CMSIS-NN/Tests/UnitTest

Unit tests for CMSIS-NN

Unit test CMSIS-NN functions on any Arm Mbed OS supported HW or using a fixed virtual platform (FVP) based on Arm Corstone-300 software.

The Unity test framework is used for running the actual unit tests.

Requirements

Python3 is required. It has been tested with Python 3.6 and it has been tested on Ubuntu 16, 18 and 20.

Make sure to use the latest pip version before starting. If in a virtual environment just start by upgrading pip.

pip install --upgrade pip

See below for what pip packages are needed.

Executing unit tests

If using the script unittest_targets.py for executing unit tests, the following packages are needed.

pip install pyserial mbed-ls mbed-cli termcolor mercurial pyelftools==0.29 pyyaml jsonschema jinja2 mbed_host_tests mbed_greentea pycryptodome pyusb cmsis_pack_manager psutil cryptography click cbor

Python packages mbed-cli and and mbed-ls are command line tools so it should not matter if those are installed under Python2 or Python3. These packages have been tested for Python2, with the following versions: mbed-ls(1.7.9) and mbed-cli(1.10.1). They have also been tested for Python3, with the following versions: mbed-ls(1.7.12) and mbed-cli(1.10.5). Package mercurial is needed for package mbed-cli.

Generating new test data

For generating new test data, the following packages are needed.

pip install numpy packaging tensorflow tf-keras~=2.16

For generating new data, the python3 packages tensorflow, numpy and packaging are required. Most unit tests use a Keras generated model for reference. The SVDF unit test use a json template as input for generating a model. To do so flatc compiler is needed and it requires a schema file.

Get flatc and schema

Note this is only needed for generating SVDF unit tests.

For flatc compiler clone this repo and build:

cd flatbuffers
cmake -G "Unix Makefiles" -DCMAKE_BUILD_TYPE=Release
make

Remember to add the built flatc binary to the path.

For schema file download schema.fbs.

Interpreter for generating reference output

Python package tensorflow is always needed however the script has the option to use other interpreters than the tensorflow default, which will generate the actual reference output.

tflite_runtime

Python package tflite_runtime can be installed with pip and it can also be built locally. Check this link on how to do that. Use the -h flag to get more info on supported interpreters.

tflite_micro

Python package tflite_micro can be installed with pip and it can also be built locally. See this comment for more info: https://github.com/tensorflow/tflite-micro/issues/1484#issuecomment-1677842603. This interpreter is only partially supported, see Tests depending on TFLM interpreter.

Getting started

Using Arm Mbed OS supported hardware

Connect any HW (e.g. NUCLEO_F746ZG) that is supported by Arm Mbed OS. Multiple boards are supported. Note that board must be mounted, which may or may not be done automatically depending on OS. If all requirements are satisfied you can just run:

./unittest_targets.py

Use the -h flag to get more info.

Using FVP based on Arm Corstone-300 software

The build for unit tests differs from the build of CMSIS-NN as a standalone library in that, there is a dependency to CMSIS project for the startup files from CMSIS-Core. This is specified by the mandatory CMSIS_PATH CMake argument.

Here is an example for testing on Arm Cortex-M55:

cd </path/to/CMSIS_NN/Tests/Unittest>
mkdir build
cd build
cmake .. -DCMAKE_TOOLCHAIN_FILE=</path/to/ethos-u-core-platform>/cmake/toolchain/arm-none-eabi-gcc.cmake -DTARGET_CPU=cortex-m55 -DCMSIS_PATH=</path/to/CMSIS>
make

This will build all unit tests. You can also just build a specific unit test only, for example:

make test_arm_depthwise_conv_s8_opt

Then you need to download and install the FVP based Arm Corstone-300 software, for example:

mkdir -p /home/$user/FVP
wget https://developer.arm.com/-/media/Arm%20Developer%20Community/Downloads/OSS/FVP/Corstone-300/FVP_Corstone_SSE-300_Ethos-U55_11.12_57.tgz
tar -xvzf FVP_Corstone_SSE-300_Ethos-U55_11.12_57.tgz
./FVP_Corstone_SSE-300_Ethos-U55.sh --i-agree-to-the-contained-eula --no-interactive -d /home/$user/FVP
export PATH="/home/$user/FVP/models/Linux64_GCC-6.4:$PATH"

Finally you can run the unit tests. For example:

FVP_Corstone_SSE-300_Ethos-U55 --cpulimit 2 -C mps3_board.visualisation.disable-visualisation=1 -C mps3_board.telnetterminal0.start_telnet=0 -C mps3_board.uart0.out_file="-" -C mps3_board.uart0.unbuffered_output=1 ./TestCases/test_arm_depthwise_conv_s8_opt/test_arm_depthwise_conv_s8_opt.elf

Generating new test data

Generating new test data is done with the following script. Use the -h flag to get more info.

./generate_test_data.py -h

The script use a concept of test data sets, i.e. it need a test set data name as input. It will then generate files with that name as prefix. Multiple header files of different test sets can then be included in the actual unit test files. When adding a new test data set, new c files should be added or existing c files should be updated to use the new data set. See overview of the folders on how/where to add new c files.

The steps to add a new unit test are as follows. Add a new test test in the load_all_testdatasets() function. Run the generate script with that new test set as input. Add the new generated header files to an existing or new unit test.

Tests depending on TFLM interpreter

SVDF INT8

This test is depending on tflite_micro for its reference data. This is because the operator is only supported by TFLM.

LSTM

This test is depending on tflite_micro for its reference data. This is because the operator differs between TFLM and TFLite.

Note that tflite_micro interpreter is currently only supported for SVDF and LSTM.

Overview of the Folders

• Corstone-300 - These are dependencies, like linker files etc, needed when building binaries targeting the FVP based on Arm Corstone-300 software. This is mostly taken from Arm Ethos-U Core Platform project.
• Mbed - These are the Arm Mbed OS settings that are used. See Mbed/README.md.
• Output - This will be created when building.
• PregeneratedData - Host local(Not part of GitHub) test data for model creation using Keras in unit tests. It can be used for debug purposes when adding new operators or debugging existing ones.
• TestCases - Here are the actual unit tests. For each function under test there is a folder under here.
• TestCases/<cmsis-nn function name> - For each function under test there is a folder with the same name with test_ prepended to the name and it contains a c-file with the actual unit tests. For example for arm_convolve_s8() the file is called test_arm_convolve_s8.c
• TestCases/<cmsis-nn function name>/Unity - This folder contains a Unity file that calls the actual unit tests. For example for arm_convolve_s8() the file is called unity_test_arm_convolve_s8.c.
• TestCases/<cmsis-nn function name>/Unity/TestRunner - This folder will contain the autogenerated Unity test runner.
• TestCases/TestData - This is auto generated test data in .h files that the unit tests are using. The data in PregenrateData folder has fp32 data of a network whereas here it is the quantized equivalent of the same. They are not the same. All data can regenerated or only parts of it (e.g. only bias data). Of course even the config can be regenerated. This partial/full regeneration is useful during debugging.
• TestCases/Common - Common files used in test data generation is placed here.

Formatting

The python test scripts should be formatted with yapf.

pip install --upgrade yapf

The following settings are used.

python -m yapf --in-place --style='{based_on_style:pep8,column_limit:120,indent_width:4}' *.py