-
Notifications
You must be signed in to change notification settings - Fork 85
/
Copy pathdemo.py
153 lines (125 loc) · 5.84 KB
/
demo.py
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
# Copyright 2023 HPC-AI Tech Inc.
# Copyright 2021 AlQuraishi Laboratory
# Copyright 2021 DeepMind Technologies Limited
#
# 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 argparse
import os
import time
import fastfold
import numpy as np
import torch
import torch.multiprocessing as mp
from fastfold.config import model_config
from fastfold.data import data_transforms
from fastfold.model.fastnn import set_chunk_size
from fastfold.model.hub import AlphaFold
from fastfold.utils.inject_fastnn import inject_fastnn
from fastfold.utils.tensor_utils import tensor_tree_map
if int(torch.__version__.split(".")[0]) >= 1 and int(torch.__version__.split(".")[1]) > 11:
torch.backends.cuda.matmul.allow_tf32 = True
def random_template_feats(n_templ, n):
b = []
batch = {
"template_mask": np.random.randint(0, 2, (*b, n_templ)),
"template_pseudo_beta_mask": np.random.randint(0, 2, (*b, n_templ, n)),
"template_pseudo_beta": np.random.rand(*b, n_templ, n, 3),
"template_aatype": np.random.randint(0, 22, (*b, n_templ, n)),
"template_all_atom_mask": np.random.randint(0, 2, (*b, n_templ, n, 37)),
"template_all_atom_positions": np.random.rand(*b, n_templ, n, 37, 3) * 10,
"template_torsion_angles_sin_cos": np.random.rand(*b, n_templ, n, 7, 2),
"template_alt_torsion_angles_sin_cos": np.random.rand(*b, n_templ, n, 7, 2),
"template_torsion_angles_mask": np.random.rand(*b, n_templ, n, 7),
}
batch = {k: v.astype(np.float32) for k, v in batch.items()}
batch["template_aatype"] = batch["template_aatype"].astype(np.int64)
return batch
def random_extra_msa_feats(n_extra, n):
b = []
batch = {
"extra_msa": np.random.randint(0, 22, (*b, n_extra, n)).astype(np.int64),
"extra_has_deletion": np.random.randint(0, 2, (*b, n_extra, n)).astype(np.float32),
"extra_deletion_value": np.random.rand(*b, n_extra, n).astype(np.float32),
"extra_msa_mask": np.random.randint(0, 2, (*b, n_extra, n)).astype(np.float32),
}
return batch
def generate_batch(n_res):
batch = {}
tf = torch.randint(21, size=(n_res,))
batch["target_feat"] = torch.nn.functional.one_hot(tf, 22).float()
batch["aatype"] = torch.argmax(batch["target_feat"], dim=-1)
batch["residue_index"] = torch.arange(n_res)
batch["msa_feat"] = torch.rand((128, n_res, 49))
t_feats = random_template_feats(4, n_res)
batch.update({k: torch.tensor(v) for k, v in t_feats.items()})
extra_feats = random_extra_msa_feats(5120, n_res)
batch.update({k: torch.tensor(v) for k, v in extra_feats.items()})
batch["msa_mask"] = torch.randint(low=0, high=2, size=(128, n_res)).float()
batch["seq_mask"] = torch.randint(low=0, high=2, size=(n_res,)).float()
batch.update(data_transforms.make_atom14_masks(batch))
batch["no_recycling_iters"] = torch.tensor(2.)
add_recycling_dims = lambda t: (t.unsqueeze(-1).expand(*t.shape, 3))
batch = tensor_tree_map(add_recycling_dims, batch)
return batch
def inference_model(rank, world_size, result_q, batch, args):
os.environ['RANK'] = str(rank)
os.environ['LOCAL_RANK'] = str(rank)
os.environ['WORLD_SIZE'] = str(world_size)
# init distributed for Dynamic Axial Parallelism
fastfold.distributed.init_dap()
torch.cuda.set_device(rank)
config = model_config(args.model_name)
if args.chunk_size:
config.globals.chunk_size = args.chunk_size
config.globals.inplace = args.inplace
config.globals.is_multimer = False
model = AlphaFold(config)
model = inject_fastnn(model)
model = model.eval()
model = model.cuda()
set_chunk_size(model.globals.chunk_size)
with torch.no_grad():
batch = {k: torch.as_tensor(v).cuda() for k, v in batch.items()}
t = time.perf_counter()
out = model(batch)
print(f"Inference time: {time.perf_counter() - t}")
out = tensor_tree_map(lambda x: np.array(x.cpu()), out)
result_q.put(out)
torch.distributed.barrier()
torch.cuda.synchronize()
def inference_monomer_model(args):
batch = generate_batch(args.n_res)
manager = mp.Manager()
result_q = manager.Queue()
torch.multiprocessing.spawn(inference_model, nprocs=args.gpus, args=(args.gpus, result_q, batch, args))
out = result_q.get()
# get unrelexed pdb and save
# batch = tensor_tree_map(lambda x: np.array(x[..., -1].cpu()), batch)
# plddt = out["plddt"]
# plddt_b_factors = np.repeat(plddt[..., None], residue_constants.atom_type_num, axis=-1)
# unrelaxed_protein = protein.from_prediction(features=batch,
# result=out,
# b_factors=plddt_b_factors)
# with open('demo_unrelex.pdb', 'w+') as fp:
# fp.write(unrelaxed_protein)
def main(args):
inference_monomer_model(args)
if __name__ == "__main__":
parser = argparse.ArgumentParser()
parser.add_argument("--gpus", type=int, default=1, help="""Number of GPUs with which to run inference""")
parser.add_argument("--n_res", type=int, default=50, help="virtual residue number of random data")
parser.add_argument("--model_name", type=str, default="model_1", help="model name of alphafold")
parser.add_argument('--chunk_size', type=int, default=None)
parser.add_argument('--inplace', default=False, action='store_true')
args = parser.parse_args()
main(args)