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| import unittest |
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| from transformers import is_torch_available |
| from transformers.testing_utils import require_torch |
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|
| if is_torch_available(): |
| import torch |
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| from transformers.activations import gelu_new, gelu_python, get_activation |
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| @require_torch |
| class TestActivations(unittest.TestCase): |
| def test_gelu_versions(self): |
| x = torch.tensor([-100, -1, -0.1, 0, 0.1, 1.0, 100]) |
| torch_builtin = get_activation("gelu") |
| torch.testing.assert_close(gelu_python(x), torch_builtin(x)) |
| self.assertFalse(torch.allclose(gelu_python(x), gelu_new(x))) |
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|
| def test_gelu_10(self): |
| x = torch.tensor([-100, -1, -0.1, 0, 0.1, 1.0, 100]) |
| torch_builtin = get_activation("gelu") |
| gelu10 = get_activation("gelu_10") |
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| y_gelu = torch_builtin(x) |
| y_gelu_10 = gelu10(x) |
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| clipped_mask = torch.where(y_gelu_10 < 10.0, 1, 0) |
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| self.assertTrue(torch.max(y_gelu_10).item() == 10.0) |
| torch.testing.assert_close(y_gelu * clipped_mask, y_gelu_10 * clipped_mask) |
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|
| def test_get_activation(self): |
| get_activation("gelu") |
| get_activation("gelu_10") |
| get_activation("gelu_fast") |
| get_activation("gelu_new") |
| get_activation("gelu_python") |
| get_activation("gelu_pytorch_tanh") |
| get_activation("linear") |
| get_activation("mish") |
| get_activation("quick_gelu") |
| get_activation("relu") |
| get_activation("sigmoid") |
| get_activation("silu") |
| get_activation("swish") |
| get_activation("tanh") |
| with self.assertRaises(KeyError): |
| get_activation("bogus") |
| with self.assertRaises(KeyError): |
| get_activation(None) |
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|
| def test_activations_are_distinct_objects(self): |
| act1 = get_activation("gelu") |
| act1.a = 1 |
| act2 = get_activation("gelu") |
| self.assertEqual(act1.a, 1) |
| with self.assertRaises(AttributeError): |
| _ = act2.a |
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