elasticai.creator 0.19.0

Design, train and compile neural networks optimized specifically for FPGAs.

ElasticAi.creator

Design, train and compile neural networks optimized specifically for FPGAs. Obtaining a final model is typically a three stage process.

• design and train it using the layers provided in the elasticai.creator.qat package.
• translate the model to a target representation, e.g. VHDL
• compile the intermediate representation with a third party tool, e.g. Xilinx Vivado (TM)

This version currently only supports parts of VHDL as target representations.

The project is part of the elastic ai ecosystem developed by the Embedded Systems Department of the University Duisburg-Essen. For more details checkout the slides at researchgate.

Table of contents

• Users Guide
  • Install
• Structure of the Project
• General Limitations
• Developers Guide
  • Install Dev Dependencies

Users Guide

Install

You can install the ElasticAI.creator as a dependency using pip:

python3 -m pip install "elasticai.creator"

Structure of the Project

The structure of the project is as follows. The creator folder includes all main concepts of our project, especially the qtorch implementation which is our implementation of quantized PyTorch layer. It also includes the supported target representations, like the subfolder vhdl is for the translation to vhdl. Additionally, we have folders for unit tests and integration tests.

General Limitations

By now we only support Sequential models for our translations.

Developers Guide

Install Dev Dependencies

• poetry
• recommended:
  • pre-commit
  • commitlint to help following our conventional commit guidelines poetry can be installed in the following way:

pip install poetry
poetry install
poetry shell
pre-commit install
npm install --save-dev @commitlint/{config-conventional,cli}
sudo apt install ghdl

Commit Message Scopes

• qat: quantization-aware-training
  • Examples: QConv1D, QLSTM, autograd functions, etc.
• readme
• precomputation: entities that deal with the precomputation of ML components
  • Examples: the precomputable decorator or the IOTable class
• vhdl: vhdl code generation
  • Examples: vhdl.TruthTable, vhdl.LSTMCell
• gh-workflow
• pyproject: changes to the pyproject.toml file will typically either update run or dev dependencies
• typing: changing type annotations and changes to code to allow consistent type annotations
• pre-commit

Adding new translation targets

New translation targets should be located in their own folder, e.g. vhdl for translating from any language to vhdl. Workflow for adding a new translation:

1. Obtain a structure, such as a list in a sequential case, which will describe the connection between every component.
2. Identify and label relevant structures, in the base cases it can be simply separate layers.
3. Map each structure to its function which will convert it.
4. Do such conversions.
5. Recreate connections based on 1.

Each sub-step should be separable and it helps for testing if common functions are wrapped around an adapter.

Syntax Checking

GHDL supports a syntax checking which checks the syntax of a vhdl file without generating code. The command is as follows:

ghdl -s path/to/vhdl/file

So, for example for checking the sigmoid source vhdl files in our project we can run:

ghdl -s elasticai/creator/vhdl/source/sigmoid.vhd

For checking all vhdl files together in our project we can just run:

ghdl -s elasticai/creator/**/*.vhd

Tests

Our implementation is fully tested with unit, integration and system tests. Please refer to the system tests as examples of how to use the Elastic Ai Creator Translator. You can run one explicit test with the following statement:

python -m unittest discover -p "test_*.py" elasticai/creator/path/to/test.py

If you want to run all tests, give the path to the tests:

python -m unittest discover -p "test_*.py" elasticai/creator/path/to/testfolder

You can also run all tests together:

python -m unittest discover -p "test_*.py" elasticai/creator/translator/path/to/language/

If you want to add more tests please refer to the Test Guidelines in the following.

Test style Guidelines

File IO

In general try to avoid interaction with the filesystem. In most cases instead of writing to or reading from a file you can use a StringIO object or a StringReader. If you absolutely have to create files, be sure to use pythons tempfile module and cleanup after the tests.

Diectory structure and file names

Files containing tests for a python module should be located in a test directory for the sake of separation of concerns. Each file in the test directory should contain tests for one and only one class/function defined in the module. Files containing tests should be named according to the rubric test_ClassName.py. Next, if needed for more specific tests define a class. Then subclass it, in this class define a setUp method (and possibly tearDown) to create the global environment. It avoids introducing the category of bugs associated with copying and pasting code for reuse. This class should be named similarly to the file name. There's a category of bugs that appear if the initialization parameters defined at the top of the test file are directly used: some tests require the initialization parameters to be changed slightly. Its possible to define a parameter and have it change in memory as a result of a test. Subsequent tests will therefore throw errors. Each class contains methods that implement a test. These methods are named according to the rubric test_name_condition

Unit tests

In those tests each functionality of each function in the module is tested, it is the entry point when adding new functions. It assures that the function behaves correctly independently of others. Each test has to be fast, so use of heavier libraries is discouraged. The input used is the minimal one needed to obtain a reproducible output. Dependencies should be replaced with mocks as needed.

Integration Tests

Here the functions' behaviour with other modules is tested. In this repository each integration function is in the correspondent folder. Then the integration with a single class of the target, or the minimum amount of classes for a functionality, is tested in each separated file.

System tests

Those tests will use every component of the system, comprising multiple classes. Those tests include expected use cases and unexpected or stress tests.

Adding new functionalities and tests required

When adding new functions to an existing module, add unit tests in the correspondent file in the same order of the module, if a new module is created a new file should be created. When a bug is solved created the respective regression test to ensure that it will not return. Proceed similarly with integration tests. Creating a new file if a functionality completely different from the others is created e.g. support for a new layer. System tests are added if support for a new library is added.

Updating tests

If new functionalities are changed or removed the tests are expected to reflect that, generally the ordering is unit tests -> integration tests-> system tests. Also, unit tests that change the dependencies should be checked, since this system is fairly small the internal dependencies are not always mocked.

references: https://jrsmith3.github.io/python-testing-style-guidelines.html