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https://github.com/facebookresearch/fairseq.git
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65 lines
2.0 KiB
Python
65 lines
2.0 KiB
Python
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import torch
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import numpy as np
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import unittest
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from fairseq.modules.rotary_positional_embedding import apply_rotary_pos_emb
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from fairseq.modules import RotaryPositionalEmbedding
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class TestRotaryPositionalEmbedding(unittest.TestCase):
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def setUp(self) -> None:
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self.T = 3
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self.B = 1
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self.C = 2
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torch.manual_seed(0)
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self.sample = torch.randn(self.T, self.B, self.C) # TBC
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self.rope_pos_emd = RotaryPositionalEmbedding(dim=self.C)
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def test_forward(self):
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expected_cos = torch.tensor(
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[[[[1.0000, 1.0000]]], [[[0.5403, 0.5403]]], [[[-0.4161, -0.4161]]]]
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)
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expected_sin = torch.tensor(
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[[[[0.0000, 0.0000]]], [[[0.8415, 0.8415]]], [[[0.9093, 0.9093]]]]
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)
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cos, sin = self.rope_pos_emd(self.sample, self.T)
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self.assertTrue(
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np.allclose(
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expected_cos.cpu().detach().numpy(),
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cos.cpu().detach().numpy(),
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atol=1e-4,
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)
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)
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self.assertTrue(
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np.allclose(
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expected_sin.cpu().detach().numpy(),
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sin.cpu().detach().numpy(),
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atol=1e-4,
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)
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)
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def test_apply_rotary_pos_emb(self):
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cos, sin = self.rope_pos_emd(self.sample, self.T)
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query = self.sample.view(self.T, self.B, 1, self.C)
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expected_query = torch.tensor(
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[[[[1.5410, -0.2934]]], [[[-1.6555, -1.5263]]], [[[1.7231, -0.4041]]]]
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)
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new_query, new_key = apply_rotary_pos_emb(query, query, cos, sin)
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self.assertTrue(
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np.allclose(
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expected_query.cpu().detach().numpy(),
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new_query.cpu().detach().numpy(),
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atol=1e-4,
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)
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)
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self.assertTrue(
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np.allclose(
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expected_query.cpu().detach().numpy(),
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new_key.cpu().detach().numpy(),
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atol=1e-4,
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)
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)
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if __name__ == "__main__":
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unittest.main()
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