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Merge pull request #561 from ModECI/development
To v0.4.13; restructure workflows, use pyproject.toml for dependencies
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‎.github/workflows/ci.yml‎

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pull_request:
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push:
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release:
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types:
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- published
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types: [published]
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concurrency:
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group: ${{ github.workflow }}-${{ github.event.pull_request.number || github.ref }}
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cancel-in-progress: true
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jobs:
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pre-commit:
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name: Format
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runs-on: ubuntu-latest
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steps:
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- uses: actions/checkout@v4
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- uses: actions/setup-python@v5
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with:
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python-version: "3.10"
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- uses: pre-commit/action@v3.0.1
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with:
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extra_args: --hook-stage manual --all-files
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checks:
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name: Check Python ${{ matrix.python-version }} on ${{ matrix.runs-on }}
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runs-on: ${{ matrix.runs-on }}
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- uses: actions/checkout@v4
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- uses: actions/setup-python@v5
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with:
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python-version: '3.10'
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- uses: pre-commit/action@v3.0.1
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with:
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extra_args: --hook-stage manual --all-files
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tests:
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name: ${{ matrix.test-suite }}, py ${{ matrix.python-version }}, ${{ matrix.os }}
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runs-on: ${{ matrix.os }}
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strategy:
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fail-fast: false
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matrix:
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python-version: [ "3.9", "3.11", "3.12"]
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runs-on: [ubuntu-latest, macos-latest, windows-latest]
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python-version: ['3.10', '3.13']
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os: [ubuntu-latest, macos-latest, windows-latest]
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test-suite: [core, actr, pytorch, neuroml, tensorflow, psyneulink]
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exclude:
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- runs-on: macos-latest
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python-version: "3.9"
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- os: windows-latest
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test-suite: tensorflow
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steps:
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- uses: actions/checkout@v4
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- uses: actions/setup-python@v5
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with:
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python-version: ${{ matrix.python-version }}
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# Rely on version of modelspec from PyPI as set by setup.cfg...
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#- name: Install modelspec
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# run: python -m pip install git+https://github.com/ModECI/modelspec.git@main
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# Rely on version of NeuroMLlite from PyPI as set by setup.py...
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# - name: Install NeuroMLlite
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# run: python -m pip install NeuroMLlite>=0.5.0
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# Rely on version of PsyNeuLink from PyPI as set by setup.py...
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#- name: Install specific PsyNeuLink branch
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# run: python -m pip install git+https://github.com/ModECI/PsyNeuLink@devel
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- name: Install HDF5 for pytables on macos-14/latest
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if: ${{ matrix.runs-on == 'macos-latest' }}
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run: |
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brew install hdf5
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- name: Install core package
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run: |
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pip install 'numpy<2.0.0' # due to lingering issues with other modules & numpy v2...
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python -m pip install .[dev]
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- name: Version info for installed packages
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run: |
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pip list
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- name: Test core package
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run: |
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python -m pytest -m coremdf tests/
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- name: Install most optional dependencies
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run: |
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python -m pip install .[optional]
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- name: Version info for optional installed packages
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run: |
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pip list
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- name: Install graphviz
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uses: ts-graphviz/setup-graphviz@v2
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with:
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# Skip to run brew update command on macOS.
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macos-skip-brew-update: 'true' # default false
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- name: Test interface ACT-R
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run: |
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python -m pytest -v -m "actr" tests/
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- name: Test interface PyTorch
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run: |
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python -m pytest -v -m "pytorch" tests/
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- name: Test interface NeuroML
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run: |
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python -m pip install .[neuroml]
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python -m pytest -v -m "neuroml" tests/
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- name: Test interface TensorFlow
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run: |
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python -m pip install .[tensorflow]
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dot -V
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python -m pytest -v -m "tensorflow" tests/
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- name: Test interface PsyNeuLink
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run: |
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python -m pip install .[psyneulink]
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python -m pytest -v -m "psyneulink" tests/
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- name: Build Documentation
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run: |
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cd docs
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python generate.py
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cd sphinx
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make clean
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make html
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- name: Final version info for optional installed packages
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run: |
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pip list
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- uses: actions/checkout@v4
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- name: Setup Python
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uses: actions/setup-python@v5
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with:
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python-version: ${{ matrix.python-version }}
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- name: Install uv and set the python version
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uses: astral-sh/setup-uv@v6
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with:
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python-version: ${{ matrix.python-version }}
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- name: Install HDF5 for pytables on macos-14/latest
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if: ${{ matrix.runs-on == 'macos-latest' }}
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run: |
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brew install hdf5
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- name: Install graphviz
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uses: ts-graphviz/setup-graphviz@v2
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with:
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# Skip to run brew update command on macOS.
