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test_template.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.template import (
TemplatePointwiseAttention,
TemplatePairStack,
)
from openfold.utils.tensor_utils import tree_map
import tests.compare_utils as compare_utils
from tests.config import consts
from tests.data_utils import random_template_feats
if compare_utils.alphafold_is_installed():
alphafold = compare_utils.import_alphafold()
import jax
import haiku as hk
class TestTemplatePointwiseAttention(unittest.TestCase):
def test_shape(self):
batch_size = consts.batch_size
n_seq = consts.n_seq
c_t = consts.c_t
c_z = consts.c_z
c = 26
no_heads = 13
n_res = consts.n_res
inf = 1e7
tpa = TemplatePointwiseAttention(
c_t, c_z, c, no_heads, inf=inf
)
t = torch.rand((batch_size, n_seq, n_res, n_res, c_t))
z = torch.rand((batch_size, n_res, n_res, c_z))
z_update = tpa(t, z, chunk_size=None)
self.assertTrue(z_update.shape == z.shape)
class TestTemplatePairStack(unittest.TestCase):
def test_shape(self):
batch_size = consts.batch_size
c_t = consts.c_t
c_hidden_tri_att = 7
c_hidden_tri_mul = 7
no_blocks = 2
no_heads = 4
pt_inner_dim = 15
dropout = 0.25
n_templ = consts.n_templ
n_res = consts.n_res
blocks_per_ckpt = None
chunk_size = 4
inf = 1e7
eps = 1e-7
tpe = TemplatePairStack(
c_t,
c_hidden_tri_att=c_hidden_tri_att,
c_hidden_tri_mul=c_hidden_tri_mul,
no_blocks=no_blocks,
no_heads=no_heads,
pair_transition_n=pt_inner_dim,
dropout_rate=dropout,
blocks_per_ckpt=None,
inf=inf,
eps=eps,
)
t = torch.rand((batch_size, n_templ, n_res, n_res, c_t))
mask = torch.randint(0, 2, (batch_size, n_templ, n_res, n_res))
shape_before = t.shape
t = tpe(t, mask, chunk_size=chunk_size)
shape_after = t.shape
self.assertTrue(shape_before == shape_after)
@compare_utils.skip_unless_alphafold_installed()
def test_compare(self):
def run_template_pair_stack(pair_act, pair_mask):
config = compare_utils.get_alphafold_config()
c_ee = config.model.embeddings_and_evoformer
tps = alphafold.model.modules.TemplatePairStack(
c_ee.template.template_pair_stack,
config.model.global_config,
name="template_pair_stack",
)
act = tps(pair_act, pair_mask, is_training=False)
ln = hk.LayerNorm([-1], True, True, name="output_layer_norm")
act = ln(act)
return act
f = hk.transform(run_template_pair_stack)
n_res = consts.n_res
pair_act = np.random.rand(n_res, n_res, consts.c_t).astype(np.float32)
pair_mask = np.random.randint(
low=0, high=2, size=(n_res, n_res)
).astype(np.float32)
params = compare_utils.fetch_alphafold_module_weights(
"alphafold/alphafold_iteration/evoformer/template_embedding/"
+ "single_template_embedding/template_pair_stack"
)
params.update(
compare_utils.fetch_alphafold_module_weights(
"alphafold/alphafold_iteration/evoformer/template_embedding/"
+ "single_template_embedding/output_layer_norm"
)
)
out_gt = f.apply(
params, jax.random.PRNGKey(42), pair_act, pair_mask
).block_until_ready()
out_gt = torch.as_tensor(np.array(out_gt))
model = compare_utils.get_global_pretrained_openfold()
out_repro = model.template_pair_stack(
torch.as_tensor(pair_act).unsqueeze(-4).cuda(),
torch.as_tensor(pair_mask).unsqueeze(-3).cuda(),
chunk_size=None,
_mask_trans=False,
).cpu()
self.assertTrue(torch.max(torch.abs(out_gt - out_repro)) < consts.eps)
class Template(unittest.TestCase):
@compare_utils.skip_unless_alphafold_installed()
def test_compare(self):
def test_template_embedding(pair, batch, mask_2d):
config = compare_utils.get_alphafold_config()
te = alphafold.model.modules.TemplateEmbedding(
config.model.embeddings_and_evoformer.template,
config.model.global_config,
)
act = te(pair, batch, mask_2d, is_training=False)
return act
f = hk.transform(test_template_embedding)
n_res = consts.n_res
n_templ = consts.n_templ
pair_act = np.random.rand(n_res, n_res, consts.c_z).astype(np.float32)
batch = random_template_feats(n_templ, n_res)
batch["template_all_atom_masks"] = batch["template_all_atom_mask"]
pair_mask = np.random.randint(0, 2, (n_res, n_res)).astype(np.float32)
# Fetch pretrained parameters (but only from one block)]
params = compare_utils.fetch_alphafold_module_weights(
"alphafold/alphafold_iteration/evoformer/template_embedding"
)
out_gt = f.apply(
params, jax.random.PRNGKey(42), pair_act, batch, pair_mask
).block_until_ready()
out_gt = torch.as_tensor(np.array(out_gt))
inds = np.random.randint(0, 21, (n_res,))
batch["target_feat"] = np.eye(22)[inds]
model = compare_utils.get_global_pretrained_openfold()
out_repro = model.embed_templates(
{k: torch.as_tensor(v).cuda() for k, v in batch.items()},
torch.as_tensor(pair_act).cuda(),
torch.as_tensor(pair_mask).cuda(),
templ_dim=0,
)
out_repro = out_repro["template_pair_embedding"]
out_repro = out_repro.cpu()
self.assertTrue(torch.max(torch.abs(out_gt - out_repro) < consts.eps))
if __name__ == "__main__":
unittest.main()