53 lines
1.7 KiB
Python
53 lines
1.7 KiB
Python
# Copyright 2021 AlQuraishi Laboratory
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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import torch
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import unittest
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from openfold.model.primitives import (
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lecun_normal_init_,
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Attention,
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)
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from tests.config import consts
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from tests.data_utils import random_attention_inputs
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class TestLMA(unittest.TestCase):
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def test_lma_vs_attention(self):
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c_hidden = 32
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no_heads = 4
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q, kv, _, biases = random_attention_inputs(batch_size=consts.batch_size,
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n_seq=consts.n_seq,
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n=2 ** 12,
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no_heads=no_heads,
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c_hidden=c_hidden)
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a = Attention(
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c_hidden, c_hidden, c_hidden, c_hidden, no_heads
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).cuda()
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with torch.no_grad():
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lecun_normal_init_(a.linear_g.weight)
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lecun_normal_init_(a.linear_o.weight)
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l = a(q, kv, biases=biases, use_lma=True).cpu()
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real = a(q, kv, biases=biases).cpu()
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err = torch.max(torch.abs(l - real))
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self.assertTrue(err < consts.eps, f'Error: {err}')
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if __name__ == "__main__":
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unittest.main() |