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macos-skip-brew-update: 'true' # default false
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- name: Install extras for ${{ matrix.test-suite }}
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shell: bash
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run: |
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uv sync --dev
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case "${{ matrix.test-suite }}" in
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pytorch) uv sync --extra "optional" --dev ;;
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neuroml) uv sync --extra "neuroml" --dev ;;
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tensorflow) uv sync --extra "tensorflow" --dev ;;
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psyneulink) uv sync --extra "psyneulink" --dev ;;
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esac
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- name: Run ${{ matrix.test-suite }} tests
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shell: bash
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run: |
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MARKER="${{ matrix.test-suite }}"
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if [ "${{ matrix.test-suite }}" = "core" ]; then
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MARKER="coremdf"
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fi
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uv run pytest -v -m "${MARKER}" tests/
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docs:
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name: Build Documentation
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needs: tests
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runs-on: ubuntu-latest
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steps:
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- uses: actions/checkout@v4
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- uses: actions/setup-python@v5
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with:
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python-version: '3.10'
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- name: Install uv and set the python version
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uses: astral-sh/setup-uv@v6
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with:
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python-version: '3.10'
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- name: Generate and Build Docs
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run: |
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uv sync --group docs
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cd docs
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uv run generate.py
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cd sphinx
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uv run make clean
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uv run make html
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dist:
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runs-on: ubuntu-latest
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steps:
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- uses: actions/checkout@v4
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- name: Build sdist and wheel
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run: pipx run --spec build pyproject-build
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- uses: actions/upload-artifact@v4
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with:
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path: dist
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- uses: pypa/gh-action-pypi-publish@v1.4.1
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if: github.event_name == 'release' && github.event.action == 'published'
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with:
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user: __token__
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# Remember to generate this and set it in "GitHub Secrets"
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password: ${{ secrets.pypi_password }}
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- uses: actions/checkout@v4
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- name: Build sdist & wheel
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run: pipx run --spec build pyproject-build
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- uses: actions/upload-artifact@v4
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with:
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path: dist
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- uses: pypa/gh-action-pypi-publish@v1.4.1
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if: github.event_name == 'release' && github.event.action == 'published'
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with:
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user: __token__
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password: ${{ secrets.pypi_password }}
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all-passed:
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runs-on: ubuntu-latest
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needs:
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- pre-commit
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- tests
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- docs
