ci.yml

name: CI

on:
  push:
    branches:
      - main
  pull_request:
    branches:
      - main
  schedule:
    - cron: '4 4 * * *'  # This schedule runs the nightly job every night at 4:04AM


jobs:
  lint_py39_torch_release:
    runs-on: ubuntu-latest
    steps:
      - name: Checkout
        uses: actions/checkout@v2
      - name: Set up Python
        uses: actions/setup-python@v2
        with:
          python-version: 3.9
      - name: Install dependencies
        run: |
          python -m pip install --upgrade pip
          pip install flake8 black isort
          ./scripts/install_via_pip.sh
      - name: Lint with flake8
        run: flake8 --config ./.github/workflows/flake8_config.ini
      - name: Lint with black
        run: black --check --diff --color .
      - name: Check import order with isort
        run: isort -v -l 88 -o opacus --lines-after-imports 2 -m 3 --trailing-comma --check-only .

  lint_torch_release:
    strategy:
      matrix:
        python_version: ["3.9"]
      fail-fast: false
    uses: pytorch/test-infra/.github/workflows/linux_job.yml@main
    with:
      runner: linux.2xlarge
      repository: pytorch/opacus
      timeout: 60
      script: |
        echo '::group::Create conda env with correct Python version'
        set -x
        PYTHON_VERSION="${{ matrix.python_version }}"
        conda create --yes --quiet -n test python="${PYTHON_VERSION}"
        conda activate test
        python --version | grep "${PYTHON_VERSION}"
        echo '::endgroup::'

        echo '::group::Install dependencies via pip, including extra deps.'
        apt-get install apt
        python -m pip install --upgrade pip
        pip install flake8 black isort
        ./scripts/install_via_pip.sh
        echo '::endgroup::'

        echo '::group::Lint with flake8'
        flake8 --config ./.github/workflows/flake8_config.ini
        echo '::endgroup::'

        echo '::group::Lint with black'
        black --check --diff --color .
        echo '::endgroup::'

        echo '::group::Check import order with isort'
        isort -v -l 88 -o opacus --lines-after-imports 2 -m 3 --trailing-comma --check-only .
        echo '::endgroup::'

  unittest_py38_torch_release:
    runs-on: ubuntu-latest
    steps:
      - name: Checkout
        uses: actions/checkout@v2
      - name: Set up Python
        uses: actions/setup-python@v2
        with:
          python-version: 3.8
      - name: Install dependencies
        run: |
          python -m pip install --upgrade pip
          pip install pytest coverage coveralls
          ./scripts/install_via_pip.sh
      - name: Run unit tests
        run: |
          mkdir unittest-py38-release-reports
          coverage run -m pytest --doctest-modules -p conftest --junitxml=unittest-py38-release-reports/junit.xml opacus
          coverage report -i -m
      - name: Store test results
        uses: actions/upload-artifact@v2
        with:
          name: unittest-py38-release-reports
          path: unittest-py38-release-reports

  unittest_py39_torch_release:
    runs-on: ubuntu-latest
    steps:
      - name: Checkout
        uses: actions/checkout@v2
      - name: Set up Python
        uses: actions/setup-python@v2
        with:
          python-version: 3.9
      - name: Install dependencies
        run: |
          python -m pip install --upgrade pip
          pip install pytest coverage coveralls
          ./scripts/install_via_pip.sh
      - name: Run unit tests
        run: |
          mkdir unittest-py39-release-reports
          coverage run -m pytest --doctest-modules -p conftest --junitxml=unittest-py39-release-reports/junit.xml opacus
          coverage report -i -m
      - name: Store test results
        uses: actions/upload-artifact@v2
        with:
          name: unittest-py39-release-reports
          path: unittest-py39-release-reports

  unittest_py39_torch_nightly:
    runs-on: ubuntu-latest
    if: ${{ github.event_name == 'schedule' }}
    steps:
      - name: Checkout
        uses: actions/checkout@v2
      - name: Set up Python
        uses: actions/setup-python@v2
        with:
          python-version: 3.9
      - name: Install dependencies
        run: |
          python -m pip install --upgrade pip
          pip install pytest coverage coveralls
          ./scripts/install_via_pip.sh -n
      - name: Run unit tests
        run: |
          mkdir unittest-py39-nightly-reports
          coverage run -m pytest --doctest-modules -p conftest --junitxml=unittest-py39-nightly-reports/junit.xml opacus
          coverage report -i -m
      - name: Store test results
        uses: actions/upload-artifact@v2
        with:
          name: unittest-py39-nightly-reports
          path: unittest-py39-nightly-reports

  prv_accountant_values:
    runs-on: ubuntu-latest
    steps:
      - name: Checkout
        uses: actions/checkout@v2
      - name: Set up Python
        uses: actions/setup-python@v2
        with:
          python-version: 3.9
      - name: Install dependencies
        run: |
          python -m pip install --upgrade pip
          ./scripts/install_via_pip.sh
      - name: Run prv accountant unit tests
        run: |
          python -m unittest opacus.tests.prv_accountant

