File size: 6,472 Bytes
d149fb1 | 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 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 | # Some functions borrowed from [ESM](https://www.github.com/facebookresearch/esm)
import argparse
import logging
import os
import torch
from onescience.datapipes.openfold import parsers
logging.basicConfig(level=logging.INFO)
class SequenceDataset(object):
def __init__(self, labels, sequences) -> None:
self.labels = labels
self.sequences = sequences
@classmethod
def from_file(cls, fasta_file):
labels, sequences = [], []
with open(fasta_file, "r") as infile:
fasta_str = infile.read()
sequences, labels = parsers.parse_fasta(fasta_str)
assert len(set(labels)) == len(labels),\
"Sequence labels need to be unique. Duplicates found!"
return cls(labels, sequences)
def __len__(self):
return len(self.labels)
def __getitem__(self, idx):
return self.labels[idx], self.sequences[idx]
def get_batch_indices(self, toks_per_batch, extra_toks_per_seq):
sizes = [(len(s), i) for i, s in enumerate(self.sequences)]
sizes.sort()
batches = []
buf = []
max_len = 0
def _flush_current_buf():
nonlocal max_len, buf
if len(buf) == 0:
return
batches.append(buf)
buf = []
max_len = 0
for sz, i in sizes:
sz += extra_toks_per_seq
if max(sz, max_len) * (len(buf)+1) > toks_per_batch:
_flush_current_buf()
max_len = max(max_len, sz)
buf.append(i)
_flush_current_buf()
return batches
class EmbeddingGenerator:
"""Generates the ESM-1b embeddings for the single sequence model"""
def __init__(self,
toks_per_batch: int = 4096,
truncate: bool = True,
use_local_esm: str = None,
nogpu: bool = False,
):
self.toks_per_batch = toks_per_batch
self.truncate = truncate
self.use_local_esm = use_local_esm
self.nogpu = nogpu
# Generate embeddings in bulk
if self.use_local_esm:
self.model, self.alphabet = torch.hub.load(self.use_local_esm, "esm1b_t33_650M_UR50S", source='local')
else:
self.model, self.alphabet = torch.hub.load("facebookresearch/esm:main", "esm1b_t33_650M_UR50S")
if torch.cuda.is_available() and not self.nogpu:
self.model = self.model.to(device="cuda")
def parse_sequences(self, fasta_dir, output_dir):
labels = []
seqs = []
# Generate a single bulk file
for f in os.listdir(fasta_dir):
f_name, ext = os.path.splitext(f)
if ext != '.fasta' and ext != '.fa':
logging.warning(f"Ignoring non-FASTA file: {f}")
continue
with open(os.path.join(fasta_dir, f), 'r') as infile:
seq = infile.readlines()[1].strip()
labels.append(f_name)
seqs.append(seq)
lines = []
for label, seq in zip(labels, seqs):
lines += f'>{label}\n'
lines += f'{seq}\n'
os.makedirs(output_dir, exist_ok=True)
temp_fasta_file = os.path.join(output_dir, 'temp.fasta')
with open(temp_fasta_file, 'w') as outfile:
outfile.writelines(lines)
return temp_fasta_file
def run(
self,
fasta_file,
output_dir,
):
dataset = SequenceDataset.from_file(fasta_file)
batches = dataset.get_batch_indices(self.toks_per_batch, extra_toks_per_seq=1)
data_loader = torch.utils.data.DataLoader(
dataset, collate_fn=self.alphabet.get_batch_converter(), batch_sampler=batches
)
logging.info("Loaded all sequences")
repr_layers = [33]
with torch.no_grad():
for batch_idx, (labels, strs, toks) in enumerate(data_loader):
logging.info(f"Processing {batch_idx + 1} of {len(batches)} batches ({toks.size(0)} sequences)")
if torch.cuda.is_available() and not self.nogpu:
toks = toks.to(device="cuda", non_blocking=True)
if self.truncate:
toks = toks[:1022]
out = self.model(toks, repr_layers=repr_layers, return_contacts=False)
representations = {
33: out["representations"][33].to(device="cpu")
}
for i, label in enumerate(labels):
os.makedirs(os.path.join(output_dir, label), exist_ok=True)
result = {"label": label}
result["representations"] = {
33: representations[33][i, 1: len(strs[i]) + 1].clone()
}
torch.save(
result,
os.path.join(output_dir, label, label+".pt")
)
def main(args):
logging.info("Loading the model...")
embedding_generator = EmbeddingGenerator(
args.toks_per_batch,
args.truncate,
args.use_local_esm,
args.nogpu)
logging.info("Loading the sequences and running the inference...")
temp_fasta_file = embedding_generator.parse_sequences(
args.fasta_dir,
args.output_dir
)
embedding_generator.run(
temp_fasta_file,
args.output_dir
)
os.remove(temp_fasta_file)
logging.info("Completed.")
if __name__ == "__main__":
parser = argparse.ArgumentParser()
parser.add_argument(
"fasta_dir", type=str,
help="""Path to directory containing FASTA files."""
)
parser.add_argument(
"output_dir", type=str,
help="Directory in which to output embeddings"
)
parser.add_argument(
"--toks_per_batch", type=int, default=4096,
help="maximum tokens in a batch"
)
parser.add_argument(
"--truncate", action="store_true", default=True,
help="Truncate sequences longer than 1022 (ESM restriction). Default: True"
)
parser.add_argument(
"--use_local_esm", type=str, default=None,
help="Use a local ESM repository instead of cloning from Github"
)
parser.add_argument(
"--nogpu", action="store_true",
help="Do not use GPU"
)
args = parser.parse_args()
main(args)
|