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82482b3e96
Based on feat/0131/nlp_args branch, the original code review: https://code.alibaba-inc.com/Ali-MaaS/MaaS-lib/codereview/11408570 Support for running finetuning from the command line with training args, Compatible with the configuration optimization.
49 lines
1.8 KiB
Python
49 lines
1.8 KiB
Python
# Copyright (c) Alibaba, Inc. and its affiliates.
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import unittest
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from modelscope.trainers.default_config import DEFAULT_CONFIG
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from modelscope.trainers.training_args import CliArgumentParser, TrainingArgs
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from modelscope.utils.test_utils import test_level
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class TrainingArgsTest(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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def tearDown(self):
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super().tearDown()
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@unittest.skipUnless(test_level() >= 0, 'skip test in current test level')
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def test_define_args(self):
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myparser = CliArgumentParser(TrainingArgs())
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input_args = [
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'--max_epochs', '100', '--work_dir', 'ddddd',
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'--per_device_train_batch_size', '8', '--unkown', 'unkown'
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]
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args, remainning = myparser.parse_known_args(input_args)
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myparser.print_help()
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self.assertTrue(args.max_epochs == 100)
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self.assertTrue(args.work_dir == 'ddddd')
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self.assertTrue(args.per_device_train_batch_size == 8)
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@unittest.skipUnless(test_level() >= 0, 'skip test in current test level')
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def test_flatten_args(self):
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cfg = DEFAULT_CONFIG
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input_args = [
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'--optimizer_params',
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'weight_decay=0.8,eps=1e-6,correct_bias=False',
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'--lr_scheduler_params', 'initial_lr=3e-5,niter_decay=1'
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]
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training_args = TrainingArgs.from_cli(input_args)
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cfg = training_args(cfg)
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self.assertAlmostEqual(cfg.train.optimizer.weight_decay, 0.8)
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self.assertAlmostEqual(cfg.train.optimizer.eps, 1e-6)
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self.assertFalse(cfg.train.optimizer.correct_bias)
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self.assertAlmostEqual(cfg.train.lr_scheduler.initial_lr, 3e-5)
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self.assertEqual(cfg.train.lr_scheduler.niter_decay, 1)
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if __name__ == '__main__':
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unittest.main()
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