Debugger#

The torch_tensorrt.dynamo.Debugger context manager provides visibility into the Torch-TensorRT compilation pipeline. Use it to capture FX graph visualizations around lowering passes, monitor engine building, save profiling data, and control logging verbosity — all without modifying your model code.

Usage#

Wrap your torch_tensorrt.dynamo.compile (or torch.compile) call inside the Debugger context:

import torch_tensorrt

with torch_tensorrt.dynamo.Debugger(
    log_level="debug",
    logging_dir="/tmp/trt_debug",
    engine_builder_monitor=True,
    capture_fx_graph_before=["remove_detach"],
    capture_fx_graph_after=["complex_graph_detection"],
):
    trt_model = torch_tensorrt.dynamo.compile(exported_program, arg_inputs=inputs)
    output = trt_model(*inputs)

On exit the context restores all previous logging state and lowering-pass lists automatically.

Options#

Parameter	Default	Description
`log_level`	`"debug"`	Verbosity level: `"debug"`, `"info"`, `"warning"`, `"error"`, `"internal_errors"`, or `"graphs"`.
`logging_dir`	`/tmp/torch_tensorrt_<user>/debug_logs`	Directory where logs, profiles, and graph SVGs are written.
`capture_fx_graph_before`	`None`	List of lowering-pass names. An SVG of the FX graph is saved before each named pass runs.
`capture_fx_graph_after`	`None`	List of lowering-pass names. An SVG of the FX graph is saved after each named pass runs.
`engine_builder_monitor`	`True`	Stream TensorRT engine-build progress to the console.
`save_engine_profile`	`False`	Save per-layer profiling data after the first inference run.
`profile_format`	`"perfetto"`	Format for saved profiles. C++ runtime supports `"perfetto"` and `"trex"`; Python runtime supports `"cudagraph"` only.
`capture_tensorrt_api_recording`	`False`	Record TensorRT API calls for replay-based debugging (Linux only). Requires `TORCHTRT_ENABLE_TENSORRT_API_CAPTURE=1`.
`save_layer_info`	`False`	Write a JSON file containing per-layer metadata for the compiled engine.

Output layout#

After the context exits, logging_dir contains:

<logging_dir>/
    torch_tensorrt_logging.log          # always written
    lowering_passes_visualization/      # when capture_fx_graph_before/after used
        before_<pass_name>.svg
        after_<pass_name>.svg
    engine_visualization_profile/       # when save_engine_profile=True
    engine_layer_info.json              # when save_layer_info=True

TensorRT API recordings (capture_tensorrt_api_recording=True) are written separately to /tmp/torch_tensorrt_<user>/shim/ and are independent of logging_dir.

API Reference#