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test_msa.py
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# 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.msa import (
MSARowAttentionWithPairBias,
MSAColumnAttention,
MSAColumnGlobalAttention,
)
from openfold.utils.tensor_utils import tree_map
import tests.compare_utils as compare_utils
from tests.config import consts
if compare_utils.alphafold_is_installed():
alphafold = compare_utils.import_alphafold()
import jax
import haiku as hk
class TestMSARowAttentionWithPairBias(unittest.TestCase):
def test_shape(self):
batch_size = consts.batch_size
n_seq = consts.n_seq
n_res = consts.n_res
c_m = consts.c_m
c_z = consts.c_z
c = 52
no_heads = 4
chunk_size = None
mrapb = MSARowAttentionWithPairBias(c_m, c_z, c, no_heads)
m = torch.rand((batch_size, n_seq, n_res, c_m))
z = torch.rand((batch_size, n_res, n_res, c_z))
shape_before = m.shape
m = mrapb(m, z=z, chunk_size=chunk_size)
shape_after = m.shape
self.assertTrue(shape_before == shape_after)
@compare_utils.skip_unless_alphafold_installed()
def test_compare(self):
def run_msa_row_att(msa_act, msa_mask, pair_act):
config = compare_utils.get_alphafold_config()
c_e = config.model.embeddings_and_evoformer.evoformer
msa_row = alphafold.model.modules.MSARowAttentionWithPairBias(
c_e.msa_row_attention_with_pair_bias, config.model.global_config
)
act = msa_row(msa_act=msa_act, msa_mask=msa_mask, pair_act=pair_act)
return act
f = hk.transform(run_msa_row_att)
n_res = consts.n_res
n_seq = consts.n_seq
msa_act = np.random.rand(n_seq, n_res, consts.c_m).astype(np.float32)
msa_mask = np.random.randint(low=0, high=2, size=(n_seq, n_res)).astype(
np.float32
)
pair_act = np.random.rand(n_res, n_res, consts.c_z).astype(np.float32)
# Fetch pretrained parameters (but only from one block)]
params = compare_utils.fetch_alphafold_module_weights(
"alphafold/alphafold_iteration/evoformer/evoformer_iteration/"
+ "msa_row_attention"
)
params = tree_map(lambda n: n[0], params, jax.Array)
out_gt = f.apply(
params, None, msa_act, msa_mask, pair_act
).block_until_ready()
out_gt = torch.as_tensor(np.array(out_gt))
model = compare_utils.get_global_pretrained_openfold()
out_repro = (
model.evoformer.blocks[0].msa_att_row(
torch.as_tensor(msa_act).cuda(),
z=torch.as_tensor(pair_act).cuda(),
chunk_size=4,
mask=torch.as_tensor(msa_mask).cuda(),
)
).cpu()
compare_utils.assert_mean_abs_diff_small(out_gt, out_repro, consts.eps)
class TestMSAColumnAttention(unittest.TestCase):
def test_shape(self):
batch_size = consts.batch_size
n_seq = consts.n_seq
n_res = consts.n_res
c_m = consts.c_m
c = 44
no_heads = 4
msaca = MSAColumnAttention(c_m, c, no_heads)
x = torch.rand((batch_size, n_seq, n_res, c_m))
shape_before = x.shape
x = msaca(x, chunk_size=None)
shape_after = x.shape
self.assertTrue(shape_before == shape_after)
@compare_utils.skip_unless_alphafold_installed()
def test_compare(self):
def run_msa_col_att(msa_act, msa_mask):
config = compare_utils.get_alphafold_config()
c_e = config.model.embeddings_and_evoformer.evoformer
msa_col = alphafold.model.modules.MSAColumnAttention(
c_e.msa_column_attention, config.model.global_config
)
act = msa_col(msa_act=msa_act, msa_mask=msa_mask)
return act
f = hk.transform(run_msa_col_att)
n_res = consts.n_res
n_seq = consts.n_seq
msa_act = np.random.rand(n_seq, n_res, consts.c_m).astype(np.float32)
msa_mask = np.random.randint(low=0, high=2, size=(n_seq, n_res)).astype(
np.float32
)
# Fetch pretrained parameters (but only from one block)]
params = compare_utils.fetch_alphafold_module_weights(
"alphafold/alphafold_iteration/evoformer/evoformer_iteration/"
+ "msa_column_attention"
)
params = tree_map(lambda n: n[0], params, jax.Array)
out_gt = f.apply(params, None, msa_act, msa_mask).block_until_ready()
out_gt = torch.as_tensor(np.array(out_gt))
model = compare_utils.get_global_pretrained_openfold()
out_repro = (
model.evoformer.blocks[0].msa_att_col(
torch.as_tensor(msa_act).cuda(),
chunk_size=4,
mask=torch.as_tensor(msa_mask).cuda(),
)
).cpu()
compare_utils.assert_mean_abs_diff_small(out_gt, out_repro, consts.eps)
class TestMSAColumnGlobalAttention(unittest.TestCase):
def test_shape(self):
batch_size = consts.batch_size
n_seq = consts.n_seq
n_res = consts.n_res
c_m = consts.c_m
c = 44
no_heads = 4
msagca = MSAColumnGlobalAttention(c_m, c, no_heads)
x = torch.rand((batch_size, n_seq, n_res, c_m))
shape_before = x.shape
x = msagca(x, chunk_size=None)
shape_after = x.shape
self.assertTrue(shape_before == shape_after)
@compare_utils.skip_unless_alphafold_installed()
def test_compare(self):
def run_msa_col_global_att(msa_act, msa_mask):
config = compare_utils.get_alphafold_config()
c_e = config.model.embeddings_and_evoformer.evoformer
msa_col = alphafold.model.modules.MSAColumnGlobalAttention(
c_e.msa_column_attention,
config.model.global_config,
name="msa_column_global_attention",
)
act = msa_col(msa_act=msa_act, msa_mask=msa_mask)
return act
f = hk.transform(run_msa_col_global_att)
n_res = consts.n_res
n_seq = consts.n_seq
c_e = consts.c_e
msa_act = np.random.rand(n_seq, n_res, c_e)
msa_mask = np.random.randint(low=0, high=2, size=(n_seq, n_res))
# Fetch pretrained parameters (but only from one block)]
params = compare_utils.fetch_alphafold_module_weights(
"alphafold/alphafold_iteration/evoformer/extra_msa_stack/"
+ "msa_column_global_attention"
)
params = tree_map(lambda n: n[0], params, jax.Array)
out_gt = f.apply(params, None, msa_act, msa_mask).block_until_ready()
out_gt = torch.as_tensor(np.array(out_gt.block_until_ready()))
model = compare_utils.get_global_pretrained_openfold()
out_repro = (
model.extra_msa_stack.blocks[0].msa_att_col(
torch.as_tensor(msa_act, dtype=torch.float32).cuda(),
chunk_size=4,
mask=torch.as_tensor(msa_mask, dtype=torch.float32).cuda(),
)
.cpu()
)
compare_utils.assert_max_abs_diff_small(out_gt, out_repro, consts.eps)
if __name__ == "__main__":
unittest.main()