mirror of
https://github.com/firestar5683/StarPilot.git
synced 2026-10-28 00:53:45 +08:00
Daniel Koepping
64 lines
2.2 KiB
Python
64 lines
2.2 KiB
Python
import unittest
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import torch
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from tinygrad import Tensor, nn, Context
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class TestEmptyTensorEdgeCases(unittest.TestCase):
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# we don't need more of these
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@unittest.expectedFailure
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def test_max_empty(self):
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# Max on an empty tensor should also raise an error.
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with self.assertRaises(RuntimeError):
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torch.tensor([]).max()
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with self.assertRaises(RuntimeError):
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Tensor([]).max()
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@unittest.expectedFailure
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def test_argmax_empty(self):
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# Argmax on an empty tensor should raise an error like torch does.
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with self.assertRaises(RuntimeError):
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torch.tensor([]).argmax()
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with self.assertRaises(RuntimeError):
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Tensor([]).argmax()
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class TestDropoutProbabilityEdgeCases(unittest.TestCase):
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# we don't need more of these
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def test_dropout_invalid_prob(self):
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with self.assertRaises(ValueError):
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torch.nn.functional.dropout(torch.ones(10), -0.1, True)
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with self.assertRaises(ValueError):
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with Context(TRAINING=1):
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Tensor.ones(10).dropout(-0.1)
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class TestInputValidation(unittest.TestCase):
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# we don't need more of these, input validation bugs are not very interesting, many are WONTFIX
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def test_repeat_negative(self):
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# repeating with a negative value should error like PyTorch
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with self.assertRaises(RuntimeError):
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torch.tensor([1, 2, 3]).repeat(-1, 2)
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with self.assertRaises(RuntimeError):
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Tensor([1, 2, 3]).repeat(-1, 2)
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def test_negative_weight_decay(self):
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with self.assertRaises(ValueError):
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torch.optim.AdamW([torch.tensor([1.], requires_grad=True)], lr=0.1, weight_decay=-0.1)
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with self.assertRaises(ValueError):
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nn.optim.AdamW([Tensor([1.])], lr=0.1, weight_decay=-0.1)
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def test_negative_lr(self):
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with self.assertRaises(ValueError):
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torch.optim.SGD([torch.tensor([1.], requires_grad=True)], lr=-0.1)
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with self.assertRaises(ValueError):
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nn.optim.SGD([Tensor([1.])], lr=-0.1)
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def test_negative_momentum(self):
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with self.assertRaises(ValueError):
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torch.optim.SGD([torch.tensor([1.], requires_grad=True)], lr=0.1, momentum=-0.1)
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with self.assertRaises(ValueError):
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nn.optim.SGD([Tensor([1.])], lr=0.1, momentum=-0.1)
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if __name__ == '__main__':
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unittest.main()
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