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#!/usr/bin/env python3 -u
# Copyright (c) Facebook, Inc. and its 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 sys
print("using", sys.executable)
sys.path.insert( 0,"/home/user/.local/lib/python3.8/site-packages")
sys.path.insert( 0,"/home/user/app/esm/")
import os
print(os.environ)
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 main(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}
# 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 : len(strs[i]) + 1].clone()
for layer, t in representations.items()
}
if "mean" in args.include:
result["mean_representations"] = {
layer: t[i, 1 : len(strs[i]) + 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, : len(strs[i]), : len(strs[i])].clone()
torch.save(
result,
args.output_file,
)
if __name__ == "__main__":
parser = create_parser()
args = parser.parse_args()
main(args)
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