translation/OpenNMT-py/.github/workflows/push.yml

name: Lint & Tests

on: [push, pull_request]

jobs:
  lint-and-tests:

    runs-on: ubuntu-latest
    strategy:
      matrix:
        python-version: [3.6] # build only for 3.6 for now

    steps:
    - uses: actions/checkout@v2
    - name: Set up Python ${{ matrix.python-version }}
      uses: actions/setup-python@v2
      with:
        python-version: ${{ matrix.python-version }}
    - name: Install dependencies
      run: |
        python -m pip install --upgrade pip
        pip install --upgrade setuptools
        pip install -e .
        pip install -r requirements.opt.txt
        pip install flake8
        if [ -f requirements.txt ]; then pip install -r requirements.txt; fi
    - name: Lint with flake8
      run: |
        flake8 .
    - name: Unit tests
      run: |
        python -m unittest discover
    - name: Test vocabulary build
      run: |
        python onmt/bin/build_vocab.py \
          -config data/data.yaml \
          -save_data /tmp/onmt \
          -n_sample 5000 \
          -src_vocab /tmp/onmt.vocab.src \
          -tgt_vocab /tmp/onmt.vocab.tgt \
          && rm -rf /tmp/sample
    - name: Test vocabulary build with features
      run: |
        python onmt/bin/build_vocab.py \
          -config data/features_data.yaml \
          -save_data /tmp/onmt_feat \
          -src_vocab /tmp/onmt_feat.vocab.src \
          -tgt_vocab /tmp/onmt_feat.vocab.tgt \
          -src_feats_vocab '{"feat0": "/tmp/onmt_feat.vocab.feat0"}' \
          -n_sample -1 \
          && rm -rf /tmp/sample
    - name: Test field/transform dump
      run: |
        # The dumped fields are used later when testing tools
        python train.py \
          -config data/data.yaml \
          -save_data /tmp/onmt.train.check \
          -dump_fields \
          -dump_transforms \
          -n_sample 30 \
          -src_vocab /tmp/onmt.vocab.src \
          -tgt_vocab /tmp/onmt.vocab.tgt \
          -src_vocab_size 1000 \
          -tgt_vocab_size 1000
    - name: Test RNN training
      run: |
        python train.py \
          -config data/data.yaml \
          -src_vocab /tmp/onmt.vocab.src \
          -tgt_vocab /tmp/onmt.vocab.tgt \
          -src_vocab_size 1000 \
          -tgt_vocab_size 1000 \
          -rnn_size 2 \
          -batch_size 10 \
          -word_vec_size 5 \
          -report_every 5\
          -rnn_size 10 \
          -train_steps 10
    - name: Test RNN training with copy
      run: |
        python train.py \
          -config data/data.yaml \
          -src_vocab /tmp/onmt.vocab.src \
          -tgt_vocab /tmp/onmt.vocab.tgt \
          -src_vocab_size 1000 \
          -tgt_vocab_size 1000 \
          -rnn_size 2 \
          -batch_size 10 \
          -word_vec_size 5 \
          -report_every 5 \
          -rnn_size 10 \
          -train_steps 10 \
          -copy_attn
    - name: Test RNN training with coverage
      run: |
        python train.py \
          -config data/data.yaml \
          -src_vocab /tmp/onmt.vocab.src \
          -tgt_vocab /tmp/onmt.vocab.tgt \
          -src_vocab_size 1000 \
          -tgt_vocab_size 1000 \
          -rnn_size 2 -batch_size 10 \
          -word_vec_size 5 -report_every 5        \
          -coverage_attn true -lambda_coverage 0.1 \
          -rnn_size 10 -train_steps 10
    - name: Test Transformer training with align
      run: |
        python train.py \
          -config data/align_data.yaml \
          -src_vocab /tmp/onmt.vocab.src \
          -tgt_vocab /tmp/onmt.vocab.tgt \
          -src_vocab_size 1000 \
          -tgt_vocab_size 1000 \
          -max_generator_batches 0 \
          -encoder_type transformer \
          -decoder_type transformer \
          -layers 4 \
          -word_vec_size 16 \
          -rnn_size 16 \
          -heads 2 \
          -transformer_ff 64 \
          -lambda_align 0.05 \
          -alignment_layer 2 \
          -alignment_heads 0 \
          -report_every 5 \
          -train_steps 10
    - name: Test LM training
      run: |
        python train.py \
            -config data/lm_data.yaml \
            -src_vocab /tmp/onmt.vocab.src \
            -tgt_vocab /tmp/onmt.vocab.src \
            -model_task lm \
            -encoder_type transformer_lm \
            -decoder_type transformer_lm \
            -src_vocab_size 1000 \
            -tgt_vocab_size 1000 \
            -dec_layers 2 -batch_size 10 \
            -heads 4 -transformer_ff 64 \
            -word_vec_size 16 -report_every 5 \
            -rnn_size 16 -train_steps 10
    - name: Test LM training with copy
      run: |
