test_structure_module.py

# 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.data.data_transforms import make_atom14_masks_np
from openfold.np.residue_constants import (
    restype_atom14_mask,
    restype_atom37_mask,
)
from openfold.model.structure_module import (
    StructureModule,
    StructureModuleTransition,
    AngleResnet,
    InvariantPointAttention,
)
from openfold.utils.rigid_utils import Rotation, Rigid
from openfold.utils.geometry.rigid_matrix_vector import Rigid3Array
from openfold.utils.geometry.rotation_matrix import Rot3Array
from openfold.utils.geometry.vector import Vec3Array
import tests.compare_utils as compare_utils
from tests.config import consts
from tests.data_utils import (
    random_affines_4x4,
)

if compare_utils.alphafold_is_installed():
    alphafold = compare_utils.import_alphafold()
    import jax
    import haiku as hk


class TestStructureModule(unittest.TestCase):
    @classmethod
    def setUpClass(cls):
        if consts.is_multimer:
            cls.am_atom = alphafold.model.all_atom_multimer
            cls.am_fold = alphafold.model.folding_multimer
            cls.am_modules = alphafold.model.modules_multimer
            cls.am_rigid = alphafold.model.geometry
        else:
            cls.am_atom = alphafold.model.all_atom
            cls.am_fold = alphafold.model.folding
            cls.am_modules = alphafold.model.modules
            cls.am_rigid = alphafold.model.r3

    def test_structure_module_shape(self):
        batch_size = consts.batch_size
        n = consts.n_res
        c_s = consts.c_s
        c_z = consts.c_z
        c_ipa = 13
        c_resnet = 17
        no_heads_ipa = 6
        no_query_points = 4
        no_value_points = 4
        dropout_rate = 0.1
        no_layers = 3
        no_transition_layers = 3
        no_resnet_layers = 3
        ar_epsilon = 1e-6
        no_angles = 7
        trans_scale_factor = 10
        inf = 1e5

        sm = StructureModule(
            c_s,
            c_z,
            c_ipa,
            c_resnet,
            no_heads_ipa,
            no_query_points,
            no_value_points,
            dropout_rate,
            no_layers,
            no_transition_layers,
            no_resnet_layers,
            no_angles,
            trans_scale_factor,
            ar_epsilon,
            inf,
            is_multimer=consts.is_multimer
        )

        s = torch.rand((batch_size, n, c_s))
        z = torch.rand((batch_size, n, n, c_z))
        f = torch.randint(low=0, high=21, size=(batch_size, n)).long()

        out = sm(s, z, f)

        if consts.is_multimer:
            self.assertTrue(out["frames"].shape == (no_layers, batch_size, n, 4, 4))
        else:
            self.assertTrue(out["frames"].shape == (no_layers, batch_size, n, 7))

        self.assertTrue(
            out["angles"].shape == (no_layers, batch_size, n, no_angles, 2)
        )
        self.assertTrue(
            out["positions"].shape == (no_layers, batch_size, n, 14, 3)
        )

    def test_structure_module_transition_shape(self):
        batch_size = 2
        n = 5
        c = 7
        num_layers = 3
        dropout = 0.1

        smt = StructureModuleTransition(c, num_layers, dropout)

        s = torch.rand((batch_size, n, c))

        shape_before = s.shape
        s = smt(s)
        shape_after = s.shape

        self.assertTrue(shape_before == shape_after)

    @compare_utils.skip_unless_alphafold_installed()
    def test_structure_module_compare(self):
        config = compare_utils.get_alphafold_config()
        c_sm = config.model.heads.structure_module
        c_global = config.model.global_config

        def run_sm(representations, batch):
            sm = self.am_fold.StructureModule(c_sm, c_global)
            representations = {
                k: jax.lax.stop_gradient(v) for k, v in representations.items()
            }
            batch = {k: jax.lax.stop_gradient(v) for k, v in batch.items()}

            if consts.is_multimer:
                return sm(representations, batch, is_training=False, compute_loss=True)
            return sm(representations, batch, is_training=False)

        f = hk.transform(run_sm)

        n_res = 200

        representations = {
            "single": np.random.rand(n_res, consts.c_s).astype(np.float32),
            "pair": np.random.rand(n_res, n_res, consts.c_z).astype(np.float32),
        }

        batch = {
            "seq_mask": np.random.randint(0, 2, (n_res,)).astype(np.float32),
            "aatype": np.random.randint(0, 21, (n_res,)),
        }

        batch["atom14_atom_exists"] = np.take(
            restype_atom14_mask, batch["aatype"], axis=0
        )

        batch["atom37_atom_exists"] = np.take(
            restype_atom37_mask, batch["aatype"], axis=0
        )

        batch.update(make_atom14_masks_np(batch))

        params = compare_utils.fetch_alphafold_module_weights(
            "alphafold/alphafold_iteration/structure_module"
        )

        key = jax.random.PRNGKey(42)
        out_gt = f.apply(params, key, representations, batch)
        out_gt = torch.as_tensor(
            np.array(out_gt["final_atom14_positions"].block_until_ready())
        )

        model = compare_utils.get_global_pretrained_openfold()
        out_repro = model.structure_module(
            torch.as_tensor(representations["single"]).cuda(),
            torch.as_tensor(representations["pair"]).cuda(),
            torch.as_tensor(batch["aatype"]).cuda(),
            mask=torch.as_tensor(batch["seq_mask"]).cuda(),
        )
        out_repro = out_repro["positions"][-1].cpu()

