push.yml

name: Lint & Tests

on: [push, pull_request]

jobs:
  lint-and-tests:

    runs-on: ubuntu-latest
    strategy:
      matrix:
        python-version: ['3.10']

    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 sacrebleu
        pip install flake8
        pip install rich
        python -m pip install black==22.* flake8==3.8.*
        if [ -f requirements.txt ]; then pip install -r requirements.txt; fi
    - name: Check code with Black
      run: |
        black --check .
    - name: Lint with flake8
      run: |
        flake8 .
    - name: Unit tests
      run: |
        python -m unittest discover
    - name: Recipes config check
      run: |
        python eole/tests/test_recipes.py recipes
    - name: Test vocabulary build
      run: |
        python eole/bin/main.py build_vocab \
          -config eole/tests/data/data.yaml \
          -save_data /tmp/eole \
          -n_sample 5000 \
          -src_vocab /tmp/eole.vocab.src \
          -tgt_vocab /tmp/eole.vocab.tgt \
          && rm -rf /tmp/sample
    - name: Test field/transform dump
      run: |
        # The dumped fields are used later when testing tools
        python eole/bin/main.py train \
          -config eole/tests/data/data.yaml \
          -save_data /tmp/eole.train.check \
          -n_sample 30 \
          -model '{"architecture": "rnn"}' \
          -training '{"num_workers": 0, "bucket_size": 1024}' \
          -src_vocab /tmp/eole.vocab.src \
          -tgt_vocab /tmp/eole.vocab.tgt \
          -src_vocab_size 1000 \
          -tgt_vocab_size 1000
    - name: Test RNN training
      run: |
        python eole/bin/main.py train \
          -config eole/tests/data/data.yaml \
          -src_vocab /tmp/eole.vocab.src \
          -tgt_vocab /tmp/eole.vocab.tgt \
          -src_vocab_size 1000 \
          -tgt_vocab_size 1000 \
          -model '{"architecture": "rnn", "hidden_size": 10, "embeddings": {"word_vec_size": 5, "position_encoding_type": None}}' \
          -training '{"batch_size": 10, "num_workers": 0, "bucket_size": 1024, "train_steps": 10}' \
          -report_every 5\
          -tensorboard \
          -tensorboard_log_dir /tmp/logs_train
        python eole/tests/test_events.py --logdir /tmp/logs_train -tensorboard_checks train
    - name: Test RNN training and validation
      run: |
        python eole/bin/main.py train \
          -config eole/tests/data/data.yaml \
          -src_vocab /tmp/eole.vocab.src \
          -tgt_vocab /tmp/eole.vocab.tgt \
          -src_vocab_size 1000 \
          -tgt_vocab_size 1000 \
          -model '{"architecture": "rnn", "hidden_size": 10, "embeddings": {"word_vec_size": 5, "position_encoding_type": None}}' \
          -training '{"batch_size": 10, "num_workers": 0, "bucket_size": 1024, "train_steps": 10, "valid_steps": 5}' \
          -report_every 5 \
          -tensorboard \
          -tensorboard_log_dir /tmp/logs_train_and_valid
        python eole/tests/test_events.py --logdir /tmp/logs_train_and_valid -tensorboard_checks train
                python eole/tests/test_events.py --logdir /tmp/logs_train_and_valid -tensorboard_checks valid
    - name: Test RNN training with coverage
      run: |
        python eole/bin/main.py train \
          -config eole/tests/data/data.yaml \
          -src_vocab /tmp/eole.vocab.src \
          -tgt_vocab /tmp/eole.vocab.tgt \
          -src_vocab_size 1000 \
          -tgt_vocab_size 1000 \
          -report_every 5 \
          -model '{"architecture": "rnn", "hidden_size": 10, "embeddings": {"word_vec_size": 5, "position_encoding_type": None}, "decoder": {"coverage_attn": True, "lambda_coverage": 0.1}}' \
