acetone-nnet 0.1

Predictable programming framework for ML applications in safety-critical systems.

ACETONE

Predictable programming framework for ML applications in safety-critical systems.

This repo contains the code of the framework presented in the ECRTS'22 paper "ACETONE: Predictable programming framework for ML applications in safety-critical systems".

This framework generate a C code corresponding to a neural network given as input.

Code architecture

You'll find in the home directory the files regarding the licencing and copyright of the framework:

• AUTHORS.md
• LICENSE

This directory also contains the requirements.txt which list the package versionning used in the framework.

The test directory includes several tests for the framework and the data to run them.

The src folder contains the backend code of ACETONE.

Installation

Clone the GitHub repo on your computer

git clone https://github.com/onera/acetone.git

Then install the packages listed in requirements.txt

pip install -r acetone/requirements.txt

Code Generation

The following commands generate a test neural network before generating the corresponding C code using ACETONE.

Generating the neural network

In the acetone directory

• Run the initial_setup.py code

python3 tests/models/lenet5/lenet5_example/initial_setup.py

This script defines a neural network with a Lenet-5 architecture using the framework Keras. It then save the model in .h5 and .json files. The later one is created using a specific function, developped by us, to write the keras model in ACETONE's format. The scripts also creates a random input to test the neural network. Finally, the scripts saves and prints, as a reference, the output of the inference done by the Keras framework.

Generating the C code with ACETONE

Then, generate the C code with ACETONE.

• Call ACETONE with the following arguments:
  • The file describing the model
  • The name of the function to generate (here 'lenet5')
  • The number of test to run (here 1)
  • The algorithm used for the convolution layer ('6loops','indirect_gemm_'+TYPE, 'std_gemm_'+TYPE, with TYPE being amongst 'nn','nt', 'tn','tt')
  • The directory in which the code will be generated
  • The input file with the test data

python3 src/cli_acetone.py tests/models/lenet5/lenet5_example/lenet5.h5  lenet5 1 std_gemm_nn tests/models/lenet5/lenet5_example/lenet5_generated .tests/models/lenet5/lenet5_example/test_input_lenet5.txt

• Compile the code

make -C tests/models/lenet5/lenet5_example all

• Execute the file with the path to the directory of the output file as argument

./tests/models/lenet5/lenet5_example/lenet5 ./tests/models/lenet5/lenet5_example/output_acetone.txt

• Compare the output given by Keras and ACETONE

python3 src/cli_compare.py ./tests/models/lenet5/lenet5_example/output_keras.txt ./tests/models/lenet5/lenet5_example/output_acetone.txt 1

Tests

Tests are implemented in the folder tests.

To run all of them, use the following command:

python3 -m unittest discover tests/test_inference/test_layer tests/tests_network tests/tests_importer

To only run a test, use the command

python3 -m unittest PATH_TO_TEST

where PATH_TO_TEST is the path to your test.

Reproduce the paper's experiments

To reproduce the result of semantic experiment with ACETONE as described in the paper, use the following commands:

• For the acas_decr128 model

pyhton3 src/cli_acteone.py tests/models/acas_decr128/acas_decr128.json acas_dcre128 1000 std_gemm_nn tests/models/acas_decr128/output_acetone tests/models/acas_decr128/test_input_acas_decr128.txt
make -C tests/models/acas_decr128/output_acetone all
./tests/models/acas_decr128/output_acetone/acas_decr128 tests/models/acas_decr128/output_acetone/output_acetone.txt
python3 src/cli_compare.py tests/models/acas_decr128/output_keras.txt tests/models/acas_decr128/output_acetone/output_acetone.txt

• For the lent5 model

pyhton3 src/cli_acteone.py tests/models/lenet5/lenet5_trained/lenet5_trained.json lenet5_trained 1000 std_gemm_nn tests/models/lenet5/lenet5_trained/output_acetone tests/models/lenet5_trained/test_input_lenet5.txt
make -C tests/models/lenet5/lenet5_trained/output_acetone all
./tests/models/lenet5/lenet5_trained/output_acetone/lenet5_trained tests/models/lenet5/lenet5_trained/output_acetone/output_acetone.txt
python3 src/cli_compare.py tests/models/lenet5/lenet5_trained/output_keras.txt tests/models/lenet5/lenet5_trained/output_acetone/output_acetone.txt

Capability

As of the 26/03/2024, the framework can generate code for neural network meeting the following condition:

• The neural network is Sequential and Feedforward

• The neural-network is describeb in one of the following formats:

  • JSON (specifique decription mad efor the framework. Confer to the file JSON_from_keras_model.py)
  • NNET
  • ONNX
  • H5 (by transforming into a JSON file)

• Its layers are amongst the following (both Keras and Onnx layers):

  • Dense
  • Convolutional (Conv2D) (3 option for the algorithm)
  • Pooling (AveragePooling2D, MaxPooling2D)
  • Flatten
  • Normalization (Normalization layers of Keras)
  • Dropout (Dropout layers of Keras)
  • Reshape
  • Permute
  • MatMul
  • Gemm
  • Gather
  • Dot
  • Concatenate
  • Clip
  • Add (where one of the inputs is an ONNX initializer)
  • Resize (Cubic interpolation, Linear interpolation, Nearest)
  • Pad (Wrap padding, Edge padding, Reflect padding, Constant padding)
  • Broadcast (Add, Average, Divide, Maximum, Minimum, Multiply, Subtract)
  • Unsqueeze (ONNX layer)
  • LRN (ONNX layer)
  • Shape (ONNX layer)
  • BatchNormalization (ONNX layer)

• Its activation layers are amongst the following:

  • Linear
  • Tanh
  • ReLu
  • Sigmoid
  • Softmax
  • Exp
  • Log

License

The project is under the GNU Lesser General Public License as published by the Free Software Foundation ; either version 3 of the License or (at your option) any later version.

See LICENSE for details.