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- dist
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if: always()
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steps:
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- name: Fail if any dependency did not succeed
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run: |
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echo "Upstream results: ${{ join(needs.*.result, ', ') }}"
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# If any result is failure/cancelled/skipped, fail this job
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if [[ "${{ contains(needs.*.result, 'failure') || contains(needs.*.result, 'cancelled') || contains(needs.*.result, 'skipped') }}" == "true" ]]; then
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exit 1
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fi

‎.github/workflows/ci_test_all.yml‎

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on:
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push:
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branches: [ main, development, experimental, test*, nml* ]
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branches: [ main, development, experimental, test*, nml*, version* ]
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pull_request:
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branches: [ main, development, experimental, test*, nml* ]
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branches: [ main, development, experimental, test*, nml*, version* ]
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jobs:
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strategy:
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fail-fast: false
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matrix:
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python-version: [ "3.10"]
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python-version: [ "3.13"]
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runs-on: [ubuntu-latest]
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steps:
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- name: Version info for installed packages
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- name: Final version info for optional installed packages
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pip list

‎README.md‎

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**Note: MDF is still in development! See the [open issues related to the specification](https://github.com/ModECI/MDF/issues?q=is%3Aissue+is%3Aopen+label%3Aspecification) or go [here](http://modeci.org/#contactPage) to get in contact regarding MDF.**
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*The MDF format was first proposed following a meeting organised at Princeton in July 2019 by Russ Poldrack of the Center for Reproducible Neuroscience (CRN) at Stanford and the [Brain Imaging Data Standard (BIDS)](https://bids.neuroimaging.io/) initiative. For more on the previous work in this area, see [here](https://github.com/OpenSourceBrain/PsyNeuLinkShowcase/tree/master/BIDS-MDF).*
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## Paper introducing MDF
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The background to the ModECI project, the motivation for developing the Model Description Format, and the initial Python implementation of the language have been described in a NeuroView article in the Neuron journal:
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*<b>Integrating model development across computational neuroscience, cognitive science, and machine learning</b>*
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Padraig Gleeson, Sharon Crook, David Turner, Katherine Mantel, Mayank Raunak, Ted Willke and Jonathan D. Cohen, April 25, 2023 DOI: [https://doi.org/10.1016/j.neuron.2023.03.037](https://doi.org/10.1016/j.neuron.2023.03.037)
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## Overview
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‎docs/MDF_function_specifications.json‎

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"expression_string": "onnx_ops.lppool(X, auto_pad, kernel_shape, p, pads, strides)"
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},
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"onnx::MatMul": {
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"description": "\nMatrix product that behaves like numpy.matmul: https://docs.scipy.org/doc/numpy-1.13.0/reference/generated/numpy.matmul.html\n",
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"description": "\nMatrix product that behaves like [numpy.matmul](https://numpy.org/doc/stable/reference/generated/numpy.matmul.html).\n",
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"arguments": [
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"A",
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"B"
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],
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"expression_string": "onnx_ops.matmul(A, B)"
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},
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"onnx::MatMulInteger": {
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"description": "\nMatrix product that behaves like numpy.matmul: https://docs.scipy.org/doc/numpy-1.13.0/reference/generated/numpy.matmul.html.\nThe production MUST never overflow. The accumulation may overflow if and only if in 32 bits.\n",
