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https://github.com/facebookresearch/fairseq.git
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995c204337
Summary: Preliminaries for data2vec release, include some minor improvements and bug fixes Most important change is that we now default to raising an exception when fields in config do not have a corresponding field in the model dataclass Pull Request resolved: https://github.com/fairinternal/fairseq-py/pull/2929 Reviewed By: wnhsu Differential Revision: D33649708 Pulled By: alexeib fbshipit-source-id: 629bdb4c361550740b451c570c2005bb956c6fcb
89 lines
2.9 KiB
Python
89 lines
2.9 KiB
Python
# Copyright (c) Facebook, Inc. and its affiliates.
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#
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# This source code is licensed under the MIT license found in the
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# LICENSE file in the root directory of this source tree.
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import unittest
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import torch
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from fairseq.modules.multihead_attention import MultiheadAttention
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class TestMultiheadAttention(unittest.TestCase):
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def test_append_prev_key_padding_mask(self):
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bsz = 1
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src_len = 4
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cases = [
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# no padding mask
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(None, None, None),
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# current padding mask only
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(
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torch.tensor([[1]]).bool(),
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None,
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torch.tensor([[0, 0, 0, 1]]).bool(),
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),
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# previous padding mask only
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(
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None,
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torch.tensor([[0, 1, 0]]).bool(),
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torch.tensor([[0, 1, 0, 0]]).bool(),
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),
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# both padding masks
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(
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torch.tensor([[1]]).bool(),
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torch.tensor([[0, 1, 0]]).bool(),
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torch.tensor([[0, 1, 0, 1]]).bool(),
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),
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# prev_key_padding_mask already full
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(
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torch.tensor([[0, 1, 0, 1]]).bool(),
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None,
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torch.tensor([[0, 1, 0, 1]]).bool(),
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),
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# key_padding_mask already full
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(
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None,
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torch.tensor([[0, 1, 0, 1]]).bool(),
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torch.tensor([[0, 1, 0, 1]]).bool(),
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),
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]
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for c in cases:
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key_padding_mask = MultiheadAttention._append_prev_key_padding_mask(
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c[0],
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c[1],
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batch_size=bsz,
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src_len=src_len,
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static_kv=False,
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)
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if key_padding_mask is not None:
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self.assertTrue(
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torch.all(torch.eq(key_padding_mask, c[2])),
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f"Unexpected resultant key padding mask: {key_padding_mask}"
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f" given current: {c[0]} and previous: {c[1]}",
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)
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self.assertEqual(key_padding_mask.size(0), bsz)
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self.assertEqual(key_padding_mask.size(1), src_len)
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else:
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self.assertIsNone(c[2])
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def test_pruning_heads(self):
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embed_dim = 768
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num_heads = 12
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num_heads_to_keep = 8
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dummy_input = torch.randn(32, 2, embed_dim)
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mha = MultiheadAttention(embed_dim=embed_dim, num_heads=num_heads)
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reserve_head_index = mha._get_reserve_head_index(
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num_heads_to_keep=num_heads_to_keep
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)
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mha._adaptive_prune_heads(reserve_head_index=reserve_head_index)
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mha._set_skip_embed_dim_check()
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mha(query=dummy_input, key=dummy_input, value=dummy_input)
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self.assertEqual(mha.head_dim, embed_dim / num_heads)
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self.assertEqual(mha.num_heads, num_heads_to_keep)
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if __name__ == "__main__":
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unittest.main()
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