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run_pretrained_openfold.py
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run_pretrained_openfold.py
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# 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 logging
import math
import numpy as np
import os
from openfold.utils.script_utils import load_models_from_command_line, parse_fasta, run_model, prep_output, \
update_timings, relax_protein
logging.basicConfig()
logger = logging.getLogger(__file__)
logger.setLevel(level=logging.INFO)
import pickle
import random
import time
import torch
torch_versions = torch.__version__.split(".")
torch_major_version = int(torch_versions[0])
torch_minor_version = int(torch_versions[1])
if(
torch_major_version > 1 or
(torch_major_version == 1 and torch_minor_version >= 12)
):
# Gives a large speedup on Ampere-class GPUs
torch.set_float32_matmul_precision("high")
torch.set_grad_enabled(False)
from openfold.config import model_config
from openfold.data import templates, feature_pipeline, data_pipeline
from openfold.np import residue_constants, protein
import openfold.np.relax.relax as relax
from openfold.utils.tensor_utils import (
tensor_tree_map,
)
from openfold.utils.trace_utils import (
pad_feature_dict_seq,
trace_model_,
)
from scripts.utils import add_data_args
TRACING_INTERVAL = 50
def precompute_alignments(tags, seqs, alignment_dir, args):
for tag, seq in zip(tags, seqs):
tmp_fasta_path = os.path.join(args.output_dir, f"tmp_{os.getpid()}.fasta")
with open(tmp_fasta_path, "w") as fp:
fp.write(f">{tag}\n{seq}")
local_alignment_dir = os.path.join(alignment_dir, tag)
if(args.use_precomputed_alignments is None and not os.path.isdir(local_alignment_dir)):
logger.info(f"Generating alignments for {tag}...")
os.makedirs(local_alignment_dir)
alignment_runner = data_pipeline.AlignmentRunner(
jackhmmer_binary_path=args.jackhmmer_binary_path,
hhblits_binary_path=args.hhblits_binary_path,
hhsearch_binary_path=args.hhsearch_binary_path,
uniref90_database_path=args.uniref90_database_path,
mgnify_database_path=args.mgnify_database_path,
bfd_database_path=args.bfd_database_path,
uniclust30_database_path=args.uniclust30_database_path,
pdb70_database_path=args.pdb70_database_path,
no_cpus=args.cpus,
)
alignment_runner.run(
tmp_fasta_path, local_alignment_dir
)
else:
logger.info(
f"Using precomputed alignments for {tag} at {alignment_dir}..."
)
# Remove temporary FASTA file
os.remove(tmp_fasta_path)
def round_up_seqlen(seqlen):
return int(math.ceil(seqlen / TRACING_INTERVAL)) * TRACING_INTERVAL
def generate_feature_dict(
tags,
seqs,
alignment_dir,
data_processor,
args,
):
tmp_fasta_path = os.path.join(args.output_dir, f"tmp_{os.getpid()}.fasta")
if len(seqs) == 1:
tag = tags[0]
seq = seqs[0]
with open(tmp_fasta_path, "w") as fp:
fp.write(f">{tag}\n{seq}")
local_alignment_dir = os.path.join(alignment_dir, tag)
feature_dict = data_processor.process_fasta(
fasta_path=tmp_fasta_path, alignment_dir=local_alignment_dir
)
else:
with open(tmp_fasta_path, "w") as fp:
fp.write(
'\n'.join([f">{tag}\n{seq}" for tag, seq in zip(tags, seqs)])
)
feature_dict = data_processor.process_multiseq_fasta(
fasta_path=tmp_fasta_path, super_alignment_dir=alignment_dir,
)
# Remove temporary FASTA file
os.remove(tmp_fasta_path)
return feature_dict
def list_files_with_extensions(dir, extensions):
return [f for f in os.listdir(dir) if f.endswith(extensions)]
def main(args):
# Create the output directory
os.makedirs(args.output_dir, exist_ok=True)
config = model_config(args.config_preset)
if(args.trace_model):
