230 lines
7.7 KiB
Python
230 lines
7.7 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.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()
|