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Summary: # Before submitting - [ ] Was this discussed/approved via a Github issue? (no need for typos, doc improvements) - [ ] Did you read the [contributor guideline](https://github.com/pytorch/fairseq/blob/main/CONTRIBUTING.md)? - [ ] Did you make sure to update the docs? - [ ] Did you write any new necessary tests? ## What does this PR do? Pulling out some changes from https://github.com/fairinternal/fairseq-py/pull/2263 unrelated to xformers to make the PR cleaner ## PR review Anyone in the community is free to review the PR once the tests have passed. If we didn't discuss your PR in Github issues there's a high chance it will not be merged. ## Did you have fun? Make sure you had fun coding � X-link: https://github.com/fairinternal/fairseq-py/pull/3068 Reviewed By: blefaudeux Differential Revision: D34149016 Pulled By: dianaml0 fbshipit-source-id: 6442a5f451d56cc47106227298a624516b19a9ad
173 lines
5.6 KiB
Python
173 lines
5.6 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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def test_mask_padding_parity():
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def old_padding_code(key_padding_mask, attn_mask):
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if attn_mask is not None:
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attn_mask = torch.cat(
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[attn_mask, attn_mask.new_zeros(attn_mask.size(0), 1)], dim=1
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)
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if key_padding_mask is not None:
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key_padding_mask = torch.cat(
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[
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key_padding_mask,
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torch.zeros(key_padding_mask.size(0), 1).type_as(
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key_padding_mask
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),
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],
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dim=1,
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)
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return key_padding_mask, attn_mask
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# values don't matter for this test.
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mha = MultiheadAttention(
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embedding=8,
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num_heads=2,
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dropout=0.0,
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add_bias_kv=True,
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add_zero_attn=True,
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)
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key_padding_mask = torch.rand((8, 64))
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attn_mask = torch.rand((64, 64))
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kp_mask_orig, a_mask_orig = old_padding_code(key_padding_mask, attn_mask)
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kp_mask_new, a_mask_new = mha._pad_masks(key_padding_mask, attn_mask)
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assert kp_mask_orig.size() == kp_mask_new.size()
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assert a_mask_orig.size() == a_mask_new.size()
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assert torch.equal(kp_mask_orig, kp_mask_new)
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assert torch.equal(a_mask_orig, a_mask_new)
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def test_add_bias_parity():
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# values don't matter for this test.
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mha = MultiheadAttention(
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embedding=8,
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num_heads=2,
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dropout=0.0,
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add_bias_kv=True,
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add_zero_attn=True,
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)
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def old_bias_code(k, v, key_padding_mask, attn_mask, bsz):
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k = torch.cat([k, mha.bias_k.repeat(1, bsz, 1)])
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v = torch.cat([v, mha.bias_v.repeat(1, bsz, 1)])
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if attn_mask is not None:
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attn_mask = torch.cat(
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[attn_mask, attn_mask.new_zeros(attn_mask.size(0), 1)], dim=1
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)
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if key_padding_mask is not None:
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key_padding_mask = torch.cat(
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[
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key_padding_mask,
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key_padding_mask.new_zeros(key_padding_mask.size(0), 1),
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],
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dim=1,
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)
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return k, v, key_padding_mask, attn_mask
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seq_len = 64
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bsz = 8
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embedding = 8
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key_padding_mask = torch.rand((bsz, seq_len))
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attn_mask = torch.rand((seq_len, seq_len))
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k = torch.rand((seq_len, bsz, embedding))
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v = torch.rand((seq_len, bsz, embedding))
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k_orig, v_orig, kp_mask_orig, a_mask_orig = old_bias_code(k, v, key_padding_mask, attn_mask, bsz)
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k_new, v_new, kp_mask_new, a_mask_new = mha._add_bias(k, v, key_padding_mask, attn_mask, bsz)
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assert torch.equal(k_orig, k_new)
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assert torch.equal(v_orig, v_new)
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assert torch.equal(kp_mask_orig, kp_mask_new)
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assert torch.equal(a_mask_orig, a_mask_new)
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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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