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TVM Command Line Interface

A command line tool that compiles neural network models using TVM.

Usage

In all the subsequent commands, if a GPU needs to be enabled, the docker image must be run with the correct flags, e.g. [--gpus 0] or [--gpus all] when running an Nvidia GPU with proprietary drivers and [--device /dev/dri --group-add video] otherwise.

$ docker run -it --rm -v `pwd`:`pwd` -w `pwd` \
    -u $(id -u ${USER}):$(id -g ${USER}) \
    autoware/model-zoo-tvm-cli:latest -h

Compiling a model in the model zoo

$ export MODEL_DIR=<path to the folder containing definition.yaml>
$ docker run \
    -it --rm \
    -v ${MODEL_DIR}:${MODEL_DIR} -w ${MODEL_DIR} \
    -u $(id -u ${USER}):$(id -g ${USER}) \
    autoware/model-zoo-tvm-cli:latest \
        compile \
        --config ${MODEL_DIR}/definition.yaml \
        --output_path <output folder>

The output will consist of these file:

  • deploy_lib.so contains compiled operators required by the network to be used with the TVM runtime
  • deploy_param.params contains trained weights for the network to be used with the TVM runtime
  • deploy_graph.json contains the compute graph defining relationship between the operators to be used with the TVM runtime
  • inference_engine_tvm_config.hpp contains declaration of a structure with configuration for the TVM runtime C++ API.

The target device can be set with the --target parameter.

Tuning a model in the model zoo

$ export MODEL_DIR=<path to the folder containing definition.yaml and \
                    AutoTVM_config.py>
$ docker run \
    -it --rm \
    -v ${MODEL_DIR}:${MODEL_DIR} -w ${MODEL_DIR} \
    -u $(id -u ${USER}):$(id -g ${USER}) \
    autoware/model-zoo-tvm-cli:latest \
        tune \
        --config ${MODEL_DIR}/definition.yaml \
        --output_path <output folder> \
        --autotvm_config ${MODEL_DIR}/AutoTVM_config.py

The output will consist of a .log file whose name can be specified in the AutoTVM_config.py file. Examples of this file are provided in /modelzoo/scripts/tvm_cli/AutoTVM_config_example_cuda.py and /modelzoo/scripts/tvm_cli/AutoTVM_config_example_llvm.py

Validation script

A testing script is provided. The script automatically detects all the .yaml definition files in a user-specified path, executes the compilation of the model corresponding to each file, and checks the output afterwards. The test corresponding to a certain .yaml file is only executed if the 'enable_testing' field is set to true.

The tests need to be run inside a container. The user is required to specify the folder containing the .yaml files using the -v option. This folder will be searched recursively for all the definition.yaml files.

$ docker run -it --rm \
    -u $(id -u ${USER}):$(id -g ${USER}) \
    -v /abs_path_to/modelzoo/:/tmp \
    autoware/model-zoo-tvm-cli:latest \
    test

The output will contain information regarding which tests were successful and which weren't.

Building the docker image

Instead of pulling the docker image, it can be built locally using the build.sh script:

Usage: ./scripts/tvm_cli/build.sh [OPTIONS]
    -c,--cuda              Build TVM cli with cuda enabled.
    -h,--help              Display the usage and exit.
    -i,--image-name <name> Set docker images name.
                           Default: autoware/model-zoo-tvm-cli
    -t,--tag <tag>         Tag use for the docker images.
                           Default: local
    --platform <platform>  Set target platform. Possible values: amd64, arm64.
                           Default: {native platform}
    --ci                   Enable CI-relevant build options

Here an example to build the image with default parameters:

$ # From root of the model zoo repo
$ ./scripts/tvm_cli/build.sh

...

Successfully built 547afbbfd193
Successfully tagged autoware/model-zoo-tvm-cli:local

The previous commands are then used with :local instead of :latest.

Note: If CUDA is needed, the build.sh script must be invoked with the -c argument. In all the docker commands shown, if CUDA needs to be enabled, the docker image must be run with a flag which exposes the gpu, e.g. [--gpus 0] or [--gpus all].

Note: Cross-compilation is possible but experimental. Follow the buildx instructions to setup your system.