mirror of
https://github.com/aqlaboratory/openfold.git
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118 lines
3.8 KiB
Python
118 lines
3.8 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 copy
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import torch
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import numpy as np
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import unittest
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from openfold.model.triangular_attention import TriangleAttention
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from openfold.utils.tensor_utils import tree_map
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import tests.compare_utils as compare_utils
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from tests.config import consts
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if compare_utils.alphafold_is_installed():
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alphafold = compare_utils.import_alphafold()
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import jax
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import haiku as hk
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class TestTriangularAttention(unittest.TestCase):
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def test_shape(self):
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c_z = consts.c_z
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c = 12
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no_heads = 4
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starting = True
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tan = TriangleAttention(c_z, c, no_heads, starting)
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batch_size = consts.batch_size
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n_res = consts.n_res
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x = torch.rand((batch_size, n_res, n_res, c_z))
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shape_before = x.shape
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x = tan(x, chunk_size=None)
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shape_after = x.shape
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self.assertTrue(shape_before == shape_after)
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def _tri_att_compare(self, starting=False):
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name = (
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"triangle_attention_"
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+ ("starting" if starting else "ending")
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+ "_node"
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)
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def run_tri_att(pair_act, pair_mask):
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config = compare_utils.get_alphafold_config()
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c_e = config.model.embeddings_and_evoformer.evoformer
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tri_att = alphafold.model.modules.TriangleAttention(
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c_e.triangle_attention_starting_node
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if starting
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else c_e.triangle_attention_ending_node,
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config.model.global_config,
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name=name,
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)
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act = tri_att(pair_act=pair_act, pair_mask=pair_mask)
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return act
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f = hk.transform(run_tri_att)
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n_res = consts.n_res
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pair_act = np.random.rand(n_res, n_res, consts.c_z) * 100
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pair_mask = np.random.randint(low=0, high=2, size=(n_res, n_res))
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# Fetch pretrained parameters (but only from one block)]
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params = compare_utils.fetch_alphafold_module_weights(
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"alphafold/alphafold_iteration/evoformer/evoformer_iteration/"
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+ name
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)
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params = tree_map(lambda n: n[0], params, jax.Array)
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out_gt = f.apply(params, None, pair_act, pair_mask).block_until_ready()
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out_gt = torch.as_tensor(np.array(out_gt))
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model = compare_utils.get_global_pretrained_openfold()
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module = (
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model.evoformer.blocks[0].pair_stack.tri_att_start
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if starting
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else model.evoformer.blocks[0].pair_stack.tri_att_end
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)
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# To save memory, the full model transposes inputs outside of the
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# triangle attention module. We adjust the module here.
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module = copy.deepcopy(module)
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module.starting = starting
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out_repro = module(
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torch.as_tensor(pair_act, dtype=torch.float32).cuda(),
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mask=torch.as_tensor(pair_mask, dtype=torch.float32).cuda(),
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chunk_size=None,
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).cpu()
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compare_utils.assert_mean_abs_diff_small(out_gt, out_repro, consts.eps)
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@compare_utils.skip_unless_alphafold_installed()
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def test_tri_att_end_compare(self):
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self._tri_att_compare()
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@compare_utils.skip_unless_alphafold_installed()
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def test_tri_att_start_compare(self):
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self._tri_att_compare(starting=True)
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if __name__ == "__main__":
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unittest.main()
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