2. microTVM TFLite Tutorial

Author: Tom Gall

This tutorial is an introduction to working with microTVM and a TFLite model with Relay.

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.

%%shell
pip install pyserial==3.5 tflite==2.1

import os

# By default, this tutorial runs on x86 CPU using TVM's C runtime. If you would like
# to run on real Zephyr hardware, you must export the `TVM_MICRO_USE_HW` environment
# variable. Otherwise (if you are using the C runtime), you can skip installing
# Zephyr. It takes ~20 minutes to install Zephyr.
use_physical_hw = bool(os.getenv("TVM_MICRO_USE_HW"))

Install Zephyr

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

# Install ZephyrProject
ZEPHYR_PROJECT_PATH="/content/zephyrproject"
export ZEPHYR_BASE=${ZEPHYR_PROJECT_PATH}/zephyr
west init ${ZEPHYR_PROJECT_PATH}
cd ${ZEPHYR_BASE}
git checkout v3.2-branch
cd ..
west update
west zephyr-export
chmod -R o+w ${ZEPHYR_PROJECT_PATH}

# Install Zephyr SDK
cd /content
ZEPHYR_SDK_VERSION="0.15.2"
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"

Import Python dependencies

import json
import tarfile
import pathlib
import tempfile
import numpy as np

import tvm
import tvm.micro
import tvm.micro.testing
from tvm import relay
import tvm.contrib.utils
from tvm.micro import export_model_library_format
from tvm.contrib.download import download_testdata

model_url = (
    "https://github.com/tlc-pack/web-data/raw/main/testdata/microTVM/model/sine_model.tflite"
)
model_file = "sine_model.tflite"
model_path = download_testdata(model_url, model_file, module="data")

tflite_model_buf = open(model_path, "rb").read()

Using the buffer, transform into a tflite model python object

try:
    import tflite

    tflite_model = tflite.Model.GetRootAsModel(tflite_model_buf, 0)
except AttributeError:
    import tflite.Model

    tflite_model = tflite.Model.Model.GetRootAsModel(tflite_model_buf, 0)

Print out the version of the model

version = tflite_model.Version()
print("Model Version: " + str(version))

Model Version: 3

Parse the python model object to convert it into a relay module and weights. It is important to note that the input tensor name must match what is contained in the model.

If you are unsure what that might be, this can be discovered by using the visualize.py script within the Tensorflow project. See How do I inspect a .tflite file?

input_tensor = "dense_4_input"
input_shape = (1,)
input_dtype = "float32"

mod, params = relay.frontend.from_tflite(
    tflite_model, shape_dict={input_tensor: input_shape}, dtype_dict={input_tensor: input_dtype}
)

Defining the target

Now we create a build config for relay, turning off two options and then calling relay.build which will result in a C source file for the selected TARGET. When running on a simulated target of the same architecture as the host (where this Python script is executed) choose “crt” below for the TARGET, the C Runtime as the RUNTIME and a proper board/VM to run it (Zephyr will create the right QEMU VM based on BOARD. In the example below the x86 arch is selected and a x86 VM is picked up accordingly:

RUNTIME = tvm.relay.backend.Runtime("crt", {"system-lib": True})
TARGET = tvm.micro.testing.get_target("crt")

# When running on physical hardware, choose a TARGET and a BOARD that describe the hardware. The
# STM32L4R5ZI Nucleo target and board is chosen in the example below. You could change the testing
# board by simply exporting `TVM_MICRO_BOARD` variable with a different Zephyr supported board.

if use_physical_hw:
    BOARD = os.getenv("TVM_MICRO_BOARD", default="nucleo_l4r5zi")
    SERIAL = os.getenv("TVM_MICRO_SERIAL", default=None)
    TARGET = tvm.micro.testing.get_target("zephyr", BOARD)

# For some boards, Zephyr runs them emulated by default, using QEMU. For example, below is the
# TARGET and BOARD used to build a microTVM firmware for the mps2-an521 board.
#
# `mps2_an521 = "mps2_an521"`
# `TARGET = tvm.micro.testing.get_target("zephyr", BOARD)`

Now, compile the model for the target. If you do not specify Executor, by default it uses GraphExecutor.

with tvm.transform.PassContext(opt_level=3, config={"tir.disable_vectorize": True}):
    module = relay.build(mod, target=TARGET, runtime=RUNTIME, params=params)

