mirror of
https://github.com/aqlaboratory/openfold.git
synced 2026-06-07 06:14:25 +08:00
54 lines
1.6 KiB
Python
54 lines
1.6 KiB
Python
# Copyright 2021 AlQuraishi Laboratory
|
|
#
|
|
# Licensed under the Apache License, Version 2.0 (the "License");
|
|
# you may not use this file except in compliance with the License.
|
|
# You may obtain a copy of the License at
|
|
#
|
|
# http://www.apache.org/licenses/LICENSE-2.0
|
|
#
|
|
# Unless required by applicable law or agreed to in writing, software
|
|
# distributed under the License is distributed on an "AS IS" BASIS,
|
|
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
|
|
# See the License for the specific language governing permissions and
|
|
# limitations under the License.
|
|
|
|
import torch
|
|
import numpy as np
|
|
import unittest
|
|
|
|
from openfold.model.primitives import (
|
|
Attention,
|
|
)
|
|
from tests.config import consts
|
|
|
|
|
|
class TestLMA(unittest.TestCase):
|
|
def test_lma_vs_attention(self):
|
|
batch_size = consts.batch_size
|
|
c_hidden = 32
|
|
n = 2**12
|
|
no_heads = 4
|
|
|
|
q = torch.rand(batch_size, n, c_hidden).cuda()
|
|
kv = torch.rand(batch_size, n, c_hidden).cuda()
|
|
|
|
bias = [torch.rand(no_heads, 1, n)]
|
|
bias = [b.cuda() for b in bias]
|
|
|
|
gating_fill = torch.rand(c_hidden * no_heads, c_hidden)
|
|
o_fill = torch.rand(c_hidden, c_hidden * no_heads)
|
|
|
|
a = Attention(
|
|
c_hidden, c_hidden, c_hidden, c_hidden, no_heads
|
|
).cuda()
|
|
|
|
with torch.no_grad():
|
|
l = a(q, kv, biases=bias, use_lma=True)
|
|
real = a(q, kv, biases=bias)
|
|
|
|
self.assertTrue(torch.max(torch.abs(l - real)) < consts.eps)
|
|
|
|
|
|
if __name__ == "__main__":
|
|
unittest.main()
|