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import unittest
import torch
from fairseq.modules import RelPositionalEncoding
import numpy as np
class TestRelPositionalEncoding(unittest.TestCase):
def setUp(self) -> None:
self.T = 3
self.B = 1
self.C = 2
torch.manual_seed(0)
self.sample = torch.randn(self.T, self.B, self.C) # TBC
self.rel_pos_enc = RelPositionalEncoding(max_len=4, d_model=self.C)
def test_extend_pe(self):
inp = self.sample.transpose(0, 1)
self.rel_pos_enc.extend_pe(inp)
expected_pe = torch.tensor(
[
[
[0.1411, -0.9900],
[0.9093, -0.4161],
[0.8415, 0.5403],
[0.0000, 1.0000],
[-0.8415, 0.5403],
[-0.9093, -0.4161],
[-0.1411, -0.9900],
]
]
)
self.assertTrue(
np.allclose(
expected_pe.cpu().detach().numpy(),
self.rel_pos_enc.pe.cpu().detach().numpy(),
atol=1e-4,
)
)
def test_forward(self):
pos_enc = self.rel_pos_enc(self.sample)
expected_pos_enc = torch.tensor(
[
[[0.9093, -0.4161]],
[[0.8415, 0.5403]],
[[0.0000, 1.0000]],
[[-0.8415, 0.5403]],
[[-0.9093, -0.4161]],
]
)
self.assertTrue(
np.allclose(
pos_enc.cpu().detach().numpy(),
expected_pos_enc.cpu().detach().numpy(),
atol=1e-4,
)
)
if __name__ == "__main__":
unittest.main()
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