecuTorch
model with the ExecuTorch SDK. It also showed how to use the Inspector APIs
to analyze the model run results.

Links Mentioned
^^^^^^^^^^^^^^^

- `ExecuTorch SDK <../sdk-overview.html>`__
- `ETRecord <../sdk-etrecord>`__
- `ETDump <../sdk-etdump.html>`__


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