Added debugger example

docsrc/index.rst

@@ -71,6 +71,7 @@ Tutorials
 * :ref:`mutable_torchtrt_module_example`
 * :ref:`weight_streaming_example`
 * :ref:`pre_allocated_output_example`
+* :ref:`debugger_example`
 
 .. toctree::
    :caption: Tutorials

examples/dynamo/README.rst

@@ -22,3 +22,4 @@ Model Zoo
 * :ref:`_torch_export_llama2`: Compiling a Llama2 model using AOT workflow (`ir=dynamo`)
 * :ref:`_torch_export_sam2`: Compiling SAM2 model using AOT workflow (`ir=dynamo`)
 * :ref:`_torch_export_flux_dev`: Compiling FLUX.1-dev model using AOT workflow (`ir=dynamo`)
+* :ref:`debugger_example`: Debugging Torch-TensorRT Compilation

examples/dynamo/debugger_example.py

@@ -0,0 +1,75 @@
+"""
+.. _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
+"""
+
+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
+"""