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import random |
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import unittest |
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import torch |
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from torch.nn import functional as F |
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from megatron.model.glu_activations import GLU_ACTIVATIONS, geglu, liglu, reglu, swiglu |
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class TestActivations(unittest.TestCase): |
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def setUp(self): |
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"""setup an input of reasonable size""" |
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seed = 11 |
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torch.manual_seed(seed) |
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torch.cuda.manual_seed_all(seed) |
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self.batch_size = random.randint(2, 64) |
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self.seq_len = random.randint(256, 1025) |
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self.num_channels = random.randint(1, 384) * 2 |
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self.x = torch.randn(self.batch_size, self.seq_len, self.num_channels) |
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self.x1, self.x2 = self.x.chunk(2, dim=-1) |
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self.output_shape = [self.batch_size, self.seq_len, self.num_channels // 2] |
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def test_shapes(self): |
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for activation_fn in GLU_ACTIVATIONS.values(): |
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output = activation_fn(self.x) |
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self.assertEqual(list(output.shape), self.output_shape) |
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def test_liglu(self): |
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expected = self.x1 * self.x2 |
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assert torch.allclose(liglu(self.x), expected) |
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def test_geglu(self): |
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expected = self.x1 * F.gelu(self.x2) |
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assert torch.allclose(geglu(self.x), expected) |
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def test_reglu(self): |
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expected = self.x1 * F.relu(self.x2) |
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assert torch.allclose(reglu(self.x), expected) |
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def test_swiglu(self): |
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expected = self.x1 * F.silu(self.x2) |
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assert torch.allclose(swiglu(self.x), expected) |
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if __name__ == "__main__": |
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ta = TestActivations() |
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ta.setUp() |
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ta.test_reglu() |
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