          -training '{"batch_size": 10, "num_workers": 0, "bucket_size": 1024, "train_steps": 10}'
    - name: Test Transformer training with align
      run: |
        python eole/bin/main.py train \
          -config eole/tests/data/align_data.yaml \
          -src_vocab /tmp/eole.vocab.src \
          -tgt_vocab /tmp/eole.vocab.tgt \
          -src_vocab_size 1000 \
          -tgt_vocab_size 1000 \
          -model '{"layers": 4, "hidden_size": 16, "transformer_ff": 64, "embeddings": {"word_vec_size": 16}, "encoder": {"encoder_type": "transformer", "heads": 2}, "decoder": {"decoder_type": "transformer", "lambda_align": 0.05, "alignment_layer": 2, "alignment_heads": 0, "heads": 2}}' \
          -training '{"batch_size": 10, "num_workers": 0, "bucket_size": 1024, "train_steps": 10, "dropout_steps": [0, 3, 7], "dropout": [0.3, 0.2, 0.1], "attention_dropout": [0.2, 0.2, 0.1]}' \
          -report_every 5 \
    - name : Test Transformer training and validation with dynamic scoring
      run: |
        python3 eole/bin/main.py train \
          -config eole/tests/data/data.yaml \
          -src_vocab /tmp/eole.vocab.src \
          -tgt_vocab /tmp/eole.vocab.tgt \
          -src_vocab_size 1000 \
          -tgt_vocab_size 1000 \
          -model '{"layers": 4, "hidden_size": 16, "transformer_ff": 16, "embeddings": {"word_vec_size": 16, "position_encoding_type": "SinusoidalInterleaved"}, "encoder": {"encoder_type": "transformer", "heads": 2}, "decoder": {"decoder_type": "transformer", "heads": 2}}' \
          -training '{"batch_size": 10, "num_workers": 0, "bucket_size": 1024, "train_steps": 10, "valid_steps": 5, "accum_count": [2, 4, 8], "accum_steps": [0, 3, 7], "model_path": "/tmp/eole.model"}' \
          -report_every 2 \
          -valid_metrics "BLEU" "TER" \
          -tensorboard \
          -scoring_debug \
          -tensorboard_log_dir /tmp/logs_dynamic-scoring \
          -dump_preds /tmp/dump_preds
        python eole/tests/test_events.py --logdir /tmp/logs_dynamic-scoring -tensorboard_checks valid_metrics 
    - name : Test Transformer training and validation with dynamic scoring and maxrelative
      run: |
        python3 eole/bin/main.py train \
          -config eole/tests/data/data.yaml \
          -src_vocab /tmp/eole.vocab.src \
          -tgt_vocab /tmp/eole.vocab.tgt \
          -src_vocab_size 1000 \
          -tgt_vocab_size 1000 \
          -model '{"architecture": "transformer", "layers": 4, "heads": 2, "hidden_size": 16, "transformer_ff": 64, "embeddings": {"word_vec_size": 16, "position_encoding_type": "Relative", "n_positions": 8}}' \
          -training '{"batch_size": 10, "num_workers": 0, "bucket_size": 1024, "train_steps": 10, "valid_steps": 5}' \
          -report_every 2 \
          -valid_metrics "BLEU" "TER" \
          -tensorboard \
          -scoring_debug \
          -tensorboard_log_dir /tmp/logs_dynamic-scoring_and_relative \
          -dump_preds /tmp/dump_preds
        python eole/tests/test_events.py --logdir /tmp/logs_dynamic-scoring_and_relative -tensorboard_checks valid_metrics 
    - name : Test Transformer training and validation with dynamic scoring and rotary
      run: |
        python3 eole/bin/main.py train \
          -config eole/tests/data/data.yaml \
          -src_vocab /tmp/eole.vocab.src \
          -tgt_vocab /tmp/eole.vocab.tgt \
          -src_vocab_size 1000 \
          -tgt_vocab_size 1000 \
          -model '{"architecture": "transformer", "layers": 4, "heads": 2, "hidden_size": 16, "transformer_ff": 64, "embeddings": {"word_vec_size": 16, "position_encoding_type": "Rotary"}}' \