if(not config.data.predict.fixed_size):
raise ValueError(
"Tracing requires that fixed_size mode be enabled in the config"
)
template_featurizer = templates.TemplateHitFeaturizer(
mmcif_dir=args.template_mmcif_dir,
max_template_date=args.max_template_date,
max_hits=config.data.predict.max_templates,
kalign_binary_path=args.kalign_binary_path,
release_dates_path=args.release_dates_path,
obsolete_pdbs_path=args.obsolete_pdbs_path
)
data_processor = data_pipeline.DataPipeline(
template_featurizer=template_featurizer,
)
output_dir_base = args.output_dir
random_seed = args.data_random_seed
if random_seed is None:
random_seed = random.randrange(2**32)
np.random.seed(random_seed)
torch.manual_seed(random_seed + 1)
feature_processor = feature_pipeline.FeaturePipeline(config.data)
if not os.path.exists(output_dir_base):
os.makedirs(output_dir_base)
if args.use_precomputed_alignments is None:
alignment_dir = os.path.join(output_dir_base, "alignments")
else:
alignment_dir = args.use_precomputed_alignments
tag_list = []
seq_list = []
for fasta_file in list_files_with_extensions(args.fasta_dir, (".fasta", ".fa")):
# Gather input sequences
with open(os.path.join(args.fasta_dir, fasta_file), "r") as fp:
data = fp.read()
tags, seqs = parse_fasta(data)
# assert len(tags) == len(set(tags)), "All FASTA tags must be unique"
tag = '-'.join(tags)
tag_list.append((tag, tags))
seq_list.append(seqs)
seq_sort_fn = lambda target: sum([len(s) for s in target[1]])
sorted_targets = sorted(zip(tag_list, seq_list), key=seq_sort_fn)
feature_dicts = {}
model_generator = load_models_from_command_line(
config,
args.model_device,
args.openfold_checkpoint_path,
args.jax_param_path,
args.output_dir)
for model, output_directory in model_generator:
cur_tracing_interval = 0
for (tag, tags), seqs in sorted_targets:
output_name = f'{tag}_{args.config_preset}'
if args.output_postfix is not None:
output_name = f'{output_name}_{args.output_postfix}'
# Does nothing if the alignments have already been computed
precompute_alignments(tags, seqs, alignment_dir, args)
feature_dict = feature_dicts.get(tag, None)
if(feature_dict is None):
feature_dict = generate_feature_dict(
tags,
seqs,
alignment_dir,
data_processor,
args,
)
if(args.trace_model):
n = feature_dict["aatype"].shape[-2]
rounded_seqlen = round_up_seqlen(n)
feature_dict = pad_feature_dict_seq(
feature_dict, rounded_seqlen,
)
feature_dicts[tag] = feature_dict
processed_feature_dict = feature_processor.process_features(
feature_dict, mode='predict',
)
processed_feature_dict = {
k:torch.as_tensor(v, device=args.model_device)
for k,v in processed_feature_dict.items()
}
if(args.trace_model):
if(rounded_seqlen > cur_tracing_interval):
logger.info(
f"Tracing model at {rounded_seqlen} residues..."
)
t = time.perf_counter()
trace_model_(model, processed_feature_dict)
tracing_time = time.perf_counter() - t
logger.info(
f"Tracing time: {tracing_time}"
)
cur_tracing_interval = rounded_seqlen
out = run_model(model, processed_feature_dict, tag, args.output_dir)
# Toss out the recycling dimensions --- we don't need them anymore
processed_feature_dict = tensor_tree_map(
lambda x: np.array(x[..., -1].cpu()),
processed_feature_dict
)
out = tensor_tree_map(lambda x: np.array(x.cpu()), out)
unrelaxed_protein = prep_output(
out,
processed_feature_dict,
feature_dict,
feature_processor,
args.config_preset,
args.multimer_ri_gap
)
unrelaxed_output_path = os.path.join(
output_directory, f'{output_name}_unrelaxed.pdb'
)
with open(unrelaxed_output_path, 'w') as fp:
fp.write(protein.to_pdb(unrelaxed_protein))
logger.info(f"Output written to {unrelaxed_output_path}...")