        # The structure module, thanks to angle normalization, is very volatile
        # We only assess the mean here. Heuristically speaking, it seems to
        # have lower error in general on real rather than synthetic data.
        self.assertTrue(torch.mean(torch.abs(out_gt - out_repro)) < 0.05)


class TestInvariantPointAttention(unittest.TestCase):
    @classmethod
    def setUpClass(cls):
        if consts.is_multimer:
            cls.am_atom = alphafold.model.all_atom_multimer
            cls.am_fold = alphafold.model.folding_multimer
            cls.am_modules = alphafold.model.modules_multimer
            cls.am_rigid = alphafold.model.geometry
        else:
            cls.am_atom = alphafold.model.all_atom
            cls.am_fold = alphafold.model.folding
            cls.am_modules = alphafold.model.modules
            cls.am_rigid = alphafold.model.r3

    def test_shape(self):
        c_m = 13
        c_z = 17
        c_hidden = 19
        no_heads = 5
        no_qp = 7
        no_vp = 11

        batch_size = 2
        n_res = 23

        s = torch.rand((batch_size, n_res, c_m))
        z = torch.rand((batch_size, n_res, n_res, c_z))
        mask = torch.ones((batch_size, n_res))

        rot_mats = torch.rand((batch_size, n_res, 3, 3))
        trans = torch.rand((batch_size, n_res, 3))

        if consts.is_multimer:
            rotation = Rot3Array.from_array(rot_mats)
            translation = Vec3Array.from_array(trans)
            r = Rigid3Array(rotation, translation)
        else:
            rots = Rotation(rot_mats=rot_mats, quats=None)
            r = Rigid(rots, trans)

        ipa = InvariantPointAttention(
            c_m, c_z, c_hidden, no_heads, no_qp, no_vp, is_multimer=consts.is_multimer
        )

        shape_before = s.shape
        s = ipa(s, z, r, mask)

        self.assertTrue(s.shape == shape_before)

    @compare_utils.skip_unless_alphafold_installed()
    def test_ipa_compare(self):
        def run_ipa(act, static_feat_2d, mask, affine):
            config = compare_utils.get_alphafold_config()
            ipa = self.am_fold.InvariantPointAttention(
                config.model.heads.structure_module,
                config.model.global_config,
            )

            if consts.is_multimer:
                attn = ipa(
                    inputs_1d=act,
                    inputs_2d=static_feat_2d,
                    mask=mask,
                    rigid=affine
                )
            else:
                attn = ipa(
                    inputs_1d=act,
                    inputs_2d=static_feat_2d,
                    mask=mask,
                    affine=affine
                )

            return attn

        f = hk.transform(run_ipa)

        n_res = consts.n_res
        c_s = consts.c_s
        c_z = consts.c_z

        sample_act = np.random.rand(n_res, c_s)
        sample_2d = np.random.rand(n_res, n_res, c_z)
        sample_mask = np.ones((n_res, 1))

        affines = random_affines_4x4((n_res,))

        if consts.is_multimer:
            rigids = self.am_rigid.Rigid3Array.from_array4x4(affines)
            transformations = Rigid3Array.from_tensor_4x4(
                torch.as_tensor(affines).float()
            )
            sample_affine = rigids
        else:
            rigids = self.am_rigid.rigids_from_tensor4x4(affines)
            quats = self.am_rigid.rigids_to_quataffine(rigids)
            transformations = Rigid.from_tensor_4x4(
                torch.as_tensor(affines).float().cuda()
            )
            sample_affine = quats

        ipa_params = compare_utils.fetch_alphafold_module_weights(
            "alphafold/alphafold_iteration/structure_module/"
            + "fold_iteration/invariant_point_attention"
        )

        out_gt = f.apply(
            ipa_params, None, sample_act, sample_2d, sample_mask, sample_affine
        ).block_until_ready()
        out_gt = torch.as_tensor(np.array(out_gt))

        with torch.no_grad():
            model = compare_utils.get_global_pretrained_openfold()
            out_repro = model.structure_module.ipa(
                torch.as_tensor(sample_act).float().cuda(),
                torch.as_tensor(sample_2d).float().cuda(),
                transformations,
                torch.as_tensor(sample_mask.squeeze(-1)).float().cuda(),
            ).cpu()

        self.assertTrue(torch.max(torch.abs(out_gt - out_repro)) < consts.eps)


class TestAngleResnet(unittest.TestCase):
    def test_shape(self):
        batch_size = 2
        n = 3
        c_s = 13
        c_hidden = 11
        no_layers = 5
        no_angles = 7
        epsilon = 1e-12

        ar = AngleResnet(c_s, c_hidden, no_layers, no_angles, epsilon)
        a = torch.rand((batch_size, n, c_s))
        a_initial = torch.rand((batch_size, n, c_s))

        _, a = ar(a, a_initial)

        self.assertTrue(a.shape == (batch_size, n, no_angles, 2))


if __name__ == "__main__":
    unittest.main()