        python train.py \
          -config data/lm_data.yaml \
          -src_vocab /tmp/onmt.vocab.src \
          -tgt_vocab /tmp/onmt.vocab.src \
          -model_task lm \
          -encoder_type transformer_lm \
          -decoder_type transformer_lm \
          -src_vocab_size 1000 \
          -tgt_vocab_size 1000 \
          -dec_layers 2 -batch_size 10 \
          -heads 4 -transformer_ff 64 \
          -word_vec_size 16 -report_every 5        \
          -rnn_size 16 -train_steps 10 \
          -copy_attn
    - name: Test Graph neural network training
      run: |
        python train.py \
          -config data/ggnn_data.yaml \
          -src_seq_length 1000 \
          -tgt_seq_length 30 \
          -encoder_type ggnn \
          -layers 2 \
          -decoder_type rnn \
          -rnn_size 256 \
          -learning_rate 0.1 \
          -learning_rate_decay 0.8 \
          -global_attention general \
          -batch_size 32 \
          -word_vec_size 256 \
          -bridge \
          -train_steps 10 \
          -n_edge_types 9 \
          -state_dim 256 \
          -n_steps 10 \
          -n_node 64
    - name: Testing training with features
      run: |
        python onmt/bin/train.py \
          -config data/features_data.yaml \
          -src_vocab /tmp/onmt_feat.vocab.src \
          -tgt_vocab /tmp/onmt_feat.vocab.tgt \
          -src_feats_vocab '{"feat0": "/tmp/onmt_feat.vocab.feat0"}' \
          -src_vocab_size 1000 -tgt_vocab_size 1000 \
          -rnn_size 2 -batch_size 10 \
          -word_vec_size 5 -rnn_size 10 \
          -report_every 5 -train_steps 10 \
          -save_model /tmp/onmt.model \
          -save_checkpoint_steps 10
    - name: Testing translation with features
      run: |
        python translate.py \
            -model /tmp/onmt.model_step_10.pt \
            -src data/data_features/src-test.txt \
            -src_feats "{'feat0': 'data/data_features/src-test.feat0'}" \
            -verbose
    - name: Test RNN translation
      run: |
        head data/src-test.txt > /tmp/src-test.txt
        python translate.py \
          -model onmt/tests/test_model.pt \
          -src /tmp/src-test.txt \
          -verbose
    - name: Test RNN ensemble translation
      run: |
        head data/src-test.txt > /tmp/src-test.txt
        python translate.py \
          -model onmt/tests/test_model.pt \
          onmt/tests/test_model.pt \
          -src /tmp/src-test.txt \
          -verbose
    - name: Test RNN translation with beam search
      run: |
        python translate.py \
          -model onmt/tests/test_model2.pt \
          -src data/morph/src.valid \
          -verbose \
          -batch_size 10 \
          -beam_size 10 \
          -tgt data/morph/tgt.valid \
          -out /tmp/trans
        diff data/morph/tgt.valid /tmp/trans && rm /tmp/trans
    - name: Test RNN translation with random sampling
      run: |
        python translate.py \
          -model onmt/tests/test_model2.pt \
          -src data/morph/src.valid \
          -verbose \
          -batch_size 10 \
          -beam_size 1 \
          -seed 1 \
          -random_sampling_topk "-1" \
          -random_sampling_temp 0.0001 \
          -tgt data/morph/tgt.valid \
          -out /tmp/trans
        diff data/morph/tgt.valid /tmp/trans && rm /tmp/trans
    - name: Test LM generation
      run: |
        head data/src-test.txt > /tmp/src-test.txt
        python translate.py \
          -model onmt/tests/test_model_lm.pt \
          -src data/src-test.txt \
          -verbose
    - name: Test LM generation with beam search
      run: |
        python translate.py \
          -model onmt/tests/test_model_lm.pt \
          -src data/data_lm/src-gen.txt \
          -verbose -batch_size 10 \
          -beam_size 10 \
          -ban_unk_token \
          -out /tmp/gen
        diff data/data_lm/gen-beam-sol.txt /tmp/gen && rm /tmp/gen
    - name: Test LM generation with random sampling
      run: |
        python translate.py -model onmt/tests/test_model_lm.pt \
          -src data/data_lm/src-gen.txt \
          -verbose -batch_size 10 \
          -beam_size 1 \
          -seed 1 \
          -random_sampling_topk -1 \
          -random_sampling_temp 0.0001 \
          -ban_unk_token \
          -out /tmp/gen
        diff data/data_lm/gen-sampling-sol.txt /tmp/gen && rm /tmp/gen
    - name: Test LM generation with random top-k/nucleus sampling
      run: |
        python translate.py -model onmt/tests/test_model_lm.pt \
          -src data/data_lm/src-gen.txt \
          -verbose -batch_size 10 \
          -beam_size 1 \
          -seed 3 \