          -training '{"batch_size": 10, "num_workers": 0, "bucket_size": 1024, "train_steps": 10, "valid_steps": 5}' \
          -report_every 2 \
          -valid_metrics "BLEU" "TER" \
          -tensorboard \
          -scoring_debug \
          -tensorboard_log_dir /tmp/logs_dynamic-scoring_and_rotary \
          -dump_preds /tmp/dump_preds
        python eole/tests/test_events.py --logdir /tmp/logs_dynamic-scoring_and_rotary -tensorboard_checks valid_metrics 
    - name : Test Transformer training and validation with dynamic scoring and alibi
      run: |
        python3 eole/bin/main.py train \
          -config eole/tests/data/data.yaml \
          -src_vocab /tmp/eole.vocab.src \
          -tgt_vocab /tmp/eole.vocab.tgt \
          -src_vocab_size 1000 \
          -tgt_vocab_size 1000 \
          -model '{"architecture": "transformer", "layers": 4, "heads": 2, "hidden_size": 16, "transformer_ff": 64, "embeddings": {"word_vec_size": 16, "position_encoding_type": "Alibi"}}' \
          -training '{"batch_size": 10, "num_workers": 0, "bucket_size": 1024, "train_steps": 10, "valid_steps": 5}' \
          -report_every 2 \
          -valid_metrics "BLEU" "TER" \
          -tensorboard \
          -scoring_debug \
          -tensorboard_log_dir /tmp/logs_dynamic-scoring_and_alibi \
          -dump_preds /tmp/dump_preds
        python eole/tests/test_events.py --logdir /tmp/logs_dynamic-scoring_and_alibi -tensorboard_checks valid_metrics 
    - name: Test LM training
      run: |
        python eole/bin/main.py train \
            -config eole/tests/data/lm_data.yaml \
            -src_vocab /tmp/eole.vocab.src \
            -tgt_vocab /tmp/eole.vocab.src \
            -model '{"hidden_size": 16, "transformer_ff": 64, "embeddings": {"word_vec_size": 16}, "encoder": None, "decoder": {"decoder_type": "transformer_lm", "layers": 2, "heads": 4}}' \
            -training '{"batch_size": 10, "num_workers": 0, "bucket_size": 1024, "train_steps": 10}' \
            -src_vocab_size 1000 \
            -tgt_vocab_size 1000 \
            -report_every 5
    - name: Test RNN translation
      run: |
        head eole/tests/data/src-test.txt > /tmp/src-test.txt
        python eole/bin/main.py predict \
          -model_path eole/tests/test_model \
          -src /tmp/src-test.txt \
          -verbose
    - name: Test RNN ensemble translation
      run: |
        head eole/tests/data/src-test.txt > /tmp/src-test.txt
        python eole/bin/main.py predict \
          -model_path eole/tests/test_model \
          eole/tests/test_model \
          -src /tmp/src-test.txt \
          -verbose
    - name: Test RNN translation with beam search
      run: |
        python eole/bin/main.py predict \
          -model_path eole/tests/test_model2 \
          -src eole/tests/data/morph/src.valid \
          -verbose \
          -batch_size 10 \
          -beam_size 10 \
          -tgt eole/tests/data/morph/tgt.valid \
          -out /tmp/trans
        diff eole/tests/data/morph/tgt.valid /tmp/trans && rm /tmp/trans
    - name: Test RNN translation with random sampling
      run: |
        python eole/bin/main.py predict \
          -model_path eole/tests/test_model2 \
          -src eole/tests/data/morph/src.valid \
          -verbose \
          -batch_size 10 \
          -beam_size 1 \
          -seed 1 \
          -top_k "-1" \
          -temperature 0.0001 \
          -tgt eole/tests/data/morph/tgt.valid \
          -out /tmp/trans
        diff eole/tests/data/morph/tgt.valid /tmp/trans && rm /tmp/trans
    - name: Test LM generation
