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40ff55abbe
Summary: **This PR** - Adds conformer layer based on https://arxiv.org/pdf/2005.08100.pdf. - Conformer implementation supports multihead attention based on 3 different positional embedding types - absolute positional embedding, relative positional encoding and rotational positional embedding. - Adds conformer encoder with conv1d subsampling, positional embedding followed by N conformer layers - Adds S2T_Conformer model based on the conformer encoder and transformer decoder. - Add conformer support in Wav2Vec2 - Add unit tests for core modules **Verfication** - Verified the set up on MUST-C En-De S2T, Covost2 Es-En S2T, Librispeech ASR to ensure the implementation is correct. - For S2T setups, the performance is either similar to the transformer based models or better. - Wav2vec2 pretraining and finetuning based on librispeech showed improvements over corresponding transformer baselines. - [WIP] Experiment log: https://docs.google.com/document/d/1QI-ROWVenUEXPJoHTaKD85Fq7T8ZXNc8bc54MzgwJjA/edit# **Next steps** - Add regression tests - Add README and open source checkpoints Pull Request resolved: https://github.com/fairinternal/fairseq-py/pull/2859 Reviewed By: kahne Differential Revision: D33434092 Pulled By: sravyapopuri388 fbshipit-source-id: 62f22b917a332481370750e04a439e05832a2282
64 lines
1.7 KiB
Python
64 lines
1.7 KiB
Python
import unittest
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import torch
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from fairseq.modules import RelPositionalEncoding
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import numpy as np
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class TestRelPositionalEncoding(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.rel_pos_enc = RelPositionalEncoding(max_len=4, d_model=self.C)
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def test_extend_pe(self):
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inp = self.sample.transpose(0, 1)
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self.rel_pos_enc.extend_pe(inp)
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expected_pe = torch.tensor(
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[
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[
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[0.1411, -0.9900],
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[0.9093, -0.4161],
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[0.8415, 0.5403],
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[0.0000, 1.0000],
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[-0.8415, 0.5403],
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[-0.9093, -0.4161],
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[-0.1411, -0.9900],
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]
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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_pe.cpu().detach().numpy(),
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self.rel_pos_enc.pe.cpu().detach().numpy(),
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atol=1e-4,
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)
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)
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def test_forward(self):
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pos_enc = self.rel_pos_enc(self.sample)
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expected_pos_enc = torch.tensor(
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[
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[[0.9093, -0.4161]],
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[[0.8415, 0.5403]],
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[[0.0000, 1.0000]],
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[[-0.8415, 0.5403]],
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[[-0.9093, -0.4161]],
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]
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)
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self.assertTrue(
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np.allclose(
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pos_enc.cpu().detach().numpy(),
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expected_pos_enc.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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