File size: 5,161 Bytes
e740833 |
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 |
#!/usr/bin/env python3 -u
# Copyright (c) Meta Platforms, Inc. and affiliates.
#
# This source code is licensed under the MIT license found in the
# LICENSE file in the root directory of this source tree.
import argparse
import pathlib
import torch
from esm import Alphabet, FastaBatchedDataset, ProteinBertModel, pretrained, MSATransformer
def create_parser():
parser = argparse.ArgumentParser(
description="Extract per-token representations and model outputs for sequences in a FASTA file" # noqa
)
parser.add_argument(
"model_location",
type=str,
help="PyTorch model file OR name of pretrained model to download (see README for models)",
)
parser.add_argument(
"fasta_file",
type=pathlib.Path,
help="FASTA file on which to extract representations",
)
parser.add_argument(
"output_dir",
type=pathlib.Path,
help="output directory for extracted representations",
)
parser.add_argument("--toks_per_batch", type=int, default=4096, help="maximum batch size")
parser.add_argument(
"--repr_layers",
type=int,
default=[-1],
nargs="+",
help="layers indices from which to extract representations (0 to num_layers, inclusive)",
)
parser.add_argument(
"--include",
type=str,
nargs="+",
choices=["mean", "per_tok", "bos", "contacts"],
help="specify which representations to return",
required=True,
)
parser.add_argument(
"--truncation_seq_length",
type=int,
default=1022,
help="truncate sequences longer than the given value",
)
parser.add_argument("--nogpu", action="store_true", help="Do not use GPU even if available")
return parser
def run(args):
model, alphabet = pretrained.load_model_and_alphabet(args.model_location)
model.eval()
if isinstance(model, MSATransformer):
raise ValueError(
"This script currently does not handle models with MSA input (MSA Transformer)."
)
if torch.cuda.is_available() and not args.nogpu:
model = model.cuda()
print("Transferred model to GPU")
dataset = FastaBatchedDataset.from_file(args.fasta_file)
batches = dataset.get_batch_indices(args.toks_per_batch, extra_toks_per_seq=1)
data_loader = torch.utils.data.DataLoader(
dataset, collate_fn=alphabet.get_batch_converter(args.truncation_seq_length), batch_sampler=batches
)
print(f"Read {args.fasta_file} with {len(dataset)} sequences")
args.output_dir.mkdir(parents=True, exist_ok=True)
return_contacts = "contacts" in args.include
assert all(-(model.num_layers + 1) <= i <= model.num_layers for i in args.repr_layers)
repr_layers = [(i + model.num_layers + 1) % (model.num_layers + 1) for i in args.repr_layers]
with torch.no_grad():
for batch_idx, (labels, strs, toks) in enumerate(data_loader):
print(
f"Processing {batch_idx + 1} of {len(batches)} batches ({toks.size(0)} sequences)"
)
if torch.cuda.is_available() and not args.nogpu:
toks = toks.to(device="cuda", non_blocking=True)
out = model(toks, repr_layers=repr_layers, return_contacts=return_contacts)
logits = out["logits"].to(device="cpu")
representations = {
layer: t.to(device="cpu") for layer, t in out["representations"].items()
}
if return_contacts:
contacts = out["contacts"].to(device="cpu")
for i, label in enumerate(labels):
args.output_file = args.output_dir / f"{label}.pt"
args.output_file.parent.mkdir(parents=True, exist_ok=True)
result = {"label": label}
truncate_len = min(args.truncation_seq_length, len(strs[i]))
# Call clone on tensors to ensure tensors are not views into a larger representation
# See https://github.com/pytorch/pytorch/issues/1995
if "per_tok" in args.include:
result["representations"] = {
layer: t[i, 1 : truncate_len + 1].clone()
for layer, t in representations.items()
}
if "mean" in args.include:
result["mean_representations"] = {
layer: t[i, 1 : truncate_len + 1].mean(0).clone()
for layer, t in representations.items()
}
if "bos" in args.include:
result["bos_representations"] = {
layer: t[i, 0].clone() for layer, t in representations.items()
}
if return_contacts:
result["contacts"] = contacts[i, : truncate_len, : truncate_len].clone()
torch.save(
result,
args.output_file,
)
def main():
parser = create_parser()
args = parser.parse_args()
run(args)
if __name__ == "__main__":
main()
|