if not args.skip_relaxation:
# Relax the prediction.
logger.info(f"Running relaxation on {unrelaxed_output_path}...")
relax_protein(config, args.model_device, unrelaxed_protein, output_directory, output_name)
if args.save_outputs:
output_dict_path = os.path.join(
output_directory, f'{output_name}_output_dict.pkl'
)
with open(output_dict_path, "wb") as fp:
pickle.dump(out, fp, protocol=pickle.HIGHEST_PROTOCOL)
logger.info(f"Model output written to {output_dict_path}...")
if __name__ == "__main__":
parser = argparse.ArgumentParser()
parser.add_argument(
"fasta_dir", type=str,
help="Path to directory containing FASTA files, one sequence per file"
)
parser.add_argument(
"template_mmcif_dir", type=str,
)
parser.add_argument(
"--use_precomputed_alignments", type=str, default=None,
help="""Path to alignment directory. If provided, alignment computation
is skipped and database path arguments are ignored."""
)
parser.add_argument(
"--output_dir", type=str, default=os.getcwd(),
help="""Name of the directory in which to output the prediction""",
)
parser.add_argument(
"--model_device", type=str, default="cpu",
help="""Name of the device on which to run the model. Any valid torch
device name is accepted (e.g. "cpu", "cuda:0")"""
)
parser.add_argument(
"--config_preset", type=str, default="model_1",
help="""Name of a model config preset defined in openfold/config.py"""
)
parser.add_argument(
"--jax_param_path", type=str, default=None,
help="""Path to JAX model parameters. If None, and openfold_checkpoint_path
is also None, parameters are selected automatically according to
the model name from openfold/resources/params"""
)
parser.add_argument(
"--openfold_checkpoint_path", type=str, default=None,
help="""Path to OpenFold checkpoint. Can be either a DeepSpeed
checkpoint directory or a .pt file"""
)
parser.add_argument(
"--save_outputs", action="store_true", default=False,
help="Whether to save all model outputs, including embeddings, etc."
)
parser.add_argument(
"--cpus", type=int, default=4,
help="""Number of CPUs with which to run alignment tools"""
)
parser.add_argument(
"--preset", type=str, default='full_dbs',
choices=('reduced_dbs', 'full_dbs')
)
parser.add_argument(
"--output_postfix", type=str, default=None,
help="""Postfix for output prediction filenames"""
)
parser.add_argument(
"--data_random_seed", type=str, default=None
)
parser.add_argument(
"--skip_relaxation", action="store_true", default=False,
)
parser.add_argument(
"--multimer_ri_gap", type=int, default=200,
help="""Residue index offset between multiple sequences, if provided"""
)
parser.add_argument(
"--trace_model", action="store_true", default=False,
help="""Whether to convert parts of each model to TorchScript.
Significantly improves runtime at the cost of lengthy
'compilation.' Useful for large batch jobs."""
)
parser.add_argument(
"--subtract_plddt", action="store_true", default=False,
help=""""Whether to output (100 - pLDDT) in the B-factor column instead
of the pLDDT itself"""
)
add_data_args(parser)
args = parser.parse_args()
if(args.jax_param_path is None and args.openfold_checkpoint_path is None):
args.jax_param_path = os.path.join(
"openfold", "resources", "params",
"params_" + args.config_preset + ".npz"
)
if(args.model_device == "cpu" and torch.cuda.is_available()):
logging.warning(
"""The model is being run on CPU. Consider specifying
--model_device for better performance"""
)
main(args)