Inspecting the compilation output

The compilation process has produced some C code implementing the operators in this graph. We can inspect it by printing the CSourceModule contents (for the purposes of this tutorial, let’s just print the first 10 lines):

c_source_module = module.get_lib().imported_modules[0]
assert c_source_module.type_key == "c", "tutorial is broken"

c_source_code = c_source_module.get_source()
first_few_lines = c_source_code.split("\n")[:10]
assert any(
    l.startswith("TVM_DLL int32_t tvmgen_default_") for l in first_few_lines
), f"tutorial is broken: {first_few_lines!r}"
print("\n".join(first_few_lines))

// tvm target: c -keys=cpu -model=host
#define TVM_EXPORTS
#include "tvm/runtime/c_runtime_api.h"
#include "tvm/runtime/c_backend_api.h"
#include <math.h>
#include <stdbool.h>
#ifdef __cplusplus
extern "C"
#endif
TVM_DLL int32_t tvmgen_default_fused_nn_contrib_dense_pack_add(void* args, int32_t* arg_type_ids, int32_t num_args, void* out_ret_value, int32_t* out_ret_tcode, void* resource_handle);

Compiling the generated code

Now we need to incorporate the generated C code into a project that allows us to run inference on the device. The simplest way to do this is to integrate it yourself, using microTVM’s standard output format model library format. This is a tarball with a standard layout.

# Get a temporary path where we can store the tarball (since this is running as a tutorial).

temp_dir = tvm.contrib.utils.tempdir()
model_tar_path = temp_dir / "model.tar"
export_model_library_format(module, model_tar_path)

with tarfile.open(model_tar_path, "r:*") as tar_f:
    print("\n".join(f" - {m.name}" for m in tar_f.getmembers()))

# TVM also provides a standard way for embedded platforms to automatically generate a standalone
# project, compile and flash it to a target, and communicate with it using the standard TVM RPC
# protocol. The Model Library Format serves as the model input to this process. When embedded
# platforms provide such an integration, they can be used directly by TVM for both host-driven
# inference and autotuning . This integration is provided by the
# `microTVM Project API` <https://github.com/apache/tvm-rfcs/blob/main/rfcs/0008-microtvm-project-api.md>_,
#
# Embedded platforms need to provide a Template Project containing a microTVM API Server (typically,
# this lives in a file ``microtvm_api_server.py`` in the root directory). Let's use the example ``host``
# project in this tutorial, which simulates the device using a POSIX subprocess and pipes:

template_project_path = pathlib.Path(tvm.micro.get_microtvm_template_projects("crt"))
project_options = {}  # You can use options to provide platform-specific options through TVM.

#  For physical hardware, you can try out the Zephyr platform by using a different template project
#  and options:

if use_physical_hw:
    template_project_path = pathlib.Path(tvm.micro.get_microtvm_template_projects("zephyr"))
    project_options = {
        "project_type": "host_driven",
        "board": BOARD,
        "serial_number": SERIAL,
        "config_main_stack_size": 4096,
        "zephyr_base": os.getenv("ZEPHYR_BASE", default="/content/zephyrproject/zephyr"),
    }

# Create a temporary directory
temp_dir = tvm.contrib.utils.tempdir()
generated_project_dir = temp_dir / "generated-project"
generated_project = tvm.micro.generate_project(
    template_project_path, module, generated_project_dir, project_options
)

# Build and flash the project
generated_project.build()
generated_project.flash()

- .
- ./codegen
- ./codegen/host
- ./codegen/host/src
- ./codegen/host/src/default_lib0.c
- ./codegen/host/src/default_lib1.c
- ./executor-config
- ./executor-config/graph
- ./executor-config/graph/default.graph
- ./metadata.json
- ./parameters
- ./parameters/default.params
- ./src
- ./src/default.relay

Next, establish a session with the simulated device and run the computation. The with session line would typically flash an attached microcontroller, but in this tutorial, it simply launches a subprocess to stand in for an attached microcontroller.

with tvm.micro.Session(transport_context_manager=generated_project.transport()) as session:
    graph_mod = tvm.micro.create_local_graph_executor(
        module.get_graph_json(), session.get_system_lib(), session.device
    )

    # Set the model parameters using the lowered parameters produced by `relay.build`.
    graph_mod.set_input(**module.get_params())

    # The model consumes a single float32 value and returns a predicted sine value.  To pass the
    # input value we construct a tvm.nd.array object with a single contrived number as input. For
    # this model values of 0 to 2Pi are acceptable.
    graph_mod.set_input(input_tensor, tvm.nd.array(np.array([0.5], dtype="float32")))
    graph_mod.run()

    tvm_output = graph_mod.get_output(0).numpy()
    print("result is: " + str(tvm_output))

result is: [[0.4443792]]