  integrationtest_py39_torch_release_cpu:
    runs-on: ubuntu-latest
    steps:
      - name: Checkout
        uses: actions/checkout@v2
      - name: Set up Python
        uses: actions/setup-python@v2
        with:
          python-version: 3.9
      - name: Install dependencies
        run: |
          python -m pip install --upgrade pip
          pip install pytest coverage coveralls
          ./scripts/install_via_pip.sh
      - name: Run MNIST integration test (CPU)
        run: |
          mkdir -p runs/mnist/data
          mkdir -p runs/mnist/test-reports
          coverage run examples/mnist.py --lr 0.25 --sigma 0.7 -c 1.5 --batch-size 64 --epochs 1 --data-root runs/mnist/data --n-runs 1 --device cpu
          python -c "import torch; accuracy = torch.load('run_results_mnist_0.25_0.7_1.5_64_1.pt'); exit(0) if (accuracy[0]>0.78 and accuracy[0]<0.95) else exit(1)"
          coverage report -i -m
      - name: Store test results
        uses: actions/upload-artifact@v2
        with:
          name: mnist-cpu-reports
          path: runs/mnist/test-reports

  integrationtest_py39_torch_release_cuda:
    runs-on: ubuntu-latest
    needs: [unittest_py39_torch_release]
    container:
      # https://hub.docker.com/r/nvidia/cuda
      image: nvidia/cuda:12.3.1-base-ubuntu22.04
      options: --gpus all
    env:
      TZ: 'UTC'
    steps:
      - name: Checkout
        uses: actions/checkout@v2

      - name: Set up Python
        uses: actions/setup-python@v2
        with:
          python-version: 3.9

      - name: Install dependencies
        run: |
          python -m pip install --upgrade pip
          pip install pytest coverage coveralls
          ./scripts/install_via_pip.sh

      - name: Install CUDA toolkit and cuDNN
        run: |
          apt-get update
          apt-get install -y --no-install-recommends \
            cuda-toolkit-11-1 \
            libcudnn8=8.1.1.33-1+cuda11.1 \
            libcudnn8-dev=8.1.1.33-1+cuda11.1

      - name: Run MNIST integration test (CUDA)
        run: |
          mkdir -p runs/mnist/data
          mkdir -p runs/mnist/test-reports
          python examples/mnist.py --lr 0.25 --sigma 0.7 -c 1.5 --batch-size 64 --epochs 1 --data-root runs/mnist/data --n-runs 1 --device cuda
          python -c "import torch; accuracy = torch.load('run_results_mnist_0.25_0.7_1.5_64_1.pt'); exit(0) if (accuracy[0]>0.78 and accuracy[0]<0.95) else exit(1)"

      - name: Store MNIST test results
        uses: actions/upload-artifact@v2
        with:
          name: mnist-gpu-reports
          path: runs/mnist/test-reports

      - name: Run CIFAR10 integration test (CUDA)
        run: |
          mkdir -p runs/cifar10/data
          mkdir -p runs/cifar10/logs
          mkdir -p runs/cifar10/test-reports
          pip install tensorboard
          python examples/cifar10.py --lr 0.1 --sigma 1.5 -c 10 --batch-size 2000 --epochs 10 --data-root runs/cifar10/data --log-dir runs/cifar10/logs --device cuda
          python -c "import torch; model = torch.load('model_best.pth.tar'); exit(0) if (model['best_acc1']>0.4 and model['best_acc1']<0.49) else exit(1)"
          python examples/cifar10.py --lr 0.1 --sigma 1.5 -c 10 --batch-size 2000 --epochs 10 --data-root runs/cifar10/data --log-dir runs/cifar10/logs --device cuda --grad_sample_mode no_op
          python -c "import torch; model = torch.load('model_best.pth.tar'); exit(0) if (model['best_acc1']>0.4 and model['best_acc1']<0.49) else exit(1)"

      - name: Store CIFAR10 test results
        uses: actions/upload-artifact@v2
        with:
          name: cifar10-gpu-reports
          path: runs/cifar10/test-reports

      - name: Run IMDb integration test (CUDA)
        run: |
          mkdir -p runs/imdb/data
          mkdir -p runs/imdb/test-reports
          pip install --user datasets transformers
          python examples/imdb.py --lr 0.02 --sigma 1.0 -c 1.0 --batch-size 64 --max-sequence-length 256 --epochs 2 --data-root runs/imdb/data --device cuda
          python -c "import torch; accuracy = torch.load('run_results_imdb_classification.pt'); exit(0) if (accuracy>0.54 and accuracy<0.66) else exit(1)"

      - name: Store IMDb test results
        uses: actions/upload-artifact@v2
        with:
          name: imdb-gpu-reports
          path: runs/imdb/test-reports

