.. DO NOT EDIT.
.. THIS FILE WAS AUTOMATICALLY GENERATED BY SPHINX-GALLERY.
.. TO MAKE CHANGES, EDIT THE SOURCE PYTHON FILE:
.. "tutorials/_rendered_examples/dynamo/debugger_example.py"
.. LINE NUMBERS ARE GIVEN BELOW.

.. only:: html

    .. note::
        :class: sphx-glr-download-link-note

        :ref:`Go to the end <sphx_glr_download_tutorials__rendered_examples_dynamo_debugger_example.py>`
        to download the full example code

.. rst-class:: sphx-glr-example-title

.. _sphx_glr_tutorials__rendered_examples_dynamo_debugger_example.py:


.. _debugger_example:

Debugging Torch-TensorRT Compilation
===================================================================

TensorRT conversion can perform many graph transformations and backend specific
optimizations that are sometimes hard to inspect. Torch-TensorRT provides a
Debugger utility to help visualize FX graphs around lowering passes, monitor
engine building, and capture profiling or TensorRT API traces.

In this example, we demonstrate how to:

    1. Enable the Torch-TensorRT Debugger context
    2. Capture and visualize FX graphs before and/or after specific lowering passes
    3. Configure logging directory and verbosity

.. GENERATED FROM PYTHON SOURCE LINES 18-76

.. code-block:: python


    import os
    import tempfile

    import numpy as np
    import torch
    import torch_tensorrt as torch_trt
    import torchvision.models as models

    temp_dir = os.path.join(tempfile.gettempdir(), "torch_tensorrt_debugger_example")

    np.random.seed(0)
    torch.manual_seed(0)
    inputs = [torch.rand((1, 3, 224, 224)).to("cuda")]


    model = models.resnet18(pretrained=False).to("cuda").eval()
    exp_program = torch.export.export(model, tuple(inputs))
    enabled_precisions = {torch.float}
    workspace_size = 20 << 30
    min_block_size = 0
    use_python_runtime = False
    torch_executed_ops = {}

    with torch_trt.dynamo.Debugger(
        log_level="debug",
        logging_dir=temp_dir,
        engine_builder_monitor=False,  # whether to monitor the engine building process
        capture_fx_graph_after=[
            "complex_graph_detection"
        ],  # fx graph visualization after certain lowering pass
        capture_fx_graph_before=[
            "remove_detach"
        ],  # fx graph visualization before certain lowering pass
    ):

        trt_gm = torch_trt.dynamo.compile(
            exp_program,
            tuple(inputs),
            use_python_runtime=use_python_runtime,
            enabled_precisions=enabled_precisions,
            min_block_size=min_block_size,
            torch_executed_ops=torch_executed_ops,
            immutable_weights=False,
            reuse_cached_engines=False,
        )

        trt_output = trt_gm(*inputs)


    """
    The logging directory will contain the following files:
    - /tmp/torch_tensorrt_debugger_example/
        torch_tensorrt_logging.log
        - /lowering_passes_visualization/
            after_complex_graph_detection.svg
            before_remove_detach.svg
    """


.. rst-class:: sphx-glr-timing

   **Total running time of the script:** ( 0 minutes  0.000 seconds)


.. _sphx_glr_download_tutorials__rendered_examples_dynamo_debugger_example.py:

.. only:: html

  .. container:: sphx-glr-footer sphx-glr-footer-example




    .. container:: sphx-glr-download sphx-glr-download-python

      :download:`Download Python source code: debugger_example.py <debugger_example.py>`

    .. container:: sphx-glr-download sphx-glr-download-jupyter

      :download:`Download Jupyter notebook: debugger_example.ipynb <debugger_example.ipynb>`


.. only:: html

 .. rst-class:: sphx-glr-signature

    `Gallery generated by Sphinx-Gallery <https://sphinx-gallery.github.io>`_