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"description": "\nMatrix product that behaves like [numpy.matmul](https://numpy.org/doc/stable/reference/generated/numpy.matmul.html).\nThe production MUST never overflow. The accumulation may overflow if and only if in 32 bits.\n",
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"arguments": [
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"expression_string": "onnx_ops.max(data_0)"
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},
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"onnx::MaxPool": {
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"description": "\n MaxPool consumes an input tensor X and applies max pooling across\n the tensor according to kernel sizes, stride sizes, and pad lengths.\n max pooling consisting of computing the max on all values of a\n subset of the input tensor according to the kernel size and downsampling the\n data into the output tensor Y for further processing. The output spatial shape is calculated differently\n depending on whether explicit padding is used, where pads is employed, or auto padding is used, where auto_pad is utilized.\n With explicit padding (https://pytorch.org/docs/stable/generated/torch.nn.MaxPool2d.html?highlight=maxpool#torch.nn.MaxPool2d):\n ```\n output_spatial_shape[i] = floor((input_spatial_shape[i] + pad_shape[i] - dilation[i] * (kernel_shape[i] - 1) - 1) / strides_spatial_shape[i] + 1)\n ```\n or\n ```\n output_spatial_shape[i] = ceil((input_spatial_shape[i] + pad_shape[i] - dilation[i] * (kernel_shape[i] - 1) - 1) / strides_spatial_shape[i] + 1)\n ```\n if ceil_mode is enabled. `pad_shape[i]` is the sum of pads along axis `i`. Sliding windows that would start in the right padded region are ignored.\n\n `auto_pad` is a DEPRECATED attribute. If you are using them currently, the output spatial shape will be following when ceil_mode is enabled:\n ```\n VALID: output_spatial_shape[i] = ceil((input_spatial_shape[i] - ((kernel_spatial_shape[i] - 1) * dilations[i] + 1) + 1) / strides_spatial_shape[i])\n SAME_UPPER or SAME_LOWER: output_spatial_shape[i] = ceil(input_spatial_shape[i] / strides_spatial_shape[i])\n ```\n or when ceil_mode is disabled (https://www.tensorflow.org/api_docs/python/tf/keras/layers/AveragePooling2D):\n ```\n VALID: output_spatial_shape[i] = floor((input_spatial_shape[i] - ((kernel_spatial_shape[i] - 1) * dilations[i] + 1)) / strides_spatial_shape[i]) + 1\n SAME_UPPER or SAME_LOWER: output_spatial_shape[i] = floor((input_spatial_shape[i] - 1) / strides_spatial_shape[i]) + 1\n ```\n And pad shape will be following if `SAME_UPPER` or `SAME_LOWER`:\n ```\n pad_shape[i] = (output_spatial_shape[i] - 1) * strides_spatial_shape[i] + ((kernel_spatial_shape[i] - 1) * dilations[i] + 1) - input_spatial_shape[i]\n ```\n The output of each pooling window is maximum number of elements exclude pad. \n ",
714+
"description": "\n MaxPool consumes an input tensor X and applies max pooling across\n the tensor according to kernel sizes, stride sizes, and pad lengths.\n max pooling consisting of computing the max on all values of a\n subset of the input tensor according to the kernel size and downsampling the\n data into the output tensor Y for further processing. The output spatial shape is calculated differently\n depending on whether explicit padding is used, where pads is employed, or auto padding is used, where auto_pad is utilized.\n With explicit padding (https://pytorch.org/docs/stable/generated/torch.nn.MaxPool2d.html?highlight=maxpool#torch.nn.MaxPool2d):\n ```\n output_spatial_shape[i] = floor((input_spatial_shape[i] + pad_shape[i] - dilation[i] * (kernel_shape[i] - 1) - 1) / strides_spatial_shape[i] + 1)\n ```\n or\n ```\n output_spatial_shape[i] = ceil((input_spatial_shape[i] + pad_shape[i] - dilation[i] * (kernel_shape[i] - 1) - 1) / strides_spatial_shape[i] + 1)\n ```\n if ceil_mode is enabled. `pad_shape[i]` is the sum of pads along axis `i`.\n\n `auto_pad` is a DEPRECATED attribute. If you are using them currently, the output spatial shape will be following when ceil_mode is enabled:\n ```\n VALID: output_spatial_shape[i] = ceil((input_spatial_shape[i] - ((kernel_spatial_shape[i] - 1) * dilations[i] + 1) + 1) / strides_spatial_shape[i])\n SAME_UPPER or SAME_LOWER: output_spatial_shape[i] = ceil(input_spatial_shape[i] / strides_spatial_shape[i])\n ```\n or when ceil_mode is disabled (https://www.tensorflow.org/api_docs/python/tf/keras/layers/AveragePooling2D):\n ```\n VALID: output_spatial_shape[i] = floor((input_spatial_shape[i] - ((kernel_spatial_shape[i] - 1) * dilations[i] + 1)) / strides_spatial_shape[i]) + 1\n SAME_UPPER or SAME_LOWER: output_spatial_shape[i] = floor((input_spatial_shape[i] - 1) / strides_spatial_shape[i]) + 1\n ```\n And pad shape will be following if `SAME_UPPER` or `SAME_LOWER`:\n ```\n pad_shape[i] = (output_spatial_shape[i] - 1) * strides_spatial_shape[i] + ((kernel_spatial_shape[i] - 1) * dilations[i] + 1) - input_spatial_shape[i]\n ```\n The output of each pooling window is maximum number of elements exclude pad. \n ",
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"arguments": [
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"X"
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],
@@ -900,7 +900,7 @@
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"expression_string": "onnx_ops.qlinearconv(x, x_scale, x_zero_point, w, w_scale, w_zero_point, y_scale, y_zero_point, B, auto_pad, dilations, group, kernel_shape, pads, strides)"
901901
},
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"onnx::QLinearMatMul": {