          -random_sampling_topk -1 \
          -random_sampling_topp 0.95 \
          -random_sampling_temp 1 \
          -ban_unk_token \
          -out /tmp/gen
        diff data/data_lm/gen-nucleus-sampling-sol.txt /tmp/gen && rm /tmp/gen
    - name: Test LM generation with random sampling multi-beams
      run: |
        python translate.py -model onmt/tests/test_model_lm.pt \
          -src data/data_lm/src-gen.txt \
          -verbose -batch_size 10 \
          -beam_size 10 \
          -seed 2 \
          -random_sampling_topk 50 \
          -random_sampling_topp 0.95 \
          -random_sampling_temp 1 \
          -length_penalty avg \
          -ban_unk_token \
          -min_length 5 \
          -out /tmp/gen
        diff data/data_lm/gen-sampling-beams-sol.txt /tmp/gen && rm /tmp/gen
    - name: Test extract_vocabulary tool
      run: |
        python tools/extract_vocabulary.py \
          -file /tmp/onmt.train.check.vocab.pt \
          -file_type field \
          -side src \
          -out_file /tmp/onmt.vocab.txt
        if ! wc -l /tmp/onmt.vocab.txt | grep -qF  "1002"
          then echo "wrong word count" && exit 1
        else
          echo "create vocabulary pass"
        fi
    - name: Test embeddings_to_torch tool
      run: |
        python tools/embeddings_to_torch.py \
          -emb_file_enc onmt/tests/sample_glove.txt \
          -emb_file_dec onmt/tests/sample_glove.txt \
          -dict_file /tmp/onmt.train.check.vocab.pt \
          -output_file /tmp/q_gloveembeddings \
          && rm /tmp/q_gloveembeddings*
        rm /tmp/onmt.train.check.*.pt
    - name: Test extract_embeddings tool
      run: |
        python tools/extract_embeddings.py \
          -model onmt/tests/test_model.pt
    - name: Test checkpoint vocabulary update
      run: |
        python train.py \
          -config data/data.yaml \
          -src_vocab /tmp/onmt.vocab.src \
          -tgt_vocab /tmp/onmt.vocab.tgt \
          -src_vocab_size 1000 \
          -tgt_vocab_size 1000 \
          -rnn_size 2 \
          -batch_size 10 \
          -word_vec_size 5 \
          -report_every 5\
          -rnn_size 10 \
          -train_steps 10 \
          -save_model /tmp/onmt.model \
          -save_checkpoint_steps 10
        sed -i '1s/^/new_tok\t100000000\n/' /tmp/onmt.vocab.src
        python train.py \
          -config data/data.yaml \
          -src_vocab /tmp/onmt.vocab.src \
          -tgt_vocab /tmp/onmt.vocab.tgt \
          -src_vocab_size 1000 \
          -tgt_vocab_size 1000 \
          -rnn_size 2 \
          -batch_size 10 \
          -word_vec_size 5 \
          -report_every 5\
          -rnn_size 10 \
          -train_steps 20 \
          -update_vocab \
          -reset_optim "states" \
          -train_from /tmp/onmt.model_step_10.pt
    - name: Test checkpoint vocabulary update with LM
      run: |
        python train.py \
          -config data/lm_data.yaml \
          -src_vocab /tmp/onmt.vocab.src \
          -tgt_vocab /tmp/onmt.vocab.tgt \
          -model_task lm \
          -encoder_type transformer_lm \
          -decoder_type transformer_lm \
          -src_vocab_size 1000 \
          -tgt_vocab_size 1000 \
          -dec_layers 2 -batch_size 10 \
          -heads 4 -transformer_ff 64 \
          -word_vec_size 16 -report_every 5 \
          -save_model /tmp/lm.onmt.model \
          -save_checkpoint_steps 10 \
          -rnn_size 16 -train_steps 10
        sed -i '1s/^/new_tok\t100000000\n/' /tmp/onmt.vocab.src
        python train.py \
          -config data/lm_data.yaml \
          -src_vocab /tmp/onmt.vocab.src \
          -tgt_vocab /tmp/onmt.vocab.tgt \
          -model_task lm \
          -encoder_type transformer_lm \
          -decoder_type transformer_lm \
          -src_vocab_size 1000 \
          -tgt_vocab_size 1000 \
          -dec_layers 2 -batch_size 10 \
          -heads 4 -transformer_ff 64 \
          -word_vec_size 16 -report_every 5 \
          -rnn_size 16  -train_steps 20 \
          -update_vocab -reset_optim "states" \
          -train_from /tmp/lm.onmt.model_step_10.pt

  build-docs:
    runs-on: ubuntu-latest
    steps:
    - uses: actions/checkout@v2
    - name: Set up Python 3.6
      uses: actions/setup-python@v2
      with:
        python-version: 3.6
    - name: Install dependencies
      run: |
        python -m pip install --upgrade pip
        pip install --upgrade setuptools
        pip install -e .
        pip install -r docs/requirements.txt
    - name: Build docs
      run: |
        set -e
        # Check that docs are built without errors
        cd docs/ && make html && cd ..