ModelScope/tests/trainers/test_trainer_with_nlp.py

# Copyright (c) Alibaba, Inc. and its affiliates.
import os
import shutil
import tempfile
import unittest

from modelscope.hub.snapshot_download import snapshot_download
from modelscope.metainfo import Metrics
from modelscope.models.base import Model
from modelscope.models.nlp.sequence_classification import \
    SbertForSequenceClassification
from modelscope.msdatasets import MsDataset
from modelscope.pipelines import pipeline
from modelscope.trainers import build_trainer
from modelscope.utils.constant import ModelFile, Tasks
from modelscope.utils.hub import read_config
from modelscope.utils.test_utils import test_level


class TestTrainerWithNlp(unittest.TestCase):
    sentence1 = '今天气温比昨天高么？'
    sentence2 = '今天湿度比昨天高么？'

    def setUp(self):
        print(('Testing %s.%s' % (type(self).__name__, self._testMethodName)))
        self.tmp_dir = tempfile.TemporaryDirectory().name
        if not os.path.exists(self.tmp_dir):
            os.makedirs(self.tmp_dir)

        self.dataset = MsDataset.load(
            'afqmc_small', namespace='userxiaoming', split='train')

    def tearDown(self):
        shutil.rmtree(self.tmp_dir)
        super().tearDown()

    @unittest.skipUnless(test_level() >= 0, 'skip test in current test level')
    def test_trainer(self):
        model_id = 'damo/nlp_structbert_sentence-similarity_chinese-base'
        kwargs = dict(
            model=model_id,
            train_dataset=self.dataset,
            eval_dataset=self.dataset,
            work_dir=self.tmp_dir,
            model_revision='beta')

        trainer = build_trainer(default_args=kwargs)
        trainer.train()
        results_files = os.listdir(self.tmp_dir)
        self.assertIn(f'{trainer.timestamp}.log.json', results_files)
        for i in range(10):
            self.assertIn(f'epoch_{i+1}.pth', results_files)

        output_files = os.listdir(
            os.path.join(self.tmp_dir, ModelFile.TRAIN_OUTPUT_DIR))
        self.assertIn(ModelFile.CONFIGURATION, output_files)
        self.assertIn(ModelFile.TORCH_MODEL_BIN_FILE, output_files)
        copy_src_files = os.listdir(trainer.model_dir)

        print(f'copy_src_files are {copy_src_files}')
        print(f'output_files are {output_files}')
        for item in copy_src_files:
            if not item.startswith('.'):
                self.assertIn(item, output_files)

        def pipeline_sentence_similarity(model_dir):
            model = Model.from_pretrained(model_dir)
            pipeline_ins = pipeline(
                task=Tasks.sentence_similarity, model=model)
            print(pipeline_ins(input=(self.sentence1, self.sentence2)))

        output_dir = os.path.join(self.tmp_dir, ModelFile.TRAIN_OUTPUT_DIR)
        pipeline_sentence_similarity(output_dir)

    @unittest.skipUnless(test_level() >= 1, 'skip test in current test level')
    def test_trainer_with_backbone_head(self):
        model_id = 'damo/nlp_structbert_sentiment-classification_chinese-base'
        kwargs = dict(
            model=model_id,
            train_dataset=self.dataset,
            eval_dataset=self.dataset,
            work_dir=self.tmp_dir,
            model_revision='beta')

        trainer = build_trainer(default_args=kwargs)
        trainer.train()
        results_files = os.listdir(self.tmp_dir)
        self.assertIn(f'{trainer.timestamp}.log.json', results_files)
        for i in range(10):
            self.assertIn(f'epoch_{i+1}.pth', results_files)

        eval_results = trainer.evaluate(
            checkpoint_path=os.path.join(self.tmp_dir, 'epoch_10.pth'))
        self.assertTrue(Metrics.accuracy in eval_results)

    @unittest.skipUnless(test_level() >= 1, 'skip test in current test level')
    def test_trainer_with_user_defined_config(self):
        model_id = 'damo/nlp_structbert_sentiment-classification_chinese-base'
        cfg = read_config(model_id, revision='beta')
        cfg.train.max_epochs = 20
        cfg.train.work_dir = self.tmp_dir
        cfg_file = os.path.join(self.tmp_dir, 'config.json')
        cfg.dump(cfg_file)
        kwargs = dict(
            model=model_id,
            train_dataset=self.dataset,
            eval_dataset=self.dataset,
            cfg_file=cfg_file,
            model_revision='beta')

        trainer = build_trainer(default_args=kwargs)
        trainer.train()
        results_files = os.listdir(self.tmp_dir)
        self.assertIn(f'{trainer.timestamp}.log.json', results_files)
        for i in range(20):
            self.assertIn(f'epoch_{i+1}.pth', results_files)

        eval_results = trainer.evaluate(
            checkpoint_path=os.path.join(self.tmp_dir, 'epoch_10.pth'))
        self.assertTrue(Metrics.accuracy in eval_results)

    @unittest.skipUnless(test_level() >= 2, 'skip test in current test level')
    def test_trainer_with_model_and_args(self):
        tmp_dir = tempfile.TemporaryDirectory().name
        if not os.path.exists(tmp_dir):
            os.makedirs(tmp_dir)

        model_id = 'damo/nlp_structbert_sentence-similarity_chinese-base'
        cache_path = snapshot_download(model_id, revision='beta')
        model = SbertForSequenceClassification.from_pretrained(cache_path)
        kwargs = dict(
            cfg_file=os.path.join(cache_path, ModelFile.CONFIGURATION),
            model=model,
            train_dataset=self.dataset,
            eval_dataset=self.dataset,
            max_epochs=2,
            work_dir=self.tmp_dir)

        trainer = build_trainer(default_args=kwargs)
        trainer.train()
        results_files = os.listdir(self.tmp_dir)
        self.assertIn(f'{trainer.timestamp}.log.json', results_files)
        for i in range(2):
            self.assertIn(f'epoch_{i+1}.pth', results_files)


if __name__ == '__main__':
    unittest.main()