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)