      run: |
        head eole/tests/data/src-test.txt > /tmp/src-test.txt
        python eole/bin/main.py predict \
          -model_path eole/tests/test_model_lm \
          -src /tmp/src-test.txt \
          -verbose
    - name: Test LM generation with beam search
      run: |
        python eole/bin/main.py predict \
          -model_path eole/tests/test_model_lm \
          -src eole/tests/data/data_lm/src-gen.txt \
          -verbose -batch_size 1 \
          -beam_size 10 \
          -ban_unk_token \
          -length_penalty none \
          -out /tmp/gen
        diff eole/tests/data/data_lm/gen-beam-sol.txt /tmp/gen && rm /tmp/gen
    - name: Test LM generation with random sampling
      run: |
        python eole/bin/main.py predict -model_path eole/tests/test_model_lm \
          -src eole/tests/data/data_lm/src-gen.txt \
          -verbose -batch_size 1 \
          -beam_size 1 \
          -seed 1 \
          -top_k -1 \
          -temperature 0.0001 \
          -ban_unk_token \
          -length_penalty none \
          -out /tmp/gen
        diff eole/tests/data/data_lm/gen-sampling-sol.txt /tmp/gen && rm /tmp/gen
    - name: Test LM generation with random top-k/nucleus sampling
      run: |
        python eole/bin/main.py predict -model_path eole/tests/test_model_lm \
          -src eole/tests/data/data_lm/src-gen.txt \
          -verbose -batch_size 1 \
          -beam_size 1 \
          -seed 3 \
          -top_k -1 \
          -top_p 0.95 \
          -temperature 1 \
          -ban_unk_token \
          -length_penalty none \
          -out /tmp/gen
        diff eole/tests/data/data_lm/gen-nucleus-sampling-sol$(python -c "import torch; print(torch.__version__[0])").txt /tmp/gen && rm /tmp/gen
    - name: Test LM generation with random sampling multi-beams
      run: |
        python eole/bin/main.py predict -model_path eole/tests/test_model_lm \
          -src eole/tests/data/data_lm/src-gen.txt \
          -verbose -batch_size 1 \
          -beam_size 10 \
          -seed 2 \
          -top_k 50 \
          -top_p 0.95 \
          -temperature 1 \
          -length_penalty avg \
          -ban_unk_token \
          -min_length 5 \
          -out /tmp/gen
        diff eole/tests/data/data_lm/gen-sampling-beams-sol$(python -c "import torch; print(torch.__version__[0])").txt /tmp/gen && rm /tmp/gen
    - name: Test py-LM inference engine
      run: |
        head eole/tests/data/src-test.txt > /tmp/src-test.txt
        python eole/tests/test_inference_engines.py \
          -model eole/tests/test_model_lm \
          -model_type decoder \
          -input_file /tmp/src-test.txt \
          -inference_config_file eole/tests/data/inference-engine_py.yaml \
          -inference_mode py \
          -out /tmp/inference_engine_lm_py_outputs
    - name: Test ct2-LM inference engine
      run: |
        head eole/tests/data/src-test.txt > /tmp/src-test.txt
        python eole/tests/test_inference_engines.py \
          -model eole/tests/test_model_lm_ct2 \
          -model_type decoder \
          -input_file /tmp/src-test.txt \
          -inference_config_file eole/tests/data/inference-engine_py.yaml \
          -inference_mode ct2 \
          -out /tmp/inference_engine_lm_py_outputs
    - name: Test py-SEQ2SEQ inference engine
      run: |
        head eole/tests/data/src-test.txt > /tmp/src-test.txt
        python eole/tests/test_inference_engines.py \
          -model eole/tests/test_model \
          -model_type encoder_decoder \
          -input_file /tmp/src-test.txt \
          -inference_config_file eole/tests/data/inference-engine_py.yaml \
