Debugging guide for TensorRT

docsrc/index.rst

@@ -39,6 +39,7 @@ User Guide
 * :ref:`runtime`
 * :ref:`using_dla`
 * :ref:`mixed_precision`
+* :ref:`debugging`
 
 .. toctree::
    :caption: User Guide
@@ -51,6 +52,7 @@ User Guide
    user_guide/runtime
    user_guide/using_dla
    user_guide/mixed_precision
+   user_guide/debugging
 
 
 Tutorials

docsrc/user_guide/debugging_torch_tensorrt.rst

@@ -0,0 +1,30 @@
+.. _debugging:
+
+Debugging Torch-TensorRT Compilation
+====================================
+
+
+FX Graph Visualization
+----------------------
+
+Debug Mode
+-------------
+
+
+Profiling TensorRT Engines
+--------------------------
+
+There are some profiling tools built into Torch-TensorRT to measure the performance of TensorRT sub blocks in compiled modules.
+This can be used in conjunction with PyTorch profiling tools to get a picture of the performance of your model.
+Profiling for any particular sub block can be enabled by the ``enabled_profiling()`` method of any
+`` __torch__.classes.tensorrt.Engine`` attribute, or of any ``torch_tensorrt.runtime.TorchTensorRTModule``. The profiler will
+dump trace files by default in /tmp, though this path can be customized by either setting the
+profile_path_prefix of ``__torch__.classes.tensorrt.Engine`` or as an argument to
+torch_tensorrt.runtime.TorchTensorRTModule.enable_precision(profiling_results_dir="").
+Traces can be visualized using the Perfetto tool (https://perfetto.dev)
+
+.. image:: /user_guide/images/perfetto.png
+   :width: 512px
+   :height: 512px
+   :scale: 50 %
+   :align: right