      - name: Run charlstm integration test (CUDA)
        run: |
          mkdir -p runs/charlstm/data
          wget https://download.pytorch.org/tutorial/data.zip -O runs/charlstm/data/data.zip
          unzip runs/charlstm/data/data.zip -d runs/charlstm/data
          rm runs/charlstm/data/data.zip
          mkdir -p runs/charlstm/test-reports
          pip install scikit-learn
          python examples/char-lstm-classification.py --epochs=20 --learning-rate=2.0 --hidden-size=128 --delta=8e-5 --batch-size 400 --n-layers=1 --sigma=1.0 --max-per-sample-grad-norm=1.5 --data-root="runs/charlstm/data/data/names/" --device cuda --test-every 5
          python -c "import torch; accuracy = torch.load('run_results_chr_lstm_classification.pt'); exit(0) if (accuracy>0.60 and accuracy<0.80) else exit(1)"

      - name: Store test results
        uses: actions/upload-artifact@v2
        with:
          name: charlstm-gpu-reports
          path: runs/charlstm/test-reports

  micro_benchmarks_py39_torch_release_cuda:
    runs-on: ubuntu-latest
    needs: [integrationtest_py39_torch_release_cuda]
    container:
      # https://hub.docker.com/r/nvidia/cuda
      image: nvidia/cuda:12.3.1-base-ubuntu22.04
      options: --gpus all
    env:
      TZ: 'UTC'
    steps:
      - name: Checkout
        uses: actions/checkout@v2

      - name: Set up Python
        uses: actions/setup-python@v2
        with:
          python-version: 3.9

      - name: Install dependencies
        run: |
          python -m pip install --upgrade pip
          pip install pytest coverage coveralls
          ./scripts/install_via_pip.sh

      - name: Install CUDA toolkit and cuDNN
        run: |
          apt-get update
          apt-get install -y --no-install-recommends \
            cuda-toolkit-11-1 \
            libcudnn8=8.1.1.33-1+cuda11.1 \
            libcudnn8-dev=8.1.1.33-1+cuda11.1

      - name: Run benchmark integration tests (CUDA)
        run: |
          mkdir -p benchmarks/results/raw
          python benchmarks/run_benchmarks.py --batch_size 16 --layers "groupnorm instancenorm layernorm" --config_file ./benchmarks/config.json --root ./benchmarks/results/raw/ --cont
          IFS=$' ';layers=("groupnorm" "instancenorm" "layernorm"); rm -rf /tmp/report_layers; mkdir -p /tmp/report_layers; IFS=$'\n'; files=`( echo "${layers[*]}" ) | sed 's/.*/.\/benchmarks\/results\/raw\/&*/'`
          cp -v ${files[@]} /tmp/report_layers
          report_id=`IFS=$'-'; echo "${layers[*]}"`
          python benchmarks/generate_report.py --path-to-results /tmp/report_layers --save-path benchmarks/results/report-${report_id}.csv --format csv
          python benchmarks/generate_report.py --path-to-results /tmp/report_layers --save-path benchmarks/results/report-${report_id}.pkl --format pkl
          python benchmarks/check_threshold.py --report-path "./benchmarks/results/report-"$report_id".pkl" --metric runtime --threshold 3.0 --column "hooks/baseline"
          python benchmarks/check_threshold.py --report-path "./benchmarks/results/report-"$report_id".pkl" --metric memory --threshold 1.6 --column "hooks/baseline"

      - name: Store artifacts
        uses: actions/upload-artifact@v2
        with:
          name: benchmarks-reports
          path: benchmarks/results/

  unittest_multi_gpu:
    runs-on: ubuntu-latest
    container:
      # https://hub.docker.com/r/nvidia/cuda
      image: nvidia/cuda:12.3.1-base-ubuntu22.04
      options: --gpus all
    env:
      TZ: 'UTC'
    steps:
      - name: Checkout
        uses: actions/checkout@v2

      - name: Set up Python
        uses: actions/setup-python@v2
        with:
          python-version: 3.9

      - name: Install dependencies
        run: |
          python -m pip install --upgrade pip
          pip install pytest coverage coveralls
          ./scripts/install_via_pip.sh

      - name: Run multi-GPU unit tests
        run: |
          nvidia-smi
          mkdir unittest-multigpu-reports
          coverage run -m unittest opacus.tests.multigpu_gradcheck.GradientComputationTest.test_gradient_correct
          coverage report -i -m

      - name: Store test results
        uses: actions/upload-artifact@v2
        with:
          name: unittest-multigpu-reports
          path: unittest-multigpu-reports

  finish_coveralls_parallel:
    runs-on: ubuntu-latest
    steps:
      - name: Checkout
        uses: actions/checkout@v2
      - name: Finish Coveralls Parallel
        run: |
          python -m pip install --upgrade pip
          pip install coveralls --user
          coveralls --finish
      - name: coveralls upload
        timeout-minutes: 5
        run: |
          python -m pip install --upgrade pip
          pip install coveralls --user
          COVERALLS_PARALLEL=true COVERALLS_FLAG_NAME="${GITHUB_JOB}" coveralls