Debugging MLIR-TensorRT backendΒΆ

  1. Install new python bindings for compiler and runtime. Assuming tripy/mlir-tensorrt directory exists. No need to update LD_LIBRARY_PATH.

     ```bash
 python3 -m pip install --force-reinstall mlir-tensorrt/build/wheels/trt100/**/*.whl
 ```

  2. Set environment flags for debugging:

  • export TRIPY_MLIR_DEBUG_ENABLED=1 to enable MLIR-TRT debugging. It will enable debugging prints in MLIR-TRT as well as dump all intermediate IRs after each pass.

  • export TRIPY_MLIR_DEBUG_PATH=<mlir-debug-path> to set debug path for MLIR-TRT dumps. Default path is mlir-dumps under the repo directory. This will create one or more folders named like module_ins_t1_outs_t2_1.

  • export TRIPY_TRT_DEBUG_ENABLED=1 to enable TensorRT debugging. It will dump TensorRT engines and their layer information (if there are any TensorRT built during compilation).

  • export TRIPY_TRT_DEBUG_PATH=<trt-debug-path> to set debug path for TensorRT dumps. Default path is tensorrt-dumps under the repo directory.

  1. Use LLDB for debugging MLIR-TensorRT backend. In order to use lldb in tripy container, launch the container with extra security options:

docker run --gpus all --cap-add=SYS_PTRACE \
        --security-opt seccomp=unconfined --security-opt apparmor=unconfined \
        -p 8080:8080 -v $(pwd):/tripy/ -it --rm tripy:latest

See https://forums.swift.org/t/debugging-using-lldb/18046 for more details.