1. microTVM CLI Tool

Author: Mehrdad Hessar

This tutorial explains how to compile a tiny model for a micro device, build a program on Zephyr platform to execute this model, flash the program and run the model all using tvmc micro command. You need to install python and Zephyr dependencies before processing with this tutorial.

Install microTVM Python dependencies

TVM does not include a package for Python serial communication, so we must install one before using microTVM. We will also need TFLite to load models.

pip install pyserial==3.5 tflite==2.1

Install Zephyr

# Install west and ninja
python3 -m pip install west
apt-get install -y ninja-build

# Install ZephyrProject
git checkout v3.2-branch
cd ..
west update
west zephyr-export

# Install Zephyr SDK
cd /content
wget "https://github.com/zephyrproject-rtos/sdk-ng/releases/download/v${ZEPHYR_SDK_VERSION}/zephyr-sdk-${ZEPHYR_SDK_VERSION}_linux-x86_64.tar.gz"
tar xvf "zephyr-sdk-${ZEPHYR_SDK_VERSION}_linux-x86_64.tar.gz"
mv "zephyr-sdk-${ZEPHYR_SDK_VERSION}" zephyr-sdk
rm "zephyr-sdk-${ZEPHYR_SDK_VERSION}_linux-x86_64.tar.gz"

# Install python dependencies
python3 -m pip install -r "${ZEPHYR_BASE}/scripts/requirements.txt"

Using TVMC Micro

TVMC is a command-line tool which is installed as a part of TVM Python packages. Accessing this package varies based on your machine setup. In many cases, you can use the tvmc command directly. Alternatively, if you have TVM as a Python module on your $PYTHONPATH, you can access this driver with python -m tvm.driver.tvmc command. This tutorial will use TVMC command as tvmc for simplicity.

To check if you have TVMC command installed on your machine, you can run:

tvmc --help

To compile a model for microtvm we use tvmc compile subcommand. The output of this command is used in next steps with tvmc micro subcommands. You can check the availability of TVMC Micro using:

tvmc micro --help

The main tasks that you can perform using tvmc micro are create, build and flash. To read about specific options under a givern subcommand, use tvmc micro <subcommand> --help. We will use each subcommand in this tutorial.

Obtain a Tiny Model

For this tutorial, we will use Micro Speech model from tflite micro. Micro Speech is a Depthwise Convolution Layer model to recognize keywords in speech.

For this tutorial we will be using the model in tflite format.

wget https://github.com/tensorflow/tflite-micro/raw/a56087ffa2703b4d5632f024a8a4c899815c31bb/tensorflow/lite/micro/examples/micro_speech/micro_speech.tflite

Compiling a TFLite model to a Model Library Format

Model Library Format (MLF) is an output format that TVM provides for micro targets. MLF is a tarball containing a file for each piece of the TVM compiler output which can be used on micro targets outside TVM environment. Read more about Model Library Format.

Here, we generate a MLF file for qemu_x86 Zephyr board. You can chooses aot or graph executor type to run this tutorial, however, we recommend to use aot for microTVM targets since aot uses ahead of time compilation with static memory allocation. To generate MLF output for the micro_speech tflite model:

tvmc compile micro_speech.tflite \
    --target='c -keys=cpu -model=host' \
    --runtime=crt \
    --runtime-crt-system-lib 1 \
    --executor='aot' \
    --output model.tar \
    --output-format mlf \
    --pass-config tir.disable_vectorize=1

This will generate a model.tar file which contains TVM compiler output files. To run this command for a different Zephyr device, you need to update target. For instance, for nrf5340dk_nrf5340_cpuapp board the target is --target='c -keys=cpu -model=nrf5340dk'.

Create a Zephyr Project Using Model Library Format

To generate a Zephyr project we use TVM Micro subcommand create. We pass the MLF format and the path for the project to create subcommand along with project options. Project options for each platform (Zephyr/Arduino) are defined in their Project API server file. To build Zephyr project for a different Zephyr board, change zephyr_board project option. To generate Zephyr project, run:

tvmc micro create \
    project \
    model.tar \
    zephyr \
    --project-option project_type=host_driven board=qemu_x86

This will generate a Host-Driven Zephyr project for qemu_x86 Zephyr board. In Host-Driven template project, the Graph Executor will run on host and perform the model execution on Zephyr device by issuing commands to the device using an RPC mechanism. Read more about Host-Driven Execution.

To get more information about TVMC Micro create subcommand:

tvmc micro create --help

Build and Flash Zephyr Project Using TVMC Micro

Next step is to build the Zephyr project which includes TVM generated code for running the tiny model, Zephyr template code to run a model in Host-Driven mode and TVM runtime source/header files. To build the project:

tvmc micro build \
    project \

This will build the project in project directory and generates binary files under project/build.

Next, we flash the Zephyr binary file to Zephyr device. For qemu_x86 Zephyr board this step does not actually perform any action since QEMU will be used, however you need this step for physical hardware.

tvmc micro flash \
    project \

Run Tiny Model on Micro Target

After flashing the device, the compiled model and TVM RPC server are programmed on the device. The Zephyr board is waiting for host to open a communication channel. MicroTVM devices typicall communicate using a serial communication (UART). To run the flashed model on the device using TVMC, we use tvmc run subcommand and pass --device micro to specify the device type. This command will open a communication channel, set input values using Graph Executor on host and run full model on the device. Then it gets output from the device.

tvmc run \
    --device micro \
    project \
    --fill-mode ones \
    --print-top 4

Specifically, this command sets the input of the model to all ones and shows the four values of the output with their indices.

# Output:
# INFO:__main__:b'[100%] [QEMU] CPU: qemu32,+nx,+pae\n'
# remote: microTVM Zephyr runtime - running
# INFO:__main__:b'[100%] Built target run\n'
# [[   3    2    1    0]
#  [ 113 -120 -121 -128]]

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