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Link: https://code.alibaba-inc.com/Ali-MaaS/MaaS-lib/codereview/9644184 * fix ditributed training and eval
124 lines
4.6 KiB
Python
124 lines
4.6 KiB
Python
# Copyright (c) Alibaba, Inc. and its affiliates.
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import os
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import shutil
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import tempfile
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import unittest
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from modelscope.hub.snapshot_download import snapshot_download
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from modelscope.metainfo import Metrics
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from modelscope.models.nlp.sequence_classification import \
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SbertForSequenceClassification
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from modelscope.msdatasets import MsDataset
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from modelscope.trainers import build_trainer
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from modelscope.utils.constant import ModelFile
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from modelscope.utils.hub import read_config
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from modelscope.utils.test_utils import test_level
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class TestTrainerWithNlp(unittest.TestCase):
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def setUp(self):
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print(('Testing %s.%s' % (type(self).__name__, self._testMethodName)))
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self.tmp_dir = tempfile.TemporaryDirectory().name
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if not os.path.exists(self.tmp_dir):
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os.makedirs(self.tmp_dir)
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self.dataset = MsDataset.load(
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'afqmc_small', namespace='userxiaoming', split='train')
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def tearDown(self):
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shutil.rmtree(self.tmp_dir)
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super().tearDown()
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@unittest.skipUnless(test_level() >= 1, 'skip test in current test level')
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def test_trainer(self):
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model_id = 'damo/nlp_structbert_sentence-similarity_chinese-base'
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kwargs = dict(
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model=model_id,
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train_dataset=self.dataset,
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eval_dataset=self.dataset,
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work_dir=self.tmp_dir,
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model_revision='beta')
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trainer = build_trainer(default_args=kwargs)
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trainer.train()
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results_files = os.listdir(self.tmp_dir)
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self.assertIn(f'{trainer.timestamp}.log.json', results_files)
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for i in range(10):
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self.assertIn(f'epoch_{i+1}.pth', results_files)
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@unittest.skipUnless(test_level() >= 1, 'skip test in current test level')
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def test_trainer_with_backbone_head(self):
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model_id = 'damo/nlp_structbert_sentiment-classification_chinese-base'
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kwargs = dict(
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model=model_id,
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train_dataset=self.dataset,
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eval_dataset=self.dataset,
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work_dir=self.tmp_dir,
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model_revision='beta')
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trainer = build_trainer(default_args=kwargs)
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trainer.train()
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results_files = os.listdir(self.tmp_dir)
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self.assertIn(f'{trainer.timestamp}.log.json', results_files)
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for i in range(10):
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self.assertIn(f'epoch_{i+1}.pth', results_files)
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eval_results = trainer.evaluate(
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checkpoint_path=os.path.join(self.tmp_dir, 'epoch_10.pth'))
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self.assertTrue(Metrics.accuracy in eval_results)
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@unittest.skipUnless(test_level() >= 1, 'skip test in current test level')
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def test_trainer_with_user_defined_config(self):
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model_id = 'damo/nlp_structbert_sentiment-classification_chinese-base'
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cfg = read_config(model_id, revision='beta')
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cfg.train.max_epochs = 20
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cfg.train.work_dir = self.tmp_dir
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cfg_file = os.path.join(self.tmp_dir, 'config.json')
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cfg.dump(cfg_file)
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kwargs = dict(
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model=model_id,
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train_dataset=self.dataset,
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eval_dataset=self.dataset,
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cfg_file=cfg_file,
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model_revision='beta')
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trainer = build_trainer(default_args=kwargs)
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trainer.train()
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results_files = os.listdir(self.tmp_dir)
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self.assertIn(f'{trainer.timestamp}.log.json', results_files)
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for i in range(20):
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self.assertIn(f'epoch_{i+1}.pth', results_files)
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eval_results = trainer.evaluate(
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checkpoint_path=os.path.join(self.tmp_dir, 'epoch_10.pth'))
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self.assertTrue(Metrics.accuracy in eval_results)
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@unittest.skipUnless(test_level() >= 2, 'skip test in current test level')
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def test_trainer_with_model_and_args(self):
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tmp_dir = tempfile.TemporaryDirectory().name
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if not os.path.exists(tmp_dir):
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os.makedirs(tmp_dir)
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model_id = 'damo/nlp_structbert_sentence-similarity_chinese-base'
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cache_path = snapshot_download(model_id, revision='beta')
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model = SbertForSequenceClassification.from_pretrained(cache_path)
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kwargs = dict(
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cfg_file=os.path.join(cache_path, ModelFile.CONFIGURATION),
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model=model,
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train_dataset=self.dataset,
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eval_dataset=self.dataset,
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max_epochs=2,
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work_dir=self.tmp_dir)
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trainer = build_trainer(default_args=kwargs)
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trainer.train()
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results_files = os.listdir(self.tmp_dir)
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self.assertIn(f'{trainer.timestamp}.log.json', results_files)
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for i in range(2):
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self.assertIn(f'epoch_{i+1}.pth', results_files)
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if __name__ == '__main__':
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unittest.main()
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