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import unittest |
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import torch |
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from protenix.model.modules.primitives import ( |
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rearrange_qk_to_dense_trunk, |
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rearrange_to_dense_trunk, |
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) |
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def create_qkv(batch_size_dims, n_q, n_kv, d): |
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q = torch.rand(size=(*batch_size_dims, n_q, d)) |
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k = torch.rand(size=(*batch_size_dims, n_kv, d)) |
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v = torch.rand(size=(*batch_size_dims, n_kv, d)) |
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return q, k, v |
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class TestUtils(unittest.TestCase): |
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def setUp(self): |
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return super().setUp() |
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def test_equivalence(self): |
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batch_size_dims = (3, 5) |
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n = 128 * 2 + 18 |
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d = 9 |
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n_queries = 32 |
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n_keys = 128 |
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inf = 10e10 |
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torch.random.manual_seed(42) |
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q, k, v = create_qkv(batch_size_dims, n, n, d) |
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q_trunked, k_trunked, _, attn_bias_trunked, q_pad_length = ( |
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rearrange_to_dense_trunk( |
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q, |
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k, |
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v, |
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n_queries, |
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n_keys, |
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inf=inf, |
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) |
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) |
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q_b, k_b, padding_info = rearrange_qk_to_dense_trunk( |
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q, k, dim_q=-2, dim_k=-2, n_queries=n_queries, n_keys=n_keys |
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) |
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self.assertTrue( |
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torch.allclose( |
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padding_info["mask_trunked"] > 0, attn_bias_trunked[0, 0] > -1 |
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) |
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) |
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self.assertTrue(torch.allclose(q_b, q_trunked)) |
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self.assertTrue(torch.allclose(k_b, k_trunked)) |
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if __name__ == "__main__": |
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unittest.main() |
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