fairseq/tests/test_positional_encoding.py

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()