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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
65 lines
2.0 KiB
Python
65 lines
2.0 KiB
Python
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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