          -inference_mode py \
          -out /tmp/inference_engine_lm_py_outputs
    - name: Test embeddings_to_torch tool
      run: |
        python eole/bin/main.py tools embeddings_to_torch \
          -emb_file_enc eole/tests/sample_glove.txt \
          -emb_file_dec eole/tests/sample_glove.txt \
          -model_path eole/tests/test_model \
          -output_file /tmp/q_gloveembeddings \
          && rm /tmp/q_gloveembeddings*
    - name: Test extract_embeddings tool
      run: |
        python eole/bin/main.py model extract_embeddings \
          -model eole/tests/test_model
    - name: Test checkpoint vocabulary update
      run: |
        python eole/bin/main.py train \
          -config eole/tests/data/data.yaml \
          -src_vocab /tmp/eole.vocab.src \
          -tgt_vocab /tmp/eole.vocab.tgt \
          -src_vocab_size 1000 \
          -tgt_vocab_size 1000 \
          -model '{"architecture": "rnn", "hidden_size": 10, "embeddings": {"word_vec_size": 5, "position_encoding_type": None}}' \
          -training '{"batch_size": 10, "num_workers": 0, "bucket_size": 1024, "train_steps": 10, "model_path": "/tmp/eole.model", "save_checkpoint_steps": 10}' \
          -report_every 5
        sed -i '1s/^/new_tok\t100000000\n/' /tmp/eole.vocab.src
        python eole/bin/main.py train \
          -config eole/tests/data/data.yaml \
          -src_vocab /tmp/eole.vocab.src \
          -tgt_vocab /tmp/eole.vocab.tgt \
          -src_vocab_size 1000 \
          -tgt_vocab_size 1000 \
          -training '{"batch_size": 10, "num_workers": 0, "bucket_size": 1024, "train_steps": 20, "train_from": "/tmp/eole.model/step_10", "save_checkpoint_steps": 10, "update_vocab": True, "reset_optim": "states"}' \
          -report_every 5
    - name: Test checkpoint vocabulary update with LM
      run: |
        python eole/bin/main.py train \
          -config eole/tests/data/lm_data.yaml \
          -src_vocab /tmp/eole.vocab.src \
          -tgt_vocab /tmp/eole.vocab.src \
          -model '{"layers": 2, "hidden_size": 16, "transformer_ff": 64, "embeddings": {"word_vec_size": 16}, "encoder": None, "decoder": {"decoder_type": "transformer_lm", "heads": 4}}' \
          -training '{"batch_size": 10, "num_workers": 0, "bucket_size": 1024, "train_steps": 10, "model_path": "/tmp/lm.eole.model", "save_checkpoint_steps": 10}' \
          -src_vocab_size 1000 \
          -tgt_vocab_size 1000 \
          -report_every 5
        sed -i '1s/^/new_tok2\t100000000\n/' /tmp/eole.vocab.src
        python eole/bin/main.py train \
          -config eole/tests/data/lm_data.yaml \
          -src_vocab /tmp/eole.vocab.src \
          -tgt_vocab /tmp/eole.vocab.src \
          -training '{"batch_size": 10, "num_workers": 0, "bucket_size": 1024, "train_steps": 20, "train_from": "/tmp/lm.eole.model/step_10", "save_checkpoint_steps": 10, "update_vocab": True, "reset_optim": "states"}' \
          -src_vocab_size 1000 \
          -tgt_vocab_size 1000 \
          -report_every 5

  build-docs:
    runs-on: ubuntu-latest
    steps:
    - uses: actions/checkout@v2
    - name: Set up Python 3.10
      uses: actions/setup-python@v2
      with:
        python-version: '3.10'
    - name: Install dependencies
      run: |
        python -m pip install --upgrade pip
        pip install --upgrade setuptools
        pip install -e .
        pip install -r docs/requirements.txt
        pip install -r requirements.opt.txt
        pip install rich
    - name: Build docs
      run: |
        set -e
        # Check that docs are built without errors
        cd docs/ && make html && cd ..