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"description": "\nMatrix product that behaves like numpy.matmul: https://docs.scipy.org/doc/numpy-1.13.0/reference/generated/numpy.matmul.html.\nIt consumes two quantized input tensors, their scales and zero points, scale and zero point of output,\nand computes the quantized output. The quantization formula is y = saturate((x / y_scale) + y_zero_point).\nFor (x / y_scale), it is rounding to nearest ties to even. Refer to https://en.wikipedia.org/wiki/Rounding for details.\nScale and zero point must have same shape. They must be either scalar (per tensor) or N-D tensor\n(per row for 'a' and per column for 'b'). Scalar refers to per tensor quantization whereas N-D refers to per row\nor per column quantization. If the input is 2D of shape [M, K] then zero point and scale tensor may be\nan M element vector [v_1, v_2, ..., v_M] for per row quantization and K element vector of shape [v_1, v_2, ..., v_K]\nfor per column quantization. If the input is N-D tensor with shape [D1, D2, M, K] then zero point and scale tensor may\nhave shape [D1, D2, M, 1] for per row quantization and shape [D1, D2, 1, K] for per column quantization.\nProduction must never overflow, and accumulation may overflow if and only if in 32 bits.\n",
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"description": "\nMatrix product that behaves like [numpy.matmul](https://numpy.org/doc/stable/reference/generated/numpy.matmul.html).\nIt consumes two quantized input tensors, their scales and zero points, scale and zero point of output,\nand computes the quantized output. The quantization formula is y = saturate((x / y_scale) + y_zero_point).\nFor (x / y_scale), it is rounding to nearest ties to even. Refer to https://en.wikipedia.org/wiki/Rounding for details.\nScale and zero point must have same shape. They must be either scalar (per tensor) or N-D tensor\n(per row for 'a' and per column for 'b'). Scalar refers to per tensor quantization whereas N-D refers to per row\nor per column quantization. If the input is 2D of shape [M, K] then zero point and scale tensor may be\nan M element vector [v_1, v_2, ..., v_M] for per row quantization and K element vector of shape [v_1, v_2, ..., v_K]\nfor per column quantization. If the input is N-D tensor with shape [D1, D2, M, K] then zero point and scale tensor may\nhave shape [D1, D2, M, 1] for per row quantization and shape [D1, D2, 1, K] for per column quantization.\nProduction must never overflow, and accumulation may overflow if and only if in 32 bits.\n",
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"arguments": [
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‎docs/MDF_function_specifications.md‎

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@@ -1685,7 +1685,7 @@ Python version: `onnx_ops.lppool(X, auto_pad, kernel_shape, p, pads, strides)`
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## MatMul
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<p><i>
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Matrix product that behaves like numpy.matmul: https://docs.scipy.org/doc/numpy-1.13.0/reference/generated/numpy.matmul.html
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Matrix product that behaves like [numpy.matmul](https://numpy.org/doc/stable/reference/generated/numpy.matmul.html).
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</i></p>
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## MatMulInteger
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<p><i>
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Matrix product that behaves like numpy.matmul: https://docs.scipy.org/doc/numpy-1.13.0/reference/generated/numpy.matmul.html.
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Matrix product that behaves like [numpy.matmul](https://numpy.org/doc/stable/reference/generated/numpy.matmul.html).
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```
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if ceil_mode is enabled. `pad_shape[i]` is the sum of pads along axis `i`. Sliding windows that would start in the right padded region are ignored.
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if ceil_mode is enabled. `pad_shape[i]` is the sum of pads along axis `i`.
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`auto_pad` is a DEPRECATED attribute. If you are using them currently, the output spatial shape will be following when ceil_mode is enabled:
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```
@@ -2237,7 +2237,7 @@ Python version: `onnx_ops.qlinearconv(x, x_scale, x_zero_point, w, w_scale, w_ze
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## QLinearMatMul
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<p><i>
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Matrix product that behaves like numpy.matmul: https://docs.scipy.org/doc/numpy-1.13.0/reference/generated/numpy.matmul.html.
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Matrix product that behaves like [numpy.matmul](https://numpy.org/doc/stable/reference/generated/numpy.matmul.html).
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It consumes two quantized input tensors, their scales and zero points, scale and zero point of output,
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and computes the quantized output. The quantization formula is y = saturate((x / y_scale) + y_zero_point).
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For (x / y_scale), it is rounding to nearest ties to even. Refer to https://en.wikipedia.org/wiki/Rounding for details.

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