# microTVM with TFLite Models¶

Author: Tom Gall

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

Note

If you want to run this tutorial on the microTVM Reference VM, download the Jupyter notebook using the link at the bottom of this page and save it into the TVM directory. Then:

1. Login to the reference VM with a modified vagrant ssh command:

$vagrant ssh -- -L8888:localhost:8888 2. Install jupyter: pip install jupyterlab 3. cd to the TVM directory. 4. Install tflite: poetry install -E importer-tflite 5. Launch Jupyter Notebook: jupyter notebook 6. Copy the localhost URL displayed, and paste it into your browser. 7. Navigate to saved Jupyter Notebook (.ipynb file). ## Setup¶ ### Install TFLite¶ To get started, TFLite package needs to be installed as prerequisite. You can do this in two ways: 1. Install tflite with pip pip install tflite=2.1.0 --user  2. Generate the TFLite package yourself. The steps are the following: Get the flatc compiler. Please refer to https://github.com/google/flatbuffers for details and make sure it is properly installed. flatc --version  Get the TFLite schema. wget https://raw.githubusercontent.com/tensorflow/tensorflow/r1.13/tensorflow/lite/schema/schema.fbs  Generate TFLite package. flatc --python schema.fbs  Add the current folder (which contains generated tflite module) to PYTHONPATH. export PYTHONPATH=${PYTHONPATH:+$PYTHONPATH:}$(pwd)


To validate that the TFLite package was installed successfully, python -c "import tflite"

### Install Zephyr (physical hardware only)¶

When running this tutorial with a host simulation (the default), you can use the host gcc to build a firmware image that simulates the device. When compiling to run on physical hardware, you need to install a toolchain plus some target-specific dependencies. microTVM allows you to supply any compiler and runtime that can launch the TVM RPC server, but to get started, this tutorial relies on the Zephyr RTOS to provide these pieces.

You can install Zephyr by following the Installation Instructions.

Aside: Recreating your own Pre-Trained TFLite model

The tutorial downloads a pretrained TFLite model. When working with microcontrollers you need to be mindful these are highly resource constrained devices as such standard models like MobileNet may not fit into their modest memory.

For this tutorial, we’ll make use of one of the TF Micro example models.

If you wish to replicate the training steps see: https://github.com/tensorflow/tensorflow/tree/master/tensorflow/lite/micro/examples/hello_world/train

Note

wget https://storage.googleapis.com/download.tensorflow.org/models/tflite/micro/hello_world_2020_04_13.zip

this will fail due to an unimplemented opcode (114)

## Load and prepare the Pre-Trained Model¶

Load the pretrained TFLite model from a file in your current directory into a buffer

import os
import numpy as np
import logging

import tvm
import tvm.micro as micro
from tvm.contrib import graph_executor, utils
from tvm import relay

model_url = "https://people.linaro.org/~tom.gall/sine_model.tflite"
model_file = "sine_model.tflite"



Out:

File /workspace/.tvm_test_data/data/sine_model.tflite exists, skip.


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))


Out:

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. When running on a simulated target, choose “host” below: TARGET = tvm.target.target.micro(“host”)

# %%
# Compiling for physical hardware
#  When running on physical hardware, choose a target and a board that
#  describe the hardware. The STM32F746 Nucleo target and board is chosen in
#  this commented code. Another option would be to choose the same target but
#  the STM32F746 Discovery board instead. The disco board has the same
#  microcontroller as the Nucleo board but a couple of wirings and configs
#  differ, so it's necessary to select the "stm32f746g_disco" board below.
#
#  .. code-block:: python
#
TARGET = tvm.target.target.micro("host")
# BOARD = "nucleo_f746zg" # or "stm32f746g_disco"
BOARD = "qemu_x86"


Now, compile the model for the target:

with tvm.transform.PassContext(
opt_level=3, config={"tir.disable_vectorize": True}, disabled_pass=["FuseOps", "AlterOpLayout"]
):
graph, c_mod, c_params = relay.build(mod, target=TARGET, params=params)

# %%
# Compiling for a simulated device
# --------------------------------
#
# First, compile a static microTVM runtime for the targeted device. In this case, the host simulated
# device is used.
compiler = tvm.micro.DefaultCompiler(target=TARGET)
opts = tvm.micro.default_options(
os.path.join(tvm.micro.get_standalone_crt_dir(), "template", "host")
)

# %%
# Compiling for physical hardware
#  For physical hardware, comment out the previous section and use this compiler definition instead.
#
#  .. code-block:: python
#
#     import subprocess
#     from tvm.micro.contrib import zephyr
#
#     repo_root = subprocess.check_output(["git", "rev-parse", "--show-toplevel"], encoding='utf-8').strip()
#     project_dir = f"{repo_root}/tests/micro/qemu/zephyr-runtime"
#     compiler = zephyr.ZephyrCompiler(
#         project_dir=project_dir,
#         board=BOARD if "stm32f746" in str(TARGET) else "qemu_x86",
#         zephyr_toolchain_variant="zephyr",
#     )
#
#     opts = tvm.micro.default_options(f"{project_dir}/crt")
#
# enable printing memory usage statistics of the runtime image
# generated by Zephyr compiler for the physical hardware
# logging.basicConfig(level="INFO")

workspace = tvm.micro.Workspace()
micro_binary = tvm.micro.build_static_runtime(
workspace,
compiler,
c_mod,
opts,
# Use the microTVM memory manager. If, in your main.cc, you change TVMPlatformMemoryAllocate and
# TVMPlatformMemoryFree to use e.g. malloc() and free(), you can omit this extra library.
extra_libs=[tvm.micro.get_standalone_crt_lib("memory")],
)


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.

flasher = compiler.flasher()
with tvm.micro.Session(binary=micro_binary, flasher=flasher) as session:
graph_mod = tvm.micro.create_local_graph_executor(
graph, session.get_system_lib(), session.device
)

# Set the model parameters using the lowered parameters produced by relay.build.
graph_mod.set_input(**c_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).asnumpy()
print("result is: " + str(tvm_output))


Out:

result is: [[0.4443792]]


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